Pyviz

The major feature of this release is support of raster data via `Canvas.raster`. To use this feature, you must install the optional dependencies via `conda install rasterio scikit-image`. `rasterio` relies on `gdal`, whose conda package has some known bugs, including a missing dependancy for `conda install krb5`. InteractiveImage in this release requires bokeh 0.11.1 or earlier, and will not work with bokeh 0.12.
- **PR 160 187** Improved example notebooks and dashboard
- **PR 186 184 178** Add datashader-download-data cli command for grabbing example datasets
- **PR 176 177** Changed census example data to use HDF5 format (slower but more portable)
- **PR 156 173 174** Added Landsat8 and race/ethnicity vs. elevation example notebooks
- **PR 172 159 157 149** Added support for images using `Canvas.raster` (requires `rasterio` and `scikit-image`)
- **PR 169** Added legends notebook demonstrating `create_categorical_legend` and `create_ramp_legend`
- **PR 162** Added notebook example for `datashader.bokeh_ext.HoverLayer`
- **PR 152** Added `alpha` arg to `tf.interpolate`
- **PR 151 150, etc.** Small bugfixes
- **PR 146 145 144 143** Added streaming example
- Added `hold` decorator to utils, `summarize_aggregate_values` helper function
- Added [FAQ](http://datashader.readthedocs.io/en/latest/faq) to docs

Backwards compatibility:
- Removed `memoize_method`
- Renamed `datashader.callbacks` --> `datashader.bokeh_ext`
- Renamed `examples/plotting_problems.ipynb` --> `examples/plotting_pitfalls.ipynb`

A major release with significant new functionality and some small backwards-incompatible changes.

New features:
- **PR 124, [census](https://anaconda.org/jbednar/census/notebook):** New census notebook example, showing how to work with categorical data.
- **PR 79, [tseries](https://anaconda.org/jbednar/tseries), [trajectory](https://anaconda.org/jbednar/trajectory):** Added line glyph and `.any()` reduction, used in new time series and trajectory notebook examples.
- **PR 76, 77, 131, etc.:** Updated all of the other notebooks in examples/, including [nyc_taxi](https://anaconda.org/jbednar/nyc_taxi/notebook).
- **PR 100, 125:** Improved dashboard example: added categorical data support, census and osm datasets, legend and hover support, better performance, out of core option, and more
- **PR 109, 111:** Add full colormap support via a new `cmap` argument to `interpolate` and `colorize`; supports color ranges as lists, plus Bokeh palettes and matplotlib colormaps
- **PR 98:** Added `set_background` to make it easier to work with images having a different background color than the default white notebooks
- **PR 119, 121:** Added eq_hist option for `how` in interpolate, performing histogram equalization on the data to reveal structure at every intensity level
- **PR 80, 83, 128:** Greatly improved InteractiveImage performance and responsiveness
- **PR 74, 123:** Added operators for spreading pixels (to make individual datapoints visible, as circles, squares, or arbitrary mask shapes) and compositing (for simple and flexible composition of images)

Backwards compatibility:
- The `low` and `high` color options to `interpolate` and `colorize` are now deprecated and will be removed in the next release; use `cmap=[low,high]` instead.
- The transfer function `merge` has been removed to avoid confusion. `stack` and others can be used instead, depending on the use case.
- The default `how` for `interpolate` and `colorize` is now `eq_hist`, to reveal the structure automatically regardless of distribution.
- `Pipeline` now has a default `dynspread` step, to make isolated points visible when zooming in, and the default sizes have changed.

Initial public release