def test_instantiate_PETVolume():
from clinica.pipelines.pet_volume.pet_volume_pipeline import PETVolume
from os.path import dirname, join, abspath
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'PETVolume')
pipeline = PETVolume(bids_directory=join(root, 'in', 'bids'),
caps_directory=join(root, 'in', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'),
group_id='UnitTest',
fwhm_tsv=None)
pipeline.build()
def test_run_PETVolume(cmdopt):
from os.path import dirname, join, abspath
import shutil
from clinica.pipelines.pet_volume.pet_volume_pipeline import PETVolume
working_dir = cmdopt
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'PETVolume')
clean_folder(join(root, 'out', 'caps'), recreate=False)
clean_folder(join(working_dir, 'PETVolume'))
shutil.copytree(join(root, 'in', 'caps'), join(root, 'out', 'caps'))
parameters = {
'group_label': 'UnitTest',
'acq_label': 'fdg',
'suvr_reference_region': 'pons'
}
pipeline = PETVolume(bids_directory=join(root, 'in', 'bids'),
caps_directory=join(root, 'out', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'),
base_dir=join(working_dir, 'PETVolume'),
parameters=parameters)
pipeline.build()
pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': 4},
bypass_check=True)
subjects = [
'sub-ADNI011S4105', 'sub-ADNI023S4020', 'sub-ADNI035S4082',
'sub-ADNI128S4832'
]
out_files = [
join(
root, 'out/caps/subjects/' + sub +
'/ses-M00/pet/preprocessing/group-UnitTest', sub +
'_ses-M00_task-rest_acq-fdg_pet_space-Ixi549Space_suvr-pons_mask-brain_fwhm-8mm_pet.nii.gz'
) for sub in subjects
]
ref_files = [
join(
root, 'ref', sub +
'_ses-M00_task-rest_acq-fdg_pet_space-Ixi549Space_suvr-pons_mask-brain_fwhm-8mm_pet.nii.gz'
) for sub in subjects
]
for i in range(len(out_files)):
assert likeliness_measure(out_files[i], ref_files[i], (1e-2, 0.25),
(1e-1, 0.001))
clean_folder(join(root, 'out', 'caps'), recreate=False)
clean_folder(join(working_dir, 'PETVolume'), recreate=False)
def test_instantiate_PETVolume():
from os.path import dirname, join, abspath
from clinica.pipelines.pet_volume.pet_volume_pipeline import PETVolume
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'PETVolume')
parameters = {
'group_label': 'UnitTest',
'acq_label': 'fdg',
'suvr_reference_region': 'pons'
}
pipeline = PETVolume(bids_directory=join(root, 'in', 'bids'),
caps_directory=join(root, 'in', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'),
parameters=parameters)
pipeline.build()
def test_instantiate_PETVolume(cmdopt):
from clinica.pipelines.pet_volume.pet_volume_pipeline import PETVolume
input_dir = Path(cmdopt["input"])
root = input_dir / "PETVolume"
parameters = {
"group_label": "UnitTest",
"acq_label": "fdg",
"suvr_reference_region": "pons",
"skip_question": True,
}
pipeline = PETVolume(
bids_directory=fspath(root / "in" / "bids"),
caps_directory=fspath(root / "in" / "caps"),
tsv_file=fspath(root / "in" / "subjects.tsv"),
parameters=parameters,
)
pipeline.build()
def test_instantiate_PETVolume():
from os.path import abspath, dirname, join
from clinica.pipelines.pet_volume.pet_volume_pipeline import PETVolume
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, "data", "PETVolume")
