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.
Sherpa is easily extensible to include user models, statistics and optimization methods.
It was originally developed by the Chandra X-ray Center for use in analysing X-ray data (both spectral and imaging) from the Chandra X-ray telescope, but it’s designed to be a general-purpose package, which can be enhanced with domain-specific tasks (such as X-ray Astronomy).
Sherpa is compatible with Python versions 2.7, 3.5, 3.6, and 3.7.
Key Features
- 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.
Website: cxc.cfa.harvard.edu/contrib/sherpa
Support: Documentation, GitHub Code Repository
Developer: Smithsonian Astrophysical Observatory
License: GNU General Public License v3.0
Sherpa is written in Python. Learn Python with our recommended free books and free tutorials.
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