1028 lines
48 KiB
C++
1028 lines
48 KiB
C++
#ifndef REDUCEDMODELOPTIMIZER_STRUCTS_HPP
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#define REDUCEDMODELOPTIMIZER_STRUCTS_HPP
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#include "csvfile.hpp"
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#include "drmsimulationmodel.hpp"
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#include "linearsimulationmodel.hpp"
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#include "simulation_structs.hpp"
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#include "unordered_map"
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#include <string>
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namespace ReducedModelOptimization {
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enum BaseSimulationScenario {
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Axial,
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Shear,
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Bending,
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Dome,
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Saddle,
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NumberOfBaseSimulationScenarios
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};
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inline static std::vector<std::string> baseSimulationScenarioNames{"Axial",
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"Shear",
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"Bending",
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"Dome",
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"Saddle"};
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struct Colors
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{
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inline static std::array<double, 3> fullInitial{0.094, 0.094, 0.094};
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// inline static std::array<double, 3> fullDeformed{0.583333, 0.890196, 0.109804};
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inline static std::array<double, 3> fullDeformed{0.094, 0.094, 0.094};
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inline static std::array<double, 3> reducedInitial{0.890196, 0.109804, 0.193138};
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inline static std::array<double, 3> reducedDeformed{0.262, 0.627, 0.910};
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};
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struct xRange
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{
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std::string label{};
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double min{0};
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double max{0};
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inline bool operator<(const xRange &other) { return label < other.label; }
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std::string toString() const
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{
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return label + "=[" + std::to_string(min) + "," + std::to_string(max) + "]";
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}
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void fromString(const std::string &s)
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{
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const std::size_t equalPos = s.find("=");
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label = s.substr(0, equalPos);
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const std::size_t commaPos = s.find(",");
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const size_t minBeginPos = equalPos + 2;
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min = std::stod(s.substr(minBeginPos, commaPos - minBeginPos));
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const size_t maxBeginPos = commaPos + 1;
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const std::size_t closingBracketPos = s.find("]");
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max = std::stod(s.substr(maxBeginPos, closingBracketPos - maxBeginPos));
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}
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bool operator==(const xRange &xrange) const
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{
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return label == xrange.label && min == xrange.min && max == xrange.max;
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}
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std::tuple<std::string, double, double> toTuple() const
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{
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return std::make_tuple(label, min, max);
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}
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void set(const std::tuple<std::string, double, double> &inputTuple)
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{
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if (std::get<1>(inputTuple) > std::get<2>(inputTuple)) {
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std::cerr
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<< "Invalid xRange tuple. Second argument(min) cant be smaller than the third(max)"
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<< std::endl;
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std::terminate();
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// return;
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}
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std::tie(label, min, max) = inputTuple;
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}
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};
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enum OptimizationParameterIndex { E, A, I2, I3, J, R, Theta, NumberOfOptimizationVariables };
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inline int getParameterIndex(const std::string &s)
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{
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if ("E" == s) {
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return E;
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} else if ("A" == s) {
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return A;
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} else if ("I2" == s) {
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return I2;
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} else if ("I3" == s) {
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return I3;
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} else if ("J" == s) {
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return J;
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} else if ("R" == s || "HexSize" == s) {
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return R;
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} else if ("Theta" == s || "HexAngle" == s) {
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return Theta;
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} else {
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std::cerr << "Input is not recognized as a valid optimization variable index:" << s
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<< std::endl;
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return -1;
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}
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}
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struct Settings