parameters = {
"group_label": "UnitTest",
"acq_label": "fdg",
"suvr_reference_region": "pons",
}
pipeline = PETVolume(
bids_directory=join(root, "in", "bids"),
caps_directory=join(root, "in", "caps"),
tsv_file=join(root, "in", "subjects.tsv"),
parameters=parameters,
)
pipeline.build()
def test_run_PETVolume(cmdopt):
import shutil
from os.path import abspath, dirname, join
from clinica.pipelines.pet_volume.pet_volume_pipeline import PETVolume
working_dir = cmdopt
root = dirname(abspath(join(abspath(__file__), pardir, pardir)))
root = join(root, "data", "PETVolume")
clean_folder(join(root, "out", "caps"), recreate=False)
clean_folder(join(working_dir, "PETVolume"))
shutil.copytree(join(root, "in", "caps"), join(root, "out", "caps"))
parameters = {
"group_label": "UnitTest",
"acq_label": "fdg",
"suvr_reference_region": "pons",
}
pipeline = PETVolume(
bids_directory=join(root, "in", "bids"),
caps_directory=join(root, "out", "caps"),
tsv_file=join(root, "in", "subjects.tsv"),
base_dir=join(working_dir, "PETVolume"),
parameters=parameters,
)
pipeline.build()
pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": 4},
bypass_check=True)
subjects = [
"sub-ADNI011S4105",
"sub-ADNI023S4020",
"sub-ADNI035S4082",
"sub-ADNI128S4832",
]
out_files = [
join(
root,
"out/caps/subjects/" + sub +
"/ses-M00/pet/preprocessing/group-UnitTest",
sub +
"_ses-M00_task-rest_acq-fdg_pet_space-Ixi549Space_suvr-pons_mask-brain_fwhm-8mm_pet.nii.gz",
) for sub in subjects
]
ref_files = [
join(
root,
"ref",
sub +
"_ses-M00_task-rest_acq-fdg_pet_space-Ixi549Space_suvr-pons_mask-brain_fwhm-8mm_pet.nii.gz",
) for sub in subjects
]
for i in range(len(out_files)):
assert likeliness_measure(out_files[i], ref_files[i], (1e-2, 0.25),
(1e-1, 0.001))
clean_folder(join(root, "out", "caps"), recreate=False)
clean_folder(join(working_dir, "PETVolume"), recreate=False)
def run_PETVolume(
input_dir: Path, output_dir: Path, ref_dir: Path, working_dir: Path
) -> None:
import shutil
from clinica.pipelines.pet_volume.pet_volume_pipeline import PETVolume
# Arrange
shutil.copytree(input_dir / "caps", output_dir / "caps", copy_function=shutil.copy)
parameters = {
"group_label": "UnitTest",
"acq_label": "fdg",
"suvr_reference_region": "pons",
"skip_question": False,
}
pipeline = PETVolume(
bids_directory=fspath(input_dir / "bids"),
caps_directory=fspath(output_dir / "caps"),
tsv_file=fspath(input_dir / "subjects.tsv"),
base_dir=fspath(working_dir),
parameters=parameters,
)
# Acte
pipeline.build()
pipeline.run(plugin="MultiProc", plugin_args={"n_procs": 4}, bypass_check=True)
# Assert
subjects = [
"sub-ADNI011S4105",
"sub-ADNI023S4020",
"sub-ADNI035S4082",
"sub-ADNI128S4832",
]
out_files = [
fspath(
output_dir
/ "caps"
/ "subjects"
/ sub
/ "ses-M00/pet/preprocessing/group-UnitTest"
/ (
sub
+ "_ses-M00_task-rest_acq-fdg_pet_space-Ixi549Space_suvr-pons_mask-brain_fwhm-8mm_pet.nii.gz"
)
)
for sub in subjects
]
ref_files = [
fspath(
ref_dir
/ (
sub
+ "_ses-M00_task-rest_acq-fdg_pet_space-Ixi549Space_suvr-pons_mask-brain_fwhm-8mm_pet.nii.gz"
)
)
for sub in subjects
]
for i in range(len(out_files)):
assert likeliness_measure(
out_files[i], ref_files[i], (1e-2, 0.25), (1e-1, 0.001)
)