pep8 changes

skimage/graph/graph_cut.py

@@ -4,11 +4,12 @@ except ImportError:
     import warnings
     warnings.warn('"cut_threshold" requires networkx')
 import numpy as np
-import _ncut
-import _ncut_cy
+from . import _ncut
+from . import _ncut_cy
 from scipy.sparse import linalg
-from scipy.sparse.linalg.eigen.arpack.arpack import ArpackNoConvergence as ANC
-from scipy.sparse.linalg.eigen.arpack.arpack import ArpackError as APE
+from scipy.sparse.linalg.eigen.arpack.arpack import ArpackNoConvergence
+from scipy.sparse.linalg.eigen.arpack.arpack import ArpackError
+
 
 def cut_threshold(labels, rag, thresh):
     """Combine regions seperated by weight less than threshold.
@@ -68,57 +69,59 @@ def cut_threshold(labels, rag, thresh):
 
 def cut_n(labels, rag, thresh):
 
-    _ncut_relabel(rag,thresh)
-    
-    from_ = range(labels.max()+1)
-    to = [ rag.node[x]['ncut label']  for x in from_ ]
+    _ncut_relabel(rag, thresh)
+
+    from_ = range(labels.max() + 1)
+    to = [rag.node[x]['ncut label'] for x in from_]
     map_array = np.array(to)
-    
+
     return map_array[labels]
 
-def _ncut_relabel(rag, cut_thresh = 0.0001):
+
+def _ncut_relabel(rag, cut_thresh=0.0001):
     d, w = _ncut.DW_matrix(rag)
     error = False
 
     try:
         m = w.shape[0]
-        vals,vectors = linalg.eigsh(d-w,M=d,which='SM',k = min(100,m-2))
-    except ANC as e:
+        vals, vectors = linalg.eigsh(d - w, M=d, which='SM',
+                                     k=min(100, m - 2))
+    except ArpackNoConvergence as e:
         vals = e.eigenvalues
         vectors = e.eigenvectors
         if len(vals) == 0:
             error = True
     except ValueError:
         error = True
-    except APE:
+    except ArpackError:
         error = True
-    
-    if not error :
-        vals,vectors = np.real(vals), np.real(vectors)
+
+    if not error:
+        vals, vectors = np.real(vals), np.real(vectors)
         index2 = _ncut_cy.argmin2(vals)
 
-        ev = np.real(vectors[:,index2])
+        ev = np.real(vectors[:, index2])
         ev = _ncut.norml(ev)
 
         mcut = np.inf
         thresh = None
-        for t in np.arange(0,1,0.1):
+        for t in np.arange(0, 1, 0.1):
             mask = ev > t
-            cost = _ncut.ncut_cost(mask,d,w)
-            if cost < mcut :
+            cost = _ncut.ncut_cost(mask, d, w)
+            if cost < mcut:
                 mcut = cost
                 thresh = t
 
-        if ( mcut < cut_thresh ):
+        if (mcut < cut_thresh):
             mask = ev > thresh
 
-            nodes1 = [ n for i,n in enumerate(rag.nodes()) if mask[i]]
-            nodes2 = [ n for i,n in enumerate(rag.nodes()) if not mask[i]]
+            nodes1 = [n for i, n in enumerate(rag.nodes()) if mask[i]]
+            nodes2 = [n for i, n in enumerate(rag.nodes()) if not mask[i]]
 
             sub1 = rag.subgraph(nodes1)
             sub2 = rag.subgraph(nodes2)
 
-            _ncut_relabel(sub1,cut_thresh)
+            _ncut_relabel(sub1, cut_thresh)
             _ncut_relabel(sub2, cut_thresh)
             return
 
@@ -126,5 +129,3 @@ def _ncut_relabel(rag, cut_thresh = 0.0001):
     new_label = rag.node[node]['labels'][0]
     for n in rag.nodes():
         rag.node[n]['ncut label'] = new_label
-
-