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发表于 2013-2-16 00:07
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本帖最后由 Rainyboy 于 2013-2-16 00:15 编辑
自己按照那个网址的代码和说明玩了一下。顺便把代码写了个注释。
======原始信号的噪声=====
======原始信号的小波系数=====
======噪声信号的小波系数=====
======去噪之后的小波系数=====
======最终结果=====
======所有代码=====
- # -*- coding: cp936 -*-
- import pywt
- import matplotlib.pyplot as plt
- import numpy as np
- from scipy import stats
- from statsmodels.robust import stand_mad
- wavtag = 'db8'
- #===============================================================================
- # 图1：绘出Haar小波母函数
- #===============================================================================
- #这里不是“函数调用”，二是“对象声明和创建”
- #创建了一个pywt.Wavelet类，用以描述小波母函数的各种性质
- w = pywt.Wavelet('Haar')
- #调用Wavefun()成员函数，返回：
- #phi - scaling function 尺度函数
- #psi - wavelet function 母函数
- phi,psi,x = w.wavefun(level=10)
- #注意，此处采用“面对对象”的方式使用matplotlib
- #而不是“状态机”的方式
- fig = plt.figure()
- ax = fig.add_subplot(111)
- ax.set_xlim(-0.02,1.02)
- ax.plot(x,psi)
- ax.grid(True)
- plt.show()
- #===============================================================================
- # 图2：Debauchies小波的尺度函数和母函数
- #===============================================================================
- db8 = pywt.Wavelet(wavtag)
- scaling, wavelet, x = db8.wavefun()
- fig = plt.figure(2)
- ax1 = fig.add_subplot(121)
- ax1.plot(x,scaling)
- ax1.set_title('Scaling function,' + wavtag)
- ax1.set_ylim(-1.2,1.2)
- ax1.grid(True)
- ax2 = fig.add_subplot(122,sharey = ax1)
- ax2.set_title('Wavelet,'+wavtag)
- ax2.plot(x,wavelet)
- ax2.tick_params(labelleft=False)
- ax2.grid(True)
- plt.tight_layout()
- plt.show()
- #===============================================================================
- # 图3：小波去噪模拟，原始信号和混合噪声的信号
- #===============================================================================
- def Blocks(x):
- K = lambda x : (1+np.sign(x))/2
- t = np.array([[0.1,0.13,0.15,0.23,0.25,0.4,0.44,0.65,0.76,0.78,0.81]]).T
- h = np.array([[4,-5,3,-4,5,-4.2,2.1,4.3,-3.1,2.1,-4.2]]).T
- return 3.655606*np.sum(h*K(x-t), axis=0)
- def bumps(x):
- K = lambda x : (1. + np.abs(x)) ** -4.
- t = np.array([[.1, .13, .15, .23, .25, .4, .44, .65, .76, .78, .81]]).T
- h = np.array([[4, 5, 3, 4, 5, 4.2, 2.1, 4.3, 3.1, 2.1, 4.2]]).T
- w = np.array([[.005, .005, .006, .01, .01, .03, .01, .01, .005, .008, .005]]).T
- return np.sum(h*K((x-t)/w), axis=0)
- #构造原始数据
- x = np.linspace(0,1,2**15)
- blk = bumps(x)
- #构造含噪声的数据
- np.random.seed(12345)
- nblk = blk + stats.norm().rvs(2**15)*0.3
- fig = plt.figure(3)
- ax31 = fig.add_subplot(211)
- ax31.plot(x,blk)
- ax31.grid(True)
- ax31.set_title('Original Data')
- ax31.tick_params(labelbottom=False)
- ax32 = fig.add_subplot(212)
- ax32.plot(x,nblk)
- ax32.grid(True)
- ax32.set_title('Nosy Data')
- plt.show()
- #===============================================================================
- # 图4,5：小波分析，及数据展示
- #===============================================================================
- def coef_pyramid_plot(coefs, first=0, scale='uniform', ax=None):
- """
- Parameters
- ----------
- coefs : array-like
- Wavelet Coefficients. Expects an iterable in order Cdn, Cdn-1, ...,
- Cd1, Cd0.
- first : int, optional
- The first level to plot.
- scale : str {'uniform', 'level'}, optional
- Scale the coefficients using the same scale or independently by
- level.
