def test_my_default_nested_parser():
input_data = {
'a': 5
}
argschema.ArgSchemaParser(input_data=input_data,
schema_type=MySchema2,
args=[])
def main():
logging.basicConfig(
format='%(asctime)s - %(process)s - %(levelname)s - %(message)s')
args = sys.argv[1:]
try:
parser = argschema.ArgSchemaParser(
args=args,
schema_type=InputSchema,
output_schema_type=OutputSchema,
)
logging.info('Input successfully parsed')
except marshmallow.exceptions.ValidationError as err:
logging.error('Parsing failure')
print(err)
raise err
try:
output = write_behavior_nwb(parser.args['session_data'],
parser.args['output_path'])
logging.info('File successfully created')
except Exception as err:
logging.error('NWB write failure')
print(err)
raise err
write_or_print_outputs(output, parser)
def main():
module = ags.ArgSchemaParser(schema_type=OptimizeParameters)
preprocess_results = ju.read(module.args["paths"]["preprocess_results"])
passive_results = ju.read(module.args["paths"]["passive_results"])
fit_style_data = ju.read(module.args["paths"]["fit_style"])
results = optimize(
hoc_files=module.args["paths"]["hoc_files"],
compiled_mod_library=module.args["paths"]["compiled_mod_library"],
morphology_path=module.args["paths"]["swc"],
features=preprocess_results["features"],
targets=preprocess_results["target_features"],
stim_params=StimParams(preprocess_results["stimulus"]),
passive_results=passive_results,
fit_type=module.args["fit_type"],
fit_style_data=fit_style_data,
seed=module.args["seed"],
ngen=module.args["ngen"],
mu=module.args["mu"],
storage_directory=module.args["paths"]["storage_directory"],
starting_population=module.args["paths"].get("starting_population",
None))
logging.info("Writing optimization output")
ju.write(module.args["output_json"], results)
def test_simple_description():
d = {
'a': "hello",
'b': 1,
'd': [1, 5, 4]
}
mod = argschema.ArgSchemaParser(input_data = d, schema_type=MyShorterExtension)
def test_SessionUploadSchema(tmpdir):
src_file = Path(tmpdir) / 'src.csv'
src_file.touch()
dst_file = Path(tmpdir) / 'dst.csv'
output_json = Path(tmpdir) / 'output.json'
test_data = {
'files': [{
'source': str(src_file),
'destination': str(dst_file),
'key': ''
}],
'output_json':
str(output_json)
}
parser = argschema.ArgSchemaParser(
test_data,
schema_type=SessionUploadInputSchema,
output_schema_type=SessionUploadOutputSchema,
args=[])
# Mocking the functionality of the main method
shutil.copy(src_file, dst_file)
parser.output({'files': test_data['files']})
def main():
module = ags.ArgSchemaParser(schema_type=MergeParameters)
project = module.args["project"]
gmm_types = module.args["gmm_types"]
sub_dirs = [
s.format(project) for s in ["all_{:s}", "exc_{:s}", "inh_{:s}"]
]
piecewise_components = [2, 2, 3]
for sub_dir, gmm_type, pw_comp in zip(sub_dirs, gmm_types,
piecewise_components):
print("merging for ", sub_dir, "with", gmm_type)
merge_info, new_labels, tau_merged, _ = entropy_merges(
sub_dir, project, gmm_type=gmm_type, piecewise_components=pw_comp)
print(merge_info)
data = pd.read_csv(os.path.join(
sub_dir, "sparse_pca_components_{:s}.csv".format(project)),
index_col=0)
new_labels, tau_merged, _, _ = order_new_labels(
