Sherpa: Modeling and Fitting in Python

Sherpa is a modeling and fitting application for Python. It contains a powerful language for combining simple models into complex expressions that can be fit to the data using a variety of statistics and optimization methods. It is easily extensible to include user models, statistics and optimization methods.

Sherpa 4.12.1 was released on July 14, 2020 and is compatible with Python versions 3.5, 3.6, and 3.7. It is expected that it will work with Python 3.8 but testing has been limited. This version is available for use outside of CIAO, and can be installed with conda, pip, or built from source.

Check the complete 4.12.1 Release Notes and the Sherpa 4.12.1 documentation pages.

Information on recent releases and citation information is available using the Digital Object Identifier (DOI).

Install Sherpa

Learn How to Install Sherpa?

What can you do with Sherpa?

  • Model generic 1D/2D (N-D) data arrays.
  • Fit 1D (multiple) data including: spectra, surface brightness profiles, light curves, arrays.
  • Fit 2D images/surfaces in Poisson/Gaussian regime.
  • Build complex model expressions.
  • Import, define and use your own models.
  • Simulate predicted data based on defined models.
  • Use appropriate statistics for modeling Poisson or Gaussian data
  • Use Classic Maximum Likelihood or Bayesian Framework.
  • Import, define the new statistics, with priors if required by analysis.
  • Visualize a parameter space with simulations or using 1D/2D cuts of the parameter space
  • Calculate confidence levels on the best fit model parameters
  • Use a robust optimization method for the fit: Levenberg-Marquardt, Nelder-Mead Simplex or Monte Carlo/Differential Evolution.
  • Sherpa supports wcs, responses, psf, convolution.
  • Use Sherpa as part of astropy.modeling with Sherpa Bridge to Astropy - SABA

See the quick guide to modeling and fitting in Sherpa for a simple introduction to Sherpa’s capabilities.

Citing Sherpa

Please follow the Digital Object Identifier (DOI) <https://doi.org/10.5281/zenodo.593753> for information on how to cite Sherpa.