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)