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This release of Sherpa introduces several functional improvements and bug fixes,\nin particular Sherpa now has support for:
\nget_int_proj did not work when recalc=True on Python 3. This has now been fixed.\n
\n615 Asymmetric Errors\nSherpa now supports asymmetric error bars. Errors can be read through a new\n
\nload_ascii_with_errors
high level function, or through the new\nData1DAsymmetricErrs
class. Sherpa uses bootstrap for estimating the uncertainties.
Updates to utilize the appropriate response files (ARF and RMF) for X-ray spectra\nand changes to the p_value output to 1/(number of simulations) when p_value is 0\nand the number of simulations in not large enough.\n
\n596 Run optimization algorithms over multiple cores\nThis PR enables the user to run the optimization algorithms (DifEvo, LevMar,\n
\nand NelderMead) on multi-cores.
\n607 PSF rebinning (fix #43)\nSherpa now supports using a PSF with a finer resolution than 2D images. If Sherpa\n
\ndetects that the PSF has a smaller pixel size than the data, it will evaluate the\nmodel on a \"PSF Space\" that has the same resolution as the PSF and the same footprint as\nthe data, then rebin the evaluated model back to the data space for calculating the statistic.
\n614 Refactor data classes (fix #563, #627, #628)\nSherpa's basic data classes have been refactored and cleaned up to help\n
\nfacilitating fixing bugs and implementing new features. New tests were added to reduce\nthe chances of introducing regressions in the future.
\n612 Fix #609 User Model: unable to change parameter values\nA regression introduced in Sherpa 4.10.0 presented users from change\n
\nuser-model parameter values through direct access. This issue has been\nfixed. Several tests were added to reduce the chance of regressions in\nthe future.
\nFix #514 - the command line tool sherpa_smoke
in the conda package now correctly honors\ncommand line arguments.