- ax : Axes, optional
- Matplotlib Axes instance
-
- Returns
- -------
- Figure : Matplotlib figure instance
- Either the parent figure of `ax` or a new pyplot.Figure instance if
- `ax` is None.
- """
-
- if ax is None:
- import matplotlib.pyplot as plt
- fig = plt.figure()
- ax = fig.add_subplot(111, axisbg='lightgrey')
- else:
- fig = ax.figure
-
- n_levels = len(coefs)
- n = 2**(n_levels - 1) # assumes periodic
-
- if scale == 'uniform':
- biggest = [np.max(np.abs(np.hstack(coefs)))] * n_levels
- else:
- # multiply by 2 so the highest bars only take up .5
- biggest = [np.max(np.abs(i))*2 for i in coefs]
-
- for i in range(first,n_levels):
- x = np.linspace(2**(n_levels - 2 - i), n - 2**(n_levels - 2 - i), 2**i)
- ymin = n_levels - i - 1 + first
- yheight = coefs[i]/biggest[i]
- ymax = yheight + ymin
- ax.vlines(x, ymin, ymax, linewidth=1.1)
-
- ax.set_xlim(0,n)
- ax.set_ylim(first - 1, n_levels)
- ax.yaxis.set_ticks(np.arange(n_levels-1,first-1,-1))
- ax.yaxis.set_ticklabels(np.arange(first,n_levels))
- ax.tick_params(top=False, right=False, direction='out', pad=6)
- ax.set_ylabel("Levels", fontsize=14)
- ax.grid(True, alpha=.85, color='white', axis='y', linestyle='-')
- ax.set_title('Wavelet Detail Coefficients', fontsize=16,
- position=(.5,1.05))
- fig.subplots_adjust(top=.89)
-
- return fig
- fig = plt.figure(4)
- ax4 = fig.add_subplot(111, axisbg='lightgrey')
- fig = plt.figure(5)
- ax5 = fig.add_subplot(111, axisbg='lightgrey')
- #调用wavedec()函数对数据进行小波变换
- #mode指定了数据补齐的方式
- #‘per’指周期延拓数据
- true_coefs = pywt.wavedec(blk,wavtag,level=15,mode='per')
- noisy_coefs = pywt.wavedec(nblk,wavtag,level=15,mode='per')
- #绘出‘coefficient pyramid’
- #注意，这里只绘出了detail coefficients
- #而没有展示approximation coefficient(s),该数据存在true_coefs[0]中
- fig1 = coef_pyramid_plot(true_coefs[1:],scale = 'level',ax=ax4)
- fig1.axes[0].set_title('Original Wavelet Detail Coefficients')
- fig2 = coef_pyramid_plot(noisy_coefs[1:],scale = 'level',ax=ax5)
- fig2.axes[0].set_title('Noisy Wavelet Detail Coefficients')
- plt.show()
- #===============================================================================
- # 图6：降噪——全局阈值
- # 图7：重构数据——对比效果
- #===============================================================================
- sigma = stand_mad(noisy_coefs[-1])
- uthresh = sigma*np.sqrt(2*np.log(len(nblk)))
- denoised_coefs = noisy_coefs[:]
- denoised_coefs[1:] = (pywt.thresholding.soft(data,value=uthresh) for data in denoised_coefs[1:])
- fig = plt.figure(6)
- ax6 = fig.add_subplot(111, axisbg='lightgrey')
- fig3 = coef_pyramid_plot(denoised_coefs[1:],scale = 'level',ax=ax6)
- fig3.axes[0].set_title('Denoised Wavelet Detail Coefficients')
- signal = pywt.waverec(denoised_coefs, wavtag, mode='per')
- fig = plt.figure(7)
- ax71 = fig.add_subplot(211)
- ax71.plot(x,nblk)
- ax71.grid(True)
- ax71.set_title('Noisy Data')
- ax71.tick_params(labelbottom=False)
- ax72 = fig.add_subplot(212)
- ax72.plot(x,signal,label = 'Denoised')
- ax72.plot(x,blk,color='red',lw = 0.5, label = 'Original')
- ax72.grid(True)
- ax72.set_title('Denoised Data')
- ax72.legend()
- plt.show()
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