How to use the clinica.utils.ux.print_end_pipeline function in clinica

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github aramis-lab / clinica / clinica / pipelines / dwi_preprocessing_using_phasediff_fieldmap / dwi_preprocessing_using_phasediff_fieldmap_cli.py View on Github external
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),
            low_bval=args.low_bval,
        )

        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)
github aramis-lab / clinica / clinica / pipelines / statistics_surface / statistics_surface_cli.py View on Github external
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)
github aramis-lab / clinica / clinica / pipelines / t1_volume_existing_dartel / t1_volume_existing_dartel_cli.py View on Github external
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
            base_dir=self.absolute_path(args.working_directory),
            group_id=args.group_id)

        pipeline.parameters.update({
            'tissues': args.tissues
        })

        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)
github aramis-lab / clinica / clinica / pipelines / machine_learning_spatial_svm / spatial_svm_cli.py View on Github external
pipeline.parameters = {
            'group_id': args.group_id,
            'fwhm': args.full_width_half_maximum,
            'image_type': args.image_type,
            'pet_type': args.pet_tracer,
            'no_pvc': args.no_pvc
        }

        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)
github aramis-lab / clinica / clinica / pipelines / dwi_preprocessing_using_t1 / dwi_preprocessing_using_t1_cli.py View on Github external
pipeline = DwiPreprocessingUsingT1(
            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),
            low_bval=args.low_bval,
        )

        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)
github aramis-lab / clinica / clinica / pipelines / t1_volume_parcellation / t1_volume_parcellation_cli.py View on Github external
# of the computed atlases
        args.atlases = verify_cat12_atlases(args.atlases)

        pipeline.parameters = {
            'group_id': args.group_id,
            'atlases': args.atlases,
        }

        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)
github aramis-lab / clinica / clinica / pipelines / dwi_connectome / dwi_connectome_cli.py View on Github external
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)
        )
        pipeline.parameters = {
            'n_tracks': args.n_tracks or 1000000,
        }

        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)
github aramis-lab / clinica / clinica / pipelines / pet_surface / pet_surface_cli.py View on Github external
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)
        )
        pipeline.parameters = {
            'pet_type': args.pet_tracer,
        }

        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)
github aramis-lab / clinica / clinica / pipelines / t1_freesurfer / t1_freesurfer_cli.py View on Github external
name=self.name,
            overwrite_caps=args.overwrite_outputs
        )

        pipeline.parameters = {
            'recon_all_args': args.recon_all_args
        }

        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)
github aramis-lab / clinica / clinica / pipelines / pet_volume / pet_volume_cli.py View on Github external
'pet_type': args.pet_tracer,
            'mask_tissues': args.mask_tissues,
            'mask_threshold': args.mask_threshold,
            'pvc_mask_tissues': args.pvc_mask_tissues,
            'smooth': args.smooth,
            'atlas_list': args.atlases
        })

        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)