04_epoching.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# epoching\n",
    "\n",
    "Here we form epochs out of the continuous, cleaned data.\n",
    "\n",
    "The workflow is as follows:\n",
    "\n",
    "1. Load the behavioral data for a participant. Each row in the dataframe reflects one drawn sample (across all tasks)\n",
    "    1. we drop the description task for now\n",
    "1. Load the events for that participant, and refine the events so that each row in the events array reflects one drawn sample\n",
    "1. Define an `EVENT_PARAMS` dictionary that contains all parameters of interest for each event. for example, a \"sample\" event may be described along multiple other dimensions such as what the following outcome was, the number of the sample in its sequence, etc. (this is defined in `utils.py`)\n",
    "1. Using the events array and the dataframe for a subject, re-define the \"code\" of each event to encode information along all dimensions of the `EVENT_PARAMS` dictionary. For example, the code `1` (reflecting a sample drawn) can become a `12443` (reflecting a sample drawn on the left side, with an outcome of \"9\", etc.) (this is defined in `utils.py`)\n",
    "1. Use the refined events array to form epochs for further analysis\n",
    "\n",
    "---\n",
    "\n",
    "The meaning of the markers in the events file can be taken from the\n",
    "`provide_trigger_dict` function.\n",
    "\n",
    "Note that the markers are shifted to distinguish by tasks:\n",
    "\n",
    "- AF          += 100\n",
    "- AV          += 200\n",
    "- YF          += 300\n",
    "- YV          += 400\n",
    "- description += 500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-27T10:04:24.083405Z",
     "start_time": "2021-01-27T10:04:23.649022Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib qt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-27T10:04:26.490643Z",
     "start_time": "2021-01-27T10:04:24.084854Z"
    }
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import multiprocessing\n",
    "import os.path as op\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import mne\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from utils import (\n",
    "    BIDS_ROOT,\n",
    "    EVENT_ID,\n",
    "    EVENT_PARAMS,\n",
    "    TASK_BASED_INCREMENTS,\n",
    "    TASK_NAME_MAP,\n",
    "    bin_outcomes,\n",
    "    find_bad_epochs,\n",
    "    provide_trigger_dict,\n",
    "    spm_orth,\n",
    "    task_not_present_for_subject,\n",
    "    add_binned_outcomes_to_df,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-27T10:04:26.588390Z",
     "start_time": "2021-01-27T10:04:26.584134Z"
    }
   },
   "outputs": [],
   "source": [
    "# IO: Where to find the data\n",
    "# Where to find event data, and behavioral data\n",
    "fname_events_template = op.join(\n",
    "    BIDS_ROOT, \"derivatives\", \"sub-{0:02}\", \"sub-{0:02}_concat_eeg-eve.fif.gz\"\n",
    ")\n",
    "\n",
    "fname_cleanraw_template = op.join(\n",
    "    BIDS_ROOT, \"derivatives\", \"sub-{0:02}\", \"sub-{0:02}_clean_eeg-raw.fif.gz\"\n",
    ")\n",
    "\n",
    "fname_behavioral = op.join(BIDS_ROOT, \"code\", \"behavioral_data.csv\")\n",
    "\n",
    "# Where to save the epochs\n",
    "fname_epo_template = op.join(\n",
    "    BIDS_ROOT, \"derivatives\", \"sub-{0:02}\", \"sub-{0:02}_epochs-epo.fif.gz\"\n",
    ")\n",
    "\n",
    "\n",
    "# Pack all names in a dict\n",
    "name_templates = dict()\n",
    "name_templates[\"events\"] = fname_events_template\n",
    "name_templates[\"cleanraw\"] = fname_cleanraw_template\n",
    "name_templates[\"epochs\"] = fname_epo_template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-27T10:04:26.895445Z",
     "start_time": "2021-01-27T10:04:26.589790Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define other variables, read overall behavioral data\n",
    "tasks = [\"ActiveFixed\", \"ActiveVariable\", \"YokedFixed\", \"YokedVariable\", \"description\"]\n",
    "\n",
    "df = pd.read_csv(fname_behavioral)\n",
    "\n",
    "\n",
    "def get_df_for(df, subject, task):\n",
    "    return df[(df[\"subject\"] == subject) & (df[\"task\"] == task)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-27T09:10:00.581816Z",
     "start_time": "2021-01-27T09:10:00.562226Z"
    }
   },
   "outputs": [],
   "source": [
    "def drop_error_events(events):\n",
    "    \"\"\"Drop error rows from events array.\"\"\"\n",
    "    trigger_dict = provide_trigger_dict()\n",
    "\n",
    "    # Collect indices of events to delete\n",
    "    del_idxs = list()\n",
    "    for task, increment in TASK_BASED_INCREMENTS.items():\n",
    "        ERROR_CODE = ord(trigger_dict[\"trig_error\"]) + increment\n",
    "        NEW_TRL_CODE = ord(trigger_dict[\"trig_new_trl\"]) + increment\n",
    "\n",
    "        err_idxs = np.nonzero(events[:, -1] == ERROR_CODE)[0]\n",
    "        new_trl_idxs = np.nonzero(events[:, -1] == NEW_TRL_CODE)[0]\n",
    "\n",
    "        for err_i in err_idxs:\n",
