920 lines
41 KiB
Markdown
920 lines
41 KiB
Markdown
<p align="center">
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<img src="https://github.com/4D-STAR/GridFire/blob/main/assets/logo/GridFire.png?raw=true" width="300" alt="OPAT Core Libraries Logo">
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</p>
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---
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'&style=for-the-badge&label=GitHub%20Main%20Branch)
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---
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# Table of Contents
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- [Introduction](#introduction)
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- [Design Philosophy and Workflow](#design-philosophy-and-workflow)
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- [Funding](#funding)
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- [Usage](#usage)
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- [Python installation](#python-installation)
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- [pypi](#pypi)
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- [source](#source)
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- [source for developers](#source-for-developers)
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- [patching shared object files](#patching-shared-object-files)
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- [Automatic Build and Installation](#automatic-build-and-installation)
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- [Script Build and Installation Instructions](#script-build-and-installation-instructions)
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- [Currently, known good platforms](#currently-known-good-platforms)
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- [Manual Build Instructions](#manual-build-instructions)
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- [Prerequisites](#prerequisites)
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- [Install Scripts](#install-scripts)
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- [Dependency Installation on Common Platforms](#dependency-installation-on-common-platforms)
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- [Building the C++ Library](#building-the-c-library)
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- [Installing the Library](#installing-the-library)
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- [Minimum compiler versions](#minimum-compiler-versions)
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- [Code Architecture and Logical Flow](#code-architecture-and-logical-flow)
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- [Engines](#engines)
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- [GraphEngine](#graphengine)
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- [GraphEngine Configuration Options](#graphengine-configuration-options)
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- [Available Partition Functions](#available-partition-functions)
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- [AutoDiff](#autodiff)
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- [Reaclib in GridFire](#reaclib-in-gridfire)
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- [Engine Views](#engine-views)
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- [A Note about composability](#a-note-about-composability)
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- [Numerical Solver Strategies](#numerical-solver-strategies)
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- [NetworkSolverStrategy<EngineT>](#networksolverstrategyenginet)
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- [NetIn and NetOut](#netin-and-netout)
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- [DirectNetworkSolver (Implicit Rosenbrock Method)](#directnetworksolver-implicit-rosenbrock-method)
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- [Algorithmic Workflow in DirectNetworkSolver](#algorithmic-workflow-in-directnetworksolver)
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- [Future Solver Implementations](#future-solver-implementations)
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- [Python Extensibility](#python-extensibility)
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- [Usage Examples](#usage-examples)
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- [C++](#c)
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- [GraphEngine Initialization](#graphengine-initialization)
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- [Adaptive Network View](#adaptive-network-view)
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- [Composition Initialization](#composition-initialization)
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- [Common Workflow Example](#common-workflow-example)
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- [Callback Example](#callback-example)
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- [Python](#python)
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- [Common Workflow Example](#common-workflow-example-1)
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- [Python callbacks](#python-callbacks)
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- [Related Projects](#related-projects)
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# Version and Notes
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This repository is currently tracking GridFire version v0.7.0-alpha. Note that this
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is a development version which has known scientific and build issues:
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1. Over long runs (say 10Gyr) repartitioning stages can introduce discontinuities into abundances
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2. We do not currently produce He-4 at a rate consistent with literature values. This is a known issue and is being addressed.
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3. When using Weak Rate Library (WRL) weak reactions the network becomes pathologically stiff. Reaclib includes a limited set of reactions which can be used to close the CNO cycle. Network construction then defaults to using all reaclib reactions while we address pathological stiffness with WRL rates.
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4. WRL reactions do track energy loss due to neutrinos and neutrino flux; however, these are not currently reported to the user. They will be in the final v0.7.0 release.
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5. There is a current bug in meson-python which results in multiple duplicate LC_RPATH entries in any shared object files compiled for python linking. Recent versions of the macos dynamic loader (XCode command line tools versions >= 16) refuse to load shared object files with duplicat rpath entries. Because of this running `pip install`. in the root will result in a broken gridfire python install. Instead we have bundled a helper script `pip_install_mac_patch.sh` which should be used to install python bindings on macos for the time being.