new_labels, tau_merged, data)
np.savetxt(os.path.join(sub_dir, "post_merge_proba.txt"), tau_merged)
np.save(os.path.join(sub_dir, "post_merge_cluster_labels.npy"),
new_labels)
df = pd.read_csv(
os.path.join(sub_dir, "all_tsne_coords_{:s}.csv".format(project)))
df["clustering_3"] = new_labels
df.to_csv(
os.path.join(sub_dir,
"all_tsne_coords_{:s}_plus.csv".format(project)))
def main():
logging.basicConfig(
format='%(asctime)s - %(process)s - %(levelname)s - %(message)s')
# TODO replace with argschema implementation of multisource parser
remaining_args = sys.argv[1:]
input_data = {}
if '--get_inputs_from_lims' in sys.argv:
lims_parser = argparse.ArgumentParser(add_help=False)
lims_parser.add_argument('--host', type=str, default='http://lims2')
lims_parser.add_argument('--job_queue', type=str, default=None)
lims_parser.add_argument('--strategy', type=str, default=None)
lims_parser.add_argument('--image_series_id', type=int, default=None)
lims_parser.add_argument('--output_root', type=str, default=None)
lims_args, remaining_args = lims_parser.parse_known_args(
remaining_args)
remaining_args = [
item for item in remaining_args if item != '--get_inputs_from_lims'
]
input_data = get_inputs_from_lims(**lims_args.__dict__)
parser = argschema.ArgSchemaParser(
args=remaining_args,
input_data=input_data,
schema_type=InputParameters,
output_schema_type=OutputParameters,
)
output = run_grid(parser.args)
write_or_print_outputs(output, parser)
def main():
module = ags.ArgSchemaParser(schema_type=PassiveFittingParameters)
info = ju.read(module.args["paths"]["passive_info"])
if not info["should_run"]:
ju.write(module.args["output_json"], { "paths": {} })
return
swc_path = module.args["paths"]["swc"].encode('ascii', 'ignore')
up_data = np.loadtxt(module.args["paths"]["up"])
down_data = np.loadtxt(module.args["paths"]["down"])
passive_fit_type = module.args["passive_fit_type"]
results_file = module.args["paths"]["passive_fit_results_file"]
npf.initialize_neuron(swc_path, module.args["paths"]["fit"])
if passive_fit_type == npf.PASSIVE_FIT_1:
results = npf.passive_fit_1(info, up_data, down_data)
elif passive_fit_type == npf.PASSIVE_FIT_2:
results = npf.passive_fit_2(info, up_data, down_data)
elif passive_fit_type == npf.PASSIVE_FIT_ELEC:
results = npf.passive_fit_elec(info, up_data, down_data)
else:
raise Exception("unknown passive fit type: %s" % passive_fit_type)
ju.write(results_file, results)
ju.write(module.args["output_json"], { "paths": { passive_fit_type: results_file } })
def main():
"""
Convert nwb version 1 to nwb version 2
Usage:
-----
python run_nwb1_to_nwb2_conversion.py --input_nwb_file /path/to/version1/file.nwb
or
python -m ipfx.bin.run_nwb1_to_nwb2_conversion --input_nwb_file /path/to/version1/file.nwb
Will produce nwb version 2 file in the same folder
"""
module = args.ArgSchemaParser(schema_type=ConvertNWBParameters)
nwb1_file_name = module.args["input_nwb_file"]
dir_name = os.path.dirname(nwb1_file_name)
base_name = os.path.basename(nwb1_file_name)
nwb2_file_name = make_nwb2_file_name(dir_name, base_name)
if not os.path.exists(nwb1_file_name):
raise ValueError(f"The file {nwb1_file_name} does not exist.")
