from __future__ import annotations

from scripts.research.holdout_v4_build import MIN_DOMAINS, MIN_EVENTS, build
from scripts.research.minute_resolution_lib import (
    aggregate_samples,
    dynamic_signature_layers,
    rescale_varga_points,
    shannon_entropy,
    subtract_event_floor,
    varga_divisor,
)
from scripts.research.minute_resolution_sweep import all_variants, verdict


def test_v4_cases_meet_event_and_domain_floors() -> None:
    payload = build()
    assert payload["ayanamsa"] == "raman"
    assert payload["node_mode"] == "mean"
    assert len(payload["cases"]) >= 8
    for case in payload["cases"]:
        domains = {item["domain"] for item in case["events"]}
        assert len(case["events"]) >= MIN_EVENTS, case["case_id"]
        assert len(domains) >= MIN_DOMAINS, case["case_id"]
        assert case["birth"]["source"]["rodden_rating"] == "AA"
        assert str(case["birth"]["time"])[:5] == case["true_minute"]


def test_varga_divisor_modes() -> None:
    assert varga_divisor(1, "2len") == 2.0
    assert varga_divisor(3, "2len") == 6.0
    assert varga_divisor(3, "len") == 3.0
    assert abs(varga_divisor(4, "sqrt") - 2.0) < 1e-9
    assert varga_divisor(3, "fixed2") == 2.0


def test_year_aggregation_keeps_peak() -> None:
    samples = [1.0, 1.0, 12.0]
    assert aggregate_samples(samples, "mean") == 4.6667
    assert aggregate_samples(samples, "max") == 12.0
    lse = aggregate_samples(samples, "lse", 1.0)
    assert lse > aggregate_samples(samples, "mean")
    assert lse < 12.0


def test_subtract_event_floor_zeroes_the_min() -> None:
    out = subtract_event_floor({"04:50": 11.0, "04:51": 13.5, "04:52": 11.0})
    assert out["04:50"] == 0.0
    assert out["04:51"] == 2.5


def test_entropy_is_zero_for_a_spike() -> None:
    assert shannon_entropy([0, 0, 5]) == 0.0
    assert shannon_entropy([1, 1]) > 0.9


def test_varga_rescale_doubles_single_chart_hit() -> None:
    points = rescale_varga_points(
        1.0,
        ["vim_md_domain_varga", "event_kind:career_change"],
        "career",
        "career_change",
        "day",
        "len",
    )
    assert points > 1.0


def test_dynamic_layers_drop_md_and_follow_domains() -> None:
    layers = dynamic_signature_layers(["career", "health_pressure"])
    assert layers[0] == "d1"
    assert "d10" in layers
    assert "d30" in layers
    assert "md" not in layers


def test_variant_grid_covers_pairs() -> None:
    names = {item.name for item in all_variants()}
    assert "baseline" in names
    assert "R1@len" in names
    assert "R3@1.0" in names
    assert "R1+R3" in names
    pair = next(item for item in all_variants() if item.name == "R1+R3")
    assert pair.r1 == "len"
    assert pair.r3 == 1.0


def test_verdict_requires_hit_and_coverage() -> None:
    baseline = {
        "n": 10, "top1": 0.2, "coverage": 0.9, "width_median": 21, "tie": 0.8,
        "squeezed": 1, "entropy0": 3.0, "entropy6": 2.0, "too_narrow": 0, "replay_top1": 0.5,
    }
    better = {
        **baseline, "top1": 0.4, "tie": 0.4, "width_median": 12, "squeezed": 0,
    }
    worse = {**baseline, "top1": 0.1, "coverage": 0.7, "squeezed": 3}
    assert verdict(baseline, better) == "benefit"
    assert verdict(baseline, worse) == "no_benefit"