sax
Autograd and XLA for S-parameters
Decision gist · record as of 2026-08-14
Yes, if you work with S-parameter circuit simulation or photonic design. SAX is actively maintained, has no known vulnerabilities, and offers a clean functional interface built on JAX's autodiff and XLA compilation. The dependency footprint is large but manageable. Install with caution if your environment has strict dependency constraints; otherwise, it is a solid choice for frequency-domain circuit work.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.11.0; JAX installation may require additional system dependencies depending on your platform (CPU vs.
- GPU/TPU support).
- Low install friction with a pure-wheel distribution.
License · maintenance · safety
Apache Software License (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes, but there are no copyleft obligations.
last release 2026-06-09 (66 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 136,305 downloads/mo, #11,400 on PyPI
Alternatives
Verify before relying
pip install sax
import sax
import jax.numpy as jnp
def coupler(coupling=0.5):
kappa = coupling**0.5
tau = (1-coupling)**0.5
return sax.reciprocal({
("in0", "out0"): tau,
("in0", "out1"): 1j*kappa,
("in1", "out0"): 1j*kappa,
("in1", "out1"): tau,
})
result = coupler(coupling=0.3)- Whether the package's 19 dependencies can be installed together without version conflicts in typical environments.
- Performance characteristics and scalability limits for large circuit topologies or high-dimensional parameter sweeps.
What it is and what it does
SAX wraps JAX to provide a functional, dictionary-based framework for simulating and optimizing circuits described by S-parameters (scattering matrices). It was built for photonic integrated circuits but works with any frequency-domain circuit model. Rather than defining custom data structures, SAX stays close to JAX's functional paradigm: you write component models as functions returning S-dictionaries, compose them into circuits via netlists, and optimize them using JAX's autodiff and XLA compilation.
The package handles the boilerplate of circuit composition and parameter passing, allowing you to define a directional coupler, waveguide, or other component once and then combine them into larger systems like Mach-Zehnder interferometers. You can then sweep parameters (wavelength, coupling strength, length) across ranges and plot transmission spectra or use JAX's optimization tools to tune component parameters for a target response.
Use it for
- Design and simulate photonic integrated circuits by composing waveguides, couplers, and other optical components into larger systems.
- Optimize photonic circuit parameters (coupling ratios, waveguide lengths, phase shifts) to achieve target transmission or reflection spectra.
- Perform frequency-domain S-parameter analysis on arbitrary circuits without writing custom matrix algebra.
- Leverage JAX autodiff to compute gradients for circuit optimization or sensitivity analysis across wavelength ranges.
- Combine circuit simulation with machine learning workflows via JAX's ecosystem (Optax, Flax, etc.).
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with S-parameter circuit simulation or photonic design.
SAX is actively maintained, has no known vulnerabilities, and offers a clean functional interface built on JAX's autodiff and XLA compilation. The dependency footprint is large but manageable. Install with caution if your environment has strict dependency constraints; otherwise, it is a solid choice for frequency-domain circuit work.
Install
sax on PyPI
Before you install
Low install friction with a pure-wheel distribution. Active maintenance (66 days since last release) and support for current Python versions (3.11–3.14). Requires 19 runtime dependencies including JAX, NumPy, and scientific libraries; installation is straightforward but the dependency stack is substantial.
Requires Python >=3.11.0; JAX installation may require additional system dependencies depending on your platform (CPU vs. GPU/TPU support).
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes, but there are no copyleft obligations.
Quickstart
pip install sax
import sax
import jax.numpy as jnp
def coupler(coupling=0.5):
kappa = coupling**0.5
tau = (1-coupling)**0.5
return sax.reciprocal({
("in0", "out0"): tau,
("in0", "out1"): 1j*kappa,
("in1", "out0"): 1j*kappa,
("in1", "out1"): tau,
})
result = coupler(coupling=0.3)Verify before relying
- Whether the package's 19 dependencies can be installed together without version conflicts in typical environments.
- Performance characteristics and scalability limits for large circuit topologies or high-dimensional parameter sweeps.
Package facts
| License | Apache Software License permissive |
| Python support | Supports the current Python release >=3.11.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 19 packagesjaxjaxellipjaxtypingklujaxlarkmatplotlibnatsortnetworkxnumpyoptaxorjsonpandaspydanticpyyamlscikit-rfsympytqdmtyping-extensionsxarray |
| Maintenance | Actively maintained 66 days since the last release |
| First released | |
| Downloads | 136,305 / month, #11,400 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Physics |
Evidence: sax-0.18.2-py3-none-any.whl
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