Contributing to TomoJAX

Make scientific behavior reviewable: describe the input and geometry, the expected result, the change, and how you checked it. A lower residual or faster kernel is not sufficient evidence for a more accurate or faster reconstruction.

Set up developmentCopy Anchor Link

From a checkout with Python 3.12 and uv:

uv sync --locked --extra cpu --dev

On Linux CUDA, select --extra cuda12 instead. Add --group examples only when generating figures, or --group benchmark for optional comparison dependencies. See bench/README.md for external solver setup.

The just recipes below use uv run --no-sync: setup is explicit, so checks preserve the selected CPU/CUDA extras. uv sync can remove manually installed packages, including locally built comparison libraries; use a separate environment for those or reinstall them deliberately. You can also run each recipe's commands directly from the justfile.

Run checksCopy Anchor Link

just check also formats files before running checks. Use just ci when you want validation without automatic source edits. Package recipes replace dist/; keep release artifacts elsewhere. GPU tests are separate from CPU CI and must be run on suitable hardware for kernel or accelerator behavior changes.

The installed-wheel check imports outside the checkout and exercises simulation, the README’s automatic reconstruction, and labelled slice export. It installs without a package cache; --extra cuda12 also requires a real CUDA device. A source import passing is not enough to establish that the wheel works.

Engineering conventionsCopy Anchor Link

• Import application code through public module roots or .api facades. Keep CLI orchestration separate from numerical implementation and honor the import contracts.
• Keep geometry explicit: Python volumes are (x, y, z), projections are (view, v, u), and voxel/detector lengths use consistent physical units. Document parameter frames and units at API boundaries.
• Preserve matched forward/adjoint semantics. Test numerical changes against independent physical or dense references, including shifted, anisotropic, tilted, odd-sized, and boundary cases when applicable.
• Validate user inputs before expensive work. Do not silently discard malformed projection metadata or convert a failed solve into a success status.
• Keep solver defaults and checkpoint semantics stable unless a change is justified, tested, and recorded in the changelog.
• Add regression tests for behavior and correctness bugs. Avoid tests that only repeat implementation details or timing thresholds that depend on the host.

Design rulesCopy Anchor Link

TomoJAX should feel like one small, consistent tool. Before adding to its surface, check the change against these rules.

• One vocabulary. A concept has one name in Python, on the command line and in the docs: method, iterations, tv_weight, nonnegative, mode, quality, freeze, poses. CLI options are the Python keywords with hyphens. Internal names (profiles, schedules, solver stages) stay internal.
• One way to do a thing. No aliases, no second spelling of an option, no mode that duplicates another with different defaults.
• Small interfaces, deep modules. A function or package should hide much more than it exposes. Prefer computing a setting from the data to adding an option; add a configuration field only with a stated reason. Package roots export what users call; expert helpers live in .api.
• Options are keyword-only, after at most the one or two arguments a call is about (tj.reconstruct(scan, "cgls", iterations=50)). No positional flags.
• Results carry their context. Return objects that hold the geometry and settings they were made with, not bare arrays plus metadata to keep in sync.
• Fix the method, don't wrap it. When a method gets a case wrong, change its model or its statistics so that the case comes out right. Do not detect the case and bypass the method. For example, the nano-tomography aligner once reported about 0.1 px of motion on scans that were already aligned. A "consistency guard" was built that reconstructed two half-datasets, compared their resolution and discarded the alignment unless it improved. It hid the bias rather than removing it, flipped between "no motion" and a full correction at a threshold, judged the method by a second objective, and still let a worse result through on drifted data. The fix belongs in the estimator: weight residuals by their noise, regularise each pose by how well the data determine it, and report the pose uncertainties.
• The CLI shape is fixed: tomojax <command> INPUT -o OUTPUT, existing outputs refused without --force, exit status 0/1/2. --help shows what most runs need; expert settings are --config keys.

tests/test_architecture.py enforces what can be checked mechanically:

• the public API matches tests/guardrails/api_surface.txt, so every change to it shows in review;
• the measures in tests/guardrails/ratchets.json may fall but never rise: configuration fields, exported names, CLI flags, the length of every file over 800 lines and every function over 100 (excluding docstrings, but counting comments and code), lint suppressions, functions over the complexity limits, type errors in modules CI does not yet type-check, public functions, methods and *Config classes taking options positionally, code that assumes the first device, private imports in tests, and other known debt;
• CLI options map to Python keywords, and --help stays short.

After an intended change, run just guardrails and commit the updated records with it. When a ratchet falls, lock it in the same way. Do not raise a ratchet to make a change pass; fix the change instead, or explain in the review why the debt is worth taking on.

Documentation and figuresCopy Anchor Link

Public API docstrings use NumPy-style Parameters, Returns (or Yields), and Raises sections, with shapes, frames and units at the boundary. Main workflow entry points include short Examples. just docstrings checks the exported callables and their public methods/properties, including documented .api facades. Constants and typing aliases have checked prose reference entries. Related APIs and extended prose are optional when the summary and parameters suffice. just docs builds the same surface into HTML.

Start from the reader's task. Provide prerequisites, complete commands, expected outputs, and material limitations alongside each feature. Check commands against the actual CLI or public API. Keep user guides in docs/; module-level READMEs explain the corresponding Python interfaces.

Use examples/plot_reconstruction.py for the README's synthetic figure. Keep scientific image scales explicit and retain its JSON provenance. Record missing provenance for historical assets in images/README.md; visual quality is not an accuracy metric.

Repository layout and measurement recordsCopy Anchor Link

Measurement records stay out of the repository. The benchmark scripts write them under bench/results/, which Git ignores; attach the records a reviewer needs to the pull request or issue instead. Measurements gives each headline result with the command, commit and date needed to rerun it, so a result is reproducible without its raw files in the tree. Do not combine measurements from different timing protocols or silently replace historical data. New performance claims should account for accepted-result quality, fresh-process and warm time, and process GPU memory across the declared geometry/object set. See measurement scope.