from __future__ import annotations

from datetime import datetime

import numpy as np

from app.models.mongodb.telemetry import TelemetryData


async def analyze_trend(
    device_id: str, start: datetime, end: datetime
) -> dict:
    """Analyze telemetry data trends using linear regression."""
    docs = await (
        TelemetryData.find(
            TelemetryData.device_id == device_id,
            TelemetryData.timestamp >= start,
            TelemetryData.timestamp <= end,
        )
        .sort("+timestamp")
        .to_list()
    )

    if len(docs) < 2:
        return {"status": "insufficient_data", "count": len(docs)}

    timestamps = np.array([d.timestamp.timestamp() for d in docs])
    values = np.array([d.metrics.get("value", 0) for d in docs], dtype=float)

    # Normalize timestamps
    t_norm = timestamps - timestamps[0]

    # Linear regression
    coeffs = np.polyfit(t_norm, values, 1)
    slope = float(coeffs[0])

    return {
        "count": len(docs),
        "mean": float(np.mean(values)),
        "std": float(np.std(values)),
        "min": float(np.min(values)),
        "max": float(np.max(values)),
        "slope": slope,
        "trend": "increasing" if slope > 0.001 else "decreasing" if slope < -0.001 else "stable",
    }