DjOdZdZgdZddlZddlmZddlmZdd l m Z m Z m Z m Z dd lmZdd lmZmZmZmZmZmZdd lmZGd deZdZdZGddee ZGddeeZy)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_matveccpeZdZdZddZdZdZdZddZe jje_ e jje_ ddZ e jje _ d Z d Z d Zdd Zdd Ze j je_ ddZe j"je_ ddZe j$je_ ddZe j&je_ ddZe j(je_ ddZdZe j,je_ y) _dia_basediaNctj||t|r|jdk(rP|r|j }|j |_|j |_t|j|_ nl|j|jk(r|r|j }n|j}|j |_|j |_t|j|_ nt|tr5t|r}t||_ tjdt!|t"|_|j%t'|j}tjd||_nY |\}}| t)d|st*}tj,tj.|d|||_tj.|d |j%t'||}tj0||_t||_ n tj4|}t|t6r(|j8d k7rt)d |j8d|j;|||j}|j |_|j |_t|j|_ | |j j=||_|j j8d k7r t)d|j j8d k7r t)d|j jdt?|j k7r:t)d|j jdt?|j fzt?tj@|j t?|j k7r t)dy#t2$r} d } t)| | d} ~ wwxYw#t2$r} t)d |jz| d} ~ wwxYw)Nr)rr)defaultmaxvalrdtypezexpected a shape argument)rcopyrz+unrecognized form for dia_array constructorz+unrecognized form for %s_matrix constructorrzDIA arrays don't support zD input. Use 2D)rshapezoffsets array must have rank 1zdata array must have rank 2zBnumber of diagonals (%d) does not match the number of offsets (%d)z&offset array contains duplicate values)!r__init__r formatrdataoffsetsrr _shapetodia isinstancetuplernpzerosrfloat_get_index_dtypemax ValueErrorr atleast_2darray atleast_1d Exceptionasarrayr ndim_coo_containerastypelenunique) selfarg1r rrA idx_dtyper#r$emessages UC:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\scipy/sparse/_dia.pyr!z_dia_base.__init__sgdD) D>{{e#99;D II #|| )$**5 ;;$++-$ A AFF  yy )!''2 e $t}*$/ HHUHUE,JK  11TZZ1I !xx9= 5$(MD' }()DEE- " bhhtAweRV.W XDI hhtAw-1-B-B#e*-B-U,02G$&==#9DL"-e"4DK Gzz$'$(TYY!^ # > 99>>Q :; ; 99??1 T\\!2 2@yyq)3t||+<=>? ? ryy& '3t||+< <EF F =W!5KG$W-145$ G "137;;"?@EFG Gs0P P) P&P!!P&) Q2Q  Qc t|j\}}t|trdnd}|jj d}d|d|d|j d|jd|d |j d S) Nr0matrixr)r r"r'r r#r rnnz)r9_fmt sparse_clsds r?__repr__z_dia_base.__repr__bs|$++&3 *4 9Wx IIOOA uHZL DJJ<@hhZ1!4J4::,VW Y c|j\}}tj|jjd}||jdddfz }|dk\}|||kz}|||kz}|S)z~Returns a mask of the same shape as self.data, where mask[i,j] is True when data[i,j] corresponds to a stored element.rNr)r r)aranger#r$)r9num_rowsnum_cols offset_indsrowmasks r? _data_maskz_dia_base._data_maskkso"ZZ(ii  23 DLL400q x  x'( rJcf|j}tj|j|SN)rRr) count_nonzeror#)r9rQs r?rUz_dia_base.count_nonzerovs'  $00rJc| td|j\}}d}|jD],}|dkDr|t|||z z }|t||z|z }.t |S)Nz6_getnnz over an axis is not implemented for DIA formatr)NotImplementedErrorr r$minint)r9axisMNrDks r?_getnnzz_dia_base._getnnzzsy  %'78 8jj! "A1us1QqSz!s1Q3qz!  " 3xrJc t|| |dkr|dz }t|j}|j\}}d}|dk(r|j }|j |zj d} | jd|k(r| } n3tj|| j} | | d| jd|j| |}ntj|df|} tj||} t||t|j|j jd|j|j | | |j| } || j ||S|j| j |}|$|j|jk7r td|j d|| S) NrrrZrr)routzdimensions do not match)rZrra)rrrr rRr#sumr)r* _ascontaineronesrr7r$r.) r9rZrra res_dtyperMrNretrQxresrow_sumsones r?rcz _dia_base.sumsT  q AID!$**- !ZZ( 19??$DT!&&A&.AwwqzX%hhxqww7#$KQWWQZ ##Cy#9Cxx1 Y?H''()4C x3t||+<yyq)4<<C S((2H|||%S|99##HLLdL$;>$,, 6??499uzz#9: :jju}}= ??; =OOK? #**""1% W [!1S5F!Fyyx!89H Y\ "ejj 0 " w 3{#3s5==7I#Izz- ":;H Xq[ !TYY . !DJJqMKO3TZZ]CAxx[!1%nnTYY ;H Xww& '499QU+; ; ' Y( )UZZ2A2-> > )""Hk#:$**"MMrJc>|j|j|zSrT)rqr#)r9rws r? _mul_scalarz_dia_base._mul_scalarstyy5011rJc |}tj|jdt|jj |jj }|j jd}|j\}}t||t|j||j|j |j|j|S)Nrrr) r)r*r rrcharr#rr7r$ravel)r9rwrhyLr[r\s r?