def main(args):
p = CompoundParser([
execution_parser, application_parser, registration_parser,
AnnotatedParser(parser=mk_mbm_parser(), namespace="mbm")
])
options = parse(p, args[1:])
stages = asymmetry_pipeline(options).stages
execute(stages, options)
def main(args):
p = CompoundParser(
[execution_parser,
application_parser,
registration_parser,
AnnotatedParser(parser=mk_mbm_parser(), namespace="mbm")])
options = parse(p, args[1:])
stages = asymmetry_pipeline(options).stages
execute(stages, options)
def main(args):
# TODO rewrite using `mk_application`
p = CompoundParser(
[execution_parser,
application_parser,
registration_parser,
tamarack_parser,
AnnotatedParser(parser=mk_mbm_parser(with_common_space=False,
with_maget=True),
namespace="mbm")])
options = parse(p, args[1:])
stages = tamarack_pipeline(options).stages
execute(stages, options)
def main(args):
p = CompoundParser(
[execution_parser,
application_parser,
registration_parser,
#twolevel_parser,
AnnotatedParser(parser=mk_mbm_parser(with_common_space=False), namespace="mbm"), # TODO use this before 1st-/2nd-level args
# TODO to combine the information from all three MBM parsers,
# could use `ConfigArgParse`r `_source_to_settings` (others?) to check whether an option was defaulted
# or user-specified, allowing the first/second-level options to override the general mbm settings
#AnnotatedParser(parser=mbm_parser, namespace="first_level", prefix="first-level"),
#AnnotatedParser(parser=mbm_parser, namespace="second_level", prefix="second-level"),
#stats_parser
#segmentation_parser
]) # TODO add more stats parsers?
options = parse(p, args[1:])
execute(two_level_pipeline(options).stages, options)
def main(args):
p = CompoundParser([
execution_parser,
application_parser,
registration_parser,
#twolevel_parser,
AnnotatedParser(
parser=mk_mbm_parser(with_common_space=False),
namespace="mbm"), # TODO use this before 1st-/2nd-level args
# TODO to combine the information from all three MBM parsers,
# could use `ConfigArgParse`r `_source_to_settings` (others?) to check whether an option was defaulted
# or user-specified, allowing the first/second-level options to override the general mbm settings
#AnnotatedParser(parser=mbm_parser, namespace="first_level", prefix="first-level"),
#AnnotatedParser(parser=mbm_parser, namespace="second_level", prefix="second-level"),
#stats_parser
#segmentation_parser
]) # TODO add more stats parsers?
options = parse(p, args[1:])
execute(two_level_pipeline(options).stages, options)
for img, xfm in zip(df["anatomical_lsq6_MincAtom"], df["lsq6_to_atlas_XfmAtom"])],
count_targetspace_MincAtom=lambda df:
[s.defer(mincresample_new(img=img, xfm=xfm, like=atlas_target))
for img, xfm in zip(df["count_lsq6_MincAtom"], df["lsq6_to_atlas_XfmAtom"])],
atlas_lsq6space_MincAtom=lambda df:
[s.defer(mincresample_new(img=atlas_target_label, xfm=xfm, like=like, invert=True,
interpolation=Interpolation.nearest_neighbour,
extra_flags=('-keep_real_range',)))
for xfm, like in zip( df["lsq6_to_atlas_XfmAtom"], df["count_lsq6_MincAtom"])]
)
csv.applymap(maybe_deref_path).to_csv("analysis.csv",index=False)
s.defer(create_quality_control_images(imgs=csv.count_targetspace_MincAtom.tolist(), montage_dir=output_dir,
montage_output=os.path.join(output_dir, pipeline_name + "_resampled",
"count_montage"),
auto_range=True,
message="count_mincs"))
return Result(stages=s, output=())
lsq6_parser = AnnotatedParser(parser=BaseParser(_mk_lsq6_parser(with_nuc=False, with_inormalize=False), "LSQ6"),
namespace="lsq6", cast=to_lsq6_conf)
tv_pipeline_application = mk_application(parsers=[AnnotatedParser(parser=mk_mbm_parser(with_common_space=False,
lsq6_parser=lsq6_parser),
namespace="mbm"),
consensus_to_atlas_parser],
pipeline=tissue_vision_pipeline)
if __name__ == "__main__":
tv_pipeline_application()