pyproject.toml

[build-system]
requires		= ['setuptools>=61.0']
build-backend	= 'setuptools.build_meta'

[project]
name			= 'mercury-settrie'
version			= '1.4.7'
description		= 'A SetTrie is a container of sets that performs efficient subset and superset queries.'
license			= {file = "LICENSE.txt"}
requires-python = '>=3.8'
classifiers		= ['Programming Language :: Python :: 3',
					'License :: OSI Approved :: Apache Software License',
					'Operating System :: OS Independent']
keywords		= ['event detection', 'event prediction', 'time series']
authors			= [{name = 'Mercury Team', email = 'mercury.group@bbva.com'}]
readme			= 'README.md'

[tool.setuptools]
package-dir		= {'' = 'src'}

[tool.setuptools.packages.find]
where			= ['src']


# setuptools.setup(
# ...
#	long_description = """    Settrie was born from the need of a better implementation of the algorithm for our recommender system. It has
#		direct application to text indexing for search in logarithmic time of documents containing a set of words. Think of a collection of
#		documents as a container of the set of words that each document has. Finding all the documents that contain a set of words is
#		finding the superset of the set of words in the query. A SetTrie will give you the answer --the list of document names-- in
#		logarithmic time. The data structure is also used in auto-complete applications.""",
#	url				 = 'https://github.com/BBVA/mercury-settrie',
# 	platforms		 = ['Linux', 'MacOS', 'Windows'],
# ...
# )