From 5c08081c6902f4fe45f389203f1033be9d1a8d0b Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 13:06:58 +0200 Subject: [PATCH 01/96] init --- src/squidpy/gr/__init__.py | 16 +- src/squidpy/gr/_build.py | 531 +++++++++++++++++++++----- src/squidpy/gr/neighbors.py | 7 + tests/graph/test_spatial_neighbors.py | 38 +- 4 files changed, 505 insertions(+), 87 deletions(-) create mode 100644 src/squidpy/gr/neighbors.py diff --git a/src/squidpy/gr/__init__.py b/src/squidpy/gr/__init__.py index 7bb9bb4a2..9a5b454c8 100644 --- a/src/squidpy/gr/__init__.py +++ b/src/squidpy/gr/__init__.py @@ -2,7 +2,16 @@ from __future__ import annotations -from squidpy.gr._build import SpatialNeighborsResult, mask_graph, spatial_neighbors +from squidpy.gr._build import ( + DelaunayBuilder, + GraphBuilder, + GridBuilder, + KNNBuilder, + RadiusBuilder, + SpatialNeighborsResult, + mask_graph, + spatial_neighbors, +) from squidpy.gr._ligrec import ligrec from squidpy.gr._nhood import ( NhoodEnrichmentResult, @@ -17,6 +26,11 @@ __all__ = [ "SpatialNeighborsResult", + "GraphBuilder", + "KNNBuilder", + "RadiusBuilder", + "DelaunayBuilder", + "GridBuilder", "NhoodEnrichmentResult", "mask_graph", "spatial_neighbors", diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 32630955d..104ad040c 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -3,6 +3,7 @@ from __future__ import annotations import warnings +from abc import ABC, abstractmethod from collections.abc import Iterable from functools import partial from itertools import chain @@ -49,7 +50,15 @@ _save_data, ) -__all__ = ["spatial_neighbors"] +__all__ = [ + "SpatialNeighborsResult", + "GraphBuilder", + "KNNBuilder", + "RadiusBuilder", + "DelaunayBuilder", + "GridBuilder", + "spatial_neighbors", +] class SpatialNeighborsResult(NamedTuple): @@ -59,6 +68,358 @@ class SpatialNeighborsResult(NamedTuple): distances: csr_matrix +class GraphBuilder(ABC): + """Base class for spatial graph construction strategies.""" + + def __init__( + self, + transform: str | Transform | None = None, + set_diag: bool = False, + percentile: float | None = None, + ) -> None: + self.transform = Transform.NONE if transform is None else Transform(transform) + self.set_diag = set_diag + self.percentile = percentile + + @property + @abstractmethod + def coord_type(self) -> CoordType: + """Coordinate system supported by this builder.""" + + @property + def legacy_params(self) -> dict[str, Any]: + """Return parameters expressed in the legacy spatial_neighbors API.""" + return { + "coord_type": self.coord_type, + "n_neighs": 6, + "radius": None, + "delaunay": False, + "n_rings": 1, + "percentile": self.percentile, + "transform": self.transform, + "set_diag": self.set_diag, + } + + @property + def metadata(self) -> dict[str, Any]: + """Return metadata stored in adata.uns after graph construction.""" + params = self.legacy_params + return { + "n_neighbors": params["n_neighs"], + "coord_type": self.coord_type.v, + "radius": params["radius"], + "transform": self.transform.v, + } + + def build(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + with warnings.catch_warnings(): + warnings.simplefilter("ignore", SparseEfficiencyWarning) + Adj, Dst = self._build_graph(coords) + + self._apply_filters(Adj, Dst) + self._apply_percentile(Adj, Dst) + Adj.eliminate_zeros() + Dst.eliminate_zeros() + + return self._apply_transform(Adj), Dst + + @abstractmethod + def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + """Construct raw adjacency and distance matrices.""" + + def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + """Apply builder-specific post-processing filters.""" + return None + + def _apply_percentile(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + if self.percentile is not None and self.coord_type == CoordType.GENERIC: + threshold = np.percentile(Dst.data, self.percentile) + Adj[Dst > threshold] = 0.0 + Dst[Dst > threshold] = 0.0 + + def _apply_transform(self, Adj: csr_matrix) -> csr_matrix: + if self.transform == Transform.SPECTRAL: + return cast(csr_matrix, _transform_a_spectral(Adj)) + if self.transform == Transform.COSINE: + return cast(csr_matrix, _transform_a_cosine(Adj)) + if self.transform == Transform.NONE: + return Adj + + raise NotImplementedError(f"Transform `{self.transform}` is not yet implemented.") + + +class KNNBuilder(GraphBuilder): + """Build a generic k-nearest-neighbor spatial graph.""" + + def __init__( + self, + n_neighs: int = 6, + transform: str | Transform | None = None, + set_diag: bool = False, + percentile: float | None = None, + ) -> None: + assert_positive(n_neighs, name="n_neighs") + super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) + self.n_neighs = n_neighs + + @property + def coord_type(self) -> CoordType: + return CoordType.GENERIC + + @property + def legacy_params(self) -> dict[str, Any]: + return super().legacy_params | {"n_neighs": self.n_neighs} + + def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + return cast( + tuple[csr_matrix, csr_matrix], + _build_connectivity( + coords, + n_neighs=self.n_neighs, + return_distance=True, + set_diag=self.set_diag, + ), + ) + + +class RadiusBuilder(GraphBuilder): + """Build a generic radius-based spatial graph.""" + + def __init__( + self, + radius: float | tuple[float, float], + n_neighs: int = 6, + transform: str | Transform | None = None, + set_diag: bool = False, + percentile: float | None = None, + ) -> None: + assert_positive(n_neighs, name="n_neighs") + super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) + self.radius = radius + self.n_neighs = n_neighs + + @property + def coord_type(self) -> CoordType: + return CoordType.GENERIC + + @property + def legacy_params(self) -> dict[str, Any]: + return super().legacy_params | {"n_neighs": self.n_neighs, "radius": self.radius} + + def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + return cast( + tuple[csr_matrix, csr_matrix], + _build_connectivity( + coords, + n_neighs=self.n_neighs, + radius=self.radius, + return_distance=True, + set_diag=self.set_diag, + ), + ) + + def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + if isinstance(self.radius, Iterable): + _filter_by_radius_interval(Adj, Dst, self.radius) + + +class DelaunayBuilder(GraphBuilder): + """Build a generic spatial graph from a Delaunay triangulation.""" + + def __init__( + self, + radius: float | tuple[float, float] | None = None, + n_neighs: int = 6, + transform: str | Transform | None = None, + set_diag: bool = False, + percentile: float | None = None, + ) -> None: + assert_positive(n_neighs, name="n_neighs") + super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) + self.radius = radius + self.n_neighs = n_neighs + + @property + def coord_type(self) -> CoordType: + return CoordType.GENERIC + + @property + def legacy_params(self) -> dict[str, Any]: + return super().legacy_params | {"n_neighs": self.n_neighs, "radius": self.radius, "delaunay": True} + + def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + return cast( + tuple[csr_matrix, csr_matrix], + _build_connectivity( + coords, + n_neighs=self.n_neighs, + radius=self.radius, + delaunay=True, + return_distance=True, + set_diag=self.set_diag, + ), + ) + + def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + if isinstance(self.radius, Iterable): + _filter_by_radius_interval(Adj, Dst, self.radius) + + +class GridBuilder(GraphBuilder): + """Build a grid-based spatial graph.""" + + def __init__( + self, + n_neighs: int = 6, + n_rings: int = 1, + delaunay: bool = False, + transform: str | Transform | None = None, + set_diag: bool = False, + percentile: float | None = None, + ) -> None: + assert_positive(n_neighs, name="n_neighs") + assert_positive(n_rings, name="n_rings") + super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) + self.n_neighs = n_neighs + self.n_rings = n_rings + self.delaunay = delaunay + + @property + def coord_type(self) -> CoordType: + return CoordType.GRID + + @property + def legacy_params(self) -> dict[str, Any]: + return super().legacy_params | { + "n_neighs": self.n_neighs, + "delaunay": self.delaunay, + "n_rings": self.n_rings, + } + + def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + return _build_grid( + coords, + n_neighs=self.n_neighs, + n_rings=self.n_rings, + delaunay=self.delaunay, + set_diag=self.set_diag, + ) + + +def _filter_by_radius_interval( + Adj: csr_matrix, + Dst: csr_matrix, + radius: Iterable[float], +) -> None: + minn, maxx = sorted(radius)[:2] + mask = (Dst.data < minn) | (Dst.data > maxx) + a_diag = Adj.diagonal() + + Dst.data[mask] = 0.0 + Adj.data[mask] = 0.0 + Adj.setdiag(a_diag) + + +def _normalize_builder_param(name: str, value: Any) -> Any: + if name == "coord_type": + return None if value is None else CoordType(value) + if name == "transform": + return Transform.NONE if value is None else Transform(value) + if name == "radius" and isinstance(value, tuple): + return tuple(sorted(value)[:2]) + + return value + + +def _validate_builder_compatibility( + builder: GraphBuilder, + *, + coord_type: str | CoordType | None, + n_neighs: int, + radius: float | tuple[float, float] | None, + delaunay: bool, + n_rings: int, + percentile: float | None, + transform: str | Transform | None, + set_diag: bool, +) -> None: + defaults = { + "coord_type": None, + "n_neighs": 6, + "radius": None, + "delaunay": False, + "n_rings": 1, + "percentile": None, + "transform": None, + "set_diag": False, + } + provided = { + "coord_type": coord_type, + "n_neighs": n_neighs, + "radius": radius, + "delaunay": delaunay, + "n_rings": n_rings, + "percentile": percentile, + "transform": transform, + "set_diag": set_diag, + } + + for key, value in provided.items(): + if value == defaults[key]: + continue + + if _normalize_builder_param(key, value) != _normalize_builder_param(key, builder.legacy_params[key]): + raise ValueError( + f"Parameter `{key}` conflicts with `{type(builder).__name__}`. " + "Leave graph-construction arguments at their defaults or make them match the builder." + ) + + +def _resolve_graph_builder( + *, + coord_type: CoordType, + n_neighs: int, + radius: float | tuple[float, float] | None, + delaunay: bool, + n_rings: int, + percentile: float | None, + transform: Transform, + set_diag: bool, +) -> GraphBuilder: + if coord_type == CoordType.GRID: + return GridBuilder( + n_neighs=n_neighs, + n_rings=n_rings, + delaunay=delaunay, + transform=transform, + set_diag=set_diag, + percentile=percentile, + ) + if delaunay: + return DelaunayBuilder( + radius=radius, + n_neighs=n_neighs, + transform=transform, + set_diag=set_diag, + percentile=percentile, + ) + if radius is not None: + return RadiusBuilder( + radius=radius, + n_neighs=n_neighs, + transform=transform, + set_diag=set_diag, + percentile=percentile, + ) + + return KNNBuilder( + n_neighs=n_neighs, + transform=transform, + set_diag=set_diag, + percentile=percentile, + ) + + @d.dedent @inject_docs(t=Transform, c=CoordType) def spatial_neighbors( @@ -75,6 +436,7 @@ def spatial_neighbors( percentile: float | None = None, transform: str | Transform | None = None, set_diag: bool = False, + builder: GraphBuilder | None = None, key_added: str = "spatial", copy: bool = False, ) -> SpatialNeighborsResult | None: @@ -128,10 +490,47 @@ def spatial_neighbors( - `{t.NONE.v}` - no transformation of the adjacency matrix. set_diag Whether to set the diagonal of the spatial connectivities to `1.0`. + builder + Advanced graph construction strategy. When provided, graph-construction arguments + (``coord_type``, ``n_neighs``, ``radius``, ``delaunay``, ``n_rings``, ``percentile``, + ``transform``, ``set_diag``) must either be left at their defaults or match the builder. key_added Key which controls where the results are saved if ``copy = False``. %(copy)s + Notes + ----- + ``spatial_neighbors`` has 4 graph-construction modes: + + - Grid mode: + ``coord_type='grid'``. Uses ``n_neighs`` and ``n_rings``. + ``radius`` is ignored. ``delaunay`` is forwarded to the + underlying grid connectivity builder. This is the mode used + for Visium-like grid coordinates. + - Generic k-nearest-neighbor mode: + ``coord_type='generic'``, ``delaunay=False``, ``radius=None``. + Uses ``n_neighs``. + - Generic radius mode: + ``coord_type='generic'``, ``delaunay=False``, ``radius`` set. + Uses ``radius`` and builds a radius-based neighbor graph. + If ``radius`` is a tuple, the graph is built with the maximum + radius and then pruned to the interval + ``[min(radius), max(radius)]``. + - Generic Delaunay mode: + ``coord_type='generic'``, ``delaunay=True``. + Builds a Delaunay triangulation graph. ``n_neighs`` is not + used for the triangulation itself, but is still accepted for + backward compatibility. If ``radius`` is a tuple, it is used + only as a post-construction pruning interval. + + Across these modes: + + - ``percentile`` only affects generic graphs. + - ``transform`` and ``set_diag`` apply to all modes. + - If ``builder`` is provided, it determines the mode directly. + The legacy graph-construction arguments must then stay at + their defaults or match the builder configuration. + Returns ------- If ``copy = True``, returns a :class:`~squidpy.gr.SpatialNeighborsResult` with the spatial connectivities and distances matrices. @@ -191,20 +590,47 @@ def spatial_neighbors( adata = adata.tables[table_key] library_key = region_key - assert_positive(n_rings, name="n_rings") - assert_positive(n_neighs, name="n_neighs") _assert_spatial_basis(adata, spatial_key) - transform = Transform.NONE if transform is None else Transform(transform) - if coord_type is None: - if radius is not None: - logg.warning( - f"Graph creation with `radius` is only available when `coord_type = {CoordType.GENERIC!r}` specified. " - f"Ignoring parameter `radius = {radius}`." - ) - coord_type = CoordType.GRID if Key.uns.spatial in adata.uns else CoordType.GENERIC + if builder is not None: + if not isinstance(builder, GraphBuilder): + raise TypeError(f"Expected `builder` to be a `GraphBuilder`, found `{type(builder)}`.") + _validate_builder_compatibility( + builder, + coord_type=coord_type, + n_neighs=n_neighs, + radius=radius, + delaunay=delaunay, + n_rings=n_rings, + percentile=percentile, + transform=transform, + set_diag=set_diag, + ) else: - coord_type = CoordType(coord_type) + assert_positive(n_rings, name="n_rings") + assert_positive(n_neighs, name="n_neighs") + + transform = Transform.NONE if transform is None else Transform(transform) + if coord_type is None: + if radius is not None: + logg.warning( + f"Graph creation with `radius` is only available when `coord_type = {CoordType.GENERIC!r}` specified. " + f"Ignoring parameter `radius = {radius}`." + ) + coord_type = CoordType.GRID if Key.uns.spatial in adata.uns else CoordType.GENERIC + else: + coord_type = CoordType(coord_type) + + builder = _resolve_graph_builder( + coord_type=coord_type, + n_neighs=n_neighs, + radius=radius, + delaunay=delaunay, + n_rings=n_rings, + percentile=percentile, + transform=transform, + set_diag=set_diag, + ) if library_key is not None: _assert_categorical_obs(adata, key=library_key) @@ -214,19 +640,12 @@ def spatial_neighbors( libs = [None] start = logg.info( - f"Creating graph using `{coord_type}` coordinates and `{transform}` transform and `{len(libs)}` libraries." + f"Creating graph using `{builder.coord_type}` coordinates and `{builder.transform}` transform and `{len(libs)}` libraries." ) _build_fun = partial( _spatial_neighbor, spatial_key=spatial_key, - coord_type=coord_type, - n_neighs=n_neighs, - radius=radius, - delaunay=delaunay, - n_rings=n_rings, - transform=transform, - set_diag=set_diag, - percentile=percentile, + builder=builder, ) if library_key is not None: @@ -248,12 +667,7 @@ def spatial_neighbors( neighbors_dict = { "connectivities_key": conns_key, "distances_key": dists_key, - "params": { - "n_neighbors": n_neighs, - "coord_type": coord_type.v, - "radius": radius, - "transform": transform.v, - }, + "params": builder.metadata, } if copy: @@ -267,66 +681,13 @@ def spatial_neighbors( def _spatial_neighbor( adata: AnnData, spatial_key: str = Key.obsm.spatial, - coord_type: str | CoordType | None = None, - n_neighs: int = 6, - radius: float | tuple[float, float] | None = None, - delaunay: bool = False, - n_rings: int = 1, - transform: str | Transform | None = None, - set_diag: bool = False, - percentile: float | None = None, + builder: GraphBuilder | None = None, ) -> tuple[csr_matrix, csr_matrix]: - coords = adata.obsm[spatial_key] - with warnings.catch_warnings(): - warnings.simplefilter("ignore", SparseEfficiencyWarning) - if coord_type == CoordType.GRID: - Adj, Dst = _build_grid( - coords, - n_neighs=n_neighs, - n_rings=n_rings, - delaunay=delaunay, - set_diag=set_diag, - ) - elif coord_type == CoordType.GENERIC: - Adj, Dst = _build_connectivity( - coords, - n_neighs=n_neighs, - radius=radius, - delaunay=delaunay, - return_distance=True, - set_diag=set_diag, - ) - else: - raise NotImplementedError(f"Coordinate type `{coord_type}` is not yet implemented.") - - if coord_type == CoordType.GENERIC and isinstance(radius, Iterable): - minn, maxx = sorted(radius)[:2] - mask = (Dst.data < minn) | (Dst.data > maxx) - a_diag = Adj.diagonal() - - Dst.data[mask] = 0.0 - Adj.data[mask] = 0.0 - Adj.setdiag(a_diag) - - if percentile is not None and coord_type == CoordType.GENERIC: - threshold = np.percentile(Dst.data, percentile) - Adj[Dst > threshold] = 0.0 - Dst[Dst > threshold] = 0.0 - - Adj.eliminate_zeros() - Dst.eliminate_zeros() - - # check transform - if transform == Transform.SPECTRAL: - Adj = _transform_a_spectral(Adj) - elif transform == Transform.COSINE: - Adj = _transform_a_cosine(Adj) - elif transform == Transform.NONE: - pass - else: - raise NotImplementedError(f"Transform `{transform}` is not yet implemented.") + if builder is None: + raise ValueError("No graph builder was provided.") - return Adj, Dst + coords = adata.obsm[spatial_key] + return builder.build(coords) def _build_grid( diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py new file mode 100644 index 000000000..3e9d1d14c --- /dev/null +++ b/src/squidpy/gr/neighbors.py @@ -0,0 +1,7 @@ +"""Public builder API for spatial neighbor graph construction.""" + +from __future__ import annotations + +from squidpy.gr._build import DelaunayBuilder, GraphBuilder, GridBuilder, KNNBuilder, RadiusBuilder + +__all__ = ["GraphBuilder", "KNNBuilder", "RadiusBuilder", "DelaunayBuilder", "GridBuilder"] diff --git a/tests/graph/test_spatial_neighbors.py b/tests/graph/test_spatial_neighbors.py index db638132b..57b6528c0 100644 --- a/tests/graph/test_spatial_neighbors.py +++ b/tests/graph/test_spatial_neighbors.py @@ -11,8 +11,9 @@ from spatialdata.datasets import blobs from squidpy._constants._pkg_constants import Key -from squidpy.gr import mask_graph, spatial_neighbors +from squidpy.gr import DelaunayBuilder, GridBuilder, KNNBuilder, RadiusBuilder, mask_graph, spatial_neighbors from squidpy.gr._build import _build_connectivity +from squidpy.gr.neighbors import KNNBuilder as PublicKNNBuilder class TestSpatialNeighbors: @@ -213,6 +214,41 @@ def test_copy(self, non_visium_adata: AnnData): np.testing.assert_allclose(result.distances.toarray(), self._gt_ddist) np.testing.assert_allclose(result.connectivities.toarray(), self._gt_dgraph) + def test_builder_module_export(self): + assert PublicKNNBuilder is KNNBuilder + + def test_knn_builder_matches_legacy(self, non_visium_adata: AnnData): + legacy = spatial_neighbors(non_visium_adata, n_neighs=3, coord_type="generic", copy=True) + builder = spatial_neighbors(non_visium_adata, builder=KNNBuilder(n_neighs=3), copy=True) + + np.testing.assert_array_equal(legacy.connectivities.toarray(), builder.connectivities.toarray()) + np.testing.assert_allclose(legacy.distances.toarray(), builder.distances.toarray()) + + def test_radius_builder_matches_legacy(self, non_visium_adata: AnnData): + legacy = spatial_neighbors(non_visium_adata, radius=5.0, coord_type="generic", copy=True) + builder = spatial_neighbors(non_visium_adata, builder=RadiusBuilder(radius=5.0), copy=True) + + np.testing.assert_array_equal(legacy.connectivities.toarray(), builder.connectivities.toarray()) + np.testing.assert_allclose(legacy.distances.toarray(), builder.distances.toarray()) + + def test_delaunay_builder_matches_legacy(self, non_visium_adata: AnnData): + legacy = spatial_neighbors(non_visium_adata, delaunay=True, coord_type="generic", copy=True) + builder = spatial_neighbors(non_visium_adata, builder=DelaunayBuilder(), copy=True) + + np.testing.assert_array_equal(legacy.connectivities.toarray(), builder.connectivities.toarray()) + np.testing.assert_allclose(legacy.distances.toarray(), builder.distances.toarray()) + + def test_grid_builder_matches_legacy(self, adata_squaregrid: AnnData): + legacy = spatial_neighbors(adata_squaregrid, n_neighs=4, n_rings=2, coord_type="grid", copy=True) + builder = spatial_neighbors(adata_squaregrid, builder=GridBuilder(n_neighs=4, n_rings=2), copy=True) + + np.testing.assert_array_equal(legacy.connectivities.toarray(), builder.connectivities.toarray()) + np.testing.assert_allclose(legacy.distances.toarray(), builder.distances.toarray()) + + def test_builder_argument_conflict(self, non_visium_adata: AnnData): + with pytest.raises(ValueError, match="conflicts"): + spatial_neighbors(non_visium_adata, builder=RadiusBuilder(radius=5.0), delaunay=True) + @pytest.mark.parametrize("percentile", [99.0, 95.0]) def test_percentile_filtering(self, adata_hne: AnnData, percentile: float, coord_type="generic"): result = spatial_neighbors(adata_hne, coord_type=coord_type, copy=True) From 5847d8406b26ad1cf79ab9fcc342cb6c3a228c96 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 13:11:33 +0200 Subject: [PATCH 02/96] doc clarifications --- src/squidpy/gr/_build.py | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 104ad040c..25f9de2c0 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -530,6 +530,39 @@ def spatial_neighbors( - If ``builder`` is provided, it determines the mode directly. The legacy graph-construction arguments must then stay at their defaults or match the builder configuration. + - By default, observations are not treated as their own + neighbors. The distance matrix always has a zero diagonal. + The connectivity matrix only gets a nonzero diagonal when + ``set_diag=True``. + + Argument precedence + ------------------- + When ``builder`` is not provided, the mode is resolved as follows: + + - If ``coord_type`` resolves to ``'grid'``, grid mode is used. + In that case ``radius`` is ignored. + - Otherwise, if ``delaunay=True``, Delaunay mode is used. + In this mode ``n_neighs`` does not affect the triangulation. + A tuple ``radius`` is only used afterward as a pruning + interval. A scalar ``radius`` is ignored. + - Otherwise, if ``radius`` is set, radius mode is used. + In this mode ``n_neighs`` does not act as a second cutoff. + - Otherwise, k-nearest-neighbor mode is used. + + Grid-specific behavior + ---------------------- + Grid mode currently does not validate ``n_neighs`` to a fixed set + such as ``{4, 6}``. Internally it first queries the + ``n_neighs`` nearest candidates and then applies a distance-based + correction tuned for grid-like coordinates. As a result: + + - values such as ``n_neighs=4`` and ``n_neighs=6`` are the + intended square-grid and hex-grid choices, respectively; + - other values are accepted for backward compatibility, but + their geometric interpretation is not guaranteed to match a + continuous ring on the grid; + - no clockwise or other within-ring ordering is part of the + public API. Returns ------- From 179ba06a0ae6d54eb7029c4b4d167b780ba05367 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 13:35:54 +0200 Subject: [PATCH 03/96] don't expose the builders --- src/squidpy/gr/__init__.py | 10 ---------- src/squidpy/gr/_build.py | 9 ++++++--- 2 files changed, 6 insertions(+), 13 deletions(-) diff --git a/src/squidpy/gr/__init__.py b/src/squidpy/gr/__init__.py index 9a5b454c8..d4f39958e 100644 --- a/src/squidpy/gr/__init__.py +++ b/src/squidpy/gr/__init__.py @@ -3,11 +3,6 @@ from __future__ import annotations from squidpy.gr._build import ( - DelaunayBuilder, - GraphBuilder, - GridBuilder, - KNNBuilder, - RadiusBuilder, SpatialNeighborsResult, mask_graph, spatial_neighbors, @@ -26,11 +21,6 @@ __all__ = [ "SpatialNeighborsResult", - "GraphBuilder", - "KNNBuilder", - "RadiusBuilder", - "DelaunayBuilder", - "GridBuilder", "NhoodEnrichmentResult", "mask_graph", "spatial_neighbors", diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 25f9de2c0..3823c22f7 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -275,11 +275,10 @@ def __init__( delaunay: bool = False, transform: str | Transform | None = None, set_diag: bool = False, - percentile: float | None = None, ) -> None: assert_positive(n_neighs, name="n_neighs") assert_positive(n_rings, name="n_rings") - super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) + super().__init__(transform=transform, set_diag=set_diag, percentile=None) self.n_neighs = n_neighs self.n_rings = n_rings self.delaunay = delaunay @@ -387,13 +386,17 @@ def _resolve_graph_builder( set_diag: bool, ) -> GraphBuilder: if coord_type == CoordType.GRID: + if percentile is not None: + raise ValueError( + "`percentile` is not supported for grid coordinates. " + "It only applies to generic (non-grid) graphs." + ) return GridBuilder( n_neighs=n_neighs, n_rings=n_rings, delaunay=delaunay, transform=transform, set_diag=set_diag, - percentile=percentile, ) if delaunay: return DelaunayBuilder( From e187abafd210691740163c04d5639445eb878b0d Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 2 Apr 2026 11:37:14 +0000 Subject: [PATCH 04/96] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/squidpy/gr/_build.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 3823c22f7..b7bea6dfa 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -388,8 +388,7 @@ def _resolve_graph_builder( if coord_type == CoordType.GRID: if percentile is not None: raise ValueError( - "`percentile` is not supported for grid coordinates. " - "It only applies to generic (non-grid) graphs." + "`percentile` is not supported for grid coordinates. It only applies to generic (non-grid) graphs." ) return GridBuilder( n_neighs=n_neighs, From cdb569923c259dbb720d71816c9363236afeb65d Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 13:50:49 +0200 Subject: [PATCH 05/96] deduplicate --- src/squidpy/gr/_build.py | 37 +++--- tests/graph/test_spatial_neighbors.py | 183 ++++++++++++++++---------- 2 files changed, 133 insertions(+), 87 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 3823c22f7..be724e369 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -182,7 +182,22 @@ def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: ) -class RadiusBuilder(GraphBuilder): +class _RadiusFilterBuilder(GraphBuilder): + """Intermediate base for builders that support radius-interval pruning.""" + + radius: float | tuple[float, float] | None + n_neighs: int + + @property + def coord_type(self) -> CoordType: + return CoordType.GENERIC + + def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + if isinstance(self.radius, Iterable): + _filter_by_radius_interval(Adj, Dst, self.radius) + + +class RadiusBuilder(_RadiusFilterBuilder): """Build a generic radius-based spatial graph.""" def __init__( @@ -198,10 +213,6 @@ def __init__( self.radius = radius self.n_neighs = n_neighs - @property - def coord_type(self) -> CoordType: - return CoordType.GENERIC - @property def legacy_params(self) -> dict[str, Any]: return super().legacy_params | {"n_neighs": self.n_neighs, "radius": self.radius} @@ -218,12 +229,8 @@ def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: ), ) - def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: - if isinstance(self.radius, Iterable): - _filter_by_radius_interval(Adj, Dst, self.radius) - -class DelaunayBuilder(GraphBuilder): +class DelaunayBuilder(_RadiusFilterBuilder): """Build a generic spatial graph from a Delaunay triangulation.""" def __init__( @@ -239,10 +246,6 @@ def __init__( self.radius = radius self.n_neighs = n_neighs - @property - def coord_type(self) -> CoordType: - return CoordType.GENERIC - @property def legacy_params(self) -> dict[str, Any]: return super().legacy_params | {"n_neighs": self.n_neighs, "radius": self.radius, "delaunay": True} @@ -260,10 +263,6 @@ def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: ), ) - def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: - if isinstance(self.radius, Iterable): - _filter_by_radius_interval(Adj, Dst, self.radius) - class GridBuilder(GraphBuilder): """Build a grid-based spatial graph.""" @@ -555,7 +554,7 @@ def spatial_neighbors( Grid-specific behavior ---------------------- Grid mode currently does not validate ``n_neighs`` to a fixed set - such as ``{4, 6}``. Internally it first queries the + such as ``{{4, 6}}``. Internally it first queries the ``n_neighs`` nearest candidates and then applies a distance-based correction tuned for grid-like coordinates. As a result: diff --git a/tests/graph/test_spatial_neighbors.py b/tests/graph/test_spatial_neighbors.py index 57b6528c0..b99c7c751 100644 --- a/tests/graph/test_spatial_neighbors.py +++ b/tests/graph/test_spatial_neighbors.py @@ -10,6 +10,7 @@ from shapely import Point from spatialdata.datasets import blobs +from squidpy._constants._constants import Transform from squidpy._constants._pkg_constants import Key from squidpy.gr import DelaunayBuilder, GridBuilder, KNNBuilder, RadiusBuilder, mask_graph, spatial_neighbors from squidpy.gr._build import _build_connectivity @@ -47,6 +48,22 @@ def _adata_concat(adata1, adata2): ) return adata_concat, batch1, batch2 + @staticmethod + def _assert_library_key_block_diagonal(adata, **neighbor_kwargs): + adata2 = adata.copy() + adata_concat, batch1, batch2 = TestSpatialNeighbors._adata_concat(adata, adata2) + spatial_neighbors(adata2, **neighbor_kwargs) + spatial_neighbors(adata_concat, library_key="library_id", **neighbor_kwargs) + np.testing.assert_array_equal( + adata_concat[adata_concat.obs["library_id"] == batch1].obsp[Key.obsp.spatial_conn()].toarray(), + adata.obsp[Key.obsp.spatial_conn()].toarray(), + ) + np.testing.assert_array_equal( + adata_concat[adata_concat.obs["library_id"] == batch2].obsp[Key.obsp.spatial_conn()].toarray(), + adata2.obsp[Key.obsp.spatial_conn()].toarray(), + ) + return adata_concat + # TODO: add edge cases # TODO(giovp): test with reshuffling @pytest.mark.parametrize(("n_rings", "n_neigh", "sum_dist"), [(1, 6, 0), (2, 18, 30), (3, 36, 84)]) @@ -66,20 +83,8 @@ def test_spatial_neighbors_visium( if n_rings > 1: assert visium_adata.obsp[Key.obsp.spatial_dist()][0].sum() == sum_dist - # test for library_key - visium_adata2 = visium_adata.copy() - adata_concat, batch1, batch2 = TestSpatialNeighbors._adata_concat(visium_adata, visium_adata2) - spatial_neighbors(visium_adata2, n_rings=n_rings) - spatial_neighbors(adata_concat, library_key="library_id", n_rings=n_rings) + adata_concat = self._assert_library_key_block_diagonal(visium_adata, n_rings=n_rings) assert adata_concat.obsp[Key.obsp.spatial_conn()][0].sum() == n_neigh - np.testing.assert_array_equal( - adata_concat[adata_concat.obs["library_id"] == batch1].obsp[Key.obsp.spatial_conn()].toarray(), - visium_adata.obsp[Key.obsp.spatial_conn()].toarray(), - ) - np.testing.assert_array_equal( - adata_concat[adata_concat.obs["library_id"] == batch2].obsp[Key.obsp.spatial_conn()].toarray(), - visium_adata2.obsp[Key.obsp.spatial_conn()].toarray(), - ) @pytest.mark.parametrize(("n_rings", "n_neigh", "sum_neigh"), [(1, 4, 4), (2, 4, 12), (3, 4, 24)]) def test_spatial_neighbors_squaregrid(self, adata_squaregrid: AnnData, n_rings: int, n_neigh: int, sum_neigh: int): @@ -91,26 +96,10 @@ def test_spatial_neighbors_squaregrid(self, adata_squaregrid: AnnData, n_rings: assert np.diff(adata.obsp[Key.obsp.spatial_conn()].indptr).max() == sum_neigh assert adata.uns[Key.uns.spatial_neighs()]["distances_key"] == Key.obsp.spatial_dist() - # test for library_key - adata2 = adata.copy() - adata_concat, batch1, batch2 = TestSpatialNeighbors._adata_concat(adata, adata2) - spatial_neighbors(adata2, n_neighs=n_neigh, n_rings=n_rings, coord_type="grid") - spatial_neighbors( - adata_concat, - library_key="library_id", - n_neighs=n_neigh, - n_rings=n_rings, - coord_type="grid", + adata_concat = self._assert_library_key_block_diagonal( + adata, n_neighs=n_neigh, n_rings=n_rings, coord_type="grid", ) assert np.diff(adata_concat.obsp[Key.obsp.spatial_conn()].indptr).max() == sum_neigh - np.testing.assert_array_equal( - adata_concat[adata_concat.obs["library_id"] == batch1].obsp[Key.obsp.spatial_conn()].toarray(), - adata.obsp[Key.obsp.spatial_conn()].toarray(), - ) - np.testing.assert_array_equal( - adata_concat[adata_concat.obs["library_id"] == batch2].obsp[Key.obsp.spatial_conn()].toarray(), - adata2.obsp[Key.obsp.spatial_conn()].toarray(), - ) @pytest.mark.parametrize("type_rings", [("grid", 1), ("grid", 6), ("generic", 1)]) @pytest.mark.parametrize("set_diag", [False, True]) @@ -162,20 +151,7 @@ def test_spatial_neighbors_non_visium(self, non_visium_adata: AnnData): np.testing.assert_array_equal(spatial_graph, self._gt_dgraph) np.testing.assert_allclose(spatial_dist, self._gt_ddist) - # test for library_key - non_visium_adata2 = non_visium_adata.copy() - adata_concat, batch1, batch2 = TestSpatialNeighbors._adata_concat(non_visium_adata, non_visium_adata2) - spatial_neighbors(adata_concat, library_key="library_id", delaunay=True, coord_type=None) - spatial_neighbors(non_visium_adata2, delaunay=True, coord_type=None) - - np.testing.assert_array_equal( - adata_concat[adata_concat.obs["library_id"] == batch1].obsp[Key.obsp.spatial_conn()].toarray(), - non_visium_adata.obsp[Key.obsp.spatial_conn()].toarray(), - ) - np.testing.assert_array_equal( - adata_concat[adata_concat.obs["library_id"] == batch2].obsp[Key.obsp.spatial_conn()].toarray(), - non_visium_adata2.obsp[Key.obsp.spatial_conn()].toarray(), - ) + self._assert_library_key_block_diagonal(non_visium_adata, delaunay=True, coord_type=None) @pytest.mark.parametrize("set_diag", [False, True]) @pytest.mark.parametrize("radius", [(0, np.inf), (2.0, 4.0), (-42, -420), (100, 200)]) @@ -217,37 +193,108 @@ def test_copy(self, non_visium_adata: AnnData): def test_builder_module_export(self): assert PublicKNNBuilder is KNNBuilder - def test_knn_builder_matches_legacy(self, non_visium_adata: AnnData): - legacy = spatial_neighbors(non_visium_adata, n_neighs=3, coord_type="generic", copy=True) - builder = spatial_neighbors(non_visium_adata, builder=KNNBuilder(n_neighs=3), copy=True) + @pytest.mark.parametrize( + ("legacy_kwargs", "builder"), + [ + ({"n_neighs": 3, "coord_type": "generic"}, KNNBuilder(n_neighs=3)), + ({"radius": 5.0, "coord_type": "generic"}, RadiusBuilder(radius=5.0)), + ({"delaunay": True, "coord_type": "generic"}, DelaunayBuilder()), + ], + ids=["knn", "radius", "delaunay"], + ) + def test_generic_builder_matches_legacy( + self, non_visium_adata: AnnData, legacy_kwargs: dict, builder: object + ): + legacy = spatial_neighbors(non_visium_adata, **legacy_kwargs, copy=True) + result = spatial_neighbors(non_visium_adata, builder=builder, copy=True) - np.testing.assert_array_equal(legacy.connectivities.toarray(), builder.connectivities.toarray()) - np.testing.assert_allclose(legacy.distances.toarray(), builder.distances.toarray()) + np.testing.assert_array_equal(legacy.connectivities.toarray(), result.connectivities.toarray()) + np.testing.assert_allclose(legacy.distances.toarray(), result.distances.toarray()) - def test_radius_builder_matches_legacy(self, non_visium_adata: AnnData): - legacy = spatial_neighbors(non_visium_adata, radius=5.0, coord_type="generic", copy=True) - builder = spatial_neighbors(non_visium_adata, builder=RadiusBuilder(radius=5.0), copy=True) + @pytest.mark.parametrize( + ("legacy_kwargs", "builder"), + [ + ({"n_neighs": 4, "n_rings": 2, "coord_type": "grid"}, GridBuilder(n_neighs=4, n_rings=2)), + ({"n_neighs": 6, "n_rings": 1, "coord_type": "grid"}, GridBuilder(n_neighs=6, n_rings=1)), + ], + ids=["4neighs_2rings", "6neighs_1ring"], + ) + def test_grid_builder_matches_legacy( + self, adata_squaregrid: AnnData, legacy_kwargs: dict, builder: object + ): + legacy = spatial_neighbors(adata_squaregrid, **legacy_kwargs, copy=True) + result = spatial_neighbors(adata_squaregrid, builder=builder, copy=True) - np.testing.assert_array_equal(legacy.connectivities.toarray(), builder.connectivities.toarray()) - np.testing.assert_allclose(legacy.distances.toarray(), builder.distances.toarray()) + np.testing.assert_array_equal(legacy.connectivities.toarray(), result.connectivities.toarray()) + np.testing.assert_allclose(legacy.distances.toarray(), result.distances.toarray()) - def test_delaunay_builder_matches_legacy(self, non_visium_adata: AnnData): - legacy = spatial_neighbors(non_visium_adata, delaunay=True, coord_type="generic", copy=True) - builder = spatial_neighbors(non_visium_adata, builder=DelaunayBuilder(), copy=True) + def test_builder_argument_conflict(self, non_visium_adata: AnnData): + with pytest.raises(ValueError, match="conflicts"): + spatial_neighbors(non_visium_adata, builder=RadiusBuilder(radius=5.0), delaunay=True) - np.testing.assert_array_equal(legacy.connectivities.toarray(), builder.connectivities.toarray()) - np.testing.assert_allclose(legacy.distances.toarray(), builder.distances.toarray()) + def test_builder_matching_non_default_legacy_args(self, non_visium_adata: AnnData): + builder = RadiusBuilder(radius=5.0, n_neighs=3, percentile=95.0, transform="cosine", set_diag=True) - def test_grid_builder_matches_legacy(self, adata_squaregrid: AnnData): - legacy = spatial_neighbors(adata_squaregrid, n_neighs=4, n_rings=2, coord_type="grid", copy=True) - builder = spatial_neighbors(adata_squaregrid, builder=GridBuilder(n_neighs=4, n_rings=2), copy=True) + baseline = spatial_neighbors(non_visium_adata, builder=builder, copy=True) + matched = spatial_neighbors( + non_visium_adata, + builder=builder, + coord_type="generic", + n_neighs=3, + radius=5.0, + percentile=95.0, + transform=Transform.COSINE, + set_diag=True, + copy=True, + ) - np.testing.assert_array_equal(legacy.connectivities.toarray(), builder.connectivities.toarray()) - np.testing.assert_allclose(legacy.distances.toarray(), builder.distances.toarray()) + np.testing.assert_allclose(baseline.connectivities.toarray(), matched.connectivities.toarray()) + np.testing.assert_allclose(baseline.distances.toarray(), matched.distances.toarray()) - def test_builder_argument_conflict(self, non_visium_adata: AnnData): - with pytest.raises(ValueError, match="conflicts"): - spatial_neighbors(non_visium_adata, builder=RadiusBuilder(radius=5.0), delaunay=True) + def test_builder_compatibility_normalizes_none_transform(self, non_visium_adata: AnnData): + builder = KNNBuilder(n_neighs=3, transform=Transform.NONE) + + baseline = spatial_neighbors(non_visium_adata, builder=builder, copy=True) + matched = spatial_neighbors(non_visium_adata, builder=builder, transform=None, copy=True) + + np.testing.assert_array_equal(baseline.connectivities.toarray(), matched.connectivities.toarray()) + np.testing.assert_allclose(baseline.distances.toarray(), matched.distances.toarray()) + + def test_builder_compatibility_normalizes_tuple_radius_order(self, non_visium_adata: AnnData): + builder = RadiusBuilder(radius=(4.0, 2.0)) + + baseline = spatial_neighbors(non_visium_adata, builder=builder, copy=True) + matched = spatial_neighbors( + non_visium_adata, + builder=builder, + coord_type="generic", + radius=(2.0, 4.0), + copy=True, + ) + + np.testing.assert_array_equal(baseline.connectivities.toarray(), matched.connectivities.toarray()) + np.testing.assert_allclose(baseline.distances.toarray(), matched.distances.toarray()) + + def test_grid_mode_ignores_radius(self, adata_squaregrid: AnnData): + default = spatial_neighbors(adata_squaregrid, coord_type="grid", n_neighs=4, n_rings=2, copy=True) + ignored = spatial_neighbors( + adata_squaregrid, + coord_type="grid", + n_neighs=4, + n_rings=2, + radius=(0.1, 0.2), + copy=True, + ) + + np.testing.assert_array_equal(default.connectivities.toarray(), ignored.connectivities.toarray()) + np.testing.assert_allclose(default.distances.toarray(), ignored.distances.toarray()) + + def test_delaunay_mode_ignores_scalar_radius(self, non_visium_adata: AnnData): + default = spatial_neighbors(non_visium_adata, coord_type="generic", delaunay=True, copy=True) + ignored = spatial_neighbors(non_visium_adata, coord_type="generic", delaunay=True, radius=5.0, copy=True) + + np.testing.assert_array_equal(default.connectivities.toarray(), ignored.connectivities.toarray()) + np.testing.assert_allclose(default.distances.toarray(), ignored.distances.toarray()) @pytest.mark.parametrize("percentile", [99.0, 95.0]) def test_percentile_filtering(self, adata_hne: AnnData, percentile: float, coord_type="generic"): From 69cda1acbe05444be285b9b471de60357470ca74 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 2 Apr 2026 11:51:15 +0000 Subject: [PATCH 06/96] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- tests/graph/test_spatial_neighbors.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/tests/graph/test_spatial_neighbors.py b/tests/graph/test_spatial_neighbors.py index b99c7c751..be1d897e7 100644 --- a/tests/graph/test_spatial_neighbors.py +++ b/tests/graph/test_spatial_neighbors.py @@ -97,7 +97,10 @@ def test_spatial_neighbors_squaregrid(self, adata_squaregrid: AnnData, n_rings: assert adata.uns[Key.uns.spatial_neighs()]["distances_key"] == Key.obsp.spatial_dist() adata_concat = self._assert_library_key_block_diagonal( - adata, n_neighs=n_neigh, n_rings=n_rings, coord_type="grid", + adata, + n_neighs=n_neigh, + n_rings=n_rings, + coord_type="grid", ) assert np.diff(adata_concat.obsp[Key.obsp.spatial_conn()].indptr).max() == sum_neigh @@ -202,9 +205,7 @@ def test_builder_module_export(self): ], ids=["knn", "radius", "delaunay"], ) - def test_generic_builder_matches_legacy( - self, non_visium_adata: AnnData, legacy_kwargs: dict, builder: object - ): + def test_generic_builder_matches_legacy(self, non_visium_adata: AnnData, legacy_kwargs: dict, builder: object): legacy = spatial_neighbors(non_visium_adata, **legacy_kwargs, copy=True) result = spatial_neighbors(non_visium_adata, builder=builder, copy=True) @@ -219,9 +220,7 @@ def test_generic_builder_matches_legacy( ], ids=["4neighs_2rings", "6neighs_1ring"], ) - def test_grid_builder_matches_legacy( - self, adata_squaregrid: AnnData, legacy_kwargs: dict, builder: object - ): + def test_grid_builder_matches_legacy(self, adata_squaregrid: AnnData, legacy_kwargs: dict, builder: object): legacy = spatial_neighbors(adata_squaregrid, **legacy_kwargs, copy=True) result = spatial_neighbors(adata_squaregrid, builder=builder, copy=True) From fddc027d835485f3e5629b31a3be4e40c0df03bc Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 13:53:06 +0200 Subject: [PATCH 07/96] arrange builders --- src/squidpy/gr/_build.py | 5 ----- tests/graph/test_spatial_neighbors.py | 4 ++-- 2 files changed, 2 insertions(+), 7 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index d2602cfe1..d4506b8a8 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -52,11 +52,6 @@ __all__ = [ "SpatialNeighborsResult", - "GraphBuilder", - "KNNBuilder", - "RadiusBuilder", - "DelaunayBuilder", - "GridBuilder", "spatial_neighbors", ] diff --git a/tests/graph/test_spatial_neighbors.py b/tests/graph/test_spatial_neighbors.py index b99c7c751..94b70f387 100644 --- a/tests/graph/test_spatial_neighbors.py +++ b/tests/graph/test_spatial_neighbors.py @@ -12,8 +12,8 @@ from squidpy._constants._constants import Transform from squidpy._constants._pkg_constants import Key -from squidpy.gr import DelaunayBuilder, GridBuilder, KNNBuilder, RadiusBuilder, mask_graph, spatial_neighbors -from squidpy.gr._build import _build_connectivity +from squidpy.gr import mask_graph, spatial_neighbors +from squidpy.gr._build import DelaunayBuilder, GridBuilder, KNNBuilder, RadiusBuilder, _build_connectivity from squidpy.gr.neighbors import KNNBuilder as PublicKNNBuilder From f194b1081a9dcaabc8acf63f5be3c93c2444138a Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 14:09:15 +0200 Subject: [PATCH 08/96] better behaviour --- src/squidpy/gr/_build.py | 105 +++++++++----------------- tests/graph/test_spatial_neighbors.py | 53 ++++++------- 2 files changed, 57 insertions(+), 101 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index d4506b8a8..140e06b26 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -313,59 +313,13 @@ def _filter_by_radius_interval( Adj.setdiag(a_diag) -def _normalize_builder_param(name: str, value: Any) -> Any: - if name == "coord_type": - return None if value is None else CoordType(value) - if name == "transform": - return Transform.NONE if value is None else Transform(value) - if name == "radius" and isinstance(value, tuple): - return tuple(sorted(value)[:2]) - - return value - - -def _validate_builder_compatibility( - builder: GraphBuilder, - *, - coord_type: str | CoordType | None, - n_neighs: int, - radius: float | tuple[float, float] | None, - delaunay: bool, - n_rings: int, - percentile: float | None, - transform: str | Transform | None, - set_diag: bool, -) -> None: - defaults = { - "coord_type": None, - "n_neighs": 6, - "radius": None, - "delaunay": False, - "n_rings": 1, - "percentile": None, - "transform": None, - "set_diag": False, - } - provided = { - "coord_type": coord_type, - "n_neighs": n_neighs, - "radius": radius, - "delaunay": delaunay, - "n_rings": n_rings, - "percentile": percentile, - "transform": transform, - "set_diag": set_diag, - } - - for key, value in provided.items(): - if value == defaults[key]: - continue - - if _normalize_builder_param(key, value) != _normalize_builder_param(key, builder.legacy_params[key]): - raise ValueError( - f"Parameter `{key}` conflicts with `{type(builder).__name__}`. " - "Leave graph-construction arguments at their defaults or make them match the builder." - ) +def _validate_no_legacy_params(**kwargs: Any) -> None: + conflicts = [k for k, v in kwargs.items() if v is not None] + if conflicts: + raise ValueError( + "When `builder` is provided, graph-construction arguments must not be set. " + f"Got non-default values for: {', '.join(conflicts)}." + ) def _resolve_graph_builder( @@ -425,13 +379,13 @@ def spatial_neighbors( table_key: str | None = None, library_key: str | None = None, coord_type: str | CoordType | None = None, - n_neighs: int = 6, + n_neighs: int | None = None, radius: float | tuple[float, float] | None = None, - delaunay: bool = False, - n_rings: int = 1, + delaunay: bool | None = None, + n_rings: int | None = None, percentile: float | None = None, transform: str | Transform | None = None, - set_diag: bool = False, + set_diag: bool | None = None, builder: GraphBuilder | None = None, key_added: str = "spatial", copy: bool = False, @@ -452,11 +406,10 @@ def spatial_neighbors( table_key Key in :attr:`spatialdata.SpatialData.tables` where the spatialdata table is stored. Must not be `None` if `adata` is a :class:`spatialdata.SpatialData`. - mask_polygon - The Polygon or MultiPolygon element. %(library_key)s coord_type - Type of coordinate system. Valid options are: + Type of coordinate system. Must not be set when ``builder`` is given. + Valid options are: - `{c.GRID.s!r}` - grid coordinates. - `{c.GENERIC.s!r}` - generic coordinates. @@ -467,29 +420,37 @@ def spatial_neighbors( - `{c.GRID.s!r}` - number of neighboring tiles. - `{c.GENERIC.s!r}` - number of neighborhoods for non-grid data. Only used when ``delaunay = False``. + + Defaults to ``6`` when no ``builder`` is provided. Must not be set when ``builder`` is given. radius - Only available when ``coord_type = {c.GENERIC.s!r}``. Depending on the type: + Only available when ``coord_type = {c.GENERIC.s!r}``. Must not be set when ``builder`` is given. + Depending on the type: - :class:`float` - compute the graph based on neighborhood radius. - :class:`tuple` - prune the final graph to only contain edges in interval `[min(radius), max(radius)]`. delaunay Whether to compute the graph from Delaunay triangulation. Only used when ``coord_type = {c.GENERIC.s!r}``. + Defaults to ``False`` when no ``builder`` is provided. Must not be set when ``builder`` is given. n_rings Number of rings of neighbors for grid data. Only used when ``coord_type = {c.GRID.s!r}``. + Defaults to ``1`` when no ``builder`` is provided. Must not be set when ``builder`` is given. percentile Percentile of the distances to use as threshold. Only used when ``coord_type = {c.GENERIC.s!r}``. + Must not be set when ``builder`` is given. transform - Type of adjacency matrix transform. Valid options are: + Type of adjacency matrix transform. Must not be set when ``builder`` is given. + Valid options are: - `{t.SPECTRAL.s!r}` - spectral transformation of the adjacency matrix. - `{t.COSINE.s!r}` - cosine transformation of the adjacency matrix. - `{t.NONE.v}` - no transformation of the adjacency matrix. set_diag Whether to set the diagonal of the spatial connectivities to `1.0`. + Defaults to ``False`` when no ``builder`` is provided. Must not be set when ``builder`` is given. builder - Advanced graph construction strategy. When provided, graph-construction arguments - (``coord_type``, ``n_neighs``, ``radius``, ``delaunay``, ``n_rings``, ``percentile``, - ``transform``, ``set_diag``) must either be left at their defaults or match the builder. + Advanced graph construction strategy. When provided, all other graph-construction + arguments (``coord_type``, ``n_neighs``, ``radius``, ``delaunay``, ``n_rings``, + ``percentile``, ``transform``, ``set_diag``) must be left as ``None``. key_added Key which controls where the results are saved if ``copy = False``. %(copy)s @@ -524,8 +485,8 @@ def spatial_neighbors( - ``percentile`` only affects generic graphs. - ``transform`` and ``set_diag`` apply to all modes. - If ``builder`` is provided, it determines the mode directly. - The legacy graph-construction arguments must then stay at - their defaults or match the builder configuration. + All other graph-construction arguments must be left as + ``None``. - By default, observations are not treated as their own neighbors. The distance matrix always has a zero diagonal. The connectivity matrix only gets a nonzero diagonal when @@ -622,10 +583,7 @@ def spatial_neighbors( _assert_spatial_basis(adata, spatial_key) if builder is not None: - if not isinstance(builder, GraphBuilder): - raise TypeError(f"Expected `builder` to be a `GraphBuilder`, found `{type(builder)}`.") - _validate_builder_compatibility( - builder, + _validate_no_legacy_params( coord_type=coord_type, n_neighs=n_neighs, radius=radius, @@ -636,6 +594,11 @@ def spatial_neighbors( set_diag=set_diag, ) else: + n_neighs = n_neighs if n_neighs is not None else 6 + delaunay = delaunay if delaunay is not None else False + n_rings = n_rings if n_rings is not None else 1 + set_diag = set_diag if set_diag is not None else False + assert_positive(n_rings, name="n_rings") assert_positive(n_neighs, name="n_neighs") diff --git a/tests/graph/test_spatial_neighbors.py b/tests/graph/test_spatial_neighbors.py index 851a81ced..6908a36c2 100644 --- a/tests/graph/test_spatial_neighbors.py +++ b/tests/graph/test_spatial_neighbors.py @@ -228,29 +228,26 @@ def test_grid_builder_matches_legacy(self, adata_squaregrid: AnnData, legacy_kwa np.testing.assert_allclose(legacy.distances.toarray(), result.distances.toarray()) def test_builder_argument_conflict(self, non_visium_adata: AnnData): - with pytest.raises(ValueError, match="conflicts"): + with pytest.raises(ValueError, match="must not be set"): spatial_neighbors(non_visium_adata, builder=RadiusBuilder(radius=5.0), delaunay=True) - def test_builder_matching_non_default_legacy_args(self, non_visium_adata: AnnData): + def test_builder_rejects_any_legacy_args(self, non_visium_adata: AnnData): builder = RadiusBuilder(radius=5.0, n_neighs=3, percentile=95.0, transform="cosine", set_diag=True) - baseline = spatial_neighbors(non_visium_adata, builder=builder, copy=True) - matched = spatial_neighbors( - non_visium_adata, - builder=builder, - coord_type="generic", - n_neighs=3, - radius=5.0, - percentile=95.0, - transform=Transform.COSINE, - set_diag=True, - copy=True, - ) - - np.testing.assert_allclose(baseline.connectivities.toarray(), matched.connectivities.toarray()) - np.testing.assert_allclose(baseline.distances.toarray(), matched.distances.toarray()) + with pytest.raises(ValueError, match="must not be set"): + spatial_neighbors( + non_visium_adata, + builder=builder, + coord_type="generic", + n_neighs=3, + radius=5.0, + percentile=95.0, + transform="cosine", + set_diag=True, + copy=True, + ) - def test_builder_compatibility_normalizes_none_transform(self, non_visium_adata: AnnData): + def test_builder_allows_none_legacy_args(self, non_visium_adata: AnnData): builder = KNNBuilder(n_neighs=3, transform=Transform.NONE) baseline = spatial_neighbors(non_visium_adata, builder=builder, copy=True) @@ -259,20 +256,16 @@ def test_builder_compatibility_normalizes_none_transform(self, non_visium_adata: np.testing.assert_array_equal(baseline.connectivities.toarray(), matched.connectivities.toarray()) np.testing.assert_allclose(baseline.distances.toarray(), matched.distances.toarray()) - def test_builder_compatibility_normalizes_tuple_radius_order(self, non_visium_adata: AnnData): + def test_builder_rejects_radius(self, non_visium_adata: AnnData): builder = RadiusBuilder(radius=(4.0, 2.0)) - baseline = spatial_neighbors(non_visium_adata, builder=builder, copy=True) - matched = spatial_neighbors( - non_visium_adata, - builder=builder, - coord_type="generic", - radius=(2.0, 4.0), - copy=True, - ) - - np.testing.assert_array_equal(baseline.connectivities.toarray(), matched.connectivities.toarray()) - np.testing.assert_allclose(baseline.distances.toarray(), matched.distances.toarray()) + with pytest.raises(ValueError, match="must not be set"): + spatial_neighbors( + non_visium_adata, + builder=builder, + radius=(2.0, 4.0), + copy=True, + ) def test_grid_mode_ignores_radius(self, adata_squaregrid: AnnData): default = spatial_neighbors(adata_squaregrid, coord_type="grid", n_neighs=4, n_rings=2, copy=True) From ae1eba1358d4dc866fb447fac81eeb149c5b44a5 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 14:22:20 +0200 Subject: [PATCH 09/96] move classes --- src/squidpy/gr/_build.py | 445 +------------------------ src/squidpy/gr/neighbors.py | 456 +++++++++++++++++++++++++- tests/graph/test_spatial_neighbors.py | 12 +- 3 files changed, 472 insertions(+), 441 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 140e06b26..1c54fda49 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -2,11 +2,7 @@ from __future__ import annotations -import warnings -from abc import ABC, abstractmethod -from collections.abc import Iterable from functools import partial -from itertools import chain from typing import Any, NamedTuple, cast import geopandas as gpd @@ -14,20 +10,13 @@ import pandas as pd from anndata import AnnData from anndata.utils import make_index_unique -from fast_array_utils import stats as fau_stats -from numba import njit, prange +from numba import njit from scipy.sparse import ( - SparseEfficiencyWarning, block_diag, - csr_array, csr_matrix, - isspmatrix_csr, spmatrix, ) -from scipy.spatial import Delaunay from shapely import LineString, MultiPolygon, Polygon -from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances -from sklearn.neighbors import NearestNeighbors from spatialdata import SpatialData from spatialdata._core.centroids import get_centroids from spatialdata._core.query.relational_query import get_element_instances, match_element_to_table @@ -49,6 +38,13 @@ _assert_spatial_basis, _save_data, ) +from squidpy.gr.neighbors import ( + DelaunayBuilder, + GraphBuilder, + GridBuilder, + KNNBuilder, + RadiusBuilder, +) __all__ = [ "SpatialNeighborsResult", @@ -63,256 +59,6 @@ class SpatialNeighborsResult(NamedTuple): distances: csr_matrix -class GraphBuilder(ABC): - """Base class for spatial graph construction strategies.""" - - def __init__( - self, - transform: str | Transform | None = None, - set_diag: bool = False, - percentile: float | None = None, - ) -> None: - self.transform = Transform.NONE if transform is None else Transform(transform) - self.set_diag = set_diag - self.percentile = percentile - - @property - @abstractmethod - def coord_type(self) -> CoordType: - """Coordinate system supported by this builder.""" - - @property - def legacy_params(self) -> dict[str, Any]: - """Return parameters expressed in the legacy spatial_neighbors API.""" - return { - "coord_type": self.coord_type, - "n_neighs": 6, - "radius": None, - "delaunay": False, - "n_rings": 1, - "percentile": self.percentile, - "transform": self.transform, - "set_diag": self.set_diag, - } - - @property - def metadata(self) -> dict[str, Any]: - """Return metadata stored in adata.uns after graph construction.""" - params = self.legacy_params - return { - "n_neighbors": params["n_neighs"], - "coord_type": self.coord_type.v, - "radius": params["radius"], - "transform": self.transform.v, - } - - def build(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: - with warnings.catch_warnings(): - warnings.simplefilter("ignore", SparseEfficiencyWarning) - Adj, Dst = self._build_graph(coords) - - self._apply_filters(Adj, Dst) - self._apply_percentile(Adj, Dst) - Adj.eliminate_zeros() - Dst.eliminate_zeros() - - return self._apply_transform(Adj), Dst - - @abstractmethod - def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: - """Construct raw adjacency and distance matrices.""" - - def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: - """Apply builder-specific post-processing filters.""" - return None - - def _apply_percentile(self, Adj: csr_matrix, Dst: csr_matrix) -> None: - if self.percentile is not None and self.coord_type == CoordType.GENERIC: - threshold = np.percentile(Dst.data, self.percentile) - Adj[Dst > threshold] = 0.0 - Dst[Dst > threshold] = 0.0 - - def _apply_transform(self, Adj: csr_matrix) -> csr_matrix: - if self.transform == Transform.SPECTRAL: - return cast(csr_matrix, _transform_a_spectral(Adj)) - if self.transform == Transform.COSINE: - return cast(csr_matrix, _transform_a_cosine(Adj)) - if self.transform == Transform.NONE: - return Adj - - raise NotImplementedError(f"Transform `{self.transform}` is not yet implemented.") - - -class KNNBuilder(GraphBuilder): - """Build a generic k-nearest-neighbor spatial graph.""" - - def __init__( - self, - n_neighs: int = 6, - transform: str | Transform | None = None, - set_diag: bool = False, - percentile: float | None = None, - ) -> None: - assert_positive(n_neighs, name="n_neighs") - super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) - self.n_neighs = n_neighs - - @property - def coord_type(self) -> CoordType: - return CoordType.GENERIC - - @property - def legacy_params(self) -> dict[str, Any]: - return super().legacy_params | {"n_neighs": self.n_neighs} - - def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: - return cast( - tuple[csr_matrix, csr_matrix], - _build_connectivity( - coords, - n_neighs=self.n_neighs, - return_distance=True, - set_diag=self.set_diag, - ), - ) - - -class _RadiusFilterBuilder(GraphBuilder): - """Intermediate base for builders that support radius-interval pruning.""" - - radius: float | tuple[float, float] | None - n_neighs: int - - @property - def coord_type(self) -> CoordType: - return CoordType.GENERIC - - def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: - if isinstance(self.radius, Iterable): - _filter_by_radius_interval(Adj, Dst, self.radius) - - -class RadiusBuilder(_RadiusFilterBuilder): - """Build a generic radius-based spatial graph.""" - - def __init__( - self, - radius: float | tuple[float, float], - n_neighs: int = 6, - transform: str | Transform | None = None, - set_diag: bool = False, - percentile: float | None = None, - ) -> None: - assert_positive(n_neighs, name="n_neighs") - super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) - self.radius = radius - self.n_neighs = n_neighs - - @property - def legacy_params(self) -> dict[str, Any]: - return super().legacy_params | {"n_neighs": self.n_neighs, "radius": self.radius} - - def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: - return cast( - tuple[csr_matrix, csr_matrix], - _build_connectivity( - coords, - n_neighs=self.n_neighs, - radius=self.radius, - return_distance=True, - set_diag=self.set_diag, - ), - ) - - -class DelaunayBuilder(_RadiusFilterBuilder): - """Build a generic spatial graph from a Delaunay triangulation.""" - - def __init__( - self, - radius: float | tuple[float, float] | None = None, - n_neighs: int = 6, - transform: str | Transform | None = None, - set_diag: bool = False, - percentile: float | None = None, - ) -> None: - assert_positive(n_neighs, name="n_neighs") - super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) - self.radius = radius - self.n_neighs = n_neighs - - @property - def legacy_params(self) -> dict[str, Any]: - return super().legacy_params | {"n_neighs": self.n_neighs, "radius": self.radius, "delaunay": True} - - def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: - return cast( - tuple[csr_matrix, csr_matrix], - _build_connectivity( - coords, - n_neighs=self.n_neighs, - radius=self.radius, - delaunay=True, - return_distance=True, - set_diag=self.set_diag, - ), - ) - - -class GridBuilder(GraphBuilder): - """Build a grid-based spatial graph.""" - - def __init__( - self, - n_neighs: int = 6, - n_rings: int = 1, - delaunay: bool = False, - transform: str | Transform | None = None, - set_diag: bool = False, - ) -> None: - assert_positive(n_neighs, name="n_neighs") - assert_positive(n_rings, name="n_rings") - super().__init__(transform=transform, set_diag=set_diag, percentile=None) - self.n_neighs = n_neighs - self.n_rings = n_rings - self.delaunay = delaunay - - @property - def coord_type(self) -> CoordType: - return CoordType.GRID - - @property - def legacy_params(self) -> dict[str, Any]: - return super().legacy_params | { - "n_neighs": self.n_neighs, - "delaunay": self.delaunay, - "n_rings": self.n_rings, - } - - def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: - return _build_grid( - coords, - n_neighs=self.n_neighs, - n_rings=self.n_rings, - delaunay=self.delaunay, - set_diag=self.set_diag, - ) - - -def _filter_by_radius_interval( - Adj: csr_matrix, - Dst: csr_matrix, - radius: Iterable[float], -) -> None: - minn, maxx = sorted(radius)[:2] - mask = (Dst.data < minn) | (Dst.data > maxx) - a_diag = Adj.diagonal() - - Dst.data[mask] = 0.0 - Adj.data[mask] = 0.0 - Adj.setdiag(a_diag) - - def _validate_no_legacy_params(**kwargs: Any) -> None: conflicts = [k for k, v in kwargs.items() if v is not None] if conflicts: @@ -682,181 +428,6 @@ def _spatial_neighbor( return builder.build(coords) -def _build_grid( - coords: NDArrayA, - n_neighs: int, - n_rings: int, - delaunay: bool = False, - set_diag: bool = False, -) -> tuple[csr_matrix, csr_matrix]: - if n_rings > 1: - Adj: csr_matrix = _build_connectivity( - coords, - n_neighs=n_neighs, - neigh_correct=True, - set_diag=True, - delaunay=delaunay, - return_distance=False, - ) - Res, Walk = Adj, Adj - for i in range(n_rings - 1): - Walk = Walk @ Adj - Walk[Res.nonzero()] = 0.0 - Walk.eliminate_zeros() - Walk.data[:] = i + 2.0 - Res = Res + Walk - Adj = Res - Adj.setdiag(float(set_diag)) - Adj.eliminate_zeros() - - Dst = Adj.copy() - Adj.data[:] = 1.0 - else: - Adj = _build_connectivity( - coords, - n_neighs=n_neighs, - neigh_correct=True, - delaunay=delaunay, - set_diag=set_diag, - ) - Dst = Adj.copy() - - Dst.setdiag(0.0) - - return Adj, Dst - - -def _build_connectivity( - coords: NDArrayA, - n_neighs: int, - radius: float | tuple[float, float] | None = None, - delaunay: bool = False, - neigh_correct: bool = False, - set_diag: bool = False, - return_distance: bool = False, -) -> csr_matrix | tuple[csr_matrix, csr_matrix]: - N = coords.shape[0] - if delaunay: - tri = Delaunay(coords) - indptr, indices = tri.vertex_neighbor_vertices - Adj = csr_matrix((np.ones_like(indices, dtype=np.float32), indices, indptr), shape=(N, N)) - - if return_distance: - # fmt: off - dists = np.array(list(chain(*( - euclidean_distances(coords[indices[indptr[i] : indptr[i + 1]], :], coords[np.newaxis, i, :]) - for i in range(N) - if len(indices[indptr[i] : indptr[i + 1]]) - )))).squeeze() - Dst = csr_matrix((dists, indices, indptr), shape=(N, N)) - # fmt: on - else: - r = 1 if radius is None else radius if isinstance(radius, int | float) else max(radius) - tree = NearestNeighbors(n_neighbors=n_neighs, radius=r, metric="euclidean") - tree.fit(coords) - - if radius is None: - dists, col_indices = tree.kneighbors() - dists, col_indices = dists.reshape(-1), col_indices.reshape(-1) - row_indices = np.repeat(np.arange(N), n_neighs) - if neigh_correct: - dist_cutoff = np.median(dists) * 1.3 # there's a small amount of sway - mask = dists < dist_cutoff - row_indices, col_indices, dists = ( - row_indices[mask], - col_indices[mask], - dists[mask], - ) - else: - dists, col_indices = tree.radius_neighbors() - row_indices = np.repeat(np.arange(N), [len(x) for x in col_indices]) - dists = np.concatenate(dists) - col_indices = np.concatenate(col_indices) - - Adj = csr_matrix( - (np.ones_like(row_indices, dtype=np.float32), (row_indices, col_indices)), - shape=(N, N), - ) - if return_distance: - Dst = csr_matrix((dists, (row_indices, col_indices)), shape=(N, N)) - - # radius-based filtering needs same indices/indptr: do not remove 0s - Adj.setdiag(1.0 if set_diag else Adj.diagonal()) - if return_distance: - Dst.setdiag(0.0) - return Adj, Dst - - return Adj - - -@njit -def _csr_bilateral_diag_scale_helper( - mat: csr_array | csr_matrix, - degrees: NDArrayA, -) -> NDArrayA: - """ - Return an array F aligned with CSR non-zeros such that - F[k] = d[i] * data[k] * d[j] for the k-th non-zero (i, j) in CSR order. - - Parameters - ---------- - - data : array of float - CSR `data` (non-zero values). - indices : array of int - CSR `indices` (column indices). - indptr : array of int - CSR `indptr` (row pointer). - degrees : array of float, shape (n,) - Diagonal scaling vector. - - Returns - ------- - array of float - Length equals len(data). Entry-wise factors d_i * d_j * data[k] - """ - - res = np.empty_like(mat.data, dtype=np.float32) - for i in prange(len(mat.indptr) - 1): - ixs = mat.indices[mat.indptr[i] : mat.indptr[i + 1]] - res[mat.indptr[i] : mat.indptr[i + 1]] = degrees[i] * degrees[ixs] * mat.data[mat.indptr[i] : mat.indptr[i + 1]] - - return res - - -def symmetric_normalize_csr(adj: spmatrix) -> csr_matrix: - """ - Return D^{-1/2} * A * D^{-1/2}, where D = diag(degrees(A)) and A = adj. - - - Parameters - ---------- - adj : scipy.sparse.csr_matrix - - Returns - ------- - scipy.sparse.csr_matrix - """ - degrees = np.squeeze(np.array(np.sqrt(1.0 / fau_stats.sum(adj, axis=0)))) - if adj.shape[0] != len(degrees): - raise ValueError("len(degrees) must equal number of rows of adj") - res_data = _csr_bilateral_diag_scale_helper(adj, degrees) - return csr_matrix((res_data, adj.indices, adj.indptr), shape=adj.shape) - - -def _transform_a_spectral(a: spmatrix) -> spmatrix: - if not isspmatrix_csr(a): - a = a.tocsr() - if not a.nnz: - return a - - return symmetric_normalize_csr(a) - - -def _transform_a_cosine(a: spmatrix) -> spmatrix: - return cosine_similarity(a, dense_output=False) - - @d.dedent def mask_graph( sdata: SpatialData, diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 3e9d1d14c..79cf62195 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -1,7 +1,459 @@ -"""Public builder API for spatial neighbor graph construction.""" +"""Builder classes and helpers for spatial neighbor graph construction.""" from __future__ import annotations -from squidpy.gr._build import DelaunayBuilder, GraphBuilder, GridBuilder, KNNBuilder, RadiusBuilder +import warnings +from abc import ABC, abstractmethod +from collections.abc import Iterable +from itertools import chain +from typing import Any, cast + +import numpy as np +from fast_array_utils import stats as fau_stats +from numba import njit, prange +from scipy.sparse import ( + SparseEfficiencyWarning, + csr_array, + csr_matrix, + isspmatrix_csr, + spmatrix, +) +from scipy.spatial import Delaunay +from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances +from sklearn.neighbors import NearestNeighbors + +from squidpy._constants._constants import CoordType, Transform +from squidpy._utils import NDArrayA +from squidpy._validators import assert_positive __all__ = ["GraphBuilder", "KNNBuilder", "RadiusBuilder", "DelaunayBuilder", "GridBuilder"] + + +class GraphBuilder(ABC): + """Base class for spatial graph construction strategies.""" + + def __init__( + self, + transform: str | Transform | None = None, + set_diag: bool = False, + percentile: float | None = None, + ) -> None: + self.transform = Transform.NONE if transform is None else Transform(transform) + self.set_diag = set_diag + self.percentile = percentile + + @property + @abstractmethod + def coord_type(self) -> CoordType: + """Coordinate system supported by this builder.""" + + @property + def legacy_params(self) -> dict[str, Any]: + """Return parameters expressed in the legacy spatial_neighbors API.""" + return { + "coord_type": self.coord_type, + "n_neighs": 6, + "radius": None, + "delaunay": False, + "n_rings": 1, + "percentile": self.percentile, + "transform": self.transform, + "set_diag": self.set_diag, + } + + @property + def metadata(self) -> dict[str, Any]: + """Return metadata stored in adata.uns after graph construction.""" + params = self.legacy_params + return { + "n_neighbors": params["n_neighs"], + "coord_type": self.coord_type.v, + "radius": params["radius"], + "transform": self.transform.v, + } + + def build(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + with warnings.catch_warnings(): + warnings.simplefilter("ignore", SparseEfficiencyWarning) + Adj, Dst = self._build_graph(coords) + + self._apply_filters(Adj, Dst) + self._apply_percentile(Adj, Dst) + Adj.eliminate_zeros() + Dst.eliminate_zeros() + + return self._apply_transform(Adj), Dst + + @abstractmethod + def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + """Construct raw adjacency and distance matrices.""" + + def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + """Apply builder-specific post-processing filters.""" + return None + + def _apply_percentile(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + if self.percentile is not None and self.coord_type == CoordType.GENERIC: + threshold = np.percentile(Dst.data, self.percentile) + Adj[Dst > threshold] = 0.0 + Dst[Dst > threshold] = 0.0 + + def _apply_transform(self, Adj: csr_matrix) -> csr_matrix: + if self.transform == Transform.SPECTRAL: + return cast(csr_matrix, _transform_a_spectral(Adj)) + if self.transform == Transform.COSINE: + return cast(csr_matrix, _transform_a_cosine(Adj)) + if self.transform == Transform.NONE: + return Adj + + raise NotImplementedError(f"Transform `{self.transform}` is not yet implemented.") + + +class KNNBuilder(GraphBuilder): + """Build a generic k-nearest-neighbor spatial graph.""" + + def __init__( + self, + n_neighs: int = 6, + transform: str | Transform | None = None, + set_diag: bool = False, + percentile: float | None = None, + ) -> None: + assert_positive(n_neighs, name="n_neighs") + super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) + self.n_neighs = n_neighs + + @property + def coord_type(self) -> CoordType: + return CoordType.GENERIC + + @property + def legacy_params(self) -> dict[str, Any]: + return super().legacy_params | {"n_neighs": self.n_neighs} + + def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + return cast( + tuple[csr_matrix, csr_matrix], + _build_connectivity( + coords, + n_neighs=self.n_neighs, + return_distance=True, + set_diag=self.set_diag, + ), + ) + + +class _RadiusFilterBuilder(GraphBuilder): + """Intermediate base for builders that support radius-interval pruning.""" + + radius: float | tuple[float, float] | None + n_neighs: int + + @property + def coord_type(self) -> CoordType: + return CoordType.GENERIC + + def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + if isinstance(self.radius, Iterable): + _filter_by_radius_interval(Adj, Dst, self.radius) + + +class RadiusBuilder(_RadiusFilterBuilder): + """Build a generic radius-based spatial graph.""" + + def __init__( + self, + radius: float | tuple[float, float], + n_neighs: int = 6, + transform: str | Transform | None = None, + set_diag: bool = False, + percentile: float | None = None, + ) -> None: + assert_positive(n_neighs, name="n_neighs") + super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) + self.radius = radius + self.n_neighs = n_neighs + + @property + def legacy_params(self) -> dict[str, Any]: + return super().legacy_params | {"n_neighs": self.n_neighs, "radius": self.radius} + + def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + return cast( + tuple[csr_matrix, csr_matrix], + _build_connectivity( + coords, + n_neighs=self.n_neighs, + radius=self.radius, + return_distance=True, + set_diag=self.set_diag, + ), + ) + + +class DelaunayBuilder(_RadiusFilterBuilder): + """Build a generic spatial graph from a Delaunay triangulation.""" + + def __init__( + self, + radius: float | tuple[float, float] | None = None, + n_neighs: int = 6, + transform: str | Transform | None = None, + set_diag: bool = False, + percentile: float | None = None, + ) -> None: + assert_positive(n_neighs, name="n_neighs") + super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) + self.radius = radius + self.n_neighs = n_neighs + + @property + def legacy_params(self) -> dict[str, Any]: + return super().legacy_params | {"n_neighs": self.n_neighs, "radius": self.radius, "delaunay": True} + + def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + return cast( + tuple[csr_matrix, csr_matrix], + _build_connectivity( + coords, + n_neighs=self.n_neighs, + radius=self.radius, + delaunay=True, + return_distance=True, + set_diag=self.set_diag, + ), + ) + + +class GridBuilder(GraphBuilder): + """Build a grid-based spatial graph.""" + + def __init__( + self, + n_neighs: int = 6, + n_rings: int = 1, + delaunay: bool = False, + transform: str | Transform | None = None, + set_diag: bool = False, + ) -> None: + assert_positive(n_neighs, name="n_neighs") + assert_positive(n_rings, name="n_rings") + super().