ELF@F@8@0HL   $$Ptd@@@llQtdRtdGNU|2ק~Lz,vvvm :1W.T.o d$=F"{E!s$@Hcq_~ 5j P}YzU%bzFu?i .N1_, m 7FV G __gmon_start___ITM_deregisterTMCloneTable_ITM_registerTMCloneTable__cxa_finalize_PyDict_GetItem_KnownHashPyMethod_TypePyTuple_New_Py_DeallocPy_EnterRecursiveCallPy_LeaveRecursiveCallPyErr_OccurredPyExc_NameErrorPyErr_FormatPyObject_GetAttrPyCFunction_TypePyType_IsSubtypePyObject_CallPyExc_SystemErrorPyErr_SetString__stack_chk_guard_Py_TrueStructPyExc_TypeErrorPyDict_Size__stack_chk_failPyDict_NewPyDict_SetItemPyFloat_TypePyObject_RichCompare_Py_FalseStruct_Py_NoneStructPyList_TypePyTuple_TypePyNumber_InPlaceOrPyObject_IsTruePyLong_FromSsize_tPyObject_GetItemPyObject_SetAttrPyLong_FromLongPyBool_TypePyExc_IndexErrorPyErr_FetchPyBuffer_ReleasePyErr_RestorePyFloat_FromDoublePyObject_SetItemPyBaseObject_TypePyList_NewPyFloat_AsDoublePyNumber_InvertPyNumber_AddPySlice_NewPyObject_GetAttrStringPyDict_SetItemStringPyExc_AttributeErrorPyErr_ExceptionMatchesPyErr_ClearPyThreadState_GetPyInterpreterState_GetIDPyExc_ImportErrorPyModule_NewObjectPyModule_GetDictPyExc_ValueErrorPyOS_snprintfPyErr_WarnExPyUnicode_AsUTF8AndSizePyCode_NewEmptyPyInit__toolsPyModuleDef_InitPyLong_TypePyLong_AsSsize_tPyNumber_IndexPyExc_OverflowErrorPyErr_GivenExceptionMatchesPyImport_ImportModuleLevelObject_PyObject_GetDictPtrPyObject_NotPyFrame_NewPyTraceBack_HerePyUnicode_FromFormatPyUnicode_AsUTF8memmovePyMem_ReallocPyMem_MallocPyLong_AsLongPyExc_DeprecationWarningPyErr_WarnFormatPyObject_GetBufferPyUnicode_TypememcmpPyErr_PrintExPyUnicode_FromStringPyErr_WriteUnraisablePyErr_SetObjectPyExc_RuntimeErrorPy_GetVersionPyBytes_FromStringAndSizePyUnicode_FromStringAndSizePyImport_AddModulePyObject_SetAttrStringPyUnicode_InternFromStringPyUnicode_DecodePyObject_HashPyImport_GetModuleDictPyDict_GetItemStringPyTuple_PackPyImport_ImportModulePyErr_GetExcInfoPyCapsule_TypePyCapsule_GetPointerPyExc_ExceptionPyErr_NormalizeExceptionPyException_SetTracebackPyErr_SetExcInfoPyCMethod_NewPyDict_NextPyUnicode_Comparelibc.so.6ld-linux-aarch64.so.1GLIBC_2.17 b0b dgggpExE E(E@pEHxEPEXE`Epgg@pEHFPFXxEp8GxxE8GFF2w23I3403pw83H3/P3eX3psh3)p3xdx3Pj3`%3f3d33Xw3b3`30g3 3 3e3444A(4 H@4xFH4Эh4Ep4ȭ4F44(H44H45H505G85X5G`55E55F5x5G5p5F6h 6H(6 H6FP6p6Gx66F66H6Ь6`I67hI787E@7`7Fh77`G778G77H7p8 I80(8(I08P8EX8x8I8 8I88E88I89I 9P@9PHH9 h9HHp99XH9Д9XE990I9@:8I: 0:H8:X:@I`:p:HI:P:hG:@:0G:0:F;  ; G(;H;GP;p;Hx;;G;;F;;H;؂<F<Ђ8<F@<Ȃ`<Gh<<H<<PG<<G<=pE=(=`H0=pP=@HX=Px=8H= =H==I==G=>G >@>FH>h>Ep>؁>H>Ё>E>ȁ>E>?@G?0?XG8?X?HE`??I?? F??F?x?PE@h @F(@XH@GP@Pp@Ix@@@`E@8@E@(@E@ AHA8A0E@A`AxEhAAxIAAhHAЀAxHABpFB(BF0BPBHXBxBFBpB HBhBHBPBPIByCXI Cy@CHHCpyhCHpCPyCGC8yCEC0yCGC(yDpIDy0DxG8DyXDG`DyDFDxDFDxDEDx   ("0'8(@)H*P8X9`:h=pGxMOPSUV[]_filu ( 0 8 @HPX`hpx !#$%&+,-./0 1(20384@5H6P7X9`;h<p>x?@ABCDEFHIJKLNQRTWXYZ \(^0`8a@bHcPdXe`ghhpjxkmnopqrst { {_{pG?    p@ p@" p @B p@b p@ p@ p@ p@ p"@ p&@" p*@B p.@b p2@ p6@ p:@ p>@ pB@ pF@" pJ@B pN@b pR@ pV@ pZ@ p^@ pb@ pf@" pj@B pn@b pr@ pv@ pz@ p~@ p@ p@" p@B p@b p@ p@ p@ p@ p@ p@" p@B p@b p@ p@ p@ p@ p@ p@" p@B p@b p@ p@ p@ p@ p@ p@" p@B p@b p@ p@ p@ p@ pA pA" p AB pAb pA pA pA pA p"A p&A" p*AB p.Ab p2A p6A p:A p>A pBA pFA" pJAB pNAb pRA pVA pZA p^A ?#{S*[5@GT*@7р`* @G@4RSA[B{è#_?#{S[ @bAXB?T@X T T@G!@%@GC@1@T?!&@7!ar@Ђc&B '$R`6Ђc@'B'$R7Ђc'B'$R7Ђc(B@(Rw7?[BSA{è#_?#CEG{ CS[*@g2 @T@@7@G!5@%eB iT@G! 6@qEbTB8U6@Gg@@B@Te{MSN[O@C#_?#HG{C[*Kg@'S*ck s @'@ҭ~@&!9`5!: 5!`:5~@!:u@5`!;k5`!@;a` 5@!;[ 5!;U 5!;O 5!  @"@>&@>*=.@  2@  BBG BBGC 6@  @:@  >@  | ҁC@ hG@ һK O@ Ҷ J$CC&CCB!R@B[@R@ R%BW@GC$BCB¾BBA R@B[@R@RsCBB¾BBo@`RR[@_@*@Z҃W@%CACBCC¾BS RBR@[@@R@<AC <@@`*R!R[@c@*@'@fLeW@CPCio@` C 'CCACWCBLCb@3RaR`[@`s@`Rg@afw@#PCaWC"LCľBB g@>Rb@`[@R`{@R֢ X`B$RsҁւR7RC`B**@C @7!a &@G!@CZւR7RCe`6ւXR7R@7Ѡ`b @B DR 0B DRI(B DRF DRB  a DB@$RB`$RB>$RB$RB$RB$RB $RB`$RB$RB$RB DRҾ@@7Ѡ4Rւ7R6ւR7R0ւ8R7R*xR@c?zyAB B7@7Ѡ`@ҡ{aC @1@T @wAC @1@T AR|@;?@7Ѡ`aC?aC B} 7@7Ѡ`Cr?`C Bl7@7Ѡ`@7`[@!w@7!au@@G?T@G!@@7Ѡ`tH@@7!ѡag@@G!@~j@6R r?kT@@G!@@ ?**! W@LC?0qT@@G!LC@ ?*!`R E@HC?*5@G!@M9qT@G!@D0RւRKւXRwREւXRwR?RwRւ9RwRւ38RwRւ-xRwRւ'ւRR!@G@" 4#@##uRZR>HD'@7@1@T@@1@T#@@1@T+@K@;@ @]RuR#@'@A@7_RuR[_RuRB@trp*#*@#ւRRk>қZRuR@]@Z@W@BV;7@E;`!@7`la B!B' 7@7Ѡ`_@B:;v!@';@7Ѡ`N`y|C B7@7`@‚x# B@Pa B!B7@7Ѡ`)‚# B@:`a B!B`7@7Ѡ`ڂ# B@$@A_@ B`7@7Ѡ`‚# B@@a B!B@7@7Ѡ`ڂ# B@ Ac@ B@7@7Ѡ`ڂ# B@ As@ B@7@7Ѡ``B:a!lC:@7!ѡaۂa# :Hb@Ҽ`a{@ B^7@7Ѡ`5ւRP7SL7TH7UD7V@7W<7X87`@B27@7Ѡ@jRWR"xR"WR"RR"RR"XRRxւR7RrւR7RkւRW ReւXRW R^ւRwRXւRwRQւRRKւXRRDւRw*R>ւRw*R7ւR3R1ւXR3R*ւR7?R$ւR7?RւR>RRւ>RւR Rւ 8RւXRւxRւRւRւRւRւ"RWRւ8R7RւR7RA!G@"@c@T{SST[UcVkWsXC#_@G@_  `a!?TA!Ga_`a!!"A !ABBGb_ { s`T9@7@G@`AY R`9 @{¨_     ?#{[vSӚBc@b@k"@1@T@"H@ @b$@?@S#7р`a@"H@@'@?`@t 7` @@@&@1@T`ؚB @@`&@1@T@H@@'@?'@7Ѡ`@XG T@bR@ @7р`@7`@7Ѡ@7@@7@*c"0R;[BSAcCkD{Ũ#_ր7`R`@XGaTRz@@@y @1@T@ @1@T `@7` `$B!Һ"@1@T#@s@@ 6 @]5`?@7Ѡ @7 @a T@5 @ @1@T@1@T@7р8@7!ѡ @7@@7@IoFC@=[ @@"H@b@?@G!@RH9R&A!G T58 @ @63@ (7@@ 5`? @7` @@"H@ @? U @@"H@ @?5@G!@9RyR|ҋ R*@|G!@RYR@|G!@@7YRҚ҈~^@G!@\Rn9RhuwҹRs?#C@G{Stc[k@@@GV@y @ ?T T?T? Tv@sy@U3@@@@ @1@T`6@@B@!@1@T@ZZG %TҌǁR `@7`4@7Ѡ @7р @7р`&*c@R9sH@G@@B!:T{CSD[EcFkGC#_?Tx@sw@v@@G@c`+$B!ARc@bR9@Gc)@s}x@sdC" @1@T @19 @T@1V @T@1x @T`@@@ u@z5?