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dia.cpython-35.pyc
�B�Wb1 � @ s� d Z d d l m Z m Z m 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 d d l m Z Gd d � d e � Z d d � Z d S)zSparse DIAgonal format� )�division�print_function�absolute_importzrestructuredtext en� dia_matrix�isspmatrix_diaN� )� isspmatrix�_formats�spmatrix)� _data_matrix)�isshape� upcast_char�getdtype�get_index_dtype� get_sum_dtype� validateaxis)� dia_matvecc @ sr e Z d Z d Z d Z d d 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 d d � Z e j j e _ d d � Z d d � Z d d d � Z d d d � Z e j j e _ d d d d � Z e j j e _ d d � Z e j j e _ d d d � Z e j j e _ d d d! � Z e j j e _ d" d# d$ � Z d S)%r 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 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]]) ZdiaNFc C s9 t j | � t | � rR | r+ | j � } | j | _ | j | _ | j | _ n t | � r� t | � r | r | j � } n | j � } | j | _ | j | _ | j | _ n�t | t � r�t | � r*| | _ t j d t | d t �� | _ t d t | j � � } t j d d | �| _ q]y | \ } } Wn t d � � Yq]X| d k rit d � � t j t j | d d | d | �� | _ t j t j | d d t d t | � � d | �� | _ | | _ n� y t j | � } Wn t d | j � � Yn Xd d l m } | | d | d | �j � } | j | _ | j | _ | j | _ | d k r~| j j | � | _ | j j d k r�t d � � | j j d k r�t d � � | j j d t | j � k rt d | j j d t | j � f � � t t j | j � � t | j � k r5t d � � d S)Nr �default�maxval�dtypez,unrecognized form for dia_matrix constructorzexpected a shape argument�copyr z+unrecognized form for %s_matrix constructor)� coo_matrix�shapez offsets array must have rank 1� zdata array must have rank 2zBnumber of diagonals (%d) does not match the number of offsets (%d)z&offset array contains duplicate values)r r ) r �__init__r r �data�offsetsr r �todia� isinstance�tupler �np�zerosr �floatr �max� ValueErrorZ atleast_2d�arrayZ atleast_1dZasarray�format�coor �astype�ndim�len�unique) �selfZarg1r r r �A� idx_dtyper r r � r/ �/dia.pyr N sh ! + #'zdia_matrix.__init__c C sB t | j � 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 Z getformatr r �type�nnzr )r, r& r/ r/ r0 �__repr__� s "zdia_matrix.__repr__c C su | j \ } } t j | 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 �aranger r )r, �num_rows�num_cols� offset_inds�row�maskr/ r/ r0 � _data_mask� s zdia_matrix._data_maskc C s | j � } t j | j | � S)N)r: r � count_nonzeror )r, r9 r/ r/ r0 r; � s zdia_matrix.count_nonzeroc C s� | d k r t d � � | j \ } } d } xN | j D]C } | d k rc | t | | | � 7} q7 | t | | | � 7} q7 Wt | � S)Nz5getnnz over an axis is not implemented for DIA formatr )�NotImplementedErrorr r �min�int)r, �axis�M�Nr2 �kr/ r/ r0 �getnnz� s zdia_matrix.getnnzc C s� t | � | d k r, | d k r, | d 7} t | j � } | j \ } } d } | d k r� | j � } | j | j d d � } | j d | k r� | } n/ t j | d | j �} | | d | j d � <t j | d | �} n� t j | d | �} t j | d | �} t | | t | j � | j j d | j | j | | � t j | � } | d k rw| j d | d | � S| d k r�| j } t j | j d | � � } | d k r�| j | j k r�t d � � | j d f d | d | � S)Nr r r? r r �outzdimensions do not match)r r r r r: r �sumr r! ZmatrixZonesr r* r �Tr$ ) r, r? r rD Z res_dtyper5 r6 Zretr9 �x�resZrow_sumsZoner/ r/ r0 rE � s6 # zdia_matrix.sumc C s� | } t j | j d d t | j j | j j � �} | j j d } | j \ } } t | | t | j � | | j | j | j � | j � � | S)Nr r r ) r r! r r r �charr r r* r �ravel)r, �otherrG �y�Lr@ rA r/ r/ r0 � _mul_vector� s :zdia_matrix._mul_vectorc s# t j � f d d � | j D� � S)Nc s+ g | ]! } � j | � j d d � � q S)r ���)rN Zreshape)�.0�col)r, r/ r0 � <listcomp>� s z/dia_matrix._mul_multimatrix.<locals>.<listcomp>)r �hstackrF )r, rK r/ )r, r0 �_mul_multimatrix� s zdia_matrix._mul_multimatrixr c C s� | j \ } } | j d k r* t j } n t | � } | d k rg t | | | | � } d } | } n& t | | | | � } | } | | } | j d k r� | d | � } | | j k r� | | j | j | k | | � f <n� t j | j | j j j | � � | _ t | | j j d � } t j | j j d d | f d | j j �} | j | d d � d | j j d � f <| | d | | � f <| | _ d S)Nr r r rO rO ) r r) r �infr* r= r r �appendr r1 r# r! ) r, �valuesrB r@ rA Zvalues_n�nZ min_indexZ max_index�mr r/ r/ r0 �_setdiag� s* %'/)zdia_matrix._setdiagc C s | r | j � S| Sd S)N)r )r, r r/ r/ r0 r s zdia_matrix.todiac C s* | d k r t d � � | j \ } } t | j � } | j } t j t | � d t j �d d � d f } t j | d t j �| | d d � d f } t d | | j j d � } t j | j t j | j j d | f d | j j �f � } | | | f } t | | f d | | 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 r4 r* Zintcr rS r! r r ) r, Zaxesr