"""
Optimize orbit data by downsampling excessively detailed orbits
灶神星(Vesta)的轨道数据被过度采样了（31,825个点），降采样到合理的数量
"""
import asyncio
from sqlalchemy import text
from app.database import engine


async def optimize_vesta_orbit():
    """Downsample Vesta orbit from 31,825 points to ~1,326 points (every 24th point)"""
    async with engine.begin() as conn:
        # Get current Vesta orbit data
        result = await conn.execute(text("""
            SELECT points, num_points
            FROM orbits
            WHERE body_id = '2000004'
        """))
        row = result.fetchone()

        if not row:
            print("❌ Vesta orbit not found")
            return

        points = row[0]  # JSONB array
        current_count = row[1]

        print(f"当前Vesta轨道点数: {current_count}")
        print(f"实际数组长度: {len(points)}")

        # Downsample: take every 24th point (0.04 days * 24 ≈ 1 day per point)
        downsampled = points[::24]
        new_count = len(downsampled)

        print(f"降采样后点数: {new_count}")
        print(f"数据大小减少: {current_count - new_count} 点")
        print(f"降采样比例: {current_count / new_count:.1f}x")

        # Calculate size reduction
        import json
        old_size = len(json.dumps(points))
        new_size = len(json.dumps(downsampled))
        print(f"JSON大小: {old_size:,} -> {new_size:,} bytes ({old_size/new_size:.1f}x)")

        # Update database
        await conn.execute(text("""
            UPDATE orbits
            SET points = :points, num_points = :num_points
            WHERE body_id = '2000004'
        """), {"points": json.dumps(downsampled), "num_points": new_count})

        print("✅ Vesta轨道数据已优化")


if __name__ == "__main__":
    asyncio.run(optimize_vesta_orbit())