more

5 files changed, 247 insertions, 243 deletions

diff --git a/examples/plot_OTDA_2D.py b/examples/plot_OTDA_2D.py
index a1fb804..1bda59c 100644
--- a/examples/plot_OTDA_2D.py
+++ b/examples/plot_OTDA_2D.py
@@ -11,110 +11,112 @@ import matplotlib.pylab as pl
 import ot
 
 
-
 #%% parameters
 
-n=150 # nb bins
+n = 150  # nb bins
 
-xs,ys=ot.datasets.get_data_classif('3gauss',n)
-xt,yt=ot.datasets.get_data_classif('3gauss2',n)
+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)
+a, b = ot.unif(n), ot.unif(n)
 # loss matrix
-M=ot.dist(xs,xt)
-#M/=M.max()
+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.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.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.imshow(M, interpolation='nearest')
 pl.title('Cost matrix M')
 
 
 #%% OT estimation
 
 # EMD
-G0=ot.emd(a,b,M)
+G0 = ot.emd(a, b, M)
 
 # sinkhorn
-lambd=1e-1
-Gs=ot.sinkhorn(a,b,M,lambd)
+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)
+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.subplot(2, 3, 1)
+pl.imshow(G0, interpolation='nearest')
 pl.title('OT matrix ')
 
-pl.subplot(2,3,2)
-pl.imshow(Gs,interpolation='nearest')
+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.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, 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, 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')
+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')
+pl.tight_layout()
 
 #%% 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)
-
+xst0 = n * G0.dot(xt)
+xsts = n * Gs.dot(xt)
+xstg = n * Gg.dot(xt)
 
-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.figure(4, figsize=(8, 3))
+pl.subplot(1, 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.subplot(1, 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
+pl.subplot(1, 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')
+pl.tight_layout()
+pl.show()
diff --git a/examples/plot_OTDA_classes.py b/examples/plot_OTDA_classes.py
index 089b45b..4d3846a 100644
--- a/examples/plot_OTDA_classes.py
+++ b/examples/plot_OTDA_classes.py
@@ -10,29 +10,25 @@ import matplotlib.pylab as pl
 import ot
 
 
-
-
 #%% parameters
 
-n=150 # nb samples in source and target datasets
-
-xs,ys=ot.datasets.get_data_classif('3gauss',n)
-xt,yt=ot.datasets.get_data_classif('3gauss2',n)
-
+n = 150  # nb samples in source and target datasets
 
+xs, ys = ot.datasets.get_data_classif('3gauss', n)
+xt, yt = ot.datasets.get_data_classif('3gauss2', n)
 
 
 #%% plot samples
 
-pl.figure(1)
+pl.figure(1, figsize=(6.4, 3))
 
-pl.subplot(2,2,1)
-pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')
+pl.subplot(1, 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.subplot(1, 2, 2)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples')
 pl.legend(loc=0)
 pl.title('target  distributions')
 
@@ -40,73 +36,78 @@ pl.title('target  distributions')
 #%% OT estimation
 
 # LP problem
-da_emd=ot.da.OTDA()     # init class
-da_emd.fit(xs,xt)       # fit distributions
-xst0=da_emd.interp()    # interpolation of source samples
-
+da_emd = ot.da.OTDA()     # init class
+da_emd.fit(xs, xt)       # fit distributions
+xst0 = da_emd.interp()    # interpolation of source samples
 
 # sinkhorn regularization
-lambd=1e-1
-da_entrop=ot.da.OTDA_sinkhorn()
-da_entrop.fit(xs,xt,reg=lambd)
-xsts=da_entrop.interp()
+lambd = 1e-1
+da_entrop = ot.da.OTDA_sinkhorn()
+da_entrop.fit(xs, xt, reg=lambd)
+xsts = da_entrop.interp()
 
 # non-convex Group lasso regularization
-reg=1e-1
-eta=1e0
-da_lpl1=ot.da.OTDA_lpl1()
-da_lpl1.fit(xs,ys,xt,reg=reg,eta=eta)
-xstg=da_lpl1.interp()
-
+reg = 1e-1
+eta = 1e0
+da_lpl1 = ot.da.OTDA_lpl1()
+da_lpl1.fit(xs, ys, xt, reg=reg, eta=eta)
+xstg = da_lpl1.interp()
 
 # True Group lasso regularization
-reg=1e-1
-eta=2e0
-da_l1l2=ot.da.OTDA_l1l2()
-da_l1l2.fit(xs,ys,xt,reg=reg,eta=eta,numItermax=20,verbose=True)
-xstgl=da_l1l2.interp()
-
+reg = 1e-1
+eta = 2e0
+da_l1l2 = ot.da.OTDA_l1l2()
+da_l1l2.fit(xs, ys, xt, reg=reg, eta=eta, numItermax=20, verbose=True)
+xstgl = da_l1l2.interp()
 
