scipy.stsci.convolve.boxcar¶
- scipy.stsci.convolve.boxcar(data, boxshape, output=None, mode='nearest', cval=0.0)¶
boxcar computes a 1D or 2D boxcar filter on every 1D or 2D subarray of data.
‘boxshape’ is a tuple of integers specifying the dimensions of the filter: e.g. (3,3)
if ‘output’ is specified, it should be the same shape as ‘data’ and None will be returned.
- supported ‘mode’s include:
- ‘nearest’ elements beyond boundary come from nearest edge pixel. ‘wrap’ elements beyond boundary come from the opposite array edge. ‘reflect’ elements beyond boundary come from reflection on same array edge. ‘constant’ elements beyond boundary are set to ‘cval’
>>> boxcar(np.array([10, 0, 0, 0, 0, 0, 1000]), (3,), mode="nearest").astype(np.longlong) array([ 6, 3, 0, 0, 0, 333, 666], dtype=int64) >>> boxcar(np.array([10, 0, 0, 0, 0, 0, 1000]), (3,), mode="wrap").astype(np.longlong) array([336, 3, 0, 0, 0, 333, 336], dtype=int64) >>> boxcar(np.array([10, 0, 0, 0, 0, 0, 1000]), (3,), mode="reflect").astype(np.longlong) array([ 6, 3, 0, 0, 0, 333, 666], dtype=int64) >>> boxcar(np.array([10, 0, 0, 0, 0, 0, 1000]), (3,), mode="constant").astype(np.longlong) array([ 3, 3, 0, 0, 0, 333, 333], dtype=int64) >>> a = np.zeros((10,10)) >>> a[0,0] = 100 >>> a[5,5] = 1000 >>> a[9,9] = 10000 >>> boxcar(a, (3,3)).astype(np.longlong) array([[ 44, 22, 0, 0, 0, 0, 0, 0, 0, 0], [ 22, 11, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 111, 111, 111, 0, 0, 0], [ 0, 0, 0, 0, 111, 111, 111, 0, 0, 0], [ 0, 0, 0, 0, 111, 111, 111, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 1111, 2222], [ 0, 0, 0, 0, 0, 0, 0, 0, 2222, 4444]], dtype=int64) >>> boxcar(a, (3,3), mode="wrap").astype(np.longlong) array([[1122, 11, 0, 0, 0, 0, 0, 0, 1111, 1122], [ 11, 11, 0, 0, 0, 0, 0, 0, 0, 11], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 111, 111, 111, 0, 0, 0], [ 0, 0, 0, 0, 111, 111, 111, 0, 0, 0], [ 0, 0, 0, 0, 111, 111, 111, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1111, 0, 0, 0, 0, 0, 0, 0, 1111, 1111], [1122, 11, 0, 0, 0, 0, 0, 0, 1111, 1122]], dtype=int64) >>> boxcar(a, (3,3), mode="reflect").astype(np.longlong) array([[ 44, 22, 0, 0, 0, 0, 0, 0, 0, 0], [ 22, 11, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 111, 111, 111, 0, 0, 0], [ 0, 0, 0, 0, 111, 111, 111, 0, 0, 0], [ 0, 0, 0, 0, 111, 111, 111, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 1111, 2222], [ 0, 0, 0, 0, 0, 0, 0, 0, 2222, 4444]], dtype=int64)
>>> boxcar(a, (3,3), mode="constant").astype(np.longlong) array([[ 11, 11, 0, 0, 0, 0, 0, 0, 0, 0], [ 11, 11, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 111, 111, 111, 0, 0, 0], [ 0, 0, 0, 0, 111, 111, 111, 0, 0, 0], [ 0, 0, 0, 0, 111, 111, 111, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 1111, 1111], [ 0, 0, 0, 0, 0, 0, 0, 0, 1111, 1111]], dtype=int64)
>>> a = np.zeros((10,10)) >>> a[3:6,3:6] = 111 >>> boxcar(a, (3,3)).astype(np.longlong) array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 12, 24, 37, 24, 12, 0, 0, 0], [ 0, 0, 24, 49, 74, 49, 24, 0, 0, 0], [ 0, 0, 37, 74, 111, 74, 37, 0, 0, 0], [ 0, 0, 24, 49, 74, 49, 24, 0, 0, 0], [ 0, 0, 12, 24, 37, 24, 12, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int64)
