biP36ddlmZddlZddlZddlZddlZddlmZddlm Z ddl m Z ddl Z ddlmZmZmZddlmZddlmZdd lmZmZmZmZmZdd lmZdd lmZm Z m!Z!dd l"m#Z#dd l$m%Z%ddl&m'Z'ddl(m)Z)m*Z*m+Z+ ddZ, ddZ- ddZ.y)) annotationsN)partial)product)Integral)composeget partition_all)config) blockwise)Array _concatenate2 asanyarray broadcast_to handle_out)ComplexWarning) compute_meta is_arraylike validate_axis)tokenize) lol_tuples)HighLevelGraph)funcname getargspecis_series_likec  tt|jttrft |j| t ddt|jvr t||}dt|jvr t||}t|r |j}tt|j}||f}| )t| } t||j}|||fz }t||g|d||xst d}t fd t#|j$D|_| 3t)|d r' t+||j,|j.dd }nd}t3||||||||  }|r2 dk7r-t fdt#|j$D|_| | |_t5| |S#t $r&t d|jd|jwxYw#t0$r't+||j,|j.d }Yt $rYwxYw)anGeneral version of reductions Parameters ---------- x: Array Data being reduced along one or more axes chunk: callable(x_chunk, [weights_chunk=None], axis, keepdims) First function to be executed when resolving the dask graph. This function is applied in parallel to all original chunks of x. See below for function parameters. combine: callable(x_chunk, axis, keepdims), optional Function used for intermediate recursive aggregation (see split_every below). If omitted, it defaults to aggregate. If the reduction can be performed in less than 3 steps, it will not be invoked at all. aggregate: callable(x_chunk, axis, keepdims) Last function to be executed when resolving the dask graph, producing the final output. It is always invoked, even when the reduced Array counts a single chunk along the reduced axes. axis: int or sequence of ints, optional Axis or axes to aggregate upon. If omitted, aggregate along all axes. keepdims: boolean, optional Whether the reduction function should preserve the reduced axes, leaving them at size ``output_size``, or remove them. dtype: np.dtype data type of output. This argument was previously optional, but leaving as ``None`` will now raise an exception. split_every: int >= 2 or dict(axis: int), optional Determines the depth of the recursive aggregation. If set to or more than the number of input chunks, the aggregation will be performed in two steps, one ``chunk`` function per input chunk and a single ``aggregate`` function at the end. If set to less than that, an intermediate ``combine`` function will be used, so that any one ``combine`` or ``aggregate`` function has no more than ``split_every`` inputs. The depth of the aggregation graph will be :math:`log_{split_every}(input chunks along reduced axes)`. Setting to a low value can reduce cache size and network transfers, at the cost of more CPU and a larger dask graph. Omit to let dask heuristically decide a good default. A default can also be set globally with the ``split_every`` key in :mod:`dask.config`. name: str, optional Prefix of the keys of the intermediate and output nodes. If omitted it defaults to the function names. out: Array, optional Another dask array whose contents will be replaced. Omit to create a new one. Note that, unlike in numpy, this setting gives no performance benefits whatsoever, but can still be useful if one needs to preserve the references to a previously existing Array. concatenate: bool, optional If True (the default), the outputs of the ``chunk``/``combine`` functions are concatenated into a single np.array before being passed to the ``combine``/``aggregate`` functions. If False, the input of ``combine`` and ``aggregate`` will be either a list of the raw outputs of the previous step or a single output, and the function will have to concatenate it itself. It can be useful to set this to False if the chunk and/or combine steps do not produce np.arrays. output_size: int >= 1, optional Size of the output of the ``aggregate`` function along the reduced axes. Ignored if keepdims is False. weights : array_like, optional Weights to be used in the reduction of `x`. Will be automatically broadcast to the shape of `x`, and so must have a compatible shape. For instance, if `x` has shape ``(3, 4)`` then acceptable shapes for `weights` are ``(3, 4)``, ``(4,)``, ``(3, 1)``, ``(1, 1)``, ``(1)``, and ``()``. Returns ------- dask array **Function Parameters** x_chunk: numpy.ndarray Individual input chunk. For ``chunk`` functions, it