SUGC 0.1.1

Sparse Unbiased Galaxy Clustering - high-performance hybrid Rust/Python correlation functions

SUGC

Correlation function counters for galaxy catalogues drawn from sparse partitions of periodic N-body simulations. Written in Rust with a Python interface via PyO3.

Supports real-space ξ(r), redshift-space ξ(s, μ) multipoles, and high-order N-point functions (N ≥ 3). Pair counts are corrected for the statistical under-sampling that arises when working with m-of-k independent partitions of a simulation box.

Installation

# pip / uv
uv add sugc

# conda
conda install -c conda-forge sugc

Usage

import numpy as np
from sugc import count_pairs_1d

coords = np.random.uniform(0, 500.0, size=(100_000, 3))
partition_ids = np.zeros(100_000, dtype=np.int32)  # single partition
r_bins = np.linspace(0.1, 50.0, 20)

counts, weights = count_pairs_1d(coords, partition_ids, r_bins, box_size=500.0)

See examples/ for redshift-space distortions and 3-point correlation functions.

Citation

If you use this in published work, please cite:

Hickman, O. et al. (2026). Fast and Unbiased Clustering Estimators for Sparse Partitions. (In Prep).

A CITATION.cff is included for software citation via GitHub or Zenodo.

License

MIT — see LICENSE.