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# Introduction
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GridFire is a C++ library designed to perform general nuclear network
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evolution. It is part of the larger SERiF project within the 4D-STAR
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collaboration. GridFire is primarily focused on modeling the most relevant
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burning stages for stellar evolution modeling. Currently, there is limited
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support for inverse reactions. Therefore, GridFire has a limited set of tools
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to evolve a fusing plasma in NSE; however, this is not the primary focus of
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the library and has therefore not had significant development. For those
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interested in modeling super nova, neutron star mergers, or other high-energy
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astrophysical phenomena, we **strongly** recommend using
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[SkyNet](https://bitbucket.org/jlippuner/skynet/src/master/).
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## Design Philosophy and Workflow
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GridFire is architected to balance physical fidelity, computational efficiency,
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and extensibility when simulating complex nuclear reaction networks. Users
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begin by defining a composition, which is used to construct a full GraphEngine
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representation of the reaction network. A GraphNetwork uses [JINA
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Reaclib](https://reaclib.jinaweb.org/index.php) reaction rates ([Cyburt et al., ApJS 189
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(2010) 240.](https://iopscience.iop.org/article/10.1088/0067-0049/189/1/240)) along
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with a dynamically constructed network topology. To manage the inherent
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stiffness and multiscale nature of these networks, GridFire employs a layered
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view strategy: partitioning algorithms isolate fast and slow processes,
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adaptive culling removes negligible reactions at runtime, and implicit solvers
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stably integrate the remaining stiff system. This modular pipeline allows
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researchers to tailor accuracy versus performance trade-offs, reuse common
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engine components, and extend screening or partitioning models without
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modifying core integration routines.
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## Funding
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GridFire is a part of the 4D-STAR collaboration.
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4D-STAR is funded by European Research Council (ERC) under the Horizon Europe
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programme (Synergy Grant agreement No. 101071505: 4D-STAR) Work for this
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project is funded by the European Union. Views and opinions expressed are
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however those of the author(s) only and do not necessarily reflect those of the
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European Union or the European Research Council.
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# Usage
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## Python installation
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By far the easiest way to install is with pip. This will install either
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pre-compiled wheels or, if your system has not had a wheel compiled for it, it
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will try to build locally (this may take **a long time**). The python bindings
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are just that and should maintain nearly the same speed as the C++ code. End
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users are strongly encouraged to use the python module rather than the C++ code.
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### pypi
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Installing from pip is as simple as. Note that this will install gridfire v0.5.0, currently the latest version on pip. Once
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v0.7.0 is released this will be pushed to pip.
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```bash
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pip install gridfire
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```
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These wheels have been compiled on many systems
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| Version | Platform | Architecture | CPython Versions | PyPy Versions |
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|---------|----------|--------------|------------------------------------------------------------|---------------|
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| 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 |
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| 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 |
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| 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 |
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> **Note**: Currently macOS x86\_64 does **not** have a precompiled wheel. Due
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> to that platform being phased out it is likely that there will never be
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> precompiled wheels or releases for it.
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> **Note:** macOS wheels were targeted to macOS 12 Monterey and should work on
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> any version more recent than that (at least as of August 2025).
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> **Note:** Linux wheels were compiled using manylinux_2_28 and are expected to
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> work on Debian 10+, Ubuntu 18.10+, Fedora 29+, or CentOS/RHEL 8+
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> **Note:** If your system does not have a prebuilt wheel the source
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> distribution will download from pypi and try to build. This may simply not
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> work if you do not have the correct system dependencies installed. If it
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> fails the best bet is to try to build boost >= 1.83.0 from source and install
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> (https://www.boost.org/) as that is the most common broken dependency.
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### source
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The user may also build the python bindings directly from source
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```bash
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git clone https://github.com/4D-STAR/GridFire
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cd GridFire
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pip install .
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```
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> **Note:** that if you do not have all system dependencies installed this will
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> fail, the steps in further sections address these in more detail.
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> **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.
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### source for developers
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If you are a developer and would like an editable and incremental python
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install `meson-python` makes this very easy
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```bash
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git clone https://github.com/4D-STAR/GridFire
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cd GridFire
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pip install -e . --no-build-isolation -vv
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```
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This will generate incremental builds whenever source code changes, and you run
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a python script automatically (note that since `meson setup` must run for each
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of these it does still take a few seconds to recompile regardless of how small
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a source code change you have made). It is **strongly** recommended that
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developers use this approach and end users *do not*.
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#### Patching Shared Object Files
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If you need to patch shared object files generated by meson-python directly you should first located the shared object file
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these will be in the site-packages and site-packages/fourdst directories for your python enviroment.