$@7р`@7``@ TA!GTP57@7 4`@7``sHntsHja @ @ 64@ (7u@@A 5?@7 [XU? TT?`T?!Ta<`<T@sV@W@@ @@"H@@?@G!@R@ @@"H@@?@G!@`@`7ÁR`@7@`@[́R`6Ҝ@@ @1@T`@1@T`@7Ѡ`ScB!Ҝ,ҫʁRu@@w @1@T@1@T`@7` 6@7BѢb?;9 ?!Ta@~ T!$@" @&@T!(@" @@ T!,@" @@ @Q@|Ga!@@ 6`@ 7` [́R }Yzm`@6ÁRd@|Ga!@`<a@A =.*"@" @@Gy @@?Tcec)e`<=dA!@ ;6R[;n%!s[́RǁR?#@G{Ssc x[k s @@/TGWS+ @Bq?@^TMVT?^T?[T@@@@ @`@1@T`@1@T @ @1@T @1@TwBz@@@d`q@1@T !@`"H@<@s@? @r7  W 6 s`@1@T`;n`ta!8@X7@@@`@5?66@7р i @7 h@7h`@7``h@`"H@@@€@?`D@?RT@@G_PTbR@x@*7!hA!G@G@@@z`]T@7!сJ5@`"H@P@@?@ZZGfTA!GtT4@ @ƒ@?@@7`p@`"H@P@‰@?@dTA!GsT4@@k @k!@?@@7`q@bRA@7рr[{GA!Gq[A*iT@7`5@`Ty:[@ @B@@1@T@Y9G렮TA!G@]T]54@@7`@7Ѡ zBu@B@` @1@T@`"H@\@b@?|@7@TA!GmTm5@`4@7р@q*[AT@7Wv4@1@T @ @7 @ @1@T @ @7 @G `Buu3@@f@BH@"@?@@7 @`"H@P@@? @ a"M2@7u3@`@7 @7 @7`Bu43@@BH@"@?f@7р@`"H@p@b@?@\@7@җ@1@T@@ 1@T@@aBBG!8@@@7$3@@7р@@7 @7 @7UG@q[A@T 4@`"H@x@@?\`By2 @!c@BH@b@?@&7@@G? T3@@7O @7р @7 @`#L@x@`? 7@7 @@?q$[$BA7T4@`"H@x@@?`Bty2 @^@BH@@?@7р`!@@G? Tj3 @7 @7@@7р@@sL@az@?!@@77!!@1@T? T? T@ P@G@cec`+$baB!dRc`cB R5@G/@@BҡT@{FSG[HcIkJsK#_@Gcec)@!F  @` `T\G7R*@6[{G*lRR @7 @ @7р @7@7 **c`c4@@@@7@ @@7@@7`@7Ѡ  @ @7 @7y@@@ *`6nRtR?PTLT?`T? GTD#@" @ PNT cD WC+@jjgda@ @@"H@P@?9 Ga!@#ғjRRe4ijRRC=3kRR> @@@ @1`T kRR(@ T @@@1`T @ @6<@ H(7@`@@5?q|RtR@C@6RTR ~6RR +@@@7!сa@|]*@6uRRSnRtR @@ @1 T _x@ 3lRRt@T@@1TfnRtR@i?T?aT@+@#<@'=L?aTTa!8@" @+TA!@ d!6`6aaR& |Ga!@ @ @`6:@=(7@`@/@5@@?@F@RRG҅ >`@{7!сa{4st^I@@@J`@P5`?%^@ҳ~RRA@ғRR;@:K@@ @1@T@@1@T@7р,@S.B@7BBbI @Ci[*@6@RR@ғRR|RtR5еGRR@ҙ pRR@@RR@ғRR@P |Ga!@pRR5еG3RR@=?5еGsRR}@@ |Ga!@@@z@0c5еGӐRRmjg5еGRRp^["X@RRn5еGҳRRNHEB@SRR<#BsRR25еG3RR+SRtR@"X`@3RR%W@7@U @1@T@1@T@@7@@=-@7Bb@ vRR m3RRDғRtR5еGRR5еGRRӟRtRsRRRtR@ @:@@1@T@ @1@T @7@,@87BB8~ҳRR5еG3RRRtRl0ғRR};@=`@4 @1@T`@1@T @7 ` ,b@7BbbZRtR_5еGRRf;@5еGRRL5еGRRFRRH#o 5еGSRR0 |Ga!@Cy |Ga!@8r?#{$ЄGS[c36ֺG@@`V &TSss @ V9@!'TSss  @  @V'TSss @ O @V'TS`!!@ _1@twB@d @ @@@-@/@1@T@A@`"H@@5@?@@447@%@`4&@&[ # + z"BGAo@D 7BT@ 7@@@:3`@Q 5@? @7р/@7р.`@7`-_'TG @814@"T/@Q @xi?kT@*_"!T_@xbh 8? T@ +T @! T@ @ah`xb aTkT@?"T_@#xah 8?BT @HT"R Ga!@@ |Ga!@< Հ@7р@@7р `@7``AAAA#@@ @ ?`T@@ @ ?Tc_@@ @ ? T/@O@ @ ? TcC'@`0@7р G@@B!)TSA[BcC3@{Ǩ#_"B@ B#$RRq/1 T@V9@ T"B@ B #$RR_/1 Tw@V @T"B@ B #c$RRN/1`TG@V @`T"B@ B#cDRR=/1@TTSOvfs]p@6\if\`h (`@7`@`6EAAR T@! _-T/@)@xa?aT  GaR!@@AAAA6 @@"H@B @?Z Ga!@AA!sb@F@_Tì@e@MTc` ax`_TaT GaC @!@ @AA @@7@AAR@Ҡ6 @?⇟AA:A`` 7`@7`@@ @ ?T@@ @ ?Tc_@@ @ ? T/@O@ @ ?@TcAAAA |Ga!`@@_T G_T}OAAt?#{SSky[csC#@ G:@@7G"L_T_T_ @GT_,HTccD Geb)B@a!Rc`c>R!, G@@BTCSA[BcCkDsE{Ǩ#_ր@ @A`@1@T`@1@T Q`@`@1@T`@`"R@}+P@7р@a!@+P@1@T@7р?@@7@>@T`N`@1@T`@ @[1@T @@]cyX@`@"@T@`]@1@Ta!l@J7@7 8a99G!@@;7@"BG?q$Y$B4T`35@1@T8Ga!@l@^7@7V@@@8``@x``5?8j@@7@ X@7 W`@7` VxB{`@@_@1@T@`"H@@"g@?@d7T@@!!GeT!!G`T,`B4A @ @B6;@ Z(7V@`@2`m5`?k@@7@P@7рOa@`"H@P@bf@?8e@`a@"H@P@g@?fbRBi@7=@@7@<@qY@AKT@7<y5yBw@"@`}@1@T@`"H@d@B@?@~7`@`a@"H@P@@? Q7  eX@@B@\@@1@T a!l@~7 @7 @p@@@`@5 ?Az@7r@7`q@7 qA롃T@T_T@,*1T @@ G?PT @+@`~!`TA ?T@"B@ BCDRRD,1,T X@7d@X@ ?T"B@ B DRR2,1@*TR@@@@@ @@(@_@T"B@ BcDRR,1 'TL@@@C@@*@@#@@N4O@q͈T@@@ / }@*"R k T iT0 1T`~i` k T~@ӟZ ? T 4 hT .T|a@3Bha!(bak@TR @1@T 8Gfcc6ÂRSHR&> _ 3TT_)T_ATT<< L<T@@ @u‚RHR@7%C@7cC@7c#@7c#c`**c)@7р@@7@ `@7` @@7рlҤRGRҴҕRGRRGRҢҵRHRң5RHRғ#_T@8a!@" @`XTa!@" @Ѡ Ta!$@" @(ѡa&[@ɂRsHRh_?<9@ҕЂRIRM!ET4R Ga*!@@#O@o@ @ ?=Tc@@ @ ?`;TC@@ @ ?:TGG@@ +@@1@T@@@97A6ҵRHRJY`F@`@@"H@"F@? GA!R@HRNF>_yT*6˂R@HRÂRHReT "R GA!@@<@ k=mGc5łRHRҴ`#c@ 0`#c@   ` @@"H@U@? GA!@D@ƂRsHRґ`#c@  o<k=#!#@" @ @@5ǂRsHRc |GA!@_)V@6@X @1@T@1@T@@7@@:c%@7Bb @@{@ʂRHR9!!@ D!J+6aRʂR@HR$@c` |GA!@ @ɂRsHR 5˂R@HRuB`@#@R#DE3@`C@!1@T@`#"H@3@E@?#DE3@@uE7!<sBR@#a@3 f@ 7`@#DE3@7`<@7Ѡ:q2T@b|Uˎ/#@k-LT@76T? T-<T?T@`ip a&T0 3 kq`i0#@!)BkTc1Rb@7Bbb]@7!ѡAV/CcO}s`J@a@R$7B*@@7@,u͂R@HRa%,oAt.t+v(i  @@"H@3@? GA!@@ҵςRIR5@ςRIR"i&?T /TA@@@0 `"T @?ǟ@@@UЂRIR ǟ1@тRIR@5тRIR> @@@"H@B)@?} GA!@@uтRIRRHRs@}@тRIR GEC)cB@BA&!DlaRP`@%@%@T@!d@"Tc`!!TbxaaT@T_zT`@E@`yT@&d@MTc` bxaxT!aT"BGA @!@@ @5ԂR@3IR҂X-`wA@UՂRSIRut5ՂR@SIRl`@e@T@d@Tc`!Tbxa_aT@_oT |GA!@Bu m4 T Th@)k#@ kyT@c@#@klxTO@o@ @ ? Tc@@ @ ?TC@@ @ ?`T`@@ |GA!`@!)BU#3#DE3@?ǟR#3#DE3@"#DE3@#3#F#DE@@BH@"@?#DE3@ GA!@uz?ǟ#DE3@7!j@̂RHRұti"m!@`T!!GT"BGA @!@@ @BӂR@3IRҏ |GA!`@ |GA!`@zӂR@3IR{!@?@T!!GT"BGA @!@@ @!@ RT!!GQTE:o/O@#3#DE3@}?#{@7B{#_{# ?#{ @S1[cks@T UB@@y@1@T@A@"H@ @@?@@7@va@"H@@@?`@7`t@@!G;TV@֑@W @1@T@1@T@@7@@r"@7!Ar t@@7@``@7``@"H@@@¥@?ܣD@?T@G_qTbRG@*7!сq!GGd@@zrTa@7!aak65@"H@P@b@?@@G`T!GT4@ @@?3@@7@ @"H@P@B@?|@뀊T!GT4@ @¦!@?6@7р bRQ@@7!GGqA@!T{@7 {@ 5@"H@@B@?@;@T@@ @1@T@@1@T@@7@@!G`T[4A @ @`[66@@(7X@@@@5?q@7@@7@`@"H@x@b@?ج @7B @@J @1@T@A@"H@@b@?@@7@ @1@TVT8[@ @@$@@1@T7!l@`7@@7@7`A@B@T7@@@@@e5?77@@7р@@7@ `@7` @7@B7@@7@ @1@T@7C"H@@b@?7@C@@7@77@``@1Ta1 @T` 7C7@C@` 77@ C@ 7@C@ n@7C7@C@7@7р7C 7@C@`@7`@@7@@7@B77@ @@BH@@?7@ @7  7CL7@C@``@1@T`7C7@C@ @7@C@n@7@C@@7`@7`7C7@C@ @7@7р@@7@@ 8@@7!@`?7@7`@7``B7w7@@e7@``@7`7!7@ 7@ 7@@V7@n@7C07@C@7@7@7A@B@"7@7e7@@@7@@7р`@7`B7#7@ f@C7@C@@7@b@G7!@_@T7@ 7C7@C@ 7@ C@7@C@n@7C7@C@7@@7@` 7H `@7` @7р@7C!@C@`@;@TV@@\ @1@T@1@T@@7@CB@ C@H&@7р`!@H@7@@@;@ATV@@\ @1@T@1@T@@7@@CC@CtC@:@7р7@@ 8@@C!