diff --git a/run_sim_different_parcels_equal_groups.ipynb b/run_sim_different_parcels_equal_groups.ipynb
new file mode 100644
index 0000000..4dc3ed2
--- /dev/null
+++ b/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
+}
diff --git a/run_sim_different_parcels_unequal_groups.ipynb b/run_sim_different_parcels_unequal_groups.ipynb
new file mode 100644
index 0000000..13cb1bc
--- /dev/null
+++ b/run_sim_different_parcels_unequal_groups.ipynb
@@ -0,0 +1,252 @@
+{
+ "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": "7224abed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_proportion(N_cases, N_controls):\n",
+    "    proportion = N_controls / (N_cases + N_controls)\n",
+    "    return proportion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "29b420b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MCI+ proportion of controls: 0.8570306362922231\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"MCI+ proportion of controls:\", calculate_proportion(182, 1091))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "10933a97",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MCI- proportion of controls: 0.8536776212832551\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"MCI- proportion of controls:\", calculate_proportion(187, 1091))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a38be7fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ADD+ proportion of controls: 0.8721023181454837\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"ADD+ proportion of controls:\", calculate_proportion(160, 1091))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "395d5fe8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ADD- proportion of controls: 0.9495213228894691\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"ADD- proportion of controls:\", calculate_proportion(58, 1091))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5b39c0b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SCHZ proportion of controls: 0.6771894093686355\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"SCHZ proportion of controls:\", calculate_proportion(317, 665))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "129b05d7",
+   "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=0.8, 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": 9,
+   "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": 10,
+   "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": 11,
+   "id": "a44f6133",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a range of N values\n",
+    "N_values = range(300, 901, 50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6d54cf99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_20parcels_80split = simulation_loop(N_values, conn_20parcels_df)\n",
+    "result_36parcels_80split = simulation_loop(N_values, conn_36parcels_df)\n",
+    "result_64parcels_80split = simulation_loop(N_values, conn_64parcels_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d08cf2e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('sim_result_20parcels_80split.pkl', 'wb') as f:\n",
+    "    pickle.dump(result_20parcels_80split, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "96090de1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('sim_result_36parcels_80split.pkl', 'wb') as f:\n",
+    "    pickle.dump(result_36parcels_80split, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "fe070bbd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('sim_result_64parcels_80split.pkl', 'wb') as f:\n",
+    "    pickle.dump(result_64parcels_80split, 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
+}
diff --git a/run_simulation_80power.ipynb b/run_simulation_80power.ipynb
new file mode 100644
index 0000000..31a30bc
--- /dev/null
+++ b/run_simulation_80power.ipynb
@@ -0,0 +1,139 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "71e121d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import simulation_tools as sim\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fb17ca19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Path to a .csv file with connectomes in upper triangular form\n",
+    "path_conn = \"/home/neuromod/ad_sz/data/abide/abide1_2_controls_concat.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8b6c3bf5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load control connectomes from ABIDE\n",
+    "conn_df = pd.read_csv(path_conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5df7cb69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a range of N values\n",
+    "N_values = range(400, 501, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3b4565ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Simulation ran for N=400.\n",
+      "Simulation ran for N=410.\n",
+      "Simulation ran for N=420.\n",
+      "Simulation ran for N=430.\n",
+      "Simulation ran for N=440.\n",
+      "Simulation ran for N=450.\n",
+      "Simulation ran for N=460.\n",
