MZ@ !L!This program cannot be run in DOS mode. $s8kkkjSkk jk jk jk jkjkjkkokjkjkjkRichkPEdai" ,D0@``hxP fd@0.text8 `.rdataT0@@.data=@.pdataP@@.reloc @BHL$USWATAUAVAWHHpE3NL}HH+HEML}M,MMaLeMQIMtHHHHEHIAHt$hHuIM+L}LuXH~-MA@fHHK|.HHtJH98tBHAHHuHGMH;E#L}PuELMPH\$ LHUHLuPHHtIEHUH+HHHDHuH G#H(LH L A|HHt4HHAH %A;HCHHuHEH]H;t0@ffH AHH $u^HH;uAH<,uqIELEHJDHLuXIILEL;EEHELL+I뮃u!H U"L+LHH SAA;H &"H/LLH (IIg tpHL9>tRL;~HHIHHtHHUHH+HHHDH a"Hu)HL9;uL;EL *+LHUIAHt$hELEHIMHLD$0HhIHH?L wHHHDMHD$(HlHL$ L*H !LIHfH  L}HMtI/u I"L N3AH \3HpA_A^A]A\_[]LILEHMuL2 ILEHHE@H HHP LHH HD$ HHu1HtH+u H!L 3AH \膉H+u H!LHMHHt H)u!IHpA_A^A]A\_[]@USWATAUAVAWHl$HH1H3HEE3HMLMLL}J L}H]HEL,MMaLeMXIMtHHHHEIAH$HuHM+L}LMH)MAHHK|)HHtJH98tBHAHHuHGMH;L}uELMH\$ LHUHLuHHtIEHUH+HHHDͷuH HhLH A|HHt4HHAHJ A;HCHHuHEH]H;t0@ffH AHH ubHH;uAHP(uxIELELMHJDIILEL;ELuEHELL+I몃u!H L'LH0H A|A;tH cHlLLH eLuIIttHLmL9>tRL;~HHIHHtHHUHH+HHHDͷ Hu)HL9;uL;EL 3'LHUI(AH$EH]IMHLD$0HdIHH?L HHHDMHD$(HHL$ L&H ;LIHH ?L}H\MtI/u ILH W3AI3HHH]HuHlHH]IWLuE#HHuHMtsI.umI@bHGHUHGLIHH H]HMtI.u IH/u HHu HlLH "W3AmLHMHt H)uIHMH3dHİA_A^A]A\_[]@USVWATAUAWHl$HH"H3HE3LMHMHHEH?HEHLHEWH}MH}N,EM^MyL}MGIMt6Ht&HtH@HBHHEHBHHEHHHEIAL$LuO4HEM+H]LML*fffIIKt)HHtJH90tBHAHHuHFLH;5H}uELD$ LMMHUHoL}HHtIEHUH+HHHDuH 7HH ?mIHt4HHAHHCHHuHEH]I;t"H AHH u`HI;uދH]ǃu|IEL)LMHJDL}HIH]I;}HELL+I뫃u!H RL;)LHH P{H L)LHH IH[ttILmI9>tRL;~HHIHHtHHUHH+HHHD QHu)HH9;uL;}L z(MHUIEL$9}LEMuLILEI"J9|tII| Ld$0H HL$(L UHD$ QIHHILd$0HH 0L HILMH #HL$(HD$ H L'H H HLHt H)u2HH|L 3AH R31HBHHEHBHHEHHLEHEMuLILELMHMHEHHPHHHHD$pH3|$hHD$`H HD$XHD$PHD$HHHL$@H D$8HD$0LL$(LD$ 4HHu4HtH+u H(L 3AH Q~HH+u HHLHt H)uHH|HHMH3HA_A]A\_^[]@USWATAUAWHl$HHSH3HE3LMHMHHEHHEH}HEWH}MH}N,EMyMyL}MbIMt6Ht&HtH[HBHHEHBHHEHHHEIAH$L$LuO4HEM+H]LML4@IIKt)HHtJH90tBHAHHuHFLH;eH}uELD$ LMMHUH蟵L}HHtIEHUH+HHHDuH gHHH omIHt4HHAH+HCHHuHEH]I;t"H AHH u`HI;uދH]ǃu|IEL2LMHJDL}HIH]I;} HELL+I뫃u!H L2LH!H {H NLO2LHMH LIH{ILmI9>tYL;~HHIHHtHHUHH+HHHD vHu)HH9;uL;}L 1MHUIEL$H$9}LMMuL ILMI(J9|tII| Ld$0H HL$(L kHD$ QIHHILd$0HH FL ,HILMH 9HL$(HD$ H L0H"H  HfHLHt H)uBHH|LH 7R3A( y3mHBHHEHBHHEHHLMHEMuL ILMLEHUHMQHHLHt H)uHH|HHMH3mHA_A]A\_[]@USVWATAUAWHl$HH"H3HEHHMMHE3HXHEWH}N M,EMM|$L}M\IMt%HtHHBHHEHHHEID$L$LuMM+H}LeH9ALHEIIJt(HHtJH90tBHAHHuHFLH;QH}uELMH\$ MHUH苰L}HHtIEHUH+HHHDͷuH SH4LH X AwIHttRL;~HHIH HtHHUHH+HHHDͷ s Hu)HH9;uL;DL T7MHUIAL$EH9}IMHHLD$0H IL H?HHHML6LIH HL$(H  HD$ H  HLHt H)u HH|L W3AH H#u3.HBHHEHHHHEuHHHELEMuL$ ILELMHEHHPHHH H HD$pH}|$hHL$`HD$X|$PHL$HH HD$@H{D$8HD$0LL$(LD$ HHu4HtH+u H L ]3ADH &G)tHH+uHm DHLHt H)uR HH|HHMH3HA_A]A\_^[]HL$USWATAUAVAWHl$HE3NL}oHHEML}M,MMaLeMOIMtHHHHEoIAH$HuIM+L}LuH8MADHHK|.HHtJH98tBHAHHuHGMH;% L}wuELMwH\$ LHUH_LuwHHtIEHUH+HHHDouH ' HLH ,A|HHt4HHAH A;HCHHuHEH]H;t0@ffH AHH  u^HH;uAH|7uqIELEHJDoLuIILEL;EEHELL+I뮃u!H 5L7LHH 3AA;H HLLH IIGtpHL9>tRL;~HHIHoHtHHUHH+HHHDo AHu)HL9;uL;EL j6LHUIAH$ELEoIMHLD$0HNIHH?L THHHDMHD$(HIHL$ L5H LIHCH L}oMtI/u IjL +3AJH $Co3LILEoMuLILEoHEgH hHHPHLHHHD$pD|$hHD$`HD$XD|$PHD$HHD$@D|$8HD$0HD$(D|$ HHu1HtH+u HL p3ApH iBtYL;~HHIHHtHHUHH+HHHD Hu)HH9;uL;}L ?MHUI8EL$H$9}H9}uH;HHEH9}uH'HHEHEHuHHHEIvJ9|tII|aLd$0H DHL$(L zHD$ oIHHHHILd$0H yDHHHLL HD$(MHLMHL$ H L>HH HHLHt H)u2HH|LH W93Ae3HBHHEHBHHEHBHHEHHHEH9}uHHHEH9}uHHHEHEHuHHHELMLEHUHMHD$ _HHLHt H)unHH|HHMH3HA_A]A\_[]@USVWATAUAVAWHl$HHМH3HE3LMHMH(HEHHEHHEWH}MH}N,EMVMyL}M?IMt6Ht&HtH8HBHHEHBHHEHHHEIALuO4HEM+H]LMLaKIIKt)HHtJH90tBHAHHuHFLH;H}uELD$ LMMHUH/L}HHtIEHUH+HHHDuH HH mIHt4HHAHHCHHuHEH]I;t"H AHH zu`HI;uދH]ǃu|IEL7JLMHJDL}HIH]I;} HELL+I뫃u!H LILHH {H LILHH IH{ILmI9>tYL;~HHIH4HtHHUHH+HHHD Hu)HH9;uL;}L IMHUI蘻E9}LEMuLILEI+J9|tII|Ld$0H 7HL$(L HD$ QIHHILd$0HH L HILMH HL$(HD$ H hL9HHH iHfHLHt H)uHH|LH 93A j`3eHBHHEHBHHEHHLEHEMuLsILELMHMHEH|$pHHPHHHHD$hHȶHD$`HlD$XHD$PHXHL$HH ܾD$@HD$8LL$0Lˉ|$(D$ LHu#L5~AHt@H+u:H/H+u HIIPHHu)L59AMH 7E37_HHtH.u HxHLHt H)ubHH|HHMH3HA_A^A]A\_^[]@USVWATAUAVAWHl$HHH3HEHHMMHE3HHEWH}N M,EMM|$L}MRIMt%HtHHBHHEHHHEID$LuMM+H}LeHOAL@fHEIIJt(HHtJH90tBHAHHuHFLH;H}uELMH\$ MHUH+L}HHtIEHUH+HHHDͷuH HLH AwIHttRL;~HHIHAHtHHUHH+HHHDͷ Hu)HH9;uL;DL lMMHUI襵AEH9}IMHHLD$0H IIL /H?HLHHMLLLIH 'HL$(H HD$ H HHLHt H)u@HH|LH 33AZ3cHBHHEHHHHEuHxHHELEMuLILELMHEH|$pHHP HH FH'HL$hHD$`|$XHL$PH HD$HHD$@ HD$8LL$0Lˉ|$(D$ ZLHu#L5AjHt@H+u:H9/H+u H(IIHHu)L5AoMH n2E3YHHtH.uHDHLHt H)uHH|HHMH3rHA_A^A]A\_^[]@USVWATAUAVAWHl$HH H3HEHLM3HEHMHUHEWH}MN,EMEMyL}M.IMt%HtH-HBHHEHHHEIALuO4HEM+H]LMLUIIKt)HHtJH90tBHAHHuHFLH;eH}uELD$ LMMHUH蟐L}HHtIEHUH+HHHDuH gHHH omIHt4HHAH+HCHHuHEH]I;t"H AHH u`HI;uދH]u|IELTLMHJDL}HIH]I;} HELL+I뫃u!H L{TLH!H {H NLGTLHMH LIH{ILmI9>tYL;~HHIHHtHHUHH+HHHD vHu)HH9;uL;}L SMHUIE9}LEMuLILEI0J9|tII|Ld$0H3HD$(L {HD$ [IHHILd$0H3H bHLHGHHL$(L %HIHD$ LMH LRH(H HHLHt H)uBHH|LH -3AqT3KHBHHEHHLEHEMuLILELMHEH|$pHHP HH [H<HL$hHD$`|$XHL$PH HD$HHD$@HD$8LL$0Lˉ|$(D$ oLHu#L5AHt@H+u:HN/H+u H=IIHHu)L5AMH ,E3SHHtH.u HHLHt H)uHH|HHMH3HA_A^A]A\_^[]@USVWATAUAVAWHl$HH@H3HEHLM3HEHMHuHEWH}MN,EMEMyL}M.IMt%HtH-HBHHEHHHEIALuO4HEM+H]LMLWIIKt)HHtJH90tBHAHHuHFLH;H}uELD$ LMMHUH迊L}HHtIEHUH+HHHDuH HhH mIHt4HHAHKHCHHuHEH]I;t"H AHH  u`HI;uދH]u|IELwVLMHJDL}HIH]I;} HELL+I뫃u!H L#VLHAH {H nLULHmH lIH{ILmI9>tYL;~HHIHHtHHUHH+HHHD Hu)HH9;uL;}L WUMHUI(E9}LEMuL8ILEI0J9|tII|Ld$0H-HD$(L HD$ [IHHILd$0H-H HLHgHHL$(L EHIHD$ LMH LoTHHH HHLHt H)ubHH|LH (3AN3KHBHHEHHLEHEMuLILELMHEH|$pHHP HH {H\HL$hHD$`|$XHL$PH HD$HHD$@HD$8LL$0Lˉ|$(D$ LHu#L5AHt@H+u:Hn/H+u H]IIHHu)L5ӱA MH &E3MHHtH.u HHLHt H)uHH|HHMH3HA_A^A]A\_^[]@USWATAUAWHl$HHcH3HEHHMHEJHڝH]HE3H}H-HEHzHEWWMMEMMMyL}MpIMtGHt7Ht&HtHcHBHHEHBHHEHBHHEHHHEIAH$L$LuO4L+H}LmL bALfHEIIHtHHtJH90tBHAHHuHFLH;QH}uDLL$ HULMMH苄L}HHtHHUH+HHHDuH TH5MH YAxIHt=HfHAHA;HCHHuHEH]I;t$fH AHH ucHI;uދH]u}HL `LEHJDL}IHLEH]M;DHELL+I먃u!H `Lq`LHH ^AzA;H .L?`LH-H ,IHktpII9>tRL;~HHIHHtHHUHH+HHHD eHu)HH9;uL;DL _MHUIAL$H$EHEHuHHHEI6J9|tII|Ld$0H HL$(L [HD$ QIHHILd$0HH 6L HILMH )HL$(HD$ H L^HH HڬfHLHt H)u2HH|LH "3AH3HBHHEHBHHEHBHHEHHHEHEHuHHHELMLEHUHMHD$ 7:H@HLHt H)uHH|HHMH3BHA_A]A\_[]HL$USWATAUAVAWHHpE3HwL}HMHEML}HN$M IYH]HfIMtHHHHEHIAHt$hHuJ4M+L}LmXL _AM@ffHEXHHI|HHtGH98t?HAHHuHGH;dL}PuELL$ HULMPLHL}PHHtI$HUH+HHHDHuH fHGMH kAzHHt?HfffHAHA;HCHHuHEH]H;t$fH AHH u\HH;u3H]؃usI$L ^LEHJDHE3IILEL;HELL+I믃u!H wL8^LHH uAA;H HL ^LHGH FIItyHL9>t[LeDL;~HHIHHtHHUHH+HHHDH vHu)HL9;uL;EL w]LHUIAEHt$hEI}J|HtII|H}@QItAH _H"Lt$0L HD$(L]HHD$ H ?'HHHEHHMHHHuPHtEH7HMHHt H)uLH 3A8#D3HpA_A^A]A\_[]HHfHMHHHt H)uFHHpA_A^A]A\_[]@USWATAUAWHl$HH{H3HEHULMHEN,HHMHE3H}HHEHHEWWMEMMMyL}M'IMtGHt7Ht&HtHHBHHEHBHHEHBHHEHHHEIAH$L$LuO4HEM+H]LMLj@IIKt)HHtJH90tBHAHHuHFLH;H}uELD$ LMMHUHzL}HHtIEHUH+HHHDuH HH mIHt4HHAH{HCHHuHEH]I;t"H AHH :u`HI;uދH]u|IELhLMHJDL}HIH]I;} HELL+I뫃u!H LhLHqH {H LKhLHH IH{ILmI9>tYL;~HHIHHtHHUHH+HHHD Hu)HH9;uL;}L gMHUIXEL$H$9}H9}uH[HHEH9}uHHHEHEHuH0HHEIvJ9|tII|aLd$0H HL$(L HD$ oIHHHHILd$0H HhHLL =HD$(MH9LMHL$ H LfH3H HHLHt H)uRHH|LH 3A\>3HBHHEHBHHEHBHHEHHHEH9}uHHHEH9}uHoHHEHEHuHHHELMLEHUHMHD$ HHLHt H)uHH|HHMH3>HA_A]A\_[]@USVWATAUAVAWHl$HHuH3HEHRLM3HEHMH%HEWH}MN,EMEMyL}M.IMt%HtH-HBHHEHHHEIALuO4HEM+H]LMLpIIKt)HHtJH90tBHAHHuHFLH;5H}uELD$ LMMHUHouL}HHtIEHUH+HHHDuH 7HH ?mIHt4HHAHHCHHuHEH]I;t"H AHH u`HI;uދH]u|IEL_oLMHJDL}HIH]I;} HELL+I뫃u!H RL oLHH P{H LnLHH IH[{ILmI9>tYL;~HHIHtHtHHUHH+HHHD FHu)HH9;uL;}L ?nMHUIؔE9}LEMuLILEI0J9|tII|Ld$0HHD$(L KHD$ [IHHILd$0H^H 2HLHHHL$(L HIHD$ LMH LWmHH HHLHt H)uHH|LH 3A93KHBHHEHHLEHEMuLILELMHEH|$pHHP HH +H HL$hHD$`|$XHL$PH HD$HHD$@HD$8LL$0Lˉ|$(D$ ?LHu#L5ȜAHt@H+u:H/H+u H II蚯HHu)L5AMH E38HHtH.u HHLHt H)uHH|HHMH3\HA_A^A]A\_^[]@USWATAUAWHl$HHpH3HE3LMHMHHEHHEH=HEHHEHϋHEWH}WH}MN,EMMMyL}M'IH {BkHHB HHEHBHHEHBHHEHBHHEHHHEIAH$L$LuO4AHEM+H]LMLffIIKt)HHtJH90tBHAHHuHFLH;H}uELMLT$ MHUHoL}HHtIEHUH+HHHDͷu!H HȞMH yIHt>HffHHHCHAHuHEH]I;t$fH HH aupHAI;uދH]IEL\LMAHJDL}HIH]I;}HELL+I롃u!H LLHH qH L~LHH IHtvILmI9>tTfL;~HHIHHtHHUHH+HHHDͷ Hu)HH9;uL;}L ?~MHUIxEL$H$9}H9}uH{HHEH9}uH߈HHEHMHuH HHMIaJ9|tII|LL ƜMIHHHHL L;MHLMHLd$0HD$(L?}HL$ H{H H H<@HLHt H)uHH|LH 3A33HB HHEHBHHEHBHHEHBHHEHHHEH9}uHHHEH9}uHtHHEHMHuH HHMHELMLEHUHL$(HMHD$ KHHLHt H)uHH|HHMH3bHA_A]A\_[]Ðffffff@USVWATAUAWHl$HHjH3HE3LMHMHZHEHσHEH,HEWH}MH}HN,EMMyL}MsIMt6Ht&HtHdHBHHEHBHHEHHHEIAL$LuO4HEM+H]LMLhIIKt)HHtJH90tBHAHHuHFLH;H}uELD$ LMMHUHOiL}HHtIEHUH+HHHDuH HH mIHt4HHAHHCHHuHEH]I;t"H AHH u`HI;uދH]ǃu|IEL7LMHJDL}HIH]I;} HELL+I뫃u!H 2LLHїH 0{H LLHH IH;{ILmI9>tYL;~HHIHTHtHHUHH+HHHD &Hu)HH9;uL;}L MHUI踈EL$9}H9}uHHHEI}fDJ9|tII|HuLd$0H SHL$(L 'HD$ H LH1H IHtMHttYL;~HHIHDHtHHUHH+HHHDͿ Hu)HH9;uL;}L MHUI訃EL$H$9}LEMuLILEI-J9|tII|Ld$0H\HD$(L HD$ [IHHILd$0HH HLHבHHL$(L HIHD$ LMH ^LHH _HHLHt H)uHH|LH w3ABZ(3^HBHHEHHLEHEMuLqILEHUHM}HHLHt H)u\HH|HHMH3 HĸA_A]A\_[]@USWATAUAWHl$HH_H3HE3HMH}HwHEHuHEHzHEWH}J L,MEMMyL}MIMt6Ht&HtHHBHHEHBHHEHHHEIAH$L$LuLM+H}LeHәALfHEIIJt(HHtJH90tBHAHHuHFLH;ѼH}uELMH\$ MHUH _L}HHtIEHUH+HHHDuH ӼHLH عAwIHttRL;~HHIH!HtHHUHH+HHHD Hu)HH9;uL;DL MHUI~AL$H$EH9}IMHHHLD$0H IL H?HHHMLLIH HL$(H HD$ H H@HLHt H)uHH|LH 3A2#3HBHHEHBHHEHHHHEuH'zHHEH9}uHzHHELMMuL lILMLEHUHMtHؐHLHt H)uRHH|HHMH3HA_A]A\_[]H H%@SH HAXHHu9HAHHHtHCXHuH [HŸHHH [HHCXH [H(HHDHLAXHQXMtI(u I3H(@SH HAHHHuHIH ɹHCHHuH [HHCHH [H(LHt3HBt#HHIHIPHHt H)u3H(H BH;H H(HAPHHAPH(LHt3HBt#HHIPIPPHt H)u3H(H ҷHH rH(HA`HHA`H9HH/HQhHHHEHH\$WH HHHuSHH eHƏAH HHHHt H)u3H\$0H _H;tHBuH H1H H\$0H _@SH HHHu1H9t!=Fy3H [HHH [H HH [H\$WH HHHuSHH eHVAH HHHHt H)u3H\$0H _H;tHB uH HH H\$0H _@SH HHHu1H9t!=Ey3H [HHH [H HH [HL$USWATAUAWHHx3HzsH]@MHEMH]N,MIyH}H;IMtHHHHE@IAHt$pLt$hLuO4M+H]LePL LDIIKt,HHtJH90tBHAHHuHFLH;%H]HuELL$ HULMHMH_VLeHHHtIEHUH+HHHD@u!H 'HMH ,yIHt>HffHAH޵HGHHuHEH}I;t$fHAHH u]HI;uދH}ЃuvIEL PLEHJD@LePIILEL;HELL+I뮃u!H 6LLHՄH 4끃t!H LҘLHH IHAttII9tVLmАL;~HHIHdHtHHUHH+HHHD@ 6Hu(HH9uL;L @MHUIuLt$hHt$pxiIJ|@tII|IH HL|$0L `HD$(L̗H]HD$ H H]@H=HtH+u HtLH R3A3HxA_A]A\_[]HHHE@HU@HM8HM@HHt H)uHHxA_A]A\_[]@SVWH`HRH3HD$PIHHMt&IHx~HH s3MHHOH$Hu HHHHuNHL$0HT$(H wlE3IH\$(/H+Hu HUHWH\$8D$@HHuH+H-}uoHdH mHT$8HFLIHH kH|$@H+Hu HH.u HHu"H-0}LH nD343H/u H{HH$HL$PH3+H`_^[H(LHt?H;-t6HB u!H |HH H(H3HIHt H)u3H(@SH HHHuHuH [HHH [@VH@HHHt HH@^H\$XHl$`H|$8Lt$0DAH-f#HHHELHHE33D$ H)H cH+Hu HHthHOHHHHtH/Hu HƯHt1HHH tHHAHHu HHH4zH EHEHHHu3 HH HH|$8Hl$`H\$XLt$0H@^@SVWH`HNH3HD$PIHHMt&I"Hx~HH Yo3MHHOH$Hu HHHHuNHL$0HT$(H iE3IH\$(_H+Hu HHWH\$8D$@HHuH+H-yuoH6dH=iHT$8HFLIHH ]iH|$@ʫH+Hu HH.u HHu"H-`yLH D3d3H/u HHH$HL$PH3[H`_^[HuHHH%@USWATAUAWHl$HHMH3HE3LMHMHdHEH0cHEH-hHEWH}MH}N,EMMyL}MrIMt6Ht&HtHuHBHHEHBHHEHHHEIAH$L$LuO4HEM+H]LMLD@IIKt)HHtJH90tBHAHHuHFLH;H}uELD$ LMMHUHOLL}HHtIEHUH+HHHDuH H{H mIHt4HHAH۫HCHHuHEH]I;t"H AHH u`HI;uދH]ǃu|IELLMHJDL}HIH]I;} HELL+I뫃u!H 2LäLHzH 0{H LLHzH IH;{ILmI9>tYL;~HHIHTHtHHUHH+HHHD &Hu)HH9;uL;}L MHUIkEL$H$9}H9}uHHHELMMuL ILMIMJ9|tII|5Ld$0H \HL$(L zHD$ eIHHHILd$0H HyHLL yHD$(MHyLMHL$ H TLHyH UHvtHLHt H)uШHH|LH 3A X3HBHHEHBHHEHHHEH9}uHdHHELMMuL MILMLEHUHMHfHLHt H)u2HH|HHMH3HA_A]A\_[]H\$WH H3HIpHtH{pH)uHK HtH{ H)uǧHK@HtH{@H)uHKHHtH{HH)uHKPHtH{PH)u|HKXHtH{XH)ucHK`HtH{`H)uJHKhHtH{hH)u1HK8H{8Ht H)uHHtHH)uHHtHH)uڦHHtHH)uHHtHH)uHKxHtH{xH)uH\$83H _@SH HH{(t HYH9HH [H%H\$Ht$WH HIHIpHHt IօHK Ht HօHK@Ht HօHKXHt HօHK`Ht HօutHK8Ht HօubHHt HօuMHHt Hօu8HHt Hօu#HKxHtHHH\$0Ht$8H _H3H\$0Ht$8H _HQPLH R}H%Hl$Ht$ WH0HA0IHHHtLLBHHt*H}t#MHl$ LH`Hl$PHt$XH0_E3Hl$PHt$XH0_H$H\$H<u}LBHκ8HHt:3HHu.H+u HAH H|LGPH 3:LLHH+H+HuHHQLL+HHH\$HHl$PHt$XH0_HL$USWATAUAVAWHl$HE3NL}oH^HEML}M,MMaLeMOIMtHHHHEoIAH$HuIM+L}LuHMADHHK|.HHtJH98tBHAHHuHGMH;ŠL}wuELMwH\$ LHUHBLuwHHtIEHUH+HHHDouH ǠHrLH ̝A|HHt4HHAHA;HCHHuHEH]H;t0@ffH AHH ;u^HH;uAHTuqIELEHJDoLuIILEL;EEHELL+I뮃u!H ՟LLHtqH ӜAA;H HqLLH IItpHL9>tRL;~HHIHHtHHUHH+HHHDo Hu)HL9;uL;EL BLHUIsbAH$ELEoIMHLD$0HNIHqH?L pHHHDMHD$(HpHL$ LH LIHpH L}oMtI/u I L k3AH L3LILEoMuLILEoHEgH cHHPHLHHHD$pD|$hHD$`HD$XD|$PHD$HHD$@D|$8HD$0HD$(D|$ [`HHu1HtH+u HOL k3AH H+u H#LHMoHt H)u IHİA_A^A]A\_[]@USVWATAUAWHl$HH>H3HE3HMH}HVHEHGYHEHYHEWH}J L$MEMMyL}M{IMt6Ht&HtHHBHHEHBHHEHHHEIAL$LuLM+H}LmHALfHEIIJt HHtJH90tBHAHHuHFLH;H}uELMH\$ MHUH=L}HHtI$HUH+HHHDuH HtmLH AwIHttRL;~HHIHHtHHUHH+HHHD Hu)HH9;uL;DL @MHUIE]AL$EH9}IMHHHLD$0H kIL kH?HkHHMLLIH kHL$(H IHD$ H SHLHt H)uҚHH|L f3AH ;V3QHBHHEHBHHEHHHHEuHXHHEH9}uHXHHELEMuL8ILELULMHEHHPHHH HH HD$pHX|$hHL$`HD$XHTD$PHD$HLL$@Lˉ|$8HL$0H ccLT$(D$ ZHHu4HtH+u HL me3A+H 9HH+uH}DHLHt H)ubHH|HHMH3uHA_A]A\_^[]@USVWATAUAWHl$HH8H3HEHSLM3HEHMHTHEWH}MN,EMMMyL}M6IMt%HtH5HBHHEHHHEIAL$LuO4HEM+H]LML+fffIIKt)HHtJH90tBHAHHuHFLH;H}uELD$ LMMHUH?8L}HHtIEHUH+HHHDͿuH HgH mIHt4HHAH˗HCHHuHEH]I;t"H AHH u`HI;uދH]u|IELLMHJDL}HIH]I;}HELL+I뫃u!H "LLHfH  {H LoLHfH IH+ttILmI9>tRL;~HHIHOHtHHUHH+HHHDͿ !Hu)HH9;uL;}L MHUIWEL$9}LEMuLILEI'J9|tII|Ld$0HvHD$(L %fHD$ [IHHILd$0H8H fHLHeHHL$(L eHIHD$ LMH xLHeH yfHLHt H)uHH|L `3A1H v3HBHHEHHLEHEMuLILELMHEHHPHHH CH_HD$pHR|$hHL$`HD$X|$PHL$HH dHD$@H)OD$8HD$0LL$(LD$ UHHu4HtH+u HL _3A}H HH+u HӓHLHt H)uHH|HHMH3moHA_A]A\_^[]H(u H03HDE3Hz<L8AHHuH(HSHAHHAHHHHHHHH(@USVWATAUAWHl$HH2H3HE3LMHMHMHEHWMHEHMHEWH}MH}N,AEMwMyL}M`IMt3I+t$I+tI;cHBHHEHBHHEHHHEIAL$LuO4HEM+H]LMLIIKt)HHtJH90tBHAHHuHFLH;H}uELD$ LMMHUH1L}HHtIEHUH+HHHDuH HaH mIHt4HHAHkHCHHuHEH]I;t"H AHH *u`HI;uދH]ǃu|IELLMHJDL}HIH]I;} HELL+I뫃u!H ŽLcLHa`H {H L/LH`H IHˌ{ILmI9>tYL;~HHIHHtHHUHH+HHHD Hu)HH9;uL;}L MHUIHQEL$9}H9}uHMHHELEMuL<ILEIGJ9|tII|2Ld$0H HL$(L _HD$ ]II+I+I;M;Ld$0H H_HLL \_HD$(LMH W_LMLD$ H LHQ_H HLHt H)urHH|L *Z3AH 33DHBHHEHBHHEHHHEH9}uHLHHELEMuLILELMHMHEAHHPHHHHD$pHY<|$hHD$`H1LHD$XHMHDT$PHD$HHHHL$@H QDT$8HD$0LL$(LD$ `NHHu4HtH+u HTL Y3AH HH+u H%HLHt H)uHH|HHMH3hHA_A]A\_^[]@USVWATAUAWHl$HHr,H3HE3LMHMHAHEHoAHEHGHEWH}MH}N,EM^MyL}MGIMt6Ht&HtH@HBHHEHBHHEHHHEIAL$LuO4HEM+H]LMLfffIIKt)HHtJH90tBHAHHuHFLH;H}uELD$ LMMHUH+L}HHtIEHUH+HHHDuH Hh[H mIHt4HHAHKHCHHuHEH]I;t"H AHH  u`HI;uދH]ǃu|IELLMHJDL}HIH]I;}HELL+I뫃u!H LcLHAZH {H nL/LHmZH lIHttILmI9>tRL;~HHIHυHtHHUHH+HHHD Hu)HH9;uL;}L MHUI3KEL$9}LEMuL;ILEI"J9|tII| Ld$0H YHL$(L YHD$ QIHHILd$0HH YL fYHILMH sYHL$(HD$ H LH\YH HLHt H)uHH|L :T3AH k31HBHHEHBHHEHHLEHEMuLILELMHMHEHHPHHHHD$pH6|$hHD$`H[FHD$XH<D$PHD$HH<HL$@H ?KD$8HD$0LL$(LD$ HHHu4HtH+u HxL 9S3A0H jHH+u HIHLHt H)u2HH|HHMH3bHA_A]A\_^[]HL$USWATAUAWHHx3HjCH]@MHEMH]N,MIyH}H;IMtHHHHE@IAHt$pLt$hLuO4M+H]LePL LDIIKt,HHtJH90tBHAHHuHFLH;H]HuELL$ HULMHMHO&LeHHHtIEHUH+HHHD@u!H HUMH yIHt>HffHAH΅HGHHuHEH}I;t$fHAHH u]HI;uދH}ЃuvIEL @LEHJD@LePIILEL;HELL+I뮃u!H &LLHTH $끃t!H LLHTH IH1ttII9tVLmАL;~HHIHTHtHHUHH+HHHD@ &Hu(HH9uL;L 0MHUIELt$hHt$pxiIJ|@tII|IH HL|$0L PTHD$(LHMTHD$ H ~H]@H=OHtH+u HdLH j3AT3HxA_A]A\_[]HHHE@HU@HM8>HM@HHt H)uHHxA_A]A\_[]@USVWATAUAWHl$HH"H3HEH7LMHEN,H7HMHE3H}H4;HEH=HEWWMEMMoMyL}MXIMtGHt7Ht&HtHKHBHHEHBHHEHBHHEHHHEIAL$LuO4HEM+H]LMLfffIIKt)HHtJH90tBHAHHuHFLH;H}uELD$ LMMHUH!L}HHtIEHUH+HHHDͰuH HhQH |mIHt4HHAHKHCHHuHEH]I;t"H AHH  u`HI;uދH]u|IELLMHJDL}HIH]I;}HELL+I뫃u!H ~LLHAPH {{H n~L_LHmPH l{IH|ttILmI9>tRL;~HHIH{HtHHUHH+HHHDͰ ~Hu)HH9;uL;}L MHUI3AEL$9}LEMuL;}ILEI-J9|tII|Ld$0H OHL$(L OHD$ QIHHILd$0HH OL fOHILMH sOHL$(HD$ H }LH\OH zHLHt H)u~HH|L :J3A5H 3AHBHHEHBHHEHBHHEHHLEHEMuL|ILELULMHMHEHHPHHH{HD$pHt6D$hHD$`H2HL$XH ED$PHD$HH2LL$@LD$8HD$0LT$(D$ x>HHu4HtH+u Hl}L -I3AH HH+uH=}DHLHt H)u"}HH|HHMH3XHA_A]A\_^[]@USVWATAUAWHl$HHH3HEH1LM3HEHMH7HEWH}MN,EMMMyL}M6IMt%HtH5HBHHEHHHEIAL$LuO4HEM+H]LMLfffIIKt)HHtJH90tBHAHHuHFLH;yH}uELD$ LMMHUHL}HHtIEHUH+HHHDͿuH yHKH vmIHt4HHAH{HCHHuHEH]I;t"H AHH J{u`HI;uދH]u|IELoLMHJDL}HIH]I;}HELL+I뫃u!H xLLHJH u{H xLLHJH uIHvttILmI9>tRL;~HHIHvHtHHUHH+HHHDͿ xHu)HH9;uL;}L ZMHUIs;EL$9}LEMuL{wILEI'J9|tII|Ld$0H6HD$(L IHD$ [IHHILd$0HH IHLHIHHL$(L IHIHD$ LMH 8wLqHIH 9tfHLHt H)uxHH|L jD3AH 63HBHHEHHLEHEMuLJvILELMHEHHPHHH &HvHD$pH6|$hHL$`HD$X|$PHL$HH %;HD$@H,D$8HD$0LL$(LD$ 8HHu4HtH+u HwL C3AH OHH+u HwHLHt H)u}wHH|HHMH3-SHA_A]A\_^[]@USVWATAUAWHl$HHH3HE3LMHMH+HEH/HEH 2HEWH}MH}N,EM^MyL}MGIMt6Ht&HtH@HBHHEHBHHEHHHEIAL$LuO4HEM+H]LMLfffIIKt)HHtJH90tBHAHHuHFLH;sH}uELD$ LMMHUH/L}HHtIEHUH+HHHDuH sHEH pmIHt4HHAHuHCHHuHEH]I;t"H AHH zuu`HI;uދH]ǃu|IELWLMHJDL}HIH]I;}HELL+I뫃u!H sLLHDH p{H rLLHDH oIHqttILmI9>tRL;~HHIH?pHtHHUHH+HHHD sHu)HH9;uL;}L BMHUI5EL$9}LEMuLqILEI"J9|tII| Ld$0H ADHL$(L DHD$ QIHHILd$0HH CL CHILMH CHL$(HD$ H rqLcHCH snHLHt H)urHH|L >3AH cv31HBHHEHBHHEHHLEHEMuLpILELMHMHEHHPHHHWpHD$pH |$hHD$`H0HD$XH*D$PHD$HH'HL$@H /9D$8HD$0LL$(LD$ 2HHu4HtH+u HqL =3A&H buHH+u HqHLHt H)uqHH|HHMH3RMHA_A]A\_^[]LD$HL$USVWATAUAWHHpHZH,3HEHuHMHuHMIDmHELH"HHHtHHGHHEHID$Lt$hLuM4LL-ʨfHEPIIJ|HHtJH98tBHAHHuHGLH;nHuXuILMXLl$ MHUHL}XHHtJHUH+HHHDHu!H nH`@MH kqIHt2HfHAH>ptwHCHHuHEH]I;t(fDH AHH ouJHI;uދƃucJHJDHL}IM;HELL+IuH mHR?LMH j똃H |mH?LMH ~jIHktoII96tQL;~fHIHjHHtHHUHH+HHHLH mHu$HH93uL;~L MHUIJ0Lt$hHuHHuHHHHH+H\$0HH>H?L >HL1HHDHHD$(H>HL$ LIH ZlH>H biH9HMHHt H)umLH 3AoHpA_A]A\_^[]HwHHuHHuH5kHHuHHM@H!HMHHt H)urmHpA_A]A\_^[]HL$USWATAUAVAWHl$HE3NL}oH6(HEML}M,MMaLeMOIMtHHHHEoIAH$HuIM+L}LuHVMADHHK|.HHtJH98tBHAHHuHGMH;UjL}wuELMwH\$ LHUH LuwHHtIEHUH+HHHDouH WjH8<LH \gA|HHt4HHAHlA;HCHHuHEH]H;t0@ffH AHH ku^HH;uAHuqIELEHJDoLuIILEL;EEHELL+I뮃u!H eiLLH;H cfAA;H 6iH?;LLH 8fIIwgtpHL9>tRL;~HHIHfHtHHUHH+HHHDo qiHu)HL9;uL;EL LHUI,AH$ELEoIMHLD$0HޯIH:H?L :HHHDMHD$(Hy:HL$ L}H hLIHs:H eL}oMtI/u IiL [53A+H L'3LILEoMuLJgILEoHEgHHPHLHH H gHD$pH'D|$hHL$`HD$XD|$PHL$HHD$@D|$8HL$0H 9HD$(D|$ )HHu1HtH+u HhL 43AsH ^H+u HhLHMoHt H)uhIHİA_A^A]A\_[]HL$USWATAUAWHHx3NH]@H"HEMH]M$MMiLmMWIMtHHHHE@IAHt$pLt$hLuMM+H]L}PH=5LAfffIIKt<HHtJH90tBHAHHuHFLH;eeH]HuELMHH|$ MHUHL}HHHtI$HUH+HHHD@uH geHH7LH lbA{IHt4HHAH*gA;HGHHuHEH}I;t0@ffHAHH fuZHI;uދH=3unI$LEHJD@L}PIILEL;EHELL+I뱃u!H ydL3LH6H waAA;H JdHS6LLH LaIHbtpII9tRL;~HHIHaHtHHUHH+HHHD@ dHu)HH9uL;DL 2MHUI'AALt$hHt$pHU@IMHLD$0HIH5H?L 5HHHDMHD$(H5HL$ L=2H &cLIH5H *`H]@H=G0HtH+u HdLH ٦3A43HxA_A]A\_[]HHHU@HuHNbHHU@HM8nHM@HHt H)u>dHHxA_A]A\_[]@USVWATAUAWHl$HHH3HEHLM3HEHMHHEWH}MN,EMMMyL}M6IMt%HtH5HBHHEHHHEIAL$LuO4HEM+H]LML3fffIIKt)HHtJH90tBHAHHuHFLH;`H}uELD$ LMMHUH/L}HHtIEHUH+HHHDͿuH `H2H ]mIHt4HHAHbHCHHuHEH]I;t"H AHH zbu`HI;uދH]u|IELLMHJDL}HIH]I;}HELL+I뫃u!H `LLH1H ]{H _LwLH1H \IH^ttILmI9>tRL;~HHIH?]HtHHUHH+HHHDͿ `Hu)HH9;uL;}L MHUI"EL$9}LEMuL^ILEI'J9|tII|Ld$0HfHD$(L 1HD$ [IHHILd$0H(H 0HLH0HHL$(L 0HIHD$ LMH h^LH0H i[fHLHt H)u_HH|L +3AvH f3HBHHEHHLEHEMuLz]ILELMHEHHPHHH HO]HD$pH|$hHL$`HD$X|$PHL$HH E2HD$@HD$8HD$0LL$(LD$ HHu4HtH+u H^L *3AH ԜHH+u H^HLHt H)u^HH|HHMH3]:HA_A]A\_^[];@USVWATAUAWHl$HHH3HE3LMHMH:HEHHEH,HEWH}MH}N,EM^MyL}MGIMt6Ht&HtH@HBHHEHBHHEHHHEIAL$LuO4HEM+H]LML{fffIIKt)HHtJH90tBHAHHuHFLH;[H}uELD$ LMMHUHOL}HHtIEHUH+HHHDuH [H,H XmIHt4HHAH\HCHHuHEH]I;t"H AHH \u`HI;uދH]ǃu|IELGLMHJDL}HIH]I;}HELL+I뫃u!H 2ZLLH+H 0W{H YLLH+H VIH;XttILmI9>tRL;~HHIH_WHtHHUHH+HHHD 1ZHu)HH9;uL;}L 2MHUIEL$9}LEMuLXILEI"J9|tII| Ld$0H a+HL$(L 5+HD$ QIHHILd$0HH +L *HILMH +HL$(HD$ H XLSH*H UHLHt H)uZHH|L %3AH 31HBHHEHBHHEHHLEHEMuLWILELMHMHEHHP HHwWHD$pH|$hHD$`HHD$XHOD$PHD$HH[HL$@H ,|$8HD$0LL$(LD$ HHu4HtH+u H YL $3A= H HH+uHXDHLHt H)uXHH|HHMH3r4HA_A]A\_^[]雂@SVHxH2H3HD$`HM~DH VH )LD$0L (3HD$(LjHt$ H H(S3Mt!IAHx~IH 43HWH|$pD$PH\$HSHHuH=`#Ht|H+uvHWkH ;VHT$HHLHGIHHL$PH g IUHHtH+u HjWH/u H[WHu H="LH ݏ3A3H|$pHHL$`H32Hx^[H\$WH H HAH9uHHtHH=@H=HLGSHHHHH HHtHBUH HF HHu%xUHuH TH$LH QHu H=!KHCHHe LMtA^VHHHHHu9H=!Hu HVLH ^3A虽3H\$0H _Hu HUHH\$0H _@USVWATAUAWHl$HHbH3HEHLM3HEHMHHEWH}MN,EMMMyL}M6IMt%HtH5HBHHEHHHEIAL$LuO4HEM+H]LMLfffIIKt)HHtJH90tBHAHHuHFLH;RH}uELD$ LMMHUHL}HHtIEHUH+HHHDͿuH RHx$H OmIHt4HHAH[THCHHuHEH]I;t"H AHH Tu`HI;uދH]u|IELLMHJDL}HIH]I;}HELL+I뫃u!H QLLHQ#H N{H ~QLWLH}#H |NIHOttILmI9>tRL;~HHIHNHtHHUHH+HHHDͿ QHu)HH9;uL;}L MHUICEL$9}LEMuLKPILEI'J9|tII|Ld$0HHD$(L "HD$ [IHHILd$0HȗH "HLH"HHL$(L _"HIHD$ LMH PLHb"H  MfHLHt H)uQHH|L :3AB H 3HBHHEHHLEHEMuLOILELMHEHHPHHH HNHD$pHs|$hHL$`HD$X|$PHL$HH HD$@H)D$8HD$0LL$(LD$ HHu4HtH+u HPL S3A H ԎHH+u HcPHLHt H)uMPHH|HHMH3+HA_A]A\_^[]HL$USWATAUAWHHx3H"H]@MHEMH]N,MIyH}H;IMtHHHHE@IAHt$pLt$hLuO4M+H]LePL `LDIIKt,HHtJH90tBHAHHuHFLH;%MH]HuELL$ HULMHMH_LeHHHtIEHUH+HHHD@u!H 'MHMH ,JyIHt>HffHAHNHGHHuHEH}I;t$fHAHH Nu]HI;uދH}ЃuvIEL LEHJD@LePIILEL;HELL+I뮃u!H 6LLLHH 4I끃t!H LLLHH IIHAJttII9tVLmАL;~HHIHdIHtHHUHH+HHHD@ 6LHu(HH9uL;L MHUILt$hHt$pxiIJ|@tII|IH KHL|$0L `HD$(LH]HD$ H GH]@H=HtH+u HtLLH 3A3HxA_A]A\_[]HHHE@HU@HM8.HM@HHt H)uLHHxA_A]A\_[]HL$USWATAUAVAWHHE3HDLePMHEMLeHN,M IYH]HBIMtHHHHEPIAHt$xHuJ4M+LeL}XL dMEHffHHK|/HHtJH98tBHAHHuHGMH;ILeuELL$ HULMLH?L}HHtIEHUH+HHHDPu!H IHMH  FzHHt>HffHAHJHCHHuHEH]H;t$fH AHH {JubHH;uAH]Ѓu}IEL LEHJDPL}XIILEL;DeHHELL+I몃u!H HLLHH EzH GLLHH DIIFtuHL9&tWLmL;~HHIH>EHtHHUHH+HHHDP HHu*HL9#uL;DeHL LHUI EHHt$xD9eH|dI}N9dPtII|H}@ItzH FHyLt$0L @HD$(LH=HD$ H CLePMtI,$u IZHL 3AH 3HHHEPHHUHEPE3H IHEH}EHHtH/u HGHuL 3AH rH+u HGL%EI$HMPHt H)uGIHĀA_A^A]A\_[]@USVWATAUAWHl$HHH3HEHLM3HEHMHGHEWH}MN,EMMMyL}M6IMt%HtH5HBHHEHHHEIAL$LuO4HEM+H]LMLfffIIKt)HHtJH90tBHAHHuHFLH;EDH}uELD$ LMMHUHL}HHtIEHUH+HHHDͿuH GDH(H OAmIHt4HHAH FHCHHuHEH]I;t"H AHH Eu`HI;uދH]u|IELgLMHJDL}HIH]I;}HELL+I뫃u!H bCLLHH `@{H .CLLH-H ,@IHkAttILmI9>tRL;~HHIH@HtHHUHH+HHHDͿ aCHu)HH9;uL;}L RMHUIEL$9}LEMuLAILEI'J9|tII|Ld$0HHD$(L eHD$ [IHHILd$0HxH LHLH1HHL$(L HIHD$ LMH ALiHH >fHLHt H)u2CHH|L 3A H 趪3HBHHEHHLEHEMuL@ILELMHEHHPHHH NH@HD$pH#|$hHL$`HD$X|$PHL$HH HD$@HD$8HD$0LL$(LD$ NHHu4HtH+u HBBL 3A H ϩHH+u HBHLHt H)uAHH|HHMH3HA_A]A\_^[]HHM~FH @HaLD$0L MHD$(LHJHD$ H <3HHMt!IAHx~IH R3HHHHҳH\$Ht$WH IHHMH6=H~sHCu H=tdH =HHtSHLHHlH/Hu H@H+u H@HH\$0Ht$@H _<HHuH\$03Ht$@H _H\$Ht$WH IHHMHf<H~sHCu H<tdH=<HHtSHLHH\H/Hu H?H+u H?HH\$0Ht$@H _;HHuH\$03Ht$@H _@USVWATAUAWHl$HHBH3HEHLM3HEHMHwHEWH}MN,EMMMyL}M6IMt%HtH5HBHHEHHHEIAL$LuO4HEM+H]LMLfffIIKt)HHtJH90tBHAHHuHFLH;u<H}uELD$ LMMHUHL}HHtIEHUH+HHHDͿuH w<HXH 9mIHt4HHAH;>HCHHuHEH]I;t"H AHH =u`HI;uދH]u|IELLMHJDL}HIH]I;}HELL+I뫃u!H ;L3LH1 H 8{H ^;LLH] H \8IH9ttILmI9>tRL;~HHIH8HtHHUHH+HHHDͿ ;Hu)HH9;uL;}L rMHUI#EL$9}LEMuL+:ILEI'J9|tII|Ld$0HHD$(L HD$ [IHHILd$0HH | HLHa HHL$(L ? HIHD$ LMH 9LHB H 6fHLHt H)ub;HH|L 3A H y3HBHHEHHLEHEMuL8ILELMHEHHPHHH ~H8HD$pHS|$hHL$`HD$X|$PHL$HH HD$@H D$8HD$0LL$(LD$ ~HHu4HtH+u Hr:L 33Ay H yHH+u HC:HLHt H)u-:HH|HHMH3HA_A]A\_^[]@SH HAHHt(9uHCH H9H0u H9u_Hp9HKHtHCH)u9HHtHǃH)up9HCHH@H [HH [H\$Ht$WH HIHIHHt Iօu&HHtHHH\$0Ht$8H _H3H\$0Ht$8H _@SH H6HHIHCH6HHt H)u8H6HHH6HHt H)u83H [@USVWATAUAWHl$HHH3HE3HMH}HHEHHEH<HEWH}J L$MEMMyL}M{IMt6Ht&HtHHBHHEHBHHEHHHEIAL$LuLM+H}LmHZALfHEIIJt HHtJH90tBHAHHuHFLH;!5H}uELMH\$ MHUH[L}HHtI$HUH+HHHDuH #5HLH (2AwIHttRL;~HHIHq1HtHHUHH+HHHD C4Hu)HH9;uL;DL MHUIAL$EH9}IMHHHLD$0H gIL MH?HjHHMLLIH EHL$(H 2HD$ H /HLHt H)ub4HH|L 3A~ H s3SHBHHEHBHHEHHHHEuHHHEH9}uHwHHELEMuL1ILELMHMHEHHP HH1HD$pH<|$hHD$`HHD$XH0D$PHD$HHHL$@H |$8HD$0LL$(LD$ AHHu4HtH+u H53L 3A H qšHH+u H3HLHt H)u2HH|HHMH3HA_A]A\_^[]HAHHAH\$WH HHHt$HHIH)u2H{3H\$0H _HP0HHIH)uU2H60HC3H\$0H _HL$USWATAUAWHHx3NH]@HHEMH]M$MMiLmMWIMtHHHHE@IAHt$pLt$hLuMM+H]L}PH=LAfffIIKt<HHtJH90tBHAHHuHFLH;5/H]HuELMHH|$ MHUHoL}HHHtI$HUH+HHHD@uH 7/HLH <,A{IHt4HHAH0A;HGHHuHEH}I;t0@ffHAHH 0uZHI;uދH=unI$LEHJD@L}PIILEL;EHELL+I뱃u!H I.LRLHH G+AA;H .H#LLH +IH[,tpII9tRL;~HHIH+HtHHUHH+HHHD@ U.Hu)HH9uL;DL MHUIAALt$hHt$pHU@IMHLD$0HtIHH?L dHHHDMHD$(HYHL$ L H ,LIHSH )H]@H=HtH+u Hs.LH h3A3HxA_A]A\_[]HHHU@HuH,HHU@HM8ޣHM@HHt H)u.HHxA_A]A\_[]@USVWATAUAWHl$HHH3HE3HMH}H"HEHWHEHHEWH}J L$MEMMyL}M{IMt6Ht&HtHHBHHEHBHHEHHHEIAL$LuLM+H}LmH ALfHEIIJt HHtJH90tBHAHHuHFLH;*H}uELMH\$ MHUHL}HHtI$HUH+HHHDuH *HLH 'AwIHttRL;~HHIH&HtHHUHH+HHHD )Hu)HH9;uL;DL 8 MHUIUAL$EH9}IMHHHLD$0H IL H?HHHML LIH HL$(H Y(HD$ H c%HLHt H)u)HH|L 3A H hf3SHBHHEHBHHEHHHHEuHHHEH9}uHHHELEMuLH'ILELMHMHEHHP HH 'HD$pH|$hHD$`HHD$XHD$PHD$HH\HL$@H |$8HD$0LL$(LD$ HHu4HtH+u H(L v3AS H gBHH+u H(HLHt H)up(HH|HHMH3 HA_A]A\_^[]@USVWATAUAWHl$HHH3HE3HMH}HbHEHHEHHEWH}J L$MEMMyL}M{IMt6Ht&HtHHBHHEHBHHEHHHEIAL$LuLM+H}LmHzALfHEIIJt HHtJH90tBHAHHuHFLH;$H}uELMH\$ MHUHL}HHtI$HUH+HHHDuH $HLH !AwIHttRL;~HHIH1!HtHHUHH+HHHD $Hu)HH9;uL;DL ,MHUIAL$EH9}IMHHHLD$0H 'IL H?H*HHMLLIH HL$(H "HD$ H HLHt H)u"$HH|L 3AX H #c見3SHBHHEHBHHEHHHHEuHCHHEH9}uH7HHELEMuL!ILELMHMHEHHP HH`!HD$pH|$hHD$`HHD$XHD$PHD$HHHL$@H |$8HD$0LL$(LD$ HHu4HtH+u H"L 3A H a肊HH+u H"HLHt H)u"HH|HHMH3`HA_A]A\_^[]HL$USWATAUAWHHx3NH]@HnHEMH]M$MMiLmMWIMtHHHHE@IAHt$pLt$hLuMM+H]L}PH=LAfffIIKt<HHtJH90tBHAHHuHFLH;uH]HuELMHH|$ MHUHL}HHHtI$HUH+HHHD@uH wHXLH |A{IHt4HHAH:!A;HGHHuHEH}I;t0@ffHAHH  uZHI;uދH=unI$LEHJD@L}PIILEL;EHELL+I뱃u!H LzLH(H AA;H ZHcLLH \IHtpII9tRL;~HHIHHtHHUHH+HHHD@ Hu)HH9uL;DL MHUI'AALt$hHt$pHU@IMHLD$0HdIHH?L HHHDMHD$(HHL$ L5H 6LIHH :H]@H=WHtH+u HLH iX3AD3HxA_A]A\_[]HHHU@HuHHHU@HM8HM@HHt H)uNHHxA_A]A\_[]@USVWATAUAWHl$HHҽH3HE3HMH}HHEHHEHHEWH}J L$MEMMyL}M{IMt6Ht&HtHHBHHEHBHHEHHHEIAL$LuLM+H}LmHb*ALfHEIIJt HHtJH90tBHAHHuHFLH;H}uELMH\$ MHUHL}HHtI$HUH+HHHDuH HLH AwIHttRL;~HHIH1HtHHUHH+HHHD Hu)HH9;uL;DL (MHUIAL$EH9}IMHHHLD$0H 'IL H?H*HHML'LIH HL$(H HD$ H HLHt H)u"HH|L 3A H SY要3SHBHHEHBHHEHHHHEuHCHHEH9}uH7HHELEMuLILELMHMHEHHPHHH`HD$pH|$hHD$`HHD$XHPD$PHD$HHHL$@H x|$8HD$0LL$(LD$ HHu4HtH+u HL 3A H /X肀HH+u HHLHt H)uHH|HHMH3`HA_A]A\_^[]@USVWATAUAWHl$HHH3HEHHMMHE3HHHEWH}N M,EMM|$L}M\IMt%HtHHBHHEHHHEID$L$LuMM+H}LeH.ALHEIIJt(HHtJH90tBHAHHuHFLH;AH}uELMH\$ MHUH{L}HHtIEHUH+HHHDͷuH CH$LH HAwIHttRL;~HHIHHtHHUHH+HHHDͷ cHu)HH9;uL;DL ,MHUIAL$EH9}IMHHLD$0H IL wH?HHHML",LIH oHL$(H HD$ H  HLHt H)uHH|L G3A" H S|3.HBHHEHHHHEuHHHELEMuLILELMHEHHP HH HHD$pHm|$hHL$`HD$X|$PHL$HH HD$@HkD$8HD$0LL$(LD$ HHu4HtH+u HL M3Aj H R{HH+uH]DHLHt H)uBHH|HHMH3HA_A]A\_^[]@VH0H!HHHtEHHuHHHt-HH0^H;tH HEWH 3H0^H\$@HWHl$HHH|$(Lt$ HHHH/Hu HaHtbHHHtBH@WH{HHtULHWHPH/Du HEyEH+u H3H|$(Hl$HH\$@Lt$ H0^H H  tHVHHHt0LHVH H/Du HEuH }H  [NHVHHHt0LH\VHc H/Du H!EH H > H9VH(3HHt6H;tLHVH H/u Hy#H H  zH@USWATAWH$PHHH3HHE33E3Ht/H;u32hH iHUH HhHH$L$ L$(H = HH.gHH R HgHH jUH HfHHZULH <HAfHBL-3U%= t_LZAH/L$@HMT$8HD$0Ll$(D$ s EHU3 Kf3 HH3f3H TmHHf3H T>HϷHeH eHS IH5)EDu< HHt2LLH3HL)#u H3H9=u HMe H>eHLH E3跜HHu He9=t#LmHH o e HHdIH HuL7IH dH5H mH HHu3? Hu(H k H|LH H<pH3Hu]H5xH H詿HHu. Hu#H  HLH ,H HHu3H3H cH: H KH |H HfHHHH zH SHwcH 8vcH $ncH +ZcH HHuDeA_HHhHHu A^HvLH y A^L)7u H3L)6u HuDeAj_HYGHHuDeA^HLH gzy A&^L)6u HUH>H LиE3HD$0H4HD$(HHD$ >HHuDeA^^HLH ްy A]L)6u HHH LWE3HD$0HHD$(HgHD$ 赏HHuDeA]HOLH Uhy A]L)6u HCH<H LƷE3HD$0H"HD$(H޾HD$ ,HHuDeAL]H&LH ̯y A\L)6u HHH dLeE3HD$0HHD$(HUHD$ 裎HHuDeA\HLHHHH 2Ey A[L)6u H H)H L E3HD$0HHD$(HHD$ HHuDeA)\HLHHHH y AW[L)6u HHH L9E3HD$0HeHD$(H!HD$ oHHuDeA>[HLH "y A>ZL)6u HHH LE3HD$0HܬHD$(HHD$ HHuDeA[HXLHHHH uy A4ZL)6u HcHH -LE3HD$0HBHD$(HHD$ LHHuDeAlZHLHHHڼH ۬y AYL)6u HHH LdE3HD$0HHD$(HdHD$ 貋HHuDeAYH$LHHHXH ATy AYL)6u H/H`H YLE3HD$0HHD$(HʺHD$ HHuDeA8YHLHHHH y AfXL)6u HHάH ߋL`E3HD$0HtHD$(H0HD$ ~HHuDeAJXHLHHHdH  y AJWL)6u HH<H eLE3HD$0HکHD$(HHD$ HHuDeAvXHVLHHHJH sy Av2WL)6u HaHHH3L LL  HHuDeA|WHfH LE3HD$0HHD$(HHD$ HHu AVHHHHu HHLH yHAbVL)7u HvHǪH L!E3HD$0HUHD$(HHD$ _HHuDeA8VHaLH yHA8UL)7u HH?H PLE3HD$0HŧHD$(HHD$ χHHuDeA\UHYLHHHH ^qyHA\1UL)7u HEHH OL@E3HD$0H$HD$(HHD$ .HHuDeA2NUHLHHHܹH yHMA2TL)7u HH H ~LE3HD$0HHD$(H?HD$ 荆HHuDeATHLH -@yHATL)7u HHH LE3HD$0HHD$(HHD$ HHuDeATHLH yH-ApSL)7u HHH LE3HD$0HcHD$(HHD$ mHHuDeA SHLHHHH yHA RL)7u HHdH -LE3HD$0H¤HD$(H~HD$ ̄HHuDeARH>LHHH¶H [nyHA.RL)7u HBH˦H lLE3HD$0H!HD$(HݳHD$ +HHuDeAKRHLHHHyH yHJAQL)7u HH2H LtE3HD$0HHD$(H<HD$ 芃HHuDeA1QHLHHHxH ,yHA1PL)7u HHH jL۫E3HD$0HߢHD$(HHD$ HHuDeA QHkLHHHGH xyHAKPL)7u H_HH L2E3HD$0H>HD$(HHD$ HHHuDeAhPHLHHHNH עyHgAOL)7u HHgH LE3HD$0HHD$(HYHD$ 见HHuDeA5OHLHHHH 6IyHA5 OL)7u HHΣH LЩE3HD$0HHD$(HHD$ HHuDeA&OHxLHHHH yH%AhNL)7u H|H5H LE3HD$0H[HD$(HHD$ eHHuDeANHסLHHHH yHAML)7u HHH LE3HD$0HHD$(HvHD$ HHuDeA+MH6LHHHH SfyHA+&ML)7u H:HH LE3HD$0HHD$(HծHD$ #HHuDeAvCMHLHHH!H ſyHBAvLL)7u HHjH #LE3HD$0HxHD$(H4HD$ ~HHuDeALHLHHHxH $yHAKL)7u HHѠH LKE3HD$0HםHD$(HHD$ }HHuDeAB LHSLHHHǭH p胾yHAB CKL)7u HWH8H LjE3HD$0H6HD$(HHD$ @}HHuDeA `KHLHHHVH ϝyH_A JL)7u HHH L!E3HD$0HHD$(HQHD$ |HHuDeA JHLHHHլH .AyHA JL)7u HHH LE3HD$0HHD$(HHD$ {HHuDeA~ JHLHHHH 蠼yHA~ `IL)7u HtHmH >LgE3HD$0HSHD$(HHD$ ]{HHuDeA }IHLHHHH yH|A HL)7u HHԝH LޣE3HD$0HHD$(HnHD$ zHHuDeAX HHNLHHHڨH K^yHۼAX HL)7u H2H;H DLEE3HD$0HHD$(HͩHD$ zHHuDeA ;HHLHHHAH 轺yH:A }GL)7u HHH ÎL4E3HD$0HpHD$(H,HD$ zyHHuDeA" GHLHHHPH yHA" FL)7u HH H LE3HD$0HϘHD$(HHD$ xHHuDeAo FHKLHHH׫H h{yHAo ;FL)7u HOHpH LBE3HD$0H.HD$(HHD$ 8xHHuDeA XFHLHHHH ǘڸyHWA EL)7u HHךH xLQE3HD$0HHD$(HIHD$ wHHuDeA( EH LHHHMH &9yHA( DL)7u H H>H xL@E3HD$0HHD$(HHD$ vHHuDeA EHhLHHHH 蘷yHA XDL)7u HlHH 6xLϟE3HD$0HKHD$(HHD$ UvHHuDeAuDHחLHHHcH yHtACL)7u HH H wLE3HD$0HHD$(HfHD$ uHHuDeAqCH&LHHHH CVyHӷAqCL)7u H*HsH 4wL E3HD$0H HD$(HŤHD$ uHHuDeA3CHLHHHH 赵yH2AuBL)7u HHڗH vLE3HD$0HhHD$(H$HD$ rtHHuDeABHLHHH0H yHAAL)7u HHAH rvLE3HD$0HǓHD$(HHD$ sHHuDeAAHCLHHHH `syHA3AL)7u HGHH uLrE3HD$0H&HD$(HHD$ 0sHHuDeAPAHҔLHHHH ҳyHOA@L)7u HHH puLɛE3HD$0HHD$(HAHD$ rHHuDeA@HLHHHMH 1yHA?L)7u HHvH tLȚE3HD$0HHD$(HHD$ qHHuDeAB@H`LHHHH }萲yH ABP?L)7u HdHݔH uLE3HD$0HCHD$(HHD$ MqHHuDeAm?HLH yH}A>L)7u HHUH ~zL/E3HD$0HHD$(HoHD$ pHHuDeAT>HǠLH ]pyHAT0>L)7u HDH -HUWE3IE觘HHuDeAg>HѠLH yHvA=L)7u HHΏHHH9 SuH=RHtH+H qTxH cL,HȬHHuDeA=HGHH[LMtAtHHuHűA=L)7u HHLH 3 y A<L)6u HHHHH9 |uH5{HtH+H mwH |LUHFHHuDeA<HFHHLMtAHHu A <L)6u H<HELH 3-yHA;L)7u HHHHH9 uH=HtH+H vH LHqHHuDeA;HGHHטLMtAHHuHA<;L)7u HPHLH G3?y A:L)6u HHHHH9 ЄuH5τHtH+H uH LHHHuDeA;HFHHLMtAHHu AA:L)6u HpHٗLH gayHޮA!:L)7u H5H6HHH9 uH=HtH+H ٜtH ˜LԃHŃ0HHuDeA0:HGHH;LMtAHHuH-Ap9L)7u HHLH {3sy A9L)6u HNHOHHH9 $uH5#HtH+H sH LHIHHuDeAI9HFHH̖LMtAHHu Au8L)6u HHLH yHAU8L)7u HiHjHHH9 OuH=NHtH+H rH L(HdHHuDeAd8HGHHOLMtAHHuHaA7L)7u HH LH 3y AS7L)6u HHHHH9 xuH5wHtH+H & rH LQHB}HHuDeA}7HFHHpLMtA)HHu A6L)6u HH1LH ψyHFA6L)7u HHHHH9 uH=HtH+H A$qH 3L|Hm蘥HHuDeA6HGHHLMtADHHuHA5L)7u HHLH 3y A5L)6u HHHHH9 uH5HtH+H Z=pH LLH豤HHuDeA5HFHH$LMtA]HHu A4L)6u H HLH yHzA4L)7u HH҆HHH9 ~uH=~HtH+H uXoH gL~H~̣HHuDeA4HGHH'LMtAxHHuHɨA 4L)7u H HLH 3y A3L)6u HHHHH9 ~uH5~HtH+H qnH L}H}HHuDeA3HFHHpLMtAHHu A3L)6u H@H1LH 71yHA2L)7u HHHHH9 K}uH=J}HtH+H mH L$}H}HHuDeA3HGHHLMtAHHuHA@2L)7u HTHeLH K3Cy A1L)6u HHHHH9 t|uH5s|HtH+H ”lH LM|H>|HHuDeA2HFHHLMtAHHu AE1L)6u HtH}LH keyHA%1L)7u H9H:HHH9 {uH={HtH+H ݓkH ϓL{Hi{4HHuDeA41HGHH?LMtAHHuH1At0L)7u HHLH 3wy A#0L)6u HRHSHHH9 zuH5zHtH+H jH LzHzMHHuDeAM0HFHH`LMtAHHu Ay/L)6u HH!LH yHAY/L)7u HmHnHHH9 zuH=zHtH+H iH LyHyhHHuDeAh/HGHHLMtAHHuHeA.L)7u HH=LH 3y AW.L)6u HHHHH9 zHHH9 suH=sHtH+H bH ӊL\sHMs8HHuDeA8(HGHHLMtAHHuH5Ax'L)7u HHՉLH y3{y A''L)6u HVHWyHHH9 ruH5rHtH+H aH LrHvrQHHuDeAQ'HFHHLLMtAHHu A}&L)6u HH LH xyHA]&L)7u HqHrxHHH9 quH=qHtH+H `H LqHqlHHuDeAl&HGHHLMtAHHuHiA%L)7u HHILH w3y A[%L)6u HHwHHH9 quH5pHtH+H .