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diff --git a/docs/source/auto_examples/demo_OT_1D_test.py b/docs/source/auto_examples/demo_OT_1D_test.py
deleted file mode 100644
index 9edc377..0000000
--- a/docs/source/auto_examples/demo_OT_1D_test.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Demo for 1D optimal transport
-
-@author: rflamary
-"""
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-from ot.datasets import get_1D_gauss as gauss
-
-
-#%% parameters
-
-n=100 # nb bins
-
-# bin positions
-x=np.arange(n,dtype=np.float64)
-
-# Gaussian distributions
-a=gauss(n,m=n*.2,s=5) # m= mean, s= std
-b=gauss(n,m=n*.6,s=10)
-
-# loss matrix
-M=ot.dist(x.reshape((n,1)),x.reshape((n,1)))
-M/=M.max()
-
-#%% plot the distributions
-
-pl.figure(1)
-pl.plot(x,a,'b',label='Source distribution')
-pl.plot(x,b,'r',label='Target distribution')
-pl.legend()
-
-#%% plot distributions and loss matrix
-
-pl.figure(2)
-ot.plot.plot1D_mat(a,b,M,'Cost matrix M')
-
-#%% EMD
-
-G0=ot.emd(a,b,M)
-
-pl.figure(3)
-ot.plot.plot1D_mat(a,b,G0,'OT matrix G0')
-
-#%% Sinkhorn
-
-lambd=1e-3
-Gs=ot.sinkhorn(a,b,M,lambd,verbose=True)
-
-pl.figure(4)
-ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn')
-
-#%% Sinkhorn
-
-lambd=1e-4
-Gss,log=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True)
-Gss2,log2=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True,warmstart=log['warmstart'])
-
-pl.figure(5)
-ot.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized')
-
-#%% Sinkhorn
-
-lambd=1e-11
-Gss=ot.bregman.sinkhorn_epsilon_scaling(a,b,M,lambd,verbose=True)
-
-pl.figure(5)
-ot.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized')