def run_feature_extraction(input_nwb_file, stimulus_ontology_file,
output_nwb_file, qc_fig_dir, sweep_info, cell_info):
lu.log_pretty_header("Extract ephys features", level=1)
sp.drop_failed_sweeps(sweep_info)
if len(sweep_info) == 0:
raise er.FeatureError(
"There are no QC-passed sweeps available to analyze")
if not stimulus_ontology_file:
stimulus_ontology_file = StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE
logging.info(
F"Ontology is not provided, using default {StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE}"
)
ont = StimulusOntology(ju.read(stimulus_ontology_file))
data_set = create_data_set(sweep_info=sweep_info,
nwb_file=input_nwb_file,
ontology=ont,
api_sweeps=False)
try:
cell_features, sweep_features, cell_record, sweep_records = dsft.extract_data_set_features(
data_set)
if cell_info: cell_record.update(cell_info)
cell_state = {"failed_fx": False, "fail_fx_message": None}
feature_data = {
'cell_features': cell_features,
'sweep_features': sweep_features,
'cell_record': cell_record,
'sweep_records': sweep_records,
'cell_state': cell_state
}
except (er.FeatureError, IndexError) as e:
cell_state = {"failed_fx": True, "fail_fx_message": str(e)}
logging.warning(e)
feature_data = {'cell_state': cell_state}
if not cell_state["failed_fx"]:
sweep_spike_times = collect_spike_times(sweep_features)
embed_spike_times(input_nwb_file, output_nwb_file, sweep_spike_times)
if qc_fig_dir is None:
logging.info("qc_fig_dir is not provided, will not save figures")
else:
plotqc.display_features(qc_fig_dir, data_set, feature_data)
# On Windows int64 keys of sweep numbers cannot be converted to str by json.dump when serializing.
# Thus, we are converting them here:
feature_data["sweep_features"] = {
str(k): v
for k, v in feature_data["sweep_features"].items()
}
return feature_data
def run_qc(stimulus_ontology_file, cell_features, sweep_features, qc_criteria):
"""
Parameters
----------
stimulus_ontology_file : str
ontology file name
cell_features: dict
cell features
sweep_features : list of dicts
sweep features
qc_criteria: dict
qc criteria
Returns
-------
dict
containing state of the cell and sweeps
"""
lu.log_pretty_header("Perform QC checks", level=1)
if not stimulus_ontology_file:
stimulus_ontology_file = StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE
logging.info(
F"Ontology is not provided, using default {StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE}"
)
ont = StimulusOntology(ju.read(stimulus_ontology_file))
cell_state, sweep_states = qcp.qc_experiment(ont, cell_features,
sweep_features, qc_criteria)
qc_summary(sweep_features, sweep_states, cell_features, cell_state)
return dict(cell_state=cell_state, sweep_states=sweep_states)
def run_sweep_extraction(input_nwb_file,
input_h5_file,
stimulus_ontology_file,
input_manual_values=None):
"""
Parameters
----------
input_nwb_file
input_h5_file
stimulus_ontology_file
input_manual_values
Returns
-------
"""
lu.log_pretty_header("Extract QC features", level=1)
if input_manual_values is None:
input_manual_values = {}
manual_values = {}
for mk in MANUAL_KEYS:
if mk in input_manual_values:
manual_values[mk] = input_manual_values[mk]
if stimulus_ontology_file:
mso.make_stimulus_ontology_from_lims(stimulus_ontology_file)
else:
stimulus_ontology_file = StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE
logging.info(
F"Ontology is not provided, using default {StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE}"
)
ont = StimulusOntology(ju.read(stimulus_ontology_file))
ds = create_data_set(nwb_file=input_nwb_file,
h5_file=input_h5_file,
ontology=ont)
cell_features, cell_tags = qcfe.cell_qc_features(ds, manual_values)
for tag in cell_tags:
logging.warning(tag)
sweep_features = qcfe.sweep_qc_features(ds)
return dict(
cell_features=cell_features,
