o 5BiI@sdZdZgdZddlZddlmZddlmZdd l m Z m Z m Z m Z dd lmZdd lmZmZmZmZmZmZdd lmZGd ddeZddZGdddee 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&ddZddZdd Zd d Zd'd d Ze jj e_ e jj e_ d(ddZ e j j e _ ddZ ddZ d)ddZd*ddZe jj e_ d+ddZe jj e_ d)ddZe jj e_ d*ddZe jj e_ d*dd Ze jj e_ d,d"d#Zd$d%Ze jj e_ dS)- _dia_basediaNFc Cst|t|rC|jdkr#|r|}|j|_|j|_t|j|_ n|j|jkr0|r0|}n| }|j|_|j|_t|j|_ nt |t rt |rnt||_ tdt|td|_|jt|jd}tjd|d|_nz|\}}Wnty} zd} t| | d} ~ ww|durtd|st}ttj|d||d |_tj|d |jt|d|d }t||_t||_ nGzt|}Wnty} ztd |j| d} ~ wwt |tr|jd krtd |jd|j|||d }|j|_|j|_t|j|_ |dur|j||_|jjd krtd|jjd kr'td|jjdt|jkrBtd|jjdt|jftt |jt|jkrTtddS)Nr)rr)defaultmaxvalrdtypez+unrecognized form for dia_array constructorzexpected a shape argument)rcopyrz+unrecognized form for %s_matrix constructorrzDIA arrays don't support zD input. Use 2D)rshapezoffsets array must have rank 1zdata array must have rank 2zBnumber of diagonals (%d) does not match the number of offsets (%d)z&offset array contains duplicate values)!r __init__r formatrdataoffsetsrr_shapetodia isinstancetuplernpzerosrfloat_get_index_dtypemax Exception ValueErrorr atleast_2darray atleast_1dasarrayr ndim_coo_containerastypelenunique) selfarg1rrrA idx_dtyper r!emessager<Q/var/www/html/Trade-python/venv/lib/python3.10/site-packages/scipy/sparse/_dia.pyrs            z_dia_base.__init__c Csrt|j\}}t|trdnd}ddd|jD}|jjd}d|d|d |jjd |j d |d |d S)Nr.matrixxcss|]}t|VqdSN)str).0r?r<r<r= esz%_dia_base.__repr__..r) r rr$r joinrr rtypennz)r6_fmt sparse_cls shape_strndiagr<r<r=__repr__bs 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!)r6num_rowsnum_cols offset_indsrowmaskr<r<r= _data_maskls   z_dia_base._data_maskcCs|}t|j|Sr@)rTr& count_nonzeror )r6rSr<r<r=rUwsz_dia_base.count_nonzerocCs`|durtd|j\}}d}|jD]}|dkr"|t|||7}q|t|||7}qt|S)Nz6_getnnz over an axis is not implemented for DIA formatr)NotImplementedErrorrr!minint)r6axisMNrGkr<r<r=_getnnz{s  z_dia_base._getnnzc CsFt||dur|dkr|d7}t|j}|j\}}d}|dkrP|}|j|jdd} | jd|kr7| } ntj|| jd} | | d| jd<|j | |d}n=tj|df|d} tj ||d} t ||t |j |jjd|j |j| | | | } |dur| j||dS| | j|d}|dur|j|jkrtd|jd||d S) NrrrYrr)routzdimensions do not matchr<)rYrr_)rrrrrTr sumr&r' _ascontaineronesrr4r!r,) r6rYrr_ res_dtyperOrPretrSr?resrow_sumsoner<r<r=r`s4   z _dia_base.sumcCst|t|s||St|j|j}|j|dd}|jD]}||jvr6| | || ||q | | ||q |S)NTr) r$rFtocsr _add_sparser& promote_typesrr3r!setdiagdiagonal)r6otherrmdr<r<r=rjs  z_dia_base._add_sparsec Csh|}tj|jdt|jj|jjd}|jjd}|j\}}t||t|j ||j |j| | |S)Nrrr) r&r'rrrcharr rr4r!ravel)r6rnr?yLrZr[r<r<r=_matmul_vectors   z_dia_base._matmul_vectorrc