Wasserstein convolutional barycenter

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diff --git a/examples/plot_convolutional_barycenter.py b/examples/plot_convolutional_barycenter.py
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+    
+#%%
+# -*- coding: utf-8 -*-
+"""
+============================================
+Convolutional Wasserstein Barycenter example
+============================================
+
+This example is designed to illustrate how the Convolutional Wasserstein Barycenter
+function of POT works.
+"""
+
+# Author: Nicolas Courty <ncourty@irisa.fr>
+#
+# License: MIT License
+
+
+import numpy as np
+import pylab as pl
+import ot
+
+##############################################################################
+# Data preparation
+# ----------------
+#
+# The four distributions are constructed from 4 simple images
+
+
+f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]
+f2 = 1 - pl.imread('../data/duck.png')[:, :, 2]
+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)
+A.append(f1)
+A.append(f2)
+A.append(f3)
+A.append(f4)
+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))
+
+
+##############################################################################
+# Barycenter computation and visualization
+# ----------------------------------------
+#
+
+pl.figure(figsize=(10,10))
+pl.title('Convolutional Wasserstein Barycenters in POT')
+cm='Blues'
+# regularization parameter
+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)
+        
+        # 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')
+        else:
+            # call to barycenter computation
+            pl.imshow(ot.convolutional_barycenter2d(A,reg,weights),cmap=cm)
+            pl.axis('off')
+pl.show()
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