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_feature_extraction(self):
self.status_message.emit("Computing features, please wait.")
drop_failed_sweeps(self.sweep_info)
data_set = create_ephys_data_set(sweep_info=self.sweep_info,
nwb_file=self.input_nwb_file,
ontology=self.ontology)
try:
cell_features, sweep_features, cell_record, sweep_records,\
cell_state, feature_states = extract_data_set_features(data_set)
self.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
}
self.new_state()
self.status_message.emit("Done computing features!")
except (FeatureError, IndexError) as ferr:
exception_message("Feature extraction error",
f"failed feature extraction",
ferr
)
Run all analyses on NWB file
"""
from __future__ import print_function
import os
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from allensdk.api.queries.cell_types_api import CellTypesApi
from ipfx.aibs_data_set import AibsDataSet
from ipfx.data_set_features import extract_data_set_features
# download a specific experiment NWB file via AllenSDK
ct = CellTypesApi()
specimen_id = 595570553
nwb_file = "%d.nwb" % specimen_id
if not os.path.exists(nwb_file):
ct.save_ephys_data(specimen_id, nwb_file)
sweep_info = ct.get_ephys_sweeps(specimen_id)
data_set = AibsDataSet(
sweep_info=sweep_info,
nwb_file=nwb_file) # Download and access the experimental data
cell_features, sweep_features, cell_record, sweep_records = \
extract_data_set_features(data_set, subthresh_min_amp=-100.0)
print(cell_record)
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