
    g+                     H   d Z dgZddlZddlmZ ddlmZ ddlm	Z	 ddlm
Z
 dd	lmZ dd
lmZ e	j                  e
j                  ej                  ej                   dZe	j$                  e
j&                  ej(                  ej*                  dZdddZ G d d      Z	 	 	 	 ddZy)a  
Python wrapper for PROPACK
--------------------------

PROPACK is a collection of Fortran routines for iterative computation
of partial SVDs of large matrices or linear operators.

Based on BSD licensed pypropack project:
  http://github.com/jakevdp/pypropack
  Author: Jake Vanderplas <vanderplas@astro.washington.edu>

PROPACK source is BSD licensed, and available at
  http://soi.stanford.edu/~rmunk/PROPACK/
_svdp    N)aslinearoperator)LinAlgError   )	_spropack)	_dpropack)	_cpropack)	_zpropack)fdFDLS)LMSMc                   <    e Zd ZdZd Zd Zed        Zed        Zy)_AProdz
    Wrapper class for linear operator

    The call signature of the __call__ method matches the callback of
    the PROPACK routines.
    c                     	 t        |      | _        y # t        $ r& t        t        j                  |            | _        Y y w xY wN)r   A	TypeErrornpasarray)selfr   s     L/var/www/html/venv/lib/python3.12/site-packages/scipy/sparse/linalg/_svdp.py__init__z_AProd.__init__9   s6    	5%a(DF 	5%bjjm4DF	5s    ,AAc                     |dk(  r| j                   j                  |      |d d  y | j                   j                  |      |d d  y )Nn)r   matvecrmatvec)r   transamr   xysparmiparms           r   __call__z_AProd.__call__?   s5    S=66==#AaD66>>!$AaD    c                 .    | j                   j                  S r   )r   shaper   s    r   r+   z_AProd.shapeE   s    vv||r)   c                     	 | j                   j                  S # t        $ rR | j                   j                  t	        j
                  | j                   j                  d               j                  cY S w xY w)Nr   )r   dtypeAttributeErrorr    r   zerosr+   r,   s    r   r.   z_AProd.dtypeI   sU    	B66<< 	B66==$&&,,q/!:;AAA	Bs    AA32A3N)	__name__
__module____qualname____doc__r   r(   propertyr+   r.    r)   r   r   r   2   s;    5%   B Br)   r   c                 
   |t        d      |j                         }|dvrt        d      |s|dk(  rt        d      t        |       }|j                  j                  }	 t
        |   }t        |   }|j                  \  }}|d	k  s|t        ||      kD  rt        d
      |d|z  }|d}t        |d	z   |d	z   |      }||k  rt        d| d| d      |rdnd}|rdnd}t        j                  ||d	z   fd|      }t        j                  ||fd|      }|h|j!                  |      |dddf<   t        j                  t        j                  d|            r/|dddfxx   d|j!                  |      z  z  cc<   n
	 ||dddf<   |
2t        j"                  t        j$                  |      j&                        }
|"t        j$                  |      j&                  dz  }|r^t        j(                  |
|||f|j+                               }|||z
  }|t        ||z
  ||      kD  rt        d      |dk  r3t        d      t        j(                  |
||f|j+                               }t        j(                  t-        t/        |            t-        t/        |            fd      }d}|s|rF||z   d|z  z   d|z  |z  z   dz   t1        d|z  |z  d|z  z   dz   |t1        ||      z        z   } d |z  }!n5||z   d|z  z   d!|z  |z  z   dz   t1        ||z   d|z  dz         z   } d!|z  d	z   }!t        j                  | |j+                               }"t        j                  |!t        j2                        }#t        j                  d	|j+                               }$t        j                  d	t        j2                        }%|j5                         r&t        j                  ||z   d"|z  z   |      }&|"|&|#f}'n|"|#f}'|r* |t6        |   |||||||||||	g|'|||$|% \  }}(})}}*n ||||||||||	g	|'|||$|% \  }}(})}}*|*dkD  rt9        d#|* d$      |*dk  rt9        d%| d&| d'      |ddd|f   |(|ddd|f   j;                         j<                  |)fS # t        $ r t        j                  t        j                  d|            r$t        j                  t              j                  }n#t        j                  t              j                  }t
        |   }t        |   }Y w xY w# t         $ r t        d|       w xY w)(ax  
    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   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%   r   r   )orderr.   )sizey              ?zv0 must be of length g      ?z0shifts must satisfy k <= min(kmax-shifts, m, n)!zshifts must be >= 0!i   	                      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emptycomplexfloatr+   minr0   uniformsqrtfinfoepsarraylowerintboolmaxint32isupper_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doptionioption	blocksizelworkliworkworkiworkdparmr'   zworkworkssigmabndinfos+                                              r   r   r   Q   s   r {UVVKKMEL >??>??1IE
++
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 3D3D 	!TAXc5A
!T#S1A
 
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 }))*
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c"#V  	:4QC899	:s   !R 	T2 BT/.T/2U
)r   TNTTNFr   NNr   FTgMb`?NNN)r4   __all__numpyr   scipy.sparse.linalgr   scipy.linalgr   _propackr   r   r	   r
   slansvddlansvdclansvdzlansvdrI   slansvd_irldlansvd_irlclansvd_irlzlansvd_irlrH   r[   r   r   r6   r)   r   <module>r      s    )  0 $     
												 
												  
 B B> 15JK8<;?d3r)   