import pandas as pd
import pytest

from reporting import (
    agp_percentile_bands,
    detect_events,
    gmi_percent,
    minutes_per_day,
    percent_cv,
    sensor_active_percent,
    summarize_events,
    tir_breakdown_5band,
)


def _series(pairs):
    """Bouw een glucose-DataFrame uit (tijd-string, mmol)-paren."""
    timestamps, values = zip(*pairs, strict=True)
    return pd.DataFrame({"timestamp": pd.to_datetime(list(timestamps)), "mmol": list(values)})


def test_gmi_percent_matches_bergenstal_formula():
    # 3.31 + 0.4311 * 7.0 = 6.3277 -> 6.3
    assert gmi_percent(7.0) == pytest.approx(6.3, abs=0.05)


def test_percent_cv_handles_empty_input():
    empty = pd.DataFrame(columns=["timestamp", "mmol"])
    assert percent_cv(empty) == 0.0


def test_percent_cv_computes_stdev_over_mean():
    glucose = _series(
        [
            ("2026-01-01 00:00", 6.0),
            ("2026-01-01 00:05", 8.0),
            ("2026-01-01 00:10", 6.0),
            ("2026-01-01 00:15", 8.0),
        ]
    )
    expected = glucose["mmol"].std() / glucose["mmol"].mean() * 100
    assert percent_cv(glucose) == pytest.approx(round(expected, 1))


def test_tir_breakdown_5band_covers_all_bands_and_sums_to_100():
    glucose = _series(
        [
            ("2026-01-01 00:00", 2.5),  # very_low
            ("2026-01-01 00:05", 3.5),  # low
            ("2026-01-01 00:10", 6.0),  # in_range
            ("2026-01-01 00:15", 11.0),  # high
            ("2026-01-01 00:20", 14.0),  # very_high
        ]
    )
    result = tir_breakdown_5band(glucose)
    assert result == {
        "very_low": 20.0,
        "low": 20.0,
        "in_range": 20.0,
        "high": 20.0,
        "very_high": 20.0,
    }
    assert sum(result.values()) == pytest.approx(100.0)


def test_tir_breakdown_5band_handles_empty_input():
    empty = pd.DataFrame(columns=["timestamp", "mmol"])
    result = tir_breakdown_5band(empty)
    assert result == {"very_low": 0.0, "low": 0.0, "in_range": 0.0, "high": 0.0, "very_high": 0.0}


def test_minutes_per_day_converts_percentage():
    assert minutes_per_day(50.0) == 720.0
    assert minutes_per_day(100.0) == 1440.0


def test_sensor_active_percent_full_coverage():
    # 12 metingen om de 5 minuten = precies 1 uur dekking
    timestamps = pd.date_range("2026-01-01 00:00", periods=12, freq="5min")
    glucose = pd.DataFrame({"timestamp": timestamps, "mmol": [6.0] * 12})
    result = sensor_active_percent(glucose, "2026-01-01 00:00", "2026-01-01 01:00")
    assert result == pytest.approx(100.0, abs=1.0)


def test_sensor_active_percent_handles_gaps():
    timestamps = pd.date_range("2026-01-01 00:00", periods=6, freq="5min")  # maar 30 min dekking
    glucose = pd.DataFrame({"timestamp": timestamps, "mmol": [6.0] * 6})
    result = sensor_active_percent(glucose, "2026-01-01 00:00", "2026-01-01 01:00")
    assert result == pytest.approx(50.0, abs=1.0)


def test_detect_events_ignores_dip_shorter_than_minimum_duration():
    # dip van 10 minuten (< 15 min drempel) mag niet meetellen
    glucose = _series(
        [
            ("2026-01-01 00:00", 6.0),
            ("2026-01-01 00:05", 3.5),
            ("2026-01-01 00:10", 3.4),
            ("2026-01-01 00:15", 6.0),
            ("2026-01-01 00:20", 6.1),
            ("2026-01-01 00:25", 6.2),
        ]
    )
    events = detect_events(glucose, threshold=3.9, direction="below")
    assert events.empty


def test_detect_events_counts_dip_of_exactly_20_minutes():
    glucose = _series(
        [
            ("2026-01-01 00:00", 6.0),
            ("2026-01-01 00:05", 3.5),
            ("2026-01-01 00:10", 3.4),
            ("2026-01-01 00:15", 3.3),
            ("2026-01-01 00:20", 3.2),
            ("2026-01-01 00:25", 6.0),
            ("2026-01-01 00:30", 6.1),
            ("2026-01-01 00:35", 6.2),
            ("2026-01-01 00:40", 6.3),
        ]
    )
    events = detect_events(glucose, threshold=3.9, direction="below")
    assert len(events) == 1
    assert events.iloc[0]["duration_minutes"] == pytest.approx(20.0)


