Within-Subject Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "def df_to_arr(df):\n",
    "    \n",
    "    vals = []\n",
    "    for _, row in df.iterrows():\n",
    "        vals.extend(row.tolist())\n",
    "    return np.array([x for x in vals if str(x) != 'nan'])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Pre-CP/IP Unpermuted vs. Permuted"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "source": [
    "ROI = 'mt'\n",
    "cond = 'pre_ip'\n",
    "\n",
    "num_runs = 30\n",
    "sub_num_per_run = 200\n",
    "num_subjects = 13\n",
    "\n",
    "scrambled_data = np.load(f'output/{ROI}/{cond}/within_outer_perms.npy')\n",
    "avg_data_unscrambled = pd.read_csv(f'output/{ROI}/{cond}/within_outer_accs.csv', index_col=0)['Average'].tolist()\n",
    "\n",
    "avg_data_scrambled = []\n",
    "for i in range(num_subjects):\n",
    "    subj_data = []\n",
    "    for j in range(num_runs):\n",
    "        subj_data.extend(scrambled_data[sub_num_per_run*num_subjects*j+sub_num_per_run*i:sub_num_per_run*num_subjects*j+sub_num_per_run*(i+1)])\n",
    "    avg_data_scrambled.append(np.mean(subj_data))\n",
    "    \n",
    "stats.ttest_rel(avg_data_unscrambled, avg_data_scrambled)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=18.582103100617424, pvalue=3.2853905583364515e-10)"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Post-CP/IP Unpermuted vs. Permuted"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "ROI = 'mt'\n",
    "cond = 'post_ip'\n",
    "\n",
    "num_runs = 30\n",
    "sub_num_per_run = 200\n",
    "num_subjects = 8\n",
    "\n",
    "scrambled_data = np.load(f'output/{ROI}/{cond}/within_outer_perms.npy')\n",
    "avg_data_unscrambled = pd.read_csv(f'output/{ROI}/{cond}/within_outer_accs.csv', index_col=0)['Average'].tolist()\n",
    "\n",
    "avg_data_scrambled = []\n",
    "for i in range(num_subjects):\n",
    "    subj_data = []\n",
    "    for j in range(num_runs):\n",
    "        subj_data.extend(scrambled_data[sub_num_per_run*num_subjects*j+sub_num_per_run*i:sub_num_per_run*num_subjects*j+sub_num_per_run*(i+1)])\n",
    "    avg_data_scrambled.append(np.mean(subj_data))\n",
    "    \n",
    "stats.ttest_rel(avg_data_unscrambled, avg_data_scrambled)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=8.291285936877607, pvalue=7.248954911277882e-05)"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Large&Small Unpermuted vs. Permuted"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "ROI = 'mt'\n",
    "cond = 'small'\n",
    "\n",
    "num_runs = 30\n",
    "num_subjects = 9\n",
    "sub_num_per_run = 200\n",
    "\n",
    "scrambled_data = np.load(f'output/{ROI}/{cond}/within_outer_perms.npy')\n",
    "avg_data_unscrambled = pd.read_csv(f'output/{ROI}/{cond}/within_outer_accs.csv', index_col=0)['Average'].tolist()\n",
    "\n",
    "avg_data_scrambled = []\n",
    "for i in range(num_subjects):\n",
    "    subj_data = []\n",
    "    for j in range(num_runs):\n",
    "        subj_data.extend(scrambled_data[sub_num_per_run*num_subjects*j+sub_num_per_run*i:sub_num_per_run*num_subjects*j+sub_num_per_run*(i+1)])\n",
    "    avg_data_scrambled.append(np.mean(subj_data))\n",
    "    \n",
    "stats.ttest_rel(avg_data_unscrambled, avg_data_scrambled)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=11.143643088003197, pvalue=3.761310544546336e-06)"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Post-Training vs. Pre-Training CP/IP"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "ROI = 'mt'"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Separate Training"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "'''\n",
    "IP Data\n",
    "'''\n",
    "\n",
    "post_data = pd.read_csv(f'output/{ROI}/post_ip/within_outer_accs.csv', index_col=0)\n",
    "pre_data = pd.read_csv(f'output/{ROI}/pre_ip/within_outer_accs.csv', index_col=0)\n",
    "common = post_data.index.intersection(pre_data.index)\n",
    "\n",
    "avg_data_post = post_data['Average'].tolist()\n",
    "avg_data_pre = pre_data.loc[common]['Average'].tolist()\n",
    "\n",
    "stats.ttest_rel(avg_data_post, avg_data_pre)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=-1.1968580969476674, pvalue=0.2703186955294861)"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "'''\n",
    "CP Data\n",
    "'''\n",
    "\n",
    "post_data = pd.read_csv(f'output/{ROI}/post_cp/within_outer_accs.csv', index_col=0)\n",
    "pre_data = pd.read_csv(f'output/{ROI}/pre_cp/within_outer_accs.csv', index_col=0)\n",
    "common = post_data.index.intersection(pre_data.index)\n",
    "\n",
    "avg_data_post = post_data['Average'].tolist()\n",
    "avg_data_pre = pre_data.loc[common]['Average'].tolist()\n",
    "\n",
    "stats.ttest_rel(avg_data_post, avg_data_pre)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=-2.4713472158564054, pvalue=0.042748522816239705)"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Combined Training"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "'''\n",
    "IP Data\n",
    "'''\n",
    "\n",
    "post_data = pd.read_csv(f'output/{ROI}/post_ip/within_outer_accs_post.csv', index_col=0)\n",
    "pre_data = pd.read_csv(f'output/{ROI}/post_ip/within_outer_accs_pre.csv', index_col=0)\n",
    "\n",
    "avg_data_post = post_data['Average'].tolist()\n",
    "avg_data_pre = pre_data.loc[common]['Average'].tolist()\n",
    "\n",
    "stats.ttest_rel(avg_data_post, avg_data_pre)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=-1.0653611089770083, pvalue=0.32207416102741493)"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "'''\n",
    "CP Data\n",
    "'''\n",
    "\n",
    "post_data = pd.read_csv(f'output/{ROI}/post_cp/within_outer_accs_post.csv', index_col=0)\n",
    "pre_data = pd.read_csv(f'output/{ROI}/post_cp/within_outer_accs_pre.csv', index_col=0)\n",
    "\n",
    "avg_data_post = post_data['Average'].tolist()\n",
    "avg_data_pre = pre_data.loc[common]['Average'].tolist()\n",
    "\n",
    "stats.ttest_rel(avg_data_post, avg_data_pre)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=0.2788760494354379, pvalue=0.7883989671364511)"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
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