from __future__ import annotations

from pathlib import Path

import matplotlib
import numpy as np
from matplotlib import colors

matplotlib.use("Agg")
import matplotlib.pyplot as plt

DEFAULT_PANEL_SIZE_MULTIPLIER = 1.5
DEFAULT_BILLBOARD_ALPHA = 1.0
DEFAULT_PANEL_SCALE = 0.12
DEFAULT_DAY_STEP = 1

DEFAULT_DIVERGING_CMAP = colors.LinearSegmentedColormap.from_list(
    "zone_load_diverging",
    ["#82B0D2", "#FFFFFF", "#FA7F6F"],
    N=256,
)

# Keep color mapping consistent with cuger/__analyse/visualise.py.
TYPE_COLORS = {
    "window": "#FFBE7A",
    "shading": "#999999",
    "floor": "#82B0D2",
    "wall": "#8ECFC9",
    "airwall": "#E7DAD2",
    "space": "#FA7F6F",
    "void": "#FFFFFF",
    None: "#FFFFFF",
}


def _axis_limits_from_points(points: np.ndarray) -> tuple[tuple[float, float], tuple[float, float], tuple[float, float]]:
    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]))
    z_min, z_max = float(np.min(points[:, 2])), float(np.max(points[:, 2]))

    max_range = max(x_max - x_min, y_max - y_min, z_max - z_min) / 2.0
    max_range = max(max_range, 1e-6) * 1.08

    x_mid = (x_max + x_min) / 2.0
    y_mid = (y_max + y_min) / 2.0
    z_mid = (z_max + z_min) / 2.0

    return (
        (x_mid - max_range, x_mid + max_range),
        (y_mid - max_range, y_mid + max_range),
        (z_mid - max_range, z_mid + max_range),
    )


def _first_existing(data: dict[str, np.ndarray], keys: list[str]) -> np.ndarray | None:
    for k in keys:
        if k in data:
            return np.asarray(data[k])
    return None


def _as_2d_points(arr: np.ndarray | None) -> np.ndarray:
    if arr is None:
        return np.zeros((0, 3), dtype=float)
    pts = np.asarray(arr, dtype=float)
    if pts.ndim != 2 or pts.shape[1] < 3:
        return np.zeros((0, 3), dtype=float)
    return pts[:, :3]


def _camera_basis(elev: float, azim: float) -> tuple[np.ndarray, np.ndarray]:
    az = np.deg2rad(float(azim))
    el = np.deg2rad(float(elev))

    forward = np.array(
        [
            np.cos(el) * np.cos(az),
            np.cos(el) * np.sin(az),
            np.sin(el),
        ],
        dtype=float,
    )
    forward_norm = np.linalg.norm(forward)
    if forward_norm < 1e-9:
        forward = np.array([1.0, 0.0, 0.0], dtype=float)
    else:
        forward = forward / forward_norm

    world_up = np.array([0.0, 0.0, 1.0], dtype=float)
    right = np.cross(forward, world_up)
    right_norm = np.linalg.norm(right)
    if right_norm < 1e-9:
        right = np.array([1.0, 0.0, 0.0], dtype=float)
    else:
        right = right / right_norm

    up = np.cross(right, forward)
    up_norm = np.linalg.norm(up)
    if up_norm < 1e-9:
        up = np.array([0.0, 1.0, 0.0], dtype=float)
    else:
        up = up / up_norm

    return right, up


def _isometric_panel_basis(elev: float, azim: float) -> tuple[np.ndarray, np.ndarray]:
    cam_right, cam_up = _camera_basis(elev=elev, azim=azim)
    c30 = np.cos(np.deg2rad(30.0))
    s30 = np.sin(np.deg2rad(30.0))

    u = c30 * cam_right + s30 * cam_up
    v = -c30 * cam_right + s30 * cam_up
    u = u / max(np.linalg.norm(u), 1e-9)
    v = v / max(np.linalg.norm(v), 1e-9)
    return u, v


