Run autopep8 on misc.py

In-place run of the standard tool. Ref #662

Author: oesteban

nipype/algorithms/misc.py

@@ -55,6 +55,7 @@ class PickAtlasOutputSpec(TraitedSpec):
 
 
 class PickAtlas(BaseInterface):
+
     '''
     Returns ROI masks given an atlas and a list of labels. Supports dilation
     and left right masking (assuming the atlas is properly aligned).
@@ -88,9 +89,9 @@ def _get_brodmann_area(self):
         for lab in labels:
             newdata[origdata == lab] = 1
         if self.inputs.hemi == 'right':
-            newdata[floor(float(origdata.shape[0]) / 2):, :, :] = 0
+            newdata[floor(float(origdata.shape[0]) / 2):, :,:] = 0
         elif self.inputs.hemi == 'left':
-            newdata[:ceil(float(origdata.shape[0]) / 2), :, :] = 0
+            newdata[:ceil(float(origdata.shape[0]) / 2), :,:] = 0
 
         if self.inputs.dilation_size != 0:
             newdata = grey_dilation(
@@ -162,6 +163,7 @@ class ModifyAffineOutputSpec(TraitedSpec):
 
 
 class ModifyAffine(BaseInterface):
+
     '''
     Left multiplies the affine matrix with a specified values. Saves the volume as a nifti file.
     '''
@@ -216,6 +218,7 @@ class DistanceOutputSpec(TraitedSpec):
 
 
 class Distance(BaseInterface):
+
     '''
     Calculates distance between two volumes.
     '''
@@ -231,7 +234,7 @@ def _find_border(self, data):
 
     def _get_coordinates(self, data, affine):
         if len(data.shape) == 4:
-            data = data[:, :, :, 0]
+            data = data[:, :,:, 0]
         indices = np.vstack(np.nonzero(data))
         indices = np.vstack((indices, np.ones(indices.shape[1])))
         coordinates = np.dot(affine, indices)
@@ -251,7 +254,7 @@ def _eucl_min(self, nii1, nii2):
         dist_matrix = cdist(set1_coordinates.T, set2_coordinates.T)
         (point1, point2) = np.unravel_index(
             np.argmin(dist_matrix), dist_matrix.shape)
-        return (euclidean(set1_coordinates.T[point1, :], set2_coordinates.T[point2, :]), set1_coordinates.T[point1, :], set2_coordinates.T[point2, :])
+        return (euclidean(set1_coordinates.T[point1, :], set2_coordinates.T[point2,:]), set1_coordinates.T[point1,:], set2_coordinates.T[point2,:])
 
     def _eucl_cog(self, nii1, nii2):
         origdata1 = nii1.get_data().astype(np.bool)
@@ -376,6 +379,7 @@ class OverlapOutputSpec(TraitedSpec):
 
 
 class Overlap(BaseInterface):
+
     """
     Calculates various overlap measures between two maps.
 
@@ -438,25 +442,30 @@ 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", desc='""none": no class-overlap weighting is performed\
                             "volume": computed class-overlaps are weighted by class volume\
-                            "squared_vol": computed class-overlaps are weighted by the squared volume of the class',usedefault=True)
-    out_file = File("diff.nii", desc="alternative name for resulting difference-map", usedefault=True)
+                            "squared_vol": computed class-overlaps are weighted by the squared volume of the class', usedefault=True)
+    out_file = File(
+        "diff.nii", desc="alternative name for resulting difference-map", usedefault=True)
 
 
 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" )
-    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" )
+    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 FuzzyOverlap(BaseInterface):
+
     """
     Calculates various overlap measures between two maps, using the fuzzy definition
     proposed in: Crum et al., Generalized Overlap Measures for Evaluation and Validation
@@ -475,76 +484,72 @@ class FuzzyOverlap(BaseInterface):
     >>> res = overlap.run() # doctest: +SKIP
     """
 
