- 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.
Changed in version 4.15.0: The arf argument can now be set to
Nonewhen the data uses a RSP file (combined RMF and ARF).
arf (None or filename or ARF object or list of filenames) – The name of the ARF, or an ARF data object (e.g. as returned by
unpack_arf). A list of filenames can be passed in for instruments that require multiple ARFs. Set this to
Noneto use any arf that is already set for the data set given by id or for instruments that do not use an ARF separate from the RMF (e.g. XMM-Newton/RGS).
rmf (filename or RMF object or list of filenames) – The name of the RMF, or an RMF data object (e.g. as returned by
unpack_rmf). A list of filenames can be passed in for instruments that require multiple RMFs. Set this to
Noneto use any rmf that is already set for the data set given by id.
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
quality (array, optional) – The quality array for the data (see
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. To use background model, set
bkg="model"`. In that case a background dataset with ``bkg_id=1has to be set before calling
fake_pha. That background dataset needs to include the data itself (not used in this function), the background model, and the response.
sherpa.utils.err.ArgumentErr – If the data set already exists and does not contain PHA data.
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_sourcefor the supplied
idparameter). 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 (exposure time, area scaling, and the backscal value) before it is passed to a Poisson random number generator. The simulated background is added to the simulated data.
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')
Sometimes, the background dataset is noisy because there are not enough photons in the background region. In this case, the background model can be used to generate the photons that the background contributes to the source spectrum. To do this, a background model must be passed in. This model is then convolved with the ARF and RMF (which must be set before) of the default background data set:
>>> set_bkg_source('sim', 'const1d.con1') >>> load_arf('sim', 'bkg.arf.fits', bkg_id=1) >>> load_rmf('sim', 'bkg_rmf.fits', bkg_id=1) >>> fake_pha('sim', arf, rmf, texp, backscal=bscal, bkg='model', ... grouping=grp, quality=qual, grouped=True) >>> save_pha('sim', 'sim.pi')