o
    ?HhH                     @   s8   d dl Zd dlZddgZeddZd	ddZdd ZdS )
    Nsave_npzload_npzF)allow_pickleTc                 C   s   i }|j dv r|j|j|jd n'|j dkr|j|jd n|j dkr-|j|j|jd nd|j  d}t||j|j d	|j	|j
d
 t|tjjrS|jdd |r`tj| fi | dS tj| fi | dS )aj   Save a sparse matrix or array to a file using ``.npz`` format.

    Parameters
    ----------
    file : str or file-like object
        Either the file name (string) or an open file (file-like object)
        where the data will be saved. If file is a string, the ``.npz``
        extension will be appended to the file name if it is not already
        there.
    matrix: spmatrix or sparray
        The sparse matrix or array to save.
        Supported formats: ``csc``, ``csr``, ``bsr``, ``dia`` or ``coo``.
    compressed : bool, optional
        Allow compressing the file. Default: True

    See Also
    --------
    scipy.sparse.load_npz: Load a sparse matrix from a file using ``.npz`` format.
    numpy.savez: Save several arrays into a ``.npz`` archive.
    numpy.savez_compressed : Save several arrays into a compressed ``.npz`` archive.

    Examples
    --------
    Store sparse matrix to disk, and load it again:

    >>> import numpy as np
    >>> import scipy as sp
    >>> sparse_matrix = sp.sparse.csc_matrix([[0, 0, 3], [4, 0, 0]])
    >>> sparse_matrix
    <Compressed Sparse Column sparse matrix of dtype 'int64'
        with 2 stored elements and shape (2, 3)>
    >>> sparse_matrix.toarray()
    array([[0, 0, 3],
           [4, 0, 0]], dtype=int64)

    >>> sp.sparse.save_npz('/tmp/sparse_matrix.npz', sparse_matrix)
    >>> sparse_matrix = sp.sparse.load_npz('/tmp/sparse_matrix.npz')

    >>> sparse_matrix
    <Compressed Sparse Column sparse matrix of dtype 'int64'
        with 2 stored elements and shape (2, 3)>
    >>> sparse_matrix.toarray()
    array([[0, 0, 3],
           [4, 0, 0]], dtype=int64)
    csccsrbsr)indicesindptrdia)offsetscoo)rowcolz4Save is not implemented for sparse matrix of format .ascii)formatshapedataT)	_is_arrayN)r   updater	   r
   r   r   r   NotImplementedErrorencoder   r   
isinstancespsparsesparraynpsavez_compressedsavez)filematrix
compressedarrays_dictmsg r%   W/home/air/sanwanet/gpt-api/venv/lib/python3.10/site-packages/scipy/sparse/_matrix_io.pyr      s&   .



c                 C   st  t j| fi t}|d}|du rtd|  d| }t|ts)|d}|dr3|d }n|d }z	t	t
j| }W n tyU } z	td	| d
|d}~ww |dv rr||d |d |d f|d dW  d   S |dkr||d |d f|d dW  d   S |dkr||d |d |d ff|d dW  d   S td| d1 sw   Y  dS )a   Load a sparse array/matrix from a file using ``.npz`` format.

    Parameters
    ----------
    file : str or file-like object
        Either the file name (string) or an open file (file-like object)
        where the data will be loaded.

    Returns
    -------
    result : csc_array, csr_array, bsr_array, dia_array or coo_array
        A sparse array/matrix containing the loaded data.

    Raises
    ------
    OSError
        If the input file does not exist or cannot be read.

    See Also
    --------
    scipy.sparse.save_npz: Save a sparse array/matrix to a file using ``.npz`` format.
    numpy.load: Load several arrays from a ``.npz`` archive.

    Examples
    --------
    Store sparse array/matrix to disk, and load it again:

    >>> import numpy as np
    >>> import scipy as sp
    >>> sparse_array = sp.sparse.csc_array([[0, 0, 3], [4, 0, 0]])
    >>> sparse_array
    <Compressed Sparse Column sparse array of dtype 'int64'
        with 2 stored elements and shape (2, 3)>
    >>> sparse_array.toarray()
    array([[0, 0, 3],
           [4, 0, 0]], dtype=int64)

    >>> sp.sparse.save_npz('/tmp/sparse_array.npz', sparse_array)
    >>> sparse_array = sp.sparse.load_npz('/tmp/sparse_array.npz')

    >>> sparse_array
    <Compressed Sparse Column sparse array of dtype 'int64'
        with 2 stored elements and shape (2, 3)>
    >>> sparse_array.toarray()
    array([[0, 0, 3],
           [4, 0, 0]], dtype=int64)

    In this example we force the result to be csr_array from csr_matrix
    >>> sparse_matrix = sp.sparse.csc_matrix([[0, 0, 3], [4, 0, 0]])
    >>> sp.sparse.save_npz('/tmp/sparse_matrix.npz', sparse_matrix)
    >>> tmp = sp.sparse.load_npz('/tmp/sparse_matrix.npz')
    >>> sparse_array = sp.sparse.csr_array(tmp)
    r   Nz	The file z+ does not contain a sparse array or matrix.r   r   _array_matrixzUnknown format ""r   r   r	   r
   r   )r   r   r   r   r   r   z4Load is not implemented for sparse matrix of format r   )r   loadPICKLE_KWARGSget
ValueErroritemr   strdecodegetattrr   r   AttributeErrorr   )r    loadedsparse_formatsparse_typeclser%   r%   r&   r   P   sF   6




 
)T)	numpyr   scipyr   __all__dictr+   r   r   r%   r%   r%   r&   <module>   s    

E