__init__(transform=transform, set_diag=set_diag, percentile=None) + self.n_neighs = n_neighs + self.n_rings = n_rings + self.delaunay = delaunay + + @property + def coord_type(self) -> CoordType: + return CoordType.GRID + + @property + def legacy_params(self) -> dict[str, Any]: + return super().legacy_params | { + "n_neighs": self.n_neighs, + "delaunay": self.delaunay, + "n_rings": self.n_rings, + } + + def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + return _build_grid( + coords, + n_neighs=self.n_neighs, + n_rings=self.n_rings, + delaunay=self.delaunay, + set_diag=self.set_diag, + ) + + +# --------------------------------------------------------------------------- +# Private helpers used by the builder classes +# --------------------------------------------------------------------------- + + +def _filter_by_radius_interval( + Adj: csr_matrix, + Dst: csr_matrix, + radius: Iterable[float], +) -> None: + minn, maxx = sorted(radius)[:2] + mask = (Dst.data < minn) | (Dst.data > maxx) + a_diag = Adj.diagonal() + + Dst.data[mask] = 0.0 + Adj.data[mask] = 0.0 + Adj.setdiag(a_diag) + + +def _build_grid( + coords: NDArrayA, + n_neighs: int, + n_rings: int, + delaunay: bool = False, + set_diag: bool = False, +) -> tuple[csr_matrix, csr_matrix]: + if n_rings > 1: + Adj: csr_matrix = _build_connectivity( + coords, + n_neighs=n_neighs, + neigh_correct=True, + set_diag=True, + delaunay=delaunay, + return_distance=False, + ) + Res, Walk = Adj, Adj + for i in range(n_rings - 1): + Walk = Walk @ Adj + Walk[Res.nonzero()] = 0.0 + Walk.eliminate_zeros() + Walk.data[:] = i + 2.0 + Res = Res + Walk + Adj = Res + Adj.setdiag(float(set_diag)) + Adj.eliminate_zeros() + + Dst = Adj.copy() + Adj.data[:] = 1.0 + else: + Adj = _build_connectivity( + coords, + n_neighs=n_neighs, + neigh_correct=True, + delaunay=delaunay, + set_diag=set_diag, + ) + Dst = Adj.copy() + + Dst.setdiag(0.0) + + return Adj, Dst + + +def _build_connectivity( + coords: NDArrayA, + n_neighs: int, + radius: float | tuple[float, float] | None = None, + delaunay: bool = False, + neigh_correct: bool = False, + set_diag: bool = False, + return_distance: bool = False, +) -> csr_matrix | tuple[csr_matrix, csr_matrix]: + N = coords.shape[0] + if delaunay: + tri = Delaunay(coords) + indptr, indices = tri.vertex_neighbor_vertices + Adj = csr_matrix((np.ones_like(indices, dtype=np.float32), indices, indptr), shape=(N, N)) + + if return_distance: + # fmt: off + dists = np.array(list(chain(*( + euclidean_distances(coords[indices[indptr[i] : indptr[i + 1]], :], coords[np.newaxis, i, :]) + for i in range(N) + if len(indices[indptr[i] : indptr[i + 1]]) + )))).squeeze() + Dst = csr_matrix((dists, indices, indptr), shape=(N, N)) + # fmt: on + else: + r = 1 if radius is None else radius if isinstance(radius, int | float) else max(radius) + tree = NearestNeighbors(n_neighbors=n_neighs, radius=r, metric="euclidean") + tree.fit(coords) + + if radius is None: + dists, col_indices = tree.kneighbors() + dists, col_indices = dists.reshape(-1), col_indices.reshape(-1) + row_indices = np.repeat(np.arange(N), n_neighs) + if neigh_correct: + dist_cutoff = np.median(dists) * 1.3 # there's a small amount of sway + mask = dists < dist_cutoff + row_indices, col_indices, dists = ( + row_indices[mask], + col_indices[mask], + dists[mask], + ) + else: + dists, col_indices = tree.radius_neighbors() + row_indices = np.repeat(np.arange(N), [len(x) for x in col_indices]) + dists = np.concatenate(dists) + col_indices = np.concatenate(col_indices) + + Adj = csr_matrix( + (np.ones_like(row_indices, dtype=np.float32), (row_indices, col_indices)), + shape=(N, N), + ) + if return_distance: + Dst = csr_matrix((dists, (row_indices, col_indices)), shape=(N, N)) + + # radius-based filtering needs same indices/indptr: do not remove 0s + Adj.setdiag(1.0 if set_diag else Adj.diagonal()) + if return_distance: + Dst.setdiag(0.0) + return Adj, Dst + + return Adj + + +@njit +def _csr_bilateral_diag_scale_helper( + mat: csr_array | csr_matrix, + degrees: NDArrayA, +) -> NDArrayA: + """ + Return an array F aligned with CSR non-zeros such that + F[k] = d[i] * data[k] * d[j] for the k-th non-zero (i, j) in CSR order. + + Parameters + ---------- + + data : array of float + CSR `data` (non-zero values). + indices : array of int + CSR `indices` (column indices). + indptr : array of int + CSR `indptr` (row pointer). + degrees : array of float, shape (n,) + Diagonal scaling vector. + + Returns + ------- + array of float + Length equals len(data). Entry-wise factors d_i * d_j * data[k] + """ + + res = np.empty_like(mat.data, dtype=np.float32) + for i in prange(len(mat.indptr) - 1): + ixs = mat.indices[mat.indptr[i] : mat.indptr[i + 1]] + res[mat.indptr[i] : mat.indptr[i + 1]] = degrees[i] * degrees[ixs] * mat.data[mat.indptr[i] : mat.indptr[i + 1]] + + return res + + +def symmetric_normalize_csr(adj: spmatrix) -> csr_matrix: + """ + Return D^{-1/2} * A * D^{-1/2}, where D = diag(degrees(A)) and A = adj. + + + Parameters + ---------- + adj : scipy.sparse.csr_matrix + + Returns + ------- + scipy.sparse.csr_matrix + """ + degrees = np.squeeze(np.array(np.sqrt(1.0 / fau_stats.sum(adj, axis=0)))) + if adj.shape[0] != len(degrees): + raise ValueError("len(degrees) must equal number of rows of adj") + res_data = _csr_bilateral_diag_scale_helper(adj, degrees) + return csr_matrix((res_data, adj.indices, adj.indptr), shape=adj.shape) + + +def _transform_a_spectral(a: spmatrix) -> spmatrix: + if not isspmatrix_csr(a): + a = a.tocsr() + if not a.nnz: + return a + + return symmetric_normalize_csr(a) + + +def _transform_a_cosine(a: spmatrix) -> spmatrix: + return cosine_similarity(a, dense_output=False) diff --git a/tests/graph/test_spatial_neighbors.py b/tests/graph/test_spatial_neighbors.py index 6908a36c2..8cf32c2c5 100644 --- a/tests/graph/test_spatial_neighbors.py +++ b/tests/graph/test_spatial_neighbors.py @@ -13,8 +13,16 @@ from squidpy._constants._constants import Transform from squidpy._constants._pkg_constants import Key from squidpy.gr import mask_graph, spatial_neighbors -from squidpy.gr._build import DelaunayBuilder, GridBuilder, KNNBuilder, RadiusBuilder, _build_connectivity -from squidpy.gr.neighbors import KNNBuilder as PublicKNNBuilder +from squidpy.gr.neighbors import ( + DelaunayBuilder, + GridBuilder, + KNNBuilder, + RadiusBuilder, + _build_connectivity, +) +from squidpy.gr.neighbors import ( + KNNBuilder as PublicKNNBuilder, +) class TestSpatialNeighbors: From 039b8527d857ce3816aba661db7e2dd1d735d4a6 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 14:29:01 +0200 Subject: [PATCH 10/96] cleanup --- src/squidpy/gr/__init__.py | 2 ++ src/squidpy/gr/_build.py | 7 +++++- src/squidpy/gr/neighbors.py | 47 +------------------------------------ 3 files changed, 9 insertions(+), 47 deletions(-) diff --git a/src/squidpy/gr/__init__.py b/src/squidpy/gr/__init__.py index d4f39958e..6053b2743 100644 --- a/src/squidpy/gr/__init__.py +++ b/src/squidpy/gr/__init__.py @@ -7,6 +7,7 @@ mask_graph, spatial_neighbors, ) +from squidpy.gr import neighbors from squidpy.gr._ligrec import ligrec from squidpy.gr._nhood import ( NhoodEnrichmentResult, @@ -22,6 +23,7 @@ __all__ = [ "SpatialNeighborsResult", "NhoodEnrichmentResult", + "neighbors", "mask_graph", "spatial_neighbors", "ligrec", diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 1c54fda49..5bfbb1ceb 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -405,7 +405,12 @@ def spatial_neighbors( neighbors_dict = { "connectivities_key": conns_key, "distances_key": dists_key, - "params": builder.metadata, + "params": { + "n_neighbors": getattr(builder, "n_neighs", 6), + "coord_type": builder.coord_type.v, + "radius": getattr(builder, "radius", None), + "transform": builder.transform.v, + }, } if copy: diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 79cf62195..00a630168 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -6,7 +6,7 @@ from abc import ABC, abstractmethod from collections.abc import Iterable from itertools import chain -from typing import Any, cast +from typing import cast import numpy as np from fast_array_utils import stats as fau_stats @@ -47,31 +47,6 @@ def __init__( def coord_type(self) -> CoordType: """Coordinate system supported by this builder.""" - @property - def legacy_params(self) -> dict[str, Any]: - """Return parameters expressed in the legacy spatial_neighbors API.""" - return { - "coord_type": self.coord_type, - "n_neighs": 6, - "radius": None, - "delaunay": False, - "n_rings": 1, - "percentile": self.percentile, - "transform": self.transform, - "set_diag": self.set_diag, - } - - @property - def metadata(self) -> dict[str, Any]: - """Return metadata stored in adata.uns after graph construction.""" - params = self.legacy_params - return { - "n_neighbors": params["n_neighs"], - "coord_type": self.coord_type.v, - "radius": params["radius"], - "transform": self.transform.v, - } - def build(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: with warnings.catch_warnings(): warnings.simplefilter("ignore", SparseEfficiencyWarning) @@ -127,10 +102,6 @@ def __init__( def coord_type(self) -> CoordType: return CoordType.GENERIC - @property - def legacy_params(self) -> dict[str, Any]: - return super().legacy_params | {"n_neighs": self.n_neighs} - def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: return cast( tuple[csr_matrix, csr_matrix], @@ -174,10 +145,6 @@ def __init__( self.radius = radius self.n_neighs = n_neighs - @property - def legacy_params(self) -> dict[str, Any]: - return super().legacy_params | {"n_neighs": self.n_neighs, "radius": self.radius} - def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: return cast( tuple[csr_matrix, csr_matrix], @@ -207,10 +174,6 @@ def __init__( self.radius = radius self.n_neighs = n_neighs - @property - def legacy_params(self) -> dict[str, Any]: - return super().legacy_params | {"n_neighs": self.n_neighs, "radius": self.radius, "delaunay": True} - def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: return cast( tuple[csr_matrix, csr_matrix], @@ -247,14 +210,6 @@ def __init__( def coord_type(self) -> CoordType: return CoordType.GRID - @property - def legacy_params(self) -> dict[str, Any]: - return super().legacy_params | { - "n_neighs": self.n_neighs, - "delaunay": self.delaunay, - "n_rings": self.n_rings, - } - def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: return _build_grid( coords, From b5dcf9f59a7bf4086a021b2d36174dd0ce86d5da Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 2 Apr 2026 12:29:39 +0000 Subject: [PATCH 11/96] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/squidpy/gr/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/squidpy/gr/__init__.py b/src/squidpy/gr/__init__.py index 6053b2743..a64930622 100644 --- a/src/squidpy/gr/__init__.py +++ b/src/squidpy/gr/__init__.py @@ -2,12 +2,12 @@ from __future__ import annotations +from squidpy.gr import neighbors from squidpy.gr._build import ( SpatialNeighborsResult, mask_graph, spatial_neighbors, ) -from squidpy.gr import neighbors from squidpy.gr._ligrec import ligrec from squidpy.gr._nhood import ( NhoodEnrichmentResult, From 4c11adfd096cd4db9eb50bc7d8d5f7af67f13f03 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 14:36:56 +0200 Subject: [PATCH 12/96] add docs --- docs/api.md | 17 +++++++++++++++++ src/squidpy/gr/neighbors.py | 19 ++++++++++++++++++- 2 files changed, 35 insertions(+), 1 deletion(-) diff --git a/docs/api.md b/docs/api.md index fc9922a31..59b08f632 100644 --- a/docs/api.md +++ b/docs/api.md @@ -109,3 +109,20 @@ import squidpy as sq datasets.visium_hne_image_crop datasets.visium_fluo_image_crop ``` +## Types + + + +## Extensibility +```{eval-rst} +.. module:: squidpy.gr.neighbors +.. currentmodule:: squidpy +.. autosummary:: + :toctree: api + + gr.neighbors.GraphBuilder + gr.neighbors.KNNBuilder + gr.neighbors.RadiusBuilder + gr.neighbors.DelaunayBuilder + gr.neighbors.GridBuilder +``` diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 00a630168..0c71aedc7 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -1,4 +1,21 @@ -"""Builder classes and helpers for spatial neighbor graph construction.""" +"""Graph construction strategies for :func:`squidpy.gr.spatial_neighbors`. + +This module provides the builder classes that control how spatial neighbor +graphs are constructed. :func:`~squidpy.gr.spatial_neighbors` accepts a +``builder`` argument; when supplied, the builder fully determines the +graph-construction algorithm and its parameters. + +Built-in builders cover the common cases: + +- :class:`KNNBuilder` -- k-nearest-neighbor graph. +- :class:`RadiusBuilder` -- radius-based graph. +- :class:`DelaunayBuilder` -- Delaunay triangulation graph. +- :class:`GridBuilder` -- grid (Visium-style) graph. + +To implement a custom strategy, subclass :class:`GraphBuilder` and override +:meth:`~GraphBuilder._build_graph`. The returned object can then be passed +to ``spatial_neighbors(adata, builder=my_builder)``. +""" from __future__ import annotations From ea1ce936aafb0185db05e2cd56c3484f28a55a82 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 14:39:10 +0200 Subject: [PATCH 13/96] remove leftover --- docs/api.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/docs/api.md b/docs/api.md index 59b08f632..37610f75a 100644 --- a/docs/api.md +++ b/docs/api.md @@ -109,8 +109,6 @@ import squidpy as sq datasets.visium_hne_image_crop datasets.visium_fluo_image_crop ``` -## Types - ## Extensibility From ebb7fd52cb648873c02d373a9bb10cf7f6a180ae Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 14:41:41 +0200 Subject: [PATCH 14/96] resolution in reolve func --- src/squidpy/gr/_build.py | 28 +++++++++++++++------------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 5bfbb1ceb..ab611b496 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -70,15 +70,27 @@ def _validate_no_legacy_params(**kwargs: Any) -> None: def _resolve_graph_builder( *, - coord_type: CoordType, + coord_type: str | CoordType | None, n_neighs: int, radius: float | tuple[float, float] | None, delaunay: bool, n_rings: int, percentile: float | None, - transform: Transform, + transform: str | Transform | None, set_diag: bool, + has_spatial_uns: bool = False, ) -> GraphBuilder: + transform = Transform.NONE if transform is None else Transform(transform) + if coord_type is None: + if radius is not None: + logg.warning( + f"Graph creation with `radius` is only available when `coord_type = {CoordType.GENERIC!r}` specified. " + f"Ignoring parameter `radius = {radius}`." + ) + coord_type = CoordType.GRID if has_spatial_uns else CoordType.GENERIC + else: + coord_type = CoordType(coord_type) + if coord_type == CoordType.GRID: if percentile is not None: raise ValueError( @@ -348,17 +360,6 @@ def spatial_neighbors( assert_positive(n_rings, name="n_rings") assert_positive(n_neighs, name="n_neighs") - transform = Transform.NONE if transform is None else Transform(transform) - if coord_type is None: - if radius is not None: - logg.warning( - f"Graph creation with `radius` is only available when `coord_type = {CoordType.GENERIC!r}` specified. " - f"Ignoring parameter `radius = {radius}`." - ) - coord_type = CoordType.GRID if Key.uns.spatial in adata.uns else CoordType.GENERIC - else: - coord_type = CoordType(coord_type) - builder = _resolve_graph_builder( coord_type=coord_type, n_neighs=n_neighs, @@ -368,6 +369,7 @@ def spatial_neighbors( percentile=percentile, transform=transform, set_diag=set_diag, + has_spatial_uns=Key.uns.spatial in adata.uns, ) if library_key is not None: From 69a939492b0f18409d1793600560b315bb024ea6 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 14:45:03 +0200 Subject: [PATCH 15/96] reduce dup code --- src/squidpy/gr/_build.py | 38 ++++++++++---------------------------- 1 file changed, 10 insertions(+), 28 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index ab611b496..f61da6df5 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -91,41 +91,23 @@ def _resolve_graph_builder( else: coord_type = CoordType(coord_type) + common: dict[str, Any] = { + "n_neighs": n_neighs, + "transform": transform, + "set_diag": set_diag, + } + if coord_type == CoordType.GRID: if percentile is not None: raise ValueError( "`percentile` is not supported for grid coordinates. It only applies to generic (non-grid) graphs." ) - return GridBuilder( - n_neighs=n_neighs, - n_rings=n_rings, - delaunay=delaunay, - transform=transform, - set_diag=set_diag, - ) + return GridBuilder(**common, n_rings=n_rings, delaunay=delaunay) if delaunay: - return DelaunayBuilder( - radius=radius, - n_neighs=n_neighs, - transform=transform, - set_diag=set_diag, - percentile=percentile, - ) + return DelaunayBuilder(**common, radius=radius, percentile=percentile) if radius is not None: - return RadiusBuilder( - radius=radius, - n_neighs=n_neighs, - transform=transform, - set_diag=set_diag, - percentile=percentile, - ) - - return KNNBuilder( - n_neighs=n_neighs, - transform=transform, - set_diag=set_diag, - percentile=percentile, - ) + return RadiusBuilder(**common, radius=radius, percentile=percentile) + return KNNBuilder(**common, percentile=percentile) @d.dedent From 77755ed844266555d4c6b99bc87f8cb16ccc6b4f Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 17:11:53 +0200 Subject: [PATCH 16/96] deprecate invalid n_neighs --- src/squidpy/gr/_build.py | 10 +++++----- src/squidpy/gr/neighbors.py | 11 ++++++++++- 2 files changed, 15 insertions(+), 6 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index f61da6df5..d1060bef9 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -215,10 +215,10 @@ def spatial_neighbors( ``[min(radius), max(radius)]``. - Generic Delaunay mode: ``coord_type='generic'``, ``delaunay=True``. - Builds a Delaunay triangulation graph. ``n_neighs`` is not - used for the triangulation itself, but is still accepted for - backward compatibility. If ``radius`` is a tuple, it is used - only as a post-construction pruning interval. + Builds a Delaunay triangulation graph. ``n_neighs`` is + ignored by the triangulation and passing it is deprecated. + If ``radius`` is a tuple, it is used only as a + post-construction pruning interval. Across these modes: @@ -239,7 +239,7 @@ def spatial_neighbors( - If ``coord_type`` resolves to ``'grid'``, grid mode is used. In that case ``radius`` is ignored. - Otherwise, if ``delaunay=True``, Delaunay mode is used. - In this mode ``n_neighs`` does not affect the triangulation. + ``n_neighs`` is ignored (deprecated). A tuple ``radius`` is only used afterward as a pruning interval. A scalar ``radius`` is ignored. - Otherwise, if ``radius`` is set, radius mode is used. diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 0c71aedc7..c65a3c7b8 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -181,11 +181,20 @@ class DelaunayBuilder(_RadiusFilterBuilder): def __init__( self, radius: float | tuple[float, float] | None = None, - n_neighs: int = 6, + n_neighs: int | None = None, transform: str | Transform | None = None, set_diag: bool = False, percentile: float | None = None, ) -> None: + if n_neighs is not None: + warnings.warn( + "Parameter `n_neighs` of `DelaunayBuilder` is ignored by " + "Delaunay triangulation and will be removed in squidpy v2.0.0.", + FutureWarning, + stacklevel=2, + ) + else: + n_neighs = 6 assert_positive(n_neighs, name="n_neighs") super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) self.radius = radius From d8a81451663227aa5d8db362209474ebd551f2ee Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 17:28:37 +0200 Subject: [PATCH 17/96] expose new functions and deprecate the flat one --- docs/api.md | 4 + src/squidpy/_docs.py | 30 +++ src/squidpy/gr/__init__.py | 8 + src/squidpy/gr/_build.py | 376 ++++++++++++++++++++++++++++++++----- 4 files changed, 373 insertions(+), 45 deletions(-) diff --git a/docs/api.md b/docs/api.md index 37610f75a..cfbc16256 100644 --- a/docs/api.md +++ b/docs/api.md @@ -18,6 +18,10 @@ import squidpy as sq :toctree: api gr.spatial_neighbors + gr.spatial_neighbors_knn + gr.spatial_neighbors_radius + gr.spatial_neighbors_delaunay + gr.spatial_neighbors_grid gr.SpatialNeighborsResult gr.mask_graph gr.nhood_enrichment diff --git a/src/squidpy/_docs.py b/src/squidpy/_docs.py index a4596df75..4580b34ef 100644 --- a/src/squidpy/_docs.py +++ b/src/squidpy/_docs.py @@ -359,6 +359,33 @@ def decorator2(obj: Any) -> Any: If multiple `library_id`, column in :attr:`anndata.AnnData.obs` which stores mapping between ``library_id`` and obs.""" +_sdata_params = """\ +elements_to_coordinate_systems + A dictionary mapping element names of the SpatialData object to coordinate systems. + The elements can be either Shapes or Labels. For compatibility, the spatialdata table must annotate + all regions keys. Must not be ``None`` if ``adata`` is a :class:`spatialdata.SpatialData`. +table_key + Key in :attr:`spatialdata.SpatialData.tables` where the spatialdata table is stored. Must not be ``None`` if + ``adata`` is a :class:`spatialdata.SpatialData`.""" +_graph_common_params = """\ +percentile + Percentile of the distances to use as threshold. +transform + Adjacency matrix transform (``'spectral'``, ``'cosine'``, or ``None``). +set_diag + Whether to set the diagonal of the connectivities to ``1.0``. +key_added + Key which controls where the results are saved if ``copy = False``.""" +_spatial_neighbors_returns = """\ +If ``copy = True``, returns a :class:`~squidpy.gr.SpatialNeighborsResult` with the +spatial connectivities and distances matrices. + +Otherwise, modifies the ``adata`` with the following keys: + + - :attr:`anndata.AnnData.obsp` ``['{key_added}_connectivities']`` - the spatial connectivities. + - :attr:`anndata.AnnData.obsp` ``['{key_added}_distances']`` - the spatial distances. + - :attr:`anndata.AnnData.uns` ``['{key_added}']`` - :class:`dict` containing parameters.""" + d = DocstringProcessor( adata=_adata, img_container=_img_container, @@ -401,4 +428,7 @@ def decorator2(obj: Any) -> Any: groups=_groups, plotting_library_id=_plotting_library_id, library_key=_library_key, + sdata_params=_sdata_params, + graph_common_params=_graph_common_params, + spatial_neighbors_returns=_spatial_neighbors_returns, ) diff --git a/src/squidpy/gr/__init__.py b/src/squidpy/gr/__init__.py index a64930622..a5dd26973 100644 --- a/src/squidpy/gr/__init__.py +++ b/src/squidpy/gr/__init__.py @@ -7,6 +7,10 @@ SpatialNeighborsResult, mask_graph, spatial_neighbors, + spatial_neighbors_delaunay, + spatial_neighbors_grid, + spatial_neighbors_knn, + spatial_neighbors_radius, ) from squidpy.gr._ligrec import ligrec from squidpy.gr._nhood import ( @@ -26,6 +30,10 @@ "neighbors", "mask_graph", "spatial_neighbors", + "spatial_neighbors_knn", + "spatial_neighbors_radius", + "spatial_neighbors_delaunay", + "spatial_neighbors_grid", "ligrec", "centrality_scores", "interaction_matrix", diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index d1060bef9..7572fad39 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -2,6 +2,7 @@ from __future__ import annotations +import warnings from functools import partial from typing import Any, NamedTuple, cast @@ -49,6 +50,10 @@ __all__ = [ "SpatialNeighborsResult", "spatial_neighbors", + "spatial_neighbors_knn", + "spatial_neighbors_radius", + "spatial_neighbors_delaunay", + "spatial_neighbors_grid", ] @@ -110,6 +115,66 @@ def _resolve_graph_builder( return KNNBuilder(**common, percentile=percentile) +def _resolve_spatial_data( + adata: AnnData | SpatialData, + *, + spatial_key: str, + elements_to_coordinate_systems: dict[str, str] | None, + table_key: str | None, + library_key: str | None, +) -> tuple[AnnData, str | None]: + """Resolve SpatialData to AnnData, returning (adata, library_key).""" + if isinstance(adata, SpatialData): + assert elements_to_coordinate_systems is not None, ( + "Since `adata` is a :class:`spatialdata.SpatialData`, `elements_to_coordinate_systems` must not be `None`." + ) + assert table_key is not None, ( + "Since `adata` is a :class:`spatialdata.SpatialData`, `table_key` must not be `None`." + ) + elements, table = match_element_to_table(adata, list(elements_to_coordinate_systems), table_key) + assert table.obs_names.equals(adata.tables[table_key].obs_names), ( + "The spatialdata table must annotate all elements keys. Some elements are missing, please check the `elements_to_coordinate_systems` dictionary." + ) + regions, region_key, instance_key = get_table_keys(adata.tables[table_key]) + regions = [regions] if isinstance(regions, str) else regions + ordered_regions_in_table = adata.tables[table_key].obs[region_key].unique() + + # TODO: remove this after https://github.com/scverse/spatialdata/issues/614 + remove_centroids = {} + elem_instances = [] + for e in regions: + schema = get_model(elements[e]) + element_instances = get_element_instances(elements[e]).to_series() + if np.isin(0, element_instances.values) and (schema in (Labels2DModel, Labels3DModel)): + element_instances = element_instances.drop(index=0) + remove_centroids[e] = True + else: + remove_centroids[e] = False + elem_instances.append(element_instances) + + element_instances = pd.concat(elem_instances) + if (not np.all(element_instances.values == adata.tables[table_key].obs[instance_key].values)) or ( + not np.all(ordered_regions_in_table == regions) + ): + raise ValueError( + "The spatialdata table must annotate all elements keys. Some elements are missing or not ordered correctly, please check the `elements_to_coordinate_systems` dictionary." + ) + centroids = [] + for region_ in ordered_regions_in_table: + cs = elements_to_coordinate_systems[region_] + centroid = get_centroids(adata[region_], coordinate_system=cs)[["x", "y"]].compute() + + # TODO: remove this after https://github.com/scverse/spatialdata/issues/614 + if remove_centroids[region_]: + centroid = centroid[1:].copy() + centroids.append(centroid) + + adata.tables[table_key].obsm[spatial_key] = np.concatenate(centroids) + adata = adata.tables[table_key] + library_key = region_key + return adata, library_key + + @d.dedent @inject_docs(t=Transform, c=CoordType) def spatial_neighbors( @@ -133,6 +198,18 @@ def spatial_neighbors( """ Create a graph from spatial coordinates. + .. deprecated:: 1.6.0 + The flat-parameter API of ``spatial_neighbors`` is deprecated and will + be removed in squidpy v1.7.0. Use one of the mode-specific functions + instead: + + - :func:`spatial_neighbors_knn` + - :func:`spatial_neighbors_radius` + - :func:`spatial_neighbors_delaunay` + - :func:`spatial_neighbors_grid` + + Passing a ``builder`` instance directly remains supported. + Parameters ---------- %(adata)s @@ -271,54 +348,59 @@ def spatial_neighbors( - :attr:`anndata.AnnData.obsp` ``['{{key_added}}_distances']`` - the spatial distances. - :attr:`anndata.AnnData.uns` ``['{{key_added}}']`` - :class:`dict` containing parameters. """ - if isinstance(adata, SpatialData): - assert elements_to_coordinate_systems is not None, ( - "Since `adata` is a :class:`spatialdata.SpatialData`, `elements_to_coordinate_systems` must not be `None`." - ) - assert table_key is not None, ( - "Since `adata` is a :class:`spatialdata.SpatialData`, `table_key` must not be `None`." - ) - elements, table = match_element_to_table(adata, list(elements_to_coordinate_systems), table_key) - assert table.obs_names.equals(adata.tables[table_key].obs_names), ( - "The spatialdata table must annotate all elements keys. Some elements are missing, please check the `elements_to_coordinate_systems` dictionary." + if builder is None: + warnings.warn( + "Calling `spatial_neighbors` without a `builder` argument is deprecated " + "and will be removed in squidpy v1.7.0. Use one of the mode-specific " + "functions instead: `spatial_neighbors_knn`, `spatial_neighbors_radius`, " + "`spatial_neighbors_delaunay`, or `spatial_neighbors_grid`.", + FutureWarning, + stacklevel=2, ) - regions, region_key, instance_key = get_table_keys(adata.tables[table_key]) - regions = [regions] if isinstance(regions, str) else regions - ordered_regions_in_table = adata.tables[table_key].obs[region_key].unique() - - # TODO: remove this after https://github.com/scverse/spatialdata/issues/614 - remove_centroids = {} - elem_instances = [] - for e in regions: - schema = get_model(elements[e]) - element_instances = get_element_instances(elements[e]).to_series() - if np.isin(0, element_instances.values) and (schema in (Labels2DModel, Labels3DModel)): - element_instances = element_instances.drop(index=0) - remove_centroids[e] = True - else: - remove_centroids[e] = False - elem_instances.append(element_instances) - - element_instances = pd.concat(elem_instances) - if (not np.all(element_instances.values == adata.tables[table_key].obs[instance_key].values)) or ( - not np.all(ordered_regions_in_table == regions) - ): - raise ValueError( - "The spatialdata table must annotate all elements keys. Some elements are missing or not ordered correctly, please check the `elements_to_coordinate_systems` dictionary." - ) - centroids = [] - for region_ in ordered_regions_in_table: - cs = elements_to_coordinate_systems[region_] - centroid = get_centroids(adata[region_], coordinate_system=cs)[["x", "y"]].compute() + return _spatial_neighbors( + adata, + spatial_key=spatial_key, + elements_to_coordinate_systems=elements_to_coordinate_systems, + table_key=table_key, + library_key=library_key, + coord_type=coord_type, + n_neighs=n_neighs, + radius=radius, + delaunay=delaunay, + n_rings=n_rings, + percentile=percentile, + transform=transform, + set_diag=set_diag, + builder=builder, + key_added=key_added, + copy=copy, + ) - # TODO: remove this after https://github.com/scverse/spatialdata/issues/614 - if remove_centroids[region_]: - centroid = centroid[1:].copy() - centroids.append(centroid) - adata.tables[table_key].obsm[spatial_key] = np.concatenate(centroids) - adata = adata.tables[table_key] - library_key = region_key +def _spatial_neighbors( + adata: AnnData | SpatialData, + *, + spatial_key: str = Key.obsm.spatial, + elements_to_coordinate_systems: dict[str, str] | None = None, + table_key: str | None = None, + library_key: str | None = None, + coord_type: str | CoordType | None = None, + n_neighs: int | None = None, + radius: float | tuple[float, float] | None = None, + delaunay: bool | None = None, + n_rings: int | None = None, + percentile: float | None = None, + transform: str | Transform | None = None, + set_diag: bool | None = None, + builder: GraphBuilder | None = None, + key_added: str = "spatial", + copy: bool = False, +) -> SpatialNeighborsResult | None: + """Internal implementation of spatial_neighbors (no deprecation warning).""" + adata, library_key = _resolve_spatial_data( + adata, spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, + table_key=table_key, library_key=library_key, + ) _assert_spatial_basis(adata, spatial_key) @@ -354,6 +436,210 @@ def spatial_neighbors( has_spatial_uns=Key.uns.spatial in adata.uns, ) + return _run_spatial_neighbors(adata, builder=builder, spatial_key=spatial_key, library_key=library_key, key_added=key_added, copy=copy) + + +@d.dedent +def spatial_neighbors_knn( + adata: AnnData | SpatialData, + spatial_key: str = Key.obsm.spatial, + elements_to_coordinate_systems: dict[str, str] | None = None, + table_key: str | None = None, + library_key: str | None = None, + n_neighs: int = 6, + percentile: float | None = None, + transform: str | Transform | None = None, + set_diag: bool = False, + key_added: str = "spatial", + copy: bool = False, +) -> SpatialNeighborsResult | None: + """Create a k-nearest-neighbor graph from spatial coordinates. + + Parameters + ---------- + %(adata)s + %(spatial_key)s + %(sdata_params)s + %(library_key)s + n_neighs + Number of nearest neighbors. Defaults to ``6``. + %(graph_common_params)s + %(copy)s + + Returns + ------- + %(spatial_neighbors_returns)s + """ + transform_enum = Transform.NONE if transform is None else Transform(transform) + builder = KNNBuilder( + n_neighs=n_neighs, percentile=percentile, + transform=transform_enum, set_diag=set_diag, + ) + return _spatial_neighbors( + adata, spatial_key=spatial_key, + elements_to_coordinate_systems=elements_to_coordinate_systems, + table_key=table_key, library_key=library_key, + builder=builder, key_added=key_added, copy=copy, + ) + + +@d.dedent +def spatial_neighbors_radius( + adata: AnnData | SpatialData, + spatial_key: str = Key.obsm.spatial, + elements_to_coordinate_systems: dict[str, str] | None = None, + table_key: str | None = None, + library_key: str | None = None, + radius: float | tuple[float, float] = 1.0, + n_neighs: int = 6, + percentile: float | None = None, + transform: str | Transform | None = None, + set_diag: bool = False, + key_added: str = "spatial", + copy: bool = False, +) -> SpatialNeighborsResult | None: + """Create a radius-based graph from spatial coordinates. + + Parameters + ---------- + %(adata)s + %(spatial_key)s + %(sdata_params)s + %(library_key)s + radius + Neighborhood radius. If a :class:`tuple`, the graph is built with the + maximum radius and then pruned to the interval ``[min(radius), max(radius)]``. + n_neighs + Number of nearest neighbors used internally by the radius graph builder. + Defaults to ``6``. + %(graph_common_params)s + %(copy)s + + Returns + ------- + %(spatial_neighbors_returns)s + """ + transform_enum = Transform.NONE if transform is None else Transform(transform) + builder = RadiusBuilder( + n_neighs=n_neighs, radius=radius, percentile=percentile, + transform=transform_enum, set_diag=set_diag, + ) + return _spatial_neighbors( + adata, spatial_key=spatial_key, + elements_to_coordinate_systems=elements_to_coordinate_systems, + table_key=table_key, library_key=library_key, + builder=builder, key_added=key_added, copy=copy, + ) + + +@d.dedent +def spatial_neighbors_delaunay( + adata: AnnData | SpatialData, + spatial_key: str = Key.obsm.spatial, + elements_to_coordinate_systems: dict[str, str] | None = None, + table_key: str | None = None, + library_key: str | None = None, + radius: tuple[float, float] | None = None, + percentile: float | None = None, + transform: str | Transform | None = None, + set_diag: bool = False, + key_added: str = "spatial", + copy: bool = False, +) -> SpatialNeighborsResult | None: + """Create a Delaunay triangulation graph from spatial coordinates. + + Parameters + ---------- + %(adata)s + %(spatial_key)s + %(sdata_params)s + %(library_key)s + radius + If a :class:`tuple`, used as a post-construction pruning interval + ``[min(radius), max(radius)]``. + %(graph_common_params)s + %(copy)s + + Returns + ------- + %(spatial_neighbors_returns)s + """ + transform_enum = Transform.NONE if transform is None else Transform(transform) + builder = DelaunayBuilder( + radius=radius, percentile=percentile, + transform=transform_enum, set_diag=set_diag, + ) + return _spatial_neighbors( + adata, spatial_key=spatial_key, + elements_to_coordinate_systems=elements_to_coordinate_systems, + table_key=table_key, library_key=library_key, + builder=builder, key_added=key_added, copy=copy, + ) + + +@d.dedent +def spatial_neighbors_grid( + adata: AnnData | SpatialData, + spatial_key: str = Key.obsm.spatial, + elements_to_coordinate_systems: dict[str, str] | None = None, + table_key: str | None = None, + library_key: str | None = None, + n_neighs: int = 6, + n_rings: int = 1, + delaunay: bool = False, + transform: str | Transform | None = None, + set_diag: bool = False, + key_added: str = "spatial", + copy: bool = False, +) -> SpatialNeighborsResult | None: + """Create a grid-based graph from spatial coordinates. + + This is the mode used for Visium-like grid coordinates. + + Parameters + ---------- + %(adata)s + %(spatial_key)s + %(sdata_params)s + %(library_key)s + n_neighs + Number of neighboring tiles. Defaults to ``6``. + n_rings + Number of rings of neighbors. Defaults to ``1``. + delaunay + Whether to compute the grid graph from Delaunay triangulation. + %(graph_common_params)s + %(copy)s + + Returns + ------- + %(spatial_neighbors_returns)s + """ + assert_positive(n_rings, name="n_rings") + assert_positive(n_neighs, name="n_neighs") + transform_enum = Transform.NONE if transform is None else Transform(transform) + builder = GridBuilder( + n_neighs=n_neighs, n_rings=n_rings, delaunay=delaunay, + transform=transform_enum, set_diag=set_diag, + ) + return _spatial_neighbors( + adata, spatial_key=spatial_key, + elements_to_coordinate_systems=elements_to_coordinate_systems, + table_key=table_key, library_key=library_key, + builder=builder, key_added=key_added, copy=copy, + ) + + +def _run_spatial_neighbors( + adata: AnnData, + *, + builder: GraphBuilder, + spatial_key: str = Key.obsm.spatial, + library_key: str | None = None, + key_added: str = "spatial", + copy: bool = False, +) -> SpatialNeighborsResult | None: + """Shared core: build the graph from a resolved builder and save results.""" if library_key is not None: _assert_categorical_obs(adata, key=library_key) libs = adata.obs[library_key].cat.categories From 2e75b3f3413513711fef56addf49d9b7a4f6c464 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 17:40:49 +0200 Subject: [PATCH 18/96] move branches to classes --- src/squidpy/gr/neighbors.py | 239 +++++++++++--------------- tests/graph/test_spatial_neighbors.py | 3 +- 2 files changed, 99 insertions(+), 143 deletions(-) diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index c65a3c7b8..67d8089d6 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -120,15 +120,23 @@ def coord_type(self) -> CoordType: return CoordType.GENERIC def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: - return cast( - tuple[csr_matrix, csr_matrix], - _build_connectivity( - coords, - n_neighs=self.n_neighs, - return_distance=True, - set_diag=self.set_diag, - ), + N = coords.shape[0] + tree = NearestNeighbors(n_neighbors=self.n_neighs, radius=1, metric="euclidean") + tree.fit(coords) + + dists, col_indices = tree.kneighbors() + dists, col_indices = dists.reshape(-1), col_indices.reshape(-1) + row_indices = np.repeat(np.arange(N), self.n_neighs) + + Adj = csr_matrix( + (np.ones_like(row_indices, dtype=np.float32), (row_indices, col_indices)), + shape=(N, N), ) + Dst = csr_matrix((dists, (row_indices, col_indices)), shape=(N, N)) + + Adj.setdiag(1.0 if self.set_diag else Adj.diagonal()) + Dst.setdiag(0.0) + return Adj, Dst class _RadiusFilterBuilder(GraphBuilder): @@ -163,16 +171,25 @@ def __init__( self.n_neighs = n_neighs def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: - return cast( - tuple[csr_matrix, csr_matrix], - _build_connectivity( - coords, - n_neighs=self.n_neighs, - radius=self.radius, - return_distance=True, - set_diag=self.set_diag, - ), + N = coords.shape[0] + r = self.radius if isinstance(self.radius, int | float) else max(self.radius) + tree = NearestNeighbors(n_neighbors=self.n_neighs, radius=r, metric="euclidean") + tree.fit(coords) + + dists, col_indices = tree.radius_neighbors() + row_indices = np.repeat(np.arange(N), [len(x) for x in col_indices]) + dists = np.concatenate(dists) + col_indices = np.concatenate(col_indices) + + Adj = csr_matrix( + (np.ones_like(row_indices, dtype=np.float32), (row_indices, col_indices)), + shape=(N, N), ) + Dst = csr_matrix((dists, (row_indices, col_indices)), shape=(N, N)) + + Adj.setdiag(1.0 if self.set_diag else Adj.diagonal()) + Dst.setdiag(0.0) + return Adj, Dst class DelaunayBuilder(_RadiusFilterBuilder): @@ -201,17 +218,23 @@ def __init__( self.n_neighs = n_neighs def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: - return cast( - tuple[csr_matrix, csr_matrix], - _build_connectivity( - coords, - n_neighs=self.n_neighs, - radius=self.radius, - delaunay=True, - return_distance=True, - set_diag=self.set_diag, - ), - ) + N = coords.shape[0] + tri = Delaunay(coords) + indptr, indices = tri.vertex_neighbor_vertices + Adj = csr_matrix((np.ones_like(indices, dtype=np.float32), indices, indptr), shape=(N, N)) + + # fmt: off + dists = np.array(list(chain(*( + euclidean_distances(coords[indices[indptr[i] : indptr[i + 1]], :], coords[np.newaxis, i, :]) + for i in range(N) + if len(indices[indptr[i] : indptr[i + 1]]) + )))).squeeze() + # fmt: on + Dst = csr_matrix((dists, indices, indptr), shape=(N, N)) + + Adj.setdiag(1.0 if self.set_diag else Adj.diagonal()) + Dst.setdiag(0.0) + return Adj, Dst class GridBuilder(GraphBuilder): @@ -237,13 +260,53 @@ def coord_type(self) -> CoordType: return CoordType.GRID def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: - return _build_grid( - coords, - n_neighs=self.n_neighs, - n_rings=self.n_rings, - delaunay=self.delaunay, - set_diag=self.set_diag, - ) + if self.n_rings > 1: + Adj = self._base_adjacency(coords, set_diag=True) + Res, Walk = Adj, Adj + for i in range(self.n_rings - 1): + Walk = Walk @ Adj + Walk[Res.nonzero()] = 0.0 + Walk.eliminate_zeros() + Walk.data[:] = i + 2.0 + Res = Res + Walk + Adj = Res + Adj.setdiag(float(self.set_diag)) + Adj.eliminate_zeros() + + Dst = Adj.copy() + Adj.data[:] = 1.0 + else: + Adj = self._base_adjacency(coords, set_diag=self.set_diag) + Dst = Adj.copy() + + Dst.setdiag(0.0) + return Adj, Dst + + def _base_adjacency(self, coords: NDArrayA, *, set_diag: bool) -> csr_matrix: + """KNN adjacency with median-distance correction for grid coordinates.""" + N = coords.shape[0] + if self.delaunay: + tri = Delaunay(coords) + indptr, indices = tri.vertex_neighbor_vertices + Adj = csr_matrix((np.ones_like(indices, dtype=np.float32), indices, indptr), shape=(N, N)) + else: + tree = NearestNeighbors(n_neighbors=self.n_neighs, radius=1, metric="euclidean") + tree.fit(coords) + dists, col_indices = tree.kneighbors() + dists, col_indices = dists.reshape(-1), col_indices.reshape(-1) + row_indices = np.repeat(np.arange(N), self.n_neighs) + + dist_cutoff = np.median(dists) * 1.3 + mask = dists < dist_cutoff + row_indices, col_indices = row_indices[mask], col_indices[mask] + + Adj = csr_matrix( + (np.ones_like(row_indices, dtype=np.float32), (row_indices, col_indices)), + shape=(N, N), + ) + + Adj.setdiag(1.0 if set_diag else Adj.diagonal()) + return Adj # --------------------------------------------------------------------------- @@ -265,112 +328,6 @@ def _filter_by_radius_interval( Adj.setdiag(a_diag) -def _build_grid( - coords: NDArrayA, - n_neighs: int, - n_rings: int, - delaunay: bool = False, - set_diag: bool = False, -) -> tuple[csr_matrix, csr_matrix]: - if n_rings > 1: - Adj: csr_matrix = _build_connectivity( - coords, - n_neighs=n_neighs, - neigh_correct=True, - set_diag=True, - delaunay=delaunay, - return_distance=False, - ) - Res, Walk = Adj, Adj - for i in range(n_rings - 1): - Walk = Walk @ Adj - Walk[Res.nonzero()] = 0.0 - Walk.eliminate_zeros() - Walk.data[:] = i + 2.0 - Res = Res + Walk - Adj = Res - Adj.setdiag(float(set_diag)) - Adj.eliminate_zeros() - - Dst = Adj.copy() - Adj.data[:] = 1.0 - else: - Adj = _build_connectivity( - coords, - n_neighs=n_neighs, - neigh_correct=True, - delaunay=delaunay, - set_diag=set_diag, - ) - Dst = Adj.copy() - - Dst.setdiag(0.0) - - return Adj, Dst - - -def _build_connectivity( - coords: NDArrayA, - n_neighs: int, - radius: float | tuple[float, float] | None = None, - delaunay: bool = False, - neigh_correct: bool = False, - set_diag: bool = False, - return_distance: bool = False, -) -> csr_matrix | tuple[csr_matrix, csr_matrix]: - N = coords.shape[0] - if delaunay: - tri = Delaunay(coords) - indptr, indices = tri.vertex_neighbor_vertices - Adj = csr_matrix((np.ones_like(indices, dtype=np.float32), indices, indptr), shape=(N, N)) - - if return_distance: - # fmt: off - dists = np.array(list(chain(*( - euclidean_distances(coords[indices[indptr[i] : indptr[i + 1]], :], coords[np.newaxis, i, :]) - for i in range(N) - if len(indices[indptr[i] : indptr[i + 1]]) - )))).squeeze() - Dst = csr_matrix((dists, indices, indptr), shape=(N, N)) - # fmt: on - else: - r = 1 if radius is None else radius if isinstance(radius, int | float) else max(radius) - tree = NearestNeighbors(n_neighbors=n_neighs, radius=r, metric="euclidean") - tree.fit(coords) - - if radius is None: - dists, col_indices = tree.kneighbors() - dists, col_indices = dists.reshape(-1), col_indices.reshape(-1) - row_indices = np.repeat(np.arange(N), n_neighs) - if neigh_correct: - dist_cutoff = np.median(dists) * 1.3 # there's a small amount of sway - mask = dists < dist_cutoff - row_indices, col_indices, dists = ( - row_indices[mask], - col_indices[mask], - dists[mask], - ) - else: - dists, col_indices = tree.radius_neighbors() - row_indices = np.repeat(np.arange(N), [len(x) for x in col_indices]) - dists = np.concatenate(dists) - col_indices = np.concatenate(col_indices) - - Adj = csr_matrix( - (np.ones_like(row_indices, dtype=np.float32), (row_indices, col_indices)), - shape=(N, N), - ) - if return_distance: - Dst = csr_matrix((dists, (row_indices, col_indices)), shape=(N, N)) - - # radius-based filtering needs same indices/indptr: do not remove 0s - Adj.setdiag(1.0 if set_diag else Adj.diagonal()) - if return_distance: - Dst.setdiag(0.0) - return Adj, Dst - - return Adj - @njit def _csr_bilateral_diag_scale_helper( diff --git a/tests/graph/test_spatial_neighbors.py b/tests/graph/test_spatial_neighbors.py index 8cf32c2c5..1f6bdbd03 100644 --- a/tests/graph/test_spatial_neighbors.py +++ b/tests/graph/test_spatial_neighbors.py @@ -18,7 +18,6 @@ GridBuilder, KNNBuilder, RadiusBuilder, - _build_connectivity, ) from squidpy.gr.neighbors import ( KNNBuilder as PublicKNNBuilder, @@ -305,7 +304,7 @@ def test_percentile_filtering(self, adata_hne: AnnData, percentile: float, coord assert not ((result.connectivities != result_filtered.connectivities).nnz == 0) assert result.distances.max() > result_filtered.distances.max() - Adj, Dst = _build_connectivity(adata_hne.obsm["spatial"], n_neighs=6, return_distance=True, set_diag=False) + Adj, Dst = KNNBuilder(n_neighs=6, set_diag=False)._build_graph(adata_hne.obsm["spatial"]) threshold = np.percentile(Dst.data, percentile) Adj[Dst > threshold] = 0.0 Dst[Dst > threshold] = 0.0 From 8c1c2ef94c474d142f5b40801c6ef7f889f8884a Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 2 Apr 2026 15:41:01 +0000 Subject: [PATCH 19/96] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/squidpy/gr/_build.py | 77 ++++++++++++++++++++++++++----------- src/squidpy/gr/neighbors.py | 1 - 2 files changed, 54 insertions(+), 24 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 7572fad39..203ecc0d7 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -398,8 +398,11 @@ def _spatial_neighbors( ) -> SpatialNeighborsResult | None: """Internal implementation of spatial_neighbors (no deprecation warning).""" adata, library_key = _resolve_spatial_data( - adata, spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, - table_key=table_key, library_key=library_key, + adata, + spatial_key=spatial_key, + elements_to_coordinate_systems=elements_to_coordinate_systems, + table_key=table_key, + library_key=library_key, ) _assert_spatial_basis(adata, spatial_key) @@ -436,7 +439,9 @@ def _spatial_neighbors( has_spatial_uns=Key.uns.spatial in adata.uns, ) - return _run_spatial_neighbors(adata, builder=builder, spatial_key=spatial_key, library_key=library_key, key_added=key_added, copy=copy) + return _run_spatial_neighbors( + adata, builder=builder, spatial_key=spatial_key, library_key=library_key, key_added=key_added, copy=copy + ) @d.dedent @@ -472,14 +477,20 @@ def spatial_neighbors_knn( """ transform_enum = Transform.NONE if transform is None else Transform(transform) builder = KNNBuilder( - n_neighs=n_neighs, percentile=percentile, - transform=transform_enum, set_diag=set_diag, + n_neighs=n_neighs, + percentile=percentile, + transform=transform_enum, + set_diag=set_diag, ) return _spatial_neighbors( - adata, spatial_key=spatial_key, + adata, + spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, - table_key=table_key, library_key=library_key, - builder=builder, key_added=key_added, copy=copy, + table_key=table_key, + library_key=library_key, + builder=builder, + key_added=key_added, + copy=copy, ) @@ -521,14 +532,21 @@ def spatial_neighbors_radius( """ transform_enum = Transform.NONE if transform is None else Transform(transform) builder = RadiusBuilder( - n_neighs=n_neighs, radius=radius, percentile=percentile, - transform=transform_enum, set_diag=set_diag, + n_neighs=n_neighs, + radius=radius, + percentile=percentile, + transform=transform_enum, + set_diag=set_diag, ) return _spatial_neighbors( - adata, spatial_key=spatial_key, + adata, + spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, - table_key=table_key, library_key=library_key, - builder=builder, key_added=key_added, copy=copy, + table_key=table_key, + library_key=library_key, + builder=builder, + key_added=key_added, + copy=copy, ) @@ -566,14 +584,20 @@ def spatial_neighbors_delaunay( """ transform_enum = Transform.NONE if transform is None else Transform(transform) builder = DelaunayBuilder( - radius=radius, percentile=percentile, - transform=transform_enum, set_diag=set_diag, + radius=radius, + percentile=percentile, + transform=transform_enum, + set_diag=set_diag, ) return _spatial_neighbors( - adata, spatial_key=spatial_key, + adata, + spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, - table_key=table_key, library_key=library_key, - builder=builder, key_added=key_added, copy=copy, + table_key=table_key, + library_key=library_key, + builder=builder, + key_added=key_added, + copy=copy, ) @@ -619,14 +643,21 @@ def spatial_neighbors_grid( assert_positive(n_neighs, name="n_neighs") transform_enum = Transform.NONE if transform is None else Transform(transform) builder = GridBuilder( - n_neighs=n_neighs, n_rings=n_rings, delaunay=delaunay, - transform=transform_enum, set_diag=set_diag, + n_neighs=n_neighs, + n_rings=n_rings, + delaunay=delaunay, + transform=transform_enum, + set_diag=set_diag, ) return _spatial_neighbors( - adata, spatial_key=spatial_key, + adata, + spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, - table_key=table_key, library_key=library_key, - builder=builder, key_added=key_added, copy=copy, + table_key=table_key, + library_key=library_key, + builder=builder, + key_added=key_added, + copy=copy, ) diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 67d8089d6..a4726ce85 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -328,7 +328,6 @@ def _filter_by_radius_interval( Adj.setdiag(a_diag) - @njit def _csr_bilateral_diag_scale_helper( mat: csr_array | csr_matrix, From 268d30cb9e88b97a5e391e0696871504614b6b38 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 17:42:42 +0200 Subject: [PATCH 20/96] remove radius abstraction --- src/squidpy/gr/neighbors.py | 35 ++++++++++++++++++----------------- 1 file changed, 18 insertions(+), 17 deletions(-) diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 67d8089d6..8606dd644 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -139,22 +139,7 @@ def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: return Adj, Dst -class _RadiusFilterBuilder(GraphBuilder): - """Intermediate base for builders that support radius-interval pruning.""" - - radius: float | tuple[float, float] | None - n_neighs: int - - @property - def coord_type(self) -> CoordType: - return CoordType.GENERIC - - def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: - if isinstance(self.radius, Iterable): - _filter_by_radius_interval(Adj, Dst, self.radius) - - -class RadiusBuilder(_RadiusFilterBuilder): +class RadiusBuilder(GraphBuilder): """Build a generic radius-based spatial graph.""" def __init__( @@ -170,6 +155,10 @@ def __init__( self.radius = radius self.n_neighs = n_neighs + @property + def coord_type(self) -> CoordType: + return CoordType.GENERIC + def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: N = coords.shape[0] r = self.radius if isinstance(self.radius, int | float) else max(self.radius) @@ -191,8 +180,12 @@ def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: Dst.setdiag(0.0) return Adj, Dst + def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + if isinstance(self.radius, Iterable): + _filter_by_radius_interval(Adj, Dst, self.radius) -class DelaunayBuilder(_RadiusFilterBuilder): + +class DelaunayBuilder(GraphBuilder): """Build a generic spatial graph from a Delaunay triangulation.""" def __init__( @@ -217,6 +210,10 @@ def __init__( self.radius = radius self.n_neighs = n_neighs + @property + def coord_type(self) -> CoordType: + return CoordType.GENERIC + def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: N = coords.shape[0] tri = Delaunay(coords) @@ -236,6 +233,10 @@ def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: Dst.setdiag(0.0) return Adj, Dst + def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + if isinstance(self.radius, Iterable): + _filter_by_radius_interval(Adj, Dst, self.radius) + class GridBuilder(GraphBuilder): """Build a grid-based spatial graph.""" From aa9734ab0bb6ae446da7ab6fd363d8a1c3339b22 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 17:47:33 +0200 Subject: [PATCH 21/96] add extensibility page --- docs/api.md | 3 ++ docs/extensibility.md | 65 +++++++++++++++++++++++++++++++++++++ docs/index.md | 1 + src/squidpy/gr/neighbors.py | 18 ++-------- 4 files changed, 71 insertions(+), 16 deletions(-) create mode 100644 docs/extensibility.md diff --git a/docs/api.md b/docs/api.md index cfbc16256..60434e4be 100644 --- a/docs/api.md +++ b/docs/api.md @@ -116,6 +116,9 @@ import squidpy as sq ## Extensibility + +See the {doc}`extensibility guide ` for how to implement a custom graph builder. + ```{eval-rst} .. module:: squidpy.gr.neighbors .. currentmodule:: squidpy diff --git a/docs/extensibility.md b/docs/extensibility.md new file mode 100644 index 000000000..b6605f61b --- /dev/null +++ b/docs/extensibility.md @@ -0,0 +1,65 @@ +# Extensibility + +## Custom graph builders + +The `squidpy.gr.neighbors` module exposes a {class}`~squidpy.gr.neighbors.GraphBuilder` +base class that all built-in graph construction strategies inherit from. +You can implement your own strategy by subclassing it. + +### Minimal example + +```python +import numpy as np +from scipy.sparse import csr_matrix +from squidpy.gr.neighbors import GraphBuilder +from squidpy._constants._constants import CoordType + + +class MyBuilder(GraphBuilder): + """Example: connect every point to its single nearest neighbor.""" + + @property + def coord_type(self) -> CoordType: + return CoordType.GENERIC + + def _build_graph(self, coords): + from sklearn.neighbors import NearestNeighbors + + N = coords.shape[0] + tree = NearestNeighbors(n_neighbors=2, metric="euclidean") + tree.fit(coords) + dists, indices = tree.kneighbors() + + # drop self-neighbor (index 0), keep only the closest other point + row = np.arange(N) + col = indices[:, 1] + d = dists[:, 1] + + Adj = csr_matrix((np.ones(N, dtype=np.float32), (row, col)), shape=(N, N)) + Dst = csr_matrix((d, (row, col)), shape=(N, N)) + return Adj, Dst +``` + +### Using a custom builder + +Pass an instance to any of the spatial-neighbor functions via the ``builder`` +argument: + +```python +import squidpy as sq + +sq.gr.spatial_neighbors_knn(adata) # built-in KNN mode +sq.gr.spatial_neighbors(adata, builder=MyBuilder()) # custom builder +``` + +### What to override + +| Method / property | Required | Purpose | +|---|---|---| +| {attr}`~squidpy.gr.neighbors.GraphBuilder.coord_type` | yes | Return the {class}`~squidpy._constants._constants.CoordType` this builder supports. | +| {meth}`~squidpy.gr.neighbors.GraphBuilder._build_graph` | yes | Construct and return ``(Adj, Dst)`` as {class}`~scipy.sparse.csr_matrix` pair. | +| {meth}`~squidpy.gr.neighbors.GraphBuilder._apply_filters` | no | Post-processing on the raw ``Adj``/``Dst`` (e.g. radius-interval pruning). Called before percentile filtering and transform. | + +The base class handles percentile filtering ({meth}`~squidpy.gr.neighbors.GraphBuilder._apply_percentile`), +adjacency transforms ({meth}`~squidpy.gr.neighbors.GraphBuilder._apply_transform`), and +{class}`~scipy.sparse.SparseEfficiencyWarning` suppression automatically. diff --git a/docs/index.md b/docs/index.md index e44bc2995..7ddaff4d7 100644 --- a/docs/index.md +++ b/docs/index.md @@ -63,6 +63,7 @@ We are happy about any contributions! Before you start, check out our [contribut installation api classes + extensibility release_notes references contributing diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 886ec0659..d167aabbc 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -1,20 +1,6 @@ -"""Graph construction strategies for :func:`squidpy.gr.spatial_neighbors`. +"""Graph construction strategies for spatial neighbor graphs. -This module provides the builder classes that control how spatial neighbor -graphs are constructed. :func:`~squidpy.gr.spatial_neighbors` accepts a -``builder`` argument; when supplied, the builder fully determines the -graph-construction algorithm and its parameters. - -Built-in builders cover the common cases: - -- :class:`KNNBuilder` -- k-nearest-neighbor graph. -- :class:`RadiusBuilder` -- radius-based graph. -- :class:`DelaunayBuilder` -- Delaunay triangulation graph. -- :class:`GridBuilder` -- grid (Visium-style) graph. - -To implement a custom strategy, subclass :class:`GraphBuilder` and override -:meth:`~GraphBuilder._build_graph`. The returned object can then be passed -to ``spatial_neighbors(adata, builder=my_builder)``. +See the :doc:`/extensibility` guide for how to implement a custom builder. """ from __future__ import annotations From 67bad43323b553da20fc69c546cb61a8cc26b821 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 17:53:46 +0200 Subject: [PATCH 22/96] unprivate methods and add extensibility page --- docs/extensibility.md | 73 +++++++++++++++------------ src/squidpy/gr/neighbors.py | 20 ++++---- tests/graph/test_spatial_neighbors.py | 2 +- 3 files changed, 53 insertions(+), 42 deletions(-) diff --git a/docs/extensibility.md b/docs/extensibility.md index b6605f61b..1ea9b0704 100644 --- a/docs/extensibility.md +++ b/docs/extensibility.md @@ -6,60 +6,71 @@ The `squidpy.gr.neighbors` module exposes a {class}`~squidpy.gr.neighbors.GraphB base class that all built-in graph construction strategies inherit from. You can implement your own strategy by subclassing it. -### Minimal example +### What to override + +| Method / property | Required | Purpose | +|---|---|---| +| {attr}`~squidpy.gr.neighbors.GraphBuilder.coord_type` | yes | Return the {class}`~squidpy._constants._constants.CoordType` this builder supports. | +| {meth}`~squidpy.gr.neighbors.GraphBuilder.build_graph` | yes | Construct and return ``(Adj, Dst)`` as {class}`~scipy.sparse.csr_matrix` pair. | +| {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_filters` | no | Post-processing on the raw ``Adj``/``Dst`` (e.g. radius-interval pruning). Called before percentile filtering and transform. | + +The base class handles percentile filtering, adjacency transforms, and +{class}`~scipy.sparse.SparseEfficiencyWarning` suppression automatically. + +### Example: mutual KNN + +The built-in {class}`~squidpy.gr.neighbors.KNNBuilder` keeps all k-nearest +edges regardless of reciprocity. A mutual KNN graph retains an edge only when +both endpoints consider each other a neighbor, producing a sparser and more +conservative graph: ```python import numpy as np from scipy.sparse import csr_matrix -from squidpy.gr.neighbors import GraphBuilder +from sklearn.neighbors import NearestNeighbors + from squidpy._constants._constants import CoordType +from squidpy.gr.neighbors import GraphBuilder + +class MutualKNNBuilder(GraphBuilder): + """KNN graph keeping only mutual (reciprocal) edges.""" -class MyBuilder(GraphBuilder): - """Example: connect every point to its single nearest neighbor.""" + def __init__(self, n_neighs: int = 6, **kwargs): + super().__init__(**kwargs) + self.n_neighs = n_neighs @property def coord_type(self) -> CoordType: return CoordType.GENERIC - def _build_graph(self, coords): - from sklearn.neighbors import NearestNeighbors - + def build_graph(self, coords): N = coords.shape[0] - tree = NearestNeighbors(n_neighbors=2, metric="euclidean") + tree = NearestNeighbors(n_neighbors=self.n_neighs, metric="euclidean") tree.fit(coords) dists, indices = tree.kneighbors() - # drop self-neighbor (index 0), keep only the closest other point - row = np.arange(N) - col = indices[:, 1] - d = dists[:, 1] + row = np.repeat(np.arange(N), self.n_neighs) + col = indices.reshape(-1) + d = dists.reshape(-1) - Adj = csr_matrix((np.ones(N, dtype=np.float32), (row, col)), shape=(N, N)) + Adj = csr_matrix((np.ones_like(d, dtype=np.float32), (row, col)), shape=(N, N)) Dst = csr_matrix((d, (row, col)), shape=(N, N)) + + # keep only mutual edges + mutual = Adj.multiply(Adj.T) + Adj = mutual.tocsr() + Dst = Dst.multiply(mutual).tocsr() + + Adj.setdiag(1.0 if self.set_diag else Adj.diagonal()) + Dst.setdiag(0.0) return Adj, Dst ``` -### Using a custom builder - -Pass an instance to any of the spatial-neighbor functions via the ``builder`` -argument: +Use it like any other builder: ```python import squidpy as sq -sq.gr.spatial_neighbors_knn(adata) # built-in KNN mode -sq.gr.spatial_neighbors(adata, builder=MyBuilder()) # custom builder +sq.gr.spatial_neighbors(adata, builder=MutualKNNBuilder(n_neighs=6)) ``` - -### What to override - -| Method / property | Required | Purpose | -|---|---|---| -| {attr}`~squidpy.gr.neighbors.GraphBuilder.coord_type` | yes | Return the {class}`~squidpy._constants._constants.CoordType` this builder supports. | -| {meth}`~squidpy.gr.neighbors.GraphBuilder._build_graph` | yes | Construct and return ``(Adj, Dst)`` as {class}`~scipy.sparse.csr_matrix` pair. | -| {meth}`~squidpy.gr.neighbors.GraphBuilder._apply_filters` | no | Post-processing on the raw ``Adj``/``Dst`` (e.g. radius-interval pruning). Called before percentile filtering and transform. | - -The base class handles percentile filtering ({meth}`~squidpy.gr.neighbors.GraphBuilder._apply_percentile`), -adjacency transforms ({meth}`~squidpy.gr.neighbors.GraphBuilder._apply_transform`), and -{class}`~scipy.sparse.SparseEfficiencyWarning` suppression automatically. diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index d167aabbc..9a15ef3b8 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -53,9 +53,9 @@ def coord_type(self) -> CoordType: def build(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: with warnings.catch_warnings(): warnings.simplefilter("ignore", SparseEfficiencyWarning) - Adj, Dst = self._build_graph(coords) + Adj, Dst = self.build_graph(coords) - self._apply_filters(Adj, Dst) + self.apply_filters(Adj, Dst) self._apply_percentile(Adj, Dst) Adj.eliminate_zeros() Dst.eliminate_zeros() @@ -63,10 +63,10 @@ def build(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: return self._apply_transform(Adj), Dst @abstractmethod - def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: """Construct raw adjacency and distance matrices.""" - def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + def apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: """Apply builder-specific post-processing filters.""" return None @@ -105,7 +105,7 @@ def __init__( def coord_type(self) -> CoordType: return CoordType.GENERIC - def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: N = coords.shape[0] tree = NearestNeighbors(n_neighbors=self.n_neighs, radius=1, metric="euclidean") tree.fit(coords) @@ -145,7 +145,7 @@ def __init__( def coord_type(self) -> CoordType: return CoordType.GENERIC - def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: N = coords.shape[0] r = self.radius if isinstance(self.radius, int | float) else max(self.radius) tree = NearestNeighbors(n_neighbors=self.n_neighs, radius=r, metric="euclidean") @@ -166,7 +166,7 @@ def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: Dst.setdiag(0.0) return Adj, Dst - def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + def apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: if isinstance(self.radius, Iterable): _filter_by_radius_interval(Adj, Dst, self.radius) @@ -200,7 +200,7 @@ def __init__( def coord_type(self) -> CoordType: return CoordType.GENERIC - def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: N = coords.shape[0] tri = Delaunay(coords) indptr, indices = tri.vertex_neighbor_vertices @@ -219,7 +219,7 @@ def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: Dst.setdiag(0.0) return Adj, Dst - def _apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + def apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: if isinstance(self.radius, Iterable): _filter_by_radius_interval(Adj, Dst, self.radius) @@ -246,7 +246,7 @@ def __init__( def coord_type(self) -> CoordType: return CoordType.GRID - def _build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: + def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: if self.n_rings > 1: Adj = self._base_adjacency(coords, set_diag=True) Res, Walk = Adj, Adj diff --git a/tests/graph/test_spatial_neighbors.py b/tests/graph/test_spatial_neighbors.py index 1f6bdbd03..39be507cb 100644 --- a/tests/graph/test_spatial_neighbors.py +++ b/tests/graph/test_spatial_neighbors.py @@ -304,7 +304,7 @@ def test_percentile_filtering(self, adata_hne: AnnData, percentile: float, coord assert not ((result.connectivities != result_filtered.connectivities).nnz == 0) assert result.distances.max() > result_filtered.distances.max() - Adj, Dst = KNNBuilder(n_neighs=6, set_diag=False)._build_graph(adata_hne.obsm["spatial"]) + Adj, Dst = KNNBuilder(n_neighs=6, set_diag=False).build_graph(adata_hne.obsm["spatial"]) threshold = np.percentile(Dst.data, percentile) Adj[Dst > threshold] = 0.0 Dst[Dst > threshold] = 0.0 From 65fa2453a5edb7ca9bb7fdaf1a3775687f7c3205 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 2 Apr 2026 18:00:38 +0200 Subject: [PATCH 23/96] give a better example --- docs/extensibility.md | 50 +++++++++++++++++++++---------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/docs/extensibility.md b/docs/extensibility.md index 1ea9b0704..00bf73478 100644 --- a/docs/extensibility.md +++ b/docs/extensibility.md @@ -17,28 +17,29 @@ You can implement your own strategy by subclassing it. The base class handles percentile filtering, adjacency transforms, and {class}`~scipy.sparse.SparseEfficiencyWarning` suppression automatically. -### Example: mutual KNN +### Example: fast radius search with SNN -The built-in {class}`~squidpy.gr.neighbors.KNNBuilder` keeps all k-nearest -edges regardless of reciprocity. A mutual KNN graph retains an edge only when -both endpoints consider each other a neighbor, producing a sparser and more -conservative graph: +The built-in {class}`~squidpy.gr.neighbors.RadiusBuilder` uses scikit-learn's +``NearestNeighbors``. The [snnpy](https://github.com/nla-group/snn) library +provides a faster exact fixed-radius search based on PCA-based pruning. The +example below swaps the backend while keeping full compatibility with the rest +of the Squidpy graph pipeline: ```python import numpy as np from scipy.sparse import csr_matrix -from sklearn.neighbors import NearestNeighbors +from snnpy import build_snn_model from squidpy._constants._constants import CoordType from squidpy.gr.neighbors import GraphBuilder -class MutualKNNBuilder(GraphBuilder): - """KNN graph keeping only mutual (reciprocal) edges.""" +class SNNRadiusBuilder(GraphBuilder): + """Radius graph using the SNN fixed-radius search backend.""" - def __init__(self, n_neighs: int = 6, **kwargs): + def __init__(self, radius: float, **kwargs): super().__init__(**kwargs) - self.n_neighs = n_neighs + self.radius = radius @property def coord_type(self) -> CoordType: @@ -46,22 +47,21 @@ class MutualKNNBuilder(GraphBuilder): def build_graph(self, coords): N = coords.shape[0] - tree = NearestNeighbors(n_neighbors=self.n_neighs, metric="euclidean") - tree.fit(coords) - dists, indices = tree.kneighbors() - - row = np.repeat(np.arange(N), self.n_neighs) - col = indices.reshape(-1) - d = dists.reshape(-1) - - Adj = csr_matrix((np.ones_like(d, dtype=np.float32), (row, col)), shape=(N, N)) + model = build_snn_model(coords, verbose=0) + indices, dists = model.batch_query_radius( + coords, self.radius, return_distance=True, + ) + + row = np.repeat(np.arange(N), [len(idx) for idx in indices]) + col = np.concatenate(indices) + d = np.concatenate(dists).astype(np.float64) + + Adj = csr_matrix( + (np.ones(len(row), dtype=np.float32), (row, col)), + shape=(N, N), + ) Dst = csr_matrix((d, (row, col)), shape=(N, N)) - # keep only mutual edges - mutual = Adj.multiply(Adj.T) - Adj = mutual.tocsr() - Dst = Dst.multiply(mutual).tocsr() - Adj.setdiag(1.0 if self.set_diag else Adj.diagonal()) Dst.setdiag(0.0) return Adj, Dst @@ -72,5 +72,5 @@ Use it like any other builder: ```python import squidpy as sq -sq.gr.spatial_neighbors(adata, builder=MutualKNNBuilder(n_neighs=6)) +sq.gr.spatial_neighbors(adata, builder=SNNRadiusBuilder(radius=100.0)) ``` From 85bfdebd98a5a65b551f7cb88234ca59fe07f1ce Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Apr 2026 10:59:30 +0200 Subject: [PATCH 24/96] apply signature suggestion --- src/squidpy/gr/_build.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 203ecc0d7..5ef948ef9 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -447,6 +447,7 @@ def _spatial_neighbors( @d.dedent def spatial_neighbors_knn( adata: AnnData | SpatialData, + *, spatial_key: str = Key.obsm.spatial, elements_to_coordinate_systems: dict[str, str] | None = None, table_key: str | None = None, @@ -497,6 +498,7 @@ def spatial_neighbors_knn( @d.dedent def spatial_neighbors_radius( adata: AnnData | SpatialData, + *, spatial_key: str = Key.obsm.spatial, elements_to_coordinate_systems: dict[str, str] | None = None, table_key: str | None = None, @@ -553,6 +555,7 @@ def spatial_neighbors_radius( @d.dedent def spatial_neighbors_delaunay( adata: AnnData | SpatialData, + *, spatial_key: str = Key.obsm.spatial, elements_to_coordinate_systems: dict[str, str] | None = None, table_key: str | None = None, @@ -604,6 +607,7 @@ def spatial_neighbors_delaunay( @d.dedent def spatial_neighbors_grid( adata: AnnData | SpatialData, + *, spatial_key: str = Key.obsm.spatial, elements_to_coordinate_systems: dict[str, str] | None = None, table_key: str | None = None, From 79f584f82db52da62cdf264bae03c56cbed9f96b Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Apr 2026 11:49:08 +0200 Subject: [PATCH 25/96] reorganize abstractions --- src/squidpy/gr/_build.py | 76 +++++++++++++++++++++++++--------------- 1 file changed, 47 insertions(+), 29 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 5ef948ef9..bfc9aeadd 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -3,7 +3,6 @@ from __future__ import annotations import warnings -from functools import partial from typing import Any, NamedTuple, cast import geopandas as gpd @@ -377,6 +376,26 @@ def spatial_neighbors( ) +def _prepare_spatial_neighbors_input( + adata: AnnData | SpatialData, + *, + spatial_key: str, + elements_to_coordinate_systems: dict[str, str] | None, + table_key: str | None, + library_key: str | None, +) -> tuple[AnnData, str | None]: + """Resolve input data and validate the requested spatial basis.""" + adata, library_key = _resolve_spatial_data( + adata, + spatial_key=spatial_key, + elements_to_coordinate_systems=elements_to_coordinate_systems, + table_key=table_key, + library_key=library_key, + ) + _assert_spatial_basis(adata, spatial_key) + return adata, library_key + + def _spatial_neighbors( adata: AnnData | SpatialData, *, @@ -397,7 +416,7 @@ def _spatial_neighbors( copy: bool = False, ) -> SpatialNeighborsResult | None: """Internal implementation of spatial_neighbors (no deprecation warning).""" - adata, library_key = _resolve_spatial_data( + adata, library_key = _prepare_spatial_neighbors_input( adata, spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, @@ -405,8 +424,6 @@ def _spatial_neighbors( library_key=library_key, ) - _assert_spatial_basis(adata, spatial_key) - if builder is not None: _validate_no_legacy_params( coord_type=coord_type, @@ -483,13 +500,18 @@ def spatial_neighbors_knn( transform=transform_enum, set_diag=set_diag, ) - return _spatial_neighbors( + adata, library_key = _prepare_spatial_neighbors_input( adata, spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, table_key=table_key, library_key=library_key, + ) + return _run_spatial_neighbors( + adata, builder=builder, + spatial_key=spatial_key, + library_key=library_key, key_added=key_added, copy=copy, ) @@ -540,13 +562,18 @@ def spatial_neighbors_radius( transform=transform_enum, set_diag=set_diag, ) - return _spatial_neighbors( + adata, library_key = _prepare_spatial_neighbors_input( adata, spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, table_key=table_key, library_key=library_key, + ) + return _run_spatial_neighbors( + adata, builder=builder, + spatial_key=spatial_key, + library_key=library_key, key_added=key_added, copy=copy, ) @@ -592,13 +619,18 @@ def spatial_neighbors_delaunay( transform=transform_enum, set_diag=set_diag, ) - return _spatial_neighbors( + adata, library_key = _prepare_spatial_neighbors_input( adata, spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, table_key=table_key, library_key=library_key, + ) + return _run_spatial_neighbors( + adata, builder=builder, + spatial_key=spatial_key, + library_key=library_key, key_added=key_added, copy=copy, ) @@ -653,13 +685,18 @@ def spatial_neighbors_grid( transform=transform_enum, set_diag=set_diag, ) - return _spatial_neighbors( + adata, library_key = _prepare_spatial_neighbors_input( adata, spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, table_key=table_key, library_key=library_key, + ) + return _run_spatial_neighbors( + adata, builder=builder, + spatial_key=spatial_key, + library_key=library_key, key_added=key_added, copy=copy, ) @@ -685,23 +722,17 @@ def _run_spatial_neighbors( start = logg.info( f"Creating graph using `{builder.coord_type}` coordinates and `{builder.transform}` transform and `{len(libs)}` libraries." ) - _build_fun = partial( - _spatial_neighbor, - spatial_key=spatial_key, - builder=builder, - ) - if library_key is not None: mats: list[tuple[spmatrix, spmatrix]] = [] ixs: list[int] = [] for lib in libs: ixs.extend(np.where(adata.obs[library_key] == lib)[0]) - mats.append(_build_fun(adata[adata.obs[library_key] == lib])) + mats.append(builder.build(adata[adata.obs[library_key] == lib].obsm[spatial_key])) ixs = cast(list[int], np.argsort(ixs).tolist()) Adj = block_diag([m[0] for m in mats], format="csr")[ixs, :][:, ixs] Dst = block_diag([m[1] for m in mats], format="csr")[ixs, :][:, ixs] else: - Adj, Dst = _build_fun(adata) + Adj, Dst = builder.build(adata.obsm[spatial_key]) neighs_key = Key.uns.spatial_neighs(key_added) conns_key = Key.obsp.spatial_conn(key_added) @@ -725,19 +756,6 @@ def _run_spatial_neighbors( _save_data(adata, attr="obsp", key=dists_key, data=Dst, prefix=False) _save_data(adata, attr="uns", key=neighs_key, data=neighbors_dict, prefix=False, time=start) - -def _spatial_neighbor( - adata: AnnData, - spatial_key: str = Key.obsm.spatial, - builder: GraphBuilder | None = None, -) -> tuple[csr_matrix, csr_matrix]: - if builder is None: - raise ValueError("No graph builder was provided.") - - coords = adata.obsm[spatial_key] - return builder.build(coords) - - @d.dedent def mask_graph( sdata: SpatialData, From 43cdbb64c280a40e6e32045a54d3e29d4647de0c Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 9 Apr 2026 09:49:32 +0000 Subject: [PATCH 26/96] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/squidpy/gr/_build.