def test_detect_events_merges_across_brief_recovery():
    # twee dips gescheiden door een kort (5 min) herstel moeten één
    # doorlopend event vormen volgens de persistentieregel
    glucose = _series(
        [
            ("2026-01-01 00:00", 6.0),
            ("2026-01-01 00:05", 3.5),
            ("2026-01-01 00:10", 3.4),
            ("2026-01-01 00:15", 6.0),  # kort herstel, 5 min
            ("2026-01-01 00:20", 3.3),
            ("2026-01-01 00:25", 3.2),
            ("2026-01-01 00:30", 3.1),
            ("2026-01-01 00:35", 6.0),
            ("2026-01-01 00:40", 6.1),
            ("2026-01-01 00:45", 6.2),
            ("2026-01-01 00:50", 6.3),  # bevestigd herstel, 15 min
        ]
    )
    events = detect_events(glucose, threshold=3.9, direction="below")
    assert len(events) == 1
    assert events.iloc[0]["start"] == pd.Timestamp("2026-01-01 00:05")
    assert events.iloc[0]["end"] == pd.Timestamp("2026-01-01 00:35")
    assert events.iloc[0]["duration_minutes"] == pytest.approx(30.0)


def test_detect_events_does_not_inflate_duration_on_trailing_brief_blip():
    # regressie: een out-periode van 10 min gevolgd door een niet-bevestigd
    # (kort, en aan het eind van de data) herstel mag niet meetellen
    glucose = _series(
        [
            ("2026-01-01 00:00", 6.0),
            ("2026-01-01 00:05", 3.5),
            ("2026-01-01 00:10", 3.4),
            ("2026-01-01 00:15", 6.0),
            ("2026-01-01 00:20", 6.1),
            ("2026-01-01 00:25", 6.2),
        ]
    )
    events = detect_events(glucose, threshold=3.9, direction="below")
    assert events.empty


def test_detect_events_supports_hyperglycemia_direction():
    glucose = _series(
        [
            ("2026-01-01 00:00", 6.0),
            ("2026-01-01 00:05", 12.0),
            ("2026-01-01 00:10", 12.5),
            ("2026-01-01 00:15", 13.0),
            ("2026-01-01 00:20", 12.8),
            ("2026-01-01 00:25", 6.0),
            ("2026-01-01 00:30", 6.1),
            ("2026-01-01 00:35", 6.2),
            ("2026-01-01 00:40", 6.3),
        ]
    )
    events = detect_events(glucose, threshold=10.0, direction="above")
    assert len(events) == 1
    assert events.iloc[0]["duration_minutes"] == pytest.approx(20.0)


def test_detect_events_rejects_invalid_direction():
    glucose = _series([("2026-01-01 00:00", 6.0)])
    with pytest.raises(ValueError, match="direction"):
        detect_events(glucose, threshold=3.9, direction="sideways")


def test_detect_events_handles_empty_input():
    empty = pd.DataFrame(columns=["timestamp", "mmol"])
    events = detect_events(empty, threshold=3.9, direction="below")
    assert events.empty
    assert list(events.columns) == ["start", "end", "duration_minutes"]


def test_summarize_events_computes_expected_stats():
    events = pd.DataFrame(
        {
            "start": pd.to_datetime(["2026-01-01 00:00", "2026-01-03 00:00"]),
            "end": pd.to_datetime(["2026-01-01 00:20", "2026-01-03 01:00"]),
            "duration_minutes": [20.0, 60.0],
        }
    )
    result = summarize_events(events, period_days=7)
    assert result["count"] == 2
    assert result["avg_duration_minutes"] == pytest.approx(40.0)
    assert result["longest_duration_minutes"] == pytest.approx(60.0)
    assert result["total_minutes"] == pytest.approx(80.0)
    assert result["events_per_week"] == pytest.approx(2.0)


def test_agp_percentile_bands_groups_by_time_of_day_across_days():
    # zelfde tijdstip (08:00) op twee verschillende dagen, met bekende waarden
    glucose = _series(
        [
            ("2026-01-01 08:00", 6.0),
            ("2026-01-02 08:00", 8.0),
            ("2026-01-01 20:00", 10.0),
            ("2026-01-02 20:00", 10.0),
        ]
    )
    result = agp_percentile_bands(glucose, bin_minutes=30)
    assert list(result.columns) == ["minute_of_day", "p5", "p25", "p50", "p75", "p95"]

    morning_bin = 8 * 60
    row = result[result["minute_of_day"] == morning_bin].iloc[0]
    assert row["p50"] == pytest.approx(7.0)  # mediaan van 6.0 en 8.0

    evening_bin = 20 * 60
    evening_row = result[result["minute_of_day"] == evening_bin].iloc[0]
    assert evening_row["p50"] == pytest.approx(10.0)


def test_agp_percentile_bands_handles_empty_input():
    empty = pd.DataFrame(columns=["timestamp", "mmol"])
    result = agp_percentile_bands(empty)
    assert result.empty
    assert list(result.columns) == ["minute_of_day", "p5", "p25", "p50", "p75", "p95"]


def test_summarize_events_handles_empty_input():
    empty = pd.DataFrame(columns=["start", "end", "duration_minutes"])
    result = summarize_events(empty, period_days=14)
    assert result == {
        "count": 0,
        "avg_duration_minutes": 0.0,
        "longest_duration_minutes": 0.0,
        "total_minutes": 0.0,
        "events_per_week": 0.0,
    }