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              "value": "One should be very careful with rolling views when it comes to memory usage.  Indeed, although a 'view' has the same memory footprint as its base array, the actual array that emerges when this 'view' is used in a computation is generally a (much) larger array than the original, especially for 2-dimensional arrays and above."
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              "value": "For example, let us consider a 3 dimensional array of size (100, 100, 100) of "
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              "value": ". This array takes about 8*100**3 Bytes for storage which is just 8 MB. If one decides to build a rolling view on this array with a window of (3, 3, 3) the hypothetical size of the rolling view (if one was to reshape the view for example) would be 8*(100-3+1)**3*3**3 which is about 203 MB! The scaling becomes even worse as the dimension of the input array becomes larger."
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                      "value": "(rolling) window view of the input array."
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                      "value": "Defines the shape of the elementary n-dimensional orthotope (better know as hyperrectangle "
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                      "value": ") of the rolling window view. If an integer is given, the shape will be a hypercube of sidelength given by its value."
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              "value": "Windows are overlapping views of the input array, with adjacent windows shifted by a single row or column (or an index of a higher dimension)."
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        "value": "import numpy as np\nfrom skimage.util.shape import view_as_windows\nA = np.arange(4*4).reshape(4,4)\nA\nwindow_shape = (2, 2)\nB = view_as_windows(A, window_shape)\nB[0, 0]\nB[0, 1]\n",
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        "value": "A = np.arange(5*4).reshape(5, 4)\nA\nwindow_shape = (4, 3)\nB = view_as_windows(A, window_shape)\nB.shape\nB  # doctest: +NORMALIZE_WHITESPACE\n",
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    ".. [1] https://en.wikipedia.org/wiki/Hyperrectangle"
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