a jL@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$d%Ze jj e_ dS)0 _dia_basediaNFc Cst|t|r|jdkrF|r(|}|j|_|j|_t|j|_ n>|j|jkr`|r`|}n| }|j|_|j|_t|j|_ nt |t rt |rt||_ tdt|td|_|jt|jd}tjd|d|_nz |\}}Wn4ty } zd} t| | WYd} ~ nxd} ~ 00|dur4td|s>t}ttj|d||d |_tj|d |jt|d|d }t||_t||_ nzt|}Wn6ty} ztd |j| WYd} ~ n d} ~ 00t |tr|jd krtd |jd|j|||d }|j|_|j|_t|j|_ |durJ|j||_|jjd kr`td|jjd krvtd|jjdt|jkrtd|jjdt|jftt |jt|jkrtddS)Nr)rr)defaultmaxvalrdtypez+unrecognized form for dia_array constructorzexpected a shape argument)rcopyrz+unrecognized form for %s_matrix constructorrzDIA arrays don't support zD input. Use 2D)rshapezoffsets array must have rank 1zdata array must have rank 2zBnumber of diagonals (%d) does not match the number of offsets (%d)z&offset array contains duplicate values)!r __init__r formatrdataoffsetsrr_shapetodia isinstancetuplernpzerosrfloat_get_index_dtypemax Exception ValueErrorrZ atleast_2darrayZ atleast_1dZasarrayr ndim_coo_containerastypelenunique) selfZarg1rrrA idx_dtyper r!emessager87E:\soccer\ml_env\Lib\site-packages\scipy/sparse/_dia.pyrs|             z_dia_base.__init__c Csrt|j\}}t|trdnd}ddd|jD}|jjd}d|d|d |jjd |j d |d |d S)Nr-matrixxcss|]}t|VqdSN)str).0r;r8r8r9 ez%_dia_base.__repr__..r) r rr$r joinrr rtypennz)r3_fmtZ sparse_clsZ shape_strZndiagr8r8r9__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!)r3num_rowsnum_cols offset_indsrowmaskr8r8r9 _data_maskls   z_dia_base._data_maskcCs|}t|j|Sr<)rNr& count_nonzeror )r3rMr8r8r9rOwsz_dia_base.count_nonzerocCs`|durtd|j\}}d}|jD]2}|dkrD|t|||7}q$|t|||7}q$t|S)Nz6_getnnz over an axis is not implemented for DIA formatr)NotImplementedErrorrr!minint)r3axisMNrDkr8r8r9_getnnz{s  z_dia_base._getnnzc CsLt||dur |dkr |d7}t|j}|j\}}d}|dkr|}|j|jdd} | jd|krn| } n"tj|| jd} | | d| jd<|j | |d}n|tj|df|d} tj ||d} t ||t |j |jjd|j |j| | | | } |dur | j||dS| | j|d}|dur<|j|jkr}||jvrl| | || ||q@| | ||q@|S)NTr) r$rCZtocsr _add_sparser&Z promote_typesrr0r!Zsetdiagdiagonal)r3otherrmdr8r8r9r_s  z_dia_base._add_sparsec Csh|}tj|jdt|jj|jjd}|jjd}|j\}}t||t|j ||j |j| | |S)Nrrr) r&r'rrrcharr rr1r!ravel)r3rar;yLrTrUr8r8r9_matmul_vectors   z_dia_base._matmul_vectorrc CsP|j\}}|jdkrtj}nt|}|dkrFt||||}d}|}nt||||}|}||}|jdkrx|d|}|jj\} } ||jvr|| krtj| |f|jj d} |j| ddd| f<| |_||j|j|k||f<njt |j|jj ||_t || } tj| d| f|jj d} |j| ddd| f<|| d||f<| |_dS)Nrrr) rr.r&infr1rQr r!r'rappendrCr*) r3valuesrVrTrUZvalues_nn min_indexZ max_indexZ data_rowsZ data_colsr rbr8r8r9_setdiags4       z_dia_base._setdiagcCs|r |S|SdSr<r^)r3rr8r8r9r#sz_dia_base.todiac Cs|dur|dkrtd|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&rHr1Zintcr Zhstackr'r_dia_container) r3ZaxesrrIrJZmax_dimr!rcZ pad_amountr r8r8r9 transpose s"   $   z_dia_base.transposec Cs|j\}}|| ks||kr.tjd|jjdSt|j|k\}td|}t|||}||}|j dkr|tj ||jjdS|j|d||f}|t |} | dkrtj |d| fdd}|S)NrrZconstant)mode) rr&emptyr rZnonzeror!r*rQsizer'r1pad) r3rVrowscolsidxZ first_colZlast_colZ result_sizeresultpaddingr8r8r9r`!s    z_dia_base.diagonalc Cs$|jdkr|j|j|jdS|j\}}|jj\}}t|}||jdddf}|dk}|||kM}|||kM}||jdkM}|jt |jd} tj |d| d} t |j ddd|| d|d<||kr| || |dd<|j |j j| dd} |jj |j } |j| | | f|j|jdS) NrrrrrXFr^)rr)rDZ_csc_containerrrr r&rHr!r)r*r'ZcumsumrZTr0) r3rrIrJ num_offsets offset_lenrKrLrMr5Zindptrindicesr r8r8r9tocsc3s(      &z_dia_base.tocscc Cs|j\}}|jj\}}t|}||jdddf}|dk}|||kM}|||kM}||jdkM}||}t|||} |j|jft|jd} |j | dd}| j | dd} |j|} |j | || ff|j|j ddS)Nr)ZarraysrFr^)rrr) rr r&rHr!Ztilerer)r*r0r/r) r3rrIrJr~rrKrLrMcolr5r r8r8r9tocooMs&      z_dia_base.tocooTcCs8|r|j||jf|jdS|j||jf|jdSdS)zReturns a matrix with the same sparsity structure as self, but with different data. By default the structure arrays are copied. )rN)rpr!rr)r3r rr8r8r9 _with_datahs z_dia_base._with_datacGst|}|\}}|jddd|f|_||jdkrt|j|jd|jjdkr|jdddf|jdt|jjdk}d|j|<||_dS)Nrr)rr rr&anyr!rHr")r3rrTrUrMr8r8r9resizeus  z_dia_base.resize)NNF)N)NNN)r)F)NF)r)F)F)T)__name__ __module__ __qualname___formatrrGrNrOrWr __doc__rZr_rhror#rsr`rrrrr8r8r8r9rs4 L     &  %           rcCs t|tS)aIs `x` of dia_matrix type? Parameters ---------- x object to check for being a dia matrix Returns ------- bool True if `x` is a dia matrix, False otherwise Examples -------- >>> from scipy.sparse import dia_array, dia_matrix, coo_matrix, isspmatrix_dia >>> isspmatrix_dia(dia_matrix([[5]])) True >>> isspmatrix_dia(dia_array([[5]])) False >>> isspmatrix_dia(coo_matrix([[5]])) False )r$r)r;r8r8r9rsrc@seZdZdZdS)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.]]) Nrrrrr8r8r8r9rsrc@seZdZdZdS)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.]]) Nrr8r8r8r9rsr)r __docformat____all__numpyr&Z _lib._utilrZ_matrixrZ_baser r r r _datar Z_sputilsrrrrrrZ _sparsetoolsrrrrrr8r8r8r9s     vK