Website update (#599)

* Increased cell timeout for long website builds * Used full versions of logo * Emphasized InteractiveImage limitations * Made labels plot on top of data for visibility * Restored Dask visualization for OSM * Fixed typos
holoviz · May 18, 2018 · eb9218c · eb9218c
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diff --git a/doc/_static/datashader-logo.png b/doc/_static/datashader-logo.png
diff --git a/doc/_static/favicon.ico b/doc/_static/favicon.ico
diff --git a/doc/_static/logo_stacked.png b/doc/_static/logo_stacked.png
diff --git a/doc/conf.py b/doc/conf.py
@@ -15,7 +15,7 @@
 html_theme_options = {
     'logo':'datashader-logo.png',
     'favicon':'favicon.ico',
-#    'css':'datashader.css'
+#    'css':'site.css'
 }
 
 _NAV =  (
@@ -45,4 +45,4 @@
     'numpydoc',
 ]
 
-nbbuild_cell_timeout=500
+nbbuild_cell_timeout=2000
diff --git a/doc/index.rst b/doc/index.rst
@@ -1,13 +1,10 @@
-**********
-Datashader
-**********
+.. image:: _static/logo_stacked.png
+   :height: 220px
+   :align: left
 
 .. raw:: html
 
-   <div style="width: 65%; float:left">
-
-
-**Turns even the largest data into images, accurately.**
+   <p style="font-size:20px"><b>Turns even the largest data into images, accurately.</b></p>
 
 Datashader is a graphics pipeline system for creating meaningful
 representations of large datasets quickly and flexibly. Datashader
@@ -18,13 +15,18 @@ automatically without trial-and-error parameter tuning, and also makes
 it simple for data scientists to focus on particular data and
 relationships of interest in a principled way.
 
+.. raw:: html
+  :file: latest_news.html
+
 The computation-intensive steps in this process are written in Python
 but transparently compiled to machine code using `Numba`_ and flexibly
 distributed across cores and processors using `Dask`_, providing a
 highly optimized rendering pipeline that makes it practical to work
 with extremely large datasets even on standard hardware.
 
+.. _`Dask`: http://dask.pydata.org
 .. _`Numba`: http://numba.pydata.org
+.. _`PyViz.org`: http://pyviz.org
 
 To make it concrete, here's an example of what datashader code looks like:
 
@@ -50,35 +52,27 @@ And here are some sample outputs for 300 million points of data (one
 per person in the USA) from the 2010 census, each constructed using
 code like the above:
 
-.. image:: images/usa_census.jpg
+.. image:: assets/images/usa_census.jpg
 
-.. image:: images/nyc_races.jpg
+.. image:: assets/images/nyc_races.jpg
 
 
 Installation
 ------------
 
 Please follow the instructions on the `Github repo <https://github.com/bokeh/datashader/tree/master/examples>`_
-if you want to reproduce the specific examples on this website, or the ones at `PyViz.org <http://pyviz.org`_ if you want
+if you want to reproduce the specific examples on this website, or the ones at `PyViz.org <http://pyviz.org>`_ if you want
 to try out Datashader together with related plotting tools.
 
 
-.. raw:: html
-
-   </div>
-
-
-.. raw:: html
-  :file: latest_news.html
-
 
 Other resources
 ---------------
 
 You can watch a short talk about datashader on YouTube:
 `Datashader: Revealing the Structure of Genuinely Big Data`_.
-The video `Visualizing Billions of Points of Data,`_ and its `slides`_
-from a February 2016 one-hour talk introducing Datashader are also
+The video `Visualizing Billions of Points of Data`_ (and its `slides`_)
+from a February 2016 one-hour talk first introducing Datashader are also
 available, but do not cover more recent extensions to the library.
 
 .. _`Datashader: Revealing the Structure of Genuinely Big Data`: https://www.youtube.com/watch?v=6m3CFbKmK_c
@@ -93,7 +87,7 @@ name Abstract Rendering, which is described in a `2014 SPIE VDA paper`_.
 The source code for datashader is maintained at our `Github site,`_ and
 is documented using the API link on this page.
 
