Set the source model expression for a data set.
The function is available as both set_model and set_source. The model fit to the data can be further modified by instrument responses which can be set explicitly - e.g. by set_psf - or be defined automatically by the type of data being used (e.g. the ARF and RMF of a PHA data set). The set_full_model command can be used to explicitly include the instrument response if necessary.
id (int or str, optional) – The data set containing the source expression. If not given then the default identifier is used, as returned by get_default_id.
model (str or sherpa.models.Model object) – This defines the model used to fit the data. It can be a Python expression or a string version of it.
Delete the model expression from a data set.
Fit one or more data sets.
Fix model parameters so they are not changed by a fit.
Return the source model expression for a data set.
Integrate 1D source expressions.
Set the background model expression for a data set.
Define the convolved model expression for a data set.
Display the source model expression for a data set.
Set the value, limits, or behavior of a model parameter.
Allow model parameters to be varied during a fit.
The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.
PHA data sets will automatically apply the instrumental response (ARF and RMF) to the source expression. For some cases this is not useful - for example, when different responses should be applied to different model components - in which case set_full_model should be used instead.
Model caching is available via the model
cacheattribute. A non-zero value for this attribute means that the results of evaluating the model will be cached if all the parameters are frozen, which may lead to a reduction in the time taken to evaluate a fit. A zero value turns off the cacheing. The default setting for X-Spec and 1D analytic models is that
0for the 2D analytic models.
The integrate1d model can be used to apply a numerical integration to an arbitrary model expression.
Create an instance of the powlaw1d model type, called
pl, and use it as the model for the default data set.
Create a model for the default dataset which is the xsphabs model multiplied by the sum of an xsapec and powlaw1d models (the model components are identified by the labels
>>> set_model(xsphabs.gal * (xsapec.clus + powlaw1d.pl))
Repeat the previous example, using a string to define the model expression:
>>> set_model('xsphabs.gal * (xsapec.clus + powlaw1d.pl)')
Use the same model component (
src, a gauss2d model) for the two data sets (‘src1’ and ‘src2’).
>>> set_model('src1', gauss2d.src + const2d.bgnd1) >>> set_model('src2', src + const2d.bgnd2)
Share an expression - in this case three gaussian lines - between three data sets. The normalization of this line complex is allowed to vary in data sets 2 and 3 (the
norm3components of the const1d model), and each data set has a separate polynom1d component (
c1parameters of the polynom1d model components are thawed and then linked together (to reduce the number of free parameters):
>>> lines = gauss1d.l1 + gauss1d.l2 + gauss1d.l3 >>> set_model(1, lines + polynom1d.bgnd1) >>> set_model(2, lines * const1d.norm2 + polynom1d.bgnd2) >>> set_model(3, lines * const1d.norm3 + polynom1d.bgnd3) >>> thaw(bgnd1.c1, bgnd2.c1, bgnd3.c1) >>> link(bgnd2.c2, bgnd1.c1) >>> link(bgnd3.c3, bgnd1.c1)
For this expression, the
galcomponent is frozen, so it is not varied in the fit. The
cacheattribute is set to a non-zero value to ensure that it is cached during a fit (this is actually the default value for this model so it not normally needed).
>>> set_model(xsphabs.gal * (xsapec.clus + powlaw1d.pl)) >>> gal.nh = 0.0971 >>> freeze(gal) >>> gal.cache = 1