complex_visibilities_tests.cpp File Reference
Test rig for developing and testing the likelihood_small_gain_correction_visibility_amplitude class. More...
#include <mpi.h>#include <memory>#include <string>#include <iostream>#include <iomanip>#include <vector>#include <fstream>#include "data_visibility.h"#include "model_symmetric_gaussian.h"#include "likelihood_visibility.h"#include "likelihood_optimal_complex_gain_visibility.h"#include "utils.h"#include "likelihood.h"#include "sampler_differential_evolution_deo_tempered_MCMC.h"#include "optimizer_kickout_powell.h"
Include dependency graph for complex_visibilities_tests.cpp:
Functions | |
| int | main (int argc, char *argv[]) |
Detailed Description
- Date
- February 2020
Runs various tests for various sets of test data to determine how well the gains can be reconstructed and mitigated in a simple parameter estimation study.
The test data set to use can be set on the command line. Options are:
- 0 ... An idealized Gaussian data set with amplitude 2.0 Jy and sigma 15 uas without thermal noise. (DEFAULT)
- 1 ... An idealized Gaussian data set with amplitude 2.0 Jy and sigma 15 uas without thermal noise, including constant gain errors of order 10%, with the LMT at 90%.
- 2 ... An idealized Gaussian data set with amplitude 2.0 Jy and sigma 15 uas without thermal noise, including variable gain errors of order 10%, with the LMT at 90%.
- 3 ... An idealized Gaussian data set with amplitude 2.0 Jy and sigma 15 uas with thermal noise.
- 4 ... An idealized Gaussian data set with amplitude 2.0 Jy and sigma 15 uas with thermal noise, including constant gain errors of order 10%, with the LMT at 90%.
- 5 ... An idealized Gaussian data set with amplitude 2.0 Jy and sigma 15 uas with thermal noise, including variable gain errors of order 10%, with the LMT at 90%.
- -? .. Get the best fit for each case