"""Pixel-level change detection: z-score rasters, hotspot masks, clustering."""
from __future__ import annotations

from typing import Any

import numpy as np
from scipy import ndimage


def compute_zscore_raster(
    current: np.ndarray,
    baseline_mean: np.ndarray,
    baseline_std: np.ndarray,
    min_std: float,
) -> np.ndarray:
    """Compute per-pixel z-score raster."""
    effective_std = np.maximum(baseline_std, min_std)
    return (current - baseline_mean) / effective_std


def detect_hotspots(
    zscore_raster: np.ndarray,
    threshold: float = 2.0,
) -> tuple[np.ndarray, float]:
    """Create boolean hotspot mask and compute percentage of area affected."""
    valid = ~np.isnan(zscore_raster)
    mask = valid & (np.abs(zscore_raster) > threshold)
    n_valid = np.sum(valid)
    pct = float(np.sum(mask) / n_valid * 100) if n_valid > 0 else 0.0
    return mask, pct


def cluster_hotspots(
    mask: np.ndarray,
    zscore_raster: np.ndarray,
    pixel_area_ha: float,
    min_pixels: int = 4,
    top_n: int = 3,
) -> list[dict[str, Any]]:
    """Find connected hotspot clusters and return the top N by area."""
    labeled, n_features = ndimage.label(mask)

    clusters: list[dict[str, Any]] = []
    for label_id in range(1, n_features + 1):
        pixels = labeled == label_id
        n_pixels = int(np.sum(pixels))
        if n_pixels < min_pixels:
            continue

        rows, cols = np.where(pixels)
        clusters.append({
            "area_ha": n_pixels * pixel_area_ha,
            "centroid_row": float(np.mean(rows)),
            "centroid_col": float(np.mean(cols)),
            "mean_zscore": float(np.nanmean(zscore_raster[pixels])),
            "n_pixels": n_pixels,
        })

    clusters.sort(key=lambda c: c["area_ha"], reverse=True)
    return clusters[:top_n]