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Look for files named
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- `site-packages/gridfire.cpython-3*-darwin.so`
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- `site-packages/fourdst/_phys.cpython-3*-darwin.so`
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then, for each of these files, run
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```bash
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otool -l <Path/to/file> | grep RPATH -A2
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```
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count the number of occurences 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.
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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
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```bash
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install_name_tool -delete_rpath @loader_path/../.fourdst.mesonpy.libs site-packages/fourdst/_phys.cpython-314-darwin.so
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```
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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).
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## Automatic Build and Installation
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### Script Build and Installation Instructions
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The easiest way to build GridFire is using the `install.sh` or `install-tui.sh`
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scripts in the root directory. To use these scripts, simply run:
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```bash
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./install.sh
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# or
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./install-tui.sh
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```
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The regular installation script will select a standard "ideal" set of build
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options for you. If you want more control over the build options, you can use
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the `install-tui.sh` script, which will provide a text-based user interface to
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select the build options you want.
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Generally, both are intended to be easy to use and will prompt you
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automatically to install any missing dependencies.
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### Currently, known good platforms
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The installation script has been tested and found to work on clean
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installations of the following platforms:
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- MacOS 15.3.2 (Apple Silicon + brew installed)
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- Fedora 42.0 (aarch64)
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- Ubuntu 25.04 (aarch64)
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- Ubuntu 22.04 (X86_64)
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> **Note:** On Ubuntu 22.04 the user needs to install boost libraries manually
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> as the versions in the Ubuntu repositories
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> are too old. The installer automatically detects this and will instruct the
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> user in how to do this.
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## Manual Build Instructions
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### Prerequisites
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These only need to be manually installed if the user is not making use of the
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`install.sh` or `install-tui.sh`
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#### Required
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- C++ compiler supporting C++23 standard
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- Meson build system (>= 1.5.0)
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- Python 3.8 or newer
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- CMake 3.20 or newer
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- ninja 1.10.0 or newer
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- Python packages: `meson-python>=0.15.0`
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- Boost libraries (>= 1.83.0) installed system-wide (or at least findable by
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meson with pkg-config)
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#### Optional
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- dialog (used by the `install.sh` script, not needed if using pip or meson
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directly)
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- pip (used by the `install.sh` script or by calling pip directly, not needed
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if using meson directly)
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> **Note:** Boost is the only external library dependency used by GridFire directly.
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> **Note:** Windows is not supported at this time and *there are no plans to
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> support it in the future*. Windows users are encouraged to use WSL2 or a
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> Linux VM.
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> **Note:** If `install-tui.sh` is not able to find a usable version of boost
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> it will provide directions to fetch, compile, and install a usable version.
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### Install Scripts
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GridFire ships with an installer (`install.sh`) which is intended to make the
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process of installation both easier and more repeatable.
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#### Ease of Installation
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Both scripts are intended to automate installation more or less completely. This
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includes dependency checking. In the event that a dependency cannot be found
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they try to install (after explicitly asking for user permission). If that does
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not work they will provide a clear message as to what went wrong.
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#### Reproducibility
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The TUI mode provides easy modification of meson build system and compiler
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settings which can then be saved to a config file. This config file can then be
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loaded by either tui mode or cli mode (with the `--config`) flag meaning that
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build configurations can be made and reused. Note that this is **not** a
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deterministically reproducible build system as it does not interact with any
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system dependencies or settings, only meson and compiler settings.
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#### Examples
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##### TUI config and saving
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[](https://asciinema.org/a/ahIrQPL71ErZv5EKKujfO1ZEW)
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##### TUI config loading and meson setup
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[](https://asciinema.org/a/zGdzt9kYsETltG0TJKC50g3BK)
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##### CLI config loading, setup, and build
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[](https://asciinema.org/a/GYaWTXZbDJRD4ohde0s3DkFMC)
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> **Note:** `install-tui.sh` is simply a script which calls `install.sh` with
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> the `--tui` flag. You can get the exact same results by running `install.sh
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> --tui`.
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> **Note:** Call `install.sh` with the `--help` or `--h` flag to see command
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> line options
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> **Note:** `clang` tends to compile GridFire much faster than `gcc` thus why I
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> select it in the above asciinema recording.
