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

Release files for pyzfn 1.0.6

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pyzfn 1.0.6
File	Size	Uploaded
pyzfn-1.0.6.tar.gz	42.0 kB	Oct 6, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for pyzfn 1.0.6
File	Interpreter	ABI	Platform	Reset
pyzfn-1.0.6-py3-none-any.whl 45.9 kB Oct 6, 2025	Python 3	none	any	Details

Total release size: 87.9 kB

Release files / pyzfn-1.0.6.tar.gz

Download URL	pyzfn-1.0.6.tar.gz
Size	42.0 kB
Tags	Source
SHA-256 checksum How to use checksums	`d88eee88c5b0b8cc42df27a81a632c6c828c9444691cd2b512565d53eb742740`
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Publishing platform: GitHub Actions
Publishing repository: github.com/MathieuMoalic/pyzfn
Publishing commit: github.com/MathieuMoalic/pyzfn/tree/e5ce4f304c60e1cd92d399d307e28df7225f1c1e

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Publishing configuration: github.com/MathieuMoalic/pyzfn/blob/e5ce4f304c60e1cd92d399d307e28df7225f1c1e/.github/workflows/ci-cd.yml
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SHA-256 checksum: d88eee88c5b0b8cc42df27a81a632c6c828c9444691cd2b512565d53eb742740 Verifying attestations

Release files / pyzfn-1.0.6-py3-none-any.whl

Download URL	pyzfn-1.0.6-py3-none-any.whl
Size	45.9 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`aa27ba0056586f25a9894cf87a966694b203fbe822d013d1fa6f49988e77d55e`
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Upload date	Oct 6, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/MathieuMoalic/pyzfn
Publishing commit: github.com/MathieuMoalic/pyzfn/tree/e5ce4f304c60e1cd92d399d307e28df7225f1c1e

Workflow

Publishing configuration: github.com/MathieuMoalic/pyzfn/blob/e5ce4f304c60e1cd92d399d307e28df7225f1c1e/.github/workflows/ci-cd.yml
Publishing logs: github.com/MathieuMoalic/pyzfn/actions/runs/18280037431/attempts/1

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Subject: pyzfn-1.0.6-py3-none-any.whl
SHA-256 checksum: aa27ba0056586f25a9894cf87a966694b203fbe822d013d1fa6f49988e77d55e Verifying attestations