"""Extract the graph for frame 42 as a plain dict, then reconstruct and visualize it."""

import json
from pathlib import Path

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import networkx as nx
from PIL import Image

from gnn_disassembly_loader import load_frame_data

# ─────────────────────────────────────────────────────────────────────────────
# Step 1: Extract graph as a plain dict
# ─────────────────────────────────────────────────────────────────────────────

episode = Path("session_0408_162129/episode_00")
frame_idx = 42

fd = load_frame_data(episode, frame_idx)
graph_json = fd.graph

# Build graph_dict — a self-contained dict describing this frame's graph
graph_dict = {
    "frame_idx": frame_idx,
    "nodes": [],
    "edges": [],
}

# Nodes: one per product component + robot
for comp in graph_json["components"]:
    cid = comp["id"]
    centroid_key = f"{cid}_centroid"
    depth_valid_key = f"{cid}_depth_valid"
    has_depth = (depth_valid_key in fd.depth_info
                 and int(fd.depth_info[depth_valid_key][0]) == 1)
    graph_dict["nodes"].append({
        "id": cid,
        "type": comp["type"],
        "color": comp["color"],
        "visible": fd.visibility.get(cid, True),
        "centroid_3d": fd.depth_info[centroid_key].tolist() if has_depth else [0, 0, 0],
        "embedding_norm": float(np.linalg.norm(fd.embeddings[cid])) if cid in fd.embeddings else 0.0,
        "mask_area": int(fd.depth_info[f"{cid}_area"][0]) if f"{cid}_area" in fd.depth_info else 0,
    })

# Robot node
if fd.robot is not None:
    graph_dict["nodes"].append({
        "id": "robot",
        "type": "robot",
        "color": "#F5F5F5",
        "visible": True,
        "centroid_3d": fd.robot["centroid"].tolist(),
        "embedding_norm": float(np.linalg.norm(fd.robot["embedding"])),
        "mask_area": int(fd.robot["area"][0]),
    })

# Edges: only physical constraints (the meaningful ones for visualization)
for edge in graph_json["edges"]:
    constraint_key = f"{edge['src']}->{edge['dst']}"
    is_locked = fd.constraints.get(constraint_key, True)
    graph_dict["edges"].append({
        "src": edge["src"],
        "dst": edge["dst"],
        "is_locked": is_locked,
    })

# Save to JSON
dict_path = Path("frame_042_graph.json")
with open(dict_path, "w") as f:
    json.dump(graph_dict, f, indent=2)
print(f"Saved graph dict to {dict_path}")
print(f"  {len(graph_dict['nodes'])} nodes, {len(graph_dict['edges'])} constraint edges")

# ─────────────────────────────────────────────────────────────────────────────
# Step 2: Read the dict back and reconstruct as a networkx graph
# ─────────────────────────────────────────────────────────────────────────────

with open(dict_path) as f:
    gd = json.load(f)

G = nx.DiGraph()

for node in gd["nodes"]:
    G.add_node(node["id"], **node)

for edge in gd["edges"]:
    G.add_edge(edge["src"], edge["dst"], is_locked=edge["is_locked"])

print(f"\nReconstructed graph: {G.number_of_nodes()} nodes, {G.number_of_edges()} directed edges")

# ─────────────────────────────────────────────────────────────────────────────
# Step 3: Visualize — two panels: RGB image + graph overlay
# ─────────────────────────────────────────────────────────────────────────────

# Use node_positions from side_graph.json for layout (if available), else spring layout
stored_pos = graph_json.get("node_positions", {})

# Build positions dict — use stored positions, place missing nodes with spring layout
pos = {}
for nid in G.nodes:
    if nid in stored_pos:
        x, y = stored_pos[nid]
        pos[nid] = (x, -y)  # flip y so it matches visual top-down convention
    elif nid == "robot":
        # Place robot off to the side
        pos[nid] = (450, 0)

