Migrate Track 1 threshold layer into renunney

README.md

@@ -23,6 +23,7 @@ plane and the Track 1 runner/API boundary are now local to `renunney`.
 - local paper-scale Figure 1 submission configs,
 - a local Track 1 runner and config/API layer,
 - a local Track 1 analysis layer for tracking summaries and loci-regression,
+- a local Track 1 threshold/search layer for Nunney-style threshold checks,
 - a Makefile for common tasks,
 - migration notes for pulling code into this repo in stages.
 
@@ -84,6 +85,7 @@ The current state is split:
 - orchestration control plane: local to `renunney`
 - Track 1 runner and config/API layer: local to `renunney`
 - Track 1 analysis layer: local to `renunney`
+- Track 1 threshold/search layer: local to `renunney`
 - Track 1 simulation backend: still in the older `cost_of_substitution`
   directory and imported through the local compatibility layer
 

docs/MIGRATION.md

@@ -29,17 +29,19 @@ Operational code still lives in:
    - `src/renunney/track1_api.py`
 4. Track 1 analysis boundary has been migrated locally:
    - `src/renunney/track1_analysis.py`
-5. Keep the Track 1 simulation backend in the legacy path until real multi-host runs are stable.
-6. Migrate the Track 1 simulation core after the runner path is stable:
+5. Track 1 threshold/search boundary has been migrated locally:
+   - `src/renunney/track1_threshold.py`
+6. Keep the Track 1 simulation backend in the legacy path until real multi-host runs are stable.
+7. Migrate the Track 1 simulation core after the runner path is stable:
    - `python/track1_reference.py`
    - `python/track1_threshold.py`
    - `python/track1_analysis.py`
-7. Migrate report, dataset, fit, and orchestration-adjacent Track 1 modules after the kernel boundary is stable:
+8. Migrate report, dataset, fit, and orchestration-adjacent Track 1 modules after the kernel boundary is stable:
    - `python/track1_report.py`
    - `python/track1_dataset.py`
    - `python/track1_fit.py`
    - `python/track1_extinction.py`
-8. Migrate docs and example configs last, after path references are updated.
+9. Migrate docs and example configs last, after path references are updated.
 
 ## Constraint
 

docs/WORKFLOW.md

@@ -47,8 +47,8 @@ make status
 ## Current Assumption
 
 The Makefile now drives the local orchestration code in `renunney`, while the
-Track 1 runner/API boundary and analysis layer are also local to `renunney`.
-The simulation kernel is still imported from the legacy `cost_of_substitution`
-directory through the compatibility layer in `src/renunney/legacy.py`. The
-paper-scale Figure 1 configs used for submission are now local to
-`renunney/config`.
+Track 1 runner/API boundary, analysis layer, and threshold/search layer are
+also local to `renunney`. The simulation kernel is still imported from the
+legacy `cost_of_substitution` directory through the compatibility layer in
+`src/renunney/legacy.py`. The paper-scale Figure 1 configs used for submission
+are now local to `renunney/config`.

src/renunney/__init__.py

@@ -28,6 +28,15 @@ from .track1_analysis import (
     sweep_number_of_loci,
 )
 from .track1_api import Track1RunConfig, config_from_mapping, load_config, run_config, save_payload
+from .track1_threshold import (
+    ThresholdCheck,
+    ThresholdSearchResult,
+    evaluate_threshold_candidate,
+    nunney_threshold_accepts,
+    published_threshold_accepts,
+    run_extinction_check,
+    search_threshold_over_candidates,
+)
 
 __all__ = [
     "ClaimedJob",
@@ -52,13 +61,20 @@ __all__ = [
     "run_worker_loop",
     "submit_job_manifest",
     "submit_track1_figure1_jobs",
+    "ThresholdCheck",
+    "ThresholdSearchResult",
     "TrackingSummary",
     "Track1RunConfig",
     "config_from_mapping",
+    "evaluate_threshold_candidate",
     "fit_linear_cost_by_loci",
     "load_config",
+    "nunney_threshold_accepts",
+    "published_threshold_accepts",
     "run_config",
+    "run_extinction_check",
     "save_payload",
+    "search_threshold_over_candidates",
     "summarize_tracking",
     "sweep_number_of_loci",
 ]

src/renunney/track1_analysis.py

@@ -15,11 +15,11 @@ from typing import Iterable, Optional
 import numpy as np
 
 from .legacy import ensure_legacy_python_path
+from .track1_threshold import ThresholdSearchResult, search_threshold_over_candidates
 
 ensure_legacy_python_path()
 
 from track1_reference import GenerationSummary, Track1Parameters
-from track1_threshold import ThresholdSearchResult, search_threshold_over_candidates
 
