def test_run_fMRIPreprocessing(cmdopt):
from os.path import dirname, join, abspath
import shutil
from clinica.pipelines.fmri_preprocessing.fmri_preprocessing_pipeline import fMRIPreprocessing
working_dir = cmdopt
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'fMRIPreprocessing')
clean_folder(join(root, 'out', 'caps'), recreate=False)
clean_folder(join(working_dir, 'fMRIPreprocessing'))
shutil.copytree(join(root, 'in', 'caps'), join(root, 'out', 'caps'))
pipeline = fMRIPreprocessing(
bids_directory=join(root, 'in', 'bids'),
caps_directory=join(root, 'out', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'),
base_dir=join(working_dir, 'fMRIPreprocessing'),
)
pipeline.build()
pipeline.run(bypass_check=True)
subject_id = 'sub-01001TMM'
out_files = [join(root, 'out', 'caps', 'subjects', subject_id, 'ses-M00', 'fmri', 'preprocessing', subject_id + '_ses-M00_task-rest_bold_space-Ixi549Space_preproc.nii.gz'),
join(root, 'out', 'caps', 'subjects', subject_id, 'ses-M00', 'fmri', 'preprocessing', subject_id + '_ses-M00_task-rest_bold_space-meanBOLD_preproc.nii.gz')]
ref_files = [join(root, 'ref', subject_id + '_ses-M00_task-rest_bold_space-Ixi549Space_preproc.nii.gz'),
join(root, 'ref', subject_id + '_ses-M00_task-rest_bold_space-meanBOLD_preproc.nii.gz')]
for i in range(len(out_files)):
assert similarity_measure(out_files[i], ref_files[i], 0.99)
clean_folder(join(root, 'out', 'caps'), recreate=False)
clean_folder(join(working_dir, 'fMRIPreprocessing'), recreate=False)
def test_instantiate_fMRIPreprocessing():
from os.path import dirname, join, abspath
from clinica.pipelines.fmri_preprocessing.fmri_preprocessing_pipeline import fMRIPreprocessing
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'fMRIPreprocessing')
pipeline = fMRIPreprocessing(
bids_directory=join(root, 'in', 'bids'),
caps_directory=join(root, 'in', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'),
)
pipeline.build()
def test_run_fMRIPreprocessing(cmdopt):
from clinica.pipelines.fmri_preprocessing.fmri_preprocessing_pipeline import fMRIPreprocessing
from .comparison_functions import similarity_measure
from os.path import dirname, join, abspath
import shutil
working_dir = cmdopt
root = join(dirname(abspath(__file__)), 'data', 'fMRIPreprocessing')
clean_folder(join(root, 'out', 'caps'), recreate=False)
clean_folder(join(working_dir, 'fMRIPreprocessing'))
shutil.copytree(join(root, 'in', 'caps'), join(root, 'out', 'caps'))
pipeline = fMRIPreprocessing(bids_directory=join(root, 'in', 'bids'),
caps_directory=join(root, 'out', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'))
pipeline.parameters = {
'full_width_at_half_maximum': [8, 8, 8],
't1_native_space': False,
'freesurfer_brain_mask': False,
'unwarping': False
}
pipeline.base_dir = join(working_dir, 'fMRIPreprocessing')
pipeline.build()
pipeline.run(bypass_check=True)
subject_id = 'sub-01001TMM'
out_files = [
join(
root, 'out', 'caps', 'subjects', subject_id, 'ses-M00', 'fmri',
'preprocessing', subject_id +
'_ses-M00_task-rest_bold_space-Ixi549Space_preproc.nii.gz'),
join(
root, 'out', 'caps', 'subjects', subject_id, 'ses-M00', 'fmri',
'preprocessing', subject_id +
'_ses-M00_task-rest_bold_space-meanBOLD_preproc.nii.gz')
]
ref_files = [
join(
root, 'ref', subject_id +
'_ses-M00_task-rest_bold_space-Ixi549Space_preproc.nii.gz'),
join(
root, 'ref', subject_id +
'_ses-M00_task-rest_bold_space-meanBOLD_preproc.nii.gz')
]
for i in range(len(out_files)):
assert similarity_measure(out_files[i], ref_files[i], 0.99)
clean_folder(join(root, 'out', 'caps'), recreate=False)
def test_instantiate_fMRIPreprocessing():
# Need to add json file in BIDS
from clinica.pipelines.fmri_preprocessing.fmri_preprocessing_pipeline import fMRIPreprocessing
from os.path import dirname, join, abspath
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'fMRIPreprocessing')
pipeline = fMRIPreprocessing(bids_directory=join(root, 'in', 'bids'),
caps_directory=join(root, 'in', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'))
pipeline.parameters = {
'full_width_at_half_maximum': [8, 8, 8],
't1_native_space': True,
'freesurfer_brain_mask': True,
'unwarping': True
}
pipeline.build()