A Python reference implementation of the CF data model at CF-1.11.
https://ncas-cms.github.io/cfdm
https://ncas-cms.github.io/cfdm/installation
https://ncas-cms.github.io/cfdm/tutorial
The cfdm package fully implements the CF data
model
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 elements defined by the CF data model are the field construct, which corresponds to CF-netCDF data variable with all of its metadata; and the domain contruct, which may be the domain of a field construct or corresponds to a CF-netCDF domain 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 and domain constructs from netCDF and CDL datasets,
- create new field and domain constructs in memory,
- write and append field and domain constructs to netCDF datasets on disk,
- read, write, and manipulate UGRID mesh topologies,
- 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 and domain constructs,
- test whether two constructs are the same,
- modify field and domain construct metadata and data,
- create subspaces of field and domain constructs, from indices or metadata values,
- 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, or coordinate arrays compressed by subsampling), whilst presenting a view of the data in its uncompressed form.
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 are run from within the cfdm/test directory:
$ python run_tests.py
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}
}