我正在尝试列出4个不同数据集的最小值和最大值。这些数据集是我在实验室做的几个测试的四次多项式拟合。我在下面做了一个示例代码来说明困难是什么。数据集的数组具有不同的长度，并且从不同的x值开始。这就是为什么我没能用一个简单的for循环解决这个问题。
绘制的蓝色和红色线条显示最小数组和最大数组在打印时的外观。
我希望示例代码中的一切都清楚了。
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 4 10:49:21 2021
@author: Lodewijk
"""
import math
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from numpy import arange
#%% Creating X and Y example values
XTest1=list(range(0,40))
YTest1=np.empty(len(XTest1))
XTest2=list(range(10,40))
YTest2=np.empty(len(XTest2))
XTest3=list(range(2,40))
YTest3=np.empty(len(XTest3))
XTest4=list(range(5,38))
YTest4=np.empty(len(XTest4))
for i in range(len(XTest1)):
YTest1[i]=math.sin(XTest1[i])
for i in range(len(XTest2)):
YTest2[i]=3*math.sin(XTest2[i])
for i in range(len(XTest3)):
YTest3[i]=2*math.sin(XTest3[i])-0.5
for i in range(len(XTest4)):
YTest4[i]=0.5*math.sin(XTest4[i])+1
plt.plot(XTest1,YTest1, label='Data 1')
plt.plot(XTest2,YTest2, label='Data 2')
plt.plot(XTest3,YTest3, label='Data 3')
plt.plot(XTest4,YTest4, label='Data 4')
plt.legend()
plt.show()
#%% Making a 4th order polynomial best fit graph through the data sets
def objective_4(x,a,b,c,d,e):
return a * x**4 +b*x**3 +c*x**2+d*x+e
pars, cov = curve_fit(objective_4, XTest1,YTest1)
x_line1 = arange(min(XTest1), max(XTest1), 1)
a, b, c, d, e = pars
y_line1 = objective_4(x_line1, a, b, c, d, e)
pars, cov = curve_fit(objective_4, XTest2,YTest2)
x_line2 = arange(min(XTest2), max(XTest2), 1)
a, b, c, d, e = pars
y_line2 = objective_4(x_line2, a, b, c, d, e)
pars, cov = curve_fit(objective_4, XTest3,YTest3)
x_line3 = arange(min(XTest3), max(XTest3), 1)
a, b, c, d, e = pars
y_line3 = objective_4(x_line3, a, b, c, d, e)
pars, cov = curve_fit(objective_4, XTest4,YTest4)
x_line4 = arange(min(XTest4), max(XTest4), 1)
a, b, c, d, e = pars
y_line4 = objective_4(x_line4, a, b, c, d, e)
plt.plot(x_line1,y_line1, label='Test1')
plt.plot(x_line2,y_line2, label='Test2')
plt.plot(x_line3,y_line3, label='Test3')
plt.plot(x_line4,y_line4, label='Test4')
plt.legend()
plt.show()
执行此操作的一个选项是使用np.nan填充您的数据，以确保它们都具有相同的维度。一旦这样做了，您就可以使用np.nanmin和np.nanmax计算最小和最大值，同时丢弃np.nan值。
总体而言，代码如下所示：
import math
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from numpy import arange
#%% Creating X and Y example values
XTest1=list(range(0,40))
YTest1=np.empty(len(XTest1))
XTest2=list(range(10,40))
YTest2=np.empty(len(XTest2))
XTest3=list(range(2,40))
YTest3=np.empty(len(XTest3))
XTest4=list(range(5,38))
YTest4=np.empty(len(XTest4))
for i in range(len(XTest1)):
YTest1[i]=math.sin(XTest1[i])
for i in range(len(XTest2)):
YTest2[i]=3*math.sin(XTest2[i])
for i in range(len(XTest3)):
YTest3[i]=2*math.sin(XTest3[i])-0.5
for i in range(len(XTest4)):
YTest4[i]=0.5*math.sin(XTest4[i])+1
plt.plot(XTest1,YTest1, label='Data 1')
plt.plot(XTest2,YTest2, label='Data 2')
plt.plot(XTest3,YTest3, label='Data 3')
plt.plot(XTest4,YTest4, label='Data 4')
plt.legend()
plt.show()
#%% Making a 4th order polynomial best fit graph through the data sets
def objective_4(x,a,b,c,d,e):
return a * x**4 +b*x**3 +c*x**2+d*x+e
pars, cov = curve_fit(objective_4, XTest1,YTest1)
x_line1 = arange(min(XTest1), max(XTest1), 1)
a, b, c, d, e = pars
y_line1 = objective_4(x_line1, a, b, c, d, e)
pars, cov = curve_fit(objective_4, XTest2,YTest2)
x_line2 = arange(min(XTest2), max(XTest2), 1)
a, b, c, d, e = pars
y_line2 = objective_4(x_line2, a, b, c, d, e)
pars, cov = curve_fit(objective_4, XTest3,YTest3)
x_line3 = arange(min(XTest3), max(XTest3), 1)
a, b, c, d, e = pars
y_line3 = objective_4(x_line3, a, b, c, d, e)
pars, cov = curve_fit(objective_4, XTest4,YTest4)
x_line4 = arange(min(XTest4), max(XTest4), 1)
a, b, c, d, e = pars
y_line4 = objective_4(x_line4, a, b, c, d, e)
plt.plot(x_line1,y_line1, label='Test1')
plt.plot(x_line2,y_line2, label='Test2')
plt.plot(x_line3,y_line3, label='Test3')
plt.plot(x_line4,y_line4, label='Test4')
######## Padding start #######
min_x=min(XTest1[0],XTest2[0],XTest3[0],XTest4[0])
max_x=max(XTest1[-1],XTest2[-1],XTest3[-1],XTest4[-1])
x=np.arange(min_x,max_x)
y_line1_pad=(XTest1[0]-min_x)*[np.nan]+list(y_line1)+(max_x-XTest1[-1])*[np.nan]
y_line2_pad=(XTest2[0]-min_x)*[np.nan]+list(y_line2)+(max_x-XTest2[-1])*[np.nan]
y_line3_pad=(XTest3[0]-min_x)*[np.nan]+list(y_line3)+(max_x-XTest3[-1])*[np.nan]
y_line4_pad=(XTest4[0]-min_x)*[np.nan]+list(y_line4)+(max_x-XTest4[-1])*[np.nan]
y_line_pad_all=np.array([y_line1_pad,y_line2_pad,y_line3_pad,y_line4_pad])
####### Compute min and max ######
min_y=np.nanmin(y_line_pad_all,axis=0)
max_y=np.nanmax(y_line_pad_all,axis=0)
####### PLot min and max ######
plt.plot(x,min_y,color='r',ls='--',lw=2,label='min')
plt.plot(x,max_y,color='b',ls='--',lw=2,label='max')
plt.legend()
plt.show()
，输出为：
这篇关于提取每个x和y的多个数组的最小值和最大值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持跟版网！