r5 r6 Zmax_dimr �r�cZ pad_amountr r/ r/ r0 � transpose s .0 (zdia_matrix.transposec C sm t j | j d k � \ } t | j � } | j d k rR t j | d | j j �S| j | d d | � f S)Nr r ) r Zwherer r= r �sizer! r r )r, �idxrX r/ r/ r0 �diagonal6 s zdia_matrix.diagonalc C s� d d l m } | j d k r5 | | j d | j �S| j \ } } | j j \ } } t j | � } | | j d d � d f } | d k } | | | k M} | | | k M} | | j d k M} t d t | j � � } t j | d d | �} t j | j d d � � | d | d � <| | | | d d � <| j | j j | d d �} | j j | j } | | | | f d | j d | j �S) Nr )� csc_matrixr r r r? r Fr )Zcscra r2 r r r r r4 r r r# r! ZcumsumrE rF r( )r, r ra r5 r6 � num_offsets� offset_lenr7 r8 r9 r. Zindptr�indicesr r/ r/ r0 �tocsc? s& ) zdia_matrix.tocscc C s | j \ } } | j j \ } } t j | � } | | j d d � d f } | d k } | | | k M} | | | k M} | | j d k M} | | } t j | | � | j � } | j | } d d l m } | | | | f f d | j d | j �} d | _ | S)Nr r )r r r T) r r r r4 r ZtilerJ r' r r Zhas_canonical_format) r, r r5 r6 rb rc r7 r8 r9 rQ r r r- r/ r/ r0 �tocooY s * zdia_matrix.tocooTc C sH | r( t | | j j � f d | j �St | | j f d | j �Sd 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)r r r r )r, r r r/ r/ r0 � _with_datao s "zdia_matrix._with_data)�__name__� __module__� __qualname__�__doc__r& r r3 r: r; rC r rE rN rT rZ r r] r` re rf rg r/ r/ r/ r0 r s2 9F ) c C s t | t � S)N)r r )rG r/ r/ r0 r y s )rk Z __future__r r r Z __docformat__�__all__Znumpyr �baser r r r r Zsputilsr r r r r r Z _sparsetoolsr r r r/ r/ r/ r0 �<module> s .� h
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__init__.cpython-35.opt-1.pyc
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__init__.cpython-35.pyc
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base.cpython-35.opt-1.pyc
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base.cpython-35.pyc
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bsr.cpython-35.opt-1.pyc
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bsr.cpython-35.pyc
18341 bytes
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compressed.cpython-35.opt-1.pyc
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compressed.cpython-35.pyc
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construct.cpython-35.opt-1.pyc
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construct.cpython-35.pyc
22666 bytes
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coo.cpython-35.opt-1.pyc
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coo.cpython-35.pyc
15485 bytes
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csc.cpython-35.opt-1.pyc
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csc.cpython-35.pyc
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csr.cpython-35.opt-1.pyc
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csr.cpython-35.pyc
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data.cpython-35.opt-1.pyc
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data.cpython-35.pyc
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dia.cpython-35.opt-1.pyc
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dia.cpython-35.pyc
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dok.cpython-35.opt-1.pyc
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dok.cpython-35.pyc
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extract.cpython-35.opt-1.pyc
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extract.cpython-35.pyc
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generate_sparsetools.cpython-35.opt-1.pyc
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generate_sparsetools.cpython-35.pyc
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lil.cpython-35.opt-1.pyc
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lil.cpython-35.pyc
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setup.cpython-35.opt-1.pyc
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setup.cpython-35.pyc
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sparsetools.cpython-35.opt-1.pyc
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sparsetools.cpython-35.pyc
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spfuncs.cpython-35.opt-1.pyc
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spfuncs.cpython-35.pyc
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sputils.cpython-35.opt-1.pyc
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sputils.cpython-35.pyc
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