+ nDjXRtRt.ROt^RIHt^RIt^RIHt^RI H t H t H t ^RI HtHtHt^R IHtHtHtHtHt^R IHt!R R ] ]4tRR ltRt!RR]] 4t!RR]]4tR#)z"List of Lists sparse matrix class zrestructuredtext en lil_array lil_matrix) bisect_leftN)spmatrix)_spbasesparrayissparse) IndexMixin INT_TYPES_broadcast_arrays)getdtypeisshape isscalarlike upcast_scalar check_shape) _csparsetoolsca]tRt^toRtR+RR/RlltRtRtRtR t R,R lt ] PP] n R,R lt ] PP] n R tR tRtRtRtRtRtRtRtRtRtRtRtRtRtRtRtRtRt Rt!R t"] PDP]"n R!R"R#R/R$lt#] PFP]#n R%t$] PHP]$n R-R&lt%] PJP]%n R.R'lt&] PLP]&n R/R(lt'] PNP]'n R/R)lt(] PPP](n R*t)Vt*R#)0 _lil_baselilNFmaxprintc@\P!WVR7\W1\R7Vn\ V4'dVP R8XdV'dVP4pMVP4pVe\V4pVPVRR7p\VP4Vn VPVnVPVn VPVnR#\V\ 4'd\#V4'dVe \%R4hVwr\W34Vn \&P(!V3\*R7Vn \&P(!V3\*R7Vn\-V4F!p .VPV &.VPV &K# R#\/R4hVP1V4p\T\24'd+TP4^8wd\%R TP4 R 24hTP7YcR7P4p\TP4Tn \TP4TnTPTn TPTnR# \.dp \/R 4T hRp ? ii;i) )r)defaultrNFcopyzinvalid use of shape parameterdtypez(unrecognized lil_array constructor usagezunsupported matrix typezLIL arrays don't support zD input. Use 2D)r__init__r floatrrformatrtolilastypershape_shaperowsdata isinstancetupler ValueErrornpemptyobjectrange TypeError _ascontainerrndim_csr_container) selfarg1r!rrrAnewdtypeMNies &&&&&$ K/data/cameron/venvs/s3viz/lib/python3.14/site-packages/scipy/sparse/_lil.pyr_lil_base.__init__sh7e59  D>>{{e#IIKJJL #E?HHXEH2%agg.DKDJDIDI U # #t}}$$%EFF)1&1 HHaT8 HHaT8 qA#%DIIaL#%DIIaL" JKK B%%d+$((QVVq[ #s&&r8__isub___lil_base.__isub__JrBrCcJ\V4'dW,VR&V#\#r;rNotImplementedr>s&&r8__imul___lil_base.__imul__N#    DIK! !rCcJ\V4'dW, VR&V#\#r;rHr>s&&r8 __itruediv___lil_base.__itruediv__UrLrCcVf/\VPUu.uFp\V4NK up4#V^8d V^, pV^8Xda\P!VP ^,\P R7pVPFpW4;;,^, uu&K V#V^8XdJ\P!VPUu.uFp\V4NK up\P R7#\R4huupiuupi)Nraxis out of bounds) sumr$lenr(zerosr!intpr#arrayr')r0axisrowvalsoutrows&& r8_getnnz_lil_base._getnnz_s <DII>IG I>? ? !8 AID 19((4::a=8CyyA !J QY88CgS\C277S S12 2?Ds DD cVf\RVP44#V^8d V^, pV^8Xd\P!VP^,\P R7p\ VPVP4F@wr4\ W44UUu.uFwrVV^8wgKVNK pppW';;,^, uu&KB V#V^8XdU\P!VPUu.uFp\P!V4NK up\P R7#\R4huuppiuupi)Nc3N"TFp\P!V4xK R#5iN)r( count_nonzero).0rXs& r8 *_lil_base.count_nonzero..rsJ Wr''00 s#%rrQ) rRr$r(rTr!rUzipr#rVr`r') r0rWrYrZr$cdmaskrXs && r8r`_lil_base.count_nonzerops <J JJ J !8 AID 19((4::a=8C DII6 &)#n?ndaQn? Q 7J QY88:>))D)w!!'*)DBGG 12 2@ Es" D;2D;:Ec VP^VP^,3VPR7pVPV,VP^&VPV,VP^&V#)zReturns a view of the 'i'th row (without copying). Parameters ---------- i : int Row to return view of. Returns ------- lil_array or lil_matrix A view of the 'i'th row. r)_lil_containerr!rr#r$)r0r6news&& r8 getrowview_lil_base.getrowviewsY!!1djjm"4DJJ!Giil iil  rCc JVPwr#V^8d W, pV^8gW8d \R4hVP^V3VPR7pVPV,R,VP^&VP V,R,VP ^&V#)zReturns a copy of the 'i'th row. Parameters ---------- i : int Row to return a copy of. Returns ------- lil_array or lil_matrix A copy of the 'i'th row. zrow index out of boundsrr<)r! IndexErrorrjrr#r$)r0r6r4r5rks&& r8getrow_lil_base.getrowszz q5 FA q5AF67 7!!1a& !;iil1o iil1o  rCc\V\4'd[\V4^8XdK\V^,\4'd.\V^,\4'dVP!V!