Vpf&ddlmZddlmZddlmZmZddlZddlm Z ddl m Z ddl m Z mZmZmZmZmZmZmZddlmZmZddlZgd ZddZGddZeZGddZeZeee e!fZ"GddZ#Gdde#Z$e$Z%Gdde#Z&e&Z'ej(Z(ej)Z)dS)) annotations)Iterable)AnyUnionN)core)promote_dtypes)arangearray concatenate expand_dimslinspacemeshgridstack transpose)Array ArrayLike)c_ index_expmgridogridr_s_ssliceop_namestrreturnrc dtjd|jd|pd}tjd|jd|}tjd|jd|pd}t j|r,t||tt|}nt|||}|S)Nzslice start of jnp.rzslice stop of jnp.zslice step of jnp.) rconcrete_or_errorstartstopstepnp iscomplexr intabsr )rrr!r"r#newobjs [/var/www/html/nettyfy-visnx/env/lib/python3.11/site-packages/jax/_src/numpy/index_tricks.py_make_1d_grid_from_slicer*"s  qw!@w!@!@ B B GEF af >W > > @ @$ af >W > > @ @ ECD\$' eT3s4yy>> 2 2FF E4 & &F -ceZdZdZddZdS) _Mgrida?Return dense multi-dimensional "meshgrid". LAX-backend implementation of :obj:`numpy.mgrid`. This is a convenience wrapper for functionality provided by :func:`jax.numpy.meshgrid` with ``sparse=False``. See Also: jnp.ogrid: open/sparse version of jnp.mgrid Examples: Pass ``[start:stop:step]`` to generate values similar to :func:`jax.numpy.arange`: >>> jnp.mgrid[0:4:1] Array([0, 1, 2, 3], dtype=int32) Passing an imaginary step generates values similar to :func:`jax.numpy.linspace`: >>> jnp.mgrid[0:1:4j] Array([0. , 0.33333334, 0.6666667 , 1. ], dtype=float32) Multiple slices can be used to create broadcasted grids of indices: >>> jnp.mgrid[:2, :3] Array([[[0, 0, 0], [1, 1, 1]], [[0, 1, 2], [0, 1, 2]]], dtype=int32) keyslice | tuple[slice, ...]rrcNt|trt|dSd|D}tjd5t |}dddn #1swxYwYt |ddd}t|dkrtdSt|dS) Nrrc38K|]}t|dVdS)rr1Nr*.0ks r) z%_Mgrid.__getitem__..Q1YYPQ77KKKYYYYYYr+standardijFindexingsparser) isinstancerr*jaxnumpy_dtype_promotionrrlenr r)selfr.output output_arrs r) __getitem__z_Mgrid.__getitem__Ns#u< %c7 ; ; ;;YYUXYYYF ": . .''v&f'''''''''''''''6D???J :! AYY Q   AA!$A!N)r.r/rr__name__ __module__ __qualname____doc__rEr+r)r-r-1s28       r+r-ceZdZdZddZdS) _OgridaReturn open multi-dimensional "meshgrid". LAX-backend implementation of :obj:`numpy.ogrid`. This is a convenience wrapper for functionality provided by :func:`jax.numpy.meshgrid` with ``sparse=True``. See Also: jnp.mgrid: dense version of jnp.ogrid Examples: Pass ``[start:stop:step]`` to generate values similar to :func:`jax.numpy.arange`: >>> jnp.ogrid[0:4:1] Array([0, 1, 2, 3], dtype=int32) Passing an imaginary step generates values similar to :func:`jax.numpy.linspace`: >>> jnp.ogrid[0:1:4j] Array([0. , 0.33333334, 0.6666667 , 1. ], dtype=float32) Multiple slices can be used to create sparse grids of indices: >>> jnp.ogrid[:2, :3] [Array([[0], [1]], dtype=int32), Array([[0, 1, 2]], dtype=int32)] r.r/rArray | list[Array]ct|trt|dSd|D}tjd5t |}dddn #1swxYwYt |dddS)Nrr1c38K|]}t|dVdS)rr1Nr3r4s r)r7z%_Ogrid.__getitem__..~r8r+r9r:Tr;)r>rr*r?r@rr)rBr.rCs r)rEz_Ogrid.__getitem__ys#u< %c7 ; ; ;;YYUXYYYF ": . .''v&f''''''''''''''' Vd4 8 8 88rFN)r.r/rrOrGrLr+r)rNrN]s26999999r+rNcLeZdZUdZded<ded<ded<ded<dd Zdd ZdS) _AxisConcatzGConcatenates slices, scalars and array-like objects along a given axis.r&axisndmintrans1drrr.#_IndexType | tuple[_IndexType, ...]rrct|tr|n|f}|j|j|jdg}|d}t|t r|dd}|dkrd|d<n|dkrd|d<n|d}t|}|dkr|||dz }n|dd|dgz} ttt|}n%#t$r}td ||d}~wwxYw|\}} } } g} |D] } t| trt| |j }d}ntuplerTrUrVrsplitrAlistmapr& ValueErrorrr*rr ndimranger'rappendr r )rBr.key_tupparams directivevecr6errrTrUrVmatrixrCitemr( item_ndim shape_obj num_lshiftsress r)rEz_AxisConcat.