def run_command(self, args):
from networkx import Graph
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .statistics_volume_correction_pipeline import StatisticsVolumeCorrection
parameters = {
"t_map": args.t_map,
"height_threshold": args.height_threshold,
"FWEp": args.FWEp,
"FDRp": args.FDRp,
"FWEc": args.FWEp,
"FDRc": args.FDRp,
"n_cuts": args.n_cuts,
}
pipeline = StatisticsVolumeCorrection(
caps_directory=self.absolute_path(args.caps_directory),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name,
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .dwi_preprocessing_using_phasediff_fieldmap_pipeline import (
DwiPreprocessingUsingPhaseDiffFieldmap,
)
parameters = {
'low_bval': args.low_bval
}
pipeline = DwiPreprocessingUsingPhaseDiffFieldmap(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir, pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .t1_freesurfer_pipeline import T1FreeSurfer
parameters = {
"recon_all_args": args.recon_all_args,
}
pipeline = T1FreeSurfer(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name,
overwrite_caps=args.overwrite_outputs,
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .dwi_dti_pipeline import DwiDti
pipeline = DwiDti(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
name=self.name,
)
if args.n_procs:
exec_pipeline = pipeline.run(
plugin="MultiProc", plugin_args={"n_procs": args.n_procs}
)
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(
self.name, pipeline.base_dir, pipeline.base_dir_was_specified
)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from .t1_volume_register_dartel_pipeline import T1VolumeRegisterDartel
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {
'group_label': args.group_label,
'tissues': args.tissues,
}
pipeline = T1VolumeRegisterDartel(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from .t1_linear_pipeline import T1Linear
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {'crop_image': args.crop_image}
# Most of the time, you will want to instantiate your pipeline with a
# BIDS and CAPS directory as inputs:
pipeline = T1Linear(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.pipelines.pet_volume.pet_volume_pipeline import PETVolume
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {
'group_label': args.group_label,
'acq_label': args.acq_label,
'suvr_reference_region': args.suvr_reference_region,
'pvc_psf_tsv': self.absolute_path(args.pvc_psf_tsv),
'mask_tissues': args.mask_tissues,
'mask_threshold': args.mask_threshold,
'pvc_mask_tissues': args.pvc_mask_tissues,
'smooth': args.smooth,
}
pipeline = PETVolume(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
from networkx import Graph
from .statistics_volume_pipeline import StatisticsVolume
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {
'contrast': args.contrast,
'feature_type': args.feature_type,
'group_id': args.group_id,
'custom_files': args.custom_files,
'cluster_threshold': args.cluster_threshold,
'group_id_caps': args.group_id_caps,
'full_width_at_half_maximum': args.full_width_at_half_maximum
}
pipeline = StatisticsVolume(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(
args.subject_visits_with_covariates_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from .spatial_svm_pipeline import SpatialSVM
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {
'group_id': args.group_id,
'fwhm': args.fwhm,
'image_type': args.image_type,
'pet_tracer': args.pet_tracer,
'no_pvc': args.no_pvc
}
pipeline = SpatialSVM(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from .pet_surface_pipeline import PetSurface
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {'pet_tracer': args.pet_tracer, 'longitudinal': False}
pipeline = PetSurface(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from .dwi_connectome_pipeline import DwiConnectome
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {'n_tracks': args.n_tracks}
pipeline = DwiConnectome(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .t1_volume_parcellation_pipeline import T1VolumeParcellation
parameters = {
"group_label": args.group_label,
}
pipeline = T1VolumeParcellation(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name,
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from .t1_volume_tissue_segmentation_pipeline import T1VolumeTissueSegmentation
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {
'tissue_classes': args.tissue_classes,
'dartel_tissues': args.dartel_tissues,
'tissue_probability_maps': args.tissue_probability_maps,
'save_warped_unmodulated': not args.dont_save_warped_unmodulated,
'save_warped_modulated': args.save_warped_modulated,
}
pipeline = T1VolumeTissueSegmentation(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir, pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .pet_surface_pipeline import PetSurface
parameters = {
"acq_label": args.acq_label,
"suvr_reference_region": args.suvr_reference_region,
"pvc_psf_tsv": self.absolute_path(args.pvc_psf_tsv),
"longitudinal": False,
}
pipeline = PetSurface(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name,
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from fmri_preprocessing_pipeline import fMRIPreprocessing
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {
'full_width_at_half_maximum': args.full_width_at_half_maximum,
't1_native_space': args.t1_native_space,
'freesurfer_brain_mask': args.freesurfer_brain_mask,
'unwarping': args.unwarping,
}
