def run_command(self, args):
"""
"""
from tempfile import mkdtemp
from clinica.utils.stream import cprint
from clinica.pipelines.t1_volume_parcellation.t1_volume_parcellation_pipeline import T1VolumeParcellation
pipeline = T1VolumeParcellation(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv))
assert args.modulation in ['on', 'off']
pipeline.parameters = {
'group_id': args.group_id,
'atlases': args.atlases,
'wd': self.absolute_path(args.working_directory),
'n_procs': args.n_procs,
'modulate': args.modulation
}
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 + ").")
def test_run_T1VolumeParcellation(cmdopt):
import shutil
from os.path import abspath, dirname, join
import numpy as np
import pandas as pds
from clinica.pipelines.t1_volume_parcellation.t1_volume_parcellation_pipeline import (
T1VolumeParcellation, )
working_dir = cmdopt
root = dirname(abspath(join(abspath(__file__), pardir, pardir)))
root = join(root, "data", "T1VolumeParcellation")
clean_folder(join(root, "out", "caps"), recreate=False)
clean_folder(join(working_dir, "T1VolumeParcellation"))
# Copy data for use of pipeline
shutil.copytree(join(root, "in", "caps"), join(root, "out", "caps"))
# Instantiate pipeline
parameters = {"group_label": "UnitTest"}
pipeline = T1VolumeParcellation(
caps_directory=join(root, "in", "caps"),
tsv_file=join(root, "in", "subjects.tsv"),
base_dir=join(working_dir, "T1VolumeParcellation"),
parameters=parameters,
)
pipeline.build()
pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": 4},
bypass_check=True)
out_files = [
join(
root,
"out/caps/subjects/sub-ADNI018S4696/ses-M00/t1/spm/dartel/group-UnitTest/atlas_statistics",
"sub-ADNI018S4696_ses-M00_T1w_segm-graymatter_space-Ixi549Space_modulated-on_probability_space-"
+ atlas + "_map-graymatter_statistics.tsv",
) for atlas in pipeline.parameters["atlases"]
]
ref_files = [
join(
root,
"ref/sub-ADNI018S4696_ses-M00_T1w_segm-graymatter_space-Ixi549Space_modulated-on_probability_space-"
+ atlas + "_map-graymatter_statistics.tsv",
) for atlas in pipeline.parameters["atlases"]
]
for i in range(len(out_files)):
out_csv = pds.read_csv(out_files[i], sep="\t")
ref_csv = pds.read_csv(ref_files[i], sep="\t")
assert np.allclose(
np.array(out_csv.mean_scalar),
np.array(ref_csv.mean_scalar),
rtol=1e-8,
equal_nan=True,
)
clean_folder(join(root, "out", "caps"), recreate=False)
clean_folder(join(working_dir, "T1VolumeParcellation"), recreate=False)
def test_run_T1VolumeParcellation(cmdopt):
from clinica.pipelines.t1_volume_parcellation.t1_volume_parcellation_pipeline import T1VolumeParcellation
from os.path import dirname, join, abspath, exists
import shutil
import pandas as pds
import numpy as np
working_dir = cmdopt
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'T1VolumeParcellation')
clean_folder(join(root, 'out', 'caps'), recreate=False)
clean_folder(join(working_dir, 'T1VolumeParcellation'))
# Copy data for use of pipeline
shutil.copytree(join(root, 'in', 'caps'), join(root, 'out', 'caps'))
# Instantiate pipeline
pipeline = T1VolumeParcellation(caps_directory=join(root, 'out', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'))
pipeline.parameters['group_id'] = 'UnitTest'
pipeline.parameters['atlases'] = [
'AAL2', 'LPBA40', 'Neuromorphometrics', 'AICHA', 'Hammers'
]
pipeline.parameters['modulate'] = 'on'
pipeline.base_dir = join(working_dir, 'T1VolumeParcellation')
pipeline.build()
pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': 4},
bypass_check=True)
out_files = [
join(
root,
'out/caps/subjects/sub-ADNI018S4696/ses-M00/t1/spm/dartel/group-UnitTest/atlas_statistics',
'sub-ADNI018S4696_ses-M00_T1w_segm-graymatter_space-Ixi549Space_modulated-on_probability_space-'
+ atlas + '_map-graymatter_statistics.tsv')
for atlas in pipeline.parameters['atlases']
]
ref_files = [
join(
root,
'ref/sub-ADNI018S4696_ses-M00_T1w_segm-graymatter_space-Ixi549Space_modulated-on_probability_space-'
