Choosing precision (float32 vs float64)¶

magpylib_jax follows your JAX precision setting and never changes the global JAX config on import — you pick the precision exactly like any other JAX code. The one rule: set it before you build any array (that is, before the first getB/getFT/object construction).

float64 (parity)

Full Magpylib parity and the tightest gradients — recommended for scientific work:

import jax
jax.config.update("jax_enable_x64", True)   # do this first
import magpylib_jax as mpj

mpj.magnet.Cuboid(polarization=(0, 0, 1.0), dimension=(1, 1, 1)).getB((2, 0, 0)).dtype
# -> float64

float32 (default)

JAX’s default — faster and lower-memory, ideal on GPU/TPU and in ML pipelines. Just don’t enable x64:

import magpylib_jax as mpj   # no x64 -> float32

mpj.magnet.Cuboid(polarization=(0, 0, 1.0), dimension=(1, 1, 1)).getB((2, 0, 0)).dtype
# -> float32

	float32 (default)	float64 (`jax_enable_x64=True`)
speed / memory	faster, lighter (especially GPU/TPU)	slower, 2× memory
Magpylib parity	~1e-6 relative	bit-level (~1e-15)
how to select	do nothing	`jax.config.update("jax_enable_x64", True)` before use

Internally, the kernels never hard-code float64: array dtypes use JAX’s canonical default (jax.numpy.float_), so a single flag switches the entire library between single and double precision with no truncation warning. The test suite runs with x64 enabled via conftest.py.

Note

Why not enable x64 on import? Because jax.config.update is process-global: it would force every JAX computation in your program into float64 (slower on GPU/TPU) and override other libraries’ choices. A library should respect your configuration, not mutate it.