head_motion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimate head motion for each subject"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use root-mean-square: prefiltered_func_data_mcf_rel_mean.rms gives the values between one image and the next image in the series. Mean Motion represents the mean absolute displacement of each brain volume as relative to the previous volume. The mean and maximum displacements in 3D space for each brain volume were computed as the root-mean-square (RMS) of the translation parameters (displacement = square root (x2 + y2 + z2)) and expressed in mm. \n",
    "\n",
    "Another measure, Number of Movements, was estimated as the number of relative displacements > 0.1 mm in 3D space between adjacent volumes. Thus, the metric is expressed as an integer with the minimum possible value being 0 and the maximum n-1 where n is the number of acquired volumes in the study. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import csv\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/project/3013104.01'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir('/project/3013104.01/')\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenate .rms files across all subjects and sessions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the file 'prefiltered_func_data_mcf_rel_mean.rms' in each subject-session's 'mc' dir. Each file contains 1 value in mm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File not found for subject: subject-0126, task: mri_movietask_1\n",
      "File not found for subject: subject-0116, task: mri_movietask_1\n",
      "File not found for subject: subject-0231, task: mri_movietask_3\n",
      "File not found for subject: subject-0123, task: mri_movietask_2\n",
      "File not found for subject: subject-0031, task: mri_movietask_1\n",
      "File not found for subject: subject-0162, task: mri_movietask_1\n",
      "File not found for subject: subject-0002, task: mri_movietask_2\n",
      "File not found for subject: subject-0002, task: mri_movietask_3\n",
      "File not found for subject: subject-0181, task: mri_movietask_1\n",
      "File not found for subject: subject-0033, task: mri_movietask_2\n",
      "File not found for subject: subject-0066, task: mri_movietask_2\n",
      "File not found for subject: subject-0089, task: mri_movietask_1\n",
      "File not found for subject: subject-0237, task: mri_movietask_2\n",
      "File not found for subject: subject-0062, task: mri_movietask_2\n",
      "File not found for subject: subject-0062, task: mri_movietask_3\n",
      "File not found for subject: subject-0155, task: mri_movietask_1\n",
      "File not found for subject: subject-0134, task: mri_movietask_3\n",
      "File not found for subject: subject-0147, task: mri_movietask_2\n",
      "File not found for subject: subject-0147, task: mri_movietask_3\n",
      "File not found for subject: subject-0124, task: mri_movietask_3\n",
      "File not found for subject: subject-0027, task: mri_movietask_3\n",
      "File not found for subject: subject-0040, task: mri_movietask_2\n",
      "File not found for subject: subject-0209, task: mri_movietask_2\n",
      "File not found for subject: subject-0085, task: mri_movietask_1\n",
      "File not found for subject: subject-0216, task: mri_movietask_1\n",
      "File not found for subject: subject-0136, task: mri_movietask_1\n",
      "File not found for subject: subject-0016, task: mri_movietask_1\n",
      "File not found for subject: subject-0016, task: mri_movietask_2\n",
      "File not found for subject: subject-0041, task: mri_movietask_1\n",
      "File not found for subject: subject-0088, task: mri_movietask_1\n",
      "File not found for subject: subject-0160, task: mri_movietask_1\n",
      "File not found for subject: subject-0199, task: mri_movietask_2\n",
      "Data processing and writing completed.\n"
     ]
    }
   ],
   "source": [
    "working_directory = 'pulled-data-MRI'\n",
    "output_file = 'analysis/head_motion.csv'\n",
    "tasks = ['mri_movietask_1', 'mri_movietask_2', 'mri_movietask_3']\n",
    "\n",
    "# Function to find sub-directories starting with 'subject-*'\n",
    "def find_subject_directories(directory):\n",
    "    return [subdir for subdir in os.listdir(directory) if subdir.startswith('subject-') and os.path.isdir(os.path.join(directory, subdir))]\n",
    "\n",
    "# Function to concatenate files and extract data\n",
    "def process_subject_task_data(subject_directory, task):\n",
    "    data = []\n",
    "    file_pattern = os.path.join(working_directory, subject_directory, task, 'mc', 'prefiltered_func_data_mcf_rel_mean.rms')\n",
    "\n",
    "    if os.path.exists(file_pattern):\n",
    "        with open(file_pattern, 'r') as file:\n",
    "            for line in file:\n",
    "                rel_mean_rms = float(line.strip())  # Assuming the data in the file is numeric\n",
    "                data.append((subject_directory, task, rel_mean_rms))\n",
    "    else:\n",
    "        print(f\"File not found for subject: {subject_directory}, task: {task}\")\n",
    "\n",
    "    return data\n",
    "\n",
    "all_data = []\n",
    "\n",
    "for subject_directory in find_subject_directories(working_directory):\n",
    "    for task in tasks:\n",
    "        subject_task_data = process_subject_task_data(subject_directory, task)\n",
    "        all_data.extend(subject_task_data)\n",
    "\n",
    "# Write data to the output CSV file\n",
    "with open(output_file, 'w', newline='') as csvfile:\n",
    "    csv_writer = csv.writer(csvfile)\n",
