Sherpa 4.11.1 release (not-so-belated version)

Sherpa 4.11.1 has just been released (August 1 2019) and is available via our Conda channel (https://conda.anaconda.org/sherpa/), pip (https://pypi.python.org/pypi/sherpa/4.11.1), Zenodo (https://zenodo.org/record/3358134), and GitHub (https://github.com/sherpa/sherpa/tree/4.11.1).

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

The documentation for this release can be found at https://sherpa.readthedocs.io/en/4.11.1/

OVERVIEW

This release of Sherpa introduces several functional improvements and bug fixes,
in particular Sherpa now has support for:

• asymmetric error bars
• PSFs with better pixel resolution than the data
• running optimization in parallel

This version of Sherpa has been tested with Python 2.7, 3.5, 3.6, and 3.7. Support for Python 2.7 is deprecated and will be dropped in a future release. Please upgrade to Python 3.5 or later!

Details

Documentation and infrastructure fixes are not shown.

#630 Fix "get_int_proj does not work when recalc=True" (#543)

get_int_proj did not work when recalc=True on Python 3. This has now been fixed.

#615 Asymmetric Errors

Sherpa now supports asymmetric error bars. Errors can be read through a new

load_ascii_with_errors high level function, or through the new
Data1DAsymmetricErrs class. Sherpa uses bootstrap for estimating the uncertainties.

#585 plot pvalue

Updates to utilize the appropriate response files (ARF and RMF) for X-ray spectra

and changes to the p_value output to 1/(number of simulations) when p_value is 0
and the number of simulations in not large enough.

#596 Run optimization algorithms over multiple cores

This PR enables the user to run the optimization algorithms (DifEvo, LevMar,
and NelderMead) on multi-cores.

#607 PSF rebinning (fix #43)

Sherpa now supports using a PSF with a finer resolution than 2D images. If Sherpa
detects that the PSF has a smaller pixel size than the data, it will evaluate the
model on a "PSF Space" that has the same resolution as the PSF and the same footprint as
the data, then rebin the evaluated model back to the data space for calculating the statistic.

#614 Refactor data classes (fix #563, #627, #628)

Sherpa's basic data classes have been refactored and cleaned up to help
facilitating fixing bugs and implementing new features. New tests were added to reduce
the chances of introducing regressions in the future.

#612 Fix #609 User Model: unable to change parameter values

A regression introduced in Sherpa 4.10.0 presented users from change
user-model parameter values through direct access. This issue has been
fixed. Several tests were added to reduce the chance of regressions in
the future.

Fix #514 - the command line tool sherpa_smoke in the conda package now correctly honors
command line arguments.