get_error

sherpa.astro.ui.get_error(id=None, filter=False, bkg_id=None)

Return the errors on the dependent axis of a data set.

The function returns the total errors (a quadrature addition of the statistical and systematic errors) on the values (dependent acis) of a data set or its background. The individual components can be retrieved with the get_staterror and get_syserror functions.

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.

  • filter (bool, optional) – Should the filter attached to the data set be applied to the return value or not. The default is False.

  • bkg_id (int or str, optional) – Set if the values returned should be from the given background component, instead of the source data set.

Returns

errors – The error for each data point, formed by adding the statistical and systematic errors in quadrature. For PHA data sets, the return array will match the grouping scheme applied to the data set. The size of this array depends on the filter argument.

Return type

array

Raises

sherpa.utils.err.IdentifierErr – If the data set does not exist.

See also

get_dep()

Return the dependent axis of a data set.

get_staterror()

Return the statistical errors on the dependent axis of a data set.

get_syserror()

Return the systematic errors on the dependent axis of a data set.

list_data_ids()

List the identifiers for the loaded data sets.

Notes

The default behavior is to not apply any filter defined on the independent axes to the results, so that the return value is for all points (or bins) in the data set. Set the filter argument to True to apply this filter.

Examples

Return the error values for the default data set, ignoring any filter applied to it:

>>> err = get_error()

Ensure that the return values are for the selected (filtered) points in the default data set (the return array may be smaller than in the previous example):

>>> err = get_error(filter=True)

Find the errors for the “core” data set and its two background components:

>>> err = get_error('core', filter=True)
>>> berr1 = get_error('core', bkg_id=1, filter=True)
>>> berr2 = get_error('core', bkg_id=2, filter=True)