import json
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import make_interp_spline
from scipy import signal
def prepare(data):
data = data[1:-1].split(',')
xs, ys = [], []
for i in data:
if 'x:' in i:
x = i.strip().split(' ')[1]
y = i.strip().split(' ')[3]
xs.append(float(x) * 100 + 100)
ys.append(float(y) * 100)
return np.asarray(xs), np.asarray(ys)
def extrema(x, yvals):
print('Extreme max x:', x[signal.argrelextrema(yvals, np.greater)[0]])
print('Extreme min x:', x[signal.argrelextrema(yvals, np.less)[0]])
plt.plot(x[signal.argrelextrema(yvals, np.greater)[0]], yvals[signal.argrelextrema(yvals, np.greater)], 'o', markersize=10) #极大值点
plt.plot(x[signal.argrelextrema(yvals, np.less)[0]], yvals[signal.argrelextrema(yvals, np.less)],'+', markersize=10) #极小值点
def polyder(yvals):
yyyd = np.polyder(yvals, 1) # 1表示一阶导
print('grad=0:', yyyd)
flag = {
'1': 'resultDataSmallWave',
'2': 'resultDataReady',
'3': 'resultDataTest',
'4': 'resultDataNextWave',
'5': 'resultDataSmall'
}
with open('checkResultData.json','r',encoding='utf-8') as f:
file = json.load(f)
x, y = prepare(file[flag['2']])
# 最小二乘法计算拟合多项式系数
z = np.polyfit(x, y, 20)
# yvals = np.polyval(z, x)
p = np.poly1d(z)
yvals = p(x)
plt.legend(loc=4)
plt.plot(x, y, '*', label='original values')
plt.plot(x, yvals,'r',label='polyfit values')
extrema(x, yvals)
plt.show()
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