Cellseg-gsontools

Fixes
- Fix a bug in `read_gdf` method when the column values are stringified.

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Chore
- Update geopandas version ^1.0.0

Features

- Add a new methods for clustering: `cluster_cells`, `find_lisa_clusters`, `connected_components` and `label_connected_components`. The `cluster_cells` works as a polished runner function to cluster spatially the cells of interest with local moran lisa analysis.

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Fixes
- Fix a bug `gdf_to_file`. Failed when qupath data fields were added to gdf.
- Remove `matplotlib.cm.get_cmap` function from source code. It was not used.

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Features

- Add support for querying a WSI with bbox coords.
- Add support for rasterizing a gdf into a instance or semantic segmentation mask
- add support for intensity features

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Docs

- Add cell clustering example
- Add cell regionalization example
- Add medial line extraction example

Features

- Add medial line extraction funtion `cellseg_gsontools.geometry.medial_lines`. Extracts the medial lines using voronoi diagram.
- Add perpendicular line extraction funtion `cellseg_gsontools.geometry.perpendicular_lines`. Extracts the perpendicular lines from a given line gdf.
- Add `num_processes` arguments in `local_character`, `local_distances`, `local_diversity` and `shape_metric` functions.

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Breaking changes

- The SpatialContext API now takes params: `parallel` and `num_processes` in the class constructor instead of the `fit()`-method.

Features

- Added support for new backends (python libraries) to compute the spatial joins in the `SpatialContext`-classes. This allows for more efficient computation of the spatial joins especially for very large tissue areas.
- Backends:
- dask-geopandas
- spatialpandas

- Add `h3` hexgrid support in grid fitting for spatial context classes

Performance
- sjoin operations replaced with sindex ops. This makes subsetting 10-100x faster.

Docs
New documentation website: https://okunator.github.io/cellseg_gsontools/

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