from __future__ import annotations

import json
from pathlib import Path

import numpy as np
from sklearn.model_selection import train_test_split

PROJECT_DIR = Path(__file__).resolve().parent
DATA_DIR = PROJECT_DIR / "data"


def build_graph(nodes: int = 600, seed: int = 2033) -> dict[str, np.ndarray]:
    rng = np.random.default_rng(seed)
    subnets = np.repeat(np.arange(6), nodes // 6)
    adjacency = np.zeros((nodes, nodes), dtype=np.float32)
    for left in range(nodes):
        same_subnet = subnets == subnets[left]
        probabilities = np.where(same_subnet, 0.045, 0.0025)
        links = rng.random(nodes) < probabilities
        links[: left + 1] = False
        adjacency[left, links] = 1
    adjacency = np.maximum(adjacency, adjacency.T)

    compromised = np.zeros(nodes, dtype=bool)
    seeds = rng.choice(nodes, size=14, replace=False)
    compromised[seeds] = True
    for _ in range(4):
        exposure = adjacency @ compromised.astype(np.float32)
        infection_probability = 1 - np.exp(-0.22 * exposure)
        new_infections = (rng.random(nodes) < infection_probability) & ~compromised
        compromised |= new_infections

    base = rng.normal(0, 1, (nodes, 8)).astype(np.float32)
    labels = compromised.astype(np.int64)
    signal = labels[:, None].astype(np.float32)
    features = base.copy()
    features[:, 0:1] += signal * rng.normal(1.0, 0.5, (nodes, 1))
    features[:, 1:2] += signal * rng.normal(0.8, 0.6, (nodes, 1))
    features[:, 2:3] += signal * rng.normal(0.7, 0.6, (nodes, 1))
    features[:, 3:4] += signal * rng.normal(0.5, 0.7, (nodes, 1))
    features[:, 4] += subnets * 0.12

    indices = np.arange(nodes)
    train, remainder = train_test_split(
        indices,
        test_size=0.40,
        stratify=labels,
        random_state=seed,
    )
    validation, test = train_test_split(
        remainder,
        test_size=0.50,
        stratify=labels[remainder],
        random_state=seed,
    )
    return {
        "features": features,
        "adjacency": adjacency,
        "labels": labels,
        "subnets": subnets.astype(np.int64),
        "train_indices": train,
        "validation_indices": validation,
        "test_indices": test,
        "seed_nodes": seeds.astype(np.int64),
    }


def main() -> None:
    DATA_DIR.mkdir(parents=True, exist_ok=True)
    graph = build_graph()
    np.savez_compressed(DATA_DIR / "meshgraph.npz", **graph)
    manifest = {
        "nodes": len(graph["labels"]),
        "edges": int(graph["adjacency"].sum() // 2),
        "features": graph["features"].shape[1],
        "subnets": len(np.unique(graph["subnets"])),
        "compromised_rate": float(graph["labels"].mean()),
        "train_nodes": len(graph["train_indices"]),
        "validation_nodes": len(graph["validation_indices"]),
        "test_nodes": len(graph["test_indices"]),
        "path": "meshgraph.npz",
    }
    (DATA_DIR / "manifest.json").write_text(
        json.dumps(manifest, indent=2),
        encoding="utf-8",
    )
    print(json.dumps(manifest, indent=2))


if __name__ == "__main__":
    main()