"""Cross-EO-product compound signal detection."""
from __future__ import annotations

import numpy as np

from app.models import CompoundSignal


def compute_overlap_pct(mask_a: np.ndarray, mask_b: np.ndarray) -> float:
    """Compute overlap percentage: intersection / min(count_a, count_b) * 100."""
    intersection = np.sum(mask_a & mask_b)
    min_count = min(np.sum(mask_a), np.sum(mask_b))
    if min_count == 0:
        return 0.0
    return float(intersection / min_count * 100)


def _tag_confidence(n_products: int, overlap_pct: float) -> str:
    if n_products >= 3 and overlap_pct > 20:
        return "strong"
    if n_products >= 2 and overlap_pct >= 10:
        return "moderate"
    return "weak"


def detect_compound_signals(
    zscore_rasters: dict[str, np.ndarray],
    pixel_area_ha: float,
    threshold: float = 2.0,
) -> list[CompoundSignal]:
    """Test for compound signal patterns across EO product z-score rasters."""
    decline: dict[str, np.ndarray] = {}
    increase: dict[str, np.ndarray] = {}
    for product_id, z in zscore_rasters.items():
        decline[product_id] = z < -threshold
        increase[product_id] = z > threshold

    signals: list[CompoundSignal] = []

    # 1. Land conversion: NDVI decline + Settlement growth
    if "ndvi" in decline and "buildup" in increase:
        overlap = compute_overlap_pct(decline["ndvi"], increase["buildup"])
        triggered = overlap > 10
        affected = float(np.sum(decline["ndvi"] & increase["buildup"])) * pixel_area_ha
        signals.append(CompoundSignal(
            name="land_conversion", triggered=triggered,
            confidence=_tag_confidence(2, overlap) if triggered else "weak",
            description=(
                f"NDVI decline overlaps with settlement growth ({overlap:.0f}% overlap, "
                f"{affected:.1f} ha affected). Suggests possible vegetation loss to urbanization."
            ) if triggered else "No land conversion signal detected.",
            indicators=["ndvi", "buildup"], overlap_pct=overlap, affected_ha=affected,
        ))

    # 2. Flood event: SAR decrease + Water increase
    if "sar" in decline and "water" in increase:
        overlap = compute_overlap_pct(decline["sar"], increase["water"])
        triggered = overlap > 10
        affected = float(np.sum(decline["sar"] & increase["water"])) * pixel_area_ha
        signals.append(CompoundSignal(
            name="flood_event", triggered=triggered,
            confidence=_tag_confidence(2, overlap) if triggered else "weak",
            description=(
                f"SAR backscatter decrease coincides with water extent increase "
                f"({overlap:.0f}% overlap, {affected:.1f} ha). Suggests potential flooding."
            ) if triggered else "No flood signal detected.",
            indicators=["sar", "water"], overlap_pct=overlap, affected_ha=affected,
        ))

    # 3. Drought stress: NDVI decline + Water decline + SAR increase
    if "ndvi" in decline and "water" in decline and "sar" in increase:
        combined = decline["ndvi"] & decline["water"] & increase["sar"]
        n_combined = int(np.sum(combined))
        min_single = min(np.sum(decline["ndvi"]), np.sum(decline["water"]), np.sum(increase["sar"]))
        overlap = float(n_combined / min_single * 100) if min_single > 0 else 0.0
        triggered = overlap > 10
        affected = n_combined * pixel_area_ha
        signals.append(CompoundSignal(
            name="drought_stress", triggered=triggered,
            confidence=_tag_confidence(3, overlap) if triggered else "weak",
            description=(
                f"NDVI decline, water decline, and SAR increase co-occur "
                f"({overlap:.0f}% overlap, {affected:.1f} ha). Suggests possible drought."
            ) if triggered else "No drought signal detected.",
            indicators=["ndvi", "water", "sar"], overlap_pct=overlap, affected_ha=affected,
        ))

    # 4. Displacement pressure: Settlement growth + NDVI decline adjacent
    if "buildup" in increase and "ndvi" in decline:
        from scipy.ndimage import binary_dilation
        expanded_buildup = binary_dilation(increase["buildup"], iterations=1)
        adjacent_decline = expanded_buildup & decline["ndvi"] & ~increase["buildup"]
        n_adjacent = int(np.sum(adjacent_decline))
        n_buildup = int(np.sum(increase["buildup"]))
        overlap = float(n_adjacent / max(n_buildup, 1) * 100)
        triggered = overlap > 10 and n_adjacent > 0
        affected = n_adjacent * pixel_area_ha
        signals.append(CompoundSignal(
            name="displacement_pressure", triggered=triggered,
            confidence=_tag_confidence(2, overlap) if triggered else "weak",
            description=(
                f"Settlement growth hotspots are adjacent to NDVI decline areas "
                f"({affected:.1f} ha of surrounding vegetation loss). "
                f"Suggests expansion into previously vegetated land."
            ) if triggered else "No displacement pressure signal detected.",
            indicators=["ndvi", "buildup"], overlap_pct=overlap, affected_ha=affected,
        ))

    return signals