FlexibleModelFittingTaskFactory.cpp

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/*
 * FlexibleModelFittingTaskFactory.cpp
 *
 *  Created on: Sep 17, 2018
 *      Author: mschefer
 */
 
#include <ElementsKernel/Logging.h>
#include "SEImplementation/Plugin/FlexibleModelFitting/FlexibleModelFitting.h"
#include "SEImplementation/Plugin/FlexibleModelFitting/FlexibleModelFittingTask.h"
#include "SEImplementation/Plugin/FlexibleModelFitting/FlexibleModelFittingIterativeTask.h"
#include "SEImplementation/Plugin/FlexibleModelFitting/FlexibleModelFittingTaskFactory.h"
 
#include "SEImplementation/Configuration/ModelFittingConfig.h"
 
#include "SEImplementation/Configuration/SamplingConfig.h"
#include "SEImplementation/Configuration/MeasurementImageConfig.h"
 
namespace SourceXtractor {
 
static auto logger = Elements::Logging::getLogger("FlexibleModelFitting");
 
std::shared_ptr<Task> FlexibleModelFittingTaskFactory::createTask(const PropertyId& property_id) const {
  if (property_id == PropertyId::create<FlexibleModelFitting>()) {
    if (m_use_iterative_fitting) {
      return std::make_shared<FlexibleModelFittingIterativeTask>(m_least_squares_engine, m_max_iterations,
          m_modified_chi_squared_scale, m_parameters, m_frames, m_priors, m_should_renormalize, m_scale_factor,
          m_meta_iterations, m_deblend_factor, m_meta_iteration_stop, m_max_fit_size, m_window_type, m_ellipse_scale);
    } else {
      return std::make_shared<FlexibleModelFittingTask>(m_least_squares_engine, m_max_iterations,
          m_modified_chi_squared_scale, m_parameters, m_frames, m_priors, m_scale_factor);
    }
  } else {
    return nullptr;
  }
}
 
void FlexibleModelFittingTaskFactory::reportConfigDependencies(Euclid::Configuration::ConfigManager& manager) const {
  manager.registerConfiguration<ModelFittingConfig>();
  manager.registerConfiguration<SamplingConfig>();
  manager.registerConfiguration<MeasurementImageConfig>();
}
 
void FlexibleModelFittingTaskFactory::configure(Euclid::Configuration::ConfigManager& manager) {
  auto& model_fitting_config = manager.getConfiguration<ModelFittingConfig>();
  auto& sampling_config = manager.getConfiguration<SamplingConfig>();
 
  for (auto const& i : model_fitting_config.getParameters()) {
    m_parameters.push_back(i.second);
  }
 
  m_frames = model_fitting_config.getFrames();
 
  for (auto const& p : model_fitting_config.getPriors()) {
    m_priors.push_back(p.second);
  }
 
  m_least_squares_engine = model_fitting_config.getLeastSquaresEngine();
  m_max_iterations = model_fitting_config.getMaxIterations();
  m_modified_chi_squared_scale = model_fitting_config.getModifiedChiSquaredScale();
  m_use_iterative_fitting = model_fitting_config.getUseIterativeFitting();
  m_meta_iterations = model_fitting_config.getMetaIterations();
  m_deblend_factor = model_fitting_config.getDeblendFactor();
  m_meta_iteration_stop = model_fitting_config.getMetaIterationStop();
  m_window_type = model_fitting_config.getWindowType();
  m_ellipse_scale = model_fitting_config.getEllipseScale();
 
  std::string approach;
  if (m_use_iterative_fitting) {
    approach = "iterative";
  } else {
    approach = "classic";
  }
 
  logger.info() << "Using engine " << m_least_squares_engine << " with the "
                << approach << " implementation and "
                << m_max_iterations << " maximum number of iterations, window type: "
                << static_cast<int>(m_window_type);
 
  m_outputs = model_fitting_config.getOutputs();
 
  m_scale_factor = sampling_config.getScaleFactor();
  m_max_fit_size = sampling_config.getMaxFitSize();
 
  const auto& image_infos = manager.getConfiguration<MeasurementImageConfig>().getImageInfos();
  for (const auto& info : image_infos) {
    m_should_renormalize.push_back(info.m_psf_renormalize);
  }
}
 
void FlexibleModelFittingTaskFactory::registerPropertyInstances(OutputRegistry& registry) {
  for (auto& p : m_outputs) {
    std::string name = p.first;
    std::vector<int> properties = p.second;
    if (properties.size() == 1) {
      registry.registerColumnConverter<FlexibleModelFitting, double>(name,
          [properties](const FlexibleModelFitting& prop) {
            return prop.getParameterValue(properties[0]);
          }
      );
      registry.registerColumnConverter<FlexibleModelFitting, double>(name+"_err",
          [properties](const FlexibleModelFitting& prop) {
            return prop.getParameterSigma(properties[0]);
          }
      );
    } else {
      registry.registerColumnConverter<FlexibleModelFitting, std::vector<double>>(name,
          [properties](const FlexibleModelFitting& prop) {
            std::vector<double> result;
            for (int id : properties) {
              result.push_back(prop.getParameterValue(id));
            }
            return result;
          }
      );
      registry.registerColumnConverter<FlexibleModelFitting, std::vector<double>>(name+"_err",
          [properties](const FlexibleModelFitting& prop) {
            std::vector<double> result;
            for (int id : properties) {
              result.push_back(prop.getParameterSigma(id));
            }
            return result;
          }
      );
    }
  }
}
 
}