    "            # event after error is always new_trl\n",
    "            new_trl_i = err_i + 1\n",
    "            assert events[new_trl_i, -1] == NEW_TRL_CODE\n",
    "\n",
    "            # find idx of new_trl event before error\n",
    "            prev = list(new_trl_idxs).index(new_trl_i) - 1\n",
    "            idx = new_trl_idxs[prev]\n",
    "\n",
    "            # we need to delete all rows from that new trial\n",
    "            # to the error (inclusively)\n",
    "            del_idxs.extend(np.arange(idx, err_i + 1))\n",
    "\n",
    "    # Delete the triggers associated with errors\n",
    "    new_events = np.delete(events, del_idxs, axis=0)\n",
    "    print(\"deleted {} events.\".format(len(del_idxs)))\n",
    "\n",
    "    return new_events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-27T09:10:00.752221Z",
     "start_time": "2021-01-27T09:10:00.746603Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_codes_across_tasks(codes):\n",
    "    \"\"\"Get list of corresponding codes.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    codes : list of int\n",
    "        The event codes we are interested to find in each task.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    codes_across_tasks : list of int\n",
    "        The original codes mapped to the value they have in the\n",
    "        different tasks. For example, the event code 1 is 101\n",
    "        in the AF task, 201 in the AV task, 301 in the YF task, etc.\n",
    "\n",
    "    See also\n",
    "    --------\n",
    "    utils.TASK_BASED_INCREMENTS\n",
    "\n",
    "    \"\"\"\n",
    "    codes_across_tasks = list()\n",
    "    for code in codes:\n",
    "        for task, increment in TASK_BASED_INCREMENTS.items():\n",
    "            codes_across_tasks.append(code + increment)\n",
    "\n",
    "    return codes_across_tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-27T09:10:00.952029Z",
     "start_time": "2021-01-27T09:10:00.935903Z"
    }
   },
   "outputs": [],
   "source": [
    "def _classify_switch(df, event_idx):\n",
    "    \"\"\"Classify a sampling action into a kind of switch behavior.\n",
    "\n",
    "    Each row in `df` as indexed by `event_idx` corresponds to a sampling\n",
    "    action. A sampling action can be either left (0) or right (1) and is\n",
    "    part of a sequence of sampling actions.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df : pd.DataFrame\n",
    "        The data for all events.\n",
    "    event_idx : int\n",
    "        The event_idx into the data for the current event to be classified.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    switch : str\n",
    "        One of ['no_switch', 'pre_switch', 'post_switch', 'mixed_switch'].\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    Each sampling action (left or right) is first evaluated whether it is\n",
    "    the same as the previous (=stay), or different(=switch). Next, the\n",
    "    following sampling action is evaluated according to whether it is the\n",
    "    same (=stay) as or different (=switch) from the current sampling action.\n",
    "    This set of two binary values is triaged into a kind of switch behavior:\n",
    "    stay+stay=no_switch, stay+switch=pre_switch, switch+stay=post_switch, and\n",
    "    switch+switch=mixed_switch.\n",
    "\n",
    "    For the first sample in a sequence it is assumed that the previous\n",
    "    sampling action was the same (=stay). For the last sample in a sequence\n",
    "    it is assumed that the next action would be the same (=stay).\n",
    "\n",
    "    \"\"\"\n",
    "    # Get current sample index in the sequence to know if we can look\n",
    "    # ahead or behind in the sequence\n",
    "    sample_1idx = df.loc[event_idx][\"sample\"] + 1  # adjust for 0-based indexing\n",
    "    n_samples = df.loc[event_idx][\"n_samples\"]\n",
    "    this_action = df.loc[event_idx][\"action\"]\n",
    "\n",
    "    # What happend PRIOR to current index, and what does this mean to current\n",
    "    # action\n",
    "    current_action = \"stay\"\n",
    "    if sample_1idx == 1:\n",
    "        pass\n",
    "    elif this_action != df.loc[event_idx - 1][\"action\"]:\n",
    "        current_action = \"switch\"\n",
    "\n",
    "    # What happend AFTER current index\n",
    "    next_action = \"stay\"\n",
    "    if sample_1idx == n_samples:\n",
    "        pass\n",
    "    elif this_action != df.loc[event_idx + 1][\"action\"]:\n",
    "        next_action = \"switch\"\n",
    "\n",
    "    # Classify current action given the previous and next action\n",
    "    if current_action == \"stay\" and next_action == \"stay\":\n",
    "        switch = \"no_switch\"\n",
    "    elif current_action == \"stay\" and next_action == \"switch\":\n",
    "        switch = \"pre_switch\"\n",
    "    elif current_action == \"switch\" and next_action == \"stay\":\n",
    "        switch = \"post_switch\"\n",
