"""
ObjectPropertiesProcessor — 3dSAGER 25 geometric property computation.

Given a dict of {building_id: mesh_record} for each side (cands/index),
computes exactly the 25 properties used in the SIGMOD 2026 paper and
applies log(1+x) normalisation if requested.

mesh_record format (from crawl_3dbag_multilod.py):
    {'polygon_mesh': [[[x,y,z],...], ...],  # list of surfaces
     'vertices': np.array unique vertices,
     'centroid': np.array([x,y,z])}
"""
import time, numpy as np
from scipy.spatial import ConvexHull

PROP_NAMES = [
    "bounding_box_width", "bounding_box_length", "area", "perimeter",
    "perimeter_ind", "volume", "convex_hull_area", "convex_hull_volume",
    "ave_centroid_distance", "height_diff", "num_floors", "axes_symmetry",
    "compactness_2d", "compactness_3d", "density", "elongation",
    "shape_ind", "hemisphericality", "fractality", "cubeness",
    "circumference", "aligned_bounding_box_width",
    "aligned_bounding_box_length", "aligned_bounding_box_height",
    "num_vertices"
]


def _extract_faces(mesh_rec):
    """Return (list-of-face-vertex-arrays, all-vertices-array)."""
    pm = mesh_rec.get("polygon_mesh", [])
    faces = []
    all_pts = []
    for surf in pm:
        if len(surf) < 3:
            continue
        verts = np.asarray(surf, dtype=np.float64)
        faces.append(verts)
        all_pts.append(verts)
    if not faces:
        all_v = mesh_rec.get("vertices", np.zeros((0, 3)))
        return [], np.asarray(all_v, dtype=np.float64)
    all_v = np.concatenate(all_pts, axis=0)
    return faces, all_v


def _footprint_vertices(all_v):
    """Return 2D vertices (x,y only) for the building footprint."""
    if len(all_v) < 3:
        return all_v[:, :2]
    return all_v[:, :2]


def _unique_2d(pts):
    """Deduplicate 2D points."""
    if len(pts) <= 1:
        return pts
    _, idx = np.unique(np.round(pts, 6), axis=0, return_index=True)
    return pts[np.sort(idx)]


def _triangle_area(a, b, c):
    """Area of triangle a-b-c."""
    return 0.5 * np.linalg.norm(np.cross(b - a, c - a))


def _face_area(verts):
    """Area of a planar polygon (triangulation from first vertex)."""
    if len(verts) < 3:
        return 0.0
    a = 0.0
    p0 = verts[0]
    for j in range(1, len(verts) - 1):
        a += _triangle_area(p0, verts[j], verts[j + 1])
    return a


def _face_perimeter(verts):
    """Perimeter of a polygon."""
    if len(verts) < 2:
        return 0.0
    p = 0.0
    for j in range(len(verts)):
        p += np.linalg.norm(verts[j] - verts[(j + 1) % len(verts)])
    return p


def _mesh_volume(faces):
    """Signed volume via divergence theorem: V = 1/3 * sum over faces
    of (face centroid · face normal) * face area. Works for closed meshes."""
    vol = 0.0
    for fv in faces:
        if len(fv) < 3:
            continue
        p0 = fv[0]
        c = fv.mean(axis=0)
        for j in range(1, len(fv) - 1):
            n = np.cross(fv[j] - p0, fv[j + 1] - p0)
            vol += np.dot(c, n)
    return abs(vol) / 6.0


def _convex_hull_volume(all_v):
    """Volume of 3D convex hull."""
    if len(all_v) < 4:
        return 0.0
    try:
        hull = ConvexHull(all_v)
        return hull.volume
    except Exception:
        return 0.0


def _convex_hull_area_2d(pts_2d):
    """Area of 2D convex hull (footprint)."""
    pts_u = _unique_2d(pts_2d)
    if len(pts_u) < 3:
        return np.ptp(pts_u[:, 0]) * np.ptp(pts_u[:, 1]) if len(pts_u) > 1 else 0.0
    try:
        hull = ConvexHull(pts_u)
        return hull.volume  # in 2D, hull.volume = area
    except Exception:
        return 0.0


def _footprint_perimeter_and_area(pts_2d):
    """Footprint (2D convex hull) perimeter and area."""
    pts_u = _unique_2d(pts_2d)
    if len(pts_u) < 3:
        return 0.0, 0.0
    try:
        hull = ConvexHull(pts_u)
        return hull.area, hull.volume  # perimeter = hull.area, area = hull.volume
    except Exception:
        return 0.0, 0.0


def _pca_axes(all_v):
    """Return sorted eigenvalues (variances along principal axes)."""
    if len(all_v) < 3:
        return np.array([1.0, 1.0, 1.0])
    c = all_v.mean(axis=0)
    X = all_v - c
    cov = X.T @ X / (len(X) - 1 + 1e-12)
    try:
        w, _ = np.linalg.eigh(cov)
        return np.sort(np.maximum(w, 0))[::-1]
    except Exception:
        return np.array([1.0, 1.0, 1.0])


def _compute_all(all_v, faces, mesh_rec):
    """Compute all 25 property values from vertices and faces."""
    eps = 1e-9
    props = {}

