gridfire 1.0.1

Python interface to the GridFire nuclear network code

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Introduction

GridFire is a C++ library designed to perform general nuclear network evolution. It is part of the larger SERiF project within the 4D-STAR collaboration. GridFire is primarily focused on modeling the most relevant burning stages for stellar evolution modeling. Currently, there is limited support for inverse reactions. Therefore, GridFire has a limited set of tools to evolve a fusing plasma in NSE; however, this is not the primary focus of the library and has therefore not had significant development. For those interested in modeling super nova, neutron star mergers, or other high-energy astrophysical phenomena, we strongly recommend using SkyNet.

Design Philosophy and Workflow

GridFire is architected to balance physical fidelity, computational efficiency, and extensibility when simulating complex nuclear reaction networks. Users begin by defining a composition, which is used to construct a full GraphEngine representation of the reaction network. A GraphNetwork uses JINA Reaclib reaction rates (Cyburt et al., ApJS 189 (2010) 240.) along with a dynamically constructed network topology. To manage the inherent stiffness and multiscale nature of these networks, GridFire employs a layered view strategy: partitioning algorithms isolate fast and slow processes, adaptive culling removes negligible reactions at runtime, and implicit solvers stably integrate the remaining stiff system. This modular pipeline allows researchers to tailor accuracy versus performance trade-offs, reuse common engine components, and extend screening or partitioning models without modifying core integration routines.

Funding

GridFire is a part of the 4D-STAR collaboration.

4D-STAR is funded by European Research Council (ERC) under the Horizon Europe programme (Synergy Grant agreement No. 101071505: 4D-STAR) Work for this project is funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council.

Usage

Python installation

By far the easiest way to install is with pip. This will install either pre-compiled wheels or, if your system has not had a wheel compiled for it, it will try to build locally (this may take a long time). The python bindings are just that and should maintain nearly the same speed as the C++ code. End users are strongly encouraged to use the python module rather than the C++ code.

pypi

Installing from pip is as simple as.

pip install gridfire

These wheels have been compiled on many systems

Version	Platform	Architecture	CPython Versions	PyPy Versions
0.7.0_rc1	macOS	arm64	3.8, 3.9, 3.10, 3.11, 3.12, 3.13 (std & t), 3.14 (std & t)	3.10, 3.11
0.7.0_rc1	Linux	aarch64	3.8, 3.9, 3.10, 3.11, 3.12, 3.13 (std & t), 3.14 (std & t)	3.10, 3.11
0.7.0_rc1	Linux	x86_64	3.8, 3.9, 3.10, 3.11, 3.12, 3.13 (std & t), 3.14 (std & t)	3.10, 3.11
0.5.0	macOS	arm64	3.8, 3.9, 3.10, 3.11, 3.12, 3.13 (std & t), 3.14 (std & t)	3.10, 3.11
0.5.0	Linux	aarch64	3.8, 3.9, 3.10, 3.11, 3.12, 3.13 (std & t), 3.14 (std & t)	3.10, 3.11
0.5.0	Linux	x86_64	3.8, 3.9, 3.10, 3.11, 3.12, 3.13 (std & t), 3.14 (std & t)	3.10, 3.11

Note: Currently macOS x86_64 does not have a precompiled wheel. Due to that platform being phased out it is likely that there will never be precompiled wheels or releases for it.

Note: macOS wheels were targeted to macOS 12 Monterey and should work on any version more recent than that (at least as of August 2025).

Note: Linux wheels were compiled using manylinux_2_28 and are expected to work on Debian 10+, Ubuntu 18.10+, Fedora 29+, or CentOS/RHEL 8+

Note: If your system does not have a prebuilt wheel the source distribution will download from pypi and try to build. This may simply not work if you do not have the correct system dependencies installed. If it fails the best bet is to try to build boost >= 1.83.0 from source and install (https://www.boost.org/) as that is the most common broken dependency.

source

The user may also build the python bindings directly from source

git clone https://github.com/4D-STAR/GridFire
cd GridFire
pip install .

Note: that if you do not have all system dependencies installed this will fail, the steps in further sections address these in more detail.

Note: If you are using macos you should use the included pip_install_mac_patch.sh script instead of pip install . as this will automatically patch the build shared object libraries such that they can be loaded by the macos dynamic loader.

source for developers

If you are a developer and would like an editable and incremental python install meson-python makes this very easy

git clone https://github.com/4D-STAR/GridFire
cd GridFire
pip install -e . --no-build-isolation -vv

This will generate incremental builds whenever source code changes, and you run a python script automatically (note that since meson setup must run for each of these it does still take a few seconds to recompile regardless of how small a source code change you have made). It is strongly recommended that developers use this approach and end users do not.

