+
    LV-jçã  ã                   ó$  € R t ^ RIHt ^ RIt^ RIt^ RIt^RIHtH	t	H
t
HtHtHtHtHtHtHtHt ^ RIHtHt ^RIHt . R?Ot ! R	 R]4      t ! R
 R]4      t ! R R]4      t/ R^ R.bR^R.bR^R.bR^R.bR^R.bR^R.bR^R.bR^R.bR^R.bR^	R .bR!^
R".bR#^R$.bR%^R&.bR'^R(.bR)^R*.bR+^R,.bR-^R..bR/^R0.R1^R2.R3^R4./Ct]! ]P>                  ]P@                  ]PB                  ]PD                  ]PF                  ]PH                  ]PJ                  ]PL                  ]PN                  ]PP                  ]PR                  ]PT                  ]PV                  ]PX                  ]PZ                  ]P\                  ]P^                  ]P`                  .4      t1^2t2]Pf                  ]Ph                  ]Ph                  ]Pf                  ]Pj                  ]Pl                  ]Pl                  ]Pj                  ]Pn                  ]Pp                  ]Pp                  ]Pn                  /t9]Pf                  R5]Pl                  R6]Pp                  R7]Ph                  R8]Pn                  R9]Pj                  R:/t: ! R; R<]4      t; ! R= R4      t<];P                   ]<n         R> t=R# )@zBase class for sparse matrices)ÚwarnN)ÚasmatrixÚcheck_reshape_kwargsÚcheck_shapeÚget_sum_dtypeÚisdenseÚisscalarlikeÚ_todataÚmatrixÚvalidateaxisÚgetdtypeÚis_pydata_spmatrix)Ú	SparseABCÚissparse)ÚspmatrixÚsparrayÚSparseWarningÚSparseEfficiencyWarningc                   ó   € ] tR t^tRtRtR# )r   z(General warning for :mod:`scipy.sparse`.© N©Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú__static_attributes__r   ó    Úc/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/scipy/sparse/_base.pyr   r      s   † Ù2Ûr   c                   ó   € ] tR t^tRtR# )ÚSparseFormatWarningr   N)r   r   r   r   r   r   r   r   r    r       s   † Ûr   r    c                   ó   € ] tR t^tRtRtR# )r   zKThe warning emitted when the operation is
inefficient for sparse matrices.
r   Nr   r   r   r   r   r      s   † ñó 	r   ÚcsczCompressed Sparse ColumnÚcsrzCompressed Sparse RowÚdokzDictionary Of KeysÚlilzList of ListsÚdodzDictionary of DictionariesÚssszSymmetric Sparse SkylineÚcooÚ
COOrdinateÚlbazLinpack BAndedÚegdz#Ellpack-itpack Generalized DiagonalÚdiaÚDIAgonalÚbsrzBlock Sparse RowÚmsrzModified compressed Sparse RowÚbsczBlock Sparse ColumnÚmscz!Modified compressed Sparse ColumnÚsskzSymmetric SKylineÚnskzNonsymmetric SKylineÚjadzJAgged DiagonalÚusszUnsymmetric Sparse SkylineÚvbrzVariable Block RowÚundÚ	Undefinedz==z>=z<=z!=Ú>Ú<c                   ó  a € ] tR t^Ut o RtRtRtRst]V 3R lR l4       t	]R 4       t
]R 4       t]R 4       t]R	 4       t]R
 4       t]R 4       t]R 4       t]R 4       tRR/R lt]R 4       tR tR tRtR lt]R 4       t]R 4       tR tR tR tRuR ltRuR lt]V 3R lR l4       t]V 3R lR l4       t ]V 3R  lR! l4       t!]R" 4       t"]R# 4       t#]R$ 4       t$R% t%R& t&R' t'R( t(RvR* lt)R+ t*R, t+R- t,R. t-R/ t.RuR0 lt/RvR1 lt0R2 t1R3 t2R4 t3R5 t4R6 t5R7 t6R8 t7R9 t8RwR: lt9R; t:R< t;R= t<R> t=R? t>R@ t?RA t@RB tARC tBRD tCRE tDRF tERG tFRH tGRI tHRJ tIRK tJRL tKRM tLRNR)/RO ltMRP tNRQ tORR tPRS tQRT tRRU tSRV tTRW tURxRX ltVRyRY ltWRyRZ ltX]WP                  ]Xn        R[ tYR\ tZR] t[R^ t\R_ t]RzR` lt^RzRa lt_RvRb lt`RvRc ltaRvRd ltbRvRe ltcRvRf ltdRxRg lteRvRh ltfRi tgR{Rj lthR{Rk ltiRwRl ltjRwRm ltkRwRn ltlRo tmRp tnR|Rq ltoRrtpV tqR# )}Ú_spbasez�This class provides a base class for all sparse arrays.  It
cannot be instantiated.  Most of the work is provided by subclasses.
g333333$@r7   c                ó    <€ V ^8„  d   QhRS[ /# ©é   Úreturn©Úint)ÚformatÚ__classdict__s   "€r   Ú__annotate__Ú_spbase.__annotate___   s   ø€ ÷  ñ  ‘cñ  r   c                ó,   € \        V P                  4      # ©N)ÚlenÚ_shape©Úselfs   &r   ÚndimÚ_spbase.ndim^   s   € ä�4—;‘;ÓÐr   c                óT   € V P                   p\        V4      ^8X  d   ^VR,          3# T# )é   éÿÿÿÿ)rJ   rI   )rL   Úss   & r   Ú_shape_as_2dÚ_spbase._shape_as_2db   s(   € à�K‰KˆÜ  ›V qœ[��1�R•5ˆzÐ/¨aÐ/r   c                ó   € ^RI Hp V# )rP   )Ú	bsr_array)Ú_bsrrV   )rL   rV   s   & r   Ú_bsr_containerÚ_spbase._bsr_containerg   ó   € å#ØÐr   c                ó   € ^RI Hp V# )rP   )Ú	coo_array)Ú_coor\   )rL   r\   s   & r   Ú_coo_containerÚ_spbase._coo_containerl   rZ   r   c                ó   € ^RI Hp V# )rP   )Ú	csc_array)Ú_cscra   )rL   ra   s   & r   Ú_csc_containerÚ_spbase._csc_containerq   rZ   r   c                ó   € ^RI Hp V# )rP   )Ú	csr_array)Ú_csrrf   )rL   rf   s   & r   Ú_csr_containerÚ_spbase._csr_containerv   rZ   r   c                ó   € ^RI Hp V# )rP   )Ú	dia_array)Ú_diark   )rL   rk   s   & r   Ú_dia_containerÚ_spbase._dia_container{   rZ   r   c                ó   € ^RI Hp V# )rP   )Ú	dok_array)Ú_dokrp   )rL   rp   s   & r   Ú_dok_containerÚ_spbase._dok_container€   rZ   r   c                ó   € ^RI Hp V# )rP   )Ú	lil_array)Ú_lilru   )rL   ru   s   & r   Ú_lil_containerÚ_spbase._lil_container…   rZ   r   ÚmaxprintNc               ó
  € R V n         V P                  P                  R8X  d   \        R4      h\	        V \
        4      '       d(   \        P                  ! V4      '       d   \        R4      hVf   \        V n	        R # TV n	        R # )Nr<   z7This class is not intended to be instantiated directly.zEscipy sparse array classes do not support instantiation from a scalar)
rJ   Ú	__class__r   Ú
ValueErrorÚ
isinstancer   ÚnpÚisscalarÚMAXPRINTry   )rL   Úarg1ry   s   &&$r   Ú__init__Ú_spbase.__init__Š   sl   € ØˆŒØ�>‰>×"Ñ" iÔ/Üð =ó >ð >ä�dœG×$Ò$¬¯ª°T×):Ò):ÜØWóð ð %-Ò$4œˆŽ¸(ˆŽr   c                ó   € V P                   # rH   )rJ   rK   s   &r   ÚshapeÚ_spbase.shape•   s   € à�{‰{Ðr   c                óø   € \        WP                  \        ^^A4      R7      p\        V4      w  rEW0P                  8X  d   V'       d   V P	                  4       # V # V P                  VR7      P                  W4RR7      # )aü  reshape(self, shape, order='C', copy=False)

Gives a new shape to a sparse array/matrix without changing its data.

Parameters
----------
shape : tuple of ints
    The new shape should be compatible with the original shape.
order : {'C', 'F'}, optional
    Read the elements using this index order. 'C' means to read and
    write the elements using C-like index order; e.g., read entire first
    row, then second row, etc. 'F' means to read and write the elements
    using Fortran-like index order; e.g., read entire first column, then
    second column, etc.
copy : bool, optional
    Indicates whether or not attributes of self should be copied
    whenever possible. The degree to which attributes are copied varies
    depending on the type of sparse array being used.

Returns
-------
reshaped : sparse array/matrix
    A sparse array/matrix with the given `shape`, not necessarily of the same
    format as the current object.

See Also
--------
numpy.reshape : NumPy's implementation of 'reshape' for ndarrays
)Úallow_nd©ÚcopyF)ÚorderrŠ   )r   r…   Úranger   rŠ   ÚtocooÚreshape)rL   ÚargsÚkwargsr…   r‹   rŠ   s   &*,   r   rŽ   Ú_spbase.reshape™   sf   € ôB ˜D§*¡*´u¸QÀ³|ÔDˆÜ*¨6Ó2‰ˆØ—J‘JÔßØ—y‘y“{Ð"à�à�z‰z˜tˆzÓ$×,Ñ,¨UÀeÐ,ÓLÐLr   c                óD   € \        \        V 4      P                   R24      h)a  Resize the array/matrix in-place to dimensions given by ``shape``

Any elements that lie within the new shape will remain at the same
indices, while non-zero elements lying outside the new shape are
removed.

Parameters
----------
shape : (int, int)
    number of rows and columns in the new array/matrix

Notes
-----
The semantics are not identical to `numpy.ndarray.resize` or
`numpy.resize`. Here, the same data will be maintained at each index
before and after reshape, if that index is within the new bounds. In
numpy, resizing maintains contiguity of the array, moving elements
around in the logical array but not within a flattened representation.

We give no guarantees about whether the underlying data attributes
(arrays, etc.) will be modified in place or replaced with new objects.
z.resize is not implemented)ÚNotImplementedErrorÚtyper   )rL   r…   s   &&r   ÚresizeÚ_spbase.resizeÄ   s)   € ô0 "Ü�D‹z×"Ñ"Ð#Ð#=Ð>ó@ð 	@r   c                óâ   € \        V4      pV P                  V8w  d;   V P                  4       P                  WVR7      P	                  V P
                  4      # V'       d   V P                  4       # V # )aq  Cast the array/matrix elements to a specified type.

Parameters
----------
dtype : string or numpy dtype
    Typecode or data-type to which to cast the data.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
    Controls what kind of data casting may occur.
    Defaults to 'unsafe' for backwards compatibility.
    'no' means the data types should not be cast at all.
    'equiv' means only byte-order changes are allowed.
    'safe' means only casts which can preserve values are allowed.
    'same_kind' means only safe casts or casts within a kind,
    like float64 to float32, are allowed.
    'unsafe' means any data conversions may be done.
copy : bool, optional
    If `copy` is `False`, the result might share some memory with this
    array/matrix. If `copy` is `True`, it is guaranteed that the result and
    this array/matrix do not share any memory.
)ÚcastingrŠ   )r   ÚdtypeÚtocsrÚastypeÚasformatrC   rŠ   )rL   r™   r˜   rŠ   s   &&&&r   r›   Ú_spbase.astypeß   s_   € ô, ˜“ˆØ�:‰:˜ÔØ—:‘:“<×&Ñ&Ø¨Tð 'ó 3ß3;±8¸D¿K¹KÓ3HðIçØ—9‘9“;ÐàˆKr   c                ót   € \        V \        4      '       d   \        P                  ! V3/ VB # \	        V3/ VB # rH   )Ú
issubclassr   r~   Úasarrayr   ©ÚclsÚXr�   s   &&,r   Ú_ascontainerÚ_spbase._ascontainerþ   s3   € ä�cœ7×#Ò#Ü—:’:˜aÑ* 6Ñ*Ð*ä˜AÑ( Ñ(Ð(r   c                ót   € \        V \        4      '       d   \        P                  ! V3/ VB # \	        V3/ VB # rH   )rŸ   r   r~   Úarrayr
   r¡   s   &&,r   Ú
_containerÚ_spbase._container  s3   € ä�cœ7×#Ò#Ü—8’8˜AÑ( Ñ(Ð(ä˜!Ñ&˜vÑ&Ð&r   c                ó  € . ROpV P                   P                  V9   d   V # V F=  pV P                   \        P                   ! V4      8:  g   K*  V P                  VRR7      u # 	  \	        RV P                   P
                   R24      h)z6Upcast array to a floating point format (if necessary)Fr‰   zcannot upcast [z] to a floating point format)ÚfÚdÚFÚD)r™   Úcharr~   r›   Ú	TypeErrorÚname)rL   Úfp_typesÚfp_types   &  r   Ú	_asfptypeÚ_spbase._asfptype  su   € ò (ˆà�:‰:�?‰?˜hÔ&ØˆKã#�Ø—:‘:¤§¢¨'Ó!2Ö2ØŸ;™; w°U˜;Ó;Ò;ñ $ô Ø! $§*¡*§/¡/Ð!2Ð2NÐOóð r   c              #  óh   "  € \        V P                  ^ ,          4       F  pW,          x € K  	  R# 5i)é    N)rŒ   r…   )rL   Úrs   & r   Ú__iter__Ú_spbase.__iter__  s#   é € Ü�t—z‘z !•}Ö%ˆAØ•'ŒMó &ùs   ‚02c                ó   € V P                   # )z3Maximum number of elements to display when printed.)ry   rK   s   &r   Ú_getmaxprintÚ_spbase._getmaxprint   s   € à�}‰}Ðr   c                óL   € V P                   P                  p\        RV R24      h)aj  Number of non-zero entries, equivalent to

np.count_nonzero(a.toarray(), axis=axis)

Unlike the nnz property, which return the number of stored
entries (the length of the data attribute), this method counts the
actual number of non-zero entries in data.

Duplicate entries are summed before counting.

Parameters
----------
axis : {-2, -1, 0, 1, None} optional
    Count nonzeros for the whole array, or along a specified axis.

    .. versionadded:: 1.15.0

Returns
-------
numpy array
    A reduced array (no axis `axis`) holding the number of nonzero values
    for each of the indices of the nonaxis dimensions.

Notes
-----
If you want to count nonzero and explicit zero stored values (e.g. nnz)
along an axis, two fast idioms are provided by `numpy` functions for the
common CSR, CSC, COO formats.

For the major axis in CSR (rows) and CSC (cols) use `np.diff`:

    >>> import numpy as np
    >>> import scipy as sp
    >>> A = sp.sparse.csr_array([[4, 5, 0], [7, 0, 0]])
    >>> major_axis_stored_values = np.diff(A.indptr)  # -> np.array([2, 1])

For the minor axis in CSR (cols) and CSC (rows) use `numpy.bincount` with
minlength ``A.shape[1]`` for CSR and ``A.shape[0]`` for CSC:

    >>> csr_minor_stored_values = np.bincount(A.indices, minlength=A.shape[1])

For COO, use the minor axis approach for either `axis`:

    >>> A = A.tocoo()
    >>> coo_axis0_stored_values = np.bincount(A.coords[0], minlength=A.shape[1])
    >>> coo_axis1_stored_values = np.bincount(A.coords[1], minlength=A.shape[0])

Examples
--------

    >>> A = sp.sparse.csr_array([[4, 5, 0], [7, 0, 0]])
    >>> A.count_nonzero(axis=0)
    array([2, 1, 0])
z"count_nonzero not implemented for Ú.©r{   r   r“   ©rL   ÚaxisÚclsnames   && r   Úcount_nonzeroÚ_spbase.count_nonzero$  s)   € ðn —.‘.×)Ñ)ˆÜ!Ð$FÀwÀiÈqÐ"QÓRÐRr   c                óL   € V P                   P                  p\        RV R24      h)zöNumber of stored values, including explicit zeros.

Parameters
----------
axis : {-2, -1, 0, 1, None} optional
    Report stored values for the whole array, or along a specified axis.

See also
--------
count_nonzero : Number of non-zero entries
zgetnnz not implemented for r¿   rÀ   rÁ   s   && r   Ú_getnnzÚ_spbase._getnnz^  s(   € ð —.‘.×)Ñ)ˆÜ!Ð$?À¸yÈÐ"JÓKÐKr   c                ó    <€ V ^8„  d   QhRS[ /# r>   rA   )rC   rD   s   "€r   rE   rF   n  s   ø€ ÷ ñ ‘Sñ r   c                ó"   € V P                  4       # )zqNumber of stored values, including explicit zeros.

See also
--------
count_nonzero : Number of non-zero entries
©rÇ   rK   s   &r   ÚnnzÚ_spbase.nnzm  ó   € ð �|‰|‹~Ðr   c                ó    <€ V ^8„  d   QhRS[ /# r>   rA   )rC   rD   s   "€r   rE   rF   x  s   ø€ ÷ ñ ‘cñ r   c                ó"   € V P                  4       # )zWNumber of stored values.

See also
--------
count_nonzero : Number of non-zero values.
rË   rK   s   &r   ÚsizeÚ_spbase.sizew  rÎ   r   c                ó    <€ V ^8„  d   QhRS[ /# r>   )Ústr)rC   rD   s   "€r   rE   rF   ‚  s   ø€ ÷ ñ ™ñ r   c                ó   € V P                   # )zFormat string for matrix.)Ú_formatrK   s   &r   rC   Ú_spbase.format�  s   € ð �|‰|Ðr   c                ó"   € V P                  4       # )z
Transpose.)Ú	transposerK   s   &r   ÚTÚ	_spbase.T†  s   € ð �~‰~ÓÐr   c                ó"   € V P                  4       # rH   )Ú_realrK   s   &r   ÚrealÚ_spbase.real‹  ó   € à�z‰z‹|Ðr   c                ó"   € V P                  4       # rH   )Ú_imagrK   s   &r   ÚimagÚ_spbase.imag�  rà   r   c                óÈ   € \         V P                  ,          w  r\        V \        4      '       d   R MRpRV RV RV P                   RV P
                   RV P                   R2# )r§   r
   r:   z sparse z of dtype 'z'
	with z stored elements and shape r9   )Ú_formatsrC   r}   r   r™   rÌ   r…   )rL   Ú_Úformat_nameÚ
sparse_clss   &   r   Ú__repr__Ú_spbase.__repr__“  sc   € Ü! $§+¡+Õ.‰ˆÜ *¨4´× 9Ò 9‘W¸xˆ
à�ˆ}˜H Z L°¸D¿J¹J¸<ð HØ—h‘h�ZÐ:¸4¿:¹:¸,ÀaðIð	
r   c                óÀ  a€ V P                  4       pV P                  4       pR  p\        V 4      pV P                  ^ 8X  d   V# VR,          pV P                  V8”  dß   V^,          oYC! \        ;QJ d!    . V3R lVP
                   4       F  NK  	  5M! V3R lVP
                   4       4      VP                  RS 4      ,          pVR,          pVS,
          oYC! \        ;QJ d!    . V3R lVP
                   4       F  NK  	  5M! V3R lVP
                   4       4      VP                  S) R 4      ,          pV# WC! VP
                  VP                  4      ,          pV# )c                 óf   € \        \        R  V  4       !  V4      pRP                  R V 4       4      # )c              3   ó@   "  € T F  qP                  4       x € K  	  R # 5irH   )Útolist)Ú.0Úcs   & r   Ú	<genexpr>Ú1_spbase.__str__.<locals>.tostr.<locals>.<genexpr>¢  s   é € Ð9±&¨QŸh™hŸj˜j³&ùs   ‚Ú
c              3   ó6   "  € T F  w  rR V RV 2x € K  	  R# 5i)z  Ú	Nr   )rð   ÚidxÚvals   &  r   rò   ró   £  s   é € ÐE¹u±8°3˜r #  b¨¨Õ.»uùs   ‚)ÚzipÚjoin)ÚcoordsÚdataÚpairss   && r   ÚtostrÚ_spbase.__str__.<locals>.tostr¡  s.   € ÜœÑ9±&Ó9Ñ:¸DÓAˆEØ—9‘9ÑE¹uÓEÓEÐEr   z
  Coords	Values
c              3   ó,   <"  € T F	  qR S x € K  	  R # 5irH   r   ©rð   rñ   Úhalfs   & €r   rò   Ú"_spbase.__str__.<locals>.<genexpr>¬  s   øé € Ð:±¨A  $�x³ùs   ƒNz
  :	:
c              3   ó.   <"  € T F
  qS) R  x € K  	  R # 5irH   r   r  s   & €r   rò   r  ¯  s   øé € Ð;±(¨Q $  �y³(ùs   ƒ)r¼   r�   ÚreprrÌ   Útuplerû   rü   )rL   ry   ÚArþ   Úoutr  s   &    @r   Ú__str__Ú_spbase.__str__›  s  ø€ Ø×$Ñ$Ó&ˆà�J‰J‹Lˆò	Fô �4‹jˆØ�8‰8�qŒ=ØˆJàÐ%Õ%ˆØ�8‰8�hÔØ˜q•=ˆDØ�5ŸœÔ:°·²Ó:Ÿ™Ô:°·²Ó:Ó:¸A¿F¹FÀ5ÀD¸MÓJÕJˆCØ�<ÕˆCØ˜d•?ˆDØ�5ŸœÔ;°!·(²(Ó;Ÿ™Ô;°!·(²(Ó;Ó;¸Q¿V¹VÀTÀEÀF¸^ÓLÕLˆCð ˆ
ð �5˜Ÿ™ 1§6¡6Ó*Õ*ˆCàˆ
r   c                óX   € V P                   R8X  d   V P                  ^ 8g  # \        R4      h)rP   z\The truth value of an array with more than one element is ambiguous. Use a.any() or a.all().)rP   rP   )r…   rÌ   r|   rK   s   &r   Ú__bool__Ú_spbase.__bool__µ  s1   € Ø�:‰:˜ÔØ—8‘8˜q‘=Ð äð Mó Nð Nr   c                ó   € \        R 4      h)z:sparse array length is ambiguous; use getnnz() or shape[0])r°   rK   s   &r   Ú__len__Ú_spbase.__len__¿  s   € Üð 'ó (ð 	(r   Fc                ó  € Ve   WP                   8X  d   V'       d   V P                  4       # V #  \        V RV,           4      p T! TR7      #   \         d   p\	        RT R24      ThRp?ii ; i  \
         d    T! 4       u # i ; i)aY  Return this array/matrix in the passed format.