-    input_spec =  FuzzyOverlapInputSpec
+    input_spec = FuzzyOverlapInputSpec
     output_spec = FuzzyOverlapOutputSpec
 
     def _run_interface(self, runtime):
         ncomp = len(self.inputs.in_ref)
-        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 ] )
+        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])
 
         msk = np.sum(img_ref, axis=0)
-        msk[msk>0] = 1.0
+        msk[msk > 0] = 1.0
         tst_msk = np.sum(img_tst, axis=0)
-        tst_msk[tst_msk>0] = 1.0
-
-        #check that volumes are normalized
-        #img_ref[:][msk>0] = img_ref[:][msk>0] / (np.sum( img_ref, axis=0 ))[msk>0]
-        #img_tst[tst_msk>0] = img_tst[tst_msk>0] / np.sum( img_tst, axis=0 )[tst_msk>0]
+        tst_msk[tst_msk > 0] = 1.0
 
         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 )
-            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 ) )
+        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))
 
-        self._dices = 2.0*np.array(self._jaccards) / (np.array(self._jaccards) +1.0 )
+        self._dices = 2.0 * \
+            np.array(self._jaccards) / (np.array(self._jaccards) + 1.0)
 
         if self.inputs.weighting != "none":
             weights = 1.0 / np.array(volumes)
             if self.inputs.weighting == "squared_vol":
-                weights = weights**2
+                weights = weights ** 2
 
-        weights = weights / np.sum( weights )
+        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
 
-        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.load( self.inputs.in_ref[0]).get_header()), self.inputs.out_file )
+        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)
 
- 
         return runtime
 
     def _list_outputs(self):
         outputs = self._outputs().get()
         for method in ("dice", "jaccard"):
             outputs[method] = getattr(self, '_' + method)
-        #outputs['volume_difference'] = self._volume
         outputs['diff_file'] = os.path.abspath(self.inputs.out_file)
-        outputs['class_fji'] =  np.array(self._jaccards).astype(float).tolist();
-        outputs['class_fdi']=  self._dices.astype(float).tolist();
+        outputs['class_fji'] = np.array(
+            self._jaccards).astype(float).tolist();
+        outputs['class_fdi'] = self._dices.astype(float).tolist();
         return outputs
 
 
-
 class CreateNiftiInputSpec(BaseInterfaceInputSpec):
     data_file = File(exists=True, mandatory=True, desc="ANALYZE img file")
     header_file = File(
@@ -599,6 +604,7 @@ class TSNROutputSpec(TraitedSpec):
 
 
 class TSNR(BaseInterface):
+
     """Computes the time-course SNR for a time series
 
     Typically you want to run this on a realigned time-series.
@@ -641,7 +647,7 @@ def _run_interface(self, runtime):
             betas = np.dot(np.linalg.pinv(X), np.rollaxis(data, 3, 2))
             datahat = np.rollaxis(np.dot(X[:, 1:],
                                          np.rollaxis(
-                                             betas[1:, :, :, :], 0, 3)),
+                                             betas[1:, :,:,:], 0, 3)),
                                   0, 4)
             data = data - datahat
             img = nb.Nifti1Image(data, img.get_affine(), header)
@@ -676,6 +682,7 @@ class GunzipOutputSpec(TraitedSpec):
 
 
 class Gunzip(BaseInterface):
+
     """
 
     """
@@ -717,7 +724,7 @@ def matlab2csv(in_array, name, reshape):
     if reshape == True:
         if len(np.shape(output_array)) > 1:
             output_array = np.reshape(output_array, (
-                np.shape(output_array)[0]*np.shape(output_array)[1], 1))
+                np.shape(output_array)[0] * np.shape(output_array)[1], 1))
             iflogger.info(np.shape(output_array))
     output_name = op.abspath(name + '.csv')
     np.savetxt(output_name, output_array, delimiter=',')
@@ -736,6 +743,7 @@ class Matlab2CSVOutputSpec(TraitedSpec):
 
 
 class Matlab2CSV(BaseInterface):
+
     """
     Simple interface to save the components of a MATLAB .mat file as a text file with comma-separated values (CSVs).
 
