diff --git a/binder/environment.yml b/binder/environment.yml index 59ff6e5..e26feea 100644 --- a/binder/environment.yml +++ b/binder/environment.yml @@ -8,7 +8,7 @@ dependencies: - python>=3.8 - jupyter - jupyterlab>=3.0 -- pymc3 +- pymc=4.0 - pip - seaborn - matplotlib @@ -36,6 +36,7 @@ dependencies: - pre-commit - networkx - mamba +- "python-graphviz" - pyprojroot - faker - pip: @@ -43,4 +44,5 @@ dependencies: - mkdocs-material - mknotebooks - daft + - gapminder # Need to add pip install from github for repo, so solutions can be executed. diff --git a/datasets/Batting.csv b/datasets/Batting.csv new file mode 100644 index 0000000..b90217c --- /dev/null +++ b/datasets/Batting.csv @@ -0,0 +1,108790 @@ +playerID,yearID,stint,teamID,lgID,G,AB,R,H,2B,3B,HR,RBI,SB,CS,BB,SO,IBB,HBP,SH,SF,GIDP +abercda01,1871,1,TRO,NA,1,4,0,0,0,0,0,0,0,0,0,0,,,,,0 +addybo01,1871,1,RC1,NA,25,118,30,32,6,0,0,13,8,1,4,0,,,,,0 +allisar01,1871,1,CL1,NA,29,137,28,40,4,5,0,19,3,1,2,5,,,,,1 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+gallibe01,1920,2,PHI,NL,19,23,1,4,0,0,0,3,0,0,1,6,,0,1,, +galloch01,1920,1,PHA,AL,98,298,28,60,9,3,0,18,2,2,22,22,,1,14,, +gardnla01,1920,1,CLE,AL,154,597,72,185,31,11,3,118,3,20,53,25,,1,32,, +gastoal01,1920,1,NY1,NL,4,10,2,1,0,0,0,1,0,0,1,2,,0,1,, +gawch01,1920,1,CHN,NL,6,4,1,1,0,0,0,0,0,0,0,1,,0,0,, +gedeojo01,1920,1,SLA,AL,153,606,95,177,33,6,0,61,1,3,55,36,,4,48,, +gerbewa01,1920,1,SLA,AL,154,584,70,163,26,2,2,60,4,13,58,32,,2,19,, +gharrpa01,1920,1,WS1,AL,131,428,51,105,18,3,3,44,6,5,37,52,,1,19,, +gilhage01,1920,1,SLN,NL,1,3,0,0,0,0,0,0,0,0,0,1,,0,0,, +glaisjo01,1920,1,DET,AL,8,3,0,0,0,0,0,0,0,0,1,0,,0,0,, +glaseno01,1920,1,DET,AL,1,0,0,0,0,0,0,0,0,0,0,0,,0,0,, +glaznwh01,1920,1,PIT,NL,2,3,0,0,0,0,0,0,0,0,0,0,,0,0,, +gleasjo01,1920,1,WS1,AL,3,2,0,0,0,0,0,0,0,0,3,0,,0,0,, +gleicfr01,1920,1,NYA,AL,24,41,6,5,0,0,0,3,0,0,6,10,,0,1,, +glennbo01,1920,1,SLN,NL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gonzami01,1920,1,NY1,NL,11,13,1,3,0,0,0,0,1,0,3,1,,0,0,, 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+hilleho02,1920,1,BOS,AL,17,29,4,5,1,1,0,2,0,3,2,5,,0,2,, +hinchbi01,1920,1,PIT,NL,18,16,0,3,0,0,0,1,0,0,1,3,,1,0,, +hocked01,1920,1,SLN,NL,1,0,0,0,0,0,0,0,0,0,0,0,,0,0,, +hodgesh01,1920,1,CHA,AL,4,6,0,0,0,0,0,0,0,0,0,3,,0,0,, +hofmafr01,1920,1,NYA,AL,15,24,3,7,0,0,0,1,0,0,1,2,,1,0,, +holkewa01,1920,1,BSN,NL,144,551,53,162,15,11,3,64,4,11,28,31,,1,23,, +hollabi01,1920,1,WS1,AL,3,4,0,1,0,0,0,1,1,0,1,2,,0,1,, +holloch01,1920,1,CHN,NL,80,301,53,96,17,2,0,22,20,14,41,15,,3,24,, +hoodwa01,1920,1,BRO,NL,7,14,4,2,1,0,0,1,2,0,4,4,,0,2,, +hoodwa01,1920,2,PIT,NL,2,1,1,0,0,0,0,0,1,0,1,0,,0,0,, +hoopeha01,1920,1,BOS,AL,139,536,91,167,30,17,7,53,16,18,88,27,,2,5,, +hornsro01,1920,1,SLN,NL,149,589,96,218,44,20,9,94,12,15,60,50,,3,8,, +hoytwa01,1920,1,BOS,AL,22,43,3,5,0,1,0,0,0,0,1,11,,0,2,, +hubbebi01,1920,1,NY1,NL,14,5,1,1,0,0,0,0,0,0,0,2,,0,0,, +hubbebi01,1920,2,PHI,NL,24,53,4,7,0,0,0,1,0,0,3,19,,0,2,, +hubercl01,1920,1,DET,AL,11,42,4,9,2,1,0,5,0,0,0,5,,0,0,, +huntehe01,1920,1,BOS,AL,4,12,2,1,0,0,0,0,0,0,1,1,,0,1,, +jacksjo01,1920,1,CHA,AL,146,570,105,218,42,20,12,121,9,12,56,14,,7,16,, +jacobba01,1920,1,SLA,AL,154,609,97,216,34,14,9,122,11,7,46,37,,2,16,, +jacobel01,1920,1,SLN,NL,23,26,2,5,1,0,0,2,0,0,1,7,,0,3,, +jaegejo01,1920,1,CHN,NL,2,1,0,0,0,0,0,0,0,0,0,1,,0,0,, +jamiech01,1920,1,CLE,AL,108,370,69,118,17,7,1,40,2,9,41,26,,1,13,, +janvrha01,1920,1,SLN,NL,87,270,33,74,8,4,1,28,5,6,17,19,,0,8,, +johnsdo01,1920,1,CLE,AL,147,535,68,156,24,10,2,71,13,7,28,32,,5,25,, +johnsed01,1920,1,WS1,AL,4,13,1,3,0,0,0,2,0,0,3,2,,0,1,, +johnsji01,1920,1,BRO,NL,155,635,87,185,17,12,1,52,19,15,43,23,,2,28,, +johnspa01,1920,1,PHA,AL,18,72,6,15,0,0,0,5,1,1,4,8,,0,1,, +johnswa01,1920,1,WS1,AL,33,64,6,17,1,3,1,7,0,0,3,10,,0,2,, +jonesbo01,1920,1,DET,AL,81,265,35,66,6,3,1,18,3,4,22,13,,1,11,, +jonesjo01,1920,1,BSN,NL,3,4,0,1,0,1,0,0,0,0,0,2,,0,0,, +jonespe01,1920,1,CHN,NL,4,2,0,0,0,0,0,0,0,0,0,2,,0,0,, +jonessa01,1920,1,BOS,AL,44,92,12,20,3,1,0,5,0,0,4,27,,2,5,, +jonnabu01,1920,1,CHA,AL,2,5,0,0,0,0,0,0,0,0,0,1,,0,0,, +jourdte01,1920,1,CHA,AL,48,150,16,36,5,2,0,8,3,2,17,17,,5,3,, +judgejo01,1920,1,WS1,AL,126,493,103,164,19,15,5,51,12,12,65,34,,5,13,, +karrbe01,1920,1,BOS,AL,57,75,8,21,5,0,1,15,0,0,6,18,,0,1,, +kauffbe01,1920,1,NY1,NL,55,157,31,43,12,3,3,26,3,7,25,14,,2,4,, +kearnte01,1920,1,PHA,AL,1,1,0,0,0,0,0,0,0,0,0,0,,0,0,, +keefeda01,1920,1,PHA,AL,32,40,3,10,1,0,0,1,0,0,1,5,,0,1,, +keenaji02,1920,1,PHI,NL,1,1,0,0,0,0,0,0,0,0,0,0,,0,0,, +kellybi03,1920,1,PHA,AL,9,13,0,3,1,0,0,0,0,0,0,2,,0,0,, +kellyge01,1920,1,NY1,NL,155,590,69,157,22,11,11,94,6,16,41,92,,6,14,, +kerrdi01,1920,1,CHA,AL,46,90,12,14,1,1,0,5,1,0,3,9,,1,4,, +kiefejo01,1920,1,CHA,AL,2,2,0,0,0,0,0,0,0,0,0,1,,0,0,, +kildupe01,1920,1,BRO,NL,141,478,62,130,26,8,0,58,2,9,58,43,,0,22,, +killebi01,1920,1,CHN,NL,62,191,16,42,7,1,0,16,2,2,8,5,,8,5,, +kimeha01,1920,1,SLN,NL,4,1,0,0,0,0,0,0,0,0,0,1,,0,0,, 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+leecl01,1920,1,PIT,NL,37,76,9,18,2,2,0,8,0,1,4,14,,0,1,, +leedu01,1920,1,SLA,AL,1,2,2,2,0,0,0,1,1,0,0,0,,0,1,, +lefeval01,1920,1,NY1,NL,17,27,5,4,0,1,0,0,0,0,0,13,,0,0,, +leibone01,1920,1,CHA,AL,108,413,61,91,16,3,1,28,7,15,55,30,,3,9,, +leifile01,1920,1,SLA,AL,4,2,0,0,0,0,0,0,0,0,0,2,,0,0,, +leonadu01,1920,1,DET,AL,28,57,2,12,3,0,0,5,0,0,8,8,,0,3,, +leonajo01,1920,1,WS1,AL,1,0,0,0,0,0,0,0,0,0,0,0,,0,0,, +leverho01,1920,1,SLA,AL,3,3,0,0,0,0,0,0,0,0,0,0,,0,0,, +lewisdu01,1920,1,NYA,AL,107,365,34,99,8,1,4,61,2,8,24,32,,2,16,, +lovesl01,1920,1,DET,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +lowdegr01,1920,1,CHA,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +lowege01,1920,1,CIN,NL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +luceyjo01,1920,1,NYA,AL,3,3,0,0,0,0,0,0,0,0,0,0,,0,0,, +luderfr01,1920,1,PHI,NL,16,32,1,5,2,0,0,4,0,1,3,6,,0,1,, +lunteha01,1920,1,CLE,AL,23,71,6,14,0,0,0,7,0,1,5,6,,0,0,, +luquedo01,1920,1,CIN,NL,37,64,6,17,3,0,0,3,2,1,8,7,,0,5,, +lynchad01,1920,1,SLA,AL,5,9,2,2,0,0,0,1,0,0,1,2,,0,0,, 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+toneyfr01,1920,1,NY1,NL,42,96,6,23,4,2,0,8,0,1,3,19,,0,2,, +torphre01,1920,1,BSN,NL,3,15,1,3,2,0,0,2,0,0,0,1,,0,0,, +torreri01,1920,1,WS1,AL,16,30,8,10,1,0,0,3,0,0,1,4,,0,0,, +townsir01,1920,1,BSN,NL,4,2,0,0,0,0,0,0,0,0,0,1,,0,0,, +townsle01,1920,1,BSN,NL,7,6,1,1,0,0,0,0,0,0,1,3,,0,1,, +tragewa01,1920,1,PHI,NL,62,176,17,37,11,1,6,26,4,0,4,36,,2,1,, +traynpi01,1920,1,PIT,NL,17,52,6,11,3,1,0,2,1,3,3,6,,1,0,, +tueroos01,1920,1,SLN,NL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +turnete02,1920,1,CHN,NL,1,1,0,0,0,0,0,0,0,0,0,1,,0,0,, +twombba01,1920,1,CHN,NL,78,183,25,43,1,1,2,14,5,9,17,20,,1,4,, +tylerle01,1920,1,CHN,NL,29,65,6,17,3,1,0,6,0,0,9,7,,0,2,, +uhlege01,1920,1,CLE,AL,27,32,4,11,0,0,0,2,1,0,2,2,,0,0,, +vangiel01,1920,1,SLA,AL,24,30,3,4,0,0,0,1,0,0,2,3,,0,0,, +vaughhi01,1920,1,CHN,NL,40,102,14,22,3,1,1,12,0,0,12,23,,0,2,, +veachbo01,1920,1,DET,AL,154,612,92,188,39,15,11,113,11,7,36,22,,7,15,, +veddelo01,1920,1,DET,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +vicksa01,1920,1,NYA,AL,51,118,21,26,7,1,0,11,1,1,14,20,,2,1,, +vittos01,1920,1,BOS,AL,87,296,50,65,10,4,1,28,5,4,43,10,,1,26,, +walkecu01,1920,1,NY1,NL,8,14,0,1,0,0,0,0,0,0,1,3,,0,0,, +walkefr01,1920,1,PHA,AL,24,91,10,21,2,2,0,10,0,2,5,14,,2,2,, +walkejo01,1920,1,PHA,AL,9,22,0,5,1,0,0,5,0,0,0,1,,0,2,, +walketi01,1920,1,PHA,AL,149,585,79,157,23,7,17,82,8,3,41,59,,4,18,, +walshwa01,1920,1,PHI,NL,2,0,0,0,0,0,0,0,0,0,0,0,,0,0,, +waltero01,1920,1,BOS,AL,88,258,25,51,11,1,0,28,2,2,30,21,,9,11,, +wambsbi01,1920,1,CLE,AL,153,565,83,138,16,11,1,55,9,18,54,26,,5,40,, +wardaa01,1920,1,NYA,AL,127,496,62,127,18,7,11,55,7,5,33,84,,1,17,, +wardch01,1920,1,BRO,NL,19,71,7,11,1,0,0,4,1,0,3,3,,1,6,, +watsomu01,1920,1,BSN,NL,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,0 +watsomu01,1920,2,PIT,NL,5,3,0,0,0,0,0,0,0,0,0,2,,0,0,, +watsomu01,1920,3,BSN,NL,12,23,2,3,0,0,0,1,0,0,1,9,,0,1,, +wattal01,1920,1,WS1,AL,1,1,0,1,1,0,0,1,0,0,0,0,,0,0,, +weavebu01,1920,1,CHA,AL,151,629,102,208,34,8,2,75,19,17,28,23,,6,27,, +weilmca01,1920,1,SLA,AL,30,63,6,11,0,0,0,6,0,0,4,12,,1,4,, +weinele01,1920,1,PHI,NL,10,5,0,0,0,0,0,0,0,0,0,1,,0,0,, +welchfr01,1920,1,PHA,AL,100,360,43,93,17,5,4,40,2,9,26,41,,2,8,, +wetzedu01,1920,1,SLA,AL,7,21,5,9,1,1,0,5,0,1,4,1,,0,1,, +wheatma01,1920,1,PHI,NL,78,230,15,52,10,3,3,20,3,1,8,35,,3,3,, +wheatza01,1920,1,BRO,NL,148,583,89,191,26,13,9,73,8,10,48,21,,6,6,, +whelato01,1920,1,BSN,NL,1,1,0,0,0,0,0,0,0,0,1,1,,0,0,, +whittpo01,1920,1,PIT,NL,134,494,53,129,11,12,1,74,11,11,35,36,,3,23,, +wilkiro01,1920,1,CHA,AL,34,48,5,7,0,1,0,2,0,0,1,12,,0,1,, +willicy01,1920,1,PHI,NL,148,590,88,192,36,10,15,72,18,12,32,45,,4,17,, +willike01,1920,1,SLA,AL,141,521,90,160,34,13,10,72,18,8,41,26,,4,26,, +willile01,1920,1,CHA,AL,39,101,5,22,2,1,0,11,0,0,7,16,,0,8,, +wilsoar01,1920,1,BSN,NL,16,19,0,1,0,0,0,0,0,0,1,1,,1,0,, +wilsomu01,1920,1,DET,AL,3,4,0,1,0,0,0,1,0,0,0,1,,0,1,, +wingoed01,1920,1,PHA,AL,1,4,0,1,0,0,0,1,0,0,0,0,,0,0,, +wingoiv01,1920,1,CIN,NL,108,364,32,96,11,5,2,38,6,4,19,13,,0,8,, +winteje01,1920,1,NY1,NL,21,7,0,0,0,0,0,0,0,0,0,1,,0,0,, +wisneja01,1920,1,PIT,NL,17,7,0,0,0,0,0,0,0,0,0,3,,1,0,, +withrfr01,1920,1,PHI,NL,48,132,8,24,4,1,0,12,0,0,8,26,,2,2,, +wittwh01,1920,1,PHA,AL,65,218,29,70,11,3,1,25,2,3,27,16,,0,8,, +woodala01,1920,1,DET,AL,18,49,4,12,1,0,0,5,0,0,2,6,,0,3,, +woodjo02,1920,1,CLE,AL,61,137,25,37,11,2,1,30,1,1,25,16,,2,12,, +wrighru01,1920,1,PHI,NL,76,206,23,54,6,1,3,17,3,2,10,25,,2,2,, +youngra01,1920,1,DET,AL,150,594,84,173,21,6,0,33,8,13,85,30,,2,22,, +youngro01,1920,1,NY1,NL,153,581,92,204,27,14,6,78,18,18,75,55,,2,13,, +zachato01,1920,1,WS1,AL,51,111,7,29,6,1,0,11,0,1,10,27,,0,0,, +zinnji01,1920,1,PIT,NL,8,15,2,3,0,1,0,1,0,0,1,5,,0,0,, +acostjo01,1921,1,WS1,AL,33,30,2,2,0,0,0,0,1,0,6,14,,0,1,, +adamsba01,1921,1,PIT,NL,25,63,7,16,2,4,0,6,1,0,1,7,,0,2,, +ainsmed01,1921,1,DET,AL,35,98,6,27,5,2,0,12,1,0,13,7,,0,4,, +ainsmed01,1921,2,SLN,NL,27,62,5,18,0,1,0,5,0,0,3,4,,0,1,, +alexape01,1921,1,CHN,NL,31,95,8,29,3,1,1,14,0,1,2,13,,0,5,, +austiji01,1921,1,SLA,AL,27,66,8,18,2,1,0,2,2,1,4,7,,1,4,, +ayersdo01,1921,1,DET,AL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bagbyji01,1921,1,CLE,AL,41,76,8,15,7,1,0,11,0,0,0,8,,0,2,, +bailebi01,1921,1,SLN,NL,19,22,0,2,0,0,0,0,0,0,1,5,,0,0,, +bailesw01,1921,1,CHN,NL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bailesw01,1921,2,BRO,NL,7,5,0,0,0,0,0,0,0,0,1,4,,0,0,, +bakerfr01,1921,1,NYA,AL,94,330,46,97,16,2,9,71,8,5,26,12,,4,9,, +bancrda01,1921,1,NY1,NL,153,606,121,193,26,15,6,67,17,10,66,23,,4,22,, +barbawa01,1921,1,BSN,NL,134,550,66,166,22,7,0,49,11,4,24,28,,0,26,, +barbetu01,1921,1,CHN,NL,127,452,73,142,14,4,1,54,5,9,41,24,,6,14,, +barneje01,1921,1,NY1,NL,42,92,8,19,0,1,0,6,1,2,3,3,,0,4,, +barnesa01,1921,1,DET,AL,7,11,2,2,1,0,0,0,0,0,2,1,,1,0,, +barnhcl01,1921,1,PIT,NL,124,449,66,116,15,13,3,62,3,3,32,36,,3,21,, +barrebi02,1921,1,PHA,AL,14,30,3,7,2,1,0,3,0,0,0,5,,0,0,, +bassljo01,1921,1,DET,AL,119,388,37,119,18,5,0,56,2,1,58,16,,3,14,, +baumgst01,1921,1,PHI,NL,31,30,5,6,1,0,0,1,0,0,5,4,,0,0,, +baynebi01,1921,1,SLA,AL,47,60,11,18,1,1,1,5,0,0,6,8,,3,0,, +behanpe01,1921,1,PHI,NL,2,4,0,0,0,0,0,0,0,0,0,2,,0,0,, +bentoru01,1921,1,NY1,NL,18,21,1,3,1,0,0,1,0,0,1,4,,0,2,, +berryjo02,1921,1,NY1,NL,9,6,0,2,0,1,0,2,0,0,1,1,,0,0,, +bettshu01,1921,1,PHI,NL,32,30,2,8,1,0,0,0,0,0,1,8,,0,0,, +bigbeca01,1921,1,PIT,NL,147,632,100,204,23,17,3,42,21,20,41,19,,0,11,, +bigbely01,1921,1,PIT,NL,5,2,0,0,0,0,0,0,0,0,0,1,,0,0,, +billijo01,1921,1,SLA,AL,20,46,2,10,0,0,0,4,0,0,0,7,,0,1,, +birdre01,1921,1,WS1,AL,1,1,0,0,0,0,0,0,0,0,0,1,,0,0,, +bishobi02,1921,1,PHA,AL,2,3,0,0,0,0,0,0,0,0,0,1,,0,0,, +blackch01,1921,1,CHA,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bluelu01,1921,1,DET,AL,153,585,103,180,33,11,5,75,13,17,103,47,,5,16,, +bodiepi01,1921,1,NYA,AL,31,87,5,15,2,2,0,12,0,1,8,8,,0,2,, +boeckto01,1921,1,BSN,NL,153,592,93,185,20,13,10,84,20,15,52,41,,2,17,, +boehlge01,1921,1,SLA,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bohnesa01,1921,1,CIN,NL,153,613,98,175,28,16,3,44,26,22,54,38,,4,20,, +bolanbe01,1921,1,SLA,AL,7,10,1,1,0,0,0,0,0,0,1,2,,0,0,, +booneda01,1921,1,DET,AL,1,1,0,0,0,0,0,0,0,0,0,0,,0,0,, +bratsfr01,1921,1,CHA,AL,16,28,0,8,1,0,0,3,0,0,0,2,,0,0,, +braxtga01,1921,1,BSN,NL,17,7,0,0,0,0,0,0,0,0,0,6,,0,0,, +brazifr01,1921,1,PHA,AL,66,177,17,48,3,1,0,19,2,4,23,21,,2,4,, +brently01,1921,1,CIN,NL,17,15,1,2,1,0,0,2,0,0,2,4,,0,3,, +bressru01,1921,1,CIN,NL,109,323,41,99,18,6,1,54,5,5,39,20,,2,17,, +brottto01,1921,1,WS1,AL,4,7,1,1,0,0,0,0,0,0,2,1,,0,0,, +brottto01,1921,2,PIT,NL,30,91,6,22,2,0,0,9,0,1,3,11,,0,1,, +browefr01,1921,1,WS1,AL,83,203,31,53,12,3,1,35,1,1,18,7,,3,6,, +browned02,1921,1,NY1,NL,70,128,16,36,6,2,0,12,1,0,4,11,,4,3,, +bruggfr01,1921,1,PHI,NL,96,277,28,86,11,2,5,28,6,2,23,37,,3,5,, +burnsge01,1921,1,NY1,NL,149,605,111,181,28,9,4,61,19,20,80,24,,6,7,, +burnsge02,1921,1,CLE,AL,84,244,52,88,21,4,0,49,3,1,13,19,,2,6,, +burwebi01,1921,1,SLA,AL,33,25,3,6,1,0,0,3,0,0,1,1,,0,0,, +bushdo01,1921,1,DET,AL,104,402,72,113,6,5,0,27,8,11,45,23,,1,40,, 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+colebe01,1921,1,DET,AL,30,46,12,13,2,3,0,8,0,0,1,4,,0,3,, +collied01,1921,1,CHA,AL,139,526,79,177,20,10,2,58,12,10,66,11,,2,13,, +collipa01,1921,1,SLA,AL,58,111,9,27,3,0,1,10,1,0,16,17,,0,3,, +colliri01,1921,1,NYA,AL,28,56,4,11,0,1,0,6,0,0,3,18,,0,2,, +collish01,1921,1,BOS,AL,141,542,63,155,29,12,4,69,15,8,18,38,,4,20,, +collizi01,1921,1,PHA,AL,24,71,14,20,5,1,0,5,1,2,6,5,,2,1,, +connasa01,1921,1,CHA,AL,5,8,2,4,0,1,0,0,0,0,0,1,,0,0,, +conneto01,1921,1,NYA,AL,4,5,0,1,0,0,0,0,0,0,1,0,,0,0,, +connojo05,1921,1,NY1,NL,2,4,0,0,0,0,0,0,0,0,1,1,,0,0,, +coonejo01,1921,1,BSN,NL,8,5,0,1,0,0,0,0,0,0,0,1,,0,0,, +coopewi01,1921,1,PIT,NL,38,122,11,31,5,2,0,9,0,1,3,7,,2,4,, +coumbfr01,1921,1,CIN,NL,31,25,2,8,1,0,0,4,0,0,0,7,,0,1,, +courtha01,1921,1,WS1,AL,32,47,5,14,3,0,0,5,0,0,0,4,,0,1,, +covelst01,1921,1,CLE,AL,43,116,3,18,5,1,0,16,0,0,4,26,,0,11,, +cranesa02,1921,1,CIN,NL,73,215,20,50,10,2,0,16,2,5,14,14,,4,9,, +cruiswa01,1921,1,BSN,NL,108,344,47,119,16,7,8,55,10,8,48,24,,2,9,, 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+hansool01,1921,1,CHN,NL,2,3,0,0,0,0,0,0,0,0,0,2,,0,0,, +hargrbu01,1921,1,CIN,NL,93,263,28,76,17,8,1,38,4,2,12,15,,3,6,, +harpeha01,1921,1,NYA,AL,8,16,1,2,0,0,0,1,0,0,1,4,,0,0,, +harribu01,1921,1,WS1,AL,154,584,82,169,22,8,0,54,29,9,54,39,,18,27,, +harrisl01,1921,1,PHA,AL,39,81,1,12,1,0,0,7,0,0,0,31,,0,5,, +hastybo01,1921,1,PHA,AL,35,68,5,20,4,0,0,7,0,0,2,17,,0,1,, +hawksch01,1921,1,NYA,AL,41,73,16,21,2,3,2,15,0,1,5,12,,0,1,, +heathcl01,1921,1,SLN,NL,62,156,18,38,6,2,0,9,7,5,10,9,,1,10,, +heilmha01,1921,1,DET,AL,149,602,114,237,43,14,19,139,2,6,53,37,,2,15,, +heimafr01,1921,1,PHA,AL,1,4,0,1,0,0,0,1,0,0,0,1,,0,0,, +heinebu01,1921,1,NY1,NL,1,2,0,0,0,0,0,0,0,0,0,0,,0,0,, +hendebe01,1921,1,CLE,AL,3,1,0,0,0,0,0,0,0,0,1,0,,0,0,, +hendrti01,1921,1,BOS,AL,49,137,10,33,8,2,0,22,1,1,24,13,,2,6,, +henlibu01,1921,1,NY1,NL,1,1,0,0,0,0,0,0,0,0,0,1,,0,0,, +henlibu01,1921,2,PHI,NL,33,111,8,34,2,0,0,8,1,0,2,6,,0,2,, +henrydu01,1921,1,SLA,AL,1,1,0,1,0,0,0,0,0,0,0,0,,0,0,, 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+shortch01,1921,1,DET,AL,92,217,33,59,11,3,0,23,2,3,20,11,,0,6,, +shottbu01,1921,1,SLN,NL,38,48,9,12,1,1,1,7,0,2,7,4,,1,0,, +sislege01,1921,1,SLA,AL,138,582,125,216,38,18,12,104,35,11,34,27,,5,14,, +skiffbi01,1921,1,PIT,NL,16,45,7,13,2,0,0,11,1,1,0,4,,0,2,, +smithea01,1921,1,SLA,AL,25,78,7,26,4,2,2,14,0,0,3,4,,1,1,, +smithea01,1921,2,WS1,AL,59,180,20,39,5,2,2,13,1,0,10,19,,2,4,, +smithea02,1921,1,NY1,NL,89,229,35,77,8,4,10,51,4,3,27,8,,1,4,, +smithel02,1921,1,CLE,AL,129,431,98,125,28,9,16,85,0,2,56,46,,2,16,, +smithge02,1921,1,PHI,NL,39,71,1,4,1,0,0,3,0,0,2,29,,0,1,, +smithja03,1921,1,SLN,NL,116,411,86,135,22,9,7,33,11,6,21,24,,0,7,, +smithji01,1921,1,PHI,NL,67,247,31,57,8,1,4,22,2,8,11,28,,1,11,, +smithsh01,1921,1,BRO,NL,35,57,2,13,3,0,1,2,0,0,1,12,,0,1,, +snydefr01,1921,1,NY1,NL,108,309,36,99,13,2,8,45,3,4,27,24,,4,4,, +sothoal01,1921,1,SLA,AL,5,9,0,1,0,0,0,0,0,0,0,0,,0,1,, +sothoal01,1921,2,BOS,AL,2,2,0,1,1,0,0,0,0,0,0,1,,0,0,, 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+twombba01,1921,1,CHN,NL,87,175,22,66,8,1,1,18,4,6,11,10,,0,4,, +twombcy01,1921,1,CHA,AL,7,10,0,0,0,0,0,0,0,0,0,4,,0,0,, +tylerle01,1921,1,CHN,NL,19,26,4,6,2,0,0,2,0,0,1,5,,0,1,, +uhlege01,1921,1,CLE,AL,48,94,21,23,2,3,1,18,0,0,6,9,,0,1,, +vangiel01,1921,1,SLA,AL,31,65,5,13,0,1,1,4,0,0,1,9,,0,2,, +vaughhi01,1921,1,CHN,NL,17,41,2,10,2,0,1,5,0,0,2,12,,0,1,, +veachbo01,1921,1,DET,AL,150,612,110,207,43,13,16,128,14,10,48,31,,1,27,, +vicksa01,1921,1,BOS,AL,44,77,5,20,3,1,0,9,0,1,1,10,,0,2,, +vittos01,1921,1,BOS,AL,78,232,29,44,11,1,0,13,1,2,45,13,,0,13,, +walkecu01,1921,1,NY1,NL,64,192,30,55,13,5,3,35,4,3,15,8,,0,7,, +walkecu01,1921,2,PHI,NL,21,77,11,26,2,1,0,8,0,2,5,5,,0,3,, +walkefr01,1921,1,PHA,AL,19,66,6,15,3,0,1,6,1,0,8,11,,0,2,, +walkejo01,1921,1,PHA,AL,113,423,41,109,14,5,2,46,5,0,9,29,,3,8,, +walkero01,1921,1,SLN,NL,38,54,2,11,2,0,0,2,0,0,2,20,,0,4,, +walketi01,1921,1,PHA,AL,142,556,89,169,32,5,23,101,3,5,73,41,,4,7,, +walshji03,1921,1,DET,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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+fordho01,1923,1,BSN,NL,111,380,27,103,16,7,2,50,1,1,31,30,,0,14,, +fosteed02,1923,1,SLA,AL,27,100,9,18,2,0,0,4,0,0,7,7,,1,7,, +fothebo01,1923,1,DET,AL,101,241,34,76,18,2,1,49,5,4,12,19,,4,11,, +fournja01,1923,1,BRO,NL,133,515,91,181,30,13,22,102,11,4,43,28,,9,11,, +fowlebo01,1923,1,CIN,NL,11,33,9,11,0,1,1,6,1,0,1,3,,0,0,, +francra01,1923,1,DET,AL,37,21,5,3,0,0,0,1,0,0,0,6,,0,0,, +freigho01,1923,1,SLN,NL,113,358,30,94,18,1,1,35,5,4,25,36,,2,7,, +frencra01,1923,1,BRO,NL,43,73,14,16,2,1,0,7,0,0,4,7,,1,4,, +frencwa01,1923,1,PHA,AL,16,39,7,9,3,0,0,2,0,1,5,7,,0,0,, +fribebe01,1923,1,CHN,NL,146,547,91,174,27,11,12,88,13,19,45,49,,2,23,, +fridask01,1923,1,WS1,AL,7,9,1,2,0,0,0,0,0,0,0,1,,1,0,, +friscfr01,1923,1,NY1,NL,151,641,116,223,32,10,12,111,29,12,46,12,,4,11,, +fryjo01,1923,1,CLE,AL,1,1,0,1,0,0,0,0,0,0,0,0,,1,0,, +fullecu01,1923,1,BOS,AL,37,37,2,11,0,0,0,3,0,0,3,10,,0,1,, +fullefr01,1923,1,BOS,AL,6,21,3,5,0,0,0,0,1,1,1,1,,0,0,, +fussefr01,1923,1,CHN,NL,28,20,2,4,1,0,0,0,0,0,1,7,,0,0,, 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+barneje01,1924,1,BSN,NL,37,90,6,20,1,1,0,9,0,1,1,6,,0,9,, +barnevi01,1924,1,NY1,NL,35,77,8,14,0,0,0,4,0,1,6,26,,0,4,, +barnhcl01,1924,1,PIT,NL,102,344,49,95,6,11,3,51,8,4,30,17,,2,14,, +barnhed01,1924,1,SLA,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +barrebi02,1924,1,CHA,AL,119,406,52,110,18,5,2,56,15,10,30,38,,3,8,, +barrebo01,1924,1,CHN,NL,54,133,12,32,2,3,5,21,1,0,7,29,,0,4,, +bassljo01,1924,1,DET,AL,124,379,43,131,20,3,1,68,2,1,62,11,,3,12,, +batchjo01,1924,1,BSN,NL,3,1,0,0,0,0,0,0,0,0,0,1,,0,0,, +baumgst01,1924,1,PHA,AL,36,60,5,13,1,1,0,3,0,0,3,7,,0,5,, +baynebi01,1924,1,SLA,AL,22,14,3,6,2,0,0,3,0,0,2,1,,0,0,, +beallwa01,1924,1,NYA,AL,4,7,0,1,0,0,0,2,0,0,0,0,,0,0,, +beckbo01,1924,1,SLA,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +begleji01,1924,1,CIN,NL,2,5,1,1,0,0,0,0,0,0,2,0,,0,0,, +bellhi01,1924,1,SLN,NL,28,31,1,2,0,0,0,0,0,0,5,14,,0,0,, +bellle01,1924,1,SLN,NL,17,57,5,14,3,2,1,5,0,0,3,7,,1,1,, +bengobe01,1924,1,NYA,AL,11,16,4,5,1,1,0,3,0,0,2,0,,0,1,, +bennehe01,1924,1,SLA,AL,41,94,16,31,4,3,1,11,1,0,3,6,,2,2,, +bentlja01,1924,1,NY1,NL,46,98,12,26,5,1,0,6,0,0,3,13,,0,1,, +bentola01,1924,1,BSN,NL,30,33,4,3,1,0,0,0,0,0,2,10,,0,2,, +bentoru01,1924,1,CIN,NL,32,46,5,12,2,0,0,3,0,0,5,10,,0,0,, +berlyja01,1924,1,SLN,NL,4,2,0,0,0,0,0,0,0,0,0,2,,0,0,, +bettshu01,1924,1,PHI,NL,38,45,7,7,0,0,0,2,0,0,4,8,,0,6,, +bigbeca01,1924,1,PIT,NL,89,282,42,74,4,1,0,15,15,7,26,12,,3,8,, +bishoji01,1924,1,PHI,NL,7,5,1,1,0,0,0,0,0,0,0,2,,0,0,, +bishoma01,1924,1,PHA,AL,91,294,52,75,13,2,2,21,4,3,54,30,,5,5,, +blackbi01,1924,1,CHA,AL,6,5,0,1,0,0,0,0,0,0,0,0,,0,0,, +bladera01,1924,1,SLN,NL,131,456,86,142,21,13,11,68,7,9,35,38,,10,13,, +blakesh01,1924,1,CHN,NL,29,31,5,9,0,1,0,3,0,1,1,1,,0,1,, +blankte01,1924,1,CHA,AL,25,46,10,15,2,1,1,5,0,0,4,6,,1,3,, +blottja01,1924,1,CIN,NL,2,1,0,0,0,0,0,0,0,0,0,0,,0,0,, +bluegos01,1924,1,WS1,AL,117,402,59,113,15,4,2,49,7,5,39,36,,9,15,, +bluelu01,1924,1,DET,AL,108,395,81,123,26,7,2,53,9,4,64,26,,4,13,, +bohnesa01,1924,1,CIN,NL,100,349,42,89,15,9,4,46,9,6,18,24,,1,10,, +booneik01,1924,1,BOS,AL,128,487,72,164,31,4,13,98,2,2,54,32,,1,3,, +bottoji01,1924,1,SLN,NL,137,528,87,167,31,12,14,111,5,4,35,35,,3,17,, +bratcjo01,1924,1,SLN,NL,4,1,1,0,0,0,0,0,0,0,0,0,,0,0,, +bressru01,1924,1,CIN,NL,115,383,41,133,14,13,4,49,9,10,22,20,,4,6,, +bretthe01,1924,1,CHN,NL,1,2,0,0,0,0,0,0,0,0,0,0,,0,0,, +browefr01,1924,1,CLE,AL,66,107,16,30,10,1,3,20,1,1,27,9,,2,0,, +browned02,1924,1,BRO,NL,114,455,56,140,30,4,5,78,3,5,26,15,,0,8,, +bruggfr01,1924,1,PHA,AL,50,113,9,30,6,0,0,8,4,0,8,15,,0,3,, +burkele01,1924,1,DET,AL,72,241,30,61,10,4,0,17,2,4,22,20,,2,8,, +burkepa01,1924,1,SLA,AL,1,3,0,0,0,0,0,1,0,0,0,0,,0,0,, +burnsde01,1924,1,PHA,AL,37,42,1,6,0,0,0,3,0,0,5,7,,0,2,, +burnsge01,1924,1,CIN,NL,93,336,43,86,19,2,2,33,3,6,29,21,,0,6,, +burnsge02,1924,1,CLE,AL,129,462,64,143,37,5,4,68,14,5,29,27,,15,23,, +burnsjo02,1924,1,CHA,AL,8,19,1,2,0,0,0,0,0,0,0,2,,0,1,, 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+cobbty01,1924,1,DET,AL,155,625,115,211,38,10,4,78,23,14,85,18,,1,15,, +colebe01,1924,1,DET,AL,33,37,4,10,0,0,0,2,0,1,4,6,,1,2,, +collied01,1924,1,CHA,AL,152,556,108,194,27,7,6,86,42,17,89,16,,3,28,, +collipa01,1924,1,SLA,AL,32,54,9,17,2,0,1,11,0,1,11,14,,0,2,, +colliri01,1924,1,DET,AL,37,76,5,11,1,0,0,2,0,0,1,23,,2,5,, +collish01,1924,1,BOS,AL,89,240,37,70,17,5,0,28,4,6,18,17,,3,10,, +combsea01,1924,1,NYA,AL,24,35,10,14,5,0,0,2,0,1,4,2,,0,0,, +connasa01,1924,1,CHA,AL,44,50,3,11,1,0,0,0,0,0,1,10,,0,1,, +connojo05,1924,1,BOS,AL,14,10,1,1,0,0,0,1,0,0,2,2,,0,1,, +cooneji02,1924,1,SLN,NL,110,383,44,113,20,8,1,57,12,10,20,20,,0,11,, +coonejo01,1924,1,BSN,NL,55,130,10,33,2,1,0,4,0,4,9,5,,0,5,, +coopewi01,1924,1,PIT,NL,38,104,11,36,5,2,0,15,2,2,5,8,,0,1,, +cotteho01,1924,1,CHN,NL,98,310,39,81,16,4,4,33,3,5,36,31,,0,7,, +couchjo01,1924,1,PHI,NL,37,49,3,10,0,0,2,3,1,0,1,12,,0,2,, +couside01,1924,1,BSN,NL,3,2,0,0,0,0,0,0,0,0,0,0,,0,0,, +covelst01,1924,1,CLE,AL,37,82,3,11,1,0,0,4,0,0,8,32,,0,4,, 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+faberre01,1924,1,CHA,AL,21,54,6,8,0,0,0,1,0,0,7,15,,0,2,, +falkbi01,1924,1,CHA,AL,138,526,77,185,37,8,6,99,6,6,47,21,,1,20,, +felixgu01,1924,1,BSN,NL,59,204,25,43,7,1,1,10,0,3,18,16,,0,3,, +fergual01,1924,1,BOS,AL,41,86,2,12,2,0,0,5,0,0,2,32,,0,8,, +fewstch01,1924,1,CLE,AL,101,322,36,86,12,2,0,36,12,12,24,36,,3,8,, +fishesh01,1924,1,WS1,AL,15,41,7,9,1,0,0,6,2,0,6,6,,0,0,, +fitzgho01,1924,1,CHN,NL,7,19,1,3,0,0,0,2,0,0,0,2,,0,0,, +fitzkpa01,1924,1,CLE,AL,1,1,0,0,0,0,0,0,0,0,0,1,,0,0,, +flackma01,1924,1,SLN,NL,67,209,31,55,11,3,2,21,3,5,21,5,,0,4,, +flagsir01,1924,1,BOS,AL,149,560,106,172,35,7,5,43,10,13,77,41,,11,13,, +fonsele01,1924,1,CIN,NL,20,57,5,13,2,1,0,9,1,0,4,4,,0,2,, +fordho01,1924,1,PHI,NL,145,530,58,144,27,5,3,53,1,9,27,40,,1,18,, +foremha01,1924,1,CHA,AL,5,2,0,0,0,0,0,0,0,0,0,1,,0,0,, +fothebo01,1924,1,DET,AL,54,166,28,50,8,3,0,15,2,3,5,13,,1,7,, +fournja01,1924,1,BRO,NL,154,563,93,188,25,4,27,116,7,5,83,46,,10,13,, +fowlebo01,1924,1,CIN,NL,59,129,20,43,6,1,0,9,2,2,5,15,,0,0,, +fowleje01,1924,1,SLN,NL,13,9,1,2,0,0,0,0,0,0,1,2,,0,0,, +freigho01,1924,1,SLN,NL,98,376,35,101,17,6,2,39,10,3,19,24,,1,10,, +frencra01,1924,1,CHA,AL,37,112,13,20,4,0,0,11,3,1,10,13,,0,2,, +fribebe01,1924,1,CHN,NL,142,495,67,138,19,3,5,82,19,27,66,53,,5,21,, +friscfr01,1924,1,NY1,NL,145,603,121,198,33,15,7,69,22,9,56,24,,2,14,, +fuhros01,1924,1,BOS,AL,23,22,2,4,0,0,0,4,0,0,0,1,,1,1,, +fullecu01,1924,1,BOS,AL,33,42,3,3,0,0,0,2,0,0,7,11,,0,2,, +gagnoch01,1924,1,WS1,AL,4,5,1,1,0,0,0,1,0,0,0,0,,0,0,, +galloch01,1924,1,PHA,AL,129,464,41,128,16,4,2,48,11,12,23,23,,1,17,, +gardnla01,1924,1,CLE,AL,38,50,3,10,0,0,0,4,0,1,5,1,,0,0,, +gastomi01,1924,1,NYA,AL,29,27,3,6,0,0,0,1,0,0,0,6,,0,2,, +gearidi01,1924,1,NY1,NL,10,9,2,3,0,0,0,1,0,0,0,1,,0,0,, +gearidi01,1924,2,BSN,NL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gehrich01,1924,1,DET,AL,5,13,2,6,0,0,0,1,1,1,0,2,,0,0,, +gehrilo01,1924,1,NYA,AL,10,12,2,6,1,0,0,5,0,0,1,3,,0,0,, +genewjo01,1924,1,BSN,NL,34,60,4,10,1,0,0,2,0,0,3,9,,1,5,, +gerbewa01,1924,1,SLA,AL,148,496,61,135,20,4,0,55,4,5,43,34,,9,18,, +geygach01,1924,1,BOS,AL,33,82,7,21,5,2,0,4,0,2,4,16,,2,4,, +gibsoch02,1924,1,PHA,AL,12,15,1,2,0,0,0,1,0,0,2,0,,0,0,, +gibsofr01,1924,1,BSN,NL,90,229,25,71,15,6,1,30,1,1,10,23,,1,1,, +glaznwh01,1924,1,PHI,NL,35,51,2,8,1,0,0,5,0,0,0,17,,0,3,, +gonzami01,1924,1,SLN,NL,120,402,34,119,27,1,3,53,1,5,24,22,,1,7,, +goochjo01,1924,1,PIT,NL,70,224,26,65,6,5,0,25,1,3,16,12,,2,0,, +gosligo01,1924,1,WS1,AL,154,579,100,199,30,17,12,129,15,14,68,29,,9,17,, +gowdyha01,1924,1,NY1,NL,87,191,25,62,9,1,4,37,1,0,26,11,,2,0,, +grabojo01,1924,1,CHA,AL,20,56,10,14,3,0,0,3,0,0,2,4,,0,4,, +grahask01,1924,1,BSN,NL,5,7,0,0,0,0,0,0,0,0,0,4,,0,2,, +grantge01,1924,1,CHN,NL,127,469,85,148,19,6,12,60,21,21,55,63,,2,13,, +grantge02,1924,1,SLA,AL,22,13,0,0,0,0,0,0,0,0,0,6,,0,0,, +graydo02,1924,1,PHA,AL,34,57,1,10,1,1,0,4,0,0,1,11,,0,0,, +greenjo02,1924,1,PHA,AL,1,1,0,0,0,0,0,0,0,0,0,0,,0,0,, +greenke01,1924,1,NY1,NL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +greenne01,1924,1,BRO,NL,4,1,0,0,0,0,0,0,0,0,0,0,,0,0,, +griffbe01,1924,1,WS1,AL,6,8,1,1,0,0,0,0,0,0,0,1,,0,1,, +griffto01,1924,1,BRO,NL,140,482,43,121,19,5,3,67,0,5,34,19,,0,15,, +grigsde01,1924,1,CHN,NL,124,411,58,123,18,2,3,48,10,19,31,47,,6,12,, +grimebu01,1924,1,BRO,NL,40,124,11,37,3,0,0,7,1,1,5,13,,1,3,, +grimera01,1924,1,CHN,NL,51,177,33,53,6,5,5,34,4,2,28,15,,2,4,, +grimmch01,1924,1,PIT,NL,151,542,53,156,25,12,2,63,3,6,37,22,,2,18,, +grohhe01,1924,1,NY1,NL,145,559,82,157,32,3,2,46,8,6,52,29,,11,13,, +gulleto01,1924,1,CLE,AL,8,20,4,3,0,1,0,1,0,0,3,2,,0,0,, +hafeych01,1924,1,SLN,NL,24,91,10,23,5,2,2,22,1,0,4,8,,1,4,, +haineje01,1924,1,SLN,NL,35,74,3,14,1,0,0,7,0,0,2,20,,0,6,, +halesa01,1924,1,PHA,AL,80,261,41,83,14,2,2,17,3,2,17,19,,3,6,, +hamilea01,1924,1,PHI,NL,3,2,0,0,0,0,0,0,0,0,0,1,,0,0,, +haneyfr01,1924,1,DET,AL,86,256,54,79,11,1,1,30,7,4,39,13,,0,11,, 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+alexape01,1925,1,CHN,NL,32,79,7,19,4,1,2,12,0,2,1,5,,0,9,, +anderbi01,1925,1,BSN,NL,2,1,0,0,0,0,0,0,0,0,0,0,,0,0,, +andreel01,1925,1,PHA,AL,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +archdma01,1925,1,CHA,AL,10,9,2,1,0,0,0,0,0,0,2,1,,0,1,, +ashke01,1925,1,CHA,AL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +austiji01,1925,1,SLA,AL,1,1,0,0,0,0,0,0,0,0,0,0,,0,0,, +bagwebi01,1925,1,PHA,AL,36,50,4,15,2,1,0,10,0,0,2,2,,0,2,, +baldwha01,1925,1,NY1,NL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ballowi01,1925,1,WS1,AL,10,7,1,1,1,0,0,0,0,0,0,3,,0,0,, +bancrda01,1925,1,BSN,NL,128,479,75,153,29,8,2,49,7,4,64,22,,0,17,, +barneje01,1925,1,BSN,NL,32,81,11,16,0,0,1,10,0,0,3,6,,0,3,, +barnevi01,1925,1,NY1,NL,32,89,4,9,0,0,0,0,0,1,0,26,,0,2,, +barnhcl01,1925,1,PIT,NL,142,539,85,175,32,11,4,114,9,5,59,25,,0,19,, +barrebi02,1925,1,CHA,AL,81,245,44,89,23,3,3,40,5,6,24,27,,0,8,, +barrebo01,1925,1,CHN,NL,14,32,1,10,1,0,0,7,1,2,1,4,,0,2,, +barrebo01,1925,2,BRO,NL,1,1,0,0,0,0,0,1,0,0,0,0,,0,0,, +bassljo01,1925,1,DET,AL,121,344,40,96,19,3,0,52,1,1,74,6,,1,14,, +batchjo01,1925,1,BSN,NL,4,1,0,0,0,0,0,0,0,0,0,1,,0,0,, +baumgst01,1925,1,PHA,AL,37,30,3,7,2,0,0,3,0,0,1,7,,0,1,, +beallwa01,1925,1,NYA,AL,8,3,0,0,0,0,0,0,0,0,0,1,,0,0,, +bedfoge01,1925,1,CLE,AL,2,3,1,0,0,0,0,0,0,0,0,1,,0,0,, +bellle01,1925,1,SLN,NL,153,586,80,167,29,9,11,88,4,5,43,47,,0,10,, +bendech01,1925,1,CHA,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bengera01,1925,1,CLE,AL,2,5,1,2,0,0,0,0,0,0,0,1,,0,0,, +bengobe01,1925,1,NYA,AL,95,283,17,73,14,2,0,23,0,2,19,9,,0,6,, +bennehe01,1925,1,SLA,AL,93,298,46,83,11,6,2,37,4,10,18,16,,2,7,, +bentlja01,1925,1,NY1,NL,64,99,10,30,5,2,3,18,0,0,9,11,,0,1,, +bentola01,1925,1,BSN,NL,32,58,6,14,2,0,0,1,1,0,3,20,,0,8,, +bentoru01,1925,1,CIN,NL,33,45,0,9,2,0,0,4,0,0,4,3,,0,4,, +berrych02,1925,1,PHA,AL,10,14,1,3,1,0,0,3,0,0,0,2,,0,0,, +bettshu01,1925,1,PHI,NL,37,34,5,10,2,0,0,6,0,0,3,4,,0,0,, +biemiha01,1925,1,CIN,NL,23,9,0,0,0,0,0,0,0,0,3,4,,0,0,, +bigbeca01,1925,1,PIT,NL,66,126,31,30,7,0,0,8,2,2,7,8,,0,4,, +bischjo01,1925,1,CHA,AL,7,11,1,1,0,0,0,0,0,0,1,5,,0,0,, +bischjo01,1925,2,BOS,AL,41,133,13,37,9,1,1,16,1,2,6,11,,0,3,, +bishoma01,1925,1,PHA,AL,105,368,66,103,18,4,4,27,5,9,87,37,,2,9,, +bladera01,1925,1,SLN,NL,122,462,112,158,37,8,12,57,6,8,59,47,,6,5,, +blaehge01,1925,1,SLA,AL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +blakesh01,1925,1,CHN,NL,36,79,4,12,1,0,0,7,0,0,2,21,,0,3,, +blankte01,1925,1,CHA,AL,40,88,7,18,2,2,2,15,0,0,4,23,,0,4,, +bluegos01,1925,1,WS1,AL,145,522,77,150,27,4,4,79,16,15,59,56,,2,24,, +bluelu01,1925,1,DET,AL,150,532,91,163,18,9,3,94,19,5,83,29,,3,34,, +bohnesa01,1925,1,CIN,NL,73,214,24,55,9,1,2,24,6,4,14,14,,0,7,, +booneik01,1925,1,BOS,AL,133,476,79,157,34,5,9,68,1,4,60,19,,1,8,, +bottoji01,1925,1,SLN,NL,153,619,92,227,44,12,21,128,3,4,47,36,,2,13,, +bradyne01,1925,1,CIN,NL,20,25,4,6,0,1,0,2,0,0,1,8,,0,0,, +braxtga01,1925,1,NYA,AL,3,6,1,2,0,0,0,1,0,0,0,1,,0,0,, 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+careyma01,1925,1,PIT,NL,133,542,109,186,39,13,5,44,46,11,66,19,,4,8,, +carlsha01,1925,1,PHI,NL,38,93,9,17,1,0,2,8,0,1,5,14,,0,2,, +carlyro01,1925,1,WS1,AL,1,1,0,0,0,0,0,0,0,0,0,1,,0,0,, +carlyro01,1925,2,BOS,AL,93,276,36,90,20,3,7,49,1,1,16,28,,1,7,, +carroow01,1925,1,DET,AL,11,16,4,6,1,0,0,4,0,0,0,1,,0,0,, +cartebl01,1925,1,NY1,NL,1,4,0,0,0,0,0,0,0,0,0,1,,0,0,, +cavenik01,1925,1,CIN,NL,115,358,38,89,9,5,2,47,2,0,28,31,,0,15,, +churrjo01,1925,1,CHN,NL,3,6,1,3,0,0,0,1,0,0,0,0,,0,0,, +clancbu01,1925,1,CHA,AL,4,3,0,0,0,0,0,0,0,0,1,0,,0,0,, +clouged01,1925,1,SLN,NL,3,4,0,1,0,0,0,0,0,0,0,0,,0,0,, +cobbty01,1925,1,DET,AL,121,415,97,157,31,12,12,102,13,9,65,12,,5,5,, +cochrmi01,1925,1,PHA,AL,134,420,69,139,21,5,6,55,7,4,44,19,,2,8,, +colebe01,1925,1,DET,AL,14,11,1,3,0,0,0,1,0,0,1,2,,0,0,, +colebe01,1925,2,CLE,AL,13,13,2,2,0,0,0,0,0,0,3,2,,0,0,, +collied01,1925,1,CHA,AL,118,425,80,147,26,3,3,80,19,6,87,8,,4,17,, +colliri01,1925,1,DET,AL,26,42,0,5,0,0,0,3,0,0,1,15,,0,2,, 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+arrieja01,2018,1,PHI,NL,31,45,3,6,0,0,1,3,0,0,3,27,0,0,2,0,1 +arroych01,2018,1,TBA,AL,20,53,5,14,2,1,1,6,0,0,6,16,0,0,0,0,0 +asheral01,2018,1,MIL,NL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +astudwi01,2018,1,MIN,AL,30,93,9,33,4,1,3,21,0,0,2,3,0,1,0,1,4 +asuajca01,2018,1,SDN,NL,79,189,15,37,8,1,2,19,1,1,24,46,1,1,1,3,5 +austity01,2018,1,NYA,AL,34,121,16,27,6,0,8,23,1,1,8,53,0,2,0,1,2 +austity01,2018,2,MIN,AL,35,123,18,29,4,0,9,24,0,1,11,42,0,0,0,2,1 +aveliab01,2018,1,SFN,NL,6,11,1,3,0,0,0,0,0,0,0,3,0,0,0,0,0 +avilaal01,2018,1,ARI,NL,80,194,13,32,6,0,7,20,0,0,37,90,2,1,0,2,4 +avilalu01,2018,1,CHA,AL,58,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +avilalu01,2018,2,PHI,NL,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +axforjo01,2018,1,TOR,AL,45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +axforjo01,2018,2,LAN,NL,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +baderha01,2018,1,SLN,NL,138,379,61,100,20,2,12,37,15,3,31,125,3,11,2,4,1 +baezja01,2018,1,CHN,NL,160,606,101,176,40,9,34,111,21,9,29,167,8,5,1,4,10 +baezpe01,2018,1,LAN,NL,55,4,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +baezsa01,2018,1,DET,AL,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +baileho02,2018,1,CIN,NL,20,26,0,3,0,0,0,0,0,0,1,13,0,0,7,0,0 +bandaan01,2018,1,TBA,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bandyje01,2018,1,MIL,NL,24,64,5,12,2,0,1,1,0,0,3,23,0,4,0,0,0 +barbajo01,2018,1,DET,AL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bardlu01,2018,1,LAA,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +barlosc01,2018,1,KCA,AL,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +barneau01,2018,1,LAN,NL,100,200,32,41,5,0,4,14,4,3,31,67,4,6,1,0,7 +barnebr02,2018,1,CLE,AL,19,19,2,5,0,0,1,2,0,0,2,5,0,0,0,0,1 +barneda02,2018,1,TOR,AL,47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +barneja01,2018,1,MIL,NL,49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +barnema01,2018,1,BOS,AL,62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +barneto01,2018,1,TEX,AL,22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +barnhtu01,2018,1,CIN,NL,138,460,50,114,21,3,10,46,0,4,54,96,2,2,3,3,13 +baronst01,2018,1,SLN,NL,2,5,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0 +barraky01,2018,1,MIA,NL,61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +barrefr02,2018,1,OAK,AL,32,73,10,17,4,0,5,16,0,0,1,29,0,1,0,0,3 +barreja01,2018,1,ARI,NL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +barrija01,2018,1,LAA,AL,26,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1 +bashlty01,2018,1,NYN,NL,24,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +bassan01,2018,1,CHN,NL,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bassich01,2018,1,OAK,AL,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bauerja01,2018,1,TBA,AL,96,323,48,65,22,2,11,48,6,6,54,104,0,3,2,6,3 +bauertr01,2018,1,CLE,AL,28,3,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +baumabu01,2018,1,SDN,NL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +baumabu01,2018,2,NYN,NL,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bautige01,2018,1,NYN,NL,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bautijo02,2018,1,ATL,NL,12,35,3,5,1,0,2,5,0,0,5,12,0,0,0,0,0 +bautijo02,2018,2,NYN,NL,83,245,37,50,13,0,9,37,2,2,51,84,2,5,0,1,2 +bautijo02,2018,3,PHI,NL,27,45,12,11,4,0,2,6,2,1,11,15,0,1,0,0,0 +bautira01,2018,1,WAS,NL,9,6,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1 +beckch02,2018,1,CHA,AL,14,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +beckch02,2018,2,NYN,NL,6,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +beckhgo01,2018,1,SEA,AL,22,44,3,8,1,0,0,1,1,0,4,11,0,0,2,0,1 +beckhti01,2018,1,BAL,AL,96,369,45,85,17,0,12,35,1,2,27,100,0,3,1,2,10 +bedroca01,2018,1,LAA,AL,71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +beedety01,2018,1,SFN,NL,2,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +beeksja01,2018,1,BOS,AL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +beeksja01,2018,2,TBA,AL,12,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +belisma01,2018,1,CLE,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +belisma01,2018,2,MIN,AL,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +belivje01,2018,1,CLE,AL,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bellch02,2018,1,DET,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bellico01,2018,1,LAN,NL,162,557,84,145,28,7,25,76,14,1,69,151,9,3,0,3,7 +belljo02,2018,1,PIT,NL,148,501,74,131,31,4,12,62,2,5,77,104,2,0,0,5,12 +beltbr01,2018,1,SFN,NL,112,399,50,101,18,2,14,46,4,0,49,107,6,6,0,2,2 +beltrad01,2018,1,TEX,AL,119,433,49,118,23,1,15,65,1,0,34,96,2,6,0,8,13 +beninan01,2018,1,BOS,AL,148,579,103,168,41,6,16,87,21,3,71,106,1,2,2,7,9 +bergmch01,2018,1,SEA,AL,3,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0 +berrijo01,2018,1,MIN,AL,32,4,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +bertijo01,2018,1,TOR,AL,4,15,2,4,1,1,0,2,1,0,0,4,0,0,0,0,0 +betande01,2018,1,NYA,AL,66,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +bettich01,2018,1,COL,NL,27,34,2,3,0,0,0,2,0,0,2,18,0,0,3,0,0 +bettsmo01,2018,1,BOS,AL,136,520,129,180,47,5,32,80,30,6,81,91,8,8,0,5,5 +biagijo01,2018,1,TOR,AL,50,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bibenau01,2018,1,TEX,AL,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +biddlje01,2018,1,ATL,NL,60,2,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0 +biebesh01,2018,1,CLE,AL,20,4,0,1,1,0,0,0,0,0,1,2,0,0,0,0,0 +birdgr01,2018,1,NYA,AL,82,272,23,54,16,1,11,38,0,0,30,78,1,5,0,4,4 +blachty01,2018,1,SFN,NL,50,29,0,1,0,0,0,0,0,0,2,15,0,0,3,0,1 +blackch02,2018,1,COL,NL,156,626,119,182,31,7,29,70,12,4,59,134,2,8,1,2,10 +blackpa01,2018,1,OAK,AL,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +blackra01,2018,1,SFN,NL,26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +blancgr01,2018,1,SFN,NL,68,189,19,41,7,3,2,12,6,2,12,58,0,0,1,1,2 +blandal01,2018,1,CIN,NL,69,128,14,30,4,0,1,8,0,0,13,41,1,4,2,0,4 +blashja01,2018,1,LAA,AL,24,39,4,4,1,0,0,1,2,1,5,24,0,0,0,1,0 +bleicje01,2018,1,OAK,AL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bleieri01,2018,1,BAL,AL,31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +blevije01,2018,1,NYN,NL,64,2,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0 +bogaexa01,2018,1,BOS,AL,136,513,72,148,45,3,23,103,8,2,55,102,4,6,0,6,14 +bonifjo01,2018,1,KCA,AL,69,236,31,53,16,2,4,23,0,1,29,71,1,2,1,2,3 +borucry01,2018,1,TOR,AL,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bostich01,2018,1,PIT,NL,2,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +bostich01,2018,2,MIA,NL,13,14,0,3,1,0,0,2,0,0,2,6,0,0,0,0,0 +boteda01,2018,1,CHN,NL,74,184,23,44,9,2,6,33,3,4,19,60,1,4,0,3,3 +bourjpe01,2018,1,ATL,NL,36,44,5,9,2,1,1,4,0,0,2,15,0,0,1,0,0 +bourju01,2018,1,MIA,NL,112,374,43,85,10,1,19,54,1,0,69,111,6,1,0,3,8 +bourju01,2018,2,PHI,NL,29,49,6,11,3,0,1,5,1,0,4,13,0,1,0,0,2 +bowmama01,2018,1,SLN,NL,22,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +boxbebr01,2018,1,ARI,NL,60,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +boydma01,2018,1,DET,AL,31,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +boyerbl01,2018,1,KCA,AL,21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +brachbr01,2018,1,BAL,AL,42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +brachbr01,2018,2,ATL,NL,27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +brachsi01,2018,1,ARI,NL,31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bradfch02,2018,1,SEA,AL,46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bradlar01,2018,1,ARI,NL,76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bradlja02,2018,1,BOS,AL,144,474,76,111,33,4,13,59,17,1,46,137,3,11,0,4,6 +brantmi02,2018,1,CLE,AL,143,570,89,176,36,2,17,76,12,3,48,60,0,5,1,6,15 +brasiry01,2018,1,BOS,AL,34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +braulst01,2018,1,PIT,NL,46,20,0,3,1,0,0,0,0,0,1,5,0,0,0,0,0 +braunry02,2018,1,MIL,NL,125,405,59,103,25,1,20,64,11,5,34,85,5,2,0,3,8 +brebbjo01,2018,1,SLN,NL,45,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +bregmal01,2018,1,HOU,AL,157,594,105,170,51,1,31,103,10,4,96,85,2,12,0,3,15 +breweco01,2018,1,SDN,NL,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +briceau01,2018,1,CIN,NL,33,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +bricejo01,2018,1,LAA,AL,46,117,12,28,2,0,5,10,0,1,8,35,0,2,1,0,2 +bridwpa01,2018,1,LAA,AL,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +brighje01,2018,1,MIA,NL,4,3,1,1,0,0,0,0,0,0,0,1,0,0,1,0,0 +brinsle01,2018,1,MIA,NL,109,382,31,76,10,5,11,42,2,1,17,120,2,4,0,2,6 +britoso01,2018,1,ARI,NL,24,40,3,7,0,0,1,3,0,1,3,9,0,0,0,1,0 +brittza01,2018,1,BAL,AL,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +brittza01,2018,2,NYA,AL,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +brookaa01,2018,1,OAK,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +brothre01,2018,1,ATL,NL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +broxtke01,2018,1,MIL,NL,51,78,15,14,2,2,4,11,5,1,11,28,0,0,0,0,0 +bruceja01,2018,1,NYN,NL,94,319,31,71,18,1,9,37,2,3,41,75,4,0,0,1,3 +bryankr01,2018,1,CHN,NL,102,389,59,106,28,3,13,52,2,4,48,107,6,17,0,3,5 +buchhcl01,2018,1,ARI,NL,17,31,0,2,1,0,0,1,0,0,1,17,0,0,5,0,0 +buchtry01,2018,1,OAK,AL,54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +buehlwa01,2018,1,LAN,NL,24,43,1,7,0,0,0,1,0,0,2,21,0,0,2,0,0 +bumgama01,2018,1,SFN,NL,22,44,2,7,2,0,0,4,0,0,1,20,0,0,0,1,0 +bummeaa01,2018,1,CHA,AL,37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bundydy01,2018,1,BAL,AL,31,5,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0 +burdini01,2018,1,PIT,NL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +burneco01,2018,1,MIL,NL,30,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +burrry01,2018,1,CHA,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +busenal01,2018,1,MIN,AL,23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +bushma01,2018,1,TEX,AL,21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +buterdr01,2018,1,KCA,AL,52,149,11,28,9,0,2,18,0,0,13,37,0,2,0,2,4 +buterdr01,2018,2,COL,NL,10,14,2,3,0,0,1,3,0,0,2,2,0,0,0,0,0 +butleda01,2018,1,BOS,AL,2,6,0,1,0,0,0,1,0,0,0,2,0,0,0,1,0 +butleed01,2018,1,CHN,NL,8,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +butleed01,2018,2,TEX,AL,22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +buttrty01,2018,1,LAA,AL,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +buxtoby01,2018,1,MIN,AL,28,90,8,14,4,0,0,4,5,0,3,28,0,0,1,0,1 +cabreas01,2018,1,NYN,NL,98,375,48,104,23,1,18,58,0,0,29,81,1,1,0,2,12 +cabreas01,2018,2,PHI,NL,49,171,20,39,13,0,5,17,0,0,12,38,0,2,0,0,4 +cabreme01,2018,1,CLE,AL,78,250,28,70,17,0,6,39,1,1,20,38,0,3,0,5,10 +cabremi01,2018,1,DET,AL,38,134,17,40,11,0,3,22,0,0,22,27,4,0,0,1,6 +cahiltr01,2018,1,OAK,AL,21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +cainlo01,2018,1,MIL,NL,141,539,90,166,25,2,10,38,30,7,71,94,1,8,0,2,10 +calhoko01,2018,1,LAA,AL,137,491,71,102,18,2,19,57,6,2,53,133,2,1,0,6,9 +calhowi01,2018,1,TEX,AL,35,99,8,22,5,0,2,11,0,0,6,24,0,1,0,2,2 +camarjo01,2018,1,ATL,NL,134,464,63,126,27,1,19,76,1,1,51,108,4,6,0,3,13 +candeje01,2018,1,DET,AL,144,539,78,121,28,3,19,54,3,2,66,160,1,9,0,5,4 +canhama01,2018,1,OAK,AL,122,365,60,91,22,0,17,52,1,2,34,88,3,10,0,2,11 +canoro01,2018,1,SEA,AL,80,310,44,94,22,0,10,50,0,0,32,47,2,4,0,2,9 +caratvi01,2018,1,CHN,NL,76,181,21,42,7,0,2,21,0,0,12,42,0,4,2,1,5 +carlesh01,2018,1,ATL,NL,53,4,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0 +carpema01,2018,1,SLN,NL,156,564,111,145,42,0,36,81,4,1,102,158,17,6,0,4,0 +carpery01,2018,1,DET,AL,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +carraca01,2018,1,CLE,AL,32,4,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0 +carroco01,2018,1,BAL,AL,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +casalcu01,2018,1,CIN,NL,52,140,15,41,10,0,4,16,0,2,12,32,1,2,1,1,5 +cashnan01,2018,1,BAL,AL,29,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +casteni01,2018,1,DET,AL,157,620,88,185,46,5,23,89,2,1,49,151,5,6,0,3,8 +castidi01,2018,1,TBA,AL,43,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +castijo03,2018,1,SDN,NL,37,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +castilu02,2018,1,CIN,NL,31,59,4,6,1,0,0,2,0,0,2,23,0,0,4,0,0 +castiwe01,2018,1,CHA,AL,49,170,17,44,7,0,6,15,1,0,9,46,0,2,0,0,7 +castrda01,2018,1,COL,NL,18,46,2,8,1,0,1,6,0,0,1,7,1,0,0,0,2 +castrha01,2018,1,DET,AL,6,10,2,3,0,0,0,0,1,0,0,2,0,0,0,0,0 +castrja01,2018,1,MIN,AL,19,63,4,9,3,0,1,3,0,0,9,26,0,1,0,1,2 +castrmi01,2018,1,BAL,AL,63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +castrst01,2018,1,MIA,NL,154,593,76,165,32,2,12,54,6,4,48,124,3,0,0,6,18 +caveja01,2018,1,MIN,AL,91,283,54,75,16,2,13,45,2,1,18,102,2,3,2,3,2 +cecilbr01,2018,1,SLN,NL,40,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +cedenxa01,2018,1,CHA,AL,33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +cedenxa01,2018,2,MIL,NL,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +centeju01,2018,1,TEX,AL,10,37,3,6,1,0,1,3,0,0,1,7,0,0,0,0,2 +cervefr01,2018,1,PIT,NL,104,332,39,86,15,3,12,57,2,3,51,84,1,15,2,4,7 +cespeyo01,2018,1,NYN,NL,38,141,20,37,6,0,9,29,3,0,13,50,2,1,0,2,1 +cessalu01,2018,1,NYA,AL,16,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +chacijh01,2018,1,MIL,NL,36,56,2,9,1,0,0,1,0,0,3,17,0,0,9,0,0 +chafian01,2018,1,ARI,NL,77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +chapmar01,2018,1,NYA,AL,55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +chapmma01,2018,1,OAK,AL,145,547,100,152,42,6,24,68,1,2,58,146,0,9,0,2,18 +chargjt01,2018,1,LAN,NL,39,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +chatwty01,2018,1,CHN,NL,25,25,3,4,0,0,0,2,0,0,1,6,0,0,6,0,0 +chaveje01,2018,1,TEX,AL,30,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +chaveje01,2018,2,CHN,NL,32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +chenwe02,2018,1,MIA,NL,27,40,2,6,1,0,0,2,0,0,0,18,0,0,2,0,0 +chiriro01,2018,1,TEX,AL,113,360,48,80,15,1,18,65,2,0,45,140,0,19,0,2,7 +chiriyo01,2018,1,TBA,AL,18,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +chiselo01,2018,1,CLE,AL,29,84,11,27,6,1,1,9,1,0,8,12,1,2,1,0,2 +choiji01,2018,1,MIL,NL,12,30,4,7,2,0,2,5,0,0,2,14,1,0,0,0,1 +choiji01,2018,2,TBA,AL,49,160,21,43,12,1,8,27,2,0,24,41,0,3,0,2,0 +choosh01,2018,1,TEX,AL,146,560,83,148,30,1,21,62,6,1,92,156,2,10,1,2,11 +cimbead01,2018,1,SDN,NL,42,4,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0 +cimbead01,2018,2,CLE,AL,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +cingrto01,2018,1,LAN,NL,30,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +cishest01,2018,1,CHN,NL,80,6,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0 +ciuffni01,2018,1,TBA,AL,16,37,3,7,1,0,1,5,0,0,3,12,0,1,2,1,1 +claudal01,2018,1,TEX,AL,66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +clevimi01,2018,1,CLE,AL,32,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0 +clippty01,2018,1,TOR,AL,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +cloydty01,2018,1,MIA,NL,7,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +cobbal01,2018,1,BAL,AL,28,6,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0 +coleaj01,2018,1,WAS,NL,4,3,1,1,0,0,1,1,0,0,0,1,0,0,1,0,0 +coleaj01,2018,2,NYA,AL,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +colege01,2018,1,HOU,AL,32,9,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0 +colemlo01,2018,1,DET,AL,51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +coleta01,2018,1,LAA,AL,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +colliti01,2018,1,WAS,NL,38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +colomal01,2018,1,TBA,AL,23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +colomal01,2018,2,SEA,AL,47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +colonba01,2018,1,TEX,AL,28,4,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0 +confomi01,2018,1,NYN,NL,153,543,78,132,25,1,28,82,3,4,84,159,8,7,0,4,10 +conlead01,2018,1,MIA,NL,52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +conlopj01,2018,1,NYN,NL,3,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +contrwi01,2018,1,CHN,NL,138,474,50,118,27,5,10,54,4,1,53,121,2,13,2,2,14 +cookry01,2018,1,SEA,AL,19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +coopega03,2018,1,MIA,NL,14,33,2,7,1,0,0,2,0,0,4,12,0,1,0,0,1 +copelsc01,2018,1,NYN,NL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +corbipa01,2018,1,ARI,NL,33,61,1,12,1,0,0,5,0,0,2,25,0,0,4,0,3 +corcida01,2018,1,LAN,NL,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +cordefr02,2018,1,SDN,NL,40,139,19,33,5,1,7,19,5,2,14,55,0,0,1,0,1 +cordeji01,2018,1,WAS,NL,22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +cordery01,2018,1,CHA,AL,19,37,3,4,1,0,1,4,0,0,0,15,0,1,0,2,0 +correca01,2018,1,HOU,AL,110,402,60,96,20,1,15,65,3,0,53,111,3,2,0,11,17 +cortene01,2018,1,BAL,AL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +couloda01,2018,1,OAK,AL,27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +coveydy01,2018,1,CHA,AL,27,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +cowarka01,2018,1,LAA,AL,47,112,7,15,7,1,1,10,1,0,10,44,0,1,0,1,2 +cozarza01,2018,1,LAA,AL,58,224,29,49,13,2,5,18,0,0,19,42,0,7,0,3,7 +cozendy01,2018,1,PHI,NL,26,38,2,6,2,0,1,2,1,0,6,24,0,0,0,0,0 +crawfbr01,2018,1,SFN,NL,151,531,63,135,28,2,14,54,4,5,50,122,13,8,0,5,12 +crawfjp01,2018,1,PHI,NL,49,117,17,25,6,3,3,12,2,0,13,37,0,5,2,0,2 +crickky01,2018,1,PIT,NL,64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +crockky01,2018,1,CIN,NL,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +croncj01,2018,1,TBA,AL,140,501,68,127,28,1,30,74,1,2,37,145,2,17,0,5,11 +cruzne02,2018,1,SEA,AL,144,519,70,133,18,1,37,97,1,0,55,122,5,14,0,3,15 +cruzrh01,2018,1,TOR,AL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +cruzto03,2018,1,CIN,NL,9,26,2,4,1,0,1,2,0,0,0,11,0,0,0,0,4 +cuetojo01,2018,1,SFN,NL,9,12,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0 +cuevano01,2018,1,COL,NL,75,146,16,34,4,1,2,10,1,0,6,24,1,1,0,0,1 +cuevawi01,2018,1,BOS,AL,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +culbech01,2018,1,ATL,NL,113,296,47,80,18,2,12,45,4,2,21,85,5,4,0,1,5 +cumptbr01,2018,1,TOR,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +curtijo02,2018,1,MIN,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +curtiza01,2018,1,PHI,NL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +curtiza01,2018,2,TEX,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +cuthbch01,2018,1,KCA,AL,30,103,11,20,2,0,3,7,0,1,11,23,0,2,0,1,5 +dahlda01,2018,1,COL,NL,77,249,31,68,11,3,16,48,5,3,19,68,4,1,0,2,4 +danisty01,2018,1,CHA,AL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +darnach01,2018,1,SFN,NL,42,93,9,20,5,0,3,9,2,1,4,37,0,1,1,1,0 +darnatr01,2018,1,NYN,NL,4,15,1,3,0,0,1,3,0,0,1,5,0,0,0,0,0 +darviyu01,2018,1,CHN,NL,8,12,0,1,1,0,0,0,0,0,0,7,0,0,2,0,0 +davidma02,2018,1,CHA,AL,126,434,51,99,23,0,20,62,0,0,52,165,0,7,0,3,8 +davieza01,2018,1,MIL,NL,13,15,0,1,0,0,0,0,0,0,0,8,0,0,4,0,0 +davisau01,2018,1,PHI,NL,32,3,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0 +davisch02,2018,1,BAL,AL,128,470,40,79,12,0,16,49,2,0,41,192,2,7,0,4,5 +davisjd01,2018,1,HOU,AL,42,103,9,18,2,0,1,5,0,0,10,29,0,0,0,0,3 +davisjo05,2018,1,TOR,AL,20,25,3,5,1,0,0,0,3,0,1,6,0,1,0,0,2 +daviskh01,2018,1,OAK,AL,151,576,98,142,28,1,48,123,0,0,59,175,5,12,0,7,16 +davisra01,2018,1,CLE,AL,101,196,33,44,6,1,1,6,21,7,11,48,0,4,4,1,2 +davista01,2018,1,CHN,NL,5,5,0,2,0,0,0,2,0,0,0,1,0,0,0,1,0 +daviswa01,2018,1,COL,NL,69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +deanau01,2018,1,MIA,NL,34,113,16,25,4,0,4,14,1,0,7,22,0,2,0,0,1 +deetzde01,2018,1,HOU,AL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +degroja01,2018,1,NYN,NL,32,67,1,11,1,0,0,5,0,0,4,25,0,0,3,0,1 +dejonch01,2018,1,MIN,AL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +dejonpa01,2018,1,SLN,NL,115,436,68,105,25,1,19,68,1,1,36,123,2,12,0,5,6 +delarjo01,2018,1,ARI,NL,43,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +delarjo01,2018,2,CHN,NL,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +delgara01,2018,1,ARI,NL,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +delmoni01,2018,1,CHA,AL,88,284,31,61,11,5,8,25,1,2,27,80,1,6,0,1,7 +delosen01,2018,1,PHI,NL,7,3,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0 +dendema01,2018,1,NYN,NL,8,18,0,0,0,0,0,1,0,0,2,9,0,0,0,1,0 +descada01,2018,1,ARI,NL,138,349,54,83,22,4,13,57,0,1,64,110,2,2,0,7,2 +desclan01,2018,1,CIN,NL,21,42,3,6,0,0,1,4,0,0,0,25,0,0,4,0,0 +deshide02,2018,1,TEX,AL,106,334,52,72,14,1,2,22,20,4,43,83,0,3,12,1,1 +desmoia01,2018,1,COL,NL,160,555,82,131,21,8,22,88,20,6,53,146,0,6,1,4,17 +despaod01,2018,1,MIA,NL,11,4,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0 +despaod01,2018,2,LAA,AL,8,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +detwiro01,2018,1,SEA,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +devench02,2018,1,HOU,AL,50,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +deverra01,2018,1,BOS,AL,121,450,59,108,24,0,21,66,5,2,38,121,6,0,0,2,9 +diazal02,2018,1,TOR,AL,130,422,55,111,26,0,18,55,3,4,23,62,2,3,0,4,9 +diazed04,2018,1,SEA,AL,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +diazel01,2018,1,PIT,NL,82,252,33,72,12,0,10,34,0,1,21,40,1,1,0,3,4 +diazmi02,2018,1,SDN,NL,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +diazya01,2018,1,CLE,AL,39,109,15,34,5,2,1,15,0,0,11,19,1,0,0,0,6 +dickeco01,2018,1,PIT,NL,135,504,65,151,35,7,13,55,8,3,21,80,4,4,0,4,14 +diekmja01,2018,1,TEX,AL,47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +diekmja01,2018,2,ARI,NL,24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +dietrde01,2018,1,MIA,NL,149,499,72,132,26,2,16,45,2,0,29,140,2,21,0,2,5 +difowi01,2018,1,WAS,NL,148,408,55,94,14,7,7,42,10,3,39,82,5,2,3,4,8 +dixonbr01,2018,1,CIN,NL,74,118,14,21,6,0,5,10,0,0,6,43,0,0,0,0,2 +dominse01,2018,1,PHI,NL,53,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0 +donaljo02,2018,1,TOR,AL,36,137,22,32,11,0,5,16,2,0,21,44,2,0,0,1,1 +donaljo02,2018,2,CLE,AL,16,50,8,14,3,0,3,7,0,0,10,10,0,0,0,0,2 +doolise01,2018,1,WAS,NL,43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +doziebr01,2018,1,MIN,AL,104,410,65,93,21,2,16,52,8,3,46,96,1,3,0,3,7 +doziebr01,2018,2,LAN,NL,47,143,16,26,9,0,5,20,4,0,24,33,0,1,0,2,5 +doziehu01,2018,1,KCA,AL,102,362,36,83,19,4,11,34,2,3,24,109,0,1,0,1,12 +drakeol01,2018,1,MIL,NL,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +drakeol01,2018,2,CLE,AL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +drakeol01,2018,3,LAA,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +drakeol01,2018,4,TOR,AL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +drakeol01,2018,5,MIN,AL,19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +drurybr01,2018,1,NYA,AL,18,51,2,9,2,0,1,7,0,0,5,12,0,1,0,0,4 +drurybr01,2018,2,TOR,AL,8,26,3,4,2,0,0,3,0,0,2,8,0,1,0,0,1 +dudalu01,2018,1,KCA,AL,87,310,34,75,12,1,13,48,1,0,24,95,2,8,0,3,4 +dudalu01,2018,2,ATL,NL,20,18,1,4,2,0,1,2,0,0,4,7,0,0,0,0,1 +duensbr01,2018,1,CHN,NL,48,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0 +duffety01,2018,1,MIN,AL,19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +duffyda01,2018,1,KCA,AL,28,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +duffyma01,2018,1,TBA,AL,132,503,59,148,22,1,4,44,12,6,47,93,1,7,1,2,12 +duggast01,2018,1,SFN,NL,41,141,20,36,11,1,2,17,5,1,10,44,1,0,0,1,0 +dukeza01,2018,1,MIN,AL,45,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +dukeza01,2018,2,SEA,AL,27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +dullry01,2018,1,OAK,AL,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +dunnmi01,2018,1,COL,NL,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +duvalad01,2018,1,CIN,NL,105,331,40,68,19,0,15,61,2,2,34,100,3,4,0,1,8 +duvalad01,2018,2,ATL,NL,33,53,8,7,1,0,0,0,0,0,3,17,0,1,0,0,1 +dysonja01,2018,1,ARI,NL,67,206,29,39,4,2,2,12,16,3,27,34,2,0,3,1,3 +dysonsa01,2018,1,SFN,NL,74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +eatonad02,2018,1,WAS,NL,95,319,55,96,18,1,5,33,9,1,38,64,0,11,2,0,2 +edwarca01,2018,1,CHN,NL,58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +edwarjo02,2018,1,CLE,AL,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +eflinza01,2018,1,PHI,NL,25,44,3,4,0,0,1,1,0,0,0,31,0,0,6,0,0 +eickhje01,2018,1,PHI,NL,3,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +eliasro01,2018,1,SEA,AL,23,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0 +ellisaj01,2018,1,SDN,NL,66,151,19,41,8,0,1,15,0,0,26,37,1,1,3,2,2 +encared01,2018,1,CLE,AL,137,500,74,123,16,1,32,107,3,0,63,132,2,8,0,7,14 +engelad01,2018,1,CHA,AL,143,429,49,101,17,4,6,29,16,8,18,129,0,8,7,1,1 +eovalna01,2018,1,TBA,AL,11,7,1,1,0,0,0,1,0,0,0,5,0,0,0,0,0 +eovalna01,2018,2,BOS,AL,12,2,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0 +erlinro01,2018,1,SDN,NL,40,23,1,5,0,0,0,0,0,0,1,11,0,0,3,0,0 +ervinph01,2018,1,CIN,NL,78,218,27,55,10,1,7,31,6,1,20,60,1,5,0,4,5 +escobal02,2018,1,KCA,AL,140,485,54,112,22,3,4,34,8,2,29,74,1,5,8,4,14 +escobed01,2018,1,MIN,AL,97,368,45,101,37,3,15,63,1,3,34,91,6,3,0,3,7 +escobed01,2018,2,ARI,NL,54,198,30,53,11,0,8,21,1,1,18,35,2,2,0,5,5 +estrama01,2018,1,TOR,AL,28,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +evansph01,2018,1,NYN,NL,15,21,1,3,0,0,0,1,1,0,2,8,0,0,0,0,1 +familje01,2018,1,NYN,NL,40,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +familje01,2018,2,OAK,AL,30,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +fariaja01,2018,1,TBA,AL,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +farmebu01,2018,1,DET,AL,66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +farmeky01,2018,1,LAN,NL,39,68,1,16,4,1,0,9,0,0,5,15,1,3,0,1,1 +farquda01,2018,1,CHA,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +farrelu01,2018,1,CHN,NL,20,4,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0 +feddeer01,2018,1,WAS,NL,11,16,1,1,0,0,0,0,0,0,1,5,0,0,0,0,1 +federti01,2018,1,HOU,AL,10,34,4,7,3,0,0,2,0,0,1,13,0,0,0,0,1 +federti01,2018,2,CIN,NL,5,6,1,2,1,0,1,2,0,0,1,3,0,0,0,0,0 +felizmi01,2018,1,PIT,NL,47,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +ferguca01,2018,1,LAN,NL,30,5,0,1,0,0,0,0,0,0,0,3,0,0,0,0,1 +fernajo03,2018,1,LAA,AL,36,116,9,31,8,0,2,11,1,0,6,15,0,1,0,0,2 +fernajo04,2018,1,TOR,AL,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +festama01,2018,1,SEA,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +fieldjo03,2018,1,LAN,NL,45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +fieldjo04,2018,1,TBA,AL,63,169,20,36,9,0,6,14,4,0,7,58,0,2,1,0,3 +fieldjo04,2018,2,MIN,AL,21,52,8,13,4,0,3,7,0,0,0,14,0,1,0,1,0 +fiersmi01,2018,1,DET,AL,21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +fiersmi01,2018,2,OAK,AL,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +fillmhe01,2018,1,KCA,AL,17,4,0,1,1,0,0,1,0,0,0,3,0,0,1,0,0 +finnebr01,2018,1,CIN,NL,5,7,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +fishede01,2018,1,HOU,AL,42,79,13,13,2,2,4,11,2,0,5,42,0,0,0,2,0 +fistedo01,2018,1,TEX,AL,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +flaheja01,2018,1,SLN,NL,31,46,2,7,0,0,0,0,0,0,2,20,0,0,4,0,0 +flahery01,2018,1,ATL,NL,81,161,17,35,6,0,2,13,4,2,18,41,4,1,1,1,5 +fletcda02,2018,1,LAA,AL,80,284,35,78,18,2,1,25,3,0,15,34,0,3,3,2,7 +flexech01,2018,1,NYN,NL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +florewi01,2018,1,NYN,NL,126,386,43,103,25,0,11,51,0,0,29,42,1,5,0,9,8 +floripe01,2018,1,PHI,NL,50,71,13,16,6,1,2,5,1,2,5,30,0,0,0,0,0 +florody01,2018,1,CIN,NL,25,4,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0 +florody01,2018,2,LAN,NL,29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +flowety01,2018,1,ATL,NL,82,251,34,57,9,0,8,30,0,0,35,76,0,9,0,1,6 +flynnbr01,2018,1,KCA,AL,48,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +foltymi01,2018,1,ATL,NL,31,58,3,3,1,0,0,4,0,0,0,41,0,0,8,0,0 +fontano01,2018,1,LAA,AL,8,11,1,1,0,0,1,1,0,0,1,6,0,0,0,0,0 +fontwi01,2018,1,LAN,NL,6,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0 +fontwi01,2018,2,OAK,AL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +fontwi01,2018,3,TBA,AL,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +forsylo01,2018,1,LAN,NL,70,193,18,40,10,0,2,13,2,0,17,43,1,0,0,1,5 +forsylo01,2018,2,MIN,AL,50,178,19,46,6,0,0,14,1,0,24,40,0,3,0,0,5 +fowlede01,2018,1,SLN,NL,90,289,40,52,10,0,8,31,5,2,38,75,0,3,0,4,2 +fowledu01,2018,1,OAK,AL,69,192,19,43,3,2,6,23,6,4,8,47,0,1,0,2,2 +francma02,2018,1,PHI,NL,131,433,48,117,17,1,22,68,1,0,29,62,7,0,0,3,15 +frankni01,2018,1,MIL,NL,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +frareca01,2018,1,CHA,AL,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +fraziad01,2018,1,PIT,NL,113,318,52,88,23,2,10,35,1,3,29,53,2,3,1,1,3 +frazicl01,2018,1,NYA,AL,15,34,9,9,3,0,0,1,0,0,5,13,0,2,0,0,3 +frazito01,2018,1,NYN,NL,115,408,54,87,18,0,18,59,9,4,48,112,1,8,0,8,10 +freelky01,2018,1,COL,NL,33,65,5,6,2,0,0,2,0,0,2,40,0,0,4,0,0 +freemfr01,2018,1,ATL,NL,162,618,94,191,44,4,23,98,10,3,76,132,12,7,0,6,11 +freemmi01,2018,1,CHN,NL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +freemsa01,2018,1,ATL,NL,63,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +freesda01,2018,1,PIT,NL,94,241,29,68,10,1,9,42,0,0,18,56,1,3,0,3,10 +freesda01,2018,2,LAN,NL,19,39,9,15,2,1,2,9,0,0,6,16,0,2,0,0,0 +freitda01,2018,1,SEA,AL,36,93,9,20,6,0,1,5,0,0,8,25,0,0,1,0,2 +friedma01,2018,1,ATL,NL,14,8,1,1,0,0,0,0,0,0,0,3,0,0,0,0,0 +fryja01,2018,1,CHA,AL,59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +frypa01,2018,1,BAL,AL,35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +fulmeca01,2018,1,CHA,AL,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +fulmemi01,2018,1,DET,AL,24,6,0,0,0,0,0,0,0,0,0,4,0,0,1,0,1 +gagnodr01,2018,1,NYN,NL,5,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0 +galero01,2018,1,LAN,NL,3,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +gallaca01,2018,1,KCA,AL,22,63,5,13,3,0,1,7,0,0,3,15,0,1,1,1,1 +gallayo01,2018,1,CIN,NL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gallayo01,2018,2,TEX,AL,18,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +gallegi01,2018,1,NYA,AL,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gallegi01,2018,2,SLN,NL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gallois01,2018,1,MIA,NL,43,64,7,13,3,0,3,7,1,1,9,21,0,0,1,0,0 +gallojo01,2018,1,TEX,AL,148,500,82,103,24,1,40,92,3,4,74,207,4,3,0,0,3 +galvifr01,2018,1,SDN,NL,162,602,62,149,31,5,13,67,8,6,45,147,2,2,1,6,8 +gamelbe01,2018,1,SEA,AL,101,257,37,70,14,4,1,19,7,3,31,61,1,4,0,1,4 +gantjo01,2018,1,SLN,NL,28,31,2,2,0,0,2,3,0,0,0,14,0,0,3,0,0 +garciad02,2018,1,SLN,NL,21,17,3,2,1,0,0,1,0,0,0,7,0,0,0,0,0 +garciar01,2018,1,SFN,NL,19,63,8,18,1,0,4,9,0,0,2,31,0,0,0,0,1 +garciav01,2018,1,CHA,AL,93,356,47,84,11,2,19,49,3,1,20,102,2,4,0,5,9 +garcigr01,2018,1,SLN,NL,114,181,15,40,6,0,3,15,3,1,20,37,1,4,1,2,3 +garcija01,2018,1,OAK,AL,26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +garcija02,2018,1,TOR,AL,25,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0 +garcija02,2018,2,CHN,NL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +garcija04,2018,1,MIA,NL,29,9,0,0,0,0,0,0,0,0,1,7,0,0,1,0,0 +garcile02,2018,1,CHA,AL,82,258,23,70,7,4,4,32,12,1,9,69,0,3,4,1,2 +garcilu03,2018,1,PHI,NL,59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +garciyi01,2018,1,LAN,NL,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gardeni01,2018,1,TEX,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gardnbr01,2018,1,NYA,AL,140,530,95,125,20,7,12,45,16,2,65,107,0,5,4,5,6 +garnedu01,2018,1,CHA,AL,1,2,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0 +garream01,2018,1,CIN,NL,66,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +garvemi01,2018,1,MIN,AL,103,302,38,81,19,2,7,45,0,0,29,72,2,2,1,1,8 +gattiev01,2018,1,HOU,AL,128,407,49,92,17,0,25,78,1,0,33,101,2,3,0,8,13 +gausmke01,2018,1,BAL,AL,21,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +gausmke01,2018,2,ATL,NL,11,19,0,1,0,0,0,0,0,0,4,9,0,0,0,0,0 +gavigsa01,2018,1,TOR,AL,26,3,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0 +gearrco01,2018,1,SFN,NL,35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gearrco01,2018,2,TEX,AL,21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gearrco01,2018,3,OAK,AL,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gennesc01,2018,1,CIN,NL,154,584,86,181,30,3,23,92,4,2,42,125,3,4,3,5,14 +gentrcr01,2018,1,BAL,AL,68,156,13,42,5,2,1,11,12,3,11,31,0,1,1,0,2 +gerbemi01,2018,1,DET,AL,18,42,2,4,1,0,0,2,0,0,4,21,0,0,0,1,1 +germado01,2018,1,NYA,AL,21,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0 +gibsoky01,2018,1,MIN,AL,32,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0 +gileske01,2018,1,HOU,AL,34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gileske01,2018,2,TOR,AL,21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gilmase01,2018,1,BAL,AL,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gimench01,2018,1,CHN,NL,12,28,1,4,0,0,0,1,1,0,3,7,0,0,0,1,1 +gimench01,2018,2,MIN,AL,13,29,6,8,1,0,2,6,0,0,3,9,0,1,0,1,1 +giolilu01,2018,1,CHA,AL,32,6,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0 +givenmy01,2018,1,BAL,AL,69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +glasnty01,2018,1,PIT,NL,34,3,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0 +glasnty01,2018,2,TBA,AL,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gloveko01,2018,1,WAS,NL,22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +godleza01,2018,1,ARI,NL,33,53,1,3,0,0,0,1,0,0,4,30,0,0,8,0,0 +goedder01,2018,1,SEA,AL,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +goedder01,2018,2,LAN,NL,26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +goharlu01,2018,1,ATL,NL,9,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +goinsry01,2018,1,KCA,AL,41,115,10,26,8,1,0,6,0,0,4,29,0,0,1,0,2 +goldspa01,2018,1,ARI,NL,158,593,95,172,35,5,33,83,7,4,90,173,11,6,0,0,7 +gombeau01,2018,1,SLN,NL,29,19,1,4,1,0,0,4,0,0,0,3,0,0,2,0,0 +gomesya01,2018,1,CLE,AL,112,403,52,107,26,0,16,48,0,0,21,119,2,8,0,3,4 +gomezca01,2018,1,TBA,AL,118,360,42,75,15,2,9,32,12,3,25,103,0,21,2,0,6 +gomezje01,2018,1,CHA,AL,26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gomezmi01,2018,1,SFN,NL,9,15,3,4,0,0,0,1,0,0,0,5,0,0,0,0,0 +gomezro01,2018,1,SFN,NL,5,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +gonsast01,2018,1,MIN,AL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gonzaad01,2018,1,NYN,NL,54,169,15,40,5,0,6,26,0,0,15,34,3,1,0,2,5 +gonzaal04,2018,1,CHA,AL,3,9,0,1,0,0,0,1,0,0,0,4,0,0,0,0,0 +gonzaca01,2018,1,COL,NL,132,463,71,128,32,4,16,64,5,2,37,113,4,1,0,3,5 +gonzaer01,2018,1,CLE,AL,81,136,17,36,10,1,1,16,3,0,5,34,0,2,0,0,0 +gonzagi01,2018,1,WAS,NL,27,44,1,3,1,0,0,0,0,0,0,27,0,0,3,0,0 +gonzagi01,2018,2,MIL,NL,5,7,2,1,0,0,0,1,0,0,1,4,0,0,0,0,0 +gonzama01,2018,1,HOU,AL,145,489,61,121,25,3,16,68,2,3,53,126,3,3,5,2,14 +gonzama02,2018,1,SEA,AL,29,2,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0 +gonzame01,2018,1,MIA,NL,8,4,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0 +gonzami03,2018,1,CHA,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +goodrni01,2018,1,DET,AL,131,444,55,109,29,3,16,53,12,4,42,132,1,4,0,2,9 +goodwbr01,2018,1,WAS,NL,48,65,9,13,1,0,3,12,3,1,10,26,0,2,1,1,0 +goodwbr01,2018,2,KCA,AL,27,94,11,25,5,0,3,13,1,1,6,31,0,1,0,0,0 +goodyni01,2018,1,CLE,AL,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gordoal01,2018,1,KCA,AL,141,506,56,124,24,0,13,54,12,2,50,124,4,10,0,2,7 +gordode01,2018,1,SEA,AL,141,556,62,149,17,8,4,36,30,12,9,80,0,9,9,5,10 +gorete01,2018,1,CHN,NL,14,5,5,1,0,0,0,0,6,0,0,1,0,0,0,0,0 +gosseda01,2018,1,OAK,AL,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gosseph01,2018,1,CIN,NL,20,24,5,3,0,0,1,2,0,0,4,8,1,0,0,0,1 +gotttr01,2018,1,WAS,NL,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gourryu01,2018,1,HOU,AL,136,537,70,156,33,1,13,85,5,1,23,63,0,6,0,7,22 +gracema02,2018,1,WAS,NL,56,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +grandcu01,2018,1,TOR,AL,104,302,48,74,21,1,11,35,2,1,42,96,1,3,1,1,3 +grandcu01,2018,2,MIL,NL,19,41,12,9,1,1,2,3,0,0,12,10,0,1,0,0,0 +grandya01,2018,1,LAN,NL,140,440,65,106,23,2,24,68,2,1,72,124,1,3,0,3,12 +grateju01,2018,1,LAA,AL,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +grateju01,2018,2,MIN,AL,3,7,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0 +gravebr01,2018,1,MIA,NL,21,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +graveke01,2018,1,OAK,AL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +grayjo02,2018,1,COL,NL,31,52,2,4,2,0,0,2,0,0,3,30,0,0,6,0,0 +grayso01,2018,1,NYA,AL,30,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +greench03,2018,1,NYA,AL,63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +greensh02,2018,1,DET,AL,66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gregelu01,2018,1,SLN,NL,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gregodi01,2018,1,NYA,AL,134,504,89,135,23,5,27,86,10,6,48,69,3,7,1,9,8 +greingr01,2018,1,DET,AL,30,96,9,21,6,0,0,12,0,1,17,32,0,0,0,3,0 +greinza01,2018,1,ARI,NL,34,64,6,15,4,0,0,5,3,0,3,16,0,0,4,0,3 +grichra01,2018,1,TOR,AL,124,424,60,104,32,1,25,61,3,2,27,122,0,8,0,3,5 +grimmju01,2018,1,KCA,AL,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +grimmju01,2018,2,SEA,AL,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +grossro01,2018,1,MIN,AL,129,396,50,108,27,1,5,48,0,1,60,83,0,2,2,5,2 +gsellro01,2018,1,NYN,NL,68,3,1,0,0,0,0,0,0,0,0,2,0,1,0,0,0 +guduare01,2018,1,HOU,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +guerrga01,2018,1,CIN,NL,14,18,1,3,0,0,1,1,0,0,0,8,0,0,0,0,0 +guerrja01,2018,1,MIA,NL,32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +guerrja02,2018,1,SDN,NL,13,16,2,2,0,0,0,1,0,0,3,9,0,0,0,0,0 +guerrju02,2018,1,MIL,NL,31,35,2,4,3,0,0,0,0,0,0,13,0,0,2,0,0 +guerrta01,2018,1,MIA,NL,60,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +guerrta02,2018,1,TOR,AL,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +guilllu01,2018,1,NYN,NL,35,67,4,14,2,0,0,5,1,0,7,3,0,0,0,0,1 +guilmpr01,2018,1,SLN,NL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +guilmpr01,2018,2,TOR,AL,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +gurrilo01,2018,1,TOR,AL,65,249,30,70,8,0,11,35,1,2,9,59,1,2,1,2,2 +guyerbr01,2018,1,CLE,AL,104,194,25,40,11,0,7,27,1,1,15,48,0,11,1,0,5 +guzmaro01,2018,1,TEX,AL,123,387,46,91,18,2,16,58,1,0,33,121,2,7,0,1,8 +gyorkje01,2018,1,SLN,NL,125,351,49,92,19,1,11,47,2,0,44,77,0,3,0,4,12 +haaseer01,2018,1,CLE,AL,9,16,0,2,0,0,0,1,0,0,0,6,0,1,0,0,1 +haderjo01,2018,1,MIL,NL,55,2,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0 +haleda02,2018,1,NYA,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +haleda02,2018,2,MIN,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +haleda02,2018,3,NYA,AL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +haleyju01,2018,1,BOS,AL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hallma02,2018,1,DET,AL,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hamelco01,2018,1,TEX,AL,20,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hamelco01,2018,2,CHN,NL,12,23,1,3,0,0,1,1,0,0,1,12,0,0,1,0,0 +hamilbi02,2018,1,CIN,NL,153,504,74,119,16,9,4,29,34,10,46,132,0,1,1,4,1 +hamilia01,2018,1,CHA,AL,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hammeja01,2018,1,KCA,AL,39,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +hampsga01,2018,1,COL,NL,24,40,3,11,3,1,0,4,2,0,7,12,0,1,0,0,0 +hancoju01,2018,1,CHN,NL,10,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +handbr01,2018,1,SDN,NL,41,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +handbr01,2018,2,CLE,AL,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hanhoer01,2018,1,NYN,NL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hanigmi01,2018,1,SEA,AL,157,596,90,170,38,4,26,93,8,2,70,148,4,10,0,7,8 +hansoal01,2018,1,SFN,NL,110,294,36,74,17,5,8,39,7,3,9,71,0,1,3,3,4 +happia01,2018,1,CHN,NL,142,387,56,90,19,2,15,44,8,4,70,167,9,3,0,2,6 +happja01,2018,1,TOR,AL,20,6,2,2,0,0,0,0,0,0,1,3,0,0,0,0,0 +happja01,2018,2,NYA,AL,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hardybl01,2018,1,DET,AL,30,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +harpebr03,2018,1,WAS,NL,159,550,103,137,34,0,34,100,13,3,130,169,16,6,0,9,7 +harrijo05,2018,1,PIT,NL,97,344,41,86,13,1,8,37,3,0,18,68,1,5,2,5,8 +harriwi02,2018,1,HOU,AL,61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hartdo01,2018,1,BAL,AL,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +harvema01,2018,1,NYN,NL,8,7,0,2,0,0,0,0,0,0,0,2,0,0,0,0,1 +harvema01,2018,2,CIN,NL,24,46,1,2,0,0,0,1,0,0,2,17,0,0,2,0,1 +hatchch02,2018,1,OAK,AL,34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hauscmi01,2018,1,TOR,AL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +headlch01,2018,1,SDN,NL,27,52,2,6,1,0,0,4,0,0,6,20,0,2,0,0,3 +healyry01,2018,1,SEA,AL,133,493,51,116,15,0,24,73,0,0,27,113,0,2,0,2,21 +heanean01,2018,1,LAA,AL,30,8,1,1,0,0,0,0,0,0,0,6,0,0,0,0,0 +hechaad01,2018,1,TBA,AL,61,217,29,56,7,0,3,26,1,0,12,37,1,0,2,6,4 +hechaad01,2018,2,PIT,NL,15,43,2,10,4,0,1,3,0,0,3,11,1,0,0,1,3 +hechaad01,2018,3,NYA,AL,18,36,3,7,0,0,2,2,1,0,1,10,0,0,0,0,0 +hedgeau01,2018,1,SDN,NL,91,303,29,70,14,2,14,37,3,0,21,90,3,1,0,1,9 +hellije01,2018,1,WAS,NL,19,32,0,2,1,0,0,1,0,0,0,13,0,0,3,0,0 +hembrhe01,2018,1,BOS,AL,67,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +hendrky01,2018,1,CHN,NL,33,60,1,4,1,0,0,1,0,0,2,31,0,0,4,0,0 +hendrli01,2018,1,OAK,AL,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +heredgu01,2018,1,SEA,AL,125,292,29,69,14,1,5,19,2,4,32,52,0,4,7,2,4 +hermomi01,2018,1,LAA,AL,31,57,7,12,4,0,1,1,0,1,3,17,0,2,0,0,0 +hernace02,2018,1,PHI,NL,161,605,91,153,15,3,15,60,19,6,95,155,4,4,1,3,12 +hernada01,2018,1,CIN,NL,57,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hernael01,2018,1,MIA,NL,32,11,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0 +hernaen02,2018,1,LAN,NL,145,402,67,103,17,3,21,52,3,0,50,78,5,1,4,5,3 +hernafe02,2018,1,SEA,AL,29,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2 +hernago01,2018,1,SFN,NL,142,414,52,97,16,2,15,40,8,5,27,113,1,4,2,4,14 +hernate01,2018,1,TOR,AL,134,476,67,114,29,7,22,57,5,5,41,163,0,3,0,3,14 +herredi01,2018,1,CIN,NL,53,87,11,16,5,0,5,11,0,0,8,39,0,2,0,0,0 +herreke01,2018,1,KCA,AL,27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +herreke01,2018,2,WAS,NL,21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +herreod01,2018,1,PHI,NL,148,550,64,140,19,3,22,71,5,2,38,122,3,7,1,1,11 +herrero02,2018,1,CIN,NL,11,13,0,2,0,0,0,0,0,1,0,5,0,0,0,0,0 +herrero02,2018,2,KCA,AL,75,265,25,63,14,3,1,20,3,4,19,52,0,2,1,2,6 +herrmch01,2018,1,SEA,AL,36,76,6,18,4,2,2,7,0,0,10,24,0,0,0,1,0 +hessda01,2018,1,BAL,AL,21,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +heywaja01,2018,1,CHN,NL,127,440,67,119,23,4,8,57,1,1,42,60,1,2,2,2,7 +hicksaa01,2018,1,NYA,AL,137,480,90,119,18,3,27,79,11,2,90,111,1,3,2,6,1 +hicksjo02,2018,1,DET,AL,81,288,35,75,12,1,9,32,0,1,22,84,0,0,1,1,9 +hicksjo03,2018,1,SLN,NL,73,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +higasky01,2018,1,NYA,AL,29,72,6,12,2,0,3,6,0,0,6,16,0,1,0,0,2 +hildetr01,2018,1,MIN,AL,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hillri01,2018,1,LAN,NL,25,43,2,4,0,0,0,2,0,0,0,15,0,0,2,0,1 +hillti01,2018,1,KCA,AL,70,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +hiranyo01,2018,1,ARI,NL,75,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hoffmje02,2018,1,COL,NL,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +holadbr01,2018,1,MIA,NL,61,151,7,31,5,0,1,16,0,0,10,29,2,2,1,2,4 +holdejo02,2018,1,NYA,AL,60,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hollade01,2018,1,SFN,NL,37,53,2,3,1,0,0,0,0,0,1,33,0,0,2,0,1 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+hugheph01,2018,1,MIN,AL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hugheph01,2018,2,SDN,NL,16,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hundlni01,2018,1,SFN,NL,96,282,34,68,13,2,10,31,2,1,22,85,2,1,0,0,8 +hunteto02,2018,1,PHI,NL,65,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +hutchdr01,2018,1,PHI,NL,11,3,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0 +hutchdr01,2018,2,TEX,AL,5,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +iannech01,2018,1,COL,NL,110,299,36,67,13,1,11,36,0,0,50,87,5,7,1,3,13 +iglesjo01,2018,1,DET,AL,125,432,43,116,31,3,5,48,15,6,19,47,0,8,3,2,11 +iglesra01,2018,1,CIN,NL,66,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +inciaen01,2018,1,ATL,NL,156,597,83,158,27,6,10,61,28,14,49,86,1,6,4,4,6 +infangr01,2018,1,CHA,AL,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +jacksau01,2018,1,SFN,NL,59,149,12,36,8,0,0,13,2,1,14,59,2,1,0,1,4 +jacksau01,2018,2,NYN,NL,57,198,17,49,9,1,3,19,1,2,12,74,0,0,0,0,4 +jacksed01,2018,1,OAK,AL,17,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +jackslu01,2018,1,ATL,NL,35,1,1,0,0,0,0,0,0,0,1,0,0,0,1,0,1 +jamesjo02,2018,1,HOU,AL,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +jankotr01,2018,1,SDN,NL,117,347,45,90,12,3,4,17,24,7,37,73,0,1,2,0,7 +janseda01,2018,1,TOR,AL,31,81,12,20,6,0,3,8,0,0,9,17,0,4,0,1,1 +janseke01,2018,1,LAN,NL,69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +jayjo02,2018,1,KCA,AL,59,238,28,73,9,2,1,18,3,2,19,39,0,3,4,2,4 +jayjo02,2018,2,ARI,NL,84,289,46,68,10,5,2,22,1,1,14,56,0,15,1,1,7 +jeffrje01,2018,1,MIL,NL,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +jennida01,2018,1,MIL,NL,72,3,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0 +jepseke01,2018,1,TEX,AL,21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +jerezwi01,2018,1,LAA,AL,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +jewelja01,2018,1,LAA,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +jimenjo02,2018,1,DET,AL,68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +johnsbr02,2018,1,BOS,AL,38,4,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0 +johnsdj01,2018,1,COL,NL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +johnsji04,2018,1,LAA,AL,62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +johnspi01,2018,1,SFN,NL,37,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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+kelake01,2018,2,PIT,NL,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +kellebr01,2018,1,KCA,AL,41,3,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0 +kellesh01,2018,1,WAS,NL,35,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +kellesh01,2018,2,OAK,AL,19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +kellyca01,2018,1,SFN,NL,7,4,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0 +kellyca02,2018,1,SLN,NL,19,35,1,4,0,0,0,3,0,0,3,7,0,1,3,0,0 +kellyjo05,2018,1,BOS,AL,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +kellyty01,2018,1,NYN,NL,9,11,1,1,0,0,0,0,0,0,1,2,0,0,0,0,1 +kempma01,2018,1,LAN,NL,146,462,62,134,25,0,21,85,0,0,36,115,2,1,0,7,14 +kempto01,2018,1,HOU,AL,97,255,37,67,15,0,6,30,9,3,32,44,1,3,3,1,7 +kendrho01,2018,1,WAS,NL,40,152,17,46,14,0,4,12,1,1,5,29,1,2,0,1,6 +kennebr02,2018,1,SDN,NL,6,8,0,0,0,0,0,0,0,0,0,4,0,0,1,0,1 +kenneia01,2018,1,KCA,AL,22,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +keplema01,2018,1,MIN,AL,156,532,80,119,30,4,20,58,4,5,71,96,2,5,0,3,8 +kershcl01,2018,1,LAN,NL,27,46,1,11,2,0,0,7,0,0,7,11,0,0,4,0,0 +keuchda01,2018,1,HOU,AL,34,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +kiebosp01,2018,1,WAS,NL,52,125,16,29,5,0,2,13,0,0,16,28,0,1,0,1,2 +kiekhde01,2018,1,OAK,AL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +kiermke01,2018,1,TBA,AL,88,332,44,72,12,9,7,29,10,5,25,91,2,6,2,2,4 +kimbrcr01,2018,1,BOS,AL,63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +kineris01,2018,1,TEX,AL,111,356,43,93,18,2,4,34,7,5,28,62,1,6,5,1,14 +kingesc01,2018,1,PHI,NL,147,452,55,102,23,2,8,35,10,3,24,126,1,3,0,5,2 +kinghni01,2018,1,PIT,NL,18,23,1,2,1,0,0,4,0,0,1,10,0,1,0,0,0 +kinlety01,2018,1,MIN,AL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +kinlety01,2018,2,MIA,NL,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +kinslia01,2018,1,LAA,AL,91,355,49,85,20,0,13,32,9,4,30,40,0,4,0,2,10 +kinslia01,2018,2,BOS,AL,37,132,17,32,6,0,1,16,7,3,10,24,0,0,0,1,5 +kintzbr01,2018,1,WAS,NL,45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +kintzbr01,2018,2,CHN,NL,25,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +kipnija01,2018,1,CLE,AL,147,530,65,122,28,1,18,75,7,1,60,112,1,7,1,3,5 +kittran01,2018,1,TBA,AL,33,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +kivlepa01,2018,1,ARI,NL,9,13,3,3,0,2,0,0,0,0,0,6,0,1,0,0,0 +klubeco01,2018,1,CLE,AL,33,6,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0 +knappan01,2018,1,PHI,NL,84,187,19,37,6,2,4,15,1,0,24,75,1,2,1,1,2 +knebeco01,2018,1,MIL,NL,57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +kochma01,2018,1,ARI,NL,19,17,1,1,0,0,0,0,0,0,3,11,0,0,3,0,0 +kolarad01,2018,1,TBA,AL,31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +kontoge01,2018,1,PIT,NL,21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +kontoge01,2018,2,CLE,AL,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +kontoge01,2018,3,NYA,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +kopecmi01,2018,1,CHA,AL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +kozmape01,2018,1,DET,AL,27,69,7,15,4,1,1,8,0,1,2,15,0,0,1,1,2 +krameke01,2018,1,PIT,NL,21,37,5,5,0,0,0,4,0,0,2,20,0,0,0,1,0 +kratzer01,2018,1,MIL,NL,67,203,18,48,6,0,6,23,1,0,6,40,0,7,1,2,8 +krehbjo01,2018,1,ARI,NL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +krolia01,2018,1,LAA,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +kuhlch01,2018,1,PIT,NL,18,21,2,3,0,0,0,1,0,0,0,5,0,0,6,0,0 +lagarju01,2018,1,NYN,NL,30,59,9,20,1,1,0,6,3,1,3,9,1,1,0,1,2 +lamarry01,2018,1,MIN,AL,43,99,7,26,5,0,0,8,1,1,8,33,0,1,0,1,3 +lamarry01,2018,2,CHA,AL,33,66,8,20,6,0,2,10,1,1,2,20,0,1,0,2,0 +lambja01,2018,1,ARI,NL,56,207,34,46,8,0,6,31,1,2,26,65,0,1,0,4,4 +lambjo02,2018,1,LAA,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +lasteto01,2018,1,CHN,NL,123,169,23,45,8,0,1,19,0,1,17,27,1,2,0,0,5 +lauerer01,2018,1,SDN,NL,23,30,1,2,1,0,0,1,0,0,1,15,0,0,5,1,0 +laurera01,2018,1,OAK,AL,48,156,27,45,12,1,5,19,7,1,16,50,0,2,0,2,0 +lavarry01,2018,1,PIT,NL,6,6,1,4,1,0,0,1,0,0,0,1,0,0,0,0,0 +lawde01,2018,1,SFN,NL,7,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +lawreca01,2018,1,SEA,AL,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +leakemi01,2018,1,SEA,AL,31,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0 +leblawa01,2018,1,SEA,AL,32,3,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0 +leclejo01,2018,1,TEX,AL,59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +leebr01,2018,1,MIA,NL,8,17,0,3,0,0,0,2,0,0,1,8,0,0,0,0,0 +leitema02,2018,1,PHI,NL,12,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +leitema02,2018,2,TOR,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +lemahdj01,2018,1,COL,NL,128,533,90,147,32,2,15,62,6,5,37,82,0,2,2,7,14 +leonedo01,2018,1,SLN,NL,30,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +leonsa01,2018,1,BOS,AL,89,265,30,47,12,0,5,22,1,0,15,75,0,4,3,1,6 +lestejo01,2018,1,CHN,NL,34,59,4,7,0,0,1,9,0,0,0,22,0,0,6,0,1 +lewicar01,2018,1,DET,AL,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +liberad01,2018,1,LAN,NL,17,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +lindofr01,2018,1,CLE,AL,158,661,129,183,42,2,38,92,25,10,70,107,7,8,3,3,5 +lintz01,2018,1,BOS,AL,37,65,15,16,6,1,1,6,0,1,8,17,0,0,0,0,0 +liriafr01,2018,1,DET,AL,27,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +litteza01,2018,1,MIN,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +livelbe01,2018,1,PHI,NL,6,7,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +livelbe01,2018,2,KCA,AL,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loaisjo01,2018,1,NYA,AL,9,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +lobatjo01,2018,1,NYN,NL,22,49,3,7,2,1,0,4,0,0,7,15,0,0,0,1,2 +locasti01,2018,1,LAN,NL,18,11,6,2,1,0,0,0,4,0,2,5,0,1,0,0,0 +lockean01,2018,1,SDN,NL,4,4,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0 +loganbo02,2018,1,MIL,NL,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +longoev01,2018,1,SFN,NL,125,480,51,117,25,4,16,54,3,1,22,101,3,5,0,5,11 +lopezjo02,2018,1,MIL,NL,10,2,1,1,1,0,0,2,0,0,0,1,0,0,0,0,0 +lopezjo02,2018,2,KCA,AL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +lopezpa01,2018,1,MIA,NL,12,16,1,3,0,0,0,0,0,0,1,8,0,0,1,0,0 +lopezra02,2018,1,SDN,NL,37,102,11,18,2,0,3,13,1,0,13,43,3,0,0,2,1 +lopezre01,2018,1,CHA,AL,32,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +lopezyo01,2018,1,ARI,NL,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +lorenmi01,2018,1,CIN,NL,55,31,5,9,1,0,4,10,0,0,2,9,0,0,1,0,4 +loupaa01,2018,1,TOR,AL,50,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loupaa01,2018,2,PHI,NL,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +lowebr01,2018,1,TBA,AL,43,129,16,30,6,2,6,25,2,1,16,38,0,2,0,1,3 +lowrije01,2018,1,OAK,AL,157,596,78,159,37,1,23,99,0,0,78,128,1,3,0,3,8 +lucasjo02,2018,1,OAK,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +lucchjo01,2018,1,SDN,NL,26,34,2,2,0,0,0,1,0,0,4,21,0,0,0,1,0 +lucrojo01,2018,1,OAK,AL,126,415,41,100,21,1,4,51,0,0,29,65,1,3,1,6,12 +lugoda01,2018,1,DET,AL,27,94,10,20,4,1,1,8,0,0,7,20,0,0,0,0,2 +lugose01,2018,1,NYN,NL,54,11,1,1,0,0,0,0,0,0,1,8,0,0,1,0,0 +luplojo01,2018,1,PIT,NL,37,92,16,17,1,3,3,7,2,2,10,18,0,1,0,0,7 +lylesjo01,2018,1,SDN,NL,26,13,2,0,0,0,0,1,0,0,2,7,0,0,2,0,1 +lylesjo01,2018,2,MIL,NL,11,2,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0 +lynnla01,2018,1,MIN,AL,20,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +lynnla01,2018,2,NYA,AL,11,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +lyonsty01,2018,1,SLN,NL,27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +machadi01,2018,1,DET,AL,67,214,20,44,13,1,1,21,1,1,14,41,1,3,1,1,2 +machama01,2018,1,BAL,AL,96,365,48,115,21,1,24,65,8,1,45,51,12,0,0,3,14 +machama01,2018,2,LAN,NL,66,267,36,73,14,2,13,42,6,1,25,53,6,2,0,2,12 +madsory01,2018,1,WAS,NL,49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +madsory01,2018,2,LAN,NL,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +maedake01,2018,1,LAN,NL,41,32,0,3,0,0,0,0,0,0,0,9,0,0,8,0,1 +magilma01,2018,1,MIN,AL,40,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +mahlety01,2018,1,CIN,NL,24,29,0,3,1,0,0,0,0,0,2,10,0,0,3,0,0 +mahtomi01,2018,1,DET,AL,67,223,24,45,4,2,9,29,4,1,21,66,1,3,0,3,3 +mailelu01,2018,1,TOR,AL,68,202,22,50,13,1,3,27,2,0,25,67,0,2,0,2,4 +makitka01,2018,1,SDN,NL,27,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +maldoma01,2018,1,LAA,AL,78,265,24,59,14,0,5,32,0,1,13,73,0,10,1,1,3 +maldoma01,2018,2,HOU,AL,41,108,15,25,4,1,4,12,0,0,3,25,0,1,1,1,5 +manaese01,2018,1,OAK,AL,27,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +mancitr01,2018,1,BAL,AL,156,582,69,141,23,3,24,58,0,1,44,153,1,5,0,5,17 +mannbr01,2018,1,TEX,AL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mapledi01,2018,1,CHN,NL,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +margoma01,2018,1,SDN,NL,141,477,50,117,26,8,8,51,11,10,32,88,4,2,1,7,9 +marinjh01,2018,1,BAL,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +marisja01,2018,1,HOU,AL,103,213,34,45,8,1,10,28,6,2,15,84,1,4,1,1,6 +marjami01,2018,1,SEA,AL,10,27,1,3,3,0,0,0,0,1,2,6,0,0,0,0,2 +markani01,2018,1,ATL,NL,162,623,78,185,43,2,14,93,1,1,72,80,10,1,0,9,18 +marquge01,2018,1,COL,NL,33,60,4,18,0,0,1,5,0,0,0,11,0,0,5,0,1 +marrede01,2018,1,ARI,NL,49,78,11,13,1,1,0,7,3,0,6,23,0,0,0,1,5 +marshev01,2018,1,CLE,AL,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +marteje01,2018,1,LAA,AL,90,194,28,42,7,1,7,22,1,1,13,41,0,2,0,0,6 +marteke01,2018,1,ARI,NL,153,520,68,135,26,12,14,59,6,1,54,79,3,3,1,2,12 +martest01,2018,1,PIT,NL,145,559,81,155,32,5,20,72,33,14,35,109,2,8,1,3,11 +martica04,2018,1,SLN,NL,34,33,1,8,2,0,1,4,0,0,0,11,0,0,2,1,0 +martich02,2018,1,TEX,AL,46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +martijd02,2018,1,BOS,AL,150,569,111,188,37,2,43,130,6,1,69,146,11,4,0,7,19 +martijo08,2018,1,SLN,NL,152,534,64,163,30,0,17,83,0,3,49,104,0,2,2,3,15 +martile01,2018,1,DET,AL,78,303,45,76,15,3,9,29,7,3,29,75,1,3,0,1,1 +martile01,2018,2,CLE,AL,6,15,3,5,0,0,2,4,0,1,1,2,0,0,0,1,1 +martini02,2018,1,OAK,AL,55,152,26,45,9,3,1,19,0,0,21,36,1,5,0,1,0 +martiru01,2018,1,TOR,AL,90,289,37,56,8,0,10,25,0,3,56,82,0,7,0,0,7 +martivi01,2018,1,DET,AL,133,467,32,117,21,0,9,54,0,0,32,49,4,2,0,7,19 +mathije01,2018,1,ARI,NL,69,195,15,39,9,1,1,20,0,0,20,66,1,0,1,2,7 +matonph01,2018,1,SDN,NL,45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +matzst01,2018,1,NYN,NL,30,46,2,5,1,0,2,4,0,0,3,13,0,0,4,0,1 +mauerjo01,2018,1,MIN,AL,127,486,64,137,27,1,6,48,0,1,51,86,5,2,1,3,9 +maurebr01,2018,1,KCA,AL,37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +maxwebr01,2018,1,OAK,AL,18,55,5,10,4,0,1,6,0,0,2,13,0,0,0,1,3 +maybica01,2018,1,MIA,NL,99,251,20,63,12,1,3,20,8,5,32,55,1,2,0,2,3 +maybica01,2018,2,SEA,AL,30,91,12,22,2,1,1,8,2,0,6,20,0,0,0,0,4 +mayermi01,2018,1,SLN,NL,50,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0 +maytr01,2018,1,MIN,AL,24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mayzati01,2018,1,TOR,AL,37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mazarno01,2018,1,TEX,AL,128,489,61,126,25,1,20,77,1,0,40,116,2,4,0,3,13 +mazzoco01,2018,1,CHN,NL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mcallza01,2018,1,CLE,AL,41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mcallza01,2018,2,DET,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mccanbr01,2018,1,HOU,AL,63,189,22,40,3,0,7,23,0,1,19,40,0,6,0,2,7 +mccanja02,2018,1,DET,AL,118,427,31,94,16,0,8,39,0,3,26,116,0,2,0,2,9 +mccarbr01,2018,1,ATL,NL,15,29,1,3,2,0,0,2,0,0,0,19,0,0,2,0,0 +mccarke01,2018,1,KCA,AL,65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mccread01,2018,1,ATL,NL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mcculla02,2018,1,HOU,AL,25,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +mccutan01,2018,1,SFN,NL,130,482,65,123,28,2,15,55,13,6,73,123,1,7,0,6,11 +mccutan01,2018,2,NYA,AL,25,87,18,22,2,1,5,10,1,3,22,22,0,4,0,1,1 +mcfartj01,2018,1,ARI,NL,47,7,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0 +mcgeeja01,2018,1,COL,NL,61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mcgowky01,2018,1,WAS,NL,5,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mcgraky01,2018,1,SDN,NL,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mcguide02,2018,1,TOR,AL,4,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +mcguide02,2018,2,LAA,AL,17,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +mcguire01,2018,1,TOR,AL,14,31,5,9,3,0,2,4,1,0,2,9,0,0,0,0,0 +mchugco01,2018,1,HOU,AL,58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mckinbi01,2018,1,NYA,AL,2,4,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0 +mckinbi01,2018,2,TOR,AL,36,115,14,29,7,0,6,13,1,0,11,32,0,1,0,1,0 +mcmahry01,2018,1,COL,NL,91,181,17,42,9,1,5,19,1,0,18,64,2,2,0,1,0 +mcneije01,2018,1,NYN,NL,63,225,35,74,11,6,3,19,7,1,14,24,1,5,4,0,2 +mcraeal01,2018,1,PIT,NL,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +meadoau01,2018,1,PIT,NL,49,154,16,45,8,2,5,13,4,1,8,35,2,1,0,2,1 +meadoau01,2018,2,TBA,AL,10,24,3,6,1,0,1,4,1,0,2,5,0,0,0,0,0 +meansjo01,2018,1,BAL,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +medlekr01,2018,1,ARI,NL,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +meisiry01,2018,1,BAL,AL,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mejiaad01,2018,1,MIN,AL,5,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +mejiafr01,2018,1,CLE,AL,1,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0 +mejiafr01,2018,2,SDN,NL,20,54,6,10,2,0,3,8,0,0,3,19,0,1,0,0,2 +melanma01,2018,1,SFN,NL,41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mellake01,2018,1,CIN,NL,4,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +mendeyo01,2018,1,TEX,AL,8,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +mengdda01,2018,1,OAK,AL,22,3,1,1,0,0,0,1,0,0,0,1,0,0,0,0,0 +mercejo03,2018,1,PIT,NL,117,394,43,99,29,2,6,39,2,0,32,87,9,6,1,3,12 +merriwh01,2018,1,KCA,AL,158,632,88,192,43,3,12,60,45,10,61,114,2,6,2,6,12 +mesorde01,2018,1,CIN,NL,18,41,1,9,2,0,1,3,0,0,2,10,0,2,0,0,2 +mesorde01,2018,2,NYN,NL,66,203,23,45,8,0,10,30,0,0,23,42,3,2,0,1,7 +meyerbe02,2018,1,MIA,NL,13,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +middlke01,2018,1,LAA,AL,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mikolmi01,2018,1,SLN,NL,34,63,4,9,1,0,2,6,0,0,2,33,0,0,5,1,0 +mileywa01,2018,1,MIL,NL,16,27,1,5,0,0,0,0,0,0,1,11,0,0,0,0,0 +millean01,2018,1,CLE,AL,37,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +millebr02,2018,1,TBA,AL,48,156,16,40,10,1,5,21,0,0,16,51,0,0,0,2,2 +millebr02,2018,2,MIL,NL,27,74,5,17,3,1,2,8,0,0,6,31,1,0,0,0,2 +milleju02,2018,1,WAS,NL,51,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +millesh01,2018,1,ARI,NL,5,3,0,0,0,0,0,1,0,0,1,1,0,0,0,1,0 +millsal02,2018,1,CHN,NL,7,5,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0 +milneho01,2018,1,PHI,NL,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +milneho01,2018,2,TBA,AL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +milonto01,2018,1,WAS,NL,5,7,0,0,0,0,0,0,0,0,1,3,0,0,1,0,0 +minayju01,2018,1,CHA,AL,52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +minormi01,2018,1,TEX,AL,28,3,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0 +minteaj01,2018,1,ATL,NL,65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +miranar01,2018,1,SEA,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mitchbr01,2018,1,SDN,NL,16,18,0,2,0,0,0,0,0,0,0,10,0,0,1,0,0 +molinya01,2018,1,SLN,NL,123,459,55,120,20,0,20,74,4,3,29,66,0,9,0,6,15 +moncayo01,2018,1,CHA,AL,149,578,73,136,32,6,17,61,12,6,67,217,1,1,2,2,4 +mondera02,2018,1,KCA,AL,75,275,47,76,13,3,14,37,32,7,11,77,0,1,3,1,2 +montafr01,2018,1,OAK,AL,13,5,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0 +montemi01,2018,1,WAS,NL,4,11,0,0,0,0,0,0,0,0,2,3,1,0,0,0,0 +montgjo01,2018,1,NYA,AL,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +montgmi01,2018,1,CHN,NL,39,33,1,3,0,0,0,1,0,0,1,20,0,0,1,0,0 +mooread01,2018,1,TBA,AL,8,18,2,4,1,0,1,2,0,0,1,7,0,0,1,0,0 +moorema02,2018,1,TEX,AL,39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +moralke01,2018,1,TOR,AL,130,413,47,103,15,0,21,57,2,3,50,95,5,3,0,5,15 +moralos01,2018,1,LAA,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +moranco01,2018,1,PIT,NL,144,415,49,115,19,1,11,58,0,2,39,82,4,4,0,7,6 +morelmi01,2018,1,BOS,AL,124,404,57,99,23,4,15,68,2,0,50,102,2,0,0,5,12 +morgaad01,2018,1,PHI,NL,67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +morinmi01,2018,1,SEA,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +morofma01,2018,1,PIT,NL,26,59,7,11,1,0,3,9,0,0,7,24,1,1,0,0,0 +moronre01,2018,1,SFN,NL,69,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +morriak01,2018,1,LAA,AL,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +morrilo01,2018,1,MIN,AL,95,318,41,59,13,0,15,39,1,0,34,80,0,6,0,1,3 +morrobr01,2018,1,CHN,NL,35,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +mortoch02,2018,1,HOU,AL,30,6,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0 +motteta01,2018,1,SEA,AL,8,15,2,4,0,0,1,1,0,0,2,5,0,0,0,0,0 +motteta01,2018,2,MIN,AL,9,19,0,1,0,0,0,1,1,0,2,3,0,0,0,0,0 +moustmi01,2018,1,KCA,AL,98,378,46,94,21,1,20,62,3,0,30,63,3,5,0,4,10 +moustmi01,2018,2,MIL,NL,54,195,20,50,12,0,8,33,1,1,19,40,2,2,0,2,3 +moyaga01,2018,1,MIN,AL,35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +moylape01,2018,1,ATL,NL,39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mullice01,2018,1,BAL,AL,45,170,23,40,9,0,4,11,2,3,17,37,0,2,2,0,1 +muncyma01,2018,1,LAN,NL,137,395,75,104,17,2,35,79,3,0,79,131,6,5,0,2,4 +munozya01,2018,1,SLN,NL,108,293,39,81,16,0,8,42,5,6,30,71,7,4,0,2,3 +murphda08,2018,1,WAS,NL,56,190,17,57,9,0,6,29,1,0,13,17,2,0,0,2,4 +murphda08,2018,2,CHN,NL,35,138,23,41,6,0,6,13,2,0,7,23,0,0,0,1,1 +murphjr01,2018,1,ARI,NL,87,208,19,42,9,0,9,24,0,0,11,71,1,1,2,1,1 +murphto04,2018,1,COL,NL,37,93,5,21,7,1,2,11,0,1,3,44,1,0,0,0,2 +musgrha01,2018,1,COL,NL,35,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +musgrjo01,2018,1,PIT,NL,20,31,2,5,1,0,0,0,0,0,0,12,0,0,4,0,0 +myerswi01,2018,1,SDN,NL,83,312,39,79,25,1,11,39,13,1,30,94,1,0,0,1,10 +naquity01,2018,1,CLE,AL,61,174,22,46,7,0,3,23,1,1,6,42,1,2,0,1,1 +narvaom01,2018,1,CHA,AL,97,280,30,77,14,1,9,30,0,2,38,65,1,2,2,0,5 +navaref01,2018,1,CHN,NL,4,6,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0 +nealza01,2018,1,LAN,NL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +negrokr01,2018,1,ARI,NL,2,3,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0 +negrokr01,2018,2,SEA,AL,18,29,6,6,0,0,1,3,2,0,1,9,0,0,0,0,1 +nerishe01,2018,1,PHI,NL,53,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +neshepa01,2018,1,PHI,NL,30,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +neverdo01,2018,1,PIT,NL,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +newbeja01,2018,1,KCA,AL,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +newcose01,2018,1,ATL,NL,31,45,3,2,1,0,0,1,0,0,1,32,0,0,7,0,0 +newmake01,2018,1,PIT,NL,31,91,7,19,2,0,0,6,0,1,4,23,1,1,0,1,2 +ngoepgi01,2018,1,TOR,AL,13,18,2,1,0,0,0,0,0,0,1,12,0,0,0,0,0 +nicasju01,2018,1,SEA,AL,46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +nidoto01,2018,1,NYN,NL,34,84,10,14,3,0,1,9,0,0,4,27,0,0,0,2,4 +nimmobr01,2018,1,NYN,NL,140,433,77,114,28,8,17,47,9,6,80,140,2,22,0,0,8 +nixja02,2018,1,SDN,NL,9,15,0,0,0,0,0,0,0,0,0,11,0,0,1,0,0 +nolaaa01,2018,1,PHI,NL,33,62,0,3,1,0,0,4,0,0,1,36,0,0,7,0,3 +norribu01,2018,1,SLN,NL,64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +norrida01,2018,1,DET,AL,11,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +norwoja01,2018,1,CHN,NL,11,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1 +nottija01,2018,1,MIL,NL,9,20,2,4,1,0,0,0,0,0,4,8,0,0,0,0,0 +novaiv01,2018,1,PIT,NL,29,53,1,1,0,0,0,0,0,0,0,30,0,0,3,0,1 +nunezed02,2018,1,BOS,AL,127,480,56,127,23,3,10,44,7,2,16,69,0,2,1,3,17 +nunezre01,2018,1,TEX,AL,13,36,2,6,1,0,1,2,0,0,3,12,0,1,0,1,0 +nunezre01,2018,2,BAL,AL,60,200,26,55,13,0,7,20,0,0,16,50,0,3,0,1,4 +nunovi01,2018,1,TBA,AL,17,2,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0 +oakstr01,2018,1,KCA,AL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +obergsc01,2018,1,COL,NL,56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +obriepe04,2018,1,MIA,NL,22,66,8,18,5,0,4,10,0,0,7,22,0,0,0,1,2 +odayda01,2018,1,BAL,AL,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +odorija01,2018,1,MIN,AL,33,6,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0 +odorro01,2018,1,TEX,AL,129,474,76,120,23,2,18,63,12,12,43,127,2,11,2,5,5 +ogandal01,2018,1,CLE,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ogradch01,2018,1,MIA,NL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ohearry01,2018,1,KCA,AL,44,149,23,39,10,2,12,30,0,0,20,45,0,1,0,0,0 +ohse01,2018,1,TOR,AL,48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ohse01,2018,2,COL,NL,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ohtansh01,2018,1,LAA,AL,114,326,59,93,21,2,22,61,10,4,37,102,2,2,0,1,2 +okertst01,2018,1,SFN,NL,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +olsonma02,2018,1,OAK,AL,162,580,85,143,33,0,29,84,2,1,70,163,3,8,0,2,13 +olsonty01,2018,1,CLE,AL,43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +oneilty01,2018,1,SLN,NL,61,130,29,33,5,0,9,23,2,0,7,57,0,3,0,2,0 +orfna01,2018,1,MIL,NL,15,21,4,2,0,0,1,1,1,0,3,8,0,1,0,0,1 +orlanpa01,2018,1,KCA,AL,25,90,6,15,3,0,0,5,0,0,3,25,0,0,0,0,3 +ortegra01,2018,1,MIA,NL,41,133,10,31,3,1,0,7,5,2,10,23,0,0,0,0,5 +ortizlu02,2018,1,BAL,AL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +osichjo01,2018,1,SFN,NL,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +osunajo01,2018,1,PIT,NL,51,106,14,24,9,0,3,11,0,0,3,22,0,1,0,1,2 +osunaro01,2018,1,TOR,AL,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +osunaro01,2018,2,HOU,AL,23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +oswalco01,2018,1,NYN,NL,17,18,0,0,0,0,0,0,0,0,0,10,0,0,1,0,0 +oteroda01,2018,1,CLE,AL,61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ottavad01,2018,1,COL,NL,75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +owingch01,2018,1,ARI,NL,106,281,34,58,15,0,4,22,11,4,24,75,4,2,0,2,4 +ozunama01,2018,1,SLN,NL,148,582,69,163,16,2,23,88,3,0,38,110,2,3,0,4,10 +paganem01,2018,1,OAK,AL,55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +palkada01,2018,1,CHA,AL,124,417,56,100,15,3,27,67,2,1,30,153,3,2,0,0,7 +panikjo01,2018,1,SFN,NL,102,358,38,91,14,1,4,24,4,2,26,30,3,3,1,4,10 +pannoth01,2018,1,TOR,AL,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +pareded01,2018,1,LAA,AL,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +pareded02,2018,1,LAN,NL,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +parkebl01,2018,1,LAA,AL,67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +parrage01,2018,1,COL,NL,142,401,52,114,17,0,6,53,11,4,32,75,3,5,2,3,6 +parsowe01,2018,1,ATL,NL,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +paulida01,2018,1,TOR,AL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +paxtoja01,2018,1,SEA,AL,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +pazosja01,2018,1,SEA,AL,60,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +peacobr01,2018,1,HOU,AL,61,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +pearcst01,2018,1,TOR,AL,26,79,16,23,6,0,4,16,0,0,7,14,0,0,0,0,1 +pearcst01,2018,2,BOS,AL,50,136,19,38,8,1,7,26,0,0,22,27,0,5,0,2,1 +pederjo01,2018,1,LAN,NL,148,395,65,98,27,3,25,56,1,5,40,85,3,4,1,3,6 +pedrodu01,2018,1,BOS,AL,3,11,1,1,0,0,0,0,0,0,2,1,0,0,0,0,0 +pelhacd01,2018,1,TEX,AL,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +penafe01,2018,1,LAA,AL,19,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +penafr01,2018,1,SLN,NL,58,133,10,27,3,0,2,8,1,0,6,43,3,1,0,2,5 +pencehu01,2018,1,SFN,NL,97,235,19,53,11,1,4,24,5,1,11,59,1,0,0,2,1 +pennicl01,2018,1,CIN,NL,16,29,1,4,0,0,0,0,0,0,5,13,0,0,0,0,1 +peralda01,2018,1,ARI,NL,146,560,75,164,25,5,30,87,4,0,48,124,4,4,0,2,14 +peralfr01,2018,1,MIL,NL,16,26,0,1,0,0,0,1,0,0,0,13,0,0,1,0,0 +peralwa01,2018,1,CIN,NL,59,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0 +peralwi01,2018,1,KCA,AL,37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +perazjo01,2018,1,CIN,NL,157,632,85,182,31,4,14,58,23,6,29,75,4,9,8,5,12 +perdolu02,2018,1,SDN,NL,12,8,0,2,0,0,0,1,0,0,0,3,0,0,0,1,0 +perezca02,2018,1,ATL,NL,8,21,0,3,0,0,0,0,0,0,1,6,0,0,0,0,1 +perezca02,2018,2,TEX,AL,20,49,1,7,2,0,1,3,1,0,1,15,0,1,2,0,3 +perezci01,2018,1,HOU,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +perezhe01,2018,1,MIL,NL,132,316,36,80,11,2,9,29,11,3,17,71,1,0,0,1,6 +perezma02,2018,1,TEX,AL,22,3,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0 +perezmi03,2018,1,TBA,AL,24,74,9,21,5,0,1,11,0,0,3,19,0,0,1,2,0 +perezol01,2018,1,CLE,AL,51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +perezro02,2018,1,CLE,AL,62,179,16,30,9,1,2,19,1,0,21,70,0,1,7,2,6 +perezsa02,2018,1,KCA,AL,129,510,52,120,23,0,27,80,1,1,17,108,0,12,0,5,19 +peterdi01,2018,1,MIA,NL,7,7,1,1,0,0,0,0,0,0,1,2,0,0,1,0,0 +peterdu01,2018,1,ATL,NL,2,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +peterja01,2018,1,NYA,AL,3,10,0,3,0,0,0,0,0,1,1,3,0,0,0,0,0 +peterja01,2018,2,BAL,AL,93,200,21,39,13,2,3,28,13,2,30,55,0,3,1,1,8 +peterti01,2018,1,NYN,NL,22,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +petitgr01,2018,1,MIN,AL,26,61,7,15,2,0,0,3,3,1,6,14,0,0,0,0,0 +petityu01,2018,1,OAK,AL,74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +petrija01,2018,1,TOR,AL,41,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +phamth01,2018,1,SLN,NL,98,351,67,87,11,0,14,41,10,6,42,97,1,2,0,1,12 +phamth01,2018,2,TBA,AL,39,143,35,49,7,6,7,22,5,1,25,43,1,4,0,2,6 +phegljo01,2018,1,OAK,AL,39,93,13,19,7,0,2,15,0,0,6,27,0,1,0,2,3 +phillbr01,2018,1,BOS,AL,9,23,4,3,0,0,1,2,0,0,4,7,0,0,0,0,1 +phillbr02,2018,1,MIL,NL,15,22,2,4,0,1,0,4,0,0,2,11,0,0,0,0,1 +phillbr02,2018,2,KCA,AL,36,112,13,21,4,2,2,7,1,1,9,50,0,1,0,1,1 +phillev01,2018,1,ATL,NL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +phillev01,2018,2,BAL,AL,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +pillake01,2018,1,TOR,AL,142,512,65,129,40,2,15,59,14,3,18,98,0,6,0,6,8 +pinama01,2018,1,MIL,NL,98,306,39,77,13,2,9,28,2,0,21,62,3,5,1,4,13 +pindech01,2018,1,OAK,AL,110,298,43,77,12,1,13,27,0,2,27,88,1,6,2,0,4 +pireljo01,2018,1,SDN,NL,146,438,54,109,23,2,5,32,6,3,30,89,1,3,0,2,10 +piscost01,2018,1,OAK,AL,151,546,78,146,41,0,27,88,2,0,42,114,0,12,0,5,21 +pivetni01,2018,1,PHI,NL,34,40,3,4,1,0,0,1,0,0,2,20,0,0,3,0,2 +plaweke01,2018,1,NYN,NL,79,238,33,50,13,2,7,30,0,1,28,65,2,9,1,1,12 +plouftr01,2018,1,PHI,NL,7,12,1,3,0,0,1,3,0,0,0,6,0,0,0,0,0 +plutkad01,2018,1,CLE,AL,17,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0 +polangr01,2018,1,PIT,NL,130,461,75,117,32,6,23,81,12,2,61,117,5,3,0,7,11 +polanjo01,2018,1,MIN,AL,77,302,38,87,18,3,6,42,7,7,25,62,0,2,3,1,5 +polloaj01,2018,1,ARI,NL,113,413,61,106,21,5,21,65,13,2,31,100,2,8,1,7,6 +pomerdr01,2018,1,BOS,AL,26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +pompeda01,2018,1,TOR,AL,5,10,0,2,0,0,0,0,0,0,1,6,0,0,0,0,0 +ponceda01,2018,1,SLN,NL,12,9,0,1,0,1,0,1,0,0,0,4,0,0,1,0,0 +porceri01,2018,1,BOS,AL,33,7,1,3,2,0,0,3,0,0,0,3,0,0,0,0,0 +poseybu01,2018,1,SFN,NL,105,398,47,113,22,1,5,41,3,2,45,53,3,3,0,2,12 +poundbr01,2018,1,COL,NL,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +powelbo02,2018,1,OAK,AL,7,24,3,4,1,1,0,0,1,1,1,6,0,0,0,0,1 +poynebo01,2018,1,BOS,AL,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +pradoma01,2018,1,MIA,NL,54,197,16,48,9,0,1,18,1,1,11,35,0,1,0,0,5 +pressry01,2018,1,MIN,AL,51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +pressry01,2018,2,HOU,AL,26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +priceda01,2018,1,BOS,AL,30,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +profaju01,2018,1,TEX,AL,146,524,82,133,35,6,20,77,10,0,54,88,1,12,0,4,9 +pruitau01,2018,1,TBA,AL,23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +puigya01,2018,1,LAN,NL,125,405,60,108,21,1,23,63,15,5,36,87,2,1,0,2,15 +pujolal01,2018,1,LAA,AL,117,465,50,114,20,0,19,64,1,0,28,65,3,2,0,3,12 +quackke01,2018,1,CIN,NL,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +quinnro01,2018,1,PHI,NL,50,131,13,34,6,4,2,12,10,4,10,35,0,1,1,0,1 +quintjo01,2018,1,CHN,NL,32,52,0,4,0,0,0,0,0,0,0,26,0,0,6,0,3 +raineta01,2018,1,CIN,NL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ramirca01,2018,1,TOR,AL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ramirca01,2018,2,OAK,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ramirer02,2018,1,SEA,AL,10,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +ramirha01,2018,1,BOS,AL,44,177,25,45,7,0,6,29,4,1,14,35,0,2,0,2,9 +ramirjc01,2018,1,LAA,AL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ramirjo01,2018,1,CLE,AL,157,578,110,156,38,4,39,105,34,6,106,80,15,8,0,6,2 +ramirjo02,2018,1,ATL,NL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ramirne01,2018,1,CLE,AL,47,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +ramirno01,2018,1,LAA,AL,69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ramirye01,2018,1,BAL,AL,17,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +ramosaj01,2018,1,NYN,NL,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ramosed02,2018,1,PHI,NL,52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ramoswi01,2018,1,TBA,AL,78,293,30,87,14,0,14,53,0,0,22,61,1,0,0,0,17 +ramoswi01,2018,2,PHI,NL,33,89,9,30,8,1,1,17,0,0,10,19,1,0,0,2,3 +rasmuco01,2018,1,BAL,AL,18,45,5,6,1,0,1,1,0,0,3,19,0,1,0,0,0 +ravinjo01,2018,1,ATL,NL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +rayro02,2018,1,ARI,NL,25,39,2,3,1,0,0,2,0,0,0,24,0,0,4,1,1 +realmjt01,2018,1,MIA,NL,125,477,74,132,30,3,21,74,3,2,38,104,0,10,0,4,9 +reddijo01,2018,1,HOU,AL,134,433,63,105,13,2,17,47,7,2,49,77,3,0,1,2,9 +reedad01,2018,1,MIN,AL,55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +reedaj01,2018,1,HOU,AL,1,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +reedco01,2018,1,CIN,NL,17,11,0,0,0,0,0,0,0,0,1,10,0,0,0,0,0 +reedmi02,2018,1,ATL,NL,7,7,1,2,0,0,0,0,0,0,0,3,0,0,0,0,0 +refsnro01,2018,1,TBA,AL,40,84,10,14,3,0,2,5,0,2,18,26,0,0,0,0,5 +reidfse01,2018,1,TOR,AL,7,3,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0 +reinhja01,2018,1,NYN,NL,21,30,4,5,0,0,0,0,1,1,5,9,0,0,0,0,1 +reiniza01,2018,1,DET,AL,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +rendato01,2018,1,BOS,AL,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +rendoan01,2018,1,WAS,NL,136,529,88,163,44,2,24,92,2,1,55,82,5,5,0,8,5 +renfrhu01,2018,1,SDN,NL,117,403,53,100,23,1,26,68,2,1,30,109,2,3,0,5,9 +reyesal02,2018,1,SLN,NL,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +reyesfr01,2018,1,SDN,NL,87,261,36,73,9,0,16,31,0,0,24,80,0,0,0,0,5 +reyesje01,2018,1,CIN,NL,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +reyesjo01,2018,1,NYN,NL,110,228,30,43,12,3,4,16,5,2,22,39,1,0,1,0,5 +reyespa01,2018,1,PIT,NL,18,58,9,17,2,0,3,7,0,1,5,11,0,0,0,0,2 +reyesvi01,2018,1,DET,AL,100,212,35,47,5,3,1,12,9,1,5,46,0,0,1,1,4 +reynoma01,2018,1,WAS,NL,86,206,26,51,8,0,13,40,0,0,24,64,1,2,0,3,8 +reynoma03,2018,1,WAS,NL,12,13,1,2,0,0,0,1,0,0,1,4,0,0,0,0,0 +rhameja01,2018,1,NYN,NL,30,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +richacl01,2018,1,SDN,NL,28,42,3,4,1,0,1,3,0,0,2,24,0,1,6,0,0 +richaga01,2018,1,LAA,AL,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +richatr01,2018,1,MIA,NL,25,31,1,2,0,0,0,2,0,0,1,17,0,0,4,0,0 +rickajo01,2018,1,BAL,AL,79,213,27,52,10,1,8,23,4,2,15,55,0,2,0,0,3 +riddljt01,2018,1,MIA,NL,102,308,28,71,10,4,9,36,0,3,20,67,1,0,3,1,4 +riosya01,2018,1,PHI,NL,36,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +riverfe01,2018,1,PIT,NL,70,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +riverre01,2018,1,LAA,AL,30,82,8,20,4,0,4,11,0,0,4,32,0,1,0,0,3 +riverre01,2018,2,ATL,NL,3,4,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0 +riverya01,2018,1,MIA,NL,111,139,13,24,3,0,1,9,2,1,19,51,3,0,0,2,3 +rizzoan01,2018,1,CHN,NL,153,566,74,160,29,1,25,101,6,4,70,80,15,20,0,9,11 +roarkta01,2018,1,WAS,NL,31,58,6,11,2,1,0,8,0,0,1,19,0,1,5,0,1 +roberda08,2018,1,NYA,AL,69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +roberda10,2018,1,TBA,AL,88,282,46,74,16,0,9,34,2,2,43,77,0,13,0,2,7 +robindr01,2018,1,TEX,AL,47,109,20,20,3,0,3,9,2,1,16,57,0,0,0,0,0 +robinsh01,2018,1,NYA,AL,25,49,8,7,1,0,1,2,1,0,4,6,0,0,1,0,2 +robleha01,2018,1,NYN,NL,16,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +robleha01,2018,2,LAA,AL,37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +roblevi01,2018,1,WAS,NL,21,59,8,17,3,1,3,10,3,2,4,12,0,2,0,1,2 +rodnefe01,2018,1,MIN,AL,46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +rodnefe01,2018,2,OAK,AL,22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +rodonca01,2018,1,CHA,AL,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +rodride01,2018,1,SFN,NL,21,32,0,3,1,0,0,1,0,0,0,16,0,0,7,0,0 +rodried05,2018,1,BOS,AL,27,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +rodrije01,2018,1,WAS,NL,14,16,2,3,1,0,0,1,0,0,0,8,0,0,2,0,0 +rodriri04,2018,1,TEX,AL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +rodriri05,2018,1,PIT,NL,63,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +rodriro03,2018,1,DET,AL,62,191,17,42,7,0,5,20,2,0,10,42,0,0,3,2,7 +rodrise01,2018,1,PIT,NL,66,150,21,25,5,1,5,19,1,0,22,60,1,1,0,0,1 +roech01,2018,1,TBA,AL,61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +rogerjo01,2018,1,BAL,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +rogerta01,2018,1,MIN,AL,72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +rojasmi02,2018,1,MIA,NL,153,488,44,123,13,0,11,53,6,3,24,69,2,9,2,4,23 +romansa01,2018,1,CIN,NL,39,36,1,2,1,0,0,2,0,0,0,24,0,0,4,0,0 +romeren01,2018,1,WAS,NL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +romeren01,2018,2,PIT,NL,2,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0 +romeren01,2018,3,KCA,AL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +romerfe01,2018,1,MIN,AL,11,3,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0 +rominan01,2018,1,SEA,AL,75,119,15,25,2,1,0,2,1,0,7,39,0,1,4,0,2 +rominau01,2018,1,NYA,AL,77,242,30,59,12,0,10,42,1,0,17,67,0,2,1,3,10 +romose01,2018,1,TBA,AL,73,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +rondobr01,2018,1,CHA,AL,35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +rondohe01,2018,1,HOU,AL,63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +rondojo02,2018,1,CHA,AL,42,100,15,23,6,0,6,14,2,1,7,30,0,0,0,0,3 +rosalad01,2018,1,CLE,AL,13,19,4,4,1,0,1,2,0,0,1,5,0,0,1,0,0 +rosaram01,2018,1,NYN,NL,154,554,76,142,26,8,9,51,24,11,29,119,4,3,3,3,9 +rosared01,2018,1,MIN,AL,138,559,87,161,31,2,24,77,8,2,30,104,5,0,1,2,4 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+sierrmo01,2018,1,WAS,NL,27,54,4,9,2,0,0,4,1,1,2,20,0,2,0,2,2 +simmoan01,2018,1,LAA,AL,146,554,68,162,26,5,11,75,10,2,35,44,2,5,1,5,17 +simslu01,2018,1,ATL,NL,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +simslu01,2018,2,CIN,NL,3,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +sippto01,2018,1,HOU,AL,54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +siscoch01,2018,1,BAL,AL,63,160,13,29,8,0,2,16,1,0,13,66,0,11,0,0,2 +skaggty01,2018,1,LAA,AL,24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +skogler01,2018,1,KCA,AL,14,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +skolema01,2018,1,CHA,AL,4,11,2,3,0,0,1,1,0,0,2,3,0,0,0,0,0 +slateau01,2018,1,SFN,NL,74,199,21,50,6,1,1,23,7,0,20,69,2,5,0,1,5 +slegeaa01,2018,1,MIN,AL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +smithbu03,2018,1,KCA,AL,38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +smithca02,2018,1,BOS,AL,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +smithca03,2018,1,MIA,NL,16,22,0,3,0,0,0,1,0,0,0,13,0,0,4,0,1 +smithdo02,2018,1,NYN,NL,56,143,14,32,11,1,5,11,0,0,4,47,0,2,0,0,2 +smithdr01,2018,1,NYN,NL,27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +smithdw02,2018,1,TOR,AL,35,65,9,17,8,0,2,8,0,0,7,13,0,2,0,1,1 +smithjo05,2018,1,HOU,AL,56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +smithke04,2018,1,CHA,AL,52,171,21,50,6,0,3,21,1,0,10,18,0,5,0,1,5 +smithma05,2018,1,TBA,AL,141,480,65,142,27,10,2,40,40,12,47,98,0,8,7,2,11 +smithmu01,2018,1,TOR,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +smithwi04,2018,1,SFN,NL,54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +smoakju01,2018,1,TOR,AL,147,505,67,122,34,0,25,77,0,1,83,156,2,3,0,3,11 +smokejo01,2018,1,PIT,NL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +smokejo01,2018,2,DET,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +smolija01,2018,1,OAK,AL,20,39,2,5,1,1,0,2,1,0,1,10,0,1,0,0,4 +snellbl01,2018,1,TBA,AL,31,1,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0 +sneltdj01,2018,1,SFN,NL,4,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +snydebr03,2018,1,TBA,AL,2,6,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0 +sobotch01,2018,1,ATL,NL,14,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +socolmi01,2018,1,ATL,NL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +sogarer01,2018,1,MIL,NL,55,97,7,13,3,0,0,2,3,0,12,23,1,2,1,1,3 +solarya01,2018,1,TOR,AL,122,468,50,106,20,0,17,54,1,3,31,72,1,3,1,3,21 +solerjo01,2018,1,KCA,AL,61,223,27,59,18,0,9,28,3,1,28,69,0,4,0,2,6 +solissa01,2018,1,WAS,NL,56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +soriajo01,2018,1,CHA,AL,40,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +soriajo01,2018,2,MIL,NL,26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +sorokmi01,2018,1,ATL,NL,5,8,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0 +sosaed01,2018,1,SLN,NL,3,2,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0 +sotoju01,2018,1,WAS,NL,116,414,77,121,25,1,22,70,5,2,79,99,10,0,1,0,9 +souzast01,2018,1,ARI,NL,72,241,21,53,15,3,5,29,6,1,28,75,0,3,0,0,4 +spande01,2018,1,TBA,AL,43,143,27,34,7,1,4,28,6,2,28,24,0,1,0,1,5 +spande01,2018,2,SEA,AL,94,294,36,80,15,6,7,30,3,2,23,55,0,5,0,6,5 +spangco01,2018,1,SDN,NL,116,298,35,70,9,4,7,25,6,1,25,108,0,2,4,0,6 +sparkgl01,2018,1,KCA,AL,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +springe01,2018,1,HOU,AL,140,544,102,144,26,0,22,71,6,4,64,122,0,5,0,3,12 +sprinje01,2018,1,TEX,AL,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +stallja01,2018,1,PIT,NL,14,37,2,8,0,0,0,5,0,0,3,9,0,0,0,1,2 +stammcr01,2018,1,SDN,NL,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +stanery01,2018,1,TBA,AL,59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +stantmi03,2018,1,NYA,AL,158,617,102,164,34,1,38,100,5,0,70,211,5,8,0,10,17 +stassma01,2018,1,HOU,AL,88,221,28,50,13,0,8,27,0,0,23,74,0,6,0,0,6 +steckdr01,2018,1,MIA,NL,71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +stephja01,2018,1,CIN,NL,29,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0 +stephro01,2018,1,CIN,NL,4,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0 +stevean01,2018,1,WAS,NL,57,75,9,19,2,0,1,13,1,1,6,23,0,1,1,3,0 +stewabr01,2018,1,LAN,NL,9,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +stewach01,2018,1,ATL,NL,5,14,3,3,0,0,0,3,0,0,1,1,0,0,0,1,2 +stewach01,2018,2,ARI,NL,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +stewach02,2018,1,DET,AL,17,60,7,16,1,1,2,10,0,0,10,13,0,1,0,1,1 +stewadj01,2018,1,BAL,AL,17,40,8,10,3,0,3,10,2,1,4,12,0,2,0,1,0 +stewako01,2018,1,MIN,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +stockro01,2018,1,SDN,NL,32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +storytr01,2018,1,COL,NL,157,598,88,174,42,6,37,108,27,6,47,168,3,7,0,4,12 +stouter01,2018,1,KCA,AL,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +strahma01,2018,1,SDN,NL,41,8,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0 +straida01,2018,1,MIA,NL,26,31,2,5,1,0,0,1,0,0,8,19,0,1,3,0,1 +strasst01,2018,1,WAS,NL,22,41,0,5,0,0,0,1,0,0,2,12,0,0,8,0,3 +stratch01,2018,1,SFN,NL,28,43,0,6,1,0,0,5,0,0,0,26,0,0,6,1,0 +strawmy01,2018,1,HOU,AL,9,9,4,3,0,0,1,1,2,0,1,0,0,0,0,0,0 +strichu01,2018,1,SFN,NL,49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +stripro01,2018,1,LAN,NL,36,33,0,3,0,0,0,2,1,0,0,13,0,0,4,0,0 +stromma01,2018,1,TOR,AL,19,3,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0 +stroppe01,2018,1,CHN,NL,60,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +stumpda01,2018,1,DET,AL,56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +suarean01,2018,1,SFN,NL,29,47,1,3,0,0,0,0,0,0,2,27,0,0,2,0,1 +suareeu01,2018,1,CIN,NL,143,527,79,149,22,2,34,104,1,1,64,142,7,9,0,6,20 +suarera01,2018,1,PHI,NL,4,6,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0 +sucreje01,2018,1,TBA,AL,73,182,9,38,5,0,1,17,1,0,9,29,2,1,4,2,4 +suerowa01,2018,1,WAS,NL,40,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +susacan01,2018,1,BAL,AL,9,26,1,3,1,0,0,0,0,0,0,12,0,0,0,0,0 +suterbr01,2018,1,MIL,NL,21,26,4,5,1,0,1,3,0,0,4,6,0,1,1,0,1 +suzukic01,2018,1,SEA,AL,15,44,5,9,0,0,0,0,0,0,3,7,0,0,0,0,0 +suzukku01,2018,1,ATL,NL,105,347,45,94,24,0,12,50,0,0,22,43,0,13,0,6,6 +swansda01,2018,1,ATL,NL,136,478,51,114,25,4,14,59,10,4,44,122,15,2,6,3,5 +swarzan01,2018,1,NYN,NL,29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +sweenda02,2018,1,TOR,AL,2,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0 +swihabl01,2018,1,BOS,AL,82,192,28,44,10,0,3,18,6,1,15,57,0,0,0,0,4 +syndeno01,2018,1,NYN,NL,25,47,0,6,2,0,0,3,0,0,1,32,0,0,3,1,0 +szczuma01,2018,1,SDN,NL,57,75,11,14,3,0,1,6,3,0,8,24,0,0,1,0,2 +taillja01,2018,1,PIT,NL,32,57,0,4,1,0,0,3,0,0,1,22,0,0,5,0,1 +tanakma01,2018,1,NYA,AL,27,6,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +tapiara01,2018,1,COL,NL,25,25,6,5,2,1,1,6,0,0,2,7,0,0,0,0,0 +tarplst01,2018,1,NYA,AL,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +tauchmi01,2018,1,COL,NL,21,32,5,3,1,0,0,0,1,0,4,15,0,0,1,0,0 +taylobe03,2018,1,CLE,AL,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +taylobe04,2018,1,OAK,AL,7,5,0,1,1,0,0,0,0,0,1,2,0,0,0,0,0 +tayloch03,2018,1,LAN,NL,155,536,85,136,35,8,17,63,9,6,55,178,0,9,0,4,5 +taylomi02,2018,1,WAS,NL,134,353,46,80,22,3,6,28,24,6,29,116,2,1,2,0,9 +tazawju01,2018,1,MIA,NL,22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +tazawju01,2018,2,LAA,AL,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +teherju01,2018,1,ATL,NL,31,40,2,7,0,0,1,4,0,0,1,8,0,0,12,1,0 +telisto01,2018,1,MIA,NL,23,29,2,6,1,0,0,1,0,0,2,8,0,0,0,0,1 +tellero01,2018,1,TOR,AL,23,70,10,22,9,0,4,14,0,0,2,21,0,0,0,1,0 +teperry01,2018,1,TOR,AL,68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +thameer01,2018,1,MIL,NL,96,247,41,54,10,3,16,37,7,0,29,97,4,2,0,0,3 +thompja03,2018,1,PHI,NL,9,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0 +thomptr01,2018,1,OAK,AL,3,7,1,1,0,0,0,0,0,0,0,4,0,0,0,0,0 +thomptr01,2018,2,CHA,AL,48,121,14,14,3,0,3,9,3,1,7,46,1,0,1,1,0 +thornty01,2018,1,BOS,AL,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +tillmch01,2018,1,BAL,AL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +tilsoch01,2018,1,CHA,AL,41,106,7,28,1,1,0,11,2,3,10,20,0,1,2,1,2 +toccica01,2018,1,TEX,AL,66,120,11,27,3,2,0,5,0,3,7,39,0,1,6,1,2 +tolesan01,2018,1,LAN,NL,17,30,5,7,2,0,0,4,1,0,2,8,1,0,0,0,1 +tomlijo01,2018,1,CLE,AL,32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +tomlike01,2018,1,SFN,NL,63,140,9,29,4,2,0,10,0,2,9,35,0,2,1,0,3 +torreca01,2018,1,WAS,NL,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +torregl01,2018,1,NYA,AL,123,431,54,117,16,1,24,77,6,2,42,122,3,5,1,5,8 +torrera01,2018,1,KCA,AL,9,28,4,5,1,0,0,1,0,0,1,3,0,0,0,0,1 +torrero01,2018,1,NYA,AL,41,100,9,28,7,1,0,7,0,0,2,16,0,0,0,0,3 +toussto01,2018,1,ATL,NL,8,10,1,1,0,0,0,0,0,0,0,5,0,0,0,0,0 +trahabl01,2018,1,CIN,NL,11,14,2,3,0,0,0,0,0,0,0,4,0,0,0,0,0 +travide01,2018,1,TOR,AL,103,357,41,83,14,3,11,44,3,2,16,64,1,5,0,0,8 +travisa01,2018,1,BOS,AL,19,36,5,8,3,0,1,7,0,0,2,10,0,0,0,0,1 +treinbl01,2018,1,OAK,AL,68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +trevijo01,2018,1,TEX,AL,3,8,0,2,0,0,0,3,0,0,0,1,0,0,0,0,1 +triggan01,2018,1,OAK,AL,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +trivilo01,2018,1,OAK,AL,69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +tropeni01,2018,1,LAA,AL,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +troutmi01,2018,1,LAA,AL,140,471,101,147,24,4,39,79,24,2,122,124,25,10,0,4,5 +trumbma01,2018,1,BAL,AL,90,330,41,86,12,0,17,44,0,0,24,87,0,2,0,2,10 +tsengje01,2018,1,CHN,NL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +tuckeky01,2018,1,HOU,AL,28,64,10,9,2,1,0,4,1,1,6,13,0,2,0,0,1 +tuckepr01,2018,1,ATL,NL,62,117,14,30,10,0,4,22,0,0,7,33,0,2,0,1,2 +tuckepr01,2018,2,CIN,NL,17,37,4,7,1,0,2,5,0,0,4,9,0,1,0,0,0 +tuckepr01,2018,3,ATL,NL,18,12,1,1,0,0,0,0,0,0,2,1,0,1,0,0,0 +tuivasa01,2018,1,SLN,NL,31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +tuivasa01,2018,2,SEA,AL,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +turnbsp01,2018,1,DET,AL,4,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +turneja01,2018,1,MIA,NL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +turneja01,2018,2,DET,AL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +turneju01,2018,1,LAN,NL,103,365,62,114,31,1,14,52,2,1,47,54,3,12,0,2,10 +turnetr01,2018,1,WAS,NL,162,664,103,180,27,6,19,73,43,9,69,132,3,5,2,0,7 +underdu01,2018,1,CHN,NL,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +uptonju01,2018,1,LAA,AL,145,533,80,137,18,1,30,85,8,2,64,176,1,10,0,6,12 +urenajo01,2018,1,MIA,NL,32,48,1,2,1,0,0,1,0,0,1,35,0,0,3,0,0 +urenari01,2018,1,TOR,AL,40,99,10,29,4,0,1,6,2,1,7,32,0,0,2,0,3 +uriasju01,2018,1,LAN,NL,3,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +uriaslu01,2018,1,SDN,NL,12,48,5,10,1,0,2,5,1,0,3,10,0,1,0,1,0 +urshegi01,2018,1,TOR,AL,19,43,7,10,1,0,1,3,0,0,2,10,0,1,0,0,1 +utleych01,2018,1,LAN,NL,87,164,18,35,10,1,1,14,3,1,17,34,2,5,0,1,3 +valaipa01,2018,1,COL,NL,68,122,8,19,5,0,2,5,0,0,9,30,0,0,2,0,4 +valbulu01,2018,1,LAA,AL,96,266,23,53,9,0,9,33,3,0,19,100,3,1,0,2,2 +valdefr01,2018,1,HOU,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +valdejo03,2018,1,SFN,NL,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +valenda01,2018,1,BAL,AL,79,255,28,67,8,1,9,28,1,1,22,53,2,0,0,5,8 +valenje01,2018,1,PHI,NL,46,79,8,14,5,1,1,6,0,0,8,24,0,1,0,1,1 +valerbr01,2018,1,LAN,NL,20,29,4,5,0,0,0,4,0,0,4,4,0,0,1,0,0 +valerbr01,2018,2,BAL,AL,12,35,4,10,0,1,0,4,1,0,3,9,0,0,1,2,0 +vargail01,2018,1,ARI,NL,14,19,2,4,0,0,1,4,1,0,1,4,0,0,0,0,1 +vargaja01,2018,1,NYN,NL,21,27,2,4,0,0,0,0,0,0,1,8,0,0,3,0,0 +vasquan02,2018,1,MIN,AL,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +vastoje01,2018,1,COL,NL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +vastoje01,2018,2,KCA,AL,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +vazquch01,2018,1,BOS,AL,80,251,24,52,10,0,3,16,4,1,13,41,1,4,1,0,5 +velasvi01,2018,1,PHI,NL,32,40,2,9,1,0,0,1,0,0,0,11,0,0,5,0,2 +velazan01,2018,1,TBA,AL,13,10,3,3,1,0,0,0,1,0,1,3,0,1,0,0,0 +velazhe01,2018,1,BOS,AL,47,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +vendipa01,2018,1,LAN,NL,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ventejo01,2018,1,TBA,AL,22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +ventejo01,2018,2,ATL,NL,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +verdual01,2018,1,LAN,NL,37,77,11,20,6,0,1,4,0,0,8,14,0,0,1,0,4 +verhadr01,2018,1,DET,AL,41,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +verlaju01,2018,1,HOU,AL,34,5,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0 +vieirth01,2018,1,CHA,AL,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +vielmen01,2018,1,BAL,AL,6,7,1,1,0,0,0,0,0,0,1,4,0,0,0,0,0 +villach01,2018,1,SDN,NL,110,351,42,83,15,0,20,46,3,0,23,104,0,9,0,1,12 +villajo01,2018,1,MIL,NL,87,257,26,67,10,1,6,22,14,2,19,80,0,2,0,1,9 +villajo01,2018,2,BAL,AL,54,209,28,54,4,0,8,24,21,3,22,58,0,3,1,1,4 +vilorme01,2018,1,KCA,AL,10,27,4,7,2,0,0,4,0,0,1,9,0,0,1,0,1 +vinceni01,2018,1,SEA,AL,62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +vinceza01,2018,1,SEA,AL,1,4,0,2,0,0,0,1,0,0,0,1,0,0,0,0,0 +vizcaar01,2018,1,ATL,NL,39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +vogelda01,2018,1,SEA,AL,37,87,9,18,2,0,4,13,0,0,13,26,0,2,0,0,4 +voitlu01,2018,1,SLN,NL,8,11,2,2,0,0,1,3,0,0,2,4,0,0,0,0,0 +voitlu01,2018,2,NYA,AL,39,132,28,44,5,0,14,33,0,0,15,39,0,1,0,0,3 +volstch01,2018,1,CHA,AL,33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +vothau01,2018,1,WAS,NL,4,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +vottojo01,2018,1,CIN,NL,145,503,67,143,28,2,12,67,2,0,108,101,6,9,0,3,15 +wachami01,2018,1,SLN,NL,15,25,3,2,0,0,0,1,0,0,1,9,0,0,6,0,0 +wadety01,2018,1,NYA,AL,36,66,8,11,4,0,1,5,1,0,4,23,0,0,0,0,1 +wahlbo01,2018,1,NYN,NL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +wainwad01,2018,1,SLN,NL,8,11,0,1,0,0,0,1,0,0,0,6,0,0,1,0,0 +waldema01,2018,1,BOS,AL,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +waldimi01,2018,1,PHI,NL,13,17,1,1,0,0,1,2,0,0,2,12,0,0,0,0,1 +walkech02,2018,1,ARI,NL,37,49,6,8,2,0,3,6,1,0,3,22,0,1,0,0,1 +walkene01,2018,1,NYA,AL,113,347,48,76,12,1,11,46,0,0,42,87,3,5,0,4,3 +walketa01,2018,1,ARI,NL,3,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1 +wallach01,2018,1,MIA,NL,15,45,4,8,1,0,1,5,0,0,4,23,0,2,1,0,0 +wardta01,2018,1,LAA,AL,40,135,14,24,3,0,6,15,2,0,9,45,0,3,0,0,0 +warread01,2018,1,NYA,AL,24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +warread01,2018,2,SEA,AL,23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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+zobribe01,2018,1,CHN,NL,139,455,67,139,28,3,9,58,3,4,55,60,1,2,1,7,8 +zuninmi01,2018,1,SEA,AL,113,373,37,75,18,0,20,44,0,0,24,150,0,6,0,2,7 +abadfe01,2019,1,SFN,NL,21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +abreubr01,2019,1,HOU,AL,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +abreujo02,2019,1,CHA,AL,159,634,85,180,38,1,33,123,2,2,36,152,4,13,0,10,24 +acunaro01,2019,1,ATL,NL,156,626,127,175,22,2,41,101,37,9,76,188,4,9,0,1,8 +adamecr01,2019,1,SFN,NL,10,22,1,7,1,0,0,2,0,0,2,8,0,0,0,0,0 +adamewi01,2019,1,TBA,AL,152,531,69,135,25,1,20,52,4,2,46,153,1,3,3,1,9 +adamja01,2019,1,TOR,AL,23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +adamsau01,2019,1,MIN,AL,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +adamsau01,2019,2,DET,AL,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +adamsau02,2019,1,WAS,NL,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +adamsau02,2019,2,SEA,AL,29,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +adamsch01,2019,1,NYA,AL,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +adamsma01,2019,1,WAS,NL,111,310,42,70,14,0,20,56,0,0,20,115,1,2,0,1,7 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+humphbe01,1915,1,CHN,NL,8,13,31,22,10,4,3,515,183,44,6,23,45,0.28,2.31,,3,5,0,702,6,69,,, +jamesbi01,1915,1,SLA,AL,7,10,34,23,8,0,1,511,155,68,2,92,58,0.25,3.59,,5,7,0,708,5,89,,, +jamesbi01,1915,2,DET,AL,7,3,11,9,3,1,0,201,57,18,1,33,24,0.24,2.42,,1,0,0,268,2,26,,, +jamesbi02,1915,1,BSN,NL,5,4,13,10,4,0,0,205,68,23,3,22,23,0.26,3.03,,3,2,0,287,3,28,,, +jaspehi01,1915,1,CHA,AL,0,1,3,2,1,0,0,47,8,8,2,9,15,0.15,4.6,,0,0,0,60,0,8,,, +johnsch02,1915,1,KCF,FL,17,17,46,34,19,4,2,844,253,86,5,71,118,9.99,2.75,,13,8,0,1126,6,121,,, +johnsra01,1915,1,CHF,FL,2,4,11,6,3,0,1,171,58,28,2,23,19,9.99,4.42,,1,2,0,0,4,34,,, +johnsra01,1915,2,BLF,FL,7,11,23,19,12,2,1,452,143,56,3,58,62,9.99,3.35,,4,0,1,0,3,68,,, +johnswa01,1915,1,WS1,AL,27,13,47,39,35,7,4,1010,258,58,1,56,203,0.21,1.55,,7,19,0,1305,8,83,,, +johnswa02,1915,1,CHA,AL,0,0,1,0,0,0,0,6,3,2,0,0,3,0.33,9,,0,0,0,9,0,2,,, +jonessa01,1915,1,CLE,AL,4,9,48,9,2,0,4,437,131,59,0,63,42,0.25,3.65,,3,1,0,583,24,78,,, +kaisege01,1915,1,NEW,FL,15,15,41,29,16,5,2,784,246,65,1,73,75,9.99,2.24,,4,9,0,1039,9,90,,, +kantler01,1915,1,PIT,NL,5,12,29,18,10,1,3,489,135,41,1,58,64,0.23,2.26,,1,4,0,669,6,60,,, +keatira01,1915,1,NYA,AL,3,6,11,10,8,1,0,238,66,32,3,45,37,0.22,3.63,,4,3,0,338,1,41,,, +kellyhe01,1915,1,PIT,NL,1,1,5,1,0,0,0,33,10,5,0,4,6,0.25,4.09,,0,1,0,48,3,9,,, +kirbyla01,1915,1,SLF,FL,0,0,1,0,0,0,0,21,7,4,1,2,7,9.99,5.14,,0,0,0,0,1,5,,, +klepfed01,1915,1,CHA,AL,1,0,3,2,1,0,0,38,11,4,0,5,3,0.23,2.84,,1,0,0,52,1,4,,, +klepfed01,1915,2,CLE,AL,1,6,8,7,2,0,0,129,47,10,0,11,13,0.28,2.09,,0,0,0,177,1,25,,, +knetzel01,1915,1,PTF,FL,18,14,41,33,22,3,3,837,256,80,5,89,120,9.99,2.58,,4,1,0,1111,8,105,,, +knowlto01,1915,1,PHA,AL,4,6,18,9,8,0,0,302,99,39,1,60,24,0.27,3.49,,4,6,1,428,7,53,,, +koober01,1915,1,SLA,AL,4,5,28,13,6,0,1,401,119,35,2,50,37,0.25,2.36,,6,10,0,528,9,50,,, +krappge01,1915,1,BUF,FL,9,19,38,30,14,1,0,693,188,90,6,123,93,9.99,3.51,,6,4,1,946,7,106,,, 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+craftmo01,1916,1,WS1,AL,0,1,2,1,1,0,0,33,12,4,0,6,9,0.31,3.27,,3,0,0,44,0,5,,, +cranddo01,1916,1,SLA,AL,0,0,2,0,0,0,0,4,7,4,0,1,0,0.63,27,,0,1,0,13,1,9,,, +croweca01,1916,1,PHA,AL,0,5,9,6,1,0,0,119,43,22,0,34,15,0.28,4.99,,1,2,0,185,2,33,,, +culloni01,1916,1,NYA,AL,13,6,28,22,9,0,1,501,151,38,4,32,77,0.24,2.05,,3,3,0,656,4,60,,, +cunnige01,1916,1,DET,AL,7,10,35,14,5,0,2,451,146,46,0,74,68,0.26,2.75,,7,3,0,619,14,71,,, +currimu01,1916,1,SLN,NL,0,0,6,0,0,0,0,43,7,3,1,9,8,0.14,1.88,,1,0,0,59,5,4,,, +dalege01,1916,1,CIN,NL,3,4,17,5,2,0,0,209,80,40,3,33,23,0.3,5.17,,0,2,0,309,8,44,,, +danfoda01,1916,1,CHA,AL,6,5,28,8,1,0,2,281,87,34,1,37,49,0.25,3.27,,1,3,3,376,12,43,,, +daussho01,1916,1,DET,AL,19,12,39,29,18,1,4,716,220,85,2,90,95,0.25,3.21,,8,16,1,961,9,102,,, +davenda01,1916,1,SLA,AL,12,11,59,31,13,1,2,872,267,92,4,100,129,0.25,2.85,,5,8,0,1149,17,112,,, +dellwh01,1916,1,BRO,NL,8,9,32,16,9,2,1,465,143,39,2,43,76,0.25,2.26,,2,4,0,623,9,52,,, 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+mccabti01,1916,1,SLA,AL,2,0,13,0,0,0,0,77,29,9,0,7,7,0.28,3.16,,1,2,0,112,5,20,,, +mcconge01,1916,1,CHN,NL,4,12,28,21,8,1,0,514,137,49,8,35,82,0.22,2.57,,8,5,0,682,7,66,,, +mchalma01,1916,1,BOS,AL,0,1,2,1,0,0,0,18,7,2,0,4,1,0.28,3,,0,1,0,30,1,7,,, +mchalma01,1916,2,CLE,AL,0,0,5,0,0,0,0,34,10,7,1,6,2,0.27,5.56,,0,0,0,43,2,7,,, +mckenli01,1916,1,CIN,NL,1,1,6,1,0,0,0,44,14,7,0,8,2,0.25,4.3,,0,2,0,66,1,8,,, +mcquige01,1916,1,PHI,NL,1,7,21,3,1,0,2,186,58,19,2,15,22,0.25,2.76,,1,3,0,260,15,33,,, +mctigbi01,1916,1,DET,AL,0,0,3,0,0,0,0,16,5,3,0,5,1,0.27,5.06,,0,0,0,23,1,6,,, +meadole01,1916,1,SLN,NL,12,23,51,36,11,1,2,867,261,83,3,119,120,0.24,2.58,,11,14,1,1217,10,117,,, +millefr02,1916,1,PIT,NL,7,10,30,20,10,2,1,519,135,44,4,49,88,0.22,2.29,,3,7,0,677,7,55,,, +mitchcl01,1916,1,CIN,NL,11,10,29,24,17,1,0,584,211,68,4,45,52,0.28,3.14,,3,10,0,819,5,87,,, +mitchwi01,1916,1,CLE,AL,2,5,12,6,1,0,1,131,55,25,1,19,24,0.3,5.15,,3,0,0,197,5,35,,, +mitchwi01,1916,2,DET,AL,7,5,23,17,7,2,0,383,119,47,1,48,60,0.25,3.31,,2,5,0,523,6,53,,, +mogrige01,1916,1,NYA,AL,6,12,30,21,10,2,0,584,174,50,3,45,66,0.25,2.31,,2,7,0,742,5,71,,, +morribi01,1916,1,PHA,AL,0,0,1,0,0,0,0,12,6,3,0,5,2,0.42,6.75,,0,0,0,19,1,3,,, +mortogu01,1916,1,CLE,AL,12,8,27,18,9,0,0,449,139,48,1,42,88,0.24,2.89,,2,3,1,610,5,63,,, +moselea01,1916,1,CIN,NL,7,10,31,15,7,0,1,451,145,65,5,69,60,0.25,3.89,,8,0,0,653,13,75,,, +myersel01,1916,1,PHA,AL,14,23,44,35,31,2,1,945,280,128,7,168,182,0.24,3.66,,13,14,0,1310,8,169,,, +naborja01,1916,1,PHA,AL,1,20,40,30,11,0,1,638,206,82,2,95,74,0.26,3.47,,4,3,0,871,8,110,,, +nehfar01,1916,1,BSN,NL,7,5,22,12,6,1,0,363,110,27,1,20,36,0.24,2.01,,2,3,0,484,5,40,,, +oeschjo01,1916,1,PHI,NL,1,0,14,0,0,0,0,91,18,8,2,14,17,0.18,2.37,,1,1,0,119,8,8,,, +package01,1916,1,CHN,NL,10,6,37,16,5,2,5,466,154,48,4,38,36,0.25,2.78,,2,3,0,663,17,60,,, +palmeem01,1916,1,NY1,NL,0,3,4,1,0,0,0,47,17,14,2,8,8,0.28,8.04,,1,1,0,71,1,14,,, +parkji01,1916,1,SLA,AL,1,4,26,6,1,0,0,237,69,23,2,25,26,0.24,2.62,,1,1,0,309,11,28,,, +parnhru01,1916,1,PHA,AL,2,1,4,3,2,0,0,74,27,11,0,13,8,0.3,4.01,,2,0,0,103,1,14,,, +pearcge01,1916,1,CHN,NL,0,0,4,1,0,0,0,13,6,1,0,1,0,0.3,2.08,,0,1,0,23,1,5,,, +penneke01,1916,1,CLE,AL,1,0,4,2,0,0,0,38,14,6,0,4,5,0.3,4.26,,0,0,0,50,1,6,,, +pennohe01,1916,1,BOS,AL,0,2,9,2,0,0,1,80,23,9,0,8,12,0.24,3.04,,1,1,0,103,6,11,,, +perripo01,1916,1,NY1,NL,18,11,40,28,17,5,2,753,243,73,11,56,115,0.25,2.62,,3,7,0,1030,7,82,,, +perrysc01,1916,1,CHN,NL,2,1,4,3,2,1,0,85,30,8,0,3,10,0.29,2.54,,1,0,0,114,1,9,,, +pfeffje01,1916,1,BRO,NL,25,11,41,37,30,6,1,986,274,70,5,63,128,0.23,1.92,,6,17,0,1298,4,91,,, +planked01,1916,1,SLA,AL,16,15,37,26,17,3,3,707,203,61,2,67,88,0.23,2.33,,2,6,0,950,8,78,,, +prendmi01,1916,1,CHN,NL,6,11,35,10,4,2,2,456,127,39,5,23,56,0.22,2.31,,5,1,0,597,14,53,,, +raganpa01,1916,1,BSN,NL,9,9,28,23,14,3,0,546,143,42,3,47,94,0.21,2.08,,1,0,0,717,4,53,,, 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+willile01,1916,1,CHA,AL,13,7,43,26,10,2,1,673,220,72,5,65,138,0.26,2.89,,2,8,0,897,9,99,,, +willima01,1916,1,PHA,AL,0,6,10,4,3,0,0,154,71,45,4,31,17,0.35,7.89,,2,0,2,234,4,53,,, +willist01,1916,1,SLN,NL,6,7,36,8,5,0,1,315,121,49,6,27,25,0.29,4.2,,1,1,1,458,21,63,,, +wolfgme01,1916,1,CHA,AL,4,6,27,14,6,1,0,381,103,28,2,42,36,0.22,1.98,,2,2,0,496,8,39,,, +wyckowe01,1916,1,PHA,AL,0,1,7,2,1,0,0,64,20,13,1,20,4,0.24,5.48,,2,1,0,102,4,16,,, +wyckowe01,1916,2,BOS,AL,0,0,8,0,0,0,1,68,19,12,0,18,18,0.23,4.76,,0,0,0,100,4,13,,, +aldrivi01,1917,1,CHN,NL,6,6,30,6,1,1,2,320,100,37,1,37,44,0.25,3.12,,5,2,0,445,13,52,,, +alexape01,1917,1,PHI,NL,30,13,45,44,34,8,0,1164,336,79,4,56,200,0.23,1.83,,2,6,0,1529,0,107,,, +allenfr01,1917,1,BSN,NL,3,11,29,14,2,0,0,336,124,49,3,47,56,0.29,3.94,,4,6,0,490,6,61,,, +amesre01,1917,1,SLN,NL,15,10,43,19,10,2,3,627,189,63,2,57,62,0.24,2.71,,5,3,0,847,17,75,,, +anderfr01,1917,1,NY1,NL,8,8,38,18,8,1,3,486,122,26,1,34,69,0.2,1.44,,5,2,0,632,15,40,,, 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+bradyne01,1917,1,NYA,AL,1,0,2,1,0,0,0,27,6,2,0,5,4,0.18,2,,0,0,0,37,1,2,,, +bressru01,1917,1,CIN,NL,0,0,2,1,0,0,0,27,15,6,0,5,2,0.42,6,,0,0,0,44,1,11,,, +bushjo01,1917,1,PHA,AL,11,17,37,31,17,4,2,700,207,64,3,111,121,0.24,2.47,,7,1,0,1025,5,101,,, +cadorle01,1917,1,BRO,NL,13,13,37,30,21,1,3,792,231,72,3,63,115,0.24,2.45,,4,7,0,1067,6,86,,, +caldwra02,1917,1,NYA,AL,13,16,32,29,21,1,0,708,199,75,8,76,102,0.23,2.86,,0,6,0,935,2,92,,, +carlsha01,1917,1,PIT,NL,7,11,34,17,9,1,1,484,140,52,0,49,68,0.24,2.9,,2,4,0,657,14,64,,, +cartepa01,1917,1,CHN,NL,5,8,23,13,6,0,2,340,115,41,2,19,34,0.27,3.26,,1,3,1,453,3,47,,, +chenela01,1917,1,BRO,NL,8,12,35,24,14,1,2,631,185,55,4,73,102,0.23,2.35,,11,5,0,869,4,80,,, +cicoted01,1917,1,CHA,AL,28,12,49,35,29,7,4,1040,246,59,2,70,150,0.2,1.53,,1,3,2,1288,9,76,,, +coombja01,1917,1,BRO,NL,7,11,31,14,9,0,0,423,147,62,7,49,34,0.28,3.96,,0,8,1,600,15,76,,, +coopewi01,1917,1,PIT,NL,17,11,40,34,23,7,1,893,276,78,4,54,99,0.25,2.36,,1,4,0,1159,5,96,,, +couchjo01,1917,1,DET,AL,0,0,3,0,0,0,0,40,13,4,0,1,1,0.25,2.7,,0,0,0,52,0,16,,, +coumbfr01,1917,1,CLE,AL,8,6,34,10,4,1,5,403,119,32,0,35,30,0.25,2.14,,2,3,1,514,14,54,,, +covelha01,1917,1,DET,AL,4,6,16,11,2,0,0,207,70,20,0,14,15,0.26,2.61,,1,2,0,280,1,39,,, +covelst01,1917,1,CLE,AL,19,14,45,36,24,9,4,895,202,60,3,94,133,0.19,1.81,,3,1,0,1142,8,78,,, +craftmo01,1917,1,WS1,AL,0,0,8,0,0,0,1,42,17,6,0,8,2,0.31,3.86,,2,0,0,62,5,10,,, +crumca01,1917,1,BSN,NL,0,0,1,0,0,0,0,3,1,0,0,1,0,0.25,0,,1,0,0,22,1,5,,, +culloni01,1917,1,NYA,AL,5,9,30,18,5,2,1,439,161,54,2,31,27,0.3,3.32,,4,2,0,559,7,70,,, +cunnige01,1917,1,DET,AL,2,7,44,8,4,0,4,417,113,45,2,51,49,0.22,2.91,,4,4,0,561,24,73,,, +danfoda01,1917,1,CHA,AL,11,6,50,9,1,1,9,519,155,51,1,74,79,0.24,2.65,,2,3,1,716,26,56,,, +daussho01,1917,1,DET,AL,17,14,37,31,22,6,2,812,243,73,3,87,102,0.24,2.43,,4,7,1,1090,5,105,,, +davenda01,1917,1,SLA,AL,17,17,47,39,20,2,2,842,273,96,5,105,100,0.26,3.08,,11,8,1,1178,6,137,,, +dellwh01,1917,1,BRO,NL,0,4,17,4,0,0,1,174,55,24,3,25,28,0.26,3.72,,2,2,0,246,6,35,,, +demaral01,1917,1,CHN,NL,5,9,24,18,6,1,1,424,125,40,5,37,43,0.24,2.55,,3,2,0,569,4,53,,, +demaral01,1917,2,NY1,NL,4,5,15,11,1,0,0,235,70,23,1,17,23,0.23,2.64,,0,1,0,321,2,33,,, +dickege01,1917,1,CLE,AL,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,,0,0,0,3,1,0,,, +doakbi01,1917,1,SLN,NL,16,20,44,37,16,3,2,844,257,97,6,85,111,0.25,3.1,,2,9,0,1152,7,123,,, +douglph01,1917,1,CHN,NL,14,20,51,37,20,5,1,880,269,83,13,50,151,0.25,2.55,,11,6,0,1161,11,123,,, +dumonge01,1917,1,WS1,AL,5,14,37,23,8,2,2,614,171,58,3,76,65,0.22,2.55,,7,6,1,839,8,76,,, +durniri01,1917,1,BRO,NL,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,,0,0,0,3,1,0,,, +ehmkeho01,1917,1,DET,AL,10,15,35,25,13,4,2,618,174,68,3,88,90,0.24,2.97,,4,5,0,808,10,84,,, +ellerho01,1917,1,CIN,NL,10,5,37,11,7,1,1,457,131,40,2,37,77,0.23,2.36,,1,3,0,614,21,60,,, +engeljo01,1917,1,CIN,NL,0,1,1,1,1,0,0,24,12,5,0,6,2,0.35,5.63,,0,0,0,42,0,8,,, 