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### Dependency Installation on Common Platforms
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- **Ubuntu/Debian:**
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```bash
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sudo apt-get update
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sudo apt-get install -y build-essential meson python3 python3-pip libboost-all-dev
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```
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> **Note:** Depending on the ubuntu version you have the libboost-all-dev
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> libraries may be too old. If this is the case refer to the boost
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> documentation for how to download and install a version `>=1.83.0`
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> **Note:** On recent versions of ubuntu python has switched to being
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> externally managed by the system. We **strongly** recommend that if you
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> install manually all python packages are installed inside some kind of
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> virtual environment (e.g. `pyenv`, `conda`, `python-venv`, etc...). When using
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> the installer script this is handled automatically using `python-venv`.
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- **Fedora/CentOS/RHEL:**
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```bash
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sudo dnf install -y gcc-c++ meson python3 python3-pip boost-devel
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```
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- **macOS (Homebrew):**
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```bash
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brew update
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brew install boost meson python
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```
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### Building the C++ Library
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```bash
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meson setup build
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meson compile -C build
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```
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#### Clang vs. GCC
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As noted above `clang` tends to compile GridFire much faster than `gcc`. If
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your system has both `clang` and `gcc` installed you may force meson to use
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clang via environmental variables
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```bash
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CC=clang CXX=clang++ meson setup build_clang
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meson compile -C build_clang
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```
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### Installing the Library
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```bash
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meson install -C build
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```
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### Minimum compiler versions
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GridFire uses C++23 features and therefore only compilers and standard library
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implementations which support C++23 are supported. Generally we have found that
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`gcc >= 13.0.0` or `clang >= 16.0.0` work well.
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## Code Architecture and Logical Flow
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GridFire is organized into a series of composable modules, each responsible for
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a specific aspect of nuclear reaction network modeling. The core components
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include:
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- **Engine Module:** Core interfaces and implementations (e.g., `GraphEngine`)
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that evaluate reaction network rate equations and energy generation. Also
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implemented `Views` submodule.
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- **Engine::Views Module:** Composable engine optimization and modification
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(e.g. `MultiscalePartitioningEngineView`) which can be used to make a problem
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more tractable or applicable.
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- **Screening Module:** Implements nuclear reaction screening corrections (e.g.
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`WeakScreening` ([Salpeter,
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1954](https://adsabs.harvard.edu/full/1954AuJPh...7..373S)), `BareScreening`)
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affecting reaction rates.
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- **Reaction Module:** Parses and manages Reaclib reaction rate data, providing
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temperature- and density-dependent rate evaluations.
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- **Partition Module:** Implements partition functions (e.g.,
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`GroundStatePartitionFunction`, `RauscherThielemannPartitionFunction`
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([Rauscher & Thielemann,
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2000](https://www.sciencedirect.com/science/article/pii/S0092640X00908349?via%3Dihub]))
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to weight reaction rates based on nuclear properties.
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- **Solver Module:** Defines numerical integration strategies (e.g.,
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`CVODESolverStrategy`) for solving the stiff ODE systems arising from reaction
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networks.
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- **io Module:** Defines shared interface for parsing network data from files
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- **trigger Module:** Defines interface for complex trigger logic so that repartitioning can be followed.
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- **Python Interface:** Exposes *almost* all C++ functionality to Python,
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allowing users to define compositions, configure engines, and run simulations
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directly from Python scripts.
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Generally a user will start by selecting a base engine (currently we only offer
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`GraphEngine`), which constructs the full reaction network graph from a given
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composition. The user can then apply various engine views to adapt the network
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topology, such as partitioning fast and slow reactions, adaptively culling
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low-flow pathways, or priming the network with specific species. Finally, a
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numerical solver is selected to integrate the network over time, producing
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updated
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abundances and diagnostics.
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## Engines
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GridFire is, at its core, based on a series of `Engines`. These are constructs
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which know how to report information on series of ODEs which need to be solved
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to evolve abundances. The important thing to understand about `Engines` is
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that they contain all the detailed physics GridFire uses. For example a
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`Solver` takes an `Engine` but does not compute physics itself. Rather, it asks
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the `Engine` for stuff like the jacobian matrix, stoichiometry, nuclear energy
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generation rate, and change in abundance with time.
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Refer to the API documentation for the exact interface which an `Engine` must
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implement to be compatible with GridFire solvers.
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Currently, we only implement `GraphEngine` which is intended to be a very general and
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adaptable `Engine`.
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### GraphEngine
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In GridFire the `GraphEngine` will generally be the most fundamental building
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block of a nuclear network. A `GraphEngine` represents a directional hypergraph
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connecting some set of atomic species through reactions listed in the [JINA
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Reaclib database](https://reaclib.jinaweb.org/index.php).
