"""B04 등고선 추출 엔진.

5종 표현(regular_grid/triangular_mesh/bspline_surface/local_rbf_height_field/
meshfree_surfels)의 npz 모델에서 표고 격자를 환원하고, marching squares로
지정 간격 등고선 라인을 추출한다. DTM valid_mask를 footprint로 사용해
경계 누출을 차단한다.
"""

from pathlib import Path
from typing import Any

import numpy as np
from scipy.interpolate import RBFInterpolator, RectBivariateSpline
from skimage import measure

# 등고선 캐시 형식/추출 규칙이 바뀔 때 증가시킨다.
CONTOUR_EXTRACTOR_VERSION = 3


def extract_contours_from_grid(
    x_coords: np.ndarray,
    y_coords: np.ndarray,
    z_grid: np.ndarray,
    valid_mask: np.ndarray | None,
    interval: float,
    min_interval: float = 0.5,
    scene_center: tuple[float, float, float] | None = None,
) -> list[dict[str, Any]]:
    """정규 표고 격자로부터 등고선 라인을 추출한다."""
    interval = max(interval, min_interval)
    finite_mask = np.isfinite(z_grid)
    if valid_mask is not None:
        finite_mask &= valid_mask
    if not finite_mask.any():
        return []

    z_min = float(np.min(z_grid[finite_mask]))
    z_max = float(np.max(z_grid[finite_mask]))
    start_level = np.ceil(z_min / interval) * interval
    levels = np.arange(start_level, z_max, interval)
    if len(levels) == 0:
        return []
    if len(levels) > 500:
        new_interval = (z_max - z_min) / 100.0
        levels = np.arange(np.ceil(z_min / new_interval) * new_interval, z_max, new_interval)
        interval = new_interval

    contours_geojson_list: list[dict[str, Any]] = []

    # marching squares의 NaN 문제 예방: 무효 영역을 sentinel(z_min-1000)로 채운다.
    z_grid_masked = z_grid.copy()
    if valid_mask is not None:
        z_grid_masked[~valid_mask] = z_min - 1000.0
    invalid_mask = ~np.isfinite(z_grid_masked)
    if invalid_mask.any():
        z_grid_masked[invalid_mask] = z_min - 1000.0

    cx, cy, cz = scene_center if scene_center is not None else (0.0, 0.0, 0.0)

    for level in levels:
        for contour in measure.find_contours(z_grid_masked, level):
            current_segment: list[list[float]] = []
            for y_idx, x_idx in contour:
                x_idx_c = np.clip(x_idx, 0, len(x_coords) - 1)
                y_idx_c = np.clip(y_idx, 0, len(y_coords) - 1)
                x_0, x_1 = int(np.floor(x_idx_c)), int(np.ceil(x_idx_c))
                y_0, y_1 = int(np.floor(y_idx_c)), int(np.ceil(y_idx_c))

                is_valid = True
                if valid_mask is not None and not (
                    valid_mask[y_0, x_0]
                    and valid_mask[y_0, x_1]
                    and valid_mask[y_1, x_0]
                    and valid_mask[y_1, x_1]
                ):
                    is_valid = False

                if not is_valid:
                    if len(current_segment) >= 2:
                        mid_idx = len(current_segment) // 2
                        contours_geojson_list.append(
                            {
                                "level": float(level),
                                "coordinates": current_segment,
                                "label_position": current_segment[mid_idx],
                            }
                        )
                    current_segment = []
                    continue

                tx = x_idx_c - x_0
                ty = y_idx_c - y_0
                x_val = (1.0 - tx) * x_coords[x_0] + tx * x_coords[x_1]
                y_val = (1.0 - ty) * y_coords[y_0] + ty * y_coords[y_1]

                if scene_center is not None:
                    current_segment.append(
                        [
                            round(float(x_val - cx), 3),
                            round(float(level - cz), 3),
                            round(float(-(y_val - cy)), 3),
                        ]
                    )
                else:
                    current_segment.append(
                        [round(float(x_val), 3), round(float(y_val), 3), round(float(level), 3)]
                    )

            if len(current_segment) >= 2:
                mid_idx = len(current_segment) // 2
                contours_geojson_list.append(
                    {
                        "level": float(level),
                        "coordinates": current_segment,
                        "label_position": current_segment[mid_idx],
                    }
                )

    return contours_geojson_list


def _load_footprint_mask(
    model_npz_path: Path, x_coords: np.ndarray, y_coords: np.ndarray
) -> np.ndarray | None:
    """같은 source filter의 DTM valid_mask를 현재 격자에 최근접 리샘플한다."""
    stem = Path(model_npz_path).stem
    if stem.endswith("_smooth"):
        stem = stem[:-7]
    parts = stem.split("_", 1)
    if len(parts) < 2:
        return None
    filter_key = parts[1]
    dtm_path = Path(model_npz_path).parent / f"dtm_{filter_key}.npz"
    if not dtm_path.exists():
        return None
    try:
        d = np.load(dtm_path)
        dtm_x = np.asarray(d["x"]).ravel()
        dtm_y = np.asarray(d["y"]).ravel()
        dtm_mask = np.asarray(d["valid_mask"], dtype=bool)
    except Exception:
        return None
    if len(dtm_x) < 2 or len(dtm_y) < 2:
        return None

