摘要：import pywtimport matplotlib.pylab as pltplt.rcParams.update({"font.size": 12, "font.weight": 'bold', "lines.linewidth": 1.5})
import pywtimport matplotlib.pylab as pltplt.rcParams.update({"font.size": 12, "font.weight": 'bold', "lines.linewidth": 1.5})# Plot discrete Mayer wavelet Functionphi, psi, x = pywt.Wavelet('dmey').wavefun(level = 5)plt.figure(figsize=(4, 3), dpi=100, layout='constrained')plt.plot(x, psi, '-b', label = 'Wavelet')# plt.plot(x, phi, '-b', label = 'Scaling')plt.xlim([20, 40])#plt.legend(frameon = False, loc = 'upper right')# plt.title('Meyer Wavelet', fontsize=14, fontweight='bold')plt.xticks()plt.yticks()# plt.savefig('wav_dmey.png', dpi=400, bbox_inches='tight', pad_inches=0.02)plt.show
# Coiflet waveletphi, psi, x = pywt.Wavelet('coif2').wavefun(level = 5)plt.figure(figsize=(4, 3), dpi=100, layout='constrained')plt.plot(x, psi, '-b', label = 'Wavelet')# plt.plot(x, phi, '-b', label = 'Scaling')#plt.text(0.005, 1.14, "(c)", fontdict = font)#plt.legend(frameon = False, loc = 'upper right')# plt.title('Coif3: Scaling Function', fontsize=14, fontweight='bold')plt.xlim([0, 10])plt.savefig('wav_coif2.png', dpi=400, bbox_inches='tight', pad_inches=0.02)plt.show
# Daubechies waveletphi, psi, x = pywt.Wavelet('db2').wavefun(level = 5)plt.figure(figsize=(4, 3), dpi=100, layout='constrained')plt.plot(x, psi, '-b', label = 'Wavelet')# plt.plot(x, phi, '-b',label = 'Scaling')#plt.text(0.005, 1.14, "(c)", fontdict = font)#plt.legend(frameon = False, loc = 'upper right')# plt.title('Db6: Scaling Function', fontsize=14, fontweight='bold')plt.xlim([0, 3])plt.savefig('wav_db2.png', dpi=400, bbox_inches='tight', pad_inches=0.02)plt.show
# Symlet waveletphi, psi, x = pywt.Wavelet('sym3').wavefun(level = 5)plt.figure(figsize=(4, 3), dpi=100, layout='constrained')plt.plot(x, psi, '-b', label = 'Wavelet')# plt.plot(x, phi, '-b', label = 'Scaling')#plt.text(0.005, 1.14, "(c)", fontdict = font)#plt.legend(frameon = False, loc = 'upper right')# plt.title('Symlet Wavelet', fontsize=14, fontweight='bold')plt.xlim([0, 5])plt.savefig('wav_sym3.png', dpi=400, bbox_inches='tight', pad_inches=0.02)plt.show
# Biorthogonal wavelet_, psi, _, _, x = pywt.Wavelet('bior1.3').wavefun(level = 5)plt.figure(figsize=(4, 3), dpi=100, layout='constrained')plt.plot(x, psi, '-b', label = 'Wavelet')# plt.plot(x, phi, '-b', label = 'Scaling')#plt.text(0.005, 1.14, "(c)", fontdict = font)#plt.legend(frameon = False, loc = 'upper right')# plt.title('Symlet Wavelet', fontsize=14, fontweight='bold')plt.xlim([0, 5])plt.savefig('wav_bior1.3.png', dpi=400, bbox_inches='tight', pad_inches=0.02)plt.show
# Reverse Biorthogonal wavelet_, psi, _, _, x = pywt.Wavelet('rbio1.3').wavefun(level = 5)plt.figure(figsize=(4, 3), dpi=100, layout='constrained')plt.plot(x, psi, '-b', label = 'Wavelet')# plt.plot(x, phi, '-b', label = 'Scaling')#plt.text(0.005, 1.14, "(c)", fontdict = font)#plt.legend(frameon = False, loc = 'upper right')# plt.title('Symlet Wavelet', fontsize=14, fontweight='bold')plt.xlim([0, 5])plt.savefig('wav_rbior1.3.png', dpi=400, bbox_inches='tight', pad_inches=0.02)plt.show
phid, psid, xd = pywt.Wavelet('db6').wavefun(level = 5) # Daubechies waveletphic, psic, xc = pywt.Wavelet('coif3').wavefun(level = 5) # Coiflet waveletphis, psis, xs = pywt.Wavelet('sym10').wavefun(level = 5) # Symlet waveletphim, psim, xm = pywt.Wavelet('dmey').wavefun(level = 5) # Meyer waveletfig, ax = plt.subplots(2, 2)ax[0, 0].plot(xd, psid, '-b')ax[0, 1].plot(xc, psic, '-b')ax[1, 0].plot(xs, psis, '-b')ax[1, 1].plot(xm, psim, '-b')plt.show
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https://www.zhihu.com/consult/people/792359672131756032?isMe=1担任《Mechanical System and Signal Processing》《中国电机工程学报》等期刊审稿专家，擅长领域：信号滤波/降噪，机器学习/深度学习，时间序列预分析/预测，设备故障诊断/缺陷检测/异常检测。
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