def extract_all(session_path, save=False, bpod_trials=False, settings=False):
"""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)
if not settings:
settings = raw.load_settings(session_path)
# Standard datasets that may be saved as ALFs
params = dict(session_path=session_path,
bpod_trials=bpod_trials,
settings=settings)
out, fil = run_extractor_classes(HabituationTrials, save=save, **params)
# The extra datasets
non_standard = [
ItiInTimes, StimOffTriggerTimes, StimCenterTriggerTimes,
StimCenterTimes
]
data, _ = run_extractor_classes(non_standard, save=False, **params)
# Merge the extracted data
out.update(data)
fil.extend([None for _ in data.keys()])
return out, fil
def extract_all(session_path, save=False, bpod_trials=None, settings=None):
if not bpod_trials:
bpod_trials = raw.load_data(session_path)
if not settings:
settings = raw.load_settings(session_path)
if settings is None or settings['IBLRIG_VERSION_TAG'] == '':
settings = {'IBLRIG_VERSION_TAG': '100.0.0'}
base = [
FeedbackType, ContrastLR, ProbabilityLeft, Choice, RepNum,
RewardVolume, FeedbackTimes, Intervals, ResponseTimes,
GoCueTriggerTimes, GoCueTimes
]
# Version check
if version.ge(settings['IBLRIG_VERSION_TAG'], '5.0.0'):
base.extend([
StimOnTriggerTimes, StimOnOffFreezeTimes, ItiInTimes,
StimOffTriggerTimes, StimFreezeTriggerTimes, ErrorCueTriggerTimes
])
else:
base.extend([IncludedTrials, ItiDuration, StimOnTimes])
out, fil = run_extractor_classes(base,
save=save,
session_path=session_path,
bpod_trials=bpod_trials,
settings=settings)
return out, fil
def extract_all(session_path, save=True, bin_exists=False):
"""
For the IBL ephys task, reads ephys binary file and extract:
- sync
- wheel
- behaviour
- video time stamps
:param session_path: '/path/to/subject/yyyy-mm-dd/001'
:param save: Bool, defaults to False
:return: outputs, files
"""
extractor_type = extractors_base.get_session_extractor_type(session_path)
_logger.info(f"Extracting {session_path} as {extractor_type}")
basecls = [FpgaTrials]
if extractor_type in ['ephys', 'mock_ephys', 'sync_ephys']:
basecls.extend([ProbaContrasts])
elif extractor_type in ['ephys_biased_opto']:
from ibllib.io.extractors import opto_trials
basecls.extend([
biased_trials.ProbabilityLeft, biased_trials.ContrastLR,
opto_trials.LaserBool
])
sync, chmap = get_main_probe_sync(session_path, bin_exists=bin_exists)
outputs, files = extractors_base.run_extractor_classes(
basecls, session_path=session_path, save=save, sync=sync, chmap=chmap)
return outputs, files
def _extract(self, extractor_classes=None, **kwargs):
base = [Intervals, GoCueTimes, ResponseTimes, Choice, StimOnOffFreezeTimes, ContrastLR, FeedbackTimes, FeedbackType,
RewardVolume, ProbabilityLeft, Wheel]
out, _ = run_extractor_classes(
base, session_path=self.session_path, bpod_trials=self.bpod_trials, settings=self.settings, save=False
)
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')
def extract_all(session_path, save=True, bin_exists=False):
"""
For the IBL ephys task, reads ephys binary file and extract:
- sync
- wheel
- behaviour
- video time stamps
:param session_path: '/path/to/subject/yyyy-mm-dd/001'
:param save: Bool, defaults to False
:return: outputs, files
"""
sync, chmap = get_main_probe_sync(session_path, bin_exists=bin_exists)
outputs, files = run_extractor_classes([CameraTimestamps, FpgaTrials],
session_path=session_path,
save=save,
sync=sync,
chmap=chmap)
return outputs, files
def extract_all(session_path, save=True, bin_exists=False):
"""
For the IBL ephys task, reads ephys binary file and extract:
- sync
- wheel
- behaviour
- video time stamps
:param session_path: '/path/to/subject/yyyy-mm-dd/001'
:param save: Bool, defaults to False
:return: outputs, files
"""
extractor_type = extractors_base.get_session_extractor_type(session_path)
_logger.info(f"Extracting {session_path} as {extractor_type}")
basecls = [FpgaTrials]
sync, chmap = get_main_probe_sync(session_path, bin_exists=bin_exists)
outputs, files = extractors_base.run_extractor_classes(
basecls, session_path=session_path, save=save, sync=sync, chmap=chmap)
return outputs, files
def extract_all(session_path, save=False, bpod_trials=None, settings=None):
"""Extract trials and wheel data.