 #%% plot interpolated source samples
-pl.figure(4,(15,8))
 
-param_img={'interpolation':'nearest','cmap':'jet'}
+param_img = {'interpolation': 'nearest', 'cmap': 'spectral'}
 
-pl.subplot(2,4,1)
-pl.imshow(da_emd.G,**param_img)
+pl.figure(2, figsize=(8, 4.5))
+pl.subplot(2, 4, 1)
+pl.imshow(da_emd.G, **param_img)
 pl.title('OT matrix')
 
+pl.subplot(2, 4, 2)
+pl.imshow(da_entrop.G, **param_img)
+pl.title('OT matrix\nsinkhorn')
 
-pl.subplot(2,4,2)
-pl.imshow(da_entrop.G,**param_img)
-pl.title('OT matrix sinkhorn')
-
-pl.subplot(2,4,3)
-pl.imshow(da_lpl1.G,**param_img)
-pl.title('OT matrix non-convex Group Lasso')
-
-pl.subplot(2,4,4)
-pl.imshow(da_l1l2.G,**param_img)
-pl.title('OT matrix Group Lasso')
+pl.subplot(2, 4, 3)
+pl.imshow(da_lpl1.G, **param_img)
+pl.title('OT matrix\nnon-convex Group Lasso')
 
+pl.subplot(2, 4, 4)
+pl.imshow(da_l1l2.G, **param_img)
+pl.title('OT matrix\nGroup Lasso')
 
-pl.subplot(2,4,5)
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)
-pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30)
+pl.subplot(2, 4, 5)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
+           label='Target samples', alpha=0.3)
+pl.scatter(xst0[:, 0], xst0[:, 1], c=ys,
+           marker='+', label='Transp samples', s=30)
 pl.title('Interp samples')
 pl.legend(loc=0)
 
-pl.subplot(2,4,6)
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)
-pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30)
-pl.title('Interp samples Sinkhorn')
-
-pl.subplot(2,4,7)
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)
-pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30)
-pl.title('Interp samples non-convex Group Lasso')
-
-pl.subplot(2,4,8)
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)
-pl.scatter(xstgl[:,0],xstgl[:,1],c=ys,marker='+',label='Transp samples',s=30)
-pl.title('Interp samples Group Lasso')
\ No newline at end of file
+pl.subplot(2, 4, 6)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
+           label='Target samples', alpha=0.3)
+pl.scatter(xsts[:, 0], xsts[:, 1], c=ys,
+           marker='+', label='Transp samples', s=30)
+pl.title('Interp samples\nSinkhorn')
+
+pl.subplot(2, 4, 7)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
+           label='Target samples', alpha=0.3)
+pl.scatter(xstg[:, 0], xstg[:, 1], c=ys,
+           marker='+', label='Transp samples', s=30)
+pl.title('Interp samples\nnon-convex Group Lasso')
+
+pl.subplot(2, 4, 8)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
+           label='Target samples', alpha=0.3)
+pl.scatter(xstgl[:, 0], xstgl[:, 1], c=ys,
+           marker='+', label='Transp samples', s=30)
+pl.title('Interp samples\nGroup Lasso')
+pl.tight_layout()
+pl.show()
diff --git a/examples/plot_OTDA_color_images.py b/examples/plot_OTDA_color_images.py
index 68eee44..a8861c6 100644
--- a/examples/plot_OTDA_color_images.py
+++ b/examples/plot_OTDA_color_images.py
@@ -4,142 +4,143 @@
 OT for domain adaptation with image color adaptation [6]
 ========================================================
 
-[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
+[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).
+Regularized discrete optimal transport.
+SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
 """
 
 import numpy as np
-import scipy.ndimage as spi
+from scipy import ndimage
 import matplotlib.pylab as pl
 import ot
 
 
 #%% Loading images
 
-I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256
-I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256
+I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256
+I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
 
 #%% Plot images
 
-pl.figure(1)
+pl.figure(1, figsize=(6.4, 3))
 
-pl.subplot(1,2,1)
+pl.subplot(1, 2, 1)
 pl.imshow(I1)
+pl.axis('off')
 pl.title('Image 1')
 
-pl.subplot(1,2,2)
+pl.subplot(1, 2, 2)
 pl.imshow(I2)
+pl.axis('off')
 pl.title('Image 2')
 
 pl.show()
 