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+causere01,1918,1,NY1,NL,11,6,29,18,10,2,2,475,143,49,2,42,48,0.24,2.79,,4,7,0,648,10,58,,, +chenela01,1918,1,BRO,NL,11,13,32,21,15,0,1,602,177,67,2,74,83,0.24,3,,13,10,0,850,10,84,,, +cicoted01,1918,1,CHA,AL,12,19,38,30,24,1,2,798,275,82,2,40,104,0.27,2.77,,2,2,0,1057,8,102,,, +cobbty01,1918,1,DET,AL,0,0,2,0,0,0,0,12,6,2,0,2,0,0.4,4.5,,0,0,0,17,1,2,,, +comstra01,1918,1,PIT,NL,5,6,15,8,6,0,1,243,78,27,0,14,44,0.25,3,,1,2,0,329,6,33,,, +conlesn01,1918,1,CIN,NL,2,0,5,0,0,0,1,41,17,8,2,5,2,0.32,5.27,,0,0,0,60,4,10,,, +coombja01,1918,1,BRO,NL,8,14,27,20,16,2,0,567,191,80,10,49,44,0.26,3.81,,4,2,0,792,4,97,,, +coopewi01,1918,1,PIT,NL,19,14,38,29,26,2,3,820,219,64,2,65,117,0.22,2.11,,4,10,0,1078,9,86,,, +coreyed01,1918,1,CHA,AL,0,0,1,0,0,0,0,6,2,1,0,1,0,0.33,4.5,,0,0,0,7,1,1,,, +coumbfr01,1918,1,CLE,AL,13,7,30,17,9,0,3,450,164,51,4,52,41,0.28,3.06,,0,1,0,607,10,60,,, +covelha01,1918,1,DET,AL,0,1,3,1,1,0,0,42,17,6,0,6,3,0.31,3.86,,0,0,0,62,2,9,,, +covelst01,1918,1,CLE,AL,22,13,38,33,25,2,1,933,261,63,2,76,87,0.22,1.82,,2,4,0,1254,5,90,,, +craftmo01,1918,1,WS1,AL,0,0,3,0,0,0,0,21,5,1,0,1,5,0.2,1.29,,0,0,0,25,3,1,,, +cranddo01,1918,1,BSN,NL,1,2,5,3,3,0,0,102,39,9,1,4,4,0.3,2.38,,0,1,0,136,1,11,,, +crumca01,1918,1,BSN,NL,0,1,1,1,0,0,0,7,6,4,0,3,0,0.6,15.43,,0,1,0,15,0,4,,, +cunnige01,1918,1,DET,AL,6,7,27,14,10,0,1,420,131,49,0,38,39,0.25,3.15,,2,5,0,578,12,68,,, +danfoda01,1918,1,CHA,AL,6,15,39,11,5,0,2,417,148,53,1,40,48,0.28,3.43,,1,5,0,567,19,73,,, +daussho01,1918,1,DET,AL,12,16,33,26,21,1,3,749,243,83,3,58,73,0.26,2.99,,5,9,0,1027,7,105,,, +davenda01,1918,1,SLA,AL,10,11,31,22,12,2,1,540,182,65,0,69,60,0.27,3.25,,3,7,0,759,3,84,,, +davisdi01,1918,1,PHI,NL,0,2,17,2,1,0,0,141,43,16,1,30,18,0.24,3.06,,1,0,0,214,13,25,,, +demaral01,1918,1,NY1,NL,8,6,26,14,8,2,1,426,143,39,5,25,39,0.26,2.47,,0,2,0,585,7,56,,, +doakbi01,1918,1,SLN,NL,9,15,31,23,16,1,1,633,191,57,3,60,74,0.24,2.43,,2,4,1,865,7,76,,, 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+fillida01,1918,1,BSN,NL,7,6,14,13,10,4,0,339,99,28,0,28,29,0.24,2.23,,2,5,0,454,1,37,,, +finneha01,1918,1,DET,AL,0,2,5,2,0,0,1,41,22,15,0,8,2,0.39,9.88,,1,0,0,64,3,17,,, +finneha01,1918,2,NYA,AL,3,6,23,13,4,0,0,343,134,48,7,35,34,0.3,3.78,,1,2,0,433,7,52,,, +fortuga01,1918,1,PHI,NL,0,2,5,2,1,0,0,93,41,28,2,19,10,0.33,8.13,,1,1,0,151,2,30,,, +gallibe01,1918,1,SLA,AL,8,6,19,17,10,1,0,372,126,48,1,61,48,0.26,3.48,,2,6,0,551,1,63,,, +gearybo01,1918,1,PHA,AL,2,5,16,7,6,2,4,261,94,26,0,31,22,0.28,2.69,,1,3,0,368,8,37,,, +georgle01,1918,1,BSN,NL,1,5,9,5,4,0,0,163,56,14,0,21,22,0.28,2.32,,1,3,0,232,4,23,,, +greggve01,1918,1,PHA,AL,9,14,30,25,17,3,2,598,180,69,4,67,63,0.25,3.12,,1,5,0,814,4,85,,, +grimebu01,1918,1,BRO,NL,19,9,40,30,19,7,1,810,210,64,3,76,113,0.21,2.13,,11,4,2,1078,9,94,,, +grineda01,1918,1,BRO,NL,1,5,11,6,3,1,0,163,47,13,0,15,22,0.26,2.15,,1,7,0,205,3,16,,, +groombo01,1918,1,CLE,AL,2,2,14,5,0,0,0,130,70,34,0,18,8,0.38,7.06,,0,1,0,203,4,42,,, +haineje01,1918,1,CIN,NL,0,0,1,0,0,0,0,15,5,1,0,1,2,0.29,1.8,,0,0,0,20,1,1,,, +hallch02,1918,1,DET,AL,0,1,6,1,0,0,0,40,14,10,1,6,2,0.26,6.75,,0,0,0,61,4,10,,, +hallhe01,1918,1,DET,AL,0,0,3,0,0,0,0,18,12,10,0,7,1,0.5,15,,0,2,0,33,0,11,,, +hamilea01,1918,1,PIT,NL,6,0,6,6,6,1,0,162,47,5,0,13,20,0.24,0.83,,1,0,0,210,0,7,,, +hansero01,1918,1,WS1,AL,1,0,5,0,0,0,0,27,10,3,0,3,2,0.27,3,,0,1,0,40,2,4,,, +harmobo01,1918,1,PIT,NL,2,7,16,9,5,0,0,247,76,24,3,12,7,0.25,2.62,,0,0,0,321,5,30,,, +harpeha01,1918,1,WS1,AL,11,10,35,32,14,3,1,732,182,59,1,104,78,0.21,2.18,,13,8,0,993,2,77,,, +hearnbu01,1918,1,BSN,NL,5,6,17,12,9,1,0,379,119,35,2,29,30,0.25,2.49,,0,0,0,509,5,43,,, +hehlja01,1918,1,BRO,NL,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,,0,1,0,4,1,0,,, +heitmha01,1918,1,BRO,NL,0,1,1,1,0,0,0,1,4,4,0,0,0,1,108,,0,0,0,5,0,4,,, +hendrcl01,1918,1,CHN,NL,20,7,32,27,21,3,0,699,229,72,2,54,86,0.25,2.78,,8,5,0,966,4,87,,, +herrmma01,1918,1,BRO,NL,0,0,1,0,0,0,0,3,0,0,0,1,0,0,0,,0,0,0,4,1,0,,, +hillca01,1918,1,PIT,NL,2,3,6,4,3,0,0,131,24,6,0,17,15,0.16,1.24,,0,0,0,170,1,11,,, +hoggbr01,1918,1,PHI,NL,13,13,29,25,17,3,1,684,201,64,3,61,81,0.24,2.53,,5,6,1,924,2,83,,, +holmech01,1918,1,PHA,AL,0,0,2,0,0,0,0,6,4,3,0,1,0,0.4,13.5,,0,1,0,12,2,5,,, +horstos01,1918,1,SLN,NL,0,2,9,2,0,0,0,69,29,14,0,14,6,0.34,5.48,,1,0,0,102,4,18,,, +houckby01,1918,1,SLA,AL,2,4,27,2,0,0,2,215,58,19,0,29,29,0.22,2.39,,1,0,0,291,22,24,,, +hovlied01,1918,1,WS1,AL,2,1,8,2,1,0,0,84,25,4,0,10,10,0.27,1.29,,1,0,0,106,4,10,,, +howarea01,1918,1,SLN,NL,0,0,1,0,0,0,0,6,0,0,0,2,0,0,0,,0,0,0,7,1,0,,, +hoytwa01,1918,1,NY1,NL,0,0,1,0,0,0,0,3,0,0,0,0,2,0,0,,0,0,0,3,1,0,,, +hugheto02,1918,1,BSN,NL,0,2,3,3,1,0,0,55,17,7,0,6,9,0.25,3.44,,0,0,0,77,0,10,,, +jacobel01,1918,1,PIT,NL,0,1,8,4,0,0,0,70,31,15,0,14,2,0.34,5.79,,0,0,0,111,1,18,,, +jacobel01,1918,2,PHI,NL,9,5,18,14,12,4,1,369,91,33,3,42,33,0.21,2.41,,2,4,0,495,2,39,,, +jacobla01,1918,1,CIN,NL,0,1,5,0,0,0,0,52,25,11,0,1,8,0.36,5.71,,0,0,0,74,4,12,,, 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+koneted01,1918,1,BSN,NL,0,1,1,1,1,0,0,24,14,6,1,2,3,0.37,6.75,,0,0,0,40,0,8,,, +lambeot01,1918,1,CLE,AL,0,0,2,0,0,0,0,21,10,5,0,6,3,0.37,6.43,,0,0,0,33,1,5,,, +leifile01,1918,1,SLA,AL,2,6,15,6,3,1,0,201,61,19,1,19,22,0.25,2.55,,0,2,0,266,6,23,,, +leonadu01,1918,1,BOS,AL,8,6,16,16,12,3,0,377,119,38,0,53,47,0.25,2.72,,7,2,0,542,0,51,,, +lovesl01,1918,1,NYA,AL,13,12,38,29,13,1,1,686,207,78,3,116,95,0.25,3.07,,4,10,1,963,5,92,,, +lowdegr01,1918,1,SLA,AL,2,6,13,11,4,0,0,240,74,28,1,38,25,0.25,3.15,,2,3,0,341,1,44,,, +luquedo01,1918,1,CIN,NL,6,3,12,10,9,1,0,249,84,35,1,32,26,0.27,3.8,,4,1,1,349,1,44,,, +mainal01,1918,1,PHI,NL,2,2,8,4,1,1,0,105,30,18,1,16,14,0.24,4.63,,0,5,0,152,2,20,,, +mamaual01,1918,1,BRO,NL,0,1,2,1,0,0,0,24,14,6,0,2,2,0.43,6.75,,0,0,0,36,1,6,,, +marquru01,1918,1,BRO,NL,9,18,34,29,19,4,0,717,231,70,7,59,89,0.26,2.64,,3,1,0,980,3,97,,, +martisp01,1918,1,CHN,NL,5,2,9,5,4,1,1,161,47,11,0,14,16,0.24,1.84,,2,1,0,219,3,19,,, +matteed01,1918,1,WS1,AL,5,3,14,6,2,0,0,203,57,13,2,15,17,0.23,1.73,,0,1,0,257,5,20,,, +mayerer01,1918,1,PHI,NL,7,4,13,13,7,0,0,312,108,36,2,26,16,0.27,3.12,,1,4,0,432,0,46,,, +mayerer01,1918,2,PIT,NL,9,3,15,14,11,1,0,370,122,31,1,27,25,0.26,2.26,,0,4,0,508,0,40,,, +mayja01,1918,1,SLN,NL,5,6,29,15,6,0,0,458,149,65,2,69,61,0.26,3.83,,1,13,0,670,8,83,,, +maysca01,1918,1,BOS,AL,21,13,35,33,30,8,0,880,230,72,2,81,114,0.22,2.21,,6,11,0,1162,1,94,,, +mcavowi01,1918,1,PHA,AL,0,0,1,0,0,0,0,2,1,1,1,0,0,0.5,13.5,,0,0,0,3,1,1,,, +mccabdi01,1918,1,BOS,AL,0,1,3,1,0,0,0,29,13,3,0,2,3,0.35,2.79,,0,0,0,40,2,4,,, +mccabti01,1918,1,SLA,AL,0,0,1,0,0,0,0,4,2,2,0,1,0,0.33,13.5,,0,0,0,8,0,2,,, +mcgrabo01,1918,1,NYA,AL,0,1,1,1,0,0,0,0,0,0,0,4,0,0,,,0,0,0,4,0,4,,, +mcquige01,1918,1,CLE,AL,0,1,5,1,0,0,1,69,25,6,0,4,7,0.28,2.35,,0,0,0,95,2,10,,, +mcquihu01,1918,1,BSN,NL,1,0,1,1,1,0,0,27,7,3,0,5,1,0.21,3,,0,0,0,39,0,3,,, +meadole01,1918,1,SLN,NL,8,14,30,23,12,0,1,496,176,66,1,56,49,0.28,3.59,,1,10,0,714,6,91,,, +millefr02,1918,1,PIT,NL,11,8,23,23,14,2,0,511,152,45,1,37,47,0.25,2.38,,1,7,0,677,0,60,,, +mitchcl01,1918,1,BRO,NL,0,1,1,1,0,0,0,1,4,4,0,0,0,1,108,,0,0,0,4,0,4,,, +mitchro01,1918,1,CHA,AL,0,1,2,2,0,0,0,36,18,10,1,4,3,0.34,7.5,,0,0,0,59,0,14,,, +mitchro01,1918,2,CIN,NL,4,0,5,3,3,2,0,109,27,3,0,5,9,0.2,0.74,,0,0,0,137,1,3,,, +mitchwi01,1918,1,DET,AL,0,1,1,1,0,0,0,12,3,4,0,5,2,0.2,9,,0,0,0,20,0,4,,, +mogrige01,1918,1,NYA,AL,16,13,45,19,13,1,7,718,232,58,6,43,62,0.26,2.18,,1,8,1,951,23,78,,, +molynvi01,1918,1,BOS,AL,1,0,6,0,0,0,0,32,3,4,0,8,1,0.08,3.38,,2,0,0,43,4,4,,, +monroed01,1918,1,NYA,AL,0,0,1,0,0,0,0,6,1,1,0,2,1,0.14,4.5,,1,0,0,9,1,2,,, +morribu01,1918,1,SLA,AL,0,2,4,2,0,0,0,31,12,4,1,7,0,0.3,3.48,,0,0,0,48,1,7,,, +mortogu01,1918,1,CLE,AL,14,8,30,28,13,1,0,644,189,63,1,77,123,0.24,2.64,,4,3,0,892,1,87,,, +myersel01,1918,1,PHA,AL,4,8,18,15,5,1,1,286,101,49,4,42,17,0.28,4.63,,6,4,0,416,3,66,,, +napiebu01,1918,1,CHN,NL,0,0,1,0,0,0,0,20,10,4,0,4,2,0.35,5.4,,0,0,0,32,1,4,,, +nehfar01,1918,1,BSN,NL,15,15,32,31,28,2,0,853,274,85,2,76,96,0.25,2.69,,4,6,1,1167,1,107,,, +northja01,1918,1,BSN,NL,5,1,7,4,4,1,0,120,26,6,0,3,4,0.18,1.35,,0,0,0,150,3,9,,, +oeschjo01,1918,1,PHI,NL,6,18,30,23,13,2,3,552,159,62,3,83,60,0.23,3.03,,4,7,0,778,7,87,,, +ogdenja01,1918,1,NY1,NL,0,0,5,0,0,0,0,26,8,3,0,3,1,0.29,3.12,,0,2,0,35,2,4,,, +package01,1918,1,SLN,NL,12,12,30,23,10,1,2,547,184,71,6,33,46,0.26,3.5,,0,5,0,744,7,84,,, +paulege01,1918,1,SLN,NL,0,0,1,0,0,0,0,1,1,0,0,0,0,0.5,0,,0,0,0,2,1,0,,, +perripo01,1918,1,NY1,NL,18,13,35,31,19,6,0,699,212,71,5,38,60,0.24,2.74,,3,1,0,928,4,82,,, +perrysc01,1918,1,PHA,AL,20,19,44,36,30,3,2,997,295,73,1,111,81,0.24,1.98,,4,2,0,1342,6,97,,, +pertibi01,1918,1,BOS,AL,0,0,1,0,0,0,0,9,3,1,0,0,1,0.27,3,,0,0,0,11,0,1,,, +pfeffje01,1918,1,BRO,NL,1,0,1,1,1,1,0,27,2,0,0,3,1,0.07,0,,0,0,0,31,0,0,,, +pierswi01,1918,1,PHA,AL,0,1,8,1,0,0,0,65,20,8,0,20,6,0.28,3.32,,0,2,0,93,5,10,,, +plittno01,1918,1,BRO,NL,0,0,1,0,0,0,0,6,3,1,0,1,0,0.42,4.5,,0,0,0,8,1,1,,, +prendmi01,1918,1,PHI,NL,13,14,33,30,20,0,1,757,257,81,6,46,41,0.27,2.89,,5,1,0,1030,3,102,,, +quinnja01,1918,1,CHA,AL,5,1,6,5,5,0,0,153,38,13,0,7,22,0.21,2.29,,1,0,1,190,1,13,,, +raganpa01,1918,1,BSN,NL,8,17,30,25,15,2,0,619,212,74,4,54,68,0.27,3.23,,1,4,0,867,3,95,,, +reesst01,1918,1,WS1,AL,1,0,2,0,0,0,0,6,3,0,0,4,1,0.5,0,,0,0,0,10,0,0,,, +reganmi01,1918,1,CIN,NL,5,5,22,6,4,3,2,240,77,29,0,29,15,0.26,3.26,,2,0,0,336,9,38,,, +ringji01,1918,1,CIN,NL,9,5,21,18,13,4,0,427,130,45,5,48,26,0.24,2.85,,1,3,0,589,3,57,,, +roberdi01,1918,1,BRO,NL,3,6,13,9,7,1,0,261,87,25,0,28,18,0.27,2.59,,0,0,0,354,4,34,,, +robinha01,1918,1,NYA,AL,2,4,11,3,1,0,0,144,47,16,0,16,14,0.26,3,,1,3,0,201,5,21,,, +rogerto01,1918,1,SLA,AL,8,10,29,16,11,0,2,462,148,56,3,49,29,0.26,3.27,,1,3,0,627,8,66,,, +rossge01,1918,1,NY1,NL,0,0,1,0,0,0,0,7,2,0,0,3,2,0.22,0,,0,0,0,12,0,0,,, +rudoldi01,1918,1,BSN,NL,9,10,21,20,15,3,0,462,144,44,2,30,48,0.25,2.57,,1,0,0,615,1,63,,, +ruethdu01,1918,1,CIN,NL,0,1,2,2,1,0,0,30,10,3,0,3,10,0.24,2.7,,0,1,0,47,0,9,,, +russeal01,1918,1,NYA,AL,7,11,27,18,7,2,4,423,139,51,6,73,54,0.26,3.26,,1,5,0,616,8,68,,, +russejo01,1918,1,BRO,NL,0,0,1,0,0,0,0,3,2,2,0,1,0,0.5,18,,0,0,0,6,0,2,,, +russere01,1918,1,CHA,AL,7,5,19,15,10,2,0,374,117,36,0,33,38,0.25,2.6,,2,0,0,506,3,45,,, +ruthba01,1918,1,BOS,AL,13,7,20,19,18,1,0,499,125,41,1,49,40,0.21,2.22,,3,2,1,660,0,51,,, +sallesl01,1918,1,NY1,NL,8,8,18,16,12,1,2,396,122,33,3,12,33,0.24,2.25,,1,0,1,531,2,44,,, +sandero01,1918,1,PIT,NL,7,9,28,14,6,1,1,468,135,45,1,52,55,0.23,2.6,,4,2,1,644,14,59,,, +sandero02,1918,1,NYA,AL,0,2,6,2,0,0,0,77,28,12,0,16,8,0.3,4.21,,0,2,0,113,2,15,,, +schnepe01,1918,1,CIN,NL,10,15,33,30,17,2,0,651,213,85,2,117,51,0.27,3.53,,5,11,0,944,0,106,,, +schupfe01,1918,1,NY1,NL,0,1,10,2,1,0,0,100,42,28,1,27,22,0.32,7.56,,3,3,0,165,6,34,,, +shawji01,1918,1,WS1,AL,16,12,41,30,14,4,1,724,201,65,2,90,129,0.22,2.42,,10,1,3,986,7,88,,, +shawkbo01,1918,1,NYA,AL,1,1,3,2,1,1,0,48,7,2,0,10,3,0.14,1.13,,0,0,0,60,1,2,,, +sheare01,1918,1,PHA,AL,0,0,3,0,0,0,0,27,14,4,0,2,2,0.37,4,,0,0,0,39,2,8,,, +shellfr01,1918,1,CHA,AL,9,12,28,21,10,2,2,548,180,54,1,74,47,0.26,2.66,,0,4,0,779,5,77,,, +sherdbi01,1918,1,SLN,NL,6,12,35,16,9,1,0,547,174,55,3,49,40,0.25,2.71,,0,3,1,744,14,78,,, +shockur01,1918,1,SLA,AL,6,5,14,9,7,0,2,284,69,19,0,40,33,0.2,1.81,,2,1,0,377,5,26,,, +sislege01,1918,1,SLA,AL,0,0,2,1,0,0,1,24,10,4,0,4,4,0.28,4.5,,1,1,0,41,1,6,,, +slapncy01,1918,1,PIT,NL,1,4,7,6,4,0,1,148,50,26,2,22,3,0.26,4.74,,1,5,0,220,1,33,,, +smithge02,1918,1,CIN,NL,2,3,10,6,4,1,0,166,71,25,3,11,19,0.32,4.07,,2,0,0,239,3,36,,, +smithge02,1918,2,NY1,NL,2,3,5,2,1,0,0,80,26,12,0,6,4,0.25,4.05,,2,1,0,111,2,12,,, +smithge02,1918,3,BRO,NL,4,1,8,5,4,0,0,150,43,13,0,5,18,0.24,2.34,,2,2,0,185,3,14,,, +sothoal01,1918,1,SLA,AL,12,12,29,24,14,2,0,627,152,45,3,67,71,0.2,1.94,,0,3,0,832,4,64,,, +steelbo01,1918,1,PIT,NL,2,3,10,4,2,1,1,147,44,18,2,17,21,0.24,3.31,,0,2,0,209,3,25,,, +steelbo01,1918,2,NY1,NL,3,5,12,7,5,1,1,198,56,19,1,11,24,0.22,2.59,,3,3,0,270,3,29,,, +tesreje01,1918,1,NY1,NL,4,4,12,9,3,1,0,221,61,19,1,21,31,0.22,2.32,,2,0,0,298,1,27,,, +thormha01,1918,1,NYA,AL,7,3,16,12,5,2,0,338,85,31,1,52,22,0.21,2.48,,4,6,0,455,2,39,,, +tincube01,1918,1,PHI,NL,0,1,8,1,0,0,0,50,24,14,0,6,6,0.32,7.56,,1,0,0,82,6,20,,, +toneyfr01,1918,1,CIN,NL,6,10,21,19,9,1,2,410,148,44,2,31,32,0.28,2.9,,1,0,1,571,2,61,,, +toneyfr01,1918,2,NY1,NL,6,2,11,9,7,1,1,256,55,16,1,7,19,0.19,1.69,,0,2,0,307,2,19,,, +tueroos01,1918,1,SLN,NL,1,2,11,3,2,0,0,133,32,5,0,10,13,0.2,1.02,,1,3,0,174,8,12,,, +tylerle01,1918,1,CHN,NL,19,8,33,30,22,6,1,808,218,60,1,67,102,0.22,2,,1,5,0,1066,3,72,,, +uphambi01,1918,1,BSN,NL,1,1,3,2,2,0,0,62,28,12,2,1,8,0.32,5.23,,0,0,0,90,1,14,,, 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+bushjo01,1919,1,BOS,AL,0,0,3,2,0,0,0,27,11,5,0,4,3,0.32,5,,1,0,0,38,0,5,,, +cadorle01,1919,1,BRO,NL,14,12,35,27,16,3,0,752,228,66,5,39,94,0.24,2.37,,3,6,0,996,7,80,,, +caldwra02,1919,1,BOS,AL,7,4,18,12,6,1,0,259,92,38,1,31,23,0.27,3.96,,2,3,0,363,5,66,,, +caldwra02,1919,2,CLE,AL,5,1,6,6,4,1,0,158,33,10,1,19,24,0.18,1.71,,1,2,0,203,0,18,,, +cantwmi01,1919,1,PHI,NL,1,3,5,3,2,0,0,82,36,17,1,9,6,0.34,5.6,,0,2,0,119,2,19,,, +carlsha01,1919,1,PIT,NL,8,10,22,14,7,1,0,423,114,35,0,39,49,0.24,2.23,,1,2,0,531,7,41,,, +cartepa01,1919,1,CHN,NL,5,4,28,7,2,0,1,255,81,25,1,28,17,0.25,2.65,,0,2,0,359,20,36,,, +causere01,1919,1,NY1,NL,9,3,19,16,6,0,0,315,99,43,5,38,25,0.25,3.69,,0,2,0,444,2,52,,, +causere01,1919,2,BSN,NL,4,5,10,10,3,0,0,207,81,35,1,20,14,0.3,4.57,,1,1,0,291,0,38,,, +chenela01,1919,1,BRO,NL,1,3,9,4,2,0,0,117,45,18,1,14,14,0.3,4.15,,2,2,0,173,2,21,,, +chenela01,1919,2,BSN,NL,0,2,8,2,0,0,0,99,35,13,0,15,13,0.29,3.55,,3,0,0,136,4,20,,, +chenela01,1919,3,PHI,NL,2,5,9,6,5,0,0,172,69,29,2,28,25,0.31,4.55,,3,1,0,261,2,34,,, +cicoted01,1919,1,CHA,AL,29,7,40,35,30,5,1,920,256,62,5,49,110,0.22,1.82,,1,2,0,1176,5,77,,, +coopewi01,1919,1,PIT,NL,19,13,35,32,27,4,1,860,229,85,10,74,106,0.22,2.67,,5,15,0,1147,3,97,,, +coumbfr01,1919,1,CLE,AL,1,1,8,2,0,0,1,71,32,14,2,9,7,0.34,5.32,,0,0,0,101,4,15,,, +courtha01,1919,1,WS1,AL,3,0,4,3,2,1,0,79,25,8,0,19,6,0.26,2.73,,0,0,0,112,1,9,,, +covelst01,1919,1,CLE,AL,24,12,43,34,24,4,4,858,286,83,2,60,118,0.26,2.61,,5,5,0,1138,9,99,,, +craftmo01,1919,1,WS1,AL,0,3,16,2,0,0,1,146,59,21,2,18,17,0.3,3.88,,1,2,0,211,9,28,,, +cunnige01,1919,1,DET,AL,1,1,17,0,0,0,1,143,54,26,0,15,11,0.29,4.91,,2,5,0,205,15,36,,, +danfoda01,1919,1,CHA,AL,1,2,15,1,0,0,1,125,58,36,1,20,17,0.33,7.78,,0,1,0,195,9,44,,, +daussho01,1919,1,DET,AL,21,9,34,32,22,2,0,769,262,101,9,63,73,0.26,3.55,,1,5,0,1049,1,125,,, +davenda01,1919,1,SLA,AL,2,11,24,16,5,0,0,370,135,54,4,41,37,0.28,3.94,,1,2,0,525,3,74,,, +demaral01,1919,1,BSN,NL,6,6,25,13,6,0,3,384,147,54,8,35,34,0.3,3.8,,2,1,1,544,11,66,,, +doakbi01,1919,1,SLN,NL,13,14,31,29,13,3,0,608,182,70,5,55,69,0.24,3.11,,5,2,0,822,2,87,,, +douglph01,1919,1,CHN,NL,10,6,25,19,8,4,0,485,133,36,0,34,63,0.23,2,,3,2,1,641,5,52,,, +douglph01,1919,2,NY1,NL,2,4,8,6,4,0,0,154,53,12,0,6,21,0.26,2.1,,0,1,0,216,2,22,,, +dubucje01,1919,1,NY1,NL,6,4,36,5,1,0,3,396,119,39,4,37,32,0.24,2.66,,1,2,0,542,22,49,,, +dumonge01,1919,1,BOS,AL,0,4,13,2,0,0,0,106,45,17,1,19,12,0.32,4.33,,5,1,0,158,5,21,,, +eckerch01,1919,1,PHA,AL,0,1,2,1,1,0,0,48,17,7,1,3,6,0.27,3.94,,0,0,0,66,1,9,,, +ehmkeho01,1919,1,DET,AL,17,10,33,31,20,2,0,746,255,88,5,107,79,0.27,3.18,,3,6,0,1043,2,114,,, +ellerho01,1919,1,CIN,NL,19,9,38,30,16,7,2,745,216,66,7,50,137,0.23,2.39,,0,4,1,987,8,80,,, +engeljo01,1919,1,CLE,AL,0,0,1,0,0,0,0,0,0,0,0,3,0,0,,,0,0,0,3,0,3,,, +enzmajo01,1919,1,CLE,AL,3,2,14,4,2,0,0,166,67,14,0,8,13,0.31,2.28,,2,2,0,225,4,29,,, +ericker01,1919,1,DET,AL,0,2,3,2,0,0,0,44,17,11,0,10,4,0.29,6.75,,3,1,0,69,1,17,,, +ericker01,1919,2,WS1,AL,6,11,20,15,7,1,0,396,130,58,7,63,86,0.25,3.95,,5,7,1,581,5,69,,, +evansbi01,1919,1,PIT,NL,0,4,7,3,2,0,0,110,41,23,1,18,15,0.29,5.65,,0,0,0,166,3,25,,, +faberre01,1919,1,CHA,AL,11,9,25,20,9,0,0,487,185,69,7,45,45,0.28,3.83,,2,8,0,718,3,92,,, +faethto01,1919,1,CLE,AL,0,0,6,0,0,0,0,55,13,1,0,10,7,0.22,0.49,,0,0,0,68,4,4,,, +faircra01,1919,1,PHI,NL,0,0,2,0,0,0,0,6,5,2,0,0,0,0.62,9,,1,0,0,10,2,2,,, +fillida01,1919,1,BSN,NL,6,13,32,19,9,0,2,559,185,70,2,39,50,0.27,3.38,,1,2,0,774,12,80,,, +fishecl01,1919,1,WS1,AL,0,0,2,0,0,0,0,12,8,6,0,3,1,0.42,13.5,,0,0,0,22,1,6,,, +fishera01,1919,1,CIN,NL,14,5,26,20,12,5,1,523,141,42,5,38,41,0.22,2.17,,1,1,1,682,3,55,,, +gallibe01,1919,1,SLA,AL,12,14,34,25,14,1,1,667,220,89,10,92,83,0.26,3.6,,5,8,1,932,9,106,,, +gearybo01,1919,1,PHA,AL,0,3,9,2,1,0,0,97,32,17,1,18,9,0.26,4.73,,0,0,1,139,5,22,,, 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+hastybo01,1920,1,PHA,AL,1,3,19,4,1,0,0,215,91,40,5,28,12,0.32,5.02,,2,0,2,310,10,53,,, +hearnbu01,1920,1,BSN,NL,0,3,11,4,2,0,0,129,54,27,3,11,9,0.32,5.65,,0,1,0,189,7,34,,, +heathsp01,1920,1,CHA,AL,0,0,4,0,0,0,0,21,19,12,1,2,0,0.47,15.43,,2,0,0,42,1,12,,, +heimafr01,1920,1,PHA,AL,0,1,1,1,0,0,0,15,13,8,0,1,0,0.54,14.4,,0,0,1,25,0,9,,, +hendrcl01,1920,1,CHN,NL,9,12,27,23,12,0,0,611,216,81,6,54,72,0.27,3.58,,4,3,0,872,2,101,,, +hodgesh01,1920,1,CHA,AL,1,1,4,2,1,0,0,59,15,5,0,12,5,0.22,2.29,,0,0,0,79,0,14,,, +hoytwa01,1920,1,BOS,AL,6,6,22,11,6,2,1,364,123,59,2,47,45,0.27,4.38,,3,1,0,548,10,72,,, +hubbebi01,1920,1,NY1,NL,0,1,14,0,0,0,2,90,26,7,2,15,8,0.23,2.1,,0,1,2,130,8,12,,, +hubbebi01,1920,2,PHI,NL,9,9,24,20,9,1,2,450,176,64,3,42,26,0.3,3.84,,4,4,1,660,3,77,,, +jacobel01,1920,1,SLN,NL,4,8,23,9,1,0,1,233,91,45,2,33,21,0.29,5.21,,4,5,1,359,7,56,,, +jaegejo01,1920,1,CHN,NL,0,0,2,0,0,0,0,9,6,4,0,4,0,0.5,12,,0,0,0,18,1,6,,, +johnswa01,1920,1,WS1,AL,8,10,21,15,12,4,3,431,135,50,5,27,78,0.24,3.13,,5,5,1,594,6,68,,, +jonesjo01,1920,1,BSN,NL,1,0,3,1,0,0,0,29,16,7,1,5,6,0.37,6.52,,0,0,0,48,2,7,,, +jonespe01,1920,1,CHN,NL,0,0,4,0,0,0,0,21,15,9,1,3,0,0.45,11.57,,0,1,0,37,4,10,,, +jonessa01,1920,1,BOS,AL,13,16,37,33,21,3,0,822,302,120,9,79,86,0.28,3.94,,6,4,2,1149,4,143,,, +karrbe01,1920,1,BOS,AL,3,8,26,2,0,0,1,275,109,49,3,24,21,0.3,4.81,,1,1,0,390,20,55,,, +keefeda01,1920,1,PHA,AL,6,7,31,13,7,1,0,391,129,43,2,30,41,0.26,2.97,,1,6,1,537,15,60,,, +keenaji02,1920,1,PHI,NL,0,0,1,0,0,0,0,9,3,1,0,1,2,0.33,3,,0,0,0,12,1,1,,, +kerrdi01,1920,1,CHA,AL,21,9,45,28,19,3,5,761,266,95,7,72,72,0.27,3.37,,3,4,1,1045,13,116,,, +kiefejo01,1920,1,CHA,AL,0,1,2,1,0,0,0,14,7,8,0,5,1,0.33,15.43,,0,1,0,27,1,8,,, +kimeha01,1920,1,SLN,NL,0,0,4,0,0,0,0,21,9,2,0,2,1,0.33,2.57,,0,1,0,30,2,4,,, +kinnewa01,1920,1,PHA,AL,2,4,10,8,5,1,0,183,59,21,3,28,19,0.26,3.1,,0,1,0,255,2,38,,, +kirchmi01,1920,1,SLN,NL,2,1,9,3,1,0,0,110,50,22,0,5,5,0.33,5.4,,0,2,0,161,6,23,,, +knowlbi01,1920,1,PHA,AL,0,1,1,1,0,0,0,17,9,3,0,3,5,0.34,4.76,,0,3,0,32,0,9,,, +leifile01,1920,1,SLA,AL,0,0,4,0,0,0,0,27,17,7,0,3,3,0.4,7,,1,0,0,45,3,12,,, +leonadu01,1920,1,DET,AL,10,17,28,27,10,3,0,574,192,92,8,63,76,0.27,4.33,,13,8,0,781,1,107,,, +leverho01,1920,1,SLA,AL,0,2,3,2,0,0,0,31,9,6,1,12,0,0.25,5.23,,0,0,0,48,1,6,,, +lovesl01,1920,1,DET,AL,0,0,1,0,0,0,0,13,6,4,0,4,2,0.37,8.31,,2,0,0,20,0,4,,, +lowdegr01,1920,1,CHA,AL,0,0,3,0,0,0,0,16,9,4,0,5,0,0.4,6.75,,1,0,0,27,2,4,,, +lowege01,1920,1,CIN,NL,0,0,1,0,0,0,0,6,1,0,0,1,0,0.16,0,,0,0,0,7,1,0,,, +luquedo01,1920,1,CIN,NL,13,9,37,23,10,1,1,623,168,58,5,60,72,0.22,2.51,,2,4,0,825,11,65,,, +lynchad01,1920,1,SLA,AL,2,0,5,3,1,0,0,67,23,13,1,17,8,0.27,5.24,,1,1,0,109,2,15,,, +lyonsge01,1920,1,SLN,NL,2,1,7,2,1,0,0,70,21,8,2,9,5,0.26,3.09,,0,1,0,93,2,8,,, +mailsdu01,1920,1,CLE,AL,7,0,9,8,6,2,0,190,54,13,1,18,25,0.23,1.85,,1,0,0,253,1,18,,, +mamaual01,1920,1,BRO,NL,12,8,41,18,9,2,4,572,172,57,2,63,101,0.25,2.69,,3,4,1,764,17,70,,, +marquru01,1920,1,BRO,NL,10,7,28,26,10,1,0,569,181,68,5,35,89,0.25,3.23,,5,1,0,779,1,83,,, +martipa01,1920,1,PHA,AL,1,4,8,5,2,0,0,97,48,22,2,25,14,0.36,6.12,,2,4,1,161,0,36,,, +martisp01,1920,1,CHN,NL,4,15,35,13,6,0,2,408,165,73,2,50,44,0.3,4.83,,5,1,0,618,10,96,,, +mayja01,1920,1,SLN,NL,1,4,16,5,3,0,0,212,65,24,0,37,33,0.25,3.06,,2,7,0,317,4,38,,, +maysca01,1920,1,NYA,AL,26,11,45,37,26,6,2,936,310,106,13,84,92,0.26,3.06,,2,7,0,1301,8,127,,, +mcgrabo01,1920,1,NYA,AL,0,0,15,0,0,0,0,81,24,14,1,20,11,0.24,4.67,,1,1,0,123,13,18,,, +mcquihu01,1920,1,BSN,NL,11,15,38,27,17,1,5,677,230,89,3,70,53,0.27,3.55,,4,2,1,950,9,110,,, +meadojo01,1920,1,PIT,NL,0,2,12,2,0,0,0,109,48,17,1,7,5,0.34,4.21,,1,0,0,153,3,18,,, +meadole01,1920,1,PHI,NL,16,14,35,33,19,3,0,741,249,78,5,90,95,0.27,2.84,,3,8,0,1043,1,104,,, +miljujo01,1920,1,BRO,NL,1,0,9,0,0,0,0,70,24,8,2,4,9,0.26,3.09,,1,0,1,99,8,10,,, +mitchcl01,1920,1,BRO,NL,5,2,19,7,3,1,1,236,85,27,1,23,18,0.28,3.09,,0,0,1,329,5,35,,, +mogrige01,1920,1,NYA,AL,5,9,26,15,7,0,1,376,146,60,4,36,35,0.28,4.31,,2,3,0,550,6,83,,, +moharge01,1920,1,BRO,NL,0,1,13,1,0,0,0,107,33,7,0,7,13,0.25,1.77,,2,3,0,145,8,17,,, +moorero01,1920,1,PHA,AL,1,13,24,14,5,0,0,398,161,69,6,64,45,0.31,4.68,,2,3,0,580,8,89,,, +morribi01,1920,1,DET,AL,1,1,8,3,1,0,0,81,25,13,0,19,15,0.24,4.33,,0,3,0,126,4,21,,, +morrijo04,1920,1,PIT,NL,1,0,2,1,1,1,0,21,4,0,0,1,3,0.16,0,,0,0,0,25,1,0,,, +mortogu01,1920,1,CLE,AL,8,6,29,17,6,1,1,411,140,68,2,57,72,0.27,4.47,,5,1,0,584,8,80,,, +murchti01,1920,1,CLE,AL,0,0,2,0,0,0,0,15,3,0,0,4,0,0.2,0,,0,0,0,19,1,1,,, +myersel01,1920,1,CLE,AL,2,4,16,7,2,0,1,215,93,38,1,23,16,0.31,4.77,,2,4,0,321,2,52,,, +myersel01,1920,2,BOS,AL,9,1,12,10,9,1,0,291,90,23,1,24,34,0.24,2.13,,0,2,0,402,2,30,,, +napiebu01,1920,1,CIN,NL,4,2,9,5,5,1,0,147,47,7,0,7,17,0.25,1.29,,0,1,0,196,4,12,,, 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+slappjo01,1920,1,PHA,AL,0,1,3,1,0,0,0,19,15,5,0,4,1,0.44,7.11,,0,0,0,38,1,12,,, +smithge02,1920,1,PHI,NL,13,18,43,28,10,1,2,752,265,96,10,51,51,0.28,3.45,,1,6,0,1037,13,115,,, +smithsh01,1920,1,BRO,NL,11,9,33,12,6,2,3,409,134,28,1,27,33,0.26,1.85,,1,2,0,552,15,42,,, +snydebi01,1920,1,WS1,AL,2,1,16,4,1,0,1,162,59,25,1,28,17,0.28,4.17,,5,6,0,245,6,33,,, +sothoal01,1920,1,SLA,AL,8,15,36,26,12,1,2,655,263,114,6,89,81,0.3,4.7,,10,6,0,975,10,151,,, +swartmo01,1920,1,CIN,NL,0,1,1,1,1,0,0,36,17,6,0,2,2,0.33,4.5,,1,0,0,53,0,6,,, +theisja01,1920,1,CIN,NL,0,0,1,0,0,0,0,6,1,0,0,3,0,0.14,0,,0,0,0,10,1,0,,, +thormha01,1920,1,NYA,AL,9,6,29,15,6,0,1,430,178,66,5,43,35,0.31,4.14,,1,2,0,621,7,86,,, +toneyfr01,1920,1,NY1,NL,21,11,42,37,17,4,1,835,266,82,8,57,81,0.25,2.65,,6,6,0,1120,5,101,,, +townsir01,1920,1,BSN,NL,0,0,4,1,0,0,0,20,10,1,0,2,1,0.37,1.35,,0,1,0,30,2,3,,, +townsle01,1920,1,BSN,NL,2,2,7,1,1,0,0,73,18,4,1,2,0,0.22,1.48,,1,0,0,87,6,4,,, +tueroos01,1920,1,SLN,NL,0,0,2,0,0,0,0,2,5,4,0,1,0,0.83,54,,0,0,0,8,0,4,,, +turnete02,1920,1,CHN,NL,0,0,1,0,0,0,0,4,2,2,0,1,0,0.4,13.5,,0,0,0,6,0,2,,, +tylerle01,1920,1,CHN,NL,11,12,27,27,18,2,0,579,193,71,6,57,57,0.26,3.31,,2,3,0,803,0,83,,, +uhlege01,1920,1,CLE,AL,4,5,27,6,2,0,1,254,98,49,3,29,27,0.29,5.21,,2,8,0,381,11,52,,, +vangiel01,1920,1,SLA,AL,3,8,24,13,4,0,0,314,131,64,7,40,25,0.31,5.5,,0,3,1,466,8,83,,, +vaughhi01,1920,1,CHN,NL,19,16,40,38,24,4,0,903,301,85,8,81,131,0.26,2.54,,3,8,1,1255,2,113,,, +veddelo01,1920,1,DET,AL,0,0,1,0,0,0,0,6,0,0,0,0,1,0,0,,0,0,0,6,1,0,,, +watsomu01,1920,1,BSN,NL,0,0,1,0,0,0,0,9,0,0,0,0,0,0,0,,0,0,0,9,1,0,,, +watsomu01,1920,2,PIT,NL,0,0,5,0,0,0,0,34,15,11,2,7,1,0.32,8.74,,0,0,0,55,2,11,,, +watsomu01,1920,3,BSN,NL,5,4,12,10,4,2,0,215,79,30,0,17,16,0.29,3.77,,1,1,0,296,2,33,,, +weilmca01,1920,1,SLA,AL,9,13,30,24,13,1,2,550,201,91,6,61,45,0.29,4.47,,2,3,2,772,3,103,,, 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+alexape01,1921,1,CHN,NL,15,13,31,29,21,3,1,756,286,95,10,33,77,0.29,3.39,,1,1,0,1033,1,110,,, +ayersdo01,1921,1,DET,AL,0,0,2,1,0,0,0,12,9,4,0,2,0,0.45,9,,0,0,1,23,1,6,,, +bagbyji01,1921,1,CLE,AL,14,12,40,26,13,0,4,575,238,100,14,44,37,0.3,4.7,,0,4,0,841,13,112,,, +bailebi01,1921,1,SLN,NL,2,5,19,6,3,1,0,222,95,35,1,22,20,0.33,4.26,,4,2,1,330,3,41,,, +bailesw01,1921,1,CHN,NL,0,0,3,0,0,0,0,15,6,2,0,2,2,0.3,3.6,,0,1,0,24,2,2,,, +bailesw01,1921,2,BRO,NL,0,0,7,0,0,0,0,73,35,14,1,7,6,0.36,5.18,,1,1,0,107,4,15,,, +barneje01,1921,1,NY1,NL,15,9,42,31,15,1,6,776,298,89,13,44,56,0.29,3.1,,2,3,0,1076,6,108,,, +barrebi02,1921,1,PHA,AL,1,0,4,0,0,0,0,15,2,4,0,9,2,0.13,7.2,,1,0,0,25,2,4,,, +baumgst01,1921,1,PHI,NL,3,6,22,7,2,0,0,200,103,52,8,22,13,0.35,7.02,,0,2,0,329,11,72,,, +baynebi01,1921,1,SLA,AL,11,5,47,14,6,1,3,492,167,86,8,80,82,0.27,4.72,,1,5,2,727,14,103,,, +behanpe01,1921,1,PHI,NL,0,1,2,2,1,0,0,32,17,7,0,1,3,0.35,5.91,,0,0,0,50,0,8,,, +bentoru01,1921,1,NY1,NL,5,2,18,9,3,1,0,216,72,23,2,17,11,0.26,2.88,,2,0,0,297,4,28,,, +bettshu01,1921,1,PHI,NL,3,7,32,2,1,0,4,302,141,50,8,14,28,0.33,4.47,,3,4,0,455,22,65,,, +bigbely01,1921,1,PIT,NL,0,0,5,0,0,0,0,24,4,1,0,4,1,0.15,1.13,,0,0,0,30,5,1,,, +birdre01,1921,1,WS1,AL,0,0,1,0,0,0,0,15,5,3,0,1,2,0.29,5.4,,0,1,0,21,1,3,,, +bishobi02,1921,1,PHA,AL,0,0,2,0,0,0,0,21,8,7,0,10,4,0.26,9,,1,0,0,41,1,9,,, +blackch01,1921,1,CHA,AL,0,0,1,0,0,0,0,3,0,0,0,1,0,0,0,,0,0,0,3,1,0,,, +boehlge01,1921,1,SLA,AL,0,0,1,0,0,0,0,3,1,0,0,0,0,0.5,0,,0,0,0,3,1,0,,, +bolanbe01,1921,1,SLA,AL,1,4,7,6,0,0,0,81,34,28,2,28,6,0.3,9.33,,0,2,0,149,1,36,,, +booneda01,1921,1,DET,AL,0,0,1,0,0,0,0,6,1,0,0,2,0,0.2,0,,0,0,0,9,1,1,,, +braxtga01,1921,1,BSN,NL,1,3,17,2,0,0,0,112,44,20,0,17,16,0.31,4.82,,0,2,0,165,8,26,,, +brently01,1921,1,CIN,NL,1,8,17,9,2,0,1,180,80,27,0,17,19,0.34,4.05,,0,1,2,265,7,35,,, +burwebi01,1921,1,SLA,AL,2,4,33,3,1,0,2,253,102,48,2,29,17,0.3,5.12,,1,2,2,378,21,62,,, 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+greenke01,1927,2,BSN,NL,11,14,27,26,11,1,0,570,203,81,3,59,59,0.28,3.84,,2,5,0,817,1,92,,, +grimebu01,1927,1,NY1,NL,19,8,39,34,15,2,2,779,274,102,12,87,102,0.27,3.54,,4,4,0,1103,4,116,,, +grovele01,1927,1,PHA,AL,20,13,51,28,14,1,9,787,251,93,6,79,174,0.25,3.19,,5,2,1,1106,18,116,,, +hadlebu01,1927,1,WS1,AL,14,6,30,27,13,0,0,596,177,63,2,86,60,0.24,2.85,,4,9,1,843,2,72,,, +haineje01,1927,1,SLN,NL,24,10,38,36,25,6,1,902,273,91,11,77,89,0.24,2.72,,1,5,0,1228,1,114,,, +hankido01,1927,1,DET,AL,2,1,20,1,0,0,2,128,67,30,1,13,10,0.38,6.33,,0,0,0,198,9,39,,, +harrisl01,1927,1,BOS,AL,14,21,44,27,11,1,1,653,253,101,8,66,77,0.29,4.18,,1,9,0,962,13,127,,, +hearnbu02,1927,1,BSN,NL,0,2,8,0,0,0,0,38,16,6,0,9,5,0.32,4.26,,1,0,0,59,3,9,,, +henrydu01,1927,1,NY1,NL,11,6,45,15,7,1,4,491,184,77,6,31,40,0.27,4.23,,2,0,0,711,17,93,,, +hillca01,1927,1,PIT,NL,22,11,43,31,22,2,3,833,260,100,12,80,95,0.24,3.24,,2,4,0,1165,7,125,,, +hollamu01,1927,1,NY1,NL,1,0,2,0,0,0,0,6,0,0,0,3,0,0,0,,1,0,0,9,2,0,,, +holloke01,1927,1,DET,AL,11,12,36,23,11,1,6,550,210,83,10,61,36,0.29,4.07,,2,4,0,804,10,103,,, +hopkipa01,1927,1,WS1,AL,1,0,2,1,0,0,0,27,13,5,1,4,5,0.36,5,,0,0,0,42,1,6,,, +hoytwa01,1927,1,NYA,AL,22,7,36,32,23,3,1,769,242,75,10,54,86,0.25,2.63,,1,4,0,1045,4,90,,, +hudliwi01,1927,1,CLE,AL,18,12,43,30,18,1,0,794,291,118,3,83,65,0.28,4.01,,6,11,1,1156,9,132,,, +jacobel01,1927,1,CHA,AL,2,4,25,8,2,1,0,223,105,38,3,37,22,0.35,4.6,,0,4,1,353,10,49,,, +jeanete01,1927,1,NY1,NL,0,0,1,0,0,0,0,3,2,1,0,2,0,0.4,9,,1,0,0,7,1,1,,, +johnsar01,1927,1,NY1,NL,0,0,1,0,0,0,0,9,1,0,0,1,0,0.12,0,,0,0,0,11,0,1,,, +johnsau01,1927,1,DET,AL,0,0,1,0,0,0,0,3,1,1,0,1,1,0.33,9,,0,0,0,5,1,1,,, +johnsji02,1927,1,PHA,AL,4,2,17,3,2,0,0,155,42,20,2,16,16,0.23,3.48,,1,4,0,205,6,20,,, +johnssy01,1927,1,SLN,NL,0,0,2,0,0,0,0,9,3,2,1,0,2,0.25,6,,0,0,0,12,1,2,,, +johnswa01,1927,1,WS1,AL,5,6,18,15,7,1,0,323,113,61,7,26,48,0.27,5.1,,1,7,0,460,2,70,,, +jonesdi01,1927,1,WS1,AL,0,0,2,0,0,0,0,10,8,8,0,5,1,0.44,21.6,,0,0,0,24,0,11,,, +jonespe01,1927,1,CHN,NL,7,8,30,11,5,1,0,338,123,51,3,72,37,0.28,4.07,,2,6,0,529,11,67,,, +jonessa01,1927,1,SLA,AL,8,14,30,26,11,0,0,569,211,91,13,102,72,0.28,4.32,,3,3,0,877,2,121,,, +juddra01,1927,1,WS1,AL,0,0,1,0,0,0,1,12,8,3,0,2,2,0.4,6.75,,0,0,0,22,1,3,,, +karrbe01,1927,1,CLE,AL,3,3,22,5,1,0,2,230,92,43,5,32,17,0.31,5.05,,0,1,0,341,6,49,,, +kaufmto01,1927,1,CHN,NL,3,3,9,6,3,0,0,160,75,38,8,19,21,0.33,6.41,,1,4,0,254,1,44,,, +kaufmto01,1927,2,PHI,NL,0,3,5,5,1,0,0,56,37,22,2,8,4,0.42,10.61,,1,0,0,100,1,25,,, +kaufmto01,1927,3,SLN,NL,0,0,1,0,0,0,0,1,4,3,0,1,0,1,81,,0,0,0,5,0,3,,, +keenvi01,1927,1,SLN,NL,2,1,21,0,0,0,0,101,39,18,3,8,12,0.29,4.81,,2,2,0,152,7,21,,, +knighja01,1927,1,BSN,NL,0,0,3,0,0,0,0,9,6,5,0,2,0,0.42,15,,0,0,0,16,0,5,,, +kolpra01,1927,1,CIN,NL,3,3,24,5,2,1,3,247,86,28,5,29,28,0.27,3.06,,1,1,0,352,11,38,,, +kremera01,1927,1,PIT,NL,19,8,35,28,18,3,2,678,205,62,9,53,63,0.24,2.47,,2,0,0,927,6,73,,, +levsedu01,1927,1,CLE,AL,3,7,25,13,2,1,0,241,96,49,1,37,15,0.3,5.49,,0,2,0,365,4,54,,, +lisenho01,1927,1,WS1,AL,18,9,39,34,17,4,0,726,221,96,6,78,105,0.24,3.57,,0,3,0,1018,4,114,,, +littlca01,1927,1,SLN,NL,3,1,14,2,1,0,0,126,47,21,4,14,16,0.29,4.5,,1,0,0,178,10,21,,, +lucasre01,1927,1,CIN,NL,18,11,37,23,19,4,2,719,231,90,6,39,51,0.25,3.38,,0,0,0,974,11,96,,, +lundgde01,1927,1,BOS,AL,5,12,30,17,5,2,0,409,160,95,7,87,39,0.3,6.27,,7,4,2,640,7,100,,, +luquedo01,1927,1,CIN,NL,13,12,29,27,17,2,0,692,225,82,10,56,76,0.26,3.2,,6,0,0,949,1,103,,, +lyonste01,1927,1,CHA,AL,22,14,39,34,30,2,2,923,291,97,7,67,71,0.25,2.84,,2,0,1,1262,4,125,,, +macfada01,1927,1,BOS,AL,5,8,34,16,6,1,2,481,176,76,9,59,42,0.29,4.27,,0,6,0,692,15,88,,, +mahafro01,1927,1,PIT,NL,1,0,2,1,0,0,0,28,9,8,0,9,4,0.3,7.71,,1,0,0,45,1,8,,, +marbefi01,1927,1,WS1,AL,10,7,56,10,2,0,9,466,177,80,4,68,74,0.29,4.64,,2,3,0,694,30,92,,, +mayja01,1927,1,CIN,NL,15,12,44,28,17,2,1,707,242,92,4,70,121,0.27,3.51,,8,14,0,1003,10,110,,, 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+ellioju01,1930,1,BRO,NL,10,7,35,21,6,2,1,595,204,87,16,70,59,0.27,3.95,,2,5,1,849,8,100,,, +erickra01,1930,1,PIT,NL,1,0,7,0,0,0,0,42,21,11,1,10,2,0.37,7.07,,1,0,0,71,2,12,,, +faberre01,1930,1,CHA,AL,8,13,29,26,10,0,1,507,188,79,7,49,62,0.28,4.21,,3,5,0,742,2,101,,, +faulkji01,1930,1,BRO,NL,0,0,2,1,0,0,1,1,2,3,1,1,0,0.66,81,,0,0,0,4,1,3,,, +ferrewe01,1930,1,CLE,AL,25,13,43,35,25,1,3,890,299,109,14,106,143,0.26,3.31,,5,0,1,1274,8,141,,, +fischca01,1930,1,WS1,AL,1,1,8,4,1,0,1,100,37,18,0,18,21,0.28,4.86,,3,2,0,157,3,22,,, +fitzsfr01,1930,1,NY1,NL,19,7,41,29,17,1,1,673,230,106,26,59,76,0.26,4.25,,6,1,0,946,9,125,,, +frankfr02,1930,1,SLN,NL,2,3,8,1,0,0,0,59,31,16,1,11,4,0.37,7.32,,0,0,0,95,5,16,,, +frankfr02,1930,2,BSN,NL,7,6,27,11,3,1,0,332,138,69,13,43,30,0.31,5.61,,2,2,1,493,10,72,,, +frencla01,1930,1,PIT,NL,17,18,42,35,21,3,1,824,325,133,20,89,90,0.29,4.36,,3,6,0,1232,5,163,,, +freybe01,1930,1,CIN,NL,11,18,44,28,14,2,1,735,295,128,15,62,43,0.3,4.7,,6,3,1,1060,9,148,,, 