pipeline = fMRIPreprocessing(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
# elif args.slurm:
# exec_pipeline = pipeline.run(plugin='SLURMGraph',
# plugin_args={'dont_resubmit_completed_jobs': True,
# 'sbatch_args': args.sbatch_args})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir, pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from .t1_volume_parcellation_pipeline import T1VolumeParcellation
from clinica.utils.check_dependency import verify_cat12_atlases
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
# If the user wants to use any of the atlases of cat12 and has not installed it, we just remove it from the list
# of the computed atlases
args.atlases = verify_cat12_atlases(args.atlases)
parameters = {
'group_id': args.group_id,
'atlases': args.atlases,
}
pipeline = T1VolumeParcellation(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from colorama import Fore
from networkx import Graph
from clinica.utils.exceptions import ClinicaException
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .spatial_svm_pipeline import SpatialSVM
if args.orig_input_data == "pet-volume":
if args.acq_label is None:
raise ClinicaException(
f"{Fore.RED}You selected pet-volume pipeline without setting --acq_label flag. "
f"Clinica will now exit.{Fore.RESET}")
if args.suvr_reference_region is None:
raise ClinicaException(
f"{Fore.RED}You selected pet-volume pipeline without setting --suvr_reference_region flag. "
f"Clinica will now exit.{Fore.RESET}")
parameters = {
# Clinica compulsory arguments
"group_label": args.group_label,
"orig_input_data": args.orig_input_data,
# Optional arguments for inputs from pet-volume pipeline
"acq_label": args.acq_label,
"use_pvc_data": args.use_pvc_data,
"suvr_reference_region": args.suvr_reference_region,
# Advanced arguments
"fwhm": args.full_width_half_maximum,
}
pipeline = SpatialSVM(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name,
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.pipelines.pet_volume.pet_volume_pipeline import PETVolume
from clinica.utils.check_dependency import verify_cat12_atlases
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {
'group_id': args.group_id,
'psf_tsv': self.absolute_path(args.psf_tsv),
'pet_tracer': args.pet_tracer,
'mask_tissues': args.mask_tissues,
'mask_threshold': args.mask_threshold,
'pvc_mask_tissues': args.pvc_mask_tissues,
'smooth': args.smooth,
'atlases': args.atlases,
}
# If the user wants to use any of the atlases of CAT12 and has not installed it, we just remove it from the list
# of the computed atlases
parameters['atlases'] = verify_cat12_atlases(args.atlases)
pipeline = PETVolume(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .deeplearning_prepare_data_pipeline import DeepLearningPrepareData
parameters = {
# Add your own pipeline parameters here to use them inside your
# pipeline. See the file `deeplearning_prepare_data_pipeline.py` to
# see an example of use.
"modality": args.modality,
"extract_method": args.extract_method,
"patch_size": args.patch_size,
"stride_size": args.stride_size,
"slice_direction": args.slice_direction,
"slice_mode": args.slice_mode,
"use_uncropped_image": args.use_uncropped_image,
"custom_suffix": args.custom_suffix,
}
pipeline = DeepLearningPrepareData(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name,
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
import os
from colorama import Fore
from networkx import Graph
from clinica.utils.stream import cprint
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .pet_linear_pipeline import PETLinear
parameters = {
"acq_label": args.acq_label,
"suvr_reference_region": args.suvr_reference_region,
"uncropped_image": args.uncropped_image,
"save_PETinT1w": args.save_pet_in_t1w_space,
}
pipeline = PETLinear(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name,
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from .t1_freesurfer_template_pipeline import T1FreeSurferTemplate
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
pipeline = T1FreeSurferTemplate(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
name='t1-freesurfer-template',
overwrite_caps=args.overwrite_outputs)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from colorama import Fore
from .statistics_surface_pipeline import StatisticsSurface
from .statistics_surface_utils import (get_t1_freesurfer_custom_file,
get_pet_surface_custom_file)
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
from clinica.utils.exceptions import ClinicaException
# PET-Surface pipeline
if args.orig_input_data == 'pet-surface':
if args.acq_label is None:
raise ClinicaException(
f"{Fore.RED}You selected pet-surface pipeline without setting --acq_label flag. "
f"Clinica will now exit.{Fore.RESET}")
if args.suvr_reference_region is None:
raise ClinicaException(
f"{Fore.RED}You selected pet-surface pipeline without setting --suvr_reference_region flag. "
f"Clinica will now exit.{Fore.RESET}")
# FreeSurfer cortical thickness
if args.orig_input_data == 't1-freesurfer':
args.custom_file = get_t1_freesurfer_custom_file()
args.measure_label = 'ct'
# PET cortical projection
elif args.orig_input_data == 'pet-surface':
args.custom_file = get_pet_surface_custom_file(
args.acq_label, args.suvr_reference_region)
args.measure_label = args.acq_label
else:
if (args.custom_file is None) or (args.measure_label is None):
raise ClinicaException(
'You must set --measure_label and --custom_file flags.')