CsR|j\}}|jdkrtj}nt|}|dkr#t||||}d}|}nt||||}|}||}|jdkr<|d|}|jj\} } ||jvrr|| krdtj| |f|jj d} |j| ddd| f<| |_||j|j|k||f<dSt |j|jj ||_t || } tj| d| f|jj d} |j| ddd| f<|| d||f<| |_dS)Nrrr) rr1r&infr4rWr r!r'rappendrFr*) r6valuesr\rZr[values_nn min_index max_index data_rows data_colsr ror<r<r=_setdiags4        z_dia_base._setdiagcCs|r|S|Sr@rh)r6rr<r<r=r#sz_dia_base.todiac Cs|dur |dkr td|j\}}t|j}|j }tjt|tjddddf}tj|tjd||dddf}td||jjd} t |jtj |jjd| f|jj df} | ||f} |j | |f||f|dS)N)rrzvSparse arrays/matrices do not support an 'axes' parameter because swapping dimensions is the only logical permutation.rrr)rr) r,rr*r!r&rNr4intcr hstackr'r_dia_container) r6axesrrOrPmax_dimr!rc pad_amountr r<r<r= transpose s"   $  z_dia_base.transposec Cs|j\}}|| ks||krtjd|jjdSt|j|k\}td|}t|||}||}|j dkr>tj ||jjdS|j|d||f}|t |} | dkr]tj |d| fdd}|S)Nrrconstant)mode) rr&emptyr rnonzeror!r*rWsizer'r4pad) r6r\rowscolsidx first_collast_col result_sizeresultpaddingr<r<r=rm!s    z_dia_base.diagonalc Cs$|jdkr|j|j|jdS|j\}}|jj\}}t|}||jdddf}|dk}|||kM}|||kM}||jdkM}|jt |jd} tj |d| d} t |j ddd|| d|d<||krs| || |dd<|j |j j| dd} |jj |j } |j| | | f|j|jdS) Nrrrrr^Frh)rr)rG_csc_containerrrr r&rNr!r)r*r'cumsumr`Tr3) r6rrOrP num_offsets offset_lenrQrRrSr9indptrindicesr r<r<r=tocsc3s(      &z_dia_base.tocscc Cs|j\}}|jj\}}t|}||jdddf}|dk}|||kM}|||kM}||jdkM}||}t|||} |j|jft|jd} |j | dd}| j | dd} |j|} |j | || ff|j|j ddS)Nr)arraysrFrh)rrr) rr r&rNr!tilerrr)r*r3r2r) r6rrOrPrrrQrRrScolr9r r<r<r=tocooMs&      z_dia_base.tocooTcCs4|r|j||jf|jdS|j||jf|jdS)zReturns a matrix with the same sparsity structure as self, but with different data. By default the structure arrays are copied. )r)rr!rr)r6r rr<r<r= _with_datahs z_dia_base._with_datacGst|}|\}}|jddd|f|_||jdkrHt|j|jd|jjdkrH|jdddf|jdt|jjdk}d|j|<||_dS)Nrr)rr rr&anyr!rNr")r6rrZr[rSr<r<r=resizeus   z_dia_base.resize)NNFr@)NNN)r)F)NF)T)__name__ __module__ __qualname___formatrrMrTrUr]r __doc__r`rjrurr#rrmrrrrr<r<r<r=rs6 L     &  %           rcCs t|tS)aIs `x` of dia_matrix type? Parameters ---------- x object to check for being a dia matrix Returns ------- bool True if `x` is a dia matrix, False otherwise Examples -------- >>> from scipy.sparse import dia_array, dia_matrix, coo_matrix, isspmatrix_dia >>> isspmatrix_dia(dia_matrix([[5]])) True >>> isspmatrix_dia(dia_array([[5]])) False >>> isspmatrix_dia(coo_matrix([[5]])) False )r$r)r?r<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. 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)ra  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. 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 _sparsetoolsrrrrrr<r<r<r=s      vK