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diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.py b/docs/source/auto_examples/plot_otda_mapping_colors_images.py
deleted file mode 100644
index a20eca8..0000000
--- a/docs/source/auto_examples/plot_otda_mapping_colors_images.py
+++ /dev/null
@@ -1,174 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-=====================================================
-OT for image color adaptation with mapping estimation
-=====================================================
-
-OT for domain adaptation with image color adaptation [6] with mapping
-estimation [8].
-
-[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized
-    discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),
-    1853-1882.
-[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for
-    discrete optimal transport", Neural Information Processing Systems (NIPS),
-    2016.
-
-"""
-
-# Authors: Remi Flamary <remi.flamary@unice.fr>
-#          Stanislas Chambon <stan.chambon@gmail.com>
-#
-# License: MIT License
-
-import numpy as np
-from scipy import ndimage
-import matplotlib.pylab as pl
-import ot
-
-r = np.random.RandomState(42)
-
-
-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)
-
-
-def minmax(I):
-    return np.clip(I, 0, 1)
-
-
-##############################################################################
-# Generate data
-# -------------
-
-# Loading images
-I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256
-I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
-
-
-X1 = im2mat(I1)
-X2 = im2mat(I2)
-
-# training samples
-nb = 1000
-idx1 = r.randint(X1.shape[0], size=(nb,))
-idx2 = r.randint(X2.shape[0], size=(nb,))
-
-Xs = X1[idx1, :]
-Xt = X2[idx2, :]
-
-
-##############################################################################
-# Domain adaptation for pixel distribution transfer
-# -------------------------------------------------
-
-# EMDTransport
-ot_emd = ot.da.EMDTransport()
-ot_emd.fit(Xs=Xs, Xt=Xt)
-transp_Xs_emd = ot_emd.transform(Xs=X1)
-Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape))
-
-# SinkhornTransport
-ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
-ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
-transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)
-Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))
-
-ot_mapping_linear = ot.da.MappingTransport(
-    mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)
-ot_mapping_linear.fit(Xs=Xs, Xt=Xt)
-
-X1tl = ot_mapping_linear.transform(Xs=X1)
-Image_mapping_linear = minmax(mat2im(X1tl, I1.shape))
-
-ot_mapping_gaussian = ot.da.MappingTransport(
-    mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)
-ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)
-
-X1tn = ot_mapping_gaussian.transform(Xs=X1)  # use the estimated mapping
-Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))
-
-
-##############################################################################
-# Plot original 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.tight_layout()
-
-
-##############################################################################
-# Plot pixel values distribution
-# ------------------------------
-
-pl.figure(2, figsize=(6.4, 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.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()
-
-
-##############################################################################
-# Plot transformed images
-# -----------------------
-
-pl.figure(2, figsize=(10, 5))
-
-pl.subplot(2, 3, 1)
-pl.imshow(I1)
-pl.axis('off')
-pl.title('Im. 1')
-
-pl.subplot(2, 3, 4)
-pl.imshow(I2)
-pl.axis('off')
-pl.title('Im. 2')
-
-pl.subplot(2, 3, 2)
-pl.imshow(Image_emd)
-pl.axis('off')
-pl.title('EmdTransport')
-
-pl.subplot(2, 3, 5)
-pl.imshow(Image_sinkhorn)
-pl.axis('off')
-pl.title('SinkhornTransport')
-
-pl.subplot(2, 3, 3)
-pl.imshow(Image_mapping_linear)
-pl.axis('off')
-pl.title('MappingTransport (linear)')
-
-pl.subplot(2, 3, 6)
-pl.imshow(Image_mapping_gaussian)
-pl.axis('off')
-pl.title('MappingTransport (gaussian)')
-pl.tight_layout()
-
-pl.show()