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,
does one specific task: compute the one-dimensional discrete Fourier Transform.
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
Diagnostics have a much broader scope and leverage the tools
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
xarrayarrays and datasets via custom diagnostic accessors.
xarrayprovides labelled multi-dimensional arrays and datasets.
Diagnostics self-manage the computed analysis data within a
xarraydataset while maintaining the computed data’s relation to the original data.
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.