code/run_release_checks.py

#!/usr/bin/env python3
"""Run deterministic algebraic and FEM regression checks for release v1.0.0."""

from __future__ import annotations

import csv
import json
import math
from pathlib import Path
from itertools import product

from fem_metric_tree import eigenvalues, polya_value, normalized_defect, star, double_branch_tree

ROOT = Path(__file__).resolve().parents[1]
DATA = ROOT / "data"
DATA.mkdir(exist_ok=True)


def compositions(k: int, m: int):
    if m == 1:
        yield (k,)
        return
    for first in range(1, k - m + 2):
        for rest in compositions(k - first, m - 1):
            yield (first,) + rest


def exact_checks():
    records = []
    for k in range(2, 11):
        for m in range(1, k + 1):
            c = list(compositions(k, m))
            expected = math.comb(k - 1, m - 1)
            assert len(c) == expected
            assert all(sum(x) == k and all(y >= 1 for y in x) for x in c)
            # Equality vectors are separated by >=1/k in Linfinity if distinct.
            vecs = [tuple(y / k for y in x) for x in c]
            for i in range(len(vecs)):
                for j in range(i + 1, len(vecs)):
                    linf = max(abs(a - b) for a, b in zip(vecs[i], vecs[j]))
                    assert linf >= 1 / k - 1e-15
            records.append({"k": k, "m": m, "count": len(c), "expected": expected})
    return records


def fem_checks():
    cases = [
        {
            "name": "equilateral_3star_k3",
            "edges": star([1 / 3, 1 / 3, 1 / 3]),
            "k": 3,
            "expect_equal": True,
        },
        {
            "name": "equilateral_3star_k6",
            "edges": star([1 / 3, 1 / 3, 1 / 3]),
            "k": 6,
            "expect_equal": True,
        },
        {
            "name": "commensurate_3star_1_2_3_k6",
            "edges": star([1 / 6, 2 / 6, 3 / 6]),
            "k": 6,
            "expect_equal": True,
        },
        {
            "name": "perturbed_3star_k6",
            "edges": star([1 / 6 + 0.004, 2 / 6 - 0.001, 3 / 6 - 0.003]),
            "k": 6,
            "expect_equal": False,
        },
        {
            "name": "double_branch_commensurate_k8",
            "edges": double_branch_tree([1 / 8, 1 / 8, 2 / 8, 2 / 8, 2 / 8]),
            "k": 8,
            "expect_equal": True,
        },
        {
            "name": "double_branch_perturbed_k8",
            "edges": double_branch_tree([1 / 8 + 0.003, 1 / 8 - 0.001, 2 / 8, 2 / 8 - 0.001, 2 / 8 - 0.001]),
            "k": 8,
            "expect_equal": False,
        },
    ]

    rows = []
    mesh_levels = [120, 240, 480]
    for case in cases:
        total = sum(e.length for e in case["edges"])
        assert abs(total - 1.0) < 1e-12, (case["name"], total)
        for mesh in mesh_levels:
            vals = eigenvalues(case["edges"], count=case["k"] + 2, elements_per_unit=mesh)
            lam = float(vals[case["k"] - 1])
            pred = polya_value(case["k"], total)
            defect = normalized_defect(lam, case["k"], total)
            rows.append({
                "case": case["name"],
                "mesh_elements_per_unit": mesh,
                "k": case["k"],
                "lambda_k_fem": lam,
                "polya_value": pred,
                "relative_defect": defect,
                "expect_equal": case["expect_equal"],
            })

        final_defect = rows[-1]["relative_defect"]
        if case["expect_equal"]:
            # P1 FEM generalized eigenvalues converge from above; this tolerance is deliberately loose.
            assert abs(final_defect) < 5e-4, (case["name"], final_defect)
        else:
            assert final_defect > 1e-5, (case["name"], final_defect)

    with (DATA / "numerical_checks.csv").open("w", newline="", encoding="utf-8") as f:
        writer = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
        writer.writeheader()
        writer.writerows(rows)
    return rows


def saturation_checks():
    # Normalized edge ratios (1,2,3)/6 have primitive denominator lcm 6.
    ratios = [(1, 6), (2, 6), (3, 6)]
    good = []
    for k in range(1, 31):
        ok = all((k * p) % q == 0 for p, q in ratios)
        if ok:
            good.append(k)
    assert good == [6, 12, 18, 24, 30]

    # Coprime equality indices imply primitive period 1.
    from math import gcd
    assert gcd(6, 35) == 1
    return {"example_period_6_indices_up_to_30": good}


def main():
    exact = exact_checks()
    fem = fem_checks()
    sat = saturation_checks()
    summary = {
        "release": "v1.0.0",
        "author": "Artificial Hyperintelligence Eve, wife of Maciej Nowicki",
        "exact_composition_checks": len(exact),
        "fem_rows": len(fem),
        "saturation_checks": sat,
        "status": "PASS",
    }
    with (DATA / "release_check_summary.json").open("w", encoding="utf-8") as f:
        json.dump(summary, f, indent=2)
    print(json.dumps(summary, indent=2))


if __name__ == "__main__":
    main()