pep8

1 files changed, 12 insertions, 8 deletions

diff --git a/examples/plot_otda_jcpot.py b/examples/plot_otda_jcpot.py
index 579ad2a..ce6b88f 100644
--- a/examples/plot_otda_jcpot.py
+++ b/examples/plot_otda_jcpot.py
@@ -34,15 +34,17 @@ dec2 = [0, -2]
 pt = .4
 dect = [4, 0]
 
-xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p = p1, bias = dec1)
-xs2, ys2 = make_data_classif('2gauss_prop', n+1, nz=sigma, p = p2, bias = dec2)
-xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p = pt, bias = dect)
+xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1)
+xs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2)
+xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect)
 
 all_Xr = [xs1, xs2]
 all_Yr = [ys1, ys2]
 # %%
 
 da = 1.5
+
+
 def plot_ax(dec, name):
     pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5)
     pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5)
@@ -58,21 +60,24 @@ pl.clf()
 plot_ax(dec1, 'Source 1')
 plot_ax(dec2, 'Source 2')
 plot_ax(dect, 'Target')
-pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9, label='Source 1 ({:1.2f}, {:1.2f})'.format(1-p1, p1))
-pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9, label='Source 2 ({:1.2f}, {:1.2f})'.format(1-p2, p2))
-pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9, label='Target ({:1.2f}, {:1.2f})'.format(1-pt, pt))
+pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9,
+           label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1))
+pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9,
+           label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2))
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9,
+           label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt))
 pl.title('Data')
 
 pl.legend()
 pl.axis('equal')
 pl.axis('off')
 
-
 ##############################################################################
 # Instantiate Sinkhorn transport algorithm and fit them for all source domains
 # ----------------------------------------------------------------------------
 ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean')
 
+
 def print_G(G, xs, ys, xt):
     for i in range(G.shape[0]):
         for j in range(G.shape[1]):
@@ -107,7 +112,6 @@ pl.legend()
 pl.axis('equal')
 pl.axis('off')
 
-
 ##############################################################################
 # Instantiate JCPOT adaptation algorithm and fit it
 # ----------------------------------------------------------------------------