1-Ph>ddlZdgZdZddZ d dddddZdS) Napply_parallelc $ddlm}g}t||dt|z z}d}|D]J}||kr%||z}|||zz}|dkr|f} n|f|dz z|fz} n|f} || ||z}Kt |S)aSplit the array into equal sized chunks based on the number of available processors. The last chunk in each dimension absorbs the remainder array elements if the number of CPUs does not divide evenly into the number of array elements. Examples -------- >>> _get_chunks((4, 4), 4) ((2, 2), (2, 2)) >>> _get_chunks((4, 4), 2) ((2, 2), (4,)) >>> _get_chunks((5, 5), 2) ((2, 3), (5,)) >>> _get_chunks((2, 4), 2) ((1, 1), (4,)) r)ceilg?)mathrintlenappendtuple) shapencpurchunksnchunks_per_dim used_chunksi regular_chunkremainder_chunk chunk_lenss [/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/skimage/util/apply_parallel.py _get_chunksrs& F$$tc%jj(89::;;OK ''   0M+q?/BCO!!-/ +-11DE#I J j!!!& ==cjddlm}t||jr|S|||S)Nrr) dask.arrayarray isinstanceArray from_array)rrdas r_ensure_dask_arrayr 2sC%"" ==v= . ..r)dtypecompute channel_axiscR ddlm} n#t$rtdwxYwi|t || j }| | |jz} ||j} ddlm } | } n#t$rd} YnwxYw| a| d| | | dzdz}tt|| }| | | | t|}nmt| | }n\| Zt||jdz kr?t|}| | |j| t|}|dkrd}n|d krd }n |d krd }n|d }| qt!j|r|g|jdz z}t|}t||jdz kr| | dt|}fd }t%||}|||||}|r|}|S)ab Map a function in parallel across an array. Split an array into possibly overlapping chunks of a given depth and boundary type, call the given function in parallel on the chunks, combine the chunks and return the resulting array. Parameters ---------- function : function Function to be mapped which takes an array as an argument. array : numpy array or dask array Array which the function will be applied to. chunks : int, tuple, or tuple of tuples, optional A single integer is interpreted as the length of one side of a square chunk that should be tiled across the array. One tuple of length ``array.ndim`` represents the shape of a chunk, and it is tiled across the array. A list of tuples of length ``ndim``, where each sub-tuple is a sequence of chunk sizes along the corresponding dimension. If None, the array is broken up into chunks based on the number of available cpus. More information about chunks is in the documentation `here `_. When `channel_axis` is not None, the tuples can be length ``ndim - 1`` and a single chunk will be used along the channel axis. depth : int or sequence of int, optional The depth of the added boundary cells. A tuple can be used to specify a different depth per array axis. Defaults to zero. When `channel_axis` is not None, and a tuple of length ``ndim - 1`` is provided, a depth of 0 will be used along the channel axis. mode : {'reflect', 'symmetric', 'periodic', 'wrap', 'nearest', 'edge'}, optional Type of external boundary padding. extra_arguments : tuple, optional Tuple of arguments to be passed to the function. extra_keywords : dictionary, optional Dictionary of keyword arguments to be passed to the function. dtype : data-type or None, optional The data-type of the `function` output. If None, Dask will attempt to infer this by calling the function on data of shape ``(1,) * ndim``. For functions expecting RGB or multichannel data this may be problematic. In such cases, the user should manually specify this dtype argument instead. .. versionadded:: 0.18 ``dtype`` was added in 0.18. compute : bool, optional If ``True``, compute eagerly returning a NumPy Array. If ``False``, compute lazily returning a Dask Array. If ``None`` (default), compute based on array type provided (eagerly for NumPy Arrays and lazily for Dask Arrays). channel_axis : int or None, optional If None, the image is assumed to be a grayscale (single channel) image. Otherwise, this parameter indicates which axis of the array corresponds to channels. Returns ------- out : ndarray or dask Array Returns the result of the applying the operation. Type is dependent on the ``compute`` argument. Notes ----- Numpy edge modes 'symmetric', 'wrap', and 'edge' are converted to the equivalent ``dask`` boundary modes 'reflect', 'periodic' and 'nearest', respectively. Setting ``compute=False`` can be useful for chaining later operations. For example region selection to preview a result or storing large data to disk instead of loading in memory. rNzACould not import 'dask'. Please install using 'pip install dask') cpu_countrwrapperiodic symmetricreflectedgenearestc|gRiSNr!)arrextra_argumentsextra_keywordsfunctions r wrapped_funcz$apply_parallel..wrapped_funcs#x@o@@@@@@rr)boundaryr")rr ImportError RuntimeErrorrrndimr multiprocessingr&NotImplementedErrorlistrinsertr r numpyisscalarr map_overlapr#)r3rrdepthmoder1r2r"r#r$rr r&r spatial_shaper4darrress` `` rrr;sd       R     111#ej0  ~   2 1 1 1 1 19;;DD"   DDD   #!-<-059I9K9K3LLM+mT::;;F MM,l(; < < <6]]FF --FF  !c&kkUZ!^&C&Cf lEK $=>>>v v~~      >%  /GuzA~.EU  u::a ' ' LLq ) ) )e AAAAAAA eF 3 3 3D   <U  K KCkkmm Js &A,, A;:A;r/)NrNr!N)r=__all__rr rr!rrrFs  )))X////  Z  ZZZZZZZr