FIX: PEP8 - E201 whitespace after '(' '[' '{'

Author: miykael

examples/smri_antsregistration_build_template.py

@@ -103,7 +103,7 @@
 4. Define data sources. In real life these would be replace by DataGrabbers
 """
 
-InitialTemplateInputs=[ mdict['T1'] for mdict in ListOfImagesDictionaries ]
+InitialTemplateInputs=[mdict['T1'] for mdict in ListOfImagesDictionaries ]
 
 datasource = pe.Node(interface=util.IdentityInterface(fields=
                                                       ['InitialTemplateInputs', 'ListOfImagesDictionaries',

nipype/algorithms/metrics.py

@@ -359,9 +359,9 @@ def _list_outputs(self):
 
 
 class FuzzyOverlapInputSpec(BaseInterfaceInputSpec):
-    in_ref = InputMultiPath( File(exists=True), mandatory=True,
+    in_ref = InputMultiPath(File(exists=True), mandatory=True,
                              desc='Reference image. Requires the same dimensions as in_tst.')
-    in_tst = InputMultiPath( File(exists=True), mandatory=True,
+    in_tst = InputMultiPath(File(exists=True), mandatory=True,
                              desc='Test image. Requires the same dimensions as in_ref.')
     weighting = traits.Enum('none', 'volume', 'squared_vol', usedefault=True,
                             desc=('\'none\': no class-overlap weighting is '
@@ -374,11 +374,11 @@ class FuzzyOverlapInputSpec(BaseInterfaceInputSpec):
 
 
 class FuzzyOverlapOutputSpec(TraitedSpec):
-    jaccard = traits.Float( desc='Fuzzy Jaccard Index (fJI), all the classes' )
-    dice = traits.Float( desc='Fuzzy Dice Index (fDI), all the classes' )
+    jaccard = traits.Float(desc='Fuzzy Jaccard Index (fJI), all the classes' )
+    dice = traits.Float(desc='Fuzzy Dice Index (fDI), all the classes' )
     diff_file = File(exists=True, desc='resulting difference-map of all classes, using the chosen weighting' )
-    class_fji = traits.List( traits.Float(), desc='Array containing the fJIs of each computed class' )
-    class_fdi = traits.List( traits.Float(), desc='Array containing the fDIs of each computed class' )
+    class_fji = traits.List(traits.Float(), desc='Array containing the fJIs of each computed class' )
+    class_fdi = traits.List(traits.Float(), desc='Array containing the fDIs of each computed class' )
 
 
 class FuzzyOverlap(BaseInterface):
@@ -406,11 +406,11 @@ class FuzzyOverlap(BaseInterface):
 
     def _run_interface(self, runtime):
         ncomp = len(self.inputs.in_ref)
-        assert( ncomp == len(self.inputs.in_tst) )
-        weights = np.ones( shape=ncomp )
+        assert(ncomp == len(self.inputs.in_tst) )
+        weights = np.ones(shape=ncomp )
 
-        img_ref = np.array( [ nb.load( fname ).get_data() for fname in self.inputs.in_ref ] )
-        img_tst = np.array( [ nb.load( fname ).get_data() for fname in self.inputs.in_tst ] )
+        img_ref = np.array([nb.load(fname ).get_data() for fname in self.inputs.in_ref ] )
+        img_tst = np.array([nb.load(fname ).get_data() for fname in self.inputs.in_tst ] )
 
 
         msk = np.sum(img_ref, axis=0)
@@ -425,11 +425,11 @@ def _run_interface(self, runtime):
         self._jaccards = []
         volumes = []
 
-        diff_im = np.zeros( img_ref.shape )
+        diff_im = np.zeros(img_ref.shape )
 
-        for ref_comp, tst_comp, diff_comp in zip( img_ref, img_tst, diff_im ):
-            num = np.minimum( ref_comp, tst_comp )
-            ddr = np.maximum( ref_comp, tst_comp )
+        for ref_comp, tst_comp, diff_comp in zip(img_ref, img_tst, diff_im ):
+            num = np.minimum(ref_comp, tst_comp )
+            ddr = np.maximum(ref_comp, tst_comp )
             diff_comp[ddr>0] += 1.0 - (num[ddr>0] / ddr[ddr>0])
             self._jaccards.append(np.sum(num) / np.sum(ddr))
             volumes.append(np.sum(ref_comp))
@@ -444,17 +444,17 @@ def _run_interface(self, runtime):
 
         weights = weights / np.sum(weights)
 
-        setattr(self, '_jaccard',  np.sum( weights * self._jaccards ) )
-        setattr(self, '_dice', np.sum( weights * self._dices ) )
+        setattr(self, '_jaccard',  np.sum(weights * self._jaccards ) )
+        setattr(self, '_dice', np.sum(weights * self._dices ) )
 
