template<typename _realT>
struct mx::AO::analysis::clAOLinearPredictor< _realT >

Class to manage the calculation of linear predictor coefficients for a closed-loop AO system.

Template Parameters

_realT the real floating point type in which to do all arithmetic.

Definition at line 36 of file clAOLinearPredictor.hpp.

#include <ao/analysis/clAOLinearPredictor.hpp>

Public Member Functions
int	calcCoefficients (std::vector< realT > &PSDt, std::vector< realT > &PSDn, realT PSDreg, int Nc, realT condition=0)
	Calculate the LP coefficients for a turbulence PSD and a noise PSD. More...

template<bool printout = false>
int	_regularizeCoefficients (realT &min_var, realT &min_sc, realT precision, realT max_sc, clGainOpt< realT > &go_lp, std::vector< realT > &PSDt, std::vector< realT > &PSDn, int Nc)
	Worker function for regularizing the PSD for coefficient calculation. More...

template<bool printout = false>
int	regularizeCoefficients (realT &gmax_lp, realT &gopt_lp, realT &var_lp, clGainOpt< realT > &go_lp, std::vector< realT > &PSDt, std::vector< realT > &PSDn, int Nc)
	Regularize the PSD and calculate the associated LP coefficients. More...

template<bool printout = false>
int	optimizeNc (realT &gmax_lp, realT &gopt_lp, int &Nc, realT &var_lp, clGainOpt< realT > &go_lp, std::vector< realT > &PSDt, std::vector< realT > &PSDn, int minNc, int maxNc)
	Regularize the PSD and calculate the associated LP coefficients. More...

Public Attributes
realT	m_gmax_lp {5}
	The maximum allowable gain for LP. More...

Member Function Documentation

◆ _regularizeCoefficients()

template<typename _realT >

template<bool printout = false>

int mx::AO::analysis::clAOLinearPredictor< _realT >::_regularizeCoefficients	(	realT &	min_var,
		realT &	min_sc,
		realT	precision,
		realT	max_sc,
		clGainOpt< realT > &	go_lp,
		std::vector< realT > &	PSDt,
		std::vector< realT > &	PSDn,
		int	Nc
	)

inline

Worker function for regularizing the PSD for coefficient calculation.

Template Parameters

printout if true then the results are printed to stdout as they are calculated.

On first call (min_var = 0): loop over scale factors from min_sc to max_sc (<=) in steps of precision.

On subsequent calls, when min_var and min_sc are passed back in loop over scale factors from min_sc-precision to max_sc in steps of

Parameters

	min_var	[in.out] the minimum variance found. Set to 0 on initial call
	min_sc	[in.out] the scale factor at the minimum variance.
[in]	precision	the step-size for the scale factor
[in]	max_sc	the maximum scale factor to test
[in]	go_lp	the gain optimization object
[in]	PSDt	the turbulence PSD
[in]	PSDn	the WFS noise PSD
[in]	Nc	the number of coefficients

Definition at line 111 of file clAOLinearPredictor.hpp.

References mx::AO::analysis::clAOLinearPredictor< _realT >::calcCoefficients(), and mx::AO::analysis::clAOLinearPredictor< _realT >::m_gmax_lp.

◆ calcCoefficients()

template<typename _realT >

int mx::AO::analysis::clAOLinearPredictor< _realT >::calcCoefficients	(	std::vector< realT > &	PSDt,
		std::vector< realT > &	PSDn,
		realT	PSDreg,
		int	Nc,
		realT	condition = `0`
	)

inline

Calculate the LP coefficients for a turbulence PSD and a noise PSD.

This combines the two PSDs, augments to two-sided, and calls the linearPredictor.calcCoefficients method.

A regularization constant can be added to the PSD as well.

Parameters

[in]	PSDt	the turbulence PSD
[in]	PSDn	the WFS noise PSD
[in]	PSDreg	the regularizing constant. Set to 0 to not use.
[in]	Nc	the number of LP coefficients.

Definition at line 75 of file clAOLinearPredictor.hpp.

References mx::sigproc::augment1SidedPSD(), and mx::sigproc::linearPredictor< _realT >::calcCoefficients().

Referenced by mx::AO::analysis::clAOLinearPredictor< _realT >::_regularizeCoefficients(), and mx::AO::analysis::clAOLinearPredictor< _realT >::regularizeCoefficients().

◆ optimizeNc()

template<typename _realT >

template<bool printout = false>

int mx::AO::analysis::clAOLinearPredictor< _realT >::optimizeNc	(	realT &	gmax_lp,
		realT &	gopt_lp,
		int &	Nc,
		realT &	var_lp,
		clGainOpt< realT > &	go_lp,
		std::vector< realT > &	PSDt,
		std::vector< realT > &	PSDn,
		int	minNc,
		int	maxNc
	)

inline

Regularize the PSD and calculate the associated LP coefficients.

The PSD is regularized by adding a constant to it. This constant is found by minimizing the variance of the residual PSD.

Template Parameters

printout if true then the results are printed to stdout as they are calculated.

Parameters

[out]	gmax_lp	the maximum gain calculated for the regularized PSD
[out]	gopt_lp	the optimum gain calculated for the regularized PSD
[out]	var_lp	the variance at the optimum gain.
[in]	go_lp	the gain optimization object
[in]	PSDt	the turbulence PSD
[in]	PSDn	the WFS noise PSD
[in]	minNc	the number of coefficients

Definition at line 253 of file clAOLinearPredictor.hpp.

◆ regularizeCoefficients()

template<typename _realT >

template<bool printout = false>

int mx::AO::analysis::clAOLinearPredictor< _realT >::regularizeCoefficients	(	realT &	gmax_lp,
		realT &	gopt_lp,
		realT &	var_lp,
		clGainOpt< realT > &	go_lp,
		std::vector< realT > &	PSDt,
		std::vector< realT > &	PSDn,
		int	Nc
	)

inline

Regularize the PSD and calculate the associated LP coefficients.

The PSD is regularized by adding a constant to it. This constant is found by minimizing the variance of the residual PSD.

Template Parameters

printout if true then the results are printed to stdout as they are calculated.

Parameters

[out]	gmax_lp	the maximum gain calculated for the regularized PSD
[out]	gopt_lp	the optimum gain calculated for the regularized PSD
[out]	var_lp	the variance at the optimum gain.
[in]	go_lp	the gain optimization object
[in]	PSDt	the turbulence PSD
[in]	PSDn	the WFS noise PSD
[in]	Nc	the number of coefficients