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diff --git a/examples/plot_OTDA_2D.py b/examples/plot_OTDA_2D.py
deleted file mode 100644
index f2108c6..0000000
--- a/examples/plot_OTDA_2D.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-==============================
-OT for empirical distributions
-==============================
-
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-
-
-#%% parameters
-
-n = 150  # nb bins
-
-xs, ys = ot.datasets.get_data_classif('3gauss', n)
-xt, yt = ot.datasets.get_data_classif('3gauss2', n)
-
-a, b = ot.unif(n), ot.unif(n)
-# loss matrix
-M = ot.dist(xs, xt)
-# M/=M.max()
-
-#%% plot samples
-
-pl.figure(1)
-pl.subplot(2, 2, 1)
-pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples')
-pl.legend(loc=0)
-pl.title('Source  distributions')
-
-pl.subplot(2, 2, 2)
-pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples')
-pl.legend(loc=0)
-pl.title('target  distributions')
-
-pl.figure(2)
-pl.imshow(M, interpolation='nearest')
-pl.title('Cost matrix M')
-
-
-#%% OT estimation
-
-# EMD
-G0 = ot.emd(a, b, M)
-
-# sinkhorn
-lambd = 1e-1
-Gs = ot.sinkhorn(a, b, M, lambd)
-
-
-# Group lasso regularization
-reg = 1e-1
-eta = 1e0
-Gg = ot.da.sinkhorn_lpl1_mm(a, ys.astype(np.int), b, M, reg, eta)
-
-
-#%% visu matrices
-
-pl.figure(3)
-
-pl.subplot(2, 3, 1)
-pl.imshow(G0, interpolation='nearest')
-pl.title('OT matrix ')
-
-pl.subplot(2, 3, 2)
-pl.imshow(Gs, interpolation='nearest')
-pl.title('OT matrix Sinkhorn')
-
-pl.subplot(2, 3, 3)
-pl.imshow(Gg, interpolation='nearest')
-pl.title('OT matrix Group lasso')
-
-pl.subplot(2, 3, 4)
-ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])
-pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples')
-pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples')
-
-
-pl.subplot(2, 3, 5)
-ot.plot.plot2D_samples_mat(xs, xt, Gs, c=[.5, .5, 1])
-pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples')
-pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples')
-
-pl.subplot(2, 3, 6)
-ot.plot.plot2D_samples_mat(xs, xt, Gg, c=[.5, .5, 1])
-pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples')
-pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples')
-pl.tight_layout()
-
-#%% sample interpolation
-
-xst0 = n * G0.dot(xt)
-xsts = n * Gs.dot(xt)
-xstg = n * Gg.dot(xt)
-
-pl.figure(4, figsize=(8, 3))
-pl.subplot(1, 3, 1)
-pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.5)
-pl.scatter(xst0[:, 0], xst0[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.title('Interp samples')
-pl.legend(loc=0)
-
-pl.subplot(1, 3, 2)
-pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.5)
-pl.scatter(xsts[:, 0], xsts[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.title('Interp samples Sinkhorn')
-
-pl.subplot(1, 3, 3)
-pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.5)
-pl.scatter(xstg[:, 0], xstg[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.title('Interp samples Grouplasso')
-pl.tight_layout()
-pl.show()