pyproject.toml

[project]
name = "pvlearn"
dynamic = ["version"]
description = "Self-learning PV production forecast library and service — teach your home to predict its own solar production."
readme = { file = "README.md", content-type = "text/markdown" }
requires-python = ">=3.12,<4"
license = "MIT"
authors = [
    { name = "Johannes Ott", email = "info@johannes-ott.net" }
]
keywords = ["photovoltaic", "solar", "forecast", "machine learning", "home automation"]
classifiers = [
    "Development Status :: 2 - Pre-Alpha",
    "Topic :: Home Automation",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
    "Natural Language :: English",
    "Programming Language :: Python :: 3 :: Only",
    "Programming Language :: Python :: 3.12",
    "Programming Language :: Python :: 3.13",
]
# Runtime and optional dependencies are pinned exactly, matching solaredge2mqtt
# and learninghouse. Updates arrive as individual dependabot pull requests that
# run the full test suite, rather than silently on whatever day a transitive
# resolve changes. The build-system requirements below stay on lower bounds:
# they shape how the wheel is built, not how the installed package behaves, and
# pinning them breaks builds on newer setuptools for no reproducibility gain.
#
# scikit-learn is the one pin that is load-bearing rather than merely tidy: the
# frozen Phase 0 baseline is only reproducible against 1.9.0. Verified that the
# baseline reproduces bit-identically across numpy 2.4.6/2.5.1, pandas
# 3.0.3/3.0.5 and scipy 1.17.1/1.18.0 as long as scikit-learn does not move, so
# a scikit-learn bump must regenerate the baseline and say so in its changelog.
#
# numpy 2.5 and scipy 1.18 require Python 3.12, which is why 3.11 is not
# supported.
dependencies = [
    "numpy==2.5.1",
    "pandas==3.0.5",
    "scikit-learn==1.9.0",
    "scipy==1.18.0",
    "pydantic==2.13.4",
    "astral==3.2",
    "joblib==1.5.3",
    # Season feature in TimeEncoder. Phase 1b drops this in favor of astral
    # alone (see chapter 3.2/6 of the Umsetzungsplan); kept for Phase 1a's
    # bit-identical extraction.
    "ephem==4.2.1",
]

[project.urls]
Homepage = "https://github.com/LearningHouseService/pvlearn"
Documentation = "https://github.com/LearningHouseService/pvlearn/blob/main/README.md"
Repository = "https://github.com/LearningHouseService/pvlearn"
Issues = "https://github.com/LearningHouseService/pvlearn/issues"

[project.optional-dependencies]
dev = [
    "setuptools-scm[toml]==10.2.1",
    "ruff==0.16.1",
    "tomli==2.4.1",
    "pyright==1.1.411",
    "pytest==9.1.1",
    "pytest-asyncio==1.4.0",
    "pytest-cov==7.1.0",
    "pytest-xdist==3.8.0",
    # Reading the Parquet reference fixture. The library itself takes DataFrames
    # from its caller and never touches Parquet, so this stays a test dependency.
    "pyarrow==25.0.0",
]
service = [
    "fastapi==0.141.1",
    "uvicorn[standard]==0.52.1",
    "httpx==0.28.1",
    "pyjwt==2.13.0",
]

[build-system]
requires = [
    "setuptools>=64",
    "wheel",
    "setuptools-scm[toml]>=8"
]
build-backend = "setuptools.build_meta"

[tool.setuptools.packages.find]
where = ["."]
include = ["pvlearn*"]
exclude = ["tests*"]

[tool.setuptools_scm]
version_scheme = "post-release"
local_scheme = "node-and-date"
fallback_version = "0.0.0"

[tool.ruff]
line-length = 88
target-version = "py312"

[tool.ruff.lint]
select = ["E", "F", "I"]
ignore = []
fixable = ["ALL"]
unfixable = []

[tool.ruff.format]
quote-style = "double"
skip-magic-trailing-comma = false
indent-style = "space"

[tool.pytest.ini_options]
testpaths = ["tests"]
asyncio_mode = "auto"
asyncio_default_fixture_loop_scope = "function"
addopts = "-v --tb=short -n auto --dist=worksteal"
markers = [
    "slow"
]

[tool.coverage.run]
source = ["pvlearn"]
branch = true
omit = [
    "*/tests/*",
    "*/__pycache__/*",
]

[tool.coverage.report]
fail_under = 90
exclude_lines = [
    "pragma: no cover",
    "def __repr__",
    "raise NotImplementedError",
    "if TYPE_CHECKING:",
]