SignifiKANTE/pyproject.toml at main · bionetslab/SignifiKANTE · 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
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
[build-system]
requires = ["hatchling >= 1.26"]
build-backend = "hatchling.build"

[project]
name = "signifikante"
version = "0.1.2"
description = "Scalable gene regulatory network inference using tree-based ensemble regressors with p-values"
readme = "README.rst"
requires-python = ">=3.10,<3.14"


authors = [
    { name="Anne Hartebrodt", email="anne.hartebrodt@fau.de" },
    { name="Fabian Woller", email="fabian.woller@fau.de" },
    { name="Paul Martini", email="paul.martini@fau.de" },
	{ name = "Thomas Moerman", email = "thomas.moerman@gmail.com" },
]

license = { text = "GNU General Public License v3 (GPLv3)" }

urls = {Homepage = "https://github.com/bionetslab/SignifiKANTE"}

classifiers = [
    "Programming Language :: Python :: 3",
    "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
    "Operating System :: OS Independent",
]

# Dependencies for pip installs
dependencies = [
	"dask",
	"distributed",
	"numpy<2.0",
	"pandas",
	"scikit-learn",
	"scipy",
	"scikit-learn-extra",
	"setuptools",  # required for scikit-learn-extra under python 3.13
	"pyarrow",
	"numba",
	"pytest",
	"xgboost",
	"statsmodels",
	"sparse"
]


[tool.pixi.workspace]
channels = ["conda-forge"]
platforms = ["linux-64"]

[tool.pixi.dependencies]
python = "*" # Ensure Python is defined as a dependency
dask = "*"
distributed = "*"
numpy = "*"
pandas = "*"
scikit-learn = "*"
scipy = "*"
scikit-learn-extra = "*"
pyarrow = "*"
numba = "*"
pytest = "*"
xgboost = "*"
statsmodels = "*"
sparse = "*"

[tool.pixi.pypi-dependencies]
# This section remains for pure-Python packages not available or preferred from Conda,
# but it currently only contains the local editable install.
signifikante = { path = ".", editable = true }

[tool.pixi.environments]
kernel = ["kernel"]


# --- KERNEL ENVIRONMENT  ---
# Defines a new, separate environment named 'kernel'.
[tool.pixi.feature.kernel.dependencies]
python = "*"
dask = "*"
distributed = "*"
numpy = "*"
pandas = "*"
scikit-learn = "*"
scipy = "*"
scikit-learn-extra = "*"
pyarrow = "*"
ipykernel = "*"
numba = "*"
xgboost="*"
statsmodels = "*"
sparse = "*"


[tool.pixi.feature.kernel.pypi-dependencies]
# This section remains for pure-Python packages not available or preferred from Conda,
# but it currently only contains the local editable install.
signifikante = { path = ".", editable = true }

[tool.pixi.feature.kernel.tasks]
install-kernel = "python -m ipykernel install --user --name pixi_alternet_new --display-name pixi_alternet_new"
uninstall-kernel = "jupyter kernelspec uninstall pixi_alternet_new"