sherpa.astro.ui.notice(lo=None, hi=None, **kwargs)

Include data in the fit.

Select one or more ranges of data to include by filtering on the independent axis value. The filter is applied to all data sets.

  • lo (number or str, optional) – 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 include everything up to, and including b, and a: means include everything that is higher than, or equal to, a.
  • hi (number, optional) – The upper bound of the filter when lo is not a string.

See also

Include data for a data set.
Include a spatial region in an image.
Exclude data from the fit.
Show any filters applied to a data set.


The order of ignore and notice calls is important, and the results are a union, rather than intersection, of the combination.

If notice is called on an un-filtered data set, then the ranges outside the noticed range are excluded: it can be thought of as if ignore had been used to remove all data points. If notice is called after a filter has been applied then the filter is applied to the existing data.

For binned data sets, the bin is included if the noticed range falls anywhere within the bin.

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

To filter a 2D data set by a shape use notice2d.


Since the notice call is applied to an un-filtered data set, the filter choses only those points that lie within the range 12 <= X <= 18.

>>> load_arrays(1, [10, 15, 20, 30], [5, 10, 7, 13])
>>> notice(12, 28)
>>> get_dep(filter=True)
array([10,  7])

As no limits are given, the whole data set is included:

>>> notice()
>>> get_dep(filter=True)
array([ 5, 10,  7, 13])

The ignore call excludes the first two points, but the notice call adds back in the second point:

>>> ignore(None, 17)
>>> notice(12, 16)
>>> get_dep(filter=True)
array([10,  7, 13])

Only include data points in the range 8<=X<=12 and 18<=X=22:

>>> ignore()
>>> notice("8:12, 18:22")
>>> get_dep(filter=True)
array([5, 7])