Merge 835510c into be5c269

examples/user_guide/15-Large_Data.ipynb

@@ -194,7 +194,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](http://assets.holoviews.org/gifs/guides/user_guide/Large_Data/rasterize_color_range.gif)"
+    "<img src=\"http://assets.holoviews.org/gifs/guides/user_guide/Large_Data/rasterize_color_range.gif\"></img>"
    ]
   },
   {
@@ -363,7 +363,13 @@
     "\n",
     "# Hover info\n",
     "\n",
-    "As you can see in the examples above, converting the data to an image using Datashader makes it feasible to work with even very large datasets interactively.  One unfortunate side effect is that the original datapoints and line segments can no longer be used to support \"tooltips\" or \"hover\" information directly for RGB images generated with `datashade`; that data simply is not present at the browser level, and so the browser cannot unambiguously report information about any specific datapoint. Luckily, you can still provide hover information that reports properties of a subset of the data in a separate layer (as above), or you can provide information for a spatial region of the plot rather than for specific datapoints.  For instance, in some small rectangle you can provide statistics such as the mean, count, standard deviation, etc:"
+    "As you can see in the examples above, converting the data to an image using Datashader makes it feasible to work with even very large datasets interactively.  One unfortunate side effect is that the original datapoints and line segments can no longer be used to support \"tooltips\" or \"hover\" information directly for RGB images generated with `datashade`; that data simply is not present at the browser level, and so the browser cannot unambiguously report information about any specific datapoint. \n",
+    "\n",
+    "Additionally once the ``shade`` operation is applied any detail about the underlying data is obscured. In order to get hover information there are therefore two options:\n",
+    "\n",
+    "1) Use the ``rasterize`` operation without `shade`\n",
+    "\n",
+    "2) Overlay a separate layer as a ``QuadMesh`` or ``Image`` containing the hover information"
    ]
   },
   {
@@ -374,20 +380,24 @@
    "source": [
     "from holoviews.streams import RangeXY\n",
     "\n",
+    "rasterized = rasterize(points, width=400, height=400)\n",
+    "\n",
     "fixed_hover = (datashade(points, width=400, height=400) *  \n",
     "               hv.QuadMesh(rasterize(points, width=10, height=10, dynamic=False)))\n",
     "\n",
     "dynamic_hover = (datashade(points, width=400, height=400) * \n",
-    "                 hv.util.Dynamic(rasterize(points, width=10, height=10, streams=[RangeXY]), operation=hv.QuadMesh))\n",
+    "                 rasterize(points, width=10, height=10, streams=[RangeXY]).apply(hv.QuadMesh))\n",
     "\n",
-    "(fixed_hover + dynamic_hover).opts(opts.QuadMesh(tools=['hover'], alpha=0, hover_alpha=0.2))"
+    "(rasterized + fixed_hover + dynamic_hover).opts(\n",
+    "    opts.QuadMesh(tools=['hover'], alpha=0, hover_alpha=0.2), \n",
+    "    opts.Image(tools=['hover']))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the above examples, the plot on the left provides hover information at a fixed spatial scale, while the one on the right reports on an area that scales with the zoom level so that arbitrarily small regions of data space can be examined, which is generally more useful (but requires a live Python server). Note that you can activate the hover tool for `Image` elements output by the `rasterize` operation."
+    "In the above examples, the plot on the left provides hover information directly on the aggregated ``Image``. The middle plot displays hover information as a ``QuadMesh`` at a fixed spatial scale, while the one on the right reports on an area that scales with the zoom level so that arbitrarily small regions of data space can be examined, which is generally more useful (but requires a live Python server)."
    ]
   },
   {
@@ -444,7 +454,7 @@
     "opts.defaults(\n",
     "    opts.Image(aspect=1, axiswise=True, xaxis='bare', yaxis='bare'),\n",
     "    opts.RGB(aspect=1, axiswise=True, xaxis='bare', yaxis='bare'),\n",
-    "    opts.Layout(vspace=0.1, hspace=0.1, sublabel_format=\"\"))\n",
+    "    opts.Layout(vspace=0.1, hspace=0.1, sublabel_format=\"\", fig_size=80))\n",
     "\n",
     "np.random.seed(12)\n",
     "N=100\n",
@@ -464,6 +474,9 @@
     "\n",
     "shadeable  = [elemtype(pts) for elemtype in [hv.Curve, hv.Scatter, hv.Points]]\n",
     "shadeable += [hv.Path([pts])]\n",
+    "shadeable += [hv.Spikes(np.random.randn(10000))]\n",
+    "shadeable += [hv.Area(np.random.randn(10000).cumsum())]\n",
+    "shadeable += [hv.Spread((np.arange(10000), np.random.randn(10000).cumsum(), np.random.randn(10000)*10))]\n",
     "shadeable += [hv.Image((x,y,z)), hv.QuadMesh((x,y,z))]\n",
     "shadeable += [hv.Graph(((np.zeros(N), np.arange(N)),))]\n",
     "shadeable += [tri.edgepaths]\n",
@@ -491,8 +504,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "rgb_opts = opts.RGB(aspect=1, axiswise=True, xaxis='bare', yaxis='bare')\n",
-    "hv.Layout([e.relabel(e.__class__.name).opts(rgb_opts) for e in shadeable + rasterizable]).cols(6)"
+    "el_opts = dict(aspect=1, axiswise=True, xaxis='bare', yaxis='bare')\n",
+    "hv.Layout([e.relabel(e.__class__.name).opts(**el_opts) for e in shadeable + rasterizable]).cols(6)"
    ]
   },
   {