pyzfn

Pyzfn is a Python library for post-processing micromagnetic simulation data generated by amumax, a fork of mumax3. It is designed for use with Zarr-backed datasets and supports spatially resolved frequency-domain analysis, visualization, and export of magnetization dynamics.

Features

🧊 Zarr-native API for reading/writing simulation results
⚡ FFT-based mode analysis across 5D datasets ((t, z, y, x, c))
📈 Interactive spectrum visualization with peak-picking
🌈 Mode visualization in amplitude and phase
🖼️ Snapshot tools for spatial magnetization structure
💾 OVF (OOMMF Vector Field) export for simulation interoperability
📚 Predefined theoretical dispersion models (Kittel, Kalinikos-Slavin, DMI-included...)

Installation

pip install pyzfn

Usage

Loading a Zarr Dataset

from pyzfn import Pyzfn

zfn = Pyzfn("my_simulation.zarr")
zfn.p  # Show tree structure

Calculate Modes

zfn.calc_modes(dset_in_str="m", dset_out_str="modes")

Performs time-FFT over magnetization and stores spatially resolved modes in modes/....

Visualize Frequency Spectrum + Modes

zfn.ispec(dset_str="modes")

Left: Spectral line plot
Right: Mode amplitude, phase, and composite maps
Click to explore frequency slices interactively

Visualize a Snapshot

zfn.snapshot(dset_str="m", t=-1, z=0)

Visualizes the magnetization vector field at a given time and layer.

Dispersion Models

You can compute theoretical resonance frequencies for comparison:

from pyzfn.equations import kittel_1948

f_res = kittel_1948(B=0.23, Ms=1.15e6, Ku=0.938e6)
print(f_res, "Hz")

Other supported models:

kalinikos_1986, kalinikos_1986_no_approx
bottcher_2021
kim_2016 (DMI included)
cortes_ortuno_2013

OVF Support

Save to OVF

from pyzfn.ovf import save_ovf

save_ovf("mode.ovf", array, dx=1e-9, dy=1e-9, dz=1e-9)

Load from OVF

from pyzfn.ovf import load_ovf

arr = load_ovf("mode.ovf")

Development

Run Tests

pytest

Run Type Checks

mypy pyzfn

Format & Lint

We use pre-commit hooks:

pre-commit install

Pyzfn

Install / Use

README