Here we demonstrate that PyCPET is a widely-applicable tool for computing 3-D electric fields, generating topological features, and analyzing their dynamics. We built the tool with flexibility in mind as users can uniquely specify relevant active sites in their own protein systems. This approach bridges the gap between electrostatic preorganization and a 3-D perspective on electronic structure with important extensions toward sampling dynamics. In addition, PyCPET features clustering algorithms, PCA, and baseline methods to compute 1-dimensional 𝐸𝐸 "⃗ (𝑟𝑟) at points for cursory analysis. We have provided a compelling set of use cases for our toolkit, that when combined with previous studies , highlight how users can functionalize these methods in their own studies. To facilitate future work, we implemented several benchmarking tools to ensure that parameters for clustering, streamline distribution analysis, and 3-D electric fields yield desirable accuracy for studied systems. Benchmarking on a few representative structures of new systems can save a considerable amount of compute while equivalent or better signal. This project is open-source source, modular, and can be configured for several modes of analysis and highthroughput studies. We provide several example notebooks in our repository (). Finally, the tool is compatible with the popular biomolecular software ChimeraX for easy visualization of 3D fields.