pep8

Author: rflamary

test/test_partial.py

@@ -9,6 +9,7 @@
 import scipy as sp
 import ot
 
+
 def test_partial_wasserstein_lagrange():
 
     n_samples = 20  # nb samples (gaussian)
@@ -29,9 +30,7 @@ def test_partial_wasserstein_lagrange():
 
     m = 0.5
 
-    w0, log0 = ot.partial.partial_wasserstein_lagrange(p, q, M, 1, log=True)  
-
-    
+    w0, log0 = ot.partial.partial_wasserstein_lagrange(p, q, M, 1, log=True)
 
 
 def test_partial_wasserstein():

test/test_stochastic.py

@@ -71,7 +71,7 @@ def test_stochastic_asgd():
     M = ot.dist(x, x)
 
     G, log = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "asgd",
-                                               numItermax=numItermax, log=True)
+                                                    numItermax=numItermax, log=True)
 
     # check constratints
     np.testing.assert_allclose(
@@ -146,7 +146,7 @@ def test_stochastic_dual_sgd():
     M = ot.dist(x, x)
 
     G, log = ot.stochastic.solve_dual_entropic(u, u, M, reg, batch_size,
-                                          numItermax=numItermax, log=True)
+                                               numItermax=numItermax, log=True)
 
     # check constratints
     np.testing.assert_allclose(

test/test_unbalanced.py

@@ -35,7 +35,7 @@ def test_unbalanced_convergence(method):
                                                verbose=True)
     loss = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m,
                                               method=method,
-                                               verbose=True)
+                                              verbose=True)
     # check fixed point equations
     # in log-domain
     fi = reg_m / (reg_m + epsilon)