def test_instantiate_StatisticsSurface():
from os.path import dirname, join, abspath
from clinica.pipelines.statistics_surface.statistics_surface_pipeline import StatisticsSurface
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'StatisticsSurface')
parameters = {
'design_matrix': '1 + group + age + sex',
'contrast': 'group',
'str_format': '%s %s %s %f %s',
'group_label': 'UnitTest',
'glm_type': 'group_comparison',
'custom_file':
'@subject/@session/t1/freesurfer_cross_sectional/@subject_@session/surf/@[email protected]',
'feature_label': 'cortical_thickness',
'full_width_at_half_maximum': 20,
'threshold_uncorrected_pvalue': 0.001,
'threshold_corrected_pvalue': 0.05,
'cluster_threshold': 0.001
}
pipeline = StatisticsSurface(caps_directory=join(root, 'in', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'),
parameters=parameters)
pipeline.build()
def test_instantiate_StatisticsSurface():
from os.path import abspath, dirname, join
from clinica.pipelines.statistics_surface.statistics_surface_pipeline import (
StatisticsSurface, )
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, "data", "StatisticsSurface")
parameters = {
# Clinica compulsory parameters
"group_label": "UnitTest",
"orig_input_data": "t1-freesurfer",
"glm_type": "group_comparison",
"contrast": "group",
# Optional parameters
"covariates": "age sex",
}
pipeline = StatisticsSurface(
caps_directory=join(root, "in", "caps"),
tsv_file=join(root, "in", "subjects.tsv"),
parameters=parameters,
)
pipeline.build()
def test_run_StatisticsSurface(cmdopt):
from clinica.pipelines.statistics_surface.statistics_surface_pipeline import StatisticsSurface
from os.path import dirname, join, abspath, exists
import shutil
import numpy as np
from scipy.io import loadmat
working_dir = cmdopt
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'StatisticsSurface')
clean_folder(join(root, 'out', 'caps'), recreate=False)
clean_folder(join(working_dir, 'StatisticsSurface'))
shutil.copytree(join(root, 'in', 'caps'), join(root, 'out', 'caps'))
pipeline = StatisticsSurface(caps_directory=join(root, 'out', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'))
pipeline.parameters = {
'design_matrix': '1 + group + age + sex',
'contrast': 'group',
'str_format': '%s %s %s %f %s',
'group_label': 'UnitTest',
'glm_type': 'group_comparison',
'custom_file':
'@subject/@session/t1/freesurfer_cross_sectional/@subject_@session/surf/@[email protected]',
'feature_label': 'cortical_thickness',
'full_width_at_half_maximum': 20,
'threshold_uncorrected_pvalue': 0.001,
'threshold_corrected_pvalue': 0.05,
'cluster_threshold': 0.001
}
pipeline.base_dir = join(working_dir, 'StatisticsSurface')
pipeline.build()
pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': 8},
bypass_check=True)
# Check files
out_file = join(
root,
'out/caps/groups/group-UnitTest/statistics/surfstat_group_comparison/group-UnitTest_AD-lt-CN_measure-cortical_thickness_fwhm-20_correctedPValue.mat'
)
ref_file = join(
root,
'ref/group-UnitTest_AD-lt-CN_measure-cortical_thickness_fwhm-20_correctedPValue.mat'
)
out_file_mat = loadmat(out_file)['correctedpvaluesstruct']
ref_file_mat = loadmat(ref_file)['correctedpvaluesstruct']
for i in range(4):