_matmul_vectorz_dia_base._matmul_vectors  HHTZZ]+djjoo78ww||+E F IIOOA jj!1QDLL)1dllDII779aggi )rJc|j\}}|jdk(rtj}n t |}|dkrt ||z||}d}|}nt |||z |}|}||z}|jdk7r|d|}|j j\} } ||jvro|| kDrItj| |f|j j} |j | ddd| f<| |_||j |j|k(||f<ytj|j|jjj||_t|| } tj| dz| f|j j} |j | ddd| f<|| d||f<| |_y)Nrrrrm) r r4r)infr7rXr#r$r*rappendtyper-) r9valuesr]r[r\values_nn min_index max_index data_rows data_colsr#ms r?_setdiagz_dia_base._setdiagszz1 ;;! vvH6{H q5AE1h'AIIAq1uh'AIAI ;;! BQZF#yy 9  9$xxI 6diiooN&*iiQ  ]#  @FDIIdlla'9)<< =99T\\4<<3E3E3J3J13MNDLIy)A88Y]A.diiooFD$(IID"jyj !,2DYy(( )DIrJc*|r|jS|SrTr)r9rs r?r&z_dia_base.todias 99; KrJc||dk7r td|j\}}t|j}|j }t j t |tjdddf}t j |tj||zdddfz }td||jjdz } t j|jt j|jjd| f|jjf} | ||f} |j| |f||f|S)N)rrzvSparse arrays/matrices do not support an 'axes' parameter because swapping dimensions is the only logical permutation.rrr)r r) r.r r-r$r)rLr7intcr#hstackr*rrv) r9axesrrMrNmax_dimr$rc pad_amountr#s r? transposez_dia_base.transposes3  LM M"ZZ(djj/<<- IIc'l"'' 21d7 ; IIhbgg .'G2CQW1M MGDIIOOA$667 yy$))RXXtyyq/A:.N48IIOO&EFGAqDz""D'? h; &*#, ,rJc|j\}}|| ks||k\r+tjd|jjStj |j |k(\}td|}t||z|}||z }|jdk(r+tj||jjS|j|d||f}|t|z } | dkDrtj|d| fd}|S)Nrrconstant)mode) r r)emptyr#rnonzeror$r-rXsizer*r7pad) r9r]rowscolsidx first_collast_col result_sizeresultpaddings r?diagonalz_dia_base.diagonal.sZZ d :d88ATYY__5 5zz$,,!+,1I tax&* 88q=88Ktyy? ?3q69X#556F + Q;VVFQLzBF rJcL|jdk(r'|j|j|jS|j\}}|jj\}}t j |}||jdddfz }|dk\}|||kz}|||kz}||jdk7z}|jt|j} t j|dz| } t j|jdd|| d|dz||kr | || |dzd|j|jj| d} |jj|j} |j| | | f|j|jS) Nrrrrr`Fr)r r)rD_csc_containerr rr#r)rLr$r,r-r*cumsumrcTr6) r9rrMrN num_offsets offset_lenrOrPrQr<indptrindicesr#s r?tocscz_dia_base.tocsc@s 88q=&&tzz&D D!ZZ("&))// Zii + DLL400q x  x'( a ))TZZ)A (Q,i8!#4888+;IX+F!GqA  $*:$6F:a<= !%%-&&yu&=yy{{466"""D'6#:$**)-#5 5rJct|j\}}|jj\}}tj|}||jdddfz }|dk\}|||kz}|||kz}||jdk7z}||}tj |||j } |j|jft|j} |j| d}| j| d} |j|} |j| || ff|j|jdS)Nr)arraysrFr)r rr) r r#r)rLr$tilerr,r-r6r5r) r9rrMrNrrrOrPrQcolr<r#s r?tocooz_dia_base.tocooZs2!ZZ("&))// Zii + DLL400q x  x'( a $iggk;/ =))LL?3tzz?* jjj/jjj/yy"" C: djj #  rJc|r7|j||jjf|jS|j||jf|jS)zReturns a matrix with the same sparsity structure as self, but with different data. By default the structure arrays are copied. rn)rvr$rr )r9r#rs r?rqz_dia_base._with_datausf &&t||((*+4::' &&t||$DJJ' rJct|}|\}}|jddd|f|_||jdkDrtj|j |jdz|jjdkr_|j dddf|jdztj |jjdk}d|j|<||_y)Nrr)rr#r r)anyr$rLr%)r9r r[r\rQs r?resizez_dia_base.resizesE"1IIa!e$  1 t||djjm3diiooa6HHILLD)DJJqM9IIdiiooa012DDIIdO rJ)NNFrT)NNN)r)F)NF)T)__name__ __module__ __qualname___formatr!rIrRrUr^r __doc__rcrorrrr&rrrrrqrrbrJr?rrsGJGX  1 oo--GO#1199M$6L++%%CK#NJ2 #J MM))EM,, ))11I ''//H50MM))EM 0MM))EM  ^^++FNrJrcrtj|}tjt|||<|S)z)Helper function to invert an index array.)r) zeros_likerLr7)rinvs r?rtrts+ -- CyyS"CH JrJc"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)rhs r?rrs. a $$rJceZdZdZy)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.]]) NrrrrrbrJr?rrGrJrceZdZdZy)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.]]) NrrbrJr?rrrrJr)r __docformat____all__numpyr) _lib._utilr_matrixr _baser r r r _datar_sputilsrrrrrr _sparsetoolsrrrtrrrrbrJr?rsm% 7'77%~, ~,B %6H 7HVH9HrJ