def _to_hourly_zone(values: np.ndarray) -> np.ndarray:
    arr = np.asarray(values)
    if arr.ndim != 2:
        raise ValueError(f"`values` must be 2D, got shape={arr.shape}")

    if arr.shape[0] == 8760:
        return arr.astype(np.float32)

    if arr.shape[1] == 8760:
        return arr.T.astype(np.float32)

    raise ValueError(f"Neither axis equals 8760, shape={arr.shape}")


def _try_parse_space_indices(raw_values: np.ndarray | None, zone_count: int, space_count: int) -> np.ndarray | None:
    if raw_values is None:
        return None

    vals = np.asarray(raw_values).reshape(-1)
    if vals.size < zone_count:
        return None

    out: list[int] = []
    for i in range(zone_count):
        v = vals[i]
        idx: int | None = None

        if isinstance(v, (int, np.integer)):
            idx = int(v)
        elif isinstance(v, (float, np.floating)):
            vf = float(v)
            if np.isfinite(vf) and float(vf).is_integer():
                idx = int(vf)
        else:
            text = v.decode("utf-8", errors="ignore") if isinstance(v, (bytes, np.bytes_)) else str(v)
            text = text.strip()
            try:
                idx = int(text)
            except ValueError:
                return None

        if idx is None or not (0 <= idx < space_count):
            return None
        out.append(idx)

    return np.asarray(out, dtype=np.int64)


def _zone_day_hour_matrix(zone_hourly: np.ndarray, day_step: int = 1) -> np.ndarray:
    series = np.asarray(zone_hourly, dtype=np.float32).reshape(-1)
    if series.size < 8760:
        raise ValueError(f"Zone series length must be >= 8760, got {series.size}")

    day_hour = series[:8760].reshape(365, 24)
    step = max(1, int(day_step))
    if step == 1:
        return day_hour

    rows: list[np.ndarray] = []
    for s in range(0, 365, step):
        e = min(365, s + step)
        rows.append(np.mean(day_hour[s:e, :], axis=0))
    return np.asarray(rows, dtype=np.float32)


def _decode_types(type_arr: np.ndarray | None, expected_count: int, fallback: str) -> list[str]:
    if type_arr is None:
        return [fallback] * expected_count

    raw = np.asarray(type_arr).reshape(-1)
    out: list[str] = []
    for val in raw[:expected_count]:
        if isinstance(val, (bytes, np.bytes_)):
            text = val.decode("utf-8", errors="ignore").strip().lower()
        else:
            text = str(val).strip().lower()

        if text in TYPE_COLORS:
            out.append(text)
            continue

        # Numeric fallback for compact encodings.
        try:
            num = float(text)
            if np.isfinite(num):
                if num <= 0:
                    out.append("wall")
                else:
                    out.append("window")
                continue
        except ValueError:
            pass

        out.append(fallback)

    if len(out) < expected_count:
        out.extend([fallback] * (expected_count - len(out)))
    return out


def _face_colors_from_binary_t(face_feats: np.ndarray | None, face_count: int) -> list[str] | None:
    """Use binary t from face_feats last column when available.

    Rule requested by user: t == 0 -> yellow(window color).
    """
    if face_feats is None:
        return None

    feats = np.asarray(face_feats, dtype=float)
    if feats.ndim != 2 or feats.shape[1] < 1 or feats.shape[0] < face_count:
        return None

    t_col = np.rint(feats[:face_count, -1]).astype(np.int32)
    if not np.all(np.isin(t_col, [0, 1])):
        return None

    out: list[str] = []
    for t_val in t_col:
        if t_val == 0:
            out.append(TYPE_COLORS["window"])
        else:
            out.append(TYPE_COLORS["wall"])
    return out


def _plot_edges(ax, starts: np.ndarray, ends: np.ndarray, color: str, linewidth: float, linestyle: str, alpha: float) -> None:
    for p0, p1 in zip(starts, ends):
        ax.plot(
            [p0[0], p1[0]],
            [p0[1], p1[1]],
            [p0[2], p1[2]],
            color=color,
            linewidth=linewidth,
            linestyle=linestyle,
            alpha=alpha,
        )


