From b0c1daada4d6bae2f5a9a2673a7e3e682540a129 Mon Sep 17 00:00:00 2001 From: Emily Boudreaux Date: Thu, 31 Jul 2025 15:41:27 -0400 Subject: [PATCH] docs(docs): added more callback docs --- README.md | 262 ++++++++++++++------------ docs/html/doxygen_crawl.html | 11 +- docs/html/index.html | 83 ++++---- docs/html/md_docs_2static_2usage.html | 16 +- docs/html/navtreedata.js | 30 +-- docs/html/navtreeindex5.js | 26 +-- docs/html/navtreeindex6.js | 4 +- docs/html/navtreeindex7.js | 1 + docs/html/search/all_0.js | 8 +- docs/html/search/all_1.js | 4 +- docs/html/search/all_10.js | 2 +- docs/html/search/all_13.js | 2 +- docs/html/search/all_15.js | 10 +- docs/html/search/all_17.js | 6 +- docs/html/search/all_18.js | 6 +- docs/html/search/all_19.js | 4 +- docs/html/search/all_1b.js | 4 +- docs/html/search/all_1c.js | 7 +- docs/html/search/all_2.js | 2 +- docs/html/search/all_3.js | 2 +- docs/html/search/all_4.js | 2 +- docs/html/search/all_6.js | 2 +- docs/html/search/all_7.js | 6 +- docs/html/search/all_8.js | 39 ++-- docs/html/search/all_9.js | 2 +- docs/html/search/all_a.js | 21 +-- docs/html/search/all_b.js | 2 +- docs/html/search/all_c.js | 2 +- docs/html/search/all_e.js | 2 +- docs/static/mainpage.md | 132 +++++++------ 30 files changed, 371 insertions(+), 329 deletions(-) diff --git a/README.md b/README.md index 8cd0911d..8f2620a5 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,16 @@ OPAT Core Libraries Logo

