Skip to main content

FitBenchmarking: A tool for comparing fitting software

Project description

Build Status Tests Status Install Status Documentation Status Coverage Status Chat Zenodo JOSS

FitBenchmarking

FitBenchmarking is an open source tool for comparing different minimizers/fitting frameworks. FitBenchmarking is cross platform and we support Windows, Linux and Mac OS. For questions, feature requests or any other inquiries, please open an issue on GitHub.

The package is the result of a collaboration between STFC’s Scientific Computing Department and ISIS Neutron and Muon Facility and the Diamond Light Source. We also would like to acknowledge support from:

  • EU SINE2020 WP-10, which received funding from the European Union’s Horizon2020 research and innovation programme under grant agreement No 654000.
  • EPSRC Grant EP/M025179/1 Least Squares: Fit for the Future.
  • The Ada Lovelace Centre (ALC). ALC is an integrated, cross-disciplinary data intensive science centre, for better exploitation of research carried out at our large scale National Facilities including the Diamond Light Source (DLS), the ISIS Neutron and Muon Facility, the Central Laser Facility (CLF) and the Culham Centre for Fusion Energy (CCFE).

Project details


Download files

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

Source Distribution

fitbenchmarking-1.5.0.tar.gz (6.4 MB view details)

Uploaded Source

Built Distribution

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

fitbenchmarking-1.5.0-py3-none-any.whl (6.5 MB view details)

Uploaded Python 3

File details

Details for the file fitbenchmarking-1.5.0.tar.gz.

File metadata

  • Download URL: fitbenchmarking-1.5.0.tar.gz
  • Upload date:
  • Size: 6.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for fitbenchmarking-1.5.0.tar.gz
Algorithm Hash digest
SHA256 5f9bf266d7dfeffe5ae81dead96b7b1ac5776e481ba350dafafe412b25ffac17
MD5 15c39ffc86db2d88109ca25a554bb602
BLAKE2b-256 8a3ebabbbac1eb9da3d460141482da2969973efecf49a6b8405ef925b6a82a89

See more details on using hashes here.

File details

Details for the file fitbenchmarking-1.5.0-py3-none-any.whl.

File metadata

File hashes

Hashes for fitbenchmarking-1.5.0-py3-none-any.whl
Algorithm Hash digest
SHA256 2ccf278e7a3680791aaabd4899c8dcfde3b3b074f7ef289a47138937ddde5fa0
MD5 c88a7f409566ac0240aa3f4a8a788ab3
BLAKE2b-256 8336e01ec7a06c3a6400c8625d78e430875b5f50db1247f18e23e402f3163d6b

See more details on using hashes here.

Supported by

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