{% extends "layout.html" %}
{% set title = 'Iris documentation homepage' %}
{% block extrahead %}
{{ super() }}

<script type="text/javascript" src="{{ pathto("_static/jquery.cycle.all.latest.js", 1) }}"></script>
<script type="text/javascript" src="{{ pathto("_static/random_image.js", 1) }}" async></script>

<style type="text/css">
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</script>

{% endblock %}



{% block body %}




<p>
  Iris implements a data model based on the <a href="http://cfconventions.org/">CF conventions</a>
  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.
</p>
<p>
  <a href="http://cfconventions.org/standard-names.html">CF Standard names</a>,
  <a href="https://github.com/SciTools/cf_units">units</a>, 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:
</p>
<ul>
  <li>a visualisation interface based on <a href="https://matplotlib.org/">matplotlib</a> and
    <a href="https://scitools.org.uk/cartopy/docs/latest/">cartopy</a>,</li>
  <li>unit conversion,</li>
  <li>subsetting and extraction,</li>
  <li>merge and concatenate,</li>
  <li>aggregations and reductions (including min, max, mean and weighted averages),</li>
  <li>interpolation and regridding (including nearest-neighbor, linear and area-weighted), and</li>
  <li>operator overloads (<code>+</code>, <code>-</code>, <code>*</code>, <code>/</code>, etc.).</li>
</ul>
<p>
  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.
</p>
<p>
  Building upon <a href="http://www.numpy.org/">NumPy</a> and
  <a href="https://dask.pydata.org/en/latest/">dask</a>,
  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.
</p>

<div style="float:right; padding-top: 50px; padding-right: 10px;">
	<div class="slideshow"></div>
	<div id="sliderNav" align="center"></div>
</div>

<div style="float: left;">
	<ul style="list-style: none; margin-left: -15px; margin-right: 0px; margin-top: 50px;">
	<li>
		<p class="biglink"><a class="biglink" href="installing.html">Installation guide</a><br/>
		<span class="linkdescr">including dependency details</span></p>
	</li>
	<li>
		<p class="biglink"><a class="biglink" href="userguide/index.html">User guide</a><br/>
         	<span class="linkdescr">an introduction to Iris and its core concepts</span></p>
	</li>
	<li>
		<p class="biglink"><a class="biglink" href="iris/iris.html">Reference documentation</a><br/>
		<span class="linkdescr">complete Iris package reference help</span></p>
	</li>
	<li>
		<p class="biglink"><a class="biglink" href="gallery.html">Gallery</a><br/>
		<span class="linkdescr">a collection of images produced using Iris</span></p>
	</li>
	<li>
		<p class="biglink"><a class="biglink" href="developers_guide/index.html">Developer's guide</a><br/>
		<span class="linkdescr">guide for SciTools developers</span></p>
	</li>
    <li>
        <p class="biglink"><a class="biglink" href="whitepapers/index.html">Whitepapers</a><br/>
        <span class="linkdescr">extra information on specific technical issues</span></p>
    </li>
    <li>
        <p class="biglink"><a class="biglink" href="whatsnew/2.4.html">What's new in Iris 2.4?</a><br/>
        <span class="linkdescr">recent changes in Iris's capabilities</span></p>
    </li>
	</ul>
</div>


<div class="clearer"></div>

{% endblock %}