Source code for ibllib.io.extractors.biased_trials

from pathlib import Path, PureWindowsPath

from packaging import version
import numpy as np
from one.alf.io import AlfBunch

from ibllib.io.extractors.base import BaseBpodTrialsExtractor, run_extractor_classes
import ibllib.io.raw_data_loaders as raw
from ibllib.io.extractors.training_trials import (
    Choice, FeedbackTimes, FeedbackType, GoCueTimes, GoCueTriggerTimes,
    IncludedTrials, Intervals, ProbabilityLeft, ResponseTimes, RewardVolume,
    StimOnTriggerTimes, StimOnOffFreezeTimes, ItiInTimes,
    StimOffTriggerTimes, StimFreezeTriggerTimes, ErrorCueTriggerTimes, PhasePosQuiescence)
from ibllib.io.extractors.training_wheel import Wheel

__all__ = ['BiasedTrials', 'EphysTrials']


class ContrastLR(BaseBpodTrialsExtractor):
    """Get left and right contrasts from raw datafile."""
    save_names = ('_ibl_trials.contrastLeft.npy', '_ibl_trials.contrastRight.npy')
    var_names = ('contrastLeft', 'contrastRight')

    def _extract(self, **kwargs):
        contrastLeft = np.array([t['contrast'] if np.sign(
            t['position']) < 0 else np.nan for t in self.bpod_trials])
        contrastRight = np.array([t['contrast'] if np.sign(
            t['position']) > 0 else np.nan for t in self.bpod_trials])
        return contrastLeft, contrastRight


class ProbaContrasts(BaseBpodTrialsExtractor):
    """Bpod pre-generated values for probabilityLeft, contrastLR, phase, quiescence."""
    save_names = ('_ibl_trials.contrastLeft.npy', '_ibl_trials.contrastRight.npy', None, None,
                  '_ibl_trials.probabilityLeft.npy', '_ibl_trials.quiescencePeriod.npy')
    var_names = ('contrastLeft', 'contrastRight', 'phase',
                 'position', 'probabilityLeft', 'quiescence')

    def _extract(self, **kwargs):
        """Extracts positions, contrasts, quiescent delay, stimulus phase and probability left
        from pregenerated session files.  Used in ephysChoiceWorld extractions.
        Optional: saves alf contrastLR and probabilityLeft npy files"""
        pe = self.get_pregenerated_events(self.bpod_trials, self.settings)
        return [pe[k] for k in sorted(pe.keys())]

    @staticmethod
    def get_pregenerated_events(bpod_trials, settings):
        for k in ['PRELOADED_SESSION_NUM', 'PREGENERATED_SESSION_NUM', 'SESSION_TEMPLATE_ID']:
            num = settings.get(k, None)
            if num is not None:
                break
        if num is None:
            fn = settings.get('SESSION_LOADED_FILE_PATH', '')
            fn = PureWindowsPath(fn).name
            num = ''.join([d for d in fn if d.isdigit()])
            if num == '':
                raise ValueError("Can't extract left probability behaviour.")
        # Load the pregenerated file
        ntrials = len(bpod_trials)
        sessions_folder = Path(raw.__file__).parent.joinpath(
            "extractors", "ephys_sessions")
        fname = f"session_{num}_ephys_pcqs.npy"
        pcqsp = np.load(sessions_folder.joinpath(fname))
        pos = pcqsp[:, 0]
        con = pcqsp[:, 1]
        pos = pos[: ntrials]
        con = con[: ntrials]
        contrastRight = con.copy()
        contrastLeft = con.copy()
        contrastRight[pos < 0] = np.nan
        contrastLeft[pos > 0] = np.nan
        qui = pcqsp[:, 2]
        qui = qui[: ntrials]
        phase = pcqsp[:, 3]
        phase = phase[: ntrials]
        pLeft = pcqsp[:, 4]
        pLeft = pLeft[: ntrials]

        phase_path = sessions_folder.joinpath(f"session_{num}_stim_phase.npy")
        is_patched_version = version.parse(
            settings.get('IBLRIG_VERSION') or '0') > version.parse('6.4.0')
        if phase_path.exists() and is_patched_version:
            phase = np.load(phase_path)[:ntrials]

