def takeN_le_per_row_broadcasting(a, b, N=3): # a, b : 1D, 2D arrays respectively
# First col indices in each row of b with <= corresponding one in a
idx = (b <= a[:,None]).argmax(1)
# Get all N ranged column indices
all_idx = idx[:,None] + np.arange(N)
# Finally advanced-index with those indices into b for desired output
return b[np.arange(len(all_idx))[:,None], all_idx]
受NumPy Fancy Indexing - Crop different ROIs from different channels的解决方案启发，我们可以利用np.lib.stride_tricks.as_strided高效的补丁提取，例如-
from skimage.util.shape import view_as_windows
def takeN_le_per_row_strides(a, b, N=3): # a, b : 1D, 2D arrays respectively
# First col indices in each row of b with <= corresponding one in a
idx = (b <= a[:,None]).argmax(1)
# Get 1D sliding windows for each element off data
w = view_as_windows(b, (1,N))[:,:,0]
# Use fancy/advanced indexing to select the required ones
return w[np.arange(len(idx)), idx]
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