Numpy一维线性插值函数的用法

import numpy as np

xp = [1, 2, 3]
fp = [3, 2, 0]

print(np.interp(2.5, xp, fp))

import numpy as np

xp = [1, 2, 3]
fp = [3, 2, 0]

x = [0, 1, 1.5, 2.72, 3.14]

print(np.interp(x, xp, fp))

import numpy as np

xp = [1, 2, 3]
fp = [3, 2, 0]

x = [0, 1, 1.5, 2.72, 3.14]

print(np.interp(x, xp, fp, -99, 99))

x = 2.5     # 要插入值的横坐标
xp = [1, 2, 3]   # 要插入序列的横坐标
fp = [3, 2, 0]   # 要插入序列的纵坐标
y = np.interp(x, xp, fp) # 返回插入值的纵坐标 1.0
plt.plot(xp, fp, '-o')
plt.plot(x, y, 'x')
plt.show()

x = [0, 1, 1.5, 2.72, 3.14] # 左侧外部默认为原函数最左侧函数值，右侧默认为右侧
xp = [1, 2, 3]
fp = [3, 2, 0]
y = np.interp(x, xp, fp) # array([ 3. ,3. ,2.5 ,0.56, 0. ])
plt.plot(xp, fp, '-o')
plt.plot(x, y, 'x')
plt.show()

x = 3.14
xp = [1, 2, 3]
fp = [3, 2, 0]
UNDEF = -99.0
y = np.interp(x, xp, fp, right=UNDEF) # -99.0
plt.plot(xp, fp, '-o')
plt.plot(x, y, 'x')
plt.show()

x = np.linspace(0, 2 * np.pi, 10) # 在0到2pi的范围内均匀取10个点
y = np.sin(x)      # sin函数x横坐标对应的y值
xvals = np.linspace(0, 2 * np.pi, 50) # 均匀取50个
yinterp = np.interp(xvals, x, y) # 在映射关系为y的x中插入xvals
plt.plot(x, y, 'o')
plt.plot(xvals, yinterp, '-x')
plt.show()