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Frictionless Energy data

Common medium to facilitate the flow of data between energy and environmental models in a way that can be automated.
https://sentinel-energy.github.io/friendly_data/

Keywords

analysis datapackage python tabular-data

Last synced: over 1 year ago
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Repository metadata

Data format to interoperate between models and frameworks

README.rst

          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]

        

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Total Commits: 456
Total Committers: 2
Avg Commits per committer: 228.0
Development Distribution Score (DDS): 0.004

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Name Email Commits
Suvayu Ali f****x@g****m 454
Stefan Pfenninger s****n@p****g 2

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Issue and Pull Request metadata

Last synced: over 1 year ago

Total issues: 18
Total pull requests: 7
Average time to close issues: 9 months
Average time to close pull requests: 2 months
Total issue authors: 4
Total pull request authors: 3
Average comments per issue: 1.06
Average comments per pull request: 1.43
Merged pull request: 4
Bot issues: 0
Bot pull requests: 0

Past year issues: 0
Past year pull requests: 0
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Past year pull request authors: 0
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Past year bot pull requests: 0

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  • danielhuppmann (1)
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  • sjpfenninger (2)
  • olejandro (1)

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Package metadata

pypi.org: friendly-data

A frictionless data package implementation for energy system data.

  • Homepage: https://github.com/sentinel-energy/friendly_data
  • Documentation: https://friendly-data.readthedocs.io/
  • Licenses:
  • Latest release: 0.3.3 (published over 3 years ago)
  • Last Synced: 2024-01-24T14:39:56.888Z (over 1 year ago)
  • Versions: 8
  • Dependent Packages: 1
  • Dependent Repositories: 1
  • Downloads: 40 Last month
  • Rankings:
    • Dependent packages count: 4.766%
    • Forks count: 16.831%
    • Stargazers count: 17.077%
    • Average: 17.103%
    • Dependent repos count: 21.615%
    • Downloads: 25.226%
  • Maintainers (4)

Dependencies

requirements-dev.txt pypi
  • coverage * development
  • pytest >=6 development
  • pytest-cov * development
requirements-docs.txt pypi
  • frictionless >=4.23
  • friendly-data-registry *
  • jinja2 *
  • numpydoc *
  • pyyaml *
  • sphinx *
  • sphinx-rtd-theme *
requirements-extras.txt pypi
  • pandas-profiling *
  • pyam-iamc *
requirements.txt pypi
  • fire *
  • frictionless >=4.23
  • friendly-data-registry *
  • glom >=19.10,<22
  • jinja2 *
  • numexpr *
  • numpy *
  • pandas >=1.3
  • pyyaml *
  • requests *
  • rich *
  • tabulate *
  • xarray *

Score: 7.473069088032197