python实现线性插值的示例

numpy.interp(x, xp, fp, left=None, right=None, period=None)

import numpy as np 
import matplotlib.pyplot as plt

xp = [1, 2, 3]
fp = [3, 2, 0]
y = np.interp(2.5, xp, fp)
#1.0

y = np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp)
#array([3.  , 3.  , 2.5 , 0.56, 0.  ])

UNDEF = -99.0
y = np.interp(3.14, xp, fp, right=UNDEF)
#-99.0

#sine 函数插值
x = np.linspace(0, 2*np.pi, 10)
y = np.sin(x)
xvals = np.linspace(0, 2*np.pi, 50)
yinterp = np.interp(xvals, x, y)

plt.plot(x, y, 'o')
plt.plot(xvals, yinterp, '-x')
plt.show()

#周期 x 坐标的插值
x = [-180, -170, -185, 185, -10, -5, 0, 365]
xp = [190, -190, 350, -350]
fp = [5, 10, 3, 4]
y = np.interp(x, xp, fp, period=360)
#array([7.5 , 5.  , 8.75, 6.25, 3.  , 3.25, 3.5 , 3.75])

#复数插值Complex interpolation:
x = [1.5, 4.0]
xp = [2,3,5]
fp = [1.0j, 0, 2+3j]
y = np.interp(x, xp, fp)
#array([0.+1.j , 1.+1.5j])

import numpy as np

y = np.array([0, 38.39, 71.41, 99.66, 123.67, 143.88, 160.61, 174.03, 184.16, 190.8, 193.52])
x = np.array([0, 0.37, 0.74, 1.11, 1.48, 1.85, 2.22, 2.59, 2.96, 3.33, 3.7])
plt.plot(x, y, '-')

y_val = 30
root = np.interp(y_val, y, x)
print(root)

scipy.interpolate.interp1d(x, y, kind='linear', axis=- 1, copy=True, bounds_error=None, fill_value=nan, assume_sorted=False)

x = data['时间']
y = data['浓度']
# 构建完整的时间序列 = [1,23,...23]
xnew = np.linspace(1,23,num=23)

# 线性插值
f1 = interp1d(x,y,kind='linear')
ynew1 = f1(xnew)
plt.scatter(x,y,zorder=3)
plt.plot(xnew,ynew1,marker='s',ls='--',c='C1')
plt.legend(['data','线性插值'])
plt.xticks(range(0,24,1))
plt.grid(ls='--',alpha=0.5)
plt.xlabel('A')
plt.ylabel('B')
plt.tight_layout()
plt.show()

from scipy.interpolate import interp1d

x = [1, 2, 3]
y = [3, 2, 0]
f = interp1d(x,y,fill_value=(3,0),bounds_error=False) # 线性内插
out = f([0, 1, 1.5, 2.72, 3.14])
print(out)
#array([3. , 3. , 2.5 , 0.56, 0. ])

fe = interp1d(x,y, fill_value='extrapolate') # 线性内插+外插
out = fe([0, 1, 1.5, 2.72, 3.14])
print(out)
#array([ 4. , 3. , 2.5 , 0.56, -0.28])