Group into a minimum number of counts per bin.
group_counts(id, num=None, bkg_id=None, maxLength=None, tabStops=None) id - int or str, optional num - 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 `num` or more 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. The background is not included in this calculation; the calculation is done on the raw data even if `subtract` has been called on this data set.
Group the default data set so that each bin contains at least 20 counts:
Plot two versions of the 'jet' data set: the first uses 20 counts per group and the second is 50:
>>> group_counts('jet', 20) >>> plot_data('jet') >>> group_counts('jet', 50) >>> 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.
>>> set_analysis('energy') >>> notice(0.5, 8) >>> group_counts(30) >>> plot_data()
If a channel has more than 30 counts then do not group, otherwise group channels so that they contain at least 40 counts. The `group_adapt` and `group_adapt_snr` functions provide similar functionality to this example. A maximum length of 10 channels is enforced, to avoid bins getting too large when the signal is low.
>>> notice() >>> counts = get_data().counts >>> ign = counts > 30 >>> group_counts(40, tabStops=ign, maxLength=10)
The parameters for this function are:
|id||The identifier for the data set to use. If not given then the default identifier is used, as returned by `get_default_id` .|
|num||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 `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_counts` 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.
- 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, group_adapt_snr, group_bins, 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