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+hogsech01,1930,1,DET,AL,9,8,33,17,4,0,1,438,174,88,9,63,54,0.3,5.42,,0,9,0,670,11,102,,, +holloke01,1930,1,CLE,AL,1,1,12,2,0,0,2,90,49,28,5,14,8,0.37,8.4,,0,0,1,150,4,32,,, +holloke01,1930,2,NYA,AL,0,0,16,0,0,0,0,103,52,20,3,8,11,0.37,5.24,,2,0,0,156,6,23,,, +holshhe01,1930,1,SLA,AL,0,1,25,1,0,0,1,187,103,54,8,28,37,0.37,7.8,,0,3,0,314,12,63,,, +hoytwa01,1930,1,NYA,AL,2,2,8,7,2,0,0,143,64,24,7,9,10,0.31,4.53,,1,0,2,213,0,27,,, +hoytwa01,1930,2,DET,AL,9,8,26,20,8,1,4,407,176,72,7,47,25,0.31,4.78,,4,2,0,628,5,89,,, +hubbeca01,1930,1,NY1,NL,17,12,37,32,17,3,2,725,263,104,11,58,117,0.27,3.87,,3,11,0,1037,4,120,,, +hudliwi01,1930,1,CLE,AL,13,16,37,33,13,1,1,650,255,110,12,76,60,0.29,4.57,,1,1,2,964,4,131,,, +johnsha01,1930,1,NYA,AL,14,11,44,15,7,1,2,526,177,91,12,104,115,0.26,4.67,,11,2,1,791,19,112,,, +johnssi01,1930,1,CIN,NL,3,1,35,3,0,0,0,235,86,43,5,31,47,0.28,4.94,,2,4,0,345,17,54,,, +johnssy01,1930,1,SLN,NL,12,10,32,24,9,2,2,563,215,97,13,38,92,0.29,4.65,,2,4,0,793,6,105,,, +joneske01,1930,1,BSN,NL,0,1,8,1,0,0,0,59,28,13,1,4,4,0.35,5.95,,0,0,0,91,6,16,,, +jonespe01,1930,1,PIT,NL,0,1,9,2,0,0,0,57,26,14,3,11,3,0.32,6.63,,1,3,0,96,4,20,,, +jonessa01,1930,1,WS1,AL,15,7,25,25,14,1,0,550,195,83,4,61,60,0.27,4.07,,1,3,2,787,0,95,,, +juddra01,1930,1,NY1,NL,0,0,2,0,0,0,0,23,13,5,0,3,0,0.39,5.87,,0,0,0,39,1,8,,, +kaufmto01,1930,1,SLN,NL,0,1,2,1,0,0,0,31,15,9,2,4,2,0.35,7.84,,0,0,0,48,1,9,,, +kimsech01,1930,1,SLA,AL,6,10,42,4,1,0,1,340,139,80,8,45,32,0.31,6.35,,1,2,0,505,30,87,,, +klinebo01,1930,1,BOS,AL,0,0,1,0,0,0,0,3,1,0,0,0,0,0.33,0,,0,0,0,4,1,0,,, +koenima01,1930,2,DET,AL,0,1,2,1,0,0,0,27,11,10,0,8,6,0.31,10,,2,1,0,44,1,10,,, +kolpra01,1930,1,CIN,NL,7,12,37,19,5,2,3,505,180,79,10,34,40,0.27,4.22,,3,0,1,707,14,86,,, +koupalo01,1930,1,PHI,NL,0,4,13,4,1,0,0,110,52,35,4,17,11,0.34,8.59,,4,1,0,175,4,35,,, +kremera01,1930,1,PIT,NL,20,12,39,38,18,1,0,828,366,154,29,63,58,0.32,5.02,,3,1,0,1232,1,181,,, +langma01,1930,1,PIT,NL,0,0,2,0,0,0,0,5,9,10,2,3,2,0.69,54,,1,0,0,16,0,10,,, +lawsoro01,1930,1,CLE,AL,1,2,7,4,2,0,0,101,46,23,1,23,10,0.32,6.15,,1,0,0,170,2,27,,, +liebhgl02,1930,1,PHA,AL,0,1,5,0,0,0,0,27,14,11,2,8,2,0.35,11,,0,0,1,48,2,12,,, +lindsji01,1930,1,SLN,NL,7,5,39,6,3,0,5,317,131,52,6,46,50,0.31,4.43,,3,4,0,489,19,59,,, +lisenho01,1930,1,BOS,AL,10,17,37,31,15,0,0,712,254,116,20,86,47,0.28,4.4,,0,5,0,1028,5,130,,, +liskaad01,1930,1,WS1,AL,9,7,32,16,7,1,1,452,140,55,6,71,40,0.25,3.29,,0,5,0,648,8,69,,, +lucasra01,1930,1,NY1,NL,0,0,6,0,0,0,0,31,9,8,2,10,1,0.26,6.97,,1,1,0,49,2,8,,, +lucasre01,1930,1,CIN,NL,14,16,33,28,18,1,1,632,270,126,15,44,53,0.31,5.38,,0,1,2,927,4,135,,, +luquedo01,1930,1,BRO,NL,14,8,31,24,16,2,2,597,221,95,18,58,62,0.28,4.3,,5,0,0,849,5,107,,, +lyonste01,1930,1,CHA,AL,22,15,42,36,29,1,1,893,331,125,12,57,69,0.28,3.78,,2,2,1,1258,6,160,,, +macfada01,1930,1,BOS,AL,11,14,36,33,18,1,2,808,293,126,9,93,76,0.28,4.21,,2,6,0,1169,3,141,,, +mahafro01,1930,1,PHA,AL,9,5,33,16,6,0,0,458,186,85,16,53,38,0.29,5.01,,4,4,0,697,5,108,,, +mahonal01,1930,1,PHA,AL,0,0,3,0,0,0,0,13,11,11,0,7,0,0.57,22.85,,0,0,0,29,0,11,,, +malonpa02,1930,1,CHN,NL,20,9,45,35,22,1,4,815,290,119,14,96,142,0.27,3.94,,4,6,1,1197,8,145,,, +marbefi01,1930,1,WS1,AL,15,5,33,22,9,2,1,555,190,84,15,53,56,0.27,4.09,,0,0,0,774,9,92,,, +mayja01,1930,1,CIN,NL,3,11,26,18,5,1,0,337,147,72,6,41,44,0.32,5.77,,3,6,0,519,4,83,,, +mcafebi01,1930,1,CHN,NL,0,0,2,0,0,0,0,3,3,0,0,2,0,0.37,0,,1,0,0,10,1,5,,, +mcevolo01,1930,1,NYA,AL,1,3,28,1,0,0,3,157,64,39,4,29,14,0.28,6.71,,1,2,0,265,15,51,,, +mckaiha01,1930,1,CHA,AL,6,4,32,5,0,0,5,267,108,55,0,42,52,0.29,5.56,,2,3,0,416,19,67,,, +mcweedo01,1930,1,CIN,NL,0,2,8,2,0,0,0,77,28,21,0,20,10,0.28,7.36,,0,0,0,122,2,23,,, +meinehe01,1930,1,PIT,NL,6,8,20,16,4,0,1,352,168,80,6,44,18,0.34,6.14,,1,5,0,551,2,89,,, +milleja02,1930,1,CLE,AL,4,4,24,9,1,0,0,265,147,70,6,38,31,0.37,7.13,,1,4,1,451,6,89,,, +millijo02,1930,1,PHI,NL,1,2,9,2,1,0,0,85,26,10,0,21,7,0.25,3.18,,1,2,0,129,4,16,,, +mitchcl01,1930,1,SLN,NL,1,0,1,1,0,0,0,9,5,2,0,2,1,0.35,6,,0,0,0,16,0,2,,, +mitchcl01,1930,2,NY1,NL,10,3,24,16,5,0,0,387,151,57,10,36,40,0.29,3.98,,1,1,0,557,3,68,,, +mooreca01,1930,1,WS1,AL,0,0,4,0,0,0,0,35,9,3,0,4,2,0.22,2.31,,0,0,0,47,4,3,,, +moorecy01,1930,1,BRO,NL,0,0,1,0,0,0,0,0,2,0,0,0,0,1,,,0,0,0,2,0,1,,, +mooreji01,1930,1,CHA,AL,2,1,9,5,2,0,1,120,42,16,0,12,11,0.26,3.6,,0,0,0,177,2,18,,, +morrebi01,1930,1,NY1,NL,0,0,2,0,0,0,0,24,6,1,0,1,3,0.21,1.13,,0,0,0,29,1,1,,, +morried02,1930,1,BOS,AL,4,9,18,9,3,0,0,196,67,30,1,38,28,0.26,4.13,,0,0,0,307,6,42,,, +morrijo04,1930,1,BRO,NL,1,2,16,0,0,0,1,104,47,21,4,16,11,0.34,5.45,,0,0,0,159,12,29,,, +mosolji01,1930,1,PIT,NL,0,0,1,0,0,0,0,1,1,1,0,0,1,0.5,27,,0,0,0,2,1,1,,, +mossma01,1930,1,CHN,NL,0,0,12,1,0,0,1,56,18,13,0,14,4,0.25,6.27,,0,0,0,87,2,13,,, +mossra01,1930,1,BRO,NL,9,6,36,11,5,0,1,355,127,67,13,55,30,0.27,5.1,,1,4,0,538,10,78,,, +mulrofr01,1930,1,BOS,AL,0,1,2,0,0,0,0,9,3,1,0,0,2,0.27,3,,0,0,0,12,2,2,,, +nelsoly01,1930,1,CHN,NL,3,2,37,3,0,0,0,244,97,46,10,28,29,0.3,5.09,,2,6,0,365,16,52,,, +newsobo01,1930,1,BRO,NL,0,0,2,0,0,0,0,9,2,0,0,2,1,0.16,0,,1,0,0,14,2,2,,, +nichoch01,1930,1,PHI,NL,1,2,16,5,1,0,0,179,76,45,8,16,15,0.3,6.79,,0,2,0,273,3,51,,, +osborbo01,1930,1,CHN,NL,10,6,35,13,3,0,1,380,147,70,9,53,42,0.3,4.97,,1,1,0,563,15,74,,, +pageph01,1930,1,DET,AL,0,1,12,0,0,0,0,36,23,13,1,9,2,0.43,9.75,,1,0,0,64,5,16,,, +parmero01,1930,1,NY1,NL,0,1,11,1,0,0,0,63,18,22,3,26,19,0.22,9.43,,0,0,0,106,8,26,,, +pennohe01,1930,1,NYA,AL,11,7,25,19,11,1,0,469,194,75,8,20,46,0.3,4.32,,4,0,0,676,3,95,,, +perkich01,1930,1,PHA,AL,0,0,8,1,0,0,0,71,25,17,0,15,15,0.31,6.46,,3,0,1,102,4,20,,, +pettyje01,1930,1,PIT,NL,1,6,10,7,0,0,1,124,67,38,8,13,16,0.36,8.27,,0,2,0,204,3,43,,, +pettyje01,1930,2,CHN,NL,1,3,9,3,0,0,0,118,51,13,2,6,18,0.31,2.97,,0,0,0,172,1,18,,, +phelpra01,1930,1,BRO,NL,14,7,36,24,11,2,0,539,198,82,21,52,64,0.28,4.11,,1,3,0,779,5,98,,, +phillbu01,1930,1,PHI,NL,0,0,14,1,0,0,0,131,68,39,6,18,9,0.35,8.04,,1,1,0,216,8,44,,, +pipgrge01,1930,1,NYA,AL,15,15,44,30,15,3,4,663,230,101,9,70,111,0.26,4.11,,4,8,0,974,13,133,,, +pruethu01,1930,1,NY1,NL,5,4,45,8,1,0,3,407,152,72,11,63,49,0.28,4.78,,1,4,0,608,18,83,,, +quinnja01,1930,1,PHA,AL,9,7,35,7,0,0,6,269,109,44,6,22,28,0.3,4.42,,1,1,0,397,16,51,,, +rhemfl01,1930,1,SLN,NL,12,8,26,19,9,0,0,419,173,69,11,37,47,0.3,4.45,,6,3,0,626,5,90,,, +rhodego01,1930,1,NYA,AL,0,0,3,0,0,0,0,6,3,2,0,4,1,0.5,9,,1,0,0,10,1,3,,, +rixeyep01,1930,1,CIN,NL,9,13,32,21,5,0,0,492,207,93,11,47,37,0.31,5.1,,2,7,0,735,9,103,,, +rommeed01,1930,1,PHA,AL,9,4,35,9,5,0,3,391,142,62,11,27,35,0.27,4.28,,0,1,0,550,20,66,,, +rootch01,1930,1,CHN,NL,16,14,37,30,15,4,3,661,247,106,17,63,124,0.28,4.33,,1,7,0,967,6,122,,, +ruffire01,1930,1,BOS,AL,0,3,4,3,1,0,0,72,32,17,1,6,14,0.32,6.38,,0,1,2,114,1,19,,, 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+clarkwa02,1931,1,BRO,NL,14,10,34,28,16,3,1,700,243,83,4,52,96,0.26,3.2,,3,1,1,981,2,86,,, +coffmdi01,1931,1,SLA,AL,9,13,32,17,11,2,1,508,159,73,10,51,39,0.24,3.88,,2,2,0,720,8,81,,, +collior02,1931,1,DET,AL,0,1,2,2,0,0,0,31,17,9,0,7,3,0.36,7.84,,0,0,0,55,0,12,,, +colliph01,1931,1,PHI,NL,12,16,42,27,16,2,4,721,268,103,14,83,73,0.28,3.86,,1,5,0,1055,10,126,,, +colliri01,1931,1,SLA,AL,5,5,17,14,2,0,0,321,130,45,5,38,34,0.3,3.79,,1,1,0,466,1,55,,, +connasa01,1931,1,CLE,AL,5,5,17,9,5,0,1,257,87,40,7,50,37,0.25,4.2,,1,6,0,405,7,56,,, +coonebo01,1931,1,SLA,AL,0,3,5,4,1,0,0,118,46,18,1,20,13,0.29,4.12,,1,1,0,180,1,21,,, +craghho01,1931,1,CLE,AL,0,0,4,0,0,0,0,17,8,4,0,2,2,0.32,6.35,,0,0,0,27,3,4,,, +crowdal01,1931,1,WS1,AL,18,11,44,26,13,1,2,703,255,101,13,72,85,0.27,3.88,,4,1,0,1009,10,117,,, +cunnibr01,1931,1,BSN,NL,3,12,33,16,6,1,1,410,157,68,7,54,32,0.29,4.48,,1,2,0,603,16,74,,, +daype01,1931,1,BRO,NL,2,2,22,2,1,0,1,172,75,29,5,13,30,0.31,4.55,,3,0,0,254,7,38,,, +derripa01,1931,1,SLN,NL,18,8,35,23,15,4,2,635,225,79,9,65,134,0.27,3.36,,6,4,0,901,10,88,,, +donohpe01,1931,1,NY1,NL,0,1,4,1,0,0,0,34,14,7,1,4,4,0.31,5.56,,0,0,0,51,1,7,,, +donohpe01,1931,2,CLE,AL,0,0,2,0,0,0,0,16,9,5,1,5,4,0.42,8.44,,0,0,0,26,1,6,,, +doyleje01,1931,1,SLA,AL,0,0,1,0,0,0,0,3,3,3,0,1,0,0.5,27,,0,0,0,7,0,3,,, +dudlecl01,1931,1,PHI,NL,8,14,30,24,8,0,0,537,206,70,10,56,50,0.28,3.52,,7,6,3,793,3,95,,, +durhaed01,1931,1,BOS,AL,8,10,38,15,7,2,0,496,175,78,9,50,53,0.26,4.25,,1,4,0,716,11,91,,, +earnsge01,1931,1,PHA,AL,21,7,43,30,23,3,6,845,255,115,16,75,152,0.23,3.67,,5,3,0,1174,11,130,,, +eckeral01,1931,1,CIN,NL,0,1,14,1,0,0,0,56,26,19,3,9,5,0.32,9.16,,1,0,0,90,9,20,,, +ellioha01,1931,1,PHI,NL,0,2,16,4,0,0,2,99,46,35,5,19,8,0.33,9.55,,0,1,0,157,5,36,,, +ellioju01,1931,1,PHI,NL,19,14,52,30,12,2,5,747,288,118,15,83,99,0.28,4.27,,1,4,2,1098,16,138,,, +faberre01,1931,1,CHA,AL,10,14,44,19,5,1,1,552,210,78,11,57,49,0.28,3.82,,0,3,0,807,14,96,,, 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+newsobo01,1932,1,CHN,NL,0,0,1,0,0,0,0,3,1,0,0,0,0,0.33,0,,0,0,0,3,1,0,,, +nichoch01,1932,1,PHI,NL,0,2,11,0,0,0,1,58,23,15,2,14,5,0.29,6.98,,0,0,0,93,4,16,,, +ogdenja01,1932,1,CIN,NL,2,2,24,3,1,0,0,171,72,33,5,22,20,0.31,5.21,,2,0,0,259,12,40,,, +parmero01,1932,1,NY1,NL,0,3,8,3,0,0,0,76,25,11,0,14,23,0.25,3.91,,2,2,1,119,4,18,,, +pearsmo01,1932,1,CLE,AL,0,0,8,0,0,0,0,24,10,9,1,11,5,0.32,10.13,,1,0,0,43,5,9,,, +pennohe01,1932,1,NYA,AL,9,5,22,21,9,1,0,440,191,75,8,38,54,0.31,4.6,,4,0,0,664,1,94,,, +phelpra01,1932,1,BRO,NL,4,5,20,8,4,1,0,238,101,52,5,27,21,0.32,5.9,,1,3,0,353,5,58,,, +pipgred01,1932,1,BRO,NL,0,1,5,1,0,0,0,30,16,6,2,6,5,0.34,5.4,,0,0,0,52,2,11,,, +pipgrge01,1932,1,NYA,AL,16,9,32,27,14,2,0,657,235,102,15,87,111,0.26,4.19,,6,6,0,981,5,120,,, +pollilo01,1932,1,SLA,AL,0,0,5,0,0,0,0,20,13,4,0,3,5,0.4,5.4,,1,0,0,35,5,8,,, +poserbo01,1932,1,CHA,AL,0,0,1,0,0,0,0,2,3,2,0,2,1,0.6,27,,0,0,0,7,1,2,,, +pruethu01,1932,1,BSN,NL,1,5,18,7,4,0,0,189,76,36,3,30,27,0.3,5.14,,0,6,0,293,5,42,,, 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+rhodego01,1934,1,BOS,AL,12,12,44,31,10,0,2,657,247,111,10,98,79,0.28,4.56,,5,4,0,994,9,133,,, +richmbe01,1934,1,CIN,NL,1,2,6,2,1,0,0,58,23,8,0,10,9,0.3,3.72,,1,0,0,89,1,11,,, +rootch01,1934,1,CHN,NL,4,7,34,9,2,0,0,353,141,56,8,53,46,0.29,4.28,,2,5,0,538,14,62,,, +rowesc01,1934,1,DET,AL,24,8,45,30,20,3,1,798,259,102,12,81,149,0.25,3.45,,1,1,0,1111,13,110,,, +ruffire01,1934,1,NYA,AL,19,11,36,31,19,5,0,769,232,112,18,104,149,0.23,3.93,,1,1,0,1100,3,134,,, +russeja01,1934,1,WS1,AL,5,10,54,9,3,0,7,473,179,73,6,56,38,0.28,4.17,,2,2,2,692,20,86,,, +salveja01,1934,1,NY1,NL,3,1,12,4,0,0,0,115,43,15,2,13,18,0.28,3.52,,0,0,0,168,4,16,,, +schumha02,1934,1,NY1,NL,23,10,41,36,18,2,0,891,299,105,16,89,112,0.25,3.18,,10,2,0,1254,2,131,,, +shautjo01,1934,1,CIN,NL,0,2,8,1,0,0,1,52,19,8,1,3,2,0.26,4.15,,0,0,0,74,6,9,,, +smithal03,1934,1,NY1,NL,3,5,30,5,0,0,5,200,70,32,2,21,27,0.26,4.32,,1,0,1,289,15,40,,, +smithbo02,1934,1,BSN,NL,6,9,39,5,3,0,5,365,133,63,9,36,26,0.27,4.66,,0,0,1,526,25,69,,, 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+mungova01,1936,1,BRO,NL,18,19,45,37,22,2,3,935,275,116,8,118,238,0.23,3.35,,10,3,0,1313,8,137,,, +munnsle01,1936,1,SLN,NL,0,3,7,1,0,0,1,72,23,8,2,12,4,0.24,3,,2,0,0,109,5,18,,, +murphjo04,1936,1,NYA,AL,9,3,27,5,2,0,5,264,90,33,5,36,34,0.26,3.38,,1,1,0,382,12,38,,, +murraam01,1936,1,BSN,NL,0,0,4,1,0,0,0,33,15,5,1,3,2,0.31,4.09,,0,0,0,51,1,5,,, +naktepe01,1936,1,PHA,AL,0,1,7,1,0,0,0,56,24,26,2,27,18,0.32,12.54,,2,2,0,104,3,26,,, +nelsoem01,1936,1,CIN,NL,1,0,6,1,0,0,0,51,24,6,1,4,3,0.33,3.18,,0,1,0,78,3,8,,, +newsobo01,1936,1,WS1,AL,17,15,43,38,24,4,2,857,294,137,13,146,156,0.26,4.32,,8,3,1,1258,3,160,,, +olsonte01,1936,1,BOS,AL,1,1,5,3,1,0,0,55,24,15,3,8,5,0.32,7.36,,2,0,0,85,1,16,,, +osborwa01,1936,1,BSN,NL,1,1,5,3,0,0,0,60,31,13,1,9,8,0.35,5.85,,0,0,0,98,1,13,,, +osterfr01,1936,1,BOS,AL,10,16,43,23,7,1,2,542,210,98,8,84,90,0.28,4.88,,8,3,0,826,14,115,,, +parmero01,1936,1,SLN,NL,11,11,37,28,9,0,2,663,226,112,13,107,79,0.27,4.56,,8,10,0,971,6,125,,, 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+johnssy01,1937,1,PHI,NL,4,10,32,15,4,0,3,414,155,77,20,22,46,0.28,5.02,,1,2,0,574,10,81,,, +jorgeor01,1937,1,PHI,NL,3,4,52,11,1,0,3,422,159,69,12,68,34,0.29,4.41,,3,5,0,621,19,83,,, +jungeke01,1937,1,CLE,AL,0,0,2,0,0,0,0,9,3,0,0,1,0,0.27,0,,0,0,0,12,2,1,,, +kalfabi01,1937,1,PHA,AL,1,0,3,1,1,0,0,36,10,4,0,10,9,0.23,3,,0,0,0,54,2,4,,, +kelleha01,1937,1,PHA,AL,13,21,41,29,14,0,0,615,267,122,16,79,68,0.3,5.36,,6,3,0,964,8,154,,, +kelleha02,1937,1,PHI,NL,2,4,27,2,1,0,0,175,72,43,3,31,20,0.3,6.63,,0,7,0,277,11,51,,, +kenneve01,1937,1,CHA,AL,14,13,32,30,15,1,0,663,238,125,16,124,114,0.27,5.09,,8,3,0,1008,2,150,,, +kimbane01,1937,1,CHN,NL,0,0,2,0,0,0,0,15,12,6,1,1,0,0.44,10.8,,0,0,0,28,1,8,,, +kimbeha01,1937,1,SLA,AL,0,2,3,2,1,0,0,46,16,4,2,9,5,0.25,2.35,,1,0,0,72,1,13,,, +kleinnu01,1937,1,SLN,NL,1,1,5,2,1,0,0,62,25,11,0,7,9,0.32,4.79,,0,0,0,87,1,14,,, +kleinte01,1937,1,CIN,NL,1,2,7,3,1,0,0,82,29,7,1,12,13,0.27,2.3,,0,1,0,121,2,13,,, +knottja01,1937,1,SLA,AL,8,18,38,22,8,0,2,574,220,104,25,91,74,0.29,4.89,,3,5,0,866,10,117,,, +kohlmjo01,1937,1,WS1,AL,1,0,2,2,1,0,0,39,15,6,0,3,3,0.28,4.15,,0,0,0,56,0,7,,, +koupalo01,1937,1,SLA,AL,4,9,26,13,6,0,0,317,150,77,10,55,24,0.33,6.56,,1,0,0,505,9,87,,, +krakajo01,1937,1,WS1,AL,4,1,5,4,3,0,0,120,33,12,0,22,18,0.22,2.7,,0,0,0,168,1,14,,, +kristho01,1937,1,SLN,NL,3,1,6,4,1,0,0,83,34,13,0,10,6,0.3,4.23,,0,0,0,123,2,13,,, +lamaswa01,1937,1,PHI,NL,15,19,50,30,10,1,4,661,255,130,24,82,135,0.29,5.31,,1,2,0,974,11,139,,, +lanahdi01,1937,1,WS1,AL,0,1,6,2,0,0,0,34,16,16,2,13,2,0.32,12.71,,2,1,0,65,3,16,,, +lannijo01,1937,1,BSN,NL,5,7,32,11,4,1,2,350,107,51,10,40,37,0.23,3.93,,3,1,0,499,13,59,,, +lawsoro01,1937,1,DET,AL,18,7,37,29,15,0,1,652,236,127,17,115,68,0.27,5.26,,6,1,1,995,5,141,,, +leebi02,1937,1,CHN,NL,14,15,42,33,17,2,3,817,289,107,14,73,108,0.27,3.54,,2,0,0,1146,5,122,,, +leeth01,1937,1,CHA,AL,12,10,30,25,13,2,0,614,209,80,17,60,80,0.26,3.52,,1,1,0,876,4,91,,, +lindsji01,1937,1,BRO,NL,0,1,20,0,0,0,2,115,43,15,4,12,15,0.29,3.52,,1,1,0,164,11,22,,, +linkeed01,1937,1,WS1,AL,6,1,36,7,0,0,3,386,158,80,11,59,61,0.3,5.6,,2,4,1,591,18,89,,, +lipscni01,1937,1,SLA,AL,0,0,3,0,0,0,0,29,13,7,3,5,1,0.33,6.52,,0,0,0,45,3,9,,, +loganbo01,1937,1,DET,AL,0,0,1,0,0,0,0,2,1,0,0,1,1,0.33,0,,0,0,0,4,0,0,,, +loganbo01,1937,2,CHN,NL,0,0,4,0,0,0,1,19,6,1,0,4,2,0.26,1.42,,0,0,0,28,3,1,,, +lohrmbi01,1937,1,NY1,NL,1,0,2,1,1,0,1,30,5,1,0,2,3,0.15,0.9,,0,0,0,35,1,1,,, +lucasre01,1937,1,PIT,NL,8,10,20,20,9,1,0,379,150,60,12,23,20,0.29,4.27,,0,1,0,549,0,69,,, +lyonste01,1937,1,CHA,AL,12,7,22,22,11,0,0,508,182,78,21,45,45,0.27,4.15,,2,1,0,706,0,86,,, +macfada01,1937,1,BSN,NL,14,14,32,32,16,2,0,738,250,80,5,60,70,0.26,2.93,,0,2,0,1015,0,96,,, +makosfr01,1937,1,NYA,AL,5,2,26,1,1,0,3,174,64,32,6,24,27,0.27,4.97,,1,0,0,265,15,42,,, +malonpa02,1937,1,NYA,AL,4,4,28,9,3,0,6,276,109,56,5,35,49,0.29,5.48,,1,4,0,420,16,65,,, 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+lamaswa01,1938,2,BRO,NL,0,1,3,0,0,0,0,34,17,6,0,3,3,0.34,4.76,,0,0,0,53,2,6,,, +laniema01,1938,1,SLN,NL,0,3,18,3,1,0,0,135,57,21,1,28,14,0.31,4.2,,0,2,0,215,4,30,,, +lannijo01,1938,1,BSN,NL,8,7,32,18,4,1,0,414,146,57,5,52,39,0.26,3.72,,4,1,0,607,9,74,,, +lannito01,1938,1,PHI,NL,0,1,3,1,0,0,0,21,9,5,0,2,2,0.3,6.43,,0,0,0,33,1,7,,, +lawsoro01,1938,1,DET,AL,8,9,27,16,5,0,1,381,154,77,13,82,39,0.29,5.46,,2,0,0,610,8,85,,, +leebi02,1938,1,CHN,NL,22,9,44,37,19,9,2,873,281,86,18,74,121,0.25,2.66,,3,2,1,1194,5,95,,, +leeth01,1938,1,CHA,AL,13,12,33,30,18,1,1,736,252,95,12,94,77,0.26,3.49,,2,3,0,1074,3,123,,, +lefebbi01,1938,1,BOS,AL,0,0,1,0,0,0,0,12,8,6,2,0,0,0.4,13.5,,0,1,0,21,1,6,,, +leonadu02,1938,1,WS1,AL,12,15,33,31,15,3,0,670,221,85,11,53,68,0.25,3.43,,11,7,0,939,1,109,,, +liebhgl02,1938,1,SLA,AL,0,0,2,0,0,0,0,9,4,2,1,0,1,0.3,6,,0,0,0,13,1,2,,, +linkeed01,1938,1,SLA,AL,1,7,21,2,0,0,0,119,60,35,6,33,18,0.35,7.94,,1,0,0,206,12,37,,, 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+whiteea01,1938,1,CLE,AL,9,8,26,23,4,0,0,481,187,99,18,83,60,0.28,5.56,,4,9,0,746,1,109,,, +whitejo02,1938,1,CHA,AL,10,11,32,24,10,2,2,550,218,97,12,80,38,0.29,4.76,,1,3,0,830,6,108,,, +wickeke01,1938,1,NYA,AL,1,0,1,0,0,0,0,3,0,0,0,1,0,0,0,,0,0,0,4,0,0,,, +willial02,1938,1,PHA,AL,0,7,30,8,1,0,0,280,128,72,6,54,25,0.32,6.94,,4,1,1,456,7,93,,, +wilsoja01,1938,1,BOS,AL,15,15,37,27,11,3,1,584,200,93,16,91,96,0.26,4.3,,2,2,0,869,7,108,,, +winfoji01,1938,1,BRO,NL,0,1,2,1,0,0,0,17,9,7,1,4,4,0.34,11.12,,0,0,0,31,0,10,,, +wittijo01,1938,1,NY1,NL,2,3,13,6,2,0,0,118,41,21,4,26,14,0.26,4.81,,1,0,0,183,4,22,,, +zuberbi01,1938,1,CLE,AL,0,3,15,0,0,0,1,86,33,16,0,20,14,0.29,5.02,,1,0,0,134,6,18,,, +allenjo02,1939,1,CLE,AL,9,7,28,26,9,2,0,525,199,89,9,56,79,0.29,4.58,,5,3,2,763,2,96,,, +andrena01,1939,1,SLN,NL,1,2,11,1,0,0,0,48,24,12,0,12,6,0.34,6.75,,3,0,0,85,5,14,,, +applepe01,1939,1,WS1,AL,5,10,40,4,2,0,6,308,104,52,7,48,50,0.26,4.56,,2,4,0,459,27,62,,, 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+grovele01,1939,1,BOS,AL,15,4,23,23,17,2,0,573,180,54,8,58,81,0.24,2.54,,0,1,0,798,0,63,,, +gumbeha01,1939,1,NY1,NL,18,11,36,34,14,2,0,731,257,117,21,81,81,0.27,4.32,,5,1,1,1047,1,132,,, +hadlebu01,1939,1,NYA,AL,12,6,26,18,7,1,2,462,132,51,10,85,65,0.23,2.98,,4,3,0,658,6,62,,, +hafeybu01,1939,2,PHI,NL,0,0,2,0,0,0,0,4,7,5,0,1,1,0.7,33.75,,0,0,0,11,1,5,,, +hamlilu01,1939,1,BRO,NL,20,13,40,36,19,2,0,809,255,109,27,54,88,0.24,3.64,,2,0,0,1100,2,115,,, +hannilo01,1939,1,SLA,AL,0,1,4,1,0,0,0,30,6,4,1,4,8,0.15,3.6,,0,0,0,43,1,5,,, +hardeme01,1939,1,CLE,AL,15,9,29,26,12,1,1,624,213,81,15,64,67,0.26,3.5,,1,3,0,877,3,89,,, +harrera01,1939,1,CHN,NL,0,2,4,2,0,0,0,52,29,16,2,6,5,0.38,8.31,,1,0,1,82,0,16,,, +harrera01,1939,2,PHI,NL,3,7,22,10,4,0,0,284,101,57,6,56,35,0.27,5.42,,4,4,0,445,5,77,,, +harribo01,1939,1,DET,AL,1,1,5,1,0,0,0,54,18,8,4,8,9,0.26,4,,2,0,0,76,2,8,,, +harribo01,1939,2,SLA,AL,3,12,28,16,6,0,0,378,162,80,5,71,48,0.32,5.71,,8,0,0,591,5,88,,, 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+hollial01,1939,1,PHI,NL,1,9,15,10,3,0,0,180,78,39,2,27,24,0.31,5.85,,3,0,0,283,0,48,,, +hollial01,1939,2,BRO,NL,1,2,8,5,1,0,0,82,33,16,1,11,11,0.31,5.27,,0,1,0,122,2,17,,, +hubbeca01,1939,1,NY1,NL,11,9,29,18,10,0,2,462,150,47,11,24,62,0.24,2.75,,2,2,0,639,8,60,,, +hudliwi01,1939,1,CLE,AL,9,10,27,20,7,0,3,429,175,78,6,42,28,0.3,4.91,,3,1,0,643,5,85,,, +humphjo02,1939,1,CLE,AL,2,4,15,1,0,0,2,85,30,26,0,32,12,0.29,8.26,,0,1,0,144,6,30,,, +hutchfr01,1939,1,DET,AL,3,6,13,12,3,0,0,254,95,49,9,51,22,0.28,5.21,,1,0,0,391,0,56,,, +hutchir01,1939,1,BRO,NL,5,2,41,1,0,0,1,317,103,51,9,51,46,0.26,4.34,,1,1,1,459,21,54,,, +jacobar01,1939,1,CIN,NL,0,0,1,0,0,0,1,3,2,1,0,1,0,0.4,9,,0,0,0,6,1,1,,, +jacobbu01,1939,1,WS1,AL,0,0,2,0,0,0,0,9,1,0,0,0,1,0.1,0,,0,0,0,10,0,0,,, +jeffcge01,1939,1,BRO,NL,0,0,1,0,0,0,0,6,2,0,0,0,1,0.28,0,,0,0,0,8,1,0,,, +johnsfr01,1939,1,SLA,AL,0,1,5,2,1,0,0,42,23,10,0,9,2,0.38,6.43,,0,0,0,70,2,12,,, +johnsha01,1939,1,CIN,NL,0,3,20,0,0,0,1,94,30,7,1,13,10,0.26,2.01,,1,0,0,133,15,10,,, +johnssy01,1939,1,PHI,NL,8,8,22,14,6,0,2,333,112,47,10,15,37,0.26,3.81,,0,1,0,453,4,50,,, +joycebo01,1939,1,PHA,AL,3,5,30,6,1,0,0,323,156,80,13,37,25,0.33,6.69,,3,1,0,517,11,91,,, +kelleha01,1939,1,WS1,AL,4,3,15,3,2,0,1,161,69,28,2,14,20,0.31,4.7,,3,3,0,241,7,32,,, +kenneve01,1939,1,DET,AL,0,3,4,4,1,0,0,63,25,15,4,9,9,0.3,6.43,,0,1,0,95,0,15,,, +kenneve01,1939,2,SLA,AL,9,17,33,27,12,1,0,575,229,122,18,115,55,0.29,5.73,,3,1,0,906,5,130,,, +kerksbi01,1939,1,PHI,NL,0,2,23,2,1,0,0,188,81,50,13,32,13,0.32,7.18,,4,0,0,290,16,52,,, +kimbeha01,1939,1,SLA,AL,1,2,17,3,0,0,0,123,59,25,6,19,11,0.32,5.49,,0,2,0,205,6,35,,, +klingbo01,1939,1,PIT,NL,14,17,37,33,10,2,0,675,251,109,11,81,64,0.28,4.36,,6,3,0,989,3,120,,, +knottja01,1939,1,CHA,AL,11,6,25,23,8,0,0,449,157,69,13,41,56,0.26,4.15,,4,1,1,641,0,71,,, +krakajo01,1939,1,WS1,AL,11,17,39,29,12,0,1,652,230,111,13,114,110,0.27,4.6,,5,1,1,966,8,125,,, 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+fryech01,1940,1,PHI,NL,0,6,15,5,1,0,0,151,58,26,3,26,18,0.29,4.65,,0,0,1,228,6,32,,, +galehde01,1940,1,BOS,AL,6,6,25,20,5,0,0,360,155,69,10,41,53,0.31,5.18,,4,0,1,543,3,77,,, +gelbech01,1940,1,WS1,AL,0,0,2,0,0,0,0,12,5,4,2,3,1,0.27,9,,0,0,0,21,2,4,,, +giebefl01,1940,1,DET,AL,2,0,2,2,2,1,0,54,14,2,2,4,11,0.2,1,,1,0,0,73,0,2,,, +gomezle01,1940,1,NYA,AL,3,3,9,5,0,0,0,82,37,20,2,18,14,0.32,6.59,,3,1,0,134,3,20,,, +gorsijo01,1940,1,DET,AL,7,7,29,20,5,2,0,480,170,77,10,57,68,0.27,4.33,,2,4,1,692,2,85,,, +grissle01,1940,1,NYA,AL,0,0,5,0,0,0,0,14,4,0,0,2,1,0.25,0,,0,0,0,19,5,0,,, +grissle01,1940,2,BRO,NL,2,5,14,10,3,1,0,221,59,23,3,34,56,0.21,2.81,,1,0,0,311,1,30,,, +grovele01,1940,1,BOS,AL,7,6,22,21,9,1,0,460,159,68,20,50,62,0.26,3.99,,1,1,0,652,1,73,,, +groveor01,1940,1,CHA,AL,0,0,3,0,0,0,0,18,4,2,0,4,1,0.18,3,,0,0,0,26,3,2,,, +guisele01,1940,1,CIN,NL,0,0,2,0,0,0,0,23,8,1,0,5,1,0.29,1.17,,0,1,0,34,0,2,,, +gumbeha01,1940,1,NY1,NL,12,14,35,30,14,2,2,711,230,99,17,81,77,0.25,3.76,,8,3,1,1007,4,110,,, +hadlebu01,1940,1,NYA,AL,3,5,25,2,0,0,2,240,88,51,4,52,39,0.27,5.74,,5,1,0,382,9,62,,, +halleja01,1940,1,CHA,AL,1,1,2,2,1,0,0,42,15,10,1,6,9,0.27,6.43,,0,1,0,63,0,10,,, +hamlilu01,1940,1,BRO,NL,9,8,33,25,9,2,0,547,183,62,17,34,91,0.25,3.06,,0,2,0,758,2,77,,, +hardeme01,1940,1,CLE,AL,12,11,31,25,5,0,0,559,200,84,16,59,76,0.27,4.06,,2,5,0,797,2,96,,, +harrera01,1940,1,PIT,NL,0,0,3,0,0,0,0,10,5,3,0,2,3,0.33,8.1,,2,0,0,17,2,5,,, +harribo01,1940,1,SLA,AL,11,15,35,28,8,1,1,581,225,106,24,85,49,0.29,4.93,,8,3,0,877,5,120,,, +harrimi01,1940,1,BOS,AL,4,2,13,9,3,0,0,205,83,38,8,26,36,0.29,5,,3,2,0,313,2,40,,, +hashhe01,1940,1,BOS,AL,7,7,34,12,3,1,3,360,123,66,11,84,36,0.26,4.95,,3,5,0,558,15,68,,, +haynejo01,1940,1,WS1,AL,3,6,22,7,1,0,0,190,85,46,4,34,23,0.32,6.54,,3,1,0,298,6,50,,, +headed01,1940,1,BRO,NL,1,2,13,5,2,0,0,118,40,18,0,18,13,0.26,4.12,,0,0,0,176,3,21,,, +heintke01,1940,1,PIT,NL,8,8,39,16,5,2,3,495,193,82,7,65,71,0.29,4.47,,7,4,1,742,12,86,,, +heussed01,1940,1,PHA,AL,6,13,41,6,2,0,5,330,144,61,11,42,39,0.3,4.99,,1,2,0,518,26,84,,, +hevinjo02,1940,1,BOS,AL,12,7,39,7,4,0,3,357,129,53,7,42,55,0.27,4.01,,1,3,0,525,21,63,,, +higbeki01,1940,1,PHI,NL,14,19,41,36,20,1,1,849,242,117,12,121,137,0.23,3.72,,9,3,1,1194,4,126,,, +hildeor01,1940,1,NYA,AL,1,1,13,0,0,0,0,58,19,4,1,14,5,0.26,1.86,,0,1,0,88,8,7,,, +hoersle01,1940,1,PHI,NL,1,0,6,0,0,0,0,36,12,7,1,8,3,0.25,5.25,,0,0,0,54,6,7,,, +hollial01,1940,1,WS1,AL,1,0,3,2,0,0,0,54,18,11,0,11,7,0.26,5.5,,0,0,0,81,1,12,,, +howeldi01,1940,1,CLE,AL,0,0,3,0,0,0,0,15,2,1,0,4,2,0.14,1.8,,0,0,0,19,3,1,,, +hubbeca01,1940,1,NY1,NL,11,12,31,27,11,2,0,643,220,87,22,59,86,0.25,3.65,,3,2,0,916,3,102,,, +hudliwi01,1940,1,CLE,AL,2,1,4,4,2,0,0,71,31,13,3,2,8,0.31,4.94,,0,0,1,100,0,13,,, +hudliwi01,1940,2,WS1,AL,1,2,8,6,1,0,0,112,50,27,9,5,9,0.31,6.51,,1,2,0,169,1,33,,, +hudliwi01,1940,3,SLA,AL,0,1,6,1,0,0,0,34,19,14,0,8,4,0.35,11.12,,0,0,0,61,3,16,,, +hudliwi01,1940,4,NY1,NL,0,1,1,1,0,0,0,15,9,6,1,1,1,0.4,10.8,,0,0,0,23,0,6,,, +hudsosi01,1940,1,WS1,AL,17,16,38,31,19,3,1,756,272,128,20,81,96,0.27,4.57,,6,3,1,1090,3,149,,, +humphjo02,1940,1,CLE,AL,0,2,19,1,1,0,1,101,35,31,5,29,17,0.26,8.29,,0,2,0,166,4,35,,, +hutchfr01,1940,1,DET,AL,3,7,17,10,1,0,0,228,85,48,6,26,32,0.28,5.68,,0,2,1,335,6,52,,, +hutchir01,1940,1,SLN,NL,4,2,20,2,1,0,1,190,68,22,3,19,19,0.27,3.13,,1,0,0,274,9,27,,, +hutchjo02,1940,1,CIN,NL,2,1,19,4,0,0,0,162,53,21,3,18,18,0.26,3.5,,2,1,0,228,9,21,,, +jacobbu01,1940,1,WS1,AL,0,1,9,0,0,0,0,45,16,10,1,9,6,0.27,6,,0,2,0,72,8,11,,, +javeral01,1940,1,BSN,NL,2,4,29,4,1,0,1,250,99,51,2,36,42,0.29,5.51,,4,2,2,379,17,62,,, +johnsar02,1940,1,BSN,NL,0,1,2,1,0,0,0,18,10,7,0,3,1,0.34,10.5,,0,1,0,33,0,7,,, +johnsea01,1940,1,BOS,AL,6,2,17,10,2,0,0,211,69,32,0,39,26,0.26,4.09,,3,2,0,309,2,33,,, +johnssi01,1940,1,PHI,NL,5,14,37,14,5,0,1,415,145,75,13,42,58,0.26,4.88,,2,2,0,594,10,81,,, +johnssy01,1940,1,PHI,NL,2,2,17,2,2,0,2,122,37,19,6,5,13,0.23,4.2,,1,0,0,165,13,22,,, +joinero01,1940,1,NY1,NL,3,2,30,2,0,0,1,159,66,20,8,17,25,0.3,3.4,,1,5,0,242,15,26,,, +jungeke01,1940,1,CLE,AL,0,0,2,0,0,0,0,10,3,1,0,1,1,0.27,2.7,,0,0,0,13,0,1,,, +kenneve01,1940,1,SLA,AL,12,17,34,32,18,0,0,667,263,138,18,122,70,0.29,5.59,,5,3,1,1020,1,149,,, +kimbane01,1940,1,BRO,NL,3,1,21,0,0,0,1,101,29,12,2,15,21,0.23,3.21,,0,0,0,142,10,15,,, +kimbane01,1940,2,SLN,NL,1,0,2,1,1,0,0,42,11,4,1,6,6,0.2,2.57,,0,0,0,59,1,5,,, +klingbo01,1940,1,PIT,NL,8,13,39,22,3,0,3,426,196,85,5,53,48,0.32,5.39,,5,5,0,662,6,102,,, +knottja01,1940,1,CHA,AL,11,9,25,23,4,2,0,474,166,80,12,52,44,0.26,4.56,,1,2,1,686,2,88,,, +krakajo01,1940,1,WS1,AL,1,6,32,10,2,0,2,327,137,78,7,73,68,0.3,6.44,,7,0,0,527,16,90,,, +krameja01,1940,1,SLA,AL,3,7,16,9,1,0,0,194,86,45,4,26,12,0.32,6.26,,4,0,0,293,4,48,,, +lamanfr02,1940,1,BSN,NL,1,0,5,1,1,0,0,40,13,7,1,8,3,0.27,4.73,,0,0,0,57,2,8,,, +lanahdi01,1940,1,PIT,NL,6,8,40,8,4,0,2,324,121,51,8,42,45,0.27,4.25,,1,1,1,481,17,63,,, +laniema01,1940,1,SLN,NL,9,6,35,11,4,2,3,315,113,39,1,38,49,0.27,3.34,,4,1,0,458,14,50,,, +lannijo01,1940,1,PIT,NL,8,4,38,7,2,0,2,347,119,52,8,39,42,0.26,4.05,,1,0,0,489,16,59,,, +lawsoro01,1940,1,SLA,AL,5,3,30,2,0,0,4,216,77,41,5,54,18,0.27,5.13,,1,0,1,337,16,45,,, +leebi02,1940,1,CHN,NL,9,17,37,30,9,1,0,634,246,118,12,70,70,0.29,5.03,,2,2,1,927,6,129,,, +leeth01,1940,1,CHA,AL,12,13,28,27,24,1,0,684,223,88,13,56,87,0.25,3.47,,4,2,1,948,0,100,,, +leonadu02,1940,1,WS1,AL,14,19,35,35,23,2,0,867,328,112,19,78,124,0.28,3.49,,10,2,1,1245,0,136,,, +lillage01,1940,1,SLN,NL,0,1,2,1,0,0,0,14,8,7,1,4,2,0.36,13.5,,0,1,0,27,0,7,,, +lohrmbi01,1940,1,NY1,NL,10,15,31,28,11,4,1,585,200,82,19,43,73,0.26,3.78,,3,3,1,810,2,98,,, +lynnre01,1940,1,NY1,NL,4,3,33,0,0,0,3,127,40,18,3,24,25,0.24,3.83,,0,1,0,189,18,21,,, +lyonste01,1940,1,CHA,AL,12,8,22,22,17,4,0,559,188,67,17,37,72,0.25,3.24,,6,0,0,789,0,85,,, +macfada01,1940,1,PIT,NL,5,4,35,8,0,0,2,274,112,36,5,27,24,0.3,3.55,,0,4,0,409,13,47,,, +maconma01,1940,1,BRO,NL,1,0,2,0,0,0,0,6,5,5,2,0,1,0.45,22.5,,0,0,0,11,1,5,,, +mahanar01,1940,1,PHI,NL,0,0,1,0,0,0,0,3,1,0,0,0,0,0.33,0,,0,0,0,3,1,0,,, +marchph01,1940,1,PHA,AL,0,2,2,2,1,0,0,30,12,8,1,8,4,0.28,7.2,,2,0,0,51,0,9,,, +mastepa01,1940,1,PHI,NL,0,0,2,0,0,0,0,15,5,4,0,2,3,0.26,7.2,,0,0,0,21,2,4,,, +mastewa02,1940,1,WS1,AL,3,13,31,19,3,0,2,391,128,71,6,88,68,0.25,4.9,,5,3,0,601,9,92,,, +mccrale01,1940,1,PHA,AL,0,0,4,0,0,0,0,35,19,9,2,2,4,0.36,6.94,,0,1,0,58,3,13,,, +mcgeebi01,1940,1,SLN,NL,16,10,38,31,11,3,0,654,222,92,13,96,78,0.26,3.8,,9,2,2,955,1,108,,, +mckaiar01,1940,1,DET,AL,5,0,27,0,0,0,3,153,48,16,2,25,24,0.24,2.82,,1,0,0,224,17,18,,, +mclaupa01,1940,1,PHA,AL,0,0,1,0,0,0,0,5,4,3,1,1,0,0.44,16.2,,0,0,0,10,0,3,,, +meltocl01,1940,1,NY1,NL,10,11,37,21,4,1,2,500,185,91,9,68,91,0.28,4.91,,1,3,0,737,6,103,,, +milesca01,1940,1,PHA,AL,0,0,2,0,0,0,0,24,9,12,2,8,6,0.28,13.5,,0,0,0,41,2,12,,, +millsle01,1940,1,SLA,AL,0,6,26,5,1,0,0,177,64,51,7,52,18,0.27,7.78,,6,3,0,291,9,55,,, +milnaal01,1940,1,CLE,AL,18,10,37,33,15,4,3,727,242,88,14,99,99,0.25,3.27,,3,1,0,1049,3,120,,, +montere01,1940,1,WS1,AL,2,6,27,8,3,0,2,302,128,68,7,52,64,0.31,6.08,,0,3,1,468,15,70,,, +moorewh01,1940,1,CIN,NL,8,8,25,15,5,1,1,350,100,47,8,56,60,0.23,3.63,,0,7,0,502,7,48,,, +mootyja01,1940,1,CHN,NL,6,6,20,12,6,0,1,342,101,37,11,49,42,0.24,2.92,,5,1,0,482,5,45,,, +mulcahu01,1940,1,PHI,NL,13,22,36,36,21,3,0,840,283,112,12,91,82,0.26,3.6,,6,3,2,1189,0,141,,, +mungova01,1940,1,BRO,NL,1,0,7,0,0,0,1,66,24,6,1,10,9,0.28,2.45,,0,0,0,96,2,6,,, +murphjo04,1940,1,NYA,AL,8,4,35,1,0,0,9,190,58,26,5,15,23,0.24,3.69,,1,0,0,255,28,27,,, +mustaal01,1940,1,BOS,AL,0,1,6,1,0,0,0,45,15,15,1,15,6,0.25,9,,1,0,0,76,3,18,,, +naymimi01,1940,1,CLE,AL,1,2,13,4,0,0,0,90,36,17,1,17,15,0.29,5.1,,2,3,0,145,8,17,,, +nelsoly01,1940,1,DET,AL,1,1,6,2,0,0,0,42,23,17,5,9,7,0.37,10.93,,0,0,0,73,4,19,,, +newhoha01,1940,1,DET,AL,9,9,28,20,7,0,0,400,149,72,12,76,89,0.28,4.86,,1,2,1,613,3,81,,, +newlima01,1940,1,SLA,AL,1,0,1,1,0,0,0,18,4,4,1,2,3,0.19,6,,0,0,0,23,0,4,,, +newsobo01,1940,1,DET,AL,21,5,36,34,20,3,0,792,235,83,19,100,164,0.23,2.83,,2,3,2,1101,1,110,,, +niggejo01,1940,1,SLA,AL,7,11,28,20,10,0,0,461,148,76,9,69,82,0.25,4.45,,8,5,0,670,6,88,,, +olsenve01,1940,1,CHN,NL,13,9,34,20,9,4,0,518,172,57,5,62,71,0.26,2.97,,0,2,0,734,6,64,,, +osterfr01,1940,1,BOS,AL,5,9,31,16,5,0,0,431,166,79,7,70,80,0.28,4.95,,4,0,0,662,7,86,,, +pageva01,1940,1,CHN,NL,1,3,30,1,0,0,2,177,65,29,1,26,22,0.27,4.42,,1,0,0,278,17,38,,, +passecl01,1940,1,CHN,NL,20,13,46,31,20,4,5,842,259,78,8,59,124,0.23,2.5,,0,4,0,1172,13,97,,, +pearsik01,1940,1,PHI,NL,3,14,29,20,5,1,1,436,160,88,13,57,43,0.27,5.45,,4,3,1,648,7,91,,, +pearsmo01,1940,1,NYA,AL,7,5,16,16,7,1,0,329,108,45,8,44,43,0.26,3.69,,3,0,0,464,0,48,,, +piechal01,1940,1,BSN,NL,2,5,21,8,2,0,0,183,68,39,6,41,18,0.27,5.75,,2,0,0,289,6,45,,, +pippeco01,1940,1,DET,AL,1,2,4,3,0,0,0,64,29,16,3,10,9,0.32,6.75,,1,1,0,102,0,16,,, +podgajo01,1940,1,PHI,NL,1,3,4,4,3,0,0,105,33,11,0,1,12,0.25,2.83,,1,1,0,135,0,14,,, +posedbi01,1940,1,BSN,NL,12,17,35,32,18,0,1,699,263,107,16,81,86,0.28,4.13,,3,1,0,1008,2,118,,, +pottene01,1940,1,PHA,AL,9,14,31,25,13,0,0,602,213,99,18,71,73,0.26,4.44,,10,1,2,876,5,115,,, +pressto01,1940,1,BRO,NL,6,5,24,4,1,1,2,205,58,28,4,17,21,0.22,3.69,,1,2,0,285,11,31,,, +rachust01,1940,1,BRO,NL,0,1,2,1,1,0,0,30,9,5,0,5,10,0.24,4.5,,0,0,0,43,1,5,,, +raffeke01,1940,1,CHN,NL,7,9,43,10,3,0,3,344,120,43,10,29,55,0.27,3.38,,1,2,1,480,16,54,,, +rambepe01,1940,1,PIT,NL,0,1,3,1,0,0,0,25,12,7,0,4,0,0.33,7.56,,0,3,0,44,1,8,,, +richwo01,1940,1,BOS,AL,1,0,3,1,1,0,0,35,9,1,2,1,8,0.21,0.77,,0,0,0,44,1,3,,, +riddlel01,1940,1,CIN,NL,1,2,15,1,1,0,2,101,30,7,0,17,9,0.25,1.87,,0,0,0,142,12,12,,, +rignejo01,1940,1,CHA,AL,14,18,39,33,19,2,3,842,240,97,22,90,141,0.23,3.11,,5,2,0,1152,6,117,,, +rootch01,1940,1,CHN,NL,2,4,36,8,1,0,1,336,118,48,9,33,50,0.26,3.86,,0,1,0,490,10,61,,, +rossbu02,1940,1,PHA,AL,5,10,24,19,10,0,1,469,160,76,15,60,43,0.25,4.38,,4,0,0,690,3,91,,, +rowesc01,1940,1,DET,AL,16,3,27,23,11,1,0,507,170,65,15,43,61,0.25,3.46,,2,1,0,706,4,68,,, +ruffire01,1940,1,NYA,AL,15,12,30,30,20,3,0,678,218,85,24,76,97,0.25,3.38,,1,3,0,954,0,98,,, +russeja01,1940,1,SLN,NL,3,4,26,0,0,0,1,162,53,15,1,26,16,0.25,2.5,,0,3,0,242,8,22,,, +russoma01,1940,1,NYA,AL,14,8,30,24,15,0,1,568,181,69,17,55,87,0.24,3.28,,1,1,0,786,2,79,,, +salvoma01,1940,1,BSN,NL,10,9,21,20,14,5,0,482,151,55,9,43,60,0.24,3.08,,2,2,2,662,1,63,,, +schumha02,1940,1,NY1,NL,13,13,34,30,12,1,1,681,218,82,14,96,123,0.25,3.25,,4,0,0,974,4,93,,, +seatsto01,1940,1,DET,AL,2,2,26,2,0,0,1,167,67,29,4,21,25,0.29,4.69,,2,0,0,256,10,43,,, +sewelri01,1940,1,PIT,NL,16,5,33,23,14,2,1,569,169,59,6,67,60,0.23,2.8,,2,3,0,790,3,71,,, +shoffmi01,1940,1,CIN,NL,1,0,20,0,0,0,0,163,56,34,3,18,17,0.26,5.63,,0,0,0,232,5,35,,, +shouncl01,1940,1,SLN,NL,13,11,54,19,13,1,5,592,193,86,13,46,82,0.25,3.92,,4,2,1,816,22,96,,, +smithal03,1940,1,CLE,AL,15,7,31,24,11,1,2,549,187,70,12,55,46,0.27,3.44,,3,6,1,761,5,79,,, +smithcl01,1940,1,DET,AL,1,1,14,1,0,0,0,85,32,16,3,13,14,0.28,5.08,,2,1,0,128,5,18,,, +smithed04,1940,1,CHA,AL,14,9,32,28,12,0,0,622,179,74,16,95,119,0.22,3.21,,4,3,0,892,3,92,,, +smollle01,1940,1,PHI,NL,2,8,33,9,0,0,0,327,145,65,6,36,31,0.32,5.37,,3,4,0,500,12,77,,, +strinni01,1940,1,BSN,NL,4,8,32,14,5,0,1,386,142,79,17,63,54,0.27,5.53,,2,8,0,587,7,89,,, +sullijo05,1940,1,BSN,NL,10,14,36,22,7,0,1,532,157,70,9,89,64,0.24,3.55,,2,8,0,766,9,89,,, +sundrst01,1940,1,NYA,AL,4,6,27,8,2,0,2,298,121,61,11,42,26,0.29,5.53,,2,1,0,457,10,68,,, +swiftbi01,1940,1,BSN,NL,1,1,4,0,0,0,1,28,12,3,0,7,7,0.3,2.89,,0,0,0,46,2,7,,, +swigaoa01,1940,1,PIT,NL,0,2,7,2,0,0,0,67,27,11,1,10,9,0.29,4.43,,0,0,0,102,3,14,,, +tamulvi01,1940,1,BRO,NL,8,5,41,12,4,1,2,463,147,53,5,34,55,0.24,3.09,,3,3,0,644,17,60,,, +terryya01,1940,1,BOS,AL,1,0,4,1,0,0,0,58,24,19,2,11,9,0.3,8.84,,1,0,0,94,0,19,,, +thomabu01,1940,1,DET,AL,0,1,3,0,0,0,0,12,8,4,1,3,0,0.42,9,,0,0,0,24,2,5,,, +thompju01,1940,1,CIN,NL,16,9,33,31,17,3,0,676,197,83,10,96,103,0.23,3.32,,3,2,2,951,1,90,,, +thumalo01,1940,1,WS1,AL,0,1,2,0,0,0,0,15,10,8,0,7,0,0.4,14.4,,0,0,0,33,0,11,,, +tobinji01,1940,1,BSN,NL,7,3,15,11,9,0,0,289,102,41,8,24,29,0.26,3.83,,2,0,0,412,3,41,,, +torregi01,1940,1,WS1,AL,0,0,2,0,0,0,0,8,3,0,0,0,1,0.27,0,,0,0,0,11,1,1,,, +trottbi01,1940,1,SLA,AL,7,6,36,4,1,0,2,294,117,41,5,31,29,0.3,3.77,,1,1,0,427,19,56,,, +troutdi01,1940,1,DET,AL,3,7,33,10,1,0,2,302,125,50,4,54,64,0.3,4.47,,3,3,0,469,6,60,,, +turneji01,1940,1,CIN,NL,14,7,24,23,11,0,0,561,187,60,9,32,53,0.26,2.89,,1,0,0,749,1,70,,, +uhlbo01,1940,1,DET,AL,0,0,1,0,0,0,0,0,4,0,0,2,0,1,,,1,0,0,6,0,5,,, +vandehy01,1940,1,NY1,NL,1,1,13,3,1,0,1,97,27,14,2,16,17,0.22,3.9,,1,1,0,139,8,15,,, +vandejo01,1940,1,CIN,NL,3,1,10,7,2,0,1,144,38,20,3,41,41,0.21,3.75,,1,1,1,224,2,24,,, +vaughpo01,1940,1,PHA,AL,2,9,18,15,5,0,2,298,104,59,9,61,46,0.26,5.35,,12,3,2,464,3,74,,, +wagnech01,1940,1,BOS,AL,1,0,12,1,0,0,0,88,45,18,5,8,13,0.34,5.52,,1,0,0,140,7,22,,, +waltebu01,1940,1,CIN,NL,22,10,36,36,29,3,0,915,241,84,19,92,115,0.22,2.48,,3,5,0,1207,0,95,,, +warnelo01,1940,1,SLN,NL,16,10,33,31,17,1,0,696,235,81,17,47,85,0.25,3.14,,3,3,0,969,2,103,,, +weilabo01,1940,1,SLN,NL,0,0,1,0,0,0,0,3,3,3,1,0,0,0.6,27,,0,0,0,5,1,3,,, +weilaed01,1940,1,CHA,AL,0,0,5,0,0,0,0,43,15,14,5,7,3,0.26,8.79,,1,0,0,65,2,15,,, +whiteer01,1940,1,SLN,NL,1,1,8,1,0,0,0,65,29,10,0,14,15,0.31,4.15,,0,1,0,110,5,13,,, +whitejo02,1940,1,SLA,AL,1,3,15,4,1,1,0,120,46,24,3,14,11,0.28,5.4,,3,0,0,177,7,25,,, 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+dorseca01,1941,1,CLE,AL,0,1,5,2,0,0,0,34,21,13,0,10,5,0.38,10.32,,0,0,1,65,2,15,,, +draketo01,1941,1,BRO,NL,1,1,10,2,0,0,0,74,26,12,2,9,12,0.28,4.38,,2,0,0,104,3,13,,, +earleto01,1941,1,BSN,NL,6,8,33,13,6,1,3,416,120,39,9,46,54,0.23,2.53,,1,3,0,570,12,52,,, +easthu01,1941,1,NY1,NL,1,1,2,2,0,0,0,47,19,6,0,9,4,0.29,3.45,,1,0,0,74,0,12,,, +eavesva01,1941,1,CHN,NL,3,3,12,7,4,0,0,176,56,23,4,21,24,0.25,3.53,,0,3,0,246,1,27,,, +eisenha01,1941,1,CLE,AL,1,1,21,0,0,0,2,102,43,16,2,16,11,0.31,4.24,,0,2,0,157,14,16,,, +embrere01,1941,1,CLE,AL,0,1,1,1,0,0,0,12,7,3,0,3,4,0.43,6.75,,0,1,0,21,0,3,,, +erickpa01,1941,1,CHN,NL,5,7,32,15,7,1,1,423,126,58,2,64,85,0.23,3.7,,4,2,0,615,9,70,,, +erricdi01,1941,1,BSN,NL,6,12,38,23,5,2,1,497,192,88,12,62,45,0.28,4.78,,3,4,0,741,9,100,,, +feldmha01,1941,1,NY1,NL,1,1,3,3,1,1,0,61,21,9,0,6,9,0.28,3.98,,0,0,0,85,0,10,,, +fellebo01,1941,1,CLE,AL,25,13,44,40,28,6,2,1029,284,120,15,194,260,0.22,3.15,,6,5,0,1466,4,129,,, 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+gomezle01,1941,1,NYA,AL,15,5,23,23,8,2,0,469,151,65,10,103,76,0.25,3.74,,5,1,0,712,0,76,,, +gorniha01,1941,1,SLN,NL,1,0,4,1,1,1,0,34,6,4,0,9,6,0.15,3.18,,0,1,0,49,0,4,,, +gorniha01,1941,2,CHN,NL,0,0,1,0,0,0,0,6,3,1,0,0,2,0.37,4.5,,0,0,0,8,0,1,,, +gorsijo01,1941,1,DET,AL,9,11,33,21,8,1,2,513,193,85,14,55,59,0.28,4.47,,5,2,1,753,10,98,,, +grissle01,1941,1,BRO,NL,0,0,4,1,0,0,1,34,10,3,2,8,5,0.23,2.38,,0,0,0,52,2,3,,, +grissle01,1941,2,PHI,NL,2,13,29,18,2,0,0,394,120,58,4,70,74,0.24,3.97,,8,2,0,574,5,69,,, +grodzjo01,1941,1,SLN,NL,2,1,5,1,0,0,0,40,6,2,0,11,10,0.13,1.35,,0,0,0,58,3,7,,, +gromest01,1941,1,CLE,AL,1,1,9,2,1,0,2,70,25,11,0,11,19,0.26,4.24,,0,0,0,106,5,12,,, +grovele01,1941,1,BOS,AL,7,7,21,21,10,0,0,402,155,65,8,42,54,0.28,4.37,,3,2,0,598,0,84,,, +groveor01,1941,1,CHA,AL,0,0,2,0,0,0,0,21,9,8,2,5,5,0.32,10.29,,1,0,0,35,0,8,,, +gumbeha01,1941,1,NY1,NL,1,1,5,5,1,0,0,97,34,16,3,18,9,0.26,4.45,,1,0,0,148,0,20,,, +gumbeha01,1941,2,SLN,NL,11,5,33,17,8,3,1,433,139,44,7,30,53,0.25,2.74,,3,1,0,596,11,52,,, +hadlebu01,1941,1,NY1,NL,1,0,3,2,0,0,0,39,19,9,1,9,4,0.34,6.23,,0,0,0,64,1,10,,, +hadlebu01,1941,2,PHA,AL,4,6,25,9,1,0,3,307,131,57,13,47,31,0.31,5.01,,7,5,0,479,12,69,,, +halleja01,1941,1,CHA,AL,5,5,22,6,3,0,0,224,96,50,7,38,25,0.3,6.03,,4,3,0,362,11,57,,, +hamlilu01,1941,1,BRO,NL,8,8,30,20,5,1,1,408,139,64,14,41,58,0.26,4.24,,0,2,1,581,5,75,,, +hardeme01,1941,1,CLE,AL,5,4,15,10,1,0,1,206,76,40,8,37,21,0.27,5.24,,1,2,0,317,1,43,,, +harmabi01,1941,1,PHI,NL,0,0,5,0,0,0,0,39,15,7,0,8,3,0.31,4.85,,0,0,0,57,5,8,,, +harribo01,1941,1,SLA,AL,12,14,34,29,9,2,1,560,237,108,18,85,57,0.31,5.21,,5,2,0,853,3,117,,, +harrilu01,1941,1,PHA,AL,4,4,33,10,5,0,2,395,134,70,16,51,49,0.26,4.78,,4,2,0,572,15,77,,, +harrimi01,1941,1,BOS,AL,8,14,35,22,11,1,1,582,189,70,6,86,111,0.25,3.25,,6,2,1,855,7,86,,, +hashhe01,1941,1,BOS,AL,1,0,4,0,0,0,1,25,7,5,1,7,3,0.22,5.4,,0,0,0,39,2,5,,, 