+      "Simulation ran for N=470.\n",
+      "Simulation ran for N=480.\n",
+      "Simulation ran for N=490.\n",
+      "Simulation ran for N=500.\n"
+     ]
+    }
+   ],
+   "source": [
+    "result_list = []\n",
+    "# Loop through the values of N and run simulation with specififed parameters\n",
+    "for N in N_values:\n",
+    "    result = sim.run_multiple_simulation(conn_df, N=N, pi=0.20, d=0.3, q=0.1, num_sample=100)\n",
+    "    print(f\"Simulation ran for N={N}.\")\n",
+    "    result_list.append(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ebf2916e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=400: 0.71, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=410: 0.72, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=420: 0.75, with mean specificity of 0.99.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=430: 0.75, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=440: 0.78, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=450: 0.79, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=460: 0.8, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=470: 0.81, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=480: 0.81, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=490: 0.83, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=500: 0.84, with mean specificity of 0.98.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for result in result_list:\n",
+    "    print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd6f422f",
+   "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
+}
diff --git a/simulation_results.ipynb b/simulation_results.ipynb
new file mode 100644
index 0000000..382bd7b
--- /dev/null
+++ b/simulation_results.ipynb
@@ -0,0 +1,370 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "af8f7250",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8788dd48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_result(result_dict, n_parcels):\n",
+    "    for i in range(len(result_dict['N'])):\n",
+    "        print(f\"With {n_parcels} parcels, for N={result_dict['N'][i]} estimated mean sensitivty: {round(result_dict['mean_sensitivity'][i], 2)}, with mean specificity of {round(result_dict['mean_specificity'][i], 2)}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2f8fcb8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pkl_path = Path(\"/home/neuromod/cwas_simulation\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dcf526f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(pkl_path / 'sim_result_20parcels.pkl', 'rb') as f:\n",
+    "    result_20parcels = pickle.load(f)\n",
+    "    \n",
+    "with open(pkl_path / 'sim_result_36parcels.pkl', 'rb') as f:\n",
+    "    result_36parcels = pickle.load(f)\n",
+    "    \n",
+    "with open(pkl_path / 'sim_result_64parcels.pkl', 'rb') as f:\n",
+    "    result_64parcels = pickle.load(f)\n",
+    "    \n",
+    "with open(pkl_path / 'sim_result_20parcels_80split.pkl', 'rb') as f:\n",
+    "    result_20parcels_80split = pickle.load(f)\n",
+    "\n",
+    "with open(pkl_path / 'sim_result_36parcels_80split.pkl', 'rb') as f:\n",
+    "    result_36parcels_80split = pickle.load(f)\n",
+    "    \n",
+    "with open(pkl_path / 'sim_result_64parcels_80split.pkl', 'rb') as f:\n",
+    "    result_64parcels_80split = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a80ed889",
+   "metadata": {},
+   "source": [
+    "# Results for equal sized groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fd2a96e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "With 20 parcels, for N=300 estimated mean sensitivty: 0.47, with mean specificity of 0.99.\n",
+      "With 20 parcels, for N=350 estimated mean sensitivty: 0.55, with mean specificity of 0.99.\n",
+      "With 20 parcels, for N=400 estimated mean sensitivty: 0.65, with mean specificity of 0.99.\n",
+      "With 20 parcels, for N=450 estimated mean sensitivty: 0.7, with mean specificity of 0.99.\n",
+      "With 20 parcels, for N=500 estimated mean sensitivty: 0.75, with mean specificity of 0.98.\n",
+      "With 20 parcels, for N=550 estimated mean sensitivty: 0.79, with mean specificity of 0.98.\n",
+      "With 20 parcels, for N=600 estimated mean sensitivty: 0.82, with mean specificity of 0.98.\n",
+      "With 20 parcels, for N=650 estimated mean sensitivty: 0.83, with mean specificity of 0.98.\n",
+      "With 20 parcels, for N=700 estimated mean sensitivty: 0.86, with mean specificity of 0.98.\n",
+      "With 20 parcels, for N=750 estimated mean sensitivty: 0.87, with mean specificity of 0.98.\n",
+      "With 20 parcels, for N=800 estimated mean sensitivty: 0.88, with mean specificity of 0.98.\n",
+      "With 20 parcels, for N=850 estimated mean sensitivty: 0.88, with mean specificity of 0.98.\n",
