Scipy 教程-SciPy 频谱图

信号处理工具箱包括一些滤波函数、一套有限的滤波器设计工具。它还包含用于一维和二维数据的少量B样条插值算法。

from scipy import signal      import matplotlib.pyplot as plt  import 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 Frequency  N = 1e5  amp = 2 * np.sqrt(2)  noise_power = 0.001 * fs / 2  time = np.arange(N) / fs  freq = 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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scipy.signal.spectogram() 返回以下数组：

f: ndarray

样本频率数组。

t: ndarray

段时间数组。

Sxx: ndarray

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