"""
Layout Engine - Efficient graph layout precomputation for large graphs.

Uses igraph (C-based) for maximum performance, with networkx fallback.
Supports multiple layout algorithms optimized for different graph sizes and structures.
"""

import numpy as np
import time
import logging
from collections import defaultdict

logger = logging.getLogger(__name__)

try:
    import igraph as ig
    HAS_IGRAPH = True
except ImportError:
    HAS_IGRAPH = False
    logger.warning("igraph not available, falling back to networkx layouts")

import networkx as nx


def compute_layout(nodes_dict, edges, algorithm='auto', spacing=1.0, iterations=300):
    """
    Precompute node positions using efficient graph layout algorithms.
    
    Strategy based on graph size:
    - Small (<300 nodes): High-quality force-directed with many iterations
    - Medium (300-3000): Force-directed with optimized parameters
    - Large (3000-20000): Community-based layout (Louvain + per-community layout)
    - Very large (>20000): DrL or spectral layout
    
    Args:
        spacing: Multiplier for target_range (1.0 = default, >1 = more spread)
        iterations: Number of layout iterations (higher = better quality, slower)
    
    Returns dict of {node_id: {'x': float, 'y': float}}
    """
    if not nodes_dict:
        return {}

    start_time = time.time()
    node_ids = list(nodes_dict.keys())
    n = len(node_ids)

    if n == 0:
        return {}

    if n == 1:
        return {node_ids[0]: {'x': 0.0, 'y': 0.0}}

    # Build adjacency
    node_index = {nid: i for i, nid in enumerate(node_ids)}
    edge_list = []
    for edge in edges:
        src = edge.get('source')
        tgt = edge.get('target')
        if src in node_index and tgt in node_index:
            si, ti = node_index[src], node_index[tgt]
            if si != ti:  # skip self-loops for layout
                edge_list.append((si, ti))

    # Choose algorithm
    if algorithm == 'auto':
        if n > 20000:
            algorithm = 'drl' if HAS_IGRAPH else 'spectral'
        elif n > 3000:
            algorithm = 'community'
        elif n > 300:
            algorithm = 'force_directed'
        else:
            algorithm = 'force_directed_hq'

    logger.info(f"Computing layout for {n} nodes, {len(edge_list)} edges using '{algorithm}' (spacing={spacing}, iterations={iterations})")

    if HAS_IGRAPH and algorithm != 'spectral':
        positions = _layout_igraph(node_ids, edge_list, n, algorithm, iterations)
    else:
        positions = _layout_networkx(node_ids, edge_list, n, algorithm, iterations)

    # Apply spacing multiplier by re-normalizing with scaled range
    target_range = 2000 * spacing
    elapsed = time.time() - start_time
    logger.info(f"Layout computed in {elapsed:.2f}s")

    # Re-normalize if spacing != 1.0
    if abs(spacing - 1.0) > 0.01:
        coords_list = [[positions[nid]['x'], positions[nid]['y']] for nid in node_ids]
        coords = np.array(coords_list)
        return _normalize_and_scale(node_ids, coords, target_range=target_range)

    return positions


def _layout_igraph(node_ids, edge_list, n, algorithm, iterations=300):
    """Use igraph's C-based layout algorithms for maximum speed."""
    g = ig.Graph(n=n, edges=edge_list, directed=False)

    iters = max(50, int(iterations))

    if algorithm == 'drl':
        # DrL: Distributed Recursive Layout - excellent for very large graphs
        layout = g.layout_drl()
    elif algorithm == 'community':
        # Community-based: detect communities, then layout
        layout = _community_layout_igraph(g, iterations=iters)
    elif algorithm == 'force_directed_hq':
        # High quality Fruchterman-Reingold with more iterations
        layout = g.layout_fruchterman_reingold(niter=max(iters, 500))
    elif algorithm == 'fruchterman_reingold':
        layout = g.layout_fruchterman_reingold(niter=iters)
    elif algorithm == 'kamada_kawai':
        if n < 1000:
            layout = g.layout_kamada_kawai()
        else:
            layout = g.layout_fruchterman_reingold(niter=iters)
    elif algorithm == 'circle':
        layout = g.layout_circle()
    else:
        # Default: Fruchterman-Reingold
        layout = g.layout_fruchterman_reingold(niter=iters)

    coords = np.array(layout.coords)
    return _normalize_and_scale(node_ids, coords)


def _community_layout_igraph(g, iterations=200):
    """
    Community-based layout: detect communities with Louvain,
    arrange communities in a circle, then layout nodes within each community.
    """
    try:
        communities = g.community_multilevel()
        membership = communities.membership
    except Exception:
        return g.layout_fruchterman_reingold(niter=200)

    n = g.vcount()
    positions = np.zeros((n, 2))

    # Group nodes by community
    comm_nodes = defaultdict(list)
    for i, comm_id in enumerate(membership):
        comm_nodes[comm_id].append(i)

    num_communities = len(comm_nodes)

    # Arrange communities in a circle
    comm_positions = {}
    radius = max(500, num_communities * 50)
    for idx, comm_id in enumerate(sorted(comm_nodes.keys())):
        angle = 2 * np.pi * idx / max(num_communities, 1)
        comm_positions[comm_id] = (radius * np.cos(angle), radius * np.sin(angle))

