localization.md

Sub-cellular localisation

{mod}raven_python.localization assigns reactions to compartments by MILP — deterministic (not simulated annealing), predictor-agnostic, and partial-update friendly.

1. Load predictor scores into the gene × compartment {class}raven_python.localization.LocalizationScores frame: {func}raven_python.localization.load_wolfpsort (WoLF PSORT summary output) or {func}raven_python.localization.load_deeploc (DeepLoc 2 per-protein CSV). raven-python does not shell out to the predictor — run it separately and feed in its output.
2. Predict / apply: {func}raven_python.localization.predict_localization is the MILP entry point. Pass the set of reactions to relocate (everything else is pinned); extra compartments beyond a reaction's primary one pay a multi_compartment_penalty. With apply=False you get a {class}raven_python.localization.LocalizationProposal diff to inspect before committing; {func}raven_python.localization.apply_localization applies a result.

The defaults and accuracy (including a predictor-noise sweep) are validated against curated yeast-GEM in the yeast localization benchmark.