exmaples in sections

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diff --git a/examples/plot_free_support_barycenter.py b/examples/plot_free_support_barycenter.py
deleted file mode 100644
index 64b89e4..0000000
--- a/examples/plot_free_support_barycenter.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-====================================================
-2D free support Wasserstein barycenters of distributions
-====================================================
-
-Illustration of 2D Wasserstein barycenters if discributions that are weighted
-sum of diracs.
-
-"""
-
-# Author: Vivien Seguy <vivien.seguy@iip.ist.i.kyoto-u.ac.jp>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-
-
-##############################################################################
-# Generate data
-# -------------
-#%% parameters and data generation
-N = 3
-d = 2
-measures_locations = []
-measures_weights = []
-
-for i in range(N):
-
-    n_i = np.random.randint(low=1, high=20)  # nb samples
-
-    mu_i = np.random.normal(0., 4., (d,))  # Gaussian mean
-
-    A_i = np.random.rand(d, d)
-    cov_i = np.dot(A_i, A_i.transpose())  # Gaussian covariance matrix
-
-    x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i)  # Dirac locations
-    b_i = np.random.uniform(0., 1., (n_i,))
-    b_i = b_i / np.sum(b_i)  # Dirac weights
-
-    measures_locations.append(x_i)
-    measures_weights.append(b_i)
-
-
-##############################################################################
-# Compute free support barycenter
-# -------------
-
-k = 10  # number of Diracs of the barycenter
-X_init = np.random.normal(0., 1., (k, d))  # initial Dirac locations
-b = np.ones((k,)) / k  # weights of the barycenter (it will not be optimized, only the locations are optimized)
-
-X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b)
-
-
-##############################################################################
-# Plot data
-# ---------
-
-pl.figure(1)
-for (x_i, b_i) in zip(measures_locations, measures_weights):
-    color = np.random.randint(low=1, high=10 * N)
-    pl.scatter(x_i[:, 0], x_i[:, 1], s=b_i * 1000, label='input measure')
-pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter')
-pl.title('Data measures and their barycenter')
-pl.legend(loc=0)
-pl.show()