FIX: PEP8 - E251 unexpected spaces around keyword / parameter equals

Author: miykael

examples/dmri_camino_dti.py

@@ -92,7 +92,7 @@ def get_affine(volume):
 
 datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
                                                outfields=list(info.keys())),
-                     name = 'datasource')
+                     name='datasource')
 
 datasource.inputs.template = "%s/%s"
 
@@ -200,7 +200,7 @@ def get_affine(volume):
 trace = pe.Node(interface=camino.ComputeTensorTrace(), name='trace')
 dteig = pe.Node(interface=camino.ComputeEigensystem(), name='dteig')
 
-analyzeheader_fa = pe.Node(interface= camino.AnalyzeHeader(), name = "analyzeheader_fa")
+analyzeheader_fa = pe.Node(interface=camino.AnalyzeHeader(), name="analyzeheader_fa")
 analyzeheader_fa.inputs.datatype = "double"
 analyzeheader_trace = analyzeheader_fa.clone('analyzeheader_trace')
 

examples/dmri_connectivity.py

@@ -147,7 +147,7 @@ def select_aparc_annot(list_of_files):
 
 datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
                                                outfields=list(info.keys())),
-                     name = 'datasource')
+                     name='datasource')
 
 datasource.inputs.template = "%s/%s"
 datasource.inputs.base_directory = data_dir
@@ -185,24 +185,24 @@ def select_aparc_annot(list_of_files):
 FSL's Brain Extraction tool is used to create a mask from the b0 image
 """
 
-b0Strip = pe.Node(interface=fsl.BET(mask = True), name = 'bet_b0')
+b0Strip = pe.Node(interface=fsl.BET(mask=True), name='bet_b0')
 
 """
 FSL's FLIRT function is used to coregister the b0 mask and the structural image.
 A convert_xfm node is then used to obtain the inverse of the transformation matrix.
 FLIRT is used once again to apply the inverse transformation to the parcellated brain image.
 """
 
-coregister = pe.Node(interface=fsl.FLIRT(dof=6), name = 'coregister')
+coregister = pe.Node(interface=fsl.FLIRT(dof=6), name='coregister')
 coregister.inputs.cost = ('corratio')
 
-convertxfm = pe.Node(interface=fsl.ConvertXFM(), name = 'convertxfm')
+convertxfm = pe.Node(interface=fsl.ConvertXFM(), name='convertxfm')
 convertxfm.inputs.invert_xfm = True
 
-inverse = pe.Node(interface=fsl.FLIRT(), name = 'inverse')
+inverse = pe.Node(interface=fsl.FLIRT(), name='inverse')
 inverse.inputs.interp = ('nearestneighbour')
 
-inverse_AparcAseg = pe.Node(interface=fsl.FLIRT(), name = 'inverse_AparcAseg')
+inverse_AparcAseg = pe.Node(interface=fsl.FLIRT(), name='inverse_AparcAseg')
 inverse_AparcAseg.inputs.interp = ('nearestneighbour')
 
 """
@@ -334,7 +334,7 @@ def select_aparc_annot(list_of_files):
 """
 
 roigen = pe.Node(interface=cmtk.ROIGen(), name="ROIGen")
-cmp_config = cmp.configuration.PipelineConfiguration(parcellation_scheme = "NativeFreesurfer")
+cmp_config = cmp.configuration.PipelineConfiguration(parcellation_scheme="NativeFreesurfer")
 cmp_config.parcellation_scheme = "NativeFreesurfer"
 roigen.inputs.LUT_file = cmp_config.get_freeview_lut("NativeFreesurfer")['freesurferaparc']
 roigen_structspace = roigen.clone('ROIGen_structspace')

examples/dmri_connectivity_advanced.py

@@ -115,7 +115,7 @@
 
 datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
                                                outfields=list(info.keys())),
-                     name = 'datasource')
+                     name='datasource')
 
 datasource.inputs.template = "%s/%s"
 datasource.inputs.base_directory = data_dir
@@ -244,7 +244,7 @@
 thresholding it at a reasonably high level.
 """
 