    "    elif current_action == \"switch\" and next_action == \"switch\":\n",
    "        switch = \"mixed_switch\"\n",
    "\n",
    "    return switch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-27T09:10:01.155421Z",
     "start_time": "2021-01-27T09:10:01.148609Z"
    }
   },
   "outputs": [],
   "source": [
    "def _classify_timing(event_info, first_last_n):\n",
    "    \"\"\"Classify timing of an event in a sampling sequence.\n",
    "\n",
    "    Each event is considered as a part of a sampling sequence. The\n",
    "    `first_last_n` first and last samples in the sequence will be classified\n",
    "    as \"early\" and \"late\" respectively. The samples in between will be\n",
    "    classified as \"mid\". For sampling sequences that are too short as to\n",
    "    clearly divide the samples into \"early\" and \"late\" (i.e., without having\n",
    "    samples that would be both early and late), all events are classified as\n",
    "    \"mixed_timing\". This latter issue will occur often for large values in\n",
    "    `first_last_n`.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    event_info : pd.Series | dict\n",
    "        The information about this event\n",
    "    first_last_n : positive int > 0\n",
    "        First n events to be classified as \"early\", and correspondingly\n",
    "        last n events to be classified as \"late\".\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    timing : str\n",
    "        One of ['early', 'mid', 'late', 'mixed_timing'].\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    Setting `first_last_n` to 1 does not make `_classify_timing` equal to\n",
    "    `_classify_order`! Most notably, the latter will ignore all trials with\n",
    "    1 or 2 samples. The \"mid\" and \"midth\" classes are different as well, with\n",
    "    `_classify_timing` summarizing across all \"middle\" samples and\n",
    "    `_classify_order` only taking the mid most sample. Hence, the\n",
    "    \"mixed_timing\" and \"mixed_order\" classes do not lign up either. However,\n",
    "    for coarse comparisons, the \"early\"/\"first\" and \"late\"/\"last\" classes\n",
    "    are approaching one another, when the `first_last_n` parameter is 1.\n",
    "\n",
    "    See also\n",
    "    --------\n",
    "    _classify_order\n",
    "\n",
    "    \"\"\"\n",
    "    if (event_info[\"sample\"] >= event_info[\"n_samples\"]) or (event_info[\"sample\"] < 0):\n",
    "        raise RuntimeError(\n",
    "            \"Encountered implausible sample value: {}/{}\\n\"\n",
    "            \"Samples are 0-based indexed and must be < n_samples \"\n",
    "            \"and > 0.\".format(event_info[\"sample\"], event_info[\"n_samples\"])\n",
    "        )\n",
    "\n",
    "    # Cannot divide the sampling sequence into two non-overlapping parts\n",
    "    if first_last_n * 2 > event_info[\"n_samples\"]:\n",
    "        return \"mixed_timing\"\n",
    "\n",
    "    # Classify the timing\n",
    "    # NOTE: nth \"sample\" is 0-indexed ... n_samples is a count\n",
    "    diff = event_info[\"n_samples\"] - event_info[\"sample\"]\n",
    "    if diff <= first_last_n:\n",
    "        timing = \"late\"\n",
    "    elif diff >= ((event_info[\"n_samples\"] + 1) - first_last_n):\n",
    "        timing = \"early\"\n",
    "    else:\n",
    "        timing = \"mid\"\n",
    "\n",
    "    return timing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-27T09:10:02.376546Z",
     "start_time": "2021-01-27T09:10:02.368292Z"
    }
   },
   "outputs": [],
   "source": [
    "def _classify_order(event_info):\n",
    "    \"\"\"Classify a sample to be the first, last, or 'midth' in a sequence.\n",
    "\n",
    "    Sequences with an even number of samples have an ambiguous middle. In\n",
    "    these cases, the later of the two middle samples will be classified as\n",
    "    the \"midth\" sample. All other samples and samples of sequences with\n",
    "    length smaller than 3 are classified as \"mixed_order\".\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    event_info : pd.Series | dict\n",
    "        The information about this event.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    order : str\n",
    "        One of ['first', 'midth', 'last', 'mixed_order']\n",
    "\n",
    "    See also\n",
    "    --------\n",
    "    _classify_timing\n",
    "\n",
    "    \"\"\"\n",
    "    if event_info[\"n_samples\"] < 3:\n",
    "        return \"mixed_order\"\n",
    "    elif event_info[\"sample\"] == 0:\n",
    "        return \"first\"\n",
    "    elif event_info[\"sample\"] + 1 == event_info[\"n_samples\"]:\n",
    "        return \"last\"\n",
    "    elif event_info[\"sample\"] == np.floor(event_info[\"n_samples\"] / 2):\n",
    "        return \"midth\"\n",
    "    else:\n",
    "        return \"mixed_order\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-27T09:10:02.668662Z",
     "start_time": "2021-01-27T09:10:02.653759Z"
    }
   },
   "outputs": [],
