Model

class sherpa.models.model.Model(name: str, pars: Sequence[Parameter] = ())[source] [edit on github]

Bases: NoNewAttributesAfterInit

The base class for Sherpa models.

A model contains zero or more parameters that control the predictions of the model when given one or more coordinates. These parameters may represent some variable that describes the model, such as the temperature of a black body, or the computation, such as what form of interpolation to use.

Parameters:

name (str) – A label for the model instance.
pars (sequence of sherpa.parameter.Parameter objects) – The parameters of the model.

Notes

Parameters can be accessed via the pars attribute, but it is expected that they will generally be accessed directly, as the class provides case-insensitive access to the parameter names as object attributes. That is, if the model contains parameters called breakFreq and norm, and the instance is stored in the variable mdl, then the following can be used to access the parameters:

print(f"Break frequency = {mdl.breakfreq.val}")
mdl.norm = 1.2e-3

Attributes Summary

`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

`calc`(p, args, *kwargs)	Evaluate the model on a grid.
`freeze`()	Freeze any thawed parameters of the model.
`get_center`()
`get_thawed_pars`()	Return the thawed parameter objects.
`guess`(dep, args, *kwargs)	Set an initial guess for the parameter values.
`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

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

calc(p: Sequence[SupportsFloat], *args, **kwargs) → ndarray[source] [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).
**kwargs – Any model-specific values that are not parameters.

freeze() → None[source] [edit on github]: Freeze any thawed parameters of the model.

get_center()[source] [edit on github]

get_thawed_pars() → list[Parameter][source] [edit on github]

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)[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.

reset() → None[source] [edit on github]