"""Unit tests for V35 market-anchored calibration (pure, no DB/model deps)."""

import os
import sys

sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))

from models.market_anchor import devig, home_favorite_delta, apply_home_correction


def _approx(a, b, tol=1e-9):
    return abs(a - b) <= tol


def test_devig_sums_to_one_and_orders_by_odds():
    p = devig([2.0, 3.5, 4.0])
    assert p is not None
    assert _approx(sum(p), 1.0)
    assert p[0] > p[1] > p[2]  # shorter odds -> higher prob


def test_devig_removes_bookmaker_margin():
    # 1.61 / 3.15 / 3.77 carries ~20% margin; fair home prob must be BELOW the
    # raw implied 1/1.61, and the three must sum to exactly 1.
    p = devig([1.61, 3.15, 3.77])
    assert p is not None
    assert p[0] < 1.0 / 1.61
    assert _approx(sum(p), 1.0)


def test_devig_rejects_missing_or_placeholder_legs():
    assert devig([1.0, 3.0, 4.0]) is None      # 1.0 leg = no real price
    assert devig([None, 3.0, 4.0]) is None      # missing leg
    assert devig([1.005, 3.0]) is None          # <= 1.01 placeholder
    assert devig([]) is None
    assert devig([1.90, 1.90]) is not None       # valid 2-way


def test_home_correction_only_lifts_favorites():
    assert home_favorite_delta(0.30) == 0.0      # underdog/level: no bias
    assert home_favorite_delta(0.50) > 0.0
    assert home_favorite_delta(0.80) >= home_favorite_delta(0.60)  # monotone


def test_apply_home_correction_keeps_distribution_valid():
    p1, px, p2 = apply_home_correction(0.70, 0.18, 0.12)
    assert p1 > 0.70                              # favourite lifted
    assert _approx(p1 + px + p2, 1.0)             # still a valid distribution
    # underdog vector untouched
    q = apply_home_correction(0.30, 0.30, 0.40)
    assert _approx(q[0], 0.30)


if __name__ == "__main__":
    fns = [v for k, v in sorted(globals().items()) if k.startswith("test_")]
    for fn in fns:
        fn()
        print(f"PASS {fn.__name__}")
    print(f"\nAll {len(fns)} tests passed.")