assert np.allclose(out_file_mat[0][0][i],
ref_file_mat[0][0][i],
rtol=1e-8,
equal_nan=True)
clean_folder(join(root, 'out', 'caps'), recreate=False)
def test_instantiate_StatisticsSurface():
from os.path import dirname, join, abspath
from clinica.pipelines.statistics_surface.statistics_surface_pipeline import StatisticsSurface
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'StatisticsSurface')
parameters = {
# Clinica compulsory parameters
'group_label': 'UnitTest',
'orig_input_data': 't1-freesurfer',
'glm_type': 'group_comparison',
'contrast': 'group',
# Optional parameters
'covariates': 'age sex',
}
pipeline = StatisticsSurface(caps_directory=join(root, 'in', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'),
parameters=parameters)
pipeline.build()
def test_run_StatisticsSurface(cmdopt):
import shutil
from os.path import abspath, dirname, join
import numpy as np
from scipy.io import loadmat
from clinica.pipelines.statistics_surface.statistics_surface_pipeline import (
StatisticsSurface,
)
working_dir = cmdopt
root = dirname(abspath(join(abspath(__file__), pardir, pardir)))
root = join(root, "data", "StatisticsSurface")
clean_folder(join(root, "out", "caps"), recreate=False)
clean_folder(join(working_dir, "StatisticsSurface"))
shutil.copytree(join(root, "in", "caps"), join(root, "out", "caps"))
parameters = {
# Clinica compulsory parameters
"group_label": "UnitTest",
"orig_input_data": "t1-freesurfer",
"glm_type": "group_comparison",
"contrast": "group",
# Optional parameters
"covariates": "age sex",
}
pipeline = StatisticsSurface(
caps_directory=join(root, "out", "caps"),
tsv_file=join(root, "in", "subjects.tsv"),
base_dir=join(working_dir, "StatisticsSurface"),
parameters=parameters,
)
pipeline.build()
pipeline.run(plugin="MultiProc", plugin_args={"n_procs": 1}, bypass_check=True)
# Check files
filename = "group-UnitTest_AD-lt-CN_measure-ct_fwhm-20_correctedPValue.mat"
out_file = join(
root,
"out",
"caps",
"groups",
"group-UnitTest",
"statistics",
"surfstat_group_comparison",
filename,
)
ref_file = join(root, "ref", filename)
out_file_mat = loadmat(out_file)["correctedpvaluesstruct"]
ref_file_mat = loadmat(ref_file)["correctedpvaluesstruct"]
for i in range(4):
assert np.allclose(
out_file_mat[0][0][i], ref_file_mat[0][0][i], rtol=1e-8, equal_nan=True
)
clean_folder(join(root, "out", "caps"), recreate=False)
clean_folder(join(working_dir, "StatisticsSurface"), recreate=False)
def test_instantiate_StatisticsSurface(cmdopt):
from clinica.pipelines.statistics_surface.statistics_surface_pipeline import (
StatisticsSurface, )
input_dir = Path(cmdopt["input"])
root = input_dir / "StatisticsSurface"
parameters = {
# Clinica compulsory parameters
"group_label": "UnitTest",
"orig_input_data": "t1-freesurfer",
"glm_type": "group_comparison",
"contrast": "group",
# Optional parameters
"covariates": "age sex",
}
pipeline = StatisticsSurface(
caps_directory=fspath(root / "in" / "caps"),
tsv_file=fspath(root / "in" / "subjects.tsv"),
parameters=parameters,
)
pipeline.build()
def run_StatisticsSurface(input_dir: Path, output_dir: Path, ref_dir: Path,
working_dir: Path) -> None:
import shutil
import numpy as np
from scipy.io import loadmat
from clinica.pipelines.statistics_surface.statistics_surface_pipeline import (
