MGD-RSS/pyproject.toml at main · UsmanovSla/MGD-RSS · GitHub

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

[project]
name = "mgd-rss"
version = "0.1.0"
description = "Hybrid Bayesian Generative Model for Synthetic Data Generation in Civil Engineering"
readme = "README.md"
requires-python = ">=3.9"
license = {text = "GPLv3"}
authors = [
    { name = "Vjačeslav Usmanov", email = "vjaceslav.usmanov@cvut.cz" }
]
keywords = [
    "synthetic data",
    "bayesian calibration",
    "MCMC",
    "hybrid modeling",
    "AI training"
]
classifiers = [
    "Programming Language :: Python :: 3",
    "License :: OSI Approved :: GPLv3 License",
    "Intended Audience :: Science/Research",
    "Topic :: Scientific/Engineering :: Civil Engineering",
    "Topic :: Scientific/Engineering :: Artificial Intelligence"
]

dependencies = [
    "numpy",
    "pandas",
    "scipy",
    "matplotlib",
    "seaborn",
    "scikit-learn",
    "pymc",
    "arviz",
    "xarray",
    "netcdf4"
]

[project.urls]
Homepage = "https://github.com/UsmanovSla/MGD-RSS"
Repository = "https://github.com/UsmanovSla/MGD-RSS"
Documentation = "https://github.com/UsmanovSla/MGD-RSS#readme"

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

[tool.black]
line-length = 88
target-version = ["py39"]

[tool.isort]
profile = "black"

[tool.pytest.ini_options]
testpaths = ["tests"]

[tool.mgd-rss.metadata]
data_format = ["CSV", "NetCDF"]
model_type = "Hybrid Bayesian Generative Model"
calibration_method = "MCMC (NUTS)"
data_provenance = "Time study measurements of robotic masonry process"
license_data = "GPLv3"