` ` __text__TEXT  __data__DATA@  __cstring__TEXT __literal4__TEXTd__literal8__TEXTX(x__picsymbolstub2__TEXThz__la_sym_ptr2__DATA'G8__nl_symbol_ptr__DATA$4D~__textcoal_nt__TEXTXx @\+E4$Y<$]U|[^_]ËE,$`<$24$1̉<$=덓ǕT$$'ϐUVSUԃpM̍uЋ&T$ŽE̋:T$ EԋEۙD$E T$EEt$L$$1҅MЃƒ!Шt uȋẺt$u؉$t$耍Eԉ$UD$ EЉT$D$E؉D$E܉D$ ED$ED$ED$E$蔌u 4$,M $蔌ƒp[^]4$nj1"L$$΍ِUWVMuS\qEUȍUȉT$ D$E T$E}Et$L$$豎1҅tqMăƒ!Шtur}D$<$&EĉD$E*ML$|$$lu&D$<$裋Ɖ<$迍\[^_]<$覍1卋ǘÒL$$UXE]UtuM̉T$ D$E T$}uEL$$貍1҅tX}D$<$AE*MD$L$|$$膊u+D$<$轊Ɖ<$ٌ]u}]<$軌1UXE]UuM̉T$ D$0E ЖT$}uEL$$1҅tX}D$<$uE*MD$L$|$$躉u+D$<$Ɖ<$ ]u}])<$1USDUM;T$ӊEEL$T$ M썓TD$E T$L$$1҅tEE$E*UD$E*MUT$L$T$$шt脋ƒD[],UYT$ $蛊UVS*M0&urT$ EEED$E T$t$L$$_1҅t`E$茉ƋE$UD$Et$D$D$ D$T$$u,M $?ƒ0[^]蠊1UX]^MԋfuEԋET$ D$E T$}L$$蚊1҅t[}D$uȉ<$&E|$t$$/uKEԉ$藈t+D$<$袇Ɖ<$辉]u}],}ȉ<$aɉ<$菉1UWV}Sl ΍UUD$EMT$ M'D$E T$uEE|$L$$蜉1҅tYE}ȉ|$$2u^4$D$t"$EVD$|$$u<$軅l[^_]<$袅1即Bt$$ÇUVSU T$E T$u$謇1҅t=4$D$t$$;*M,эNT$ $u [^]<1UWVSuUMMUčUQ}MT$$UEE}ȋEM̍}MT$U|$ L$T$ MD$}E T$uEEE|$L$$輇1҅EE19PtT$@$i5E$蹃M؉ED$M $D$4$ti$ZEZuf.u|zzE$UƋE$H*]D$*MEWt$D$\$ L$$ uE$NČ[^_]lE$/1ߋE$ۄƋE$΄t$,Z}D$ *uEZm*ew|$$D$t$l$ d$4$bkEI9Pt T$}؋@$ }EMD$ D$ML$$Å1҅}D$}<$u9t$$qUWVS--rUЍUĉ}ȉM̍5T$(UE}ԉM؍}}T$Uك%sL$$|$ L$T$ MtD$}E T$uEU]EEE|$L$$1҅MMx]U$Z%sf.UMZ)sf.wZ-sf.v)}D$i$&1ҁČ[^_]É4$D$tU$u19E9EL$щT$t7E*m|$~D$l$<$u蜃w*EDžx 9pB͐D$e7Mf.Mt.z̐UWVSUUUE}EuM uMT$U}t$(ML$$T$ MD$uE T$E|$ |$t$L$$1҅EUE}ȉ$~|$EE$P;<$_~<$<~9u <$I~;Et1!D$=$<$~1ҁĬ[^_]ÍUED$ D$|T$$MD$ $tU$|E$'*]D$$*U|*MV|$ D$\$T$ L$$F}u<$~|$7:<$}|<$9t$$=L$ߐUVSPU荓MUMUEL$ T$UD$L$L$T$ M܍E T$uEEEL$$覀1҅t`E܅xk4$D$t@$19U*ML$L$19UL$ N ${u P[^]1ʋt$$~UVSPU荓UU؉EMEMT$UL$T$ MԍD$E T$uEL$$1҅tU4$D$t<$E$}U*MԍND$T$ L$ $zu P[^]~1UVS`U܍M؉U䍋UЉMT$MԍŰEL$$T$U؉L$ L$T$ Mȍ"D$E T$uEEEL$$~1҅tvEȅE̅xv4$D$tK$19U*U*MT$ L$19UVL$L$$tyu `[^]}1.D$$|ΉUVSff@U荓EUUMT$MUL$T$ M܍!D$E T$uEEEL$$p}1҅tsU܍x~E9}w_4$D$t4$*UE*M܍NT$D$ L$ $4xu @[^]|1&D$${fUWVS=koUsEMEE}EE|UȍU}Mč}MT$D$UE|$$L$ T$ MD$}E T$uEE|$L$$|1҅E}؉$)zEE$zEE$ zD$ EED$|$${{1҅u\4$D$t;$ED$E*MD$VED$L$|$$wvu<$zČ[^_]É<$zz1UWVS~UEEM~Ux|}EuM}u|T$ U|$,t$(L$$~pT$ D$E tT$Et$|$t$L$$_z1҅pSt}ȉ$v|$hx$v<$u<$u9u<$u;ht1ɆD$~$x<$Jv1ҁ[^_]ÍMtD$ D$`L$$bynED$$l|Dždt5;~t-*pUDžd,ɉ$L$vl $E$FwƋE$9wdt$$D$ 1E*hE`*pD$lt$|$\$ T$$s%du!<$u`4$wlUD$\$u|;~tu&t\$w돋F4$Pt$D$lA\@$rutG<$^t`$Ww\<$IwlB$PF4$P뮉<$ t`$w\$'w<$s`<$vdu4$vp<$s`$vvP))i~|$$u&Ʌ~t$ߐUWVS]]z?dtz\`hElpLd`T$ hxt$(L$$zTT$ D$ЄE XT$Dž|t$|$t$L$$ v1҅TX}ȉ$q|$L\$xrS<$q<$dq9u<$qq;Lt1}D$z$=t<$q1ҁ[^_]ÍuXHt$$qED$$P`1tY;ztQ*TU,ɉD$L$rR*TU,ʉ$L$qrP$;d$r1ɉD$$EEȍE*LL$ 1ɅEȋP*TL$H|$L$d$ \$$oQu!<$pH$pPEM@D$< $n`;ztu7t!<$Ms@4$?syF4$Pԉt$D$PA\@$|nutU<$oH$o<$r@$rPB$PF4$P렉<$oH4$o<$r@<$ruU$ar4$Yr<$JoH$A<$4oH$&oD$rLr<$oH<$nލ݄zL$$q}zT$ߐUWVSz|_}MUUiU||zU̍U v}ĉMȍ|MT$$UEE}ЋEL$(}MT$U|$ L$T$ MpD$}E T$L$uEEE|$$hq1҅E_Eu9PtT$@$qE$mmM؉ED$M $nD$4$tu$sE$oƋE$oZmD$(*eE*]ZU*MWt$4l$,D$$d$\$T$ L$$#kuE$oĬ[^_]pE$o1ߋEv9Pt T$}؋@$p}EMD$ D$ML$$o1҅냍|ut$$nUWVSU]xlUxU̍}MȉUԍ}UsMЉT$ MčUEEL$(EsT$UȋsL$$L$L$T$ M~D$E T$u}EL$$6o1҅tfExr4$D$tF$19}*M19}T$19}~L$L$<$T$ Xiu l[^_]pn1퍳uzst$$XmUVSw@|U荓xEUUMT$MUL$T$ M܍|D$E T$uEEL$$'n1҅t^E܅}w_4$D$t4$yE*U*M܍ND$T$ L$ $8hu @[^]jm1|D$Rr$Rl6yUWVUSl"|&|ET$U}MT$ Rq)|D$}E T$uEEEL$|$L$$m1҅E}D$ D$|$$l1҅EMD$ D$ML$$lu|4$D$t*$END$ D$|$ $fu<$k}<$kl[^_]k<$kE$k1ډ<$UD$U$idUVSt@zU荓tEUUMT$MUL$T$ M܍uD$E T$uEEL$$k1҅t`E܃wg4$D$t<$E$i*U*M܍ND$T$ L$ $|eu @[^]j1zot$$iUVSxpyU؍xM܉UUЍuT$UȋEMT$M̍U؉L$D$MĉT$ E yuET$EL$$j1҅t\4$D$tC$e*]*U*MčNd$\$T$ L$ $Odu p[^]i1US4(tMEUUT$ D$yE T$EEL$$i1҅t:UJwHEMT$L$$cu"Etmƒ4[]Ëm#i1獓ymT$$ hUS2sD5sUUET$UMT$ MnxD$E T$EL$$h1҅tCEM*U*ML$T$ L$$b1҅uE܅tmƒD[]ËmUVSM䍓"q@rUUET$UuL$bljlT$ D$wE T$EuuL$L$$+h1҅tB19uE19u‰L$ M؉T$$L$auM $aƒ@[^]g1UWVMuSG|(qOpkU؍U؉EkT$ EEqD$E T$}EEt$L$$_g1҅tMMEut$ML$$d}D$<$dtE$dfƒ|[^_]ËE$5e|$D$ED$ED$ ED$ED$ED$E$e`uFEu,D$<$dƉ<$fE$d{E $6`ыE$cf1QUWVSuo!jnUЉEUjuȉM̍uu|MET$Uȉ}ԉu1T9TL$ T$ |uD$}E uT$EEUMDžx|$t$L$$e1҅tYx}|T$t|$$$c}D$<$bt"t$bd[^_]ËE$UcmD$EeD$E*]D$ El$0D$Ed$(D$E\$ D$E|$$K^uLxu/D$<$aƉ<$dt$bQE04$/^΋t$ad1$UWVUMSmEET$ ]nD$E T$uEEL$$c1҅U}ET$}|$$au}ȃ4$bD$]<$M,ɉL$ ]ugE|$4$D$ED$ ED$ED$ED$\u4D$<$\Ɖ<$_E$`Ĝ[^_]ËE$`b1ߐUWVMuS|pl(mfU؍U؉EET$ D$TrE T$}EEEt$L$$b1҅t\EuE}|$ut$$9`}D$<$`tE$_aƒ|[^_]ËE|$D$ED$ED$ ED$ED$ED$E$+[uCEu)D$<$C_Ɖ<$_aE$`_놋E $|[ԋE$>__a1\orfL$$D`ݐUWVUMS|kEET$ tpD$E T$uEL$$5a1҅Eu4$-`]M,|$}؉<$^MȉM $D$ ^EUD$ET$ D$|$4$Yu1D$<$^Ɖ<$!`E$`|[^_]7`<$_E$_1ڍ{p7e|$$ _UWVU؍MSoiEu܉T$ d>jD$E T$}EuEt$L$$_1҅}D$<$o] UZMEL$D$ D$UT$4$XMEut$ML$$-]E|$D$ED$ED$ ED$ED$ED$E$Xu?D$<$q\ƋE$\<$^E$w^Č[^_]^E$d\<$P^E$E^1n^<$b^鐐Uxuu}V}T$<$MWuAFD$$Ɖ$VD$X<$T$Vu}]]1UWV}SlmKhEmUU؉u؉M܉T$ MmD$uE T$EEEE|$t$L$$]1҅EUD$U$ VEĉD$ED$ ED$ED$E$UM $UE$tUEEE9E|}<$UUl[^_]ËMEȋuM>T4UWVMuSC$^^EU؍U؉T$ D$dE T$}EEEEt$L$$\T1҅tzEuE}|$ut$$Qu}4$1SD$]<$M,щT$gMtE$rQSĜ[^_]ËE|$4$D$ED$ED$ ED$ED$ED$Ju)D$<$LƉ<$OE$P됉<$OE$P S1qdXL$$QݐUWVplS衸bDžtVT$ bD$ET$D$E upDžlL$$R1҅ltE$NV*ȉ\$`Q݅`l}N|$ $BIuZFD$$tPD$X<$T$Jluļ[^_]Íx $~QQlu1Ѝx<$[Q썽xD$ D$|$$Q1҅.u|$\}|$$fH8!cVt$$0P뉐UX]u2UZaU܉EЉEԍU؋EM؉T$ MЍe[D$uԋE }T$}Et$L$$Q1҅t4Eԉ$0OƋEЉ$#Ot$wD$4$Gu]u}]jP1UWVMuS'lQ`ZEU؍U؉T$ D$S`E T$}EEt$L$$QP1҅tREăEx u*ȃM4$=OEr&aD$cT$N1҃l[^_]*U}D$U<$zMEE|$D$\$4$XFuD$<$MƉ<$OFO<$ O댍gamUVSW@YUUET$UMT$ M]`D$E T$uEEEL$$O1҅t.Eu9*MND$ L$ $pEu@[^]N1a`6St$$oMUWVPLS!XDžTET$ WD$E T$uPL$$VN1҅L}D$ D$|$$M1҅X|$$LXxVD$\HD$ `L$D$d$D$hD$)DuKFD$$tHHD$XL$$-F<$Le[^_]M<$L1吐USײ$UET$$CuM[^L$$L$[]L1񐐐UWVUMS|`VEET$ WD$E T$uEEL$$L1҅tMU}ET$}|$$QJ}D$<$JtE$ILƒ|[^_]ËE|$D$ED$ ED$EĉD$EȉD$E$\BuCEu)D$<$bIƋE$I<$sK덋E $EKE$XI<$DK1[jK򐐐UXE]U$uM̉T$ D$ZE UT$}uEEL$$[K1҅t}D$<$HtJ‹]u}]ËẺ|$D$E$2AuD$<$XHƉ<$tJJ<$cJ1US[D[M싃NUUEET$ D$TE T$EL$$J1҅tCE$HMD$EL$$m@u#[M$L$3IƒD[]I1US误DSM싃NUUEET$ D$ETE T$EL$$I1҅tCE$HMD$EL$$?u#3[M$L$HƒD[]@I1U8}}؉uD$D$<$BuDE|$$?u&D$<$vBƉ<$Eu}]É<$EH1U8}}؉uD$D$<$ZBuDE|$$>u&D$<$AƉ<$-Eu}]É<$EMH1U8}}؉uD$D$<$AuDE|$$=u&D$<$AƉ<$Du}]É<$DG1U8}}؉uD$D$<$jAuDE|$$l=u&D$<$AƉ<$=Du}]É<$)D]G1UWVS!XXuȍXXu؋uX}̉MЉUEE4$iFUݝpEZ-5p}Mf(T$UYYZ155|$ L$T$ MXD$}E T$m]UM|$L$$F1҅}D$D$<$@ux]UM}*u\$(T$ L$|$t$|$4$;u)D$<$?Ɖ<$Bļ[^_]É<$BE1UWVS葫$W,Wuȍ4Wux]UM}*u\$(T$ L$|$t$|$4$:u)D$<$=Ɖ<$ Aļ[^_]É<$ A=D1UWVSUUuȍUUu؋uU}̉MЉUEE4$ICUݝpE}pMT$f(ڍUZ2Yډ|$ L$]MYډT$ D$UE }T$UM]|$L$$C1҅}D$D$<$<]}um*eD$0\$(|$ t$l$d$|$4$x8u)D$<$e<Ɖ<$?[^_]É<$}?B1UWVSuT5Tuȍ:T Tu؋uET}̉MЉUEE4$AUݝpE}pMT$f(ڍUZ0Yډ|$ L$]MYډT$ D$.TE }T$UM]|$L$$"B1҅}D$D$<$r;]}um*eD$0\$(|$ t$l$d$|$4$6u)D$<$:Ɖ<$>[^_]É<$=%A1UWVSR|Ru̍RRu܋uMȍRRUЉ}ԉMEDž|4$%@UEݝpT$$Up}Mf(ډT$UYZ /Z/]|$ YډL$T$ |RD$}E T$Ue]M|$L$$s@1҅}D$D$<$9U]M}u*|D$8T$0\$(L$ |$t$l$|$4$4u)D$<$9Ɖ<$K<[^_]É<$4<h?1␐UWVS)PPuЍQPuuPUȉ}̍PQMԉU؉}EDžtE4$U>UMݝ`T$$U`EL$(f(ڍ}MYډT$U]ZU-YZY-|$ L$T$ tQD$xE T$xe]M|$L$$>1҅}D$D$<$7*eU]M}x*td$8T$0\$(L$ |$t$l$|$4$3u)D$<$97Ɖ<$h:[^_]É<$Q:=1UhE]U@uMT$ D$LE 9OT$}uEEL$$w=1҅tX}D$D$<$6uPE|$D$E$2u+D$<$h6Ɖ<$9]u}]É<$~9<1UWVUMSolYFEET$ mND$E T$uEL$$<1҅}}<$;<$]U,;U]t$UM$,ɉL$5uZE<$D$ED$0u,ED$$c5NjE$8l[^_];E$s81;N@|$$:UWVUMSGlME?T$ EEET$D$E uL$$;1҅}<$:<$]U,r:U]t$UM$,ɉL$4u{E$`9<$D$ED$/uEE$>9t,D$E$,4NjE$X7l[^_]D$v:E$271b:UWV}0u4$9<$]M,щT$8D$|$4$.u<$.<$90^_]9<$91萐US诟48M=T$ EDEET$D$E L$$91҅t4E$8Eщ$T$$.u=ƒ4[]\91US478Mg=T$ CEET$D$E L$$^91҅t4E$7Eщ$T${-u_=ƒ4[]81UWVdS臞AK<lhdEZ&hT$ `KD$E T$uDž`PXDžp|$L$$r81҅?K`|$$,RUD$H$5*d$U6XD$L$ HxPD$T$|$D $+HD$$5L4$D$t%$VDD$XT$$"0<$Q+H$6L,X,Pt$KD$L$ |$4$6[^_]ËH$6<$*166+$*D$<$5ɐUWVtSCK?H:|xtExT$ pHD$E T$uDžpE|$L$$I61҅Hp|$$V*t$M4UZ{$D$|$L$l$)uS4$D$t%$MNlD$XL$$U.<$)Ĭ[^_]A5<$h)1)$n)D$o:$4אUWVS՚^GkG9hd\`dDE=T$hlptT$ XqGD$`E \T$DžXDžx|$t$L$$41҅dGXL$$(Td$2Nj`$2Ƌ\$}2|$D$ }Tt$D$D$<$'uVED$$t$xVD$XT$<$,T$'[^_]l3T$'1'$'D$9$=2UWVtSBE?7EUptxEx|T$ lED$pE T$DžlE|$t$L$$21҅El|$$&t$0Ƌp$0Ut$ D$|$h$&uVMD$ $t%$VhD$XT$$*<$&Ĭ[^_]1<$%1&&$&D$#7$0UWVU|SdCDž|ET$ DD$E T$u}EL$$11҅t}C|T$$%t}MD$x $$uS4$D$t%$VxD$XT$$)<$$Ĝ[^_]0<$$1%$$D$5$/אU]CEc/MD$T$ E BuT$}uEL$$~01҅ttBE|$$$tyUD$U$#uR4$D$t"$谕NED$XL$$(<$#]u}]/<$#1#$#D$o4$|.אUWVtS3AAE|xxT$ pAD$E T$uDžpDžtE|$L$$;/1҅Ap|$$H#Ut|$D$l$%"uS4$D$t%$UNlD$XL$$]'<$"Ĭ[^_]I.<$p"1"$v"D$_3$ -אUWVSݓ|f@@2UUE@E@T$U}uMMĉT$ M@D$}E uT$EEE|$t$L$$-1҅@EL$$!EZ}؉D$EL$D$ |$$n&29EE|$t$D$ ED$E$> uH<$,4$ !EtEɉt2ƒ|[^_]ËEp$V,<$e,4$ EtE00t1뺋Ex$WEE$E:4$u A, $x D$ 2$"+렐UVSUߑ0M썳h>ET$ >D$E T$EuEL$$,1҅tD>ET$$* tCD$E$u4$0ƒ0[^]s+4$1$D$ 0$J*אUW1VUԍMSuЃ\EEA/T$)*EԉEЋET$ D$=E T$Et$L$$ +1҅=ẺL$$0 EԋM/9PtT$@$*t(Eԍ}D$ D$|$$*1҅uqE;A/t$lEȃEċEĉ|$t$D$ E$-uIt<$)EȅtUȋt4$,A/ƒ\[^_]ËB$P؅t<$)}ȅtUȋt)4$1빋B$P$D$-/$(̐UVSB;@ <%<UUEuM䍳;E9T$UuMT$ M܍,<D$uE T$EEt$L$$D)1҅tR<E܉T$$XtQEt$D$ ED$E$u4$-ƒ@[^](4$1$D$n-$j'אUVS* ;P;UЍ-8r,M̍::UUЉEԉu؋EM܍;MԉT$U؉uL$T$ Mȍ";D$űE T$EEt$L$$(1҅ta:Eȉt$$'t`Eԉ$%&t$D$EЉD$ ẺD$E$Iu4$j,ƒP[^]S'4$z1$D$V,$*&אUVSU0M썳p9ET$ 9D$E T$EuEL$$'1҅tD9ET$$2tCD$E$Xu4$'+ƒ0[^]{&4$1$D$+$R%אUWVUЍMS \/EW*T$ 16D$ET$D$E }EuEL$$1&1҅tpE̅$!E̅tyuD$ D$t$$%u:UȍOt$T$ $ uEȉ$#ƒ\[^_]a%1퍃 9D$G*$I$MȍwL$4$묍K9΍W$$]M,8D$E T$E}Et$L$$$1҅tyu}܃4$$Z MEzt"4$#ZUEuIzGM؍W4$L$T$u$E؅t)ƒL[^_]Ë)$1M؍W4$L$T$T뷐UE@\$"US诉E D$E@\$t []t1퍓%7'T$$"UVuUE tDt$D$B\$1҃t^]ËtF4$PD$B\$ƐUE@\$UE@\@$ÐUE@\@$ÐUVuFdt Fd^]É4$bFd琐UVuFht Fh^]É4$Fh琐U]}0} u<$1xD$<$t(t"]u}]ËF4$PE<$D$dt t <$븋F4$P|$4$tjU|$t$u<$:MAD$$t%$~8tD$X|$$C)F4$P닉<$t1F4$P7Gp*9tT$$(!uT&EWT$D$Et$- 덐U]}p} u<$CxD$<$Nt(t[ ]u}]ËF4$PE<$D$t t <$븋F4$P|$4$[t~U|$t$uQ<$zMAD$$t%$~xtD$X|$$)<$)w1F4$Ptt <$֋F4$PeG(9tT$$Vu$EWT$D$Et$gU]}蠄} u<$KCxD$<$~t(t]u}]ËF4$PE<$D$t t <$븋F4$P|$4$t~U|$t$uQ<$MAD$$t%$~訃tD$X|$$)<$Y1F4$Ptt <$,֋F4$PG&9tT$$u"EWT$D$Et$ gU]u}͂u }F %9t&T$$ u ]u}]ÍWNT$UL$U$7 u@GD$$t"$~OED$X|$$]\1느U]U_ T$&EE uT$}u$51҅t`E}$ND$L$<$h uJFD$$t$蜁VD$XT$<$]u}]1萐UWV}puD$<$VT$ uBFD$$t$VD$XT$<$+p^_]%1U]u}݀u }F#9t&T$$u]u}]ÍWNT$UL$U$ u@GD$$t"$~_ED$X|$$ml1느UWVMuS'-$EU؍U؉T$ D$-E T$}EEt$L$$N1҅tCEx u*ȃM4$IEv)-cL$$1ҁĜ[^_]É4$}]}<$U,҉T$zv4$}]}<$],ӉT$L)4$}]}<$e,ԉT$E*m4$D$El$D$ED$ED$ ; ED$$D$NjE$lD$ƋE$W…!ШtHtDt$-L$ 4$|$ƋE$@E$5E$*1L-WL$$3-WL$$-WL$$kUS}B+DL"U荓t+UUMEMET$UL$T$ M܍}+D$E T$EEL$$1҅t*E$D$ ED$E܉D$E$sƒD[]ÐUWVUԍMS|uЃlEE;T$3EԋET$ D$h!E T$}t$L$$!1҅t{*]*UZCEԍu]UL$D$ |$t$$u0EUO\$Et$\$T$ $}t4$*Wƒl[^_]É4$( e<$d$bѐUWVUԍMS{}ЁEE'T$/EԋET$ D$)E T$u|$L$$ 1҅#Z3Eԍ}L$D$ t$|$$p*UZ7*Mf.uzf.pf.xf.B<x4$Z;ݝpEEpx,f(*f.uv#MMf.z\uZ_}X f.wf.evEE<$k'uM4$L$Ĭ[^_]ËEL$T$Mx|$D$4$<Mxu/]ff.wEf.KEAE<$ sUN|$L$T$T$ $(.M4$.EݝpEMp,Z;*f(f.].UUf.zu_mXf.wEL$T$Mx|$D$4$Mx.]ff.wEf.vEE.UN|$T$ $UWVMЍuSx\EET$ g&D$E T$Et$L$$1҅t_}D$}ĉ<$< E*U*MЉD$ET$L$$tuĉ4$ ƒ\[^_]ËEĉ$ Eȉ$o 1҅E.t1;u}-E؋<<$t)M̋A <$;u|ӋEĉ$X U;u|+Mĉ $C M̋t1mA $PE؋$3;u|뽍"T$$J뺐UWVUSwi>@? e`?Bf@BDh EElEGn@GGnHIpJLqLO sOEPs`PQ$uQRwRS,w@SSHwSS}TT TT} U_UwUVL{ VV|yVHW`WX Y[d[G]`]^_+`<@``t ac c@e$`egLhhTimm[p$p@u,uz(z~ؐ~I`0vHvv~L{|yfpH0Edl4nedgesdirected|iO!O!Directed graph (|V| = %ld, |E| = %ld)Undirected graph (|V| = %ld, |E| = %ld)lNumber of vertices to be added can't be negative.Oby_indexO|Overticestypeloops|OiOdtype should be either ALL or IN or OUTvertexl|itype should be either ALL or IN or OUTv1v2ii|Oiunconn|OOreturn_shortest_circle|OmodematrixO!|iError while converting adjacency matrixmoutprefpowerzero_appeall|OOOffNumber of vertices must be positive.pl|dlO!O!p must be between 0 and 1.m must be between 0 and n^2.Only one must be given from m and p.Either m or p must be given.pref_matrixktype_distllO!O!|OPreference matrix must have exactly the same rows and columns as the number of typesError while converting type distribution vectorError while converting preference matrixNumber of vertices and the amount of connection trials per step must be positive.l|O!O!radiustorusld|Ocitationll|O!O!Number of new edges per iteration must be positive.centerl|llMode should be either STAR_IN, STAR_OUT or STAR_UNDIRECTED.Central vertex ID should be between 0 and n-1neidimcircularmutualO!|lOOOattributelO!O!|OOOtype_dist_matrixlO!O!|OOError while converting type distribution matrixwindowlOl|OOffl|O!O!O!childrenll|lMode should be either TREE_IN, TREE_OUT or TREE_UNDIRECTED.outinO!|O!classOnly graphs with 3 or 4 vertices are supportedsizellld|lmode must be either STRONG or WEAKll|O!O!niterepsdamping|OOldd|Olmode must be one of IN, OUT or ALLweightsminelementsmaxcompno|lll|O!vl|lmode must be either IN or OUT or ALLWeight list must have at least |V| elements (|V| = node count in the graph)multiplevertex ID must be non-negative and less than the number of edgesmode must be either IN, OUT or ALL|llmode must be REWIRING_SIMPLEdignore_loopsmaxiterinitempkkconstcoolexpsigma|lddddarearepulseradmaxdeltacellsize|ldddddproot|ldddddlroot|itype must be either GET_ADJACENCY_LOWER or GET_ADJACENCY_UPPER or GET_ADJACENCY_BOTHnormalizedcollapsefs|OrNiiNnamess|OOOs|OOsindexs|icapacitysourcetargetsii|Owcreatoridsnameweights|zzisolatess|zzOError while converting PyList to igraph_vector_tGraph or subgraph must have 3 or 4 vertices.otherO!Attribute does not existvidi|iinvalid vertex id(OOO)not enough memoryadvancedO|iO|iiOminmax|iireturn_removed_edgesreturn_ebsreturn_mergesreturn_bridges|OOOOO|iOOOreturn_qdestructorThe destructor must be callable!vsThe sequence of vertices in the graph.esThe sequence of edges in the graph.vcountvcount() Counts the number of vertices. @return: the number of vertices in the graph. @rtype: integerecountecount() Counts the number of edges. @return: the number of edges in the graph. @rtype: integeris_directedis_directed() Checks whether the graph is directed.@return: C{True} if it is directed, C{False} otherwise. @rtype: booleanadd_verticesadd_vertices(n) Adds vertices to the graph. @param n: the number of vertices to be added @return: the same graph object delete_verticesdelete_vertices(vs) Deletes vertices and all its edges from the graph. @param vs: a single vertex ID or the list of vertex IDs to be deleted. @return: the same graph object add_edgesadd_edges(es) Adds edges to the graph. @param es: the list of edges to be added. Every edge is represented with a tuple, containing the vertex IDs of the two endpoints. Vertices are enumerated from zero. It is allowed to provide a single pair instead of a list consisting of only one pair. @return: the same graph object delete_edgesdelete_edges(es, by_index=False) Removes edges from the graph. All vertices will be kept, even if they lose all their edges. Nonexistent edges will be silently ignored. @param es: the list of edges to be removed. @param by_index: determines how edges are identified. If C{by_index} is C{False}, every edge is represented with a tuple, containing the vertex IDs of the two endpoints. Vertices are enumerated from zero. It is allowed to provide a single pair instead of a list consisting of only one pair. If C{by_index} is C{True}, edges are identified by their IDs starting from zero. @return: the same graph object degreedegree(vertices, type=ALL, loops=False) Returns some vertex degrees from the graph. This method accepts a single vertex ID or a list of vertex IDs as a parameter, and returns the degree of the given vertices (in the form of a single integer or a list, depending on the input parameter). @param vertices: a single vertex ID or a list of vertex IDs @param type: the type of degree to be returned (L{OUT} for out-degrees, L{IN} IN for in-degrees or L{ALL} for the sum of them). @param loops: whether self-loops should be counted. neighborsneighbors(vertex, type=ALL) Returns adjacent vertices to a given vertex. @param vertex: a vertex ID @param type: whether to return only predecessors (L{OUT}), successors (L{OUT}) or both (L{ALL}). Ignored for undirected graphs.successorssuccessors(vertex) Returns the successors of a given vertex. Equivalent to calling the L{Graph.neighbors} method with type=L{OUT}.predecessorspredecessors(vertex) Returns the predecessors of a given vertex. Equivalent to calling the L{Graph.neighbors} method with type=L{IN}.get_eidget_eid(v1, v2) Returns the edge ID of an arbitrary edge between vertices v1 and v2 @param v1: the first vertex ID @param v2: the second vertex ID @return: the edge ID of an arbitrary edge between vertices v1 and v2 AdjacencyAdjacency(matrix, mode=ADJ_DIRECTED) Generates a graph from its adjacency matrix. @param matrix: the adjacency matrix @param mode: the mode to be used. Possible values are: - C{ADJ_DIRECTED} - the graph will be directed and a matrix element gives the number of edges between two vertex. - C{ADJ_UNDIRECTED} - alias to C{ADJ_MAX} for convenience. - C{ADJ_MAX} - undirected graph will be created and the number of edges between vertex M{i} and M{j} is M{max(A(i,j), A(j,i))} - C{ADJ_MIN} - like C{ADJ_MAX}, but with M{min(A(i,j), A(j,i))} - C{ADJ_PLUS} - like C{ADJ_MAX}, but with M{A(i,j) + A(j,i)} - C{ADJ_UPPER} - undirected graph with the upper right triangle of the matrix (including the diagonal) - C{ADJ_LOWER} - undirected