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Introduce TermDef (key, term, filename) as a base dataclass and make TermSpec inherit from it, adding the notebook-specific mean_axes and err_axes fields. Update all pipeline_utils function signatures to accept Sequence[TermDef] since none of them use the axes fields directly. Add pipeline.py with run_pipeline(), which wires together the existing pipeline_utils building blocks into the full prepare→save→forecast→ reconstruct→save sequence. This gives the CLI and tests a single importable entry point for the end-to-end pipeline.
Replace emulate() and jump() in cli.py with a call to run_pipeline(),
passing data_path=args.path and out_dir separately so the raw data
root and the run output directory are no longer conflated.
Fix a path mismatch between Simulation.save() and load_ts_all():
save() writes to {prepared_path}/{term}/{term}.npz (nested), but
load_ts_all was passing prepared_path directly to load_ts, which
looked for {prepared_path}/{term}.npz (flat). Update load_ts_all to
pass the term subdirectory, and update the Jumper notebook save cell
to match the same nested convention.
A test which runs the full pipeline end-to-end using the existing test data. Asserts that output .npy files are written for each term, each array has exactly `steps` time steps, and no output is entirely NaN.
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Change Description
Refactors
cli.pyto make use ofpipeline_utils.pyTask List
TermDefbase classrun_pipelinefunction which makes use of functions frompipeline_utils.pyrun_pipelineCloses #104, #105