XSkerrdisk

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

Bases: sherpa.astro.xspec.XSAdditiveModel

The XSPEC kerrdisk model: accretion disk line emission with BH spin as free parameter.

The model is described at 1.

Note

Deprecated in Sherpa 4.10.0

The r_brg, Rinms, and Routms parameters have been renamed to r_br_g, Rin_ms, and Rout_ms respectively to match the XSPEC definition. The names r_brg, Rinms, and Routms can still be used to access the parameters, but they will be removed in a future release.

lineE

The rest-frame line energy, in keV.

Index1

The emissivity index for the inner disk.

Index2

The emissivity index for the outer disk.

r_br_g

The break radius separating the inner and outer portions of the disk, in gravitational radii.

a

The dimensionless black hole spin.

Incl

The disk inclination angle, in degrees. A face-on disk has Incl=0.

Rin_ms

The inner radius of the disk, in units of the radius of marginal stability.

Rout_ms

The outer radius of the disk, in units of the radius of marginal stability.

z

The redshift of the source.

norm

The flux in the line, in units of photon/cm^2/s.

References

1

https://heasarc.gsfc.nasa.gov/xanadu/xspec/manual/XSmodelKerrdisk.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