def drop_failed_sweeps(
dataset: EphysDataSet,
stimulus_ontology: Optional[StimulusOntology] = None,
qc_criteria: Optional[Dict] = None
) -> List[Dict]:
"""A convenience which extracts and QCs sweeps in preparation for dataset
feature extraction. This function:
1. extracts sweep qc features
2. removes sweeps tagged with failure messages
3. sets sweep states based on qc results
Parameters
----------
dataset : dataset from which to draw sweeps
Returns
-------
sweep_features : a list of dictionaries, each describing a sweep
"""
if stimulus_ontology is None:
stimulus_ontology = StimulusOntology.default()
if qc_criteria is None:
qc_criteria = qcp.load_default_qc_criteria()
sweep_features = sweep_qc_features(dataset)
sweep_props.drop_tagged_sweeps(sweep_features)
sweep_props.remove_sweep_feature("tags", sweep_features)
sweep_states = qcp.qc_sweeps(
stimulus_ontology, sweep_features, qc_criteria
)
sweep_props.assign_sweep_states(sweep_states, sweep_features)
dataset.sweep_info = sweep_features
import pandas as pd
from ipfx.dataset.create import create_ephys_data_set
from ipfx.qc_feature_extractor import sweep_qc_features
import ipfx.sweep_props as sweep_props
import ipfx.qc_feature_evaluator as qcp
from ipfx.stimulus import StimulusOntology
# Download and access the experimental data from DANDI archive per instructions in the documentation
# Example below will use an nwb file provided with the package
nwb_file = os.path.join(os.path.dirname(os.getcwd()), "data",
"nwb2_H17.03.008.11.03.05.nwb")
data_set = create_ephys_data_set(nwb_file=nwb_file)
# Compute sweep QC features
sweep_features = sweep_qc_features(data_set)
# Drop sweeps that failed to compute QC criteria
sweep_props.drop_tagged_sweeps(sweep_features)
sweep_props.remove_sweep_feature("tags", sweep_features)
stimulus_ontology = StimulusOntology.default()
qc_criteria = qcp.load_default_qc_criteria()
sweep_states = qcp.qc_sweeps(stimulus_ontology, sweep_features, qc_criteria)
# print a few sweeps and states
print(pd.DataFrame(sweep_features).head())
print(sweep_states[0:len(pd.DataFrame(sweep_features).head())])