Patching Shared Object Files

If you need to patch shared object files generated by meson-python directly you should first locate the shared object file these will be in the site-packages and site-packages/fourdst directories for your python environment.

Look for files named

• site-packages/gridfire.cpython-3*-darwin.so
• site-packages/fourdst/_phys.cpython-3*-darwin.so

then, for each of these files, run

otool -l <Path/to/file> | grep RPATH -A2

count the number of occurrences of duplicate RPATH entries (these should look like @loaderpath/.gridfire.mesonpy.libs or @loaderpath/../.fourdst.mesonpy.libs). Then use install_name_tool to remove all but one of these from each shared object file.

If for example there are 4 occurrences of the path @loader_path/../.fourdst.mesonpy.libs in _phys.cpython-3*-darwin.so then you should run the following command 3 times

install_name_tool -delete_rpath @loader_path/../.fourdst.mesonpy.libs site-packages/fourdst/_phys.cpython-314-darwin.so

the same for the other shared object file (make sure to count the duplicate rpath entries for each separately as there may be a different number of duplicates in each shared object file).

We also include a script at pip_install_mac_patch.sh which will do this automatically for you.

Automatic Build and Installation

Currently, known good platforms

The installation script has been tested and found to work on clean installations of the following platforms:

• MacOS 15.3.2 (Apple Silicon + brew installed)
• Fedora 42.0 (aarch64)
• Ubuntu 24.04 (X86_64)

Manual Build Instructions

Required

• C++ compiler supporting C++23 standard
  • clang >= 17.0.0
  • gcc >= 14.0
• Meson build system (>= 1.5.0)
• Python 3.8 or newer
• CMake 3.20 or newer
• ninja 1.10.0 or newer
• Python packages: meson-python>=0.15.0

Note: Windows is not supported at this time and there are no plans to support it in the future. Windows users are encouraged to use WSL2 or a Linux VM.

Dependency Installation on Common Platforms

• Ubuntu/Debian:

sudo apt-get update
sudo apt install pipx pkg-config gcc-14 g++-14
pipx install meson ninja cmake
pipx ensurepath

Note: Ubuntu tends to ship with older versions of compilers, you may need to adjust the specific compiler name or add additional repositories if you are running a version of ubuntu older than 24.04 LTS

• Fedora/CentOS/RHEL:

sudo dnf install pkgconf-pkg-config meson ninja-build cmake

• Arch:

sudo pacman -Syu pkgconf meson ninja cmake

• macOS (Homebrew):

brew update
brew install pkg-config meson ninja cmake gcc

Note: macos >= 15.2.0 ships with a version of clang which works for compilation (and therefore you dont need gcc. I have not tested on versions of macos prior to this).

Building the C++ Library

meson setup build
meson compile -C build

Note: On Ubuntu (or any system which does not have a supported version of the compiler as the default) you should use

CC=gcc-14 CXX=g++-14 meson setup build
meson compile -C build

Clang vs. GCC

As noted above clang tends to compile GridFire much faster than gcc. If your system has both clang and gcc installed you may force meson to use clang via environmental variables

Note: There is an issue I have not resolved on ubuntu 24.04 where clang++-18 fails to find the expected header therefore we reccomend the use of gcc-14 and g++-14 on ubuntu 24.04

CC=clang CXX=clang++ meson setup build_clang
meson compile -C build_clang

Installing the Library

meson install -C build

Minimum compiler versions

GridFire uses C++23 features and therefore only compilers and standard library implementations which support C++23 are supported. Generally we have found that gcc >= 14.0.0 or clang >= 17.0.0 work well.

Code Architecture and Logical Flow

GridFire is organized into a series of composable modules, each responsible for a specific aspect of nuclear reaction network modeling. The core components include:

• Engine Module: Core interfaces and implementations (e.g., GraphEngine) that evaluate reaction network rate equations and energy generation. Also implemented Views submodule.
• Engine::Views Module: Composable engine optimization and modification (e.g. MultiscalePartitioningEngineView) which can be used to make a problem more tractable or applicable.
• Screening Module: Implements nuclear reaction screening corrections (e.g. WeakScreening (Salpeter, 1954), BareScreening) affecting reaction rates.
• Reaction Module: Parses and manages Reaclib reaction rate data, providing temperature- and density-dependent rate evaluations.
• Partition Module: Implements partition functions (e.g., GroundStatePartitionFunction, RauscherThielemannPartitionFunction (Rauscher & Thielemann, 2000) to weight reaction rates based on nuclear properties.
• Solver Module: Defines numerical integration strategies (e.g., CVODESolverStrategy) for solving the stiff ODE systems arising from reaction networks.
• io Module: Defines shared interface for parsing network data from files
• trigger Module: Defines interface for complex trigger logic so that repartitioning can be followed.
• Policy Module: Contains "policies" which are small modular units of code that enforce certain contracts. For example the ProtonProtonReactionChainPolicy enforces than an engine must include at least all the reactions in the proton-proton chain. This module exposes the primary construction interface for users. I.e. select a policy (such as MainSequencePolicy), provide a composition, and get back an engine which satisfies that policy.
• Python Interface: Exposes almost all C++ functionality to Python, allowing users to define compositions, configure engines, and run simulations directly from Python scripts.

