Xray: N-dimensional, labeled arrays for analyzing physical datasets in Python

Abstract:

Efficient analysis of geophysical datasets requires tools that both preserve and utilize metadata, and that transparently scale to process large datas. Xray is such a tool, in the form of an open source Python library for analyzing the labeled, multi-dimensional array (tensor) datasets that are ubiquitous in the Earth sciences.

Xray’s approach pairs Python data structures based on the data model of the netCDF file format with the proven design and user interface of pandas, the popular Python data analysis library for labeled tabular data. On top of the NumPy array, xray adds labeled dimensions (e.g., "time") and coordinate values (e.g., "2015-04-10"), which it uses to enable a host of operations powered by these labels: selection, aggregation, alignment, broadcasting, split-apply-combine, interoperability with pandas and serialization to netCDF/HDF5. Many of these operations are enabled by xray’s tight integration with pandas. Finally, to allow for easy parallelism and to enable its labeled data operations to scale to datasets that does not fit into memory, xray integrates with the parallel processing library dask.