wroking make!

1 files changed, 79 insertions, 72 deletions

diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst
index 4e94bef..ba52bfe 100644
--- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst
+++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst
@@ -7,11 +7,10 @@
 2D Optimal transport for different metrics
 ==========================================
 
-Stole the figure idea from Fig. 1 and 2 in 
+Stole the figure idea from Fig. 1 and 2 in
 https://arxiv.org/pdf/1706.07650.pdf
 
 
-@author: rflamary
 
 
 
@@ -56,6 +55,10 @@ https://arxiv.org/pdf/1706.07650.pdf
 .. code-block:: python
 
 
+    # Author: Remi Flamary <remi.flamary@unice.fr>
+    #
+    # License: MIT License
+
     import numpy as np
     import matplotlib.pylab as pl
     import ot
@@ -65,94 +68,98 @@ https://arxiv.org/pdf/1706.07650.pdf
     for data in range(2):
 
         if data:
-            n=20 # nb samples
-            xs=np.zeros((n,2))
-            xs[:,0]=np.arange(n)+1
-            xs[:,1]=(np.arange(n)+1)*-0.001 # to make it strictly convex...
-        
-            xt=np.zeros((n,2))
-            xt[:,1]=np.arange(n)+1
+            n = 20  # nb samples
+            xs = np.zeros((n, 2))
+            xs[:, 0] = np.arange(n) + 1
+            xs[:, 1] = (np.arange(n) + 1) * -0.001  # to make it strictly convex...
+
+            xt = np.zeros((n, 2))
+            xt[:, 1] = np.arange(n) + 1
         else:
-        
-            n=50 # nb samples
-            xtot=np.zeros((n+1,2))
-            xtot[:,0]=np.cos((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi)
-            xtot[:,1]=np.sin((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi)
-        
-            xs=xtot[:n,:]
-            xt=xtot[1:,:]
-        
-        
-    
-        a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples
-    
+
+            n = 50  # nb samples
+            xtot = np.zeros((n + 1, 2))
+            xtot[:, 0] = np.cos(
+                (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)
+            xtot[:, 1] = np.sin(
+                (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)
+
+            xs = xtot[:n, :]
+            xt = xtot[1:, :]
+
+        a, b = ot.unif(n), ot.unif(n)  # uniform distribution on samples
+
         # loss matrix
-        M1=ot.dist(xs,xt,metric='euclidean')
-        M1/=M1.max()
-    
+        M1 = ot.dist(xs, xt, metric='euclidean')
+        M1 /= M1.max()
+
         # loss matrix
-        M2=ot.dist(xs,xt,metric='sqeuclidean')
-        M2/=M2.max()
-    
+        M2 = ot.dist(xs, xt, metric='sqeuclidean')
+        M2 /= M2.max()
+
         # loss matrix
-        Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean'))
-        Mp/=Mp.max()
-    
+        Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))
+        Mp /= Mp.max()
+
         #%% plot samples
-    
-        pl.figure(1+3*data)
+
+        pl.figure(1 + 3 * data, figsize=(7, 3))
         pl.clf()
-        pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')
-        pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')
+        pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+        pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
         pl.axis('equal')
         pl.title('Source and traget distributions')
-    
-        pl.figure(2+3*data,(15,5))
-        pl.subplot(1,3,1)
-        pl.imshow(M1,interpolation='nearest')
-        pl.title('Eucidean cost')
-        pl.subplot(1,3,2)
-        pl.imshow(M2,interpolation='nearest')
+
+        pl.figure(2 + 3 * data, figsize=(7, 3))
+
+        pl.subplot(1, 3, 1)
+        pl.imshow(M1, interpolation='nearest')
+        pl.title('Euclidean cost')
+
+        pl.subplot(1, 3, 2)
+        pl.imshow(M2, interpolation='nearest')
         pl.title('Squared Euclidean cost')
-    
-        pl.subplot(1,3,3)
-        pl.imshow(Mp,interpolation='nearest')
+
+        pl.subplot(1, 3, 3)
+        pl.imshow(Mp, interpolation='nearest')
         pl.title('Sqrt Euclidean cost')
+        pl.tight_layout()
+
         #%% EMD
-    
-        G1=ot.emd(a,b,M1)
-        G2=ot.emd(a,b,M2)
-        Gp=ot.emd(a,b,Mp)
-    
-        pl.figure(3+3*data,(15,5))
-    
-        pl.subplot(1,3,1)
-        ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1])
-        pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')
-        pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')
+        G1 = ot.emd(a, b, M1)
+        G2 = ot.emd(a, b, M2)
+        Gp = ot.emd(a, b, Mp)
+
+        pl.figure(3 + 3 * data, figsize=(7, 3))
+
+        pl.subplot(1, 3, 1)
+        ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])
+        pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+        pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
         pl.axis('equal')
-        #pl.legend(loc=0)
+        # pl.legend(loc=0)
         pl.title('OT Euclidean')
-    
-        pl.subplot(1,3,2)
-    
-        ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1])
-        pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')
-        pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')
+
+        pl.subplot(1, 3, 2)
+        ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])
+        pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+        pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
         pl.axis('equal')
-        #pl.legend(loc=0)
+        # pl.legend(loc=0)
         pl.title('OT squared Euclidean')
-    
-        pl.subplot(1,3,3)
-    
-        ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1])
-        pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')
-        pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')
+
+        pl.subplot(1, 3, 3)
+        ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])
+        pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+        pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
         pl.axis('equal')
-        #pl.legend(loc=0)
+        # pl.legend(loc=0)
         pl.title('OT sqrt Euclidean')
+        pl.tight_layout()
+
+    pl.show()
 
-**Total running time of the script:** ( 0 minutes  1.417 seconds)
+**Total running time of the script:** ( 0 minutes  1.906 seconds)