pybbfmr 0.2.1

Usage

The basic concept is to load the measurement data stored in the binary NI-TDMS file format using one of the Measurement classes. (The base Measurement class can also be fed directly with 2D data.) Several processing operations are included that can be applied to the data by using Measurement().add_operation(). The data can be plotted (using matplotlib) by using Measurement().plot().

Several lmfit models for fitting the data to bbFMR models such as the Polder susceptibility and various damping and dispersion models.

Finally, a graphical user interface to visualize and process the 2D data using guidata and guiqwt is included.

See the data [1] of [2] for a usage example and for a detailed description of the physics behind the modeling and the “derivative divide” processing method in particular.

Requirements

The following packages are required. (The versions in brackets are the tested versions. Other versions will probably work just as well):

• Python (3.5.2)

• lmfit (0.9.5)

• matplotlib (1.5.3)

• npTDMS (0.8.2)

• numpy (1.11.2)

• scipy (0.18.1)

• guiqwt (3.0.3)

• guidata (1.7.6)

• pyqt (>4)

Contribute

Please use the issue tracker to report problems and suggest changes and new features. Get in touch if you want to know more about the package.

Contributors

• Hannes Maier-Flaig

• Lukas Liensberger

• Stefan Weichselbaumer

References

1. 8. Maier-Flaig, “Analysis of broadband FMR in the frequency domain - dataset and reference implemenation of derivative divide,” (2017), [https://osf.io/u27sf/…](https://osf.io/u27sf/?view_only=bc9d8bd783324875960eab1e0286e77a)

2. Hannes Maier-Flaig, Sebastian T. B. Goennenwein, Ryo Ohshima, Masashi Shiraishi, Rudolf Gross, Hans Huebl: “Analysis of broadband ferromagnetic resonance in the frequency domain”, 2017; [arXiv:1705.05694](http://arxiv.org/abs/1705.05694).