    "    csv_writer.writerow(['subject', 'task', 'rel_mean_rms'])\n",
    "    csv_writer.writerows(all_data)\n",
    "\n",
    "print(\"Data processing and writing completed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of 'rel_mean_rms': 0.12027273491379312\n",
      "Standard deviation of 'rel_mean_rms': 0.04544833560951867\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('analysis/head_motion.csv')\n",
    "\n",
    "# Filter rows where 'task' is 'mri_movietask_1'\n",
    "filtered_df = df[df['task'] == 'mri_movietask_3']\n",
    "\n",
    "# Calculate the mean and standard deviation of 'rel_mean_rms'\n",
    "mean_rel_mean_rms = filtered_df['rel_mean_rms'].mean()\n",
    "std_rel_mean_rms = filtered_df['rel_mean_rms'].std()\n",
    "\n",
    "print(f\"Mean of 'rel_mean_rms': {mean_rel_mean_rms}\")\n",
    "print(f\"Standard deviation of 'rel_mean_rms': {std_rel_mean_rms}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task: mri_movietask_1\n",
      "Mean of 'rel_mean_rms': 0.12224729733333334\n",
      "Standard deviation of 'rel_mean_rms': 0.052325449814512495\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task: mri_movietask_2\n",
      "Mean of 'rel_mean_rms': 0.1200188153508772\n",
      "Standard deviation of 'rel_mean_rms': 0.04583321305039493\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task: mri_movietask_3\n",
      "Mean of 'rel_mean_rms': 0.12027273491379308\n",
      "Standard deviation of 'rel_mean_rms': 0.045448335609518664\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the CSV file into a DataFrame\n",
    "df = pd.read_csv('analysis/head_motion.csv')\n",
    "\n",
    "# List of unique tasks\n",
    "unique_tasks = df['task'].unique()\n",
    "\n",
    "for task in unique_tasks:\n",
    "    # Filter rows for the current task\n",
    "    filtered_df = df[df['task'] == task]\n",
    "\n",
    "    # Calculate the mean and standard deviation of 'rel_mean_rms' for the current task\n",
    "    mean_rel_mean_rms = filtered_df['rel_mean_rms'].mean()\n",
    "    std_rel_mean_rms = filtered_df['rel_mean_rms'].std()\n",
    "\n",
    "    print(f\"Task: {task}\")\n",
    "    print(f\"Mean of 'rel_mean_rms': {mean_rel_mean_rms}\")\n",
    "    print(f\"Standard deviation of 'rel_mean_rms': {std_rel_mean_rms}\")\n",
    "\n",
    "    # Create a histogram of 'rel_mean_rms' for the current task\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.hist(filtered_df['rel_mean_rms'], bins=20)\n",
    "    plt.xlabel('rel_mean_rms')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.title(f'Histogram of rel_mean_rms for {task}')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assess the relationship between motion and neural data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files not found for subject-0008 and mri_movietask_2\n",
      "Files not found for subject-0008 and mri_movietask_2\n",
      "Files not found for subject-0016 and mri_movietask_1\n",
      "Files not found for subject-0016 and mri_movietask_2\n",
      "Files not found for subject-0023 and mri_movietask_2\n",
      "Files not found for subject-0023 and mri_movietask_2\n",
      "Files not found for subject-0027 and mri_movietask_1\n",
      "Files not found for subject-0027 and mri_movietask_3\n",
      "Files not found for subject-0031 and mri_movietask_1\n",
      "Files not found for subject-0031 and mri_movietask_1\n",
      "Files not found for subject-0033 and mri_movietask_2\n",
      "Files not found for subject-0033 and mri_movietask_2\n",
      "Files not found for subject-0040 and mri_movietask_2\n",
      "Files not found for subject-0040 and mri_movietask_2\n",
      "Files not found for subject-0041 and mri_movietask_1\n",
      "Files not found for subject-0041 and mri_movietask_1\n",
      "Files not found for subject-0047 and mri_movietask_2\n",
      "Files not found for subject-0047 and mri_movietask_2\n",
      "Files not found for subject-0058 and mri_movietask_2\n",
      "Files not found for subject-0058 and mri_movietask_2\n",
      "Files not found for subject-0060 and mri_movietask_1\n",
      "Files not found for subject-0060 and mri_movietask_1\n",
      "Files not found for subject-0062 and mri_movietask_2\n",
      "Files not found for subject-0062 and mri_movietask_3\n",
      "Files not found for subject-0066 and mri_movietask_2\n",
      "Files not found for subject-0066 and mri_movietask_2\n",
      "Files not found for subject-0071 and mri_movietask_2\n",
      "Files not found for subject-0071 and mri_movietask_2\n",
      "Files not found for subject-0073 and mri_movietask_3\n",
      "Files not found for subject-0073 and mri_movietask_3\n",
      "Files not found for subject-0079 and mri_movietask_3\n",
      "Files not found for subject-0079 and mri_movietask_3\n",
      "Files not found for subject-0084 and mri_movietask_3\n",
      "Files not found for subject-0084 and mri_movietask_3\n",
      "Files not found for subject-0085 and mri_movietask_1\n",
      "Files not found for subject-0085 and mri_movietask_2\n",
      "Files not found for subject-0088 and mri_movietask_1\n",
      "Files not found for subject-0088 and mri_movietask_1\n",
      "Files not found for subject-0089 and mri_movietask_1\n",
      "Files not found for subject-0089 and mri_movietask_1\n",
      "Files not found for subject-0102 and mri_movietask_3\n",
      "Files not found for subject-0102 and mri_movietask_3\n",
      "Files not found for subject-0112 and mri_movietask_1\n",
      "Files not found for subject-0112 and mri_movietask_1\n",
      "Files not found for subject-0114 and mri_movietask_3\n",
