pyproject.toml

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

[project]
name = "deepSTRF"
description = "A PyTorch-based library and benchmark for fitting sensory neural responses with deep neural network models"
readme = "README.md"
requires-python = ">=3.10"
license = { text = "GPL-3.0-or-later" }
authors = [
    { name = "Ulysse Rançon", email = "ulysse.rancon@gmail.com" },
]
maintainers = [
    { name = "Ulysse Rançon", email = "ulysse.rancon@gmail.com" },
]
keywords = [
    "pytorch",
    "computational neuroscience",
    "sensory neurons",
    "STRF",
    "receptive fields",
    "neural encoding",
    "electrophysiology",
    "auditory cortex",
    "visual cortex",
]
classifiers = [
    "Development Status :: 3 - Alpha",
    "Intended Audience :: Science/Research",
    "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
    "Operating System :: OS Independent",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
]

dynamic = ["version"]

# Runtime dependencies — loose upper bounds so pip's resolver has room,
# lower bounds set to the earliest versions we know work with the code.
dependencies = [
    "numpy>=1.24,<3",
    "scipy>=1.10",
    "torch>=2.0,<3",
    "torchaudio>=2.0",
    "torchcodec>=0.1",  # backend for torchaudio.load() since torchaudio 2.2+
    "torchvision>=0.15",
    "pytorch-lightning>=2.0",
    "matplotlib>=3.5",
    "Pillow>=9.0",
    "einops>=0.6",
    "mambapy>=1.2",         # pure-PyTorch Mamba backbone for StateNet(rnn_type='Mamba')
    "soundfile>=0.12",
    "scikit-image>=0.20",
    "h5py>=3.7",
    "tables>=3.7",
    "mat73>=0.65",
    "pandas>=1.5",
    "tqdm>=4.60",
    "wandb>=0.15",
    "tensorboard>=2.10",
    "requests>=2.28",       # auto-download (OSF, etc.)
    "platformdirs>=3.0",    # default cache dir for downloaded datasets
    "huggingface_hub>=0.20", # pretrained model weights — download / upload
    "safetensors>=0.4",     # state_dict serialization for HF Hub checkpoints
]

[project.optional-dependencies]
# Documentation build (Sphinx + MyST + notebooks on RTD).
docs = [
    "sphinx>=6",
    "sphinx-rtd-theme>=1.3",
    "myst-parser>=2",
    "nbsphinx>=0.9",
    "pandocfilters>=1.5",
]

# Development: test runner + linters + notebook tooling.
dev = [
    "pytest>=7",
    "pytest-cov>=4",
    "ruff>=0.4",
    "pre-commit>=3",
    "ipython>=8",
    "ipykernel>=6",
    "nbstripout>=0.7",
]

# Allen Brain Observatory (visual datasets): allensdk is heavy so it's opt-in.
allen = [
    "allensdk>=2.15",
    "xarray>=2023.1",
]

# Optional speed-ups for the S4 model (JIT-compiled CUDA kernels).
# Pure-PyTorch fallback works without this.
s4 = [
    "pykeops>=2.1",
]

# EEG datasets (currently: Alice EEG). MNE-Python parses the .fif files
# Brodbeck 2023 rereleased, and exposes channel montages + bad-channel /
# artifact annotations natively. The gammatone package provides the
# paper-faithful Heeris time-domain filterbank used by
# ``AliceEEGDataset(spec_backend='heeris')`` to match Brodbeck via
# eelbrain's ``gammatone_bank``.
eeg = [
    "mne>=1.5",
    "gammatone>=1.0",
]

# Le 2025 (zebra finch occluded-song restoration). Uses the paper's
# gammatone filter bank (Methods p. 10) for stimulus spectrograms.
le = [
    "gammatone>=1.0",
]

[project.urls]
Homepage = "https://github.com/urancon/deepSTRF"
Documentation = "https://deepstrf.readthedocs.io/"
Repository = "https://github.com/urancon/deepSTRF"
Issues = "https://github.com/urancon/deepSTRF/issues"

# -----------------------------------------------------------------------------
# setuptools backend configuration
# -----------------------------------------------------------------------------

[tool.setuptools.dynamic]
version = { attr = "deepSTRF._version.__version__" }

[tool.setuptools.packages.find]
where = ["."]
include = ["deepSTRF*"]
exclude = ["tests*", "docs*", "scripts*"]

# Ship the PEP 561 marker so downstream type-checkers see deepSTRF's type hints.
[tool.setuptools.package-data]
deepSTRF = ["py.typed"]

# -----------------------------------------------------------------------------
# pytest
# -----------------------------------------------------------------------------

[tool.pytest.ini_options]
minversion = "7.0"
testpaths = ["tests"]
python_files = ["test_*.py"]
addopts = [
    "-ra",               # short summary for everything except passed
    "--strict-markers",  # unknown @pytest.mark.foo -> error
    "--strict-config",
]
markers = [
    "slow: real-dataset integration test (local data, skipped in CI). Auto-applied to the heavy dataset modules by tests/conftest.py; run the fast loop with `pytest -m 'not slow'`.",
]