[3/3] cp_measure features + coordinate alignment for calculate_image_features

docs/release/notes-dev.md

@@ -2,6 +2,7 @@
 
 ## Features
 
+- {func}`squidpy.experimental.im.calculate_image_features` now featurizes tiles on a shared dask engine: `n_jobs > 1` runs worker processes via a `dask.distributed.LocalCluster` (or an active `Client`), and per-tile BLAS/OpenMP threads are pinned to avoid oversubscription. This also speeds up the serial path. {func}`squidpy.experimental.tl.calculate_tiling_qc` shares the same engine. Adds `distributed` and `threadpoolctl` as dependencies.
 - Fix {func}`squidpy.tl.var_by_distance` behaviour when providing {mod}`numpy` arrays of coordinates as anchor point.
 - Update :attr:`squidpy.pl.var_by_distance` to show multiple variables on same plot.
   [@LLehner](https://github.com/LLehner)

pyproject.toml

@@ -45,8 +45,10 @@ dynamic = [
 dependencies = [
   "aiohttp>=3.8.1",
   "anndata>=0.9",
+  "centrosome>=1.2.3",
+  "cp-measure>=0.1.19,<0.2",
   "cycler>=0.11",
-  "dask[array]>=2021.2",
+  "dask[array,distributed]>=2021.2",  # distributed is the only scheduler that forks for GIL-bound featurization
   "dask-image>=0.5",
   "docrep>=0.3.1",
   "fast-array-utils",
@@ -69,6 +71,7 @@ dependencies = [
   "spatialdata>=0.7.2",  # 0.7.2 dropped xarray-schema (pkg_resources break, #1115)
   "spatialdata-plot>=0.3.3",
   "statsmodels>=0.12",
+  "threadpoolctl>=3",  # clamp BLAS/OpenMP per worker in tiled featurization
   # https://github.com/scverse/squidpy/issues/526
   "tifffile!=2022.4.22",
   "tqdm>=4.50.2",