bi0dZddlmZddlZddlmZmZmZddlm Z ddl m Z ddl Z ddlmZddlmZd Ze j&Ze j(Ze j*Ze j,Zee j.Zee j0Zee j2Zee j4Ze j6Ze j8Ze j:Ze j<Ze j>Ze j@Z e jBZ!e jDZ"e jFZ#ejHe%5e jLZ&ddde jNZ'ejHe%5e jPZ(ddde jRZ)ejHe%5e jTZ*ddddd Z+dd Z,d Z-d Z.dZ/dZ0dZ1ddZ2ddZ3ddZ4ddZ5dZ6dZ7dZ8y#1swYxYw#1swYyxYw#1swYRxYw)z+A set of NumPy functions to apply per chunk) annotationsN) ContainerIterableSequencewraps)Integral)concat)flattenc0tdfd }|S)zU A wrapper for functions that don't provide keepdims to ensure that they do. c |g|d|i|}|s|S|}|t|j}t|ttt fs|g}t }t|jD]}||vr|dz } |tdfz }||}|S)NaxisN)rangendim isinstancerrrtupleslice) xrkeepdimsargskwargsraxesr_slice each_axis a_callables K/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/dask/array/chunk.pykeepdims_wrapped_callablez3keepdims_wrapper..keepdims_wrapped_callables q 54 5d 5f 5H <=D$Hh ?@6D'qvv *ID 7"E$K>)  * gJ)NNr)rrs` rkeepdims_wrapperr!s$  :2 %$r c t|jD] }|vsd|<|r,tfdt|jD}||}tt t|jDcgc]}|j||z|fc}}||j |fdttd|jdzdi|Scc}w)aCoarsen array by applying reduction to fixed size neighborhoods Parameters ---------- reduction: function Function like np.sum, np.mean, etc... x: np.ndarray Array to be coarsened axes: dict Mapping of axis to coarsening factor Examples -------- >>> x = np.array([1, 2, 3, 4, 5, 6]) >>> coarsen(np.sum, x, {0: 2}) array([ 3, 7, 11]) >>> coarsen(np.max, x, {0: 3}) array([3, 6]) Provide dictionary of scale per dimension >>> x = np.arange(24).reshape((4, 6)) >>> x 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]]) >>> coarsen(np.min, x, {0: 2, 1: 3}) array([[ 0, 3], [12, 15]]) You must avoid excess elements explicitly >>> x = np.array([1, 2, 3, 4, 5, 6, 7, 8]) >>> coarsen(np.min, x, {0: 3}, trim_excess=True) array([1, 4]) c3rK|].\}}||zrtd||z n tdd0yw)rNr).0idrs r zcoarsen..sD 1)*DG E!q47{^ $tT9J J s47r)rrr enumerateshaper reshape) reductionrr trim_excessrr'indnewshapes ` rcoarsenr2UsP166] D=DG !!''*   cFVuQVV}U!aggajDG3T!W=UVWH QYYx( XuU1affqj!5L/M XQW XXVs5!C ct|tr|g|jz}t|tr2t |jDcgc]}|j |d}}|t d|DScc}w)aCTrim boundaries off of array >>> x = np.arange(24).reshape((4, 6)) >>> trim(x, axes={0: 0, 1: 1}) array([[ 1, 2, 3, 4], [ 7, 8, 9, 10], [13, 14, 15, 16], [19, 20, 21, 22]]) >>> trim(x, axes={0: 1, 1: 1}) array([[ 7, 8, 9, 10], [13, 14, 15, 16]]) rc3>K|]}t||r| ndywrr%)r&axs rr)ztrim..sAb5BbSD1As)rr rdictrgetr)rrr's rtrimr8sg$!v$(-aff 61A66 UADA A BB7sA8c|dusJdt||jk\r|Stj|| }|dkDr t | dn t | |t fdt |jDS)aChunk and combine function of topk Extract the k largest elements from a on the given axis. If k is negative, extract the -k smallest elements instead. Note that, unlike in the parent function, the returned elements are not sorted internally. TrrNc3BK|]}|k(rn tdywrr%r&r'rk_slices rr)ztopk..s PQa4i7U4[8P)absr,np partitionrrrr)akrrr=s ` @rtopkrDs t   7D 1v Q&A!"QeQBoE1"IG UP%-P P QQr c|dusJt|||}dtj|}|dkr|S|tfdt |j DS)zmFinal aggregation function of topk Invoke topk one final time and then sort the results internally. Trr:c3XK|]!}|k(r tdddn td#ywNr%r&r'rs rr)z!topk_aggregate... DEQ$YE$b !E$K ? '*)rDr@sortrrr)rBrCrrs ` rtopk_aggregaterMsr t   Q4"A 7D A1u   INqvv   r c ||fS)z^Preparatory step for argtopk Put data together with its original indices in a tuple. )rBidxs rargtopk_preprocessrQs c6Mr c  |dusJdt|trtt|}tj|Dcgc]\}}| c}}}tj|Dcgc]%\}}tj ||j 'c}}}n|\}}t||j k\r|Stj|| } |dkDr t| dn t|  | t fdt|jD} tj|| tj|| fScc}}wcc}}w)a)Chunk and combine function of argtopk Extract the indices of the k largest elements from a on the given axis. If k is negative, extract the indices of the -k smallest elements instead. Note that, unlike in the parent function, the returned elements are not sorted internally. Trr:Nc3BK|]}|k(rn tdywrr%r<s rr)zargtopk..s Sdgd ;Sr>)rlistr r@ concatenate broadcast_tor,r? argpartitionrrrrtake_along_axis) a_plus_idxrCrrai_rBidxirPidx2r=s ` @rargtopkr^s' t   7D*d#'*-. NNJ752qB7 >nn=G HTR__T288 , H$ 3 1v ??1qbt ,D!"QeQBoE1"IG SU166]SS TD  at ,b.