`H LpHp腔HHuDeA%HFHHLMtA1HHu A$L)6u HHLH vyHNA$L)7u HHvHHH9 +puH=*pHtH+H I,_H ;LpHo蠓HHuDeA$HGHH LMtALHHuHA#L)7u HHņLH u3y A#L)6u HHuHHH9 TouH5SoHtH+H bE^H TL-oHo蹒HHuDeA#HFHH,LMtAeHHu A"L)6u HHLH uyHA"L)7u HHtHHH9 nuH=~nHtH+H }`]H oLXnHInԑHHuDeA"HGHHOLMtAHHuHіA"L)7u H(H LH t3y A!L)6u HHsHHH9 muH5mHtH+H y\H LmHrmHHuDeA!HFHHpLMtAHHu A!L)6u HHH1LH ?s9yHA L)7u H HsHHH9 luH=lHtH+H [H LlHlHHuDeA!HGHHLMtAHHuHAH L)7u H\HMLH Sr3Ky AL)6u H&H'rHHH9 luH5lHtH+H ʂZH LkHk!HHuDeA! HFHHLMtAHHu AML)6u H|HLH sqmyHA-L)7u HAHBqHHH9 GkuH=FkHtH+H YH ׁL kHk<HHuDeA<HGHHLMtAHHuH9A|L)7u HHYLH p3y A+L)6u HZH[pHHH9 juH5jHtH+H XH LYjHJjUHHuDeAUHFHH؁LMtAHHu AL)6u HHLH oyHAaL)7u HuHvoHHH9 iuH=iHtH+H WH LiHipHHuDeApHGHH;LMtAHHuHmAL)7u HHLH n3y A_L)6u HHnHHH9 huH5hHtH+H 2WH $LhHh艋HHuDeAHFHH\LMtA5HHu AL)6u HHLH myHRAL)7u HHmHHH9 huH=hHtH+H M~0VH ?~LgHg褊HHuDeAHGHHLMtAPHHuHAL)7u HH9LH l3y AL)6u HHlHHH9 8guH57gHtH+H f}IUH X}LgHg轉HHuDeAHFHH~LMtAiHHu AL)6u HHa~LH l yHAL)7u HHkHHH9 cfuH=bfHtH+H |dTH s|Li!CH 0iLUHUwHHL"HH [ALHkL)7u HHhMH Z3<M)7u IHYHHH9 UuL=UMtI+H ~hiBH phLaUHRUvLME3L;=gAM)7u ImECHeYHHH9 *UuL=)UMtI+H hAH gLUHT_vLMtEIGIL!`HgLMtA xM)7I3^AlAkwAjlAj^Ht'HHHXu Ai2\L)6u H^H WE3HgHALMtAZyDeAn~H VE3HfHALMtAyDeAn?-HHuHVzE3Lw_HH\_LH_HHf\@L]HH[q!L:_HHP\RL[_HHQ\3L^HH[L\HHZL\HHZ֩L^HH[gL0^HHN[HL!^HH?[y)L ^HH([Z L3^HHI[;L,]HHZL]HH[L6\HHZިL\HHYoL\HH&ZPL[HHY1Lb\HHYbL#]HH9ZCL$]HH:Z$LM]HH;ZL>\HH|YL]HH ZǧwL\HHVYXL[HHX9LZHHXjLZHHXKLZHHX,L[HHX L>\HH,YL[HHXϦLYHHW`LZHHXALZHHXr"L[HHXSLYHHbW4LYHHSWLYHHTWLYHHEWץLYHHWxlLXHHVxQLZZHHpWx6LYHHWgxHdLH RRLyGE3HtH.u H'HtH/u HMtI/u IDe_L)6HM)uu IӨE-D5/iHPtH)iHQHQAH*tH=JPtHH=QtEtLH gE3H PHt;H)H Pu*J":HuH FHH &HOL$(HL$ H$HH3跃HA_A\_[]@USVWATAUAWHl$HHrGH3HE3LMHMHb_HEH7bHEHbHEWH}MH}N,EM^MyL}MGIMt6Ht&HtH@HBHHEHBHHEHHHEIAL$LuO4HEM+H]LMLfffIIKt)HHtJH90tBHAHHuHFLH;H}uELD$ LMMHUHFL}HHtIEHUH+HHHDuH HhvH mIHt4HHAHKHCHHuHEH]I;t"H AHH  u`HI;uދH]ǃu|IELLMHJDL}HIH]I;}HELL+I뫃u!H L/LHAuH {H nLLHmuH lIHttILmI9>tRL;~HHIHϠHtHHUHH+HHHD Hu)HH9;uL;}L nMHUI3fEL$9}LEMuL;ILEI"J9|tII| Ld$0H tHL$(L tHD$ QIHHILd$0HH tL ftHILMH stHL$(HD$ H LH\tH HLHt H)uHH|L :o3Ao H  31HBHHEHBHHEHHLEHEMuLILELMHMHEHHPHHHHD$pHQ|$hHD$`H[aHD$XHw]D$PHD$HHZHL$@H /yD$8HD$0LL$(LD$ cHHu4HtH+u HxL 9n3A H  HH+u HIHLHt H)u2HH|HHMH3}HA_A]A\_^[]HL$USWATAUAWHHx3HJ]H]@MHEMH]N,MIyH}H;IMtHHHHE@IAHt$pLt$hLuO4M+H]LePL hLDIIKt,HHtJH90tBHAHHuHFLH;H]HuELL$ HULMHMHOALeHHHtIEHUH+HHHD@u!H HpMH yIHt>HffHAHΠHGHHuHEH}I;t$fHAHH u]HI;uދH}ЃuvIEL (LEHJD@LePIILEL;HELL+I뮃u!H &LLHoH $끃t!H LLHoH IH1ttII9tVLmАL;~HHIHTHtHHUHH+HHHD@ &Hu(HH9uL;L MHUI`Lt$hHt$pxiIJ|@tII|IH HL|$0L PoHD$(LHMoHD$ H H]@H=jHtH+u HdLH J3A>3HxA_A]A\_[]HHHE@HU@HM8~'HM@HHt H)uHHxA_A]A\_[]@USWATAUAWHl$HH=H3HEHTHMHEJHUH]HE3H}H%XHEHXHEWWMMEMMMyL}MpIMtGHt7Ht&HtHcHBHHEHBHHEHBHHEHHHEIAH$L$LuO4L+H}LmL {ALfHEIIHtHHtJH90tBHAHHuHFLH;H}uDLL$ HULMMH<L}HHtHHUH+HHHDuH HelMH AxIHt=HfHAH>A;HCHHuHEH]I;t$fH AHH ucHI;uދH]u}HL 9LEHJDL}IHLEH]M;DHELL+I먃u!H LLH/kH AzA;H ^LLH]kH \IHtpII9>tRL;~HHIHÖHtHHUHH+HHHD Hu)HH9;uL;DL &MHUI'\AL$H$EHEHuH(HHEI6J9|tII|Ld$0H jHL$(L jHD$ QIHHILd$0HH fjL LjHILMH YjHL$(HD$ H L9HBjH H efHLHt H)ubHH|LH '3A 3HBHHEHBHHEHBHHEHHHEHEHuHHHELMLEHUHMHD$ 'H@HLHt H)u˜HH|HHMH3rtHA_A]A\_[]H )7H%̋Q @tÃuÃDLL$ DD$SVATAUHHALHHl$p3H|$@LLt$8DL|$0tAHVHt.VHp;}HHD9DuHHH$Im`DM}hIMupH$Iu`IuhIupӖHHuDHtHmu HMtI/u IzMI.IIM`MehI}pIm`M}hMupHt H)u6MtI,$u I!HtH/u H D$H=UEHu>eHHUU@UD`HHtUEHoLcD;}-MMF9du J JHH)q2U;u0Dp@HIcHƖHHtdUH UD5THcLI;~#HHHDAHHIAI;IHDdLTID$L>E3HIHHt HD`(OHtH+u HHtH/u HL|$0Lt$8H|$@Hl$pHHA]A\^[H\$H|$ UHl$HH@HHHHUwL L0HEwHEo! EsHMoH|$(HD$ 9H?H^HHULL0HEHXHEHhEoB EsHMoH|$(HD$ ?9H?HHHUL8L0HEHHEHEoA EsHMoH|$(HD$ 8H0?HHHULL0HEH EoB EsHMoH|$(HD$ LM8H>HEHHUL L0HEHHEH HEH8Eob EsHMoH|$(HD$ 8H|>HHHULL0HEHXHEH HEH HEH HEHEo Es>HMoH|$(HD$ 7H>HYHHULL0HEHHEH EoB EsHMoH|$(HD$ :7H=HHHULL0HEHHEHXEoB EsHMoH|$(HD$ 6HS=HHHUL L0HEHP HEH HEHHEHEo EsHMoH|$(HD$ d6H<H#HHULP L0HEHHEHHEHEoc EsHMoH|$(HD$ 5H<HHHUL8L0HEHHEHEoB EsJHMoH|$(HD$ 5H*<HXHHULL0HEHHEH HEHHEHHEH HEHEo EsvHMoH|$(HD$ 5H;HHHULL0HEHHEH HEHHEH HEH HEHHEH( HEH`HEH@Eo% EsHMoH|$(HD$ `4H;HHHUL@ L0HEH(HEH0HEHEob Es8HMoH|$(HD$ 3H:HHHULp L0HEHP HEHHEH@HEH HEHxHEH HEH HEHXHEH0HEH HEHHEHX HEHHEH( HEH8HEH HEHx HEHxHE'H`HE/HH HE7HHE?HHHEGH`EoEs\HMoH|$(HD$ 2H_9HmHHULL0HEHHEH HEHHEH HEHh HEH HEHHEH HEHHEHHEHEod Es2HMoH|$(HD$ 1H8HHHULL0HEH HEH@EoAEsHMoH|$(HD$ 1HL8HJHHULL0HEH HEHXEoAEsHMoH|$(HD$ +1H7HHHULL0HEHHEH HEHHEHEo Es HMoH|$(HD$ 0H7HtHHULHL0HEHHEHEoB EsHMoH|$(HD$ U0H.7HHHULL0HEHXHEHHEHHEHEo EsHMoH|$(HD$ /H6HHHUL@L0HEHHEHHEHEoc Es1HMoH|$(HD$ t/H]6H3HHUL L0HEHHEHHEHHEHEo EsHMoH|$(HD$ .H5HHHULX L0HEH HEH HEHHEHEo EsHMoH|$(HD$ .H5HGHHULL0HEH HEH HEHHEHHEHEo Es5HMoH|$(HD$ .H5HHHULh L0HEH HEHHEHEoc EsHMoH|$(HD$ -H4H[HHULL0HEH HEHHEHHEHEo EsHMoH|$(HD$ &-H74HHHUL0L0HEHHEHEoB Es+HMoH|$(HD$ ,H3HHHULPL0HEH HEHHEHEoc EsvHMoH|$(HD$ [,H|3HHHULHL0HEHHEH HEHHEHHEo EsHMoH|$(HD$ +H3HHHUL8L0HEHP HEHHEHEoc EsB HMoH|$(HD$ z+H2H9HHULhL0HEHP HEHHEHEoc Es HMoH|$(HD$ +HH2H HHULL0HEHP HEHHEHHEoc Es HMoH|$(HD$ *H1Hc HHULL0HEHXHEHHEHHEHEo Es~ HMoH|$(HD$ .*Hw1H HHUL L0HEHXHEHHEHHEHEo Es HMoH|$(HD$ )H 1Hw HHULpL0HEHXHEHHEHHEH(Eo EsX HMoH|$(HD$ B)H0H HHULxL0HEHHEHHEHHEHPEo Es HMoH|$(HD$ (H-0H HHUL(L0HEHHEHHEHEoc Es" HMoH|$(HD$ a(H/H HHULPL0HEHHEHHEHHEHEo Eso HMoH|$(HD$ 'H\/H HHULL0HEHHEHHEHxHEHHEH HEH HEH HEHHEHHEHHEHEoe Es HMoH|$(HD$ ('H.H HHUL L0HEH HEH HEHHEH HEH8 HEH HEH HEH HEHHEHHEH HEH(HEH(HEH`Eo Es( HMoH|$(HD$ D&H-H HHULXL0HEH HEH HEHHEHHEHEo Es HMoH|$(HD$ %HL-HHHUL`L0HEHHEHHEHHEHPEo EsHMoH|$(HD$ M%H,H HHULL0HEHP HEHHEHHEHEo EsqHMoH|$(HD$ $Hp,HHHUL L0HEH HEHHEHHEH0Eo EsHMoH|$(HD$ a$H,H HHUL L0HEHpHEHHHEHHEHHEHHEHHEHHEHHHEH@HEHPHEHHEH8Eo EsHMoH|$(HD$ #H<+HRHHULL0HEH HEHHEHHEHEo EsHMoH|$(HD$ #H*HHHULL0HEHHEH HEHHEH` HEHHEHHEHHEH` HEHxHEHHEHHEH(HEHHEHEo EsHMoH|$(HD$ 9"H)HHHULL0HEH HEHHEHHEH HEH HEHHEHHEHHEH@HEHHEHXHEHHEHxHEHHEHHEHHEH(Eo$EsHMoH|$(HD$ 4!H(HHHUL L0HEH HEHHEH HEHHEH HEH HEH HEH0HEH HEH8HEHX HEHp HEHHEH HEHEo EsBHMoH|$(HD$ E H(HHHULL0HEHxHEH HEH HEH HEH`HEH HEHHEH8 HEH0 HEHpEoB EsHMoH|$(HD$ H^'HLHHULHL0HEHxHEH HEH( HEHxEo EsTHMoH|$(HD$ H&HH HULL0HEHhEoA EsHMoH|$(HD$ LMH&H}HHUwL L0Eo EsHMoH|$(HEwHD$ lHU&H+H HULL0HEHHEHEoA EsHMoH|$(HD$ H%HH HULL0HEH HEH0Eo@<EsHMoH|$(HD$ H%HtoH HULL0HEH HEH0Eo@<Es#HMoH|$(HD$ PHQ%HtH/u Hx3H/u HzxL$I[I{(I]H\$Hl$Ht$WH0HAIHHDPHpAAAAtAtH LvH%PH v3MtIsHHCHu HSLEL 0PH vHOHD$ MH s3MtIsHu[HCHu3zLEL OH uHYOHD$ MH r3QMHHDMt7IAsHt)LEL OH buH#OMH gr3HHH\$@Hl$HHt$PH0_H\$Hl$Ht$H|$ AVH0LIH q5IvvHHu 33Hx(LpHXHtHHppHD$`HtHHC HD$hH{@H{HHEHC`HkPH{XH{8HHD$pHtHHChH{xHHHHHANtdtXtJzt<t+H #tHMH tH+u HuH2H=H=?H=H=HH{0PuHH\$@Hl$HHt$PH|$XH0A^@SH LYMHE3L#Ћ$I[<u5Is)H ~sL NMH5MH |p3H [AEt&At3H [H HIMHH [HHIMHH [HH\$WH0LQHHY8L#M$E3Iz<u:Is.H rL gMMHLH o3H\$@H0_AEtAt 3H\$@H0_H HIHIML\$ LHH\$@H0_@SH0MHL#LHIME3AHY$<u5Is)LL LH rHKH o3H0[AMt0I{t)LL !LH qHKH n3H0[EtAu;H IIHMt2M@L KH qH5KLT$ MH n3H0[3HH0[H@SH0MHL#LHIME3AHY$<u5Is)LL KH qHJH n3H0[AMt0I{t)LL !KH pHJH m3H0[EtAu@H HIIHIt2M@L JH pH0JLT$ MH m3H0[HHH0[H@SH HHHHu H [H@HHHA HHH)uq3H [H\$WH LQ`HyhHYpHQ`LAhLIpMtI*u IrqHtH/u H^qHtH+u HJqH\$8H _H\$ UVWATAWH H.IIM3ZHHtHI,$HpLHuH\$hH A_A\_^]H FLt$XmLHHHLl$PmLHIHlHH|oHLIIpHHHtHtHHHHxH)uEpHtELIIolHHt]HH;t-H.u HpMcD$HHHe4y@Ht9(H(u3Ho(HHMcD$H64yH+u Ho3I.Ll$Pu IoI/Lt$Xu IoHH\$hH A_A\_^]H\$ WH HBIt,"HHu@nHuHHHH\$HH _YoHHl$0%nH mHHHH`H;t8Ht!LBAt%uHl$03H\$HH _HM`3H}pHt$@HuhHE`HEhHEpHt H)unHtH.u HnHt$@HtH/u HjnHHHl$0H\$HH _H\$WH HHH A HHu(mHuH BlHS<LH giHH\$0H _H\$Hl$Ht$WH0HHEHAH;blH;Mlu@H¹H?EDtHHGHÃ|$`t H;GHDHhpHphHt?H}t8HhHHt|LEHHAH+Hu H'mHHH~EtFHyAHHt9HxHHHFHH kH hu3ykHFHHeHùH?EDtHHOH˃|$`t.H;Or(HgHHu3HHiHGHHH\$@Hl$HHt$PH0_@SH H%HNiHHuH [3H|$8HhHHu"jHuH &jHw?H jH+u HkHH|$8H [@SUWH HAHHLMtAkHHH iH fUjHgHHjHHt~HHHt$HjHHtHHLt$PHtjLHtHgHI.u IjH.Lt$Pu HjH/Ht$Hu HjHuH hH<LH fHH _][H\$Hl$Ht$ ATAVAWHPLIH?ILjLH IHvfHHHHHfH΅uRHuhH|$pH8fIHkfL$Hd?H\$0LHl$(HL|$ IeH|$pfHQfHHtUI.u IiH.u Hi3[HgIHeL$H>LL|$ HdI.u IeiHtH.u HQiL\$PI[(Ik0Is8IA_A^A\H\$Hl$Ht$WHPHIIIBiHHHH|"H 2gH<LLH 4dLH(L@ MtH$3HHHHEL;LLKH;s)H fH<HD$(LLH|$ H cs$u7L;H fH<HD$0LLD$(LH|$ H c1uRL;vMLD$0LH|$(L2<3Ht$ 3dy&H+u Hg3H\$`Hl$hHt$pHP_HH(HeLLcH-Hu'L&H>PcHHu'L%HJcHHu'XL%H%bHHub'Y$H dLHcHSHu4'LdHйbHSHu'LheH%VbH'Hu&1HDd)bHHu&eHdaHHuv&8LcHv$aHHuB&HcLRdLaHHu &\L cL $H#VaHOHu%2HDcL&aH'Hu % eL #L!Hc`HHu h%-HbH #LaH`Hu"9%iHZ0H3%H(3H(H\$Hl$ VWATAVAWH LH?HL$@AMH =cHHH =GaHHfHHgdLHN,H 7A_HHuI.MIc?E3HI`HmHu HcI.u IcH+u HcH/u HcH3DDd@HN AIAE3bH$X HpHrH$X bH$X HHI߰MHH;|D\@HN I΋bLIHHH8|H.u HbHDI߰aHHH8|foFIpfo FHl$ D$ H+L$0+^HHH|IfDŽ$X HƄ$Z H$` H$` H+H$X sHHL+]HHtdHH|3oH.uLHAH _L7;AHJ;H !]HtH+u HaH/u Ha!H-H!L$ I[0IkHIA_A^A\_^LH;}I;txHXHt/LR3M~!HJ@HI;tQI;tLHHI;|3HHI;t-HuH^L;tHI;t HuL;t3øH;tMLXMt#MA3M~IIH9t-HHI;|3HH;tHu3H;Z^ø@VH l_HHH`HHE^H u H ^HN`3H\$8H^pH|$@H~hHFhHF`HFpHt H)u_HtH/u H_H|$@HtH+u H_H\$83H ^H\$Hl$Ht$H|$ ATAVAWH HiMMHHHuf^HHIIAHH@H H;iuwLQL;SumQ DDC AAAD;uM t@0IDHHIHA tA@0IDHHSHEMgH3ɅuuHOHHoI;fDHHH;iLYL;[DA ADK A;ugA t$A@A0MDLH+HI?LLQHA tA@0IDHHKHDHMIG3Ʌu.HI;P3H\$@Hl$HHt$PH|$XH A_A^A\H 0\H-LD$`LH 0YHL$SWATAUAWHPH3HPHH$HtHHuLcHl$HHt$@Lt$8J ]LAHHCH$I;VHK H$H HHAZHH3HzYLHu"[HuH [Hq0H [H+u H\MH3MIHuqHEHYHHu3D3HXHHu"[[HuH ZH/H G[H/u H8\IIDI;tHtpHHI;hH$H$AHIcH$H;$ZI?\3Lt$8Ht$@Hl$HHPA_A]A\_[L$Hb/L$IcMMLH YM@MIH VI[LD$HL$SUVWAUAVAWHHHH3MHDVLHL$Ht%IH+fDHHHHGHDHuH$MI WLHH$HtRHG3H4EHH?ZLHH"ZHL3HCLI LIILlˋ$DL$%H$DÉD$xH$Ll$pLl$hHD$`LT$XLT$PLd$HLT$@LT$8 Ht$0L$(AAAAl$ XHMtI.u IYI/L$u IYHHĐA_A^A]_^][H\$WH HeUHHuOXHuHHOHtC UHHt"H MWHH !XH/u H YHH\$0H _H WH WHH\$0H _HH\$0H _H\$Hl$Ht$H|$ AVH LrE3HMHrHH;IHM;|H=XV3HH;HCS@CHByl@t`HXHt+L@3M HH9t2HHI;|3HHH;tHuH;LZ3MHBH;tHHI;|E3LRfI HA}n@tbH;tHXHt*L@3M~FH@H9vHHI;|'HfDHH;OHuH;AIIM;r3HSH=Tu-HHI;l3H\$0Hl$8Ht$@H|$HH A^øH(H;u H(HA@HBDE}@tqHXHt0L@E3AM~NHH9t?HHI;|AH(@HH;tHuE3H;SAAH(AAH(AH|$ E3HzEH\$0Ht$8HHZHH;IHL;|H5|SMِHHBD9}f@tZH;tgHXHt&LPMM~=HH9tCIHM;|#HffHH;tHuH;tIHL;zAH|$ AHt$8H\$0H(H(H%QHL$HT$LD$LL$ H(PLD$8LHt53H9T$0~,IIHM@HIAH HH;T$0|IH(H(HAuPH\$8HHuH\$8H(HH|$ H+u H%TH\$8H|$ H(HQHt&HtHt H(H%|OAHyH(3H(H(HAuTH\$8HHuH\$8HH(HH|$ H+Hu HSH\$8HH|$ H(HQHtmHtVHtPHHw;LAXIAIHH HH(ËAIHH H(H(H%NAHy HH(3HH(Ð22 222 22H(LH;u H(HAH;QHAHHHHEu3H‹H(yEHyEAEAEt=3HuJA?E9AE9Q u-H(Hy3H(Hu A?E9At3ҋH(H;Pu"AfAnf.zuغH(A)RHH( H(LH;u3H(HAH;"PHAHHHHEu3H‹H(yEHyEAEAEt@Hu3A?E9AʋE9Q tH(HyH(HuA?E9Au3ҋH(H;OuAfAnf.zu3ҋH(A,QHH(H\$WH HA3tHHH\$8H _H@`HHHHHH@H;NL@t1H NMLd*H Lu%HH\$8H _H eNH*H mKH+u HOHH\$8H _NHuH &NH*H NHH\$8H _H\$Hl$Ht$WH HAIHHHHuLSH 6Lt3>LHHH"LHu"GNHuH MHH 3NHH\$0Hl$8Ht$@H _H\$Ht$WH HAH@ Hpt3HYH QKt3H\$0Ht$8H _HHHKHu"MHuH LH&H MHt$8HH\$0H _H\$Hl$H|$AVH HIIILLH#u=HLLH HItZIC@tP3IHHu2LLH HItIC@t IIiICH;| u0tH@8MMtE3LIIA+HuHLILLIIKH\$0Hl$8H|$@H A^H(HALL;JMuAE3H(H%MHt$HMtA7MHHHl$@LH JHHHH`H;t*HLBAttgHM`3H\$8H]pH|$ H}hHEhHE`HEpHt H)u{LHtH/u HgLH|$ HtH+u HNLH\$8Hl$@HHt$HH(@UWHHHHHQLBpHBhMtM@Mt HHH_]IH\$hHt$pHPHxEHHJ3Lt$@H9GuwHOHtgHAHtLHHw4HUHH_GHH k_GHH HYHFHKGHy HcHEHIH.Hu HHHH FHM@H C3H\$HHl$PHt$XH A_A^_E3H; FAAH; &FH; FAA uH%*H@SH HHu H [3H|$8H;F@H;EH;`F u HGH+u HGH|$8H [H\$Hl$Ht$ WH 3AHHH;HiEH9AH9FG uHFF uHyFLWL;VHGHNH;tHt HO DDF AAAD;0AH t@ADHHOHA t A@IDHHVHAu DAu DDA;u|It{EM/uieL@DI;uGI;u;DHHFHHu*HH+u H F ËH\$8Hl$@Ht$HH _H\$HT$HL$UVWATAUAVAWH@ID;IAFًDLHwwAAAEFwEg@ AH t@0ADIHT$0 IFHHD$0AIIL;H$3HH$HC u H-DH$AHH{HtsIH+H;|C  t@0IDHHSHA;uAHHLHD$0IH.E3H|$ LHICH$HHAHH;@I(H tBH]H ,CI.u ID3H$H@A_A^A]A\_^]HT$HL$SVWAUAVH`L$3MH\$8IMvLt$@K H7DHHu ?3H`A^A]_^[L|$PLMtWIr0J HQHGHHH;wH;sLHH,!H+J II+HHHIHuI>LHuHCL|$P3H`A^A]_^[H$Ld$XN$I+H\$0H$LIfDLL$ HL$HT$0>uH$HHD$ Ht%H$HA#IL>HD$ HH뢅H$uH @H@H WAH|$8H$MMH$HI/Lt$@u IBM~I H)uBHI;|HfBLd$XHL|$PH`A^A]_^[H\$UVWAVAWH E3LH ?IIHAL;u0AIVH;Ld$PIDߋsVHtFHgMtH>@I9@tH ?HH H\$XH A_A^_^]H%@@ILA@HthLcA$@t1M;t#II>tH >MMHCtH$Hӹ<3g<HH4E3HI=H+Hu Hz@H LOHA@uH >HwMH ;H >HtMH;tHt'>HHpH;tHHppHt H)u?HtAH/u;H?0H =H9H >HH =H g>Ld$PH\$XH A_A^_^]HHLL H =HLDH H%:H\$WH HBH3HHL$8tH@h3HL@AHD$8:HL$@Ht;t[E3LD$8HT$@H:HD$8uHHD$8Ht.H <HLLH 9HL$8H)u]>H\$0H _Hl$VAVAWHPIHD$8IHD$0LLD$0E3HT$8L:H\$pH|$xHHHt)@HD$0H9tHJHHu H HH|$0HGH;;uLL$@Hl$ LIHu!H ;H LH 8sHHHt1@HAH=trHCHHuIL;t-fffH AHH K=u7HH;u3uPE3LD$0HT$8I8SԃuH ;H LLH 8뫅uH :H LL$0LH 7H\$pH|$xH$HPA_A^^H(HuH :H H 8;3H(LQL;|IXHt#L@3M~?HH9t[HHI;|'IMtDHH;t4HuH;9t&H :LJHd MBH 73H(øH(H(HA|$LH]H 9H 6H(MtL9A t LH^3H(H\$Ht$H|$ UAVAWHH@HA`E3HEIHAhLE HELHApHULypHLy`ILyhHMHE 6L9{`HE Ht%HMH6HE HtHHE HMHt HHMHE HUHtHHMHE HUIHHHCxHHHMHt H)uL:HM Ht H)u7:HtH+u H#:3MHMM>L>L?Ht H)u:HMHt H)u9HM Ht H)u9H\$hHt$pH|$xH@A_A^]H\$Ht$WH IHLAHHH 5L MQLHHtHH\$0Ht$8H _M8H fHHHu%88HuH d7HuLH 4Ht$8HH\$0H _H\$Hl$VWATAVAWH0HLE3IMLIEH5HHu7HMtE3HIH8tHH;|HH+u Hx83ID$ E3.H7HHL3H7HHjH+u H8H4HHu 6HH/u H7&3HHMtRH33LH7Hr3LHHHL+J9HHIHHu싄$H$uT.H } HtuH+u H33H+u H2L$I[0Is@IA_A^A\_]H\$Hl$VWAVH@HH u/HH L5uHI2HHHH|&H 0L`uMHfH -HH H@(HtH;HLHHs1H 0LuHT$(MH=HD$ H k-8HvKHL$0L tHD$(L3Lt$ 3}.yH.u H1HG0Hw0H+u H1H t.HHD$(LvtA Hl$ HPtH0HG8HH+u H<1H %t'.HHD$(L$tA Hl$ HsHHG@H\H+u H0H s-HH/HsH0HHHH|'H .L sLsHH +PHH H@(HtH;HLHH sOHL$(L lsH .LVsH_HD$ H +H.u H0HGHxH*sHwHH50HHHH|'H %.L rLrHH +SHH H@(HtH;HLHHH sOHL$(L rH -LrHHD$ H H *H.u HS/HGPHwrHwPHr/HHHH|'H b-L CrLrHH \*SHH H@(HtH;HLHH0sOHL$(L qH -LqHHD$ 0H *H.u H.HGXHqHwXH.HHHH|'H ,L qLYqH:H )PHH H@(HtH;HLHH`sOHL$(L NqH W,LqHHD$ `H G)H.u H-HG`2D$(LqAHl$ HpHw`HnHGhHD$(LpAHl$ HpH8HGpHD$(LpAHl$ HJpHHGxHD$(LvpAHl$ HpHHHSD$(LMpAHl$ HoHHHD$(L$pAHl$ HoHZHHD$(LoAHl$ HioH!HHD$(LoAHl$ H0oHHHoD$(LoAHl$ HnHHH6D$(LxoAHl$ HnHvHHHSoH+HHHH|'H )L #oLhnHIH &SHH H@(HtH;HLHHsRHL$(L nH c)LnH%HD$ H S&H.u H*HLJ;HH+u H*H n'HHD$(LnA`Hl$ HTnHLHHD$(L]nA@Hl$ HnHHHHHHD$(LnAHl$ HmHHHtMD$(LmAHl$ HmHHHtH+u H)3HtH+u H)H\$`Hl$pH@A^_^Ht$WH HH\$0HH=HHHcHH]HHWHHDHPuHHuHI)H sHtPHQH;~BL )H DLAGMu M9 HHH;|X( fHϋ(%Qt5(HH uHM(H9uHH E3H3$HHHLH#H HH Hu H'HLH0H Y'3ɅAH\$0Ht$8H _H %H[MHLH "+H %HM@H "Hu H2'H\$0Ht$8H _H\$E3DZHEExIcHD;D~H\$E~-AA+yF IcHD;D}E~$EQE;|IcAIHD;DANɋH\$H\$AH\$Hl$VWAVH0H3HHHIH)uY&HuHHFHLMtA`&LHu AAH(II"uoH W$HT$(HE3H\$ IHD$(H \HHu AE3E33HH.u H%AH(I"LHuq$Ht AAHE HANHAF @ HEHHFHLMtAQ%HHuA5HH)u$HEHHHPHHu"AL eH ]3oHu%H(u$H"HH/u H$Hl$`HH\$XH0A^_^H\$Hl$Ht$ WAVAWH0HLH=HHnH7$HHH@LMuHLHHAHHI3H=.HLuI#LH"H@LMuIMHIALIMH c"I9IuIqIyHHI)u Iz#3LHt$ IHD$(H+I?HD$ L HE3HLHtH.u H/#H/u H #MtvI.u I #AGPE3IGXHHw2H+Hu H"HtYH/u H"H H^H HH !H=.H+u%H"H HH !H=LH [3A3H\$XHl$`Ht$hH0A_A^_@UVAVH0HrIMHH\$PHH|$XLd$`Ld$pLl$(Li HL|$ L|$xIfHI5HMIJ< HQ MHHC MIH5 HH+H}L|$ Ll$(Ld$`H|$XH\$PHgHH0A^^]H\$WAVAWH`H H3HD$PLE3H FHAH9{uHzHtHH=@H=HLG8HHHHH 3H4HtHBH H}HHu%HuH HLH H$Hu sHHCHHLMtA HHHH+u H> HH9GuLHwIHH)u H 3HL|$(HLt$0H?HT$(HAL+E3L HbHMtI/u IH.u HH 3H;2@3H;}3H; u Hy$H+sH5)HRH+u H>tAIZHHuHt tHuIHHD$@3HD$HLt$8HHu)HTvHMI.u{IpH HT$8HLHCIHHL$HH O1HMtI.u IRH+u HCHu$vH5LH VD3ƅ3HH$HL$PH3H$H`A_A^_HUVWATAUH`3HpDHp HpHH$HHxfDH9HtH;u HIHuLt$PL|$HHtH;tHHLwIL HLLH RH\$XCLHu7H TH kH QLHHQI<I,$Hu IHH H9Kt,H HQH H+H{3HH+HuHHHuHQH H c5=v~HHHAHQDHL%HH$HHI`H;HHBH}H H\QDAH qHu HRt HRMtI.u IaHtH/u HMMtI/u I9uMH RD3蹂H$L$L$H$ybH HT$0HE3Ht$(IHD$0H 7HHt#E3E33HHmu HHL%H$L$H$HAxHH8Ht H)uEMtI.u I1MtI/u IHtH.u H H$Ht H)uMtImu IHtHmu HMH |QD3ZL|$HLt$PH\$XH`A]A\_^]@USVWATAUAVAWH$HHH3HHE3IEHEHHL$0H`HMH /HMHLL$XHT$hLD$pLeLd$xLeLeLeLeLeLeLd$@Ld$PLd$HLd$8H]HEHHsHrH57HD$pH YLmLL}DLu3H}HD$pHD$hHD$hHEHEHEHEHEHEHD$xHD$xH\$hHt$pHtH/u H/HL$xHt H)uMtI.u IMtI/u IHMHt H)uHMHt H)uMtImu IHMHt H)uHtH+u HHtH.u HuIHH3+HA_A^A]A\_^[]I@HTHZtHH|H/u HIHHQLHt6HHLHULDd$ HHMH^ImHu IH.u HHH$HܪLHu rI HLmIEHu IfH/u HWH XIHAH9 uL5 MtIH@HHLCGLH HHH »L5ûMtIBH HLHu%HuH HLH Mu uIFIHZLMtAHLHuH5uI.u IHHH9CuH{L{HIH) u HILt$hHUHH}HLuH?HʾE3DL+IL gHHtH/u HI/u IHu uI.IH\$huH MHAH9uL-Mt IEHAH HLCLHKHHH @L-AMtIEBH )HLHu%HuH 'H8LH LMu v'IEIHLMtAHLHuH5.vImu IH=MH9CuL{LsIIH) u HQIH\$pHUHL}HH]H?HLE3L+IL HEHMtI)?u II)>Mu IMHu v0H);HHt$puH\$XH;u3Hu xHD$x`HKAԋrH豱Ѕt,HJHHu zHH@HL$xHHH\$xHL$hHHALMtAHHHu ~XH%HHu ~IH/u HHtmH #HT$`HE3Ld$XIHD$`H (HHu E3E33HH+u HoHFHH?LMtApHHu HMHHu I;H/u HHAH]HHHCLMtAHHu HGH;RuHGL0IH;2u LwI}HppH@hHt$L9ft3 HHt_HFHH6HtH@Ht 3HL23 HHtHHP H+Lu HH]Mu I$H/u HHCHHLMtAHHuH5ELd$HLd$8HGH;>uHGLxIH;u L IHppH@hHt+L9ft% HH@HFHHHHH HHtHFHT$HH 軟HHu'HuH H*LD$HH <HHCHHLMtAHD$@HLHH+u HIH9IߺH9Au!HyLqHIH)uRILt$@HEAHEL+HH}HE3H?IL HEH蝝LHtH/u HHL$@IH)uMMt)HMpLHtHHFHBHD$@MtI.u IL}HtH/u HL}HtH+u HiIMtI,$u IQLL$@H ,3L|$HA/RLt$PLL$HILEHUy 0xHMHH9AuHH |HHtLHMrHD$`HHt5HD$pHHtHHxHHD$`HHup1L5HT$PHMHRxLH MtI(u IeHL$0Ht H)uOHMHt H)u:HMH}Ld$HH.u HH+u H HMH|$hHt H)uHMHt H)uHL$HHt H)uMIFxHMHHHtH*u HHL$0Ht H)uHMHtgH)uauYHL$0L`MLp Ht H)uRHtH.u H>HMH|$hHtH)H|$hu H|$hH IH\$0HAH9uH=HtHH5@H5HLFHHˑHHH PH=QHtHBH HJHHu%|HuH HLH Hu2L|$`L|$pHGHHΤLMtAWHD$`HHu2L|$pH/u HH HAH9uH=HtHL5}@L5tIMFHHHHH JH=KHtHB{H I4HHu%fHuH HMH Hu2L|$pHGHHLMtAFHD$pHu 2H/u HHAH9Fu)H^HFH\$0HD$`HHL)6u HMHD$hHE3HEH]YHHu 2#HHUHt$pJHGAM+LHILt$`HH?IL HuLHtH+u H*H.L|$0u HH/L|$pu HI.Iu IL|$`Mu 2xI$IHL$xMHAQHI HEHLAHcMAHĝHu 7 HMHtHHHHD$PL|$PHMHDHHEHHHEDHbMH蓮HHHHAAH9Cu(LsH{IHH) uHAIHHLuL+L}HHUH?HL E3H+LMtI.u IH+L|$`u HxL|$HIMu7L5;H.u HPHD$xIHL$0HD$xL|$`I,$u I$ML}Lt$PHEM~`HELeH]H}Lx`Hp JLULL|$(LHLd$ IIuH|$HE3LeLl$8HuHEHxH!E3H֓H.Hu H|HHD$x;HD$xIHL$0L5HD$hHD$hHEHEHt H)u2HtH/u HHtH.u H MtI,$u IHL$`Ht H)uHL$pHt H)uMH G$D3]JH\$xHuH|$hMt5#H+u HH\$xH|$hLHuHImu ImHtH+u HYHtH.u HEHtH/u H1HtH+u HIHM H3ֽH8A_A^A]A\_^[]LUSWIkH`HH3HE3Is McMkDMsDHMDM{HLLEH|$8H}H|$xH|$HH|$hHEHu H HhH(HH|$(D$ DDIHD$@HHD$PLHu aHHHu HH HL|$8H\$@HAH9uQH5HtHH{H ԉHHD$`oHHHLD$`nH>HHD$XL@HHzHHH 'H5(HtHF@HT$XH THHu')HuLD$XH PHaH xHuHD$P=HFHHLMtAHEHD$HHuHD$PH.u HH HHAH9fuRL%eMt I$H(H HHD$`LHJHLD$`oHHHD$XL@aLH'HHH L%MtI$FHT$XH 裑LHu'HuLD$XH H H $MuHD$PID$IHٕLMtAHD$XHHD$hHuHD$PI,$uIQHL$XHLH9Au"HALiHD$hIEHH)uHHAHEL+HL}L|$hHH?E3L LmHEIH\HMtImu II/Lu IH|$hHuHD$P HMHAH9Au!HAHYHD$HHHH)u]HHL$HHHH]H?AL+HuL E3HEH诎LHtH+u HH.LH|$@u HHL$HHH)uH|$HMuHD$P Il؅yHD$P I.u ItwH HUHZE3H}IHEH LHuHD$P E3E33InI.u I7HD$PLf HD$PHHHEH;HMuXHMLHuHD$P+ HD$HHuHD$P LpH|$HHD$xHHD$`HHxDL1MtL;u HIHuMt$L;tIIMfLd$XI$HE LH|$XH}H NHAH9+uH*HtHL-.@L-%IME@HHHHH HHtHBH I腍HHu%HuH HMH Hu Ll$HHCHHːLMtALLHu Ll$HH+u HHHH߹I9Eu#IuM}HII)Mu IHMHEHUHEHHHuHAL+H?L E3IcHD$HHtH.u HImHu ILl$HLMt)HM0LHtEHHKHHD$pMtI/u IYH|$HHtH.u H@H|$PHtH+u H'HMtImu ILL$pH 3H|$@AH|$hLL@L|$`LL$@ILD$HHT$P蒝ysHMHTH9AuHH 0HHtGHM&HD$hHHt06LHtHHXHHD$hHHupL=գHD$`H@xHL0Ht H)u%HL$XHt H)uHMHt H)uHD$@HL$HLt$PHD$H3H.u HImu IHL$PH\$xHt H)uHL$HHt H)uHL$@Ht H)u{H|$HIGxHL0Ht H)u[HL$XHt H)uEHMHthH)ub0ZLhLx H|$HMtI,$u I MtI.u IHMHt$xHtH)Ht$xu Ht$xH HH\$@HAH9~uL5~MtIH5M@H5DHLFLHHHH ~L5~MtIBKH dHLHu%6HuH bHsLH Mu"HT$8HT$8HLH|$hL>IFIHzLMtAHD$hHLHu LI.u IH ~HAH9}uJL5}MtIL%(H ~I!LHOHMeL-IMEmLH3~HHH @}L5A}MtIBH ~I貇LHu%HuMH HH 5Mu LIFIH=LMtALHu I.u IuH.ALH}AH9FuH^L~HIH)u H<HHD$xHUHEHHH]HLmH?AL+E3L I臅HD$HHtL)#u HM)eH|$@u IM)'Lu IHT$HH|$hHu HHHBHD$`HyHI HURLH|$HH|$PAHuEHHEHΜHELHwHwEHH襜HLHHIH9AuHYLiHIEH)uHLH]IH}L+I?HEM HE3INHD$HHHtH+u HImH|$hu ILHuCL=HMH)uqHMHHD$8LHMHL$@H|$hHD$8H.u H;H|$HHD$0H9}HD$`HLMH L]Ll$`I+LULuNHD$pHHL$p)$0)$ =ܬHT$XWMyIL$ If INHB#XH(HuLM(L^IrrIIHHƒH+H+HD$p@@H@@I(fYH(@fYH(@fYH(@fYHL;|M;HT$PII+H|XHBIH II+IHHHMfDAHI fYAAfYAHuM;IH III+HI(YAAHuHD$PHL$pNHT$XLUL]IIHD$PHL$pI;b($ L($0LuHD$0HHzE3I:I/Hu IHHMHMHD$8LHHD$8HL$@L=>HD$xHD$xHt H)uMtI.u IHtH.u HkHL$HHt H)uUMtImu I@MtI,$u I+HL$hHt H)uMH D36HL$8H]Ht$xHt9+HH+u HH]HL$8HHt$xHH)uL$@L$HL$PL$XHtH+u HHtH.u HmH$HtH+u HQHHMH3 H`_[]H\$UVWATAUAVAWHl$HPHmH3HEHE3HL$hLLuHLt$0ALt$XEHT$HEHHEHuH5JLl$00 LNHwL;taIXHt-L@IMtHH9t9HHI;|WIMtfHH;tHuH; /IHHHtJLHuH5 Ll$0~IFIHfLMtAHHuH5X Ll$0XI.u IHJ؅xH/u HE3AuqH HUHI{E3LuIHEH }HHuH5՗ Ll$0E3E33HNH/u HIHsuHFAH;ALHH9MtAHHuH5R Ll$05HTE3EHx؅yH5! Ll$0L)7u Ht3HFHHlLMtAuHHuH5Ɩ Ll$0Hǂ؅yH5 Ll$0L)7u HHVt3t2H;5t^HuRH5\Ll$0?HtLVIiIHHH HFHHHD$xHFLMtAHHuH5ԕLl$0HGH;uHGL0IH;u LwIHppHHhHt%L9ft3iHHtfLFHHA;HtHAHt 3HL330HHuE3HHH+Lu HHt$HMuH5Ll$0H/u HV3I΋ruHD$pHu!1HtH5Ll$0I.u IHFHH~LMtALHuH5kLl$0NIFIHLMtAHHuH5(Ll$0(I.u IzHtHD$HHHu!VHtH5Ll$0H/u H4H 5qHLHAH9lpu"HkpHD$0HtHHEHHLCHHD$0HpHHH pHpHtHDH pH\zHD$0Hu%HuH HɕLH Ll$0MuH5IEIHl|LMtAeHLHuH5Imu I HHD$0LHuH5|gH oHAH90ouL=/oMtIH5c@H5ZHLFLHoHHH nL=nMtIBaH zoHyLHu%LHuH xHLH MuH5IGIHy|LMtA*HD$XHuH5ydI/u IHAAH9Cu#LsLcII$L);u H3ۋ33LuIHELm>LHu-H5MI.INHRzHUIGHAML+IHHD$XHEHHH?L HMtI.u IHL$0H)uHL$XLH\$0H)uI/H\$Xu II,$Lu IH\$PLHH5Ld$PE3HtH/u HgMtImu IRMtI,$u I=MtI/u I)HL$XHt H)uLH D3+HH}H;5Ht$HHmH耇uH5]@HFAH\$hEHxHEHHD$`HHuH5Ld$PLl$0HxEHԌHLHuE3H3IDH9Cu%LsL{MIIH) u HHILLuIHUL+I?HE3M LuE3IitHMtI,$u IH+IHD$PHEu HLuMHu,H5+HL$`H)uLd$PLl$0H/u HgqHBHHD$`HHxfDL1MtL;u HIHuMt L;tIIMfI$dL3 3DDDHELD$HLL$pHUHL$hHD$(HD$xHD$ Iu號HH!A耟HHA L /H 3H|$HH|$0/)LMHLEHT$H:H|$HL}y HEH]MLH\$PH׹HD$XHE3HHkqH.HEu HHD$XH(u HHuHD$XHtLHxH.D$Hu HD$Hx)ӿLMHHO3DHD$PH5'HD$`H@xHL0Ht H)urMtI,$u I]MImI@Ld$PLl$0HtH/u HMtI/u I HtH+u HHD$`H@xHL0Ht H)uտMtI,$u IMImIH(uMtI,$u IMtI.u IoMtImu IZHiE3HoH.Hu H6HH+u HL5IHEHtH(u HIHMHH3赚H$HPA_A^A]A\_^]LHuH5M"Ll$00H?}AL;tiID$H;u 3I9\$5H;uAL$Wf.zt433EIH7v؅yH5҉"Ll$0AM)4$u I!tHHHFHHXvLMtALHuH5j&Ll$0MHl|E3EIj؅yH59&Ll$0M)4$u IbHFHHrLMtALHuH5߈&Ll$0ID$IH yLMtAJLHHuH5&Ll$0{M)4$u IH eHAH9Xeu"H_eHD$PHtHHfuEH]uHLCظHHD$PHeHHH eHeHtHD_H xeHoHD$PHu%HHuH tHLH Ld$PMuH5&Ll$0ID$IHtLMtAHHHuH5i&Ll$0LM)4$u IM)7u IE3L)7Eu HIH;gH dHAH9du"HdHD$PHtHHwEHwHLC{HHD$PHIMMH5{2IExHT$pHHHt H)uHL$`Ht H)uHL$xH$HtH/u HMtI,$u IMtI/u IIExHT$pHHHt H)uwHL$`Ht H)uaHL$xH5H)+ HL$`Ht H)u.HL$pHt H)uHL$xHt H)uHuE3H|YH4_H.Hu HڮHH5Zz2Ll$0@H WHAH9WuL=WMtIHa@HaHLCLHtWHHH AWL=BWMtIB:H SWH`LHu%%HuH QHb|LH vMuH5y<Ll$0uIH؃uH5iy<Ll$0LM)7u IE3H VHAH9VuH=VHtHHi@HiHLCHHjVHHH GVH=HVHtHB0H IVH_HHu%HuH GHX{LH lHuH5x>Ll$0kHGHH;iLMtAHD$XHHuH5@x>Ll$0#L)7u HHL$HHdHIHALMtAHHuH5w?Ll$0HGH;/u}HHH|$0L)7u H$H}]LD$0HEIHJ\HELEuM@HMIDȺfHHuyH5_w?Ll$0BH 1H;u HAXH'H :H;u HAXHHTH¨HD$0HH?H5v?LL)3u HOHIH9Fu$HFLfHD$XI$HH)u H3HtLe(H}0H3HM8IHE@HD$0HHuH5rv>LWH viHU(HeHHFAL+LHHHHM@HL$XH?L HLMtM)4$u Iy3L)7Du HfL)6Hu HUHL$XH\$0L)1u@H\$XMuH5u>Ll$0M)7u ILHX_EIqsHD$PHuH5xuFLl$0[H _EI;sHLHuE3HiE3IAH9Cu+HCLkHHD$XHIEH) u HuIIHD$XHULHEIL}L+I?HM E3IZLHtH.u H(E3H+AHD$XHEu H Lt$0MMu&H5tFHL$PH)gܨ\I/u IȨMϧHHEHHxHH\$pHtH;u HIHuHt$H;tHHHCHD$xHHD$`Lt$pLt$xLt$`LmIIHD$hH\$HH HD$@fIHu?HKLH; uHKLHppH@hHt0H~t)IHHLNMHL$HHAFHtH@(HtMIHЋ?IVHHu"HL$HMHH+u HH\$H)Ht$0I/u IHCE3H;u$HHCJ J4H)UHppH@hHt1L9~t+IHHLNHLD$0HL$HAJHtH@(HtLD$0IHЋAIjHHu$LD$0HHL$HH+u HH\$Hx?Ht$0H.u HE3ILt$0IHD$@SE33HL$0HoMtI/u ITHL$0E3LuHt H)u7LLu3H AI HMLMLEHUhLmHEL}HD$XLMIչHHtHt$PE3HHTH.Hu HH/u HIHtCH6[H+u H}x&u}LL$XMHI1MLt$XMH5nFHEHT$pH@xHHHt H)u#HL$xHt H)u HL$`HMtImu IMtI/u IѢHL$XHt H)uHEHT$pH@xHHHt H)uHL$xHt H)uHL$`HL$xHt H)uaHL$pHt H)uKHL$`Ht H)u5Ht$PE3HLHfRH.Hu H HH5mFLl$0rLUIkH@HAH3HE3IsI{3Mc3MkDE3HL$hHJAHD$(HD$8HD$0M{LAHT$XHt$xrfHJH;uEUHXHt%L@3M~HH9tHHI;|E3$HtHH;tHuE3H;AH JH$`L$HAEH9_uL5_MtIHnY@HeYHLCLHIHHH k_L5l_MtIBlH IH%SLHu%WHuH HnLH MuH5kLIFIHSLMtA2HLHuH5kM)&u I؟H HE3HAH9^uL-^Mt IEHUXAHLXHLCǛLHHHHH b^L-c^MtIEBRH kHH RLHu%=HuH iHzmLH MuH5jIEIHbYLMtAHHHuH5ijImu IH GHAH9]uL-]Mt IEL5?WAL56WIMFLHwGHHH \]L-]]MtIEB<H UGIPLHu%'HuH SHdlMH xMuH5iIEIH|ULMtAHD$0HuH5TiImu IHcE3H9CuLcH{I$HH) u Hx3HD$XHUH\$0HHEAHLeL+H]HE3H?HL NLMtI,$u IH+u H3H/HD$0u H3MuH5|h HH9Fu!LfL~I$IH)u H3LHD$XHE3HELeNHHuH5h* H~QHUHGHALL+IHHHLuH?L HMtI,$u I!I.u IH/u HI/u IHuH5xgMOHt$hHT$XE3Ht$XIH\$`LHH VoHHtH.u HHuH5gH/u HkHE3H9aZuH=`ZHtHHS@HSHLC^HH$DHHH ZH=ZHtHBH DHMHHu%ՙHuH HiLH &HuH5BfMHGHHoNLMtAHHHuH5ew L)'u HVH3IH9CuLsHsIHH) u H(3HLuHL}H?HUHAL+E3L H}KHD$HMtM)&u IݙE3L)&u H̙Ht$HHuH5KeL"HFLHQHHt$(LMtALHuH5eH XE3ILHuH5d I,$u I,IP؅yH5d I.u IH HT$pH;JE3H|$hIHD$pH LJHHtE3E33HVH+u HH5!dLt$(LH D3 3MtI.u IbHD$xL$L$L$ L$(H$0H$8HtH(u HHH$`HMH3sH@]HFHHCPLMtALHu *HcVAEE3ID؅yH5*cfM)&u IH @E3HA!3H9{VuL=zVMtIHO@HOHLChLH.