cfdm 1.12.2.0

The cfdm Python package is a complete reference implementation of the CF data model for CF-1.11, that identifies the fundamental elements of the CF conventions and shows how they relate to each other, independently of the netCDF encoding.

The central element defined by the CF data model is the field construct, which corresponds to a CF-netCDF data 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, CDL, and Zarr datasets with a choice of netCDF backends,

• be fully flexible with respect to dataset storage chunking,

• 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,

• 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,

• read and write that data that are quantized to eliminate false precision.