Parameters
----------
format : {str, None}
    The desired sparse format ("csr", "csc", "lil", "dok", "array", ...)
    or None for no conversion.
copy : bool, optional
    If True, the result is guaranteed to not share data with self.

Returns
-------
A : This array/matrix in the passed format.
NÚtozFormat z is unknown.r‰   )rC   rŠ   ÚgetattrÚAttributeErrorr|   r°   )rL   rC   rŠ   Úconvert_methodÚes   &&&  r   rœ   Ú_spbase.asformatÃ  s�   € ð Š>˜V§{¡{Ô2ßØ—y‘y“{Ð"à�ðHÜ!(¨¨t°f­}Ó!=�ð
(Ù%¨4Ô0Ð0øô "ô HÜ  7¨6¨(°,Ð!?Ó@ÀaÐGûðHûô ô (Ù%Ó'Ò'ð(ús)   °A ÁA0 ÁA-ÁA(Á(A-Á0BÂBc                óf  € \        V4      '       d   V P                  V4      # V P                  ^8  d    V P                  V4      # \        V4      '       gk   \        V4      '       gZ   \        P                  ! V4      pVP                  ^ 8X  d&   VP                  \        P                  8X  d   \        #  VP                   V P                  VP                  8w  d   \        R4      h\        V4      '       dv   \        P                  ! V P                   WP"                  ,          4      pV P%                  4       pVP'                  \        P(                  4      P+                  4       Vn        V# \        V4      '       dl   V P-                  ^R4      P                  4       pVP-                  ^R4      P                  4       pVP/                  VR4      P-                  V P                  4      # \        #   \         d#    T P                  4       P                  T4      u # i ; i  \         d    Tp ELni ; i)z4Element-wise multiplication by another array/matrix.zEinconsistent shapes: >2D multiply() does not yet support broadcastingÚ_elmul_rQ   )r   Ú_mul_scalarrM   Ú_multiply_2d_with_broadcastingr  rš   r   r   r~   Ú
asanyarrayr™   Úobject_ÚNotImplementedr…   r|   Úmultiplyrü   rû   rŠ   ÚviewÚndarrayÚravelrŽ   Ú_binopt)rL   ÚotherÚother_arü   ÚresultÚcsr_selfÚ	csr_others   &&     r   r  Ú_spbase.multiplyê  s¬  € ä˜×ÒØ×#Ñ# EÓ*Ð*à�9‰9�qŒ=ðJØ×:Ñ:¸5ÓAÐAô ˜—’¤7¨5§>¢>ä—m’m EÓ*ˆGØ�|‰|˜qÔ  W§]¡]´b·j±jÔ%@ô &Ð%ð Ø—’ð �:‰:˜Ÿ™Ô$Üð 4ó 5ð 5ô �5�>Š>Ü—;’;˜tŸy™y¨%·±Õ*<Ó=ˆDØ—Y‘Y“[ˆFØŸ)™)¤B§J¡JÓ/×5Ñ5Ó7ˆFŒKØˆMä�e�_Š_Ø—|‘| A rÓ*×0Ñ0Ó2ˆHØŸ™ a¨Ó,×2Ñ2Ó4ˆIØ×#Ñ# I¨yÓ9×AÑAÀ$Ç*Á*ÓMÐMô
 "Ð!øôI "ô JØ—z‘z“|×BÑBÀ5ÓIÒIðJûô "ô  Ø“ð ús#   µG/ Â5H Ç/*HÈHÈH0È/H0c                óX  € \        V4      '       g�   \        V4      '       gp   \        V4      '       g_   \        P                  ! V4      pVP
                  ^ 8X  d+   VP                  \        P                  8X  d   \        R4      h VP                   \        V4      '       Ed   V! ^ V4      '       dc   V\        P                  8X  d   RMRp\        RVP                   RV R2\        ^R7       V P                  V! V P!                  4       V4      4      # V P
                  ^8  d5   V P"                  R9   d   T MV P%                  4       pVP'                  W4      # V P)                  ^R4      P%                  4       pVP'                  W4      pVP+                  4       P)                  V P                  4      # \        V4      '       d   V! V P-                  4       V4      # \        V4      '       Ed    V P                  VP                  8w  d(   \/        RV P                  : R	VP                  : 24      hV P
                  ^8  dD   V P"                  R9   d   T MV P%                  4       pVP1                  VR
VP                   R
24      # V P)                  ^R4      P%                  4       pVP)                  ^R4      P%                  4       pVP1                  VR
VP                   R
24      pVP+                  4       P)                  V P                  4      # \/        R4      h  \         d    Tp EL–i ; i)r·   zCmaximum or minimum with an unrecognized array type is not supportedÚpositiveÚnegativezTaking z with a z" number results in a dense matrix.©Ú
stacklevelzinconsistent shapes self.shape=z other.shape=rç   zOperands not compatible.)r#   r"   rQ   )r   r   r   r~   r  rM   r™   r  r“   r…   r  Úmaximumr   r   r   r{   ÚtoarrayrC   rš   Ú_scalar_binoptrŽ   r�   Útodenser|   r#  )	rL   r$  Únp_opr%  Úpos_negÚcs_selfr'  r&  r(  s	   &&&      r   Ú_maximum_minimumÚ_spbase._maximum_minimum  sx  € Ü˜—’¤7¨5§>¢>´\À%×5HÒ5Hä—m’m EÓ*ˆGØ�|‰|˜qÔ  W§]¡]´b·j±jÔ%@ô *ð +Hó Ið Ið Ø—’ô ˜×ÓÙ�Q˜�ŠØ(-´·±Ô(;™*À�Ü�w˜uŸ~™~Ð.¨h°w°ið @&ð &Ü'>È1õNà—~‘~¡e¨D¯L©L«N¸EÓ&BÓCÐCà—9‘9˜q”=Ø&*§k¡k°^Ô&C™dÈÏÉË�GØ"×1Ñ1°%Ó?Ð?ØŸ<™<¨¨2Ó.×4Ñ4Ó6�Ø!×0Ñ0°Ó>�Ø—|‘|“~×-Ñ-¨d¯j©jÓ9Ð9Ü�U�^Š^Ù˜Ÿ™›¨Ó/Ð/Ü�e�_‹_Ø�z‰z˜UŸ[™[Ô(Ü Ð#C¸¿
¹
±}ÀNÀeÇkÁkÁ^Ð!TÓUÐUØ�y‰y˜1Œ}Ø"&§+¡+°Ô"?™$ÀTÇZÁZÃ\�Ø—‘ u°°%·.±.Ð1AÀÐ.CÓDÐDØ—|‘| A rÓ*×0Ñ0Ó2ˆHØŸ™ a¨Ó,×2Ñ2Ó4ˆIØ×%Ñ% i°1°U·^±^Ð4DÀAÐ1FÓGˆFØ—<‘<“>×)Ñ)¨$¯*©*Ó5Ð5äÐ7Ó8Ð8øô; "ô  Ø“ð ús   ÂL ÌL)Ì(L)c                óB   € V P                  V\        P                  4      # )z;Element-wise maximum between this and another array/matrix.)r6  r~   r/  ©rL   r$  s   &&r   r/  Ú_spbase.maximumC  ó   € à×$Ñ$ U¬B¯J©JÓ7Ð7r   c                óB   € V P                  V\        P                  4      # )z;Element-wise minimum between this and another array/matrix.)r6  r~   Úminimumr9  s   &&r   r=  Ú_spbase.minimumG  r;  r   c                óZ   € \         P                  ! V4      '       d	   W,          # W,          # )zçOrdinary dot product

Examples
--------
>>> import numpy as np
>>> from scipy.sparse import csr_array
>>> A = csr_array([[1, 2, 0], [0, 0, 3], [4, 0, 5]])
>>> v = np.array([1, 0, -1])
>>> A.dot(v)
array([ 1, -3, -1], dtype=int64)