def _infer_space_count(graph_data: dict[str, np.ndarray], sf_edges: np.ndarray | None) -> int:
    valid_spaces = graph_data.get("valid_energy_spaces")
    if valid_spaces is not None:
        size = int(np.asarray(valid_spaces).reshape(-1).size)
        if size > 0:
            return size

    space_feats = _first_existing(graph_data, ["space_feats", "space_c", "space_centers"])
    if space_feats is not None:
        feats = np.asarray(space_feats)
        if feats.ndim >= 1 and feats.shape[0] > 0:
            return int(feats.shape[0])

    if sf_edges is not None:
        edges = np.asarray(sf_edges, dtype=np.int64)
        if edges.ndim == 2 and edges.shape[1] >= 2 and edges.shape[0] > 0:
            c0_max = int(np.max(edges[:, 0]))
            c1_max = int(np.max(edges[:, 1]))
            return max(c0_max, c1_max) + 1

    return 0


def _extract_face_space_pairs(sf_edges: np.ndarray | None, n_faces: int, n_spaces: int) -> list[tuple[int, int]]:
    if sf_edges is None or n_faces <= 0 or n_spaces <= 0:
        return []

    edges = np.asarray(sf_edges, dtype=np.int64)
    if edges.ndim != 2 or edges.shape[1] < 2:
        return []

    # PACK building npz uses [face_idx, space_idx] in sf_edges.
    c0_max = int(np.max(edges[:, 0])) if edges.shape[0] > 0 else -1
    c1_max = int(np.max(edges[:, 1])) if edges.shape[0] > 0 else -1
    face_space_ok = c0_max < n_faces and c1_max < n_spaces
    space_face_ok = c1_max < n_faces and c0_max < n_spaces
    use_face_space = True
    if face_space_ok and not space_face_ok:
        use_face_space = True
    elif space_face_ok and not face_space_ok:
        use_face_space = False

    pairs: list[tuple[int, int]] = []
    for e in edges:
        a, b = int(e[0]), int(e[1])
        if use_face_space:
            f_idx, s_idx = a, b
        else:
            f_idx, s_idx = b, a

        if 0 <= f_idx < n_faces and 0 <= s_idx < n_spaces:
            pairs.append((f_idx, s_idx))

    return pairs


def _infer_space_centers_from_edges_indexed(face_centers: np.ndarray, sf_edges: np.ndarray | None, n_spaces: int) -> np.ndarray:
    if len(face_centers) == 0 or n_spaces <= 0:
        return np.zeros((0, 3), dtype=float)

    pairs = _extract_face_space_pairs(sf_edges, n_faces=len(face_centers), n_spaces=n_spaces)
    if not pairs:
        return np.full((n_spaces, 3), np.nan, dtype=float)

    buckets: list[list[np.ndarray]] = [[] for _ in range(n_spaces)]
    for f_idx, s_idx in pairs:
        buckets[s_idx].append(face_centers[f_idx])

    centers = np.full((n_spaces, 3), np.nan, dtype=float)
    for s_idx, pts in enumerate(buckets):
        if pts:
            centers[s_idx] = np.mean(np.asarray(pts, dtype=float), axis=0)

    return centers


def _resolve_space_layout(
    face_centers: np.ndarray,
    graph_data: dict[str, np.ndarray],
) -> tuple[np.ndarray, list[tuple[int, int]], int, dict[int, int]]:
    sf_edges_raw = _first_existing(graph_data, ["sf_edges", "face_space_edges"])
    sf_edges = np.asarray(sf_edges_raw, dtype=np.int64) if sf_edges_raw is not None else np.zeros((0, 2), dtype=np.int64)

