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Legacy doc notice #3813

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Sep 4, 2020
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Legacy doc notice #3813

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Added note for legacy documentation
tkknight Sep 1, 2020
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tkknight Sep 1, 2020
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35 changes: 21 additions & 14 deletions docs/iris/src/index.rst
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@@ -1,12 +1,18 @@
.. note:: For **Iris 2.4** and earlier documentation please see the
`legacy documentation`_

.. _legacy documentation: https://scitools.org.uk/iris/docs/v2.4.0/


Iris Documentation
==================

.. todolist::
.. todolist::

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

Iris implements a data model based on the `CF conventions <http://cfconventions.org>`_
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.
Expand All @@ -23,18 +29,19 @@ associated metadata as first-class objects includes:
* 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
* 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.
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.
`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.


.. toctree::
Expand Down Expand Up @@ -84,6 +91,6 @@ use of standard NumPy/dask arrays as its underlying data storage.
:maxdepth: 1
:caption: Reference

whatsnew/index
techpapers/index
whatsnew/index
techpapers/index
copyright