"""
scripts/seed_database.py
─────────────────────────
Seed the database with test data (PPG signals + BP predictions).

Usage:
    python scripts/seed_database.py
    python scripts/seed_database.py --users 3 --signals-per-user 5

Useful for:
  • Getting started quickly with a populated database
  • Manual testing of the frontend without sending real PPG signals
  • Demo purposes
"""
from __future__ import annotations

import argparse
import asyncio
import random
import uuid
from datetime import datetime, timedelta, timezone

from src.infrastructure.database.connection import create_all_tables, dispose_engine, get_session_factory
from src.infrastructure.database.repositories.ppg_repository import SQLAlchemyPPGRepository
from src.infrastructure.database.repositories.prediction_repository import SQLAlchemyPredictionRepository
from src.infrastructure.model.mock_model_service import MockModelService
from src.infrastructure.processing.scipy_signal_processor import ScipySignalProcessor
from src.domain.entities.ppg_signal import PPGSignal
from src.shared.logger import get_logger

logger = get_logger(__name__)


def _random_ppg(sampling_rate: float = 125.0, duration: float = 10.0) -> list[float]:
    """Generate random-ish PPG values."""
    import math
    n = int(sampling_rate * duration)
    hr = random.uniform(60, 90) / 60.0  # heart rate in Hz
    return [
        math.sin(2 * math.pi * hr * i / sampling_rate) + random.gauss(0, 0.05)
        for i in range(n)
    ]


async def seed(num_users: int, signals_per_user: int) -> None:
    logger.info("Creating DB tables…")
    await create_all_tables()

    session_factory = get_session_factory()
    processor = ScipySignalProcessor()
    model_service = MockModelService()
    await model_service.load_model()

    total_signals = 0
    total_predictions = 0

    async with session_factory() as session:
        ppg_repo = SQLAlchemyPPGRepository(session)
        prediction_repo = SQLAlchemyPredictionRepository(session)

        for u in range(1, num_users + 1):
            user_id = f"seed-user-{u:03d}"
            device_id = f"seed-device-{u:03d}"

            for s in range(1, signals_per_user + 1):
                # Spread timestamps over the last 30 days
                days_ago = random.uniform(0, 30)
                ts = datetime.now(timezone.utc) - timedelta(days=days_ago)

                ppg_signal = PPGSignal(
                    id=str(uuid.uuid4()),
                    device_id=device_id,
                    user_id=user_id,
                    sampling_rate=125.0,
                    ppg_values=_random_ppg(),
                    duration_seconds=10.0,
                    timestamp=ts,
                )

                stored = await ppg_repo.add(ppg_signal)
                total_signals += 1

                # Generate a prediction for this signal
                try:
                    segments = processor.process(stored.ppg_values, stored.sampling_rate)
                    if segments.shape[0] > 0:
                        prediction = await model_service.predict(stored.id, segments)
                        await prediction_repo.add(prediction)
                        total_predictions += 1
                except Exception as exc:
                    logger.warning("Could not generate prediction for %s: %s", stored.id, exc)

        await session.commit()

    logger.info(
        "Seeding complete: %d users, %d signals, %d predictions.",
        num_users,
        total_signals,
        total_predictions,
    )
    await dispose_engine()


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Seed BP Monitoring Database")
    parser.add_argument("--users", type=int, default=3, help="Number of test users")
    parser.add_argument("--signals-per-user", type=int, default=5, help="Signals per user")
    args = parser.parse_args()
    asyncio.run(seed(args.users, args.signals_per_user))