Generally a user will start by selecting a base engine (currently we only offer GraphEngine), which constructs the full reaction network graph from a given composition. The user can then apply various engine views to adapt the network topology, such as partitioning fast and slow reactions, adaptively culling low-flow pathways, or priming the network with specific species. Finally, a numerical solver is selected to integrate the network over time, producing updated abundances and diagnostics.

Engines

GridFire is, at its core, based on a series of Engines. These are constructs which know how to report information on series of ODEs which need to be solved to evolve abundances. The important thing to understand about Engines is that they contain all the detailed physics GridFire uses. For example a Solver takes an Engine but does not compute physics itself. Rather, it asks the Engine for stuff like the jacobian matrix, stoichiometry, nuclear energy generation rate, and change in abundance with time.

Refer to the API documentation for the exact interface which an Engine must implement to be compatible with GridFire solvers.

Currently, we only implement GraphEngine which is intended to be a very general and adaptable Engine.

GraphEngine

In GridFire the GraphEngine will generally be the most fundamental building block of a nuclear network. A GraphEngine represents a directional hypergraph connecting some set of atomic species through reactions listed in the JINA Reaclib database.

GraphEngines are constructed from a seed composition of species from which they recursively expand their topology outward, following known reaction pathways and adding new species to the tracked list as they expand.

GraphEngine Configuration Options

GraphEngine exposes runtime configuration methods to tailor network construction and rate evaluations:

• Constructor Parameters:

  • composition: The initial seed composition to start network construction from.
  • BuildDepthType (Full, Shallow, SecondOrder, etc...): controls number of recursions used to construct the network topology. Can either be a member of the NetworkBuildDepth enum or an integer.
  • partition::PartitionFunction: Partition function used when evaluating detailed balance for inverse rates.
  • NetworkConstructionFlags: A bitwise flag telling the network how to construct itself. That is, what reaction types should be used in construction. For example one might use NetworkConstructionFlags::STRONG | NetworkConstructionFlags::BETA_PLUS to use all strong reactions and β+ decay. By Default this is set to use reaclib strong and reaclib weak (no WRL included by default due to current pathological stiffness issues).

• setPrecomputation(bool precompute):

  • Enable/disable caching of reaction rates and stoichiometric data at initialization.
  • Effect: Reduces per-step overhead; increases memory and setup time.

• setScreeningModel(ScreeningType type):

  • Choose plasma screening (models: BARE, WEAK).
  • Effect: Alters rate enhancement under dense/low-T conditions, impacting stiffness.

• setUseReverseReactions(bool useReverse):

  • Toggle inclusion of reverse (detailed balance) reactions.
  • Effect: Improves equilibrium fidelity; increases network size and stiffness.

Available Partition Functions

Function Name	Identifier / Enum	Description
GroundStatePartitionFunction	"GroundState"	Weights using nuclear ground-state spin factors.
RauscherThielemannPartitionFunction	"RauscherThielemann"	Interpolates normalized g-factors per Rauscher & Thielemann.
CompositePartitionFunction	"Composite"	Combines multiple partition functions for situations where different partitions functions are used for different domains

AutoDiff

One of the primary tasks any engine must accomplish is to report the jacobian matrix of the system to the solver. GraphEngine uses CppAD, a C++ auto differentiation library, to generate analytic jacobian matrices very efficiently.

Reaclib in GridFire

All reactions in JINA Reaclib which only include reactants iron and lighter were downloaded on June 17th, 2025 where the most recent documented change on the JINA Reaclib site was on June 24th, 2021.

All of these reactions have been compiled into a header file which is then statically compiled into the gridfire binaries (specifically into lib_reaction_reaclib.cpp.o). This does increase the binary size by a few MB; however, the benefit is faster load times and more importantly no need for end users to manage resource files.

If a developer wants to add new reaclib reactions we include a script at utils/reaclib/format.py which can ingest a reaclib data file and produce the needed header file. More details on this process are included in utils/reaclib/readme.md

Engine Views

The GridFire engine supports multiple engine view strategies to adapt or restrict network topology. Generally when extending GridFire the approach is likely to be one of adding new EngineViews.

