| Name | daops JSON |
| Version |
0.13.0
JSON |
| download |
| home_page | None |
| Summary | Dataset-Aware Operations. |
| upload_time | 2025-02-07 11:03:59 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
daops
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
daops - data-aware operations
=============================
.. image:: https://img.shields.io/pypi/v/daops.svg
:target: https://pypi.python.org/pypi/daops
:alt: PyPI
.. image:: https://github.com/roocs/daops/workflows/build/badge.svg
:target: https://github.com/roocs/daops/actions
:alt: Build Status
.. image:: https://readthedocs.org/projects/daops/badge/?version=latest
:target: https://daops.readthedocs.io/en/latest/?badge=latest
:alt: Documentation
.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
:target: https://github.com/python/black
:alt: Python Black
The ``daops`` library (pronounced "day-ops") provides a python interface to a
set of operations suitable for working with climate simulation outputs. It is
typically used with ESGF data sets that are described in NetCDF files. ``daops``
is unique in that it accesses a store of *fixes* defined for datasets that are
irregular when compared with others in their *population*.
When a ``daops`` operation, such as ``subset``\ , is requested, the library will look
up a database of known fixes before performing and calculations or transformations.
The data will be loaded and *fixed* using the `xarray <http://xarray.pydata.org/>`_
library before the any actual operations are sent to its sister library
`clisops <https://github.com/roocs/clisops>`_.
* Free software: BSD
* Documentation: https://daops.readthedocs.io
Features
--------
The package has the following features:
* Ability to run *data-reduction* operations on large climate data sets.
* Knowledge of irregularities/anomalies in some climate data sets.
* Ability to apply *fixes* to those data sets before operating on them.
This process is called *normalisation* of the data sets.
Credits
=======
This package was created with ``Cookiecutter`` and the ``cedadev/cookiecutter-pypackage`` project template.
* Cookiecutter: https://github.com/audreyr/cookiecutter
* cookiecutter-pypackage: https://github.com/cedadev/cookiecutter-pypackage
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