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`GraphEngine`s are constructed from a seed composition of species from which
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they recursively expand their topology outward, following known reaction
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pathways and adding new species to the tracked list as they expand.
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### GraphEngine Configuration Options
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GraphEngine exposes runtime configuration methods to tailor network
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construction and rate evaluations:
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- **Constructor Parameters:**
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- `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 |
|
|
|----------------------------------|-----------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------|
|
|
| AdaptiveEngineView | Dynamically culls low-flow species and reactions during runtime | Iterative flux thresholding to remove reactions below a flow threshold | Large networks to reduce computational cost |
|
|
| 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
|
|
|
|
```cpp
|
|
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 energy generated by the network while evolving to `tMax`
|
|
(`NetOut::energy`)
|
|
|
|
>**Note:** Currently `GraphEngine` only considers energy due to nuclear mass
|
|
>defect and not neutrino loss.
|
|
|
|
### CVODESolverStrategy
|
|
|
|
We use the CVODE module from [SUNDIALS](https://computing.llnl.gov/projects/sundials/cvode) 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
|
|
```c++
|
|
#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
|
|
```c++
|
|
#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
|
|
```c++
|
|
#include "fourdst/composition/composition.h"
|
|
#include <vector>
|
|
#include <string>
|
|
|
|
#include <iostream>
|
|
|
|
int main() {
|
|
fourdst::composition::Composition comp;
|
|
|
|
std::vector<std::string> symbols = {"H-1", "He-4", "C-12"};
|
|
std::vector<double> massFractions = {0.7, 0.29, 0.01};
|
|
|
|
comp.registerSymbols(symbols);
|
|
comp.setMassFraction(symbols, massFractions);
|
|
|
|
comp.finalize(true);
|
|
|
|
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:
|
|
|
|
```c++
|
|
#include "gridfire/engine/engine.h" // Unified header for real usage
|
|
#include "gridfire/solver/solver.h" // Unified header for solvers
|
|
#include "fourdst/composition/composition.h"
|
|
|
|
int main(){
|
|
// 1. Define initial composition
|
|
fourdst::composition::Composition comp;
|
|
|
|
std::vector<std::string> symbols = {"H-1", "He-4", "C-12"};
|
|
std::vector<double> massFractions = {0.7, 0.29, 0.01};
|
|
|
|
comp.registerSymbols(symbols);
|
|
comp.setMassFraction(symbols, massFractions);
|
|
|
|
comp.finalize(true);
|
|
|
|
// 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 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.
|
|
|
|
```c++
|
|
#include "gridfire/engine/engine.h" // Unified header for real usage
|
|
#include "gridfire/solver/solver.h" // Unified header for solvers
|
|
#include "fourdst/composition/composition.h"
|
|
#include "fourdst/atomic/species.h"
|
|
|
|
#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(){
|
|
// 1. Define initial composition
|
|
fourdst::composition::Composition comp;
|
|
|
|
std::vector<std::string> symbols = {"H-1", "He-4", "C-12"};
|
|
std::vector<double> massFractions = {0.7, 0.29, 0.01};
|
|
|
|
comp.registerSymbols(symbols);
|
|
comp.setMassFraction(symbols, massFractions);
|
|
|
|
comp.finalize(true);
|
|
|
|
// 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::CVODESolverStrategy solver(adaptView);
|
|
solver.set_callback(callback);
|
|
|
|
// 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;
|
|
}
|
|
```
|
|
>**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
|
|
|
|
```c++
|
|
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.
|
|
|
|
```c++
|
|
...
|
|
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.
