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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.

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Sherpa for Python Users

Standalone Sherpa for Python

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

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

Requirements: Python 2.6.2 or later, NumPy 1.3.0 or later, FFTW 3.2.1 or later

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

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

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

pyBLoCXS

Developed by Sherpa Team at SAO-CXC (email: brefsdal 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

COSlsf

Developed by Hans Moritz Guenther (email: hguenther@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