- sherpa.astro.ui.load_arf(id, arg=None, resp_id=None, bkg_id=None)¶
Load an ARF from a file and add it to a PHA data set.
Load in the effective area curve for a PHA data set, or its background. The
load_bkg_arffunction can be used for setting most background ARFs.
arg – Identify the ARF: a file name, or a data structure representing the data to use, as used by the I/O backend in use by Sherpa: a
TABLECratefor crates, as used by CIAO, or a list of AstroPy HDU objects.
Return the ARF associated with a PHA data set.
Load an ARF from a file and add it to the background of a PHA data set.
Load multiple ARFs for a PHA data set.
Load a file as a PHA data set.
Load a RMF from a file and add it to a PHA data set.
Define the convolved model expression for a data set.
Set the ARF for use by a PHA data set.
Create an ARF data structure.
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
argparameter. If given two un-named arguments, then they are interpreted as the
argparameters, respectively. The remaining parameters are expected to be given as named arguments.
If a PHA data set has an associated ARF - either from when the data was loaded or explicitly with the
set_arffunction - then the model fit to the data will include the effect of the ARF when the model is created with
set_source. In this case the
get_sourcefunction returns the user model, and
get_modelthe model that is fit to the data (i.e. it includes any response information; that is the ARF and RMF, if set). To include the ARF explicitly, use
minimum_energysetting of the
ogipsection of the Sherpa configuration file determines the behavior when an ARF with a minimum energy of 0 is read in. The default is to replace the 0 by the value 1e-10, which will also cause a warning message to be displayed.
Use the contents of the file ‘src.arf’ as the ARF for the default data set.
Read in an ARF from the file ‘bkg.arf’ and set it as the ARF for the background model of data set “core”:
>>> load_arf('core', 'bkg.arf', bkg_id=1)