__getitem__s-7U-C-C&Occ#GiT\2 6F I)S!! g c  r   r "-- HH q55  ##BQB6":,&# C &&&&    ;i ; ;     $* D%& F D%  )$ EEE dC  KLLLt%(((K V%u555f B59,q00%,,'' c%'/A"566>> )KLL1Il{l4KKLL 69-- mmF eFmm$ / / /C ||A V $ $c Js<"C D)C<<DcdS)NrrL)rBs r)__len__z_AxisConcat.__len__s 1r+N)r.rWrr)rr&)rHrIrJrK__annotations__rErtrLr+r)rSrSsiOO ))) ***,,,,,,;;;;z      r+rSc"eZdZdZdZdZdZdZdS)RClassaWConcatenate slices, scalars and array-like objects along the first axis. LAX-backend implementation of :obj:`numpy.r_`. See Also: ``jnp.c_``: Concatenates slices, scalars and array-like objects along the last axis. Examples: Passing slices in the form ``[start:stop:step]`` generates ``jnp.arange`` objects: >>> jnp.r_[-1:5:1, 0, 0, jnp.array([1,2,3])] Array([-1, 0, 1, 2, 3, 4, 0, 0, 1, 2, 3], dtype=int32) An imaginary value for ``step`` will create a ``jnp.linspace`` object instead, which includes the right endpoint: >>> jnp.r_[-1:1:6j, 0, jnp.array([1,2,3])] Array([-1. , -0.6 , -0.20000002, 0.20000005, 0.6 , 1. , 0. , 1. , 2. , 3. ], dtype=float32) Use a string directive of the form ``"axis,dims,trans1d"`` as the first argument to specify concatenation axis, minimum number of dimensions, and the position of the upgraded array's original dimensions in the resulting array's shape tuple: >>> jnp.r_['0,2', [1,2,3], [4,5,6]] # concatenate along first axis, 2D output Array([[1, 2, 3], [4, 5, 6]], dtype=int32) >>> jnp.r_['0,2,0', [1,2,3], [4,5,6]] # push last input axis to the front Array([[1], [2], [3], [4], [5], [6]], dtype=int32) Negative values for ``trans1d`` offset the last axis towards the start of the shape tuple: >>> jnp.r_['0,2,-2', [1,2,3], [4,5,6]] Array([[1], [2], [3], [4], [5], [6]], dtype=int32) Use the special directives ``"r"`` or ``"c"`` as the first argument on flat inputs to create an array with an extra row or column axis, respectively: >>> jnp.r_['r',[1,2,3], [4,5,6]] Array([[1, 2, 3, 4, 5, 6]], dtype=int32) >>> jnp.r_['c',[1,2,3], [4,5,6]] Array([[1], [2], [3], [4], [5], [6]], dtype=int32) For higher-dimensional inputs (``dim >= 2``), both directives ``"r"`` and ``"c"`` give the same result. rrrYrNrHrIrJrKrTrUrVrrLr+r)rwrws1@@B $ % ' '''r+rwc"eZdZdZdZdZdZdZdS)CClassazConcatenate slices, scalars and array-like objects along the last axis. LAX-backend implementation of :obj:`numpy.c_`. See Also: ``jnp.r_``: Concatenates slices, scalars and array-like objects along the first axis. Examples: >>> a = jnp.arange(6).reshape((2,3)) >>> jnp.c_[a,a] Array([[0, 1, 2, 0, 1, 2], [3, 4, 5, 3, 4, 5]], dtype=int32) Use a string directive of the form ``"axis:dims:trans1d"`` as the first argument to specify concatenation axis, minimum number of dimensions, and the position of the upgraded array's original dimensions in the resulting array's shape tuple: >>> jnp.c_['0,2', [1,2,3], [4,5,6]] Array([[1], [2], [3], [4], [5], [6]], dtype=int32) >>> jnp.c_['0,2,-1', [1,2,3], [4,5,6]] Array([[1, 2, 3], [4, 5, 6]], dtype=int32) Use the special directives ``"r"`` or ``"c"`` as the first argument on flat inputs to create an array with inputs stacked along the last axis: >>> jnp.c_['r',[1,2,3], [4,5,6]] Array([[1, 4], [2, 5], [3, 6]], dtype=int32) rYrrNrxrLr+r)rzrzs/%%L $ % ' '''r+rz)rrrrrr)* __future__rcollections.abcrtypingrrr?jax._srcrjax._src.numpy.utilrjax._src.numpy.lax_numpyr r r r r rrrjax._src.typingrrnumpyr$__all__r*r-rrNrrr _IndexTyperSrwrrzrrrrLr+r)rsU#"""""$$$$$$ ......-,,,,,,, < ; ;    & & & & & & & & R $9$9$9$9$9$9$9$9N 9c5( ) E E E E E E E E PEEEEE[EEEP VXX*****[***Z VXXU L r+