IR-eHAdZddlZddlZddlmZddlZgdZGddZGddeej Z Gd d e Z d Z d ZdZdS)z>The ShapedLikeNDArray mixin class and shape-related functions.N) zip_longest)NDArrayShapeMethodsShapedLikeNDArraycheck_broadcastIncompatibleShapeErrorsimplify_basic_index unbroadcastcfeZdZdZdZdZdZdZdZdZ e dZ d Z d Z d ZddZd S)raMixin class to provide shape-changing methods. The class proper is assumed to have some underlying data, which are arrays or array-like structures. It must define a ``shape`` property, which gives the shape of those data, as well as an ``_apply`` method that creates a new instance in which a `~numpy.ndarray` method has been applied to those. Furthermore, for consistency with `~numpy.ndarray`, it is recommended to define a setter for the ``shape`` property, which, like the `~numpy.ndarray.shape` property allows in-place reshaping the internal data (and, unlike the ``reshape`` method raises an exception if this is not possible). This class only provides the shape-changing methods and is meant in particular for `~numpy.ndarray` subclasses that need to keep track of other arrays. For other classes, `~astropy.utils.shapes.ShapedLikeNDArray` is recommended. c.|d|S)N __getitem___applyselfitems 4lib/python3.11/site-packages/astropy/utils/shapes.pyr zNDArrayShapeMethods.__getitem__0s{{=$///c$|jdg|Ri|S)zReturn an instance containing copies of the internal data. Parameters are as for :meth:`~numpy.ndarray.copy`. copyr rargskwargss rrzNDArrayShapeMethods.copy3s& t{63D333F333rc$|jdg|Ri|S)aReturns an instance containing the same data with a new shape. Parameters are as for :meth:`~numpy.ndarray.reshape`. Note that it is not always possible to change the shape of an array without copying the data (see :func:`~numpy.reshape` documentation). If you want an error to be raise if the data is copied, you should assign the new shape to the shape attribute (note: this may not be implemented for all classes using ``NDArrayShapeMethods``). reshaper rs rrzNDArrayShapeMethods.reshape:s&t{96t666v666rc$|jdg|Ri|S)ahReturn an instance with the array collapsed into one dimension. Parameters are as for :meth:`~numpy.ndarray.ravel`. Note that it is not always possible to unravel an array without copying the data. If you want an error to be raise if the data is copied, you should should assign shape ``(-1,)`` to the shape attribute. ravelr rs rrzNDArrayShapeMethods.ravelFs&t{74T444V444rc$|jdg|Ri|S)zReturn a copy with the array collapsed into one dimension. Parameters are as for :meth:`~numpy.ndarray.flatten`. flattenr rs rrzNDArrayShapeMethods.flattenPs& t{96t666v666rc$|jdg|Ri|S)zReturn an instance with the data transposed. Parameters are as for :meth:`~numpy.ndarray.transpose`. All internal data are views of the data of the original. transposer rs rr zNDArrayShapeMethods.transposeWs& t{;8888888rcD|jdkr|S|S)zReturn an instance with the data transposed. Parameters are as for :attr:`~numpy.ndarray.T`. All internal data are views of the data of the original. )ndimr rs rTzNDArrayShapeMethods.T_s$ 9q==K>>## #rc$|jdg|Ri|S)zReturn an instance with the given axes interchanged. Parameters are as for :meth:`~numpy.ndarray.swapaxes`: ``axis1, axis2``. All internal data are views of the data of the original. swapaxesr rs rr'zNDArrayShapeMethods.swapaxesks&t{:7777777rc$|jdg|Ri|S)zReturn an instance with the specified diagonals. Parameters are as for :meth:`~numpy.ndarray.diagonal`. All internal data are views of the data of the original. diagonalr rs rr)zNDArrayShapeMethods.diagonalts& t{:7777777rc$|jdg|Ri|S)zReturn an instance with single-dimensional shape entries removed. Parameters are as for :meth:`~numpy.ndarray.squeeze`. All internal data are views of the data of the original. squeezer rs rr+zNDArrayShapeMethods.squeeze|s& t{96t666v666rNraisecV|tdS|d|||S)zReturn a new instance formed from the elements at the given indices. Parameters are as for :meth:`~numpy.ndarray.take`, except that, obviously, no output array can be given. Nz$cannot pass 'out' argument to 'take.take)axismode)NotImplementedErrorr)rindicesr/outr0s rr.zNDArrayShapeMethods.takes2 ?&'MNN N{{67D{AAAr)NNr,)__name__ __module__ __qualname____doc__r rrrrr propertyr%r'r)r+r.rrrrs6000444 7 7 7555777999 $ $X $888888777 B B B B B Brrc eZdZdZeejdZejdZedZ edZ edZ dZ dZ d Zd Zejejejejejejejejejejejejejh Zd ejj j!DZ"d e"ej#<d Z$dS)raMixin class to provide shape-changing methods. The class proper is assumed to have some underlying data, which are arrays or array-like structures. It must define a ``shape`` property, which gives the shape of those data, as well as an ``_apply`` method that creates a new instance in which a `~numpy.ndarray` method has been applied to those. Furthermore, for consistency with `~numpy.ndarray`, it is recommended to define a setter for the ``shape`` property, which, like the `~numpy.ndarray.shape` property allows in-place reshaping the internal data (and, unlike the ``reshape`` method raises an exception if this is not possible). This class also defines default implementations for ``ndim`` and ``size`` properties, calculating those from the ``shape``. These can be overridden by subclasses if there are faster ways to obtain those numbers. cdS)z!The shape of the underlying data.Nr9r$s rshapezShapedLikeNDArray.shapercdS)aCreate a new instance, with ``method`` applied to underlying data. The method is any of the shape-changing methods for `~numpy.ndarray` (``reshape``, ``swapaxes``, etc.), as well as those picking particular elements (``__getitem__``, ``take``, etc.). It will be applied to the underlying arrays (e.g., ``jd1`` and ``jd2`` in `~astropy.time.Time`), with the results used to create a new instance. Parameters ---------- method : str Method to be applied to the instance's internal data arrays. args : tuple Any positional arguments for ``method``. kwargs : dict Any keyword arguments for ``method``. Nr9)methodrrs rrzShapedLikeNDArray._applyr=rc*t|jS)z?The number of dimensions of the instance and underlying arrays.)lenr<r$s rr#zShapedLikeNDArray.ndims4:rc(d}|jD]}||z}|S)z5The size of the object, as calculated from its shape.r<)rsizeshs rrEzShapedLikeNDArray.sizes**  B BJDD rc|jdkS)Nr9rDr$s risscalarzShapedLikeNDArray.isscalarszRrcd|jrtd|jjd|jdS)NzScalar z object has no len()r)rH TypeError __class__r4r<r$s r__len__zShapedLikeNDArray.