sherpa.astro.ui.fake_pha(id, arf, rmf, exposure, backscal=None, areascal=None, grouping=None, grouped=False, quality=None, bkg=None)

Simulate a PHA data set from a model.

The function creates a simulated PHA data set based on a source model, instrument response (given as an ARF and RMF), and exposure time, along with a Poisson noise term. A background component can be included.

  • id (int or str) – The identifier for the data set to create. If it already exists then it is assumed to contain a PHA data set and the counts will be over-written.

  • arf (filename or ARF object) – The name of the ARF, or an ARF data object (e.g. as returned by get_arf or unpack_arf).

  • rmf (filename or RMF object) – The name of the RMF, or an RMF data object (e.g. as returned by get_arf or unpack_arf).

  • exposure (number) – The exposure time, in seconds.

  • backscal (number, optional) – The ‘BACKSCAL’ value for the data set.

  • areascal (number, optional) – The ‘AREASCAL’ value for the data set.

  • grouping (array, optional) – The grouping array for the data (see set_grouping).

  • grouped (bool, optional) – Should the simulated data be grouped (see group)? The default is False. This value is only used if the grouping parameter is set.

  • quality (array, optional) – The quality array for the data (see set_quality).

  • bkg (optional) – If left empty, then only the source emission is simulated. If set to a PHA data object, then the counts from this data set are scaled appropriately and added to the simulated source signal.


sherpa.utils.err.ArgumentErr – If the data set already exists and does not contain PHA data.

See also


Simulate a data set.


Return the ARF associated with a PHA data set.


Return the RMF associated with a PHA data set.


Return the dependent axis of a data set.


Create a data set from array values.


Set the source model expression for a data set.


A model expression is created by using the supplied ARF and RMF to convolve the source expression for the dataset (the return value of get_source for the supplied id parameter). This expresion is evaluated for each channel to create the expectation values, which is then passed to a Poisson random number generator to determine the observed number of counts per channel. Any background component is scaled by appropriate terms (exsposure time, area scaling, and the backscal value) before adding to the simulated date. That is, the background component is not simulated.


Estimate the signal from a 5000 second observation using the ARF and RMF from “src.arf” and “src.rmf” respectively:

>>> set_source(1, xsphabs.gal * xsapec.clus)
>>> gal.nh = 0.12
>>> clus.kt, clus.abundanc = 4.5, 0.3
>>> clus.redshift = 0.187
>>> clus.norm = 1.2e-3
>>> fake_pha(1, 'src.arf', 'src.rmf', 5000)

Simulate a 1 mega second observation for the data and model from the default data set. The simulated data will include an estimated background component based on scaling the existing background observations for the source. The simulated data set, which has the same grouping as the default set, for easier comparison, is created with the ‘sim’ label and then written out to the file ‘sim.pi’:

>>> arf = get_arf()
>>> rmf = get_rmf()
>>> bkg = get_bkg()
>>> bscal = get_backscal()
>>> grp = get_grouping()
>>> qual = get_quality()
>>> texp = 1e6
>>> set_source('sim', get_source())
>>> fake_pha('sim', arf, rmf, texp, backscal=bscal, bkg=bkg,
...          grouping=grp, quality=qual, grouped=True)
>>> save_pha('sim', 'sim.pi')