#\ P !W4#))r%r&rSr _get_intXintr __getitem__)r0keys&&r8ru_lil_base.__getitem__s] sE " "s3x1}3q69--3q69--$$c* *%%d00rCc\P!VP^,VP^,VPVPW4pVP P V4#)rlil_get1r!r#r$rtype)r0rZcolvs&&& r8rt_lil_base._get_intXintsH  " "4::a=$**Q-#'99c 8zzq!!rCc\VPVP^,4!pVPV\ W"^,44#ry)r+indicesr!_get_row_rangesslicer0rZr}s&&&r8_get_sliceXint_lil_base._get_sliceXints9S[[A/0##CsE):;;rCcVPVP4\W"^,44pVP^8dVP VP 4#V#))rravelrr.reshaper!)r0rZr}ress&&& r8_get_arrayXint_lil_base._get_arrayXintsF""399;cq50AB 88a<;;syy) ) rCc(VPV3V4#r_rrs&&&r8_get_intXslice_lil_base._get_intXslices##SFC00rCcv\VPVP^,4!pVPW4#ry)r+rr!rrs&&&r8_get_sliceXslice_lil_base._get_sliceXslices.S[[A/0##C--rCc$VPW4#r_rrs&&&r8_get_arrayXslice_lil_base._get_arrayXslices##C--rCch\P!WP^R7pVPW4#)r)rndmin)r(rVr_get_columnXarrayrs&&&r8_get_intXarray_lil_base._get_intXarrays'hhs))15%%c//rCc\P!VPVP^,4!pVP W4#ry)r(arangerr!rrs&&&r8_get_sliceXarray_lil_base._get_sliceXarrays2iiTZZ]34%%c//rCcN\VR,V4wrVPW4#)r<)r<N)r _get_arrayXarrayrs&&&r8r_lil_base._get_columnXarrays%$S[#6$$S..rCc j\\P\W44wr4VP VP VP R7p\P!VP ^,VP ^,VPVPVPVPW44V#r) mapr( atleast_2d_prepare_index_for_memoryviewrjr!rr lil_fancy_getr#r$)r0rZr}r6jrks&&& r8r_lil_base._get_arrayXarrays}2=="?"IJ!!!''!<##DJJqM4::a=$(IItyy$'HHchh$% * rCc  VPVP^,4wr4p\W4V4p\V4pVP \V4V3VP R7p\ P!VP^,VP^,VPVPVPVPVW4WW4 V#)a[ Fast path for indexing in the case where column index is slice. This gains performance improvement over brute force by more efficient skipping of zeros, by accessing the elements column-wise in order. Parameters ---------- rows : sequence or range Rows indexed. If range, must be within valid bounds. col_slice : slice Columns indexed r) rr!r+rSrjrrlil_get_row_rangesr#r$) r0r# col_slicej_startj_stopj_stride col_rangenjrks &&& r8r_lil_base._get_row_rangess %.$5$5djjm$D!'84 ^!!3t9b/!D((A 1 )-DII),388)-)0(  H  rCc \P!VP^,VP^,VPVPWV4R#rzN)r lil_insertr!r#r$)r0rZr}xs&&&&r8 _set_intXint_lil_base._set_intXints5  A 1 tyy!%Ca 9rCc \\P\WV44wrEp\P !VP ^,VP ^,VPVPWEV4R#r) rr(rrr lil_fancy_setr!r#r$)r0rZr}rr6rs&&&& r8_set_arrayXarray_lil_base._set_arrayXarray sRbmm%B3Q%OPa##DJJqM4::a=$(IItyy$%! -rCc\P!VP4VPR7p\ W14wr4VP WV4R#)rN)r(asarraytoarrayrr r)r0rZr}r_s&&&& r8_set_arrayXarray_sparse!_lil_base._set_arrayXarray_sparses8 JJqyy{$** 5 ( c*rCc\V\4'Ed\V4^8XEdVwr4\V\4'd\V\4'd\ V4'dVP 4p\V\ P4'dVP4pVPPV4pVP^8d \R4hVPW4V4#\V\4'd\V\4'dV\R48Xd}V\R48Xdm\ V4'd\VPVP8XdAVP!W PR7pVP"VnVP$VnR#\&P(!WV4R#)rsz&Trying to assign a sequence to an itemNr)r%r&rSr rrr(ndarrayitemrr|sizer'rrr!rjr#r$r __setitem__)r0rvrrZr}s&&& r8r_lil_base.__setitem__s- c5 ! !c#h!mHC#y))ji.H.HA;; Aa,,AJJOOA&66A:$%MNN((1553&&:c5+A+A5;&3%++=QKKAGGtzz$9'''<FF FF t!,rCcDV^8Xd*VPVPVPR7pV#\VPV4pVP V4p\ VP 4F*wrEVUu.uF qfV,NK upVP V&K, V#uupi)rzr)rjr!rrr enumerater$)r0r?rk res_dtyperrXvals&& r8 _mul_scalar_lil_base._mul_scalar-s A:%%djj %CC &djj%8I++i(C'1 4;**499d;""D6#:$**"MMrC)r"r$rr#)NNFr_)NN)NF)F)+__name__ __module__ __qualname____firstlineno___formatrr@rErJrNr[r__doc__r`rlrprurtrrrrrrrrrrrrrrrrrrrrrrr__static_attributes____classdictcell__) __classdict__s@r8rrsG-T-^"" 3oo--GO3&$1199M$.1" < 1..00/ :9- + -0  (<<''DLCe:oo--GO.