cf-python 3.19.0
A CF-compliant earth science data analysis library
License: MIT
Requires Python: >=3.9
Keywords: cf, netcdf, UM, data, science, oceanography, meteorology, climate


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 <https://cfconventions.org/cf-conventions/cf-conventions.html#appendix-CF-data-model>`_.

Documentation
=============

http://ncas-cms.github.io/cf-python

Dask
====

From version 3.14.0, the ``cf`` package uses `Dask
<https://docs.dask.org>`_ for all of its data manipulations.

Recipes
=======

https://ncas-cms.github.io/cf-python/recipes

Tutorial
========

https://ncas-cms.github.io/cf-python/tutorial

Installation
============

http://ncas-cms.github.io/cf-python/installation

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.

Visualization
=============

Powerful, flexible, and very simple to produce visualizations of field
constructs are available with the `cf-plot
<https://ncas-cms.github.io/cf-plot>`_ package, that needs to be
installed separately to the ``cf`` package.

Training
========

https://github.com/NCAS-CMS/cf-tools-training

Functionality
=============

The ``cf`` package implements the `CF data model
<https://cfconventions.org/cf-conventions/cf-conventions.html#appendix-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 and domain constructs from netCDF, CDL, Zarr, PP and UM datasets,

* be fully flexible with respect to dataset storage chunking,

* create new field constructs in memory,

* write and append field and domain constructs to netCDF and Zarr v3 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,

* 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 structured grid, mesh and DSG field constructs with
  (multi-)linear, nearest neighbour, first- and second-order
  conservative and higher order patch recovery methods, including 3-d
  regridding, and large-grid support,

* 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),

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