NWBConverter(
nwb1_file_name,
nwb2_file_name,
)
def main():
module = ags.ArgSchemaParser(schema_type=ModelSelectionParameters)
swc_path = module.args["paths"]["swc"]
fit_style_paths = module.args["paths"]["fit_styles"]
best_fit_json_path = module.args["paths"]["best_fit_json_path"]
passive = ju.read(module.args["paths"]["passive_results"])
preprocess = ju.read(module.args["paths"]["preprocess_results"])
fits = module.args["paths"]["fits"]
fit_results = ms.fit_info(fits)
best_fit = ms.select_model(fit_results, module.args["paths"], passive,
preprocess["v_baseline"],
module.args["noise_1_sweeps"],
module.args["noise_2_sweeps"])
if best_fit is None:
raise Exception("Failed to find acceptable optimized model")
logging.info("building fit data")
fit_style_data = ju.read(
module.args["paths"]["fit_styles"][best_fit["fit_type"]])
fit_data = ms.build_fit_data(best_fit["params"], passive, preprocess,
fit_style_data)
logging.info("writing fit data: %s", best_fit_json_path)
ju.write(best_fit_json_path, fit_data)
output = {
"paths": {
"fit_json": best_fit_json_path,
}
}
logging.info("writing output json: %s", module.args["output_json"])
ju.write(module.args["output_json"], output)
def test_david_example(tmpdir_factory):
file_ = tmpdir_factory.mktemp('test').join('testinput.json')
file_.write(json.dumps(david_data))
args = ['--input_json', str(file_)]
mod = argschema.ArgSchemaParser(schema_type=PopulationSelectionParameters,args=args)
print(mod.args)
assert(len(mod.args['paths']['fits'])==2)
def main():
module = ags.ArgSchemaParser(schema_type=CollectFeatureVectorParameters)
if module.args["input"]: # input file should be list of IDs on each line
with open(module.args["input"], "r") as f:
ids = [int(line.strip("\n")) for line in f]
run_feature_vector_extraction(ids=ids, **module.args)
else:
run_feature_vector_extraction(**module.args)
def test_training_schema_fails_nonexistent_data(self, s3_file):
args = {
"training_data": "s3://myschematest/does/not/exist.txt",
"test_data": s3_file
}
with pytest.raises(mm.ValidationError, match=r".*does not exist"):
argschema.ArgSchemaParser(input_data=args,
schema_type=train.TrainingSchema,
args=[])
def test_training_schema_fails_bad_optimizer(self, s3_file):
args = {
"training_data": s3_file,
"test_data": s3_file,
"optimizer": "CoolOptimizer"
}
with pytest.raises(mm.ValidationError, match=r"Must be one of"):
argschema.ArgSchemaParser(input_data=args,
schema_type=train.TrainingSchema,
args=[])
def test_training_schema_fails_bad_model(self, s3_file):
args = {
"training_data": s3_file,
"test_data": s3_file,
"model": "SomeRandomAlgorithm"
}
with pytest.raises(mm.ValidationError, match=r"Must be one of"):
argschema.ArgSchemaParser(input_data=args,
schema_type=train.TrainingSchema,
args=[])
def main():
module = args.ArgSchemaParser(schema_type=ConvertNWBParameters)
nwb1_file_name = module.args["input_nwb_file"]
nwb2_file_name = make_nwb2_file_name(nwb1_file_name)
if not os.path.exists(nwb1_file_name):
raise ValueError(f"The file {nwb1_file_name} does not exist.")
convert(nwb1_file=nwb1_file_name, nwb2_file=nwb2_file_name)
def main():
module = ags.ArgSchemaParser(schema_type=MorphologySummaryParameters)
run_morphology_summary(module.args["pia_transform"],
module.args["relative_soma_depth"],
module.args["soma_depth"], module.args["swc_file"],
module.args["thumbnail_file"],
module.args["cortex_thumbnail_file"],
module.args["normal_depth_thumbnail_file"],
module.args["high_resolution_thumbnail_file"])
ju.write(module.args["output_json"], {})
def main():
"""
Usage:
python run_sweep_extraction.py
--input_json INPUT_JSON --output_json OUTPUT_JSON
"""
module = ags.ArgSchemaParser(schema_type=SweepExtractionParameters)
output = run_sweep_extraction(**module.args)
json_utilities.write(module.args["output_json"], output)
def main():
"""
Usage:
python run_sweep_extraction.py --input_json INPUT_JSON --output_json OUTPUT_JSON
"""
module = ags.ArgSchemaParser(schema_type=SweepExtractionParameters)
output = run_sweep_extraction(
module.args["input_nwb_file"], module.args.get("input_h5_file", None),
module.args.get("stimulus_ontology_file", None))
ju.write(module.args["output_json"], output)
def main():
module = ags.ArgSchemaParser(schema_type=ConsolidateParameters)
preprocess_results = ju.read(module.args["paths"]["preprocess_results"])
is_spiny = preprocess_results["is_spiny"]
info = ju.read(module.args["paths"]["passive_info"])
if info["should_run"]:
fit_1_path = module.args["paths"]["passive_fit_1"]
fit_1 = ju.read(fit_1_path)
fit_2_path = module.args["paths"]["passive_fit_2"]
fit_2 = ju.read(fit_2_path)
fit_3_path = module.args["paths"]["passive_fit_elec"]
fit_3 = ju.read(fit_3_path)
ra, cm1, cm2 = cpf.compare_runs(preprocess_results, fit_1, fit_2,
fit_3)
else:
ra = 100.
