JDPileup

class sherpa.astro.models.JDPileup(name='jdpileup')[source] [edit on github]

Bases: RegriddableModel1D

A CCD pileup model for the ACIS detectors on Chandra.

This model is based on the work by John Davis ([1]). It is intended only for modeling the pileup in one-dimensional spectra obtained in imaging mode (i.e. no grating data), but can be used with the zeroth-order spectrum of a grating data set.

alpha

The alpha parameter parameterizes “grade migration” in the detector and represents the fraction of piled-up events that result in a good grade.

g0

The probabilty of assigning a grade of zero. This should remain frozen.

f

The fraction of flux falling into the pileup region. This should remain frozen.

n

The number of detection cells. This parameter can not be fit.

ftime

The frame time in seconds (as given by the EXPTIME keyword of the event file). This parameter can not be fit.

fracexp

The fractional exposure that the source experienced while dithering on the chip (as given by the FRACEXPO keyword in the ARF file). This parameter can not be fit.

nterms

The maximum number of photons considered for pileup in a single readout frame. This should not be changed from its default value of 30. This parameter can not be fit.

Notes

As the pileup model is inherently non-linear, it is strongly advised that multiple optimization methods are used to thoroughly investigate the search space for the model.

The alpha parameter should vary with photon energy and detector position, but for simplicity it is treated as independent of energy and location.

An example of using this model to fit a Chandra spectrum is provided in [2].

References

Attributes Summary

cache

The maximum size of the cache.

lpars

Return any linked parameters.

ndim

A one-dimensional model.

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.

cache_status()

Display the cache status.

calc(p, arf_source, exposure_time, ...)

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.

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.

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 = 1

A one-dimensional model.

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.

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.

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.

Methods Documentation

apply(outer, *otherargs, **otherkwargs) [edit on github]
cache_clear() None [edit on github]

Clear the cache.

cache_status() None [edit on github]

Display the cache status.

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

Example

>>> pl.cache_status()
 powlaw1d.pl                size:    5  hits:   633  misses:   240  check=  873
calc(p, arf_source, exposure_time, min_energy, max_energy, specresp, model)[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 [edit on github]

Freeze any thawed parameters of the model.

get_center() [edit on github]
get_thawed_pars() list[Parameter] [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) [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
>>> from sherpa.utils import linear_interp
>>> mybox = Box1D()
>>> request_space = np.arange(1, 10, 0.1)
>>> regrid_model = mybox.regrid(request_space, interp=linear_interp)
reset() None [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: bool = False) None [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() None [edit on github]

Called after a model may be evaluated multiple times.

See also

startup

thaw() None [edit on github]

Thaw any frozen parameters of the model.

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