def test_run_T1VolumeTissueSegmentation(cmdopt):
import os
from clinica.pipelines.t1_volume_tissue_segmentation.t1_volume_tissue_segmentation_pipeline import T1VolumeTissueSegmentation
from os.path import dirname, join, abspath
working_dir = cmdopt
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'T1VolumeTissueSegmentation')
clean_folder(join(working_dir, 'T1VolumeTissueSegmentation'))
clean_folder(join(root, 'out', 'caps'))
pipeline = T1VolumeTissueSegmentation(
bids_directory=join(root, 'in', 'bids'),
caps_directory=join(root, 'out', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'))
pipeline.base_dir = join(working_dir, 'T1VolumeTissueSegmentation')
pipeline.build()
pipeline.run(bypass_check=True)
out_file = join(
root,
'out/caps/subjects/sub-ADNI011S4105/ses-M00/t1/spm/segmentation/dartel_input/'
+ 'sub-ADNI011S4105_ses-M00_T1w_segm-graymatter_dartelinput.nii.gz')
if not os.path.exists(out_file):
raise IOError(
'Pipeline did not produce file : ' + out_file +
'. Consider rerunning test_run_T1VolumeTissueSegmentation')
ref_file = join(
root,
'ref/caps/subjects/sub-ADNI011S4105/ses-M00/t1/spm/segmentation/dartel_input/'
+ 'sub-ADNI011S4105_ses-M00_T1w_segm-graymatter_dartelinput.nii.gz')
assert likeliness_measure(out_file, ref_file, (1e-1, 0.02), (0.4, 0.01))
clean_folder(join(root, 'out', 'caps'), recreate=False)
def test_instantiate_T1VolumeTissueSegmentation():
from clinica.pipelines.t1_volume_tissue_segmentation.t1_volume_tissue_segmentation_pipeline import T1VolumeTissueSegmentation
from os.path import dirname, join, abspath
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'T1VolumeTissueSegmentation')
pipeline = T1VolumeTissueSegmentation(
bids_directory=join(root, 'in', 'bids'),
caps_directory=join(root, 'in', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'))
pipeline.build()
def test_instantiate_T1VolumeTissueSegmentation():
from os.path import abspath, dirname, join
from clinica.pipelines.t1_volume_tissue_segmentation.t1_volume_tissue_segmentation_pipeline import (
T1VolumeTissueSegmentation, )
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, "data", "T1VolumeTissueSegmentation")
pipeline = T1VolumeTissueSegmentation(
bids_directory=join(root, "in", "bids"),
caps_directory=join(root, "in", "caps"),
tsv_file=join(root, "in", "subjects.tsv"),
)
pipeline.build()
def test_instantiate_T1VolumeTissueSegmentation(cmdopt):
from clinica.pipelines.t1_volume_tissue_segmentation.t1_volume_tissue_segmentation_pipeline import (
T1VolumeTissueSegmentation, )
input_dir = Path(cmdopt["input"])
root = input_dir / "T1VolumeTissueSegmentation"
parameters = {"skip_question": True}
pipeline = T1VolumeTissueSegmentation(
bids_directory=fspath(root / "in" / "bids"),
caps_directory=fspath(root / "in" / "caps"),
tsv_file=fspath(root / "in" / "subjects.tsv"),
parameters=parameters,
)
pipeline.build()
def run_T1VolumeTissueSegmentation(
input_dir: Path, output_dir: Path, ref_dir: Path, working_dir: Path
) -> None:
from clinica.pipelines.t1_volume_tissue_segmentation.t1_volume_tissue_segmentation_pipeline import (
T1VolumeTissueSegmentation,
)
parameters = {"skip_question": False}
pipeline = T1VolumeTissueSegmentation(
bids_directory=fspath(input_dir / "bids"),
caps_directory=fspath(output_dir / "caps"),
tsv_file=fspath(input_dir / "subjects.tsv"),
parameters=parameters,
base_dir=fspath(working_dir),
)
pipeline.build()
pipeline.run(bypass_check=True)
out_file = fspath(
output_dir
/ "caps"
/ "subjects"
/ "sub-ADNI011S4105"
/ "ses-M00"
/ "t1"
/ "spm"
/ "segmentation"
/ "dartel_input"
/ "sub-ADNI011S4105_ses-M00_T1w_segm-graymatter_dartelinput.nii.gz",
)
ref_file = fspath(
ref_dir
/ "caps"
/ "subjects"
/ "sub-ADNI011S4105"
/ "ses-M00"
/ "t1"
/ "spm"
/ "segmentation"
/ "dartel_input"
/ "sub-ADNI011S4105_ses-M00_T1w_segm-graymatter_dartelinput.nii.gz",
)
assert likeliness_measure(out_file, ref_file, (1e-1, 0.02), (0.4, 0.01))
def test_run_T1VolumeTissueSegmentation(cmdopt):
import os
from os.path import abspath, dirname, join
from clinica.pipelines.t1_volume_tissue_segmentation.t1_volume_tissue_segmentation_pipeline import (
T1VolumeTissueSegmentation, )
working_dir = cmdopt
root = dirname(abspath(join(abspath(__file__), pardir, pardir)))
root = join(root, "data", "T1VolumeTissueSegmentation")
clean_folder(join(working_dir, "T1VolumeTissueSegmentation"))
clean_folder(join(root, "out", "caps"))
pipeline = T1VolumeTissueSegmentation(
bids_directory=join(root, "in", "bids"),
caps_directory=join(root, "out", "caps"),
tsv_file=join(root, "in", "subjects.tsv"),
base_dir=join(working_dir, "T1VolumeTissueSegmentation"),
)
pipeline.build()
pipeline.run(bypass_check=True)
out_file = join(
root,
"out/caps/subjects/sub-ADNI011S4105/ses-M00/t1/spm/segmentation/dartel_input/"
+ "sub-ADNI011S4105_ses-M00_T1w_segm-graymatter_dartelinput.nii.gz",
)
if not os.path.exists(out_file):
raise IOError(
"Pipeline did not produce file: " + out_file +
". Consider rerunning test_run_T1VolumeTissueSegmentation")
ref_file = join(
root,
"ref/caps/subjects/sub-ADNI011S4105/ses-M00/t1/spm/segmentation/dartel_input/"
+ "sub-ADNI011S4105_ses-M00_T1w_segm-graymatter_dartelinput.nii.gz",
)
assert likeliness_measure(out_file, ref_file, (1e-1, 0.02), (0.4, 0.01))
clean_folder(join(root, "out", "caps"), recreate=False)
clean_folder(join(working_dir, "T1VolumeTissueSegmentation"),
recreate=False)
def run_command(self, args):
"""
"""
from tempfile import mkdtemp
from clinica.utils.stream import cprint
from clinica.pipelines.t1_volume_tissue_segmentation.t1_volume_tissue_segmentation_pipeline import T1VolumeTissueSegmentation
pipeline = T1VolumeTissueSegmentation(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv))
pipeline.parameters.update({
'tissue_classes': args.tissue_classes,
'dartel_tissues': args.dartel_tissues,
'tpm': args.tissue_probability_maps,
'save_warped_unmodulated': args.save_warped_unmodulated,
'save_warped_modulated': args.save_warped_modulated,
'write_deformation_fields':
[True, True], # args.write_deformation_fields
'save_t1_mni': True
})
if args.working_directory is None:
args.working_directory = mkdtemp()
pipeline.base_dir = self.absolute_path(args.working_directory)
if args.n_procs:
pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
pipeline.run()
cprint(
"The " + self._name +
" pipeline has completed. You can now delete the working directory ("
+ args.working_directory + ").")