    # bbox in world coords
    if len(all_v) == 0:
        return {p: 0.0 for p in PROP_NAMES}

    bmin, bmax = all_v.min(axis=0), all_v.max(axis=0)
    bbox_dims = bmax - bmin  # [width_x, width_y, height]
    bbw, bbl, bbh = float(bbox_dims[0]), float(bbox_dims[1]), float(bbox_dims[2])

    props["bounding_box_width"]  = max(bbw, eps)
    props["bounding_box_length"] = max(bbl, eps)
    props["aligned_bounding_box_width"]  = max(bbw, eps)
    props["aligned_bounding_box_length"] = max(bbl, eps)
    props["aligned_bounding_box_height"] = max(bbh, eps)
    props["height_diff"] = max(bbh, eps)
    props["num_floors"]   = max(bbh / 3.0, eps)  # ~3m per floor

    # area = sum of face areas
    area = sum(_face_area(f) for f in faces)
    props["area"] = max(area, eps)

    # perimeter = sum of face perimeters
    perim = sum(_face_perimeter(f) for f in faces)
    props["perimeter"] = max(perim, eps)

    # volume via divergence theorem
    vol = _mesh_volume(faces)
    props["volume"] = max(vol, eps)

    # convex hull
    pts_2d = _footprint_vertices(all_v)
    props["convex_hull_area"] = max(_convex_hull_area_2d(pts_2d), eps)
    props["convex_hull_volume"] = max(_convex_hull_volume(all_v), eps)

    # footprint perimeter & area (for compactness / perimeter_ind)
    fp_perim, fp_area = _footprint_perimeter_and_area(pts_2d)
    props["perimeter_ind"] = max(fp_perim / (2.0 * np.sqrt(np.pi * max(fp_area, eps)) + eps), eps)
    props["circumference"] = max(fp_perim, eps)

    # centroid distance
    centroid = np.asarray(mesh_rec.get("centroid", all_v.mean(axis=0)), dtype=np.float64)
    dists = np.linalg.norm(all_v - centroid, axis=1)
    props["ave_centroid_distance"] = max(float(dists.mean()), eps)

    # PCA axes
    axes = _pca_axes(all_v)  # sorted descending
    props["elongation"]  = max(float(axes[0] / (axes[2] + eps)), eps)
    props["axes_symmetry"] = max(float((axes[1] + axes[2]) / (2.0 * max(axes[0], eps))), eps)

    # compactness
    props["compactness_2d"] = max(float(4.0 * np.pi * max(fp_area, eps) / (max(fp_perim, eps) ** 2 + eps)), eps)
    # 3D compactness: V^2 / A^3 * 36π  (sphere = 1)
    area_safe = max(area, eps)
    vol_safe = max(vol, eps)
    props["compactness_3d"] = max(float(36.0 * np.pi * vol_safe * vol_safe / (area_safe * area_safe * area_safe + eps)), eps)

    # density
    props["density"] = max(float(vol_safe / max(fp_area, eps)), eps)

    # shape index
    props["shape_ind"] = max(float(bbh / (np.sqrt(max(fp_area, eps)) + eps)), eps)

    # hemisphericality: how close to a hemisphere (min height vs max height)
    props["hemisphericality"] = max(float(1.0 - (all_v[:, 2].min() - bmin[2]) / (max(bbh, eps))), eps)

    # fractality: log(area) / log(perimeter) as a rough fractal dimension proxy
    props["fractality"] = max(float(np.log(max(area, 1.0)) / np.log(max(perim, 1.0))), eps)

    # cubeness: V / bbox_volume
    bbox_vol = max(bbw * bbl * bbh, eps)
    props["cubeness"] = max(float(vol_safe / bbox_vol), eps)

    # num_vertices
    props["num_vertices"] = max(len(all_v), eps)

    return props


class ObjectPropertiesProcessor:
    def __init__(self, object_dict, vector_normalization=True):
        self.od = object_dict
        self.vector_normalization = vector_normalization
        self.property_dict_generation_time = 0.0
        t0 = time.time()
        self.prop_vals_dict = self._compute()
        self.property_dict_generation_time = time.time() - t0

    def _resolve_sides(self):
        """Object dict may use {'cands':..., 'index':...} or arbitrary side keys.
        Each side value must be {building_id: mesh_record}."""
        if "cands" in self.od and "index" in self.od:
            return ["cands", "index"], self.od
        # Use whatever keys the dict already has as side names
        return list(self.od.keys()), self.od

    def _compute(self):
        sides, od = self._resolve_sides()
        result = {p: {s: {} for s in sides} for p in PROP_NAMES}

        for side in sides:
            for bid, rec in od[side].items():
                faces, all_v = _extract_faces(rec)
                vals = _compute_all(all_v, faces, rec)
                for p in PROP_NAMES:
                    raw = vals.get(p, 0.0)
                    if self.vector_normalization and p not in ("num_vertices", "num_floors"):
                        raw = np.log1p(raw)
                    result[p][side][bid] = float(raw)

        return result