        return {'position': pos, 'quiescence': qui, 'phase': phase, 'probabilityLeft': pLeft,
                'contrastRight': contrastRight, 'contrastLeft': contrastLeft}


class TrialsTableBiased(BaseBpodTrialsExtractor):
    """
    Extracts the following into a table from Bpod raw data:
        intervals, goCue_times, response_times, choice, stimOn_times, contrastLeft, contrastRight,
        feedback_times, feedbackType, rewardVolume, probabilityLeft, firstMovement_times
    Additionally extracts the following wheel data:
        wheel_timestamps, wheel_position, wheelMoves_intervals, wheelMoves_peakAmplitude
    """
    save_names = ('_ibl_trials.table.pqt', None, None, '_ibl_wheel.timestamps.npy', '_ibl_wheel.position.npy',
                  '_ibl_wheelMoves.intervals.npy', '_ibl_wheelMoves.peakAmplitude.npy', None, None)
    var_names = ('table', 'stimOff_times', 'stimFreeze_times', 'wheel_timestamps', 'wheel_position', 'wheelMoves_intervals',
                 'wheelMoves_peakAmplitude', 'wheelMoves_peakVelocity_times', 'is_final_movement')

    def _extract(self, extractor_classes=None, **kwargs):
        extractor_classes = extractor_classes or []
        base = [Intervals, GoCueTimes, ResponseTimes, Choice, StimOnOffFreezeTimes, ContrastLR, FeedbackTimes, FeedbackType,
                RewardVolume, ProbabilityLeft, Wheel]
        out, _ = run_extractor_classes(
            base + extractor_classes, session_path=self.session_path, bpod_trials=self.bpod_trials,
            settings=self.settings, save=False, task_collection=self.task_collection)

        table = AlfBunch({k: out.pop(k) for k in list(out.keys()) if k not in self.var_names})
        assert len(table.keys()) == 12

        return table.to_df(), *(out.pop(x) for x in self.var_names if x != 'table')


class TrialsTableEphys(BaseBpodTrialsExtractor):
    """
    Extracts the following into a table from Bpod raw data:
        intervals, goCue_times, response_times, choice, stimOn_times, contrastLeft, contrastRight,
        feedback_times, feedbackType, rewardVolume, probabilityLeft, firstMovement_times
    Additionally extracts the following wheel data:
        wheel_timestamps, wheel_position, wheelMoves_intervals, wheelMoves_peakAmplitude
    """
    save_names = ('_ibl_trials.table.pqt', None, None, '_ibl_wheel.timestamps.npy', '_ibl_wheel.position.npy',
                  '_ibl_wheelMoves.intervals.npy', '_ibl_wheelMoves.peakAmplitude.npy', None,
                  None, None, None, '_ibl_trials.quiescencePeriod.npy')
    var_names = ('table', 'stimOff_times', 'stimFreeze_times', 'wheel_timestamps', 'wheel_position', 'wheelMoves_intervals',
                 'wheelMoves_peakAmplitude', 'wheelMoves_peakVelocity_times', 'is_final_movement',
                 'phase', 'position', 'quiescence')

    def _extract(self, extractor_classes=None, **kwargs):
        extractor_classes = extractor_classes or []
        base = [Intervals, GoCueTimes, ResponseTimes, Choice, StimOnOffFreezeTimes, ProbaContrasts,
                FeedbackTimes, FeedbackType, RewardVolume, Wheel]
        # Exclude from trials table
        out, _ = run_extractor_classes(
            base + extractor_classes, session_path=self.session_path, bpod_trials=self.bpod_trials,
            settings=self.settings, save=False, task_collection=self.task_collection)
        table = AlfBunch({k: v for k, v in out.items() if k not in self.var_names})
        assert len(table.keys()) == 12

        return table.to_df(), *(out.pop(x) for x in self.var_names if x != 'table')




