jaxellip
Some elliptic integrals in JAX
Decision gist · record as of 2026-08-14
Yes, if you need elliptic integrals inside JAX-compiled or autodiff code. The package is actively maintained, has low install friction, carries no known vulnerabilities, and is MIT-licensed. The 0.1.0 release is recent and tested against mpmath and scipy.special. Install only if you actually use elliptic integrals; otherwise it adds an unnecessary dependency.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction: pure Python wheel with a single runtime dependency on jax.
- Active maintenance with a recent release (44 days old) and current Python 3.12–3.14 support.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution with minimal attribution requirements.
last release 2026-07-01 (44 days) · last repo commit 2026-08-06 · 7 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 101,503 downloads/mo, #12,928 on PyPI
Alternatives
Verify before relying
pip install jaxellip
import jaxellip
import jax.numpy as jnp
input_vals = jnp.linspace(-10, 10, 1000)
jaxellip.ellipk(input_vals) # Complete elliptic integral of the first kind
jaxellip.ellipkm1(input_vals) # Complete elliptic integral of the first kind around m=1
jaxellip.ellipe(input_vals) # Complete elliptic integral of the second kind- Whether the package is suitable for production use beyond the early 0.1.0 release stage.
- Real-world performance characteristics on different hardware (GPU, TPU) and JAX/XLA versions.
What it is and what it does
jaxellip fills a gap in JAX's scientific computing stack by providing differentiable, JIT-compatible implementations of three complete elliptic integrals: ellipk, ellipkm1, and ellipe. These functions are available in scipy.special but not in jax.scipy.special, making them incompatible with JAX's autodiff and compilation. The package uses Cephes minimax polynomials (the same method as SciPy) for forward evaluation and custom JVP rules for derivatives, enabling full differentiability in forward mode, reverse mode, and higher-order derivatives like jax.hessian.
The implementation handles numerical edge cases consistently with SciPy—ellipk(1) returns inf, ellipe(1) returns 1, and out-of-domain inputs return NaN. Results are tested against arbitrary-precision mpmath references and validated against scipy.special to about 1e-14 relative accuracy. The package requires only jax as a runtime dependency and supports Python 3.12–3.14.
Use it for
- Compute elliptic integrals inside JAX-compiled functions (jit, vmap, grad) without leaving the JAX ecosystem.
- Differentiate functions that depend on elliptic integrals using jax.grad, jax.jacobian, or jax.hessian.
- Replace scipy.special calls in numerical code when JIT compilation or autodiff is needed.
- Implement physics or engineering models (e.g., pendulum dynamics, elliptic orbits) with automatic differentiation.
- Build machine learning models that incorporate elliptic integrals as part of a larger differentiable computation graph.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need elliptic integrals inside JAX-compiled or autodiff code.
The package is actively maintained, has low install friction, carries no known vulnerabilities, and is MIT-licensed. The 0.1.0 release is recent and tested against mpmath and scipy.special. Install only if you actually use elliptic integrals; otherwise it adds an unnecessary dependency.
Install
jaxellip on PyPI
Before you install
Low friction: pure Python wheel with a single runtime dependency on jax. Active maintenance with a recent release (44 days old) and current Python 3.12–3.14 support.
License in practice
MIT license permits unrestricted use, modification, and distribution with minimal attribution requirements.
Quickstart
pip install jaxellip
import jaxellip
import jax.numpy as jnp
input_vals = jnp.linspace(-10, 10, 1000)
jaxellip.ellipk(input_vals) # Complete elliptic integral of the first kind
jaxellip.ellipkm1(input_vals) # Complete elliptic integral of the first kind around m=1
jaxellip.ellipe(input_vals) # Complete elliptic integral of the second kindVerify before relying
- Whether the package is suitable for production use beyond the early 0.1.0 release stage.
- Real-world performance characteristics on different hardware (GPU, TPU) and JAX/XLA versions.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <3.15,>=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagejax |
| Maintenance | Actively maintained 44 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 101,503 / month, #12,928 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: EducationIntended Audience :: Science/ResearchProgramming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: jaxellip-0.1.0-py3-none-any.whl
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