cell_tags=cell_tags,
sweep_features=sweep_features,
)
def run_sweep_extraction(input_nwb_file,
stimulus_ontology_file=None,
input_manual_values=None,
update_ontology=True,
**unused_args):
"""
Parameters
----------
input_nwb_file
stimulus_ontology_file
input_manual_values
Returns
-------
"""
log_pretty_header("Extract QC features", level=1)
if input_manual_values is None:
input_manual_values = {}
manual_values = {}
for mk in MANUAL_KEYS:
if mk in input_manual_values:
manual_values[mk] = input_manual_values[mk]
if stimulus_ontology_file and update_ontology:
make_stimulus_ontology_from_lims(stimulus_ontology_file)
if stimulus_ontology_file is None:
stimulus_ontology_file = \
StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE
logging.info(f"Ontology is not provided, using default "
f"{StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE}")
ont = StimulusOntology(json_utilities.read(stimulus_ontology_file))
ds = create_ephys_data_set(nwb_file=input_nwb_file, ontology=ont)
cell_features, cell_tags = cell_qc_features(ds, manual_values)
for tag in cell_tags:
logging.warning(tag)
sweep_features = sweep_qc_features(ds)
return {
"cell_features": cell_features,
"cell_tags": cell_tags,
"sweep_features": sweep_features,
}
def main():
"""
Usage:
python run_pipeline.py --input_json INPUT_JSON --output_json OUTPUT_JSON
"""
module = ags.ArgSchemaParser(schema_type=PipelineParameters)
output = run_pipeline(module.args["input_nwb_file"],
module.args.get("input_h5_file", None),
module.args["output_nwb_file"],
module.args.get("stimulus_ontology_file", None),
module.args.get("qc_fig_dir", None),
module.args.get("qc_criteria", None),
module.args.get("manual_sweep_states", None))
ju.write(module.args["output_json"], output)
lu.log_pretty_header("Analysis completed!", level=1)
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 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 run_feature_extraction(input_nwb_file,
stimulus_ontology_file,
output_nwb_file,
qc_fig_dir,
sweep_info,
cell_info,
write_spikes=True):
lu.log_pretty_header("Extract ephys features", level=1)
sp.drop_failed_sweeps(sweep_info)
if len(sweep_info) == 0:
raise er.FeatureError(
"There are no QC-passed sweeps available to analyze")
if not stimulus_ontology_file:
stimulus_ontology_file = StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE
logging.info(
F"Ontology is not provided, using default {StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE}"
)
ont = StimulusOntology(ju.read(stimulus_ontology_file))
data_set = create_ephys_data_set(sweep_info=sweep_info,
nwb_file=input_nwb_file,
ontology=ont)
(cell_features, sweep_features, cell_record, sweep_records, cell_state,
feature_states) = dsft.extract_data_set_features(data_set)
if cell_state['failed_fx']:
feature_data = {'cell_state': cell_state}
else:
if cell_info:
cell_record.update(cell_info)
feature_data = {
'cell_features': cell_features,
'sweep_features': sweep_features,
'cell_record': cell_record,
'sweep_records': sweep_records,
'cell_state': cell_state,
'feature_states': feature_states
}
if write_spikes:
if not feature_states['sweep_features_state']['failed_fx']:
sweep_spike_times = collect_spike_times(sweep_features)
append_spike_times(input_nwb_file,
sweep_spike_times,
output_nwb_path=output_nwb_file)
else:
logging.warn("extract_sweep_features failed, "
"unable to write spikes")
if qc_fig_dir is None:
logging.info("qc_fig_dir is not provided, will not save figures")
else:
plotqc.display_features(qc_fig_dir, data_set, feature_data)
# On Windows int64 keys of sweep numbers cannot be converted to str by json.dump when serializing.
# Thus, we are converting them here:
feature_data["sweep_features"] = {
str(k): v
for k, v in feature_data["sweep_features"].items()
}
return feature_data