   "source": [
    "def _classify_half(event_info, break_tie):\n",
    "    \"\"\"Classify which half of a sequence a sample belongs to.\n",
    "\n",
    "    For sequences with an uneven number of samples, the first half will be\n",
    "    one sample longer than the second half. For sequences of length 1 or less\n",
    "    we will return \"mixed_half\".\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    event_info : pd.Series | dict\n",
    "        The information about this event\n",
    "    break_tie : str\n",
    "        Method to break tie in case of sequences with uneven number of\n",
    "        samples. Can be one of ['ceil', 'floor', 'rand']. For 'ceil', the\n",
    "        first half will be longer. For 'floor', the second half will be\n",
    "        longer. For 'rand' each event is randomly either processed with\n",
    "        'ceil' or 'floor'.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    half : str\n",
    "        One of ['first_half', 'second_half', 'mixed_half']\n",
    "\n",
    "    \"\"\"\n",
    "    if event_info[\"n_samples\"] <= 1:\n",
    "        return \"mixed_half\"\n",
    "\n",
    "    if break_tie == \"ceil\":\n",
    "        tiebreak = np.ceil\n",
    "    elif break_tie == \"floor\":\n",
    "        tiebreak = np.floor\n",
    "    elif break_tie == \"rand\":\n",
    "\n",
    "        def tiebreak(inp):\n",
    "            if np.random.random() < 0.5:\n",
    "                return np.ceil(inp)\n",
    "            return np.floor(inp)\n",
    "\n",
    "    else:\n",
    "        raise ValueError(\"Unexpected value for `break_tie`\")\n",
    "\n",
    "    if event_info[\"sample\"] + 1 <= tiebreak(event_info[\"n_samples\"] / 2):\n",
    "        return \"first_half\"\n",
    "\n",
    "    return \"second_half\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-04T20:43:09.699908Z",
     "start_time": "2020-11-04T20:43:09.695358Z"
    }
   },
   "outputs": [],
   "source": [
    "def _check_mapping(mapping):\n",
    "    \"\"\"Check whether a mapping corresponds to EVENT_PARAMS.\"\"\"\n",
    "    for key, val in mapping.items():\n",
    "        if key not in EVENT_PARAMS:\n",
    "            raise ValueError(\n",
    "                'Key \"{}\" not in EVENT_PARAMS. Must be one of \"{}\"'.format(\n",
    "                    key, \", \".join(EVENT_PARAMS.keys())\n",
    "                )\n",
    "            )\n",
    "        expected_vals = EVENT_PARAMS.get(key)\n",
    "        if val not in expected_vals:\n",
    "            raise ValueError(\n",
    "                'Unexpected value \"{}\" for key \"{}\". Must be one of \"{}\"'.format(\n",
    "                    val, key, \", \".join(expected_vals)\n",
    "                )\n",
    "            )\n",
    "    return mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-04T20:43:11.036085Z",
     "start_time": "2020-11-04T20:43:11.031811Z"
    }
   },
   "outputs": [],
   "source": [
    "def _get_event_and_code(mapping):\n",
    "    \"\"\"Given a mapping, get the deterministic event and its code.\"\"\"\n",
    "    mapping = _check_mapping(mapping)\n",
    "\n",
    "    event_template = \"/\".join([\"{\" + \"{}\".format(key) + \"}\" for key in EVENT_PARAMS])\n",
    "    event = event_template.format(**mapping)\n",
    "\n",
    "    code = EVENT_ID[event]\n",
    "    return code, event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-04T20:43:12.813564Z",
     "start_time": "2020-11-04T20:43:12.791442Z"
    }
   },
   "outputs": [],
   "source": [
    "def convert_events(df, events):\n",
    "    \"\"\"Get new event codes and an event_id.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df : pd.DataFrame\n",
    "        Information about each event. Each row in the dataframe\n",
    "        corresponds to a row in the events array.\n",
    "    events : ndarray\n",
    "        The events with the original event codes.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    events : ndarray\n",
    "        The events with updated event codes.\n",
    "    event_id : dict\n",
    "        A dictionary mapping an event description to a code.\n",
    "\n",
    "    \"\"\"\n",
    "    df = df.reset_index(drop=True)\n",
    "    n_events = events.shape[0]\n",
    "    assert df.shape[0] == n_events\n",
    "    codes = list()\n",
    "    event_id = dict()\n",
    "    for event_idx in range(n_events):\n",
    "        # Get all information about this event\n",
    "        event_info = df.iloc[event_idx]\n",
    "\n",
    "        # Describe this event using our vocabulary\n",
    "        mapping = dict()\n",
    "        mapping[\"sampling\"] = \"active\" if event_info[\"task\"][0] == \"A\" else \"yoked\"\n",
    "        mapping[\"stopping\"] = \"fixed\" if event_info[\"task\"][1] == \"F\" else \"variable\"\n",
    "        mapping[\"direction\"] = \"left\" if event_info[\"action\"] == 0 else \"right\"\n",
    "        mapping[\"outcome\"] = \"out{}\".format(event_info[\"outcome\"])\n",
