(phPSrSSKrSSKrSSKJrJrJr SSKJ r J r /r "SS\5r \H"r \ RrSr\"\ \\"\ 55 M$ S r"S S 5rg) zBase class for sparse matrice with a .data attribute subclasses must provide a _with_data() method that creates a new matrix with the same sparsity pattern as self but with a different data array N)_spbasesparray _ufuncs_with_fixed_point_at_zero) isscalarlike validateaxiscV\rSrSrSS.Sjr\S5r\RS5rSrSr SS jr S r S r S r S rSrSSjr\R R$\lSSjr\R&R$\lSr\R(R$\lSSjrSrSrg) _data_matrixNmaxprintc.[R"XUS9 g)Nr )r__init__)selfarg1r s E/var/www/html/venv/lib/python3.13/site-packages/scipy/sparse/_data.pyr_data_matrix.__init__sh7c.URR$Ndatadtypers rr_data_matrix.dtypesyyrc$XRlgrr)rnewtypes rrrs ! rc\[US5(aUR5 UR$)Nsum_duplicates)hasattrrrrs r _deduped_data_data_matrix._deduped_data s& 4) * *    !yyrcRUR[UR555$r) _with_dataabsr!rs r__abs___data_matrix.__abs__%ss4#5#5#7899rcfUR[R"UR5US95$)N)decimals)r$nparoundr!)rndigitss r __round___data_matrix.__round__(s%ryy););)=PQQrcLURURR5$r)r$rrealrs r_real_data_matrix._real+tyy~~..rcLURURR5$r)r$rimagrs r_imag_data_matrix._imag.r3rcURRS:Xa [S5eURUR*5$)Nbz0negating a boolean sparse array is not supported)rkindNotImplementedErrorr$rrs r__neg___data_matrix.__neg__1s9 ::??c !%'23 3 z**rc\[U5(aU=RU-slU$[$rrrNotImplementedrothers r__imul___data_matrix.__imul__7s&    II IKrcf[U5(aSU- nU=RU-slU$[$)Ng?r?)rrBrecips r __itruediv___data_matrix.__itruediv__=s/   %KE II IK! !rc [R"U5nURU:waFURURR XSS9SS9nURUR 5SS9$U(aUR 5$U$)NT)castingcopyrKF)r*rr$rastyper!rK)rrrJrKmatrixs rrM_data_matrix.astypeEs} :: __   d C%F$$V%9%9%;%$H H 99; Krc[R"UR[R5(a(UR UR R 5US9$U(aUR5$U$)NrL)r* issubdtypercomplexfloatingr$r conjugaterK)rrKs rrS_data_matrix.conjugateTsQ ==R%7%7 8 8??499#6#6#8t?D D 99; KrcRURURR5SS9$NTrL)r$rrKrs rrK_data_matrix.copy^s tyy~~/d;;rc[U5(d [S5eU(d [S5eUR5nUbURU5nUR X1-5$)a This function performs element-wise power. Parameters ---------- n : scalar n is a non-zero scalar (nonzero avoids dense ones creation) If zero power is desired, special case it to use `np.ones` dtype : If dtype is not specified, the current dtype will be preserved. Raises ------ NotImplementedError : if n is a zero scalar If zero power is desired, special case it to use ``np.ones(A.shape, dtype=A.dtype)`` zinput is not scalarzpzero power is not supported as it would densify the matrix. Use `np.ones(A.shape, dtype=A.dtype)` for this case.)rr;r!rMr$)rnrrs rpower_data_matrix.powercsd$A%&;< <%G  !!#  ;;u%Dty))rc>URURU-5$r)r$rrAs r _mul_scalar_data_matrix._mul_scalarstyy5011r)r)unsafeT)Tr)__name__ __module__ __qualname____firstlineno__rpropertyrsetterr!r&r-r1r6r<rCrGrMr__doc__rSrKrZr]__static_attributes__r_rrr r s)-8 \\"" :R//+  " ^^++FN ))11I<<<''DL*F2rr cV^U4SjnS[S[S3Ul[UlU$)NcP>T"UR55nURUSS9$rV)r!r$)rresultops rmethod_create_method..methods)**,-F??6?5 5rz Element-wise z. See `numpy.z` for more information.)namergra)rlrms` r_create_methodrps4 6*$0((,v-DF rcV[U5Hup#X#:wdM Us $ WS- nX!:aU$g)Nr) enumerate)indrYkas r_find_missing_indexrws4# 6HFAurc\rSrSrSrSrSrSrSrSSS .S jjr SSS .S jjr SSS .S jjr SSS .S jjr SSS .Sjjr SSS .SjjrSrg) _minmax_mixinzdMixin for min and max methods. These are not implemented for dia_matrix, hence the separate class. c rURUnUS:Xa [S5eURSU- nURUS9nUS:XaUR5OUR 5nUR 5 UR U5upU(d3[R"UR5UU:n U"XS5X'U S:gn [R"X5RUSS9n[R"X5n [U[5(a!U4n U4n URX4XRS9$US:Xa=URU [R "[#U 5US9U44URSU4S 9$URX[R "[#U 5US944URUS4S 9$) Nr&zero-size array to reduction operationr)maxvalFrL)shaperr)rr~)r~ ValueError_get_index_dtypetocsctocsrr _minor_reducer*diffindptrcompressrM isinstancer_coo_containerrzeroslen)raxis min_or_maxexplicitNM idx_dtypemat major_indexvaluenot_fullmaskcoordsr~s r_min_or_max_axis_minmax_mixin._min_or_max_axiss JJt  6EF F JJq4x )))3 "aidjjlTZZ\  ..z: wwszz*;7!;H(! LMjjA'  &&bhhs5z&KLMjjA' rcPUb [S5e[U5 URS:XaUS;a [S5eSnUcSUR;a [S5eURR S5nUR S:XaU$URUR5R55nUR [R"UR5:waU(dU"XV5nU$US:aUS- nUS:XdUS:XaURXU5$[S5e)Nz0Sparse arrays do not support an 'out' parameter.rNrrraxis out of rangerr|) rrndimr~rtypennzreducer!ravelmathprodr)rroutrrzeroms r _min_or_max_minmax_mixin._min_or_maxs ?OP PT 99>=( !455D <DJJ !IJJ::??1%Dxx1} !!