get_photon_flux_hist

sherpa.astro.ui.get_photon_flux_hist(lo=None, hi=None, id=None, num=7500, bins=75, correlated=False, numcores=None, bkg_id=None, scales=None, model=None, otherids=(), recalc=True)

Return the data displayed by plot_photon_flux.

The get_photon_flux_hist() function calculates a histogram of simulated photon flux values representing the photon flux probability distribution for a model component, accounting for the errors on the model parameters.

Changed in version 4.12.2: The scales parameter is no longer ignored when set and the model and otherids parameters have been added.

Parameters
  • lo (number, optional) – The lower limit to use when summing up the signal. If not given then the lower value of the data grid is used.

  • hi (optional) – The upper limit to use when summing up the signal. If not given then the upper value of the data grid is used.

  • id (int or string, optional) – The identifier of the data set to use. If None, the default value, then all datasets with associated models are used to calculate the errors and the model evaluation is done using the default dataset.

  • num (int, optional) – The number of samples to create. The default is 7500.

  • bins (int, optional) – The number of bins to use for the histogram.

  • correlated (bool, optional) – If True (the default is False) then scales is the full covariance matrix, otherwise it is just a 1D array containing the variances of the parameters (the diagonal elements of the covariance matrix).

  • numcores (optional) – The number of CPU cores to use. The default is to use all the cores on the machine.

  • bkg_id (int or string, optional) – The identifier of the background component to use. This should only be set when the line to be measured is in the background model.

  • scales (array, optional) – The scales used to define the normal distributions for the parameters. The size and shape of the array depends on the number of free parameters in the fit (n) and the value of the correlated parameter. When the parameter is True, scales must be given the covariance matrix for the free parameters (a n by n matrix that matches the parameter ordering used by Sherpa). For un-correlated parameters the covariance matrix can be used, or a one-dimensional array of n elements can be used, giving the width (specified as the sigma value of a normal distribution) for each parameter (e.g. the square root of the diagonal elements of the covariance matrix). If the scales parameter is not given then the covariance matrix is evaluated for the current model and best-fit parameters.

  • model (model, optional) – The model to integrate. If left as None then the source model for the dataset will be used. This can be used to calculate the unabsorbed flux, as shown in the examples. The model must be part of the source expression.

  • otherids (sequence of integer and string ids, optional) – The list of other datasets that should be included when calculating the errors to draw values from.

  • recalc (bool, optional) – If True, the default, then re-calculate the values rather than use the values from the last time the function was run.

Returns

hist – An object representing the data used to create the plot by plot_photon_flux.

Return type

a sherpa.astro.plot.PhotonFluxHistogram instance

See also

get_energy_flux_hist()

Return the data displayed by plot_energy_flux.

plot_energy_flux()

Display the energy flux distribution.

plot_photon_flux()

Display the photon flux distribution.

sample_energy_flux()

Return the energy flux distribution of a model.

sample_flux()

Return the flux distribution of a model.

sample_photon_flux()

Return the photon flux distribution of a model.

Examples

Get the photon flux distribution for the range 0.5 to 7 for the default data set:

>>> phist = get_photon_flux_hist(0.5, 7, num=1000)
>>> print(phist)

Compare the 0.5 to 2 photon flux distribution from the “core” data set to the values from the “jet” data set:

>>> phist1 = get_photon_flux_hist(0.5, 2, id='jet', num=1000)
>>> phist2 = get_photon_flux_hist(0.5, 2, id='core', num=1000)

Compare the flux distribution for the full source expression (aflux) to that for just the pl component (uflux); this can be useful to calculate the unabsorbed flux distribution if the full source model contains an absorption component:

>>> aflux = get_photon_flux_hist(0.5, 2, num=1000, bins=20)
>>> uflux = get_photon_flux_hist(0.5, 2, model=pl, num=1000, bins=20)

When there are multiple datasets loaded, get_photon_flux_hist uses all datasets to evaluate the errors when the id parameter is left at its default value of None. The otherids parameter is used, along with id, to specify exactly what datasets are used:

>>> x = get_photon_flux_hist(2, 10, num=1000, bins=20, model=src)
>>> y = get_photon_flux_hist(2, 10, num=1000, bins=20, model=src,
...                          id=1, otherids=(2, 3, 4))