Source code for ibllib.qc.oneqc_metrics

import numpy as np

from ibllib.qc import base
from import is_uuid_string

[docs]class ONEQC(base.QC): def __init__(self, eid, one=None, bpod_ntrials=None, lazy=False): super().__init__(eid, one=one) self.details =, full=True) self.bpod_ntrials = bpod_ntrials or self.details["n_trials"] self.metrics = self.passed = None if not lazy: self.compute() def _set_eid_or_path(self, session_path_or_eid): """Overloading base: session path not supported""" if is_uuid_string(str(session_path_or_eid)): self.eid = session_path_or_eid # Try to set session_path if data is found locally self.session_path = else: self.log.error("Cannot run ONE QC: an experiment uuid requried") raise ValueError("'session' must be a valid session uuid")
[docs] def compute(self):"Session {self.eid}: Running QC on ONE DatasetTypes...") """(one value per metric as proportion of trial level criteria that passed)""" qcmetrics_frame = {} qcmetrics_frame.update(self.load_nDatasetTypes()) dstype_names = [ "trials.intervals", "trials.stimOnTrigger_times", "trials.stimOn_times", "trials.goCueTrigger_times", "trials.goCue_times", "trials.response_times", "trials.feedback_times", ] for name in dstype_names: qcmetrics_frame.update(self.load_dstype_qc_metrics(name)) # Split metrics and passed frames metrics = {} passed = {} for k in qcmetrics_frame: metrics[k], passed[k] = qcmetrics_frame[k] self.metrics = metrics self.passed = passed return
# ---------------------------------------------------------------------------- # # ONE qc is atm just counting nans (*_count) or # comparing the dims to the bpod "ground truth" data (*_length) # # bpod_ntrials = len(raw.load_data(one.path_from_eid(eid)))
[docs] def load_nDatasetTypes(self): """ 17. Proportion of datasetTypes extracted Variable name: nDatasetTypes Metric: len(one.load(eid, offline=True, download_only=True)) / nExpetedDatasetTypes (hardcoded per task?) """ self.log.warning("QC test not implemented: nDatasetTypes") out = {"_one_nDatasetTypes": (None, None)} return out
[docs] def load_dstype_qc_metrics(self, dstype_name: str) -> dict: """Returns dict to update to metrics or criteria frame Metrics: _length = number of trials in ONE dstype _count = number of nans in dstype Criteria: length / bpod number of trials count / length of dstype NB: Makes sense for dstypes that should have one value per trial and where nans are informative of failures Other dstypes will have nans because of the contingency of the trial, e.g. if contrastLeft has a nan it means the contrast was on the right. """ name = dstype_name.replace("trials.", "") # Add namespace and termination strings names = [f"_one_{name}_length", f"_one_{name}_count"] # Create output dict out = dict.fromkeys(names) out = {k: (None, None) for k in out} # Load dset data from ONE dset =, dataset_types=dstype_name)[0] if dset is None: self.log.warning(f"ONE datasetType not found: {dstype_name}") return out # Define length and count as metric # Define criteria is applies output normalized len and count _count = np.sum(np.isnan(dset)) _length = len(dset) _count = (_count, 1 - _count / _length) # len(dset) _length = (_length, _length / self.bpod_ntrials) # Add the values to output dict for k, v in zip(names, (_length, _count)): out[k] = v return out