您可以使用scipy.interpolate.interp2d和进行此操作numpy.meshgrid。
您需要确保新的X和Y范围与旧的范围相同，并且步长较小。这很容易np.linspace：
import numpy as np
from scipy import interpolate
mymin,mymax = 0,3
X = np.linspace(mymin,mymax,4)
Y = np.linspace(mymin,mymax,4)
x,y = np.meshgrid(X,Y)
test = np.array([[ 1.2514318 , 1.25145821, 1.25148472, 1.25151133],
[ 1.25087456, 1.25090105, 1.25092764, 1.25095435],
[ 1.25031581, 1.25034238, 1.25036907, 1.25039586],
[ 1.24975557, 1.24978222, 1.24980898, 1.24983587]])
f = interpolate.interp2d(x,y,test,kind='cubic')
# use linspace so your new range also goes from 0 to 3, with 8 intervals
Xnew = np.linspace(mymin,mymax,8)
Ynew = np.linspace(mymin,mymax,8)
test8x8 = f(Xnew,Ynew)
print test8x8
>>> [[ 1.2514318 1.25144311 1.25145443 1.25146577 1.25147714 1.25148852 1.25149991 1.25151133]
[ 1.25119317 1.25120449 1.25121583 1.25122719 1.25123856 1.25124995 1.25126137 1.25127281]
[ 1.25095426 1.2509656 1.25097695 1.25098832 1.25099971 1.25101112 1.25102255 1.25103401]
[ 1.25071507 1.25072642 1.25073779 1.25074918 1.25076059 1.25077201 1.25078346 1.25079494]
[ 1.25047561 1.25048697 1.25049835 1.25050976 1.25052119 1.25053263 1.2505441 1.25055558]
[ 1.25023587 1.25024724 1.25025864 1.25027007 1.25028151 1.25029297 1.25030446 1.25031595]
[ 1.24999585 1.25000724 1.25001866 1.2500301 1.25004156 1.25005304 1.25006453 1.25007605]
[ 1.24975557 1.24976698 1.24977841 1.24978985 1.24980132 1.24981281 1.24982433 1.24983587]]
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