    "        mapping[\"outcome_bin\"] = \"bin{}\".format(event_info[\"outcome_bin\"])\n",
    "        mapping[\"outcome_bin_orth\"] = \"orthbin{}\".format(event_info[\"outcome_bin_orth\"])\n",
    "        mapping[\"timing\"] = _classify_timing(event_info, first_last_n=FIRST_LAST_N)\n",
    "        mapping[\"switch\"] = _classify_switch(df, event_idx)\n",
    "        mapping[\"order\"] = _classify_order(event_info)\n",
    "        mapping[\"half\"] = _classify_half(event_info, \"rand\")\n",
    "\n",
    "        # some sanity checks\n",
    "        if event_info[\"task\"][0] != \"A\":\n",
    "            assert event_info[\"task\"][0] == \"Y\"\n",
    "\n",
    "        if event_info[\"task\"][1] != \"F\":\n",
    "            assert event_info[\"task\"][1] == \"V\"\n",
    "\n",
    "        if event_info[\"action\"] != 0:\n",
    "            assert event_info[\"action\"] == 1\n",
    "\n",
    "        # Get a unique code for this type of event\n",
    "        _check_mapping(mapping)\n",
    "        code, event = _get_event_and_code(mapping)\n",
    "\n",
    "        # build up codes and id\n",
    "        codes.append(code)\n",
    "        if event not in event_id:\n",
    "            event_id[event] = code\n",
    "\n",
    "    # Overwrite old event codes with new ones\n",
    "    events[:, -1] = codes\n",
    "\n",
    "    return events, event_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find good N for classifying samples into groups of early/mid/late\n",
    "\n",
    "see `_classify_timing`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T08:08:49.775931Z",
     "start_time": "2020-06-15T08:08:49.370839Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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   "source": [
    "# Calculate optimal N for early/late/classification\n",
    "# checking the tradeoff n dropped epochs vs. balanced groups early/mid/late\n",
    "# NOTE: Need to calculate \"sample\" column +1 because it is 0-indexed\n",
    "arr = (\n",
    "    df.groupby([\"subject\", \"trial\", \"task\"])[\"sample\"]\n",
    "    .max()\n",
    "    .drop([\"AF\", \"DESC\", \"YF\", \"YV\"], level=2)\n",
    "    + 1\n",
    ").values\n",
    "\n",
    "# for each potential N (1...12) number of dropped epochs, and mean squared\n",
    "# error of an equal group size\n",
    "mses = list()\n",
    "drops = list()\n",
    "ns = range(1, 13)\n",
    "for n in ns:\n",
    "\n",
    "    ndrop = 0\n",
    "\n",
    "    nmid = list()\n",
    "    for val in arr:\n",
    "\n",
    "        # Check if this trial would be discarded\n",
    "        if 2 * n > val:\n",
    "            ndrop += 1\n",
    "            continue\n",
    "\n",
    "        # how many trials would be \"mid\"?\n",
    "        nmid.append(val - 2 * n)\n",
    "\n",
    "    # Calc mean squared error\n",
    "    if np.size(nmid) == 0:\n",
    "        mses.append(np.nan)\n",
    "    else:\n",
    "        mses.append(np.mean((np.array(nmid) - n) ** 2))\n",
    "    drops.append(ndrop)\n",
    "\n",
    "# Plot results\n",
    "fig, axs = plt.subplots(2, 1, sharex=True)\n",
    "axs.flat[0].plot(ns, mses)\n",
    "axs.flat[0].set_ylabel('MSE from\\n\"equal group size\"\\nearly/mid/late')\n",
    "axs.flat[1].plot(ns, drops)\n",
    "axs.flat[1].set_ylabel(\"n dropped trials\")\n",
    "axs.flat[1].set_xlabel(\"N\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Back to epoching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T09:12:34.293282Z",
     "start_time": "2020-06-15T09:12:34.282598Z"
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   },
   "outputs": [],
   "source": [
    "# epoching settings. sample event means onset of mask\n",
    "# and 0.8s after mask onset: number reward revealed for 0.5s\n",
    "# trial done at 1.3s\n",
    "TMIN = -0.5  # see: https://chat.mpib-berlin.mpg.de/arc/pl/1kp1i3qfebfuuxfyaph8fbrngo\n",
    "TMAX = 1.6\n",
    "BASELINE = None\n",
    "DOWNSAMPLE_FREQ = 250.0\n",
    "OVERWRITE = True\n",
    "\n",
    "# Number of first/last samples to define as early/late\n",
    "FIRST_LAST_N = 2\n",
    "\n",
    "# Event codes of interest\n",
    "trigger_dict = provide_trigger_dict()\n",
    "\n",
    "SAMPLE_L_CODE = ord(trigger_dict[\"trig_mask_out_l\"])\n",
    "SAMPLE_R_CODE = ord(trigger_dict[\"trig_mask_out_r\"])\n",
    "\n",
    "event_codes_oi = [SAMPLE_L_CODE, SAMPLE_R_CODE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T09:12:35.778745Z",
     "start_time": "2020-06-15T09:12:35.769270Z"
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   "outputs": [],
   "source": [
    "def run_parallel(subj):\n",
    "    \"\"\"Helper func to run the following snippet in parallel.\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    Will take the following variables from global space:\n",
    "    name_templates, OVERWRITE, TASK_NAME_MAP, df, event_codes_oi, TMIN,\n",
    "    TMAX, BASELINE, DOWNSAMPLE_FREQ\n",
    "\n",
    "    \"\"\"\n",
    "    # Loop over subjects\n",
