How to resample a Numpy array of arbitrary dimensions?

Question

There is scipy.misc.imresize for resampling the first two dimensions of 3D arrays. It also supports bilinear interpolation. However, there does not seem to be an existing function for resizing all dimensions of arrays with any number of dimensions. How can I resample any array given a new shape of the same rank, using multi-linear interpolation?

Answer 1

You want scipy.ndimage.zoom, which can be used as follows:

>>> x = np.arange(8, dtype=np.float_).reshape(2, 2, 2)
>>> scipy.ndimage.zoom(x, 1.5, order=1)
array([[[ 0. ,  0.5,  1. ],
        [ 1. ,  1.5,  2. ],
        [ 2. ,  2.5,  3. ]],

       [[ 2. ,  2.5,  3. ],
        [ 3. ,  3.5,  4. ],
        [ 4. ,  4.5,  5. ]],

       [[ 4. ,  4.5,  5. ],
        [ 5. ,  5.5,  6. ],
        [ 6. ,  6.5,  7. ]]])

Note that this function always preserves the boundaries of the image, essentially resampling a mesh with a node at each pixel center. You might want to look at other functions in scipy.ndimage if you need more control over exactly where the resampling occurs