Metadata-Version: 2.1
Name: py-pod
Version: 1.0.3
Summary: Implementation of a Proper Orthogonal Decomposition (POD) method
Author: Christophe
Author-email: christophe.alexandre@pm.me
Requires-Python: >=3.10,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: numpy (>=1.25.2,<2.0.0)
Requires-Dist: pydoctor (>=23.4.1,<24.0.0)
Description-Content-Type: text/markdown

# 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<sub>i</sub>_ becomes the coordinate of *P* along
the axis _t<sub>i</sub>_.

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
Generated with PyDoctor:

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


## Publication
poetry build
poetry publish

