probe_extraction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data extraction for probe questions** \n",
    "\n",
    "This code aims to extract usefull information from the experiment's CSV-files. The goal is to retrive the probes and the answers for each subject and session. These needs to be aligned with the time for eeg recordings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "# Enable inline plotting for Jupyter Notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Retrieve all dataframes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the path to the directory containing the CSV files\n",
    "path = r'/home/sivert/Documents/Specialization_project/Datasets/jin2019/study1/raw/beh/'\n",
    "\n",
    "# Dict to store dataframes\n",
    "dataframes = {}\n",
    "\n",
    "# Generate all the keys for the dict\n",
    "for i in range(1, 31):\n",
    "    for j in range(1, 3):\n",
    "        file_name = f'subject-{i}_s{j}.csv'\n",
    "        file_path = os.path.join(path, file_name)\n",
    "        key = f'subject-{i}'\n",
    "        if os.path.exists(file_path):\n",
    "            if key not in dataframes:\n",
    "                dataframes[key] = {}\n",
    "            dataframes[key][f'session-{j}'] = pd.read_csv(file_path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Select relevant columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of relevant columns\n",
    "relevant_columns = ['probe_on', 'response_probe_content', 'practice_on', 'block_name']\n",
    "\n",
    "# Drop all irrelevant columns in the dataframes\n",
    "for subject, sessions in dataframes.items():\n",
    "    for session, df in sessions.items():\n",
    "        dataframes[subject][session] = dataframes[subject][session][relevant_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remove sessions with practice on**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subjects and sessions with practice on:\n",
      "Subject: 1, Session: 1\n",
      "Subject: 18, Session: 2\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probe_on</th>\n",
       "      <th>response_probe_content</th>\n",
       "      <th>block_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [probe_on, response_probe_content, block_name]\n",
       "Index: []"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize an empty list to store the subject and session numbers\n",
    "subject_session_with_practice = []\n",
    "\n",
    "# Iterate through the dataframes to find the required datasets\n",
    "for subject, sessions in dataframes.items():\n",
    "    for session, df in sessions.items():\n",
    "        if df['practice_on'].eq(1).any():\n",
    "            subject_number = int(subject.split('-')[1])\n",
    "            session_number = int(session.split('-')[1])\n",
    "            subject_session_with_practice.append((subject_number, session_number))\n",
    "\n",
    "#Print out the subject and session numbers with practice\n",
    "print('Subjects and sessions with practice on:')\n",
    "for subject, session in subject_session_with_practice:\n",
    "    print(f'Subject: {subject}, Session: {session}')\n",
    "\n",
    "# remove the subjects and sessions with practice and drop the column as it is unnecessary at this point.\n",
    "for subject, sessions in dataframes.items():\n",
    "    for session, df in sessions.items():\n",
    "        if (int(subject.split('-')[1]), int(session.split('-')[1])) in subject_session_with_practice:\n",
    "            dataframes[subject][session] = dataframes[subject][session][dataframes[subject][session].practice_on.eq(0)]\n",
    "        dataframes[subject][session] = dataframes[subject][session].drop(columns=['practice_on'])\n",
    "\n",
    "dataframes['subject-1']['session-1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Make task indicator**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "# Function to replace block_name content\n",
    "def replace_block_name(block_name):\n",
    "    if re.match(r'^vs$', block_name):\n",
    "        return 0\n",
    "    elif re.match(r'^sart\\d*$', block_name):\n",
    "        return 1\n",
    "    else:\n",
    "        return block_name\n",
    "\n",
    "# Apply the function to all dataframes\n",
    "for subject, sessions in dataframes.items():\n",
    "    for session, df in sessions.items():\n",
    "        df['block_name'] = df['block_name'].apply(replace_block_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>probe_on</th>\n",
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       "      <th>6</th>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    probe_on response_probe_content  block_name\n",
       "0          0                    NaN           1\n",
       "1          0                    NaN           1\n",
       "2          0                    NaN           1\n",
       "3          0                    NaN           1\n",
       "4          0                    NaN           1\n",
       "5          0                    NaN           1\n",
       "6          0                    NaN           1\n",
       "7          1                      2           1\n",
       "8          0                      2           1\n",
       "9          0                      2           1\n",
       "10         0                      2           1\n",
       "11         0                      2           1\n",
       "12         0                      2           1\n",
       "13         0                      2           1\n",
       "14         0                      2           1\n",
       "15         0                      2           1\n",
       "16         0                      2           1\n",
       "17         0                      2           1\n",
       "18         0                      2           1\n",
       "19         0                      2           1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframes['subject-2']['session-1'].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Deal with nan values and strings**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_715367/1055512194.py:12: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  df = df.applymap(convert_and_replace)\n"