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index bfc9aeadd..29b52269b 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -756,6 +756,7 @@ def _run_spatial_neighbors( _save_data(adata, attr="obsp", key=dists_key, data=Dst, prefix=False) _save_data(adata, attr="uns", key=neighs_key, data=neighbors_dict, prefix=False, time=start) + @d.dedent def mask_graph( sdata: SpatialData, From e632f74d7dc2bf6a808b1886d8c74124386cb995 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Apr 2026 11:53:09 +0200 Subject: [PATCH 27/96] remove unnecessary abstraction --- src/squidpy/gr/_build.py | 80 ++++++++++------------------------------ 1 file changed, 20 insertions(+), 60 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index bfc9aeadd..20eaf16b9 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -356,66 +356,6 @@ def spatial_neighbors( FutureWarning, stacklevel=2, ) - return _spatial_neighbors( - adata, - spatial_key=spatial_key, - elements_to_coordinate_systems=elements_to_coordinate_systems, - table_key=table_key, - library_key=library_key, - coord_type=coord_type, - n_neighs=n_neighs, - radius=radius, - delaunay=delaunay, - n_rings=n_rings, - percentile=percentile, - transform=transform, - set_diag=set_diag, - builder=builder, - key_added=key_added, - copy=copy, - ) - - -def _prepare_spatial_neighbors_input( - adata: AnnData | SpatialData, - *, - spatial_key: str, - elements_to_coordinate_systems: dict[str, str] | None, - table_key: str | None, - library_key: str | None, -) -> tuple[AnnData, str | None]: - """Resolve input data and validate the requested spatial basis.""" - adata, library_key = _resolve_spatial_data( - adata, - spatial_key=spatial_key, - elements_to_coordinate_systems=elements_to_coordinate_systems, - table_key=table_key, - library_key=library_key, - ) - _assert_spatial_basis(adata, spatial_key) - return adata, library_key - - -def _spatial_neighbors( - adata: AnnData | SpatialData, - *, - spatial_key: str = Key.obsm.spatial, - elements_to_coordinate_systems: dict[str, str] | None = None, - table_key: str | None = None, - library_key: str | None = None, - coord_type: str | CoordType | None = None, - n_neighs: int | None = None, - radius: float | tuple[float, float] | None = None, - delaunay: bool | None = None, - n_rings: int | None = None, - percentile: float | None = None, - transform: str | Transform | None = None, - set_diag: bool | None = None, - builder: GraphBuilder | None = None, - key_added: str = "spatial", - copy: bool = False, -) -> SpatialNeighborsResult | None: - """Internal implementation of spatial_neighbors (no deprecation warning).""" adata, library_key = _prepare_spatial_neighbors_input( adata, spatial_key=spatial_key, @@ -461,6 +401,26 @@ def _spatial_neighbors( ) +def _prepare_spatial_neighbors_input( + adata: AnnData | SpatialData, + *, + spatial_key: str, + elements_to_coordinate_systems: dict[str, str] | None, + table_key: str | None, + library_key: str | None, +) -> tuple[AnnData, str | None]: + """Resolve input data and validate the requested spatial basis.""" + adata, library_key = _resolve_spatial_data( + adata, + spatial_key=spatial_key, + elements_to_coordinate_systems=elements_to_coordinate_systems, + table_key=table_key, + library_key=library_key, + ) + _assert_spatial_basis(adata, spatial_key) + return adata, library_key + + @d.dedent def spatial_neighbors_knn( adata: AnnData | SpatialData, From c02686089318fcc79d2a8ba8e623b7993871e586 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Apr 2026 13:04:18 +0200 Subject: [PATCH 28/96] put the warning in resolve graph builder code --- src/squidpy/gr/_build.py | 44 +++++++++++++++------------ src/squidpy/gr/neighbors.py | 12 -------- tests/graph/test_spatial_neighbors.py | 4 +++ 3 files changed, 28 insertions(+), 32 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index b45a8c4c6..4186b5f51 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -75,15 +75,24 @@ def _validate_no_legacy_params(**kwargs: Any) -> None: def _resolve_graph_builder( *, coord_type: str | CoordType | None, - n_neighs: int, + n_neighs: int | None, radius: float | tuple[float, float] | None, - delaunay: bool, - n_rings: int, + delaunay: bool | None, + n_rings: int | None, percentile: float | None, transform: str | Transform | None, - set_diag: bool, + set_diag: bool | None, has_spatial_uns: bool = False, ) -> GraphBuilder: + n_neighs_was_set = n_neighs is not None + n_neighs = 6 if n_neighs is None else n_neighs + delaunay = False if delaunay is None else delaunay + n_rings = 1 if n_rings is None else n_rings + set_diag = False if set_diag is None else set_diag + + assert_positive(n_rings, name="n_rings") + assert_positive(n_neighs, name="n_neighs") + transform = Transform.NONE if transform is None else Transform(transform) if coord_type is None: if radius is not None: @@ -95,23 +104,26 @@ def _resolve_graph_builder( else: coord_type = CoordType(coord_type) - common: dict[str, Any] = { - "n_neighs": n_neighs, - "transform": transform, - "set_diag": set_diag, - } + common: dict[str, Any] = {"transform": transform, "set_diag": set_diag} if coord_type == CoordType.GRID: if percentile is not None: raise ValueError( "`percentile` is not supported for grid coordinates. It only applies to generic (non-grid) graphs." ) - return GridBuilder(**common, n_rings=n_rings, delaunay=delaunay) + return GridBuilder(n_neighs=n_neighs, **common, n_rings=n_rings, delaunay=delaunay) if delaunay: + # below check should be removed once legacy mode spatial_neighbors is deprecated + if n_neighs_was_set: + warnings.warn( + "Parameter `n_neighs` is ignored when `delaunay=True` and will be removed in squidpy v2.0.0.", + FutureWarning, + stacklevel=3, + ) return DelaunayBuilder(**common, radius=radius, percentile=percentile) if radius is not None: - return RadiusBuilder(**common, radius=radius, percentile=percentile) - return KNNBuilder(**common, percentile=percentile) + return RadiusBuilder(n_neighs=n_neighs, **common, radius=radius, percentile=percentile) + return KNNBuilder(n_neighs=n_neighs, **common, percentile=percentile) def _resolve_spatial_data( @@ -376,14 +388,6 @@ def spatial_neighbors( set_diag=set_diag, ) else: - n_neighs = n_neighs if n_neighs is not None else 6 - delaunay = delaunay if delaunay is not None else False - n_rings = n_rings if n_rings is not None else 1 - set_diag = set_diag if set_diag is not None else False - - assert_positive(n_rings, name="n_rings") - assert_positive(n_neighs, name="n_neighs") - builder = _resolve_graph_builder( coord_type=coord_type, n_neighs=n_neighs, diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 9a15ef3b8..800f1059a 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -177,24 +177,12 @@ class DelaunayBuilder(GraphBuilder): def __init__( self, radius: float | tuple[float, float] | None = None, - n_neighs: int | None = None, transform: str | Transform | None = None, set_diag: bool = False, percentile: float | None = None, ) -> None: - if n_neighs is not None: - warnings.warn( - "Parameter `n_neighs` of `DelaunayBuilder` is ignored by " - "Delaunay triangulation and will be removed in squidpy v2.0.0.", - FutureWarning, - stacklevel=2, - ) - else: - n_neighs = 6 - assert_positive(n_neighs, name="n_neighs") super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) self.radius = radius - self.n_neighs = n_neighs @property def coord_type(self) -> CoordType: diff --git a/tests/graph/test_spatial_neighbors.py b/tests/graph/test_spatial_neighbors.py index 39be507cb..5317fe79d 100644 --- a/tests/graph/test_spatial_neighbors.py +++ b/tests/graph/test_spatial_neighbors.py @@ -295,6 +295,10 @@ def test_delaunay_mode_ignores_scalar_radius(self, non_visium_adata: AnnData): np.testing.assert_array_equal(default.connectivities.toarray(), ignored.connectivities.toarray()) np.testing.assert_allclose(default.distances.toarray(), ignored.distances.toarray()) + def test_delaunay_mode_warns_on_n_neighs(self, non_visium_adata: AnnData): + with pytest.warns(FutureWarning, match=r"Parameter `n_neighs` is ignored when `delaunay=True`"): + spatial_neighbors(non_visium_adata, coord_type="generic", delaunay=True, n_neighs=3, copy=True) + @pytest.mark.parametrize("percentile", [99.0, 95.0]) def test_percentile_filtering(self, adata_hne: AnnData, percentile: float, coord_type="generic"): result = spatial_neighbors(adata_hne, coord_type=coord_type, copy=True) From 10cc2f0628350ba529cddd4c47d4035b495c37b7 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Apr 2026 13:08:53 +0200 Subject: [PATCH 29/96] remove n_neighs from RadiusBuilder --- src/squidpy/gr/_build.py | 17 ++++++++++------- src/squidpy/gr/neighbors.py | 5 +---- tests/graph/test_spatial_neighbors.py | 6 +++++- 3 files changed, 16 insertions(+), 12 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 4186b5f51..ca7d0b241 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -122,7 +122,14 @@ def _resolve_graph_builder( ) return DelaunayBuilder(**common, radius=radius, percentile=percentile) if radius is not None: - return RadiusBuilder(n_neighs=n_neighs, **common, radius=radius, percentile=percentile) + # below check should be removed once legacy mode spatial_neighbors is deprecated + if n_neighs_was_set: + warnings.warn( + "Parameter `n_neighs` is ignored when `radius` is set and will be removed in squidpy v2.0.0.", + FutureWarning, + stacklevel=3, + ) + return RadiusBuilder(**common, radius=radius, percentile=percentile) return KNNBuilder(n_neighs=n_neighs, **common, percentile=percentile) @@ -298,6 +305,7 @@ def spatial_neighbors( - Generic radius mode: ``coord_type='generic'``, ``delaunay=False``, ``radius`` set. Uses ``radius`` and builds a radius-based neighbor graph. + ``n_neighs`` is ignored and passing it is deprecated. If ``radius`` is a tuple, the graph is built with the maximum radius and then pruned to the interval ``[min(radius), max(radius)]``. @@ -331,7 +339,7 @@ def spatial_neighbors( A tuple ``radius`` is only used afterward as a pruning interval. A scalar ``radius`` is ignored. - Otherwise, if ``radius`` is set, radius mode is used. - In this mode ``n_neighs`` does not act as a second cutoff. + In this mode ``n_neighs`` is ignored (deprecated). - Otherwise, k-nearest-neighbor mode is used. Grid-specific behavior @@ -490,7 +498,6 @@ def spatial_neighbors_radius( table_key: str | None = None, library_key: str | None = None, radius: float | tuple[float, float] = 1.0, - n_neighs: int = 6, percentile: float | None = None, transform: str | Transform | None = None, set_diag: bool = False, @@ -508,9 +515,6 @@ def spatial_neighbors_radius( radius Neighborhood radius. If a :class:`tuple`, the graph is built with the maximum radius and then pruned to the interval ``[min(radius), max(radius)]``. - n_neighs - Number of nearest neighbors used internally by the radius graph builder. - Defaults to ``6``. %(graph_common_params)s %(copy)s @@ -520,7 +524,6 @@ def spatial_neighbors_radius( """ transform_enum = Transform.NONE if transform is None else Transform(transform) builder = RadiusBuilder( - n_neighs=n_neighs, radius=radius, percentile=percentile, transform=transform_enum, diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 800f1059a..175a44b03 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -131,15 +131,12 @@ class RadiusBuilder(GraphBuilder): def __init__( self, radius: float | tuple[float, float], - n_neighs: int = 6, transform: str | Transform | None = None, set_diag: bool = False, percentile: float | None = None, ) -> None: - assert_positive(n_neighs, name="n_neighs") super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) self.radius = radius - self.n_neighs = n_neighs @property def coord_type(self) -> CoordType: @@ -148,7 +145,7 @@ def coord_type(self) -> CoordType: def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: N = coords.shape[0] r = self.radius if isinstance(self.radius, int | float) else max(self.radius) - tree = NearestNeighbors(n_neighbors=self.n_neighs, radius=r, metric="euclidean") + tree = NearestNeighbors(radius=r, metric="euclidean") tree.fit(coords) dists, col_indices = tree.radius_neighbors() diff --git a/tests/graph/test_spatial_neighbors.py b/tests/graph/test_spatial_neighbors.py index 5317fe79d..46d5a4a68 100644 --- a/tests/graph/test_spatial_neighbors.py +++ b/tests/graph/test_spatial_neighbors.py @@ -239,7 +239,7 @@ def test_builder_argument_conflict(self, non_visium_adata: AnnData): spatial_neighbors(non_visium_adata, builder=RadiusBuilder(radius=5.0), delaunay=True) def test_builder_rejects_any_legacy_args(self, non_visium_adata: AnnData): - builder = RadiusBuilder(radius=5.0, n_neighs=3, percentile=95.0, transform="cosine", set_diag=True) + builder = RadiusBuilder(radius=5.0, percentile=95.0, transform="cosine", set_diag=True) with pytest.raises(ValueError, match="must not be set"): spatial_neighbors( @@ -299,6 +299,10 @@ def test_delaunay_mode_warns_on_n_neighs(self, non_visium_adata: AnnData): with pytest.warns(FutureWarning, match=r"Parameter `n_neighs` is ignored when `delaunay=True`"): spatial_neighbors(non_visium_adata, coord_type="generic", delaunay=True, n_neighs=3, copy=True) + def test_radius_mode_warns_on_n_neighs(self, non_visium_adata: AnnData): + with pytest.warns(FutureWarning, match=r"Parameter `n_neighs` is ignored when `radius` is set"): + spatial_neighbors(non_visium_adata, coord_type="generic", radius=5.0, n_neighs=3, copy=True) + @pytest.mark.parametrize("percentile", [99.0, 95.0]) def test_percentile_filtering(self, adata_hne: AnnData, percentile: float, coord_type="generic"): result = spatial_neighbors(adata_hne, coord_type=coord_type, copy=True) From 6477f18d7b2a84d03a2eb2f36980f10b2b48c658 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Apr 2026 13:20:49 +0200 Subject: [PATCH 30/96] definition of Adj, Dst and rename to adj, dst --- docs/extensibility.md | 26 ++++++-- src/squidpy/gr/_build.py | 32 +++++----- src/squidpy/gr/neighbors.py | 124 ++++++++++++++++++------------------ 3 files changed, 97 insertions(+), 85 deletions(-) diff --git a/docs/extensibility.md b/docs/extensibility.md index 00bf73478..5797804b3 100644 --- a/docs/extensibility.md +++ b/docs/extensibility.md @@ -11,12 +11,24 @@ You can implement your own strategy by subclassing it. | Method / property | Required | Purpose | |---|---|---| | {attr}`~squidpy.gr.neighbors.GraphBuilder.coord_type` | yes | Return the {class}`~squidpy._constants._constants.CoordType` this builder supports. | -| {meth}`~squidpy.gr.neighbors.GraphBuilder.build_graph` | yes | Construct and return ``(Adj, Dst)`` as {class}`~scipy.sparse.csr_matrix` pair. | -| {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_filters` | no | Post-processing on the raw ``Adj``/``Dst`` (e.g. radius-interval pruning). Called before percentile filtering and transform. | +| {meth}`~squidpy.gr.neighbors.GraphBuilder.build_graph` | yes | Construct and return ``(adj, dst)`` as {class}`~scipy.sparse.csr_matrix` pair. | +| {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_filters` | no | Post-processing on the raw ``adj``/``dst`` (e.g. radius-interval pruning). Called before percentile filtering and transform. | The base class handles percentile filtering, adjacency transforms, and {class}`~scipy.sparse.SparseEfficiencyWarning` suppression automatically. +Here ``adj`` and ``dst`` are square sparse matrices of shape ``(n_obs, n_obs)`` +with matching sparsity structure: + +- ``adj`` is the connectivity / adjacency matrix. Non-zero entries mark edges in + the graph, and built-in builders typically use ``1.0`` for present edges. +- ``dst`` is the distance matrix for those same edges. For generic graphs this is + usually the Euclidean edge length. For grid builders it may instead encode + graph-distance semantics such as ring number. +- Both should be returned as {class}`~scipy.sparse.csr_matrix`. +- By convention, ``dst`` should have a zero diagonal, and ``adj`` should only + have a non-zero diagonal when ``set_diag=True``. + ### Example: fast radius search with SNN The built-in {class}`~squidpy.gr.neighbors.RadiusBuilder` uses scikit-learn's @@ -56,15 +68,15 @@ class SNNRadiusBuilder(GraphBuilder): col = np.concatenate(indices) d = np.concatenate(dists).astype(np.float64) - Adj = csr_matrix( + adj = csr_matrix( (np.ones(len(row), dtype=np.float32), (row, col)), shape=(N, N), ) - Dst = csr_matrix((d, (row, col)), shape=(N, N)) + dst = csr_matrix((d, (row, col)), shape=(N, N)) - Adj.setdiag(1.0 if self.set_diag else Adj.diagonal()) - Dst.setdiag(0.0) - return Adj, Dst + adj.setdiag(1.0 if self.set_diag else adj.diagonal()) + dst.setdiag(0.0) + return adj, dst ``` Use it like any other builder: diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index ca7d0b241..9f71c548c 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -696,10 +696,10 @@ def _run_spatial_neighbors( ixs.extend(np.where(adata.obs[library_key] == lib)[0]) mats.append(builder.build(adata[adata.obs[library_key] == lib].obsm[spatial_key])) ixs = cast(list[int], np.argsort(ixs).tolist()) - Adj = block_diag([m[0] for m in mats], format="csr")[ixs, :][:, ixs] - Dst = block_diag([m[1] for m in mats], format="csr")[ixs, :][:, ixs] + adj = block_diag([m[0] for m in mats], format="csr")[ixs, :][:, ixs] + dst = block_diag([m[1] for m in mats], format="csr")[ixs, :][:, ixs] else: - Adj, Dst = builder.build(adata.obsm[spatial_key]) + adj, dst = builder.build(adata.obsm[spatial_key]) neighs_key = Key.uns.spatial_neighs(key_added) conns_key = Key.obsp.spatial_conn(key_added) @@ -717,10 +717,10 @@ def _run_spatial_neighbors( } if copy: - return SpatialNeighborsResult(connectivities=Adj, distances=Dst) + return SpatialNeighborsResult(connectivities=adj, distances=dst) - _save_data(adata, attr="obsp", key=conns_key, data=Adj) - _save_data(adata, attr="obsp", key=dists_key, data=Dst, prefix=False) + _save_data(adata, attr="obsp", key=conns_key, data=adj) + _save_data(adata, attr="obsp", key=dists_key, data=dst, prefix=False) _save_data(adata, attr="uns", key=neighs_key, data=neighbors_dict, prefix=False, time=start) @@ -782,11 +782,11 @@ def mask_graph( # get elements table = sdata.tables[table_key] coords = table.obsm[spatial_key] - Adj = table.obsp[conns_key] - Dst = table.obsp[dists_key] + adj = table.obsp[conns_key] + dst = table.obsp[dists_key] # convert edges to lines - lines_coords, idx_out = _get_lines_coords(Adj.indices, Adj.indptr, coords) + lines_coords, idx_out = _get_lines_coords(adj.indices, adj.indptr, coords) lines_coords, idx_out = np.array(lines_coords), np.array(idx_out) lines_df = gpd.GeoDataFrame(geometry=list(map(LineString, lines_coords))) @@ -800,12 +800,12 @@ def mask_graph( filt_idx_out = idx_out[filt_lines] # filter connectivities - Adj[filt_idx_out[:, 0], filt_idx_out[:, 1]] = 0 - Adj.eliminate_zeros() + adj[filt_idx_out[:, 0], filt_idx_out[:, 1]] = 0 + adj.eliminate_zeros() # filter_distances - Dst[filt_idx_out[:, 0], filt_idx_out[:, 1]] = 0 - Dst.eliminate_zeros() + dst[filt_idx_out[:, 0], filt_idx_out[:, 1]] = 0 + dst.eliminate_zeros() mask_conns_key = f"{key_added}_{conns_key}" mask_dists_key = f"{key_added}_{dists_key}" @@ -822,11 +822,11 @@ def mask_graph( } if copy: - return Adj, Dst + return adj, dst # save back to spatialdata - _save_data(table, attr="obsp", key=mask_conns_key, data=Adj) - _save_data(table, attr="obsp", key=mask_dists_key, data=Dst, prefix=False) + _save_data(table, attr="obsp", key=mask_conns_key, data=adj) + _save_data(table, attr="obsp", key=mask_dists_key, data=dst, prefix=False) _save_data(table, attr="uns", key=mask_neighs_key, data=neighbors_dict, prefix=False) diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 175a44b03..9e55229d9 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -53,36 +53,36 @@ def coord_type(self) -> CoordType: def build(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: with warnings.catch_warnings(): warnings.simplefilter("ignore", SparseEfficiencyWarning) - Adj, Dst = self.build_graph(coords) + adj, dst = self.build_graph(coords) - self.apply_filters(Adj, Dst) - self._apply_percentile(Adj, Dst) - Adj.eliminate_zeros() - Dst.eliminate_zeros() + self.apply_filters(adj, dst) + self._apply_percentile(adj, dst) + adj.eliminate_zeros() + dst.eliminate_zeros() - return self._apply_transform(Adj), Dst + return self._apply_transform(adj), dst @abstractmethod def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: """Construct raw adjacency and distance matrices.""" - def apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> None: """Apply builder-specific post-processing filters.""" return None - def _apply_percentile(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + def _apply_percentile(self, adj: csr_matrix, dst: csr_matrix) -> None: if self.percentile is not None and self.coord_type == CoordType.GENERIC: - threshold = np.percentile(Dst.data, self.percentile) - Adj[Dst > threshold] = 0.0 - Dst[Dst > threshold] = 0.0 + threshold = np.percentile(dst.data, self.percentile) + adj[dst > threshold] = 0.0 + dst[dst > threshold] = 0.0 - def _apply_transform(self, Adj: csr_matrix) -> csr_matrix: + def _apply_transform(self, adj: csr_matrix) -> csr_matrix: if self.transform == Transform.SPECTRAL: - return cast(csr_matrix, _transform_a_spectral(Adj)) + return cast(csr_matrix, _transform_a_spectral(adj)) if self.transform == Transform.COSINE: - return cast(csr_matrix, _transform_a_cosine(Adj)) + return cast(csr_matrix, _transform_a_cosine(adj)) if self.transform == Transform.NONE: - return Adj + return adj raise NotImplementedError(f"Transform `{self.transform}` is not yet implemented.") @@ -114,15 +114,15 @@ def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: dists, col_indices = dists.reshape(-1), col_indices.reshape(-1) row_indices = np.repeat(np.arange(N), self.n_neighs) - Adj = csr_matrix( + adj = csr_matrix( (np.ones_like(row_indices, dtype=np.float32), (row_indices, col_indices)), shape=(N, N), ) - Dst = csr_matrix((dists, (row_indices, col_indices)), shape=(N, N)) + dst = csr_matrix((dists, (row_indices, col_indices)), shape=(N, N)) - Adj.setdiag(1.0 if self.set_diag else Adj.diagonal()) - Dst.setdiag(0.0) - return Adj, Dst + adj.setdiag(1.0 if self.set_diag else adj.diagonal()) + dst.setdiag(0.0) + return adj, dst class RadiusBuilder(GraphBuilder): @@ -153,19 +153,19 @@ def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: dists = np.concatenate(dists) col_indices = np.concatenate(col_indices) - Adj = csr_matrix( + adj = csr_matrix( (np.ones_like(row_indices, dtype=np.float32), (row_indices, col_indices)), shape=(N, N), ) - Dst = csr_matrix((dists, (row_indices, col_indices)), shape=(N, N)) + dst = csr_matrix((dists, (row_indices, col_indices)), shape=(N, N)) - Adj.setdiag(1.0 if self.set_diag else Adj.diagonal()) - Dst.setdiag(0.0) - return Adj, Dst + adj.setdiag(1.0 if self.set_diag else adj.diagonal()) + dst.setdiag(0.0) + return adj, dst - def apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> None: if isinstance(self.radius, Iterable): - _filter_by_radius_interval(Adj, Dst, self.radius) + _filter_by_radius_interval(adj, dst, self.radius) class DelaunayBuilder(GraphBuilder): @@ -189,7 +189,7 @@ def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: N = coords.shape[0] tri = Delaunay(coords) indptr, indices = tri.vertex_neighbor_vertices - Adj = csr_matrix((np.ones_like(indices, dtype=np.float32), indices, indptr), shape=(N, N)) + adj = csr_matrix((np.ones_like(indices, dtype=np.float32), indices, indptr), shape=(N, N)) # fmt: off dists = np.array(list(chain(*( @@ -198,15 +198,15 @@ def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: if len(indices[indptr[i] : indptr[i + 1]]) )))).squeeze() # fmt: on - Dst = csr_matrix((dists, indices, indptr), shape=(N, N)) + dst = csr_matrix((dists, indices, indptr), shape=(N, N)) - Adj.setdiag(1.0 if self.set_diag else Adj.diagonal()) - Dst.setdiag(0.0) - return Adj, Dst + adj.setdiag(1.0 if self.set_diag else adj.diagonal()) + dst.setdiag(0.0) + return adj, dst - def apply_filters(self, Adj: csr_matrix, Dst: csr_matrix) -> None: + def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> None: if isinstance(self.radius, Iterable): - _filter_by_radius_interval(Adj, Dst, self.radius) + _filter_by_radius_interval(adj, dst, self.radius) class GridBuilder(GraphBuilder): @@ -233,26 +233,26 @@ def coord_type(self) -> CoordType: def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: if self.n_rings > 1: - Adj = self._base_adjacency(coords, set_diag=True) - Res, Walk = Adj, Adj + adj = self._base_adjacency(coords, set_diag=True) + res, walk = adj, adj for i in range(self.n_rings - 1): - Walk = Walk @ Adj - Walk[Res.nonzero()] = 0.0 - Walk.eliminate_zeros() - Walk.data[:] = i + 2.0 - Res = Res + Walk - Adj = Res - Adj.setdiag(float(self.set_diag)) - Adj.eliminate_zeros() - - Dst = Adj.copy() - Adj.data[:] = 1.0 + walk = walk @ adj + walk[res.nonzero()] = 0.0 + walk.eliminate_zeros() + walk.data[:] = i + 2.0 + res = res + walk + adj = res + adj.setdiag(float(self.set_diag)) + adj.eliminate_zeros() + + dst = adj.copy() + adj.data[:] = 1.0 else: - Adj = self._base_adjacency(coords, set_diag=self.set_diag) - Dst = Adj.copy() + adj = self._base_adjacency(coords, set_diag=self.set_diag) + dst = adj.copy() - Dst.setdiag(0.0) - return Adj, Dst + dst.setdiag(0.0) + return adj, dst def _base_adjacency(self, coords: NDArrayA, *, set_diag: bool) -> csr_matrix: """KNN adjacency with median-distance correction for grid coordinates.""" @@ -260,7 +260,7 @@ def _base_adjacency(self, coords: NDArrayA, *, set_diag: bool) -> csr_matrix: if self.delaunay: tri = Delaunay(coords) indptr, indices = tri.vertex_neighbor_vertices - Adj = csr_matrix((np.ones_like(indices, dtype=np.float32), indices, indptr), shape=(N, N)) + adj = csr_matrix((np.ones_like(indices, dtype=np.float32), indices, indptr), shape=(N, N)) else: tree = NearestNeighbors(n_neighbors=self.n_neighs, radius=1, metric="euclidean") tree.fit(coords) @@ -272,13 +272,13 @@ def _base_adjacency(self, coords: NDArrayA, *, set_diag: bool) -> csr_matrix: mask = dists < dist_cutoff row_indices, col_indices = row_indices[mask], col_indices[mask] - Adj = csr_matrix( + adj = csr_matrix( (np.ones_like(row_indices, dtype=np.float32), (row_indices, col_indices)), shape=(N, N), ) - Adj.setdiag(1.0 if set_diag else Adj.diagonal()) - return Adj + adj.setdiag(1.0 if set_diag else adj.diagonal()) + return adj # --------------------------------------------------------------------------- @@ -287,17 +287,17 @@ def _base_adjacency(self, coords: NDArrayA, *, set_diag: bool) -> csr_matrix: def _filter_by_radius_interval( - Adj: csr_matrix, - Dst: csr_matrix, + adj: csr_matrix, + dst: csr_matrix, radius: Iterable[float], ) -> None: minn, maxx = sorted(radius)[:2] - mask = (Dst.data < minn) | (Dst.data > maxx) - a_diag = Adj.diagonal() + mask = (dst.data < minn) | (dst.data > maxx) + a_diag = adj.diagonal() - Dst.data[mask] = 0.0 - Adj.data[mask] = 0.0 - Adj.setdiag(a_diag) + dst.data[mask] = 0.0 + adj.data[mask] = 0.0 + adj.setdiag(a_diag) @njit From 516b3e024079d8225f9f6297979c32e9230047d3 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Fri, 10 Apr 2026 13:48:31 +0200 Subject: [PATCH 31/96] Generalize the base class for extensibility --- docs/extensibility.md | 34 ++++++++++------ src/squidpy/gr/_build.py | 6 +-- src/squidpy/gr/neighbors.py | 77 +++++++++++++++++++++++++------------ 3 files changed, 78 insertions(+), 39 deletions(-) diff --git a/docs/extensibility.md b/docs/extensibility.md index 5797804b3..5cdec8ba2 100644 --- a/docs/extensibility.md +++ b/docs/extensibility.md @@ -2,20 +2,29 @@ ## Custom graph builders -The `squidpy.gr.neighbors` module exposes a {class}`~squidpy.gr.neighbors.GraphBuilder` -base class that all built-in graph construction strategies inherit from. -You can implement your own strategy by subclassing it. +The `squidpy.gr.neighbors` module exposes two builder base classes: + +- {class}`~squidpy.gr.neighbors.GraphBuilder` is the generic builder pipeline. + Use it when you want to plug in a custom coordinate type or sparse-matrix backend. +- {class}`~squidpy.gr.neighbors.GraphBuilderCSR` is the CSR-specialized builder used + by the built-in graph construction strategies. Use it when your builder returns + {class}`~scipy.sparse.csr_matrix` objects and should reuse the default CSR-specific + percentile filtering, transform handling, and sparse warning suppression. ### What to override -| Method / property | Required | Purpose | +| Base class | Method / property | Required | Purpose | |---|---|---| -| {attr}`~squidpy.gr.neighbors.GraphBuilder.coord_type` | yes | Return the {class}`~squidpy._constants._constants.CoordType` this builder supports. | -| {meth}`~squidpy.gr.neighbors.GraphBuilder.build_graph` | yes | Construct and return ``(adj, dst)`` as {class}`~scipy.sparse.csr_matrix` pair. | -| {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_filters` | no | Post-processing on the raw ``adj``/``dst`` (e.g. radius-interval pruning). Called before percentile filtering and transform. | +| {class}`~squidpy.gr.neighbors.GraphBuilder` | {attr}`~squidpy.gr.neighbors.GraphBuilder.coord_type` | yes | Return the {class}`~squidpy._constants._constants.CoordType` this builder supports. | +| {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.build_graph` | yes | Construct and return ``(adj, dst)`` using the coordinate and matrix types of your custom backend. | +| {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_filters` | no | Post-process the raw ``adj``/``dst`` before percentile filtering and transform. | +| {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_percentile` | no | Override percentile handling when the backend needs custom behavior. | +| {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_transform` | no | Override transform handling when the backend needs custom behavior. | +| {class}`~squidpy.gr.neighbors.GraphBuilderCSR` | {meth}`~squidpy.gr.neighbors.GraphBuilderCSR.build_graph` | yes | Construct and return ``(adj, dst)`` as a {class}`~scipy.sparse.csr_matrix` pair. | -The base class handles percentile filtering, adjacency transforms, and -{class}`~scipy.sparse.SparseEfficiencyWarning` suppression automatically. +The generic builder only defines the pipeline. The CSR-specialized builder adds the +built-in CSR behavior for percentile filtering, adjacency transforms, and +{class}`~scipy.sparse.SparseEfficiencyWarning` suppression. Here ``adj`` and ``dst`` are square sparse matrices of shape ``(n_obs, n_obs)`` with matching sparsity structure: @@ -25,7 +34,8 @@ with matching sparsity structure: - ``dst`` is the distance matrix for those same edges. For generic graphs this is usually the Euclidean edge length. For grid builders it may instead encode graph-distance semantics such as ring number. -- Both should be returned as {class}`~scipy.sparse.csr_matrix`. +- When subclassing {class}`~squidpy.gr.neighbors.GraphBuilderCSR`, both should be + returned as {class}`~scipy.sparse.csr_matrix`. - By convention, ``dst`` should have a zero diagonal, and ``adj`` should only have a non-zero diagonal when ``set_diag=True``. @@ -43,10 +53,10 @@ from scipy.sparse import csr_matrix from snnpy import build_snn_model from squidpy._constants._constants import CoordType -from squidpy.gr.neighbors import GraphBuilder +from squidpy.gr.neighbors import GraphBuilderCSR -class SNNRadiusBuilder(GraphBuilder): +class SNNRadiusBuilder(GraphBuilderCSR): """Radius graph using the SNN fixed-radius search backend.""" def __init__(self, radius: float, **kwargs): diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 9f71c548c..2ce017b10 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -83,7 +83,7 @@ def _resolve_graph_builder( transform: str | Transform | None, set_diag: bool | None, has_spatial_uns: bool = False, -) -> GraphBuilder: +) -> GraphBuilder[Any, Any]: n_neighs_was_set = n_neighs is not None n_neighs = 6 if n_neighs is None else n_neighs delaunay = False if delaunay is None else delaunay @@ -209,7 +209,7 @@ def spatial_neighbors( percentile: float | None = None, transform: str | Transform | None = None, set_diag: bool | None = None, - builder: GraphBuilder | None = None, + builder: GraphBuilder[Any, Any] | None = None, key_added: str = "spatial", copy: bool = False, ) -> SpatialNeighborsResult | None: @@ -672,7 +672,7 @@ def spatial_neighbors_grid( def _run_spatial_neighbors( adata: AnnData, *, - builder: GraphBuilder, + builder: GraphBuilder[Any, Any], spatial_key: str = Key.obsm.spatial, library_key: str | None = None, key_added: str = "spatial", diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 9e55229d9..283ed4c89 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -9,7 +9,7 @@ from abc import ABC, abstractmethod from collections.abc import Iterable from itertools import chain -from typing import cast +from typing import Generic, TypeVar, cast import numpy as np from fast_array_utils import stats as fau_stats @@ -29,10 +29,14 @@ from squidpy._utils import NDArrayA from squidpy._validators import assert_positive -__all__ = ["GraphBuilder", "KNNBuilder", "RadiusBuilder", "DelaunayBuilder", "GridBuilder"] +__all__ = ["GraphBuilder", "GraphBuilderCSR", "KNNBuilder", "RadiusBuilder", "DelaunayBuilder", "GridBuilder"] -class GraphBuilder(ABC): +CoordT = TypeVar("CoordT") +GraphMatrixT = TypeVar("GraphMatrixT") + + +class GraphBuilder(ABC, Generic[CoordT, GraphMatrixT]): """Base class for spatial graph construction strategies.""" def __init__( @@ -50,44 +54,67 @@ def __init__( def coord_type(self) -> CoordType: """Coordinate system supported by this builder.""" + def build(self, coords: CoordT) -> tuple[GraphMatrixT, GraphMatrixT]: + adj, dst = self.build_graph(coords) + adj, dst = self.apply_filters(adj, dst) + adj, dst = self.apply_percentile(adj, dst) + adj, dst = self.apply_transform(adj, dst) + return adj, dst + + + @abstractmethod + def build_graph(self, coords: CoordT) -> tuple[GraphMatrixT, GraphMatrixT]: + """Construct raw adjacency and distance matrices.""" + + def apply_filters(self, adj: GraphMatrixT, dst: GraphMatrixT) -> tuple[GraphMatrixT, GraphMatrixT]: + """Apply builder-specific post-processing filters.""" + return adj, dst + + def apply_percentile(self, adj: GraphMatrixT, dst: GraphMatrixT) -> tuple[GraphMatrixT, GraphMatrixT]: + return adj, dst + + def apply_transform(self, adj: GraphMatrixT, dst: GraphMatrixT) -> tuple[GraphMatrixT, GraphMatrixT]: + return adj, dst + + +class GraphBuilderCSR(GraphBuilder[NDArrayA, csr_matrix], ABC): + """CSR-based graph construction strategy.""" + def build(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: with warnings.catch_warnings(): warnings.simplefilter("ignore", SparseEfficiencyWarning) - adj, dst = self.build_graph(coords) - - self.apply_filters(adj, dst) - self._apply_percentile(adj, dst) - adj.eliminate_zeros() - dst.eliminate_zeros() - - return self._apply_transform(adj), dst + return super().build(coords) @abstractmethod def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: """Construct raw adjacency and distance matrices.""" - def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> None: + def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, csr_matrix]: """Apply builder-specific post-processing filters.""" - return None + return adj, dst - def _apply_percentile(self, adj: csr_matrix, dst: csr_matrix) -> None: + def apply_percentile(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, csr_matrix]: if self.percentile is not None and self.coord_type == CoordType.GENERIC: threshold = np.percentile(dst.data, self.percentile) adj[dst > threshold] = 0.0 dst[dst > threshold] = 0.0 + return adj, dst + + def apply_transform(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, csr_matrix]: + adj.eliminate_zeros() + dst.eliminate_zeros() - def _apply_transform(self, adj: csr_matrix) -> csr_matrix: if self.transform == Transform.SPECTRAL: - return cast(csr_matrix, _transform_a_spectral(adj)) + return cast(csr_matrix, _transform_a_spectral(adj)), dst if self.transform == Transform.COSINE: - return cast(csr_matrix, _transform_a_cosine(adj)) + return cast(csr_matrix, _transform_a_cosine(adj)), dst if self.transform == Transform.NONE: - return adj + return adj, dst raise NotImplementedError(f"Transform `{self.transform}` is not yet implemented.") -class KNNBuilder(GraphBuilder): +class KNNBuilder(GraphBuilderCSR): """Build a generic k-nearest-neighbor spatial graph.""" def __init__( @@ -125,7 +152,7 @@ def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: return adj, dst -class RadiusBuilder(GraphBuilder): +class RadiusBuilder(GraphBuilderCSR): """Build a generic radius-based spatial graph.""" def __init__( @@ -163,12 +190,13 @@ def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: dst.setdiag(0.0) return adj, dst - def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> None: + def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, csr_matrix]: if isinstance(self.radius, Iterable): _filter_by_radius_interval(adj, dst, self.radius) + return adj, dst -class DelaunayBuilder(GraphBuilder): +class DelaunayBuilder(GraphBuilderCSR): """Build a generic spatial graph from a Delaunay triangulation.""" def __init__( @@ -204,12 +232,13 @@ def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: dst.setdiag(0.0) return adj, dst - def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> None: + def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, csr_matrix]: if isinstance(self.radius, Iterable): _filter_by_radius_interval(adj, dst, self.radius) + return adj, dst -class GridBuilder(GraphBuilder): +class GridBuilder(GraphBuilderCSR): """Build a grid-based spatial graph.""" def __init__( From 141fdb690562e84cf7cfeeaacbe59939be2bd610 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 10 Apr 2026 11:48:44 +0000 Subject: [PATCH 32/96] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/squidpy/gr/neighbors.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 283ed4c89..eadac65d2 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -61,7 +61,6 @@ def build(self, coords: CoordT) -> tuple[GraphMatrixT, GraphMatrixT]: adj, dst = self.apply_transform(adj, dst) return adj, dst - @abstractmethod def build_graph(self, coords: CoordT) -> tuple[GraphMatrixT, GraphMatrixT]: """Construct raw adjacency and distance matrices.""" From 1fe316beb12ff04bff4e9127223f24be8db6eed3 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Fri, 10 Apr 2026 13:50:55 +0200 Subject: [PATCH 33/96] update docs --- docs/api.md | 1 + docs/extensibility.md | 2 +- src/squidpy/gr/_build.py | 3 +++ 3 files changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/api.md b/docs/api.md index 60434e4be..bf2d39f4e 100644 --- a/docs/api.md +++ b/docs/api.md @@ -126,6 +126,7 @@ See the {doc}`extensibility guide ` for how to implement a custo :toctree: api gr.neighbors.GraphBuilder + gr.neighbors.GraphBuilderCSR gr.neighbors.KNNBuilder gr.neighbors.RadiusBuilder gr.neighbors.DelaunayBuilder diff --git a/docs/extensibility.md b/docs/extensibility.md index 5cdec8ba2..f92e741ec 100644 --- a/docs/extensibility.md +++ b/docs/extensibility.md @@ -14,7 +14,7 @@ The `squidpy.gr.neighbors` module exposes two builder base classes: ### What to override | Base class | Method / property | Required | Purpose | -|---|---|---| +|---|---|---|---| | {class}`~squidpy.gr.neighbors.GraphBuilder` | {attr}`~squidpy.gr.neighbors.GraphBuilder.coord_type` | yes | Return the {class}`~squidpy._constants._constants.CoordType` this builder supports. | | {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.build_graph` | yes | Construct and return ``(adj, dst)`` using the coordinate and matrix types of your custom backend. | | {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_filters` | no | Post-process the raw ``adj``/``dst`` before percentile filtering and transform. | diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 2ce017b10..a843dfb96 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -286,6 +286,9 @@ def spatial_neighbors( Advanced graph construction strategy. When provided, all other graph-construction arguments (``coord_type``, ``n_neighs``, ``radius``, ``delaunay``, ``n_rings``, ``percentile``, ``transform``, ``set_diag``) must be left as ``None``. + Built-in builders subclass {class}`~squidpy.gr.neighbors.GraphBuilderCSR`, + while custom backends can implement the more generic + {class}`~squidpy.gr.neighbors.GraphBuilder` interface directly. key_added Key which controls where the results are saved if ``copy = False``. %(copy)s From bb1ec50c13ed9b67a889ad2843ea0db853b6d748 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Fri, 10 Apr 2026 13:55:32 +0200 Subject: [PATCH 34/96] clarify delauney vs grid --- src/squidpy/gr/_build.py | 5 ++++- src/squidpy/gr/neighbors.py | 13 +++++++++++-- 2 files changed, 15 insertions(+), 3 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index a843dfb96..943a657cd 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -637,7 +637,10 @@ def spatial_neighbors_grid( n_rings Number of rings of neighbors. Defaults to ``1``. delaunay - Whether to compute the grid graph from Delaunay triangulation. + Whether to derive the base grid connectivity from a Delaunay triangulation. + This is still grid mode: unlike :func:`spatial_neighbors_delaunay`, the + resulting distance matrix encodes grid or ring distances rather than + Euclidean edge lengths. %(graph_common_params)s %(copy)s diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index eadac65d2..83a86feb9 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -196,7 +196,11 @@ def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, c class DelaunayBuilder(GraphBuilderCSR): - """Build a generic spatial graph from a Delaunay triangulation.""" + """Build a generic point-cloud graph from a Delaunay triangulation. + + Unlike ``GridBuilder(delaunay=True)``, this builder uses the triangulation as the + graph itself and stores real Euclidean edge lengths in ``dst``. + """ def __init__( self, @@ -238,7 +242,12 @@ def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, c class GridBuilder(GraphBuilderCSR): - """Build a grid-based spatial graph.""" + """Build a grid-based spatial graph. + + When ``delaunay=True``, Delaunay triangulation is used only to derive the base + grid connectivity. The resulting ``dst`` still encodes grid or ring distances, + not geometric Euclidean edge lengths. + """ def __init__( self, From 84cbdc4becc1e54b23df4e2c936fb74b87002a31 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Fri, 10 Apr 2026 14:06:29 +0200 Subject: [PATCH 35/96] dtype rec --- docs/extensibility.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/docs/extensibility.md b/docs/extensibility.md index f92e741ec..bfcb41ee6 100644 --- a/docs/extensibility.md +++ b/docs/extensibility.md @@ -36,6 +36,12 @@ with matching sparsity structure: graph-distance semantics such as ring number. - When subclassing {class}`~squidpy.gr.neighbors.GraphBuilderCSR`, both should be returned as {class}`~scipy.sparse.csr_matrix`. +- For CSR-based builders, ``adj`` often behaves like a boolean or indicator + matrix describing whether an edge is present, even if it is stored with a + numeric dtype such as ``float32``. ``dst`` stores edge-associated values such + as distances and will often use a floating-point dtype. The exact dtype choice + is left to the builder implementation and may depend on performance, memory, + and numerical accuracy requirements. - By convention, ``dst`` should have a zero diagonal, and ``adj`` should only have a non-zero diagonal when ``set_diag=True``. From 6d942ec8dbef664a3e986d53876d6b5a621a0150 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Fri, 10 Apr 2026 14:12:42 +0200 Subject: [PATCH 36/96] be a bit more verbose on funcitons --- src/squidpy/gr/_build.py | 47 ++++++++++++++++++++++++++++++++++------ 1 file changed, 40 insertions(+), 7 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 943a657cd..ccf83655c 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -286,9 +286,9 @@ def spatial_neighbors( Advanced graph construction strategy. When provided, all other graph-construction arguments (``coord_type``, ``n_neighs``, ``radius``, ``delaunay``, ``n_rings``, ``percentile``, ``transform``, ``set_diag``) must be left as ``None``. - Built-in builders subclass {class}`~squidpy.gr.neighbors.GraphBuilderCSR`, + Built-in builders subclass {{class}}`~squidpy.gr.neighbors.GraphBuilderCSR`, while custom backends can implement the more generic - {class}`~squidpy.gr.neighbors.GraphBuilder` interface directly. + {{class}}`~squidpy.gr.neighbors.GraphBuilder` interface directly. key_added Key which controls where the results are saved if ``copy = False``. %(copy)s @@ -453,6 +453,10 @@ def spatial_neighbors_knn( ) -> SpatialNeighborsResult | None: """Create a k-nearest-neighbor graph from spatial coordinates. + Each observation is connected to its ``n_neighs`` nearest observations in + Euclidean space. This mode is typically most useful for continuous + coordinates, where you want to control neighborhood size directly. + Parameters ---------- %(adata)s @@ -460,7 +464,8 @@ def spatial_neighbors_knn( %(sdata_params)s %(library_key)s n_neighs - Number of nearest neighbors. Defaults to ``6``. + Number of nearest neighbors. Defaults to ``6``. Smaller values produce a + sparser, more local graph; larger values connect broader neighborhoods. %(graph_common_params)s %(copy)s @@ -509,6 +514,10 @@ def spatial_neighbors_radius( ) -> SpatialNeighborsResult | None: """Create a radius-based graph from spatial coordinates. + Two observations are connected when their Euclidean distance falls within the + requested radius. This mode is useful when a physical interaction scale is + more meaningful than a fixed number of neighbors. + Parameters ---------- %(adata)s @@ -518,6 +527,9 @@ def spatial_neighbors_radius( radius Neighborhood radius. If a :class:`tuple`, the graph is built with the maximum radius and then pruned to the interval ``[min(radius), max(radius)]``. + In practice, a single value defines a disk around each observation, + whereas a tuple defines an annulus by keeping only edges within the + specified distance interval. %(graph_common_params)s %(copy)s @@ -566,6 +578,11 @@ def spatial_neighbors_delaunay( ) -> SpatialNeighborsResult | None: """Create a Delaunay triangulation graph from spatial coordinates. + Delaunay triangulation connects observations into triangles such that no + other observation lies inside the circumcircle of each triangle. In + practice, this yields an adaptive geometry-driven graph rather than one + based on a fixed ``k`` or radius, and ``dst`` stores Euclidean edge lengths. + Parameters ---------- %(adata)s @@ -574,7 +591,9 @@ def spatial_neighbors_delaunay( %(library_key)s radius If a :class:`tuple`, used as a post-construction pruning interval - ``[min(radius), max(radius)]``. + ``[min(radius), max(radius)]``. This does not change the triangulation + itself; it only removes Delaunay edges whose Euclidean lengths fall + outside the interval. %(graph_common_params)s %(copy)s @@ -625,6 +644,10 @@ def spatial_neighbors_grid( """Create a grid-based graph from spatial coordinates. This is the mode used for Visium-like grid coordinates. + It assumes observations lie on an approximately regular lattice, so it is + usually not appropriate for continuous coordinates such as Xenium point + clouds. On irregular coordinates, the resulting graph and ring distances may + not have a meaningful grid interpretation. Parameters ---------- @@ -633,14 +656,24 @@ def spatial_neighbors_grid( %(sdata_params)s %(library_key)s n_neighs - Number of neighboring tiles. Defaults to ``6``. + Number of neighboring tiles used to form the base grid connectivity. + Defaults to ``6``. On a Visium-like hexagonal grid, ``6`` corresponds to + the immediate surrounding spots, while smaller values such as ``3`` make + the first-ring graph deliberately sparser. n_rings - Number of rings of neighbors. Defaults to ``1``. + Number of rings of neighbors. Defaults to ``1``. ``n_rings=1`` keeps + only immediate neighbors; larger values add progressively more distant + shells and encode the shell number in ``dst``. For example, + ``n_neighs=3`` with ``n_rings=2`` on a Visium-like grid starts from a + sparse three-neighbor base graph and then adds a second graph-distance + ring relative to that base connectivity. delaunay Whether to derive the base grid connectivity from a Delaunay triangulation. This is still grid mode: unlike :func:`spatial_neighbors_delaunay`, the resulting distance matrix encodes grid or ring distances rather than - Euclidean edge lengths. + Euclidean edge lengths. In practice, this changes how the first-ring + connectivity is inferred, but not the meaning of the resulting + distances. %(graph_common_params)s %(copy)s From ae9a4e6727d1245ac6cea68c89671b6f86e7aa0a Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Fri, 10 Apr 2026 14:41:24 +0200 Subject: [PATCH 37/96] spatial_neighbours_from_builder --- docs/api.md | 1 + docs/extensibility.md | 4 +- src/squidpy/gr/__init__.py | 2 + src/squidpy/gr/_build.py | 149 ++++++++++++++------------ tests/graph/test_spatial_neighbors.py | 43 ++------ 5 files changed, 92 insertions(+), 107 deletions(-) diff --git a/docs/api.md b/docs/api.md index bf2d39f4e..dd19bb18e 100644 --- a/docs/api.md +++ b/docs/api.md @@ -18,6 +18,7 @@ import squidpy as sq :toctree: api gr.spatial_neighbors + gr.spatial_neighbors_from_builder gr.spatial_neighbors_knn gr.spatial_neighbors_radius gr.spatial_neighbors_delaunay diff --git a/docs/extensibility.md b/docs/extensibility.md index bfcb41ee6..aa2845bf5 100644 --- a/docs/extensibility.md +++ b/docs/extensibility.md @@ -20,7 +20,7 @@ The `squidpy.gr.neighbors` module exposes two builder base classes: | {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_filters` | no | Post-process the raw ``adj``/``dst`` before percentile filtering and transform. | | {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_percentile` | no | Override percentile handling when the backend needs custom behavior. | | {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_transform` | no | Override transform handling when the backend needs custom behavior. | -| {class}`~squidpy.gr.neighbors.GraphBuilderCSR` | {meth}`~squidpy.gr.neighbors.GraphBuilderCSR.build_graph` | yes | Construct and return ``(adj, dst)`` as a {class}`~scipy.sparse.csr_matrix` pair. | + The generic builder only defines the pipeline. The CSR-specialized builder adds the built-in CSR behavior for percentile filtering, adjacency transforms, and @@ -100,5 +100,5 @@ Use it like any other builder: ```python import squidpy as sq -sq.gr.spatial_neighbors(adata, builder=SNNRadiusBuilder(radius=100.0)) +sq.gr.spatial_neighbors_from_builder(adata, SNNRadiusBuilder(radius=100.0)) ``` diff --git a/src/squidpy/gr/__init__.py b/src/squidpy/gr/__init__.py index a5dd26973..fce615b66 100644 --- a/src/squidpy/gr/__init__.py +++ b/src/squidpy/gr/__init__.py @@ -8,6 +8,7 @@ mask_graph, spatial_neighbors, spatial_neighbors_delaunay, + spatial_neighbors_from_builder, spatial_neighbors_grid, spatial_neighbors_knn, spatial_neighbors_radius, @@ -30,6 +31,7 @@ "neighbors", "mask_graph", "spatial_neighbors", + "spatial_neighbors_from_builder", "spatial_neighbors_knn", "spatial_neighbors_radius", "spatial_neighbors_delaunay", diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index ccf83655c..516dd5ed8 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -49,6 +49,7 @@ __all__ = [ "SpatialNeighborsResult", "spatial_neighbors", + "spatial_neighbors_from_builder", "spatial_neighbors_knn", "spatial_neighbors_radius", "spatial_neighbors_delaunay", @@ -63,15 +64,6 @@ class SpatialNeighborsResult(NamedTuple): distances: csr_matrix -def _validate_no_legacy_params(**kwargs: Any) -> None: - conflicts = [k for k, v in kwargs.items() if v is not None] - if conflicts: - raise ValueError( - "When `builder` is provided, graph-construction arguments must not be set. " - f"Got non-default values for: {', '.join(conflicts)}." - ) - - def _resolve_graph_builder( *, coord_type: str | CoordType | None, @@ -209,7 +201,6 @@ def spatial_neighbors( percentile: float | None = None, transform: str | Transform | None = None, set_diag: bool | None = None, - builder: GraphBuilder[Any, Any] | None = None, key_added: str = "spatial", copy: bool = False, ) -> SpatialNeighborsResult | None: @@ -217,16 +208,14 @@ def spatial_neighbors( Create a graph from spatial coordinates. .. deprecated:: 1.6.0 - The flat-parameter API of ``spatial_neighbors`` is deprecated and will - be removed in squidpy v1.7.0. Use one of the mode-specific functions - instead: + ``spatial_neighbors`` is deprecated and will be removed in squidpy + v1.7.0. Use one of the mode-specific functions instead: - :func:`spatial_neighbors_knn` - :func:`spatial_neighbors_radius` - :func:`spatial_neighbors_delaunay` - :func:`spatial_neighbors_grid` - - Passing a ``builder`` instance directly remains supported. + - :func:`spatial_neighbors_from_builder` Parameters ---------- @@ -243,8 +232,7 @@ def spatial_neighbors( `adata` is a :class:`spatialdata.SpatialData`. %(library_key)s coord_type - Type of coordinate system. Must not be set when ``builder`` is given. - Valid options are: + Type of coordinate system. Valid options are: - `{c.GRID.s!r}` - grid coordinates. - `{c.GENERIC.s!r}` - generic coordinates. @@ -256,24 +244,23 @@ def spatial_neighbors( - `{c.GRID.s!r}` - number of neighboring tiles. - `{c.GENERIC.s!r}` - number of neighborhoods for non-grid data. Only used when ``delaunay = False``. - Defaults to ``6`` when no ``builder`` is provided. Must not be set when ``builder`` is given. + Defaults to ``6``. radius - Only available when ``coord_type = {c.GENERIC.s!r}``. Must not be set when ``builder`` is given. + Only available when ``coord_type = {c.GENERIC.s!r}``. Depending on the type: - :class:`float` - compute the graph based on neighborhood radius. - :class:`tuple` - prune the final graph to only contain edges in interval `[min(radius), max(radius)]`. delaunay Whether to compute the graph from Delaunay triangulation. Only used when ``coord_type = {c.GENERIC.s!r}``. - Defaults to ``False`` when no ``builder`` is provided. Must not be set when ``builder`` is given. + Defaults to ``False``. n_rings Number of rings of neighbors for grid data. Only used when ``coord_type = {c.GRID.s!r}``. - Defaults to ``1`` when no ``builder`` is provided. Must not be set when ``builder`` is given. + Defaults to ``1``. percentile Percentile of the distances to use as threshold. Only used when ``coord_type = {c.GENERIC.s!r}``. - Must not be set when ``builder`` is given. transform - Type of adjacency matrix transform. Must not be set when ``builder`` is given. + Type of adjacency matrix transform. Valid options are: - `{t.SPECTRAL.s!r}` - spectral transformation of the adjacency matrix. @@ -281,14 +268,7 @@ def spatial_neighbors( - `{t.NONE.v}` - no transformation of the adjacency matrix. set_diag Whether to set the diagonal of the spatial connectivities to `1.0`. - Defaults to ``False`` when no ``builder`` is provided. Must not be set when ``builder`` is given. - builder - Advanced graph construction strategy. When provided, all other graph-construction - arguments (``coord_type``, ``n_neighs``, ``radius``, ``delaunay``, ``n_rings``, - ``percentile``, ``transform``, ``set_diag``) must be left as ``None``. - Built-in builders subclass {{class}}`~squidpy.gr.neighbors.GraphBuilderCSR`, - while custom backends can implement the more generic - {{class}}`~squidpy.gr.neighbors.GraphBuilder` interface directly. + Defaults to ``False``. key_added Key which controls where the results are saved if ``copy = False``. %(copy)s @@ -323,9 +303,6 @@ def spatial_neighbors( - ``percentile`` only affects generic graphs. - ``transform`` and ``set_diag`` apply to all modes. - - If ``builder`` is provided, it determines the mode directly. - All other graph-construction arguments must be left as - ``None``. - By default, observations are not treated as their own neighbors. The distance matrix always has a zero diagonal. The connectivity matrix only gets a nonzero diagonal when @@ -333,7 +310,7 @@ def spatial_neighbors( Argument precedence ------------------- - When ``builder`` is not provided, the mode is resolved as follows: + The mode is resolved as follows: - If ``coord_type`` resolves to ``'grid'``, grid mode is used. In that case ``radius`` is ignored. @@ -370,15 +347,14 @@ def spatial_neighbors( - :attr:`anndata.AnnData.obsp` ``['{{key_added}}_distances']`` - the spatial distances. - :attr:`anndata.AnnData.uns` ``['{{key_added}}']`` - :class:`dict` containing parameters. """ - if builder is None: - warnings.warn( - "Calling `spatial_neighbors` without a `builder` argument is deprecated " - "and will be removed in squidpy v1.7.0. Use one of the mode-specific " - "functions instead: `spatial_neighbors_knn`, `spatial_neighbors_radius`, " - "`spatial_neighbors_delaunay`, or `spatial_neighbors_grid`.", - FutureWarning, - stacklevel=2, - ) + warnings.warn( + "Calling `spatial_neighbors` is deprecated and will be removed in squidpy " + "v1.7.0. Use `spatial_neighbors_knn`, `spatial_neighbors_radius`, " + "`spatial_neighbors_delaunay`, `spatial_neighbors_grid`, or " + "`spatial_neighbors_from_builder` instead.", + FutureWarning, + stacklevel=2, + ) adata, library_key = _prepare_spatial_neighbors_input( adata, spatial_key=spatial_key, @@ -386,36 +362,73 @@ def spatial_neighbors( table_key=table_key, library_key=library_key, ) - - if builder is not None: - _validate_no_legacy_params( - coord_type=coord_type, - n_neighs=n_neighs, - radius=radius, - delaunay=delaunay, - n_rings=n_rings, - percentile=percentile, - transform=transform, - set_diag=set_diag, - ) - else: - builder = _resolve_graph_builder( - coord_type=coord_type, - n_neighs=n_neighs, - radius=radius, - delaunay=delaunay, - n_rings=n_rings, - percentile=percentile, - transform=transform, - set_diag=set_diag, - has_spatial_uns=Key.uns.spatial in adata.uns, - ) + builder = _resolve_graph_builder( + coord_type=coord_type, + n_neighs=n_neighs, + radius=radius, + delaunay=delaunay, + n_rings=n_rings, + percentile=percentile, + transform=transform, + set_diag=set_diag, + has_spatial_uns=Key.uns.spatial in adata.uns, + ) return _run_spatial_neighbors( adata, builder=builder, spatial_key=spatial_key, library_key=library_key, key_added=key_added, copy=copy ) +@d.dedent +def spatial_neighbors_from_builder( + adata: AnnData | SpatialData, + builder: GraphBuilder[Any, Any], + *, + spatial_key: str = Key.obsm.spatial, + elements_to_coordinate_systems: dict[str, str] | None = None, + table_key: str | None = None, + library_key: str | None = None, + key_added: str = "spatial", + copy: bool = False, +) -> SpatialNeighborsResult | None: + """Create a graph from spatial coordinates using an explicit builder instance. + + Parameters + ---------- + %(adata)s + builder + Graph construction strategy to execute. Built-in builders subclass + {{class}}`~squidpy.gr.neighbors.GraphBuilderCSR`, while custom backends + can implement the more generic + {{class}}`~squidpy.gr.neighbors.GraphBuilder` interface directly. + %(spatial_key)s + %(sdata_params)s + %(library_key)s + key_added + Key which controls where the results are saved if ``copy = False``. + %(copy)s + + Returns + ------- + %(spatial_neighbors_returns)s + """ + adata, library_key = _prepare_spatial_neighbors_input( + adata, + spatial_key=spatial_key, + elements_to_coordinate_systems=elements_to_coordinate_systems, + table_key=table_key, + library_key=library_key, + ) + return _run_spatial_neighbors( + adata, + builder=builder, + spatial_key=spatial_key, + library_key=library_key, + key_added=key_added, + copy=copy, + ) + + def _prepare_spatial_neighbors_input( adata: AnnData | SpatialData, *, diff --git a/tests/graph/test_spatial_neighbors.py b/tests/graph/test_spatial_neighbors.py index 46d5a4a68..150d211bb 100644 --- a/tests/graph/test_spatial_neighbors.py +++ b/tests/graph/test_spatial_neighbors.py @@ -12,7 +12,7 @@ from squidpy._constants._constants import Transform from squidpy._constants._pkg_constants import Key -from squidpy.gr import mask_graph, spatial_neighbors +from squidpy.gr import mask_graph, spatial_neighbors, spatial_neighbors_from_builder from squidpy.gr.neighbors import ( DelaunayBuilder, GridBuilder, @@ -214,7 +214,7 @@ def test_builder_module_export(self): ) def test_generic_builder_matches_legacy(self, non_visium_adata: AnnData, legacy_kwargs: dict, builder: object): legacy = spatial_neighbors(non_visium_adata, **legacy_kwargs, copy=True) - result = spatial_neighbors(non_visium_adata, builder=builder, copy=True) + result = spatial_neighbors_from_builder(non_visium_adata, builder=builder, copy=True) np.testing.assert_array_equal(legacy.connectivities.toarray(), result.connectivities.toarray()) np.testing.assert_allclose(legacy.distances.toarray(), result.distances.toarray()) @@ -229,51 +229,20 @@ def test_generic_builder_matches_legacy(self, non_visium_adata: AnnData, legacy_ ) def test_grid_builder_matches_legacy(self, adata_squaregrid: AnnData, legacy_kwargs: dict, builder: object): legacy = spatial_neighbors(adata_squaregrid, **legacy_kwargs, copy=True) - result = spatial_neighbors(adata_squaregrid, builder=builder, copy=True) + result = spatial_neighbors_from_builder(adata_squaregrid, builder=builder, copy=True) np.testing.assert_array_equal(legacy.connectivities.toarray(), result.connectivities.toarray()) np.testing.assert_allclose(legacy.distances.toarray(), result.distances.toarray()) - def test_builder_argument_conflict(self, non_visium_adata: AnnData): - with pytest.raises(ValueError, match="must not be set"): - spatial_neighbors(non_visium_adata, builder=RadiusBuilder(radius=5.0), delaunay=True) - - def test_builder_rejects_any_legacy_args(self, non_visium_adata: AnnData): - builder = RadiusBuilder(radius=5.0, percentile=95.0, transform="cosine", set_diag=True) - - with pytest.raises(ValueError, match="must not be set"): - spatial_neighbors( - non_visium_adata, - builder=builder, - coord_type="generic", - n_neighs=3, - radius=5.0, - percentile=95.0, - transform="cosine", - set_diag=True, - copy=True, - ) - - def test_builder_allows_none_legacy_args(self, non_visium_adata: AnnData): + def test_builder_explicit_entry_point(self, non_visium_adata: AnnData): builder = KNNBuilder(n_neighs=3, transform=Transform.NONE) - baseline = spatial_neighbors(non_visium_adata, builder=builder, copy=True) - matched = spatial_neighbors(non_visium_adata, builder=builder, transform=None, copy=True) + baseline = spatial_neighbors_from_builder(non_visium_adata, builder=builder, copy=True) + matched = spatial_neighbors_from_builder(non_visium_adata, builder=builder, copy=True) np.testing.assert_array_equal(baseline.connectivities.toarray(), matched.connectivities.toarray()) np.testing.assert_allclose(baseline.distances.toarray(), matched.distances.toarray()) - def test_builder_rejects_radius(self, non_visium_adata: AnnData): - builder = RadiusBuilder(radius=(4.0, 2.0)) - - with pytest.raises(ValueError, match="must not be set"): - spatial_neighbors( - non_visium_adata, - builder=builder, - radius=(2.0, 4.0), - copy=True, - ) - def test_grid_mode_ignores_radius(self, adata_squaregrid: AnnData): default = spatial_neighbors(adata_squaregrid, coord_type="grid", n_neighs=4, n_rings=2, copy=True) ignored = spatial_neighbors( From cd3f6fd7ea879bc6dbcc0e6ef154f4e016e1bb2c Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Fri, 10 Apr 2026 15:36:35 +0200 Subject: [PATCH 38/96] update docs to refer to each other --- src/squidpy/gr/_build.py | 37 +++++++++++++++++++++++++++ src/squidpy/gr/neighbors.py | 51 +++++++++++++++++++++++++++++++------ 2 files changed, 80 insertions(+), 8 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 516dd5ed8..bf99398e9 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -393,6 +393,14 @@ def spatial_neighbors_from_builder( ) -> SpatialNeighborsResult | None: """Create a graph from spatial coordinates using an explicit builder instance. + This function is the bridge between the high-level API (e.g., + :func:`spatial_neighbors_knn`, :func:`spatial_neighbors_radius`) and advanced + customization via builder classes. Use this when you need to: + + - Stack or chain builder behaviors + - Pass pre-configured builder instances multiple times + - Implement custom builders (see :doc:`/extensibility`) + Parameters ---------- %(adata)s @@ -411,6 +419,14 @@ def spatial_neighbors_from_builder( Returns ------- %(spatial_neighbors_returns)s + + See Also + -------- + spatial_neighbors_knn : k-nearest-neighbor graphs (wraps :class:`~squidpy.gr.neighbors.KNNBuilder`). + spatial_neighbors_radius : radius-based graphs (wraps :class:`~squidpy.gr.neighbors.RadiusBuilder`). + spatial_neighbors_delaunay : Delaunay triangulation graphs (wraps :class:`~squidpy.gr.neighbors.DelaunayBuilder`). + spatial_neighbors_grid : grid-based graphs (wraps :class:`~squidpy.gr.neighbors.GridBuilder`). + gr.neighbors.GraphBuilder : Base builder interface. Inherit from this or :class:`~squidpy.gr.neighbors.GraphBuilderCSR` to implement custom graph construction. """ adata, library_key = _prepare_spatial_neighbors_input( adata, @@ -485,6 +501,11 @@ def spatial_neighbors_knn( Returns ------- %(spatial_neighbors_returns)s + + See Also + -------- + spatial_neighbors_from_builder : Use :class:`~squidpy.gr.neighbors.KNNBuilder` directly for advanced customization. + gr.neighbors.KNNBuilder : k-nearest-neighbor builder class. """ transform_enum = Transform.NONE if transform is None else Transform(transform) builder = KNNBuilder( @@ -549,6 +570,11 @@ def spatial_neighbors_radius( Returns ------- %(spatial_neighbors_returns)s + + See Also + -------- + spatial_neighbors_from_builder : Use :class:`~squidpy.gr.neighbors.RadiusBuilder` directly for advanced customization. + gr.neighbors.RadiusBuilder : radius-based builder class. """ transform_enum = Transform.NONE if transform is None else Transform(transform) builder = RadiusBuilder( @@ -613,6 +639,11 @@ def spatial_neighbors_delaunay( Returns ------- %(spatial_neighbors_returns)s + + See Also + -------- + spatial_neighbors_from_builder : Use :class:`~squidpy.gr.neighbors.DelaunayBuilder` directly for advanced customization. + gr.neighbors.DelaunayBuilder : Delaunay triangulation builder class. """ transform_enum = Transform.NONE if transform is None else Transform(transform) builder = DelaunayBuilder( @@ -693,6 +724,11 @@ def spatial_neighbors_grid( Returns ------- %(spatial_neighbors_returns)s + + See Also + -------- + spatial_neighbors_from_builder : Use :class:`~squidpy.gr.neighbors.GridBuilder` directly for advanced customization. + gr.neighbors.GridBuilder : grid-based builder class. """ assert_positive(n_rings, name="n_rings") assert_positive(n_neighs, name="n_neighs") @@ -774,6 +810,7 @@ def _run_spatial_neighbors( _save_data(adata, attr="obsp", key=conns_key, data=adj) _save_data(adata, attr="obsp", key=dists_key, data=dst, prefix=False) _save_data(adata, attr="uns", key=neighs_key, data=neighbors_dict, prefix=False, time=start) + return None @d.dedent diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 83a86feb9..2a1e80605 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -77,7 +77,22 @@ def apply_transform(self, adj: GraphMatrixT, dst: GraphMatrixT) -> tuple[GraphMa class GraphBuilderCSR(GraphBuilder[NDArrayA, csr_matrix], ABC): - """CSR-based graph construction strategy.""" + """CSR-based graph construction strategy. + + Specializes :class:`GraphBuilder` for sparse CSR matrix output. Adds built-in handling + for percentile-based edge pruning, adjacency transforms (spectral/cosine), and + SparseEfficiencyWarning suppression. All built-in concrete builders + (:class:`KNNBuilder`, :class:`RadiusBuilder`, :class:`DelaunayBuilder`, :class:`GridBuilder`) + inherit from this class. + + Subclass this (not the generic :class:`GraphBuilder`) when implementing a builder + that returns CSR matrices. + + See Also + -------- + GraphBuilder : Generic builder interface for custom coordinate/matrix types. + KNNBuilder : Example of a concrete CSR-based builder. + """ def build(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: with warnings.catch_warnings(): @@ -114,7 +129,12 @@ def apply_transform(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, class KNNBuilder(GraphBuilderCSR): - """Build a generic k-nearest-neighbor spatial graph.""" + """Build a generic k-nearest-neighbor spatial graph. + + Each observation is connected to its k nearest neighbors. See + :func:`~squidpy.gr.spatial_neighbors_knn` for the user-facing API or + :func:`~squidpy.gr.spatial_neighbors_from_builder` for direct builder usage. + """ def __init__( self, @@ -152,7 +172,13 @@ def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: class RadiusBuilder(GraphBuilderCSR): - """Build a generic radius-based spatial graph.""" + """Build a generic radius-based spatial graph. + + Two observations are connected when their Euclidean distance falls within + the specified radius. See :func:`~squidpy.gr.spatial_neighbors_radius` for the + user-facing API or :func:`~squidpy.gr.spatial_neighbors_from_builder` for + direct builder usage. + """ def __init__( self, @@ -198,8 +224,13 @@ def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, c class DelaunayBuilder(GraphBuilderCSR): """Build a generic point-cloud graph from a Delaunay triangulation. - Unlike ``GridBuilder(delaunay=True)``, this builder uses the triangulation as the - graph itself and stores real Euclidean edge lengths in ``dst``. + Delaunay triangulation connects observations into triangles such that no + other observation lies inside the circumcircle of each triangle. Unlike + ``GridBuilder(delaunay=True)``, this builder uses geometry-based connectivity + and stores real Euclidean edge lengths. + + See :func:`~squidpy.gr.spatial_neighbors_delaunay` for the user-facing API or + :func:`~squidpy.gr.spatial_neighbors_from_builder` for direct builder usage. """ def __init__( @@ -244,9 +275,13 @@ def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, c class GridBuilder(GraphBuilderCSR): """Build a grid-based spatial graph. - When ``delaunay=True``, Delaunay triangulation is used only to derive the base - grid connectivity. The resulting ``dst`` still encodes grid or ring distances, - not geometric Euclidean edge lengths. + Assumes observations lie on an approximately regular lattice (e.g., Visium). + When ``delaunay=True``, Delaunay triangulation is used only to derive the + base connectivity; the distance matrix still encodes grid/ring distances, + not Euclidean lengths. + + See :func:`~squidpy.gr.spatial_neighbors_grid` for the user-facing API or + :func:`~squidpy.gr.spatial_neighbors_from_builder` for direct builder usage. """ def __init__( From 95012dc54ec158044d06fbf8bf54bdec2e53cec5 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Fri, 10 Apr 2026 15:40:23 +0200 Subject: [PATCH 39/96] refer to the classes properly --- src/squidpy/gr/_build.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index bf99398e9..3d1df5c9a 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -426,7 +426,7 @@ def spatial_neighbors_from_builder( spatial_neighbors_radius : radius-based graphs (wraps :class:`~squidpy.gr.neighbors.RadiusBuilder`). spatial_neighbors_delaunay : Delaunay triangulation graphs (wraps :class:`~squidpy.gr.neighbors.DelaunayBuilder`). spatial_neighbors_grid : grid-based graphs (wraps :class:`~squidpy.gr.neighbors.GridBuilder`). - gr.neighbors.GraphBuilder : Base builder interface. Inherit from this or :class:`~squidpy.gr.neighbors.GraphBuilderCSR` to implement custom graph construction. + squidpy.gr.neighbors.GraphBuilder : Base builder interface. Inherit from this or :class:`~squidpy.gr.neighbors.GraphBuilderCSR` to implement custom graph construction. """ adata, library_key = _prepare_spatial_neighbors_input( adata, @@ -505,7 +505,7 @@ def spatial_neighbors_knn( See Also -------- spatial_neighbors_from_builder : Use :class:`~squidpy.gr.neighbors.KNNBuilder` directly for advanced customization. - gr.neighbors.KNNBuilder : k-nearest-neighbor builder class. + squidpy.gr.neighbors.KNNBuilder : k-nearest-neighbor builder class. """ transform_enum = Transform.NONE if transform is None else Transform(transform) builder = KNNBuilder( @@ -574,7 +574,7 @@ def spatial_neighbors_radius( See Also -------- spatial_neighbors_from_builder : Use :class:`~squidpy.gr.neighbors.RadiusBuilder` directly for advanced customization. - gr.neighbors.RadiusBuilder : radius-based builder class. + squidpy.gr.neighbors.RadiusBuilder : radius-based builder class. """ transform_enum = Transform.NONE if transform is None else Transform(transform) builder = RadiusBuilder( @@ -643,7 +643,7 @@ def spatial_neighbors_delaunay( See Also -------- spatial_neighbors_from_builder : Use :class:`~squidpy.gr.neighbors.DelaunayBuilder` directly for advanced customization. - gr.neighbors.DelaunayBuilder : Delaunay triangulation builder class. + squidpy.gr.neighbors.DelaunayBuilder : Delaunay triangulation builder class. """ transform_enum = Transform.NONE if transform is None else Transform(transform) builder = DelaunayBuilder( @@ -728,7 +728,7 @@ def spatial_neighbors_grid( See Also -------- spatial_neighbors_from_builder : Use :class:`~squidpy.gr.neighbors.GridBuilder` directly for advanced customization. - gr.neighbors.GridBuilder : grid-based builder class. + squidpy.gr.neighbors.GridBuilder : grid-based builder class. """ assert_positive(n_rings, name="n_rings") assert_positive(n_neighs, name="n_neighs") From 5a02b5958eae9d85aee489caa71d242950dd5053 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Mon, 13 Apr 2026 07:46:25 +0200 Subject: [PATCH 40/96] mark as TODO's --- src/squidpy/gr/_build.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 3d1df5c9a..b50b5262b 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -105,7 +105,7 @@ def _resolve_graph_builder( ) return GridBuilder(n_neighs=n_neighs, **common, n_rings=n_rings, delaunay=delaunay) if delaunay: - # below check should be removed once legacy mode spatial_neighbors is deprecated + # TODO: below check should be removed once legacy mode spatial_neighbors is deprecated if n_neighs_was_set: warnings.warn( "Parameter `n_neighs` is ignored when `delaunay=True` and will be removed in squidpy v2.0.0.", @@ -114,7 +114,7 @@ def _resolve_graph_builder( ) return DelaunayBuilder(**common, radius=radius, percentile=percentile) if radius is not None: - # below check should be removed once legacy mode spatial_neighbors is deprecated + # TODO: below check should be removed once legacy mode spatial_neighbors is deprecated if n_neighs_was_set: warnings.warn( "Parameter `n_neighs` is ignored when `radius` is set and will be removed in squidpy v2.0.0.", From 3af98ccd23a4adea348b88f317d7016b5aa6f18b Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Mon, 13 Apr 2026 07:55:40 +0200 Subject: [PATCH 41/96] rename sugg --- src/squidpy/gr/_build.py | 51 +++++++++++++++++++++------------------- 1 file changed, 27 insertions(+), 24 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index b50b5262b..3da4cf9a5 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -126,7 +126,7 @@ def _resolve_graph_builder( def _resolve_spatial_data( - adata: AnnData | SpatialData, + data: AnnData | SpatialData, *, spatial_key: str, elements_to_coordinate_systems: dict[str, str] | None, @@ -134,20 +134,21 @@ def _resolve_spatial_data( library_key: str | None, ) -> tuple[AnnData, str | None]: """Resolve SpatialData to AnnData, returning (adata, library_key).""" - if isinstance(adata, SpatialData): + if isinstance(data, SpatialData): + sdata = data assert elements_to_coordinate_systems is not None, ( - "Since `adata` is a :class:`spatialdata.SpatialData`, `elements_to_coordinate_systems` must not be `None`." + "Since `data` is a :class:`spatialdata.SpatialData`, `elements_to_coordinate_systems` must not be `None`." ) assert table_key is not None, ( - "Since `adata` is a :class:`spatialdata.SpatialData`, `table_key` must not be `None`." + "Since `data` is a :class:`spatialdata.SpatialData`, `table_key` must not be `None`." ) - elements, table = match_element_to_table(adata, list(elements_to_coordinate_systems), table_key) - assert table.obs_names.equals(adata.tables[table_key].obs_names), ( + elements, table = match_element_to_table(sdata, list(elements_to_coordinate_systems), table_key) + assert table.obs_names.equals(sdata.tables[table_key].obs_names), ( "The spatialdata table must annotate all elements keys. Some elements are missing, please check the `elements_to_coordinate_systems` dictionary." ) - regions, region_key, instance_key = get_table_keys(adata.tables[table_key]) + regions, region_key, instance_key = get_table_keys(sdata.tables[table_key]) regions = [regions] if isinstance(regions, str) else regions - ordered_regions_in_table = adata.tables[table_key].obs[region_key].unique() + ordered_regions_in_table = sdata.tables[table_key].obs[region_key].unique() # TODO: remove this after https://github.com/scverse/spatialdata/issues/614 remove_centroids = {} @@ -163,7 +164,7 @@ def _resolve_spatial_data( elem_instances.append(element_instances) element_instances = pd.concat(elem_instances) - if (not np.all(element_instances.values == adata.tables[table_key].obs[instance_key].values)) or ( + if (not np.all(element_instances.values == sdata.tables[table_key].obs[instance_key].values)) or ( not np.all(ordered_regions_in_table == regions) ): raise ValueError( @@ -172,16 +173,18 @@ def _resolve_spatial_data( centroids = [] for region_ in ordered_regions_in_table: cs = elements_to_coordinate_systems[region_] - centroid = get_centroids(adata[region_], coordinate_system=cs)[["x", "y"]].compute() + centroid = get_centroids(sdata[region_], coordinate_system=cs)[["x", "y"]].compute() # TODO: remove this after https://github.com/scverse/spatialdata/issues/614 if remove_centroids[region_]: centroid = centroid[1:].copy() centroids.append(centroid) - adata.tables[table_key].obsm[spatial_key] = np.concatenate(centroids) - adata = adata.tables[table_key] + sdata.tables[table_key].obsm[spatial_key] = np.concatenate(centroids) + adata = sdata.tables[table_key] library_key = region_key + else: + adata = data return adata, library_key @@ -381,7 +384,7 @@ def spatial_neighbors( @d.dedent def spatial_neighbors_from_builder( - adata: AnnData | SpatialData, + data: AnnData | SpatialData, builder: GraphBuilder[Any, Any], *, spatial_key: str = Key.obsm.spatial, @@ -429,7 +432,7 @@ def spatial_neighbors_from_builder( squidpy.gr.neighbors.GraphBuilder : Base builder interface. Inherit from this or :class:`~squidpy.gr.neighbors.GraphBuilderCSR` to implement custom graph construction. """ adata, library_key = _prepare_spatial_neighbors_input( - adata, + data, spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, table_key=table_key, @@ -446,7 +449,7 @@ def spatial_neighbors_from_builder( def _prepare_spatial_neighbors_input( - adata: AnnData | SpatialData, + data: AnnData | SpatialData, *, spatial_key: str, elements_to_coordinate_systems: dict[str, str] | None, @@ -455,7 +458,7 @@ def _prepare_spatial_neighbors_input( ) -> tuple[AnnData, str | None]: """Resolve input data and validate the requested spatial basis.""" adata, library_key = _resolve_spatial_data( - adata, + data, spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, table_key=table_key, @@ -467,7 +470,7 @@ def _prepare_spatial_neighbors_input( @d.dedent def spatial_neighbors_knn( - adata: AnnData | SpatialData, + data: AnnData | SpatialData, *, spatial_key: str = Key.obsm.spatial, elements_to_coordinate_systems: dict[str, str] | None = None, @@ -515,7 +518,7 @@ def spatial_neighbors_knn( set_diag=set_diag, ) adata, library_key = _prepare_spatial_neighbors_input( - adata, + data, spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, table_key=table_key, @@ -533,7 +536,7 @@ def spatial_neighbors_knn( @d.dedent def spatial_neighbors_radius( - adata: AnnData | SpatialData, + data: AnnData | SpatialData, *, spatial_key: str = Key.obsm.spatial, elements_to_coordinate_systems: dict[str, str] | None = None, @@ -584,7 +587,7 @@ def spatial_neighbors_radius( set_diag=set_diag, ) adata, library_key = _prepare_spatial_neighbors_input( - adata, + data, spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, table_key=table_key, @@ -602,7 +605,7 @@ def spatial_neighbors_radius( @d.dedent def spatial_neighbors_delaunay( - adata: AnnData | SpatialData, + data: AnnData | SpatialData, *, spatial_key: str = Key.obsm.spatial, elements_to_coordinate_systems: dict[str, str] | None = None, @@ -653,7 +656,7 @@ def spatial_neighbors_delaunay( set_diag=set_diag, ) adata, library_key = _prepare_spatial_neighbors_input( - adata, + data, spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, table_key=table_key, @@ -671,7 +674,7 @@ def spatial_neighbors_delaunay( @d.dedent def spatial_neighbors_grid( - adata: AnnData | SpatialData, + data: AnnData | SpatialData, *, spatial_key: str = Key.obsm.spatial, elements_to_coordinate_systems: dict[str, str] | None = None, @@ -741,7 +744,7 @@ def spatial_neighbors_grid( set_diag=set_diag, ) adata, library_key = _prepare_spatial_neighbors_input( - adata, + data, spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, table_key=table_key, From e8a19d7ef4209fef188b87013c9d0201fdbe7468 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Mon, 13 Apr 2026 08:39:47 +0200 Subject: [PATCH 42/96] add combine into API --- src/squidpy/gr/_build.py | 22 +++++++++------------- src/squidpy/gr/neighbors.py | 22 +++++++++++++++++++++- 2 files changed, 30 insertions(+), 14 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 3da4cf9a5..e006fb7d2 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -3,7 +3,7 @@ from __future__ import annotations import warnings -from typing import Any, NamedTuple, cast +from typing import Any, Generic, NamedTuple, TypeVar import geopandas as gpd import numpy as np @@ -11,11 +11,6 @@ from anndata import AnnData from anndata.utils import make_index_unique from numba import njit -from scipy.sparse import ( - block_diag, - csr_matrix, - spmatrix, -) from shapely import LineString, MultiPolygon, Polygon from spatialdata import SpatialData from spatialdata._core.centroids import get_centroids @@ -57,11 +52,14 @@ ] -class SpatialNeighborsResult(NamedTuple): +GraphMatrixT = TypeVar("GraphMatrixT") + + +class SpatialNeighborsResult(NamedTuple, Generic[GraphMatrixT]): """Result of spatial_neighbors function.""" - connectivities: csr_matrix - distances: csr_matrix + connectivities: GraphMatrixT + distances: GraphMatrixT def _resolve_graph_builder( @@ -781,14 +779,12 @@ def _run_spatial_neighbors( f"Creating graph using `{builder.coord_type}` coordinates and `{builder.transform}` transform and `{len(libs)}` libraries." ) if library_key is not None: - mats: list[tuple[spmatrix, spmatrix]] = [] + mats: list[tuple[Any, Any]] = [] ixs: list[int] = [] for lib in libs: ixs.extend(np.where(adata.obs[library_key] == lib)[0]) mats.append(builder.build(adata[adata.obs[library_key] == lib].obsm[spatial_key])) - ixs = cast(list[int], np.argsort(ixs).tolist()) - adj = block_diag([m[0] for m in mats], format="csr")[ixs, :][:, ixs] - dst = block_diag([m[1] for m in mats], format="csr")[ixs, :][:, ixs] + adj, dst = builder.combine(mats, ixs) else: adj, dst = builder.build(adata.obsm[spatial_key]) diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 2a1e80605..eee55b61d 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -7,7 +7,7 @@ import warnings from abc import ABC, abstractmethod -from collections.abc import Iterable +from collections.abc import Iterable, Sequence from itertools import chain from typing import Generic, TypeVar, cast @@ -16,6 +16,7 @@ from numba import njit, prange from scipy.sparse import ( SparseEfficiencyWarning, + block_diag, csr_array, csr_matrix, isspmatrix_csr, @@ -75,6 +76,15 @@ def apply_percentile(self, adj: GraphMatrixT, dst: GraphMatrixT) -> tuple[GraphM def apply_transform(self, adj: GraphMatrixT, dst: GraphMatrixT) -> tuple[GraphMatrixT, GraphMatrixT]: return adj, dst + def combine( + self, + mats: Sequence[tuple[GraphMatrixT, GraphMatrixT]], + ixs: Sequence[int], + ) -> tuple[GraphMatrixT, GraphMatrixT]: + raise NotImplementedError( + "Using `library_key` with this graph builder is not implemented yet." + ) + class GraphBuilderCSR(GraphBuilder[NDArrayA, csr_matrix], ABC): """CSR-based graph construction strategy. @@ -127,6 +137,16 @@ def apply_transform(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, raise NotImplementedError(f"Transform `{self.transform}` is not yet implemented.") + def combine( + self, + mats: Sequence[tuple[csr_matrix, csr_matrix]], + ixs: Sequence[int], + ) -> tuple[csr_matrix, csr_matrix]: + order = cast(list[int], np.argsort(ixs).tolist()) + adj = block_diag([m[0] for m in mats], format="csr")[order, :][:, order] + dst = block_diag([m[1] for m in mats], format="csr")[order, :][:, order] + return cast(csr_matrix, adj), cast(csr_matrix, dst) + class KNNBuilder(GraphBuilderCSR): """Build a generic k-nearest-neighbor spatial graph. From ac682a3c12e1ce897c51641c449918d8043a1f40 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 13 Apr 2026 06:40:28 +0000 Subject: [PATCH 43/96] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/squidpy/gr/neighbors.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index eee55b61d..b6c404548 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -81,9 +81,7 @@ def combine( mats: Sequence[tuple[GraphMatrixT, GraphMatrixT]], ixs: Sequence[int], ) -> tuple[GraphMatrixT, GraphMatrixT]: - raise NotImplementedError( - "Using `library_key` with this graph builder is not implemented yet." - ) + raise NotImplementedError("Using `library_key` with this graph builder is not implemented yet.") class GraphBuilderCSR(GraphBuilder[NDArrayA, csr_matrix], ABC): From 0f3329cf93325e9b91e94ffdfc8a0f22f4b7ad92 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Mon, 13 Apr 2026 08:43:30 +0200 Subject: [PATCH 44/96] update docs --- docs/extensibility.md | 6 ++++-- src/squidpy/gr/_build.py | 3 +++ src/squidpy/gr/neighbors.py | 21 +++++++++++++++++---- 3 files changed, 24 insertions(+), 6 deletions(-) diff --git a/docs/extensibility.md b/docs/extensibility.md index aa2845bf5..798d8c714 100644 --- a/docs/extensibility.md +++ b/docs/extensibility.md @@ -20,10 +20,12 @@ The `squidpy.gr.neighbors` module exposes two builder base classes: | {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_filters` | no | Post-process the raw ``adj``/``dst`` before percentile filtering and transform. | | {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_percentile` | no | Override percentile handling when the backend needs custom behavior. | | {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_transform` | no | Override transform handling when the backend needs custom behavior. | +| {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.combine` | no | Combine per-library results when using ``library_key``. If you do not need ``library_key`` support, leaving this unimplemented is fine. | -The generic builder only defines the pipeline. The CSR-specialized builder adds the -built-in CSR behavior for percentile filtering, adjacency transforms, and +The generic builder only defines the pipeline. The CSR-specialized builder adds +the built-in CSR behavior for percentile filtering, adjacency transforms, +multi-library ``library_key`` combination, and {class}`~scipy.sparse.SparseEfficiencyWarning` suppression. Here ``adj`` and ``dst`` are square sparse matrices of shape ``(n_obs, n_obs)`` diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index e006fb7d2..25dfd0826 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -410,6 +410,9 @@ def spatial_neighbors_from_builder( {{class}}`~squidpy.gr.neighbors.GraphBuilderCSR`, while custom backends can implement the more generic {{class}}`~squidpy.gr.neighbors.GraphBuilder` interface directly. + Custom builders only need to implement multi-library support when using + ``library_key``; otherwise leaving + :meth:`~squidpy.gr.neighbors.GraphBuilder.combine` unimplemented is fine. %(spatial_key)s %(sdata_params)s %(library_key)s diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index b6c404548..94724a010 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -38,7 +38,12 @@ class GraphBuilder(ABC, Generic[CoordT, GraphMatrixT]): - """Base class for spatial graph construction strategies.""" + """Base class for spatial graph construction strategies. + + Custom builders must implement :meth:`build_graph`. Overriding + :meth:`combine` is optional and only needed to support multi-library graph + construction via ``library_key``. + """ def __init__( self, @@ -81,15 +86,23 @@ def combine( mats: Sequence[tuple[GraphMatrixT, GraphMatrixT]], ixs: Sequence[int], ) -> tuple[GraphMatrixT, GraphMatrixT]: - raise NotImplementedError("Using `library_key` with this graph builder is not implemented yet.") + """Combine per-library results into a single graph. + + Override this only if the builder should support multi-library graph + construction via ``library_key``. + """ + raise NotImplementedError( + "Using `library_key` with this graph builder is not implemented yet." + ) class GraphBuilderCSR(GraphBuilder[NDArrayA, csr_matrix], ABC): """CSR-based graph construction strategy. Specializes :class:`GraphBuilder` for sparse CSR matrix output. Adds built-in handling - for percentile-based edge pruning, adjacency transforms (spectral/cosine), and - SparseEfficiencyWarning suppression. All built-in concrete builders + for percentile-based edge pruning, adjacency transforms (spectral/cosine), + SparseEfficiencyWarning suppression, and multi-library ``library_key`` + combination. All built-in concrete builders (:class:`KNNBuilder`, :class:`RadiusBuilder`, :class:`DelaunayBuilder`, :class:`GridBuilder`) inherit from this class. From 00ec2b068525aabadaab5d04adb43f5725138f74 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 13 Apr 2026 06:46:13 +0000 Subject: [PATCH 45/96] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/squidpy/gr/neighbors.