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diff --git a/docs/source/auto_examples/plot_OTDA_color_images.py b/docs/source/auto_examples/plot_OTDA_color_images.py
deleted file mode 100644
index 68eee44..0000000
--- a/docs/source/auto_examples/plot_OTDA_color_images.py
+++ /dev/null
@@ -1,145 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-========================================================
-OT for domain adaptation with image color adaptation [6]
-========================================================
-
-[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
-"""
-
-import numpy as np
-import scipy.ndimage as spi
-import matplotlib.pylab as pl
-import ot
-
-
-#%% Loading images
-
-I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256
-I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256
-
-#%% Plot images
-
-pl.figure(1)
-
-pl.subplot(1,2,1)
-pl.imshow(I1)
-pl.title('Image 1')
-
-pl.subplot(1,2,2)
-pl.imshow(I2)
-pl.title('Image 2')
-
-pl.show()
-
-#%% Image conversion and dataset generation
-
-def im2mat(I):
-    """Converts and image to matrix (one pixel per line)"""
-    return I.reshape((I.shape[0]*I.shape[1],I.shape[2]))
-
-def mat2im(X,shape):
-    """Converts back a matrix to an image"""
-    return X.reshape(shape)
-
-X1=im2mat(I1)
-X2=im2mat(I2)
-
-# training samples
-nb=1000
-idx1=np.random.randint(X1.shape[0],size=(nb,))
-idx2=np.random.randint(X2.shape[0],size=(nb,))
-
-xs=X1[idx1,:]
-xt=X2[idx2,:]
-
-#%% Plot image distributions
-
-
-pl.figure(2,(10,5))
-
-pl.subplot(1,2,1)
-pl.scatter(xs[:,0],xs[:,2],c=xs)
-pl.axis([0,1,0,1])
-pl.xlabel('Red')
-pl.ylabel('Blue')
-pl.title('Image 1')
-
-pl.subplot(1,2,2)
-#pl.imshow(I2)
-pl.scatter(xt[:,0],xt[:,2],c=xt)
-pl.axis([0,1,0,1])
-pl.xlabel('Red')
-pl.ylabel('Blue')
-pl.title('Image 2')
-
-pl.show()
-
-
-
-#%% domain adaptation between images
-
-# LP problem
-da_emd=ot.da.OTDA()     # init class
-da_emd.fit(xs,xt)       # fit distributions
-
-
-# sinkhorn regularization
-lambd=1e-1
-da_entrop=ot.da.OTDA_sinkhorn()
-da_entrop.fit(xs,xt,reg=lambd)
-
-
-
-#%% prediction between images (using out of sample prediction as in [6])
-
-X1t=da_emd.predict(X1)
-X2t=da_emd.predict(X2,-1)
-
-
-X1te=da_entrop.predict(X1)
-X2te=da_entrop.predict(X2,-1)
-
-
-def minmax(I):
-    return np.minimum(np.maximum(I,0),1)
-
-I1t=minmax(mat2im(X1t,I1.shape))
-I2t=minmax(mat2im(X2t,I2.shape))
-
-I1te=minmax(mat2im(X1te,I1.shape))
-I2te=minmax(mat2im(X2te,I2.shape))
-
-#%% plot all images
-
-pl.figure(2,(10,8))
-
-pl.subplot(2,3,1)
-
-pl.imshow(I1)
-pl.title('Image 1')
-
-pl.subplot(2,3,2)
-pl.imshow(I1t)
-pl.title('Image 1 Adapt')
-
-
-pl.subplot(2,3,3)
-pl.imshow(I1te)
-pl.title('Image 1 Adapt (reg)')
-
-pl.subplot(2,3,4)
-
-pl.imshow(I2)
-pl.title('Image 2')
-
-pl.subplot(2,3,5)
-pl.imshow(I2t)
-pl.title('Image 2 Adapt')
-
-
-pl.subplot(2,3,6)
-pl.imshow(I2te)
-pl.title('Image 2 Adapt (reg)')
-
-pl.show()