diff --git a/GPy/core/gp.py b/GPy/core/gp.py
index 89313ae2..898c7b58 100644
--- a/GPy/core/gp.py
+++ b/GPy/core/gp.py
@@ -89,7 +89,6 @@ class GP(Model):
             assert mean_function.output_dim == self.output_dim
             self.link_parameter(mean_function)
 
-
         #find a sensible inference method
         logger.info("initializing inference method")
         if inference_method is None:
diff --git a/GPy/core/parameterization/variational.py b/GPy/core/parameterization/variational.py
index 7a58ac5e..f33e7715 100644
--- a/GPy/core/parameterization/variational.py
+++ b/GPy/core/parameterization/variational.py
@@ -176,11 +176,11 @@ class SpikeAndSlabPosterior(VariationalPosterior):
             self.mean.fix(warning=False)
             self.variance.fix(warning=False)
         if group_spike:
-            self.gamma_group = Param("binary_prob_group",binary_prob.mean(axis=0),Logistic(1e-6,1.-1e-6))
+            self.gamma_group = Param("binary_prob_group",binary_prob.mean(axis=0),Logistic(1e-10,1.-1e-10))
             self.gamma = Param("binary_prob",binary_prob, __fixed__)
             self.link_parameters(self.gamma_group,self.gamma)
         else:
-            self.gamma = Param("binary_prob",binary_prob,Logistic(1e-6,1.-1e-6))
+            self.gamma = Param("binary_prob",binary_prob,Logistic(1e-10,1.-1e-10))
             self.link_parameter(self.gamma)
             
     def propogate_val(self):
diff --git a/GPy/plotting/matplot_dep/visualize.py b/GPy/plotting/matplot_dep/visualize.py
index b57b7dfc..6903c2d2 100644
--- a/GPy/plotting/matplot_dep/visualize.py
+++ b/GPy/plotting/matplot_dep/visualize.py
@@ -408,12 +408,13 @@ class mocap_data_show_vpython(vpython_show):
 class mocap_data_show(matplotlib_show):
     """Base class for visualizing motion capture data."""
 
-    def __init__(self, vals, axes=None, connect=None):
+    def __init__(self, vals, axes=None, connect=None, color='b'):
         if axes==None:
             fig = plt.figure()
             axes = fig.add_subplot(111, projection='3d', aspect='equal')
         matplotlib_show.__init__(self, vals, axes)
 
+        self.color = color
         self.connect = connect
         self.process_values()
         self.initialize_axes()
@@ -423,7 +424,7 @@ class mocap_data_show(matplotlib_show):
         self.axes.figure.canvas.draw()
 
     def draw_vertices(self):
-        self.points_handle = self.axes.scatter(self.vals[:, 0], self.vals[:, 1], self.vals[:, 2])
+        self.points_handle = self.axes.scatter(self.vals[:, 0], self.vals[:, 1], self.vals[:, 2], color=self.color)
 
     def draw_edges(self):
         self.line_handle = []
@@ -442,7 +443,7 @@ class mocap_data_show(matplotlib_show):
                 z.append(self.vals[i, 2])
                 z.append(self.vals[j, 2])
                 z.append(np.NaN)
-            self.line_handle = self.axes.plot(np.array(x), np.array(y), np.array(z), 'b-')
+            self.line_handle = self.axes.plot(np.array(x), np.array(y), np.array(z), '-', color=self.color)
 
     def modify(self, vals):
         self.vals = vals.copy()
@@ -494,7 +495,7 @@ class stick_show(mocap_data_show):
 
 class skeleton_show(mocap_data_show):
     """data_show class for visualizing motion capture data encoded as a skeleton with angles."""
-    def __init__(self, vals, skel, axes=None, padding=0):
+    def __init__(self, vals, skel, axes=None, padding=0, color='b'):
         """data_show class for visualizing motion capture data encoded as a skeleton with angles.
         :param vals: set of modeled angles to use for printing in the axis when it's first created.
         :type vals: np.array
@@ -506,7 +507,7 @@ class skeleton_show(mocap_data_show):
         self.skel = skel
         self.padding = padding
         connect = skel.connection_matrix()
-        mocap_data_show.__init__(self, vals, axes=axes, connect=connect)
+        mocap_data_show.__init__(self, vals, axes=axes, connect=connect, color=color)
     def process_values(self):
         """Takes a set of angles and converts them to the x,y,z coordinates in the internal prepresentation of the class, ready for plotting.