Why Iris#

A powerful, format-agnostic, community-driven Python package for analysing and visualising Earth science data.

Iris implements a data model based on the CF conventions giving you a powerful, format-agnostic interface for working with your data. It excels when working with multi-dimensional Earth Science data, where tabular representations become unwieldy and inefficient.

CF Standard names, units, and coordinate metadata are built into Iris, giving you a rich and expressive interface for maintaining an accurate representation of your data. Its treatment of data and associated metadata as first-class objects includes:

  • visualisation interface based on matplotlib and cartopy,

  • unit conversion,

  • subsetting and extraction,

  • merge and concatenate,

  • aggregations and reductions (including min, max, mean and weighted averages),

  • interpolation and regridding (including nearest-neighbor, linear and area-weighted), and

  • operator overloads (+, -, *, /, etc.).

A number of file formats are recognised by Iris, including CF-compliant NetCDF, GRIB, and PP, and it has a plugin architecture to allow other formats to be added seamlessly.

Building upon NumPy and dask, Iris scales from efficient single-machine workflows right through to multi-core clusters and HPC. Interoperability with packages from the wider scientific Python ecosystem comes from Iris’ use of standard NumPy/dask arrays as its underlying data storage.

Iris is part of SciTools, for more information see https://scitools.org.uk/. For Iris 2.4 and earlier documentation please see Support.