wroking make!

1 files changed, 35 insertions, 29 deletions

diff --git a/docs/source/auto_examples/plot_compute_emd.py b/docs/source/auto_examples/plot_compute_emd.py
index c7063e8..893eecf 100644
--- a/docs/source/auto_examples/plot_compute_emd.py
+++ b/docs/source/auto_examples/plot_compute_emd.py
@@ -4,9 +4,12 @@
 1D optimal transport
 ====================
 
-@author: rflamary
 """
 
+# Author: Remi Flamary <remi.flamary@unice.fr>
+#
+# License: MIT License
+
 import numpy as np
 import matplotlib.pylab as pl
 import ot
@@ -15,60 +18,63 @@ from ot.datasets import get_1D_gauss as gauss
 
 #%% parameters
 
-n=100 # nb bins
-n_target=50 # nb target distributions
+n = 100  # nb bins
+n_target = 50  # nb target distributions
 
 
 # bin positions
-x=np.arange(n,dtype=np.float64)
+x = np.arange(n, dtype=np.float64)
 
-lst_m=np.linspace(20,90,n_target)
+lst_m = np.linspace(20, 90, n_target)
 
 # Gaussian distributions
-a=gauss(n,m=20,s=5) # m= mean, s= std
+a = gauss(n, m=20, s=5)  # m= mean, s= std
 
-B=np.zeros((n,n_target))
+B = np.zeros((n, n_target))
 
-for i,m in enumerate(lst_m):
-    B[:,i]=gauss(n,m=m,s=5)
+for i, m in enumerate(lst_m):
+    B[:, i] = gauss(n, m=m, s=5)
 
 # loss matrix and normalization
-M=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'euclidean')
-M/=M.max()
-M2=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'sqeuclidean')
-M2/=M2.max()
+M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')
+M /= M.max()
+M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')
+M2 /= M2.max()
 #%% plot the distributions
 
 pl.figure(1)
-pl.subplot(2,1,1)
-pl.plot(x,a,'b',label='Source distribution')
+pl.subplot(2, 1, 1)
+pl.plot(x, a, 'b', label='Source distribution')
 pl.title('Source distribution')
-pl.subplot(2,1,2)
-pl.plot(x,B,label='Target distributions')
+pl.subplot(2, 1, 2)
+pl.plot(x, B, label='Target distributions')
 pl.title('Target distributions')
+pl.tight_layout()
 
 #%% Compute and plot distributions and loss matrix
 
-d_emd=ot.emd2(a,B,M) # direct computation of EMD
-d_emd2=ot.emd2(a,B,M2)  # direct computation of EMD with loss M3
+d_emd = ot.emd2(a, B, M)  # direct computation of EMD
+d_emd2 = ot.emd2(a, B, M2)  # direct computation of EMD with loss M3
 
 
 pl.figure(2)
-pl.plot(d_emd,label='Euclidean EMD')
-pl.plot(d_emd2,label='Squared Euclidean EMD')
+pl.plot(d_emd, label='Euclidean EMD')
+pl.plot(d_emd2, label='Squared Euclidean EMD')
 pl.title('EMD distances')
 pl.legend()
 
 #%%
-reg=1e-2
-d_sinkhorn=ot.sinkhorn(a,B,M,reg)
-d_sinkhorn2=ot.sinkhorn(a,B,M2,reg)
+reg = 1e-2
+d_sinkhorn = ot.sinkhorn2(a, B, M, reg)
+d_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)
 
 pl.figure(2)
 pl.clf()
-pl.plot(d_emd,label='Euclidean EMD')
-pl.plot(d_emd2,label='Squared Euclidean EMD')
-pl.plot(d_sinkhorn,'+',label='Euclidean Sinkhorn')
-pl.plot(d_sinkhorn2,'+',label='Squared Euclidean Sinkhorn')
+pl.plot(d_emd, label='Euclidean EMD')
+pl.plot(d_emd2, label='Squared Euclidean EMD')
+pl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')
+pl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')
 pl.title('EMD distances')
-pl.legend()
\ No newline at end of file
+pl.legend()
+
+pl.show()