
    Cpj*                     .   S /r SSKrSSKJr  SSKJr  SSKJr  SSK	J
r
  \
R                  \
R                  \
R                  \
R                  S.r\
R                   \
R"                  \
R$                  \
R&                  S.rSSS	.r " S
 S5      r    SS jrg)_svdp    N)	HAS_ILP64)aslinearoperator)LinAlgError   )_propack)fdFD)LMSMc                   D    \ rS rSrSrS rS r\S 5       r\S 5       r	Sr
g)	_AProd"   z|
Wrapper class for linear operator

The call signature of the __call__ method matches the callback of
the PROPACK routines.
c                      [        U5      U l        g ! [         a'    [        [        R                  " U5      5      U l         g f = fN)r   A	TypeErrornpasarray)selfr   s     U/var/www/html/pdf-tiff/venv/lib/python3.13/site-packages/scipy/sparse/linalg/_svdp.py__init___AProd.__init__)   s6    	5%a(DF 	5%bjjm4DF	5s    .AAc                     US:X  a  U R                   R                  U5      US S & g U R                   R                  U5      US S & g )Nr   )r   matvecrmatvec)r   transamnxys         r   __call___AProd.__call__/   s5    Q;66==#AaD66>>!$AaD    c                 .    U R                   R                  $ r   )r   shaper   s    r   r(   _AProd.shape5   s    vv||r&   c                      U R                   R                  $ ! [         aS    U R                   R                  [        R
                  " U R                   R                  S   5      5      R                  s $ f = f)Nr   )r   dtypeAttributeErrorr   r   zerosr(   r)   s    r   r,   _AProd.dtype9   sU    	B66<< 	B66==$&&,,q/!:;AAA	Bs    AA54A5)r   N)__name__
__module____qualname____firstlineno____doc__r   r$   propertyr(   r,   __static_attributes__ r&   r   r   r   "   s;    5%   B Br&   r   c                 ^   Uc  [        S5      eUR                  5       nUS;  a  [        S5      eU(       d  US:X  a  [        S5      e[        U 5      nUR                  R                  n [
        U   n[        U   nUR                  u  nnUS:  d  U[        UU5      :  a  [        S5      eUc  SU-  nUc  Sn[        US-   US-   U5      nXA:  a  [        SU SU S35      eU(       a  SOSnU(       a  SOSn[        R                  " UUS-   4SUS9n[        R                  " UU4SUS9n[        R                  " USUR                  5       S9n[        R                  " USUR                  5       S9nUcf  UR                  US9USS2S4'   [        R                  " [        R                  " SUS95      (       a!  USS2S4==   SUR                  US9-  -  ss'   O
 UUSS2S4'   U
c4  [        R                   " [        R"                  " U5      R$                  5      n
Uc#  [        R"                  " U5      R$                  S-  nU(       a]  [        R&                  " XX4UR                  5       S9nUc  XA-
  nU[        UU-
  UU5      :  a  [        S5      eUS:  a  [        S5      eO%[        R&                  " XU4UR                  5       S9n[(        (       a  [        R*                  O[        R,                  n [        R&                  " [/        [1        U5      5      [/        [1        U5      5      4U S9n!Sn"U(       d  U(       aH  UU-   SUS-  -  -   SU-  -   S-   n#U#[3        SUS-  -  SU-  -   S-   U"[3        UU5      -  5      -  n#S U-  n$O5UU-   SU-  -   SUS-  -  -   S-   [3        UU-   SU-  S-   5      -   n#SU-  S-   n$[        R                  " U#UR                  5       S9n%[        R                  " U$U S9n&[        R                  " SUR                  5       S9n'[        R                  " SU S9n(UR5                  5       (       a!  [        R                  " UU-   U-   US9n)U%U)U&4n*OU%U&4n*[7        US!5      (       aM  UR9                  S[        R:                  " [        R*                  5      R2                  S[        R<                  S"9n+OLUR?                  S[        R:                  " [        R*                  5      R2                  S[        R<                  S"9n+U(       a(  U" [@        U   UUUUUUUU	UUUUU/U*QUPU!PU'PU(PU+P76 n,OU" UUUUXU	UUUUU/U*QUPU!PU'PU(PU+P76 n,U,S:  a  [C        S#U, S$35      eU,S:  a  [C        S%U S&U S'35      eUSS2SU24   UUSS2SU24   RE                  5       RF                  U4$ ! [         aI    [        R                  " [        R                  " SUS95      (       a  S	nOS
n[
        U   n[        U   n GNf = f! [          a    [        SU 35      ef = f)(a4  
Compute the singular value decomposition of a linear operator using PROPACK

