- sherpa.astro.ui.ungroup(id=None, bkg_id=None)
Turn off the grouping for a PHA data set.
A PHA data set can be grouped either because it contains grouping information 1, which is automatically applied when the data is read in with
load_data, or because the
group_xxxset of routines has been used to dynamically re-group the data. The
ungroupfunction removes this grouping (however it was created).
id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by
bkg_id (int or str, optional) – Set to ungroup the background associated with the data set.
sherpa.utils.err.ArgumentErr – If the data set does not contain a PHA data set.
PHA data is often grouped to improve the signal to noise of the data, by decreasing the number of bins, so that a chi-square statistic can be used when fitting the data. After calling
ungroup, anything that uses the data set - such as a plot, fit, or error analysis - will use the original data values. Models should be re-fit if
ungroupis called; this may require a change of statistic depending on the counts per channel in the spectrum.
The grouping is implemented by separate arrays to the main data - the information is stored in the
qualityarrays of the PHA data set - so that a data set can be grouped and ungrouped many times, without losing information.
groupedfield of a PHA data set is set to
Falsewhen the data is not grouped.
If subtracting the background estimate from a data set, the grouping applied to the source data set is used for both source and background data sets.
Arnaud., K. & George, I., “The OGIP Spectral File Format”, http://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/docs/spectra/ogip_92_007/ogip_92_007.html
Ungroup the data in the default data set:
>>> ungroup() >>> get_data().grouped False
Ungroup the first background component of the ‘core’ data set:
>>> ungroup('core', bkg_id=1) >>> get_bkg('core', bkg_id=1).grouped False