[plotting] new baseling

GPy/models/mrd.py

@@ -127,8 +127,6 @@ class MRD(BayesianGPLVMMiniBatch):
 
         self.unlink_parameter(self.likelihood)
         self.unlink_parameter(self.kern)
-        del self.kern
-        del self.likelihood
 
         self.num_data = Ylist[0].shape[0]
         if isinstance(batchsize, int):
@@ -156,7 +154,11 @@ class MRD(BayesianGPLVMMiniBatch):
             self.link_parameter(spgp, i+2)
             self.bgplvms.append(spgp)
 
-        self.posterior = None
+        b = self.bgplvms[0]
+        self.posterior = b.posterior
+        self.kern = b.kern
+        self.likelihood = b.likelihood
+
         self.logger.info("init done")
 
     def parameters_changed(self):

GPy/plotting/gpy_plot/latent_plots.py

@@ -131,7 +131,9 @@ def plot_latent_inducing(self,
 
     Z = self.Z.values
     labels = np.array(['inducing'] * Z.shape[0])
-    scatters = _plot_latent_scatter(canvas, Z, sig_dims, labels, marker, num_samples, projection=projection, **kwargs)
+    kwargs['marker'] = marker
+    update_not_existing_kwargs(kwargs, pl().defaults.inducing_2d)  # @UndefinedVariable
+    scatters = _plot_latent_scatter(canvas, Z, sig_dims, labels, num_samples=num_samples, projection=projection, **kwargs)
     return pl().add_to_canvas(canvas, dict(scatter=scatters), legend=legend)
 
 

GPy/plotting/matplot_dep/defaults.py

@@ -46,8 +46,8 @@ it gives back an empty default, when defaults are not defined.
 data_1d = dict(lw=1.5, marker='x', color='k')
 data_2d = dict(s=35, edgecolors='none', linewidth=0., cmap=cm.get_cmap('hot'), alpha=.5)
 inducing_1d = dict(lw=0, s=500, facecolors=Tango.colorsHex['darkRed'])
-inducing_2d = dict(s=14, edgecolors='k', linewidth=.4, facecolors='white', alpha=.5, marker='^')
-inducing_3d = dict(lw=.3, s=500, facecolors='white', edgecolors='k')
+inducing_2d = dict(s=17, edgecolor='k', linewidth=.4, color='white', alpha=.5, marker='^')
+inducing_3d = dict(lw=.3, s=500, color=Tango.colorsHex['darkRed'], edgecolor='k')
 xerrorbar = dict(color='k', fmt='none', elinewidth=.5, alpha=.5)
 yerrorbar = dict(color=Tango.colorsHex['darkRed'], fmt='none', elinewidth=.5, alpha=.5)
 
@@ -71,5 +71,5 @@ ard = dict(edgecolor='k', linewidth=1.2)
 latent = dict(aspect='auto', cmap='Greys', interpolation='bicubic')
 gradient = dict(aspect='auto', cmap='RdBu', interpolation='nearest', alpha=.7)
 magnification = dict(aspect='auto', cmap='Greys', interpolation='bicubic')
-latent_scatter = dict(s=40, linewidth=.2, edgecolor='k', alpha=.9)
+latent_scatter = dict(s=20, linewidth=.2, edgecolor='k', alpha=.9)
 annotation = dict(fontdict=dict(family='sans-serif', weight='light', fontsize=9), zorder=.3, alpha=.7)

GPy/plotting/matplot_dep/variational_plots.py

@@ -15,7 +15,9 @@ def plot(parameterized, fignum=None, ax=None, colors=None, figsize=(12, 6)):
     if ax is None:
         fig = pb.figure(num=fignum, figsize=figsize)
     if colors is None:
-        colors = pb.gca()._get_lines.color_cycle
+        from ..Tango import mediumList
+        from itertools import cycle
+        colors = cycle(mediumList)
         pb.clf()
     else:
         colors = iter(colors)
@@ -64,7 +66,9 @@ def plot_SpikeSlab(parameterized, fignum=None, ax=None, colors=None, side_by_sid
         else:
             fig = pb.figure(num=fignum, figsize=(8, min(12, (2 * parameterized.mean.shape[1]))))
     if colors is None:
-        colors = pb.gca()._get_lines.color_cycle
+        from ..Tango import mediumList
+        from itertools import cycle
+        colors = cycle(mediumList)
         pb.clf()
     else:
         colors = iter(colors)

GPy/testing/plotting_tests.py

@@ -110,7 +110,8 @@ def _image_comparison(baseline_images, extensions=['pdf','svg','png'], tol=11):
                         transparent=True,
                         edgecolor='none',
                         facecolor='none',
-                        bbox='tight')
+                        #bbox='tight'
+                        )
     for num, base in zip(plt.get_fignums(), baseline_images):
         for ext in extensions:
             #plt.close(num)