Calculate dispersion relation for dataset or array of datasets.
The output dataset (or array of data sets) will retain only the Q axes, and
the signal array(s) will contain the values of energy along the Q axes.
The dispersion relation is calculated at the bin centres.
If the function that calculates dispersion relations produces more than one
branch, then in the case of a single input dataset the output will be an array
of datasets, one for each branch. If the input is an array of datasets, then only
the first dispersion branch will be returned, so there is one output dataset per
input dataset.
>> wout=dispersion(win,dispreln,p)
Input:
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win Dataset that provides the axes and points for the calculation
If one of the plot axes is energy transfer, then the output dataset
will have dimensionality one less than the input dataset
dispreln Handle to function that calculates the dispersion relation w(Q)
Must have form:
w = dispreln (qh,qk,ql,p)
where
qh,qk,ql Arrays containing the coordinates of a set of points
in reciprocal lattice units
p Vector of parameters needed by dispersion function
e.g. [A,js,gam] as intensity, exchange, lifetime
w Array of corresponding energies, or, if more than
one dispersion relation, a cell array of arrays.
More general form is:
w = dispreln (qh,qk,ql,p,c1,c2,..)
where
p Typically a vector of parameters that we might want
to fit in a least-squares algorithm
c1,c2,... Other constant parameters e.g. file name for look-up
table
p Arguments needed by the function. Most commonly, a vector of parameter
values e.g. [A,js,gam] as intensity, exchange, lifetime. If a more general
set of parameters is required by the function, then
package these into a cell array and pass that as pars. In the example
above then pars = {p, c1, c2, ...}
Output:
=======
wdisp Output dataset or array of datasets. Output is always dnd-type.
The output dataset (or array of data sets) will retain only the Q axes, the
the signal array(s) will contain the values of energy along the Q axes, and
the error array will contain the square of the spectral weight.
weight Mirror output: the signal is the spectral weight, and the error array
contains the square of the frequency.
e.g. If win is a 2D dataset with Q and E axes, then wdisp is a 1D dataset
with just the Q axis