@`?C@7@@7@@CAWC@@`C@@1@T @ 1@T 7@ @1@T 7@@H`@1`T*as1@T`@s;@@T@;C;@C@ R!NRsw`q]YRҡ R7se xRҡ.R7sw<ZI`@@@0@@ 45?S4%"`@7`!RRs. \7@7!aRa R7s~ Rҁ"R7sRA"R7swR"R7sJ@V@@@@,@@25?,60!@7%RRs7@7@`R)Rsҏ`RA'R7sl_?Rҡ*R7s].RR7sR*R7s2 @@"H@"1@? 1R,R7s\/~ 7<7@777@727@7-7@R!-R7sY|GA!@8@6A.RR7sER-R7s;77@ 7c7@. @@"H@b,@?7@,FU7C7@C@7C7@C@7C7@C@77@7C77@C@`3 @@"H@b;@?7@C@ 1\(|GA!@.RFs`RҡR7s R0R7s7@C@>Ro7C7@C@|GA!@ R!1R7s R!2R7sA3R RY R2R7s{ R3R7ssY7@C@@Rҁ$R7s]7B7@7=7@787@ Rҁ3R7sG|GA!@G$R Ra5R7s477@zGC" @c@A! 77@ R8R7s77@  Rҡ5R7s+7@b R6R7s!7R R7s Rҡ6R7s  R6R7sxGA!@Rҁ,R7s Ra7R7s|GA!@v77@;@ R;R7s@ RA9R7s7x7@GA!7@V7@R.R7s@ Rҁ9R7s@ R9R7sf7@7a7@A;R@ Rz@ R!:R7sk7P7@L7@@ R;R7sz@ R:R7spGA!@F Rҁ0R7s<@Z @ @1@T @1@T@7р@#`CpGC@ # RA{{@; f;@@@7Ѡ @7р`0;;@*7C7@C@$ Rҁ=R7s@ Rҡ;R7s H7C7@C@HCC@ R!>R7s R?R7s Ra>R7s R=R7sa @n (`7@m R?R7s RA?R7sC!@dC@CzC@vHCqC@GC" @c@A! ;9;@ RFRsuCZC@CqC@ҶCMC@IC@ RaCRsw RҡERsn RCRsG R?Rs` RIRsX RҡGRsO RaGRs& RҡARs;@  RҁORs4CGC@ R!NRs%?#G{[5cS@@cv @B T TG@&TCEc)DBAB!A؁RC@cRG@@BҁT{DSE[FcG#_x@ks t@@1@T A@b@z@1@T@@@!G|T!G`TW4A @ @63@x(7V@@@]@5`?\@@7@q{G!GBGqAB*iT@7рӂ4@"H@A@?܈aAqˊTT@7р`N@"H@$A@?@`@vTv@v@z @1@T@@1@T@`@7``/@@!GT`5@7!uR7(R@@7@`c`@7`@a@7cC@**c3`@7`] @7] @7 \`@7`Z@7р^@7р@]kHsIks $aAqkTTaA*`7@7рg4J@"A`@@@Ug@@5?)Ӭ`@7``R(RҝCEc`+$,5߁R'Rw!A @ @63@v(7W@@@Ur5`?q@7`]`@7`a@7р`@"H@P@{@?|z@G^T!GfT4@n @Bn@?v@7рb@"H@P@{@?z@@ \T!GgT4@n @bn!@?6v@@7@`ebR4x@7e @?q*$A@$A\T @7 QTx5B@@z@1@T@A@"H@@|@?|@@7@n@"H@,A}@?Z| I~  @Z@@@S @ @1@T!l@A|7@7n "T @ q7@@@T @ @5 ?, @@7рu`@7`u@@7@@uB`@@`@1@T@ "H@@@? @@7u@"H@0A@?&  @   @@t@`@@A@1@T!l@A7@7р`t"TAw7@@@@ @A5?A@@7@@`@7`~@7~B`@@BH@Š@?`@7`@ "H@4AB@? @[ l @ A`@An@A7@@7@"TA~7A@7`@7`@7рB@@@7@`S@ @ Y @[@ @`o@lA7@7  @`@7р `@7`@@7@:AZBc@@ Q @`@7``@@@끉TT@T@C @1@T`@1@T`@@7@ @ @ 3 @@@7@@7@ `@7`@끌T@?aT@T@롇T@SG?vT@`~!aTA`@1@TS@(* 6߁R'RҀ@7р *@@@skHsI* `(T %TրAW" @J@Vl+TcBks !A3 @@"H@@@?G!!@u݁R'RS@S`@V @1@T`@1@T@@7@`b@7Bb |js>UR(RqnPk@ @ @@`@1T`^[@ @ T@@@@1T$J@A`@@@1 @`55?S49`@7`R'RR2/*65R(R  @@`@1T`R'Rx@05߁R'RR'R@ @)TV@@1TZ|G!!@@7R7(R!a@7!aaKR7(R @7!a`< =qMT!@ $!6AցRa@_ҵRw(RzuRw(Rn#~ҕR(Rb!$@" @UR(RbM"R'RDR(RA O @ҠJ@*AQ+A@7@"R(R8R)R  @@"H@".@?G!!@UR)RҕR)RR)R @QSU5R)RUR)RR'RR)RQ,:A ) @@"H@&@?A'D~@R'RAZl @ҵR)R R'R|G!R!@R7)Rx|G!!@y@|G!!@@R@` @@"H@B'@?&{G!!@8ҕR7)R]R7)RPA @5R7)R85|G!!@_UR(R 9 @5 1A-uR7)RR7)Rv @IR7)R  @{A ` @@"H@@?AA@xR7)RRW)RAҕR(RRW)RI@|G!!@@R7)RһRW)R @uRW)RҵRW)R @URW)RRW)R҆ҕRW)R{AG!!@Y@R)Rl @Lks S t @R)Rm5RW)RWf @b_UR)RE T @O@ҵR)R5R)R0R)R*R)R$ 0R)R @ @ @UR)RA G!!@@UR7)R5R)R@.x5 R)RҵR)RR)RuR)RQwS@ R)R!@ s55 R)R!@r5u R)R!@@q5U R)R?#CG{S[ c @7G3u @B TTMT#cdG%"Х)B@!!pTR# c3RuG7@@BKT{GSH[IcJC#_`@k s tA@1@T 66@`@1@T`e@"R@``@7`!@@1@T``@7` @7 @ҭ`@1@T@ @41@T @ BG!R  @7  @7Ѡ@@7@@7@7@# *cT`@7`@ @7 @@ @7 `@7Ѡ@7 @ @7 @@7@7р @7рkKsL@k s #Dc@ @@ @1T +!  a@7!a"@@@VdRb;R j @@T.TUTa@+@" @]/@w7T3@cEk s [^R:R`@7`@+#+@@#C)QcmpT#@#@++#+@@#C+@@+@ +@@+@ NkKsL3#}#@ҖZR:R6[R:RҖ[R:RQ]@@ @ @1 jT OEB!!A" @+u @1. n @@G @A@7!Aa@ @@D@*@6@6RB=Rx9@A @ @ m67@` (7U@ @ 5?[9lIt@hR;R GV]R:R;V^R:R"  @ @{6<@9(7 @ @@`55 @?@S @ z7 @ y@ |HK^Ra#T`<a@3=?T!@ $!C`6aRRf@Ҷ`R";R@CG@v`R";RF@|G!!@]:G! @! @@vhR;R !PC" @3@VbRb;R`@`7` @VdRb;RE@vbRb;Rg @`<=  J @@"H@K@?EGG!!@@eR;R 8@6fR;R ' @"W@D@U @1@T@1@T@@7@7@7Bb @@hR;R |G!!@`@6@VdRb;R Td#dcB` @@"H@E@?@R@kR;R @;?<@hR;R  @ҶlR;R  o@6nR;R   u @L @ @"H@"M@? @J @@7 @ @,@pR;R czx*X6@{RR@ҖRb=R@vRb>R@Rb>Rrr@M@ҖRb=R^B; @G `G@VRb>R@Rb>RwBY?[@VRb>Rik s 1@6R=R@R=R\@@[ @1@T`@1@T`@@7@`G@G!@@ҶsR;R n@Rb>R#{G@ҙ@@@6R=RK@R=R>@6R=R5@ҖR=R-G@֖R>R @vR>R@֔R=R@@ҶR=R@vR=RҴ@{x@R>RҠ@R">R@R>R@vR=R@R">RK@:@VR">R?@:@VR">R@pR;R ?#{qT$${#_"B@BH`8`"@֟$`$$!$ $ $?q`#!#$?q@"!"$?q"!`"$$#$!$ $$#$ !$ $@ $$$$`!$$`  ?#{#qdT#*@?kkT4RT?kJT`K  @kT*?k T_kԀ{#_֤@R_kԀ?#{BG@@A9F@b @c`@@"@Ta_{#$@@!)@O{#(!(E{#`+B$!(9AQ?qT$G?#{@!*+{#_#c cHa8a##`֟$_֟$ _֟$Հ_֟$@_֟$_ A9)4?#{S @k@@ @ _qRX@DCz$Tf@?qe9#RA'T)4[!:c+?qT  @ ZT `9cB@_DqT_q TAQ $ҵ R T R_kT R_kT 76 ! R `A9@q[zT?q)T$G!*@`A9q T! @ sA9?`T qTH[BcC+@[:cB@c+_DqT@QDqT!$D! ?T?TAQ RqTG!*R@`@bA9cB@@AQ?qHTJa8`! ֟$! @8 sA9?TkT qT q`T"q!T`A @@c_TBAc@_!cc"bT@b"pA9_LqAT"@@D@ @@d! `@bA9cB@@AQ$Ґ$՘ҍ$XҊ$q$G!,@`A9q!T`A9a@"B!!a`@`A9`i$qҀc_@`T`@Zaxd! ֟$S$8P$XM$qG$qҁA$q;!@!`@ @@@cc@B!@sc9R[BcC+@BSAkD{ƨ#_eA9R$՘AQ5 RG!.@Ϳ[BcC+@R_փ4AQuRAQU RG!+@[BcC+@G*!@,@  ?#{S[b@9_PqT_qT_q`T)T_qTT_q"T`@9sqT`@8qTsb@9_PqT _qTIT_q T_qT@k1 T9@Ԛ!ˠ`_(q`TT_qT_qTG! /@SA[B{Ĩ#_TQ! Ҡ4 ԚT_Pq T_q TG!1@J@hQ\qT4҃"?!T6b@9sAQ_q! Az!TA9k@T"1T@A9!Ҡ9`@8B95A9`4@1T@[BSA{Ĩ#_1 TB"ҡA9s99 j_qTs9ec@@`@9qT1T~w 9K!TcCJR@ T1T@e(R@@ `X@ բ@9_qD@z T_4qT_q@TCQ`$qHT@9DQ$qHT@8c  DQ?$qIT1`TkT"yf_#T@9_qDHz T_qRs9 _ q T_ qTU_4qATsA9RkTB@kTA9A9?k!TA95B"sG!0@ҽ%"AQ $qTb@9sCQ`$qhT b@8! a CQ`$qIT?1T!|@qTcCkaT"4 R!ҳ9cCG!4@G!`3@N`G**! 4@BG!2@:G! 0@~cCGB R!*@)?#{S B@"H@"@?3SA{¨#_,3G!@ SA{¨#_ ?#{@BH@{#{#?#{S B@@@1@T`SA{¨#_z@B@"H@@?3G!@ѽ?#{@[v@@S@¼5?_[BSA{è#_G[BSA{è#_[B{è#Փ|G!@  ?#{S@[@1@T@v1@T@u1@T@@@U@}5?a@7!a@7!сSA[B{è#_SA[B{è#_SA[B{è#_7|G!@?#{S @1@T@t@@T@+5?ȼa@7!a@SA{è#_>@SA{è#_֣SA@{è#_|G!@@  ?#{S@!G`Tټ4a @ @65@(7s@@޻5?|@SA{è#_SA{è#Ջ\`|G!@?#{!GS@`T4a @ @`64@(7s@@5?FSA{è#_`@5C@@t@5?0@SA{è#_@|G!@SA@{è#X|G!@@?#{@Sb8@B@SA{Ĩ#`4@ @`@G?!T? T`@BGTBG T4@@a ` @?T`@xa`@1@T`SA{Ĩ#_Gb @!@=@` @!?Ta3 @`@1T` @!s@N@7!с"` @!