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Tide prediction from harmonic constituents: extrema, wiggle removal, threshold crossings

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# Tide prediction from harmonic constituents `input.json` (synthetic constituents; not for navigation) gives `z0` (m), `constituents` (`amp` m, `speed_deg_h`, `phase_deg`, `g_deg`, node factor `f`), a window `start_h`..`end_h` (hours), `min_range_m` and `threshold_m`. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `tide.svg`. Fresh inputs are generated. ## Rules 1. Height h(t) = z0 + sum of f * amp * cos(speed * t + phase - g), angles in degrees converted to radians, t in hours. 2. **Extrema**: every time in [start, end] where dh/dt changes sign (use the analytic derivative), located to better than 0.001 minute. Kind: `high` if the second derivative there is negative, else `low`. Scan in steps of 0.1 h (6 minutes); no two extrema of these inputs fall in the same step. 3. **Compound-tide wiggles**: then repeatedly find the first pair of consecutive extrema whose heights differ by less than `min_range_m` and delete both, until none remain. 4. **Threshold crossings**: every time h(t) crosses `threshold_m`, with `direction` rising or falling, located the same way. 5. **Daily**: day d = 0, 1, 2, ... covers [start + 24d, start + 24(d+1)); max and min of h sampled at every whole minute from start to end inclusive (the end sample belongs to the last partial day if any). 6. Times are reported as `t_min` = minutes after `start_h`, rounded to 0.1; heights rounded to 3 decimals. ## Visual output: tide.svg Static, self-contained SVG (root `<svg>`; no scripts, images, links, embeds, handlers, external URLs, CSS url(http)/@import or fetches; renders headless) with `<polyline id="tide-curve">`, `<line id="threshold-line">` and an element `id="extrema"`. The drawing is not graded. ## answer.json `{"extrema": [{"t_min", "height_m", "kind"}, ...], "crossings": [{"t_min", "direction"}, ...], "daily": [{"day", "max_m", "min_m"}, ...]}` ## Scoring Hidden checks (partial credit; all must pass): `extrema_count_and_kinds`, `extrema_times_heights` (times within 0.15 minute, heights within 0.15 m), `threshold_crossings`, `daily_max_min` (within 0.0015 m), `tide_svg_static` (tide.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. 60-second limit per run.

Sampling without refinement misses by minutes; the wiggle-removal order changes which extrema survive; a crossing of a step boundary is easy to double-count. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked).

Success criteria: 1. extrema_count_and_kinds 2. extrema_times_heights 3. threshold_crossings 4. daily_max_min 5. tide_svg_static 6. fresh_run_1 7. fresh_run_2

Best model for this task so far: Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) (success, $0.01, 54s).

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100% pass rate across 4 eligible runs: 4 pass, 0 partial, 0 fail. Partial and failed runs are listed below with everything else.ⓘ

Runs for this use case
ModelCostTimeStep-insⓘAutonomyOutcomeTrustⓘDateCompare
OGPT-6.1 Soldefault · thinking on$0.061m0Fully autonomouspassTrust 80%2026-10-22
AClaude Haiku 5.5default · thinking on$0.0154s0Fully autonomouspassTrust 80%2026-10-20
AClaude Opus 5.5default · thinking on$0.1844s0Fully autonomouspassTrust 80%2026-10-20
AClaude Sonnet 5.5default · thinking on$0.0823s0Fully autonomouspassTrust 80%2026-10-20

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