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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 -*-
+"""
+====================
+1D optimal transport
+====================
+
+@author: rflamary
+"""
+
+import numpy as np
+import matplotlib.pylab as pl
+import ot
+from ot.datasets import get_1D_gauss as gauss
+
+
+#%% 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 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')
+
+#%% 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 M3
+
+
+pl.figure(2)
+pl.plot(d_emd,label='Euclidean EMD')
+pl.plot(d_emd2,label='Squared Euclidean EMD')
+pl.title('EMD distances')
+pl.legend()
+
+#%%
+reg=1e-2
+d_sinkhorn=ot.sinkhorn(a,B,M,reg)
+d_sinkhorn2=ot.sinkhorn(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()
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