)r~   r   r9  s   &&r   ÚdotÚ_spbase.dotK  s#   € ô �;Š;�u×ÒØ•<Ðà•<Ðr   c                óB   € V P                  4       P                  WR7      # )zElement-wise power.©r™   )rš   Úpower)rL   Únr™   s   &&&r   rD  Ú_spbase.power]  s   € à�z‰z‹|×!Ñ! !Ð!Ó1Ð1r   c                ó–   € V P                   V8X  d   V'       d   V P                  4       # T # V P                  4       P                  W4      # rH   )r…   rŠ   rš   Ú_broadcast_to)rL   r…   rŠ   s   &&&r   rH  Ú_spbase._broadcast_toa  s8   € Ø�:‰:˜Ôß"&�4—9‘9“;Ð0¨DÐ0à—:‘:“<×-Ñ-¨eÓ:Ð:r   c           
     ó  € \        V4      '       g“   \        V4      '       g‚   \        V4      '       gq   \        V4      '       d   \        # \
        P                  ! V4      pVP                  ^ 8X  d&   VP                  \
        P                  8X  d   \        #  VP                   \        V4      '       EdÓ   V! ^ V4      '       gç   \
        P                  ! V4      '       d,   V P                  V P                  \
        P                  R7      # V P                  ^8  d5   V P                  R
9   d   T MV P!                  4       pVP#                  W4      # V P%                  ^R4      P!                  4       pVP#                  W4      pVP'                  4       P%                  V P                  4      # \)        RV R\*        V,           R\*        \,        V,          ,           R2\.        ^R7       \
        P                  ! V4      '       d@   V P                  \
        P0                  ! V P                  \
        P                  R7      4      # V P                  ^8  dˆ   V P                  R
9   d   T MV P!                  4       pVP#                  V\,        V,          4      pVP                  \
        P0                  ! VP                  \
        P                  R7      4      pW‡,
          # V P%                  ^R4      P!                  4       pVP#                  V\,        V,          4      pVP                  \
        P0                  ! VP                  \
        P                  R7      4      pW‡,
          pVP'                  4       P%                  V P                  4      # \        V4      '       d   V! V P3                  4       V4      # \        V4      '       Ed(   V P                  VP                  8w  dC   V\4        P6                  \4        P8                  39   d   V\4        P8                  J # \;        R4      hV P                  ^8  d'   V P                  R
9   d   T MV P!                  4       pTp	M@V P%                  ^R4      P!                  4       pVP%                  ^R4      P!                  4       p	V! ^ ^ 4      '       g]   VP=                  V	RVP>                   R24      pV P                  ^8  d   V# VP'                  4       P%                  V P                  4      # \)        R	\*        V,           R\*        \,        V,          ,           R2\.        ^R7       VP=                  V	R\,        V,          P>                   R24      pVP                  \
        P0                  ! VP                  \
        P                  R7      4      pW‡,
          pV P                  ^8  d   V# VP'                  4       P%                  V P                  4      # \        #   \         d    Tp EL[i ; i)r·   rC  zComparing a sparse matrix with z using z is inefficient. Try using z	 instead.r-  zinconsistent shaperç   z$Comparing two sparse matrices using )r"   r#   rQ   ) r   r   r   r   r  r~   r  rM   r™   r  r…   r  Úisnanr{   Úbool_rC   rš   r1  rŽ   r�   r   Úop_symÚop_negr   Úonesr2  ÚoperatorÚeqÚner|   r#  r   )
rL   r$  Úopr%  r5  r'  r&  ÚinvÚall_trueÚcs_others
   &&&       r   Ú_comparisonÚ_spbase._comparisong  sN  € ô ˜—’¤7¨5§>¢>´\À%×5HÒ5HÜ! %×(Ò(ä%Ð%ä—m’m EÓ*ˆGØ�|‰|˜qÔ  W§]¡]´b·j±jÔ%@ô &Ð%ð Ø—’ô ˜×ÓÙ�a˜—<’<Ü—8’8˜E—?’?ØŸ>™>¨$¯*©*¼B¿H¹H˜>ÓEÐEØ—9‘9˜q”=Ø&*§k¡k°^Ô&C™dÈÏÉË�GØ"×1Ñ1°%Ó<Ð<ØŸ<™<¨¨2Ó.×4Ñ4Ó6�Ø!×0Ñ0°Ó;�Ø—|‘|“~×-Ñ-¨d¯j©jÓ9Ð9äÐ6°u°g¸WÄVÈBÅZÀLð Q2Ü28¼À½Õ2DÐ1EÀYðPä,¸õ<ô —8’8˜E—?’?àŸ>™>¬"¯'ª'°$·*±*ÄBÇHÁHÔ*MÓNÐNð —9‘9˜q”=Ø&*§k¡k°^Ô&C™dÈÏÉË�GØ!×0Ñ0°¼¸r½
ÓC�CØ&×0Ñ0´·²¸¿¹ÌbÏhÉhÔ1WÓX�HØ#�>Ð)àŸ<™<¨¨2Ó.×4Ñ4Ó6�Ø×-Ñ-¨e´V¸BµZÓ@�Ø#×-Ñ-¬b¯gªg°h·n±nÌBÏHÉHÔ.UÓV�Ø!��Ø—|‘|“~×-Ñ-¨d¯j©jÓ9Ð9ä�U�^Š^Ù�d—l‘l“n eÓ,Ð,ä�e�_‹_à�z‰z˜UŸ[™[Ô(ð œ(Ÿ+™+¤x§{¡{Ð3Ô3Ø¤§¡Ð,Ð,Ü Ð!5Ó6Ð6à�y‰y˜1Œ}Ø"&§+¡+°Ô"?™$ÀTÇZÁZÃ\�Ø ‘àŸ,™, q¨"Ó-×3Ñ3Ó5�Ø Ÿ=™=¨¨BÓ/×5Ñ5Ó7�Ù�a˜—8’8Ø Ÿ™¨°Q°r·{±{°mÀ1Ð3EÓF�Ø!%§¡¨Q¤�vÐV°F·L±L³N×4JÑ4JÈ4Ï:É:Ó4VÐVô Ð;¼FÀ2½J¸<ð H2Ü28¼À½Õ2DÐ1EÀYðPä,¸õ<ð —o‘o h°!´F¸2µJ×4GÑ4GÐ3HÈÐ0JÓK�Ø"×,Ñ,¬R¯WªW°W·]±]Ì"Ï(É(Ô-SÓT�Ø!��Ø!%§¡¨Q¤�vÐV°F·L±L³N×4JÑ4JÈ4Ï:É:Ó4VÐVô "Ð!øôE "ô  Ø“ð ús   ÂW/ ×/X ×?X c                óB   € V P                  V\        P                  4      # rH   )rW  rP  rQ  r9  s   &&r   Ú__eq__Ú_spbase.__eq__¼  ó   € Ø×Ñ ¤x§{¡{Ó3Ð3r   c                óB   € V P                  V\        P                  4      # rH   )rW  rP  rR  r9  s   &&r   Ú__ne__Ú_spbase.__ne__¿  r\  r   c                óB   € V P                  V\        P                  4      # rH   )rW  rP  Últr9  s   &&r   Ú__lt__Ú_spbase.__lt__Â  r\  r   c                óB   € V P                  V\        P                  4      # rH   )rW  rP  Úgtr9  s   &&r   Ú__gt__Ú_spbase.__gt__Å  r\  r   c                óB   € V P                  V\        P                  4      # rH   )rW  rP  Úler9  s   &&r   Ú__le__Ú_spbase.__le__È  r\  r   c                óB   € V P                  V\        P                  4      # rH   )rW  rP  Úger9  s   &&r   Ú__ge__Ú_spbase.__ge__Ë  r\  r   c                ó4   € \        V P                  4       4      # rH   )Úabsrš   rK   s   &r   Ú__abs__Ú_spbase.__abs__Î  s   € Ü�4—:‘:“<Ó Ð r   c                ó8   € \        V P                  4       VR 7      # ))Úndigits)Úroundrš   )rL   ru  s   &&r   Ú	__round__Ú_spbase.__round__Ñ  s   € Ü�T—Z‘Z“\¨7Ô3Ð3r   c                ó@   € V P                  4       P                  V4      # rH   )rš   Ú_add_sparser9  s   &&r   rz  Ú_spbase._add_sparseÔ  ó   € Ø�z‰z‹|×'Ñ'¨Ó.Ð.r   c                ó@   € V P                  4       P                  V4      # rH   )r�   Ú
_add_denser9  s   &&r   r~  Ú_spbase._add_dense×  s   € Ø�z‰z‹|×&Ñ& uÓ-Ð-r   c                ó@   € V P                  4       P                  V4      # rH   )rš   Ú_sub_sparser9  s   &&r   r�  Ú_spbase._sub_sparseÚ  r|  r   c                ó0   € V P                  4       V,
          # rH   ©r2  r9  s   &&r   Ú
_sub_denseÚ_spbase._sub_denseÝ  s   € Ø�|‰|‹~ Õ%Ð%r   c                ó.   € WP                  4       ,
          # rH   r„  r9  s   &&r   Ú_rsub_denseÚ_spbase._rsub_denseà  s   € à—|‘|“~Õ%Ð%r   c                óˆ  € \        V4      '       d#   V^ 8X  d   V P                  4       # \        R4      h\        V4      '       d8   VP                  V P                  8w  d   \        R4      hV P                  V4      # \        V4      '       d2   \        P                  ! WP                  4      pV P                  V4      # \        # )r·   z:adding a nonzero scalar to a sparse array is not supportedúinconsistent shapes)r   rŠ   r“   r   r…   r|   rz  r   r~   Úbroadcast_tor~  r  r9  s   &&r   Ú__add__Ú_spbase.__add__ä  s›   € Ü˜×ÒØ˜ŒzØ—y‘y“{Ð"ä%ð 'Fó Gð Gä�e�_Š_Ø�{‰{˜dŸj™jÔ(Ü Ð!6Ó7Ð7Ø×#Ñ# EÓ*Ð*Ü�U�^Š^Ü—O’O E¯:©:Ó6ˆEØ—?‘? 5Ó)Ð)ä!Ð!r   c                ó$   € V P                  V4      # rH   )r�  r9  s   &&r   Ú__radd__Ú_spbase.__radd__õ  ó   € Ø�|‰|˜EÓ"Ð"r   c                óˆ  € \        V4      '       d#   V^ 8X  d   V P                  4       # \        R4      h\        V4      '       d8   VP                  V P                  8w  d   \        R4      hV P                  V4      # \        V4      '       d2   \        P                  ! WP                  4      pV P                  V4      # \        # )r·   zAsubtracting a nonzero scalar from a sparse array is not supportedr‹  )r   rŠ   r“   r   r…   r|   r�  r   r~   rŒ  r…  r  r9  s   &&r   Ú__sub__Ú_spbase.__sub__ø  s›   € Ü˜×ÒØ˜ŒzØ—y‘y“{Ð"Ü%ð 'Fó Gð Gä�e�_Š_Ø�{‰{˜dŸj™jÔ(Ü Ð!6Ó7Ð7Ø×#Ñ# EÓ*Ð*Ü�U�^Š^Ü—O’O E¯:©:Ó6ˆEØ—?‘? 5Ó)Ð)ä!Ð!r   c                óú   € \        V4      '       d$   V^ 8X  d   V P                  4       ) # \        R4      h\        V4      '       d2   \        P
                  ! WP                  4      pV P                  V4      # \        # )r·   zAsubtracting a sparse array from a nonzero scalar is not supported)	r   rŠ   r“   r   r~   rŒ  r…   rˆ  r  r9  s   &&r   Ú__rsub__Ú_spbase.__rsub__  si   € Ü˜×ÒØ˜ŒzØŸ	™	›�|Ð#Ü%ð 'Hó Ið Iä�U�^Š^Ü—O’O E¯:©:Ó6ˆEØ×#Ñ# EÓ*Ð*ä!Ð!r   c                ó~  € V P                   w  r#VP                  \        P                  J dÄ   VP                  V38X  d   V P                  V4      # VP                  V^38X  dT   V P                  VP                  4       4      pV P                  ^8X  d   VP                  ^4      # VP                  V^4      # VP                  ^8X  d*   VP                  ^ ,          V8X  d   V P                  V4      # \        V4      '       d   V P                  V4      # Rp\        V4      '       dN   W1P                  ^ ,          8w  d&   \        V RV RVP                  ^ ,           R24      hV P                  V4      # \        P                  ! V4      pVP                  ^ 8X  d&   VP                   \        P"                  8X  d   \$        #  VP                   VP                  ^8X  g+   VP                  ^8X  Ed   VP                  ^,          ^8X  dõ   VP                  ^ ,          V8w  d&   \        V RV RVP                  ^ ,           R24      hV P                  \        P                  ! V4      4      p\)        V\        P*                  4      '       d   V P-                  V4      pVP                  ^8X  dO   VP                  ^,          ^8X  d7   V P                  ^8X  d   VP                  ^4      pV# VP                  R^4      pV# VP                  ^8X  d–   VP                  ^ ,          V8w  d&   \        V RV RVP                  ^ ,           R24      hV P                  \        P.                  ! V4      4      p\)        V\        P*                  4      '       d   V P-                  V4      pV# \        R4      h  \&         d    Tp ELði ; i)zÕnp.array-like matmul & `np.matrix`-like mul, i.e. `dot` or `NotImplemented`