+      "With 20 parcels, for N=900 estimated mean sensitivty: 0.89, with mean specificity of 0.98.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_result(result_20parcels, 20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c5031cf4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "With 36 parcels, for N=300 estimated mean sensitivty: 0.47, with mean specificity of 0.99.\n",
+      "With 36 parcels, for N=350 estimated mean sensitivty: 0.59, with mean specificity of 0.99.\n",
+      "With 36 parcels, for N=400 estimated mean sensitivty: 0.69, with mean specificity of 0.98.\n",
+      "With 36 parcels, for N=450 estimated mean sensitivty: 0.75, with mean specificity of 0.98.\n",
+      "With 36 parcels, for N=500 estimated mean sensitivty: 0.79, with mean specificity of 0.98.\n",
+      "With 36 parcels, for N=550 estimated mean sensitivty: 0.84, with mean specificity of 0.98.\n",
+      "With 36 parcels, for N=600 estimated mean sensitivty: 0.86, with mean specificity of 0.98.\n",
+      "With 36 parcels, for N=650 estimated mean sensitivty: 0.89, with mean specificity of 0.98.\n",
+      "With 36 parcels, for N=700 estimated mean sensitivty: 0.9, with mean specificity of 0.98.\n",
+      "With 36 parcels, for N=750 estimated mean sensitivty: 0.91, with mean specificity of 0.98.\n",
+      "With 36 parcels, for N=800 estimated mean sensitivty: 0.92, with mean specificity of 0.98.\n",
+      "With 36 parcels, for N=850 estimated mean sensitivty: 0.93, with mean specificity of 0.98.\n",
+      "With 36 parcels, for N=900 estimated mean sensitivty: 0.93, with mean specificity of 0.98.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_result(result_36parcels, 36)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "39042505",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "With 64 parcels, for N=300 estimated mean sensitivty: 0.52, with mean specificity of 0.99.\n",
+      "With 64 parcels, for N=350 estimated mean sensitivty: 0.63, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=400 estimated mean sensitivty: 0.72, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=450 estimated mean sensitivty: 0.78, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=500 estimated mean sensitivty: 0.84, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=550 estimated mean sensitivty: 0.88, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=600 estimated mean sensitivty: 0.91, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=650 estimated mean sensitivty: 0.93, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=700 estimated mean sensitivty: 0.95, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=750 estimated mean sensitivty: 0.96, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=800 estimated mean sensitivty: 0.98, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=850 estimated mean sensitivty: 0.98, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=900 estimated mean sensitivty: 0.99, with mean specificity of 0.98.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_result(result_64parcels, 64)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ce12c45",
+   "metadata": {},
+   "source": [
+    "# Results for unequal sized groups (80% controls)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c788f906",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "With 20 parcels, for N=300 estimated mean sensitivty: 0.21, with mean specificity of 1.0.\n",
+      "With 20 parcels, for N=350 estimated mean sensitivty: 0.3, with mean specificity of 0.99.\n",
+      "With 20 parcels, for N=400 estimated mean sensitivty: 0.38, with mean specificity of 0.99.\n",
+      "With 20 parcels, for N=450 estimated mean sensitivty: 0.45, with mean specificity of 0.99.\n",
+      "With 20 parcels, for N=500 estimated mean sensitivty: 0.51, with mean specificity of 0.99.\n",
+      "With 20 parcels, for N=550 estimated mean sensitivty: 0.56, with mean specificity of 0.99.\n",
+      "With 20 parcels, for N=600 estimated mean sensitivty: 0.6, with mean specificity of 0.99.\n",
+      "With 20 parcels, for N=650 estimated mean sensitivty: 0.67, with mean specificity of 0.98.\n",
+      "With 20 parcels, for N=700 estimated mean sensitivty: 0.7, with mean specificity of 0.98.\n",
+      "With 20 parcels, for N=750 estimated mean sensitivty: 0.74, with mean specificity of 0.98.\n",
+      "With 20 parcels, for N=800 estimated mean sensitivty: 0.76, with mean specificity of 0.98.\n",
+      "With 20 parcels, for N=850 estimated mean sensitivty: 0.78, with mean specificity of 0.98.\n",
+      "With 20 parcels, for N=900 estimated mean sensitivty: 0.8, with mean specificity of 0.98.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_result(result_20parcels_80split, 20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9e2b0364",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "With 36 parcels, for N=300 estimated mean sensitivty: 0.22, with mean specificity of 0.99.\n",
+      "With 36 parcels, for N=350 estimated mean sensitivty: 0.29, with mean specificity of 0.99.\n",
