virtual-casing-jax 0.0.2

JAX implementation of the virtual casing principle with high-order quadrature

virtual_casing_jax

virtual_casing_jax is a JAX implementation of the virtual casing principle for computing magnetic-field contributions from plasma currents using high-order singular quadrature. It is based on the C++ reference implementation in hiddenSymmetries/virtual-casing and on the SIMSOPT virtual-casing interface in hiddenSymmetries/simsopt.

Documentation is available at virtual-casing-jax.readthedocs.io.

Installation

Install the latest release from PyPI:

pip install virtual-casing-jax

Or install from a local source checkout:

git clone https://github.com/uwplasma/virtual_casing_jax.git
cd virtual_casing_jax
pip install -e .

Basic Usage

The SIMSOPT-compatible wrapper can be used as a drop-in virtual-casing calculation when SIMSOPT is installed:

from virtual_casing_jax import VirtualCasing

vc = VirtualCasing.from_vmec(
    "wout_example.nc",
    src_nphi=32,
    trgt_nphi=32,
    trgt_ntheta=32,
    filename="auto",
)

B_external_normal = vc.B_external_normal

For lower-level JAX workflows, use VirtualCasingJAX directly after preparing surface coordinates and magnetic-field arrays:

from virtual_casing_jax import VirtualCasingJAX

vc_jax = VirtualCasingJAX()
vc_jax.setup(digits, nfp, stellsym, Nt, Np, gamma, Nt, Np, Nt, Np)
B_external = vc_jax.compute_external_B(B_total)

Performance features:

• Source/target tiling with auto-tuned chunk sizes.
• Rematerialization hooks for GradB singular correction.
• Optional target-scan mode to reduce GradB peak memory (scan_targets).
• Mixed-precision POU/patch tables with float64 outputs.
• Bundled Quas3/LHD/W7X geometry assets (converted from SCTL .mat).

SIMSOPT compatibility: The package ships a SIMSOPT-compatible VirtualCasing class that mirrors simsopt.mhd.virtual_casing.VirtualCasing while using the JAX backend. Import it as from virtual_casing_jax import VirtualCasing. See docs/using_simsopt.rst and the examples in examples/ for full scripts.

Bundled test data: To make the SIMSOPT-style examples and tests self-contained, the repo includes a small subset of SIMSOPT test assets under tests/test_files/ and the VMEC input examples/inputs/input.QH_finitebeta. These files originated from the SIMSOPT repository (SIMSOPT) and are used only for validation and example runs.

Docs

Sphinx documentation lives in docs/ and is configured for ReadTheDocs. It includes the equations, numerics, implementation details, and validation strategy. Run locally:

pip install -r docs/requirements.txt
sphinx-build -b html docs docs/_build/html

Profiling

Use the profiling harness to capture JAX traces and inspect performance:

JAX_ENABLE_X64=1 python tools/profile_vc.py --case case_vc --op B --jit \
  --repeat 5 --trace-dir /tmp/vc_trace

tensorboard --logdir /tmp/vc_trace

For the new tuning knobs:

JAX_ENABLE_X64=1 XLA_FLAGS="--xla_dump_to=/tmp/vc_xla --xla_dump_hlo_as_text" \
  python tools/profile_vc.py --case case_vc_large --op GradB --jit \
  --chunk-size auto --target-chunk-size auto --pou-dtype float32 --patch-dtype float32 \
  --interp-block-size auto --remat --donate \
  --repeat 2 --trace-dir /tmp/vc_trace_case_vc_large_GradB

tensorboard --logdir /tmp/vc_trace_case_vc_large_GradB

This writes JAX traces under /tmp/vc_trace_* and HLO dumps under /tmp/vc_xla_*. See docs/performance.rst for detailed interpretation.