parameters = {
# Clinica compulsory arguments
'group_label': args.group_label,
'orig_input_data': args.orig_input_data,
'glm_type': args.glm_type,
'contrast': args.contrast,
# Optional arguments
'covariates': args.covariates,
'full_width_at_half_maximum': args.full_width_at_half_maximum,
# Optional arguments for inputs from pet-surface pipeline
'acq_label': args.acq_label,
'suvr_reference_region': args.suvr_reference_region,
# Optional arguments for custom pipeline
'custom_file': args.custom_file,
'measure_label': args.measure_label,
# Advanced arguments (i.e. tricky parameters)
'cluster_threshold': args.cluster_threshold,
}
pipeline = StatisticsSurface(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(
args.subject_visits_with_covariates_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
from networkx import Graph
from colorama import Fore
from .statistics_volume_pipeline import StatisticsVolume
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
from clinica.utils.exceptions import ClinicaException
# PET-Volume pipeline
if args.orig_input_data == 'pet-volume':
if args.acq_label is None:
raise ClinicaException(
f"{Fore.RED}You selected pet-volume pipeline without setting --acq_label flag. "
f"Clinica will now exit.{Fore.RESET}")
if args.suvr_reference_region is None:
raise ClinicaException(
f"{Fore.RED}You selected pet-volume pipeline without setting --suvr_reference_region flag. "
f"Clinica will now exit.{Fore.RESET}")
# Custom pipeline
if args.orig_input_data == 'custom-pipeline':
if (args.custom_file is None) or (args.measure_label is None):
raise ClinicaException(
'You must set --measure_label and --custom_file flags.')
parameters = {
# Clinica compulsory arguments
'group_label': args.group_label,
'orig_input_data': args.orig_input_data,
'contrast': args.contrast,
# Optional arguments
'group_label_dartel': args.group_label_dartel,
'full_width_at_half_maximum': args.full_width_at_half_maximum,
# Optional arguments for inputs from pet-volume pipeline
'acq_label': args.acq_label,
'use_pvc_data': args.use_pvc_data,
'suvr_reference_region': args.suvr_reference_region,
# Optional arguments for custom pipeline
'measure_label': args.measure_label,
'custom_file': args.custom_file,
# Advanced arguments
'cluster_threshold': args.cluster_threshold,
}
pipeline = StatisticsVolume(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(
args.subject_visits_with_covariates_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
import os
from networkx import Graph
from .statistics_surface_pipeline import StatisticsSurface
from .statistics_surface_utils import (check_inputs,
get_t1_freesurfer_custom_file,
get_fdg_pet_surface_custom_file)
from clinica.utils.stream import cprint
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
from clinica.utils.exceptions import ClinicaException
if args.feature_type is not None:
if args.custom_file is not None:
raise ClinicaException('--feature_type and --custom_file are mutually exclusive: you must choose '
'between one or the other. See documentation for more information.')
if args.feature_label is not None:
raise ClinicaException('--feature_label should not be used with --feature_type.')
# FreeSurfer cortical thickness
if args.feature_type == 'cortical_thickness':
args.custom_file = get_t1_freesurfer_custom_file()
args.feature_label = 'ct'
# PET cortical projection
elif args.feature_type == 'pet_fdg_projection':
args.custom_file = get_fdg_pet_surface_custom_file()
args.feature_label = 'fdg'
else:
raise ClinicaException('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 = get_t1_freesurfer_custom_file()
args.feature_label = 'ct'
else:
cprint('Using custom features.')
if args.feature_label is None:
raise ClinicaException('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
# Note(AR): if the user wants to compare Cortical Thickness measure with PET measure
# using the group_id, Clinica won't allow it.
# TODO: Modify this behaviour
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 pipeline'
raise ClinicaException(error_message)
parameters = {
'group_label': args.group_id,
'design_matrix': args.design_matrix,
'contrast': args.contrast,
'str_format': args.string_format,
'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 = StatisticsSurface(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subject_visits_with_covariates_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name
)
check_inputs(pipeline.caps_directory,
pipeline.parameters['custom_file'],
pipeline.parameters['full_width_at_half_maximum'],
pipeline.tsv_file)
cprint("Parameters used for this pipeline:")
cprint(pipeline.parameters)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir, pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
