EOF Analysis. reshape the dataframe to be [time, space] varimax rotated PCA analysis; unrotated EOFs analysis; compare with Eofs package (unrotated EOFs)

xeofs is a specialized Python package designed for dimensionality reduction in climate science, aimed at extracting meaningful patterns from large datasets. It provides eigenmethods such as Principal Component Analysis (EOF analysis) and several related variants.

Oct 28, 2024 · Comprehensive EOF analysis in Python with xarray: A versatile, multidimensional, and scalable tool for advanced climate data analysis. xeofs is a specialized Python package designed for dimensionality reduction in climate science, aimed at extracting meaningful patterns from large datasets.

Aug 10, 2015 · In this notebook I give a very simple (and rather uncommented) example of how to use scikit-learn to perform an Empirical Orthogonal Function decomposition (EOF analysis, often referred to as well as Principal Component Analysis or PCA) of a climate field, in this case the monthly Sea Surface Temperature (SST) anomalies in the Pacific.

Feb 22, 2021 · pyEOF is a Python package for EOF and Rotated EOF Analysis. It takes advantage of. sklearn.decomposition.PCA (for EOF) Advanced Priniciple Component Analysis (for varimax rotation // varimax rotated EOF // REOF) Installation. Step 1: create an environment:

pyEOF is a Python package for EOF and Rotated EOF Analysis. It takes advantage of. sklearn.decomposition.PCA (for EOF) Advanced Priniciple Component Analysis (for varimax rotation // varimax rotated EOF // REOF)

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)

Jul 22, 2013 · In climate studies, EOF analysis is often used to study possible spatial modes (ie, patterns) of variability and how they change with time (e.g., the North Atlantic Oscilliation). In statistics, EOF analysis is known as Principal Component Analysis (PCA). As such, EOF analysis is sometimes classified as a multivariate statistical technique.

EOF analysis¶ EOF analysis is performed using the Eofs package. In the following, the steps for the computation of an EOF decomposition is provided. The objective would be to compute the El Nino index based on the SST of the Northern Pacific.

xeofs is a specialized Python package designed for dimensionality reduction in climate science, aimed at extracting meaningful patterns from large datasets. It provides eigenmethods such as Principal Component Analysis (EOF analysis) and several related variants.