"""The paper's numbers are part of the regression.

A table transcribed by hand drifts from its data the first time the data is
regenerated.  verify_article.py asserts all 72 stated numbers against
sweep_full.json; this makes that check part of the suite, so a re-run of the
sweep that moves a number fails the build instead of quietly invalidating the
paper.
"""
from __future__ import annotations

import os
import subprocess
import sys

import pytest

ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))

# the other end of the trade the paper states: better average, one constant
PROJ_LABEL = "pure.ratio.derived3.project"


@pytest.mark.skipif(not os.path.exists(os.path.join(ROOT, "sweep_full.json")),
                    reason="sweep_full.json not present; run sweep_full.py")
def test_every_number_in_the_paper_matches_the_sweep():
    r = subprocess.run([sys.executable, "verify_article.py"], cwd=ROOT,
                       capture_output=True, text=True, timeout=600)
    tail = "\n".join(r.stdout.strip().splitlines()[-25:])
    assert r.returncode == 0, f"the paper disagrees with the data:\n{tail}"
    assert "every number stated in the paper matches" in r.stdout
    assert r.stdout.count("  OK ") >= 72, r.stdout.count("  OK ")


@pytest.mark.skipif(not os.path.exists(os.path.join(ROOT, "sweep_full.json")),
                    reason="sweep_full.json not present")
def test_the_headline_claim_is_what_the_paper_says_it_is():
    """Guard against the one mistake that would matter most: the abstract
    claiming a place the data does not support.

    It caught exactly that.  Until Chandrupatla's own method was added - a
    reader asked why the owner of half the benchmark was missing - this test
    asserted that a configuration of ours came first, and it passed only
    because the method that beats us was absent from the field.  The claim it
    now guards is the corrected one: we are not first, Chandrupatla is, and
    our margin behind it on the canonical set is under one percent."""
    import json

    from report_sweep import PROPOSED, rank
    with open(os.path.join(ROOT, "sweep_full.json"), encoding="utf8") as fh:
        rows = json.load(fh)
    by = {r["label"]: r for r in rows}
    assert "chandrupatla" in by, "the sweep predates the missing baselines"

    aps = rank(rows, "APS")
    assert aps[0]["label"] == "chandrupatla", aps[0]["label"]

    def both(lbl):
        return by[lbl]["APS"]["total"] + by[lbl]["CHA"]["total"]

    # What the paper may claim for the credit method, and nothing more.
    # It is NOT within a percent of the best on Alefeld-Potra-Shi - that was
    # true of the projection variant, which carries a constant.  Buying
    # constant-freedom and a near-bisection worst case costs average
    # performance, and the test pins the direction of that trade so the
    # abstract cannot quietly reclaim the projection variant's numbers.
    assert both(PROPOSED) > both("chandrupatla"), "behind Chandrupatla"
    for weaker in ("toms748", "brent", "itp", "ridders", "bisection"):
        assert both(PROPOSED) < both(weaker), weaker

    # On the canonical set alone it is behind the classical methods; saying
    # otherwise would be quoting the projection variant.
    assert by[PROPOSED]["APS"]["total"] > by["toms748"]["APS"]["total"]
    assert by[PROPOSED]["APS"]["total"] > by["brent"]["APS"]["total"]

    # The claim it does own: the worst single problem, over the problems it
    # solves, is close to bisection's and far below the classical methods'.
    def worst(lbl):
        w = 0
        for setname in ("APS", "CHA"):
            for n, ok in by[lbl][setname]["per"].values():
                if ok:
                    w = max(w, n)
        return w

    assert worst(PROPOSED) < 100, worst(PROPOSED)
    assert worst(PROPOSED) < worst("brent")
    assert worst(PROPOSED) < worst("toms748")
    assert worst(PROPOSED) < worst(PROJ_LABEL), (worst(PROPOSED),
                                                 worst(PROJ_LABEL))