py-pod 2.0

Implementation of a Proper Orthogonal Decomposition (POD) method

Proper Orthogonal Decomposition

Principle

The pod package is an implementation of a Proper Orthogonal Decomposition (POD) method. The POD method intention is close to the more commonly known Principal Component Analysis (PCA). The package contains processing algorithms for decomposing an input using a set of predefined signals.

Decomposition is performed by iterating projections onto the linear variety generated by the reference signals.

The proposed algorithm takes a vector space approach. A signal, or more precisely its sequence of N temporal samples, is mapped to a point P in a vector space of dimension N. A value taken by a signal P at sample time t_i becomes the coordinate of P along the axis t_i.

The set of reference signals represents a library that one can use to synthetize or approximate any kind of input. The reference points form a cloud in the space described above. A linear combination of appropriately selected reference points will approximate the target signal S.

Documentation

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pydoctor --make-html --html-output=docs/api pod

Publication

poetry build

poetry publish