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import pandas as pd

import matplotlib.pyplot as plt

import numpy as np

from scipy import signal #滤波等

xxx = np.arange(0, 1000)

yyy = np.sin(xxx*np.pi/180)

z1 = np.polyfit(xxx, yyy, 7) # 用7次多项式拟合

p1 = np.poly1d(z1) #多项式系数

print(p1) # 在屏幕上打印拟合多项式

yvals=p1(xxx)

plt.plot(xxx, yyy, '*',label='original values')

plt.plot(xxx, yvals, 'r',label='polyfit values')

plt.xlabel('x axis')

plt.ylabel('y axis')

plt.legend(loc=4)

plt.title('polyfitting')

plt.show()

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# 极值

num_peak_3 = signal.find_peaks(yvals, distance=10) #distance表极大值点的距离至少大于等于10个水平单位

print(num_peak_3[0])

print('the number of peaks is ' + str(len(num_peak_3[0])))

plt.plot(xxx, yyy, '*',label='original values')

plt.plot(xxx, yvals, 'r',label='polyfit values')

plt.xlabel('x axis')

plt.ylabel('y axis')

plt.legend(loc=4)

plt.title('polyfitting')

for ii in range(len(num_peak_3[0])):

 plt.plot(num_peak_3[0][ii], yvals[num_peak_3[0][ii]],'*',markersize=10)

plt.show()

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yyyd = np.polyder(p1,1) # 1表示一阶导

print(yyyd)

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print(yvals[signal.argrelextrema(yvals, np.greater)]) #极大值的y轴, yvals为要求极值的序列

print(signal.argrelextrema(yvals, np.greater)) #极大值的x轴

peak_ind = signal.argrelextrema(yvals,np.greater)[0] #极大值点，改为np.less即可得到极小值点

plt.plot(xxx, yyy, '*',label='original values')

plt.plot(xxx, yvals, 'r',label='polyfit values')

plt.xlabel('x axis')

plt.ylabel('y axis')

plt.legend(loc=4)

plt.title('polyfitting')

plt.plot(signal.argrelextrema(yvals,np.greater)[0],yvals[signal.argrelextrema(yvals, np.greater)],'o', markersize=10) #极大值点

plt.plot(signal.argrelextrema(yvals,np.less)[0],yvals[signal.argrelextrema(yvals, np.less)],'+', markersize=10) #极小值点

plt.show()