Metadata-Version: 2.1
Name: cf-python
Version: 3.16.0
Summary: A CF-compliant earth science data analysis library
Home-page: https://ncas-cms.github.io/cf-python
Author: David Hassell
Author-email: david.hassell@ncas.ac.uk
Maintainer: David Hassell
Maintainer-email: david.hassell@ncas.ac.uk
License: MIT
Keywords: cf,netcdf,UM,data,science,oceanography,meteorology,climate
Platform: Linux
Platform: MacOS
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
Classifier: Topic :: Utilities
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.7
Description-Content-Type: text/x-rst
Provides-Extra: required C libraries
Provides-Extra: regridding
Provides-Extra: convolution filters, derivatives, relative vorticity
Provides-Extra: subspacing with multi-dimensional construct cells
Provides-Extra: documentation
Provides-Extra: pre-commit hooks
License-File: LICENSE


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
[cfplot](http://ajheaps.github.io/cf-plot) package, that needs to be
installed seprately to the ``cf`` package.

See the `cfplot gallery
<http://ajheaps.github.io/cf-plot/gallery.html>`_ for the full range
range plotting possibilities with example code.

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 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).

