Introduction

Version 1.11.1.0 for version 1.11 of the CF conventions.

The cfdm library implements the data model of the CF (Climate and Forecast) metadata conventions (http://cfconventions.org) and so should be able to represent and manipulate all existing and conceivable CF-compliant datasets.

The CF conventions are designed to promote the creation, processing, and sharing of climate and forecasting data using Network Common Data Form (netCDF) files and libraries (https://www.unidata.ucar.edu/software/netcdf). They cater for data from model simulations as well as from observations, made in situ or by remote sensing platforms, of the planetary surface, ocean, and atmosphere. For a netCDF data variable, they provide a description of the physical meaning of data and of its spatial, temporal, and other dimensional properties. The CF data model is an abstract interpretation of the CF conventions that is independent of the netCDF encoding.

For more details see cfdm: A Python reference implementation of the CF data model in the Journal of Open Source Software: https://doi.org/10.21105/joss.02717

Functionality

The cfdm library can create field constructs ab initio, or read them from netCDF files, inspect, subspace and modify in memory, and write them to CF-netCDF dataset files. As long as it can interpret the data, cfdm does not enforce CF-compliance, allowing non-compliant datasets to be read, processed, corrected and rewritten.

It does not contain higher-level analysis functions (such as regridding) because the expectation is that other libraries will build on cfdm, inheriting its comprehensive knowledge of the CF conventions, to add more sophisticated methods.

A basic 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 and domain constructs from netCDF and CDL datasets,

  • create new field and domain constructs in memory,

  • write field and domain 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 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,

  • 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.

Note that the cfdm package enables the representation and creation of CF field constructs, but it is largely up to the user to use them in a CF-compliant way.

A command line tool is provided that allows inspection of datasets outside of a Python environment:

Inspect a dataset from the command line.
$ 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

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, D., and Bartholomew, S. L. (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}
}

References

Eaton, B., Gregory, J., Drach, B., Taylor, K., Hankin, S., Caron, J.,

Signell, R., et al. (2020). NetCDF Climate and Forecast (CF) Metadata Conventions. CF Conventions Committee. Retrieved from https://cfconventions.org/cf-conventions/cf-conventions.html

Hassell, D., and Bartholomew, S. L. (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

Hassell, D., Gregory, J., Blower, J., Lawrence, B. N., and

Taylor, K. E. (2017). A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1), Geosci. Model Dev., 10, 4619-4646, https://doi.org/10.5194/gmd-10-4619-2017

Rew, R., and Davis, G. (1990). NetCDF: An Interface for Scientific

Data Access. IEEE Computer Graphics and Applications, 10(4), 76–82. https://doi.org/10.1109/38.56302

Rew, R., Hartnett, E., and Caron, J. (2006). NetCDF-4: Software

Implementing an Enhanced Data Model for the Geosciences. In 22nd International Conference on Interactive Information Processing Systems for Meteorology, Oceanography, and Hydrology. AMS. Retrieved from https://www.unidata.ucar.edu/software/netcdf/papers/2006-ams.pdf