bayspec

Bayesian inference-based spectral fitting for high-energy astrophysical data.

BaySpec is a Python library for Bayesian inference on high-energy astrophysical spectra. It pairs MCMC and nested-sampling backends with multi-spectrum, multi-model fitting machinery, reads OGIP FITS data out of the box, and bridges to local, astromodels, and Xspec model libraries.

Features

Inference backends. Posterior sampling via MCMC (emcee) or nested sampling (MultiNest via pymultinest); maximum-likelihood fits via lmfit or iminuit for quick checks.
OGIP Type I & II FITS. Source and background PHA/PHA2 spectra, plus RMF, RSP/RSP2, and ARF/ARF2 responses — multi-extension archives are row-indexed so individual spectra plug in directly.
Multi-dimensional. Time-evolving spectra fit with time-involved physical models.
Multi-wavelength. Joint fitting across spectra at different wavelengths.
Multi-spectrum, multi-model. Simultaneously fit any number of (data, model) pairs; combine, freeze, or link parameters across pairs.
Pluggable model libraries. A local set plus optional astromodels and Xspec bridges. Register your own Python functions as new components.
Browser workbench (optional). BaySpec App is a FastAPI + HTMX front-end that exposes the same fitting machinery through a web UI.

Installation

BaySpec is available on PyPI:

pip install bayspec

Optional: `MultiNest` sampler

To enable MultiNest for nested sampling, follow the pymultinest install guide.

Optional: `astromodels` components

To pull components from astromodels, install astromodels in your Python environment.

Optional: `Xspec` components

To pull components from Xspec:

Install HEASoft and Xspec v12.12.1+.
After initialising the HEASoft environment, install xspec-models-cxc.

Note: bayspec currently supports only Additive and Multiplicative Xspec models.

BaySpec App

BaySpec App is the browser front-end for bayspec — a FastAPI + HTMX workbench that loads OGIP spectra, composes models, and runs inference without notebook glue. A public, hosted deployment runs on Hugging Face Spaces:

Documentation

Read the full documentation or browse the examples for typical workflows end to end.

Citation

If you use BaySpec in your research, please cite the software version used in your analysis. Citation metadata are provided in CITATION.cff, and a reusable BibTeX entry is available in CITATION.bib. For version 0.4.5:

Yang, J. (2026). BaySpec: Bayesian inference for high-energy astrophysical spectra (Version 0.4.5) [Computer software]. https://github.com/jyangch/bayspec/releases/tag/v0.4.5

@software{bayspec,
  author  = {Yang, Jun},
  title   = {{BaySpec}: Bayesian inference for high-energy astrophysical spectra},
  version = {0.4.5},
  year    = {2026},
  month   = oct,
  date    = {2026-10-11},
  url     = {https://github.com/jyangch/bayspec/releases/tag/v0.4.5}
}

Zenodo automatic archiving is enabled for this repository. A DOI for the first archive is pending; until it is available, use the version-specific release URL above. Once archived, cite the DOI for the version used in your analysis.

License

BaySpec is distributed under the GPL-3.0 license.

Metadata

Release files for bayspec 0.4.5

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for bayspec 0.4.5
File	Size	Uploaded
bayspec-0.4.5.tar.gz	984.8 kB	Oct 11, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for bayspec 0.4.5
File	Interpreter	ABI	Platform	Reset
bayspec-0.4.5-py3-none-any.whl 988.1 kB Oct 11, 2026	Python 3	none	any	Details

Total release size: 2.0 MB

Release files / bayspec-0.4.5.tar.gz

Download URL	bayspec-0.4.5.tar.gz
Size	984.8 kB
Tags	Source
SHA-256 checksum How to use checksums	`924c6a0e5479a4ee225529c66c1aae26566aba32685692506f5f02d4ec14cdde`
BLAKE2b-256 checksum How to use checksums	`cf20d3ce9b8d01803a4bb648db9d6411c6d20f50d44e3186e072209934b60a25`
Upload date	Oct 11, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.12.8`

Release files / bayspec-0.4.5-py3-none-any.whl

Download URL	bayspec-0.4.5-py3-none-any.whl
Size	988.1 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`c44fd1c3cfc902cc6c5ba5ca6a157304b643908798c8751726833e34ba23b93c`
BLAKE2b-256 checksum How to use checksums	`f78ca4df623ecfbb3c099641255d2404b6e21f2dc67f10a0e6f40bc54ee0d8e2`
Upload date	Oct 11, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.12.8`