Last modified: December 2021

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AHELP for CIAO 4.14 Sherpa


Context: data


Adaptively group to a minimum number of counts.


group_adapt(id, min=None, bkg_id=None, maxLength=None, tabStops=None)

id - int or str, optional
min - int
bkg_id - int or str, optional
maxLength - int, optional
tabStops - array of int or bool, optional


Combine the data so that each bin contains `min` or more counts. The difference to `group_counts` is 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 `ignore` or `notice` set of functions - is re-applied after the data has been grouped.


Example 1

Group the default data set so that each bin contains at least 20 counts:

>>> group_adapt(20)

Example 2

Plot two versions of the 'jet' data set: the first uses an adaptive scheme of 20 counts per bin, the second the `group_counts` method:

>>> group_adapt('jet', 20)
>>> plot_data('jet')
>>> group_counts('jet', 20)
>>> plot_data('jet', overplot=True)


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` .
min The number of channels to combine into a group.
bkg_id 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.
maxLength The maximum number of channels that can be combined into a single group.
tabStops 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 or False otherwise).


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

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

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 `get_quality` command.


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, 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