In both applications the mathematics is the same: One constructs the
covariance matrix of all data vectors (e.g. in an m-dimensional
time delay embedding space),
and computes its singular vectors. Then one projects onto the m-dimensional vectors corresponding to the q largest singular values. To work with flow data, q should be at least the correct embedding dimension, and m considerably larger (e.g. m=2q or larger). The result is a vector valued time series, and in  the relation of these components to temporal derivatives on the one hand and to Fourier components on the other hand were discussed. If, in the non-autonomous case, one wants to compress flow data to map data, q=1. In this case, the redundancy of the flow is implicitly used for noise reduction of the map data. The routine pca can be used for both purposes.