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+description = "Surrogate modeling." +authors = ["Miki Verma "] +readme = "README.md" + +# The priorities of these are backwards relative to what they should +# actually be. See +# https://www.notion.so/rewiringamerica/Poetry-performance-regression-when-using-private-artifact-repository-21cf6e7c7b4c496ab3526a86e7d7d641?pvs=4 +# for details. +[[tool.poetry.source]] +name = "rewiring-america-python-repo" +url = "https://us-central1-python.pkg.dev/cube-machine-learning/rewiring-america-python-repo/simple/" +priority = "supplemental" + +[[tool.poetry.source]] +name = "PyPI" +priority = "primary" + +[tool.poetry.dependencies] +python = ">=3.10, <3.11" + +# These are RA projects in an internal repo. +dmlutils = "^0.7.0" +dmlbootstrap = "^0.1.3" + +# This is to depend on essentially all the pinned +# versions of things found in the Databricks 15.4 +# runtime. This means this project should only be +# run on 15.4 runtimes, possibly including the ML +# version. +radbcluster = "==14.3" + +# These are from PyPi +usingversion = "^0.1.2" + +[tool.poetry.group.dev.dependencies] +black = "^22.6.0" +ruff = "^0.5.5" +isort = "^5.13.2" +flake8 = "^6.1.0" +keyrings-google-artifactregistry-auth = "^1.1.2" + +[tool.poetry.group.test.dependencies] +pytest = "^7.4.4" +coverage = {extras = ["toml"], version = "^7.6.0"} +pytest-cov = "^5.0.0" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + + +# Linting tools + +[tool.black] +line-length = 120 + +[tool.isort] +profile = "black" + +[tool.ruff.lint] +select = ["D"] + +[tool.ruff.lint.pydocstyle] +convention = "numpy" + +[tool.pytest.ini_options] +pythonpath = [ + "src" +] diff --git a/tests/test_constants.py b/tests/test_constants.py index a7e02cd..f021dcf 100644 --- a/tests/test_constants.py +++ b/tests/test_constants.py @@ -8,7 +8,7 @@ import unittest -from src import constants +import constants class ConstantTestCase(unittest.TestCase): @@ -56,7 +56,7 @@ def test_lb_to_kg(self): if os.environ.get("DATABRICKS_RUNTIME_VERSION", None): # If we are developing on databricks we have to manually - # instatiate a test suite and load all the tests from this module. + # instantiate a test suite and load all the tests from this module. test_suite = unittest.TestSuite() test_suite.addTests( unittest.TestLoader().loadTestsFromModule(sys.modules[__name__])