pyghl 0.1.1

Python bindings for GRHayL (EOS-first preview)

pyghl

This directory contains Python bindings for GRHayL, including enough tabulated-EOS and con2prim functionality to reproduce the neural-network dataset/testing generators in Unit_Tests/data_gen.

What is included

• pyghl.initialize_params(...)
• pyghl.initialize_metric(...)
• pyghl.compute_ADM_auxiliaries(...)
• pyghl.initialize_primitives(...)
• pyghl.initialize_diagnostics(...)
• pyghl.compute_conservs(...)
• pyghl.undensitize_conservatives(...)
• pyghl.compute_SU_Bsq_Ssq_BdotS(...)
• pyghl.limit_v_and_compute_u0(...)
• pyghl.limit_utilde_and_compute_v(...)
• pyghl.guess_primitives(...)
• pyghl.tabulated_Palenzuela1D_energy(...)
• pyghl.nn_c2p_guess(eos, ...)
• pyghl.nn_c2p_guess_x(eos, ...)
• Struct wrappers:
  • pyghl.Primitive
  • pyghl.Conservative
  • pyghl.Metric
  • pyghl.ADMAux
  • pyghl.Diagnostics
• pyghl.eos.initialize_tabulated_eos_functions_and_params(...)
• pyghl.nn helpers:
  • flat_metric()
  • guess(eos, ...)
  • guess_x(eos, ...)
  • nn_initial_guess(...)
  • iter_dataset_points(...)
  • read_training_dataset(...)
  • train_regressor(...)
  • train_on_dataset(...)
  • save_inference_bundle(...)
  • load_inference_bundle(...)
  • export_to_c_header(...)
• TabulatedEOS methods:
  • tabulated_enforce_bounds_rho_Ye_T
  • tabulated_enforce_bounds_rho_Ye_eps
  • tabulated_compute_P_from_T
  • tabulated_compute_eps_from_T
  • tabulated_compute_cs2_from_T
  • tabulated_compute_P_eps_from_T
  • tabulated_compute_P_eps_S_from_T
  • tabulated_compute_T_from_eps
  • tabulated_compute_P_T_from_eps

Build requirements

• The pinned GRHayL submodule initialized at extern/GRHayL, or GRHAYL_DIR set to a configured GRHayL checkout.
• HDF5 enabled in GRHayL build.
• Python build tooling (pip, setuptools, compiler toolchain).

Install (from repo root)

Initialize the pinned GRHayL source:

git submodule update --init --recursive

Install:

python3 -m pip install -e .

Offline or restricted-network environments:

python3 -m pip install --no-build-isolation -e .

The build step runs:

make -C extern/GRHayL grhayl

and then compiles the Python extension against extern/GRHayL/build/lib/libghl.so. Set GRHAYL_DIR=/path/to/GRHayL to build against a different local checkout.

If editable install is not available in your environment, you can work from the repository root with:

PYTHONPATH=src python3 ...

Example

import pyghl as ghl

params = ghl.initialize_params()
eos = ghl.eos.initialize_tabulated_eos_functions_and_params(
    params,
    "Unit_Tests/sample_table/Hempel_SFHoEOS_rho222_temp180_ye60_version_1.1_20120817_simple.h5",
)

rho = 1e-12
Y_e = 0.05
T = 1e2

rho, Y_e, T = eos.tabulated_enforce_bounds_rho_Ye_T(rho, Y_e, T)
P = eos.tabulated_compute_P_from_T(rho, Y_e, T)

You can also pass a short table name and set GRHAYL_EOS_TABLE_DIR.

Neural-network data generators

The following example scripts mirror the C generators in Unit_Tests/data_gen:

• examples/nn_c2p_generate_dataset.py
• examples/nn_c2p_test.py
• examples/nn_c2p_train.py

The same tools are also importable/runnable as package modules:

• pyghl train <eos-file> [dataset] ...

• pyghl append-eos <eos-file> [nn-hdf5] ...

• python -m pyghl.nn_c2p.nn_c2p_generate_dataset ...

• python -m pyghl.nn_c2p.nn_c2p_train ...

• python -m pyghl.nn_c2p.nn_c2p_test ...

• python -m pyghl.nn_c2p.append_eos_file ...

• python -m pyghl.nn_c2p.check_eos ...

• python -m pyghl.nn_c2p.list_installed_models

• python -m pyghl.nn_c2p.remove_eos_nn ...

Example:

PYTHONPATH=src python3 examples/nn_c2p_generate_dataset.py \
  Unit_Tests/sample_table/Hempel_SFHoEOS_rho222_temp180_ye60_version_1.1_20120817_simple.h5 \
  train --n-pts 2 --output /tmp/nn_training_dataset.bin

PYTHONPATH=src python3 examples/nn_c2p_test.py \
  Unit_Tests/sample_table/Hempel_SFHoEOS_rho222_temp180_ye60_version_1.1_20120817_simple.h5 \
  /tmp/nn_training_dataset.bin --limit 8

python3 -m pyghl.nn_c2p.nn_c2p_generate_dataset \
  Unit_Tests/sample_table/Hempel_SFHoEOS_rho222_temp180_ye60_version_1.1_20120817_simple.h5 \
  train

pyghl train \
  Unit_Tests/sample_table/Hempel_SFHoEOS_rho222_temp180_ye60_version_1.1_20120817_simple.h5

pyghl train \
  Unit_Tests/sample_table/Hempel_SFHoEOS_rho222_temp180_ye60_version_1.1_20120817_simple.h5 \
  /tmp/nn_training_dataset.bin

pyghl append-eos \
  Unit_Tests/sample_table/Hempel_SFHoEOS_rho222_temp180_ye60_version_1.1_20120817_simple.h5 \
  /tmp/tiny_mlp_model.h5

pyghl append-eos \
  Unit_Tests/sample_table/Hempel_SFHoEOS_rho222_temp180_ye60_version_1.1_20120817_simple.h5

python3 -m pyghl.nn_c2p.list_installed_models

python3 -m pyghl.nn_c2p.check_eos \
  Unit_Tests/sample_table/Hempel_SFHoEOS_rho222_temp180_ye60_version_1.1_20120817_simple.h5

python3 -m pyghl.nn_c2p.remove_eos_nn \
  Unit_Tests/sample_table/Hempel_SFHoEOS_rho222_temp180_ye60_version_1.1_20120817_simple.h5