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{
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inline static std::string defaultFilename{"OptimizationSettings.json"};
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std::vector<std::vector<OptimizationParameterIndex>> optimizationStrategy = {
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{E, A, I2, I3, J, R, Theta}};
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// {A, I2, I3, J, R, Theta}};
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std::optional<std::vector<double>>
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optimizationVariablesGroupsWeights; //TODO:should be removed in the future if not a splitting strategy is used for the optimization
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enum NormalizationStrategy { NonNormalized, Epsilon };
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inline static std::vector<std::string> normalizationStrategyStrings{"NonNormalized", "Epsilon"};
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NormalizationStrategy normalizationStrategy{Epsilon};
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std::array<xRange, NumberOfOptimizationVariables> variablesRanges{xRange{"E", 0.01, 100},
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xRange{"A", 0.01, 100},
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xRange{"I2", 0.01, 100},
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xRange{"I3", 0.01, 100},
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xRange{"J", 0.01, 100},
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xRange{"R", 0.05, 0.95},
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xRange{"Theta", -30, 30}};
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int numberOfFunctionCalls{100000};
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double solverAccuracy{1e-3};
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double translationEpsilon{3e-4};
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// double translationEpsilon{0};
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double angularDistanceEpsilon{vcg::math::ToRad(0.0)};
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struct ObjectiveWeights
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{
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double translational{1.2};
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double rotational{0.8};
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bool operator==(const ObjectiveWeights &other) const
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{
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return this->translational == other.translational
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&& this->rotational == other.rotational;
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}
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};
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std::array<ObjectiveWeights, NumberOfBaseSimulationScenarios> perBaseScenarioObjectiveWeights;
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std::array<std::pair<double, double>, NumberOfBaseSimulationScenarios>
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convertObjectiveWeightsToPairs() const
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{
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std::array<std::pair<double, double>, NumberOfBaseSimulationScenarios> objectiveWeightsPairs;
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for (int baseScenario = Axial; baseScenario != NumberOfBaseSimulationScenarios;
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baseScenario++) {
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objectiveWeightsPairs[baseScenario]
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= std::make_pair(perBaseScenarioObjectiveWeights[baseScenario].translational,
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perBaseScenarioObjectiveWeights[baseScenario].rotational);
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}
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return objectiveWeightsPairs;
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}
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struct JsonKeys
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{
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inline static std::string OptimizationStrategy{"OptimizationStrategy"};
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inline static std::string OptimizationStrategyGroupWeights{
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"OptimizationStrategyGroupWeights"};
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inline static std::string OptimizationVariables{"OptimizationVariables"};
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inline static std::string NumberOfFunctionCalls{"NumberOfFunctionCalls"};
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inline static std::string SolverAccuracy{"SolverAccuracy"};
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inline static std::string ObjectiveWeights{"ObjectiveWeight"};
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};
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void save(const std::filesystem::path &saveToPath)
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{
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assert(std::filesystem::is_directory(saveToPath));
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nlohmann::json json;
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json[GET_VARIABLE_NAME(optimizationStrategy)] = optimizationStrategy;
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if (optimizationVariablesGroupsWeights.has_value()) {
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json[GET_VARIABLE_NAME(ptimizationStrategyGroupWeights)]
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= optimizationVariablesGroupsWeights.value();
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}
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//write x ranges
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const std::array<std::tuple<std::string, double, double>, NumberOfOptimizationVariables>
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xRangesAsTuples = [=]() {
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std::array<std::tuple<std::string, double, double>, NumberOfOptimizationVariables>
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xRangesAsTuples;
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for (int optimizationParameterIndex = E;
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optimizationParameterIndex != NumberOfOptimizationVariables;
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optimizationParameterIndex++) {
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xRangesAsTuples[optimizationParameterIndex]
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= variablesRanges[optimizationParameterIndex].toTuple();
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}
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return xRangesAsTuples;
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}();
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json[JsonKeys::OptimizationVariables] = xRangesAsTuples;
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// for (size_t xRangeIndex = 0; xRangeIndex < variablesRanges.size(); xRangeIndex++) {
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// const auto &xRange = variablesRanges[xRangeIndex];
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// json[JsonKeys::OptimizationVariables + "_" + std::to_string(xRangeIndex)]
