pep8 normalization

1 files changed, 35 insertions, 35 deletions

diff --git a/examples/plot_convolutional_barycenter.py b/examples/plot_convolutional_barycenter.py
index d231da9..7ccdbe3 100644
--- a/examples/plot_convolutional_barycenter.py
+++ b/examples/plot_convolutional_barycenter.py
@@ -1,4 +1,4 @@
-    
+
 #%%
 # -*- coding: utf-8 -*-
 """
@@ -32,24 +32,24 @@ f3 = 1 - pl.imread('../data/heart.png')[:, :, 2]
 f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]
 
 A = []
-f1=f1/np.sum(f1)
-f2=f2/np.sum(f2)
-f3=f3/np.sum(f3)
-f4=f4/np.sum(f4)
+f1 = f1 / np.sum(f1)
+f2 = f2 / np.sum(f2)
+f3 = f3 / np.sum(f3)
+f4 = f4 / np.sum(f4)
 A.append(f1)
 A.append(f2)
 A.append(f3)
 A.append(f4)
-A=np.array(A)
+A = np.array(A)
 
 nb_images = 5
 
 # those are the four corners coordinates that will be interpolated by bilinear
 # interpolation
-v1=np.array((1,0,0,0))
-v2=np.array((0,1,0,0))
-v3=np.array((0,0,1,0))
-v4=np.array((0,0,0,1))
+v1 = np.array((1, 0, 0, 0))
+v2 = np.array((0, 1, 0, 0))
+v3 = np.array((0, 0, 1, 0))
+v4 = np.array((0, 0, 0, 1))
 
 
 ##############################################################################
@@ -57,36 +57,36 @@ v4=np.array((0,0,0,1))
 # ----------------------------------------
 #
 
-pl.figure(figsize=(10,10))
+pl.figure(figsize=(10, 10))
 pl.title('Convolutional Wasserstein Barycenters in POT')
-cm='Blues'
+cm = 'Blues'
 # regularization parameter
-reg=0.004
+reg = 0.004
 for i in range(nb_images):
     for j in range(nb_images):
-        pl.subplot(nb_images,nb_images,i*nb_images+j+1)
-        tx=float(i)/(nb_images-1)
-        ty=float(j)/(nb_images-1)
-        
+        pl.subplot(nb_images, nb_images, i * nb_images + j + 1)
+        tx = float(i) / (nb_images - 1)
+        ty = float(j) / (nb_images - 1)
+
         # weights are constructed by bilinear interpolation
-        tmp1=(1-tx)*v1+tx*v2
-        tmp2=(1-tx)*v3+tx*v4
-        weights=(1-ty)*tmp1+ty*tmp2
-        
-        if i==0 and j==0:
-           pl.imshow(f1,cmap=cm)
-           pl.axis('off')
-        elif i==0 and j==(nb_images-1):
-           pl.imshow(f3,cmap=cm)
-           pl.axis('off')
-        elif i==(nb_images-1) and j==0:
-           pl.imshow(f2,cmap=cm)
-           pl.axis('off')
-        elif i==(nb_images-1) and j==(nb_images-1):
-           pl.imshow(f4,cmap=cm)
-           pl.axis('off')
+        tmp1 = (1 - tx) * v1 + tx * v2
+        tmp2 = (1 - tx) * v3 + tx * v4
+        weights = (1 - ty) * tmp1 + ty * tmp2
+
+        if i == 0 and j == 0:
+            pl.imshow(f1, cmap=cm)
+            pl.axis('off')
+        elif i == 0 and j == (nb_images - 1):
+            pl.imshow(f3, cmap=cm)
+            pl.axis('off')
+        elif i == (nb_images - 1) and j == 0:
+            pl.imshow(f2, cmap=cm)
+            pl.axis('off')
+        elif i == (nb_images - 1) and j == (nb_images - 1):
+            pl.imshow(f4, cmap=cm)
+            pl.axis('off')
         else:
             # call to barycenter computation
-            pl.imshow(ot.convolutional_barycenter2d(A,reg,weights),cmap=cm)
+            pl.imshow(ot.convolutional_barycenter2d(A, reg, weights), cmap=cm)
             pl.axis('off')
-pl.show()
\ No newline at end of file
+pl.show()