XSbexrav

class sherpa.astro.xspec.XSbexrav(name='bexrav')[source] [edit on github]

Bases: sherpa.astro.xspec.XSAdditiveModel

The XSPEC bexrav model: reflected e-folded broken power law, neutral medium.

The model is described at 1.

Gamma1

The power-law index of the first power-law component.

breakE

The break energy, in keV.

Gamma2

The power-law index of the second power-law component.

foldE

The e-folding energy (Ec) in keV. If zero there is no cut off.

rel_refl

The reflection scaling parameter (a value of 1 for an isotropic source above the disk).

cosIncl

The cosine of the inclination angle.

abund

The abundance of the elements heavier than He relative to their solar abundance, as set by the set_xsabund function.

Fe_abund

The iron abundance relative to the solar abundance, as set by the set_xsabund function.

redshift

The redshift of the source.

norm

The normalization of the model: see 1 for an explanation of the units.

See also

XSbexriv, XSreflect

Notes

The precision of the numerical integration can be changed by using the set_xsxset function to set the value of the BEXRAV_PRECISION keyword, which defines the fractional precision. The default is 0.01 (1%).

References

1(1,2)

https://heasarc.gsfc.nasa.gov/xanadu/xspec/manual/XSmodelBexrav.html

Attributes Summary

ndim

thawedparhardmaxes

The hard maximum values for the thawed parameters.

thawedparhardmins

The hard minimum values for the thawed parameters.

thawedparmaxes

Access to the maximum limits for the thawed parameters

thawedparmins

Access to the minimum limits for the thawed parameters

thawedpars

Access to the thawed parameters of the model

version_enabled

Methods Summary

apply(outer, *otherargs, **otherkwargs)

calc(pars, xlo, *args, **kwargs)

Evaluate the model on a grid.

get_center()

guess(dep, *args, **kwargs)

Set an initial guess for the parameter values.

regrid(*args, **kwargs)

The class RegriddableModel1D allows the user to evaluate in the requested space then interpolate onto the data space.

reset()

Reset the parameter values.

set_center(*args, **kwargs)

startup([cache])

Called before a model may be evaluated multiple times.

teardown()

Called after a model may be evaluated multiple times.

Attributes Documentation

ndim = 1
thawedparhardmaxes

The hard maximum values for the thawed parameters.

thawedparhardmins

The hard minimum values for the thawed parameters.

thawedparmaxes

Access to the maximum limits for the thawed parameters

thawedparmins

Access to the minimum limits for the thawed parameters

thawedpars

Access to the thawed parameters of the model

version_enabled = True

Methods Documentation

apply(outer, *otherargs, **otherkwargs) [edit on github]
calc(pars, xlo, *args, **kwargs) [edit on github]

Evaluate the model on a grid.

Parameters
  • p (sequence of numbers) – The parameter values to use. The order matches the pars field.

  • *args – The model grid. The values can be scalar or arrays, and the number depends on the dimensionality of the model and whether it is being evaluated over an integrated grid or at a point (or points).

get_center() [edit on github]
guess(dep, *args, **kwargs)[source] [edit on github]

Set an initial guess for the parameter values.

Attempt to set the parameter values, and ranges, for the model to match the data values. This is intended as a rough guess, so it is expected that the model is only evaluated a small number of times, if at all.

regrid(*args, **kwargs) [edit on github]

The class RegriddableModel1D allows the user to evaluate in the requested space then interpolate onto the data space. An optional argument ‘interp’ enables the user to change the interpolation method.

Examples

>>> import numpy as np
>>> from sherpa.models.basic import Box1D
>>> mybox = Box1D()
>>> request_space = np.arange(1, 10, 0.1)
>>> regrid_model = mybox.regrid(request_space, interp=linear_interp)
reset() [edit on github]

Reset the parameter values.

set_center(*args, **kwargs) [edit on github]
startup(cache=False) [edit on github]

Called before a model may be evaluated multiple times.

Parameters

cache (bool, optional) – Should a cache be used when evaluating the models.

See also

teardown

teardown() [edit on github]

Called after a model may be evaluated multiple times.

See also

setup