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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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--- a/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()