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Published August 1, 2019 | Version 4.11.1
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sherpa/sherpa: Sherpa 4.11.1

  • 1. Smithsonian Astrophysical Observatory
  • 2. Center for Astrophysics | Harvard & Smithsonian
  • 3. MPI for Nuclear Physics
  • 4. DIRAC Institute, UW
  • 5. @TIBHannover


Sherpa 4.11.1

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
Details 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.



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