U
    Ë»|e¯>  ã                   @   s„   d Z dZddgZddlZddlmZmZmZ ddl	m
Z
 dd	lmZmZmZmZmZmZmZ dd
lmZ G dd„ de
ƒZdd„ ZdS )zSparse DIAgonal formatzrestructuredtext enÚ
dia_matrixÚisspmatrix_diaé    Né   )Ú
isspmatrixÚ_formatsÚspmatrix)Ú_data_matrix)ÚisshapeÚupcast_charÚgetdtypeÚget_index_dtypeÚget_sum_dtypeÚvalidateaxisÚcheck_shape)Ú
dia_matvecc                   @   s
  e Zd ZdZdZd)dd„Zdd„ Zd	d
„ Zdd„ Zd*dd„Z	e
j	je	_e
jje_d+dd„Ze
jje_dd„ Zdd„ Zdd„ Zd,dd„Zd-dd„Ze
jje_d.dd„Ze
jje_d/dd„Ze
jje_d0d d!„Ze
jje_d1d"d#„Ze
jje_d2d%d&„Zd'd(„ Ze
jje_dS )3r   a$  Sparse matrix with DIAgonal storage

    This can be instantiated in several ways:
        dia_matrix(D)
            with a dense matrix

        dia_matrix(S)
            with another sparse matrix S (equivalent to S.todia())

        dia_matrix((M, N), [dtype])
            to construct an empty matrix with shape (M, N),
            dtype is optional, defaulting to dtype='d'.

        dia_matrix((data, offsets), shape=(M, N))
            where the ``data[k,:]`` stores the diagonal entries for
            diagonal ``offsets[k]`` (See example below)

    Attributes
    ----------
    dtype : dtype
        Data type of the matrix
    shape : 2-tuple
        Shape of the matrix
    ndim : int
        Number of dimensions (this is always 2)
    nnz
        Number of stored values, including explicit zeros
    data
        DIA format data array of the matrix
    offsets
        DIA format offset array of the matrix

    Notes
    -----

    Sparse matrices can be used in arithmetic operations: they support
    addition, subtraction, multiplication, division, and matrix power.

    Examples
    --------

    >>> import numpy as np
    >>> from scipy.sparse import dia_matrix
    >>> dia_matrix((3, 4), dtype=np.int8).toarray()
    array([[0, 0, 0, 0],
           [0, 0, 0, 0],
           [0, 0, 0, 0]], dtype=int8)

    >>> data = np.array([[1, 2, 3, 4]]).repeat(3, axis=0)
    >>> offsets = np.array([0, -1, 2])
    >>> dia_matrix((data, offsets), shape=(4, 4)).toarray()
    array([[1, 0, 3, 0],
           [1, 2, 0, 4],
           [0, 2, 3, 0],
           [0, 0, 3, 4]])

    >>> from scipy.sparse import dia_matrix
    >>> n = 10
    >>> ex = np.ones(n)
    >>> data = np.array([ex, 2 * ex, ex])
    >>> offsets = np.array([-1, 0, 1])
    >>> dia_matrix((data, offsets), shape=(n, n)).toarray()
    array([[2., 1., 0., ..., 0., 0., 0.],
           [1., 2., 1., ..., 0., 0., 0.],
           [0., 1., 2., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 2., 1., 0.],
           [0., 0., 0., ..., 1., 2., 1.],
           [0., 0., 0., ..., 0., 1., 2.]])
    ÚdiaNFc           
   
   C   sŒ  t  | ¡ t|ƒr>|r| ¡ }|j| _|j| _t|jƒ| _�n¬t	|ƒr„t|ƒr\|r\| ¡ }n| 
¡ }|j| _|j| _t|jƒ| _�nft|tƒ�rvt|ƒrÚt|ƒ| _t dt|td�¡| _tt| jƒd�}tjd|d�| _nšz|\}}W n. tk
�r }	 ztdƒ|	‚W 5 d }	~	X Y n`X |d k�r(tdƒ‚t tj|d ||d�¡| _t tj|d	 tt|ƒd�|d�¡| _t|ƒ| _ntzt |¡}W n4 tk
�r¸ }	 ztd
| j ƒ|	‚W 5 d }	~	X Y nX | j|||d� 
¡ }|j| _|j| _t|jƒ| _|d k	�r| j |¡| _| jjd	k�rtdƒ‚| jjdk�r.tdƒ‚| jjd t| jƒk�rdtd| jjd t| jƒf ƒ‚tt | j¡ƒt| jƒk�rˆtdƒ‚d S )N)r   r   )Údefault©Úmaxvalr   ©Údtypez,unrecognized form for dia_matrix constructorzexpected a shape argument)r   Úcopyr   z+unrecognized form for %s_matrix constructor)r   Úshapezoffsets array must have rank 1é   zdata array must have rank 2zBnumber of diagonals (%d) does not match the number of offsets (%d)z&offset array contains duplicate values) r   Ú__init__r   r   ÚdataÚoffsetsr   r   Ú_shaper   ÚtodiaÚ
isinstanceÚtupler	   ÚnpÚzerosr   Úfloatr   ÚmaxÚ	ExceptionÚ
ValueErrorÚ
atleast_2dÚarrayÚ
atleast_1dÚasarrayÚformatÚ_coo_containerÚastypeÚndimÚlenÚunique)
ÚselfÚarg1r   r   r   ÚAÚ	idx_dtyper   r   Úe© r6   úN/var/www/website-v5/atlas_env/lib/python3.8/site-packages/scipy/sparse/_dia.pyr   Y   sp    