View Name	Purpose	Algorithm / Reference	When to Use
DefinedEngineView	Restricts the network to a user-specified subset of species and reactions	Static network masking based on user-provided species/reaction lists	Targeted pathway studies or code-to-code comparisons
FileDefinedEngineView	Load a defined engine view from a file using some parser	Same as DefinedEngineView but loads from a file	Same as DefinedEngineView
MultiscalePartitioningEngineView	Partitions the network into fast and slow subsets based on reaction timescales	Network partitioning following Hix & Thielemann Silicon Burning I & II (DOI:10.1086/177016,10.1086/306692)	Stiff, multi-scale networks requiring tailored integration
NetworkPrimingEngineView	Primes the network with an initial species or set of species for ignition studies	Single-species ignition and network priming	Investigations of ignition triggers or initial seed sensitivities

These engine views implement the common Engine interface and may be composed in any order to build complex network pipelines. New view types can be added by deriving from the EngineView base class, and linked into the composition chain without modifying core engine code.

A Note about composability

There are certain functions for which it is expected that a call to an engine view will propagate the result down the chain of engine views, eventually reaching the base engine (e.g. DynamicEngine::update). We do not strongly enforce this as it is not hard to contrive a situation where that is not the mose useful behavior; however, we do strongly encourage developers to think carefully about passing along calls to base engine methods when implementing new views.

Numerical Solver Strategies

GridFire defines a flexible solver architecture through the networkfire::solver::NetworkSolverStrategy interface, enabling multiple ODE integration algorithms to be used interchangeably with any engine that implements the Engine or DynamicEngine contract.

NetworkSolverStrategy<EngineT>:

All GridFire solvers implement the abstract strategy templated by NetworkSolverStrategy which enforces only that there is some evaluate method with the following signature

NetOut evaluate(const NetIn& netIn);

Which is intended to integrate some network over some time and returns updated abundances, temperature, density, and diagnostics.

NetIn and NetOut

GridFire solvers use a unified input and output type for their public interface (though as developers will quickly learn, internally these are immediately broken down into simpler data structures). All solvers expect a NetIn struct for the input type to the evaluate method and return a NetOut struct.

NetIn

A NetIn struct contains

• The composition to start the timestep at. (NetIn::composition)
• The temperature in Kelvin (NetIn::temperature)
• The density in g/cm^3 (NetIn::density)
• The max time to evolve the network to in seconds (NetIn::tMax)
• The initial timestep to use in seconds (NetIn::dt0)
• The initial energy in the system in ergs (NetIn::energy)

Note: It is often useful to set NetIn::dt0 to something very small and let an iterative time stepper push the timestep up. Often for main sequence burning I use ~1e-12 for dt0

Note: The composition must be a fourdst::composition::Composition object. This is made available through the foursdt library and the fourdst/composition/Composition.h header. fourdst is installed automatically with GridFire

Note: In Python composition comes from fourdst.composition.Composition and similarly is installed automatically when building GridFire python bindings.

NetOut

A NetOut struct contains

• The final composition after evolving to tMax (NetOut::composition)
• The number of steps the solver took to evolve to tmax (NetOut::num_steps)
• The final specific energy generated by the network while evolving to tMax (NetOut::energy) [erg/g]
• The derivative of energy with respect to temperature at the end of the evolution (NetOut::dEps_dT)
• The derivative of energy with respect to density at the end of the evolution (NetOut::dEps_dRho)
• The total specific energy lost to neutrinos while evolving to tMax (NetOut::total_neutrino_loss) [erg/g]
• The total flux of neutrinos while evolving to tMax (NetOut::total_neutrino_flux)

PointSolver

We use the CVODE module from SUNDIALS as our primary numerical solver. Specifically we use the BDF linear multistep method from that which includes advanced adaptive timestepping.

Further, we use a trigger system to periodically repartition the network as the state of the network changes. This keeps the stiffness of the network tractable. The algorithm we use for that is

1. Trigger every 1000th time that the simulation time exceeds the simulationTimeInterval
2. OR if any off-diagonal Jacobian entry exceeds the offDiagonalThreshold
3. OR every 10th time that the timestep growth exceeds the timestepGrowthThreshold (relative or absolute)
4. OR if the number of convergence failures grows more than 100% from one step to the next or exceeds 5 at any given step.

Moreover, callback functions can be registered in either python or C++ which will take a const CVODESolverStrategy::TimestepContext& struct as argument. This allows for more complex logging logic. Note that callbacks do not let you reach inside the solver and adjust the state of the network. They are only intended for investigation not extension of physics. If you wish to extend the physics this must be implemented at the engine or engine view level.

Python Extensibility

Through the Python bindings, users can subclass engine view classes directly in Python, override methods like evaluate or generateStoichiometryMatrix, and pass instances back into C++ solvers. This enables rapid prototyping of custom view strategies without touching C++ sources.

