cf python package

Introduction

Version 3.13.1 for version 1.9 of the CF conventions.

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

Functionality

The cf 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 that are partially conformant may nonetheless be modified in memory.

A simple example of reading a field construct from a file and inspecting it.
>>> import cf
>>> f = cf.read('file.nc')
>>> f
[<CF 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 cf package can:

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

  • 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,

  • 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 moving means from field constructs,

  • apply differential operators to field constructs,

  • create derived quantities (such as relative vorticity).

All of the above use LAMA functionality, which allows multiple fields larger than the available memory to exist and be manipulated.

Visualization

Powerful, flexible, and very simple to produce visualizations of field constructs are available with the cfplot package, that is installed separately to cf (see http://ajheaps.github.io/cf-plot for details).

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

_images/cfplot_example.png

Example output of cfplot displaying a cf field construct.

Old versions

Since version 3.0.0 (released 2019-10-01), cf is for Python 3 only and there are incompatible differences between versions 2.x and 3.x of cf.

Scripts written for version 2.x but running under version 3.x should either work as expected, or provide informative error messages on the new API usage. However, it is advised that the outputs of older scripts are checked when running with Python 3 versions of the cf library.

For version 2.x documentation, see the releases page.

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.

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