SM
- class sherpa.astro.optical.SM(name='sm')[source] [edit on github]
Bases:
RegriddableModel1D
Galactic extinction: the Savage & Mathis model.
The interstellar extinction is calculated using the formula from [1]. This model is intended to be used to modify another model (e.g. by multiplying the two together). It is for use when the independent axis is in Angstrom.
- ebv
E(B-V)
Notes
When evaluated on a binned grid, the lower-edges of the bins are used for the calculation.
References
Attributes Summary
The maximum size of the cache.
Return any linked parameters.
A one-dimensional model.
Return the parameters of the model.
The hard maximum values for the thawed parameters.
The hard minimum values for the thawed parameters.
The maximum limits of the thawed parameters.
The minimum limits of the thawed parameters.
The thawed parameters of the model.
Methods Summary
apply
(outer, *otherargs, **otherkwargs)Clear the cache.
Display the cache status.
calc
(p, x[, xhi])Evaluate the model on a grid.
freeze
()Freeze any thawed parameters of the model.
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
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'>,)
- 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
- 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
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
- 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
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.
Examples
>>> pl.cache_status() powlaw1d.pl size: 5 hits: 633 misses: 240 check= 873
- calc(p, x, xhi=None, **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).
**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() None [edit on github]
Called after a model may be evaluated multiple times.
See also
- thaw() None [edit on github]
Thaw any frozen parameters of the model.
Those parameters that are marked as “always frozen” are skipped.