【发布时间】：2021-11-26 16:34:58
【问题描述】：
我目前有一组数据，我希望从中创建一个函数，该函数是为 temp_vp 的所有可能值定义的（在这种情况下是我的 x 数据）
temp_vp = [280.0,290.0,300.0,310.0,320.0,330.0,340.0,350.0,360.0,370.0,380.0,390.0,400.0,410.0,420.0,430.0,440.0,450.0,460.0,470.0,480.0,490.0,500.0]
vp_in = [3.88e-52,5.16e-50,4.95e-48,3.53e-46,1.93e-44,8.26e-43,2.83e-41,7.93e-40,1.85e-38,3.62e-37,6.07e-36,8.79e-35,1.11e-33,1.25e-32,1.24e-31,1.11e-30,9.03e-30,6.66e-29,4.51e-28,2.81e-27,1.62e-26,8.72e-26,4.38e-25]
我之前使用过UnivariateSpline，以便在数据点之间进行插值并给出给定数据范围之外的常数值。但是，当我这次尝试将它用于这些数据时，我得到了这个：
有谁知道我怎样才能根据前面描述的要求为这些数据获得所需的插值函数？
我当前的代码如下：
import numpy as np
from scipy.interpolate import interp1d
from scipy import interp
from scipy.interpolate import UnivariateSpline
import matplotlib.pyplot as plt
temp_vp = [280.0,290.0,300.0,310.0,320.0,330.0,340.0,350.0,360.0,370.0,380.0,390.0,400.0,410.0,420.0,430.0,440.0,450.0,460.0,470.0,480.0,490.0,500.0]
vp_in = [3.88e-52,5.16e-50,4.95e-48,3.53e-46,1.93e-44,8.26e-43,2.83e-41,7.93e-40,1.85e-38,3.62e-37,6.07e-36,8.79e-35,1.11e-33,1.25e-32,1.24e-31,1.11e-30,9.03e-30,6.66e-29,4.51e-28,2.81e-27,1.62e-26,8.72e-26,4.38e-25]
tempspace = np.linspace(200,10000,10000)
vp_f = UnivariateSpline(temp_vp, vp_in, k = 1, ext = 3)
fig=plt.figure(figsize=(4.5,3.6))
ax=fig.add_subplot(1,1,1)
ax.minorticks_on() # enable minor ticks
ax.set_axisbelow(True) # put grid behind curves
ax.grid(b=True, which='major', color='black', linestyle='-', zorder=1, linewidth=0.4, alpha = 0.12) # turn on major grid
ax.grid(b=True, which='minor', color='black', linestyle='-', zorder=1, linewidth=0.4, alpha = 0.12) # turn on minor grid
ax.scatter(temp_vp,vp_in, color = 'black', label = 'data', s= 5, zorder = 3)
ax.plot(tempspace, vp_f(tempspace), color = 'blue', label = 'Fit', zorder = 2)
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_xlabel('Temperature [K]')
ax.set_ylabel('Vapor Pressure [Pa]')
ax.legend(labelspacing=0.25, fontsize = 8)
plt.xlim([250,600])
#plt.ylim([1e-10,1e5])
plt.savefig('Al_vpdata.pdf', bbox_inches='tight', format='pdf')
plt.savefig('Al_vpdata.png', dpi=300, bbox_inches='tight', format='png')
【问题讨论】：
标签： python numpy spline exponential