RMFModelPHA

class sherpa.astro.instrument.RMFModelPHA(rmf, pha, model)

Bases: RMFModel

RMF convolution model with associated PHA data set.

Notes

Scaling by the AREASCAL setting (scalar or array) is included in this model.

Attributes Summary

cache	The maximum size of the cache.
lpars	Return any linked parameters.
ndim	The dimensionality of the model, if defined, or None.
pars	Return the parameters of the model.
thawedparhardmaxes	The hard maximum values for the thawed parameters.
thawedparhardmins	The hard minimum values for the thawed parameters.
thawedparmaxes	The maximum limits of the thawed parameters.
thawedparmins	The minimum limits of the thawed parameters.
thawedpars	The thawed parameters of the model.

Methods Summary

apply(outer, *otherargs, **otherkwargs)
cache_clear()	Clear the cache for each component.
cache_status()	Display the cache status of each component.
calc(p, x[, xhi])	Evaluate the model on a grid.
filter()
freeze()	Freeze any thawed parameters of the model.
get_center()
get_thawed_pars()	Return the thawed parameter objects.
guess(dep, *args, **kwargs)	Call guess on each component.
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.
thaw()	Thaw any frozen parameters of the model.

Attributes Documentation

cache = 5

The maximum size of the cache.

lpars

Return any linked parameters.

This only returns linked parameters that are not related to the model, and each parameter is not repeated.

Added in version 4.16.1.

See also

pars

Examples

By default there are no linked parameters:

>>> from sherpa.models.basic import Gauss2D
>>> mdl = Gauss2D("mdl")
>>> len(mdl.pars)
6
>>> mdl.lpars
()

Force the model to have identical xpos and ypos parameters. Since the linked parameter value (mdl.xpos) is part of the model it is not included in lpars:

>>> mdl.ypos = mdl.xpos
>>> len(mdl.pars)
6
>>> mdl.lpars
()

Add a link to allow the sigma term to be fit rather than FWHM. Since the linked parameter - here from the Const1D model - is not a part of the model it is included in lpars:

>>> import numpy as np
>>> from sherpa.models.basic import Const1D
>>> sigma = Const1D("sigma")
>>> mdl.fwhm = 2 * np.sqrt(2 * np.log(2)) * sigma.c0
>>> len(mdl.pars)
6
>>> mdl.lpars
(<Parameter 'c0' of model 'sigma'>,)

ndim: int | None = None

The dimensionality of the model, if defined, or None.

pars

Return the parameters of the model.

This does not include any linked parameters.

Changed in version 4.16.1: The pars field can no-longer be set directly. Individual elements can still be changed.

See also

lpars

thawedparhardmaxes

The hard maximum values for the thawed parameters.

The minimum and maximum range of the parameters can be changed with thawedparmins and thawedparmaxes but only within the range given by thawedparhardmins to thawparhardmaxes.

See also

thawedparhardmins, thawedparmaxes

thawedparhardmins

The hard minimum values for the thawed parameters.

The minimum and maximum range of the parameters can be changed with thawedparmins and thawedparmaxes but only within the range given by thawedparhardmins to thawparhardmaxes.

See also

thawedparhardmaxes, thawedparmins

thawedparmaxes

The maximum limits of the thawed parameters.

Get or set the maximum limits of the thawed parameters of the model as a list of numbers. If there are no thawed parameters then [] is used. The ordering matches that of the pars attribute.

See also

thawedpars, thawedarhardmaxes, thawedparmins

thawedparmins

The minimum limits of the thawed parameters.

Get or set the minimum limits of the thawed parameters of the model as a list of numbers. If there are no thawed parameters then [] is used. The ordering matches that of the pars attribute.

See also

thawedpars, thawedarhardmins, thawedparmaxes

thawedpars

The thawed parameters of the model.

Get or set the thawed parameters of the model as a list of numbers. If there are no thawed parameters then [] is used. The ordering matches that of the pars attribute.

See also

thawedparmaxes, thawedparmins

Methods Documentation

apply(outer, *otherargs, **otherkwargs)

cache_clear() → None

Clear the cache for each component.

cache_status() → None

Display the cache status of each component.

Information on the cache - the number of “hits”, “misses”, and “requests” - is displayed at the INFO logging level.

Example

>>> mdl.cache_status()
 xsphabs.gal                size:    5  hits:   715  misses:   158  check=  873
 powlaw1d.pl                size:    5  hits:   633  misses:   240  check=  873

calc(p, x, xhi=None, *args, **kwargs)

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).

• **kwargs – Any model-specific values that are not parameters.

filter()

freeze() → None

Freeze any thawed parameters of the model.

get_center()

get_thawed_pars() → list[Parameter]

Return the thawed parameter objects.

This includes linked parameters, which complicates the min/max settings, since the range on the components of a linked parameter does not match that of the original parameter, which is an issue when the limits are exceeded.

Added in version 4.16.1.

guess(dep, *args, **kwargs)

Call guess on each component.

At the moment there is no recognition of the full model expression - e.g. cpt1 * cpt2 and cpt1 + cpt2 would ideally have different scalings applied here.

Changed in version 4.17.0: Prior to 4.17.0 the guess method could not be called on composite models.

reset() → None

Reset the parameter values.

Restores each parameter to the last value it was set to. This allows the parameters to be easily reset after a fit.

set_center(*args, **kwargs)

startup(cache=False)

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()

Called after a model may be evaluated multiple times.

See also

startup

thaw() → None

Thaw any frozen parameters of the model.

Those parameters that are marked as “always frozen” are skipped.