Fast global Radial Basis Function (RBF) interpolation.

Radial Basis Function (RBF) interpolation is a powerful but computationally expensive technique. Direct solvers (e.g. LU factorisation) require O(N²) memory and O(N³) operations, which quickly becomes impractical beyond ~10,000 points on a typical machine.

This library provides a scalable alternative by combining two key techniques:

  • Domain Decomposition - following 1, used as a preconditioner within a Flexible Generalised Minimal Residual (FGMRES) iterative solver.
  • The Fast Multipole Method (FMM) - via the ferreus_bbfmm crate, used as a fast evaluator to reduce per-iteration cost.

Together, these methods reduce the overall complexity to O(N log N), enabling efficient interpolation on datasets with millions of points in up to three dimensions.

Features

  • Written in Rust
  • Supports 1D, 2D, and 3D input domains
  • Scales efficiently to datasets with over 1,000,000 input source points
  • Optional global trend transforms to capture large-scale patterns in the data
  • Provides fast 3D isosurface extraction using a surface-following regularised marching tetrahedra method via the 'ferreus_rmt' crate
  • Optional simultaneous evaluation of RBF values and gradients
  • Built on faer for linear algebra, avoiding complex build dependencies

Install

pip install ferreus_rbf

Then in Python:

import ferreus_rbf

See the docs and examples directory for more detailed usage and API documentation.

Attribution and licensing

This package was developed while the author was working at Maptek and has been approved for open‑source distribution under the terms of the MIT license.

Unless otherwise stated, the following copyright applies:

Copyright (c) 2025 Maptek Pty Ltd.
All rights reserved.

This copyright applies to all files in this repository, whether or not an individual file contains an explicit notice.

The code is released under the MIT License – see the top‑level LICENSE file for details.

References

  1. R. K. Beatson, W. A. Light, and S. Billings. Fast solution of the radial basis function interpolation equations: domain decomposition methods. SIAM J. Sci. Comput., 22(5):1717–1740 (electronic), 2000.
  2. Haase, G., Martin, D., Schiffmann, P., Offner, G. (2018). A Domain Decomposition Multilevel Preconditioner for Interpolation with Radial Basis Functions. In: Lirkov, I., Margenov, S. (eds) Large-Scale Scientific Computing. LSSC 2017.
  3. Fasshauer, G., 2007. Meshfree Approximation Methods with Matlab. World Scientific Publishing Co.
  4. J. B. Cherrie. Fast Evaluation of Radial Basis Functions: Theory and Application. PhD thesis, University of Canterbury, 2000.
  5. G.M. Treece, R.W. Prager, and A.H. Gee. Regularised marching tetrahedra: improved iso-surface extraction. Computers & Graphics, 23(4):583–598, 1999.

Project links

Data verified by PyPI on Jun 23, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for graphic-goose from gravatar.com graphic-goose

Credits

Author: Daniel Owen

GitHub Statistics

Data verified by PyPI on Jun 23, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.8

Tags

rbf radial-basis-function isosurface interpolation

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

ferreus_rbf-0.2.2-cp38-abi3-win_amd64.whl (1.9 MB view details)

Uploaded CPython 3.8+Windows x86-64

ferreus_rbf-0.2.2-cp38-abi3-manylinux_2_39_x86_64.whl (2.1 MB view details)

Uploaded CPython 3.8+manylinux: glibc 2.39+ x86-64

ferreus_rbf-0.2.2-cp38-abi3-macosx_11_0_arm64.whl (1.5 MB view details)

Uploaded CPython 3.8+macOS 11.0+ ARM64

ferreus_rbf-0.2.2-cp38-abi3-macosx_10_12_x86_64.whl (1.9 MB view details)

Uploaded CPython 3.8+macOS 10.12+ x86-64

File details

Details for the file ferreus_rbf-0.2.2-cp38-abi3-win_amd64.whl.

File metadata

  • Download URL: ferreus_rbf-0.2.2-cp38-abi3-win_amd64.whl
  • Upload date:
  • Size: 1.9 MB
  • Tags: CPython 3.8+, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for ferreus_rbf-0.2.2-cp38-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 a0c39821a3ba2e0ac9bd12e44ca50a9dff08fa7ca4d1f49cce1d58aefcdcafb1
MD5 141816723293802898a529074aa5ddb5
BLAKE2b-256 09a84bd798c83d2fbb9375f23b491bce5b2e3005846b3e4dc4bf4e5eb343d19a

See more details on using hashes here.

Provenance

The following attestation bundles were made for ferreus_rbf-0.2.2-cp38-abi3-win_amd64.whl:

Publisher: publish-py-ferreus-rbf.yml on graphic-goose/ferreus_rbf_rs

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file ferreus_rbf-0.2.2-cp38-abi3-manylinux_2_39_x86_64.whl.

File metadata

File hashes

Hashes for ferreus_rbf-0.2.2-cp38-abi3-manylinux_2_39_x86_64.whl
Algorithm Hash digest
SHA256 deab24b76e1842bb08942be088856165fae217b0eafb63d1ddf45daf63993495
MD5 5c11512d9bf558b092f48a595699e936
BLAKE2b-256 3a8d273e1ea4241c8660cc0bb4804ddc883fb71b6dccbaaa5c30e1024b69a112

See more details on using hashes here.

Provenance

The following attestation bundles were made for ferreus_rbf-0.2.2-cp38-abi3-manylinux_2_39_x86_64.whl:

Publisher: publish-py-ferreus-rbf.yml on graphic-goose/ferreus_rbf_rs

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file ferreus_rbf-0.2.2-cp38-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for ferreus_rbf-0.2.2-cp38-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 209d4c31c532d7800e8308ac5d9c3badd76a0cf4ef2e7e0e21f97235171e89c1
MD5 2fa679f5550d6873e74508d71425e2b4
BLAKE2b-256 fd43601d5a544598fc92b252c6a53af83033de7624ee62953be6662485ce868c

See more details on using hashes here.

Provenance

The following attestation bundles were made for ferreus_rbf-0.2.2-cp38-abi3-macosx_11_0_arm64.whl:

Publisher: publish-py-ferreus-rbf.yml on graphic-goose/ferreus_rbf_rs

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file ferreus_rbf-0.2.2-cp38-abi3-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for ferreus_rbf-0.2.2-cp38-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 7f3021a18935dd8e50b72135ca6aa7c5241447054cac33032f1e259457bbb193
MD5 9f6fd534a4822fd1f9fbc62d89d521ab
BLAKE2b-256 408038f44984ebe5104c0032107ec3c18972b61f83757590d5bb6ed222ca6646

See more details on using hashes here.

Provenance

The following attestation bundles were made for ferreus_rbf-0.2.2-cp38-abi3-macosx_10_12_x86_64.whl:

Publisher: publish-py-ferreus-rbf.yml on graphic-goose/ferreus_rbf_rs

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page