["python","numpy"]
Slice 2d array into smaller 2d arrays

Is there a way to slice a 2d array in numpy into smaller 2d arrays? Example [[1,2,3,4], -> [[1,2] [3,4] [5,6,7,8]] [5,6] [7,8]] So I basically want to cut down a 2x4 array into 2 2x2 arrays. Looking for a generic solution to be used on images.
There was another question a couple of months ago which clued me in to the idea of using reshape and swapaxes. The h//nrows makes sense since this keeps the first block's rows together. It also makes sense that you'll need nrows and ncols to be part of the shape. -1 tells reshape to fill in whatever number is necessary to make the reshape valid. Armed with the form of the solution, I just tried things until I found the formula that works. You should be able to break your array into "blocks" using some combination of reshape and swapaxes: def blockshaped(arr, nrows, ncols): """ Return an array of shape (n, nrows, ncols) where n * nrows * ncols = arr.size If arr is a 2D array, the returned array should look like n subblocks with each subblock preserving the "physical" layout of arr. """ h, w = arr.shape assert h % nrows == 0, f"{h} rows is not evenly divisible by {nrows}" assert w % ncols == 0, f"{w} cols is not evenly divisible by {ncols}" return (arr.reshape(h//nrows, nrows, -1, ncols) .swapaxes(1,2) .reshape(-1, nrows, ncols)) turns c np.random.seed(365) c = np.arange(24).reshape((4, 6)) print(c) [out]: [[ 0 1 2 3 4 5] [ 6 7 8 9 10 11] [12 13 14 15 16 17] [18 19 20 21 22 23]] into print(blockshaped(c, 2, 3)) [out]: [[[ 0 1 2] [ 6 7 8]] [[ 3 4 5] [ 9 10 11]] [[12 13 14] [18 19 20]] [[15 16 17] [21 22 23]]] I've posted an inverse function, unblockshaped, here, and an N-dimensional generalization here. The generalization gives a little more insight into the reasoning behind this algorithm. Note that there is also superbatfish's blockwise_view. It arranges the blocks in a different format (using more axes) but it has the advantage of (1) always returning a view and (2) being capable of handling arrays of any dimension.