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89 changes: 5 additions & 84 deletions README.md
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<h4 align="center">
Iris is a powerful, format-agnostic, community-driven Python library for
Iris is a powerful, format-agnostic, community-driven Python package for
analysing and visualising Earth science data
</h4>

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<img src="https://api.travis-ci.org/repositories/SciTools/iris.svg?branch=master"
alt="Travis-CI" /></a>
<a href='https://scitools-iris.readthedocs.io/en/latest/?badge=latest'>
<img src='https://readthedocs.org/projects/scitools-iris/badge/?version=latest'
<img src='https://readthedocs.org/projects/scitools-iris/badge/?version=latest'
alt='Documentation Status' /></a>
<a href="https://anaconda.org/conda-forge/iris">
<img src="https://img.shields.io/conda/dn/conda-forge/iris.svg"
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<img src="https://img.shields.io/badge/code%20style-black-000000.svg"
alt="black" /></a>
</p>
<br>

<!-- NOTE: toc auto-generated with https://github.com/frnmst/md-toc:
$ md_toc github README.md -i
-->

<h1>Table of contents</h1>

[](TOC)

+ [Overview](#overview)
+ [Documentation](#documentation)
+ [Installation](#installation)
+ [Copyright and licence](#copyright-and-licence)
+ [Get in touch](#get-in-touch)
+ [Contributing](#contributing)

[](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

<a href="https://scitools.org.uk/iris/docs/latest/index.html"> <img src="https://img.shields.io/badge/docs-stable-green.svg" alt="Stable docs" /></a> The documentation for *stable released versions* of Iris, including a user guide, example code, and gallery.

<a href="https://scitools-docs.github.io/iris/master/index.html"> <img src="https://img.shields.io/badge/docs-latest-blue.svg" alt="Latest docs" /></a> The documentation for the *latest development version* of Iris.


# 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 the
[documentation](https://scitools-iris.readthedocs.io/en/latest/installing.html).

# Get in touch

* Report bugs, or suggest new features using an Issue or Pull Request on [Github](https://github.com/SciTools/iris). You can also comment on existing Issues and Pull Requests.
* For discussions from a user perspective you could join our [SciTools Users Google Group](https://groups.google.com/forum/#!forum/scitools-iris).
* For those involved in developing Iris we also have an [Iris Developers Google Group](https://groups.google.com/forum/#!forum/scitools-iris-dev).
* [StackOverflow](https://stackoverflow.com/questions/tagged/python-iris) For "How do I?".

# Copyright and licence

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

# Contributing
Information on how to contribute can be found in the [Iris developer guide](https://scitools.org.uk/iris/docs/latest/developers_guide/index.html).

(C) British Crown Copyright 2010 - 2020, Met Office
<p align="center">
See the <a href="https://scitools-iris.readthedocs.io/en/latest/">documentation</a> for the <b>latest development version</b> of Iris.
</P>
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.. todolist::

**A powerful, format-agnostic, community-driven Python library for analysing
**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 <http://cfconventions.org>`_
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Citing Iris
===========

If Iris played an important part in your research then please add us to your reference list by using one of the recommendations below.
If Iris played an important part in your research then please add us to your
reference list by using one of the recommendations below.

************
BibTeX entry
BibTeX entry
************

For example::

@manual{Iris,
author = {{Met Office}},
title = {Iris: A Python library for analysing and visualising meteorological and oceanographic data sets},
title = {Iris: A Python package for analysing and visualising meteorological and oceanographic data sets},
edition = {v1.2},
year = {2010 - 2013},
address = {Exeter, Devon },
url = {http://scitools.org.uk/}
}
}


*******************
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For example::

Iris. Met Office. [email protected]:SciTools/iris.git 06-03-2013
Iris. Met Office. [email protected]:SciTools/iris.git 06-03-2013

.. _How to cite and describe software: http://software.ac.uk/so-exactly-what-software-did-you-use

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