-.. _`GitHub site`: https://github.com/bokeh/datashader
+.. _`GitHub site,`: https://github.com/bokeh/datashader
 
 We recommend the `Getting Started Guide <getting_started>`_ to learn
 the basic concepts and start using Datashader as quickly as possible.

diff --git a/doc/user_guide/index.rst b/doc/user_guide/index.rst
@@ -54,4 +54,4 @@ Contents:
     6. Trimesh <6_Trimesh>
     7. Networks <7_Networks>
     9. Extending <9_Extending>
-    10. Peformance <10_Performance>
+    10. Performance <10_Performance>
diff --git a/examples/getting_started/3_Interactivity.ipynb b/examples/getting_started/3_Interactivity.ipynb
@@ -108,7 +108,7 @@
     "\n",
     "***Note that you'll only see an updated image on zooming in if there is a live Python process running.*** Bokeh works by taking a Python specification for a plot and generating a corresponding JavaScript-based visualization in the browser.  Whatever data has been given to the browser can be viewed interactively, but in this case only a single image of the data is given at a time, and so you will not be able to see more detail when zooming in unless the Python (and thus Datashader) process is running.  In a static HTML export of this notebook, such as those on a website, you'll only see the original pixels getting larger, not a zoomed-in rendering as in the callback plots above.\n",
     "\n",
-    "``InteractiveImage`` lets you explore any Datashader pipeline you like, but unfortunately it only works in a Jupyter notebook (not a deployed Bokeh server), and it is not typically possible to combine such a plot with other Bokeh figures.  The [dashboard.py](https://github.com/bokeh/datashader/blob/cb2f49f9/examples/dashboard/dashboard.py) from datashader 0.6 gives an example of building Bokeh+Datashader visualizations from the ground up, but this approach is quite difficult and is not recommended for most users. A much more practical approach to embedding and interactivity is to use HoloViews, as shown in the rest of this guide."
+    "``InteractiveImage`` lets you explore any Datashader pipeline you like, but unfortunately it only works in a Jupyter notebook (not a deployed Bokeh server), and it is not typically possible to combine such a plot with other Bokeh figures.  The [dashboard.py](https://github.com/bokeh/datashader/blob/cb2f49f9/examples/dashboard/dashboard.py) from datashader 0.6 gives an example of building Bokeh+Datashader visualizations from the ground up, but this approach is quite difficult and is not recommended for most users. For these reasons, we do not recommend using InteractiveImage in new projects.  Luckily, a much more practical approach to embedding and interactivity is available using HoloViews, as shown in the rest of this guide."
    ]
   },
   {

diff --git a/examples/topics/census.ipynb b/examples/topics/census.ipynb
@@ -606,7 +606,7 @@
    "source": [
     "labels = gv.WMTS(\"http://tile.stamen.com/toner-labels/{Z}/{X}/{Y}.png\")\n",
     "population = dynspread(datashade(points, cmap=fire, element_type=gv.Image))\n",
-    "geomap * labels * population"
+    "geomap * population * labels.options(level=\"annotation\")"
    ]
   },
   {

diff --git a/examples/topics/osm.ipynb b/examples/topics/osm.ipynb
@@ -105,8 +105,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#diag.visualize([prof, rprof])\n",
-    "None"
+    "diag.visualize([prof, rprof])\n"
    ]
   },
   {

diff --git a/examples/user_guide/5_Rasters.ipynb b/examples/user_guide/5_Rasters.ipynb
@@ -256,7 +256,7 @@
     "\n",
     "You can see Datashader rasters at work in the [Landsat](../topics/landsat.ipynb) example notebook, which also has examples of reading raster data from TIFF files using xarray and rasterio:\n",
     "\n",
-    "<img src=\"../assets/images/landsat.png\" width=1204 height=1150>"
+    "<img src=\"../assets/images/landsat.png\" width=602 height=575>"
    ]
   }
  ],