Merge 1ac5876 into 6e5d434

doc/Tutorials/Columnar_Data.ipynb

@@ -853,7 +853,7 @@
    },
    "outputs": [],
    "source": [
-    "%%opts Scatter [scaling_factor=1.4] (color=Palette('Set3') edgecolors='k')\n",
+    "%%opts Scatter [scaling_method='width' scaling_factor=2] (color=Palette('Set3') edgecolors='k')\n",
     "gdp_unem_scatter = macro.to.scatter('Year', ['GDP Growth', 'Unemployment'])\n",
     "gdp_unem_scatter.overlay('Country')"
    ]
@@ -873,7 +873,7 @@
    },
    "outputs": [],
    "source": [
-    "%%opts NdOverlay [legend_cols=2] Scatter [size_index=1 scaling_factor=1.3] (color=Palette('Blues'))\n",
+    "%%opts NdOverlay [legend_cols=2] Scatter [size_index=1] (color=Palette('Blues'))\n",
     "macro.to.scatter('GDP Growth', 'Unemployment', ['Year']).overlay()"
    ]
   },

doc/Tutorials/Elements.ipynb

@@ -565,7 +565,7 @@
    "source": [
     "The ``Scatter`` object expresses a dependent relationship between *x* and *y*, making it useful for combining with other similar ``Chart`` types, while the ``Points`` object expresses the relationship of two independent keys *x* and *y* with optional ``vdims`` (zero in this case), which makes ``Points`` objects meaningful to combine with the ``Raster`` types below.\n",
     "\n",
-    "Of course, the ``vdims`` need not be empty for ``Points``; here is an example with two additional quantities for each point, as ``value_dimension``s *z* and α visualized as the color and size of the dots, respectively:"
+    "Of course, the ``vdims`` need not be empty for ``Points``; here is an example with two additional quantities for each point, as ``value_dimension``s *z* and α visualized as the color and size of the dots, respectively. The point sizes can be tweaked using the option `scaling_factor`, which determines the amount by which each point width or area is scaled, depending on the value of `scaling_method`."
    ]
   },
   {
@@ -576,7 +576,7 @@
    },
    "outputs": [],
    "source": [
-    "%%opts Points [color_index=2 size_index=3 scaling_factor=50]\n",
+    "%%opts Points [color_index=2 size_index=3 scaling_method=\"width\" scaling_factor=10]\n",
     "np.random.seed(10)\n",
     "data = np.random.rand(100,4)\n",
     "\n",

holoviews/plotting/bokeh/chart.py

@@ -13,22 +13,21 @@
 
 class PointPlot(ElementPlot):
 
-    color_index = param.Integer(default=3, doc="""
+    color_index = param.Integer(default=3, allow_None=True, doc="""
       Index of the dimension from which the color will the drawn""")
 
-    size_index = param.Integer(default=2, doc="""
+    size_index = param.Integer(default=2, allow_None=True, doc="""
       Index of the dimension from which the sizes will the drawn.""")
 
-    radius_index = param.Integer(default=None, doc="""
-      Index of the dimension from which the sizes will the drawn.""")
+    scaling_method = param.ObjectSelector(default="area",
+                                          objects=["width", "area"],
+                                          doc="""
+      Determines whether the `scaling_factor` should be applied to
+      the width or area of each point (default: "area").""")
 
     scaling_factor = param.Number(default=1, bounds=(1, None), doc="""
-      If values are supplied the area of the points is computed relative
-      to the marker size. It is then multiplied by scaling_factor to the power
-      of the ratio between the smallest point and all other points.
-      For values of 1 scaling by the values is disabled, a factor of 2
-      allows for linear scaling of the area and a factor of 4 linear
-      scaling of the point width.""")
+      Scaling factor which is applied to either the width or area
+      of each point, depending on the value of `scaling_method`.""")
 
     size_fn = param.Callable(default=np.abs, doc="""
       Function applied to size values before applying scaling,
@@ -49,7 +48,7 @@ def get_data(self, element, ranges=None, empty=False):
         data = {}
 
         cmap = style.get('palette', style.get('cmap', None))
-        if self.color_index < len(dims) and cmap:
+        if self.color_index is not None and self.color_index < len(dims) and cmap:
             map_key = 'color_' + dims[self.color_index]
             mapping['color'] = map_key
             if empty:
@@ -59,16 +58,17 @@ def get_data(self, element, ranges=None, empty=False):
                 colors = element.dimension_values(self.color_index)
                 crange = ranges.get(dims[self.color_index], None)
                 data[map_key] = map_colors(colors, crange, cmap)
-        if self.size_index < len(dims) and self.scaling_factor != 1:
+        if self.size_index is not None and self.size_index < len(dims):
             map_key = 'size_' + dims[self.size_index]
             mapping['size'] = map_key
             if empty:
                 data[map_key] = []
             else:
                 ms = style.get('size', 1)
                 sizes = element.dimension_values(self.size_index)
-                data[map_key] = compute_sizes(sizes, self.size_fn,
-                                              self.scaling_factor, ms)
+                data[map_key] = np.sqrt(compute_sizes(sizes, self.size_fn,
+                                                      self.scaling_factor,
+                                                      self.scaling_method, ms))
 
