Cd@ozddlmZddlZddlmZmZddlZddlm Z m Z m Z ddl m Z ddlmZddlmZmZmZmZddlmZmZdd lmZdd lmZdd lmZdd lmZd Z dZ!d#dZ"d#dZ#dZ$dZ%dZ&dZ'dZ(dZ)d$dZ*dZ+dddZ,dZ-dddddddZ.dZ/d Z0d!Z1eej2j3d#d"Z4dS)%) annotationsN)IntegralNumber)concatgetpartial)map)chunk)Array concatenate map_blocks unify_chunks) empty_like full_like)tokenize)HighLevelGraph)ArrayOverlapLayer) derived_fromcg}t|D]\}}||d}t|tr|d}|d}n|}|}t |dkr||n|d|zg}|d|zg} g} |ddD]} | | |z|z||| z| z|S)z&Get new chunks for array with overlap.r) enumerater isinstancetuplelenappend) original_chunksaxeschunksibdsdepth left_depth right_depthleftrightmidbds 2lib/python3.11/site-packages/dask/array/overlap.py_overlap_internal_chunksr*s FO,,..3A eU # # qJ(KKJK s88q== MM#    F[()DWz)*EC!B$i : : 2 ?[89999 MM$*u, - - - - Mct||}d|z}t|j||j|j|}t j|||g}t|j|}t||||S)aJShare boundaries between neighboring blocks Parameters ---------- x: da.Array A dask array axes: dict The size of the shared boundary per axis The axes input informs how many cells to overlap between neighboring blocks {0: 2, 2: 5} means share two cells in 0 axis, 5 cells in 2 axis zoverlap-)namerr numblockstoken) dependencies)meta) rrr-rr.rfrom_collectionsr*r )xrr/r-graphrs r)overlap_internalr5+s Q  E  D  V x+    E  +D%qc J J JE %ah 5 5F f1 - - --r+cPt|j|}t|||S)zTrim sides from each block. This couples well with the ``map_overlap`` operation which may leave excess data on each block. See also -------- dask.array.overlap.map_overlap rboundary) coerce_depthndim trim_internal)r3r"r8rs r) trim_overlapr<Is*  & &D  9 9 99r+c:t|j|}g}t|jD]-\}}||d}||d}g}t|D]\} } |dkr1t |t r| t|z } n| |dzz } nvt |t r7| dkr | |dz n| } | t|dz kr | |dz n| } n*| dkr| |z n| } | t|dz kr| |z n| } | | | t |/t |} ttt|||| |j |jS)zTrim sides from each block This couples well with the overlap operation, which may leave excess data on each block See also -------- dask.array.chunk.trim dask.array.map_blocks nonerrr7)rdtyper1)coerce_boundaryr:rrrrrsumrrr r_trimr@_meta) r3rr8olistr r(bdyoverlapilistjdrs r)r;r;Zsqvx00H E18$$##2ll1f%%((1a..bMM  DAqf}}gu--(CLL(AAGaKAAgu--?*+q&&GAJaA*+s2ww{*:*:GAJAA'(AvvG 1A'(CGGaK'7'7G QA LLOOOO U5\\"""" 5\\F D8444 g W    r+cfdt|jDdD}dD}fdtt|dd|D}fdtt|dd|dd|D}t d t||D}||S) zSimilar to dask.array.chunk.trim but requires one to specificy the boundary condition. ``axes``, and ``boundary`` are assumed to have been coerced. c<g|]}|dS)rr).0r rs r) z_trim..s% 2 2 2qDHHQNN 2 2 2r+c3TK|]#}t|tr|dn|V$dSrN)rrrNaxs r) z_trim..s9HHR:b%008"Q%%bHHHHHHr+c3K|]F}t|tr|dr |d nt|tr|r| ndVGdSrN)rrrrRs r)rTz_trim..s  b%  %'U A b( # #(*bSS  r+c3lK|].\}\}}|dkr|ddkrdn|V/dS)rr>NrM)rNr chunk_locationrSr8s r)rTz_trim..sf #A#!!hll1f&=&=&G&Gbr+rzchunk-locationc3tK|]2\}\}}}||dz kr|ddkrdn|V3dS)rr>NrM)rNr rrXrSr8s r)rTz_trim..sr ,A+ fqj ( (X\\!V-D-D-N-N  r+z num-chunksc3<K|]\}}t||VdSN)slice)rNfrontbacks r)rTz_trim..s0QQ{udeT""QQQQQQr+)ranger:rzipr) r3rr8 block_info axes_front axes_back trim_front trim_backinds `` r)rCrCs* 3 2 2 2E!