def test_create_eddy_correct_pipeline(): fsl_course_dir = os.path.abspath('fsl_course_data') dwi_file = os.path.join(fsl_course_dir, "fdt/subj1/data.nii.gz") nipype_eddycorrect = create_eddy_correct_pipeline("nipype_eddycorrect") nipype_eddycorrect.inputs.inputnode.in_file = dwi_file nipype_eddycorrect.inputs.inputnode.ref_num = 0 with warnings.catch_warnings(): warnings.simplefilter("ignore") original_eddycorrect = pe.Node(interface=fsl.EddyCorrect(), name="original_eddycorrect") original_eddycorrect.inputs.in_file = dwi_file original_eddycorrect.inputs.ref_num = 0 test = pe.Node(util.AssertEqual(), name="eddy_corrected_dwi_test") pipeline = pe.Workflow(name="test_eddycorrect") pipeline.base_dir = tempfile.mkdtemp(prefix="nipype_test_eddycorrect_") pipeline.connect([(nipype_eddycorrect, test, [("outputnode.eddy_corrected", "volume1")]), (original_eddycorrect, test, [("eddy_corrected", "volume2")]), ]) pipeline.run(plugin='Linear') shutil.rmtree(pipeline.base_dir)
def test_create_eddy_correct_pipeline(): fsl_course_dir = os.path.abspath(os.environ['FSL_COURSE_DATA']) dwi_file = os.path.join(fsl_course_dir, "fdt1/subj1/data.nii.gz") trim_dwi = pe.Node(fsl.ExtractROI(t_min=0, t_size=2), name="trim_dwi") trim_dwi.inputs.in_file = dwi_file nipype_eddycorrect = create_eddy_correct_pipeline("nipype_eddycorrect") nipype_eddycorrect.inputs.inputnode.ref_num = 0 with warnings.catch_warnings(): warnings.simplefilter("ignore") original_eddycorrect = pe.Node(interface=fsl.EddyCorrect(), name="original_eddycorrect") original_eddycorrect.inputs.ref_num = 0 test = pe.Node(util.AssertEqual(), name="eddy_corrected_dwi_test") pipeline = pe.Workflow(name="test_eddycorrect") pipeline.base_dir = tempfile.mkdtemp(prefix="nipype_test_eddycorrect_") pipeline.connect([ (trim_dwi, original_eddycorrect, [("roi_file", "in_file")]), (trim_dwi, nipype_eddycorrect, [("roi_file", "inputnode.in_file")]), (nipype_eddycorrect, test, [("outputnode.eddy_corrected", "volume1")]), (original_eddycorrect, test, [("eddy_corrected", "volume2")]), ]) pipeline.run(plugin='Linear') shutil.rmtree(pipeline.base_dir)
def test_create_bedpostx_pipeline(): fsl_course_dir = os.path.abspath(os.environ['FSL_COURSE_DATA']) mask_file = os.path.join(fsl_course_dir, "fdt2/subj1.bedpostX/nodif_brain_mask.nii.gz") bvecs_file = os.path.join(fsl_course_dir, "fdt2/subj1/bvecs") bvals_file = os.path.join(fsl_course_dir, "fdt2/subj1/bvals") dwi_file = os.path.join(fsl_course_dir, "fdt2/subj1/data.nii.gz") z_min = 62 z_size = 2 slice_mask = pe.Node(fsl.ExtractROI(x_min=0, x_size=-1, y_min=0, y_size=-1, z_min=z_min, z_size=z_size), name="slice_mask") slice_mask.inputs.in_file = mask_file slice_dwi = pe.Node(fsl.ExtractROI(x_min=0, x_size=-1, y_min=0, y_size=-1, z_min=z_min, z_size=z_size), name="slice_dwi") slice_dwi.inputs.in_file = dwi_file nipype_bedpostx = create_bedpostx_pipeline("nipype_bedpostx") nipype_bedpostx.inputs.inputnode.bvecs = bvecs_file nipype_bedpostx.inputs.inputnode.bvals = bvals_file nipype_bedpostx.inputs.xfibres.n_fibres = 1 nipype_bedpostx.inputs.xfibres.fudge = 1 nipype_bedpostx.inputs.xfibres.burn_in = 0 nipype_bedpostx.inputs.xfibres.n_jumps = 1 nipype_bedpostx.inputs.xfibres.sample_every = 1 