- sherpa.astro.ui.group_adapt(id, min=None, bkg_id=None, maxLength=None, tabStops=None)
Adaptively group to a minimum number of counts.
Combine the data so that each bin contains
minor more counts. The difference to
group_countsis that this algorithm starts with the bins with the largest signal, in order to avoid over-grouping bright features, rather than at the first channel of the data. The adaptive nature means that low-count regions between bright features may not end up in groups with the minimum number of counts. The binning scheme is applied to all the channels, but any existing filter - created by the
noticeset of functions - is re-applied after the data has been grouped.
Changed in version 4.15.1: The filter is now reported, noting any changes the new grouping scheme has made.
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
min (int) – The number of channels to combine into a group.
bkg_id (int or str, optional) – Set to group the background associated with the data set. When
None(which is the default), the grouping is applied to all the associated background data sets as well as the source data set.
maxLength (int, optional) – The maximum number of channels that can be combined into a single group.
tabStops (array of int or bool, optional) – If set, indicate one or more ranges of channels that should not be included in the grouped output. The array should match the number of channels in the data set and non-zero or
Truemeans that the channel should be ignored from the grouping (use 0 or
sherpa.utils.err.ArgumentErr – If the data set does not contain a PHA data set.
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.
The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the
minparameter. If given two un-named arguments, then they are interpreted as the
minparameters, respectively. The remaining parameters are expected to be given as named arguments.
group, it is possible to call
group_adaptmultiple times on the same data set without needing to call
If channels can not be placed into a “valid” group, then a warning message will be displayed to the screen and the quality value for these channels will be set to 2. This information can be found with the
Group the default data set so that each bin contains at least 20 counts:
Plot two versions of the ‘jet’ data set: the first uses an adaptive scheme of 20 counts per bin, the second the
>>> group_adapt('jet', 20) >>> plot_data('jet') >>> group_counts('jet', 20) >>> plot_data('jet', overplot=True)