      "Files not found for subject-0114 and mri_movietask_3\n",
      "Files not found for subject-0116 and mri_movietask_1\n",
      "Files not found for subject-0116 and mri_movietask_1\n",
      "Files not found for subject-0123 and mri_movietask_2\n",
      "Files not found for subject-0123 and mri_movietask_2\n",
      "Files not found for subject-0124 and mri_movietask_3\n",
      "Files not found for subject-0124 and mri_movietask_3\n",
      "Files not found for subject-0126 and mri_movietask_1\n",
      "Files not found for subject-0126 and mri_movietask_1\n",
      "Files not found for subject-0128 and mri_movietask_2\n",
      "Files not found for subject-0128 and mri_movietask_3\n",
      "Files not found for subject-0129 and mri_movietask_2\n",
      "Files not found for subject-0129 and mri_movietask_2\n",
      "Files not found for subject-0134 and mri_movietask_3\n",
      "Files not found for subject-0134 and mri_movietask_3\n",
      "Files not found for subject-0136 and mri_movietask_1\n",
      "Files not found for subject-0136 and mri_movietask_1\n",
      "Files not found for subject-0138 and mri_movietask_2\n",
      "Files not found for subject-0138 and mri_movietask_2\n",
      "Files not found for subject-0142 and mri_movietask_2\n",
      "Files not found for subject-0142 and mri_movietask_2\n",
      "Files not found for subject-0147 and mri_movietask_2\n",
      "Files not found for subject-0147 and mri_movietask_3\n",
      "Files not found for subject-0155 and mri_movietask_1\n",
      "Files not found for subject-0155 and mri_movietask_1\n",
      "Files not found for subject-0157 and mri_movietask_1\n",
      "Files not found for subject-0157 and mri_movietask_1\n",
      "Files not found for subject-0158 and mri_movietask_2\n",
      "Files not found for subject-0158 and mri_movietask_2\n",
      "Files not found for subject-0160 and mri_movietask_1\n",
      "Files not found for subject-0160 and mri_movietask_1\n",
      "Files not found for subject-0162 and mri_movietask_1\n",
      "Files not found for subject-0162 and mri_movietask_1\n",
      "Files not found for subject-0163 and mri_movietask_1\n",
      "Files not found for subject-0163 and mri_movietask_1\n",
      "Files not found for subject-0181 and mri_movietask_1\n",
      "Files not found for subject-0181 and mri_movietask_1\n",
      "Files not found for subject-0185 and mri_movietask_2\n",
      "Files not found for subject-0185 and mri_movietask_2\n",
      "Files not found for subject-0187 and mri_movietask_2\n",
      "Files not found for subject-0187 and mri_movietask_2\n",
      "Files not found for subject-0188 and mri_movietask_2\n",
      "Files not found for subject-0188 and mri_movietask_2\n",
      "Files not found for subject-0199 and mri_movietask_2\n",
      "Files not found for subject-0199 and mri_movietask_2\n",
      "Files not found for subject-0203 and mri_movietask_1\n",
      "Files not found for subject-0203 and mri_movietask_2\n",
      "Files not found for subject-0204 and mri_movietask_2\n",
      "Files not found for subject-0204 and mri_movietask_2\n",
      "Files not found for subject-0209 and mri_movietask_2\n",
      "Files not found for subject-0209 and mri_movietask_2\n",
      "Files not found for subject-0216 and mri_movietask_1\n",
      "Files not found for subject-0216 and mri_movietask_1\n",
      "Files not found for subject-0219 and mri_movietask_1\n",
      "Files not found for subject-0219 and mri_movietask_1\n",
      "Files not found for subject-0228 and mri_movietask_2\n",
      "Files not found for subject-0228 and mri_movietask_2\n",
      "Files not found for subject-0231 and mri_movietask_3\n",
      "Files not found for subject-0231 and mri_movietask_3\n",
      "Files not found for subject-0237 and mri_movietask_2\n",
      "Files not found for subject-0237 and mri_movietask_2\n",
      "Files not found for subject-0239 and mri_movietask_2\n",
      "Files not found for subject-0239 and mri_movietask_2\n",
      "Correlation results saved to analysis/motion/correlation_results.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "# Define the list of MRI movie tasks\n",
    "mri_movietasks = ['mri_movietask_1', 'mri_movietask_2', 'mri_movietask_3']\n",
    "\n",
    "# Output directory for saving correlation results\n",
    "out_dir = 'analysis/motion'\n",
    "\n",
    "# Create the output directory if it doesn't exist\n",
    "os.makedirs(out_dir, exist_ok=True)\n",
    "\n",
    "# Create a dictionary to store correlations for each subject and MRI movie task\n",
    "correlation_results = {}\n",
    "\n",
    "# Iterate through files in the data directory\n",
    "for subject in os.listdir('analysis/roi_estimations'):\n",
    "    if not subject.endswith('.csv'):\n",
    "        continue\n",
    "    \n",
    "    # Extract the subject name from the activation data filename\n",
    "    subject_name = subject.split('_')[0]\n",
    "    \n",
    "    # Initialize subject-specific dictionary\n",
    "    if subject_name not in correlation_results:\n",
    "        correlation_results[subject_name] = {mri_task: {'r': None, 'p': None} for mri_task in mri_movietasks}\n",
    "    \n",
    "    # Loop through MRI movie tasks\n",
    "    for mri_movietask in mri_movietasks:\n",
    "        # Define paths to activation and motion data files\n",
    "        activation_data_path = f'analysis/roi_estimations/{subject_name}_{mri_movietask}_smoothed_filtered_func_data_clean_MNI.nii.csv'\n",
    "        motion_data_path = f'pulled-data-MRI/{subject_name}/{mri_movietask}/mc/prefiltered_func_data_mcf_rel.rms'\n",