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 94724a010..b5f2aad16 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -91,9 +91,7 @@ def combine( Override this only if the builder should support multi-library graph construction via ``library_key``. """ - raise NotImplementedError( - "Using `library_key` with this graph builder is not implemented yet." - ) + raise NotImplementedError("Using `library_key` with this graph builder is not implemented yet.") class GraphBuilderCSR(GraphBuilder[NDArrayA, csr_matrix], ABC): From 042f749c37d9c8d39eb0caef6b51a2f07c3a7e28 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 10 Apr 2026 22:51:22 +0200 Subject: [PATCH 46/96] [pre-commit.ci] pre-commit autoupdate (#1149) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * [pre-commit.ci] pre-commit autoupdate updates: - [github.com/biomejs/pre-commit: v2.4.9 → v2.4.10](https://github.com/biomejs/pre-commit/compare/v2.4.9...v2.4.10) - [github.com/tox-dev/pyproject-fmt: v2.20.0 → v2.21.0](https://github.com/tox-dev/pyproject-fmt/compare/v2.20.0...v2.21.0) - [github.com/astral-sh/ruff-pre-commit: v0.15.8 → v0.15.9](https://github.com/astral-sh/ruff-pre-commit/compare/v0.15.8...v0.15.9) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .pre-commit-config.yaml | 6 +++--- pyproject.toml | 42 ++++++++++++++++++++--------------------- 2 files changed, 24 insertions(+), 24 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 118cb1b8a..401c82b0f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,16 +7,16 @@ default_stages: minimum_pre_commit_version: 2.16.0 repos: - repo: https://github.com/biomejs/pre-commit - rev: v2.4.9 + rev: v2.4.10 hooks: - id: biome-format exclude: ^\.cruft\.json$ # inconsistent indentation with cruft - file never to be modified manually. - repo: https://github.com/tox-dev/pyproject-fmt - rev: v2.20.0 + rev: v2.21.0 hooks: - id: pyproject-fmt - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.15.8 + rev: v0.15.9 hooks: - id: ruff-check types_or: [python, pyi, jupyter] diff --git a/pyproject.toml b/pyproject.toml index 1e264c809..06e9dfc5a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -133,6 +133,27 @@ build.targets.wheel.packages = [ "src/squidpy" ] metadata.allow-direct-references = true version.source = "vcs" +[tool.pixi] +workspace.channels = [ "conda-forge" ] +workspace.platforms = [ "linux-64", "osx-arm64" ] +dependencies.python = ">=3.11" +pypi-dependencies.squidpy = { path = ".", editable = true } +tasks.format = "ruff format ." +tasks.kernel-install = 'python -m ipykernel install --user --name pixi-dev --display-name "squidpy (dev)"' +tasks.lab = "jupyter lab" +tasks.lint = "ruff check ." +tasks.pre-commit = "pre-commit run" +tasks.pre-commit-install = "pre-commit install" +tasks.test = "pytest -v --color=yes --tb=short --durations=10" +feature.py311.dependencies.python = "3.11.*" +feature.py313.dependencies.python = "3.13.*" +environments.default = { features = [ "py313" ], solve-group = "py313" } +environments.dev-py311 = { features = [ "dev", "test", "py311" ], solve-group = "py311" } +environments.dev-py313 = { features = [ "dev", "test", "py313" ], solve-group = "py313" } +environments.docs-py311 = { features = [ "docs", "py311" ], solve-group = "py311" } +environments.docs-py313 = { features = [ "docs", "py313" ], solve-group = "py313" } +environments.test-py313 = { features = [ "test", "py313" ], solve-group = "py313" } + [tool.ruff] line-length = 120 exclude = [ @@ -278,24 +299,3 @@ skip = [ "docs/references.md", "docs/notebooks/example.ipynb", ] - -[tool.pixi] -dependencies.python = ">=3.11" -environments.dev-py311 = { features = [ "dev", "test", "py311" ], solve-group = "py311" } -environments.docs-py311 = { features = [ "docs", "py311" ], solve-group = "py311" } -environments.default = { features = [ "py313" ], solve-group = "py313" } -environments.dev-py313 = { features = [ "dev", "test", "py313" ], solve-group = "py313" } -environments.docs-py313 = { features = [ "docs", "py313" ], solve-group = "py313" } -environments.test-py313 = { features = [ "test", "py313" ], solve-group = "py313" } -feature.py311.dependencies.python = "3.11.*" -feature.py313.dependencies.python = "3.13.*" -pypi-dependencies.squidpy = { path = ".", editable = true } -tasks.lab = "jupyter lab" -tasks.kernel-install = 'python -m ipykernel install --user --name pixi-dev --display-name "squidpy (dev)"' -tasks.test = "pytest -v --color=yes --tb=short --durations=10" -tasks.lint = "ruff check ." -tasks.format = "ruff format ." -tasks.pre-commit-install = "pre-commit install" -tasks.pre-commit = "pre-commit run" -workspace.channels = [ "conda-forge" ] -workspace.platforms = [ "osx-arm64", "linux-64" ] From 1a87841815549635e10e55c27e52dd8db33fa059 Mon Sep 17 00:00:00 2001 From: Tim Treis Date: Fri, 10 Apr 2026 21:33:40 +0000 Subject: [PATCH 47/96] Functions to QC histopathology images (#1036) --- src/squidpy/experimental/im/__init__.py | 4 + src/squidpy/experimental/im/_detect_tissue.py | 6 +- .../experimental/im/_intensity_metrics.py | 134 ++++++ src/squidpy/experimental/im/_make_tiles.py | 175 +------ src/squidpy/experimental/im/_qc_image.py | 438 ++++++++++++++++++ src/squidpy/experimental/im/_qc_metrics.py | 98 ++++ .../experimental/im/_sharpness_metrics.py | 94 ++++ src/squidpy/experimental/im/_utils.py | 199 +++++++- src/squidpy/experimental/pl/__init__.py | 4 +- src/squidpy/experimental/pl/_qc_image.py | 199 ++++++++ tests/_images/QCImage_calc_qc_image_hne.png | Bin 0 -> 21230 bytes .../_images/QCImage_calc_qc_image_not_hne.png | Bin 0 -> 21063 bytes tests/_images/QCImage_plot_qc_image.png | Bin 0 -> 17741 bytes .../_images/QCSharpness_calc_qc_sharpness.png | Bin 19379 -> 0 bytes .../_images/QCSharpness_plot_qc_sharpness.png | Bin 30929 -> 0 bytes tests/experimental/test_qc_image.py | 216 +++++++++ 16 files changed, 1406 insertions(+), 161 deletions(-) create mode 100644 src/squidpy/experimental/im/_intensity_metrics.py create mode 100644 src/squidpy/experimental/im/_qc_image.py create mode 100644 src/squidpy/experimental/im/_qc_metrics.py create mode 100644 src/squidpy/experimental/im/_sharpness_metrics.py create mode 100644 src/squidpy/experimental/pl/_qc_image.py create mode 100644 tests/_images/QCImage_calc_qc_image_hne.png create mode 100644 tests/_images/QCImage_calc_qc_image_not_hne.png create mode 100644 tests/_images/QCImage_plot_qc_image.png delete mode 100644 tests/_images/QCSharpness_calc_qc_sharpness.png delete mode 100644 tests/_images/QCSharpness_plot_qc_sharpness.png create mode 100644 tests/experimental/test_qc_image.py diff --git a/src/squidpy/experimental/im/__init__.py b/src/squidpy/experimental/im/__init__.py index b88006688..24f7cf6ee 100644 --- a/src/squidpy/experimental/im/__init__.py +++ b/src/squidpy/experimental/im/__init__.py @@ -7,12 +7,16 @@ detect_tissue, ) from ._make_tiles import make_tiles, make_tiles_from_spots +from ._qc_image import qc_image +from ._qc_metrics import QCMetric __all__ = [ "BackgroundDetectionParams", "FelzenszwalbParams", + "QCMetric", "WekaParams", "detect_tissue", "make_tiles", "make_tiles_from_spots", + "qc_image", ] diff --git a/src/squidpy/experimental/im/_detect_tissue.py b/src/squidpy/experimental/im/_detect_tissue.py index 4b8bdc98a..c8aa34590 100644 --- a/src/squidpy/experimental/im/_detect_tissue.py +++ b/src/squidpy/experimental/im/_detect_tissue.py @@ -24,7 +24,7 @@ from squidpy._utils import _ensure_dim_order, _get_scale_factors, _yx_from_shape -from ._utils import _flatten_channels, _get_element_data +from ._utils import flatten_channels, get_element_data class DetectTissueMethod(enum.Enum): @@ -357,7 +357,7 @@ def detect_tissue( # Load smallest available or explicit scale img_node = sdata.images[image_key] - img_da = _get_element_data(img_node, scale if manual_scale else "auto", "image", image_key) + img_da = get_element_data(img_node, scale if manual_scale else "auto", "image", image_key) img_src = _ensure_dim_order(img_da, "yxc") src_h = int(img_src.sizes["y"]) src_w = int(img_src.sizes["x"]) @@ -375,7 +375,7 @@ def detect_tissue( # Channel flattening (greyscale) for threshold-based methods img_grey = None if method != DetectTissueMethod.WEKA: - img_grey_da: xr.DataArray = _flatten_channels(img=img_src, channel_format=channel_format) + img_grey_da: xr.DataArray = flatten_channels(img=img_src, channel_format=channel_format) if need_downscale: logger.info("Downscaling for faster computation.") img_grey = _downscale_with_dask(img_grey=img_grey_da, target_pixels=auto_max_pixels) diff --git a/src/squidpy/experimental/im/_intensity_metrics.py b/src/squidpy/experimental/im/_intensity_metrics.py new file mode 100644 index 000000000..2657bb41a --- /dev/null +++ b/src/squidpy/experimental/im/_intensity_metrics.py @@ -0,0 +1,134 @@ +from __future__ import annotations + +import numpy as np + +# --- Intensity metrics (grayscale input) --- + + +def brightness_mean(block: np.ndarray) -> np.ndarray: + """Mean pixel intensity of a grayscale tile.""" + return np.array([[float(block.mean())]], dtype=np.float32) + + +def brightness_std(block: np.ndarray) -> np.ndarray: + """Standard deviation of pixel intensity of a grayscale tile.""" + return np.array([[float(block.std())]], dtype=np.float32) + + +def entropy(block: np.ndarray) -> np.ndarray: + """Shannon entropy of pixel intensity histogram.""" + arr = block.ravel() + lo, hi = float(arr.min()), float(arr.max()) + if hi - lo < 1e-10: + return np.array([[0.0]], dtype=np.float32) + # Quantize to 256 bins directly without storing intermediate normalized array + bins = np.clip(((arr - lo) * (255.0 / (hi - lo))).astype(np.int32), 0, 255) + counts = np.bincount(bins, minlength=256) + probs = counts[counts > 0].astype(np.float64) + probs /= probs.sum() + ent = -float(np.dot(probs, np.log2(probs))) + return np.array([[ent]], dtype=np.float32) + + +# --- Staining metrics (RGB input, H&E only) --- + + +def rgb_to_hed(block_rgb: np.ndarray) -> np.ndarray: + """Convert RGB tile to HED colour space using Beer-Lambert deconvolution. + + Parameters + ---------- + block_rgb + (ty, tx, 3) float32 array in [0, 1]. + + Returns + ------- + (ty, tx, 3) float64 array with channels H, E, D. + """ + from skimage.color import rgb2hed + + rgb_clipped = np.clip(block_rgb, 0.0, 1.0) + return rgb2hed(rgb_clipped) + + +def hed_metrics(block: np.ndarray) -> np.ndarray: + """Return all HED-derived metrics for one RGB tile.""" + hed = rgb_to_hed(block) + h = hed[..., 0] + e = hed[..., 1] + + return np.array( + [ + [ + [ + float(h.mean()), + float(h.std()), + float(e.mean()), + float(e.std()), + float(np.abs(h).mean() / (np.abs(e).mean() + 1e-10)), + ] + ] + ], + dtype=np.float32, + ) + + +def hematoxylin_mean(block: np.ndarray) -> np.ndarray: + """Mean hematoxylin channel intensity.""" + hed = rgb_to_hed(block) + return np.array([[float(hed[..., 0].mean())]], dtype=np.float32) + + +def hematoxylin_std(block: np.ndarray) -> np.ndarray: + """Std of hematoxylin channel intensity.""" + hed = rgb_to_hed(block) + return np.array([[float(hed[..., 0].std())]], dtype=np.float32) + + +def eosin_mean(block: np.ndarray) -> np.ndarray: + """Mean eosin channel intensity.""" + hed = rgb_to_hed(block) + return np.array([[float(hed[..., 1].mean())]], dtype=np.float32) + + +def eosin_std(block: np.ndarray) -> np.ndarray: + """Std of eosin channel intensity.""" + hed = rgb_to_hed(block) + return np.array([[float(hed[..., 1].std())]], dtype=np.float32) + + +def he_ratio(block: np.ndarray) -> np.ndarray: + """Ratio of hematoxylin to eosin mean intensity.""" + hed = rgb_to_hed(block) + h_mean = float(np.abs(hed[..., 0]).mean()) + e_mean = float(np.abs(hed[..., 1]).mean()) + ratio = h_mean / (e_mean + 1e-10) + return np.array([[ratio]], dtype=np.float32) + + +# --- Artifact metrics (RGB input, H&E only) --- + + +def fold_fraction(block: np.ndarray) -> np.ndarray: + """Fraction of pixels identified as tissue folds. + + Uses HSV thresholds tuned for H&E staining: saturation > 0.4 and + value < 0.3 captures the dark, saturated appearance of folded tissue. + """ + from skimage.color import rgb2hsv + + rgb_clipped = np.clip(block, 0.0, 1.0) + hsv = rgb2hsv(rgb_clipped) + sat = hsv[..., 1] + val = hsv[..., 2] + fold_mask = (sat > 0.4) & (val < 0.3) + frac = float(fold_mask.sum()) / max(fold_mask.size, 1) + return np.array([[frac]], dtype=np.float32) + + +# --- Tissue coverage (mask input) --- + + +def tissue_fraction(block: np.ndarray) -> np.ndarray: + """Fraction of pixels that are tissue (nonzero) in a binary mask tile.""" + return np.array([[float(block.mean())]], dtype=np.float32) diff --git a/src/squidpy/experimental/im/_make_tiles.py b/src/squidpy/experimental/im/_make_tiles.py index ef04d6302..6323c48ad 100644 --- a/src/squidpy/experimental/im/_make_tiles.py +++ b/src/squidpy/experimental/im/_make_tiles.py @@ -1,9 +1,5 @@ from __future__ import annotations -import itertools -from typing import Literal - -import dask.array as da import geopandas as gpd import numpy as np import pandas as pd @@ -18,101 +14,16 @@ from squidpy._utils import _yx_from_shape from squidpy._validators import assert_in_range, assert_key_in_sdata, assert_positive - -from ._utils import _get_element_data +from squidpy.experimental.im._utils import ( + TileGrid, + get_element_data, + get_mask_materialized, + save_tile_grid_to_shapes, +) __all__ = ["make_tiles", "make_tiles_from_spots"] -class _TileGrid: - """Immutable tile grid definition with cached bounds and centroids.""" - - def __init__( - self, - H: int, - W: int, - tile_size: Literal["auto"] | tuple[int, int] = "auto", - target_tiles: int = 100, - offset_y: int = 0, - offset_x: int = 0, - ): - self.H = H - self.W = W - if tile_size == "auto": - size = max(min(self.H // target_tiles, self.W // target_tiles), 100) - self.ty = int(size) - self.tx = int(size) - else: - self.ty = int(tile_size[0]) - self.tx = int(tile_size[1]) - self.offset_y = offset_y - self.offset_x = offset_x - # Calculate number of tiles needed to cover entire image, accounting for offset - # The grid starts at offset_y, offset_x (can be negative) - # We need tiles from min(0, offset_y) to at least H - # So total coverage needed is from min(0, offset_y) to H - grid_start_y = min(0, self.offset_y) - grid_start_x = min(0, self.offset_x) - total_h_needed = self.H - grid_start_y - total_w_needed = self.W - grid_start_x - self.tiles_y = (total_h_needed + self.ty - 1) // self.ty - self.tiles_x = (total_w_needed + self.tx - 1) // self.tx - # Cache immutable derived values - self._indices = np.array([[iy, ix] for iy in range(self.tiles_y) for ix in range(self.tiles_x)], dtype=int) - self._names = [f"tile_x{ix}_y{iy}" for iy in range(self.tiles_y) for ix in range(self.tiles_x)] - self._bounds = self._compute_bounds() - self._centroids_polys = self._compute_centroids_and_polygons() - - def indices(self) -> np.ndarray: - return self._indices - - def names(self) -> list[str]: - return self._names - - def bounds(self) -> np.ndarray: - return self._bounds - - def _compute_bounds(self) -> np.ndarray: - b: list[list[int]] = [] - for iy, ix in itertools.product(range(self.tiles_y), range(self.tiles_x)): - y0 = iy * self.ty + self.offset_y - x0 = ix * self.tx + self.offset_x - y1 = ((iy + 1) * self.ty + self.offset_y) if iy < self.tiles_y - 1 else self.H - x1 = ((ix + 1) * self.tx + self.offset_x) if ix < self.tiles_x - 1 else self.W - # Clamp bounds to image dimensions - y0 = max(0, min(y0, self.H)) - x0 = max(0, min(x0, self.W)) - y1 = max(0, min(y1, self.H)) - x1 = max(0, min(x1, self.W)) - b.append([y0, x0, y1, x1]) - return np.array(b, dtype=int) - - def centroids_and_polygons(self) -> tuple[np.ndarray, list[Polygon]]: - return self._centroids_polys - - def _compute_centroids_and_polygons(self) -> tuple[np.ndarray, list[Polygon]]: - cents: list[list[float]] = [] - polys: list[Polygon] = [] - for y0, x0, y1, x1 in self._bounds: - cy = (y0 + y1) / 2 - cx = (x0 + x1) / 2 - cents.append([cy, cx]) - polys.append(Polygon([(x0, y0), (x1, y0), (x1, y1), (x0, y1), (x0, y0)])) - return np.array(cents, dtype=float), polys - - def rechunk_and_pad(self, arr_yx: da.Array) -> da.Array: - if arr_yx.ndim != 2: - raise ValueError("Expected a 2D array shaped (y, x).") - pad_y = self.tiles_y * self.ty - int(arr_yx.shape[0]) - pad_x = self.tiles_x * self.tx - int(arr_yx.shape[1]) - a = arr_yx.rechunk((self.ty, self.tx)) - return da.pad(a, ((0, pad_y), (0, pad_x)), mode="edge") if (pad_y > 0 or pad_x > 0) else a - - def coarsen(self, arr_yx: da.Array, reduce: Literal["mean", "sum"] = "mean") -> da.Array: - reducer = np.mean if reduce == "mean" else np.sum - return da.coarsen(reducer, arr_yx, {0: self.ty, 1: self.tx}, trim_excess=False) - - class _SpotTileGrid: """Tile container for Visium spots, used with ``_filter_tiles``.""" @@ -176,9 +87,9 @@ def _get_largest_scale_dimensions( """ img_node = sdata.images[image_key] - # Use _get_element_data with "scale0" which is always the largest scale + # Use get_element_data with "scale0" which is always the largest scale # It handles both datatree (multiscale) and single-scale images - img_da = _get_element_data(img_node, "scale0", "image", image_key) + img_da = get_element_data(img_node, "scale0", "image", image_key) # Get spatial dimensions (y, x) if "y" in img_da.sizes and "x" in img_da.sizes: @@ -208,34 +119,12 @@ def _choose_label_scale_for_image(label_node: Labels2DModel, target_hw: tuple[in def _save_tiles_to_shapes( sdata: sd.SpatialData, - tg: _TileGrid, + tg: TileGrid, image_key: str, shapes_key: str, ) -> None: """Save a TileGrid to sdata.shapes as a GeoDataFrame.""" - tile_indices = tg.indices() - pixel_bounds = tg.bounds() - _, polys = tg.centroids_and_polygons() - - tile_gdf = gpd.GeoDataFrame( - { - "tile_id": tg.names(), - "tile_y": tile_indices[:, 0], - "tile_x": tile_indices[:, 1], - "pixel_y0": pixel_bounds[:, 0], - "pixel_x0": pixel_bounds[:, 1], - "pixel_y1": pixel_bounds[:, 2], - "pixel_x1": pixel_bounds[:, 3], - "geometry": polys, - }, - geometry="geometry", - ) - - sdata.shapes[shapes_key] = ShapesModel.parse(tile_gdf) - # we know that a) the element exists and b) it has at least an Identity transformation - transformations = get_transformation(sdata.images[image_key], get_all=True) - set_transformation(sdata.shapes[shapes_key], transformations, set_all=True) - logger.info(f"Saved tile grid as 'sdata.shapes[\"{shapes_key}\"]'") + save_tile_grid_to_shapes(sdata, tg, shapes_key, copy_transforms_from_key=image_key) def _save_spot_tiles_to_shapes( @@ -375,7 +264,7 @@ def make_tiles( mask_key_for_grid = default_mask_key else: try: - from ._detect_tissue import detect_tissue + from squidpy.experimental.im._detect_tissue import detect_tissue detect_tissue( sdata, @@ -420,7 +309,7 @@ def make_tiles( classification_mask_key, ) try: - from ._detect_tissue import detect_tissue + from squidpy.experimental.im._detect_tissue import detect_tissue detect_tissue( sdata, @@ -568,7 +457,7 @@ def make_tiles_from_spots( classification_mask_key, ) try: - from ._detect_tissue import detect_tissue + from squidpy.experimental.im._detect_tissue import detect_tissue detect_tissue( sdata, @@ -639,7 +528,7 @@ def make_tiles_from_spots( def _filter_tiles( sdata: sd.SpatialData, - tg: _TileGrid, + tg: TileGrid, image_key: str | None, *, tissue_mask_key: str | None = None, @@ -691,7 +580,7 @@ def _filter_tiles( else: raise ValueError("tissue_mask_key must be provided when image_key is None.") assert_key_in_sdata(sdata, mask_key, attr="labels") - mask = _get_mask_from_labels(sdata, mask_key, scale) + mask = get_mask_materialized(sdata, mask_key, scale) H_mask, W_mask = mask.shape # Check tissue coverage for each tile @@ -756,7 +645,7 @@ def _make_tiles( tile_size: tuple[int, int] = (224, 224), center_grid_on_tissue: bool = False, scale: str = "auto", -) -> _TileGrid: +) -> TileGrid: """Construct a tile grid for an image, optionally centered on a tissue mask. Callers must validate *image_key* before calling this helper. @@ -768,14 +657,14 @@ def _make_tiles( # Path 1: Regular grid starting from top-left if not center_grid_on_tissue or image_mask_key is None: - return _TileGrid(H, W, tile_size=tile_size) + return TileGrid(H, W, tile_size=tile_size) # Path 2: Center grid on tissue mask centroid assert_key_in_sdata(sdata, image_mask_key, attr="labels") # Get mask and compute centroid label_node = sdata.labels[image_mask_key] - mask_da = _get_element_data(label_node, scale, "label", image_mask_key) + mask_da = get_element_data(label_node, scale, "label", image_mask_key) # Convert to numpy array if needed if is_dask_collection(mask_da): @@ -807,7 +696,7 @@ def _make_tiles( mask_bool = mask > 0 if not mask_bool.any(): logger.warning("Mask is empty. Using regular grid starting from top-left.") - return _TileGrid(H, W, tile_size=tile_size) + return TileGrid(H, W, tile_size=tile_size) # Calculate centroid using center of mass y_coords, x_coords = np.where(mask_bool) @@ -822,7 +711,7 @@ def _make_tiles( offset_y = int(round(centroid_y - tile_center_y_standard)) offset_x = int(round(centroid_x - tile_center_x_standard)) - return _TileGrid(H, W, tile_size=tile_size, offset_y=offset_y, offset_x=offset_x) + return TileGrid(H, W, tile_size=tile_size, offset_y=offset_y, offset_x=offset_x) def _get_spot_coordinates( @@ -878,27 +767,3 @@ def _derive_tile_size_from_spots(coords: np.ndarray) -> tuple[int, int]: ) side = max(1, int(np.floor(row_spacing))) return side, side - - -def _get_mask_from_labels(sdata: sd.SpatialData, mask_key: str, scale: str) -> np.ndarray: - """Extract a 2D mask array from ``sdata.labels`` at the requested scale. - - Callers must validate *mask_key* before calling this helper. - """ - label_node = sdata.labels[mask_key] - mask_da = _get_element_data(label_node, scale, "label", mask_key) - - if is_dask_collection(mask_da): - mask_da = mask_da.compute() - - if isinstance(mask_da, xr.DataArray): - mask = np.asarray(mask_da.data) - else: - mask = np.asarray(mask_da) - - if mask.ndim > 2: - mask = mask.squeeze() - if mask.ndim != 2: - raise ValueError(f"Expected 2D mask with shape (y, x), got shape {mask.shape}") - - return mask diff --git a/src/squidpy/experimental/im/_qc_image.py b/src/squidpy/experimental/im/_qc_image.py new file mode 100644 index 000000000..a8010e87f --- /dev/null +++ b/src/squidpy/experimental/im/_qc_image.py @@ -0,0 +1,438 @@ +from __future__ import annotations + +from collections import defaultdict +from typing import Literal + +import dask +import dask.array as da +import numpy as np +import pandas as pd +import xarray as xr +from anndata import AnnData +from dask.diagnostics import ProgressBar +from spatialdata import SpatialData +from spatialdata._logging import logger +from spatialdata.models import TableModel + +from squidpy._utils import _ensure_dim_order +from squidpy.experimental.im._intensity_metrics import hed_metrics +from squidpy.experimental.im._qc_metrics import _HNE_METRICS, InputKind, QCMetric, get_metric_info +from squidpy.experimental.im._utils import ( + TileGrid, + get_element_data, + get_mask_dask, + resolve_tissue_mask, + save_tile_grid_to_shapes, +) + +_DEFAULT_HNE_METRICS: list[QCMetric] = [ + QCMetric.TENENGRAD, + QCMetric.VAR_OF_LAPLACIAN, + QCMetric.ENTROPY, + QCMetric.BRIGHTNESS_MEAN, + QCMetric.HEMATOXYLIN_MEAN, + QCMetric.EOSIN_MEAN, +] + +_DEFAULT_GENERIC_METRICS: list[QCMetric] = [ + QCMetric.TENENGRAD, + QCMetric.VAR_OF_LAPLACIAN, + QCMetric.ENTROPY, + QCMetric.BRIGHTNESS_MEAN, +] + + +def qc_image( + sdata: SpatialData, + image_key: str, + *, + scale: str = "scale0", + metrics: QCMetric | list[QCMetric] | None = None, + tile_size: Literal["auto"] | tuple[int, int] = "auto", + is_hne: bool = True, + detect_outliers: bool = True, + detect_tissue: bool = True, + outlier_threshold: float = 0.1, + progress: bool = True, + tissue_mask_key: str | None = None, + preview: bool = True, +) -> None: + """ + Perform quality control analysis on an image. + + Computes tile-based QC metrics including sharpness, intensity, staining + (H&E only), artifact detection, and tissue coverage. + + Parameters + ---------- + sdata + SpatialData object containing the image. + image_key + Key of the image in ``sdata.images`` to analyze. + scale + Scale level to use for processing. Defaults to ``"scale0"``. + metrics + QC metrics to compute. Can be a single metric or list of metrics. + If ``None``, uses sensible defaults based on ``is_hne``. + tile_size + Size of tiles for analysis. If ``"auto"``, automatically determines size. + is_hne + Whether the image is H&E stained. Controls which metrics are available + and which defaults are used. If ``False`` and H&E-specific metrics are + explicitly requested, raises ``ValueError``. + detect_outliers + Whether to detect outlier tiles based on QC scores. + detect_tissue + Whether to detect tissue regions for context-aware outlier detection. + outlier_threshold + Percentile threshold (0-1) for outlier detection. A tile is flagged as + an outlier if its within-tissue percentile rank falls below this value. + For example, ``0.1`` flags the bottom 10% of tissue tiles (by their + worst sharpness metric) as outliers. Default is ``0.1``. + progress + Whether to show progress bar during computation. + tissue_mask_key + Key of the tissue mask in ``sdata.labels`` to use. If ``None``, the function will + check if ``"{image_key}_tissue"`` already exists in ``sdata.labels`` and reuse it. + If it doesn't exist, tissue detection will be performed and the mask will be added + to ``sdata.labels`` with key ``"{image_key}_tissue"``. If provided, the existing + mask at this key will be used. + preview + If ``True``, render a preview showing the image overlaid with outlier tiles + highlighted in red. Only shown when ``detect_outliers=True``. + + Returns + ------- + None + Results are stored in the following locations: + + - ``sdata.tables[f"qc_img_{image_key}"]``: AnnData object with QC scores + - ``sdata.shapes[f"qc_img_{image_key}_grid"]``: GeoDataFrame with tile geometries + - ``sdata.tables[...].uns["qc_image"]``: Metadata about the analysis + """ + # Parameter validation + if image_key not in sdata.images: + raise KeyError(f"Image key '{image_key}' not found in sdata.images") + + if metrics is None: + metrics = list(_DEFAULT_HNE_METRICS if is_hne else _DEFAULT_GENERIC_METRICS) + elif isinstance(metrics, QCMetric): + metrics = [metrics] + else: + metrics = list(metrics) + + if not isinstance(metrics, list) or not all(isinstance(m, QCMetric) for m in metrics): + available = ", ".join(m.value for m in QCMetric) + raise TypeError(f"metrics must be QCMetric or list of QCMetric. Available: {available}") + + # Validate H&E constraint + if not is_hne: + hne_requested = _HNE_METRICS & set(metrics) + if hne_requested: + names = ", ".join(m.value for m in hne_requested) + raise ValueError( + f"H&E-specific metrics ({names}) cannot be used when is_hne=False. " + f"Set is_hne=True or remove these metrics." + ) + + if not 0 < outlier_threshold < 1: + raise ValueError(f"outlier_threshold must be in (0, 1), got {outlier_threshold}") + + # Compute QC metrics + img_node = sdata.images[image_key] + img_da = get_element_data(img_node, scale, "image", image_key) + img_yxc = _ensure_dim_order(img_da, "yxc") + gray = _to_gray_dask_yx(img_yxc) + H, W = int(gray.shape[0]), int(gray.shape[1]) + + tg = TileGrid(H, W, tile_size) + obs_names = tg.names() + + logger.info("Quantifying image quality.") + logger.info(f"- Input image (x, y): ({W}, {H})") + logger.info(f"- Tile size (x, y): ({tg.tx}, {tg.ty})") + logger.info(f"- Number of tiles (n_x, n_y): ({tg.tiles_x}, {tg.tiles_y})") + + # Group metrics by InputKind + groups: dict[InputKind, list[QCMetric]] = defaultdict(list) + for m in metrics: + kind, _fn = get_metric_info(m) + groups[kind].append(m) + + out_chunks = ((1,) * tg.tiles_y, (1,) * tg.tiles_x) + + # Prepare inputs lazily (only for needed kinds) + prepared_inputs: dict[InputKind, da.Array] = {} + + _tissue_binary_da: da.Array | None = None + if InputKind.MASK in groups or detect_tissue: + mask_key_resolved = resolve_tissue_mask(sdata, image_key, scale, tissue_mask_key) + raw_mask = get_mask_dask(sdata, mask_key_resolved, scale) + _tissue_binary_da = (raw_mask > 0).astype(np.float32).rechunk((tg.ty, tg.tx)) + + if InputKind.GRAYSCALE in groups: + prepared_inputs[InputKind.GRAYSCALE] = gray.rechunk((tg.ty, tg.tx)) + + if InputKind.RGB in groups: + src_dtype = img_yxc.data.dtype + rgb_arr = img_yxc.data[..., :3].astype(np.float32, copy=False) + # Normalize integer images to [0, 1] + if np.issubdtype(src_dtype, np.integer): + rgb_arr = rgb_arr / float(np.iinfo(src_dtype).max) + prepared_inputs[InputKind.RGB] = rgb_arr.rechunk((tg.ty, tg.tx, 3)) + + if InputKind.MASK in groups and _tissue_binary_da is not None: + prepared_inputs[InputKind.MASK] = _tissue_binary_da + + # Build all dask graphs lazily + delayed_scores: dict[str, da.Array] = {} + hed_metric_indices = { + QCMetric.HEMATOXYLIN_MEAN: 0, + QCMetric.HEMATOXYLIN_STD: 1, + QCMetric.EOSIN_MEAN: 2, + QCMetric.EOSIN_STD: 3, + QCMetric.HE_RATIO: 4, + } + hed_scores: da.Array | None = None + + for m in metrics: + if m in hed_metric_indices: + if hed_scores is None: + hed_scores = da.map_blocks( + hed_metrics, + prepared_inputs[InputKind.RGB], + dtype=np.float32, + chunks=out_chunks + ((5,),), + drop_axis=2, + new_axis=2, + ) + delayed_scores[m.value] = hed_scores[..., hed_metric_indices[m]] + logger.info(f"- Calculating metric: '{m.value}'") + continue + + kind, metric_func = get_metric_info(m) + source = prepared_inputs[kind] + + if kind == InputKind.RGB: + delayed_scores[m.value] = da.map_blocks( + metric_func, source, dtype=np.float32, chunks=out_chunks, drop_axis=2 + ) + else: + delayed_scores[m.value] = da.map_blocks(metric_func, source, dtype=np.float32, chunks=out_chunks) + logger.info(f"- Calculating metric: '{m.value}'") + + # Single dask.compute() across all metric types + if progress: + with ProgressBar(): + results = dask.compute(*delayed_scores.values()) + else: + results = dask.compute(*delayed_scores.values()) + all_scores: dict[str, np.ndarray] = dict(zip(delayed_scores.keys(), results, strict=True)) + + # Build AnnData + metric_names = [m.value for m in metrics] + first = next(iter(all_scores.values())) + cents, _ = tg.centroids_and_polygons() + n_tiles = first.size + X = np.column_stack([all_scores[n].ravel() for n in metric_names]) + var_names = [f"qc_{n}" for n in metric_names] + + adata = AnnData(X=X) + adata.var_names = var_names + adata.obs_names = obs_names + adata.obs["centroid_y"] = cents[:, 0] + adata.obs["centroid_x"] = cents[:, 1] + adata.obsm["spatial"] = cents + + tissue = np.zeros(n_tiles, dtype=bool) + back = ~tissue + outlier_labels = np.ones(n_tiles, dtype=int) + unfocus_scores: np.ndarray | None = None + + if detect_outliers: + if detect_tissue: + tissue, back = _classify_tiles_by_tissue( + tg, + sdata=sdata, + image_key=image_key, + scale=scale, + tissue_mask_key=tissue_mask_key, + binary_mask_da=_tissue_binary_da, + ) + logger.info(f"- Classified tiles: background: {back.sum()}, tissue: {tissue.sum()}.") + + if detect_tissue and tissue.any(): + tissue_scores = _compute_unfocus_scores(X[tissue], var_names) + unfocus_scores = np.full(n_tiles, np.nan, dtype=np.float32) + unfocus_scores[tissue] = tissue_scores + outlier_labels = np.ones(n_tiles, dtype=int) + outlier_labels[tissue] = np.where(tissue_scores >= 1 - outlier_threshold, -1, 1) + else: + all_scores_arr = _compute_unfocus_scores(X, var_names) + unfocus_scores = all_scores_arr + outlier_labels = np.where(all_scores_arr >= 1 - outlier_threshold, -1, 1) + + adata.obs["qc_outlier"] = pd.Categorical((outlier_labels == -1).astype(str), categories=["False", "True"]) + if detect_tissue: + adata.obs["is_tissue"] = pd.Categorical(tissue.astype(str), categories=["False", "True"]) + adata.obs["is_background"] = pd.Categorical(back.astype(str), categories=["False", "True"]) + adata.obs["unfocus_score"] = unfocus_scores + + logger.info(f"- Detected {int((outlier_labels == -1).sum())} outlier tiles.") + + adata.uns["qc_image"] = { + "metrics": list(all_scores.keys()), + "tile_size_y": tg.ty, + "tile_size_x": tg.tx, + "image_height": H, + "image_width": W, + "n_tiles_y": tg.tiles_y, + "n_tiles_x": tg.tiles_x, + "image_key": image_key, + "scale": scale, + "is_hne": is_hne, + "detect_tissue": detect_tissue, + "outlier_threshold": outlier_threshold, + "n_tissue_tiles": int(tissue.sum()), + "n_background_tiles": int(back.sum()), + "n_outlier_tiles": int((outlier_labels == -1).sum()), + } + + table_key = f"qc_img_{image_key}" + shapes_key = f"qc_img_{image_key}_grid" + + # Build shapes first (need the index for tile_id linkage) + save_tile_grid_to_shapes(sdata, tg, shapes_key, copy_transforms_from_key=image_key) + + # Set spatialdata linkage on adata BEFORE TableModel.parse + adata.obs["grid_name"] = pd.Categorical([shapes_key] * len(adata)) + adata.obs["tile_id"] = sdata.shapes[shapes_key].index + adata.uns["spatialdata_attrs"] = { + "region": shapes_key, + "region_key": "grid_name", + "instance_key": "tile_id", + } + + sdata.tables[table_key] = TableModel.parse(adata) + logger.info(f"- Saved QC scores as 'sdata.tables[\"{table_key}\"]'") + + if preview and detect_outliers and "qc_outlier" in adata.obs.columns: + try: + ( + sdata.pl.render_images(image_key) + .pl.render_shapes( + shapes_key, + color="qc_outlier", + groups="True", + palette="red", + fill_alpha=0.5, + table_name=table_key, + ) + .pl.show() + ) + except (AttributeError, KeyError, ValueError) as e: + logger.warning(f"Could not generate preview plot: {e}") + + +_LUMINANCE_WEIGHTS = da.from_array(np.array([0.2126, 0.7152, 0.0722], dtype=np.float32), chunks=(3,)) + + +def _to_gray_dask_yx(img_yxc: xr.DataArray) -> da.Array: + """Convert multi-channel image to grayscale using ITU-R BT.709 luminance weights.""" + arr = img_yxc.data + if arr.ndim != 3: + raise ValueError(f"Expected image with shape `(y, x, c)`, found `{arr.shape}`.") + c = arr.shape[2] + if c == 1: + return arr[..., 0].astype(np.float32, copy=False) + rgb = arr[..., :3].astype(np.float32, copy=False) + gray = da.tensordot(rgb, _LUMINANCE_WEIGHTS, axes=([2], [0])) + return gray.astype(np.float32, copy=False) + + +def _classify_tiles_by_tissue( + tg: TileGrid, + *, + sdata: SpatialData, + image_key: str, + scale: str = "scale0", + tissue_mask_key: str | None = None, + binary_mask_da: da.Array | None = None, +) -> tuple[np.ndarray, np.ndarray]: + """Classify tiles as tissue or background based on a binary mask. + + Returns + ------- + tissue, background + Boolean arrays of shape ``(n_tiles,)``. + """ + if binary_mask_da is None: + mask_key_resolved = resolve_tissue_mask(sdata, image_key, scale, tissue_mask_key) + raw_mask = get_mask_dask(sdata, mask_key_resolved, scale) + binary_mask_da = (raw_mask > 0).astype(np.float32).rechunk((tg.ty, tg.tx)) + + H, W = binary_mask_da.shape + tiles_y = (H + tg.ty - 1) // tg.ty + tiles_x = (W + tg.tx - 1) // tg.tx + + def _mean_block(block: np.ndarray) -> np.ndarray: + return np.array([[float(block.mean())]], dtype=np.float32) + + frac_da = da.map_blocks( + _mean_block, + binary_mask_da, + dtype=np.float32, + chunks=((1,) * tiles_y, (1,) * tiles_x), + ) + n_tiles = tg.tiles_y * tg.tiles_x + frac = frac_da.compute().ravel()[:n_tiles] + + tissue = frac > 0.5 + return tissue, ~tissue + + +def _compute_unfocus_scores(X: np.ndarray, var_names: list[str]) -> np.ndarray: + """Compute per-tile unfocus scores using within-group percentile ranks. + + For each sharpness metric, every tile is ranked among its peers (the tiles + in ``X``) and assigned a percentile in [0, 1] where 0 = lowest value = + worst quality and 1 = highest. The final ``unfocus_score`` for a tile is + ``1 - min_rank`` across all *sharpness* metrics, so a tile that scores + poorly on *any* sharpness axis gets a high unfocus score. + + Only gradient-based sharpness metrics (tenengrad, var_of_laplacian) + contribute to the score. Other metrics like entropy, wavelet energy, + or FFT energy correlate more with tissue structure than with actual + optical focus and are therefore excluded. + + Parameters + ---------- + X + Score matrix of shape ``(n_tiles, n_metrics)``. + var_names + Column names matching ``X`` (``qc_`` prefix expected). + + Returns + ------- + np.ndarray of shape ``(n_tiles,)`` with values in [0, 1]. + """ + from scipy.stats import rankdata + + _SHARPNESS_KEYWORDS = {"tenengrad", "laplacian"} + + sharpness_cols = [i for i, name in enumerate(var_names) if any(kw in name.lower() for kw in _SHARPNESS_KEYWORDS)] + if not sharpness_cols: + sharpness_cols = list(range(X.shape[1])) + + n = X.shape[0] + if n <= 1: + return np.zeros(n, dtype=np.float32) + + # Percentile rank per metric: 0 = worst, 1 = best + ranks = np.column_stack([(rankdata(X[:, col], method="average") - 1) / (n - 1) for col in sharpness_cols]) + + # Worst (minimum) rank across sharpness metrics governs the score + min_rank = ranks.min(axis=1) + + # Invert: high unfocus_score = low quality + return (1.0 - min_rank).astype(np.float32) diff --git a/src/squidpy/experimental/im/_qc_metrics.py b/src/squidpy/experimental/im/_qc_metrics.py new file mode 100644 index 000000000..05c6567bc --- /dev/null +++ b/src/squidpy/experimental/im/_qc_metrics.py @@ -0,0 +1,98 @@ +from __future__ import annotations + +from collections.abc import Callable +from enum import StrEnum + +import numpy as np + +from squidpy.experimental.im._intensity_metrics import ( + brightness_mean, + brightness_std, + entropy, + eosin_mean, + eosin_std, + fold_fraction, + he_ratio, + hematoxylin_mean, + hematoxylin_std, + tissue_fraction, +) +from squidpy.experimental.im._sharpness_metrics import ( + fft_high_freq_energy, + haar_wavelet_energy, + laplacian_variance, + pop_variance, + tenengrad_mean, +) + +MetricFn = Callable[[np.ndarray], np.ndarray] + + +class InputKind(StrEnum): + GRAYSCALE = "grayscale" # (ty, tx) float32 + RGB = "rgb" # (ty, tx, 3) float32 in [0,1] + MASK = "mask" # (ty, tx) binary float32 + + +class QCMetric(StrEnum): + # Sharpness (grayscale input) + TENENGRAD = "tenengrad" + VAR_OF_LAPLACIAN = "var_of_laplacian" + VARIANCE = "variance" + FFT_HIGH_FREQ_ENERGY = "fft_high_freq_energy" + HAAR_WAVELET_ENERGY = "haar_wavelet_energy" + # Intensity (grayscale input) + BRIGHTNESS_MEAN = "brightness_mean" + BRIGHTNESS_STD = "brightness_std" + ENTROPY = "entropy" + # Staining (RGB input, H&E only) + HEMATOXYLIN_MEAN = "hematoxylin_mean" + HEMATOXYLIN_STD = "hematoxylin_std" + EOSIN_MEAN = "eosin_mean" + EOSIN_STD = "eosin_std" + HE_RATIO = "he_ratio" + # Artifacts (RGB input, H&E only) + FOLD_FRACTION = "fold_fraction" + # Tissue coverage (mask input) + TISSUE_FRACTION = "tissue_fraction" + + +_HNE_METRICS: set[QCMetric] = { + QCMetric.HEMATOXYLIN_MEAN, + QCMetric.HEMATOXYLIN_STD, + QCMetric.EOSIN_MEAN, + QCMetric.EOSIN_STD, + QCMetric.HE_RATIO, + QCMetric.FOLD_FRACTION, +} + + +# --- Registry --- + +_METRIC_REGISTRY: dict[QCMetric, tuple[InputKind, MetricFn]] = { + # Sharpness (grayscale) + QCMetric.TENENGRAD: (InputKind.GRAYSCALE, tenengrad_mean), + QCMetric.VAR_OF_LAPLACIAN: (InputKind.GRAYSCALE, laplacian_variance), + QCMetric.VARIANCE: (InputKind.GRAYSCALE, pop_variance), + QCMetric.FFT_HIGH_FREQ_ENERGY: (InputKind.GRAYSCALE, fft_high_freq_energy), + QCMetric.HAAR_WAVELET_ENERGY: (InputKind.GRAYSCALE, haar_wavelet_energy), + # Intensity (grayscale) + QCMetric.BRIGHTNESS_MEAN: (InputKind.GRAYSCALE, brightness_mean), + QCMetric.BRIGHTNESS_STD: (InputKind.GRAYSCALE, brightness_std), + QCMetric.ENTROPY: (InputKind.GRAYSCALE, entropy), + # Staining (RGB, H&E only) + QCMetric.HEMATOXYLIN_MEAN: (InputKind.RGB, hematoxylin_mean), + QCMetric.HEMATOXYLIN_STD: (InputKind.RGB, hematoxylin_std), + QCMetric.EOSIN_MEAN: (InputKind.RGB, eosin_mean), + QCMetric.EOSIN_STD: (InputKind.RGB, eosin_std), + QCMetric.HE_RATIO: (InputKind.RGB, he_ratio), + # Artifacts (RGB, H&E only) + QCMetric.FOLD_FRACTION: (InputKind.RGB, fold_fraction), + # Tissue coverage (mask) + QCMetric.TISSUE_FRACTION: (InputKind.MASK, tissue_fraction), +} + + +def get_metric_info(metric: QCMetric) -> tuple[InputKind, MetricFn]: + """Look up the input kind and callable for a QCMetric.""" + return _METRIC_REGISTRY[metric] diff --git a/src/squidpy/experimental/im/_sharpness_metrics.py b/src/squidpy/experimental/im/_sharpness_metrics.py new file mode 100644 index 000000000..f48f2aeaf --- /dev/null +++ b/src/squidpy/experimental/im/_sharpness_metrics.py @@ -0,0 +1,94 @@ +from __future__ import annotations + +import numpy as np +from scipy.fft import fft2, fftfreq +from skimage.filters import laplace, sobel_h, sobel_v + + +def to_f32_2d(x: np.ndarray) -> np.ndarray: + if x.ndim != 2: + raise ValueError("block must be 2D") + return np.ascontiguousarray(x.astype(np.float32, copy=False)) + + +def tenengrad_mean(block: np.ndarray) -> np.ndarray: + """Mean Tenengrad energy (sum of squared Sobel gradients).""" + b = to_f32_2d(block) + energy = sobel_h(b) ** 2 + sobel_v(b) ** 2 + return np.array([[float(energy.mean())]], dtype=np.float32) + + +def laplacian_variance(block: np.ndarray) -> np.ndarray: + """Population variance of Laplacian response.""" + b = to_f32_2d(block) + lap = laplace(b) + var_val = float(np.var(lap)) + return np.array([[max(var_val, 0.0)]], dtype=np.float32) + + +def pop_variance(block: np.ndarray) -> np.ndarray: + """Population variance of pixel intensities.""" + b = to_f32_2d(block) + return np.array([[float(np.var(b))]], dtype=np.float32) + + +def fft_high_freq_mask(h: int, w: int) -> np.ndarray: + """Frequency-domain mask for high-frequency energy (cached per tile shape).""" + fy = fftfreq(h) + fx = fftfreq(w) + ry, rx = np.meshgrid(fy, fx, indexing="ij") + return np.hypot(ry, rx) > 0.1 + + +def fft_high_freq_energy(block: np.ndarray) -> np.ndarray: + x = to_f32_2d(block).astype(np.float64, copy=False) + m = float(x.mean()) + s = float(x.std()) + x = (x - m) / s if s > 0.0 else (x - m) + + F = fft2(x) + mag2 = (F.real * F.real) + (F.imag * F.imag) + + mask = fft_high_freq_mask(x.shape[0], x.shape[1]) + + total = float(mag2.sum()) + if not np.isfinite(total) or total <= 1e-12: + ratio = 0.0 + else: + hi = float(mag2[mask].sum()) + ratio = hi / total if np.isfinite(hi) else 0.0 + ratio = np.clip(ratio, 0.0, 1.0) + return np.array([[ratio]], dtype=np.float32) + + +def haar_wavelet_energy(block: np.ndarray) -> np.ndarray: + """Detail-band (LH+HL+HH) energy ratio of a single-level Haar transform.""" + x = to_f32_2d(block).astype(np.float64, copy=False) + m = float(x.mean()) + s = float(x.std()) + x = (x - m) / s if s > 0.0 else (x - m) + + h, w = x.shape + if h % 2 == 1: + x = np.vstack([x, x[-1:, :]]) + h += 1 + if w % 2 == 1: + x = np.hstack([x, x[:, -1:]]) + w += 1 + + cA_h = (x[::2, :] + x[1::2, :]) / 2.0 + cH_h = (x[::2, :] - x[1::2, :]) / 2.0 + + cA = (cA_h[:, ::2] + cA_h[:, 1::2]) / 2.0 # LL + cH = (cA_h[:, ::2] - cA_h[:, 1::2]) / 2.0 # LH + cV = (cH_h[:, ::2] + cH_h[:, 1::2]) / 2.0 # HL + cD = (cH_h[:, ::2] - cH_h[:, 1::2]) / 2.0 # HH + + total = float((cA * cA).sum() + (cH * cH).sum() + (cV * cV).sum() + (cD * cD).sum()) + if not np.isfinite(total) or total <= 1e-12: + ratio = 0.0 + else: + detail = float((cH * cH).sum() + (cV * cV).sum() + (cD * cD).sum()) + ratio = detail / total if np.isfinite(detail) else 0.0 + ratio = np.clip(ratio, 0.0, 1.0) + return np.array([[ratio]], dtype=np.float32) diff --git a/src/squidpy/experimental/im/_utils.py b/src/squidpy/experimental/im/_utils.py index 8ce954e04..11467da49 100644 --- a/src/squidpy/experimental/im/_utils.py +++ b/src/squidpy/experimental/im/_utils.py @@ -2,11 +2,102 @@ from typing import Any, Literal +import dask.array as da +import geopandas as gpd +import numpy as np import xarray as xr +from shapely import box +from spatialdata import SpatialData from spatialdata._logging import logger +from spatialdata.models import ShapesModel +from spatialdata.transformations import get_transformation, set_transformation -def _get_element_data( +class TileGrid: + """Immutable tile grid definition with cached bounds and centroids.""" + + def __init__( + self, + H: int, + W: int, + tile_size: Literal["auto"] | tuple[int, int] = "auto", + target_tiles: int = 100, + offset_y: int = 0, + offset_x: int = 0, + ): + self.H = int(H) + self.W = int(W) + if tile_size == "auto": + size = max(min(self.H // target_tiles, self.W // target_tiles), 100) + self.ty = int(size) + self.tx = int(size) + else: + self.ty = int(tile_size[0]) + self.tx = int(tile_size[1]) + self.offset_y = offset_y + self.offset_x = offset_x + # Calculate number of tiles needed to cover entire image, accounting for offset + grid_start_y = min(0, self.offset_y) + grid_start_x = min(0, self.offset_x) + total_h_needed = self.H - grid_start_y + total_w_needed = self.W - grid_start_x + self.tiles_y = (total_h_needed + self.ty - 1) // self.ty + self.tiles_x = (total_w_needed + self.tx - 1) // self.tx + # Cache immutable derived values (vectorized) + iy = np.repeat(np.arange(self.tiles_y), self.tiles_x) + ix = np.tile(np.arange(self.tiles_x), self.tiles_y) + self._indices = np.column_stack([iy, ix]) + self._names = [f"tile_x{x}_y{y}" for y, x in zip(iy, ix, strict=True)] + self._bounds = self._compute_bounds(iy, ix) + self._centroids, self._polys = self._compute_centroids_and_polygons() + + def indices(self) -> np.ndarray: + return self._indices + + def names(self) -> list[str]: + return self._names + + def bounds(self) -> np.ndarray: + return self._bounds + + def _compute_bounds(self, iy: np.ndarray, ix: np.ndarray) -> np.ndarray: + y0 = iy * self.ty + self.offset_y + x0 = ix * self.tx + self.offset_x + y1 = (iy + 1) * self.ty + self.offset_y + x1 = (ix + 1) * self.tx + self.offset_x + # Last row/column extends to image edge + y1[iy == self.tiles_y - 1] = self.H + x1[ix == self.tiles_x - 1] = self.W + # Clamp to image dimensions + y0 = np.clip(y0, 0, self.H) + x0 = np.clip(x0, 0, self.W) + y1 = np.clip(y1, 0, self.H) + x1 = np.clip(x1, 0, self.W) + return np.column_stack([y0, x0, y1, x1]).astype(int) + + def centroids_and_polygons(self) -> tuple[np.ndarray, list]: + return self._centroids, self._polys + + def _compute_centroids_and_polygons(self) -> tuple[np.ndarray, list]: + y0, x0, y1, x1 = self._bounds[:, 0], self._bounds[:, 1], self._bounds[:, 2], self._bounds[:, 3] + centroids = np.column_stack([(y0 + y1) / 2.0, (x0 + x1) / 2.0]) + polys = list(box(x0, y0, x1, y1)) + return centroids, polys + + def rechunk_and_pad(self, arr_yx: da.Array) -> da.Array: + if arr_yx.ndim != 2: + raise ValueError("Expected a 2D array shaped (y, x).") + pad_y = self.tiles_y * self.ty - int(arr_yx.shape[0]) + pad_x = self.tiles_x * self.tx - int(arr_yx.shape[1]) + a = arr_yx.rechunk((self.ty, self.tx)) + return da.pad(a, ((0, pad_y), (0, pad_x)), mode="edge") if (pad_y > 0 or pad_x > 0) else a + + def coarsen(self, arr_yx: da.Array, reduce: Literal["mean", "sum"] = "mean") -> da.Array: + reducer = np.mean if reduce == "mean" else np.sum + return da.coarsen(reducer, arr_yx, {0: self.ty, 1: self.tx}, trim_excess=False) + + +def get_element_data( element_node: Any, scale: str | Literal["auto"], element_type: str = "element", @@ -58,7 +149,7 @@ def _idx(k: str) -> int: return data -def _flatten_channels( +def flatten_channels( img: xr.DataArray, channel_format: Literal["infer", "rgb", "rgba", "multichannel"] = "infer", ) -> xr.DataArray: @@ -129,3 +220,107 @@ def _flatten_channels( raise ValueError( f"Invalid channel_format: {channel_format}. Must be one of 'infer', 'rgb', 'rgba', 'multichannel'." ) + + +def get_mask_dask(sdata: SpatialData, mask_key: str, scale: str) -> da.Array: + """Extract mask as a lazy dask array from ``sdata.labels``.""" + if mask_key not in sdata.labels: + raise KeyError(f"Mask key '{mask_key}' not found in sdata.labels") + label_node = sdata.labels[mask_key] + mask_xr = get_element_data(label_node, scale, "label", mask_key) + + arr = mask_xr.data if hasattr(mask_xr, "data") else mask_xr + if not isinstance(arr, da.Array): + arr = da.from_array(np.asarray(arr)) + + if arr.ndim > 2: + arr = arr.squeeze() + if arr.ndim != 2: + raise ValueError(f"Expected 2D mask with shape (y, x), got shape {arr.shape}") + return arr + + +def get_mask_materialized(sdata: SpatialData, mask_key: str, scale: str) -> np.ndarray: + """Extract a 2D mask array from ``sdata.labels`` at the requested scale (materialized).""" + arr = get_mask_dask(sdata, mask_key, scale) + return np.asarray(arr.compute()) + + +def resolve_tissue_mask( + sdata: SpatialData, + image_key: str, + scale: str, + tissue_mask_key: str | None = None, +) -> str: + """Return the key of a tissue mask in ``sdata.labels``, creating one if needed. + + If *tissue_mask_key* is given and exists, it is returned as-is. + Otherwise falls back to ``f"{image_key}_tissue"``, running ``detect_tissue`` + to create it when missing. + + Raises + ------ + KeyError + If *tissue_mask_key* is given but not found in ``sdata.labels``. + Exception + Any exception raised by ``detect_tissue`` if auto-creation fails. + Callers needing graceful fallback should wrap this call in try/except. + """ + if tissue_mask_key is not None: + if tissue_mask_key not in sdata.labels: + raise KeyError(f"Tissue mask key '{tissue_mask_key}' not found in sdata.labels") + return tissue_mask_key + + mask_key = f"{image_key}_tissue" + if mask_key not in sdata.labels: + from squidpy.experimental.im._detect_tissue import detect_tissue + + detect_tissue(sdata=sdata, image_key=image_key, scale=scale, inplace=True, new_labels_key=mask_key) + logger.info(f"Saved tissue mask as 'sdata.labels[\"{mask_key}\"]'") + return mask_key + + +def save_tile_grid_to_shapes( + sdata: SpatialData, + tg: TileGrid, + shapes_key: str, + *, + copy_transforms_from_key: str | None = None, +) -> None: + """Save a TileGrid to ``sdata.shapes`` as a GeoDataFrame. + + Parameters + ---------- + sdata + SpatialData object. + tg + TileGrid whose bounds/centroids are persisted. + shapes_key + Key under which to store the shapes. + copy_transforms_from_key + If given, copy the transformations from ``sdata.images[copy_transforms_from_key]`` + onto the new shapes element. + """ + tile_indices = tg.indices() + pixel_bounds = tg.bounds() + _, polys = tg.centroids_and_polygons() + + tile_gdf = gpd.GeoDataFrame( + { + "tile_id": tg.names(), + "tile_y": tile_indices[:, 0], + "tile_x": tile_indices[:, 1], + "pixel_y0": pixel_bounds[:, 0], + "pixel_x0": pixel_bounds[:, 1], + "pixel_y1": pixel_bounds[:, 2], + "pixel_x1": pixel_bounds[:, 3], + "geometry": polys, + }, + geometry="geometry", + ) + + sdata.shapes[shapes_key] = ShapesModel.parse(tile_gdf) + if copy_transforms_from_key is not None: + transformations = get_transformation(sdata.images[copy_transforms_from_key], get_all=True) + set_transformation(sdata.shapes[shapes_key], transformations, set_all=True) + logger.info(f"- Saved tile grid as 'sdata.shapes[\"{shapes_key}\"]'") diff --git a/src/squidpy/experimental/pl/__init__.py b/src/squidpy/experimental/pl/__init__.py index 4d21ee850..cdb8a56d1 100644 --- a/src/squidpy/experimental/pl/__init__.py +++ b/src/squidpy/experimental/pl/__init__.py @@ -1,3 +1,5 @@ from __future__ import annotations -__all__ = [] +from ._qc_image import qc_image + +__all__ = ["qc_image"] diff --git a/src/squidpy/experimental/pl/_qc_image.py b/src/squidpy/experimental/pl/_qc_image.py new file mode 100644 index 000000000..bf369b753 --- /dev/null +++ b/src/squidpy/experimental/pl/_qc_image.py @@ -0,0 +1,199 @@ +from __future__ import annotations + +from typing import Any + +import matplotlib.pyplot as plt +import numpy as np +from scipy.stats import gaussian_kde +from spatialdata import SpatialData +from spatialdata._logging import logger as logg + +from squidpy.experimental.im._qc_metrics import QCMetric + + +def qc_image( + sdata: SpatialData, + image_key: str, + metrics: QCMetric | list[QCMetric] | None = None, + figsize: tuple[int, int] | None = None, + return_ax: bool = False, + **kwargs: Any, +) -> np.ndarray | None: + """ + Plot a summary view of QC metrics from qc_image results. + + Automatically scans adata.uns for calculated metrics and plots the values. + Creates a multi-panel plot: one panel per calculated metric. + Each panel shows: spatial view, KDE plot, and statistics. + + Parameters + ---------- + sdata : SpatialData + SpatialData object containing QC results. + image_key : str + Image key used in qc_image function. + metrics : QCMetric or list of QCMetric, optional + Specific metrics to plot. If None, plots all calculated metrics. + figsize : tuple, optional + Figure size (width, height). Auto-calculated if None. + return_ax : bool + Whether to return the axes array. Default is False. + **kwargs + Additional arguments passed to render_shapes(). + + Returns + ------- + axes : numpy.ndarray of matplotlib.axes.Axes or None + The axes array if return_ax=True, otherwise None. + """ + table_key = f"qc_img_{image_key}" + shapes_key = f"qc_img_{image_key}_grid" + + if table_key not in sdata.tables: + raise ValueError(f"No QC data found for image '{image_key}'. Run sq.exp.im.qc_image() first.") + + adata = sdata.tables[table_key] + + if "qc_image" not in adata.uns: + raise ValueError("No qc_image metadata found. Run sq.exp.im.qc_image() first.") + + calculated_metrics = adata.uns["qc_image"]["metrics"] + + if not calculated_metrics: + raise ValueError("No QC metrics found in metadata.") + + if metrics is not None: + metrics_list = metrics if isinstance(metrics, list) else [metrics] + metrics_to_plot = [] + for metric in metrics_list: + metric_name = metric.value if isinstance(metric, QCMetric) else metric + if metric_name not in calculated_metrics: + raise ValueError(f"Metric '{metric_name}' not found. Available: {calculated_metrics}") + metrics_to_plot.append(metric_name) + else: + metrics_to_plot = calculated_metrics + + logg.info(f"Plotting {len(metrics_to_plot)} QC metrics: {metrics_to_plot}") + + n_metrics = len(metrics_to_plot) + ncols = 3 + nrows = n_metrics + + if figsize is None: + figsize = (12, 4 * nrows) + + _fig, axes = plt.subplots(nrows, ncols, figsize=figsize) + + if nrows == 1: + axes = axes.reshape(1, -1) + if ncols == 1: + axes = axes.reshape(-1, 1) + + var_name_to_idx = {name: idx for idx, name in enumerate(adata.var_names)} + + for i, metric_name in enumerate(metrics_to_plot): + var_name = f"qc_{metric_name}" + if var_name not in var_name_to_idx: + logg.warning(f"Variable '{var_name}' not found in adata.var_names. Skipping.") + continue + + raw_values = adata.X[:, var_name_to_idx[var_name]] + + ax_spatial = axes[i, 0] + ax_hist = axes[i, 1] + ax_stats = axes[i, 2] + + # Panel 1: Spatial plot + try: + ( + sdata.pl.render_shapes(shapes_key, color=var_name, table_name=table_key, **kwargs).pl.show( + ax=ax_spatial, title=f"{metric_name.replace('_', ' ').title()}" + ) + ) + except (ValueError, KeyError, AttributeError) as e: + logg.warning(f"Error plotting spatial view for {metric_name}: {e}") + ax_spatial.text( + 0.5, 0.5, f"Error plotting\n{metric_name}", ha="center", va="center", transform=ax_spatial.transAxes + ) + ax_spatial.set_title(f"{metric_name.replace('_', ' ').title()}") + + # Panel 2: KDE plot + x_min, x_max = float(np.min(raw_values)), float(np.max(raw_values)) + + if x_min < x_max: + x_range = np.linspace(x_min, x_max, 200) + + if "is_tissue" in adata.obs: + is_tissue = adata.obs["is_tissue"].astype(str) == "True" + tissue_values = raw_values[is_tissue] + background_values = raw_values[~is_tissue] + + if len(background_values) > 1 and np.std(background_values) > 0: + kde_background = gaussian_kde(background_values) + density_background = kde_background(x_range) + ax_hist.plot(x_range, density_background, label="Background", alpha=0.7) + ax_hist.fill_between(x_range, density_background, alpha=0.3) + + if len(tissue_values) > 1 and np.std(tissue_values) > 0: + kde_tissue = gaussian_kde(tissue_values) + density_tissue = kde_tissue(x_range) + ax_hist.plot(x_range, density_tissue, label="Tissue", alpha=0.7) + ax_hist.fill_between(x_range, density_tissue, alpha=0.3) + + ax_hist.legend() + + elif len(raw_values) > 1 and np.std(raw_values) > 0: + kde = gaussian_kde(raw_values) + density = kde(x_range) + ax_hist.plot(x_range, density, alpha=0.7) + ax_hist.fill_between(x_range, density, alpha=0.3) + else: + ax_hist.text( + 0.5, + 0.5, + f"Constant value: {x_min:.4f}", + ha="center", + va="center", + transform=ax_hist.transAxes, + ) + + ax_hist.set_xlabel(f"{metric_name.replace('_', ' ').title()}") + ax_hist.set_ylabel("Density") + ax_hist.set_title("Distribution") + ax_hist.grid(True, alpha=0.3) + + # Panel 3: Statistics + ax_stats.axis("off") + stats_text = ( + f"Raw {metric_name.replace('_', ' ').title()} Statistics:\n" + f"\n" + f"Count: {len(raw_values):,}\n" + f"Mean: {np.mean(raw_values):.4f}\n" + f"Std: {np.std(raw_values):.4f}\n" + f"Min: {np.min(raw_values):.4f}\n" + f"Max: {np.max(raw_values):.4f}\n" + f"\n" + f"Percentiles:\n" + f"5%: {np.percentile(raw_values, 5):.4f}\n" + f"25%: {np.percentile(raw_values, 25):.4f}\n" + f"50%: {np.percentile(raw_values, 50):.4f}\n" + f"75%: {np.percentile(raw_values, 75):.4f}\n" + f"95%: {np.percentile(raw_values, 95):.4f}\n" + f"\n" + f"Non-zero: {np.count_nonzero(raw_values):,}\n" + f"Zero: {np.sum(raw_values == 0):,}" + ) + + ax_stats.text( + 0.05, + 0.95, + stats_text, + transform=ax_stats.transAxes, + fontsize=9, + verticalalignment="top", + fontfamily="monospace", + ) + + plt.tight_layout() + + return axes if return_ax else None diff --git a/tests/_images/QCImage_calc_qc_image_hne.png b/tests/_images/QCImage_calc_qc_image_hne.png new file mode 100644 index 0000000000000000000000000000000000000000..c02d403082d6f656fb7ebf0c8ce609eba7424ea3 GIT binary patch literal 21230 zcmb4~Q*(2i^-1qxXRWH@OR@drY z{q6nzc7&3GBoaI>JQx@llC+eV%D*-5zXAL0-yAh#!SHY4aTV8cRdq0TbvJT01Cuv$ zb+mPGwY4%P{%PjyV&!1Z%E-dZ$VE?V>FVm}!o$R5_kTMW9h@zg`gGl<|J?=7QA*nd z4D1`#e*;`zZFK|;OhrdpOjyk$>#_^3%GOGo@x97(Nw4`A>*Hz3Z>!$0NBvr%jEqbr=2k7{X^XLpnD-ji`Batt0mko zzN;r+o9c>2#V4!Y8l>Od)2ICE)jH?8S6(B>zWSBakr0*5IX7t`AoiiP{9cu=+YADC%)D8`ZHXYd?bl`f`#Wg8lHOne1GGLlE<^Oxp|7&|( z-QV9=0-Z{9l^=wI_r``; zCDJcZzPpT>BFDIO*+bHLMr(!hZgt*ai zm}{ne`uo#E;5pO)HR)|@`(x`lSnwJ0{bT!!9~lKjoNHxe zg=oNzt9?S}0eYsk=Y66FkTm{qVIipd-nH~fiY{ag#dDFZ0OS70lS==wAxrqME4<#p86IB1_O;zuO&OvfFP5lBjoi zIrUkhTH_qyBcbwvyXM9tdc&cI;CMQFAT1h9zs(UGMev<@I)|Tb&Y0ixZ4E{sifYpb zoS8?{=bg5r>eG>bqC($bF;+a3u}@U_O*Am8N8pxvIpNpAQ|#eroM(c8oBg3Ap{Ks& zkD0ypA6M**e%5Y(mK4QbtT&Fhbw}^+&k%I9p8mMh`g|m!_g=l^TqQ^W5k))qzSqWY z`#xy;H~Tba;VXKL+nsrLao-yB#WWszzu5KppJAr0#k~?3w7nf^y(b>F<5&8{i36HT zjZ*pz{mPIvJ`KM9xao1HbxIxDe0bbCvFBS1>e0hX8^uxx5>Ftk{=6JScyRc1zIR{l ze_8$<9~c*<7-_5w4qv)Cu%U#|%05;momrvZAocBojt5<3U-=x*`Dbl-Q`kz3Mh8ru zBK}dE!#!go^7<$hfg+Y|Aw$DR6;{p$GA{p#;&_;Bna90z!CVfEK; z8zRNHQ?Z$m?q9;y%l=l|_{jSK;nFSf+-_UqRKC7sehOLUqPo6}-`#z4nB+pgtb#FT zZ6fnMzo$%|(b-z!L9u-BR{pMX-MzXR-ZaBAZoY<3BgbsC0pBxq0ts`MdE_XDy|!n# z9n@H~oZvoTv*bMqnOw5gIh!F%`(?28>~*1eGNXRX>|nX{wd6fDzB*RItUHS<7y8z)66LTH`C$AwnjZ|SscLSfI+QtUVwJ*_cUQsR%$2P&# zskjo>L8r)p;0RdcxSm#CwX1e#u#uMfE;r&J$2!4OAtRm)Bh0%n@HsYd>~E`U0%En> z{mX~GP3uS(f04ukmbj0wkxIiY%6%~7272rqh$58;@uPR!VzVGanL)$p3R*5Lb*;R# zwd&au0(vXMDHGm_!|U5bW88N<1={p0isxw0IeM%_Mb($0XjLCwEYmsL$u_&)W#<~} znSyutF_TVL=MR@j`QQ^f3;nh{!RPIk&2}u_Q(1`^_O$7)9d6=k-0$M)4|dnK#|<020?#Aqgyfif|O;x+`-G#DZPvMcN< z;jqS?62lJeO`9rdM=xCuf<rWz@0qhGoEI#VAvWs8$pCQ7bSiE6 z!Lv6*2ddt)P1+j7BL_B(;td;x@MTb9jU#6gsMtY$Hr-j4eZ^~Fs zp6_yBx(*XXQFOh?}^mpO4ye(I7=cKPWF{N5_~snlIQ2Cc$+lUa8f;c4rh>8x6Lw_I

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b/tests/experimental/test_qc_image.py new file mode 100644 index 000000000..1fd62cfe2 --- /dev/null +++ b/tests/experimental/test_qc_image.py @@ -0,0 +1,216 @@ +from __future__ import annotations + +import copy + +import numpy as np +import pytest +import spatialdata_plot as sdp + +import squidpy as sq +from tests.conftest import PlotTester, PlotTesterMeta + +_ = sdp + +# Large tile size to keep tests fast (fewer tiles = less computation). +_FAST_TILE = (1000, 1000) + + +@pytest.fixture(scope="session") +def _sdata_hne_with_tissue(): + """Load the Visium H&E dataset once and pre-compute the tissue mask. + + The tissue mask is the most expensive setup step (~16 s). Pre-computing + it here and deepcopying per test avoids paying that cost repeatedly. + """ + sdata = sq.datasets.visium_hne_sdata() + sq.experimental.im.detect_tissue(sdata, image_key="hne", scale="scale0", inplace=True, new_labels_key="hne_tissue") + return sdata + + +@pytest.fixture() +def sdata_hne(_sdata_hne_with_tissue): + """Per-test deep copy so each test gets a fresh SpatialData with tissue mask.""" + return copy.deepcopy(_sdata_hne_with_tissue) + + +class TestQCImage(PlotTester, metaclass=PlotTesterMeta): + def test_plot_calc_qc_image_hne(self, sdata_hne): + """Test QC image overlay with a single sharpness metric.""" + sq.experimental.im.qc_image( + sdata_hne, + image_key="hne", + tile_size=_FAST_TILE, + metrics=[sq.experimental.im.QCMetric.TENENGRAD], + progress=False, + ) + + ( + sdata_hne.pl.render_images() + .pl.render_shapes("qc_img_hne_grid", color="qc_outlier", groups="True", palette="red", fill_alpha=1.0) + .pl.show() + ) + + def test_plot_calc_qc_image_not_hne(self, sdata_hne): + """Test QC image overlay with is_hne=False default metrics.""" + sq.experimental.im.qc_image( + sdata_hne, + image_key="hne", + tile_size=_FAST_TILE, + is_hne=False, + metrics=None, + progress=False, + ) + + ( + sdata_hne.pl.render_images() + .pl.render_shapes("qc_img_hne_grid", color="qc_outlier", groups="True", palette="red", fill_alpha=1.0) + .pl.show() + ) + + def test_plot_plot_qc_image(self, sdata_hne): + """Test QC image plotting function.""" + sq.experimental.im.qc_image( + sdata_hne, + image_key="hne", + tile_size=_FAST_TILE, + metrics=[sq.experimental.im.QCMetric.TENENGRAD], + progress=False, + ) + + sq.experimental.pl.qc_image( + sdata_hne, + image_key="hne", + ) + + +def test_qc_image_hne_metric_without_hne_flag(sdata_hne): + """Test that H&E metrics raise ValueError when is_hne=False.""" + with pytest.raises(ValueError, match="H&E-specific metrics"): + sq.experimental.im.qc_image( + sdata_hne, + image_key="hne", + tile_size=_FAST_TILE, + is_hne=False, + metrics=[sq.experimental.im.QCMetric.HEMATOXYLIN_MEAN], + ) + + +def test_qc_image_default_metrics_hne(sdata_hne): + """Test that default H&E metrics produce expected var_names.""" + sq.experimental.im.qc_image( + sdata_hne, + image_key="hne", + tile_size=_FAST_TILE, + metrics=None, + is_hne=True, + progress=False, + ) + adata = sdata_hne.tables["qc_img_hne"] + expected = { + "qc_tenengrad", + "qc_var_of_laplacian", + "qc_entropy", + "qc_brightness_mean", + "qc_hematoxylin_mean", + "qc_eosin_mean", + } + assert set(adata.var_names) == expected + + +def test_qc_image_default_metrics_generic(sdata_hne): + """Test that default generic metrics produce expected var_names.""" + sq.experimental.im.qc_image( + sdata_hne, + image_key="hne", + tile_size=_FAST_TILE, + metrics=None, + is_hne=False, + progress=False, + ) + adata = sdata_hne.tables["qc_img_hne"] + expected = { + "qc_tenengrad", + "qc_var_of_laplacian", + "qc_entropy", + "qc_brightness_mean", + } + assert set(adata.var_names) == expected + + +def test_qc_image_rgb_metric(sdata_hne): + """Test running a single RGB metric.""" + sq.experimental.im.qc_image( + sdata_hne, + image_key="hne", + tile_size=_FAST_TILE, + is_hne=True, + metrics=[sq.experimental.im.QCMetric.HEMATOXYLIN_MEAN], + detect_tissue=False, + detect_outliers=False, + progress=False, + ) + adata = sdata_hne.tables["qc_img_hne"] + assert "qc_hematoxylin_mean" in adata.var_names + + +def test_qc_image_outlier_detection_with_tissue(sdata_hne): + """Test that outlier detection with tissue classification populates expected columns.""" + sq.experimental.im.qc_image( + sdata_hne, + image_key="hne", + tile_size=_FAST_TILE, + metrics=[sq.experimental.im.QCMetric.TENENGRAD], + detect_outliers=True, + detect_tissue=True, + progress=False, + ) + adata = sdata_hne.tables["qc_img_hne"] + assert "qc_outlier" in adata.obs.columns + assert "is_tissue" in adata.obs.columns + assert "is_background" in adata.obs.columns + assert "unfocus_score" in adata.obs.columns + assert set(adata.obs["qc_outlier"].cat.categories) == {"False", "True"} + # At least some tiles should be classified as tissue + assert (adata.obs["is_tissue"] == "True").any() + + +def test_qc_image_outlier_detection_without_tissue(sdata_hne): + """Test outlier detection without tissue classification scores all tiles.""" + sq.experimental.im.qc_image( + sdata_hne, + image_key="hne", + tile_size=_FAST_TILE, + metrics=[sq.experimental.im.QCMetric.TENENGRAD], + detect_outliers=True, + detect_tissue=False, + progress=False, + ) + adata = sdata_hne.tables["qc_img_hne"] + assert "qc_outlier" in adata.obs.columns + assert "unfocus_score" in adata.obs.columns + # Without tissue detection, these columns should not exist + assert "is_tissue" not in adata.obs.columns + assert "is_background" not in adata.obs.columns + # All tiles should have a valid unfocus score (no NaNs) + assert not np.any(np.isnan(adata.obs["unfocus_score"].values)) + + +def test_qc_image_compute_only(sdata_hne): + """Test compute-only mode without outlier detection.""" + sq.experimental.im.qc_image( + sdata_hne, + image_key="hne", + tile_size=_FAST_TILE, + metrics=[sq.experimental.im.QCMetric.TENENGRAD, sq.experimental.im.QCMetric.BRIGHTNESS_MEAN], + detect_outliers=False, + detect_tissue=False, + progress=False, + ) + adata = sdata_hne.tables["qc_img_hne"] + assert set(adata.var_names) == {"qc_tenengrad", "qc_brightness_mean"} + # No outlier-related columns + assert "qc_outlier" not in adata.obs.columns + assert "unfocus_score" not in adata.obs.columns + # Spatial coordinates should still be present + assert "centroid_y" in adata.obs.columns + assert "spatial" in adata.obsm From 1cb6fde0e4719d7e9b29fb53abeb930512a953c4 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Mon, 13 Apr 2026 09:04:03 +0200 Subject: [PATCH 48/96] remove coord_type abstraction --- docs/extensibility.md | 6 ------ src/squidpy/gr/_build.py | 5 ++--- src/squidpy/gr/neighbors.py | 25 ++----------------------- 3 files changed, 4 insertions(+), 32 deletions(-) diff --git a/docs/extensibility.md b/docs/extensibility.md index 798d8c714..5ad3c0b98 100644 --- a/docs/extensibility.md +++ b/docs/extensibility.md @@ -15,7 +15,6 @@ The `squidpy.gr.neighbors` module exposes two builder base classes: | Base class | Method / property | Required | Purpose | |---|---|---|---| -| {class}`~squidpy.gr.neighbors.GraphBuilder` | {attr}`~squidpy.gr.neighbors.GraphBuilder.coord_type` | yes | Return the {class}`~squidpy._constants._constants.CoordType` this builder supports. | | {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.build_graph` | yes | Construct and return ``(adj, dst)`` using the coordinate and matrix types of your custom backend. | | {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_filters` | no | Post-process the raw ``adj``/``dst`` before percentile filtering and transform. | | {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_percentile` | no | Override percentile handling when the backend needs custom behavior. | @@ -60,7 +59,6 @@ import numpy as np from scipy.sparse import csr_matrix from snnpy import build_snn_model -from squidpy._constants._constants import CoordType from squidpy.gr.neighbors import GraphBuilderCSR @@ -71,10 +69,6 @@ class SNNRadiusBuilder(GraphBuilderCSR): super().__init__(**kwargs) self.radius = radius - @property - def coord_type(self) -> CoordType: - return CoordType.GENERIC - def build_graph(self, coords): N = coords.shape[0] model = build_snn_model(coords, verbose=0) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 25dfd0826..5532157d6 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -87,7 +87,7 @@ def _resolve_graph_builder( if coord_type is None: if radius is not None: logg.warning( - f"Graph creation with `radius` is only available when `coord_type = {CoordType.GENERIC!r}` specified. " + "Graph creation with `radius` is only available for generic coordinates. " f"Ignoring parameter `radius = {radius}`." ) coord_type = CoordType.GRID if has_spatial_uns else CoordType.GENERIC @@ -779,7 +779,7 @@ def _run_spatial_neighbors( libs = [None] start = logg.info( - f"Creating graph using `{builder.coord_type}` coordinates and `{builder.transform}` transform and `{len(libs)}` libraries." + f"Creating graph using `{builder.transform}` transform and `{len(libs)}` libraries." ) if library_key is not None: mats: list[tuple[Any, Any]] = [] @@ -800,7 +800,6 @@ def _run_spatial_neighbors( "distances_key": dists_key, "params": { "n_neighbors": getattr(builder, "n_neighs", 6), - "coord_type": builder.coord_type.v, "radius": getattr(builder, "radius", None), "transform": builder.transform.v, }, diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index b5f2aad16..0d880192c 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -26,7 +26,7 @@ from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances from sklearn.neighbors import NearestNeighbors -from squidpy._constants._constants import CoordType, Transform +from squidpy._constants._constants import Transform from squidpy._utils import NDArrayA from squidpy._validators import assert_positive @@ -55,11 +55,6 @@ def __init__( self.set_diag = set_diag self.percentile = percentile - @property - @abstractmethod - def coord_type(self) -> CoordType: - """Coordinate system supported by this builder.""" - def build(self, coords: CoordT) -> tuple[GraphMatrixT, GraphMatrixT]: adj, dst = self.build_graph(coords) adj, dst = self.apply_filters(adj, dst) @@ -127,7 +122,7 @@ def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, c return adj, dst def apply_percentile(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, csr_matrix]: - if self.percentile is not None and self.coord_type == CoordType.GENERIC: + if self.percentile is not None: threshold = np.percentile(dst.data, self.percentile) adj[dst > threshold] = 0.0 dst[dst > threshold] = 0.0 @@ -176,10 +171,6 @@ def __init__( super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) self.n_neighs = n_neighs - @property - def coord_type(self) -> CoordType: - return CoordType.GENERIC - def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: N = coords.shape[0] tree = NearestNeighbors(n_neighbors=self.n_neighs, radius=1, metric="euclidean") @@ -219,10 +210,6 @@ def __init__( super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) self.radius = radius - @property - def coord_type(self) -> CoordType: - return CoordType.GENERIC - def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: N = coords.shape[0] r = self.radius if isinstance(self.radius, int | float) else max(self.radius) @@ -272,10 +259,6 @@ def __init__( super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) self.radius = radius - @property - def coord_type(self) -> CoordType: - return CoordType.GENERIC - def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: N = coords.shape[0] tri = Delaunay(coords) @@ -328,10 +311,6 @@ def __init__( self.n_rings = n_rings self.delaunay = delaunay - @property - def coord_type(self) -> CoordType: - return CoordType.GRID - def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: if self.n_rings > 1: adj = self._base_adjacency(coords, set_diag=True) From 8afcd030b4e559d5ae760c67377da712fb8093a5 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 13 Apr 2026 07:04:32 +0000 Subject: [PATCH 49/96] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/squidpy/gr/_build.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 5532157d6..58617d0fb 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -778,9 +778,7 @@ def _run_spatial_neighbors( else: libs = [None] - start = logg.info( - f"Creating graph using `{builder.transform}` transform and `{len(libs)}` libraries." - ) + start = logg.info(f"Creating graph using `{builder.transform}` transform and `{len(libs)}` libraries.") if library_key is not None: mats: list[tuple[Any, Any]] = [] ixs: list[int] = [] From 21f02c376059b06c7579365fb1b0a79b6246dce3 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Mon, 13 Apr 2026 23:27:43 +0200 Subject: [PATCH 50/96] make builder positional consistently --- src/squidpy/gr/_build.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 58617d0fb..bac3aa6fe 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -376,7 +376,7 @@ def spatial_neighbors( ) return _run_spatial_neighbors( - adata, builder=builder, spatial_key=spatial_key, library_key=library_key, key_added=key_added, copy=copy + adata, builder, spatial_key=spatial_key, library_key=library_key, key_added=key_added, copy=copy ) @@ -441,7 +441,7 @@ def spatial_neighbors_from_builder( ) return _run_spatial_neighbors( adata, - builder=builder, + builder, spatial_key=spatial_key, library_key=library_key, key_added=key_added, @@ -527,7 +527,7 @@ def spatial_neighbors_knn( ) return _run_spatial_neighbors( adata, - builder=builder, + builder, spatial_key=spatial_key, library_key=library_key, key_added=key_added, @@ -596,7 +596,7 @@ def spatial_neighbors_radius( ) return _run_spatial_neighbors( adata, - builder=builder, + builder, spatial_key=spatial_key, library_key=library_key, key_added=key_added, @@ -665,7 +665,7 @@ def spatial_neighbors_delaunay( ) return _run_spatial_neighbors( adata, - builder=builder, + builder, spatial_key=spatial_key, library_key=library_key, key_added=key_added, @@ -753,7 +753,7 @@ def spatial_neighbors_grid( ) return _run_spatial_neighbors( adata, - builder=builder, + builder, spatial_key=spatial_key, library_key=library_key, key_added=key_added, @@ -763,8 +763,8 @@ def spatial_neighbors_grid( def _run_spatial_neighbors( adata: AnnData, - *, builder: GraphBuilder[Any, Any], + *, spatial_key: str = Key.obsm.spatial, library_key: str | None = None, key_added: str = "spatial", From 744c757e2825ddc525420e4d00588b4f584b05a2 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Tue, 14 Apr 2026 00:10:38 +0200 Subject: [PATCH 51/96] make more abstractions --- docs/api.md | 4 + docs/extensibility.md | 20 +++-- src/squidpy/gr/_build.py | 4 + src/squidpy/gr/neighbors.py | 168 ++++++++++++++++++++++-------------- 4 files changed, 125 insertions(+), 71 deletions(-) diff --git a/docs/api.md b/docs/api.md index dd19bb18e..4d23a6ed5 100644 --- a/docs/api.md +++ b/docs/api.md @@ -128,6 +128,10 @@ See the {doc}`extensibility guide ` for how to implement a custo gr.neighbors.GraphBuilder gr.neighbors.GraphBuilderCSR + gr.neighbors.GraphPostprocessor + gr.neighbors.DistanceIntervalPostprocessor + gr.neighbors.PercentilePostprocessor + gr.neighbors.TransformPostprocessor gr.neighbors.KNNBuilder gr.neighbors.RadiusBuilder gr.neighbors.DelaunayBuilder diff --git a/docs/extensibility.md b/docs/extensibility.md index 5ad3c0b98..befd61b23 100644 --- a/docs/extensibility.md +++ b/docs/extensibility.md @@ -8,24 +8,28 @@ The `squidpy.gr.neighbors` module exposes two builder base classes: Use it when you want to plug in a custom coordinate type or sparse-matrix backend. - {class}`~squidpy.gr.neighbors.GraphBuilderCSR` is the CSR-specialized builder used by the built-in graph construction strategies. Use it when your builder returns - {class}`~scipy.sparse.csr_matrix` objects and should reuse the default CSR-specific - percentile filtering, transform handling, and sparse warning suppression. + {class}`~scipy.sparse.csr_matrix` objects and should reuse Squidpy's CSR-specific + postprocessors, sparse warning suppression, and multi-library combination. +- Reusable postprocessors such as + {class}`~squidpy.gr.neighbors.DistanceIntervalPostprocessor`, + {class}`~squidpy.gr.neighbors.PercentilePostprocessor`, and + {class}`~squidpy.gr.neighbors.TransformPostprocessor` are also exposed for + custom builder composition. ### What to override | Base class | Method / property | Required | Purpose | |---|---|---|---| | {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.build_graph` | yes | Construct and return ``(adj, dst)`` using the coordinate and matrix types of your custom backend. | -| {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_filters` | no | Post-process the raw ``adj``/``dst`` before percentile filtering and transform. | -| {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_percentile` | no | Override percentile handling when the backend needs custom behavior. | -| {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.apply_transform` | no | Override transform handling when the backend needs custom behavior. | +| {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.postprocessors` | no | Return post-build processing steps for ``(adj, dst)``. You can either override this or pass ``postprocessors=...`` to ``super().