For task versions >= 5.0.0, outputs wheel data and trials.table dataset (+ some extra datasets)
Parameters
----------
session_path : str, pathlib.Path
The path to the session
save : bool
If true save the data files to ALF
bpod_trials : list of dicts
The Bpod trial dicts loaded from the _iblrig_taskData.raw dataset
settings : dict
The Bpod settings loaded from the _iblrig_taskSettings.raw dataset
Returns
-------
A list of extracted data and a list of file paths if save is True (otherwise None)
"""
if not bpod_trials:
bpod_trials = raw.load_data(session_path)
if not settings:
settings = raw.load_settings(session_path)
if settings is None or settings['IBLRIG_VERSION_TAG'] == '':
settings = {'IBLRIG_VERSION_TAG': '100.0.0'}
base = [RepNum, GoCueTriggerTimes]
# Version check
if version.ge(settings['IBLRIG_VERSION_TAG'], '5.0.0'):
# We now extract a single trials table
base.extend([
StimOnTriggerTimes, ItiInTimes, StimOffTriggerTimes, StimFreezeTriggerTimes,
ErrorCueTriggerTimes, TrialsTable, PhasePosQuiescence
])
else:
base.extend([
Intervals, Wheel, FeedbackType, ContrastLR, ProbabilityLeft, Choice, IncludedTrials, ItiDuration,
StimOnTimes_deprecated, RewardVolume, FeedbackTimes, ResponseTimes, GoCueTimes, PhasePosQuiescence
])
out, fil = run_extractor_classes(
base, save=save, session_path=session_path, bpod_trials=bpod_trials, settings=settings)
return out, fil
def extract_all(session_path, save=False, bpod_trials=False, settings=False):
"""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)
if not settings:
settings = raw.load_settings(session_path)
# Standard datasets that may be saved as ALFs
params = dict(session_path=session_path, bpod_trials=bpod_trials, settings=settings)
out, fil = run_extractor_classes(HabituationTrials, save=save, **params)
return out, fil
def extract_all(session_path, save=True, bin_exists=False):
"""
For the IBL ephys task, reads ephys binary file and extract:
- sync
- wheel
- behaviour
- video time stamps
:param session_path: '/path/to/subject/yyyy-mm-dd/001'
:param save: Bool, defaults to False
:param version: bpod version, defaults to None
:return: outputs, files
"""
assert save # fixme with wheel positions, this function can't work without saving the data
sync, chmap = _get_main_probe_sync(session_path, bin_exists=bin_exists)
outputs, files = run_extractor_classes([CameraTimestamps, FpgaTrials],
session_path=session_path,
save=save,
sync=sync,
chmap=chmap)
return outputs, files
def extract_all(session_path, save=False, bpod_trials=False, settings=False, extra_classes=None):
"""
Same as training_trials.extract_all except...
- there is no RepNum
- ContrastLR is extracted differently
- IncludedTrials is only extracted for 5.0.0 or greater
:param session_path:
:param save:
:param bpod_trials:
:param settings:
:param extra_classes: additional BaseBpodTrialsExtractor subclasses for custom extractions
:return:
"""
if not bpod_trials:
bpod_trials = raw.load_data(session_path)
if not settings:
settings = raw.load_settings(session_path)
if settings is None or settings['IBLRIG_VERSION_TAG'] == '':
settings = {'IBLRIG_VERSION_TAG': '100.0.0'}
base = [GoCueTriggerTimes]
# Version check
if version.ge(settings['IBLRIG_VERSION_TAG'], '5.0.0'):
# We now extract a single trials table
base.extend([
StimOnTriggerTimes, ItiInTimes, StimOffTriggerTimes, StimFreezeTriggerTimes, ErrorCueTriggerTimes,
TrialsTableBiased, IncludedTrials, PhasePosQuiescence
])
else:
base.extend([
Intervals, Wheel, FeedbackType, ContrastLR, ProbabilityLeft, Choice, ItiDuration,
StimOnTimes_deprecated, RewardVolume, FeedbackTimes, ResponseTimes, GoCueTimes, PhasePosQuiescence
])
if extra_classes:
base.extend(extra_classes)
out, fil = run_extractor_classes(
base, save=save, session_path=session_path, bpod_trials=bpod_trials, settings=settings)
return out, fil
def extract_all(session_path, bpod_trials=None, settings=None, save=False):
"""Extract the wheel data.