Usage Examples

C++

GraphEngine Initialization

#include "gridfire/engine/engine_graph.h"
#include "fourdst/composition/composition.h"

int main(){
    // Define a composition and initialize the engine
    fourdst::composition::Composition comp;
    gridfire::GraphEngine engine(comp);
}

Adaptive Network View

#include "gridfire/engine/views/engine_adaptive.h"
#include "gridfire/engine/engine_graph.h"

int main(){
    fourdst::composition::Composition comp;
    gridfire::GraphEngine baseEngine(comp);
    // Dynamically adapt network topology based on reaction flows
    gridfire::AdaptiveEngineView adaptiveView(baseEngine);
}

Composition Initialization

#include "fourdst/composition/composition.h"
#include "fourdst/composition/utils.h" // for buildCompositionFromMassFractions
#include <vector>
#include <string>

#include <iostream>

int main() {

    std::vector<std::string> symbols = {"H-1", "He-4", "C-12"};
    std::vector<double> massFractions = {0.7, 0.29, 0.01};
    
    const fourdst::composition::Composition comp = fourdst::composition::buildCompositionFromMassFractions(symbols, massFractions);

    std::cout << comp << std::endl;
}

Common Workflow Example

A representative workflow often composes multiple engine views to balance accuracy, stability, and performance when integrating stiff nuclear networks:

#include "gridfire/gridfire.h" // Unified header for real usage

#include "fourdst/composition/composition.h"
#include "fourdst/composition/utils.h" // for buildCompositionFromMassFractions

int main(){
    // 1. Define initial composition
    std::unordered_map<std::string, double> initialMassFractions = {
        {"H-1", 0.7},
        {"He-4", 0.29},
        {"C-12", 0.01}
    };
    const fourdst::composition::Composition composition = fourdst::composition::buildCompositionFromMassFractions(initialMassFractions);
    
    // In this example we will not use the policy module (for sake of demonstration of what is happening under the hood)
    //  however, for end users we **strongly** recommend using the policy module to construct engines. It will
    //  ensure that you are not missing important reactions or seed species.

    // 2. Create base network engine (full reaction graph)
    gridfire::GraphEngine baseEngine(comp, NetworkBuildDepth::SecondOrder)

    // 3. Partition network into fast/slow subsets (reduces stiffness)
    gridfire::MultiscalePartitioningEngineView msView(baseEngine);

    // 4. Adaptively cull negligible flux pathways (reduces dimension & stiffness)
    gridfire::AdaptiveEngineView adaptView(msView);

    // 5. Construct implicit solver (handles remaining stiffness)
    gridfire::CVODESolverStrategey solver(adaptView);

    // 6. Prepare input conditions
    NetIn input{
        comp,     // composition
        1.5e7,      // temperature [K]
        1.5e2,      // density [g/cm^3]
        1e-12,     // initial timestep [s]
        3e17      // integration end time [s]
    };

    // 7. Execute integration
    NetOut output = solver.evaluate(input);
    std::cout << "Final results are: " << output << std::endl;
}

Callback and Policy Example

Custom callback functions can be registered with any solver. Because it might make sense for each solver to provide different context to the callback function, you should use the struct gridfire::solver::<SolverName>::TimestepContext as the argument type for the callback function. This struct contains all the information provided by that solver to the callback function.

#include "gridfire/gridfire.h" // Unified header for real usage

#include "fourdst/composition/composition.h" // for Composition
#include "fourdst/composition/utils.h" // for buildCompositionFromMassFractions
#include "fourdst/atomic/species.h" // For strongly typed species

#include <iostream>

void callback(const gridfire::solver::CVODESolverStrategy::TimestepContext& context) {
    int H1Index = context.engine.getSpeciesIndex(fourdst::atomic::H_1);
    int He4Index = context.engine.getSpeciesIndex(fourdst::atomic::He_4);

    std::cout << context.t << "," << context.state(H1Index) << "," << context.state(He4Index) << "\n";
}

int main(){
    std::vector<std::string> symbols = {"H-1", "He-4", "C-12"};
    std::vector<double> X = {0.7, 0.29, 0.01};


    const fourdst::composition::Composition composition = fourdst::composition::buildCompositionFromMassFractions(symbols, X);
    gridfire::policy::MainSequencePolicy stellarPolicy(netIn.composition);
    gridfire::engine::DynamicEngine& engine = stellarPolicy.construct();
    
    gridfire::solver::CVODESolverStrategy solver(adaptView);
    solver.set_callback(callback);

    // 6. Prepare input conditions
    gridfire::NetIn input{
        comp,     // composition
        1.5e7,      // temperature [K]
        1.5e2,      // density [g/cm^3]
        1e-12,     // initial timestep [s]
        3e17      // integration end time [s]
    };

    // 7. Execute integration
    gridfire::NetOut output = solver.evaluate(input);
    std::cout << "Final results are: " << output << std::endl;
}

Note: If you want to see exactly why each repartitioning stage was triggered in a human readable manner add the flag True to solver.evaluate (solver.evaluate(input, true)).

