Various fixes and cleanup of bokeh BarsPlot

holoviews/plotting/bokeh/chart.py

@@ -726,10 +726,12 @@ def get_stack(self, xvals, yvals, baselines, sign='positive'):
             if sign == 'positive':
                 bottom = baseline
                 top = bottom+y
+                baseline = top
             else:
                 top = baseline
                 bottom = top+y
-            baselines[x][sign] = top
+                baseline = bottom
+            baselines[x][sign] = baseline
             bottoms.append(bottom)
             tops.append(top)
         return bottoms, tops
@@ -739,6 +741,31 @@ def _glyph_properties(self, *args):
         del props['width']
         return props
 
+
+    def _add_color_data(self, ds, ranges, style, cdim, data, mapping, factors, colors):
+        # Get colormapper
+        cdata, cmapping = self._get_color_data(ds, ranges, dict(style),
+                                               factors=factors, colors=colors)
+        if 'color' not in cmapping:
+            return
+
+        # Enable legend if colormapper is categorical
+        cmapper = cmapping['color']['transform']
+        if ('color' in cmapping and self.show_legend and
+            isinstance(cmapper, CategoricalColorMapper)):
+            mapping['legend'] = cdim.name
+
+        # Merge data and mappings
+        mapping.update(cmapping)
+        for k, cd in cdata.items():
+            if self.color_index is None and cd.dtype.kind in 'if':
+                cd = categorize_array(cd, color_dim)
+            if k not in data or len(data[k]) != [len(data[key]) for key in data if key != k][0]:
+                data[k].append(cd)
+            else:
+                data[k][-1] = cd
+
+
     def get_data(self, element, ranges, style):
         # Get x, y, group, stack and color dimensions
         grouping = None
@@ -817,10 +844,8 @@ def get_data(self, element, ranges, style):
             k = k[0] if isinstance(k, tuple) else k
             if group_dim:
                 gval = k if isinstance(k, basestring) else group_dim.pprint_value(k)
-
             # Apply stacking or grouping
             if grouping == 'stacked':
-                ds = ds.sort(ds.vdims[0])
                 for sign, slc in [('negative', (None, 0)), ('positive', (0, None))]:
                     slc_ds = ds.select(**{ds.vdims[0].name: slc})
                     xs = slc_ds.dimension_values(xdim)
@@ -829,7 +854,10 @@ def get_data(self, element, ranges, style):
                     data['bottom'].append(bs)
                     data['top'].append(ts)
                     data[xdim.name].append(xs)
+                    data[stack_dim.name].append(slc_ds.dimension_values(stack_dim))
                     if hover: data[ydim.name].append(ys)
+                    self._add_color_data(slc_ds, ranges, style, cdim, data,
+                                         mapping, factors, colors)
             elif grouping == 'grouped':
                 xs = ds.dimension_values(xdim)
                 ys = ds.dimension_values(ydim)
@@ -838,44 +866,20 @@ def get_data(self, element, ranges, style):
                 data['xoffsets'].append(xoffsets)
                 data[ydim.name].append(ys)
                 if hover: data[xdim.name].append(xs)
+                if group_dim not in ds.dimensions():
+                    ds = ds.add_dimension(group_dim.name, ds.ndims, gval)
+                data[group_dim.name].append(ds.dimension_values(group_dim))
             else:
                 data[xdim.name].append(ds.dimension_values(xdim))
                 data[ydim.name].append(ds.dimension_values(ydim))
 
-            # Add group dimension to data
-            if group_dim and group_dim not in ds.dimensions():
-                ds = ds.add_dimension(group_dim.name, ds.ndims, gval)
-
             if hover:
-                # Fill in missing hover data if dimension other than group_dim is colormapped
-                if group_dim and cdim != group_dim:
-                    data[group_dim.name].append(ds.dimension_values(group_dim))
-                # Add other hover info
                 for vd in ds.vdims[1:]:
                     data[vd.name].append(ds.dimension_values(vd))
 
-            # Get colormapper
-            cdata, cmapping = self._get_color_data(ds, ranges, dict(style),
-                                                   factors=factors, colors=colors)
-
-            # Skip if no colormapper applied
-            if 'color' not in cmapping:
-                continue
-
-            # Enable legend if colormapper is categorical
-            cmapper = cmapping['color']['transform']
-            if ('color' in cmapping and self.show_legend and
-                isinstance(cmapper, CategoricalColorMapper)):
-                mapping['legend'] = cdim.name
-
-            # Merge data and mappings
-            mapping.update(cmapping)
-            for k, cd in cdata.items():
-                # If values have already been added, skip
-                if no_cidx and cd.dtype.kind in 'if':
-                    cd = categorize_array(cd, color_dim)
-                if not len(data[k]) == i+1:
-                    data[k].append(cd)
+            if not grouping == 'stacked':
+                self._add_color_data(ds, ranges, style, cdim, data,
+                                     mapping, factors, colors)
 
         # Concatenate the stacks or groups
         sanitized_data = {}

tests/testplotinstantiation.py

@@ -985,7 +985,7 @@ def test_bars_grouped_categories(self):
         source = plot.handles['source']
         self.assertEqual([tuple(x) for x in source.data['xoffsets']],
                          [('A', '0'), ('B', '0'), ('A', '1')])
-        self.assertEqual(list(source.data['Category']), ['0', '0', '1'])
+        self.assertEqual(list(source.data['Category']), [0, 0, 1])
         self.assertEqual(source.data['Value'], np.array([1, 2, -1]))
         x_range = plot.handles['x_range']
         self.assertEqual(x_range.factors, [('A', '0'), ('A', '1'), ('B', '0'), ('B', '1')])
@@ -995,7 +995,7 @@ def test_bars_positive_negative_mixed(self):
                     kdims=['Index', 'Category'], vdims=['Value'])
         plot = bokeh_renderer.get_plot(bars.opts(plot=dict(stack_index=1)))
         source = plot.handles['source']
-        self.assertEqual(list(source.data['Category']), ['1', '0', '0'])
+        self.assertEqual(list(source.data['Category']), [1, 0, 0])
         self.assertEqual(list(source.data['Index']), ['A', 'A', 'B'])
         self.assertEqual(source.data['top'], np.array([0, 1, 2]))
         self.assertEqual(source.data['bottom'], np.array([-1, 0, 0]))