Merge 6c567a6 into 17ce0cc

examples/user_guide/Geographic_Data.ipynb

@@ -203,7 +203,7 @@
    "source": [
     "Note that when displaying raster data in a projection other than the one in which the data is stored, it is more accurate to render it as a ``quadmesh`` rather than an ``image``. As you can see above, a QuadMesh will project each original bin or pixel into the correct non-rectangular shape determined by the projection, accurately showing the geographic extent covered by each sample. An Image, on the other hand, will always be rectangularly aligned in the 2D plane, which requires warping and resampling the data in a way that allows efficient display but loses accuracy at the pixel level. Unfortunately, rendering a large QuadMesh using Bokeh can be very slow, but there are two useful alternatives for datasets too large to be practical as native QuadMeshes.\n",
     "\n",
-    "The first is using the ``datashade`` or ``rasterize`` options to regrid the data before rendering it, i.e., rendering the data on the backend and then sending a more efficient image-based representation to the browser. One thing to note when using these operations is that it may be necessary to project the data **before** rasterizing it, e.g. to address wrapping issues. To do this provide ``project=True``, which will project the data before it is rasterized (this also works for other types and even when not using these operations). Another reason why this is important when rasterizing the data is that if the the CRS of the data does not match the displayed projection, all the data will be projected every time you zoom or pan, which can be very slow. Deciding whether to ``project`` is therefore a tradeoff between projecting the raw data ahead of time or accepting the overhead on dynamic zoom and pan actions."
+    "The first is using the ``rasterize`` or ``datashade`` options to regrid the data before rendering it, i.e., rendering the data on the backend and then sending a more efficient image-based representation to the browser. One thing to note when using these operations is that it may be necessary to project the data **before** rasterizing it, e.g. to address wrapping issues. To do this provide ``project=True``, which will project the data before it is rasterized (this also works for other types and even when not using these operations). Another reason why this is important when rasterizing the data is that if the CRS of the data does not match the displayed projection, all the data will be projected every time you zoom or pan, which can be very slow. Deciding whether to ``project`` is therefore a tradeoff between projecting the raw data ahead of time or accepting the overhead on dynamic zoom and pan actions."
    ]
   },
   {

examples/user_guide/Gridded_Data.ipynb

@@ -261,11 +261,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Datashading\n",
+    "## Rasterizing\n",
     "\n",
-    "If you are plotting a large amount of data at once, you can consider using the hvPlot interface to [Datashader](http://datashader.org), which can be enabled simply by setting `datashade=True`.\n",
+    "If you are plotting a large amount of data at once, you can consider using the hvPlot interface to [Datashader](http://datashader.org), which can be enabled simply by setting `rasterize=True`.\n",
     "\n",
-    "Note that be declaring that the data should not be grouped by another coordinate variable, by setting `groupby=[]`, we can plot all the datapoints, showing us the spread of air temperatures in the dataset:"
+    "Note that by declaring that the data should not be grouped by another coordinate variable, i.e. by setting `groupby=[]`, we can plot all the datapoints, showing us the spread of air temperatures in the dataset:"
    ]
   },
   {
@@ -274,7 +274,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "air.hvplot.scatter('time', groupby=[], datashade=True) *\\\n",
+    "air.hvplot.scatter('time', groupby=[], rasterize=True) *\\\n",
     "air.mean(['lat', 'lon']).hvplot.line('time', color='indianred')"
    ]
   },

examples/user_guide/Pandas_API.ipynb

@@ -537,9 +537,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plot_1 = df.plot.scatter(x='a', y='b', color='DarkBlue', label='Group 1')\n",
@@ -1013,9 +1011,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "(mean_std.plot.bar(y='mean', alpha=0.7) * \\\n",
@@ -1058,27 +1054,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Doesn't currently work as expected. Work-around:\n",
-    "\n",
-    "from holoviews.plotting.util import process_cmap\n",
-    "cmap = process_cmap('cubehelix', ncolors=10, categorical=True)\n",
-    "# cmap = 'cubehelix'\n",
-    "\n",
     "df = pd.DataFrame(np.random.randn(1000, 10), index=ts.index)\n",
     "df = df.cumsum()\n",
-    "df.plot(colormap=cmap)"
+    "df.plot(colormap='cubehelix')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Colormaps can also be used other plot types, like bar charts:"
+    "Colormaps can also be used with other plot types, like bar charts:"
    ]
   },
   {
@@ -1102,9 +1090,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from bokeh.sampledata import iris\n",

hvplot/converter.py

@@ -185,14 +185,17 @@ class HoloViewsConverter(object):
         (warning: for large groupby operations embedded data can become
         very large if dynamic=False)
     datashade (default=False):
-        Whether to apply rasterization and shading using datashader
-        library returning an RGB object
+        Whether to apply rasterization and shading (colormapping) using
+        the Datashader library, returning an RGB object instead of 
+        individual points
     dynspread (default=False):
-        Allows plots generated with datashade=True or rasterize=True 
-        to increase the point size to make sparse regions more visible
+        For plots generated with datashade=True or rasterize=True, 
+        automatically increase the point size when the data is sparse
+        so that individual points become more visible
     rasterize (default=False):
-        Whether to apply rasterization using the datashader library
-        returning an aggregated Image
+        Whether to apply rasterization using the Datashader library,
+        returning an aggregated Image (to be colormapped by the 
+        plotting backend) instead of individual points
     x_sampling/y_sampling (default=None):
         Specifies the smallest allowed sampling interval along the x/y axis.
 

hvplot/tests/testoperations.py

@@ -19,7 +19,7 @@ def setUp(self):
         except:
             raise SkipTest('Datashader not available')
         if sys.maxsize < 2**32:
-            raise SkipTest('Datashader does not work on 32-bit systems')
+            raise SkipTest('Datashader does not support 32-bit systems')
         import hvplot.pandas # noqa
         self.df = pd.DataFrame([[1, 2, 'A', 0.1], [3, 4, 'B', 0.2], [5, 6, 'C', 0.3]],
                                columns=['x', 'y', 'category', 'number'])
@@ -138,7 +138,7 @@ def setUp(self):
         except:
             raise SkipTest('xarray or datashader not available')
         if sys.maxsize < 2**32:
-            raise SkipTest('Datashader does not work on 32-bit systems')
+            raise SkipTest('Datashader does not support 32-bit systems')
         import hvplot.xarray  # noqa
         data = np.arange(0, 60).reshape(6, 10)
         x = np.arange(10)