pep8

8 files changed, 18 insertions, 19 deletions

diff --git a/examples/README.txt b/examples/README.txt
index c3d556d..b08d3f1 100644
--- a/examples/README.txt
+++ b/examples/README.txt
@@ -1,4 +1,4 @@
 POT Examples
 ============
 
-This is a gallery of all the POT example files. 
+This is a gallery of all the POT example files.
diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py
index a63f29a..b6ffa5f 100644
--- a/examples/plot_OT_1D.py
+++ b/examples/plot_OT_1D.py
@@ -4,7 +4,7 @@
 1D optimal transport
 ====================
 
-This example illustrates the computation of EMD and Sinkhorn transport plans 
+This example illustrates the computation of EMD and Sinkhorn transport plans
 and their visualization.
 
 """
@@ -20,7 +20,8 @@ from ot.datasets import get_1D_gauss as gauss
 
 ##############################################################################
 # Generate data
-##############################################################################
+# #############
+
 
 #%% parameters
 
@@ -40,7 +41,7 @@ M /= M.max()
 
 ##############################################################################
 # Plot distributions and loss matrix
-##############################################################################
+###################################
 
 #%% plot the distributions
 
diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py
index 77bde22..49d37e1 100644
--- a/examples/plot_OT_L1_vs_L2.py
+++ b/examples/plot_OT_L1_vs_L2.py
@@ -76,7 +76,6 @@ pl.tight_layout()
 ##############################################################################
 
 
-
 #%% EMD
 G1 = ot.emd(a, b, M1)
 G2 = ot.emd(a, b, M2)
@@ -172,7 +171,6 @@ pl.tight_layout()
 ##############################################################################
 
 
-
 #%% EMD
 G1 = ot.emd(a, b, M1)
 G2 = ot.emd(a, b, M2)
diff --git a/examples/plot_WDA.py b/examples/plot_WDA.py
index 06a2e38..5928621 100644
--- a/examples/plot_WDA.py
+++ b/examples/plot_WDA.py
@@ -7,7 +7,7 @@ Wasserstein Discriminant Analysis
 This example illustrate the use of WDA as proposed in [11].
 
 
-[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). 
+[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016).
 Wasserstein Discriminant Analysis.
 
 """
diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py
index 142b05e..eef8536 100644
--- a/examples/plot_barycenter_1D.py
+++ b/examples/plot_barycenter_1D.py
@@ -4,11 +4,11 @@
 1D Wasserstein barycenter demo
 ==============================
 
-This example illustrates the computation of regularized Wassersyein Barycenter 
+This example illustrates the computation of regularized Wassersyein Barycenter
 as proposed in [3].
 
 
-[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). 
+[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015).
 Iterative Bregman projections for regularized transportation problems
 SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
 
diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py
index b688f93..a84b249 100644
--- a/examples/plot_compute_emd.py
+++ b/examples/plot_compute_emd.py
@@ -4,7 +4,7 @@
 Plot multiple EMD
 =================
 
-Shows how to compute multiple EMD and Sinkhorn with two differnt 
+Shows how to compute multiple EMD and Sinkhorn with two differnt
 ground metrics and plot their values for diffeent distributions.
 
 
diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py
index b362662..d753414 100644
--- a/examples/plot_optim_OTreg.py
+++ b/examples/plot_optim_OTreg.py
@@ -5,20 +5,20 @@ Regularized OT with generic solver
 ==================================
 
 Illustrates the use of the generic solver for regularized OT with
-user-designed regularization term. It uses Conditional gradient as in [6] and 
+user-designed regularization term. It uses Conditional gradient as in [6] and
 generalized Conditional Gradient as proposed in [5][7].
 
 
-[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for 
-Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine 
+[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for
+Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine
 Intelligence , vol.PP, no.99, pp.1-1.
 
-[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). 
-Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 
+[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).
+Regularized discrete optimal transport. SIAM Journal on Imaging Sciences,
 7(3), 1853-1882.
 
-[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized 
-conditional gradient: analysis of convergence and applications. 
+[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized
+conditional gradient: analysis of convergence and applications.
 arXiv preprint arXiv:1510.06567.
 
 
diff --git a/examples/plot_otda_mapping_colors_images.py b/examples/plot_otda_mapping_colors_images.py
index 162c24b..5590286 100644
--- a/examples/plot_otda_mapping_colors_images.py
+++ b/examples/plot_otda_mapping_colors_images.py
@@ -1,10 +1,10 @@
 # -*- coding: utf-8 -*-
 """
 =====================================================
-OT for image color adaptation with mapping estimation 
+OT for image color adaptation with mapping estimation
 =====================================================
 
-OT for domain adaptation with image color adaptation [6] with mapping 
+OT for domain adaptation with image color adaptation [6] with mapping
 estimation [8].
 
 [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized