scitools-iris 2.1.0

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

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Iris is a powerful, format-agnostic, and community-driven Python library for
analysing and visualising Earth science data
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<h1>Table of contents</h1>

[](TOC)

+ [Overview](#overview)
+ [Documentation](#documentation)
+ [Installation](#installation)
+ [Copyright and licence](#copyright-and-licence)

[](TOC)

# Overview

Iris implements a data model based on the [CF conventions](http://cfconventions.org/)
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](http://cfconventions.org/standard-names.html),
[units](https://github.com/SciTools/cf_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](https://matplotlib.org/) and
[cartopy](https://scitools.org.uk/cartopy/docs/latest/),
* 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](http://www.numpy.org/) and [dask](https://dask.pydata.org/en/latest/),
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](https://conda.io/miniconda.html):

conda install -c conda-forge iris

Detailed instructions, including information on installing from source,
are available in [INSTALL](INSTALL).


# Copyright and licence

Iris may be freely distributed, modified and used commercially under the terms
of its [GNU LGPLv3 license](COPYING.LESSER).


(C) British Crown Copyright 2010 - 2018, Met Office