@`@!GT!GT4@t@`@@?@@!J@7BѢb@@@!G!@4:҂@G!=B @@@ZG@4$@@O?#{@?T@#d@MTc`Tbx`?aT R{#_|G!`@ͺR @?TBG R?`T G# @A@ @kR?#{S[*Cl%*a@7!aa[BSA@{Ĩ#_*CM *@7!VS[BSA@{Ĩ#_@7`B?#{@SBH@"@?SSA{¨#_3 G@]`4G!>@?#CЄG{S*[*cs@p @ kcC @`"@@" @r@!G TGT. 5A@kGK@ @*%*kTCc\, BG A:A@@ @** *k TkGR||@kaThd`@1@T`BB(`@7`@@7р G@@BaT{CSD[EcFsHC#_G!GaTA@\`@7``Cc3 4*B? '   *w@7!@ AK4@fk* @**?kT||@ @k`T$@?kT#K |cQ@b!`B|`@9ST 1"@TkG`6kG*4A*@@$@?k! Ta|L 8 @$kTkGYe@7 @@7!A@@7!A @@7!aIA@BH@b@?"@`5@@@7МG!G@@Ը@иgŹ! Ga@!1`Ta 8|@@7!Ghah!@a7!kGk?#{@S"T@B6* T*SA{¨#_!0@!@@ ?`@!GT@*a7!с`TG!`@G!@ٸT@ @A6G!B@\`4@7р WG!@! ?#ưG{CS[*@'ҟ*ٸ1 T&@kT =`~!rA9O =#_LqT@cd@pA9Lq T#|@C @@_T"@R!G'@"@cT{ESF[G#_@ "G*!@Ǹ@"@@ ?Tʷ!@ ~"G_@@!)`+ð0! "$?#{S T@BG&@qD@zT@3R @_ATa @ @?$AAT`@@S&SkT (7`@(7@q TqT@$@3Rk!TR_T*|q*SA{è#_ДG_q3RTTq T`TbRBG!GqATT@7Ѡ @R*SA{è#_g@@y$@y*?r!rf`@9$@9@  ?#!GC{ @#@@!1@T@"@B1@T" @C@c1@TC RU߷@ @,`@7` G@@BҡT{B@#_G _  ?#{S@T@7A6`V@6W @7!ѡa@`T@@6@7!сSA@{è#_SA{è#۶GSA!{è#@@SA{è#ĶG!`@?#G{S3 [Cc#k;@  F4b@@"Ō@@T@j&25!G@"@ca T{BSC[DcEkF;@#_ @T@@ 6@"@TB@@@TA@@@j!@qTT@ɶ @T@`6!T@"@@T{#T`@@TG! @ڶq+T@@T{#aTG!@G!@@w {{_name '%U' is not defined while calling a Python objectNULL result without error in PyObject_Call_tools.pyxscipy.sparse.csgraph._tools.csgraph_to_maskedat leastat mostcsgraph_from_densescipy.sparse.csgraph._tools.csgraph_from_dense%.200s() takes %.8s %zd positional argument%.1s (%zd given)csgraph_masked_from_densescipy.sparse.csgraph._tools.csgraph_masked_from_denseMissing type objectCannot convert %.200s to %.200sOut of bounds on buffer access (axis %d)scipy.sparse.csgraph._tools._populate_graphconstruct_dist_matrixscipy.sparse.csgraph._tools._construct_dist_matrixscipy.sparse.csgraph._tools.construct_dist_matrixassignment'%.200s' object does not support slice %.10sscipy.sparse.csgraph._tools.csgraph_from_maskedcsgraph_to_densescipy.sparse.csgraph._tools.csgraph_to_densereconstruct_path'%.200s' object is unsliceablescipy.sparse.csgraph._tools.reconstruct_pathITYPE_tDTYPE_t__init__.pxdtype.pxdparameters.pxi'bool''char''signed char''unsigned char''short''unsigned short''int''unsigned int''long''unsigned long''long long''unsigned long long''complex float''float''complex double''double''complex long double''long double'a structPython objecta pointera stringendunparseable format stringInterpreter change detected - this module can only be loaded into one interpreter per process.name__loader__loader__file__origin__package__parent__path__submodule_search_locations'Buffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'Unexpected format string character: '%c'Expected a dimension of size %zu, got %zuExpected %d dimensions, got %dPython does not define a standard format string size for long double ('g')..Buffer dtype mismatch; next field is at offset %zd but %zd expectedBig-endian buffer not supported on little-endian compilerBuffer acquisition: Expected '{' after 'T'Cannot handle repeated arrays in format stringDoes not understand character buffer dtype format string ('%c')Expected a dimension of size %zu, got %dExpected a comma in format string, got '%c'Expected %d dimension(s), got %dUnexpected end of format string, expected ')'%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectco_argcountco_posonlyargcountco_kwonlyargcountco_nlocalsco_stacksizeco_flagsco_codeco_constsco_namesco_varnamesco_freevarsco_cellvarsco_linetablereplace'%.200s' object is not subscriptablecannot fit '%.200s' into an index-sized integercannot import name %Sscipy/sparse/csgraph/_tools.cpython-312-aarch64-linux-gnu.so.p/_tools.c%s (%s:%d)value too large to convert to int__int__ returned non-int (type %.200s). The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.int__%.4s__ returned non-%.4s (type %.200s)an integer is requiredBuffer has wrong number of dimensions (expected %d, got %d)buffer dtypeItem size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s)calling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionModule '_tools' has already been imported. Re-initialisation is not supported.%d.%dscipy.sparse.csgraph._toolscompiletime version %s of module '%.100s' does not match runtime version %sbuiltinscython_runtime__builtins__numpydtypeflatiterbroadcastndarraygenericnumbersignedintegerunsignedintegerinexactfloatingcomplexfloatingflexiblecharacterufuncnumpy.core._multiarray_umath_ARRAY_API_ARRAY_API is not PyCapsule object_ARRAY_API is NULL pointermodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule compiled against API version 0x%x but this version of numpy is 0x%x . Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem .FATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimenumpy.import_arrayinit scipy.sparse.csgraph._tools%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'csgraph_to_maskedcsgraph_from_masked_toolsLXHD@<840,(T  P  <99666?