pyghl 0.4.0

Python bindings for GRHayL (EOS-first preview)

pyghl

Python bindings and neural-network tooling for GRHayL.

pyghl packages a pinned GRHayL build together with a CPython extension and Python utilities for tabulated equations of state, conservative-to-primitive experiments, and neural-network initial guesses.

Warning

While revised by humans, the majority of the code in this repository has leveraged AI assistance. Users should review results carefully and use this package at their own risk.

At a high level:

• pyghl exposes selected GRHayL C structs and routines to Python.
• pyghl.eos provides tabulated-EOS loading and interpolation helpers.
• pyghl.nn provides neural-network con2prim data, training, export, and EOS embedding helpers.
• pyghl train trains a neural-network model for an EOS table.
• pyghl append embeds a trained model into an EOS HDF5 file.

Installation

For end users installing from PyPI:

python -m pip install pyghl

Check the installed package:

pyghl --version

For contributors working from a clone of this repository:

git clone https://github.com/GRHayL/pyghl.git
cd pyghl
git submodule update --init --recursive
python -m pip install -e .

The local build compiles the pinned GRHayL submodule in extern/GRHayL and then builds the Python extension against the resulting libghl.

If you need to build against a different local GRHayL checkout:

GRHAYL_DIR=/path/to/GRHayL python -m pip install -e .

Prerequisites by Workflow

python -m pip install pyghl should install a prebuilt wheel on supported platforms. Source builds need additional system tools:

• Local extension builds: a C compiler, make, HDF5, and Python build tooling.
• Editable contributor installs: initialized extern/GRHayL submodule.
• Neural-network training: torch, numpy, and h5py from package dependencies.
• EOS workflows: a GRHayL-compatible tabulated EOS HDF5 file.

Current CI builds wheels for Linux x86_64 and macOS arm64.

EOS format support

Currently, pyghl supports only tables in the StellarCollapse HDF5 format. Support for tables in the CompOSE format is planned for a future release.

Downloading an EOS Table

stellarcollapse.org provides GRHayL-compatible tabulated EOS files. For example, the APR EOS page provides an APR table with NSE (3335 nuclides). Download and extract it before passing the HDF5 file to pyghl:

curl -fL \
  'https://stockholmuniversity.box.com/s/xatxe62v9ywxr5sl2vf94e180uf1zkik?download=1' \
  -o APR_3335_rho393_temp133_ye66_gitM180edd5_20190225.h5.tar.bz2
tar -xjf APR_3335_rho393_temp133_ye66_gitM180edd5_20190225.h5.tar.bz2

This APR table does not currently have an installed pyghl model, so train one and embed it in the extracted EOS file:

pyghl train APR_3335_rho393_temp133_ye66_gitM180edd5_20190225.h5
pyghl check-eos APR_3335_rho393_temp133_ye66_gitM180edd5_20190225.h5

pyghl train generates a training dataset, trains and saves the model, and embeds the resulting neural-network data into the EOS file.

Basic EOS Neural-Network Workflow

The quickest useful workflow is to make sure the EOS file itself contains the neural-network data that GRHayL loads when enable_neural_net_c2p=True.

1. Try the installed model cache first

pyghl append path/to/eos_table.h5

This looks for an installed neural-network model whose recorded EOS hash matches the EOS file, then embeds it into the EOS under the grhayl_nn_c2p group. This is the preferred first command because it is fast and avoids retraining known EOS tables.

2. Train if the EOS is unknown

Run training without an EOS path to choose from the tables published through the StellarCollapse microphysics index:

pyghl train

The command discovers EOS-family catalog pages from that index (including nested EOS catalog links). It first presents the category/paper names used by the StellarCollapse microphysics page, then opens a searchable list of the specific tables in the selected category. Table search supports arrow-key navigation, paging, and type-to-filter matching across family, variant, and filename.

APR tables remain advertised by StellarCollapse, but their Stockholm University Box downloads currently return HTTP 404. The category and table screens mark these entries unavailable before selection. Other selected bzip2 or tar+bzip2 HDF5 tables are downloaded, decompressed into the current directory, and reused on later runs. Downloads display transferred size and speed; servers that report the file size also get a percentage bar and ETA. Standard tables are hundreds of MB compressed; some high-resolution SRO tables are 4.3-4.5 GB.

To use an EOS table already on disk instead:

pyghl train path/to/eos_table.h5

If no installed model matches, pyghl train creates training data when no dataset is supplied, trains a small neural network, writes model artifacts such as tiny_mlp_model.h5, registers the model by the EOS canonical MD5 hash, and embeds the result into the EOS file by default.

To train from an existing dataset:

pyghl train path/to/eos_table.h5 path/to/nn_training_dataset.bin

3. Retrain a known EOS deliberately

pyghl train path/to/eos_table.h5 --force_retrain

Use this when the EOS is already known but you want to replace the installed model artifacts with a fresh training run. If the EOS already contains embedded neural-network data and you want to replace it too, add --overwrite_eos.

Other Useful Commands

Inspect whether an EOS file already contains embedded neural-network data:

pyghl check-eos path/to/eos_table.h5

List installed models that can be matched by EOS hash:

pyghl list-models

Append a specific model file instead of using the installed model cache:

pyghl append path/to/eos_table.h5 path/to/tiny_mlp_model.h5

Remove embedded neural-network data from an EOS file:

pyghl remove-eos-nn path/to/eos_table.h5

Python API Example

import pyghl as ghl

params = ghl.initialize_params()
eos = ghl.eos.initialize_tabulated_eos_functions_and_params(
    params,
    "path/to/eos_table.h5",
)

rho = 1.0e-12
Y_e = 0.05
T = 1.0e2

rho, Y_e, T = eos.tabulated_enforce_bounds_rho_Ye_T(rho, Y_e, T)
pressure = eos.tabulated_compute_P_from_T(rho, Y_e, T)
print(pressure)

Command-Line Tools

Primary commands:

pyghl train [<eos-file>] [dataset]
pyghl append <eos-file> [nn-hdf5]
pyghl check-eos <eos-file>
pyghl list-models
pyghl remove-eos-nn <eos-file>
pyghl --version

What Is Exposed?

Selected top-level bindings include:

• initialize_params
• initialize_metric
• compute_ADM_auxiliaries
• initialize_primitives
• initialize_diagnostics
• compute_conservs
• undensitize_conservatives
• compute_SU_Bsq_Ssq_BdotS
• limit_v_and_compute_u0
• limit_utilde_and_compute_v
• guess_primitives
• tabulated_Palenzuela1D_energy
• tabulated_con2prim_multi_method
• nn_c2p_guess
• nn_c2p_guess_x

Struct wrappers include:

• Primitive
• Conservative
• Metric
• ADMAux
• Diagnostics
• Params
• TabulatedEOS

Repository Map

• csrc/: CPython extension source.
• src/pyghl/: Python package.
• src/pyghl/nn_c2p/: command-line neural-network workflows.
• examples/: direct example scripts.
• extern/GRHayL/: pinned GRHayL submodule used for builds.
• .github/workflows/: wheel and PyPI publishing automation.
• PUBLISHING.md: release and PyPI publishing checklist.

Contributor Setup

Recommended local workflow:

git submodule update --init --recursive
python -m pip install -e .
python -m compileall -q src setup.py

Build a local wheel:

python -m pip wheel . -w /tmp/pyghl-wheel --no-deps --no-build-isolation