Labeled ND Data

xeofs is specifically designed for xarray objects. This design choice allows you to apply dimensionality reduction techniques to multi-dimensional data while maintaining data labels. The only requirement for the user is to specify the sample dimensions. By default, xeofs assumes all other dimensions present in the data are feature dimensions. For example:

model.fit(data, dim="time")

This will assume that the data object has a sample dimension named time, while all other dimensions are feature dimensions to be expressed as latent variables.

Note

In this context, sample refers to the dimension representing the number of observations. In contrast, feature dimensions denote the variables. The main goal of dimensionality reduction techniques is to minimize the number of feature dimensions without altering the sample dimensions.

Note

Multiple sample dimensions can be specified as long as at least one feature dimension remains.

Input Data Compatibility

xeofs is tailored to function harmoniously with xarray objects. Currently, it supports:

1. Single instances of xarray.DataArray or xarray.Dataset

2. Lists comprising xarray.DataArray or xarray.Dataset

An intelligent feature of xeofs is its ability to deliver the appropriate output based on the input type. For instance, executing PCA on a single xarray.DataArray will yield a single xarray.DataArray for the PC components. Conversely, if a list of xarray.DataArray is inputted, xeofs will return a list of xarray.DataArray as PC components.

Warning

A mixed list containing both xr.DataArray and xr.Dataset objects is not currently supported.

Warning

Some methods like PCA/EOF analysis can also handle complex-valued input data. However, due to a limitation in the underlying dask library, complex-valued data is not supported when using dask.