    explicit_space_centers = _as_2d_points(_first_existing(graph_data, ["space_c", "space_centers"]))
    if len(explicit_space_centers) > 0:
        raw_space_count = int(explicit_space_centers.shape[0])
        raw_to_compact = {int(i): int(i) for i in range(raw_space_count)}
        space_centers = explicit_space_centers
    else:
        raw_space_count = _infer_space_count(graph_data, sf_edges)
        raw_space_centers = _infer_space_centers_from_edges_indexed(face_centers, sf_edges, n_spaces=raw_space_count)
        if len(raw_space_centers) == 0:
            return np.zeros((0, 3), dtype=float), [], raw_space_count, {}

        valid_mask = np.isfinite(raw_space_centers).all(axis=1)
        valid_raw_idx = np.where(valid_mask)[0].astype(np.int64)
        raw_to_compact = {int(raw_idx): int(compact_idx) for compact_idx, raw_idx in enumerate(valid_raw_idx.tolist())}
        space_centers = raw_space_centers[valid_mask]

    pairs_raw = _extract_face_space_pairs(sf_edges, n_faces=len(face_centers), n_spaces=raw_space_count)
    pairs_compact = [(f_idx, raw_to_compact[s_idx]) for f_idx, s_idx in pairs_raw if s_idx in raw_to_compact]
    return space_centers, pairs_compact, raw_space_count, raw_to_compact


def _plot_generic_graph(ax, graph_data: dict[str, np.ndarray]) -> bool:
    centers = _first_existing(graph_data, ["c", "center", "centers", "node_c", "node_centers", "face_c"])
    points = _as_2d_points(centers)
    if len(points) == 0:
        return False

    type_arr = _first_existing(graph_data, ["t", "type", "node_t", "node_type", "types"])
    node_types = _decode_types(type_arr, expected_count=len(points), fallback="wall")
    node_colors = [TYPE_COLORS.get(t, "#8ECFC9") for t in node_types]

    ax.scatter(points[:, 0], points[:, 1], points[:, 2], c=node_colors, s=30, edgecolors="k", alpha=0.95)

    edge_arr = _first_existing(graph_data, ["edges", "edge_index", "ff_edges"])
    if edge_arr is not None:
        edges = np.asarray(edge_arr, dtype=np.int64)
        if edges.ndim == 2 and edges.shape[1] >= 2:
            valid = (edges[:, 0] >= 0) & (edges[:, 1] >= 0) & (edges[:, 0] < len(points)) & (edges[:, 1] < len(points))
            valid_edges = edges[valid]
            if len(valid_edges) > 0:
                starts = points[valid_edges[:, 0]]
                ends = points[valid_edges[:, 1]]
                _plot_edges(ax, starts, ends, color="#666666", linewidth=1.0, linestyle="-", alpha=0.45)

    x_lim, y_lim, z_lim = _axis_limits_from_points(points)
    ax.set_xlim(*x_lim)
    ax.set_ylim(*y_lim)
    ax.set_zlim(*z_lim)
    return True


def _plot_pack_graph(ax, graph_data: dict[str, np.ndarray], geometry_npz: Path | None) -> bool:
    face_centers = _as_2d_points(_first_existing(graph_data, ["face_c"]))

    if len(face_centers) == 0 and geometry_npz is not None:
        with np.load(geometry_npz, allow_pickle=True) as g_data:
            if "face_c" in g_data:
                face_centers = _as_2d_points(np.asarray(g_data["face_c"]))

    space_centers, pairs_compact, _, _ = _resolve_space_layout(face_centers, graph_data)

    if len(face_centers) == 0 and len(space_centers) == 0:
        return False

    face_types = _decode_types(_first_existing(graph_data, ["face_t", "face_type", "t", "type"]), len(face_centers), "wall")
    face_colors = [TYPE_COLORS.get(t, "#8ECFC9") for t in face_types]
    binary_colors = _face_colors_from_binary_t(_first_existing(graph_data, ["face_feats"]), len(face_centers))
    if binary_colors is not None:
        face_colors = binary_colors