&MM 2 2 2DHH4HHHJ  I'0  1 ./ < <( ( J 09  1 l+Z];K-Li X X0 0 I QQc*i6P6PQQQ Q QC S6Mr+ctdddf|ztd|fztdddf|j|z dz zz}tdddf|zt| dfztdddf|j|z dz zz}||}||}t||||\}}t|||g|S)zuCopy a slice of an array around to its other side Useful to create periodic boundary conditions for overlap Nrraxisr\r:_remove_overlap_boundariesr r3rir"r%r&lrs r)periodicros tT4 "T) E??   tT " " $ (9 : ; tT4 "T) %    ! tT " " $ (9 : ; $A %A %aD% 8 8DAq 1ayt , , ,,r+cD|dkrItdddf|ztddfztdddf|j|z dz zz}nLtdddf|zt|dz ddfztdddf|j|z dz zz}tdddf|ztd| dz dfztdddf|j|z dz zz}||}||}t||||\}}t|||g|S)z[Reflect boundaries of array on the same side This is the converse of ``periodic`` rNrrrhrjrls r)reflectrqsj  zz 4t $ $ & -Q{{n T4&&(AFTMA,=> ? 4t $ $ & -UQYb))+ ,T4&&(AFTMA,=> ? tT4 "T) eVaZ $ $ & ' tT " " $ (9 : ; $A %A %aD% 8 8DAq 1ayt , , ,,r+ctdddf|ztddfztdddf|j|z dz zz}tdddf|ztdddfztdddf|j|z dz zz}t||g|z|}t||g|z|}t||||\}}t|||g|S)zEach reflect each boundary value outwards This mimics what the skimage.filters.gaussian_filter(... mode="nearest") does. Nrrrrh)r\r:r rkrls r)nearestrts! tT4 "T) A;;.  tT " " $ (9 : ; tT4 "T) R     tT " " $ (9 : ; QtWI%D111AQuXJ&T222A %aD% 8 8DAq 1ayt , , ,,r+c t|j}|f||<t||tt t |t||j}t|||g|S)z*Add constant slice to either side of arrayshaperr@rh)listrrrr rBr@r )r3rir"valuercs r)constantr{sr !(^^F8F4L  CV$$%%V}}g    A 1ayt , , ,,r+ct|j}|f||<t|j}|f||<|t|}|t|}||fSr[)rxrrechunkr)rmrnrir"lchunksrchunkss r)rkrk si18nnGHGDM18nnGHGDM %..!!A %..!!A a4Kr+cHtts fdt|jDtts fdt|jDt|jD]}|d}|dkr|d}|dkr<|dkrt |||}T|dkrt |||}l|dkrt|||}|vrt||||}|S)zoAdd boundary conditions to an array before overlaping See Also -------- periodic constant ci|]}|Sr)rNr kinds r) zboundaries..s///A4///r+ci|]}|Srr)rNr r"s r)rzboundaries..!s111aE111r+rr>rorqrt) rdictr_r:rrorqrtr{)r3r"rr rJ this_kinds `` r) boundariesrsA dD ! !0////qv/// eT " "211115==111 16]]++ IIaOO 66 HHQ''     * $ $Aq!!AA ) # #1a  AA ) # #1a  AA $YYAq$q'**A Hr+c|t|kr|Sg}d}|D]_}||kr/||||z zkr||||z z |}n||z }||kr||d}||kr||z }`||kr||nGt|dkr|dxx|z cc<n#td|dt |dt |S)aDetermine new chunks to ensure that every chunk >= size Parameters ---------- size: int The maximum size of any chunk. chunks: tuple Chunks along one axis, e.g. ``(3, 3, 2)`` Examples -------- >>> ensure_minimum_chunksize(10, (20, 20, 1)) (20, 11, 10) >>> ensure_minimum_chunksize(3, (1, 1, 3)) (5,) See Also -------- overlap rrrzThe overlapping depth z is larger than your array .)minrr ValueErrorrBr)sizeroutputnewrzs r)ensure_minimum_chunksizer7s3* s6{{ F C    t88TTAX&&& cTAX.///q $;; MM#   C 99 1HC d{{ c V  r c  XT X X#f++ X X X    ==r+T) allow_rechunkcb t|j|}t|j| d|D}|rBt dt ||jD}||}nKtdt ||jD}|rtd|d|jd|}t|| } t| |} fd| D} tj| | } | S)a Share boundaries between neighboring blocks Parameters ---------- x: da.Array A dask array depth: dict The size of the shared boundary per axis boundary: dict The boundary condition on each axis. Options are 'reflect', 'periodic', 'nearest', 'none', or an array value. Such a value will fill the boundary with that value. allow_rechunk: bool, keyword only Allows rechunking, otherwise chunk sizes need to match and core dimensions are to consist