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 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 main():
"""
Runs pipeline from the nwb file
Usage:
python pipeline_from_nwb_file.py INPUT_NWB_FILE
User must specify the OUTPUT_DIR
"""
input_nwb_file = sys.argv[1]
input_nwb_file_basename = os.path.basename(input_nwb_file)
cell_name = os.path.splitext(input_nwb_file_basename)[0]
cell_dir = os.path.join(OUTPUT_DIR, cell_name)
if not os.path.exists(cell_dir):
os.makedirs(cell_dir)
lu.configure_logger(cell_dir)
pipe_input = gpi.generate_pipeline_input(cell_dir,
input_nwb_file=input_nwb_file)
input_json = os.path.join(cell_dir, INPUT_JSON)
ju.write(input_json, pipe_input)
# reading back from disk
pipe_input = ju.read(input_json)
pipe_output = run_pipeline(pipe_input["input_nwb_file"],
pipe_input.get("input_h5_file", None),
pipe_input["output_nwb_file"],
pipe_input.get("stimulus_ontology_file", None),
pipe_input.get("qc_fig_dir",
None), pipe_input["qc_criteria"],
pipe_input["manual_sweep_states"])
ju.write(os.path.join(cell_dir, OUTPUT_JSON), pipe_output)
def main():
"""
Runs pipeline from the specimen_id
Usage:
python pipeline_from_nwb_file.py SPECIMEN_ID
User must specify the OUTPUT_DIR
"""
specimen_id = sys.argv[1]
cell_name = specimen_id
cell_dir = os.path.join(OUTPUT_DIR, cell_name)
if not os.path.exists(cell_dir):
os.makedirs(cell_dir)
lu.configure_logger(cell_dir)
pipe_input = gpi.generate_pipeline_input(cell_dir,
specimen_id=int(specimen_id))
input_json = os.path.join(cell_dir, INPUT_JSON)
ju.write(input_json, pipe_input)
# reading back from disk
pipe_input = ju.read(input_json)
pipe_output = run_pipeline(pipe_input["input_nwb_file"],
pipe_input.get("input_h5_file", None),
pipe_input["output_nwb_file"],
pipe_input.get("stimulus_ontology_file", None),
pipe_input.get("qc_fig_dir",
None), pipe_input["qc_criteria"],
pipe_input["manual_sweep_states"])
ju.write(os.path.join(cell_dir, OUTPUT_JSON), pipe_output)
def main():
"""
Convenience script for running ephys pipeline from a given nwb file
It generates the pipeline input and then calls the run_pipeline executable
Usage:
python run_pipeline_from_nwb_file.py <input_nwb_file> <output_dir>
"""
parser = argparse.ArgumentParser(
description="Process an nwb file through the ephys pipeline"
)
parser.add_argument(
"input_nwb_file", type=str, help="process this NWB2 file"
)
parser.add_argument(
"output_dir", type=str, help="outputs will be written here"
)
parser.add_argument(
"--write_spikes", type=bool, default=False,
help="If true will attempt to append spike times to the nwb file",
)
parser.add_argument(
"--input_json", type=str, default="input.json",
help=(
"write pipeline input json file here (relative to "
"OUTPUT_DIR/cell_name, where cell_name is the extensionless "
"basename of the input NWB file)"
)
)
parser.add_argument(
"--output_json", type=str, default="output.json",
help=(
"write output json file here (relative to OUTPUT_DIR/cell_name, "
"where cell_name is the extensionless basename of the input NWB "
"file)"
)
)
parser.add_argument(
"--qc_fig_dir", type=str, default=None, const="qc_figs", nargs="?",
help=(
"Generate qc figures and store them here (relative to "
"OUTPUT_DIR/cell_name, where cell_name is the extensionless "
"basename of the input nwb file). If you supply --qc_fig_dir with "
"no arguments, the path will be OUTPUT_DIR/cell_name/qc_figs. If "
"this argument is not supplied, no figures will be generated."
)
)
args = vars(parser.parse_args())
output_dir = args["output_dir"]
input_nwb_file = args["input_nwb_file"]
input_json = args["input_json"]
output_json = args["output_json"]
input_nwb_file_basename = os.path.basename(input_nwb_file)
cell_name = os.path.splitext(input_nwb_file_basename)[0]
cell_dir = os.path.join(output_dir, cell_name)
os.makedirs(cell_dir, exist_ok=True)
lu.configure_logger(cell_dir)
pipeline_input = generate_pipeline_input(
cell_dir=cell_dir,
input_nwb_file=input_nwb_file,
plot_figures=args["qc_fig_dir"] is not None,
qc_fig_dirname=args["qc_fig_dir"]
)
input_json = os.path.join(cell_dir, input_json)
ju.write(input_json, pipeline_input)
# reading back from disk
pipeline_input = ju.read(input_json)
pipeline_output = run_pipeline(pipeline_input["input_nwb_file"],
pipeline_input["output_nwb_file"],
pipeline_input.get("stimulus_ontology_file", None),
pipeline_input.get("qc_fig_dir", None),
pipeline_input["qc_criteria"],
pipeline_input["manual_sweep_states"],
args["write_spikes"])
ju.write(os.path.join(cell_dir, output_json), pipeline_output)