Parameters
----------
A : array_like, sparse matrix, or LinearOperator
    Operator for which SVD will be computed.  If `A` is a LinearOperator
    object, it must define both ``matvec`` and ``rmatvec`` methods.
k : int
    Number of singular values/vectors to compute
which : {"LM", "SM"}
    Which singular triplets to compute:
    - 'LM': compute triplets corresponding to the `k` largest singular
            values
    - 'SM': compute triplets corresponding to the `k` smallest singular
            values
    `which='SM'` requires `irl_mode=True`.  Computes largest singular
    values by default.
irl_mode : bool, optional
    If `True`, then compute SVD using IRL (implicitly restarted Lanczos)
    mode.  Default is `True`.
kmax : int, optional
    Maximal number of iterations / maximal dimension of the Krylov
    subspace. Default is ``10 * k``.
compute_u : bool, optional
    If `True` (default) then compute left singular vectors, `u`.
compute_v : bool, optional
    If `True` (default) then compute right singular vectors, `v`.
tol : float, optional
    The desired relative accuracy for computed singular values.
    If not specified, it will be set based on machine precision.
v0 : array_like, optional
    Starting vector for iterations: must be of length ``A.shape[0]``.
    If not specified, PROPACK will generate a starting vector.
full_output : bool, optional
    If `True`, then return sigma_bound.  Default is `False`.
delta : float, optional
    Level of orthogonality to maintain between Lanczos vectors.
    Default is set based on machine precision.
eta : float, optional
    Orthogonality cutoff.  During reorthogonalization, vectors with
    component larger than `eta` along the Lanczos vector will be purged.
    Default is set based on machine precision.
anorm : float, optional
    Estimate of ``||A||``.  Default is ``0``.
cgs : bool, optional
    If `True`, reorthogonalization is done using classical Gram-Schmidt.
    If `False` (default), it is done using modified Gram-Schmidt.
elr : bool, optional
    If `True` (default), then extended local orthogonality is enforced
    when obtaining singular vectors.
min_relgap : float, optional
    The smallest relative gap allowed between any shift in IRL mode.
    Default is ``0.001``.  Accessed only if ``irl_mode=True``.
shifts : int, optional
    Number of shifts per restart in IRL mode.  Default is determined
    to satisfy ``k <= min(kmax-shifts, m, n)``.  Must be
    >= 0, but choosing 0 might lead to performance degradation.
    Accessed only if ``irl_mode=True``.
maxiter : int, optional
    Maximum number of restarts in IRL mode.  Default is ``1000``.
    Accessed only if ``irl_mode=True``.
rng : `numpy.random.Generator`, optional
    Pseudorandom number generator state. When `rng` is None, a new
    `numpy.random.Generator` is created using entropy from the
    operating system. Types other than `numpy.random.Generator` are
    passed to `numpy.random.default_rng` to instantiate a ``Generator``.

Returns
-------
u : ndarray
    The `k` largest (``which="LM"``) or smallest (``which="SM"``) left
    singular vectors, ``shape == (A.shape[0], 3)``, returned only if
    ``compute_u=True``.
sigma : ndarray
    The top `k` singular values, ``shape == (k,)``
vt : ndarray
    The `k` largest (``which="LM"``) or smallest (``which="SM"``) right
    singular vectors, ``shape == (3, A.shape[1])``, returned only if
    ``compute_v=True``.
sigma_bound : ndarray
    the error bounds on the singular values sigma, returned only if
    ``full_output=True``.

Nz:`rng` must be a normalized numpy.random.Generator instance>   r   r   z#`which` must be either 'LM' or 'SM'r   z#`which`='SM' requires irl_mode=Truer   )r,   r   r
   r   z.k must be positive and not greater than m or n
   i  z3kmax must be greater than or equal to k, but kmax (z) < k ()r   )orderr,   )sizey              ?zv0 must be of length g      ?z0shifts must satisfy k <= min(kmax-shifts, m, n)!zshifts must be >= 0!          	            integers)lowhighr<   r,   z#An invariant subspace of dimension z was found.zk=z0 singular triplets did not converge within kmax=z iterations)$
ValueErrorupperr   r,   char_lansvd_irl_dict_lansvd_dictKeyErrorr   iscomplexobjemptyr(   minr.   loweruniformsqrtfinfoepsarrayr   int64int32intboolmaxisupperhasattrrD   iinfouint64randint_which_converterr   conjT)-r   kwhichirl_modekmax	compute_u	compute_vv0full_outputtoldeltaetaanormcgselr
min_relgapshiftsmaxiterrngaprodtyp
lansvd_irllansvdr    r!   jobujobvuvsigmabnddoption	int_dtypeioptionNBlworkliworkworkiworkdparmiparmzworkworks	rng_stateinfos-                                                r   r   r   A   sN   r {UVVKKMEL >??>??1IE
++

C
#%c*
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 1qD1qD 	!TAXc5A
!T#S1AHHQc5E
((1Csyy{
3C
 
z++1+%!Q$??288AS122adGrCKKQK///G	:AadG
 }))*
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	 KK*..Qbii   
	 *5141fa#E3<49<;B< "< $)< +0< 2;<
 dD!QeQsA II%I'.I05I7<I>GI ax1${CE 	E	 6&' 	' QU8UAa!eHMMO--s22O  #??288AS122CC%c*
c"#Z  	:4QC899	:s   *V= 	X =AXXX,)r   TNTTNFr   NNr   FTgMb`?NNN)__all__numpyr   scipy.linalg.lapackr   scipy.sparse.linalgr   scipy.linalgr    r   slansvddlansvdclansvdzlansvdrK   slansvd_irldlansvd_irlclansvd_irlzlansvd_irlrJ   r`   r   r   r7   r&   r   <module>r      s   )  ) 0 $  
												 
												  
 B B> 15JK8<;?o3r&   