Skip to main content

Data fitting with bayesian uncertainty analysis

Project description

Bumps provides data fitting and Bayesian uncertainty modeling for inverse problems. It has a variety of optimization algorithms available for locating the most like value for function parameters given data, and for exploring the uncertainty around the minimum.

Installation is with the usual python installation command:

pip install bumps

Once the system is installed, you can verify that it is working with:

bumps doc/examples/peaks/model.py --chisq
bumps -h

To start the GUI use:

bumps

Documentation is available at readthedocs. See CHANGES.rst for details on recent changes.

If a compiler is available, then significant speedup is possible for DREAM using:

python -m bumps.dream.build_compiled

If you have installed from source, you must first check out the random123 library:

git clone --branch v1.14.0 https://github.com/DEShawResearch/random123.git bumps/dream/random123
python -m bumps.dream.build_compiled

Build status Documentation status DOI tag

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

bumps-1.0.5.tar.gz (1.5 MB view details)

Uploaded Source

Built Distribution

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

bumps-1.0.5-py3-none-any.whl (1.6 MB view details)

Uploaded Python 3

File details

Details for the file bumps-1.0.5.tar.gz.

File metadata

  • Download URL: bumps-1.0.5.tar.gz
  • Upload date:
  • Size: 1.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for bumps-1.0.5.tar.gz
Algorithm Hash digest
SHA256 9a97ca9f31bc4d5dfae2413b56659ad91cdcd3fea664e98cbdcccf107103aa3a
MD5 0d1ff979d4e7dd96a212b35343fed378
BLAKE2b-256 3d906890849d4b9c25d93b2366a406a07ab87634f4f964f8adeef6d37bed75ff

See more details on using hashes here.

Provenance

The following attestation bundles were made for bumps-1.0.5.tar.gz:

Publisher: test-publish.yml on bumps/bumps

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

File details

Details for the file bumps-1.0.5-py3-none-any.whl.

File metadata

  • Download URL: bumps-1.0.5-py3-none-any.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for bumps-1.0.5-py3-none-any.whl
Algorithm Hash digest
SHA256 816fa422cfebd9d1eb66c9b2384ac507bf4a2a7ad2abe926b958a679d9a7b966
MD5 419b2fdd9a5a21b128556b1b9597dcc4
BLAKE2b-256 82f52f77f0bc663c13371d1c00ab8e550e2c9b11fec3c63ebf12a8336c0f534e

See more details on using hashes here.

Provenance

The following attestation bundles were made for bumps-1.0.5-py3-none-any.whl:

Publisher: test-publish.yml on bumps/bumps

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 Pingdom Monitoring Sentry Error logging StatusPage Status page