@HHH 3VL=4VMtIBH @HILHu%ߕHuH HeLH 0Mu !IGIHcNLMtAĖHHHuH5bbM)'u IjH#E3IH9CuHsH{HHH) u H;3HD$HHUHEHHD$XAHEL+HHuHE3H?HL GLHtL)&u HL)'u H֕3Mu -IeM؅yH5@a|M)&u IE3H >HAH9TuL%TMt I$H NAHNHLCLHE>HHH ZTL%[TMtI$B H #>HGLHu%HuH !H2cLH FMu 7ID$IHHLMtAٔHLHuH5'`'I,$u I}H6H9Cu!H{L{HIH+u HS3Ht$HHHH|$8H?AL+Ht$@L E3HD$8IHEHHtH/u HI/u IHu DH.LuHʓLt$(H|$hHT$HE3H|$HILt$PHH NZHHtH/u H{HuH5^H+u HVIIH9|RuH={RHtHHK@HKHLCIHH<HHH 4RH=5RHtHBՑH ;HEHHu%HuH H`LH Hu HGHHDFLMtAHLHuH5]lH/u HJHD$HH&MHL$(H@LMtANHHuH5]HGH;uHGL(Ll$8IEH;|uLoLl$8IELhpHHhMt:M9ut43-HHuH5.]HD$8)IEHHEHtHAHt3HLHD$8=3ڌHHu E3Ll$8#HHlH+LHD$8u H%MuH5\H/u HH :HAH9FPuH=EPHtHHI@HxIHLCHH9HHH OH=OHtHBH 9H8CHHu%jHuH H^LH HuH5[HGHHFLMtAHHD$0HuH5[H/u HHH9FuL~LfII$H)u H33L}HELmdHHH5![HD$HE3HD$(HD$8HD$8HD$0HD$0M;t"I,$Au IPLd$8MtLd$8I,$u I/MtI.u IMtI/u IMtImu IHtH/u HގHL$0H?H)5*HCHUHt$0HHGAL+LHIHHuHH?L 2HMtI/u IcImu ISH.u HDH/u H5I,$u I%Hu ~H|$hHUE3H\$xIH}HH]HH HHHtH/u HɍHu "H+u HLt$(HIsAHHH5YH\$Hl$Ht$ WATAUAVAWHpH -H3HD$`HFLH D6E3HBGE3HL$@3HHD$0HI=HH\$8HEHHusHH-tXLH D3x3MtI/u IHHL$`H3rhL\$pI[8Ik@IsHIA_A^A]A\_3fHT8HtHH|H/u HY3ILHUHHt;HHLHT$@MD$ HHL$0HHmHu HI.u IHHEH LHu HMIHu IE3H/u HJHHu IEWLl$HMD$PHIEHGIE HHtxH‰LHPBIHFHH @HT$PHT$HЈImLu IE3H.u H3MtHHueH/H-IVu HMtI.u IMtI,$u IHH.HiILxHx Lp(HT$HL$USVWAUAVAWHHLqE3HL}PE3IH 3HAH9JuHJHtHH=W;@H=N;HLG HH2HHH IHIHtHBH 2HNHHtH+u H3HuA/H/u HHM@HAHPHHRAL RH 3H]H] HMLMXLEHUKLuXH]LmMMHӹHEPHH}E3HHM6H/HEXu HHEPH(u H߅H}X3HEPHtFHp=H/E@u HE@x%MMHH3E3E3HQHE@HEH@xHH0Ht H)u[MtI/u IGMtI,$u I2Hu@L}PMtI.u IHtH+u HMtImu IMtI/u IلLH gD3m3/HtH+u HMtImu IMtI.u IHEH@xHH0Ht H)ubMtI/u INMI,$IH(u&MtI/u IHtH.u HMtI,$u IHuE3Hc.H4H.Hu HHuH5EOE3~H/u HHzHLd$xHĀA_A^A]_^[]H\$UVWATAUAVAWHl$HH #H3HEH3HMDHIHUDH>H]HALMtA=HELHu += HIH6I9Guc)HELHu , H.4AI;Dy , I,$u IH]AE`HM:y , CH X+HAH9Bu_L5BMtIL%>H 1+I4LHu%HuH /H@PMH T}A|L-E>IME~LH*HHH BL5BMtIBH *IM4LHu%HuH HOMH |Mu - IFIH=LMtAdLHHu -b I.u IHLII9Eu$I}IuHHImu I߀HLH:0AHEL+H.E3H}HHHHMH?HL HEH2HELHtH/u HxImHu IeHMu -I,$u ICH~HMH]HH~H)HEuALuEHV5IrIHELHu 1E3$H5AI@ILHHHr~AALH9Gu+HwHLHHI)uIAHLHHuL+H]HHUH?HL E3H0HELMtImu I.I.Hu IHMu+LeL-J1I,$u I~ME36I,$u I~}H|HEHHxL)LmMtL;u HIHuMt$L;tIEIMeLeI$yHELH]LH]H]HMߋIPyLHuH5I2HB4MIn{y2TI.u I~HE@XyLHuH5I36H3MI{3I.H5\Iu I}LH]DH]3H]H fLFHMLMLEHUQBH}Huy HEHELeLILeLǹ,yLHHME3H-HMHEH)u%}I.u I}HELHtKH4HMЉEH)u |Ux'{LLHI! H]HHL-HH1HEHMH@xHHHtH*u H|HMHt H)ux|HMHt H)uc|MMtI,$u IF|H]HtH/u H.|HHtH.u H|HHEH@xHL(Ht H)u{HMHt H)u{HMHH)I.u I{H]MtI,$u I{MtImu I{HMHt H)ur{LeE3H%I+I,$Lu II{Mu 1}I.u I+{LmI2y 4E3QHEHpHH #HAH9C;uH=B;HtHL5>,@L55,IMFvHH#HHH :H=:HtHB|yH #I5-HHu%gyHuH xHHMH uHu 4E3HHwDu 4E3`H.u HzH/Hu HyHEusH ExHUH)E3H]IHEH M+HHu 5E3E3E33H7H/u HyHE35I!1y 9E3ZH xIHi-I9GunvHHuE3lHwIH}4I9Gu:vHHuE38HwHH/H9FufvHEHHuE3H.u HxH\wIH4I9GuuHHuE3H(wHH1H9FuuLHH.u H@xHvIH/.I9GuLuHHu;PHvIH-I9GuyuHELHu;sH:ML-CCL}HEHEMtI/u IwHtH/u HwMtI.u IkwHtH.u HWwHMHt H)uBwMH D3Lm3Mt4"HxHX HLp(Hp0L`8IIMI,$u IvMtImu IvHHMH3RH$HİA_A^A]A\_^]HHHUATHhHHXE3HpHHRHxALhALpEDLxE)pLewLeAHU*Hsy st/H}1Hsy sctHHH]w[H FtHUH&E3LeIHEH N'HHu tE3E33H3L)/u HuIt AAAAE#ACȅulH sHUH^&E3LeIHEH &HHu x E3E33H?3L)/u H uIxY H;suL9cvtHCH8HH;wsuL9cvVH{H{LjpHBhMt%M9et3+pHHt]MEHHA7HtL@Mt 3HAH.3oHHtHHqH.Hu HKtHu z H%AH,y zt H/u HttmH UrHUH$E3LeIHEH ]%HHu { E3E33H1H/u HsI{ oHHu } HCH;ruL9cvsHCL0IH;quL9cvULsIzLhpHHhMt%M9et3nHHt\MEHHA6HtHAHt 3HL.3qnHHtHH pH.Lu HrMu } H&MHny } I.u Ir~nLHu ~ HCH;qu!H{HCALxIH;puH{L{ AILhpHHhMtDM9et>mHHMEHHAH.Lu HqAUHtHAHtAHAL4AAmHHtHHnL).Lu HzqMu ~ HT(MI`my ~ I/u I;qHCH;ouH{HCLxIH;ouH{vjL{(ILxpHHhMt4M9gt.YlHHu M~! IGHHAHtHAHtHL6lHHuMHHmH.Lu HcpMu ~H)MIIly ~I/u I$pH}+MHMly }]I.u IoHH}wHIMmHu (H'HCH;^nuH{v~HCHxHH;6nuH{v^H{0HLhpHHhMt(M9et"jHHtfMEHHAXHHu i3H;V@3H;nV3H;U u HWy E32H+u HW(H 3HAH9uL=MtIH@HHLCSLHUHHH bL=kMtIBVH 4H LHu%VHuH 2UHC%LH WRMu MIGIH2LMtAVHHHu EI/u IVHPUH9CuHsH{HHH)u HiV3HHAHEL+HTE3HuHHHHMH?HL HEHHHtH.u H VH/u HUHu YH+u HUI$HUH >E3ILeLm{SHMtI,$u IUHu I.ILuzUL}H wHAH9uHHtHH= @H= HLGiQHH/HHH HHtHBSH HHHu%SHuH SH#LH 1PHu 'HCHH LMtATHHu E3H+u HqTAHEITHHu ,H/u H;TH3 y E3H+u HTtOIIG HuAHT$PLHL$HHH\$0HD$(|$ HH  H HAH92uH=1HtHH= @H4 HLCOHHuHHH H=HtHB;RH THHHu%&RHuH RQHc!LH wNHu mHGHHR LMtA SHHu H/u HREHIRHHu E3 H+u HRH' ؅y QH/u H`RtjIIG HuAHT$PLHL$HHH|$0HD$(Dl$ HHuL5! H/u HQLJ H HAH9uHHtHH=s @H=j HLGMHHHHH @HAHtHBqPH H*HHu%\PHuH OHLH LHu "HCHHxLMtAAQHHu "E3y H+u HPEHI.QHHu " H/u HPH3[y "E3 H+u HPtOIIG HuAHT$PLHL$HHH\$0HD$(|$ HH #l H ?HAH9uH=HtHH@HHLC1LHHHHH H=HtHBNH HvHHu%NHuH MHLH JHu $ HGHHLMtAOHHu $- H/u HH+u HFLHD3H;uHUH;Et 3H;D.H w3HAH9uHHtHH=@H=HLGgBHH-HHH RHSHtHBDH HHHu%DHuH DHLH /AHu 4%HCHHzLMtAEHLHu)4L5 H+u HmEMH+u HPEH DH9GuHwLHIH)u H"E3HHuHLmH?HUHML+E3L IzHHtH.u HDI/u IDHu 4*HGHHLMtADLHtmH/u HDHEIDHHu&4L5I/u IKDH)I/u I1DH؅y$4L5H/HDsH/u HCHM3HHHBH9AuHYHyHHH)uC3LH]ILmL+I?HUM HE3H HHtH+u HjCH/u H[CH5IEIH H; At%ID$t H;Au IYB If@HHt^H ?AHUH}E3HEIH VH+Hu HBHtE3E33HH/u HB1L5MH }D3 3MtI,$u IaBMtImu ILBHD$HHtH(u H3BHD$PH(u HBHHMH3HA_A^A]A\_^[]@SVWAUAVH@HBLH33HHAHHLMtAAHl$xLL$L$HuH- Yv=LHHK<HH|HeLIQ=aH/u H2AH 3HAH9huH=gHtHH@HHLC%=HHHHH H=!HtHB?H HjHHu%?HuH >HLH ;HuH- ZZHGHHQLMtAz@HHuH- ZH/u H"@H+LI3<x)H.u H?HMI0HHuH-i YI.u I?I,$u I?HHT$ HE3H wIHD$(E3Ht$ B=H.Hu Hh?H.u HY?3HuH- ZxHHT$0H E3IH\$0Ht$8<H+Lu H?H+u H>MtMGIhpHH9uH<ID:LHL<HHE;I.Hu I>HtVLEHIAH+Lu Ho>Mt5I/u I[>IH <HM@H 9E3I/ZH- u I>MtLI.u I>MtI,$u I=HtH/u H=HtH.u H=LH yD3]3L$L$Hl$xH@A^A]_^[LUWIxHxH&H3HHE3HAII[HIsHMcEMkMsLHM`H .HEHEM{EHAH9LL$pLELU(LU0LUHLULULULULU LUpLT$xLU8LULUPLUXLU@LT$PLT$hLT$`LUuL-:Mt IEHEAH<HLC8LH}HHH L-MtIEBB;H [HLHu%-;HuH Y:Hj LH ~7)$0)$ Mu BLIEIHLMtA<HLHu LImu I;Hf:E3H9Cu#HCLcHEHI$H) u Hw;3LEHUxLExHHHHAL+H?L E3IHHD$XHD$PHEHtH(u H;I,$H}u I;E3Hu =KH.u H:HCHH-HLMtA:HD$PHHu JH5WH;t[HAH;9u 3H9Y5H;9uAf.zt-3.A:H7HL$P؅yJH)u):3HD$PHE 9H7LHEHHxfDHH]HtH;u HIHuHtH;tHHHCHEH 5 33H]HEH HEHAH91uH50HtHL5@L5IMF5HHdHHH H5HtHB*8H CIHHu%8HuH A7HRMH f4HyHFHH/LMtA9LLHu$H.L5E?H81H.u H8L|$XHE3LIHIH !#6I/Hu II8E3HH6I9Fu MfMnI$IEI)u I 83HLHHH?HHAL+E3L IYLMtI,$u I7E3H.u H7Imu I7E3M3HD$PHEL}L53HuHuMtImu IQ7H5E3IO`E3LmHH;t-HHBtNWMH r3A蘞LMILEHUyLELD$PsH 5HUhHvE3Hu`IHEhH !LHu LELD$P,E3E33II,$u If6HEE3HD$PHEL5LmHEIGxHMHHHtH*u H 6HMHt H)u 6HMHt H)u5Ht$XE3;FHMLu(H|$PHt H)u5HtH+u H5HMHt H)u5H3AI5HEHu EH؅y EHMH)uQ53HuH DHAH9u"HHD$PHtHHEHHLC11HHD$PHHHH HHtHD3H HqHD$PHu%3HuH 2HLH /Ht$PHu DHFHHLMtA4HLHu DH.u H-4H2H|$PH9Cu LkLcIEI$H) u H33HEHHHHLHAL+H?L E3I@HEMtImu I3E3I,$u I3LUE3Mu CHL;taIBH;1u 3I9Z;H;1uABf.zt2-AIu3HLU؅y OC3I*u I23HutpH ?1HUHE3HuIHEH GHEHu BE3E33HHMH)u2HuBLuLHFHHLMtA}2HELHu BHE3AIW؅y ZBHMH)u1trH L0HUH3H]E3HEIH RHEHu BE3E33HHMH)u1H]AHFHHcLMtA1HELHu AIBH;/uIBHHH;/u IZHHXpH@hHt%H9{t3,HHthHCHHM;HtH@Ht 3IH33I,HHu3HMH-H.Hu H0LULHu @I*u Iy03H}E3H](H;@GH \HAH9uL-Mt IEHAHHLCM,LHHHH L-MtIEB.H HLHu%.HuH -HLH +Mu ?IEIH'LMtA/HD$PHHHu ?Imu IN/H.E3H9Cu%HCHsHEHHH) u H/3HHEHLEHHE3LHHAL+H?L HZHMLHt H)u.H+H}u H.H|$PMu >H$L;taID$H;,u 3I9t$@8H;,uAD$f.zt.)AI.Hy l>3I,$u I.E3tkH ^,HUHE3H}IHEH fLHu >E3E33II,$u I-=3H+LeL;I*HEHu E3=H mHAH92u!H1HEHtHHDHHLC[)HHEHHHH HHtHC+H HHEHu%+HuH *H LH (LUMu E3<IBIHLMtA,LLHu E3<HMH)uS,H TE3H}HAH9"u"L!LD$hMtIHEHHLC:(LHD$hHHHH LMtIL*H HzHD$hLHu**HuH )HLH &LD$hMu ;I@IHLMtA+HHHu ;HL$hH)u1+H 2HAH9u"LLD$hMtIHEHHLC'LHD$hHHHH LMtIL)H H_HD$hLHu*)HuH (HLH %LD$hMu :I@IHLMtAl*HD$`LHu k:HL$hH)u*H(3ɺHL$hH9FuL~H~IHH)u H)3H\$`HHLH?DHL+E3L HHH$HEMtI/u I)E3H+u Hr)3H/HL$`u H\)LU3Mu 9IBIHyLMtAR)HHu V9HMH)u)H'L}I9Fu!I^MnHIEI)u I(3H\$PHHHHH?HHLL+E3L ILLHtH+u Hs(H/L|$Pu H_(3Imu IM(E3Mu 8HuE3HLu0HNXH HAH9uYH=HtHHH HHHur&HugH %HLH #KHTHLC#HHHHH H=HtH[&HHHu 7HGHHLMtA\'HD$PHHuL5Ht$X`7H/u H&HFHHLMtA'HHu  7H|%H9Cu$HCLkHD$PIEHH) u H&3H&HH\$PHHAHLL+HHE3H?HL LMtImu I(&E3H/u H&3H+u H&H|$PMu 76I؅y 6I,$u I%E34HHHH9 uH=HtH+H BEH 4LHHHu 5HGHHqLMtAj%HLHu k5H/u H%H3HHH9 :u$L9LD$`MtI0H 薶H LH HD$`LMu 4I@IH LMtA$HLHu 4HL$`H)u`$HMHeHALMtAj$HD$`LHu i4H"H9Cu'HCL{HEHIH) uH#LD$`3H]HLHHHHAL+H?L E3I4HHtH+u H#HL$`LeH)u#3I/HL$`u Ij#E3Hu 3HGHHLMtAc#LHu g3H/u H#H!3H9Fu!H^LnHIEH)u H"3H\$PHHHLH?HHAL+E3L I&LHtH+u H"I/H|$Pu Is"E3Imu I`"E3Mu 2IAIIM"HD$PHHu e2H؅y M2H.u H!H|$PI΅t I$II IIHHH]0Hu!E3I,$u I!HHHu H!I.u I!HuH H(HhHHD$(E3D$ E3HHLHuE3L|$`x1HHHu H!HEE3HH}LH(u!E3H;=t$HVHuLd$`31HGHHwHLMtA LHuLd$`30H0E3AI؅yLd$`30I/u IQ t{H HUHE3LeIHEH LHuLd$`3K0E3E33II/u IE3Ld$`30HGHHLMtALHuLd$`3/HU(AILHuHD$`3/I/u IYIE3؅yL|$`3}/I,$u I'E3t|H qHUHE3LeIHEH yLHuL|$`3/E3E33II,$u IE3Ld$`3.HUHoH(HHH9 u L-MtIE+H H LHLMuLd$`3s.IEIHLMtA8HD$PHHHuLd$`3-.Imu I(vLHuLd$`3-HnH9CuL{HsIHH) u H3HLHLH?HHAL+E3L HLMtI/u I4E3Imu I!E3H.u HLl$PMuLl$`3:-I؅yLl$`3-I,$u IE3t|H HUH$E3LeIHEH LHuLl$`3,E3E33II,$u IVE3Ld$`3,HCHHH9 u L-MtIE+H ȭH LHqHHxLpHHGILdH nH euE3PHGIHL4IHHuE3HHH/Lu HMu!E3HD$h3HD$`"IH/HEHHD$PHu I/u I}H+u HnHu`HXHMIH`E3MHXHH /HEHHD$PHtH.u HE3HuE3H}3L|$hL|$`2"H3L|$PH`LHPHX LHuHUػHUH}H HPHMIEMHH`H 3H/LHEHD$Pu HPE3Imu I=E3Mu%HUE3H}HU3Ll$hLl$`V!H 3Ll$PHAH9ϷuZL%ηMt I$HH HLHusHuhH HLH  LHDHLC LHHHH JL%KMtI$JH LMu,HMHUHMHMHMH|$hH|$`HUd ID$IHLMtA(HD$`HHu'H|$hHMHUHMHMHMHU I,$u IHmE3H9Fu H~H^HHH)u H3HHHhHLpH?HhHAL+E3L HLHtH/u H+3H+u HH|$`M!IMHHHH9 u$LLD$`MtI0H 舠H wLHHD$`LMI@IH%LMtAHHu Ld$hHL$`H)u[3LhHxHp HD$`LHu Ld$hHE3H]pIH|$pHLH+Hu HLl$`HuHE HELl$hHUE3HM LH|$xHHX 3HHE3HE%؅: H/Iu H]3 H]`HU HMH\$`HLH H HE3HIHHHtH+u HI/HD$`u IE3H HE8H}8HHt H.uH8AHM HH H$؅ H/u HctlHL$x3D$@E3ɉD$8E3D$03HD$(HEHD$ QHH2LHIBH/u HH3HHH9 'u$L&LD$`MtI0H psH bLHHD$`LM I@IH@LMtAHLH HL$`H)uIHH|$`H9Cu"L{LkIIEH) u H3IHLHLH?HHAL+E3L I^HEHHMtI/u IE3H+u HE3H HMHt H)uD$(AE3Dl$ IH|HHW HHHEHLH H/u H*H.u HHE8HE3HH3HHLHH YHHúMIGIHHH QkH+LHELu H3I/u IyE3MHMHt H)uXHYE3HHH9 uH=HtH+H ٷܐH ˷LtHePHHSHGHHLMtAHD$`HHH/u HHpAH9Fu&H^L~H\$`IHL)6u HE3H3LHLHHH HHJHGDLM+HIHHH?HL LMtI/u IE3H/u H3H+u HH|$`MIUL OI;H;GILH8Imu ItIGIE33HHD$`HIϾHHIHH`轜{I)7ugI\IMH;MELD$`II;uHII} H%HHD$`HIEHxHHImu IHUPHL$`HMPHtHEH(u HHMXHHD$`H]XHtHEH(ucHHH jE3IHHDžH/Lu H3MImu II$HH E3ILHI,$LHELu IM9I,$u IIFIHLMtALHLe HIHHImu I=L|$xE3MGIppHRL9nHLHIHHKLNMHIAH+u H"H/u HIFIHŵLMtAHHHILHkH/u HkI,$IH] IH]uPHUE3HE3HHHEXHHu8HEHEPHE轱؅LeHEHEL|$h. HELD$xHELD$xL|$h H)0uH HyAH  Ll$hHELD$xHMHUHELD$xHMHU I/u IbE3uHH|$hHUHULuHME3L|$xELL$hEHM LL$`U HD$hL|$xHEE3HUE3HMHEHUHM Ll$hH L M@HH Ll$hLD$xLD$xHD$hLD$xLD$xH|$huHME3HEE3HT$xHMHMHMH|$hHEHT$xHMػHEHT$xHMHMHML|$hHEHT$xE~H QHLH N Hx蒸HMػHEHT$xHMHMHMH|$hHEHT$xHMػHEHT$xHMHMHMLl$hHEHT$xHEHT$xHMHEHT$xHMH|$hHEHT$xHEHT$xLl$hH}mHEHT$xHMHEHT$xHML|$hDHEHT$xHMHEHT$xHMLl$hLl$hHEHL$xHEHL$xH|$hݻL|$hHMHMHL$xHL$xL|$hHMHMHL$xHL$xH|$hHD$pHELeL|$xHH]`HE3HIL HH hLHtH+u H3MuE3H|$hH|$` MEIXpHH9{H ILTHHLCHIAH/HHD$pu HHHImu IhHCE3HB3LHLMtAcH]ЅHL$pE3ELL$hLL$`EHMлOLeH`IL$&I$H(HlE3H IH0L(tI,$Hu IE3HuHEE3Ld$hLHD$` I,$ILH}u \H}Ht$XHHHFLMtA^HHu#HEE3Ld$hLHD$`SHH;tQHGH;iu 3H9_3H;uuGf.zt#3$AH H蛫؅y녻H/u H3t I$II%yHE&LHD$`E3H|$h3ۅÅulID$IHLMtAZHHu &HE3E3H;؅y &H/u HHݛE3HHH9 Ou$LNLD$`MtI0H X[H JL#HϸHD$`LMuL}E3Ld$h3,I@IHtLMtAmHLHuE3,Ld$h3iHL$`H)u HFHHLMtAHD$`LHuE3,Ld$h3HH9CuLkL{IEIH);u H3HH3HL-HD$PLHuE3,Ld$h3HUHH\$`HIFAL+MHIHHHH?L HMtImu IE3H+u H3I.HL$`u II/Ll$Pu IE3HuE3,Ld$hHIHH]@]HHuE3-Ld$hHLHyE3-3HL$hH/u H1HHLuHDIHHH)HEE33HD$`HL$h0L9H 7HPM@H ;3E33HL$hHL$`E3Ht$XL5>HD$PHtH(u HL$PHMHt H)u~MtImu IiMtI,$u ITMtI/u I@HL$hHt H)u*HtH/u HHL$`Ht H)uMH ~*D3VLe3LuHH]@HM(($ ($0L$HL$XHt H)uHM0Ht H)uMtI.u IHML$PHt H)ucHMHt H)uNHMHt H)u9MtI,$u I$HM L$`Ht H)uHMpHt H)uHL$xHt H)uHM8Ht H)uHMHt H)uHMPHt H)uHMXHt H)uHtH+u HtH$pHtH.u HXHMH$hHt H)u;HEHtH(u H#HHH3Hx_]LUSATHl$HpHH3HEHIsHI{IMkHhH(MsE3M{EHMȹ A){E)CLuLuLuLuLMHLt$(E3D$ E3HHELHuL5۷ HIEHu I,Hm LmH(HhHLt$(E3D$ E3HLHuL5rv HIHu IEGAE9EL}u EA…HFf.D(zuxHtL5 Hf.y(zuDHtL5ж H 7A\E3HAH9uH=HtHH@HHLC!HHHHH H=HtHBH ƓHfHHu%HuH HոLH HuL5 HGHHLMtAvHHHuL5ĵv H/u H(HHuL5M\ HH9CuLsHsIHH) u HIHLuHH}H?HUHAL+E3L HLMtI.u I}H/u HnH.Iu H\IMuL5ݴI؅yL5VI/u IuxH BHUHWE3LmIHEH jHHuL5cgE3E33HH+u HL537H]A(LI)HHu (HHu zH ݖHS H*MLEHD$pHDl$hHL$`HD$XDl$PHL$HH|$@Dl$8HL$0H HHt$(D$ ݨHHu I/u IH.u HH/u HIH HAH9uH5HtHH'@HHLCHH_HHH LH5MHtHB%H >HޙHHu%HuH <HMLH aHuL5}HFHHMLMtAHHHuL5<E3H.u HHIH9CuLsH{IHH) u Hb3HLuHL}H?HUHLmAE3L+HL 賗HMtI.u IH/u HHuL5H0HHH]H0HEHHuL5RVHHHu HH E3H]HAH9uH=HtHH@HHLCHHVHHH sH=tHtHBH 5H՗HHu%HuH 3HDLH XHuL5txHGHHDLMtALHHuL53H/u HH 3HAH9uL=MtIH @HHLC{LHAHHH nL=oMtIBH HLHu%HuH H/LH CMuL5_+IGIHGLMtAHLHuL5I/u IuH.H9CuH{LcHI$H) u HF3HEHUHEHHH}HAL+H?L E3I藔HHtH/u HI,$u IHuL5lE3E3仨2HI9Fu"MnIvIEHI.u I3HLmHH]H?HUHAL+E3L HLMtImu IOH+u H@E3H.Au H+IMuL5I貙؅yL5%I/u IuxH HUH&E3LmIHEH 9HHuL526E3E33H谟H+u HyL5HELELHP MIH ؏H)HD$pHDl$hHL$`HD$XHEDl$PHL$HHD$@HEDl$8HL$0H FHD$(D$ ۡHHL5cMtI/u IMMHtH/MMu HHtH.u HMtI/u IwMtI,$u IbLeH H}MHuDLm3G3Mt4"I/u I&H}HLeLmHuImu ID($ ($0L$HL$PL$XHtH.u HH$hHtH/u HH$`MtI,$u IHHMH3FHpA\[]LUWAUIkHHH3HEHE3IIL;I[ AIsAMcLHMH MsM{ELMHAHULEH9]uH\HtHL58@L5/IMFHHHHH HHtHB~H I7HHu%iHuH HMH HuL%֩qHCHHLMtAGHELHuL%qH+u HID$H;u I$IAH EH;u HAXI'H NH;u HAXIHφIHHuL%r&HދHHEH(HEH]DLCHMǃIADȺgHHuL%qH+u HHMHH9AuLyLqIIH)uIHFAL+L}HuE3HHHHMH?IL HEH.HMtI/u IH.u HI.u IqHuL%qH+u HLI$HILeIH)u/HКIHL%ÚH+Leu 3H]LHHpH9GTHLUH9dMu$H5hHtHH$HEHHHHHEL@HHHHH H5HtHD_HUH tHHu&IHuH uHLEH HuL%xHFHHmLMtA&HELHuL%sxH.u HID$H;u I$IAH $H;u HAXI'H -H;u HAXIHIHHu yHGH;HuHHAH H;u HAXH'H H;u HAXHHIHPHHu ywHgHHEHHEHHEHuH]EAHD:AHND LCH@LAHMߺƓLHu xH.u H}H+u HnHMHH9Au LqLiIIEH)u>33HAL+LuL}E3HHHHMH?IL HEHHMtI.u II/u IImu IHu x&H+HH LMuLIGH L5H;usIWHuIHHHHHuAOHAOOeHtHA`IILAMAOAGHH HHH+H;uAGX IILMt5I/u IWH]߹LeELuWLHu||HImL% u IpHtH+u H\MtI.u IHHtH.u H4MI/IHMIGIHHHEHEHIG HH HUHUH+Lu HI.u II/u IMu+|L%E3MH D3>H}LeL$L$H$H$MtI,$u I1L$HtH/u HIHMH3αHA]_]Hl$ WAVAWH HAE3H\HELMtAH\$HHHt$PHuH-6wHGHH֍LMtAHHHu H/u HSHMLHʼnHALMtAZHHuH-HCHHKLMtAHHuH-mH+u HH uHHHtXH/u HHHHHuH-CH+u HhHHHHuJH/H-ԟu H9HtH+u H%LH D3;Mu)6H/u HH.Lu HHLI.u IHt$PIH\$HHl$XH A_A^_LUSWIHHUsH3HH03Is3McIMkMMsE3M{LHEE33A)sHEHEHyHM A){H]HhH]H(E)CHH|$(E3D$ E3IHEHHuH5 ,HHHu HHy H]H(HhHH|$(E3D$ E3IHEHHuH5 HHHu HhHx H]H(HhHH|$(E3D$ E3HHEHHuH5 THHHu HHMHEH]Q39PuC3;u3ɅGIof.׬D(zuHtH5- H:f.(zumHtH5 If.n(zu9HtH5Ŝ o3fD/txH nHUHE3H}IHEH vHHuH5o E3E33HH+u HH5? 3f/txH HUHVE3H}IHEH HHuH5 E3E33HhH+u H1H5 dfD.zzuxH hHUHE3H}IHEH pHHuH5i E3E33HH+u HH59 H]A(LI/LHuH5 W ( HHu  % (LHu  HHS LE3HD$pMH}L$hHD$`L|$XL$PHD$HH|$@L$8H )HD$0Ld$(D$ ɏHHu  I.u II,$u IH/u HI/u IH}H wL}HAH9nuH=nHtHH@HHLCpHH6wHHH [nH=\nHtHBH wH赀HHu%HuH H$LH 8HuH5T HGHH\LMtAHELHuH5 z H/u HiH jv3E3HAH9muL-mMt IEHAHۅHLCVLHvHHH QmL-RmMtIEBH uHLHu%HuH H LH Mu  x IEIHLMtAHEHHHu  : Imu IXHE3H9FuLvH^IHH)u H'3HEHU@HEHHHEAHEPL+HLu@HE3H?HL p}HMtI.u IE3H+u H3HEHu  |HMHZH9Au LyLaL}II$H)uv3HL}HH}H?AL+E3L IHEH|HMLHt H)u+H/u HI,$u I MuH5 : I薂؅yH5q I/u ItxH HUHBzE3LmIHEH |HHuH5 E3E33H蔈H+u H]H5 H MsHAH9juH=jHtHH͂@HĂHLC?HHsHHH JjH=KjHtHBH rH|HHu%HuH HLH HuH5# HGHH+}LMtAHEHLHuH5ޔ FH/u H5H 6rE33LeHAH9iuL5iMtIH@HHLCLHqHHH :iL5;iMtIBH qHd{LHu%HuH HӖLH MuH5 IFIHK~LMtAtHLHuH5“ I.u IHE3H9Cu LcLkI$IEH) u H3HEHUXHE`HHEAHEhL+HLeXHE3H?IL -yHMtI)<$u I3I)}HEu IyE3HuH5 vHHH9Fu$L~H~L}HIH)u H03IHH} HH](H?HU HAL+E3L IxLHtH/u HH+u HHMH)uMuH5F IL~؅yH5' I/u I~txH HUH8vE3LmIHEH wHHuH5̑ vE3E33HJH+u HH5 FH oE3L}HAH9afuH=`fHtHH|~@Hs~HLCHHnHHH fH=fHtHBzH nH3xHHu%eHuH HLH HuH5Ґ |HGHHxLMtACHELHuH5 H/u HH m3HAH9[eu!HZeH]HtHH5b}DH5Y}HLFHHEHmHHH eH eHtHF\H umHwHEHHu%CHuH oHLH HHCHH]yLMtA.HLHH+u H3HEHH9FuLnLvIEIH)u H3HEHUpHuHHExAHLmpL+HHE3H?IL tHMtImu IQE3I.u I?E3H I,$H5u IMtI.u IHtH/u HMtI/u IMtImu IHMHH)HMH_H9Au#LyLaL}II$H)uz3HL}0HH}8H?HU0HAL+E3L IsHMLHt H)u(H/u HI,$u I MuH5 7Iy؅yH5n I/u ItxH HUHGqE3LmIHEH sHHuH5 E3E33HH+u HZH5 HEE3HMDD$`LHP MIHoHD$XHt$PDD$HHD$@HL$8H 9{DD$0LEHD$(HEHD$ HHuIH5e" MtI/u IH}H LuLL}D3D(3Mt+I/u IHL}LuI/u IjD($($($L$L$L$H$MtI.u IL$HtH/u HHHH3跛H_[]LUSIkHXHy_H3HEIs3I{3Mc3MkMMsE3M{MLHD$`3HEHD$XE3HeHT$8HM A)sHhH(LMHt$pLD$PHHt$(E3D$ E3IHD$@HHuLd$8 3HHHu HsH8e HhH(HH|$(E3D$ E3IHD$HHHD$PHuLd$8 HHHu HE3H]LD$P 9{ If.}(zuHHtLd$8 \I\jHD$HHHuHtLd$8 ,H-wA(uLd$8 HvWH*A ~uLd$8 L}L;=A HuEHrHHD$0LHuLd$8 HHrHLfI;HD$PHH{H@LMuH MHAHHL$PHLHYH3IH9Cu*HCLcHHD$XHI$H) u HƼ3IHD$XHUHEHHH}HML+H?L E3InHD$8LHtL).u Hs3L)+HD$XHD$xu HY3HD$PHD$hMM),$u I5?HHEHHxL)MtL;u HIHuMtL;tIEIIuH<LE33E3HE(LD$HLH`HH >H53HHT$8HT$0HUL .H A 4#HMLL$xLD$hHT$0iI/Hu IHuLd$8 H.u HHt$@H HH L=- HL$0L))uHt$@Ld$8H"qEHqHLCHHD$8HUaHHH j[Hk[HtHDH 4aHjHD$8Hu%HuH 0HALH ULd$8Mu ID$IHlLMtAHLHu 3I,$u IH `HAH9Zu"HZHD$8HtHH=pEH=oHLGzHHD$8H;`HHH pZHqZHtHDH `HiHD$8Hu%HuH H'LH ;Ld$8Mu 3ID$IH>nLMtAǶHHu I,$u IuH.E3ILd$8H9Cu$HCLsHD$PHIH+u H;3H\$PHULH]IL}H+I?HH}L E3IgHD$XHtH+u HE3H/LD$Pu HյI.u IƵHL$XE3Hu 3 HHHL$pHHuH[VHN Ld$XLAL}LHFEHjHD$0I~HD$hHHuLd$8 U HfjEI~HHHHɳ3IH9Cu$HCH[HHD$XHHH)uܴ3HD$XLIHEL+I?HELuM HE3H2fLHtH.u HH+HD$Xu H{Mu,L= H/u H[Ht$@3Ld$8v I.u I;E33HD$xLM~3H|$0Ll$HMO`M(IO }~HI;|H|$hLl$xIHh^E3HdH/Hu HóHu Ld$8 3 Ht$@AHlH^tuLd$8 HkA H4tuLd$8 u L;-1H D\HAH9TuH=THtHHk@HkHLC6HH[HHH iTH=rTHtHB±H [H{eHHu%HuH ٰHLH HuLd$8 HGHHgLMtAHLHu m H/u H9H :[HAH9SuH=SHtHH5j@H5jHLF,HHZHHH wSH=xSHtHBH ZHqdHHu%HuH ϯHLH Ht$@Hu HGHHhLMtALHu f H/u H2HAAH9Cu!H{LcHI$H) u H3H|$PHH}HLmH?HUHLuAE3L+IL IbHHtL)?u HM)>HD$Pu IE3M)<$u IE3Hu 3HHHu HZHIHVLHֹLALHu*L |3A H Ld$8 &L;-MtHNYIsu H XE33HAH9QuL5QMtIHTh@HKhHLCƫLHXHHH QQL5RQMtIBRH kXH bLHu%=HuH iHz}LH Mu Ll$`2IFIH$dLMtAHD$PHHu Ll$`I.u I®IEIHiLMtAͮLHu Ll$`H WHAH9lPuH=kPHtHL=g@L=fIMGyHH?WHHH $PH=%PHtHBH WI`HHu%HuH H-|MH AHu Ll$`HGHHGeLMtAЭHD$XHHu Ll$`H/u HuH.3AAH9Cu#HCHsHD$PHHH) u H<3HD$XHUH\$PHHEAHHuL+LuHE3H?HL ^LHtL)>u HM)>u I׬HL$XE3L)9uĬL);H|$Xu HE3LD$PMu Ll$`I$I$Hu IIIH\$`Ht$pHRE3VHN LAH RHH|$@LL$HLHD$0LALHu-L x3AH H 3E3L;-թLl$Pt%H?UIouH 3E3HtH+u HIEIHfLMtAHD$PHHuHLl$`3 E3HH@lLHtH+u HAE3I,$Lt$Pu I)HuEHk`E3HtHD$HHuHLl$`3 H`EHPtHD$XHLHH}3IH9Cu/LsLSILT$XIIH+uHLT$X3ILLt$xIHT$xL+I?HE3M LuE3I[LHtH/u H<H+Lt$Pu H(Lt$XMu2H|$@L=u HL$HH)uHLl$`II,$u IܩMH|$0MHD$hLl$`HLl$`I0LN`H0I@L0I8MH0 HN sE3IE E9]+OM0@fIH@ILcQEuH(H0IH@(D88t"H(HP8| HB(HcB AuAHA0H;0}HHA0IH0xLa0IH@(IH(H+0ZMExYJ(IHH9|%L$AIHHH)0IyIcHD(IH(H0AIE;]HzHD$hMHH|$HE3HRHMXH/Hu HH 3E3Ht$@L=dsLl$PMtImu IHtH/u HMtI,$u IMtI.u I}HL$XHt H)ugMH eD3HL$p3Lt$`H+u H6HL$pHt$@HHHt H)uMtI.u IHtH.u HHE($L$(L$0L$8L$@H$HH$PHtH(u HHHMH3VHX[]@USVWATAUAVAWHl$HXHFH3HEHH3HEL3HM3HEHEMHEIHhL3HME3HME3H] HhH]H(LMHHt$(E3D$ E3IHEHHu u HHHu HHK H]H(HhHHt$(E3D$ E3HHEHHu v9 HHHu HEHK H]H(HhHHt$(E3D$ E3IHEHHu w HHHu HHU3HEH]R9PuC3;u3Ʌ5IPLHuHt zf HPHHugHt {@ HMPHHu@Ht | J'H;}jH HUHTE3HuIHEH UHHu E3E33HbH+u HУL}IMIGLHuH=0oH%HHu H HEHHu oH \H pHUMLEH D$pHD$hH{[H\$`D$XHD$PH[Ht$HD$@ HD$8Ld$0D$(Dl$ cHHu I/u IǢI,$u IH.u HH+u HHH}%HHWH xK3H]HAH9GuH5GHtHH=Z@H=ZHLGdHH*KHHH oGH5pGHtHBH KHTHHu%۠HuH HpLH ,Hu HFHHWULMtAHELHuH= mH.u HdH eJ3E3HAH9FuL5FMtIH=Y@H=YHLGRLHJHHH mFL5nFMtIBޟH IHSLHu%ɟHuH HoLH MIFIHWLMtAHEHEHI.u IdH eI3HEE3HAH9EuL-EMt IEH=XAH=XHLGMLHIHHH xEL-yEMtIEB؞H HHRLHu%ÞHuH HnLH MIEIHRLMtAHEHH|Imu I^HE3H9Gu'HGHOHEHMHHH/u H$3HEHUH}HHE AHEL+HE(E3HH}HMHH?L hPLHtH/u HɞHMH)uH]MHMH\H9AuLyHyIHH)u}3H}HEAHE@L+HL}0HLu8H?E3L HHE0HOHMtI/u I%E3I.u IE3H/u H3HMHI,$H=qiu I֝MtI/u IHtH.u HHMHt H)uMtI.u IHMHt H)upMtImu I[HMH5H)+> HUHH9BuHZLbHI$H) u H3HH]HHuH?HUHAL+E3L I[NLHtH+u HH.u HI,$u IMu nI.T؅yH= hI.u I`tjH HUH:LE3LmIHEH MHHu E3E33H3ZH+u HHEH iLED$`LHP MIH{THD$XHEHD$PHSD$HHD$@HEHD$8H TD$0 HD$(HEHD$ }\HHu"H=fMtCI.u=IM2I.u I<HH]HyHHuAH=fHEH HuLLuDL}3HEH}3Mt L}LuHuI/u IɚMtI.u IHtH.u HHtH/u HHHMHH3FvHXA_A^A]A\_^[]HVWHhHX3H;:HhL` LLhLLpLxeH 1CDHAH9YuHYHtHH-nK@H-eKHLE HHBHHH ;YH3LHAH9UuHUHtHH-O@H-OHLEܑHH>HHH wUHxUHtHBhH >H!HHHu%SHuH HcLH HuYH;t0H?HXuH+L5`u2H'HCIHHLAHHu'L5``MH ND3dHu%H(uH5HH/u HH\$@HHt$XHl$HH A_A^_H\$Hl$Ht$WH H U=IHHAH94TuH3THtHH=M@H=MHLGAHH=HHH SHSHtHB͒H <HFHHu%HuH HaLH  Hu H5%_HCHHzMLMtAHHt?H+u HTHkHHu H5^1LHHtCHHuEH+H5^u HHtH/u HLH 3A!3!H/u HВH+u HHH\$0Hl$8Ht$@H _H\$Hl$Ht$WH H ;IHHAH9RuHRHtHH=/L@H=&LHLGHHG;HHH HMtI.u IH.u H HuH-X HXH9LuHLHtHH=F@H=FHLGHH5HHH ULHVLHtHBvH 5H/?HHu%aHuH HZLH HuH-WJHCHHF@LMtA?HHu H+u HHGHH/Lgu HԋE3L|$0IHl$8HT$8I8=HHuH-1WH@HHFLMtAHHuH-V`H/u HJH K4HAH9KuH=JHtHH5D@H5DHLF=HH4HHH JH=JHtHBɉH 3H=HHu%HuH HXLH HtkHGHH>LMtALHt>H/u H\IFIHELILMtAYyYH+H-Uu HMtI.u IHtH/u HLH D3~(H+u HʼnI.u IL=IMtI,$u IILt$pH\$xH@A_A\_^]H\$Ht$WH HAHzHHH HLHH;s,H3HHHH;sLCH AHLHH;rH\$0HHt$8IHH _H\$Ht$WH IHHHCH H#H;wH\$0Ht$8H _H\$Hl$Ht$WAVAWH A8IDIHEwuHAH ЉAAAAAEAfA;sOA+ǙDf;s<EuHFHЉA G DfA;rH\$@Hl$HHt$PH A_A^_H\$Hl$Ht$WH HAjHًH DLD;s$3ҋD;sHCH DLD;rH\$0Hl$8Ht$@I AH _H\$Hl$Ht$WAVAWH A8IDIHAouHAH ЉA A)AEDDED:sS@͸A+ǙD:s=EuHFHЉA/ DAD@:rH\$@Hl$HHt$PfAAH A_A^_@SH)$HD)L$p(D)T$`D(DHafD/fD/Rf/v1f/v+HAH 3(AXYf/fn)$D)$EWD)\$PD)d$@D)l$0E(D)t$ E(E^D^HCH HCD(H D(A(A(Io(A(A(9o(XfD/rA(AXfA/vfA/D(t$ D(l$0vfA/v ^(JA(n(A(^nA^\fA/v[W=(nnWnD(\$PD($($D(d$@($D(L$pD(T$`Hİ[(Nn\(0A(H(/X^(@SH IMWf.zWf/H*r'Yf/(r = HH [`HH [_\(Y(f/r HcH+HH [#HcH+HH [3H [HPSUWAVH8A9I)pȋ)xLD)@D)HD)PDx^D)xD)hD%D)XD)HD)8A9QAAf.AA@EAEy EY8Ay0Eq`EipAIxD$(AAHD$ AAPD$HAAXD$PAAhfDnED$@AD$0D$`D$xD\$hDL$`E(AQD\AiAfD/D$`v $`D(fDnE(E\EAEWWA(D$xAYEy DL$`(AYAXf.f: ,Aq(Ay0wfQ(kYA(Y ݮEW (\(fD: EXfnD\$h(D[8XˮAX^(l$(XA\k@AXD(\$P\d$ (cHAYDXl$HkP[X\(AYEX^(EY\D(EYDkp^EX(AYDY(AXDs`Y(A^^AXD$@ChXKxT$0EYH$0fnEYL$8Dl$pDt$xD$D$XfD$xfDIFIIF(IYt$0fA/(fA/id$8(f/Wv>\$@L$H^\f: ,;Wf.zt\YYA^XT$ f: ,؅ZWf.zKA\YAY\D$XA\^t$PXt$ ((Yt$P\AXAXTScA^\fA/(Wf: ,ً+HcHHHHȃA(fnAYA\f/(^=EW(X=fD*YA^X=0A^AXYA(AXD^7hA(D(\f/DXfA/D$`DL$`WDt$`EXDD$X*D\L$XEXD\D(EXA(A(A(AYEXAYE(EYA^E(EYgD(WA(A^+*X eDYkgYD$(A(Y$xDXA(Y$`^=gW%-*Y(DX(^\(^\(^\^5Ū(\(A^DA^DX(^\(^\(^\(^\A^A^(DX(A^\(A^\(A^\(A^\(A^A^DX(A^\(A^\(A^\(A^\A^A^DXfE/D%ިD/WD$D$`D\$hDl$pDt$xEA(fnA(A^Y;'O;G+DCQ@WB*B((^W*\^W\*Y(Y(^W\*BY(^W\*BY(^W\*BY(^W\*BY(^W\*Y(^\YA;,;Q@fn(^\Y;~+%K;+DGQWB*((^W\*B^(^W\*^^W(*B\^W\*B^^(^W\*B^(^W\*B^(^W\*^(^\^A;/;(ffffn(^\^;~f/I L$(WAY\Xf: ,$pL$8H$0+A(sA({E(CE(KE(SE([E(xE(hE(XfA/E(cFIA^_][HHXHhHpWHA9I)p)xHD)@D(D)Pt)A9Qu#AAf.zuEQ AyAq0D{SA(WxE\D)L$@fDnEAD)\$ EQ AiAErAYaC(E(WEYA(D[XAYXSf.wfQaY2AXD(\$ fA/wD(A,D(L$@s0HG3H(f/(vR;~HG3H((,\fnË+fnAYAYY^f/w(t$pL$IkI[Is E(CE(S(|$`I_MHt$H|$AVH Lt$X3IHDEuMIHt$8H|$@H A^_`H\$0HEuHGHAAȀA HH;|H\$0Ht$8H|$@H A^H\$ WATAUAVAWH E3EDMHEfEu*MH|$xAIfH\$hH A_A^A]A\_øfD;uUIML|$xEuHCH DAAAC+fAHI;|H\$hH A_A^A]A\_Ht$XD8T$p}IIHI HHH HHH HHf ML|$xfEuHCH DAAAAf#fA;wfAfAwHI;|MMAvHl$Pft$`EuHCH DAAAAf;s@A+ƙf;s.fEuHCH DAAAAf;rt$`HD$xfAfBu HAH ЋY|$PtfDHCH #;w;E3E3*Hu HAH |$PHt LkHH\$0Hl$8Ht$@H _H\$ UVWAVAWH L|$xIILHMu&MIHIHH\$hH A_A^_^]øLd$PH;u.3H@HCH ЋIIHH;|3@8|$ptlHHH HHH HHH HHH HHH LI L H^@HCH A#;wIIHH;|6H-E`Ll$XDd$`EfHCH ЋIA;s)3ҋAD;sHCH ЋIA;rDd$`H II HH;|Ll$X3Hu+HDHCH IIHH;||$ptbHHH HHH HHH HHH HHH LI L H~FHCH I#H;wIIHH;|$H~DHHIIHH;|Ld$PH\$hH A_A^_^]@SUAUAVAWH E3AMDHEEu"MHL$xMH A_A^A]][0YH|$X@uSIMqLt$xEuHCH DAAAC+A>HI;|H|$XH A_A^A]][Ht$PID8T$ptkHHH HHH HH@ MLt$xEuHCH DAAAA@"@:wAB6HI;|MLd$`DeEEuHCH DAAAEEE:sFA+řD:s1EuHCH DAAAAAD@:rHD$xfAEDHI;pLd$`Ht$PH|$XH A_A^A]][H(EuH(H\$ H\$X;u%HAH HL$`H\$ $H(HL$`) H\$ $H(Hl$VAVAWH EALfEuHl$PH A_A^^øH\$@f;u=H\$h;uHAH HL$pf1sHL$pA f1^|$`tBH\$hH|$HH|$p;uIFIЉG Af#f;wH|$HLL$pLD$hfH\$@Hl$PH A_A^^H\$Hl$Ht$WH AAHEuH\$hH|$HH|$p;uIFIЉ/ A"@:wH|$HLL$p@LD$h@H\$@Hl$PH A_A^^H(Y ̗XH(H8)t$ (Y(t$ H8@SHP)|$@HD)D$0D(D)L$ D(Y n1A(HY Z(XXAYD(D$0AYD(L$ ^((|$@HP[H8)t$ (Y(t$ H8H8)t$ (Y(t$ H8HHf/ )|$ (reH(|$ HH)t$0(WW`dWH^f: f/ (t$0sH,(|$ HH@SHPHQH )t$@()|$0(D)D$ DDD\f/vfAYXf/w(t$@(|$0D(D$ HP[@SHPHAHH )t$@)|$0(D)D$ D(5D(\f/wfHCH (\f/v(RWR(t$@AYD(D$ \((|$0HP[H\$WH HHHu 3H\$8H _Ht$0HHH HHH HHH HHH HHH HH H H;w'@HCH ЋH#H;wHt$0H\$8H _HCH H#H;wHt$0H\$8H _@SH@HAHH )t$0()|$ (f/n(r0\\dQY\((t$0(|$ H@[Wf/v$X(4QYX(t$0(|$ H@[((H(t$0(|$ H@[WHD)L$@D 2BfA.D)T$0D(zt fD.zuWD(L$@D(T$0HĈH$)t$p)|$`D)D$PD)\$ DfE/vA(A\,3WA(*AX^(YLP(D(Y -(\"fE/X D(\$ DYYX YX YX YX YX YX YX vY(t$pX ]^X DXD\v|A\(OD\Hu(|$`A(D(D$PH$D(L$@D(T$0HĈ@SHPHAHH )t$@)|$0(D)D$ D(W(f/wHCH f/(vI@\^(N(t$@Y(|$0AXD(D$ HP[HH)t$0()|$ (GYX(t$0(|$ HHNHSH)p()xHD)@D)HD(D)PD)XDD)`AWD)l$ ^HCD(H fA/(D%e?EWHCH AY^D(EWA(AYf/rO(M(A(M^WAXf: H,H| fA.z(u&HCH fA/(r fA/rL$A(sA({E(CE(KE(SE([E(cE(kI[HHXHhHpWATAUAVAWHp)p3)xMD)@MD)HD)PLH$DG>A(LhMH$EWD =yA^ƅtpfA.ztgfD/L͋Ifnr!Y(f/r Hc;Hc1A(Hc\Yf/r HcH+HcH+3+A<~#A\4HI;dH$~A\(t$`L\$pI[0Ik8Is@E(CE(KE(S(|$PIA_A^A]A\_@SH0)t$ H(z  =H\^Y(t$ H0[@SHPf.H)|$0(D)D$ D(<u(|$0D(D$ HP[Wf.z)u'DY5A( X(|$0D(D$ HP[o<fD/)t$@vWD\DYA( HD(W(f.w WQ(JXEXYAX(3Y=(HfnAXY E X(t$@(|$0D(D$ HP[@SHP)t$@H)|$0(D)D$ (D((HYY5( (t$@XAYD(D$ ^((|$0HP[HH)t$0()|$ (wY(t$0X(|$ HHH8)t$ (^(t$ H8H%[Z@SH@f/ "Hr H@[Wf.z u3H@[W H|$P()t$0)|$ 8IHC(H 3f/(vHSH Yf/w(|$ H|$P(t$0H@[HSVWH)p()xHD)@D)HD)PD)d$`D)l$PD)t$@D(D)|$0HW$fA.w EWEQ A(HD(DY= Dp=PDX=gD%9D)\$pA(A(\Y^\X 4A(\8D($$DX E^DL$ D\D$HFHHF(H\(D(T BD(D\WA(fD/[A^AXYAXX `f: ,r fE/xfA/v fE/pfDnEE(EXfE.zt fD. Fz u EWfA/v A\,3WA(*AX^(YF(D(Y(\ 3fA/XDYYXuYXaYXMYX9YX%YXYXYX^X5DXD\v)|$ffA\( FD\Hu=A(E($EXEY$A^AXEDY$\E\E\fD/sD$DL$ D(\$pL$A(sA({E(CE(KE(SE(cE(kD(t$@D(|$0I_^[H(HAH ЋHH(H(HAH H(H(HAH HH(H8)|$ (WrU 6W^(|$ H8DH8)t$ (o(WXf.wWQY(t$ H8(rDY(t$ H8@SH0)t$ H H( ^((t$ H0[H\$Ht$WH@HAHH )|$ HLfjHWHHxH*HHHH H*XAYrsH HC@uWJT= \J)t$0NxN\Y(WX6Cf/(t$0w H(H\$P((|$ Ht$XH@_H\$Ht$WH@HAHH )|$ H5"WH*YZ;VH HC@u0WH*Y(W cS=\\)t$0^W|^\H*Y ݄YX(W:B/(t$0w H(H\$P((|$ Ht$XH@_H?HHX UWAVHPHpML`HLxL%_h)xL=!D)@HD#3)pHGHHWHHHxH*HHHH H*XAYrcHHG@uAWR=߄\=ANAxN\Y(AWXAf/w H(AL (A\\AYYWf.w WQ(.\((t$`(|$PD(D$@D(L$0D(T$ HxH(HAH ЋH(HHHAH )t$0()|$ (Y(t$0X(|$ HH@SHf.H)$(D)l$0D(<u $rpf/v"HAH X\,Yq_pf/)$D)$D)L$pvD E(D^DXqf/ D (Y pWYAXf.w WQ (]-(AXW(Xf.wQ:-\(X^D(DYXEXD^HCH D)T$`D)\$PD)d$@D)t$ D%pAY,HCD(H AXEYA(EXD^A\YD5oEWA((\Y\fA/~(^\,AX\fA/s[HCH AY!,HCD(H AXEYA(EXD^A\YA((\Y\fA/rA(+HC(H  nf/D(t$ D(T$`vW5p +oAX(T &AX+3A\D(d$@fD/D(\$PtiY_ (^Wf.wfQ(^+YzoAXf/vX5nf/5gnvX5]o(D($($D(L$p($D(l$0Hİ[@SHP)t$@H)|$0(D)D$ D(KDYn(=YYWD^DXfA.w WAQ A(* mXHCH ^\Y((X^f/r( Y^((t$@(|$0D(D$ HP[H8W)t$ f.(zt 4^)(t$ H8LSHf/ mHE)CD(rE(CHİ[l)$)$=D)L$pD\D)T$`D)\$PD)d$@D)l$0A(D)t$ N)D-ImA(W mD(A(-)D%xE(E^D\D(HCH HC(H D((\(AYXA((Wf: fA/wf/w(A(^X(((\AYA^YA^f/zD(t$ D(l$0D(d$@D(\$PD(T$`D(L$p($D($,($Hİ[HSH)pH5^f/D)PD(D)XD(&f/)xD)@D(D)HD(D)`D^D)l$ D^DH HAH H D(HAH D(A(A('(A(A('Xf/rD(L$`Wf/D(D$pv8^(D(d$0($D(l$ ($D(T$PD(\$@HĠ[A('(A(A^ 'A^(_\\&((&X&\(&w A(H(p X^(gH(Y iO XH(H8H )t$ (HAH \(\&WjY(t$ H8@SHP)|$@HD)D$0D(D)L$ D(Y h A(HY h( XXAYD(D$0AYD(L$ ^((|$@HP[H8)t$ (_ Y(t$ H8@SHpyHtA3AHAHp[)t$`)|$PD)D$@D)L$0EWD)T$ D@H HAH H (XHAH A\D((EXYE\A(AYXfA/sfA.zt($D(T$ (Y iiWD(L$0^(t$`f.w WQ($(CY(|$PAYD(D$@KHp[HH)t$0()|$ (YX(t$0(|$ HH2$HSH)pH)xD)@D)HD(D)PD)XDQA(D)`\#HCD(H fA/(EWHCH AY#E(D\A(AYf/rQ(#(A(#^WAXf: ,| fA.zuHCH fA/(rHfA/r(|$pL$A(sE(CE(KE(SE([E(cI[@SH0)t$ H( H (4\^Y;(t$ HH0[@SHP)|$0Wf.HD)D$ (D(z'u%Y=ze(: X(|$0D(D$ HP[f/)t$@vT\Y=>e(H(W(fA.w WAQ A(3"XXYX(JH A(Y dZHfnXY dfE.X<ue(t$@(|$0D(D$ HP[@SH)t$pWf.H)|$`(D)D$PD(D)L$@D)\$ D(z!uD AdAY(XfD/D dvW\D)T$0AYHD(kW(f.w WQ( XEXYAXD(T$0FH (AY$HfnAXAYXf.(X<u5dA(HAY-(|$`XD(L$@AYD(\$ AYD(D$P^((t$pHĀ[HH)t$0()|$ (Y(t$0X(|$ HHH8H )|$ (HAH (5\W-d^\(|$ H8HHH )t$0)|$ (HAH 5((\WcWc_(^\(((t$0(|$ HHY@SH *bIf/bfnr'Yf/(r UHH [蘱HH [@\(Y(f/r+HcH [_+HcH [HHXHhHpWHA9I)p)xHD)HD(D)Pt)A9Qu#AAf.zuEQ AyAq0DD)D$PE\fDnEID)\$ AiA(EQ AEAYC(E(WEYA(D[XAYX+f.wfQYZaAXD(\$ fA/wD(A,D(D$Ps0HG3H(f/(vK;~HG3H((,\fnË+fnAYAYY^f/w(t$pL$IkI[Is E(KE(S(|$`I_HHf/ _)|$ (rH(|$ HHHAH )t$0W$a-( \(|$ W^f: (t$0,HHHH\$UAVAWHPIILLA ~ [ Ht$pA;)t$@AL)|$0D)D$ WfDnEfD/A(vPH|$xB<IFIfn(W^Xf: ,fn\tf/wH|$x(|$0D\(t$@Ht$pA,D(D$ +D;NHcH$HPA_A^]3H$HHPA_A^]+H(HH(HSHm^H)p)x= D)@D(D)HD\D)PD)XD)`D)l$ A( D%X D(D-^D\D(E^DHCH HC(H \D(A((Wf: fA/wf/w(A(^X((\AYA^YA^f/rL$A({E(CE(KE(SE([E(cE(k,A(sHI[H8HAH )t$ (WT^*(WYx^f.wWQY(t$ H8(Y(t$ H8@SH0)t$ HH(^((t$ H0[H(H HAH  \(TW]H(@SHD)L$pHD } fA.D)\$PD(z(u&H HAH D\A(WQ]D)T$`EWfE.z uWfE/)$)$D)$D)d$@D%\E(E(E\E^H HAH H (HAH A(\(ifD/(AWrA(([f/rHA(\A^2(A(AW(AYAX!Xf/sD\ZWD)l$0D)t$ A(Y n[f.w WQ(D-vZE(D5ZD^@HA(D(YAXfD/sH (YHAH YA(AY(AYYA(\f/w=I((>A(\XA(AYAYAYXf/iD(t$ D(l$0DYA(($D($($D(d$@D(T$`D(L$pD(\$PHİ[@SHP)t$@H)|$0(D)D$ Y=YWD(f.w WQ (((AYHWf.wQb(|$0D(D$ ^((t$@HP[@SHf.HD)$D(D)t$ D(<u ZmXfA/v"HAH X\Y YNLXfA/)$)$D)L$pD D)T$`D)\$PD)d$@D)l$0vA(A^AXxA(WY XAYAXf.w WQ (N(AXW(Xf.wQ+\A(AX^(YXAX^HCH D%$XAYHCD(H XDY(EXD^A\AYD-WEWA((\Y\fA/s|fD(^lAX\fA/sZHCH AY1HCD(H XDY(EXD^A\AYA((\Y\fA/rA(HC(H  Vf/D(l$0D(T$`D(L$p($vW5X .WAX(TAX3A\D(d$@fD/D(\$P($tYD($D(t$ Hİ[@SHP)t$@H)|$0(D)D$ D((A(Y |VYY(YYXWf.w WQ(H \EX(HAH A^YX((X^f/r( Y^((t$@(|$0D(D$ HP[HHW)|$ f.