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diff --git a/examples/plot_OTDA_color_images.py b/examples/plot_OTDA_color_images.py
deleted file mode 100644
index c5ff873..0000000
--- a/examples/plot_OTDA_color_images.py
+++ /dev/null
@@ -1,152 +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.
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#
-# License: MIT License
-
-import numpy as np
-from scipy import ndimage
-import matplotlib.pylab as pl
-import ot
-
-
-#%% Loading images
-
-I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256
-I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
-
-#%% Plot images
-
-pl.figure(1, figsize=(6.4, 3))
-
-pl.subplot(1, 2, 1)
-pl.imshow(I1)
-pl.axis('off')
-pl.title('Image 1')
-
-pl.subplot(1, 2, 2)
-pl.imshow(I2)
-pl.axis('off')
-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, figsize=(6.4, 3))
-
-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.scatter(xt[:, 0], xt[:, 2], c=xt)
-pl.axis([0, 1, 0, 1])
-pl.xlabel('Red')
-pl.ylabel('Blue')
-pl.title('Image 2')
-pl.tight_layout()
-
-#%% 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.clip(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, figsize=(8, 4))
-
-pl.subplot(2, 3, 1)
-pl.imshow(I1)
-pl.axis('off')
-pl.title('Image 1')
-
-pl.subplot(2, 3, 2)
-pl.imshow(I1t)
-pl.axis('off')
-pl.title('Image 1 Adapt')
-
-pl.subplot(2, 3, 3)
-pl.imshow(I1te)
-pl.axis('off')
-pl.title('Image 1 Adapt (reg)')
-
-pl.subplot(2, 3, 4)
-pl.imshow(I2)
-pl.axis('off')
-pl.title('Image 2')
-
-pl.subplot(2, 3, 5)
-pl.imshow(I2t)
-pl.axis('off')
-pl.title('Image 2 Adapt')
-
-pl.subplot(2, 3, 6)
-pl.imshow(I2te)
-pl.axis('off')
-pl.title('Image 2 Adapt (reg)')
-pl.tight_layout()
-
-pl.show()