+ LV-jQRt^RIt^RIt^RIHtHtHt^RIH t H t .t !RR]4t ]F"t ] PtRt]!] ]]!] 44K$ Rt!RR 4tR#) 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 validateaxiscba]tRt^toRR/Rlt]R4t]PR4tRtRt RRlt R t R t R t R tR tRRlt]P P$]nRRlt]P&P$]nRt]P(P$]nRRltRtRtVtR#) _data_matrixmaxprintNc6\P!WVR7R#))r N)r__init__)selfarg1r s&&$c/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/scipy/sparse/_data.pyr _data_matrix.__init__sh7c.VPP#N)datadtyper s&rr_data_matrix.dtypesyyrcFVPPV4VnR#r)rview)r newtypes&&rrrsIINN7+ rc^\VR4'dVP4VP#)sum_duplicates)hasattrrrrs&r _deduped_data_data_matrix._deduped_data s& 4) * *    !yyrcRVP\VP444#r) _with_dataabsrrs&r__abs___data_matrix.__abs__%ss4#5#5#7899rclVP\P!VP4VR74#))decimals)r nparoundr)r ndigitss&&r __round___data_matrix.__round__(s%ryy););)=PQQrcLVPVPP4#r)r rrealrs&r_real_data_matrix._real+tyy~~..rcLVPVPP4#r)r rimagrs&r_imag_data_matrix._imag.r/rcVPPR8Xd \R4hVPVP)4#)bz0negating a boolean sparse array is not supported)rkindNotImplementedErrorr rrs&r__neg___data_matrix.__neg__1s9 ::??c !%'23 3 z**rcf\V4'dV;PV,unV#\#rrrNotImplementedr others&&r__imul___data_matrix.__imul__7s&    II IKrcx\V4'd%RV, pV;PV,unV#\#)g?r;)r r>recips&& r __itruediv___data_matrix.__itruediv__=s/   %KE II IK! !rc"\P!V4pVPV8wdPVPVPP WRR7RR7pVPVP 4RR7#V'dVP 4#V#)T)castingcopyrGF)r&rr rastyperrG)r rrFrGmatrixs&&&& rrI_data_matrix.astypeEs} :: __   d C%F$$V%9%9%;%$H H 99; Krc\P!VP\P4'd,VP VP P 4VR7#V'dVP4#V#)rH)r& issubdtypercomplexfloatingr r conjugaterG)r rGs&&rrO_data_matrix.conjugateTsQ ==R%7%7 8 8??499#6#6#8t?D D 99; KrcXVPVPP4RR7#TrH)r rrGrs&rrG_data_matrix.copy^s tyy~~/d;;rc \V4'g \R4hV'g \R4hVP4pVeVPVRR7pVP W1,4#)a This function performs element-wise power. Parameters ---------- n : scalar n is a non-zero scalar (nonzero avoids dense ones creation) If zero power is desired, special case it to use `np.ones` dtype : If dtype is not specified, the current dtype will be preserved. Raises ------ NotImplementedError : if n is a zero scalar If zero power is desired, special case it to use ``np.ones(A.shape, dtype=A.dtype)`` zinput is not scalarzpzero power is not supported as it would densify the matrix. Use `np.ones(A.shape, dtype=A.dtype)` for this case.FrH)rr7rrIr )r nrrs&&& rpower_data_matrix.powercsh$A%&;< <%G  !!#  ;;u5;1Dty))rcFVPVPV,4#r)r rr=s&&r _mul_scalar_data_matrix._mul_scalarstyy5011r)r))unsafeT)Tr)__name__ __module__ __qualname____firstlineno__r propertyrsetterrr"r)r-r2r8r?rCrIr__doc__rOrGrVrY__static_attributes____classdictcell__ __classdict__s@rrrs88 \\,, :R//+  " ^^++FN ))11I<<<''DL*F22rrcVaV3RlpR\ R\ R2Vn\VnV#)cV<S!VP44pVPVRR7#rR)rr )r resultops& rmethod_create_method..methods)**,-F??6?5 5rz Element-wise z. See `numpy.z` for more information.)namercr])rkrlsf r_create_methodros4 6*$0((,v-DF rcd\V4Fwr#W#8wgK Vu# X^, pW!8dV#R#)) enumerate)indrUkas&& r_find_missing_indexrws6# 6HFAu rca]tRt^toRtRtRtRtRtRRR/R llt RRR/R llt RRR/R llt RRR/R llt RRR/R llt RRR/RlltRtVtR#) _minmax_mixinzdMixin for min and max methods. These are not implemented for dia_matrix, hence the separate class. c VPV,pVP^V, ,pVPVR7pV^8XdVP4MVP4pVP 4VP V4wrV'g<\ P!VP4V,V8p V!W,^4W&V ^8gp \ P!W4PVRR7p\ P!W4p \V\4'd%V3p V3p VPW3WPR7#V^8XdDVPV \ P!