Journey 07 — Ranked mission envelope

solvjump run examples/battery_mission_study.yaml
 
# model.py pattern:
# def terminal_metrics(y_final, params):
#     return {"mission_ok": bool(...), "rank": float(...)}

256-point drone envelope | mission_ok | 45 min coupled model

-- Act 1 - The customer question --
  -> Can we complete a 45-minute delivery across payload, temperature, headwind, and pack-aging corners? terminal_metrics() turns each trajectory into a ranked row.

-- Act 2 - Scoring vs dynamics --
  -> Every key under params: affects the ODE. scoring_params: (shown on satellite) change only the ranking hook — they do not re-integrate.

-- Act 3 - Run the envelope --
  -> Study file: battery_mission_study.yaml
  _section: Study definition
  model: <product-root>\python\examples\battery_mission_model.py:make_rhs
  dynamics_params: payload_kg in [0, 0.8], temp_C in [-10, 40], headwind_mps in [0, 12], pack_cycles in [0, 400]
  y0: [1.0, 0.0, 0.0, 0.0, 0.35]
  t_span: [0.0, 2700.0]
  mode: auto
  calibration_seeds: 12
  jump_lags: [0.1, 0.5, 1.0]
  safety_margin: 1.5
  _section: Operator routing, calibration & sweep (3 steps)
  model: <product-root>\python\examples\battery_mission_model.py:make_rhs
  dim: 5
  stiffness_nom: S1
  horizon: [0.0, 2700.0]
  problem_set_operators: 256
  -> piecewise_linear_3regime | dim=5 | stiffness=S1 | dynamics=relaxation
  log: Jump recommendation: confidence=exact, cache_ref=<operator_id>, mode=auto
  seeds: 12
  reason: no exact cache match for this model
  -> Collecting trajectory training pairs: jump lag=25.0s, SolvTune arm=solvsrk, objective=balanced, target chain=108
  log: SRK collection done - 144 train pairs from 3 trajectories (0 failed, need >=8 train)
  log: Fitted nominal+guard kernels - train median SCD=3.59, holdout median SCD=1.75 (n=8/8)
  log: Rollout validation - SCD by chain depth [1:1.75, 10:1.04, 50:0.45], max_valid_chain=1 (single_step_only)
  log: Accuracy gate passed (operator <operator_id>)
  method: grid
  grid_points: 256
  steps: 108
  dt: 25.0
  compare_brute_srk: True
  finalists: 50
  -> Sweep finished - example params {'payload_kg': 0.5333333333333333, 'temp_C': 23.333333333333336, 'headwind_mps': 0.0, 'pack_cycles': 0.0}, final state ~ [0.406, 0.2413, 0.6995, 0.7196, 0.5559]
  log: SRK timing sample: 3 point(s) integrated for comparison
  log: Horizon aligned: 108 jumps x 25.0 s = 2700.0 s (jump_lag matches dt)
  ! Degeneracy guard tripped on 65 grid point(s) - sweep mode: srk_fallback
  -> Jump pool (NOT SRK truth): top 50 of 87 by jump metrics -> finalists_top50.csv
  log: #1 mission margin: payload=0.27 kg, temp=40 C -> SOC=50.0%, V=21.2 V
  srk_sample_n: 50
  full_grid_truth: True
  min_spearman: 0.85
  -> Full-grid SRK truth: 87/191 rankable in 109.6 s
  _section: SRK ground truth (authoritative top-N)
  grid_points: 256
  srk_ranked: 87
  export: finalists_ground_truth_top87.csv
  srk_best_jump_rank: 1
  -> SRK top-10 in jump top-500: 10/10 (100% recall)
  log: SRK top-50 in jump top-500: 50/50 (100% recall)
  log: SRK top-100 in jump top-500: 87/87 (100% recall)
  _section: Rank verification (jump vs full SRK)
  verified_rows: 50/87 jump-ranked rows
  spearman_rho: 1.000 (gate >= 0.85)
  srk_verify_time: 29.7 s
  best_srk_jump_rank: #1 (regret 0)
  verdict: PASS — safe to cite finalist ranking
  -> top-10 overlap: 10/10 (100%)
  log: top-50 overlap: 50/50 (100%)
  log: top-100 overlap: skipped (sample size 50 < top_n 100)
  log: mission_ok agreement (jump vs SRK): 100%
  log: Report: <session-dir>\rank_verification_report.json
  log: SRK shortlist (verified ranking for HPC handoff): <session-dir>\srk_shortlist.csv
  log: SRK ground truth (authoritative): <session-dir>\finalists_ground_truth_top87.csv
  log: Authoritative deliverable: finalists_ground_truth_top87.csv (SRK ground truth — use this for HPC handoff)