__init__()``. | | {class}`~squidpy.gr.neighbors.GraphBuilder` | {meth}`~squidpy.gr.neighbors.GraphBuilder.combine` | no | Combine per-library results when using ``library_key``. If you do not need ``library_key`` support, leaving this unimplemented is fine. | The generic builder only defines the pipeline. The CSR-specialized builder adds -the built-in CSR behavior for percentile filtering, adjacency transforms, -multi-library ``library_key`` combination, and -{class}`~scipy.sparse.SparseEfficiencyWarning` suppression. +multi-library ``library_key`` combination and +{class}`~scipy.sparse.SparseEfficiencyWarning` suppression, while built-in and +custom CSR builders can compose the public reusable postprocessors for +distance-interval pruning, percentile filtering, and adjacency transforms. Here ``adj`` and ``dst`` are square sparse matrices of shape ``(n_obs, n_obs)`` with matching sparsity structure: diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index bac3aa6fe..e2778fa62 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -410,6 +410,10 @@ def spatial_neighbors_from_builder( {{class}}`~squidpy.gr.neighbors.GraphBuilderCSR`, while custom backends can implement the more generic {{class}}`~squidpy.gr.neighbors.GraphBuilder` interface directly. + Reusable post-build operations are also exposed via + :class:`~squidpy.gr.neighbors.DistanceIntervalPostprocessor`, + :class:`~squidpy.gr.neighbors.PercentilePostprocessor`, and + :class:`~squidpy.gr.neighbors.TransformPostprocessor`. Custom builders only need to implement multi-library support when using ``library_key``; otherwise leaving :meth:`~squidpy.gr.neighbors.GraphBuilder.combine` unimplemented is fine. diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 0d880192c..9a3078e64 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -7,7 +7,8 @@ import warnings from abc import ABC, abstractmethod -from collections.abc import Iterable, Sequence +from collections.abc import Callable, Sequence +from dataclasses import dataclass from itertools import chain from typing import Generic, TypeVar, cast @@ -30,19 +31,32 @@ from squidpy._utils import NDArrayA from squidpy._validators import assert_positive -__all__ = ["GraphBuilder", "GraphBuilderCSR", "KNNBuilder", "RadiusBuilder", "DelaunayBuilder", "GridBuilder"] +__all__ = [ + "GraphBuilder", + "GraphBuilderCSR", + "GraphPostprocessor", + "DistanceIntervalPostprocessor", + "PercentilePostprocessor", + "TransformPostprocessor", + "KNNBuilder", + "RadiusBuilder", + "DelaunayBuilder", + "GridBuilder", +] CoordT = TypeVar("CoordT") GraphMatrixT = TypeVar("GraphMatrixT") +GraphPostprocessor = Callable[[GraphMatrixT, GraphMatrixT], tuple[GraphMatrixT, GraphMatrixT]] class GraphBuilder(ABC, Generic[CoordT, GraphMatrixT]): """Base class for spatial graph construction strategies. Custom builders must implement :meth:`build_graph`. Overriding - :meth:`combine` is optional and only needed to support multi-library graph - construction via ``library_key``. + :meth:`postprocessors` and :meth:`combine` is optional. Postprocessors can + be provided directly via ``__init__`` or by overriding + :meth:`postprocessors`. """ def __init__( @@ -50,31 +64,26 @@ def __init__( transform: str | Transform | None = None, set_diag: bool = False, percentile: float | None = None, + postprocessors: Sequence[GraphPostprocessor[GraphMatrixT]] = (), ) -> None: self.transform = Transform.NONE if transform is None else Transform(transform) self.set_diag = set_diag self.percentile = percentile + self._postprocessors: list[GraphPostprocessor[GraphMatrixT]] = list(postprocessors) def build(self, coords: CoordT) -> tuple[GraphMatrixT, GraphMatrixT]: adj, dst = self.build_graph(coords) - adj, dst = self.apply_filters(adj, dst) - adj, dst = self.apply_percentile(adj, dst) - adj, dst = self.apply_transform(adj, dst) + for postprocessor in self.postprocessors(): + adj, dst = postprocessor(adj, dst) return adj, dst @abstractmethod def build_graph(self, coords: CoordT) -> tuple[GraphMatrixT, GraphMatrixT]: """Construct raw adjacency and distance matrices.""" - def apply_filters(self, adj: GraphMatrixT, dst: GraphMatrixT) -> tuple[GraphMatrixT, GraphMatrixT]: - """Apply builder-specific post-processing filters.""" - return adj, dst - - def apply_percentile(self, adj: GraphMatrixT, dst: GraphMatrixT) -> tuple[GraphMatrixT, GraphMatrixT]: - return adj, dst - - def apply_transform(self, adj: GraphMatrixT, dst: GraphMatrixT) -> tuple[GraphMatrixT, GraphMatrixT]: - return adj, dst + def postprocessors(self) -> Sequence[GraphPostprocessor[GraphMatrixT]]: + """Return post-build processing steps for ``(adj, dst)``.""" + return self._postprocessors def combine( self, @@ -92,12 +101,12 @@ def combine( class GraphBuilderCSR(GraphBuilder[NDArrayA, csr_matrix], ABC): """CSR-based graph construction strategy. - Specializes :class:`GraphBuilder` for sparse CSR matrix output. Adds built-in handling - for percentile-based edge pruning, adjacency transforms (spectral/cosine), - SparseEfficiencyWarning suppression, and multi-library ``library_key`` - combination. All built-in concrete builders + Specializes :class:`GraphBuilder` for sparse CSR matrix output. Adds + SparseEfficiencyWarning suppression and multi-library ``library_key`` + combination. Built-in concrete builders (:class:`KNNBuilder`, :class:`RadiusBuilder`, :class:`DelaunayBuilder`, :class:`GridBuilder`) - inherit from this class. + inherit from this class and declare their postprocessors explicitly in + ``__init__`` using the reusable public postprocessor classes. Subclass this (not the generic :class:`GraphBuilder`) when implementing a builder that returns CSR matrices. @@ -117,30 +126,6 @@ def build(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: """Construct raw adjacency and distance matrices.""" - def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, csr_matrix]: - """Apply builder-specific post-processing filters.""" - return adj, dst - - def apply_percentile(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, csr_matrix]: - if self.percentile is not None: - threshold = np.percentile(dst.data, self.percentile) - adj[dst > threshold] = 0.0 - dst[dst > threshold] = 0.0 - return adj, dst - - def apply_transform(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, csr_matrix]: - adj.eliminate_zeros() - dst.eliminate_zeros() - - if self.transform == Transform.SPECTRAL: - return cast(csr_matrix, _transform_a_spectral(adj)), dst - if self.transform == Transform.COSINE: - return cast(csr_matrix, _transform_a_cosine(adj)), dst - if self.transform == Transform.NONE: - return adj, dst - - raise NotImplementedError(f"Transform `{self.transform}` is not yet implemented.") - def combine( self, mats: Sequence[tuple[csr_matrix, csr_matrix]], @@ -168,7 +153,16 @@ def __init__( percentile: float | None = None, ) -> None: assert_positive(n_neighs, name="n_neighs") - super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) + postprocessors: list[GraphPostprocessor[csr_matrix]] = [] + if percentile is not None: + postprocessors.append(PercentilePostprocessor(percentile)) + postprocessors.append(TransformPostprocessor(Transform.NONE if transform is None else Transform(transform))) + super().__init__( + transform=transform, + set_diag=set_diag, + percentile=percentile, + postprocessors=postprocessors, + ) self.n_neighs = n_neighs def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: @@ -207,7 +201,18 @@ def __init__( set_diag: bool = False, percentile: float | None = None, ) -> None: - super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) + postprocessors: list[GraphPostprocessor[csr_matrix]] = [] + if isinstance(radius, tuple): + postprocessors.append(DistanceIntervalPostprocessor(tuple(sorted(radius)))) + if percentile is not None: + postprocessors.append(PercentilePostprocessor(percentile)) + postprocessors.append(TransformPostprocessor(Transform.NONE if transform is None else Transform(transform))) + super().__init__( + transform=transform, + set_diag=set_diag, + percentile=percentile, + postprocessors=postprocessors, + ) self.radius = radius def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: @@ -231,12 +236,6 @@ def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: dst.setdiag(0.0) return adj, dst - def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, csr_matrix]: - if isinstance(self.radius, Iterable): - _filter_by_radius_interval(adj, dst, self.radius) - return adj, dst - - class DelaunayBuilder(GraphBuilderCSR): """Build a generic point-cloud graph from a Delaunay triangulation. @@ -256,7 +255,18 @@ def __init__( set_diag: bool = False, percentile: float | None = None, ) -> None: - super().__init__(transform=transform, set_diag=set_diag, percentile=percentile) + postprocessors: list[GraphPostprocessor[csr_matrix]] = [] + if isinstance(radius, tuple): + postprocessors.append(DistanceIntervalPostprocessor(tuple(sorted(radius)))) + if percentile is not None: + postprocessors.append(PercentilePostprocessor(percentile)) + postprocessors.append(TransformPostprocessor(Transform.NONE if transform is None else Transform(transform))) + super().__init__( + transform=transform, + set_diag=set_diag, + percentile=percentile, + postprocessors=postprocessors, + ) self.radius = radius def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: @@ -278,12 +288,6 @@ def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: dst.setdiag(0.0) return adj, dst - def apply_filters(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, csr_matrix]: - if isinstance(self.radius, Iterable): - _filter_by_radius_interval(adj, dst, self.radius) - return adj, dst - - class GridBuilder(GraphBuilderCSR): """Build a grid-based spatial graph. @@ -306,7 +310,8 @@ def __init__( ) -> None: assert_positive(n_neighs, name="n_neighs") assert_positive(n_rings, name="n_rings") - super().__init__(transform=transform, set_diag=set_diag, percentile=None) + postprocessors = [TransformPostprocessor(Transform.NONE if transform is None else Transform(transform))] + super().__init__(transform=transform, set_diag=set_diag, percentile=None, postprocessors=postprocessors) self.n_neighs = n_neighs self.n_rings = n_rings self.delaunay = delaunay @@ -369,9 +374,9 @@ def _base_adjacency(self, coords: NDArrayA, *, set_diag: bool) -> csr_matrix: def _filter_by_radius_interval( adj: csr_matrix, dst: csr_matrix, - radius: Iterable[float], + radius: tuple[float, float], ) -> None: - minn, maxx = sorted(radius)[:2] + minn, maxx = radius mask = (dst.data < minn) | (dst.data > maxx) a_diag = adj.diagonal() @@ -379,6 +384,43 @@ def _filter_by_radius_interval( adj.data[mask] = 0.0 adj.setdiag(a_diag) +@dataclass(frozen=True) +class DistanceIntervalPostprocessor: + interval: tuple[float, float] + + def __call__(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, csr_matrix]: + _filter_by_radius_interval(adj, dst, self.interval) + return adj, dst + + +@dataclass(frozen=True) +class PercentilePostprocessor: + percentile: float + + def __call__(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, csr_matrix]: + threshold = np.percentile(dst.data, self.percentile) + adj[dst > threshold] = 0.0 + dst[dst > threshold] = 0.0 + return adj, dst + + +@dataclass(frozen=True) +class TransformPostprocessor: + transform: Transform + + def __call__(self, adj: csr_matrix, dst: csr_matrix) -> tuple[csr_matrix, csr_matrix]: + adj.eliminate_zeros() + dst.eliminate_zeros() + + if self.transform == Transform.SPECTRAL: + return cast(csr_matrix, _transform_a_spectral(adj)), dst + if self.transform == Transform.COSINE: + return cast(csr_matrix, _transform_a_cosine(adj)), dst + if self.transform == Transform.NONE: + return adj, dst + + raise NotImplementedError(f"Transform `{self.transform}` is not yet implemented.") + @njit def _csr_bilateral_diag_scale_helper( From dbc7f0d806ac1e8c60798384e7ba1b8c326b32a3 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 13 Apr 2026 22:10:38 +0000 Subject: [PATCH 52/96] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/squidpy/gr/neighbors.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 9a3078e64..d99646734 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -236,6 +236,7 @@ def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: dst.setdiag(0.0) return adj, dst + class DelaunayBuilder(GraphBuilderCSR): """Build a generic point-cloud graph from a Delaunay triangulation. @@ -288,6 +289,7 @@ def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: dst.setdiag(0.0) return adj, dst + class GridBuilder(GraphBuilderCSR): """Build a grid-based spatial graph. @@ -384,6 +386,7 @@ def _filter_by_radius_interval( adj.data[mask] = 0.0 adj.setdiag(a_diag) + @dataclass(frozen=True) class DistanceIntervalPostprocessor: interval: tuple[float, float] From d7e5e5f0c58d4043414bb3eb33b896c4844189a8 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Tue, 14 Apr 2026 00:20:16 +0200 Subject: [PATCH 53/96] graphmatrixT exposure --- docs/api.md | 2 ++ pyproject.toml | 2 +- src/squidpy/gr/__init__.py | 2 ++ src/squidpy/gr/_build.py | 7 +++---- src/squidpy/gr/neighbors.py | 1 + 5 files changed, 9 insertions(+), 5 deletions(-) diff --git a/docs/api.md b/docs/api.md index 4d23a6ed5..e975967b4 100644 --- a/docs/api.md +++ b/docs/api.md @@ -23,6 +23,7 @@ import squidpy as sq gr.spatial_neighbors_radius gr.spatial_neighbors_delaunay gr.spatial_neighbors_grid + gr.GraphMatrixT gr.SpatialNeighborsResult gr.mask_graph gr.nhood_enrichment @@ -128,6 +129,7 @@ See the {doc}`extensibility guide ` for how to implement a custo gr.neighbors.GraphBuilder gr.neighbors.GraphBuilderCSR + gr.neighbors.GraphMatrixT gr.neighbors.GraphPostprocessor gr.neighbors.DistanceIntervalPostprocessor gr.neighbors.PercentilePostprocessor diff --git a/pyproject.toml b/pyproject.toml index 06e9dfc5a..5dd9499c8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -261,7 +261,7 @@ addopts = [ markers = [ "internet: tests that require internet", "gpu: tests that require GPU", -] + ] [tool.coverage] run.branch = true diff --git a/src/squidpy/gr/__init__.py b/src/squidpy/gr/__init__.py index fce615b66..5a9a489fc 100644 --- a/src/squidpy/gr/__init__.py +++ b/src/squidpy/gr/__init__.py @@ -4,6 +4,7 @@ from squidpy.gr import neighbors from squidpy.gr._build import ( + GraphMatrixT, SpatialNeighborsResult, mask_graph, spatial_neighbors, @@ -26,6 +27,7 @@ from squidpy.gr._sepal import sepal __all__ = [ + "GraphMatrixT", "SpatialNeighborsResult", "NhoodEnrichmentResult", "neighbors", diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index e2778fa62..2d9d1b3bd 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -3,7 +3,7 @@ from __future__ import annotations import warnings -from typing import Any, Generic, NamedTuple, TypeVar +from typing import Any, Generic, NamedTuple import geopandas as gpd import numpy as np @@ -36,12 +36,14 @@ from squidpy.gr.neighbors import ( DelaunayBuilder, GraphBuilder, + GraphMatrixT, GridBuilder, KNNBuilder, RadiusBuilder, ) __all__ = [ + "GraphMatrixT", "SpatialNeighborsResult", "spatial_neighbors", "spatial_neighbors_from_builder", @@ -52,9 +54,6 @@ ] -GraphMatrixT = TypeVar("GraphMatrixT") - - class SpatialNeighborsResult(NamedTuple, Generic[GraphMatrixT]): """Result of spatial_neighbors function.""" diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index d99646734..422fbf77a 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -32,6 +32,7 @@ from squidpy._validators import assert_positive __all__ = [ + "GraphMatrixT", "GraphBuilder", "GraphBuilderCSR", "GraphPostprocessor", From 13768e2c72b5307ff67706c810b76d9c3800fafe Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 13 Apr 2026 22:20:18 +0000 Subject: [PATCH 54/96] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 5dd9499c8..06e9dfc5a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -261,7 +261,7 @@ addopts = [ markers = [ "internet: tests that require internet", "gpu: tests that require GPU", - ] +] [tool.coverage] run.branch = true From 21f248e8423ecc1a24c2079bfd8be4aba7966a06 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Tue, 12 May 2026 13:58:47 +0200 Subject: [PATCH 55/96] add radius: float | int | tuple t --- src/squidpy/gr/_build.py | 22 ++++++++++++++++------ src/squidpy/gr/neighbors.py | 9 ++++++++- tests/graph/test_spatial_neighbors.py | 15 ++++++++++++++- 3 files changed, 38 insertions(+), 8 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 2d9d1b3bd..8e3e3c342 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -109,7 +109,13 @@ def _resolve_graph_builder( FutureWarning, stacklevel=3, ) - return DelaunayBuilder(**common, radius=radius, percentile=percentile) + # Preserve the documented legacy contract: under the deprecated + # `spatial_neighbors`, a scalar `radius` with `delaunay=True` is silently + # ignored. The new `DelaunayBuilder(radius=r)` interprets a scalar as + # the interval `(0.0, r)`, so we strip scalars here before delegating + # and only forward tuple intervals to keep the legacy behavior intact. + legacy_radius = radius if isinstance(radius, tuple) else None + return DelaunayBuilder(**common, radius=legacy_radius, percentile=percentile) if radius is not None: # TODO: below check should be removed once legacy mode spatial_neighbors is deprecated if n_neighs_was_set: @@ -615,7 +621,7 @@ def spatial_neighbors_delaunay( elements_to_coordinate_systems: dict[str, str] | None = None, table_key: str | None = None, library_key: str | None = None, - radius: tuple[float, float] | None = None, + radius: float | tuple[float, float] | None = None, percentile: float | None = None, transform: str | Transform | None = None, set_diag: bool = False, @@ -636,10 +642,14 @@ def spatial_neighbors_delaunay( %(sdata_params)s %(library_key)s radius - If a :class:`tuple`, used as a post-construction pruning interval - ``[min(radius), max(radius)]``. This does not change the triangulation - itself; it only removes Delaunay edges whose Euclidean lengths fall - outside the interval. + Post-construction edge pruning of the Delaunay graph. The triangulation + itself is never changed; only the resulting edges are filtered. + + - :class:`tuple` ``(min, max)``: keep edges with Euclidean length + in ``[min(radius), max(radius)]``. + - :class:`float` / :class:`int` ``r``: shorthand for ``(0.0, r)``, + i.e. keep edges with length at most ``r``. + - ``None``: keep every Delaunay edge. %(graph_common_params)s %(copy)s diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 422fbf77a..12165455c 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -246,6 +246,11 @@ class DelaunayBuilder(GraphBuilderCSR): ``GridBuilder(delaunay=True)``, this builder uses geometry-based connectivity and stores real Euclidean edge lengths. + ``radius`` only controls post-construction edge pruning; the triangulation + itself is unchanged. A tuple ``(min, max)`` keeps edges with Euclidean + length in that interval. A scalar ``r`` is shorthand for ``(0.0, r)``, + i.e. keep edges with length at most ``r``. ``None`` keeps every edge. + See :func:`~squidpy.gr.spatial_neighbors_delaunay` for the user-facing API or :func:`~squidpy.gr.spatial_neighbors_from_builder` for direct builder usage. """ @@ -257,8 +262,10 @@ def __init__( set_diag: bool = False, percentile: float | None = None, ) -> None: + if isinstance(radius, int | float): + radius = (0.0, float(radius)) postprocessors: list[GraphPostprocessor[csr_matrix]] = [] - if isinstance(radius, tuple): + if radius is not None: postprocessors.append(DistanceIntervalPostprocessor(tuple(sorted(radius)))) if percentile is not None: postprocessors.append(PercentilePostprocessor(percentile)) diff --git a/tests/graph/test_spatial_neighbors.py b/tests/graph/test_spatial_neighbors.py index 150d211bb..63b441c84 100644 --- a/tests/graph/test_spatial_neighbors.py +++ b/tests/graph/test_spatial_neighbors.py @@ -209,8 +209,12 @@ def test_builder_module_export(self): ({"n_neighs": 3, "coord_type": "generic"}, KNNBuilder(n_neighs=3)), ({"radius": 5.0, "coord_type": "generic"}, RadiusBuilder(radius=5.0)), ({"delaunay": True, "coord_type": "generic"}, DelaunayBuilder()), + ( + {"delaunay": True, "radius": (2.0, 4.0), "coord_type": "generic"}, + DelaunayBuilder(radius=(2.0, 4.0)), + ), ], - ids=["knn", "radius", "delaunay"], + ids=["knn", "radius", "delaunay", "delaunay_interval"], ) def test_generic_builder_matches_legacy(self, non_visium_adata: AnnData, legacy_kwargs: dict, builder: object): legacy = spatial_neighbors(non_visium_adata, **legacy_kwargs, copy=True) @@ -264,6 +268,15 @@ def test_delaunay_mode_ignores_scalar_radius(self, non_visium_adata: AnnData): np.testing.assert_array_equal(default.connectivities.toarray(), ignored.connectivities.toarray()) np.testing.assert_allclose(default.distances.toarray(), ignored.distances.toarray()) + def test_delaunay_builder_scalar_radius_equals_zero_max_tuple(self, non_visium_adata: AnnData): + scalar = spatial_neighbors_from_builder(non_visium_adata, builder=DelaunayBuilder(radius=5.0), copy=True) + interval = spatial_neighbors_from_builder( + non_visium_adata, builder=DelaunayBuilder(radius=(0.0, 5.0)), copy=True + ) + + np.testing.assert_array_equal(scalar.connectivities.toarray(), interval.connectivities.toarray()) + np.testing.assert_allclose(scalar.distances.toarray(), interval.distances.toarray()) + def test_delaunay_mode_warns_on_n_neighs(self, non_visium_adata: AnnData): with pytest.warns(FutureWarning, match=r"Parameter `n_neighs` is ignored when `delaunay=True`"): spatial_neighbors(non_visium_adata, coord_type="generic", delaunay=True, n_neighs=3, copy=True) From 3bbc535342b07887dfb3d4b789619aa14b24085f Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Tue, 12 May 2026 14:08:45 +0200 Subject: [PATCH 56/96] 4 or 6 --- src/squidpy/gr/_build.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 8e3e3c342..37bab7544 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -3,7 +3,7 @@ from __future__ import annotations import warnings -from typing import Any, Generic, NamedTuple +from typing import Any, Generic, Literal, NamedTuple import geopandas as gpd import numpy as np @@ -694,7 +694,7 @@ def spatial_neighbors_grid( elements_to_coordinate_systems: dict[str, str] | None = None, table_key: str | None = None, library_key: str | None = None, - n_neighs: int = 6, + n_neighs: Literal[4, 6] = 6, n_rings: int = 1, delaunay: bool = False, transform: str | Transform | None = None, @@ -718,9 +718,10 @@ def spatial_neighbors_grid( %(library_key)s n_neighs Number of neighboring tiles used to form the base grid connectivity. - Defaults to ``6``. On a Visium-like hexagonal grid, ``6`` corresponds to - the immediate surrounding spots, while smaller values such as ``3`` make - the first-ring graph deliberately sparser. + Defaults to ``6``. Use ``4`` for a square grid or ``6`` for a hexagonal + grid such as Visium; other values are accepted at runtime but their + geometric interpretation on a regular grid is not part of the public + API. n_rings Number of rings of neighbors. Defaults to ``1``. ``n_rings=1`` keeps only immediate neighbors; larger values add progressively more distant From 77627dcd1b45e6fba18d33c380d888555460f7a5 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 21 May 2026 10:27:31 +0200 Subject: [PATCH 57/96] mark snn as ilustrative --- docs/extensibility.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/extensibility.md b/docs/extensibility.md index befd61b23..c137ffa1c 100644 --- a/docs/extensibility.md +++ b/docs/extensibility.md @@ -59,6 +59,7 @@ example below swaps the backend while keeping full compatibility with the rest of the Squidpy graph pipeline: ```python +# Following code is only illustrative and probably not runnable. import numpy as np from scipy.sparse import csr_matrix from snnpy import build_snn_model From 0afbe3c9ef665fc99b6d0ef3b7dcad9e5b97d1ff Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 21 May 2026 10:56:02 +0200 Subject: [PATCH 58/96] add uns params --- src/squidpy/gr/_build.py | 15 ++++++++------- src/squidpy/gr/neighbors.py | 38 +++++++++++++++++++++++++++++++++++-- 2 files changed, 44 insertions(+), 9 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 37bab7544..4ef266f8d 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -381,7 +381,12 @@ def spatial_neighbors( ) return _run_spatial_neighbors( - adata, builder, spatial_key=spatial_key, library_key=library_key, key_added=key_added, copy=copy + adata, + builder, + spatial_key=spatial_key, + library_key=library_key, + key_added=key_added, + copy=copy, ) @@ -549,10 +554,10 @@ def spatial_neighbors_radius( data: AnnData | SpatialData, *, spatial_key: str = Key.obsm.spatial, + radius: float | tuple[float, float], elements_to_coordinate_systems: dict[str, str] | None = None, table_key: str | None = None, library_key: str | None = None, - radius: float | tuple[float, float] = 1.0, percentile: float | None = None, transform: str | Transform | None = None, set_diag: bool = False, @@ -810,11 +815,7 @@ def _run_spatial_neighbors( neighbors_dict = { "connectivities_key": conns_key, "distances_key": dists_key, - "params": { - "n_neighbors": getattr(builder, "n_neighs", 6), - "radius": getattr(builder, "radius", None), - "transform": builder.transform.v, - }, + "params": builder.uns_params(), } if copy: diff --git a/src/squidpy/gr/neighbors.py b/src/squidpy/gr/neighbors.py index 12165455c..c09b9bf9b 100644 --- a/src/squidpy/gr/neighbors.py +++ b/src/squidpy/gr/neighbors.py @@ -10,7 +10,7 @@ from collections.abc import Callable, Sequence from dataclasses import dataclass from itertools import chain -from typing import Generic, TypeVar, cast +from typing import Any, Generic, TypeVar, cast import numpy as np from fast_array_utils import stats as fau_stats @@ -27,7 +27,7 @@ from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances from sklearn.neighbors import NearestNeighbors -from squidpy._constants._constants import Transform +from squidpy._constants._constants import CoordType, Transform from squidpy._utils import NDArrayA from squidpy._validators import assert_positive @@ -86,6 +86,10 @@ def postprocessors(self) -> Sequence[GraphPostprocessor[GraphMatrixT]]: """Return post-build processing steps for ``(adj, dst)``.""" return self._postprocessors + @abstractmethod + def uns_params(self) -> dict[str, Any]: + """Parameters stored in :attr:`anndata.AnnData.uns` after graph construction.""" + def combine( self, mats: Sequence[tuple[GraphMatrixT, GraphMatrixT]], @@ -166,6 +170,13 @@ def __init__( ) self.n_neighs = n_neighs + def uns_params(self) -> dict[str, Any]: + return { + "coord_type": CoordType.GENERIC.v, + "n_neighbors": self.n_neighs, + "transform": self.transform.v, + } + def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: N = coords.shape[0] tree = NearestNeighbors(n_neighbors=self.n_neighs, radius=1, metric="euclidean") @@ -216,6 +227,13 @@ def __init__( ) self.radius = radius + def uns_params(self) -> dict[str, Any]: + return { + "coord_type": CoordType.GENERIC.v, + "radius": self.radius, + "transform": self.transform.v, + } + def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: N = coords.shape[0] r = self.radius if isinstance(self.radius, int | float) else max(self.radius) @@ -278,6 +296,13 @@ def __init__( ) self.radius = radius + def uns_params(self) -> dict[str, Any]: + return { + "coord_type": CoordType.GENERIC.v, + "radius": self.radius, + "transform": self.transform.v, + } + def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: N = coords.shape[0] tri = Delaunay(coords) @@ -326,6 +351,15 @@ def __init__( self.n_rings = n_rings self.delaunay = delaunay + def uns_params(self) -> dict[str, Any]: + return { + "coord_type": CoordType.GRID.v, + "n_neighbors": self.n_neighs, + "n_rings": self.n_rings, + "delaunay": self.delaunay, + "transform": self.transform.v, + } + def build_graph(self, coords: NDArrayA) -> tuple[csr_matrix, csr_matrix]: if self.n_rings > 1: adj = self._base_adjacency(coords, set_diag=True) From 1711a62326bc9b94056f2bac66701b013b152a77 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 21 May 2026 11:09:08 +0200 Subject: [PATCH 59/96] deprecation warnings alignment --- src/squidpy/gr/_build.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 4ef266f8d..5b4adc5c1 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -102,10 +102,10 @@ def _resolve_graph_builder( ) return GridBuilder(n_neighs=n_neighs, **common, n_rings=n_rings, delaunay=delaunay) if delaunay: - # TODO: below check should be removed once legacy mode spatial_neighbors is deprecated + # TODO: below check should be removed when spatial_neighbors is deprecated if n_neighs_was_set: warnings.warn( - "Parameter `n_neighs` is ignored when `delaunay=True` and will be removed in squidpy v2.0.0.", + "Parameter `n_neighs` is ignored when `delaunay=True` use `spatial_neighbors_delaunay` instead.", FutureWarning, stacklevel=3, ) @@ -117,10 +117,10 @@ def _resolve_graph_builder( legacy_radius = radius if isinstance(radius, tuple) else None return DelaunayBuilder(**common, radius=legacy_radius, percentile=percentile) if radius is not None: - # TODO: below check should be removed once legacy mode spatial_neighbors is deprecated + # TODO: below check should be removed when spatial_neighbors is deprecated if n_neighs_was_set: warnings.warn( - "Parameter `n_neighs` is ignored when `radius` is set and will be removed in squidpy v2.0.0.", + "Parameter `n_neighs` is ignored when `radius` is set use `spatial_neighbors_radius` instead.", FutureWarning, stacklevel=3, ) @@ -213,9 +213,9 @@ def spatial_neighbors( """ Create a graph from spatial coordinates. - .. deprecated:: 1.6.0 + .. deprecated:: 1.7.0 ``spatial_neighbors`` is deprecated and will be removed in squidpy - v1.7.0. Use one of the mode-specific functions instead: + v1.9.0. Use one of the mode-specific functions instead: - :func:`spatial_neighbors_knn` - :func:`spatial_neighbors_radius` @@ -294,14 +294,14 @@ def spatial_neighbors( - Generic radius mode: ``coord_type='generic'``, ``delaunay=False``, ``radius`` set. Uses ``radius`` and builds a radius-based neighbor graph. - ``n_neighs`` is ignored and passing it is deprecated. + ``n_neighs`` is ignored and will throw a warning if passed. If ``radius`` is a tuple, the graph is built with the maximum radius and then pruned to the interval ``[min(radius), max(radius)]``. - Generic Delaunay mode: ``coord_type='generic'``, ``delaunay=True``. Builds a Delaunay triangulation graph. ``n_neighs`` is - ignored by the triangulation and passing it is deprecated. + ignored by the triangulation and will throw a warning if passed. If ``radius`` is a tuple, it is used only as a post-construction pruning interval. @@ -321,11 +321,11 @@ def spatial_neighbors( - If ``coord_type`` resolves to ``'grid'``, grid mode is used. In that case ``radius`` is ignored. - Otherwise, if ``delaunay=True``, Delaunay mode is used. - ``n_neighs`` is ignored (deprecated). + ``n_neighs`` is ignored (deprecated, removed in v1.9.0). A tuple ``radius`` is only used afterward as a pruning interval. A scalar ``radius`` is ignored. - Otherwise, if ``radius`` is set, radius mode is used. - In this mode ``n_neighs`` is ignored (deprecated). + In this mode ``n_neighs`` is ignored (deprecated, removed in v1.9.0). - Otherwise, k-nearest-neighbor mode is used. Grid-specific behavior @@ -355,7 +355,7 @@ def spatial_neighbors( """ warnings.warn( "Calling `spatial_neighbors` is deprecated and will be removed in squidpy " - "v1.7.0. Use `spatial_neighbors_knn`, `spatial_neighbors_radius`, " + "v1.9.0. Use `spatial_neighbors_knn`, `spatial_neighbors_radius`, " "`spatial_neighbors_delaunay`, `spatial_neighbors_grid`, or " "`spatial_neighbors_from_builder` instead.", FutureWarning, From 4632b74e92bb05ff327b5a039887c06be10c96c6 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 21 May 2026 11:16:50 +0200 Subject: [PATCH 60/96] set diag isnt defaulted to none --- src/squidpy/gr/_build.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 5b4adc5c1..8ceeb248b 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -206,7 +206,7 @@ def spatial_neighbors( n_rings: int | None = None, percentile: float | None = None, transform: str | Transform | None = None, - set_diag: bool | None = None, + set_diag: bool = False, key_added: str = "spatial", copy: bool = False, ) -> SpatialNeighborsResult | None: @@ -259,7 +259,6 @@ def spatial_neighbors( - :class:`tuple` - prune the final graph to only contain edges in interval `[min(radius), max(radius)]`. delaunay Whether to compute the graph from Delaunay triangulation. Only used when ``coord_type = {c.GENERIC.s!r}``. - Defaults to ``False``. n_rings Number of rings of neighbors for grid data. Only used when ``coord_type = {c.GRID.s!r}``. Defaults to ``1``. From f4805c19beb0c3388f5536d57b11e02a2ccc1fb9 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 21 May 2026 11:17:55 +0200 Subject: [PATCH 61/96] fix --- src/squidpy/gr/_build.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 8ceeb248b..abc389dc6 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -259,6 +259,7 @@ def spatial_neighbors( - :class:`tuple` - prune the final graph to only contain edges in interval `[min(radius), max(radius)]`. delaunay Whether to compute the graph from Delaunay triangulation. Only used when ``coord_type = {c.GENERIC.s!r}``. + Defaults to ``False``. n_rings Number of rings of neighbors for grid data. Only used when ``coord_type = {c.GRID.s!r}``. Defaults to ``1``. @@ -273,7 +274,6 @@ def spatial_neighbors( - `{t.NONE.v}` - no transformation of the adjacency matrix. set_diag Whether to set the diagonal of the spatial connectivities to `1.0`. - Defaults to ``False``. key_added Key which controls where the results are saved if ``copy = False``. %(copy)s From f2b0fa055f7d82502d461649672c78134611df90 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 21 May 2026 11:20:12 +0200 Subject: [PATCH 62/96] radius is also positional --- src/squidpy/gr/_build.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index abc389dc6..f64a1f115 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -551,9 +551,9 @@ def spatial_neighbors_knn( @d.dedent def spatial_neighbors_radius( data: AnnData | SpatialData, + radius: float | tuple[float, float], *, spatial_key: str = Key.obsm.spatial, - radius: float | tuple[float, float], elements_to_coordinate_systems: dict[str, str] | None = None, table_key: str | None = None, library_key: str | None = None, From 0ed1604669a863ead9e3c400eec961c40af8cc54 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 21 May 2026 11:25:44 +0200 Subject: [PATCH 63/96] n_neighs on grid mode --- src/squidpy/gr/_build.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index f64a1f115..92a374c2e 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -698,7 +698,7 @@ def spatial_neighbors_grid( elements_to_coordinate_systems: dict[str, str] | None = None, table_key: str | None = None, library_key: str | None = None, - n_neighs: Literal[4, 6] = 6, + n_neighs: int = 6, n_rings: int = 1, delaunay: bool = False, transform: str | Transform | None = None, @@ -722,10 +722,7 @@ def spatial_neighbors_grid( %(library_key)s n_neighs Number of neighboring tiles used to form the base grid connectivity. - Defaults to ``6``. Use ``4`` for a square grid or ``6`` for a hexagonal - grid such as Visium; other values are accepted at runtime but their - geometric interpretation on a regular grid is not part of the public - API. + Defaults to ``6`` for Visium-like grid coordinates. n_rings Number of rings of neighbors. Defaults to ``1``. ``n_rings=1`` keeps only immediate neighbors; larger values add progressively more distant From c85ba43914623854ca5349a87f9d4c82574f82e4 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Tue, 26 May 2026 15:15:30 +0200 Subject: [PATCH 64/96] make radius kw only --- src/squidpy/gr/_build.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 92a374c2e..54c8c3a0c 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -551,8 +551,8 @@ def spatial_neighbors_knn( @d.dedent def spatial_neighbors_radius( data: AnnData | SpatialData, - radius: float | tuple[float, float], *, + radius: float | tuple[float, float], spatial_key: str = Key.obsm.spatial, elements_to_coordinate_systems: dict[str, str] | None = None, table_key: str | None = None, From d9264a8503d4253bf61bc66bf5ebee31cfbb8a6c Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Wed, 27 May 2026 11:17:13 +0200 Subject: [PATCH 65/96] put a working example --- docs/extensibility.md | 73 +++++++++++++++++++++++++------------------ 1 file changed, 42 insertions(+), 31 deletions(-) diff --git a/docs/extensibility.md b/docs/extensibility.md index c137ffa1c..5b06b3d38 100644 --- a/docs/extensibility.md +++ b/docs/extensibility.md @@ -50,50 +50,61 @@ with matching sparsity structure: - By convention, ``dst`` should have a zero diagonal, and ``adj`` should only have a non-zero diagonal when ``set_diag=True``. -### Example: fast radius search with SNN +### Example: approximate kNN search with pynndescent -The built-in {class}`~squidpy.gr.neighbors.RadiusBuilder` uses scikit-learn's -``NearestNeighbors``. The [snnpy](https://github.com/nla-group/snn) library -provides a faster exact fixed-radius search based on PCA-based pruning. The -example below swaps the backend while keeping full compatibility with the rest -of the Squidpy graph pipeline: +The built-in {class}`~squidpy.gr.neighbors.KNNBuilder` uses scikit-learn's +``NearestNeighbors``. The [pynndescent](https://github.com/lmcinnes/pynndescent) +library provides an approximate nearest-neighbor search backend that is often +faster on larger datasets. The example below swaps the backend while keeping +the Squidpy graph pipeline contract intact. + +To run the example, install the optional backend into the same environment as +Squidpy: + +```bash +python -m pip install pynndescent +``` ```python -# Following code is only illustrative and probably not runnable. +# Following code is illustrative and requires ``pynndescent`` to be installed. import numpy as np from scipy.sparse import csr_matrix -from snnpy import build_snn_model +from pynndescent import NNDescent from squidpy.gr.neighbors import GraphBuilderCSR -class SNNRadiusBuilder(GraphBuilderCSR): - """Radius graph using the SNN fixed-radius search backend.""" +class PynndescentKNNBuilder(GraphBuilderCSR): + """KNN graph using the pynndescent approximate nearest-neighbor backend.""" + + def __init__(self, n_neighs: int = 6, **kwargs): + super().__init__(**kwargs) + self.n_neighs = n_neighs - def __init__(self, radius: float, **kwargs): - super().__init__(**kwargs) - self.radius = radius + def uns_params(self): + return { + "n_neighbors": self.n_neighs, + "set_diag": self.set_diag, + } - def build_graph(self, coords): - N = coords.shape[0] - model = build_snn_model(coords, verbose=0) - indices, dists = model.batch_query_radius( - coords, self.radius, return_distance=True, - ) + def build_graph(self, coords): + n_obs = coords.shape[0] + model = NNDescent(coords, metric="euclidean") + indices, dists = model.query(coords, k=self.n_neighs) - row = np.repeat(np.arange(N), [len(idx) for idx in indices]) - col = np.concatenate(indices) - d = np.concatenate(dists).astype(np.float64) + row_indices = np.repeat(np.arange(n_obs), self.n_neighs) + col_indices = indices.reshape(-1) + dists = dists.reshape(-1).astype(np.float64) - adj = csr_matrix( - (np.ones(len(row), dtype=np.float32), (row, col)), - shape=(N, N), - ) - dst = csr_matrix((d, (row, col)), shape=(N, N)) + adj = csr_matrix( + (np.ones_like(row_indices, dtype=np.float32), (row_indices, col_indices)), + shape=(n_obs, n_obs), + ) + dst = csr_matrix((dists, (row_indices, col_indices)), shape=(n_obs, n_obs)) - adj.setdiag(1.0 if self.set_diag else adj.diagonal()) - dst.setdiag(0.0) - return adj, dst + adj.setdiag(1.0 if self.set_diag else adj.diagonal()) + dst.setdiag(0.0) + return adj, dst ``` Use it like any other builder: @@ -101,5 +112,5 @@ Use it like any other builder: ```python import squidpy as sq -sq.gr.spatial_neighbors_from_builder(adata, SNNRadiusBuilder(radius=100.0)) +sq.gr.spatial_neighbors_from_builder(adata, PynndescentKNNBuilder(n_neighs=6)) ``` From 52b4e79d0e0faf39e07998b09abdd7d8f434b282 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Wed, 27 May 2026 11:25:20 +0200 Subject: [PATCH 66/96] fix api.md --- docs/api.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/api.md b/docs/api.md index e30c23d89..af9a90e7b 100644 --- a/docs/api.md +++ b/docs/api.md @@ -148,4 +148,5 @@ See the {doc}`extensibility guide ` for how to implement a custo :toctree: api experimental.tl.calculate_tiling_qc - experimental.pl.tiling_qc \ No newline at end of file + experimental.pl.tiling_qc +``` \ No newline at end of file From bfbb3e8a263751bac4c69c52873417c8f253f90e Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 28 May 2026 14:45:53 +0200 Subject: [PATCH 67/96] be more compatible with main --- src/squidpy/gr/_build.py | 108 +++++++++++++++++++-------------------- 1 file changed, 52 insertions(+), 56 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 54c8c3a0c..819d9a09b 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -128,67 +128,63 @@ def _resolve_graph_builder( return KNNBuilder(n_neighs=n_neighs, **common, percentile=percentile) -def _resolve_spatial_data( + + + +def _resolve_data( data: AnnData | SpatialData, - *, - spatial_key: str, elements_to_coordinate_systems: dict[str, str] | None, - table_key: str | None, - library_key: str | None, + table_key: str | None = None, + spatial_key: str = Key.obsm.spatial, + library_key: str | None = None, ) -> tuple[AnnData, str | None]: - """Resolve SpatialData to AnnData, returning (adata, library_key).""" - if isinstance(data, SpatialData): - sdata = data - assert elements_to_coordinate_systems is not None, ( - "Since `data` is a :class:`spatialdata.SpatialData`, `elements_to_coordinate_systems` must not be `None`." - ) - assert table_key is not None, ( - "Since `data` is a :class:`spatialdata.SpatialData`, `table_key` must not be `None`." - ) - elements, table = match_element_to_table(sdata, list(elements_to_coordinate_systems), table_key) - assert table.obs_names.equals(sdata.tables[table_key].obs_names), ( - "The spatialdata table must annotate all elements keys. Some elements are missing, please check the `elements_to_coordinate_systems` dictionary." + if not isinstance(data, SpatialData): + return data, library_key + + assert elements_to_coordinate_systems is not None, ( + "Since input is a :class:`spatialdata.SpatialData`, `elements_to_coordinate_systems` must not be `None`." + ) + table = extract_adata_if_sdata(data, table_key=table_key) + elements, matched_table = match_element_to_table(data, list(elements_to_coordinate_systems), table_key) + assert matched_table.obs_names.equals(table.obs_names), ( + "The spatialdata table must annotate all elements keys. Some elements are missing, please check the `elements_to_coordinate_systems` dictionary." + ) + regions, region_key, instance_key = get_table_keys(table) + regions = [regions] if isinstance(regions, str) else regions + ordered_regions_in_table = table.obs[region_key].unique() + + # TODO: remove this after https://github.com/scverse/spatialdata/issues/614 + remove_centroids = {} + elem_instances = [] + for e in regions: + schema = get_model(elements[e]) + element_instances = get_element_instances(elements[e]).to_series() + if np.isin(0, element_instances.values) and (schema in (Labels2DModel, Labels3DModel)): + element_instances = element_instances.drop(index=0) + remove_centroids[e] = True + else: + remove_centroids[e] = False + elem_instances.append(element_instances) + + element_instances = pd.concat(elem_instances) + if (not np.all(element_instances.values == table.obs[instance_key].values)) or ( + not np.all(ordered_regions_in_table == regions) + ): + raise ValueError( + "The spatialdata table must annotate all elements keys. Some elements are missing or not ordered correctly, please check the `elements_to_coordinate_systems` dictionary." ) - regions, region_key, instance_key = get_table_keys(sdata.tables[table_key]) - regions = [regions] if isinstance(regions, str) else regions - ordered_regions_in_table = sdata.tables[table_key].obs[region_key].unique() + centroids = [] + for region_ in ordered_regions_in_table: + cs = elements_to_coordinate_systems[region_] + centroid = get_centroids(data[region_], coordinate_system=cs)[["x", "y"]].compute() # TODO: remove this after https://github.com/scverse/spatialdata/issues/614 - remove_centroids = {} - elem_instances = [] - for e in regions: - schema = get_model(elements[e]) - element_instances = get_element_instances(elements[e]).to_series() - if np.isin(0, element_instances.values) and (schema in (Labels2DModel, Labels3DModel)): - element_instances = element_instances.drop(index=0) - remove_centroids[e] = True - else: - remove_centroids[e] = False - elem_instances.append(element_instances) - - element_instances = pd.concat(elem_instances) - if (not np.all(element_instances.values == sdata.tables[table_key].obs[instance_key].values)) or ( - not np.all(ordered_regions_in_table == regions) - ): - raise ValueError( - "The spatialdata table must annotate all elements keys. Some elements are missing or not ordered correctly, please check the `elements_to_coordinate_systems` dictionary." - ) - centroids = [] - for region_ in ordered_regions_in_table: - cs = elements_to_coordinate_systems[region_] - centroid = get_centroids(sdata[region_], coordinate_system=cs)[["x", "y"]].compute() - - # TODO: remove this after https://github.com/scverse/spatialdata/issues/614 - if remove_centroids[region_]: - centroid = centroid[1:].copy() - centroids.append(centroid) - - sdata.tables[table_key].obsm[spatial_key] = np.concatenate(centroids) - adata = sdata.tables[table_key] - library_key = region_key - else: - adata = data - return adata, library_key + if remove_centroids[region_]: + centroid = centroid[1:].copy() + centroids.append(centroid) + + table.obsm[spatial_key] = np.concatenate(centroids) + return table, region_key @d.dedent @@ -471,7 +467,7 @@ def _prepare_spatial_neighbors_input( library_key: str | None, ) -> tuple[AnnData, str | None]: """Resolve input data and validate the requested spatial basis.""" - adata, library_key = _resolve_spatial_data( + adata, library_key = _resolve_data( data, spatial_key=spatial_key, elements_to_coordinate_systems=elements_to_coordinate_systems, From 9dd330eed4e386c29a88e401890841376dedda67 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 28 May 2026 14:49:09 +0200 Subject: [PATCH 68/96] align more to main --- src/squidpy/gr/_build.py | 113 +++++++++++++++++++-------------------- 1 file changed, 56 insertions(+), 57 deletions(-) diff --git a/src/squidpy/gr/_build.py b/src/squidpy/gr/_build.py index 726be35eb..b3b82f18c 100644 --- a/src/squidpy/gr/_build.py +++ b/src/squidpy/gr/_build.py @@ -131,63 +131,6 @@ def _resolve_graph_builder( - -def _resolve_data( - data: AnnData | SpatialData, - elements_to_coordinate_systems: dict[str, str] | None, - table_key: str | None = None, - spatial_key: str = Key.obsm.spatial, - library_key: str | None = None, -) -> tuple[AnnData, str | None]: - if not isinstance(data, SpatialData): - return data, library_key - - assert elements_to_coordinate_systems is not None, ( - "Since input is a :class:`spatialdata.SpatialData`, `elements_to_coordinate_systems` must not be `None`." - ) - table = extract_adata_if_sdata(data, table_key=table_key) - elements, matched_table = match_element_to_table(data, list(elements_to_coordinate_systems), table_key) - assert matched_table.obs_names.equals(table.obs_names), ( - "The spatialdata table must annotate all elements keys. Some elements are missing, please check the `elements_to_coordinate_systems` dictionary." - ) - regions, region_key, instance_key = get_table_keys(table) - regions = [regions] if isinstance(regions, str) else regions - ordered_regions_in_table = table.obs[region_key].unique() - - # TODO: remove this after https://github.com/scverse/spatialdata/issues/614 - remove_centroids = {} - elem_instances = [] - for e in regions: - schema = get_model(elements[e]) - element_instances = get_element_instances(elements[e]).to_series() - if np.isin(0, element_instances.values) and (schema in (Labels2DModel, Labels3DModel)): - element_instances = element_instances.drop(index=0) - remove_centroids[e] = True - else: - remove_centroids[e] = False - elem_instances.append(element_instances) - - element_instances = pd.concat(elem_instances) - if (not np.all(element_instances.values == table.obs[instance_key].values)) or ( - not np.all(ordered_regions_in_table == regions) - ): - raise ValueError( - "The spatialdata table must annotate all elements keys. Some elements are missing or not ordered correctly, please check the `elements_to_coordinate_systems` dictionary." - ) - centroids = [] - for region_ in ordered_regions_in_table: - cs = elements_to_coordinate_systems[region_] - centroid = get_centroids(data[region_], coordinate_system=cs)[["x", "y"]].compute() - - # TODO: remove this after https://github.com/scverse/spatialdata/issues/614 - if remove_centroids[region_]: - centroid = centroid[1:].copy() - centroids.append(centroid) - - table.obsm[spatial_key] = np.concatenate(centroids) - return table, region_key - - @d.dedent @inject_docs(t=Transform, c=CoordType) def spatial_neighbors( @@ -386,6 +329,62 @@ def spatial_neighbors( ) +def _resolve_data( + data: AnnData | SpatialData, + elements_to_coordinate_systems: dict[str, str] | None, + table_key: str | None = None, + spatial_key: str = Key.obsm.spatial, + library_key: str | None = None, +) -> tuple[AnnData, str | None]: + if not isinstance(data, SpatialData): + return data, library_key + + assert elements_to_coordinate_systems is not None, ( + "Since input is a :class:`spatialdata.SpatialData`, `elements_to_coordinate_systems` must not be `None`." + ) + table = extract_adata_if_sdata(data, table_key=table_key) + elements, matched_table = match_element_to_table(data, list(elements_to_coordinate_systems), table_key) + assert matched_table.obs_names.equals(table.obs_names), ( + "The spatialdata table must annotate all elements keys. Some elements are missing, please check the `elements_to_coordinate_systems` dictionary." + ) + regions, region_key, instance_key = get_table_keys(table) + regions = [regions] if isinstance(regions, str) else regions + ordered_regions_in_table = table.obs[region_key].unique() + + # TODO: remove this after https://github.com/scverse/spatialdata/issues/614 + remove_centroids = {} + elem_instances = [] + for e in regions: + schema = get_model(elements[e]) + element_instances = get_element_instances(elements[e]).to_series() + if np.isin(0, element_instances.values) and (schema in (Labels2DModel, Labels3DModel)): + element_instances = element_instances.drop(index=0) + remove_centroids[e] = True + else: + remove_centroids[e] = False + elem_instances.append(element_instances) + + element_instances = pd.concat(elem_instances) + if (not np.all(element_instances.values == table.obs[instance_key].values)) or ( + not np.all(ordered_regions_in_table == regions) + ): + raise ValueError( + "The spatialdata table must annotate all elements keys. Some elements are missing or not ordered correctly, please check the `elements_to_coordinate_systems` dictionary." + ) + centroids = [] + for region_ in ordered_regions_in_table: + cs = elements_to_coordinate_systems[region_] + centroid = get_centroids(data[region_], coordinate_system=cs)[["x", "y"]].compute() + + # TODO: remove this after https://github.com/scverse/spatialdata/issues/614 + if remove_centroids[region_]: + centroid = centroid[1:].copy() + centroids.append(centroid) + + table.obsm[spatial_key] = np.concatenate(centroids) + return table, region_key + + @d.dedent def spatial_neighbors_from_builder( data: AnnData | SpatialData, From 6c61ddce64de4b971d3673c90c27a83b2e44bdff Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Mon, 1 Jun 2026 09:21:34 +0200 Subject: [PATCH 69/96] save state --- hatch.toml | 4 + pyproject.toml | 3 + src/squidpy/experimental/__init__.py | 4 +- src/squidpy/experimental/_fit/__init__.py | 46 +++ src/squidpy/experimental/_fit/_estimator.py | 58 +++ .../experimental/_fit/_methods/__init__.py | 12 + .../_fit/_methods/_stalign/_core.py | 371 ++++++++++++++++++ .../_fit/_methods/_stalign/_estimator.py | 130 ++++++ .../_fit/_methods/_stalign/_helpers.py | 160 ++++++++ .../_fit/_methods/_stalign/_tools.py | 231 +++++++++++ src/squidpy/experimental/_fit/_registry.py | 46 +++ src/squidpy/experimental/_fit/_result.py | 32 ++ tests/experimental/fit/__init__.py | 0 tests/experimental/fit/test_core.py | 95 +++++ tests/experimental/fit/test_stalign.py | 95 +++++ 15 files changed, 1285 insertions(+), 2 deletions(-) create mode 100644 src/squidpy/experimental/_fit/__init__.py create mode 100644 src/squidpy/experimental/_fit/_estimator.py create mode 100644 src/squidpy/experimental/_fit/_methods/__init__.py create mode 100644 src/squidpy/experimental/_fit/_methods/_stalign/_core.py create mode 100644 src/squidpy/experimental/_fit/_methods/_stalign/_estimator.py create mode 100644 src/squidpy/experimental/_fit/_methods/_stalign/_helpers.py create mode 100644 src/squidpy/experimental/_fit/_methods/_stalign/_tools.py create mode 100644 src/squidpy/experimental/_fit/_registry.py create mode 100644 src/squidpy/experimental/_fit/_result.py create mode 100644 tests/experimental/fit/__init__.py create mode 100644 tests/experimental/fit/test_core.py create mode 100644 tests/experimental/fit/test_stalign.py diff --git a/hatch.toml b/hatch.toml index 065fe2dfb..f2672cd35 100644 --- a/hatch.toml +++ b/hatch.toml @@ -17,10 +17,14 @@ extra-dependencies = ["diff-cover"] matrix = [ { deps = ["stable"], python = ["3.11", "3.12", "3.13"] }, { deps = ["pre"], python = ["3.13"] }, + { deps = ["stable"], python = ["3.13"], extras = ["jax"] }, ] overrides.matrix.deps.env-vars = [ { key = "UV_PRERELEASE", value = "allow", if = ["pre"] }, ] +overrides.matrix.extras.features = [ + { value = "jax", if = ["jax"] }, +] # default commands (only `cov-report` is overridden) scripts.run = "pytest{env:HATCH_TEST_ARGS:} -p no:cov {args}" scripts.run-cov = "coverage run -m pytest{env:HATCH_TEST_ARGS:} -p no:cov {args}" diff --git a/pyproject.toml b/pyproject.toml index 06ffac1bf..208f6ee10 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -76,6 +76,9 @@ dependencies = [ "xarray>=2024.10", "zarr>=3", ] +optional-dependencies.jax = [ + "jax", +] optional-dependencies.leiden = [ "leidenalg", "spatialleiden>=0.4", diff --git a/src/squidpy/experimental/__init__.py b/src/squidpy/experimental/__init__.py index 3c5b1f537..a33215238 100644 --- a/src/squidpy/experimental/__init__.py +++ b/src/squidpy/experimental/__init__.py @@ -6,6 +6,6 @@ from __future__ import annotations -from . import im, pl, tl, utils +from . import fit, im, pl, tl, utils -__all__ = ["im", "pl", "tl", "utils"] +__all__ = ["fit", "im", "pl", "tl", "utils"] diff --git a/src/squidpy/experimental/_fit/__init__.py b/src/squidpy/experimental/_fit/__init__.py new file mode 100644 index 000000000..b56d9df80 --- /dev/null +++ b/src/squidpy/experimental/_fit/__init__.py @@ -0,0 +1,46 @@ +"""In-memory model-fitting core for experimental squidpy tools. + +This subpackage factors the *fit* half of fitting-style tools (alignment, +imputation, ...) out of any particular container or write-back path: + +- :class:`Estimator` -- a model whose :meth:`~Estimator.fit` takes in-memory + data and returns a :class:`FitResult` (scikit-learn-style; training is + assumed to fit in memory). +- :class:`FitResult` -- the fitted parameters plus an optional, pure in-memory + :meth:`~FitResult.transform`. Container write-back lives in the calling + function, not here. +- :class:`Registry` -- a flat ``name -> Estimator`` map, one per family. + +Concrete estimators live in :mod:`._methods` and pull their optional +dependencies (e.g. JAX) in lazily, so importing this package stays cheap. + +Example +------- +>>> import numpy as np +>>> from squidpy.experimental.fit import Estimator, FitResult, Registry +>>> demo = Registry("demo") +>>> class Shift(FitResult): +... def __init__(self, delta): +... self.metadata, self.delta = {}, delta +... +... def transform(self, x): +... return np.asarray(x) + self.delta +>>> @demo.register("mean_shift") +... class MeanShift(Estimator): +... name = "mean_shift" +... +... def fit(self, ref, query): +... return Shift(ref.mean(0) - query.mean(0)) +>>> est = demo.get("mean_shift")() +>>> result = est.fit(np.ones((3, 2)), np.zeros((3, 2))) +>>> result.transform(np.zeros((3, 2))).tolist() +[[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]] +""" + +from __future__ import annotations + +from squidpy.experimental.fit._estimator import Estimator +from squidpy.experimental.fit._registry import Registry +from squidpy.experimental.fit._result import FitResult + +__all__ = ["Estimator", "FitResult", "Registry"] diff --git a/src/squidpy/experimental/_fit/_estimator.py b/src/squidpy/experimental/_fit/_estimator.py new file mode 100644 index 000000000..c73a3173f --- /dev/null +++ b/src/squidpy/experimental/_fit/_estimator.py @@ -0,0 +1,58 @@ +"""Estimator contract: a pure, in-memory fit.""" + +from __future__ import annotations + +import importlib.util +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING, Any, ClassVar + +if TYPE_CHECKING: + from squidpy.experimental.fit._result import FitResult + + +class Estimator(ABC): + """Base class for an in-memory model fit. + + Subclasses implement :meth:`fit`, which takes already-extracted in-memory + data (NumPy arrays, sparse matrices, or an :class:`~anndata.AnnData`) and + returns a :class:`FitResult`. Following scikit-learn, ``fit`` assumes the + training data fits in memory -- there is no streaming / out-of-core + contract (scikit-learn's ``partial_fit`` is the analogous opt-in exception + and is intentionally not modelled here). + + Reading data out of a container and writing results back are *not* part of + this contract; both are handled by the calling function. + + Attributes + ---------- + name + Stable identifier used as the registry key. + requires + Optional third-party packages the estimator needs at fit time (e.g. + ``("jax",)``). Advertised here so :meth:`check_requirements` can fail + early with an actionable message before any heavy import fires. + """ + + name: ClassVar[str] + requires: ClassVar[tuple[str, ...]] = () + + @abstractmethod + def fit(self, *args: Any, **kwargs: Any) -> FitResult: + """Fit the model on in-memory data and return a :class:`FitResult`.""" + + def check_requirements(self) -> None: + """Raise :class:`ImportError` if any package in :attr:`requires` is missing. + + Estimators should call this at the top of :meth:`fit`, before importing + their optional backend, so callers get a clean message instead of a + deep ``ModuleNotFoundError``. + """ + missing = [pkg for pkg in self.requires if importlib.util.find_spec(pkg) is None] + if missing: + verb = "is" if len(missing) == 1 else "are" + names = ", ".join(repr(p) for p in missing) + extras = ",".join(missing) + raise ImportError( + f"Estimator {self.name!r} requires {names}, which {verb} not installed. " + f'Install with `pip install "squidpy[{extras}]"`.' + ) diff --git a/src/squidpy/experimental/_fit/_methods/__init__.py b/src/squidpy/experimental/_fit/_methods/__init__.py new file mode 100644 index 000000000..8b15faa6b --- /dev/null +++ b/src/squidpy/experimental/_fit/_methods/__init__.py @@ -0,0 +1,12 @@ +"""Concrete estimators for :mod:`squidpy.experimental.fit`. + +Importing this package registers the bundled estimators (e.g. STalign in the +``align`` family) but stays cheap: optional dependencies such as JAX are pulled +in lazily, only when an estimator's ``fit`` is actually called. +""" + +from __future__ import annotations + +from squidpy.experimental.fit._methods._stalign import ALIGN, StalignEstimator, StalignFitResult + +__all__ = ["ALIGN", "StalignEstimator", "StalignFitResult"] diff --git a/src/squidpy/experimental/_fit/_methods/_stalign/_core.py b/src/squidpy/experimental/_fit/_methods/_stalign/_core.py new file mode 100644 index 000000000..8ac139b6c --- /dev/null +++ b/src/squidpy/experimental/_fit/_methods/_stalign/_core.py @@ -0,0 +1,371 @@ +"""Core JAX implementation for experimental STalign point registration. + +Lifted byte-for-byte from scverse/squidpy#1150 (Selman Özleyen). +""" + +from __future__ import annotations + +from typing import Any, Literal + +import jax +import jax.numpy as jnp +import jax.scipy as jsp +import numpy as np + +__all__ = ["jax_dtype", "lddmm", "transform_points_row_col"] + + +def jax_dtype() -> jnp.dtype: + """Resolve the active JAX float dtype at call time, not import time.""" + return jnp.float64 if jax.config.x64_enabled else jnp.float32 + + +def _to_affine(linear: Any, translation: Any) -> Any: + return jnp.array( + [ + [linear[0, 0], linear[0, 1], translation[0]], + [linear[1, 0], linear[1, 1], translation[1]], + [0.0, 0.0, 1.0], + ], + dtype=linear.dtype, + ) + + +def _grid_points(x: tuple[Any, Any]) -> Any: + yy, xx = jnp.meshgrid(x[0], x[1], indexing="ij") + return jnp.stack((yy, xx)) + + +def _interp( + x: tuple[Any, Any], + image: Any, + phii: Any, + *, + mode: str = "nearest", +) -> Any: + """Interpolate a channels-first image on physical row-column coordinates.""" + arr = jnp.asarray(image) + coords = jnp.asarray(phii) + if coords.shape[0] != 2: + raise ValueError(f"Expected interpolation coordinates to have leading axis of size 2, found `{coords.shape}`.") + if arr.ndim == 2: + arr = arr[None, ...] + + row_step = x[0][1] - x[0][0] + col_step = x[1][1] - x[1][0] + row_idx = (coords[0] - x[0][0]) / row_step + col_idx = (coords[1] - x[1][0]) / col_step + idx = jnp.stack((row_idx.reshape(-1), col_idx.reshape(-1))) + + def _sample(channel: Any) -> Any: + values = jsp.ndimage.map_coordinates(channel, idx, order=1, mode=mode) + return values.reshape(coords.shape[1:]) + + return jax.vmap(_sample)(arr) + + +def transform_points_row_col( + xv: tuple[Any, Any], + velocity: Any, + affine: Any, + points: Any, + *, + direction: Literal["forward", "backward"] = "forward", +) -> Any: + pts = jnp.asarray(points) + n_steps = velocity.shape[0] + time_steps = range(n_steps) + flow_sign = 1.0 + if direction == "backward": + affine = jnp.linalg.inv(affine) + pts = pts @ affine[:2, :2].T + affine[:2, -1] + flow_sign = -1.0 + time_steps = reversed(time_steps) + + for t in time_steps: + disp = _interp( + xv, + jnp.moveaxis(flow_sign * velocity[t], -1, 0), + pts.T[:, :, None], + mode="nearest", + )[:, :, 0].T + pts = pts + disp / n_steps + + if direction == "forward": + pts = pts @ affine[:2, :2].T + affine[:2, -1] + + return pts + + +def _transform_grid_backward( + x_target: tuple[Any, Any], + xv: tuple[Any, Any], + velocity: Any, + affine: Any, +) -> Any: + target_grid = _grid_points(x_target) + affine_inv = jnp.linalg.inv(affine) + source_grid = jnp.einsum("ij,jhw->ihw", affine_inv[:2, :2], target_grid) + affine_inv[:2, -1][:, None, None] + + for t in range(velocity.shape[0] - 1, -1, -1): + disp = _interp(xv, jnp.moveaxis(-velocity[t], -1, 0), source_grid, mode="nearest") + source_grid = source_grid + disp / velocity.shape[0] + + return source_grid + + +def _contrast_transform(source_image: Any, target_image: Any, weights: Any) -> Any: + flat_source = source_image.reshape(source_image.shape[0], -1) + flat_target = target_image.reshape(target_image.shape[0], -1) + flat_weights = weights.reshape(-1) + + design = jnp.concatenate((jnp.ones((1, flat_source.shape[1]), dtype=source_image.dtype), flat_source), axis=0) + weighted_design = design * flat_weights[None, :] + design_cov = weighted_design @ design.T + target_cov = weighted_design @ flat_target.T + regularized = design_cov + 0.1 * jnp.eye(design_cov.shape[0], dtype=design_cov.dtype) + coefficients = jnp.linalg.solve(regularized, target_cov) + return (coefficients.T @ design).reshape(target_image.shape) + + +def _build_velocity_grid(x_source: tuple[Any, Any], *, a: float, expand: float) -> tuple[Any, Any]: + minimum = jnp.array([x_source[0][0], x_source[1][0]]) + maximum = jnp.array([x_source[0][-1], x_source[1][-1]]) + center = (minimum + maximum) / 2.0 + half_width = (maximum - minimum) * expand / 2.0 + step = a * 0.5 + return ( + jnp.arange(center[0] - half_width[0], center[0] + half_width[0] + step, step), + jnp.arange(center[1] - half_width[1], center[1] + half_width[1] + step, step), + ) + + +def _build_regularizer( + xv: tuple[Any, Any], + *, + a: float, + p: float, +) -> tuple[Any, Any, Any]: + dv = jnp.array([xv[0][1] - xv[0][0], xv[1][1] - xv[1][0]]) + shape = (xv[0].shape[0], xv[1].shape[0]) + fy = jnp.arange(shape[0], dtype=xv[0].dtype) / (shape[0] * dv[0]) + fx = jnp.arange(shape[1], dtype=xv[1].dtype) / (shape[1] * dv[1]) + frequency_grid = jnp.stack(jnp.meshgrid(fy, fx, indexing="ij"), axis=-1) + ll = (1.0 + 2.0 * a**2 * jnp.sum((1.0 - jnp.cos(2.0 * np.pi * frequency_grid * dv)) / (dv**2), axis=-1)) ** ( + 2.0 * p + ) + kernel = 1.0 / ll + dv_prod = jnp.prod(dv) + return kernel, ll, dv_prod + + +def _update_mixture_weights( + transformed_source: Any, + target_image: Any, + match_weights: Any, + artifact_weights: Any, + background_weights: Any, + *, + sigmaM: float, + sigmaA: float, + sigmaB: float, + estimate_muA: bool, + estimate_muB: bool, + muA: Any, + muB: Any, + iteration: int, +) -> tuple[Any, Any, Any, Any, Any]: + if estimate_muA: + muA = jnp.sum(artifact_weights * target_image, axis=(-1, -2)) / jnp.maximum(jnp.sum(artifact_weights), 1e-12) + if estimate_muB: + muB = jnp.sum(background_weights * target_image, axis=(-1, -2)) / jnp.maximum( + jnp.sum(background_weights), 1e-12 + ) + + if iteration < 50: + return match_weights, artifact_weights, background_weights, muA, muB + + weights = jnp.stack((match_weights, artifact_weights, background_weights)) + mixing = jnp.sum(weights, axis=(1, 2)) + mixing = mixing + jnp.max(mixing) * 1e-6 + mixing = mixing / jnp.sum(mixing) + + n_channels = target_image.shape[0] + norm_match = (2.0 * np.pi * sigmaM**2) ** (n_channels / 2.0) + norm_artifact = (2.0 * np.pi * sigmaA**2) ** (n_channels / 2.0) + norm_background = (2.0 * np.pi * sigmaB**2) ** (n_channels / 2.0) + + match_weights = mixing[0] * jnp.exp(-jnp.sum((transformed_source - target_image) ** 2, axis=0) / (2.0 * sigmaM**2)) + match_weights = match_weights / norm_match + artifact_weights = mixing[1] * jnp.exp( + -jnp.sum((muA[:, None, None] - target_image) ** 2, axis=0) / (2.0 * sigmaA**2) + ) + artifact_weights = artifact_weights / norm_artifact + background_weights = mixing[2] * jnp.exp( + -jnp.sum((muB[:, None, None] - target_image) ** 2, axis=0) / (2.0 * sigmaB**2) + ) + background_weights = background_weights / norm_background + + total = match_weights + artifact_weights + background_weights + total = total + jnp.max(total) * 1e-6 + return match_weights / total, artifact_weights / total, background_weights / total, muA, muB + + +def _lddmm_loss( + linear: Any, + translation: Any, + velocity: Any, + *, + x_source: tuple[Any, Any], + source_image: Any, + x_target: tuple[Any, Any], + target_image: Any, + xv: tuple[Any, Any], + match_weights: Any, + ll: Any, + dv_prod: Any, + points_source: Any, + points_target: Any, + sigmaM: float, + sigmaR: float, + sigmaP: float, +) -> tuple[Any, tuple[Any, Any, Any, Any, Any]]: + affine = _to_affine(linear, translation) + source_grid = _transform_grid_backward(x_target, xv, velocity, affine) + warped_source = _interp(x_source, source_image, source_grid, mode="nearest") + contrast_source = _contrast_transform(warped_source, target_image, match_weights) + + match_energy = jnp.sum((contrast_source - target_image) ** 2 * match_weights) / (2.0 * sigmaM**2) + fft_velocity = jnp.fft.fftn(velocity, axes=(1, 2)) + reg_energy = ( + jnp.sum(jnp.sum(jnp.abs(fft_velocity) ** 2, axis=(0, 3)) * ll) + * dv_prod + / 2.0 + / velocity.shape[1] + / velocity.shape[2] + / sigmaR**2 + ) + + transformed_points = transform_points_row_col(xv, velocity, affine, points_source, direction="forward") + if points_source.shape[0] == 0: + point_energy = jnp.array(0.0, dtype=source_image.dtype) + else: + point_energy = jnp.sum((transformed_points - points_target) ** 2) / (2.0 * sigmaP**2) + + total = match_energy + reg_energy + point_energy + return total, (contrast_source, transformed_points, match_energy, reg_energy, point_energy) + + +def lddmm( + xI: tuple[Any, Any], + I: Any, + xJ: tuple[Any, Any], + J: Any, + *, + L: Any, + T: Any, + points_source: Any | None = None, + points_target: Any | None = None, + a: float = 500.0, + p: float = 2.0, + expand: float = 2.0, + nt: int = 3, + niter: int = 5000, + diffeo_start: int = 0, + epL: float = 2e-8, + epT: float = 2e-1, + epV: float = 2e3, + sigmaM: float = 1.0, + sigmaB: float = 2.0, + sigmaA: float = 5.0, + sigmaR: float = 5e5, + sigmaP: float = 2e1, +) -> dict[str, Any]: + x_source = (jnp.asarray(xI[0]), jnp.asarray(xI[1])) + x_target = (jnp.asarray(xJ[0]), jnp.asarray(xJ[1])) + source_image = jnp.asarray(I, dtype=jax_dtype()) + target_image = jnp.asarray(J, dtype=jax_dtype()) + linear = jnp.asarray(L, dtype=jax_dtype()) + translation = jnp.asarray(T, dtype=jax_dtype()) + + if points_source is None: + source_landmarks = jnp.zeros((0, 2), dtype=jax_dtype()) + target_landmarks = jnp.zeros((0, 2), dtype=jax_dtype()) + else: + source_landmarks = jnp.asarray(points_source, dtype=jax_dtype()) + target_landmarks = jnp.asarray(points_target, dtype=jax_dtype()) + + xv = _build_velocity_grid(x_source, a=a, expand=expand) + velocity = jnp.zeros((nt, xv[0].shape[0], xv[1].shape[0], 2), dtype=jax_dtype()) + kernel, ll, dv_prod = _build_regularizer(xv, a=a, p=p) + + match_weights = jnp.full(target_image.shape[1:], 0.5, dtype=target_image.dtype) + background_weights = jnp.full(target_image.shape[1:], 0.4, dtype=target_image.dtype) + artifact_weights = jnp.full(target_image.shape[1:], 0.1, dtype=target_image.dtype) + muA = jnp.mean(target_image, axis=(1, 2)) + muB = jnp.zeros_like(muA) + estimate_muA = True + estimate_muB = True + + loss_and_grad = jax.jit(jax.value_and_grad(_lddmm_loss, argnums=(0, 1, 2), has_aux=True)) + + for iteration in range(niter): + (energy, aux), (grad_linear, grad_translation, grad_velocity) = loss_and_grad( + linear, + translation, + velocity, + x_source=x_source, + source_image=source_image, + x_target=x_target, + target_image=target_image, + xv=xv, + match_weights=match_weights, + ll=ll, + dv_prod=dv_prod, + points_source=source_landmarks, + points_target=target_landmarks, + sigmaM=sigmaM, + sigmaR=sigmaR, + sigmaP=sigmaP, + ) + contrast_source, transformed_points, _, _, _ = aux + + affine_scale = 1.0 + 9.0 * float(iteration >= diffeo_start) + linear = linear - (epL / affine_scale) * grad_linear + translation = translation - (epT / affine_scale) * grad_translation + + grad_velocity = jnp.fft.ifftn( + jnp.fft.fftn(grad_velocity, axes=(1, 2)) * kernel[None, ..., None], + axes=(1, 2), + ).real + if iteration >= diffeo_start: + velocity = velocity - epV * grad_velocity + + if iteration % 5 == 0: + match_weights, artifact_weights, background_weights, muA, muB = _update_mixture_weights( + contrast_source, + target_image, + match_weights, + artifact_weights, + background_weights, + sigmaM=sigmaM, + sigmaA=sigmaA, + sigmaB=sigmaB, + estimate_muA=estimate_muA, + estimate_muB=estimate_muB, + muA=muA, + muB=muB, + iteration=iteration, + ) + + affine = _to_affine(linear, translation) + return { + "A": affine, + "v": velocity, + "xv": xv, + "WM": match_weights, + "WB": background_weights, + "WA": artifact_weights, + "E": energy, + "points": transformed_points, + } diff --git a/src/squidpy/experimental/_fit/_methods/_stalign/_estimator.py b/src/squidpy/experimental/_fit/_methods/_stalign/_estimator.py new file mode 100644 index 000000000..e9cec4735 --- /dev/null +++ b/src/squidpy/experimental/_fit/_methods/_stalign/_estimator.py @@ -0,0 +1,130 @@ +"""STalign estimator: JAX LDDMM point-cloud registration. + +The JAX solver is lifted from scverse/squidpy#1150 (Selman Özleyen); see +:mod:`._stalign_core`, :mod:`._stalign_helpers` and :mod:`._stalign_tools`. +This module only adds the thin :class:`Estimator` / :class:`FitResult` adapter +onto the :mod:`squidpy.experimental.fit` core. JAX is imported lazily inside +:meth:`StalignEstimator.fit`, so importing this module is cheap and does not +require JAX. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, Any + +import numpy as np + +from squidpy.experimental.fit._estimator import Estimator +from squidpy.experimental.fit._registry import Registry +from squidpy.experimental.fit._result import FitResult + +if TYPE_CHECKING: + from ._stalign_tools import STalignConfig + +#: Registry for the ``align`` family of estimators. +ALIGN = Registry("align") + + +@dataclass +class StalignFitResult(FitResult): + """Fitted STalign deformation, in ``(x, y)`` convention. + + ``deltas`` is the per-point displacement that maps the *query* points into + the *reference* frame; :meth:`transform` bakes it in. STalign produces a + diffeomorphic (non-affine) map, so ``affine`` is ``None`` here -- the full + map (velocity field, velocity grid, affine initialisation) is kept under + ``metadata['stalign_result']`` for power users who need to transform + arbitrary points via ``stalign_result.transform_points``. + """ + + deltas: np.ndarray + affine: np.ndarray | None = None + metadata: dict[str, Any] = field(default_factory=dict) + + def transform(self, x: np.ndarray) -> np.ndarray: + """Apply the fitted displacement to the ``(N, 2)`` ``(x, y)`` points it was fit on. + + ``deltas`` is per-point, so ``x`` must have the same shape as the query + cloud passed to :meth:`StalignEstimator.fit`. To map *arbitrary* points, + use ``metadata['stalign_result'].transform_points``. + """ + coords = np.asarray(x, dtype=float) + if coords.shape != self.deltas.shape: + raise ValueError( + f"`transform` expects coordinates of shape {self.deltas.shape} (the fitted query cloud), " + f"found {coords.shape}. Use `metadata['stalign_result'].transform_points` for arbitrary points." + ) + return coords + self.deltas + + +@ALIGN.register("stalign") +class StalignEstimator(Estimator): + """Align a query point cloud onto a reference with a JAX LDDMM solver.""" + + name = "stalign" + requires = ("jax",) + + def fit( + self, + ref: np.ndarray, + query: np.ndarray, + *, + config: STalignConfig | None = None, + landmarks_source: np.ndarray | None = None, + landmarks_target: np.ndarray | None = None, + ) -> StalignFitResult: + """Fit a deformation mapping ``query`` onto ``ref``. + + Parameters + ---------- + ref + ``(N, 2)`` reference point cloud in ``(x, y)`` order. + query + ``(M, 2)`` query point cloud in ``(x, y)`` order, to be aligned to + ``ref``. Both are plain in-memory arrays; extracting them from an + ``AnnData`` / ``SpatialData`` is the caller's responsibility. + config + Optional :class:`STalignConfig` of solver hyperparameters. + landmarks_source, landmarks_target + Optional corresponding ``(x, y)`` landmark arrays used to + initialise the affine. Must be provided together. + + Returns + ------- + A :class:`StalignFitResult` whose ``deltas`` move ``query`` into the + reference frame. + """ + self.check_requirements() + # Import the JAX-backed solver only after requirements pass, so callers + # without JAX get the clean ImportError from check_requirements rather + # than a confusing failure from a module-level `import jax`. + from ._stalign_tools import stalign_points + + ref_xy = np.asarray(ref, dtype=float) + query_xy = np.asarray(query, dtype=float) + for arr_name, arr in (("ref", ref_xy), ("query", query_xy)): + if arr.ndim != 2 or arr.shape[1] != 2: + raise ValueError(f"Expected `{arr_name}` to be an (N, 2) array, found shape {arr.shape}.") + + # The solver runs internally in row-col (y, x); inputs are (x, y) -- swap at the boundary. + src_rc = query_xy[:, [1, 0]] + tgt_rc = ref_xy[:, [1, 0]] + lm_src_rc = None if landmarks_source is None else np.asarray(landmarks_source, dtype=float)[:, [1, 0]] + lm_tgt_rc = None if landmarks_target is None else np.asarray(landmarks_target, dtype=float)[:, [1, 0]] + + result = stalign_points( + source_points=src_rc, + target_points=tgt_rc, + config=config, + landmarks_source=lm_src_rc, + landmarks_target=lm_tgt_rc, + ) + + aligned_xy = np.asarray(result.aligned_points)[:, [1, 0]] + deltas_xy = aligned_xy - query_xy + return StalignFitResult( + deltas=deltas_xy, + affine=None, + metadata={"method": "stalign", "stalign_result": result}, + ) diff --git a/src/squidpy/experimental/_fit/_methods/_stalign/_helpers.py b/src/squidpy/experimental/_fit/_methods/_stalign/_helpers.py new file mode 100644 index 000000000..6d3304188 --- /dev/null +++ b/src/squidpy/experimental/_fit/_methods/_stalign/_helpers.py @@ -0,0 +1,160 @@ +"""Numeric helpers for STalign point-cloud registration. + +Lifted from scverse/squidpy#1150 (Selman Özleyen). Container readers +(``extract_points`` / ``extract_landmarks``) were dropped on the move into +:mod:`squidpy.experimental.fit`: reading coordinates out of an ``AnnData`` / +``SpatialData`` is the caller's concern, so everything here operates purely on +in-memory NumPy arrays. +""" + +from __future__ import annotations + +from typing import Literal + +import numpy as np + +PointOrder = Literal["row_col", "xy"] + +__all__ = [ + "PointOrder", + "affine_from_points", + "from_row_col", + "rasterize", + "to_row_col", +] + + +def _validate_points(points: np.ndarray, *, name: str) -> np.ndarray: + arr = np.asarray(points, dtype=float) + if arr.ndim != 2 or arr.shape[1] != 2: + raise ValueError(f"Expected `{name}` to have shape `(n, 2)`, found `{arr.shape}`.") + if not np.all(np.isfinite(arr)): + raise ValueError(f"Expected `{name}` to contain only finite values.") + return arr + + +def to_row_col(points: np.ndarray, *, point_order: PointOrder) -> np.ndarray: + """Convert coordinates to row-column order.""" + arr = _validate_points(points, name="points") + if point_order == "row_col": + return arr + if point_order == "xy": + return arr[:, [1, 0]] + raise ValueError(f"Unknown `point_order`: `{point_order}`.") + + +def from_row_col(points: np.ndarray, *, point_order: PointOrder) -> np.ndarray: + """Convert row-column coordinates to the requested order.""" + arr = _validate_points(points, name="points") + if point_order == "row_col": + return arr + if point_order == "xy": + return arr[:, [1, 0]] + raise ValueError(f"Unknown `point_order`: `{point_order}`.") + + +def _normalize(values: np.ndarray) -> np.ndarray: + values = np.asarray(values, dtype=float) + vmin = np.min(values) + vmax = np.max(values) + if np.isclose(vmin, vmax): + return np.ones_like(values, dtype=float) + return (values - vmin) / (vmax - vmin) + + +def rasterize( + x: np.ndarray, + y: np.ndarray, + *, + g: np.ndarray | None = None, + dx: float = 30.0, + blur: float | list[float] = 1.0, + expand: float = 1.1, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Rasterize a point cloud into a multi-scale density image.""" + x = np.asarray(x, dtype=float) + y = np.asarray(y, dtype=float) + if x.ndim != 1 or y.ndim != 1 or x.shape != y.shape: + raise ValueError("Expected `x` and `y` to be 1D arrays with the same length.") + if x.size == 0: + raise ValueError("Expected at least one point to rasterize.") + if dx <= 0: + raise ValueError("Expected `dx` to be positive.") + if expand <= 0: + raise ValueError("Expected `expand` to be positive.") + + blur_values = np.atleast_1d(np.asarray(blur, dtype=float)) + if blur_values.ndim != 1 or np.any(blur_values <= 0): + raise ValueError("Expected `blur` to be a positive scalar or a 1D sequence of positive values.") + + if g is None: + weights = np.ones_like(x, dtype=float) + else: + weights = np.asarray(g, dtype=float) + if weights.shape != x.shape: + raise ValueError("Expected `g` to have the same shape as `x` and `y`.") + if not np.allclose(weights, 1.0): + weights = _normalize(weights) + + min_x = float(np.min(x)) + max_x = float(np.max(x)) + min_y = float(np.min(y)) + max_y = float(np.max(y)) + + center_x = (min_x + max_x) / 2.0 + center_y = (min_y + max_y) / 2.0 + half_x = (max_x - min_x) * expand / 2.0 + half_y = (max_y - min_y) * expand / 2.0 + + grid_x = np.arange(center_x - half_x, center_x + half_x + dx, dx, dtype=float) + grid_y = np.arange(center_y - half_y, center_y + half_y + dx, dx, dtype=float) + if grid_x.size < 2 or grid_y.size < 2: + raise ValueError("Rasterized grid is too small. Increase the point spread or lower `dx`.") + + mesh_x, mesh_y = np.meshgrid(grid_x, grid_y) + out = np.zeros((len(blur_values), grid_y.size, grid_x.size), dtype=float) + radius = int(np.ceil(float(np.max(blur_values)) * 4.0)) + + for x_i, y_i, w_i in zip(x, y, weights, strict=False): + col = int(np.rint((x_i - grid_x[0]) / dx)) + row = int(np.rint((y_i - grid_y[0]) / dx)) + + row0 = max(row - radius, 0) + row1 = min(row + radius, out.shape[1] - 1) + col0 = max(col - radius, 0) + col1 = min(col + radius, out.shape[2] - 1) + + patch_x = mesh_x[row0 : row1 + 1, col0 : col1 + 1] + patch_y = mesh_y[row0 : row1 + 1, col0 : col1 + 1] + denom = 2.0 * (dx * blur_values * 2.0) ** 2 + + kernels = np.exp(-((patch_x[..., None] - x_i) ** 2 + (patch_y[..., None] - y_i) ** 2) / denom) + kernels_sum = kernels.sum(axis=(0, 1), keepdims=True) + kernels /= np.where(kernels_sum == 0.0, 1.0, kernels_sum) + out[:, row0 : row1 + 1, col0 : col1 + 1] += np.moveaxis(kernels * w_i, -1, 0) + + return grid_x, grid_y, out + + +def affine_from_points( + points_source: np.ndarray, + points_target: np.ndarray, +) -> tuple[np.ndarray, np.ndarray]: + """Compute an affine initialization from corresponding landmarks.""" + source = _validate_points(points_source, name="points_source") + target = _validate_points(points_target, name="points_target") + if source.shape != target.shape: + raise ValueError( + f"Expected `points_source` and `points_target` to have the same shape, found " + f"`{source.shape}` and `{target.shape}`." + ) + + if source.shape[0] < 3: + linear = np.eye(2, dtype=float) + translation = np.mean(target, axis=0) - np.mean(source, axis=0) + return linear, translation + + source_h = np.concatenate((source, np.ones((source.shape[0], 1), dtype=float)), axis=1) + target_h = np.concatenate((target, np.ones((target.shape[0], 1), dtype=float)), axis=1) + affine = np.linalg.lstsq(source_h, target_h, rcond=None)[0].T + return affine[:2, :2], affine[:2, -1] diff --git a/src/squidpy/experimental/_fit/_methods/_stalign/_tools.py b/src/squidpy/experimental/_fit/_methods/_stalign/_tools.py new file mode 100644 index 000000000..be73201fe --- /dev/null +++ b/src/squidpy/experimental/_fit/_methods/_stalign/_tools.py @@ -0,0 +1,231 @@ +"""Low-level point-cloud tools for experimental STalign. + +Lifted from scverse/squidpy#1150 (Selman Özleyen) with import paths adjusted +and minor cleanups (config unpacking, lazy dtype resolution). The container +write-back path (``STalignResult.transform_adata``) was dropped on the move +into :mod:`squidpy.experimental.fit`: results stay in memory and the caller +owns write-back. +""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, field +from typing import TYPE_CHECKING, Any, Literal, TypeAlias + +import jax.numpy as jnp +import numpy as np + +from ._stalign_core import jax_dtype, lddmm, transform_points_row_col +from ._stalign_helpers import ( + PointOrder, + affine_from_points, + from_row_col, + rasterize, + to_row_col, +) + +if TYPE_CHECKING: + import jax + + JaxArray = jax.Array +else: # pragma: no cover - typing only + JaxArray = Any + +BlurScales: TypeAlias = float | tuple[float, ...] | list[float] + +__all__ = [ + "STalignConfig", + "STalignPreprocessConfig", + "STalignPreprocessResult", + "STalignRegistrationConfig", + "STalignResult", + "stalign_points", + "stalign_preprocess", + "transform_points", +] + + +@dataclass(slots=True) +class STalignPreprocessConfig: + dx: float = 30.0 + blur: BlurScales = (2.0, 1.0, 0.5) + expand: float = 1.1 + + +@dataclass(slots=True) +class STalignRegistrationConfig: + """LDDMM registration hyperparameters. + + Field names (``sigmaM``, ``epL``, etc.) preserve the conventions from + the STalign paper and reference implementation to keep them + recognisable when cross-referencing the literature. + """ + + a: float = 500.0 + p: float = 2.0 + expand: float = 2.0 + nt: int = 3 + niter: int = 5000 + diffeo_start: int = 0 + epL: float = 2e-8 + epT: float = 2e-1 + epV: float = 2e3 + sigmaM: float = 1.0 + sigmaB: float = 2.0 + sigmaA: float = 5.0 + sigmaR: float = 5e5 + sigmaP: float = 2e1 + + +@dataclass(slots=True) +class STalignConfig: + preprocess: STalignPreprocessConfig = field(default_factory=STalignPreprocessConfig) + registration: STalignRegistrationConfig = field(default_factory=STalignRegistrationConfig) + + +@dataclass(slots=True) +class STalignPreprocessResult: + source_grid: tuple[np.ndarray, np.ndarray] + source_image: np.ndarray + target_grid: tuple[np.ndarray, np.ndarray] + target_image: np.ndarray + + +@dataclass(slots=True) +class STalignResult: + affine: JaxArray + velocity: JaxArray + velocity_grid: tuple[JaxArray, JaxArray] + aligned_points: JaxArray + point_order: PointOrder = "row_col" + + def transform_points( + self, + points: np.ndarray, + *, + direction: Literal["forward", "backward"] = "forward", + point_order: PointOrder | None = None, + ) -> JaxArray: + """Transform arbitrary point arrays with the fitted map.""" + return transform_points( + self.velocity_grid, + self.velocity, + self.affine, + points, + direction=direction, + point_order=self.point_order if point_order is None else point_order, + ) + + +def stalign_preprocess( + source_points: np.ndarray, + target_points: np.ndarray, + *, + config: STalignPreprocessConfig | None = None, +) -> STalignPreprocessResult: + """Rasterize source and target point clouds for LDDMM registration.""" + config = STalignPreprocessConfig() if config is None else config + source_points = to_row_col(source_points, point_order="row_col") + target_points = to_row_col(target_points, point_order="row_col") + + source_x, source_y, source_image = rasterize( + source_points[:, 1], + source_points[:, 0], + dx=config.dx, + blur=config.blur, + expand=config.expand, + ) + target_x, target_y, target_image = rasterize( + target_points[:, 1], + target_points[:, 0], + dx=config.dx, + blur=config.blur, + expand=config.expand, + ) + + return STalignPreprocessResult( + source_grid=(source_y, source_x), + source_image=source_image, + target_grid=(target_y, target_x), + target_image=target_image, + ) + + +def transform_points( + xv: tuple[JaxArray, JaxArray], + v: JaxArray, + A: JaxArray, + points: np.ndarray, + *, + direction: Literal["forward", "backward"] = "forward", + point_order: PointOrder = "row_col", +) -> JaxArray: + """Transform point arrays with a fitted STalign map.""" + points_rc = to_row_col(points, point_order=point_order) + transformed = transform_points_row_col( + xv, + jnp.asarray(v), + jnp.asarray(A), + jnp.asarray(points_rc, dtype=jax_dtype()), + direction=direction, + ) + return jnp.asarray(from_row_col(np.asarray(transformed), point_order=point_order)) + + +def stalign_points( + source_points: np.ndarray, + target_points: np.ndarray, + *, + preprocessed: STalignPreprocessResult | None = None, + config: STalignConfig | None = None, + landmarks_source: np.ndarray | None = None, + landmarks_target: np.ndarray | None = None, +) -> STalignResult: + """Align source point cloud to target with a JAX LDDMM solver.""" + config = STalignConfig() if config is None else config + registration = config.registration + source_points = to_row_col(source_points, point_order="row_col") + target_points = to_row_col(target_points, point_order="row_col") + if preprocessed is None: + preprocessed = stalign_preprocess(source_points, target_points, config=config.preprocess) + + if (landmarks_source is None) != (landmarks_target is None): + raise ValueError("Expected both landmark arrays to be provided together.") + + if landmarks_source is None: + linear = np.eye(2, dtype=float) + translation = np.zeros(2, dtype=float) + source_landmarks = None + target_landmarks = None + else: + source_landmarks = to_row_col(landmarks_source, point_order="row_col") + target_landmarks = to_row_col(landmarks_target, point_order="row_col") + linear, translation = affine_from_points(source_landmarks, target_landmarks) + + dtype = jax_dtype() + result = lddmm( + preprocessed.source_grid, + preprocessed.source_image, + preprocessed.target_grid, + preprocessed.target_image, + L=jnp.asarray(linear, dtype=dtype), + T=jnp.asarray(translation, dtype=dtype), + points_source=None if source_landmarks is None else jnp.asarray(source_landmarks, dtype=dtype), + points_target=None if target_landmarks is None else jnp.asarray(target_landmarks, dtype=dtype), + **asdict(registration), + ) + aligned_points = transform_points( + result["xv"], + result["v"], + result["A"], + source_points, + direction="forward", + point_order="row_col", + ) + return STalignResult( + affine=result["A"], + velocity=result["v"], + velocity_grid=result["xv"], + aligned_points=aligned_points, + point_order="row_col", + ) diff --git a/src/squidpy/experimental/_fit/_registry.py b/src/squidpy/experimental/_fit/_registry.py new file mode 100644 index 000000000..0df6b0d5f --- /dev/null +++ b/src/squidpy/experimental/_fit/_registry.py @@ -0,0 +1,46 @@ +"""A flat registry mapping method names to estimator classes.""" + +from __future__ import annotations + +from collections.abc import Callable +from typing import TYPE_CHECKING, TypeVar + +if TYPE_CHECKING: + from squidpy.experimental.fit._estimator import Estimator + +E = TypeVar("E", bound="type[Estimator]") + + +class Registry: + """A flat ``name -> Estimator`` registry for one *family* of methods. + + One :class:`Registry` is created per family (e.g. ``align``, ``impute``), + so keys are plain method names -- there is no ``(method, mode)`` compound + key, because the family already pins the rest. + """ + + def __init__(self, name: str) -> None: + self.name = name + self._estimators: dict[str, type[Estimator]] = {} + + def register(self, key: str) -> Callable[[E], E]: + """Return a class decorator registering an :class:`Estimator` under ``key``.""" + + def decorator(cls: E) -> E: + if key in self._estimators: + raise ValueError(f"Estimator {key!r} is already registered in the {self.name!r} registry.") + self._estimators[key] = cls + return cls + + return decorator + + def get(self, key: str) -> type[Estimator]: + """Return the :class:`Estimator` class registered under ``key``.""" + try: + return self._estimators[key] + except KeyError: + raise ValueError(f"Unknown {self.name} method {key!r}. Available: {sorted(self._estimators)}.") from None + + def keys(self) -> tuple[str, ...]: + """Return the registered method names.""" + return tuple(self._estimators) diff --git a/src/squidpy/experimental/_fit/_result.py b/src/squidpy/experimental/_fit/_result.py new file mode 100644 index 000000000..861f3cbe4 --- /dev/null +++ b/src/squidpy/experimental/_fit/_result.py @@ -0,0 +1,32 @@ +"""Result contract for fitted estimators.""" + +from __future__ import annotations + +from abc import ABC +from typing import Any + + +class FitResult(ABC): + """Base class for the output of :meth:`Estimator.fit`. + + A ``FitResult`` carries the *fitted parameters* of a model -- pointers or + parametric forms (an affine matrix, a displacement field, neighbour + weights), not bulk data -- together with free-form :attr:`metadata`. + + It is deliberately container-agnostic. Turning a result into mutations on + an :class:`~anndata.AnnData` / :class:`~spatialdata.SpatialData` + (write-back) is the caller's responsibility, not the result's: write-back + is a general squidpy concern with several unrelated shapes (set a field, + apply a transformation to a lazy element, ...) and does not belong on the + fit result. + """ + + metadata: dict[str, Any] + + def transform(self, x: Any) -> Any: + """Apply the fitted model to in-memory data ``x`` and return the result. + + Optional. Estimators whose fit does not yield a reusable map (or that + only produce side outputs) need not implement it. + """ + raise NotImplementedError(f"{type(self).__name__} does not implement `transform`.") diff --git a/tests/experimental/fit/__init__.py b/tests/experimental/fit/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/experimental/fit/test_core.py b/tests/experimental/fit/test_core.py new file mode 100644 index 000000000..9d9302bd3 --- /dev/null +++ b/tests/experimental/fit/test_core.py @@ -0,0 +1,95 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + +import numpy as np +import pytest + +from squidpy.experimental.fit import Estimator, FitResult, Registry + + +@dataclass +class _MeanShiftResult(FitResult): + """Toy result: a constant per-axis offset baked into ``transform``.""" + + delta: np.ndarray + metadata: dict[str, Any] = field(default_factory=dict) + + def transform(self, x: np.ndarray) -> np.ndarray: + return np.asarray(x, dtype=float) + self.delta + + +class _MeanShiftEstimator(Estimator): + """Toy estimator: fit the offset that maps the query centroid onto the ref centroid.""" + + name = "mean_shift" + + def fit(self, ref: np.ndarray, query: np.ndarray) -> _MeanShiftResult: + delta = np.asarray(ref, dtype=float).mean(0) - np.asarray(query, dtype=float).mean(0) + return _MeanShiftResult(delta=delta, metadata={"method": self.name}) + + +def test_fit_then_transform_round_trip() -> None: + ref = np.array([[1.0, 1.0], [3.0, 3.0]]) # centroid (2, 2) + query = np.array([[0.0, 0.0], [2.0, 2.0]]) # centroid (1, 1) + + result = _MeanShiftEstimator().fit(ref, query) + + assert isinstance(result, FitResult) + np.testing.assert_allclose(result.delta, [1.0, 1.0]) + np.testing.assert_allclose(result.transform(query), query + 1.0) + assert result.metadata == {"method": "mean_shift"} + + +def test_fit_result_transform_default_not_implemented() -> None: + @dataclass + class _Bare(FitResult): + metadata: dict[str, Any] = field(default_factory=dict) + + with pytest.raises(NotImplementedError, match="does not implement `transform`"): + _Bare().transform(np.zeros((2, 2))) + + +def test_registry_register_get_keys() -> None: + reg = Registry("demo") + + @reg.register("mean_shift") + class _Registered(_MeanShiftEstimator): + pass + + assert reg.keys() == ("mean_shift",) + assert reg.get("mean_shift") is _Registered + assert isinstance(reg.get("mean_shift")().fit(np.ones((2, 2)), np.zeros((2, 2))), FitResult) + + +def test_registry_unknown_key_lists_available() -> None: + reg = Registry("demo") + reg.register("a")(_MeanShiftEstimator) + + with pytest.raises(ValueError, match=r"Unknown demo method 'b'. Available: \['a'\]"): + reg.get("b") + + +def test_registry_rejects_duplicate_key() -> None: + reg = Registry("demo") + reg.register("dup")(_MeanShiftEstimator) + + with pytest.raises(ValueError, match="already registered"): + reg.register("dup")(_MeanShiftEstimator) + + +def test_check_requirements_passes_when_none() -> None: + # _MeanShiftEstimator has the default empty `requires`. + _MeanShiftEstimator().check_requirements() + + +def test_check_requirements_raises_for_missing_dependency() -> None: + class _NeedsGhost(_MeanShiftEstimator): + name = "needs_ghost" + requires = ("squidpy_nonexistent_pkg_xyz",) + + with pytest.raises( + ImportError, match=r"requires 'squidpy_nonexistent_pkg_xyz'.*squidpy\[squidpy_nonexistent_pkg_xyz\]" + ): + _NeedsGhost().check_requirements() diff --git a/tests/experimental/fit/test_stalign.py b/tests/experimental/fit/test_stalign.py new file mode 100644 index 000000000..eb4b84a3d --- /dev/null +++ b/tests/experimental/fit/test_stalign.py @@ -0,0 +1,95 @@ +"""Integration tests for the ported STalign estimator. + +Tiny synthetic fixtures with ``niter=1`` keep these fast; they verify wiring +and shapes (dispatch -> JAX LDDMM -> StalignFitResult), not solver quality. +""" + +from __future__ import annotations + +import numpy as np +import pytest + +pytest.importorskip("jax") + +from squidpy.experimental.fit._methods import ALIGN, StalignEstimator, StalignFitResult +from squidpy.experimental.fit._methods._stalign_tools import ( + STalignConfig, + STalignPreprocessConfig, + STalignRegistrationConfig, +) + + +def _points_xy() -> np.ndarray: + return np.array( + [ + [10.0, 1.0], + [12.0, 1.0], + [11.0, 2.0], + [10.0, 3.0], + [12.0, 3.0], + ] + ) + + +def _tiny_config() -> STalignConfig: + """Single-iteration LDDMM hyperparameters - the smallest possible solve.""" + return STalignConfig( + preprocess=STalignPreprocessConfig(dx=0.5, blur=1.0), + registration=STalignRegistrationConfig(a=1.0, expand=1.0, nt=1, niter=1, epV=1.0), + ) + + +def test_stalign_registered_in_align_family() -> None: + assert "stalign" in ALIGN.keys() + assert ALIGN.get("stalign") is StalignEstimator + + +def test_stalign_fit_returns_displacement_result() -> None: + ref, query = _points_xy(), _points_xy() + + result = StalignEstimator().fit(ref, query, config=_tiny_config()) + + assert isinstance(result, StalignFitResult) + assert result.deltas.shape == query.shape + assert np.all(np.isfinite(result.deltas)) + assert result.affine is None + assert result.metadata["method"] == "stalign" + # Escape hatch: the full diffeomorphic map is preserved for power users. + assert "stalign_result" in result.metadata + + +def test_stalign_transform_bakes_in_deltas() -> None: + ref, query = _points_xy(), _points_xy() + + result = StalignEstimator().fit(ref, query, config=_tiny_config()) + + np.testing.assert_allclose(result.transform(query), query + result.deltas) + + +def test_stalign_transform_rejects_wrong_shape() -> None: + ref, query = _points_xy(), _points_xy() + result = StalignEstimator().fit(ref, query, config=_tiny_config()) + + with pytest.raises(ValueError, match="expects coordinates of shape"): + result.transform(np.zeros((1, 2))) + + +def test_stalign_fit_with_landmarks() -> None: + ref, query = _points_xy(), _points_xy() + landmarks = ref[:3] + + result = StalignEstimator().fit( + ref, + query, + config=_tiny_config(), + landmarks_source=landmarks, + landmarks_target=landmarks, + ) + + assert result.deltas.shape == query.shape + assert "stalign_result" in result.metadata + + +def test_stalign_fit_rejects_non_2d_input() -> None: + with pytest.raises(ValueError, match=r"Expected `query` to be an \(N, 2\) array"): + StalignEstimator().fit(_points_xy(), np.zeros((5, 3)), config=_tiny_config()) From e2266dec67cb33b3fc8e5e11855769864e94d7e8 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Mon, 1 Jun 2026 09:21:40 +0200 Subject: [PATCH 70/96] save state --- src/squidpy/experimental/__init__.py | 4 +- src/squidpy/experimental/_fit/__init__.py | 8 +- src/squidpy/experimental/_fit/_estimator.py | 2 +- .../experimental/_fit/_methods/__init__.py | 27 +- .../experimental/_fit/_methods/_families.py | 16 + .../experimental/_fit/_methods/_landmark.py | 142 + .../_fit/_methods/_stalign/__init__.py | 11 + .../_fit/_methods/_stalign/_estimator.py | 19 +- .../_fit/_methods/_stalign/_helpers.py | 2 +- .../_fit/_methods/_stalign/_tools.py | 6 +- src/squidpy/experimental/_fit/_registry.py | 2 +- tests/experimental/fit/test_core.py | 2 +- tests/experimental/fit/test_stalign.py | 4 +- uv.lock | 6445 +++++++++++++++++ 14 files changed, 6658 insertions(+), 32 deletions(-) create mode 100644 src/squidpy/experimental/_fit/_methods/_families.py create mode 100644 src/squidpy/experimental/_fit/_methods/_landmark.py create mode 100644 src/squidpy/experimental/_fit/_methods/_stalign/__init__.py create mode 100644 uv.lock diff --git a/src/squidpy/experimental/__init__.py b/src/squidpy/experimental/__init__.py index a33215238..3c5b1f537 100644 --- a/src/squidpy/experimental/__init__.py +++ b/src/squidpy/experimental/__init__.py @@ -6,6 +6,6 @@ from __future__ import annotations -from . import fit, im, pl, tl, utils +from . import im, pl, tl, utils -__all__ = ["fit", "im", "pl", "tl", "utils"] +__all__ = ["im", "pl", "tl", "utils"] diff --git a/src/squidpy/experimental/_fit/__init__.py b/src/squidpy/experimental/_fit/__init__.py index b56d9df80..66f88a475 100644 --- a/src/squidpy/experimental/_fit/__init__.py +++ b/src/squidpy/experimental/_fit/__init__.py @@ -17,7 +17,7 @@ Example ------- >>> import numpy as np ->>> from squidpy.experimental.fit import Estimator, FitResult, Registry +>>> from squidpy.experimental._fit import Estimator, FitResult, Registry >>> demo = Registry("demo") >>> class Shift(FitResult): ... def __init__(self, delta): @@ -39,8 +39,8 @@ from __future__ import annotations -from squidpy.experimental.fit._estimator import Estimator -from squidpy.experimental.fit._registry import Registry -from squidpy.experimental.fit._result import FitResult +from squidpy.experimental._fit._estimator import Estimator +from squidpy.experimental._fit._registry import Registry +from squidpy.experimental._fit._result import FitResult __all__ = ["Estimator", "FitResult", "Registry"] diff --git a/src/squidpy/experimental/_fit/_estimator.py b/src/squidpy/experimental/_fit/_estimator.py index c73a3173f..aea3abc93 100644 --- a/src/squidpy/experimental/_fit/_estimator.py +++ b/src/squidpy/experimental/_fit/_estimator.py @@ -7,7 +7,7 @@ from typing import TYPE_CHECKING, Any, ClassVar if TYPE_CHECKING: - from squidpy.experimental.fit._result import FitResult + from squidpy.experimental._fit._result import FitResult class Estimator(ABC): diff --git a/src/squidpy/experimental/_fit/_methods/__init__.py b/src/squidpy/experimental/_fit/_methods/__init__.py index 8b15faa6b..f3b6864ca 100644 --- a/src/squidpy/experimental/_fit/_methods/__init__.py +++ b/src/squidpy/experimental/_fit/_methods/__init__.py @@ -1,12 +1,27 @@ -"""Concrete estimators for :mod:`squidpy.experimental.fit`. +"""Concrete estimators for :mod:`squidpy.experimental._fit`. -Importing this package registers the bundled estimators (e.g. STalign in the -``align`` family) but stays cheap: optional dependencies such as JAX are pulled -in lazily, only when an estimator's ``fit`` is actually called. +Importing this package registers the bundled estimators into their family +registries (``ALIGN``: STalign; ``LANDMARK``: similarity / affine) but stays +cheap: optional dependencies such as JAX are pulled in lazily, only when an +estimator's ``fit`` is actually called. """ from __future__ import annotations -from squidpy.experimental.fit._methods._stalign import ALIGN, StalignEstimator, StalignFitResult +from squidpy.experimental._fit._methods._families import ALIGN, LANDMARK +from squidpy.experimental._fit._methods._landmark import ( + AffineFitResult, + LandmarkAffineEstimator, + LandmarkSimilarityEstimator, +) +from squidpy.experimental._fit._methods._stalign import StalignEstimator, StalignFitResult -__all__ = ["ALIGN", "StalignEstimator", "StalignFitResult"] +__all__ = [ + "ALIGN", + "LANDMARK", + "AffineFitResult", + "LandmarkAffineEstimator", + "LandmarkSimilarityEstimator", + "StalignEstimator", + "StalignFitResult", +] diff --git a/src/squidpy/experimental/_fit/_methods/_families.py b/src/squidpy/experimental/_fit/_methods/_families.py new file mode 100644 index 000000000..867f6fa02 --- /dev/null +++ b/src/squidpy/experimental/_fit/_methods/_families.py @@ -0,0 +1,16 @@ +"""Estimator family registries. + +Defined in their own module so individual estimator modules can register into +them without importing :mod:`squidpy.experimental._fit._methods` (which imports +the estimator modules in turn -- that would be circular). +""" + +from __future__ import annotations + +from squidpy.experimental._fit._registry import Registry + +#: Data-driven alignment estimators (consumed by ``experimental.tl.align``). +ALIGN = Registry("align") + +#: Closed-form landmark estimators (consumed by ``experimental.tl.align_by_landmarks``). +LANDMARK = Registry("landmark") diff --git a/src/squidpy/experimental/_fit/_methods/_landmark.py b/src/squidpy/experimental/_fit/_methods/_landmark.py new file mode 100644 index 000000000..3dc475235 --- /dev/null +++ b/src/squidpy/experimental/_fit/_methods/_landmark.py @@ -0,0 +1,142 @@ +"""Closed-form landmark alignment estimators. + +Ported from scverse/squidpy#1162 (``fit_landmark_affine``). Two pure models, no +JAX: + +- ``"similarity"`` (4 DOF: rotation + uniform scale + translation) via + :func:`spatialdata.transformations.get_transformation_between_landmarks`. +- ``"affine"`` (6 DOF) via :func:`skimage.transform.estimate_transform`. + +Both consume **pre-paired** ``(N, 2)`` ``(x, y)`` landmark arrays (row ``i`` of +the query matches row ``i`` of the reference); ``N`` must be at least 3. No +automatic correspondence matching is performed. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, ClassVar, Literal + +import numpy as np + +from squidpy.experimental._fit._estimator import Estimator +from squidpy.experimental._fit._methods._families import LANDMARK +from squidpy.experimental._fit._result import FitResult + + +@dataclass +class AffineFitResult(FitResult): + """A fitted ``(3, 3)`` homogeneous affine mapping query onto ref, in ``(x, y)``.""" + + matrix: np.ndarray + source_cs: str | None = None + target_cs: str | None = None + metadata: dict[str, Any] = field(default_factory=dict) + + def __post_init__(self) -> None: + if self.matrix.shape != (3, 3): + raise ValueError(f"Expected a (3, 3) homogeneous matrix, found shape {self.matrix.shape}.") + + def transform(self, x: np.ndarray) -> np.ndarray: + """Apply the affine to an ``(N, 2)`` ``(x, y)`` coordinate array.""" + coords = np.asarray(x, dtype=float) + if coords.ndim != 2 or coords.shape[1] != 2: + raise ValueError(f"Expected an (N, 2) coordinate array, found shape {coords.shape}.") + return coords @ self.matrix[:2, :2].T + self.matrix[:2, 2] + + +class _LandmarkEstimator(Estimator): + """Base for closed-form landmark fits; subclasses pin :attr:`model`.""" + + model: ClassVar[Literal["similarity", "affine"]] + + def fit( + self, + landmarks_ref: np.ndarray, + landmarks_query: np.ndarray, + *, + source_cs: str | None = None, + target_cs: str | None = None, + ) -> AffineFitResult: + """Fit an affine mapping ``landmarks_query`` onto ``landmarks_ref``. + + Parameters + ---------- + landmarks_ref, landmarks_query + Pre-paired ``(N, 2)`` ``(x, y)`` landmark arrays (``N >= 3``). + source_cs, target_cs + Optional coordinate-system labels stamped onto the result for + traceability; they do not affect the fit. + """ + ref = _validate_landmarks(landmarks_ref, name="landmarks_ref") + query = _validate_landmarks(landmarks_query, name="landmarks_query") + if ref.shape != query.shape: + raise ValueError( + f"`landmarks_ref` and `landmarks_query` must have the same shape; got {ref.shape} and {query.shape}." + ) + if ref.shape[0] < 3: + raise ValueError(f"`model={self.model!r}` needs at least 3 landmark pairs, got {ref.shape[0]}.") + + matrix = _fit_similarity(ref, query) if self.model == "similarity" else _fit_affine(ref, query) + return AffineFitResult( + matrix=matrix, + source_cs=source_cs, + target_cs=target_cs, + metadata={"method": self.model}, + ) + + +@LANDMARK.register("similarity") +class LandmarkSimilarityEstimator(_LandmarkEstimator): + """4-DOF similarity fit (rotation + uniform scale + translation).""" + + name = "similarity" + model = "similarity" + + +@LANDMARK.register("affine") +class LandmarkAffineEstimator(_LandmarkEstimator): + """6-DOF affine fit (rotation + non-uniform scale + shear + translation).""" + + name = "affine" + model = "affine" + + +def _validate_landmarks(points: np.ndarray, *, name: str) -> np.ndarray: + arr = np.asarray(points, dtype=float) + if arr.ndim != 2 or arr.shape[1] != 2: + raise ValueError(f"`{name}` must be a sequence of (x, y) pairs, got shape {arr.shape}.") + if not np.all(np.isfinite(arr)): + raise ValueError(f"`{name}` must contain only finite values.") + return arr + + +def _fit_similarity(ref_xy: np.ndarray, query_xy: np.ndarray) -> np.ndarray: + """4-DOF similarity fit, delegated to spatialdata.""" + from spatialdata.models import PointsModel + from spatialdata.transformations import get_transformation_between_landmarks + + refs_pts = PointsModel.parse(ref_xy) + moving_pts = PointsModel.parse(query_xy) + sd_transform = get_transformation_between_landmarks(refs_pts, moving_pts) + return _extract_affine_matrix(sd_transform) + + +def _fit_affine(ref_xy: np.ndarray, query_xy: np.ndarray) -> np.ndarray: + """Full 6-DOF affine fit, delegated to skimage's least-squares estimator.""" + from skimage.transform import estimate_transform + + model_obj = estimate_transform("affine", src=query_xy, dst=ref_xy) + return np.asarray(model_obj.params) + + +def _extract_affine_matrix(sd_transform: object) -> np.ndarray: + """Pull a ``(3, 3)`` homogeneous matrix out of a spatialdata transformation.""" + from spatialdata.transformations import Affine as SDAffine + from spatialdata.transformations import Sequence as SDSequence + + if isinstance(sd_transform, SDAffine): + return np.asarray(sd_transform.matrix) + if isinstance(sd_transform, SDSequence): + return np.asarray(sd_transform.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))) + raise TypeError(f"Unexpected transformation type from spatialdata: {type(sd_transform).__name__}.") diff --git a/src/squidpy/experimental/_fit/_methods/_stalign/__init__.py b/src/squidpy/experimental/_fit/_methods/_stalign/__init__.py new file mode 100644 index 000000000..78000bd3e --- /dev/null +++ b/src/squidpy/experimental/_fit/_methods/_stalign/__init__.py @@ -0,0 +1,11 @@ +"""Private grouping of the ported STalign solver and its estimator adapter. + +Importing this package registers :class:`StalignEstimator` in the ``align`` +family and stays cheap -- JAX is pulled in lazily, only when ``fit`` runs. +""" + +from __future__ import annotations + +from squidpy.experimental._fit._methods._stalign._estimator import StalignEstimator, StalignFitResult + +__all__ = ["StalignEstimator", "StalignFitResult"] diff --git a/src/squidpy/experimental/_fit/_methods/_stalign/_estimator.py b/src/squidpy/experimental/_fit/_methods/_stalign/_estimator.py index e9cec4735..1d11f29c2 100644 --- a/src/squidpy/experimental/_fit/_methods/_stalign/_estimator.py +++ b/src/squidpy/experimental/_fit/_methods/_stalign/_estimator.py @@ -1,9 +1,9 @@ """STalign estimator: JAX LDDMM point-cloud registration. The JAX solver is lifted from scverse/squidpy#1150 (Selman Özleyen); see -:mod:`._stalign_core`, :mod:`._stalign_helpers` and :mod:`._stalign_tools`. -This module only adds the thin :class:`Estimator` / :class:`FitResult` adapter -onto the :mod:`squidpy.experimental.fit` core. JAX is imported lazily inside +:mod:`._core`, :mod:`._helpers` and :mod:`._tools`. This module only adds the +thin :class:`Estimator` / :class:`FitResult` adapter onto the +:mod:`squidpy.experimental._fit` core. JAX is imported lazily inside :meth:`StalignEstimator.fit`, so importing this module is cheap and does not require JAX. """ @@ -15,15 +15,12 @@ import numpy as np -from squidpy.experimental.fit._estimator import Estimator -from squidpy.experimental.fit._registry import Registry -from squidpy.experimental.fit._result import FitResult +from squidpy.experimental._fit._estimator import Estimator +from squidpy.experimental._fit._methods._families import ALIGN +from squidpy.experimental._fit._result import FitResult if TYPE_CHECKING: - from ._stalign_tools import STalignConfig - -#: Registry for the ``align`` family of estimators. -ALIGN = Registry("align") + from ._tools import STalignConfig @dataclass @@ -99,7 +96,7 @@ def fit( # Import the JAX-backed solver only after requirements pass, so callers # without JAX get the clean ImportError from check_requirements rather # than a confusing failure from a module-level `import jax`. - from ._stalign_tools import stalign_points + from ._tools import stalign_points ref_xy = np.asarray(ref, dtype=float) query_xy = np.asarray(query, dtype=float) diff --git a/src/squidpy/experimental/_fit/_methods/_stalign/_helpers.py b/src/squidpy/experimental/_fit/_methods/_stalign/_helpers.py index 6d3304188..0be6a63ec 100644 --- a/src/squidpy/experimental/_fit/_methods/_stalign/_helpers.py +++ b/src/squidpy/experimental/_fit/_methods/_stalign/_helpers.py @@ -2,7 +2,7 @@ Lifted from scverse/squidpy#1150 (Selman Özleyen). Container readers (``extract_points`` / ``extract_landmarks``) were dropped on the move into -:mod:`squidpy.experimental.fit`: reading coordinates out of an ``AnnData`` / +:mod:`squidpy.experimental._fit`: reading coordinates out of an ``AnnData`` / ``SpatialData`` is the caller's concern, so everything here operates purely on in-memory NumPy arrays. """ diff --git a/src/squidpy/experimental/_fit/_methods/_stalign/_tools.py b/src/squidpy/experimental/_fit/_methods/_stalign/_tools.py index be73201fe..4501ac291 100644 --- a/src/squidpy/experimental/_fit/_methods/_stalign/_tools.py +++ b/src/squidpy/experimental/_fit/_methods/_stalign/_tools.py @@ -3,7 +3,7 @@ Lifted from scverse/squidpy#1150 (Selman Özleyen) with import paths adjusted and minor cleanups (config unpacking, lazy dtype resolution). The container write-back path (``STalignResult.transform_adata``) was dropped on the move -into :mod:`squidpy.experimental.fit`: results stay in memory and the caller +into :mod:`squidpy.experimental._fit`: results stay in memory and the caller owns write-back. """ @@ -15,8 +15,8 @@ import jax.numpy as jnp import numpy as np -from ._stalign_core import jax_dtype, lddmm, transform_points_row_col -from ._stalign_helpers import ( +from ._core import jax_dtype, lddmm, transform_points_row_col +from ._helpers import ( PointOrder, affine_from_points, from_row_col, diff --git a/src/squidpy/experimental/_fit/_registry.py b/src/squidpy/experimental/_fit/_registry.py index 0df6b0d5f..4df835f1d 100644 --- a/src/squidpy/experimental/_fit/_registry.py +++ b/src/squidpy/experimental/_fit/_registry.py @@ -6,7 +6,7 @@ from typing import TYPE_CHECKING, TypeVar if TYPE_CHECKING: - from squidpy.experimental.fit._estimator import Estimator + from squidpy.experimental._fit._estimator import Estimator E = TypeVar("E", bound="type[Estimator]") diff --git a/tests/experimental/fit/test_core.py b/tests/experimental/fit/test_core.py index 9d9302bd3..89968f64a 100644 --- a/tests/experimental/fit/test_core.py +++ b/tests/experimental/fit/test_core.py @@ -6,7 +6,7 @@ import numpy as np import pytest -from squidpy.experimental.fit import Estimator, FitResult, Registry +from squidpy.experimental._fit import Estimator, FitResult, Registry @dataclass diff --git a/tests/experimental/fit/test_stalign.py b/tests/experimental/fit/test_stalign.py index eb4b84a3d..56769e729 100644 --- a/tests/experimental/fit/test_stalign.py +++ b/tests/experimental/fit/test_stalign.py @@ -11,8 +11,8 @@ pytest.importorskip("jax") -from squidpy.experimental.fit._methods import ALIGN, StalignEstimator, StalignFitResult -from squidpy.experimental.fit._methods._stalign_tools import ( +from squidpy.experimental._fit._methods import ALIGN, StalignEstimator, StalignFitResult +from squidpy.experimental._fit._methods._stalign._tools import ( STalignConfig, STalignPreprocessConfig, STalignRegistrationConfig, diff --git a/uv.lock b/uv.lock new file mode 100644 index 000000000..a8b388cdf --- /dev/null +++ b/uv.lock @@ -0,0 +1,6445 @@ +version = 1 +requires-python = ">=3.11" 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+0200 Subject: [PATCH 71/96] refactor --- src/squidpy/experimental/_fit/__init__.py | 5 +- src/squidpy/experimental/_fit/_families.py | 20 +++ .../experimental/_fit/_methods/__init__.py | 27 --- .../experimental/_fit/_methods/_families.py | 16 -- .../_fit/_methods/_stalign/__init__.py | 11 -- .../_fit/align_landmarks/__init__.py | 22 +++ .../_landmark.py | 39 +++-- .../_fit/align_samples/__init__.py | 13 ++ .../_stalign.py} | 17 +- .../align_samples/_stalign_impl/__init__.py | 10 ++ .../_stalign_impl}/_core.py | 0 .../_stalign_impl}/_helpers.py | 0 .../_stalign_impl}/_tools.py | 0 src/squidpy/experimental/tl/__init__.py | 3 +- .../experimental/tl/_align/__init__.py | 7 + src/squidpy/experimental/tl/_align/_api.py | 161 ++++++++++++++++++ src/squidpy/experimental/tl/_align/_io.py | 158 +++++++++++++++++ .../experimental/tl/_align/_validation.py | 16 ++ tests/experimental/fit/test_stalign.py | 8 +- tests/experimental/tl/__init__.py | 0 tests/experimental/tl/test_align.py | 83 +++++++++ .../tl/test_align_by_landmarks.py | 108 ++++++++++++ 22 files changed, 641 insertions(+), 83 deletions(-) create mode 100644 src/squidpy/experimental/_fit/_families.py delete mode 100644 src/squidpy/experimental/_fit/_methods/__init__.py delete mode 100644 src/squidpy/experimental/_fit/_methods/_families.py delete mode 100644 src/squidpy/experimental/_fit/_methods/_stalign/__init__.py create mode 100644 src/squidpy/experimental/_fit/align_landmarks/__init__.py rename src/squidpy/experimental/_fit/{_methods => align_landmarks}/_landmark.py (80%) create mode 100644 src/squidpy/experimental/_fit/align_samples/__init__.py rename src/squidpy/experimental/_fit/{_methods/_stalign/_estimator.py => align_samples/_stalign.py} (90%) create mode 100644 src/squidpy/experimental/_fit/align_samples/_stalign_impl/__init__.py rename src/squidpy/experimental/_fit/{_methods/_stalign => align_samples/_stalign_impl}/_core.py (100%) rename src/squidpy/experimental/_fit/{_methods/_stalign => align_samples/_stalign_impl}/_helpers.py (100%) rename src/squidpy/experimental/_fit/{_methods/_stalign => align_samples/_stalign_impl}/_tools.py (100%) create mode 100644 src/squidpy/experimental/tl/_align/__init__.py create mode 100644 src/squidpy/experimental/tl/_align/_api.py create mode 100644 src/squidpy/experimental/tl/_align/_io.py create mode 100644 src/squidpy/experimental/tl/_align/_validation.py create mode 100644 tests/experimental/tl/__init__.py create mode 100644 tests/experimental/tl/test_align.py create mode 100644 tests/experimental/tl/test_align_by_landmarks.py diff --git a/src/squidpy/experimental/_fit/__init__.py b/src/squidpy/experimental/_fit/__init__.py index 66f88a475..68440899d 100644 --- a/src/squidpy/experimental/_fit/__init__.py +++ b/src/squidpy/experimental/_fit/__init__.py @@ -11,8 +11,9 @@ function, not here. - :class:`Registry` -- a flat ``name -> Estimator`` map, one per family. -Concrete estimators live in :mod:`._methods` and pull their optional -dependencies (e.g. JAX) in lazily, so importing this package stays cheap. +Concrete estimators live in per-family subpackages (``align_samples``, +``align_landmarks``, ...) and pull their optional dependencies (e.g. JAX) in +lazily, so importing this package stays cheap. Example ------- diff --git a/src/squidpy/experimental/_fit/_families.py b/src/squidpy/experimental/_fit/_families.py new file mode 100644 index 000000000..1399d9d2c --- /dev/null +++ b/src/squidpy/experimental/_fit/_families.py @@ -0,0 +1,20 @@ +"""Estimator family registries. + +A *family* is the set of interchangeable algorithms behind one public function, +selected by ``method=`` and sharing one ``fit`` contract. One :class:`Registry` +per family. Registries live in this small module so the estimator modules can +register into them without importing their package ``__init__`` (which imports +the estimator modules in turn -- that would be circular). +""" + +from __future__ import annotations + +from squidpy.experimental._fit._registry import Registry + +#: Sample-to-sample alignment estimators -- ref/query point clouds in, transform out. +#: Consumed by ``squidpy.experimental.tl.align``. +ALIGN_SAMPLES = Registry("align_samples") + +#: Closed-form landmark alignment estimators -- paired landmarks in, affine out. +#: Consumed by ``squidpy.experimental.tl.align_by_landmarks``. +ALIGN_LANDMARKS = Registry("align_landmarks") diff --git a/src/squidpy/experimental/_fit/_methods/__init__.py b/src/squidpy/experimental/_fit/_methods/__init__.py deleted file mode 100644 index f3b6864ca..000000000 --- a/src/squidpy/experimental/_fit/_methods/__init__.py +++ /dev/null @@ -1,27 +0,0 @@ -"""Concrete estimators for :mod:`squidpy.experimental._fit`. - -Importing this package registers the bundled estimators into their family -registries (``ALIGN``: STalign; ``LANDMARK``: similarity / affine) but stays -cheap: optional dependencies such as JAX are pulled in lazily, only when an -estimator's ``fit`` is actually called. -""" - -from __future__ import annotations - -from squidpy.experimental._fit._methods._families import ALIGN, LANDMARK -from squidpy.experimental._fit._methods._landmark import ( - AffineFitResult, - LandmarkAffineEstimator, - LandmarkSimilarityEstimator, -) -from squidpy.experimental._fit._methods._stalign import StalignEstimator, StalignFitResult - -__all__ = [ - "ALIGN", - "LANDMARK", - "AffineFitResult", - "LandmarkAffineEstimator", - "LandmarkSimilarityEstimator", - "StalignEstimator", - "StalignFitResult", -] diff --git a/src/squidpy/experimental/_fit/_methods/_families.py b/src/squidpy/experimental/_fit/_methods/_families.py deleted file mode 100644 index 867f6fa02..000000000 --- a/src/squidpy/experimental/_fit/_methods/_families.py +++ /dev/null @@ -1,16 +0,0 @@ -"""Estimator family registries. - -Defined in their own module so individual estimator modules can register into -them without importing :mod:`squidpy.experimental._fit._methods` (which imports -the estimator modules in turn -- that would be circular). -""" - -from __future__ import annotations - -from squidpy.experimental._fit._registry import Registry - -#: Data-driven alignment estimators (consumed by ``experimental.tl.align``). -ALIGN = Registry("align") - -#: Closed-form landmark estimators (consumed by ``experimental.tl.align_by_landmarks``). -LANDMARK = Registry("landmark") diff --git a/src/squidpy/experimental/_fit/_methods/_stalign/__init__.py b/src/squidpy/experimental/_fit/_methods/_stalign/__init__.py deleted file mode 100644 index 78000bd3e..000000000 --- a/src/squidpy/experimental/_fit/_methods/_stalign/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -"""Private grouping of the ported STalign solver and its estimator adapter. - -Importing this package registers :class:`StalignEstimator` in the ``align`` -family and stays cheap -- JAX is pulled in lazily, only when ``fit`` runs. -""" - -from __future__ import annotations - -from squidpy.experimental._fit._methods._stalign._estimator import StalignEstimator, StalignFitResult - -__all__ = ["StalignEstimator", "StalignFitResult"] diff --git a/src/squidpy/experimental/_fit/align_landmarks/__init__.py b/src/squidpy/experimental/_fit/align_landmarks/__init__.py new file mode 100644 index 000000000..e4f528e93 --- /dev/null +++ b/src/squidpy/experimental/_fit/align_landmarks/__init__.py @@ -0,0 +1,22 @@ +"""``align_landmarks`` family: closed-form alignment from paired landmarks. + +Importing this package registers the family's estimators into +:data:`~squidpy.experimental._fit._families.ALIGN_LANDMARKS`. Pure NumPy / +spatialdata / skimage -- no JAX. +""" + +from __future__ import annotations + +from squidpy.experimental._fit._families import ALIGN_LANDMARKS +from squidpy.experimental._fit.align_landmarks._landmark import ( + AffineFitResult, + LandmarkAffineEstimator, + LandmarkSimilarityEstimator, +) + +__all__ = [ + "ALIGN_LANDMARKS", + "AffineFitResult", + "LandmarkAffineEstimator", + "LandmarkSimilarityEstimator", +] diff --git a/src/squidpy/experimental/_fit/_methods/_landmark.py b/src/squidpy/experimental/_fit/align_landmarks/_landmark.py similarity index 80% rename from src/squidpy/experimental/_fit/_methods/_landmark.py rename to src/squidpy/experimental/_fit/align_landmarks/_landmark.py index 3dc475235..2fffb3f5e 100644 --- a/src/squidpy/experimental/_fit/_methods/_landmark.py +++ b/src/squidpy/experimental/_fit/align_landmarks/_landmark.py @@ -14,13 +14,14 @@ from __future__ import annotations +from abc import abstractmethod from dataclasses import dataclass, field -from typing import Any, ClassVar, Literal +from typing import Any import numpy as np from squidpy.experimental._fit._estimator import Estimator -from squidpy.experimental._fit._methods._families import LANDMARK +from squidpy.experimental._fit._families import ALIGN_LANDMARKS from squidpy.experimental._fit._result import FitResult @@ -46,9 +47,13 @@ def transform(self, x: np.ndarray) -> np.ndarray: class _LandmarkEstimator(Estimator): - """Base for closed-form landmark fits; subclasses pin :attr:`model`.""" + """Shared logic for closed-form landmark fits. - model: ClassVar[Literal["similarity", "affine"]] + The base :meth:`fit` does all the work that is common across models -- + validate inputs, enforce ``N >= 3``, build the :class:`AffineFitResult`. + Subclasses only provide the matrix solver via :meth:`_solve`, so there is + no per-model branching here. + """ def fit( self, @@ -75,31 +80,39 @@ def fit( f"`landmarks_ref` and `landmarks_query` must have the same shape; got {ref.shape} and {query.shape}." ) if ref.shape[0] < 3: - raise ValueError(f"`model={self.model!r}` needs at least 3 landmark pairs, got {ref.shape[0]}.") + raise ValueError(f"`{self.name}` needs at least 3 landmark pairs, got {ref.shape[0]}.") - matrix = _fit_similarity(ref, query) if self.model == "similarity" else _fit_affine(ref, query) + matrix = self._solve(ref, query) return AffineFitResult( matrix=matrix, source_cs=source_cs, target_cs=target_cs, - metadata={"method": self.model}, + metadata={"method": self.name}, ) + @abstractmethod + def _solve(self, ref_xy: np.ndarray, query_xy: np.ndarray) -> np.ndarray: + """Return the ``(3, 3)`` matrix mapping ``query_xy`` onto ``ref_xy``.""" -@LANDMARK.register("similarity") + +@ALIGN_LANDMARKS.register("similarity") class LandmarkSimilarityEstimator(_LandmarkEstimator): - """4-DOF similarity fit (rotation + uniform scale + translation).""" + """4-DOF similarity fit (rotation + uniform scale + translation), via spatialdata.""" name = "similarity" - model = "similarity" + + def _solve(self, ref_xy: np.ndarray, query_xy: np.ndarray) -> np.ndarray: + return _fit_similarity(ref_xy, query_xy) -@LANDMARK.register("affine") +@ALIGN_LANDMARKS.register("affine") class LandmarkAffineEstimator(_LandmarkEstimator): - """6-DOF affine fit (rotation + non-uniform scale + shear + translation).""" + """6-DOF affine fit (rotation + non-uniform scale + shear + translation), via skimage.""" name = "affine" - model = "affine" + + def _solve(self, ref_xy: np.ndarray, query_xy: np.ndarray) -> np.ndarray: + return _fit_affine(ref_xy, query_xy) def _validate_landmarks(points: np.ndarray, *, name: str) -> np.ndarray: diff --git a/src/squidpy/experimental/_fit/align_samples/__init__.py b/src/squidpy/experimental/_fit/align_samples/__init__.py new file mode 100644 index 000000000..f3abcc568 --- /dev/null +++ b/src/squidpy/experimental/_fit/align_samples/__init__.py @@ -0,0 +1,13 @@ +"""``align_samples`` family: align two samples' point clouds (STalign). + +Importing this package registers the family's estimators into +:data:`~squidpy.experimental._fit._families.ALIGN_SAMPLES`. It stays cheap -- +JAX is pulled in lazily, only when an estimator's ``fit`` runs. +""" + +from __future__ import annotations + +from squidpy.experimental._fit._families import ALIGN_SAMPLES +from squidpy.experimental._fit.align_samples._stalign import StalignEstimator, StalignFitResult + +__all__ = ["ALIGN_SAMPLES", "StalignEstimator", "StalignFitResult"] diff --git a/src/squidpy/experimental/_fit/_methods/_stalign/_estimator.py b/src/squidpy/experimental/_fit/align_samples/_stalign.py similarity index 90% rename from src/squidpy/experimental/_fit/_methods/_stalign/_estimator.py rename to src/squidpy/experimental/_fit/align_samples/_stalign.py index 1d11f29c2..749370a06 100644 --- a/src/squidpy/experimental/_fit/_methods/_stalign/_estimator.py +++ b/src/squidpy/experimental/_fit/align_samples/_stalign.py @@ -1,11 +1,10 @@ """STalign estimator: JAX LDDMM point-cloud registration. The JAX solver is lifted from scverse/squidpy#1150 (Selman Özleyen); see -:mod:`._core`, :mod:`._helpers` and :mod:`._tools`. This module only adds the -thin :class:`Estimator` / :class:`FitResult` adapter onto the -:mod:`squidpy.experimental._fit` core. JAX is imported lazily inside -:meth:`StalignEstimator.fit`, so importing this module is cheap and does not -require JAX. +:mod:`._stalign_impl`. This module only adds the thin :class:`Estimator` / +:class:`FitResult` adapter onto the :mod:`squidpy.experimental._fit` core. JAX +is imported lazily inside :meth:`StalignEstimator.fit`, so importing this module +is cheap and does not require JAX. """ from __future__ import annotations @@ -16,11 +15,11 @@ import numpy as np from squidpy.experimental._fit._estimator import Estimator -from squidpy.experimental._fit._methods._families import ALIGN +from squidpy.experimental._fit._families import ALIGN_SAMPLES from squidpy.experimental._fit._result import FitResult if TYPE_CHECKING: - from ._tools import STalignConfig + from ._stalign_impl._tools import STalignConfig @dataclass @@ -55,7 +54,7 @@ def transform(self, x: np.ndarray) -> np.ndarray: return coords + self.deltas -@ALIGN.register("stalign") +@ALIGN_SAMPLES.register("stalign") class StalignEstimator(Estimator): """Align a query point cloud onto a reference with a JAX LDDMM solver.""" @@ -96,7 +95,7 @@ def fit( # Import the JAX-backed solver only after requirements pass, so callers # without JAX get the clean ImportError from check_requirements rather # than a confusing failure from a module-level `import jax`. - from ._tools import stalign_points + from ._stalign_impl._tools import stalign_points ref_xy = np.asarray(ref, dtype=float) query_xy = np.asarray(query, dtype=float) diff --git a/src/squidpy/experimental/_fit/align_samples/_stalign_impl/__init__.py b/src/squidpy/experimental/_fit/align_samples/_stalign_impl/__init__.py new file mode 100644 index 000000000..958810ce7 --- /dev/null +++ b/src/squidpy/experimental/_fit/align_samples/_stalign_impl/__init__.py @@ -0,0 +1,10 @@ +"""Ported STalign JAX LDDMM solver (provenance: scverse/squidpy#1150). + +Pure numerics only -- no :class:`~squidpy.experimental._fit.Estimator`. The +estimator adapter lives one level up in +:mod:`squidpy.experimental._fit.align_samples._stalign`. JAX is imported at +module load here, so this subpackage must only be imported lazily (the adapter +does so inside ``fit``, after checking requirements). +""" + +from __future__ import annotations diff --git a/src/squidpy/experimental/_fit/_methods/_stalign/_core.py b/src/squidpy/experimental/_fit/align_samples/_stalign_impl/_core.py similarity index 100% rename from src/squidpy/experimental/_fit/_methods/_stalign/_core.py rename to src/squidpy/experimental/_fit/align_samples/_stalign_impl/_core.py diff --git a/src/squidpy/experimental/_fit/_methods/_stalign/_helpers.py b/src/squidpy/experimental/_fit/align_samples/_stalign_impl/_helpers.py similarity index 100% rename from src/squidpy/experimental/_fit/_methods/_stalign/_helpers.py rename to src/squidpy/experimental/_fit/align_samples/_stalign_impl/_helpers.py diff --git a/src/squidpy/experimental/_fit/_methods/_stalign/_tools.py b/src/squidpy/experimental/_fit/align_samples/_stalign_impl/_tools.py similarity index 100% rename from src/squidpy/experimental/_fit/_methods/_stalign/_tools.py rename to src/squidpy/experimental/_fit/align_samples/_stalign_impl/_tools.py diff --git a/src/squidpy/experimental/tl/__init__.py b/src/squidpy/experimental/tl/__init__.py index e4e529771..9dc9150f4 100644 --- a/src/squidpy/experimental/tl/__init__.py +++ b/src/squidpy/experimental/tl/__init__.py @@ -1,5 +1,6 @@ from __future__ import annotations +from ._align import align, align_by_landmarks from ._tiling_qc import calculate_tiling_qc -__all__ = ["calculate_tiling_qc"] +__all__ = ["align", "align_by_landmarks", "calculate_tiling_qc"] diff --git a/src/squidpy/experimental/tl/_align/__init__.py b/src/squidpy/experimental/tl/_align/__init__.py new file mode 100644 index 000000000..d386fa112 --- /dev/null +++ b/src/squidpy/experimental/tl/_align/__init__.py @@ -0,0 +1,7 @@ +"""Public alignment API for :mod:`squidpy.experimental.tl`.""" + +from __future__ import annotations + +from squidpy.experimental.tl._align._api import align, align_by_landmarks + +__all__ = ["align", "align_by_landmarks"] diff --git a/src/squidpy/experimental/tl/_align/_api.py b/src/squidpy/experimental/tl/_align/_api.py new file mode 100644 index 000000000..7d31a3ad5 --- /dev/null +++ b/src/squidpy/experimental/tl/_align/_api.py @@ -0,0 +1,161 @@ +"""Public alignment functions built on the :mod:`squidpy.experimental._fit` core. + +These are thin orchestrators: resolve inputs to in-memory arrays, dispatch to a +fit-core estimator, write the result back. All container I/O and write-back live +in :mod:`._io`; the estimators themselves never see a container. +""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING, Any, Literal + +import numpy as np +from anndata import AnnData +from spatialdata import SpatialData + +from squidpy.experimental._fit.align_landmarks import ALIGN_LANDMARKS +from squidpy.experimental._fit.align_samples import ALIGN_SAMPLES +from squidpy.experimental.tl._align._io import ( + get_coords, + resolve_obs_pair, + writeback_affine_sdata, + writeback_obs, +) +from squidpy.experimental.tl._align._validation import validate_on, validate_output_mode + +if TYPE_CHECKING: + from squidpy.experimental._fit import FitResult + +__all__ = ["align", "align_by_landmarks"] + + +def align( + data_ref: AnnData | SpatialData, + data_query: AnnData | SpatialData | None = None, + *, + method: str = "stalign", + on: Literal["obs", "image"] = "obs", + ref_key: str | None = None, + query_key: str | None = None, + spatial_key: str = "spatial", + output_mode: Literal["object", "copy", "inplace"] = "object", + key_added: str | None = None, + **method_kwargs: Any, +) -> FitResult | AnnData | SpatialData | None: + """Align a query sample onto a reference sample. + + Parameters + ---------- + data_ref, data_query + Both :class:`~anndata.AnnData`, or both :class:`~spatialdata.SpatialData`, + or ``data_ref`` a SpatialData with ``data_query=None`` to align two of its + own tables (selected by ``ref_key`` / ``query_key``). + method + Estimator in the ``align_samples`` family. ``"stalign"`` (JAX LDDMM). + on + ``"obs"`` aligns the ``obsm`` point clouds. ``"image"`` is reserved and + currently raises :class:`NotImplementedError`. + ref_key, query_key + Table keys, required (and only valid) for SpatialData inputs. + spatial_key + ``obsm`` key holding the ``(x, y)`` coordinates. Defaults to ``"spatial"``. + output_mode + - ``"object"`` (default) -- return the raw :class:`FitResult`; nothing is written. + - ``"inplace"`` -- write the aligned coordinates into the query and return ``None``. + - ``"copy"`` -- write into a copy of the query and return the copy. + key_added + Destination ``obsm`` key for the aligned coordinates. If ``None`` it + defaults to ``f"aligned_{spatial_key}"``; if that key already exists and + ``key_added`` was not given explicitly, a :class:`ValueError` is raised + (pass ``key_added`` to overwrite intentionally). + method_kwargs + Forwarded to the estimator's ``fit`` (e.g. ``config=STalignConfig(...)``). + """ + validate_on(on) + validate_output_mode(output_mode) + if on == "image": + raise NotImplementedError("`align(on='image')` is not implemented yet; use `on='obs'`.") + + ref_adata, query_adata, container, element_key = resolve_obs_pair(data_ref, data_query, ref_key, query_key) + ref_xy = get_coords(ref_adata, spatial_key) + query_xy = get_coords(query_adata, spatial_key) + + result = ALIGN_SAMPLES.get(method)().fit(ref_xy, query_xy, **method_kwargs) + + return writeback_obs( + result, + output_mode=output_mode, + query_adata=query_adata, + container=container, + element_key=element_key, + spatial_key=spatial_key, + key_added=key_added, + ) + + +def align_by_landmarks( + landmarks_ref: np.ndarray | Sequence[tuple[float, float]], + landmarks_query: np.ndarray | Sequence[tuple[float, float]], + *, + method: Literal["similarity", "affine"] = "similarity", + data: AnnData | SpatialData | None = None, + cs_name_ref: str | None = None, + cs_name_query: str | None = None, + spatial_key: str = "spatial", + output_mode: Literal["object", "copy", "inplace"] = "object", + key_added: str | None = None, +) -> FitResult | AnnData | SpatialData | None: + """Align by a closed-form fit on pre-paired landmarks. + + Parameters + ---------- + landmarks_ref, landmarks_query + Equal-length ``(N, 2)`` ``(x, y)`` landmark arrays (``N >= 3``), paired by + row order. No automatic correspondence matching is performed. + method + Estimator in the ``align_landmarks`` family: ``"similarity"`` (4 DOF) or + ``"affine"`` (6 DOF). + data + Target to write the alignment into. Required for ``output_mode`` other + than ``"object"``. + cs_name_ref, cs_name_query + Coordinate-system names. For a SpatialData ``data`` the fitted affine is + registered on every element in ``cs_name_query``, mapping into + ``cs_name_ref``. + spatial_key + ``obsm`` key when ``data`` is an :class:`~anndata.AnnData`. + output_mode + See :func:`align`. ``"object"`` (default) returns the :class:`FitResult`. + key_added + Destination ``obsm`` key when ``data`` is an AnnData (see :func:`align`). + """ + validate_output_mode(output_mode) + + result = ALIGN_LANDMARKS.get(method)().fit( + landmarks_ref, + landmarks_query, + source_cs=cs_name_query, + target_cs=cs_name_ref, + ) + + if output_mode == "object": + return result + if data is None: + raise ValueError("`data` is required when `output_mode` is 'copy' or 'inplace'.") + + if isinstance(data, SpatialData): + return writeback_affine_sdata( + result, data, output_mode=output_mode, moving_cs=cs_name_query, target_cs=cs_name_ref + ) + if isinstance(data, AnnData): + return writeback_obs( + result, + output_mode=output_mode, + query_adata=data, + container=None, + element_key=None, + spatial_key=spatial_key, + key_added=key_added, + ) + raise TypeError(f"`data` must be AnnData or SpatialData, got {type(data).__name__}.") diff --git a/src/squidpy/experimental/tl/_align/_io.py b/src/squidpy/experimental/tl/_align/_io.py new file mode 100644 index 000000000..ff3437065 --- /dev/null +++ b/src/squidpy/experimental/tl/_align/_io.py @@ -0,0 +1,158 @@ +"""Input resolvers and output write-back for the public align functions. + +This is the *only* layer that knows about the ``AnnData | SpatialData`` argument +shapes and the ``output_mode`` write-back strategies. The fit estimators in +:mod:`squidpy.experimental._fit` operate on plain arrays and never see a +container. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +from anndata import AnnData +from spatialdata import SpatialData + +from squidpy._validators import assert_key_in_sdata + +if TYPE_CHECKING: + from squidpy.experimental._fit import FitResult + from squidpy.experimental._fit.align_landmarks import AffineFitResult + + +# --------------------------------------------------------------------------- +# Read side +# --------------------------------------------------------------------------- + + +def resolve_obs_pair( + data_ref: AnnData | SpatialData, + data_query: AnnData | SpatialData | None, + ref_key: str | None, + query_key: str | None, +) -> tuple[AnnData, AnnData, SpatialData | None, str | None]: + """Normalise ``align(on="obs")`` inputs. + + Returns ``(ref_adata, query_adata, query_container, query_key)`` where + ``query_container`` is the SpatialData to write back into (``None`` for plain + AnnData inputs). + """ + if isinstance(data_ref, AnnData): + if data_query is None: + raise ValueError("`data_query` is required when `data_ref` is an AnnData.") + if not isinstance(data_query, AnnData): + raise TypeError( + f"Mixed AnnData/SpatialData inputs are not supported; `data_query` is {type(data_query).__name__}." + ) + if ref_key is not None or query_key is not None: + raise ValueError("`ref_key`/`query_key` are only valid for SpatialData inputs.") + return data_ref, data_query, None, None + + if not isinstance(data_ref, SpatialData): + raise TypeError(f"`data_ref` must be AnnData or SpatialData, got {type(data_ref).__name__}.") + + sdata_query = data_ref if data_query is None else data_query + if not isinstance(sdata_query, SpatialData): + raise TypeError( + f"Mixed AnnData/SpatialData inputs are not supported; `data_query` is {type(data_query).__name__}." + ) + if ref_key is None or query_key is None: + raise ValueError("`ref_key` and `query_key` are required for SpatialData inputs.") + assert_key_in_sdata(data_ref, ref_key, attr="tables") + assert_key_in_sdata(sdata_query, query_key, attr="tables") + return data_ref.tables[ref_key], sdata_query.tables[query_key], sdata_query, query_key + + +def get_coords(adata: AnnData, spatial_key: str) -> np.ndarray: + """Return a validated ``(N, 2)`` ``(x, y)`` coordinate array from ``obsm``.""" + if spatial_key not in adata.obsm: + raise KeyError(f"`obsm[{spatial_key!r}]` not found; pass `spatial_key=` to select the coordinate key.") + arr = np.asarray(adata.obsm[spatial_key], dtype=float) + if arr.ndim != 2 or arr.shape[1] != 2: + raise ValueError(f"`obsm[{spatial_key!r}]` must be an (N, 2) array, found shape {arr.shape}.") + return arr + + +# --------------------------------------------------------------------------- +# Write side +# --------------------------------------------------------------------------- + + +def writeback_obs( + result: FitResult, + *, + output_mode: str, + query_adata: AnnData, + container: SpatialData | None, + element_key: str | None, + spatial_key: str, + key_added: str | None, +) -> FitResult | AnnData | SpatialData | None: + """Bake ``result.transform(coords)`` into the query ``obsm`` per ``output_mode``.""" + if output_mode == "object": + return result + + dest = _resolve_dest(query_adata, spatial_key=spatial_key, key_added=key_added) + new_coords = result.transform(get_coords(query_adata, spatial_key)) + + if container is None: + target = query_adata if output_mode == "inplace" else query_adata.copy() + target.obsm[dest] = new_coords + return None if output_mode == "inplace" else target + + sdata = container if output_mode == "inplace" else shallow_copy_sdata(container) + sdata.tables[element_key].obsm[dest] = new_coords + return None if output_mode == "inplace" else sdata + + +def writeback_affine_sdata( + result: AffineFitResult, + sdata: SpatialData, + *, + output_mode: str, + moving_cs: str | None, + target_cs: str | None, +) -> SpatialData | None: + """Register the fitted affine on every element living in ``moving_cs``. + + Non-destructive: it adds a transformation into ``target_cs`` so the whole + coordinate system inherits the alignment. Nothing is materialised. + """ + from spatialdata.transformations import Affine, get_transformation, set_transformation + + if moving_cs is None or target_cs is None: + raise ValueError("`cs_name_query` and `cs_name_ref` are required to register a transform on a SpatialData.") + + out = sdata if output_mode == "inplace" else shallow_copy_sdata(sdata) + sd_affine = Affine(np.asarray(result.matrix), input_axes=("x", "y"), output_axes=("x", "y")) + touched = False + for _etype, _name, element in out.gen_elements(): + if isinstance(element, AnnData): + continue + if moving_cs not in get_transformation(element, get_all=True): + continue + set_transformation(element, sd_affine, to_coordinate_system=target_cs) + touched = True + if not touched: + raise KeyError(f"No elements in the SpatialData are registered to coordinate system {moving_cs!r}.") + return None if output_mode == "inplace" else out + + +def _resolve_dest(adata: AnnData, *, spatial_key: str, key_added: str | None) -> str: + """Resolve the destination obsm key, guarding against silent overwrite.""" + if key_added is not None: + return key_added + dest = f"aligned_{spatial_key}" + if dest in adata.obsm: + raise ValueError( + f"`obsm[{dest!r}]` already exists. Pass `key_added` explicitly to choose the destination " + f"(or to overwrite it intentionally)." + ) + return dest + + +def shallow_copy_sdata(sdata: SpatialData) -> SpatialData: + """Shallow copy of a SpatialData for ``output_mode='copy'`` (via ``subset``).""" + names = [name for _, name, _ in sdata.gen_elements()] + return sdata.subset(names, filter_tables=False, include_orphan_tables=True) diff --git a/src/squidpy/experimental/tl/_align/_validation.py b/src/squidpy/experimental/tl/_align/_validation.py new file mode 100644 index 000000000..d501d8208 --- /dev/null +++ b/src/squidpy/experimental/tl/_align/_validation.py @@ -0,0 +1,16 @@ +"""Argument validation for the public ``align`` / ``align_by_landmarks`` functions.""" + +from __future__ import annotations + +from squidpy._validators import assert_one_of + +OUTPUT_MODES = ("object", "copy", "inplace") +ON_VALUES = ("obs", "image") + + +def validate_output_mode(value: str) -> None: + assert_one_of(value, OUTPUT_MODES, name="output_mode") + + +def validate_on(value: str) -> None: + assert_one_of(value, ON_VALUES, name="on") diff --git a/tests/experimental/fit/test_stalign.py b/tests/experimental/fit/test_stalign.py index 56769e729..f1d8f3bfb 100644 --- a/tests/experimental/fit/test_stalign.py +++ b/tests/experimental/fit/test_stalign.py @@ -11,8 +11,8 @@ pytest.importorskip("jax") -from squidpy.experimental._fit._methods import ALIGN, StalignEstimator, StalignFitResult -from squidpy.experimental._fit._methods._stalign._tools import ( +from squidpy.experimental._fit.align_samples import ALIGN_SAMPLES, StalignEstimator, StalignFitResult +from squidpy.experimental._fit.align_samples._stalign_impl._tools import ( STalignConfig, STalignPreprocessConfig, STalignRegistrationConfig, @@ -40,8 +40,8 @@ def _tiny_config() -> STalignConfig: def test_stalign_registered_in_align_family() -> None: - assert "stalign" in ALIGN.keys() - assert ALIGN.get("stalign") is StalignEstimator + assert "stalign" in ALIGN_SAMPLES.keys() + assert ALIGN_SAMPLES.get("stalign") is StalignEstimator def test_stalign_fit_returns_displacement_result() -> None: diff --git a/tests/experimental/tl/__init__.py b/tests/experimental/tl/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/experimental/tl/test_align.py b/tests/experimental/tl/test_align.py new file mode 100644 index 000000000..30a2051d5 --- /dev/null +++ b/tests/experimental/tl/test_align.py @@ -0,0 +1,83 @@ +"""Integration tests for the public ``align`` (STalign) function. + +Tiny synthetic fixtures with ``niter=1`` keep these fast; they verify wiring, +write-back modes, and the key guard -- not solver quality. +""" + +from __future__ import annotations + +import numpy as np +import pytest +from anndata import AnnData + +pytest.importorskip("jax") + +from squidpy.experimental._fit.align_samples import StalignFitResult +from squidpy.experimental._fit.align_samples._stalign_impl._tools import ( + STalignConfig, + STalignPreprocessConfig, + STalignRegistrationConfig, +) +from squidpy.experimental.tl import align + + +def _adata(*, key: str = "spatial") -> AnnData: + pts = np.array([[10.0, 1.0], [12.0, 1.0], [11.0, 2.0], [10.0, 3.0], [12.0, 3.0]]) + adata = AnnData(np.zeros((pts.shape[0], 1))) + adata.obsm[key] = pts + return adata + + +def _tiny() -> STalignConfig: + return STalignConfig( + preprocess=STalignPreprocessConfig(dx=0.5, blur=1.0), + registration=STalignRegistrationConfig(a=1.0, expand=1.0, nt=1, niter=1, epV=1.0), + ) + + +def test_object_mode_returns_result_and_touches_nothing() -> None: + ref, query = _adata(), _adata() + result = align(ref, query, method="stalign", output_mode="object", config=_tiny()) + assert isinstance(result, StalignFitResult) + assert result.deltas.shape == query.obsm["spatial"].shape + assert "aligned_spatial" not in query.obsm + + +def test_inplace_writes_explicit_key() -> None: + ref, query = _adata(), _adata() + out = align(ref, query, output_mode="inplace", key_added="spatial_aligned", config=_tiny()) + assert out is None + assert query.obsm["spatial_aligned"].shape == query.obsm["spatial"].shape + + +def test_inplace_default_key() -> None: + ref, query = _adata(), _adata() + align(ref, query, output_mode="inplace", config=_tiny()) + assert "aligned_spatial" in query.obsm + + +def test_copy_leaves_original_untouched() -> None: + ref, query = _adata(), _adata() + out = align(ref, query, output_mode="copy", key_added="aligned", config=_tiny()) + assert isinstance(out, AnnData) and out is not query + assert "aligned" in out.obsm + assert "aligned" not in query.obsm + + +def test_existing_default_key_requires_explicit_key_added() -> None: + ref, query = _adata(), _adata() + query.obsm["aligned_spatial"] = np.zeros_like(query.obsm["spatial"]) + with pytest.raises(ValueError, match="aligned_spatial.*already exists"): + align(ref, query, output_mode="inplace", config=_tiny()) + + +def test_image_not_implemented() -> None: + ref, query = _adata(), _adata() + with pytest.raises(NotImplementedError, match="on='image'"): + align(ref, query, on="image", config=_tiny()) + + +def test_missing_spatial_key() -> None: + ref, query = _adata(), _adata() + with pytest.raises(KeyError, match="missing.*not found"): + align(ref, query, spatial_key="missing", config=_tiny()) diff --git a/tests/experimental/tl/test_align_by_landmarks.py b/tests/experimental/tl/test_align_by_landmarks.py new file mode 100644 index 000000000..05e3e472d --- /dev/null +++ b/tests/experimental/tl/test_align_by_landmarks.py @@ -0,0 +1,108 @@ +from __future__ import annotations + +import numpy as np +import pytest +from anndata import AnnData + +from squidpy.experimental._fit.align_landmarks import AffineFitResult +from squidpy.experimental.tl import align_by_landmarks + +# square corners; query = ref shifted by (5, 7) -> a pure translation both models recover +_REF = np.array([[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]]) +_SHIFT = np.array([5.0, 7.0]) +_QUERY = _REF + _SHIFT + + +def _adata(coords: np.ndarray = _QUERY, *, key: str = "spatial") -> AnnData: + adata = AnnData(np.zeros((coords.shape[0], 1))) + adata.obsm[key] = coords.copy() + return adata + + +@pytest.mark.parametrize("method", ["similarity", "affine"]) +def test_object_mode_returns_affine_result(method: str) -> None: + result = align_by_landmarks(_REF, _QUERY, method=method, output_mode="object") + assert isinstance(result, AffineFitResult) + assert result.matrix.shape == (3, 3) + # affine maps query -> ref + np.testing.assert_allclose(result.transform(_QUERY), _REF, atol=1e-6) + assert result.metadata["method"] == method + + +def test_object_is_default() -> None: + assert isinstance(align_by_landmarks(_REF, _QUERY), AffineFitResult) + + +def test_anndata_inplace_writes_default_key() -> None: + adata = _adata() + out = align_by_landmarks(_REF, _QUERY, method="affine", data=adata, output_mode="inplace") + assert out is None + assert "aligned_spatial" in adata.obsm + np.testing.assert_allclose(adata.obsm["aligned_spatial"], _REF, atol=1e-6) + # source coords untouched + np.testing.assert_allclose(adata.obsm["spatial"], _QUERY) + + +def test_anndata_copy_leaves_original_untouched() -> None: + adata = _adata() + out = align_by_landmarks(_REF, _QUERY, method="affine", data=adata, output_mode="copy", key_added="xy_aligned") + assert isinstance(out, AnnData) and out is not adata + assert "xy_aligned" in out.obsm + assert "xy_aligned" not in adata.obsm + + +def test_custom_spatial_key() -> None: + adata = _adata(key="loc") + align_by_landmarks(_REF, _QUERY, method="affine", data=adata, spatial_key="loc", output_mode="inplace") + assert "aligned_loc" in adata.obsm + + +def test_existing_default_key_requires_explicit_key_added() -> None: + adata = _adata() + adata.obsm["aligned_spatial"] = np.zeros_like(_QUERY) + with pytest.raises(ValueError, match="aligned_spatial.*already exists"): + align_by_landmarks(_REF, _QUERY, method="affine", data=adata, output_mode="inplace") + # explicit key_added overwrites without error + align_by_landmarks(_REF, _QUERY, method="affine", data=adata, output_mode="inplace", key_added="aligned_spatial") + np.testing.assert_allclose(adata.obsm["aligned_spatial"], _REF, atol=1e-6) + + +def test_write_mode_requires_data() -> None: + with pytest.raises(ValueError, match="`data` is required"): + align_by_landmarks(_REF, _QUERY, method="affine", output_mode="inplace") + + +def test_too_few_landmarks() -> None: + with pytest.raises(ValueError, match="at least 3 landmark pairs"): + align_by_landmarks(_REF[:2], _QUERY[:2], method="affine", output_mode="object") + + +def test_length_mismatch() -> None: + with pytest.raises(ValueError, match="same shape"): + align_by_landmarks(_REF, _QUERY[:3], method="affine", output_mode="object") + + +def test_unknown_method_lists_available() -> None: + with pytest.raises(ValueError, match=r"Unknown align_landmarks method 'nope'"): + align_by_landmarks(_REF, _QUERY, method="nope", output_mode="object") + + +def test_spatialdata_registers_transformation() -> None: + sd = pytest.importorskip("spatialdata") + from spatialdata.models import PointsModel + from spatialdata.transformations import Identity, get_transformation + + pts = PointsModel.parse(_QUERY, transformations={"query_cs": Identity()}) + sdata = sd.SpatialData(points={"pts": pts}) + + out = align_by_landmarks( + _REF, + _QUERY, + method="affine", + data=sdata, + cs_name_query="query_cs", + cs_name_ref="ref_cs", + output_mode="inplace", + ) + assert out is None + assert "ref_cs" in get_transformation(sdata.points["pts"], get_all=True) From 14d36b0d2b8795a5ba1b380906a062394e957a26 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Mon, 1 Jun 2026 10:05:17 +0200 Subject: [PATCH 72/96] undo lock file --- uv.lock | 6445 ------------------------------------------------------- 1 file changed, 6445 deletions(-) delete mode 100644 uv.lock diff --git a/uv.lock 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+0200 Subject: [PATCH 73/96] undo changes with main --- src/squidpy/experimental/utils/_labels.py | 28 +++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 src/squidpy/experimental/utils/_labels.py diff --git a/src/squidpy/experimental/utils/_labels.py b/src/squidpy/experimental/utils/_labels.py new file mode 100644 index 000000000..5d18e8370 --- /dev/null +++ b/src/squidpy/experimental/utils/_labels.py @@ -0,0 +1,28 @@ +"""Shared internal helpers for working with labels elements. + +Not part of the public API - symbols here are private and may change +without notice. +""" + +from __future__ import annotations + +import spatialdata as sd +import xarray as xr +from spatialdata._logging import logger as logg + + +def resolve_labels_array(sdata: sd.SpatialData, labels_key: str, scale: str | None) -> xr.DataArray: + """Resolve a labels element to its 2-D ``xarray.DataArray``. + + Single-scale elements pass through; multi-scale (``xarray.DataTree``) + elements require an explicit ``scale`` and raise otherwise. Passing + ``scale`` for a single-scale element logs a warning and is ignored. + """ + node = sdata.labels[labels_key] + if isinstance(node, xr.DataTree): + if scale is None: + raise ValueError(f"Labels `{labels_key}` is multi-scale; pass `scale` (e.g. 'scale0').") + return node[scale].ds["image"] + if scale is not None: + logg.warning(f"`scale={scale!r}` ignored: labels at `{labels_key}` are single-scale.") + return node From 9a489f2fd316adc324401c671e16a9a46757196a Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Mon, 1 Jun 2026 10:19:40 +0200 Subject: [PATCH 74/96] resuse resolve_labels_array --- src/squidpy/experimental/tl/_tiling_qc.py | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/src/squidpy/experimental/tl/_tiling_qc.py b/src/squidpy/experimental/tl/_tiling_qc.py index 1c6145610..ae0773576 100644 --- a/src/squidpy/experimental/tl/_tiling_qc.py +++ b/src/squidpy/experimental/tl/_tiling_qc.py @@ -50,6 +50,7 @@ compute_cell_info_tiled, extract_labels_tile_lazy, ) +from squidpy.experimental.utils._labels import resolve_labels_array __all__ = ["calculate_tiling_qc"] @@ -563,15 +564,7 @@ def calculate_tiling_qc( if n_neighbors < 1: raise ValueError(f"n_neighbors must be >= 1, got {n_neighbors}.") - labels_node = sdata.labels[labels_key] - if isinstance(labels_node, xr.DataTree): - if scale is None: - raise ValueError("When using multi-scale labels, please specify the scale.") - labels_da = labels_node[scale].ds["image"] - else: - if scale is not None: - logg.warning(f"`scale={scale!r}` ignored: labels at {labels_key!r} are single-scale.") - labels_da = labels_node + labels_da = resolve_labels_array(sdata, labels_key, scale) cell_info = _compute_centroids_for_labels(sdata, labels_key, labels_da, scale) if not cell_info: From 759e09e2fcf7429464fdc97570c8776dea507cd7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Selman=20=C3=96zleyen?= <32667648+selmanozleyen@users.noreply.github.com> Date: Mon, 1 Jun 2026 16:26:05 +0200 Subject: [PATCH 75/96] Update api.md --- docs/api.md | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/api.md b/docs/api.md index 508b33285..1c8fdde61 100644 --- a/docs/api.md +++ b/docs/api.md @@ -154,4 +154,3 @@ See the {doc}`extensibility guide ` for how to implement a custo experimental.im.apply_stain_normalization experimental.im.StainReference experimental.im.ReinhardParams -``` \ No newline at end of file From ad200ea6808027e65ce17d482f8573960be6e1e1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Selman=20=C3=96zleyen?= <32667648+selmanozleyen@users.noreply.github.com> Date: Mon, 1 Jun 2026 16:27:05 +0200 Subject: [PATCH 76/96] Update api.md --- docs/api.