fix: check before commit

Author: satra

examples/fmri_openfmri.py

@@ -351,7 +351,7 @@ def get_subs(subject_id, conds, model_id, task_id):
         'mean'))
         subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_flirt',
         'affine'))
-        
+
         for i in range(len(conds)):
             subs.append(('_flameo%d/cope1.' % i, 'cope%02d.' % (i + 1)))
             subs.append(('_flameo%d/varcope1.' % i, 'varcope%02d.' % (i + 1)))
@@ -392,7 +392,7 @@ def get_subs(subject_id, conds, model_id, task_id):
     wf.connect(registration, 'outputspec.transformed_mean', datasink, 'mean.mni')
     wf.connect(registration, 'outputspec.func2anat_transform', datasink, 'xfm.mean2anat')
     wf.connect(registration, 'outputspec.anat2target_transform', datasink, 'xfm.anat2target')
-    
+
     """
     Set processing parameters
     """

nipype/external/provcopy.py

@@ -346,7 +346,7 @@ def provn_representation(self):
 
     def json_representation(self):
         return {'$': self._uri, 'type': u'xsd:anyURI'}
-    
+
     def rdf_representation(self):
         return URIRef(self.get_uri())
 
@@ -787,7 +787,7 @@ def rdf(self, graph=None, subj=None):
                     pred = attr.rdf_representation()
                 graph.add((subj, pred, obj))
         return graph
-        
+
     def is_asserted(self):
         return self._asserted
 
@@ -802,7 +802,7 @@ def is_relation(self):
 class ProvElement(ProvRecord):
     def is_element(self):
         return True
-    
+
     def rdf(self, graph=None):
         if graph is None:
             graph = Graph()
@@ -1673,15 +1673,15 @@ def rdf(self, graph=None):
             # graph should not None here
             uri = self.get_identifier().rdf_representation()
             graph = Graph(graph.store, uri)
-        
+
         for prefix, namespace in self._namespaces.items():
             graph.bind(prefix, namespace.get_uri())
-        
+
         for record in self._records:
             if record.is_asserted():
                 record.rdf(graph)
         return graph
-    
+
     def get_provjson(self, **kw):
         """Return the `PROV-JSON <http://www.w3.org/Submission/prov-json/>`_ representation for the bundle/document.
 

nipype/interfaces/freesurfer/__init__.py

@@ -9,7 +9,7 @@
                          SynthesizeFLASH)
 from .model import (MRISPreproc, GLMFit, OneSampleTTest, Binarize, Concatenate,
                     SegStats, Label2Vol, MS_LDA)
-from .utils import (SampleToSurface, SurfaceSmooth, SurfaceTransform, Surface2VolTransform, 
+from .utils import (SampleToSurface, SurfaceSmooth, SurfaceTransform, Surface2VolTransform,
                     SurfaceSnapshots,ApplyMask, MRIsConvert, MRITessellate,
                     MRIMarchingCubes, SmoothTessellation, MakeAverageSubject,
                     ExtractMainComponent)

nipype/interfaces/fsl/preprocess.py

@@ -678,17 +678,17 @@ def _list_outputs(self):
                                                       '_variance.ext', cwd=cwd)
             outputs['std_img'] = self._gen_fname(outputs['out_file'] +
                                                  '_sigma.ext', cwd=cwd)
-                                                  
+
         # The mean image created if -stats option is specified ('meanvol')
-        # is missing the top and bottom slices. Therefore we only expose the 
-        # mean image created by -meanvol option ('mean_reg') which isn't 
+        # is missing the top and bottom slices. Therefore we only expose the
+        # mean image created by -meanvol option ('mean_reg') which isn't
         # corrupted.
         # Note that the same problem holds for the std and variance image.
-                                                  
+
         if isdefined(self.inputs.mean_vol) and self.inputs.mean_vol:
             outputs['mean_img'] = self._gen_fname(outputs['out_file'] +
                                                   '_mean_reg.ext', cwd=cwd)
-                                                  
+
         if isdefined(self.inputs.save_mats) and self.inputs.save_mats:
             _, filename = os.path.split(outputs['out_file'])
             matpathname = os.path.join(cwd, filename + '.mat')

nipype/interfaces/mrtrix/tensors.py

@@ -327,10 +327,10 @@ class GenerateDirections(CommandLine):
     input_spec=GenerateDirectionsInputSpec
     output_spec=GenerateDirectionsOutputSpec
 
