pimp + pep8 on plot_OT_L1_vs_L2

1 files changed, 86 insertions, 82 deletions

diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py
index 9bb92fe..e11d6ad 100644
--- a/examples/plot_OT_L1_vs_L2.py
+++ b/examples/plot_OT_L1_vs_L2.py
@@ -4,7 +4,7 @@
 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
 
 
@@ -12,7 +12,7 @@ https://arxiv.org/pdf/1706.07650.pdf
 """
 
 import numpy as np
-import matplotlib.pylab as pl
+import matplotlib.pylab as plt
 import ot
 
 #%% parameters and data generation
@@ -20,89 +20,93 @@ import ot
 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.clf()
-    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.title('Squared Euclidean cost')
-    
-    pl.subplot(1,3,3)
-    pl.imshow(Mp,interpolation='nearest')
-    pl.title('Sqrt Euclidean cost')
+
+    plt.figure(1 + 3 * data, figsize=(7, 3))
+    plt.clf()
+    plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+    plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
+    plt.axis('equal')
+    plt.title('Source and traget distributions')
+
+    plt.figure(2 + 3 * data, figsize=(7, 3))
+
+    plt.subplot(1, 3, 1)
+    plt.imshow(M1, interpolation='nearest')
+    plt.title('Euclidean cost')
+
+    plt.subplot(1, 3, 2)
+    plt.imshow(M2, interpolation='nearest')
+    plt.title('Squared Euclidean cost')
+
+    plt.subplot(1, 3, 3)
+    plt.imshow(Mp, interpolation='nearest')
+    plt.title('Sqrt Euclidean cost')
+    plt.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')
-    pl.axis('equal')
-    #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.axis('equal')
-    #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.axis('equal')
-    #pl.legend(loc=0)
-    pl.title('OT sqrt Euclidean')
+    G1 = ot.emd(a, b, M1)
+    G2 = ot.emd(a, b, M2)
+    Gp = ot.emd(a, b, Mp)
+
+    plt.figure(3 + 3 * data, figsize=(7, 3))
+
+    plt.subplot(1, 3, 1)
+    ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])
+    plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+    plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
+    plt.axis('equal')
+    # plt.legend(loc=0)
+    plt.title('OT Euclidean')
+
+    plt.subplot(1, 3, 2)
+    ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])
+    plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+    plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
+    plt.axis('equal')
+    # plt.legend(loc=0)
+    plt.title('OT squared Euclidean')
+
+    plt.subplot(1, 3, 3)
+    ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])
+    plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+    plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
+    plt.axis('equal')
+    # plt.legend(loc=0)
+    plt.title('OT sqrt Euclidean')
+    plt.tight_layout()
+
+plt.show()