\!V 4VR7V33VP^V3R7#VPW\ P!\!V 4VR733VPV^3R7#)rq)maxvalFrH)shaperr)rr|)r|_get_index_dtypetocsctocsrr _minor_reducer&diffindptrcompressrI isinstancer_coo_containerrzeroslen)r axis min_or_maxexplicitNM idx_dtypemat major_indexvaluenot_fullmaskcoordsr|s&&&& r_min_or_max_axis_minmax_mixin._min_or_max_axiss JJt  JJq4x )))3 "aidjjlTZZ\  ..z: wwszz*;7!;H(! LMjjA'  &&bhhs5z&KLMjjA' rcaVe \R4h\VSPR7pVf^SP9d \R4hSPP ^4pSP ^8XdV#VPSP4P44pSP \P!SP48wdV'g V!WV4pV#\;QJdV3RlV4F 'gK RM RM!V3RlV44'd \R4hSP^8XdSPV^,W44#SPWV4#)Nz3Sparse min/max does not support an 'out' parameter.ndimz&zero-size array to reduction operationc3P<"TFpSPV,^8HxK R#5ir[Nr|).0dr s& r ,_minmax_mixin._min_or_max..s04atzz!}!4#&TF) ValueErrorrrr|rtypennzreducerravelmathprodanyr_min_or_max_axis_nd)r routrrzeromsf&&&& r _min_or_max_minmax_mixin._min_or_maxs ?RS SDtyy1 <DJJ !IJJ::??1%Dxx1} !!$"4"4"6"<"<">?Axx499TZZ00t'H 3040333040 0 0EF F 99>((a*G G''(CCrcVPP^4pV^8XdVP4MVP4pVP 4VP VP 4wrx\P!V\R7p \P!\P!VP44wp V Fp VPW^,wrVPWpVPWpV!V4pVV,pV'dW, ^8dVV,W&KiKkV!VV4'gW, V8XdVV,W&K\W4pVV8Xd\!VV4W&KVW&K \#V\$4'dV #V^8XdV P'R^4p VP)V 4#)r[r}rr)rrrrr_swapr|r&rintnonzerorrrindicesrwminrrreshape _ascontainer)r r argminmaxcomparerrrret_size line_sizeretnz_linesipqrr extreme_index extreme_valuezero_inds&&&&& r_argminmax_axis_minmax_mixin._argminmax_axisskzzq!"aidjjlTZZ\ !ii 2hhxs+JJrwwszz23 A::aA&DA88A=Dkk!&G%dOM /M519$]3CF=$//15I3E$]3CF27FH$,!$]H!=!)#& dG $ $J 19++b!$C  %%rcfaVe)V\P8XdRMRp\RV R24h\VSPR7pVe\ ;QJdV3RlV4F 'gK RM RM!V3RlV44'd)V\P8XdRMRp\R V R 24hSP^8XdSP V^,W4V4#SPWWE4#^SP9d)V\P8XdRMRp\R V R 24hSP^8Xd3V'd)V\P8XdRMRp\R V R 24h^#SPP^4pSP4pVP4V!VP4p V'dV #VPV ,p VP^8dVPR 4p\ P"!SP4p V!W4'gVPV 8XdVP^8Xd\%VP&V ,4#VPR ,p \%VP(V ,4V ,\%VP&V ,4,#VP^8XdVP*R ,p M7VPR ,p VP(V ,VP&,p \-W4pW8Xd \/W4#V#)NargminargmaxzSparse z% does not support an 'out' parameter.rc3P<"TFpSPV,^8HxK R#5irr)rrr s& rr+_minmax_mixin._argminmax..s4t!4::a=A%trTFz Cannot apply z along a zero-sized dimension.z to an empty matrix.z# to zero matrix when explicit=True.rr)r&rrrrrr_argminmax_axis_ndr|rrrtocoorrrrrrcolrowrrwr)r rrrrrminmaxrrrrmaxnnznum_collinear_indicesfirst_implicit_zero_indexsf&&&&& r _argminmax_minmax_mixin._argminmaxs ?!*bii!7XXFwvh.STU UDtyy1  s4t4sss4t444%."))%; =8V!WXXyyA~++DGYRR**4GN N  ?!*bii!7XXF}VH4HIJ J 88q=%."))%; =97"788zzq!jjl !#((+  / 88a<++b/C4::& = ' '377f+<xx1}377=122iimGsww}-.83sww}?U;VV V 88q= ZZ^NiimG WWw.8N$7$O!  0@ @((rNrFc DVPW\PV4#)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&maximumr rrrs&&&$rmax_minmax_mixin.maxOL2::x@@rc DVPW\PV4#)aReturn the minimum of the array/matrix or maximum along an axis. By default, all elements are taken into account, not just the non-zero ones. But with `explicit` set, only the stored elements are considered. Parameters ---------- axis : {-2, -1, 0, 1, None} optional Axis along which the sum is computed. The default is to compute the minimum over all elements, returning a scalar (i.e., `axis` = `None`). out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. explicit : {False, True} optional (default: False) When set to True, only the stored elements will be considered. If