 
-        diff = np.zeros( diff_im[0].shape )
+        diff = np.zeros(diff_im[0].shape )
 
         for w,ch in zip(weights,diff_im):
             ch[msk==0] = 0
             diff+= w* ch
 
-        nb.save(nb.Nifti1Image(diff, nb.load( self.inputs.in_ref[0]).get_affine(),
+        nb.save(nb.Nifti1Image(diff, nb.load(self.inputs.in_ref[0]).get_affine(),
                                nb.load(self.inputs.in_ref[0]).get_header()), self.inputs.out_file)
 
 
@@ -519,7 +519,7 @@ def _run_interface(self, runtime):
             mapshape = ref_data.shape[:-1]
 
         if isdefined(self.inputs.mask):
-            msk = nb.load( self.inputs.mask ).get_data()
+            msk = nb.load(self.inputs.mask ).get_data()
             if (mapshape != msk.shape):
                 raise RuntimeError("Mask should match volume shape, \
                                    mask is %s and volumes are %s" %
@@ -596,7 +596,7 @@ class SimilarityInputSpec(BaseInterfaceInputSpec):
 
 
 class SimilarityOutputSpec(TraitedSpec):
-    similarity = traits.List( traits.Float(desc="Similarity between volume 1 and 2, frame by frame"))
+    similarity = traits.List(traits.Float(desc="Similarity between volume 1 and 2, frame by frame"))
 
 
 class Similarity(BaseInterface):
@@ -645,14 +645,14 @@ def _run_interface(self, runtime):
         dims = vol1_nii.get_data().ndim
 
         if dims==3 or dims==2:
-            vols1 = [ vol1_nii ]
-            vols2 = [ vol2_nii ]
+            vols1 = [vol1_nii ]
+            vols2 = [vol2_nii ]
         if dims==4:
-            vols1 = nb.four_to_three( vol1_nii )
-            vols2 = nb.four_to_three( vol2_nii )
+            vols1 = nb.four_to_three(vol1_nii )
+            vols2 = nb.four_to_three(vol2_nii )
 
         if dims<2 or dims>4:
-            raise RuntimeError( 'Image dimensions not supported (detected %dD file)' % dims )
+            raise RuntimeError('Image dimensions not supported (detected %dD file)' % dims )
 
         if isdefined(self.inputs.mask1):
             mask1 = nb.load(self.inputs.mask1).get_data() == 1
@@ -666,13 +666,13 @@ def _run_interface(self, runtime):
 
         self._similarity = []
 
-        for ts1,ts2 in zip( vols1, vols2 ):
+        for ts1,ts2 in zip(vols1, vols2 ):
             histreg = HistogramRegistration(from_img = ts1,
                                             to_img = ts2,
                                             similarity=self.inputs.metric,
                                             from_mask = mask1,
                                             to_mask = mask2)
-            self._similarity.append( histreg.eval(Affine()) )
+            self._similarity.append(histreg.eval(Affine()) )
 
         return runtime
 

nipype/algorithms/misc.py

@@ -1269,12 +1269,12 @@ def normalize_tpms(in_files, in_mask=None, out_files=[]):
     weights = np.sum(img_data, axis=0)
 
     msk = np.ones_like(imgs[0].get_data())
-    msk[ weights<= 0 ] = 0
+    msk[weights<= 0 ] = 0
 
     if not in_mask is None:
         msk = nib.load(in_mask).get_data()
-        msk[ msk<=0 ] = 0
-        msk[ msk>0 ] = 1
+        msk[msk<=0 ] = 0
+        msk[msk>0 ] = 1
 
     msk = np.ma.masked_equal(msk, 0)
 

nipype/algorithms/tests/test_normalize_tpms.py

@@ -20,7 +20,7 @@ def test_normalize_tpms():
     tempdir = mkdtemp()
 
     in_mask = example_data('tpms_msk.nii.gz' )
-    mskdata = nb.load( in_mask ).get_data()
+    mskdata = nb.load(in_mask ).get_data()
     mskdata[mskdata>0.0] = 1.0
 
     mapdata = []
@@ -34,21 +34,21 @@ def test_normalize_tpms():
 
         im = nb.load(mapname)
         data = im.get_data()
-        mapdata.append( data.copy() )
+        mapdata.append(data.copy() )
 
         nb.Nifti1Image(2.0 * (data * mskdata), im.get_affine(),
                        im.get_header() ).to_filename(filename)
-        in_files.append( filename )
+        in_files.append(filename )
 
-    normalize_tpms( in_files, in_mask, out_files=out_files )
+    normalize_tpms(in_files, in_mask, out_files=out_files )
 
     sumdata = np.zeros_like(mskdata)
 