    "        \n",
    "        # Check if both files exist before proceeding\n",
    "        if os.path.exists(activation_data_path) and os.path.exists(motion_data_path):\n",
    "            # Load activation and motion data into DataFrames\n",
    "            activation_df = pd.read_csv(activation_data_path, header=0)  # Assuming header is in the first row\n",
    "            motion_df = pd.read_csv(motion_data_path, header=None, names=['motion_parameter'])\n",
    "            \n",
    "            # Remove the first row (corresponding to the first time point) from activation data\n",
    "            activation_df = activation_df.iloc[1:]\n",
    "            \n",
    "            # Ensure that the dimensions match (number of rows)\n",
    "            if activation_df.shape[0] == motion_df.shape[0]:\n",
    "                # Calculate Pearson correlation and p-value between mean activation data and motion data\n",
    "                corr_coefficient, p_value = pearsonr(activation_df.mean(axis=1), motion_df['motion_parameter'])\n",
    "                \n",
    "                # Store the correlation coefficient and p-value in the results dictionary\n",
    "                correlation_results[subject_name][mri_movietask]['r'] = corr_coefficient\n",
    "                correlation_results[subject_name][mri_movietask]['p'] = p_value\n",
    "            else:\n",
    "                print(f\"Dimensions of {activation_data_path} and {motion_data_path} do not match for {subject_name} and {mri_movietask}\")\n",
    "        else:\n",
    "            print(f\"Files not found for {subject_name} and {mri_movietask}\")\n",
    "\n",
    "# Create a DataFrame with subjects, MRI tasks, correlation values, and p-values\n",
    "result_data = []\n",
    "\n",
    "for subject_name, tasks_data in correlation_results.items():\n",
    "    for mri_movietask, corr_data in tasks_data.items():\n",
    "        result_data.append([subject_name, mri_movietask, corr_data['r'], corr_data['p']])\n",
    "\n",
    "result_df = pd.DataFrame(result_data, columns=['subject', 'MRI_movie_task', 'correlation_r', 'p_value'])\n",
    "\n",
    "# Save the results to a CSV file\n",
    "correlation_file = os.path.join(out_dir, 'correlation_results.csv')\n",
    "result_df.to_csv(correlation_file, index=False)\n",
    "\n",
    "print(f\"Correlation results saved to {correlation_file}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make sure excluded data is excluded\n",
    "import os\n",
    "import glob\n",
    "\n",
    "# Data to exclude because they are repeats\n",
    "directory = 'temp_dir/'\n",
    "file_patterns = [\n",
    "    'subject-0022_mri_movietask_2*',\n",
    "    'subject-0100_mri_movietask_2*',\n",
    "    'subject-0166_mri_movietask_2*',\n",
    "    'subject-0174_mri_movietask_2*',\n",
    "    'subject-0213_mri_movietask_3*'\n",
    "]\n",
    "\n",
    "# Iterate over the list of file patterns and remove matching files in temp_dir\n",
    "for pattern in file_patterns:\n",
    "    files_to_remove = glob.glob(os.path.join(directory, pattern))\n",
    "    for file_to_remove in files_to_remove:\n",
    "        os.remove(file_to_remove)\n",
    "        \n",
    "# Also refer to: excluded_data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files not found for subject-0008 and mri_movietask_2\n",
      "Files not found for subject-0008 and mri_movietask_2\n",
      "Files not found for subject-0016 and mri_movietask_1\n",
      "Files not found for subject-0016 and mri_movietask_2\n",
      "Files not found for subject-0023 and mri_movietask_2\n",
      "Files not found for subject-0023 and mri_movietask_2\n",
      "Files not found for subject-0027 and mri_movietask_1\n",
      "Files not found for subject-0027 and mri_movietask_3\n",
      "Files not found for subject-0031 and mri_movietask_1\n",
      "Files not found for subject-0031 and mri_movietask_1\n",
      "Files not found for subject-0033 and mri_movietask_2\n",
      "Files not found for subject-0033 and mri_movietask_2\n",
      "Files not found for subject-0040 and mri_movietask_2\n",
      "Files not found for subject-0040 and mri_movietask_2\n",
      "Files not found for subject-0041 and mri_movietask_1\n",
      "Files not found for subject-0041 and mri_movietask_1\n",
      "Files not found for subject-0047 and mri_movietask_2\n",
      "Files not found for subject-0047 and mri_movietask_2\n",
      "Files not found for subject-0058 and mri_movietask_2\n",
      "Files not found for subject-0058 and mri_movietask_2\n",
      "Files not found for subject-0060 and mri_movietask_1\n",
      "Files not found for subject-0060 and mri_movietask_1\n",
      "Files not found for subject-0062 and mri_movietask_2\n",
      "Files not found for subject-0062 and mri_movietask_3\n",
      "Files not found for subject-0066 and mri_movietask_2\n",
      "Files not found for subject-0066 and mri_movietask_2\n",
      "Files not found for subject-0071 and mri_movietask_2\n",
      "Files not found for subject-0071 and mri_movietask_2\n",
      "Files not found for subject-0073 and mri_movietask_3\n",
      "Files not found for subject-0073 and mri_movietask_3\n",
      "Files not found for subject-0079 and mri_movietask_3\n",
      "Files not found for subject-0079 and mri_movietask_3\n",
      "Files not found for subject-0084 and mri_movietask_3\n",
      "Files not found for subject-0084 and mri_movietask_3\n",
      "Files not found for subject-0085 and mri_movietask_1\n",
