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phase1-exploration

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Atualizado7 de julho de 2026 às 07:52

Run Phase 1 (EXPLORATION) of the A2MC calibration workflow as the offline agent — the human-in-the-loop analog of the orchestrator's `_run_exploration()` / `reasoning.analyze_sensitivity_results()`. Extract the Y matrix from completed simulations, run Morris sensitivity analysis, and interpret μ* to decide what parameters matter for each target/PFT. Use when the user says "run the sensitivity analysis", "extract the Y matrix", "which parameters matter", "run Phase 1", "Morris rankings", after the Phase 0 ensemble finishes.

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jingtao-lbl

jingtao-lbl/A2MC-elm

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name	phase1-exploration
description	Run Phase 1 (EXPLORATION) of the A2MC calibration workflow as the offline agent — the human-in-the-loop analog of the orchestrator's `_run_exploration()` / `reasoning.analyze_sensitivity_results()`. Extract the Y matrix from completed simulations, run Morris sensitivity analysis, and interpret μ* to decide what parameters matter for each target/PFT. Use when the user says "run the sensitivity analysis", "extract the Y matrix", "which parameters matter", "run Phase 1", "Morris rankings", after the Phase 0 ensemble finishes.
modes	{"requires_fates":false,"nutrient_pathway":"any","scope":["calibration"],"summary":"Offline analog of Phase 1 (extract Y matrix, Morris sensitivity). Applies in every calibration mode."}

Phase 1: Exploration (offline agent)

The offline analog of _run_exploration(). Online, reasoning.analyze_sensitivity_results() interprets the Morris output automatically; offline, YOU interpret it — attribute each ranked parameter to a FATES mechanism, spot cross-PFT vs PFT-specific importance, and decide what's worth tuning. The extraction + Morris scripts are the same the online agent uses.

Floor, not ceiling. Morris μ* is the online agent's one lens. As the offline agent, reach for more when the round warrants it: check σ/μ* for interaction-heavy parameters, look for edge effects (params pinned near bounds → a Phase 0 redesign signal), compute a second output variable the loop didn't rank, or run a targeted correlation scientific-analysis the fixed pipeline has no step for. Produce the ranking the online agent would, then go past it.

Inputs (from Phase 0): completed TRANS outputs, the X matrix ($A2MC_ENSEMBLE_MATRIX_FILE), the SALib problem ($A2MC_SALIB_PROBLEM_FILE). Deliverable: Morris rankings (μ, μ*, σ per parameter per PFT) as CSV + plots, plus your interpretation of what to tune (captured in the phase log), handed to Phase 2.

Step 0 — completion gate

tools/diagnose_ensemble_status.py --cases 1-N — proceed when >95% of cases have TRANS complete. Failed cases become NaN rows downstream.

Step 1 — extract the Y matrix (reads existing outputs — no new simulations)

phases/phase1_exploration/extract_sensitivity_outputs.py --output-var leaf_biomass --cases 1-N --validation-period 2010 2019 → Morris{Var}_{N}cases.txt (cases × PFTs). Runs on the login node; large ensembles can be memory-heavy — extract in --cases ranges, and use --resume to continue an interrupted run.

Footgun — row-order alignment. The X-matrix row order MUST match the Y-matrix row order (same cases, same order) or SALib Morris is meaningless. If you filter failed cases, filter both sides with completed_cases_<TS>.txt. Audit for NaN rows — >5% NaN, investigate failures first.

Step 2 — run Morris (analysis of the extracted matrices — no new simulations)

phases/phase1_exploration/morris_sensitivity_analysis.py --output-var leaf_biomass --y-matrix <Y> --x-matrix <X> --problem <salib_problem> → per-PFT morris_*.csv (parameter, μ, μ*, σ, rank) + color-coded *.png. analyze_ensemble.py is the higher-level driver that chains extract → Morris.

Step 3 — interpret (you are the reasoning)

Produce the same shape as the online analyze_sensitivity_results output: key_parameters (each attributed to a FATES mechanism — verify against RAG, never from the name), interactions (high σ/μ*), cross_pft_patterns (generic-to-all vs PFT-specific), edge_effects (near-bound → redesign candidates), and a calibration strategy (tune order, PFT-by-PFT vs global).

Step 4 — log and hand off

Log via calibration-log (phase log → PhaseLogger.log_exploration): top parameters per PFT, the mechanism attributions, edge effects, and the tuning recommendation. Hand off to phase2-screening.

Related skills / next phase

Standardized single-round / cross-round figures → summarize-calibration-round, compare-calibration-rounds (they invoke the same Morris pipeline).
A one-off sensitivity question with a figure + ana_log → scientific-analysis.
Next: phase2-screening (rank the ensemble against targets).

Changelog

2026-07-02: Created — offline Phase 1 routine mirroring reasoning.analyze_sensitivity_results(); drives extract_sensitivity_outputs → morris_sensitivity_analysis, hands off to phase2-screening.