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: ====== 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

- dispersion Calculate dispersion relation for dataset or array of datasets.
- sqw Convert input dnd-type object into sqw object

- dispersion Calculate dispersion relation for dataset or array of datasets.

Generated on Mon 26-Nov-2018 15:08:42 by