 #%% Image conversion and dataset generation
 
+
 def im2mat(I):
     """Converts and image to matrix (one pixel per line)"""
-    return I.reshape((I.shape[0]*I.shape[1],I.shape[2]))
+    return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
+
 
-def mat2im(X,shape):
+def mat2im(X, shape):
     """Converts back a matrix to an image"""
     return X.reshape(shape)
 
-X1=im2mat(I1)
-X2=im2mat(I2)
+X1 = im2mat(I1)
+X2 = im2mat(I2)
 
 # training samples
-nb=1000
-idx1=np.random.randint(X1.shape[0],size=(nb,))
-idx2=np.random.randint(X2.shape[0],size=(nb,))
+nb = 1000
+idx1 = np.random.randint(X1.shape[0], size=(nb,))
+idx2 = np.random.randint(X2.shape[0], size=(nb,))
 
-xs=X1[idx1,:]
-xt=X2[idx2,:]
+xs = X1[idx1, :]
+xt = X2[idx2, :]
 
 #%% Plot image distributions
 
 
-pl.figure(2,(10,5))
+pl.figure(2, figsize=(6.4, 3))
 
-pl.subplot(1,2,1)
-pl.scatter(xs[:,0],xs[:,2],c=xs)
-pl.axis([0,1,0,1])
+pl.subplot(1, 2, 1)
+pl.scatter(xs[:, 0], xs[:, 2], c=xs)
+pl.axis([0, 1, 0, 1])
 pl.xlabel('Red')
 pl.ylabel('Blue')
 pl.title('Image 1')
 
-pl.subplot(1,2,2)
-#pl.imshow(I2)
-pl.scatter(xt[:,0],xt[:,2],c=xt)
-pl.axis([0,1,0,1])
+pl.subplot(1, 2, 2)
+pl.scatter(xt[:, 0], xt[:, 2], c=xt)
+pl.axis([0, 1, 0, 1])
 pl.xlabel('Red')
 pl.ylabel('Blue')
 pl.title('Image 2')
-
-pl.show()
-
-
+pl.tight_layout()
 
 #%% domain adaptation between images
 
 # LP problem
-da_emd=ot.da.OTDA()     # init class
-da_emd.fit(xs,xt)       # fit distributions
-
+da_emd = ot.da.OTDA()     # init class
+da_emd.fit(xs, xt)       # fit distributions
 
 # sinkhorn regularization
-lambd=1e-1
-da_entrop=ot.da.OTDA_sinkhorn()
-da_entrop.fit(xs,xt,reg=lambd)
-
-
+lambd = 1e-1
+da_entrop = ot.da.OTDA_sinkhorn()
+da_entrop.fit(xs, xt, reg=lambd)
 
 #%% prediction between images (using out of sample prediction as in [6])
 
-X1t=da_emd.predict(X1)
-X2t=da_emd.predict(X2,-1)
+X1t = da_emd.predict(X1)
+X2t = da_emd.predict(X2, -1)
 
-
-X1te=da_entrop.predict(X1)
-X2te=da_entrop.predict(X2,-1)
+X1te = da_entrop.predict(X1)
+X2te = da_entrop.predict(X2, -1)
 
 
 def minmax(I):
-    return np.minimum(np.maximum(I,0),1)
+    return np.clip(I, 0, 1)
 
-I1t=minmax(mat2im(X1t,I1.shape))
-I2t=minmax(mat2im(X2t,I2.shape))
+I1t = minmax(mat2im(X1t, I1.shape))
+I2t = minmax(mat2im(X2t, I2.shape))
 
-I1te=minmax(mat2im(X1te,I1.shape))
-I2te=minmax(mat2im(X2te,I2.shape))
+I1te = minmax(mat2im(X1te, I1.shape))
+I2te = minmax(mat2im(X2te, I2.shape))
 
 #%% plot all images
 
-pl.figure(2,(10,8))
-
-pl.subplot(2,3,1)
+pl.figure(2, figsize=(8, 4))
 
+pl.subplot(2, 3, 1)
 pl.imshow(I1)
+pl.axis('off')
 pl.title('Image 1')
 
-pl.subplot(2,3,2)
+pl.subplot(2, 3, 2)
 pl.imshow(I1t)
+pl.axis('off')
 pl.title('Image 1 Adapt')
 
-
-pl.subplot(2,3,3)
+pl.subplot(2, 3, 3)
 pl.imshow(I1te)
+pl.axis('off')
 pl.title('Image 1 Adapt (reg)')
 
-pl.subplot(2,3,4)
-
+pl.subplot(2, 3, 4)
 pl.imshow(I2)
+pl.axis('off')
 pl.title('Image 2')
 