 
 @dataclass(frozen=True)

src/renunney/track1_api.py

@@ -16,6 +16,7 @@ from typing import Any, Optional
 
 from .legacy import ensure_legacy_python_path
 from .track1_analysis import summarize_tracking, sweep_number_of_loci
+from .track1_threshold import evaluate_threshold_candidate, search_threshold_over_candidates
 
 ensure_legacy_python_path()
 
@@ -23,7 +24,6 @@ from track1_dataset import generate_extinction_dataset
 from track1_fit import class_balance, fit_payload_from_jsonl, load_jsonl
 from track1_reference import Track1Parameters, simulate_run
 from track1_report import generate_report_bundle
-from track1_threshold import evaluate_threshold_candidate, search_threshold_over_candidates
 
 
 @dataclass(frozen=True, init=False)

src/renunney/track1_threshold.py

@@ -0,0 +1,262 @@
+"""
+track1_threshold.py
+
+Local Track 1 threshold-search layer for renunney.
+
+This stage keeps the simulation kernel in the legacy tree while moving the
+historical threshold heuristic inward.
+"""
+
+from __future__ import annotations
+
+from concurrent.futures import ProcessPoolExecutor
+from dataclasses import dataclass
+import json
+from pathlib import Path
+from typing import Iterable, Optional
+
+from .legacy import ensure_legacy_python_path
+
+ensure_legacy_python_path()
+
+from track1_reference import Track1Parameters, simulate_run
+
+
+@dataclass(frozen=True)
+class ThresholdCheck:
+    """Summary for one T value under the Track 1 heuristic."""
+
+    T: float
+    runs: int
+    extinctions: int
+
+    @property
+    def survived_all(self) -> bool:
+        return self.extinctions == 0
+
+
+@dataclass(frozen=True)
+class ThresholdSearchResult:
+    """Result of Nunney-style threshold search."""
+
+    threshold_T: float
+    baseline_check: ThresholdCheck
+    check_1_02: ThresholdCheck
+    check_1_05: ThresholdCheck
+    check_1_10: ThresholdCheck
+    retest_check: Optional[ThresholdCheck]
+
+
+def _cache_key(params: Track1Parameters, T_value: float, runs: int, seed_start: int) -> str:
+    return json.dumps(
+        {
+            "K": params.K,
+            "N0": params.N0,
+            "n": params.n,
+            "u": params.u,
+            "R": params.R,
+            "T_value": T_value,
+            "epochs": params.epochs,
+            "p": params.p,
+            "a_max": params.a_max,
+            "runs": runs,
+            "seed_start": seed_start,
+        },
+        sort_keys=True,
+    )
+
+
+def _load_cache(path: str | Path | None) -> dict:
+    if path is None:
+        return {}
+    cache_path = Path(path)
+    if not cache_path.exists():
+        return {}
+    return json.loads(cache_path.read_text(encoding="utf-8"))
+
+
+def _save_cache(path: str | Path | None, cache: dict) -> None:
+    if path is None:
+        return
+    cache_path = Path(path)
+    cache_path.parent.mkdir(parents=True, exist_ok=True)
+    cache_path.write_text(json.dumps(cache, indent=2, sort_keys=True) + "\n", encoding="utf-8")
+
+
+def _single_run_extinct(params: Track1Parameters, sim_T: int, seed: int) -> bool:
+    run_params = Track1Parameters(
+        K=params.K,
+        N0=params.N0,
+        n=params.n,
+        u=params.u,
+        R=params.R,
+        T=sim_T,
+        epochs=params.epochs,
+        p=params.p,
+        a_max=params.a_max,
+    )
+    summaries = simulate_run(run_params, seed=seed)
+    return bool(summaries and summaries[-1].extinct)
+
+
+def run_extinction_check(
+    params: Track1Parameters,
+    T_value: float,
+    runs: int = 20,
+    seed_start: int = 0,
+    cache_path: str | Path | None = None,
+    jobs: int = 1,
+) -> ThresholdCheck:
+    """
+    Run repeated simulations at one T value.
+
+    reconstruction_choice:
+    T is rounded to the nearest integer generation count for the simulator.
+    """
+
+    cache = _load_cache(cache_path)
+    key = _cache_key(params, T_value, runs, seed_start)
+    if key in cache:
+        cached = cache[key]
+        return ThresholdCheck(T=cached["T"], runs=cached["runs"], extinctions=cached["extinctions"])
+
+    sim_T = int(round(T_value))
+    seeds = [seed_start + run_index for run_index in range(runs)]
+    if jobs <= 1 or runs <= 1:
+        extinctions = sum(1 for seed in seeds if _single_run_extinct(params, sim_T, seed))
