better pep8 but not solved

Author: rflamary

examples/plot_barycenter_1D.py

@@ -25,7 +25,7 @@
 from mpl_toolkits.mplot3d import Axes3D  # noqa
 from matplotlib.collections import PolyCollection
 
-##############################################################################
+#
 # Generate data
 # -------------
 
@@ -48,7 +48,7 @@
 M = ot.utils.dist0(n)
 M /= M.max()
 
-##############################################################################
+#
 # Plot data
 # ---------
 
@@ -60,7 +60,7 @@
 pl.title('Distributions')
 pl.tight_layout()
 
-##############################################################################
+#
 # Barycenter computation
 # ----------------------
 
@@ -90,7 +90,7 @@
 pl.title('Barycenters')
 pl.tight_layout()
 
-##############################################################################
+#
 # Barycentric interpolation
 # -------------------------
 

examples/plot_gromov.py

@@ -20,7 +20,7 @@
 import ot
 
 
-##############################################################################
+#
 # Sample two Gaussian distributions (2D and 3D)
 # ---------------------------------------------
 #
@@ -43,7 +43,7 @@
 xt = np.random.randn(n_samples, 3).dot(P) + mu_t
 
 
-##############################################################################
+#
 # Plotting the distributions
 # --------------------------
 
@@ -56,7 +56,7 @@
 pl.show()
 
 
-##############################################################################
+#
 # Compute distance kernels, normalize them and then display
 # ---------------------------------------------------------
 
@@ -74,33 +74,32 @@
 pl.imshow(C2)
 pl.show()
 
-##############################################################################
+#
 # Compute Gromov-Wasserstein plans and distance
 # ---------------------------------------------
 
-#%%
 p = ot.unif(n_samples)
 q = ot.unif(n_samples)
 
-gw0,log0 = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss', verbose=True,log=True)
+gw0, log0 = ot.gromov.gromov_wasserstein(
+    C1, C2, p, q, 'square_loss', verbose=True, log=True)
 
-gw,log= ot.gromov.entropic_gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4,log=True,verbose=True)
+gw, log = ot.gromov.entropic_gromov_wasserstein(
+    C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)
 
 
 print('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))
 print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))
 
 
-pl.figure(1,(10,5))
+pl.figure(1, (10, 5))
 
-pl.subplot(1,2,1)
+pl.subplot(1, 2, 1)
 pl.imshow(gw0, cmap='jet')
-pl.colorbar()
 pl.title('Gromov Wasserstein')
 
-pl.subplot(1,2,2)
-pl.imshow(gw0, cmap='jet')
-pl.colorbar()
+pl.subplot(1, 2, 2)
+pl.imshow(gw, cmap='jet')
 pl.title('Entropic Gromov Wasserstein')
 
 pl.show()

ot/__init__.py

@@ -16,21 +16,19 @@
 from . import optim
 from . import utils
 from . import datasets
-from . import plot
 from . import da
 from . import gromov
 
 # OT functions
 from .lp import emd, emd2
 from .bregman import sinkhorn, sinkhorn2, barycenter
 from .da import sinkhorn_lpl1_mm
-from .gromov import gromov_wasserstein, gromov_wasserstein2
 
 # utils functions
 from .utils import dist, unif, tic, toc, toq
 
 __version__ = "0.4.0"
 
 __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets',
-           'bregman', 'lp', 'plot', 'tic', 'toc', 'toq',
+           'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov',
            'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim']

ot/da.py

@@ -933,6 +933,7 @@ def distribution_estimation_uniform(X):
 
 
 class BaseTransport(BaseEstimator):
+
     """Base class for OTDA objects
 
     Notes
@@ -1180,6 +1181,7 @@ class label
 
 
 class SinkhornTransport(BaseTransport):
+
     """Domain Adapatation OT method based on Sinkhorn Algorithm
 
     Parameters
@@ -1289,6 +1291,7 @@ class label
 
 
 class EMDTransport(BaseTransport):
+
     """Domain Adapatation OT method based on Earth Mover's Distance
 
     Parameters
@@ -1377,6 +1380,7 @@ class label
 
 
 class SinkhornLpl1Transport(BaseTransport):
+
     """Domain Adapatation OT method based on sinkhorn algorithm +
     LpL1 class regularization.
 
@@ -1486,6 +1490,7 @@ class label
 
 
 class SinkhornL1l2Transport(BaseTransport):
+
     """Domain Adapatation OT method based on sinkhorn algorithm +
     l1l2 class regularization.
 
@@ -1608,6 +1613,7 @@ class label
 
 
 class MappingTransport(BaseEstimator):
+
     """MappingTransport: DA methods that aims at jointly estimating a optimal
     transport coupling and the associated mapping
 

ot/gpu/__init__.py

@@ -5,7 +5,7 @@
 from .bregman import sinkhorn
 
 # Author: Remi Flamary <remi.flamary@unice.fr>
-#         Leo Gautheron <https://github.com/aje> 
+#         Leo Gautheron <https://github.com/aje>
 #
 # License: MIT License
 

ot/gpu/da.py

@@ -188,6 +188,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10,
 
 
 class OTDA_GPU(OTDA):
+
     def normalizeM(self, norm):
         if norm == "median":
             self.M_GPU.divide(float(np.median(self.M_GPU.asarray())))
@@ -204,6 +205,7 @@ def normalizeM(self, norm):
 
 
 class OTDA_sinkhorn(OTDA_GPU):
+
     def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs):
         cudamat.init()
         xs = np.asarray(xs, dtype=np.float64)
@@ -228,6 +230,7 @@ def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs):
 
 
 class OTDA_lpl1(OTDA_GPU):
+
     def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None,
             **kwargs):
         cudamat.init()