class BiasedTrials(BaseBpodTrialsExtractor):
    """
    Same as training_trials.TrainingTrials except...
     - there is no RepNum
     - ContrastLR is extracted differently
     - IncludedTrials is only extracted for 5.0.0 or greater
    """
    save_names = ('_ibl_trials.goCueTrigger_times.npy', '_ibl_trials.stimOnTrigger_times.npy', None,
                  '_ibl_trials.stimOffTrigger_times.npy', None, None, '_ibl_trials.table.pqt',
                  '_ibl_trials.stimOff_times.npy', None, '_ibl_wheel.timestamps.npy', '_ibl_wheel.position.npy',
                  '_ibl_wheelMoves.intervals.npy', '_ibl_wheelMoves.peakAmplitude.npy', None, None,
                  '_ibl_trials.included.npy', None, None, '_ibl_trials.quiescencePeriod.npy')
    var_names = ('goCueTrigger_times', 'stimOnTrigger_times', 'itiIn_times', 'stimOffTrigger_times', 'stimFreezeTrigger_times',
                 'errorCueTrigger_times', 'table', 'stimOff_times', 'stimFreeze_times', 'wheel_timestamps', 'wheel_position',
                 'wheelMoves_intervals', 'wheelMoves_peakAmplitude', 'wheelMoves_peakVelocity_times', 'is_final_movement',
                 'included', 'phase', 'position', 'quiescence')

    def _extract(self, extractor_classes=None, **kwargs) -> dict:
        extractor_classes = extractor_classes or []
        base = [GoCueTriggerTimes, StimOnTriggerTimes, ItiInTimes, StimOffTriggerTimes, StimFreezeTriggerTimes,
                ErrorCueTriggerTimes, TrialsTableBiased, IncludedTrials, PhasePosQuiescence]
        # Exclude from trials table
        out, _ = run_extractor_classes(
            base + extractor_classes, session_path=self.session_path, bpod_trials=self.bpod_trials,
            settings=self.settings, save=False, task_collection=self.task_collection)
        return {k: out[k] for k in self.var_names}





class EphysTrials(BaseBpodTrialsExtractor):
    """
    Same as BiasedTrials except...
     - Contrast, phase, position, probabilityLeft and quiescence is extracted differently
    """
    save_names = ('_ibl_trials.goCueTrigger_times.npy', '_ibl_trials.stimOnTrigger_times.npy', None,
                  '_ibl_trials.stimOffTrigger_times.npy', None, None,
                  '_ibl_trials.table.pqt', '_ibl_trials.stimOff_times.npy', None, '_ibl_wheel.timestamps.npy',
                  '_ibl_wheel.position.npy', '_ibl_wheelMoves.intervals.npy', '_ibl_wheelMoves.peakAmplitude.npy', None, None,
                  '_ibl_trials.included.npy', None, None, '_ibl_trials.quiescencePeriod.npy')
    var_names = ('goCueTrigger_times', 'stimOnTrigger_times', 'itiIn_times', 'stimOffTrigger_times', 'stimFreezeTrigger_times',
                 'errorCueTrigger_times', 'table', 'stimOff_times', 'stimFreeze_times', 'wheel_timestamps', 'wheel_position',
                 'wheelMoves_intervals', 'wheelMoves_peakAmplitude', 'wheelMoves_peakVelocity_times', 'is_final_movement',
                 'included', 'phase', 'position', 'quiescence')

    def _extract(self, extractor_classes=None, **kwargs) -> dict:
        extractor_classes = extractor_classes or []

        # For iblrig v8 we use the biased trials table instead. ContrastLeft, ContrastRight and ProbabilityLeft are
        # filled from the values in the bpod data itself rather than using the pregenerated session number
        iblrig_version = self.settings.get('IBLRIG_VERSION', self.settings.get('IBLRIG_VERSION_TAG', '0'))
        if version.parse(iblrig_version) >= version.parse('8.0.0'):
            TrialsTable = TrialsTableBiased
        else:
            TrialsTable = TrialsTableEphys

        base = [GoCueTriggerTimes, StimOnTriggerTimes, ItiInTimes, StimOffTriggerTimes, StimFreezeTriggerTimes,
                ErrorCueTriggerTimes, TrialsTable, IncludedTrials, PhasePosQuiescence]
        # Get all detected TTLs. These are stored for QC purposes
        self.frame2ttl, self.audio = raw.load_bpod_fronts(self.session_path, data=self.bpod_trials)
        # Exclude from trials table
        out, _ = run_extractor_classes(
            base + extractor_classes, session_path=self.session_path, bpod_trials=self.bpod_trials,
            settings=self.settings, save=False, task_collection=self.task_collection)
        return {k: out[k] for k in self.var_names}