tboudreaux/GridFire
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

build(boost): added auto call to boost dep script

Browse Source
This commit is contained in:
Emily Boudreaux
2025-07-29 08:22:31 -04:00
parent e3d7bed39c
commit a9c4a2faff
3 changed files with 79 additions and 29 deletions
Download Patch File Download Diff File Expand all files Collapse all files

39
README.md
View File

@@ -5,10 +5,23 @@
---
GridFire is a C++ library designed to perform general nuclear network evolution using the Reaclib library. It is part of the larger SERiF project within the 4D-STAR collaboration.
GridFire is a C++ library designed to perform general nuclear network evolution using the Reaclib library. 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 evolves a fusing plasma in NSE; however,
this is not the primary focus of the library and has therefor not had significant development. For those interested in
modeling super nova, neutron star mergers, or other
high-energy astrophysical phenomena, we **strongly** recomment
using [SkyNet](https://bitbucket.org/jlippuner/skynet/src/master/).
**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. To manage the inherent stiffness and multi-scale 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.
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. 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.
@@ -16,7 +29,7 @@ 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.
only and do not necessarily reflect those of the European Union or the European Research Council.
## Build and Installation Instructions
@@ -27,9 +40,9 @@ only and do not necessarily reflect those of the European Union or the European
- Python packages: `meson-python>=0.15.0`
- Boost libraries (>= 1.75.0) installed system-wide
> Note: Boost is the only external library dependency; no additional libraries are required beyond a C++ compiler, Meson, Python, and Boost.
>
> Windows is not supported at this time.
> **Note:** Boost is the only external library dependency; no additional libraries are required beyond a C++ compiler, Meson, Python, and Boost.
> **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
@@ -82,12 +95,20 @@ pip install .
## Code Architecture and Logical Flow
GridFire is organized into modular components that collaborate to simulate nuclear reaction networks with high fidelity:
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.
- **Screening Module:** Implements nuclear reaction screening corrections (`WeakScreening`, `BareScreening`, etc.) affecting reaction rates.
- **Rates Module:** Parses and manages Reaclib reaction rate data, providing temperature- and density-dependent rate evaluations.
- **Python Interface:** Exposes C++ functionality to Python, enabling rapid prototyping and integration into workflows.
- **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`) to weight reaction rates based on nuclear properties.
- **Solver Module:** Defines numerical integration strategies (e.g., `DirectNetworkSolver`) for solving the stiff ODE systems arising from reaction networks.
- **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.
### GraphEngine Configuration Options

9
build-config/boost/meson.build
View File

@@ -1 +1,10 @@
boost_dep = dependency('boost', version: '>=1.8.0')
if not boost_dep.found()
message('❌ Boost library not found. Note that Boost is a system dependency and must be installed seperatly.')
message('GridFire includes a Boost auto installation script, attempting to run it now (this will require some user input)...')
run_res = run_command('./install.sh', check: true)
boost_dep = dependency('boost', version: '>=1.8.0', required: true)
endif

40
docs/static/mainpage.md vendored
View File

@@ -2,11 +2,23 @@
![GridFire Logo](../../assets/logo/GridFire.png)
---
GridFire is a C++ library designed to perform general nuclear network evolution using the Reaclib library. It is part of the larger SERiF project within the 4D-STAR collaboration.
GridFire is a C++ library designed to perform general nuclear network evolution using the Reaclib library. 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 evolves a fusing plasma in NSE; however,
this is not the primary focus of the library and has therefor not had significant development. For those interested in
modeling super nova, neutron star mergers, or other
high-energy astrophysical phenomena, we **strongly** recomment
using [SkyNet](https://bitbucket.org/jlippuner/skynet/src/master/).
**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. To manage the inherent stiffness and multi-scale 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.
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. 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.
@@ -14,7 +26,7 @@ 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.
only and do not necessarily reflect those of the European Union or the European Research Council.
## Build and Installation Instructions
@@ -25,9 +37,9 @@ only and do not necessarily reflect those of the European Union or the European
- Python packages: `meson-python>=0.15.0`
- Boost libraries (>= 1.75.0) installed system-wide
> Note: Boost is the only external library dependency; no additional libraries are required beyond a C++ compiler, Meson, Python, and Boost.
>
> Windows is not supported at this time.
> **Note:** Boost is the only external library dependency; no additional libraries are required beyond a C++ compiler, Meson, Python, and Boost.
> **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
@@ -80,12 +92,20 @@ pip install .
## Code Architecture and Logical Flow
GridFire is organized into modular components that collaborate to simulate nuclear reaction networks with high fidelity:
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.
- **Screening Module:** Implements nuclear reaction screening corrections (`WeakScreening`, `BareScreening`, etc.) affecting reaction rates.
- **Rates Module:** Parses and manages Reaclib reaction rate data, providing temperature- and density-dependent rate evaluations.
- **Python Interface:** Exposes C++ functionality to Python, enabling rapid prototyping and integration into workflows.
- **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`) to weight reaction rates based on nuclear properties.
- **Solver Module:** Defines numerical integration strategies (e.g., `DirectNetworkSolver`) for solving the stiff ODE systems arising from reaction networks.
- **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.
### GraphEngine Configuration Options