#include <py_engine.h>

Inheritance diagram for PyDynamicEngine:

Public Member Functions
const std::vector< fourdst::atomic::Species > &	getNetworkSpecies () const override
	PyDynamicEngine Implementation ///.

std::expected< gridfire::StepDerivatives< double >, gridfire::expectations::StaleEngineError >	calculateRHSAndEnergy (const std::vector< double > &Y, double T9, double rho) const override
	Calculate the right-hand side (dY/dt) and energy generation.

void	generateJacobianMatrix (const std::vector< double > &Y_dynamic, double T9, double rho) const override
	Generate the Jacobian matrix for the current state.

void	generateJacobianMatrix (const std::vector< double > &Y_dynamic, double T9, double rho, const gridfire::SparsityPattern &sparsityPattern) const override

double	getJacobianMatrixEntry (int i, int j) const override
	Get an entry from the previously generated Jacobian matrix.

void	generateStoichiometryMatrix () override
	Generate the stoichiometry matrix for the network.

int	getStoichiometryMatrixEntry (int speciesIndex, int reactionIndex) const override
	Get an entry from the stoichiometry matrix.

double	calculateMolarReactionFlow (const gridfire::reaction::Reaction &reaction, const std::vector< double > &Y, double T9, double rho) const override
	Calculate the molar reaction flow for a given reaction.

const gridfire::reaction::LogicalReactionSet &	getNetworkReactions () const override
	Get the set of logical reactions in the network.

void	setNetworkReactions (const gridfire::reaction::LogicalReactionSet &reactions) override

std::expected< std::unordered_map< fourdst::atomic::Species, double >, gridfire::expectations::StaleEngineError >	getSpeciesTimescales (const std::vector< double > &Y, double T9, double rho) const override
	Compute timescales for all species in the network.

std::expected< std::unordered_map< fourdst::atomic::Species, double >, gridfire::expectations::StaleEngineError >	getSpeciesDestructionTimescales (const std::vector< double > &Y, double T9, double rho) const override

fourdst::composition::Composition	update (const gridfire::NetIn &netIn) override
	Update the internal state of the engine.

bool	isStale (const gridfire::NetIn &netIn) override

void	setScreeningModel (gridfire::screening::ScreeningType model) override
	Set the electron screening model.

gridfire::screening::ScreeningType	getScreeningModel () const override
	Get the current electron screening model.

int	getSpeciesIndex (const fourdst::atomic::Species &species) const override
	Get the index of a species in the network.

std::vector< double >	mapNetInToMolarAbundanceVector (const gridfire::NetIn &netIn) const override
	Map a NetIn object to a vector of molar abundances.

gridfire::PrimingReport	primeEngine (const gridfire::NetIn &netIn) override
	Prime the engine with initial conditions.

gridfire::BuildDepthType	getDepth () const override
	Get the depth of the network.

void	rebuild (const fourdst::composition::Composition &comp, gridfire::BuildDepthType depth) override
	Rebuild the network with a specified depth.

Public Member Functions inherited from gridfire::Engine
virtual	~Engine ()=default
	Virtual destructor.

Private Attributes
std::vector< fourdst::atomic::Species >	m_species_cache

Member Function Documentation

◆ calculateMolarReactionFlow()

double PyDynamicEngine::calculateMolarReactionFlow	(	const gridfire::reaction::Reaction &	reaction,
		const std::vector< double > &	Y,
		double	T9,
		double	rho ) const

overridevirtual

Calculate the molar reaction flow for a given reaction.

Parameters

reaction	The reaction for which to calculate the flow.
Y	Vector of current abundances.
T9	Temperature in units of 10^9 K.
rho	Density in g/cm^3.

Returns: Molar flow rate for the reaction (e.g., mol/g/s).

This method computes the net rate at which the given reaction proceeds under the current state.

Implements gridfire::DynamicEngine.

◆ calculateRHSAndEnergy()

std::expected< gridfire::StepDerivatives< double >, gridfire::expectations::StaleEngineError > PyDynamicEngine::calculateRHSAndEnergy	(	const std::vector< double > &	Y,
		double	T9,
		double	rho ) const

overridevirtual

Calculate the right-hand side (dY/dt) and energy generation.

Parameters

Y	Vector of current abundances for all species.
T9	Temperature in units of 10^9 K.
rho	Density in g/cm^3.

Returns: StepDerivatives<double> containing dY/dt and energy generation rate.

This function must be implemented by derived classes to compute the time derivatives of all species and the specific nuclear energy generation rate for the current state.

Implements gridfire::Engine.