graph with the lower left triangle of the matrix (including the diagonal) Asymmetric_PreferenceAsymmetric_Preference(n, type_dist_matrix, pref_matrix, attribute=None, loops=False) Generates a graph based on asymmetric vertex types and connection probabilities. This is the asymmetric variant of L{Graph.Preference}. A given number of vertices are generated. Every vertex is assigned to an "incoming" and an "outgoing" vertex type according to the given joint type probabilities. Finally, every vertex pair is evaluated and a directed edge is created between them with a probability depending on the "outgoing" type of the source vertex and the "incoming" type of the target vertex. @param n: the number of vertices in the graph @param type_dist_matrix: matrix giving the joint distribution of vertex types @param pref_matrix: matrix giving the connection probabilities for different vertex types. @param attribute: the vertex attribute name used to store the vertex types. If C{None}, vertex types are not stored. @param loops: whether loop edges are allowed. AtlasAtlas(idx) Generates a graph from the Graph Atlas. @param idx: The index of the graph to be generated. Indices start from zero, graphs are listed: 1. in increasing order of number of nodes; 2. for a fixed number of nodes, in increasing order of the number of edges; 3. for fixed numbers of nodes and edges, in increasing order of the degree sequence, for example 111223 < 112222; 4. for fixed degree sequence, in increasing number of automorphisms. @newfield ref: Reference @ref: I{An Atlas of Graphs} by Ronald C. Read and Robin J. Wilson, Oxford University Press, 1998.BarabasiBarabasi(n, m, outpref=False, directed=False, power=1) Generates a graph based on the Barabasi-Albert model. @param n: the number of vertices @param m: either the number of outgoing edges generated for each vertex or a list containing the number of outgoing edges for each vertex explicitly. @param outpref: C{True} if the out-degree of a given vertex should also increase its citation probability (as well as its in-degree), but it defaults to C{False}. @param directed: C{True} if the generated graph should be directed (default: C{False}). @param power: the power constant of the nonlinear model. It can be omitted, and in this case the usual linear model will be used. @newfield ref: Reference @ref: Barabasi, A-L and Albert, R. 1999. Emergence of scaling in random networks. Science, 286 509-512.EstablishmentEstablishment(n, k, type_dist, pref_matrix, directed=False) Generates a graph based on a simple growing model with vertex types. A single vertex is added at each time step. This new vertex tries to connect to k vertices in the graph. The probability that such a connection is realized depends on the types of the vertices involved. @param n: the number of vertices in the graph @param k: the number of connections tried in each step @param type_dist: list giving the distribution of vertex types @param pref_matrix: matrix (list of lists) giving the connection probabilities for different vertex types @param directed: whether to generate a directed graph. Erdos_RenyiErdos_Renyi(n, p, m, directed=False, loops=False) Generates a graph based on the Erdos-Renyi model. @param n: the number of vertices. @param p: the probability of edges. If given, C{m} must be missing. @param m: the number of edges. If given, C{p} must be missing. @param directed: whether to generate a directed graph. @param loops: whether self-loops are allowed. FullFull(n, directed=False, loops=False) Generates a full graph (directed or undirected, with or without loops). @param n: the number of vertices. @param directed: whether to generate a directed graph. @param loops: whether self-loops are allowed. GRGGRG(n, radius, torus=False) Generates a growing random geometric graph. The algorithm drops the vertices randomly on the 2D unit square and connects them if they are closer to each other than the given radius. @param n: The number of vertices in the graph @param radius: The given radius @param torus: This should be C{True} if we want to use a torus instead of a square.Growing_RandomGrowing_Random(n, m, directed=False, citation=False) Generates a growing random graph. @param n: The number of vertices in the graph @param m: The number of edges to add in each step (after adding a new vertex) @param directed: whether the graph should be directed. @param citation: whether the new edges should originate from the most recently added vertex. PreferencePreference(n, type_dist, pref_matrix, attribute=None, directed=False, loops=False) Generates a graph based on vertex types and connection probabilities. This is practically the nongrowing variant of L{Graph.Establishment}. A given number of vertices are generated. Every vertex is assigned to a vertex type according to the given type probabilities. Finally, every vertex pair is evaluated and an edge is created between them with a probability depending on the types of the vertices involved. @param n: the number of vertices in the graph @param type_dist: list giving the distribution of vertex types @param pref_matrix: matrix giving the connection probabilities for different vertex types. @param attribute: the vertex attribute name used to store the vertex types. If C{None}, vertex types are not stored. @param directed: whether to generate a directed graph. @param loops: whether loop edges are allowed. Recent_DegreeRecent_Degree(n, m, window, outpref=False, directed=False, power=1) Generates a graph based on a stochastic model where the probability of an edge gaining a new node is proportional to the edges gained in a given time window. @param n: the number of vertices @param m: either the number of outgoing edges generated for each vertex or a list containing the number of outgoing edges for each vertex explicitly. @param window: size of the window in time steps @param outpref: C{True} if the out-degree of a given vertex should also increase its citation probability (as well as its in-degree), but it defaults to C{False}. @param directed: C{True} if the generated graph should be directed (default: C{False}). @param power: the power constant of the nonlinear model. It can be omitted, and in this case the usual linear model will be used. StarStar(n, mode=STAR_UNDIRECTED, center=0) Generates a star graph. @param n: the number of vertices in the graph @param mode: Gives the type of the star graph to create. Should be one of the constants C{STAR_OUT}, C{STAR_IN} and C{STAR_UNDIRECTED}. @param center: Vertex ID for the central vertex in the star. LatticeLattice(dim, nei=1, directed=False, mutual=True, circular=True) Generates a regular lattice. @param dim: list with the dimensions of the lattice @param nei: value giving the distance (number of steps) within which two vertices will be connected. Not implemented yet. @param directed: whether to create a directed graph. @param mutual: whether to create all connections as mutual in case of a directed graph. @param circular: whether the generated lattice is periodic. RingRing(n, directed=False, mutual=False, circular=True) Generates a ring graph. @param n: the number of vertices in the ring @param directed: whether to create a directed ring. @param mutual: whether to create mutual edges in a directed ring. @param circular: whether to create a closed ring. TreeTree(n, children, type=TREE_UNDIRECTED) Generates a tree in which almost all vertices have the same number of children. @param n: the number of vertices in the graph @param children: the number of children of a vertex in the graph @param type: determines whether the tree should be directed, and if this is the case, also its orientation. Must be one of C{TREE_IN}, C{TREE_OUT} and C{TREE_UNDIRECTED}. Degree_SequenceDegree_Sequence(out, in=None) Generates a graph with a given degree sequence. @param out: the out-degree sequence for a directed graph. If the in-degree sequence is omitted, the generated graph will be undirected, so this will be the in-degree sequence as well @param in: the in-degree sequence for a directed graph. If omitted, the generated graph will be undirected. IsoclassIsoclass(n, class, directed=False) Generates a graph with a given isomorphy class. @param n: the number of vertices in the graph (3 or 4) @param class: the isomorphy class @param directed: whether the graph should be directed. Watts_StrogatzWatts_Strogatz(dim, nei, p) @param dim: list with the dimensions of the lattice @param nei: value giving the distance (number of steps) within which two vertices will be connected. Not implemented yet, should be 1. @param p: rewiring probability @see: L{Lattice()}, L{rewire()} if more flexibility is needed @newfield ref: Reference @ref: Duncan J Watts and Steven H Strogatz: I{Collective dynamics of small world networks}, Nature 393, 440-442, 1998 are_connectedare_connected(v1, v2) Decides whether two given vertices are directly connected. @param v1: the first vertex @param v2: the second vertex @return: C{True} if there exists an edge from v1 to v2, C{False} otherwise. average_path_lengthaverage_path_length(directed=True, unconn=True) Calculates the average path length in a graph. @param directed: whether to consider directed paths in case of a directed graph. Ignored for undirected graphs. @param unconn: what to do when the graph is unconnected. If C{True}, the average of the geodesic lengths in the components is calculated. Otherwise for all unconnected vertex pairs, a path length equal to the number of vertices is used. @return: the average path length in the graph betweennessbetweenness(vertices=None, directed=True) Calculates the betweenness of nodes in a graph. Keyword arguments: @param vertices: the vertices for which the betweennesses must be returned. If C{None}, assumes all of the vertices in the graph. @param directed: whether to consider directed paths. @return: the betweenness of the given nodes in a list bibcouplingbibcoupling(vertices) Calculates bibliographic coupling values for given vertices in a graph. @param vertices: the vertices to be analysed. @return: bibliographic coupling values for all given vertices in a matrix. closenesscloseness(vertices=None, mode=ALL) Calculates the closeness centralities of given nodes in a graph. The closeness centerality of a vertex measures how easily other vertices can be reached from it (or the other way: how easily it can be reached from the other vertices). It is defined as the number of the number of vertices minus one divided by the sum of the lengths of all geodesics from/to the given vertex. If the graph is not connected, and there is no path between two vertices, the number of vertices is used instead the length of the geodesic. This is always longer than the longest possible geodesic. @param vertices: the vertices for which the closenesses must be returned. If C{None}, uses all of the vertices in the graph. @param mode: must be one of C{IN}, C{OUT} and C{ALL}. C{IN} means that the length of the incoming paths, C{OUT} means that the length of the outgoing paths must be calculated. C{ALL} means that both of them must be calculated. @return: the calculated closenesses in a list clustersclusters(mode=STRONG) Calculates the (strong or weak) clusters for a given graph. @attention: this function has a more convenient interface in class L{Graph} which wraps the result in a L{VertexClustering} object. It is advised to use that. @param mode: must be either C{STRONG} or C{WEAK}, depending on the clusters being sought. Optional, defaults to C{STRONG}. @return: the component index for every node in the graph. componentscomponents(mode=STRONG) Alias for L{Graph.clusters}. See the documentation of L{Graph.clusters} for details.copycopy() Creates an exact deep copy of the graph.decomposedecompose(mode=STRONG, maxcompno=None, minelements=1) Decomposes the graph into subgraphs. @param mode: must be either STRONG or WEAK, depending on the clusters being sought. @param maxcompno: maximum number of components to return. C{None} means all possible components. @param minelements: minimum number of vertices in a component. By setting this to 2, isolated vertices are not returned as separate components. @return: a list of the subgraphs. Every returned subgraph is a copy of the original. cocitationcocitation(vertices) Calculates cocitation scores for given vertices in a graph. @param vertices: the vertices to be analysed. @return: cocitation scores for all given vertices in a matrix.constraintcocitation(vertices=None, weights=None) Calculates Burt's constraint scores for given vertices in a graph. Burt's constraint is higher if ego has less, or mutually stronger related (i.e. more redundant) contacts. Burt's measure of constraint, C[i], of vertex i's ego network V[i], is defined for directed and valued graphs as follows: C[i] = sum( sum( (p[i,q] p[q,j])^2, q in V[i], q != i,j ), j in V[], j != i) for a graph of order (ie. number od vertices) N, where proportional tie strengths are defined as follows: p[i,j]=(a[i,j]+a[j,i]) / sum(a[i,k]+a[k,i], k in V[i], k != i), a[i,j] are elements of A and the latter being the graph adjacency matrix. For isolated vertices, constraint is undefined. @param vertices: the vertices to be analysed or C{None} for all vertices. @param weights: weights associated to the edges. Can be an attribute name as well. If C{None}, every edge will have the same weight. @return: cocitation scores for all given vertices in a matrix.densitydensity(loops=False) Calculates the density of the graph. @param loops: whether to take loops into consideration. If C{True}, the algorithm assumes that there might be some loops in the graph and calculates the density accordingly. If C{False}, the algorithm assumes that there can't be any loops. @return: the reciprocity of the graph.diameterdiameter(directed=True, unconn=True) Calculates the diameter of the graph. @param directed: whether to consider directed paths. @param unconn: if C{True} and the graph is unconnected, the longest geodesic within a component will be returned. If C{False} and the graph is unconnected, the result is the number of vertices.edge_betweennessedge_betweenness(directed=True) Calculates the edge betweennesses in a graph. @param directed: whether to consider directed paths. @return: a list with the edge betweennesses of all specified edges. get_shortest_pathsget_shortest_paths(v, mode=OUT) Calculates the shortest paths from/to a given node in a graph. @param v: the source/destination for the calculated paths @param mode: the directionality of the paths. C{IN} means to calculate incoming paths, C{OUT} means to calculate outgoing paths, C{ALL} means to calculate both ones. @return: at most one shortest path for every node in the graph in a list. For unconnected graphs, some of the list elements will be empty lists. Note that in case of mode=C{IN}, the nodes in a path are returned in reversed order!get_all_shortest_pathsget_all_shortest_paths(v, mode=OUT) Calculates all of the shortest paths from/to a given node in a graph. @param v: the source/destination for the calculated paths @param mode: the directionality of the paths. C{IN} means to calculate incoming paths, C{OUT} means to calculate outgoing paths, C{ALL} means to calculate both ones. @return: all of the shortest path from the given node to every other reachable node in the graph in a list. Note that in case of mode=C{IN}, the nodes in a path are returned in reversed order!girthgirth(return_shortest_circle=False) Returns the girth of the graph. The girth of a graph is the length of the shortest circle in it. @param return_shortest_circle: whether to return one of the shortest circles found in the graph. @return: the length of the shortest circle or (if C{return_shortest_circle}) is true, the shortest circle itself as a list is_connectedis_connected(mode=STRONG) Decides whether a graph is connected. @param mode: whether we should calculate strong or weak