Implemented and documented Graph edge_color_index

examples/reference/elements/bokeh/Graph.ipynb

@@ -90,7 +90,7 @@
    "source": [
     "#### Additional features\n",
     "\n",
-    "Next we will extend this example by supplying explicit edges:"
+    "Next we will extend this example by supplying explicit edges, node information and edge weights. By constructing the ``Nodes`` explicitly we can declare an additional value dimensions, which are revealed when hovering and/or can be mapped to the color by specifying the ``color_index``. We can also associate additional information with each edge by supplying a value dimension to the ``Graph`` itself, which we can map to a color using the ``edge_color_index``."
    ]
   },
   {
@@ -100,8 +100,10 @@
    "outputs": [],
    "source": [
     "# Node info\n",
+    "np.random.seed(7)\n",
     "x, y = simple_graph.nodes.array([0, 1]).T\n",
     "node_labels = ['Output']+['Input']*(N-1)\n",
+    "edge_weights = np.random.rand(8)\n",
     "\n",
     "# Compute edge paths\n",
     "def bezier(start, end, control, steps=np.linspace(0, 1, 100)):\n",
@@ -114,10 +116,10 @@
     "\n",
     "# Declare Graph\n",
     "nodes = hv.Nodes((x, y, node_indices, node_labels), vdims='Type')\n",
-    "graph = hv.Graph(((source, target), nodes, paths))\n",
+    "graph = hv.Graph(((source, target, edge_weights), nodes, paths), vdims='Weight')\n",
     "\n",
-    "graph.redim.range(**padding).opts(plot=dict(color_index='Type'),\n",
-    "                                  style=dict(cmap=['blue', 'yellow']))"
+    "graph.redim.range(**padding).opts(plot=dict(color_index='Type', edge_color_index='Weight'),\n",
+    "                                  style=dict(cmap=['blue', 'red'], edge_cmap='viridis'))"
    ]
   }
  ],

examples/reference/elements/matplotlib/Graph.ipynb

@@ -90,7 +90,8 @@
    "source": [
     "#### Additional features\n",
     "\n",
-    "Next we will extend this example by supplying explicit edges:"
+    "\n",
+    "Next we will extend this example by supplying explicit edges, node information and edge weights. By constructing the ``Nodes`` explicitly we can declare an additional value dimensions, which are revealed when hovering and/or can be mapped to the color by specifying the ``color_index``. We can also associate additional information with each edge by supplying a value dimension to the ``Graph`` itself, which we can map to a color using the ``edge_color_index``."
    ]
   },
   {
@@ -99,11 +100,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from matplotlib.colors import ListedColormap\n",
-    "\n",
     "# Node info\n",
+    "np.random.seed(7)\n",
     "x, y = simple_graph.nodes.array([0, 1]).T\n",
     "node_labels = ['Output']+['Input']*(N-1)\n",
+    "edge_weights = np.random.rand(8)\n",
     "\n",
     "# Compute edge paths\n",
     "def bezier(start, end, control, steps=np.linspace(0, 1, 100)):\n",
@@ -116,10 +117,10 @@
     "\n",
     "# Declare Graph\n",
     "nodes = hv.Nodes((x, y, node_indices, node_labels), vdims='Type')\n",
-    "graph = hv.Graph(((source, target), nodes, paths))\n",
+    "graph = hv.Graph(((source, target, edge_weights), nodes, paths), vdims='Weight')\n",
     "\n",
-    "graph.redim.range(**padding).opts(plot=dict(color_index='Type'),\n",
-    "                                  style=dict(cmap=ListedColormap(['blue', 'yellow'])))"
+    "graph.redim.range(**padding).opts(plot=dict(color_index='Type', edge_color_index='Weight'),\n",
+    "                                  style=dict(cmap=['blue', 'red'], edge_cmap='viridis'))"
    ]
   }
  ],

