Defines a likelihood that non-linearly optimizes over gain corrections with a Gaussian prior. More...

#include "likelihood/likelihood_optimal_complex_gain_constrained_crosshand_visibilities.h"

Inheritance diagram for likelihood_optimal_complex_gain_constrained_crosshand_visibilities:

Collaboration diagram for likelihood_optimal_complex_gain_constrained_crosshand_visibilities:

Public Member Functions
	likelihood_optimal_complex_gain_constrained_crosshand_visibilities (data_crosshand_visibilities &data, model_crosshand_visibilities &model, std::vector< std::string > station_codes, std::vector< double > sigma_g)
	Construct to do scan-by-scan correction by default.

	likelihood_optimal_complex_gain_constrained_crosshand_visibilities (data_crosshand_visibilities &data, model_crosshand_visibilities &model, std::vector< std::string > station_codes, std::vector< double > sigma_g, std::vector< double > t_ge)
	Specify the times of the gain-correction epochs by hand.

	likelihood_optimal_complex_gain_constrained_crosshand_visibilities (data_crosshand_visibilities &data, model_crosshand_visibilities &model, std::vector< std::string > station_codes, std::vector< double > sigma_g, std::vector< double > t_ge, std::vector< double > max_g)
	Specify the times of the gain-correction epochs by hand.

virtual double	operator() (std::vector< double > &x)
	Returns the log-likelihood of a vector of parameters \( \mathbf{x} \).

virtual std::vector< double >	gradient (std::vector< double > &x, prior &Pr)

virtual double	chi_squared (std::vector< double > &x)
	Returns the \( \chi^2 \) of a vector of parameters \( \mathbf{x} \).

std::vector< double >	get_gain_times ()
	Returns the start time of each gain-correction epoch.

std::vector< std::vector< std::complex< double > > >	get_gains ()
	Returns the set of gain corrections for each epoch.

void	output_gains (std::ostream &out)
	Outputs the set of complex gains in a structure fashion to an output stream.

void	output_gains (std::string outname)
	Outputs the set of complex gains in a structure fashion to a file.

void	output_gain_corrections (std::ostream &out)
	Outputs the set of gain corrections in a structure fashion to an output stream.

void	output_gain_corrections (std::string outname)
	Outputs the set of gain corrections in a structure fashion to a file.

virtual void	set_mpi_communicator (MPI_Comm comm)
	Defines a set of processors provided to the model for parallel computation via an MPI communicator. Only facilates code parallelization if the model computation is parallelized via MPI.

size_t	number_of_independent_gains ()
	Returns the number of independent gains, which is generally less than product of the number of stations and number of epochs.

void	set_iteration_limit (int itermax)
	Set the maximum number of iterations in the likelihood maximizer. More iterations results in higher accuracy for the gains, but at the expense of more time.

void	read_gain_file (std::string gain_file_name)
	Reads gain file.

void	solve_for_gains ()
	Turns on gain solver (default is on)

void	fix_gains ()
	Fixes the gains to last value.

void	fix_gains_during_gradient ()
	Fixes the gains during gradient evaluation.

void	solve_for_gains_during_gradient ()
	Fixes the gains during gradient evaluation.

void	use_prior_gain_solutions ()
	Assume that the previous solutions for the complex gains yield an approximate solution for the next iteration. Note that this does make subsequent gain solves dependent on the prior history. However, it can also make the solution of the gains much faster. It overrides assume_smoothly_varying_gains() and assume_independently_varying gains().

void	assume_smoothly_varying_gains ()
	Assume that the gain corrections are correlated, yielding an approximate solution for subsequent times (default).

void	assume_independently_varying_gains ()
	Assume that the gain corrections are not correlated.

Public Member Functions inherited from likelihood_base
void	output_model_data_comparison (std::ostream &out)

void	output_model_data_comparison (std::string filename)

Protected Member Functions
virtual void	output (std::ostream &out)