-bet = pe.Node(interface=fsl.BET(mask = True), name = 'bet_b0')
+bet = pe.Node(interface=fsl.BET(mask=True), name='bet_b0')
 gen_WM_mask = pe.Node(interface=mrtrix.GenerateWhiteMatterMask(), name='gen_WM_mask')
 threshold_wmmask = pe.Node(interface=mrtrix.Threshold(), name='threshold_wmmask')
 threshold_wmmask.inputs.absolute_threshold_value = 0.4
@@ -287,7 +287,7 @@
 so that they are in the same space as the regions of interest.
 """
 
-coregister = pe.Node(interface=fsl.FLIRT(dof=6), name = 'coregister')
+coregister = pe.Node(interface=fsl.FLIRT(dof=6), name='coregister')
 coregister.inputs.cost = ('normmi')
 
 """

examples/dmri_dtk_dti.py

@@ -99,7 +99,7 @@
 
 datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
                                                outfields=list(info.keys())),
-                     name = 'datasource')
+                     name='datasource')
 
 datasource.inputs.template = "%s/%s"
 

examples/dmri_dtk_odf.py

@@ -99,7 +99,7 @@
 
 datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
                                                outfields=list(info.keys())),
-                     name = 'datasource')
+                     name='datasource')
 
 datasource.inputs.template = "%s/%s"
 

examples/dmri_fsl_dti.py

@@ -74,8 +74,8 @@
 info = dict(dwi=[['subject_id', 'data']],
             bvecs=[['subject_id', 'bvecs']],
             bvals=[['subject_id', 'bvals']],
-            seed_file = [['subject_id', 'MASK_average_thal_right']],
-            target_masks = [['subject_id', ['MASK_average_M1_right',
+            seed_file=[['subject_id', 'MASK_average_thal_right']],
+            target_masks=[['subject_id', ['MASK_average_M1_right',
                                            'MASK_average_S1_right',
                                            'MASK_average_occipital_right',
                                            'MASK_average_pfc_right',
@@ -108,7 +108,7 @@
 
 datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
                                                outfields=list(info.keys())),
-                     name = 'datasource')
+                     name='datasource')
 
 datasource.inputs.template = "%s/%s"
 

examples/dmri_mrtrix_dti.py

@@ -58,7 +58,7 @@
 
 datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
                                                outfields=list(info.keys())),
-                     name = 'datasource')
+                     name='datasource')
 
 datasource.inputs.template = "%s/%s"
 datasource.inputs.base_directory = data_dir
@@ -135,7 +135,7 @@
 thresholding it at a reasonably high level.
 """
 
-bet = pe.Node(interface=fsl.BET(mask = True), name = 'bet_b0')
+bet = pe.Node(interface=fsl.BET(mask=True), name='bet_b0')
 gen_WM_mask = pe.Node(interface=mrtrix.GenerateWhiteMatterMask(), name='gen_WM_mask')
 threshold_wmmask = pe.Node(interface=mrtrix.Threshold(), name='threshold_wmmask')
 threshold_wmmask.inputs.absolute_threshold_value = 0.4

examples/fmri_ants_openfmri.py

@@ -370,7 +370,7 @@ def create_fs_reg_workflow(name='registration'):
     binarize = Node(fs.Binarize(min=0.5, out_type="nii.gz", dilate=1), name="binarize_aparc")
     register.connect(fssource, ("aparc_aseg", get_aparc_aseg), binarize, "in_file")
 
-    stripper = Node(fsl.ApplyMask(), name ='stripper')
+    stripper = Node(fsl.ApplyMask(), name='stripper')
     register.connect(binarize, "binary_file", stripper, "mask_file")
     register.connect(convert, 'out_file', stripper, 'in_file')
 

examples/fmri_freesurfer_smooth.py

@@ -163,8 +163,8 @@
 
 """
 
-volsmooth = pe.Node(interface=spm.Smooth(), name = "volsmooth")
-surfsmooth = pe.MapNode(interface=fs.Smooth(proj_frac_avg=(0, 1, 0.1)), name = "surfsmooth",
+volsmooth = pe.Node(interface=spm.Smooth(), name="volsmooth")
+surfsmooth = pe.MapNode(interface=fs.Smooth(proj_frac_avg=(0, 1, 0.1)), name="surfsmooth",
                         iterfield=['in_file'])
 
