likelihood_optimal_gain_correction_visibility_amplitude Class Reference

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

#include "likelihood/likelihood_optimal_gain_correction_visibility_amplitude.h"

Inheritance diagram for likelihood_optimal_gain_correction_visibility_amplitude:

Collaboration diagram for likelihood_optimal_gain_correction_visibility_amplitude:

Public Member Functions
	likelihood_optimal_gain_correction_visibility_amplitude (data_visibility_amplitude &data, model_visibility_amplitude &model, std::vector< std::string > station_codes, std::vector< double > sigma_g)
	Construct to do scan-by-scan correction by default.
	likelihood_optimal_gain_correction_visibility_amplitude (data_visibility_amplitude &data, model_visibility_amplitude &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_gain_correction_visibility_amplitude (data_visibility_amplitude &data, model_visibility_amplitude &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 double	chi_squared (std::vector< double > &x)
	Returns the \( \chi^2 \) of a vector of parameters \( \mathbf{x} \).
std::vector< double >	get_gain_correction_times ()
	Returns the start time of each gain-correction epoch.
std::vector< std::vector< double > >	get_gain_corrections ()
	Returns the set of gain corrections for each epoch.
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	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	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
virtual std::vector< double >	gradient (std::vector< double > &x, prior &Pr)
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_gain_corrections (std::vector< double > &y, std::vector< double > &yb, std::vector< size_t > &is1, std::vector< size_t > &is2, std::vector< double > &gest)
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	covsrt (double **covar, int ma, int mfit)
void	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	mrqmin (double y[], int ndata, double a[], int ma, double **covar, double **alpha, double *chisq, double *alamda)

Private Attributes
data_visibility_amplitude &	_data
model_visibility_amplitude &	_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< double > >	_g
std::vector< double >	_sqrt_detC
std::vector< double >	_x
bool	_smoothly_varying_gains
bool	_solve_for_gains
std::vector< std::vector< size_t > >	_datum_index_list
std::vector< std::vector< double > >	_y_list
std::vector< std::vector< size_t > >	_is1_list
std::vector< std::vector< size_t > >	_is2_list
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 visibility amplitude data object and a visibility amplitude model object, and then returns the log likelihood after minimalization of the log-likelihood within the gain-correction sub-space. That is, it sets the model to \( V_{AB}(g_A,g_B;p) = (1+g_A)(1+g_B) \bar{V}_{AB}(p) \), and numerically maximizes the likelihood independently for each observation epoch, assuming Gaussian priors on the \(g_A\) and Gaussian errors. 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

Note that this fails if low-SNR points are included in the analysis, which drives erroneous gain reconstructions. Typically, a minimum SNR or 2 is sufficient, which is required for the visibility amplitude error distribution to be well approximated by a Gaussian.

Member Function Documentation

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

private

_sigma_g.size();

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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:

• likelihood/likelihood_optimal_gain_correction_visibility_amplitude.h
• likelihood/likelihood_optimal_gain_correction_visibility_amplitude.cpp