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EDIT: ah crap, my answer is just writing <code>im[i-d:i+d+1, j-d:j+d+1].flatten()</code> but written in a incomprehensible way :) <hr> The good old sliding window trick may help here: <pre><code>import numpy as np from numpy.lib.stride_tricks import as_strided def sliding_window(arr, window_size): """ Construct a sliding window view of the array""" arr = np.asarray(arr) window_size = int(window_size) if arr.ndim != 2: raise ValueError("need 2-D input") if not (window_size > 0): raise ValueError("need a positive window size") shape = (arr.shape[0] - window_size + 1, arr.shape[1] - window_size + 1, window_size, window_size) if shape[0] <= 0: shape = (1, shape[1], arr.shape[0], shape[3]) if shape[1] <= 0: shape = (shape[0], 1, shape[2], arr.shape[1]) strides = (arr.shape[1]*arr.itemsize, arr.itemsize, arr.shape[1]*arr.itemsize, arr.itemsize) return as_strided(arr, shape=shape, strides=strides) def cell_neighbors(arr, i, j, d): """Return d-th neighbors of cell (i, j)""" w = sliding_window(arr, 2*d+1) ix = np.clip(i - d, 0, w.shape[0]-1) jx = np.clip(j - d, 0, w.shape[1]-1) i0 = max(0, i - d - ix) j0 = max(0, j - d - jx) i1 = w.shape[2] - max(0, d - i + ix) j1 = w.shape[3] - max(0, d - j + jx) return w[ix, jx][i0:i1,j0:j1].ravel() x = np.arange(8*8).reshape(8, 8) print x for d in [1, 2]: for p in [(0,0), (0,1), (6,6), (8,8)]: print "-- d=%d, %r" % (d, p) print cell_neighbors(x, p[0], p[1], d=d) </code></pre> Didn't do any timings here, but it's possible this version has reasonable performance. For more info, search the net with phrases "rolling window numpy" or "sliding window numpy".
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