Metadata-Version: 2.1
Name: sherpa
Version: 4.13.1
Summary: Modeling and fitting package for scientific data analysis
Home-page: http://cxc.harvard.edu/sherpa/
Author: Smithsonian Astrophysical Observatory / Chandra X-Ray Center
Author-email: cxchelp@head.cfa.harvard.edu
License: GNU GPL v3
Description: ![Build Status: Conda](https://github.com/sherpa/sherpa/workflows/Conda%20CI/badge.svg)
        ![Build Status: Pip](https://github.com/sherpa/sherpa/workflows/Pip%20CI/badge.svg)
        [![Documentation Status](https://readthedocs.org/projects/sherpa/badge/)](https://sherpa.readthedocs.io/)
        [![DOI](https://zenodo.org/badge/683/sherpa/sherpa.svg)](https://zenodo.org/badge/latestdoi/683/sherpa/sherpa)
        [![GPLv3+ License](https://img.shields.io/badge/license-GPLv3+-blue.svg)](https://www.gnu.org/copyleft/gpl.html)
        ![Python version](https://img.shields.io/badge/Python-3.6,3.7,3.8-green.svg?style=flat)
        
        <!-- TOC *generated with [DocToc](https://github.com/thlorenz/doctoc)* -->
        **Table of Contents**
        
        - [Sherpa](#sherpa)
        - [License](#license)
        - [How To Install Sherpa](#how-to-install-sherpa)
          - [Using Anaconda](#using-anaconda)
          - [Using pip](#using-pip)
          - [Building from source](#building-from-source)
        - [History](#history)
          - [Release History](#release-history)
          
        <!-- END doctoc generated TOC please keep comment here to allow auto update -->
        
        
        Sherpa
        ======
        
        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.  It provides a high-level User
        Interface for interactive data-analysis work, such as within a
        Jupyter notebook, and it can also be used as a library component,
        providing fitting and modeling capabilities to an application.
        
        What can you do with Sherpa?
        
        - fit 1D (multiple) data including: spectra, surface brightness profiles, light curves, general ASCII arrays
        - fit 2D images/surfaces in Poisson/Gaussian regime
        - build complex model expressions
        - import and use your own models
        - use appropriate statistics for modeling Poisson or Gaussian data
        - import new statistics, with priors if required by analysis
        - visualize the parameter space with simulations or using 1D/2D cuts of the parameter space
        - calculate confidence levels on the best fit model parameters
        - choose a robust optimization method for the fit: Levenberg-Marquardt, Nelder-Mead Simplex or Monte Carlo/Differential Evolution.
        
        Documentation for Sherpa is available at
        [Read The Docs](https://sherpa.readthedocs.io/)
        and also for [Sherpa in CIAO](http://cxc.harvard.edu/sherpa/).
        
        A [Quick Start Tutorial](http://nbviewer.ipython.org/github/sherpa/sherpa/tree/main/notebooks/SherpaQuickStart.ipynb)
        is included in the `notebooks` folder and can be opened with an `ipython notebook`.
        
        License
        =======
        
        This program is free software: you can redistribute it and/or modify it under
        the terms of the GNU General Public License as published by the Free Software
        Foundation, either version 3 of the License, or (at your option) any later
        version. A copy of the GNU General Public License can be found in the
        `LICENSE` file provided with the source code, or from the
        [Free Software Foundation](http://www.gnu.org/licenses/).
        
        How To Install Sherpa
        =====================
        
        [Full installation instructions](https://sherpa.readthedocs.io/en/latest/install.html)
        are part of the [Read The Docs](https://sherpa.readthedocs.io/)
        documentation, and should be read if the following is not sufficient.
        
        It is strongly recommended that some form of *virtual environment* is
        used with Sherpa.
        
        Sherpa is tested against Python versions 3.6, 3.7, and 3.8.
        
        The last version of Sherpa which supported Python 2.7 is
        [Sherpa 4.11.1](https://doi.org/10.5281/zenodo.3358134).
        
        Using Anaconda
        --------------
        
        Sherpa is provided for both Linux and macOS operating systems running
        Python 3.6, 3.7, and 3.8. It can be installed with the `conda`
        package manager by saying
        
            $ conda install -c sherpa sherpa
        
        Using pip
        ---------
        
        Sherpa is also available
        [on PyPI](https://pypi.python.org/pypi/sherpa) and so can be installed
        with the following command (which requires that the NumPy package is
        already installed).
        
            % pip install sherpa
        
        Building from source
        --------------------
        
        Source installation is available for platforms incompatible with the
        binary builds, or for when the default build options are not sufficient
        (such as including support for the
        [`XSPEC` model library](https://heasarc.gsfc.nasa.gov/xanadu/xspec/)).
        The steps are described in the
        [building from source](https://sherpa.readthedocs.io/en/latest/install.html#building-from-source)
        documentation.
        
        History
        =======
        
        Sherpa is developed by the [Chandra X-ray
        Observatory](http://chandra.harvard.edu/) to provide fitting and modelling
        capabilities to the [CIAO](http://cxc.harvard.edu/ciao/) analysis package. It
        has been released onto [GitHub](https://github.com/sherpa/sherpa) for users to
        extend (whether to other areas of Astronomy or in other domains).
        
        Release History
        ---------------
        
        4.13.0: 08 January 2021 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4428938.svg)](https://doi.org/10.5281/zenodo.4428938)
        
        4.12.2: 27 October 2020 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4141888.svg)](https://doi.org/10.5281/zenodo.4141888)
        
        4.12.1: 14 July 2020 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3944985.svg)](https://doi.org/10.5281/zenodo.3944985) 
        
        4.12.0: 30 January 2020 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3631574.svg)](https://doi.org/10.5281/zenodo.3631574)
        
        4.11.1: 1 August 2019 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3358134.svg)](https://doi.org/10.5281/zenodo.3358134)
        
        4.11.0: 20 February 2019 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2573885.svg)](https://doi.org/10.5281/zenodo.2573885)
        
        4.10.2: 14 December 2018 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2275738.svg)](https://doi.org/10.5281/zenodo.2275738)
        
        4.10.1: 16 October 2018 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1463962.svg)](https://doi.org/10.5281/zenodo.1463962)
        
        4.10.0: 11 May 2018 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1245678.svg)](https://doi.org/10.5281/zenodo.1245678)
        
        4.9.1: 01 August 2017 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.838686.svg)](https://doi.org/10.5281/zenodo.838686)
        
        4.9.0: 27 January 2017 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.260416.svg)](https://doi.org/10.5281/zenodo.260416)
        
        4.8.2: 23 September 2016 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.154744.svg)](https://doi.org/10.5281/zenodo.154744)
        
        4.8.1: 15 April 2016 [![DOI](https://zenodo.org/badge/doi/10.5281/zenodo.49832.svg)](https://doi.org/10.5281/zenodo.49832)
        
        4.8.0: 27 January 2016 [![DOI](https://zenodo.org/badge/doi/10.5281/zenodo.45243.svg)](https://doi.org/10.5281/zenodo.45243)
        
        
Platform: Linux
Platform: Mac OS X
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: C
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: ~=3.5
Description-Content-Type: text/markdown