    "    # for subj in tqdm(subjects):\n",
    "\n",
    "    # Handle overwriting\n",
    "    fname_epochs = name_templates[\"epochs\"].format(subj)\n",
    "    if op.exists(fname_epochs):\n",
    "        if not OVERWRITE:\n",
    "            # continue\n",
    "            return\n",
    "\n",
    "    # Get behavioral data\n",
    "    # Going through tasks, ignoring description for now\n",
    "    # Append all tasks for this subj into a single data frame\n",
    "    dfs = list()\n",
    "    for task in [\"ActiveFixed\", \"ActiveVariable\", \"YokedFixed\", \"YokedVariable\"]:\n",
    "\n",
    "        if task_not_present_for_subject(subj, task):\n",
    "            continue\n",
    "\n",
    "        task_short = TASK_NAME_MAP[task]\n",
    "        dfs.append(get_df_for(df, subj, task_short))\n",
    "\n",
    "    df_ = pd.concat(dfs)\n",
    "\n",
    "    # Read EEG Events for this subj\n",
    "    fname_events = name_templates[\"events\"].format(subj)\n",
    "    events = mne.read_events(fname_events)\n",
    "    events = drop_error_events(events)\n",
    "\n",
    "    # Get events of interest\n",
    "    codes = get_codes_across_tasks(event_codes_oi)\n",
    "    event_mask = np.isin(events[:, -1], codes)\n",
    "    events_oi = events[event_mask, :]\n",
    "\n",
    "    # Enrich the events of interest with additional information\n",
    "    events, event_id = convert_events(df_, events_oi)\n",
    "\n",
    "    # load raw data\n",
    "    fname_cleanraw = name_templates[\"cleanraw\"].format(subj)\n",
    "    raw = mne.io.read_raw_fif(fname_cleanraw, preload=True, verbose=0)\n",
    "\n",
    "    # Try to \"decimate\" the data instead of resampling (i.e., taking every\n",
    "    # nth sample). Data is already filtered at this point, so no\n",
    "    # aliasing artifacts occur\n",
    "    assert raw.info[\"lowpass\"] <= (DOWNSAMPLE_FREQ / 3)\n",
    "    sfreq = raw.info[\"sfreq\"]\n",
    "    assert sfreq == 1000\n",
    "    assert sfreq % DOWNSAMPLE_FREQ == 0\n",
    "    decim = int(sfreq / DOWNSAMPLE_FREQ)\n",
    "\n",
    "    # Make epochs and drop those overlapping with \"BAD\" annotated segments\n",
    "    # Dropped epochs can be seen in epochs.drop_log\n",
    "    epochs = mne.Epochs(\n",
    "        raw=raw,\n",
    "        events=events,\n",
    "        event_id=event_id,\n",
    "        tmin=TMIN,\n",
    "        tmax=TMAX,\n",
    "        baseline=BASELINE,\n",
    "        decim=decim,\n",
    "        reject_by_annotation=True,\n",
    "        preload=True,\n",
    "    )\n",
    "\n",
    "    # Find additional bad epochs and drop them\n",
    "    bad_epos = find_bad_epochs(epochs)\n",
    "    epochs.drop(bad_epos, reason=\"FASTER_AUTOMATIC\")\n",
    "\n",
    "    # Downsample epochs\n",
    "    # replaced the \"epochs.resample(...)\"\" call with decimation on 2020-07-11\n",
    "    # see `decim` parameter in mne.Epochs\n",
    "\n",
    "    # Save epochs\n",
    "    epochs.save(fname_epochs, overwrite=OVERWRITE)"
   ]
  },
  {
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   "execution_count": 112,
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    "ExecuteTime": {
     "end_time": "2020-06-15T09:35:47.760479Z",
     "start_time": "2020-06-15T09:12:36.194241Z"
    }
   },
   "outputs": [
    {
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     "text": [
      "deleted 127 events.\n",
      "deleted 339 events.\n",
      "1086 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 1086 events and 2001 original time points ...\n",
      "77 bad epochs dropped\n",
      "Dropped 17 epochs\n",
      "Overwriting existing file.\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "71 bad epochs dropped\n",
      "deleted 227 events.\n",
      "Dropped 63 epochs\n",
      "Overwriting existing file.\n",
      "deleted 52 events.\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "169 bad epochs dropped\n",
      "Dropped 46 epochs\n",
      "1099 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 1099 events and 2001 original time points ...\n",
      "30 bad epochs dropped\n",
      "Dropped 26 epochs\n",
      "Overwriting existing file.\n",
      "Overwriting existing file.\n",
      "deleted 500 events.\n",
      "deleted 1037 events.\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "84 bad epochs dropped\n",
      "2432 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2432 events and 2001 original time points ...\n",
      "Dropped 69 epochs\n",
      "86 bad epochs dropped\n",
      "Dropped 70 epochs\n",
      "Overwriting existing file.\n",
      "Overwriting existing file.\n",
      "deleted 135 events.\n",
      "deleted 8 events.\n",
      "1679 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 1679 events and 2001 original time points ...\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "89 bad epochs dropped\n",
      "195 bad epochs dropped\n",
      "Dropped 44 epochs\n",
      "Dropped 88 epochs\n",