connectivity. @return: C{True} if the graph is connected, C{False} otherwise. maxdegreemaxdegree(vertices=None, type=ALL, loops=False) Returns the maximum degree of a vertex set in the graph. This method accepts a single vertex ID or a list of vertex IDs as a parameter, and returns the degree of the given vertices (in the form of a single integer or a list, depending on the input parameter). @param vertices: a single vertex ID or a list of vertex IDs or C{None} meaning all the vertices in the graph. @param type: the type of degree to be returned (L{OUT} for out-degrees, L{IN} IN for in-degrees or L{ALL} for the sum of them). @param loops: whether self-loops should be counted. pagerankpagerank(vertices=None, directed=True, niter=1000, eps=0.001, damping=0.85) Calculates the Google PageRank values of a graph. @param vertices: the indices of the vertices being queried. C{None} means all of the vertices. @param directed: whether to consider directed paths. @param niter: the maximum number of iterations to be performed. @param eps: the iteration stops if all of the PageRank values change less than M{eps} between two iterations. @param damping: the damping factor. M{1-damping} is the PageRank value for nodes with no incoming links. @return: a list with the Google PageRank values of the specified vertices. @newfield ref: Reference @ref: Sergey Brin and Larry Page: I{The Anatomy of a Large-Scale Hypertextual Web Search Engine}. Proceedings of the 7th World-Wide Web Conference, Brisbane, Australia, April 1998. reciprocityreciprocity() @return: the reciprocity of the graph.rewirerewire(n=1000, mode=REWIRING_SIMPLE) Randomly rewires the graph while preserving the degree distribution. Please note that the rewiring is done "in-place", so the original graph will be modified. If you want to preserve the original graph, use the L{copy} method before. @param n: the number of rewiring trials. @param mode: the rewiring algorithm to use. As for now, only C{REWIRING_SIMPLE} is supported. @return: the modified graph. shortest_pathsshortest_paths(vertices, mode=OUT) Calculates shortest path lengths for given nodes in a graph. @param vertices: a list containing the vertex IDs which should be included in the result. @param mode: the type of shortest paths to be used for the calculation in directed graphs. C{OUT} means only outgoing, C{IN} means only incoming paths. C{ALL} means to consider the directed graph as an undirected one. @return: the shortest path lengths for given nodes in a matrix simplifysimplify(multiple=True, loops=True) Simplifies a graph by removing self-loops and/or multiple edges. @param multiple: whether to remove multiple edges. @param loops: whether to remove loops. spanning_treespanning_tree(weights=None) Calculates a minimum spanning tree for a graph (weighted or unweighted) @param weights: a vector containing weights for every edge in the graph. C{None} means that the graph is unweighted. @return: the spanning tree as a L{Graph} object. @newfield ref: Reference @ref: Prim, R.C.: I{Shortest connection networks and some generalizations}, Bell System Technical Journal, Vol. 36., 1957, 1389--1401.subcomponentsubcomponent(v, mode=ALL) Determines the indices of vertices which are in the same component as a given vertex. @param v: the index of the vertex used as the source/destination @param mode: if equals to C{IN}, returns the vertex IDs from where the given vertex can be reached. If equals to C{OUT}, returns the vertex IDs which are reachable from the given vertex. If equals to C{ALL}, returns all vertices within the same component as the given vertex, ignoring edge directions. Note that this is not equal to calculating the union of the results of C{IN} and C{OUT}. @return: the indices of vertices which are in the same component as a given vertex. subgraphsubgraph(vertices) Returns a subgraph based on the given vertices. @param vertices: a list containing the vertex IDs which should be included in the result. @return: a copy of the subgraph topological_sortingtopological_sorting(mode=OUT) Calculates a possible topological sorting of the graph. Returns a partial sorting and issues a warning if the graph is not a directed acyclic graph. @param mode: if C{OUT}, vertices are returned according to the forward topological order -- all vertices come before their successors. If C{IN}, all vertices come before their ancestors. @return: a possible topological ordering as a listtransitivity_undirectedtransitivity_undirected() Calculates the transitivity (clustering coefficient) of the graph. @return: the transitivity transitivity_local_undirectedtransitivity_local_undirected(vertices=None) Calculates the local transitivity of given vertices in the graph. @param vertices: a list containing the vertex IDs which should be included in the result. C{None} means all of the vertices. @return: the transitivities for the given vertices in a list layout_circlelayout_circle() Places the vertices of the graph uniformly on a circle. @return: the calculated coordinate pairs in a list.layout_spherelayout_sphere() Places the vertices of the graph uniformly on a sphere. @return: the calculated coordinate triplets in a list.layout_kamada_kawailayout_kamada_kawai(maxiter=1000, sigma=None, initemp=10, coolexp=0.99, kkconst=None) Places the vertices on a plane according to the Kamada-Kawai algorithm. This is a force directed layout, see Kamada, T. and Kawai, S.: An Algorithm for Drawing General Undirected Graphs. Information Processing Letters, 31/1, 7--15, 1989. @param maxiter: the number of iterations to perform. @param sigma: the standard base deviation of the position change proposals. C{None} means the number of vertices * 0.25 @param initemp: initial temperature of the simulated annealing. @param coolexp: cooling exponent of the simulated annealing. @param kkconst: the Kamada-Kawai vertex attraction constant. C{None} means the square of the number of vertices. @return: the calculated coordinate pairs in a list.layout_kamada_kawai_3dlayout_kamada_kawai_3d(maxiter=1000, sigma=None, initemp=10, coolexp=0.99, kkconst=None) Places the vertices in the 3D space according to the Kamada-Kawai algorithm. This is a force directed layout, see Kamada, T. and Kawai, S.: An Algorithm for Drawing General Undirected Graphs. Information Processing Letters, 31/1, 7--15, 1989. @param maxiter: the number of iterations to perform. @param sigma: the standard base deviation of the position change proposals. C{None} means the number of vertices * 0.25 @param initemp: initial temperature of the simulated annealing. @param coolexp: cooling exponent of the simulated annealing. @param kkconst: the Kamada-Kawai vertex attraction constant. C{None} means the square of the number of vertices. @return: the calculated coordinate triplets in a list.layout_fruchterman_reingoldlayout_fruchterman_reingold(maxiter=500, maxdelta=None, area=None, coolexp=0.99, repulserad=maxiter*maxdelta) Places the vertices on a 2D plane according to the Fruchterman-Reingold algorithm. This is a force directed layout, see Fruchterman, T. M. J. and Reingold, E. M.: Graph Drawing by Force-directed Placement. Software -- Practice and Experience, 21/11, 1129--1164, 1991 @param maxiter: the number of iterations to perform. @param maxdelta: the maximum distance to move a vertex in an iteration. C{None} means the number of vertices. @param area: the area of the square on which the vertices will be placed. C{None} means the square of M{maxdelta}. @param coolexp: the cooling exponent of the simulated annealing. @param repulserad: determines the radius at which vertex-vertex repulsion cancels out attraction of adjacent vertices. C{None} means M{maxiter*maxdelta}. @return: the calculated coordinate pairs in a list.layout_fruchterman_reingold_3dlayout_fruchterman_reingold_3d(maxiter=500, maxdelta=None, area=None, coolexp=0.99, repulserad=maxiter*maxdelta) Places the vertices in the 3D space according to the Fruchterman-Reingold grid algorithm. This is a force directed layout, see Fruchterman, T. M. J. and Reingold, E. M.: Graph Drawing by Force-directed Placement. Software -- Practice and Experience, 21/11, 1129--1164, 1991 @param maxiter: the number of iterations to perform. @param maxdelta: the maximum distance to move a vertex in an iteration. C{None} means the number of vertices. @param area: the area of the square on which the vertices will be placed. C{None} means the square of M{maxdelta}. @param coolexp: the cooling exponent of the simulated annealing. @param repulserad: determines the radius at which vertex-vertex repulsion cancels out attraction of adjacent vertices. C{None} means M{maxiter*maxdelta}. @return: the calculated coordinate triplets in a list.layout_grid_fruchterman_reingoldlayout_grid_fruchterman_reingold(maxiter=500, maxdelta=None, area=None, coolexp=0.99, repulserad=maxiter*maxdelta, cellsize=1.0) Places the vertices on a 2D plane according to the Fruchterman-Reingold grid algorithm. This is a modified version of a force directed layout, see Fruchterman, T. M. J. and Reingold, E. M.: Graph Drawing by Force-directed Placement. Software -- Practice and Experience, 21/11, 1129--1164, 1991. The algorithm partitions the 2D space to a grid and vertex repulsion is then calculated only for vertices nearby. @param maxiter: the number of iterations to perform. @param maxdelta: the maximum distance to move a vertex in an iteration. C{None} means the number of vertices. @param area: the area of the square on which the vertices will be placed. C{None} means the square of M{maxdelta}. @param coolexp: the cooling exponent of the simulated annealing. @param repulserad: determines the radius at which vertex-vertex repulsion cancels out attraction of adjacent vertices. C{None} means M{maxiter*maxdelta}. @param cellsize: the size of the grid cells. @return: the calculated coordinate pairs in a list.layout_lgllayout_lgl(maxiter=500, maxdelta=None, area=None, coolexp=0.99, repulserad=maxiter*maxdelta, cellsize=1.0, proot=None) Places the vertices on a 2D plane according to the Large Graph Layout. @param maxiter: the number of iterations to perform. @param maxdelta: the maximum distance to move a vertex in an iteration. C{None} means the number of vertices. @param area: the area of the square on which the vertices will be placed. C{None} means the square of M{maxdelta}. @param coolexp: the cooling exponent of the simulated annealing. @param repulserad: determines the radius at which vertex-vertex repulsion cancels out attraction of adjacent vertices. C{None} means M{maxiter*maxdelta}. @param cellsize: the size of the grid cells. @param proot: the root vertex, this is placed first, its neighbors in the first iteration, second neighbors in the second, etc. C{None} means a random vertex. @return: the calculated coordinate pairs in a list.layout_reingold_tilfordlayout_reingold_tilford(root) Places the vertices on a 2D plane according to the Reingold-Tilford layout algorithm. @param root: the root of the tree. @return: the calculated coordinate pairs in a list. @see: EM Reingold, JS Tilford: I{Tidier Drawings of Trees.} IEEE Transactions on Software Engineering 7:22, 223-228, 1981.layout_randomlayout_random() Places the vertices of the graph randomly in a 2D space. @return: the "calculated" coordinate pairs in a list.layout_random_3dlayout_random_3d() Places the vertices of the graph randomly in a 3D space. @return: the "calculated" coordinate triplets in a list.bfsbfs(vid, mode=OUT) Conducts a breadth first search (BFS) on the graph. @param vid: the root vertex ID @param mode: either C{IN} or C{OUT} or C{ALL}, ignored for undirected graphs. @return: a tuple with the following items: - The vertex IDs visited (in order) - The start indices of the layers in the vertex list - The parent of every vertex in the BFS bfsiterbfsiter(vid, mode=OUT, advanced=False) Constructs a breadth first search (BFS) iterator of the graph. @param vid: the root vertex ID @param mode: either C{IN} or C{OUT} or C{ALL}. @param advanced: if C{False}, the iterator returns the next vertex in BFS order in every step. If C{True}, the iterator returns the distance of the vertex from the root and the parent of the vertex in the BFS tree as well. @return: the BFS iterator as an L{igraph.BFSIter} object. get_adjacencyget_adjacency(type=GET_ADJACENCY_BOTH) Returns the adjacency matrix of a graph. @param type: either C{GET_ADJACENCY_LOWER} (uses the lower triangle of the matrix) or C{GET_ADJACENCY_UPPER} (uses the upper triangle) or C{GET_ADJACENCY_BOTH} (uses both parts). Ignored for directed graphs. @return: the adjacency matrix. get_edgelistget_edgelist() Returns the edge list of a graph.to_directedto_directed(mutual=True) Converts an undirected graph to directed. @param mutual: C{True} if mutual directed edges should be created for every undirected edge. If C{False}, a directed edge with arbitrary direction is created. to_undirectedto_undirected(collapse=True) Converts a directed graph to undirected. @param collapse: C{True} if only a single edge should be created from multiple directed edges going between the same vertex pair. If C{False}, the edge count is kept constant. laplacianlaplacian(normalized=False) Returns the Laplacian matrix of a graph. The Laplacian matrix is similar to the adjacency matrix, but the edges are denoted with -1 and the diagonal contains the node degrees. Normalized Laplacian matrices have 1 or 0 in their diagonals (0 for nodes with no edges), edges are denoted by 1 / sqrt(d_i * d_j) where d_i is the degree of node i. Multiple edges and self-loops are silently ignored. Although it is possible to calculate the Laplacian matrix of a directed graph, it does not make much sense. @param normalized: whether to return the normalized Laplacian matrix. @return: the Laplacian matrix. Read_DIMACSRead_DIMACS(f, directed=False) Reads a graph from a file conforming to the DIMACS minimum-cost flow file format. For the exact description of the format, see U{http://lpsolve.sourceforge.net/5.5/DIMACS.htm} Restrictions compared to the official description of the format: - igraph's DIMACS reader requires only three fields in an arc definition, describing the edge's source and target node and its capacity. - Source nodes are identified by 's' in the FLOW field, target nodes are identified by 't'. - Node indices start from 1. Only a single source and target node is allowed. @param f: the name of the file @param directed: whether the generated graph should be directed. @return: the generated graph, the source and the target of the flow and the edge capacities in a tuple Read_EdgelistRead_Edgelist(f, directed=True) Reads an edge list from a file and creates a graph based on it. Please note that the vertex indices are zero-based. @param f: the name of the file @param directed: whether the generated graph should be directed. Read_GraphMLRead_GraphML(f, directed=True, index=0) Reads a GraphML format file and creates a graph based on it. @param f: the name of the file @param index: if the GraphML file contains multiple graphs, specifies the one that should be loaded. Graph indices start from zero, so if you want to load the first graph, specify 0 here. Read_GMLRead_GML(f) Reads a GML file and creates a graph based on it. @param f: the name of the file Read_NcolRead_Ncol(f, names=True, weights=True) Reads an .ncol file used by LGL. It is also useful for creating graphs from "named" (and optionally weighted) edge lists. This format is used by the Large Graph Layout program. See the U{documentation of LGL } regarding the exact format description. LGL originally cannot deal with graphs containing multiple or loop edges, but this condition is not checked here, as igraph is happy with these. @param f: the name of the file @param names: If C{True}, the vertex names are added as a vertex attribute called 'name'. @param weights: If True, the edge weights are added as an edge attribute called 