a row/column is empty, the sparse.coo_array returned has no stored element (i.e. an implicit zero) for that row/column. .. versionadded:: 1.15.0 Returns ------- amin : coo_matrix or scalar Minimum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is a sparse.coo_array of dimension ``a.ndim - 1``. See Also -------- max : The maximum value of a sparse array/matrix along a given axis. numpy.min : NumPy's implementation of 'min' )rr&minimumrs&&&$rr_minmax_mixin.minwrrc DVPW\PV4#)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&&&$rnanmax_minmax_mixin.nanmaxX277H==rc DVPW\PV4#)aReturn the minimum, ignoring any Nans, along an axis. Return the minimum, ignoring any Nans, of the array/matrix along an axis. By default this takes all elements into account, but with `explicit` set, only stored elements are considered. .. versionadded:: 1.11.0 Parameters ---------- axis : {-2, -1, 0, 1, None} optional Axis along which the minimum is computed. The default is to compute the minimum over all elements, returning a scalar (i.e., `axis` = `None`). out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. explicit : {False, True} optional (default: False) When set to True, only the stored elements will be considered. If a row/column is empty, the sparse.coo_array returned has no stored element (i.e. an implicit zero) for that row/column. .. versionadded:: 1.15.0 Returns ------- amin : coo_array or scalar Minimum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is a sparse.coo_array of dimension ``a.ndim - 1``. See Also -------- nanmax : The maximum value of a sparse array/matrix along a given axis, ignoring NaNs. min : The minimum value of a sparse array/matrix along a given axis, propagating NaNs. numpy.nanmin : NumPy's implementation of 'nanmin'. )rr&fminrs&&&$rnanmin_minmax_mixin.nanminrrc bVPW\P\PV4#)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 an axis 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&rgreaterrs&&&$rr_minmax_mixin.argmaxs!<t"))RZZJJrc bVPW\P\PV4#)a&Return indices of minimum elements along an axis. By default, implicit zero elements are taken into account. If there are several minimum values, the index of the first occurrence is returned. If `explicit` is set, only explicitly stored elements will be considered. Parameters ---------- axis : {-2, -1, 0, 1, None}, optional Axis along which the argmin is computed. If None (default), index of the minimum element in the flatten data is returned. out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. explicit : {False, True} optional (default: False) When set to True, only explicitly stored elements will be considered. If axis is not None and an axis has no stored elements, argmin is undefined, so the index ``0`` is returned for that row/column. .. versionadded:: 1.15.0 Returns ------- ind : numpy.matrix or int Indices of minimum elements. If matrix, its size along `axis` is 1. )rr&rlessrs&&&$rr_minmax_mixin.argmins!<t"))RWWhGGr)NN)r]r^r_r`rcrrrrrrrrrrrdrerfs@rryrys} DD4#&J>)@&A5&AP&A5&AP,>e,>\,>e,>\KeK@HeHHrry)rcrnumpyr&_baserrr_sputilsrr__all__rnpfuncr]rnrosetattrrwryrrrrso EE0  s27s2n/F ??D  L$v 67/" QHQHr