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.

What’s new in Sherpa 4.10.2

Sherpa 4.10.2 released on December 13, 2018 provides a bug fix to error catching (#PR 551). This release supports Python 2.7, 3.5-3.7, and includes all the features provided by the 4.10.1 version, such as a partial support for fitting models defined on arbitrary grid, uncertainties on the line equivalent width, generation of diagonal response matrix, updated optimization code for ‘levmar’ and several bug fixes.

Check the complete Release Notes.

Learn more on the Sherpa documentation pages.

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

Citing Sherpa

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