Joint-mode models define the function argument, on which a model expression is evaluated,
to be a particular data axis.
A joint-mode model, <jointmode_model>, may appear as an element of a
model expression, <modelExpr>, and is constructed using one of the
following syntax options:
<sherpa_modelname>{<axis>}
<sherpa_modelname>[<modelname>]{<axis>}
<modelname>{<axis>}
where <axis> must be enclosed in curly braces, { }, and where <axis> is
a data column name. Note that <axis> defines the particular data axis
on which the model expression is to be evaluated.
Create 2-D joint-mode models that define data axes as the arguments for model components:
sherpa> DATA example_img.fits FITSIMAGE
sherpa> LORENTZ[SpatialModelAxis0](98:5:200, 70:50:90, 1:1:200)
sherpa> POWLAW1D[SpecModelAxis1]
sherpa> SRC 1 = SpatialModelAxis0{x1}*SpecModelAxis1{x2}
The command
LORENTZ[SpatialModelAxis0](98:5:200, 70:50:90, 1:1:200)
assigns the name SpatialModelAxis0
to the Sherpa model component
LORENTZ,
and assigns various parameters values and
ranges. Similarly, a second model component is established and assigned a name.
The final command assigns the model expression
SpatialModelAxis0{x1}*SpecModelAxis1{x2},
to the source model for dataset number 1. This source model expression is an algebraic
combination of joint-mode models, where these joint-mode models each define a specific
data axis as the argument for
a model component. So with this source model expression, the
joint-mode model SpatialModelAxis0{x1}
will fit model SpatialModelAxis0 to
Axis 0 (x1) of the data, and the joint-mode
model SpecModelAxis1{x2}
will jointly fit model
SpecModelAxis1 to Axis 1 (x2) of the data, etc.
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