SciPy：检测时间序列数据的频率

Question

I have a file containing several hundred thousand (time, value) points and I would like to estimate the frequency of the waveform from the data points. I have been looking at using SciPy's find_peaks function to select a single point for each peak and average the time between peaks to find the frequency. However, the find_peaks function is selecting many peaks instead of just one as seen below.

Close up of a single peak:

I'm trying to use the prominence argument to filter out the noise, but I can't understand why it's not working. Here is the relevant code:

    peak_idxs = list(
        sig.find_peaks(
            data,
            prominence=0.15,
            width=(0, 0.05 * sample_freq),
        )[0]
    )
    troph_idxs = list(
        sig.find_peaks(
            np.negative(data),
            prominence=0.15,
            width=(0, 0.05 * sample_freq),
        )[0]
    )
    peak_criticals = [timestamps[i] for i in peak_idxs]
    troph_criticals = [timestamps[i] for i in troph_idxs]
    t_criticals_freqs = [
        1 / (b - a) for a, b in zip(peak_criticals[0:], peak_criticals[1:])
    ] + [1 / (b - a) for a, b in zip(troph_criticals[0:], troph_criticals[1:])]

    freq = statistics.mean(t_criticals_freqs)

    ax.plot(timestamps, data)
    ax.plot(
        [timestamps[i] for i in peak_idxs + troph_idxs],
        [data[i] for i in peak_idxs + troph_idxs],
        "r*",
    )

Answer 1

It will be much more robust and easier to compute the spectrogram:

from scipy.signal import welch

frequencies, power = welch(my_signal, my_signals_sampling_rate)
dominant_frequency = frequencies[np.argmax(power)]

The sampling rate is 1/dt where dt is the time in seconds between consecutive samples.