IR-endZddlZddlmZgdZdZdZeejfdZ ed d Z dS) z= This module includes helper functions for array operations. N)support_nddata)reshape_as_blocks block_reduceblock_replicatectj|}tj|}tj|dkrt d|jdkr-t |dkrtj||j}t ||jkrt d|t}tj||krt d||fS)Nrz-block_size elements must be strictly positiverzTblock_size must be a scalar or have the same length as the number of data dimensionsz$block_size elements must be integers) np asanyarray atleast_1dany ValueErrorndimlenrepeatastypeint)data block_sizeblock_size_ints 5lib/python3.11/site-packages/astropy/nddata/blocks.py_process_block_inputsr s =  Dz**J vjAoJHIII y1}}ZA--Yz4955  :$)## 6    &&s++N vn *++A?@@@  c.t||\}}tjtj|j|dkrt dtj|j|z}tdt||D}ttdt|d}ttdt|d}| | ||zS)a Reshape a data array into blocks. This is useful to efficiently apply functions on block subsets of the data instead of using loops. The reshaped array is a view of the input data array. .. versionadded:: 4.1 Parameters ---------- data : ndarray The input data array. block_size : int or array-like (int) The integer block size along each axis. If ``block_size`` is a scalar and ``data`` has more than one dimension, then ``block_size`` will be used for for every axis. Each dimension of ``block_size`` must divide evenly into the corresponding dimension of ``data``. Returns ------- output : ndarray The reshaped array as a view of the input ``data`` array. Examples -------- >>> import numpy as np >>> from astropy.nddata import reshape_as_blocks >>> data = np.arange(16).reshape(4, 4) >>> data array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) >>> reshape_as_blocks(data, (2, 2)) array([[[[ 0, 1], [ 4, 5]], [[ 2, 3], [ 6, 7]]], [[[ 8, 9], [12, 13]], [[10, 11], [14, 15]]]]) rzXEach dimension of block_size must divide evenly into the corresponding dimension of datac3$K|] }|D]}|V dS)N).0ijks r z$reshape_as_blocks..\s/GGBBGGqaGGGGGGGrr) rr r modshaper arraytupleziprangerreshape transpose)rrnblocks new_shape nblocks_idx block_idxs rrr$s^-T:>>D* vbfTZ,,122  7   htz""j0GGG#gz":":GGGGGIaY3344KeAs9~~q1122I << " " , ,[9-D E EErct||\}}tj|j|z}||z}t |jD]U}|j|||kr<|d|}|d||}|d|}Vt||}tt |j|j}|||S)a% Downsample a data array by applying a function to local blocks. If ``data`` is not perfectly divisible by ``block_size`` along a given axis then the data will be trimmed (from the end) along that axis. Parameters ---------- data : array-like The data to be resampled. block_size : int or array-like (int) The integer block size along each axis. If ``block_size`` is a scalar and ``data`` has more than one dimension, then ``block_size`` will be used for for every axis. func : callable, optional The method to use to downsample the data. Must be a callable that takes in a 4D `~numpy.ndarray` (the 2D `~numpy.ndarray` input into `block_reduce` gets reshaped as 4D) and has an ``axis`` keyword that accepts tuples. This function will be called with ``axis=(2, 3)`` and it should return a 2D array. The default is `~numpy.sum`, which provides block summation (and conserves the data sum). Returns ------- output : array-like The resampled data. Note the depending on the input ``func``, the dtype of the output array may not match the input array. Examples -------- >>> import numpy as np >>> from astropy.nddata import block_reduce >>> data = np.arange(16).reshape(4, 4) >>> block_reduce(data, 2) # doctest: +FLOAT_CMP array([[10, 18], [42, 50]]) >>> block_reduce(data, 2, func=np.mean) # doctest: +FLOAT_CMP array([[ 2.5, 4.5], [ 10.5, 12.5]]) rNaxis) rr r#r"r&rswapaxesrr$)rrfuncr) size_initr/reshapeds rrrcs^-T:>>D*htz""j0G*$Idi  ** :d y . .==D))D))D/)*D==D))D z22H ty(-00 1 1D 4t $ $ $$rTct||\}}t|jD]}tj||||} |r|tj|z }|S)a Upsample a data array by block replication. Parameters ---------- data : array-like The data to be block replicated. block_size : int or array-like (int) The integer block size along each axis. If ``block_size`` is a scalar and ``data`` has more than one dimension, then ``block_size`` will be used for for every axis. conserve_sum : bool, optional If `True` (the default) then the sum of the output block-replicated data will equal the sum of the input ``data``. Returns ------- output : array-like The block-replicated data. Note that when ``conserve_sum`` is `True`, the dtype of the output array will be float. Examples -------- >>> import numpy as np >>> from astropy.nddata import block_replicate >>> data = np.array([[0., 1.], [2., 3.]]) >>> block_replicate(data, 2) # doctest: +FLOAT_CMP array([[0. , 0. , 0.25, 0.25], [0. , 0. , 0.25, 0.25], [0.5 , 0.5 , 0.75, 0.75], [0.5 , 0.5 , 0.75, 0.75]]) >>> block_replicate(data, 2, conserve_sum=False) # doctest: +FLOAT_CMP array([[0., 0., 1., 1.], [0., 0., 1., 1.], [2., 2., 3., 3.], [2., 2., 3., 3.]]) r.)rr&rr rprod)rr conserve_sumis rrrsoT-T:>>D* 49  66yz!}1555*bgj))) Kr)T) __doc__numpyr decoratorsr__all__rrsumrrrrrr=s&&&&&& B B B   .<F<F<F~(*<%<%<%<%~111111r