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+hutchjo02,1941,1,CIN,NL,0,0,8,0,0,0,0,33,12,5,0,4,5,0.27,4.09,,0,0,0,48,5,6,,, +hutchjo02,1941,2,BSN,NL,1,6,36,7,1,1,2,287,110,44,6,22,36,0.28,4.14,,2,4,0,418,17,59,,, +iottho01,1941,1,SLA,AL,0,0,2,0,0,0,0,6,2,2,0,1,1,0.25,9,,0,0,0,9,1,2,,, +javeral01,1941,1,BSN,NL,10,11,34,23,9,1,1,482,181,77,5,65,54,0.28,4.31,,2,6,0,720,5,88,,, +johnsar02,1941,1,BSN,NL,7,15,43,18,6,0,1,550,189,72,7,71,70,0.27,3.53,,2,5,0,793,13,92,,, +johnsea01,1941,1,BOS,AL,4,5,17,12,4,0,0,281,90,47,4,51,46,0.24,4.52,,2,3,0,420,3,57,,, +johnsra02,1941,1,PHA,AL,1,0,7,0,0,0,0,30,14,4,0,3,0,0.32,3.6,,0,0,0,47,5,10,,, +johnssi01,1941,1,PHI,NL,5,12,39,21,6,1,2,490,207,82,8,54,80,0.3,4.52,,1,1,0,738,8,91,,, +jonesda02,1941,1,PHI,NL,0,1,2,1,0,0,0,25,13,7,0,6,2,0.34,7.56,,0,0,0,45,0,11,,, +juddos01,1941,1,BOS,AL,0,0,7,0,0,0,1,37,15,12,1,10,5,0.3,8.76,,2,0,0,60,4,12,,, +jungeke01,1941,1,CLE,AL,0,0,6,0,0,0,0,41,17,11,4,8,6,0.29,7.24,,0,1,0,67,4,12,,, +kenneve01,1941,1,SLA,AL,2,4,6,6,2,0,0,135,44,22,5,27,6,0.25,4.4,,0,0,0,198,0,27,,, +kenneve01,1941,2,WS1,AL,1,7,17,7,2,0,0,199,77,42,5,39,22,0.29,5.7,,2,2,0,307,9,49,,, +kimbane01,1941,1,BRO,NL,3,1,15,5,1,0,1,156,43,21,0,29,17,0.22,3.63,,0,0,1,225,6,22,,, +klingbo01,1941,1,PIT,NL,9,4,35,9,3,0,4,350,127,51,5,30,36,0.27,3.93,,2,1,0,499,18,58,,, +knottja01,1941,1,PHA,AL,13,11,27,26,11,0,0,583,212,95,20,81,54,0.27,4.4,,8,2,0,857,1,108,,, +kosloda01,1941,1,NY1,NL,1,2,4,3,2,0,0,71,17,5,0,10,12,0.2,1.9,,1,0,0,94,0,6,,, +krakajo01,1941,1,CLE,AL,1,2,12,5,0,0,0,125,39,19,3,29,25,0.24,4.1,,1,0,0,189,5,25,,, +krameja01,1941,1,SLA,AL,4,3,29,3,0,0,2,178,69,34,5,40,20,0.28,5.16,,1,0,0,284,14,48,,, +kristho01,1941,1,SLN,NL,10,0,37,8,2,0,2,342,107,51,10,35,36,0.24,4.03,,1,1,0,476,17,57,,, +kushem01,1941,1,CHN,NL,0,0,2,0,0,0,0,12,2,1,0,0,2,0.14,2.25,,0,0,0,14,1,1,,, +lamanfr02,1941,1,BSN,NL,5,4,35,4,0,0,1,218,77,43,5,56,23,0.28,5.33,,5,1,0,342,15,52,,, +lambege01,1941,1,PHI,NL,0,1,2,1,0,0,0,27,11,2,0,2,3,0.29,2,,0,0,0,40,0,2,,, +lanahdi01,1941,1,PIT,NL,0,1,7,0,0,0,0,36,13,7,1,3,5,0.28,5.25,,0,2,0,52,2,9,,, +lanfrwa01,1941,1,CHN,NL,0,1,2,1,0,0,0,18,7,2,0,2,1,0.28,3,,0,0,0,27,0,3,,, +laniema01,1941,1,SLN,NL,10,8,35,18,8,2,3,459,126,48,4,59,93,0.22,2.82,,2,1,2,628,6,59,,, +lannijo01,1941,1,PIT,NL,11,11,34,23,9,0,1,527,175,61,6,47,41,0.25,3.13,,2,0,0,742,8,72,,, +leebi02,1941,1,CHN,NL,8,14,28,22,12,0,1,502,179,70,6,43,62,0.27,3.76,,6,2,0,723,1,87,,, +leeth01,1941,1,CHA,AL,22,11,35,34,30,3,1,901,258,79,18,92,130,0.23,2.37,,2,4,0,1224,1,98,,, +leonadu02,1941,1,WS1,AL,18,13,34,33,19,4,0,768,271,98,6,54,91,0.27,3.45,,9,3,0,1073,1,117,,, +loganbo01,1941,1,CIN,NL,0,1,2,0,0,0,0,10,5,3,0,5,0,0.33,8.1,,1,0,0,20,0,5,,, +lohrmbi01,1941,1,NY1,NL,9,10,33,20,6,2,3,477,184,71,7,40,61,0.28,4.02,,3,0,1,692,6,87,,, +lyonshe01,1941,1,SLN,NL,0,0,1,0,0,0,0,4,1,0,0,3,1,0.2,0,,0,0,0,8,0,0,,, +lyonste01,1941,1,CHA,AL,12,10,22,22,19,2,0,562,199,77,9,37,63,0.26,3.7,,2,4,0,795,0,87,,, +macfada01,1941,1,WS1,AL,0,1,5,0,0,0,0,21,12,8,1,5,3,0.37,10.29,,0,0,0,37,3,9,,, +mandeha01,1941,1,DET,AL,1,0,8,0,0,0,0,46,13,4,0,8,7,0.23,2.35,,0,1,0,65,7,5,,, +marchph01,1941,1,PHA,AL,10,15,30,27,14,1,0,613,188,81,15,118,74,0.24,3.57,,12,3,3,898,1,94,,, +mastepa01,1941,1,PHI,NL,1,0,2,1,1,0,0,34,11,6,0,6,8,0.25,4.76,,0,0,0,50,1,6,,, +mastewa02,1941,1,WS1,AL,4,3,34,6,1,0,3,235,101,52,3,53,40,0.32,5.97,,4,1,0,376,13,56,,, +mccrale01,1941,1,PHA,AL,9,13,26,23,11,1,2,472,188,96,16,49,40,0.29,5.49,,0,1,0,706,3,105,,, +mcgeebi01,1941,1,SLN,NL,0,1,4,3,0,0,0,42,17,8,1,13,2,0.29,5.14,,0,1,0,71,1,9,,, +mcgeebi01,1941,2,NY1,NL,2,9,22,14,1,0,0,318,117,58,9,54,41,0.28,4.92,,1,0,0,475,3,68,,, +mckaiar01,1941,1,DET,AL,2,1,15,0,0,0,0,129,58,24,3,11,14,0.33,5.02,,2,0,0,193,9,24,,, +mckaiar01,1941,2,SLA,AL,0,1,8,0,0,0,1,30,16,9,2,4,2,0.36,8.1,,0,1,0,49,5,9,,, +meersru01,1941,1,CHN,NL,0,1,1,1,0,0,0,24,5,1,0,0,5,0.17,1.13,,0,1,0,30,0,2,,, +meltocl01,1941,1,NY1,NL,8,11,42,22,9,3,1,583,181,65,14,61,100,0.24,3.01,,2,2,0,818,10,80,,, +meltoru01,1941,1,PHI,NL,1,5,25,5,2,0,0,251,81,44,7,47,57,0.25,4.73,,5,0,0,369,15,48,,, +millero02,1941,1,WS1,AL,0,0,1,0,0,0,0,6,2,1,0,1,0,0.33,4.5,,0,0,0,7,1,1,,, +milnaal01,1941,1,CLE,AL,12,19,35,30,9,1,0,688,236,111,9,116,82,0.26,4.36,,5,1,0,1018,3,128,,, +moorewh01,1941,1,CIN,NL,2,1,23,4,1,0,0,185,62,30,2,45,17,0.25,4.38,,0,3,0,300,9,35,,, +mootyja01,1941,1,CHN,NL,8,9,33,14,7,1,4,460,143,57,9,56,45,0.25,3.35,,3,2,0,643,12,69,,, +muellle01,1941,1,DET,AL,0,0,4,0,0,0,0,39,9,7,1,10,8,0.2,4.85,,0,0,0,57,1,9,,, +mullidi01,1941,1,WS1,AL,0,1,1,1,1,0,0,27,11,5,0,2,2,0.3,5,,0,0,0,39,0,5,,, +muncrbo01,1941,1,SLA,AL,13,9,36,24,12,2,1,643,221,87,18,53,67,0.26,3.65,,2,5,0,898,8,95,,, +mungova01,1941,1,BRO,NL,0,0,2,0,0,0,0,6,1,1,0,2,0,0.14,4.5,,0,0,0,9,1,1,,, +murphjo04,1941,1,NYA,AL,8,3,35,0,0,0,15,232,68,17,1,40,29,0.23,1.98,,3,0,0,333,31,20,,, +nahemsa01,1941,1,SLN,NL,5,2,26,8,2,0,1,245,76,27,2,38,31,0.24,2.98,,4,2,0,359,9,35,,, +newhoha01,1941,1,DET,AL,9,11,33,27,5,1,0,519,166,92,6,137,106,0.24,4.79,,5,1,0,810,5,109,,, +newlima01,1941,1,SLA,AL,0,2,14,0,0,0,1,83,43,20,4,12,10,0.36,6.51,,2,0,0,135,9,24,,, +newsobo01,1941,1,DET,AL,12,20,43,36,12,2,2,751,265,128,15,118,175,0.26,4.6,,3,3,0,1130,5,140,,, +newsodi01,1941,1,BOS,AL,19,10,36,29,17,2,0,641,235,98,13,79,58,0.27,4.13,,6,7,0,944,4,115,,, +niggejo01,1941,1,SLA,AL,7,9,24,20,13,1,0,505,168,71,17,63,68,0.25,3.8,,1,1,0,733,3,83,,, +olsenve01,1941,1,CHN,NL,10,8,37,23,10,2,1,557,202,65,7,59,73,0.27,3.15,,2,1,0,807,10,84,,, +osterfr01,1941,1,SLA,AL,0,3,15,2,0,0,0,138,45,23,3,23,20,0.25,4.5,,4,0,0,200,8,26,,, +pageva01,1941,1,CHN,NL,2,2,25,3,1,0,1,145,48,23,2,30,17,0.25,4.28,,2,2,0,223,16,24,,, +passecl01,1941,1,CHN,NL,14,14,34,30,20,3,0,693,262,86,10,52,80,0.28,3.35,,2,1,0,993,4,99,,, +pearsik01,1941,1,PHI,NL,4,14,46,10,0,0,6,408,139,54,8,70,38,0.26,3.57,,2,8,0,616,30,75,,, +pearsmo01,1941,1,CIN,NL,1,3,7,4,1,0,0,73,22,14,3,15,8,0.24,5.18,,0,0,0,108,3,15,,, +peekst01,1941,1,NYA,AL,4,2,17,8,2,0,0,240,85,45,6,39,18,0.27,5.06,,1,0,0,350,2,48,,, +piechal01,1941,1,BSN,NL,0,0,1,0,0,0,0,3,0,0,0,1,0,0,0,,0,0,0,4,1,0,,, +podgajo01,1941,1,PHI,NL,9,12,34,24,8,0,0,544,191,93,8,70,53,0.27,4.62,,4,4,0,793,7,96,,, +polleho01,1941,1,SLN,NL,5,2,9,8,6,2,0,210,55,15,1,27,37,0.21,1.93,,4,1,0,295,0,18,,, +posedbi01,1941,1,BSN,NL,4,4,18,9,3,0,0,172,61,31,6,30,10,0.27,4.87,,2,0,0,253,4,36,,, +pottene01,1941,1,PHA,AL,1,1,10,3,1,0,2,70,35,24,3,16,7,0.33,9.26,,3,0,0,121,3,26,,, +pottene01,1941,2,BOS,AL,2,0,10,0,0,0,0,60,21,10,0,16,6,0.28,4.5,,1,0,1,92,6,10,,, +pressto01,1941,1,CHN,NL,5,3,29,1,0,0,1,210,69,24,2,23,27,0.25,3.09,,0,4,0,301,11,26,,, +quinnwi01,1941,1,CHN,NL,0,0,3,0,0,0,0,15,3,4,0,3,2,0.15,7.2,,1,0,0,22,2,4,,, 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+lyonste01,1942,1,CHA,AL,14,6,20,20,20,1,0,541,167,42,11,26,50,0.24,2.1,,3,2,0,720,0,52,,, +maconma01,1942,1,BRO,NL,5,3,14,8,4,1,1,252,67,18,3,33,27,0.22,1.93,,0,2,0,348,3,22,,, +mandeha01,1942,1,DET,AL,2,0,18,0,0,0,0,99,39,15,4,15,14,0.3,4.09,,0,1,0,146,11,19,,, +marchph01,1942,1,PHA,AL,17,14,38,31,18,1,1,732,215,114,14,140,110,0.23,4.2,,13,4,0,1081,7,126,,, +mastepa01,1942,1,PHI,NL,0,0,4,0,0,0,0,25,10,6,1,5,3,0.3,6.48,,0,0,0,39,3,6,,, +mastewa02,1942,1,WS1,AL,5,9,25,15,8,4,2,428,138,53,6,54,63,0.25,3.34,,3,2,0,618,7,75,,, +mccrale01,1942,1,PHA,AL,0,0,1,0,0,0,0,12,14,14,2,2,0,0.56,31.5,,1,1,0,29,1,14,,, +mcculph01,1942,1,WS1,AL,0,0,1,0,0,0,0,9,5,2,0,2,2,0.33,6,,0,0,0,17,1,4,,, +mcgeebi01,1942,1,NY1,NL,6,3,31,8,2,1,1,312,95,34,8,46,40,0.24,2.94,,0,1,0,447,11,50,,, +meltocl01,1942,1,NY1,NL,11,5,23,17,12,2,1,431,122,42,9,33,61,0.22,2.63,,4,2,0,575,2,51,,, +meltoru01,1942,1,PHI,NL,9,20,42,29,10,1,4,628,180,86,7,114,107,0.23,3.7,,8,3,0,903,10,95,,, +milnaal01,1942,1,CLE,AL,6,8,28,19,8,2,0,471,146,72,3,85,35,0.25,4.13,,4,4,1,685,4,82,,, +moorewh01,1942,1,CIN,NL,0,0,1,0,0,0,0,3,0,0,0,1,0,0,0,,0,0,0,4,1,0,,, +moorewh01,1942,2,SLN,NL,0,1,9,0,0,0,0,37,10,6,0,11,1,0.21,4.38,,1,1,0,58,4,6,,, +mootyja01,1942,1,CHN,NL,2,5,19,10,1,0,1,253,89,44,11,44,28,0.26,4.7,,4,0,2,384,5,48,,, +muncrbo01,1942,1,SLA,AL,6,8,24,18,7,1,0,403,149,58,11,31,39,0.28,3.89,,2,0,0,572,2,61,,, +mungova01,1942,1,NY1,NL,1,2,9,5,0,0,0,109,38,24,4,21,27,0.27,5.94,,2,0,0,162,0,32,,, +murphjo04,1942,1,NYA,AL,4,10,31,0,0,0,11,174,66,22,2,23,24,0.29,3.41,,1,2,0,260,26,27,,, +nahemsa01,1942,1,PHI,NL,1,3,35,2,0,0,0,224,72,41,2,40,38,0.25,4.94,,4,2,1,333,16,48,,, +nayloea01,1942,1,PHI,NL,0,5,20,4,1,0,0,181,68,41,5,29,19,0.28,6.12,,2,0,1,272,12,43,,, +newhoha01,1942,1,DET,AL,8,14,38,23,11,1,5,551,137,50,4,114,103,0.2,2.45,,2,2,0,789,14,73,,, +newsobo01,1942,1,WS1,AL,11,17,30,29,15,2,0,641,236,117,5,92,113,0.28,4.93,,2,3,0,957,1,135,,, 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+bithohi01,1943,1,CHN,NL,18,12,39,30,19,7,2,749,227,72,8,65,86,0.24,2.6,,3,2,0,1020,7,79,,, +blackdo01,1943,1,PHA,AL,6,16,33,26,12,1,1,624,193,97,8,110,65,0.24,4.2,,4,6,0,917,4,105,,, +bonhati01,1943,1,NYA,AL,15,8,28,26,17,4,1,677,197,57,13,52,71,0.23,2.27,,0,1,1,902,1,63,,, +borowha01,1943,1,NYA,AL,14,9,29,27,14,3,0,652,195,68,11,72,113,0.24,2.82,,4,2,1,896,1,75,,, +bowlech01,1943,1,PHA,AL,1,1,2,2,2,0,0,54,17,6,0,4,6,0.25,3,,0,0,0,72,0,10,,, +brandbi01,1943,1,PIT,NL,4,1,29,3,0,0,0,172,57,20,3,19,17,0.24,3.14,,2,1,1,254,12,25,,, +brazlal01,1943,1,SLN,NL,8,2,13,9,8,1,0,264,74,15,0,29,26,0.23,1.53,,2,0,0,352,2,18,,, +brechha01,1943,1,SLN,NL,9,6,29,13,8,1,4,406,98,34,4,39,68,0.2,2.26,,2,3,0,527,12,41,,, +breuema01,1943,1,NYA,AL,0,1,5,1,0,0,0,42,22,13,0,6,6,0.34,8.36,,0,0,0,70,4,16,,, +bridgto01,1943,1,DET,AL,12,7,25,22,11,3,0,575,159,51,9,61,124,0.22,2.39,,3,0,0,774,3,57,,, +brownma01,1943,1,BOS,AL,6,6,49,0,0,0,9,280,71,22,2,51,40,0.22,2.12,,2,0,0,382,41,26,,, 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+kimbane01,1943,2,PHI,NL,1,6,34,6,2,0,2,269,85,41,4,42,33,0.25,4.12,,2,1,0,393,18,47,,, +kliemed01,1943,1,CLE,AL,0,1,1,1,1,0,0,27,8,1,0,5,2,0.28,1,,0,0,0,34,0,1,,, +klingbo01,1943,1,PIT,NL,11,8,33,25,14,3,0,585,185,59,6,58,65,0.25,2.72,,0,0,0,809,6,77,,, +krameja01,1943,1,SLA,AL,0,0,3,0,0,0,0,27,11,8,0,8,4,0.29,8,,2,1,0,46,2,8,,, +krausja01,1943,1,PHI,NL,9,15,34,25,10,1,2,599,197,70,7,78,48,0.25,3.16,,3,0,0,863,7,83,,, +kristho01,1943,1,SLN,NL,11,5,34,17,9,2,3,493,141,53,5,62,57,0.23,2.9,,3,4,1,683,10,57,,, +kuczybe01,1943,1,PHA,AL,0,1,6,1,0,0,0,74,36,11,2,9,8,0.33,4.01,,3,2,0,119,5,15,,, +lamacal01,1943,1,SLA,AL,0,1,1,1,0,0,0,12,9,5,0,2,2,0.45,11.25,,2,0,0,23,0,6,,, +laniema01,1943,1,SLN,NL,15,7,32,25,14,2,3,640,195,45,3,75,123,0.24,1.9,,0,2,0,882,5,62,,, +lannijo01,1943,1,PIT,NL,4,1,12,2,0,0,2,81,23,7,0,9,11,0.22,2.33,,0,0,0,114,4,10,,, +lapihan01,1943,1,PHI,NL,0,0,1,0,0,0,0,7,5,6,1,3,0,0.41,23.14,,0,0,0,15,0,6,,, +leebi02,1943,1,CHN,NL,3,7,13,12,4,0,0,235,83,31,4,27,18,0.27,3.56,,3,0,0,336,1,37,,, +leebi02,1943,2,PHI,NL,1,5,13,7,2,0,3,182,70,31,4,21,17,0.29,4.6,,1,1,0,261,4,35,,, +leeth01,1943,1,CHA,AL,5,9,19,19,7,1,0,381,129,59,8,50,35,0.26,4.18,,0,4,0,550,0,66,,, +lefebbi01,1943,1,WS1,AL,2,0,6,3,1,0,0,97,33,16,3,16,10,0.26,4.45,,0,0,0,140,3,18,,, +leonadu02,1943,1,WS1,AL,11,13,31,30,15,2,1,659,218,80,9,46,51,0.25,3.28,,6,4,2,912,1,96,,, +lindqca01,1943,1,BSN,NL,0,2,2,2,0,0,0,39,17,9,3,4,1,0.31,6.23,,0,0,0,58,0,10,,, +lohrmbi01,1943,1,NY1,NL,5,6,17,12,3,0,1,241,110,46,7,25,16,0.32,5.15,,0,2,0,370,3,51,,, +lohrmbi01,1943,2,BRO,NL,0,2,6,2,2,0,0,83,29,11,2,10,5,0.27,3.58,,1,1,0,121,3,14,,, +lowrysa01,1943,1,PHA,AL,0,0,5,0,0,0,0,54,18,10,1,9,3,0.26,5,,0,0,0,79,5,10,,, +lucielo01,1943,1,BOS,AL,3,4,16,9,3,0,0,222,94,32,1,33,23,0.32,3.89,,0,2,0,332,3,35,,, +macfada01,1943,1,BSN,NL,2,1,10,1,0,0,0,64,31,14,1,9,5,0.34,5.91,,0,1,0,100,5,14,,, 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+terryya01,1943,1,BOS,AL,7,9,30,22,7,0,1,491,147,64,8,63,63,0.24,3.52,,4,1,4,684,6,70,,, +tobinji01,1943,1,BSN,NL,14,14,33,30,24,1,0,750,241,74,12,69,52,0.25,2.66,,1,2,0,1049,3,96,,, +tostlo01,1943,1,BSN,NL,0,1,3,1,0,0,0,20,10,4,2,4,3,0.35,5.4,,0,0,0,32,1,5,,, +trinkke01,1943,1,NY1,NL,1,5,11,6,1,0,0,137,51,19,3,15,10,0.27,3.74,,0,1,0,205,1,23,,, +troutdi01,1943,1,DET,AL,20,12,44,30,18,5,6,740,204,68,6,101,111,0.22,2.48,,6,0,0,1019,14,83,,, +truckvi01,1943,1,DET,AL,16,10,33,25,10,2,2,608,170,64,11,52,118,0.22,2.84,,1,1,1,817,6,72,,, +turneji01,1943,1,NYA,AL,3,0,18,0,0,0,1,130,44,17,1,13,15,0.26,3.53,,0,0,0,183,12,22,,, +vandejo01,1943,1,CIN,NL,15,16,36,36,21,3,0,867,228,92,5,162,174,0.22,2.87,,6,3,0,1204,0,102,,, +voisebi01,1943,1,NY1,NL,1,2,4,4,3,0,0,93,18,7,1,14,19,0.15,2.03,,0,0,0,134,0,10,,, +wadeja01,1943,1,CHA,AL,3,7,21,9,3,1,0,251,66,28,3,54,41,0.22,3.01,,0,4,0,364,11,34,,, +waltebu01,1943,1,CIN,NL,15,15,34,34,21,5,0,739,244,97,8,109,80,0.26,3.54,,8,1,1,1054,0,105,,, 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+cuccuco01,1944,1,PIT,NL,2,1,32,4,0,0,4,319,110,48,5,44,31,0.27,4.06,,0,3,0,463,14,65,,, +curtive01,1944,1,WS1,AL,0,1,3,1,0,0,0,29,8,3,0,3,2,0.23,2.79,,0,0,0,37,2,3,,, +daviscu01,1944,1,BRO,NL,10,11,31,23,12,1,4,582,207,72,12,39,49,0.27,3.34,,0,5,0,822,7,84,,, +delacto01,1944,1,CIN,NL,9,9,34,20,9,0,1,574,170,69,9,45,65,0.23,3.25,,0,1,0,770,10,73,,, +derripa01,1944,1,CHN,NL,7,13,42,16,7,0,3,540,205,83,13,39,69,0.28,4.15,,2,0,0,772,17,96,,, +dietrbi01,1944,1,CHA,AL,16,17,36,36,15,2,0,738,269,99,15,68,70,0.27,3.62,,3,2,1,1061,0,132,,, +donahde01,1944,1,PHI,NL,0,2,6,0,0,0,0,28,18,8,0,2,2,0.42,7.71,,0,0,0,45,4,8,,, +donalat01,1944,1,NYA,AL,13,10,30,19,9,0,0,477,173,59,13,59,48,0.28,3.34,,3,2,0,693,9,77,,, +donnebl01,1944,1,SLN,NL,2,1,27,4,2,1,2,229,61,18,2,34,45,0.21,2.12,,3,2,0,322,12,26,,, +dreiscl01,1944,1,BOS,AL,2,4,7,7,3,0,0,146,52,22,2,9,9,0.26,4.07,,0,0,0,207,0,25,,, +dubiemo01,1944,1,NYA,AL,13,13,30,28,19,3,0,696,217,87,12,86,79,0.24,3.38,,1,1,1,984,1,93,,, +eatonze01,1944,1,DET,AL,0,0,6,0,0,0,0,47,19,10,2,8,4,0.32,5.74,,0,0,1,68,4,12,,, +eisenja01,1944,1,CIN,NL,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,,0,0,0,2,1,0,,, +embrere01,1944,1,CLE,AL,0,1,3,1,0,0,0,10,2,5,0,5,4,0.16,13.5,,1,0,0,17,1,5,,, +erickpa01,1944,1,CHN,NL,5,9,33,15,5,3,1,373,113,49,5,67,82,0.24,3.55,,5,0,0,547,6,59,,, +fausebu01,1944,1,CIN,NL,0,0,2,0,0,0,0,32,13,7,0,7,3,0.29,5.91,,1,2,0,54,0,8,,, +feldmha01,1944,1,NY1,NL,11,13,40,27,8,1,2,616,214,95,18,91,70,0.26,4.16,,4,2,0,913,7,120,,, +fergubo02,1944,1,CIN,NL,0,3,9,2,0,0,1,48,24,16,3,10,9,0.35,9,,0,2,0,82,6,17,,, +fickjo01,1944,1,PHI,NL,0,0,4,0,0,0,0,16,3,2,0,3,2,0.15,3.38,,0,1,0,24,1,2,,, +fischru01,1944,1,NY1,NL,6,14,38,18,2,1,2,386,128,74,7,87,39,0.26,5.18,,2,6,0,591,10,83,,, +flemibi01,1944,1,CHN,NL,9,10,39,18,9,1,0,475,163,55,6,62,42,0.26,3.13,,4,1,0,688,10,74,,, +floreje01,1944,1,PHA,AL,9,11,27,25,11,2,0,557,172,70,8,49,65,0.24,3.39,,2,4,0,771,1,75,,, +flowewe01,1944,1,BRO,NL,1,1,9,1,0,0,0,52,26,15,3,13,3,0.33,7.79,,0,1,0,93,2,17,,, +foxho01,1944,1,CIN,NL,0,0,2,0,0,0,0,7,2,0,0,0,0,0.22,0,,0,0,0,9,1,0,,, +frankja01,1944,1,BRO,NL,0,0,1,0,0,0,0,6,2,3,1,4,0,0.25,13.5,,0,2,0,14,1,3,,, +fuchsch01,1944,1,BRO,NL,1,0,8,0,0,0,0,47,25,10,2,9,5,0.34,5.74,,1,1,0,84,1,16,,, +galehde01,1944,1,SLA,AL,9,10,24,19,6,2,0,459,162,53,6,44,80,0.26,3.12,,0,1,0,664,1,64,,, +gassach01,1944,1,CHN,NL,0,1,2,2,0,0,0,35,20,10,3,10,7,0.38,7.71,,1,0,0,62,0,11,,, +geejo01,1944,1,PIT,NL,0,0,4,0,0,0,0,34,20,9,0,5,3,0.37,7.15,,1,0,0,59,2,10,,, +geejo01,1944,2,NY1,NL,0,0,4,0,0,0,0,14,5,0,0,0,3,0.26,0,,0,0,0,19,3,1,,, +gentrru01,1944,1,DET,AL,12,14,37,30,10,3,0,611,211,96,9,108,68,0.27,4.24,,4,4,0,901,5,104,,, +gerheal01,1944,1,PHI,NL,8,16,30,29,10,2,0,548,210,93,8,65,66,0.28,4.58,,6,1,0,811,1,102,,, +gillebo01,1944,1,DET,AL,0,1,7,0,0,0,0,33,7,8,0,12,4,0.19,6.55,,2,0,0,49,2,8,,, +gilmole01,1944,1,PIT,NL,0,1,1,1,1,0,0,24,13,7,2,0,0,0.36,7.88,,0,0,0,36,0,7,,, +gorsijo01,1944,1,DET,AL,6,14,34,19,8,1,4,486,192,74,5,32,47,0.29,4.11,,1,4,1,696,8,88,,, +greggha01,1944,1,BRO,NL,9,16,39,31,6,0,2,593,201,120,12,137,92,0.25,5.46,,10,9,0,941,7,142,,, +gromest01,1944,1,CLE,AL,10,9,35,21,12,2,1,611,160,58,5,70,115,0.21,2.56,,2,3,0,820,8,74,,, +groveor01,1944,1,CHA,AL,14,15,34,33,11,2,0,704,237,97,11,71,105,0.26,3.72,,8,8,0,1006,1,112,,, +gumbeha01,1944,1,SLN,NL,4,2,10,7,3,0,1,184,60,17,1,19,16,0.25,2.49,,0,0,0,255,1,23,,, +gumbeha01,1944,2,CIN,NL,10,8,24,19,11,2,2,466,157,57,7,40,40,0.26,3.3,,0,2,0,653,3,61,,, +haefnmi01,1944,1,WS1,AL,12,15,31,28,18,3,1,684,221,77,7,71,86,0.25,3.04,,0,4,1,974,2,94,,, +hamlilu01,1944,1,PHA,AL,6,12,29,23,9,2,0,570,204,79,13,38,58,0.27,3.74,,4,3,0,809,2,94,,, +hansean01,1944,1,NY1,NL,3,3,23,4,0,0,1,158,63,38,3,32,15,0.3,6.49,,3,3,0,249,7,39,,, +hanskdo01,1944,1,CHA,AL,0,0,2,0,0,0,0,9,5,4,0,2,0,0.35,12,,0,0,1,16,2,4,,, +hanyzed01,1944,1,CHN,NL,2,5,14,7,3,0,0,175,61,29,6,20,19,0.26,4.47,,0,1,0,257,2,33,,, +hardeme01,1944,1,CLE,AL,12,10,30,27,12,2,0,589,211,81,5,69,64,0.27,3.71,,3,3,0,843,0,95,,, +harrilu01,1944,1,PHA,AL,10,9,23,22,12,2,0,523,193,64,8,26,33,0.28,3.3,,6,0,0,724,1,70,,, +hausmcl01,1944,1,BOS,AL,4,7,32,12,3,0,2,411,139,52,6,69,43,0.26,3.42,,1,3,0,602,9,55,,, +haynejo01,1944,1,CHA,AL,5,6,33,12,8,0,2,463,148,44,5,43,44,0.25,2.57,,2,0,0,640,14,55,,, +headed01,1944,1,BRO,NL,4,3,9,8,5,1,0,190,54,19,2,19,17,0.23,2.7,,0,0,0,225,0,21,,, +henryea01,1944,1,CLE,AL,1,1,2,2,1,0,0,53,18,9,0,3,5,0.26,4.58,,1,0,0,71,0,9,,, +henshro01,1944,1,DET,AL,0,0,7,1,0,0,0,37,17,12,0,6,10,0.31,8.76,,0,0,0,60,2,12,,, +herriar01,1944,1,BRO,NL,3,4,12,6,3,1,1,166,59,21,3,17,19,0.27,3.42,,0,1,0,238,5,28,,, +heussed01,1944,1,CIN,NL,13,11,30,23,17,4,2,578,165,51,9,42,42,0.23,2.38,,1,1,0,769,6,59,,, +hevinjo02,1944,1,CLE,AL,8,3,63,1,0,0,10,359,106,26,2,41,46,0.23,1.96,,1,2,0,499,38,42,,, +hickeji01,1944,1,BSN,NL,0,0,8,0,0,0,0,28,15,5,0,5,3,0.36,4.82,,0,1,0,47,7,9,,, 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+johnsvi01,1944,1,BOS,AL,0,3,7,5,0,0,0,82,42,19,0,15,7,0.36,6.26,,1,0,0,135,2,22,,, +juddos01,1944,1,BOS,AL,1,1,9,6,1,0,0,90,30,12,1,15,9,0.26,3.6,,0,0,1,132,2,16,,, +jurisal01,1944,1,SLN,NL,7,9,30,14,5,2,1,390,102,49,7,65,53,0.22,3.39,,2,5,0,540,7,53,,, +karlan01,1944,1,PHI,NL,3,2,38,0,0,0,2,267,76,23,1,21,26,0.23,2.33,,1,1,0,351,20,32,,, +katzbo01,1944,1,CIN,NL,0,1,6,2,0,0,0,55,17,8,0,7,4,0.25,3.93,,0,0,0,75,1,9,,, +kenneve01,1944,1,CLE,AL,2,5,12,10,2,0,0,177,66,33,0,37,17,0.28,5.03,,4,0,0,273,2,36,,, +kenneve01,1944,2,PHI,NL,1,5,12,7,3,0,0,166,60,26,3,20,23,0.26,4.23,,2,0,0,244,2,31,,, +kingcl01,1944,1,BRO,NL,2,1,14,3,1,0,0,131,42,15,1,12,14,0.25,3.09,,0,1,0,179,8,18,,, +kleinha01,1944,1,CLE,AL,1,2,11,6,1,0,0,122,38,26,0,36,13,0.24,5.75,,0,0,0,189,2,29,,, +kliemed01,1944,1,CLE,AL,11,13,47,19,5,1,5,535,185,67,4,70,44,0.27,3.38,,2,7,0,764,14,73,,, +kloppst01,1944,1,BSN,NL,1,2,24,0,0,0,0,139,47,22,1,33,17,0.27,4.27,,3,0,0,212,13,36,,, 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+schanch01,1944,1,PHI,NL,13,16,40,30,13,1,3,724,231,89,6,103,84,0.25,3.32,,5,6,2,1040,8,108,,, +scheica01,1944,1,PHA,AL,0,0,15,0,0,0,0,109,36,17,1,11,13,0.25,4.21,,1,4,2,156,9,18,,, +schmifr01,1944,1,SLN,NL,7,3,37,9,3,2,5,343,94,40,5,58,58,0.22,3.15,,0,1,0,488,17,48,,, +sewarfr01,1944,1,NY1,NL,3,2,25,7,2,0,0,235,98,47,8,32,16,0.3,5.4,,3,2,2,360,9,51,,, +sewelri01,1944,1,PIT,NL,21,12,38,33,24,3,2,858,263,101,15,99,87,0.24,3.18,,1,3,1,1218,4,112,,, +shirlte01,1944,1,SLA,AL,5,4,23,11,2,1,0,241,59,37,4,64,35,0.2,4.15,,2,1,0,360,6,45,,, +shouncl01,1944,1,CIN,NL,13,10,38,21,12,1,2,608,193,68,10,42,55,0.24,3.02,,2,3,0,834,13,83,,, +shumaha01,1944,1,PHI,NL,0,0,18,0,0,0,0,80,26,12,1,11,4,0.24,4.05,,1,0,0,118,12,15,,, +smithal03,1944,1,CLE,AL,7,13,28,26,7,1,0,545,197,69,6,69,44,0.28,3.42,,4,3,0,792,2,83,,, +speerfl01,1944,1,CHA,AL,0,0,2,0,0,0,0,6,4,2,0,0,1,0.5,9,,0,0,0,9,2,2,,, +starrra01,1944,1,PIT,NL,6,5,27,12,5,0,3,269,116,50,6,36,25,0.31,5.02,,1,1,0,413,6,60,,, 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+lopatar01,1945,1,SLN,NL,1,0,4,1,1,0,0,35,7,2,0,3,5,0.15,1.54,,0,1,0,48,2,4,,, +lopated01,1945,1,CHA,AL,10,13,26,24,17,1,1,598,226,91,8,56,74,0.28,4.11,,3,6,1,874,2,101,,, +lucielo01,1945,1,PHI,NL,0,1,13,0,0,0,1,61,14,5,1,5,5,0.19,2.21,,0,0,0,80,10,9,,, +maglisa01,1945,1,NY1,NL,5,4,13,10,7,3,0,253,72,22,2,22,32,0.23,2.35,,0,2,1,341,1,22,,, +marchph01,1945,1,PHA,AL,0,1,3,2,0,0,0,27,5,4,0,11,2,0.17,4,,1,0,0,41,0,5,,, +mastewa02,1945,1,WS1,AL,1,2,4,2,1,1,0,75,21,3,1,10,14,0.22,1.08,,0,0,0,104,1,8,,, +maunedi01,1945,1,PHI,NL,6,10,20,16,6,2,1,368,127,42,7,27,35,0.26,3.08,,2,2,0,509,3,54,,, +mclaupa01,1945,1,DET,AL,0,0,1,0,0,0,0,3,2,1,0,0,0,0.4,9,,0,0,0,5,1,2,,, +milleox01,1945,1,SLA,AL,2,1,4,3,3,0,0,85,23,5,2,5,4,0.21,1.59,,1,0,0,111,0,5,,, +modakmi01,1945,1,CIN,NL,1,2,20,3,1,1,1,127,52,27,0,23,7,0.3,5.74,,1,0,0,195,9,27,,, +montere01,1945,1,PHI,NL,0,0,14,0,0,0,0,137,67,38,1,28,16,0.34,7.49,,1,2,0,232,7,42,,, 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+adamsac01,1946,1,NY1,NL,0,1,3,0,0,0,0,8,9,5,2,1,3,0.5,16.88,,0,0,0,19,1,5,,, +adamsre01,1946,1,CHN,NL,0,1,8,0,0,0,0,36,18,11,1,7,8,0.35,8.25,,0,0,0,59,4,12,,, +albosed01,1946,1,PIT,NL,0,6,17,6,0,0,0,119,41,27,3,35,19,0.26,6.13,,1,1,0,195,3,34,,, +andrena01,1946,1,CIN,NL,2,4,7,7,3,0,0,130,50,19,2,8,13,0.28,3.95,,0,1,0,190,0,29,,, +andrena01,1946,2,NY1,NL,1,0,3,2,0,0,0,36,17,8,2,4,5,0.36,6,,0,0,0,53,0,9,,, +bagbyji02,1946,1,BOS,AL,7,6,21,11,6,1,0,320,117,44,4,49,16,0.27,3.71,,1,1,0,476,4,55,,, +bahred01,1946,1,PIT,NL,8,6,27,14,7,0,0,410,128,40,8,52,44,0.25,2.63,,2,5,0,577,7,57,,, +barnere02,1946,1,BRO,NL,2,5,16,9,1,0,0,161,46,35,2,51,36,0.24,5.87,,1,0,0,249,1,42,,, +barrefr01,1946,1,BSN,NL,2,4,23,0,0,0,1,106,35,20,2,17,12,0.25,5.09,,0,1,0,160,11,21,,, +barrere01,1946,1,SLN,NL,3,2,23,9,1,1,2,201,75,30,5,24,22,0.28,4.03,,2,2,0,298,9,35,,, +bauerru01,1946,1,CHN,NL,2,1,15,2,2,0,1,130,45,17,1,19,22,0.27,3.53,,1,1,0,189,8,17,,, +beazljo01,1946,1,SLN,NL,7,5,19,18,5,0,0,309,109,51,6,55,36,0.27,4.46,,3,4,1,460,0,55,,, +beggsjo01,1946,1,CIN,NL,12,10,28,22,14,2,1,570,175,49,15,39,38,0.24,2.32,,0,1,0,760,4,63,,, +behrmha01,1946,1,BRO,NL,11,5,47,11,2,0,4,452,138,49,3,69,78,0.24,2.93,,2,2,2,655,12,63,,, +bentoal01,1946,1,DET,AL,11,7,28,15,6,1,1,422,132,57,9,58,60,0.24,3.65,,2,1,0,606,6,69,,, +berryjo03,1946,1,PHA,AL,0,1,5,0,0,0,0,39,15,4,1,3,5,0.28,2.77,,0,1,0,57,4,5,,, +berryjo03,1946,2,CLE,AL,3,6,21,0,0,0,1,112,32,14,4,21,16,0.23,3.38,,0,0,0,160,13,18,,, +bessehe01,1946,1,PHA,AL,0,2,7,3,0,0,1,62,19,12,1,9,10,0.24,5.23,,0,0,0,89,2,12,,, +bevenbi01,1946,1,NYA,AL,16,13,31,31,18,3,0,749,213,62,11,78,120,0.23,2.23,,0,1,0,1014,0,73,,, +biscafr01,1946,1,SLA,AL,1,1,16,0,0,0,1,68,28,13,0,22,9,0.31,5.16,,0,0,0,112,5,13,,, +bithohi01,1946,1,CHN,NL,6,5,26,7,2,1,1,260,97,37,5,25,34,0.28,3.84,,0,0,0,376,9,42,,, +blackdo01,1946,1,CLE,AL,1,2,18,4,0,0,0,131,45,22,5,21,15,0.27,4.53,,2,1,0,191,5,26,,, 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+houttar01,1946,1,DET,AL,0,1,1,1,0,0,0,24,15,8,1,0,2,0.38,9,,0,0,0,40,0,8,,, +howarle01,1946,1,PIT,NL,0,1,3,2,1,0,0,40,14,3,0,9,6,0.28,2.03,,0,0,0,61,1,3,,, +hudsosi01,1946,1,WS1,AL,8,11,31,15,6,1,1,427,160,57,9,37,35,0.28,3.6,,6,4,0,616,14,75,,, +hugheto04,1946,1,PHI,NL,6,9,29,13,3,2,1,333,123,54,5,44,34,0.28,4.38,,2,1,0,491,6,64,,, +hughste01,1946,1,BOS,AL,20,11,39,35,21,6,3,834,252,85,15,51,172,0.23,2.75,,1,2,0,1128,4,89,,, +humphjo02,1946,1,PHI,NL,0,0,10,1,0,0,0,74,24,11,1,9,10,0.25,4.01,,0,1,0,103,4,17,,, +hutchfr01,1946,1,DET,AL,14,11,28,26,16,3,2,621,184,71,14,66,138,0.23,3.09,,0,0,1,853,2,78,,, +hutchjo02,1946,1,BSN,NL,0,1,1,1,0,0,0,9,5,3,1,1,1,0.35,9,,0,0,0,15,0,3,,, +javeral01,1946,1,BSN,NL,0,1,2,1,0,0,0,10,5,5,0,5,0,0.41,13.5,,0,0,0,18,1,5,,, +johnsch03,1946,1,SLA,AL,0,0,5,3,0,0,0,54,20,10,0,13,8,0.28,5,,0,0,0,84,2,12,,, +johnsea01,1946,1,BOS,AL,5,4,29,5,0,0,3,240,78,33,5,39,40,0.25,3.71,,1,2,0,356,14,39,,, +johnssi01,1946,1,PHI,NL,0,0,1,0,0,0,0,9,7,1,1,0,2,0.53,3,,0,0,0,16,0,4,,, +johnssi01,1946,2,BSN,NL,6,5,28,12,5,1,1,381,134,39,8,35,41,0.27,2.76,,2,4,0,544,6,47,,, +johnsvi01,1946,1,CLE,AL,0,1,9,1,0,0,0,41,20,14,1,8,3,0.35,9.22,,0,0,0,64,3,14,,, +jonessh01,1946,1,NY1,NL,1,2,6,4,1,0,0,84,21,10,4,17,24,0.2,3.21,,0,1,0,122,1,10,,, +joycebo01,1946,1,NY1,NL,3,4,14,7,2,0,0,182,79,36,3,20,24,0.31,5.34,,1,0,0,275,2,43,,, +juddos01,1946,1,PHI,NL,11,12,30,24,12,1,2,520,169,68,6,90,65,0.26,3.53,,4,1,0,760,3,86,,, +jurisal01,1946,1,PHI,NL,4,3,13,10,2,1,1,205,71,28,9,31,34,0.26,3.69,,3,1,0,305,2,30,,, +karlan01,1946,1,PHI,NL,3,7,39,0,0,0,5,196,84,36,6,22,15,0.32,4.96,,0,1,0,292,23,37,,, +karpehe01,1946,1,NYA,AL,0,0,2,0,0,0,0,5,4,2,0,0,0,0.5,10.8,,0,0,0,8,0,2,,, +kennebi01,1946,1,WS1,AL,1,2,21,2,0,0,3,117,40,26,1,29,18,0.27,6,,1,0,0,182,12,29,,, +kennemo01,1946,1,NY1,NL,9,10,38,27,10,1,1,560,153,71,14,116,71,0.22,3.42,,5,4,1,814,4,80,,, +kindeel01,1946,1,SLA,AL,3,3,33,7,1,0,1,260,78,32,8,36,59,0.24,3.32,,1,0,0,366,12,35,,, +kliemed01,1946,1,CLE,AL,0,0,9,0,0,0,0,45,18,11,0,10,2,0.29,6.6,,1,0,0,72,3,13,,, +klingbo01,1946,1,BOS,AL,3,2,28,1,0,0,9,171,49,15,1,25,16,0.23,2.37,,1,1,0,236,20,16,,, +knerrlo01,1946,1,PHA,AL,3,16,30,22,6,0,0,445,171,89,13,67,58,0.28,5.4,,1,1,1,674,5,95,,, +knottja01,1946,1,PHA,AL,0,1,3,1,0,0,0,19,7,4,1,1,2,0.28,5.68,,0,1,1,28,1,4,,, +koechdi01,1946,1,PHI,NL,0,1,1,1,0,0,0,8,7,3,0,1,2,0.46,10.13,,0,0,0,16,0,3,,, +konstji01,1946,1,BSN,NL,0,1,10,1,0,0,0,46,17,9,2,7,9,0.28,5.28,,2,0,0,67,3,9,,, +kosloda01,1946,1,NY1,NL,14,19,40,35,17,3,1,796,251,107,15,101,121,0.24,3.63,,5,5,0,1136,5,119,,, +krakajo01,1946,1,CLE,AL,2,5,29,5,0,0,1,142,60,29,2,25,20,0.31,5.51,,1,0,0,221,11,31,,, +krameja01,1946,1,SLA,AL,13,11,31,28,13,3,0,584,190,69,6,68,69,0.25,3.19,,4,0,2,818,2,84,,, +krausja01,1946,1,NY1,NL,2,1,17,1,0,0,0,75,25,17,4,15,7,0.26,6.12,,3,1,0,114,8,17,,, +kressre01,1946,1,NY1,NL,0,0,1,0,0,0,0,11,5,5,1,1,1,0.33,12.27,,0,1,0,17,0,5,,, +kretllo01,1946,1,DET,AL,1,0,1,1,1,0,0,27,7,3,2,2,4,0.2,3,,0,0,0,36,0,3,,, +kristho01,1946,1,SLN,NL,0,2,15,0,0,0,0,56,22,14,3,8,3,0.3,6.75,,0,1,0,84,5,15,,, +kushem01,1946,1,CHN,NL,9,2,40,6,1,1,2,389,120,44,4,43,50,0.25,3.05,,1,3,0,536,19,47,,, +kuzavbo01,1946,1,CLE,AL,1,0,2,2,0,0,0,36,9,4,0,11,4,0.19,3,,0,1,0,60,0,7,,, +ladedo01,1946,1,CHN,NL,0,2,3,2,0,0,0,46,15,7,0,3,8,0.23,4.11,,0,1,0,67,0,8,,, +lamacal01,1946,1,SLA,AL,0,0,8,0,0,0,0,45,17,10,2,7,3,0.27,6,,2,0,0,70,5,10,,, +lamacal01,1946,2,WS1,AL,0,1,2,0,0,0,0,8,6,5,1,2,0,0.46,16.88,,0,0,0,15,1,5,,, +lambecl01,1946,1,CIN,NL,2,2,23,4,2,0,1,158,48,25,3,20,20,0.25,4.27,,0,1,0,214,8,27,,, +laniema01,1946,1,SLN,NL,6,0,6,6,6,2,0,168,45,12,1,19,36,0.22,1.93,,0,1,0,221,0,13,,, +lannijo01,1946,1,PIT,NL,4,5,27,9,3,0,1,273,97,31,3,31,16,0.26,3.07,,2,1,0,400,12,36,,, +leebi02,1946,1,BSN,NL,10,9,25,21,8,0,0,420,148,65,7,45,32,0.27,4.18,,0,1,0,602,3,73,,, +leeth01,1946,1,CHA,AL,2,4,7,7,2,0,0,130,39,17,1,23,23,0.24,3.53,,2,1,0,187,0,24,,, +lemonbo01,1946,1,CLE,AL,4,5,32,5,1,0,1,282,77,26,1,68,39,0.22,2.49,,5,0,0,412,17,40,,, +leonadu02,1946,1,WS1,AL,10,10,26,23,7,2,0,485,182,64,9,36,62,0.28,3.56,,2,4,0,695,1,85,,, +libkeal01,1946,1,CIN,NL,0,0,1,1,0,0,0,15,4,2,0,3,2,0.23,3.6,,1,0,0,20,0,2,,, +lombavi01,1946,1,BRO,NL,13,10,41,25,13,2,3,579,170,62,10,84,60,0.23,2.89,,3,2,1,829,10,76,,, +lopatar01,1946,1,PHI,NL,0,1,4,1,0,0,0,16,13,10,1,4,4,0.44,16.88,,1,0,0,33,2,11,,, +lopated01,1946,1,CHA,AL,13,13,29,29,20,2,0,693,216,70,18,48,89,0.24,2.73,,1,1,1,934,0,80,,, +lyonsal01,1946,1,NYA,AL,0,1,2,1,0,0,0,25,11,5,0,6,4,0.31,5.4,,0,1,0,42,1,5,,, +lyonste01,1946,1,CHA,AL,1,4,5,5,5,0,0,128,38,11,2,9,10,0.23,2.32,,0,0,0,174,0,17,,, +mallobo01,1946,1,CIN,NL,2,5,27,3,1,0,2,216,71,22,2,26,24,0.26,2.75,,1,2,0,306,17,29,,, +maltzgo01,1946,1,CHA,AL,2,0,19,0,0,0,2,119,30,7,3,6,17,0.2,1.59,,1,1,0,154,10,7,,, +mandeha01,1946,1,DET,AL,0,0,2,0,0,0,0,18,8,7,1,2,3,0.36,10.5,,0,1,0,26,1,7,,, +mandeha01,1946,2,CHN,NL,0,1,2,1,0,0,0,18,11,6,1,3,4,0.42,9,,0,1,0,30,0,6,,, +marchph01,1946,1,PHA,AL,13,16,36,29,16,1,1,680,197,88,14,114,95,0.23,3.49,,7,4,2,963,6,104,,, +marshcu01,1946,1,NYA,AL,3,4,23,11,1,0,0,243,96,48,4,56,32,0.3,5.33,,1,0,1,374,5,49,,, +martifr02,1946,1,SLN,NL,2,1,6,3,2,0,0,86,29,13,0,8,19,0.25,4.08,,0,0,0,123,2,13,,, +mastewa02,1946,1,WS1,AL,5,6,29,9,2,0,1,274,105,61,8,67,61,0.29,6.01,,2,3,0,435,11,70,,, +maunedi01,1946,1,PHI,NL,6,4,24,7,3,1,2,270,98,27,4,18,31,0.27,2.7,,0,3,0,378,8,36,,, +mccabra01,1946,1,CLE,AL,0,1,1,1,0,0,0,12,5,5,3,2,3,0.31,11.25,,0,1,0,19,0,5,,, +mccahbi01,1946,1,PHA,AL,1,1,4,2,2,1,0,54,16,2,0,9,6,0.24,1,,1,0,0,77,1,2,,, +mclisca01,1946,1,BRO,NL,0,0,1,0,0,0,0,0,1,0,0,0,0,1,,,0,0,0,2,0,2,,, +meersru01,1946,1,CHN,NL,1,2,7,2,0,0,0,34,10,4,0,10,2,0.23,3.18,,4,0,0,53,2,6,,, +meltoru01,1946,1,BRO,NL,6,3,24,12,3,2,1,299,72,22,3,52,44,0.2,1.99,,1,3,0,412,8,27,,, +meyerru01,1946,1,CHN,NL,0,0,4,1,0,0,1,51,21,6,2,10,10,0.3,3.18,,1,0,0,78,1,7,,, +milleox01,1946,1,SLA,AL,1,3,11,3,0,0,1,106,52,27,5,15,12,0.33,6.88,,1,0,0,171,2,28,,, +milnaal01,1946,1,SLA,AL,1,1,4,2,1,1,0,44,15,4,1,6,1,0.27,2.45,,0,0,0,61,2,4,,, +milnaal01,1946,2,PHI,NL,0,0,1,1,0,0,0,0,2,0,0,2,0,1,,,0,0,0,4,0,4,,, +minnepa01,1946,1,BRO,NL,0,1,3,0,0,0,0,12,6,3,1,3,3,0.33,6.75,,0,0,0,22,0,4,,, +mouldgl01,1946,1,BRO,NL,0,0,1,0,0,0,0,6,2,1,1,1,1,0.28,4.5,,0,0,0,8,1,1,,, +mulcahu01,1946,1,PHI,NL,2,4,16,5,1,0,0,188,69,31,3,33,12,0.29,4.45,,5,5,0,284,5,34,,, +mullidi01,1946,1,PHI,NL,2,2,19,5,1,0,1,164,61,29,0,27,16,0.28,4.77,,2,4,0,248,9,32,,, +mullidi01,1946,2,BSN,NL,1,0,4,0,0,0,0,46,6,4,1,9,4,0.12,2.35,,0,0,0,58,2,4,,, +muncrbo01,1946,1,SLA,AL,3,12,29,14,4,1,0,346,149,64,6,31,49,0.31,4.99,,2,0,0,514,4,75,,, +mungere01,1946,1,SLN,NL,2,2,10,7,2,0,1,146,47,18,0,12,28,0.25,3.33,,1,0,0,199,1,19,,, +murphjo04,1946,1,NYA,AL,4,2,27,0,0,0,7,135,40,17,4,19,19,0.24,3.4,,1,0,0,192,24,22,,, +newhoha01,1946,1,DET,AL,26,9,37,34,29,6,1,878,215,63,10,98,275,0.2,1.94,,8,1,0,1176,2,77,,, +newsobo01,1946,1,PHA,AL,3,5,10,9,3,1,0,176,61,22,2,30,32,0.26,3.38,,0,5,0,267,0,27,,, +newsobo01,1946,2,WS1,AL,11,8,24,22,14,2,1,534,163,55,5,60,82,0.24,2.78,,3,2,0,744,1,63,,, +niggejo01,1946,1,WS1,AL,3,2,8,6,3,0,0,114,39,17,1,21,10,0.26,4.03,,0,1,0,173,1,22,,, +niggejo01,1946,2,BSN,NL,2,5,8,8,3,0,0,174,54,21,2,21,24,0.24,3.26,,3,1,0,247,0,23,,, +olsenve01,1946,1,CHN,NL,0,0,5,0,0,0,0,29,10,3,0,9,8,0.29,2.79,,0,0,0,44,3,3,,, +oneilem01,1946,1,CHN,NL,0,0,1,0,0,0,0,3,0,0,0,3,1,0,0,,0,0,0,6,0,0,,, +oneilem01,1946,2,CHA,AL,0,0,2,0,0,0,0,11,4,0,0,5,0,0.33,0,,0,0,0,18,1,2,,, +osterfr01,1946,1,PIT,NL,13,10,27,25,16,2,0,580,193,61,5,56,57,0.26,2.84,,1,3,0,810,2,70,,, +overmst01,1946,1,DET,AL,5,7,24,13,3,0,1,292,106,50,6,29,34,0.27,4.62,,1,0,0,422,7,54,,, +pagejo01,1946,1,NYA,AL,9,8,31,17,6,1,3,408,126,54,7,72,77,0.25,3.57,,1,4,2,588,8,66,,, +papisfr01,1946,1,CHA,AL,7,5,31,15,6,2,0,414,122,42,7,63,66,0.24,2.74,,2,1,0,573,7,52,,, +passecl01,1946,1,CHN,NL,9,8,21,21,10,2,0,388,118,45,5,42,47,0.23,3.13,,1,1,0,546,0,53,,, +pearsik01,1946,1,PHI,NL,1,0,5,2,1,1,0,43,16,6,1,8,6,0.27,3.77,,0,1,0,69,1,8,,, +permele01,1946,1,CHA,AL,0,0,4,0,0,0,0,13,6,4,0,7,2,0.31,8.31,,0,0,0,26,2,4,,, +pierema01,1946,1,WS1,AL,2,2,30,2,1,0,0,186,70,41,9,40,20,0.29,5.95,,2,2,0,291,10,48,,, +podgajo01,1946,1,CLE,AL,0,0,6,0,0,0,0,27,13,5,0,2,4,0.3,5,,0,0,0,45,4,8,,, +polleho01,1946,1,SLN,NL,21,10,40,32,22,4,5,798,228,62,12,86,107,0.23,2.1,,1,5,0,1083,7,84,,, +posedbi01,1946,1,BSN,NL,2,0,19,0,0,0,4,85,34,22,4,13,9,0.3,6.99,,1,0,0,127,10,24,,, +posselo01,1946,1,PHI,NL,1,2,4,4,0,0,0,41,19,9,0,10,4,0.33,5.93,,0,1,0,68,0,9,,, +pottene01,1946,1,SLA,AL,8,9,23,19,10,0,0,435,152,60,9,59,72,0.26,3.72,,4,3,0,641,2,72,,, +primra01,1946,1,CHN,NL,2,3,14,2,0,0,1,70,28,15,5,10,10,0.28,5.79,,0,0,0,108,6,17,,, +queenme01,1946,1,NYA,AL,1,1,14,3,1,0,0,91,40,22,2,21,26,0.31,6.53,,0,0,1,152,3,28,,, +raffeke01,1946,1,PHI,NL,8,15,39,23,14,2,6,588,203,79,10,39,73,0.26,3.63,,2,1,0,819,10,89,,, +raschvi01,1946,1,NYA,AL,2,0,2,2,2,0,0,48,14,7,0,5,11,0.23,3.94,,0,0,0,67,0,7,,, +reidea01,1946,1,BSN,NL,1,0,2,0,0,0,0,9,4,1,0,3,2,0.3,3,,0,0,0,16,1,3,,, +reynoal01,1946,1,CLE,AL,11,15,31,28,9,3,0,550,180,79,10,108,107,0.25,3.88,,4,1,0,815,2,93,,, +rignejo01,1946,1,CHA,AL,5,5,15,11,3,2,0,248,76,37,6,35,51,0.24,4.03,,1,2,0,357,2,37,,, +ripplch01,1946,1,PHI,NL,1,0,6,0,0,0,0,10,5,4,0,6,3,0.38,10.8,,0,0,0,19,1,4,,, +roepr01,1946,1,PIT,NL,3,8,21,10,1,0,2,210,83,40,5,25,28,0.29,5.14,,0,2,0,315,8,50,,, +roserst01,1946,1,NYA,AL,1,1,4,1,0,0,0,10,7,6,0,4,1,0.43,16.2,,0,0,0,20,2,6,,, +roserst01,1946,2,BSN,NL,1,1,14,1,0,0,1,105,33,14,1,18,18,0.25,3.6,,1,0,1,153,7,15,,, +rowesc01,1946,1,PHI,NL,11,4,17,16,9,2,0,408,112,32,3,21,51,0.22,2.12,,0,6,0,538,0,39,,, +royje01,1946,1,BRO,NL,0,0,3,1,0,0,0,19,5,7,2,5,6,0.2,9.95,,1,0,0,30,0,7,,, +ruffire01,1946,1,NYA,AL,5,1,8,8,4,2,0,183,37,12,2,23,19,0.17,1.77,,0,0,0,243,0,13,,, +russoma01,1946,1,NYA,AL,0,2,8,3,0,0,0,56,26,9,1,11,7,0.33,4.34,,0,0,0,89,2,9,,, +rybami01,1946,1,BOS,AL,0,1,9,0,0,0,1,38,12,5,1,5,5,0.26,3.55,,1,0,0,53,5,7,,, +sainjo01,1946,1,BSN,NL,20,14,37,34,24,3,2,795,225,65,8,87,129,0.23,2.21,,3,2,2,1086,2,80,,, +sanfofr01,1946,1,SLA,AL,2,1,3,3,2,2,0,66,19,5,0,9,8,0.23,2.05,,0,0,0,91,0,7,,, +savagbo01,1946,1,PHA,AL,3,15,40,19,7,1,2,492,164,74,5,93,78,0.25,4.06,,0,2,0,746,14,80,,, +scarbra01,1946,1,WS1,AL,7,11,32,20,6,1,1,467,176,70,8,74,46,0.28,4.05,,2,1,1,695,5,85,,, +schanch01,1946,1,PHI,NL,6,6,32,15,4,0,4,349,130,75,8,71,47,0.28,5.8,,6,5,0,535,12,82,,, +schmifr01,1946,1,SLN,NL,1,0,16,0,0,0,0,82,27,10,0,15,14,0.27,3.29,,0,3,0,121,5,11,,, +schmijo01,1946,1,CHN,NL,11,11,41,31,14,2,2,673,184,65,6,94,135,0.22,2.61,,3,2,1,945,8,77,,, 