^^++FNoo--GOX ))11I MM))EMN@MM))EMMrCrcVPVP8dVPVP4pM6VPVP8dVPVP4pVPP'd0VP\P \P 39d VP\P4pVPP'd0VP\P \P 39d VP\P4pVe1VPP'gVP4pWV3#W3#)a Convert index and data arrays to form suitable for passing to the Cython fancy getset routines. The conversions are necessary since to (i) ensure the integer index arrays are in one of the accepted types, and (ii) to ensure the arrays are writable so that Cython memoryview support doesn't choke on them. Parameters ---------- i, j Index arrays x : optional Data arrays Returns ------- i, j, x Re-formatted arrays (x is omitted, if input was None) ) rr flags writeabler(rrrUr)r6rrs&&&r8rrs. ww HHQWW  177  HHQWW  77   "((/C C HHRWW  77   "((/C C HHRWW }ww   AQwt rCc"\V\4#)a0Is `x` of lil_matrix type? .. warning:: SciPy sparse is shifting from a sparse matrix interface to a sparse array interface. In the next few releases we expect to deprecate the sparse matrix interface. For documentation of the matrix interface, see the :ref:`spmatrix interface docs `. For guidance on converting existing code to sparse arrays, see :ref:`Migration from spmatrix to sparray `. Parameters ---------- x object to check for being a lil matrix Returns ------- bool True if `x` is a lil matrix, False otherwise Examples -------- >>> from scipy.sparse import lil_array, lil_matrix, coo_matrix, isspmatrix_lil >>> isspmatrix_lil(lil_matrix([[5]])) True >>> isspmatrix_lil(lil_array([[5]])) False >>> isspmatrix_lil(coo_matrix([[5]])) False )r%r)rs&r8isspmatrix_lilrs@ a $$rCc]tRtRtRtRtR#)ria@ Row-based LIst of Lists sparse array. This is a structure for constructing sparse arrays incrementally. Note that inserting a single item can take linear time in the worst case; to construct the array efficiently, make sure the items are pre-sorted by index, per row. This can be instantiated in several ways: lil_array(D) where D is a 2-D ndarray lil_array(S) with another sparse array or matrix S (equivalent to S.tolil()) lil_array((M, N), [dtype]) to construct an empty array with shape (M, N) dtype is optional, defaulting to dtype='d'. Attributes ---------- data : ndarray LIL format data array of the array rows : ndarray LIL format row index array of the array dtype : dtype Data type of the array shape : 2-tuple Shape of the array ndim : int Number of dimensions (this is always 2) format : str Three letter code for the format of the array storage, e.g. 'lil' nnz : int Number of values stored in the array size : int Number of values stored in the array T : lil_array The transpose of the array mT : lil_array The matrix transpose of the array Notes ----- Sparse arrays can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Advantages of the LIL format - supports flexible slicing - changes to the array sparsity structure are efficient Disadvantages of the LIL format - arithmetic operations LIL + LIL are slow (consider CSR or