eofs

Spec sheet

Purpose

eofs is a Python package for empirical orthogonal function analysis of spatial-temporal data.Read 8 Oct 2026 · ajdawson.github.io

Platforms

The package runs on Linux, Windows, and MacOS where Python and NumPy are available.Read 8 Oct 2026 · github.com

Deployment

desktopRead 24 Sep 2026 · ajdawson.github.io

Free plan

YesRead 24 Sep 2026 · ajdawson.github.io

Support

The project directs users to GitHub Issues for bug reports and feature requests.Read 8 Oct 2026 · ajdawson.github.io

License

The repository describes eofs as licensed under GNU GPLv3.Read 8 Oct 2026 · github.com

Analysis outputs

Solver methods return EOFs, principal component time series, eigenvalues, variance fractions, and reconstructed fields, among other quantities.Read 8 Oct 2026 · ajdawson.github.io

Bayesian analysis

NoRead 24 Sep 2026 · ajdawson.github.io

Citation

The project asks users of eofs in published research to cite its peer-reviewed paper and optionally a version-specific Zenodo DOI.Read 8 Oct 2026 · ajdawson.github.io

Data import formats

NetCDF via Iris example; NumPy arrays; xarray objectsRead 24 Sep 2026 · ajdawson.github.io

Data interfaces

The package provides interfaces for NumPy and Dask arrays, plus metadata-enabled interfaces for Iris and xarray.Read 8 Oct 2026 · ajdawson.github.io

Dependencies

NumPy is required; dask is optional, while Iris and xarray are needed only for their respective metadata interfaces.Read 8 Oct 2026 · ajdawson.github.io

Examples

The documentation includes standard, Iris, and xarray examples for North Atlantic Oscillation and El Niño analyses.Read 8 Oct 2026 · ajdawson.github.io

Implementation

The implementation is pure Python using NumPy and has no Fortran or C dependencies.Read 8 Oct 2026 · ajdawson.github.io

Installation

The project documents installation through conda-forge, pip, or a source archive.Read 8 Oct 2026 · ajdawson.github.io

Intended use

The package is presented for analyzing spatial-temporal data, with examples including climate patterns.Read 8 Oct 2026 · ajdawson.github.io

Interfaces

The standard interface accepts NumPy or dask arrays, and metadata interfaces support Iris or xarray data.Read 8 Oct 2026 · ajdawson.github.io

Large datasets

The package is designed to compute efficiently on large output datasets from modern climate models.Read 8 Oct 2026 · ajdawson.github.io

Metadata

Optional Iris and xarray interfaces carry metadata from input fields into output fields.Read 8 Oct 2026 · github.com

Missing values

Missing values are removed during computations and placed back into output fields.Read 8 Oct 2026 · ajdawson.github.io

Regression models

NoRead 24 Sep 2026 · ajdawson.github.io

Requirements

NumPy is required; Dask is optional, while the Iris interface requires Iris 1.2 or higher and the xarray interface requires xarray.Read 8 Oct 2026 · ajdawson.github.io

Research citation

The documentation asks users of eofs in published research to cite its peer-reviewed paper.Read 8 Oct 2026 · ajdawson.github.io

Scripting support

YesRead 24 Sep 2026 · ajdawson.github.io

Statistical tests

NoRead 24 Sep 2026 · ajdawson.github.io

Usage

A solver class computes the EOF solution when it is created, and its methods retrieve quantities of interest.Read 8 Oct 2026 · ajdawson.github.io

Use case

EOF analysis reduces the dimensionality of spatial-temporal data by transforming it into a basis expressed in terms of variance.Read 8 Oct 2026 · ajdawson.github.io

Where Specifiction places it

• Statistical Analysis Software#82 of 1067.3