cfdm package

Introduction

Version 1.8.3 for version 1.8 of the CF conventions.

The Python cfdm package is a complete implementation of the CF data model.

Functionality

The cfdm package implements the CF data model 1 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.

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,

  • 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,

  • 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, and

  • read, write, and create coordinates defined by geometry cells (new in version 1.8.0).

Note that cfdm enables the creation of CF field constructs, but it’s up to the user to use them in a CF-compliant way.

The cfdm package has, with few exceptions, only the functionality required to read and write datasets, and to create, modify and inspect field constructs in memory.

Hierarchical groups

Hierarchical groups provide a powerful mechanism to structure variables within datasets. A future 1.8.x release of cfdm will include support for netCDF4 files containing data organised in hierarchical groups, but this is not available in version 1.8.3 (even though it is allowed in CF-1.8).

Additional functionality

The cf-python and cf-plot packages, which are both are built on top of the cfdm package, include higher level functionality, such as regridding, statistical operations and comprehensive visualisation.

1

Hassell, D., Gregory, J., Blower, J., Lawrence, B. N., and Taylor, K. E.: 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, 2017.