NB: Wheel extraction is now called through ibllib.io.training_trials.extract_all
Parameters
----------
session_path : str, pathlib.Path
The path to the session
save : bool
If true save the data files to ALF
bpod_trials : list of dicts
The Bpod trial dicts loaded from the _iblrig_taskData.raw dataset
settings : dict
The Bpod settings loaded from the _iblrig_taskSettings.raw dataset
Returns
-------
A list of extracted data and a list of file paths if save is True (otherwise None)
"""
return run_extractor_classes(Wheel, save=save, session_path=session_path,
bpod_trials=bpod_trials, settings=settings)
def extract_all(session_path, session_type=None, save=True, **kwargs):
"""
For the IBL ephys task, reads ephys binary file and extract:
- video time stamps
:param session_path: '/path/to/subject/yyyy-mm-dd/001'
:param session_type: the session type to extract, i.e. 'ephys', 'training' or 'biased'. If
None the session type is inferred from the settings file.
:param save: Bool, defaults to False
:param kwargs: parameters to pass to the extractor
:return: outputs, files
"""
if session_type is None:
session_type = get_session_extractor_type(session_path)
if not session_type or session_type not in _get_task_types_json_config(
).values():
raise ValueError(
f"Session type {session_type} has no matching extractor")
elif 'ephys' in session_type: # assume ephys == FPGA
labels = assert_valid_label(
kwargs.pop('labels', ('left', 'right', 'body')))
labels = (labels, ) if isinstance(labels,
str) else labels # Ensure list/tuple
extractor = [partial(CameraTimestampsFPGA, label) for label in labels]
if 'sync' not in kwargs:
kwargs['sync'], kwargs['chmap'] = \
get_main_probe_sync(session_path, bin_exists=kwargs.pop('bin_exists', False))
else: # assume Bpod otherwise
assert kwargs.pop('labels',
'left'), 'only left camera is currently supported'
extractor = CameraTimestampsBpod
outputs, files = run_extractor_classes(extractor,
session_path=session_path,
save=save,
**kwargs)
return outputs, files
def _extract(self):
# Extract all trials...
# Get all stim_sync events detected
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, Intervals, GoCueTimes,
StimOnTriggerTimes
]
out, _ = run_extractor_classes(training,
session_path=self.session_path,
save=False,
bpod_trials=self.bpod_trials,
settings=self.settings)
# GoCueTriggerTimes is the same event as StimOnTriggerTimes
out['goCueTrigger_times'] = out['stimOnTrigger_times'].copy()
# StimOn times
stimOn_times = np.full(out['stimOnTrigger_times'].shape, np.nan)
stim_center_triggers, _ = (StimCenterTriggerTimes(
self.session_path).extract(self.bpod_trials, self.settings))
for i, (sync, last) in enumerate(zip(ttls, stim_center_triggers)):
"""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)
# StimOffTrigger times (not saved)
stimOffTriggers, _ = (StimOffTriggerTimes(self.session_path).extract(
self.bpod_trials, self.settings))
# 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, off in zip(ttls[1:], stimOffTriggers)
])
# FeedbackType is always positive
out['feedbackType'] = np.ones(len(out['feedback_times']),
dtype=np.int8)
# NB: We lose the last trial because the stim off event occurs at trial_num + 1
n_trials = out['stimOff_times'].size
return [out[k][:n_trials] for k in self.var_names]
def extract_all(session_path, bpod_trials=None, settings=None, save=False):
return run_extractor_classes(Wheel,
save=save,
session_path=session_path,
bpod_trials=bpod_trials,
settings=settings)