interpret other and call one of the following
self._mul_scalar()
self._matmul_vector()
self._matmul_multivector()
self._matmul_sparse()
z)matmul: dimension mismatch with signaturez (n,k=z),(k=z
,m)->(n,m)z,1?)->(n,1?)zcould not interpret dimensionsrQ   )rS   r{   r~   r!  r…   Ú_matmul_vectorr"  rM   rŽ   Ú_matmul_multivectorr   r  r   r|   Ú_matmul_sparser  r™   r  r  r  r}   r
   r¤   r    )rL   r$  ÚMÚNr&  Ú
err_prefixr%  s   &&     r   Ú_matmul_dispatchÚ_spbase._matmul_dispatch  s  € ð × Ñ ‰ˆà�?‰?œbŸj™jÓ(à�{‰{˜q˜dÔ"Ø×*Ñ*¨5Ó1Ð1Ø—‘  A Ô&Ø×,Ñ,¨U¯[©[«]Ó;�Ø—9‘9 ”>Ø!Ÿ>™>¨!Ó,Ð,Ø—~‘~ a¨Ó+Ð+Ø—‘˜q” U§[¡[°¥^°qÔ%8Ø×/Ñ/°Ó6Ð6ä˜×Òà×#Ñ# EÓ*Ð*à@ˆ
Ü�E�?Š?Ø—K‘K •NÔ"Ü Ø!�l &¨¨¨5°·±¸QµÐ0@À
ÐKóð ð ×&Ñ& uÓ-Ð-ô —-’- Ó&ˆØ�<‰<˜1Ô §¡´"·*±*Ô!<ô "Ð!ð	Ø�KŠKð �:‰:˜Œ?˜eŸj™j¨A�o°%·+±+¸aµ.ÀAÔ2Eà�{‰{˜1�~ Ô"Ü Ø!�l &¨¨¨5°·±¸QµÐ0@ÀÐMóð ð ×(Ñ(¬¯ª°%«Ó9ˆFä˜%¤§¡×+Ò+Ø×*Ñ*¨6Ó2�à�z‰z˜QŒ 5§;¡;¨q¥>°QÔ#6à—9‘9 ”>Ø#Ÿ^™^¨AÓ.�Fð ˆMð $Ÿ^™^¨B°Ó2�FàˆMà�Z‰Z˜1Œ_ð �{‰{˜1�~ Ô"Ü Ø!�l &¨¨¨5°·±¸QµÐ0@À
ÐKóð ð ×-Ñ-¬b¯jªj¸Ó.?Ó@ˆFä˜%¤§¡×+Ò+Ø×*Ñ*¨6Ó2�àˆMô Ð=Ó>Ð>øôS ô 	Ø‹Eð	ús   Æ?N+ Î+N<Î;N<c                ó$   € V P                  V4      # rH   ©r  r9  s   &&r   Ú__mul__Ú_spbase.__mul__r  ó   € Ø�}‰}˜UÓ#Ð#r   c                ó$   € V P                  V4      # rH   r£  r9  s   &&r   Ú__rmul__Ú_spbase.__rmul__u  r¦  r   c                ó@   € V P                  4       P                  V4      # rH   )rš   r  r9  s   &&r   r  Ú_spbase._mul_scalary  r|  r   c                ó@   € V P                  4       P                  V4      # rH   )rš   rš  r9  s   &&r   rš  Ú_spbase._matmul_vector|  ó   € Ø�z‰z‹|×*Ñ*¨5Ó1Ð1r   c                ó@   € V P                  4       P                  V4      # rH   )rš   r›  r9  s   &&r   r›  Ú_spbase._matmul_multivector  s   € Ø�z‰z‹|×/Ñ/°Ó6Ð6r   c                ó@   € V P                  4       P                  V4      # rH   )rš   rœ  r9  s   &&r   rœ  Ú_spbase._matmul_sparse‚  r®  r   c                óP  € \        V4      '       d   V P                  V4      #  VP                  4       pV P                  4       P                  V4      pV\        J d   \        # VP                  4       #   \         d(    \        P
                  ! T4      P                  4       p Lpi ; irH   )r   r  rÙ   r  r~   r    r   r  )rL   r$  ÚtrÚrets   &&  r   Ú_rmatmul_dispatchÚ_spbase._rmatmul_dispatch…  sŠ   € Ü˜×ÒØ×#Ñ# EÓ*Ð*ð3Ø—_‘_Ó&�ð —.‘.Ó"×3Ñ3°BÓ7ˆCØ”nÓ$Ü%Ð%Ø—=‘=“?Ð"øô "ô 3Ü—Z’Z Ó&×0Ñ0Ó2’ð3ús   ¤A3 Á3/B%Â$B%c                ó\   € \        V4      '       d   \        R 4      hV P                  V4      # ©z0Scalar operands are not allowed, use '*' instead)r   r|   r   r9  s   &&r   Ú
__matmul__Ú_spbase.__matmul__—  s0   € Ü˜×ÒÜð /ó 0ð 0à×$Ñ$ UÓ+Ð+r   c                ó\   € \        V4      '       d   \        R 4      hV P                  V4      # r¹  )r   r|   r¶  r9  s   &&r   Ú__rmatmul__Ú_spbase.__rmatmul__�  s0   € Ü˜×ÒÜð /ó 0ð 0à×%Ñ% eÓ,Ð,r   Úrdividec               ó&  € \        V4      '       g|   \        V4      '       gk   \        V4      '       gZ   \        P                  ! V4      pVP
                  ^ 8X  d&   VP                  \        P                  8X  d   \        #  VP                   \        V4      '       EdP   V'       d%   \        P                  ! WP                  4       4      # \        P                  ! V P                  \        P                  4      '       d8   V P                  \        P                  RR7      P!                  ^V,          4      # V P!                  ^V,          4      p\        P"                  ! V4      P                  p\        P$                  ! V P                  \        P&                  4      '       dI   \        P$                  ! V\        P&                  4      '       d   VP                  V P                  RR7      # V# \        V4      '       dS   V'       d%   \        P                  ! WP                  4       4      # V P)                  \        P                  ! ^V4      4      # \        V4      '       Ed   V'       d   VP+                  V RR7      # V P
                  ^8  d   T MV P-                  ^R4      P/                  4       pV P
                  ^8  d   TMVP-                  ^R4      P/                  4       p\        P                  ! V P                  \        P                  4      '       d"   VP                  \        P                  RR7      pVP1                  V4      pV P
                  ^8  d   V# VP-                  V P                  4      # \        #   \         d    Tp ELi ; i)r·   Fr‰   )r¿  rQ   )r   r   r   r~   r  rM   r™   r  r  r…   r  Údivider2  Úcan_castÚfloat64r›   r  r    Ú
issubdtypeÚintegerr  Ú_dividerŽ   rš   Ú_divide_sparse)	rL   r$  r¿  r%  r¸   Úscalar_dtyper'  r(  r&  s	   &&$      r   rÆ  Ú_spbase._divide§  sT  € Ü˜—’¤7¨5§>¢>´\À%×5HÒ5Hä—m’m EÓ*ˆGØ�|‰|˜qÔ  W§]¡]´b·j±jÔ%@ô &Ð%ð Ø—’ô ˜×ÓßÜ—y’y ¯©«Ó7Ð7ä�{Š{˜4Ÿ:™:¤r§z¡z×2Ò2Ø—{‘{¤2§:¡:°E�{Ó:×FÑFÀqÈ5ÅyÓQÐQà×$Ñ$ Q¨¥YÓ/�ä!Ÿzšz¨%Ó0×6Ñ6�Ü—M’M $§*¡*¬b¯j©j×9Ò9ÜŸš l´B·J±J×?Ò?ØŸ8™8 D§J¡J°U˜8Ó;Ð;à�Hä�U�^Š^ßÜ—y’y ¯©«Ó7Ð7à—=‘=¤§¢¨1¨eÓ!4Ó5Ð5ä�e�_‹_ßØ—}‘} T°5�}Ó9Ð9à $§	¡	¨A¤™°4·<±<ÀÀ2Ó3F×MÑMÓOˆHØ"&§)¡)¨a¤-™°U·]±]À1ÀbÓ5I×PÑPÓRˆIÜ�{Š{˜4Ÿ:™:¤r§z¡z×2Ò2Ø#Ÿ?™?¬2¯:©:¸E˜?ÓB�Ø×,Ñ,¨YÓ7ˆFØ!ŸY™Y¨œ]�6ÐJ°·±¸t¿z¹zÓ0JÐJô "Ð!øôM "ô  Ø“ð ús   ÂM? Í?NÎNc                ó$   € V P                  V4      # rH   )rÆ  r9  s   &&r   Ú__truediv__Ú_spbase.__truediv__Û  r’  r   c                ó   € \         # rH   ©r  r9  s   &&r   Ú__rtruediv__Ú_spbase.__rtruediv__Þ  s   € äÐr   c                ó$   € V P                  4       ) # rH   )rš   rK   s   &r   Ú__neg__Ú_spbase.__neg__â  s   € Ø—
‘
“ˆ}Ðr   c                ó   € \         # rH   rÎ  r9  s   &&r   Ú__iadd__Ú_spbase.__iadd__å  ó   € ÜÐr   c                ó   € \         # rH   rÎ  r9  s   &&r   Ú__isub__Ú_spbase.__isub__è  r×  r   c                ó   € \         # rH   rÎ  r9  s   &&r   Ú__imul__Ú_spbase.__imul__ë  r×  r   c                ó   € \         # rH   rÎ  r9  s   &&r   Ú__itruediv__Ú_spbase.__itruediv__î  r×  r   c                ó&   € V P                   ! V/ VB # rH   )rD  )rL   r�   r�   s   &*,r   Ú__pow__Ú_spbase.__pow__ñ  s   € Ø�zŠz˜4Ð* 6Ñ*Ð*r   c                óH   € V P                  VR7      P                  VRR7      # )aâ  
Reverses the dimensions of the sparse array/matrix.