+--- +![PyPI - Version](https://img.shields.io/pypi/v/gridfire?style=for-the-badge) +![PyPI - Wheel](https://img.shields.io/pypi/wheel/gridfire?style=for-the-badge) + +![GitHub License](https://img.shields.io/github/license/4D-STAR/GridFire?style=for-the-badge) +![ERC](https://img.shields.io/badge/Funded%20by-ERC-blue?style=for-the-badge&logo=europeancommission) + +![Dynamic Regex Badge](https://img.shields.io/badge/dynamic/regex?url=https%3A%2F%2Fgithub.com%2F4D-STAR%2FGridFire%2Fblob%2Fmain%2Fmeson.build&search=version%3A%20'(%5B0-9a-zA-Z%5C.%5D%2B)'&style=for-the-badge&label=GitHub%20Main%20Branch) +![GitHub commit activity](https://img.shields.io/github/commit-activity/w/4D-STAR/GridFire?style=for-the-badge) + --- @@ -49,10 +59,10 @@ 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 encorages to use the python module rather than the C++ code. +users are strongly encourages to use the python module rather than the C++ code. ### pypi -Installing from pip is as simple as +Installing from pip is as simple as ```bash pip install gridfire ``` @@ -60,16 +70,16 @@ pip install gridfire These wheels have been compiled on many systems | Version | Platform | Architecture | CPython Versions | PyPy Versions | -| ------- | -------- | ------------ | ---------------------------------------------------------- | ------------- | +|---------|----------|--------------|------------------------------------------------------------|---------------| | 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 +> **Note**: Currently macOS x86\_64 does **not** have a precompiled wheel. Do > to that platform being phased out it is likely that there will never be -> precompiled wheels or releases for it. +> precompiled wheels or releases for it. -> **Note:** macOS wheels were targeted to MacOS 12 Monterey and should work on +> **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 @@ -82,7 +92,7 @@ These wheels have been compiled on many systems > (https://www.boost.org/) as that is the most common broken dependency. ### source -The user may also build the python bindings directly from source +The user may also build the python bindings directly from source ```bash git clone https://github.com/4D-STAR/GridFire @@ -94,7 +104,7 @@ pip install . > fail, the steps in further sections address these in more detail. ### source for developers -If you are a developer and would like an editable and incrimental python +If you are a developer and would like an editable and incremental python install `meson-python` makes this very easy ```bash @@ -103,10 +113,10 @@ cd GridFire pip install -e . --no-build-isolation -vv ``` -This will generate incrimental builds whenever source code changes and you run -a python script automartically (note that since `meson setup` must run for each +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** reccomended that +a source code change you have made). It is **strongly** recommended that developers use this approach and end users *do not*. @@ -130,7 +140,7 @@ Generally, both are intended to be easy to use and will prompt you automatically to install any missing dependencies. -### Currently known good platforms +### 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) @@ -157,13 +167,13 @@ These only need to be manually installed if the user is not making use of the - ninja 1.10.0 or newer - Python packages: `meson-python>=0.15.0` - Boost libraries (>= 1.83.0) installed system-wide (or at least findable by -meson with pkg-config) + meson with pkg-config) #### Optional - dialog (used by the `install.sh` script, not needed if using pip or meson -directly) + directly) - pip (used by the `install.sh` script or by calling pip directly, not needed -if using meson directly) + if using meson directly) > **Note:** Boost is the only external library dependency used by GridFire directly. @@ -176,20 +186,20 @@ if using meson directly) ### Install Scripts GridFire ships with an installer (`install.sh`) which is intended to make the -process of installation both easier and more repetable. +process of installation both easier and more repeatable. #### Ease of Installation -Both scripts are intended to automate installation more or less completly. This +Both scripts are intended to automate installation more or less completely. This includes dependency checking. In the event that a dependency cannot be found they try to install (after explicitly asking for user permission). If that does -not work they will provide a clear message as to what went wrong. +not work they will provide a clear message as to what went wrong. #### Reproducibility The TUI mode provides easy modification of meson build system and compiler settings which can then be saved to a config file. This config file can then be loaded by either tui mode or cli mode (with the `--config`) flag meaning that build configurations can be made and reused. Note that this is **not** a -deterministicly reproducible build system as it does not interact with any +deterministically reproducible build system as it does not interact with any system dependencies or settings, only meson and compiler settings. #### Examples @@ -204,12 +214,12 @@ system dependencies or settings, only meson and compiler settings. [![asciicast](https://asciinema.org/a/GYaWTXZbDJRD4ohde0s3DkFMC.svg)](https://asciinema.org/a/GYaWTXZbDJRD4ohde0s3DkFMC) -> **Note:** `install-tui.sh` is simply a script which calles `install.sh` with +> **Note:** `install-tui.sh` is simply a script which calls `install.sh` with > the `--tui` flag. You can get the exact same results by running `install.sh > --tui`. > **Note:** Call `install.sh` with the `--help` or `--h` flag to see command -> line options +> line options > **Note:** `clang` tends to compile GridFire much faster than `gcc` thus why I > select it in the above asciinema recording. @@ -224,12 +234,12 @@ sudo apt-get install -y build-essential meson python3 python3-pip libboost-all-d > **Note:** Depending on the ubuntu version you have the libboost-all-dev > libraries may be too old. If this is the case refer to the boost -> documentation for how to download and install a version `>=1.83.0` +> documentation for how to download and install a version `>=1.83.0` > **Note:** On recent versions of ubuntu python has switched to being -> externally managed by the system. We **strongly** recomend that if you -> install manaully all python pacakges are installed inside some kind of -> virtual enviroment (e.g. `pyenv`, `conda`, `python-venv`, etc...). When using +> externally managed by the system. We **strongly** recommend that if you +> install manually all python packages are installed inside some kind of +> virtual environment (e.g. `pyenv`, `conda`, `python-venv`, etc...). When using > the installer script this is handled automatically using `python-venv`. - **Fedora/CentOS/RHEL:** @@ -252,7 +262,7 @@ 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 enviromental variables +clang via environmental variables ```bash CC=clang CXX=clang++ meson setup build_clang @@ -266,8 +276,8 @@ meson install -C build ### Minimum compiler versions GridFire uses C++23 features and therefore only compilers and standard library -implimentations which support C++23 are supported. Generally we have found that -`gcc >= 13.0.0` or `clang >= 16.0.0` work well. +implementations which support C++23 are supported. Generally we have found that +`gcc >= 13.0.0` or `clang >= 16.0.0` work well. ## Code Architecture and Logical Flow @@ -277,28 +287,28 @@ 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 -implimented `Views` submodule. + 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. + (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](https://adsabs.harvard.edu/full/1954AuJPh...7..373S)), `BareScreening`) -affecting reaction rates. + `WeakScreening` ([Salpeter, + 1954](https://adsabs.harvard.edu/full/1954AuJPh...7..373S)), `BareScreening`) + affecting reaction rates. - **Reaction Module:** Parses and manages Reaclib reaction rate data, providing -temperature- and density-dependent rate evaluations. + temperature- and density-dependent rate evaluations. - **Partition Module:** Implements partition functions (e.g., -`GroundStatePartitionFunction`, `RauscherThielemannPartitionFunction` -([Rauscher & Thielemann, -2000](https://www.sciencedirect.com/science/article/pii/S0092640X00908349?via%3Dihub])) -to weight reaction rates based on nuclear properties. + `GroundStatePartitionFunction`, `RauscherThielemannPartitionFunction` + ([Rauscher & Thielemann, + 2000](https://www.sciencedirect.com/science/article/pii/S0092640X00908349?via%3Dihub])) + 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. + `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. + 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 @@ -312,27 +322,27 @@ 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 evolver abundnances. The important thing to understand about `Engines` is -that they contain all of the detailed physics GridFire uses. For example a +to evolver 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 -impliment to be compatible with GridFire solvers. +implement to be compatible with GridFire solvers. -Currently we only impliment `GraphEngine` which is intended to be a very general and +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](https://reaclib.jinaweb.org/index.php). +Reaclib database](https://reaclib.jinaweb.org/index.php). `GraphEngine`s are constructed from a seed composition of species from which -they recursivley expand their topology outward, following known reaction -pathways and adding new species to the tracked list as they expand. +they recursively expand their topology outward, following known reaction +pathways and adding new species to the tracked list as they expand. ### GraphEngine Configuration Options @@ -341,38 +351,38 @@ 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 an - member of the `NetworkBuildDepth` enum or an integerl. - - `partition::PartitionFunction`: Partition function used when evlauating - detailed balance for inverse rates. + - `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. - **setPrecomputation(bool precompute):** - - Enable/disable caching of reaction rates and stoichiometric data at initialization. - - *Effect:* Reduces per-step overhead; increases memory and setup time. + - 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. + - 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. + - 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 | +| 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 matricies very -efficiently. +differentiation library, to generate analytic jacobian matrices very +efficiently. ## Reaclib in GridFire @@ -380,14 +390,14 @@ 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 thes reactions have been compiled into a header file which is then +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 benafit is faster load times and more importantly no need for end +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 injest a reaclib data file and produce the +`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` @@ -398,13 +408,13 @@ 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| +| 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 @@ -413,11 +423,11 @@ 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 propegate the result down the chain of engine views, eventually +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 encorage developers to think -carefully about passing along calls to base engine methods when implimenting +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 @@ -428,7 +438,7 @@ integration algorithms to be used interchangeably with any engine that implements the `Engine` or `DynamicEngine` contract. ### NetworkSolverStrategy<EngineT>: -All GridFire solvers impliment the abstract strategy templated by +All GridFire solvers implement the abstract strategy templated by `NetworkSolverStrategy` which enforces only that there is some `evaluate` method with the following signature @@ -440,7 +450,7 @@ 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 immediatly +(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. @@ -454,11 +464,11 @@ A `NetIn` struct contains - 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 timestepper push the timestep up. Often for main sequence +>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 avalible through the `foursdt` library and the +>object. This is made available through the `foursdt` library and the >`fourdst/composition/Composition.h` header. `fourdst` is installed >automatically with GridFire @@ -471,7 +481,7 @@ 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`) + (`NetOut::energy`) >**Note:** Currently `GraphEngine` only considers energy due to nuclear mass >defect and not neutrino loss. @@ -480,18 +490,18 @@ A `NetOut` struct contains ### DirectNetworkSolver (Implicit Rosenbrock Method) - **Integrator:** Implicit Rosenbrock4 scheme (order 4) via `Boost.Odeint`’s -`rosenbrock4`, optimized for stiff reaction networks with adaptive step -size control using configurable absolute and relative tolerances. + `rosenbrock4`, optimized for stiff reaction networks with adaptive step + size control using configurable absolute and relative tolerances. - **Jacobian Assembly:** Asks the base engine for the Jacobian Matrix -- **RHS Evaluation:** Assk the base engine for RHS of the abundance evolution -equations +- **RHS Evaluation:** Asks the base engine for RHS of the abundance evolution + equations - **Linear Algebra:** Utilizes `Boost.uBLAS` for state vectors and dense Jacobian -matrices, with sparse access patterns supported via coordinate lists of nonzero -entries. + matrices, with sparse access patterns supported via coordinate lists of nonzero + entries. - **Error Control and Logging:** Absolute and relative tolerance parameters -(`absTol`, `relTol`) are read from configuration; Quill loggers, which run in a -seperate non blocking thread, capture integration diagnostics and step -statistics. + (`absTol`, `relTol`) are read from configuration; Quill loggers, which run in a + separate non blocking thread, capture integration diagnostics and step + statistics. ### Algorithmic Workflow in DirectNetworkSolver 1. **Initialization:** Convert input temperature to T9 units, retrieve @@ -499,21 +509,21 @@ statistics. 2. **Integrator Setup:** Construct the controlled Rosenbrock4 stepper and bind `RHSManager` and `JacobianFunctor`. 3. **Adaptive Integration Loop:** - - Perform `integrate_adaptive` advancing until `tMax`, catching any - `StaleEngineTrigger` to repartition the network and update composition. - - On each substep, observe states and log via `RHSManager::observe`. + - Perform `integrate_adaptive` advancing until `tMax`, catching any + `StaleEngineTrigger` to repartition the network and update composition. + - On each substep, observe states and log via `RHSManager::observe`. 4. **Finalization:** Assemble final mass fractions, compute accumulated energy, and populate `NetOut` with updated composition and diagnostics. ### Future Solver Implementations - **Operator Splitting Solvers:** Strategies to decouple thermodynamics, -screening, and reaction substeps for performance on stiff, multi-scale -networks. + screening, and reaction substeps for performance on stiff, multiscale + networks. - **GPU-Accelerated Solvers:** Planned use of CUDA/OpenCL backends for -large-scale network integration. + large-scale network integration. - **Callback observer support:** Currently we use an observer built into our -`RHSManager` (`RHSManager::observe`); however, we intend to inlucde support for -custom, user defined, observer method. + `RHSManager` (`RHSManager::observe`); however, we intend to include support for + custom, user defined, observer method. These strategies can be developed by inheriting from `NetworkSolverStrategy` and registering against the same engine types without modifying existing engine @@ -629,22 +639,22 @@ int main(){ #### Workflow Components and Effects - **GraphEngine** constructs the full reaction network, capturing all species -and reactions. + and reactions. - **MultiscalePartitioningEngineView** segregates reactions by characteristic -timescales (Hix & Thielemann), reducing the effective stiffness by treating -fast processes separately. + timescales (Hix & Thielemann), reducing the effective stiffness by treating + fast processes separately. - **AdaptiveEngineView** prunes low-flux species/reactions at runtime, -decreasing dimensionality and improving computational efficiency. + decreasing dimensionality and improving computational efficiency. - **DirectNetworkSolver** employs an implicit Rosenbrock method to stably -integrate the remaining stiff system with adaptive step control. + integrate the remaining stiff system with adaptive step control. This layered approach enhances stability for stiff networks while maintaining accuracy and performance. ### 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::::TimestepContext` -as the argument type for the callback function. This struct contains all of the information provided by that solver to +different context to the callback function, you should use the struct `gridfire::solver::::TimestepContext` +as the argument type for the callback function. This struct contains all the information provided by that solver to the callback function. ```c++ @@ -704,20 +714,32 @@ int main(){ >**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 +>**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). +#### 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 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 +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 Examople -This example impliments the same logic as the above C++ example +### 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 DirectNetworkSolver @@ -820,11 +842,11 @@ print(f"Final H-1 mass fraction {results.composition.getMassFraction("H-1")}") GridFire integrates with and builds upon several key 4D-STAR libraries: - [fourdst](https://github.com/4D-STAR/fourdst): hub module managing versioning -of `libcomposition`, `libconfig`, `liblogging`, and `libconstants` + of `libcomposition`, `libconfig`, `liblogging`, and `libconstants` - [libcomposition](https://github.com/4D-STAR/libcomposition) -([docs](https://4d-star.github.io/libcomposition/)): Composition management -toolkit. + ([docs](https://4d-star.github.io/libcomposition/)): Composition management + toolkit. - [libconfig](https://github.com/4D-STAR/libconfig): Configuration file parsing -utilities. + utilities. - [liblogging](https://github.com/4D-STAR/liblogging): Flexible logging framework. - [libconstants](https://github.com/4D-STAR/libconstants): Physical constants diff --git a/docs/html/doxygen_crawl.html b/docs/html/doxygen_crawl.html index 09bc71f1..a63fc04e 100644 --- a/docs/html/doxygen_crawl.html +++ b/docs/html/doxygen_crawl.html @@ -854,6 +854,7 @@ + @@ -867,13 +868,13 @@ - - - - - + + + + + diff --git a/docs/html/index.html b/docs/html/index.html index a2672c36..87ad08cc 100644 --- a/docs/html/index.html +++ b/docs/html/index.html @@ -118,7 +118,7 @@ Funding 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 encorages to use the python module rather than the C++ code.

