import pytest
import pandas as pd
from src.features import engineer_features

@pytest.fixture
def sample_data():
    data = {
        "timestamp": pd.date_range(start="2025-01-01", periods=10, freq="1min"),
        "node": ["agave1"] * 10,
        "cpu_usage": [45, 46, 47, 48, 49, 50, 51, 52, 53, 54],
        "rpc_error_rate": [0.1] * 10,
        "rpc_latency_ms": [60] * 10,
        "failure_imminent": [0]  *10
    }
    return pd.DataFrame(data)

def test_engineer_features(sample_data):
    processed_df = engineer_features(sample_data)
    assert "cpu_trend" in processed_df.columns
    assert processed_df["cpu_trend"].iloc[1] == 1  # 46 - 45
    assert "cpu_rolling_mean" in processed_df.columns
    assert len(processed_df) == 10
    assert processed_df["cpu_rolling_mean"].iloc[4] == pytest.approx(47.0)  # Mean of first 5 entries

def test_engineer_features_empty():
    df = pd.DataFrame()
    with pytest.raises(KeyError):
        engineer_features(df)