我从模拟中得到了numpy.array数据集，但我错过了边缘处的点（x = 0.1），如何将z中的数据插值/外推到边缘？我有：
x = [ 0. 0.00667 0.02692 0.05385 0.08077]
y = [ 0. 10. 20. 30. 40. 50.]
# 0. 0.00667 0.02692 0.05385 0.08077
z = [[ 25. 25. 25. 25. 25. ] # 0.
[ 25.301 25.368 25.617 26.089 26.787] # 10.
[ 25.955 26.094 26.601 27.531 28.861] # 20.
[ 26.915 27.126 27.887 29.241 31.113] # 30.
[ 28.106 28.386 29.378 31.097 33.402] # 40.
[ 29.443 29.784 30.973 32.982 35.603]] # 50.
我想在z中添加一个对应于x = 0.1的新列，以便我的新x将是
x_new = [ 0. 0.00667 0.02692 0.05385 0.08077 0.1]
# 0. 0.00667 0.02692 0.05385 0.08077 0.01
z = [[ 25. 25. 25. 25. 25. ? ] # 0.
[ 25.301 25.368 25.617 26.089 26.787 ? ] # 10.
[ 25.955 26.094 26.601 27.531 28.861 ? ] # 20.
[ 26.915 27.126 27.887 29.241 31.113 ? ] # 30.
[ 28.106 28.386 29.378 31.097 33.402 ? ] # 40.
[ 29.443 29.784 30.973 32.982 35.603 ? ]] # 50.
所有'？'替换为插值/外插数据。 谢谢你的帮助！
答案 0 :(得分：2)
您是否看过scipy.interpolate2d.interp2d（使用样条线）？
from scipy.interpolate import interp2d
fspline = interp2d(x,y,z) # maybe need to switch x and y around
znew = fspline([0.1], y)
z = np.c_[[z, znew] # to join arrays
修改：
@dnalow和我想象的方法大致如下：
import numpy as np
import matplotlib.pyplot as plt
# make some test data
def func(x, y):
return np.sin(np.pi*x) + np.sin(np.pi*y)
xx, yy = np.mgrid[0:2:20j, 0:2:20j]
zz = func(xx[:], yy[:]).reshape(xx.shape)
fig, (ax1, ax2, ax3, ax4) = plt.subplots(1,4, figsize=(13, 3))
ax1.imshow(zz, interpolation='nearest')
ax1.set_title('Original')
# remove last column
zz[:,-1] = np.nan
ax2.imshow(zz, interpolation='nearest')
ax2.set_title('Missing data')
# compute missing column using simplest imaginable model: first order Taylor
gxx, gyy = np.gradient(zz[:, :-1])
zz[:, -1] = zz[:, -2] + gxx[:, -1] + gyy[:,-1]
ax3.imshow(zz, interpolation='nearest')
ax3.set_title('1st order Taylor approx')
# add curvature to estimate
ggxx, _ = np.gradient(gxx)
_, ggyy = np.gradient(gyy)
zz[:, -1] = zz[:, -2] + gxx[:, -1] + gyy[:,-1] + ggxx[:,-1] + ggyy[:, -1]
ax4.imshow(zz, interpolation='nearest')
ax4.set_title('2nd order Taylor approx')
fig.tight_layout()
fig.savefig('extrapolate_2d.png')
plt.show()
http://dev.gamesmart.com/mobile
您可以通过增加估算
（a）增加高阶导数（又名泰勒展开）或
（b）计算比x和y更多方向的渐变（然后相应地加权渐变）。
此外，如果预先平滑图像，您将获得更好的渐变（现在我们有一个完整的Sobel滤镜......）。