examples/user_guide/Network_Graphs.ipynb

@@ -43,8 +43,8 @@
    "source": [
     "# Declare abstract edges\n",
     "N = 8\n",
-    "node_indices = np.arange(N)\n",
-    "source = np.zeros(N)\n",
+    "node_indices = np.arange(N, dtype=np.int32)\n",
+    "source = np.zeros(N, dtype=np.int32)\n",
     "target = node_indices\n",
     "\n",
     "padding = dict(x=(-1.2, 1.2), y=(-1.2, 1.2))\n",
@@ -148,7 +148,7 @@
    "source": [
     "#### Additional information\n",
     "\n",
-    "We can also associate additional information with the nodes and edges of a graph. By constructing the ``Nodes`` explicitly we can declare an additional value dimensions, which are revealed when hovering and/or can be mapped to the color by specifying the ``color_index``. We can also associate additional information with each edge by supplying a value dimension to the ``Graph`` itself."
+    "We can also associate additional information with the nodes and edges of a graph. By constructing the ``Nodes`` explicitly we can declare an additional value dimensions, which are revealed when hovering and/or can be mapped to the color by specifying the ``color_index``. We can also associate additional information with each edge by supplying a value dimension to the ``Graph`` itself, which we can map to a color using the ``edge_color_index``."
    ]
   },
   {
@@ -157,12 +157,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts Graph [color_index='Type'] (cmap='Set1')\n",
+    "%%opts Graph [color_index='Type' edge_color_index='Weight'] (cmap='Set1' edge_cmap='viridis')\n",
     "node_labels = ['Output']+['Input']*(N-1)\n",
-    "edge_labels = list('ABCDEFGH')\n",
+    "np.random.seed(7)\n",
+    "edge_labels = np.random.rand(8)\n",
     "\n",
     "nodes = hv.Nodes((x, y, node_indices, node_labels), vdims='Type')\n",
-    "graph = hv.Graph(((source, target, edge_labels), nodes, paths), vdims='Label').redim.range(**padding)\n",
+    "graph = hv.Graph(((source, target, edge_labels), nodes, paths), vdims='Weight').redim.range(**padding)\n",
     "graph + graph.opts(plot=dict(inspection_policy='edges'))"
    ]
   },

holoviews/core/util.py

@@ -1569,3 +1569,12 @@ def dt_to_int(value, time_unit='us'):
     except:
         # Handle python2
         return (time.mktime(value.timetuple()) + value.microsecond / 1e6) * tscale
+
+
+def search_indices(values, source):
+    """
+    Given a set of values returns the indices of each of those values
+    in the source array.
+    """
+    orig_indices = source.argsort()
+    return orig_indices[np.searchsorted(source[orig_indices], values)]

holoviews/plotting/bokeh/element.py

@@ -5,7 +5,6 @@
 import numpy as np
 import bokeh
 import bokeh.plotting
-from bokeh import palettes
 from bokeh.core.properties import value
 from bokeh.models import (HoverTool, Renderer, Range1d, DataRange1d, Title,
                           FactorRange, FuncTickFormatter, Tool, Legend)
@@ -20,7 +19,7 @@
 from bokeh.plotting.helpers import _known_tools as known_tools
 
 from ...core import DynamicMap, CompositeOverlay, Element, Dimension
-from ...core.options import abbreviated_exception, SkipRendering, Cycle
+from ...core.options import abbreviated_exception, SkipRendering
 from ...core import util
 from ...streams import Stream, Buffer
 from ..plot import GenericElementPlot, GenericOverlayPlot
@@ -1102,7 +1101,7 @@ def _draw_colorbar(self, plot, color_mapper):
 
 
     def _get_colormapper(self, dim, element, ranges, style, factors=None, colors=None,
-                         cycle=None, name='color_mapper'):
+                         name='color_mapper'):
         # The initial colormapper instance is cached the first time
         # and then only updated
         if dim is None and colors is None:
@@ -1124,7 +1123,7 @@ def _get_colormapper(self, dim, element, ranges, style, factors=None, colors=Non
         else:
             low, high = None, None
 
-        cmap = colors or cycle or style.pop('cmap', 'viridis')
+        cmap = colors or style.pop('cmap', 'viridis')
         palette = process_cmap(cmap, ncolors)
         nan_colors = {k: rgba_tuple(v) for k, v in self.clipping_colors.items()}
         colormapper, opts = self._get_cmapper_opts(low, high, factors, nan_colors)
@@ -1145,7 +1144,7 @@ def _get_colormapper(self, dim, element, ranges, style, factors=None, colors=Non
 
 
     def _get_color_data(self, element, ranges, style, name='color', factors=None, colors=None,
-                        cycle=None, int_categories=False):
+                        int_categories=False):
         data, mapping = {}, {}
         cdim = element.get_dimension(self.color_index)
         if not cdim:
@@ -1162,7 +1161,7 @@ def _get_color_data(self, element, ranges, style, name='color', factors=None, co
             factors = [str(f) for f in factors]
 
         mapper = self._get_colormapper(cdim, element, ranges, style,
-                                       factors, colors, cycle)
+                                       factors, colors)
         data[field] = cdata
         if factors is not None:
             mapping['legend'] = {'field': field}