XSConvolutionModel¶
- class sherpa.astro.xspec.XSConvolutionModel(model, wrapper)[source] [edit on github]¶
Bases:
sherpa.models.model.CompositeModel
,sherpa.astro.xspec.XSModel
Evaluate a model and pass it to an XSPEC convolution model.
Calculate the convolved data - that is evaluate the model and then pass it to the wrapper model which applies the convolution model.
New in version 4.12.2.
- Parameters
model (sherpa.models.model.ArithmeticModel instance) – The model whose results, when evaluated, are passed to the convolution model.
wrapper (sherpa.astro.xspec.XSConvolutionKernel instance) – The XSPEC convolution model.
Examples
The following evaluates two models (creating the y1 and y2 arrays), where y1 applies the XScfux convolution model to the combined absorption times powerlaw model, and y2 applies the convolution model to only the power-law model, and then multiples this by the absorption model. In the following mdl1 and mdl2 are instances of XSConvolutionModel:
>>> import numpy as np >>> from sherpa.astro import xspec >>> cmdl = xspec.XScflux() >>> gal = xspec.XSphabs() >>> pl = xspec.XSpowerlaw() >>> pl.norm.freeze() >>> mdl1 = cmdl(gal * pl) >>> mdl2 = gal * cmdl(pl) >>> cmdl.emin = 0.5 >>> cmdl.emax = 7.0 >>> cmdl.lg10flux = -12.1 >>> egrid = np.arange(0.1, 10, 0.01) >>> elo, ehi = egrid[:-1], egrid[1:] >>> y1 = mdl1(elo, ehi) >>> y2 = mdl2(elo, ehi)
Display the combined model:
>>> print(mdl1) xscflux((phabs * powerlaw)) Param Type Value Min Max Units ----- ---- ----- --- --- ----- xscflux.Emin frozen 0.5 0 1e+06 keV xscflux.Emax frozen 10 0 1e+06 keV xscflux.lg10Flux thawed -12 -100 100 cgs phabs.nH thawed 1 0 100000 10^22 atoms / cm^2 powerlaw.PhoIndex thawed 1 -2 9 powerlaw.norm frozen 1 0 1e+24
>>> print(mdl2) (phabs * xscflux(powerlaw)) Param Type Value Min Max Units ----- ---- ----- --- --- ----- phabs.nH thawed 1 0 100000 10^22 atoms / cm^2 xscflux.Emin frozen 0.5 0 1e+06 keV xscflux.Emax frozen 10 0 1e+06 keV xscflux.lg10Flux thawed -12 -100 100 cgs powerlaw.PhoIndex thawed 1 -2 9 powerlaw.norm frozen 1 0 1e+24
Attributes Summary
The maximum size of the cache.
A one-dimensional model.
The hard maximum values for the thawed parameters.
The hard minimum values for the thawed parameters.
Access to the maximum limits for the thawed parameters
Access to the minimum limits for the thawed parameters
Access to the thawed parameters of the model
Methods Summary
apply
(outer, *otherargs, **otherkwargs)Clear the cache for each component.
Display the cache status of each component.
calc
(p, *args, **kwargs)Evaluate the convolved model on a grid.
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.
wrapobj
(obj)Attributes Documentation
- cache = 5¶
The maximum size of the cache.
- ndim = 1¶
A one-dimensional model.
- 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]¶
- cache_clear() [edit on github]¶
Clear the cache for each component.
- cache_status() [edit on github]¶
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, *args, **kwargs)[source] [edit on github]¶
Evaluate the convolved model on a grid.
- Parameters
p (sequence of numbers) – The parameters of the model, matching the
pars
field. This will start with the convolution model parameters (if any) and then the model.*args – The model grid. There should be two arrays (the low and high edges of the bin) to make sure the wrapped model is evaluated correctly.
**kwargs – Additional keyword arguments.
- get_center() [edit on github]¶
- 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 >>> 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() [edit on github]¶
Called after a model may be evaluated multiple times.
See also
- static wrapobj(obj)[source] [edit on github]¶