o iRhN@sdZdZgdZddlZddlmZddlmZdd l m Z m Z m Z m Z dd lmZdd lmZmZmZmZmZmZdd lmZGd ddeZddZddZGdddee ZGdddeeZdS)zSparse DIAgonal formatzrestructuredtext en) dia_array dia_matrixisspmatrix_diaN)copy_if_needed)spmatrix)issparse_formats_spbasesparray) _data_matrix)isshape upcast_chargetdtype get_sum_dtype validateaxis check_shape) dia_matvecc@seZdZdZd)ddddZddZd d Zd*d d Zejj e_ d*d dZ ej j e _ d+ddZ ej j e _ ddZ ddZ ddZd,ddZd-ddZejj e_ d.ddZejj e_ d,ddZejj e_ d-d d!Zejj e_ d-d"d#Zejj e_ d/d%d&Zd'd(Zejj e_ dS)0 _dia_basediaNFmaxprintc Cstj|||dt|rF|jdkr&|r|}|j|_|j|_t|j|_ n|j|jkr3|r3|}n| }|j|_|j|_t|j|_ nt |t rt |rqt||_ tdt|td|_|jt|jd}tjd|d|_nz|\}} Wnty} zd} t| | d} ~ ww|durtd |st}ttj|d||d |_tj|d |jt|d|d } t| |_t||_ nIzt|}Wnty} z td |jd | d} ~ wwt |tr|jdkrtd|jd|j|||d }|j|_|j|_t|j|_ |durt|} |j| |_|jjd kr%td|jjdkr0td|jjdt|jkrKtd|jjdt|jftt |jt|jkr]tddS)Nrr)rr)defaultmaxvalrdtypez+unrecognized form for dia_array constructorzexpected a shape argument)rcopyrzunrecognized form for z_matrix constructorrzDIA arrays don't support zD input. Use 2D)rshapezoffsets array must have rank 1zdata array must have rank 2zBnumber of diagonals (%d) does not match the number of offsets (%d)z&offset array contains duplicate values)!r __init__r formatrdataoffsetsrr_shapetodia isinstancetuplernpzerosrfloat_get_index_dtypemax Exception ValueErrorr atleast_2darray atleast_1dasarrayr ndim_coo_containerastypelenunique) selfarg1rrrrA idx_dtyper"r#emessagenewdtyper?S/home/air/sanwanet/backup_V2/venv/lib/python3.10/site-packages/scipy/sparse/_dia.pyr s           z_dia_base.__init__c Cs\t|j\}}t|trdnd}|jjd}d|d|d|jd|jd|d |jd S) Nr0matrixr)r r!r&r r"rrnnz)r8_fmt sparse_clsdr?r?r@__repr__cs z_dia_base.__repr__cCsV|j\}}t|jjd}||jdddf}|dk}|||kM}|||kM}|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.rNr)rr(aranger"r#)r8num_rowsnum_cols offset_indsrowmaskr?r?r@ _data_maskls   z_dia_base._data_maskcCs(|durtd|}t|j|S)Nztj ||jjdS|j|d||f}|t |} | dkr]tj |d| fdd}|S)Nrrconstant)mode) rr(emptyr"rnonzeror#r,rTsizer)r6pad) r8rXrowscolsidx first_collast_col result_sizeresultpaddingr?r?r@diagonal4s    z_dia_base.diagonalc Cs$|jdkr|j|j|jdS|j\}}|jj\}}t|}||jdddf}|dk}|||kM}|||kM}||jdkM}|jt |jd} tj |d| d} t |j ddd|| d|d<||krs| || |dd<|j |j j| dd} |jj |j } |j| | | f|j|jdS) NrrrrrZFr)rr)rD_csc_containerrrr"r(rJr#r+r,r)cumsumr\Tr5) r8rrKrL num_offsets offset_lenrMrNrOr;indptrindicesr"r?r?r@tocscFs(      &z_dia_base.tocscc Cs|j\}}|jj\}}t|}||jdddf}|dk}|||kM}|||kM}||jdkM}||}t|||} |j|jft|jd} |j | dd}| j | dd} |j|} |j | || ff|j|j ddS)Nr)arraysrFr)rrr) rr"r(rJr#tileryr+r,r5r4r) r8rrKrLrrrMrNrOcolr;r"r?r?r@tocoo`s&      z_dia_base.tocooTcCs4|r|j||jf|jdS|j||jf|jdS)zReturns a matrix with the same sparsity structure as self, but with different data. By default the structure arrays are copied. rf)rnr#rr)r8r"rr?r?r@ri{s z_dia_base._with_datacGst|}|\}}|jddd|f|_||jdkrHt|j|jd|jjdkrH|jdddf|jdt|jjdk}d|j|<||_dS)Nrr)rr"rr(anyr#rJr$)r8rrVrWrOr?r?r@resizes   z_dia_base.resize)NNFrv)NNN)r)F)NF)T)__name__ __module__ __qualname___formatr rIrPrRr __doc__rYr\rgrwr|rr%rrrrrirr?r?r?r@rs8M    &%  %           rcCs t|}tt|||<|S)z)Helper function to invert an index array.)r( zeros_likerJr6)rinvr?r?r@rls rlcCs t|tS)aIs `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_array, dia_matrix, coo_matrix, isspmatrix_dia >>> isspmatrix_dia(dia_matrix([[5]])) True >>> isspmatrix_dia(dia_array([[5]])) False >>> isspmatrix_dia(coo_matrix([[5]])) False )r&r)rar?r?r@rs rc@eZdZdZdS)ra< Sparse array with DIAgonal storage. This can be instantiated in several ways: dia_array(D) where D is a 2-D ndarray dia_array(S) with another sparse array or matrix S (equivalent to S.todia()) dia_array((M, N), [dtype]) to construct an empty array with shape (M, N), dtype is optional, defaulting to dtype='d'. dia_array((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 array shape : 2-tuple Shape of the array ndim : int Number of dimensions (this is always 2) nnz size data DIA format data array of the array offsets DIA format offset array of the array T Notes ----- Sparse arrays can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Sparse arrays with DIAgonal storage do not support slicing. Examples -------- >>> import numpy as np >>> from scipy.sparse import dia_array >>> dia_array((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_array((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_array >>> n = 10 >>> ex = np.ones(n) >>> data = np.array([ex, 2 * ex, ex]) >>> offsets = np.array([-1, 0, 1]) >>> dia_array((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.]]) Nrrrrr?r?r?r@rrc@r)raO Sparse matrix with DIAgonal storage. This can be instantiated in several ways: dia_matrix(D) where D is a 2-D ndarray dia_matrix(S) with another sparse array or 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 size data DIA format data array of the matrix offsets DIA format offset array of the matrix T Notes ----- Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Sparse matrices with DIAgonal storage do not support slicing. 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.]]) Nrr?r?r?r@rrr)r __docformat____all__numpyr( _lib._utilr_matrixr_baser r r r _datar _sputilsrrrrrr _sparsetoolsrrrlrrrr?r?r?r@s$      L