Dear CF Community,
I would like to announce a new CF-aware python library: *cfdm* (
https://ncas-cms.github.io/cfdm).
This is a reference implementation of the CF data model, and so it is
guaranteed to be able to read and write any CF-compliant dataset. It is not
strict about CF-compliance, however, so that partially conformant datasets
may be ingested from existing datasets and written to new datasets.This is
so that datasets which are partially conformant may nonetheless be modified
in memory.
The package fulfills a promise made in the CF data model GMD paper (
https://doi.org/10.5194/gmd-10-4619-2017) to create such a library, along
with a commitment to keep the library up to date with with new release of
the CF conventions. It currently support all features up to and including
CF-1.7.
(As an aside, I would like to advertise the proposal for formally
incorporating the CF data model into the CF conventions:
https://github.com/cf-convention/cf-conventions/issues/159)
Unlike some similar packages, it has no high-level functionality required
for data analysis (such as functions for regridding, statistical collapses,
etc.), rather it focuses on reading, writing, creating and editing
CF-compliant field constructs (i.e. CF-netCDF data variables) and datasets.
It can:
- - read field constructs from netCDF datasets,
- - create new field constructs in memory,
- - inspect field constructs,
- - test whether two field constructs are the same,
- - modify field construct metadata and data,
- - create subspaces of field constructs,
- - write field constructs to netCDF datasets on disk,
- - incorporate, and create, metadata stored in external files, and
- - read, write, and create data that have been compressed by convention
(i.e. ragged or gathered arrays), whilst presenting a view of the data in
its uncompressed form.
It also provides a shell command line tool call *cfdump* that is like a
CF-aware ncdump, in that it provides a view of a dataset organized into CF
constructs.
It has comprehensive documentation at
https://ncas-cms.github.io/cfdm,
including some background on the CF data model, installation instructions,
a full tutorial, a reference section and guidance on using cfdm within
other libraries.
It is my hope that this new library may prove useful, and I welcome any
form of feedback at
https://github.com/NCAS-CMS/cfdm/issues
Finally, I would like to thank my colleagues at NCAS for their invaluable
reviews of beta versions of the code and documentation (but any mistakes
are mine alone).
Many thanks and all the best,
David
--
David Hassell
National Centre for Atmospheric Science
Department of Meteorology, University of Reading,
Earley Gate, PO Box 243, Reading RG6 6BB
Tel: +44 118 3785183
http://www.met.reading.ac.uk/
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Received on Tue Apr 02 2019 - 05:24:06 BST
