tnia-ops

tnia-python's utilities, as ops: plain Python functions declared with opspec, so a runner can run them in their own environments and a front end can show them.

Each op wraps a tnia-python function and changes nothing about it.

Namespaces are laid out like scikit-ops': one scikit-ops has as a folder (deconvolve, detect, kernels, segment) is a folder here too, one file per op, even if there is only one; the rest are single files. Either way, ops are imported from the namespace: from tnia_ops.deconvolve import richardson_lucy.

Environments are in envs/<id>/pixi.toml: skimage (CPU), cupy (GPU), and numpy_1x, for the simulation ops, whose raster-geometry needs NumPy 1, and photo (OpenCV and PyTorch) for the photo, restore, detect and ocr ops.

The photo ops are not wrappers of tnia-python: they came from a race-photo app, which calls them directly. Model weights are parameters (weights: Path), never downloaded by the op.

Some names are the same as scikit-ops' on purpose -- ops called richardson_lucy and connect_2d_in_3d, environments called skimage and cupy -- to find out how a runner copes with two collections that clash.

uv sync
uv run pytest      # every op reads as a valid spec; no environment needed

Examples

tnia-ops has no runner of its own. examples/hybrid.ipynb runs it with scikit-ops' runner, in one pipeline with a scikit-ops op, and shows which environment each op runs in and where it is built.

The notebook's kernel is tnia-ops (editable): Jupyter, scikit-ops' runner, and tnia-ops, both editable from sibling checkouts. With pixi:

cd pixi/tnia-ops-editable
pixi install
pixi run register-kernel     # once; then pick the kernel in Jupyter or VS Code
pixi run lab

Or with uv, from the top folder (the examples group has the same things):

uv sync
uv run python -m ipykernel install --user --name=tnia-ops-examples --display-name 'tnia-ops (examples)'