cfdm
A Python reference implementation of the CF data model.
Documentation
https://ncas-cms.github.io/cfdm
Tutorial
https://ncas-cms.github.io/cfdm/tutorial
Installation
https://ncas-cms.github.io/cfdm/installation
Functionality
The cfdm package implements the CF data model
(https://doi.org/10.5194/gmd-10-4619-2017) for its internal data
structures and so is able to process 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 central element defined by the CF data model is the field
construct, which corresponds to a CF-netCDF data variable with all
of its metadata.
A simple example of reading a field construct from a file and
inspecting it:
>>> import cfdm
>>> f = cfdm.read('file.nc')
>>> f
[<Field: air_temperature(time(12), latitude(64), longitude(128)) K>]
>>> print(f[0])
Field: air_temperature (ncvar%tas)
----------------------------------
Data : air_temperature(time(12), latitude(64), longitude(128)) K
Cell methods : time(12): mean (interval: 1.0 month)
Dimension coords: time(12) = [0450-11-16 00:00:00, ..., 0451-10-16 12:00:00] noleap
: latitude(64) = [-87.8638, ..., 87.8638] degrees_north
: longitude(128) = [0.0, ..., 357.1875] degrees_east
: height(1) = [2.0] m
The cfdm package can:
- read field constructs from netCDF and CDL datasets,
- create new field constructs in memory,
- write and append field constructs to netCDF datasets on disk,
- read, write, and create coordinates defined by geometry cells,
- read and write netCDF4 string data-type variables,
- read, write, and create netCDF and CDL datasets containing
hierarchical groups, - inspect field constructs,
- test whether two field constructs are the same,
- modify field construct metadata and data,
- create subspaces of field constructs,
- 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.
Command line utility
During installation the cfdump command line tool is also installed,
which generates text descriptions of the field constructs contained in
a netCDF dataset:
$ cfdump file.nc
Field: air_temperature (ncvar%tas)
----------------------------------
Data : air_temperature(time(12), latitude(64), longitude(128)) K
Cell methods : time(12): mean (interval: 1.0 month)
Dimension coords: time(12) = [0450-11-16 00:00:00, ..., 0451-10-16 12:00:00] noleap
: latitude(64) = [-87.8638, ..., 87.8638] degrees_north
: longitude(128) = [0.0, ..., 357.1875] degrees_east
: height(1) = [2.0] m
Tests
Tests are run from within the cfdm/test directory:
$ python run_tests.py
Citation
If you use cfdm, either as a stand-alone application or to provide a CF
data model implementation to another software library, please consider
including the reference:
Hassell et al., (2020). cfdm: A Python reference implementation of the
CF data model. Journal of Open Source Software, 5(54), 2717,
https://doi.org/10.21105/joss.02717
@article{Hassell2020,
doi = {10.21105/joss.02717},
url = {https://doi.org/10.21105/joss.02717},
year = {2020},
publisher = {The Open Journal},
volume = {5},
number = {54},
pages = {2717},
author = {David Hassell and Sadie L. Bartholomew},
title = {cfdm: A Python reference implementation of the CF data model},
journal = {Journal of Open Source Software}
}
