pep8

Author: agramfort

examples/plot_OTDA_color_images.py

@@ -48,6 +48,7 @@ def mat2im(X, shape):
     """Converts back a matrix to an image"""
     return X.reshape(shape)
 
+
 X1 = im2mat(I1)
 X2 = im2mat(I2)
 
@@ -102,6 +103,7 @@ def mat2im(X, shape):
 def minmax(I):
     return np.clip(I, 0, 1)
 
+
 I1t = minmax(mat2im(X1t, I1.shape))
 I2t = minmax(mat2im(X2t, I2.shape))
 

examples/plot_OTDA_mapping_color_images.py

@@ -48,6 +48,7 @@ def mat2im(X, shape):
     """Converts back a matrix to an image"""
     return X.reshape(shape)
 
+
 X1 = im2mat(I1)
 X2 = im2mat(I2)
 
@@ -85,6 +86,7 @@ def mat2im(X, shape):
 def minmax(I):
     return np.clip(I, 0, 1)
 
+
 # LP problem
 da_emd = ot.da.OTDA()     # init class
 da_emd.fit(xs, xt)       # fit distributions

examples/plot_optim_OTreg.py

@@ -44,6 +44,7 @@ def f(G):
 def df(G):
     return G
 
+
 reg = 1e-1
 
 Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)
@@ -61,6 +62,7 @@ def f(G):
 def df(G):
     return np.log(G) + 1.
 
+
 reg = 1e-3
 
 Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True)
@@ -78,6 +80,7 @@ def f(G):
 def df(G):
     return G
 
+
 reg1 = 1e-3
 reg2 = 1e-1
 

ot/utils.py

@@ -2,11 +2,13 @@
 """
 Various function that can be usefull
 """
+import multiprocessing
+from functools import reduce
+import time
+
 import numpy as np
 from scipy.spatial.distance import cdist
-import multiprocessing
 
-import time
 __time_tic_toc = time.time()
 
 

test/test_gpu_sinkhorn_lpl1.py

@@ -8,6 +8,7 @@ def describeRes(r):
     print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}"
           .format(np.min(r), np.max(r), np.mean(r), np.std(r)))
 
+
 for n in [5000, 10000, 15000, 20000]:
     print(n)
     a = np.random.rand(n // 4, 100)