test: golden-master regression for scenario #0 (#38)#47
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Re-runs scenario #0 (deterministic: static balloons, fixed seed, ideal sensors, no Monte Carlo) with the default agent and compares against a committed baseline, guarding against unintended physics changes when ActiveRocketPy is updated. - Compares popped count (exact, == 10), rocket position trajectory (rtol 3%, atol 1 m), and flight duration (within 2%). - Baseline is downsampled post-launch positions; regenerate deliberately via tests/baselines/regenerate_scenario_0.py and review the diff. - Side-effect free runner; guarded with skipUnless like the other runtime tests.
This was referenced Jun 27, 2026
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Adds a golden-master regression test for scenario #0. Part of #38.
Scenario #0 is deterministic (static balloons, fixed seed, ideal sensors, no Monte Carlo), so the default agent produces a reproducible rocket trajectory. The test re-runs it and compares against a committed baseline, guarding against unintended physics changes when ActiveRocketPy is updated.
== 10), the rocket position trajectory (rtol=0.03,atol=1 m), and flight duration (within 2%).tests/baselines/scenario_0.json; regenerate deliberately viatests/baselines/regenerate_scenario_0.pyand review the diff when a physics change is intended.skipUnlesslike the other runtime tests.Scope: scenario #0 only; scenario #1 (Monte Carlo) is left for a follow-up nightly test. Measured ~12 s, and the baseline is identical across numpy 2.4.5/2.5.0 and Python 3.13/3.14.