Parameters
----------
axes : None, optional
    This argument is in the signature *solely* for NumPy
    compatibility reasons. Do not pass in anything except
    for the default value.
copy : bool, optional
    Indicates whether or not attributes of `self` should be
    copied whenever possible. The degree to which attributes
    are copied varies depending on the type of sparse array/matrix
    being used.

Returns
-------
p : `self` with the dimensions reversed.

Notes
-----
If `self` is a `csr_array` or a `csc_array`, then this will return a
`csc_array` or a `csr_array`, respectively.

See Also
--------
numpy.transpose : NumPy's implementation of 'transpose' for ndarrays
r‰   F)ÚaxesrŠ   )rš   rÙ   )rL   rå  rŠ   s   &&&r   rÙ   Ú_spbase.transposeô  s%   € ð: �z‰z˜tˆzÓ$×.Ñ.°D¸uÐ.ÓEÐEr   c                óä   € \         P                  ! V P                  \         P                  4      '       d#   V P	                  VR7      P                  RR7      # V'       d   V P                  4       # V # )aG  Element-wise complex conjugation.

If the array/matrix is of non-complex data type and `copy` is False,
this method does nothing and the data is not copied.

Parameters
----------
copy : bool, optional
    If True, the result is guaranteed to not share data with self.

Returns
-------
A : The element-wise complex conjugate.

r‰   F)r~   rÄ  r™   Úcomplexfloatingrš   Ú	conjugaterŠ   ©rL   rŠ   s   &&r   ré  Ú_spbase.conjugate  sR   € ô  �=Š=˜Ÿ™¤R×%7Ñ%7×8Ò8Ø—:‘: 4�:Ó(×2Ñ2¸Ð2Ó>Ð>ßØ—9‘9“;ÐàˆKr   c                ó&   € V P                  VR 7      # )r‰   )ré  rê  s   &&r   ÚconjÚ_spbase.conj*  s   € Ø�~‰~ 4ˆ~Ó(Ð(r   c                ó>   € V P                  4       P                  4       # rH   )rš   rÝ   rK   s   &r   rÝ   Ú_spbase._real/  ó   € Ø�z‰z‹|×!Ñ!Ó#Ð#r   c                ó>   € V P                  4       P                  4       # rH   )rš   râ   rK   s   &r   râ   Ú_spbase._imag2  rñ  r   c                óÎ   a€ V P                  4       pVP                  ^ 8g  o\        ;QJ d!    . V3R lVP                   4       F  NK  	  5# ! V3R lVP                   4       4      # )aS  Nonzero indices of the array/matrix.

Returns a tuple of arrays (row,col) containing the indices
of the non-zero elements of the array.

Examples
--------
>>> from scipy.sparse import csr_array
>>> A = csr_array([[1, 2, 0], [0, 0, 3], [4, 0, 5]])
>>> A.nonzero()
(array([0, 0, 1, 2, 2], dtype=int32), array([0, 1, 2, 0, 2], dtype=int32))

c              3   ó4   <"  € T F  qS,          x € K  	  R # 5irH   r   )rð   r÷   Únz_masks   & €r   rò   Ú"_spbase.nonzero.<locals>.<genexpr>G  s   øé € Ð6©X c˜—\’\«Xùs   ƒ)r�   rü   r  rû   )rL   r  rö  s   & @r   ÚnonzeroÚ_spbase.nonzero5  sG   ø€ ð  �J‰J‹LˆØ—&‘&˜A‘+ˆßŒuÔ6¨Q¯XªXÓ6�uÐ6ˆuÔ6¨Q¯XªXÓ6Ó6Ð6r   c                ó  € V P                   ^8X  d   \        R4      hV P                  R,          pV^ 8  d	   W,          pV^ 8  g   W8¼  d   \        R4      hV P	                  ^.V.^ ..3V^3V P
                  R7      pW,          pV# )zUReturns a copy of column j of the array, as an (m x 1) sparse
array (column vector).
z4getcol not provided for 1d arrays. Use indexing A[j]úindex out of bounds©r…   r™   rQ   )rM   r|   r…   Ú
IndexErrorrc   r™   )rL   Újrž  Úcol_selectorr&  s   &&   r   Ú_getcolÚ_spbase._getcolI  s�   € ð �9‰9˜Œ>ÜÐSÓTÐTð �J‰J�r�NˆØˆqŒ5Ø�FˆAØˆqŒ5�A”FÜÐ2Ó3Ð3Ø×*Ñ*¨Q¨C°1°#¸°s°Ð+<Ø23°Q°¸t¿z¹zð +ó KˆàÕ$ˆØˆr   c                ó  € V P                   ^8X  d   \        R4      hV P                  ^ ,          pV^ 8  d	   W,          pV^ 8  g   W8¼  d   \        R4      hV P	                  ^.^ .V..3^V3V P
                  R7      pW0,          # )zNReturns a copy of row i of the array, as a (1 x n) sparse
array (row vector).
z$getrow not meaningful for a 1d arrayrû  rü  )rM   r|   r…   rý  rh   r™   )rL   Úir�  Úrow_selectors   &&  r   Ú_getrowÚ_spbase._getrow\  sŒ   € ð �9‰9˜Œ>ÜÐCÓDÐDð �J‰J�q�MˆØˆqŒ5Ø�FˆAØˆqŒ5�A”FÜÐ2Ó3Ð3Ø×*Ñ*¨Q¨C°1°#¸°s°Ð+<Ø23°Q°¸t¿z¹zð +ó KˆàÕ"Ð"r   c                óD   € V P                  V P                  WR7      4      # )a~  
Return a dense representation of this sparse array.

Parameters
----------
order : {'C', 'F'}, optional
    Whether to store multi-dimensional data in C (row-major)
    or Fortran (column-major) order in memory. The default
    is 'None', which provides no ordering guarantees.
    Cannot be specified in conjunction with the `out`
    argument.

out : ndarray, 2-D, optional
    If specified, uses this array as the output buffer
    instead of allocating a new array to return. The
    provided array must have the same shape and dtype as
    the sparse array on which you are calling the method.

Returns
-------
arr : ndarray, 2-D
    An array with the same shape and containing the same
    data represented by the sparse array, with the requested
    memory order. If `out` was passed, the same object is
    returned after being modified in-place to contain the
    appropriate values.
©r‹   r  )r¤   r0  ©rL   r‹   r  s   &&&r   r2  Ú_spbase.todense‚  s    € ð8 × Ñ  §¡°E Ó!CÓDÐDr   c                óF   € V P                  RR7      P                  WR7      # )a  
Return a dense ndarray representation of this sparse array/matrix.

Parameters
----------
order : {'C', 'F'}, optional
    Whether to store multidimensional data in C (row-major)
    or Fortran (column-major) order in memory. The default
    is 'None', which provides no ordering guarantees.
    Cannot be specified in conjunction with the `out`
    argument.

out : ndarray, 2-D, optional
    If specified, uses this array as the output buffer
    instead of allocating a new array to return. The provided
    array must have the same shape and dtype as the sparse
    array/matrix on which you are calling the method. For most
    sparse types, `out` is required to be memory contiguous
    (either C or Fortran ordered).

Returns
-------
arr : ndarray, 2-D
    An array with the same shape and containing the same
    data represented by the sparse array/matrix, with the requested
    memory order. If `out` was passed, the same object is
    returned after being modified in-place to contain the
    appropriate values.
Fr‰   r  )r�   r0  r	  s   &&&r   r0  Ú_spbase.toarray   s#   € ð< �z‰z˜uˆzÓ%×-Ñ-°EÐ-ÓCÐCr   c                óF   € V P                  VR7      P                  RR7      # )zªConvert this array/matrix to Compressed Sparse Row format.