(zt@H )t$0HAH 5((\9WV^(2(t$0(|$ HHHHX D@UVWATAUAVAWH)pB)xA;D)@ED)HDLD)T$pAD)\$`ODA+fAnD;fnD)d$PD)l$@D)t$0D)|$ ED%gTDMɋEA+ƋHfEn^fnЍCfAnEYA(\DYYEXYfn^WAXf.wfDQ (D(WAF*AGWW*ȍCE(DY5LU*AYWDX5-U^Wf: ,+*蟿(CW*荿XA+W*v(EWXA+*[DYTXE;AEWWALEX$D*fA: EXfA/wD(D=jSEWDHGHHGD(HA\AYA^AXfD/wfA/sWAGf: ,W*襾(EW*蓾(AWXC*z(A+XWA*]X$\A(A\AY\nRf/s/A(\AYfA/A(Xf/;$L$A(sA({NE(C+E(KE;E(SE([MI[XE(cE(kD(t$0D(|$ IA_A^A]A\_^]%h%%R%H(MA8HI H(@SEHALALtA@McPLHcL#IcJHCHHCDtDH#LL3I[ffH; uHfuHH(t9t(tt H(vGH(IH(MH(H\$Ht$H|$ AVH HL3m؈D$@@=qatO HR$H C$8 u)Ut H"$H $  @2@u?HH8t$HtLƺIHL #A%3H\$0Ht$8H|$HH A^ùH\$WH0@ 3H\$@H0_ȉS؈D$ =Zu6g?3@ j룹4HHX L@PHHVWAVH@ILu9H3Bw@H@PHuX"؉\$0LƋI؉D$0LƋI ؉D$0u6u2L3IHHOHtL3I"tu >>> np.random.random_sample((5,)) array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428]) # random Three-by-two array of random numbers from [-5, 0): >>> 5 * np.random.random_sample((3, 2)) - 5 array([[-3.99149989, -0.52338984], # random [-2.99091858, -0.79479508], [-1.23204345, -1.75224494]]) random_sample random(size=None) Return random floats in the half-open interval [0.0, 1.0). Alias for `random_sample` to ease forward-porting to the new random API. random beta(a, b, size=None) Draw samples from a Beta distribution. The Beta distribution is a special case of the Dirichlet distribution, and is related to the Gamma distribution. It has the probability distribution function .. math:: f(x; a,b) = \frac{1}{B(\alpha, \beta)} x^{\alpha - 1} (1 - x)^{\beta - 1}, where the normalization, B, is the beta function, .. math:: B(\alpha, \beta) = \int_0^1 t^{\alpha - 1} (1 - t)^{\beta - 1} dt. It is often seen in Bayesian inference and order statistics. .. note:: New code should use the `~numpy.random.Generator.beta` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- a : float or array_like of floats Alpha, positive (>0). b : float or array_like of floats Beta, positive (>0). size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``a`` and ``b`` are both scalars. Otherwise, ``np.broadcast(a, b).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized beta distribution. See Also -------- random.Generator.beta: which should be used for new code. beta binomial(n, p, size=None) Draw samples from a binomial distribution. Samples are drawn from a binomial distribution with specified parameters, n trials and p probability of success where n an integer >= 0 and p is in the interval [0,1]. (n may be input as a float, but it is truncated to an integer in use) .. note:: New code should use the `~numpy.random.Generator.binomial` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- n : int or array_like of ints Parameter of the distribution, >= 0. Floats are also accepted, but they will be truncated to integers. p : float or array_like of floats Parameter of the distribution, >= 0 and <=1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``n`` and ``p`` are both scalars. Otherwise, ``np.broadcast(n, p).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized binomial distribution, where each sample is equal to the number of successes over the n trials. See Also -------- scipy.stats.binom : probability density function, distribution or cumulative density function, etc. random.Generator.binomial: which should be used for new code. Notes ----- The probability mass function (PMF) for the binomial distribution is .. math:: P(N) = \binom{n}{N}p^N(1-p)^{n-N}, where :math:`n` is the number of trials, :math:`p` is the probability of success, and :math:`N` is the number of successes. When estimating the standard error of a proportion in a population by using a random sample, the normal distribution works well unless the product p*n <=5, where p = population proportion estimate, and n = number of samples, in which case the binomial distribution is used instead. For example, a sample of 15 people shows 4 who are left handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4, so the binomial distribution should be used in this case. References ---------- .. [1] Dalgaard, Peter, "Introductory Statistics with R", Springer-Verlag, 2002. .. [2] Glantz, Stanton A. "Primer of Biostatistics.", McGraw-Hill, Fifth Edition, 2002. .. [3] Lentner, Marvin, "Elementary Applied Statistics", Bogden and Quigley, 1972. .. [4] Weisstein, Eric W. "Binomial Distribution." From MathWorld--A Wolfram Web Resource. https://mathworld.wolfram.com/BinomialDistribution.html .. [5] Wikipedia, "Binomial distribution", https://en.wikipedia.org/wiki/Binomial_distribution Examples -------- Draw samples from the distribution: >>> n, p = 10, .5 # number of trials, probability of each trial >>> s = np.random.binomial(n, p, 1000) # result of flipping a coin 10 times, tested 1000 times. A real world example. A company drills 9 wild-cat oil exploration wells, each with an estimated probability of success of 0.1. All nine wells fail. What is the probability of that happening? Let's do 20,000 trials of the model, and count the number that generate zero positive results. >>> sum(np.random.binomial(9, 0.1, 20000) == 0)/20000. # answer = 0.38885, or 38%. binomial exponential(scale=1.0, size=None) Draw samples from an exponential distribution. Its probability density function is .. math:: f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}), for ``x > 0`` and 0 elsewhere. :math:`\beta` is the scale parameter, which is the inverse of the rate parameter :math:`\lambda = 1/\beta`. The rate parameter is an alternative, widely used parameterization of the exponential distribution [3]_. The exponential distribution is a continuous analogue of the geometric distribution. It describes many common situations, such as the size of raindrops measured over many rainstorms [1]_, or the time between page requests to Wikipedia [2]_. .. note:: New code should use the `~numpy.random.Generator.exponential` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- scale : float or array_like of floats The scale parameter, :math:`\beta = 1/\lambda`. Must be non-negative. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``scale`` is a scalar. Otherwise, ``np.array(scale).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized exponential distribution. Examples -------- A real world example: Assume a company has 10000 customer support agents and the average time between customer calls is 4 minutes. >>> n = 10000 >>> time_between_calls = np.random.default_rng().exponential(scale=4, size=n) What is the probability that a customer will call in the next 4 to 5 minutes? >>> x = ((time_between_calls < 5).sum())/n >>> y = ((time_between_calls < 4).sum())/n >>> x-y 0.08 # may vary See Also -------- random.Generator.exponential: which should be used for new code. References ---------- .. [1] Peyton Z. Peebles Jr., "Probability, Random Variables and Random Signal Principles", 4th ed, 2001, p. 57. .. [2] Wikipedia, "Poisson process", https://en.wikipedia.org/wiki/Poisson_process .. [3] Wikipedia, "Exponential distribution", https://en.wikipedia.org/wiki/Exponential_distribution exponential standard_exponential(size=None) Draw samples from the standard exponential distribution. `standard_exponential` is identical to the exponential distribution with a scale parameter of 1. .. note:: New code should use the `~numpy.random.Generator.standard_exponential` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Default is None, in which case a single value is returned. Returns ------- out : float or ndarray Drawn samples. See Also -------- random.Generator.standard_exponential: which should be used for new code. Examples -------- Output a 3x8000 array: >>> n = np.random.standard_exponential((3, 8000)) standard_exponential tomaxint(size=None) Return a sample of uniformly distributed random integers in the interval [0, ``np.iinfo("long").max``]. .. warning:: This function uses the C-long dtype, which is 32bit on windows and otherwise 64bit on 64bit platforms (and 32bit on 32bit ones). Since NumPy 2.0, NumPy's default integer is 32bit on 32bit platforms and 64bit on 64bit platforms. Parameters ---------- size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Default is None, in which case a single value is returned. Returns ------- out : ndarray Drawn samples, with shape `size`. See Also -------- randint : Uniform sampling over a given half-open interval of integers. random_integers : Uniform sampling over a given closed interval of integers. Examples -------- >>> rs = np.random.RandomState() # need a RandomState object >>> rs.tomaxint((2,2,2)) array([[[1170048599, 1600360186], # random [ 739731006, 1947757578]], [[1871712945, 752307660], [1601631370, 1479324245]]]) >>> rs.tomaxint((2,2,2)) < np.iinfo(np.int_).max array([[[ True, True], [ True, True]], [[ True, True], [ True, True]]]) tomaxint randint(low, high=None, size=None, dtype=int) Return random integers from `low` (inclusive) to `high` (exclusive). Return random integers from the "discrete uniform" distribution of the specified dtype in the "half-open" interval [`low`, `high`). If `high` is None (the default), then results are from [0, `low`). .. note:: New code should use the `~numpy.random.Generator.integers` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- low : int or array-like of ints Lowest (signed) integers to be drawn from the distribution (unless ``high=None``, in which case this parameter is one above the *highest* such integer). high : int or array-like of ints, optional If provided, one above the largest (signed) integer to be drawn from the distribution (see above for behavior if ``high=None``). If array-like, must contain integer values size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Default is None, in which case a single value is returned. dtype : dtype, optional Desired dtype of the result. Byteorder must be native. The default value is long. .. warning:: This function defaults to the C-long dtype, which is 32bit on windows and otherwise 64bit on 64bit platforms (and 32bit on 32bit ones). Since NumPy 2.0, NumPy's default integer is 32bit on 32bit platforms and 64bit on 64bit platforms. Which corresponds to `np.intp`. (`dtype=int` is not the same as in most NumPy functions.) Returns ------- out : int or ndarray of ints `size`-shaped array of random integers from the appropriate distribution, or a single such random int if `size` not provided. See Also -------- random_integers : similar to `randint`, only for the closed interval [`low`, `high`], and 1 is the lowest value if `high` is omitted. random.Generator.integers: which should be used for new code. Examples -------- >>> np.random.randint(2, size=10) array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0]) # random >>> np.random.randint(1, size=10) array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) Generate a 2 x 4 array of ints between 0 and 4, inclusive: >>> np.random.randint(5, size=(2, 4)) array([[4, 0, 2, 1], # random [3, 2, 2, 0]]) Generate a 1 x 3 array with 3 different upper bounds >>> np.random.randint(1, [3, 5, 10]) array([2, 2, 9]) # random Generate a 1 by 3 array with 3 different lower bounds >>> np.random.randint([1, 5, 7], 10) array([9, 8, 7]) # random Generate a 2 by 4 array using broadcasting with dtype of uint8 >>> np.random.randint([1, 3, 5, 7], [[10], [20]], dtype=np.uint8) array([[ 8, 6, 9, 7], # random [ 1, 16, 9, 12]], dtype=uint8) randint negative_binomial(n, p, size=None) Draw samples from a negative binomial distribution. Samples are drawn from a negative binomial distribution with specified parameters, `n` successes and `p` probability of success where `n` is > 0 and `p` is in the interval [0, 1]. .. note:: New code should use the `~numpy.random.Generator.negative_binomial` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- n : float or array_like of floats Parameter of the distribution, > 0. p : float or array_like of floats Parameter of the distribution, >= 0 and <=1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``n`` and ``p`` are both scalars. Otherwise, ``np.broadcast(n, p).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized negative binomial distribution, where each sample is equal to N, the number of failures that occurred before a total of n successes was reached. .. warning:: This function returns the C-long dtype, which is 32bit on windows and otherwise 64bit on 64bit platforms (and 32bit on 32bit ones). Since NumPy 2.0, NumPy's default integer is 32bit on 32bit platforms and 64bit on 64bit platforms. See Also -------- random.Generator.negative_binomial: which should be used for new code. Notes ----- The probability mass function of the negative binomial distribution is .. math:: P(N;n,p) = \frac{\Gamma(N+n)}{N!\Gamma(n)}p^{n}(1-p)^{N}, where :math:`n` is the number of successes, :math:`p` is the probability of success, :math:`N+n` is the number of trials, and :math:`\Gamma` is the gamma function. When :math:`n` is an integer, :math:`\frac{\Gamma(N+n)}{N!\Gamma(n)} = \binom{N+n-1}{N}`, which is the more common form of this term in the pmf. The negative binomial distribution gives the probability of N failures given n successes, with a success on the last trial. If one throws a die repeatedly until the third time a "1" appears, then the probability distribution of the number of non-"1"s that appear before the third "1" is a negative binomial distribution. References ---------- .. [1] Weisstein, Eric W. "Negative Binomial Distribution." From MathWorld--A Wolfram Web Resource. https://mathworld.wolfram.com/NegativeBinomialDistribution.html .. [2] Wikipedia, "Negative binomial distribution", https://en.wikipedia.org/wiki/Negative_binomial_distribution Examples -------- Draw samples from the distribution: A real world example. A company drills wild-cat oil exploration wells, each with an estimated probability of success of 0.1. What is the probability of having one success for each successive well, that is what is the probability of a single success after drilling 5 wells, after 6 wells, etc.? >>> s = np.random.negative_binomial(1, 0.1, 100000) >>> for i in range(1, 11): # doctest: +SKIP ... probability = sum(s>> import numpy as np >>> s = np.random.poisson(5, 10000) Display histogram of the sample: >>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, 14, density=True) >>> plt.show() Draw each 100 values for lambda 100 and 500: >>> s = np.random.poisson(lam=(100., 500.), size=(100, 2)) poisson zipf(a, size=None) Draw samples from a Zipf distribution. Samples are drawn from a Zipf distribution with specified parameter `a` > 1. The Zipf distribution (also known as the zeta distribution) is a discrete probability distribution that satisfies Zipf's law: the frequency of an item is inversely proportional to its rank in a frequency table. .. note:: New code should use the `~numpy.random.Generator.zipf` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- a : float or array_like of floats Distribution parameter. Must be greater than 1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``a`` is a scalar. Otherwise, ``np.array(a).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized Zipf distribution. See Also -------- scipy.stats.zipf : probability density function, distribution, or cumulative density function, etc. random.Generator.zipf: which should be used for new code. Notes ----- The probability mass function (PMF) for the Zipf distribution is .. math:: p(k) = \frac{k^{-a}}{\zeta(a)}, for integers :math:`k \geq 1`, where :math:`\zeta` is the Riemann Zeta function. It is named for the American linguist George Kingsley Zipf, who noted that the frequency of any word in a sample of a language is inversely proportional to its rank in the frequency table. References ---------- .. [1] Zipf, G. K., "Selected Studies of the Principle of Relative Frequency in Language," Cambridge, MA: Harvard Univ. Press, 1932. Examples -------- Draw samples from the distribution: >>> a = 4.0 >>> n = 20000 >>> s = np.random.zipf(a, n) Display the histogram of the samples, along with the expected histogram based on the probability density function: >>> import matplotlib.pyplot as plt >>> from scipy.special import zeta # doctest: +SKIP `bincount` provides a fast histogram for small integers. >>> count = np.bincount(s) >>> k = np.arange(1, s.max() + 1) >>> plt.bar(k, count[1:], alpha=0.5, label='sample count') >>> plt.plot(k, n*(k**-a)/zeta(a), 'k.-', alpha=0.5, ... label='expected count') # doctest: +SKIP >>> plt.semilogy() >>> plt.grid(alpha=0.4) >>> plt.legend() >>> plt.title(f'Zipf sample, a={a}, size={n}') >>> plt.show() zipf geometric(p, size=None) Draw samples from the geometric distribution. Bernoulli trials are experiments with one of two outcomes: success or failure (an example of such an experiment is flipping a coin). The geometric distribution models the number of trials that must be run in order to achieve success. It is therefore supported on the positive integers, ``k = 1, 2, ...``. The probability mass function of the geometric distribution is .. math:: f(k) = (1 - p)^{k - 1} p where `p` is the probability of success of an individual trial. .. note:: New code should use the `~numpy.random.Generator.geometric` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- p : float or array_like of floats The probability of success of an individual trial. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``p`` is a scalar. Otherwise, ``np.array(p).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized geometric distribution. See Also -------- random.Generator.geometric: which should be used for new code. Examples -------- Draw ten thousand values from the geometric distribution, with the probability of an individual success equal to 0.35: >>> z = np.random.geometric(p=0.35, size=10000) How many trials succeeded after a single run? >>> (z == 1).sum() / 10000. 0.34889999999999999 #random geometric hypergeometric(ngood, nbad, nsample, size=None) Draw samples from a Hypergeometric distribution. Samples are drawn from a hypergeometric distribution with specified parameters, `ngood` (ways to make a good selection), `nbad` (ways to make a bad selection), and `nsample` (number of items sampled, which is less than or equal to the sum ``ngood + nbad``). .. note:: New code should use the `~numpy.random.Generator.hypergeometric` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- ngood : int or array_like of ints Number of ways to make a good selection. Must be nonnegative. nbad : int or array_like of ints Number of ways to make a bad selection. Must be nonnegative. nsample : int or array_like of ints Number of items sampled. Must be at least 1 and at most ``ngood + nbad``. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if `ngood`, `nbad`, and `nsample` are all scalars. Otherwise, ``np.broadcast(ngood, nbad, nsample).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized hypergeometric distribution. Each sample is the number of good items within a randomly selected subset of size `nsample` taken from a set of `ngood` good items and `nbad` bad items. See Also -------- scipy.stats.hypergeom : probability density function, distribution or cumulative density function, etc. random.Generator.hypergeometric: which should be used for new code. Notes ----- The probability mass function (PMF) for the Hypergeometric distribution is .. math:: P(x) = \frac{\binom{g}{x}\binom{b}{n-x}}{\binom{g+b}{n}}, where :math:`0 \le x \le n` and :math:`n-b \le x \le g` for P(x) the probability of ``x`` good results in the drawn sample, g = `ngood`, b = `nbad`, and n = `nsample`. Consider an urn with black and white marbles in it, `ngood` of them are black and `nbad` are white. If you draw `nsample` balls without replacement, then the hypergeometric distribution describes the distribution of black balls in the drawn sample. Note that this distribution is very similar to the binomial distribution, except that in this case, samples are drawn without replacement, whereas in the Binomial case samples are drawn with replacement (or the sample space is infinite). As the sample space becomes large, this distribution approaches the binomial. References ---------- .. [1] Lentner, Marvin, "Elementary Applied Statistics", Bogden and Quigley, 1972. .. [2] Weisstein, Eric W. "Hypergeometric Distribution." From MathWorld--A Wolfram Web Resource. https://mathworld.wolfram.com/HypergeometricDistribution.html .. [3] Wikipedia, "Hypergeometric distribution", https://en.wikipedia.org/wiki/Hypergeometric_distribution Examples -------- Draw samples from the distribution: >>> ngood, nbad, nsamp = 100, 2, 10 # number of good, number of bad, and number of samples >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000) >>> from matplotlib.pyplot import hist >>> hist(s) # note that it is very unlikely to grab both bad items Suppose you have an urn with 15 white and 15 black marbles. If you pull 15 marbles at random, how likely is it that 12 or more of them are one color? >>> s = np.random.hypergeometric(15, 15, 15, 100000) >>> sum(s>=12)/100000. + sum(s<=3)/100000. # answer = 0.003 ... pretty unlikely! hypergeometric bytes(length) Return random bytes. .. note:: New code should use the `~numpy.random.Generator.bytes` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- length : int Number of random bytes. Returns ------- out : bytes String of length `length`. See Also -------- random.Generator.bytes: which should be used for new code. Examples -------- >>> np.random.bytes(10) b' eh\x85\x022SZ\xbf\xa4' #random bytes choice(a, size=None, replace=True, p=None) Generates a random sample from a given 1-D array .. note:: New code should use the `~numpy.random.Generator.choice` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. .. warning:: This function uses the C-long dtype, which is 32bit on windows and otherwise 64bit on 64bit platforms (and 32bit on 32bit ones). Since NumPy 2.0, NumPy's default integer is 32bit on 32bit platforms and 64bit on 64bit platforms. Parameters ---------- a : 1-D array-like or int If an ndarray, a random sample is generated from its elements. If an int, the random sample is generated as if it were ``np.arange(a)`` size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Default is None, in which case a single value is returned. replace : boolean, optional Whether the sample is with or without replacement. Default is True, meaning that a value of ``a`` can be selected multiple times. p : 1-D array-like, optional The probabilities associated with each entry in a. If not given, the sample assumes a uniform distribution over all entries in ``a``. Returns ------- samples : single item or ndarray The generated random samples Raises ------ ValueError If a is an int and less than zero, if a or p are not 1-dimensional, if a is an array-like of size 0, if p is not a vector of probabilities, if a and p have different lengths, or if replace=False and the sample size is greater than the population size See Also -------- randint, shuffle, permutation random.Generator.choice: which should be used in new code Notes ----- Setting user-specified probabilities through ``p`` uses a more general but less efficient sampler than the default. The general sampler produces a different sample than the optimized sampler even if each element of ``p`` is 1 / len(a). Sampling random rows from a 2-D array is not possible with this function, but is possible with `Generator.choice` through its ``axis`` keyword. Examples -------- Generate a uniform random sample from np.arange(5) of size 3: >>> np.random.choice(5, 3) array([0, 3, 4]) # random >>> #This is equivalent to np.random.randint(0,5,3) Generate a non-uniform random sample from np.arange(5) of size 3: >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0]) array([3, 3, 0]) # random Generate a uniform random sample from np.arange(5) of size 3 without replacement: >>> np.random.choice(5, 3, replace=False) array([3,1,0]) # random >>> #This is equivalent to np.random.permutation(np.arange(5))[:3] Generate a non-uniform random sample from np.arange(5) of size 3 without replacement: >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0]) array([2, 3, 0]) # random Any of the above can be repeated with an arbitrary array-like instead of just integers. For instance: >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher'] >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3]) array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], # random dtype='>> a = .6 >>> s = np.random.logseries(a, 10000) >>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s) # plot against distribution >>> def logseries(k, p): ... return -p**k/(k*np.log(1-p)) >>> plt.plot(bins, logseries(bins, a)*count.max()/ ... logseries(bins, a).max(), 'r') >>> plt.show() logseries multivariate_normal(mean, cov, size=None, check_valid='warn', tol=1e-8) Draw random samples from a multivariate normal distribution. The multivariate normal, multinormal or Gaussian distribution is a generalization of the one-dimensional normal distribution to higher dimensions. Such a distribution is specified by its mean and covariance matrix. These parameters are analogous to the mean (average or "center") and variance (standard deviation, or "width," squared) of the one-dimensional normal distribution. .. note:: New code should use the `~numpy.random.Generator.multivariate_normal` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- mean : 1-D array_like, of length N Mean of the N-dimensional distribution. cov : 2-D array_like, of shape (N, N) Covariance matrix of the distribution. It must be symmetric and positive-semidefinite for proper sampling. size : int or tuple of ints, optional Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are generated, and packed in an `m`-by-`n`-by-`k` arrangement. Because each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``. If no shape is specified, a single (`N`-D) sample is returned. check_valid : { 'warn', 'raise', 'ignore' }, optional Behavior when the covariance matrix is not positive semidefinite. tol : float, optional Tolerance when checking the singular values in covariance matrix. cov is cast to double before the check. Returns ------- out : ndarray The drawn samples, of shape *size*, if that was provided. If not, the shape is ``(N,)``. In other words, each entry ``out[i,j,...,:]`` is an N-dimensional value drawn from the distribution. See Also -------- random.Generator.multivariate_normal: which should be used for new code. Notes ----- The mean is a coordinate in N-dimensional space, which represents the location where samples are most likely to be generated. This is analogous to the peak of the bell curve for the one-dimensional or univariate normal distribution. Covariance indicates the level to which two variables vary together. From the multivariate normal distribution, we draw N-dimensional samples, :math:`X = [x_1, x_2, ... x_N]`. The covariance matrix element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`. The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its "spread"). Instead of specifying the full covariance matrix, popular approximations include: - Spherical covariance (`cov` is a multiple of the identity matrix) - Diagonal covariance (`cov` has non-negative elements, and only on the diagonal) This geometrical property can be seen in two dimensions by plotting generated data-points: >>> mean = [0, 0] >>> cov = [[1, 0], [0, 100]] # diagonal covariance Diagonal covariance means that the variables are independent, and the probability density contours have their axes aligned with the coordinate axes: >>> import matplotlib.pyplot as plt >>> x, y = np.random.multivariate_normal(mean, cov, 5000).T >>> plt.plot(x, y, 'x') >>> plt.axis('equal') >>> plt.show() Note that the covariance matrix must be positive semidefinite (a.k.a. nonnegative-definite). Otherwise, the behavior of this method is undefined and backwards compatibility is not guaranteed. References ---------- .. [1] Papoulis, A., "Probability, Random Variables, and Stochastic Processes," 3rd ed., New York: McGraw-Hill, 1991. .. [2] Duda, R. O., Hart, P. E., and Stork, D. G., "Pattern Classification," 2nd ed., New York: Wiley, 2001. Examples -------- >>> mean = (1, 2) >>> cov = [[1, 0], [0, 1]] >>> x = np.random.multivariate_normal(mean, cov, (3, 3)) >>> x.shape (3, 3, 2) Here we generate 800 samples from the bivariate normal distribution with mean [0, 0] and covariance matrix [[6, -3], [-3, 3.5]]. The expected variances of the first and second components of the sample are 6 and 3.5, respectively, and the expected correlation coefficient is -3/sqrt(6*3.5) ≈ -0.65465. >>> cov = np.array([[6, -3], [-3, 3.5]]) >>> pts = np.random.multivariate_normal([0, 0], cov, size=800) Check that the mean, covariance, and correlation coefficient of the sample are close to the expected values: >>> pts.mean(axis=0) array([ 0.0326911 , -0.01280782]) # may vary >>> np.cov(pts.T) array([[ 5.96202397, -2.85602287], [-2.85602287, 3.47613949]]) # may vary >>> np.corrcoef(pts.T)[0, 1] -0.6273591314603949 # may vary We can visualize this data with a scatter plot. The orientation of the point cloud illustrates the negative correlation of the components of this sample. >>> import matplotlib.pyplot as plt >>> plt.plot(pts[:, 0], pts[:, 1], '.', alpha=0.5) >>> plt.axis('equal') >>> plt.grid() >>> plt.show() multivariate_normal multinomial(n, pvals, size=None) Draw samples from a multinomial distribution. The multinomial distribution is a multivariate generalization of the binomial distribution. Take an experiment with one of ``p`` possible outcomes. An example of such an experiment is throwing a dice, where the outcome can be 1 through 6. Each sample drawn from the distribution represents `n` such experiments. Its values, ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome was ``i``. .. note:: New code should use the `~numpy.random.Generator.multinomial` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. .. warning:: This function defaults to the C-long dtype, which is 32bit on windows and otherwise 64bit on 64bit platforms (and 32bit on 32bit ones). Since NumPy 2.0, NumPy's default integer is 32bit on 32bit platforms and 64bit on 64bit platforms. Parameters ---------- n : int Number of experiments. pvals : sequence of floats, length p Probabilities of each of the ``p`` different outcomes. These must sum to 1 (however, the last element is always assumed to account for the remaining probability, as long as ``sum(pvals[:-1]) <= 1)``. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Default is None, in which case a single value is returned. Returns ------- out : ndarray The drawn samples, of shape *size*, if that was provided. If not, the shape is ``(N,)``. In other words, each entry ``out[i,j,...,:]`` is an N-dimensional value drawn from the distribution. See Also -------- random.Generator.multinomial: which should be used for new code. Examples -------- Throw a dice 20 times: >>> np.random.multinomial(20, [1/6.]*6, size=1) array([[4, 1, 7, 5, 2, 1]]) # random It landed 4 times on 1, once on 2, etc. Now, throw the dice 20 times, and 20 times again: >>> np.random.multinomial(20, [1/6.]*6, size=2) array([[3, 4, 3, 3, 4, 3], # random [2, 4, 3, 4, 0, 7]]) For the first run, we threw 3 times 1, 4 times 2, etc. For the second, we threw 2 times 1, 4 times 2, etc. A loaded die is more likely to land on number 6: >>> np.random.multinomial(100, [1/7.]*5 + [2/7.]) array([11, 16, 14, 17, 16, 26]) # random The probability inputs should be normalized. As an implementation detail, the value of the last entry is ignored and assumed to take up any leftover probability mass, but this should not be relied on. A biased coin which has twice as much weight on one side as on the other should be sampled like so: >>> np.random.multinomial(100, [1.0 / 3, 2.0 / 3]) # RIGHT array([38, 62]) # random not like: >>> np.random.multinomial(100, [1.0, 2.0]) # WRONG Traceback (most recent call last): ValueError: pvals < 0, pvals > 1 or pvals contains NaNs multinomial dirichlet(alpha, size=None) Draw samples from the Dirichlet distribution. Draw `size` samples of dimension k from a Dirichlet distribution. A Dirichlet-distributed random variable can be seen as a multivariate generalization of a Beta distribution. The Dirichlet distribution is a conjugate prior of a multinomial distribution in Bayesian inference. .. note:: New code should use the `~numpy.random.Generator.dirichlet` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- alpha : sequence of floats, length k Parameter of the distribution (length ``k`` for sample of length ``k``). size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n)``, then ``m * n * k`` samples are drawn. Default is None, in which case a vector of length ``k`` is returned. Returns ------- samples : ndarray, The drawn samples, of shape ``(size, k)``. Raises ------ ValueError If any value in ``alpha`` is less than or equal to zero See Also -------- random.Generator.dirichlet: which should be used for new code. Notes ----- The Dirichlet distribution is a distribution over vectors :math:`x` that fulfil the conditions :math:`x_i>0` and :math:`\sum_{i=1}^k x_i = 1`. The probability density function :math:`p` of a Dirichlet-distributed random vector :math:`X` is proportional to .. math:: p(x) \propto \prod_{i=1}^{k}{x^{\alpha_i-1}_i}, where :math:`\alpha` is a vector containing the positive concentration parameters. The method uses the following property for computation: let :math:`Y` be a random vector which has components that follow a standard gamma distribution, then :math:`X = \frac{1}{\sum_{i=1}^k{Y_i}} Y` is Dirichlet-distributed References ---------- .. [1] David McKay, "Information Theory, Inference and Learning Algorithms," chapter 23, https://www.inference.org.uk/mackay/itila/ .. [2] Wikipedia, "Dirichlet distribution", https://en.wikipedia.org/wiki/Dirichlet_distribution Examples -------- Taking an example cited in Wikipedia, this distribution can be used if one wanted to cut strings (each of initial length 1.0) into K pieces with different lengths, where each piece had, on average, a designated average length, but allowing some variation in the relative sizes of the pieces. >>> s = np.random.dirichlet((10, 5, 3), 20).transpose() >>> import matplotlib.pyplot as plt >>> plt.barh(range(20), s[0]) >>> plt.barh(range(20), s[1], left=s[0], color='g') >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r') >>> plt.title("Lengths of Strings") dirichlet(@`xnumpy/random/mtrand.pyxcpython/contextvars.pxdnumpy/__init__.cython-30.pxdcpython/type.pxdcpython/bool.pxdcpython/complex.pxdnumpy/random/bit_generator.pxd uniform(low=0.0, high=1.0, size=None) Draw samples from a uniform distribution. Samples are uniformly distributed over the half-open interval ``[low, high)`` (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by `uniform`. .. note:: New code should use the `~numpy.random.Generator.uniform` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- low : float or array_like of floats, optional Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0. high : float or array_like of floats Upper boundary of the output interval. All values generated will be less than or equal to high. The high limit may be included in the returned array of floats due to floating-point rounding in the equation ``low + (high-low) * random_sample()``. The default value is 1.0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``low`` and ``high`` are both scalars. Otherwise, ``np.broadcast(low, high).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized uniform distribution. See Also -------- randint : Discrete uniform distribution, yielding integers. random_integers : Discrete uniform distribution over the closed interval ``[low, high]``. random_sample : Floats uniformly distributed over ``[0, 1)``. random : Alias for `random_sample`. rand : Convenience function that accepts dimensions as input, e.g., ``rand(2,2)`` would generate a 2-by-2 array of floats, uniformly distributed over ``[0, 1)``. random.Generator.uniform: which should be used for new code. Notes ----- The probability density function of the uniform distribution is .. math:: p(x) = \frac{1}{b - a} anywhere within the interval ``[a, b)``, and zero elsewhere. When ``high`` == ``low``, values of ``low`` will be returned. If ``high`` < ``low``, the results are officially undefined and may eventually raise an error, i.e. do not rely on this function to behave when passed arguments satisfying that inequality condition. The ``high`` limit may be included in the returned array of floats due to floating-point rounding in the equation ``low + (high-low) * random_sample()``. For example: >>> x = np.float32(5*0.99999999) >>> x np.float32(5.0) Examples -------- Draw samples from the distribution: >>> s = np.random.uniform(-1,0,1000) All values are within the given interval: >>> np.all(s >= -1) True >>> np.all(s < 0) True Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, 15, density=True) >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r') >>> plt.show() uniform__module___cython_3_2_4._common_types_metatype shuffle(x) Modify a sequence in-place by shuffling its contents. This function only shuffles the array along the first axis of a multi-dimensional array. The order of sub-arrays is changed but their contents remains the same. .. note:: New code should use the `~numpy.random.Generator.shuffle` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- x : ndarray or MutableSequence The array, list or mutable sequence to be shuffled. Returns ------- None See Also -------- random.Generator.shuffle: which should be used for new code. Examples -------- >>> arr = np.arange(10) >>> np.random.shuffle(arr) >>> arr [1 7 5 2 9 4 3 6 0 8] # random Multi-dimensional arrays are only shuffled along the first axis: >>> arr = np.arange(9).reshape((3, 3)) >>> np.random.shuffle(arr) >>> arr array([[3, 4, 5], # random [6, 7, 8], [0, 1, 2]]) rand(d0, d1, ..., dn) Random values in a given shape. .. note:: This is a convenience function for users porting code from Matlab, and wraps `random_sample`. That function takes a tuple to specify the size of the output, which is consistent with other NumPy functions like `numpy.zeros` and `numpy.ones`. Create an array of the given shape and populate it with random samples from a uniform distribution over ``[0, 1)``. Parameters ---------- d0, d1, ..., dn : int, optional The dimensions of the returned array, must be non-negative. If no argument is given a single Python float is returned. Returns ------- out : ndarray, shape ``(d0, d1, ..., dn)`` Random values. See Also -------- random Examples -------- >>> np.random.rand(3,2) array([[ 0.14022471, 0.96360618], #random [ 0.37601032, 0.25528411], #random [ 0.49313049, 0.94909878]]) #random shufflerand randn(d0, d1, ..., dn) Return a sample (or samples) from the "standard normal" distribution. .. note:: This is a convenience function for users porting code from Matlab, and wraps `standard_normal`. That function takes a tuple to specify the size of the output, which is consistent with other NumPy functions like `numpy.zeros` and `numpy.ones`. .. note:: New code should use the `~numpy.random.Generator.standard_normal` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. If positive int_like arguments are provided, `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled with random floats sampled from a univariate "normal" (Gaussian) distribution of mean 0 and variance 1. A single float randomly sampled from the distribution is returned if no argument is provided. Parameters ---------- d0, d1, ..., dn : int, optional The dimensions of the returned array, must be non-negative. If no argument is given a single Python float is returned. Returns ------- Z : ndarray or float A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from the standard normal distribution, or a single such float if no parameters were supplied. See Also -------- standard_normal : Similar, but takes a tuple as its argument. normal : Also accepts mu and sigma arguments. random.Generator.standard_normal: which should be used for new code. Notes ----- For random samples from the normal distribution with mean ``mu`` and standard deviation ``sigma``, use:: sigma * np.random.randn(...) + mu Examples -------- >>> np.random.randn() 2.1923875335537315 # random Two-by-four array of samples from the normal distribution with mean 3 and standard deviation 2.5: >>> 3 + 2.5 * np.random.randn(2, 4) array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random randnfunc_doc__doc__func_name__name____qualname__func_dict__dict__func_globals__globals__func_closure__closure__func_code__code__func_defaults__defaults____kwdefaults____annotations___is_coroutine__dictoffset____vectorcalloffset____weaklistoffset__ random_integers(low, high=None, size=None) Random integers of type `numpy.int_` between `low` and `high`, inclusive. Return random integers of type `numpy.int_` from the "discrete uniform" distribution in the closed interval [`low`, `high`]. If `high` is None (the default), then results are from [1, `low`]. The `numpy.int_` type translates to the C long integer type and its precision is platform dependent. This function has been deprecated. Use randint instead. .. deprecated:: 1.11.0 Parameters ---------- low : int Lowest (signed) integer to be drawn from the distribution (unless ``high=None``, in which case this parameter is the *highest* such integer). high : int, optional If provided, the largest (signed) integer to be drawn from the distribution (see above for behavior if ``high=None``). size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Default is None, in which case a single value is returned. Returns ------- out : int or ndarray of ints `size`-shaped array of random integers from the appropriate distribution, or a single such random int if `size` not provided. See Also -------- randint : Similar to `random_integers`, only for the half-open interval [`low`, `high`), and 0 is the lowest value if `high` is omitted. Notes ----- To sample from N evenly spaced floating-point numbers between a and b, use:: a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.) Examples -------- >>> np.random.random_integers(5) 4 # random >>> type(np.random.random_integers(5)) >>> np.random.random_integers(5, size=(3,2)) array([[5, 4], # random [3, 3], [4, 5]]) Choose five random numbers from the set of five evenly-spaced numbers between 0 and 2.5, inclusive (*i.e.*, from the set :math:`{0, 5/8, 10/8, 15/8, 20/8}`): >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4. array([ 0.625, 1.25 , 0.625, 0.625, 2.5 ]) # random Roll two six sided dice 1000 times and sum the results: >>> d1 = np.random.random_integers(1, 6, 1000) >>> d2 = np.random.random_integers(1, 6, 1000) >>> dsums = d1 + d2 Display results as a histogram: >>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(dsums, 11, density=True) >>> plt.show() random_integers standard_normal(size=None) Draw samples from a standard Normal distribution (mean=0, stdev=1). .. note:: New code should use the `~numpy.random.Generator.standard_normal` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Default is None, in which case a single value is returned. Returns ------- out : float or ndarray A floating-point array of shape ``size`` of drawn samples, or a single sample if ``size`` was not specified. See Also -------- normal : Equivalent function with additional ``loc`` and ``scale`` arguments for setting the mean and standard deviation. random.Generator.standard_normal: which should be used for new code. Notes ----- For random samples from the normal distribution with mean ``mu`` and standard deviation ``sigma``, use one of:: mu + sigma * np.random.standard_normal(size=...) np.random.normal(mu, sigma, size=...) Examples -------- >>> np.random.standard_normal() 2.1923875335537315 #random >>> s = np.random.standard_normal(8000) >>> s array([ 0.6888893 , 0.78096262, -0.89086505, ..., 0.49876311, # random -0.38672696, -0.4685006 ]) # random >>> s.shape (8000,) >>> s = np.random.standard_normal(size=(3, 4, 2)) >>> s.shape (3, 4, 2) Two-by-four array of samples from the normal distribution with mean 3 and standard deviation 2.5: >>> 3 + 2.5 * np.random.standard_normal(size=(2, 4)) array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random standard_normalBitGenerator_cython_3_2_4.cython_function_or_method normal(loc=0.0, scale=1.0, size=None) Draw random samples from a normal (Gaussian) distribution. The probability density function of the normal distribution, first derived by De Moivre and 200 years later by both Gauss and Laplace independently [2]_, is often called the bell curve because of its characteristic shape (see the example below). The normal distributions occurs often in nature. For example, it describes the commonly occurring distribution of samples influenced by a large number of tiny, random disturbances, each with its own unique distribution [2]_. .. note:: New code should use the `~numpy.random.Generator.normal` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- loc : float or array_like of floats Mean ("centre") of the distribution. scale : float or array_like of floats Standard deviation (spread or "width") of the distribution. Must be non-negative. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``loc`` and ``scale`` are both scalars. Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized normal distribution. See Also -------- scipy.stats.norm : probability density function, distribution or cumulative density function, etc. random.Generator.normal: which should be used for new code. Notes ----- The probability density for the Gaussian distribution is .. math:: p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }} e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} }, where :math:`\mu` is the mean and :math:`\sigma` the standard deviation. The square of the standard deviation, :math:`\sigma^2`, is called the variance. The function has its peak at the mean, and its "spread" increases with the standard deviation (the function reaches 0.607 times its maximum at :math:`x + \sigma` and :math:`x - \sigma` [2]_). This implies that normal is more likely to return samples lying close to the mean, rather than those far away. References ---------- .. [1] Wikipedia, "Normal distribution", https://en.wikipedia.org/wiki/Normal_distribution .. [2] P. R. Peebles Jr., "Central Limit Theorem" in "Probability, Random Variables and Random Signal Principles", 4th ed., 2001, pp. 51, 51, 125. Examples -------- Draw samples from the distribution: >>> mu, sigma = 0, 0.1 # mean and standard deviation >>> s = np.random.normal(mu, sigma, 1000) Verify the mean and the standard deviation: >>> abs(mu - np.mean(s)) 0.0 # may vary >>> abs(sigma - np.std(s, ddof=1)) 0.0 # may vary Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, 30, density=True) >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) * ... np.exp( - (bins - mu)**2 / (2 * sigma**2) ), ... linewidth=2, color='r') >>> plt.show() Two-by-four array of samples from the normal distribution with mean 3 and standard deviation 2.5: >>> np.random.normal(3, 2.5, size=(2, 4)) array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random normal standard_gamma(shape, size=None) Draw samples from a standard Gamma distribution. Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated "k") and scale=1. .. note:: New code should use the `~numpy.random.Generator.standard_gamma` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- shape : float or array_like of floats Parameter, must be non-negative. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``shape`` is a scalar. Otherwise, ``np.array(shape).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized standard gamma distribution. See Also -------- scipy.stats.gamma : probability density function, distribution or cumulative density function, etc. random.Generator.standard_gamma: which should be used for new code. Notes ----- The probability density for the Gamma distribution is .. math:: p(x) = x^{k-1}\frac{e^{-x/\theta}}{\theta^k\Gamma(k)}, where :math:`k` is the shape and :math:`\theta` the scale, and :math:`\Gamma` is the Gamma function. The Gamma distribution is often used to model the times to failure of electronic components, and arises naturally in processes for which the waiting times between Poisson distributed events are relevant. References ---------- .. [1] Weisstein, Eric W. "Gamma Distribution." From MathWorld--A Wolfram Web Resource. https://mathworld.wolfram.com/GammaDistribution.html .. [2] Wikipedia, "Gamma distribution", https://en.wikipedia.org/wiki/Gamma_distribution Examples -------- Draw samples from the distribution: >>> shape, scale = 2., 1. # mean and width >>> s = np.random.standard_gamma(shape, 1000000) Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt >>> import scipy.special as sps # doctest: +SKIP >>> count, bins, ignored = plt.hist(s, 50, density=True) >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ # doctest: +SKIP ... (sps.gamma(shape) * scale**shape)) >>> plt.plot(bins, y, linewidth=2, color='r') # doctest: +SKIP >>> plt.show() standard_gamma gamma(shape, scale=1.0, size=None) Draw samples from a Gamma distribution. Samples are drawn from a Gamma distribution with specified parameters, `shape` (sometimes designated "k") and `scale` (sometimes designated "theta"), where both parameters are > 0. .. note:: New code should use the `~numpy.random.Generator.gamma` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- shape : float or array_like of floats The shape of the gamma distribution. Must be non-negative. scale : float or array_like of floats, optional The scale of the gamma distribution. Must be non-negative. Default is equal to 1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``shape`` and ``scale`` are both scalars. Otherwise, ``np.broadcast(shape, scale).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized gamma distribution. See Also -------- scipy.stats.gamma : probability density function, distribution or cumulative density function, etc. random.Generator.gamma: which should be used for new code. Notes ----- The probability density for the Gamma distribution is .. math:: p(x) = x^{k-1}\frac{e^{-x/\theta}}{\theta^k\Gamma(k)}, where :math:`k` is the shape and :math:`\theta` the scale, and :math:`\Gamma` is the Gamma function. The Gamma distribution is often used to model the times to failure of electronic components, and arises naturally in processes for which the waiting times between Poisson distributed events are relevant. References ---------- .. [1] Weisstein, Eric W. "Gamma Distribution." From MathWorld--A Wolfram Web Resource. https://mathworld.wolfram.com/GammaDistribution.html .. [2] Wikipedia, "Gamma distribution", https://en.wikipedia.org/wiki/Gamma_distribution Examples -------- Draw samples from the distribution: >>> shape, scale = 2., 2. # mean=4, std=2*sqrt(2) >>> s = np.random.gamma(shape, scale, 1000) Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt >>> import scipy.special as sps # doctest: +SKIP >>> count, bins, ignored = plt.hist(s, 50, density=True) >>> y = bins**(shape-1)*(np.exp(-bins/scale) / # doctest: +SKIP ... (sps.gamma(shape)*scale**shape)) >>> plt.plot(bins, y, linewidth=2, color='r') # doctest: +SKIP >>> plt.show() gamma f(dfnum, dfden, size=None) Draw samples from an F distribution. Samples are drawn from an F distribution with specified parameters, `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom in denominator), where both parameters must be greater than zero. The random variate of the F distribution (also known as the Fisher distribution) is a continuous probability distribution that arises in ANOVA tests, and is the ratio of two chi-square variates. .. note:: New code should use the `~numpy.random.Generator.f` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- dfnum : float or array_like of floats Degrees of freedom in numerator, must be > 0. dfden : float or array_like of float Degrees of freedom in denominator, must be > 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``dfnum`` and ``dfden`` are both scalars. Otherwise, ``np.broadcast(dfnum, dfden).