    if len(face_centers) > 0:
        ax.scatter(face_centers[:, 0], face_centers[:, 1], face_centers[:, 2], c=face_colors, s=28, marker="D", edgecolors="k", alpha=0.9)
    if len(space_centers) > 0:
        ax.scatter(space_centers[:, 0], space_centers[:, 1], space_centers[:, 2], c=TYPE_COLORS["space"], s=70, edgecolors="k", alpha=0.95)

    ff_edges = _first_existing(graph_data, ["ff_edges", "face_edges"])
    if ff_edges is not None and len(face_centers) > 0:
        edges = np.asarray(ff_edges, dtype=np.int64)
        if edges.ndim == 2 and edges.shape[1] >= 2:
            valid = (edges[:, 0] >= 0) & (edges[:, 1] >= 0) & (edges[:, 0] < len(face_centers)) & (edges[:, 1] < len(face_centers))
            edge_ok = edges[valid]
            if len(edge_ok) > 0:
                _plot_edges(
                    ax,
                    face_centers[edge_ok[:, 0]],
                    face_centers[edge_ok[:, 1]],
                    color="#999999",
                    linewidth=1.2,
                    linestyle="-",
                    alpha=0.35,
                )

    if len(face_centers) > 0 and len(space_centers) > 0 and pairs_compact:
        starts = np.asarray([face_centers[f_idx] for f_idx, _ in pairs_compact], dtype=float)
        ends = np.asarray([space_centers[s_idx] for _, s_idx in pairs_compact], dtype=float)
        _plot_edges(
            ax,
            starts,
            ends,
            color="#555555",
            linewidth=1.8,
            linestyle="--",
            alpha=0.9,
        )

    all_points = face_centers if len(space_centers) == 0 else np.vstack([face_centers, space_centers])
    x_lim, y_lim, z_lim = _axis_limits_from_points(all_points)
    ax.set_xlim(*x_lim)
    ax.set_ylim(*y_lim)
    ax.set_zlim(*z_lim)
    return True


def _plot_pack_graph_overlay(
    ax,
    graph_data: dict[str, np.ndarray],
    energy_data: dict[str, np.ndarray],
    geometry_npz: Path | None,
    *,
    elev: float,
    azim: float,
    panel_scale: float,
    day_step: int,
) -> bool:
    face_centers = _as_2d_points(_first_existing(graph_data, ["face_c"]))
    if len(face_centers) == 0 and geometry_npz is not None:
        with np.load(geometry_npz, allow_pickle=True) as g_data:
            if "face_c" in g_data:
                face_centers = _as_2d_points(np.asarray(g_data["face_c"]))

    if len(face_centers) == 0:
        return False

    if "values" not in energy_data:
        return False

    space_centers, pairs_compact, raw_space_count, raw_to_compact = _resolve_space_layout(face_centers, graph_data)
    if len(space_centers) == 0:
        return False

    hourly_zone = _to_hourly_zone(np.asarray(energy_data["values"], dtype=np.float32))
    zone_count = int(hourly_zone.shape[1])
    valid_energy_spaces_raw = graph_data.get("valid_energy_spaces")
    valid_energy_spaces = _try_parse_space_indices(valid_energy_spaces_raw, zone_count=zone_count, space_count=raw_space_count)

    zone_to_space: list[tuple[int, int]] = []
    if valid_energy_spaces is not None and valid_energy_spaces.size >= zone_count:
        for z_idx in range(zone_count):
            raw_s_idx = int(valid_energy_spaces[z_idx])
            compact_s_idx = raw_to_compact.get(raw_s_idx)
            if compact_s_idx is not None:
                zone_to_space.append((z_idx, compact_s_idx))
    else:
        for z_idx in range(min(zone_count, raw_space_count)):
            compact_s_idx = raw_to_compact.get(z_idx)
            if compact_s_idx is not None:
                zone_to_space.append((z_idx, compact_s_idx))

