[tests working now?]

GPy/plotting/__init__.py

@@ -43,20 +43,20 @@ if config.get('plotting', 'library') is not 'none':
     
     from ..models import GPLVM, BayesianGPLVM, bayesian_gplvm_minibatch, SSGPLVM, SSMRD
     GPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
-    GPLVM.plot_latent_scatter = gpy_plot.latent_plots.plot_latent_scatter
-    GPLVM.plot_latent_inducing = gpy_plot.latent_plots.plot_latent_inducing
+    GPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
+    GPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
     GPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
     BayesianGPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
-    BayesianGPLVM.plot_latent_scatter = gpy_plot.latent_plots.plot_latent_scatter
-    BayesianGPLVM.plot_latent_inducing = gpy_plot.latent_plots.plot_latent_inducing
+    BayesianGPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
+    BayesianGPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
     BayesianGPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
     bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_latent = gpy_plot.latent_plots.plot_latent
-    bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_latent_scatter = gpy_plot.latent_plots.plot_latent_scatter
-    bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_latent_inducing = gpy_plot.latent_plots.plot_latent_inducing
+    bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
+    bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
     bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
     SSGPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
-    SSGPLVM.plot_latent_scatter = gpy_plot.latent_plots.plot_latent_scatter
-    SSGPLVM.plot_latent_inducing = gpy_plot.latent_plots.plot_latent_inducing
+    SSGPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
+    SSGPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
     SSGPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
     
     from ..kern import Kern

GPy/plotting/gpy_plot/latent_plots.py

@@ -52,7 +52,7 @@ def _plot_latent_scatter(canvas, X, visible_dims, labels, marker, num_samples, p
     Tango.reset()
     X, labels = subsample_X(X, labels, num_samples)
     scatters = []   
-    generate_colors = 'color' not in kwargs 
+    generate_colors = 'color' not in kwargs
     for x, y, z, this_label, _, m in scatter_label_generator(labels, X, visible_dims, marker):
         update_not_existing_kwargs(kwargs, pl.defaults.latent_scatter)
         if generate_colors:
@@ -89,7 +89,7 @@ def plot_latent_scatter(self, labels=None,
         labels = np.ones(self.num_data)
         legend = False
     else:
-        legend = find_best_layout_for_subplots(len(np.unique(labels)))
+        legend = find_best_layout_for_subplots(len(np.unique(labels)))[1]
     scatters = _plot_latent_scatter(canvas, X, sig_dims, labels, marker, num_samples, projection=projection, **kwargs)
     if projection == '3d':
         return pl.show_canvas(canvas, dict(scatter=scatters), legend=legend,
@@ -126,9 +126,9 @@ def plot_latent_inducing(self,
     if 'color' not in kwargs:
         kwargs['color'] = 'white'
     canvas, kwargs = pl.get_new_canvas(projection=projection, **kwargs)
-    X, _, _ = get_x_y_var(self)
-    labels = np.ones(self.num_data)
-    scatters = _plot_latent_scatter(canvas, X, sig_dims, labels, marker, num_samples, projection=projection, **kwargs)
+    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)
     if projection == '3d':
         return pl.show_canvas(canvas, dict(scatter=scatters), legend=legend,
                               xlabel='latent dimension %i' % input_1, 

GPy/plotting/gpy_plot/plot_util.py

@@ -138,7 +138,6 @@ def scatter_label_generator(labels, X, visible_dims, marker=None):
     for lab in labels:
         if not lab in ulabels:
             ulabels.append(lab)
-
     if marker is not None:
         marker = itertools.cycle(list(marker))    
     else:
@@ -154,19 +153,20 @@ def scatter_label_generator(labels, X, visible_dims, marker=None):
         except:
             input_1 = visible_dims
             input_2 = input_3 = None
-    
+
     for ul in ulabels:
-        if type(ul) is np.string_:
-            this_label = ul
-        elif type(ul) is np.int64:
-            this_label = 'class %i' % ul
-        else:
+        from numbers import Number
+        if isinstance(ul, str):
             try:
                 this_label = unicode(ul)
             except NameError:
                 #python3
                 this_label = ul
-                
+        elif isinstance(ul, Number):
+            this_label = 'class {!s}'.format(ul)
+        else:
+            this_label = ul
+        
         if marker is not None:
             m = next(marker)
 

GPy/plotting/matplot_dep/plot_definitions.py

@@ -76,7 +76,13 @@ class MatplotlibPlots(AbstractPlottingLibrary):
             legend_ontop(ax, ncol=legend, fontdict=fontdict)
         if zlim is not None:
             ax.set_zlim(zlim)
-        #ax.figure.show()
+        ax.figure.canvas.draw()
+        ax.figure.show()
+        #try:
+        #    ax.figure.tight_layout()
+        #except:
+        #    # couldnt do tight layout, python 2.7 on MacOSX
+        #    pass
         ax.figure.canvas.draw()
         return plots
     

GPy/plotting/matplot_dep/util.py

@@ -35,7 +35,7 @@ def legend_ontop(ax, mode='expand', ncol=3, fontdict=None):
     from mpl_toolkits.axes_grid1 import make_axes_locatable
     handles, labels = ax.get_legend_handles_labels()
     divider = make_axes_locatable(ax)
-    cax = divider.append_axes("top", "5%", pad="1%")
+    cax = divider.append_axes("top", "5%", pad=0)
     lgd = cax.legend(handles, labels, bbox_to_anchor=(0., 0., 1., 1.), loc=3,
             ncol=ncol, mode=mode, borderaxespad=0., prop=fontdict or {})
     cax.set_axis_off()