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized Fisher distribution. See Also -------- scipy.stats.f : probability density function, distribution or cumulative density function, etc. random.Generator.f: which should be used for new code. Notes ----- The F statistic is used to compare in-group variances to between-group variances. Calculating the distribution depends on the sampling, and so it is a function of the respective degrees of freedom in the problem. The variable `dfnum` is the number of samples minus one, the between-groups degrees of freedom, while `dfden` is the within-groups degrees of freedom, the sum of the number of samples in each group minus the number of groups. References ---------- .. [1] Glantz, Stanton A. "Primer of Biostatistics.", McGraw-Hill, Fifth Edition, 2002. .. [2] Wikipedia, "F-distribution", https://en.wikipedia.org/wiki/F-distribution Examples -------- An example from Glantz[1], pp 47-40: Two groups, children of diabetics (25 people) and children from people without diabetes (25 controls). Fasting blood glucose was measured, case group had a mean value of 86.1, controls had a mean value of 82.2. Standard deviations were 2.09 and 2.49 respectively. Are these data consistent with the null hypothesis that the parents diabetic status does not affect their children's blood glucose levels? Calculating the F statistic from the data gives a value of 36.01. Draw samples from the distribution: >>> dfnum = 1. # between group degrees of freedom >>> dfden = 48. # within groups degrees of freedom >>> s = np.random.f(dfnum, dfden, 1000) The lower bound for the top 1% of the samples is : >>> np.sort(s)[-10] 7.61988120985 # random So there is about a 1% chance that the F statistic will exceed 7.62, the measured value is 36, so the null hypothesis is rejected at the 1% level. permutation(x) Randomly permute a sequence, or return a permuted range. If `x` is a multi-dimensional array, it is only shuffled along its first index. .. note:: New code should use the `~numpy.random.Generator.permutation` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- x : int or array_like If `x` is an integer, randomly permute ``np.arange(x)``. If `x` is an array, make a copy and shuffle the elements randomly. Returns ------- out : ndarray Permuted sequence or array range. See Also -------- random.Generator.permutation: which should be used for new code. Examples -------- >>> np.random.permutation(10) array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6]) # random >>> np.random.permutation([1, 4, 9, 12, 15]) array([15, 1, 9, 4, 12]) # random >>> arr = np.arange(9).reshape((3, 3)) >>> np.random.permutation(arr) array([[6, 7, 8], # random [0, 1, 2], [3, 4, 5]]) fpermutation noncentral_f(dfnum, dfden, nonc, size=None) Draw samples from the noncentral F distribution. Samples are drawn from an F distribution with specified parameters, `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom in denominator), where both parameters > 1. `nonc` is the non-centrality parameter. .. note:: New code should use the `~numpy.random.Generator.noncentral_f` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- dfnum : float or array_like of floats Numerator degrees of freedom, must be > 0. dfden : float or array_like of floats Denominator degrees of freedom, must be > 0. nonc : float or array_like of floats Non-centrality parameter, the sum of the squares of the numerator means, must be >= 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``dfnum``, ``dfden``, and ``nonc`` are all scalars. Otherwise, ``np.broadcast(dfnum, dfden, nonc).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized noncentral Fisher distribution. See Also -------- random.Generator.noncentral_f: which should be used for new code. Notes ----- When calculating the power of an experiment (power = probability of rejecting the null hypothesis when a specific alternative is true) the non-central F statistic becomes important. When the null hypothesis is true, the F statistic follows a central F distribution. When the null hypothesis is not true, then it follows a non-central F statistic. References ---------- .. [1] Weisstein, Eric W. "Noncentral F-Distribution." From MathWorld--A Wolfram Web Resource. https://mathworld.wolfram.com/NoncentralF-Distribution.html .. [2] Wikipedia, "Noncentral F-distribution", https://en.wikipedia.org/wiki/Noncentral_F-distribution Examples -------- In a study, testing for a specific alternative to the null hypothesis requires use of the Noncentral F distribution. We need to calculate the area in the tail of the distribution that exceeds the value of the F distribution for the null hypothesis. We'll plot the two probability distributions for comparison. >>> dfnum = 3 # between group deg of freedom >>> dfden = 20 # within groups degrees of freedom >>> nonc = 3.0 >>> nc_vals = np.random.noncentral_f(dfnum, dfden, nonc, 1000000) >>> NF = np.histogram(nc_vals, bins=50, density=True) >>> c_vals = np.random.f(dfnum, dfden, 1000000) >>> F = np.histogram(c_vals, bins=50, density=True) >>> import matplotlib.pyplot as plt >>> plt.plot(F[1][1:], F[0]) >>> plt.plot(NF[1][1:], NF[0]) >>> plt.show() noncentral_f chisquare(df, size=None) Draw samples from a chi-square distribution. When `df` independent random variables, each with standard normal distributions (mean 0, variance 1), are squared and summed, the resulting distribution is chi-square (see Notes). This distribution is often used in hypothesis testing. .. note:: New code should use the `~numpy.random.Generator.chisquare` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- df : float or array_like of floats Number of degrees of freedom, must be > 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``df`` is a scalar. Otherwise, ``np.array(df).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized chi-square distribution. Raises ------ ValueError When `df` <= 0 or when an inappropriate `size` (e.g. ``size=-1``) is given. See Also -------- random.Generator.chisquare: which should be used for new code. Notes ----- The variable obtained by summing the squares of `df` independent, standard normally distributed random variables: .. math:: Q = \sum_{i=1}^{\mathtt{df}} X^2_i is chi-square distributed, denoted .. math:: Q \sim \chi^2_k. The probability density function of the chi-squared distribution is .. math:: p(x) = \frac{(1/2)^{k/2}}{\Gamma(k/2)} x^{k/2 - 1} e^{-x/2}, where :math:`\Gamma` is the gamma function, .. math:: \Gamma(x) = \int_0^{-\infty} t^{x - 1} e^{-t} dt. References ---------- .. [1] NIST "Engineering Statistics Handbook" https://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm Examples -------- >>> np.random.chisquare(2,4) array([ 1.89920014, 9.00867716, 3.13710533, 5.62318272]) # random chisquare noncentral_chisquare(df, nonc, size=None) Draw samples from a noncentral chi-square distribution. The noncentral :math:`\chi^2` distribution is a generalization of the :math:`\chi^2` distribution. .. note:: New code should use the `~numpy.random.Generator.noncentral_chisquare` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- df : float or array_like of floats Degrees of freedom, must be > 0. nonc : float or array_like of floats Non-centrality, must be non-negative. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``df`` and ``nonc`` are both scalars. Otherwise, ``np.broadcast(df, nonc).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized noncentral chi-square distribution. See Also -------- random.Generator.noncentral_chisquare: which should be used for new code. Notes ----- The probability density function for the noncentral Chi-square distribution is .. math:: P(x;df,nonc) = \sum^{\infty}_{i=0} \frac{e^{-nonc/2}(nonc/2)^{i}}{i!} P_{Y_{df+2i}}(x), where :math:`Y_{q}` is the Chi-square with q degrees of freedom. References ---------- .. [1] Wikipedia, "Noncentral chi-squared distribution" https://en.wikipedia.org/wiki/Noncentral_chi-squared_distribution Examples -------- Draw values from the distribution and plot the histogram >>> import matplotlib.pyplot as plt >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000), ... bins=200, density=True) >>> plt.show() Draw values from a noncentral chisquare with very small noncentrality, and compare to a chisquare. >>> plt.figure() >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000), ... bins=np.arange(0., 25, .1), density=True) >>> values2 = plt.hist(np.random.chisquare(3, 100000), ... bins=np.arange(0., 25, .1), density=True) >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob') >>> plt.show() Demonstrate how large values of non-centrality lead to a more symmetric distribution. >>> plt.figure() >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000), ... bins=200, density=True) >>> plt.show() noncentral_chisquare standard_cauchy(size=None) Draw samples from a standard Cauchy distribution with mode = 0. Also known as the Lorentz distribution. .. note:: New code should use the `~numpy.random.Generator.standard_cauchy` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Default is None, in which case a single value is returned. Returns ------- samples : ndarray or scalar The drawn samples. See Also -------- random.Generator.standard_cauchy: which should be used for new code. Notes ----- The probability density function for the full Cauchy distribution is .. math:: P(x; x_0, \gamma) = \frac{1}{\pi \gamma \bigl[ 1+ (\frac{x-x_0}{\gamma})^2 \bigr] } and the Standard Cauchy distribution just sets :math:`x_0=0` and :math:`\gamma=1` The Cauchy distribution arises in the solution to the driven harmonic oscillator problem, and also describes spectral line broadening. It also describes the distribution of values at which a line tilted at a random angle will cut the x axis. When studying hypothesis tests that assume normality, seeing how the tests perform on data from a Cauchy distribution is a good indicator of their sensitivity to a heavy-tailed distribution, since the Cauchy looks very much like a Gaussian distribution, but with heavier tails. References ---------- .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, "Cauchy Distribution", https://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm .. [2] Weisstein, Eric W. "Cauchy Distribution." From MathWorld--A Wolfram Web Resource. https://mathworld.wolfram.com/CauchyDistribution.html .. [3] Wikipedia, "Cauchy distribution" https://en.wikipedia.org/wiki/Cauchy_distribution Examples -------- Draw samples and plot the distribution: >>> import matplotlib.pyplot as plt >>> s = np.random.standard_cauchy(1000000) >>> s = s[(s>-25) & (s<25)] # truncate distribution so it plots well >>> plt.hist(s, bins=100) >>> plt.show() standard_cauchy seed(seed=None) Reseed the singleton RandomState instance. Notes ----- This is a convenience, legacy function that exists to support older code that uses the singleton RandomState. Best practice is to use a dedicated ``Generator`` instance rather than the random variate generation methods exposed directly in the random module. See Also -------- numpy.random.Generator standard_t(df, size=None) Draw samples from a standard Student's t distribution with `df` degrees of freedom. A special case of the hyperbolic distribution. As `df` gets large, the result resembles that of the standard normal distribution (`standard_normal`). .. note:: New code should use the `~numpy.random.Generator.standard_t` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- df : float or array_like of floats Degrees of freedom, must be > 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``df`` is a scalar. Otherwise, ``np.array(df).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized standard Student's t distribution. See Also -------- random.Generator.standard_t: which should be used for new code. Notes ----- The probability density function for the t distribution is .. math:: P(x, df) = \frac{\Gamma(\frac{df+1}{2})}{\sqrt{\pi df} \Gamma(\frac{df}{2})}\Bigl( 1+\frac{x^2}{df} \Bigr)^{-(df+1)/2} The t test is based on an assumption that the data come from a Normal distribution. The t test provides a way to test whether the sample mean (that is the mean calculated from the data) is a good estimate of the true mean. The derivation of the t-distribution was first published in 1908 by William Gosset while working for the Guinness Brewery in Dublin. Due to proprietary issues, he had to publish under a pseudonym, and so he used the name Student. References ---------- .. [1] Dalgaard, Peter, "Introductory Statistics With R", Springer, 2002. .. [2] Wikipedia, "Student's t-distribution" https://en.wikipedia.org/wiki/Student's_t-distribution Examples -------- From Dalgaard page 83 [1]_, suppose the daily energy intake for 11 women in kilojoules (kJ) is: >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \ ... 7515, 8230, 8770]) Does their energy intake deviate systematically from the recommended value of 7725 kJ? Our null hypothesis will be the absence of deviation, and the alternate hypothesis will be the presence of an effect that could be either positive or negative, hence making our test 2-tailed. Because we are estimating the mean and we have N=11 values in our sample, we have N-1=10 degrees of freedom. We set our significance level to 95% and compute the t statistic using the empirical mean and empirical standard deviation of our intake. We use a ddof of 1 to base the computation of our empirical standard deviation on an unbiased estimate of the variance (note: the final estimate is not unbiased due to the concave nature of the square root). >>> np.mean(intake) 6753.636363636364 >>> intake.std(ddof=1) 1142.1232221373727 >>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake))) >>> t -2.8207540608310198 We draw 1000000 samples from Student's t distribution with the adequate degrees of freedom. >>> import matplotlib.pyplot as plt >>> s = np.random.standard_t(10, size=1000000) >>> h = plt.hist(s, bins=100, density=True) Does our t statistic land in one of the two critical regions found at both tails of the distribution? >>> np.sum(np.abs(t) < np.abs(s)) / float(len(s)) 0.018318 #random < 0.05, statistic is in critical region The probability value for this 2-tailed test is about 1.83%, which is lower than the 5% pre-determined significance threshold. Therefore, the probability of observing values as extreme as our intake conditionally on the null hypothesis being true is too low, and we reject the null hypothesis of no deviation. standard_t vonmises(mu, kappa, size=None) Draw samples from a von Mises distribution. Samples are drawn from a von Mises distribution with specified mode (mu) and concentration (kappa), on the interval [-pi, pi]. The von Mises distribution (also known as the circular normal distribution) is a continuous probability distribution on the unit circle. It may be thought of as the circular analogue of the normal distribution. .. note:: New code should use the `~numpy.random.Generator.vonmises` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- mu : float or array_like of floats Mode ("center") of the distribution. kappa : float or array_like of floats Concentration of the distribution, has to be >=0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``mu`` and ``kappa`` are both scalars. Otherwise, ``np.broadcast(mu, kappa).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized von Mises distribution. See Also -------- scipy.stats.vonmises : probability density function, distribution, or cumulative density function, etc. random.Generator.vonmises: which should be used for new code. Notes ----- The probability density for the von Mises distribution is .. math:: p(x) = \frac{e^{\kappa cos(x-\mu)}}{2\pi I_0(\kappa)}, where :math:`\mu` is the mode and :math:`\kappa` the concentration, and :math:`I_0(\kappa)` is the modified Bessel function of order 0. The von Mises is named for Richard Edler von Mises, who was born in Austria-Hungary, in what is now the Ukraine. He fled to the United States in 1939 and became a professor at Harvard. He worked in probability theory, aerodynamics, fluid mechanics, and philosophy of science. References ---------- .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). "Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, 9th printing," New York: Dover, 1972. .. [2] von Mises, R., "Mathematical Theory of Probability and Statistics", New York: Academic Press, 1964. Examples -------- Draw samples from the distribution: >>> mu, kappa = 0.0, 4.0 # mean and concentration >>> s = np.random.vonmises(mu, kappa, 1000) Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt >>> from scipy.special import i0 # doctest: +SKIP >>> plt.hist(s, 50, density=True) >>> x = np.linspace(-np.pi, np.pi, num=51) >>> y = np.exp(kappa*np.cos(x-mu))/(2*np.pi*i0(kappa)) # doctest: +SKIP >>> plt.plot(x, y, linewidth=2, color='r') # doctest: +SKIP >>> plt.show() vonmises__getstate__ Returns the singleton RandomState's bit generator Returns ------- BitGenerator The bit generator that underlies the singleton RandomState instance Notes ----- The singleton RandomState provides the random variate generators in the ``numpy.random`` namespace. This function, and its counterpart set method, provides a path to hot-swap the default MT19937 bit generator with a user provided alternative. These function are intended to provide a continuous path where a single underlying bit generator can be used both with an instance of ``Generator`` and with the singleton instance of RandomState. See Also -------- set_bit_generator numpy.random.Generator get_bit_generator pareto(a, size=None) Draw samples from a Pareto II or Lomax distribution with specified shape. The Lomax or Pareto II distribution is a shifted Pareto distribution. The classical Pareto distribution can be obtained from the Lomax distribution by adding 1 and multiplying by the scale parameter ``m`` (see Notes). The smallest value of the Lomax distribution is zero while for the classical Pareto distribution it is ``mu``, where the standard Pareto distribution has location ``mu = 1``. Lomax can also be considered as a simplified version of the Generalized Pareto distribution (available in SciPy), with the scale set to one and the location set to zero. The Pareto distribution must be greater than zero, and is unbounded above. It is also known as the "80-20 rule". In this distribution, 80 percent of the weights are in the lowest 20 percent of the range, while the other 20 percent fill the remaining 80 percent of the range. .. note:: New code should use the `~numpy.random.Generator.pareto` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- a : float or array_like of floats Shape of the distribution. Must be positive. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``a`` is a scalar. Otherwise, ``np.array(a).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized Pareto distribution. See Also -------- scipy.stats.lomax : probability density function, distribution or cumulative density function, etc. scipy.stats.genpareto : probability density function, distribution or cumulative density function, etc. random.Generator.pareto: which should be used for new code. Notes ----- The probability density for the Pareto distribution is .. math:: p(x) = \frac{am^a}{x^{a+1}} where :math:`a` is the shape and :math:`m` the scale. The Pareto distribution, named after the Italian economist Vilfredo Pareto, is a power law probability distribution useful in many real world problems. Outside the field of economics it is generally referred to as the Bradford distribution. Pareto developed the distribution to describe the distribution of wealth in an economy. It has also found use in insurance, web page access statistics, oil field sizes, and many other problems, including the download frequency for projects in Sourceforge [1]_. It is one of the so-called "fat-tailed" distributions. References ---------- .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of Sourceforge projects. .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne. .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme Values, Birkhauser Verlag, Basel, pp 23-30. .. [4] Wikipedia, "Pareto distribution", https://en.wikipedia.org/wiki/Pareto_distribution Examples -------- Draw samples from the distribution: >>> a, m = 3., 2. # shape and mode >>> s = (np.random.pareto(a, 1000) + 1) * m Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt >>> count, bins, _ = plt.hist(s, 100, density=True) >>> fit = a*m**a / bins**(a+1) >>> plt.plot(bins, max(count)*fit/max(fit), linewidth=2, color='r') >>> plt.show() pareto Sets the singleton RandomState's bit generator Parameters ---------- bitgen A bit generator instance Notes ----- The singleton RandomState provides the random variate generators in the ``numpy.random``namespace. This function, and its counterpart get method, provides a path to hot-swap the default MT19937 bit generator with a user provided alternative. These function are intended to provide a continuous path where a single underlying bit generator can be used both with an instance of ``Generator`` and with the singleton instance of RandomState. See Also -------- get_bit_generator numpy.random.Generator set_bit_generator__setstate__ weibull(a, size=None) Draw samples from a Weibull distribution. Draw samples from a 1-parameter Weibull distribution with the given shape parameter `a`. .. math:: X = (-ln(U))^{1/a} Here, U is drawn from the uniform distribution over (0,1]. The more common 2-parameter Weibull, including a scale parameter :math:`\lambda` is just :math:`X = \lambda(-ln(U))^{1/a}`. .. note:: New code should use the `~numpy.random.Generator.weibull` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- a : float or array_like of floats Shape parameter of the distribution. Must be nonnegative. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``a`` is a scalar. Otherwise, ``np.array(a).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized Weibull distribution. See Also -------- scipy.stats.weibull_max scipy.stats.weibull_min scipy.stats.genextreme gumbel random.Generator.weibull: which should be used for new code. Notes ----- The Weibull (or Type III asymptotic extreme value distribution for smallest values, SEV Type III, or Rosin-Rammler distribution) is one of a class of Generalized Extreme Value (GEV) distributions used in modeling extreme value problems. This class includes the Gumbel and Frechet distributions. The probability density for the Weibull distribution is .. math:: p(x) = \frac{a} {\lambda}(\frac{x}{\lambda})^{a-1}e^{-(x/\lambda)^a}, where :math:`a` is the shape and :math:`\lambda` the scale. The function has its peak (the mode) at :math:`\lambda(\frac{a-1}{a})^{1/a}`. When ``a = 1``, the Weibull distribution reduces to the exponential distribution. References ---------- .. [1] Waloddi Weibull, Royal Technical University, Stockholm, 1939 "A Statistical Theory Of The Strength Of Materials", Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939, Generalstabens Litografiska Anstalts Forlag, Stockholm. .. [2] Waloddi Weibull, "A Statistical Distribution Function of Wide Applicability", Journal Of Applied Mechanics ASME Paper 1951. .. [3] Wikipedia, "Weibull distribution", https://en.wikipedia.org/wiki/Weibull_distribution Examples -------- Draw samples from the distribution: >>> a = 5. # shape >>> s = np.random.weibull(a, 1000) Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt >>> x = np.arange(1,100.)/50. >>> def weib(x,n,a): ... return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a) >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000)) >>> x = np.arange(1,100.)/50. >>> scale = count.max()/weib(x, 1., 5.).max() >>> plt.plot(x, weib(x, 1., 5.)*scale) >>> plt.show() weibull This is an alias of `random_sample`. See `random_sample` for the complete documentation. __reduce__sample This is an alias of `random_sample`. See `random_sample` for the complete documentation. ranf power(a, size=None) Draws samples in [0, 1] from a power distribution with positive exponent a - 1. Also known as the power function distribution. .. note:: New code should use the `~numpy.random.Generator.power` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- a : float or array_like of floats Parameter of the distribution. Must be non-negative. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``a`` is a scalar. Otherwise, ``np.array(a).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized power distribution. Raises ------ ValueError If a <= 0. See Also -------- random.Generator.power: which should be used for new code. Notes ----- The probability density function is .. math:: P(x; a) = ax^{a-1}, 0 \le x \le 1, a>0. The power function distribution is just the inverse of the Pareto distribution. It may also be seen as a special case of the Beta distribution. It is used, for example, in modeling the over-reporting of insurance claims. References ---------- .. [1] Christian Kleiber, Samuel Kotz, "Statistical size distributions in economics and actuarial sciences", Wiley, 2003. .. [2] Heckert, N. A. and Filliben, James J. "NIST Handbook 148: Dataplot Reference Manual, Volume 2: Let Subcommands and Library Functions", National Institute of Standards and Technology Handbook Series, June 2003. https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf Examples -------- Draw samples from the distribution: >>> a = 5. # shape >>> samples = 1000 >>> s = np.random.power(a, samples) Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, bins=30) >>> x = np.linspace(0, 1, 100) >>> y = a*x**(a-1.) >>> normed_y = samples*np.diff(bins)[0]*y >>> plt.plot(x, normed_y) >>> plt.show() Compare the power function distribution to the inverse of the Pareto. >>> from scipy import stats # doctest: +SKIP >>> rvs = np.random.power(5, 1000000) >>> rvsp = np.random.pareto(5, 1000000) >>> xx = np.linspace(0,1,100) >>> powpdf = stats.powerlaw.pdf(xx,5) # doctest: +SKIP >>> plt.figure() >>> plt.hist(rvs, bins=50, density=True) >>> plt.plot(xx,powpdf,'r-') # doctest: +SKIP >>> plt.title('np.random.power(5)') >>> plt.figure() >>> plt.hist(1./(1.+rvsp), bins=50, density=True) >>> plt.plot(xx,powpdf,'r-') # doctest: +SKIP >>> plt.title('inverse of 1 + np.random.pareto(5)') >>> plt.figure() >>> plt.hist(1./(1.