    if not zone_to_space:
        fallback_n = min(zone_count, len(space_centers))
        zone_to_space = [(z_idx, z_idx) for z_idx in range(fallback_n)]

    if not zone_to_space:
        return False

    if hasattr(ax, "computed_zorder"):
        ax.computed_zorder = True

    ax.scatter(face_centers[:, 0], face_centers[:, 1], face_centers[:, 2], c="#A8A8A8", s=8, alpha=0.24, zorder=2)
    ax.scatter(space_centers[:, 0], space_centers[:, 1], space_centers[:, 2], c="#6E6E6E", s=14, alpha=0.5, zorder=3)

    if pairs_compact:
        starts = np.asarray([space_centers[s_idx] for _, s_idx in pairs_compact], dtype=float)
        ends = np.asarray([face_centers[f_idx] for f_idx, _ in pairs_compact], dtype=float)
        _plot_edges(
            ax,
            starts,
            ends,
            color="#777777",
            linewidth=0.8,
            linestyle="--",
            alpha=0.3,
        )

    all_points = np.vstack([face_centers, space_centers])
    x_lim, y_lim, z_lim = _axis_limits_from_points(all_points)
    span = max(x_lim[1] - x_lim[0], y_lim[1] - y_lim[0], z_lim[1] - z_lim[0])
    panel_w = max(span * float(panel_scale) * DEFAULT_PANEL_SIZE_MULTIPLIER, 1e-4)
    panel_h = panel_w * 0.75

    right, up = _isometric_panel_basis(elev=elev, azim=azim)

    for z_idx, s_idx in zone_to_space:
        center = space_centers[s_idx]
        mat = _zone_day_hour_matrix(hourly_zone[:, z_idx], day_step=day_step)

        zmin = float(np.min(mat))
        zmax = float(np.max(mat))
        if zmin < 0.0 < zmax:
            zone_norm = colors.TwoSlopeNorm(vmin=zmin, vcenter=0.0, vmax=zmax)
        elif abs(zmax - zmin) < 1e-12:
            zone_norm = colors.Normalize(vmin=zmin - 1.0, vmax=zmax + 1.0)
        else:
            zone_norm = colors.Normalize(vmin=zmin, vmax=zmax)

        facecolors_rgba = DEFAULT_DIVERGING_CMAP(zone_norm(mat))

        n_rows, n_cols = mat.shape
        u = np.linspace(-0.5, 0.5, n_cols, dtype=float) * panel_w
        v = np.linspace(-0.5, 0.5, n_rows, dtype=float) * panel_h
        uu, vv = np.meshgrid(u, v)

        x = center[0] + right[0] * uu + up[0] * vv
        y = center[1] + right[1] * uu + up[1] * vv
        z = center[2] + right[2] * uu + up[2] * vv

        surf = ax.plot_surface(
            x,
            y,
            z,
            facecolors=facecolors_rgba,
            shade=False,
            linewidth=0.0,
            antialiased=False,
            alpha=DEFAULT_BILLBOARD_ALPHA,
        )
        if hasattr(surf, "set_zsort"):
            surf.set_zsort("average")

        c1 = center - 0.5 * panel_w * right - 0.5 * panel_h * up
        c2 = center + 0.5 * panel_w * right - 0.5 * panel_h * up
        c3 = center + 0.5 * panel_w * right + 0.5 * panel_h * up
        c4 = center - 0.5 * panel_w * right + 0.5 * panel_h * up
        border = np.asarray([c1, c2, c3, c4, c1], dtype=float)
        ax.plot(
            border[:, 0],
            border[:, 1],
            border[:, 2],
            color="#000000",
            linewidth=0.85,
            alpha=1.0,
        )

    ax.set_xlim(*x_lim)
    ax.set_ylim(*y_lim)
    ax.set_zlim(*z_lim)
    return True


def _looks_like_pack_graph(graph_data: dict[str, np.ndarray]) -> bool:
    pack_keys = {"face_c", "sf_edges", "face_space_edges", "space_c", "space_centers", "face_feats", "ff_edges"}
    return any(k in graph_data for k in pack_keys)


