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The reprocessing of radiation belt electron flux measurements into phase space density (PSD) as a function of the adiabatic invariants is a widely-used method to address major questions regarding electron energization and loss in the outer radiation belt. In this reprocessing, flux measurements j (α, E) at local pitch angles α, energies E, and optionally magnetometer measurements B, are combined with a global magnetic field model to express the phase space density f (L*) in terms of the third invariant Φ ∝ 1/L* at fixed first and second invariants M and K. While the general framework of the calculation is agreed upon, implementation details vary amongst the literature, and the issue of magnetic field model dependence is rarely addressed. This work reviews the steps of the calculation with lists of commonly used implementation options. For the first time, analysis is presented to display the effect of doing the calculation with different implementation options and with different backing models (including both empirical and MHD-driven models). The results are summarized to inform evaluation of existing results and future efforts calculating and analyzing radiation belt electron phase space density. Three events are analyzed, and while differences are found, the primary structural interpretations of the phase space density analysis exhibit model independence.

In this article, we describe the free, open-source Python-based Space Physics Environment Data Analysis System (PySPEDAS), a platform for multi-mission, multi-instrument retrieval, analysis, and visualization of Heliophysics data. PySPEDAS currently contains load routines for data from 23 space missions, as well as a variety of data from ground-based observatories. The load routines are built from a common set of general routines that provide access to datasets in different ways (e.g., downloading and caching CDF files or accessing data hosted on web services), making the process of adding additional datasets simple. In addition to load routines, PySPEDAS contains numerous analysis tools for working with the dataset once it is loaded. We describe how these load routines and analysis tools are built by utilizing other free, open-source Python projects (e.g., PyTplot, cdflib, hapiclient, etc.) to make tools for space and solar physicists that are extremely powerful, yet easy-to-use. After discussing the code in detail, we show numerous examples of code using PySPEDAS, and discuss limitations and future plans.