    # Layout nodes within each community
    for comm_id, node_indices in comm_nodes.items():
        cx, cy = comm_positions[comm_id]

        if len(node_indices) == 1:
            positions[node_indices[0]] = [cx, cy]
            continue

        # Create subgraph
        subgraph = g.subgraph(node_indices)

        # Use FR layout for the subgraph
        sub_layout = subgraph.layout_fruchterman_reingold(niter=iterations)
        sub_coords = np.array(sub_layout.coords)

        # Scale based on community size
        scale = max(30, np.sqrt(len(node_indices)) * 15)
        if sub_coords.std() > 0:
            sub_coords = (sub_coords - sub_coords.mean(axis=0)) / max(sub_coords.std(), 1e-6) * scale

        # Offset to community position
        for local_idx, global_idx in enumerate(node_indices):
            positions[global_idx] = [cx + sub_coords[local_idx][0], cy + sub_coords[local_idx][1]]

    return ig.Layout(positions.tolist())


def _layout_networkx(node_ids, edge_list, n, algorithm, iterations=300):
    """Fallback to networkx layouts."""
    G = nx.Graph()
    G.add_nodes_from(range(n))
    G.add_edges_from(edge_list)

    iters = max(50, int(iterations))

    if algorithm == 'spectral':
        try:
            pos = nx.spectral_layout(G, dim=2, scale=1000)
        except Exception:
            pos = nx.spring_layout(G, seed=42, scale=1000, iterations=iters)
    elif algorithm in ('force_directed_hq', 'force_directed'):
        k = 2.0 / np.sqrt(max(n, 1))
        pos = nx.spring_layout(G, k=k, iterations=iters, scale=1000, seed=42)
    elif algorithm == 'community':
        pos = _community_layout_networkx(G, n)
    elif algorithm == 'kamada_kawai':
        if n < 500:
            pos = nx.kamada_kawai_layout(G, scale=1000)
        else:
            pos = nx.spring_layout(G, seed=42, scale=1000, iterations=iters)
    elif algorithm == 'circle':
        pos = nx.circular_layout(G, scale=1000)
    else:
        k = 2.0 / np.sqrt(max(n, 1))
        pos = nx.spring_layout(G, k=k, iterations=iters, scale=1000, seed=42)

    coords = np.array([pos[i] for i in range(n)])
    return _normalize_and_scale(node_ids, coords)


def _community_layout_networkx(G, n):
    """Community-based layout using networkx."""
    try:
        from networkx.algorithms.community import greedy_modularity_communities
        communities = list(greedy_modularity_communities(G))
    except Exception:
        return nx.spring_layout(G, seed=42, scale=1000, iterations=200)

    positions = {}
    num_communities = len(communities)
    radius = max(500, num_communities * 50)

    for idx, comm in enumerate(communities):
        angle = 2 * np.pi * idx / max(num_communities, 1)
        cx = radius * np.cos(angle)
        cy = radius * np.sin(angle)

        comm_nodes = list(comm)
        if len(comm_nodes) == 1:
            positions[comm_nodes[0]] = np.array([cx, cy])
            continue

        subG = G.subgraph(comm_nodes)
        sub_pos = nx.spring_layout(subG, seed=42, scale=max(20, np.sqrt(len(comm_nodes)) * 15), iterations=200)

        for node, pos in sub_pos.items():
            positions[node] = pos + np.array([cx, cy])

    return positions


def _normalize_and_scale(node_ids, coords, target_range=2000):
    """Normalize coordinates to a centered range for consistent rendering."""
    if len(coords) == 0:
        return {}

    # Center
    center = coords.mean(axis=0)
    coords = coords - center

    # Scale to target range
    max_extent = max(np.abs(coords).max(), 1e-6)
    coords = coords / max_extent * (target_range / 2)

    # Add slight jitter to prevent perfect overlaps
    jitter = np.random.RandomState(42).uniform(-1, 1, coords.shape) * 2
    coords += jitter

    positions = {}
    for i, node_id in enumerate(node_ids):
        positions[node_id] = {'x': float(coords[i][0]), 'y': float(coords[i][1])}

    return positions


def get_available_algorithms():
    """Return list of available layout algorithms."""
    algos = [
        {'id': 'auto', 'name': 'Auto (best for size)', 'description': 'Automatically selects based on graph size'},
        {'id': 'force_directed', 'name': 'Force-Directed', 'description': 'Classic spring-electric layout'},
        {'id': 'force_directed_hq', 'name': 'Force-Directed (HQ)', 'description': 'Higher quality, more iterations'},
        {'id': 'community', 'name': 'Community-Based', 'description': 'Groups by community detection'},
        {'id': 'circle', 'name': 'Circular', 'description': 'Nodes arranged in a circle'},
    ]

    if HAS_IGRAPH:
        algos.extend([
            {'id': 'drl', 'name': 'DrL (Large Graphs)', 'description': 'Distributed Recursive Layout for 10k+ nodes'},
            {'id': 'kamada_kawai', 'name': 'Kamada-Kawai', 'description': 'Energy-based layout (small graphs)'},
        ])
    else:
        algos.append(
            {'id': 'spectral', 'name': 'Spectral', 'description': 'Eigenvalue-based layout (fast for large graphs)'},
        )

    return algos