$"4"4"6"<"<">?Axx499TZZ00t'H !8 AID AI419((8D D01 1rcURUS:Xa [S5eUS:aUS- nURRS5nUS:XaUR 5OUR 5nUR 5 URUR5upx[R"U[S9n [R"[R"UR55un U Hn URXS-upURXnURXnU"U5nUUnU(aX- S:a UUX'MWMYU"UU5(dX- U:Xa UUX'Mx[!X5nUU:Xa[#UU5X'MUX'M [%U[&5(aU $US:XaU R)SS5n UR+U 5$)Nrz8Cannot apply the operation along a zero-sized dimension.rrrrr)r~rrrrrr_swapr*rintnonzerorrrindicesrwminrrreshape _ascontainer)rrargmin_or_argmaxcomparerrrret_size line_sizeretnz_linesipqrr extreme_index extreme_valuezero_inds r_arg_min_or_max_axis"_minmax_mixin._arg_min_or_max_axiss ::d q WX X !8 AIDzzq!"aidjjlTZZ\ !ii 2hhxs+JJrwwszz23 A::aA&DA88A=Dkk!&G,T2M /M519$]3CF=$//15I3E$]3CF27FH$,!$]H!=!)#& dG $ $J 19++b!$C  %%rcUb [S5e[U5 URS:XaUS;a [S5eSnUbURXXE5$SUR;a [S5eUR S:XaU(a [S5egUR RS5nUR5nUR5 U"UR5nU(aU$URUn URSn U"X5(a4[URU5U -[URU5-$[R"UR5n XR :Xa4[URU5U -[URU5-$URU -UR-n [!X5n X:Xa [#X5$U $) Nz/Sparse types do not support an 'out' parameter.rrrrz.Cannot apply the operation to an empty matrix.z=Cannot apply the operation to zero matrix when explicit=True.rr)rrrrr~rrrtocoorrrrowcolrrrwr)rrrrrrrrrrnum_colsizelinear_indicesfirst_implicit_zero_indexs r_arg_min_or_max_minmax_mixin._arg_min_or_maxs ?NO OT 99>=( !455D  ,,TWW W  ?MN N 88q= "788zzq!jjl (2  / ))B- = ' 'sww}-.83sww}?U;VV Vyy$ 77?sww}-.83sww}?U;VV V 7*SWW4$7$M!  0@ @((rNF)rcDURX[RU5$)aReturn the maximum of the array/matrix or maximum along an axis. By default, all elements are taken into account, not just the non-zero ones. But with `explicit` set, only the stored elements are considered. Parameters ---------- axis : {-2, -1, 0, 1, None} optional Axis along which the sum is computed. The default is to compute the maximum over all elements, returning a scalar (i.e., `axis` = `None`). out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. explicit : {False, True} optional (default: False) When set to True, only the stored elements will be considered. If a row/column is empty, the sparse.coo_array returned has no stored element (i.e. an implicit zero) for that row/column. .. versionadded:: 1.15.0 Returns ------- amax : coo_array or scalar Maximum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is a sparse.coo_array of dimension ``a.ndim - 1``. See Also -------- min : The minimum value of a sparse array/matrix along a given axis. numpy.max : NumPy's implementation of 'max' )rr*maximumrrrrs rmax_minmax_mixin.maxOL2::x@@rcDURX[RU5$)aReturn the minimum of the array/matrix or maximum along an axis. By default, all elements are taken into account, not just the non-zero ones. But with `explicit` set, only the stored elements are considered. Parameters ---------- axis : {-2, -1, 0, 1, None} optional Axis along which the sum is computed. The default is to compute the minimum over all elements, returning a scalar (i.e., `axis` = `None`). out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. explicit : {False, True} optional (default: False) When set to True, only the stored elements will be considered. If a row/column is empty, the sparse.coo_array returned has no stored element (i.e. an implicit zero) for that row/column. .. versionadded:: 1.15.0 Returns ------- amin : coo_matrix or scalar Minimum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is a sparse.coo_array of dimension ``a.ndim - 1``. See Also -------- max : The maximum value of a sparse array/matrix along a given axis. numpy.min : NumPy's implementation of 'min' )rr*minimumrs rr_minmax_mixin.minwrrcDURX[RU5$)aReturn the maximum, ignoring any Nans, along an axis. Return the maximum, ignoring any Nans, of the array/matrix along an axis. By default this takes all elements into account, but with `explicit` set, only stored elements are considered. .. versionadded:: 1.11.0 Parameters ---------- axis : {-2, -1, 0, 1, None} optional Axis along which the maximum is computed. The default is to compute the maximum over all elements, returning a scalar (i.e., `axis` = `None`). out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. explicit : {False, True} optional (default: False) When set to True, only the stored elements will be considered. If a row/column is empty, the sparse.coo_array returned has no stored element (i.e. an implicit zero) for that row/column. .. versionadded:: 1.15.0 Returns ------- amax : coo_array or scalar Maximum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is a sparse.coo_array of dimension ``a.ndim - 1``. See Also -------- nanmin : The minimum value of a sparse array/matrix along a given axis, ignoring NaNs. max : The maximum value of a sparse array/matrix along a given axis, propagating NaNs. numpy.nanmax : NumPy's implementation of 'nanmax'. )rr*fmaxrs rnanmax_minmax_mixin.nanmaxX277H==rcDURX[RU5$)aReturn the minimum, ignoring any Nans, along an axis. Return the minimum, ignoring any Nans, of the array/matrix along an axis. By default this takes all elements into account, but with `explicit` set, only stored elements are considered. .. versionadded:: 1.11.0 Parameters ---------- axis : {-2, -1, 0, 1, None} optional Axis along which the minimum is computed. The default is to compute the minimum over all elements, returning a scalar (i.e., `axis` = `None`). out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. explicit : {False, True} optional (default: False) When set to True, only the stored elements will be considered. If a row/column is empty, the sparse.coo_array returned has no stored element (i.e. an implicit zero) for that row/column. .. versionadded:: 1.15.0 Returns ------- amin : coo_array or scalar Minimum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is a sparse.coo_array of dimension ``a.ndim - 1``. See Also -------- nanmax : The maximum value of a sparse array/matrix along a given axis, ignoring NaNs. min : The minimum value of a sparse array/matrix along a given axis, propagating NaNs. numpy.nanmin : NumPy's implementation of 'nanmin'. )rr*fminrs rnanmin_minmax_mixin.nanminrrcbURX[R[RU5$)a*Return indices of maximum elements along an axis. By default, implicit zero elements are taken into account. If there are several minimum values, the index of the first occurrence is returned. If `explicit` is set, only explicitly stored elements will be considered. Parameters ---------- axis : {-2, -1, 0, 1, None}, optional Axis along which the argmax is computed. If None (default), index of the maximum element in the flatten data is returned. out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. explicit : {False, True} optional (default: False) When set to True, only explicitly stored elements will be considered. If axis is not None and a row/column has no stored elements, argmax is undefined, so the index ``0`` is returned for that row/column. .. versionadded:: 1.15.0 Returns ------- ind : numpy.matrix or int Indices of maximum elements. If matrix, its size along `axis` is 1. )rr*argmaxgreaterrs rr_minmax_mixin.argmaxs#<##Dryy"**hOOrcbURX[R[RU5$)a+Return indices of minimum elements along an axis. By default, implicit zero elements are taken into account. If there are several minimum values, the index of the first occurrence is returned. If `explicit` is set, only explicitly stored elements will be considered. Parameters ---------- axis : {-2, -1, 0, 1, None}, optional Axis along which the argmin is computed. If None (default), index of the minimum element in the flatten data is returned. out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. explicit : {False, True} optional (default: False) When set to True, only explicitly stored elements will be considered. If axis is not None and a row/column has no stored elements, argmin is undefined, so the index ``0`` is returned for that row/column. .. versionadded:: 1.15.0 Returns ------- ind : numpy.matrix or int Indices of minimum elements. If matrix, its size along `axis` is 1. )rr*argminlessrs rr_minmax_mixin.argmins#<##Dryy"''8LLrr_)NN)rarbrcrdrgrrrrrrrrrrrhr_rrryrysy !F2<)&V3)j&A5&AP&A5&AP,>e,>\,>e,>\PeP@MeMMrry)rgrnumpyr*_baserrr_sputilsrr__all__r npfuncrarorpsetattrrwryr_rrrso EE0  s27s2n/F ??D  L$v 67/" QMQMr