'weight'. Read_LglRead_Lgl(f, names=True, weights=True) Reads an .lgl file used by LGL. It is also useful for creating graphs from "named" (and optionally weighted) edge lists. This format is used by the Large Graph Layout program. See the U{documentation of LGL } regarding the exact format description. LGL originally cannot deal with graphs containing multiple or loop edges, but this condition is not checked here, as igraph is happy with these. @param f: the name of the file @param names: If C{True}, the vertex names are added as a vertex attribute called 'name'. @param weights: If True, the edge weights are added as an edge attribute called 'weight'. Read_PajekRead_Pajek(f) Reads a Pajek format file and creates a graph based on it. @param f: the name of the file write_dimacswrite_dimacs(f, source, target, capacity=None) Writes the graph in DIMACS format to the given file. @param f: the name of the file to be written @param source: the source vertex ID @param target: the target vertex ID @param capacity: the capacities of the edges in a list. If it is not a list, the corresponding edge attribute will be used to retrieve capacities.write_edgelistwrite_edgelist(f) Writes the edge list of a graph to a file. Directed edges are written in (from, to) order. @param f: the name of the file to be written write_gmlwrite_gml(f, creator=None, ids=None) Writes the graph in GML format to the given file. @param f: the name of the file to be written @param creator: optional creator information to be written to the file. If C{None}, the current date and time is added. @param ids: optional numeric vertex IDs to use in the file. This must be a list of integers or C{None}. If C{None}, the C{id} attribute of the vertices are used, or if they don't exist, numeric vertex IDs will be generated automatically.write_ncolwrite_ncol(f, names="name", weights="weights") Writes the edge list of a graph to a file in .ncol format. Note that multiple edges and/or loops break the LGL software, but igraph does not check for this condition. Unless you know that the graph does not have multiple edges and/or loops, it is wise to call L{simplify()} before saving. @param f: the name of the file to be written @param names: the name of the vertex attribute containing the name of the vertices. If you don't want to store vertex names, supply C{None} here. @param weights: the name of the edge attribute containing the weight of the vertices. If you don't want to store weights, supply C{None} here. write_lglwrite_lgl(f, names="name", weights="weights", isolates=True) Writes the edge list of a graph to a file in .lgl format. Note that multiple edges and/or loops break the LGL software, but igraph does not check for this condition. Unless you know that the graph does not have multiple edges and/or loops, it is wise to call L{simplify()} before saving. @param f: the name of the file to be written @param names: the name of the vertex attribute containing the name of the vertices. If you don't want to store vertex names, supply C{None} here. @param weights: the name of the edge attribute containing the weight of the vertices. If you don't want to store weights, supply C{None} here. @param isolates: whether to include isolated vertices in the output. write_graphmlwrite_graphml(f) Writes the graph to a GraphML file. @param f: the name of the file to be written isoclassisoclass(vertices) Returns the isomorphy class of the graph or its subgraph. Isomorphy class calculations are implemented only for graphs with 3 or 4 nodes. @param vertices: a list of vertices if we want to calculate the isomorphy class for only a subset of vertices. C{None} means to use the full graph. @return: the isomorphy class of the (sub)graph isomorphicisomorphic(other) Checks whether the graph is isomorphic with another graph. @param other: the other graph with which we want to compare the graph. @return: C{True} if the graphs are isomorphic, C{False} if not. attributesattributes() @return: the attribute name list of the graph vertex_attributesvertex_attributes() @return: the attribute name list of the graph's vertices edge_attributesedge_attributes() @return: the attribute name list of the graph's edges complementercomplementer(loops=False) Returns the complementer of the graph @param loops: whether to include loop edges in the complementer. @return: the complementer of the graph composecompose(other) Returns the composition of two graphs.differencedifference(other) Subtracts the given graph from the originaldisjoint_uniondisjoint_union(graphs) Creates the disjoint union of two (or more) graphs. @param graphs: the list of graphs to be united with the current one. intersectionintersection(graphs) Creates the intersection of two (or more) graphs. @param graphs: the list of graphs to be intersected with the current one. unionunion(graphs) Creates the union of two (or more) graphs. @param graphs: the list of graphs to be intersected with the current one. maxflow_valuemaxflow_value(source, target, capacity=None) Returns the maximum flow between the source and target vertices. @param source: the source vertex ID @param target: the target vertex ID @param capacity: the capacity of the edges. It must be a list or a valid attribute name or C{None}. In the latter case, every edge will have the same capacity. @return: the value of the maximum flow between the given vertices mincut_valuemincut_value(source=-1, target=-1, capacity=None) Returns the minimum cut between the source and target vertices. @param source: the source vertex ID. If negative, the calculation is done for every vertex except the target and the minimum is returned. @param target: the target vertex ID. If negative, the calculation is done for every vertex except the source and the minimum is returned. @param capacity: the capacity of the edges. It must be a list or a valid attribute name or C{None}. In the latter case, every edge will have the same capacity. @return: the value of the minimum cut between the given vertices cliquescliques(min=0, max=0) Returns some or all cliques of the graph as a list of tuples. A clique is a complete subgraph -- a set of vertices where an edge is present between any two of them (excluding loops) @param min: the minimum size of cliques to be returned. If zero or negative, no lower bound will be used. @param max: the maximum size of cliques to be returned. If zero or negative, no upper bound will be used.largest_cliqueslargest_cliques() Returns the largest cliques of the graph as a list of tuples. Quite intuitively a clique is considered largest if there is no clique with more vertices in the whole graph. All largest cliques are maximal (i.e. nonextendable) but not all maximal cliques are largest. @see: L{clique_number()} for the size of the largest cliques or L{maximal_cliques()} for the maximal cliquesmaximal_cliquesmaximal_cliques() Returns the maximal cliques of the graph as a list of tuples. A maximal clique is a clique which can't be extended by adding any other vertex to it. A maximal clique is not necessarily one of the largest cliques in the graph. @see: L{largest_cliques()} for the largest cliques.clique_numberclique_number() Returns the clique number of the graph. The clique number of the graph is the size of the largest clique. @see: L{largest_cliques()} for the largest cliques.independent_vertex_setsindependent_vertex_sets(min=0, max=0) Returns some or all independent vertex sets of the graph as a list of tuples. Two vertices are independent if there is no edge between them. Members of an independent vertex set are mutually independent. @param min: the minimum size of sets to be returned. If zero or negative, no lower bound will be used. @param max: the maximum size of sets to be returned. If zero or negative, no upper bound will be used.largest_independent_vertex_setslargest_independent_vertex_sets() Returns the largest independent vertex sets of the graph as a list of tuples. Quite intuitively an independent vertex set is considered largest if there is no other set with more vertices in the whole graph. All largest sets are maximal (i.e. nonextendable) but not all maximal sets are largest. @see: L{independence_number()} for the size of the largest independent vertex sets or L{maximal_independent_vertex_sets()} for the maximal (nonextendable) independent vertex setsmaximal_independent_vertex_setsmaximal_independent_vertex_sets() Returns the maximal independent vertex sets of the graph as a list of tuples. A maximal independent vertex set is an independent vertex set which can't be extended by adding any other vertex to it. A maximal independent vertex set is not necessarily one of the largest independent vertex sets in the graph. @see: L{largest_independent_vertex_sets()} for the largest independent vertex sets @newfield ref: Reference @ref: S. Tsukiyama, M. Ide, H. Ariyoshi and I. Shirawaka: I{A new algorithm for generating all the maximal independent sets}. SIAM J Computing, 6:505--517, 1977.independence_numberindependence_number() Returns the independence number of the graph. The independence number of the graph is the size of the largest independent vertex set. @see: L{largest_independent_vertex_sets()} for the largest independent vertex setsmodularitymodularity(membership) Calculates the modularity of the graph with respect to some vertex types. The modularity of a graph w.r.t. some division measures how good the division is, or how separated are the different vertex types from each other. It is defined as M{Q=1/(2m) * sum(Aij-ki*kj/(2m)delta(ci,cj),i,j)}. M{m} is the number of edges, M{Aij} is the element of the M{A} adjacency matrix in row M{i} and column M{j}, M{ki} is the degree of node M{i}, M{kj} is the degree of node M{j}, and M{Ci} and C{cj} are the types of the two vertices (M{i} and M{j}). M{delta(x,y)} is one iff M{x=y}, 0 otherwise. @attention: method overridden in L{Graph} to allow L{VertexClustering} objects as a parameter. This method is not strictly necessary, since the L{VertexClustering} class provides a variable called C{modularity}. @param membership: the membership vector, e.g. the vertex type index for each vertex. @return: the modularity score. Score larger than 0.3 usually indicates strong community structure. @newfield ref: Reference @ref: MEJ Newman and M Girvan: Finding and evaluating community structure in networks. Phys Rev E 69 026113, 2004. corenesscoreness(mode=ALL) Finds the coreness (shell index) of the vertices of the network. The M{k}-core of a graph is a maximal subgraph in which each vertex has at least degree k. (Degree here means the degree in the subgraph of course). The coreness of a vertex is M{k} if it is a member of the M{k}-core but not a member of the M{k+1}-core. @param mode: whether to compute the in-corenesses (L{IN}), the out-corenesses (L{OUT}) or the undirected corenesses (L{ALL}). Ignored and assumed to be L{ALL} for undirected graphs. @return: the corenesses for each vertex. @newfield ref: Reference @ref: Vladimir Batagelj, Matjaz Zaversnik: I{An M{O(m)} Algorithm for Core Decomposition of Networks.}community_fastgreedycommunity_fastgreedy(return_qs=True) Finds the community structure of the graph according to the algorithm of Clauset et al based on the greedy optimization of modularity. This is a bottom-up algorithm: initially every vertex belongs to a separate community, and communities are merged one by one. In every step, the two communities being merged are the ones which result in the maximal increase in modularity. @attention: this function is wrapped in a more convenient syntax in the derived class L{Graph}. It is advised to use that instead of this version. @param return_qs: if C{True}, returns the modularity achieved before each merge during the algorithm, so the first element of the list returned will be the initial modularity (when every vertex belongs to a separate community), the second one is the modularity after the first join and so on. @return: a tuple with the following elements: 1. The list of merges 2. The modularity scores before each merge if C{return_qs} is C{True}, or C{None} otherwise @newfield ref: Reference @ref: A. Clauset, M. E. J. Newman and C. Moore: I{Finding community structure in very large networks.} Phys Rev E 70, 066111 (2004). @see: modularity() community_leading_eigenvector_naivecommunity_leading_eigenvector_naive(n=-1, return_merges=False) A naive implementation of Newman's eigenvector community structure detection. This function splits the network into two components according to the leading eigenvector of the modularity matrix and then recursively takes the given number of steps by splitting the communities as individual networks. This is not the correct way, however, see the reference for explanation. Consider using the correct L{community_leading_eigenvector} method instead. @attention: this function is wrapped in a more convenient syntax in the derived class L{Graph}. It is advised to use that instead of this version. @param n: the desired number of communities. If negative, the algorithm tries to do as many splits as possible. Note that the algorithm won't split a community further if the signs of the leading eigenvector are all the same. @param return_merges: if C{True}, returns the order in which the individual vertices are merged into communities. @return: a tuple where the first element is the membership vector of the clustering and the second element is the merge matrix. @newfield ref: Reference @ref: MEJ Newman: Finding community structure in networks using the eigenvectors of matrices, arXiv:physics/0605087 community_leading_eigenvectorcommunity_leading_eigenvector(n=-1, return_merges=False) A proper implementation of Newman's eigenvector community structure detection. Each split is done by maximizing the modularity regarding the original network. See the reference for details. @attention: this function is wrapped in a more convenient syntax in the derived class L{Graph}. It is advised to use that instead of this version. @param n: the desired number of communities. If negative, the algorithm tries to do as many splits as possible. Note that the algorithm won't split a community further if the signs of the leading eigenvector are all the same. @param return_merges: if C{True}, returns the order in which the individual vertices are merged into communities. @return: a tuple where the first element is the membership vector of the clustering and the second element is the merge matrix. @newfield ref: Reference @ref: MEJ Newman: Finding community structure in networks using the eigenvectors of matrices, arXiv:physics/0605087 community_edge_betweennesscommunity_edge_betweenness(directed=True, return_removed_edges=False, return_merges=True, return_ebs=False, return_bridges=False) Community structure detection based on the betweenness of the edges in the network. This algorithm was invented by M Girvan and MEJ Newman, see: M Girvan and MEJ Newman: Community structure in social and biological networks, Proc. Nat. Acad. Sci. USA 99, 7821-7826 (2002). The idea is that the betweenness of the edges connecting two communities is typically high. So we gradually remove the edge with the highest betweenness from the network and recalculate edge betweenness after every removal, as long as all edges are removed. @attention: this function is wrapped in a more convenient syntax in the derived class L{Graph}. It is advised to use that instead of this version. @param directed: whether to take into account the directedness of the edges when we calculate the betweenness values. @param return_removed_edges: whether to return the IDs of the edges in the order of removal. @param return_merges: if C{True}, returns the order in which the individual vertices are merged into communities. @param return_ebs: if C{True}, returns the edge betweenness of the removed edges at the time of the removal. @param return_bridges: if C{True}, returns the IDs of the edges whose removal increased the number of connected components (these are the so-called bridges). @return: a tuple with the removed edges IDs, the merge matrix, the edge betweennesses of the removed edges and the IDs of the bridges. Any of these elements can be equal to C{None} based on the C{return_*} arguments.