`E<996LLLLLLL9LLLLLLLLLLLLLLLLLLLLL 9LLLLLLzeros_validationvalidate_graphtodensetocsr__test__sumshapesearchsortedscipy.sparse.csgraph._toolsscipy.sparsereconstruct_path (line 413) reconstruct_path(csgraph, predecessors, directed=True) Construct a tree from a graph and a predecessor list. .. versionadded:: 0.11.0 Parameters ---------- csgraph : array_like or sparse matrix The N x N matrix representing the directed or undirected graph from which the predecessors are drawn. predecessors : array_like, one dimension The length-N array of indices of predecessors for the tree. The index of the parent of node i is given by predecessors[i]. directed : bool, optional If True (default), then operate on a directed graph: only move from point i to point j along paths csgraph[i, j]. If False, then operate on an undirected graph: the algorithm can progress from point i to j along csgraph[i, j] or csgraph[j, i]. Returns ------- cstree : csr matrix The N x N directed compressed-sparse representation of the tree drawn from csgraph which is encoded by the predecessor list. Examples -------- >>> import numpy as np >>> from scipy.sparse import csr_matrix >>> from scipy.sparse.csgraph import reconstruct_path >>> graph = [ ... [0, 1, 2, 0], ... [0, 0, 0, 1], ... [0, 0, 0, 3], ... [0, 0, 0, 0] ... ] >>> graph = csr_matrix(graph) >>> print(graph) (0, 1) 1 (0, 2) 2 (1, 3) 1 (2, 3) 3 >>> pred = np.array([-9999, 0, 0, 1], dtype=np.int32) >>> cstree = reconstruct_path(csgraph=graph, predecessors=pred, directed=False) >>> cstree.todense() matrix([[0., 1., 2., 0.], [0., 0., 0., 1.], [0., 0., 0., 0.], [0., 0., 0., 0.]]) reconstruct_pathrangepredecessorspindorderonesnumpy.core.umath failed to importnumpy.core.multiarray failed to importnumpynull_valuenpnnullndimnan_nullnan__name__minimummasked_valuesmasked_invalidmasked_arraymask__main__malilissparseisnanisinfint32infinity_nullinfindptrindices__import__idx_gridgraph should have two dimensionsgraph should be a square arraygraph and predecessors must have the same shapegraphgetA1formatfloat64fillemptydtypedist_matrixdirecteddense_outputdata2datacumsumcsr_outputcsr_matrixcsrcsgraph_to_masked (line 339) csgraph_to_masked(csgraph) Convert a sparse graph representation to a masked array representation .. versionadded:: 0.11.0 Parameters ---------- csgraph : csr_matrix, csc_matrix, or lil_matrix Sparse representation of a graph. Returns ------- graph : MaskedArray The masked dense representation of the sparse graph. Examples -------- >>> from scipy.sparse import csr_matrix >>> from scipy.sparse.csgraph import csgraph_to_masked >>> graph = csr_matrix( [ ... [0, 1, 2, 0], ... [0, 0, 0, 1], ... [0, 0, 0, 3], ... [0, 0, 0, 0] ... ]) >>> graph <4x4 sparse matrix of type '' with 4 stored elements in Compressed Sparse Row format> >>> csgraph_to_masked(graph) masked_array( data=[[ --, 1.0, 2.0, --], [ --, --, --, 1.0], [ --, --, --, 3.0], [ --, --, --, --]], mask=[[ True, False, False, True], [ True, True, True, False], [ True, True, True, False], [ True, True, True, True]], fill_value=1e+20) csgraph_to_maskedcsgraph_to_dense (line 221) csgraph_to_dense(csgraph, null_value=0) Convert a sparse graph representation to a dense representation .. versionadded:: 0.11.0 Parameters ---------- csgraph : csr_matrix, csc_matrix, or lil_matrix Sparse representation of a graph. null_value : float, optional The value used to indicate null edges in the dense representation. Default is 0. Returns ------- graph : ndarray The dense representation of the sparse graph. Notes ----- For normal sparse graph representations, calling csgraph_to_dense with null_value=0 produces an equivalent result to using dense format conversions in the main sparse package. When the sparse representations have repeated values, however, the results will differ. The tools in scipy.sparse will add repeating values to obtain a final value. This function will select the minimum among repeating values to obtain a final value. For example, here we'll create a two-node directed sparse graph with multiple edges from node 0 to node 1, of weights 2 and 3. This illustrates the difference in behavior: >>> from scipy.sparse import csr_matrix, csgraph >>> import numpy as np >>> data = np.array([2, 3]) >>> indices = np.array([1, 1]) >>> indptr = np.array([0, 2, 2]) >>> M = csr_matrix((data, indices, indptr), shape=(2, 2)) >>> M.toarray() array([[0, 5], [0, 0]]) >>> csgraph.csgraph_to_dense(M) array([[0., 2.], [0., 0.]]) The reason for this difference is to allow a compressed sparse graph to represent multiple edges between any two nodes. As most sparse graph algorithms are concerned with the single lowest-cost edge between any two nodes, the default scipy.sparse behavior of summming multiple weights does not make sense in this context. The other reason for using this routine is to allow for graphs with zero-weight edges. Let's look at the example of a two-node directed graph, connected by an edge of weight zero: >>> from scipy.sparse import csr_matrix, csgraph >>> data = np.array([0.0]) >>> indices = np.array([1]) >>> indptr = np.array([0, 1, 1]) >>> M = csr_matrix((data, indices, indptr), shape=(2, 2)) >>> M.toarray() array([[0, 0], [0, 0]]) >>> csgraph.csgraph_to_dense(M, np.inf) array([[inf, 