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/api.md b/docs/api.md index 1c8fdde61..1bf4fdf92 100644 --- a/docs/api.md +++ b/docs/api.md @@ -154,3 +154,4 @@ See the {doc}`extensibility guide ` for how to implement a custo experimental.im.apply_stain_normalization experimental.im.StainReference experimental.im.ReinhardParams +``` From c9eb7e43444cd7d245fa09e2fb292714be9a3ad4 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 10:03:44 +0200 Subject: [PATCH 77/96] rename file and refactor --- src/squidpy/experimental/_fit/_estimator.py | 58 -------- src/squidpy/experimental/_fit/_registry.py | 46 ------ .../_fit/align_samples/_stalign.py | 126 ---------------- .../{_fit => _methods}/__init__.py | 20 +-- .../{_fit => _methods}/_families.py | 0 .../experimental/_methods/_registry.py | 65 +++++++++ .../{_fit => _methods}/_result.py | 0 .../align_landmarks/__init__.py | 8 +- .../align_landmarks/_landmark.py | 135 ++++++++++-------- .../align_samples/__init__.py | 4 +- .../_methods/align_samples/_stalign.py | 117 +++++++++++++++ .../align_samples/_stalign_impl/__init__.py | 0 .../align_samples/_stalign_impl/_core.py | 0 .../align_samples/_stalign_impl/_helpers.py | 0 .../align_samples/_stalign_impl/_tools.py | 0 src/squidpy/experimental/tl/__init__.py | 3 +- src/squidpy/experimental/tl/_align/_api.py | 4 +- tests/experimental/fit/test_core.py | 49 +++---- tests/experimental/fit/test_stalign.py | 14 +- 19 files changed, 303 insertions(+), 346 deletions(-) delete mode 100644 src/squidpy/experimental/_fit/_estimator.py delete mode 100644 src/squidpy/experimental/_fit/_registry.py delete mode 100644 src/squidpy/experimental/_fit/align_samples/_stalign.py rename src/squidpy/experimental/{_fit => _methods}/__init__.py (62%) rename src/squidpy/experimental/{_fit => _methods}/_families.py (100%) create mode 100644 src/squidpy/experimental/_methods/_registry.py rename src/squidpy/experimental/{_fit => _methods}/_result.py (100%) rename src/squidpy/experimental/{_fit => _methods}/align_landmarks/__init__.py (79%) rename src/squidpy/experimental/{_fit => _methods}/align_landmarks/_landmark.py (60%) rename src/squidpy/experimental/{_fit => _methods}/align_samples/__init__.py (81%) create mode 100644 src/squidpy/experimental/_methods/align_samples/_stalign.py rename src/squidpy/experimental/{_fit => _methods}/align_samples/_stalign_impl/__init__.py (100%) rename src/squidpy/experimental/{_fit => _methods}/align_samples/_stalign_impl/_core.py (100%) rename src/squidpy/experimental/{_fit => _methods}/align_samples/_stalign_impl/_helpers.py (100%) rename src/squidpy/experimental/{_fit => _methods}/align_samples/_stalign_impl/_tools.py (100%) diff --git a/src/squidpy/experimental/_fit/_estimator.py b/src/squidpy/experimental/_fit/_estimator.py deleted file mode 100644 index aea3abc93..000000000 --- a/src/squidpy/experimental/_fit/_estimator.py +++ /dev/null @@ -1,58 +0,0 @@ -"""Estimator contract: a pure, in-memory fit.""" - -from __future__ import annotations - -import importlib.util -from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any, ClassVar - -if TYPE_CHECKING: - from squidpy.experimental._fit._result import FitResult - - -class Estimator(ABC): - """Base class for an in-memory model fit. - - Subclasses implement :meth:`fit`, which takes already-extracted in-memory - data (NumPy arrays, sparse matrices, or an :class:`~anndata.AnnData`) and - returns a :class:`FitResult`. Following scikit-learn, ``fit`` assumes the - training data fits in memory -- there is no streaming / out-of-core - contract (scikit-learn's ``partial_fit`` is the analogous opt-in exception - and is intentionally not modelled here). - - Reading data out of a container and writing results back are *not* part of - this contract; both are handled by the calling function. - - Attributes - ---------- - name - Stable identifier used as the registry key. - requires - Optional third-party packages the estimator needs at fit time (e.g. - ``("jax",)``). Advertised here so :meth:`check_requirements` can fail - early with an actionable message before any heavy import fires. - """ - - name: ClassVar[str] - requires: ClassVar[tuple[str, ...]] = () - - @abstractmethod - def fit(self, *args: Any, **kwargs: Any) -> FitResult: - """Fit the model on in-memory data and return a :class:`FitResult`.""" - - def check_requirements(self) -> None: - """Raise :class:`ImportError` if any package in :attr:`requires` is missing. - - Estimators should call this at the top of :meth:`fit`, before importing - their optional backend, so callers get a clean message instead of a - deep ``ModuleNotFoundError``. - """ - missing = [pkg for pkg in self.requires if importlib.util.find_spec(pkg) is None] - if missing: - verb = "is" if len(missing) == 1 else "are" - names = ", ".join(repr(p) for p in missing) - extras = ",".join(missing) - raise ImportError( - f"Estimator {self.name!r} requires {names}, which {verb} not installed. " - f'Install with `pip install "squidpy[{extras}]"`.' - ) diff --git a/src/squidpy/experimental/_fit/_registry.py b/src/squidpy/experimental/_fit/_registry.py deleted file mode 100644 index 4df835f1d..000000000 --- a/src/squidpy/experimental/_fit/_registry.py +++ /dev/null @@ -1,46 +0,0 @@ -"""A flat registry mapping method names to estimator classes.""" - -from __future__ import annotations - -from collections.abc import Callable -from typing import TYPE_CHECKING, TypeVar - -if TYPE_CHECKING: - from squidpy.experimental._fit._estimator import Estimator - -E = TypeVar("E", bound="type[Estimator]") - - -class Registry: - """A flat ``name -> Estimator`` registry for one *family* of methods. - - One :class:`Registry` is created per family (e.g. ``align``, ``impute``), - so keys are plain method names -- there is no ``(method, mode)`` compound - key, because the family already pins the rest. - """ - - def __init__(self, name: str) -> None: - self.name = name - self._estimators: dict[str, type[Estimator]] = {} - - def register(self, key: str) -> Callable[[E], E]: - """Return a class decorator registering an :class:`Estimator` under ``key``.""" - - def decorator(cls: E) -> E: - if key in self._estimators: - raise ValueError(f"Estimator {key!r} is already registered in the {self.name!r} registry.") - self._estimators[key] = cls - return cls - - return decorator - - def get(self, key: str) -> type[Estimator]: - """Return the :class:`Estimator` class registered under ``key``.""" - try: - return self._estimators[key] - except KeyError: - raise ValueError(f"Unknown {self.name} method {key!r}. Available: {sorted(self._estimators)}.") from None - - def keys(self) -> tuple[str, ...]: - """Return the registered method names.""" - return tuple(self._estimators) diff --git a/src/squidpy/experimental/_fit/align_samples/_stalign.py b/src/squidpy/experimental/_fit/align_samples/_stalign.py deleted file mode 100644 index 749370a06..000000000 --- a/src/squidpy/experimental/_fit/align_samples/_stalign.py +++ /dev/null @@ -1,126 +0,0 @@ -"""STalign estimator: JAX LDDMM point-cloud registration. - -The JAX solver is lifted from scverse/squidpy#1150 (Selman Özleyen); see -:mod:`._stalign_impl`. This module only adds the thin :class:`Estimator` / -:class:`FitResult` adapter onto the :mod:`squidpy.experimental._fit` core. JAX -is imported lazily inside :meth:`StalignEstimator.fit`, so importing this module -is cheap and does not require JAX. -""" - -from __future__ import annotations - -from dataclasses import dataclass, field -from typing import TYPE_CHECKING, Any - -import numpy as np - -from squidpy.experimental._fit._estimator import Estimator -from squidpy.experimental._fit._families import ALIGN_SAMPLES -from squidpy.experimental._fit._result import FitResult - -if TYPE_CHECKING: - from ._stalign_impl._tools import STalignConfig - - -@dataclass -class StalignFitResult(FitResult): - """Fitted STalign deformation, in ``(x, y)`` convention. - - ``deltas`` is the per-point displacement that maps the *query* points into - the *reference* frame; :meth:`transform` bakes it in. STalign produces a - diffeomorphic (non-affine) map, so ``affine`` is ``None`` here -- the full - map (velocity field, velocity grid, affine initialisation) is kept under - ``metadata['stalign_result']`` for power users who need to transform - arbitrary points via ``stalign_result.transform_points``. - """ - - deltas: np.ndarray - affine: np.ndarray | None = None - metadata: dict[str, Any] = field(default_factory=dict) - - def transform(self, x: np.ndarray) -> np.ndarray: - """Apply the fitted displacement to the ``(N, 2)`` ``(x, y)`` points it was fit on. - - ``deltas`` is per-point, so ``x`` must have the same shape as the query - cloud passed to :meth:`StalignEstimator.fit`. To map *arbitrary* points, - use ``metadata['stalign_result'].transform_points``. - """ - coords = np.asarray(x, dtype=float) - if coords.shape != self.deltas.shape: - raise ValueError( - f"`transform` expects coordinates of shape {self.deltas.shape} (the fitted query cloud), " - f"found {coords.shape}. Use `metadata['stalign_result'].transform_points` for arbitrary points." - ) - return coords + self.deltas - - -@ALIGN_SAMPLES.register("stalign") -class StalignEstimator(Estimator): - """Align a query point cloud onto a reference with a JAX LDDMM solver.""" - - name = "stalign" - requires = ("jax",) - - def fit( - self, - ref: np.ndarray, - query: np.ndarray, - *, - config: STalignConfig | None = None, - landmarks_source: np.ndarray | None = None, - landmarks_target: np.ndarray | None = None, - ) -> StalignFitResult: - """Fit a deformation mapping ``query`` onto ``ref``. - - Parameters - ---------- - ref - ``(N, 2)`` reference point cloud in ``(x, y)`` order. - query - ``(M, 2)`` query point cloud in ``(x, y)`` order, to be aligned to - ``ref``. Both are plain in-memory arrays; extracting them from an - ``AnnData`` / ``SpatialData`` is the caller's responsibility. - config - Optional :class:`STalignConfig` of solver hyperparameters. - landmarks_source, landmarks_target - Optional corresponding ``(x, y)`` landmark arrays used to - initialise the affine. Must be provided together. - - Returns - ------- - A :class:`StalignFitResult` whose ``deltas`` move ``query`` into the - reference frame. - """ - self.check_requirements() - # Import the JAX-backed solver only after requirements pass, so callers - # without JAX get the clean ImportError from check_requirements rather - # than a confusing failure from a module-level `import jax`. - from ._stalign_impl._tools import stalign_points - - ref_xy = np.asarray(ref, dtype=float) - query_xy = np.asarray(query, dtype=float) - for arr_name, arr in (("ref", ref_xy), ("query", query_xy)): - if arr.ndim != 2 or arr.shape[1] != 2: - raise ValueError(f"Expected `{arr_name}` to be an (N, 2) array, found shape {arr.shape}.") - - # The solver runs internally in row-col (y, x); inputs are (x, y) -- swap at the boundary. - src_rc = query_xy[:, [1, 0]] - tgt_rc = ref_xy[:, [1, 0]] - lm_src_rc = None if landmarks_source is None else np.asarray(landmarks_source, dtype=float)[:, [1, 0]] - lm_tgt_rc = None if landmarks_target is None else np.asarray(landmarks_target, dtype=float)[:, [1, 0]] - - result = stalign_points( - source_points=src_rc, - target_points=tgt_rc, - config=config, - landmarks_source=lm_src_rc, - landmarks_target=lm_tgt_rc, - ) - - aligned_xy = np.asarray(result.aligned_points)[:, [1, 0]] - deltas_xy = aligned_xy - query_xy - return StalignFitResult( - deltas=deltas_xy, - affine=None, - metadata={"method": "stalign", "stalign_result": result}, - ) diff --git a/src/squidpy/experimental/_fit/__init__.py b/src/squidpy/experimental/_methods/__init__.py similarity index 62% rename from src/squidpy/experimental/_fit/__init__.py rename to src/squidpy/experimental/_methods/__init__.py index 68440899d..5bf65cc3f 100644 --- a/src/squidpy/experimental/_fit/__init__.py +++ b/src/squidpy/experimental/_methods/__init__.py @@ -3,13 +3,10 @@ This subpackage factors the *fit* half of fitting-style tools (alignment, imputation, ...) out of any particular container or write-back path: -- :class:`Estimator` -- a model whose :meth:`~Estimator.fit` takes in-memory - data and returns a :class:`FitResult` (scikit-learn-style; training is - assumed to fit in memory). - :class:`FitResult` -- the fitted parameters plus an optional, pure in-memory :meth:`~FitResult.transform`. Container write-back lives in the calling function, not here. -- :class:`Registry` -- a flat ``name -> Estimator`` map, one per family. +- :class:`Registry` -- a flat ``name -> function`` map, one per family. Concrete estimators live in per-family subpackages (``align_samples``, ``align_landmarks``, ...) and pull their optional dependencies (e.g. JAX) in @@ -18,7 +15,7 @@ Example ------- >>> import numpy as np ->>> from squidpy.experimental._fit import Estimator, FitResult, Registry +>>> from squidpy.experimental._fit import FitResult, Registry >>> demo = Registry("demo") >>> class Shift(FitResult): ... def __init__(self, delta): @@ -27,21 +24,16 @@ ... def transform(self, x): ... return np.asarray(x) + self.delta >>> @demo.register("mean_shift") -... class MeanShift(Estimator): -... name = "mean_shift" -... -... def fit(self, ref, query): -... return Shift(ref.mean(0) - query.mean(0)) ->>> est = demo.get("mean_shift")() ->>> result = est.fit(np.ones((3, 2)), np.zeros((3, 2))) +... def fit_mean_shift(ref, query): +... return Shift(ref.mean(0) - query.mean(0)) +>>> result = demo.get("mean_shift")(np.ones((3, 2)), np.zeros((3, 2))) >>> result.transform(np.zeros((3, 2))).tolist() [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]] """ from __future__ import annotations -from squidpy.experimental._fit._estimator import Estimator from squidpy.experimental._fit._registry import Registry from squidpy.experimental._fit._result import FitResult -__all__ = ["Estimator", "FitResult", "Registry"] +__all__ = ["FitResult", "Registry"] diff --git a/src/squidpy/experimental/_fit/_families.py b/src/squidpy/experimental/_methods/_families.py similarity index 100% rename from src/squidpy/experimental/_fit/_families.py rename to src/squidpy/experimental/_methods/_families.py diff --git a/src/squidpy/experimental/_methods/_registry.py b/src/squidpy/experimental/_methods/_registry.py new file mode 100644 index 000000000..8026c14fe --- /dev/null +++ b/src/squidpy/experimental/_methods/_registry.py @@ -0,0 +1,65 @@ +"""A flat registry mapping method names to fitting functions.""" + +from __future__ import annotations + +import functools +import importlib.util +from collections.abc import Callable +from typing import Any, TypeVar + +F = TypeVar("F", bound=Callable[..., Any]) + + +class Registry: + """A flat ``name -> function`` registry for one *family* of methods. + + One :class:`Registry` is created per family (e.g. ``align``, ``impute``), + so keys are plain method names -- there is no ``(method, mode)`` compound + key, because the family already pins the rest. + """ + + def __init__(self, name: str) -> None: + self.name = name + self._registry: dict[str, Callable[..., Any]] = {} + + def register(self, key: str, *, requires: tuple[str, ...] = ()) -> Callable[[F], F]: + """Return a decorator registering a method/function under ``key``.""" + + def decorator(func: F) -> F: + if key in self._registry: + raise ValueError(f"Method {key!r} is already registered in the {self.name!r} registry.") + + if requires: + @functools.wraps(func) + def wrapped(*args: Any, **kwargs: Any) -> Any: + missing = [pkg for pkg in requires if importlib.util.find_spec(pkg) is None] + if missing: + verb = "is" if len(missing) == 1 else "are" + names = ", ".join(repr(p) for p in missing) + extras = ",".join(missing) + raise ImportError( + f"Method {key!r} requires {names}, which {verb} not installed. " + f'Install with `pip install "squidpy[{extras}]"`.' + ) + return func(*args, **kwargs) + + wrapped.__requires__ = requires # type: ignore[attr-defined] + self._registry[key] = wrapped + return wrapped # type: ignore[return-value] + else: + self._registry[key] = func + return func + + return decorator + + def get(self, key: str) -> Callable[..., Any]: + """Return the function registered under ``key``.""" + try: + return self._registry[key] + except KeyError: + raise ValueError(f"Unknown {self.name} method {key!r}. Available: {sorted(self._registry)}.") from None + + def keys(self) -> tuple[str, ...]: + """Return the registered method names.""" + return tuple(self._registry) + diff --git a/src/squidpy/experimental/_fit/_result.py b/src/squidpy/experimental/_methods/_result.py similarity index 100% rename from src/squidpy/experimental/_fit/_result.py rename to src/squidpy/experimental/_methods/_result.py diff --git a/src/squidpy/experimental/_fit/align_landmarks/__init__.py b/src/squidpy/experimental/_methods/align_landmarks/__init__.py similarity index 79% rename from src/squidpy/experimental/_fit/align_landmarks/__init__.py rename to src/squidpy/experimental/_methods/align_landmarks/__init__.py index e4f528e93..36d355977 100644 --- a/src/squidpy/experimental/_fit/align_landmarks/__init__.py +++ b/src/squidpy/experimental/_methods/align_landmarks/__init__.py @@ -10,13 +10,13 @@ from squidpy.experimental._fit._families import ALIGN_LANDMARKS from squidpy.experimental._fit.align_landmarks._landmark import ( AffineFitResult, - LandmarkAffineEstimator, - LandmarkSimilarityEstimator, + fit_affine, + fit_similarity, ) __all__ = [ "ALIGN_LANDMARKS", "AffineFitResult", - "LandmarkAffineEstimator", - "LandmarkSimilarityEstimator", + "fit_affine", + "fit_similarity", ] diff --git a/src/squidpy/experimental/_fit/align_landmarks/_landmark.py b/src/squidpy/experimental/_methods/align_landmarks/_landmark.py similarity index 60% rename from src/squidpy/experimental/_fit/align_landmarks/_landmark.py rename to src/squidpy/experimental/_methods/align_landmarks/_landmark.py index 2fffb3f5e..f3a7635c2 100644 --- a/src/squidpy/experimental/_fit/align_landmarks/_landmark.py +++ b/src/squidpy/experimental/_methods/align_landmarks/_landmark.py @@ -14,13 +14,12 @@ from __future__ import annotations -from abc import abstractmethod +from collections.abc import Callable from dataclasses import dataclass, field from typing import Any import numpy as np -from squidpy.experimental._fit._estimator import Estimator from squidpy.experimental._fit._families import ALIGN_LANDMARKS from squidpy.experimental._fit._result import FitResult @@ -46,73 +45,87 @@ def transform(self, x: np.ndarray) -> np.ndarray: return coords @ self.matrix[:2, :2].T + self.matrix[:2, 2] -class _LandmarkEstimator(Estimator): - """Shared logic for closed-form landmark fits. - - The base :meth:`fit` does all the work that is common across models -- - validate inputs, enforce ``N >= 3``, build the :class:`AffineFitResult`. - Subclasses only provide the matrix solver via :meth:`_solve`, so there is - no per-model branching here. - """ - - def fit( - self, - landmarks_ref: np.ndarray, - landmarks_query: np.ndarray, - *, - source_cs: str | None = None, - target_cs: str | None = None, - ) -> AffineFitResult: - """Fit an affine mapping ``landmarks_query`` onto ``landmarks_ref``. - - Parameters - ---------- - landmarks_ref, landmarks_query - Pre-paired ``(N, 2)`` ``(x, y)`` landmark arrays (``N >= 3``). - source_cs, target_cs - Optional coordinate-system labels stamped onto the result for - traceability; they do not affect the fit. - """ - ref = _validate_landmarks(landmarks_ref, name="landmarks_ref") - query = _validate_landmarks(landmarks_query, name="landmarks_query") - if ref.shape != query.shape: - raise ValueError( - f"`landmarks_ref` and `landmarks_query` must have the same shape; got {ref.shape} and {query.shape}." - ) - if ref.shape[0] < 3: - raise ValueError(f"`{self.name}` needs at least 3 landmark pairs, got {ref.shape[0]}.") - - matrix = self._solve(ref, query) - return AffineFitResult( - matrix=matrix, - source_cs=source_cs, - target_cs=target_cs, - metadata={"method": self.name}, +def _fit_landmark_relation( + landmarks_ref: np.ndarray, + landmarks_query: np.ndarray, + *, + method: str, + solve_fn: Callable[[np.ndarray, np.ndarray], np.ndarray], + source_cs: str | None = None, + target_cs: str | None = None, +) -> AffineFitResult: + ref = _validate_landmarks(landmarks_ref, name="landmarks_ref") + query = _validate_landmarks(landmarks_query, name="landmarks_query") + if ref.shape != query.shape: + raise ValueError( + f"`landmarks_ref` and `landmarks_query` must have the same shape; got {ref.shape} and {query.shape}." ) + if ref.shape[0] < 3: + raise ValueError(f"`{method}` needs at least 3 landmark pairs, got {ref.shape[0]}.") - @abstractmethod - def _solve(self, ref_xy: np.ndarray, query_xy: np.ndarray) -> np.ndarray: - """Return the ``(3, 3)`` matrix mapping ``query_xy`` onto ``ref_xy``.""" + matrix = solve_fn(ref, query) + return AffineFitResult( + matrix=matrix, + source_cs=source_cs, + target_cs=target_cs, + metadata={"method": method}, + ) @ALIGN_LANDMARKS.register("similarity") -class LandmarkSimilarityEstimator(_LandmarkEstimator): - """4-DOF similarity fit (rotation + uniform scale + translation), via spatialdata.""" - - name = "similarity" - - def _solve(self, ref_xy: np.ndarray, query_xy: np.ndarray) -> np.ndarray: - return _fit_similarity(ref_xy, query_xy) +def fit_similarity( + landmarks_ref: np.ndarray, + landmarks_query: np.ndarray, + *, + source_cs: str | None = None, + target_cs: str | None = None, +) -> AffineFitResult: + """4-DOF similarity fit (rotation + uniform scale + translation), via spatialdata. + + Parameters + ---------- + landmarks_ref, landmarks_query + Pre-paired ``(N, 2)`` ``(x, y)`` landmark arrays (``N >= 3``). + source_cs, target_cs + Optional coordinate-system labels stamped onto the result for + traceability; they do not affect the fit. + """ + return _fit_landmark_relation( + landmarks_ref, + landmarks_query, + method="similarity", + solve_fn=_fit_similarity, + source_cs=source_cs, + target_cs=target_cs, + ) @ALIGN_LANDMARKS.register("affine") -class LandmarkAffineEstimator(_LandmarkEstimator): - """6-DOF affine fit (rotation + non-uniform scale + shear + translation), via skimage.""" - - name = "affine" - - def _solve(self, ref_xy: np.ndarray, query_xy: np.ndarray) -> np.ndarray: - return _fit_affine(ref_xy, query_xy) +def fit_affine( + landmarks_ref: np.ndarray, + landmarks_query: np.ndarray, + *, + source_cs: str | None = None, + target_cs: str | None = None, +) -> AffineFitResult: + """6-DOF affine fit (rotation + non-uniform scale + shear + translation), via skimage. + + Parameters + ---------- + landmarks_ref, landmarks_query + Pre-paired ``(N, 2)`` ``(x, y)`` landmark arrays (``N >= 3``). + source_cs, target_cs + Optional coordinate-system labels stamped onto the result for + traceability; they do not affect the fit. + """ + return _fit_landmark_relation( + landmarks_ref, + landmarks_query, + method="affine", + solve_fn=_fit_affine, + source_cs=source_cs, + target_cs=target_cs, + ) def _validate_landmarks(points: np.ndarray, *, name: str) -> np.ndarray: diff --git a/src/squidpy/experimental/_fit/align_samples/__init__.py b/src/squidpy/experimental/_methods/align_samples/__init__.py similarity index 81% rename from src/squidpy/experimental/_fit/align_samples/__init__.py rename to src/squidpy/experimental/_methods/align_samples/__init__.py index f3abcc568..5c38b9cc3 100644 --- a/src/squidpy/experimental/_fit/align_samples/__init__.py +++ b/src/squidpy/experimental/_methods/align_samples/__init__.py @@ -8,6 +8,6 @@ from __future__ import annotations from squidpy.experimental._fit._families import ALIGN_SAMPLES -from squidpy.experimental._fit.align_samples._stalign import StalignEstimator, StalignFitResult +from squidpy.experimental._fit.align_samples._stalign import StalignFitResult, fit_stalign -__all__ = ["ALIGN_SAMPLES", "StalignEstimator", "StalignFitResult"] +__all__ = ["ALIGN_SAMPLES", "fit_stalign", "StalignFitResult"] diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign.py b/src/squidpy/experimental/_methods/align_samples/_stalign.py new file mode 100644 index 000000000..003957390 --- /dev/null +++ b/src/squidpy/experimental/_methods/align_samples/_stalign.py @@ -0,0 +1,117 @@ +"""STalign estimator: JAX LDDMM point-cloud registration. + +The JAX solver is lifted from scverse/squidpy#1150 (Selman Özleyen); see +:mod:`._stalign_impl`. This module only adds the thin :class:`Estimator` / +:class:`FitResult` adapter onto the :mod:`squidpy.experimental._fit` core. JAX +is imported lazily inside :meth:`StalignEstimator.fit`, so importing this module +is cheap and does not require JAX. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, Any + +import numpy as np + +from squidpy.experimental._fit._families import ALIGN_SAMPLES +from squidpy.experimental._fit._result import FitResult + +if TYPE_CHECKING: + from ._stalign_impl._tools import STalignConfig + + +@dataclass +class StalignFitResult(FitResult): + """Fitted STalign deformation, in ``(x, y)`` convention. + + ``deltas`` is the per-point displacement that maps the *query* points into + the *reference* frame; :meth:`transform` bakes it in. STalign produces a + diffeomorphic (non-affine) map, so ``affine`` is ``None`` here -- the full + map (velocity field, velocity grid, affine initialisation) is kept under + ``metadata['stalign_result']`` for power users who need to transform + arbitrary points via ``stalign_result.transform_points``. + """ + + deltas: np.ndarray + affine: np.ndarray | None = None + metadata: dict[str, Any] = field(default_factory=dict) + + def transform(self, x: np.ndarray) -> np.ndarray: + """Apply the fitted displacement to the ``(N, 2)`` ``(x, y)`` points it was fit on. + + ``deltas`` is per-point, so ``x`` must have the same shape as the query + cloud passed to :meth:`fit_stalign`. To map *arbitrary* points, + use ``metadata['stalign_result'].transform_points``. + """ + coords = np.asarray(x, dtype=float) + if coords.shape != self.deltas.shape: + raise ValueError( + f"`transform` expects coordinates of shape {self.deltas.shape} (the fitted query cloud), " + f"found {coords.shape}. Use `metadata['stalign_result'].transform_points` for arbitrary points." + ) + return coords + self.deltas + + +@ALIGN_SAMPLES.register("stalign", requires=("jax",)) +def fit_stalign( + ref: np.ndarray, + query: np.ndarray, + *, + config: STalignConfig | None = None, + landmarks_source: np.ndarray | None = None, + landmarks_target: np.ndarray | None = None, +) -> StalignFitResult: + """Fit a deformation mapping ``query`` onto ``ref``. + + Parameters + ---------- + ref + ``(N, 2)`` reference point cloud in ``(x, y)`` order. + query + ``(M, 2)`` query point cloud in ``(x, y)`` order, to be aligned to + ``ref``. Both are plain in-memory arrays; extracting them from an + ``AnnData`` / ``SpatialData`` is the caller's responsibility. + config + Optional :class:`STalignConfig` of solver hyperparameters. + landmarks_source, landmarks_target + Optional corresponding ``(x, y)`` landmark arrays used to + initialise the affine. Must be provided together. + + Returns + ------- + A :class:`StalignFitResult` whose ``deltas`` move ``query`` into the + reference frame. + """ + # Import the JAX-backed solver only after requirements pass, so callers + # without JAX get the clean ImportError from check_requirements rather + # than a confusing failure from a module-level `import jax`. + from ._stalign_impl._tools import stalign_points + + ref_xy = np.asarray(ref, dtype=float) + query_xy = np.asarray(query, dtype=float) + for arr_name, arr in (("ref", ref_xy), ("query", query_xy)): + if arr.ndim != 2 or arr.shape[1] != 2: + raise ValueError(f"Expected `{arr_name}` to be an (N, 2) array, found shape {arr.shape}.") + + # The solver runs internally in row-col (y, x); inputs are (x, y) -- swap at the boundary. + src_rc = query_xy[:, [1, 0]] + tgt_rc = ref_xy[:, [1, 0]] + lm_src_rc = None if landmarks_source is None else np.asarray(landmarks_source, dtype=float)[:, [1, 0]] + lm_tgt_rc = None if landmarks_target is None else np.asarray(landmarks_target, dtype=float)[:, [1, 0]] + + result = stalign_points( + source_points=src_rc, + target_points=tgt_rc, + config=config, + landmarks_source=lm_src_rc, + landmarks_target=lm_tgt_rc, + ) + + aligned_xy = np.asarray(result.aligned_points)[:, [1, 0]] + deltas_xy = aligned_xy - query_xy + return StalignFitResult( + deltas=deltas_xy, + affine=None, + metadata={"method": "stalign", "stalign_result": result}, + ) diff --git a/src/squidpy/experimental/_fit/align_samples/_stalign_impl/__init__.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/__init__.py similarity index 100% rename from src/squidpy/experimental/_fit/align_samples/_stalign_impl/__init__.py rename to src/squidpy/experimental/_methods/align_samples/_stalign_impl/__init__.py diff --git a/src/squidpy/experimental/_fit/align_samples/_stalign_impl/_core.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_core.py similarity index 100% rename from src/squidpy/experimental/_fit/align_samples/_stalign_impl/_core.py rename to src/squidpy/experimental/_methods/align_samples/_stalign_impl/_core.py diff --git a/src/squidpy/experimental/_fit/align_samples/_stalign_impl/_helpers.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py similarity index 100% rename from src/squidpy/experimental/_fit/align_samples/_stalign_impl/_helpers.py rename to src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py diff --git a/src/squidpy/experimental/_fit/align_samples/_stalign_impl/_tools.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py similarity index 100% rename from src/squidpy/experimental/_fit/align_samples/_stalign_impl/_tools.py rename to src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py diff --git a/src/squidpy/experimental/tl/__init__.py b/src/squidpy/experimental/tl/__init__.py index 1949aa6f9..772cf72e8 100644 --- a/src/squidpy/experimental/tl/__init__.py +++ b/src/squidpy/experimental/tl/__init__.py @@ -1,8 +1,7 @@ from __future__ import annotations from ._align import align, align_by_landmarks -from ._tiling_qc import , calculate_tiling_qc, TilingQCParams +from ._tiling_qc import TilingQCParams, calculate_tiling_qc -__all__ = ["TilingQCParams", "calculate_tiling_qc"] __all__ = ["align", "align_by_landmarks", "calculate_tiling_qc", "TilingQCParams"] diff --git a/src/squidpy/experimental/tl/_align/_api.py b/src/squidpy/experimental/tl/_align/_api.py index 7d31a3ad5..e1e18dc99 100644 --- a/src/squidpy/experimental/tl/_align/_api.py +++ b/src/squidpy/experimental/tl/_align/_api.py @@ -81,7 +81,7 @@ def align( ref_xy = get_coords(ref_adata, spatial_key) query_xy = get_coords(query_adata, spatial_key) - result = ALIGN_SAMPLES.get(method)().fit(ref_xy, query_xy, **method_kwargs) + result = ALIGN_SAMPLES.get(method)(ref_xy, query_xy, **method_kwargs) return writeback_obs( result, @@ -132,7 +132,7 @@ def align_by_landmarks( """ validate_output_mode(output_mode) - result = ALIGN_LANDMARKS.get(method)().fit( + result = ALIGN_LANDMARKS.get(method)( landmarks_ref, landmarks_query, source_cs=cs_name_query, diff --git a/tests/experimental/fit/test_core.py b/tests/experimental/fit/test_core.py index 89968f64a..b6c25a290 100644 --- a/tests/experimental/fit/test_core.py +++ b/tests/experimental/fit/test_core.py @@ -6,7 +6,7 @@ import numpy as np import pytest -from squidpy.experimental._fit import Estimator, FitResult, Registry +from squidpy.experimental._fit import FitResult, Registry @dataclass @@ -20,21 +20,17 @@ def transform(self, x: np.ndarray) -> np.ndarray: return np.asarray(x, dtype=float) + self.delta -class _MeanShiftEstimator(Estimator): - """Toy estimator: fit the offset that maps the query centroid onto the ref centroid.""" - - name = "mean_shift" - - def fit(self, ref: np.ndarray, query: np.ndarray) -> _MeanShiftResult: - delta = np.asarray(ref, dtype=float).mean(0) - np.asarray(query, dtype=float).mean(0) - return _MeanShiftResult(delta=delta, metadata={"method": self.name}) +def fit_mean_shift(ref: np.ndarray, query: np.ndarray) -> _MeanShiftResult: + """Toy estimator function: fit the offset that maps the query centroid onto the ref centroid.""" + delta = np.asarray(ref, dtype=float).mean(0) - np.asarray(query, dtype=float).mean(0) + return _MeanShiftResult(delta=delta, metadata={"method": "mean_shift"}) def test_fit_then_transform_round_trip() -> None: ref = np.array([[1.0, 1.0], [3.0, 3.0]]) # centroid (2, 2) query = np.array([[0.0, 0.0], [2.0, 2.0]]) # centroid (1, 1) - result = _MeanShiftEstimator().fit(ref, query) + result = fit_mean_shift(ref, query) assert isinstance(result, FitResult) np.testing.assert_allclose(result.delta, [1.0, 1.0]) @@ -55,17 +51,17 @@ def test_registry_register_get_keys() -> None: reg = Registry("demo") @reg.register("mean_shift") - class _Registered(_MeanShiftEstimator): - pass + def _registered(ref: np.ndarray, query: np.ndarray) -> _MeanShiftResult: + return fit_mean_shift(ref, query) assert reg.keys() == ("mean_shift",) - assert reg.get("mean_shift") is _Registered - assert isinstance(reg.get("mean_shift")().fit(np.ones((2, 2)), np.zeros((2, 2))), FitResult) + assert reg.get("mean_shift") is _registered + assert isinstance(reg.get("mean_shift")(np.ones((2, 2)), np.zeros((2, 2))), FitResult) def test_registry_unknown_key_lists_available() -> None: reg = Registry("demo") - reg.register("a")(_MeanShiftEstimator) + reg.register("a")(fit_mean_shift) with pytest.raises(ValueError, match=r"Unknown demo method 'b'. Available: \['a'\]"): reg.get("b") @@ -73,23 +69,28 @@ def test_registry_unknown_key_lists_available() -> None: def test_registry_rejects_duplicate_key() -> None: reg = Registry("demo") - reg.register("dup")(_MeanShiftEstimator) + reg.register("dup")(fit_mean_shift) with pytest.raises(ValueError, match="already registered"): - reg.register("dup")(_MeanShiftEstimator) + reg.register("dup")(fit_mean_shift) def test_check_requirements_passes_when_none() -> None: - # _MeanShiftEstimator has the default empty `requires`. - _MeanShiftEstimator().check_requirements() + reg = Registry("demo") + # By default, registering without requires parameter does not wrap/check. + reg.register("mean_shift")(fit_mean_shift) + result = reg.get("mean_shift")(np.ones((2, 2)), np.zeros((2, 2))) + assert isinstance(result, FitResult) def test_check_requirements_raises_for_missing_dependency() -> None: - class _NeedsGhost(_MeanShiftEstimator): - name = "needs_ghost" - requires = ("squidpy_nonexistent_pkg_xyz",) + reg = Registry("demo") + + @reg.register("needs_ghost", requires=("squidpy_nonexistent_pkg_xyz",)) + def _needs_ghost(ref: np.ndarray, query: np.ndarray) -> _MeanShiftResult: + return fit_mean_shift(ref, query) with pytest.raises( - ImportError, match=r"requires 'squidpy_nonexistent_pkg_xyz'.*squidpy\[squidpy_nonexistent_pkg_xyz\]" + ImportError, match=r"Method 'needs_ghost' requires 'squidpy_nonexistent_pkg_xyz'.*squidpy\[squidpy_nonexistent_pkg_xyz\]" ): - _NeedsGhost().check_requirements() + reg.get("needs_ghost")(np.ones((2, 2)), np.zeros((2, 2))) diff --git a/tests/experimental/fit/test_stalign.py b/tests/experimental/fit/test_stalign.py index f1d8f3bfb..382d2c4aa 100644 --- a/tests/experimental/fit/test_stalign.py +++ b/tests/experimental/fit/test_stalign.py @@ -11,7 +11,7 @@ pytest.importorskip("jax") -from squidpy.experimental._fit.align_samples import ALIGN_SAMPLES, StalignEstimator, StalignFitResult +from squidpy.experimental._fit.align_samples import ALIGN_SAMPLES, StalignFitResult, fit_stalign from squidpy.experimental._fit.align_samples._stalign_impl._tools import ( STalignConfig, STalignPreprocessConfig, @@ -41,13 +41,13 @@ def _tiny_config() -> STalignConfig: def test_stalign_registered_in_align_family() -> None: assert "stalign" in ALIGN_SAMPLES.keys() - assert ALIGN_SAMPLES.get("stalign") is StalignEstimator + assert ALIGN_SAMPLES.get("stalign") is fit_stalign def test_stalign_fit_returns_displacement_result() -> None: ref, query = _points_xy(), _points_xy() - result = StalignEstimator().fit(ref, query, config=_tiny_config()) + result = fit_stalign(ref, query, config=_tiny_config()) assert isinstance(result, StalignFitResult) assert result.deltas.shape == query.shape @@ -61,14 +61,14 @@ def test_stalign_fit_returns_displacement_result() -> None: def test_stalign_transform_bakes_in_deltas() -> None: ref, query = _points_xy(), _points_xy() - result = StalignEstimator().fit(ref, query, config=_tiny_config()) + result = fit_stalign(ref, query, config=_tiny_config()) np.testing.assert_allclose(result.transform(query), query + result.deltas) def test_stalign_transform_rejects_wrong_shape() -> None: ref, query = _points_xy(), _points_xy() - result = StalignEstimator().fit(ref, query, config=_tiny_config()) + result = fit_stalign(ref, query, config=_tiny_config()) with pytest.raises(ValueError, match="expects coordinates of shape"): result.transform(np.zeros((1, 2))) @@ -78,7 +78,7 @@ def test_stalign_fit_with_landmarks() -> None: ref, query = _points_xy(), _points_xy() landmarks = ref[:3] - result = StalignEstimator().fit( + result = fit_stalign( ref, query, config=_tiny_config(), @@ -92,4 +92,4 @@ def test_stalign_fit_with_landmarks() -> None: def test_stalign_fit_rejects_non_2d_input() -> None: with pytest.raises(ValueError, match=r"Expected `query` to be an \(N, 2\) array"): - StalignEstimator().fit(_points_xy(), np.zeros((5, 3)), config=_tiny_config()) + fit_stalign(_points_xy(), np.zeros((5, 3)), config=_tiny_config()) From 77f3e7d940486f6cd54295b10644be65a816df82 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 10:06:14 +0200 Subject: [PATCH 78/96] move files --- tests/experimental/{fit => _methods}/__init__.py | 0 tests/experimental/{fit => _methods}/test_core.py | 0 tests/experimental/{fit => _methods}/test_stalign.py | 0 3 files changed, 0 insertions(+), 0 deletions(-) rename tests/experimental/{fit => _methods}/__init__.py (100%) rename tests/experimental/{fit => _methods}/test_core.py (100%) rename tests/experimental/{fit => _methods}/test_stalign.py (100%) diff --git a/tests/experimental/fit/__init__.py b/tests/experimental/_methods/__init__.py similarity index 100% rename from tests/experimental/fit/__init__.py rename to tests/experimental/_methods/__init__.py diff --git a/tests/experimental/fit/test_core.py b/tests/experimental/_methods/test_core.py similarity index 100% rename from tests/experimental/fit/test_core.py rename to tests/experimental/_methods/test_core.py diff --git a/tests/experimental/fit/test_stalign.py b/tests/experimental/_methods/test_stalign.py similarity index 100% rename from tests/experimental/fit/test_stalign.py rename to tests/experimental/_methods/test_stalign.py From 57f9d21d52007ed98e338db6c18610bd99b7b711 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 10:06:21 +0200 Subject: [PATCH 79/96] fix imports --- src/squidpy/experimental/_methods/_families.py | 2 +- .../experimental/_methods/align_landmarks/_landmark.py | 4 ++-- src/squidpy/experimental/_methods/align_samples/_stalign.py | 6 +++--- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/src/squidpy/experimental/_methods/_families.py b/src/squidpy/experimental/_methods/_families.py index 1399d9d2c..2ef6b01a5 100644 --- a/src/squidpy/experimental/_methods/_families.py +++ b/src/squidpy/experimental/_methods/_families.py @@ -9,7 +9,7 @@ from __future__ import annotations -from squidpy.experimental._fit._registry import Registry +from squidpy.experimental._methods._registry import Registry #: Sample-to-sample alignment estimators -- ref/query point clouds in, transform out. #: Consumed by ``squidpy.experimental.tl.align``. diff --git a/src/squidpy/experimental/_methods/align_landmarks/_landmark.py b/src/squidpy/experimental/_methods/align_landmarks/_landmark.py index f3a7635c2..5da8f7ff4 100644 --- a/src/squidpy/experimental/_methods/align_landmarks/_landmark.py +++ b/src/squidpy/experimental/_methods/align_landmarks/_landmark.py @@ -20,8 +20,8 @@ import numpy as np -from squidpy.experimental._fit._families import ALIGN_LANDMARKS -from squidpy.experimental._fit._result import FitResult +from squidpy.experimental._methods._families import ALIGN_LANDMARKS +from squidpy.experimental._methods._result import FitResult @dataclass diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign.py b/src/squidpy/experimental/_methods/align_samples/_stalign.py index 003957390..e207a49f4 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign.py @@ -2,7 +2,7 @@ The JAX solver is lifted from scverse/squidpy#1150 (Selman Özleyen); see :mod:`._stalign_impl`. This module only adds the thin :class:`Estimator` / -:class:`FitResult` adapter onto the :mod:`squidpy.experimental._fit` core. JAX +:class:`FitResult` adapter onto the :mod:`squidpy.experimental._methods` core. JAX is imported lazily inside :meth:`StalignEstimator.fit`, so importing this module is cheap and does not require JAX. """ @@ -14,8 +14,8 @@ import numpy as np -from squidpy.experimental._fit._families import ALIGN_SAMPLES -from squidpy.experimental._fit._result import FitResult +from squidpy.experimental._methods._families import ALIGN_SAMPLES +from squidpy.experimental._methods._result import FitResult if TYPE_CHECKING: from ._stalign_impl._tools import STalignConfig From 5d69264c4518a1bafa9a18139c17b2e2e456ca92 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 10:07:47 +0200 Subject: [PATCH 80/96] fixes --- src/squidpy/experimental/_methods/__init__.py | 6 +++--- .../experimental/_methods/align_landmarks/__init__.py | 6 +++--- .../experimental/_methods/align_samples/__init__.py | 6 +++--- .../_methods/align_samples/_stalign_impl/__init__.py | 4 ++-- .../_methods/align_samples/_stalign_impl/_helpers.py | 2 +- .../_methods/align_samples/_stalign_impl/_tools.py | 2 +- src/squidpy/experimental/tl/_align/_api.py | 8 ++++---- src/squidpy/experimental/tl/_align/_io.py | 6 +++--- tests/experimental/_methods/test_core.py | 2 +- tests/experimental/_methods/test_stalign.py | 4 ++-- tests/experimental/tl/test_align.py | 4 ++-- tests/experimental/tl/test_align_by_landmarks.py | 2 +- 12 files changed, 26 insertions(+), 26 deletions(-) diff --git a/src/squidpy/experimental/_methods/__init__.py b/src/squidpy/experimental/_methods/__init__.py index 5bf65cc3f..0e0839690 100644 --- a/src/squidpy/experimental/_methods/__init__.py +++ b/src/squidpy/experimental/_methods/__init__.py @@ -15,7 +15,7 @@ Example ------- >>> import numpy as np ->>> from squidpy.experimental._fit import FitResult, Registry +>>> from squidpy.experimental._methods import FitResult, Registry >>> demo = Registry("demo") >>> class Shift(FitResult): ... def __init__(self, delta): @@ -33,7 +33,7 @@ from __future__ import annotations -from squidpy.experimental._fit._registry import Registry -from squidpy.experimental._fit._result import FitResult +from squidpy.experimental._methods._registry import Registry +from squidpy.experimental._methods._result import FitResult __all__ = ["FitResult", "Registry"] diff --git a/src/squidpy/experimental/_methods/align_landmarks/__init__.py b/src/squidpy/experimental/_methods/align_landmarks/__init__.py index 36d355977..6b04a86f2 100644 --- a/src/squidpy/experimental/_methods/align_landmarks/__init__.py +++ b/src/squidpy/experimental/_methods/align_landmarks/__init__.py @@ -1,14 +1,14 @@ """``align_landmarks`` family: closed-form alignment from paired landmarks. Importing this package registers the family's estimators into -:data:`~squidpy.experimental._fit._families.ALIGN_LANDMARKS`. Pure NumPy / +:data:`~squidpy.experimental._methods._families.ALIGN_LANDMARKS`. Pure NumPy / spatialdata / skimage -- no JAX. """ from __future__ import annotations -from squidpy.experimental._fit._families import ALIGN_LANDMARKS -from squidpy.experimental._fit.align_landmarks._landmark import ( +from squidpy.experimental._methods._families import ALIGN_LANDMARKS +from squidpy.experimental._methods.align_landmarks._landmark import ( AffineFitResult, fit_affine, fit_similarity, diff --git a/src/squidpy/experimental/_methods/align_samples/__init__.py b/src/squidpy/experimental/_methods/align_samples/__init__.py index 5c38b9cc3..e874c852e 100644 --- a/src/squidpy/experimental/_methods/align_samples/__init__.py +++ b/src/squidpy/experimental/_methods/align_samples/__init__.py @@ -1,13 +1,13 @@ """``align_samples`` family: align two samples' point clouds (STalign). Importing this package registers the family's estimators into -:data:`~squidpy.experimental._fit._families.ALIGN_SAMPLES`. It stays cheap -- +:data:`~squidpy.experimental._methods._families.ALIGN_SAMPLES`. It stays cheap -- JAX is pulled in lazily, only when an estimator's ``fit`` runs. """ from __future__ import annotations -from squidpy.experimental._fit._families import ALIGN_SAMPLES -from squidpy.experimental._fit.align_samples._stalign import StalignFitResult, fit_stalign +from squidpy.experimental._methods._families import ALIGN_SAMPLES +from squidpy.experimental._methods.align_samples._stalign import StalignFitResult, fit_stalign __all__ = ["ALIGN_SAMPLES", "fit_stalign", "StalignFitResult"] diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/__init__.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/__init__.py index 958810ce7..61cb3d357 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/__init__.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/__init__.py @@ -1,8 +1,8 @@ """Ported STalign JAX LDDMM solver (provenance: scverse/squidpy#1150). -Pure numerics only -- no :class:`~squidpy.experimental._fit.Estimator`. The +Pure numerics only -- no :class:`~squidpy.experimental._methods.Estimator`. The estimator adapter lives one level up in -:mod:`squidpy.experimental._fit.align_samples._stalign`. JAX is imported at +:mod:`squidpy.experimental._methods.align_samples._stalign`. JAX is imported at module load here, so this subpackage must only be imported lazily (the adapter does so inside ``fit``, after checking requirements). """ diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py index 0be6a63ec..aa173d1c4 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py @@ -2,7 +2,7 @@ Lifted from scverse/squidpy#1150 (Selman Özleyen). Container readers (``extract_points`` / ``extract_landmarks``) were dropped on the move into -:mod:`squidpy.experimental._fit`: reading coordinates out of an ``AnnData`` / +:mod:`squidpy.experimental._methods`: reading coordinates out of an ``AnnData`` / ``SpatialData`` is the caller's concern, so everything here operates purely on in-memory NumPy arrays. """ diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py index 4501ac291..7e2c9d6f2 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py @@ -3,7 +3,7 @@ Lifted from scverse/squidpy#1150 (Selman Özleyen) with import paths adjusted and minor cleanups (config unpacking, lazy dtype resolution). The container write-back path (``STalignResult.transform_adata``) was dropped on the move -into :mod:`squidpy.experimental._fit`: results stay in memory and the caller +into :mod:`squidpy.experimental._methods`: results stay in memory and the caller owns write-back. """ diff --git a/src/squidpy/experimental/tl/_align/_api.py b/src/squidpy/experimental/tl/_align/_api.py index e1e18dc99..48cf5d062 100644 --- a/src/squidpy/experimental/tl/_align/_api.py +++ b/src/squidpy/experimental/tl/_align/_api.py @@ -1,4 +1,4 @@ -"""Public alignment functions built on the :mod:`squidpy.experimental._fit` core. +"""Public alignment functions built on the :mod:`squidpy.experimental._methods` core. These are thin orchestrators: resolve inputs to in-memory arrays, dispatch to a fit-core estimator, write the result back. All container I/O and write-back live @@ -14,8 +14,8 @@ from anndata import AnnData from spatialdata import SpatialData -from squidpy.experimental._fit.align_landmarks import ALIGN_LANDMARKS -from squidpy.experimental._fit.align_samples import ALIGN_SAMPLES +from squidpy.experimental._methods.align_landmarks import ALIGN_LANDMARKS +from squidpy.experimental._methods.align_samples import ALIGN_SAMPLES from squidpy.experimental.tl._align._io import ( get_coords, resolve_obs_pair, @@ -25,7 +25,7 @@ from squidpy.experimental.tl._align._validation import validate_on, validate_output_mode if TYPE_CHECKING: - from squidpy.experimental._fit import FitResult + from squidpy.experimental._methods import FitResult __all__ = ["align", "align_by_landmarks"] diff --git a/src/squidpy/experimental/tl/_align/_io.py b/src/squidpy/experimental/tl/_align/_io.py index ff3437065..c55ebfa22 100644 --- a/src/squidpy/experimental/tl/_align/_io.py +++ b/src/squidpy/experimental/tl/_align/_io.py @@ -2,7 +2,7 @@ This is the *only* layer that knows about the ``AnnData | SpatialData`` argument shapes and the ``output_mode`` write-back strategies. The fit estimators in -:mod:`squidpy.experimental._fit` operate on plain arrays and never see a +:mod:`squidpy.experimental._methods` operate on plain arrays and never see a container. """ @@ -17,8 +17,8 @@ from squidpy._validators import assert_key_in_sdata if TYPE_CHECKING: - from squidpy.experimental._fit import FitResult - from squidpy.experimental._fit.align_landmarks import AffineFitResult + from squidpy.experimental._methods import FitResult + from squidpy.experimental._methods.align_landmarks import AffineFitResult # --------------------------------------------------------------------------- diff --git a/tests/experimental/_methods/test_core.py b/tests/experimental/_methods/test_core.py index b6c25a290..284b588d4 100644 --- a/tests/experimental/_methods/test_core.py +++ b/tests/experimental/_methods/test_core.py @@ -6,7 +6,7 @@ import numpy as np import pytest -from squidpy.experimental._fit import FitResult, Registry +from squidpy.experimental._methods import FitResult, Registry @dataclass diff --git a/tests/experimental/_methods/test_stalign.py b/tests/experimental/_methods/test_stalign.py index 382d2c4aa..a0a42af93 100644 --- a/tests/experimental/_methods/test_stalign.py +++ b/tests/experimental/_methods/test_stalign.py @@ -11,8 +11,8 @@ pytest.importorskip("jax") -from squidpy.experimental._fit.align_samples import ALIGN_SAMPLES, StalignFitResult, fit_stalign -from squidpy.experimental._fit.align_samples._stalign_impl._tools import ( +from squidpy.experimental._methods.align_samples import ALIGN_SAMPLES, StalignFitResult, fit_stalign +from squidpy.experimental._methods.align_samples._stalign_impl._tools import ( STalignConfig, STalignPreprocessConfig, STalignRegistrationConfig, diff --git a/tests/experimental/tl/test_align.py b/tests/experimental/tl/test_align.py index 30a2051d5..a8d762ebf 100644 --- a/tests/experimental/tl/test_align.py +++ b/tests/experimental/tl/test_align.py @@ -12,8 +12,8 @@ pytest.importorskip("jax") -from squidpy.experimental._fit.align_samples import StalignFitResult -from squidpy.experimental._fit.align_samples._stalign_impl._tools import ( +from squidpy.experimental._methods.align_samples import StalignFitResult +from squidpy.experimental._methods.align_samples._stalign_impl._tools import ( STalignConfig, STalignPreprocessConfig, STalignRegistrationConfig, diff --git a/tests/experimental/tl/test_align_by_landmarks.py b/tests/experimental/tl/test_align_by_landmarks.py index 05e3e472d..507f04e58 100644 --- a/tests/experimental/tl/test_align_by_landmarks.py +++ b/tests/experimental/tl/test_align_by_landmarks.py @@ -4,7 +4,7 @@ import pytest from anndata import AnnData -from squidpy.experimental._fit.align_landmarks import AffineFitResult +from squidpy.experimental._methods.align_landmarks import AffineFitResult from squidpy.experimental.tl import align_by_landmarks # square corners; query = ref shifted by (5, 7) -> a pure translation both models recover From 6587efed9395f9e77fb6ede2d14504666135936d Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 10:15:58 +0200 Subject: [PATCH 81/96] remove bloat code --- .../align_samples/_stalign_impl/_tools.py | 45 ++++++------------- 1 file changed, 13 insertions(+), 32 deletions(-) diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py index 7e2c9d6f2..f31177c14 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py @@ -41,7 +41,6 @@ "STalignResult", "stalign_points", "stalign_preprocess", - "transform_points", ] @@ -107,14 +106,16 @@ def transform_points( point_order: PointOrder | None = None, ) -> JaxArray: """Transform arbitrary point arrays with the fitted map.""" - return transform_points( + order = self.point_order if point_order is None else point_order + points_rc = to_row_col(points, point_order=order) + transformed = transform_points_row_col( self.velocity_grid, - self.velocity, - self.affine, - points, + jnp.asarray(self.velocity), + jnp.asarray(self.affine), + jnp.asarray(points_rc, dtype=jax_dtype()), direction=direction, - point_order=self.point_order if point_order is None else point_order, ) + return jnp.asarray(from_row_col(np.asarray(transformed), point_order=order)) def stalign_preprocess( @@ -151,27 +152,6 @@ def stalign_preprocess( ) -def transform_points( - xv: tuple[JaxArray, JaxArray], - v: JaxArray, - A: JaxArray, - points: np.ndarray, - *, - direction: Literal["forward", "backward"] = "forward", - point_order: PointOrder = "row_col", -) -> JaxArray: - """Transform point arrays with a fitted STalign map.""" - points_rc = to_row_col(points, point_order=point_order) - transformed = transform_points_row_col( - xv, - jnp.asarray(v), - jnp.asarray(A), - jnp.asarray(points_rc, dtype=jax_dtype()), - direction=direction, - ) - return jnp.asarray(from_row_col(np.asarray(transformed), point_order=point_order)) - - def stalign_points( source_points: np.ndarray, target_points: np.ndarray, @@ -214,14 +194,15 @@ def stalign_points( points_target=None if target_landmarks is None else jnp.asarray(target_landmarks, dtype=dtype), **asdict(registration), ) - aligned_points = transform_points( + points_rc = to_row_col(source_points, point_order="row_col") + transformed = transform_points_row_col( result["xv"], - result["v"], - result["A"], - source_points, + jnp.asarray(result["v"]), + jnp.asarray(result["A"]), + jnp.asarray(points_rc, dtype=dtype), direction="forward", - point_order="row_col", ) + aligned_points = jnp.asarray(from_row_col(np.asarray(transformed), point_order="row_col")) return STalignResult( affine=result["A"], velocity=result["v"], From e45e247d6e848dfd7feda7c5beaf8060e18955a1 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 10:18:48 +0200 Subject: [PATCH 82/96] typr hints --- .../align_samples/_stalign_impl/__init__.py | 8 +- .../align_samples/_stalign_impl/_core.py | 103 +++++++++--------- 2 files changed, 54 insertions(+), 57 deletions(-) diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/__init__.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/__init__.py index 61cb3d357..26c72f7e2 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/__init__.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/__init__.py @@ -1,10 +1,6 @@ -"""Ported STalign JAX LDDMM solver (provenance: scverse/squidpy#1150). +"""Ported STalign JAX LDDMM solver -Pure numerics only -- no :class:`~squidpy.experimental._methods.Estimator`. The -estimator adapter lives one level up in -:mod:`squidpy.experimental._methods.align_samples._stalign`. JAX is imported at -module load here, so this subpackage must only be imported lazily (the adapter -does so inside ``fit``, after checking requirements). +Pure numerics only and these functions are gated by the jax requirement. """ from __future__ import annotations diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_core.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_core.py index 8ac139b6c..0b98d4255 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_core.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_core.py @@ -20,7 +20,7 @@ def jax_dtype() -> jnp.dtype: return jnp.float64 if jax.config.x64_enabled else jnp.float32 -def _to_affine(linear: Any, translation: Any) -> Any: +def _to_affine(linear: jax.Array, translation: jax.Array) -> jax.Array: return jnp.array( [ [linear[0, 0], linear[0, 1], translation[0]], @@ -31,23 +31,24 @@ def _to_affine(linear: Any, translation: Any) -> Any: ) -def _grid_points(x: tuple[Any, Any]) -> Any: +def _grid_points(x: tuple[jax.Array, jax.Array]) -> jax.Array: yy, xx = jnp.meshgrid(x[0], x[1], indexing="ij") return jnp.stack((yy, xx)) def _interp( - x: tuple[Any, Any], - image: Any, - phii: Any, + x: tuple[jax.Array, jax.Array], + image: jax.Array, + phii: jax.Array, *, mode: str = "nearest", -) -> Any: +) -> jax.Array: """Interpolate a channels-first image on physical row-column coordinates.""" arr = jnp.asarray(image) coords = jnp.asarray(phii) if coords.shape[0] != 2: raise ValueError(f"Expected interpolation coordinates to have leading axis of size 2, found `{coords.shape}`.") + if arr.ndim == 2: arr = arr[None, ...] @@ -57,7 +58,7 @@ def _interp( col_idx = (coords[1] - x[1][0]) / col_step idx = jnp.stack((row_idx.reshape(-1), col_idx.reshape(-1))) - def _sample(channel: Any) -> Any: + def _sample(channel: jax.Array) -> jax.Array: values = jsp.ndimage.map_coordinates(channel, idx, order=1, mode=mode) return values.reshape(coords.shape[1:]) @@ -65,13 +66,13 @@ def _sample(channel: Any) -> Any: def transform_points_row_col( - xv: tuple[Any, Any], - velocity: Any, - affine: Any, - points: Any, + xv: tuple[jax.Array, jax.Array], + velocity: jax.Array, + affine: jax.Array, + points: np.ndarray | jax.Array, *, direction: Literal["forward", "backward"] = "forward", -) -> Any: +) -> jax.Array: pts = jnp.asarray(points) n_steps = velocity.shape[0] time_steps = range(n_steps) @@ -98,11 +99,11 @@ def transform_points_row_col( def _transform_grid_backward( - x_target: tuple[Any, Any], - xv: tuple[Any, Any], - velocity: Any, - affine: Any, -) -> Any: + x_target: tuple[jax.Array, jax.Array], + xv: tuple[jax.Array, jax.Array], + velocity: jax.Array, + affine: jax.Array, +) -> jax.Array: target_grid = _grid_points(x_target) affine_inv = jnp.linalg.inv(affine) source_grid = jnp.einsum("ij,jhw->ihw", affine_inv[:2, :2], target_grid) + affine_inv[:2, -1][:, None, None] @@ -114,7 +115,7 @@ def _transform_grid_backward( return source_grid -def _contrast_transform(source_image: Any, target_image: Any, weights: Any) -> Any: +def _contrast_transform(source_image: jax.Array, target_image: jax.Array, weights: jax.Array) -> jax.Array: flat_source = source_image.reshape(source_image.shape[0], -1) flat_target = target_image.reshape(target_image.shape[0], -1) flat_weights = weights.reshape(-1) @@ -128,7 +129,7 @@ def _contrast_transform(source_image: Any, target_image: Any, weights: Any) -> A return (coefficients.T @ design).reshape(target_image.shape) -def _build_velocity_grid(x_source: tuple[Any, Any], *, a: float, expand: float) -> tuple[Any, Any]: +def _build_velocity_grid(x_source: tuple[jax.Array, jax.Array], *, a: float, expand: float) -> tuple[jax.Array, jax.Array]: minimum = jnp.array([x_source[0][0], x_source[1][0]]) maximum = jnp.array([x_source[0][-1], x_source[1][-1]]) center = (minimum + maximum) / 2.0 @@ -141,11 +142,11 @@ def _build_velocity_grid(x_source: tuple[Any, Any], *, a: float, expand: float) def _build_regularizer( - xv: tuple[Any, Any], + xv: tuple[jax.Array, jax.Array], *, a: float, p: float, -) -> tuple[Any, Any, Any]: +) -> tuple[jax.Array, jax.Array, float | jax.Array]: dv = jnp.array([xv[0][1] - xv[0][0], xv[1][1] - xv[1][0]]) shape = (xv[0].shape[0], xv[1].shape[0]) fy = jnp.arange(shape[0], dtype=xv[0].dtype) / (shape[0] * dv[0]) @@ -160,21 +161,21 @@ def _build_regularizer( def _update_mixture_weights( - transformed_source: Any, - target_image: Any, - match_weights: Any, - artifact_weights: Any, - background_weights: Any, + transformed_source: jax.Array, + target_image: jax.Array, + match_weights: jax.Array, + artifact_weights: jax.Array, + background_weights: jax.Array, *, sigmaM: float, sigmaA: float, sigmaB: float, estimate_muA: bool, estimate_muB: bool, - muA: Any, - muB: Any, + muA: jax.Array, + muB: jax.Array, iteration: int, -) -> tuple[Any, Any, Any, Any, Any]: +) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]: if estimate_muA: muA = jnp.sum(artifact_weights * target_image, axis=(-1, -2)) / jnp.maximum(jnp.sum(artifact_weights), 1e-12) if estimate_muB: @@ -212,24 +213,24 @@ def _update_mixture_weights( def _lddmm_loss( - linear: Any, - translation: Any, - velocity: Any, + linear: jax.Array, + translation: jax.Array, + velocity: jax.Array, *, - x_source: tuple[Any, Any], - source_image: Any, - x_target: tuple[Any, Any], - target_image: Any, - xv: tuple[Any, Any], - match_weights: Any, - ll: Any, - dv_prod: Any, - points_source: Any, - points_target: Any, + x_source: tuple[jax.Array, jax.Array], + source_image: jax.Array, + x_target: tuple[jax.Array, jax.Array], + target_image: jax.Array, + xv: tuple[jax.Array, jax.Array], + match_weights: jax.Array, + ll: jax.Array, + dv_prod: float | jax.Array, + points_source: jax.Array, + points_target: jax.Array, sigmaM: float, sigmaR: float, sigmaP: float, -) -> tuple[Any, tuple[Any, Any, Any, Any, Any]]: +) -> tuple[jax.Array, tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]]: affine = _to_affine(linear, translation) source_grid = _transform_grid_backward(x_target, xv, velocity, affine) warped_source = _interp(x_source, source_image, source_grid, mode="nearest") @@ -257,15 +258,15 @@ def _lddmm_loss( def lddmm( - xI: tuple[Any, Any], - I: Any, - xJ: tuple[Any, Any], - J: Any, + xI: tuple[np.ndarray | jax.Array, np.ndarray | jax.Array], + I: np.ndarray | jax.Array, + xJ: tuple[np.ndarray | jax.Array, np.ndarray | jax.Array], + J: np.ndarray | jax.Array, *, - L: Any, - T: Any, - points_source: Any | None = None, - points_target: Any | None = None, + L: np.ndarray | jax.Array, + T: np.ndarray | jax.Array, + points_source: np.ndarray | jax.Array | None = None, + points_target: np.ndarray | jax.Array | None = None, a: float = 500.0, p: float = 2.0, expand: float = 2.0, From beb1476f154e2cf6196743dfac66a666af1e0328 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 10:34:45 +0200 Subject: [PATCH 83/96] add tests --- .../align_samples/_stalign_impl/_helpers.py | 7 ++++--- tests/experimental/tl/test_align.py | 19 +++++++++++++++++++ 2 files changed, 23 insertions(+), 3 deletions(-) diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py index aa173d1c4..78d509ba1 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py @@ -154,7 +154,8 @@ def affine_from_points( translation = np.mean(target, axis=0) - np.mean(source, axis=0) return linear, translation - source_h = np.concatenate((source, np.ones((source.shape[0], 1), dtype=float)), axis=1) - target_h = np.concatenate((target, np.ones((target.shape[0], 1), dtype=float)), axis=1) - affine = np.linalg.lstsq(source_h, target_h, rcond=None)[0].T + from skimage.transform import estimate_transform + + model_obj = estimate_transform("affine", src=source, dst=target) + affine = np.asarray(model_obj.params) return affine[:2, :2], affine[:2, -1] diff --git a/tests/experimental/tl/test_align.py b/tests/experimental/tl/test_align.py index a8d762ebf..213d2bbd0 100644 --- a/tests/experimental/tl/test_align.py +++ b/tests/experimental/tl/test_align.py @@ -81,3 +81,22 @@ def test_missing_spatial_key() -> None: ref, query = _adata(), _adata() with pytest.raises(KeyError, match="missing.*not found"): align(ref, query, spatial_key="missing", config=_tiny()) + + +def test_align_with_landmarks() -> None: + ref, query = _adata(), _adata() + landmarks = ref.obsm["spatial"][:3] + + result = align( + ref, + query, + method="stalign", + output_mode="object", + landmarks_source=landmarks, + landmarks_target=landmarks, + config=_tiny(), + ) + + assert isinstance(result, StalignFitResult) + assert result.deltas.shape == query.obsm["spatial"].shape + assert "stalign_result" in result.metadata From 5eeb95d4a6516045e610844e88a4590f9472fef5 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 10:50:27 +0200 Subject: [PATCH 84/96] reduce bloat --- src/squidpy/experimental/_methods/__init__.py | 33 +--- src/squidpy/experimental/_methods/_result.py | 32 ---- .../_methods/align_landmarks/_landmark.py | 3 +- .../_methods/align_samples/__init__.py | 4 +- .../_methods/align_samples/_stalign.py | 95 +++------- .../align_samples/_stalign_impl/_helpers.py | 170 +++++++++--------- .../align_samples/_stalign_impl/_tools.py | 129 ++++++------- src/squidpy/experimental/tl/_align/_api.py | 11 +- src/squidpy/experimental/tl/_align/_io.py | 8 +- tests/experimental/_methods/test_core.py | 18 +- tests/experimental/_methods/test_stalign.py | 42 +++-- tests/experimental/tl/test_align.py | 11 +- 12 files changed, 235 insertions(+), 321 deletions(-) delete mode 100644 src/squidpy/experimental/_methods/_result.py diff --git a/src/squidpy/experimental/_methods/__init__.py b/src/squidpy/experimental/_methods/__init__.py index 0e0839690..ab5d10038 100644 --- a/src/squidpy/experimental/_methods/__init__.py +++ b/src/squidpy/experimental/_methods/__init__.py @@ -1,39 +1,20 @@ """In-memory model-fitting core for experimental squidpy tools. This subpackage factors the *fit* half of fitting-style tools (alignment, -imputation, ...) out of any particular container or write-back path: - -- :class:`FitResult` -- the fitted parameters plus an optional, pure in-memory - :meth:`~FitResult.transform`. Container write-back lives in the calling - function, not here. -- :class:`Registry` -- a flat ``name -> function`` map, one per family. +imputation, ...) out of any particular container or write-back path. Each +family registers its estimators into a flat :class:`Registry` (``name -> +function``). An estimator returns a plain result dataclass that carries the +fitted parameters and a ``transform`` method; turning that into mutations on an +:class:`~anndata.AnnData` / :class:`~spatialdata.SpatialData` is the calling +function's job, not the result's. Concrete estimators live in per-family subpackages (``align_samples``, ``align_landmarks``, ...) and pull their optional dependencies (e.g. JAX) in lazily, so importing this package stays cheap. - -Example -------- ->>> import numpy as np ->>> from squidpy.experimental._methods import FitResult, Registry ->>> demo = Registry("demo") ->>> class Shift(FitResult): -... def __init__(self, delta): -... self.metadata, self.delta = {}, delta -... -... def transform(self, x): -... return np.asarray(x) + self.delta ->>> @demo.register("mean_shift") -... def fit_mean_shift(ref, query): -... return Shift(ref.mean(0) - query.mean(0)) ->>> result = demo.get("mean_shift")(np.ones((3, 2)), np.zeros((3, 2))) ->>> result.transform(np.zeros((3, 2))).tolist() -[[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]] """ from __future__ import annotations from squidpy.experimental._methods._registry import Registry -from squidpy.experimental._methods._result import FitResult -__all__ = ["FitResult", "Registry"] +__all__ = ["Registry"] diff --git a/src/squidpy/experimental/_methods/_result.py b/src/squidpy/experimental/_methods/_result.py deleted file mode 100644 index 861f3cbe4..000000000 --- a/src/squidpy/experimental/_methods/_result.py +++ /dev/null @@ -1,32 +0,0 @@ -"""Result contract for fitted estimators.""" - -from __future__ import annotations - -from abc import ABC -from typing import Any - - -class FitResult(ABC): - """Base class for the output of :meth:`Estimator.fit`. - - A ``FitResult`` carries the *fitted parameters* of a model -- pointers or - parametric forms (an affine matrix, a displacement field, neighbour - weights), not bulk data -- together with free-form :attr:`metadata`. - - It is deliberately container-agnostic. Turning a result into mutations on - an :class:`~anndata.AnnData` / :class:`~spatialdata.SpatialData` - (write-back) is the caller's responsibility, not the result's: write-back - is a general squidpy concern with several unrelated shapes (set a field, - apply a transformation to a lazy element, ...) and does not belong on the - fit result. - """ - - metadata: dict[str, Any] - - def transform(self, x: Any) -> Any: - """Apply the fitted model to in-memory data ``x`` and return the result. - - Optional. Estimators whose fit does not yield a reusable map (or that - only produce side outputs) need not implement it. - """ - raise NotImplementedError(f"{type(self).__name__} does not implement `transform`.") diff --git a/src/squidpy/experimental/_methods/align_landmarks/_landmark.py b/src/squidpy/experimental/_methods/align_landmarks/_landmark.py index 5da8f7ff4..dde043a9d 100644 --- a/src/squidpy/experimental/_methods/align_landmarks/_landmark.py +++ b/src/squidpy/experimental/_methods/align_landmarks/_landmark.py @@ -21,11 +21,10 @@ import numpy as np from squidpy.experimental._methods._families import ALIGN_LANDMARKS -from squidpy.experimental._methods._result import FitResult @dataclass -class AffineFitResult(FitResult): +class AffineFitResult: """A fitted ``(3, 3)`` homogeneous affine mapping query onto ref, in ``(x, y)``.""" matrix: np.ndarray diff --git a/src/squidpy/experimental/_methods/align_samples/__init__.py b/src/squidpy/experimental/_methods/align_samples/__init__.py index e874c852e..eb73741f0 100644 --- a/src/squidpy/experimental/_methods/align_samples/__init__.py +++ b/src/squidpy/experimental/_methods/align_samples/__init__.py @@ -8,6 +8,6 @@ from __future__ import annotations from squidpy.experimental._methods._families import ALIGN_SAMPLES -from squidpy.experimental._methods.align_samples._stalign import StalignFitResult, fit_stalign +from squidpy.experimental._methods.align_samples._stalign import fit_stalign -__all__ = ["ALIGN_SAMPLES", "fit_stalign", "StalignFitResult"] +__all__ = ["ALIGN_SAMPLES", "fit_stalign"] diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign.py b/src/squidpy/experimental/_methods/align_samples/_stalign.py index e207a49f4..8b82bd813 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign.py @@ -1,67 +1,31 @@ """STalign estimator: JAX LDDMM point-cloud registration. The JAX solver is lifted from scverse/squidpy#1150 (Selman Özleyen); see -:mod:`._stalign_impl`. This module only adds the thin :class:`Estimator` / -:class:`FitResult` adapter onto the :mod:`squidpy.experimental._methods` core. JAX -is imported lazily inside :meth:`StalignEstimator.fit`, so importing this module -is cheap and does not require JAX. +:mod:`._stalign_impl`. This module only adds the thin registry entry that swaps +``(x, y)`` inputs into the solver's row-column frame and returns the fitted +:class:`StalignResult`. JAX is imported lazily inside :func:`fit_stalign`, so +importing this module is cheap and does not require JAX. """ from __future__ import annotations -from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any -import numpy as np - from squidpy.experimental._methods._families import ALIGN_SAMPLES -from squidpy.experimental._methods._result import FitResult if TYPE_CHECKING: - from ._stalign_impl._tools import STalignConfig - - -@dataclass -class StalignFitResult(FitResult): - """Fitted STalign deformation, in ``(x, y)`` convention. - - ``deltas`` is the per-point displacement that maps the *query* points into - the *reference* frame; :meth:`transform` bakes it in. STalign produces a - diffeomorphic (non-affine) map, so ``affine`` is ``None`` here -- the full - map (velocity field, velocity grid, affine initialisation) is kept under - ``metadata['stalign_result']`` for power users who need to transform - arbitrary points via ``stalign_result.transform_points``. - """ - - deltas: np.ndarray - affine: np.ndarray | None = None - metadata: dict[str, Any] = field(default_factory=dict) - - def transform(self, x: np.ndarray) -> np.ndarray: - """Apply the fitted displacement to the ``(N, 2)`` ``(x, y)`` points it was fit on. - - ``deltas`` is per-point, so ``x`` must have the same shape as the query - cloud passed to :meth:`fit_stalign`. To map *arbitrary* points, - use ``metadata['stalign_result'].transform_points``. - """ - coords = np.asarray(x, dtype=float) - if coords.shape != self.deltas.shape: - raise ValueError( - f"`transform` expects coordinates of shape {self.deltas.shape} (the fitted query cloud), " - f"found {coords.shape}. Use `metadata['stalign_result'].transform_points` for arbitrary points." - ) - return coords + self.deltas + from ._stalign_impl._tools import STalignConfig, StalignResult @ALIGN_SAMPLES.register("stalign", requires=("jax",)) def fit_stalign( - ref: np.ndarray, - query: np.ndarray, + ref: Any, + query: Any, *, config: STalignConfig | None = None, - landmarks_source: np.ndarray | None = None, - landmarks_target: np.ndarray | None = None, -) -> StalignFitResult: + landmarks_source: Any | None = None, + landmarks_target: Any | None = None, +) -> StalignResult: """Fit a deformation mapping ``query`` onto ``ref``. Parameters @@ -80,38 +44,29 @@ def fit_stalign( Returns ------- - A :class:`StalignFitResult` whose ``deltas`` move ``query`` into the - reference frame. + A :class:`StalignResult` whose :meth:`~StalignResult.transform` maps + ``(x, y)`` points into the reference frame; ``aligned_points`` is the fitted + ``query`` already mapped. """ # Import the JAX-backed solver only after requirements pass, so callers # without JAX get the clean ImportError from check_requirements rather # than a confusing failure from a module-level `import jax`. + import jax.numpy as jnp + + from ._stalign_impl._helpers import validate_points from ._stalign_impl._tools import stalign_points - ref_xy = np.asarray(ref, dtype=float) - query_xy = np.asarray(query, dtype=float) - for arr_name, arr in (("ref", ref_xy), ("query", query_xy)): - if arr.ndim != 2 or arr.shape[1] != 2: - raise ValueError(f"Expected `{arr_name}` to be an (N, 2) array, found shape {arr.shape}.") + ref_xy = validate_points(ref, name="ref") + query_xy = validate_points(query, name="query") # The solver runs internally in row-col (y, x); inputs are (x, y) -- swap at the boundary. - src_rc = query_xy[:, [1, 0]] - tgt_rc = ref_xy[:, [1, 0]] - lm_src_rc = None if landmarks_source is None else np.asarray(landmarks_source, dtype=float)[:, [1, 0]] - lm_tgt_rc = None if landmarks_target is None else np.asarray(landmarks_target, dtype=float)[:, [1, 0]] + lm_src = None if landmarks_source is None else jnp.asarray(landmarks_source)[:, ::-1] + lm_tgt = None if landmarks_target is None else jnp.asarray(landmarks_target)[:, ::-1] - result = stalign_points( - source_points=src_rc, - target_points=tgt_rc, + return stalign_points( + source_points=query_xy[:, ::-1], + target_points=ref_xy[:, ::-1], config=config, - landmarks_source=lm_src_rc, - landmarks_target=lm_tgt_rc, - ) - - aligned_xy = np.asarray(result.aligned_points)[:, [1, 0]] - deltas_xy = aligned_xy - query_xy - return StalignFitResult( - deltas=deltas_xy, - affine=None, - metadata={"method": "stalign", "stalign_result": result}, + landmarks_source=lm_src, + landmarks_target=lm_tgt, ) diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py index 78d509ba1..3b2011b62 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py @@ -4,77 +4,62 @@ (``extract_points`` / ``extract_landmarks``) were dropped on the move into :mod:`squidpy.experimental._methods`: reading coordinates out of an ``AnnData`` / ``SpatialData`` is the caller's concern, so everything here operates purely on -in-memory NumPy arrays. +in-memory arrays. Rasterization runs on JAX so the point cloud never leaves the +device between preprocessing and the LDDMM solve. """ from __future__ import annotations -from typing import Literal +from typing import TYPE_CHECKING, Any +import jax.numpy as jnp import numpy as np -PointOrder = Literal["row_col", "xy"] +from ._core import jax_dtype + +if TYPE_CHECKING: + import jax + + JaxArray = jax.Array +else: # pragma: no cover - typing only + JaxArray = Any __all__ = [ - "PointOrder", "affine_from_points", - "from_row_col", "rasterize", - "to_row_col", + "validate_points", ] -def _validate_points(points: np.ndarray, *, name: str) -> np.ndarray: - arr = np.asarray(points, dtype=float) +def validate_points(points: Any, *, name: str) -> JaxArray: + """Coerce ``points`` to a finite ``(n, 2)`` JAX array.""" + arr = jnp.asarray(points, dtype=jax_dtype()) if arr.ndim != 2 or arr.shape[1] != 2: raise ValueError(f"Expected `{name}` to have shape `(n, 2)`, found `{arr.shape}`.") - if not np.all(np.isfinite(arr)): + if not bool(jnp.all(jnp.isfinite(arr))): raise ValueError(f"Expected `{name}` to contain only finite values.") return arr -def to_row_col(points: np.ndarray, *, point_order: PointOrder) -> np.ndarray: - """Convert coordinates to row-column order.""" - arr = _validate_points(points, name="points") - if point_order == "row_col": - return arr - if point_order == "xy": - return arr[:, [1, 0]] - raise ValueError(f"Unknown `point_order`: `{point_order}`.") - - -def from_row_col(points: np.ndarray, *, point_order: PointOrder) -> np.ndarray: - """Convert row-column coordinates to the requested order.""" - arr = _validate_points(points, name="points") - if point_order == "row_col": - return arr - if point_order == "xy": - return arr[:, [1, 0]] - raise ValueError(f"Unknown `point_order`: `{point_order}`.") - - -def _normalize(values: np.ndarray) -> np.ndarray: - values = np.asarray(values, dtype=float) - vmin = np.min(values) - vmax = np.max(values) - if np.isclose(vmin, vmax): - return np.ones_like(values, dtype=float) - return (values - vmin) / (vmax - vmin) - - def rasterize( - x: np.ndarray, - y: np.ndarray, + x: JaxArray, + y: JaxArray, *, - g: np.ndarray | None = None, + g: JaxArray | None = None, dx: float = 30.0, blur: float | list[float] = 1.0, expand: float = 1.1, -) -> tuple[np.ndarray, np.ndarray, np.ndarray]: - """Rasterize a point cloud into a multi-scale density image.""" - x = np.asarray(x, dtype=float) - y = np.asarray(y, dtype=float) - if x.ndim != 1 or y.ndim != 1 or x.shape != y.shape: +) -> tuple[JaxArray, JaxArray, JaxArray]: + """Rasterize a point cloud into a multi-scale Gaussian density image. + + Each point splats a normalized Gaussian over a fixed ``(2r + 1)`` patch and + the patches are scatter-added onto the grid -- the JAX analogue of the + original per-point NumPy loop. Out-of-grid patch pixels are masked out. + """ + dtype = jax_dtype() + x = jnp.asarray(x, dtype=dtype).reshape(-1) + y = jnp.asarray(y, dtype=dtype).reshape(-1) + if x.shape != y.shape: raise ValueError("Expected `x` and `y` to be 1D arrays with the same length.") if x.size == 0: raise ValueError("Expected at least one point to rasterize.") @@ -83,66 +68,91 @@ def rasterize( if expand <= 0: raise ValueError("Expected `expand` to be positive.") - blur_values = np.atleast_1d(np.asarray(blur, dtype=float)) - if blur_values.ndim != 1 or np.any(blur_values <= 0): + blur_values = jnp.atleast_1d(jnp.asarray(blur, dtype=dtype)) + if blur_values.ndim != 1 or bool(jnp.any(blur_values <= 0)): raise ValueError("Expected `blur` to be a positive scalar or a 1D sequence of positive values.") if g is None: - weights = np.ones_like(x, dtype=float) + weights = jnp.ones_like(x) else: - weights = np.asarray(g, dtype=float) + weights = jnp.asarray(g, dtype=dtype).reshape(-1) if weights.shape != x.shape: raise ValueError("Expected `g` to have the same shape as `x` and `y`.") - if not np.allclose(weights, 1.0): + if not bool(jnp.allclose(weights, 1.0)): weights = _normalize(weights) - min_x = float(np.min(x)) - max_x = float(np.max(x)) - min_y = float(np.min(y)) - max_y = float(np.max(y)) + min_x = float(jnp.min(x)) + max_x = float(jnp.max(x)) + min_y = float(jnp.min(y)) + max_y = float(jnp.max(y)) center_x = (min_x + max_x) / 2.0 center_y = (min_y + max_y) / 2.0 half_x = (max_x - min_x) * expand / 2.0 half_y = (max_y - min_y) * expand / 2.0 - grid_x = np.arange(center_x - half_x, center_x + half_x + dx, dx, dtype=float) - grid_y = np.arange(center_y - half_y, center_y + half_y + dx, dx, dtype=float) - if grid_x.size < 2 or grid_y.size < 2: + grid_x = jnp.arange(center_x - half_x, center_x + half_x + dx, dx, dtype=dtype) + grid_y = jnp.arange(center_y - half_y, center_y + half_y + dx, dx, dtype=dtype) + n_cols = int(grid_x.shape[0]) + n_rows = int(grid_y.shape[0]) + if n_cols < 2 or n_rows < 2: raise ValueError("Rasterized grid is too small. Increase the point spread or lower `dx`.") - mesh_x, mesh_y = np.meshgrid(grid_x, grid_y) - out = np.zeros((len(blur_values), grid_y.size, grid_x.size), dtype=float) - radius = int(np.ceil(float(np.max(blur_values)) * 4.0)) + radius = int(np.ceil(float(jnp.max(blur_values)) * 4.0)) + offsets = jnp.arange(-radius, radius + 1) + + # Nearest grid cell for every point, then its fixed (2r+1) patch indices. + col0 = jnp.round((x - grid_x[0]) / dx).astype(jnp.int32) # (N,) + row0 = jnp.round((y - grid_y[0]) / dx).astype(jnp.int32) # (N,) + patch_cols = col0[:, None] + offsets[None, :] # (N, K) + patch_rows = row0[:, None] + offsets[None, :] # (N, K) - for x_i, y_i, w_i in zip(x, y, weights, strict=False): - col = int(np.rint((x_i - grid_x[0]) / dx)) - row = int(np.rint((y_i - grid_y[0]) / dx)) + patch_x = grid_x[0] + patch_cols.astype(dtype) * dx # (N, K) + patch_y = grid_y[0] + patch_rows.astype(dtype) * dx # (N, K) - row0 = max(row - radius, 0) - row1 = min(row + radius, out.shape[1] - 1) - col0 = max(col - radius, 0) - col1 = min(col + radius, out.shape[2] - 1) + # (N, K, K) squared distance from each patch pixel to its source point. + dist2 = (patch_x[:, None, :] - x[:, None, None]) ** 2 + (patch_y[:, :, None] - y[:, None, None]) ** 2 - patch_x = mesh_x[row0 : row1 + 1, col0 : col1 + 1] - patch_y = mesh_y[row0 : row1 + 1, col0 : col1 + 1] - denom = 2.0 * (dx * blur_values * 2.0) ** 2 + denom = 2.0 * (dx * blur_values * 2.0) ** 2 # (B,) + kernels = jnp.exp(-dist2[None] / denom[:, None, None, None]) # (B, N, K, K) + kernels_sum = kernels.sum(axis=(2, 3), keepdims=True) + kernels = kernels / jnp.where(kernels_sum == 0.0, 1.0, kernels_sum) + kernels = kernels * weights[None, :, None, None] - kernels = np.exp(-((patch_x[..., None] - x_i) ** 2 + (patch_y[..., None] - y_i) ** 2) / denom) - kernels_sum = kernels.sum(axis=(0, 1), keepdims=True) - kernels /= np.where(kernels_sum == 0.0, 