      "Overwriting existing file.\n",
      "Overwriting existing file.\n",
      "deleted 186 events.\n",
      "deleted 225 events.\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "94 bad epochs dropped\n",
      "1834 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 1834 events and 2001 original time points ...\n",
      "Dropped 99 epochs\n",
      "44 bad epochs dropped\n",
      "Dropped 20 epochs\n",
      "Overwriting existing file.\n",
      "Overwriting existing file.\n",
      "deleted 159 events.\n",
      "deleted 134 events.\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "46 bad epochs dropped\n",
      "2288 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2288 events and 2001 original time points ...\n",
      "Dropped 62 epochs\n",
      "38 bad epochs dropped\n",
      "Dropped 52 epochs\n",
      "Overwriting existing file.\n",
      "Overwriting existing file.\n",
      "deleted 23 events.\n",
      "deleted 60 events.\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "2170 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2170 events and 2001 original time points ...\n",
      "278 bad epochs dropped\n",
      "175 bad epochs dropped\n",
      "Dropped 86 epochs\n",
      "Dropped 51 epochs\n",
      "Overwriting existing file.\n",
      "Overwriting existing file.\n",
      "deleted 872 events.\n",
      "deleted 73 events.\n",
      "1710 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 1710 events and 2001 original time points ...\n",
      "13 bad epochs dropped\n",
      "Dropped 35 epochs\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "72 bad epochs dropped\n",
      "Dropped 37 epochs\n",
      "Overwriting existing file.\n",
      "deleted 216 events.\n",
      "Overwriting existing file.\n",
      "deleted 191 events.\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "6 bad epochs dropped\n",
      "Dropped 44 epochs\n",
      "1406 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 1406 events and 2001 original time points ...\n",
      "26 bad epochs dropped\n",
      "Dropped 37 epochs\n",
      "Overwriting existing file.\n",
      "Overwriting existing file.\n",
      "deleted 85 events.\n",
      "deleted 198 events.\n",
      "1261 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 1261 events and 2001 original time points ...\n",
      "13 bad epochs dropped\n",
      "Dropped 67 epochs\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "22 bad epochs dropped\n",
      "Dropped 72 epochs\n",
      "Overwriting existing file.\n",
      "deleted 232 events.\n",
      "Overwriting existing file.\n",
      "deleted 67 events.\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "19 bad epochs dropped\n",
      "Dropped 45 epochs\n",
      "1246 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 1246 events and 2001 original time points ...\n",
      "21 bad epochs dropped\n",
      "Dropped 20 epochs\n",
      "Overwriting existing file.\n",
      "Overwriting existing file.\n",
      "deleted 295 events.\n",
      "deleted 64 events.\n",
      "1610 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 1610 events and 2001 original time points ...\n",
      "108 bad epochs dropped\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "Dropped 45 epochs\n",
      "65 bad epochs dropped\n",
      "Dropped 68 epochs\n",
      "Overwriting existing file.\n",
      "deleted 54 events.\n",
      "Overwriting existing file.\n",
      "deleted 172 events.\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "3 bad epochs dropped\n",
      "1328 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 1328 events and 2001 original time points ...\n",
      "8 bad epochs dropped\n",
      "Dropped 55 epochs\n",
      "Dropped 33 epochs\n",
      "Overwriting existing file.\n",
      "deleted 193 events.\n",
      "Overwriting existing file.\n",
      "deleted 105 events.\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "195 bad epochs dropped\n",
      "Dropped 39 epochs\n",
      "2390 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2390 events and 2001 original time points ...\n",
      "76 bad epochs dropped\n",
      "Overwriting existing file.\n",
      "Dropped 67 epochs\n",
      "deleted 296 events.\n",
      "Overwriting existing file.\n",
      "deleted 60 events.\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "170 bad epochs dropped\n",
      "Dropped 48 epochs\n",
      "1868 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 1868 events and 2001 original time points ...\n",
      "128 bad epochs dropped\n",
      "Dropped 53 epochs\n",
      "Overwriting existing file.\n",
      "Overwriting existing file.\n",
      "deleted 563 events.\n",
      "deleted 0 events.\n",
      "1497 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 1497 events and 2001 original time points ...\n",
      "7 bad epochs dropped\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "Dropped 49 epochs\n",
      "4 bad epochs dropped\n",