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// = xRange.toString();
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// }
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json[GET_VARIABLE_NAME(numberOfFunctionCalls)] = numberOfFunctionCalls;
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json[GET_VARIABLE_NAME(solverAccuracy)] = solverAccuracy;
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//Objective weights
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std::array<std::pair<double, double>, NumberOfBaseSimulationScenarios> objectiveWeightsPairs;
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std::transform(perBaseScenarioObjectiveWeights.begin(),
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perBaseScenarioObjectiveWeights.end(),
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objectiveWeightsPairs.begin(),
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[](const ObjectiveWeights &objectiveWeights) {
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return std::make_pair(objectiveWeights.translational,
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objectiveWeights.rotational);
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});
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json[JsonKeys::ObjectiveWeights] = objectiveWeightsPairs;
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json[GET_VARIABLE_NAME(translationEpsilon)] = translationEpsilon;
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json[GET_VARIABLE_NAME(angularDistanceEpsilon)] = vcg::math::ToDeg(
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angularDistanceEpsilon);
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std::filesystem::path jsonFilePath(
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std::filesystem::path(saveToPath).append(defaultFilename));
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std::ofstream jsonFile(jsonFilePath.string());
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jsonFile << json;
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jsonFile.close();
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}
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bool load(const std::filesystem::path &loadFromPath)
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{
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assert(std::filesystem::is_directory(loadFromPath));
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//Load optimal X
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nlohmann::json json;
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std::filesystem::path jsonFilepath(
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std::filesystem::path(loadFromPath).append(defaultFilename));
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if (!std::filesystem::exists(jsonFilepath)) {
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std::cerr << "Optimization settings could not be loaded because input filepath does "
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"not exist:"
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<< jsonFilepath << std::endl;
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return false;
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}
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std::ifstream ifs(jsonFilepath);
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ifs >> json;
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if (json.contains(GET_VARIABLE_NAME(optimizationStrategy))) {
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optimizationStrategy
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= std::vector<std::vector<ReducedModelOptimization::OptimizationParameterIndex>>(
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(json.at(GET_VARIABLE_NAME(optimizationStrategy))));
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}
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if (json.contains(GET_VARIABLE_NAME(optimizationStrategyGroupWeights))) {
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optimizationVariablesGroupsWeights = std::vector<double>(
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json[GET_VARIABLE_NAME(optimizationStrategyGroupWeights)]);
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}
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//read x ranges
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if (json.contains(JsonKeys::OptimizationVariables)) {
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const std::array<std::tuple<std::string, double, double>, NumberOfOptimizationVariables>
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xRangesAsTuples = json.at(JsonKeys::OptimizationVariables);
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for (const auto &rangeTuple : xRangesAsTuples) {
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variablesRanges[getParameterIndex(std::get<0>(rangeTuple))].set(rangeTuple);
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}
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} else { //NOTE:legacy compatibility
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size_t xRangeIndex = 0;
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while (true) {
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const std::string jsonXRangeKey = JsonKeys::OptimizationVariables + "_"
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+ std::to_string(xRangeIndex++);
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if (!json.contains(jsonXRangeKey)) {
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break;
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}
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xRange x;
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x.fromString(json.at(jsonXRangeKey));
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variablesRanges[getParameterIndex(x.label)] = x;
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}
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}
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if (json.contains(GET_VARIABLE_NAME(numberOfFunctionCalls))) {
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numberOfFunctionCalls = json.at(GET_VARIABLE_NAME(numberOfFunctionCalls));
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}
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if (json.contains(GET_VARIABLE_NAME(solverAccuracy))) {
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solverAccuracy = json.at(GET_VARIABLE_NAME(solverAccuracy));
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}
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//Objective weights
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if (json.contains(JsonKeys::ObjectiveWeights)) {
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std::array<std::pair<double, double>, NumberOfBaseSimulationScenarios>
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objectiveWeightsPairs = json.at(JsonKeys::ObjectiveWeights);
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std::transform(objectiveWeightsPairs.begin(),
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objectiveWeightsPairs.end(),
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perBaseScenarioObjectiveWeights.begin(),
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[](const std::pair<double, double> &objectiveWeightsPair) {
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return ObjectiveWeights{objectiveWeightsPair.first,
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objectiveWeightsPair.second};
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});
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}