0.], [inf, inf]]) In the first case, the zero-weight edge gets lost in the dense representation. In the second case, we can choose a different null value and see the true form of the graph. Examples -------- >>> from scipy.sparse import csr_matrix >>> from scipy.sparse.csgraph import csgraph_to_dense >>> graph = csr_matrix( [ ... [0, 1, 2, 0], ... [0, 0, 0, 1], ... [0, 0, 0, 3], ... [0, 0, 0, 0] ... ]) >>> graph <4x4 sparse matrix of type '' with 4 stored elements in Compressed Sparse Row format> >>> csgraph_to_dense(graph) array([[0., 1., 2., 0.], [0., 0., 0., 1.], [0., 0., 0., 3.], [0., 0., 0., 0.]]) csgraph_to_densecsgraph should be a square matrixcsgraph must be sparsecsgraph must be lil, csr, or csc formatcsgraph_masked_from_dense (line 82) csgraph_masked_from_dense(graph, null_value=0, nan_null=True, infinity_null=True, copy=True) Construct a masked array graph representation from a dense matrix. .. versionadded:: 0.11.0 Parameters ---------- graph : array_like Input graph. Shape should be (n_nodes, n_nodes). null_value : float or None (optional) Value that denotes non-edges in the graph. Default is zero. infinity_null : bool If True (default), then infinite entries (both positive and negative) are treated as null edges. nan_null : bool If True (default), then NaN entries are treated as non-edges Returns ------- csgraph : MaskedArray masked array representation of graph Examples -------- >>> from scipy.sparse.csgraph import csgraph_masked_from_dense >>> graph = [ ... [0, 1, 2, 0], ... [0, 0, 0, 1], ... [0, 0, 0, 3], ... [0, 0, 0, 0] ... ] >>> csgraph_masked_from_dense(graph) masked_array( data=[[--, 1, 2, --], [--, --, --, 1], [--, --, --, 3], [--, --, --, --]], mask=[[ True, False, False, True], [ True, True, True, False], [ True, True, True, False], [ True, True, True, True]], fill_value=0) csgraph_masked_from_densecsgraph_from_masked (line 17) csgraph_from_masked(graph) Construct a CSR-format graph from a masked array. .. versionadded:: 0.11.0 Parameters ---------- graph : MaskedArray Input graph. Shape should be (n_nodes, n_nodes). Returns ------- csgraph : csr_matrix Compressed sparse representation of graph, Examples -------- >>> import numpy as np >>> from scipy.sparse.csgraph import csgraph_from_masked >>> graph_masked = np.ma.masked_array(data =[ ... [0, 1, 2, 0], ... [0, 0, 0, 1], ... [0, 0, 0, 3], ... [0, 0, 0, 0] ... ], ... mask=[[ True, False, False, True], ... [ True, True, True, False], ... [ True, True, True, False], ... [ True, True, True, True]], ... fill_value = 0) >>> csgraph_from_masked(graph_masked) <4x4 sparse matrix of type '' with 4 stored elements in Compressed Sparse Row format> csgraph_from_maskedcsgraph_from_dense (line 171) csgraph_from_dense(graph, null_value=0, nan_null=True, infinity_null=True) Construct a CSR-format sparse graph from a dense matrix. .. versionadded:: 0.11.0 Parameters ---------- graph : array_like Input graph. Shape should be (n_nodes, n_nodes). null_value : float or None (optional) Value that denotes non-edges in the graph. Default is zero. infinity_null : bool If True (default), then infinite entries (both positive and negative) are treated as null edges. nan_null : bool If True (default), then NaN entries are treated as non-edges Returns ------- csgraph : csr_matrix Compressed sparse representation of graph, Examples -------- >>> from scipy.sparse.csgraph import csgraph_from_dense >>> graph = [ ... [0, 1, 2, 0], ... [0, 0, 0, 1], ... [0, 0, 0, 3], ... [0, 0, 0, 0] ... ] >>> csgraph_from_dense(graph) <4x4 sparse matrix of type '' with 4 stored elements in Compressed Sparse Row format> csgraph_from_densecsgraphcsccopy_if_densecopyconstruct_dist_matrix (line 502) construct_dist_matrix(graph, predecessors, directed=True, null_value=np.inf) Construct distance matrix from a predecessor matrix .. versionadded:: 0.11.0 Parameters ---------- graph : array_like or sparse The N x N matrix representation of a directed or undirected graph. If dense, then non-edges are indicated by zeros or infinities. predecessors : array_like The N x N matrix of predecessors of each node (see Notes below). directed : bool, optional If True (default), then operate on a directed graph: only move from point i to point j along paths csgraph[i, j]. If False, then operate on an undirected graph: the algorithm can progress from point i to j along csgraph[i, j] or csgraph[j, i]. null_value : bool, optional value to use for distances between unconnected nodes. Default is np.inf Returns ------- dist_matrix : ndarray The N x N matrix of distances between nodes along the path specified by the predecessor matrix. If no path exists, the distance is zero. Notes ----- The predecessor matrix is of the form returned by `shortest_path`. Row i of the predecessor matrix contains information on the shortest paths from point i: each entry predecessors[i, j] gives the index of the previous node in the path from point i to point j. If no path exists between point i and j, then