PyCPET includes the PCA functionality to compare 3-D, multicomponent fields across different sets of proteins, or between different dynamics frames of the same protein. PCA is a popular dimensionality reduction tool that constructs eigenvectors of the covariance matrix with maximum eigenvalues. This corresponds to producing the direction along which there is maximum variability in the data. Our implementation takes in a set of 3-D 𝐸𝐸 "⃗ (𝑟𝑟) vectors of total size (3, Nx, Ny, Nz, T), where Nx, Ny, and Nz are the number of points in each dimension of the electric field volume, and T is the number of electric field frames provided. From PCA, we obtain eigenvectors that maximally explain the variance in the data of shape (3, Nx, Ny, Nz), which we can then visualize using PyCPET's built-in tools.