diff --git a/GPy/plotting/matplot_dep/variational_plots.py b/GPy/plotting/matplot_dep/variational_plots.py
index 3f20efeb..17481a80 100644
--- a/GPy/plotting/matplot_dep/variational_plots.py
+++ b/GPy/plotting/matplot_dep/variational_plots.py
@@ -1,4 +1,6 @@
-from matplotlib import pyplot as pb, numpy as np
+from matplotlib import pyplot as pb
+import numpy as np
+
 
 def plot(parameterized, fignum=None, ax=None, colors=None, figsize=(12, 6)):
     """
@@ -17,6 +19,7 @@ def plot(parameterized, fignum=None, ax=None, colors=None, figsize=(12, 6)):
     if colors is None:
         from ..Tango import mediumList
         from itertools import cycle
+
         colors = cycle(mediumList)
         pb.clf()
     else:
@@ -33,21 +36,30 @@ def plot(parameterized, fignum=None, ax=None, colors=None, figsize=(12, 6)):
             a = ax[i]
         else:
             raise ValueError("Need one ax per latent dimension input_dim")
-        bg_lines.append(a.plot(means, c='k', alpha=.3))
-        lines.extend(a.plot(x, means.T[i], c=next(colors), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
-        fills.append(a.fill_between(x,
-                        means.T[i] - 2 * np.sqrt(variances.T[i]),
-                        means.T[i] + 2 * np.sqrt(variances.T[i]),
-                        facecolor=lines[-1].get_color(),
-                        alpha=.3))
-        a.legend(borderaxespad=0.)
+        bg_lines.append(a.plot(means, c="k", alpha=0.3))
+        lines.extend(
+            a.plot(
+                x, means.T[i], c=next(colors), label=r"$\mathbf{{X_{{{}}}}}$".format(i)
+            )
+        )
+        fills.append(
+            a.fill_between(
+                x,
+                means.T[i] - 2 * np.sqrt(variances.T[i]),
+                means.T[i] + 2 * np.sqrt(variances.T[i]),
+                facecolor=lines[-1].get_color(),
+                alpha=0.3,
+            )
+        )
+        a.legend(borderaxespad=0.0)
         a.set_xlim(x.min(), x.max())
         if i < means.shape[1] - 1:
-            a.set_xticklabels('')
+            a.set_xticklabels("")
     pb.draw()
-    a.figure.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
+    a.figure.tight_layout(h_pad=0.01)  # , rect=(0, 0, 1, .95))
     return dict(lines=lines, fills=fills, bg_lines=bg_lines)
 
+
 def plot_SpikeSlab(parameterized, fignum=None, ax=None, colors=None, side_by_side=True):
     """
     Plot latent space X in 1D:
@@ -62,45 +74,60 @@ def plot_SpikeSlab(parameterized, fignum=None, ax=None, colors=None, side_by_sid
     """
     if ax is None:
         if side_by_side:
-            fig = pb.figure(num=fignum, figsize=(16, min(12, (2 * parameterized.mean.shape[1]))))
+            fig = pb.figure(
+                num=fignum, figsize=(16, min(12, (2 * parameterized.mean.shape[1])))
+            )
         else:
-            fig = pb.figure(num=fignum, figsize=(8, min(12, (2 * parameterized.mean.shape[1]))))
+            fig = pb.figure(
+                num=fignum, figsize=(8, min(12, (2 * parameterized.mean.shape[1])))
+            )
     if colors is None:
         from ..Tango import mediumList
         from itertools import cycle
+
         colors = cycle(mediumList)
         pb.clf()
     else:
         colors = iter(colors)
     plots = []
-    means, variances, gamma = parameterized.mean, parameterized.variance, parameterized.binary_prob
+    means, variances, gamma = (
+        parameterized.mean,
+        parameterized.variance,
+        parameterized.binary_prob,
+    )
     x = np.arange(means.shape[0])
     for i in range(means.shape[1]):
         if side_by_side:
-            sub1 = (means.shape[1],2,2*i+1)
-            sub2 = (means.shape[1],2,2*i+2)
+            sub1 = (means.shape[1], 2, 2 * i + 1)
+            sub2 = (means.shape[1], 2, 2 * i + 2)
         else:
-            sub1 = (means.shape[1]*2,1,2*i+1)
-            sub2 = (means.shape[1]*2,1,2*i+2)
+            sub1 = (means.shape[1] * 2, 1, 2 * i + 1)
+            sub2 = (means.shape[1] * 2, 1, 2 * i + 2)
 
         # mean and variance plot
         a = fig.add_subplot(*sub1)
-        a.plot(means, c='k', alpha=.3)
-        plots.extend(a.plot(x, means.T[i], c=next(colors), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
-        a.fill_between(x,
-                        means.T[i] - 2 * np.sqrt(variances.T[i]),
-                        means.T[i] + 2 * np.sqrt(variances.T[i]),
-                        facecolor=plots[-1].get_color(),
-                        alpha=.3)
-        a.legend(borderaxespad=0.)
+        a.plot(means, c="k", alpha=0.3)
+        plots.extend(
+            a.plot(
+                x, means.T[i], c=next(colors), label=r"$\mathbf{{X_{{{}}}}}$".format(i)
+            )
+        )
+        a.fill_between(
+            x,
+            means.T[i] - 2 * np.sqrt(variances.T[i]),
+            means.T[i] + 2 * np.sqrt(variances.T[i]),
+            facecolor=plots[-1].get_color(),
+            alpha=0.3,
+        )
+        a.legend(borderaxespad=0.0)
         a.set_xlim(x.min(), x.max())
         if i < means.shape[1] - 1:
-            a.set_xticklabels('')
+            a.set_xticklabels("")
         # binary prob plot
         a = fig.add_subplot(*sub2)
-        a.bar(x,gamma[:,i],bottom=0.,linewidth=1.,width=1.0,align='center')
+        a.bar(x, gamma[:, i], bottom=0.0, linewidth=1.0, width=1.0, align="center")
         a.set_xlim(x.min(), x.max())
-        a.set_ylim([0.,1.])
+        a.set_ylim([0.0, 1.0])
     pb.draw()
-    fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
+    fig.tight_layout(h_pad=0.01)  # , rect=(0, 0, 1, .95))
     return fig