group_snr
- sherpa.astro.ui.group_snr(id, snr=None, bkg_id=None, maxLength=None, tabStops=None, errorCol=None)
Group into a minimum signal-to-noise ratio.
Combine the data so that each bin has a signal-to-noise ratio of at least
snr
. The background is not included in this calculation; the calculation is done on the raw data even ifsubtract
has been called on this data set. The binning scheme is, by default, applied to only the noticed data range. It is suggested that filtering is done before calling group_snr.Changed in version 4.16.0: Grouping now defaults to only using the noticed channel range. The tabStops argument can be set to “nofilter” to use the previous behaviour.
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
.snr (number) – The minimum signal-to-noise ratio that must be reached to form a group 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.maxLength (int, optional) – The maximum number of channels that can be combined into a single group.
tabStops (array of int or bool, optional) – If not set then it will be based on the filtering of the data set, so that the grouping only uses the filtered data. If set it can be the string “nofilter”, which means that no filter is applied (and matches the behavior prior to the 4.16 release), or an array of booleans where True indicates that the channel should not be used in the grouping (this array must match the number of channels in the data set).
errorCol (array of num, optional) – 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.
- 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_bins
Group into a fixed number of bins.
group_counts
Group into a minimum number of counts per bin.
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
snr
parameter. If given two un-named arguments, then they are interpreted as theid
andsnr
parameters, respectively. The remaining parameters are expected to be given as named arguments.Unlike
group
, it is possible to callgroup_snr
multiple times on the same data set without needing to callungroup
.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.Examples
Group the default data set so that each bin has a signal-to-noise ratio of at least 5:
>>> group_snr(20)
Plot two versions of the ‘jet’ data set: the first uses a signal-to-noise ratio of 3 and the second 5:
>>> group_snr('jet', 3) >>> plot_data('jet') >>> group_snr('jet', 5) >>> plot_data('jet', overplot=True)
The grouping is applied to only the data within the 0.5 to 8 keV range (this behaviour is new in 4.16):
>>> set_analysis('energy') >>> notice() >>> notice(0.5, 8) >>> group_snr(3) >>> plot_data()
Group the full channel range and then apply the existing filter (0.5 to 8 keV) so that the noticed range may be larger (this was the default behaviour before 4.16):
>>> group_snr(3, tabStops="nofilter")