def visualize_graph(
    graph_npz: str | Path,
    output_png: str | Path,
    *,
    geometry_npz: str | Path | None = None,
    elev: float = 35.0,
    azim: float = 15.0,
    dpi: int = 300,
) -> Path:
    """Render graph nodes/edges from graph npz using c/t/type conventions when available."""
    graph_npz = Path(graph_npz)
    output_png = Path(output_png)
    geometry_path = Path(geometry_npz) if geometry_npz is not None else None

    with np.load(graph_npz, allow_pickle=True) as data:
        graph_data = {k: np.asarray(data[k]) for k in data.files}

    fig = plt.figure(figsize=(4, 4))
    ax = fig.add_subplot(111, projection="3d")
    ax.view_init(elev=elev, azim=azim)
    fig.subplots_adjust(left=0, right=1, top=1, bottom=0)

    if _looks_like_pack_graph(graph_data):
        ok = _plot_pack_graph(ax, graph_data, geometry_npz=geometry_path)
        if not ok:
            ok = _plot_generic_graph(ax, graph_data)
    else:
        ok = _plot_generic_graph(ax, graph_data)
        if not ok:
            ok = _plot_pack_graph(ax, graph_data, geometry_npz=geometry_path)

    if not ok:
        keys = ", ".join(sorted(graph_data.keys()))
        plt.close(fig)
        raise ValueError(f"Cannot parse graph centers/edges from {graph_npz}; keys=[{keys}]")

    ax.set_box_aspect([1, 1, 1])
    ax.set_axis_off()

    output_png.parent.mkdir(parents=True, exist_ok=True)
    fig.savefig(output_png, dpi=dpi, bbox_inches="tight", pad_inches=0.04, transparent=True)
    plt.close(fig)
    return output_png


def visualize_graph_overlap(
    graph_npz: str | Path,
    energy_npz: str | Path,
    output_png: str | Path,
    *,
    geometry_npz: str | Path | None = None,
    elev: float = 35.0,
    azim: float = 15.0,
    panel_scale: float = DEFAULT_PANEL_SCALE,
    day_step: int = DEFAULT_DAY_STEP,
    dpi: int = 300,
) -> Path:
    """Render PACK graph with per-space energy billboard overlays."""
    graph_npz = Path(graph_npz)
    energy_npz = Path(energy_npz)
    output_png = Path(output_png)
    geometry_path = Path(geometry_npz) if geometry_npz is not None else None

    with np.load(graph_npz, allow_pickle=True) as data:
        graph_data = {k: np.asarray(data[k]) for k in data.files}
    with np.load(energy_npz, allow_pickle=True) as data:
        energy_data = {k: np.asarray(data[k]) for k in data.files}

    fig = plt.figure(figsize=(4, 4))
    ax = fig.add_subplot(111, projection="3d")
    ax.view_init(elev=elev, azim=azim)
    fig.subplots_adjust(left=0, right=1, top=1, bottom=0)

    ok = _plot_pack_graph_overlay(
        ax,
        graph_data,
        energy_data,
        geometry_npz=geometry_path,
        elev=elev,
        azim=azim,
        panel_scale=panel_scale,
        day_step=day_step,
    )
    if not ok:
        ok = _plot_pack_graph(ax, graph_data, geometry_npz=geometry_path)
    if not ok:
        ok = _plot_generic_graph(ax, graph_data)
    if not ok:
        keys = ", ".join(sorted(graph_data.keys()))
        plt.close(fig)
        raise ValueError(f"Cannot parse graph centers/edges from {graph_npz}; keys=[{keys}]")

    ax.set_box_aspect([1, 1, 1])
    ax.set_axis_off()

    output_png.parent.mkdir(parents=True, exist_ok=True)
    fig.savefig(output_png, dpi=dpi, bbox_inches="tight", pad_inches=0.04, transparent=True)
    plt.close(fig)
    return output_png