__len__s8 = WUdn&=UUUVV Vz!}rc|jdkS)z>Any instance should evaluate to True, except when it is empty.r)rEr$s r__bool__zShapedLikeNDArray.__bool__sy1}rc |d|S#t$r&|jrtd|jjdwxYw)Nr scalar z object is not subscriptable.)r IndexErrorrHrJ_ShapedLikeNDArray__classr4rs rr zShapedLikeNDArray.__getitem__sj ;;}d33 3   } Tdl3TTT  s 0Acjjrtdjjdfd}|S)NrPz object is not iterable.c3^KttD] }|V dSN)rangerA)idxrs r self_iterz-ShapedLikeNDArray.__iter__..self_iters=SYY''  3i  r)rHrJrKr4)rrXs` r__iter__zShapedLikeNDArray.__iter__sY = M$.1MMM      y{{rc xi|]7}|dvtt|ddddddd||8S))alensort partitionmaxminroundallany)amaxaminaroundround_alltruesometrue)getattrnpget).0names r zShapedLikeNDArray. se     4 4 4 D    #dD// 4 4 4rrc|jvrtjurddnTtjtjtjhvr/t|dkrtfd|DS||durtS|j g|ddRiS||durH|j vr?t||j d}|!t|r||ddiS|Sj|iS)z%Wrap numpy functions that make sense.subokTrCc3*K|] }|fiVdSrUr9)rlargfunctionrs r z7ShapedLikeNDArray.__array_function__../s5EEXXc44V44EEEEEErrN)_APPLICABLE_FUNCTIONSrj broadcast_to setdefault atleast_1d atleast_2d atleast_3drAtupleNotImplementedr_METHOD_FUNCTIONSricallable __wrapped__)rrstypesrrr?s ` ` r__array_function__z$ShapedLikeNDArray.__array_function__$sO t1 1 12?**!!'40000R]BM2=IIIIIMMEEEEEEEEEEE47""%%4;x=$qrr(===f== = 47??x4+AAAT4#9(#CTJJF!F##"!6486v666"M$x#T4V444rN)%r4r5r6r7r8abcabstractmethodr<rr#rErHrLrNr rYrjmoveaxisrollaxisrxryrz expand_dimsrvflipfliplrflipudrot90rolldeleterucore fromnumeric__all__r}rrr9rrrrs400X0    (XX  X        "             &  G'/   "(bg55555rr) metaclassceZdZfdZxZS)rcPt||||dSrU)super__init__)rshape_a shape_a_idxshape_b shape_b_idxrKs rrzIncompatibleShapeError.__init__Gs' +w DDDDDr)r4r5r6r __classcell__)rKs@rrrFsAEEEEEEEEErrct|dkrdSt|dkr|dSd|D}g}t|ddiD]f}d}d}t|D];\}}|dkr |dkr|}|}||krt||||||<||gt |dddS)a Determines whether two or more Numpy arrays can be broadcast with each other based on their shape tuple alone. Parameters ---------- *shapes : tuple All shapes to include in the comparison. If only one shape is given it is passed through unmodified. If no shapes are given returns an empty `tuple`. Returns ------- broadcast : `tuple` If all shapes are mutually broadcastable, returns a tuple of the full broadcast shape. rr9rCc34K|]}t|VdSrU)reversed)rlr<s rrtz"check_broadcast..bs(;;5x;;;;;;r fillvalueN)rAr enumeraterappendr{)shapesreversed_shapes full_shapedimsmax_dim max_dim_idxrWdims rrrKs$ 6{{ar V  ay;;F;;;OJ_:::## !$  HCaxx!||! ,;'fSk3 '"""" DDbD! " ""rc |jdkr|S|td|jD}tdt |jD|j}||j|dS)a Given an array, return a new array that is the smallest subset of the original array that can be re-broadcasted back to the original array. See https://stackoverflow.com/questions/40845769/un-broadcasting-numpy-arrays for more details. rc3`K|])}|dkrtddntdV*dS)rrCN)slice)rlstrides rrtzunbroadcast..s< X XfkkuQ{{{uT{{ X X X X X Xrc3,K|]\}}|dk |VdS)rCNr9)rliss rrtzunbroadcast..s*99v11q55555599rN)r#r{stridesnextrr<r)arrayfirst_not_unitys rr r {s zQ  X X%- X X XXX E 995;//9995:O ==_%5%56 7 77rc t|}t|ttfs|f}t|}t|vr|tdkrt d|t}|t|t|z }t|D]2}||z}| |td||d3t||krtd|d|dt|D] }|t|kr||}t|trFt| ||} | ddkrd| d<t| ||<yt|tjrrs2DD !!!!!!   yByByByByByByByBxs5s5s5s5s5+s{s5s5s5s5lEEEEEZEEE -#-#-#`888.@@@@@r