Last modified: December 2023

AHELP for CIAO 4.16 Sherpa


Context: data


Turn on the grouping for a PHA data set.


group(id=None, bkg_id=None)

id - int or str, optional
bkg_id - int or str, optional


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_pha` or `load_data` , or because the `group` set of routines has been used to dynamically re-group the data. The `ungroup` function removes this grouping (however it was created). The `group` function re-applies this grouping. The grouping scheme can be changed dynamically, using the group_xxx series of routines.


Example 1

Group the data in the default data set:

>>> group()
>>> get_data().grouped

Example 2

Group the first background component of the 'core' data set:

>>> group('core', bkg_id=1)
>>> get_bkg('core', bkg_id=1).grouped

Example 3

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()


The parameters for this function are:

Parameter Definition
id The identifier for the data set to use. If not given then the default identifier is used, as returned by `get_default_id` .
bkg_id Set to group the background associated with the 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 `group` is 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 grouping and quality arrays of the PHA data set - so that a data set can be grouped and ungrouped many times, without losing information. The `group` command does not create this information; this is either created by modifying the PHA file before it is read in, or by using the group_xxx routines once the data has been loaded.

The grouped field of a PHA data set is set to True when the data is grouped.


Changes in CIAO

Changed in CIAO 4.16

The filter is now reported, noting any changes the new grouping scheme has made.


See the bugs pages on the Sherpa website for an up-to-date listing of known bugs.

See Also

copy_data, dataspace1d, dataspace2d, datastack, delete_data, fake, get_axes, get_bkg_chisqr_plot, get_bkg_delchi_plot, get_bkg_fit_plot, get_bkg_model_plot, get_bkg_plot, get_bkg_ratio_plot, get_bkg_resid_plot, get_bkg_source_plot, get_counts, get_data, get_data_contour, get_data_contour_prefs, get_data_image, get_data_plot, get_data_plot_prefs, get_dep, get_dims, get_error, get_grouping, get_quality, get_specresp, get_staterror, get_syserror, group_adapt, group_adapt_snr, group_bins, group_counts, group_snr, group_width, load_ascii, load_data, load_grouping, load_quality, set_data, set_grouping, set_quality, ungroup, unpack_ascii, unpack_data
get_filter, ignore, ignore2d, ignore2d_id, ignore_bad, ignore_id, load_filter, notice, notice2d, notice2d_id, notice_id, set_filter, show_filter
get_default_id, list_data_ids, list_response_ids
plot_data, set_xlinear, set_xlog, set_ylinear, set_ylog
save_error, save_filter, save_grouping, save_quality, save_staterror, save_syserror
calc_data_sum, calc_data_sum2d, calc_ftest, calc_kcorr, calc_mlr, calc_model_sum2d, calc_source_sum2d, get_rate
contour, contour_data, contour_ratio, histogram1d, histogram2d, image_data, rebin