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/
_______________________________________________ CF-metadata mailing list [email protected] http://mailman.cgd.ucar.edu/mailman/listinfo/cf-metadata
