def main():
"""Main entry point for running sg-prototype."""
mod = ArgSchemaParser(schema_type=InputParameters,
output_schema_type=OutputParameters)
output = {}
# YOUR STUFF GOES HERE
output.update({"input_parameters": mod.args})
if "output_json" in mod.args:
mod.output(output)
else:
print(mod.get_output_json(output))
def test_bad_output(tmpdir):
file_out = tmpdir.join('test_output_bad.json')
input_parameters = {'output_json': str(file_out)}
mod = ArgSchemaParser(input_data=input_parameters,
output_schema_type=MyOutputSchema,
args=[])
M = [[5, 5], [7, 2]]
Mnp = np.array(M)
output = {"a": "example", "b": "not a number", "M": Mnp}
with pytest.raises(mm.ValidationError):
mod.output(output)
def test_output_unvalidated(tmpdir):
file_out = tmpdir.join('test_output_unvalidated.json')
input_parameters = {'output_json': str(file_out)}
mod = ArgSchemaParser(input_data=input_parameters, args=[])
output = {
"a": "example",
}
mod.output(output)
with open(str(file_out), 'r') as fp:
actual_output = json.load(fp)
assert actual_output == output
def main():
"""Main entry point for running AllenSDK Eye Tracking."""
try:
mod = ArgSchemaParser(schema_type=InputParameters,
output_schema_type=OutputParameters)
istream = CvInputStream(mod.args["input_source"])
ostream = setup_annotation(
istream.frame_shape,
**mod.args.get("annotation", DEFAULT_ANNOTATION))
qc_params = mod.args.get("qc", {})
generate_plots = qc_params.get("generate_plots",
EyeTracker.DEFAULT_GENERATE_QC_OUTPUT)
tracker = EyeTracker(istream, ostream, mod.args.get("starburst", {}),
mod.args.get("ransac",
{}), mod.args["pupil_bounding_box"],
mod.args["cr_bounding_box"], generate_plots,
**mod.args.get("eye_params", {}))
cr_params, pupil_params, cr_err, pupil_err = tracker.process_stream(
start=mod.args.get("start_frame", 0),
stop=mod.args.get("stop_frame", None),
step=mod.args.get("frame_step", 1))
output = write_output(mod.args["output_dir"], cr_params, pupil_params,
tracker.mean_frame)
pupil_intensity = None
if tracker.adaptive_pupil:
pupil_intensity = tracker.pupil_colors
if generate_plots:
write_QC_output(tracker.annotator,
cr_params,
pupil_params,
cr_err,
pupil_err,
tracker.mean_frame,
pupil_intensity=pupil_intensity,
**mod.args)
output["input_parameters"] = mod.args
if "output_json" in mod.args:
mod.output(output, indent=1)
else:
print(json.dumps(mod.get_output_json(output), indent=1))
except marshmallow.ValidationError as e:
print(e)
argparser = schema_argparser(InputParameters())
argparser.print_usage()
def main():
from ._schemas import InputParameters, OutputParameters
mod = ArgSchemaParser(schema_type=InputParameters,
output_schema_type=OutputParameters)
output = calculate_quality_metrics(mod.args)
output.update({"input_parameters": mod.args})
if "output_json" in mod.args:
mod.output(output, indent=2)
else:
print(mod.get_output_json(output))
def main():
from ._schemas import InputParameters, OutputParameters
mod = ArgSchemaParser(schema_type=InputParameters,
output_schema_type=OutputParameters)
# output = calculate_stimulus_metrics_ondisk(mod.args)
output = calculate_stimulus_metrics_gather(mod.args)
if MPI_rank == 0:
output.update({"input_parameters": mod.args})
if "output_json" in mod.args:
mod.output(output, indent=2)
else:
log_info(mod.get_output_json(output))
barrier()
def main():
from ._schemas import InputParameters, OutputParameters
mod = ArgSchemaParser(schema_type=InputParameters,
output_schema_type=OutputParameters)
output = classify_noise_templates(mod.args)
output.update({"input_parameters": mod.args})
if "output_json" in mod.args:
mod.output(output, indent=2)
else:
print(mod.get_output_json(output))
def main():
from ._schemas import InputParameters, OutputParameters
"""Main entry point:"""
mod = ArgSchemaParser(schema_type=InputParameters,
output_schema_type=OutputParameters)
