Sherpa 4.10.1 release

Sherpa 4.10.1  has now been released and is available via our Conda channel (https://conda.anaconda.org/sherpa), pip (https://pypi.python.org/pypi/sherpa/4.10.1), Zenodo (https://zenodo.org/record/1463962#.W8jkmCSYUeM), and GitHub (https://github.com/sherpa/sherpa/tree/4.10.1).

This release supports Python versions 2.7, 3.5, 3.6, and 3.7.

We thank everyone who contributed to this release with feature requests, bug reports, testing, code contributions, or questions.  Please join in the fun on GitHub: https://github.com/sherpa/sherpa/

Overview

This release fixes several bugs and introduces a few new features, notably the ability to evaluate model components on arbitrary grids and generate user-defined ARFs and RMFs. Also, as of this release Sherpa will no longer rely on any Fortran code. See the following section for details.

It is now possible to build the Sherpa documentation using Sphinx. Additionally, the Sphinx documentation is automatically built and hosted on ReadTheDocs. The version for 4.10.1 can be found at https://sherpa.readthedocs.io/en/4.10.1/

Details

#407  Ensure 0-length array is an error in filter_resp (fix #405)

Add an explicit check in the C++ filter_resp code to error out if the noticed channels array is empty.

#422 Improve error message with wrong xspec version

Improve the handling of XSPEC versions mismatch.

#466 Fix bounding box out-of-bounds memory read

Avoid an out-of-bounds memory read when calling pad_bounding_box (when the data is not matching the expected conditions for this call).

#469 Evaluate model on finer grid

Sherpa users can now define arbitrary grids, called evaluation spaces, on which to evaluate individual model components, both in 1D and 2D. This can be useful in a number of cases, for instance when it is desirable to evaluate models on a finer grid than the one defined by the data, or in convolution models where information outside of the data range can be used to reduce boundary effects or to inform the evaluation inside the data space. 

Also, when plotting individual 1D model components (plot_source_component), if a specific evaluation space was attached to the model then the plot will be of the model evaluated over that evaluation space, not the data space. Other plotting commands should be unaffected by this change.

#470 Order for np.ones() should be a one length string not a bool

Fixed a DeprecationWarning in the optimization module.

#471 Using overwrite rather than clobber of astropy.io.fits

The clobber option in astropy has been deprecated for a while now in favour of overwrite and thus issues a DeprecationWarning. Sherpa now uses overwrite instead.

#475 Fix unecessary runtime warning (fix #402)

A condition check in the optimization code was modified so as not to produce a warning when the value is a NaN.

#481 Remove Fortran code

Sherpa does not rely on any Fortran routines anymore, except those compiled in XSPEC. The existing Fortran code, which was used mainly in some of the optimization routines, has  been replaced by equivalent C++ code. This means that gfortran is no longer required for building Sherpa, although a version of libgfortran compatible with the one used to link the XSPEC libraries would still be needed at runtime if the XSPEC extension is built.

#482 ARF/RMF creation functions

The create_rmf and create_arf functions now allow users to easily generate user defined response objects.

#486 ZeroDivisionError in calc_stat_info (fix #476)

Sherpa did not catch divisions by zero in the calc_stat_info command. That has been fixed.

#484 Equivalent Width errors

Several optional arguments (params=None, error=False, otherids=(), niter=1000), were added to eqwidth. If error = True, then get_draws shall be run if the fit stat is one of the following: (Cash,  CStat, WStat) otherwise a multivariate normal distribution shall be run to generate the samples. The optional niter parameter is used to generate the number of samples. The optional parameter otherids is only used if get_draws is used internally when multiple data sets were used to fit. Alternatively, the user can enter the samples via the params option where the samples must be a numpy.ndarray of dimension (num, npar).

#487 PSF bin size warning

Sherpa assumes that the PSF image and the data have the same pixel size. When this is not true Sherpa ignores the difference, which results in a larger PSF being applied. From now on Sherpa will issue a warning when the PSF image pixel size and the data image pixel size are different.