+

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 encourages to use the python module rather than the C++ code.

pypi

Installing from pip is as simple as

pip install gridfire
@@ -134,10 +134,10 @@ pypi 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: Currently macOS x86_64 does not have a precompiled wheel. Do 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: 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+

@@ -156,11 +156,11 @@ source

source for developers

-

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

+

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 incrimental builds whenever source code changes and you run a python script automartically (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 reccomended that developers use this approach and end users do not.

+

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.

Automatic Build and Installation

@@ -172,7 +172,7 @@ Script Build and Installation Instructions

The regular installation script will select a standard "ideal" set of build options for you. If you want more control over the build options, you can use the install-tui.sh script, which will provide a text-based user interface to select the build options you want.

Generally, both are intended to be easy to use and will prompt you automatically to install any missing dependencies.

-Currently known good platforms

+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)
    • @@ -215,13 +215,13 @@ Optional

    Install Scripts

    -

    GridFire ships with an installer (install.sh) which is intended to make the process of installation both easier and more repetable.

    +

    GridFire ships with an installer (install.sh) which is intended to make the process of installation both easier and more repeatable.

    Ease of Installation

    -

    Both scripts are intended to automate installation more or less completly. This includes dependency checking. In the event that a dependency cannot be found they try to install (after explicitly asking for user permission). If that does not work they will provide a clear message as to what went wrong.

    +

    Both scripts are intended to automate installation more or less completely. This includes dependency checking. In the event that a dependency cannot be found they try to install (after explicitly asking for user permission). If that does not work they will provide a clear message as to what went wrong.