With copy=False, the data/indices may be shared between this array/matrix and
the resultant csr_array/matrix.
r‰   F)r�   rš   rê  s   &&r   rš   Ú_spbase.tocsrÃ  ó#   € ð �z‰z˜tˆzÓ$×*Ñ*°Ð*Ó6Ð6r   c                óF   € V P                  VR7      P                  RR7      # )z§Convert this array/matrix to Dictionary Of Keys format.

With copy=False, the data/indices may be shared between this array/matrix and
the resultant dok_array/matrix.
r‰   F)r�   Útodokrê  s   &&r   r  Ú_spbase.todokË  r  r   c                óF   € V P                  RR7      P                  VR7      # )zŸConvert this array/matrix to COOrdinate format.

With copy=False, the data/indices may be shared between this array/matrix and
the resultant coo_array/matrix.
Fr‰   )rš   r�   rê  s   &&r   r�   Ú_spbase.tocooÓ  ó#   € ð �z‰z˜uˆzÓ%×+Ñ+°Ð+Ó6Ð6r   c                óF   € V P                  RR7      P                  VR7      # )z¢Convert this array/matrix to List of Lists format.

With copy=False, the data/indices may be shared between this array/matrix and
the resultant lil_array/matrix.
Fr‰   )rš   Útolilrê  s   &&r   r  Ú_spbase.tolilÛ  r  r   c                óF   € V P                  VR7      P                  RR7      # )z¤Convert this array/matrix to sparse DIAgonal format.

With copy=False, the data/indices may be shared between this array/matrix and
the resultant dia_array/matrix.
r‰   F)r�   Útodiarê  s   &&r   r  Ú_spbase.todiaã  r  r   c                óF   € V P                  RR7      P                  WR7      # )a  Convert this array/matrix to Block Sparse Row format.

With copy=False, the data/indices may be shared between this array/matrix and
the resultant bsr_array/matrix.

When blocksize=(R, C) is provided, it will be used for construction of
the bsr_array/matrix.
Fr‰   )Ú	blocksizerŠ   )rš   Útobsr)rL   r  rŠ   s   &&&r   r  Ú_spbase.tobsrë  s#   € ð �z‰z˜uˆzÓ%×+Ñ+°iÐ+ÓKÐKr   c                óF   € V P                  VR7      P                  RR7      # )z­Convert this array/matrix to Compressed Sparse Column format.

With copy=False, the data/indices may be shared between this array/matrix and
the resultant csc_array/matrix.
r‰   F)rš   Útocscrê  s   &&r   r!  Ú_spbase.tocscö  r  r   c                ó(   € V P                  V RR7      # )zzReturns a copy of this array/matrix.

No data/indices will be shared between the returned value and current
array/matrix.
Tr‰   )r{   rK   s   &r   rŠ   Ú_spbase.copyþ  s   € ð �~‰~˜d¨ˆ~Ó.Ð.r   c                óÚ  a a€ \        SS P                  R7      o\        S P                  4      pSfu   S P                  ^ 8X  d4   \
        P                  ! S P                  ^ .4      T;'       g    TVR7      # \
        P                  ! S P                  \        S 4      4      W#R7      # \        S 4      '       d3   SR8X  d   ^S P                  ^,          3MS P                  ^ ,          ^3pMZ\        ;QJ d+    . VV 3R l\        S P                  4       4       F  NK  	  5M$! VV 3R l\        S P                  4       4       4      pVf2   S P                  \
        P                  ! YR;'       g    TR7      4      pMVP                  V8w  d   \        R4      hS P                  ^8”  d   S P                  SWC4      # SR8X  dX   S P                  \
        P                   ! ^S P                  ^ ,          3VR7      4      pVS ,          P#                  V4      VR&   V# S P                  \
        P                   ! S P                  ^,          ^3VR7      4      pS V,          P#                  V4      VR&   V# )a¡  
Sum the array/matrix elements over a given axis.

Parameters
----------
axis : {-2, -1, 0, 1, None} optional
    Axis along which the sum is computed. The default is to
    compute the sum of all the array/matrix elements, returning a scalar
    (i.e., `axis` = `None`).
dtype : dtype, optional
    The type of the returned array/matrix and of the accumulator in which
    the elements are summed.  The dtype of `a` is used by default
    unless `a` has an integer dtype of less precision than the default
    platform integer.  In that case, if `a` is signed then the platform
    integer is used while if `a` is unsigned then an unsigned integer
    of the same precision as the platform integer is used.

    .. versionadded:: 0.18.0

out : np.matrix, optional
    Alternative output matrix in which to place the result. It must
    have the same shape as the expected output, but the type of the
    output values will be cast if necessary.

    .. versionadded:: 0.18.0

Returns
-------
sum_along_axis : np.matrix
    A matrix with the same shape as `self`, with the specified
    axis removed.

See Also
--------
numpy.matrix.sum : NumPy's implementation of 'sum' for matrices

©rM   )r™   r  c              3   óZ   <"  € T F   qS9  g   K  SP                   V,          x € K"  	  R # 5irH   ©r…   )rð   r  rÂ   rL   s   & €€r   rò   Ú_spbase.sum.<locals>.<genexpr>:  s$   øé € ÐWÑ5E°ÐRVÉœm˜dŸj™j¨ŸmšmÓ5Eùs   ƒ+�+rC  z!out dimensions do not match shape.©r·   )r   rM   r   r™   rÌ   r~   Úsumr¤   r	   Ú
isspmatrixr…   r  rŒ   Úzerosr|   Ú_sum_ndrO  rŽ   )rL   rÂ   r™   r  Ú	res_dtypeÚ	new_shaperO  s   ff&&   r   r+  Ú_spbase.sum  sÕ  ù€ ôL ˜D t§y¡yÔ1ˆô " $§*¡*Ó-ˆ	ð Š<Ø�x‰x˜1Œ}Ü—v’v˜d×/Ñ/°°Ó4¸E×<NÐ<NÀYÐTWÔXÐXÜ—6’6˜$×+Ñ+¬G°D«MÓ:À%ÔQÐQÜ˜×Òà.2°d¬l˜˜DŸJ™J q�MÑ*ÀÇÁÈAÅÐPQÐ@R‰IçœÕW´U¸4¿9¹9Ô5EÓWŸ™ÕW´U¸4¿9¹9Ô5EÓWÓWˆIàŠ;à×#Ñ#¤B§H¢H¨Y×>PÐ>PÀyÔ$QÓR‰Cà�y‰y˜IÔ%Ü Ð!DÓEÐEà�9‰9�qŒ=Ø—<‘<  iÓ5Ð5ð
 �4Œ<Ø×$Ñ$¤R§W¢W¨a°·±¸AµÐ-?ÀyÔ%QÓRˆDà˜t�×,Ñ,¨YÓ7ˆC�‰Hð
 ˆ
ð ×$Ñ$¤R§W¢W¨d¯j©j¸­m¸QÐ-?ÀyÔ%QÓRˆDà˜t�×,Ñ,¨YÓ7ˆC�‰HØˆ
r   c                ór  a € \        VS P                  R7      p\        P                  ! S P                  \        P
                  4      ;'       g0    \        P                  ! S P                  \        P                  4      pV'       d   \        P                  MS P                  pS P                  VRR7      pVf"   \        P                  ! S P                  4      pM \        P                  ! V 3R lV 4       4      pVRV,          ,          P                  WVR7      pVe   Vf   VP                  VRR7      # V# )a  
Compute the arithmetic mean along the specified axis.

Returns the average of the array/matrix elements. The average is taken
over all elements in the array/matrix by default, otherwise over the
specified axis. `float64` intermediate and return values are used
for integer inputs.

Parameters
----------
axis : {-2, -1, 0, 1, None} optional
    Axis along which the mean is computed. The default is to compute
    the mean of all elements in the array/matrix (i.e., `axis` = `None`).
dtype : data-type, optional
    Type to use in computing the mean. For integer inputs, the default
    is `float64`; for floating point inputs, it is the same as the
    input dtype.

    .. versionadded:: 0.18.0

out : np.matrix, optional
    Alternative output matrix in which to place the result. It must
    have the same shape as the expected output, but the type of the
    output values will be cast if necessary.

    .. versionadded:: 0.18.0

Returns
-------
m : np.matrix

See Also
--------
numpy.matrix.mean : NumPy's implementation of 'mean' for matrices

r&  Fr‰   c              3   óJ   <"  € T F  pSP                   V,          x € K  	  R # 5irH   r(  )rð   ÚaxrL   s   & €r   rò   Ú_spbase.mean.<locals>.<genexpr>„  s   øé € Ð<±t°˜dŸj™j¨Ÿnšn³tùs   ƒ #g      ð?)rÂ   r™   r  )r   rM   r~   rÄ  r™   rÅ  rL  rÃ  r›   ÚmathÚprodr…   r+  )	rL   rÂ   r™   r  ÚintegralÚinter_dtypeÚ
inter_selfÚdenomÚress	   f&&&     r   ÚmeanÚ_spbase.meanS  sâ   ø€ ôJ ˜D t§y¡yÔ1ˆä—M’M $§*¡*¬b¯j©jÓ9÷ 8ð 8Ü—M’M $§*¡*¬b¯h©hÓ7ð 	÷ %-”b—j’j°$·*±*ˆØ—[‘[ °5�[Ó9ˆ
àŠ<Ü—I’I˜dŸj™jÓ)‰Eä—I’IÔ<±tÓ<Ó<ˆEØ˜S 5�[Õ)×.Ñ.°DÐQTÐ.ÓUˆØÒ ¢Ø—:‘:˜e¨%�:Ó0Ð0Øˆ
r   c                óB   € V P                  4       P                  VR7      # )aÈ  Returns the kth diagonal of the array/matrix.

Parameters
----------
k : int, optional
    Which diagonal to get, corresponding to elements a[i, i+k].
    Default: 0 (the main diagonal).

    .. versionadded:: 1.0

See also
--------
numpy.diagonal : Equivalent numpy function.

Examples
--------
>>> from scipy.sparse import csr_array
>>> A = csr_array([[1, 2, 0], [0, 0, 3], [4, 0, 5]])
>>> A.diagonal()
array([1, 0, 5])
>>> A.diagonal(k=1)
array([2, 3])
©Úk)rš   Údiagonal)rL   rA  s   &&r   rB  Ú_spbase.diagonal‹  s   € ð0 �z‰z‹|×$Ñ$ qÐ$Ó)Ð)r   c                óB   € V P                  VR7      P                  4       # )zÔReturns the sum along diagonals of the sparse array/matrix.