-    
+
 class FindShPeaksInputSpec(CommandLineInputSpec):
     in_file = File(exists=True, argstr='%s', mandatory=True, position=-3, desc='the input image of SH coefficients.')
-    directions_file = File(exists=True, argstr='%s', mandatory=True, position=-2, desc='the set of directions to use as seeds for the peak finding')    
+    directions_file = File(exists=True, argstr='%s', mandatory=True, position=-2, desc='the set of directions to use as seeds for the peak finding')
     peaks_image = File(exists=True, argstr='-peaks %s', desc='the program will try to find the peaks that most closely match those in the image provided')
     num_peaks = traits.Int(argstr='-num %s', desc='the number of peaks to extract (default is 3)')
     peak_directions = traits.List(traits.Float, argstr='-direction %s', sep=' ', minlen=2, maxlen=2,
@@ -342,7 +342,7 @@ class FindShPeaksInputSpec(CommandLineInputSpec):
     display_debug = traits.Bool(argstr='-debug', desc='Display debugging messages.')
     out_file = File(name_template="%s_peak_dirs.mif", keep_extension=False, argstr='%s', hash_files=False, position= -1,
                     desc='the output image. Each volume corresponds to the x, y & z component of each peak direction vector in turn', name_source=["in_file"])
-    
+
 class FindShPeaksOutputSpec(TraitedSpec):
     out_file = File(exists=True, desc='Peak directions image')
 
@@ -368,7 +368,7 @@ class FindShPeaks(CommandLine):
 
 
 class Directions2AmplitudeInputSpec(CommandLineInputSpec):
-    in_file = File(exists=True, argstr='%s', mandatory=True, position=-2, desc='the input directions image. Each volume corresponds to the x, y & z component of each direction vector in turn.')    
+    in_file = File(exists=True, argstr='%s', mandatory=True, position=-2, desc='the input directions image. Each volume corresponds to the x, y & z component of each direction vector in turn.')
     peaks_image = File(exists=True, argstr='-peaks %s', desc='the program will try to find the peaks that most closely match those in the image provided')
     num_peaks = traits.Int(argstr='-num %s', desc='the number of peaks to extract (default is 3)')
     peak_directions = traits.List(traits.Float, argstr='-direction %s', sep=' ', minlen=2, maxlen=2,
@@ -379,7 +379,7 @@ class Directions2AmplitudeInputSpec(CommandLineInputSpec):
     display_debug = traits.Bool(argstr='-debug', desc='Display debugging messages.')
     out_file = File(name_template="%s_amplitudes.mif", keep_extension=False, argstr='%s', hash_files=False, position= -1,
                     desc='the output amplitudes image', name_source=["in_file"])
-    
+
 class Directions2AmplitudeOutputSpec(TraitedSpec):
     out_file = File(exists=True, desc='amplitudes image')
 

nipype/interfaces/mrtrix/tracking.py

@@ -75,22 +75,22 @@ class StreamlineTrackInputSpec(CommandLineInputSpec):
     in_file = File(exists=True, argstr='%s', mandatory=True, position=-2, desc='the image containing the source data.' \
     'The type of data required depends on the type of tracking as set in the preceeding argument. For DT methods, ' \
     'the base DWI are needed. For SD methods, the SH harmonic coefficients of the FOD are needed.')
-    
+
     seed_xor = ['seed_file', 'seed_spec']
     seed_file = File(exists=True, argstr='-seed %s', desc='seed file', xor = seed_xor)
     seed_spec = traits.List(traits.Float, desc='seed specification in mm and radius (x y z r)', position=2,
         argstr='-seed %s', minlen=4, maxlen=4, sep=',', units='mm', xor = seed_xor)
-    
+
     include_xor = ['include_file', 'include_spec']
     include_file = File(exists=True, argstr='-include %s', desc='inclusion file', xor = include_xor)
     include_spec = traits.List(traits.Float, desc='inclusion specification in mm and radius (x y z r)', position=2,
         argstr='-include %s', minlen=4, maxlen=4, sep=',', units='mm', xor = include_xor)
-    
+
     exclude_xor = ['exclude_file', 'exclude_spec']
     exclude_file = File(exists=True, argstr='-exclude %s', desc='exclusion file', xor = exclude_xor)
     exclude_spec = traits.List(traits.Float, desc='exclusion specification in mm and radius (x y z r)', position=2,
         argstr='-exclude %s', minlen=4, maxlen=4, sep=',', units='mm', xor = exclude_xor)
-    
+
     mask_xor = ['mask_file', 'mask_spec']
     mask_file = File(exists=True, argstr='-mask %s', desc='mask file. Only tracks within mask.', xor = mask_xor)
     mask_spec = traits.List(traits.Float, desc='Mask specification in mm and radius (x y z r). Tracks will be terminated when they leave the ROI.', position=2,
@@ -127,7 +127,7 @@ class StreamlineTrackInputSpec(CommandLineInputSpec):
 
