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.
http://ncas-cms.github.io/cf-python
http://ncas-cms.github.io/cf-python/installation.html
https://ncas-cms.github.io/cf-python/cheat_sheet.html
https://ncas-cms.github.io/cf-python/recipes
https://ncas-cms.github.io/cf-python/tutorial.html
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.
A simple example of reading a field construct from a file and inspecting it:
>>> import cf
>>> f = cf.read('file.nc')
>>> 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) = [1991-11-16 00:00:00, ..., 1991-10-16 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 uses
Dask for all
of its array manipulation and can:
Powerful and flexible visualizations of cf
field constructs,
designed to be produced and configured in as few lines of code as
possible, are available with the cf-plot
package, which
needs to be installed separately to the cf
package.
See the cf-plot gallery for a range of plotting possibilities with example code.
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.
Tests are run from within the cf/test
directory:
python run_tests.py