 """
@@ -198,15 +198,15 @@
 :class:`nipype.interfaces.spm.SpecifyModel`.
 """
 
-modelspec = pe.Node(interface=model.SpecifySPMModel(), name= "modelspec")
+modelspec = pe.Node(interface=model.SpecifySPMModel(), name="modelspec")
 modelspec.inputs.concatenate_runs = True
 
 """
 Generate a first level SPM.mat file for analysis
 :class:`nipype.interfaces.spm.Level1Design`.
 """
 
-level1design = pe.Node(interface=spm.Level1Design(), name= "level1design")
+level1design = pe.Node(interface=spm.Level1Design(), name="level1design")
 level1design.inputs.bases = {'hrf': {'derivs': [0, 0]}}
 
 """
@@ -222,7 +222,7 @@
 first level contrasts specified in a few steps above.
 """
 
-contrastestimate = pe.Node(interface = spm.EstimateContrast(), name="contrastestimate")
+contrastestimate = pe.Node(interface=spm.EstimateContrast(), name="contrastestimate")
 
 volanalysis.connect([(modelspec, level1design, [('session_info', 'session_info')]),
                      (level1design, level1estimate, [('spm_mat_file', 'spm_mat_file')]),
@@ -417,7 +417,7 @@
 
 datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
                                                outfields=['func', 'struct']),
-                     name = 'datasource')
+                     name='datasource')
 datasource.inputs.base_directory = data_dir
 datasource.inputs.template = '%s/%s.nii'
 datasource.inputs.template_args = info

examples/fmri_fsl.py

@@ -73,7 +73,7 @@
 """
 
 img2float = pe.MapNode(interface=fsl.ImageMaths(out_data_type='float',
-                                                op_string = '',
+                                                op_string='',
                                                 suffix='_dtype'),
                        iterfield=['in_file'],
                        name='img2float')
@@ -84,7 +84,7 @@
 """
 
 extract_ref = pe.Node(interface=fsl.ExtractROI(t_size=1),
-                      name = 'extractref')
+                      name='extractref')
 
 """
 Define a function to pick the first file from a list of files
@@ -116,10 +116,10 @@ def getmiddlevolume(func):
 Realign the functional runs to the middle volume of the first run
 """
 
-motion_correct = pe.MapNode(interface=fsl.MCFLIRT(save_mats = True,
-                                                  save_plots = True),
+motion_correct = pe.MapNode(interface=fsl.MCFLIRT(save_mats=True,
+                                                  save_plots=True),
                             name='realign',
-                            iterfield = ['in_file'])
+                            iterfield=['in_file'])
 preproc.connect(img2float, 'out_file', motion_correct, 'in_file')
 preproc.connect(extract_ref, 'roi_file', motion_correct, 'ref_file')
 
@@ -138,7 +138,7 @@ def getmiddlevolume(func):
 Extract the mean volume of the first functional run
 """
 
-meanfunc = pe.Node(interface=fsl.ImageMaths(op_string = '-Tmean',
+meanfunc = pe.Node(interface=fsl.ImageMaths(op_string='-Tmean',
                                             suffix='_mean'),
                    name='meanfunc')
 preproc.connect(motion_correct, ('out_file', pickfirst), meanfunc, 'in_file')
@@ -147,10 +147,10 @@ def getmiddlevolume(func):
 Strip the skull from the mean functional to generate a mask
 """
 
-meanfuncmask = pe.Node(interface=fsl.BET(mask = True,
+meanfuncmask = pe.Node(interface=fsl.BET(mask=True,
                                          no_output=True,
-                                         frac = 0.3),
-                       name = 'meanfuncmask')
+                                         frac=0.3),
+                       name='meanfuncmask')
 preproc.connect(meanfunc, 'out_file', meanfuncmask, 'in_file')
 
 """
@@ -160,7 +160,7 @@ def getmiddlevolume(func):
 maskfunc = pe.MapNode(interface=fsl.ImageMaths(suffix='_bet',
                                                op_string='-mas'),
                       iterfield=['in_file'],
-                      name = 'maskfunc')
+                      name='maskfunc')
 preproc.connect(motion_correct, 'out_file', maskfunc, 'in_file')
 preproc.connect(meanfuncmask, 'mask_file', maskfunc, 'in_file2')
 
