Collection of EOF analysis and related techniques for climate science
Project description
Overview
xeofs is a Python package designed for Empirical Orthogonal Function (EOF) analysis, also known as Principal Component Analysis (PCA), and related variants. The package stands out due to its capacity to handle multi-dimensional Earth observation data, thereby optimizing the speed and efficiency of EOF analysis. Here are the key strengths of xeofs:
The benefits of using xeofs include:
Multi-dimensional Analysis: Execute labeled EOF analysis with the extensive features of xarray.
Scalability: Handle large datasets effectively with dask.
Speed: Enjoy quick EOF analysis using scipy’s randomized SVD.
Variety of Methods: Perform diverse variants of EOF analysis, including complex and rotated version, along with related techniques such as Maximum Covariance Analysis (MCA).
Model Validation: Validate models through bootstrapping.
Modular Code Structure: Incorporate new EOF variants with ease due to the package’s modular structure.
Flexible Data Formats: Accepts a variety of xarray input types (DataArray, Dataset, list of DataArray).
Compared to similar packages like eofs, pyEOF, and xMCA, xeofs is more comprehensive and flexible, providing unique capabilities like handling fully multidimensional dimensions (both samples and features) and a simple interface for bootstrapping. This makes xeofs a powerful one-stop-shop for most of your EOF analysis needs.
Installation
To install the package, use either of the following commands:
pip install xeofs
or
conda install -c conda-forge xeofs
Quickstart
In order to get started with xeofs, follow these simple steps:
Import the package
import xeofs as xe
Instantiate the model
Select the type of analysis you want to perform (in this case, EOF analysis) and set the parameters. For example, if you want to analyze the first 10 modes, you would use the following code:
model = xe.models.EOF(n_modes=10)
Fit the model to your data
Fit the model to your data by specifying the dimensions along which the analysis should be performed. Replace ‘your_data’ and ‘your_dimension’ with your specific data and dimension:
model.fit(your_data, dim=your_dimension)
Congratulations! You have performed your first analysis with xeofs. To further explore the capabilities of xeofs, check the documentation and examples.
Documentation
For a more comprehensive overview and usage examples, visit the documentation.
Contributing
Contributions are highly welcomed and appreciated. If you’re interested in improving xeofs or fixing issues, please open a Github issue.
License
This project is licensed under the terms of the MIT license.
Contact
For questions or support, please open a Github issue.
Credits
I want to acknowledge
Andrew Dawson, for his foundational Python package for EOF analysis.
Yefee, whose work provided useful references for implementing MCA in xeofs.
James Chapman, creator of a Python package for Canonical Correlation Analysis.
Diego Bueso, for his open-source ROCK-PCA implementation in Matlab.
The developers of NumPy, pandas, and xarray for their indispensable tools for scientific computations in Python.
How to cite?
When utilizing xeofs, kindly remember to cite the original creators of the methods employed in your work. Additionally, if xeofs is proving useful in your research, I’d appreciate if you could acknowledge its use with the following citation:
@software{rieger_xeofs_2023,
title = {xeofs: Multi-dimensional {EOF} analysis and variants in xarray},
url = {https://github.com/nicrie/xeofs}
version = {1.0.0},
author = {Rieger, Niclas},
date = {2023},
doi = {10.5281/zenodo.6323011}
}
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