__graph_as_cobject__graph_as_cobject() Returns the igraph graph encapsulated by the Python object as a PyCObject .A PyObject is barely a regular C pointer. This function should not be used directly by igraph users, it is useful only in the case when the underlying igraph object must be passed to another C code through Python. __register_destructor__register_destructor(destructor) Registers a destructor to be called when the object is freed by Python. This function should not be used directly by igraph users.igraph.GraphLow-level representation of a graph. Don't use it directly, use L{igraph.Graph} instead. @deffield ref: Reference??> A> A???????@@@??MbP?333333?Gz?Gz??{w⍀Pgwbw⍀PNwIwx⍀xP5w0wc⍀cPwwN⍀NPwv9⍀9Pvv$⍀$Pvv⍀Pvv⍀Pvv⍀Pvv⍀Pmvhv⍀PTvOv⍀P;v6v⍀P"vv|⍀|P vvg⍀gPuuR⍀RPuu=⍀=Puu(⍀(Puu⍀Puu⍀Psunu⍀PZuUu⍀PAui9i⍀P%i i⍀P ii~⍀~Phhi⍀iPhhT⍀TPhh?⍀?Phh*⍀*Phh⍀Pvhqh⍀P]hXh⍀PDh?h⍀P+h&h⍀Ph h⍀Pgg⍀Pgg⍀Pggm⍀mPggX⍀XPggC⍀CP|gwg.⍀.Pcg^g⍀PJgEg⍀P1g,g⍀Pgg⍀Pff⍀Pff⍀Pff⍀Pff⍀Pffq⍀qPf}f\⍀\PifdfG⍀GPPfKf2⍀2P7f2f⍀Pff⍀Pff⍀Pee⍀Pee⍀Pee⍀Pee⍀Pee⍀Poejeu⍀uPVeQe`⍀`P=e8eK⍀KP$ee6⍀6P ee!⍀!Pdd ⍀ Pd؈ #<Un҉6Oh̊0Ib{Ƌߋ*C\uٌ $=VoӍ7Pi͎1Jc|Ǐ+D]vڐ %>Wpԑ8QjΒ2Kd}ȓ,E^w”۔ &?XqՕ 9Rkϖ3Le~ɗ-F_xØܘ'@Yr֙!:SlК$Ë$æ8@@bQ4ң4 Q ՗@2ƗK;(21$ ޖӖǖgG2=T272.Q ϕ@9zj(9ZM(ޔU$9KT9>95Q ӓ@=~n(=^Q,!Y$=OT=B=9Q !ܑjI' xHTQ ΐa]Q J@܏ȏpQ {lcF802202%ߎώupaD.Q 0 0 ̍k`MH9Q 00Ԍ~dE:#܋ܭdQ k\S6(0""0"ϊxe`Q4Q 00}[P=8) Q ׈0uш0uĈ~nT5*̇ܭuzXuqQ YxTeDЅ}PxTP|LjPϭ<H2@Q ҃̓kHS9m%8@Q ǂʭMgTMaMXMOMIQ :,0& 00݁́zeP5Ѐm_<YJ&Q tH$#>.@#Q ~~~~w~`~:~'~~}}}H}}Q }}}}X}$3}N}>}@3}/}Q }|$`{||@`{|||}|n|T|,| |{{{{{j{\{Q 9{{$y{z@yzzzzzz\zPz(zzyyyyyQ tygy=y$w3y#y@wyxxxxxhxOx8xxwwwQ ww[w?w&w wvvv(QvvvQvyvgvMvQ Bv)vvvT5uuH5uuuD5uuu@5uuyuMus5u@um5u:u@5u1uQ uu@sttt<ststttt\tCtt&stLssssQ ss,ssssss@sssqsbsЬsVs.ss!sssQ sr,qrrrrr@qrrr|rmrЬqar5rqrqqqqqqHqqqqެqqqqQ qq,pqqqqq@p}qqqKq[[٫Z [ūZ[ZZЫZZZZZZZZQ ZZZZZ&ZZYYY(YY$YnYIYūYCYY4YY.Y٫Y%YYYЫYYQ YXXXXwXRX)XWX WWWЫWWWWūWWWWWWQ xWsW\WRW>WVVVUwVUJV(VЫUVUVūU VUVUUQ UUUUUkUFUUoTTpoTToTToTTToTToTToT}ToTqToTkTQ \TWT@T6T"TSSSRoShRgSR>S R*SSRRRRRRRRRRQ RRRRRRTROR;R1RRRQQQQQQdQ_QKQAQ-QQPPPQPPPPyPQP_PTQPVPQPMPQ =P.PPOPOOOOOHOOOOQ OOOyOeO;O2OONNNyNNQ NNNNNN|NoNTNNNMMMMMMM}MQ eMZMLM)M=M%MQ M MLLLiLMLLKңKKKKQ KK<KKgKKK`K1KcKKKKKKQ J@IJJJJJJyJoJ<IeJIVJ#JI,IIII*IIQ IIIIqIFI{I'I̪I!IIIHIIQ HH<_GH_GHHHHyHRHHGGGG&_GwGL_GgG_G[GQ AG3G<E-G@E GGGGFFFFFFbFIFFEũEEEEEEQ EEE~EvEEE=EDDDDDDdDGD7DD DCCCjCBC,C+CC%C*CCQ CB0:@B0:@BBBBSB@BBBAAAA{A5A%A@@@@,:@m@H@:@B@*:@6@Q $@@ @@??HY????~?PY?u?&Y?k?HY?^?Y?U?Q ;?6?+??? ?>>>>>k>I>!>> >>>Q ==<N<=N<=====M=0=%==<<{<!N<b<N<Y<N<S< N<J<Q 7<&<<;;;;;;;;u;m;`;L;;::=:::a:{=:[:@=:K:=:E:=:9:Q #::<9:9:99999999f9?99y9 999Q 88<q78q7888888s8+8&88777ũq77@q7|7q7v7q7m7Q W7R787-7#77666}6U696A69656Q 6665555]5C5>5,5544'44`'44X'4b4'4\4'4P4@'4A4'4;4H'454'4/4'4#4Q 4 433333}3f3a3R37332{22@22H222222Q 22|2I2%212P12H11t1111Q 1T511H511~1`151@1j5171g5111Q $11<q01q010Tq00Hq0000yq0v0q0m0Q ]0I00//tz//Ez//.z//oz/|/2z/v/Q g/Y/<r.S/@r.I/5///..mr..r.}.)r.t.r.n.Q ].>.3.+....---_-%-#,-L,- ,,,,Q , +,,<+,+,,,M,%,Ҩ++++ɨ++++Q ++<*+ *++j+>++*+H**P**T**?**6******Q **<G(* G(t*D*-*LG( ***))))j)Q)>)*)4G( )(G((G((TG((G(|(G(v(G(m(G([(G(U(G(O(G(C(Q 0(<#*( #((8# (#(#''''''''''x'j'\'&'' '&&&&&@#u&L&>&*&%%%%`%@#$%$$$<#$ #$$$$$i$:$s#&$L####@####b##T####N##Q #<W# W{#m#8Wg#W\#WQ#L#>#1## ##"""""z"Q"2"@W"!!!y!l!a!K!$!@W    <W W| p f V >  XWLWWNWW@WtWkTWeW_WSQ C>'qBF ?L@H6T2.Q <wO Q  Rx<RnRdPHRtRPRrRfkR]RTRNQ @,frTr`r}YrtrnQ `R<VL VB.HVRVPVjVaVXVRQ @<:/!8V < eTL:T Q ť8k=H7 <P >G%PEQ < O8L*j]J=4JD8T/  Q }Q l^8~X~NF5)~L~~~zQ h`W9/ TQ yh3&{HQ rm\Em.$TQ      q (X h X T Q < 4       ( Q o a <y [ @y Q I 8 .     /y y ~ (y u Q a S < M  E < 4 $     t K  ; T 2 ܘ ( @  Q   <u u         x 8 %  Tuu@u}̘uqQ [: [F"ݣ T   Q pQ#ңQ wO:ң1-Q  DQ TaxHan]Q LD!.Q Q `Gt GnZNCQ &HHV'LPTQ wk`rU               |tpld`\TPLD@<40,$  |tpld`\TPLD@<40,$  |tpld`\TPLD@<40,$  |tpld`\TPLD@<40,$  |tpld`\TPLD@<40,$  |tpld`\TPLD@<40,$  |tpld`\TPLD@<40,$  |tpld`\TPLD@  |xplh`XPH@<40,$   P  |vP q kc]P X RȚJȚDP ? 91+P &   P  }}P  ddP  KKP  22P  P  |P w qicP ^ XΙPΙJP E ?71P , &P  ߝߝP  ۝j۝jP  םQםQP  ӝ8ӝ8P  ϝϝP  ˝˝P } wǝoǝiP d ^ÝԘVÝԘPP K E=7P 2 ,$P   P  ppP  WWP  >>P  %%P    P  }uoP j dڗ\ڗVP Q KC=P 8 2*$P   P  vvP  ]]P  DDP  ++P  {{P  w{wuP p jsbs\P W QoǖIoǖCP > 8k0k*P % ggP  c| c| P  _c _c P  [J [J P  W1 W1 P  S S P  O O{ P v  p Kh Kb P ]  W G͕O G͕I P D  > C6 C0 P +  % ? ? P   ; ; P  7i 7i P  3P 3P P  /7 /7 P  + + P  ' ' P |  v #n #h P c  ] ӔU ӔO P J  D < 6 P 1  + #  P      P  o o P  V V P  = = P  $ $ P      P  | t n P i  c ٓ[ ٓU P P  J B < P 7  1 ) # P      P   u u P  \ \ P  ߜC ߜC P  ۜ* ۜ* P  ל ל P  Ӝz Ӝt P o  i Ϝߒa Ϝߒ[ P V  P ˜ƒH ˜ƒB P =  7 ǜ/ ǜ) P $   Ü Ü P   { { P  b b P  I I P  0 0 P    P   z P u  o g a P \  V ̑N ̑H P C  = 5 / P *  $   P    P  hhP  OOP  66P  P  P { umgP b \{ҐT{ҐNP I Cw;w5P 0 *s"sP  o oP  knknP  gUgUP  c<c<P  _#_#P  [ [ P  {WsWmP h bS؏ZS؏TP O IOAO;P 6 0K(K"P  GG P  CtCtP  ?[?[P  ;B;BP  7)7)P  33P  /y/sP n h+ގ`+ގZP U O'ŎG'ŎAP < 6#.#(P # P  zzP  aaP  HHP  //P    P  yP t nf`P [ UˍMˍGP B <4.P ) #P  P  ggP  NNP  55P  P  ߛߛP z tۛlۛfP a [כьSכьMP H Bӛ:ӛ4P / )ϛ!ϛP  ˛˛P  ǛmǛmP  ÛTÛTP  ;;P  ""P    P  zrlP g a׋Y׋SP N H@:P 5 /'!P  P  ssP  ZZP  AAP  ((P  P  xrP m g݊_݊YP T NĊFĊ@P ; 5-'P " {{P  wywyP  s`s`P  oGoGP  k.k.P  ggP  c~cxP s m_e__P Z T[ʉL[ʉFP A ;W3W-P ( "SSP  OOP  KfKfP  GMGMP  C4C4P  ??P  ;;~P y s7k7eP ` Z3ЈR3ЈLP G A/9/3P . (+ +P  ''P |xtplhd`\XTPLHD@<840,($  |xtplhd`\XTPLHD@<840,($  |xtplhd`\XTPLHD@<840,($  g#BcOCbNyda -DE./LKHJonrpqml }~<M:ZWTSRVUYX[Q*t|u{xefw; >18%"!]jFI'vskPzA@97^_3i\ ?,=h`&4(56 ) 0G2$+g#BcOCbNyda -DE./LKHJonrpqml }~<M:ZWTSRVUYX[Q*t|u{xefw; >18%"!]jFI'vskPzA@97^_3i\ ?,=h`&4(56 ) 0G2$+#d#d#<D/D0 D2 D3D4D5"D6)D70#$/$$0$.A%4a%l%3_)3!4h24o4|444444455)5~;5K5\5v55555556!626G6\6r66666677+7788888 99-9.G9W90n9!99 :: ,:/C:$4DP4DS>DZQD]YD:$P4c:N:N :O:G:@Q:>:\:$0DddDfnDhvDjyDi{DjDlDmDnDpDqDrD|:$dd:c:@e:n::@e;;;$DDDDD DD D%D3D@;$+;7; ^;k;~;@;@;;F;$\DHDRDYDdDsD{DDDDDDD;$H;;@;R;;$|DDDDDDDDDD DD.D1D5D<DCDJDgDDDDDDDD;$;; <<@#<@0<z<<<<<<<<= ==!=6=7=08=$lD0D9D<D?DBDJD VDD 9=$0X=d=$DDDe=$==@===$@D$D&$D(P=$$=#=@%='=V=$@D0XD1dD2vD9D6=$0X >/>$8DBDEDBDEDBDEDGDMDRDTDUDNDODIDK>$BA>@M>A Z>Ag>@At>C}>~>>C>>>$Da De&Da)De1Da>DeADaEDeKDgYDmDsDuDwDxDnDoDp>$a >_>` >`>@`>b>c>1??b ?c???$DDDDDDDDD-D0D3D6D]DxDDDDDDDDD?$??K? X?e?@r?~????????$?DDDDDDD&D)D7D:DTDgDDDDDDDDDD DDDDDE?$?? ??@@@@!@1@;@]@tr@AlBB&5B6B7BCBSB]BhB&xByBdzB$h{B̘DdDiDoDuDxD{DDDDDDDDDDDDD D1 D D D D D D D D  D  D  D  D D D D D D B$dBB BB@B@BBB CC=CRC.DDD&̘iD{jD kDwDDDDDD&̘D D D$DܘD! D+ D! D+ D"& D+, D"6 D$9 D+? D$C D+F D#Z D'a D+h D/ D5 D: DA DC DD* D<3 D=8 D>@ D6D D7I D0K D2e D$! EE  E (E@ 5E@ BE"NE#ZE$gE%vE&E'E&)ܘE& E- E"E#E$E%E&E'F&)ܘ#F3 $Fg %F$XDSh D^m DSs DY| D^ DY D^ DU DY D^ Da Dg Dh Dn" Do4 Dq< Dr> DiH DjM DkU DbY Dds &F$Sh KFQWFR dFRqF@R~F@RF@TFUFVFWFYF| FA F@TFUFVGWGY&GH 'Gu (G$Dx D~ D D D D D D D D D D D D D D" D$ D3 D8 D@ )G$x OG}[G~ hG~uG@~G@~G@GGGG G/ G@GGHH3 HD H$DD DJ DM DP DS D[ Do Du Dy D| D D D D D D D D DD H$D =HIH VHcH@pH@}H@HHHH^ H H@HHHI II$IDDD"D(D2D5DfDDD I$0IJJJ WJdJ@qJ@~JJJJ&JJJJJ K&KKK$KDDDDDDDDDDDDDDD #D 5D =D?D ND_DdDl&K$GKSK `KmK@zK@KK&KKKK@K#KJKJKK&KLLNLpL$DpDuDxDDDDDDDDDDD#D)D, D) D, D-D.D51D79D8;D/ED0JD1RD$VD&pL$p N@N N.N;NJNEKNrLN$D@tDDyD@|DDD@DDDGDJDGDJDKDLDRDSDMDNMN$@tnN?|N? N@?NAN@BNNNAN@BNNN$D`Dg DcDgDc$Dg*Dj3De6Dg<DjNDeUDg[Dj^DgaDjdD`DbDeDgDjDoDtDyDzD{ Dz D{D"D3D5D7D9DADVDDDDDDDD|3D}aDuDpDqN$`N^N_ O_O@_O@_+O@a7Ob@ObIOccOrOcOdOeOeOeOgO OO@aObObPcPc'Pd6PeCPeRPebPgqPrPsP$DDDDDDDD DD#D)DPDSDZD`DnDuDDDDDDD D DDD-D/D1D3D;DDDDDDDtP$PP PP@P@P@PPPQ@MQ|kQvQQQQQQ@QQQQ@QQ RRRR$,DDDDD&D)D,D/D2D>DKDNDwD~DD DD DD DDDDD#D-#D4%D2D!bD$vD!xD$zD%|D&D1D2D3D*D+D,DDD3D8R$DRRR _RlR@yR@R@RRRRRRRRRS S+!S@-S6SCSLS]SpSSSSS2SJS$XD=LDBVD=\DB_DDtD=D@DBDDDIDNDQDNDQDRDSDZ6D[8DUADVFDJJDKdS$=LS;S< S<S@<S@< T@>T?T@,T@nT?wT@T@TBTATfT$DdhDjrDdxDj{DhDjDlDhDjDlDdDjDlDpDsDpDsDtDuD|4D}6Dw?DxDT$dhTbTc TcT@cT@cU@eUfUg&Uh3UjWUrXU9YU@eeUfnUgxUhUjU?UHU$DHDRDXD[DvDyD|DDDDDDDDD D D DDXDZDcDhDlDDU$HUU UU@U@V VV%V5V@AVeVRfV]gVpVyVVV@VVcVV$HDDDDDDDDDDDDDDDDD.D4DEDGDIDKDSDDDDDDDV$VV VV@ W@W"W0WWvWW@WWWWWWX@ XXXX$(DDDDDDDDDDD DDDDTDWD^DeDDDDDDDDDDDDDDD #D+D-D:DBDGX$BXPX ]XjX@wX@XXXXXXXXYY@%YIYJY3KY]YhYyYYYYYYY@YY:YKY$DLD%]DiD%oDxD{D%~D"D%D)D"D%D)D%D)D%D) D/# D31 D57 D3: D5B D3F D5L D;b D= DE DF Dt D DB DI DL!DI!DL!DM!DN*!DM5!DN?!DUW!DVe!Dc!D}!D~!D!Dd"De "Dd""De$"Dl*"Dx<"DyG"Dmb"Do"Dp"Dq"Dr"Ds"Df"Dg"Dh"D[#D\#D]#D^#D_*#DO:#DPB#DQP#DRU#DCZ#D6e#D8#D0#Y$L$Z2Z ?ZLZ@YZ@fZlqZpzZhZtZxZZZZZ Z@!["|[#d-[%Q[]R[ S[l^[pg[ht[t[x[[[[[ [@!["|\#d\%)\ *\#+\$LD#D#D#D#D#D#D#D#D#D#D#D#D$D$D$DK$DU$Dw$D$D$D$D$D$D$D$D$D$D$D$D%D %D0%DD%DF%DL%DR%Df%Dq%Dv%D%D%D%D6&D:&DB&DP&D[&Dy&D{&D}&D&D&D&D&D&D&D&D'D'D'D*'DX'D`'Dn'D|'D'D'D'D'D'D'D'D'D'D'D'D'D'D'D'D (D#(D((,\$#]\k\ x\\@\@\P\T\L\X\\\d\]]"]7]@J]`_]@v]h]#]%]P]T]L]X]\]d]^ ^^4^@G^`\^@s^h^ %^:(^$D<(D M(D Y(D _(D e(D k(D(D (D (D(D (D (D(D(D(D (D)D)D%)DM)DU)DX)D[)D'n)D*)D')D*)D+)D,)D9)D;*D<*D3*D4*D5$*D (*D!V*D*D*^$<(^^ ^^@^@^@^___ *_ >_ M_ Z_ i_ y_ _M(_ *_@____ _ _ _  ` ` (` 7`*8`*9`$dDE*DJ*DE*DJ*DL*DG*DJ*DL*DG*DL*DG*DL+DE)+DG,+DJ/+DL6+DRH+DWO+DZ`+DWb+DZd+D[f+D\n+Dc+Dd+D^+D_+DS+DT+:`$E*Z`Ch`D u`D`@D`@D`F`G`G`G`@H`Ja*a+aFaGaG,aG,DtE,DxW,D}b,Dh,Dy,D{,D},D,D,D,D,D,D,D,D,Dy,Za$m+zakal ala@la@lananao)byEbPb@p\brb+b,bnbnbob@pbrb,b,b$ D,D,D,D,D -D-D-D7-D:-DH-DO-Dm-D-D-D-D-D-D-D-D-D .D.D.D .D*.D/.D7.DB.DF.DK.b$,bb cc@&c@3c@?cNc\cjcwcc,c#.c@cccccc*.cf.c$|Dh.Dr.Dx.D{.D.D.D.D.D.D.D.D.D.D.D.D.D.D.D/D /D=/D?/DH/DM/DQ/Dk/c$h.d%d 2d?d@Ld@Ydbdrdd@ddr.dB/dddd@ddH/dm/d$Dp/Dz/D/D/D/D/D/D/D/D/D/D/D/D/D /D 0D  0D  0D0D0DQ0DS0D\0Da0d$p/%e3e @eMe@Ze@gere}eee@eez/eV0eeeee@e f\0 fe0f$Dh0D t0D$z0D 0D$0D 0D!0D$0D'0D,0D00D10D60D80D50D-1D. 1D(1D)(1f$h07fCf Pf]f@jf@wf f!fnff"gt0 g0 g g!&g"2g03g*14g$DA,1DB51DA;1DB>1DFG1DBT1DFW1DBo1DFv1DI1DM1DN1DS1DU1DR15g$A,1^g?jg@ wg@g@@g@@gBgCgCgDg51g1gBgCgChDh1h1h$D_1Dd1D`1D_1D`1Dd1D`2Dd 2Da2Dd2D`02Da72Dd=2DiS2Do2Dp2Dk2Dl2h$_1Eh]Qh] ^h^kh@]xh@^h`hahahbh1h2h`hahaibi2i2i$Dy2D2Dy2Dz2D{2Dz2D2D{2D|2D2D|2D2Dz2D~3D 3D3DC3DZ3De3Dl3Dv3D3D3D3D3D3D3D4D4D4i$y28iwDix Qix^i@xki@xxizi{i|i@|i}i~ii2io3izj{j|j@|,j}8j~NjYjv3Zj4[j$tD4D-4D94D?4DH4DK4DN4DT4Df4Dl4Dy4D4D4D4D4D4D4D4D4D5D45DB5DI5DV5D5D5D5D5D5D5D 6D6D6\j$4jj jj@j@jjj|k@ kk&k6kBkxXkck-4dkO5ektkk|k@kkkkkxkkV5k%6k$ D(6D-6D36D?6DL6Dd6Dg6Dn6Du6D6D6D6D7D)7D17D<7D>7DK7DV7D[7k$(6l)l 6lCl@Pl@]lll@llll?6lD7lll@llmmN7m_7 m$8D`7D e7Dk7Dt7D7D7D 7D7D 7D7D 7D 7D 7D7D7D8D8D*8D.18D;8D%{8D&8D*8D+8D-8D(8D 8D!8D"8D8D8!m$`7FmRm _mlm@ym@mmm@mm m m mt7m48m nn@%n1n >n Rn ]n;8^n8_n$D78D>8D78D89D>9D8-9D909D>79DAM9DF\9DG9DI9DP9DQ9DR9DS9DT9DJ9DK9DL9DM :DB :DC':`n$78n5n6 n6n@6n@6n8n9n:o:o;o@<'o9(o9)o88o9Eo:Ro:_o;jo@<wo9xo):yo$8D\,:Dc1:D\7:D]C:DcO:D]R:DcU:D^Y:Dc_:D]p:D^z:Da}:Dc:Dg:Dl:Dv:D};DT;Df;Dq;Dy;D;D;D~;D;D;D;D;Dw;Dx;Do;Dh;Di;zo$\,:oZo[ o[o@[o@[o]p^p@^!p_0p_@p`Mpacp1:dp;ep]tp^p@^p_p_p`pap;p;p$D;D;D;D <D<D*<D,<D6<D;<p$;pq@qq;q?<q$pD@<DE<DH<DQ<D]<D`<Di<Dl<Dr<D<D<D<D<D<D<D<D=D,=D7=DF=DQ=DT=D^=Dd=Dg=Dj=D=D=D=D=D=D=D=D=D=D=q$@<@qLq Yqfq@sq@qqqq@qqqqq'ryDrr@rQ<rW=rrrr@rrss$s7s@Bsa=Cs=Ds$D=D=D=D >D>D0>D3>D:>DA>DS>Dw>D>D>D >D>D>D ?D ?D #?D'?D/?D:?D??Es$=ksws ss@s@sss@sstt >t>t$t0t@=tIt]tht>itF?jt$TD H?D M?D S?D \?D i?D o?D y?D |?D ?D ?D ?D ?D ?D @D! @D" @D @D @D (@kt$ H?t t t t@ t@ t t u@ u $u\?%u@&u 5u Au@ Nu ^u@_u,@`u$D, ,@D6 1@D, 4@D- @@D3 L@D- O@D3 U@D- X@D/ _@D3 f@D6 w@D3 }@D6 @D3 @D6 @D3 @D6 @D9 @D; @D9 @D; @D< @D; @D< @DA @DF ADG ADF ADH ADF !ADH $ADF )ADK .ADT ADU ADT ADU AD\ AD] AD^ ADe AD\ ADn ADn ADo ADn ADp ADq BDr BDs BDf !BDg /BDg 9BDh N ^@ moTnUo ~ Ç Ӈ@ UU$D UD UD VD VD #VD &VD ,VD /VD ?VD FVD NVD QVD WVD ]VD eVD lVD pVD sVD {VD VD VD VD VD VD VD VD VD VD VD$ VD+ FWD, XWD- `WD. bWD& oWD' wWD( |W$ U ( 5 B@ O@ \      ň ؈@ UhW     - = P@ _oW`Wa$D9 WD: WD9 WD: WDA WD: WDA WD: WD= WDA WDF WDA WDF WDA WDF WDB WDF WDC WDB XDF XDB XDF XDD XDF XDA 7XDC (> 5> E> X@? gWhXi: x< = > > > > Њ@? ߊX Y$Dd YDe YDd YDe #YDm ;YDe >YDm MYDe PYDi `YDm jYDs rYDm xYDs ~YDm YDs YDn YDs YDn YDo YDq YDn YDs YDp YDs YDm YDo YDp YDq YDs YDx ZD} 2ZD ZD ZD ZD ZD ZD ZD Z$d Yc *c 7c D@c Q@c ^e h i |j j j Njj ڋj @k YZe h i |"j 3j @j Pj cj t@k ZZ$D ZD ZD ZD ZD ZD ZD [D [D )[D :[D B[D H[D N[D U[D ][D d[D h[D n[D r[D y[D ~[D [D [D [D [D [D [D [D [D [D \D r\D \D \D \D \D \D \$ Z  Ō Ҍ@ ߌ@  @ ) t8 E xV c s Z\ @  tЍ ݍ x  /\0\1$D \D \D \D \D \D \D \D \D \D \D \D \D ]D &]D C]D U]D ]]D _]D n]D v]D {]2$ \e q ~ @ @ Ɏ ӎ ߎ@ \j] @ $n]%]&$D ]D ]D ]D ]D ]D ]D ]D ]D ]D ]D +^D E^D Z^D e^D g^D q^D v^D ^D ^D ^D ^D ^'$ ]P \ i v@ @ ?J T@ c]dj^e t ~ @ q^^$(D ^D ^D ^D ^D ^D ^D ^D ^D ^D ^D _D K_D m_D |_D" _D# _D$ _D _D _D _D _D _D _$ ^ ː ؐ @ @  # -@ < N^O_P _ i@ x __$(D- _D1 _D- _D1 _D2 `D8 `D9 !`D8 $`D9 &`D; +`D< -`D3 4`D4 9`D5 A`$- _+ , Α, ۑ. @/ _E`$xDF H`DJ T`DG ]`DJ c`DG m`DJ p`DL `DO `DL `DO `DL `DO `DS `DT `DP `DQ `$F H`1D =E JE W@E d@E qG @H ֒&I &T`'`(G 8@H F&I V`W`X$YD_ `Dc `D` `Dc `D` `Dc aDe aDh -aDe 0aDh 3aDe 6aDh 9aDl IaDm TaDi \aDj aac$_ `] ^ ^ @^ @^ ʓ` ؓ@a ,JU&b z`{Wa|` @a &b \aea$Dv haD maDv saD aD} aD aD aD} aD aDz aD aD aDv aDx aDz aD aD aD bD (bD *bD ,bD 2bD SbD bD bD bD bD bD bD bD bD bD bD cD cD cD OcD \cD jcD rcD vcD {cD }cD cD cD c$v haҔt u u @u @u @w  x `-@y Tb%<OTq(Øژz Pz X{ | x} d!} L5 hYaZUc[@w gx `t@y z Pz X{ | x} dƙ} Lڙ hbcc$DD cD cD cD cD cD cD cD cD cD cD cD dD dD dD dD 9dD QdD SdD UdD [dD dD dD dD dD dD dD dD dD dD dD dD dD dD e$ c # 0 =@ J@ W@ c pp@ { t xcd@ Ś pҚ@ ݚ t xde$tD eD 1eD =eD CeD IeD OeD aeD geD jeD peD zeD eD eD eD eD eD eD eD fD fD ffD zfD |fD ~fD fD fD fDfDfD fD fD fD fD f$ e, : G T@ a@ n@ z X T \ ` d ś h1ef@  X T \ `* d: B hQfRfS$D fDfD gDgDgD!gD*gD6gD?gDKgD lgDvgD}gDgDgDgDgDgD%gD&hD%hD&hD(hD)hD+;hD-ChD.EhD!RhD"WhD#_hDchDhT$ fx   @ @ @ Ɯ lӜ@ޜptx&g'Kh(@ 4 lA@LpYthpxRhh$D7hDAhD7hD=hD9hDAhD7hD=hDAhDDhDEiDDiDEiDIiDN iDO1iDN3iDO5iDQ7iDR?iDT\iDVdiDWfiDJsiDKxiDLiDFiDGi$7h55 Ý6Н@5ݝ@6@89|@:;=:h;li<@8H9|U@:`;h=wsixiy$$z D`iDiiD`iDiiD`iDbiDiiDljDmjDljDmjDqjDv1jDwBjDvDjDwFjDyHjDzPjD|jjD~rjDtjDrjDsjDtjDnjDoj$`i^_ Þ_О@_ݞ@_@ab@ce&g ;i<j=@aIbV@caei&g yjzj{$DjDjDjDjDjDjDjDjDjDkDkDkD&kD-kDGkD_kDakDckDikDkDkDkDkDkDkDkDkDkDkDkDkDkDl|$j ̟@ٟ@@p@ txCjDkE@Qp^@itv~xkl$\DlD&lD2lD8lD;lD>lDAlDGlDJlDPlDWlDclDlDlDlDlDlDlDlDlDlD mDmDAmDImDQmDXmDfmDqmDmDmDmDmDmDmDmDmDmDmDmDmDn$lĠ Ѡޠ@@@,@Qn&ltm@ȡܡ {mn$DnDnDnD$nD'nD-nDEnDLnDVnDhnD}nDnDnDnDnDnDnDnDnDnDnDn$nFR _l@y@@¢'nânĢѢ@ܢnn$(DnDnDnDnDnDnDoDoD oDoDoD#oD&oD)oDAoDHoDboDwoDyoD{oD$oD%oD&oD)oD*oD+oD.oD5pD6pD7(pD90pD:;pD/PpD0\pD1qpD2vpD3~pD pD!p$n ) 6C@P@]j@u@ˣ&(n>p@ $-;@M`&(pEpqpr$DCpDIpDCpDEpDFpDIpDMpDEpDFpDIpDMpDIpDMpDDqDIqDM$qDQ:qDROqDQQqDRSqDVUqD[yqD]qD^qDWqDXqDYqDSqDTqs$CpAB B@B̤@B٤DEF@G I1p2q3D@EMF\@GgIvqwqx$DgqDjqDgqDiqDjqDnqDkqDiqDnqDrrDk rDn rDrrDnrDrrDn%rDr(rDhGrDnNrDrUrDvkrDwrDvrDwrD{rDrDrDrD}rD~rDrDxrDy sy$gqef fĥ@fѥ@fޥhijk@l"nFqGrHhUibjqk@lnr s$@D sDsDsDsDsD%sD=sDDsDNsD`sDusDwsDysD{sDsDsDsDsDsDsDsDs$ sǦӦ @@@CsDsER@]lsmsn$DsDsDsDsDsDtD'tD*tD1tD;tDMtDXtD`tDltDstDtDtDtDtDtDtDtDtDuDuo$s @Ƨ@ӧ@s,st-t.t/>@GSbtcstttu$uv$@D$uD)uD/uD8uD>uDDuD\uDcuDmuDuDuDuDuDuDuDuDuDvDvDvDvw$$u è@Ш@ݨ@&8u'u(7@@M\v]/v^$ D0vD6vDFv_$0v$D HvD TvDkvDovDrvDrvDxvDvDv$ Hv˩ թ@ߩ@ Wvv$\D"vD#vD&vD'vD,vD-vD)vD*vD$v$"v %! /!9@!C@!M$pD3wD4wD5*wN$3wt2$D<,wD=2wD>Cw$<,w;$DDHwDENwDF_w$DHwC$DMdwDNnwDQuwDRxwDS{wDOw$MdwK'L 8$,DZwD[wD^wD_wD`wD\w9$Zw^XjY {$,DgwDmwDnwDmwDnwDqwDrxD xDxDu*xDv@xDKxDz`xDtxDxDxDxDxDxDxDxDxDxDxDxD{xD|xDyDyDyD;yDDyDIyDsy|$gwef @fΫ@h٫@ijwpxwxpxx y pxyyzy$D|yDyDyDyDyDyDyDyDyDyDzD zD zD4zD?zDXzD`zDwzDyzD{zD}zDzDzDzDzDzDzDzDzDzDzDzDzDzD {D{D{$|y:F T@b@m@{yxyczxzzxzzJ{$DL{Df{Dn{Dp{Dr{Dx{D{D{D+{D{D{D{D{D|D |D(|D0|D"G|DI|D"K|D&M|D'P|D&S|D'X|D*r|D y|D |D|D|D|D|D|D|D|D|D|D|$L{լ @@@f{x*x{+3|,x7y|8|9xD|E|F}G$D2}D69}D7V}D8_}DM_}D<l}DA}DD}DA}DD}DH}DI}DH}DI}DL}D=}D>}H$2}n0z1 @1@349}}$DT}DY}DV}DY}DV}DY}DT~DY~DT ~DY~D[~D`B~DcV~D`X~DcZ~Dg\~Dhd~Dk{~Dl}~D\~D]~$T}׭RS @S@U VW}~@U,V5W=~>~?