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SHSCouplingMatrixCap() |
spherical harmonics software package, spherical harmonic transform, legendre functions, multitaper spectral analysis, Python, gravity, magnetic field |
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pyshscouplingmatrixcap.html |
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This routine returns the spherical harmonic coupling matrix for a given set of spherical-cap 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.
kij = SHSCouplingMatrixCap (galpha, galpha_order, nmax)
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 spherical-cap Slepian functions to the expectation of the global power spectrum.
galpha : float, dimension (lmax+1, nmax) : An array of spherical-cap Slepian functions, arranged in columns from best to worst localized.
galpha_order : integer, dimension (kmaxin) : The angular order of the spherical-cap Slepian functions in galpha.
nmax : input, integer : The number of Slepian functions used in reconstructing the function.
SHSCouplingMatrixCap returns the spherical harmonic coupling matrix that relates the power spectrum expectation of the function expressed in a subset of the best-localized spherical-cap Slepian functions to the expectation of the global power spectrum (assumed to be stationary). The Slepian functions are determined by a call to SHReturnTapers and each row of galpha contains the (lmax+1) spherical harmonic coefficients for the single angular order as given in galpha_order.
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=-mmax}^mmax ( Sum_{alpha=1}^nmax g_{l'm}(alpha) g_{lm}(alpha) )**2
where mmax is min(l, l').