      "Files not found for subject-0085 and mri_movietask_2\n",
      "Files not found for subject-0088 and mri_movietask_1\n",
      "Files not found for subject-0088 and mri_movietask_1\n",
      "Files not found for subject-0089 and mri_movietask_1\n",
      "Files not found for subject-0089 and mri_movietask_1\n",
      "Files not found for subject-0102 and mri_movietask_3\n",
      "Files not found for subject-0102 and mri_movietask_3\n",
      "Files not found for subject-0112 and mri_movietask_1\n",
      "Files not found for subject-0112 and mri_movietask_1\n",
      "Files not found for subject-0114 and mri_movietask_3\n",
      "Files not found for subject-0114 and mri_movietask_3\n",
      "Files not found for subject-0116 and mri_movietask_1\n",
      "Files not found for subject-0116 and mri_movietask_1\n",
      "Files not found for subject-0123 and mri_movietask_2\n",
      "Files not found for subject-0123 and mri_movietask_2\n",
      "Files not found for subject-0124 and mri_movietask_3\n",
      "Files not found for subject-0124 and mri_movietask_3\n",
      "Files not found for subject-0126 and mri_movietask_1\n",
      "Files not found for subject-0126 and mri_movietask_1\n",
      "Files not found for subject-0128 and mri_movietask_2\n",
      "Files not found for subject-0128 and mri_movietask_3\n",
      "Files not found for subject-0129 and mri_movietask_2\n",
      "Files not found for subject-0129 and mri_movietask_2\n",
      "Files not found for subject-0134 and mri_movietask_3\n",
      "Files not found for subject-0134 and mri_movietask_3\n",
      "Files not found for subject-0136 and mri_movietask_1\n",
      "Files not found for subject-0136 and mri_movietask_1\n",
      "Files not found for subject-0138 and mri_movietask_2\n",
      "Files not found for subject-0138 and mri_movietask_2\n",
      "Files not found for subject-0142 and mri_movietask_2\n",
      "Files not found for subject-0142 and mri_movietask_2\n",
      "Files not found for subject-0147 and mri_movietask_2\n",
      "Files not found for subject-0147 and mri_movietask_3\n",
      "Files not found for subject-0155 and mri_movietask_1\n",
      "Files not found for subject-0155 and mri_movietask_1\n",
      "Files not found for subject-0157 and mri_movietask_1\n",
      "Files not found for subject-0157 and mri_movietask_1\n",
      "Files not found for subject-0158 and mri_movietask_2\n",
      "Files not found for subject-0158 and mri_movietask_2\n",
      "Files not found for subject-0160 and mri_movietask_1\n",
      "Files not found for subject-0160 and mri_movietask_1\n",
      "Files not found for subject-0162 and mri_movietask_1\n",
      "Files not found for subject-0162 and mri_movietask_1\n",
      "Files not found for subject-0163 and mri_movietask_1\n",
      "Files not found for subject-0163 and mri_movietask_1\n",
      "Files not found for subject-0181 and mri_movietask_1\n",
      "Files not found for subject-0181 and mri_movietask_1\n",
      "Files not found for subject-0185 and mri_movietask_2\n",
      "Files not found for subject-0185 and mri_movietask_2\n",
      "Files not found for subject-0187 and mri_movietask_2\n",
      "Files not found for subject-0187 and mri_movietask_2\n",
      "Files not found for subject-0188 and mri_movietask_2\n",
      "Files not found for subject-0188 and mri_movietask_2\n",
      "Files not found for subject-0199 and mri_movietask_2\n",
      "Files not found for subject-0199 and mri_movietask_2\n",
      "Files not found for subject-0203 and mri_movietask_1\n",
      "Files not found for subject-0203 and mri_movietask_2\n",
      "Files not found for subject-0204 and mri_movietask_2\n",
      "Files not found for subject-0204 and mri_movietask_2\n",
      "Files not found for subject-0209 and mri_movietask_2\n",
      "Files not found for subject-0209 and mri_movietask_2\n",
      "Files not found for subject-0216 and mri_movietask_1\n",
      "Files not found for subject-0216 and mri_movietask_1\n",
      "Files not found for subject-0219 and mri_movietask_1\n",
      "Files not found for subject-0219 and mri_movietask_1\n",
      "Files not found for subject-0228 and mri_movietask_2\n",
      "Files not found for subject-0228 and mri_movietask_2\n",
      "Files not found for subject-0231 and mri_movietask_3\n",
      "Files not found for subject-0231 and mri_movietask_3\n",
      "Files not found for subject-0237 and mri_movietask_2\n",
      "Files not found for subject-0237 and mri_movietask_2\n",
      "Files not found for subject-0239 and mri_movietask_2\n",
      "Files not found for subject-0239 and mri_movietask_2\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Could not interpret input 'activation_mean'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-49ff4768ace9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmri_movietask\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmri_movietasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Create subplots for each task\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m     \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mviolinplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'activation_mean'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'motion_parameter'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtask_dfs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"quart\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Violin Plot for {mri_movietask}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Activation (Mean)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mviolinplot\u001b[0;34m(data, x, y, hue, order, hue_order, bw, cut, scale, scale_hue, gridsize, width, inner, split, dodge, orient, linewidth, color, palette, saturation, ax, **kwargs)\u001b[0m\n\u001b[1;32m   2303\u001b[0m ):\n\u001b[1;32m   2304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2305\u001b[0;31m     plotter = _ViolinPlotter(x, y, hue, data, order, hue_order,\n\u001b[0m\u001b[1;32m   2306\u001b[0m                              \u001b[0mbw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale_hue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2307\u001b[0m                              \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdodge\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, data, order, hue_order, bw, cut, scale, scale_hue, gridsize, width, inner, split, dodge, orient, linewidth, color, palette, saturation)\u001b[0m\n\u001b[1;32m    899\u001b[0m                  color, palette, saturation):\n\u001b[1;32m    900\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 901\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    902\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    903\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimate_densities\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale_hue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[0;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[1;32m    539\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m                     \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Could not interpret input '{var}'\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m             \u001b[0;31m# Figure out the plotting orientation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Could not interpret input 'activation_mean'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the directory containing the CSV files with correlation data\n",
    "data_dir = 'analysis/motion'\n",
    "\n",
    "# Initialize an empty list to store the separate DataFrames for each 'mri_movietask'\n",
    "task_dfs = []\n",
    "\n",
    "# Iterate through files in the data directory\n",
    "for subject_name in os.listdir('analysis/roi_estimations'):\n",
    "    if not subject_name.endswith('.csv'):\n",
    "        continue\n",
    "    \n",
    "    # Extract the subject name from the activation data filename\n",
    "    subject_name = subject_name.split('_')[0]\n",
    "    \n",
    "    # Initialize an empty DataFrame to store the combined data for the current subject\n",
    "    combined_df = pd.DataFrame()\n",
    "    \n",
    "    # Loop through MRI movie tasks\n",
    "    for mri_movietask in ['mri_movietask_1', 'mri_movietask_2', 'mri_movietask_3']:\n",
    "        # Define paths to activation and motion data files\n",
    "        activation_data_path = f'analysis/roi_estimations/{subject_name}_{mri_movietask}_smoothed_filtered_func_data_clean_MNI.nii.csv'\n",
    "        motion_data_path = f'pulled-data-MRI/{subject_name}/{mri_movietask}/mc/prefiltered_func_data_mcf_rel.rms'\n",
    "        \n",
    "        # Check if both files exist before proceeding\n",
    "        if os.path.exists(activation_data_path) and os.path.exists(motion_data_path):\n",
    "            # Load activation and motion data into DataFrames\n",
    "            activation_df = pd.read_csv(activation_data_path, header=0)  # Assuming header is in the first row\n",
    "            motion_df = pd.read_csv(motion_data_path, header=None, names=['motion_parameter'])\n",
    "            \n",
    "            # Remove the first row (corresponding to the first time point) from activation data\n",
    "            activation_df = activation_df.iloc[1:]\n",
    "            \n",
    "            # Ensure that the dimensions match (number of rows)\n",
    "            if activation_df.shape[0] == motion_df.shape[0]:\n",
    "                # Calculate the mean of activation data across regions\n",
    "                activation_mean = activation_df.mean(axis=1)\n",
    "                \n",
    "                # Combine the activation and motion data into one DataFrame\n",
    "                combined_df = pd.concat([combined_df, pd.concat([activation_mean, motion_df], axis=1)], axis=0)\n",
    "            else:\n",
    "                print(f\"Dimensions of {activation_data_path} and {motion_data_path} do not match for {subject_name} and {mri_movietask}\")\n",
    "        else:\n",
    "            print(f\"Files not found for {subject_name} and {mri_movietask}\")\n",
    "    \n",
    "    # Append the combined DataFrame for the current subject to the list\n",
    "    task_dfs.append(combined_df)\n",
    "\n",
    "# Create a list of 'mri_movietask' labels\n",
    "mri_movietasks = ['mri_movietask_1', 'mri_movietask_2', 'mri_movietask_3']\n",
    "\n",
    "# Create a separate violin plot for each 'mri_movietask'\n",
    "plt.figure(figsize=(12, 6))\n",
    "for i, mri_movietask in enumerate(mri_movietasks):\n",
    "    plt.subplot(1, 3, i + 1)  # Create subplots for each task\n",
    "    sns.violinplot(x='activation_mean', y='motion_parameter', data=task_dfs[i], split=True, inner=\"quart\")\n",
    "    plt.title(f'Violin Plot for {mri_movietask}')\n",
    "    plt.xlabel('Activation (Mean)')\n",
