{ "info": { "author": "Hannes Maier-Flaig", "author_email": "hannes@maier-flaig.de", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3.5", "Topic :: Scientific/Engineering :: Physics" ], "description": "pybbfmr\n=======\n\npybbfmr is a python package to load, process and model \n2D (field-frequency) broadband ferromagnetic resonance (bbfmr) measurements.\n\n\nUsage\n--------\n\nThe basic concept is to load the measurement data stored in the\nbinary NI-TDMS file format using one of the Measurement classes.\n(The base Measurement class can also be fed directly with 2D \ndata.) Several processing operations are included that can be \napplied to the data by using Measurement().add_operation().\nThe data can be plotted (using matplotlib) by using \nMeasurement().plot().\n\nSeveral lmfit models for fitting the data to bbFMR models\nsuch as the Polder susceptibility and various damping and \ndispersion models.\n\nFinally, a graphical user interface to visualize and process \nthe 2D data using guidata and guiqwt is included.\n\nSee the data [1] of [2] for a usage example and for a detailed\ndescription of the physics behind the modeling and the \n\"derivative divide\" processing method in particular. \n\n\nRequirements\n------------\n\nThe following packages are required. (The versions in brackets are the tested \nversions. Other versions will probably work just as well):\n\n + Python (3.5.2)\n + lmfit (0.9.5)\n + matplotlib (1.5.3)\n + npTDMS (0.8.2)\n + numpy (1.11.2)\n + scipy (0.18.1)\n + guiqwt (3.0.3)\n + guidata (1.7.6)\n + pyqt (>4)\n\n\nContribute\n-------------\n\nPlease use the issue tracker to report problems and suggest changes \nand new features. Get in touch if you want to know more about the \npackage.\n\n\nContributors\n------------\n\n + Hannes Maier-Flaig\n + Lukas Liensberger\n + Stefan Weichselbaumer\n\n\nReferences\n----------\n\n 1. H. 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)\n 2. Hannes Maier-Flaig, Sebastian T. B. 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