◆ generateJacobianMatrix() [1/2]

void PyDynamicEngine::generateJacobianMatrix	(	const std::vector< double > &	Y_dynamic,
		double	T9,
		double	rho ) const

overridevirtual

Generate the Jacobian matrix for the current state.

Parameters

Y_dynamic	Vector of current abundances.
T9	Temperature in units of 10^9 K.
rho	Density in g/cm^3.

This method must compute and store the Jacobian matrix (∂(dY/dt)_i/∂Y_j) for the current state. The matrix can then be accessed via getJacobianMatrixEntry().

Implements gridfire::DynamicEngine.

◆ generateJacobianMatrix() [2/2]

void PyDynamicEngine::generateJacobianMatrix	(	const std::vector< double > &	Y_dynamic,
		double	T9,
		double	rho,
		const gridfire::SparsityPattern &	sparsityPattern ) const

overridevirtual

Reimplemented from gridfire::DynamicEngine.

◆ generateStoichiometryMatrix()

void PyDynamicEngine::generateStoichiometryMatrix ( )

overridevirtual

Generate the stoichiometry matrix for the network.

This method must compute and store the stoichiometry matrix, which encodes the net change of each species in each reaction.

Implements gridfire::DynamicEngine.

◆ getDepth()

gridfire::BuildDepthType PyDynamicEngine::getDepth ( ) const

inlineoverridevirtual

Get the depth of the network.

Returns: The depth of the network, which may indicate the level of detail or complexity in the reaction network.

This method is intended to provide information about the network's structure, such as how many layers of reactions or species are present. It can be useful for diagnostics and understanding the network's complexity.

Reimplemented from gridfire::DynamicEngine.

◆ getJacobianMatrixEntry()

double PyDynamicEngine::getJacobianMatrixEntry	(	int	i,
		int	j ) const

overridevirtual

Get an entry from the previously generated Jacobian matrix.

Parameters

i	Row index (species index).
j	Column index (species index).

Returns: Value of the Jacobian matrix at (i, j).

The Jacobian must have been generated by generateJacobianMatrix() before calling this.

Implements gridfire::DynamicEngine.

◆ getNetworkReactions()

const gridfire::reaction::LogicalReactionSet & PyDynamicEngine::getNetworkReactions ( ) const

overridevirtual

Get the set of logical reactions in the network.

Returns: Reference to the LogicalReactionSet containing all reactions.

Implements gridfire::DynamicEngine.

◆ getNetworkSpecies()

const std::vector< fourdst::atomic::Species > & PyDynamicEngine::getNetworkSpecies ( ) const

overridevirtual

PyDynamicEngine Implementation ///.

Implements gridfire::Engine.

◆ getScreeningModel()

gridfire::screening::ScreeningType PyDynamicEngine::getScreeningModel ( ) const

overridevirtual

Get the current electron screening model.

Returns: The currently active screening model type.

Usage Example:: screening::ScreeningType currentModel = myEngine.getScreeningModel();

Implements gridfire::DynamicEngine.

◆ getSpeciesDestructionTimescales()

std::expected< std::unordered_map< fourdst::atomic::Species, double >, gridfire::expectations::StaleEngineError > PyDynamicEngine::getSpeciesDestructionTimescales	(	const std::vector< double > &	Y,
		double	T9,
		double	rho ) const

overridevirtual

Implements gridfire::DynamicEngine.

◆ getSpeciesIndex()

int PyDynamicEngine::getSpeciesIndex ( const fourdst::atomic::Species & species ) const

overridevirtual

Get the index of a species in the network.

Parameters

species The species to look up.

This method allows querying the index of a specific species in the engine's internal representation. It is useful for accessing species data efficiently.

Implements gridfire::DynamicEngine.

◆ getSpeciesTimescales()

std::expected< std::unordered_map< fourdst::atomic::Species, double >, gridfire::expectations::StaleEngineError > PyDynamicEngine::getSpeciesTimescales	(	const std::vector< double > &	Y,
		double	T9,
		double	rho ) const

overridevirtual

Compute timescales for all species in the network.

Parameters

Y	Vector of current abundances.
T9	Temperature in units of 10^9 K.
rho	Density in g/cm^3.

Returns: Map from Species to their characteristic timescales (s).

This method estimates the timescale for abundance change of each species, which can be used for timestep control, diagnostics, and reaction network culling.

Implements gridfire::DynamicEngine.

◆ getStoichiometryMatrixEntry()

int PyDynamicEngine::getStoichiometryMatrixEntry	(	int	speciesIndex,
		int	reactionIndex ) const

overridevirtual

Get an entry from the stoichiometry matrix.