only of one chunk. The depth input informs how many cells to overlap between neighboring blocks ``{0: 2, 2: 5}`` means share two cells in 0 axis, 5 cells in 2 axis. Axes missing from this input will not be overlapped. Any axis containing chunks smaller than depth will be rechunked if possible, provided the keyword ``allow_rechunk`` is True (recommended). Examples -------- >>> import numpy as np >>> import dask.array as da >>> x = np.arange(64).reshape((8, 8)) >>> d = da.from_array(x, chunks=(4, 4)) >>> d.chunks ((4, 4), (4, 4)) >>> g = da.overlap.overlap(d, depth={0: 2, 1: 1}, ... boundary={0: 100, 1: 'reflect'}) >>> g.chunks ((8, 8), (6, 6)) >>> np.array(g) array([[100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100], [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100], [ 0, 0, 1, 2, 3, 4, 3, 4, 5, 6, 7, 7], [ 8, 8, 9, 10, 11, 12, 11, 12, 13, 14, 15, 15], [ 16, 16, 17, 18, 19, 20, 19, 20, 21, 22, 23, 23], [ 24, 24, 25, 26, 27, 28, 27, 28, 29, 30, 31, 31], [ 32, 32, 33, 34, 35, 36, 35, 36, 37, 38, 39, 39], [ 40, 40, 41, 42, 43, 44, 43, 44, 45, 46, 47, 47], [ 16, 16, 17, 18, 19, 20, 19, 20, 21, 22, 23, 23], [ 24, 24, 25, 26, 27, 28, 27, 28, 29, 30, 31, 31], [ 32, 32, 33, 34, 35, 36, 35, 36, 37, 38, 39, 39], [ 40, 40, 41, 42, 43, 44, 43, 44, 45, 46, 47, 47], [ 48, 48, 49, 50, 51, 52, 51, 52, 53, 54, 55, 55], [ 56, 56, 57, 58, 59, 60, 59, 60, 61, 62, 63, 63], [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100], [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100]]) cZg|](}t|trt|n|)Sr)rrmaxrNrJs r)rOzoverlap..s1 M M M 1e,,3c!fff! M M Mr+c3<K|]\}}t||VdSr[r)rNrrzs r)rTzoverlap..sB  29$ $T1 - -      r+c:g|]\}}t||kSr)rrNrJrzs r)rOzoverlap..s&(V(V(V1Q!(V(V(Vr+zOverlap depth is larger than smallest chunksize. Please set allow_rechunk=True to rechunk automatically. Overlap depths required: z Input chunks:  cZi|]'\}}||ddkr|dznd(S)r>r?rrM)rNkv boundary2s r)rzoverlap..sL   BF!QIMM!V,,661q55A   r+)r9r:rAvaluesrr`rr}anyrrr5itemsr trim)r3r"r8rdepth2depths new_chunksx1original_chunks_too_smallx2x3rx4rs @r)rGrGjsst!&% ( (F11I M MV]]__ M M MF  =@=R=R     YYz " "%((V(VFAH@U@U(V(V(V$W$W! $ .,2.."#...   B * *B "f % %B    JP,,..   D B  B Ir+c>|D]\}}||d}|dkr|dkrt|j}|||<t|j}|f||<t t |d||||j}t|j} t| |} | dxx|z cc<| dxx|z cc<t| | |<t|||g|}| | }|S)a? Pads an array which has 'none' as the boundary type. Used to simplify trimming arrays which use 'none'. >>> import dask.array as da >>> x = da.arange(6, chunks=3) >>> add_dummy_padding(x, {0: 1}, {0: 'none'}).compute() # doctest: +NORMALIZE_WHITESPACE array([..., 0, 1, 2, 3, 4, 5, ...]) rr>rDrvrrh) rrrxrwrrgetattrr@rr r}) r3r"r8rrrJ empty_shape empty_chunksempty out_chunks ax_chunkss r)add_dummy_paddingrs,  &&1 IIaOO ;;1q55qw--KKN>>L dLO7A&&!