Adaptively group to a minimum signal-to-noise ratio.
group_adapt_snr(id, min=None, bkg_id=None, maxLength=None, tabStops=None, errorCol=None) id - int or str, optional num - number bkg_id - int or str, optional maxLength - int, optional tabStops - array of int or bool, optional errorCol - array of num, optional
Combine the data so that each bin has a signal-to-noise ratio of at least `num` . The difference to `group_snr` 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.
Group the default data set so that each bin contains a signal-to-noise ratio of at least 5:
Plot two versions of the 'jet' data set: the first uses an adaptive scheme and the second the non-adaptive version:
>>> group_adapt_snr('jet', 4) >>> plot_data('jet') >>> group_snr('jet', 4) >>> plot_data('jet', overplot=True)
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 minimum signal-to-noise ratio that must be reached to form a group of channels.|
|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).|
|errorCol||If set, the error to use for each channel when calculating the signal-to-noise ratio. If not given then Poisson statistics is assumed. A warning is displayed for each zero-valued error estimate.|
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_adapt_snr` 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_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