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diff --git a/docs/source/auto_examples/plot_compute_emd.py b/docs/source/auto_examples/plot_compute_emd.py
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+# -*- coding: utf-8 -*-
+"""
+=================
+Plot multiple EMD
+=================
+
+Shows how to compute multiple EMD and Sinkhorn with two differnt
+ground metrics and plot their values for diffeent distributions.
+
+
+"""
+
+# Author: Remi Flamary <remi.flamary@unice.fr>
+#
+# License: MIT License
+
+import numpy as np
+import matplotlib.pylab as pl
+import ot
+from ot.datasets import make_1D_gauss as gauss
+
+
+##############################################################################
+# Generate data
+# -------------
+
+#%% parameters
+
+n = 100  # nb bins
+n_target = 50  # nb target distributions
+
+
+# bin positions
+x = np.arange(n, dtype=np.float64)
+
+lst_m = np.linspace(20, 90, n_target)
+
+# Gaussian distributions
+a = gauss(n, m=20, s=5)  # m= mean, s= std
+
+B = np.zeros((n, n_target))
+
+for i, m in enumerate(lst_m):
+    B[:, i] = gauss(n, m=m, s=5)
+
+# loss matrix and normalization
+M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')
+M /= M.max()
+M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')
+M2 /= M2.max()
+
+##############################################################################
+# Plot data
+# ---------
+
+#%% plot the distributions
+
+pl.figure(1)
+pl.subplot(2, 1, 1)
+pl.plot(x, a, 'b', label='Source distribution')
+pl.title('Source distribution')
+pl.subplot(2, 1, 2)
+pl.plot(x, B, label='Target distributions')
+pl.title('Target distributions')
+pl.tight_layout()
+
+
+##############################################################################
+# Compute EMD for the different losses
+# ------------------------------------
+
+#%% Compute and plot distributions and loss matrix
+
+d_emd = ot.emd2(a, B, M)  # direct computation of EMD
+d_emd2 = ot.emd2(a, B, M2)  # direct computation of EMD with loss M2
+
+
+pl.figure(2)
+pl.plot(d_emd, label='Euclidean EMD')
+pl.plot(d_emd2, label='Squared Euclidean EMD')
+pl.title('EMD distances')
+pl.legend()
+
+##############################################################################
+# Compute Sinkhorn for the different losses
+# -----------------------------------------
+
+#%%
+reg = 1e-2
+d_sinkhorn = ot.sinkhorn2(a, B, M, reg)
+d_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)
+
+pl.figure(2)
+pl.clf()
+pl.plot(d_emd, label='Euclidean EMD')
+pl.plot(d_emd2, label='Squared Euclidean EMD')
+pl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')
+pl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')
+pl.title('EMD distances')
+pl.legend()
+
+pl.show()