SpinWaveToolkit 1.3.0

Set of tools useful in spin wave research.

SpinWaveToolkit

SpinWaveToolkit is an open-source Python package which provides analytical tools for spin-wave physics and research.

[!TIP] This package could use some updating. If you want to contrubute, see CONTRIBUTING GUIDELINES.

Installation

Currently you can either

1. (recommended) install latest release from PyPI via pip by typing in the command line

py -m pip install SpinWaveToolkit --user

2. or install from GitHub any branch via pip by typing in the command line

py -m pip install https://github.com/CEITECmagnonics/SpinWaveToolkit/tarball/<branch-name> --user

older installation approaches (not recommended)

3. or copy the SpinWaveToolkit folder to your site-packages folder manually. Usually (on Windows machines) located at

C:\Users\<user>\AppData\Roaming\Python\Python<python-version>\site-packages

for user-installed modules, or at

C:\<python-installation-folder>\Python<python-version>\Lib\site-packages

for global modules.

Dependencies

The SpinWaveToolkit package is compatible with Python >3.7, and uses the following modules:

• numpy >1.20 (>2.0 is also ok, bugs be reported in Issues)
• scipy >1.8
• tqdm >4.66

[!NOTE] If you encounter compatibility errors in contradiction with this list, let us know by posting your findings in a new Issue.

About

This package provides analytical tools in spin-wave physics. This section gives an overview of its capabilites. All functionalities are described in the SpinWaveToolkit Documentation.

Features:

• Calculation of the dispersion relation and derived quantities for several systems using analytical, semi-analytical, and numerical models. These include
  • single magnetic layer (thin film) surrounded by dielectrics [^1] [^2],
  • coupled magnetic double layer (e.g. a synthetic antiferromagnet) [^3],
  • single magnetic layer inductively coupled to a superconducting layer from one side [^4].
• Simple magnetic material management using a Material class.
• Functions for modelling Brillouin light scattering (BLS) signal and experiments.

Example

Example of calculation of the spin-wave dispersion relation f(k_xi), and other important quantities, for the lowest-order mode in a 30 nm thick NiFe (Permalloy) layer.

import numpy as np
import SpinWaveToolkit as SWT

kxi = np.linspace(1e-6, 150e6, 150)

PyChar = SWT.SingleLayer(Bext=20e-3, kxi=kxi, theta=np.pi/2,
                         phi=np.pi/2, d=30e-9, weff=2e-6,
                         boundary_cond=2, material=SWT.NiFe)
DispPy = PyChar.GetDispersion()*1e-9/(2*np.pi)  # GHz
vgPy = PyChar.GetGroupVelocity()*1e-3  # km/s
lifetimePy = PyChar.GetLifetime()*1e9  # ns
decLen = PyChar.GetDecLen()*1e6  # um

For more examples (with images) look here.

Cite us

If you use SpinWaveToolkit in your work, please cite it as follows:

[1] Klíma, J. et al. "SpinWaveToolkit: Python package for (semi-)analytical calculations in the field of spin-wave physics" (2026) arXiv:2601.23227 [cond-mat.mes-hall] [2] Klíma, J., Krčma, J., & Wojewoda, O. "SpinWaveToolkit: Set of tools useful in spin wave research." GitHub, 2026. https://github.com/CEITECmagnonics/SpinWaveToolkit

BibTeX entry:

@misc{klima2026,
    title={{SpinWaveToolkit}: {Python} package for (semi-)analytical calculations in the field of spin-wave physics},
    author={Klíma, Jan and Wojewoda, Ondřej and Krčma, Jakub and Hrtoň, Martin and Pavelka, Dominik and Holobrádek, Jakub and Urbánek, Michal},
    year={2026},
    eprint={2601.23227},
    archivePrefix={arXiv},
    primaryClass={cond-mat.mes-hall},
    url={https://arxiv.org/abs/2601.23227},
}

@online{swt,
    author = {Klíma, Jan and Krčma, Jakub and Wojewoda, Ondřej},
    title = {SpinWaveToolkit: Set of tools useful in spin wave research},
    year = {2026},
    publisher = {GitHub},
    version = {1.3.0},
    url = {https://github.com/CEITECmagnonics/SpinWaveToolkit},
    language = {en},
}

All sources of models used within the SpinWaveToolkit are cited in their respective documentation. Consider citing them as well if you use these models.

[^1]: B. A. Kalinikos and A. N. Slavin, J. Phys. C: Solid State Phys., 19, 7013 (1986). [^2]: S. Tacchi et al., Phys. Rev. B, 100, 104406 (2019). [^3]: R. A. Gallardo et al., Phys. Rev. Applied, 12, 034012 (2019). [^4]: X.-H. Zhou et al., Phys. Rev. B, 110, L020404 (2024).