+rvsp), bins=50, density=True) >>> plt.plot(xx,powpdf,'r-') # doctest: +SKIP >>> plt.title('inverse of stats.pareto(5)') power laplace(loc=0.0, scale=1.0, size=None) Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay). The Laplace distribution is similar to the Gaussian/normal distribution, but is sharper at the peak and has fatter tails. It represents the difference between two independent, identically distributed exponential random variables. .. note:: New code should use the `~numpy.random.Generator.laplace` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- loc : float or array_like of floats, optional The position, :math:`\mu`, of the distribution peak. Default is 0. scale : float or array_like of floats, optional :math:`\lambda`, the exponential decay. Default is 1. Must be non- negative. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``loc`` and ``scale`` are both scalars. Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized Laplace distribution. See Also -------- random.Generator.laplace: which should be used for new code. Notes ----- It has the probability density function .. math:: f(x; \mu, \lambda) = \frac{1}{2\lambda} \exp\left(-\frac{|x - \mu|}{\lambda}\right). The first law of Laplace, from 1774, states that the frequency of an error can be expressed as an exponential function of the absolute magnitude of the error, which leads to the Laplace distribution. For many problems in economics and health sciences, this distribution seems to model the data better than the standard Gaussian distribution. References ---------- .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). "Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, 9th printing," New York: Dover, 1972. .. [2] Kotz, Samuel, et. al. "The Laplace Distribution and Generalizations, " Birkhauser, 2001. .. [3] Weisstein, Eric W. "Laplace Distribution." From MathWorld--A Wolfram Web Resource. https://mathworld.wolfram.com/LaplaceDistribution.html .. [4] Wikipedia, "Laplace distribution", https://en.wikipedia.org/wiki/Laplace_distribution Examples -------- Draw samples from the distribution >>> loc, scale = 0., 1. >>> s = np.random.laplace(loc, scale, 1000) Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, 30, density=True) >>> x = np.arange(-8., 8., .01) >>> pdf = np.exp(-abs(x-loc)/scale)/(2.*scale) >>> plt.plot(x, pdf) Plot Gaussian for comparison: >>> g = (1/(scale * np.sqrt(2 * np.pi)) * ... np.exp(-(x - loc)**2 / (2 * scale**2))) >>> plt.plot(x,g) laplace seed(seed=None) Reseed a legacy MT19937 BitGenerator Notes ----- This is a convenience, legacy function. The best practice is to **not** reseed a BitGenerator, rather to recreate a new one. This method is here for legacy reasons. This example demonstrates best practice. >>> from numpy.random import MT19937 >>> from numpy.random import RandomState, SeedSequence >>> rs = RandomState(MT19937(SeedSequence(123456789))) # Later, you want to restart the stream >>> rs = RandomState(MT19937(SeedSequence(987654321))) seed gumbel(loc=0.0, scale=1.0, size=None) Draw samples from a Gumbel distribution. Draw samples from a Gumbel distribution with specified location and scale. For more information on the Gumbel distribution, see Notes and References below. .. note:: New code should use the `~numpy.random.Generator.gumbel` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- loc : float or array_like of floats, optional The location of the mode of the distribution. Default is 0. scale : float or array_like of floats, optional The scale parameter of the distribution. Default is 1. Must be non- negative. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``loc`` and ``scale`` are both scalars. Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized Gumbel distribution. See Also -------- scipy.stats.gumbel_l scipy.stats.gumbel_r scipy.stats.genextreme weibull random.Generator.gumbel: which should be used for new code. Notes ----- The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value Type I) distribution is one of a class of Generalized Extreme Value (GEV) distributions used in modeling extreme value problems. The Gumbel is a special case of the Extreme Value Type I distribution for maximums from distributions with "exponential-like" tails. The probability density for the Gumbel distribution is .. math:: p(x) = \frac{e^{-(x - \mu)/ \beta}}{\beta} e^{ -e^{-(x - \mu)/ \beta}}, where :math:`\mu` is the mode, a location parameter, and :math:`\beta` is the scale parameter. The Gumbel (named for German mathematician Emil Julius Gumbel) was used very early in the hydrology literature, for modeling the occurrence of flood events. It is also used for modeling maximum wind speed and rainfall rates. It is a "fat-tailed" distribution - the probability of an event in the tail of the distribution is larger than if one used a Gaussian, hence the surprisingly frequent occurrence of 100-year floods. Floods were initially modeled as a Gaussian process, which underestimated the frequency of extreme events. It is one of a class of extreme value distributions, the Generalized Extreme Value (GEV) distributions, which also includes the Weibull and Frechet. The function has a mean of :math:`\mu + 0.57721\beta` and a variance of :math:`\frac{\pi^2}{6}\beta^2`. References ---------- .. [1] Gumbel, E. J., "Statistics of Extremes," New York: Columbia University Press, 1958. .. [2] Reiss, R.-D. and Thomas, M., "Statistical Analysis of Extreme Values from Insurance, Finance, Hydrology and Other Fields," Basel: Birkhauser Verlag, 2001. Examples -------- Draw samples from the distribution: >>> mu, beta = 0, 0.1 # location and scale >>> s = np.random.gumbel(mu, beta, 1000) Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, 30, density=True) >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta) ... * np.exp( -np.exp( -(bins - mu) /beta) ), ... linewidth=2, color='r') >>> plt.show() Show how an extreme value distribution can arise from a Gaussian process and compare to a Gaussian: >>> means = [] >>> maxima = [] >>> for i in range(0,1000) : ... a = np.random.normal(mu, beta, 1000) ... means.append(a.mean()) ... maxima.append(a.max()) >>> count, bins, ignored = plt.hist(maxima, 30, density=True) >>> beta = np.std(maxima) * np.sqrt(6) / np.pi >>> mu = np.mean(maxima) - 0.57721*beta >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta) ... * np.exp(-np.exp(-(bins - mu)/beta)), ... linewidth=2, color='r') >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi)) ... * np.exp(-(bins - mu)**2 / (2 * beta**2)), ... linewidth=2, color='g') >>> plt.show() gumbel_bit_generatornumpy.random.mtrand.RandomStateRandomState(seed=None) -- Container for the slow Mersenne Twister pseudo-random number generator. Consider using a different BitGenerator with the Generator container instead. `RandomState` and `Generator` expose a number of methods for generating random numbers drawn from a variety of probability distributions. In addition to the distribution-specific arguments, each method takes a keyword argument `size` that defaults to ``None``. If `size` is ``None``, then a single value is generated and returned. If `size` is an integer, then a 1-D array filled with generated values is returned. If `size` is a tuple, then an array with that shape is filled and returned. **Compatibility Guarantee** A fixed bit generator using a fixed seed and a fixed series of calls to 'RandomState' methods using the same parameters will always produce the same results up to roundoff error except when the values were incorrect. `RandomState` is effectively frozen and will only receive updates that are required by changes in the internals of Numpy. More substantial changes, including algorithmic improvements, are reserved for `Generator`. Parameters ---------- seed : {None, int, array_like, BitGenerator}, optional Random seed used to initialize the pseudo-random number generator or an instantized BitGenerator. If an integer or array, used as a seed for the MT19937 BitGenerator. Values can be any integer between 0 and 2**32 - 1 inclusive, an array (or other sequence) of such integers, or ``None`` (the default). If `seed` is ``None``, then the `MT19937` BitGenerator is initialized by reading data from ``/dev/urandom`` (or the Windows analogue) if available or seed from the clock otherwise. Notes ----- The Python stdlib module "random" also contains a Mersenne Twister pseudo-random number generator with a number of methods that are similar to the ones available in `RandomState`. `RandomState`, besides being NumPy-aware, has the advantage that it provides a much larger number of probability distributions to choose from. See Also -------- Generator MT19937 numpy.random.BitGenerator logistic(loc=0.0, scale=1.0, size=None) Draw samples from a logistic distribution. Samples are drawn from a logistic distribution with specified parameters, loc (location or mean, also median), and scale (>0). .. note:: New code should use the `~numpy.random.Generator.logistic` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- loc : float or array_like of floats, optional Parameter of the distribution. Default is 0. scale : float or array_like of floats, optional Parameter of the distribution. Must be non-negative. Default is 1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``loc`` and ``scale`` are both scalars. Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized logistic distribution. See Also -------- scipy.stats.logistic : probability density function, distribution or cumulative density function, etc. random.Generator.logistic: which should be used for new code. Notes ----- The probability density for the Logistic distribution is .. math:: P(x) = P(x) = \frac{e^{-(x-\mu)/s}}{s(1+e^{-(x-\mu)/s})^2}, where :math:`\mu` = location and :math:`s` = scale. The Logistic distribution is used in Extreme Value problems where it can act as a mixture of Gumbel distributions, in Epidemiology, and by the World Chess Federation (FIDE) where it is used in the Elo ranking system, assuming the performance of each player is a logistically distributed random variable. References ---------- .. [1] Reiss, R.-D. and Thomas M. (2001), "Statistical Analysis of Extreme Values, from Insurance, Finance, Hydrology and Other Fields," Birkhauser Verlag, Basel, pp 132-133. .. [2] Weisstein, Eric W. "Logistic Distribution." From MathWorld--A Wolfram Web Resource. https://mathworld.wolfram.com/LogisticDistribution.html .. [3] Wikipedia, "Logistic-distribution", https://en.wikipedia.org/wiki/Logistic_distribution Examples -------- Draw samples from the distribution: >>> loc, scale = 10, 1 >>> s = np.random.logistic(loc, scale, 10000) >>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, bins=50) # plot against distribution >>> def logist(x, loc, scale): ... return np.exp((loc-x)/scale)/(scale*(1+np.exp((loc-x)/scale))**2) >>> lgst_val = logist(bins, loc, scale) >>> plt.plot(bins, lgst_val * count.max() / lgst_val.max()) >>> plt.show() logistic get_state(legacy=True) Return a tuple representing the internal state of the generator. For more details, see `set_state`. Parameters ---------- legacy : bool, optional Flag indicating to return a legacy tuple state when the BitGenerator is MT19937, instead of a dict. Raises ValueError if the underlying bit generator is not an instance of MT19937. Returns ------- out : {tuple(str, ndarray of 624 uints, int, int, float), dict} If legacy is True, the returned tuple has the following items: 1. the string 'MT19937'. 2. a 1-D array of 624 unsigned integer keys. 3. an integer ``pos``. 4. an integer ``has_gauss``. 5. a float ``cached_gaussian``. If `legacy` is False, or the BitGenerator is not MT19937, then state is returned as a dictionary. See Also -------- set_state Notes ----- `set_state` and `get_state` are not needed to work with any of the random distributions in NumPy. If the internal state is manually altered, the user should know exactly what he/she is doing. get_state lognormal(mean=0.0, sigma=1.0, size=None) Draw samples from a log-normal distribution. Draw samples from a log-normal distribution with specified mean, standard deviation, and array shape. Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from. .. note:: New code should use the `~numpy.random.Generator.lognormal` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- mean : float or array_like of floats, optional Mean value of the underlying normal distribution. Default is 0. sigma : float or array_like of floats, optional Standard deviation of the underlying normal distribution. Must be non-negative. Default is 1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``mean`` and ``sigma`` are both scalars. Otherwise, ``np.broadcast(mean, sigma).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized log-normal distribution. See Also -------- scipy.stats.lognorm : probability density function, distribution, cumulative density function, etc. random.Generator.lognormal: which should be used for new code. Notes ----- A variable `x` has a log-normal distribution if `log(x)` is normally distributed. The probability density function for the log-normal distribution is: .. math:: p(x) = \frac{1}{\sigma x \sqrt{2\pi}} e^{(-\frac{(ln(x)-\mu)^2}{2\sigma^2})} where :math:`\mu` is the mean and :math:`\sigma` is the standard deviation of the normally distributed logarithm of the variable. A log-normal distribution results if a random variable is the *product* of a large number of independent, identically-distributed variables in the same way that a normal distribution results if the variable is the *sum* of a large number of independent, identically-distributed variables. References ---------- .. [1] Limpert, E., Stahel, W. A., and Abbt, M., "Log-normal Distributions across the Sciences: Keys and Clues," BioScience, Vol. 51, No. 5, May, 2001. https://stat.ethz.ch/~stahel/lognormal/bioscience.pdf .. [2] Reiss, R.D. and Thomas, M., "Statistical Analysis of Extreme Values," Basel: Birkhauser Verlag, 2001, pp. 31-32. Examples -------- Draw samples from the distribution: >>> mu, sigma = 3., 1. # mean and standard deviation >>> s = np.random.lognormal(mu, sigma, 1000) Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, 100, density=True, align='mid') >>> x = np.linspace(min(bins), max(bins), 10000) >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2)) ... / (x * sigma * np.sqrt(2 * np.pi))) >>> plt.plot(x, pdf, linewidth=2, color='r') >>> plt.axis('tight') >>> plt.show() Demonstrate that taking the products of random samples from a uniform distribution can be fit well by a log-normal probability density function. >>> # Generate a thousand samples: each is the product of 100 random >>> # values, drawn from a normal distribution. >>> b = [] >>> for i in range(1000): ... a = 10. + np.random.standard_normal(100) ... b.append(np.prod(a)) >>> b = np.array(b) / np.min(b) # scale values to be positive >>> count, bins, ignored = plt.hist(b, 100, density=True, align='mid') >>> sigma = np.std(np.log(b)) >>> mu = np.mean(np.log(b)) >>> x = np.linspace(min(bins), max(bins), 10000) >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2)) ... / (x * sigma * np.sqrt(2 * np.pi))) >>> plt.plot(x, pdf, color='r', linewidth=2) >>> plt.show() lognormal rayleigh(scale=1.0, size=None) Draw samples from a Rayleigh distribution. The :math:`\chi` and Weibull distributions are generalizations of the Rayleigh. .. note:: New code should use the `~numpy.random.Generator.rayleigh` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- scale : float or array_like of floats, optional Scale, also equals the mode. Must be non-negative. Default is 1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``scale`` is a scalar. Otherwise, ``np.array(scale).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized Rayleigh distribution. See Also -------- random.Generator.rayleigh: which should be used for new code. Notes ----- The probability density function for the Rayleigh distribution is .. math:: P(x;scale) = \frac{x}{scale^2}e^{\frac{-x^2}{2 \cdotp scale^2}} The Rayleigh distribution would arise, for example, if the East and North components of the wind velocity had identical zero-mean Gaussian distributions. Then the wind speed would have a Rayleigh distribution. References ---------- .. [1] Brighton Webs Ltd., "Rayleigh Distribution," https://web.archive.org/web/20090514091424/http://brighton-webs.co.uk:80/distributions/rayleigh.asp .. [2] Wikipedia, "Rayleigh distribution" https://en.wikipedia.org/wiki/Rayleigh_distribution Examples -------- Draw values from the distribution and plot the histogram >>> from matplotlib.pyplot import hist >>> values = hist(np.random.rayleigh(3, 100000), bins=200, density=True) Wave heights tend to follow a Rayleigh distribution. If the mean wave height is 1 meter, what fraction of waves are likely to be larger than 3 meters? >>> meanvalue = 1 >>> modevalue = np.sqrt(2 / np.pi) * meanvalue >>> s = np.random.rayleigh(modevalue, 1000000) The percentage of waves larger than 3 meters is: >>> 100.*sum(s>3)/1000000. 0.087300000000000003 # random rayleighmtrand wald(mean, scale, size=None) Draw samples from a Wald, or inverse Gaussian, distribution. As the scale approaches infinity, the distribution becomes more like a Gaussian. Some references claim that the Wald is an inverse Gaussian with mean equal to 1, but this is by no means universal. The inverse Gaussian distribution was first studied in relationship to Brownian motion. In 1956 M.C.K. Tweedie used the name inverse Gaussian because there is an inverse relationship between the time to cover a unit distance and distance covered in unit time. .. note:: New code should use the `~numpy.random.Generator.wald` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- mean : float or array_like of floats Distribution mean, must be > 0. scale : float or array_like of floats Scale parameter, must be > 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``mean`` and ``scale`` are both scalars. Otherwise, ``np.broadcast(mean, scale).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized Wald distribution. See Also -------- random.Generator.wald: which should be used for new code. Notes ----- The probability density function for the Wald distribution is .. math:: P(x;mean,scale) = \sqrt{\frac{scale}{2\pi x^3}}e^ \frac{-scale(x-mean)^2}{2\cdotp mean^2x} As noted above the inverse Gaussian distribution first arise from attempts to model Brownian motion. It is also a competitor to the Weibull for use in reliability modeling and modeling stock returns and interest rate processes. References ---------- .. [1] Brighton Webs Ltd., Wald Distribution, https://web.archive.org/web/20090423014010/http://www.brighton-webs.co.uk:80/distributions/wald.asp .. [2] Chhikara, Raj S., and Folks, J. Leroy, "The Inverse Gaussian Distribution: Theory : Methodology, and Applications", CRC Press, 1988. .. [3] Wikipedia, "Inverse Gaussian distribution" https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution Examples -------- Draw values from the distribution and plot the histogram: >>> import matplotlib.pyplot as plt >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, density=True) >>> plt.show() wald set_state(state) Set the internal state of the generator from a tuple. For use if one has reason to manually (re-)set the internal state of the bit generator used by the RandomState instance. By default, RandomState uses the "Mersenne Twister"[1]_ pseudo-random number generating algorithm. Parameters ---------- state : {tuple(str, ndarray of 624 uints, int, int, float), dict} The `state` tuple has the following items: 1. the string 'MT19937', specifying the Mersenne Twister algorithm. 2. a 1-D array of 624 unsigned integers ``keys``. 3. an integer ``pos``. 4. an integer ``has_gauss``. 5. a float ``cached_gaussian``. If state is a dictionary, it is directly set using the BitGenerators `state` property. Returns ------- out : None Returns 'None' on success. See Also -------- get_state Notes ----- `set_state` and `get_state` are not needed to work with any of the random distributions in NumPy. If the internal state is manually altered, the user should know exactly what he/she is doing. For backwards compatibility, the form (str, array of 624 uints, int) is also accepted although it is missing some information about the cached Gaussian value: ``state = ('MT19937', keys, pos)``. References ---------- .. [1] M. Matsumoto and T. Nishimura, "Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator," *ACM Trans. on Modeling and Computer Simulation*, Vol. 8, No. 1, pp. 3-30, Jan. 1998. set_state triangular(left, mode, right, size=None) Draw samples from the triangular distribution over the interval ``[left, right]``. The triangular distribution is a continuous probability distribution with lower limit left, peak at mode, and upper limit right. Unlike the other distributions, these parameters directly define the shape of the pdf. .. note:: New code should use the `~numpy.random.Generator.triangular` method of a `~numpy.random.Generator` instance instead; please see the :ref:`random-quick-start`. Parameters ---------- left : float or array_like of floats Lower limit. mode : float or array_like of floats The value where the peak of the distribution occurs. The value must fulfill the condition ``left <= mode <= right``. right : float or array_like of floats Upper limit, must be larger than `left`. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``left``, ``mode``, and ``right`` are all scalars. Otherwise, ``np.broadcast(left, mode, right).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized triangular distribution. See Also -------- random.Generator.triangular: which should be used for new code. Notes ----- The probability density function for the triangular distribution is .. math:: P(x;l, m, r) = \begin{cases} \frac{2(x-l)}{(r-l)(m-l)}& \text{for $l \leq x \leq m$},\\ \frac{2(r-x)}{(r-l)(r-m)}& \text{for $m \leq x \leq r$},\\ 0& \text{otherwise}. \end{cases} The triangular distribution is often used in ill-defined problems where the underlying distribution is not known, but some knowledge of the limits and mode exists. Often it is used in simulations. References ---------- .. [1] Wikipedia, "Triangular distribution" https://en.wikipedia.org/wiki/Triangular_distribution Examples -------- Draw values from the distribution and plot the histogram: >>> import matplotlib.pyplot as plt >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=200, ... density=True) >>> plt.show() triangularnumpy._core._multiarray_umathnumpy.core._multiarray_umath_ARRAY_API_ARRAY_API is not PyCapsule object_ARRAY_API is NULL pointermodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule was compiled against NumPy C-API version 0x%x (NumPy 1.23) but the running NumPy has C-API version 0x%x. Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem.FATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimenumpy.PyArray_MultiIterNew2numpy.PyArray_MultiIterNew3numpy.import_arraynumpy.random.mtrand.int64_to_long__init__numpy.random.mtrand.RandomState.__init__numpy.random.mtrand.RandomState.__repr__numpy.random.mtrand.RandomState.__str__numpy.random.mtrand.RandomState.__getstate__numpy.random.mtrand.RandomState.__setstate__numpy.random.mtrand.RandomState.__reduce__numpy.random.mtrand.RandomState._initialize_bit_generatornumpy.random.mtrand.RandomState._reset_gaussnumpy.random.mtrand.RandomState.seednumpy.random.mtrand.RandomState.get_statenumpy.random.mtrand.RandomState.set_statenumpy.random.mtrand.RandomState.random_samplenumpy.random.mtrand.RandomState.randomnumpy.random.mtrand.RandomState.betanumpy.random.mtrand.RandomState.exponentialnumpy.random.mtrand.RandomState.standard_exponentialnumpy.random.mtrand.RandomState.tomaxintnumpy.random.mtrand.RandomState.randintnumpy.random.mtrand.RandomState.bytesnumpy.random.mtrand.RandomState.choicenumpy.random.mtrand.RandomState.uniformnumpy.random.mtrand.RandomState.randnumpy.random.mtrand.RandomState.randnnumpy.random.mtrand.RandomState.random_integersnumpy.random.mtrand.RandomState.standard_normalnumpy.random.mtrand.RandomState.normalnumpy.random.mtrand.RandomState.standard_gammanumpy.random.mtrand.RandomState.gammanumpy.random.mtrand.RandomState.fnumpy.random.mtrand.RandomState.noncentral_fnumpy.random.mtrand.RandomState.chisquarenumpy.random.mtrand.RandomState.noncentral_chisquarenumpy.random.mtrand.RandomState.standard_cauchynumpy.random.mtrand.RandomState.standard_tnumpy.random.mtrand.RandomState.vonmisesnumpy.random.mtrand.RandomState.paretonumpy.random.mtrand.RandomState.weibullnumpy.random.mtrand.RandomState.powernumpy.random.mtrand.RandomState.laplacenumpy.random.mtrand.RandomState.gumbelnumpy.random.mtrand.RandomState.logisticnumpy.random.mtrand.RandomState.lognormalnumpy.random.mtrand.RandomState.rayleighnumpy.random.mtrand.RandomState.waldnumpy.random.mtrand.RandomState.triangularnumpy.random.mtrand.RandomState.binomialnumpy.random.mtrand.RandomState.negative_binomialnumpy.random.mtrand.RandomState.poissonnumpy.random.mtrand.RandomState.zipfnumpy.random.mtrand.RandomState.geometricnumpy.random.mtrand.RandomState.hypergeometricnumpy.random.mtrand.RandomState.logseriesnumpy.random.mtrand.RandomState.multivariate_normalnumpy.random.mtrand.RandomState.multinomialnumpy.random.mtrand.RandomState.dirichletnumpy.random.mtrand.RandomState.shufflenumpy.random.mtrand.RandomState.permutationnumpy.random.mtrand.seednumpy.random.mtrand.get_bit_generatornumpy.random.mtrand.set_bit_generatornumpy.random.mtrand.samplenumpy.random.mtrand.ranfbuiltinstypeboolcomplexnumpydtypeflatiterbroadcastndarraygenericnumberintegersignedintegerunsignedintegerinexactfloatingcomplexfloatingflexiblecharacterufuncnumpy.random.bit_generatorBitGeneratorSeedSequenceSeedlessSeedSequenceSeedlessSequencenumpy.random._commonnumpy.random._bounded_integersInterpreter change detected - this module can only be loaded into one interpreter per process.name__loader__loader__file__origin__package__parent__path__submodule_search_locationsModule 'mtrand' has already been imported. Re-initialisation is not supported.cython_runtime__builtins__numpy.random.mtrandinit numpy.random.mtrandx~F/=OQ H:J_w'n؝t- R@ my.U*DYvv2 E u]_ "<'8NrgE䧳 ?_DOX,20i4XD88zGY$8q~ DqA߂[? 'bbAIꊗ/\t> f ` @(J΃4yNxS䗋@)㊹2t 3j-$ T\&K^3/&Ɠ`h1l:qx2_B7oG눅^1H0eُ4Mҟx_~0NFƁvMwqYOK\&;H±u׶~i8>\yEê}aqoo1HA~`9ꝝ=SǠ߷|:s2 \7{c8z权v,[ p00_F~[? uvXޏywqeUz2.