[EXPERIMENTAL]: Integrate cp-measure#982
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Introduces _tiling.py with build_tile_specs() and extract_tile() that split a label image into overlapping tiles where each cell is assigned to exactly one tile by centroid. Non-owned cells are zeroed out so downstream processing never double-counts. Includes 31 tests: deterministic brick-pattern grid (touching and non-touching), coverage verification, and visual regression tests. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Refactoring in anticipation of afermg/cp_measure#38 being merged so we can upstream behaviour. |
This was referenced Apr 10, 2026
Wires the in-progress cp_measure.featurizer + lazy-tiling refactor onto a
working _tiling.py and closes out the six open notes on the PR.
_tiling.py:
* build_tile_specs now takes (shape, cell_info), so it is agnostic to
whether labels are in memory, dask-backed, or multiscale.
* compute_cell_info is public; new compute_cell_info_multiscale (read
coarsest scale, rescale to target) and compute_cell_info_tiled
(stream tiles, merge boundary-spanning cells via additive accumulators).
* extract_tile_lazy slices an xr.DataArray and materializes only the crop;
extract_tile retained for in-memory callers.
* verify_coverage takes a label_ids set.
_feature.py:
* Channel names: read via spatialdata.models.get_channel_names so c_coords
set at parse time flow through to output column suffixes.
* Progress: tqdm wrapper around joblib.Parallel(return_as='generator_unordered')
+ periodic logg.info('Tile {n}/{total} done (elapsed ...)') so non-TTY
runs (CI, slurm) also see progress.
* Alignment: _align_to_image_grid replaces the dim-mismatch raise with a
coordinate-system aware crop. Identity-or-integer-pixel-translation is
honored as a 1-to-1 pixel alignment; the overlap rectangle is processed
and out-of-extent cells are counted, not crashed on. Non-pixel-aligned
transforms either raise with a spatialdata.rasterize hint
(align_mode='strict', default) or trigger materialization via
spatialdata.rasterize (align_mode='rasterize') with a warning.
* DropReport: per-run counter for cells dropped due to extent, partial
boundary intersection, cp_measure no-data, or empty tiles. Emitted via
logg.info(report.summary()) at the end of every run.
Tests: 39 in test_tiling.py (was 30; new coverage for the lazy/multiscale
helpers + verify_coverage edge cases), 35 in test_calculate_image_features
including a TestPR982Concerns class with one regression test per open note.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* compute_cell_info_tiled: replace per-id np.where with scipy.ndimage.find_objects and np.bincount sums. One vectorized pass per tile instead of O(n_cells) scans. * _zero_non_owned: replace per-id rewrite loop with np.isin + np.where. * _classify_dropped_cells: drop the full-array .values + per-cell np.where; use compute_cell_info_tiled bboxes for inside/partial/outside classification, so the full label array is no longer materialized. * CellInfo: add bbox_y0/bbox_x0 fields so callers can do bbox math without reconstructing from the centroid (which is area-weighted, not bbox-centered). * _relabel_contiguous: replaced by skimage.segmentation.relabel_sequential. * _align_to_image_grid: flatten nested if/else with elif chain; extract _rasterize_to_image_grid so the shapes-key path and the align_mode='rasterize' path no longer duplicate the rasterize call. * DropReport: empty_tile_drop -> empty_tiles (the counter increments per tile). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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Five Sphinx warnings (treated as errors by ``-W``) on the docs build: - ``:mod:`spatialdata_plot``` and ``:meth:`spatialdata.SpatialData.pl.show``` have no intersphinx targets. - ``:class:`dask.distributed.Client``` has no intersphinx target either. - ``:func:`~squidpy.experimental.im.calculate_image_features``` pointed at a function that does not exist on this branch (planned for PR #982). Downgrade all five to plain double-backtick literals. Re-phrase the calculate_image_features reference to describe the shared tiling infrastructure (``squidpy.experimental.im._tiling``) without claiming a public function that has not yet shipped.