# For nodes without stored positions, use spring layout seeded by known positions
missing = [n for n in G.nodes if n not in pos]
if missing:
    sub = nx.spring_layout(G, pos=pos, fixed=list(pos.keys()), seed=42)
    for n in missing:
        pos[n] = sub[n]

fig, axes = plt.subplots(1, 2, figsize=(20, 8))

# Panel 1: RGB image
rgb_path = episode / "side" / "rgb" / f"frame_{frame_idx:06d}.png"
if rgb_path.exists():
    img = np.array(Image.open(rgb_path))
    axes[0].imshow(img)
    axes[0].set_title(f"Frame {frame_idx} — RGB", fontsize=14)
    axes[0].axis("off")
else:
    axes[0].text(0.5, 0.5, "RGB image not found", ha="center", va="center", fontsize=14)
    axes[0].set_title(f"Frame {frame_idx} — RGB", fontsize=14)

# Panel 2: constraint graph
ax = axes[1]

# Separate constraint edges by lock state
locked_edges = [(e["src"], e["dst"]) for e in gd["edges"] if e["is_locked"]]
unlocked_edges = [(e["src"], e["dst"]) for e in gd["edges"] if not e["is_locked"]]

# Node colors and sizes
node_colors = []
node_sizes = []
for nid in G.nodes:
    ndata = G.nodes[nid]
    node_colors.append(ndata["color"])
    if ndata["type"] == "robot":
        node_sizes.append(800)
    elif ndata["type"] == "motherboard":
        node_sizes.append(1200)
    else:
        node_sizes.append(600)

# Draw nodes
nx.draw_networkx_nodes(G, pos, ax=ax, node_color=node_colors,
                       node_size=node_sizes, edgecolors="black", linewidths=1.5)

# Draw locked constraint edges (solid red arrows)
if locked_edges:
    nx.draw_networkx_edges(G, pos, edgelist=locked_edges, ax=ax,
                           edge_color="#E74C3C", width=2.0, alpha=0.8,
                           arrows=True, arrowsize=15, arrowstyle="-|>",
                           connectionstyle="arc3,rad=0.1")

# Draw unlocked constraint edges (dashed green arrows)
if unlocked_edges:
    nx.draw_networkx_edges(G, pos, edgelist=unlocked_edges, ax=ax,
                           edge_color="#2ECC71", width=2.0, alpha=0.8,
                           style="dashed", arrows=True, arrowsize=15,
                           arrowstyle="-|>", connectionstyle="arc3,rad=0.1")

# Labels
labels = {}
for nid in G.nodes:
    ndata = G.nodes[nid]
    vis_marker = "" if ndata["visible"] else " (hidden)"
    labels[nid] = f"{nid}{vis_marker}"

nx.draw_networkx_labels(G, pos, labels, ax=ax, font_size=7, font_weight="bold")

# Legend
legend_handles = [
    mpatches.Patch(color="#E74C3C", label="Constraint (locked)"),
    mpatches.Patch(color="#2ECC71", label="Constraint (unlocked)"),
]
# Add type color legend
type_colors_seen = {}
for node in gd["nodes"]:
    if node["type"] not in type_colors_seen:
        type_colors_seen[node["type"]] = node["color"]
for t, c in type_colors_seen.items():
    legend_handles.append(mpatches.Patch(facecolor=c, edgecolor="black", label=t))

ax.legend(handles=legend_handles, loc="upper left", fontsize=8, framealpha=0.9)
ax.set_title(f"Frame {frame_idx} — Constraint Graph ({len(locked_edges)} locked, "
             f"{len(unlocked_edges)} unlocked)", fontsize=14)
ax.axis("off")

plt.tight_layout()
out_path = Path("frame_042_graph_viz.png")
plt.savefig(out_path, dpi=150, bbox_inches="tight")
print(f"\nSaved visualization to {out_path}")
plt.close()