.. sherpa documentation master file, created by sphinx-quickstart on Fri Jan 22 12:56:22 2016. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. .. image:: sherpa_logo.gif Sherpa: Modeling and Fitting in Python ========================================== .. toctree:: :maxdepth: 2 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 4.13 was released on January 8, 2021. It contains a few minor documentation updates, a version number update to coincide with CIAO version 4.13.0, and infrastructure changes to migrate from Travis-CI to GitHub Actions for testing. This version is compatible with Python 3.6, 3.7 and 3.8. It is available for use outside of `CIAO `_, and can be installed with conda, pip, or built from source. Check the complete `4.13.0 Release Notes `_ and the `Sherpa 4.13 documentation `_ pages. Information on recent releases and citation information is available using the `Digital Object Identifier (DOI) `_. Install Sherpa --------------- Check `How to Install Sherpa? `_ for installation. There is a set of new `Notebooks `_ with examples of Sherpa in Python. See the `quick guide to modeling and fitting in Sherpa `_ for a simple introduction to Sherpa’s capabilities. 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) for information on how to cite Sherpa. Release Notes ------------- Release Notes for the previous version of Sherpa: * `4.12.2 Release Notes `_. * `4.12.1 Release Notes `_. * `4.12.0 Release Notes `_. * `4.11.1 Release Notes `_. * `4.11.0 Release Notes `_. * `4.10.2 Release Notes `_ * `4.10.1 Release Notes `_ * `4.10.0 Release Notes `_ * `4.9.1 Release Notes `_ * `4.9.0 Release Notes `_ * `4.8.2 Release Notes `_ * `4.8.1 Release Notes `_ * `4.8.0 Release Notes `_