import pandas as pd

def extract_second_order_features(intraday_features, so_features_names):
    if not intraday_features.empty:
        so_features = pd.DataFrame()
        if "mean" in so_features_names:
            so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).mean().add_suffix("_SO_mean")], axis=1)
        if "median" in so_features_names:
            so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).mean().add_suffix("_SO_median")], axis=1)
        if "sd" in so_features_names:
            so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).std().add_suffix("_SO_sd")], axis=1)
        if "max" in so_features_names:
            so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).max().add_suffix("_SO_max")], axis=1)
        if "min" in so_features_names:
            so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).min().add_suffix("_SO_min")], axis=1)
        
        so_features.reset_index(inplace=True)

    else:
        so_features = pd.DataFrame(columns=["local_segment"])

    return so_features

def get_sample_rate(data):
    try:
        timestamps_diff = data['timestamp'].diff().dropna().mean()
    except:
        raise Exception("Error occured while trying to get the mean sample rate from the data.")

    return int(1000/timestamps_diff)