#g EahJZ]++I aLLLA LLL bMMMQ MMM!),,JqMUAu-A666A *%%A Hr+)r"r8r align_arraysrc t|trt|drtjdt gd}t |d|t |d|t |d||}|d|g}}t|s4td t|j td|Ds,td d |Dd } | |t| |t|rCd |D} tt!t#t%|| d di\} }tdDrt'|g|Ri|St)|D]\} } t+| jD]e}t| |t.rB| |dkr0t1d || | |fd}|||sd|vr|dj|d<fdt%|D}||t'|g|Ri|} || g|r t5t)|ddd} | | |ddtt8rgt;d|DfdDt/fdt+|| jD}fdt)|Dfdt)|Dt=| S| S)aMap a function over blocks of arrays with some overlap We share neighboring zones between blocks of the array, map a function, and then trim away the neighboring strips. If depth is larger than any chunk along a particular axis, then the array is rechunked. Note that this function will attempt to automatically determine the output array type before computing it, please refer to the ``meta`` keyword argument in ``map_blocks`` if you expect that the function will not succeed when operating on 0-d arrays. Parameters ---------- func: function The function to apply to each extended block. If multiple arrays are provided, then the function should expect to receive chunks of each array in the same order. args : dask arrays depth: int, tuple, dict or list, keyword only The number of elements that each block should share with its neighbors If a tuple or dict then this can be different per axis. If a list then each element of that list must be an int, tuple or dict defining depth for the corresponding array in `args`. Asymmetric depths may be specified using a dict value of (-/+) tuples. Note that asymmetric depths are currently only supported when ``boundary`` is 'none'. The default value is 0. boundary: str, tuple, dict or list, keyword only How to handle the boundaries. Values include 'reflect', 'periodic', 'nearest', 'none', or any constant value like 0 or np.nan. If a list then each element must be a str, tuple or dict defining the boundary for the corresponding array in `args`. The default value is 'reflect'. trim: bool, keyword only Whether or not to trim ``depth`` elements from each block after calling the map function. Set this to False if your mapping function already does this for you align_arrays: bool, keyword only Whether or not to align chunks along equally sized dimensions when multiple arrays are provided. This allows for larger chunks in some arrays to be broken into smaller ones that match chunk sizes in other arrays such that they are compatible for block function mapping. If this is false, then an error will be thrown if arrays do not already have the same number of blocks in each dimension. allow_rechunk: bool, keyword only Allows rechunking, otherwise chunk sizes need to match and core dimensions are to consist only of one chunk. **kwargs: Other keyword arguments valid in ``map_blocks`` Examples -------- >>> import numpy as np >>> import dask.array as da >>> x = np.array([1, 1, 2, 3, 3, 3, 2, 1, 1]) >>> x = da.from_array(x, chunks=5) >>> def derivative(x): ... return x - np.roll(x, 1) >>> y = x.map_overlap(derivative, depth=1, boundary=0) >>> y.compute() array([ 1, 0, 1, 1, 0, 0, -1, -1, 0]) >>> x = np.arange(16).reshape((4, 4)) >>> d = da.from_array(x, chunks=(2, 2)) >>> d.map_overlap(lambda x: x + x.size, depth=1, boundary='reflect').compute() array([[16, 17, 18, 19], [20, 21, 22, 23], [24, 25, 26, 27], [28, 29, 30, 31]]) >>> func = lambda x: x + x.size >>> depth = {0: 1, 