xmca 0.1.0

Maximum Covariance Analysis in Python

Maximum Covariance Analysis in Python

Maximum Covariance Analysis (MCA) maximises the temporal covariance between two different data fields and is closely related to Principal Component Analysis (PCA) / Empirical Orthogonal Function (EOF) analysis, which maximises the variance within a single data field. MCA allows to extract the dominant co-varying patterns between two different data fields.

The module xmca works with numpy.ndarray and xarray.DataArray as input fields.

Installation

pip install xmca

Testing

After cloning the repository

python -m unittest discover -v -s tests/

Core Features

• Standard PCA/MCA
• Rotated PCA/MCA
  • Orthogonal Varimax rotation
  • Oblique Promax rotation
• Complex PCA/MCA (also known as Hilbert EOF analysis)
  • Optimised Theta model extension
• normalization of input data
• latitude correction to compensate for stretched areas in higher latitutes