+ atlas + '_map-graymatter_statistics.tsv')
for atlas in pipeline.parameters['atlases']
]
for i in range(len(out_files)):
out_csv = pds.read_csv(out_files[i], sep='\t')
ref_csv = pds.read_csv(ref_files[i], sep='\t')
assert np.allclose(np.array(out_csv.mean_scalar),
np.array(ref_csv.mean_scalar),
rtol=1e-8,
equal_nan=True)
clean_folder(join(root, 'out', 'caps'), recreate=False)
def test_instantiate_T1VolumeParcellation():
from os.path import dirname, join, abspath
from clinica.pipelines.t1_volume_parcellation.t1_volume_parcellation_pipeline import T1VolumeParcellation
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'T1VolumeParcellation')
parameters = {'group_label': 'UnitTest'}
pipeline = T1VolumeParcellation(caps_directory=join(root, 'in', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'),
parameters=parameters)
pipeline.build()
def test_instantiate_T1VolumeParcellation():
from clinica.pipelines.t1_volume_parcellation.t1_volume_parcellation_pipeline import T1VolumeParcellation
from os.path import dirname, join, abspath
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'T1VolumeParcellation')
pipeline = T1VolumeParcellation(caps_directory=join(root, 'in', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'))
pipeline.parameters['group_id'] = 'UnitTest'
pipeline.parameters['atlases'] = [
'AAL2', 'LPBA40', 'Neuromorphometrics', 'AICHA', 'Hammers'
]
pipeline.parameters['modulate'] = 'on'
pipeline.build()
def test_instantiate_T1VolumeParcellation(cmdopt):
from clinica.pipelines.t1_volume_parcellation.t1_volume_parcellation_pipeline import (
T1VolumeParcellation, )
input_dir = Path(cmdopt["input"])
root = input_dir / "T1VolumeParcellation"
parameters = {"group_label": "UnitTest"}
pipeline = T1VolumeParcellation(
caps_directory=fspath(root / "in" / "caps"),
tsv_file=fspath(root / "in" / "subjects.tsv"),
parameters=parameters,
)
pipeline.build()
def test_instantiate_T1VolumeParcellation():
from os.path import abspath, dirname, join
from clinica.pipelines.t1_volume_parcellation.t1_volume_parcellation_pipeline import (
T1VolumeParcellation, )
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, "data", "T1VolumeParcellation")
parameters = {"group_label": "UnitTest"}
pipeline = T1VolumeParcellation(
caps_directory=join(root, "in", "caps"),
tsv_file=join(root, "in", "subjects.tsv"),
parameters=parameters,
)
pipeline.build()
def run_T1VolumeParcellation(
input_dir: Path, output_dir: Path, ref_dir: Path, working_dir: Path
) -> None:
import shutil
import numpy as np
import pandas as pds
from clinica.pipelines.t1_volume_parcellation.t1_volume_parcellation_pipeline import (
T1VolumeParcellation,
)
# Copy necessary data from in to out
shutil.copytree(input_dir / "caps", output_dir / "caps", copy_function=shutil.copy)
# Instantiate pipeline
parameters = {"group_label": "UnitTest"}
pipeline = T1VolumeParcellation(
caps_directory=fspath(output_dir / "caps"),
tsv_file=fspath(input_dir / "subjects.tsv"),
base_dir=fspath(working_dir),
parameters=parameters,
)
pipeline.build()
pipeline.run(plugin="MultiProc", plugin_args={"n_procs": 4}, bypass_check=True)
out_files = [
fspath(
output_dir
/ "caps"
/ "subjects"
/ "sub-ADNI018S4696"
/ "ses-M00"
/ "t1"
/ "spm"
/ "dartel"
/ "group-UnitTest"
/ "atlas_statistics"
/ (
"sub-ADNI018S4696_ses-M00_T1w_segm-graymatter_space-Ixi549Space_modulated-on_probability_space-"
+ atlas
+ "_map-graymatter_statistics.tsv"
)
)
for atlas in pipeline.parameters["atlases"]
]
ref_files = [
fspath(
ref_dir
/ (
"sub-ADNI018S4696_ses-M00_T1w_segm-graymatter_space-Ixi549Space_modulated-on_probability_space-"
+ atlas
+ "_map-graymatter_statistics.tsv"
)
)
for atlas in pipeline.parameters["atlases"]
]
for i in range(len(out_files)):
out_csv = pds.read_csv(out_files[i], sep="\t")
ref_csv = pds.read_csv(ref_files[i], sep="\t")
assert np.allclose(
np.array(out_csv.mean_scalar),
np.array(ref_csv.mean_scalar),
rtol=1e-8,
equal_nan=True,
)