Iris Documentation

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 For Iris 2.4 and earlier documentation please see the legacy documentation.

Install Iris to use or for development.

Example code to create a variety of plots.

Find out what has recently changed in Iris.

Learn how to use Iris.

Browse full Iris functionality by module.

As a developer you can contribute to Iris.