Table of Contents

• Sherpa
• How To Install Sherpa
  • Binary installation
    • 1a. Anaconda
    • 1b. Starting from scratch
  • Source Build
    • 2a. Extract the source tarball
    • 2b. Get the code from the GitHub repository
    • 2c. Build Sherpa
    • 2d. Testing the build
• Custom source build
  • FFTW library
  • XSPEC
  • Other customization options
• History

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 an IPython 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.
• For detailed documentation see: http://cxc.harvard.edu/sherpa

A Quick Start Tutorial is included in the docs folder and can be opened with an ipython notebook.

Sherpa can be installed from a binary distribution or built from sources.

The binary distribution is suited for people wanting to have Sherpa up and running as soon as possible in its standard form.

Source installation is available for platforms incompatible with the binary builds, or for users wanting to customize the way Sherpa is built and installed.

If you are in doubt about which installation to perform, you should try with the Conda installation (sections 1a and 1b).

1. Binary installation (Anaconda)

2. Source build (from a source tarball or the GitHub repository)

Source builds can be customized, for instance:

• to point to a local build of the FFTW library

• to build the XSPEC extension to provide many common Astronomical X-ray spectral models

These and other customization options are descibed below.

If you already have Anaconda installed on your system, you can just follow the easy steps in section 1a.

If you don't have Anaconda you can follow the instructions in section 1b, or you can install Anaconda from: https://store.continuum.io/cshop/anaconda/ and then refer to section 1a.

Notice that section 1b. only provides instructions on how to install a minimal Anaconda-powered environment, not the full Anaconda distribution.

If you have Anaconda already installed on your system you can use it to seamlessly install Sherpa.

First you need to add the Sherpa channel to your configuration, and then install Sherpa:

$ conda config --add channels https://conda.binstar.org/sherpa
$ conda install sherpa

To test that your installation works, just type:

$ sherpa_test

To update Sherpa:

$ conda update sherpa

Miniconda is a minimal distribution of Anaconda that allows users to create isolated virtual environments in which they can mix and match specific versions of software. In this case the instructions show you how to install Sherpa and its dependencies. After that you will be able to add more packages and make them work with Sherpa. You do not need root/administrator privileges for installing Miniconda and Sherpa, and you can activate and deactivate the Sherpa environment every time you want.

The instructions cover both TCSH and BASH. However, Anaconda only supports BASH, so we recommend that you start a BASH session before installing and using Sherpa through Anaconda.

Download the Miniconda (a minimal distribution of Anaconda) installer for your platform:

• Linux 32 bit - http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86.sh
• Linux 64 bit - http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh
• OS X 64 bit (10.7 and forward) - http://repo.continuum.io/miniconda/Miniconda-latest-MacOSX-x86_64.sh

Decide where you are going to install Miniconda, e.g.:

$ export MINICONDA=/home/miniconda # BASH
$ setenv MINICONDA /home/miniconda # TCSH

Run the Miniconda installer. It is assumed that you have read and agree with the Miniconda EULA

$ bash <Miniconda file you downloaded> -b -p $MINICONDA # BASH AND TCSH

Add miniconda to your PATH:

$ export PATH=$PATH:$MINICONDA/bin # BASH
$ setenv PATH "${PATH}:${MINICONDA}/bin" # TCSH

You may add these lines to your shell's startup script, e.g. $HOME/.bash_profile for BASH or $HOME/.cshrc for TCSH.

Add the Sherpa conda repositories to your configuration:

$ conda config --add channels https://conda.binstar.org/sherpa

Create a new environment and install Sherpa:

$ conda create -n sherpa sherpa=4.7

The above command will download and install Sherpa and its dependencies in an isolated environment, so that Sherpa will not interfere with your System's Python and you will be able to install other packages in a safe environment.

You need to activate the new environment in order to start using Sherpa. This is straightforward in BASH, but it takes a little bit more work in TCSH. We recommend TCSH users to create aliases for activating and deactivating the Sherpa environment.

$ source activate sherpa # BASH
$ setenv OLDPATH $PATH; setenv PATH ${MINICONDA}/envs/sherpa/bin:${PATH} #TCSH

BASH users will be reminded that they are running in the Sherpa environment by pre-pending the string (sherpa) to their BASH prompt.

You can start using Sherpa by starting a Python shell, or you can install ipython and use it as a more convenient shell. We recommend that you also install ipython-notebook and matplotlib so that you can use the nice ipython notebook features and the seamless integration with matplotlib for plotting from Sherpa. We also recommend that you install astropy for enabling FITS I/O (Sherpa will look for pyfits if astropy is not present).

$ conda install ipython-notebook matplotlib astropy

When you are done working with Sherpa you can either close the terminal window you were working with, or you can deactivate the Sherpa environment and restore your default environment:

$ source deactivate # BASH
$ setenv $PATH $OLDPATH # TCSH

Please remember that you need to activate the Sherpa environment every time you want to work with it. After the installation, the following commands are necessary to run Sherpa:

$ source activate sherpa # BASH
$ setenv OLDPATH $PATH; setenv PATH ${MINICONDA}/envs/sherpa/bin:${PATH} #TCSH
... Run your favourite Python shell ....

When you are done and you want to go back to your environment, close the terminal or deactivate the Sherpa environment.

$ source deactivate sherpa # BASH  
$ setenv $PATH $OLDPATH

The prerequisites for building from source are:

• Python: setuptools, numpy
• System: gcc, g++, gfortran, make, flex, bison

The current Sherpa code base only works with Python 2.7.