cm1 = 1.
if is_spiny:
cm2 = 2.
else:
cm2 = 1.
passive = {
"ra": ra,
"cm": {
"soma": cm1,
"axon": cm1,
"dend": cm2
},
"e_pas": preprocess_results["v_baseline"]
}
passive["e_pas"] = preprocess_results["v_baseline"]
if preprocess_results["has_apical"]:
passive["cm"]["apic"] = cm2
passive_results_path = module.args["paths"]["passive_results"]
ju.write(passive_results_path, passive)
output = {
"paths": {
"passive_results": passive_results_path,
}
}
ju.write(module.args["output_json"], output)
def main():
"""
Usage:
python run_qc.py --input_json INPUT_JSON --output_json OUTPUT_JSON
"""
module = ags.ArgSchemaParser(schema_type=QcParameters)
output = run_qc(module.args.get("stimulus_ontology_file",
None), module.args["cell_features"],
module.args["sweep_features"], module.args["qc_criteria"])
ju.write(module.args["output_json"], output)
def main():
module = ags.ArgSchemaParser(schema_type=PopulationSelectionParameters)
print module.args
fits = module.args["paths"]["fits"]
populations = ps.population_info(fits)
starting_populations = ps.select_starting_population(populations)
output = {
"paths": {
"starting_populations": starting_populations,
}
}
ju.write(module.args["output_json"], output)
def main():
global files
global ids
module = ags.ArgSchemaParser(schema_type=CollectFeatureVectorParameters)
path = module.args["input"]
no = 0
# r=root, d=directories, f = files
for r, d, f in os.walk(path):
for file in f:
if '.nwb' in file:
files.append(os.path.join(r, file))
ids.append(no)
no += 1
run_feature_vector_extraction(ids=ids, files=files, **module.args)
def main():
"""Main sequence of pre-processing and passive fitting"""
# This argschema package reads arguments from a JSON file
module = ags.ArgSchemaParser(schema_type=PreprocessorParameters,
logger_name=None)
nwb_path = module.args["paths"][
"nwb"] # nwb - neurodata without borders (ephys data)
swc_path = module.args["paths"]["swc"] # swc - morphology data
storage_directory = module.args["paths"]["storage_directory"]
try:
paths, results, passive_info, s1_tasks, s2_tasks = \
preprocess(data_set=NwbDataSet(nwb_path),
swc_data=pd.read_table(swc_path, sep='\s+', comment='#', header=None),
dendrite_type_tag=module.args["dendrite_type_tag"],
sweeps=module.args["sweeps"],
bridge_avg=module.args["bridge_avg"],
storage_directory=storage_directory)
except NoUsableSweepsException as e:
ju.write(module.args["output_json"], {'error': e.message})
return
preprocess_results_path = os.path.join(storage_directory,
"preprocess_results.json")
ju.write(preprocess_results_path, results)
passive_info_path = os.path.join(storage_directory, "passive_info.json")
ju.write(passive_info_path, passive_info)
paths.update({
"swc": swc_path,
"nwb": nwb_path,
"storage_directory": storage_directory,
"preprocess_results": preprocess_results_path,
"passive_info": passive_info_path,
})
output = {
"paths": paths,
"stage_1_task_list": s1_tasks,
"stage_2_task_list": s2_tasks,
}
ju.write(module.args["output_json"], output)
def main():
global files
global ids
module = ags.ArgSchemaParser(schema_type=CollectFeatureVectorParameters)
path = module.args["input"]
no = 0
# r=root, d=directories, f = files
for r, d, f in os.walk(path):
for file in f:
if '.nwb' in file:
files.append(os.path.join(r, file))
ids.append(no)
no += 1
np.savetxt('test.csv', np.array([files, ids]), delimiter=',', fmt="%s")
run_feature_vector_extraction(ids=ids, nfiles=files, **module.args)
def test_annotation_job_schema_make_objects(upload_manifest, context,
tmp_path):
"""Test that the objects are properly loaded and turned into
appropriate objects."""
