cleaned up _ncut.py

skimage/graph/_ncut.py

@@ -2,37 +2,29 @@ import networkx as nx
 import numpy as np
 from scipy import sparse
 
+
 def DW_matrix(graph):
-    #print graph[4][4]['weight']
+
     W = nx.to_scipy_sparse_matrix(graph, format='csc')
     entries = W.sum(0)
-    D = sparse.dia_matrix( (entries,0),shape = W.shape).tocsc()
-    #print W[4,4]
-    
-    m,n = W.shape
-    #for i in range(n):
-    #    W[i,i] = 1.0
-    return D,W
+    D = sparse.dia_matrix((entries, 0), shape=W.shape).tocsc()
+    return D, W
 
-def ncut_cost(mask,D,W):
+
+def ncut_cost(mask, D, W):
 
     mask = np.array(mask)
-    mask_list = [ np.logical_xor(mask[i], mask) for i in range(mask.shape[0])]
+    mask_list = [np.logical_xor(mask[i], mask) for i in range(mask.shape[0])]
     mask_array = np.array(mask_list)
-       
-    cut = float(W[mask_array].sum()/2.0)
-    #print W.todense()
-    #print mask_array.astype(int)
-    #print "cut=",cut
-    
+
+    cut = float(W[mask_array].sum() / 2.0)
     assoc_a = D.data[mask].sum()
     assoc_b = D.data[np.logical_not(mask)].sum()
-    
-    #print cut
-    #print assoc_a,assoc_b
-    return (cut/assoc_a) + (cut/assoc_b)
 
-def norml(a):
+    return (cut / assoc_a) + (cut / assoc_b)
+
+
+def normalize(a):
     mi = a.min()
     mx = a.max()
-    return (a-mi)/(mx-mi)
+    return (a - mi) / (mx - mi)

skimage/graph/_ncut_cy.pyx

@@ -5,6 +5,7 @@
 cimport numpy as cnp
 import numpy as np
 
+
 def argmin2(cnp.float64_t[:] array):
     cdef cnp.float64_t min1 = np.inf
     cdef cnp.float64_t min2 = np.inf
@@ -14,14 +15,14 @@ def argmin2(cnp.float64_t[:] array):
 
     while i < array.shape[0]:
         x = array[i]
-        if x < min1 :
+        if x < min1:
             min2 = min1
             i2 = i1
             min1 = x
             i1 = i
-        elif x > min1 and x < min2 :
+        elif x > min1 and x < min2:
             min2 = x
             i2 = i
         i += 1
-        
+
     return i2