predecessors[i, j] = -9999 Examples -------- >>> import numpy as np >>> from scipy.sparse import csr_matrix >>> from scipy.sparse.csgraph import construct_dist_matrix >>> graph = [ ... [0, 1, 2, 0], ... [0, 0, 0, 1], ... [0, 0, 0, 3], ... [0, 0, 0, 0] ... ] >>> graph = csr_matrix(graph) >>> print(graph) (0, 1) 1 (0, 2) 2 (1, 3) 1 (2, 3) 3 >>> pred = np.array([[-9999, 0, 0, 2], ... [1, -9999, 0, 1], ... [2, 0, -9999, 2], ... [1, 3, 3, -9999]], dtype=np.int32) >>> construct_dist_matrix(graph=graph, predecessors=pred, directed=False) array([[0., 1., 2., 5.], [1., 0., 3., 1.], [2., 3., 0., 3.], [2., 1., 3., 0.]]) construct_dist_matrixcompressedcline_in_tracebackc/build/scipy-CRQwve/scipy-1.11.4/scipy/sparse/csgraph/_tools.pyxboolastypeasarrayarrayargsortarangeValueErrorNImportErrorITYPEDTYPEC Tools and utilities for working with compressed sparse graphs @;l,XȒd0 ̙@P,0@$:LZ$ 4t@h  D4 ` 0 `` @  X | Ф T 0 Dp$zRx 0,< @>> import numpy as np >>> from scipy.sparse import csr_matrix >>> from scipy.sparse.csgraph import construct_dist_matrix >>> graph = [ ... [0, 1, 2, 0], ... [0, 0, 0, 1], ... [0, 0, 0, 3], ... [0, 0, 0, 0] ... ] >>> graph = csr_matrix(graph) >>> print(graph) (0, 1) 1 (0, 2) 2 (1, 3) 1 (2, 3) 3 >>> pred = np.array([[-9999, 0, 0, 2], ... [1, -9999, 0, 1], ... [2, 0, -9999, 2], ... [1, 3, 3, -9999]], dtype=np.int32) >>> construct_dist_matrix(graph=graph, predecessors=pred, directed=False) array([[0., 1., 2., 5.], [1., 0., 3., 1.], [2., 3., 0., 3.], [2., 1., 3., 0.]]) reconstruct_path(csgraph, predecessors, directed=True) Construct a tree from a graph and a predecessor list. .. versionadded:: 0.11.0 Parameters ---------- csgraph : array_like or sparse matrix The N x N matrix representing the directed or undirected graph from which the predecessors are drawn. predecessors : array_like, one dimension The length-N array of indices of predecessors for the tree. The index of the parent of node i is given by predecessors[i]. directed : bool, optional If True (default), then operate on a directed graph: only move from point i to point j along paths csgraph[i, j]. If False, then operate on an undirected graph: the algorithm can progress from point i to j along csgraph[i, j] or csgraph[j, i]. Returns ------- cstree : csr matrix The N x N directed compressed-sparse representation of the tree drawn from csgraph which is encoded by the predecessor list. Examples -------- >>> import numpy as np >>> from scipy.sparse import csr_matrix >>> from scipy.sparse.csgraph import reconstruct_path >>> graph = [ ... [0, 1, 2, 0], ... [0, 0, 0, 1], ... [0, 0, 0, 3], ... [0, 0, 0, 0] ... ] >>> graph = csr_matrix(graph) >>> print(graph) (0, 1) 1 (0, 2) 2 (1, 3) 1 (2, 3) 3 >>> pred = np.array([-9999, 0, 0, 1], dtype=np.int32) >>> cstree = reconstruct_path(csgraph=graph, predecessors=pred, directed=False) >>> cstree.todense() matrix([[0., 1., 2., 0.], [0., 0., 0., 1.], [0., 0., 0., 0.], [0., 0., 0., 0.]]) csgraph_to_masked(csgraph) Convert a sparse graph representation to a masked array representation .. versionadded:: 0.11.0 Parameters ---------- csgraph : csr_matrix, csc_matrix, or lil_matrix Sparse representation of a graph. Returns ------- graph : MaskedArray The masked dense representation of the sparse graph. Examples -------- >>> from scipy.sparse import csr_matrix >>> from scipy.sparse.csgraph import csgraph_to_masked >>> graph = csr_matrix( [ ... [0, 1, 2, 0], ... [0, 0, 0, 1], ... [0, 0, 0, 3], ... [0, 0, 0, 0] ... ]) >>> graph <4x4 sparse matrix of type '' with 4 stored elements in Compressed Sparse Row format> >>> csgraph_to_masked(graph) masked_array( data=[[ --, 1.0, 2.0, --], [ --, --, --, 1.0], [ --, --, --, 3.0], [ --, --, --, --]], mask=[[ True, False, False, True], [ True, True, True, False], [ True, True, True, False], [ True, True, True, True]], fill_value=1e+20) csgraph_to_dense(csgraph, null_value=0) Convert a sparse graph representation to a dense representation .. versionadded:: 0.11.0 Parameters ---------- csgraph : csr_matrix, csc_matrix, or lil_matrix Sparse representation of a graph. null_value : float, optional The value used to indicate null edges in the dense representation. Default is 0. Returns ------- graph : ndarray The dense representation of the sparse graph. Notes ----- For normal sparse graph representations, calling csgraph_to_dense with null_value=0 produces an equivalent result to using dense format conversions in the main sparse package. When the sparse representations have repeated values, however, the results will differ. The tools in scipy.sparse will add repeating values to obtain a final value. This function will select the minimum among repeating values to obtain a final value. For example, here we'll create a two-node directed sparse graph with multiple edges from node 0 to node 1, of weights 2 and 3. This illustrates the difference in behavior: >>> from scipy.sparse import csr_matrix, csgraph >>> import numpy as np >>> data = np.array([2, 3]) >>> indices = np.array([1, 1]) >>> indptr = np.array([0, 