SciPy spectrogram（计算频谱图） _spectrogram

信号处理工具箱由很少的滤波功能和一组有限的滤波器设计工具组成。它还包含一些针对一维和二维数据的B样条插值算法。
scipy.signal.spectrogram使用连续的傅立叶变换来计算频谱图。频谱图可以用作反映非信号信号的频率内容随时间变化的一种方式。

from scipy import signal import matplotlib.pyplot as pltimport numpy as np#Generate a test signal, a 2 Vrms sine wave whose frequency linearly changes with time from 1kHz to 2kHz, corrupted by 0.001 V**2/Hz of white noise sampled at 10 kHz.fs = 10e3 # Sampling FrequencyN = 1e5amp = 2 * np.sqrt(2)noise_power = 0.001 * fs / 2time = np.arange(N) / fsfreq = np.linspace(1e3, 2e3, N) x = amp * np.sin(2*np.pi*freq*time)x += np.random.normal(scale=np.sqrt(noise_power), size=time.shape)# Compute and plot the spectrogram.f, t, Sxx = signal.spectrogram(x, fs)plt.pcolormesh(t, f, Sxx)plt.ylabel('Frequency [Hz]')plt.xlabel('Time [sec]')plt.show()

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