1: 1} >>> boundary = {0: 'reflect', 1: 'none'} >>> d.map_overlap(func, depth, boundary).compute() # doctest: +NORMALIZE_WHITESPACE array([[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23], [24, 25, 26, 27]]) The ``da.map_overlap`` function can also accept multiple arrays. >>> func = lambda x, y: x + y >>> x = da.arange(8).reshape(2, 4).rechunk((1, 2)) >>> y = da.arange(4).rechunk(2) >>> da.map_overlap(func, x, y, depth=1, boundary='reflect').compute() # doctest: +NORMALIZE_WHITESPACE array([[ 0, 2, 4, 6], [ 4, 6, 8, 10]]) When multiple arrays are given, they do not need to have the same number of dimensions but they must broadcast together. Arrays are aligned block by block (just as in ``da.map_blocks``) so the blocks must have a common chunk size. This common chunking is determined automatically as long as ``align_arrays`` is True. >>> x = da.arange(8, chunks=4) >>> y = da.arange(8, chunks=2) >>> r = da.map_overlap(func, x, y, depth=1, boundary='reflect', align_arrays=True) >>> len(r.to_delayed()) 4 >>> da.map_overlap(func, x, y, depth=1, boundary='reflect', align_arrays=False).compute() Traceback (most recent call last): ... ValueError: Shapes do not align {'.0': {2, 4}} Note also that this function is equivalent to ``map_blocks`` by default. A non-zero ``depth`` must be defined for any overlap to appear in the arrays provided to ``func``. >>> func = lambda x: x.sum() >>> x = da.ones(10, dtype='int') >>> block_args = dict(chunks=(), drop_axis=0) >>> da.map_blocks(func, x, **block_args).compute() 10 >>> da.map_overlap(func, x, **block_args, boundary='reflect').compute() 10 >>> da.map_overlap(func, x, **block_args, depth=1, boundary='reflect').compute() 12 For functions that may not handle 0-d arrays, it's also possible to specify ``meta`` with an empty array matching the type of the expected result. In the example below, ``func`` will result in an ``IndexError`` when computing ``meta``: >>> x = np.arange(16).reshape((4, 4)) >>> d = da.from_array(x, chunks=(2, 2)) >>> y = d.map_overlap(lambda x: x + x[2], depth=1, boundary='reflect', meta=np.array(())) >>> y dask.array<_trim, shape=(4, 4), dtype=float64, chunksize=(2, 2), chunktype=numpy.ndarray> >>> y.compute() array([[ 4, 6, 8, 10], [ 8, 10, 12, 14], [20, 22, 24, 26], [24, 26, 28, 30]]) Similarly, it's possible to specify a non-NumPy array to ``meta``: >>> import cupy # doctest: +SKIP >>> x = cupy.arange(16).reshape((4, 4)) # doctest: +SKIP >>> d = da.from_array(x, chunks=(2, 2)) # doctest: +SKIP >>> y = d.map_overlap(lambda x: x + x[2], depth=1, boundary='reflect', meta=cupy.array(())) # doctest: +SKIP >>> y # doctest: +SKIP dask.array<_trim, shape=(4, 4), dtype=float64, chunksize=(2, 2), chunktype=cupy.ndarray> >>> y.compute() # doctest: +SKIP array([[ 4, 6, 8, 10], [ 8, 10, 12, 14], [20, 22, 24, 26], [24, 26, 28, 30]]) rzThe use of map_overlap(array, func, **kwargs) is deprecated since dask 2.17.0 and will be an error in a future release. To silence this warning, use the syntax map_overlap(func, array0,[ array1, ...,] **kwargs) instead.)funcr"r8rr"r8rzFirst argument must be callable function, not {} Usage: da.map_overlap(function, x) or: da.map_overlap(function, x, y, z)c3@K|]}t|tVdSr[)rr rNr3s r)rTzmap_overlap..s,22z!U##222222r+z}All variadic arguments must be arrays, not {} Usage: da.map_overlap(function, x) or: da.map_overlap(function, x, y, z)c6g|]}t|jSr)type__name__rs r)rOzmap_overlap..s!000aa!000r+ct|ts|gt|z}fdt||DS)Nc8g|]\}}|j|Srr:)rNr3afns r)rOz/map_overlap..coerce..s)777$!Q161 777r+)rrxrr`)xsargrs `r)coercezmap_overlap..coercesH#t$$ "%#b''/C7777#b#,,7777r+c jg|]0}ttt|j1Sr)rxreversedr_r:rs r)rOzmap_overlap..s0<<..s&;;9 Q;;;;;;r+)allrrs r)rOzmap_overlap..s7 K K KC;; ;;; ; ; K K Kr+r>zAsymmetric overlap is currently only implemented for boundary='none', however boundary for dimension {} in array argument {} is {}c<td|DsJdS)Nc3`K|])}|jD]}|D]}t|tuV *dSr[)rrint)rNr3ccrzs r)rTz.assert_int_chunksize..sHMMaMM""MMQ477c>MMMMMMMMr+)r)rs r)assert_int_chunksizez)map_overlap..assert_int_chunksizes+MM2MMMMMMMMMMr+rc>g|]\}}}t|||S))r"r8r)rG)rNr3rJbrs r)rOzmap_overlap..s@    Aq! QmDDD   r+c.|dj|d fS)Nrrr)rs r)zmap_overlap..s1Q49qte2Dr+)keyr drop_axisNc3NK|] }t|t|jV!dSr[)rr r:)rNrs r)rTzmap_overlap..s3HHa:a3G3GH16HHHHHHr+cg|]}|zSrr)rNrJndim_outs r)rOzmap_overlap..s999!X999r+c3$K|] }|v|V dSr[r)rNrSrs r)rTzmap_overlap..s-VVR"IBUBUbBUBUBUBUVVr+c(i|]\}}||Srr)rNnrSr"s r)rzmap_overlap..s#DDDeaQb DDDr+c(i|]\}}||Srr)rNrrSr8s r)rzmap_overlap..s#JJJEAr8B<JJJr+)rr callablewarningsr FutureWarningrindex TypeErrorformatrrrr9rArrxrr`r rr_r:rNotImplementedErrorrsortedpoprrr;)rr"r8rrrargskwargssigrinds_r r3rIr kept_axesrrs `` ` @@r) map_overlaprsJ$ %8DG#4#4 %  J     433CIIg&&e44syy,,dH==399V$$dD11!Wtfd D>>  99?T @S9T9T   22T222 2 2  99?004000::   888 F4 - -EvdHo66HK<x    411   D4)$)))&))A!  9T??(D(D E E Eb I! LaA;JJ{D11  )V,, (&K HH4HHHHHH9999y999IVVVV5a+>+>VVVVVIDDDDy/C/CDDDEJJJJYy5I5IJJJHQx000r+cHdttrf|zttr*tt t |ttrfdt |Dt |S)Nrc>i|]}||SrrM)rNrSdefaultr"s r)rz coerce_depth..s)BBBUYYr7++BBBr+)rrrrr`r_coerce_depth_type)r:r"rs `@r)r9r9sG }%"" 4%.Ste,,--%CBBBBBeDkkBBB T5 ) ))r+ct|D]X}t||tr#td||D||<@t||||<Y|S)Nc34K|]}t|VdSr[)rrs r)rTz$coerce_depth_type..s(66SVV666666r+)r_rrr)r:r"r s r)rrsm 4[[%% eAh & & %66U1X66666E!HH58}}E!HH Lr+c:dtttfsf|zttr*ttt |ttrfdt |DS)Nr>c>i|]}||SrrM)rNrSr8rs r)rz#coerce_boundary.. s)HHHbB R11HHHr+)rrrr`r_)r:r8rs `@r)rArAsG h . .&;%(E""4E$KK2233(D!!IHHHHHE$KKHHH Or+cddlm}tj|rt |n|f}tj|}tj|dkrtd|jt t|j }t|t|kr(tdt|d|j dnc|||j d}t|t|kr0td t|d t|d dg|j z}t||D]\}}||xx|d z z cc<t d t||j D}| |}t dt||j Dt d|Dz} ttjjj|t d|Dd|jt|j |j t|z| dd|| S)Nr)normalize_axis_tuplez&`window_shape` must contain values > 0zOSince axis is `None`, must provide window_shape for all dimensions of `x`; got z' window_shape elements and `x.ndim` is rT)allow_duplicatez8Must provide matching length window_shape and axis; got z window_shape elements and z axes elements.rc3BK|]\}}t|dz|VdSrVrrs r)rTz&sliding_window_view..1sF/3q! 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