StatisticsSurface, )
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)
parameters = {
# Clinica compulsory parameters
"group_label": "UnitTest",
"orig_input_data": "t1-freesurfer",
"glm_type": "group_comparison",
"contrast": "group",
# Optional parameters
"covariates": ["age", "sex"],
}
pipeline = StatisticsSurface(
caps_directory=fspath(caps_dir),
tsv_file=fspath(tsv),
base_dir=fspath(working_dir),
parameters=parameters,
)
pipeline.build()
pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": 1},
bypass_check=True)
# Check files
filename = "group-UnitTest_AD-lt-CN_measure-ct_fwhm-20_correctedPValue.mat"
out_file = (caps_dir / "groups" / "group-UnitTest" / "statistics" /
"surfstat_group_comparison" / filename)
ref_file = ref_dir / filename
out_file_mat = loadmat(fspath(out_file))["correctedpvaluesstruct"]
ref_file_mat = loadmat(fspath(ref_file))["correctedpvaluesstruct"]
for i in range(4):
assert np.allclose(
out_file_mat[0][0][i],
ref_file_mat[0][0][i],
rtol=1e-8,
equal_nan=True,
)
def test_run_StatisticsSurface(cmdopt):
from clinica.pipelines.statistics_surface.statistics_surface_pipeline import StatisticsSurface
from os.path import dirname, join, abspath
import shutil
import numpy as np
from scipy.io import loadmat
working_dir = cmdopt
root = dirname(abspath(join(abspath(__file__), pardir)))
root = join(root, 'data', 'StatisticsSurface')
clean_folder(join(root, 'out', 'caps'), recreate=False)
clean_folder(join(working_dir, 'StatisticsSurface'))
shutil.copytree(join(root, 'in', 'caps'), join(root, 'out', 'caps'))
parameters = {
# Clinica compulsory parameters
'group_label': 'UnitTest',
'orig_input_data': 't1-freesurfer',
'glm_type': 'group_comparison',
'contrast': 'group',
# Optional parameters
'covariates': 'age sex',
}
pipeline = StatisticsSurface(caps_directory=join(root, 'out', 'caps'),
tsv_file=join(root, 'in', 'subjects.tsv'),
base_dir=join(working_dir,
'StatisticsSurface'),
parameters=parameters)
pipeline.build()
pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': 1},
bypass_check=True)
# Check files
filename = 'group-UnitTest_AD-lt-CN_measure-ct_fwhm-20_correctedPValue.mat'
out_file = join(root, 'out', 'caps', 'groups', 'group-UnitTest',
'statistics', 'surfstat_group_comparison', filename)
ref_file = join(root, 'ref', filename)
out_file_mat = loadmat(out_file)['correctedpvaluesstruct']
ref_file_mat = loadmat(ref_file)['correctedpvaluesstruct']
for i in range(4):
assert np.allclose(out_file_mat[0][0][i],
ref_file_mat[0][0][i],
rtol=1e-8,
equal_nan=True)
clean_folder(join(root, 'out', 'caps'), recreate=False)
clean_folder(join(working_dir, 'StatisticsSurface'), recreate=False)
def run_command(self, args):
"""
Run the pipelines with defined args
"""
from clinica.pipelines.statistics_surface.statistics_surface_pipeline import StatisticsSurface
from clinica.pipelines.statistics_surface.statistics_surface_utils import check_inputs
from clinica.utils.stream import cprint
import os
if args.feature_type is not None:
if args.custom_file is not None:
raise Exception('--feature_type and --custom_file are mutually exclusive : you must choose between one or the other. See documentation for more informations.')
