\;j6ddlZddlZddlmZddgZdZddZy)N) as_stridedview_as_blocksview_as_windowsct|ts tdtj|}|dkj r t d|j|jk7r t dtj|j}||zjdk7r t dt||zt|z}t|j|z|jz}t|||}|S)aBBlock view of the input n-dimensional array (using re-striding). Blocks are non-overlapping views of the input array. Parameters ---------- arr_in : ndarray, shape (M[, ...]) Input array. block_shape : tuple The shape of the block. Each dimension must divide evenly into the corresponding dimensions of `arr_in`. Returns ------- arr_out : ndarray Block view of the input array. Examples -------- >>> import numpy as np >>> from skimage.util.shape import view_as_blocks >>> A = np.arange(4*4).reshape(4,4) >>> A array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) >>> B = view_as_blocks(A, block_shape=(2, 2)) >>> B[0, 0] array([[0, 1], [4, 5]]) >>> B[0, 1] array([[2, 3], [6, 7]]) >>> B[1, 0, 1, 1] 13 >>> A = np.arange(4*4*6).reshape(4,4,6) >>> A # doctest: +NORMALIZE_WHITESPACE array([[[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17], [18, 19, 20, 21, 22, 23]], [[24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35], [36, 37, 38, 39, 40, 41], [42, 43, 44, 45, 46, 47]], [[48, 49, 50, 51, 52, 53], [54, 55, 56, 57, 58, 59], [60, 61, 62, 63, 64, 65], [66, 67, 68, 69, 70, 71]], [[72, 73, 74, 75, 76, 77], [78, 79, 80, 81, 82, 83], [84, 85, 86, 87, 88, 89], [90, 91, 92, 93, 94, 95]]]) >>> B = view_as_blocks(A, block_shape=(1, 2, 2)) >>> B.shape (4, 2, 3, 1, 2, 2) >>> B[2:, 0, 2] # doctest: +NORMALIZE_WHITESPACE array([[[[52, 53], [58, 59]]], [[[76, 77], [82, 83]]]]) zblock needs to be a tuplerz0'block_shape' elements must be strictly positivez9'block_shape' must have the same length as 'arr_in.shape'z-'block_shape' is not compatible with 'arr_in'shapestrides) isinstancetuple TypeErrornparrayany ValueErrorsizendimrsumr r)arr_in block_shape arr_shape new_shape new_stridesarr_outs [G:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\skimage/util/shape.pyrrsB k5 )344((;'KqKLL6;;&WXX&IK$$&!+HIIi;./% 2DDI45FKy+FG Nc(t|tjs td|j}t|t j r|f|z}t||k(s tdt|t j r|dkr td|f|z}t||k7r tdtj|j}tj||j}||z dkjr td|dz dkjr td td |D}tj|j}||j}tj|jtj|z tj|zdz}tt|t|z} tt|t|z} t!|| | } | S) a~ Rolling window view of the input n-dimensional array. 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). Parameters ---------- arr_in : ndarray, shape (M[, ...]) Input array. window_shape : integer or tuple of length arr_in.ndim Defines the shape of the elementary n-dimensional orthotope (better know as hyperrectangle [1]_) of the rolling window view. If an integer is given, the shape will be a hypercube of sidelength given by its value. step : integer or tuple of length arr_in.ndim Indicates step size at which extraction shall be performed. If integer is given, then the step is uniform in all dimensions. Returns ------- arr_out : ndarray (rolling) window view of the input array. Notes ----- 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. For example, let us consider a 3 dimensional array of size (100, 100, 100) of ``float64``. 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. References ---------- .. [1] https://en.wikipedia.org/wiki/Hyperrectangle Examples -------- >>> import numpy as np >>> from skimage.util.shape import view_as_windows >>> A = np.arange(4*4).reshape(4,4) >>> A array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) >>> window_shape = (2, 2) >>> B = view_as_windows(A, window_shape) >>> B[0, 0] array([[0, 1], [4, 5]]) >>> B[0, 1] array([[1, 2], [5, 6]]) >>> A = np.arange(10) >>> A array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> window_shape = (3,) >>> B = view_as_windows(A, window_shape) >>> B.shape (8, 3) >>> B array([[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 6], [5, 6, 7], [6, 7, 8], [7, 8, 9]]) >>> A = np.arange(5*4).reshape(5, 4) >>> A array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15], [16, 17, 18, 19]]) >>> window_shape = (4, 3) >>> B = view_as_windows(A, window_shape) >>> B.shape (2, 2, 4, 3) >>> B # doctest: +NORMALIZE_WHITESPACE array([[[[ 0, 1, 2], [ 4, 5, 6], [ 8, 9, 10], [12, 13, 14]], [[ 1, 2, 3], [ 5, 6, 7], [ 9, 10, 11], [13, 14, 15]]], [[[ 4, 5, 6], [ 8, 9, 10], [12, 13, 14], [16, 17, 18]], [[ 5, 6, 7], [ 9, 10, 11], [13, 14, 15], [17, 18, 19]]]]) z `arr_in` must be a numpy ndarrayz2`window_shape` is incompatible with `arr_in.shape`z`step` must be >= 1z*`step` is incompatible with `arr_in.shape`)dtyperz`window_shape` is too largez`window_shape` is too smallc36K|]}tdd|yw)N)slice).0sts r z"view_as_windows..s84R5tR(4sr)r r ndarrayr rnumbersNumberlenrrrrrr r listr) r window_shapesteprrsliceswindow_stridesindexing_strideswin_indices_shaperr rs rrr`s^ fbjj ):;; ;;D,/$-   %MNN$' !823 3w~ 4yDEFF&I88L @L \ !Q&++-677  Q##%677848 8FXXfnn-Nf~-- &,, "((<"8 8RXXd^K  d,-\0BBCID)*T.-AABGy'BG Nr)r)r%numpyr numpy.lib.stride_tricksr__all__rrrrr3s&. . /UpWr