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diff --git a/docs/source/auto_examples/plot_compute_emd.py b/docs/source/auto_examples/plot_compute_emd.py
deleted file mode 100644
index 7ed2b01..0000000
--- a/docs/source/auto_examples/plot_compute_emd.py
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@@ -1,102 +0,0 @@
-# -*- 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()