clif is a CLImate Fingerprinting library that calculates empirical orthogonal functions for mainly climate data.
The code is super easy to install with pip. Make sure you have numpy,
scikit-learn, and xarray. Then, after cloning, cd into the clif directory, i.e. the folder with the setup.py, and run
pip install .You can also run a suite of unit tests and regression tests before installation with
python -m pytest clif/tests
to check that the library works. That's it! Now you are ready to use clif.
Once you have successfully installed clif, you can compute EOFs of data (as a numpy array for now) as follows.[#]_
from clif import fingerprints
from sklearn import datasets
X = datasets.load_digits().data
fp = fingerprints(n_eofs=8)
fp.fit(X)
EOFs = fp.eofs_clif also has a bunch of preprocessing transforms useful for manipulating xarray DataArrays. To see more information from the documentation, go to the docs/ folder and open index.html. All transforms are templated and use the following pseudo code interface.
from clif import preprocessing
X = load_xarray_data()
xarrayTransform = preprocessing.TransformName(**init_params)))
X_transformed = xarrayTransform.fit_transform(X)