ó î&]\c@`sÓdZddlmZmZmZdZddgZddlZddl m Z m Z m Z dd l mZdd lmZmZmZmZmZmZmZdd lmZdefd „ƒYZd „ZdS(sSparse DIAgonal formati(tdivisiontprint_functiontabsolute_importsrestructuredtext ent dia_matrixtisspmatrix_diaNi(t isspmatrixt_formatstspmatrix(t _data_matrix(tisshapet upcast_chartgetdtypetget_index_dtypet get_sum_dtypet validateaxist check_shape(t dia_matveccB`sXeZdZdZdded„Zd„Zd„Zd„Z dd„Z e j je _e j je _dddd„Z e j je _d„Z d „Zd d „Zed „Ze jje_ded „Ze jje_d d„Ze jje_ed„Ze jje_ed„Ze jje_ed„Zd„Ze jje_RS(s 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 nonzero elements 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]]) tdiac C`s~tj|ƒt|ƒr[|r.|jƒ}n|j|_|j|_t|jƒ|_n8t |ƒrÁt|ƒrˆ|rˆ|jƒ}n |j ƒ}|j|_|j|_t|jƒ|_nÒt |t ƒrt |ƒr?t|ƒ|_tjdt|dtƒƒ|_tdt|jƒƒ}tjdd|ƒ|_q“y|\}}Wntk rntdƒ‚q“X|dkrŠtdƒ‚ntjtj|dd|d|ƒƒ|_tjtj|ddtdt|ƒƒd|ƒƒ|_t|ƒ|_n’ytj|ƒ}Wn$tk r:td |jƒ‚nXdd lm} | |d|d |ƒj ƒ}|j|_|j|_t|jƒ|_|dk r·|jj|ƒ|_n|jjdkrØtd ƒ‚n|jjd krùtdƒ‚n|jjdt |jƒkrDtd|jjdt |jƒfƒ‚nt tj!|jƒƒt |jƒkrztdƒ‚ndS(Nitdefaulttmaxvaltdtypes,unrecognized form for dia_matrix constructorsexpected a shape argumenttcopyis+unrecognized form for %s_matrix constructor(t coo_matrixtshapesoffsets array must have rank 1isdata array must have rank 2sBnumber of diagonals (%d) does not match the number of offsets (%d)s&offset array contains duplicate values(ii("Rt__init__RRtdatatoffsetsRRt_shapeRttodiat isinstancettupleR tnptzerosR tfloatR tmaxt Exceptiont ValueErrortNonet atleast_2dtarrayt atleast_1dtasarraytformattcooRtastypetndimtlentunique( tselftarg1RRRtAt idx_dtypeRRR((s/lib/python2.7/site-packages/scipy/sparse/dia.pyRNsh         !  +    &'cC`sBt|jƒd}d|j|jj|j|jjd|fS(NisW<%dx%d sparse matrix of type '%s' with %d stored elements (%d diagonals) in %s format>i(Rt getformatRRttypetnnzR(R0R*((s/lib/python2.7/site-packages/scipy/sparse/dia.pyt__repr__”s"cC`su|j\}}tj|jjdƒ}||jdd…df}|dk}|||kM}|||kM}|S(s~Returns a mask of the same shape as self.data, where mask[i,j] is True when data[i,j] corresponds to a stored element.iNi(RRtarangeRRR%(R0tnum_rowstnum_colst offset_indstrowtmask((s/lib/python2.7/site-packages/scipy/sparse/dia.pyt _data_mask›s cC`s |jƒ}tj|j|ƒS(N(R>Rt count_nonzeroR(R0R=((s/lib/python2.7/site-packages/scipy/sparse/dia.pyR?¦s cC`s‹|dk rtdƒ‚n|j\}}d}xN|jD]C}|dkrf|t|||ƒ7}q:|t|||ƒ7}q:Wt|ƒS(Ns5getnnz over an axis is not implemented for DIA formati(R%tNotImplementedErrorRRtmintint(R0taxistMtNR6tk((s/lib/python2.7/site-packages/scipy/sparse/dia.pytgetnnzªs  c C`sðt|ƒ|dk r/|dkr/|d7}nt|jƒ}|j\}}d}|dkrá|jƒ}|j|jddƒ} | jd|kr | } n)tj |d| jƒ} | | | jd*tj | d|ƒ}nÆtj |d|ƒ} tj |d|ƒ} t ||t |jƒ|jjd|j|j| | ƒtj | ƒ} |dkrt| jd|d|ƒS|dk rŒ| j} ntj | jd|ƒƒ}|dk rÔ|j|jkrÔtdƒ‚n|jddd|d|ƒS( NiiRCRitoutsdimensions do not match((RR%R RRR>RtsumRR tmatrixtonesRR.RtTR$( R0RCRRHt res_dtypeR9R:tretR=txtrestrow_sumstone((s/lib/python2.7/site-packages/scipy/sparse/dia.pyRIºs6     #   c C`s”|}tj|jddt|jj|jjƒƒ}|jjd}|j\}}t||t|j ƒ||j |j|j ƒ|j ƒƒ|S(NiRi( RR RR RtcharRRR.Rtravel(R0totherROtytLRDRE((s/lib/python2.7/site-packages/scipy/sparse/dia.pyt _mul_vectorås:cC`s8tjg|jD]!}|j|ƒjddƒ^qƒS(Niÿÿÿÿi(RthstackRLRXtreshape(R0RUtcol((s/lib/python2.7/site-packages/scipy/sparse/dia.pyt_mul_multimatrixósic C`s˜|j\}}|jdkr*tj}n t|ƒ}|dkrgt||||ƒ}d}|}n&t||||ƒ}|}||}|jdkr©|| }n||jkrÝ||j|j|k||…fR?RGRRIRXR\ReRRlRtR}RRR‚R„(((s/lib/python2.7/site-packages/scipy/sparse/dia.pyRs69F   )       cC`s t|tƒS(sÍIs 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 (RR(RO((s/lib/python2.7/site-packages/scipy/sparse/dia.pyRs(R‡t __future__RRRt __docformat__t__all__tnumpyRtbaseRRRRRtsputilsR R R R R RRt _sparsetoolsRRR(((s/lib/python2.7/site-packages/scipy/sparse/dia.pyts  4ÿ|