PyCPET is based on our method to extract topological data of a 3-D 𝐸𝐸 "⃗ (𝑟𝑟), inspired by fluid dynamics . In this approach, we compute a distribution of streamlines of 𝐸𝐸 "⃗ (𝑟𝑟) in a volume. Each streamline is computed by propagating a point in the volume along the field it experiences by steps of size δ, up to an arbitrary length. A streamline's topological data is encoded in it's mean of start and end curvatures (Eq. 2) and the Euclidean distance of the start and end points. This process is repeated until information about N streamlines in 𝐸𝐸 "⃗ (𝑟𝑟) are computed (the procedure is highlighted in Fig. ). The whole field, therefore, can be represented as a 2-D histogram of streamline mean curvatures and Euclidean distances. These histograms are clearly invariant under rotations of the field and scaling of field magnitudes. This makes a streamline distribution approach a robust metric for generating fingerprints of 3-D electric fields, and focuses truly on the underlying geometry.