Note: A fully detailed list of all available information in the TimestepContext struct is available in the API documentation.

Note: The order of species in the boost state vector (ctx.state) is not guaranteed to be any particular order run over run. Therefore, in order to reliably extract values from it, you must use the getSpeciesIndex method of the engine to get the index of the species you are interested in (these will always be in the same order).

If you wish to know what is provided by a solver context without investigating the code you can simply do

void callback(const gridfire::solver::SolverContextBase& context) {
    for (const auto& [parameterName, description] : context.describe()) {
        std::cout << parameterName << ": " << description << "\n";
    }
    std::cout << std::flush();
    exit(0);
}

If you set this as the callback (to any solver strategy) it will print out the available parameters and what they are and then close the code. This is useful when writing new callbacks.

Callback Context

Since each solver may provide different context to the callback function, and it may be frustrating to refer to the documentation every time, we also enforce that all solvers must implement a descripe_callback_context method which returns a vector of tuples<string, string> where the first element is the name of the field and the second is its datatype. It is on the developer to ensure that this information is accurate.

...
std::cout << solver.describe_callback_context() << std::endl;

Python

The python bindings intentionally look very similar to the C++ code. Generally all examples can be adapted to python by replacing includes of paths with imports of modules such that

#include "gridfire/engine/GraphEngine.h" becomes import gridfire.engine.GraphEngine

All GridFire C++ types have been bound and can be passed around as one would expect.

Python Example for End Users

The syntax for registration is very similar to C++. There are a few things to note about this more robust example

1. Note how I use a callback and a log object to store the state of the simulation at each timestep.
2. If you have tools such as mypy installed you will see that the python bindings are strongly typed. This is intentional to help users avoid mistakes when writing code.

from fourdst.composition import Composition
from gridfire.type import NetIn
from gridfire.policy import MainSequencePolicy
from gridfire.solver import CVODESolverStrategy
from enum import Enum
from typing import Dict, Union, SupportsFloat
import json
import dicttoxml

def init_composition() -> Composition:
  Y = [7.0262E-01, 9.7479E-06, 6.8955E-02, 2.5000E-04, 7.8554E-05, 6.0144E-04, 8.1031E-05, 2.1513E-05] # Note these are molar abundances
  S = ["H-1", "He-3", "He-4", "C-12", "N-14", "O-16", "Ne-20", "Mg-24"]
  return Composition(S, Y)

def init_netIn(temp: float, rho: float, time: float, comp: Composition) -> NetIn:
  netIn = NetIn()
  netIn.temperature = temp
  netIn.density = rho
  netIn.tMax = time
  netIn.dt0 = 1e-12
  netIn.composition = comp
  return netIn

class StepData(Enum):
  TIME = 0
  DT = 1
  COMP = 2
  CONTRIB = 3


class StepLogger:
  def __init__(self):
    self.num_steps: int = 0
    self.step_data: Dict[int, Dict[StepData, Union[SupportsFloat, Dict[str, SupportsFloat]]]] = {}

  def log_step(self, context):
    engine = context.engine
    self.step_data[self.num_steps] = {}
    self.step_data[self.num_steps][StepData.TIME] = context.t
    self.step_data[self.num_steps][StepData.DT] = context.dt
    comp_data: Dict[str, SupportsFloat] = {}
    for species in engine.getNetworkSpecies():
      sid = engine.getSpeciesIndex(species)
      comp_data[species.name()] = context.state[sid]
    self.step_data[self.num_steps][StepData.COMP] = comp_data
    self.num_steps += 1

  def to_json (self, filename: str):
    serializable_data = {
      stepNum: {
        StepData.TIME.name: step[StepData.TIME],
        StepData.DT.name: step[StepData.DT],
        StepData.COMP.name: step[StepData.COMP],
      }
      for stepNum, step in self.step_data.items()
    }
    with open(filename, 'w') as f:
      json.dump(serializable_data, f, indent=4)

  def to_xml(self, filename: str):
    serializable_data = {
      stepNum: {
        StepData.TIME.name: step[StepData.TIME],
        StepData.DT.name: step[StepData.DT],
        StepData.COMP.name: step[StepData.COMP],
      }
      for stepNum, step in self.step_data.items()
    }
    xml_data = dicttoxml.dicttoxml(serializable_data, custom_root='StepLog', attr_type=False)
    with open(filename, 'wb') as f:
      f.write(xml_data)

def main(temp: float, rho: float, time: float):
  comp = init_composition()
  netIn = init_netIn(temp, rho, time, comp)

