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An open source toolkit for implementing and developing standard methods for calculating normalized metered energy consumption (NMEC) and avoided energy use.
https://github.com/openeemeter/eemeter/blob/master/CONTRIBUTING.md
Governance file added on the project (https://github.com/openeemeter/eemeter/tree/master).
https://github.com/openeemeter/eemeter/blob/master/CODE_OF_CONDUCT.md
Added in Governance file on the project ( https://github.com/openeemeter/eemeter/tree/master)
https://github.com/openeemeter/eemeter/blob/master/MAINTAINERS.md
https://eemeter.readthedocs.io/
https://eemeter.readthedocs.io/tutorial.html
Badge is added on readme file on the project (https://github.com/openeemeter/eemeter/tree/master).
The output and input do not correspond to language, but more specifically input numbers in the form of a csv file or similar.
The output is usually in the form of an object which contains numbers as the output, i.e. the output is essentially a graph on the input values. It does not require internationalization.
https://eemeter.readthedocs.io/ Project does not require personal login details for using the software.
https://github.com/openeemeter/eemeter/issues
No vulnerabilities received.
Vulnerability process added on Security file added on the project (https://github.com/openeemeter/eemeter/tree/master).
Style guide explained at the top under "Guidelines".
Blacken is used to enforce the accepted upon style guide in Python.
https://github.com/openeemeter/eemeter/tree/master
Code is written entirely in Python.
https://github.com/openeemeter/eemeter/tree/master.
Uses PyPi.
Installation is done through PyPi as mentioned on the readme (https://github.com/openeemeter/eemeter/tree/master)
Installation done via pip as mentioned on the readme (https://github.com/openeemeter/eemeter/tree/master)
All dependencies are mentioned in the requirements.txt file, which are installed via Pypi upon installation.
The dependencies are on common and public libraries such as Numpy, Pandas, etc. No obscure or unknown libraries are used which can have potentially severe hidden vulnerabilities.
The project is available on Pypi for installation, and the requirements are installed automatically when installing the library.
Deprecated usages are avoided in the implementation.
Continuous Integration is enabled on the project via Docker Publish Github Action.
Automated test suite is added via Docker Publish (Github Actions)
Statement coverage is maintained using CodeQL.
Test cases must be added in a specific folder as written in documentation. The test cases are picked into an automated test suite.
Mentioned in the readme - https://github.com/openeemeter/eemeter/blob/master/CONTRIBUTING.md
Warnings are adhered to and fixed in the project itself.
CodeQL analysis (via Github Actions) is enabled on the project.
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