1.0, kernels_sum) - out[:, row0 : row1 + 1, col0 : col1 + 1] += np.moveaxis(kernels * w_i, -1, 0) + valid = ( + (patch_rows >= 0)[:, :, None] + & (patch_rows < n_rows)[:, :, None] + & (patch_cols >= 0)[:, None, :] + & (patch_cols < n_cols)[:, None, :] + ) # (N, K, K) + kernels = kernels * valid[None] + rows = jnp.broadcast_to(jnp.clip(patch_rows, 0, n_rows - 1)[:, :, None], dist2.shape) # (N, K, K) + cols = jnp.broadcast_to(jnp.clip(patch_cols, 0, n_cols - 1)[:, None, :], dist2.shape) # (N, K, K) + + out = jnp.zeros((blur_values.shape[0], n_rows, n_cols), dtype=dtype) + out = out.at[:, rows, cols].add(kernels) return grid_x, grid_y, out +def _normalize(values: JaxArray) -> JaxArray: + vmin = jnp.min(values) + vmax = jnp.max(values) + return jnp.where(jnp.isclose(vmin, vmax), jnp.ones_like(values), (values - vmin) / (vmax - vmin)) + + def affine_from_points( - points_source: np.ndarray, - points_target: np.ndarray, + points_source: JaxArray, + points_target: JaxArray, ) -> tuple[np.ndarray, np.ndarray]: - """Compute an affine initialization from corresponding landmarks.""" - source = _validate_points(points_source, name="points_source") - target = _validate_points(points_target, name="points_target") + """Compute an affine initialization from corresponding landmarks. + + Delegates to skimage's least-squares estimator, so this is the one place + that drops to NumPy on the CPU; the small ``(2, 2)`` / ``(2,)`` result is + lifted back to JAX by the caller. + """ + source = np.asarray(points_source, dtype=float) + target = np.asarray(points_target, dtype=float) if source.shape != target.shape: raise ValueError( f"Expected `points_source` and `points_target` to have the same shape, found " diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py index f31177c14..532b44c5a 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py @@ -1,10 +1,11 @@ """Low-level point-cloud tools for experimental STalign. Lifted from scverse/squidpy#1150 (Selman Özleyen) with import paths adjusted -and minor cleanups (config unpacking, lazy dtype resolution). The container -write-back path (``STalignResult.transform_adata``) was dropped on the move -into :mod:`squidpy.experimental._methods`: results stay in memory and the caller -owns write-back. +and minor cleanups. The fitted map is returned as a single :class:`StalignResult` +container that keeps everything on JAX -- the affine, velocity field and +velocity grid stay device-side and :meth:`StalignResult.transform` applies them +to arbitrary points without round-tripping through NumPy. Container write-back +lives in the caller, not here. """ from __future__ import annotations @@ -13,16 +14,9 @@ from typing import TYPE_CHECKING, Any, Literal, TypeAlias import jax.numpy as jnp -import numpy as np from ._core import jax_dtype, lddmm, transform_points_row_col -from ._helpers import ( - PointOrder, - affine_from_points, - from_row_col, - rasterize, - to_row_col, -) +from ._helpers import affine_from_points, rasterize, validate_points if TYPE_CHECKING: import jax @@ -38,7 +32,7 @@ "STalignPreprocessConfig", "STalignPreprocessResult", "STalignRegistrationConfig", - "STalignResult", + "StalignResult", "stalign_points", "stalign_preprocess", ] @@ -84,50 +78,58 @@ class STalignConfig: @dataclass(slots=True) class STalignPreprocessResult: - source_grid: tuple[np.ndarray, np.ndarray] - source_image: np.ndarray - target_grid: tuple[np.ndarray, np.ndarray] - target_image: np.ndarray + source_grid: tuple[JaxArray, JaxArray] + source_image: JaxArray + target_grid: tuple[JaxArray, JaxArray] + target_image: JaxArray @dataclass(slots=True) -class STalignResult: +class StalignResult: + """A fitted STalign diffeomorphism, ready to transform arbitrary points. + + The map (``affine`` + diffeomorphic ``velocity`` field on ``velocity_grid``) + is held as JAX arrays in the solver's internal row-column frame. + :meth:`transform` takes care of the ``(x, y)`` boundary, so callers work in + ``(x, y)`` throughout. ``aligned_points`` is the fitted query cloud already + mapped into the reference frame (``transform`` of the query). + """ + affine: JaxArray velocity: JaxArray velocity_grid: tuple[JaxArray, JaxArray] aligned_points: JaxArray - point_order: PointOrder = "row_col" - def transform_points( + def transform( self, - points: np.ndarray, + points: JaxArray, *, direction: Literal["forward", "backward"] = "forward", - point_order: PointOrder | None = None, ) -> JaxArray: - """Transform arbitrary point arrays with the fitted map.""" - order = self.point_order if point_order is None else point_order - points_rc = to_row_col(points, point_order=order) - transformed = transform_points_row_col( + """Map ``(N, 2)`` ``(x, y)`` points with the fitted diffeomorphism.""" + pts = jnp.asarray(points, dtype=jax_dtype()) + if pts.ndim != 2 or pts.shape[1] != 2: + raise ValueError(f"Expected an (N, 2) `(x, y)` array, found shape {pts.shape}.") + transformed_rc = transform_points_row_col( self.velocity_grid, - jnp.asarray(self.velocity), - jnp.asarray(self.affine), - jnp.asarray(points_rc, dtype=jax_dtype()), + self.velocity, + self.affine, + pts[:, ::-1], direction=direction, ) - return jnp.asarray(from_row_col(np.asarray(transformed), point_order=order)) + return transformed_rc[:, ::-1] def stalign_preprocess( - source_points: np.ndarray, - target_points: np.ndarray, + source_points: JaxArray, + target_points: JaxArray, *, config: STalignPreprocessConfig | None = None, ) -> STalignPreprocessResult: - """Rasterize source and target point clouds for LDDMM registration.""" + """Rasterize source and target point clouds (row-col) for LDDMM registration.""" config = STalignPreprocessConfig() if config is None else config - source_points = to_row_col(source_points, point_order="row_col") - target_points = to_row_col(target_points, point_order="row_col") + source_points = validate_points(source_points, name="source_points") + target_points = validate_points(target_points, name="target_points") source_x, source_y, source_image = rasterize( source_points[:, 1], @@ -153,60 +155,63 @@ def stalign_preprocess( def stalign_points( - source_points: np.ndarray, - target_points: np.ndarray, + source_points: JaxArray, + target_points: JaxArray, *, preprocessed: STalignPreprocessResult | None = None, config: STalignConfig | None = None, - landmarks_source: np.ndarray | None = None, - landmarks_target: np.ndarray | None = None, -) -> STalignResult: - """Align source point cloud to target with a JAX LDDMM solver.""" + landmarks_source: JaxArray | None = None, + landmarks_target: JaxArray | None = None, +) -> StalignResult: + """Align a source point cloud onto a target with a JAX LDDMM solver. + + All point arrays are in the solver's row-column frame; the returned + :class:`StalignResult` speaks ``(x, y)``. + """ config = STalignConfig() if config is None else config registration = config.registration - source_points = to_row_col(source_points, point_order="row_col") - target_points = to_row_col(target_points, point_order="row_col") + source_points = validate_points(source_points, name="source_points") + target_points = validate_points(target_points, name="target_points") if preprocessed is None: preprocessed = stalign_preprocess(source_points, target_points, config=config.preprocess) if (landmarks_source is None) != (landmarks_target is None): raise ValueError("Expected both landmark arrays to be provided together.") + dtype = jax_dtype() if landmarks_source is None: - linear = np.eye(2, dtype=float) - translation = np.zeros(2, dtype=float) + linear = jnp.eye(2, dtype=dtype) + translation = jnp.zeros(2, dtype=dtype) source_landmarks = None target_landmarks = None else: - source_landmarks = to_row_col(landmarks_source, point_order="row_col") - target_landmarks = to_row_col(landmarks_target, point_order="row_col") - linear, translation = affine_from_points(source_landmarks, target_landmarks) + source_landmarks = validate_points(landmarks_source, name="landmarks_source") + target_landmarks = validate_points(landmarks_target, name="landmarks_target") + linear_np, translation_np = affine_from_points(source_landmarks, target_landmarks) + linear = jnp.asarray(linear_np, dtype=dtype) + translation = jnp.asarray(translation_np, dtype=dtype) - dtype = jax_dtype() result = lddmm( preprocessed.source_grid, preprocessed.source_image, preprocessed.target_grid, preprocessed.target_image, - L=jnp.asarray(linear, dtype=dtype), - T=jnp.asarray(translation, dtype=dtype), - points_source=None if source_landmarks is None else jnp.asarray(source_landmarks, dtype=dtype), - points_target=None if target_landmarks is None else jnp.asarray(target_landmarks, dtype=dtype), + L=linear, + T=translation, + points_source=source_landmarks, + points_target=target_landmarks, **asdict(registration), ) - points_rc = to_row_col(source_points, point_order="row_col") - transformed = transform_points_row_col( + transformed_rc = transform_points_row_col( result["xv"], - jnp.asarray(result["v"]), - jnp.asarray(result["A"]), - jnp.asarray(points_rc, dtype=dtype), + result["v"], + result["A"], + source_points, direction="forward", ) - aligned_points = jnp.asarray(from_row_col(np.asarray(transformed), point_order="row_col")) - return STalignResult( + return StalignResult( affine=result["A"], velocity=result["v"], velocity_grid=result["xv"], - aligned_points=aligned_points, - point_order="row_col", + aligned_points=transformed_rc[:, ::-1], ) diff --git a/src/squidpy/experimental/tl/_align/_api.py b/src/squidpy/experimental/tl/_align/_api.py index 48cf5d062..8451bb9e4 100644 --- a/src/squidpy/experimental/tl/_align/_api.py +++ b/src/squidpy/experimental/tl/_align/_api.py @@ -25,7 +25,8 @@ from squidpy.experimental.tl._align._validation import validate_on, validate_output_mode if TYPE_CHECKING: - from squidpy.experimental._methods import FitResult + from squidpy.experimental._methods.align_landmarks import AffineFitResult + from squidpy.experimental._methods.align_samples._stalign_impl._tools import StalignResult __all__ = ["align", "align_by_landmarks"] @@ -42,7 +43,7 @@ def align( output_mode: Literal["object", "copy", "inplace"] = "object", key_added: str | None = None, **method_kwargs: Any, -) -> FitResult | AnnData | SpatialData | None: +) -> StalignResult | AnnData | SpatialData | None: """Align a query sample onto a reference sample. Parameters @@ -61,7 +62,7 @@ def align( spatial_key ``obsm`` key holding the ``(x, y)`` coordinates. Defaults to ``"spatial"``. output_mode - - ``"object"`` (default) -- return the raw :class:`FitResult`; nothing is written. + - ``"object"`` (default) -- return the raw :class:`StalignResult`; nothing is written. - ``"inplace"`` -- write the aligned coordinates into the query and return ``None``. - ``"copy"`` -- write into a copy of the query and return the copy. key_added @@ -105,7 +106,7 @@ def align_by_landmarks( spatial_key: str = "spatial", output_mode: Literal["object", "copy", "inplace"] = "object", key_added: str | None = None, -) -> FitResult | AnnData | SpatialData | None: +) -> AffineFitResult | AnnData | SpatialData | None: """Align by a closed-form fit on pre-paired landmarks. Parameters @@ -126,7 +127,7 @@ def align_by_landmarks( spatial_key ``obsm`` key when ``data`` is an :class:`~anndata.AnnData`. output_mode - See :func:`align`. ``"object"`` (default) returns the :class:`FitResult`. + See :func:`align`. ``"object"`` (default) returns the :class:`AffineFitResult`. key_added Destination ``obsm`` key when ``data`` is an AnnData (see :func:`align`). """ diff --git a/src/squidpy/experimental/tl/_align/_io.py b/src/squidpy/experimental/tl/_align/_io.py index c55ebfa22..ff8b9aef1 100644 --- a/src/squidpy/experimental/tl/_align/_io.py +++ b/src/squidpy/experimental/tl/_align/_io.py @@ -17,8 +17,8 @@ from squidpy._validators import assert_key_in_sdata if TYPE_CHECKING: - from squidpy.experimental._methods import FitResult from squidpy.experimental._methods.align_landmarks import AffineFitResult + from squidpy.experimental._methods.align_samples._stalign_impl._tools import StalignResult # --------------------------------------------------------------------------- @@ -80,7 +80,7 @@ def get_coords(adata: AnnData, spatial_key: str) -> np.ndarray: def writeback_obs( - result: FitResult, + result: StalignResult | AffineFitResult, *, output_mode: str, query_adata: AnnData, @@ -88,13 +88,13 @@ def writeback_obs( element_key: str | None, spatial_key: str, key_added: str | None, -) -> FitResult | AnnData | SpatialData | None: +) -> StalignResult | AffineFitResult | AnnData | SpatialData | None: """Bake ``result.transform(coords)`` into the query ``obsm`` per ``output_mode``.""" if output_mode == "object": return result dest = _resolve_dest(query_adata, spatial_key=spatial_key, key_added=key_added) - new_coords = result.transform(get_coords(query_adata, spatial_key)) + new_coords = np.asarray(result.transform(get_coords(query_adata, spatial_key))) if container is None: target = query_adata if output_mode == "inplace" else query_adata.copy() diff --git a/tests/experimental/_methods/test_core.py b/tests/experimental/_methods/test_core.py index 284b588d4..a657e1bd1 100644 --- a/tests/experimental/_methods/test_core.py +++ b/tests/experimental/_methods/test_core.py @@ -6,11 +6,11 @@ import numpy as np import pytest -from squidpy.experimental._methods import FitResult, Registry +from squidpy.experimental._methods import Registry @dataclass -class _MeanShiftResult(FitResult): +class _MeanShiftResult: """Toy result: a constant per-axis offset baked into ``transform``.""" delta: np.ndarray @@ -32,21 +32,11 @@ def test_fit_then_transform_round_trip() -> None: result = fit_mean_shift(ref, query) - assert isinstance(result, FitResult) np.testing.assert_allclose(result.delta, [1.0, 1.0]) np.testing.assert_allclose(result.transform(query), query + 1.0) assert result.metadata == {"method": "mean_shift"} -def test_fit_result_transform_default_not_implemented() -> None: - @dataclass - class _Bare(FitResult): - metadata: dict[str, Any] = field(default_factory=dict) - - with pytest.raises(NotImplementedError, match="does not implement `transform`"): - _Bare().transform(np.zeros((2, 2))) - - def test_registry_register_get_keys() -> None: reg = Registry("demo") @@ -56,7 +46,7 @@ def _registered(ref: np.ndarray, query: np.ndarray) -> _MeanShiftResult: assert reg.keys() == ("mean_shift",) assert reg.get("mean_shift") is _registered - assert isinstance(reg.get("mean_shift")(np.ones((2, 2)), np.zeros((2, 2))), FitResult) + assert isinstance(reg.get("mean_shift")(np.ones((2, 2)), np.zeros((2, 2))), _MeanShiftResult) def test_registry_unknown_key_lists_available() -> None: @@ -80,7 +70,7 @@ def test_check_requirements_passes_when_none() -> None: # By default, registering without requires parameter does not wrap/check. reg.register("mean_shift")(fit_mean_shift) result = reg.get("mean_shift")(np.ones((2, 2)), np.zeros((2, 2))) - assert isinstance(result, FitResult) + assert isinstance(result, _MeanShiftResult) def test_check_requirements_raises_for_missing_dependency() -> None: diff --git a/tests/experimental/_methods/test_stalign.py b/tests/experimental/_methods/test_stalign.py index a0a42af93..5974b54d4 100644 --- a/tests/experimental/_methods/test_stalign.py +++ b/tests/experimental/_methods/test_stalign.py @@ -1,7 +1,7 @@ """Integration tests for the ported STalign estimator. Tiny synthetic fixtures with ``niter=1`` keep these fast; they verify wiring -and shapes (dispatch -> JAX LDDMM -> StalignFitResult), not solver quality. +and shapes (dispatch -> JAX LDDMM -> StalignResult), not solver quality. """ from __future__ import annotations @@ -11,11 +11,12 @@ pytest.importorskip("jax") -from squidpy.experimental._methods.align_samples import ALIGN_SAMPLES, StalignFitResult, fit_stalign +from squidpy.experimental._methods.align_samples import ALIGN_SAMPLES, fit_stalign from squidpy.experimental._methods.align_samples._stalign_impl._tools import ( STalignConfig, STalignPreprocessConfig, STalignRegistrationConfig, + StalignResult, ) @@ -44,34 +45,40 @@ def test_stalign_registered_in_align_family() -> None: assert ALIGN_SAMPLES.get("stalign") is fit_stalign -def test_stalign_fit_returns_displacement_result() -> None: +def test_stalign_fit_returns_diffeomorphism() -> None: ref, query = _points_xy(), _points_xy() result = fit_stalign(ref, query, config=_tiny_config()) - assert isinstance(result, StalignFitResult) - assert result.deltas.shape == query.shape - assert np.all(np.isfinite(result.deltas)) - assert result.affine is None - assert result.metadata["method"] == "stalign" - # Escape hatch: the full diffeomorphic map is preserved for power users. - assert "stalign_result" in result.metadata + assert isinstance(result, StalignResult) + assert result.aligned_points.shape == query.shape + assert np.all(np.isfinite(np.asarray(result.aligned_points))) + assert result.affine.shape == (3, 3) + assert result.velocity.ndim == 4 -def test_stalign_transform_bakes_in_deltas() -> None: +def test_stalign_transform_matches_aligned_points() -> None: ref, query = _points_xy(), _points_xy() result = fit_stalign(ref, query, config=_tiny_config()) - np.testing.assert_allclose(result.transform(query), query + result.deltas) + np.testing.assert_allclose(np.asarray(result.transform(query)), np.asarray(result.aligned_points), atol=1e-5) -def test_stalign_transform_rejects_wrong_shape() -> None: +def test_stalign_transform_accepts_arbitrary_points() -> None: ref, query = _points_xy(), _points_xy() result = fit_stalign(ref, query, config=_tiny_config()) - with pytest.raises(ValueError, match="expects coordinates of shape"): - result.transform(np.zeros((1, 2))) + out = result.transform(np.zeros((1, 2))) + assert np.asarray(out).shape == (1, 2) + + +def test_stalign_transform_rejects_non_2d() -> None: + ref, query = _points_xy(), _points_xy() + result = fit_stalign(ref, query, config=_tiny_config()) + + with pytest.raises(ValueError, match=r"Expected an \(N, 2\)"): + result.transform(np.zeros((5, 3))) def test_stalign_fit_with_landmarks() -> None: @@ -86,10 +93,9 @@ def test_stalign_fit_with_landmarks() -> None: landmarks_target=landmarks, ) - assert result.deltas.shape == query.shape - assert "stalign_result" in result.metadata + assert result.aligned_points.shape == query.shape def test_stalign_fit_rejects_non_2d_input() -> None: - with pytest.raises(ValueError, match=r"Expected `query` to be an \(N, 2\) array"): + with pytest.raises(ValueError, match=r"Expected `query` to have shape `\(n, 2\)`"): fit_stalign(_points_xy(), np.zeros((5, 3)), config=_tiny_config()) diff --git a/tests/experimental/tl/test_align.py b/tests/experimental/tl/test_align.py index 213d2bbd0..22df2e675 100644 --- a/tests/experimental/tl/test_align.py +++ b/tests/experimental/tl/test_align.py @@ -12,11 +12,11 @@ pytest.importorskip("jax") -from squidpy.experimental._methods.align_samples import StalignFitResult from squidpy.experimental._methods.align_samples._stalign_impl._tools import ( STalignConfig, STalignPreprocessConfig, STalignRegistrationConfig, + StalignResult, ) from squidpy.experimental.tl import align @@ -38,8 +38,8 @@ def _tiny() -> STalignConfig: def test_object_mode_returns_result_and_touches_nothing() -> None: ref, query = _adata(), _adata() result = align(ref, query, method="stalign", output_mode="object", config=_tiny()) - assert isinstance(result, StalignFitResult) - assert result.deltas.shape == query.obsm["spatial"].shape + assert isinstance(result, StalignResult) + assert result.aligned_points.shape == query.obsm["spatial"].shape assert "aligned_spatial" not in query.obsm @@ -97,6 +97,5 @@ def test_align_with_landmarks() -> None: config=_tiny(), ) - assert isinstance(result, StalignFitResult) - assert result.deltas.shape == query.obsm["spatial"].shape - assert "stalign_result" in result.metadata + assert isinstance(result, StalignResult) + assert result.aligned_points.shape == query.obsm["spatial"].shape From 3c2ed02d9e2e1d9e6f26631f81fc4a4e6c1b3983 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 10:53:43 +0200 Subject: [PATCH 85/96] reduce bloat --- .../_methods/align_samples/_stalign_impl/_helpers.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py index 3b2011b62..314195d30 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py @@ -145,12 +145,7 @@ def affine_from_points( points_source: JaxArray, points_target: JaxArray, ) -> tuple[np.ndarray, np.ndarray]: - """Compute an affine initialization from corresponding landmarks. - - Delegates to skimage's least-squares estimator, so this is the one place - that drops to NumPy on the CPU; the small ``(2, 2)`` / ``(2,)`` result is - lifted back to JAX by the caller. - """ + """Compute an affine initialization from corresponding landmarks.""" source = np.asarray(points_source, dtype=float) target = np.asarray(points_target, dtype=float) if source.shape != target.shape: From 1ea9996a6f43ccd71f2fb113088ba3fe00982c6e Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 10:57:06 +0200 Subject: [PATCH 86/96] remove useless file --- src/squidpy/experimental/tl/_align/_api.py | 8 +++++--- .../experimental/tl/_align/_validation.py | 16 ---------------- 2 files changed, 5 insertions(+), 19 deletions(-) delete mode 100644 src/squidpy/experimental/tl/_align/_validation.py diff --git a/src/squidpy/experimental/tl/_align/_api.py b/src/squidpy/experimental/tl/_align/_api.py index 8451bb9e4..ffc3712d9 100644 --- a/src/squidpy/experimental/tl/_align/_api.py +++ b/src/squidpy/experimental/tl/_align/_api.py @@ -14,6 +14,7 @@ from anndata import AnnData from spatialdata import SpatialData +from squidpy._validators import assert_one_of from squidpy.experimental._methods.align_landmarks import ALIGN_LANDMARKS from squidpy.experimental._methods.align_samples import ALIGN_SAMPLES from squidpy.experimental.tl._align._io import ( @@ -22,8 +23,9 @@ writeback_affine_sdata, writeback_obs, ) -from squidpy.experimental.tl._align._validation import validate_on, validate_output_mode +OUTPUT_MODES = ("object", "copy", "inplace") +ON_VALUES = ("obs", "image") if TYPE_CHECKING: from squidpy.experimental._methods.align_landmarks import AffineFitResult from squidpy.experimental._methods.align_samples._stalign_impl._tools import StalignResult @@ -73,8 +75,8 @@ def align( method_kwargs Forwarded to the estimator's ``fit`` (e.g. ``config=STalignConfig(...)``). """ - validate_on(on) - validate_output_mode(output_mode) + assert_one_of(output_mode, OUTPUT_MODES, name="output_mode") + assert_one_of(on, ON_VALUES, name="on") if on == "image": raise NotImplementedError("`align(on='image')` is not implemented yet; use `on='obs'`.") diff --git a/src/squidpy/experimental/tl/_align/_validation.py b/src/squidpy/experimental/tl/_align/_validation.py deleted file mode 100644 index d501d8208..000000000 --- a/src/squidpy/experimental/tl/_align/_validation.py +++ /dev/null @@ -1,16 +0,0 @@ -"""Argument validation for the public ``align`` / ``align_by_landmarks`` functions.""" - -from __future__ import annotations - -from squidpy._validators import assert_one_of - -OUTPUT_MODES = ("object", "copy", "inplace") -ON_VALUES = ("obs", "image") - - -def validate_output_mode(value: str) -> None: - assert_one_of(value, OUTPUT_MODES, name="output_mode") - - -def validate_on(value: str) -> None: - assert_one_of(value, ON_VALUES, name="on") From ee741abd80d4523729f659ad23cc7d72bdff1531 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 11:01:03 +0200 Subject: [PATCH 87/96] fix --- src/squidpy/experimental/tl/_align/_api.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/squidpy/experimental/tl/_align/_api.py b/src/squidpy/experimental/tl/_align/_api.py index ffc3712d9..4b6c52930 100644 --- a/src/squidpy/experimental/tl/_align/_api.py +++ b/src/squidpy/experimental/tl/_align/_api.py @@ -133,7 +133,7 @@ def align_by_landmarks( key_added Destination ``obsm`` key when ``data`` is an AnnData (see :func:`align`). """ - validate_output_mode(output_mode) + assert_one_of(output_mode, OUTPUT_MODES, name="output_mode") result = ALIGN_LANDMARKS.get(method)( landmarks_ref, From ba91e4f4bab81c4bd0ebb59a86bd85d6e3f9e384 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 11:22:34 +0200 Subject: [PATCH 88/96] undo rasterize changes --- .../experimental/_methods/_registry.py | 1 - .../_methods/align_samples/_stalign.py | 9 +- .../align_samples/_stalign_impl/_helpers.py | 106 ++++++------------ .../align_samples/_stalign_impl/_tools.py | 76 +++---------- tests/experimental/_methods/test_stalign.py | 9 ++ 5 files changed, 61 insertions(+), 140 deletions(-) diff --git a/src/squidpy/experimental/_methods/_registry.py b/src/squidpy/experimental/_methods/_registry.py index 8026c14fe..c56f7cc7c 100644 --- a/src/squidpy/experimental/_methods/_registry.py +++ b/src/squidpy/experimental/_methods/_registry.py @@ -43,7 +43,6 @@ def wrapped(*args: Any, **kwargs: Any) -> Any: ) return func(*args, **kwargs) - wrapped.__requires__ = requires # type: ignore[attr-defined] self._registry[key] = wrapped return wrapped # type: ignore[return-value] else: diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign.py b/src/squidpy/experimental/_methods/align_samples/_stalign.py index 8b82bd813..a8a3c6acb 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign.py @@ -1,11 +1,4 @@ -"""STalign estimator: JAX LDDMM point-cloud registration. - -The JAX solver is lifted from scverse/squidpy#1150 (Selman Özleyen); see -:mod:`._stalign_impl`. This module only adds the thin registry entry that swaps -``(x, y)`` inputs into the solver's row-column frame and returns the fitted -:class:`StalignResult`. JAX is imported lazily inside :func:`fit_stalign`, so -importing this module is cheap and does not require JAX. -""" +"""STalign estimator: JAX LDDMM point-cloud registration.""" from __future__ import annotations diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py index 314195d30..9da0a2067 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_helpers.py @@ -1,12 +1,4 @@ -"""Numeric helpers for STalign point-cloud registration. - -Lifted from scverse/squidpy#1150 (Selman Özleyen). Container readers -(``extract_points`` / ``extract_landmarks``) were dropped on the move into -:mod:`squidpy.experimental._methods`: reading coordinates out of an ``AnnData`` / -``SpatialData`` is the caller's concern, so everything here operates purely on -in-memory arrays. Rasterization runs on JAX so the point cloud never leaves the -device between preprocessing and the LDDMM solve. -""" +"""Numeric helpers for STalign point-cloud registration.""" from __future__ import annotations @@ -42,23 +34,20 @@ def validate_points(points: Any, *, name: str) -> JaxArray: def rasterize( - x: JaxArray, - y: JaxArray, + x: np.ndarray, + y: np.ndarray, *, - g: JaxArray | None = None, dx: float = 30.0, blur: float | list[float] = 1.0, expand: float = 1.1, -) -> tuple[JaxArray, JaxArray, JaxArray]: +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: """Rasterize a point cloud into a multi-scale Gaussian density image. Each point splats a normalized Gaussian over a fixed ``(2r + 1)`` patch and - the patches are scatter-added onto the grid -- the JAX analogue of the - original per-point NumPy loop. Out-of-grid patch pixels are masked out. + the patches are accumulated onto the grid. """ - dtype = jax_dtype() - x = jnp.asarray(x, dtype=dtype).reshape(-1) - y = jnp.asarray(y, dtype=dtype).reshape(-1) + x = np.asarray(x, dtype=float).reshape(-1) + y = np.asarray(y, dtype=float).reshape(-1) if x.shape != y.shape: raise ValueError("Expected `x` and `y` to be 1D arrays with the same length.") if x.size == 0: @@ -68,79 +57,50 @@ def rasterize( if expand <= 0: raise ValueError("Expected `expand` to be positive.") - blur_values = jnp.atleast_1d(jnp.asarray(blur, dtype=dtype)) - if blur_values.ndim != 1 or bool(jnp.any(blur_values <= 0)): + blur_values = np.atleast_1d(np.asarray(blur, dtype=float)) + if blur_values.ndim != 1 or np.any(blur_values <= 0): raise ValueError("Expected `blur` to be a positive scalar or a 1D sequence of positive values.") - if g is None: - weights = jnp.ones_like(x) - else: - weights = jnp.asarray(g, dtype=dtype).reshape(-1) - if weights.shape != x.shape: - raise ValueError("Expected `g` to have the same shape as `x` and `y`.") - if not bool(jnp.allclose(weights, 1.0)): - weights = _normalize(weights) - - min_x = float(jnp.min(x)) - max_x = float(jnp.max(x)) - min_y = float(jnp.min(y)) - max_y = float(jnp.max(y)) + min_x = float(np.min(x)) + max_x = float(np.max(x)) + min_y = float(np.min(y)) + max_y = float(np.max(y)) center_x = (min_x + max_x) / 2.0 center_y = (min_y + max_y) / 2.0 half_x = (max_x - min_x) * expand / 2.0 half_y = (max_y - min_y) * expand / 2.0 - grid_x = jnp.arange(center_x - half_x, center_x + half_x + dx, dx, dtype=dtype) - grid_y = jnp.arange(center_y - half_y, center_y + half_y + dx, dx, dtype=dtype) - n_cols = int(grid_x.shape[0]) - n_rows = int(grid_y.shape[0]) - if n_cols < 2 or n_rows < 2: + grid_x = np.arange(center_x - half_x, center_x + half_x + dx, dx, dtype=float) + grid_y = np.arange(center_y - half_y, center_y + half_y + dx, dx, dtype=float) + if grid_x.size < 2 or grid_y.size < 2: raise ValueError("Rasterized grid is too small. Increase the point spread or lower `dx`.") - radius = int(np.ceil(float(jnp.max(blur_values)) * 4.0)) - offsets = jnp.arange(-radius, radius + 1) - - # Nearest grid cell for every point, then its fixed (2r+1) patch indices. - col0 = jnp.round((x - grid_x[0]) / dx).astype(jnp.int32) # (N,) - row0 = jnp.round((y - grid_y[0]) / dx).astype(jnp.int32) # (N,) - patch_cols = col0[:, None] + offsets[None, :] # (N, K) - patch_rows = row0[:, None] + offsets[None, :] # (N, K) + mesh_x, mesh_y = np.meshgrid(grid_x, grid_y) + out = np.zeros((len(blur_values), grid_y.size, grid_x.size), dtype=float) + radius = int(np.ceil(float(np.max(blur_values)) * 4.0)) + denom = 2.0 * (dx * blur_values * 2.0) ** 2 - patch_x = grid_x[0] + patch_cols.astype(dtype) * dx # (N, K) - patch_y = grid_y[0] + patch_rows.astype(dtype) * dx # (N, K) + for x_i, y_i in zip(x, y, strict=False): + col = int(np.rint((x_i - grid_x[0]) / dx)) + row = int(np.rint((y_i - grid_y[0]) / dx)) - # (N, K, K) squared distance from each patch pixel to its source point. - dist2 = (patch_x[:, None, :] - x[:, None, None]) ** 2 + (patch_y[:, :, None] - y[:, None, None]) ** 2 + row0 = max(row - radius, 0) + row1 = min(row + radius, out.shape[1] - 1) + col0 = max(col - radius, 0) + col1 = min(col + radius, out.shape[2] - 1) - denom = 2.0 * (dx * blur_values * 2.0) ** 2 # (B,) - kernels = jnp.exp(-dist2[None] / denom[:, None, None, None]) # (B, N, K, K) - kernels_sum = kernels.sum(axis=(2, 3), keepdims=True) - kernels = kernels / jnp.where(kernels_sum == 0.0, 1.0, kernels_sum) - kernels = kernels * weights[None, :, None, None] + patch_x = mesh_x[row0 : row1 + 1, col0 : col1 + 1] + patch_y = mesh_y[row0 : row1 + 1, col0 : col1 + 1] - valid = ( - (patch_rows >= 0)[:, :, None] - & (patch_rows < n_rows)[:, :, None] - & (patch_cols >= 0)[:, None, :] - & (patch_cols < n_cols)[:, None, :] - ) # (N, K, K) - kernels = kernels * valid[None] + kernels = np.exp(-((patch_x[..., None] - x_i) ** 2 + (patch_y[..., None] - y_i) ** 2) / denom) + kernels_sum = kernels.sum(axis=(0, 1), keepdims=True) + kernels /= np.where(kernels_sum == 0.0, 1.0, kernels_sum) + out[:, row0 : row1 + 1, col0 : col1 + 1] += np.moveaxis(kernels, -1, 0) - rows = jnp.broadcast_to(jnp.clip(patch_rows, 0, n_rows - 1)[:, :, None], dist2.shape) # (N, K, K) - cols = jnp.broadcast_to(jnp.clip(patch_cols, 0, n_cols - 1)[:, None, :], dist2.shape) # (N, K, K) - - out = jnp.zeros((blur_values.shape[0], n_rows, n_cols), dtype=dtype) - out = out.at[:, rows, cols].add(kernels) return grid_x, grid_y, out -def _normalize(values: JaxArray) -> JaxArray: - vmin = jnp.min(values) - vmax = jnp.max(values) - return jnp.where(jnp.isclose(vmin, vmax), jnp.ones_like(values), (values - vmin) / (vmax - vmin)) - - def affine_from_points( points_source: JaxArray, points_target: JaxArray, diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py index 532b44c5a..b0b2425cf 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py @@ -1,11 +1,7 @@ -"""Low-level point-cloud tools for experimental STalign. - -Lifted from scverse/squidpy#1150 (Selman Özleyen) with import paths adjusted -and minor cleanups. The fitted map is returned as a single :class:`StalignResult` -container that keeps everything on JAX -- the affine, velocity field and -velocity grid stay device-side and :meth:`StalignResult.transform` applies them -to arbitrary points without round-tripping through NumPy. Container write-back -lives in the caller, not here. +"""Point-cloud tools for experimental STalign. + +The fitted map is returned as a single :class:`StalignResult`; container +write-back lives in the caller, not here. """ from __future__ import annotations @@ -30,11 +26,9 @@ __all__ = [ "STalignConfig", "STalignPreprocessConfig", - "STalignPreprocessResult", "STalignRegistrationConfig", "StalignResult", "stalign_points", - "stalign_preprocess", ] @@ -76,23 +70,12 @@ class STalignConfig: registration: STalignRegistrationConfig = field(default_factory=STalignRegistrationConfig) -@dataclass(slots=True) -class STalignPreprocessResult: - source_grid: tuple[JaxArray, JaxArray] - source_image: JaxArray - target_grid: tuple[JaxArray, JaxArray] - target_image: JaxArray - - @dataclass(slots=True) class StalignResult: """A fitted STalign diffeomorphism, ready to transform arbitrary points. - The map (``affine`` + diffeomorphic ``velocity`` field on ``velocity_grid``) - is held as JAX arrays in the solver's internal row-column frame. - :meth:`transform` takes care of the ``(x, y)`` boundary, so callers work in - ``(x, y)`` throughout. ``aligned_points`` is the fitted query cloud already - mapped into the reference frame (``transform`` of the query). + :meth:`transform` works in ``(x, y)``; ``aligned_points`` is the fitted query + cloud already mapped into the reference frame. """ affine: JaxArray @@ -120,45 +103,22 @@ def transform( return transformed_rc[:, ::-1] -def stalign_preprocess( - source_points: JaxArray, - target_points: JaxArray, - *, - config: STalignPreprocessConfig | None = None, -) -> STalignPreprocessResult: - """Rasterize source and target point clouds (row-col) for LDDMM registration.""" - config = STalignPreprocessConfig() if config is None else config - source_points = validate_points(source_points, name="source_points") - target_points = validate_points(target_points, name="target_points") - - source_x, source_y, source_image = rasterize( - source_points[:, 1], - source_points[:, 0], - dx=config.dx, - blur=config.blur, - expand=config.expand, - ) - target_x, target_y, target_image = rasterize( - target_points[:, 1], - target_points[:, 0], +def _rasterize_cloud(points_rc: JaxArray, config: STalignPreprocessConfig) -> tuple[tuple[JaxArray, JaxArray], JaxArray]: + """Rasterize a row-col cloud into a ``((grid_y, grid_x), image)`` density.""" + grid_x, grid_y, image = rasterize( + points_rc[:, 1], + points_rc[:, 0], dx=config.dx, blur=config.blur, expand=config.expand, ) - - return STalignPreprocessResult( - source_grid=(source_y, source_x), - source_image=source_image, - target_grid=(target_y, target_x), - target_image=target_image, - ) + return (grid_y, grid_x), image def stalign_points( source_points: JaxArray, target_points: JaxArray, *, - preprocessed: STalignPreprocessResult | None = None, config: STalignConfig | None = None, landmarks_source: JaxArray | None = None, landmarks_target: JaxArray | None = None, @@ -172,8 +132,8 @@ def stalign_points( registration = config.registration source_points = validate_points(source_points, name="source_points") target_points = validate_points(target_points, name="target_points") - if preprocessed is None: - preprocessed = stalign_preprocess(source_points, target_points, config=config.preprocess) + source_grid, source_image = _rasterize_cloud(source_points, config.preprocess) + target_grid, target_image = _rasterize_cloud(target_points, config.preprocess) if (landmarks_source is None) != (landmarks_target is None): raise ValueError("Expected both landmark arrays to be provided together.") @@ -192,10 +152,10 @@ def stalign_points( translation = jnp.asarray(translation_np, dtype=dtype) result = lddmm( - preprocessed.source_grid, - preprocessed.source_image, - preprocessed.target_grid, - preprocessed.target_image, + source_grid, + source_image, + target_grid, + target_image, L=linear, T=translation, points_source=source_landmarks, diff --git a/tests/experimental/_methods/test_stalign.py b/tests/experimental/_methods/test_stalign.py index 5974b54d4..960328733 100644 --- a/tests/experimental/_methods/test_stalign.py +++ b/tests/experimental/_methods/test_stalign.py @@ -73,6 +73,15 @@ def test_stalign_transform_accepts_arbitrary_points() -> None: assert np.asarray(out).shape == (1, 2) +def test_stalign_transform_backward_inverts_forward() -> None: + ref, query = _points_xy(), _points_xy() + result = fit_stalign(ref, query, config=_tiny_config()) + + forward = result.transform(query, direction="forward") + roundtrip = result.transform(forward, direction="backward") + np.testing.assert_allclose(np.asarray(roundtrip), query, atol=1e-3) + + def test_stalign_transform_rejects_non_2d() -> None: ref, query = _points_xy(), _points_xy() result = fit_stalign(ref, query, config=_tiny_config()) From 1d65f4afaca7281336552594a59355ea3dbcdba4 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 11:23:36 +0200 Subject: [PATCH 89/96] doc landmark --- .../_methods/align_landmarks/_landmark.py | 14 +------------- 1 file changed, 1 insertion(+), 13 deletions(-) diff --git a/src/squidpy/experimental/_methods/align_landmarks/_landmark.py b/src/squidpy/experimental/_methods/align_landmarks/_landmark.py index dde043a9d..b3c3c1d98 100644 --- a/src/squidpy/experimental/_methods/align_landmarks/_landmark.py +++ b/src/squidpy/experimental/_methods/align_landmarks/_landmark.py @@ -1,16 +1,4 @@ -"""Closed-form landmark alignment estimators. - -Ported from scverse/squidpy#1162 (``fit_landmark_affine``). Two pure models, no -JAX: - -- ``"similarity"`` (4 DOF: rotation + uniform scale + translation) via - :func:`spatialdata.transformations.get_transformation_between_landmarks`. -- ``"affine"`` (6 DOF) via :func:`skimage.transform.estimate_transform`. - -Both consume **pre-paired** ``(N, 2)`` ``(x, y)`` landmark arrays (row ``i`` of -the query matches row ``i`` of the reference); ``N`` must be at least 3. No -automatic correspondence matching is performed. -""" +"""Closed-form landmark alignment estimators.""" from __future__ import annotations From a3da05a2d5e1de3888b8709af9ff74d117fb587f Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 11:23:59 +0200 Subject: [PATCH 90/96] remove bloat --- .../_methods/align_samples/_stalign_impl/_core.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_core.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_core.py index 0b98d4255..39996a099 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_core.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_core.py @@ -1,7 +1,4 @@ -"""Core JAX implementation for experimental STalign point registration. - -Lifted byte-for-byte from scverse/squidpy#1150 (Selman Özleyen). -""" +"""Core JAX implementation for experimental STalign point registration.""" from __future__ import annotations From 373ca02a62696ae3872edfeea1f41a6574428807 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 11:25:19 +0200 Subject: [PATCH 91/96] docs --- src/squidpy/experimental/_methods/__init__.py | 15 +-------------- src/squidpy/experimental/_methods/_families.py | 9 +-------- 2 files changed, 2 insertions(+), 22 deletions(-) diff --git a/src/squidpy/experimental/_methods/__init__.py b/src/squidpy/experimental/_methods/__init__.py index ab5d10038..2769ea99f 100644 --- a/src/squidpy/experimental/_methods/__init__.py +++ b/src/squidpy/experimental/_methods/__init__.py @@ -1,17 +1,4 @@ -"""In-memory model-fitting core for experimental squidpy tools. - -This subpackage factors the *fit* half of fitting-style tools (alignment, -imputation, ...) out of any particular container or write-back path. Each -family registers its estimators into a flat :class:`Registry` (``name -> -function``). An estimator returns a plain result dataclass that carries the -fitted parameters and a ``transform`` method; turning that into mutations on an -:class:`~anndata.AnnData` / :class:`~spatialdata.SpatialData` is the calling -function's job, not the result's. - -Concrete estimators live in per-family subpackages (``align_samples``, -``align_landmarks``, ...) and pull their optional dependencies (e.g. JAX) in -lazily, so importing this package stays cheap. -""" +"""In-memory model-fitting core for experimental methods.""" from __future__ import annotations diff --git a/src/squidpy/experimental/_methods/_families.py b/src/squidpy/experimental/_methods/_families.py index 2ef6b01a5..dadef5033 100644 --- a/src/squidpy/experimental/_methods/_families.py +++ b/src/squidpy/experimental/_methods/_families.py @@ -1,11 +1,4 @@ -"""Estimator family registries. - -A *family* is the set of interchangeable algorithms behind one public function, -selected by ``method=`` and sharing one ``fit`` contract. One :class:`Registry` -per family. Registries live in this small module so the estimator modules can -register into them without importing their package ``__init__`` (which imports -the estimator modules in turn -- that would be circular). -""" +"""Estimator family registries.""" from __future__ import annotations From aaf48c7eef1673ca537176fed81919542052fb11 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 11:28:20 +0200 Subject: [PATCH 92/96] doc debloatr --- .../experimental/_methods/align_landmarks/__init__.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/src/squidpy/experimental/_methods/align_landmarks/__init__.py b/src/squidpy/experimental/_methods/align_landmarks/__init__.py index 6b04a86f2..37ee80aa0 100644 --- a/src/squidpy/experimental/_methods/align_landmarks/__init__.py +++ b/src/squidpy/experimental/_methods/align_landmarks/__init__.py @@ -1,9 +1,4 @@ -"""``align_landmarks`` family: closed-form alignment from paired landmarks. - -Importing this package registers the family's estimators into -:data:`~squidpy.experimental._methods._families.ALIGN_LANDMARKS`. Pure NumPy / -spatialdata / skimage -- no JAX. -""" +"""``align_landmarks`` family: closed-form alignment from paired landmarks.""" from __future__ import annotations From 4387a61080bbd0f012ee2a1ac905b6744a1845ab Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Mon, 8 Jun 2026 11:37:21 +0200 Subject: [PATCH 93/96] expose result classes --- .../align_samples/_stalign_impl/_tools.py | 16 +++++++++++----- src/squidpy/experimental/tl/__init__.py | 13 +++++++++++-- tests/experimental/tl/test_align.py | 6 ++++++ 3 files changed, 28 insertions(+), 7 deletions(-) diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py index b0b2425cf..b5a6848bc 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign_impl/_tools.py @@ -9,11 +9,6 @@ from dataclasses import asdict, dataclass, field from typing import TYPE_CHECKING, Any, Literal, TypeAlias -import jax.numpy as jnp - -from ._core import jax_dtype, lddmm, transform_points_row_col -from ._helpers import affine_from_points, rasterize, validate_points - if TYPE_CHECKING: import jax @@ -90,6 +85,10 @@ def transform( direction: Literal["forward", "backward"] = "forward", ) -> JaxArray: """Map ``(N, 2)`` ``(x, y)`` points with the fitted diffeomorphism.""" + import jax.numpy as jnp + + from ._core import jax_dtype, transform_points_row_col + pts = jnp.asarray(points, dtype=jax_dtype()) if pts.ndim != 2 or pts.shape[1] != 2: raise ValueError(f"Expected an (N, 2) `(x, y)` array, found shape {pts.shape}.") @@ -105,6 +104,8 @@ def transform( def _rasterize_cloud(points_rc: JaxArray, config: STalignPreprocessConfig) -> tuple[tuple[JaxArray, JaxArray], JaxArray]: """Rasterize a row-col cloud into a ``((grid_y, grid_x), image)`` density.""" + from ._helpers import rasterize + grid_x, grid_y, image = rasterize( points_rc[:, 1], points_rc[:, 0], @@ -128,6 +129,11 @@ def stalign_points( All point arrays are in the solver's row-column frame; the returned :class:`StalignResult` speaks ``(x, y)``. """ + import jax.numpy as jnp + + from ._core import jax_dtype, lddmm, transform_points_row_col + from ._helpers import affine_from_points, validate_points + config = STalignConfig() if config is None else config registration = config.registration source_points = validate_points(source_points, name="source_points") diff --git a/src/squidpy/experimental/tl/__init__.py b/src/squidpy/experimental/tl/__init__.py index 772cf72e8..f44dcfc8b 100644 --- a/src/squidpy/experimental/tl/__init__.py +++ b/src/squidpy/experimental/tl/__init__.py @@ -1,7 +1,16 @@ from __future__ import annotations +from squidpy.experimental._methods.align_landmarks import AffineFitResult +from squidpy.experimental._methods.align_samples._stalign_impl._tools import StalignResult + from ._align import align, align_by_landmarks from ._tiling_qc import TilingQCParams, calculate_tiling_qc -__all__ = ["align", "align_by_landmarks", "calculate_tiling_qc", "TilingQCParams"] - +__all__ = [ + "align", + "align_by_landmarks", + "calculate_tiling_qc", + "TilingQCParams", + "AffineFitResult", + "StalignResult", +] diff --git a/tests/experimental/tl/test_align.py b/tests/experimental/tl/test_align.py index 22df2e675..b4bd135cd 100644 --- a/tests/experimental/tl/test_align.py +++ b/tests/experimental/tl/test_align.py @@ -83,6 +83,12 @@ def test_missing_spatial_key() -> None: align(ref, query, spatial_key="missing", config=_tiny()) +def test_public_exports_are_reachable() -> None: + import squidpy.experimental.tl as tl + + assert tl.StalignResult is StalignResult + + def test_align_with_landmarks() -> None: ref, query = _adata(), _adata() landmarks = ref.obsm["spatial"][:3] From 85df8fcf4857af57cabe1de364b635444bd97f5a Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 11 Jun 2026 10:25:48 +0200 Subject: [PATCH 94/96] refactoring, testing, typing and bug fix on copy mode --- src/squidpy/experimental/_methods/__init__.py | 3 +- .../experimental/_methods/_families.py | 5 +- .../experimental/_methods/_protocols.py | 59 ++++ .../experimental/_methods/_registry.py | 27 +- .../_methods/align_landmarks/_landmark.py | 4 +- .../_methods/align_samples/__init__.py | 3 +- .../_methods/align_samples/_stalign.py | 12 +- src/squidpy/experimental/tl/__init__.py | 4 +- src/squidpy/experimental/tl/_align/_api.py | 14 +- src/squidpy/experimental/tl/_align/_io.py | 22 +- tests/experimental/tl/test_align.py | 24 ++ .../tl/test_align_by_landmarks.py | 34 ++ tests/experimental/tl/test_align_io.py | 297 ++++++++++++++++++ 13 files changed, 476 insertions(+), 32 deletions(-) create mode 100644 src/squidpy/experimental/_methods/_protocols.py create mode 100644 tests/experimental/tl/test_align_io.py diff --git a/src/squidpy/experimental/_methods/__init__.py b/src/squidpy/experimental/_methods/__init__.py index 2769ea99f..1fa74d939 100644 --- a/src/squidpy/experimental/_methods/__init__.py +++ b/src/squidpy/experimental/_methods/__init__.py @@ -2,6 +2,7 @@ from __future__ import annotations +from squidpy.experimental._methods._protocols import AlignLandmarksFn, AlignResult, AlignSamplesFn from squidpy.experimental._methods._registry import Registry -__all__ = ["Registry"] +__all__ = ["Registry", "AlignResult", "AlignSamplesFn", "AlignLandmarksFn"] diff --git a/src/squidpy/experimental/_methods/_families.py b/src/squidpy/experimental/_methods/_families.py index dadef5033..6f3610f8d 100644 --- a/src/squidpy/experimental/_methods/_families.py +++ b/src/squidpy/experimental/_methods/_families.py @@ -2,12 +2,13 @@ from __future__ import annotations +from squidpy.experimental._methods._protocols import AlignLandmarksFn, AlignSamplesFn from squidpy.experimental._methods._registry import Registry #: Sample-to-sample alignment estimators -- ref/query point clouds in, transform out. #: Consumed by ``squidpy.experimental.tl.align``. -ALIGN_SAMPLES = Registry("align_samples") +ALIGN_SAMPLES: Registry[AlignSamplesFn] = Registry("align_samples") #: Closed-form landmark alignment estimators -- paired landmarks in, affine out. #: Consumed by ``squidpy.experimental.tl.align_by_landmarks``. -ALIGN_LANDMARKS = Registry("align_landmarks") +ALIGN_LANDMARKS: Registry[AlignLandmarksFn] = Registry("align_landmarks") diff --git a/src/squidpy/experimental/_methods/_protocols.py b/src/squidpy/experimental/_methods/_protocols.py new file mode 100644 index 000000000..6f2366a65 --- /dev/null +++ b/src/squidpy/experimental/_methods/_protocols.py @@ -0,0 +1,59 @@ +"""Structural contracts shared across the alignment estimator families. + +These :class:`~typing.Protocol` types are what the public API and the registries +are typed against, so the orchestration layer never names a concrete estimator +result (e.g. ``StalignResult``). A new estimator only has to satisfy +:class:`AlignResult` -- a ``transform`` that maps points into the reference +frame -- to plug into :func:`squidpy.experimental.tl.align`. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, Protocol, runtime_checkable + +import numpy.typing as npt + +from squidpy._utils import NDArrayA + +if TYPE_CHECKING: + from squidpy.experimental._methods.align_landmarks._landmark import AffineFitResult + +__all__ = ["AlignResult", "AlignSamplesFn", "AlignLandmarksFn"] + + +@runtime_checkable +class AlignResult(Protocol): + """A fitted alignment that maps ``(N, 2)`` ``(x, y)`` points into the reference frame. + + This is the only thing the public ``align*`` functions require of an + estimator's result, so ``output_mode="object"`` is agnostic to the method + that produced it. + """ + + def transform(self, points: npt.ArrayLike, /) -> NDArrayA: + """Map an ``(N, 2)`` ``(x, y)`` array into the reference frame.""" + ... + + +class AlignSamplesFn(Protocol): + """Calling convention for ``align_samples`` estimators. + + Two point clouds in (passed by keyword as ``ref`` / ``query`` so the + direction can never be silently swapped), one :class:`AlignResult` out. + Solver-specific options arrive through ``**kwargs``. + """ + + def __call__(self, ref: npt.ArrayLike, query: npt.ArrayLike, **kwargs: Any) -> AlignResult: ... + + +class AlignLandmarksFn(Protocol): + """Calling convention for ``align_landmarks`` estimators: paired landmarks in, affine out.""" + + def __call__( + self, + landmarks_ref: npt.ArrayLike, + landmarks_query: npt.ArrayLike, + *, + source_cs: str | None = ..., + target_cs: str | None = ..., + ) -> AffineFitResult: ... diff --git a/src/squidpy/experimental/_methods/_registry.py b/src/squidpy/experimental/_methods/_registry.py index c56f7cc7c..75d7c1ab0 100644 --- a/src/squidpy/experimental/_methods/_registry.py +++ b/src/squidpy/experimental/_methods/_registry.py @@ -5,31 +5,41 @@ import functools import importlib.util from collections.abc import Callable -from typing import Any, TypeVar +from typing import Any, Generic, TypeVar +#: The calling convention a family's registry advertises (returned by :meth:`Registry.get`). F = TypeVar("F", bound=Callable[..., Any]) +#: The concrete function being registered. Kept separate from ``F`` so an estimator may +#: declare specific keyword parameters (e.g. ``config=``) without having to structurally +#: match the family's open-ended ``**kwargs`` calling convention. +RegisteredT = TypeVar("RegisteredT", bound=Callable[..., Any]) -class Registry: +class Registry(Generic[F]): """A flat ``name -> function`` registry for one *family* of methods. One :class:`Registry` is created per family (e.g. ``align``, ``impute``), so keys are plain method names -- there is no ``(method, mode)`` compound key, because the family already pins the rest. + + The type parameter ``F`` is the family's calling convention (a callable + :class:`~typing.Protocol`); :meth:`get` returns it, so dispatch sites are + typed against the family contract rather than ``Callable[..., Any]``. """ def __init__(self, name: str) -> None: self.name = name - self._registry: dict[str, Callable[..., Any]] = {} + self._registry: dict[str, F] = {} - def register(self, key: str, *, requires: tuple[str, ...] = ()) -> Callable[[F], F]: + def register(self, key: str, *, requires: tuple[str, ...] = ()) -> Callable[[RegisteredT], RegisteredT]: """Return a decorator registering a method/function under ``key``.""" - def decorator(func: F) -> F: + def decorator(func: RegisteredT) -> RegisteredT: if key in self._registry: raise ValueError(f"Method {key!r} is already registered in the {self.name!r} registry.") if requires: + @functools.wraps(func) def wrapped(*args: Any, **kwargs: Any) -> Any: missing = [pkg for pkg in requires if importlib.util.find_spec(pkg) is None] @@ -43,15 +53,15 @@ def wrapped(*args: Any, **kwargs: Any) -> Any: ) return func(*args, **kwargs) - self._registry[key] = wrapped + self._registry[key] = wrapped # type: ignore[assignment] return wrapped # type: ignore[return-value] else: - self._registry[key] = func + self._registry[key] = func # type: ignore[assignment] return func return decorator - def get(self, key: str) -> Callable[..., Any]: + def get(self, key: str) -> F: """Return the function registered under ``key``.""" try: return self._registry[key] @@ -61,4 +71,3 @@ def get(self, key: str) -> Callable[..., Any]: def keys(self) -> tuple[str, ...]: """Return the registered method names.""" return tuple(self._registry) - diff --git a/src/squidpy/experimental/_methods/align_landmarks/_landmark.py b/src/squidpy/experimental/_methods/align_landmarks/_landmark.py index b3c3c1d98..714309f1b 100644 --- a/src/squidpy/experimental/_methods/align_landmarks/_landmark.py +++ b/src/squidpy/experimental/_methods/align_landmarks/_landmark.py @@ -7,7 +7,9 @@ from typing import Any import numpy as np +import numpy.typing as npt +from squidpy._utils import NDArrayA from squidpy.experimental._methods._families import ALIGN_LANDMARKS @@ -24,7 +26,7 @@ def __post_init__(self) -> None: if self.matrix.shape != (3, 3): raise ValueError(f"Expected a (3, 3) homogeneous matrix, found shape {self.matrix.shape}.") - def transform(self, x: np.ndarray) -> np.ndarray: + def transform(self, x: npt.ArrayLike) -> NDArrayA: """Apply the affine to an ``(N, 2)`` ``(x, y)`` coordinate array.""" coords = np.asarray(x, dtype=float) if coords.ndim != 2 or coords.shape[1] != 2: diff --git a/src/squidpy/experimental/_methods/align_samples/__init__.py b/src/squidpy/experimental/_methods/align_samples/__init__.py index eb73741f0..933ea7d06 100644 --- a/src/squidpy/experimental/_methods/align_samples/__init__.py +++ b/src/squidpy/experimental/_methods/align_samples/__init__.py @@ -9,5 +9,6 @@ from squidpy.experimental._methods._families import ALIGN_SAMPLES from squidpy.experimental._methods.align_samples._stalign import fit_stalign +from squidpy.experimental._methods.align_samples._stalign_impl._tools import StalignResult -__all__ = ["ALIGN_SAMPLES", "fit_stalign"] +__all__ = ["ALIGN_SAMPLES", "fit_stalign", "StalignResult"] diff --git a/src/squidpy/experimental/_methods/align_samples/_stalign.py b/src/squidpy/experimental/_methods/align_samples/_stalign.py index a8a3c6acb..59490b9c8 100644 --- a/src/squidpy/experimental/_methods/align_samples/_stalign.py +++ b/src/squidpy/experimental/_methods/align_samples/_stalign.py @@ -2,7 +2,9 @@ from __future__ import annotations -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING + +import numpy.typing as npt from squidpy.experimental._methods._families import ALIGN_SAMPLES @@ -12,12 +14,12 @@ @ALIGN_SAMPLES.register("stalign", requires=("jax",)) def fit_stalign( - ref: Any, - query: Any, + ref: npt.ArrayLike, + query: npt.ArrayLike, *, config: STalignConfig | None = None, - landmarks_source: Any | None = None, - landmarks_target: Any | None = None, + landmarks_source: npt.ArrayLike | None = None, + landmarks_target: npt.ArrayLike | None = None, ) -> StalignResult: """Fit a deformation mapping ``query`` onto ``ref``. diff --git a/src/squidpy/experimental/tl/__init__.py b/src/squidpy/experimental/tl/__init__.py index f44dcfc8b..3b6f1013d 100644 --- a/src/squidpy/experimental/tl/__init__.py +++ b/src/squidpy/experimental/tl/__init__.py @@ -1,7 +1,8 @@ from __future__ import annotations +from squidpy.experimental._methods import AlignResult from squidpy.experimental._methods.align_landmarks import AffineFitResult -from squidpy.experimental._methods.align_samples._stalign_impl._tools import StalignResult +from squidpy.experimental._methods.align_samples import StalignResult from ._align import align, align_by_landmarks from ._tiling_qc import TilingQCParams, calculate_tiling_qc @@ -11,6 +12,7 @@ "align_by_landmarks", "calculate_tiling_qc", "TilingQCParams", + "AlignResult", "AffineFitResult", "StalignResult", ] diff --git a/src/squidpy/experimental/tl/_align/_api.py b/src/squidpy/experimental/tl/_align/_api.py index 4b6c52930..a89d82270 100644 --- a/src/squidpy/experimental/tl/_align/_api.py +++ b/src/squidpy/experimental/tl/_align/_api.py @@ -27,8 +27,7 @@ OUTPUT_MODES = ("object", "copy", "inplace") ON_VALUES = ("obs", "image") if TYPE_CHECKING: - from squidpy.experimental._methods.align_landmarks import AffineFitResult - from squidpy.experimental._methods.align_samples._stalign_impl._tools import StalignResult + from squidpy.experimental._methods import AlignResult __all__ = ["align", "align_by_landmarks"] @@ -45,7 +44,7 @@ def align( output_mode: Literal["object", "copy", "inplace"] = "object", key_added: str | None = None, **method_kwargs: Any, -) -> StalignResult | AnnData | SpatialData | None: +) -> AlignResult | AnnData | SpatialData | None: """Align a query sample onto a reference sample. Parameters @@ -64,7 +63,7 @@ def align( spatial_key ``obsm`` key holding the ``(x, y)`` coordinates. Defaults to ``"spatial"``. output_mode - - ``"object"`` (default) -- return the raw :class:`StalignResult`; nothing is written. + - ``"object"`` (default) -- return the fitted :class:`~squidpy.experimental._methods.AlignResult`; nothing is written. - ``"inplace"`` -- write the aligned coordinates into the query and return ``None``. - ``"copy"`` -- write into a copy of the query and return the copy. key_added @@ -84,7 +83,7 @@ def align( ref_xy = get_coords(ref_adata, spatial_key) query_xy = get_coords(query_adata, spatial_key) - result = ALIGN_SAMPLES.get(method)(ref_xy, query_xy, **method_kwargs) + result = ALIGN_SAMPLES.get(method)(ref=ref_xy, query=query_xy, **method_kwargs) return writeback_obs( result, @@ -108,7 +107,7 @@ def align_by_landmarks( spatial_key: str = "spatial", output_mode: Literal["object", "copy", "inplace"] = "object", key_added: str | None = None, -) -> AffineFitResult | AnnData | SpatialData | None: +) -> AlignResult | AnnData | SpatialData | None: """Align by a closed-form fit on pre-paired landmarks. Parameters @@ -129,7 +128,8 @@ def align_by_landmarks( spatial_key ``obsm`` key when ``data`` is an :class:`~anndata.AnnData`. output_mode - See :func:`align`. ``"object"`` (default) returns the :class:`AffineFitResult`. + See :func:`align`. ``"object"`` (default) returns the fitted + :class:`~squidpy.experimental._methods.align_landmarks.AffineFitResult`. key_added Destination ``obsm`` key when ``data`` is an AnnData (see :func:`align`). """ diff --git a/src/squidpy/experimental/tl/_align/_io.py b/src/squidpy/experimental/tl/_align/_io.py index ff8b9aef1..84f0b5b08 100644 --- a/src/squidpy/experimental/tl/_align/_io.py +++ b/src/squidpy/experimental/tl/_align/_io.py @@ -17,8 +17,8 @@ from squidpy._validators import assert_key_in_sdata if TYPE_CHECKING: + from squidpy.experimental._methods import AlignResult from squidpy.experimental._methods.align_landmarks import AffineFitResult - from squidpy.experimental._methods.align_samples._stalign_impl._tools import StalignResult # --------------------------------------------------------------------------- @@ -80,7 +80,7 @@ def get_coords(adata: AnnData, spatial_key: str) -> np.ndarray: def writeback_obs( - result: StalignResult | AffineFitResult, + result: AlignResult, *, output_mode: str, query_adata: AnnData, @@ -88,7 +88,7 @@ def writeback_obs( element_key: str | None, spatial_key: str, key_added: str | None, -) -> StalignResult | AffineFitResult | AnnData | SpatialData | None: +) -> AlignResult | AnnData | SpatialData | None: """Bake ``result.transform(coords)`` into the query ``obsm`` per ``output_mode``.""" if output_mode == "object": return result @@ -102,7 +102,13 @@ def writeback_obs( return None if output_mode == "inplace" else target sdata = container if output_mode == "inplace" else shallow_copy_sdata(container) - sdata.tables[element_key].obsm[dest] = new_coords + target_table = sdata.tables[element_key] + if output_mode == "copy": + # `shallow_copy_sdata` shares table objects with the original; copy the single + # table we mutate so `output_mode="copy"` truly leaves the input untouched. + target_table = target_table.copy() + sdata.tables[element_key] = target_table + target_table.obsm[dest] = new_coords return None if output_mode == "inplace" else sdata @@ -119,6 +125,7 @@ def writeback_affine_sdata( Non-destructive: it adds a transformation into ``target_cs`` so the whole coordinate system inherits the alignment. Nothing is materialised. """ + from spatialdata import deepcopy as sd_deepcopy from spatialdata.transformations import Affine, get_transformation, set_transformation if moving_cs is None or target_cs is None: @@ -127,11 +134,16 @@ def writeback_affine_sdata( out = sdata if output_mode == "inplace" else shallow_copy_sdata(sdata) sd_affine = Affine(np.asarray(result.matrix), input_axes=("x", "y"), output_axes=("x", "y")) touched = False - for _etype, _name, element in out.gen_elements(): + for etype, name, element in list(out.gen_elements()): if isinstance(element, AnnData): continue if moving_cs not in get_transformation(element, get_all=True): continue + if output_mode == "copy": + # `shallow_copy_sdata` shares element objects with the original; deep-copy each + # element we register a transform on so `output_mode="copy"` leaves the input untouched. + element = sd_deepcopy(element) + getattr(out, etype)[name] = element set_transformation(element, sd_affine, to_coordinate_system=target_cs) touched = True if not touched: diff --git a/tests/experimental/tl/test_align.py b/tests/experimental/tl/test_align.py index b4bd135cd..6f0469855 100644 --- a/tests/experimental/tl/test_align.py +++ b/tests/experimental/tl/test_align.py @@ -89,6 +89,30 @@ def test_public_exports_are_reachable() -> None: assert tl.StalignResult is StalignResult +def _sdata_tables(**tables: AnnData): + sd = pytest.importorskip("spatialdata") + from spatialdata.models import TableModel + + return sd.SpatialData(tables={name: TableModel.parse(adata) for name, adata in tables.items()}) + + +def test_sdata_object_mode() -> None: + sdata = _sdata_tables(ref=_adata(), query=_adata()) + result = align(sdata, method="stalign", ref_key="ref", query_key="query", output_mode="object", config=_tiny()) + assert isinstance(result, StalignResult) + assert "aligned_spatial" not in sdata.tables["query"].obsm + + +def test_sdata_copy_leaves_original_untouched() -> None: + sd = pytest.importorskip("spatialdata") + + sdata = _sdata_tables(ref=_adata(), query=_adata()) + out = align(sdata, ref_key="ref", query_key="query", output_mode="copy", key_added="aligned", config=_tiny()) + assert isinstance(out, sd.SpatialData) and out is not sdata + assert "aligned" in out.tables["query"].obsm + assert "aligned" not in sdata.tables["query"].obsm + + def test_align_with_landmarks() -> None: ref, query = _adata(), _adata() landmarks = ref.obsm["spatial"][:3] diff --git a/tests/experimental/tl/test_align_by_landmarks.py b/tests/experimental/tl/test_align_by_landmarks.py index 507f04e58..e54d93bf7 100644 --- a/tests/experimental/tl/test_align_by_landmarks.py +++ b/tests/experimental/tl/test_align_by_landmarks.py @@ -87,6 +87,40 @@ def test_unknown_method_lists_available() -> None: align_by_landmarks(_REF, _QUERY, method="nope", output_mode="object") +def test_non_finite_landmarks_rejected() -> None: + bad = _QUERY.copy() + bad[0, 0] = np.nan + with pytest.raises(ValueError, match="must contain only finite values"): + align_by_landmarks(_REF, bad, method="affine", output_mode="object") + + +def test_bad_data_type_raises() -> None: + with pytest.raises(TypeError, match="must be AnnData or SpatialData"): + align_by_landmarks(_REF, _QUERY, method="affine", data=object(), output_mode="inplace") # type: ignore[arg-type] + + +def test_spatialdata_copy_leaves_original_untouched() -> None: + sd = pytest.importorskip("spatialdata") + from spatialdata.models import PointsModel + from spatialdata.transformations import Identity, get_transformation + + pts = PointsModel.parse(_QUERY, transformations={"query_cs": Identity()}) + sdata = sd.SpatialData(points={"pts": pts}) + + out = align_by_landmarks( + _REF, + _QUERY, + method="affine", + data=sdata, + cs_name_query="query_cs", + cs_name_ref="ref_cs", + output_mode="copy", + ) + assert out is not sdata + assert "ref_cs" in get_transformation(out.points["pts"], get_all=True) + assert "ref_cs" not in get_transformation(sdata.points["pts"], get_all=True) + + def test_spatialdata_registers_transformation() -> None: sd = pytest.importorskip("spatialdata") from spatialdata.models import PointsModel diff --git a/tests/experimental/tl/test_align_io.py b/tests/experimental/tl/test_align_io.py new file mode 100644 index 000000000..409364471 --- /dev/null +++ b/tests/experimental/tl/test_align_io.py @@ -0,0 +1,297 @@ +"""Unit tests for the I/O layer of the public ``align`` functions. + +These exercise input resolution and write-back directly -- no estimator, no JAX -- +so they cover the ``AnnData`` *and* ``SpatialData`` branches and every error guard +in :mod:`squidpy.experimental.tl._align._io` cheaply. +""" + +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np +import pytest +from anndata import AnnData + +from squidpy.experimental._methods.align_landmarks import AffineFitResult +from squidpy.experimental.tl._align._io import ( + get_coords, + resolve_obs_pair, + shallow_copy_sdata, + writeback_affine_sdata, + writeback_obs, +) + +_PTS = np.array([[10.0, 1.0], [12.0, 1.0], [11.0, 2.0], [10.0, 3.0], [12.0, 3.0]]) + + +@dataclass +class _ShiftResult: + """Minimal :class:`AlignResult`: a constant offset baked into ``transform``.""" + + delta: float = 100.0 + + def transform(self, points: np.ndarray) -> np.ndarray: + return np.asarray(points, dtype=float) + self.delta + + +def _adata(coords: np.ndarray = _PTS, *, key: str = "spatial") -> AnnData: + adata = AnnData(np.zeros((coords.shape[0], 1))) + adata.obsm[key] = coords.copy() + return adata + + +def _sdata_tables(**tables: AnnData): + sd = pytest.importorskip("spatialdata") + from spatialdata.models import TableModel + + return sd.SpatialData(tables={name: TableModel.parse(adata) for name, adata in tables.items()}) + + +def _sdata_points(cs: str = "qcs"): + sd = pytest.importorskip("spatialdata") + from spatialdata.models import PointsModel + from spatialdata.transformations import Identity + + pts = PointsModel.parse(_PTS, transformations={cs: Identity()}) + return sd.SpatialData(points={"pts": pts}) + + +# --------------------------------------------------------------------------- +# resolve_obs_pair +# --------------------------------------------------------------------------- + + +def test_resolve_anndata_pair() -> None: + ref, query = _adata(), _adata() + r_adata, q_adata, container, element_key = resolve_obs_pair(ref, query, None, None) + assert r_adata is ref and q_adata is query + assert container is None and element_key is None + + +def test_resolve_anndata_requires_query() -> None: + with pytest.raises(ValueError, match="`data_query` is required when `data_ref` is an AnnData"): + resolve_obs_pair(_adata(), None, None, None) + + +def test_resolve_anndata_rejects_mixed_query() -> None: + with pytest.raises(TypeError, match="Mixed AnnData/SpatialData"): + resolve_obs_pair(_adata(), _sdata_points(), None, None) + + +def test_resolve_anndata_rejects_keys() -> None: + with pytest.raises(ValueError, match="only valid for SpatialData"): + resolve_obs_pair(_adata(), _adata(), "ref", None) + + +def test_resolve_bad_ref_type() -> None: + with pytest.raises(TypeError, match="must be AnnData or SpatialData"): + resolve_obs_pair(object(), _adata(), None, None) # type: ignore[arg-type] + + +def test_resolve_sdata_pair() -> None: + pytest.importorskip("spatialdata") + ref_sd = _sdata_tables(ref=_adata()) + query_sd = _sdata_tables(query=_adata(_PTS + 5)) + r_adata, q_adata, container, element_key = resolve_obs_pair(ref_sd, query_sd, "ref", "query") + assert r_adata is ref_sd.tables["ref"] + assert q_adata is query_sd.tables["query"] + assert container is query_sd + assert element_key == "query" + + +def test_resolve_sdata_single_two_tables() -> None: + pytest.importorskip("spatialdata") + both = _sdata_tables(ref=_adata(), query=_adata(_PTS + 5)) + r_adata, q_adata, container, element_key = resolve_obs_pair(both, None, "ref", "query") + assert r_adata is both.tables["ref"] + assert q_adata is both.tables["query"] + assert container is both + assert element_key == "query" + + +def test_resolve_sdata_requires_keys() -> None: + pytest.importorskip("spatialdata") + both = _sdata_tables(ref=_adata(), query=_adata()) + with pytest.raises(ValueError, match="`ref_key` and `query_key` are required"): + resolve_obs_pair(both, None, None, None) + + +def test_resolve_sdata_rejects_mixed_query() -> None: + pytest.importorskip("spatialdata") + with pytest.raises(TypeError, match="Mixed AnnData/SpatialData"): + resolve_obs_pair(_sdata_tables(ref=_adata()), _adata(), "ref", "query") + + +def test_resolve_sdata_missing_key() -> None: + pytest.importorskip("spatialdata") + both = _sdata_tables(ref=_adata()) + with pytest.raises(KeyError, match="nope"): + resolve_obs_pair(both, None, "nope", "ref") + + +# --------------------------------------------------------------------------- +# get_coords +# --------------------------------------------------------------------------- + + +def test_get_coords_missing_key() -> None: + with pytest.raises(KeyError, match="missing.*not found"): + get_coords(_adata(), "missing") + + +def test_get_coords_rejects_non_2d() -> None: + adata = _adata() + adata.obsm["bad"] = np.zeros((_PTS.shape[0], 3)) + with pytest.raises(ValueError, match=r"must be an \(N, 2\) array"): + get_coords(adata, "bad") + + +# --------------------------------------------------------------------------- +# writeback_obs -- AnnData +# --------------------------------------------------------------------------- + + +def test_writeback_obs_object_mode_returns_result() -> None: + result = _ShiftResult() + out = writeback_obs( + result, + output_mode="object", + query_adata=_adata(), + container=None, + element_key=None, + spatial_key="spatial", + key_added=None, + ) + assert out is result + + +def test_writeback_obs_anndata_inplace() -> None: + query = _adata() + out = writeback_obs( + _ShiftResult(), + output_mode="inplace", + query_adata=query, + container=None, + element_key=None, + spatial_key="spatial", + key_added="aligned", + ) + assert out is None + np.testing.assert_allclose(query.obsm["aligned"], _PTS + 100.0) + + +def test_writeback_obs_anndata_copy_leaves_original_untouched() -> None: + query = _adata() + out = writeback_obs( + _ShiftResult(), + output_mode="copy", + query_adata=query, + container=None, + element_key=None, + spatial_key="spatial", + key_added="aligned", + ) + assert isinstance(out, AnnData) and out is not query + assert "aligned" in out.obsm + assert "aligned" not in query.obsm + + +# --------------------------------------------------------------------------- +# writeback_obs -- SpatialData +# --------------------------------------------------------------------------- + + +def test_writeback_obs_sdata_inplace() -> None: + pytest.importorskip("spatialdata") + sdata = _sdata_tables(query=_adata()) + out = writeback_obs( + _ShiftResult(), + output_mode="inplace", + query_adata=sdata.tables["query"], + container=sdata, + element_key="query", + spatial_key="spatial", + key_added="aligned", + ) + assert out is None + np.testing.assert_allclose(sdata.tables["query"].obsm["aligned"], _PTS + 100.0) + + +def test_writeback_obs_sdata_copy_leaves_original_untouched() -> None: + pytest.importorskip("spatialdata") + sdata = _sdata_tables(query=_adata()) + out = writeback_obs( + _ShiftResult(), + output_mode="copy", + query_adata=sdata.tables["query"], + container=sdata, + element_key="query", + spatial_key="spatial", + key_added="aligned", + ) + assert out is not sdata + assert "aligned" in out.tables["query"].obsm + # regression: copy must not leak the new key back into the input container + assert "aligned" not in sdata.tables["query"].obsm + + +# --------------------------------------------------------------------------- +# writeback_affine_sdata +# --------------------------------------------------------------------------- + + +def test_writeback_affine_inplace_registers_transform() -> None: + pytest.importorskip("spatialdata") + from spatialdata.transformations import get_transformation + + sdata = _sdata_points() + out = writeback_affine_sdata( + AffineFitResult(matrix=np.eye(3)), sdata, output_mode="inplace", moving_cs="qcs", target_cs="tcs" + ) + assert out is None + assert "tcs" in get_transformation(sdata.points["pts"], get_all=True) + + +def test_writeback_affine_copy_leaves_original_untouched() -> None: + pytest.importorskip("spatialdata") + from spatialdata.transformations import get_transformation + + sdata = _sdata_points() + out = writeback_affine_sdata( + AffineFitResult(matrix=np.eye(3)), sdata, output_mode="copy", moving_cs="qcs", target_cs="tcs" + ) + assert out is not sdata + assert "tcs" in get_transformation(out.points["pts"], get_all=True) + # regression: copy must not register the transform on the input container + assert "tcs" not in get_transformation(sdata.points["pts"], get_all=True) + + +def test_writeback_affine_requires_cs_names() -> None: + pytest.importorskip("spatialdata") + with pytest.raises(ValueError, match="`cs_name_query` and `cs_name_ref` are required"): + writeback_affine_sdata( + AffineFitResult(matrix=np.eye(3)), _sdata_points(), output_mode="inplace", moving_cs=None, target_cs="tcs" + ) + + +def test_writeback_affine_no_matching_cs() -> None: + pytest.importorskip("spatialdata") + sdata = _sdata_points(cs="qcs") + with pytest.raises(KeyError, match="No elements .* registered to coordinate system 'other'"): + writeback_affine_sdata( + AffineFitResult(matrix=np.eye(3)), sdata, output_mode="inplace", moving_cs="other", target_cs="tcs" + ) + + +# --------------------------------------------------------------------------- +# shallow_copy_sdata +# --------------------------------------------------------------------------- + + +def test_shallow_copy_sdata_preserves_elements() -> None: + pytest.importorskip("spatialdata") + sdata = _sdata_tables(ref=_adata(), query=_adata(_PTS + 5)) + copy = shallow_copy_sdata(sdata) + assert copy is not sdata + assert set(copy.tables) == {"ref", "query"} From 599186fe8e53df7e69b62734003508775faa10cf Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 11 Jun 2026 10:26:06 +0200 Subject: [PATCH 95/96] landmark is positional only now --- src/squidpy/experimental/tl/_align/_api.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/src/squidpy/experimental/tl/_align/_api.py b/src/squidpy/experimental/tl/_align/_api.py index a89d82270..53bf306c2 100644 --- a/src/squidpy/experimental/tl/_align/_api.py +++ b/src/squidpy/experimental/tl/_align/_api.py @@ -129,15 +129,16 @@ def align_by_landmarks( ``obsm`` key when ``data`` is an :class:`~anndata.AnnData`. output_mode See :func:`align`. ``"object"`` (default) returns the fitted - :class:`~squidpy.experimental._methods.align_landmarks.AffineFitResult`. + :class:`~squidpy.experimental._methods.AlignResult` (an + :class:`~squidpy.experimental._methods.align_landmarks.AffineFitResult`). key_added Destination ``obsm`` key when ``data`` is an AnnData (see :func:`align`). """ assert_one_of(output_mode, OUTPUT_MODES, name="output_mode") result = ALIGN_LANDMARKS.get(method)( - landmarks_ref, - landmarks_query, + landmarks_ref=landmarks_ref, + landmarks_query=landmarks_query, source_cs=cs_name_query, target_cs=cs_name_ref, ) From 8cfc7709fb3956382d7580259a67c556267849df Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 11 Jun 2026 11:14:24 +0200 Subject: [PATCH 96/96] expose api --- docs/api.md | 3 +++ src/squidpy/experimental/tl/__init__.py | 9 +++++---- tests/experimental/tl/test_align.py | 14 ++++++++++++-- 3 files changed, 20 insertions(+), 6 deletions(-) diff --git a/docs/api.md b/docs/api.md index 1bf4fdf92..d9da2508d 100644 --- a/docs/api.md +++ b/docs/api.md @@ -149,6 +149,9 @@ See the {doc}`extensibility guide ` for how to implement a custo experimental.tl.calculate_tiling_qc experimental.tl.TilingQCParams + experimental.tl.align + experimental.tl.align_by_landmarks + experimental.tl.AlignResult experimental.pl.tiling_qc experimental.im.fit_stain_reference experimental.im.apply_stain_normalization diff --git a/src/squidpy/experimental/tl/__init__.py b/src/squidpy/experimental/tl/__init__.py index 3b6f1013d..803c04039 100644 --- a/src/squidpy/experimental/tl/__init__.py +++ b/src/squidpy/experimental/tl/__init__.py @@ -1,8 +1,11 @@ from __future__ import annotations +# `AlignResult` is the only result type on the public surface: it is the estimator +# contract (a `transform` mapping points into the reference frame) and the declared +# return of `align` / `align_by_landmarks`. The concrete results (`StalignResult`, +# `AffineFitResult`) stay in their home modules under `squidpy.experimental._methods` +# for callers that need raw fields -- the public API stays method-agnostic. from squidpy.experimental._methods import AlignResult -from squidpy.experimental._methods.align_landmarks import AffineFitResult -from squidpy.experimental._methods.align_samples import StalignResult from ._align import align, align_by_landmarks from ._tiling_qc import TilingQCParams, calculate_tiling_qc @@ -13,6 +16,4 @@ "calculate_tiling_qc", "TilingQCParams", "AlignResult", - "AffineFitResult", - "StalignResult", ] diff --git a/tests/experimental/tl/test_align.py b/tests/experimental/tl/test_align.py index 6f0469855..7136be25b 100644 --- a/tests/experimental/tl/test_align.py +++ b/tests/experimental/tl/test_align.py @@ -83,10 +83,20 @@ def test_missing_spatial_key() -> None: align(ref, query, spatial_key="missing", config=_tiny()) -def test_public_exports_are_reachable() -> None: +def test_public_surface_is_align_result_only() -> None: import squidpy.experimental.tl as tl - assert tl.StalignResult is StalignResult + # `AlignResult` is the only result type exposed; concretes stay in their home modules. + assert "AlignResult" in tl.__all__ + assert not hasattr(tl, "StalignResult") + assert not hasattr(tl, "AffineFitResult") + + +def test_object_result_satisfies_align_result_protocol() -> None: + from squidpy.experimental.tl import AlignResult + + result = align(_adata(), _adata(), method="stalign", output_mode="object", config=_tiny()) + assert isinstance(result, AlignResult) def _sdata_tables(**tables: AnnData):