SVDEntropyTruncation
class boulderopal.noise_reconstruction.SVDEntropyTruncation(rounding_threshold=0.5)
Configuration for noise reconstruction with the singular value decomposition (SVD) method using entropy truncation.
Parameters
rounding_threshold (float , optional) – The rounding threshold of the entropy, between 0 and 1 (inclusive). Defaults to 0.5.
Notes
The singular value decomposition (SVD) method first finds a low rank approximation of the matrix of weighted filter functions
where matrices and {n{\mathrm{sv}} \times n_{\mathrm{sv}}}\Sigman_{\mathrm{sv}}
The entropy truncation method calculates the value and rounds the value to an integer . When rounding the value , the floor of plus the rounding threshold that you chose is taken. Therefore a small value leads to rounding down, while a large value leads to rounding up. The
The SVD method then estimates the noise power spectral density (PSD)
This method calculates the uncertainties in estimation using error propagation if you provide measurement uncertainties.