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diff --git a/docs/source/auto_examples/plot_partial_wass_and_gromov.py b/docs/source/auto_examples/plot_partial_wass_and_gromov.py
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--- a/docs/source/auto_examples/plot_partial_wass_and_gromov.py
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@@ -1,163 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-==================================================
-Partial Wasserstein and Gromov-Wasserstein example
-==================================================
-
-This example is designed to show how to use the Partial (Gromov-)Wassertsein
-distance computation in POT.
-"""
-
-# Author: Laetitia Chapel <laetitia.chapel@irisa.fr>
-# License: MIT License
-
-# necessary for 3d plot even if not used
-from mpl_toolkits.mplot3d import Axes3D  # noqa
-import scipy as sp
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-
-
-#############################################################################
-#
-# Sample two 2D Gaussian distributions and plot them
-# --------------------------------------------------
-#
-# For demonstration purpose, we sample two Gaussian distributions in 2-d
-# spaces and add some random noise.
-
-
-n_samples = 20  # nb samples (gaussian)
-n_noise = 20  # nb of samples (noise)
-
-mu = np.array([0, 0])
-cov = np.array([[1, 0], [0, 2]])
-
-xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
-xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2))
-xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
-xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2))
-
-M = sp.spatial.distance.cdist(xs, xt)
-
-fig = pl.figure()
-ax1 = fig.add_subplot(131)
-ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-ax2 = fig.add_subplot(132)
-ax2.scatter(xt[:, 0], xt[:, 1], color='r')
-ax3 = fig.add_subplot(133)
-ax3.imshow(M)
-pl.show()
-
-#############################################################################
-#
-# Compute partial Wasserstein plans and distance
-# ----------------------------------------------
-
-p = ot.unif(n_samples + n_noise)
-q = ot.unif(n_samples + n_noise)
-
-w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=0.5, log=True)
-w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=0.1, m=0.5,
-                                                 log=True)
-
-print('Partial Wasserstein distance (m = 0.5): ' + str(log0['partial_w_dist']))
-print('Entropic partial Wasserstein distance (m = 0.5): ' +
-      str(log['partial_w_dist']))
-
-pl.figure(1, (10, 5))
-pl.subplot(1, 2, 1)
-pl.imshow(w0, cmap='jet')
-pl.title('Partial Wasserstein')
-pl.subplot(1, 2, 2)
-pl.imshow(w, cmap='jet')
-pl.title('Entropic partial Wasserstein')
-pl.show()
-
-
-#############################################################################
-#
-# Sample one 2D and 3D Gaussian distributions and plot them
-# ---------------------------------------------------------
-#
-# The Gromov-Wasserstein distance allows to compute distances with samples that
-# do not belong to the same metric space. For demonstration purpose, we sample
-# two Gaussian distributions in 2- and 3-dimensional spaces.
-
-n_samples = 20  # nb samples
-n_noise = 10  # nb of samples (noise)
-
-p = ot.unif(n_samples + n_noise)
-q = ot.unif(n_samples + n_noise)
-
-mu_s = np.array([0, 0])
-cov_s = np.array([[1, 0], [0, 1]])
-
-mu_t = np.array([0, 0, 0])
-cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
-
-
-xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)
-xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0)
-P = sp.linalg.sqrtm(cov_t)
-xt = np.random.randn(n_samples, 3).dot(P) + mu_t
-xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0)
-
-fig = pl.figure()
-ax1 = fig.add_subplot(121)
-ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-ax2 = fig.add_subplot(122, projection='3d')
-ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')
-pl.show()
-
-
-#############################################################################
-#
-# Compute partial Gromov-Wasserstein plans and distance
-# -----------------------------------------------------
-
-C1 = sp.spatial.distance.cdist(xs, xs)
-C2 = sp.spatial.distance.cdist(xt, xt)
-
-# transport 100% of the mass
-print('-----m = 1')
-m = 1
-res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)
-res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,
-                                                          m=m, log=True)
-
-print('Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist']))
-print('Entropic Wasserstein distance (m = 1): ' + str(log['partial_gw_dist']))
-
-pl.figure(1, (10, 5))
-pl.title("mass to be transported m = 1")
-pl.subplot(1, 2, 1)
-pl.imshow(res0, cmap='jet')
-pl.title('Wasserstein')
-pl.subplot(1, 2, 2)
-pl.imshow(res, cmap='jet')
-pl.title('Entropic Wasserstein')
-pl.show()
-
-# transport 2/3 of the mass
-print('-----m = 2/3')
-m = 2 / 3
-res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)
-res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,
-                                                          m=m, log=True)
-
-print('Partial Wasserstein distance (m = 2/3): ' +
-      str(log0['partial_gw_dist']))
-print('Entropic partial Wasserstein distance (m = 2/3): ' +
-      str(log['partial_gw_dist']))
-
-pl.figure(1, (10, 5))
-pl.title("mass to be transported m = 2/3")
-pl.subplot(1, 2, 1)
-pl.imshow(res0, cmap='jet')
-pl.title('Partial Wasserstein')
-pl.subplot(1, 2, 2)
-pl.imshow(res, cmap='jet')
-pl.title('Entropic partial Wasserstein')
-pl.show()