====================================================================================== pyEOF: Empirical Orthogonal Function (EOF) analysis and Rotated EOF analysis in Python

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pyEOF is a Python package for EOF and Rotated EOF Analysis <https://climatedataguide.ucar.edu/climate-data-tools-and-analysis/empirical-orthogonal-function-eof-analysis-and-rotated-eof-analysis>_ . It takes advantage of

• sklearn.decomposition.PCA <https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html>_ (for EOF)
• Advanced Priniciple Component Analysis <https://github.com/alfredsasko/advanced-principle-component-analysis>_ (for varimax rotation // varimax rotated EOF // REOF)

Installation

Step 1: create an environment::

$ conda create -n pyEOF python=3.7
$ conda activate pyEOF
$ conda install -c conda-forge numpy pandas scipy scikit-learn rpy2

Step 2: install using pip::

$ pip install pyEOF

(optional) for jupyter notebook tutorial::

$ conda install -c conda-forge numpy pandas scipy scikit-learn rpy2 xarray matplotlib jupyter eofs pooch netcdf4

(optional) install from source::

$ git clone https://github.com/zzheng93/pyEOF.git
$ cd pyEOF
$ python setup.py install

How to use it?

You may reproduce the jupyter notebook example on Binder <https://mybinder.org/v2/gh/zzheng93/pyEOF/HEAD?filepath=docs%2Fnotebooks>_.

Please check online documentation <https://pyeof.readthedocs.io/en/latest/>_ for more information.

How to ask for help

The GitHub issue tracker <https://github.com/zzheng93/pyEOF/issues>_ is the primary place for bug reports.