+      "With 36 parcels, for N=400 estimated mean sensitivty: 0.38, with mean specificity of 0.99.\n",
+      "With 36 parcels, for N=450 estimated mean sensitivty: 0.45, with mean specificity of 0.99.\n",
+      "With 36 parcels, for N=500 estimated mean sensitivty: 0.54, with mean specificity of 0.99.\n",
+      "With 36 parcels, for N=550 estimated mean sensitivty: 0.6, with mean specificity of 0.99.\n",
+      "With 36 parcels, for N=600 estimated mean sensitivty: 0.65, with mean specificity of 0.99.\n",
+      "With 36 parcels, for N=650 estimated mean sensitivty: 0.71, with mean specificity of 0.99.\n",
+      "With 36 parcels, for N=700 estimated mean sensitivty: 0.73, with mean specificity of 0.99.\n",
+      "With 36 parcels, for N=750 estimated mean sensitivty: 0.76, with mean specificity of 0.98.\n",
+      "With 36 parcels, for N=800 estimated mean sensitivty: 0.8, with mean specificity of 0.98.\n",
+      "With 36 parcels, for N=850 estimated mean sensitivty: 0.83, with mean specificity of 0.98.\n",
+      "With 36 parcels, for N=900 estimated mean sensitivty: 0.84, with mean specificity of 0.98.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_result(result_36parcels_80split, 36)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "145748b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "With 64 parcels, for N=300 estimated mean sensitivty: 0.23, with mean specificity of 0.99.\n",
+      "With 64 parcels, for N=350 estimated mean sensitivty: 0.32, with mean specificity of 0.99.\n",
+      "With 64 parcels, for N=400 estimated mean sensitivty: 0.41, with mean specificity of 0.99.\n",
+      "With 64 parcels, for N=450 estimated mean sensitivty: 0.5, with mean specificity of 0.99.\n",
+      "With 64 parcels, for N=500 estimated mean sensitivty: 0.58, with mean specificity of 0.99.\n",
+      "With 64 parcels, for N=550 estimated mean sensitivty: 0.64, with mean specificity of 0.99.\n",
+      "With 64 parcels, for N=600 estimated mean sensitivty: 0.7, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=650 estimated mean sensitivty: 0.74, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=700 estimated mean sensitivty: 0.79, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=750 estimated mean sensitivty: 0.82, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=800 estimated mean sensitivty: 0.85, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=850 estimated mean sensitivty: 0.88, with mean specificity of 0.98.\n",
+      "With 64 parcels, for N=900 estimated mean sensitivty: 0.89, with mean specificity of 0.98.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_result(result_64parcels_80split, 64)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4faa511",
+   "metadata": {},
+   "source": [
+    "# Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "211c44d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(result_20parcels['N'], result_20parcels['mean_sensitivity'], marker='o', linestyle='-', color='b', label='20 parcels')\n",
+    "plt.plot(result_36parcels['N'], result_36parcels['mean_sensitivity'], marker='o', linestyle='-', color='r', label='36 parcels')\n",
+    "plt.plot(result_64parcels['N'], result_64parcels['mean_sensitivity'], marker='o', linestyle='-', color='g', label='64 parcels')\n",
+    "#plt.plot(result_20parcels['N'], result_20parcels['mean_specificity'], marker='s', linestyle='-', color='r', label='Mean Specificity')\n",
+    "\n",
+    "# Add titles and labels\n",
+    "plt.title('Mean sensitivity vs. N for equal sized groups')\n",
+    "plt.xlabel('N participants')\n",
+    "plt.ylabel('Sensitivity')\n",
+    "plt.ylim(0.05, 1)\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8d50e707",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(result_20parcels_80split['N'], result_20parcels_80split['mean_sensitivity'], marker='o', linestyle='-', color='b', label='20 parcels')\n",
+    "plt.plot(result_36parcels_80split['N'], result_36parcels_80split['mean_sensitivity'], marker='o', linestyle='-', color='r', label='36 parcels')\n",
+    "plt.plot(result_64parcels_80split['N'], result_64parcels_80split['mean_sensitivity'], marker='o', linestyle='-', color='g', label='64 parcels')\n",
+    "#plt.plot(result_20parcels['N'], result_20parcels['mean_specificity'], marker='s', linestyle='-', color='r', label='Mean Specificity')\n",
+    "\n",
+    "# Add titles and labels\n",
+    "plt.title('Mean sensitivity vs. N for unequal sized groups: 80% control')\n",
+    "plt.xlabel('N participants')\n",
+    "plt.ylabel('Sensitivity')\n",
+    "plt.ylim(0.05, 1)\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e801d89",
+   "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
+}
diff --git a/simulation_tools.py b/simulation_tools.py
index 33428db..360b710 100644
--- a/simulation_tools.py
+++ b/simulation_tools.py
@@ -33,15 +33,18 @@ def random_sample(df, N):
     return sampled_df
 
