sherpa.astro.ui.group_bins(id, num=None, bkg_id=None, tabStops=None)

Group into a fixed number of bins.

Combine the data so that there num equal-width bins (or groups). The binning scheme is, by default, applied to only the noticed data range. It is suggested that filtering is done before calling group_bins.

Changed in version 4.16.0: Grouping now defaults to only using the noticed channel range. The tabStops argument can be set to “nofilter” to use the previous behaviour.

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 get_default_id.

  • num (int) – The number of bins in the grouped data set. Each bin will contain the same number of channels.

  • bkg_id (int or str, optional) – Set to group the background associated with the data set. When bkg_id is None (which is the default), the grouping is applied to all the associated background data sets as well as the source data set.

  • tabStops (str or array of int or bool, optional) – If not set then it will be based on the filtering of the data set, so that the grouping only uses the filtered data. If set it can be the string “nofilter”, which means that no filter is applied (and matches the behavior prior to the 4.16 release), or an array of booleans where True indicates that the channel should not be used in the grouping (this array must match the number of channels in the data set).


sherpa.utils.err.ArgumentErr – If the data set does not contain a PHA data set.

See also


Adaptively group to a minimum number of counts.


Adaptively group to a minimum signal-to-noise ratio.


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 num parameter. If given two un-named arguments, then they are interpreted as the id and num parameters, respectively. The remaining parameters are expected to be given as named arguments.

Unlike group, it is possible to call group_bins multiple times on the same data set without needing to call ungroup.

Since the bin width is an integer number of channels, it is likely that some channels will be “left over”. This is even more likely when the tabStops parameter is set. If this happens, 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 get_quality command.


Group the default data set so that there are 50 bins.

>>> group_bins(50)

Group the ‘jet’ data set to 50 bins and plot the result, then re-bin to 100 bins and overplot the data:

>>> group_bins('jet', 50)
>>> plot_data('jet')
>>> group_bins('jet', 100)
>>> plot_data('jet', overplot=True)

The grouping is applied to only the data within the 0.5 to 8 keV range (this behaviour is new in 4.16):

>>> set_analysis('energy')
>>> notice()
>>> notice(0.5, 8)
>>> group_bins(50)
>>> plot_data()

Group the full channel range and then apply the existing filter (0.5 to 8 keV) so that the noticed range may be larger (this was the default behaviour before 4.16):

>>> group_bins(50, tabStops="nofilter")

Do not group any channels numbered less than 20 or 800 or more. Since there are 780 channels to be grouped, the width of each bin will be 20 channels and there are no “left over” channels:

>>> notice()
>>> channels = get_data().channel
>>> ign = (channels <= 20) | (channels >= 800)
>>> group_bins(39, tabStops=ign)
>>> plot_data()