$Dr~Dv~Dr~Dv~D{~D~~D{~D~~D~D~D~D~DwDx@$r~kqw@st~ $tD D)DFDODOD\D|DDDDDDDDDD$  ͮ@ۮ@)$DDDDDDDD DDD0DWDqDqDsDDD܀D DADTD\D^DiDqDsD~DDDDDDƁDсD؁D܁DDD$$ ,9@F@Sw@@@@̯ۯy@@@(@7DQ`pqCr$|DDDMDSDVDeDkDnDqDtDwDDDDDs$D @ɰ@ְM $!8DDDDDD DDD)D,DDDGD]DbDgDuDzDDDɃDуD ؃D D D D ,$Ua n{@@&8ѱ&'ۃ(&88Ogv$HDD DDDD"D0D6D@DCD[D^D xD%D'D&D'D-ЄD=ڄD>D@D>D? D>D@!D>)D@-DAFD@NDJcDKjDExDTxDU{DTDUDVDCDGDHD)D*DN&D.YD/iD.pD/vD0}D.D0D1D0D3ֆD7D82D(G$ڲ @ @&H?Vn}@@@Ƴ0dzȳ&Hس%@1@=IU@_`ba$\bXD`dDgiD`oDgxDc~DgDcDgDkDp؇DqDrDDu#Dv1Dw9Dv=Dw@DzCD{JD|PD}XD|ZD}\D^DzgDjDzrDwDD~DDDDD~DD~ȈDlψDmm$`d^_ _@_ô@_д&aXb@bcc.@d8@dBdKeZx[\&aXlbx@bcc@d@ddµeѵ&ҵ@{ߵJg@{jr@{ψ$DDDDDD1D<DADIDWDbDlDsDyDDDDDDDDDDDDDΉDDDDDD$,8D@Q@[@en}~@s@@$(DDDD%D@DYDdDiDqDDDDDDDDDDDDÊDȊDӊD؊D݊DDDDDDD D:$ض@@@)*@789@FGÊH@U؊V W<X$(D<DBD[D`DbY$<Hq$8dDtDyDDDDDDDʋD D D "D')D3DADIDMDPDSDZD`DhDjDlD nDwD"zDD$D&DDDDDnjD͌DЌD،DߌD$tٷ @ @&d>J@Wgw@@,&d@θ޸@@ 6@(Z)w*@7z89@FGߌHI$D/D4D/D4 D9(D:AD;LDUQD>YD?gD@rDC|DDDEDFDEDFDNDCDPDCDRDTDGDIōDJЍDKލDHDGDHDGD5D6"J$/.0@0@1@11ǹ2ֹ׹@D@D@D$$(D]$Db)D],Db5DgPDhiDitDyDlDmDnDqDrDsDtDsDtD|DqȎD~ˎDqӎD؎DDuDwDxDyDvDuDv!Du)Dc0DdJ$]$A\M^Y@^f@_p@_z_`)@rȎ@rˎӎ@r0L$(DLDRDkDpDrº$LX $8 pDDDDDDҏDDDD%D4DFDN$9E R_@l@y&p@ûĻ0Ż&pջ@4R$DTDYD\DdDDDDʐDҐ$T#/ 9@CViYjĐk~ʐ֐$xDؐDݐDDDDDDDDDD DDDDSDrDDDDDDDD D DDD%$ؐռ @@&x:Jf{@Խսֽ&x'=O@[j,$TD,D1D7D@DFDMDSD]D`DnDqDxDDDD̒DD D D" D D(D0D;DBDUDXDZDbDiDDD!DDDœDדDDDD$,;پ @@ &2;PZ@f@u@&տ߿@@ !"(#$$D((D1-D(3D1<D*BD+ID1OD+YD1\D(jD.mD1tD6D7D<D>ȔD?D@DADYDDDE$DF,DK7DL>DMQDLTDMVDN^DTeDUDVDXDPDQDGDHӕD8D9D8 D9/$((h(t( (@(@(&)*+,@,@,- .-/7<8 9&)I*R+g,q@,}@,-./$$D`$Dh)D`,Dh5Dc;DhADcKDhND`\Dh_DlqDmDnDoDpϖDqזD|זD}DDtDuDv DD*D5D9DRD]DhDDDwDxD{DӗDٗ$`$^_ _@_$@_1&aVcq@d|@ddef5&ac@d@ddef$DDD$EQ ^k$ DDDDD!D$D'D?DBDID[DjDmDpDsDvDzDDDl$ @@@$%}&5G@VWX$Y y    ;d X \c @p#tX,2LKhcH|h.L ha c( iE j f  e h<(&*?+p/ Ir-,1{ 0v w1'2iDd(6;dD<d`78=^ؐ$(,})~M ,:;"rH@<dN  HPd } w( Hw4H? ] C Hv) _m wHY,@L dwuLft L{h0 X' sD $u ^(P U6 Wi  YRdT ZlQ\R \JlQ  $?Th #4G4'D O.J vEIHG0 IIKx l `J H`NpMf MU |y ,wf l n n  s q p"#Q# "G7Vu  i <#s#+##C!>""/"J!!R -  &QWFk~,RB2" !fq!!c# Wb"R"^!!!~s4   bF1jq"N" )q}jE,J[;  gSv d5LB9- S""+!&!:!2w*F^ Ud "?!o"i _igraphmodule_Graph_init_internal_igraphmodule_Graph_new_igraphmodule_Graph_clear_igraphmodule_Graph_traverse_igraphmodule_Graph_dealloc_igraphmodule_Graph_init___i686.get_pc_thunk.bx_igraphmodule_Graph_str_igraphmodule_Graph_vcount_igraphmodule_Graph_ecount_igraphmodule_Graph_is_directed_igraphmodule_Graph_add_vertices_igraphmodule_Graph_delete_vertices_igraphmodule_Graph_add_edges_igraphmodule_Graph_delete_edges_igraphmodule_Graph_degree_igraphmodule_Graph_maxdegree_igraphmodule_Graph_neighbors_igraphmodule_Graph_successors_igraphmodule_Graph_predecessors_igraphmodule_Graph_get_eid_igraphmodule_Graph_diameter_igraphmodule_Graph_girth_igraphmodule_Graph_Adjacency_igraphmodule_Graph_Atlas_igraphmodule_Graph_Barabasi_igraphmodule_Graph_Erdos_Renyi_igraphmodule_Graph_Establishment_igraphmodule_Graph_Full_igraphmodule_Graph_GRG_igraphmodule_Graph_Growing_Random_igraphmodule_Graph_Star_igraphmodule_Graph_Lattice_igraphmodule_Graph_Preference_igraphmodule_Graph_Asymmetric_Preference_igraphmodule_Graph_Recent_Degree_igraphmodule_Graph_Ring_igraphmodule_Graph_Tree_igraphmodule_Graph_Degree_Sequence_igraphmodule_Graph_Isoclass_igraphmodule_Graph_Watts_Strogatz_igraphmodule_Graph_is_connected_igraphmodule_Graph_are_connected_igraphmodule_Graph_average_path_length_igraphmodule_Graph_betweenness_igraphmodule_Graph_pagerank_igraphmodule_Graph_bibcoupling_igraphmodule_Graph_closeness_igraphmodule_Graph_clusters_igraphmodule_Graph_constraint_igraphmodule_Graph_copy_igraphmodule_Graph_decompose_igraphmodule_Graph_cocitation_igraphmodule_Graph_edge_betweenness_igraphmodule_Graph_get_shortest_paths_igraphmodule_Graph_get_all_shortest_paths_igraphmodule_Graph_shortest_paths_igraphmodule_Graph_spanning_tree_igraphmodule_Graph_simplify_igraphmodule_Graph_subcomponent_igraphmodule_Graph_rewire_igraphmodule_Graph_subgraph_igraphmodule_Graph_transitivity_undirected_igraphmodule_Graph_transitivity_local_undirected_igraphmodule_Graph_topological_sorting_igraphmodule_Graph_reciprocity_igraphmodule_Graph_density_igraphmodule_Graph_layout_circle_igraphmodule_Graph_layout_sphere_igraphmodule_Graph_layout_random_igraphmodule_Graph_layout_random_3d_igraphmodule_Graph_layout_kamada_kawai_igraphmodule_Graph_layout_kamada_kawai_3d_igraphmodule_Graph_layout_fruchterman_reingold_igraphmodule_Graph_layout_fruchterman_reingold_3d_igraphmodule_Graph_layout_grid_fruchterman_reingold_igraphmodule_Graph_layout_lgl_igraphmodule_Graph_layout_reingold_tilford_igraphmodule_Graph_get_adjacency_igraphmodule_Graph_laplacian_igraphmodule_Graph_get_edgelist_igraphmodule_Graph_to_undirected_igraphmodule_Graph_to_directed_igraphmodule_Graph_Read_DIMACS_igraphmodule_Graph_Read_Edgelist_igraphmodule_Graph_Read_Ncol_igraphmodule_Graph_Read_Lgl_igraphmodule_Graph_Read_Pajek_igraphmodule_Graph_Read_GML_igraphmodule_Graph_Read_GraphML_igraphmodule_Graph_write_dimacs_igraphmodule_Graph_write_edgelist_igraphmodule_Graph_write_gml_igraphmodule_Graph_write_ncol_igraphmodule_Graph_write_lgl_igraphmodule_Graph_write_graphml_igraphmodule_Graph_isoclass_igraphmodule_Graph_isomorphic_igraphmodule_GraphType_igraphmodule_Graph_attribute_count_igraphmodule_Graph_get_attribute_igraphmodule_Graph_set_attribute_igraphmodule_Graph_attributes_igraphmodule_Graph_vertex_attributes_igraphmodule_Graph_edge_attributes_igraphmodule_Graph_get_vertices_igraphmodule_Graph_get_edges_igraphmodule_Graph_disjoint_union_igraphmodule_Graph_union_igraphmodule_Graph_intersection_igraphmodule_Graph_difference_igraphmodule_Graph_complementer_igraphmodule_Graph_complementer_op_igraphmodule_Graph_compose_igraphmodule_Graph_bfs_igraphmodule_Graph_bfsiter_igraphmodule_Graph_maxflow_value_igraphmodule_Graph_mincut_value_igraphmodule_Graph_cliques_igraphmodule_Graph_largest_cliques_igraphmodule_Graph_maximal_cliques_igraphmodule_Graph_clique_number_igraphmodule_Graph_independent_vertex_sets_igraphmodule_Graph_largest_independent_vertex_sets_igraphmodule_Graph_maximal_independent_vertex_sets_igraphmodule_Graph_independence_number_igraphmodule_Graph_coreness_igraphmodule_Graph_modularity_igraphmodule_Graph_community_edge_betweenness_igraphmodule_Graph_community_leading_eigenvector_naive_igraphmodule_Graph_community_leading_eigenvector_igraphmodule_Graph_community_fastgreedy_igraphmodule_Graph___graph_as_cobject___igraphmodule_Graph___register_destructor___igraphmodule_Graph_getseters_igraphmodule_Graph_methods_igraphmodule_Graph_as_mapping_igraphmodule_Graph_as_number___i686.get_pc_thunk.axdyld_stub_binding_helper__Py_NotImplementedStruct_PyExc_KeyError_PyExc_IOError_PyExc_MemoryError_PyInt_Type_PyExc_TypeError_PyExc_ValueError__Py_NoneStruct_PyExc_AssertionError__Py_TrueStruct_PyList_Type_PyBool_Type__Py_ZeroStruct_PyCObject_FromVoidPtr_igraph_community_fastgreedy_igraph_community_leading_eigenvector_igraph_community_leading_eigenvector_naive_igraph_community_edge_betweenness_igraph_modularity_igraph_coreness_igraph_independence_number_igraph_maximal_independent_vertex_sets_igraph_largest_independent_vertex_sets_igraph_independent_vertex_sets_igraph_clique_number_igraph_maximal_cliques_igraph_largest_cliques_igraphmodule_vector_t_to_PyTuple_igraph_cliques_igraph_st_mincut_value_igraph_mincut_value_igraph_maxflow_value_igraphmodule_BFSIter_new_igraph_bfs_igraph_compose_igraph_complementer_igraph_difference_igraph_intersection_igraph_intersection_many_igraph_union_igraph_union_many_igraph_disjoint_union_PyErr_Clear_igraph_disjoint_union_many_igraphmodule_append_PyIter_to_vector_ptr_t_igraph_vector_ptr_push_back_PyObject_GetIter_igraphmodule_EdgeSeq_New_igraphmodule_VertexSeq_New_PyDict_Keys_PyDict_DelItem_PyErr_Occurred_PyDict_GetItem_PyDict_Size_igraph_isomorphic_vf2_igraph_isomorphic_igraph_isoclass_igraph_isoclass_subgraph_igraph_write_graph_graphml_igraph_write_graph_lgl_igraph_write_graph_ncol_igraph_write_graph_gml_PyObject_Str_igraph_write_graph_edgelist_igraph_write_graph_dimacs_PyString_FromString_igraph_read_graph_graphml_igraph_read_graph_gml_igraph_read_graph_pajek_igraph_read_graph_lgl_igraph_read_graph_ncol_igraph_read_graph_edgelist_igraph_read_graph_dimacs_fclose_strerror___error_fopen_igraph_to_directed_igraph_to_undirected_igraphmodule_vector_t_to_PyList_pairs_igraph_get_edgelist_igraph_laplacian_igraph_get_adjacency_igraph_layout_reingold_tilford_igraph_layout_lgl_igraph_layout_grid_fruchterman_reingold_igraph_layout_fruchterman_reingold_3d_igraph_layout_fruchterman_reingold_igraph_layout_kamada_kawai_3d_igraph_layout_kamada_kawai_igraph_layout_random_3d_igraph_layout_random_igraph_layout_sphere_igraph_layout_circle_igraph_density_igraph_reciprocity_igraph_topological_sorting_igraph_transitivity_local_undirected_igraph_transitivity_undirected_igraph_subgraph_igraph_rewire_igraph_subcomponent_igraph_simplify_igraph_minimum_spanning_tree_prim_igraph_minimum_spanning_tree_unweighted_igraph_shortest_paths_igraph_vector_ptr_destroy_all_igraph_vector_ptr_e_igraph_get_all_shortest_paths_PyList_SetItem_free_igraph_get_shortest_paths_igraph_vss_all_calloc_igraph_edge_betweenness_igraph_cocitation_igraph_free_PyList_New_igraph_vector_ptr_size_igraph_vector_ptr_destroy_igraph_decompose_igraph_vector_ptr_init_bcopy_igraph_copy_igraph_constraint_igraphmodule_PyObject_to_attribute_values_igraph_clusters_igraph_closeness_igraphmodule_matrix_t_to_PyList_igraph_bibcoupling_igraph_matrix_init_igraph_pagerank_igraph_betweenness_PyFloat_FromDouble_igraph_average_path_length_igraph_are_connected_igraph_is_connected_igraph_watts_strogatz_game_igraph_isoclass_create_igraph_degree_sequence_game_igraph_tree_igraph_ring_igraph_recent_degree_game_igraphmodule_vector_t_pair_to_PyList_igraph_asymmetric_preference_game_PyDict_SetItem_igraph_preference_game_igraph_lattice_igraph_star_igraph_growing_random_game_igraph_grg_game_igraph_full_igraph_establishment_game_igraph_matrix_ncol_igraph_matrix_nrow_PyList_Size_igraph_erdos_renyi_game_igraph_nonlinear_barabasi_game_igraph_barabasi_game_PyInt_AsLong_igraph_atlas_igraph_matrix_destroy_igraph_adjacency_igraphmodule_PyList_to_matrix_t_igraph_girth_igraph_diameter_igraph_get_eid_igraph_neighbors_igraph_maxdegree_PyInt_FromLong_igraphmodule_vector_t_to_PyList_igraph_degree_igraph_vs_destroy_igraph_vector_init_igraphmodule_PyObject_to_vs_t_igraph_es_destroy_igraph_delete_edges_igraph_es_pairs_igraph_es_vector_PyObject_IsTrue_igraph_add_edges_igraph_delete_vertices_igraph_vss_vector_igraph_add_vertices_PyErr_SetString_PyArg_ParseTuple_Py_BuildValue_PyString_FromFormat_igraph_vcount_igraph_ecount_igraph_is_directed_igraph_empty_igraph_vector_destroy_igraph_create_igraphmodule_handle_igraph_error_igraphmodule_PyList_to_vector_t_PyType_IsSubtype_PyArg_ParseTupleAndKeywords_PyObject_GC_Del_PyObject_CallObject_PyCallable_Check_igraph_destroy_PyObject_ClearWeakRefs_PyObject_GC_UnTrack/mnt/gmirror/ports/math/py-igraph/work/igraph-0.4.1/src/graphobject.cgcc2_compiled.igraphmodule_Graph_init_internal:F(0,1)=(0,1)void:t(0,1)self:p(0,2)=*(0,3)=(0,4)=s112ob_refcnt:(0,5)=r(0,5);-2147483648;2147483647;,0,32;ob_type:(0,6)=*(0,7)=xs_typeobject:,32,32;g:(0,8)=(0,9)=xsigraph_s:,64,704;destructor:(0,10)=*(0,11)=(0,12)=xs_object:,768,32;vseq:(0,10),800,32;eseq:(0,10),832,32;weakreflist:(0,10),864,32;;igraphmodule_GraphObject:t(0,3)int:t(0,5)_typeobject:T(0,7)=s192ob_refcnt:(0,5),0,32;ob_type:(0,6),32,32;ob_size:(0,5),64,32;tp_name:(0,13)=*(0,14)=r(0,14);0;127;,96,32;tp_basicsize:(0,5),128,32;tp_itemsize:(0,5),160,32;tp_dealloc:(0,15)=(0,16)=*(0,17)=f(0,1),192,32;tp_print:(0,18)=(0,19)=*(0,20)=f(0,5),224,32;tp_getattr:(0,21)=(0,22)=*(0,23)=f(0,10),256,32;tp_setattr:(0,24)=(0,25)=*(0,26)=f(0,5),288,32;tp_compare:(0,27)=(0,28)=*(0,29)=f(0,5),320,32;tp_repr:(0,30)=(0,31)=*(0,32)=f(0,10),352,32;tp_as_number:(0,33)=*(0,34)=(0,35)=s152nb_add:(0,36)=(0,37)=*(0,38)=f(0,10),0,32;nb_subtract:(0,36),32,32;nb_multiply:(0,36),64,32;nb_divide:(0,36),96,32;nb_remainder:(0,36),128,32;nb_divmod:(0,36),160,32;nb_power:(0,39)=(0,40)=*(0,41)=f(0,10),192,32;nb_negative:(0,42)=(0,31),224,32;nb_positive:(0,42),256,32;nb_absolute:(0,42),288,32;nb_nonzero:(0,43)=(0,44)=*(0,45)=f(0,5),320,32;nb_invert:(0,42),352,32;nb_lshift:(0,36),384,32;nb_rshift:(0,36),416,32;nb_and:(0,36),448,32;nb_xor:(0,36),480,32;nb_or:(0,36),512,32;nb_coerce:(0,46)=(0,47)=*(0,48)=f(0,5),544,32;nb_int:(0,42),576,32;nb_long:(0,42),608,32;nb_float:(0,42),640,32;nb_oct:(0,42),672,32;nb_hex:(0,42),704,32;nb_inplace_add:(0,36),736,32;nb_inplace_subtract:(0,36),768,32;nb_inplace_multiply:(0,36),800,32;nb_inplace_divide:(0,36),832,32;nb_inplace_remainder:(0,36),864,32;nb_inplace_power:(0,39),896,32;nb_inplace_lshift:(0,36),928,32;nb_inplace_rshift:(0,36),960,32;nb_inplace_and:(0,36),992,32;nb_inplace_xor:(0,36),1024,32;nb_inplace_or:(0,36),1056,32;nb_floor_divide:(0,36),1088,32;nb_true_divide:(0,36),1120,32;nb_inplace_floor_divide:(0,36),1152,32;nb_inplace_true_divide:(0,36),1184,32;;,384,32;tp_as_sequence:(0,49)=*(0,50)=(0,51)=s40sq_length:(0,43),0,32;sq_concat:(0,36),32,32;sq_repeat:(0,52)=(0,53)=*(0,54)=f(0,10),64,32;sq_item:(0,52),96,32;sq_slice:(0,55)=(0,56)=*(0,57)=f(0,10),128,32;sq_ass_item:(0,58)=(0,59)=*(0,60)=f(0,5),160,32;sq_ass_slice:(0,61)=(0,62)=*(0,63)=f(0,5),192,32;sq_contains:(0,64)=(0,28),224,32;sq_inplace_concat:(0,36),256,32;sq_inplace_repeat:(0,52),288,32;;,416,32;tp_as_mapping:(0,65)=*(0,66)=(0,67)=s12mp_length:(0,43),0,32;mp_subscript:(0,36),32,32;mp_ass_subscript:(0,68)=(0,69)=*(0,70)=f(0,5),64,32;;,448,32;tp_hash:(0,71)=(0,72)=*(0,73)=f(0,74)=r(0,74);-2147483648;2147483647;,480,32;tp_call:(0,39),512,32;tp_str:(0,30),544,32;tp_getattro:(0,75)=(0,37),576,32;tp_setattro:(0,76)=(0,69),608,32;tp_as_buffer:(0,77)=*(0,78)=(0,79)=s16bf_getreadbuffer:(0,80)=(0,81)=*(0,82)=f(0,5),0,32;bf_getwritebuffer:(0,83)=(0,81),32,32;bf_getsegcount:(0,84)=(0,85)=*(0,86)=f(0,5),64,32;bf_getcharbuffer:(0,87)=(0,88)=*(0,89)=f(0,5),96,32;;,640,32;tp_flags:(0,74),672,32;tp_doc:(0,13),704,32;tp_traverse:(0,90)=(0,91)=*(0,92)=f(0,5),736,32;tp_clear:(0,43),768,32;tp_richcompare:(0,93)=(0,94)=*(0,95)=f(0,10),800,32;tp_weaklistoffset:(0,74),832,32;tp_iter:(0,96)=(0,31),864,32;tp_iternext:(0,97)=(0,31),896,32;tp_methods:(0,98)=*(0,99)=xsPyMethodDef:,928,32;tp_members:(0,100)=*(0,101)=xsPyMemberDef:,960,32;tp_getset:(0,102)=*(0,103)=xsPyGetSetDef:,992,32;tp_base:(0,6),1024,32;tp_dict:(0,10),1056,32;tp_descr_get:(0,104)=(0,40),1088,32;tp_descr_set:(0,105)=(0,69),1120,32;tp_dictoffset:(0,74),1152,32;tp_init:(0,106)=(0,69),1184,32;tp_alloc:(0,107)=(0,108)=*(0,109)=f(0,10),1216,32;tp_new:(0,110)=(0,111)=*(0,112)=f(0,10),1248,32;tp_free:(0,113)=(0,114)=*(0,115)=f(0,1),1280,32;tp_is_gc:(0,43),1312,32;tp_bases:(0,10),1344,32;tp_mro:(0,10),1376,32;tp_cache:(0,10),1408,32;tp_subclasses:(0,10),1440,32;tp_weaklist:(0,10),1472,32;tp_del:(0,15),1504,32;;igraph_t:t(0,8)igraph_s:T(0,9)=s88n:(0,116)=(0,117)=r(0,5);8;0;,0,64;directed:(0,118)=(0,5),64,32;from:(0,119)=(0,120)=xsigraph_vector_t:,96,96;to:(0,119),192,96;oi:(0,119),288,96;ii:(0,119),384,96;os:(0,119),480,96;is:(0,119),576,96;attr:(0,121)=*(0,1),672,32;;PyObject:t(0,11)_object:T(0,12)=s8ob_refcnt:(0,5),0,32;ob_type:(0,6),32,32;;char:t(0,14)destructor:t(0,15)printfunc:t(0,18)getattrfunc:t(0,21)setattrfunc:t(0,24)cmpfunc:t(0,27)reprfunc:t(0,30)PyNumberMethods:t(0,34)binaryfunc:t(0,36)ternaryfunc:t(0,39)unaryfunc:t(0,42)inquiry:t(0,43)coercion:t(0,46)PySequenceMethods:t(0,50)intargfunc:t(0,52)intintargfunc:t(0,55)intobjargproc:t(0,58)intintobjargproc:t(0,61)objobjproc:t(0,64)PyMappingMethods:t(0,66)objobjargproc:t(0,68)hashfunc:t(0,71)long