EOF
Empirical Orthogonal Functions tailored for spatiotemporal analysis, with a tutorial.
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Cite As
Greene, C. A., Thirumalai, K., Kearney, K. A., Delgado, J. M., Schwanghart, W., Wolfenbarger, N. S., et al. (2019). The Climate Data Toolbox for MATLAB. Geochemistry, Geophysics, Geosystems, 20. https://doi.org/10.1029/2019GC008392
Acknowledgements
Inspired by: PCAtool, PCA (Principial Component Analysis), Shade Anomaly, PCA (Principal Component Analysis), PCA and ICA Package, PCA, Empirical orthogonal function (PCA) estimation for EEG time series, Face recognition using PCA, Principal Component Analysis for large feature and small observation, trend, Fast SVD and PCA, pca, borders, Empirical Orthogonal Function (EOF) analysis, Empirical Orthogonal Function (EOF) with Spatiotemporal Convertion, cmocean perceptually-uniform colormaps, Principal Component Analysis, PCA, anomaly, detrend3
Inspired: xcorr3
General Information
- Version 1.2.0 (15.2 MB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.2.0 | updated citation. |
||
| 1.1.0 | Fixed the issues that arose from rounding the explained variance values, fixed the issue of results going complex for large numbers of modes, updated and expanded the Tutorial. |
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| 1.0.0 | Typo fix in the documentation. |
