Interpolation for a 2D array

Question

I was wondering if there is a way to interpolate a 2D array in python using the same principle used to interpolate a 1D array ( {np.interpolate} ).

So my aim is to increase the number of data points that is within my array ([1000,20] to [1000, 200] [Time_indexing, X]).

I am looking for a function that is capable of doing that.

A = np.array([[ 0.45717218,  0.44250104,  0.47812272,  0.49092173,  0.46002069],
   [ 0.29829681,  0.26408021,  0.3709202 ,  0.44823109,  0.49311853],
   [ 0.05469835,  0.01048596,  0.17398291,  0.30088943,  0.39783137],
   [-0.20463768, -0.24610673, -0.0713164 ,  0.08406331,  0.22047102],
   [-0.4074527 , -0.43573695, -0.31062521, -0.15750053, -0.00222392]])

This is a [5,5] array i want to interpolate it using a spacing of 0.01 hence the final product should be [500,500].

Thank you,

Answer 1

You could use interp2d :

from scipy.interpolate import interp2d
f = interp2d(np.arange(0,500,100), np.arange(0,500,100), A)
f(np.arange(500), np.arange(500))

Output:

array([[ 0.45717218,  0.45702547,  0.45687876, ...,  0.46002069,
         0.46002069,  0.46002069],
       [ 0.45558343,  0.45543476,  0.45528609, ...,  0.46035167,
         0.46035167,  0.46035167],
       [ 0.45399467,  0.45384405,  0.45369343, ...,  0.46068265,
         0.46068265,  0.46068265],
       ...,
       [-0.4074527 , -0.40773554, -0.40801839, ..., -0.00222392,
        -0.00222392, -0.00222392],
       [-0.4074527 , -0.40773554, -0.40801839, ..., -0.00222392,
        -0.00222392, -0.00222392],
       [-0.4074527 , -0.40773554, -0.40801839, ..., -0.00222392,
        -0.00222392, -0.00222392]])