Barrier-overlap objective with continuous, differentiable-ready variants

.gitignore

@@ -127,3 +127,9 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+# Optimization outputs
+out_*/
+out_s*/
+out_s*
+threed1.*

AGENTS.md

@@ -0,0 +1,50 @@
+# Repository Guidelines
+
+## Project Structure & Module Organization
+
+- `main.py`: primary EP + equilibrium optimization driver (MPI-aware); produces
+  output folders like `out_s*_NFP*` and CSV logs `output_*.csv`.
+- `initial_configs/`: VMEC input files used as starting points (e.g.
+  `initial_configs/input.nfp2_QA`).
+- `Alan_objectives.py`: objective/penalty utilities (mirror ratio, elongation).
+- `plot_opt.py`, `vmecPlot2.py`: post-processing and plotting helpers.
+- `regression_test.py`, `test_search_global_minimum.py`: standalone scripts for
+  robustness checks / optimization experiments.
+
+## Build, Test, and Development Commands
+
+This repo is script-driven (no package build step). Use a Python environment
+that has the scientific stack plus VMEC + NEAT installed.
+
+- Install dependencies (examples):
+  - `python3 -m pip install numpy scipy pandas matplotlib mpi4py simsopt booz_xform`
+  - VMEC extension (required by `simsopt.mhd.Vmec`): `python3 -m pip install git+ssh://git@github.com/hiddenSymmetries/VMEC2000.git`
+  - NEAT (required by `from neat...`): `python3 -m pip install -e ../NEAT`
+- If VMEC fails to build due to hardcoded compiler/library paths, edit the
+  VMEC2000 `cmake_config_file.json` to point at your `mpicc`/`mpifort` and
+  NetCDF include/lib directories (e.g. `/usr/include`, `/usr/lib`).
+- Run optimization: `mpirun -n 4 python3 main.py` (or `python3 main.py` for
+  serial experiments).
+- Plot results (after an optimization run): `python3 plot_opt.py`.
+
+## Coding Style & Naming Conventions
+
+- Python, 4-space indentation, keep lines reasonably short.
+- Prefer `pathlib.Path` over manual string path joins in new code.
+- Keep scripts runnable from the repository root (assume `initial_configs/` is
+  available via relative paths).
+
+## Testing Guidelines
+
+There is no pytest suite. Use quick smoke checks before larger runs:
+
+- VMEC + SIMSOPT sanity run (writes to `/tmp`): `python3 -c "from simsopt.mhd import Vmec; import os; os.chdir('/tmp'); Vmec('path/to/initial_configs/input.nfp2_QA', verbose=False).run()"`
+- Import sanity: `python3 -c "from neat.fields import Simple; from simsopt.mhd import Vmec"`
+
+## Commit & Pull Request Guidelines
+
+- Commit messages in this repo are short, imperative, and capitalized (e.g.
+  Added ..., Create ..., Initial commit); follow that style.
+- PRs should describe the physics intent, list runtime/environment assumptions
+  (MPI size, key parameters), and include plots/CSV excerpts when they help
+  reviewers reproduce results.

Alan_objectives.py

@@ -225,9 +225,9 @@ def FindBoundary(theta,phi):
         # Fit an ellipse to this cross-section shape
         perim_resid = lambda a : perim - (4*a*ellipe(1 - ( A / (np.pi * a**2 ) )**2))
         if iphi == 0:
-            a1 = fsolve(perim_resid, 1)
+            a1 = float(np.asarray(fsolve(perim_resid, 1)).item())
         else:
-            a1 = fsolve(perim_resid, a1)
+            a1 = float(np.asarray(fsolve(perim_resid, a1)).item())
         a2 = A / (np.pi * a1)
         if a1 > a2:
             maj = a1
@@ -275,4 +275,4 @@ def MirrorRatioPen(v, mirror_threshold=0.20, output_mirror=False):
     # print("Mirror =",m)
     pen = np.max([0,m-mirror_threshold])
     if output_mirror: return m
-    else: return pen
+    else: return pen

README.md

@@ -1,2 +1,16 @@
 # EPoptimization
-Energetic Particle Stellarator Optimization
+
+This branch runs a direct collisionless alpha-loss study. It minimizes the
+measured 100 ms loss fraction for 256 deterministic 3.5 MeV alpha particles
+born at `s = 0.25`. Mirror ratio and maximum elongation are broad feasibility
+constraints; proxy, classifier, Gamma-c, and effective-ripple quantities do
+not enter the objective.
+
+`main.py` primes constrained TuRBO/SCBO from completed direct observations.
+`generate_scbo_candidates.py` maps requests through the bounded ALPES-centered
+eight-dimensional chart, and `run_direct_scbo_candidate.sh` evaluates VMEC,
+geometry feasibility, and the pinned SIMPLE direct metric.
+
+The campaign contract, exact executable hashes, provenance, and cluster
+orchestration live in the companion `alpha-loss-optimization-data` repository
+under `runs/alpha_direct_turbo_v1/`.

advance_alpha_scbo.py

@@ -0,0 +1,51 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+
+def advance(args: argparse.Namespace) -> None:
+    from simsopt_dfo.scbo_checkpoint import (
+        complete_candidates,
+        issue_candidates,
+        write_checkpoint,
+    )
+
+    state = json.loads(args.state.read_text())
+    responses = pending_responses(state, args.responses)
+    state = complete_candidates(state, responses)
+    state, requests = issue_candidates(state)
+    write_checkpoint(args.state, state)
+    args.requests.write_text(
+        json.dumps({"requests": requests}, indent=2, sort_keys=True) + "\n"
+    )
+
+
+def pending_responses(state: dict, root: Path) -> list[dict]:
+    pending = {row["candidate_id"] for row in state["pending"]}
+    rows = [json.loads(path.read_text()) for path in Path(root).glob("*/response.json")]
+    selected = sorted(
+        (row for row in rows if row["candidate_id"] in pending),
+        key=lambda row: row["candidate_id"],
+    )
+    if {row["candidate_id"] for row in selected} != pending:
+        raise ValueError("SCBO response wave does not cover every pending candidate")
+    return selected
+
+
+def parser() -> argparse.ArgumentParser:
+    root = argparse.ArgumentParser()
+    root.add_argument("--state", type=Path, required=True)
+    root.add_argument("--responses", type=Path, required=True)
+    root.add_argument("--requests", type=Path, required=True)
+    return root
+
+
+def main() -> None:
+    advance(parser().parse_args())
+
+
+if __name__ == "__main__":
+    main()