+    else:
+        worker_count = min(jobs, runs)
+        with ProcessPoolExecutor(max_workers=worker_count) as executor:
+            extinctions = sum(executor.map(_single_run_extinct, [params] * runs, [sim_T] * runs, seeds))
+    result = ThresholdCheck(T=T_value, runs=runs, extinctions=extinctions)
+    cache[key] = {"T": result.T, "runs": result.runs, "extinctions": result.extinctions}
+    _save_cache(cache_path, cache)
+    return result
+
+
+def nunney_threshold_accepts(
+    baseline_check: ThresholdCheck,
+    check_1_02: ThresholdCheck,
+    check_1_05: ThresholdCheck,
+    check_1_10: ThresholdCheck,
+) -> tuple[bool, int]:
+    """Return acceptance and the number of failed higher checks."""
+
+    if not baseline_check.survived_all:
+        return False, 3
+    higher_checks = [check_1_02, check_1_05, check_1_10]
+    failures = sum(0 if check.survived_all else 1 for check in higher_checks)
+    return failures == 0, failures
+
+
+def evaluate_threshold_candidate(
+    params: Track1Parameters,
+    T_value: float,
+    runs: int = 20,
+    seed_start: int = 0,
+    cache_path: str | Path | None = None,
+    jobs: int = 1,
+) -> ThresholdSearchResult:
+    """Evaluate one candidate threshold T using Nunney's published checks."""
+
+    baseline_check = run_extinction_check(
+        params,
+        T_value,
+        runs=runs,
+        seed_start=seed_start,
+        cache_path=cache_path,
+        jobs=jobs,
+    )
+    check_1_02 = run_extinction_check(
+        params,
+        1.02 * T_value,
+        runs=runs,
+        seed_start=seed_start + 1000,
+        cache_path=cache_path,
+        jobs=jobs,
+    )
+    check_1_05 = run_extinction_check(
+        params,
+        1.05 * T_value,
+        runs=runs,
+        seed_start=seed_start + 2000,
+        cache_path=cache_path,
+        jobs=jobs,
+    )
+    check_1_10 = run_extinction_check(
+        params,
+        1.10 * T_value,
+        runs=runs,
+        seed_start=seed_start + 3000,
+        cache_path=cache_path,
+        jobs=jobs,
+    )
+
+    _, failures = nunney_threshold_accepts(
+        baseline_check=baseline_check,
+        check_1_02=check_1_02,
+        check_1_05=check_1_05,
+        check_1_10=check_1_10,
+    )
+
+    retest_check: Optional[ThresholdCheck] = None
+    if failures == 1:
+        failed = [check for check in (check_1_02, check_1_05, check_1_10) if not check.survived_all][0]
+        retest_check = run_extinction_check(
+            params,
+            failed.T,
+            runs=runs,
+            seed_start=seed_start + 4000,
+            cache_path=cache_path,
+            jobs=jobs,
+        )
+
+    return ThresholdSearchResult(
+        threshold_T=T_value,
+        baseline_check=baseline_check,
+        check_1_02=check_1_02,
+        check_1_05=check_1_05,
+        check_1_10=check_1_10,
+        retest_check=retest_check,
+    )
+
+
+def published_threshold_accepts(result: ThresholdSearchResult) -> bool:
+    """True if the candidate passes Nunney's published criterion."""
+
+    if not result.baseline_check.survived_all:
+        return False
+
+    higher_checks = [result.check_1_02, result.check_1_05, result.check_1_10]
+    failures = [check for check in higher_checks if not check.survived_all]
+    if len(failures) == 0:
+        return True
+    if len(failures) > 1:
+        return False
+    if result.retest_check is None:
+        return False
+    return result.retest_check.survived_all
+
+
+def search_threshold_over_candidates(
+    params: Track1Parameters,
+    candidate_T_values: Iterable[float],
+    runs: int = 20,
+    seed_start: int = 0,
+    cache_path: str | Path | None = None,
+    jobs: int = 1,
+) -> Optional[ThresholdSearchResult]:
+    """Search candidate T values from below and return the first accepted threshold."""
+
+    for index, T_value in enumerate(candidate_T_values):
+        result = evaluate_threshold_candidate(
+            params=params,
+            T_value=T_value,
+            runs=runs,
+            seed_start=seed_start + (index * 10000),
+            cache_path=cache_path,
+            jobs=jobs,
+        )
+        if published_threshold_accepts(result):
+            return result
+    return None

