rstar-python

Python bindings for the rstar R*-tree spatial index library.

A fast, dynamic spatial index for points and stored axis-aligned bounding boxes in 2–8 dimensions. Insert and remove items after construction, run nearest-neighbour and radius searches, and query spatial regions. Each item can carry an integer id so queries can return references to your data.

Installation

pip install rstar-python

Prebuilt abi3 wheels are published for Linux, macOS, and Windows and work on CPython 3.10+.

Why rstar-python?

	rstar-python	scipy `cKDTree` / sklearn `KDTree`	`Rtree` (libspatialindex)
Dynamic insert / remove	✅	❌ (static, rebuild)	✅
Bounding-box / region query	✅	❌ (radius only)	✅
Returns ids of matches	✅	✅ (row indices)	✅
Vectorised batch query	✅	✅	❌
Pure-Rust, no system C/C++ dep	✅	(C/Cython, bundled)	❌ (needs libspatialindex)

Reach for the KD-trees in scipy/scikit-learn when you build an index once from a static array and only need point/radius queries. Reach for rstar-python when you need to mutate the index over time or run bounding-box queries — with easy prebuilt wheels and no C/C++ system dependency.

Usage

import numpy as np
from rstar_python import PyBBoxRTree, PyRTree

# Create a 3D R-tree
tree = PyRTree(dims=3)

# Insert points. insert() returns the point's id.
tree.insert([1.0, 2.0, 3.0])            # -> 0  (auto-assigned)
tree.insert([4.0, 5.0, 6.0], data=42)   # -> 42 (explicit id)

# Or bulk-load (replaces existing contents). Accepts lists or numpy arrays,
# and an optional list of ids.
points = np.array([[1.0, 2.0, 3.0],
                   [4.0, 5.0, 6.0],
                   [7.0, 8.0, 9.0]], dtype=np.float64)
tree.bulk_load(points, data=[10, 20, 30])

# --- Coordinate-returning queries ---
tree.nearest_neighbor([1.1, 2.1, 3.1])           # -> [1.0, 2.0, 3.0]
tree.k_nearest_neighbors([1.1, 2.1, 3.1], k=2)   # -> [[...], [...]]
tree.neighbors_within_radius([1.0, 2.0, 3.0], radius=1.0)
tree.locate_in_envelope(min_corner=[0, 0, 0], max_corner=[2, 2, 2])
tree.locate_in_envelope_ids([0, 0, 0], [2, 2, 2])  # stored ids, closed AABB

# Remove an exact point+id when coordinates are shared by multiple items.
tree.remove_item([1.0, 2.0, 3.0], data=10)

# --- Vectorised, id-returning queries (scipy/sklearn style) ---
query_pts = np.array([[1.1, 2.1, 3.1], [7.0, 8.0, 9.0]], dtype=np.float64)
distances, ids = tree.query(query_pts, k=2)
# distances: (2, 2) float64 Euclidean distances
# ids:       (2, 2) int64 ids; padded with -1 / inf if fewer than k exist

within = tree.query_radius(query_pts, radius=1.0)  # list of id lists

# --- Bookkeeping ---
len(tree)                  # number of points
tree.dims                  # 3
[1.0, 2.0, 3.0] in tree    # membership test
tree.remove([1.0, 2.0, 3.0])

# --- Stored bounding boxes ---
boxes = PyBBoxRTree(dims=2)
boxes.insert([0.0, 0.0], [2.0, 2.0], data=100)
boxes.insert([2.0, 1.0], [3.0, 3.0])
# Returns both ids: intersection uses closed bounds, so touching counts.
boxes.intersection([1.0, 1.0], [2.0, 2.0])

# Query many boxes in one native call. Results use CSR-style row offsets:
# the matches for row i are ids[offsets[i]:offsets[i + 1]].
query_mins = np.array([[0.0, 0.0], [5.0, 5.0]], dtype=np.float64)
query_maxs = np.array([[2.0, 2.0], [6.0, 6.0]], dtype=np.float64)
ids, offsets = boxes.intersection_batch(query_mins, query_maxs)

boxes.remove_item([0.0, 0.0], [2.0, 2.0], data=100)

# Bulk loading replaces the stored boxes.
boxes.bulk_load(
    min_corners=[[0.0, 0.0], [5.0, 5.0]],
    max_corners=[[1.0, 1.0], [6.0, 6.0]],
    data=[10, 20],
)

Features

Points and stored axis-aligned bounding boxes in 2–8 dimensions
Dynamic insertion and exact item removal
Per-item integer ids (auto-assigned or supplied)
Nearest-neighbour and k-nearest-neighbour queries
Radius search and axis-aligned bounding-box (envelope) queries
Vectorised point query / query_radius and bounding-box intersection_batch queries over NumPy arrays
NumPy-native batched intersection results without Python integer boxing
Bulk loading (lists or NumPy arrays) for fast construction
Type stubs (py.typed) for IDE and mypy support
Built on the fast Rust rstar library

Development

Requirements:

Rust (stable)
Python 3.10+
maturin

git clone https://github.com/kephale/rstar-python
cd rstar-python

python -m venv .venv
source .venv/bin/activate  # or `.venv\Scripts\activate` on Windows

pip install maturin pytest numpy

# Build and install in development mode
maturin develop --release

# Run tests
pytest python/tests -v

# Run the bounding-box performance benchmark
python python/benchmarks/benchmark_bbox_rtree.py

License

This project is licensed under the MIT License - see the LICENSE file for details.

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