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if (json.contains(GET_VARIABLE_NAME(translationalNormalizationEpsilon))) {
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translationEpsilon
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= json[GET_VARIABLE_NAME(translationalNormalizationEpsilon)];
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}
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if (json.contains(GET_VARIABLE_NAME(angularDistanceEpsilon))) {
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angularDistanceEpsilon = vcg::math::ToRad(
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static_cast<double>(json[GET_VARIABLE_NAME(angularDistanceEpsilon)]));
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}
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// perBaseScenarioObjectiveWeights = json.at(JsonKeys::ObjectiveWeights);
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// objectiveWeights.translational = json.at(JsonKeys::ObjectiveWeights);
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// objectiveWeights.rotational = 2 - objectiveWeights.translational;
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return true;
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}
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std::string toString() const
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{
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std::string settingsString;
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if (!variablesRanges.empty()) {
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std::string xRangesString;
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for (const xRange &range : variablesRanges) {
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xRangesString += range.toString() + " ";
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}
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settingsString += xRangesString;
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}
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settingsString += "FuncCalls=" + std::to_string(numberOfFunctionCalls)
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+ " Accuracy=" + std::to_string(solverAccuracy);
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for (int baseScenario = Axial; baseScenario != NumberOfBaseSimulationScenarios;
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baseScenario++) {
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+" W_t=" + std::to_string(perBaseScenarioObjectiveWeights[baseScenario].translational)
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+ " W_r="
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+ std::to_string(perBaseScenarioObjectiveWeights[baseScenario].rotational);
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}
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return settingsString;
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}
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void writeHeaderTo(csvFile &os) const
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{
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if (!variablesRanges.empty()) {
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for (const xRange &range : variablesRanges) {
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os << range.label + " max";
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os << range.label + " min";
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}
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}
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os << "Function Calls";
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os << "Solution Accuracy";
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os << "Normalization trans epsilon";
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os << "Normalization rot epsilon(deg)";
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os << JsonKeys::ObjectiveWeights;
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os << "Optimization parameters";
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// os << std::endl;
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}
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void writeSettingsTo(csvFile &os) const
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{
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if (!variablesRanges.empty()) {
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for (const xRange &range : variablesRanges) {
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os << range.max;
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os << range.min;
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}
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}
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os << numberOfFunctionCalls;
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os << solverAccuracy;
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os << std::to_string(translationEpsilon);
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os << std::to_string(vcg::math::ToDeg(angularDistanceEpsilon));
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std::string objectiveWeightsString;
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objectiveWeightsString += "{";
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for (int baseScenario = Axial; baseScenario != NumberOfBaseSimulationScenarios;
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baseScenario++) {
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objectiveWeightsString
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+= "{" + std::to_string(perBaseScenarioObjectiveWeights[baseScenario].translational)
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+ "," + std::to_string(perBaseScenarioObjectiveWeights[baseScenario].rotational)
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+ "}";
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}
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objectiveWeightsString += "}";
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os << objectiveWeightsString;
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//export optimization parameters
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std::vector<std::vector<int>> vv;
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for (const std::vector<OptimizationParameterIndex> &v : optimizationStrategy) {
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std::vector<int> vi;
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vi.reserve(v.size());
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for (const OptimizationParameterIndex ¶meter : v) {
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vi.emplace_back(parameter);
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}
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vv.push_back(vi);
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}
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os << Utilities::toString(vv);
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}
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};
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inline bool operator==(const Settings &settings1, const Settings &settings2) noexcept
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{
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const bool haveTheSameObjectiveWeights
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= std::mismatch(settings1.perBaseScenarioObjectiveWeights.begin(),
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settings1.perBaseScenarioObjectiveWeights.end(),
