Tools and apps for processing numerical climate model data
Project description
Afterburner Package
What is Afterburner?
Afterburner is a Python package that provides functionality for analysing and processing climate model data, in particular, though not exclusively, data produced by the UK Met Office's Unified Model.
At present the Afterburner codebase is compatible with both Python 2.7 and Python 3.6 or later. However, since official support for Python 2.7 has now ceased the use of Python 3 is firmly recommended.
License
Afterburner software is made available under a 3-Clause BSD license, as detailed in the LICENSE.TXT file which can be found in the root directory of the Afterburner distribution.
Dependencies
The afterburner package depends upon the following packages, each of which have
their own dependencies.
- scitools-iris (version 2.1 or later; version 2.4 recommended)
- pyparsing
- windspharm
- sphinx (only required if you intend to build and install the documentation set)
- pytest (only required if you intend to run the unit tests)
Installation
The afterburner package can be installed from PyPI
or conda-forge. Installing from conda-forge is the
recommended method.
Installing from PyPI
Although the afterburner package can be installed from PyPI, installing certain
prerequisite packages (notably cartopy) can be challenging. Consequently, it is
recommended that you first install Iris into a suitable conda environment, and
then use pip to install afterburner into that environment, but skipping
installation of dependent packages (using the --no-deps option).
% conda activate <your-env>
% conda info -c conda-forge iris==2.4.0 # just to preview package dependencies
% conda install -c conda-forge iris==2.4.0
% conda install -c conda-forge windspharm
% pip install --no-deps metoffice-afterburner
Note: installing Iris should take care of installing the pyparsing package.
Installing from conda-forge
% conda activate <your-env>
% conda install -c conda-forge iris==2.4.0
% conda install -c conda-forge windspharm
% conda install -c conda-forge metoffice-afterburner
Installing from a tarball
You may also install directly from the source distribution. After downloading the
package tarball from PyPI, unpack it, change to the source directory, then enter
the following command (with associated options if required: use --help to see
which ones are supported):
% python setup.py install
Integration with Rose and Cylc
If you are intending to utilise the afterburner Python package in combination
with Rose and Cylc
-- for example, to incorporate an Afterburner app into a Rose suite -- then you
should also install those packages onto your system.
In addition to their respective GitHub project home pages (linked above), both packages can be found on PyPI (Rose | Cylc).
Note, however, that the afterburner Python package should build and install
successfully without the Rose and Cylc packages being installed.
Documentation
The sphinx-based documentation for Afterburner may be generated by running the
build_docs subcommand:
% python setup.py build_docs
That command will build HTML documentation within the directory doc/src/_build/html.
If desired, the documentation may then be installed into a target directory
using the install_docs subcommand:
% python setup.py install_docs --dst-dir=<destination-dir> [--host=<hostname>]
Alternatively, if preferred you can manually modify and run the shell script at
doc/src/build_docs.sh.
Testing
If desired, the Afterburner unit tests may be run using the following command:
pytest
Depending on which packages are installed in your Python runtime environment, some unit tests may be skipped.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file metoffice_afterburner-1.3.4.tar.gz.
File metadata
- Download URL: metoffice_afterburner-1.3.4.tar.gz
- Upload date:
- Size: 2.6 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.12.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
ed2bf255316587d4cc7dd418a0e035fbe7b142d3d7861f610f6497b94b08a002
|
|
| MD5 |
ca7986cda9e729559e59d10fc68f1af7
|
|
| BLAKE2b-256 |
bd3a1d3b357bfbcb4f28a9f064e4d94d24526994490a9a2aad54dd796577bbe4
|