@.@dD.Q QQ8 Hs  E /*E c$|dusJt|dkDr|n|d}t|||\}}dtj|}tj||}|dkr|S|t fdt |jDS)zFinal aggregation function of argtopk Invoke argtopk one final time, sort the results internally, drop the data and return the index only. Tr#rr:c3XK|]!}|k(r tdddn td#ywrGr%rIs rr)z$argtopk_aggregate..rJrK)lenr^r@argsortrXrrr)rYrCrrrBrPr]s ` rargtopk_aggregatercs t  ":2 1 J ZD( 3FAs 7D ::ad #D  S$ -C1u   INsxx   r cRddlm}||||||}t||kDr|ddS|S)Nr) arange_safelikerH)dask.array.utilsrera)startstopsteplengthdtypergreress rarangeros3, eT4T :C3x&(3s81c1r cddlm}t||r|j}t||r|j}t j |||||S)Nr)Array)endpointrm)dask.array.corerqrcomputer@linspace)rirjnumrrrmrqs rruru sF%% $||~ ;;udC(% HHr c (|j|fi|Sr)astype)r astype_dtypers rrxrxs 188L +F ++r ch|dk(r) tj||}|j|S tj||}|j j|j S#t$rtj|}YnwxYw#t$rtj|}YiwxYw)NCrf)r@ascontiguousarray TypeErrorviewasfortranarrayT)rrmorders rr~r~s | ($$QQ/Avve} %!!!!,Assxx    ($$Q'A (  %!!!$A %s"A,B,B  B B10B1cHddlm}m}|||jtj t j dk|z|z dk\|jkz}||tfdt|jDS)aChunk function of `slice_with_int_dask_array_on_axis`. Slice one chunk of x by one chunk of idx. Parameters ---------- x: ndarray, any dtype, any shape i-th chunk of x idx: ndarray, ndim=1, dtype=any integer j-th chunk of idx (cartesian product with the chunks of x) offset: ndarray, shape=(1, ), dtype=int64 Index of the first element along axis of the current chunk of x x_size: int Total size of the x da.Array along axis axis: int normalized axis to take elements from (0 <= axis < x.ndim) Returns ------- x sliced along axis, using only the elements of idx that fall inside the current chunk. r) asarray_safemeta_from_arrayrfc3BK|]}|k(rn tdywrr%)r&r'rrPs rr)z,slice_with_int_dask_array..Ts L!t)3t4Lr>) rhrrrxr@int64wherer,rrr)rrPoffsetx_sizerrr idx_filters ` ` rslice_with_int_dask_arrayr)s,? s!3 4C **RXX C ((37C&L# .C ,C(sQWWT]23J j/C ULeAFFmL L MMr c |jtj}tj|dk|t |z|}d}d}tj | |D]b}||k\|||zkz}tj |} tj||dz |zdz ||z }|jdkDs[||dz }d|t fdt|jDS)aSFinal aggregation function of `slice_with_int_dask_array_on_axis`. Aggregate all chunks of x by one chunk of idx, reordering the output of `slice_with_int_dask_array`. Note that there is no combine function, as a recursive aggregation (e.g. with split_every) would not give any benefit. Parameters ---------- idx: ndarray, ndim=1, dtype=any integer j-th chunk of idx chunk_outputs: ndarray concatenation along axis of the outputs of `slice_with_int_dask_array` for all chunks of x and the j-th chunk of idx x_chunks: tuple dask chunks of the x da.Array along axis, e.g. ``(3, 3, 2)`` axis: int normalized axis to take elements from (0 <= axis < x.ndim) Returns ------- Selection from all chunks of x for the j-th chunk of idx, in the correct order rr#rHc3BK|]}|k(rn tdywrr%)r&r'r idx_finals rr)z6slice_with_int_dask_array_aggregate..s% 89dId 3 r>) rxr@rrsum zeros_likecumsumsizerrr) rP chunk_outputsx_chunksrx_chunk_offsetchunk_output_offsetx_chunkridx_cumrs ` @r#slice_with_int_dask_array_aggregaterWs4 **RXX C ((37C#h-/ 5CN  c"I/^+nw6N0NO ))J'RXXj'A+8K*KQOO '! <rs1"99%F ff wwffff ")) $ R\\ * ")) $ R\\ * ffff  ** ]] LL   wwZ(jjG ffZ( YYF ffZ( YYF6YrC,R$$R:,2 I, !+N\5p#Q   s$, G G$ G0G!$G-0G9