Iris is a powerful, easy to use, and community-driven Python library for analysing and visualising Earth science data
Table of contents
Overview
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-in to Iris, giving you a rich and expressive interface for maintaining an accurate representation of your data. Its first-class treatment of data and associated metadata includes:
- aggregations and reductions (min, max, (area-)weighted mean, etc.)
- interpolation and regridding (nearest-neighbor, linear, area-weighted, etc.)
- operator overloads (
+,-,*,/, etc.) - merge and concatenate
- subsetting and extraction
- unit conversion
- a visualisation interface based on matplotlib and cartopy
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.
Documentation
The documentation for Iris is available at https://scitools.org.uk/iris/docs/latest, including a user guide, example code, and gallery.
Installation
The easiest way to install Iris is with conda:
conda install -c conda-forge iris
Detailed instructions, including information on installing from source, are available in INSTALL.
Copyright and licence
Iris may be freely distributed, modified and used commercially under the terms of its GNU LGPLv3 license.
(C) British Crown Copyright 2010 - 2018, Met Office