Datashader Versions Save

Minor compatibility release.

• Supports dask >= 0.18.
• Updated installation and usage instructions

Minor bugfix release.

• Now available to install using pip (pip install datashader) or conda defaults (conda install datashader)
• InteractiveImage is now deprecated; please use the Datashader support in HoloViews instead.
• Updated installation and example instructions to use new datashader command.
• Made package building automatic, to allow more frequent releases
• Ensured transparent (not black) image is returned when there is no data to plot (thanks to Nick Xie)
• Simplified getting-started example (thanks to David Jones)
• Various fixes and compatibility updates to examples

Major release with extensive support for triangular meshes and changes to the raster API.

New features:

• Trimesh support: Rendering of irregular triangular meshes using Canvas.trimesh() (see user guide) (#525,#552)
• Added a new website at datashader.org, with new Getting Started pages and an extensive User Guide, with about 50% new material not previously in example notebooks. Built entirely from Jupyter notebooks, which can be run in the examples/ directory. Website is now complete except for sections on points (see the nyc_taxi example in the meantime).
• Canvas.raster() now accepts xarray Dataset types, not just DataArrays, with the specific DataArray selectable from the Dataset using the column= argument of a supplied aggregation function.
• tf.Images() now displays anything with an HTML representation, to allow laying out Pandas dataframes alongside datashader output.

Bugfixes and compatibility:

• Changed Raster API to match other glyph types:
  • Now accepts a reduction function via an agg= argument like Canvas.line(), Canvas.points(), etc. The previous downsample_method is still accepted for this release, but is now deprecated.
  • upsample_method is now interpolate, accepting linear=True or linear=False; the previous spelling is now deprecated.
  • The layer= argument previously accepted a 1-based integer index, which was confusing given the standard Python 0-based indexing elsewhere. Changed to accept an xarray coordinate, which can be a 1-based index if that's what is defined on the array, but also works with arbitrary floating-point coordinates (e.g. for a depth parameter in an image stack).
  • Now auto-ranges in x and y when not given explicit ranges, instead of raising an error.
• Fixed various bugs, including one generating incorrect output in Canvas.raster(agg='mode')

Minor compatibility release to track changes in external packages.

• Updated imports for bokeh 0.12.11 (fixes #535), though there are issues in 0.12.11 itself and so 0.12.12 should be used instead (to be released shortly).
• Pinned pillow version on Windows (fixes #534).

Apart from the new website, this is a minor release primarily to catch up with changes in external libraries.

New features:

• Reorganized examples directory as the basis for a completely new website at https://bokeh.github.io/datashader-docs (#516).
• Added tf.Images() class to format multiple labeled Datashader images as a table in a Jupyter notebook, now used extensively in the new website.
• Added utility function dataframe_from_multiple_sequences(x_values, y_values) to convert large numbers of sequences stored as 2D numpy arrays to a NaN-separated pandas dataframe that can be displayed efficiently (see new example in tseries.ipynb) (#512).
• Improved streaming support (#520).

Bugfixes and compatibility:

• Added support for Dask 0.15 and 0.16 and pandas 0.21 (#523,#529) and declared minimum required Numba version.
• Improved and fixed issues with various example notebooks, primarily to update for changes in dependencies.
• Changes in network graph support: ignore id field by default to avoid surprising dependence on column name, rename directly_connect_edges to connect_edges for accuracy and conciseness.

Release with bugfixes, changes to match external libraries, and some new features.

Backwards compatibility:

• Minor changes to network graph API, e.g. to ignore weights by default in forcelayout2 (#488)
• Fix upper-bound bin error for auto-ranged data (#459). Previously, points falling on the upper bound of the plotted area were excluded from the plot, which was consistent with the behavior for individual grid cells, but which was confusing and misleading for the outer boundaries. Points falling on the very outermost boundaries are now folded into the final grid cell, which should be the least surprising behavior.

New or updated examples (.ipynb files in examples/):

• streaming-aggregation.ipynb: Illustrates combining incoming streams of data for display (also see holoviews streaming).
• landsat.ipynb: simplified using HoloViews; now includes plots of full spectrum for each point via hovering.
• Updated and simplified census-hv-dask (now called census-congressional), census-hv, packet_capture_graph.

New features and improvements

• Updated Bokeh support to work with new bokeh 0.12.10 release (#505)
• More options for network/graph plotting (configurable column names, control over weights usage; #488, #494)
• For lines plots (time series, trajectory, networ graphs), switch line-clipping algorithm from Cohen-Sutherland to Liang-Barsky. The performance gains for random lines range from 50-75% improvement for a million lines. (#495)
• Added tf.Images class to format a list of images as an HTML table (#492)
• Faster resampling/regridding operations (#486)

Known issues:

• examples/dashboard has not yet been updated to match other libraries, and is thus missing functionality like hovering and legends.
• A full website with documentation has been started but is not yet ready for deployment.

