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diff --git a/docs/source/auto_examples/plot_screenkhorn_1D.py b/docs/source/auto_examples/plot_screenkhorn_1D.py
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--- a/docs/source/auto_examples/plot_screenkhorn_1D.py
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-# -*- coding: utf-8 -*-
-"""
-===============================
-1D Screened optimal transport
-===============================
-
-This example illustrates the computation of Screenkhorn:
-Screening Sinkhorn Algorithm for Optimal transport.
-"""
-
-# Author: Mokhtar Z. Alaya <mokhtarzahdi.alaya@gmail.com>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot.plot
-from ot.datasets import make_1D_gauss as gauss
-from ot.bregman import screenkhorn
-
-##############################################################################
-# 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 Screenkhorn
-# -----------------------
-
-# Screenkhorn
-lambd = 2e-03  # entropy parameter
-ns_budget = 30  # budget number of points to be keeped in the source distribution
-nt_budget = 30  # budget number of points to be keeped in the target distribution
-
-G_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True)
-pl.figure(4, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn')
-pl.show()