Add single-case benchmark runner

benchmarks/README.md

@@ -44,9 +44,29 @@ Notebook cells produce:
 - Scatter plots and histograms
 - Summary interpretation
 
+### Debug One Case
+Use `run_single_case.py` to reproduce one SciPy baseline and one Pyomo run
+without writing JSONL or figure artifacts:
+
+```bash
+python benchmarks/run_single_case.py \
+  --task both --scenario baseline \
+  --set product.A1=20 \
+  --set ht.KC=4e-4 \
+  --method fd \
+  --n-elements 24 \
+  --tsh-ramp 40 \
+  --pch-ramp 0.05 \
+  --tee
+```
+
+The runner prints compact SciPy and Pyomo summaries with objective time, solver
+status, termination condition, trajectory size, and validation metrics.
+
 ## Core Modules
 
 - **`grid_cli.py`** — CLI for N-dimensional grid generation (replaces legacy `run_grid*.py`)
+- **`run_single_case.py`** — CLI for one-case SciPy/Pyomo debug runs without generated artifacts
 - **`adapters.py`** — Normalized scipy/Pyomo runners with discretization metadata
 - **`scenarios.py`** — Scenario definitions (vial, product, ht, eq_cap, nVial)
 - **`schema.py`** — Versioned record serialization (v2: trajectories + hashing)

benchmarks/adapters.py

@@ -16,9 +16,8 @@
 from typing import Any
 
 import numpy as np
-from lyopronto import opt_Pch, opt_Pch_Tsh, opt_Tsh
-
 from benchmarks.validate import compare_trajectories
+from lyopronto import opt_Pch, opt_Pch_Tsh, opt_Tsh
 
 DRYNESS_TARGET = 98.9  # Percentage (0-100)
 ACCEPTABLE_PYOMO_TERMINATIONS = {"optimal", "locallyoptimal"}
@@ -245,15 +244,11 @@ def ipopt_replay_adapter(
             "residual_tol": residual_tol,
             "max_constraint_residual": meta.get("max_constraint_residual"),
             "residuals": meta.get("residuals"),
-            "max_scipy_trajectory_residual": meta.get(
-                "max_scipy_trajectory_residual"
-            ),
+            "max_scipy_trajectory_residual": meta.get("max_scipy_trajectory_residual"),
             "scipy_trajectory_residuals": meta.get("scipy_trajectory_residuals"),
             "max_scipy_mesh_residual": meta.get("max_scipy_mesh_residual"),
             "scipy_mesh_residuals": meta.get("scipy_mesh_residuals"),
-            "max_replay_solution_residual": meta.get(
-                "max_replay_solution_residual"
-            ),
+            "max_replay_solution_residual": meta.get("max_replay_solution_residual"),
             "replay_solution_residuals": meta.get("replay_solution_residuals"),
             "trajectory_comparison": comparison,
         },
@@ -282,6 +277,7 @@ def pyomo_adapter(
     use_secant_ramp_constraints: bool = True,  # Add secant slope constraints for collocation
     solver_cpu_time: float | None = None,
     solver_wall_time: float | None = None,
+    tee: bool = False,
 ) -> dict[str, Any]:
     """Run Pyomo optimizer counterpart for specified task with discretization controls.
 
@@ -301,6 +297,8 @@ def pyomo_adapter(
         When using collocation with ramp constraints, add explicit secant slope constraints.
         Default True. If False, only polynomial derivative constraints are used, which may
         allow numerical derivatives between mesh points to exceed the ramp limit (by ~15%).
+    tee : bool
+        Show solver output for interactive debugging.
     """
     pyomo_opt = _load_pyomo_optimizers()
 
@@ -343,7 +341,7 @@ def pyomo_adapter(
             treat_n_elements_as_effective=treat_eff,
             warmstart_scipy=warmstart,
             return_metadata=True,
-            tee=False,
+            tee=tee,
             use_secant_ramp_constraints=use_secant_ramp_constraints,
             solver_cpu_time=solver_cpu_time,
             solver_wall_time=solver_wall_time,