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A CF-compliant earth science data analysis library

Project description

CF Python

The Python cf package is an Earth science data analysis library that is built on a complete implementation of the CF data model.



From version 3.14.0, the cf package uses Dask for all of its data manipulations.




Command line utilities

During installation the cfa command line utility is also installed, which

  • generates text descriptions of field constructs contained in files, and

  • creates new datasets aggregated from existing files.


Powerful, flexible, and very simple to produce visualizations of field constructs are available with the [cfplot]( package, that needs to be installed seprately to the cf package.

See the cfplot gallery for the full range range plotting possibilities with example code.


The cf package 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 cf package can:

  • read field constructs from netCDF, CDL, PP and UM 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 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,

  • 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, or coordinate arrays compressed by subsampling), whilst presenting a view of the data in its uncompressed form,

  • combine field constructs arithmetically,

  • manipulate field construct data by arithmetical and trigonometrical operations,

  • perform statistical collapses on field constructs,

  • perform histogram, percentile and binning operations on field constructs,

  • regrid field constructs with (multi-)linear, nearest neighbour, first- and second-order conservative and higher order patch recovery methods,

  • apply convolution filters to field constructs,

  • create running means from field constructs,

  • apply differential operators to field constructs,

  • create derived quantities (such as relative vorticity).

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