remove useless amperage reported by EnergyTrace backend

    m_duration_us = data[-1, 0] - data[0, 0]

    m_energy_nj = data[-1, 3] - data[0, 3]

    # mV * nA * us = aJ (1e-18 J) -> use factor 1e-6 to get pJ (1e-12 J)

    m_calc_energy_pj = np.sum(data[1:, 1] * data[1:, 2] * 1e-6 * (data[1:, 0] - data[:-1, 0]))

    m_energy_nj_deviation = np.abs(m_energy_nj - m_calc_energy_pj * 1e-3) / np.max([m_energy_nj, m_calc_energy_pj * 1e-3])

    print('{:d} measurements in {:.2f} s = {:.0f} Hz sample rate'.format(

        data_count, m_duration_us * 1e-6, data_count / (m_duration_us * 1e-6)))

    print('Reported energy: E = {:f} J'.format(m_energy_nj * 1e-9))

    print('Calculated energy: U*I*t = {:f} J'.format(m_calc_energy_pj * 1e-12))

    print('Energy deviation: {:.1f}%'.format(m_energy_nj_deviation * 100))

    # nA * mV = pW

    power = data[:, 1] * data[:, 2] * 1e-12

    # nJ / us = mW -> (nJ * 1e-9) / (us * 1e-6) = W

    # Do not use power = data[:, 1] * data[:, 2] * 1e-12 here: nA values provided by the EnergyTrace library in data[:, 1] are heavily filtered and mostly

    # useless for visualization and calculation. They often do not agree with the nJ values in data[:, 3].

    power = ((data[1:, 3] - data[:-1, 3]) * 1e-9) / ((data[1:, 0] - data[:-1, 0]) * 1e-6)

    if 'threshold-peakcount' in opt:

        bs_mean = np.mean(power)

        print('Average per-peak energy (delta over baseline): {:.0f} µJ         : {:.9f}'.format(

            delta_energy * 1e6 / len(peaks), delta_energy / len(peaks)))

    power_from_current = data[:, 1] * data[:, 2] * 1e-12

    power_from_energy = ((data[1:, 3] - data[:-1, 3]) * 1e-9) / ((data[1:, 0] - data[:-1, 0]) * 1e-6)

    mean_power = running_mean(power_from_energy, 10)

    if 'plot' in opt:

        # nA * mV = pW

        pwrhandle, = plt.plot(data[:, 0] * 1e-6, power_from_current, 'b-', label='P=U*I', markersize=1)

        energyhandle, = plt.plot(data[1:, 0] * 1e-6, power_from_energy, 'r-', label='P=ΔE/Δt', markersize=1)

        energyhandle2, = plt.plot(data[1:, 0] * 1e-6, mean_power, 'y-', label='E/Δt', markersize=1)

        plt.legend(handles=[pwrhandle, energyhandle])

        energyhandle, = plt.plot(data[1:, 0] * 1e-6, power_from_energy, 'b-', label='P=ΔE/Δt', markersize=1)

        meanhandle, = plt.plot(data[1:, 0] * 1e-6, mean_power, 'r-', label='mean(P, 10)', markersize=1)

        plt.legend(handles=[energyhandle, meanhandle])

        plt.xlabel('Time [s]')

        plt.ylabel('Power [W]')

        plt.grid(True)