feat(python): added robust python bindings covering the entire codebase

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2025-07-23 16:26:30 -04:00
parent 6a22cb65b8
commit f20bffc411
134 changed files with 2202 additions and 170 deletions

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#pragma once
#include "gridfire/engine/engine_graph.h"
#include "gridfire/engine/engine_abstract.h"
#include "../engine/views/engine_adaptive.h"
#include "gridfire/network.h"
#include "fourdst/logging/logging.h"
#include "fourdst/config/config.h"
#include "quill/Logger.h"
#include <vector>
namespace gridfire::solver {
/**
* @class NetworkSolverStrategy
* @brief Abstract base class for network solver strategies.
*
* This class defines the interface for network solver strategies, which are responsible
* for integrating the reaction network ODEs over a given timestep. It is templated on the
* engine type to allow for different engine implementations to be used with the same solver.
*
* @tparam EngineT The type of engine to use with this solver strategy. Must inherit from Engine.
*/
template <typename EngineT>
class NetworkSolverStrategy {
public:
/**
* @brief Constructor for the NetworkSolverStrategy.
* @param engine The engine to use for evaluating the network.
*/
explicit NetworkSolverStrategy(EngineT& engine) : m_engine(engine) {};
/**
* @brief Virtual destructor.
*/
virtual ~NetworkSolverStrategy() = default;
/**
* @brief Evaluates the network for a given timestep.
* @param netIn The input conditions for the network.
* @return The output conditions after the timestep.
*/
virtual NetOut evaluate(const NetIn& netIn) = 0;
protected:
EngineT& m_engine; ///< The engine used by this solver strategy.
};
/**
* @brief Type alias for a network solver strategy that uses a DynamicEngine.
*/
using DynamicNetworkSolverStrategy = NetworkSolverStrategy<DynamicEngine>;
/**
* @class DirectNetworkSolver
* @brief A network solver that directly integrates the reaction network ODEs.
*
* This solver uses a Runge-Kutta method to directly integrate the reaction network
* ODEs. It is simpler than the QSENetworkSolver, but it can be less efficient for
* stiff networks with disparate timescales.
*
* @implements DynamicNetworkSolverStrategy
*/
class DirectNetworkSolver final : public DynamicNetworkSolverStrategy {
public:
/**
* @brief Constructor for the DirectNetworkSolver.
* @param engine The dynamic engine to use for evaluating the network.
*/
using DynamicNetworkSolverStrategy::DynamicNetworkSolverStrategy;
/**
* @brief Evaluates the network for a given timestep using direct integration.
* @param netIn The input conditions for the network.
* @return The output conditions after the timestep.
*/
NetOut evaluate(const NetIn& netIn) override;
private:
/**
* @struct RHSFunctor
* @brief Functor for calculating the right-hand side of the ODEs.
*
* This functor is used by the ODE solver to calculate the time derivatives of the
* species abundances. It takes the current abundances as input and returns the
* time derivatives.
*/
struct RHSManager {
DynamicEngine& m_engine; ///< The engine used to evaluate the network.
const double m_T9; ///< Temperature in units of 10^9 K.
const double m_rho; ///< Density in g/cm^3.
mutable double m_cached_time;
mutable std::optional<StepDerivatives<double>> m_cached_result;
mutable double m_last_observed_time = 0.0; ///< Last time the state was observed.
quill::Logger* m_logger = LogManager::getInstance().newFileLogger("integration.log", "GridFire"); ///< Logger instance.
mutable int m_num_steps = 0;
mutable double m_last_step_time = 1e-20;
/**
* @brief Constructor for the RHSFunctor.
* @param engine The engine used to evaluate the network.
* @param T9 Temperature in units of 10^9 K.
* @param rho Density in g/cm^3.
*/
RHSManager(
DynamicEngine& engine,
const double T9,
const double rho
) :
m_engine(engine),
m_T9(T9),
m_rho(rho),
m_cached_time(0) {}
/**
* @brief Calculates the time derivatives of the species abundances.
* @param Y Vector of current abundances.
* @param dYdt Vector to store the time derivatives.
* @param t Current time.
*/
void operator()(
const boost::numeric::ublas::vector<double>& Y,
boost::numeric::ublas::vector<double>& dYdt,
double t
) const;
void observe(const boost::numeric::ublas::vector<double>& state, double t) const;
void compute_and_cache(const boost::numeric::ublas::vector<double>& state, double t) const;
};
/**
* @struct JacobianFunctor
* @brief Functor for calculating the Jacobian matrix.
*
* This functor is used by the ODE solver to calculate the Jacobian matrix of the
* ODEs. It takes the current abundances as input and returns the Jacobian matrix.
*/
struct JacobianFunctor {
DynamicEngine& m_engine; ///< The engine used to evaluate the network.
const double m_T9; ///< Temperature in units of 10^9 K.
const double m_rho; ///< Density in g/cm^3.
/**
* @brief Constructor for the JacobianFunctor.
* @param engine The engine used to evaluate the network.
* @param T9 Temperature in units of 10^9 K.
* @param rho Density in g/cm^3.
*/
JacobianFunctor(
DynamicEngine& engine,
const double T9,
const double rho
) :
m_engine(engine),
m_T9(T9),
m_rho(rho) {}
/**
* @brief Calculates the Jacobian matrix.
* @param Y Vector of current abundances.
* @param J Matrix to store the Jacobian matrix.
* @param t Current time.
* @param dfdt Vector to store the time derivatives (not used).
*/
void operator()(
const boost::numeric::ublas::vector<double>& Y,
boost::numeric::ublas::matrix<double>& J,
double t,
boost::numeric::ublas::vector<double>& dfdt
) const;
};
private:
quill::Logger* m_logger = LogManager::getInstance().getLogger("log"); ///< Logger instance.
Config& m_config = Config::getInstance(); ///< Configuration instance.
};
}