    "    plt.ylabel('Motion Parameter')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regress out motion from ROI estimations using linear regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length mismatch between ROI activation and motion data for subject-0027\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (224, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0060\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (242, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0060\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (242, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0112\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (233, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0112\n",
      "ROI activation data shape: (295, 300)\n",
      "Motion data shape: (233, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0163\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (39, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0163\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (39, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0219\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (8, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0219\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (8, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0008\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0008\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0023\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0023\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0047\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0047\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0058\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0058\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0071\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0071\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0085\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0112\n",
      "ROI activation data shape: (295, 300)\n",
      "Motion data shape: (235, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0129\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (276, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0129\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (276, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0138\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0138\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0142\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (223, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0142\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (223, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0185\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0185\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0187\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0187\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0188\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0188\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0204\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0204\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0228\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0228\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0239\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0239\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0073\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0073\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0079\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (237, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0079\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (237, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0084\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (251, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0084\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (251, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0102\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (221, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0102\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (221, 6)\n",
      "Length mismatch between ROI activation and motion data for subject-0112\n",
      "ROI activation data shape: (235, 300)\n",
      "Motion data shape: (295, 6)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Directory where ROI activation data is stored\n",
    "activation_directory = 'analysis/roi_estimations'\n",
    "\n",
    "# Directory to store motion-regressed ROI estimations\n",
    "output_directory = 'analysis/roi_estimations/motion'\n",
    "\n",
    "# Delete the output directory if it already exists (including all contents)\n",
    "if os.path.exists(output_directory):\n",
    "    shutil.rmtree(output_directory)\n",
    "\n",
    "# Create the output directory\n",
    "os.makedirs(output_directory)\n",
    "\n",
    "# Loop through MRI movie tasks\n",
    "for mri_movietask in ['mri_movietask_1', 'mri_movietask_2', 'mri_movietask_3']:\n",
    "    # Get a list of all CSV files in the activation_directory\n",
    "    csv_files = [file for file in os.listdir(activation_directory) if file.endswith('.csv')]\n",
    "\n",
    "    # Loop through the CSV files\n",
    "    for csv_file in csv_files:\n",
    "        # Extract the subject name from the activation data filename\n",
    "        subject_name = csv_file.split('_')[0]\n",
    "\n",
    "        # Check if the subject name is valid for the current MRI movie task\n",
    "        if os.path.exists(f'pulled-data-MRI/{subject_name}/{mri_movietask}'):\n",
    "            # Define paths to activation and motion data files\n",
    "            activation_data_path = os.path.join(activation_directory, csv_file)\n",
    "            motion_data_path = f'pulled-data-MRI/{subject_name}/{mri_movietask}/mc/prefiltered_func_data_mcf.par'\n",
    "\n",
    "            try:\n",
    "                # Load ROI activation data with header row\n",