@@ -170,7 +170,7 @@ def getmiddlevolume(func):
 """
 
 getthresh = pe.MapNode(interface=fsl.ImageStats(op_string='-p 2 -p 98'),
-                       iterfield = ['in_file'],
+                       iterfield=['in_file'],
                        name='getthreshold')
 preproc.connect(maskfunc, 'out_file', getthresh, 'in_file')
 
@@ -197,7 +197,7 @@ def getthreshop(thresh):
 """
 
 medianval = pe.MapNode(interface=fsl.ImageStats(op_string='-k %s -p 50'),
-                       iterfield = ['in_file'],
+                       iterfield=['in_file'],
                        name='medianval')
 preproc.connect(motion_correct, 'out_file', medianval, 'in_file')
 preproc.connect(threshold, 'out_file', medianval, 'mask_file')
@@ -318,25 +318,25 @@ def getinormscale(medianvals):
 """
 
 nosestrip = pe.Node(interface=fsl.BET(frac=0.3),
-                    name = 'nosestrip')
-skullstrip = pe.Node(interface=fsl.BET(mask = True),
-                     name = 'stripstruct')
+                    name='nosestrip')
+skullstrip = pe.Node(interface=fsl.BET(mask=True),
+                     name='stripstruct')
 
 coregister = pe.Node(interface=fsl.FLIRT(dof=6),
-                     name = 'coregister')
+                     name='coregister')
 
 """
 Use :class:`nipype.algorithms.rapidart` to determine which of the
 images in the functional series are outliers based on deviations in
 intensity and/or movement.
 """
 
-art = pe.MapNode(interface=ra.ArtifactDetect(use_differences = [True, False],
-                                             use_norm = True,
-                                             norm_threshold = 1,
-                                             zintensity_threshold = 3,
-                                             parameter_source = 'FSL',
-                                             mask_type = 'file'),
+art = pe.MapNode(interface=ra.ArtifactDetect(use_differences=[True, False],
+                                             use_norm=True,
+                                             norm_threshold=1,
+                                             zintensity_threshold=3,
+                                             parameter_source='FSL',
+                                             mask_type='file'),
                  iterfield=['realigned_files', 'realignment_parameters'],
                  name="art")
 
@@ -377,7 +377,7 @@ def getinormscale(medianvals):
 """
 
 modelgen = pe.MapNode(interface=fsl.FEATModel(), name='modelgen',
-                      iterfield = ['fsf_file', 'ev_files'])
+                      iterfield=['fsf_file', 'ev_files'])
 
 
 """
@@ -389,14 +389,14 @@ def getinormscale(medianvals):
                                                  mask_size=5,
                                                  threshold=1000),
                            name='modelestimate',
-                           iterfield = ['design_file', 'in_file'])
+                           iterfield=['design_file', 'in_file'])
 
 """
 Use :class:`nipype.interfaces.fsl.ContrastMgr` to generate contrast estimates
 """
 
 conestimate = pe.MapNode(interface=fsl.ContrastMgr(), name='conestimate',
-                         iterfield = ['tcon_file', 'param_estimates',
+                         iterfield=['tcon_file', 'param_estimates',
                                       'sigmasquareds', 'corrections',
                                       'dof_file'])
 
@@ -539,7 +539,7 @@ def num_copes(files):
 
 datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
                                                outfields=['func', 'struct']),
-                     name = 'datasource')
+                     name='datasource')
 datasource.inputs.base_directory = data_dir
 datasource.inputs.template = '%s/%s.nii'
 datasource.inputs.template_args = info
@@ -615,7 +615,7 @@ def subjectinfo(subject_id):
 ========================
 """
 
-l1pipeline = pe.Workflow(name= "level1")
+l1pipeline = pe.Workflow(name="level1")
 l1pipeline.base_dir = os.path.abspath('./fsl/workingdir')
 l1pipeline.config = {"execution": {"crashdump_dir": os.path.abspath('./fsl/crashdumps')}}