Skip to content

sssom-py

Details
GitHub mapping-commons/sssom-py
Language Python
Description Python toolkit for SSSOM mapping format

Dependencies

External Dependencies

Package Version
python ^3.8
click >=8.1.6
curies >=0.7.3
linkml-runtime >=1.5.5
pandas >1.0.3
pansql {'version': '>=0.0.1', 'extras': ['pansql']}
sssom-schema >=0.14.0
networkx {'version': '>=3.1', 'extras': ['networkx']}
sparqlwrapper >=2.0.0
validators >=0.20.0
deprecation ^2.1.0
pyyaml ^6.0.1
rdflib >=6.0.0
scipy {'version': '*', 'extras': ['scipy']}
importlib-resources ^6.1.1

Documentation

Python Utilities for SSSOM

Tests PyPI PyPI - Python Version PyPI - License Code style: black

SSSOM (Simple Standard for Sharing Ontology Mappings) is a TSV and RDF/OWL standard for ontology mappings

WARNING: 
    The export formats (json, rdf) of sssom-py are not yet finalised! 
    Please expect changes in future releases!

See https://github.com/OBOFoundry/SSSOM

This is a python library and command line toolkit for working with SSSOM. It also defines a schema for SSSOM.

Documentation

See documentation

Deploy documentation

make sphinx
make deploy-docs

Schema

See the schema/ folder for source schema in YAML, plus derivations to JSON-Schema, ShEx, etc.

Testing

tox is similar to make, but specific for Python software projects. Its configuration is stored in tox.ini in different "environments" whose headers look like [testenv:...]. All tests can be run with:

$ pip install tox
$ tox

A specific environment can be run using the -e flag, such as tox -e lint to run the linting environment.

Outstanding Contributors

Outstanding contributors are groups and institutions that have helped with organising the SSSOM Python package's development, providing funding, advice and infrastructure. We are very grateful for all your contribution - the project would not exist without you!

Harvard Medical School

Harvard Medical School Logo

The INDRA Lab, a part of the Laboratory of Systems Pharmacology and the Harvard Program in Therapeutic Science (HiTS), is interested in natural language processing and large-scale knowledge assembly. Their work on SSSOM is funded by the DARPA Young Faculty Award W911NF2010255 (PI: Benjamin M. Gyori).

https://indralab.github.io