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diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py
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--- a/docs/source/auto_examples/plot_OT_2D_samples.py
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-# -*- coding: utf-8 -*-
-"""
-====================================================
-2D Optimal transport between empirical distributions
-====================================================
-
-Illustration of 2D optimal transport between discributions that are weighted
-sum of diracs. The OT matrix is plotted with the samples.
-
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#         Kilian Fatras <kilian.fatras@irisa.fr>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-import ot.plot
-
-##############################################################################
-# Generate data
-# -------------
-
-#%% parameters and data generation
-
-n = 50  # nb samples
-
-mu_s = np.array([0, 0])
-cov_s = np.array([[1, 0], [0, 1]])
-
-mu_t = np.array([4, 4])
-cov_t = np.array([[1, -.8], [-.8, 1]])
-
-xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)
-xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)
-
-a, b = np.ones((n,)) / n, np.ones((n,)) / n  # uniform distribution on samples
-
-# loss matrix
-M = ot.dist(xs, xt)
-M /= M.max()
-
-##############################################################################
-# Plot data
-# ---------
-
-#%% plot samples
-
-pl.figure(1)
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.legend(loc=0)
-pl.title('Source and target distributions')
-
-pl.figure(2)
-pl.imshow(M, interpolation='nearest')
-pl.title('Cost matrix M')
-
-##############################################################################
-# Compute EMD
-# -----------
-
-#%% EMD
-
-G0 = ot.emd(a, b, M)
-
-pl.figure(3)
-pl.imshow(G0, interpolation='nearest')
-pl.title('OT matrix G0')
-
-pl.figure(4)
-ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.legend(loc=0)
-pl.title('OT matrix with samples')
-
-
-##############################################################################
-# Compute Sinkhorn
-# ----------------
-
-#%% sinkhorn
-
-# reg term
-lambd = 1e-3
-
-Gs = ot.sinkhorn(a, b, M, lambd)
-
-pl.figure(5)
-pl.imshow(Gs, interpolation='nearest')
-pl.title('OT matrix sinkhorn')
-
-pl.figure(6)
-ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.legend(loc=0)
-pl.title('OT matrix Sinkhorn with samples')
-
-pl.show()
-
-
-##############################################################################
-# Emprirical Sinkhorn
-# ----------------
-
-#%% sinkhorn
-
-# reg term
-lambd = 1e-3
-
-Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd)
-
-pl.figure(7)
-pl.imshow(Ges, interpolation='nearest')
-pl.title('OT matrix empirical sinkhorn')
-
-pl.figure(8)
-ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1])
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.legend(loc=0)
-pl.title('OT matrix Sinkhorn from samples')
-
-pl.show()