PSFModel

class sherpa.astro.instrument.PSFModel(name='psfmodel', kernel=None)[source] [edit on github]

Bases: PSFModel

Attributes Summary

`BETTER_RESOLUTION`
`SAME_RESOLUTION`
`WORSE_RESOLUTION`
`center`	array of size parameters
`kernel`	The kernel (sherpa.data.Data or sherpa.models.model.Model instance, callable, or None).
`model`	The model that applies the convolution.
`ndim`	The dimensionality of the model, if defined, or None.
`origin`	FFT origin
`size`	array of size parameters
`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`(args, *kwargs)	Evaluate the model on a grid.
`fold`(data)	The data to be convolved by the PSF.
`freeze`()	Freeze any thawed parameters of the model.
`get_center`()
`get_kernel`(data[, subkernel])	Return a data object representing the kernel.
`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

BETTER_RESOLUTION = 1

SAME_RESOLUTION = 0

WORSE_RESOLUTION = -1

center: array of size parameters

kernel

The kernel (sherpa.data.Data or sherpa.models.model.Model instance, callable, or None).

The kernel determines the dimensionality of the model (the ndim attribute), although it can remain as None for callable arguments. The size, origin, and center fields, if set, must match the ndim field, and will be cleared if they do not match.

model

The model that applies the convolution.

This is set by the fold method and can not be changed directly.

ndim = None: The dimensionality of the model, if defined, or None.

origin: FFT origin

size: array of size parameters

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(*args, **kwargs)[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).

fold(data)[source] [edit on github]

The data to be convolved by the PSF.

Parameters:: data (sherpa.data.Data or sherpa.models.model.Model instance or a callable) – It must match the dimensionality of the kernel.
Raises:: sherpa.utils.err.PSFErr – The kernel has not been set.

freeze() [edit on github]: Freeze any thawed parameters of the model.

get_center() [edit on github]

get_kernel(data, subkernel=True)[source] [edit on github]

Return a data object representing the kernel.

Parameters:

data (sherpa.data.Data or sherpa.models.model.Model instance) – The data to apply the kernel to. This routine will pass data to the fold method.
subkernel (bool, optional) –

Returns:

data

Return type:

sherpa.data.Data1D or sherpa.data.Data2D instance

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

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) [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

startup

thaw() [edit on github]

Thaw any frozen parameters of the model.

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