    def _nearest_idx(axis: np.ndarray, coords: np.ndarray) -> np.ndarray:
        ascending = bool(axis[0] <= axis[-1])
        a = axis if ascending else axis[::-1]
        idx = np.clip(np.searchsorted(a, coords), 1, len(a) - 1)
        idx = np.where(np.abs(a[idx - 1] - coords) <= np.abs(a[idx] - coords), idx - 1, idx)
        return idx if ascending else (len(axis) - 1 - idx)

    xi = _nearest_idx(dtm_x, np.asarray(x_coords, dtype=np.float64))
    yi = _nearest_idx(dtm_y, np.asarray(y_coords, dtype=np.float64))
    return dtm_mask[np.ix_(yi, xi)]


def _apply_footprint(
    model_npz_path: Path, x_coords: np.ndarray, y_coords: np.ndarray, valid_mask: np.ndarray
) -> np.ndarray:
    """valid_mask에 DTM footprint를 교집합으로 적용한다 (형상 다르면 최근접 리샘플)."""
    fp = _load_footprint_mask(model_npz_path, x_coords, y_coords)
    if fp is not None:
        if fp.shape == valid_mask.shape:
            return valid_mask & fp
        from scipy.ndimage import zoom

        zoom_y = valid_mask.shape[0] / fp.shape[0]
        zoom_x = valid_mask.shape[1] / fp.shape[1]
        fp_resized = zoom(fp.astype(float), (zoom_y, zoom_x), order=0) > 0.5
        if fp_resized.shape == valid_mask.shape:
            return valid_mask & fp_resized
    return valid_mask


def _tin_face_coverage_mask(
    vertices: np.ndarray, faces: np.ndarray, xx: np.ndarray, yy: np.ndarray
) -> np.ndarray:
    """저장된 TIN 면이 실제로 덮는 XY 영역만 True로 반환한다."""
    vertices = np.asarray(vertices)
    faces = np.asarray(faces, dtype=np.int64)
    if vertices.ndim != 2 or vertices.shape[1] < 2 or not len(faces):
        return np.zeros(xx.shape, dtype=bool)

    edges = np.vstack((faces[:, [0, 1]], faces[:, [1, 2]], faces[:, [2, 0]]))
    edges = np.sort(edges, axis=1)
    unique_edges, counts = np.unique(edges, axis=0, return_counts=True)
    boundary_edges = unique_edges[counts == 1]
    if not len(boundary_edges):
        return np.zeros(xx.shape, dtype=bool)

    from shapely import get_parts, intersects_xy, linestrings, polygonize, union_all

    boundary_lines = linestrings(vertices[boundary_edges, :2])
    polygons = list(get_parts(polygonize(boundary_lines)))
    if not polygons:
        return np.zeros(xx.shape, dtype=bool)
    coverage = union_all(polygons)
    xx_flat = np.asarray(xx, dtype=np.float64).ravel()
    yy_flat = np.asarray(yy, dtype=np.float64).ravel()
    res_flat = np.asarray(intersects_xy(coverage, xx_flat, yy_flat), dtype=bool)
    return res_flat.reshape(xx.shape)


def _grid_axes(x_min: float, x_max: float, y_min: float, y_max: float, target_grid_m: float):
    cols = max(2, int(np.ceil((x_max - x_min) / target_grid_m)) + 1)
    rows = max(2, int(np.ceil((y_max - y_min) / target_grid_m)) + 1)
    x_coords = np.linspace(x_min, x_max, cols, dtype=np.float32)
    y_coords = np.linspace(y_min, y_max, rows, dtype=np.float32)
    return x_coords, y_coords


def extract_contours(
    model_npz_path: Path,
    representation: str,
    interval: float,
    target_grid_m: float = 1.0,
    scene_center: tuple[float, float, float] | None = None,
) -> list[dict[str, Any]]:
    """표현별 npz 모델에서 표고 격자를 환원한 뒤 등고선 리스트를 추출한다."""
    model_npz_path = Path(model_npz_path)
    if not model_npz_path.exists():
        raise FileNotFoundError(f"모델 파일이 존재하지 않습니다: {model_npz_path}")
    data = np.load(model_npz_path)