    Reproducibility

    -

    The TUI mode provides easy modification of meson build system and compiler settings which can then be saved to a config file. This config file can then be loaded by either tui mode or cli mode (with the --config) flag meaning that build configurations can be made and reused. Note that this is not a deterministicly reproducible build system as it does not interact with any system dependencies or settings, only meson and compiler settings.

    +

    The TUI mode provides easy modification of meson build system and compiler settings which can then be saved to a config file. This config file can then be loaded by either tui mode or cli mode (with the --config) flag meaning that build configurations can be made and reused. Note that this is not a deterministically reproducible build system as it does not interact with any system dependencies or settings, only meson and compiler settings.

    Examples

    @@ -234,7 +234,7 @@ TUI config loading and meson setup
    CLI config loading, setup, and build

    asciicast

    -

    Note: install-tui.sh is simply a script which calles install.sh with the --tui flag. You can get the exact same results by running install.sh --tui.

    +

    Note: install-tui.sh is simply a script which calls install.sh with the --tui flag. You can get the exact same results by running install.sh --tui.

    Note: Call install.sh with the --help or --h flag to see command line options

    @@ -253,7 +253,7 @@ Dependency Installation on Common Platforms

    Note: Depending on the ubuntu version you have the libboost-all-dev libraries may be too old. If this is the case refer to the boost documentation for how to download and install a version >=1.83.0

    -

    Note: On recent versions of ubuntu python has switched to being externally managed by the system. We strongly recomend that if you install manaully all python pacakges are installed inside some kind of virtual enviroment (e.g. pyenv, conda, python-venv, etc...). When using the installer script this is handled automatically using python-venv.

    +

    Note: On recent versions of ubuntu python has switched to being externally managed by the system. We strongly recommend that if you install manually all python packages are installed inside some kind of virtual environment (e.g. pyenv, conda, python-venv, etc...). When using the installer script this is handled automatically using python-venv.

    • Fedora/CentOS/RHEL:
      sudo dnf install -y gcc-c++ meson python3 python3-pip boost-devel
      @@ -268,7 +268,7 @@ Building the C++ Library
      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 enviromental variables

      +

      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

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

      @@ -276,12 +276,12 @@ Installing the Library

      meson install -C build

      Minimum compiler versions

      -

      GridFire uses C++23 features and therefore only compilers and standard library implimentations which support C++23 are supported. Generally we have found that gcc >= 13.0.0 or clang >= 16.0.0 work well.

      +

      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 >= 13.0.0 or clang >= 16.0.0 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 implimented Views submodule.
        • +
      • 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.
        • @@ -292,21 +292,21 @@ Code Architecture and Logical Flow

        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 evolver abundnances. The important thing to understand about Engines is that they contain all of 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 impliment to be compatible with GridFire solvers.

        -

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

        +

        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 evolver 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 recursivley expand their topology outward, following known reaction pathways and adding new species to the tracked list as they expand.

        +

        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 an member of the NetworkBuildDepth enum or an integerl.
            • -
          • partition::PartitionFunction: Partition function used when evlauating detailed balance for inverse rates.
            • +
          • 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.
        • setPrecomputation(bool precompute):
            @@ -339,12 +339,12 @@ Available Partition Functions

            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 matricies very efficiently.

            +

            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 thes 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 benafit 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 injest a reaclib data file and produce the needed header file. More details on this process are included in utils/reaclib/readme.md

            +

            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.

            @@ -365,18 +365,18 @@ Engine Views

            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 propegate 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 encorage developers to think carefully about passing along calls to base engine methods when implimenting new views.

            +

            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 impliment the abstract strategy templated by NetworkSolverStrategy which enforces only that there is some evaluate method with the following signature

            +

            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 immediatly 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.

            +

            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

              @@ -387,8 +387,8 @@ NetIn
            • 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 timestepper 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 avalible through the foursdt library and the >fourdst/composition/Composition.h header. fourdst is installed >automatically with GridFire

            +

            >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

            @@ -403,9 +403,9 @@ DirectNetworkSolver (Implicit Rosenbrock Method)
            • Integrator: Implicit Rosenbrock4 scheme (order 4) via Boost.Odeint’s rosenbrock4<double>, optimized for stiff reaction networks with adaptive step size control using configurable absolute and relative tolerances.
            • Jacobian Assembly: Asks the base engine for the Jacobian Matrix
              • -
            • RHS Evaluation: Assk the base engine for RHS of the abundance evolution equations
              • +
            • RHS Evaluation: Asks the base engine for RHS of the abundance evolution equations
            • Linear Algebra: Utilizes Boost.uBLAS for state vectors and dense Jacobian matrices, with sparse access patterns supported via coordinate lists of nonzero entries.
              • -
            • Error Control and Logging: Absolute and relative tolerance parameters (absTol, relTol) are read from configuration; Quill loggers, which run in a seperate non blocking thread, capture integration diagnostics and step statistics.
              • +
            • Error Control and Logging: Absolute and relative tolerance parameters (absTol, relTol) are read from configuration; Quill loggers, which run in a separate non blocking thread, capture integration diagnostics and step statistics.

