ReducedModelOptimization/src/main.cpp

#include "beamformfinder.hpp"
#include "csvfile.hpp"
#include "edgemesh.hpp"
#include "reducedmodeloptimizer.hpp"
#include "simulationhistoryplotter.hpp"
#include "trianglepattterntopology.hpp"
#include <chrono>
#include <filesystem>
#include <iostream>
#include <iterator>
#include <stdexcept>
#include <string>
#include <vcg/complex/algorithms/update/position.h>

#ifdef POLYSCOPE_DEFINED
#include "polyscope/curve_network.h"
#include "polyscope/point_cloud.h"
#include "polyscope/polyscope.h"
#endif
int main(int argc, char *argv[]) {
  if (argc < 3) {
    std::cerr << "Specify at least the two pattern filepaths to be "
                 "optimized.Exiting.."
              << std::endl;
    std::terminate();
  }

  // Populate the pattern pair to be optimized
  ////Full pattern
  const std::string filepath_fullPattern = argv[1];
  PatternGeometry fullPattern(filepath_fullPattern);
  fullPattern.setLabel(
      std::filesystem::path(filepath_fullPattern).stem().string());
  fullPattern.scale(0.03);
  ////Reduced pattern
  const std::string filepath_reducedPattern = argv[2];
  PatternGeometry reducedPattern(filepath_reducedPattern);
  reducedPattern.setLabel(
      std::filesystem::path(filepath_reducedPattern).stem().string());
  reducedPattern.scale(0.03);

  // Set the optization settings
  ReducedModelOptimizer::xRange beamWidth{"B", 0.5, 1.5};
  ReducedModelOptimizer::xRange beamDimensionsRatio{"bOverh", 0.7, 1.3};
  ReducedModelOptimizer::xRange beamE{"E", 0.1, 1.9};
  ReducedModelOptimizer::xRange innerHexagonSize{"HexSize", 0.1, 0.8};
  ReducedModelOptimizer::xRange innerHexagonAngle{"HexAngle", -29.5, 29.5};
  ReducedModelOptimizer::Settings settings_optimization;
  settings_optimization.xRanges = {beamWidth, beamDimensionsRatio, beamE,
                                   innerHexagonSize, innerHexagonAngle};
  const bool input_numberOfFunctionCallsDefined = argc >= 4;
  settings_optimization.numberOfFunctionCalls =
      input_numberOfFunctionCallsDefined ? std::atoi(argv[3]) : 100;
  settings_optimization.normalizationStrategy =
      ReducedModelOptimizer::Settings::NormalizationStrategy::Epsilon;
  settings_optimization.normalizationParameter = 0.0003;
  settings_optimization.solutionAccuracy = 0.01;

  // Optimize pair
  const std::string pairName =
      fullPattern.getLabel() + "@" + reducedPattern.getLabel();

  const std::vector<size_t> numberOfNodesPerSlot{1, 0, 0, 2, 1, 2, 1};
  ReducedModelOptimizer optimizer(numberOfNodesPerSlot);
  optimizer.initializePatterns(fullPattern, reducedPattern, {});
  ReducedModelOptimizer::Results optimizationResults =
      optimizer.optimize(settings_optimization);

  // Export results
  const bool input_resultDirectoryDefined = argc >= 5;
  std::string optimizationResultsDirectory =
      input_resultDirectoryDefined ? argv[4] : "OptimizationResults";
  std::string resultsOutputDir;
  if (optimizationResults.numberOfSimulationCrashes != 0) {
    const auto crashedJobsDirPath =
        std::filesystem::path(optimizationResultsDirectory)
            .append("CrashedJobs")
            .append(pairName);
    std::filesystem::create_directories(crashedJobsDirPath);
    resultsOutputDir = crashedJobsDirPath.string();
  } else {
    std::filesystem::path convergedJobsDirPath(
        std::filesystem::path(optimizationResultsDirectory)
            .append("ConvergedJobs")
            .append(pairName));
    std::filesystem::create_directories(convergedJobsDirPath);
    resultsOutputDir = convergedJobsDirPath.string();
  }
  optimizationResults.save(resultsOutputDir);
  // Write results in csv
  csvFile csv_results({}, false);
  //    csvFile csv_results(std::filesystem::path(dirPath_thisOptimization)
  //                            .append("results.csv")
  //                            .string(),
  //                        false);
  csv_results << "Name";
  optimizationResults.writeHeaderTo(settings_optimization, csv_results);
  settings_optimization.writeHeaderTo(csv_results);
  csv_results << endrow;
  csv_results << pairName;
  optimizationResults.writeResultsTo(settings_optimization, csv_results);
  settings_optimization.writeSettingsTo(csv_results);
  csv_results << endrow;

  //  optimizationResults.draw();

  return 0;
}