class boulderopal.noise_reconstruction.SVDFixedLengthTruncation(singular_value_count=None)

Configuration for noise reconstruction with the singular value decomposition (SVD) method using fixed-length truncation.


singular_value_count (int or None , optional) – The number of singular values to retain. It must be greater or equal to 1. Defaults to None, in which case no truncation is performed.


The singular value decomposition (SVD) method first finds a low rank approximation of the matrix of weighted filter functions FF^\prime

FUΣV, F^\prime \approx U \Sigma V ,

where matrices UU and VV{n{\mathrm{sv}} \times n_{\mathrm{sv}}},and, and \Sigmaisadiagonalmatrixof is a diagonal matrix of

The SVD method then estimates the noise power spectral density (PSD) S\mathbf S

Sest=VΣ1UI. {\mathbf S}_{\mathrm{est}} = V^\dagger\Sigma^{-1}U^\dagger{\mathbf I} .

This method calculates the uncertainties in estimation using error propagation if you provide measurement uncertainties.

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