This routine returns the spherical harmonic coupling matrix for a given set of Slepian basis functions. This matrix relates the power spectrum expectation of the function expressed in a subset of the best-localized Slepian functions to the expectation of the global power spectrum.

Usage

kij = SHSCouplingMatrix (galpha, nmax)

Returns

kij : float, dimension (lmax+1, lmax+1`)
The coupling matrix that relates the power spectrum expectation of the function expressed in a subset of the best-localized Slepian functions to the expectation of the global power spectrum.

Parameters

galpha : float, dimension ((lmax+1)**2, nmax)
An array of Slepian functions, arranged in columns from best to worst localized.
nmax : input, integer
The number of Slepian functions used in reconstructing the function.

Description

SHSCouplingMatrix returns the spherical harmonic coupling matrix that relates the power spectrum expectation of the function expressed in a subset of the best-localized Slepian functions to the expectation of the global power spectrum (assumed to be stationary). The Slepian functions are determined by a call to either (1) SHReturnTapers and then SHRotateTapers, or (2) SHReturnTapersMap. Each row of galpha contains the (lmax+1)**2 spherical harmonic coefficients of a Slepian function that can be unpacked using SHVectorToCilm. The Slepian functions must be normalized to have unit power (that is the sum of the coefficients squared is 1).

The relationship between the global and localized power spectra is given by:

< S_{\tilde{f}}(l) > = \sum_{l'=0}^lmax K_{ll'} S_{f}(l')

where S_{\tilde{f}} is the expectation of the power spectrum at degree l of the function expressed in Slepian functions, S_{f}(l') is the expectation of the global power spectrum, and < ... > is the expectation operator. The coupling matrix is given explicitly by

K_{ll'} = \frac{1}{2l'+1} Sum_{m=-l}^l Sum_{m'=-l'}^l' ( Sum_{alpha=1}^nmax g_{l'm'}(alpha) g_{lm}(alpha) )**2

See also

shreturntapers, shrotatetapers, shreturntapersmap, shvectorrocilm, shscouplingmatrix, shmtcouplingmatrix

Tags: python
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