output = get_psth_events(mod.args)
output.update({"input_parameters": mod.args})
if "output_json" in mod.args:
mod.output(output, indent=2)
else:
print(mod.get_output_json(output))
def test_output(tmpdir):
file_out = tmpdir.join('test_output.json')
input_parameters = {'output_json': str(file_out)}
mod = ArgSchemaParser(input_data=input_parameters,
output_schema_type=MyOutputSchema,
args=[])
M = [[5, 5], [7, 2]]
Mnp = np.array(M)
output = {"a": "example", "M": Mnp}
expected_output = {"a": "example", "b": 5, "M": M}
mod.output(output)
with open(str(file_out), 'r') as fp:
actual_output = json.load(fp)
assert actual_output == expected_output
def main():
from ._schemas import InputParameters, OutputParameters
mod = ArgSchemaParser(schema_type=InputParameters,
output_schema_type=OutputParameters)
output = run_automerging(mod.args)
output.update({"input_parameters": mod.args})
if "output_json" in mod.args:
mod.output(output, indent=2)
else:
print(mod.get_output_json(output))
def main():
from ._schemas import InputParameters, OutputParameters
"""Main entry point:"""
mod = ArgSchemaParser(schema_type=InputParameters,
output_schema_type=OutputParameters)
if mod.args['tPrime_helper_params']['tPrime_3A']:
output = call_TPrime_3A(mod.args)
else:
output = call_TPrime(mod.args)
output.update({"input_parameters": mod.args})
if "output_json" in mod.args:
mod.output(output, indent=2)
else:
print(mod.get_output_json(output))
def main():
parser = ArgSchemaParser(
schema_type=InputParameters,
output_schema_type=OutputParameters
)
inputs_record = cp.deepcopy(parser.args)
logging.getLogger().setLevel(inputs_record.pop("log_level"))
inputs_record.pop("input_json", None)
inputs_record.pop("output_json", None)
output_table_path = inputs_record.pop("output_table_path", None)
output = {}
output.update({"inputs": parser.args})
output.update({"results": extract_multiple(**inputs_record)})
parser.output(output)
def main():
"""Main entry point for running AllenSDK Eye Tracking."""
try:
mod = ArgSchemaParser(schema_type=InputParameters,
output_schema_type=OutputParameters)
starburst_args = get_starburst_args(mod.args["starburst"])
istream = CvInputStream(mod.args["input_source"])
im_shape = istream.frame_shape
ostream = setup_annotation(im_shape, **mod.args["annotation"])
tracker = EyeTracker(im_shape, istream, ostream, starburst_args,
mod.args["ransac"],
mod.args["pupil_bounding_box"],
mod.args["cr_bounding_box"],
mod.args["qc"]["generate_plots"],
**mod.args["eye_params"])
pupil_parameters, cr_parameters = tracker.process_stream(
start=mod.args.get("start_frame", 0),
stop=mod.args.get("stop_frame", None),
step=mod.args.get("frame_step", 1))
output = write_output(mod.args["output_dir"], cr_parameters,
pupil_parameters, tracker.mean_frame)
if mod.args["qc"]["generate_plots"]:
write_QC_output(tracker.annotator, cr_parameters, pupil_parameters,
tracker.mean_frame, **mod.args)
output["input_parameters"] = mod.args
if "output_json" in mod.args:
mod.output(output, indent=1)
else:
print(json.dumps(mod.get_output_json(output), indent=1))
except marshmallow.ValidationError as e:
print(e)
argparser = schema_argparser(InputParameters())
argparser.print_usage()
def main():
parser = ArgSchemaParser(schema_type=PiaWmStreamlineSchema,
output_schema_type=OutputParameters)
args = cp.deepcopy(parser.args)
logging.getLogger().setLevel(args.pop("log_level"))
output_dir = args.pop('output_dir')
depth_field, gradient_field, translation = run_streamlines(**args)
# save results to file
depth_field_file = os.path.join(output_dir, 'depth_field.nc')
gradient_field_file = os.path.join(output_dir, 'gradient_field.nc')
depth_field.to_netcdf(depth_field_file)
gradient_field.to_netcdf(gradient_field_file)
output = {
'inputs': parser.args,
'translation': translation,