͗99HfJ?#sg50[0VG{tX7*?&hTuNcC/=sT˂`bvNuǽ~!d/ǧzjrFG/jT\W O@D c$N|-ɷA¼M9]}^s?mke~op1&> Dp1̄d2d/he VOt#?8s?dk031Lv)|')xEB<2C38|PED5?UR8Ev -.$N.l( v`P]a{ϣ/^ʾix͖́|,#@dщ,apׄQ_)&Ҹ_z.1 l'f.ILZ爍#*#o 5ۋm^N}5$c`|SZQ",fP}-y-@ZX@Ԩxh0ůES_|}$|^`UѥsѾBsi<䎞m~y⎖n1 6d.[p XqsX 2ږ^#ueW4cc7qH,:1+^XaFEaX½i#`[$OIm :h,}73NdG4YmRWxv%tӒZ i2UW6J{(Rg_N4CC|hls9؂C\:xO4+|YC)`Op XYEm_8VWC%S":F)6 I[&}@}}U,ED B#EN7zߗ9_E]߅"/:DgWacWgcp.v`/vph = NpkLiO3[TobShCSpOQo:M9 w&dg9ѣcqqW;E<( )XxD$ʢqؚZO|tmXP!X<T(6i^x&uE~~zx(75őxMmg׋7^k|s];E+^qH-hٷQ,#ȋèݡ;"=Wc~ADZ,4C[([hh`6F V ƿ cϨ2c9U|b6N3T){ MDN /#4^X << $cqnzs5~Iq,%V3>QkRymG}!!%u80fuA/ dHUR}vԏ)uołI8.$KPĦq? :Ez\voS88@!uF"JR" N ҄cq`&~O]`#a[,PA Zv7ҡyZ&e9p8Yƹ԰Kݍt D :õO@7j69mG y7wH?ФCw0k^xG,?meL(!;RxXgN_)Y˦Cp#~7ZorD#K3h[/NM__m%Oẓ& Zd>ŏ/>0\Oc|NI&#;4v69oW(Ac2ZadgpSAnwi,K'h9}@1GS)*ˍlxYH1i-%[GRRxX3r"mIDFד$>;RPKqA 3/Y7V VjC)άON嫴 PrprC:CجVpW6Pt#b Q4|>!LuҾ*!p0@]7I+& B afzuI_Bb 5GJt}9B>L^hs$(L7.kj÷y4YC:4 0.SrNTEٞ-tDc@V:jg<ȻTsqzDC7AkwǑ-tDabϤR_iSo ] %@ns8؂"g&\Zn2CvV266[)OV ڻT}JDd0"qlt`L_9&u0˓rv*}1ORu"# pj'YkH?N=zA[wNj 9^3 `=@9vWAޚrR<(z"ɲ #)9"vҳÊPW9P0aI:<4H{7}떝pKf݊h $^|c&}ovNF^1e2(&";~],tۜ,beƌv3HW͸=}1PaJwӭ'Κ]}_* CW *(\ <-xX9 {m:bS(NCг_m/ #x~Zǘ>=ǿ.gxiB}̤۰o 2.ܿ[B'8iGyn)+9D:|=D*5U[ڥlEkb:bi4{3.*+=]:O#Wa De<Rݯ砞0мEl}1dtMÈIZ~$MLc3İ_["q%) `y+A\D<ή.\qp / :-R9˼=ԭ`j4DZ~BJٿ = ˠ#S?[#@Ar&yGovxW摸0FUw|Nď㿡ti2.,d—O$臀meoǃh 3gK wt^͆t.v̿܅=rLWs7 ӽ9Fql0g1ܼ9&\314('  )Qrmc;<7sI%18%剃w? K5BH_e?k%sU2B'3?]nn9 M,F=ʏanMH@yMp )"J(K"ʭgow䧧 __ꧼc" Jңmv̶_q_<̣o ˶B<߅H~m`Bp䡿斑M~ZUY6.lM W5 o/OAB>^`Lio:f6Ni)  Jz8)39%QAG|4mK\ZIr#h%#661?)1*% eQ2[jB4r$cAs%L3ʓhOa<N &[b0Xz<#f9* $INdȅ"8XOHI˚kb]&>|M&VO">5Z;Q+Nfq.P< %$N @s %^H#3sL 'H#4mЍ1L=1W*[Dvv}X 45n$f p}@S.KQ*.\bhԌkч#7jrIoEVmA|'MXt%fe Mvl=/ |!~V1-( #1`UJpE]i9Fi*a`{{D49&[gq4;0nft) Y1ʼn-_̶DLpR՚yN:" SYiLLjX@m0,1ҩt}޳*](DHؔIXI% c&X!"NC9S͕Kg;cǝ _sqͧfflv&ҮRR8 $_/NM-SJ9:`eQS;CJSl3 `$fi x *]U:2^^U7sSUzpc&D_VKr nhPQmR|;;l-/?2kF!307C_X$\NӣtÚ`?2inհb*Eo4NA; m3H88:o Vʥ6?;S_ 9HQGdf(;|;Pܠ~6 '/Ob+VuZ7\}p]Pi;CVDK cy,̸hH)“m8!1=UjzjXp?Ceeq FZz}# ;@9ʱ17ǎ VG]m/ NɎa(g'((mQ}/4~COO ٨`p`q H ?,E]tFlhDTN)9SbC\5ʊ-FQ2%jE%Eܢlq,\~BuhWKHY7.B`kԅ%'`>%0dF7i o] Ӿob]vj6s,l余طlmhNu??Zsaj4 _x? 3QE㟛**^b=ͪ;P|Nj|﵁w hhk(ϳ ܺ)l(pdž.O>|9-#p DJkvReXᓷDj_ϗ]9;:~-Ihe|>LZX˸] T6Sï| 4?b,HuL6iRUm^OٮJIDRZ0.r' 4 =U]S䧾yTOB. [S?o &[!hՈˆ=`8W䥔qx?iEw2CGJ " \a r{Pn]a˨v0d6%B}~~KŢbL<ֺ(\wAd;f\=ɇu9 Sjd*Rt,x-'2!Y<O`|8.0@wC+KU;{= vfy dX /L2S4^\$}ɒFoܘA*Xpl\)['~*YT!e2̖Y8n)HU\l$4:UZ~=zn+Y눼 &g0fxަ+d'EcceU",kjyj@|;%SQ[~`{և֖-PMcl>`ߊ`4x絍6cw-r܍fBQV&hhgY_X&%E)2FĒE~ a).0 ח%Ud8`/$O)6䀐nv_#TDDO:_{}Xfv}su }QWg-Qbv^C#(;{$K%_ Pg$hjveT-eP/-3?SV]63@UJ(HLg9$S?'򳢷4"SR&0جL(BzJe+IE}$ %hf S>"lIKG?FPG-fW^St"ٍՀ茑B/"-ŗEz \hzuN2O)ܰxOPTR3WP08yHQ衣,@YԵxdjn\BUgl9 dXOkdujyfg&&<8Y6]WDDz0KL:i/`EtI6QY˦/p2  -JߥT&S-ʽ0? zml weMq瑥Va @,PֲjTźJ}ÞMB{Tm]`6$;&@U0DO)(.xd9 綽u2'ɥ qтj$F ~ 0V9H+UT* 2t]FAY?X L4+;3N|#Cё WK%q)-$0)@Wߔ="RT:c]6jTE:G͑"(dz>i7߃%GY(]o5qO /CUn$=y0uoSaPK:*Ws Z&]__e +4#;UWN㮵/sL^8ea"g$ =^`R/sѥ%~-b`:X'O=nc(pfdPp]n+H)Qr.[$ d<]5ը5P7i. ^eFaU4NkBզdSȽΨù,% +Iyt(Cb#ojj_Ψ$-,YR+pBahl{؋Ͳ"~1u/Ec5tC]xYFl"8Xh8;*疓.a"0b.J*44N!C66e'$6!k~hDɔmΖrAŬQdBk^t ~'M).urbGcv,7Cg³Q(MK4Qz账zvܣs?遁1 !G%p(^WDf7fzSZ"55Z3:/nֈb8pOy}ou!eL:{5cT:Z ,OelS.SݪѫShg9,;3C hU V5gu*b+v`yqv@?ܹ"Д͂"A|)[ťQ.3pa$4x[^H7[pɎ·1VslD>숟}ρbu=+iDH\]~sǧ7ij; Puʼ{K;Fevm0zn3%9]rsIxQK-nzHݤNS`jp=L`#h~2 yv: #13+ ~ +| 5;U?ނ N~+yh9mHUIz;{vwQ?[ai[8y-(LQօk3Gdp`֢Cd"vnDZznc,ѯZ /ZG/TTPĬSB"_!c'YW.qڬ+ 8GUƆ̞@HGe4cكw }"ȊCQxs,E?OBC$xT?-Q.Re FM1Z*erO*q0> % %!ĤJNz"$zM? }^ynjn$)CalMgV͙ݨA4YdX/ۙMc ?7j@P^F.$kBPID/Vޭ֏ }L?VVЃqy}٫=eP}̌ώ4p'!˹: )baAPZCaoF\KXgZ;DJ" 01 }XEO^m+? (Q*2=y%rV,DiMYmOOey/Guj'R~/?wQHq0BЛbKbc4RDcL?x7 6?娟gmc='F4.z*}07m| g-5(+hXKZr5qG)qKviΝ3£QN,ح9^TzdHój$\yK[GˡT{gAia'R>- fs V%PV́P3,ghG wQ#3J\kEZGn;"7HYιu·ekΆXhkeF ]sF:RL!;,JbI NDl;5>žJo>h^ېK{VqXk^T5OA1 ʉ )UdNz_2*}7iJ0teR–t.OCPMדJԕ#Q@K3f\Pe [MxjH+ԅQPWBFSԜ#./ӷ6Ŕ^Ʒ >D2BۀyVWE`<x/,^*AƞYqD+/N5|xveK+q8>>F=lR]3J\F˧BeFtO(ђ%ET.| zC3Kˏz"GǪre%H+}`-vdUPi {F4DK=t-R ", ꎹt\ZMPEj1~P* F%BArAsl 04Ě9$-Õq@6Q@@c8Hy0(Ds ޽5hU䐿pI|+rv̲(]"0:kW@3SUyj-ݾj1܂^,j)[]',t8 gZп,o^t`qC pDBjuwtV{c#Y7ɳִ NIގ =Mkzx#ǖ2?=̂ґ+FhDv,N6\ , 'ܺbCVkp22cxnptC$=ܮŶ0Ű\D> Ehb oD[ u {H55V6S*MQZ@8½c6M=mE(eXOB5_2뢄ϜRPe*aݞ(Cs2 rV_g+Jqb{c5H쾣n {f~b `=C[ϱ0 }~Wq?śb\=[eŰCP5uNM09 5M .ƭ"WktK'ɩ~ap鸲aqSr'n8s-E+O)Ao bbY]ga&QcKx+Z_Z9P 0ȍ l!g S[Z) q5پTm-B_%=^~97=So҅~?55;ڵ4Y ~"AK.|KNYr>X1Jxad7Dnb%Ehj*nߵv=~WmZ)zYYVpOp<,~/JjPeT?cwmI2׫hU!Г1P|oONI X!K`~7B84 &Kc,8ڲ~6tN,Z5=™HhݴuMFŜΰ&;d5߳Yt9n> fiF)|bs#ai, ǫ%柑9ju4Nu?2sq=~2~1͇zVv6et`!o^4> 7* ʱu Px}}׍=nɱBWv2܌j%[Nx#t ĽN: Bg%.dSS\jR 7|IE4 d2G ~c.ACtBPbj'0f.BYmPGyGĄLeIt!3|}hBF@$ }X+Ug~S&Jӱ?2ޏ3?M| 01)䜭6HG?ZeVL_14Jvcll99#ؒ4Ex <*S0[O,j%o`3!iE(XBuo;ƅꟊRǽs /F[9w2hM)|9}D+:eS+Nf+9:FJ[)apWpβ W7̈Ob =eՌ' mͰg*؆-+U -,zRd@opR#%KPC(;ͻm=>,bap@ΚAq w[WSZ! ڷV^;5C4'!O=l6DѩhOƖ! A\Xkj4¥(؀(CKaŏIʢ-.ѩ,:448z\ H.^8@sfԈ Pʽq"Ɋ1={| Ebt2V) IΛNec1AjXCsU14TbF`z !m-N4"t"b1K@;[# Q\<6,QM\䲻oQ8ݠ4j֦^oV&P NO+RW*PϬ{6.t`#xR*ە_ªK#^/B*(*0@PUy$UGwq-ս L&]S*bu3tx! +f"/j+[̏Mgղ/Y Mp8-kK)k/9HFT_ df1ncSg#F9W^*U Y@pM@|l8/ǙXkIm pW<(u)f>jv.Vtzv{]?~N's+ rۜ q"*/%F{9ϊ'{Ka:hr Pw<p/E2B#u#[ ,1wzfj<HA_qI6Ɯa">;!pY0daFBbډ (!^y<.kqJ0^"O4ϩx֜3-pk󙝆SNgB!)sVh#\;YZL^Y \dW?Z7R9Ų3ŵf )D8ȩ];gv CY^s >.kxhZIw$SL ֏῅ e>cyH Y[XK; `YU~;YElXgê7[͕wQ՛a) u3'[#65KvilC-&Р?Xo"qVqFܪĂet!wcVsM`N7˜ '&'IjIC&D4o6'2Z KnVF&V^/l6StA/QS{SdBU)؜#I`@rFr%&evL2$]S/]U5Y@-W,hqCסXMej%{ k4MNAs3ؼaWn3ZRGgW4^-Sx`1#ǢqP)FfT$wr 5Lj"*X%Ntn{mm1;.jVO]d_ ֮˰߿kMUK@ 3.`6; %<k3hmPk~SULy_> /L N ߕ-GrKDn .kp~òB=(T2ڋ B]K@.@Nͩ,8`ɛȑ"!D^WTRiCqm5Wŭb:\ThU;pԖX;գ.<ӯcAZ|n+stӀh~DI6G3+Ӧ"C=.ӷEk#~CX ń=dw BGgcbN+" cTv^(L:ZwNfQ?2JRa *MJ~Ly٬sU}dv.{P8Z?>iTKٟk؋oI(_Wgum9N˨{V"Ý5D>\65,ŋH2MφUr *>qu5\eiƞz(f8De)1e0W !v ;TkEJ&Bqx^[Hea4ͼϼu~ꍲEZ5jJwpӚ@%uB{~QX` Ѩ$ţoQۇ_bEPZs.AMAzzY/$:GxfDK[%':&A 2T܅gQЌIXZ0 G|x7 jpc ~09@Hу*oЮo崧Ք˯x}<6`KNEտB oB`7.gFz`]#mA0<*n3&9𔒨@[: "$ $@wɔ%06jTv/13CԖcV'K<֥> `3c_1/鋗{u$g(Uz~]Ai5J⻳$|;8f4?;S X6?݅L]=ʷb=#JDJO>VWYn9k+g^/--*tLN'=N1W˕Te݇ *uZZJDj)c5 )6J֝bxza?^U_Tws* WbPh7qq)V4:UsN[u#(7rR6sw_j7?а!ľB}ޮ7 3S˴U߳SZuF ]oѴ,x(6Euu}1 ƅXm5a\4/4\e-pja>)R JKr 0E8\TzMnXƑYH P8,%#!S82:3*AA;lOհz=Y@GUs~=k\ ˂ʽ"k[ɒgNBIH5&*w|Gj Q]9Tj'(T m^nk 0K߾ ?%uUVm3 o /h/(.싎=aT ǎ؞Lb@ioefMY=Lk #a{06_whr@H5Ⱦy5ud۪FK(jC$ь.W'"톃gƂ5{@A7 Q(wYx-ὃیG;?J6Sc{2tݾӭOzRUיYT,Z@w4jFa`|<%Kt_^AB5@ƀ  e]a)Px(AZRk%s|d!O0`ɒbK >Ah% (\ 6T/k4xF8hcGLPW ԟ|I-aX-n8m*bB$VDM9i YѬpip(P,]xX,y:$~OMm[";,L\ ڒ3[(R_5di0QuuB|?B.   78a|nql8:wnz: n okVdOhi޶H\Pi]# #jEM$fh 7k{^c3^QE-6͕&[aOua TsWfx)=Q>%d!4e0}{1pĈY<JαCl؀O[1?{[ˮJ],+mj3zd5־v!iuQ* .4)KW|&WXvc$Zc9߳&c3 oPc/D^- ~c]E_3.{on;eC?vU^{7ߺ5iۇ +9} |{nAZ͚&[jB4vB7pwr?͒]KwZ"rN_VZsO /`RTDg&u97NYiϊ+D=k5ܭ ';6TK3}!4"~bVSVViZҦ&q*% Gف%)$[ņ2Yf!dƱt뚔Uނ@O|X3 1, acK ATm[[{QQ=`%d4eHi5)\Jr=E*'QCZS7hVҘI}]4"ڃ~oAt!v 8m7?<{vYx2ܭvΠi!=)h_?7 =J<6eVpK?'P)9&> WUh,׏tMND4R'[(5*ee ,))OvLoS%eKl38i. @v;A:]|ɷ'p޵bWQ?juk3ǻ(4Z6在Z;Rg>5!q#ŒU){*5SE,J>:y +^):(@NsYFWBL[P#i\H]Ï,/ZB^fG³ҥ~)j3BI {3r m>ĬyÂR.ɖFM+{w:An !j $/ŏ/H65_-xrTZc.읈wvpC3ǢJ Y 2LH>67p`[WUN'~9b]ޭ)RK)vWA`mO^)hCahx'MCR]Lp K*:`Awx`3|]'d!P"'?pcO$Pc2&.t.i9Gmp,E4 ƦF072~:PiKB7\EQdVqaf`7ܑpt->LBePV U) Uv~}q zD[ kˏw} YVsRVHhW֚`t_O/5Ԯ. o@T !(lÀ[OpacMĻJw0;LфYwg]x=s߮kkz΢WqCZ,Z4X°O"f/Q+ӥxOGm;SYoeGz%ӇvifIdaLR:~ r8.[`oU#!e؊YT pJ  VLnSWEj*syCԈ;rEK}/4drBN/e@ L/)zbr)b4W!GjKx"hi.h?ɰ"2 tv Ts7Y@t.[1/`5!qw= .w=n׶՜/61qkٽnýU`m)c(U${ȾiuMߢ} QeS`Zh'rDQ=B] ;#V(RR3櫤aݻ-RX%_՛dW*"?9!gŃvݓiV~{1>P9[h$_l\K4Hk1_ӌHn]J,ռڊAS$z9]j#Ki)/ŶuGa(-ާD;h }S K4EU~jpUakHڢoZ5+Xb5cu5u8k^;+Ş˝Ec)zgV;E9WZV2bN_Ph5ʽFȣ |*?/,HX#iEm#!QAf% ܉vih2 ei0JC q5zpnU|XY߽1_j :j"v0,y|pg~@-RZ/Jͮ`A՘[9dN}P={]\u=̵+{Фrz1Sbc%JV ~*z:yc2AEDK6^5d8bY ?.(R[Qr$}ǎ2|bV#瘂3#u:T=(Kaഋĥt##c 7YnnϛL?Rc*"pOwog4c2ʻ\7^c?EL56v,j+iB曫VRr5q(}CA1N27I]o ܧ4ClB`Xo,m 12FUl&p9{) 'p<*l٬ \,GQ, s\|dGcr E}m]qߦy` hIͽeKԅ,r(bO" $!JYm"wW#?p/[O{==7A |´4%^, +I]4RV(탬)@1ZH8/?(Jv*~=oNq΄m,yDa)G`w"^@eӥFYGƨR?JbTt|vyF,)a1Yh;JL6cD!,)8(xPAO ,I,,SWWiS|8\|Xn!iB#[.d`M6LYq-| e< uImm1iBH]M1.<9}tٿ8$yӭ@5x uoouz5WęI+z^znh^«mT{UQ.څq~k^;0`aR9+l9pGwy\ѾuqӺޚR"D{e,\|1NCFKIL0 %8ZW/s9"FY#o5̋,i©=ʁ.;J;:TE-`#T&+~#*rM iBe"LہHr9V~"0' E=Ks9+6 0fH;)H?ɮ;A¶lӂhR&̐:Q~kvTnIyCzܐ2(x͝2ir9WT$=$ksU5Tj.Y7dQ<#j,&e$M2)qoI bN 3  N!5þb++JS( L iISQbii8Yәp ]q ?ˈc"3<0"du$C݊QܘA>)`K;Nv?_X]|)Bi-i;#j>~F>jd#2NPթk.=vH ?rd0I1[~8o#Z>y+kZ^̶aiFY@_KGy*F3C >tfR9zc"adV7U3ay2J5n ѴyQRNbc |XY%"οԠ;_//d-Kמo=˭}WSgy1ܹZLc諈2X[(x}yc؜ж h'/^I"TN8q{b vmQN.kꣵD5$ϣh\Ԭx0>z2杞 wH7#MGbm&*rZа_ii>;D~?gV{Z=?} {#m"0m|;^&x8y=0ce,qZ D73kQ-EE~fNqs)d&M bfin&ٸmp|L2ՂN <[Y,8t ^Kz$2&._7:*@=T\\ 8/7bU3S^645@|s0Kʫ\5/(zڈ{ΚHXNR ȃo?'@YIt,Zi,`ՇYLmswzI; ɱ볮f>z $]<643GH|6>k %a2~G]"4}~t캕@vv4<=.~ۮ/vD p\^dˁ'8/GKkziߝӗ`٤9 rdn"F;tC_֜g|c<'6n߱Kb)U$wxG$|!ÆJNLGn]C*XcEtNX)_ ld!Zé%М=z}}&p<3yi3)j&?:l&H3yMg}R}an;IN: fbqb8-ArhKPr /Ho*H&.s"W$5 Թp,ywĪ>qGǯc-xO^qGz;NZ_ |'Wւկڟi/dl4>m-ﾓ[]=,ܙe$6'"vZu:)IJ7\44杧74_[0sHgRPhOD@Wy^o`&Dimdt5'2yb/b|D;-iARy &01!0 H8KF4GewHds&Rwݏ{?+d(H6)- e,'O'4}L PJfP9"d1)mc/A$uH?)%C_ES%2F&įD~V*J'8f& 1* L*REaz\I"Kt"+SM/Q]I9,4FlO+I֗7,qhJzڸR>%`L<P1e(JbblE͈\pCumeI Xl#m9>B/z#F&f<=ؒ#@geC6"o c,~fVI 9y3MGm; hb=GYv9:oy] EpibQ4j sRNeОu҄[4N3OP!! M["i /@U[ZYJ= ŶυLZ7 "SE4mj`t\1&~`M)/HۼIEέIʡ2noE$'t{DDN0zn33Jw[>'ޛYFUSnIbLiLRmH'A}eO?]~nghMug;d<6ڨgdӪ)G))bb bTLx $#:m ̿.DW`"^ۺ& 7Ab?&R/c Hƛ"oiqktX1Nϧ7g緭Ӣ YBmmE۾}(4KK'}z"= hk~h;ܹ;ZpgmG{v JP =j\+m+Ӆj)O}֗3+Fpxb:}}zLO(C{Gںۗ-;sxv+B{Háݩeů +ߨY ?yK7BƏs| %k]B,@jf?\uT,'ͧ,A<^eFɪ>Ta8+?Ԃt]e* ZUv!goȹy/@[| \:]Y,T^fs׭ϩ,-]P֙VYRfv^=A탨s>QxnTRf$J`-)G+#²{RAUàڮHS&3jp/玃g,-** 6@ϹR2kמ'BVyhOSj`hfא rJѽV(2^5Pk?WXIӻ2C4jc;!qxEct$5b긠D^u^ll;:D)(<[8W=(YhKJq{$!ckI9L)_*yujHުE1l<>!X*- _6@~H'!%WRCyOՠk<ΐ{\@VaPյ=\gƌrj>[ GTQU0#Sm.kG-r?3**V Jsxր Q2&3o}T`ByG]xy!/9/0<3BU nyK'9MF58~f@PH"]d1 WQ\1}X@Xqރ@XE"MesNJKUYR8QLnArdA#b ꁆ>FfLsA-JPN@/=!/(c>t e.EɊcpl:nzls[.J||D5u9h>24[P#ېP.~7 x4W^TNuGUΫkv.) a>Af7:|9X_9f.0_hz!W/O_ҐF` F &hî==tqP .xJ }XJ2] ?yh H/-h>ל3?L&`WvHS{;*ӗ9eY=o?"9l]PV5.ҷK? HWbmBb)̺$ޢG}nkH&t" s`"}FxJFQr*AҖ/8Dj*,9ؠՏE+bFf觍+& TZ^Fd!BUM^w۩++'yŠ/p2W5Ȋ)1lj0/Β^ sb .Ȧ־ N@= lwc $?þO1 Gn9(7>~Gxpg#h⟖^̡\?{DF`+lð;Jz8"9~pĉ"}EH AW==bmp՟`ֆGhy|,$-#yyΘ9e~Xer\\*4Ө2!DMl|#k1P~;$6@gCH>I+Ӎ hdp)UVX,X3ɐY5Cm4{E `baӴP=>ec`-#H' 2kt y>S(; b &|rWgx`_޳6[FجPsvB1~un]- }@ 28,){*7Zy%'8qH_U}wǶ_CD@;w{hB)!$yatT(ߪ 9~b#M=.>Bڇޢ$mp߻KF GLspW>d" o!כENtǗZےzR[j/ {5xj+\zZ^ Ûjo4&g-md`.4_&J MG&ĴϕDѦ=n5B,QoD/<`v_PpSCCYfBV E7w@u-8QFes orjȕ́1>Xʖ;*>ȕxxоп4{qw;^s Ƶ`g E,7EllrBOfP'"7CMx_;/lgx\y^sȩO"G)5ƹfuqt*/WNK/DCyJm\=v+Ӣ%(y{k 9C{oiLx |ցCB62|Ɖu#Rٮ-[e{J#j: o+̍ƢߣJlf,(IߦFlq$Љ)h!|y.],/0gWqGy"E&gDF;P^P5}?)~n(A0)zוEgM嶋:tQQܷ9JOE#lo|%?OÕ9ʢR_߂]]&qh!ah#{(1Yname '%U' is not defined while calling a Python objectNULL result without error in PyObject_Callraise: arg 3 must be a traceback or Noneinstance exception may not have a separate valuecalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionexception causes must derive from BaseExceptionkeywords must be strings%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'at leastat mostexactlys%.200s() takes %.8s %zd positional argument%.1s (%zd given)hasattr(): attribute name must be stringjoin() result is too long for a Python stringcannot import name %S'%.200s' object is unsliceableMissing type objectCannot convert %.200s to %.200stoo many values to unpack (expected %zd)need more than %zd value%.1s to unpackassignment'%.200s' object does not support slice %.10scannot fit '%.200s' into an index-sized integer'%.200s' object is not subscriptablebase class '%.200s' is not a heap typeextension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base typeinvalid vtable found for imported typemultiple bases have vtable conflict: '%.200s' and '%.200s'%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd-%zd from PyObject__pyx_capi__%.200s does not export expected C %.8s %.200sC %.8s %.200s.%.200s has wrong signature (expected %.500s, got %.500s)variablefunction_cython_3_2_4Shared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompiling__name__ must be set to a string object__qualname__ must be set to a string object__defaults__ must be set to a tuple objectchanges to cyfunction.__defaults__ will not currently affect the values used in function calls__kwdefaults__ must be set to a dict objectchanges to cyfunction.__kwdefaults__ will not currently affect the values used in function calls__annotations__ must be set to a dict object%.200s() %s (%zd given)%.200s() %sBad call flags for CyFunctiontakes no argumentstakes exactly one argumenttakes no keyword argumentsunbound method %.200S() needs an argumentneeds an argumentdoes not matchcompile time Python version %d.%d of module '%.100s' %s runtime version %d.%dzlibdecompressFailed to import '%.20s.decompress' - cannot initialise module strings. String compression was configured with the C macro 'CYTHON_COMPRESS_STRINGS=%d'.__int__ returned non-int (type %.200s). The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__int__ returned non-int (type %.200s)an integer is required?? @%=# !!"!& !!#+),$#'$ '+&'"" =41 /%4 '35 $2a   zt z Sp  " z D  '" % "p '   v?A 5 yw   ' j .                                           r? 9f5Y9:DB8::8s(8D* =!t=<;<988::;=98B$=C9=9?:0yE>qh?j%= BT ~Q~U~KD Ga7\%aFOaSuzpD(|Wc %WM$ t`K[oT`gtSwf# Wl`0H7[z1z(zK^2#9MM0MFPrOxS왎2ȩn{TH,ҭ^p .]M[\}r;/4d6dcNQp.t@e$oX%L(xo6xu{fUY>9>{ppCBwS(:5^dܓAN}8) YfHqն&|s f2,2Ztզޗ .n ZR'ӯB)[l@u Pҍ'TȈt(5wI'L/$;nXMØT`OArW,+jtȳRfARnqӊ<KZW$eKs) 4<=>)G'QA@Y.(5bX jz>lq{2Xx{~JH҄Cc`Qz%~ )Q\HsrUb'Bkq-hnק Ψ;3Kd)P^٨Tv$Hx"$ 5..&$ŗ: L< Ŀk<4xV<=A[<'?}y<NG<~;[?l?*?%z?PՋt?4?e;?$"?zaWF}?Gz‘B?Oq1? OU?ߺH?7a?nV,? K?Xhw?հ<?Vp\?m?)?zP?ZcX?*;Q^?#*'g? U7?e&$ ?jJo?\Ȭ)?L&?FS?leZ&?g ?NIO??xRr!?P_hy?y6IJO?_5%?[X~?1>?bU?+À?PX?5:pɗ0?8d?;U?J?͓?)m?ېZ]G?/|!? ?iT??Wq?PF9 ?ߓ^??ۮY?3???i?Z8o? O5?ٸ?P?R9?igP?La;?L?!ވ?%o?{7=8?Ҁt?DvC?6?=p\?;So&?mj?W?j?$O?z5Ѽ?Ҏ?C|P?yh|?%H?/ZM?f!w;??>ǭ?MAz?G?y?.?P9կ?TT}?g4K?#$O? Y?BM?6C;?B"_U?~t$?œ߉?52?Ҙl'?DɤT?<(i?qE8 ? Uī?OQM?o^?Sq͒?Gط5?zx?1zd}?:R!?Wg?~& ~k?=~-2?ZҿҶ?'|j_]?it?[?8R?uqb?#h?z|J?G~`?\!>?GF?vJ?l󈬚?5hȩmE?㭍?-l ?uG?1i%?調?M?e*|?zè?^V?4<%F?B}u?c-@c?n? R=?Kr?*}T#?,"k>?R) ?K{o?vaӽ?命8? t;I_? h?3xk?3Ӻ?b3?vZ9S?LJisk?M$a.?ftW?+ ?"@|?&#?p>_?1fҲ? DE?} ?/?%,?0?5nl+,&?QG?b. ?,*(>?p_8?cU)?h*?'wާ?dИۦ?ԭ<ڥ?]']ۤ?ݣ?=|?j?.?ĥׁ?u? ̓0?"NR? y? ڥ?d֔?^8 ?0`4I?IrO*?O'?x A?B?/)?7h`|?] ٨v?p?gC_e?T?wE`mru\zw8xky5zz/ {ԃ{{7|3}|&|H|}C}g}ۇ}}a}g}]}~~4%~5~C~Q~g^~ij~u~>~2~~r~դ~Ƭ~N~u~C~~~~k~~~~~~t~~~6 < :#%](*.-z/13579;=?EABD:FGNIJ8LMNLPQR T=UdVWXYZ[\]^~__`;abbcod.eefLggh~~7~~/~7~~ ~ ~w~G]~>~Y~,}6}b}|O|06{xI4_h2z3+3@3aQ3i`3{am3Ay3i3*353=3r333|ϡ3ڍ3+333^33׶3iż3-¿3c3%3uY3<3L3gv3;3k3-3$3!333P3P33<3p~3չ3^3J3I34V?4=3@4A4A4qB4C4D4udE4-CF4K"G4H4H41I4J4vK4\fL4HM4+N4aO4O4bP4ٽQ4R4ԊS4crT4ZU4CV4-W4ZX4Y4UY4Z4[4(\4_]4^4_4C`4va4alb40cc47[d4~Te4Of4Jg42Hh4Fi4Fj4Hk4Kl4MPm4Vn4^o48hp4sq4r4s4 t4u4v4Cw4x4 z42{40S|4u}4~44v4@ 4L4>4ق4v444lV44R4F44p4 I44"4_44Ќ4l4L4`4ԏ4坐4y4ݖ4%44r&4k44(4444.4Q4N4t44\۶4H94̻4p44~X4w4p_4~444?V#z?u?q?}n?k?Lh?e?Rc?`?Zw^?*+\?Y?RW?U?_S?XQ?߱O?M?3K?J?GH?F?jD?`C?(`A?j??>?x,>N>>q>>>j>>k>>Π>>F>>>'>\>#>u>J>*>_F>d>+>$>w>>>JK>y>|>iݿ>>I>;>ʾ>t>5<> ~>>>O>>>~3>T>ե>(>g~>ՠ>G/>>>F>J> >:n>bԓ>Q<>>x>~>>>^>Ј>D>l>1>>%>\D>@|>?y>Bv>Hs>Qp>#^m>mj>|g>md>a>^>$[> Y>=3V>[S>P>M>J>~H>UE>B>?>=>S:>7>"4>=22>T/>d,>m+*>m'>c$>N?">,>>m>t>F>>1*> > >Y>>ʗ>>I=_={==^==&=_=g=='0===P6=˙=\= s==d= =yo=/=6=.=fЍ=x=i'=܀=a1y=p=xIh=_==W=TO=G=>=N6=.=&===-H==<א<̀<<<. NVeΙVn6nvK zicp%E tQ)2U1WQ9Lin?23F:L"3\LQ V f[_rWDdx h+*k2=Ko:qr Mu\x?A{FS~8;b=ZV`bBtu9=JE>XدGwdO 8cx AFẙi&zqVYםΡag6 X83:뇡koɣj_ۤ| Mg^ݧt|Ψ_ΓXp2X^ttH蟿W;ޭl~$\z[߁İPp:J+N!X ɦ֬ ᆴX7(. Ɉ?5}h.G{tr&oya=cA/˺DH0⤮<)9O@ᣩTrVj֋@?˷dsI^i@(0߾ta&⊂l1EA1T[n&mi#d)B}QJwt}B < EOvpc/F<Ң"Ae އ0~ Rfq(*QtH3D@M`P}hwx%ƿ8*JG+[EliPIw+ E>ҙ02yΩ4A (Nt.Ȱ--̕^&܌z#;ޖu~g6X .pmF 3n bH޵LaEZvpR(-x_b˿ӰdyQӶVg<7܆ut7$MH𯋉ld"rqտH)݄ /0 wپ}2}K D5z&R cM,}uc?Ѡp5.bJ3ʸT[vv+\[U@ضBi"7oLeiFγ>SR(D2Z> B0$y1gWr-ެ @樫(afoeW-|&aY +M]d<A]X`<+M[Ij<[5q&<.8eG< h#ឪV <;LC%K<ꆭh?{s?%;?omo?3;?J9?++?*T[?};1s?HeC?$`?vE!=?ſ-r?MBц?K=?Q}6Ei?7u? !?z}k? ~?@?`x?*?8? Qi?oTC?_(4?ָ?@je?!u v?7Zi?{ ?I?]T?9]??}?8aD:?Yζi?Ɲҷ?r^sSw?ꍰ07?d>[?%۹? Ə{?'HB>?vX#?l1&?:l?磽!O?ލ?&?ڋ?ﾀ+ j?A1?N0Z?0H?}G?(V?5$1!?pB9 ?b"FS?)vEW(?vG}rO?~ /? {^?Z? ?ބS?i"?lR?3Sn?>N?Ґ]b?,|y2?jG>?TLҫ?~>\O??@YH?/֎@?9O"H?>?1 7?8?Ox?]4?5D9g?r|?>ܸ$8? [B/?I䠟?O?y%d?bPޱ?c?PR?j?F}?9(Q1?c?(ڦ^w?0U^Q?1j?T ξ?x.BTv?Imb.??6YJ?)ِ?\C}?%d?w?SN?эv?pa?,Q&?@oű?SuFe?PV?;?I?viׯ?4D?.g?X1Iα?Jy?!dJ?پz?j»?8G;?L|{ʎ?mwn?k9:9??Ry?A&E?U?Ŗ<?k&_?G??~#? V#?_?S?Q| z? Y&?$?htQz? 3Tݜ?pXP?N梚?H*g?gS(u?1c?w@rT?Q=I?QA?]1%? RD?lj?W'n?-BU؊?h?t4? n?boQ?qvi?_)N?]tQW}?6H#z? 67w?"Ηs?C@Wi=q?ḰXl?f?$ka?%> T+Y? O?K 2=?QHqoMֻanjDotTrotou$w'xx,jyy7\z׻z{W{S{{.|3|]|ȃ|||I||}C0}F}Z}m}}S}(}}-}}"}}|}M}~i ~~~B(~o0~C8~?~F~M~T~Z~a~f~l~r~]w~v|~`~ ~~$~m~~~w~:~ަ~f~ѭ~#~Z~y~~q~K~~~^~~a~~~`~~~~~~~~f~*~~~-~~J~~=~~~\~~~$~U~}~~~~~pH`  i   6  H  A!B+m 5XttW3 `wK\ L   s   G {V~~~d~~x~~K~~~~~~~~~)~~~a~~~{~;~~A~~~m~~z~~~"~k~]~~~ԃ~|~s~j~Ua~W~K~?~2~$~~~ }}} }}i}A}}|Q|D|{3N{zeyww7mse'52V227222f23ن 33H3(&3o.3z63oN>3E3lM3FT3/[3b3i34p3fw3&~3[3B3ψ3g37!3>3T3d3n3r3Fq3j3_31P3r<3$3k 33ȸ3q3|{3P3#3C333dY3"3+3®3r35333x37333p3-33731b33l44(44h4C44 4` 4M 47 44?4nB44L4 i4a4T044542`44p. 4!4"4i$4%4@'4t(4>*4+4,4j.4/4'P1424):4454&)7484c:4;4$=4+>4@4A4KC4vD4B(F4G4:I4J4rTL4M4GuO4Q4R44T4U4EiW4Y4 Z4G\4]4_4:a4b4d4Bf4\g4ji4bk4m4n4p4\r4}"t4Yu4Hw4[y4X{46.}4 4q4a4]S4F4N<434,4+(4{%4$4o&4,*4'04m84 C4P4_4q474{4w4>ԕ44s4<4d444$4 (4a44lߢ4$4l44x 4_444{4 4EP4±4{:4귴4);4nķ4S444<*4տ44A4.44ע44f4RW4R4*Y4Fk44δ444444g44k4<444y44u4_45555@5ó 5 5]5^555q5v 5!5%5V*5s/5;S55:<5D5NO5^5Nv5?/*p?3f?(_?xY?յS?N?J?F?DB?Qt??u+ ?A?Į?"?ʝ?G??i>l>7>>*J>>>^>>F>>7P>>K{>>>u>;->>>|>eO>4(>8>L>N>ȿ>>>>·>ε>߳>>>0>U>~>|>ަ>Y>IP>w>ҟ>B>e>)>~>_>>C>{>>J>﷍>X(>'>N>Í>x >b>x>>!>^}>;z>Хw>@t>wr>byl>i>g>Sd>3a>^>]\>&Y>z)W>T>P R>O>L>5~J>3H>E>nC>@>VK>>;>9>07>4>2>>0>p->+>s)>7'>%>"> >s>L>*>= >T>T>4>>y >ϣ > >>L>>l>=+==0 =C==8==hp==0== =n==|===;=ں=Z=o,=ް=ߗ=.W===%==r=W= C=4=M,=4*=D.=y8=H=~=x=>h㈵>??9v?[ m?m{?h|?5?Q?x&?UUUUUU?UUUUUU?Q???dg?r鷯?n?$~?r?@(\@= ףp=@@-DT! @333333 @B>٬ @3 ; @@ffffff@-DT!@@~)@"@$@.@4@>@X@`@a@i@|@@@N@MAA.AACS lѿ-DT! -DT!q?3?r?0@@@67f6777 7ai 0fLf4f(_t)?+kz,}r0 \  p.text$mnp 6.text$mn$00 .text$x0.idata$568.00cfg(7.CRT$XCA07.CRT$XCZ87.CRT$XIA@7.CRT$XIZH7.CRT$XPAP7.CRT$XPZX7.CRT$XTA`7.CRT$XTZp7/.rdataft.rdata$voltmdf4.rdata$zzzdbg(i.rtc$IAA0i.rtc$IZZ8i.rtc$TAA@i.rtc$TZZHi'.xdata`.edatahd.idata$2̘.idata$3.idata$4П.idata$6.data'.bssP.pdata  t20  4 2p  R`P!) ) t 4 -`i!-`i11d B!TMi!4Mi!ti!i!Mi!Mi  4 2p td4 rPdT42pd42 ptT424 2 p`P! ptj!ptj! ptjd T42p 4rp`P T 4 R p `2pP0!d k! &k!&k!k t dT42d T 4Rp d T 4 2pB2`!4 k!t )k! )k! kpP!  d4 Sk!Szl!Szl!Szl!d4 Skd T 4 Rp//t20Q QLdGT  p0dT 4 p4 2 p`P! rl! rl!rl!rlR020  4 Rp t d T 4RB  4 2p!T(Pm!T(Pm!dpm!pm!(Pm4< <4,t%T ` 0!4pk!t4pk!pk!4k!k!t4k!d4t`k!`kT 4 R p ` d T 4 Rp 4 ` [  p`Pd4 pP  p`!) ) %  T4n!nooT d4 pF Fd >4 T 2 p+ dT4p` p ` 0 P* 4  p`P P!<p0P!hhMx0h9:; <d=bb@r!:bb@r!bb@r+0P!WWh$%&'( t)d*pvvr!pvvr' + p`0PH- ? p`0P' ' p`0P ,p0P!d3PTqT\s!() *+qTuTts!xhuTdds!uTdds!xhuTdds!qTuTts!PTqT\s* 44* p`PH(P!#4,F"$%t&d'`,t!4,`,t!"#$%t&d'4,`,t 4r p`P d T 42pp ` 0 P! Wt!Wt`0`!tSu!Su p0P!d@Lu!@Lu p0P!  du!u!  dIu!Iu!  d@u!@u p0P!d |v!|v&  p0P!dCPv!CPv%  p`0P!v!v%  p`0P!$o%v!$o%v  p0P!d **w! **w!  d/y/u!/y/u$ p0P!d@3(4w!@3(4w!d UZVv!UZVv!d Z[w! Z[w$ p0P!d vvx! vvxp`0P!T@Tx!@Tx!TbTx!bTx$ p0P!dx!x!dw!w%  p`0P!`-y!`-y%  p`0P!\y!\y!Pv!Pv!p9v!p9v% " p`0P!!P9y!P9y!P\y!P\y!ɿv!ɿv!dqw!qw! \y! \y!v!v!)\y!)\y!y\y!y\y!I\y!I\y!y!y!P y!P y!y!y!y!y!v!v!p9v!p9v!dP;w!P;w!dX x!X x'  p`0P'  p`0P!dPkQw!PkQw$ p0P!def|!ef|%  p`0P!kl }!kl }$ p0P!dqqd}!qqd}!  d0u!0u!  d@u!@u!  dYu!Yu!  d0u!0ud42 p T-4*$ p `4 d 2pT4 r p ` d4 p Pt#4  PR`!tT 40##~!tT 40##~(\p0P!eddc%>&!%>& p ` P 0!7X!7X t d T 42T `!  t4G!G dT4!t#!# p ` 0! P$! T P$! P$2 2-t(T #4 r`zz4 d T R pdT42pd T 4Rp!h!xd 4 r p!h!!h{!{4pP!; ;h)x dP!P4pP!> >h1x d@X!@X!h!dT 4 p!++xh!WWh@(xdT4pPPhC5$xdT4p` P!RRD6/x$h t4p!pTp!UUG9+$xh d4!&& 0!<!x h P!  d!d!P!<$$ 0! Z̃!% % x h Zx!  xP!xP!Zx! Z̃x h  hbxh# #h 0!!!x e!e!e!e!x e x0  hR0  hb  xbxhr0##xh0x h0hx0!!x 0!h E! E!E!E!E5 5+"xhIIxDh>4T 2 `!t < !< 884T 2 `!t 0X!0X!4Pak!4Pak4 2 p`P! a! a!̇!a!̇!a4 2 pP!d p! p(!p(!p4 2 p!d пp!T #!#!пp  2 P0!t `̈!d 05(! !!t `̈!`̈td2!40yL!0yL==6.&x hdT4pqq i a P H 72-%xh' p P 0!d&̵990(x"hdT4p!p! ! !pUU4! ! xheh!xehF<60*% x h p`0 d T 42p0!  xhp!xp  xb  x!h  T!  Tx hhb\\h x06 60&xh 0!p!p, ,#h 0!x !"" $ ! $ ! !x && 0!!x h 4!  4!4!4!## 0!h Y !/ /)#x Y a (!Y a (! Y 4 P!x hd!t!!@@.) xh 0NNHC>4+xh 033hx990(x"hdT4p!PT!x!x!PTysLC91 )x !h 4#p ` P0t d42'  2P  4 Rp     4rp`Q. .RP  brp`0  4 RP "W !P4PdT4p??0пP`P0 @` `@0p@p0p@ pppp0@'3C\wÓݓ8VgzȔ֔':So•ؕ ">\}Ŗ1Jfȗ"1AM\  !"#$%&'()*+,-./0123456789:;<=>mtrand.cp311-win_amd64.pydPyInit_mtrandrandom_betarandom_binomialrandom_bounded_bool_fillrandom_bounded_uint16_fillrandom_bounded_uint32_fillrandom_bounded_uint64random_bounded_uint64_fillrandom_bounded_uint8_fillrandom_buffered_bounded_boolrandom_buffered_bounded_uint16random_buffered_bounded_uint32random_buffered_bounded_uint8random_chisquarerandom_exponentialrandom_frandom_gammarandom_gamma_frandom_geometricrandom_geometric_searchrandom_gumbelrandom_intervalrandom_laplacerandom_logisticrandom_lognormalrandom_logseriesrandom_multinomialrandom_negative_binomialrandom_noncentral_chisquarerandom_noncentral_frandom_normalrandom_paretorandom_poissonrandom_positive_intrandom_positive_int32random_positive_int64random_powerrandom_rayleighrandom_standard_cauchyrandom_standard_exponentialrandom_standard_exponential_frandom_standard_exponential_fillrandom_standard_exponential_fill_frandom_standard_exponential_inv_fillrandom_standard_exponential_inv_fill_frandom_standard_gammarandom_standard_gamma_frandom_standard_normalrandom_standard_normal_frandom_standard_normal_fillrandom_standard_normal_fill_frandom_standard_trandom_standard_uniformrandom_standard_uniform_frandom_standard_uniform_fillrandom_standard_uniform_fill_frandom_triangularrandom_uintrandom_uniformrandom_vonmisesrandom_waldrandom_weibullrandom_zipf`1`|0081F02аp\>"&0:D\ڮҮ̮Į$>`|Ԥ $6FVh~̥0DTbr̦ڦ4H^tЧ$2BVxԨVf|ʩީ(<PfxЪ$:Tp~ī֫4HZlҬ(6HZjz̭.r`N< ܣ|hR>&֢vZ@.ҡxj>Pؠp^N2$2ПuPyObject_GenericGetDictwPyObject_GenericSetDictPyMem_MallocPyMem_ReallocPyMem_Free@PyType_FromModuleAndSpecNPyType_ReadyMPyType_ModifiedPyObject_RichComparePyObject_RichCompareBool{PyObject_GetAttrStringPyObject_SetAttrStringzPyObject_GetAttrPyObject_SetAttrPyObject_HasAttrtPyObject_GenericGetAttrPyObject_HashPyObject_IsTruegPyObject_ClearWeakRefs_Py_Dealloc_PyType_Lookup\PyObject_CallFinalizerFromDealloc"_PyObject_GenericGetAttrWithDict|PyGC_Enable{PyGC_Disable_PyObject_GC_NewqPyObject_GC_TrackrPyObject_GC_UnTracknPyObject_GC_DeloPyObject_GC_IsFinalized'PyBytes_FromStringAndSizePyBytes_AsStringPyUnicode_FromStringAndSizePyUnicode_FromStringPyUnicode_SubstringPyUnicode_FromFormatPyUnicode_InternInPlacePyUnicode_InternFromStringPyUnicode_DecodeUTF8PyUnicode_ConcatPyUnicode_FindCharPyUnicode_FormatPyUnicode_New_PyUnicode_Ready_PyUnicode_FastCopyCharactersRPyCode_NewWithPosOnlyArgsQPyCode_NewEmptyPyInterpreterState_GetID PyThreadState_GetV_PyThreadState_UncheckedGetPyErr_SetObjectPyErr_SetStringPyErr_OccurredPyErr_ClearPyErr_GivenExceptionMatchesPyErr_ExceptionMatchesPyErr_NormalizeExceptionFPyException_SetTracebackCPyException_GetTracebackDPyException_SetCausePyErr_NoMemoryPyErr_FormatOPyOS_snprintfPyLong_FromLongPyLong_FromSsize_tPyLong_AsLongPyLong_AsSsize_tPyLong_FromLongLongPyLong_AsLongLongQPyFloat_FromDoublePPyFloat_AsDoublePyMemoryView_FromMemory:PyTuple_New8PyTuple_GetItem9PyTuple_GetSlice;PyTuple_PackPyList_NewPyList_AppendPyList_AsTuplePyDict_NewPyDict_GetItemWithErrorPyDict_SetItemPyDict_NextPyDict_SizePyDict_CopyPyDict_GetItemStringPyDict_SetItemString_PyDict_GetItem_KnownHashPyDict_SetDefault_PyDict_NewPresizedPyModule_NewObject PyModule_GetDictPyModule_GetNamePyModuleDef_InitPyMethod_New=PyCapsule_New:PyCapsule_GetPointer9PyCapsule_GetName<PyCapsule_IsValid2PyTraceBack_HerePySlice_NewPyErr_WarnExPyErr_WarnFormatPyArg_ValidateKeywordArgumentsPy_EnterRecursiveCall Py_LeaveRecursiveCallPyEval_SaveThreadPyEval_RestoreThreadPyImport_GetModuleDictPyImport_GetModulePyImport_AddModulePyImport_ImportModulePyImport_ImportModuleLevelObjectZPyObject_CallbPyObject_CallObject^PyObject_CallFunctionObjArgs`PyObject_CallMethodObjArgsPyObject_Size}PyObject_GetItemPyObject_SetItemlPyObject_Format~PyObject_GetIterPyNumber_Add8PyNumber_Subtract1PyNumber_Multiply6PyNumber_Remainder,PyNumber_Index.PyNumber_LongPyNumber_InPlaceAdd*PyNumber_InPlaceTrueDividePySequence_TuplePySequence_ListPySequence_ContainsPyObject_IsInstancePyObject_IsSubclassPyObject_VectorcallPyObject_VectorcallDictPyObject_VectorcallMethodhPyFrame_NewPPyType_Type PyBaseObject_Type _Py_NoneStructPyUnicode_Type PyExc_Exception-PyExc_StopIterationPyExc_AttributeErrorPyExc_ImportErrorPyExc_ModuleNotFoundErrorPyExc_IndexErrorPyExc_KeyErrorPyExc_NameError#PyExc_OverflowError*PyExc_RuntimeError0PyExc_SystemError4PyExc_TypeError<PyExc_ValueError;PyExc_UserWarning PyExc_DeprecationWarning+PyExc_RuntimeWarningPyLong_Type PyBool_Type_Py_FalseStruct_Py_TrueStructYPyFloat_Type>PyTuple_TypePyList_TypePyDict_Type1PyCFunction_TypePyMethod_TypeBPyCapsule_Type4PyTraceBack_Type_Py_EllipsisObject%Py_Versionpython311.dll@strchrAstrrchr;memcmp<memcpy>memset__C_specific_handler%__std_type_info_destroy_listVCRUNTIME140.dllexpm1log1plog1pfJacoscosexpexpffmodloglogfpowpowfsqrtsqrtf6_initterm7_initterm_e?_seh_filter_dll_configure_narrow_argv3_initialize_narrow_environment4_initialize_onexit_table"_execute_onexit_table_cexitapi-ms-win-crt-math-l1-1-0.dllapi-ms-win-crt-runtime-l1-1-0.dll RtlCaptureContextRtlLookupFunctionEntryRtlVirtualUnwindUnhandledExceptionFilterSetUnhandledExceptionFilterAGetCurrentProcessTerminateProcessIsProcessorFeaturePresentQueryPerformanceCounterBGetCurrentProcessIdFGetCurrentThreadIdGetSystemTimeAsFileTimeBDisableThreadLibraryCallsInitializeSListHeadIsDebuggerPresentKERNEL32.dll(>p7>@>E?TE@_$Tc *P_i/cv@3ip9v ?D JP0`UL ZpX_`epPkq` v {Ih0@p@#0{{#0{{#{|#{|#p||#rx$rx$|($|8$|H$|X$}h$}x$}0}$}0}$~0~$p$# $@$0$(p|00%8@04B02PG 3@@@HpIP68MH`9 TI@`P0T`spP`hs@pnPs؉P8 `Pph0x80P@P``H@P(PpP@01,P`@@Y(>p7>@>E?@_$Tc *P_i/cv@3i`UL Zp vp@#00%8@0H`9 TI@`P0T`spP`Ps؉P8 `PxP`@P(PP, pC1MC,YpMjP``TEp9v ?D JP0X_`epPkq`h0@hs@pn0PPD``@, 1,pC1MC0#%Mp,YpMP`@@YjP``UUUUUU?llfJ?88C$+K?<ٰj_AAz?SˆB8?5gG2-+] fu|v|!,v!@vCPvCpvvv5v5vX xX #L|#$d|$o%vo%(v( * w **w*D-4wD-.Hw/y/uy/$2Xw$2;3pw@3(4w(46w69w9?t| ?DPpDJ|JxP|PkQwkQ(T|(TU|UZVvZVYwYZw Z[w[]w]_x_e|ef|fi|ik}kl }l`o@}`oqT}qqd}qxt}xtv} vvxvy,xy{Dx0{{m{{k{ |m|p|k||k0}}j}/~m0~~j~/m0uL}L;}@Tx؄hx؄|xckpm|bTxbxxxhxh?x@ҏTim &PjPuwxSy`-y-Қ8yҚLy\ye|yeyEkPvЦyЦcyp9v9y@y@u\}\K}P9y9zPzP\y,z@zɿvɿuPzudzWtWtuqwqtzzYuY}~ \yuzuzvUzUzSuS(u.&>&J,JhHp9v9|@|@u\u\KvP;w;$|<|ؠm~jpkp0mm2$m@9m@?m@m Tirlrlllm(Pm(\mpmmmmWj`pk|lk&k&b$kbv8kvHk##nn |lkX~ k )k)mkmkkl7X7pxjXk`k`UXnU`tn`Hmkn0n4@n4j0npkmm| n{kxkTi-4>Hkdptjjjj@jGGkk$j:Pj@jOXkPhlj~p ~ +t~0mnn-`i-liiXn`nAsANTd@t`X$0l0px@`@eppDkk%k0oHpp0{{,@P8`89@XXЁ4<Pd Z̃ZxxPPYY~0~@PpDpnlp{{|؂,kxkpЀpqc(pk|EEFЅF  $0x7<@Ȇ  $ $ @X<h<`X`zk<FPoppG G0`B0Pbp<Ћ<`NHPLmPTxXXjjxD::vЍvkQ`0k44 ̌ + + 1 1 F P  x Y Y a (a C <C  d  |  x  T  `  trk.0kXDDPjm܏kkUkXkkkkD