set_rmf(id, rmf=None, resp_id=None, bkg_id=None)¶
Set the RMF for use by a PHA data set.
Set the redistribution matrix for a PHA data set, or its background.
id (int or str, optional) – The data set to use. If not given then the default identifier is used, as returned by get_default_id.
rmf – An RMF, such as returned by get_rmf or unpack_rmf.
resp_id (int or str, optional) – The identifier for the RMF within this data set, if there are multiple responses.
bkg_id (int or str, optional) – Set this to identify the RMF as being for use with the background.
Return the RMF associated with 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 a RMF 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 rmf parameter. If given two un-named arguments, then they are interpreted as the id and rmf parameters, respectively. The remaining parameters are expected to be given as named arguments.
If a PHA data set has an associated RMF - either from when the data was loaded or explicitly with the set_rmf function - then the model fit to the data will include the efect of the RMF when the model is created with set_model or set_source. In this case the get_source function returns the user model, and get_model the 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 RMF explicitly, use set_full_model.
Copy the RMF from the default data set to data set 2:
>>> rmf1 = get_rmf() >>> set_rmf(2, rmf1)
Read in a RMF from the file ‘bkg.rmf’ and set it as the RMF for the background model of data set “core”:
>>> rmf = unpack_rmf('bkg.rmf') >>> set_rmf('core', rmf, bkg_id=1)