    "                roi_activation_data = pd.read_csv(activation_data_path, header=0)\n",
    "                # Read the motion data text file\n",
    "                with open(motion_data_path, 'r') as file:\n",
    "                    lines = file.readlines()\n",
    "            except FileNotFoundError as e:\n",
    "                # Handle the case where the file is not found for either motion or activation data\n",
    "                if motion_data_path not in str(e):\n",
    "                    print(f\"Motion data file not found: {motion_data_path}\")\n",
    "                if activation_data_path not in str(e):\n",
    "                    print(f\"Activation data file not found: {activation_data_path}\")\n",
    "\n",
    "            # Split each line into a list of values and store them in a DataFrame\n",
    "            motion_data = pd.DataFrame([line.split() for line in lines], dtype=float)\n",
    "\n",
    "            # Check dimensions of both datasets\n",
    "            if len(motion_data) != len(roi_activation_data):\n",
    "                print(f\"Length mismatch between ROI activation and motion data for {subject_name}\")\n",
    "                print(f\"ROI activation data shape: {roi_activation_data.shape}\")\n",
    "                print(f\"Motion data shape: {motion_data.shape}\")\n",
    "                continue  # Skip this subject and move to the next one\n",
    "\n",
    "            # Convert lists to NumPy arrays with the appropriate data type (float)\n",
    "            motion_data = np.array(motion_data, dtype=float)\n",
    "            roi_activation_data = np.array(roi_activation_data, dtype=float)\n",
    "\n",
    "            # Regress out all six motion parameters from ROI activation\n",
    "            constant_term = np.ones((len(roi_activation_data), 1)) # intercept\n",
    "            X = np.concatenate((constant_term, motion_data), axis=1)\n",
    "            model = sm.OLS(roi_activation_data, X).fit() # sm.OLS(dependent, independent)\n",
    "            residual_roi_activation = model.resid # This is the part of the ROI activation data that remains uncorrelated with the motion parameters after accounting for the linear relationship\n",
    "\n",
    "            # Save the residual ROI activation to a new CSV file\n",
    "            output_file = f'{subject_name}_{mri_movietask}_motion_regressed.csv'\n",
    "            output_path = os.path.join(output_directory, output_file)\n",
    "            pd.DataFrame(residual_roi_activation).to_csv(output_path, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There are some extra files in output directory because the above script still analyzed the subject sessions which were outliers in mean signal. See: excluded_data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prefixes in output_directory but not in activation_directory:\n",
      "subject-0128_mri_movietask_3\n",
      "subject-0203_mri_movietask_1\n",
      "subject-0157_mri_movietask_1\n",
      "subject-0128_mri_movietask_2\n",
      "subject-0203_mri_movietask_2\n",
      "subject-0158_mri_movietask_2\n",
      "subject-0114_mri_movietask_3\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "activation_directory = 'analysis/roi_estimations'\n",
    "output_directory = 'analysis/roi_estimations/motion'\n",
    "\n",
    "def extract_prefix(filename):\n",
    "    parts = filename.split('_')\n",
    "    if len(parts) >= 4:\n",
    "        return '_'.join(parts[:4])\n",
    "    else:\n",
    "        return filename\n",
    "\n",
    "# Get a list of filenames in both directories\n",
    "activation_files = os.listdir(activation_directory)\n",
    "output_files = os.listdir(output_directory)\n",
    "\n",
    "# Extract the prefixes from the filenames in both directories\n",
    "activation_prefixes = set(map(extract_prefix, activation_files))\n",
    "output_prefixes = set(map(extract_prefix, output_files))\n",
    "\n",
    "# Find the prefixes that are in output_directory but not in activation_directory\n",
    "output_only_prefixes = output_prefixes - activation_prefixes\n",
    "\n",
    "# Display the differences\n",
    "print(\"Prefixes in output_directory but not in activation_directory:\")\n",
    "for prefix in output_only_prefixes:\n",
    "    print(prefix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Iterate through the output_directory and remove files with matching prefixes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed: subject-0157_mri_movietask_1_motion_regressed.csv\n",
      "Removed: subject-0203_mri_movietask_1_motion_regressed.csv\n",
      "Removed: subject-0128_mri_movietask_2_motion_regressed.csv\n",
      "Removed: subject-0158_mri_movietask_2_motion_regressed.csv\n",
      "Removed: subject-0203_mri_movietask_2_motion_regressed.csv\n",
      "Removed: subject-0114_mri_movietask_3_motion_regressed.csv\n",
      "Removed: subject-0128_mri_movietask_3_motion_regressed.csv\n",
      "File removal completed.\n"
     ]
    }
   ],
   "source": [
    "for filename in os.listdir(output_directory):\n",
    "    prefix = extract_prefix(filename)\n",
    "    if prefix in output_only_prefixes:\n",
    "        file_path = os.path.join(output_directory, filename)\n",
    "        os.remove(file_path)\n",
    "        print(f\"Removed: {filename}\")\n",
    "\n",
    "print(\"File removal completed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next: Organize subject session data into each unique frame-topic combination"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}