nipype_bedpostx.inputs.xfibres.cnlinear = True nipype_bedpostx.inputs.xfibres.seed = 0 with warnings.catch_warnings(): warnings.simplefilter("ignore") original_bedpostx = pe.Node(interface=fsl.BEDPOSTX(), name="original_bedpostx") original_bedpostx.inputs.bvecs = bvecs_file original_bedpostx.inputs.bvals = bvals_file original_bedpostx.inputs.environ['FSLPARALLEL'] = "" original_bedpostx.inputs.n_fibres = 1 original_bedpostx.inputs.fudge = 1 original_bedpostx.inputs.burn_in = 0 original_bedpostx.inputs.n_jumps = 1 original_bedpostx.inputs.sample_every = 1 original_bedpostx.inputs.seed = 0 test_f1 = pe.Node(util.AssertEqual(), name="mean_f1_test") pipeline = pe.Workflow(name="test_bedpostx") pipeline.base_dir = tempfile.mkdtemp(prefix="nipype_test_bedpostx_") pipeline.connect([ (slice_mask, original_bedpostx, [("roi_file", "mask")]), (slice_mask, nipype_bedpostx, [("roi_file", "inputnode.mask")]), (slice_dwi, original_bedpostx, [("roi_file", "dwi")]), (slice_dwi, nipype_bedpostx, [("roi_file", "inputnode.dwi")]), (nipype_bedpostx, test_f1, [(("outputnode.mean_fsamples", list_to_filename), "volume1")]), (original_bedpostx, test_f1, [("mean_fsamples", "volume2")]), ]) pipeline.run(plugin='Linear') shutil.rmtree(pipeline.base_dir)
def test_create_bedpostx_pipeline(): fsl_course_dir = os.path.abspath('fsl_course_data') mask_file = os.path.join(fsl_course_dir, "fdt/subj1.bedpostX/nodif_brain_mask.nii.gz") bvecs_file = os.path.join(fsl_course_dir, "fdt/subj1/bvecs") bvals_file = os.path.join(fsl_course_dir, "fdt/subj1/bvals") dwi_file = os.path.join(fsl_course_dir, "fdt/subj1/data.nii.gz") nipype_bedpostx = create_bedpostx_pipeline("nipype_bedpostx") nipype_bedpostx.inputs.inputnode.dwi = dwi_file nipype_bedpostx.inputs.inputnode.mask = mask_file nipype_bedpostx.inputs.inputnode.bvecs = bvecs_file nipype_bedpostx.inputs.inputnode.bvals = bvals_file nipype_bedpostx.inputs.xfibres.n_fibres = 2 nipype_bedpostx.inputs.xfibres.fudge = 1 nipype_bedpostx.inputs.xfibres.burn_in = 1000 nipype_bedpostx.inputs.xfibres.n_jumps = 1250 nipype_bedpostx.inputs.xfibres.sample_every = 25 with warnings.catch_warnings(): warnings.simplefilter("ignore") original_bedpostx = pe.Node(interface=fsl.BEDPOSTX(), name="original_bedpostx") original_bedpostx.inputs.dwi = dwi_file original_bedpostx.inputs.mask = mask_file original_bedpostx.inputs.bvecs = bvecs_file original_bedpostx.inputs.bvals = bvals_file original_bedpostx.inputs.environ['FSLPARALLEL'] = "" original_bedpostx.inputs.fibres = 2 original_bedpostx.inputs.weight = 1 original_bedpostx.inputs.burn_period = 1000 original_bedpostx.inputs.jumps = 1250 original_bedpostx.inputs.sampling = 25 test_f1 = pe.Node(util.AssertEqual(), name="mean_f1_test") test_f2 = pe.Node(util.AssertEqual(), name="mean_f2_test") test_th1 = pe.Node(util.AssertEqual(), name="mean_th1_test") test_th2 = pe.Node(util.AssertEqual(), name="mean_th2_test") test_ph1 = pe.Node(util.AssertEqual(), name="mean_ph1_test") test_ph2 = pe.Node(util.AssertEqual(), name="mean_ph2_test") pipeline = pe.Workflow(name="test_bedpostx") pipeline.base_dir = tempfile.mkdtemp(prefix="nipype_test_bedpostx_") def pickFirst(l): return l[0] def pickSecond(l): return l[1] pipeline.connect([ (nipype_bedpostx, test_f1, [(("outputnode.mean_fsamples", pickFirst), "volume1")]), (nipype_bedpostx, test_f2, [(("outputnode.mean_fsamples", pickSecond), "volume1")]), (nipype_bedpostx, test_th1, [(("outputnode.mean_thsamples", pickFirst), "volume1")]), (nipype_bedpostx, test_th2, [(("outputnode.mean_thsamples", pickSecond), "volume1")]), (nipype_bedpostx, test_ph1, [(("outputnode.mean_phsamples", pickFirst), "volume1")]), (nipype_bedpostx, test_ph2, [(("outputnode.mean_phsamples", pickSecond), "volume1")]), (original_bedpostx, test_f1, [(("mean_fsamples", pickFirst), "volume2") ]), (original_bedpostx, test_f2, [(("mean_fsamples", pickSecond), "volume2")]), (original_bedpostx, test_th1, [(("mean_thsamples", pickFirst), "volume2")]), (original_bedpostx, test_th2, [(("mean_thsamples", pickSecond), "volume2")]), (original_bedpostx, test_ph1, [(("mean_phsamples", pickFirst), "volume2")]), (original_bedpostx, test_ph2, [(("mean_phsamples", pickSecond), "volume2")]) ]) pipeline.run(plugin='Linear') shutil.rmtree(pipeline.base_dir)
def _tbss_test_helper(estimate_skeleton): fsl_course_dir = os.path.abspath(os.environ['FSL_COURSE_DATA']) fsl.FSLCommand.set_default_output_type('NIFTI_GZ') test_dir = tempfile.mkdtemp(prefix="nipype_test_tbss_") tbss_orig_dir = os.path.join(test_dir, "tbss_all_original") os.mkdir(tbss_orig_dir) old_dir = os.getcwd() os.chdir(tbss_orig_dir) subjects = ['1260', '1549'] FA_list = [ os.path.join(fsl_course_dir, 'tbss', subject_id + '.nii.gz') for subject_id in subjects ] for f in FA_list: shutil.copy(f, os.getcwd()) call(['tbss_1_preproc'] + [subject_id + '.nii.gz' for subject_id in subjects], env=os.environ.update({'FSLOUTPUTTYPE': 'NIFTI_GZ'})) tbss1_orig_dir = os.path.join(test_dir, "tbss1_original") shutil.copytree(tbss_orig_dir, tbss1_orig_dir) call(['tbss_2_reg', '-T'], env=os.environ.update({'FSLOUTPUTTYPE': 'NIFTI_GZ'})) tbss2_orig_dir = os.path.join(test_dir, "tbss2_original") shutil.copytree(tbss_orig_dir, tbss2_orig_dir) if estimate_skeleton: call(['tbss_3_postreg', '-S'], env=os.environ.update({'FSLOUTPUTTYPE': 'NIFTI_GZ'})) else: call(['tbss_3_postreg', '-T'], env=os.environ.update({'FSLOUTPUTTYPE': 'NIFTI_GZ'})) tbss3_orig_dir = os.path.join(test_dir, "tbss3_original") shutil.copytree(tbss_orig_dir, tbss3_orig_dir) call(['tbss_4_prestats', '0.2'], env=os.environ.update({'FSLOUTPUTTYPE': 'NIFTI_GZ'})) tbss4_orig_dir = os.path.join(test_dir, "tbss4_original") shutil.copytree(tbss_orig_dir, tbss4_orig_dir) pipeline = pe.Workflow(name="test_tbss") pipeline.base_dir = os.path.join(test_dir, "tbss_nipype") tbss = create_tbss_all(estimate_skeleton=estimate_skeleton) tbss.inputs.inputnode.fa_list = FA_list tbss.inputs.inputnode.skeleton_thresh = 0.2 tbss1_original_datasource = pe.Node(nio.DataGrabber( outfields=['fa_list', 'mask_list'], sort_filelist=False), name='tbss1_original_datasource') tbss1_original_datasource.inputs.base_directory = tbss1_orig_dir tbss1_original_datasource.inputs.template = 'FA/%s_FA%s.nii.gz' tbss1_original_datasource.inputs.template_args = dict( fa_list=[[subjects, '']], mask_list=[[subjects, '_mask']]) tbss1_test_fa = pe.MapNode(util.AssertEqual(), name="tbss1_fa_test", iterfield=['volume1', 