def test_instantiate_DWIDTI():
from clinica.pipelines.dwi_dti.dwi_dti_pipeline import DwiDti
from os.path import dirname, join, abspath
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'DWIDTI')
pipeline = DwiDti(caps_directory=join(root, 'in', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'))
pipeline.build()
def test_instantiate_DWIDTI(cmdopt):
from clinica.pipelines.dwi_dti.dwi_dti_pipeline import DwiDti
input_dir = Path(cmdopt["input"])
root = input_dir / "DWIDTI"
pipeline = DwiDti(
caps_directory=fspath(root / "in" / "caps"),
tsv_file=fspath(root / "in" / "subjects.tsv"),
)
pipeline.build()
def test_instantiate_DWIDTI():
from os.path import abspath, dirname, join
from clinica.pipelines.dwi_dti.dwi_dti_pipeline import DwiDti
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, "data", "DWIDTI")
pipeline = DwiDti(
caps_directory=join(root, "in", "caps"),
tsv_file=join(root, "in", "subjects.tsv"),
)
pipeline.build()
def test_run_DWIDTI(cmdopt):
from clinica.pipelines.dwi_dti.dwi_dti_pipeline import DwiDti
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', 'DWIDTI')
clean_folder(join(root, 'out', 'caps'), recreate=False)
clean_folder(join(working_dir, 'DWIDTI'))
shutil.copytree(join(root, 'in', 'caps'), join(root, 'out', 'caps'))
pipeline = DwiDti(caps_directory=join(root, 'out', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'))
pipeline.base_dir = join(working_dir, 'DWIDTI')
pipeline.build()
pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': 4},
bypass_check=True)
# Check files
subject_id = 'sub-CAPP01001TMM'
maps = ['AD', 'FA', 'MD', 'RD']
out_files = [
join(
root, 'out', 'caps', 'subjects', subject_id, 'ses-M00', 'dwi',
'dti_based_processing', 'atlas_statistics',
subject_id + '_ses-M00_dwi_space-JHUDTI81_res-1x1x1_map-' + m +
'_statistics.tsv') for m in maps
]
ref_files = [
join(
root, 'ref',
subject_id + '_ses-M00_dwi_space-JHUDTI81_res-1x1x1_map-' + m +
'_statistics.tsv') for m in maps
]
for i in range(len(out_files)):
out_csv = pds.read_csv(out_files[i], sep='\t')
out_mean_scalar = np.array(out_csv.mean_scalar)
ref_csv = pds.read_csv(ref_files[i], sep='\t')
ref_mean_scalar = np.array(ref_csv.mean_scalar)
assert np.allclose(out_mean_scalar,
ref_mean_scalar,
rtol=0.025,
equal_nan=True)
clean_folder(join(root, 'out', 'caps'), recreate=False)
def run_DWIDTI(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.dwi_dti.dwi_dti_pipeline import DwiDti
caps_dir = output_dir / "caps"
tsv = input_dir / "subjects.tsv"
# Copy necessary data from in to out
shutil.copytree(input_dir / "caps", caps_dir, copy_function=shutil.copy)
pipeline = DwiDti(
caps_directory=fspath(caps_dir),
tsv_file=fspath(tsv),
base_dir=fspath(working_dir),
)
pipeline.build()
pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": 4},
bypass_check=True)
# Check files
subject_id = "sub-CAPP01001TMM"
maps = ["AD", "FA", "MD", "RD"]
out_files = [
fspath(caps_dir / "subjects" / subject_id / "ses-M00" / "dwi" /
"dti_based_processing" / "atlas_statistics" /
(subject_id + "_ses-M00_dwi_space-JHUDTI81_res-1x1x1_map-" + m +
"_statistics.tsv")) for m in maps
]
ref_files = [
fspath(ref_dir /
(subject_id + "_ses-M00_dwi_space-JHUDTI81_res-1x1x1_map-" + m +
"_statistics.tsv")) for m in maps
]
for i in range(len(out_files)):
out_csv = pds.read_csv(out_files[i], sep="\t")
out_mean_scalar = np.array(out_csv.mean_scalar)
ref_csv = pds.read_csv(ref_files[i], sep="\t")
ref_mean_scalar = np.array(ref_csv.mean_scalar)
assert np.allclose(out_mean_scalar,
ref_mean_scalar,
rtol=0.025,
equal_nan=True)
def test_run_DWIDTI(cmdopt):
import shutil
from os.path import abspath, dirname, join
import numpy as np
import pandas as pds
from clinica.pipelines.dwi_dti.dwi_dti_pipeline import DwiDti
working_dir = cmdopt
root = dirname(abspath(join(abspath(__file__), pardir, pardir)))
root = join(root, "data", "DWIDTI")
clean_folder(join(root, "out", "caps"), recreate=False)
clean_folder(join(working_dir, "DWIDTI"))
shutil.copytree(join(root, "in", "caps"), join(root, "out", "caps"))
pipeline = DwiDti(
caps_directory=join(root, "out", "caps"),
tsv_file=join(root, "in", "subjects.tsv"),
base_dir=join(working_dir, "DWIDTI"),
)
pipeline.build()
pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": 4},
bypass_check=True)
# Check files
subject_id = "sub-CAPP01001TMM"
maps = ["AD", "FA", "MD", "RD"]
out_files = [
join(
root,
"out",
"caps",
"subjects",
subject_id,
"ses-M00",
"dwi",
"dti_based_processing",
"atlas_statistics",
subject_id + "_ses-M00_dwi_space-JHUDTI81_res-1x1x1_map-" + m +
"_statistics.tsv",
) for m in maps
]
ref_files = [
join(
root,
"ref",
subject_id + "_ses-M00_dwi_space-JHUDTI81_res-1x1x1_map-" + m +
"_statistics.tsv",
) for m in maps
]
for i in range(len(out_files)):
out_csv = pds.read_csv(out_files[i], sep="\t")
out_mean_scalar = np.array(out_csv.mean_scalar)
ref_csv = pds.read_csv(ref_files[i], sep="\t")
ref_mean_scalar = np.array(ref_csv.mean_scalar)
assert np.allclose(out_mean_scalar,
ref_mean_scalar,
rtol=0.025,
equal_nan=True)
clean_folder(join(root, "out", "caps"), recreate=False)
clean_folder(join(working_dir, "DWIDTI"), recreate=False)