 
-def split_sampled_df(sampled_df):
+def split_sampled_df(sampled_df, proportion_split):
     """
-    Split a DataFrame into two equal-sized groups after shuffling its rows.
+    Split a DataFrame into two groups based on a specified proportion after shuffling its rows.
 
     Parameters
     ----------
     sampled_df : pandas DataFrame
         The input DataFrame to be split.
 
+    proportion_split : float, optional
+        The proportion of the data to assign to the first group.
+
     Returns
     -------
     group1 : pandas DataFrame
@@ -54,12 +57,12 @@ def split_sampled_df(sampled_df):
     # Use the sample method with frac=1 to shuffle all rows of the df
     sampled_df_shuffled = sampled_df.sample(frac=1).reset_index(drop=True)
 
-    # Calculate the number of rows to split it in half
-    half_rows = len(sampled_df_shuffled) // 2
+    # Calculate the number of rows for the first group based on the specified proportion
+    group1_rows = int(len(sampled_df_shuffled) * proportion_split)
 
-    # Split the DataFrame into two halves
-    group1 = sampled_df_shuffled.iloc[:half_rows]
-    group2 = sampled_df_shuffled.iloc[half_rows:]
+    # Split the DataFrame into two groups
+    group1 = sampled_df_shuffled.iloc[:group1_rows]
+    group2 = sampled_df_shuffled.iloc[group1_rows:]
 
     return group1, group2
 
@@ -299,7 +302,7 @@ def run_cwas(group1_conn_stand, group2_modified_stand, group1_site, group2_site)
     return pval_list
 
 
-def run_simulation(conn_df, N, pi, d):
+def run_simulation(conn_df, N, pi, d, proportion_split):
     """
     Simulate a Connectome-Wide Association Study (CWAS) experiment.
 
@@ -317,6 +320,9 @@ def run_simulation(conn_df, N, pi, d):
     d : float
         The effect size used to modify the selected connections.
 
+    proportion_split : float, optional
+        The proportion of the data to assign to the first group.
+
     Returns
     -------
     group1_conn : pandas DataFrame
@@ -333,7 +339,7 @@ def run_simulation(conn_df, N, pi, d):
     sampled_df = random_sample(conn_df, N)
 
     # Step 2: Randomly split N selected subjects into 2 groups
-    group1, group2 = split_sampled_df(sampled_df)
+    group1, group2 = split_sampled_df(sampled_df, proportion_split)
 
     group1_site, group2_site, group1_conn, group2_conn = extract_data(
         sampled_df, group1, group2
@@ -410,17 +416,16 @@ def calculate_sens_spef(group1_conn, connections_to_modify, rejected, q):
     return sensitivity, specificity
 
 
-def run_multiple_simulation(conn_df, N, pi, d, q, num_sample):
+def run_multiple_simulation(conn_df, N, pi, d, proportion_split, q, num_sample):
     sensitivity_list = []
     specificity_list = []
-    correct_rejected_count = 0
     for sample in range(num_sample):
         # Perform steps 1-4 of simulation
         (
             group1_conn,
             connections_to_modify,
             pval_list,
-        ) = run_simulation(conn_df, N, pi, d)
+        ) = run_simulation(conn_df, N, pi, d, proportion_split)
 
         # Step 5: Apply FDR correction
         rejected, corrected_pval_list = fdrcorrection(pval_list, alpha=q)
@@ -433,13 +438,8 @@ def run_multiple_simulation(conn_df, N, pi, d, q, num_sample):
         sensitivity_list.append(sensitivity)
         specificity_list.append(specificity)
 
-        # If null hypothesis rejected, plus 1
-        if np.any(corrected_pval_list < q):
-            correct_rejected_count += 1
-
-    result_summary = (
-        f"Estimated mean sensitivity to detect d={d}, with pi={pi}%, q={q} and N={N}: "
-        f"{round(np.mean(sensitivity_list), 2)}, with mean specificity of {round(np.mean(specificity_list), 2)}."
-    )
+    # result_summary = (
+    #    f"Estimated mean sensitivity to detect d={d}, with pi={pi}%, q={q} and N={N}: "
+    #    f"{round(mean_sensitivity, 2)}, with mean specificity of {round(mean_specificity, 2)}.")
 
-    return result_summary
+    return np.mean(sensitivity_list), np.mean(specificity_list)