The Analysis and Diagnostic framework is in active development at the moment. For the foreseeable future, the API will be in continuous flux as functionality is added and modified. To follow the package development please visit our GitHub Project ( https://github.com/PlasmaPy/PlasmaPy/projects/19 ) and comment on any of the relevant issues and/or pull requests.
Analysis & Diagnostic Toolkits
Think of the
plasmapy.analysis as your toolbox. It has all the tools
(functionality) you need to analyze your data. Functionality is built
around numpy arrays
and each function has a well-defined, focused task. For example,
numpy.fft.fft() does one specific task: compute the
one-dimensional discrete Fourier Transform. Similarly,
finds the floating potential for a given Langmuir trace. It does not
have smoothing. It does not do any filtering. It does not do any signal
conditioning. Its sole task is to find the floating potential of a
single Langmuir trace.
Diagnostics have a much broader scope and leverage the tools defined in
plasmapy.analysis to give a more integrated user experience when
analyzing data. Diagnostics try to enhance the analysis workflow by
focusing on some of following key areas…
A more human-friendly way of managing data by building an interface around xarray arrays and datasets via custom diagnostic accessors.
Quick viewing of analyzed data with default plotting routines.
Fully defining the physical parameters of a diagnostic with purposely designed
Probeclasses that are integrated into the analysis workflow.
Adding graphical user interfaces (GUIs) to the analysis workflow via notebook widgets.