Private Member Functions
void	check_station_codes ()

void	allocate_memory ()

void	organize_data_lists ()

double	optimal_complex_gains (std::vector< std::vector< std::complex< double > > > &y, std::vector< std::vector< std::complex< double > > > &yb, std::vector< size_t > &is1, std::vector< size_t > &is2, std::vector< std::complex< double > > &gest)

double	optimal_complex_gains_trial (std::vector< std::vector< std::complex< double > > > &y, std::vector< std::vector< std::complex< double > > > &yb, std::vector< size_t > &is1, std::vector< size_t > &is2, std::vector< std::complex< double > > &gest, double &chisq)

double	optimal_complex_gains_log_trial (std::vector< std::vector< std::complex< double > > > &y, std::vector< std::vector< std::complex< double > > > &yb, std::vector< size_t > &is1, std::vector< size_t > &is2, std::vector< std::complex< double > > &gest, double &chisq_opt)

double	matrix_determinant (double **a)

void	ludcmp (double *a, int n, int indx, double &d)

void	gain_optimization_likelihood (size_t i, const double g[], double *y, double dydg[]) const

void	gain_optimization_log_likelihood (size_t i, const double g[], double *y, double dydg[]) const

void	covsrt (double **covar, int ma, int mfit)

int	gaussj (double a, int n, double b, int m)

void	mrqcof (double y[], int ndata, double a[], int ma, double *alpha, double beta[], double chisq)

void	mrqcof_log (double y[], double sig[], int ndata, double a[], int ma, double *alpha, double beta[], double chisq)

void	mrqmin (double y[], int ndata, double a[], int ma, double covar, double alpha, double chisq, double alamda)

void	mrqmin_log (double y[], double sig[], int ndata, double a[], int ma, double covar, double alpha, double chisq, double alamda)

Private Attributes
data_crosshand_visibilities &	_data

model_crosshand_visibilities &	_model

std::vector< std::string >	_station_codes

std::vector< double >	_sigma_g

std::vector< double >	_max_g

std::vector< double >	_tge

std::vector< std::vector< std::complex< double > > >	_G

std::vector< double >	_sqrt_detC

bool	_use_prior_gain_solutions

bool	_smoothly_varying_gains

bool	_solve_for_gains

bool	_solve_for_gains_during_gradient

std::vector< std::vector< size_t > >	_datum_index_list

std::vector< std::vector< std::complex< double > > >	_yrr_list

std::vector< std::vector< std::complex< double > > >	_yll_list

std::vector< std::vector< std::complex< double > > >	_yrl_list

std::vector< std::vector< std::complex< double > > >	_ylr_list

std::vector< std::vector< size_t > >	_is1_list

std::vector< std::vector< size_t > >	_is2_list

double	_opi2

int	_itermax

double *	_ogc_y

double *	_ogc_yb

size_t *	_ogc_is1

size_t *	_ogc_is2

double	_mrq_ochisq

double *	_mrq_atry

double *	_mrq_beta

double *	_mrq_da

double **	_mrq_oneda

Additional Inherited Members
Protected Attributes inherited from likelihood_base
MPI_Comm	_comm

Detailed Description

This class takes a crosshand_visibilities data object and a crosshand_visibilities model object, and then returns the log likelihood after minimalization of the log-likelihood within the complex gain-correction sub-space. That is, it sets the model to \( V_{AB}(G_A,G_B;p) = G_A G_B \bar{V}_{AB}(p) \), for \( V = {\rm RR, LL, RL, LR}\), and numerically maximizes the likelihood independently for each observation epoch, assuming Gaussian priors on the \(|G_A|\) and Gaussian errors. Note that this likelihood is constrained in that it fixes the L and R complex gains to be identical. An approximation of the marginalized likelihood is generated using the covariance of the likelihood, though this makes little difference in practice. Gain-reconstruction epochs may be specified explicitly; by default the corrections will be performed by scan.

Warning: If the left and right gains are not the same, this will create difficulty. While there are good reasons to believe that their relative calibration is much better than their absolute calibration. Nevertheless, see likelihood_optimal_complex_gain_crosshand_visibilities for the unconstrained case.

Todo:: Implement likelihood_optimal_complex_gain_crosshand_visibilities as promised in the warning!

Member Function Documentation

std::vector< double > gradient	(	std::vector< double > &	x,
		prior &	Pr
	)

virtual

Returns the gradient of the log-likelihood of a vector of parameters \( \mathbf{x} \) The prior permits parameter checking if required during likelihood gradient evaluation, through the gradients of the prior is applied elsewhere.

Reimplemented from likelihood_base.

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void output ( std::ostream & out )

protectedvirtual

Outputs the data and model, as modified by the likelihood appropriately, to the specified output stream. Useful for comparison later.

Reimplemented from likelihood_base.

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The documentation for this class was generated from the following files:

Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Member Function Documentation