canopy-height-ml/pyproject.toml at main · vsocrates/canopy-height-ml · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
[build-system]
requires = ["flit_core >=3.2,<4"]
build-backend = "flit_core.buildapi"

[project]
name = "canopy_height_prediction"
version = "0.0.1"
description = "Use XGBoost to predict canopy height based on NASA GEDI and spectrography images"
authors = [
  { name = "Vimig Socrates" },
]
license = { file = "LICENSE" }
readme = "README.md"
classifiers = [
    "Programming Language :: Python :: 3",
    "License :: OSI Approved :: MIT License"
]
dependencies = [
    "loguru",
    "pip",
    "python-dotenv",
    "ruff",
    "tqdm",
    "typer",
    # ML / modeling
    "xgboost",
    "lightgbm",
    "scikit-learn",
    "mlflow",
    # Geospatial
    "geopandas",
    "rasterio",
    "rasterstats",
    # Google Earth Engine
    "geemap",
    # GEDI / HDF5 data
    "h5py",
    # Notebooks & visualization
    "jupyter",
    "jupyterlab",
    "matplotlib",
    "seaborn",
    "contextily",
    # Data wrangling
    "numpy",
    "pandas",
    # Agentic pipeline
    "pydantic-ai[anthropic]>=0.0.14",
    "logfire[pydantic-ai]",
    "sqlalchemy>=2.0",
    "alembic",
    # Testing
    "pytest",
    "pytest-asyncio",
    "pytest-mock",
]
requires-python = "~=3.10.0"


[tool.ruff]
line-length = 99
src = ["canopy_height_prediction"]
include = ["pyproject.toml", "canopy_height_prediction/**/*.py"]

[tool.ruff.lint]
extend-select = ["I"]  # Add import sorting

[tool.ruff.lint.isort]
known-first-party = ["canopy_height_prediction"]
force-sort-within-sections = true