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settings2.perBaseScenarioObjectiveWeights.begin())
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.first
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!= settings1.perBaseScenarioObjectiveWeights.end();
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return settings1.numberOfFunctionCalls == settings2.numberOfFunctionCalls
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&& settings1.variablesRanges == settings2.variablesRanges
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&& settings1.solverAccuracy == settings2.solverAccuracy && haveTheSameObjectiveWeights
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&& settings1.translationEpsilon
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== settings2.translationEpsilon;
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}
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inline void updateMeshWithOptimalVariables(const std::vector<double> &x, SimulationMesh &m)
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{
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assert(CrossSectionType::name == RectangularBeamDimensions::name);
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const double E = x[0];
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const double A = x[1];
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const double beamWidth = std::sqrt(A);
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const double beamHeight = beamWidth;
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const double J = x[2];
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const double I2 = x[3];
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const double I3 = x[4];
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for (EdgeIndex ei = 0; ei < m.EN(); ei++) {
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Element &e = m.elements[ei];
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e.setDimensions(CrossSectionType(beamWidth, beamHeight));
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e.setMaterial(ElementMaterial(e.material.poissonsRatio, E));
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e.dimensions.inertia.J = J;
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e.dimensions.inertia.I2 = I2;
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e.dimensions.inertia.I3 = I3;
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}
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CoordType center_barycentric(1, 0, 0);
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CoordType interfaceEdgeMiddle_barycentric(0, 0.5, 0.5);
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CoordType movableVertex_barycentric((center_barycentric + interfaceEdgeMiddle_barycentric)
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* x[x.size() - 2]);
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CoordType patternCoord0 = CoordType(0, 0, 0);
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double bottomEdgeHalfSize = 0.03 / std::tan(M_PI / 3);
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CoordType interfaceNodePosition(0, -0.03, 0);
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CoordType patternBottomRight = interfaceNodePosition + CoordType(bottomEdgeHalfSize, 0, 0);
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CoordType patternBottomLeft = interfaceNodePosition - CoordType(bottomEdgeHalfSize, 0, 0);
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vcg::Triangle3<double> baseTriangle(patternCoord0, patternBottomLeft, patternBottomRight);
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CoordType baseTriangleMovableVertexPosition = baseTriangle.cP(0)
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* movableVertex_barycentric[0]
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+ baseTriangle.cP(1)
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* movableVertex_barycentric[1]
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+ baseTriangle.cP(2)
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* movableVertex_barycentric[2];
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VectorType patternPlaneNormal(0, 0, 1);
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baseTriangleMovableVertexPosition = vcg::RotationMatrix(patternPlaneNormal,
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vcg::math::ToRad(x[x.size() - 1]))
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* baseTriangleMovableVertexPosition;
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const int fanSize = 6;
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for (int rotationCounter = 0; rotationCounter < fanSize; rotationCounter++) {
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m.vert[2 * rotationCounter + 1].P() = vcg::RotationMatrix(patternPlaneNormal,
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vcg::math::ToRad(
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60.0 * rotationCounter))
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* baseTriangleMovableVertexPosition;
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}
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m.reset();
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}
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struct Results
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{
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std::string label{"EmptyLabel"};
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double time{-1};
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bool wasSuccessful{true};
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// int numberOfSimulationCrashes{0};
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Settings settings;
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std::vector<std::pair<std::string, double>> optimalXNameValuePairs;
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std::vector<std::shared_ptr<SimulationJob>> fullPatternSimulationJobs;
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std::vector<std::shared_ptr<SimulationJob>> reducedPatternSimulationJobs;
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double fullPatternYoungsModulus{0};
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PatternGeometry baseTriangleFullPattern; //non-fanned,non-tiled full pattern
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vcg::Triangle3<double> baseTriangle;
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std::string notes;
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//Data gathered for csv exporting
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struct ObjectiveValues
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{
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double totalRaw;
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double total;
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std::vector<double> perSimulationScenario_rawTranslational;
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std::vector<double> perSimulationScenario_rawRotational;
|
|
std::vector<double> perSimulationScenario_translational;
|
|
std::vector<double> perSimulationScenario_rotational;
|
|
std::vector<double> perSimulationScenario_total;
|
|
std::vector<double> perSimulationScenario_total_unweighted;
|
|
} objectiveValue;
|
|
std::vector<double> perScenario_fullPatternPotentialEnergy;
|
|
std::vector<double> objectiveValueHistory;
|
|
std::vector<size_t> objectiveValueHistory_iteration;
|
|
|
|
struct CSVExportingSettings
|
|
{
|
|
bool exportPE{false};
|
|
bool exportIterationOfMinima{false};
|
|
bool exportRawObjectiveValue{true};
|
|