            Algorithmic Workflow in DirectNetworkSolver

            @@ -422,9 +422,9 @@ Algorithmic Workflow in DirectNetworkSolver

            Future Solver Implementations

              -
            • Operator Splitting Solvers: Strategies to decouple thermodynamics, screening, and reaction substeps for performance on stiff, multi-scale networks.
              • +
            • Operator Splitting Solvers: Strategies to decouple thermodynamics, screening, and reaction substeps for performance on stiff, multiscale networks.
            • GPU-Accelerated Solvers: Planned use of CUDA/OpenCL backends for large-scale network integration.
              • -
            • Callback observer support: Currently we use an observer built into our RHSManager (RHSManager::observe); however, we intend to inlucde support for custom, user defined, observer method.
              • +
            • Callback observer support: Currently we use an observer built into our RHSManager (RHSManager::observe); however, we intend to include support for custom, user defined, observer method.

            These strategies can be developed by inheriting from NetworkSolverStrategy and registering against the same engine types without modifying existing engine code.

            @@ -538,7 +538,7 @@ Workflow Components and Effects

            This layered approach enhances stability for stiff networks while maintaining accuracy and performance.

            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 of the information provided by that solver to the callback function.

            +

            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/engine/engine.h" // Unified header for real usage
            #include "gridfire/solver/solver.h" // Unified header for solvers
            #include "fourdst/composition/composition.h"
            @@ -600,14 +600,19 @@ Callback Example

            >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).

            -

            +

            +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.

            -

            -Common Workflow Examople

            -

            This example impliments the same logic as the above C++ example

            from gridfire.engine import GraphEngine, MultiscalePartitioningEngineView, AdaptiveEngineView
            +

            +Common Workflow Example

            +

            This example implements the same logic as the above C++ example

            from gridfire.engine import GraphEngine, MultiscalePartitioningEngineView, AdaptiveEngineView
            from gridfire.solver import DirectNetworkSolver
            from gridfire.type import NetIn
            @@ -644,7 +649,7 @@ Common Workflow Examople
            print(f"Final H-1 mass fraction {results.composition.getMassFraction("H-1")}")
            gridfire::solver
            Definition solver.h:18
            -

            +

            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++

            from gridfire.engine import GraphEngine, MultiscalePartitioningEngineView, AdaptiveEngineView
            @@ -694,7 +699,7 @@ Python callbacks
            print(f"Final H-1 mass fraction {results.composition.getMassFraction("H-1")}")
            -

            +

            Related Projects

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

              diff --git a/docs/html/md_docs_2static_2usage.html b/docs/html/md_docs_2static_2usage.html index e1ba70cb..47eb8f0f 100644 --- a/docs/html/md_docs_2static_2usage.html +++ b/docs/html/md_docs_2static_2usage.html @@ -102,15 +102,15 @@ $(function(){initNavTree('md_docs_2static_2usage.html',''); initResizable(true);
              GridFire Python Usage Guide
              -

              +

              This tutorial walks you through installing GridFire’s Python bindings, choosing engines and views thoughtfully, running a simulation, and visualizing your results.

              -

              +

              1. Installation

              -

              +

              1.1 PyPI Release

              The quickest way to get started is:

              pip install gridfire
              -

              +

              1.2 Development from Source

              If you want the cutting-edge features or need to hack the C++ backend:

              git clone https://github.com/4DSTAR/GridFire.git
              cd GridFire
              @@ -122,7 +122,7 @@ $(function(){initNavTree('md_docs_2static_2usage.html',''); initResizable(true);

              You can also build manually with Meson (generally end users will not need to do this):

              meson setup build-python
              meson compile -C build_gridfire
              -

              +

              2. Why These Engines and Views?

              GridFire’s design balances physical fidelity and performance. Here’s why we pick each component:

                @@ -162,7 +162,7 @@ $(function(){initNavTree('md_docs_2static_2usage.html',''); initResizable(true);

              By composing these views in sequence, you can tailor accuracy vs performance for your scientific question. Commonly one might use a flow like GraphEngine → Partitioning → Adaptive to capture both full-network physics and manageable stiffness.

              -

              +

              3. Step-by-Step Example

              Adapted from tests/python/test.py. Comments explain each choice.

              import matplotlib.pyplot as plt
              @@ -217,7 +217,7 @@ $(function(){initNavTree('md_docs_2static_2usage.html',''); initResizable(true);
            • Implicit solver: Rosenbrock4 handles stiff systems robustly, letting you push to longer tMax.
            -

            +

            4. Visualizing Reaction Networks

            GridFire engines and views provide built-in export methods for Graphviz DOT and CSV formats:

            # Export the base network to DOT for Graphviz
            @@ -244,7 +244,7 @@ $(function(){initNavTree('md_docs_2static_2usage.html',''); initResizable(true);
            df.to_csv('H1_evolution.csv', index=False)

            Then plot in pandas or Excel for custom figures.

