add example

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diff --git a/examples/plot_barycenter_lp_vs_entropic.py b/examples/plot_barycenter_lp_vs_entropic.py
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+++ b/examples/plot_barycenter_lp_vs_entropic.py
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+# -*- coding: utf-8 -*-
+"""
+=================================================================================
+1D Wasserstein barycenter comparison between exact LP and entropic regularization
+=================================================================================
+
+This example illustrates the computation of regularized Wassersyein Barycenter
+as proposed in [3].
+
+
+[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015).
+Iterative Bregman projections for regularized transportation problems
+SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
+
+"""
+
+# Author: Remi Flamary <remi.flamary@unice.fr>
+#
+# License: MIT License
+
+import numpy as np
+import matplotlib.pylab as pl
+import ot
+# necessary for 3d plot even if not used
+from mpl_toolkits.mplot3d import Axes3D  # noqa
+from matplotlib.collections import PolyCollection  # noqa
+
+#import ot.lp.cvx as cvx
+
+#
+# Generate data
+# -------------
+
+#%% parameters
+
+problems = []
+
+n = 100  # nb bins
+
+# bin positions
+x = np.arange(n, dtype=np.float64)
+
+# Gaussian distributions
+# Gaussian distributions
+a1 = ot.datasets.get_1D_gauss(n, m=20, s=5)  # m= mean, s= std
+a2 = ot.datasets.get_1D_gauss(n, m=60, s=8)
+
+# creating matrix A containing all distributions
+A = np.vstack((a1, a2)).T
+n_distributions = A.shape[1]
+
+# loss matrix + normalization
+M = ot.utils.dist0(n)
+M /= M.max()
+
+#
+# Plot data
+# ---------
+
+#%% plot the distributions
+
+pl.figure(1, figsize=(6.4, 3))
+for i in range(n_distributions):
+    pl.plot(x, A[:, i])
+pl.title('Distributions')
+pl.tight_layout()
+
+#
+# Barycenter computation
+# ----------------------
+
+#%% barycenter computation
+
+alpha = 0.5  # 0<=alpha<=1
+weights = np.array([1 - alpha, alpha])
+
+# l2bary
+bary_l2 = A.dot(weights)
+
+# wasserstein
+reg = 1e-3
+ot.tic()
+bary_wass = ot.bregman.barycenter(A, M, reg, weights)
+ot.toc()
+
+
+ot.tic()
+bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)
+ot.toc()
+
+pl.figure(2)
+pl.clf()
+pl.subplot(2, 1, 1)
+for i in range(n_distributions):
+    pl.plot(x, A[:, i])
+pl.title('Distributions')
+
+pl.subplot(2, 1, 2)
+pl.plot(x, bary_l2, 'r', label='l2')
+pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')
+pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')
+pl.legend()
+pl.title('Barycenters')
+pl.tight_layout()
+
+problems.append([A, [bary_l2, bary_wass, bary_wass2]])
+
+#%% parameters
+
+a1 = 1.0 * (x > 10) * (x < 50)
+a2 = 1.0 * (x > 60) * (x < 80)
+
+a1 /= a1.sum()
+a2 /= a2.sum()
+
+# creating matrix A containing all distributions
+A = np.vstack((a1, a2)).T
+n_distributions = A.shape[1]
+
+# loss matrix + normalization
+M = ot.utils.dist0(n)
+M /= M.max()
+
+
+#%% plot the distributions
+
+pl.figure(1, figsize=(6.4, 3))
+for i in range(n_distributions):
+    pl.plot(x, A[:, i])
+pl.title('Distributions')
+pl.tight_layout()
+
+#
+# Barycenter computation
+# ----------------------
+
+#%% barycenter computation
+
+alpha = 0.5  # 0<=alpha<=1
+weights = np.array([1 - alpha, alpha])
+
+# l2bary
+bary_l2 = A.dot(weights)
+
+# wasserstein
+reg = 1e-3
+ot.tic()
+bary_wass = ot.bregman.barycenter(A, M, reg, weights)
+ot.toc()
+
+
+ot.tic()
+bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)
+ot.toc()
+
+
+problems.append([A, [bary_l2, bary_wass, bary_wass2]])
+
+pl.figure(2)
+pl.clf()
+pl.subplot(2, 1, 1)
+for i in range(n_distributions):
+    pl.plot(x, A[:, i])
+pl.title('Distributions')
+
+pl.subplot(2, 1, 2)
+pl.plot(x, bary_l2, 'r', label='l2')
+pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')
+pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')
+pl.legend()
+pl.title('Barycenters')
+pl.tight_layout()
+
+#%% parameters
+
+a1 = np.zeros(n)
+a2 = np.zeros(n)
+
+a1[10] = .25
+a1[20] = .5
+a1[30] = .25
+a2[80] = 1
+
+
+a1 /= a1.sum()
+a2 /= a2.sum()
+
+# creating matrix A containing all distributions
+A = np.vstack((a1, a2)).T
+n_distributions = A.shape[1]
+
+# loss matrix + normalization
+M = ot.utils.dist0(n)
+M /= M.max()
+
+
+#%% plot the distributions
+
+pl.figure(1, figsize=(6.4, 3))
+for i in range(n_distributions):
+    pl.plot(x, A[:, i])
+pl.title('Distributions')
+pl.tight_layout()
+
+#
+# Barycenter computation
+# ----------------------
+
+#%% barycenter computation
+
+alpha = 0.5  # 0<=alpha<=1
+weights = np.array([1 - alpha, alpha])
+
+# l2bary
+bary_l2 = A.dot(weights)
+
+# wasserstein
+reg = 1e-3
+ot.tic()
+bary_wass = ot.bregman.barycenter(A, M, reg, weights)
+ot.toc()
+
+
+ot.tic()
+bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)
+ot.toc()
+
+
+problems.append([A, [bary_l2, bary_wass, bary_wass2]])
+
+pl.figure(2)
+pl.clf()
+pl.subplot(2, 1, 1)
+for i in range(n_distributions):
+    pl.plot(x, A[:, i])
+pl.title('Distributions')
+
+pl.subplot(2, 1, 2)
+pl.plot(x, bary_l2, 'r', label='l2')
+pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')
+pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')
+pl.legend()
+pl.title('Barycenters')
+pl.tight_layout()
+
+
+#
+# Final figure
+# ------------
+#
+
+#%% plot
+
+nbm = len(problems)
+nbm2 = (nbm // 2)
+
+
+pl.figure(2, (20, 6))
+pl.clf()
+
+for i in range(nbm):
+
+    A = problems[i][0]
+    bary_l2 = problems[i][1][0]
+    bary_wass = problems[i][1][1]
+    bary_wass2 = problems[i][1][2]
+
+    pl.subplot(2, nbm, 1 + i)
+    for j in range(n_distributions):
+        pl.plot(x, A[:, j])
+    if i == nbm2:
+        pl.title('Distributions')
+    pl.xticks(())
+    pl.yticks(())
+
+    pl.subplot(2, nbm, 1 + i)
+
+    pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)')
+    pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')
+    pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')
+    if i == nbm - 1:
+        pl.legend()
+    if i == nbm2:
+        pl.title('Barycenters')