input_dict = {
"slapp_upload_manifest_path": upload_manifest,
"annotation_output_manifest": str(tmp_path),
"labeling_project_key": "astley",
"annotation_id_key": "id",
"output_location": str(tmp_path)
}
with context:
result = argschema.ArgSchemaParser(
schema_type=AnnotationIngestJobInput,
input_data=input_dict,
args=[])
assert "manifest_data" in result.args
def main():
"""
Usage:
python run_feature_extraction.py --input_json INPUT_JSON --output_json OUTPUT_JSON
"""
module = ags.ArgSchemaParser(schema_type=FeatureExtractionParameters)
feature_data = run_feature_extraction(
module.args["input_nwb_file"],
module.args.get("stimulus_ontology_file",
None), module.args["output_nwb_file"],
module.args.get("qc_fig_dir", None), module.args["sweep_features"],
module.args["cell_features"])
ju.write(module.args["output_json"], feature_data)
def main():
"""
Usage:
python run_pipeline.py --input_json INPUT_JSON --output_json OUTPUT_JSON
"""
module = ags.ArgSchemaParser(schema_type=PipelineParameters)
output = run_pipeline(
module.args["input_nwb_file"],
module.args["output_nwb_file"],
module.args.get("stimulus_ontology_file", None),
module.args.get("qc_fig_dir", None),
module.args.get("qc_criteria", None),
module.args.get("manual_sweep_states", None),
module.args.get("write_spikes", None),
)
json_utilities.write(module.args["output_json"], output)
def main():
"""
Usage:
python run_pipeline.py --input_json INPUT_JSON --output_json OUTPUT_JSON
"""
module = ags.ArgSchemaParser(schema_type=PipelineParameters)
output = run_pipeline(module.args["input_nwb_file"],
module.args.get("input_h5_file", None),
module.args["output_nwb_file"],
module.args.get("stimulus_ontology_file", None),
module.args.get("qc_fig_dir", None),
module.args.get("qc_criteria", None),
module.args.get("manual_sweep_states", None))
ju.write(module.args["output_json"], output)
lu.log_pretty_header("Analysis completed!", level=1)
def load_hdf5(self):
"""
Opens hdf5 files and loads its datasets
"""
#Get input data from the json file or command line
data = argschema.ArgSchemaParser(schema_type=TopSchema)
input_args = data.args
#Open 2P video hdf5 file and load its content
video_file = h5py.File(input_args['video']['uri'], 'r')
self.buffer_video_frame = video_file.get(
input_args['video']['hdf5']['dataset'][0])
#open segemenation file and load its datasets
mask_file = h5py.File(input_args['segmentation']['uri'], 'r')
self.masks = mask_file.get(
input_args['segmentation']['hdf5']['dataset'][0])
self.masks = self.masks[:, :, :]
self.masks = self.masks.transpose(0, 2, 1)
offset = mask_file.get(
input_args['segmentation']['hdf5']['dataset'][1])
self.offset_x = offset['x']
self.offset_y = offset['y']
size = mask_file.get(input_args['segmentation']['hdf5']['dataset'][2])
self.size_x = size['x']
self.size_y = size['y']
mask_file.close()
#Open ROI Trace hdf5 file and load its content
trace_file = h5py.File(input_args['traces']['uri'], 'r')
self.traces = trace_file.get(
input_args['traces']['hdf5']['dataset'][0])
self.traces = self.traces[:, :]
self.traces = np.asarray(self.traces, dtype=np.float16)
self.trace_min = np.min(self.traces)
self.trace_max = np.max(self.traces)
trace_file.close()