CSC) - slow column slicing (consider CSC) - slow matrix vector products (consider CSR or CSC) Intended Usage - LIL is a convenient format for constructing sparse arrays - once an array has been constructed, convert to CSR or CSC format for fast arithmetic and matrix vector operations - consider using the COO format when constructing large arrays Data Structure - An array (``self.rows``) of rows, each of which is a sorted list of column indices of non-zero elements. - The corresponding nonzero values are stored in similar fashion in ``self.data``. r=Nrrrrrrr=rCr8rrsDrCc]tRtRtRtRtR#)riVa Row-based LIst of Lists sparse matrix. This is a structure for constructing sparse matrices incrementally. Note that inserting a single item can take linear time in the worst case; to construct the matrix efficiently, make sure the items are pre-sorted by index, per row. .. warning:: SciPy sparse is shifting from a sparse matrix interface to a sparse array interface. In the next few releases we expect to deprecate the sparse matrix interface. For documentation of the matrix interface, see the :ref:`spmatrix interface docs `. For guidance on converting existing code to sparse arrays, see :ref:`Migration from spmatrix to sparray `. This can be instantiated in several ways: lil_matrix(D) where D is a 2-D ndarray lil_matrix(S) with another sparse array or matrix S (equivalent to S.tolil()) lil_matrix((M, N), [dtype]) to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype='d'. Attributes ---------- data : ndarray LIL format data array of the matrix rows : ndarray LIL format row index array of the matrix dtype : dtype Data type of the matrix shape : 2-tuple Shape of the matrix ndim : int Number of dimensions (this is always 2) format : str Three letter code for the format of the matrix storage, e.g. 'lil' nnz : int Number of values stored in the matrix size : int Number of values stored in the matrix T : lil_matrix The transpose of the matrix mT : lil_array The transpose of the matrix Notes ----- Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Advantages of the LIL format - supports flexible slicing - changes to the matrix sparsity structure are efficient Disadvantages of the LIL format - arithmetic operations LIL + LIL are slow (consider CSR or CSC) - slow column slicing (consider CSC) - slow matrix vector products (consider CSR or CSC) Intended Usage - LIL is a convenient format for constructing sparse matrices - once a matrix has been constructed, convert to CSR or CSC format for fast arithmetic and matrix vector operations - consider using the COO format when constructing large matrices Data Structure - An array (``self.rows``) of rows, each of which is a sorted list of column indices of non-zero elements. - The corresponding nonzero values are stored in similar fashion in ``self.data``. r=Nrr=rCr8rrVsMrC)rrrr_)r __docformat____all__bisectrnumpyr(_matrixr_baserrr_indexr r r _sputilsr r rrrrrrrrrr=rCr8rsv& 7--<<$$j*j*Z &R %HE 7EPN9NrC