Minor bugfix release, primarily updating example notebooks to match API changes in external packages.

Backwards compatibility:

• Made edge bundling retain edge order, to allow indexing, and absolute coordinates, to allow overlaying on external data.
• Updated examples to show that xarray now requires dimension names to match before doing arithmetic or comparisons between arrays.

Known issues:

• If you use Jupyter notebook 5.0 (earlier or later versions should be ok), you will need to override a setting that prevents visualizations from appearing, e.g.: jupyter notebook --NotebookApp.iopub_data_rate_limit=100000000 census.ipynb &
• The dashboard needs to be rewritten entirely to match current Bokeh and HoloViews releases, so that hover and legend support can be restored.

New release of features that may still be in progress, but are already usable:

• Added graph/network plotting support (still may be in flux) (#385, #390, #398, #408, #415, #418, #436)
• Improved raster regridding based on gridtools and xarray (still may be in flux); no longer depends on rasterio and scikit-image (#383, #389, #423)
• Significantly improved performance for dataframes with categorical fields

New examples (.ipynb files in examples/):

• osm-1billion: 1-billion-point OSM example, for in-core processing on a 16GB laptop.
• edge_bundling: Plotting graphs using "edgehammer" bundling of edges to show structure.
• packet_capture_graph: Laying out and visualizing network packets as a graph.

Backwards compatibility:

• Remove deprecated interpolate and colorize functions
• Made raster processing consistently use bin centers to match xarray conventions (requires recent fixes to xarray; only available on a custom channel for now) (#422)
• Fixed various limitations and quirks for NaN values
• Made alpha scaling respect min_alpha consistently (#371)

Known issues:

• If you use Jupyter notebook 5.0 (earlier or later versions should be ok), you will need to override a setting that prevents visualizations from appearing, e.g.: jupyter notebook --NotebookApp.iopub_data_rate_limit=100000000 census.ipynb &
• The dashboard needs updating to match current Bokeh releases; most parts other than hover and legends, should be functional but it needs a rewrite to use currently recommended approaches.

Major release with extensive optimizations and new plotting-library support, incorporating 9 months of development from 5 main contributors:

• Extensive optimizations for speed and memory usage, providing at least 5X improvements in speed (using the latest Numba versions) and 2X improvements in peak memory requirements. Outlined in #313 and #129.
• Added HoloViews support for flexible, composable, dynamic plotting, making it simple to switch between datashaded and non-datashaded versions of a Bokeh or Matplotlib plot.
• Added examples/environment.yml to make it easy to install dependencies needed to run the examples.
• Updated examples to use the now-recommended supported and fast Apache Parquet file format
• Added support for variable alpha for non-categorical aggregates, by specifying a single color rather than a list or colormap #345
• Added datashader.utils.lnglat_to_meters utility function for working in Web Mercator coordinates with Bokeh
• Added discussion of why you should be using uniform colormaps, and examples of using uniform colormaps from the new colorcet package
• Numerous bug fixes and updates, mostly in the examples and Bokeh extension
• Updated reference manual and documentation

New examples (.ipynb files in examples/):

• holoviews_datashader: Using HoloViews to create dynamic Datashader plots easily
• census-hv-dask: Using GeoViews for overlaying shape files, demonstrating gerrymandering by race
• nyc_taxi-paramnb: Using ParamNB to make a simple dashboard
• lidar: Visualizing point clouds
• solar: Visualizing solar radiation data
• Dynamic 1D histogram example (last code cell in examples/nyc_taxi-nongeo.ipynb)
• dashboard: Now includes opensky example (python dashboard/dashboard.py -c dashboard/opensky.yml)

Backwards compatibility:

• To improve consistency with Numpy and Python data structures and eliminate issues with an empty column and row at the edge of the aggregated raster, the provided xrange,yrange bounds are now treated as upper exclusive. Results will thus differ between 0.5.0 and earlier versions. See #259 for discussion.

Known issues:

• If you use Jupyter notebook 5.0 (earlier or later versions should be ok), you will need to override a setting that prevents visualizations from appearing, e.g.: jupyter notebook --NotebookApp.iopub_data_rate_limit=100000000 census.ipynb &
• Legend and hover support is currently disabled for the dashboard, due to ongoing development of a simpler approach.

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.