- sherpa.astro.ui.group(id=None, bkg_id=None)
Turn on the grouping for a PHA data set.
A PHA data set can be grouped either because it contains grouping information , which is automatically applied when the data is read in with
load_data, or because the
groupset of routines has been used to dynamically re-group the data. The
ungroupfunction removes this grouping (however it was created). The
groupfunction re-applies this grouping. The grouping scheme can be changed dynamically, using the
group_xxxseries of routines.
sherpa.utils.err.ArgumentErr – If the data set does not contain a PHA data set.
Fit one or more data sets.
Adaptively group to a minimum number of counts.
Adaptively group to a minimum signal-to-noise ratio.
Group into a fixed number of bins.
Group into a minimum number of counts per bin.
Group into a minimum signal-to-noise ratio.
Group into a fixed bin width.
Apply a set of grouping flags to a PHA data set.
Apply a set of quality flags to a PHA data set.
Turn off the grouping for 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
group, anything that uses the data set - such as a plot, fit, or error analysis - will use the grouped data values. Models should be re-fit if
groupis called; the increase in the signal of the bins may mean that a chi-square statistic can now be used.
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. The
groupcommand does not create this information; this is either created by modifying the PHA file before it is read in, or by using the
group_xxxroutines once the data has been loaded.
groupedfield of a PHA data set is set to
Truewhen the data is grouped.
Group the data in the default data set:
>>> group() >>> get_data().grouped True
Group the first background component of the ‘core’ data set:
>>> group('core', bkg_id=1) >>> get_bkg('core', bkg_id=1).grouped True
The data is fit using the ungrouped data, and then plots of the data and best-fit, and the residuals, are created. The first plot uses the ungrouped data, and the second plot uses the grouped data.
>>> ungroup() >>> fit() >>> plot_fit_resid() >>> group() >>> plot_fit_resid()