  policy = MainSequencePolicy(comp)
  engine = policy.construct()

  solver = CVODESolverStrategy(engine)

  step_logger = StepLogger()
  solver.set_callback(lambda context: step_logger.log_step(context))

  solver.evaluate(netIn, False)
  step_logger.to_xml("log_data.xml")

if __name__ == "__main__":
  import argparse
  parser = argparse.ArgumentParser(description="Simple python example of GridFire usage")
  parser.add_argument("-t", "--temp", type=float, help="Temperature in K", default=1.5e7)
  parser.add_argument("-r", "--rho", type=float, help="Density in g/cm^3", default=1.5e2)
  parser.add_argument("--tMax", type=float, help="Time in s", default=3.1536 * 1e17)
  args = parser.parse_args()
  main(args.temp, args.rho, args.tMax)

External Usage

C++ does not have a stable ABI nor does it make any strong guarantees about stl container layouts between compiler versions. Therefore, GridFire includes a set of stable C bindings which can be used to interface with a limited subset of GridFire functionality from other languages.

Note: These bindings are not intended to allow GridFire to be extended from other languages; rather, they are intended to allow GridFire to be used as a black-box library from other languages.

Note: One assumption for external usage is that the ordering of the species list will not change. That is to say that whatever order the array used to register the species is will be assumed to always be the order used when passing abundance arrays to and from GridFire.

Note: Because the C API does not pass the general Composition object a mass_lost output parameter has been added to the evolve calls, this tracks the total mass in species which have not been registered with the C API GridFire by the caller

C API Overview

In general when using the C API the workflow is to

1. create a gf_context pointer. This object holds the state of GridFire so that it does not need to be re-initialized for each call.
2. call initialization routines on the context to set up the engine and solver you wish to use.
3. call the gf_evolve function to evolve a network over some time.
4. At each state check the ret code of the function to ensure that no errors occurred. Valid ret-codes are 0 and 1. All other ret codes indicate an error.
5. Finally, call gf_free to free the context and all associated memory.

C Example

#include "gridfire/extern/gridfire_extern.h"
#include <stdio.h>

#define NUM_SPECIES 8

// Define a macro to check return codes
#define GF_CHECK_RET_CODE(ret, ctx, msg) \
    if (ret != 0 && ret != 1) { \
        printf("Error %s: %s\n", msg, gf_get_last_error_message(ctx)); \
        gf_free(ctx); \
        return ret; \
    }

int main() {
    void* gf_context = gf_init();

    const char* species_names[NUM_SPECIES];
    species_names[0] = "H-1";
    species_names[1] = "He-3";
    species_names[2] = "He-4";
    species_names[3] = "C-12";
    species_names[4] = "N-14";
    species_names[5] = "O-16";
    species_names[6] = "Ne-20";
    species_names[7] = "Mg-24";
    const double abundances[NUM_SPECIES] = {0.702616602672027, 9.74791583949078e-06, 0.06895512307276903, 0.00025, 7.855418029399437e-05, 0.0006014411598306529, 8.103062886768109e-05, 2.151340851063217e-05};

    int ret = gf_register_species(gf_context, NUM_SPECIES, species_names);
    GF_CHECK_RET_CODE(ret, gf_context, "Species Registration");

    ret = gf_construct_engine_from_policy(gf_context, "MAIN_SEQUENCE_POLICY", abundances, NUM_SPECIES);
    GF_CHECK_RET_CODE(ret, gf_context, "Policy and Engine Construction");

    ret = gf_construct_solver_from_engine(gf_context, "CVODE");
    GF_CHECK_RET_CODE(ret, gf_context, "Solver Construction");

    // When using the C API it is assumed that the caller will ensure that the output arrays are large enough to hold the results.
    double Y_out[NUM_SPECIES];
    double energy_out;
    double dEps_dT;
    double dEps_dRho;
    double neutrino_energy_loss;
    double neutrino_flux;
    double mass_lost;

    ret = gf_evolve(
        gf_context,
        abundances,
        NUM_SPECIES,
        1.5e7,    // Temperature in K
        1.5e2,    // Density in g/cm^3
        3e17,      // Time step in seconds
        1e-12, // Initial time step in seconds
        Y_out,
        &energy_out,
        &dEps_dT,
        &dEps_dRho,
        &neutrino_energy_loss,
        &neutrino_flux,
        &mass_lost
    );

    GF_CHECK_RET_CODE(ret, gf_context, "Evolution");


    printf("Evolved abundances:\n");
    for (size_t i = 0; i < NUM_SPECIES; i++) {
        printf("Species %s: %e\n", species_names[i], Y_out[i]);
    }
    printf("Energy output: %e\n", energy_out);
    printf("dEps/dT: %e\n", dEps_dT);
    printf("dEps/dRho: %e\n", dEps_dRho);
    printf("Mass lost: %e\n", mass_lost);

    gf_free(gf_context);

    return 0;
}

Fortran API Overview

GridFire makes use of the stable C API and Fortran 2003's iso_c_bindings to provide a Fortran interface for legacy code. The fortran interface is designed to be very similar to the C API and exposes the same functionality.