int:t(0,74)getattrofunc:t(0,75)setattrofunc:t(0,76)PyBufferProcs:t(0,78)getreadbufferproc:t(0,80)getwritebufferproc:t(0,83)getsegcountproc:t(0,84)getcharbufferproc:t(0,87)traverseproc:t(0,90)richcmpfunc:t(0,93)getiterfunc:t(0,96)iternextfunc:t(0,97)PyMethodDef:T(0,99)=s16ml_name:(0,13),0,32;ml_meth:(0,122)=(0,37),32,32;ml_flags:(0,5),64,32;ml_doc:(0,13),96,32;;PyMemberDef:T(0,101)=s20name:(0,13),0,32;type:(0,5),32,32;offset:(0,5),64,32;flags:(0,5),96,32;doc:(0,13),128,32;;PyGetSetDef:T(0,103)=s20name:(0,13),0,32;get:(0,123)=(0,124)=*(0,125)=f(0,10),32,32;set:(0,126)=(0,127)=*(0,128)=f(0,5),64,32;doc:(0,13),96,32;closure:(0,121),128,32;;descrgetfunc:t(0,104)descrsetfunc:t(0,105)initproc:t(0,106)allocfunc:t(0,107)newfunc:t(0,110)freefunc:t(0,113)igraph_integer_t:t(0,116)double:t(0,117)igraph_bool_t:t(0,118)igraph_vector_t:t(0,119)igraph_vector_t:T(0,120)=s12stor_begin:(0,129)=*(0,130)=(0,117),0,32;stor_end:(0,129),32,32;end:(0,129),64,32;;PyCFunction:t(0,122)getter:t(0,123)setter:t(0,126)igraph_real_t:t(0,130)igraphmodule_Graph_new:F(0,10)type:p(0,131)=*(0,132)=(0,7)args:p(0,10)kwds:p(0,10)PyTypeObject:t(0,132)self:r(0,2)igraphmodule_Graph_clear:F(0,5)self:p(0,2)tmp:r(0,10)tmp:r(0,10)igraphmodule_Graph_traverse:F(0,5)self:p(0,2)visit:p(0,133)=(0,134)=*(0,135)=f(0,5)arg:p(0,121)visitproc:t(0,133)vret:r(0,5)i:r(0,5)igraphmodule_Graph_dealloc:F(0,1)self:p(0,2)r:r(0,10)igraphmodule_Graph_init:F(0,5)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,136)=ar(0,137)=r(0,137);0000000000000;0037777777777;;0;3;(0,13)long unsigned int:t(0,138)=r(0,138);0000000000000;0037777777777;n:(0,5)edges:(0,10)dir:(0,10)edges_vector:(0,119)kwlist:(0,136)n:(0,5)edges:(0,10)dir:(0,10)edges_vector:(0,119)igraphmodule_Graph_str:F(0,10)self:p(0,2)igraphmodule_Graph_vcount:F(0,10)self:p(0,2)result:r(0,10)igraphmodule_Graph_ecount:F(0,10)self:p(0,2)result:r(0,10)igraphmodule_Graph_is_directed:F(0,10)self:p(0,2)igraphmodule_Graph_add_vertices:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)n:(0,74)n:(0,74)igraphmodule_Graph_delete_vertices:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)list:(0,10)v:(0,119)list:(0,10)v:(0,119)igraphmodule_Graph_add_edges:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)list:(0,10)v:(0,119)list:(0,10)v:(0,119)_kwlist.0igraphmodule_Graph_delete_edges:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)list:(0,10)by_index:(0,10)v:(0,119)es:(0,139)=(0,140)=xsigraph_es_t:igraph_es_t:t(0,139)igraph_es_t:T(0,140)=s20type:(0,5),0,32;data:(0,141)=u16vid:(0,116),0,64;eid:(0,116),0,64;vecptr:(0,142)=*(0,143)=k(0,119),0,32;adj:(0,144)=s12vid:(0,116),0,64;mode:(0,145)=(0,146)=eIGRAPH_OUT:1,IGRAPH_IN:2,IGRAPH_ALL:3,IGRAPH_TOTAL:3,;,64,32;;,0,96;seq:(0,147)=s16from:(0,116),0,64;to:(0,116),64,64;;,0,128;path:(0,148)=s8ptr:(0,142),0,32;mode:(0,118),32,32;;,0,64;;,32,128;;igraph_neimode_t:t(0,145) :T(0,146)kwlist:V(0,149)=ar(0,137);0;2;(0,13)list:(0,10)by_index:(0,10)v:(0,119)es:(0,139)kwlist:V(0,149)_kwlist.1igraphmodule_Graph_degree:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)list:(0,10)dtype:(0,5)loops:(0,10)result:(0,119)vs:(0,150)=(0,151)=xsigraph_vs_t:igraph_vs_t:t(0,150)igraph_vs_t:T(0,151)=s20type:(0,5),0,32;data:(0,152)=u16vid:(0,116),0,64;vecptr:(0,142),0,32;adj:(0,153)=s12vid:(0,116),0,64;mode:(0,145),64,32;;,0,96;seq:(0,154)=s16from:(0,116),0,64;to:(0,116),64,64;;,0,128;;,32,128;;return_single:(0,118)kwlist:V(0,155)=ar(0,137);0;3;(0,13)list:(0,10)dtype:(0,5)loops:(0,10)result:(0,119)vs:(0,150)return_single:(0,118)kwlist:V(0,155)_kwlist.2igraphmodule_Graph_maxdegree:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)list:(0,10)dtype:(0,5)loops:(0,10)result:(0,116)vs:(0,150)return_single:(0,118)kwlist:V(0,156)=ar(0,137);0;3;(0,13)list:(0,10)dtype:(0,5)loops:(0,10)result:(0,116)vs:(0,150)return_single:(0,118)kwlist:V(0,156)igraphmodule_Graph_neighbors:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)list:r(0,10)dtype:(0,5)idx:(0,74)result:(0,119)kwlist:(0,157)=ar(0,137);0;2;(0,13)list:r(0,10)dtype:(0,5)idx:(0,74)result:(0,119)kwlist:(0,157)igraphmodule_Graph_successors:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)list:r(0,10)idx:(0,74)result:(0,119)kwlist:(0,158)=ar(0,137);0;1;(0,13)list:r(0,10)idx:(0,74)result:(0,119)kwlist:(0,158)igraphmodule_Graph_predecessors:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)list:r(0,10)idx:(0,74)result:(0,119)kwlist:(0,159)=ar(0,137);0;1;(0,13)list:r(0,10)idx:(0,74)result:(0,119)kwlist:(0,159)_kwlist.3igraphmodule_Graph_get_eid:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:V(0,160)=ar(0,137);0;3;(0,13)v1:(0,74)v2:(0,74)result:(0,116)directed:(0,10)kwlist:V(0,160)v1:(0,74)v2:(0,74)result:(0,116)directed:(0,10)_kwlist.4igraphmodule_Graph_diameter:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)dir:(0,10)vcount_if_unconnected:(0,10)i:(0,116)kwlist:V(0,161)=ar(0,137);0;2;(0,13)dir:(0,10)vcount_if_unconnected:(0,10)i:(0,116)kwlist:V(0,161)_kwlist.5igraphmodule_Graph_girth:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)sc:(0,10)kwlist:V(0,162)=ar(0,137);0;1;(0,13)girth:(0,116)vids:(0,119)o:r(0,10)sc:(0,10)kwlist:V(0,162)girth:(0,116)vids:(0,119)igraphmodule_Graph_Adjacency:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:r(0,2)m:(0,163)=(0,164)=xss_matrix:igraph_matrix_t:t(0,163)s_matrix:T(0,164)=s20data:(0,119),0,96;nrow:(0,74),96,32;ncol:(0,74),128,32;;matrix:(0,10)mode:(0,165)=(0,166)=eIGRAPH_ADJ_DIRECTED:0,IGRAPH_ADJ_UNDIRECTED:1,IGRAPH_ADJ_MAX:1,IGRAPH_ADJ_UPPER:2,IGRAPH_ADJ_LOWER:3,IGRAPH_ADJ_MIN:4,IGRAPH_ADJ_PLUS:5,;igraph_adjacency_t:t(0,165) :T(0,166)kwlist:(0,167)=ar(0,137);0;2;(0,13)self:r(0,2)m:(0,163)matrix:(0,10)mode:(0,165)kwlist:(0,167)igraphmodule_Graph_Atlas:F(0,10)type:p(0,131)args:p(0,10)args:r(0,10)n:(0,74)self:r(0,2)n:(0,74)self:r(0,2)igraphmodule_Graph_Barabasi:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:r(0,2)n:(0,74)m:(0,74)power:(0,168)=r(0,5);4;0;float:t(0,168)zero_appeal:(0,168)outseq:(0,119)m_obj:(0,10)outpref:(0,10)directed:(0,10)kwlist:(0,169)=ar(0,137);0;6;(0,13)self:r(0,2)n:(0,74)m:(0,74)power:(0,168)zero_appeal:(0,168)outseq:(0,119)m_obj:(0,10)outpref:(0,10)directed:(0,10)kwlist:(0,169)igraphmodule_Graph_Erdos_Renyi:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:r(0,2)n:(0,74)m:(0,74)p:(0,117)t:r(0,170)=(0,171)=eIGRAPH_ERDOS_RENYI_GNP:0,IGRAPH_ERDOS_RENYI_GNM:1,;igraph_erdos_renyi_t:t(0,170) :T(0,171)loops:(0,10)directed:(0,10)kwlist:(0,172)=ar(0,137);0;5;(0,13)self:r(0,2)n:(0,74)m:(0,74)p:(0,117)t:r(0,170)loops:(0,10)directed:(0,10)kwlist:(0,172)igraphmodule_Graph_Establishment:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:r(0,2)n:(0,74)types:(0,74)k:(0,74)type_dist:(0,10)pref_matrix:(0,10)directed:(0,10)pm:(0,163)td:(0,119)kwlist:(0,173)=ar(0,137);0;5;(0,13)self:r(0,2)n:(0,74)types:(0,74)k:(0,74)type_dist:(0,10)pref_matrix:(0,10)directed:(0,10)pm:(0,163)td:(0,119)kwlist:(0,173)igraphmodule_Graph_Full:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:r(0,2)n:(0,74)loops:(0,10)directed:(0,10)kwlist:(0,174)=ar(0,137);0;3;(0,13)self:r(0,2)n:(0,74)loops:(0,10)directed:(0,10)kwlist:(0,174)igraphmodule_Graph_GRG:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:r(0,2)n:(0,74)r:(0,117)torus:(0,10)kwlist:(0,175)=ar(0,137);0;3;(0,13)self:r(0,2)n:(0,74)r:(0,117)torus:(0,10)kwlist:(0,175)igraphmodule_Graph_Growing_Random:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)n:(0,74)m:(0,74)directed:(0,10)citation:(0,10)self:r(0,2)kwlist:(0,176)=ar(0,137);0;4;(0,13)n:(0,74)m:(0,74)directed:(0,10)citation:(0,10)self:r(0,2)kwlist:(0,176)igraphmodule_Graph_Star:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)n:(0,74)center:(0,74)mode:(0,177)=(0,178)=eIGRAPH_STAR_OUT:0,IGRAPH_STAR_IN:1,IGRAPH_STAR_UNDIRECTED:2,;igraph_star_mode_t:t(0,177) :T(0,178)self:r(0,2)kwlist:(0,179)=ar(0,137);0;3;(0,13)n:(0,74)center:(0,74)mode:(0,177)self:r(0,2)kwlist:(0,179)igraphmodule_Graph_Lattice:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)dimvector:(0,119)nei:(0,74)directed:(0,118)mutual:(0,118)circular:(0,118)o_directed:(0,10)o_mutual:(0,10)o_circular:(0,10)o_dimvector:(0,10)self:r(0,2)kwlist:(0,180)=ar(0,137);0;5;(0,13)dimvector:(0,119)nei:(0,74)directed:(0,118)mutual:(0,118)circular:(0,118)o_directed:(0,10)o_mutual:(0,10)o_circular:(0,10)o_dimvector:(0,10)self:r(0,2)kwlist:(0,180)igraphmodule_Graph_Preference:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:(0,2)n:(0,74)types:(0,74)type_dist:(0,10)pref_matrix:(0,10)directed:(0,10)loops:(0,10)pm:(0,163)td:(0,119)type_vec:(0,119)type_vec_o:r(0,10)attribute_key:(0,10)store_attribs:(0,118)kwlist:(0,181)=ar(0,137);0;6;(0,13)self:(0,2)n:(0,74)types:(0,74)type_dist:(0,10)pref_matrix:(0,10)directed:(0,10)loops:(0,10)pm:(0,163)td:(0,119)type_vec:(0,119)type_vec_o:r(0,10)attribute_key:(0,10)store_attribs:(0,118)kwlist:(0,181)igraphmodule_Graph_Asymmetric_Preference:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:(0,2)n:(0,74)types:(0,74)type_dist_matrix:(0,10)pref_matrix:(0,10)loops:(0,10)pm:(0,163)td:(0,163)in_type_vec:(0,119)out_type_vec:(0,119)type_vec_o:r(0,10)attribute_key:(0,10)store_attribs:r(0,118)kwlist:(0,182)=ar(0,137);0;5;(0,13)self:(0,2)n:(0,74)types:(0,74)type_dist_matrix:(0,10)pref_matrix:(0,10)loops:(0,10)pm:(0,163)td:(0,163)in_type_vec:(0,119)out_type_vec:(0,119)type_vec_o:r(0,10)attribute_key:(0,10)store_attribs:r(0,118)kwlist:(0,182)igraphmodule_Graph_Recent_Degree:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:r(0,2)n:(0,74)m:(0,74)window:(0,74)power:(0,168)zero_appeal:(0,168)outseq:(0,119)m_obj:(0,10)outpref:(0,10)directed:(0,10)kwlist:(0,183)=ar(0,137);0;7;(0,13)self:r(0,2)n:(0,74)m:(0,74)window:(0,74)power:(0,168)zero_appeal:(0,168)outseq:(0,119)m_obj:(0,10)outpref:(0,10)directed:(0,10)kwlist:(0,183)igraphmodule_Graph_Ring:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)n:(0,74)directed:(0,10)mutual:(0,10)circular:(0,10)self:r(0,2)kwlist:(0,184)=ar(0,137);0;4;(0,13)n:(0,74)directed:(0,10)mutual:(0,10)circular:(0,10)self:r(0,2)kwlist:(0,184)igraphmodule_Graph_Tree:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)n:(0,74)children:(0,74)mode:(0,185)=(0,186)=eIGRAPH_TREE_OUT:0,IGRAPH_TREE_IN:1,IGRAPH_TREE_UNDIRECTED:2,;igraph_tree_mode_t:t(0,185) :T(0,186)self:r(0,2)kwlist:(0,187)=ar(0,137);0;3;(0,13)n:(0,74)children:(0,74)mode:(0,185)self:r(0,2)kwlist:(0,187)igraphmodule_Graph_Degree_Sequence:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:r(0,2)outseq:(0,119)inseq:(0,119)outdeg:(0,10)indeg:(0,10)kwlist:(0,188)=ar(0,137);0;2;(0,13)self:r(0,2)outseq:(0,119)inseq:(0,119)outdeg:(0,10)indeg:(0,10)kwlist:(0,188)igraphmodule_Graph_Isoclass:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)n:(0,74)isoclass:(0,74)directed:(0,10)self:r(0,2)kwlist:(0,189)=ar(0,137);0;3;(0,13)n:(0,74)isoclass:(0,74)directed:(0,10)self:r(0,2)kwlist:(0,189)igraphmodule_Graph_Watts_Strogatz:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)nei:(0,74)dim:(0,74)size:(0,74)p:(0,117)self:r(0,2)kwlist:(0,190)=ar(0,137);0;4;(0,13)nei:(0,74)dim:(0,74)size:(0,74)p:(0,117)self:r(0,2)kwlist:(0,190)igraphmodule_Graph_is_connected:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,191)=ar(0,137);0;1;(0,13)mode:(0,192)=(0,193)=eIGRAPH_WEAK:1,IGRAPH_STRONG:2,;igraph_connectedness_t:t(0,192) :T(0,193)res:(0,118)kwlist:(0,191)mode:(0,192)res:(0,118)igraphmodule_Graph_are_connected:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,194)=ar(0,137);0;2;(0,13)v1:(0,74)v2:(0,74)res:(0,118)kwlist:(0,194)v1:(0,74)v2:(0,74)res:(0,118)igraphmodule_Graph_average_path_length:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,195)=ar(0,137);0;2;(0,13)directed:(0,10)unconn:(0,10)res:(0,130)kwlist:(0,195)directed:(0,10)unconn:(0,10)res:(0,130)igraphmodule_Graph_betweenness:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,196)=ar(0,137);0;2;(0,13)directed:(0,10)vobj:(0,10)list:r(0,10)res:(0,119)return_single:(0,118)vs:(0,150)kwlist:(0,196)directed:(0,10)vobj:(0,10)list:r(0,10)res:(0,119)return_single:(0,118)vs:(0,150)igraphmodule_Graph_pagerank:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,197)=ar(0,137);0;5;(0,13)directed:(0,10)vobj:(0,10)list:r(0,10)niter:(0,74)eps:(0,117)damping:(0,117)res:(0,119)return_single:(0,118)vs:(0,150)kwlist:(0,197)directed:(0,10)vobj:(0,10)list:r(0,10)niter:(0,74)eps:(0,117)damping:(0,117)res:(0,119)return_single:(0,118)vs:(0,150)igraphmodule_Graph_bibcoupling:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,198)=ar(0,137);0;1;(0,13)vobj:(0,10)list:r(0,10)res:(0,163)vs:(0,150)return_single:(0,118)kwlist:(0,198)vobj:(0,10)list:r(0,10)res:(0,163)vs:(0,150)return_single:(0,118)igraphmodule_Graph_closeness:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,199)=ar(0,137);0;2;(0,13)vobj:(0,10)list:r(0,10)res:(0,119)mode:(0,145)return_single:(0,5)vs:(0,150)kwlist:(0,199)vobj:(0,10)list:r(0,10)res:(0,119)mode:(0,145)return_single:(0,5)vs:(0,150)igraphmodule_Graph_clusters:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,200)=ar(0,137);0;1;(0,13)mode:(0,192)res1:(0,119)res2:(0,119)no:(0,116)list:r(0,10)kwlist:(0,200)mode:(0,192)res1:(0,119)res2:(0,119)no:(0,116)list:r(0,10)igraphmodule_Graph_constraint:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,201)=ar(0,137);0;2;(0,13)vids_obj:(0,10)list:r(0,10)result:(0,119)weights:(0,119)vids:(0,150)return_single:(0,118)kwlist:(0,201)vids_obj:(0,10)list:r(0,10)result:(0,119)weights:(0,119)vids:(0,150)return_single:(0,118)igraphmodule_Graph_copy:F(0,10)self:p(0,2)result:r(0,2)g:(0,8)igraphmodule_Graph_decompose:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,202)=ar(0,137);0;3;(0,13)mode:(0,192)list:(0,10)o:r(0,2)maxcompno:(0,74)minelements:(0,74)n:(0,74)i:(0,74)components:(0,203)=(0,204)=xss_vector_ptr:igraph_vector_ptr_t:t(0,203)s_vector_ptr:T(0,204)=s12stor_begin:(0,205)=*(0,121),0,32;stor_end:(0,205),32,32;end:(0,205),64,32;;g:r(0,206)=*(0,8)kwlist:(0,202)mode:(0,192)list:(0,10)o:r(0,2)maxcompno:(0,74)minelements:(0,74)n:(0,74)i:(0,74)components:(0,203)g:r(0,206)igraphmodule_Graph_cocitation:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,207)=ar(0,137);0;1;(0,13)vobj:(0,10)list:r(0,10)res:(0,163)return_single:(0,5)vs:(0,150)kwlist:(0,207)vobj:(0,10)list:r(0,10)res:(0,163)return_single:(0,5)vs:(0,150)igraphmodule_Graph_edge_betweenness:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,208)=ar(0,137);0;1;(0,13)res:(0,119)list:r(0,10)directed:(0,10)kwlist:(0,208)res:(0,119)list:r(0,10)directed:(0,10)igraphmodule_Graph_get_shortest_paths:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,209)=ar(0,137);0;2;(0,13)res:(0,210)=*(0,119)mode:(0,145)from0:(0,74)i:r(0,74)j:r(0,74)from:(0,116)list:(0,10)item:r(0,10)no_of_nodes:(0,74)ptrvec:(0,203)kwlist:(0,209)res:(0,210)mode:(0,145)from0:(0,74)i:r(0,74)j:r(0,74)from:(0,116)list:(0,10)item:r(0,10)no_of_nodes:(0,74)ptrvec:(0,203)igraphmodule_Graph_get_all_shortest_paths:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,211)=ar(0,137);0;2;(0,13)res:(0,203)mode:(0,145)from0:(0,74)i:r(0,74)j:(0,74)k:r(0,74)from:(0,116)list:(0,10)item:r(0,10)kwlist:(0,211)res:(0,203)mode:(0,145)from0:(0,74)i:r(0,74)j:(0,74)k:r(0,74)from:(0,116)list:(0,10)item:r(0,10)igraphmodule_Graph_shortest_paths:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,212)=ar(0,137);0;2;(0,13)vobj:(0,10)list:r(0,10)res:(0,163)mode:(0,145)return_single:(0,5)vs:(0,150)kwlist:(0,212)vobj:(0,10)list:r(0,10)res:(0,163)mode:(0,145)return_single:(0,5)vs:(0,150)igraphmodule_Graph_spanning_tree:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,213)=ar(0,137);0;1;(0,13)mst:(0,8)err:r(0,5)ws:(0,119)weights:(0,10)result:r(0,2)kwlist:(0,213)mst:(0,8)err:r(0,5)ws:(0,119)weights:(0,10)result:r(0,2)igraphmodule_Graph_simplify:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,214)=ar(0,137);0;2;(0,13)multiple:(0,10)loops:(0,10)kwlist:(0,214)multiple:(0,10)loops:(0,10)igraphmodule_Graph_subcomponent:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,215)=ar(0,137);0;2;(0,13)res:(0,119)mode:(0,145)from0:(0,74)from:(0,130)list:r(0,10)kwlist:(0,215)res:(0,119)mode:(0,145)from0:(0,74)from:(0,130)list:r(0,10)igraphmodule_Graph_rewire:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,216)=ar(0,137);0;2;(0,13)n:(0,74)mode:(0,217)=(0,218)=eIGRAPH_REWIRING_SIMPLE:0,;igraph_rewiring_t:t(0,217) :T(0,218)kwlist:(0,216)n:(0,74)mode:(0,217)igraphmodule_Graph_subgraph:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,219)=ar(0,137);0;1;(0,13)vertices:(0,119)sg:(0,8)result:r(0,2)list:(0,10)kwlist:(0,219)vertices:(0,119)sg:(0,8)result:r(0,2)list:(0,10)igraphmodule_Graph_transitivity_undirected:