CSVExportingSettings() {}
|
|
};
|
|
|
|
const CSVExportingSettings exportSettings;
|
|
struct JsonKeys
|
|
{
|
|
inline static std::string filename{"OptimizationResults.json"};
|
|
inline static std::string optimizationVariables{"OptimizationVariables"};
|
|
inline static std::string baseTriangle{"BaseTriangle"};
|
|
inline static std::string Label{"Label"};
|
|
inline static std::string FullPatternLabel{"FullPatternLabel"};
|
|
inline static std::string Settings{"OptimizationSettings"};
|
|
inline static std::string FullPatternPotentialEnergies{"PE"};
|
|
inline static std::string fullPatternYoungsModulus{"youngsModulus"};
|
|
};
|
|
|
|
void save(const std::string &saveToPath, const bool shouldExportDebugFiles = false)
|
|
{
|
|
//clear directory
|
|
if (std::filesystem::exists(saveToPath)) {
|
|
for (const auto &entry : std::filesystem::directory_iterator(saveToPath)) {
|
|
std::error_code ec;
|
|
std::filesystem::remove_all(entry.path(), ec);
|
|
}
|
|
}
|
|
std::filesystem::create_directories(saveToPath);
|
|
|
|
//Save optimal X
|
|
nlohmann::json json_optimizationResults;
|
|
json_optimizationResults[JsonKeys::Label] = label;
|
|
if (wasSuccessful) {
|
|
std::string jsonValue_optimizationVariables;
|
|
for (const auto &optimalXNameValuePair : optimalXNameValuePairs) {
|
|
jsonValue_optimizationVariables.append(optimalXNameValuePair.first + ",");
|
|
}
|
|
jsonValue_optimizationVariables.pop_back(); // for deleting the last comma
|
|
json_optimizationResults[JsonKeys::optimizationVariables]
|
|
= jsonValue_optimizationVariables;
|
|
|
|
for (const auto &optimalXNameValuePair : optimalXNameValuePairs) {
|
|
json_optimizationResults[optimalXNameValuePair.first] = optimalXNameValuePair
|
|
.second;
|
|
}
|
|
}
|
|
//base triangle
|
|
json_optimizationResults[JsonKeys::baseTriangle]
|
|
= std::vector<double>{baseTriangle.cP0(0)[0],
|
|
baseTriangle.cP0(0)[1],
|
|
baseTriangle.cP0(0)[2],
|
|
baseTriangle.cP1(0)[0],
|
|
baseTriangle.cP1(0)[1],
|
|
baseTriangle.cP1(0)[2],
|
|
baseTriangle.cP2(0)[0],
|
|
baseTriangle.cP2(0)[1],
|
|
baseTriangle.cP2(0)[2]};
|
|
baseTriangleFullPattern.save(std::filesystem::path(saveToPath).string());
|
|
json_optimizationResults[JsonKeys::FullPatternLabel] = baseTriangleFullPattern.getLabel();
|
|
|
|
//potential energies
|
|
const int numberOfSimulationJobs = fullPatternSimulationJobs.size();
|
|
std::vector<double> fullPatternPE(numberOfSimulationJobs);
|
|
for (int simulationScenarioIndex = 0; simulationScenarioIndex < numberOfSimulationJobs;
|
|
simulationScenarioIndex++) {
|
|
fullPatternPE[simulationScenarioIndex]
|
|
= perScenario_fullPatternPotentialEnergy[simulationScenarioIndex];
|
|
}
|
|
json_optimizationResults[JsonKeys::FullPatternPotentialEnergies] = fullPatternPE;
|
|
json_optimizationResults[JsonKeys::fullPatternYoungsModulus] = fullPatternYoungsModulus;
|
|
////Save to json file
|
|
std::filesystem::path jsonFilePath(
|
|
std::filesystem::path(saveToPath).append(JsonKeys::filename));
|
|
std::ofstream jsonFile_optimizationResults(jsonFilePath.string());
|
|
jsonFile_optimizationResults << json_optimizationResults;
|
|
|
|
/*TODO: Refactor since the meshes are saved for each simulation scenario although they do not change*/
|
|
//Save jobs and meshes for each simulation scenario
|
|
|
|
if (shouldExportDebugFiles) {
|
|
//Save the reduced and full patterns
|
|
const std::filesystem::path simulationJobsPath(
|
|
std::filesystem::path(saveToPath).append("SimulationJobs"));
|
|
const int numberOfSimulationJobs = fullPatternSimulationJobs.size();
|
|
for (int simulationScenarioIndex = 0;
|
|
simulationScenarioIndex < numberOfSimulationJobs;
|
|
simulationScenarioIndex++) {
|
|
const std::shared_ptr<SimulationJob> &pFullPatternSimulationJob
|
|
= fullPatternSimulationJobs[simulationScenarioIndex];
|
|
std::filesystem::path simulationJobFolderPath(
|
|
std::filesystem::path(simulationJobsPath)
|
|
.append(std::to_string(simulationScenarioIndex) + "_"
|
|
+ pFullPatternSimulationJob->label));
|
|
std::filesystem::create_directories(simulationJobFolderPath);
|
|
const auto fullPatternDirectoryPath
|
|
= std::filesystem::path(simulationJobFolderPath).append("Full");
|
|
std::filesystem::create_directory(fullPatternDirectoryPath);
|
|
pFullPatternSimulationJob->save(fullPatternDirectoryPath.string());
|
|
const std::shared_ptr<SimulationJob> &pReducedPatternSimulationJob
|
|
= reducedPatternSimulationJobs[simulationScenarioIndex];
|
|
const auto reducedPatternDirectoryPath
|
|
= std::filesystem::path(simulationJobFolderPath).append("Reduced");
|
|
if (!std::filesystem::exists(reducedPatternDirectoryPath)) {
|
|
std::filesystem::create_directory(reducedPatternDirectoryPath);
|
|
}
|
|
pReducedPatternSimulationJob->save(reducedPatternDirectoryPath.string());
|
|
}
|
|
}
|
|
|
|
//save minima info
|
|
std::filesystem::path csvFilepathMinimaInfo = std::filesystem::path(saveToPath)
|
|
.append("minimaInfo.csv");
|
|
csvFile csv_minimaInfo(csvFilepathMinimaInfo, false);
|
|
writeMinimaInfoTo(csv_minimaInfo);
|
|
|
|
settings.save(saveToPath);
|
|
|
|
#ifdef POLYSCOPE_DEFINED
|
|
std::cout << "Saved optimization results to:" << saveToPath << std::endl;
|
|
#endif
|
|
}
|
|
|
|
bool load(const std::string &loadFromPath, const bool &shouldLoadDebugFiles = false)
|
|
{
|
|
assert(std::filesystem::is_directory(loadFromPath));
|
|
std::filesystem::path jsonFilepath(
|
|
std::filesystem::path(loadFromPath).append(JsonKeys::filename));
|
|
if (!std::filesystem::exists(jsonFilepath)) {
|
|
std::cerr << "Input path does not exist:" << loadFromPath << std::endl;
|
|
return false;
|
|
}
|
|
//Load optimal X
|
|
nlohmann::json json_optimizationResults;
|
|
std::ifstream ifs(std::filesystem::path(loadFromPath).append(JsonKeys::filename));
|
|
ifs >> json_optimizationResults;
|
|
|
|
label = json_optimizationResults.at(JsonKeys::Label);
|
|
std::string optimizationVariablesString = *json_optimizationResults.find(
|
|
JsonKeys::optimizationVariables);
|
|
std::string optimizationVariablesDelimeter = ",";
|
|
size_t pos = 0;
|
|
std::vector<std::string> optimizationVariablesNames;
|
|
while ((pos = optimizationVariablesString.find(optimizationVariablesDelimeter))
|
|
!= std::string::npos) {
|
|
const std::string variableName = optimizationVariablesString.substr(0, pos);
|
|
optimizationVariablesNames.push_back(variableName);
|
|
optimizationVariablesString.erase(0, pos + optimizationVariablesDelimeter.length());
|
|
}
|
|
optimizationVariablesNames.push_back(optimizationVariablesString); //add last variable name
|
|
|
|
optimalXNameValuePairs.resize(optimizationVariablesNames.size());
|
|
for (int xVariable_index = 0; xVariable_index < optimizationVariablesNames.size();
|
|
xVariable_index++) {
|
|
const std::string xVariable_name = optimizationVariablesNames[xVariable_index];
|
|
const double xVariable_value = *json_optimizationResults.find(xVariable_name);
|
|
optimalXNameValuePairs[xVariable_index] = std::make_pair(xVariable_name,
|
|
xVariable_value);
|
|
}
|
|
|
|
const std::string fullPatternLabel = json_optimizationResults.at(
|
|
JsonKeys::FullPatternLabel);
|
|
baseTriangleFullPattern.load(
|
|
std::filesystem::path(loadFromPath).append(fullPatternLabel + ".ply").string());
|
|
|
|
std::vector<double> baseTriangleVertices = json_optimizationResults.at(
|
|
JsonKeys::baseTriangle);
|
|
baseTriangle.P0(0) = CoordType(baseTriangleVertices[0],
|
|
baseTriangleVertices[1],
|
|
baseTriangleVertices[2]);
|
|
baseTriangle.P1(0) = CoordType(baseTriangleVertices[3],
|
|
baseTriangleVertices[4],
|
|
baseTriangleVertices[5]);
|
|
baseTriangle.P2(0) = CoordType(baseTriangleVertices[6],
|
|
baseTriangleVertices[7],
|
|
baseTriangleVertices[8]);
|
|
