- sherpa.astro.ui.group_snr(id, snr=None, bkg_id=None, maxLength=None, tabStops=None, errorCol=None)
Group into a minimum signal-to-noise ratio.
Combine the data so that each bin has a signal-to-noise ratio of at least
snr. 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. The background is not included in this calculation; the calculation is done on the raw data even if
subtracthas been called on this data set.
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
snr (number) – The minimum signal-to-noise ratio that must be reached to form a group of channels.
bkg_id (int or str, optional) – Set to group the background associated with the data set. When
bkg_idis 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
errorCol (array of num, optional) – If set, the error to use for each channel when calculating the signal-to-noise ratio. If not given then Poisson statistics is assumed. A warning is displayed for each zero-valued error estimate.
sherpa.utils.err.ArgumentErr – If the data set does not contain a PHA data set.
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 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
snrparameter. If given two un-named arguments, then they are interpreted as the
snrparameters, respectively. The remaining parameters are expected to be given as named arguments.
group, it is possible to call
group_snrmultiple 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 has a signal-to-noise ratio of at least 5:
Plot two versions of the ‘jet’ data set: the first uses a signal-to-noise ratio of 3 and the second 5:
>>> group_snr('jet', 3) >>> plot_data('jet') >>> group_snr('jet', 5) >>> plot_data('jet', overplot=True)