-pl.subplot(2,3,5)
+pl.subplot(2, 3, 5)
 pl.imshow(I2t)
+pl.axis('off')
 pl.title('Image 2 Adapt')
 
-
-pl.subplot(2,3,6)
+pl.subplot(2, 3, 6)
 pl.imshow(I2te)
+pl.axis('off')
 pl.title('Image 2 Adapt (reg)')
+pl.tight_layout()
 
 pl.show()
diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py
index 3a93591..75ed7db 100644
--- a/examples/plot_OT_2D_samples.py
+++ b/examples/plot_OT_2D_samples.py
@@ -8,7 +8,7 @@
 """
 
 import numpy as np
-import matplotlib.pylab as plt
+import matplotlib.pylab as pl
 import ot
 
 #%% parameters and data generation
@@ -32,31 +32,31 @@ M /= M.max()
 
 #%% plot samples
 
-plt.figure(1)
-plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-plt.legend(loc=0)
-plt.title('Source and target distributions')
+pl.figure(1)
+pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
+pl.legend(loc=0)
+pl.title('Source and target distributions')
 
-plt.figure(2)
-plt.imshow(M, interpolation='nearest')
-plt.title('Cost matrix M')
+pl.figure(2)
+pl.imshow(M, interpolation='nearest')
+pl.title('Cost matrix M')
 
 
 #%% EMD
 
 G0 = ot.emd(a, b, M)
 
-plt.figure(3)
-plt.imshow(G0, interpolation='nearest')
-plt.title('OT matrix G0')
+pl.figure(3)
+pl.imshow(G0, interpolation='nearest')
+pl.title('OT matrix G0')
 
-plt.figure(4)
+pl.figure(4)
 ot.plot.plot2D_samples_mat(xs, xt, G0, 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.legend(loc=0)
-plt.title('OT matrix with samples')
+pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
+pl.legend(loc=0)
+pl.title('OT matrix with samples')
 
 
 #%% sinkhorn
@@ -66,15 +66,15 @@ lambd = 5e-4
 
 Gs = ot.sinkhorn(a, b, M, lambd)
 
-plt.figure(5)
-plt.imshow(Gs, interpolation='nearest')
-plt.title('OT matrix sinkhorn')
+pl.figure(5)
+pl.imshow(Gs, interpolation='nearest')
+pl.title('OT matrix sinkhorn')
 
-plt.figure(6)
+pl.figure(6)
 ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])
-plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-plt.legend(loc=0)
-plt.title('OT matrix Sinkhorn with samples')
+pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
+pl.legend(loc=0)
+pl.title('OT matrix Sinkhorn with samples')
 
-plt.show()
+pl.show()
diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py
index e11d6ad..86d902b 100644
--- a/examples/plot_OT_L1_vs_L2.py
+++ b/examples/plot_OT_L1_vs_L2.py
@@ -12,7 +12,7 @@ https://arxiv.org/pdf/1706.07650.pdf
 """
 
 import numpy as np
-import matplotlib.pylab as plt
+import matplotlib.pylab as pl
 import ot
 
 #%% parameters and data generation
@@ -55,58 +55,58 @@ for data in range(2):
 
     #%% plot samples
 
-    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')
+    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.axis('equal')
+    pl.title('Source and traget distributions')
 
-    plt.figure(2 + 3 * data, figsize=(7, 3))
+    pl.figure(2 + 3 * data, figsize=(7, 3))
 
-    plt.subplot(1, 3, 1)
-    plt.imshow(M1, interpolation='nearest')
-    plt.title('Euclidean cost')
+    pl.subplot(1, 3, 1)
+    pl.imshow(M1, interpolation='nearest')
+    pl.title('Euclidean cost')
 
-    plt.subplot(1, 3, 2)
-    plt.imshow(M2, interpolation='nearest')
-    plt.title('Squared Euclidean cost')
+    pl.subplot(1, 3, 2)
+    pl.imshow(M2, interpolation='nearest')
+    pl.title('Squared Euclidean cost')
 
-    plt.subplot(1, 3, 3)
-    plt.imshow(Mp, interpolation='nearest')
-    plt.title('Sqrt Euclidean cost')
-    plt.tight_layout()
+    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)
 
-    plt.figure(3 + 3 * data, figsize=(7, 3))
+    pl.figure(3 + 3 * data, figsize=(7, 3))
 
-    plt.subplot(1, 3, 1)
+    pl.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')
+    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')
 
-    plt.subplot(1, 3, 2)
+    pl.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')
+    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')
 
-    plt.subplot(1, 3, 3)
+    pl.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()
+    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')
+    pl.tight_layout()
+
+pl.show()