group_bins
- 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 applied to all the channels, but any existing filter - created by theignore
ornotice
set 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.
- Parameters:
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 (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
True
means that the channel should be ignored from the grouping (use 0 orFalse
otherwise).
- Raises:
sherpa.utils.err.ArgumentErr – If the data set does not contain a PHA data set.
See also
group_adapt
Adaptively group to a minimum number of counts.
group_adapt_snr
Adaptively group to a minimum signal-to-noise ratio.
group_counts
Group into a minimum number of counts per bin.
group_snr
Group into a minimum signal-to-noise ratio.
group_width
Group into a fixed bin width.
set_grouping
Apply a set of grouping flags to a PHA data set.
set_quality
Apply a set of quality flags to a PHA data set.
Notes
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 theid
andnum
parameters, respectively. The remaining parameters are expected to be given as named arguments.Unlike
group
, it is possible to callgroup_bins
multiple times on the same data set without needing to callungroup
.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 theget_quality
command.Examples
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 the full data set, and then the filter - in this case defined over the range 0.5 to 8 keV - will be applied. This means that the noticed data range will likely contain less than 50 bins.
>>> set_analysis('energy') >>> notice(0.5, 8) >>> group_bins(50) >>> plot_data()
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()