'volume2']) tbss1_test_mask = pe.MapNode(util.AssertEqual(), name="tbss1_mask_test", iterfield=['volume1', 'volume2']) pipeline.connect(tbss, 'tbss1.outputnode.fa_list', tbss1_test_fa, 'volume1') pipeline.connect(tbss, 'tbss1.outputnode.mask_list', tbss1_test_mask, 'volume1') pipeline.connect(tbss1_original_datasource, 'fa_list', tbss1_test_fa, 'volume2') pipeline.connect(tbss1_original_datasource, 'mask_list', tbss1_test_mask, 'volume2') tbss2_original_datasource = pe.Node(nio.DataGrabber( outfields=['field_list'], sort_filelist=False), name='tbss2_original_datasource') tbss2_original_datasource.inputs.base_directory = tbss2_orig_dir tbss2_original_datasource.inputs.template = 'FA/%s_FA%s.nii.gz' tbss2_original_datasource.inputs.template_args = dict( field_list=[[subjects, '_to_target_warp']]) tbss2_test_field = pe.MapNode(util.AssertEqual(), name="tbss2_test_field", iterfield=['volume1', 'volume2']) pipeline.connect(tbss, 'tbss2.outputnode.field_list', tbss2_test_field, 'volume1') pipeline.connect(tbss2_original_datasource, 'field_list', tbss2_test_field, 'volume2') tbss3_original_datasource = pe.Node(nio.DataGrabber(outfields=[ 'groupmask', 'skeleton_file', 'meanfa_file', 'mergefa_file' ], sort_filelist=False), name='tbss3_original_datasource') tbss3_original_datasource.inputs.base_directory = tbss3_orig_dir tbss3_original_datasource.inputs.template = 'stats/%s.nii.gz' tbss3_original_datasource.inputs.template_args = dict( groupmask=[['mean_FA_mask']], skeleton_file=[['mean_FA_skeleton']], meanfa_file=[['mean_FA']], mergefa_file=[['all_FA']]) tbss3_test_groupmask = pe.Node(util.AssertEqual(), name="tbss3_test_groupmask") tbss3_test_skeleton_file = pe.Node(util.AssertEqual(), name="tbss3_test_skeleton_file") tbss3_test_meanfa_file = pe.Node(util.AssertEqual(), name="tbss3_test_meanfa_file") tbss3_test_mergefa_file = pe.Node(util.AssertEqual(), name="tbss3_test_mergefa_file") pipeline.connect(tbss, 'tbss3.outputnode.groupmask', tbss3_test_groupmask, 'volume1') pipeline.connect(tbss3_original_datasource, 'groupmask', tbss3_test_groupmask, 'volume2') pipeline.connect(tbss, 'tbss3.outputnode.skeleton_file', tbss3_test_skeleton_file, 'volume1') pipeline.connect(tbss3_original_datasource, 'skeleton_file', tbss3_test_skeleton_file, 'volume2') pipeline.connect(tbss, 'tbss3.outputnode.meanfa_file', tbss3_test_meanfa_file, 'volume1') pipeline.connect(tbss3_original_datasource, 'meanfa_file', tbss3_test_meanfa_file, 'volume2') pipeline.connect(tbss, 'tbss3.outputnode.mergefa_file', tbss3_test_mergefa_file, 'volume1') pipeline.connect(tbss3_original_datasource, 'mergefa_file', tbss3_test_mergefa_file, 'volume2') tbss4_original_datasource = pe.Node(nio.DataGrabber( outfields=['all_FA_skeletonised', 'mean_FA_skeleton_mask'], sort_filelist=False), name='tbss4_original_datasource') tbss4_original_datasource.inputs.base_directory = tbss4_orig_dir tbss4_original_datasource.inputs.template = 'stats/%s.nii.gz' tbss4_original_datasource.inputs.template_args = dict( all_FA_skeletonised=[['all_FA_skeletonised']], mean_FA_skeleton_mask=[['mean_FA_skeleton_mask']]) tbss4_test_all_FA_skeletonised = pe.Node( util.AssertEqual(), name="tbss4_test_all_FA_skeletonised") tbss4_test_mean_FA_skeleton_mask = pe.Node( util.AssertEqual(), name="tbss4_test_mean_FA_skeleton_mask") pipeline.connect(tbss, 'tbss4.outputnode.projectedfa_file', tbss4_test_all_FA_skeletonised, 'volume1') pipeline.connect(tbss4_original_datasource, 'all_FA_skeletonised', tbss4_test_all_FA_skeletonised, 'volume2') pipeline.connect(tbss, 'tbss4.outputnode.skeleton_mask', tbss4_test_mean_FA_skeleton_mask, 'volume1') pipeline.connect(tbss4_original_datasource, 'mean_FA_skeleton_mask', tbss4_test_mean_FA_skeleton_mask, 'volume2') pipeline.run(plugin='Linear') os.chdir(old_dir) shutil.rmtree(test_dir)
def test_tbss_all_pipeline(): data_dir = '/nfs/s2/dticenter/data4test/tbss/mydata' # fsl_course_dir = os.getenv('FSL_COURSE_DATA') # data_dir = os.path.join(fsl_course_dir,'fsl_course_data/tbss') # subject_list = ['1260','1549','1636','1651','2078','2378'] subject_list = [ 'S0001', 'S0005', 'S0036', 'S0038', 'S0085', 'S0099', 'S0004', 'S0032', 'S0037', 'S0057', 'S0098' ] subject_list.sort() fsl_tbss_dir = '/nfs/s2/dticenter/data4test/tbss/tbss_fsl/tbss_mydata' workingdir = '/nfs/s2/dticenter/data4test/tbss/tbss_test_workingdir' """ For Nipype TBSS Workflow Get a list of all FA.nii.gz for nipype TBSS workflow """ def getFAList(subject_list): fa_list = [] for subject_id in subject_list: fa_list.append(os.path.join(data_dir, subject_id + '_FA.nii.gz')) return fa_list """ A nipype workflow for TBSS """ tbss_all = tbss.create_tbss_all(name='tbss_all') tbss_all.inputs.inputnode.target = fsl.Info.standard_image( "FMRIB58_FA_1mm.nii.gz") tbss_all.inputs.inputnode.skeleton_thresh = 0.2 tbss_all.inputs.inputnode.fa_list = getFAList(subject_list) """ For FSL_TBSS Get other FSL_TBSS results """ def getFA_prep_list(subjct_list): fa_prep_list = [] for subject_id in subject_list: fa_prep_list.append( os.path.join(fsl_tbss_dir, 'FA', subject_id + '_FA_FA.nii.gz')) return fa_prep_list def getmask_prep_list(subjct_list): mask_prep_list = [] for subject_id in subject_list: mask_prep_list.append( os.path.join(fsl_tbss_dir, 'FA', subject_id + '_FA_FA_mask.nii.gz')) return mask_prep_list def getfield_list(subjct_list): field_list = [] for subject_id in subject_list: field_list.append( os.path.join(fsl_tbss_dir, 'FA', subject_id + '_FA_FA_to_target.mat')) return field_list t3_all_FA = os.path.join(fsl_tbss_dir, 'stats/all_FA.nii.gz') t3_mean_FA = os.path.join(fsl_tbss_dir, 'stats/mean_FA.nii.gz') t3_groupmask = os.path.join(fsl_tbss_dir, 'stats/mean_FA_mask.nii.gz') t3_skeleton_file = os.path.join(fsl_tbss_dir, 'stats/mean_FA_skeleton.nii.gz') t4_all_FA_skeletonised = os.path.join(fsl_tbss_dir, 'stats/all_FA_skeletonised.nii.gz') t4_mean_FA_skeleton_mask = os.path.join( fsl_tbss_dir, 'stats/mean_FA_skeleton_mask.nii.gz') t4_mean_FA_skeleton_mask_dst = os.path.join( fsl_tbss_dir, 'stats/mean_FA_skeleton_mask_dst.nii.gz') """ """ merge_fa_list = pe.Node(fsl.Merge(dimension="t", merged_file="all_fa.nii.gz"), name="merge_fa_list") merge_mask_list = pe.Node(fsl.Merge(dimension="t", merged_file="all_mask.nii.gz"), name="merge_mask_list") """ The Test Nodes Check outputs of tbss1 """ FA_prep = pe.Node(util.AssertEqual(ignore_exception=False), name="FA_prep") merge_FA_prep = pe.Node(fsl.Merge(dimension="t", merged_file="all_FA_prep.nii.gz"), name="merge_FA_prep") merge_FA_prep.inputs.in_files = getFA_prep_list(subject_list) #FA_prep = pe.MapNode(util.AssertEqual(ignore_exception = True), name = "FA_prep", iterfield=['volume1','volume2']) #FA_prep.inputs.volume2 = getFA_prep_list(subject_list) mask_prep = pe.Node(util.AssertEqual(), name="mask_prep") merge_mask_prep = pe.Node(fsl.Merge(dimension="t", merged_file="all_mask_prep.nii.gz"), name="merge_mask_prep") merge_mask_prep.inputs.in_files = getmask_prep_list(subject_list) #mask_prep = pe.MapNode(util.AssertEqual(), name = "mask_prep", iterfield=['volume1','volume2']) #mask_prep.inputs.volume2 = getmask_prep_list(subject_list) """ Check outputs of tbss2 """ #field = pe.MapNode(util.AssertEqual(), name = "field", iterfield=['volume1','volume2']) #field.inputs.volume2 = getfield_list(subject_list) """ Check outputs of tbss3 """ all_FA = pe.Node(util.AssertEqual(ignore_exception=False), name="all_FA") all_FA.inputs.volume2 = t3_all_FA mean_FA = pe.Node(util.AssertEqual(ignore_exception=False), name="mean_FA") # OK mean_FA.inputs.volume2 = t3_mean_FA groupmask = pe.Node(util.AssertEqual(ignore_exception=False), name="groupmask") groupmask.inputs.volume2 = t3_groupmask skeleton_file = pe.Node(util.AssertEqual(ignore_exception=False), name="skeleton_file") skeleton_file.inputs.volume2 = t3_skeleton_file """ Check outputs of tbss4 """ all_FA_skeletonised = pe.Node(util.AssertEqual(ignore_exception=False), name="all_FA_skeletonised") all_FA_skeletonised.inputs.volume2 = t4_all_FA_skeletonised mean_FA_skeleton_mask = pe.Node(util.AssertEqual(ignore_exception=False), name="mean_FA_skeleton_mask") mean_FA_skeleton_mask.inputs.volume2 = t4_mean_FA_skeleton_mask mean_FA_skeleton_mask_dst = pe.Node( util.AssertEqual(ignore_exception=False), name="mean_FA_skeleton_mask_dst") mean_FA_skeleton_mask_dst.inputs.volume2 = t4_mean_FA_skeleton_mask_dst cmp_nipy2fsl = pe.Workflow(name="cmp_nipy2fsl") cmp_nipy2fsl.base_dir = workingdir cmp_nipy2fsl.connect([ (tbss_all, merge_fa_list, [('outputall_node.fa_list1', 'in_files') ]), #OK (merge_fa_list, FA_prep, [('merged_file', 'volume1')]), (merge_FA_prep, FA_prep, [('merged_file', 'volume2')]), # (tbss_all, FA_prep, [('outputall_node.fa_list1','volume1')]), (tbss_all, merge_mask_list, [('outputall_node.mask_list1', 'in_files')] ), #OK (merge_mask_list, mask_prep, [('merged_file', 'volume1')]), (merge_mask_prep, mask_prep, [('merged_file', 'volume2')]), # (tbss_all, mask_prep, [('outputall_node.mask_list1','volume1')]), # (tbss_all, field, [('outputall_node.field_list2','volume1')]), (tbss_all, all_FA, [('outputall_node.mergefa_file3', 'volume1')]), #OK (tbss_all, mean_FA, [('outputall_node.meanfa_file3', 'volume1')]), #OK (tbss_all, groupmask, [('outputall_node.groupmask3', 'volume1')]), #OK (tbss_all, skeleton_file, [('outputall_node.skeleton_file3', 'volume1') ]), #OK (tbss_all, all_FA_skeletonised, [('outputall_node.projectedfa_file4', 'volume1')]), #OK (tbss_all, mean_FA_skeleton_mask, [('outputall_node.skeleton_mask4', 'volume1')]), #OK (tbss_all, mean_FA_skeleton_mask_dst, [('outputall_node.distance_map4', 'volume1')]), #OK ]) #cmp_nipy2fsl.run(plugin=pluginName) cmp_nipy2fsl.run()