     initial_direction = traits.List(traits.Int, desc='Specify the initial tracking direction as a vector',
         argstr='-initdirection %s', minlen=2, maxlen=2, units='voxels')
-    out_file = File(argstr='%s', position= -1, name_source = ['in_file'], name_template='%s_tracked.tck', 
+    out_file = File(argstr='%s', position= -1, name_source = ['in_file'], name_template='%s_tracked.tck',
                     output_name='tracked', desc='output data file')
 
 class StreamlineTrackOutputSpec(TraitedSpec):

nipype/interfaces/vista/vista.py

@@ -40,8 +40,8 @@ class Vnifti2Image(CommandLine):
     _cmd = 'vnifti2image'
     input_spec=Vnifti2ImageInputSpec
     output_spec=Vnifti2ImageOutputSpec
-    
-    
+
+
 class VtoMatInputSpec(CommandLineInputSpec):
     in_file = File(exists=True, argstr='-in %s', mandatory=True, position=1, desc='in file')
     out_file = File(name_template="%s.mat", keep_extension=False, argstr='-out %s', hash_files=False,

nipype/workflows/smri/freesurfer/recon.py

@@ -26,9 +26,9 @@ def create_skullstripped_recon_flow(name="skullstripped_recon_all"):
 
            outputspec.subject_id : freesurfer subject id
            outputspec.subjects_dir : freesurfer subjects directory
-    """    
+    """
     wf = pe.Workflow(name=name)
-    
+
     inputnode = pe.Node(niu.IdentityInterface(fields=['subject_id',
                                                       'subjects_dir',
                                                       'T1_files']),
@@ -43,34 +43,34 @@ def create_skullstripped_recon_flow(name="skullstripped_recon_all"):
     wf.connect(inputnode, "T1_files", autorecon1, "T1_files")
     wf.connect(inputnode, "subjects_dir", autorecon1, "subjects_dir")
     wf.connect(inputnode, "subject_id", autorecon1, "subject_id")
-    
-    
+
+
     def link_masks(subjects_dir, subject_id):
         import os
         os.symlink(os.path.join(subjects_dir, subject_id, "mri", "T1.mgz"),
                    os.path.join(subjects_dir, subject_id, "mri", "brainmask.auto.mgz"))
         os.symlink(os.path.join(subjects_dir, subject_id, "mri", "brainmask.auto.mgz"),
                    os.path.join(subjects_dir, subject_id, "mri", "brainmask.mgz"))
         return subjects_dir, subject_id
-    
-    masks = pe.Node(niu.Function(input_names=['subjects_dir', 'subject_id'], 
+
+    masks = pe.Node(niu.Function(input_names=['subjects_dir', 'subject_id'],
                                   output_names=['subjects_dir', 'subject_id'],
                                   function=link_masks), name="link_masks")
-    
+
     wf.connect(autorecon1, "subjects_dir", masks, "subjects_dir")
-    wf.connect(autorecon1, "subject_id", masks, "subject_id")   
-    
-    
+    wf.connect(autorecon1, "subject_id", masks, "subject_id")
+
+
     autorecon_resume = pe.Node(fs.ReconAll(), name="autorecon_resume")
     autorecon_resume.plugin_args={'submit_specs': 'request_memory = 2500'}
     autorecon_resume.inputs.args = "-no-isrunning"
     wf.connect(masks, "subjects_dir", autorecon_resume, "subjects_dir")
     wf.connect(masks, "subject_id", autorecon_resume, "subject_id")
-    
+
     outputnode = pe.Node(niu.IdentityInterface(fields=['subject_id',
                                                       'subjects_dir']),
                         name='outputspec')
-    
+
     wf.connect(autorecon_resume, "subjects_dir", outputnode, "subjects_dir")
     wf.connect(autorecon_resume, "subject_id", outputnode, "subject_id")
     return wf