* _featurize_tile: accept a pre-built cp_config and drop the per-tile _build_cp_config rebuild. Config is now constructed once in calculate_image_features and reused across every tile (matters on 100kx100k images with thousands of tiles). * pyproject: cp-measure>=0.1.4 -> >=0.1.19 to pick up the granularity correctness fix (#44, #47), 3D-only feature filtering (#35), and static typing (#45). No upper cap left in place; bump when upstream ships a breaking release. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* pyproject: cp-measure>=0.1.19,<0.2 -- pre-1.0 dep, cap upper bound so a future 0.2.x release doesn't silently break installs. * _featurize_tile: cp_config is now keyword-only with a default of None and falls back to _build_cp_config when not supplied. Preserves the pre-hoist call signature for direct (test/notebook) callers while the caller-built reuse path in calculate_image_features still skips the per-tile rebuild. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Resolves add/add conflict on src/squidpy/experimental/im/_tiling.py between our cp_measure-driven lazy tiling refactor and PR #1157's tiling QC additions. Unified into one module: * Kept our superset definitions of CellInfo (with bbox_y0/bbox_x0 defaults), TileSpec, build_tile_specs((shape, cell_info, ...)), compute_cell_info, compute_cell_info_multiscale, compute_cell_info_tiled, extract_tile, extract_tile_lazy, verify_coverage, and the array-returning _zero_non_owned. * Added extract_labels_tile_lazy(labels_da, spec) -- the labels-only crop variant from main, needed by tl/_tiling_qc.py. Implemented on top of our _zero_non_owned return style. * __all__ now exports the new symbol. Auto-merge restored main's new files (tl/_tiling_qc.py, pl/_tiling_qc.py, conftest.py, tests/_images/TilingQCVisual_*.png, test_tiling_qc.py); our earlier deletion of the old tl/_tiling_qc.py no longer applies -- the new QC implementation supersedes it. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Aligns with main's tl/_tiling_qc.py tests that already call ``compute_cell_info_tiled(labels_da, chunk_size=...)`` and with the numpy/dask convention. Internal body and our own test in tests/experimental/test_tiling.py updated accordingly. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Convention in the scverse ecosystem is to address channels by name only. Passing an int now raises TypeError; passing a non-existent name still raises ValueError as before. A channel whose name happens to be the string "0" is still accepted -- the check discriminates on Python type (isinstance(ch, str)), not on the string contents. * _prepare_lazy: type hint list[str] | list[int] | None -> list[str] | None and add an isinstance(ch, str) guard before lookup. * calculate_image_features: same type hint update. * Docstring clarified that integer indices are not accepted. * test_channel_selection_by_index renamed to test_channel_selection_rejects_int and now asserts TypeError. * test_concern4_channel_subset_by_index renamed to ..._by_name and passes ["c0", "c2"] instead of [0, 2]. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This was referenced May 27, 2026
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- Trim DropReport to `empty_tiles` (the only field this code ever increments) - Narrow `align_mode` Literal to "strict" and add a runtime guard that catches dynamic callers passing other values - Exclude `cpmeasure:*` flag names from the "available" list in the unknown-feature error (they always raise NotImplementedError; listing them as available is misleading) - Raise on ambiguous mixes of `skimage:label` with `skimage:label:<prop>` (and same for `skimage:label+image`); the previous order-dependent behaviour silently expanded the narrowing form - Collapse `pd.concat -> replace([inf,-inf],0) -> fillna(0) -> .values.astype(float32)` into a single numpy pass via `np.nan_to_num`; saves two full-table copies - Use `pd.Categorical.from_codes` for the region column to avoid allocating an N-element Python list for a one-level categorical - Hoist the labels_key/shapes_key XOR pick to one local - Add `experimental.im.calculate_image_features` to docs/api.md Also: remove all references to the multi-PR split (follow-up PRs, PR-2, PR-4, "in this PR", "cp_measure-as-default behaviour from PR scverse#982", TestPR982Concerns, "Concern N" markers) from code and tests.
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Associated notebook: https://github.com/scverse/squidpy_notebooks/blob/add_cpmeasure_notebook/tutorials/tutorial_cpmeasure.ipynb