def METHOD_NAME(epoch, output={}):
    # Sanitize input
    if "RSP_Symmetry_PeakTrough" not in epoch:
        warn(
            "Input does not have an `RSP_Symmetry_PeakTrough` column."
            + " Will skip all symmetry-related features.",
            category=NeuroKitWarning,
        )
        return output
    # Get baseline
    zero = find_closest(0, epoch.index.values, return_index=True)  # Find index closest to 0
    baseline1 = epoch["RSP_Symmetry_PeakTrough"].iloc[zero]
    signal1 = epoch["RSP_Symmetry_PeakTrough"].values[zero + 1 : :]
    baseline2 = epoch["RSP_Symmetry_RiseDecay"].iloc[zero]
    signal2 = epoch["RSP_Symmetry_RiseDecay"].values[zero + 1 : :]
    # Mean
    output["RSP_Symmetry_PeakTrough_Baseline"] = baseline1
    output["RSP_Symmetry_RiseDecay_Baseline"] = baseline2
    output["RSP_Symmetry_PeakTrough_Mean"] = np.mean(signal1) - baseline1
    output["RSP_Symmetry_RiseDecay_Mean"] = np.mean(signal2) - baseline2
    return output