|
|
|
|
### Common Workflow Example
|
|
This example implements the same logic as the above C++ example
|
|
```python
|
|
from gridfire.engine import GraphEngine, MultiscalePartitioningEngineView, AdaptiveEngineView
|
|
from gridfire.solver import CVODESolverStrategey
|
|
from gridfire.type import NetIn
|
|
|
|
from fourdst.composition import Composition
|
|
|
|
symbols : list[str] = ["H-1", "He-3", "He-4", "C-12", "N-14", "O-16", "Ne-20", "Mg-24"]
|
|
X : list[float] = [0.708, 2.94e-5, 0.276, 0.003, 0.0011, 9.62e-3, 1.62e-3, 5.16e-4]
|
|
|
|
|
|
comp = Composition()
|
|
comp.registerSymbol(symbols)
|
|
comp.setMassFraction(symbols, X)
|
|
comp.finalize(True)
|
|
|
|
print(f"Initial H-1 mass fraction {comp.getMassFraction("H-1")}")
|
|
|
|
netIn = NetIn()
|
|
netIn.composition = comp
|
|
netIn.temperature = 1.5e7
|
|
netIn.density = 1.6e2
|
|
netIn.tMax = 1e-9
|
|
netIn.dt0 = 1e-12
|
|
|
|
baseEngine = GraphEngine(netIn.composition, 2)
|
|
baseEngine.setUseReverseReactions(False)
|
|
|
|
qseEngine = MultiscalePartitioningEngineView(baseEngine)
|
|
|
|
adaptiveEngine = AdaptiveEngineView(qseEngine)
|
|
|
|
solver = CVODESolverStrategey(adaptiveEngine)
|
|
|
|
results = solver.evaluate(netIn)
|
|
|
|
print(f"Final H-1 mass fraction {results.composition.getMassFraction("H-1")}")
|
|
```
|
|
|
|
### Python callbacks
|
|
|
|
Just like in C++, python users can register callbacks to be called at the end of each successful timestep. Note that
|
|
these may slow down code significantly as the interpreter needs to jump up into the slower python code therefore these
|
|
should likely only be used for debugging purposes.
|
|
|
|
The syntax for registration is very similar to C++
|
|
```python
|
|
from gridfire.engine import GraphEngine, MultiscalePartitioningEngineView, AdaptiveEngineView
|
|
from gridfire.solver import DirectNetworkSolver
|
|
from gridfire.type import NetIn
|
|
|
|
from fourdst.composition import Composition
|
|
from fourdst.atomic import species
|
|
|
|
symbols : list[str] = ["H-1", "He-3", "He-4", "C-12", "N-14", "O-16", "Ne-20", "Mg-24"]
|
|
X : list[float] = [0.708, 2.94e-5, 0.276, 0.003, 0.0011, 9.62e-3, 1.62e-3, 5.16e-4]
|
|
|
|
|
|
comp = Composition()
|
|
comp.registerSymbol(symbols)
|
|
comp.setMassFraction(symbols, X)
|
|
comp.finalize(True)
|
|
|
|
print(f"Initial H-1 mass fraction {comp.getMassFraction("H-1")}")
|
|
|
|
netIn = NetIn()
|
|
netIn.composition = comp
|
|
netIn.temperature = 1.5e7
|
|
netIn.density = 1.6e2
|
|
netIn.tMax = 1e-9
|
|
netIn.dt0 = 1e-12
|
|
|
|
baseEngine = GraphEngine(netIn.composition, 2)
|
|
baseEngine.setUseReverseReactions(False)
|
|
|
|
qseEngine = MultiscalePartitioningEngineView(baseEngine)
|
|
|
|
adaptiveEngine = AdaptiveEngineView(qseEngine)
|
|
|
|
solver = DirectNetworkSolver(adaptiveEngine)
|
|
|
|
|
|
data: List[Tuple[float, Dict[str, Tuple[float, float]]]] = []
|
|
def callback(context):
|
|
engine = context.engine
|
|
abundances: Dict[str, Tuple[float, float]] = {}
|
|
for species in engine.getNetworkSpecies():
|
|
sid = engine.getSpeciesIndex(species)
|
|
abundances[species.name()] = (species.mass(), context.state[sid])
|
|
data.append((context.t,abundances))
|
|
|
|
solver.set_callback(callback)
|
|
results = solver.evaluate(netIn)
|
|
|
|
print(f"Final H-1 mass fraction {results.composition.getMassFraction("H-1")}")
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```
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# Related Projects
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GridFire integrates with and builds upon several key 4D-STAR libraries:
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- [fourdst](https://github.com/4D-STAR/fourdst): hub module managing versioning
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of `libcomposition`, `libconfig`, `liblogging`, and `libconstants`
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- [libcomposition](https://github.com/4D-STAR/libcomposition)
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([docs](https://4d-star.github.io/libcomposition/)): Composition management
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toolkit.
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- [libconfig](https://github.com/4D-STAR/libconfig): Configuration file parsing
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utilities.
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- [liblogging](https://github.com/4D-STAR/liblogging): Flexible logging framework.
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- [libconstants](https://github.com/4D-STAR/libconstants): Physical constants
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