if args.feature_label is not None:
raise Exception('--feature_label should not be used with --feature_type')
# FreeSurfer cortical thickness
if args.feature_type == 'cortical_thickness':
args.custom_file = '@subject/@session/t1/freesurfer_cross_sectional/@subject_@session/surf/@[email protected]'
args.feature_label = 'ct'
# PET cortical projection
elif args.feature_type == 'pet_fdg_projection':
args.custom_file = '@subject/@session/pet/surface/@subject_@session_task-rest_acq-fdg_pet_space-fsaverage_suvr-pons_pvc-iy_hemi-@hemi_fwhm-@fwhm_projection.mgh'
args.feature_label = 'fdg'
# NODDI, NDI, ODI and FISO
elif args.feature_type == 'noddi_projection_ndi':
args.custom_file = '@subject/@session/noddi/postprocessing/noddi-register-vertex-fsaverage/cortex-projection/@subject_@session_OnFsaverage_fwhm-@[email protected]'
args.feature_label = 'NDI'
elif args.feature_type == 'noddi_projection_fiso':
args.custom_file = '@subject/@session/noddi/postprocessing/noddi-register-vertex-fsaverage/cortex-projection/@subject_@session_OnFsaverage_fwhm-@[email protected]'
args.feature_label = 'FISO'
elif args.feature_type == 'noddi_projection_odi':
args.custom_file = '@subject/@session/noddi/postprocessing/noddi-register-vertex-fsaverage/cortex-projection/@subject_@session_OnFsaverage_fwhm-@[email protected]'
args.feature_label = 'ODI'
# DTI fa, md, rd and ad
elif args.feature_type == 'dti_projection_fa':
args.custom_file = '@subject/@session/noddi/postprocessing/dti-register-vertex-fsaverage/cortex-projection/@subject_@session_OnFsaverage_fwhm-@[email protected]'
args.feature_label = 'FA'
elif args.feature_type == 'dti_projection_md':
args.custom_file = '@subject/@session/noddi/postprocessing/dti-register-vertex-fsaverage/cortex-projection/@subject_@session_OnFsaverage_fwhm-@[email protected]'
args.feature_label = 'MD'
elif args.feature_type == 'dti_projection_rd':
args.custom_file = '@subject/@session/noddi/postprocessing/dti-register-vertex-fsaverage/cortex-projection/@subject_@session_OnFsaverage_fwhm-@[email protected]'
args.feature_label = 'RD'
elif args.feature_type == 'dti_projection_ad':
args.custom_file = '@subject/@session/noddi/postprocessing/dti-register-vertex-fsaverage/cortex-projection/@subject_@session_OnFsaverage_fwhm-@[email protected]'
args.feature_label = 'AD'
else:
raise Exception('Feature type ' + args.feature_type + ' not recognized. Use --custom_file to specify your own files (without --feature_type).')
elif args.feature_type is None:
if args.custom_file is None:
cprint('No feature type selected : using cortical thickness as default value')
args.custom_file = '@subject/@session/t1/freesurfer_cross_sectional/@subject_@session/surf/@[email protected]'
args.feature_label = 'ct'
else:
cprint('Using custom features.')
if args.feature_label is None:
raise Exception('You must specify a --feature_label when using the --custom_files flag')
# Check if the group label has been existed, if yes, give an error to the users
if os.path.exists(os.path.join(os.path.abspath(self.absolute_path(args.caps_directory)), 'groups', 'group-' + args.group_id)):
error_message = 'group_id : ' + args.group_id + ' already exists, please choose another one or delete the existing folder and also the working directory and rerun the pipelines'
raise Exception(error_message)
pipeline = StatisticsSurface(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subject_visits_with_covariates_tsv))
pipeline.parameters = {
# pass these args by using self.parameters in a dictionary
'design_matrix': args.design_matrix,
'contrast': args.contrast,
'str_format': args.string_format,
'group_label': args.group_id,
'glm_type': args.glm_type,
'custom_file': args.custom_file,
'feature_label': args.feature_label,
'full_width_at_half_maximum': args.full_width_at_half_maximum,
'threshold_uncorrected_pvalue': args.threshold_uncorrected_pvalue,
'threshold_corrected_pvalue': args.threshold_corrected_pvalue,
'cluster_threshold': args.cluster_threshold
}
pipeline.base_dir = self.absolute_path(args.working_directory)
check_inputs(pipeline.caps_directory,
pipeline.parameters['custom_file'],
pipeline.parameters['full_width_at_half_maximum'],
pipeline.tsv_file)
if args.n_procs:
pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
print(pipeline.parameters)
pipeline.run()
cprint("The " + self._name + " pipeline has completed. You can now delete the working directory (" + args.working_directory + ").")
