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:
- a 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.
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Installation guide
including dependency details -
User guide
an introduction to Iris and its core concepts -
Reference documentation
complete Iris package reference help -
Gallery
a collection of images produced using Iris -
Developer's guide
guide for SciTools developers -
Whitepapers
extra information on specific technical issues -
What's new in Iris 2.4?
recent changes in Iris's capabilities