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Last modified: 1 February 2017

URL: http://cxc.harvard.edu/sherpa/contrib.html

Contributed CIAO Sherpa (Python) Extension Packages


On this page we provide links to software packages developed by the CXC and external users which extend the functionality of Sherpa.

Please note that the CIAO HelpDesk does not provide support for this software. If you need help using one of these packages, contact the package developer at the email address listed below.


Sherpa for Python Users

Sherpa for Python

Sherpa is a modeling and fitting application for Python users which can be built and used independently of CIAO.

Sherpa is an Open Source project with the source code repository placed on GitHub The complete tar files are available for download as well as the full project repository which can be 'cloned'. The Sherpa Project welcomes contributions from the users via GitHub. There are several Sherpa Python releases in each calendar year.

Requirements: Python 2.7, Python 3.5 or later, a recent version of NumPy, FFTW 3.3 or later

Primary developers are the Sherpa Team at SAO-CXC, although input - such as new features, bug or documentation fixes, and bug reports - by the community are welcomed. Post questions and issues on the Sherpa GitHub page.

Specialized Science Tools

Cosmocalc

Developed by Tom Aldcroft at SAO-CXC (email: aldcroft at head.cfa.harvard.edu)

Calculate useful values for a given cosmology. This is a Python version of the Cosmology Calculator (Ned Wright) and uses code adapted from CC.py (James Schombert).

Requirements: CIAO, Sherpa

Deproject

Developed by Tom Aldcroft at SAO-CXC (email: aldcroft at head.cfa.harvard.edu)

CIAO Sherpa extension package to facilitate deprojection of two-dimensional annular X-ray spectra to recover the three-dimensional source properties.

Requirements: CIAO, Sherpa

COSlsf

Developed by Hans Moritz Guenther (email: hguenther at cfa.harvard.edu)

A convolution model to use with HST/COS data. It will convolve the model with the local line spread functions appropriate for this data.

Requirements: CIAO, Sherpa

BXA - Bayesian X-ray Analysis

Developed by Johannes Buchner (email: jbuchner - at - mpe.mpg.de)

BXA connects the nested sampling algorithm MultiNest to the X-ray spectral analysis environment Sherpa for Bayesian Parameter Estimation and Model comparison.

Requirements: CIAO, Sherpa, PyMultiNest, and MultiNest.

Reference: http://arxiv.org/abs/1402.0004

Packages that have been moved into Sherpa

The following packages have been moved into Sherpa and no-longer need to be installed separately.

pyBLoCXS

This was moved into Sherpa at the CIAO 4.5 release: pyblocxs.

Developed by Sherpa Team at SAO-CXC (email: asiemiginowska at cfa.harvard.edu).

pyBLoCXS is a sophisticated Markov chain Monte Carlo (MCMC) based algorithm designed to carry out Bayesian Low-Count X-ray Spectral (BLoCXS) analysis in the Sherpa environment.

Requirements: Sherpa 4.2.1 or later

Datastack

This was moved into Sherpa at the CIAO 4.7 release: datastack.

Developed by Tom Aldcroft at SAO-CXC (email: aldcroft at head.cfa.harvard.edu)

Datastack is a CIAO Sherpa extension package for manipulating and fitting a stack of related data sets. It provides stack-enabled (i.e. vectorized) versions of the key Sherpa commands used to load data, set source models, get and set parameters, fit, and plot.

Requirements: CIAO, Sherpa


Last modified: 1 February 2017
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