def run_chirp_feature_vector_extraction(output_dir,
output_code,
include_failed_cells,
specimen_ids,
chirp_stimulus_codes,
data_source="lims",
run_parallel=True):
logging.info("Number of specimens to process: {:d}".format(
len(specimen_ids)))
# Include and name chirp stimulus codes in ontology
ontology_data = ju.read(StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE)
edited_ontology_data = edit_ontology_data(ontology_data,
chirp_stimulus_codes,
new_name_tag="Chirp",
new_core_tag="Core 2")
ontology = StimulusOntology(edited_ontology_data)
get_data_partial = partial(data_for_specimen_id,
data_source=data_source,
ontology=ontology)
if run_parallel:
pool = Pool()
results = pool.map(get_data_partial, specimen_ids)
else:
results = map(get_data_partial, specimen_ids)
used_ids, results, error_set = su.filter_results(specimen_ids, results)
logging.info("Finished with {:d} processed specimens".format(
len(used_ids)))
results_dict = su.organize_results(used_ids, results)
su.save_results_to_npy(used_ids, results_dict, output_dir, output_code)
su.save_errors_to_json(error_set, output_dir, output_code)
logging.info("Finished saving")
def run_feature_vector_extraction(output_dir,
data_source,
output_code,
project,
output_file_type,
sweep_qc_option,
include_failed_cells,
run_parallel,
ap_window_length,
ids=None,
file_list=None,
**kwargs):
"""
Extract feature vector from a list of cells and save result to the output file(s)
Parameters
----------
output_dir : str
see CollectFeatureVectorParameters input schema for details
data_source : str
see CollectFeatureVectorParameters input schema for details
output_code: str
see CollectFeatureVectorParameters input schema for details
project : str
see CollectFeatureVectorParameters input schema for details
output_file_type : str
see CollectFeatureVectorParameters input schema for details
sweep_qc_option: str
see CollectFeatureVectorParameters input schema for details
include_failed_cells: bool
see CollectFeatureVectorParameters input schema for details
run_parallel: bool
see CollectFeatureVectorParameters input schema for details
ap_window_length: float
see CollectFeatureVectorParameters input schema for details
ids: int
ids associated to each cell.
file_list: list of str
nwbfile names
kwargs
Returns
-------
"""
if ids is not None:
specimen_ids = ids
elif data_source == "lims":
specimen_ids = lq.project_specimen_ids(
project, passed_only=not include_failed_cells)
else:
logging.error("Must specify input file if data source is not LIMS")
if output_file_type == "h5":
# Check that we can access the specified file before processing everything
h5_file = h5py.File(
os.path.join(output_dir, "fv_{}.h5".format(output_code)))
h5_file.close()
ontology = StimulusOntology(
ju.read(StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE))
logging.info("Number of specimens to process: {:d}".format(
len(specimen_ids)))
get_data_partial = partial(data_for_specimen_id,
sweep_qc_option=sweep_qc_option,
data_source=data_source,
ontology=ontology,
ap_window_length=ap_window_length,
file_list=file_list)
if run_parallel:
pool = Pool()
results = pool.map(get_data_partial, specimen_ids)
else:
results = map(get_data_partial, specimen_ids)
used_ids, results, error_set = su.filter_results(specimen_ids, results)
logging.info("Finished with {:d} processed specimens".format(
len(used_ids)))
results_dict = su.organize_results(used_ids, results)
if output_file_type == "h5":
su.save_results_to_h5(used_ids, results_dict, output_dir, output_code)
elif output_file_type == "npy":
su.save_results_to_npy(used_ids, results_dict, output_dir, output_code)
else:
raise ValueError(
"Unknown output_file_type option {} (allowed values are h5 and npy)"
.format(output_file_type))
su.save_errors_to_json(error_set, output_dir, output_code)
logging.info("Finished saving")
data_source=data_source,
ontology=ontology,
ap_window_length=ap_window_length,
file_list=file_list)
if run_parallel:
pool = Pool()
results = pool.map(get_data_partial, specimen_ids)
else:
results = map(get_data_partial, specimen_ids)
used_ids, results, error_set = su.filter_results(specimen_ids, results)
logging.info("Finished with {:d} processed specimens".format(len(used_ids)))
results_dict = su.organize_results(used_ids, results)
if output_file_type == "h5":
su.save_results_to_h5(used_ids, results_dict, output_dir, output_code)
elif output_file_type == "npy":
su.save_results_to_npy(used_ids, results_dict, output_dir, output_code)
else:
raise ValueError("Unknown output_file_type option {} (allowed values are h5 and npy)".format(output_file_type))
su.save_errors_to_json(error_set, output_dir, output_code)
logging.info("Finished saving")
def main():
module = ags.ArgSchemaParser(schema_type=CollectFeatureVectorParameters)