þ
ÿÿ
þzdia_matrix.__init__c                 C   s4   t |  ¡  d }d| j| jj| j| jjd |f  S )Nr   zW<%dx%d sparse matrix of type '%s'
	with %d stored elements (%d diagonals) in %s format>r   )r   Ú	getformatr   r   ÚtypeÚnnzr   )r1   r+   r6   r6   r7   Ú__repr__ž   s    ÿþzdia_matrix.__repr__c                 C   sV   | j \}}t | jj d ¡}|| jdd…df  }|dk}|||k M }|||k M }|S )z~Returns a mask of the same shape as self.data, where
        mask[i,j] is True when data[i,j] corresponds to a stored element.r   Nr   )r   r!   Úaranger   r   )r1   Únum_rowsÚnum_colsÚoffset_indsÚrowÚmaskr6   r6   r7   Ú
_data_mask¥   s    
zdia_matrix._data_maskc                 C   s   |   ¡ }t | j| ¡S ©N)rB   r!   Úcount_nonzeror   )r1   rA   r6   r6   r7   rD   °   s    zdia_matrix.count_nonzeroc                 C   s`   |d k	rt dƒ‚| j\}}d}| jD ]2}|dkrD|t||| ƒ7 }q$|t|| |ƒ7 }q$t|ƒS )Nz5getnnz over an axis is not implemented for DIA formatr   )ÚNotImplementedErrorr   r   ÚminÚint)r1   ÚaxisÚMÚNr:   Úkr6   r6   r7   Úgetnnz´   s    

zdia_matrix.getnnzc              	   C   sL  t |ƒ |d k	r |dk r |d7 }t| jƒ}| j\}}d }|dkr |  ¡ }| j| jdd�}	|	jd |krn|	}
n"tj||	jd�}
|	|
d |	jd …< | j	|
|d�}n|tj|df|d�}tj
||d�}t||t| jƒ| jjd | j| j||ƒ |  	|¡}|d k�r
|j||d�S |  	|j|d�¡}|d k	�r<|j|jk�r<tdƒ‚|jd||d	�S )
Nr   r   ©rH   r   r   )r   Úoutzdimensions do not matchr6   )rH   r   rN   )r   r   r   r   rB   r   Úsumr!   r"   Ú_ascontainerÚonesr   r/   r   r&   )r1   rH   r   rN   Ú	res_dtyper=   r>   ÚretrA   ÚxÚresZrow_sumsÚoner6   r6   r7   rO   Ä   s<    


    ÿ

zdia_matrix.sumc                 C   s„   t |t| ƒƒs|  ¡  |¡S t | j|j¡}| j|dd�}|jD ]>}||jkrl| 	| 
|¡| 
|¡ |¡ q@| 	| 
|¡|¡ q@|S )NT©r   )r   r9   ÚtocsrÚ_add_sparser!   Úpromote_typesr   r-   r   ÚsetdiagÚdiagonal)r1   Úotherr   ÚmÚdr6   r6   r7   rY   ì   s    

zdia_matrix._add_sparsec              
   C   sh   |}t j| jd t| jj|jjƒd�}| jjd }| j\}}t||t| j	ƒ|| j	| j| 
¡ | 
¡ ƒ |S )Nr   r   r   )r!   r"   r   r
   r   Úcharr   r   r/   r   Úravel)r1   r]   rT   ÚyÚLrI   rJ   r6   r6   r7   Ú_mul_vector  s    ÿ
(zdia_matrix._mul_vectorc                    s   t  ‡ fdd„|jD ƒ¡S )Nc                    s   g | ]}ˆ   |¡ d d¡‘qS )éÿÿÿÿr   )rd   Úreshape)Ú.0Úcol©r1   r6   r7   Ú
<listcomp>  s     z/dia_matrix._mul_multimatrix.<locals>.<listcomp>)r!   ÚhstackÚT)r1   r]   r6   ri   r7   Ú_mul_multimatrix  s    zdia_matrix._mul_multimatrixr   c                 C   sP  | j \}}|jdkrtj}nt|ƒ}|dk rFt|| ||ƒ}d}|}nt||| |ƒ}|}|| }|jdkrx|d |… }| jj \}	}
|| jkrâ||
krÈtj|	|f| jj	d�}| j|d d …d |
…f< || _|| j| j|k||…f< njt 
| j| jj	 |¡¡| _t||
ƒ}tj|	d |f| jj	d�}| j|d d…d |
…f< ||d||…f< || _d S )Nr   r   r   re   )r   r.   r!   Úinfr/   rF   r   r   r"   r   Úappendr9   r$   )r1   ÚvaluesrK   rI   rJ   Zvalues_nÚnZ	min_indexÚ	max_indexÚ	data_rowsZ	data_colsr   r^   r6   r6   r7   Ú_setdiag  s4    