tests/test_track1_threshold.py

@@ -0,0 +1,82 @@
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+SRC_DIR = ROOT / "src"
+if str(SRC_DIR) not in sys.path:
+    sys.path.insert(0, str(SRC_DIR))
+
+import renunney.track1_threshold as thr
+
+from track1_reference import Track1Parameters
+
+
+def test_published_threshold_accepts_when_all_checks_survive():
+    result = thr.ThresholdSearchResult(
+        threshold_T=100.0,
+        baseline_check=thr.ThresholdCheck(T=100.0, runs=20, extinctions=0),
+        check_1_02=thr.ThresholdCheck(T=102.0, runs=20, extinctions=0),
+        check_1_05=thr.ThresholdCheck(T=105.0, runs=20, extinctions=0),
+        check_1_10=thr.ThresholdCheck(T=110.0, runs=20, extinctions=0),
+        retest_check=None,
+    )
+    assert thr.published_threshold_accepts(result) is True
+
+
+def test_published_threshold_accepts_single_failure_if_retest_survives():
+    result = thr.ThresholdSearchResult(
+        threshold_T=100.0,
+        baseline_check=thr.ThresholdCheck(T=100.0, runs=20, extinctions=0),
+        check_1_02=thr.ThresholdCheck(T=102.0, runs=20, extinctions=1),
+        check_1_05=thr.ThresholdCheck(T=105.0, runs=20, extinctions=0),
+        check_1_10=thr.ThresholdCheck(T=110.0, runs=20, extinctions=0),
+        retest_check=thr.ThresholdCheck(T=102.0, runs=20, extinctions=0),
+    )
+    assert thr.published_threshold_accepts(result) is True
+
+
+def test_published_threshold_rejects_multiple_failures():
+    result = thr.ThresholdSearchResult(
+        threshold_T=100.0,
+        baseline_check=thr.ThresholdCheck(T=100.0, runs=20, extinctions=0),
+        check_1_02=thr.ThresholdCheck(T=102.0, runs=20, extinctions=1),
+        check_1_05=thr.ThresholdCheck(T=105.0, runs=20, extinctions=1),
+        check_1_10=thr.ThresholdCheck(T=110.0, runs=20, extinctions=0),
+        retest_check=None,
+    )
+    assert thr.published_threshold_accepts(result) is False
+
+
+def test_search_threshold_over_candidates_uses_first_accepted(monkeypatch):
+    params = Track1Parameters(K=5000, N0=20, n=1, u=5e-6, R=10.0, T=20)
+
+    def fake_eval(params, T_value, runs=20, seed_start=0, cache_path=None, jobs=1):
+        if T_value == 60:
+            return thr.ThresholdSearchResult(
+                threshold_T=60.0,
+                baseline_check=thr.ThresholdCheck(T=60.0, runs=runs, extinctions=1),
+                check_1_02=thr.ThresholdCheck(T=61.2, runs=runs, extinctions=0),
+                check_1_05=thr.ThresholdCheck(T=63.0, runs=runs, extinctions=0),
+                check_1_10=thr.ThresholdCheck(T=66.0, runs=runs, extinctions=0),
+                retest_check=None,
+            )
+        return thr.ThresholdSearchResult(
+            threshold_T=float(T_value),
+            baseline_check=thr.ThresholdCheck(T=float(T_value), runs=runs, extinctions=0),
+            check_1_02=thr.ThresholdCheck(T=1.02 * T_value, runs=runs, extinctions=0),
+            check_1_05=thr.ThresholdCheck(T=1.05 * T_value, runs=runs, extinctions=0),
+            check_1_10=thr.ThresholdCheck(T=1.10 * T_value, runs=runs, extinctions=0),
+            retest_check=None,
+        )
+
+    monkeypatch.setattr(thr, "evaluate_threshold_candidate", fake_eval)
+    result = thr.search_threshold_over_candidates(params, [60, 80, 100], runs=3, seed_start=10)
+    assert result is not None
+    assert result.threshold_T == 80.0
+
+
+def test_run_extinction_check_parallel_matches_serial():
+    params = Track1Parameters(K=5000, N0=20, n=1, u=5e-6, R=10.0, T=20)
+    serial = thr.run_extinction_check(params, T_value=20.0, runs=2, seed_start=5, jobs=1)
+    parallel = thr.run_extinction_check(params, T_value=20.0, runs=2, seed_start=5, jobs=2)
+    assert parallel == serial