1. GridFire%gff_init: Initializes a GridFire context and returns a handle to it.
2. GridFire%register_species: Registers species with the GridFire context.
3. GridFire%setup_policy: Configures the engine using a specified policy and initial abundances.
4. GridFire%setup_solver: Sets up the solver for the engine.
5. GridFire%evolve: Evolves the network over a specified time step.
6. GridFire%get_last_error: Retrieves the last error message from the GridFire context.
7. GridFire%gff_free: Frees the GridFire context and associated resources.

Note: You must instantiate a GridFire type object to access these methods.

Note: free and init have had the gff_ prefix (GridFire Fortran) to avoid name clashes with common Fortran functions.

When building GridFire a fortran module file gridfire_mod.mod is generated which contains all the necessary bindings to use GridFire from Fortran. You must also link your code against the C API library libgridfire_extern.

Fortran Example

program main
    use iso_c_binding
    use gridfire_mod
    implicit none

    type(GridFire) :: net
    integer(c_int) :: ierr
    integer :: i

    ! --- 1. Define Species and Initial Conditions ---
    ! Note: String lengths must match or exceed the longest name.
    ! We pad with spaces, which 'trim' handles inside the module.
    character(len=5), dimension(8) :: species_names = [ &
            "H-1  ", &
            "He-3 ", &
            "He-4 ", &
            "C-12 ", &
            "N-14 ", &
            "O-16 ", &
            "Ne-20", &
            "Mg-24"  &
        ]

    ! Initial Mass Fractions (converted to Molar Abundances Y = X/A)
    ! Standard solar-ish composition
    real(c_double), dimension(8) :: Y_in = [ &
        0.702616602672027,     &
        9.74791583949078e-06,  &
        0.06895512307276903,   &
        0.00025,               &
        7.855418029399437e-05, &
        0.0006014411598306529, &
        8.103062886768109e-05, &
        2.151340851063217e-05  &
        ]

    ! Output buffers
    real(c_double), dimension(8) :: Y_out
    real(c_double) :: energy_out, dedt, dedrho, nu_E_loss, nu_flux, dmass

    ! Thermodynamic Conditions (Solar Core-ish)
    real(c_double) :: T = 1.5e7      ! 15 Million K
    real(c_double) :: rho = 150.0e0  ! 150 g/cm^3
    real(c_double) :: dt = 3.1536e17     ! ~10 Gyr timestep

    ! --- 2. Initialize GridFire ---
    print *, "Initializing GridFire..."
    call net%gff_init()

    ! --- 3. Register Species ---
    print *, "Registering species..."
    call net%register_species(species_names)

    ! --- 4. Configure Engine & Solver ---
    print *, "Setting up Main Sequence Policy..."
    call net%setup_policy("MAIN_SEQUENCE_POLICY", Y_in)

    print *, "Setting up CVODE Solver..."
    call net%setup_solver("CVODE")

    ! --- 5. Evolve ---
    print *, "Evolving system (dt =", dt, "s)..."
    call net%evolve(Y_in, T, rho, dt, Y_out, energy_out, dedt, dedrho, nu_E_loss, nu_flux, dmass, ierr)

    if (ierr /= 0) then
        print *, "Evolution Failed with error code: ", ierr
        print *, "Error Message: ", net%get_last_error()
        call net%gff_free() ! Always cleanup
        stop
    end if

    ! --- 6. Report Results ---
    print *, ""
    print *, "--- Results ---"
    print '(A, ES12.5, A)', "Energy Generation: ", energy_out, " erg/g/s"
    print '(A, ES12.5)',    "dEps/dT:           ", dedt
    print '(A, ES12.5)',    "Mass Change:       ", dmass

    print *, ""
    print *, "Abundances:"
    do i = 1, size(species_names)
        print '(A, " : ", ES12.5, " -> ", ES12.5)', &
                trim(species_names(i)), Y_in(i), Y_out(i)
    end do

    ! --- 7. Cleanup ---
    call net%gff_free()

end program main

Related Projects

GridFire integrates with and builds upon several key 4D-STAR libraries:

• fourdst: hub module managing versioning of libcomposition, libconfig, liblogging, and libconstants
• libcomposition (docs): Composition management toolkit.
• libconfig: Configuration file parsing utilities.
• liblogging: Flexible logging framework.
• libconstants: Physical constants
• libplugin: Dynamically loadable plugin framework.