pyWavelet基于阈值的小波分解重构法去噪（软硬阈值折衷法）
软硬阈值及相应的改良方法见链接：点击打开链接
数据来源：https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction
代码如下：
import pywt
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import math
####################一些参数和函数############
def sgn(num):
if(num > 0.0):
return 1.0
elif(num == 0.0):
return 0.0
else:
return -1.0
begin = 1
end = 1001
###软硬阈值折衷法 a 参数
a = 0.5
###################一些参数和函数#############
###sample###
#x = [3, 7, 1, 1, -2, 5, 4, 6]
#read data
data = pd.read_csv('energydata_complete.csv' )
#y_value为原信号
##########画图################################################
x1 = range(begin, end)
y_values = data['RH_6'][begin:end]
'''
scatter()
x:横坐标 y:纵坐标 s:点的尺寸
'''
#plt.scatter(x1, y_values, s=10)
plt.plot(x1, y_values)
## 设置图表标题并给坐标轴加上标签
#plt.title('plot Numbers', fontsize=24)
#plt.xlabel('xValue', fontsize=14)
#plt.ylabel('yValue', fontsize=14)
# 设置刻度标记的大小
#plt.tick_params(axis='both', which='major', labelsize=14)
# 设置每个坐标轴的取值范围
#plt.axis([0, 1000, 0, 100])
plt.show()
##############画图############################################
#print(data.shape)
#print(data)
#print(data['RH_6'])
##################去噪#########################
db1 = pywt.Wavelet('db1')
#[ca3, cd3, cd2, cd1] = pywt.wavedec(x, db1)
#print(ca3)
#print(cd3)
#print(cd2)
#print(cd1)
#分解为三层
coeffs = pywt.wavedec(y_values, db1, level = 3)
print("------------------len of coeffs---------------------")
print(len(coeffs))
#print(coeffs)
recoeffs = pywt.waverec(coeffs, db1)
#print(recoeffs)
thcoeffs =[]
for i in range(1, len(coeffs)):
tmp = coeffs[i].copy()
Sum = 0.0
for j in coeffs[i]:
Sum = Sum + abs(j)
N = len(coeffs[i])
Sum = (1.0 / float(N)) * Sum
sigma = (1.0 / 0.6745) * Sum
lamda = sigma * math.sqrt(2.0 * math.log(float(N), math.e))
for k in range(len(tmp)):
if(abs(tmp[k]) >= lamda):
tmp[k] = sgn(tmp[k]) * (abs(tmp[k]) - a * lamda)
else:
tmp[k] = 0.0
thcoeffs.append(tmp)
#print(thcoeffs)
usecoeffs = []
usecoeffs.append(coeffs[0])
usecoeffs.extend(thcoeffs)
#print(usecoeffs)
#recoeffs为去噪后信号
recoeffs = pywt.waverec(usecoeffs, db1)
#print(recoeffs)
##########画图################################################
x1 = range(begin, end)
y_values = recoeffs
'''
scatter()
x:横坐标 y:纵坐标 s:点的尺寸
'''
#plt.scatter(x1, y_values, s=10)
plt.plot(x1, y_values)
## 设置图表标题并给坐标轴加上标签
#plt.title('plot Numbers', fontsize=24)
#plt.xlabel('xValue', fontsize=14)
#plt.ylabel('yValue', fontsize=14)
# 设置刻度标记的大小
#plt.tick_params(axis='both', which='major', labelsize=14)
# 设置每个坐标轴的取值范围
#plt.axis([0, 1000, 0, 100])
plt.show()
##############画图############################################结果：
原信号：
去噪后信号：
