Skip to main content

No project description provided

Project description

pybbfmr is a python package to load, process and model 2D (field-frequency) broadband ferromagnetic resonance (bbfmr) measurements.

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. 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)

  1. 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).

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pybbfmr-0.2.1.tar.gz (71.4 kB view details)

Uploaded Source

Built Distribution

pybbfmr-0.2.1-py3-none-any.whl (66.9 kB view details)

Uploaded Python 3

File details

Details for the file pybbfmr-0.2.1.tar.gz.

File metadata

  • Download URL: pybbfmr-0.2.1.tar.gz
  • Upload date:
  • Size: 71.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for pybbfmr-0.2.1.tar.gz
Algorithm Hash digest
SHA256 027d70ce91e1e9b3517c95cebdebe66d055d7afd80eb606a698d0c835ae400f8
MD5 5fe3eeec7bd78124244e4c84de8110ff
BLAKE2b-256 1a0fdc0ab2c5e4fb49705c8ce2322ded9753bffb7fac3077a8b1ae23b4f0bb71

See more details on using hashes here.

File details

Details for the file pybbfmr-0.2.1-py3-none-any.whl.

File metadata

File hashes

Hashes for pybbfmr-0.2.1-py3-none-any.whl
Algorithm Hash digest
SHA256 88e445412e49bc6ba9a7b3bf89304800d7c8223a3e104093f86cff638408512e
MD5 d9b04c84fef23f6afb4239fda6b43110
BLAKE2b-256 a1b97856215b2f5a9fad45f84a325881cac7b67381c734ed6ca5a8eaa2741af4

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page