whigg/pca

Principal Component Analysis, including EOF Analysis (EOFA), Singular Spectrum Analysis (SSA), and Nonlinear Laplacian Spectral Analysis (NLSA)

PCA

pyPCA.py contains three methods, based on Principal Component Analysis (PCA), to compute spatial and temporal, or spatio-temporal patterns of variability in a given geospatial time series data set. The three methods include:

Empirical Orthogonal Function Analysis (EOFA)
Singular Spectrum Analysis (SSA)
Nonlinear Laplacian Spectral Analysis (NLSA)

The code was built around the theory outlined in sections 2.2, 2.3, and 2.4 respectively of Bushuk 2015.

See examples of each of these methods in the accompanying Jupyter Notebook. In order to use each of these methods the following Python packages are required:

NumPy
SciPy