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)res:(0,130)igraphmodule_Graph_transitivity_local_undirected:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,220)=ar(0,137);0;1;(0,13)vobj:(0,10)list:r(0,10)result:(0,119)return_single:(0,118)vs:(0,150)kwlist:(0,220)vobj:(0,10)list:r(0,10)result:(0,119)return_single:(0,118)vs:(0,150)igraphmodule_Graph_topological_sorting:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,221)=ar(0,137);0;1;(0,13)list:r(0,10)mode:(0,145)result:(0,119)kwlist:(0,221)list:r(0,10)mode:(0,145)result:(0,119)igraphmodule_Graph_reciprocity:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,222)=ar(0,137);0;1;(0,13)result:(0,130)ignore_loops:(0,10)kwlist:(0,222)result:(0,130)ignore_loops:(0,10)igraphmodule_Graph_density:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,223)=ar(0,137);0;1;(0,13)result:(0,130)loops:(0,10)kwlist:(0,223)result:(0,130)loops:(0,10)igraphmodule_Graph_layout_circle:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)m:(0,163)result:r(0,10)igraphmodule_Graph_layout_sphere:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)m:(0,163)result:r(0,10)igraphmodule_Graph_layout_random:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)m:(0,163)result:r(0,10)igraphmodule_Graph_layout_random_3d:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)m:(0,163)result:r(0,10)igraphmodule_Graph_layout_kamada_kawai:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,224)=ar(0,137);0;5;(0,13)m:(0,163)niter:(0,74)sigma:(0,117)initemp:(0,117)coolexp:(0,117)kkconst:(0,117)result:r(0,10)kwlist:(0,224)m:(0,163)niter:(0,74)sigma:(0,117)initemp:(0,117)coolexp:(0,117)kkconst:(0,117)result:r(0,10)igraphmodule_Graph_layout_kamada_kawai_3d:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,225)=ar(0,137);0;5;(0,13)m:(0,163)niter:(0,74)sigma:(0,117)initemp:(0,117)coolexp:(0,117)kkconst:(0,117)result:r(0,10)kwlist:(0,225)m:(0,163)niter:(0,74)sigma:(0,117)initemp:(0,117)coolexp:(0,117)kkconst:(0,117)result:r(0,10)igraphmodule_Graph_layout_fruchterman_reingold:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,226)=ar(0,137);0;5;(0,13)m:(0,163)niter:(0,74)maxdelta:(0,117)area:(0,117)coolexp:(0,117)repulserad:(0,117)result:r(0,10)kwlist:(0,226)m:(0,163)niter:(0,74)maxdelta:(0,117)area:(0,117)coolexp:(0,117)repulserad:(0,117)result:r(0,10)igraphmodule_Graph_layout_fruchterman_reingold_3d:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,227)=ar(0,137);0;5;(0,13)m:(0,163)niter:(0,74)maxdelta:(0,117)area:(0,117)coolexp:(0,117)repulserad:(0,117)result:r(0,10)kwlist:(0,227)m:(0,163)niter:(0,74)maxdelta:(0,117)area:(0,117)coolexp:(0,117)repulserad:(0,117)result:r(0,10)igraphmodule_Graph_layout_grid_fruchterman_reingold:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,228)=ar(0,137);0;6;(0,13)m:(0,163)niter:(0,74)maxdelta:(0,117)area:(0,117)coolexp:(0,117)repulserad:(0,117)cellsize:(0,117)result:r(0,10)kwlist:(0,228)m:(0,163)niter:(0,74)maxdelta:(0,117)area:(0,117)coolexp:(0,117)repulserad:(0,117)cellsize:(0,117)result:r(0,10)igraphmodule_Graph_layout_lgl:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,229)=ar(0,137);0;7;(0,13)m:(0,163)result:r(0,10)maxiter:(0,74)proot:(0,74)maxdelta:(0,117)area:(0,117)coolexp:(0,117)repulserad:(0,117)cellsize:(0,117)kwlist:(0,229)m:(0,163)result:r(0,10)maxiter:(0,74)proot:(0,74)maxdelta:(0,117)area:(0,117)coolexp:(0,117)repulserad:(0,117)cellsize:(0,117)igraphmodule_Graph_layout_reingold_tilford:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,230)=ar(0,137);0;1;(0,13)m:(0,163)root:(0,74)result:r(0,10)kwlist:(0,230)m:(0,163)root:(0,74)result:r(0,10)igraphmodule_Graph_get_adjacency:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,231)=ar(0,137);0;1;(0,13)t:(0,232)=(0,233)=eIGRAPH_GET_ADJACENCY_UPPER:0,IGRAPH_GET_ADJACENCY_LOWER:1,IGRAPH_GET_ADJACENCY_BOTH:2,;igraph_get_adjacency_t:t(0,232) :T(0,233)m:(0,163)result:r(0,10)kwlist:(0,231)t:(0,232)m:(0,163)result:r(0,10)igraphmodule_Graph_laplacian:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,234)=ar(0,137);0;1;(0,13)m:(0,163)result:r(0,10)normalized:(0,10)kwlist:(0,234)m:(0,163)result:r(0,10)normalized:(0,10)igraphmodule_Graph_get_edgelist:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)edgelist:(0,119)result:r(0,10)_kwlist.6igraphmodule_Graph_to_undirected:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)collapse:(0,10)mode:r(0,235)=(0,236)=eIGRAPH_TO_UNDIRECTED_EACH:0,IGRAPH_TO_UNDIRECTED_COLLAPSE:1,;igraph_to_undirected_t:t(0,235) :T(0,236)kwlist:V(0,237)=ar(0,137);0;1;(0,13)collapse:(0,10)mode:r(0,235)kwlist:V(0,237)_kwlist.7igraphmodule_Graph_to_directed:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)mutual:(0,10)mode:r(0,238)=(0,239)=eIGRAPH_TO_DIRECTED_ARBITRARY:0,IGRAPH_TO_DIRECTED_MUTUAL:1,;igraph_to_directed_t:t(0,238) :T(0,239)kwlist:V(0,240)=ar(0,137);0;1;(0,13)mutual:(0,10)mode:r(0,238)kwlist:V(0,240)igraphmodule_Graph_Read_DIMACS:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:r(0,2)fname:(0,13)f:r(0,241)=*(0,242)=(0,243)=xs__sFILE:FILE:t(0,242)__sFILE:T(0,243)=s88_p:(0,244)=*(0,245)=@s8;r(0,245);0;255;,0,32;_r:(0,5),32,32;_w:(0,5),64,32;_flags:(0,246)=@s16;r(0,246);-32768;32767;,96,16;_file:(0,246),112,16;_bf:(0,247)=xs__sbuf:,128,64;_lbfsize:(0,5),192,32;_cookie:(0,121),224,32;_close:(0,248)=*(0,249)=f(0,5),256,32;_read:(0,250)=*(0,251)=f(0,5),288,32;_seek:(0,252)=*(0,253)=f(0,254)=(0,255)=(0,256)=(0,257)=@s64;r(0,257);01000000000000000000000;0777777777777777777777;,320,32;_write:(0,258)=*(0,259)=f(0,5),352,32;_ub:(0,247),384,64;_extra:(0,260)=*(0,261)=xs__sFILEX:,448,32;_ur:(0,5),480,32;_ubuf:(0,262)=ar(0,137);0;2;(0,245),512,24;_nbuf:(0,263)=ar(0,137);0;0;(0,245),536,8;_lb:(0,247),544,64;_blksize:(0,5),608,32;_offset:(0,254),640,64;;unsigned char:t(0,245)short int:t(0,246)__sbuf:T(0,247)=s8_base:(0,244),0,32;_size:(0,5),32,32;;fpos_t:t(0,254)__darwin_off_t:t(0,255)__int64_t:t(0,256)long long int:t(0,257)source:(0,116)target:(0,116)capacity:(0,119)g:(0,8)directed:(0,10)capacity_obj:(0,10)kwlist:(0,264)=ar(0,137);0;2;(0,13)self:r(0,2)fname:(0,13)f:r(0,241)source:(0,116)target:(0,116)capacity:(0,119)g:(0,8)directed:(0,10)capacity_obj:(0,10)kwlist:(0,264)igraphmodule_Graph_Read_Edgelist:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:r(0,2)fname:(0,13)f:r(0,241)directed:(0,10)g:(0,8)kwlist:(0,265)=ar(0,137);0;2;(0,13)self:r(0,2)fname:(0,13)f:r(0,241)directed:(0,10)g:(0,8)kwlist:(0,265)igraphmodule_Graph_Read_Ncol:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:r(0,2)fname:(0,13)f:(0,241)names:(0,10)weights:(0,10)directed:(0,10)g:(0,8)kwlist:(0,266)=ar(0,137);0;4;(0,13)self:r(0,2)fname:(0,13)f:(0,241)names:(0,10)weights:(0,10)directed:(0,10)g:(0,8)kwlist:(0,266)igraphmodule_Graph_Read_Lgl:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:r(0,2)fname:(0,13)f:r(0,241)names:(0,10)weights:(0,10)g:(0,8)kwlist:(0,267)=ar(0,137);0;3;(0,13)self:r(0,2)fname:(0,13)f:r(0,241)names:(0,10)weights:(0,10)g:(0,8)kwlist:(0,267)igraphmodule_Graph_Read_Pajek:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:r(0,2)fname:(0,13)f:r(0,241)g:(0,8)kwlist:(0,268)=ar(0,137);0;1;(0,13)self:r(0,2)fname:(0,13)f:r(0,241)g:(0,8)kwlist:(0,268)_kwlist.8igraphmodule_Graph_Read_GML:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:r(0,2)fname:(0,13)f:r(0,241)g:(0,8)kwlist:V(0,269)=ar(0,137);0;1;(0,13)self:r(0,2)fname:(0,13)f:r(0,241)g:(0,8)kwlist:V(0,269)igraphmodule_Graph_Read_GraphML:F(0,10)type:p(0,131)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)self:r(0,2)fname:(0,13)f:r(0,241)index:(0,74)g:(0,8)kwlist:(0,270)=ar(0,137);0;2;(0,13)self:r(0,2)fname:(0,13)f:r(0,241)index:(0,74)g:(0,8)kwlist:(0,270)igraphmodule_Graph_write_dimacs:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)fname:(0,13)f:r(0,241)source:(0,74)target:(0,74)capacity_obj:(0,10)capacity:(0,119)capacity_obj_created:(0,118)kwlist:(0,271)=ar(0,137);0;4;(0,13)fname:(0,13)f:r(0,241)source:(0,74)target:(0,74)capacity_obj:(0,10)capacity:(0,119)capacity_obj_created:(0,118)kwlist:(0,271)igraphmodule_Graph_write_edgelist:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)fname:(0,13)f:r(0,241)kwlist:(0,272)=ar(0,137);0;1;(0,13)fname:(0,13)f:r(0,241)kwlist:(0,272)_kwlist.9igraphmodule_Graph_write_gml:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)fname:(0,13)f:r(0,241)ids:(0,10)creator:(0,10)o:(0,10)idvec:(0,119)idvecptr:r(0,210)creator_str:(0,13)kwlist:V(0,273)=ar(0,137);0;3;(0,13)fname:(0,13)f:r(0,241)ids:(0,10)creator:(0,10)o:(0,10)idvec:(0,119)idvecptr:r(0,210)creator_str:(0,13)kwlist:V(0,273)igraphmodule_Graph_write_ncol:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)fname:(0,13)names:(0,13)weights:(0,13)f:r(0,241)kwlist:(0,274)=ar(0,137);0;3;(0,13)fname:(0,13)names:(0,13)weights:(0,13)f:r(0,241)kwlist:(0,274)igraphmodule_Graph_write_lgl:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)fname:(0,13)names:(0,13)weights:(0,13)isolates:(0,10)f:r(0,241)kwlist:(0,275)=ar(0,137);0;4;(0,13)fname:(0,13)names:(0,13)weights:(0,13)isolates:(0,10)f:r(0,241)kwlist:(0,275)igraphmodule_Graph_write_graphml:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)fname:(0,13)f:r(0,241)kwlist:(0,276)=ar(0,137);0;1;(0,13)fname:(0,13)f:r(0,241)kwlist:(0,276)igraphmodule_Graph_isoclass:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)isoclass:(0,5)n:r(0,5)vids:(0,10)kwlist:(0,277)=ar(0,137);0;1;(0,13)vidsvec:(0,119)isoclass:(0,5)n:r(0,5)vids:(0,10)kwlist:(0,277)vidsvec:(0,119)igraphmodule_Graph_isomorphic:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)result:(0,118)o:(0,10)other:r(0,2)kwlist:(0,278)=ar(0,137);0;1;(0,13)result:(0,118)o:(0,10)other:r(0,2)kwlist:(0,278)igraphmodule_Graph_attribute_count:F(0,5)self:p(0,2)igraphmodule_Graph_get_attribute:F(0,10)self:p(0,2)s:p(0,10)s:r(0,10)result:r(0,10)igraphmodule_Graph_set_attribute:F(0,5)self:p(0,2)k:p(0,10)v:p(0,10)k:r(0,10)v:r(0,10)igraphmodule_Graph_attributes:F(0,10)self:p(0,2)igraphmodule_Graph_vertex_attributes:F(0,10)self:p(0,2)igraphmodule_Graph_edge_attributes:F(0,10)self:p(0,2)igraphmodule_Graph_get_vertices:F(0,10)self:p(0,2)closure:p(0,121)igraphmodule_Graph_get_edges:F(0,10)self:p(0,2)closure:p(0,121)igraphmodule_Graph_disjoint_union:F(0,10)self:p(0,2)other:p(0,10)other:r(0,10)it:r(0,10)result:r(0,2)g:(0,8)gs:(0,203)gs:(0,203)gs:(0,203)igraphmodule_Graph_union:F(0,10)self:p(0,2)other:p(0,10)other:r(0,10)it:r(0,10)result:r(0,2)g:(0,8)gs:(0,203)gs:(0,203)gs:(0,203)igraphmodule_Graph_intersection:F(0,10)self:p(0,2)other:p(0,10)other:r(0,10)it:r(0,10)result:r(0,2)g:(0,8)gs:(0,203)gs:(0,203)gs:(0,203)igraphmodule_Graph_difference:F(0,10)self:p(0,2)other:p(0,10)other:r(0,10)result:r(0,2)g:(0,8)igraphmodule_Graph_complementer:F(0,10)self:p(0,2)args:p(0,10)args:r(0,10)result:r(0,2)o:(0,10)g:(0,8)result:r(0,2)o:(0,10)g:(0,8)igraphmodule_Graph_complementer_op:F(0,10)self:p(0,2)result:r(0,2)g:(0,8)igraphmodule_Graph_compose:F(0,10)self:p(0,2)other:p(0,10)other:r(0,10)result:r(0,2)g:(0,8)igraphmodule_Graph_bfs:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,279)=ar(0,137);0;2;(0,13)vid:(0,74)l1:r(0,10)l2:r(0,10)l3:r(0,10)result:r(0,10)mode:(0,145)vids:(0,119)layers:(0,119)parents:(0,119)kwlist:(0,279)vid:(0,74)l1:r(0,10)l2:r(0,10)l3:r(0,10)result:r(0,10)mode:(0,145)vids:(0,119)layers:(0,119)parents:(0,119)igraphmodule_Graph_bfsiter:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,280)=ar(0,137);0;3;(0,13)root:(0,10)adv:(0,10)mode:(0,145)_kwlist.10igraphmodule_Graph_maxflow_value:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:V(0,281)=ar(0,137);0;3;(0,13)capacity_object:(0,10)capacity_vector:(0,119)result:(0,130)vid1:(0,74)vid2:(0,74)v1:(0,116)v2:(0,116)kwlist:V(0,281)capacity_object:(0,10)capacity_vector:(0,119)result:(0,130)vid1:(0,74)vid2:(0,74)v1:(0,116)v2:(0,116)_kwlist.11igraphmodule_Graph_mincut_value:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:V(0,282)=ar(0,137);0;3;(0,13)capacity_object:(0,10)capacity_vector:(0,119)result:(0,130)mincut:(0,130)v1:r(0,116)v2:r(0,116)vid1:(0,74)vid2:(0,74)n:r(0,74)kwlist:V(0,282)capacity_object:(0,10)capacity_vector:(0,119)result:(0,130)mincut:(0,130)v1:r(0,116)v2:r(0,116)vid1:(0,74)vid2:(0,74)n:r(0,74)_kwlist.12igraphmodule_Graph_cliques:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:V(0,283)=ar(0,137);0;2;(0,13)list:(0,10)item:r(0,10)min_size:(0,74)max_size:(0,74)i:r(0,74)j:r(0,74)n:(0,74)result:(0,203)kwlist:V(0,283)list:(0,10)item:r(0,10)min_size:(0,74)max_size:(0,74)i:r(0,74)j:r(0,74)n:(0,74)result:(0,203)vec:r(0,210)vec:r(0,210)vec:r(0,210)igraphmodule_Graph_largest_cliques:F(0,10)self:p(0,2)list:(0,10)item:r(0,10)i:r(0,74)j:r(0,74)n:(0,74)result:(0,203)vec:r(0,210)vec:r(0,210)vec:r(0,210)igraphmodule_Graph_maximal_cliques:F(0,10)self:p(0,2)list:(0,10)item:r(0,10)i:r(0,74)j:r(0,74)n:(0,74)result:(0,203)vec:r(0,210)vec:r(0,210)vec:r(0,210)igraphmodule_Graph_clique_number:F(0,10)self:p(0,2)i:(0,116)_kwlist.13igraphmodule_Graph_independent_vertex_sets:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:V(0,284)=ar(0,137);0;2;(0,13)list:(0,10)item:r(0,10)min_size:(0,74)max_size:(0,74)i:r(0,74)j:r(0,74)n:(0,74)result:(0,203)kwlist:V(0,284)list:(0,10)item:r(0,10)min_size:(0,74)max_size:(0,74)i:r(0,74)j:r(0,74)n:(0,74)result:(0,203)vec:r(0,210)vec:r(0,210)vec:r(0,210)igraphmodule_Graph_largest_independent_vertex_sets:F(0,10)self:p(0,2)list:(0,10)item:r(0,10)i:r(0,74)j:r(0,74)n:(0,74)result:(0,203)vec:r(0,210)vec:r(0,210)vec:r(0,210)igraphmodule_Graph_maximal_independent_vertex_sets:F(0,10)self:p(0,2)list:(0,10)item:r(0,10)i:r(0,74)j:r(0,74)n:(0,74)result:(0,203)vec:r(0,210)vec:r(0,210)vec:r(0,210)igraphmodule_Graph_independence_number:F(0,10)self:p(0,2)i:(0,116)_kwlist.14igraphmodule_Graph_coreness:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:V(0,285)=ar(0,137);0;1;(0,13)mode:(0,74)result:(0,119)o:r(0,10)kwlist:V(0,285)mode:(0,74)result:(0,119)o:r(0,10)igraphmodule_Graph_modularity:F(0,10)self:p(0,2)o:p(0,10)o:r(0,10)membership:(0,119)modularity:(0,130)membership:(0,119)modularity:(0,130)_kwlist.15igraphmodule_Graph_community_edge_betweenness:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:V(0,286)=ar(0,137);0;5;(0,13)directed:(0,10)return_removed_edges:(0,10)return_merges:(0,10)return_bridges:(0,10)return_ebs:(0,10)res:r(0,10)merges:(0,163)removed_edges:(0,119)kwlist:V(0,286)directed:(0,10)return_removed_edges:(0,10)return_merges:(0,10)return_bridges:(0,10)return_ebs:(0,10)res:r(0,10)merges:(0,163)removed_edges:(0,119)_kwlist.16igraphmodule_Graph_community_leading_eigenvector_naive:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:V(0,287)=ar(0,137);0;2;(0,13)n:(0,74)return_merges:(0,10)cl:(0,10)res:r(0,10)merges:r(0,10)members:(0,119)mptr:(0,288)=*(0,163)m:(0,163)kwlist:V(0,287)n:(0,74)return_merges:(0,10)cl:(0,10)res:r(0,10)merges:r(0,10)members:(0,119)mptr:(0,288)m:(0,163)_kwlist.17igraphmodule_Graph_community_leading_eigenvector:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:V(0,289)=ar(0,137);0;2;(0,13)n:(0,74)return_merges:(0,10)cl:(0,10)res:r(0,10)merges:r(0,10)members:(0,119)mptr:(0,288)m:(0,163)kwlist:V(0,289)n:(0,74)return_merges:(0,10)cl:(0,10)res:r(0,10)merges:r(0,10)members:(0,119)mptr:(0,288)m:(0,163)_kwlist.18igraphmodule_Graph_community_fastgreedy:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:V(0,290)=ar(0,137);0;1;(0,13)return_modularities:(0,10)ms:r(0,10)qs:r(0,10)res:(0,10)merges:(0,163)q:(0,119)kwlist:V(0,290)return_modularities:(0,10)ms:r(0,10)qs:r(0,10)res:(0,10)merges:(0,163)q:(0,119)igraphmodule_Graph___graph_as_cobject__:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)igraphmodule_Graph___register_destructor__:F(0,10)self:p(0,2)args:p(0,10)kwds:p(0,10)args:r(0,10)kwds:r(0,10)kwlist:(0,291)=ar(0,137);0;1;(0,13)destructor:(0,10)result:r(0,10)kwlist:(0,291)destructor:(0,10)result:r(0,10)igraphmodule_GraphType:G(0,132)igraphmodule_Graph_getseters:G(0,292)=ar(0,137);0;2;(0,103)igraphmodule_Graph_methods:G(0,293)=ar(0,137);0;120;(0,99)igraphmodule_Graph_as_mapping:G(0,66)igraphmodule_Graph_as_number:G(0,34)