zdia_matrix._setdiagc                 C   s   |r|   ¡ S | S d S rC   rW   )r1   r   r6   r6   r7   r   <  s    zdia_matrix.todiac                 C   sÔ   |d k	rt dƒ‚| j\}}t| jƒ}| j }tjt|ƒtjd�d d …d f }tj|tjd�|| d d …d f  }td|| jjd  ƒ}	t 	| jtj
| jjd |	f| jjd�f¡}
|
||f }
| j|
|f||f|d�S )NzoSparse matrices do not support an 'axes' parameter because swapping dimensions is the only logical permutation.r   r   r   )r   r   )r&   r   r$   r   r!   r<   r/   Úintcr   rk   r"   r   Ú_dia_container)r1   Úaxesr   r=   r>   Úmax_dimr   ÚrÚcZ
pad_amountr   r6   r6   r7   Ú	transposeD  s$    

 $ÿ

 ÿÿzdia_matrix.transposec           
      C   s¾   | j \}}|| ks||kr.tjd| jjd�S t | j|k¡\}td|ƒ}t|| |ƒ}|| }|j	dkr|tj
|| jjd�S | j|d ||…f }|t|ƒ }	|	dkrºtj|d|	fdd�}|S )Nr   r   Úconstant)Úmode)r   r!   Úemptyr   r   Únonzeror   r$   rF   Úsizer"   r/   Úpad)
r1   rK   ÚrowsÚcolsÚidxZ	first_colZlast_colZresult_sizeÚresultÚpaddingr6   r6   r7   r\   \  s    


zdia_matrix.diagonalc                 C   s"  | j dkr| j| j| jd�S | j\}}| jj\}}t |¡}|| jd d …d f  }|dk}|||k M }|||k M }|| jdkM }tt	| jƒd�}	tj
|d |	d�}
t |jdd�d |… ¡|
d|d …< ||k rä|
| |
|d d …< |j|j j|	dd�}| jj|j }| j|||
f| j| jd�S )	Nr   r   r   r   rM   FrW   ©r   r   )r:   Ú_csc_containerr   r   r   r!   r<   r   r   r$   r"   ÚcumsumrO   rl   r-   )r1   r   r=   r>   Únum_offsetsÚ
offset_lenr?   r@   rA   r4   ÚindptrÚindicesr   r6   r6   r7   Útocscn  s(    


&ÿzdia_matrix.tocscc                 C   s°   | j \}}| jj \}}t |¡}|| jd d …d f  }|dk}|||k M }|||k M }|| jdkM }|| }t ||¡| ¡  }	| j| }
| j|
||	ff| j | jd�}d|_	|S )Nr   r‡   T)
r   r   r!   r<   r   Útilera   r,   r   Úhas_canonical_format)r1   r   r=   r>   rŠ   r‹   r?   r@   rA   rh   r   r3   r6   r6   r7   Útocooˆ  s$    



  ÿzdia_matrix.tocooTc                 C   s8   |r| j || j ¡ f| jd�S | j || jf| jd�S dS )z‘Returns a matrix with the same sparsity structure as self,
        but with different data.  By default the structure arrays are copied.
        )r   N)rv   r   r   r   )r1   r   r   r6   r6   r7   Ú
_with_dataŸ  s     ÿ ÿzdia_matrix._with_datac                 G   sš   t |ƒ}|\}}| jd d …d |…f | _|| jd kr�t | j| jd  | jjd k ¡r�| jd d …d f | jd  t | jjd ¡k}d| j|< || _d S )Nr   r   )r   r   r   r!   Úanyr   r<   r   )r1   r   rI   rJ   rA   r6   r6   r7   Úresize¬  s     ÿÿ
zdia_matrix.resize)NNF)N)NNN)r   )F)NF)r   )F)F)T)Ú__name__Ú
__module__Ú__qualname__Ú__doc__r+   r   r;   rB   rD   rL   r   rO   rY   rd   rm   rt   r   r{   r\   rŽ   r‘   r’   r”   r6   r6   r6   r7   r      s8   F
E



&

%










c                 C   s    ddl m} t| tƒpt| |ƒS )aÍ  Is x of dia_matrix type?

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

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

    Examples
    --------
    >>> from scipy.sparse import dia_matrix, isspmatrix_dia
    >>> isspmatrix_dia(dia_matrix([[5]]))
    True

    >>> from scipy.sparse import dia_matrix, csr_matrix, isspmatrix_dia
    >>> isspmatrix_dia(csr_matrix([[5]]))
    False
    r   )Ú	dia_array)Ú_arraysr™   r   r   )rT   r™   r6   r6   r7   r   ¾  s    )r˜   Ú__docformat__Ú__all__Únumpyr!   Ú_baser   r   r   Ú_datar   Ú_sputilsr	   r
   r   r   r   r   r   Ú_sparsetoolsr   r   r   r6   r6   r6   r7   Ú<module>   s   $   1