将小波应用于信号,计算信号与Morlet小波的卷积。 defwavelet_transform(signal,f,t):""" 对信号应用Morlet小波变换 signal: 输入信号 f: 中心频率 t: 时间向量 返回值: 小波变换结果 """wavelet=morlet_wavelet(f,t)returnnp.convolve(signal,wavelet,mode='same')# 进行卷积运算# 应用小波变换transformed_signal...
importnumpyasnpimportmatplotlib.pyplotaspltdefmorlet_wavelet(t,sigma,omega_0):# 计算复Morlet小波return(1/np.sqrt(sigma*np.sqrt(np.pi)))*np.exp(-0.5*(t**2)/(sigma**2))*np.exp(1j*omega_0*t)# 定义参数sigma=1.0omega_0=5.0t=np.linspace(-5,5,1000)# 生成小波wavelet=morlet_wavelet(t...
是一种在连续小波变换(Continuous Wavelet Transform,CWT)中常用的小波函数。CWT是一种信号处理技术,用于在时间和频率域上分析非平稳信号。 Morlet小波是一种复数小...
是一种在连续小波变换(Continuous Wavelet Transform,CWT)中常用的小波函数。CWT是一种信号处理技术,用于在时间和频率域上分析非平稳信号。 Morlet小波是一种复数小波函数,由高斯窗口和复指数函数组成。它的数学表达式为: ψ(t) = π^(-1/4) * exp(j * 2π * f0 * t) * exp(-t^2 / 2) ...
我们将用Python代码实现这个公式: defmorlet_wavelet(t,f0,sigma):""" 生成复Morlet小波 :param t: 时间数组 :param f0: 中心频率 :param sigma: 标准差 :return: 复Morlet小波 """wavelet=np.exp(2j*np.pi*f0*t)*np.exp(-t**2/(2*sigma**2))returnwavelet ...
The wavelet method by Continuous Wavelet Transform (CWT) is able to clearly and simultaneously of amplitudes and frequency-energy from component between the seismogram which seismic sensor broadband recorded in the January 16, 2017 in Medan, North Sumatra. Finally, from machine learning python with ...
Python实现Morlet小波分析 我们可以使用Python中的PyWavelets库来实现Morlet小波分析。下面是一个简单的示例,显示如何使用该库进行小波变换。 首先,确保安装了所需库: AI检测代码解析 pipinstallnumpy matplotlib pywt 1. 示例代码 以下是一个简单的Python示例,演示如何生成一个信号,进行Morlet小波变换并绘制结果: ...
I don't think there is a bug in the transform as I see the same kind of spectrum when comparing to Matlab'scwt. To get a result in Matlab that matches the defaultcmorin PyWavelets, one has to set 'cmor1.0-0.5'. I think we should modify the PyWaveletsContinuousWaveletobject code ...
- **cmw (np.ndarray)**: Complex Morlet wavelet. - **cmwFFT (np.ndarray)**:Fourier transformof the wavelet. - **ssFFT (np.ndarray)**: Fourier transform of the input signal. - **nconv (int)**: Convolution length (signal + wavelet - 1). ...
plt.title('Morlet Wavelet filtered signal') plt.plot(tv,convres,'b') plt.xlabel('Time') plt.show() # frequency domain plt.figure() plt.title('Morlet Wavelet filtered signal spectrum') convresX = np.abs(scipy.fftpack.fft(convres))**2 plt.plot(hz,convresX[:len(hz)]) plt.xlim([0...