0.4.0

Minor bugfix release to support Bokeh 0.12.1, with some API and defaults changes.

• Added examples() function to obtain the notebooks and other examples corresponding to the installed datashader version; see examples/README.md.
• Updated dashboard example to match changes in Bokeh
• Added default color cycle with distinguishable colors for shading categorical data; now tf.shade(agg) with no other arguments should give a usable plot for both categorical and non-categorical data.

Backwards compatibility:

• Replaced confusing tf.interpolate() and tf.colorize() functions with a single shading function tf.shade(). The previous names are still supported, but give deprecation warnings. Calls to the previous functions using keyword arguments can simply be renamed to use tf.shade, as all the same keywords are accepted, but calls to colorize that used a positional argument for e.g. the color_key will now need to use a keyword when calling shade().
• Increased default threshold for tf.dynspread() to improve visibility of sparse dots
• Increased default min_alpha for tf.shade() (formerly tf.colorize()) to avoid undersaturation

Known issues:

• For Bokeh 0.12.1, some notebooks will give warnings for Bokeh plots when used with Jupyter's "Run All" command. Bokeh 0.12.2 will fix this problem when it is released, but for now you can either downgrade to 0.12.0 or use single-cell execution.
• There are some Bokeh compatibility issues with the dashboard example that are still being investigated and may require a new Bokeh or datashader release in this series.