'depth_field_file': depth_field_file,
'gradient_field_file': gradient_field_file,
}
parser.output(output)
def main():
mod = ArgSchemaParser(schema_type=ApplyAffineSchema,
output_schema_type=OutputParameters)
args = mod.args
if 'affine_dict' in args:
affine_transform = AffineTransform.from_dict(args['affine_dict'])
elif 'affine_list' in args:
affine_transform = AffineTransform.from_list(args['affine_list'])
else:
raise ValueError('must provide either an affine_dict or affine_list')
morph_in = morphology_from_swc(args['input_swc'])
morph_out = affine_transform.transform_morphology(morph_in)
morphology_to_swc(morph_out, args['output_swc'])
output = {
'inputs': args,
'transformed_swc': args['output_swc'],
}
mod.output(output)
def main():
mod = ArgSchemaParser(schema_type=InputSchema,
output_schema_type=OutputSchema)
if mod.args['test_mode']:
global volume_to_h5, volume_to_tif
volume_to_h5 = mock_h5
volume_to_tif = mock_tif
h5_opts = {}
if mod.args['compression_level']:
h5_opts = {"compression": "gzip",
"compression_opts": mod.args['compression_level']}
stack_tifs = set()
ready_to_archive = set()
session_storage = mod.args["storage_directory"]
output = {"column_stacks": [],
"file_metadata": []}
experiments = []
z_outs = {}
for plane_group in mod.args["plane_groups"]:
column_stack = plane_group.get("column_z_stack_tif", None)
if column_stack:
ready_to_archive.add(column_stack)
if column_stack not in stack_tifs:
try:
out, meta = convert_column(
column_stack,
session_storage,
plane_group["ophys_experiments"][0],
**h5_opts
)
output["column_stacks"].append(out)
output["file_metadata"].append(meta)
except ValueError as e:
# don't break on failed column stack conversion
logging.error(e)
stack_tifs.add(column_stack)
for exp in plane_group["ophys_experiments"]:
localz = plane_group["local_z_stack_tif"]
ready_to_archive.add(localz)
out, meta = split_z(localz, exp, **h5_opts)
if localz not in stack_tifs:
output["file_metadata"].append(meta)
stack_tifs.add(localz)
experiments.append(exp)
z_outs[exp["experiment_id"]] = out
surf_outs, surf_meta = split_image(mod.args["surface_tif"],
experiments,
"surface")
depth_outs, depth_meta = split_image(mod.args["depths_tif"],
experiments,
"depth")
ts_outs, ts_meta = split_timeseries(mod.args["timeseries_tif"],
experiments,
**h5_opts)
output["file_metadata"].extend([surf_meta, depth_meta, ts_meta])
exp_out = []
for exp in experiments:
eid = exp["experiment_id"]
sync_stride = ts_outs[eid].pop("sync_stride")
sync_offset = ts_outs[eid].pop("sync_offset")
exp_data = {"experiment_id": eid,
"local_z_stack": z_outs[eid],
"surface_2p": surf_outs[eid],
"depth_2p": depth_outs[eid],
"timeseries": ts_outs[eid],
"sync_offset": sync_offset,
"sync_stride": sync_stride}
exp_out.append(exp_data)
output["experiment_output"] = exp_out
ready_to_archive.add(mod.args["surface_tif"])
ready_to_archive.add(mod.args["depths_tif"])
ready_to_archive.add(mod.args["timeseries_tif"])
output["ready_to_archive"] = list(ready_to_archive)
mod.output(output, indent=1)
# this defines a default dictionary that will be used if input_json is not specified
example_input = {
"inc": {
"name": "from_dictionary",
"increment": 5,
"array": [0, 2, 5],
"write_output": True
},
"output_json": "output_dictionary.json"
}
# here is my ArgSchemaParser that processes my inputs
mod = ArgSchemaParser(input_data=example_input,
schema_type=MyParameters,
output_schema_type=MyOutputParams)
# pull out the inc section of the parameters
inc_params = mod.args['inc']
# do my simple addition of the parameters
inc_array = inc_params['array'] + inc_params['increment']
# define the output dictionary
output = {'name': inc_params['name'], 'inc_array': inc_array}
# if the parameters are set as such write the output
if inc_params['write_output']:
mod.output(output)
pp.pprint(mod.args)