2, 2]) >>> M = csr_matrix((data, indices, indptr), shape=(2, 2)) >>> M.toarray() array([[0, 5], [0, 0]]) >>> csgraph.csgraph_to_dense(M) array([[0., 2.], [0., 0.]]) The reason for this difference is to allow a compressed sparse graph to represent multiple edges between any two nodes. As most sparse graph algorithms are concerned with the single lowest-cost edge between any two nodes, the default scipy.sparse behavior of summming multiple weights does not make sense in this context. The other reason for using this routine is to allow for graphs with zero-weight edges. Let's look at the example of a two-node directed graph, connected by an edge of weight zero: >>> from scipy.sparse import csr_matrix, csgraph >>> data = np.array([0.0]) >>> indices = np.array([1]) >>> indptr = np.array([0, 1, 1]) >>> M = csr_matrix((data, indices, indptr), shape=(2, 2)) >>> M.toarray() array([[0, 0], [0, 0]]) >>> csgraph.csgraph_to_dense(M, np.inf) array([[inf, 0.], [inf, inf]]) In the first case, the zero-weight edge gets lost in the dense representation. In the second case, we can choose a different null value and see the true form of the graph. Examples -------- >>> from scipy.sparse import csr_matrix >>> from scipy.sparse.csgraph import csgraph_to_dense >>> graph = csr_matrix( [ ... [0, 1, 2, 0], ... [0, 0, 0, 1], ... [0, 0, 0, 3], ... [0, 0, 0, 0] ... ]) >>> graph <4x4 sparse matrix of type '' with 4 stored elements in Compressed Sparse Row format> >>> csgraph_to_dense(graph) array([[0., 1., 2., 0.], [0., 0., 0., 1.], [0., 0., 0., 3.], [0., 0., 0., 0.]]) csgraph_from_dense(graph, null_value=0, nan_null=True, infinity_null=True) Construct a CSR-format sparse graph from a dense matrix. .. versionadded:: 0.11.0 Parameters ---------- graph : array_like Input graph. Shape should be (n_nodes, n_nodes). null_value : float or None (optional) Value that denotes non-edges in the graph. Default is zero. infinity_null : bool If True (default), then infinite entries (both positive and negative) are treated as null edges. nan_null : bool If True (default), then NaN entries are treated as non-edges Returns ------- csgraph : csr_matrix Compressed sparse representation of graph, Examples -------- >>> from scipy.sparse.csgraph import csgraph_from_dense >>> graph = [ ... [0, 1, 2, 0], ... [0, 0, 0, 1], ... [0, 0, 0, 3], ... [0, 0, 0, 0] ... ] >>> csgraph_from_dense(graph) <4x4 sparse matrix of type '' with 4 stored elements in Compressed Sparse Row format> csgraph_masked_from_dense(graph, null_value=0, nan_null=True, infinity_null=True, copy=True) Construct a masked array graph representation from a dense matrix. .. versionadded:: 0.11.0 Parameters ---------- graph : array_like Input graph. Shape should be (n_nodes, n_nodes). null_value : float or None (optional) Value that denotes non-edges in the graph. Default is zero. infinity_null : bool If True (default), then infinite entries (both positive and negative) are treated as null edges. nan_null : bool If True (default), then NaN entries are treated as non-edges Returns ------- csgraph : MaskedArray masked array representation of graph Examples -------- >>> from scipy.sparse.csgraph import csgraph_masked_from_dense >>> graph = [ ... [0, 1, 2, 0], ... [0, 0, 0, 1], ... [0, 0, 0, 3], ... [0, 0, 0, 0] ... ] >>> csgraph_masked_from_dense(graph) masked_array( data=[[--, 1, 2, --], [--, --, --, 1], [--, --, --, 3], [--, --, --, --]], mask=[[ True, False, False, True], [ True, True, True, False], [ True, True, True, False], [ True, True, True, True]], fill_value=0) csgraph_from_masked(graph) Construct a CSR-format graph from a masked array. .. versionadded:: 0.11.0 Parameters ---------- graph : MaskedArray Input graph. Shape should be (n_nodes, n_nodes). Returns ------- csgraph : csr_matrix Compressed sparse representation of graph, Examples -------- >>> import numpy as np >>> from scipy.sparse.csgraph import csgraph_from_masked >>> graph_masked = np.ma.masked_array(data =[ ... [0, 1, 2, 0], ... [0, 0, 0, 1], ... [0, 0, 0, 3], ... [0, 0, 0, 0] ... ], ... mask=[[ True, False, False, True], ... [ True, True, True, False], ... [ True, True, True, False], ... [ True, True, True, True]], ... fill_value = 0) >>> csgraph_from_masked(graph_masked) <4x4 sparse matrix of type '' with 4 stored elements in Compressed Sparse Row format> wI4pw/eps)xdPj`%fdXwb`0g  e4A HxFЭEȭF(H HH GGEFxGpFhH AFGF HЬ`IhI!EF`G8GHp I0@(IEI IEIRIP$PH (HHXHД"XE0I@l 8I H@IpHIPhG@0G0 F   GGHG F H؂ FЂFȂGHPGGpE`Hp0@HP8H !H I GGFE؁HЁEȁE@G XGHEI  FFx PEhFXGPI@ `E8E( E H0ExE xIhHЀ'xH"pFFHFp  HhHPPIyXIyHpy HPyG8y E0yG(ypIy xGyGyFxFx Ex/usr/lib/debug/.dwz/aarch64-linux-gnu/python3-scipy.debug~\-ׇ!1\I!be7c8411329cd7a7a3b37e084ca7857aacda2c.debug .shstrtab.note.gnu.build-id.gnu.hash.dynsym.dynstr.gnu.version.gnu.version_r.rela.dyn.rela.plt.init.text.fini.rodata.eh_frame_hdr.eh_frame.init_array.fini_array.data.rel.ro.dynamic.got.got.plt.data.bss.gnu_debugaltlink.gnu_debuglink $o$( ( 0@ @ 8oNNEo@@@T^BX2X2@h::c::nP@P@H#tcczccJ@@l   `B E E ENpE4E