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.. image:: sherpa_logo.gif


Sherpa: Modeling and Fitting in Python
==========================================

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   :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 <https://cxc.harvard.edu/ciao/>`_, and can be installed with conda, pip, 
or built from source.

Check the complete  `4.13.0 Release Notes <https://github.com/sherpa/sherpa/releases/tag/4.13.0>`_ and
the  `Sherpa 4.13 documentation  <https://sherpa.readthedocs.io/en/latest/>`_ pages.

Information on recent releases and
citation information is available using the `Digital Object
Identifier (DOI) <https://doi.org/10.5281/zenodo.593753>`_.

Install Sherpa
---------------

Check `How to Install Sherpa? <https://sherpa.readthedocs.io/en/latest/install.html>`_ for installation.

There is a set of new `Notebooks <https://sherpa.readthedocs.io/en/latest/NotebookSupport.html>`_ with examples of Sherpa in Python.

See the `quick guide to modeling and fitting in Sherpa <https://sherpa.readthedocs.io/en/latest/quick.html>`_ 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 <https://saba.readthedocs.io/en/latest/>`_



Citing Sherpa
-------------

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


Release Notes 
-------------

Release Notes for the previous version of Sherpa:

* `4.12.2 Release Notes <https://github.com/sherpa/sherpa/releases/tag/4.12.2>`_.

* `4.12.1 Release Notes <https://github.com/sherpa/sherpa/releases/tag/4.12.1>`_.

* `4.12.0 Release Notes <https://github.com/sherpa/sherpa/releases/tag/4.12.0>`_.

* `4.11.1 Release Notes <https://github.com/sherpa/sherpa/releases/tag/4.11.1>`_.

* `4.11.0 Release Notes <https://github.com/sherpa/sherpa/releases/tag/4.11.0>`_.

* `4.10.2 Release Notes <https://github.com/sherpa/sherpa/releases/tag/4.10.2>`_

* `4.10.1 Release Notes <https://github.com/sherpa/sherpa/releases/tag/4.10.1>`_

* `4.10.0 Release Notes <https://github.com/sherpa/sherpa/releases/tag/4.10.0>`_
 
* `4.9.1 Release Notes <https://github.com/sherpa/sherpa/releases/tag/4.9.1>`_ 

* `4.9.0 Release Notes <https://github.com/sherpa/sherpa/releases/tag/4.9.0>`_

* `4.8.2 Release Notes <https://github.com/sherpa/sherpa/releases/tag/4.8.2>`_

* `4.8.1 Release Notes <https://github.com/sherpa/sherpa/releases/tag/4.8.1>`_

* `4.8.0 Release Notes <https://github.com/sherpa/sherpa/releases/tag/4.8.0>`_