FIX: PEP8 - E131 continuation line unaligned for hanging indent

Author: miykael

examples/dmri_preprocessing.py

@@ -155,14 +155,14 @@
 wf = pe.Workflow(name="dMRI_Preprocessing")
 wf.base_dir = os.path.abspath('preprocessing_dmri_tutorial')
 wf.connect([
-     (infosource,   datasource,     [('subject_id', 'subject_id')])
-    ,(datasource,   prep,           [('dwi', 'inputnode.in_file'),
+     (infosource,   datasource,     [('subject_id', 'subject_id')]),
+     (datasource,   prep,           [('dwi', 'inputnode.in_file'),
                                      ('dwi_rev', 'inputnode.alt_file'),
                                      ('bvals', 'inputnode.in_bval'),
-                                     ('bvecs', 'inputnode.in_bvec')])
-    ,(prep,         bias,           [('outputnode.out_file', 'inputnode.in_file'),
-                                     ('outputnode.out_mask', 'inputnode.in_mask')])
-    ,(datasource,   bias,           [('bvals', 'inputnode.in_bval')])
+                                     ('bvecs', 'inputnode.in_bvec')]),
+     (prep,         bias,           [('outputnode.out_file', 'inputnode.in_file'),
+                                     ('outputnode.out_mask', 'inputnode.in_mask')]),
+     (datasource,   bias,           [('bvals', 'inputnode.in_bval')])
 ])
 
 

nipype/interfaces/cmtk/parcellation.py

@@ -531,15 +531,15 @@ class ParcellateOutputSpec(TraitedSpec):
     white_matter_mask_file = File(exists=True, desc='White matter mask file')
     cc_unknown_file = File(
         desc='Image file with regions labelled as unknown cortical structures',
-                    exists=True)
+        exists=True)
     ribbon_file = File(desc='Image file detailing the cortical ribbon',
                        exists=True)
     aseg_file = File(
         desc='Automated segmentation file converted from Freesurfer "subjects" directory',
-                    exists=True)
+        exists=True)
     roi_file_in_structural_space = File(
         desc='ROI image resliced to the dimensions of the original structural image',
-                    exists=True)
+        exists=True)
     dilated_roi_file_in_structural_space = File(
         desc='dilated ROI image resliced to the dimensions of the original structural image')
 

nipype/interfaces/fsl/preprocess.py

@@ -1099,11 +1099,11 @@ class SUSANInputSpec(FSLCommandInputSpec):
                              desc='whether to use a local median filter in the cases where single-point noise is detected')
     usans = traits.List(
         traits.Tuple(File(exists=True), traits.Float), maxlen=2,
-                        argstr='', position=6, default=[], usedefault=True,
-             desc='determines whether the smoothing area (USAN) is to be '
-                  'found from secondary images (0, 1 or 2). A negative '
-                  'value for any brightness threshold will auto-set the '
-                  'threshold at 10% of the robust range')
+        argstr='', position=6, default=[], usedefault=True,
+        desc='determines whether the smoothing area (USAN) is to be '
+             'found from secondary images (0, 1 or 2). A negative '
+             'value for any brightness threshold will auto-set the '
+             'threshold at 10% of the robust range')
     out_file = File(argstr='%s', position=-1, genfile=True,
                     desc='output file name', hash_files=False)
 

nipype/interfaces/spm/model.py

@@ -275,7 +275,7 @@ class EstimateContrastInputSpec(SPMCommandInputSpec):
             session list is None or not provided, all sessions are used. For F
             contrasts, the condition list should contain previously defined
             T-contrasts.""",
-            mandatory=True)
+        mandatory=True)
     beta_images = InputMultiPath(File(exists=True),
                                  desc='Parameter estimates of the design matrix',
                                  copyfile=False,

nipype/workflows/dmri/connectivity/group_connectivity.py

@@ -190,8 +190,8 @@ def create_merge_network_results_by_group_workflow(group_list, group_id, data_di
     l2source.inputs.template = '%s/%s'
     l2source.inputs.field_template = dict(
         CFFfiles=op.join(output_dir, '%s/cff/*/connectome.cff'),
-     CSVmatrices=op.join(output_dir, '%s/cmatrices_csv/*/*.csv'), CSVnodal=op.join(output_dir, '%s/nxcsv/*/*nodal*.csv'),
-     CSVglobal=op.join(output_dir, '%s/nxcsv/*/*global*.csv'), CSVfibers=op.join(output_dir, '%s/fiber_csv/*/*fibers*.csv'))
+        CSVmatrices=op.join(output_dir, '%s/cmatrices_csv/*/*.csv'), CSVnodal=op.join(output_dir, '%s/nxcsv/*/*nodal*.csv'),
+        CSVglobal=op.join(output_dir, '%s/nxcsv/*/*global*.csv'), CSVfibers=op.join(output_dir, '%s/fiber_csv/*/*fibers*.csv'))
     l2source.inputs.sort_filelist = True
 
     l2inputnode = pe.Node(interface=util.IdentityInterface(fields=['CFFfiles',
@@ -237,7 +237,7 @@ def create_merge_network_results_by_group_workflow(group_list, group_id, data_di
 
     concat_csv_interface = Function(
         input_names=["in_files"], output_names=["out_name"],
-                             function=concatcsv)
+        function=concatcsv)
 
     concat_node_csvs = pe.Node(
         interface=concat_csv_interface, name='concat_node_csvs')

nipype/workflows/dmri/dipy/denoise.py

@@ -37,11 +37,11 @@ def nlmeans_pipeline(name='Denoise',
     wf = pe.Workflow(name=name)
     wf.connect([
          (inputnode,  nmask,       [('in_file', 'in_file'),
-                                    ('in_mask', 'in_mask')])
-        ,(inputnode,  nlmeans,     [('in_file', 'in_file'),
-                                    ('in_mask', 'in_mask')])
-        ,(nmask,      nlmeans,     [('out_file', 'noise_mask')])
-        ,(nlmeans,    outputnode,  [('out_file', 'out_file')])
+                                    ('in_mask', 'in_mask')]),
+         (inputnode,  nlmeans,     [('in_file', 'in_file'),
+                                    ('in_mask', 'in_mask')]),
+         (nmask,      nlmeans,     [('out_file', 'noise_mask')]),
+         (nlmeans,    outputnode,  [('out_file', 'out_file')])
     ])
     return wf