David,
That's great! Thanks!
Jim
On 4/2/19 7:24 AM, David Hassell wrote:
> 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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