            -

            +

            5. Beyond the Basics

            • Custom Partition Functions: In Python, subclass gridfire.partition.PartitionFunction, override evaluate, supports, and clone to implement new weighting schemes.
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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 encorages to use the python module rather than the C++ code. +users are strongly encourages to use the python module rather than the C++ code. ### pypi Installing from pip is as simple as @@ -56,16 +56,16 @@ pip install gridfire These wheels have been compiled on many systems | Version | Platform | Architecture | CPython Versions | PyPy Versions | -| ------- | -------- | ------------ | ---------------------------------------------------------- | ------------- | +|---------|----------|--------------|------------------------------------------------------------|---------------| | 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 +> **Note**: Currently macOS x86\_64 does **not** have a precompiled wheel. Do > 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 +> **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 @@ -90,7 +90,7 @@ pip install . > fail, the steps in further sections address these in more detail. ### source for developers -If you are a developer and would like an editable and incrimental python +If you are a developer and would like an editable and incremental python install `meson-python` makes this very easy ```bash @@ -99,10 +99,10 @@ cd GridFire pip install -e . --no-build-isolation -vv ``` -This will generate incrimental builds whenever source code changes and you run -a python script automartically (note that since `meson setup` must run for each +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** reccomended that +a source code change you have made). It is **strongly** recommended that developers use this approach and end users *do not*. @@ -126,7 +126,7 @@ Generally, both are intended to be easy to use and will prompt you automatically to install any missing dependencies. -### Currently known good platforms +### 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) @@ -172,10 +172,10 @@ These only need to be manually installed if the user is not making use of the ### Install Scripts GridFire ships with an installer (`install.sh`) which is intended to make the -process of installation both easier and more repetable. +process of installation both easier and more repeatable. #### Ease of Installation -Both scripts are intended to automate installation more or less completly. This +Both scripts are intended to automate installation more or less completely. This includes dependency checking. In the event that a dependency cannot be found they try to install (after explicitly asking for user permission). If that does not work they will provide a clear message as to what went wrong. @@ -185,7 +185,7 @@ The TUI mode provides easy modification of meson build system and compiler settings which can then be saved to a config file. This config file can then be loaded by either tui mode or cli mode (with the `--config`) flag meaning that build configurations can be made and reused. Note that this is **not** a -deterministicly reproducible build system as it does not interact with any +deterministically reproducible build system as it does not interact with any system dependencies or settings, only meson and compiler settings. #### Examples @@ -200,7 +200,7 @@ system dependencies or settings, only meson and compiler settings. [![asciicast](https://asciinema.org/a/GYaWTXZbDJRD4ohde0s3DkFMC.svg)](https://asciinema.org/a/GYaWTXZbDJRD4ohde0s3DkFMC) -> **Note:** `install-tui.sh` is simply a script which calles `install.sh` with +> **Note:** `install-tui.sh` is simply a script which calls `install.sh` with > the `--tui` flag. You can get the exact same results by running `install.sh > --tui`. @@ -223,9 +223,9 @@ sudo apt-get install -y build-essential meson python3 python3-pip libboost-all-d > documentation for how to download and install a version `>=1.83.0` > **Note:** On recent versions of ubuntu python has switched to being -> externally managed by the system. We **strongly** recomend that if you -> install manaully all python pacakges are installed inside some kind of -> virtual enviroment (e.g. `pyenv`, `conda`, `python-venv`, etc...). When using +> externally managed by the system. We **strongly** recommend that if you +> install manually all python packages are installed inside some kind of +> virtual environment (e.g. `pyenv`, `conda`, `python-venv`, etc...). When using > the installer script this is handled automatically using `python-venv`. - **Fedora/CentOS/RHEL:** @@ -248,7 +248,7 @@ 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 enviromental variables +clang via environmental variables ```bash CC=clang CXX=clang++ meson setup build_clang @@ -262,7 +262,7 @@ meson install -C build ### Minimum compiler versions GridFire uses C++23 features and therefore only compilers and standard library -implimentations which support C++23 are supported. Generally we have found that +implementations which support C++23 are supported. Generally we have found that `gcc >= 13.0.0` or `clang >= 16.0.0` work well. @@ -274,7 +274,7 @@ include: - **Engine Module:** Core interfaces and implementations (e.g., `GraphEngine`) that evaluate reaction network rate equations and energy generation. Also - implimented `Views` submodule. + 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. @@ -308,16 +308,16 @@ 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 evolver abundnances. The important thing to understand about `Engines` is -that they contain all of the detailed physics GridFire uses. For example a +to evolver 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 -impliment to be compatible with GridFire solvers. +implement to be compatible with GridFire solvers. -Currently we only impliment `GraphEngine` which is intended to be a very general and +Currently, we only implement `GraphEngine` which is intended to be a very general and adaptable `Engine`. ### GraphEngine @@ -327,7 +327,7 @@ connecting some set of atomic species through reactions listed in the [JINA Reaclib database](https://reaclib.jinaweb.org/index.php). `GraphEngine`s are constructed from a seed composition of species from which -they recursivley expand their topology outward, following known reaction +they recursively expand their topology outward, following known reaction pathways and adding new species to the tracked list as they expand. @@ -339,9 +339,9 @@ 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 an - member of the `NetworkBuildDepth` enum or an integerl. - - `partition::PartitionFunction`: Partition function used when evlauating + 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. - **setPrecomputation(bool precompute):** @@ -358,16 +358,16 @@ construction and rate evaluations: ### 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 | +| 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 matricies very +differentiation library, to generate analytic jacobian matrices very efficiently. @@ -376,14 +376,14 @@ 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 thes reactions have been compiled into a header file which is then +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 benafit is faster load times and more importantly no need for end +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 injest a reaclib data file and produce the +`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` @@ -394,13 +394,13 @@ 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| +| 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 @@ -409,11 +409,11 @@ 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 propegate the result down the chain of engine views, eventually +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 encorage developers to think -carefully about passing along calls to base engine methods when implimenting +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 @@ -424,7 +424,7 @@ integration algorithms to be used interchangeably with any engine that implements the `Engine` or `DynamicEngine` contract. ### NetworkSolverStrategy<EngineT>: -All GridFire solvers impliment the abstract strategy templated by +All GridFire solvers implement the abstract strategy templated by `NetworkSolverStrategy` which enforces only that there is some `evaluate` method with the following signature @@ -436,7 +436,7 @@ 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 immediatly +(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. @@ -450,11 +450,11 @@ A `NetIn` struct contains - 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 timestepper push the timestep up. Often for main sequence +>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 avalible through the `foursdt` library and the +>object. This is made available through the `foursdt` library and the >`fourdst/composition/Composition.h` header. `fourdst` is installed >automatically with GridFire @@ -479,14 +479,14 @@ A `NetOut` struct contains `rosenbrock4`, optimized for stiff reaction networks with adaptive step size control using configurable absolute and relative tolerances. - **Jacobian Assembly:** Asks the base engine for the Jacobian Matrix -- **RHS Evaluation:** Assk the base engine for RHS of the abundance evolution +- **RHS Evaluation:** Asks the base engine for RHS of the abundance evolution equations - **Linear Algebra:** Utilizes `Boost.uBLAS` for state vectors and dense Jacobian matrices, with sparse access patterns supported via coordinate lists of nonzero entries. - **Error Control and Logging:** Absolute and relative tolerance parameters (`absTol`, `relTol`) are read from configuration; Quill loggers, which run in a - seperate non blocking thread, capture integration diagnostics and step + separate non blocking thread, capture integration diagnostics and step statistics. ### Algorithmic Workflow in DirectNetworkSolver @@ -495,20 +495,20 @@ A `NetOut` struct contains 2. **Integrator Setup:** Construct the controlled Rosenbrock4 stepper and bind `RHSManager` and `JacobianFunctor`. 3. **Adaptive Integration Loop:** - - Perform `integrate_adaptive` advancing until `tMax`, catching any - `StaleEngineTrigger` to repartition the network and update composition. - - On each substep, observe states and log via `RHSManager::observe`. + - Perform `integrate_adaptive` advancing until `tMax`, catching any + `StaleEngineTrigger` to repartition the network and update composition. + - On each substep, observe states and log via `RHSManager::observe`. 4. **Finalization:** Assemble final mass fractions, compute accumulated energy, and populate `NetOut` with updated composition and diagnostics. ### Future Solver Implementations - **Operator Splitting Solvers:** Strategies to decouple thermodynamics, - screening, and reaction substeps for performance on stiff, multi-scale + screening, and reaction substeps for performance on stiff, multiscale networks. - **GPU-Accelerated Solvers:** Planned use of CUDA/OpenCL backends for large-scale network integration. - **Callback observer support:** Currently we use an observer built into our - `RHSManager` (`RHSManager::observe`); however, we intend to inlucde support for + `RHSManager` (`RHSManager::observe`); however, we intend to include support for custom, user defined, observer method. These strategies can be developed by inheriting from `NetworkSolverStrategy` @@ -640,7 +640,7 @@ accuracy and performance. ### 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::::TimestepContext` -as the argument type for the callback function. This struct contains all of the information provided by that solver to +as the argument type for the callback function. This struct contains all the information provided by that solver to the callback function. ```c++ @@ -703,6 +703,18 @@ int main(){ >**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). +#### 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 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 @@ -712,8 +724,8 @@ with imports of modules such that All GridFire C++ types have been bound and can be passed around as one would expect. -### Common Workflow Examople -This example impliments the same logic as the above C++ example +### 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 DirectNetworkSolver