is one of the original chunks of x. For ``combine`` and ``aggregate`` functions, it's the concatenation of the outputs produced by the previous ``chunk`` or ``combine`` functions. If concatenate=False, it's a list of the raw outputs from the previous functions. weights_chunk: numpy.ndarray, optional Only applicable to the ``chunk`` function. Weights, with the same shape as `x_chunk`, to be applied during the reduction of the individual input chunk. If ``weights`` have not been provided then the function may omit this parameter. When `weights_chunk` is included then it must occur immediately after the `x_chunk` parameter, and must also have a default value for cases when ``weights`` are not provided. axis: tuple Normalized list of axes to reduce upon, e.g. ``(0, )`` Scalar, negative, and None axes have been normalized away. Note that some numpy reduction functions cannot reduce along multiple axes at once and strictly require an int in input. Such functions have to be wrapped to cope. keepdims: bool Whether the reduction function should preserve the reduced axes or remove them. NzMust specify dtypedtyperzWeights with shape z' are not broadcastable to x with shape T)axiskeepdimstokenrc3NK|]\}}|vrft|zn|ywN)len.0icr output_sizes Y/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/dask/array/_reductions_generic.py zreduction..s08<119Q!3s"%_meta)rrcomputing_metarr)name concatenate reduced_metac36K|]\}}|vrfn|ywr"r$s r)r*zreduction..s' 371aa4i[NQ . s)tuplerangendim isinstancerr ValueErrorrargsrrvaluesrrshaper float enumeratechunks_chunkshasattrrrr+ TypeError _tree_reducer)xchunk aggregaterrr split_everycombiner.outr/r(metaweightsindsr9wgttmpr0results ` ` r) reductionrOsef |U166]#$!w qvv &D }-..*U#(((U+*Y',,,IU3 a HH qvv D t9D! sAGG,C d   t  !%DQV C@I#**@UCK |7+ 'qwwdTRVL     ! FK1$ ;DSZZ;P    c6 ""a %cii[1##$77)-  * 'qwwdTL   s$(G"%H"/H-II Ic |xstjdd}t|tr!|D cic]} | |j| d}} ndt|trIt j t|dt|xsdz zd} tj|| }n tdd} t|jD]\\} } | |vs || dk7stt j | tjtj| || } ^t!|xs||d}|r$t#|t!t$t'|}t)| dz D](}t+|||d||xst-|xs|d z| }*t!|||}|r$t#|t!t$t'|}t+||||||xs t-|d z| Scc} w) zPerform the tree reduction step of a reduction. Lower level, users should use ``reduction`` or ``arg_reduction`` directly. rFr1z#split_every must be a int or a dictTr-)axesz-partial)rr.r0z -aggregate)rrr.r0)r rr7dictrbuiltinsmaxintr#fromkeysr8r= numblocksmathceillogrrr sortedr5partial_reducer)rCrErrrrFrGr.r/r0kndepthr&func_s r)rBrBs"=M1!=K+t$9=>Aq+//!Q//> > K * LL[Q#d).q-ABCQ GmmD!, >?? E!++&U1  A! 3 UDIIdhhq+a.6Q,RSTEU 7'idT BDtW]FG 519       8(7#7i8JF%    94( ;DtW]FG   )hy)\ 9! 9?sG-c |xs t|dzt||||z}t|jDcgc]3\}}t t j |dt|5} }}ttttt| } t|jD cgc]*\}} |vrtdt || Dn| ,} }} |sJt|jDcgc] }|vs| c}fd} t| | } t | | } i}t| t| D]\}}t|Dcic]\}}t|dk(s|vs||d!}}}tfdt|D}t!|j"ft|j||}||f||f|z<t%j&|||g}|j(}| ||d }t1|rT|jt| k7rt@5tCjD5|jGdrtCjHdtJ|jM|}ddddddt;||| |Scc}}wcc} }wcc}wcc}}w#t*$r3 ||}n%#t,$r}d t/|vr|}Yd}~nd}~wwxYwY_t,$rYjwxYw#1swYxYw#1swYxYw)aPartial reduction across multiple axes. Parameters ---------- func : function x : Array split_every : dict Maximum reduction block sizes in each dimension. Examples -------- Reduce across axis 0 and 2, merging a maximum of 1 block in the 0th dimension, and 3 blocks in the 2nd dimension: >>> partial_reduce(np.min, x, {0: 1, 2: 3}) # doctest: +SKIP -r1c3 K|]}dyw)r1Nr3)r%ps r)r*z!partial_reduce..-s :Aa :s ct|Sr")r)r_out_axiss r)z partial_reduce..2s3x+rc32K|]}|dvs |yw)rNr3)r%r&rFs r)r*z!partial_reduce..:sD1! 0CQDs ) dependenciesNT)r,z&zero-size array to reduction operation)rr)varmomentignore)rI)'rrr=rYlistr rr5rmapr#r>r4r6ziprTrr.rfrom_collectionsr+rAr8strrsumreshapenpisscalarr contextlibsuppressAttributeErrorwarningscatch_warnings startswith simplefilterrastype)rbrCrFrrr.r0r&r`partskeysr' out_chunksgetterdskr_rgjfreedummyggraphrIeris ` @r)r^r^sJ(  $3&$;RW)XX   ,  Q ];??1a0%( ;< E  Cs3/ 0D ) Q?@;>N :{1~q9 ::TUUJ $QVV}E!0DAE+64 &,- CD'5/*%1"+A, A#a&A+!;BVAqtG  D ! DD y%-u = !9TGaK %  + +D#QC HE 77D  T:D Ddii3z?: z?a 88:D<<s: 67D {{4UD*E::   0 &(2I2I2K &01%%h?;;u%D & &UD*488q   F   ( (L) (;s1vE'D (     & & & &s8L4/L? L" L"L'1L'6L'? 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