@@ -766,7 +774,8 @@ def _run_interface(self, runtime):
                 if isinstance(in_dict[key][0], np.ndarray):
                     saved_variables.append(key)
                 else:
-                    iflogger.info('One of the keys in the input file, {k}, is not a Numpy array'.format(k=key))
+                    iflogger.info(
+                        'One of the keys in the input file, {k}, is not a Numpy array'.format(k=key))
 
         if len(saved_variables) > 1:
             iflogger.info(
@@ -828,7 +837,7 @@ def merge_csvs(in_list):
                         in_file, delimiter=',', skiprows=1, usecols=range(1, n_cols))
                 except ValueError, ex:
                     in_array = np.loadtxt(
-                        in_file, delimiter=',', skiprows=1, usecols=range(1, n_cols-1))
+                        in_file, delimiter=',', skiprows=1, usecols=range(1, n_cols - 1))
         if idx == 0:
             out_array = in_array
         else:
@@ -846,7 +855,7 @@ def remove_identical_paths(in_files):
         out_names = list()
         commonprefix = op.commonprefix(in_files)
         lastslash = commonprefix.rfind('/')
-        commonpath = commonprefix[0:(lastslash+1)]
+        commonpath = commonprefix[0:(lastslash + 1)]
         for fileidx, in_file in enumerate(in_files):
             path, name, ext = split_filename(in_file)
             in_file = op.join(path, name)
@@ -864,10 +873,10 @@ def maketypelist(rowheadings, shape, extraheadingBool, extraheading):
     if rowheadings:
         typelist.append(('heading', 'a40'))
     if len(shape) > 1:
-        for idx in range(1, (min(shape)+1)):
+        for idx in range(1, (min(shape) + 1)):
             typelist.append((str(idx), float))
     else:
-        for idx in range(1, (shape[0]+1)):
+        for idx in range(1, (shape[0] + 1)):
             typelist.append((str(idx), float))
     if extraheadingBool:
         typelist.append((extraheading, 'a40'))
@@ -881,13 +890,13 @@ def makefmtlist(output_array, typelist, rowheadingsBool, shape, extraheadingBool
         fmtlist.append('%s')
     if len(shape) > 1:
         output = np.zeros(max(shape), typelist)
-        for idx in range(1, min(shape)+1):
-            output[str(idx)] = output_array[:, idx-1]
+        for idx in range(1, min(shape) + 1):
+            output[str(idx)] = output_array[:, idx - 1]
             fmtlist.append('%f')
     else:
         output = np.zeros(1, typelist)
-        for idx in range(1, len(output_array)+1):
-            output[str(idx)] = output_array[idx-1]
+        for idx in range(1, len(output_array) + 1):
+            output[str(idx)] = output_array[idx - 1]
             fmtlist.append('%f')
     if extraheadingBool:
         fmtlist.append('%s')
@@ -917,6 +926,7 @@ class MergeCSVFilesOutputSpec(TraitedSpec):
 
 
 class MergeCSVFiles(BaseInterface):
+
     """
     This interface is designed to facilitate data loading in the R environment.
     It takes input CSV files and merges them into a single CSV file.
@@ -1051,6 +1061,7 @@ class AddCSVColumnOutputSpec(TraitedSpec):
 
 
 class AddCSVColumn(BaseInterface):
+
     """
     Short interface to add an extra column and field to a text file
 
@@ -1108,6 +1119,7 @@ class CalculateNormalizedMomentsOutputSpec(TraitedSpec):
 
 
 class CalculateNormalizedMoments(BaseInterface):
+
     """
     Calculates moments of timeseries.
 
@@ -1147,4 +1159,4 @@ def calc_moments(timeseries_file, moment):
     m2 = stats.moment(timeseries, 2, axis=0)
     m3 = stats.moment(timeseries, moment, axis=0)
     zero = (m2 == 0)
-    return np.where(zero, 0, m3 / m2**(moment/2.0))
+    return np.where(zero, 0, m3 / m2 ** (moment / 2.0))