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Accidental PR - #50
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…en<->Witten) Standalone Python subproject (GPLv3, excluded from the R package build via .Rbuildignore) that serves realistic arrival-time forecasts for the Aachen Hbf <-> Witten Hbf commute as a phone PWA. Instead of scraping and training from scratch, it self-hosts the Bahn-Vorhersage predictor image (weekly-retrained model returning full delay distributions per stop event) and adds: - journey enumeration via transport.rest (vendo world), IRIS/DB InfraGO forecasts fed to the model (its training domain), with desync between the two sources surfaced instead of hidden - explicit cancellation branch (model distributions are conditional on the train running): missed/cancelled options re-plan onto the next option, yielding effective arrival quantiles incl. that risk; empirical cancellation rates from piebro/deutsche-bahn-data - prediction log + travel-day outcome resolver + calibration report (quantile coverage, pinball loss, DB-forecast bias per source) - FastAPI backend + vanilla-JS PWA, docker-compose (predictor/app/resolver) Payload semantics verified against bahnvorhersage source: 34 classes, offset 3, pmf over deviation from current prognosis, open +30 tail; ISO weekday 1-7 as trained (upstream frontend's 0-6 off-by-one not reproduced). 21 offline tests (fixtures for transport.rest, IRIS XML, synthetic predictor responses); ruff clean. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016RWSd2ioqjj9FG6yzw3JXF
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