    Exclude data from the fit for a data set.

ignore_id(ids, lo=None, hi=None, **kwargs)
ids - int or str, or array of int or str
lo - number or str, optional
hi - number, optional
bkg_id - int or str, optional

Select one or more ranges of data to exclude by filtering on the independent axis value. The filter is applied to the given data set, or sets.

Ignore all data points with an X value (the independent axis) between 12 and 18 for data set 1:

>>> ignore_id(1, 12, 18) dataset 1: 10:30 -> 10,20:30 x

Ignore the range up to 0.5 and 7 and above, for data sets 1, 2, and 3:

>>> ignore_id([1, 2, 3], hi=0.5) dataset 1: 0.00146:14.9504 -> 0.584:14.9504 Energy (keV) dataset 2: 0.00146:14.9504 -> 0.6424:14.9504 Energy (keV) dataset 3: 0.00146:14.9504 -> 0.511:14.9504 Energy (keV) >>> ignore_id([1, 2, 3], lo=7) dataset 1: 0.584:14.9504 -> 0.584:4.4384 Energy (keV) dataset 2: 0.6424:14.9504 -> 0.6424:5.1392 Energy (keV) dataset 3: 0.511:14.9504 -> 0.511:4.526 Energy (keV)

Apply the same filter as the previous example, but to data sets "core" and "jet", and hide the screen output:

>>> from sherpa.utils.logging import SherpaVerbosity >>> with SherpaVerbsity("WARN"): ... ignore_id(["core", "jet"], ":0.5,7:") ...

PARAMETERS

The parameters for this function are:

Parameter
Definition
ids
The data set, or sets, to use.
lo
The lower bound of the filter (when a number) or a string expression listing ranges in the form a:b , with multiple ranges allowed, where the ranges are separated by a , . The term :b means exclude everything up to

b (an exclusive limit for integrated datasets), and

a: means exclude everything that is higher than, or equal to, a .
hi
The upper bound of the filter when lo is not a string.
bkg_id
The filter will be applied to the associated background component of the data set if bkg_id is set. Only PHA data sets support this option; if not given, then the filter is applied to all background components as well as the source data.

Notes

The order of `ignore` and `notice` calls is important.

The units used depend on the analysis setting of the data set, if appropriate.

To filter a 2D data set by a shape use `ignore2d` .

Changes in CIAO

The change in the filter is now reported for the dataset.

Integrated data sets - so Data1DInt and DataPHA when using energy or wavelengths - now ensure that the `hi` argument is exclusive and better handling of the `lo` argument when it matches a bin edge. This can result in the same filter selecting a smaller number of bins than in earlier versions of Sherpa.

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

December 2023