Analyses and diagnostics go hand-in-hand, but have subtle differences. Thus, PlasmaPy gives each their own sub-packages, plasmapy.analysis and plasmapy.diagnostics respectively.
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, plasmapy.analysis.swept_langmuir.find_floating_potential only 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.
- xarray provides labelled multi-dimensional arrays and datasets.
- Diagnostics self-manage the computed analysis data within a xarray dataset 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 Probe classes that are integrated into the analysis workflow.
- Adding graphical user interfaces (GUIs) to the analysis workflow via notebook widgets.
Analysis <analysis/index> Diagnostics <diagnostics/index>