Source code for

"""Habituation ChoiceWorld Bpod trials extraction."""
import logging
import numpy as np

import as raw
from import BaseBpodTrialsExtractor, run_extractor_classes
from import ContrastLR
from import FeedbackTimes, StimOnTriggerTimes, GoCueTimes

_logger = logging.getLogger(__name__)

[docs] class HabituationTrials(BaseBpodTrialsExtractor): var_names = ('feedbackType', 'rewardVolume', 'stimOff_times', 'contrastLeft', 'contrastRight', 'feedback_times', 'stimOn_times', 'stimOnTrigger_times', 'intervals', 'goCue_times', 'goCueTrigger_times', 'itiIn_times', 'stimOffTrigger_times', 'stimCenterTrigger_times', 'stimCenter_times', 'position', 'phase') def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) exclude = ['itiIn_times', 'stimOffTrigger_times', 'stimCenter_times', 'stimCenterTrigger_times', 'position', 'phase'] self.save_names = tuple(f'_ibl_trials.{x}.npy' if x not in exclude else None for x in self.var_names) def _extract(self) -> dict: """ Extract the Bpod trial events. The Bpod state machine for this task has extremely misleading names! The 'iti' state is actually the delay between valve open and trial end (the stimulus is still present during this period), and the 'trial_start' state is actually the ITI during which there is a 1s Bpod TTL and gray screen period. Returns ------- dict A dictionary of Bpod trial events. The keys are defined in the `var_names` attribute. """ # Extract all trials... # Get all detected TTLs. These are stored for QC purposes self.frame2ttl, = raw.load_bpod_fronts(self.session_path, data=self.bpod_trials) # These are the frame2TTL pulses as a list of lists, one per trial ttls = [raw.get_port_events(tr, 'BNC1') for tr in self.bpod_trials] # Report missing events n_missing = sum(len(pulses) != 3 for pulses in ttls) # Check if all stim syncs have failed to be detected if n_missing == len(ttls): _logger.error(f'{self.session_path}: Missing ALL BNC1 TTLs ({n_missing} trials)') elif n_missing > 0: # Check if any stim_sync has failed be detected for every trial _logger.warning(f'{self.session_path}: Missing BNC1 TTLs on {n_missing} trial(s)') # Extract datasets common to trainingChoiceWorld training = [ContrastLR, FeedbackTimes, GoCueTimes, StimOnTriggerTimes] out, _ = run_extractor_classes(training, session_path=self.session_path, save=False, bpod_trials=self.bpod_trials, settings=self.settings, task_collection=self.task_collection) """ The 'trial_start' state is in fact the 1s grey screen period, therefore the first timestamp is really the end of the previous trial and also the stimOff trigger time. The second timestamp is the true trial start time. """ (_, *ends), starts = zip(*[ t['behavior_data']['States timestamps']['trial_start'][-1] for t in self.bpod_trials] ) # StimOffTrigger times out['stimOffTrigger_times'] = np.array(ends) # StimOff times """ There should be exactly three TTLs per trial. stimOff_times should be the first TTL pulse. If 1 or more pulses are missing, we can not be confident of assigning the correct one. """ out['stimOff_times'] = np.array([sync[0] if len(sync) == 3 else np.nan for sync in ttls[1:]]) # Trial intervals """ In terms of TTLs, the intervals are defined by the 'trial_start' state, however the stim off time often happens after the trial end TTL front, i.e. after the 'trial_start' start begins. For these trials, we set the trial end time as the stim off time. """ # NB: We lose the last trial because the stim off event occurs at trial_num + 1 n_trials = out['stimOff_times'].size out['intervals'] = np.c_[starts, np.r_[ends, np.nan]][:n_trials, :] to_correct = ~np.isnan(out['stimOff_times']) & (out['stimOff_times'] > out['intervals'][:, 1]) if np.any(to_correct): _logger.debug( '%i/%i stim off events occurring outside trial intervals; using stim off times as trial end', sum(to_correct), len(to_correct)) out['intervals'][to_correct, 1] = out['stimOff_times'][to_correct] # itiIn times out['itiIn_times'] = np.r_[ends, np.nan] # GoCueTriggerTimes is the same event as StimOnTriggerTimes out['goCueTrigger_times'] = out['stimOnTrigger_times'].copy() # StimCenterTrigger times # Get the stim_on_state that triggers the onset of the stim stim_center_state = np.array([tr['behavior_data']['States timestamps'] ['stim_center'][0] for tr in self.bpod_trials]) out['stimCenterTrigger_times'] = stim_center_state[:, 0].T # StimCenter times stim_center_times = np.full(out['stimCenterTrigger_times'].shape, np.nan) for i, (sync, last) in enumerate(zip(ttls, out['stimCenterTrigger_times'])): """We expect there to be 3 pulses per trial; if this is the case, stim center will be the third pulse. If any pulses are missing, we can only be confident of the correct one if exactly one pulse occurs after the stim center trigger""" if len(sync) == 3 or (len(sync) > 0 and sum(pulse > last for pulse in sync) == 1): stim_center_times[i] = sync[-1] out['stimCenter_times'] = stim_center_times # StimOn times stimOn_times = np.full(out['stimOnTrigger_times'].shape, np.nan) for i, (sync, last) in enumerate(zip(ttls, out['stimCenterTrigger_times'])): """We expect there to be 3 pulses per trial; if this is the case, stim on will be the second pulse. If 1 pulse is missing, we can only be confident of the correct one if both pulses occur before the stim center trigger""" if len(sync) == 3 or (len(sync) == 2 and sum(pulse < last for pulse in sync) == 2): stimOn_times[i] = sync[1] out['stimOn_times'] = stimOn_times # RewardVolume trial_volume = [x['reward_amount'] for x in self.bpod_trials] out['rewardVolume'] = np.array(trial_volume).astype(np.float64) # FeedbackType is always positive out['feedbackType'] = np.ones(len(out['feedback_times']), dtype=np.int8) # Phase and position out['position'] = np.array([t['position'] for t in self.bpod_trials]) out['phase'] = np.array([t['stim_phase'] for t in self.bpod_trials]) # Double-check that the early and late trial events occur within the trial intervals idx = ~np.isnan(out['stimOn_times'][:n_trials]) assert not np.any(out['stimOn_times'][:n_trials][idx] < out['intervals'][idx, 0]), \ 'Stim on events occurring outside trial intervals' # Truncate arrays and return in correct order return {k: out[k][:n_trials] for k in self.var_names}
[docs] def extract_all(session_path, save=False, bpod_trials=False, settings=False, task_collection='raw_behavior_data', save_path=None): """Extract all datasets from habituationChoiceWorld Note: only the datasets from the HabituationTrials extractor will be saved to disc. :param session_path: The session path where the raw data are saved :param save: If True, the datasets that are considered standard are saved to the session path :param bpod_trials: The raw Bpod trial data :param settings: The raw Bpod sessions :returns: a dict of datasets and a corresponding list of file names """ if not bpod_trials: bpod_trials = raw.load_data(session_path, task_collection=task_collection) if not settings: settings = raw.load_settings(session_path, task_collection=task_collection) # Standard datasets that may be saved as ALFs params = dict(session_path=session_path, bpod_trials=bpod_trials, settings=settings, task_collection=task_collection, path_out=save_path) out, fil = run_extractor_classes(HabituationTrials, save=save, **params) return out, fil