Parameters

speciesIndex	Index of the species.
reactionIndex	Index of the reaction.

Returns: Stoichiometric coefficient for the species in the reaction.

The stoichiometry matrix must have been generated by generateStoichiometryMatrix().

Implements gridfire::DynamicEngine.

◆ isStale()

bool PyDynamicEngine::isStale ( const gridfire::NetIn & netIn )

overridevirtual

Implements gridfire::DynamicEngine.

◆ mapNetInToMolarAbundanceVector()

std::vector< double > PyDynamicEngine::mapNetInToMolarAbundanceVector ( const gridfire::NetIn & netIn ) const

overridevirtual

Map a NetIn object to a vector of molar abundances.

Parameters

netIn The input conditions for the network.

Returns: A vector of molar abundances corresponding to the species in the network.

This method converts the input conditions into a vector of molar abundances, which can be used for further calculations or diagnostics.

Implements gridfire::DynamicEngine.

◆ primeEngine()

gridfire::PrimingReport PyDynamicEngine::primeEngine ( const gridfire::NetIn & netIn )

overridevirtual

Prime the engine with initial conditions.

Parameters

netIn The input conditions for the network.

Returns: PrimingReport containing information about the priming process.

This method is used to prepare the engine for calculations by setting up initial conditions, reactions, and species. It may involve compiling reaction rates, initializing internal data structures, and performing any necessary pre-computation.

Implements gridfire::DynamicEngine.

◆ rebuild()

void PyDynamicEngine::rebuild	(	const fourdst::composition::Composition &	comp,
		gridfire::BuildDepthType	depth )

inlineoverridevirtual

Rebuild the network with a specified depth.

Parameters

comp	The composition to rebuild the network with.
depth	The desired depth of the network.

This method is intended to allow dynamic adjustment of the network's depth, which may involve adding or removing species and reactions based on the specified depth. However, not all engines support this operation.

Reimplemented from gridfire::DynamicEngine.

◆ setNetworkReactions()

void PyDynamicEngine::setNetworkReactions ( const gridfire::reaction::LogicalReactionSet & reactions )

overridevirtual

Implements gridfire::DynamicEngine.

◆ setScreeningModel()

void PyDynamicEngine::setScreeningModel ( gridfire::screening::ScreeningType model )

overridevirtual

Set the electron screening model.

Parameters

model The type of screening model to use for reaction rate calculations.

This method allows changing the screening model at runtime. Screening corrections account for the electrostatic shielding of nuclei by electrons, which affects reaction rates in dense stellar plasmas.

Usage Example:: myEngine.setScreeningModel(screening::ScreeningType::WEAK);

Postcondition: The engine will use the specified screening model for subsequent rate calculations.

Implements gridfire::DynamicEngine.

◆ update()

fourdst::composition::Composition PyDynamicEngine::update ( const gridfire::NetIn & netIn )

overridevirtual

Update the internal state of the engine.

Parameters

netIn A struct containing the current network input, such as temperature, density, and composition.

This method is intended to be implemented by derived classes to update their internal state based on the provided network conditions. For example, an adaptive engine might use this to re-evaluate which reactions and species are active. For other engines that do not support manually updating, this method might do nothing.

Usage Example:: NetIn input = { ... };

myEngine.update(input);

Postcondition: The internal state of the engine is updated to reflect the new conditions.

Implements gridfire::DynamicEngine.

Member Data Documentation

◆ m_species_cache

std::vector<fourdst::atomic::Species> PyDynamicEngine::m_species_cache

mutableprivate

The documentation for this class was generated from the following files:

src/python/engine/trampoline/py_engine.h
src/python/engine/trampoline/py_engine.cpp

Public Member Functions

Private Attributes

Member Function Documentation

◆ calculateMolarReactionFlow()

◆ calculateRHSAndEnergy()

◆ generateJacobianMatrix() [1/2]

◆ generateJacobianMatrix() [2/2]

◆ generateStoichiometryMatrix()

◆ getDepth()

◆ getJacobianMatrixEntry()

◆ getNetworkReactions()

◆ getNetworkSpecies()

◆ getScreeningModel()

◆ getSpeciesDestructionTimescales()

◆ getSpeciesIndex()

◆ getSpeciesTimescales()

◆ getStoichiometryMatrixEntry()

◆ isStale()

◆ mapNetInToMolarAbundanceVector()

◆ primeEngine()

◆ rebuild()

◆ setNetworkReactions()

◆ setScreeningModel()

◆ update()

Member Data Documentation

◆ m_species_cache