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AI|Python去线性化趋势

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import numpy as np import matplotlib.pyplot as plt from matplotlib.pyplot import MultipleLocator import csv from scipy import signal#去线性趋势主要用的库 import numpy as npdata = https://www.it610.com/article/[] data1 = [] data2 = [] data3 = [] # with open('D:\keyan_z\lvbo_output\lvbo.csv','r') as csvfile: with open('C://Users//Lenovo//Desktop//999.csv', 'r') as csvfile:reader = csv.reader(csvfile) for row in reader: data.append(float(row[0])) data1.append(float(row[1]))#原数据 data2.append(float(row[2]))#原数据 data3.append(float(row[3]))#原数据 # print(numpy.array(data)) a_detrend=signal.detrend(data1, axis=0, type='linear')#去线性趋势后的数据 b_detrend=signal.detrend(data2, axis=0, type='linear')#去线性趋势后的数据 c_detrend=signal.detrend(data3, axis=0, type='linear')#去线性趋势后的数据 # plt.plot(data1, color='lightcoral') # plt.plot(a_detrend+np.array(data1).mean())plt.plot(data2, color='orange') plt.plot(b_detrend)#只对比 # plt.plot(data3, color='cornflowerblue')# plt.title('model loss and acc') plt.ylabel('gait') plt.xlabel('Time(0.01s)') plt.legend(['raw-data', 'detrend'],loc='upper right') plt.show()# # print(data2) # plt.subplot(4, 1, 1) # plt.plot(data) # plt.ylabel('raw') # # plt.xlabel('Time(0.01s)') # # x_major_locator = MultipleLocator(10) # # y_major_locator = MultipleLocator(0.2)# 把y轴的刻度间隔设置为0.1，并存在变量里 # # ax = plt.gca()# ax为两条坐标轴的实例 # # ax.xaxis.set_major_locator(x_major_locator) # # ax.yaxis.set_major_locator(y_major_locator)# 把y轴的主刻度设置为0.1的倍数 # # plt.xlim(1, 100) # # plt.ylim(0.8, 1.4)# 把y轴的刻度范围设置为-5到110，同理，-5不会标出来，但是能看到一点空白 # # plt.subplot(4, 1, 2) # plt.plot(data1) # plt.ylabel('butterwolth') # # plt.subplot(4, 1, 3) # plt.plot(data2) # plt.ylabel('sliding') # # plt.subplot(4, 1, 4) # plt.plot(data3) # plt.ylabel('low') # plt.xlabel('Sample point') # # # # plt.legend() # plt.show() # # # #

【AI|Python去线性化趋势】

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