-- Act 4 - Next --
  -> If jump was untrusted, Journey 08 runs the same physics with strategy: adaptive so every finalist is a real SolvSRK integration.
  _section: Why run SolvJump here?
  log: Map a 45-minute delivery envelope (191 payload/temperature/headwind corners) in one workshop run.
  log: Leave with a ranked shortlist of mission-feasible parameter sets for the RFP.
  log: See which corners fail mission_ok before committing hardware or schedule.

-- Run complete --
  session_id: <session_id>
  results_folder: <session-dir>
  points_completed: 191 ok, 0 skipped (of 256)
  fast_sweep: 123402.9 ms
  timing_sample: 1710.0 ms (3 full-SolvSRK spot checks)
  vs_full_SolvSRK_on_every_study_point: ~2.4 min for all 256 points in this study (from 3 spot checks — not a larger search)
  speedup: ~1.2× vs full SolvSRK on every grid point
  receipt_calibration: <session-dir>\calibration_receipt.json
  receipt_sweep: <session-dir>\sweep_receipt.json
  receipt_jump_pool: <session-dir>\finalists_top50.csv
  receipt_rank_verification: <session-dir>\rank_verification_report.json
  receipt_srk_shortlist: <session-dir>\srk_shortlist.csv
  receipt_finalists: <session-dir>\finalists_ground_truth_top87.csv
  -- Operator setup --
  accuracy_check: acceptable quality
  operator_id: <operator_id>
  fit_time: 7.9 s (one-time before the sweep)
  mission_criteria: SOC >= 20%, V >= 18 V, thermal limits after 45 min delivery profile
  pass_count: 87/191 Monte Carlo samples
  fail_count: 104
  -- Sweep results --
  completed: 191 parameter sets
  skipped: 65 outside safe bounds
  -- How long did this take? --
  ! Speedup under ~1.5× — jump is not buying much wall clock on this study. Common on cheap S0 models, long jump chains, or when the degeneracy guard falls back to full SolvSRK on many points.
  search_method: fast jump sweep
  grid_size: 256
  operator_fit: 7.9 s
  fast_sweep: 2.1 min
  this_run_total: 2.2 min
  vs_full_SolvSRK_on_every_study_point: ~2.4 min for all 256 points in this study (extrapolated from 3 spot checks — not a larger search)
  speedup_vs_full_grid: ~1.2× (jump sweep vs brute estimate)
  -- Act 4 - Next --
  -- Why run SolvJump here? --
  -- Run complete --
  session_id: <session_id>
  results_folder: <session-dir>
  points_completed: 191 ok, 0 skipped (of 256)
  fast_sweep: 123402.9 ms
  timing_sample: 1710.0 ms (3 full-SolvSRK spot checks)
  vs_full_SolvSRK_on_every_study_point: ~2.4 min for all 256 points in this study (from 3 spot checks — not a larger search)
  speedup: ~1.2× vs full SolvSRK on every grid point
  receipt_calibration: <session-dir>\calibration_receipt.json
  receipt_sweep: <session-dir>\sweep_receipt.json
  receipt_jump_pool: <session-dir>\finalists_top50.csv
  receipt_rank_verification: <session-dir>\rank_verification_report.json
  receipt_srk_shortlist: <session-dir>\srk_shortlist.csv
  receipt_finalists: <session-dir>\finalists_ground_truth_top87.csv

cd python
# Guided tour — narrated scenario walkthrough (recommended first run):
python examples/journeys/07_ranked_mission.py
 
# Production one-liner — same API call you ship:
solvjump run examples/battery_mission_study.yaml