sm-rust 0.0.1

Spatial multiomics primitives (neighbour graphs, Moran's I, nhood enrichment, Ripley, co-occurrence) — Python bindings.

sm-rust — Python bindings

Spatial multiomics primitives from the sm-rust crate (neighbour graphs, Moran's I, Geary's C, neighbourhood enrichment, Ripley F/G/L, co-occurrence, ligand-receptor, sepal, niche detection), exposed to Python via PyO3 + maturin.

Layout

• sm_rust._native — the compiled extension module: Cells, Neighbors, every compute_* function returning result dataclass-like objects with numpy arrays. Mirrors the Node/wasm binding surface.
• sm_rust.gr — squidpy-shaped wrappers that consume AnnData directly: spatial_neighbors, spatial_autocorr, nhood_enrichment, interaction_matrix, centrality_scores, co_occurrence, ripley, ligrec, sepal. The output shape (column names, .uns keys) matches squidpy.gr so existing notebooks can drop this in.

Build (host)

pip install maturin
maturin develop --release --features python-bindings,parallel

Build (docker, recommended)

The repo's docker-compose.yml ships a pyo3 service that builds a wheel into python/dist/:

docker compose run --rm pyo3

Quick start

Low-level API:

import numpy as np
from sm_rust import _native as sm

cells = sm.cells_from_coords_py(np.random.rand(1000), np.random.rand(1000))
labels = np.random.randint(0, 3, 1000).astype(np.uint8)
neighbors = sm.Neighbors.knn(6)

res = sm.compute_nhood_enrichment_py(cells, labels, neighbors, n_clusters=3)
print(res.count.reshape(3, 3))
print(res.zscore.reshape(3, 3))

squidpy-style API:

import sm_rust as smr
import anndata as ad

smr.gr.spatial_neighbors(adata, coord_type="generic", n_neighs=6)
smr.gr.spatial_autocorr(adata, mode="moran", genes=adata.var_names[:50])
smr.gr.nhood_enrichment(adata, cluster_key="leiden")