Hi, I've coded a function that allows to extract a contiguous array from another one using a given shape and centered on a given position. I did not find an equivalent within numpy so I hope I did not miss it. The only interest of the function is to guarantee that the resulting sub-array will have the required shape. If some values are out of bounds, result array is padded with a fill value. Hope it can be useful to someone. Nicolas Code: ----- import numpy def extract(Z, shape, position, fill=numpy.NaN): """ Extract a sub-array from Z using given shape and centered on position. If some part of the sub-array is out of Z bounds, result is padded with fill value. **Parameters** `Z` : array_like Input array. `shape` : tuple Shape of the output array `position` : tuple Position within Z `fill` : scalar Fill value **Returns** `out` : array_like Z slice with given shape and center **Examples** >>> Z = numpy.arange(0,16).reshape((4,4)) >>> extract(Z, shape=(3,3), position=(0,0)) [[ NaN NaN NaN] [ NaN 0. 1.] [ NaN 4. 5.]] Schema: +-----------+ | 0 0 0 | = extract (Z, shape=(3,3), position=(0,0)) | +---------------+ | 0 | 0 1 | 2 3 | = Z | | | | | 0 | 4 5 | 6 7 | +---|-------+ | | 8 9 10 11 | | | | 12 13 14 15 | +---------------+ >>> Z = numpy.arange(0,16).reshape((4,4)) >>> extract(Z, shape=(3,3), position=(3,3)) [[ 10. 11. NaN] [ 14. 15. NaN] [ NaN NaN NaN]] Schema: +---------------+ | 0 1 2 3 | = Z | | | 4 5 6 7 | | +-----------+ | 8 9 |10 11 | 0 | = extract (Z, shape=(3,3), position=(3,3)) | | | | | 12 13 |14 15 | 0 | +---------------+ | | 0 0 0 | +-----------+ """ # assert(len(position) == len(Z.shape)) # if len(shape) < len(Z.shape): # shape = shape + Z.shape[len(Z.shape)-len(shape):] R = numpy.ones(shape, dtype=Z.dtype)*fill P = numpy.array(list(position)).astype(int) Rs = numpy.array(list(R.shape)).astype(int) Zs = numpy.array(list(Z.shape)).astype(int) R_start = numpy.zeros((len(shape),)).astype(int) R_stop = numpy.array(list(shape)).astype(int) Z_start = (P-Rs//2) Z_stop = (P+Rs//2)+Rs%2 R_start = (R_start - numpy.minimum(Z_start,0)).tolist() Z_start = (numpy.maximum(Z_start,0)).tolist() R_stop = (R_stop - numpy.maximum(Z_stop-Zs,0)).tolist() Z_stop = (numpy.minimum(Z_stop,Zs)).tolist() r = [slice(start,stop) for start,stop in zip(R_start,R_stop)] z = [slice(start,stop) for start,stop in zip(Z_start,Z_stop)] R[r] = Z[z] return R Z = numpy.arange(0,16).reshape((4,4)) print Z print print extract(Z, shape=(3,3), position=(0,0)) print print extract(Z, shape=(3,3), position=(3,3))