Parameters
----------
offset : int, optional
    Which diagonal to get, corresponding to elements a[i, i+offset].
    Default: 0 (the main diagonal).

r@  )rB  r+  )rL   Úoffsets   &&r   ÚtraceÚ_spbase.trace¥  s   € ð �}‰}˜vˆ}Ó&×*Ñ*Ó,Ð,r   c                ó¼   € V P                   w  r4V^ 8”  d   W$8¼  g   V^ 8  d   V) V8¼  d   \        R4      hV P                  \        P                  ! V4      V4       R# )a"  
Set diagonal or off-diagonal elements of the array/matrix.

Parameters
----------
values : array_like
    New values of the diagonal elements.

    Values may have any length. If the diagonal is longer than values,
    then the remaining diagonal entries will not be set. If values are
    longer than the diagonal, then the remaining values are ignored.

    If a scalar value is given, all of the diagonal is set to it.

k : int, optional
    Which off-diagonal to set, corresponding to elements a[i,i+k].
    Default: 0 (the main diagonal).

zk exceeds array dimensionsN)r…   r|   Ú_setdiagr~   r    )rL   ÚvaluesrA  r�  rž  s   &&&  r   ÚsetdiagÚ_spbase.setdiag±  sK   € ð( �z‰z‰ˆØ�ŒE�a”f ! a¤%¨Q¨B°!¬GÜÐ9Ó:Ð:Ø�‰”b—j’j Ó(¨!Ö,r   c                ó`  € V P                   w  r4V^ 8  dŽ   VP                  ^ 8X  d3   \        W2,           V4      p\        V4       F  pWWb,
          V3&   K  	  R# \        W2,           V\	        V4      4      pV^ 8:  d   R# \        VRV 4       F  w  rgWpWb,
          V3&   K  	  R# VP                  ^ 8X  d3   \        W4V,
          4      p\        V4       F  pWWfV,           3&   K  	  R# \        W4V,
          \	        V4      4      pV^ 8:  d   R# \        VRV 4       F  w  rgWpWfV,           3&   K  	  R# )zJThis part of the implementation gets overridden by the
different formats.
N)r…   rM   ÚminrŒ   rI   Ú	enumerate)rL   rJ  rA  r�  rž  Ú	max_indexr  Úvs   &&&     r   rI  Ú_spbase._setdiagÊ  s  € ð �z‰z‰ˆØˆqŒ5Ø�{‰{˜aÔä ¥ Q›K�	Ü˜yÖ)�AØ%+˜� ˜“Nó *ô   ¥ Q¬¨F«Ó4�	Ø ”>ÙÜ% f¨Z¨iÐ&8Ö9‘D�AØ%&˜� ˜“Nó :ð �{‰{˜aÔä  Q¥3›K�	Ü˜yÖ)�AØ%+˜ �E˜“Nó *ô    Q¥3¬¨F«Ó4�	Ø ”>ÙÜ% f¨Z¨iÐ&8Ö9‘D�AØ%&˜ �E˜“Nó :r   c                ó  € VeX   Ve   \        R4      hVP                  V P                  8w  g   VP                  V P                  8w  d   \        R4      hRVR&   V# \        P                  ! V P                  V P                  VR7      # )Nz,order cannot be specified if out is not Nonez6out array must be same dtype and shape as sparse arrayg        .)r™   r‹   )r|   r…   r™   r~   r-  r	  s   &&&r   Ú_process_toarray_argsÚ_spbase._process_toarray_argsè  sy   € ØŠ?ØÒ Ü ð "/ó 0ð 0à�y‰y˜DŸJ™JÔ&¨#¯)©)°t·z±zÔ*AÜ ð "0ó 1ð 1àˆC�‰HØˆJä—8’8˜DŸJ™J¨d¯j©jÀÔFÐFr   c                ó\   € ^RI Hp T! TTT;'       d    \        V \        4      '       * 4      # )aQ  
Determine index dtype for array.

This wraps _sputils.get_index_dtype, providing compatibility for both
array and matrix API sparse matrices. Matrix API sparse matrices would
attempt to downcast the indices - which can be computationally
expensive and undesirable for users. The array API changes this
behaviour.

See discussion: https://github.com/scipy/scipy/issues/16774

The get_index_dtype import is due to implementation details of the test
suite. It allows the decorator ``with_64bit_maxval_limit`` to mock a
lower int32 max value for checks on the matrix API's downcasting
behaviour.
)Úget_index_dtype)Ú_sputilsrW  r}   r   )rL   ÚarraysÚmaxvalÚcheck_contentsrW  s   &&&& r   Ú_get_index_dtypeÚ_spbase._get_index_dtypeõ  s2   € õ" 	.ñ ˜vØ%Ø .× PÐ P´zÀ$ÌÓ7PÔ3PóSð 	Sr   )rJ   ry   )r?   )ÚunsafeTrH   )Fr*  )NF)T)NN)NNN)r   NF)rr   r   r   r   r   Ú__array_priority__rÖ   Ú	_allow_ndÚpropertyrM   rS   rX   r^   rc   rh   rm   rr   rw   r‚   r…   rŽ   r•   r›   Úclassmethodr¤   r¨   r´   r¹   r¼   rÄ   rÇ   rÌ   rÑ   rC   rÚ   rÞ   rã   rê   r	  r  r  rœ   r  r6  r/  r=  r@  rD  rH  rW  rZ  r^  rb  rf  rj  rn  rr  rw  rz  r~  r�  r…  rˆ  r�  r�  r”  r—  r   r¤  r¨  r  rš  r›  rœ  r¶  rº  r½  rÆ  rË  rÏ  rÒ  rÕ  rÙ  rÜ  rß  râ  rÙ   ré  rí  rÝ   râ   rø  r   r  r2  r0  rš   r  r�   r  r  r  r!  rŠ   r+  r=  rB  rF  rK  rI  rT  r\  r   Ú__classdictcell__©rD   s   @r   r<   r<   U   se  ø‡ € ñð ÐØ€GØ€Ià÷ ó ð ð ñ0ó ð0ð ñó ðð ñó ðð ñó ðð ñó ðð ñó ðð ñó ðð ñó ðð	C¨ô 	Cð ñó ðò)MòV@ô6ð> ñ)ó ð)ð ñ'ó ð'òò òô8SôtLð ÷ó ðð ÷ó ðð ÷ó ðð ñ ó ð ð ñó ðð ñó ðò
òò4Nò(ô(òN,"ò\)9òV8ò8ò ô$2ô;òS"òj4ò4ò4ò4ò4ò4ò!ô4ò/ò.ò/ò&ò&ò"ò"#ò"ò 
"ò\?ò|$ò$ò/ò2ò7ò2ò#ò$,ò-ð2"¨ô 2"òh#òòòòòòò+ôFô>ô.)ð ×$Ñ$€D„Lò$ò$ò7ò(ò&#ôLEô<DôF7ô7ô7ô7ô7ô	Lô7ò/ôKôZ5ôp*ô4
-ô-ò2'ò<G÷Sò Sr   r<   c                   ó4   a € ] tR tRt o Rt]R 4       tRtV tR# )r   i  z3A namespace class to separate sparray from spmatrixc               ó   € ^ RI Hp V! W4      # )ah  
Return a parametrized wrapper around the `~scipy.sparse.sparray` type.

.. versionadded:: 1.16.0

Returns
-------
alias : types.GenericAlias
    A parametrized `~scipy.sparse.sparray` type.

Examples
--------
>>> import numpy as np
>>> from scipy.sparse import coo_array

>>> coo_array[np.int8, tuple[int]]
scipy.sparse._coo.coo_array[numpy.int8, tuple[int]]
)ÚGenericAlias)Útypesrg  )r¢   Úargrg  s   "" r   Ú__class_getitem__Úsparray.__class_getitem__  s   € õ( 	'Ù˜CÓ%Ð%r   r   N)	r   r   r   r   r   rb  rj  r   rc  rd  s   @r   r   r     s   ø‡ € Ù=àñ&ó ö&r   c                ó"   € \        V \        4      # )a¦  Is `x` of a sparse matrix type?

Parameters
----------
x
    object to check for being a sparse matrix

Returns
-------
bool
    True if `x` is a sparse matrix, False otherwise

Examples
--------
>>> import numpy as np
>>> from scipy.sparse import csr_array, csr_matrix, isspmatrix
>>> isspmatrix(csr_matrix([[5]]))
True
>>> isspmatrix(csr_array([[5]]))
False
>>> isspmatrix(np.array([[5]]))
False
>>> isspmatrix(5)
False
)r}   r   )Úxs   &r   r,  r,  ,  s   € ô4 �aœÓ"Ð"r   )r,  r   r   r   r   )>r   Úwarningsr   r6  Únumpyr~   rP  rX  r   r   r   r   r   r   r	   r
   r   r   r   Úscipy._lib._sparser   r   Ú_matrixr   Ú__all__ÚWarningr   r    r   ræ   Ú	frozensetÚsinÚtanÚarcsinÚarctanÚsinhÚtanhÚarcsinhÚarctanhÚrintÚsignÚexpm1Úlog1pÚdeg2radÚrad2degÚfloorÚceilÚtruncÚsqrtÚ _ufuncs_with_fixed_point_at_zeror€   rQ  rR  ra  rm  re  ri  rN  rM  r<   r   r,  r   r   r   Ú<module>rˆ     sù  ðÙ $å Û Û Û ÷K÷ K÷ Kñ K÷ 3å ò7€ô	�Gô 	ô
	˜-ô 	ô	˜mô 	ðˆE�AÐ1Ð2ð Ø�AÐ.Ð/ðà�AÐ+Ð,ðð �A�Ð'ðð �AÐ3Ð4ð	ð
 �AÐ1Ð2ðð �A�|Ð$ðð �AÐ'Ð(ðð �AÐ<Ð=ðð �A�z�?ðð �BÐ*Ð+ðð �BÐ8Ð9ðð �BÐ-Ð.ðð �BÐ;Ð<ðð �BÐ+Ð,ðð �BÐ.Ð/ðð  �BÐ)Ð*ð!ð" �BÐ4Ð5Ø�BÐ,Ð-Ø�B˜Ð$ñ'€ñ0 $-Ø
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