Installation#
normix requires Python ≥ 3.12 and runs on CPU or GPU through JAX.
From PyPI#
pip install normix
Optional plotting helpers:
pip install "normix[plotting]"
The docs site header shows the version of the package used to build these
pages. PyPI releases can lag the docs branch — if pip installs an older
wheel than the header, prefer the from-source path below for the matching
code.
From source (development)#
git clone https://github.com/xshi19/normix
cd normix
uv sync
uv sync installs the locked dependency set into a project virtual environment.
Run anything in that environment with uv run, e.g. uv run python,
uv run pytest, uv run jupyter lab.
Editable install with pip instead of uv:
pip install -e .
Float64 precision#
normix relies on double precision throughout — the Bessel evaluations, the GIG \(\eta \mapsto \theta\) solve, and log-density arithmetic all lose accuracy in float32. Enable it before importing normix:
import jax
jax.config.update("jax_enable_x64", True)
import normix
If float64 is not enabled, normix emits a warning on import.
Dependencies#
Package |
Role |
|---|---|
|
Array computation, autodiff, JIT, |
|
Immutable pytree modules |
|
L-BFGS/BFGS for the GIG \(\eta \mapsto \theta\) solve |
|
Reference Bessel functions |
|
CPU Bessel evaluation on the EM hot path |
|
Gradient-based fitting experiments |
Optional extras#
uv sync --extra docs --extra plotting # Sphinx site + matplotlib helpers
Once installed, head to the Quickstart for a 30-second example, or Your first model, step by step for a guided walkthrough.