if (json_optimizationResults.contains(JsonKeys::fullPatternYoungsModulus)) {
|
|
fullPatternYoungsModulus = json_optimizationResults.at(
|
|
JsonKeys::fullPatternYoungsModulus);
|
|
} else {
|
|
fullPatternYoungsModulus = 1 * 1e9;
|
|
}
|
|
if (shouldLoadDebugFiles) {
|
|
const std::filesystem::path simulationJobsFolderPath(
|
|
std::filesystem::path(loadFromPath).append("SimulationJobs"));
|
|
|
|
std::vector<std::filesystem::path> sortedByName;
|
|
for (auto &entry : std::filesystem::directory_iterator(simulationJobsFolderPath))
|
|
sortedByName.push_back(entry.path());
|
|
|
|
std::sort(sortedByName.begin(), sortedByName.end(), &Utilities::compareNat);
|
|
|
|
for (const auto &simulationScenarioPath : sortedByName) {
|
|
if (!std::filesystem::is_directory(simulationScenarioPath)) {
|
|
continue;
|
|
}
|
|
// Load full pattern files
|
|
for (const auto &fileEntry : std::filesystem::directory_iterator(
|
|
std::filesystem::path(simulationScenarioPath).append("Full"))) {
|
|
const auto filepath = fileEntry.path();
|
|
if (filepath.extension() == ".json") {
|
|
SimulationJob job;
|
|
job.load(filepath.string());
|
|
job.pMesh->setBeamMaterial(0.3, fullPatternYoungsModulus);
|
|
fullPatternSimulationJobs.push_back(std::make_shared<SimulationJob>(job));
|
|
}
|
|
}
|
|
|
|
const auto fullJobFilepath = Utilities::getFilepathWithExtension(
|
|
std::filesystem::path(simulationScenarioPath).append("Full"), ".json");
|
|
SimulationJob fullJob;
|
|
fullJob.load(fullJobFilepath.string());
|
|
fullJob.pMesh->setBeamMaterial(0.3, fullPatternYoungsModulus);
|
|
fullPatternSimulationJobs.push_back(std::make_shared<SimulationJob>(fullJob));
|
|
|
|
const auto reducedJobFilepath = Utilities::getFilepathWithExtension(
|
|
std::filesystem::path(simulationScenarioPath).append("Reduced"), ".json");
|
|
SimulationJob reducedJob;
|
|
reducedJob.load(reducedJobFilepath.string());
|
|
// applyOptimizationResults_innerHexagon(*this, baseTriangle, *job.pMesh);
|
|
applyOptimizationResults_elements(*this, reducedJob.pMesh);
|
|
reducedPatternSimulationJobs.push_back(
|
|
std::make_shared<SimulationJob>(reducedJob));
|
|
}
|
|
}
|
|
settings.load(std::filesystem::path(loadFromPath).append(Settings::defaultFilename));
|
|
|
|
return true;
|
|
}
|
|
|
|
template<typename MeshType>
|
|
static void applyOptimizationResults_innerHexagon(
|
|
const ReducedModelOptimization::Results &reducedPattern_optimizationResults,
|
|
const vcg::Triangle3<double> &patternBaseTriangle,
|
|
MeshType &reducedPattern)
|
|
{
|
|
std::unordered_map<std::string, double>
|
|
optimalXVariables(reducedPattern_optimizationResults.optimalXNameValuePairs.begin(),
|
|
reducedPattern_optimizationResults.optimalXNameValuePairs.end());
|
|
assert(
|
|
(optimalXVariables.contains("R") && optimalXVariables.contains("Theta"))
|
|
|| (optimalXVariables.contains("HexSize") && optimalXVariables.contains("HexAngle")));
|
|
if (optimalXVariables.contains("HexSize")) {
|
|
applyOptimizationResults_innerHexagon(optimalXVariables.at("HexSize"),
|
|
optimalXVariables.at("HexAngle"),
|
|
patternBaseTriangle,
|
|
reducedPattern);
|
|
return;
|
|
}
|
|
applyOptimizationResults_innerHexagon(optimalXVariables.at("R"),
|
|
optimalXVariables.at("Theta"),
|
|
patternBaseTriangle,
|
|
reducedPattern);
|
|
}
|
|
|
|
template<typename MeshType>
|
|
static void applyOptimizationResults_innerHexagon(
|
|
const double &hexSize,
|
|
const double &hexAngle,
|
|
const vcg::Triangle3<double> &patternBaseTriangle,
|
|
MeshType &reducedPattern)
|
|
{
|
|
//Set optimal geometrical params of the reduced pattern
|
|
// CoordType center_barycentric(1, 0, 0);
|
|
// CoordType interfaceEdgeMiddle_barycentric(0, 0.5, 0.5);
|
|
// CoordType movableVertex_barycentric(
|
|
// (center_barycentric * (1 - hexSize) + interfaceEdgeMiddle_barycentric));
|
|
CoordType movableVertex_barycentric(1 - hexSize, hexSize / 2, hexSize / 2);
|
|
|
|
reducedPattern.vert[0].P() = patternBaseTriangle.cP(0) * movableVertex_barycentric[0]
|
|
+ patternBaseTriangle.cP(1) * movableVertex_barycentric[1]
|
|
+ patternBaseTriangle.cP(2) * movableVertex_barycentric[2];
|
|
if (hexAngle != 0) {
|
|
reducedPattern.vert[0].P() = vcg::RotationMatrix(CoordType{0, 0, 1},
|
|
vcg::math::ToRad(hexAngle))
|
|
* reducedPattern.vert[0].cP();
|
|
}
|
|
// for (int rotationCounter = 0;
|
|
// rotationCounter < ReducedModelOptimizer::fanSize; rotationCounter++) {
|
|
// pReducedPatternSimulationMesh->vert[2 * rotationCounter].P()
|
|
// = vcg::RotationMatrix(ReducedModelOptimizer::patternPlaneNormal,
|
|
// vcg::math::ToRad(60.0 * rotationCounter))
|
|
// * baseTriangleMovableVertexPosition;
|
|
// }
|
|
// reducedPattern.registerForDrawing();
|
|
// polyscope::show();
|
|
// CoordType p0 = reducedPattern.vert[0].P();
|
|
// CoordType p1 = reducedPattern.vert[1].P();
|
|
// int i = 0;
|
|
// i++;
|
|
}
|
|
|
|
static void applyOptimizationResults_elements(
|
|
const ReducedModelOptimization::Results &reducedPattern_optimizationResults,
|
|
const std::shared_ptr<SimulationMesh> &pReducedPattern_simulationMesh)
|
|
{
|
|
assert(CrossSectionType::name == RectangularBeamDimensions::name);
|
|
// Set reduced parameters fron the optimization results
|
|
std::unordered_map<std::string, double>
|
|
optimalXVariables(reducedPattern_optimizationResults.optimalXNameValuePairs.begin(),
|
|
reducedPattern_optimizationResults.optimalXNameValuePairs.end());
|
|
|
|
const std::string ALabel = "A";
|
|
if (optimalXVariables.contains(ALabel)) {
|
|
const double A = optimalXVariables.at(ALabel);
|
|
const double beamWidth = std::sqrt(A);
|
|
const double beamHeight = beamWidth;
|
|
CrossSectionType elementDimensions(beamWidth, beamHeight);
|
|
for (int ei = 0; ei < pReducedPattern_simulationMesh->EN(); ei++) {
|
|
Element &e = pReducedPattern_simulationMesh->elements[ei];
|
|
e.setDimensions(elementDimensions);
|
|
}
|
|
}
|
|
|
|
const double poissonsRatio = 0.3;
|
|
const std::string ymLabel = "E";
|
|
if (optimalXVariables.contains(ymLabel)) {
|
|
const double E = optimalXVariables.at(ymLabel);
|
|
const ElementMaterial elementMaterial(poissonsRatio, E);
|
|
for (int ei = 0; ei < pReducedPattern_simulationMesh->EN(); ei++) {
|
|
Element &e = pReducedPattern_simulationMesh->elements[ei];
|
|
e.setMaterial(elementMaterial);
|
|
}
|
|
}
|
|
|
|
const std::string JLabel = "J";
|
|
if (optimalXVariables.contains(JLabel)) {
|
|
const double J = optimalXVariables.at(JLabel);
|
|
for (int ei = 0; ei < pReducedPattern_simulationMesh->EN(); ei++) {
|
|
Element &e = pReducedPattern_simulationMesh->elements[ei];
|
|
e.dimensions.inertia.J = J;
|
|
}
|
|
}
|
|
|
|
const std::string I2Label = "I2";
|
|
if (optimalXVariables.contains(I2Label)) {
|
|
const double I2 = optimalXVariables.at(I2Label);
|
|
for (int ei = 0; ei < pReducedPattern_simulationMesh->EN(); ei++) {
|
|
Element &e = pReducedPattern_simulationMesh->elements[ei];
|
|
e.dimensions.inertia.I2 = I2;
|
|
}
|
|
}
|
|
|
|
const std::string I3Label = "I3";
|
|
if (optimalXVariables.contains(I3Label)) {
|
|
const double I3 = optimalXVariables.at(I3Label);
|
|
for (int ei = 0; ei < pReducedPattern_simulationMesh->EN(); ei++) {
|
|
Element &e = pReducedPattern_simulationMesh->elements[ei];
|
|
e.dimensions.inertia.I3 = I3;
|
|
}
|
|
}
|
|
pReducedPattern_simulationMesh->reset();
|
|
}
|
|
|
|
#if POLYSCOPE_DEFINED
|
|
void draw() const {
|
|
PolyscopeInterface::init();
|
|
DRMSimulationModel drmSimulator;
|
|
LinearSimulationModel linearSimulator;
|
|
assert(fullPatternSimulationJobs.size() == reducedPatternSimulationJobs.size());
|
|
fullPatternSimulationJobs[0]->pMesh->registerForDrawing(Colors::fullInitial);
|
|
reducedPatternSimulationJobs[0]->pMesh->registerForDrawing(Colors::reducedInitial, false);
|
