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diff --git a/examples/sliced-wasserstein/plot_variance.py b/examples/sliced-wasserstein/plot_variance.py
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+# -*- coding: utf-8 -*-
+"""
+==============================
+2D Sliced Wasserstein Distance
+==============================
+
+This example illustrates the computation of the sliced Wasserstein Distance as
+proposed in [31].
+
+[31] Bonneel, Nicolas, et al. "Sliced and radon wasserstein barycenters of
+measures." Journal of Mathematical Imaging and Vision 51.1 (2015): 22-45
+
+"""
+
+# Author: Adrien Corenflos <adrien.corenflos@aalto.fi>
+#
+# License: MIT License
+
+import matplotlib.pylab as pl
+import numpy as np
+
+import ot
+
+##############################################################################
+# Generate data
+# -------------
+
+# %% parameters and data generation
+
+n = 500  # nb samples
+
+mu_s = np.array([0, 0])
+cov_s = np.array([[1, 0], [0, 1]])
+
+mu_t = np.array([4, 4])
+cov_t = np.array([[1, -.8], [-.8, 1]])
+
+xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)
+xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)
+
+a, b = np.ones((n,)) / n, np.ones((n,)) / n  # uniform distribution on samples
+
+##############################################################################
+# Plot data
+# ---------
+
+# %% plot samples
+
+pl.figure(1)
+pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
+pl.legend(loc=0)
+pl.title('Source and target distributions')
+
+###############################################################################
+# Sliced Wasserstein distance for different seeds and number of projections
+# -------------------------------------------------------------------------
+
+n_seed = 50
+n_projections_arr = np.logspace(0, 3, 25, dtype=int)
+res = np.empty((n_seed, 25))
+
+# %% Compute statistics
+for seed in range(n_seed):
+    for i, n_projections in enumerate(n_projections_arr):
+        res[seed, i] = ot.sliced_wasserstein_distance(xs, xt, a, b, n_projections, seed=seed)
+
+res_mean = np.mean(res, axis=0)
+res_std = np.std(res, axis=0)
+
+###############################################################################
+# Plot Sliced Wasserstein Distance
+# --------------------------------
+
+pl.figure(2)
+pl.plot(n_projections_arr, res_mean, label="SWD")
+pl.fill_between(n_projections_arr, res_mean - 2 * res_std, res_mean + 2 * res_std, alpha=0.5)
+
+pl.legend()
+pl.xscale('log')
+
+pl.xlabel("Number of projections")
+pl.ylabel("Distance")
+pl.title('Sliced Wasserstein Distance with 95% confidence inverval')
+
+pl.show()