     ]
    },
    {
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       "  <thead>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>825 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     probe_on  response_probe_content  block_name\n",
       "0           0                       0           0\n",
       "1           0                       0           0\n",
       "2           0                       0           0\n",
       "3           0                       0           0\n",
       "4           0                       0           0\n",
       "..        ...                     ...         ...\n",
       "820         0                       3           1\n",
       "821         0                       3           1\n",
       "822         0                       3           1\n",
       "823         0                       3           1\n",
       "824         1                       1           1\n",
       "\n",
       "[825 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to replace NaN values and ints outside [1,5] with zero and ensure all values are integers\n",
    "def replace_nan_and_strings(df):\n",
    "    def convert_and_replace(x):\n",
    "        if pd.isna(x):\n",
    "            return 0\n",
    "        try:\n",
    "            x = int(x)\n",
    "        except ValueError:\n",
    "            return 0\n",
    "        return x if 1 <= x <= 6 else 0\n",
    "\n",
    "    df = df.applymap(convert_and_replace)\n",
    "    return df\n",
    "\n",
    "# Apply the function to all dataframes\n",
    "for subject, sessions in dataframes.items():\n",
    "    for session, df in sessions.items():\n",
    "        dataframes[subject][session] = replace_nan_and_strings(df)\n",
    "\n",
    "# Display the updated dataframe for subject-3 session-1\n",
    "dataframes['subject-3']['session-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    probe_on  response_probe_content  block_name\n",
       "0          0                       0           1\n",
       "1          0                       0           1\n",
       "2          0                       0           1\n",
       "3          0                       0           1\n",
       "4          0                       0           1\n",
       "5          0                       0           1\n",
       "6          0                       0           1\n",
       "26         1                       0           1\n",
       "27         0                       0           1\n",
       "28         0                       0           1\n",
       "29         0                       0           1\n",
       "30         0                       0           1\n",
       "31         0                       0           1\n",
       "32         0                       0           1\n",
       "33         0                       0           1\n",
       "34         0                       0           1\n",
       "35         0                       0           1\n",
       "36         0                       0           1\n",
       "37         0                       0           1\n",
       "38         0                       0           1\n",
       "39         0                       0           1\n",
       "40         0                       0           1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the updated dataframe for subject-1 session-1\n",
    "dataframes['subject-2']['session-1']\n",
    "\n",
    "# show rows with response_probe_content equal to 0\n",
    "dataframes['subject-2']['session-1'][dataframes['subject-2']['session-1'].response_probe_content.eq(0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Export new event data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the path to the new directory\n",
    "new_path = os.path.join(path, 'new_events')\n",
    "\n",
    "# Create the directory if it does not exist\n",
    "if not os.path.exists(new_path):\n",
    "    os.makedirs(new_path)\n",
    "\n",
    "# Export each dataframe to a CSV file in the new directory\n",
    "for subject, sessions in dataframes.items():\n",
    "    for session, df in sessions.items():\n",
    "        file_name = f'{subject}_{session}_events.csv'\n",
    "        file_path = os.path.join(new_path, file_name)\n",
    "        df.to_csv(file_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     probe_on  response_probe_content  block_name\n",
      "0           0                       0           1\n",
      "1           0                       0           1\n",
      "2           0                       0           1\n",
      "3           0                       0           1\n",
      "4           0                       0           1\n",
      "..        ...                     ...         ...\n",
      "820         0                       3           0\n",
      "821         1                       1           0\n",
      "822         0                       1           0\n",
      "823         0                       1           0\n",
      "824         0                       1           0\n",
      "\n",
      "[825 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "def load_dataframes(path):\n",
    "    dataframes = {}\n",
    "    for i in range(1, 31):\n",
    "        for j in range(1, 3):\n",
    "            file_name = f'subject-{i}_session-{j}_events.csv'\n",
    "            file_path = os.path.join(path, file_name)\n",
    "            key = f'subject-{i}'\n",
    "            if os.path.exists(file_path):\n",
    "                if key not in dataframes:\n",
    "                    dataframes[key] = {}\n",
    "                dataframes[key][f'session-{j}'] = pd.read_csv(file_path)\n",
    "    return dataframes\n",
    "\n",
    "# Define the path to the directory containing the CSV files\n",
    "path = '/home/sivert/Documents/Specialization_project/Datasets/jin2019/study1/raw/beh/new_events'\n",
    "\n",
    "# Load the dataframes\n",
    "dataframes = load_dataframes(path)\n",
    "\n",
    "# Example usage: Accessing a specific dataframe\n",
    "print(dataframes['subject-2']['session-1'])"
   ]
  }
 ],
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