|
|
|
const int numberOfSimulationJobs = fullPatternSimulationJobs.size();
|
|
for (int simulationJobIndex = 0; simulationJobIndex < numberOfSimulationJobs;
|
|
simulationJobIndex++) {
|
|
// Drawing of full pattern results
|
|
const std::shared_ptr<SimulationJob> &pFullPatternSimulationJob
|
|
= fullPatternSimulationJobs[simulationJobIndex];
|
|
pFullPatternSimulationJob->registerForDrawing(
|
|
fullPatternSimulationJobs[0]->pMesh->getLabel());
|
|
DRMSimulationModel::Settings drmSettings;
|
|
SimulationResults fullModelResults
|
|
= drmSimulator.executeSimulation(pFullPatternSimulationJob, drmSettings);
|
|
fullModelResults.registerForDrawing(Colors::fullDeformed, true);
|
|
// SimulationResults fullModelLinearResults =
|
|
// linearSimulator.executeSimulation(pFullPatternSimulationJob);
|
|
// fullModelLinearResults.setLabelPrefix("linear");
|
|
// fullModelLinearResults.registerForDrawing(Colors::fullDeformed,false);
|
|
// Drawing of reduced pattern results
|
|
const std::shared_ptr<SimulationJob> &pReducedPatternSimulationJob
|
|
= reducedPatternSimulationJobs[simulationJobIndex];
|
|
// pReducedPatternSimulationJob->pMesh->registerForDrawing();
|
|
// polyscope::show();
|
|
// SimulationResults reducedModelResults = drmSimulator.executeSimulation(
|
|
// pReducedPatternSimulationJob);
|
|
// reducedModelResults.registerForDrawing(Colors::reducedDeformed, false);
|
|
// SimulationResults reducedModelResults
|
|
// = drmSimulator.executeSimulation(pReducedPatternSimulationJob,
|
|
// DRMSimulationModel::Settings());
|
|
SimulationResults reducedModelResults = linearSimulator.executeSimulation(
|
|
pReducedPatternSimulationJob);
|
|
reducedModelResults.setLabelPrefix("linear");
|
|
reducedModelResults.registerForDrawing(Colors::reducedDeformed, true);
|
|
polyscope::options::programName = fullPatternSimulationJobs[0]->pMesh->getLabel();
|
|
// polyscope::view::resetCameraToHomeView();
|
|
polyscope::show();
|
|
// Save a screensh
|
|
const std::string screenshotFilename
|
|
= "/home/iason/Coding/Projects/Approximating shapes with flat "
|
|
"patterns/RodModelOptimizationForPatterns/Results/Images/"
|
|
+ fullPatternSimulationJobs[0]->pMesh->getLabel() + "_"
|
|
+ pFullPatternSimulationJob->getLabel();
|
|
polyscope::screenshot(screenshotFilename, false);
|
|
pFullPatternSimulationJob->unregister(fullPatternSimulationJobs[0]->pMesh->getLabel());
|
|
fullModelResults.unregister();
|
|
// reducedModelResults.unregister();
|
|
reducedModelResults.unregister();
|
|
// fullModelLinearResults.unregister();
|
|
// double error = computeError(reducedModelLinearResults.displacements,
|
|
// fullModelResults.displacements);
|
|
// std::cout << "Error of simulation scenario "
|
|
// << simulationScenarioStrings[simulationScenarioIndex] << " is " << error
|
|
// << std::endl;
|
|
}
|
|
}
|
|
#endif // POLYSCOPE_DEFINED
|
|
void saveMeshFiles() const {
|
|
const int numberOfSimulationJobs = fullPatternSimulationJobs.size();
|
|
assert(numberOfSimulationJobs != 0 &&
|
|
fullPatternSimulationJobs.size() ==
|
|
reducedPatternSimulationJobs.size());
|
|
|
|
fullPatternSimulationJobs[0]->pMesh->save(
|
|
"undeformed " + fullPatternSimulationJobs[0]->pMesh->getLabel() + ".ply");
|
|
reducedPatternSimulationJobs[0]->pMesh->save(
|
|
"undeformed " + reducedPatternSimulationJobs[0]->pMesh->getLabel() + ".ply");
|
|
DRMSimulationModel simulator_drm;
|
|
LinearSimulationModel simulator_linear;
|
|
for (int simulationJobIndex = 0; simulationJobIndex < numberOfSimulationJobs;
|
|
simulationJobIndex++) {
|
|
// Drawing of full pattern results
|
|
const std::shared_ptr<SimulationJob> &pFullPatternSimulationJob
|
|
= fullPatternSimulationJobs[simulationJobIndex];
|
|
DRMSimulationModel::Settings drmSettings;
|
|
SimulationResults fullModelResults
|
|
= simulator_drm.executeSimulation(pFullPatternSimulationJob, drmSettings);
|
|
fullModelResults.saveDeformedModel();
|
|
|
|
// Drawing of reduced pattern results
|
|
const std::shared_ptr<SimulationJob> &pReducedPatternSimulationJob
|
|
= reducedPatternSimulationJobs[simulationJobIndex];
|
|
SimulationResults reducedModelResults = simulator_linear.executeSimulation(
|
|
pReducedPatternSimulationJob);
|
|
reducedModelResults.saveDeformedModel();
|
|
}
|
|
}
|
|
|
|
void writeHeaderTo(csvFile &os) const
|
|
{
|
|
if (exportSettings.exportRawObjectiveValue) {
|
|
os << "Total raw Obj value";
|
|
}
|
|
os << "Total Obj value";
|
|
if (exportSettings.exportIterationOfMinima) {
|
|
os << "Iteration of minima";
|
|
}
|
|
for (int simulationScenarioIndex = 0;
|
|
simulationScenarioIndex < fullPatternSimulationJobs.size();
|
|
simulationScenarioIndex++) {
|
|
const std::string simulationScenarioName
|
|
= fullPatternSimulationJobs[simulationScenarioIndex]->getLabel();
|
|
os << "Obj value Trans " + simulationScenarioName;
|
|
os << "Obj value Rot " + simulationScenarioName;
|
|
os << "Obj value Total " + simulationScenarioName;
|
|
}
|
|
|
|
if (exportSettings.exportPE) {
|
|
for (int simulationScenarioIndex = 0;
|
|
simulationScenarioIndex < fullPatternSimulationJobs.size();
|
|
simulationScenarioIndex++) {
|
|
const std::string simulationScenarioName
|
|
= fullPatternSimulationJobs[simulationScenarioIndex]->getLabel();
|
|
os << "PE " + simulationScenarioName;
|
|
}
|
|
}
|
|
for (const auto &nameValuePair : optimalXNameValuePairs) {
|
|
os << nameValuePair.first;
|
|
}
|
|
os << "Time(s)";
|
|
// os << "#Crashes";
|
|
// os << "notes";
|
|
}
|
|
|
|
void writeHeaderTo(std::vector<csvFile *> &vectorOfPointersToOutputStreams) const
|
|
{
|
|
for (csvFile *outputStream : vectorOfPointersToOutputStreams) {
|
|
writeHeaderTo(*outputStream);
|
|
}
|
|
}
|
|
|
|
void writeResultsTo(csvFile &os) const
|
|
{
|
|
if (exportSettings.exportRawObjectiveValue) {
|
|
os << objectiveValue.totalRaw;
|
|
}
|
|
os << objectiveValue.total;
|
|
if (exportSettings.exportIterationOfMinima && !objectiveValueHistory_iteration.empty()) {
|
|
os << objectiveValueHistory_iteration.back();
|
|
}
|
|
for (int simulationScenarioIndex = 0;
|
|
simulationScenarioIndex < fullPatternSimulationJobs.size();
|
|
simulationScenarioIndex++) {
|
|
os << objectiveValue.perSimulationScenario_translational[simulationScenarioIndex];
|
|
os << objectiveValue.perSimulationScenario_rotational[simulationScenarioIndex];
|
|
os << objectiveValue.perSimulationScenario_total[simulationScenarioIndex];
|
|
}
|
|
if (exportSettings.exportPE) {
|
|
for (int simulationScenarioIndex = 0;
|
|
simulationScenarioIndex < fullPatternSimulationJobs.size();
|
|
simulationScenarioIndex++) {
|
|
os << perScenario_fullPatternPotentialEnergy[simulationScenarioIndex];
|
|
}
|
|
}
|
|
for (const auto &optimalXNameValuePair : optimalXNameValuePairs) {
|
|
os << optimalXNameValuePair.second;
|
|
}
|
|
|
|
os << time;
|
|
// if (numberOfSimulationCrashes == 0) {
|
|
// os << "-";
|
|
// } else {
|
|
// os << numberOfSimulationCrashes;
|
|
// }
|
|
// os << notes;
|
|
}
|
|
|
|
void writeResultsTo(std::vector<csvFile *> &vectorOfPointersToOutputStreams) const
|
|
{
|
|
for (csvFile *&outputStream : vectorOfPointersToOutputStreams) {
|
|
writeResultsTo(*outputStream);
|
|
}
|
|
}
|
|
|
|
void writeMinimaInfoTo(csvFile &outputCsv)
|
|
{
|
|
outputCsv << "Iteration";
|
|
outputCsv << "Objective value";
|
|
for (int objectiveValueIndex = 0; objectiveValueIndex < objectiveValueHistory.size();
|
|
objectiveValueIndex++) {
|
|
outputCsv.endrow();
|
|
outputCsv << objectiveValueHistory_iteration[objectiveValueIndex];
|
|
outputCsv << objectiveValueHistory[objectiveValueIndex];
|
|
}
|
|
}
|
|
};
|
|
enum SimulationModelTag { DRM, Linear };
|
|
|
|
} // namespace ReducedPatternOptimization
|
|
#endif // REDUCEDMODELOPTIMIZER_STRUCTS_HPP
|