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Merge pull request #5325 from SciTools/v3.6.x
v3.6.x merge-back
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docs/src/userguide/citation.rst

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@manual{Iris,
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author = {{Met Office}},
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title = {Iris: A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data },
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edition = {v3.5},
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title = {Iris: A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data},
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edition = {v3.6},
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year = {2010 - 2023},
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address = {Exeter, Devon },
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address = {Exeter, Devon},
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url = {http://scitools.org.uk/},
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doi = {10.5281/zenodo.7871017}
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doi = {10.5281/zenodo.7948293}
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}
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docs/src/whatsnew/3.6.rst

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.. include:: ../common_links.inc
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v3.6 (03 May 2023) [release candidate]
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**************************************
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v3.6 (18 May 2023)
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******************
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This document explains the changes made to Iris for this release
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(:doc:`View all changes <index>`.)
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.. dropdown:: v3.6 Release Highlights
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:color: primary
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:icon: info
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:animate: fade-in
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:open:
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We're so excited about our recent support for **delayed saving of lazy data
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to netCDF** (:pull:`5191`) that we're celebrating this important step change
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in behavour with its very own dedicated release 🥳
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We're super keen for the community to leverage the benefit of this new
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feature within Iris that we've brought this release forward several months.
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As a result, this minor release of Iris is intentionally light in content.
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However, there are some other goodies available for you to enjoy, such as:
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* Performing lazy arithmetic with an Iris :class:`~iris.cube.Cube` and a
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:class:`dask.array.Array`, and
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* Various improvements to our documentation resulting from adoption of
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`sphinx-design`_ and `sphinx-apidoc`_.
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As always, get in touch with us on :issue:`GitHub<new/choose>`, particularly
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if you have any feedback with regards to delayed saving, or have any issues
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or feature requests for improving Iris. Enjoy!
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:color: primary
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:icon: info
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:animate: fade-in
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:open:
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We're so excited about our recent support for **delayed saving of lazy data
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to netCDF** (:pull:`5191`) that we're celebrating this important step change
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in behavour with its very own dedicated release 🥳
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By using ``iris.save(..., compute=False)`` you can now save to multiple NetCDF files
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in parallel. See the new ``compute`` keyword in :func:`iris.fileformats.netcdf.save`.
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This can share and re-use any common (lazy) result computations, and it makes much
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better use of resources during any file-system waiting (i.e., it can use such periods
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to progress the *other* saves).
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Usage example::
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# Create output files with delayed data saving.
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delayeds = [
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iris.save(cubes, filepath, compute=False)
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for cubes, filepath in zip(output_cubesets, output_filepaths)
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]
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# Complete saves in parallel.
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dask.compute(*delayeds)
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This advance also includes **another substantial benefit**, because NetCDF saves can
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now use a
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`Dask.distributed scheduler <https://docs.dask.org/en/stable/scheduling.html>`_.
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With `Distributed <https://distributed.dask.org/en/stable/>`_ you can parallelise the
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saves across a whole cluster. Whereas previously, the NetCDF saving *only* worked with
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a "threaded" scheduler, limiting it to a single CPU.
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We're so super keen for the community to leverage the benefit of this new
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feature within Iris that we've brought this release forward several months.
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As a result, this minor release of Iris is intentionally light in content.
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However, there are some other goodies available for you to enjoy, such as:
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* Performing lazy arithmetic with an Iris :class:`~iris.cube.Cube` and a
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:class:`dask.array.Array`, and
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* Various improvements to our documentation resulting from adoption of
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`sphinx-design`_ and `sphinx-apidoc`_.
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As always, get in touch with us on :issue:`GitHub<new/choose>`, particularly
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if you have any feedback with regards to delayed saving, or have any issues
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or feature requests for improving Iris. Enjoy!
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📢 Announcements

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