-    for i,tstfname in enumerate( out_files ):
-        normdata = nb.load( tstfname ).get_data()
+    for i,tstfname in enumerate(out_files ):
+        normdata = nb.load(tstfname ).get_data()
         sumdata+=normdata
-        yield assert_equal, np.all( normdata[mskdata==0]==0 ), True
-        yield assert_equal, np.allclose( normdata, mapdata[i] ), True
+        yield assert_equal, np.all(normdata[mskdata==0]==0 ), True
+        yield assert_equal, np.allclose(normdata, mapdata[i] ), True
 
     yield assert_equal, np.allclose(sumdata[sumdata>0.0], 1.0 ), True
 

nipype/external/portalocker.py

@@ -133,7 +133,7 @@ def unlock(file):
     portalocker.lock(log, portalocker.LOCK_EX)
 
     timestamp = strftime('%m/%d/%Y %H:%M:%S\n', localtime(time()))
-    log.write( timestamp )
+    log.write(timestamp )
 
     print('Wrote lines. Hit enter to release lock.')
     dummy = sys.stdin.readline()

nipype/fixes/numpy/testing/noseclasses.py

@@ -85,7 +85,7 @@ def _find(self, tests, obj, name, module, source_lines, globs, seen):
         if ismodule(obj) and self._recurse:
             for valname, val in list(obj.__dict__.items()):
                 valname1 = '%s.%s' % (name, valname)
-                if ( (isroutine(val) or isclass(val))
+                if ((isroutine(val) or isclass(val))
                      and self._from_module(module, val) ):
 
                     self._find(tests, val, valname1, module, source_lines,

nipype/interfaces/ants/base.py

@@ -59,10 +59,10 @@ def _num_threads_update(self):
         ## default behavior should be the one specified by ITKv4 rules
         ## (i.e. respect SGE $NSLOTS or environmental variables of threads, or
         ## user environmental settings)
-        if ( self.inputs.num_threads == -1 ):
-            if ( ALT_ITKv4_THREAD_LIMIT_VARIABLE in self.inputs.environ ):
+        if (self.inputs.num_threads == -1 ):
+            if (ALT_ITKv4_THREAD_LIMIT_VARIABLE in self.inputs.environ ):
                 del self.inputs.environ[ALT_ITKv4_THREAD_LIMIT_VARIABLE]
-            if ( PREFERED_ITKv4_THREAD_LIMIT_VARIABLE in self.inputs.environ ):
+            if (PREFERED_ITKv4_THREAD_LIMIT_VARIABLE in self.inputs.environ ):
                 del self.inputs.environ[PREFERED_ITKv4_THREAD_LIMIT_VARIABLE]
         else:
             self.inputs.environ.update({PREFERED_ITKv4_THREAD_LIMIT_VARIABLE:

nipype/interfaces/ants/registration.py

@@ -553,7 +553,7 @@ def _formatMetric(self, index):
                 else:
                     temp["moving_image"] = self.inputs.moving_image[i]
 
-                specs.append( temp )
+                specs.append(temp )
         else:
             specs = [stage_inputs]
 

nipype/interfaces/cmtk/cmtk.py

@@ -265,7 +265,7 @@ def cmat(track_file, roi_file, resolution_network_file, matrix_name, matrix_mat_
         fiberlabels[i, 0] = startROI
         fiberlabels[i, 1] = endROI
 
-        final_fiberlabels.append([ startROI, endROI ])
+        final_fiberlabels.append([startROI, endROI ])
         final_fibers_idx.append(i)
 
         # Add edge to graph
@@ -670,7 +670,7 @@ def _run_interface(self, runtime):
         """ Create empty grey matter mask, Populate with only those regions defined in the mapping."""
         niiGM = np.zeros(niiAPARCdata.shape, dtype=np.uint)
         for ma in MAPPING:
-            niiGM[ niiAPARCdata == ma[1]] = ma[0]
+            niiGM[niiAPARCdata == ma[1]] = ma[0]
             mapDict[ma[0]] = ma[1]
         iflogger.info('Grey matter mask created')
         greyMaskLabels = np.unique(niiGM)

nipype/interfaces/cmtk/convert.py

@@ -235,7 +235,7 @@ def _run_interface(self, runtime):
                 # metadata information
                 ne.load()
                 contitle = mycon.get_connectome_meta().get_title()
-                ne.set_name( str(i) + ': ' + contitle + ' - ' + ne.get_name() )
+                ne.set_name(str(i) + ': ' + contitle + ' - ' + ne.get_name() )
                 ne.set_src(ne.get_name())
                 extracted_networks.append(ne)