https://sentinel-energy.github.io/friendly_data/

Data format to interoperate between models and frameworks
https://sentinel-energy.github.io/friendly_data/

analysis datapackage python tabular-data

Last synced: about 2 months ago
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Data format to interoperate between models and frameworks

• Host: GitHub
• URL: https://sentinel-energy.github.io/friendly_data/
• Owner: sentinel-energy
• License: apache-2.0
• Created: 2020-02-11T11:34:03.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-07-13T12:34:41.000Z (about 2 years ago)
• Last Synced: 2024-03-21T00:02:33.917Z (6 months ago)
• Topics: analysis, datapackage, python, tabular-data
• Language: Python
• Homepage: https://sentinel-energy.github.io/friendly_data/
• Size: 4.65 MB
• Stars: 11
• Watchers: 3
• Forks: 3
• Open Issues: 9
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

Awesome Lists containing this project

• open-sustainable-technology - Frictionless Energy data - Common medium to facilitate the flow of data between energy and environmental models in a way that can be automated. (Energy Systems / Energy System Modeling Frameworks)

README

        
Frictionless Energy data - Friendly data

========================================

|unittests| |coverage| |docs|

A frictionless_ data package implementation for energy data;

maintained by the `SENTINEL collaboration`_.

This package provides a Python API and CLI utilities to read/write and

manage Energy (systems) data as a frictionless data package.  The

Python API includes easy conversion to and from standard Python data

structures common in data analysis; e.g. ``pandas.DataFrame``,

``xarray.DataArray``, ``xarray.Dataset``, etc.

It introduces energy modelling terminology by relying on an external

(but loosely coupled) registry_ that collates commonly used variables

and associated metadata.  If someone outside of the energy modelling

community wants to make use this package, it should be relatively

striaghtforward to point to a different registry while continuing to

use the same workflow.  Read more in the documentation_.

.. _frictionless:

   https://frictionlessdata.io/

.. _`SENTINEL collaboration`:

   https://sentinel.energy/

.. _registry:

   https://github.com/sentinel-energy/friendly_data_registry

.. _documentation:

   https://sentinel-energy.github.io/friendly_data/

.. |unittests| image:: https://github.com/sentinel-energy/friendly_data/workflows/Unit%20tests/badge.svg

   :target: https://github.com/sentinel-energy/friendly_data/actions?query=workflow%3A%22Unit+tests%22

.. |coverage| image:: https://codecov.io/gh/sentinel-energy/friendly_data/branch/master/graph/badge.svg

  :target: https://codecov.io/gh/sentinel-energy/friendly_data

.. |docs| image:: https://github.com/sentinel-energy/friendly_data/workflows/Publish%20docs/badge.svg

  :target: https://github.com/sentinel-energy/friendly_data/actions?query=workflow%3A%22Publish+docs%22

Installation

------------

You can install (or update) the package with ``pip``::

  $ pip install [-U] friendly-data

    

As the registry is coupled loosely, so that it can be updated much

faster.  This will accomodate to an evolving data vocabulary of the

community.  So it is recommended to update the registry regularly, eventhough you might not want to update the main package.  You can do this with::

  $ pip install -U friendly-data-registry

If your workflow also includes converting your data packages into IAMC

format, you will need the optional dependencies; which can be

installed at any time like this (leaving out the `-U` will not upgrade

the main package)::

  $ pip install [-U] friendly-data[extras]