      "Dropped 52 epochs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting existing file.\n",
      "deleted 275 events.\n",
      "Overwriting existing file.\n",
      "deleted 884 events.\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "31 bad epochs dropped\n",
      "Dropped 38 epochs\n",
      "1960 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 1960 events and 2001 original time points ...\n",
      "28 bad epochs dropped\n",
      "Overwriting existing file.\n",
      "Dropped 67 epochs\n",
      "deleted 222 events.\n",
      "Overwriting existing file.\n",
      "deleted 257 events.\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "171 bad epochs dropped\n",
      "Dropped 115 epochs\n",
      "2115 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2115 events and 2001 original time points ...\n",
      "16 bad epochs dropped\n",
      "Dropped 42 epochs\n",
      "Overwriting existing file.\n",
      "deleted 70 events.\n",
      "Overwriting existing file.\n",
      "deleted 99 events.\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "78 bad epochs dropped\n",
      "Dropped 57 epochs\n",
      "1652 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 1652 events and 2001 original time points ...\n",
      "157 bad epochs dropped\n",
      "Dropped 45 epochs\n",
      "Overwriting existing file.\n",
      "Overwriting existing file.\n",
      "deleted 46 events.\n",
      "deleted 152 events.\n",
      "1483 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 1483 events and 2001 original time points ...\n",
      "0 bad epochs dropped\n",
      "Dropped 38 epochs\n",
      "2400 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 2400 events and 2001 original time points ...\n",
      "0 bad epochs dropped\n",
      "Overwriting existing file.\n",
      "Dropped 29 epochs\n",
      "Overwriting existing file.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# multiprocessing using tqdm progress bar\n",
    "# see: https://stackoverflow.com/a/41921948/5201771\n",
    "NJOBS = max(1, multiprocessing.cpu_count() - 8)\n",
    "subjects = range(1, 41)\n",
    "with multiprocessing.Pool(NJOBS) as pool:\n",
    "    with tqdm(total=len(subjects)) as pbar:\n",
    "        for i, _ in enumerate(pool.imap_unordered(run_parallel, subjects)):\n",
    "            pbar.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate percentage dropped epochs per participant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-27T10:04:28.705065Z",
     "start_time": "2021-01-27T10:04:28.695416Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_perc_rejected_for_subject(fname):\n",
    "    \"\"\"Load epochs and return percentage of rejected epochs.\"\"\"\n",
    "    epochs = mne.read_epochs(fname, verbose=0)\n",
    "    kept_epos = np.array([False if len(i) > 0 else True for i in epochs.drop_log])\n",
    "    perc_rejected = 100 * (1 - (kept_epos.sum() / len(epochs.drop_log)))\n",
    "    return fname, perc_rejected, kept_epos.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-27T10:06:06.596560Z",
     "start_time": "2021-01-27T10:04:29.330843Z"
    }
   },
   "outputs": [],
   "source": [
    "# Get rejected epochs per participant in parallel\n",
    "subjects = range(1, 41)\n",
    "fnames = [name_templates[\"epochs\"].format(subj) for subj in subjects]\n",
    "used_names = []\n",
    "percs = []\n",
    "absolutes = []\n",
    "for fname in tqdm(fnames):\n",
    "    result = get_perc_rejected_for_subject(fname)\n",
    "    fname, perc, absolute = result\n",
    "    used_names.append(fname)\n",
    "    percs.append(perc)\n",
    "    absolutes.append(absolute)\n",
    "\n",
    "df_rejected = pd.DataFrame(\n",
    "    {\"fname\": used_names, \"percent_rejected\": percs, \"sum_kept\": absolutes}\n",
    ")\n",
    "\n",
    "df_rejected.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-27T10:07:20.236521Z",
     "start_time": "2021-01-27T10:07:20.210690Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Following this approach, between 2.00% and 15.50% (mean=6.15%) of epochs were rejected per participant.\n",
      "\n",
      "\n",
      "By this approach, 93.85% of epochs (n=1925) were retained for analysis per participant on average\n"
     ]
    }
   ],
   "source": [
    "min_rej = df_rejected[\"percent_rejected\"].min()\n",
    "max_rej = df_rejected[\"percent_rejected\"].max()\n",
    "mean_rej = df_rejected[\"percent_rejected\"].mean()\n",
    "\n",
    "mean_abs_kept = df_rejected[\"sum_kept\"].mean()\n",
    "\n",
    "print(\n",
    "    f\"Following this approach, between {min_rej:.2f}% and {max_rej:.2f}% \"\n",
    "    f\"(mean={mean_rej:.2f}%) of epochs were rejected per participant.\\n\\n\"\n",
    ")\n",
    "\n",
    "print(\n",
    "    f\"By this approach, {100-mean_rej:.2f}% of epochs (n={mean_abs_kept:.0f}) \"\n",
    "    f\"were retained for analysis per participant on average\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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