set_pileup_model

sherpa.astro.ui.set_pileup_model(id, model=None) → None

Include a model of the Chandra ACIS pile up when fitting PHA data.

Chandra observations of bright sources can be affected by pileup, so that there is a non-linear correlation between the source model and the predicted counts. This process can be modelled by including the jdpileup model for a data set, using the set_pileup_model.

Parameters:

id (int or str, optional) – The data set containing the source expression. If not given then the default identifier is used, as returned by get_default_id.
model (an instance of the sherpa.astro.models.JDPileup class)

See also

delete_pileup_model: Delete the pile up model for a data set.
fit: Fit one or more data sets.
get_pileup_model: Return the pile up model for a data set.
sherpa.models.model.JDPileup: The ACIS pile up model.
list_pileup_model_ids: List of all the data sets with a pile up model.
set_full_model: Define the convolved model expression for a data set.
set_model: Set the source model expression for a data set.

Notes

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 model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.

This is a generic function, and can be used to model other non-linear detector effects, but at present the only available model is for the ACIS pile up provided by the jdpileup model.

Examples

Plot up the model (an xsphabs model multiplied by a powlaw1d component) and then overplot the same expression but including the effects of pile up in the Chandra ACIS instrument:

>>> load_pha('src.pi')
>>> set_source(xsphabs.gal * powlaw1d.pl)
>>> plot_model()
>>> set_pileup_model(jdpileup.jpd)
>>> plot_model(overplot=True)