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diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.py b/docs/source/auto_examples/plot_OT_1D_smooth.py
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+# -*- coding: utf-8 -*-
+"""
+===========================
+1D smooth optimal transport
+===========================
+
+This example illustrates the computation of EMD, Sinkhorn and smooth OT plans
+and their visualization.
+
+"""
+
+# Author: Remi Flamary <remi.flamary@unice.fr>
+#
+# License: MIT License
+
+import numpy as np
+import matplotlib.pylab as pl
+import ot
+import ot.plot
+from ot.datasets import make_1D_gauss as gauss
+
+##############################################################################
+# Generate data
+# -------------
+
+
+#%% parameters
+
+n = 100  # nb bins
+
+# bin positions
+x = np.arange(n, dtype=np.float64)
+
+# Gaussian distributions
+a = gauss(n, m=20, s=5)  # m= mean, s= std
+b = gauss(n, m=60, s=10)
+
+# loss matrix
+M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
+M /= M.max()
+
+
+##############################################################################
+# Plot distributions and loss matrix
+# ----------------------------------
+
+#%% plot the distributions
+
+pl.figure(1, figsize=(6.4, 3))
+pl.plot(x, a, 'b', label='Source distribution')
+pl.plot(x, b, 'r', label='Target distribution')
+pl.legend()
+
+#%% plot distributions and loss matrix
+
+pl.figure(2, figsize=(5, 5))
+ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')
+
+##############################################################################
+# Solve EMD
+# ---------
+
+
+#%% EMD
+
+G0 = ot.emd(a, b, M)
+
+pl.figure(3, figsize=(5, 5))
+ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')
+
+##############################################################################
+# Solve Sinkhorn
+# --------------
+
+
+#%% Sinkhorn
+
+lambd = 2e-3
+Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)
+
+pl.figure(4, figsize=(5, 5))
+ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')
+
+pl.show()
+
+##############################################################################
+# Solve Smooth OT
+# --------------
+
+
+#%% Smooth OT with KL regularization
+
+lambd = 2e-3
+Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl')
+
+pl.figure(5, figsize=(5, 5))
+ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.')
+
+pl.show()
+
+
+#%% Smooth OT with KL regularization
+
+lambd = 1e-1
+Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2')
+
+pl.figure(6, figsize=(5, 5))
+ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.')
+
+pl.show()