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
def main():
"""
Usage:
> python generate_fx_input.py --specimen_id SPECIMEN_ID --cell_dir CELL_DIR
> python generate_fx_input.py --input_nwb_file INPUT_NWB_FILE --cell_dir CELL_DIR
"""
kwargs = parse_args()
se_input = generate_se_input(**kwargs)
cell_dir = kwargs["cell_dir"]
lu.configure_logger(cell_dir)
if not os.path.exists(cell_dir):
os.makedirs(cell_dir)
ju.write(os.path.join(cell_dir, 'se_input.json'), se_input)
se_output = run_sweep_extraction(
se_input["input_nwb_file"], se_input.get("input_h5_file", None),
se_input.get("stimulus_ontology_file", None))
ju.write(os.path.join(cell_dir, 'se_output.json'), se_output)
sp.drop_tagged_sweeps(se_output["sweep_features"])
qc_input = generate_qc_input(se_input, se_output)
ju.write(os.path.join(cell_dir, 'qc_input.json'), qc_input)
qc_output = run_qc(qc_input.get("stimulus_ontology_file",
None), qc_input["cell_features"],
qc_input["sweep_features"], qc_input["qc_criteria"])
ju.write(os.path.join(cell_dir, 'qc_output.json'), qc_output)
if kwargs["specimen_id"]:
manual_sweep_states = lq.get_sweep_states(kwargs["specimen_id"])
elif kwargs["input_nwb_file"]:
manual_sweep_states = []
sp.override_auto_sweep_states(manual_sweep_states,
qc_output["sweep_states"])
sp.assign_sweep_states(qc_output["sweep_states"],
se_output["sweep_features"])
fx_input = generate_fx_input(se_input,
se_output,
cell_dir,
plot_figures=True)
ju.write(os.path.join(cell_dir, 'fx_input.json'), fx_input)
def run_pipeline(input_nwb_file,
output_nwb_file,
stimulus_ontology_file,
qc_fig_dir,
qc_criteria,
manual_sweep_states,
write_spikes=True,
update_ontology=True):
se_output = run_sweep_extraction(input_nwb_file,
stimulus_ontology_file,
update_ontology=update_ontology)
sweep_props.drop_tagged_sweeps(se_output["sweep_features"])
sweep_props.remove_sweep_feature("tags", se_output["sweep_features"])
qc_output = run_qc(stimulus_ontology_file, se_output["cell_features"],
se_output["sweep_features"], qc_criteria)
sweep_props.override_auto_sweep_states(manual_sweep_states,
qc_output["sweep_states"])
sweep_props.assign_sweep_states(qc_output["sweep_states"],
se_output["sweep_features"])
fx_output = run_feature_extraction(
input_nwb_file,
stimulus_ontology_file,
output_nwb_file,
qc_fig_dir,
se_output['sweep_features'],
se_output['cell_features'],
write_spikes,
)
log_pretty_header("Analysis completed!", level=1)
return {
"sweep_extraction": se_output,
"qc": qc_output,
"feature_extraction": fx_output
}
def run_pipeline(input_nwb_file, input_h5_file, output_nwb_file,
stimulus_ontology_file, qc_fig_dir, qc_criteria,
manual_sweep_states):
se_output = run_sweep_extraction(input_nwb_file, input_h5_file,
stimulus_ontology_file)
sp.drop_tagged_sweeps(se_output["sweep_features"])
sp.remove_sweep_feature("tags", se_output["sweep_features"])
qc_output = run_qc(stimulus_ontology_file, se_output["cell_features"],
se_output["sweep_features"], qc_criteria)
if qc_output["cell_state"]["failed_qc"]:
logging.warning("Failed QC. No ephys features extracted.")
return dict(
sweep_extraction=se_output,
qc=qc_output,
)
sp.override_auto_sweep_states(manual_sweep_states,
qc_output["sweep_states"])
sp.assign_sweep_states(qc_output["sweep_states"],
se_output["sweep_features"])
fx_output = run_feature_extraction(
input_nwb_file,
stimulus_ontology_file,
output_nwb_file,
qc_fig_dir,
se_output['sweep_features'],
se_output['cell_features'],
)
return dict(sweep_extraction=se_output,
qc=qc_output,
feature_extraction=fx_output)
def qc_summary(sweep_features, sweep_states, cell_features, cell_state):
"""
Output QC summary
Parameters
----------
sweep_features: list of dicts
sweep_states: list of dict
cell_features: list of dicts
cell_state: dict
Returns
-------
"""
lu.log_pretty_header("QC Summary:", level=2)
logging.info("Cell State:")
for k, v in cell_state.items():
logging.info("%s:%s" % (k, v))
logging.info("Sweep States:")
sp.assign_sweep_states(sweep_states, sweep_features)
sweep_table = pd.DataFrame(sweep_features)
if sweep_features:
for stimulus_name, sg_table in sweep_table.groupby("stimulus_name"):
passed_sweep_numbers = sg_table[
sg_table.passed == True].sweep_number.sort_values().values
failed_sweep_numbers = sg_table[
sg_table.passed == False].sweep_number.sort_values().values
logging.info("{} sweeps passed: {}, failed {}".format(
stimulus_name, passed_sweep_numbers, failed_sweep_numbers))
else:
logging.warning("No current clamp sweeps available for QC")
def update_sweep_states(self):
manual_sweep_states = self.get_non_default_manual_sweep_states()
sweep_states = copy.deepcopy(self.sweep_states)
sweep_props.override_auto_sweep_states(manual_sweep_states, sweep_states)
sweep_props.assign_sweep_states(sweep_states, self.sweep_features)