Simulate a PHA data set from a model.
fake_pha(id, arf, rmf, exposure, backscal=None, areascal=None, grouping=None, grouped=False, quality=None, bkg=None) id - int or str arf - filename or ARF object or list of filenames rmf - filename or RMF object or list of filenames exposure - number backscal - number, optional areascal - number, optional grouping - array, optional grouped - bool, optional quality - array, optional bkg - optional
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.
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')
The parameters for this function are:
|id||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||The name of the ARF, or an ARF data object (e.g. as returned by `get_arf` or `unpack_arf` ). A list of filenames can be passed in for instruments that require multile ARFs. Set this to `None` to use any arf that is already set for the data set given by id.|
|rmf||The name of the RMF, or an RMF data object (e.g. as returned by `get_arf` or `unpack_arf` ). A list of filenames can be passed in for instruments that require multile RMFs. Set this to `None` to use any arf that is already set for the data set given by id.|
|exposure||The exposure time, in seconds.|
|backscal||The 'BACKSCAL' value for the data set.|
|areascal||The 'AREASCAL' value for the data set.|
|grouping||The grouping array for the data (see `set_grouping` ).|
|grouped||Should the simulated data be grouped (see `group` )? The default is False . This value is only used if the `grouping` parameter is set.|
|quality||The quality array for the data (see `set_quality` ).|
|bkg||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=1 has 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.|
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 (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.
See the bugs pages on the Sherpa website for an up-to-date listing of known bugs.