It is highly recommended that matplotlib be installed, as this is used to create graphical output (although the code can be built and used without this package), and ipython, which is for interactive analysis. Data I/O requires a FITS I/O library. Sherpa looks for astropy by default, and it falls back to pyfits if astropy is not installed.

The instructions on how to set up the prerequisites vary from system to system, and even on the same system there may be multiple ways of setting up the requirements.

There are two ways to download the source code:

• as a source tarball

• directly from GitHub as a git repository

If you donwloaded the Sherpa source tarball, you can extract it by:

$ tar xf sherpa-<version>.tar.gz
$ cd sherpa-<version>

You can clone the Sherpa repository with:

$ git clone https://github.com/sherpa/sherpa
$ cd sherpa

The most stable code is available in the stable branch. The main development code, which is unstable, is available in the master branch. New features and bug fixes or other, even less stable versions of the code may be available in other branches.

Once the code is available, it can be built and installed with:

$ python setup.py install

To test that your installation of Sherpa is working, type:

$ sherpa_test

which will run a small test suite (the script may not be in your path, depending on where the installation step chose to install Sherpa). Note that the test may report several WARNING lines and failed attempts to load modules. These messages are expected - as some of the tests require optional packages to be installed alongside Sherpa. These warnings may be ignored, as long as the test ends with an OK message.

There are several options for customizing the Sherpa build.

Sherpa ships with the fftw library source code and builds it as part of its own build process by default. However, users might want to point Sherpa to their own version of the library. This might be required because the performance of this library can be significantly increased by compiling it with optimization flags specific for some system or architecture.

In order to make Sherpa build its modules against a local fftw library, users need to change the default Sherpa build configuration as follows.

First, make sure you download the Sherpa source tarball, or get the source code from GitHub:

$ git clone https://github.com/sherpa/sherpa.git
$ cd sherpa

Then, you need to edit the setup.cfg configuration. This file is documented, so it should be easy to follow the instructions therein.

In particular, you need to make sure that you set the following configuration options:

fftw=local
fftw-include-dirs=/usr/local/include
fftw-lib-dirs=/usr/local/lib
fftw-libraries=fftw3

You might need to change the /usr/local path in the above example to the actual directories on your system that contain the header (.h) files and the libfftw3.so shared object.

Make sure there are no leading spaces or the python configuration system will not parse the configuration options correctly.

Then, build Sherpa in the standard way:

$ python setup.py install

Sherpa does not support XSPEC models by default. However, it is possible to instruct Sherpa to build its XSPEC extension module by changing the build configuration options.

The xspec_config section of the setup.cfg file will need changing to point to the libraries, and to turn on the extension. In all cases, set

with-xspec=True

The remaining settings depend on how the XSPEC libraries have been built (in the examples below, environment variables are used, but the full path should be in your own copy of the file):

1. If the full XSPEC system has been built, then use

   xspec_lib_dirs=$HEADAS/lib
   xspec_libraries=XSFunctions XSModel XSUtil XS wcs-4.20
   cfitsio_libraries=cfitsio_3.37
   ccfits_libraries=CCfits_2.4
   

   The environment variable $HEADAS should be expanded out, and the version numbers of the wcs, cfitsio, and CCfits libraries may need to be changed.

2. Use the model-only build of XSPEC, which will also require building the cfitsio, CCfits, and WCSLIB libraries (it is not clear if version 5 is supported, since XSPEC 12.8.2 uses version 4.20). If all the libraries are installed into the same location ($HEADAS/lib), then a similar set up to the full XSPEC build is used

   xspec_lib_dirs=$HEADAS/lib
   xspec_libraries=XSFunctions XSModel XSUtil XS wcs
   

   except that the library names (cfitiso, CCfits, and wcs) do not need version numbers. If placed in different directories then the cfitsio_lib_dirs, ccfits_lib_dirs, and (possibly) gfortran_lib_dirs values should be set appropriately.

3. or point to the XSPEC libraries provided by CIAO. In this case the wcs library does not need to be specified because of the way the XSPEC models-only version was built with CIAO 4.7.

   xspec_lib_dirs=$ASCDS_INSTALL/ots/lib
   xspec_libraries=XSFunctions XSModel XSUtil XS
   

In all cases, the same version of gfortran should be used to build Sherpa and XSPEC, to avoid possible incompatabilities.

If there are problems building, or using, the module, then the other options may need to be set - in particular the gfortran_lib_dirs and gfortran_libraries settings.

The XSpec module is designed for use with XSpec version 12.8.2e. It can be used with other versions - either patches to 12.8.2 or different versions - but there may be build or user issues.

In order for the module to work, the HEADAS environment variable has to be set in the shell from which the Python session is started. For the CIAO-XSPEC build, HEADAS should be set to $ASCDS_INSTALL/ots/spectral, otherwise it is the parent directory of the xspec_lib_dirs directory.

In order to check that the module is working, importing the sherpa.astro.ui module will no-longer warn you that the sherpa.astro.xspec module is not available and you can use routines such as:

>>> from sherpa.astro import xspec
>>> xspec.get_xsversion()
'12.8.2e'

Sherpa supports other build configuration options that are required to support Sherpa build in specific environments, for instance when building the CIAO analysis system. These options include:

• building Sherpa against a local version of the CIAO region and group libraries
• specify additional CFLAGS options for the group library
• building Sherpa against a local build of the wcssubs routines
• change the default ./configure command line

The setup.cfg file in the Sherpa source distribution contains more information about these options.

Sherpa is developed by the Chandra X-ray Observatory to provide fitting and modelling capabilities to the CIAO analysis package. It has been released onto GitHub under the GNU General Public Licence, version 3 for users to extend (whether to other areas of Astronomy or in other domains).