This minor release added an important bug fix from Neil Shephard (#235), preventing a crash when working with float32 images.
This minor release fixed issues with the version switcher in the documentation.
This release adds NumPy 2.0 compatibility (while remaining compatible with 1.x) (#229). It also lays the groundwork for new skeleton editing features with bidirectional Skeleton to NetworkX conversion functions (#224.
We also have a minor deprecation that should improve quality of life in the
future: column names in the summary dataframe can now use _ as the separator
(instead of -), which allows one to use the pandas attribute access for
columns (for example, summary.branch_distance instead of
summary['branch-distance']. Use the separator='_' keyword argument to
summarize to take advantage of this feature (which will become the default in
a future version), or separator='-' to maintain the current behavior even
when new versions come out (#215).
The napari plugin now lets you make a Shapes layer fully backed by a Skeleton dataset, including coloring the edges by features in the summary table (#201).
Thanks to Neil Shephard, James Ryan, Jarod Hanko, and Tim Monko for their contributions to this release! 🙏 You can find the full list of changes below:
- #215: The separators used for column
names are configurable, and will transition to
_in the future. This is to make it easier to use the dataframe attribute interface, e.g.summary.branch_distance
- #229: NumPy 2 compatibility
- #224: Create a networkx summary graph from a Skeleton
- #201: Add napari widget to generate shapes layer from a skeletonized label layer
- #220: Allow mean pixel value calculation from integer values, not just floats
- #212: Improved error reporting and tests for prune_paths methods
- #221: Fix documentation builds
- #210: Cache skeleton_image shape for use by the path_label_image method
- #231: Add 0.12 release notes