         data[dims[0]] = [] if empty else element.dimension_values(0)
         data[dims[1]] = [] if empty else element.dimension_values(1)

holoviews/plotting/mpl/chart.py

@@ -542,19 +542,21 @@ class PointPlot(ChartPlot, ColorbarPlot):
     how point magnitudes are rendered to different colors.
     """
 
-    color_index = param.Integer(default=3, doc="""
+    color_index = param.Integer(default=3, allow_None=True, doc="""
       Index of the dimension from which the color will the drawn""")
 
-    size_index = param.Integer(default=2, doc="""
+    size_index = param.Integer(default=2, allow_None=True, doc="""
       Index of the dimension from which the sizes will the drawn.""")
 
-    scaling_factor = param.Number(default=1, bounds=(1, None), doc="""
-      If values are supplied the area of the points is computed relative
-      to the marker size. It is then multiplied by scaling_factor to the power
-      of the ratio between the smallest point and all other points.
-      For values of 1 scaling by the values is disabled, a factor of 2
-      allows for linear scaling of the area and a factor of 4 linear
-      scaling of the point width.""")
+    scaling_method = param.ObjectSelector(default="area",
+                                          objects=["width", "area"],
+                                          doc="""
+      Determines whether the `scaling_factor` should be applied to
+      the width or area of each point (default: "area").""")
+
+    scaling_factor = param.Number(default=1, bounds=(0, None), doc="""
+      Scaling factor which is applied to either the width or area
+      of each point, depending on the value of `scaling_method`.""")
 
     show_grid = param.Boolean(default=True, doc="""
       Whether to draw grid lines at the tick positions.""")
@@ -584,7 +586,7 @@ def initialize_plot(self, ranges=None):
             cs = points.dimension_values(self.color_index)
 
         style = self.style[self.cyclic_index]
-        if self.size_index < ndims and self.scaling_factor > 1:
+        if self.size_index is not None and self.size_index < ndims:
             style['s'] = self._compute_size(points, style)
 
         color = style.pop('color', None)
@@ -609,18 +611,18 @@ def initialize_plot(self, ranges=None):
     def _compute_size(self, element, opts):
         sizes = element.dimension_values(self.size_index)
         ms = opts.pop('s') if 's' in opts else plt.rcParams['lines.markersize']
-        return compute_sizes(sizes, self.size_fn, self.scaling_factor, ms)
+        return compute_sizes(sizes, self.size_fn, self.scaling_factor, self.scaling_method, ms)
 
 
     def update_handles(self, axis, element, key, ranges=None):
         paths = self.handles['artist']
         paths.set_offsets(element.array(dimensions=[0, 1]))
         dims = element.dimensions(label=True)
         ndims = len(dims)
-        if self.size_index < ndims:
+        if self.size_index is not None and self.size_index < ndims:
             opts = self.style[self.cyclic_index]
             paths.set_sizes(self._compute_size(element, opts))
-        if self.color_index < ndims:
+        if self.color_index is not None and self.color_index < ndims:
             cs = element.dimension_values(self.color_index)
             val_dim = dims[self.color_index]
             paths.set_clim(ranges[val_dim])

holoviews/plotting/util.py

@@ -1,3 +1,4 @@
+import math
 import param
 
 from ..core import (HoloMap, DynamicMap, CompositeOverlay, Layout,
@@ -74,14 +75,22 @@ def undisplayable_info(obj, html=False):
             ['<b>%s</b>' % error, remedy, '<i>%s</i>' % info])))
 
 
-def compute_sizes(sizes, size_fn, scaling, base_size):
+def compute_sizes(sizes, size_fn, scaling_factor, scaling_method, base_size):
     """
     Scales point sizes according to a scaling factor,
     base size and size_fn, which will be applied before
     scaling.
     """
+    if scaling_method == 'area':
+        pass
+    elif scaling_method == 'width':
+        scaling_factor = scaling_factor**2
+    else:
+        raise ValueError(
+            'Invalid value for argument "scaling_method": "{}". '
+            'Valid values are: "width", "area".'.format(scaling_method))
     sizes = size_fn(sizes)
-    return (base_size*scaling**sizes)
+    return (base_size*scaling_factor*sizes)
 
 
 def get_sideplot_ranges(plot, element, main, ranges):