def main():
"""
Usage:
> python generate_qc_input.py --specimen_id SPECIMEN_ID --cell_dir CELL_DIR
> python generate_qc_input.py --input_nwb_file input_nwb_file --cell_dir CELL_DIR
"""
args = parse_args()
se_input = generate_se_input(**args)
cell_dir = args['cell_dir']
if not os.path.exists(cell_dir):
os.makedirs(cell_dir)
lu.configure_logger(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)
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 extract_qc_features(data_set):
cell_features, cell_tags = cell_qc_features(
data_set,
# manual_values=cell_qc_manual_values
)
sweep_features = sweep_qc_features(data_set)
drop_tagged_sweeps(sweep_features)
return cell_features, cell_tags, 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_extraction_and_auto_qc(self, nwb_path, stimulus_ontology, qc_criteria, commit=True):
data_set = create_ephys_data_set(
sweep_info=None,
nwb_file=nwb_path,
ontology=stimulus_ontology
)
cell_features, cell_tags, sweep_features = extract_qc_features(data_set)
sweep_props.drop_tagged_sweeps(sweep_features)
cell_state, cell_features, sweep_states, sweep_features = run_qc(
stimulus_ontology, cell_features, sweep_features, qc_criteria
)
if commit:
self.begin_commit_calculated.emit()
self.stimulus_ontology = stimulus_ontology
self.qc_criteria = qc_criteria
self.nwb_path = nwb_path
self.data_set = data_set
self.cell_features = cell_features
self.cell_tags = cell_tags
self.cell_state = cell_state
self.sweep_features = sweep_features
self.sweep_states = sweep_states
self.manual_qc_states = {sweep["sweep_number"]: "default" for sweep in self.sweep_features}
self.end_commit_calculated.emit(
self.sweep_features, self.sweep_states, self.manual_qc_states, self.data_set
)
self.data_changed.emit(self.nwb_path,
self.stimulus_ontology,
self.sweep_features,
self.cell_features)
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)
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())])