ó  ‰\c@ smddlmZddlZddlZddlmZddlmZddgZ d„Z dd „Z dS( i˙˙˙˙(tdivisionN(t as_strided(twarntview_as_blockstview_as_windowscC s$t|tƒstdƒ‚ntj|ƒ}|dkjƒrNtdƒ‚n|j|jkrotdƒ‚ntj|j ƒ}||j ƒdkrĤtdƒ‚n|j j sĊt tdƒƒntj|ƒ}t||ƒt|ƒ}t|j|ƒ|j}t|d|d|ƒ}|S( slBlock view of the input n-dimensional array (using re-striding). Blocks are non-overlapping views of the input array. Parameters ---------- arr_in : ndarray N-d 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. If `arr_in` is non-contiguous, a copy is made. 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]]]]) sblock needs to be a tupleis0'block_shape' elements must be strictly positives9'block_shape' must have the same length as 'arr_in.shape's-'block_shape' is not compatible with 'arr_in'sECannot provide views on a non-contiguous input array without copying.tshapetstrides(t isinstancettuplet TypeErrortnptarraytanyt ValueErrortsizetndimRtsumtflagst contiguousRtRuntimeWarningtascontiguousarrayRR(tarr_int block_shapet arr_shapet new_shapet new_stridestarr_out((s1lib/python2.7/site-packages/skimage/util/shape.pyR s"B ic C s&t|tjƒs!tdƒ‚n|j}t|tjƒrL|f|}nt|ƒ|ksmtdƒ‚nt|tjƒrŞ|dkrštdƒ‚n|f|}nt|ƒ|krËtdƒ‚ntj |j ƒ}tj |d|j ƒ}||dkj ƒrtdƒ‚n|ddkj ƒr?td ƒ‚n|j js^ttd ƒƒntj|ƒ}td „|Dƒƒ}tj |jƒ}||j}tj |j ƒtj |ƒtj |ƒd}tt|ƒt|ƒƒ} tt|ƒt|ƒƒ} t|d | d | ƒ} | S(s¨ 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 N-d 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. If `arr_in` is non-contiguous, a copy is made. 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] http://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]]]]) s `arr_in` must be a numpy ndarrays2`window_shape` is incompatible with `arr_in.shape`is`step` must be >= 1s*`step` is incompatible with `arr_in.shape`tdtypeis`window_shape` is too larges`window_shape` is too smallsECannot provide views on a non-contiguous input array without copying.cs s!|]}tdd|ƒVqdS(N(tslicetNone(t.0tst((s1lib/python2.7/site-packages/skimage/util/shape.pys üsRR(RR tndarrayR RtnumberstNumbertlenR R RRR RRRRRRRtlistR( Rt window_shapetstepRRtslicestwindow_stridestindexing_stridestwin_indices_shapeRRR((s1lib/python2.7/site-packages/skimage/util/shape.pyRks>p    ( t __future__RR!tnumpyR tnumpy.lib.stride_tricksRtwarningsRt__all__RR(((s1lib/python2.7/site-packages/skimage/util/shape.pyts    a