    if representation == "regular_grid":
        x_coords, y_coords, z_grid, valid_mask = (
            data["x"],
            data["y"],
            data["z"],
            data["valid_mask"],
        )
        current_res = (x_coords[-1] - x_coords[0]) / (len(x_coords) - 1)
        step = max(1, int(round(target_grid_m / current_res)))
        if step > 1:
            return extract_contours_from_grid(
                x_coords[::step],
                y_coords[::step],
                z_grid[::step, ::step],
                valid_mask[::step, ::step],
                interval,
                scene_center=scene_center,
            )
        return extract_contours_from_grid(
            x_coords, y_coords, z_grid, valid_mask, interval, scene_center=scene_center
        )

    if representation == "triangular_mesh":
        from scipy.interpolate import griddata

        vertices, faces = data["vertices"], data["faces"]
        x_min, x_max = float(np.min(vertices[:, 0])), float(np.max(vertices[:, 0]))
        y_min, y_max = float(np.min(vertices[:, 1])), float(np.max(vertices[:, 1]))
        x_coords, y_coords = _grid_axes(x_min, x_max, y_min, y_max, target_grid_m)
        xx, yy = np.meshgrid(x_coords, y_coords)
        z_grid = griddata(vertices[:, :2], vertices[:, 2], (xx, yy), method="linear")
        face_mask = _tin_face_coverage_mask(vertices, faces, xx, yy)
        valid_mask = np.isfinite(z_grid) & face_mask
        valid_mask = _apply_footprint(model_npz_path, x_coords, y_coords, valid_mask)
        return extract_contours_from_grid(
            x_coords, y_coords, z_grid, valid_mask, interval, scene_center=scene_center
        )

    if representation == "bspline_surface":
        control_x, control_y, control_z = data["control_x"], data["control_y"], data["control_z"]
        degree = int(data["degree"][0])
        spline = RectBivariateSpline(
            control_y,
            control_x,
            control_z,
            kx=min(degree, len(control_y) - 1),
            ky=min(degree, len(control_x) - 1),
            s=float(len(control_x) * len(control_y)) * 0.01,
        )
        x_coords, y_coords = _grid_axes(
            float(control_x[0]),
            float(control_x[-1]),
            float(control_y[0]),
            float(control_y[-1]),
            target_grid_m,
        )
        z_grid = np.asarray(spline(y_coords, x_coords), dtype=np.float32)
        valid_mask = _apply_footprint(
            model_npz_path, x_coords, y_coords, np.ones_like(z_grid, dtype=bool)
        )
        return extract_contours_from_grid(
            x_coords, y_coords, z_grid, valid_mask, interval, scene_center=scene_center
        )

    if representation == "local_rbf_height_field":
        centers_xy, center_z = data["centers_xy"], data["center_z"]
        smoothing = float(data["smoothing"][0])
        interpolator = RBFInterpolator(
            centers_xy.astype(np.float64),
            center_z.astype(np.float64),
            neighbors=min(64, len(centers_xy)),
            smoothing=smoothing,
            kernel="thin_plate_spline",
        )
        x_min, x_max = float(np.min(centers_xy[:, 0])), float(np.max(centers_xy[:, 0]))
        y_min, y_max = float(np.min(centers_xy[:, 1])), float(np.max(centers_xy[:, 1]))
        x_coords, y_coords = _grid_axes(x_min, x_max, y_min, y_max, target_grid_m)
        xx, yy = np.meshgrid(x_coords, y_coords)
        z_values = interpolator(np.column_stack([xx.ravel(), yy.ravel()])).astype(np.float32)
        z_grid = z_values.reshape(len(y_coords), len(x_coords))
        valid_mask = _apply_footprint(
            model_npz_path, x_coords, y_coords, np.ones_like(z_grid, dtype=bool)
        )
        return extract_contours_from_grid(
            x_coords, y_coords, z_grid, valid_mask, interval, scene_center=scene_center
        )

    if representation == "meshfree_surfels":
        from scipy.interpolate import griddata
        from scipy.spatial import Delaunay

        points = data["points"]
        x_min, x_max = float(np.min(points[:, 0])), float(np.max(points[:, 0]))
        y_min, y_max = float(np.min(points[:, 1])), float(np.max(points[:, 1]))
        x_coords, y_coords = _grid_axes(x_min, x_max, y_min, y_max, target_grid_m)
        xx, yy = np.meshgrid(x_coords, y_coords)
        z_grid = griddata(points[:, :2], points[:, 2], (xx, yy), method="linear")
        valid_mask = np.isfinite(z_grid)
        try:
            tri = Delaunay(points[:, :2])
            hull_inside = tri.find_simplex(np.column_stack([xx.ravel(), yy.ravel()])) >= 0
            valid_mask = valid_mask & hull_inside.reshape(xx.shape)
        except Exception:
            pass
        valid_mask = _apply_footprint(model_npz_path, x_coords, y_coords, valid_mask)
        return extract_contours_from_grid(
            x_coords, y_coords, z_grid, valid_mask, interval, scene_center=scene_center
        )

    raise ValueError(f"지원하지 않는 표현 방식입니다: {representation}")