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diff --git a/docs/source/auto_examples/plot_OTDA_2D.py b/docs/source/auto_examples/plot_OTDA_2D.py
new file mode 100644
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+++ b/docs/source/auto_examples/plot_OTDA_2D.py
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+# -*- coding: utf-8 -*-
+"""
+==============================
+OT for empirical distributions
+==============================
+
+"""
+
+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')
+
+#%% sample interpolation
+
+xst0=n*G0.dot(xt)
+xsts=n*Gs.dot(xt)
+xstg=n*Gg.dot(xt)
+
+pl.figure(4)
+pl.subplot(2,3,1)
+
+
+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(2,3,2)
+
+
+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(2,3,3)
+
+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')
+\ No newline at end of file