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+wightbi01,1946,1,NYA,AL,2,2,14,4,1,0,0,121,44,20,1,30,11,0.28,4.46,,2,1,0,189,3,22,,, +wilkile01,1946,1,PIT,NL,0,0,7,0,0,0,0,23,13,9,0,3,3,0.38,10.57,,0,0,0,37,4,9,,, +wilkste01,1946,1,SLN,NL,8,0,40,4,0,0,1,285,88,36,13,38,40,0.24,3.41,,0,2,0,404,18,41,,, +williac01,1946,1,BSN,NL,0,0,1,0,0,0,0,0,1,0,0,1,0,1,,,0,0,0,2,0,0,,, +wilsoji02,1946,1,BOS,AL,0,0,1,0,0,0,0,2,2,2,1,0,0,0.5,27,,0,0,0,4,1,2,,, +wilsoma01,1946,1,WS1,AL,0,1,9,0,0,0,0,38,16,10,1,9,8,0.32,7.11,,0,0,0,61,5,12,,, +wolffro01,1946,1,WS1,AL,5,8,21,17,6,0,0,366,115,35,8,30,50,0.24,2.58,,1,5,0,506,2,51,,, +wrighed01,1946,1,BSN,NL,12,9,36,21,8,2,0,529,164,69,8,71,44,0.25,3.52,,2,2,0,744,7,82,,, +wynnea01,1946,1,WS1,AL,8,5,17,12,9,0,0,321,112,37,8,33,36,0.26,3.11,,3,3,0,460,5,45,,, +wyseha01,1946,1,CHN,NL,14,12,40,27,12,2,1,604,206,60,7,52,52,0.26,2.68,,2,3,1,843,10,73,,, +zoldasa01,1946,1,SLA,AL,9,11,35,21,9,2,2,511,166,65,11,57,51,0.25,3.43,,2,1,0,718,7,71,,, 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+blackew01,1947,1,CIN,NL,22,8,33,33,23,6,0,819,227,75,10,95,193,0.23,2.47,,2,4,1,1086,0,91,,, +bonhati01,1947,1,PIT,NL,11,8,33,18,7,3,3,449,167,64,17,35,63,0.27,3.85,,1,2,0,642,12,67,,, +borowha01,1947,1,CHN,NL,8,12,40,25,7,1,2,549,190,89,19,63,75,0.26,4.38,,1,1,1,784,11,99,,, +brancra01,1947,1,BRO,NL,21,12,43,36,15,4,1,840,251,83,22,98,148,0.24,2.67,,7,6,1,1158,5,100,,, +brazlal01,1947,1,SLN,NL,14,8,44,19,7,0,4,504,186,53,7,48,85,0.28,2.84,,4,2,0,713,11,65,,, +brechha01,1947,1,SLN,NL,16,11,29,28,18,1,1,670,220,82,20,66,89,0.26,3.3,,3,3,0,927,1,92,,, +brisslo01,1947,1,PHA,AL,0,1,1,1,0,0,0,21,9,5,1,5,4,0.31,6.43,,2,0,0,34,0,5,,, +brownwa01,1947,1,SLA,AL,1,0,19,0,0,0,0,138,50,25,3,28,10,0.29,4.89,,1,0,0,202,13,27,,, +budnimi01,1947,1,NY1,NL,0,0,7,1,0,0,0,36,16,14,0,10,6,0.31,10.5,,0,0,0,62,0,16,,, +burkhke01,1947,1,SLN,NL,3,6,34,6,1,0,1,285,108,55,13,23,44,0.29,5.21,,3,4,1,404,9,55,,, +butlabi01,1947,1,BOS,AL,0,0,1,0,0,0,0,6,3,1,0,0,1,0.33,4.5,,0,0,0,9,0,1,,, 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+harriea01,1947,1,CHA,AL,3,8,33,4,0,0,5,281,85,37,3,49,55,0.24,3.56,,4,3,0,406,12,48,,, +harrilu01,1947,1,WS1,AL,0,0,3,0,0,0,0,19,7,2,0,7,2,0.31,2.84,,0,0,0,33,1,2,,, +harrimi01,1947,1,BOS,AL,5,4,15,6,1,0,0,155,42,14,3,23,35,0.22,2.44,,0,0,0,214,7,20,,, +hartucl01,1947,1,NY1,NL,9,7,23,20,8,1,0,414,140,70,15,69,54,0.26,4.57,,2,2,0,608,1,76,,, +hattejo01,1947,1,BRO,NL,17,8,42,32,11,3,0,676,211,91,9,105,76,0.25,3.63,,6,5,0,968,2,95,,, +haugsph01,1947,1,BRO,NL,1,0,6,1,0,0,0,38,14,4,1,4,4,0.29,2.84,,0,0,1,52,1,4,,, +haynejo01,1947,1,CHA,AL,14,6,29,22,7,2,0,546,174,49,5,61,50,0.25,2.42,,4,2,1,767,5,65,,, +hearnji01,1947,1,SLN,NL,12,7,37,21,4,1,1,486,151,58,9,63,57,0.24,3.22,,0,1,0,681,9,67,,, +heintke01,1947,1,PIT,NL,0,0,2,1,0,0,0,12,9,9,2,6,2,0.4,20.25,,0,0,0,28,0,11,,, +heintke01,1947,2,PHI,NL,7,10,24,19,8,0,1,408,144,61,12,46,55,0.27,4.04,,3,2,0,571,3,72,,, +herriar01,1947,1,PIT,NL,1,3,11,0,0,0,2,32,18,10,3,4,6,0.36,8.44,,0,0,0,55,8,11,,, 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+iottho01,1947,2,NY1,NL,3,8,20,9,2,1,0,214,67,47,3,52,46,0.25,5.93,,2,1,0,328,8,50,,, +jansela01,1947,1,NY1,NL,21,5,42,30,20,1,1,744,241,87,23,57,104,0.26,3.16,,2,1,1,985,5,102,,, +johnsdo03,1947,1,NYA,AL,4,3,15,8,2,0,0,163,57,22,2,23,16,0.27,3.64,,0,1,0,238,4,26,,, +johnsea01,1947,1,BOS,AL,12,11,45,17,6,3,8,427,129,47,7,62,65,0.24,2.97,,1,2,1,596,17,63,,, +johnske01,1947,1,SLN,NL,1,0,2,1,1,0,0,30,2,0,0,5,8,0.06,0,,0,1,0,38,1,1,,, +johnssi01,1947,1,BSN,NL,6,8,36,10,3,0,2,338,124,53,7,34,27,0.27,4.23,,1,1,0,493,15,57,,, +jonessh01,1947,1,NY1,NL,2,2,15,6,0,0,0,167,51,24,2,29,24,0.25,3.88,,1,3,0,240,5,27,,, +juddos01,1947,1,PHI,NL,4,15,32,19,8,1,0,440,155,75,6,69,54,0.27,4.6,,8,3,0,639,4,86,,, +jurisal01,1947,1,PHI,NL,1,7,34,12,5,0,3,355,110,65,15,52,48,0.25,4.94,,0,1,0,488,12,69,,, +karlan01,1947,1,BSN,NL,2,3,27,0,0,0,3,105,41,15,2,13,5,0.31,3.86,,0,0,0,147,17,18,,, +kennebi01,1947,1,WS1,AL,0,0,2,0,0,0,0,20,10,6,1,5,1,0.37,8.1,,0,0,0,34,0,8,,, 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+ladedo01,1947,1,CHN,NL,11,10,34,25,7,1,0,562,202,82,15,79,62,0.27,3.94,,5,1,1,826,5,105,,, +lambecl01,1947,1,CIN,NL,0,0,3,0,0,0,0,17,12,10,3,6,1,0.44,15.88,,0,0,0,33,0,10,,, +lanfrwa01,1947,1,BSN,NL,4,4,36,4,1,0,1,192,65,21,2,27,18,0.27,2.95,,0,0,0,271,12,23,,, +lannijo01,1947,1,BSN,NL,0,0,3,0,0,0,0,11,4,4,0,6,0,0.4,9.82,,0,0,0,18,0,5,,, +leebi02,1947,1,CHN,NL,0,2,14,2,0,0,0,72,26,12,2,14,9,0.26,4.5,,0,1,0,116,4,16,,, +leeth01,1947,1,CHA,AL,3,7,21,11,2,1,1,260,86,43,5,56,57,0.26,4.47,,1,2,0,394,5,50,,, +lemonbo01,1947,1,CLE,AL,11,5,37,15,6,1,3,502,150,64,7,97,65,0.24,3.44,,6,4,0,725,15,68,,, +leonadu02,1947,1,PHI,NL,17,12,32,29,19,3,0,705,224,70,14,57,103,0.25,2.68,,4,2,0,943,2,86,,, +lindely01,1947,1,CLE,AL,0,0,1,0,0,0,0,2,3,2,0,1,0,0.6,27,,0,0,0,6,0,2,,, +livelbu01,1947,1,CIN,NL,4,7,38,17,3,1,0,369,126,64,16,63,52,0.26,4.68,,1,0,1,553,6,75,,, +lombavi01,1947,1,BRO,NL,12,11,33,20,7,3,3,524,156,58,12,65,72,0.24,2.99,,4,2,1,726,9,73,,, +lopated01,1947,1,CHA,AL,16,13,31,31,22,3,0,758,241,79,17,73,109,0.25,2.81,,0,2,0,1047,0,88,,, +lyonsal01,1947,1,NYA,AL,1,0,6,0,0,0,0,33,18,11,2,9,7,0.36,9,,1,0,0,59,1,11,,, +lyonsal01,1947,2,PIT,NL,1,2,13,0,0,0,0,85,36,23,4,12,16,0.3,7.31,,2,1,0,133,6,24,,, +maconma01,1947,1,BSN,NL,0,0,1,0,0,0,0,6,1,0,0,1,1,0.16,0,,0,0,0,7,1,0,,, +mallobo01,1947,1,CIN,NL,0,0,1,0,0,0,0,3,3,2,1,0,1,0.6,18,,0,0,0,5,0,2,,, +maltzgo01,1947,1,CHA,AL,1,4,33,0,0,0,5,191,61,24,4,25,22,0.25,3.39,,0,1,0,271,22,26,,, +marchph01,1947,1,PHA,AL,19,9,35,35,21,2,0,830,228,99,15,141,128,0.22,3.22,,1,7,0,1172,0,110,,, +martira01,1947,1,BSN,NL,1,0,1,1,1,0,0,27,7,1,0,4,2,0.21,1,,0,0,0,37,0,1,,, +mastewa02,1947,1,WS1,AL,12,16,35,31,14,4,1,759,215,88,11,97,135,0.23,3.13,,3,2,0,1035,4,98,,, +maunedi01,1947,1,PHI,NL,0,0,9,1,0,0,1,49,15,7,1,7,6,0.28,3.86,,0,1,1,62,7,8,,, +mccahbi01,1947,1,PHA,AL,10,5,29,19,10,1,0,496,160,61,7,62,47,0.25,3.32,,3,0,0,703,5,73,,, +mclisca01,1947,1,PIT,NL,0,0,1,0,0,0,0,3,2,2,0,0,0,0.4,18,,0,1,0,6,1,2,,, +meersru01,1947,1,CHN,NL,2,0,35,1,0,0,0,193,61,32,5,38,28,0.26,4.48,,4,2,0,278,16,34,,, +meltoru01,1947,1,BRO,NL,0,1,4,1,0,0,0,14,7,7,1,7,1,0.35,13.5,,0,0,0,27,0,7,,, +meyerru01,1947,1,CHN,NL,3,2,23,2,1,0,0,135,43,17,4,14,22,0.25,3.4,,2,1,0,185,9,17,,, +milleox01,1947,1,CHN,NL,1,2,4,4,1,0,0,48,31,18,2,5,7,0.39,10.13,,0,0,0,83,0,18,,, +mouldgl01,1947,1,SLA,AL,4,2,32,2,0,0,2,219,78,31,4,43,23,0.28,3.82,,0,0,0,329,25,37,,, +mulcahu01,1947,1,PIT,NL,0,0,2,1,0,0,0,20,8,3,1,7,2,0.33,4.05,,0,0,0,31,1,7,,, +mullidi01,1947,1,BSN,NL,0,0,1,0,0,0,0,6,4,2,0,1,1,0.4,9,,0,0,1,11,1,2,,, +muncrbo01,1947,1,SLA,AL,8,14,31,23,7,0,0,529,210,96,14,51,74,0.29,4.9,,3,2,0,770,6,108,,, +mungere01,1947,1,SLN,NL,16,5,40,31,13,6,3,673,218,84,12,76,123,0.25,3.37,,4,2,0,936,6,94,,, +murphjo04,1947,1,BOS,AL,0,0,32,0,0,0,3,164,41,17,1,28,9,0.2,2.8,,2,0,0,231,16,17,,, +nagyst01,1947,1,PIT,NL,1,3,6,1,0,0,0,42,18,9,1,9,4,0.31,5.79,,2,0,0,68,1,10,,, +newhoha01,1947,1,DET,AL,17,17,40,36,24,3,2,855,268,91,9,110,176,0.24,2.87,,11,2,0,1216,4,105,,, +newsobo01,1947,1,WS1,AL,4,6,14,13,1,0,0,251,99,38,2,37,40,0.29,4.09,,1,1,1,381,0,44,,, +newsobo01,1947,2,NYA,AL,7,5,17,15,6,2,0,347,109,36,8,30,42,0.25,2.8,,0,2,0,475,1,38,,, +osterfr01,1947,1,PIT,NL,12,10,26,24,12,3,0,549,181,78,18,68,66,0.25,3.84,,5,1,0,789,0,94,,, +overmst01,1947,1,DET,AL,11,5,28,17,7,3,0,422,142,59,9,44,33,0.25,3.77,,0,1,0,600,8,62,,, +pagejo01,1947,1,NYA,AL,14,8,56,2,0,0,17,424,105,39,5,72,116,0.2,2.48,,1,1,0,584,44,41,,, +palicer01,1947,1,BRO,NL,0,1,3,0,0,0,0,9,2,1,0,2,1,0.18,3,,0,1,0,15,1,1,,, +papisfr01,1947,1,CHA,AL,12,12,38,26,6,1,3,597,185,72,6,98,79,0.24,3.26,,6,2,0,871,9,82,,, +parneme01,1947,1,BOS,AL,2,3,15,5,1,0,0,152,60,36,1,27,23,0.29,6.39,,4,1,1,232,5,41,,, +passecl01,1947,1,CHN,NL,2,6,19,6,1,1,2,190,97,44,7,24,26,0.35,6.25,,3,1,0,309,9,54,,, +perkoha01,1947,1,CIN,NL,0,0,3,1,0,0,0,22,12,3,1,3,2,0.37,3.68,,0,0,0,35,1,3,,, +peterke01,1947,1,CIN,NL,6,13,37,17,3,1,2,457,156,72,8,62,78,0.26,4.25,,2,3,1,661,13,74,,, +piconma01,1947,1,NY1,NL,0,0,2,1,0,0,0,21,10,6,1,2,1,0.34,7.71,,1,0,0,31,1,6,,, +pierema01,1947,1,WS1,AL,2,4,23,10,2,1,0,250,97,39,3,47,32,0.28,4.21,,3,2,0,394,6,50,,, +poatra01,1947,1,NY1,NL,4,3,7,7,5,0,0,180,53,17,8,13,25,0.23,2.55,,1,0,1,239,0,18,,, +polivke01,1947,1,CIN,NL,0,0,2,0,0,0,0,9,3,1,0,3,1,0.25,3,,0,0,0,15,2,1,,, +polleho01,1947,1,SLN,NL,9,11,37,24,9,0,2,529,195,85,11,87,73,0.28,4.34,,4,3,0,784,5,96,,, +posselo01,1947,1,PHI,NL,0,0,2,0,0,0,0,13,5,2,0,0,1,0.38,4.15,,0,1,0,16,1,2,,, +pottene01,1947,1,SLA,AL,4,10,32,10,3,0,2,368,130,55,13,44,65,0.27,4.04,,1,2,0,529,19,61,,, +queenme01,1947,1,NYA,AL,0,0,5,0,0,0,0,20,9,7,2,4,2,0.32,9.45,,0,1,0,33,2,7,,, +queenme01,1947,2,PIT,NL,3,7,14,12,2,0,0,222,70,33,8,51,34,0.24,4.01,,0,1,1,343,1,39,,, +raffeke01,1947,1,PHI,NL,2,6,10,7,3,1,0,123,50,25,4,8,16,0.3,5.49,,0,1,0,176,0,30,,, +raffeke01,1947,2,CIN,NL,6,5,19,15,7,0,1,320,132,49,11,29,38,0.3,4.13,,0,0,0,472,3,54,,, +ramsdwi01,1947,1,BRO,NL,1,1,2,0,0,0,0,8,4,2,0,3,3,0.33,6.75,,0,1,0,17,1,6,,, +raschvi01,1947,1,NYA,AL,7,2,15,14,6,1,0,314,89,45,11,38,51,0.22,3.87,,2,1,0,436,0,47,,, +reynoal01,1947,1,NYA,AL,19,8,34,30,17,4,2,725,207,86,23,123,129,0.22,3.2,,8,4,0,1045,3,94,,, +riddlel01,1947,1,CIN,NL,1,0,16,3,0,0,0,91,42,28,5,31,8,0.33,8.31,,0,1,0,159,6,30,,, +rignejo01,1947,1,CHA,AL,2,3,11,7,2,0,0,152,42,11,3,15,19,0.22,1.95,,1,0,0,203,3,15,,, +roepr01,1947,1,PIT,NL,4,15,38,22,4,1,2,432,156,84,19,63,59,0.27,5.25,,1,0,1,635,8,93,,, +rowesc01,1947,1,PHI,NL,14,10,31,28,15,1,1,587,232,94,22,45,74,0.29,4.32,,2,3,0,851,3,106,,, +ruffire01,1947,1,CHA,AL,3,5,9,9,1,0,0,159,63,36,7,16,11,0.29,6.11,,3,0,0,235,0,39,,, +sainjo01,1947,1,BSN,NL,21,12,38,35,22,3,1,798,265,104,19,79,132,0.25,3.52,,3,4,0,1131,3,117,,, +sanfofr01,1947,1,SLA,AL,7,16,34,23,9,0,4,560,186,77,17,76,62,0.26,3.71,,3,0,0,803,7,89,,, +savagbo01,1947,1,PHA,AL,8,10,44,8,2,1,2,438,135,61,8,55,56,0.24,3.76,,1,0,0,614,20,71,,, +scarbra01,1947,1,WS1,AL,6,13,33,18,8,2,0,483,165,61,5,67,63,0.26,3.41,,2,1,2,695,5,74,,, +schanch01,1947,1,PHI,NL,2,4,34,6,1,0,2,305,107,47,7,47,42,0.29,4.16,,2,3,0,422,15,59,,, +scheica01,1947,1,PHA,AL,4,6,21,12,6,2,0,348,121,65,11,55,26,0.27,5.04,,6,2,2,505,6,68,,, +schmifr01,1947,1,SLN,NL,0,0,2,0,0,0,0,12,5,1,1,1,2,0.33,2.25,,0,0,0,16,1,2,,, +schmifr01,1947,2,PHI,NL,5,8,29,5,0,0,0,230,76,40,4,43,24,0.28,4.7,,4,4,0,322,14,44,,, +schmifr01,1947,3,CHN,NL,0,0,1,1,0,0,0,9,4,3,0,5,0,0.33,9,,0,0,0,19,0,3,,, +schmijo01,1947,1,CHN,NL,13,18,38,28,10,3,4,621,209,74,8,80,97,0.26,3.22,,6,2,0,894,9,91,,, +schulmi01,1947,1,CIN,NL,0,0,1,0,0,0,0,6,4,1,0,2,0,0.44,4.5,,0,0,0,11,0,2,,, +sewelri01,1947,1,PIT,NL,6,4,24,12,4,1,0,363,121,48,11,36,36,0.26,3.57,,1,3,0,504,8,58,,, +sheasp01,1947,1,NYA,AL,14,5,27,23,13,3,1,536,127,61,10,89,89,0.2,3.07,,0,4,0,734,3,63,,, +shouncl01,1947,1,CIN,NL,0,0,10,0,0,0,0,43,16,8,2,5,7,0.32,5.02,,0,1,0,57,4,8,,, +shouncl01,1947,2,BSN,NL,5,3,26,3,1,1,1,221,73,36,6,21,23,0.25,4.4,,0,0,0,312,9,41,,, +simmocu01,1947,1,PHI,NL,1,0,1,1,1,0,0,27,5,1,0,6,9,0.16,1,,0,0,0,37,0,1,,, +singlel01,1947,1,PIT,NL,2,2,36,3,0,0,1,201,70,47,9,39,24,0.26,6.31,,5,2,0,309,14,49,,, +smithed04,1947,1,CHA,AL,1,3,15,5,0,0,0,100,40,27,1,24,12,0.29,7.29,,1,0,0,166,6,36,,, +smithed04,1947,2,BOS,AL,1,3,8,3,0,0,0,51,18,14,3,18,15,0.26,7.41,,1,0,0,86,3,14,,, +spahnwa01,1947,1,BSN,NL,21,10,40,35,22,7,3,869,245,75,15,84,123,0.22,2.33,,5,1,0,1174,4,87,,, +spragho01,1947,1,PHI,NL,0,0,4,0,0,0,0,16,3,4,0,3,3,0.15,6.75,,0,0,0,22,1,4,,, +stalege01,1947,1,SLN,NL,1,0,18,1,1,0,0,88,33,9,2,8,14,0.28,2.76,,0,1,0,126,8,11,,, +starrdi01,1947,1,NYA,AL,1,0,4,1,1,0,0,37,12,2,1,8,1,0.25,1.46,,0,0,0,57,2,4,,, +stephbr01,1947,1,CLE,AL,5,10,31,5,1,0,1,276,79,41,6,39,34,0.23,4.01,,4,2,0,392,8,46,,, +stobbch01,1947,1,BOS,AL,0,1,4,1,0,0,0,27,10,6,0,10,5,0.29,6,,1,0,0,45,0,6,,, +strinni01,1947,1,PIT,NL,1,6,32,7,1,0,0,267,111,52,9,37,22,0.31,5.26,,0,2,0,397,10,59,,, +swartbu01,1947,1,SLA,AL,0,0,5,0,0,0,0,16,9,4,1,7,1,0.36,6.75,,0,0,0,32,5,6,,, +tayloha03,1947,1,BRO,NL,10,5,33,20,10,2,1,486,130,56,10,83,58,0.22,3.11,,5,5,0,673,2,63,,, +thompju01,1947,1,NY1,NL,4,2,15,0,0,0,0,107,36,17,3,27,13,0.27,4.29,,1,1,0,164,6,20,,, +toeneha01,1947,1,WS1,AL,0,1,3,1,0,0,0,20,11,5,0,2,5,0.37,6.75,,0,0,0,32,1,5,,, +tostlo01,1947,1,PIT,NL,0,0,1,0,0,0,0,3,3,1,0,0,0,0.6,9,,0,0,0,6,0,1,,, +trinkke01,1947,1,NY1,NL,8,4,62,0,0,0,10,281,100,39,3,48,37,0.27,3.75,,2,1,0,416,38,47,,, +troutdi01,1947,1,DET,AL,10,11,32,26,9,2,2,559,186,72,6,65,74,0.26,3.48,,2,3,1,795,6,85,,, +truckvi01,1947,1,DET,AL,10,12,36,26,8,2,2,542,186,91,14,79,108,0.26,4.53,,5,2,0,800,5,105,,, +vancujo01,1947,1,BRO,NL,0,0,2,0,0,0,0,10,5,2,0,1,2,0.35,5.4,,0,0,0,16,1,2,,, +vandejo01,1947,1,CIN,NL,9,14,30,29,9,3,0,558,186,91,11,87,79,0.26,4.4,,3,3,0,816,0,104,,, +voisebi01,1947,1,NY1,NL,1,4,11,5,1,0,0,128,44,22,4,22,20,0.28,4.64,,4,1,0,181,1,26,,, +voisebi01,1947,2,BSN,NL,8,7,22,20,7,0,0,394,146,63,10,51,59,0.28,4.32,,1,1,0,576,0,66,,, +waltebu01,1947,1,CIN,NL,8,8,20,20,5,2,0,366,137,78,15,49,43,0.27,5.75,,0,3,1,550,0,83,,, +wehmehe01,1947,1,CIN,NL,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,,0,0,0,3,1,0,,, +wenslbu01,1947,1,NYA,AL,3,1,11,5,1,0,0,155,41,15,3,22,18,0.21,2.61,,0,0,0,213,3,17,,, +whiteer01,1947,1,BSN,NL,0,0,1,1,0,0,0,12,1,0,0,1,1,0.08,0,,0,0,0,13,0,0,,, +whiteha01,1947,1,DET,AL,4,5,35,5,0,0,2,254,91,34,5,47,33,0.27,3.61,,4,2,0,380,13,43,,, +widmaal01,1947,1,BOS,AL,0,0,2,0,0,0,0,4,1,2,1,2,1,0.2,13.5,,0,0,0,7,1,2,,, +wightbi01,1947,1,NYA,AL,1,0,1,1,1,0,0,27,8,1,0,2,3,0.24,1,,0,0,0,35,0,3,,, +wilkste01,1947,1,SLN,NL,4,0,37,0,0,0,5,151,57,28,10,11,28,0.27,5.01,,0,2,0,221,19,33,,, +willile04,1947,1,CLE,AL,0,2,22,2,0,0,1,132,58,17,3,24,10,0.32,3.48,,1,0,0,207,13,26,,, +wolffro01,1947,1,CLE,AL,0,0,7,2,0,0,0,48,15,7,1,10,5,0.25,3.94,,0,2,0,72,3,7,,, +wolffro01,1947,2,PIT,NL,1,4,13,6,1,0,0,90,49,29,4,18,7,0.36,8.7,,0,1,0,155,4,33,,, +wrighed01,1947,1,BSN,NL,3,3,23,6,1,0,0,194,80,46,9,35,14,0.3,6.4,,1,2,0,305,6,52,,, +wynnea01,1947,1,WS1,AL,17,15,33,31,22,2,0,741,251,100,13,90,73,0.26,3.64,,4,5,0,1058,2,114,,, +wyseha01,1947,1,CHN,NL,6,9,37,19,5,1,1,426,158,68,12,64,53,0.28,4.31,,1,3,0,633,6,84,,, +zoldasa01,1947,1,SLA,AL,9,10,35,19,6,1,1,513,162,66,7,76,36,0.25,3.47,,1,0,1,725,8,76,,, +zuberbi01,1947,1,BOS,AL,1,0,20,1,0,0,0,152,60,30,4,31,23,0.31,5.33,,4,0,0,230,8,32,,, +andrehu01,1948,1,NY1,NL,0,0,1,0,0,0,0,9,3,0,0,0,0,0.3,0,,0,0,0,10,0,1,,, +antonjo02,1948,1,BSN,NL,0,0,4,0,0,0,1,12,2,1,0,3,0,0.14,2.25,,0,0,0,17,4,1,,, +bantaja01,1948,1,BRO,NL,0,1,2,1,0,0,0,10,5,3,0,5,1,0.38,8.1,,0,0,0,19,1,6,,, 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+blackdo01,1948,1,CLE,AL,2,2,18,10,1,0,0,156,57,31,5,40,16,0.28,5.37,,0,1,0,249,3,33,,, +blackew01,1948,1,CIN,NL,7,9,22,20,4,1,1,416,134,70,12,52,114,0.25,4.54,,1,4,0,599,2,73,,, +blackji01,1948,1,CIN,NL,0,2,16,0,0,0,0,97,38,15,1,14,10,0.3,4.18,,0,0,0,144,5,18,,, +bonhati01,1948,1,PIT,NL,6,10,22,20,7,0,0,407,145,65,18,23,42,0.27,4.31,,0,3,0,556,0,71,,, +borowha01,1948,1,CHN,NL,5,10,39,17,2,1,1,381,156,69,9,49,50,0.3,4.89,,3,0,0,564,12,80,,, +bradlfr01,1948,1,CHA,AL,0,0,8,0,0,0,0,47,11,8,2,4,2,0.19,4.6,,0,1,0,66,4,12,,, +brancra01,1948,1,BRO,NL,14,9,36,28,11,1,1,647,189,84,24,80,122,0.23,3.51,,9,4,0,905,4,93,,, +brazlal01,1948,1,SLN,NL,10,6,42,23,6,2,1,469,171,66,8,50,55,0.28,3.8,,2,0,1,673,9,77,,, +brechha01,1948,1,SLN,NL,20,7,33,30,21,7,1,700,193,58,6,49,149,0.22,2.24,,2,2,1,931,1,62,,, +brisslo01,1948,1,PHA,AL,14,10,39,25,11,0,5,582,202,89,6,95,127,0.26,4.13,,7,2,0,862,13,100,,, +burkhke01,1948,1,SLN,NL,0,0,20,0,0,0,1,112,50,23,4,14,16,0.33,5.54,,0,2,0,171,7,24,,, +burkhke01,1948,2,CIN,NL,0,3,16,0,0,0,0,125,42,32,3,16,14,0.25,6.91,,0,1,1,182,8,34,,, +byrneto01,1948,1,NYA,AL,8,5,31,11,5,1,2,401,79,49,8,101,93,0.17,3.3,,3,9,0,579,11,55,,, +caldwea01,1948,1,CHA,AL,1,5,25,1,0,0,3,117,53,23,3,22,10,0.33,5.31,,0,2,0,185,16,25,,, +caldwea01,1948,2,BOS,AL,1,1,8,0,0,0,0,27,11,13,2,11,5,0.33,13,,0,1,0,46,3,14,,, +candimi01,1948,1,WS1,AL,2,3,35,4,1,0,3,283,96,54,1,63,23,0.26,5.15,,0,1,0,430,14,56,,, +carlsdo01,1948,1,CHN,NL,0,0,1,0,0,0,0,3,5,4,0,2,1,0.62,36,,0,0,0,10,0,4,,, +caseyhu01,1948,1,BRO,NL,3,0,22,0,0,0,4,108,59,32,6,17,7,0.39,8,,1,2,0,172,11,36,,, +chambcl01,1948,1,CHN,NL,2,9,29,12,3,1,0,311,100,51,4,48,51,0.25,4.43,,0,3,0,452,10,57,,, +chesnbo01,1948,1,PIT,NL,14,6,25,23,15,0,0,583,180,77,13,90,69,0.24,3.57,,6,4,2,829,0,92,,, +chipmbo01,1948,1,CHN,NL,2,1,34,3,0,0,4,181,73,24,5,24,16,0.29,3.58,,1,0,0,279,13,34,,, +chrisru01,1948,1,CLE,AL,3,2,45,0,0,0,17,177,55,19,3,27,14,0.24,2.9,,1,0,0,255,28,21,,, +colemjo04,1948,1,PHA,AL,14,13,33,29,13,3,0,647,224,98,11,90,86,0.26,4.09,,6,1,1,944,2,105,,, +coopeca01,1948,1,WS1,AL,0,0,1,0,0,0,0,3,5,5,1,1,0,0.62,45,,1,0,0,9,0,5,,, +cresswa01,1948,1,CIN,NL,0,1,30,2,1,0,0,180,60,30,2,42,33,0.27,4.5,,0,1,0,266,13,32,,, +dealco01,1948,1,BOS,AL,1,0,4,0,0,0,0,12,3,0,0,3,2,0.2,0,,3,0,0,18,4,0,,, +dicksmu01,1948,1,SLN,NL,12,16,42,29,11,1,1,757,257,116,39,85,113,0.26,4.14,,1,0,0,1064,8,121,,, +dietrbi01,1948,1,PHA,AL,1,2,4,2,0,0,0,46,21,10,0,9,5,0.35,5.87,,1,0,0,70,2,10,,, +doberje01,1948,1,CHN,NL,7,2,54,0,0,0,1,257,67,30,8,40,48,0.21,3.15,,0,1,0,365,27,33,,, +dobsojo01,1948,1,BOS,AL,16,10,38,32,16,5,2,736,237,97,14,92,116,0.25,3.56,,6,1,1,1044,5,115,,, +donnebl01,1948,1,PHI,NL,5,7,26,19,8,1,2,395,125,54,13,49,46,0.26,3.69,,1,0,0,536,6,65,,, +dorisha01,1948,1,BOS,AL,0,1,9,0,0,0,0,43,18,9,1,6,5,0.28,5.65,,2,0,0,71,1,13,,, +dreiscl01,1948,1,SLA,AL,0,2,13,0,0,0,1,67,28,14,6,8,6,0.31,5.64,,0,0,0,97,9,15,,, +dreiscl01,1948,2,NY1,NL,0,0,4,0,0,0,1,38,17,8,3,5,2,0.32,5.68,,0,0,0,58,2,8,,, +drewska01,1948,1,NYA,AL,2,3,19,2,0,0,1,114,35,16,3,31,11,0.24,3.79,,1,0,1,176,8,17,,, +drewska01,1948,2,SLA,AL,3,2,20,2,0,0,2,114,43,34,3,38,11,0.28,8.05,,3,0,0,189,6,35,,, +dubiemo01,1948,1,PHI,NL,8,10,37,17,6,2,4,451,139,65,13,58,42,0.24,3.89,,1,1,0,628,10,84,,, +dusaker01,1948,1,SLN,NL,0,0,1,0,0,0,0,3,0,0,0,1,0,0,0,,1,0,0,4,1,0,,, +elliogl01,1948,1,BSN,NL,1,0,1,1,0,0,0,9,5,1,0,1,2,0.35,3,,0,0,0,15,0,1,,, +embrere01,1948,1,NYA,AL,5,3,20,8,4,0,0,230,77,32,6,30,25,0.26,3.76,,1,1,0,329,6,37,,, +erauted01,1948,1,CIN,NL,0,0,2,0,0,0,0,9,3,2,0,1,0,0.25,6,,0,0,0,13,2,2,,, +erickpa01,1948,1,CHN,NL,0,0,3,0,0,0,0,17,7,4,0,6,4,0.29,6.35,,0,0,0,31,0,5,,, +erickpa01,1948,2,PHI,NL,2,0,4,2,0,0,0,52,19,10,2,17,5,0.29,5.19,,1,0,0,83,0,10,,, +erickpa01,1948,3,NY1,NL,0,0,2,0,0,0,0,3,0,0,0,2,1,0,0,,0,0,0,5,1,0,,, +erskica01,1948,1,BRO,NL,6,3,17,9,3,0,0,192,51,23,5,35,29,0.23,3.23,,0,1,0,263,4,28,,, +fannicl01,1948,1,SLA,AL,10,14,34,29,10,3,1,641,198,99,14,104,102,0.24,4.17,,3,1,0,921,3,106,,, +fellebo01,1948,1,CLE,AL,19,15,44,38,18,2,3,841,255,111,20,116,164,0.24,3.56,,2,2,0,1186,3,123,,, +ferrida01,1948,1,BOS,AL,7,3,31,9,1,0,3,346,127,67,7,61,30,0.28,5.23,,0,7,0,518,13,71,,, +ferrito01,1948,1,WS1,AL,2,5,37,0,0,0,10,221,75,34,3,38,34,0.26,4.15,,1,0,0,330,26,37,,, +fowledi01,1948,1,PHA,AL,15,8,29,26,16,2,2,614,221,86,15,76,50,0.28,3.78,,0,4,1,879,2,93,,, +foxho01,1948,1,CIN,NL,6,9,34,24,5,0,1,513,185,86,11,62,63,0.28,4.53,,4,1,0,737,3,100,,, +galehde01,1948,1,BOS,AL,8,8,27,15,6,1,3,412,152,61,10,46,38,0.28,4,,0,2,0,595,7,68,,, +garcimi01,1948,1,CLE,AL,0,0,1,0,0,0,0,6,3,0,0,0,1,0.33,0,,0,0,0,9,0,0,,, +garcira01,1948,1,WS1,AL,0,0,4,0,0,0,0,11,11,7,0,4,2,0.52,17.18,,0,1,0,26,2,7,,, +garvene01,1948,1,SLA,AL,7,11,38,24,7,0,5,594,200,75,14,95,75,0.26,3.41,,5,1,0,848,7,92,,, +gentrru01,1948,1,DET,AL,0,0,4,0,0,0,0,20,5,2,0,5,1,0.2,2.7,,0,1,0,30,2,2,,, +gerheal01,1948,1,SLA,AL,0,3,14,2,0,0,0,70,32,19,0,10,10,0.31,7.33,,0,1,0,115,5,23,,, +getteal01,1948,1,CLE,AL,0,1,5,2,0,0,0,23,15,15,2,10,4,0.38,17.61,,0,1,0,50,1,15,,, +getteal01,1948,2,CHA,AL,8,10,22,19,7,0,1,444,154,66,7,60,49,0.26,4.01,,2,4,3,646,2,76,,, +gillebo01,1948,1,CHA,AL,0,4,25,6,1,0,0,216,81,41,3,33,19,0.28,5.13,,3,1,0,319,8,45,,, +goodwji01,1948,1,CHA,AL,0,0,8,1,0,0,1,31,9,10,0,12,3,0.23,8.71,,0,1,0,53,2,11,,, +grasmlo01,1948,1,PHI,NL,0,0,2,0,0,0,0,15,3,4,1,8,2,0.17,7.2,,0,0,0,25,1,4,,, +grayte01,1948,1,DET,AL,6,2,26,11,3,1,0,256,73,40,2,72,60,0.23,4.22,,4,3,0,392,6,43,,, +greggha01,1948,1,PIT,NL,2,4,22,8,1,0,1,223,72,38,3,34,25,0.25,4.6,,0,3,0,322,9,40,,, +gromest01,1948,1,CLE,AL,9,3,38,9,4,1,2,390,109,41,10,51,50,0.22,2.84,,3,6,0,546,10,52,,, +grother02,1948,1,CLE,AL,0,0,1,0,0,0,0,3,1,1,0,2,0,0.25,9,,0,0,0,6,1,1,,, +groveor01,1948,1,CHA,AL,2,10,32,11,1,0,1,263,110,60,6,42,18,0.31,6.16,,4,3,0,404,13,64,,, +gumbeha01,1948,1,CIN,NL,10,8,61,0,0,0,17,319,123,41,5,34,25,0.29,3.47,,2,0,0,464,46,50,,, +gumpera01,1948,1,NYA,AL,1,0,15,0,0,0,0,75,27,8,0,6,12,0.26,2.88,,1,1,0,109,8,10,,, +gumpera01,1948,2,CHA,AL,2,6,16,11,6,1,0,292,103,41,6,13,31,0.27,3.79,,1,2,0,401,5,43,,, +hackewa02,1948,1,CHN,NL,0,1,3,1,0,0,0,9,7,7,0,3,0,0.43,21,,0,0,0,19,1,7,,, +haefnmi01,1948,1,WS1,AL,5,13,28,20,4,0,0,443,151,66,7,61,45,0.26,4.02,,4,6,0,648,5,86,,, +halleja01,1948,1,NY1,NL,0,0,2,0,0,0,0,12,3,2,0,4,3,0.21,4.5,,0,0,0,18,1,3,,, +halljo01,1948,1,BRO,NL,0,0,3,0,0,0,0,13,4,3,1,2,2,0.26,6.23,,0,0,0,19,1,3,,, +hamnera01,1948,1,CHN,NL,5,9,27,17,5,0,0,334,110,58,12,69,53,0.25,4.69,,2,5,1,506,4,63,,, +hansean01,1948,1,NY1,NL,5,3,36,9,3,0,1,300,96,33,4,36,27,0.25,2.97,,1,0,0,419,11,40,,, +harribu02,1948,1,PHA,AL,5,2,45,0,0,0,5,281,89,43,2,35,32,0.24,4.13,,1,1,1,398,19,51,,, +harriea01,1948,1,CHA,AL,1,3,11,1,0,0,0,69,23,15,4,13,14,0.26,5.87,,1,3,0,106,5,17,,, +harriea01,1948,2,WS1,AL,3,3,23,4,0,0,0,182,70,31,1,37,21,0.29,4.6,,0,3,0,282,10,35,,, +harrimi01,1948,1,BOS,AL,7,10,20,17,6,1,0,341,120,67,10,59,42,0.27,5.3,,2,1,1,505,1,73,,, +hartucl01,1948,1,NY1,NL,8,8,36,19,6,2,1,460,146,81,15,72,42,0.25,4.75,,2,5,0,655,7,89,,, +hattejo01,1948,1,BRO,NL,13,10,42,30,11,1,0,626,228,83,9,94,73,0.28,3.58,,4,3,0,916,8,93,,, +haugsph01,1948,1,BRO,NL,0,0,1,0,0,0,0,3,1,0,0,0,0,0.33,0,,0,0,0,4,1,0,,, +haynejo01,1948,1,CHA,AL,9,10,27,22,6,0,0,449,167,66,13,52,40,0.28,3.97,,3,2,0,653,2,79,,, +hearnji01,1948,1,SLN,NL,8,6,34,13,3,0,1,269,92,42,9,35,27,0.27,4.22,,2,2,0,384,17,44,,, +heintke01,1948,1,PHI,NL,6,11,27,16,5,2,2,390,117,62,10,45,57,0.24,4.29,,5,1,0,536,3,66,,, +hetkijo01,1948,1,CIN,NL,0,1,3,0,0,0,0,20,8,7,0,3,3,0.28,9.45,,1,0,0,33,0,7,,, +heussed01,1948,1,PHI,NL,3,2,33,0,0,0,3,222,89,41,9,28,22,0.29,4.99,,1,0,0,330,21,46,,, +higbeki01,1948,1,PIT,NL,8,7,56,8,3,0,10,474,140,59,11,83,86,0.24,3.36,,4,3,1,681,31,75,,, +hillefr01,1948,1,NYA,AL,5,2,22,5,1,0,0,187,59,28,8,30,25,0.24,4.04,,0,1,0,276,5,29,,, +hoguebo01,1948,1,BSN,NL,8,2,40,1,0,0,2,259,88,31,4,19,43,0.26,3.23,,0,2,0,359,15,34,,, +holbowa01,1948,1,PHA,AL,1,2,5,1,1,0,0,52,32,11,1,7,3,0.42,5.71,,0,0,0,88,1,12,,, +holcoke01,1948,1,CIN,NL,0,0,2,0,0,0,0,7,3,2,0,0,2,0.3,7.71,,0,0,0,10,1,2,,, +houttar01,1948,1,DET,AL,2,16,43,20,4,0,10,493,186,85,11,52,74,0.28,4.66,,3,2,0,716,15,101,,, +hudsosi01,1948,1,WS1,AL,4,16,39,29,4,0,1,546,217,119,11,107,53,0.29,5.88,,1,6,0,856,5,128,,, +hugheto04,1948,1,CIN,NL,0,4,12,4,0,0,0,81,43,27,3,24,7,0.36,9,,2,0,0,147,3,28,,, +hughste01,1948,1,BOS,AL,3,1,15,0,0,0,0,58,21,11,0,7,6,0.27,5.12,,0,0,0,84,11,14,,, +hutchfr01,1948,1,DET,AL,13,11,33,28,15,0,0,663,223,106,32,48,92,0.25,4.32,,2,1,0,925,3,119,,, +jacobto01,1948,1,CHN,NL,0,0,1,0,0,0,0,6,3,1,1,0,2,0.33,4.5,,0,0,0,9,1,1,,, +jansela01,1948,1,NY1,NL,18,12,42,36,15,4,2,831,283,111,25,54,126,0.26,3.61,,0,3,0,1137,4,125,,, +johnsea01,1948,1,BOS,AL,10,4,35,3,1,0,5,274,98,46,7,42,45,0.27,4.53,,2,0,1,402,21,49,,, +johnske01,1948,1,SLN,NL,2,4,13,4,0,0,0,136,43,24,1,30,20,0.26,4.76,,2,1,0,198,2,27,,, +jonessh01,1948,1,NY1,NL,16,8,55,21,8,1,5,604,204,75,16,90,82,0.26,3.35,,10,6,0,879,17,89,,, +juddos01,1948,1,PHI,NL,0,2,4,1,0,0,0,43,19,11,1,11,7,0.31,6.91,,0,0,0,73,1,14,,, +judsoho01,1948,1,CHA,AL,4,5,40,5,1,0,8,322,102,57,7,56,38,0.25,4.78,,2,3,1,465,18,60,,, +kellnal01,1948,1,PHA,AL,0,0,13,1,0,0,0,69,21,20,0,16,14,0.23,7.83,,3,2,0,107,5,20,,, +kennebi02,1948,1,CLE,AL,1,0,6,3,0,0,0,34,16,14,0,13,12,0.33,11.12,,0,0,0,62,0,14,,, +kennebi02,1948,2,SLA,AL,7,8,26,20,3,0,0,396,132,69,10,104,77,0.25,4.7,,5,5,0,624,5,82,,, +kennemo01,1948,1,NY1,NL,3,9,25,16,7,1,0,343,118,51,10,57,63,0.26,4.01,,3,3,0,510,3,64,,, +kindeel01,1948,1,BOS,AL,10,7,28,22,10,1,0,534,183,74,10,63,53,0.26,3.74,,2,2,0,762,3,84,,, +kingcl01,1948,1,BRO,NL,0,1,9,0,0,0,0,37,14,11,3,6,5,0.28,8.03,,0,1,0,56,3,11,,, +kliemed01,1948,1,CLE,AL,3,2,44,0,0,0,4,239,62,23,3,46,18,0.22,2.6,,1,2,0,330,18,26,,, +koechdi01,1948,1,PHI,NL,0,1,3,0,0,0,0,18,4,2,0,3,2,0.23,3,,0,0,0,21,3,2,,, +konikal01,1948,1,NY1,NL,2,3,22,1,0,0,1,100,46,28,7,17,9,0.34,7.56,,0,0,0,154,6,34,,, +konstji01,1948,1,PHI,NL,1,0,6,0,0,0,2,29,7,1,0,2,7,0.23,0.93,,0,0,0,33,5,1,,, +kosloda01,1948,1,NY1,NL,8,10,35,18,5,3,3,447,168,64,7,62,58,0.29,3.87,,1,1,0,655,6,69,,, +krameja01,1948,1,BOS,AL,18,5,29,29,14,2,0,615,233,99,12,64,72,0.28,4.35,,3,0,0,891,0,104,,, +kretllo01,1948,1,DET,AL,2,1,5,2,1,0,0,70,21,12,1,11,9,0.23,4.63,,0,0,0,101,2,14,,, +kushem01,1948,1,CHN,NL,1,4,34,1,0,0,3,216,70,35,5,37,31,0.25,4.38,,3,2,0,326,17,39,,, +ladedo01,1948,1,CHN,NL,5,6,19,12,6,0,0,262,99,39,4,31,29,0.28,4.02,,2,1,0,386,2,44,,, +lakemal01,1948,1,PHI,NL,0,0,1,0,0,0,0,2,1,1,1,0,0,0.33,13.5,,0,0,0,3,1,1,,, +leeth01,1948,1,NY1,NL,1,3,11,4,1,0,0,98,41,16,3,12,17,0.3,4.41,,0,1,0,150,0,20,,, +lemonbo01,1948,1,CLE,AL,20,14,43,37,20,10,2,881,231,92,12,129,147,0.21,2.82,,3,3,0,1214,2,104,,, +leonadu02,1948,1,PHI,NL,12,17,34,30,16,1,0,677,226,63,9,54,92,0.26,2.51,,4,4,0,920,2,85,,, +lindely01,1948,1,CLE,AL,0,0,3,0,0,0,0,30,9,6,1,4,0,0.24,5.4,,0,0,0,41,1,6,,, +livelbu01,1948,1,CIN,NL,0,0,10,0,0,0,0,68,13,6,0,11,12,0.16,2.38,,0,1,0,92,5,7,,, +lombalo01,1948,1,NY1,NL,0,0,2,0,0,0,0,16,5,4,1,5,0,0.25,6.75,,1,1,0,27,0,4,,, +lombavi01,1948,1,PIT,NL,10,9,38,17,9,0,4,489,156,67,9,67,54,0.25,3.7,,1,2,1,689,15,72,,, +lopated01,1948,1,NYA,AL,17,11,33,31,13,3,0,680,246,92,16,66,83,0.28,3.65,,3,2,2,958,1,106,,, +lyonsal01,1948,1,BSN,NL,1,0,7,0,0,0,0,38,17,11,1,8,5,0.3,7.82,,0,0,0,64,1,11,,, +mainwo01,1948,1,PIT,NL,1,1,17,0,0,0,0,81,35,25,4,19,12,0.32,8.33,,0,0,1,130,6,27,,, +marchph01,1948,1,PHA,AL,9,15,33,30,12,1,0,679,214,114,19,131,66,0.25,4.53,,7,4,0,1007,3,133,,, +marshcu01,1948,1,NYA,AL,0,0,1,0,0,0,0,3,0,0,0,3,0,0,0,,0,0,0,6,1,0,,, +martira01,1948,1,BSN,NL,0,0,2,0,0,0,0,7,0,0,0,1,0,0,0,,0,0,0,8,2,0,,, +mastewa02,1948,1,WS1,AL,8,15,33,27,9,2,2,564,171,80,12,122,72,0.24,3.83,,3,4,0,829,4,88,,, +mccahbi01,1948,1,PHA,AL,4,7,17,15,5,0,0,260,98,55,8,65,20,0.28,5.71,,3,0,0,417,2,58,,, +mccaldu01,1948,1,CHN,NL,4,13,30,20,5,0,0,454,158,81,14,85,89,0.26,4.82,,4,1,0,684,2,93,,, +mccalwi01,1948,1,BOS,AL,0,1,1,1,0,0,0,4,6,3,1,1,0,0.6,20.25,,0,0,0,11,0,3,,, +mcdermi03,1948,1,BOS,AL,0,0,7,0,0,0,0,70,16,16,2,35,17,0.2,6.17,,3,1,1,114,5,18,,, +mcgowmi01,1948,1,NY1,NL,0,0,3,0,0,0,0,11,3,3,1,4,2,0.23,7.36,,0,0,0,18,1,3,,, +mclisca01,1948,1,PIT,NL,0,0,2,1,0,0,0,15,8,5,0,2,1,0.4,9,,1,0,0,22,0,5,,, +meyerru01,1948,1,CHN,NL,10,10,29,26,8,3,0,494,157,67,8,77,89,0.25,3.66,,3,1,1,702,2,75,,, +minnepa01,1948,1,BRO,NL,4,3,28,2,0,0,1,188,61,17,5,26,23,0.25,2.44,,2,0,0,269,11,23,,, +mouldgl01,1948,1,CHA,AL,3,6,33,9,0,0,2,257,108,61,8,54,26,0.31,6.41,,5,1,1,408,11,67,,, +muncrbo01,1948,1,CLE,AL,5,4,21,9,1,1,0,217,76,32,8,31,24,0.27,3.98,,0,0,0,309,8,37,,, +mungere01,1948,1,SLN,NL,10,11,39,25,7,2,0,498,179,83,13,74,72,0.27,4.5,,2,1,0,738,3,91,,, +nahemsa01,1948,1,PHI,NL,3,3,28,1,0,0,0,177,68,46,4,45,30,0.28,7.02,,1,3,2,289,17,52,,, +newhoha01,1948,1,DET,AL,21,12,39,35,19,2,1,817,249,91,10,99,143,0.24,3.01,,5,1,0,1146,4,109,,, +newsobo01,1948,1,NY1,NL,0,4,11,4,0,0,0,77,35,12,1,13,9,0.33,4.21,,1,0,0,123,1,16,,, +osterfr01,1948,1,PIT,NL,8,11,23,22,10,2,0,403,143,66,13,41,43,0.26,4.42,,4,1,0,597,0,73,,, +ostrojo02,1948,1,SLA,AL,4,6,26,9,3,0,3,235,108,52,6,17,20,0.33,5.97,,1,0,1,347,11,54,,, +overmst01,1948,1,DET,AL,3,4,37,4,0,0,3,199,89,44,5,31,14,0.32,5.97,,3,0,0,313,16,48,,, +pagejo01,1948,1,NYA,AL,7,8,55,1,0,0,16,323,116,51,6,66,77,0.27,4.26,,2,1,0,503,38,59,,, +paigesa01,1948,1,CLE,AL,6,1,21,7,3,2,1,218,61,20,2,22,43,0.22,2.48,,1,1,2,298,5,21,,, +palicer01,1948,1,BRO,NL,6,6,41,10,3,0,3,376,111,62,13,58,74,0.23,4.45,,3,3,0,531,14,63,,, +palmmi01,1948,1,BOS,AL,0,0,3,0,0,0,0,9,6,2,0,5,1,0.4,6,,0,0,0,20,0,2,,, +papaial01,1948,1,SLN,NL,0,1,10,0,0,0,0,48,14,9,3,7,8,0.24,5.06,,1,0,0,67,7,10,,, +papisfr01,1948,1,CHA,AL,2,8,32,14,2,0,4,286,97,53,7,75,41,0.26,5,,2,3,0,453,12,65,,, +parneme01,1948,1,BOS,AL,15,8,35,27,16,1,0,636,205,74,7,90,77,0.25,3.14,,3,4,2,916,5,87,,, +pearsik01,1948,1,CHA,AL,2,3,23,2,0,0,1,159,62,29,8,27,12,0.29,4.92,,2,2,0,245,14,32,,, +peterke01,1948,1,CIN,NL,2,15,43,17,2,0,1,411,146,70,10,59,64,0.27,4.6,,2,6,0,611,12,82,,, +piercbi02,1948,1,DET,AL,3,0,22,5,0,0,0,166,47,39,5,51,36,0.23,6.34,,1,1,0,258,9,40,,, +pierema01,1948,1,WS1,AL,0,2,8,1,0,0,0,35,18,14,1,7,6,0.37,10.8,,0,0,0,56,3,14,,, +pierema01,1948,2,CHA,AL,8,10,21,18,4,0,1,360,117,66,6,52,28,0.26,4.95,,2,0,0,516,2,70,,, +poatra01,1948,1,NY1,NL,11,10,39,24,7,3,0,473,162,76,21,67,57,0.26,4.34,,1,3,1,698,8,95,,, +polleho01,1948,1,SLN,NL,13,8,36,26,11,0,0,559,216,94,10,67,80,0.28,4.54,,3,2,0,826,2,102,,, +portebo01,1948,1,NYA,AL,5,3,16,12,2,1,0,234,85,39,5,34,30,0.27,4.5,,1,0,2,349,2,42,,, +portoal01,1948,1,PHI,NL,0,0,3,0,0,0,0,12,2,0,0,1,1,0.14,0,,0,0,0,15,1,0,,, +posselo01,1948,1,PHI,NL,1,1,3,2,1,0,0,44,17,8,3,4,7,0.3,4.91,,0,0,0,60,0,8,,, +pottene01,1948,1,SLA,AL,1,1,2,2,0,0,0,31,11,6,1,4,4,0.26,5.23,,0,2,0,49,0,7,,, +pottene01,1948,2,PHA,AL,2,2,8,0,0,0,1,54,17,8,1,5,13,0.25,4,,0,0,0,75,5,8,,, +pottene01,1948,3,BSN,NL,5,2,18,7,3,0,2,255,77,22,4,8,47,0.24,2.33,,1,0,0,332,9,27,,, +prendji01,1948,1,BSN,NL,1,1,10,2,0,0,1,50,30,19,1,5,3,0.38,10.26,,0,0,0,84,4,20,,, +queenme01,1948,1,PIT,NL,4,4,25,8,0,0,1,199,82,49,8,40,34,0.3,6.65,,1,3,0,316,6,51,,, +raffeke01,1948,1,CIN,NL,11,12,40,24,7,4,0,541,187,77,15,37,57,0.25,3.84,,0,1,0,772,9,88,,, +ramsdwi01,1948,1,BRO,NL,4,4,27,1,0,0,4,151,48,29,6,41,34,0.25,5.19,,2,3,0,241,14,35,,, +raschvi01,1948,1,NYA,AL,19,8,36,31,18,6,1,668,208,95,15,74,124,0.24,3.84,,5,3,0,929,2,103,,, +reynoal01,1948,1,NYA,AL,16,7,39,31,11,1,3,709,240,99,17,111,101,0.26,3.77,,9,4,0,1017,5,108,,, +riddlel01,1948,1,PIT,NL,12,10,28,27,12,3,1,573,184,74,20,81,63,0.25,3.49,,9,3,0,828,1,83,,, +roberro01,1948,1,PHI,NL,7,9,20,20,9,0,0,440,148,52,10,61,84,0.27,3.19,,1,4,0,604,0,63,,, +roepr01,1948,1,BRO,NL,12,8,34,22,8,2,2,533,156,52,14,33,86,0.23,2.63,,5,2,0,713,7,60,,, +rotblma01,1948,1,CHA,AL,0,1,7,2,0,0,0,55,19,16,0,23,4,0.27,7.85,,0,1,0,95,3,16,,, +rowesc01,1948,1,PHI,NL,10,10,30,20,8,0,2,444,167,67,5,31,46,0.28,4.07,,1,2,1,637,8,74,,, +rushbo01,1948,1,CHN,NL,5,11,36,16,4,0,0,400,153,58,8,37,72,0.28,3.92,,3,1,0,579,9,70,,, +sainjo01,1948,1,BSN,NL,24,15,42,39,28,4,1,944,297,91,19,83,137,0.24,2.6,,2,5,0,1313,3,105,,, +sanfofr01,1948,1,SLA,AL,12,21,42,33,9,1,2,681,250,117,19,91,79,0.27,4.64,,4,2,0,997,7,123,,, +savagbo01,1948,1,PHA,AL,5,1,33,1,1,0,5,226,98,52,9,33,26,0.31,6.21,,5,0,1,346,19,55,,, +scarbra01,1948,1,WS1,AL,15,8,31,26,9,0,1,556,166,58,10,72,76,0.23,2.82,,2,3,0,790,3,71,,, +scheica01,1948,1,PHA,AL,14,8,32,24,15,1,0,596,219,87,14,76,44,0.28,3.94,,2,1,0,855,7,90,,, +schmijo01,1948,1,CHN,NL,18,13,34,30,18,2,1,726,186,71,11,97,100,0.21,2.64,,6,2,0,986,4,92,,, +schwabl01,1948,1,SLA,AL,1,1,12,5,0,0,0,95,44,30,3,21,7,0.33,8.53,,1,0,0,156,3,34,,, +sewelri01,1948,1,PIT,NL,13,3,21,17,7,0,0,365,126,47,9,37,36,0.26,3.48,,1,1,0,522,3,51,,, +sexauel01,1948,1,BRO,NL,0,0,2,0,0,0,0,2,0,1,0,2,0,0,13.5,,0,0,0,4,1,1,,, +sheasp01,1948,1,NYA,AL,9,10,28,22,8,3,1,467,117,59,10,87,71,0.2,3.41,,1,2,1,663,4,66,,, +shorera01,1948,1,SLA,AL,1,2,17,4,0,0,0,114,40,27,2,35,12,0.27,6.39,,4,4,0,190,6,30,,, +shouncl01,1948,1,BSN,NL,5,1,36,2,1,0,4,222,77,33,7,20,25,0.26,4.01,,2,0,0,310,16,37,,, +simmocu01,1948,1,PHI,NL,7,13,31,23,7,0,0,510,169,92,8,108,86,0.26,4.87,,6,2,0,758,4,110,,, +singlel01,1948,1,PIT,NL,4,6,38,5,1,0,2,277,90,51,11,40,53,0.25,4.97,,9,1,0,404,17,52,,, +sloatle01,1948,1,BRO,NL,0,1,4,1,0,0,0,22,7,5,0,8,1,0.28,6.14,,0,0,0,34,0,5,,, 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+wynnea01,1948,1,WS1,AL,8,19,33,31,15,1,0,594,236,128,18,94,49,0.29,5.82,,2,1,0,912,1,144,,, +yochira01,1948,1,SLN,NL,0,0,1,0,0,0,0,3,0,0,0,3,1,0,0,,1,0,0,6,1,0,,, +zoldasa01,1948,1,SLA,AL,2,4,11,9,0,0,0,162,64,28,4,19,13,0.29,4.67,,1,1,0,238,0,30,,, +zoldasa01,1948,2,CLE,AL,9,6,23,12,4,1,0,317,104,33,6,24,17,0.26,2.81,,0,0,1,428,5,37,,, +adkinde01,1949,1,CHN,NL,2,4,30,5,1,0,0,247,98,52,10,39,43,0.29,5.68,,3,0,0,377,14,58,,, +albreed01,1949,1,SLA,AL,1,0,1,1,1,0,0,15,1,3,0,4,1,0.06,5.4,,0,0,0,20,0,3,,, +antonjo02,1949,1,BSN,NL,3,7,22,10,3,1,0,288,99,38,6,42,48,0.27,3.56,,3,2,0,416,6,49,,, +bantaja01,1949,1,BRO,NL,10,6,48,12,2,1,3,457,125,57,12,68,97,0.22,3.37,,3,6,0,643,15,63,,, +barnere02,1949,1,BRO,NL,9,8,38,20,6,2,1,422,108,69,15,89,80,0.21,4.41,,3,3,1,594,10,75,,, +barrere01,1949,1,BSN,NL,1,1,23,0,0,0,0,133,58,28,4,10,17,0.32,5.68,,2,2,0,197,15,32,,, +beardge01,1949,1,CLE,AL,8,8,32,19,5,0,0,381,140,72,6,92,41,0.28,5.1,,11,2,0,593,0,77,,, 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+candimi01,1949,1,WS1,AL,0,0,3,0,0,0,1,17,4,3,0,1,1,0.2,4.76,,0,0,0,21,1,3,,, +carraal01,1949,1,CHA,AL,0,0,3,0,0,0,0,11,8,6,1,4,1,0.42,14.73,,0,0,0,23,1,6,,, +caseyhu01,1949,1,PIT,NL,4,1,33,0,0,0,5,116,50,20,4,14,9,0.31,4.66,,0,1,0,176,23,24,,, +caseyhu01,1949,2,NYA,AL,1,0,4,0,0,0,0,23,11,7,0,8,5,0.32,8.22,,0,0,0,42,1,10,,, +chambcl01,1949,1,PIT,NL,13,7,34,21,10,1,0,532,186,78,15,58,93,0.26,3.96,,1,5,0,765,6,89,,, +chesnbo01,1949,1,PIT,NL,7,13,27,25,8,1,1,436,153,95,15,82,49,0.27,5.88,,5,5,0,647,1,104,,, +chipmbo01,1949,1,CHN,NL,7,8,38,11,3,1,1,340,110,50,7,63,46,0.24,3.97,,3,2,0,511,15,65,,, +colemjo04,1949,1,PHA,AL,13,14,33,30,18,1,1,721,249,103,12,127,109,0.27,3.86,,3,3,1,1063,3,119,,, +coopemo01,1949,1,CHN,NL,0,0,1,0,0,0,0,0,2,3,1,1,0,1,,,1,0,0,3,0,3,,, +cresswa01,1949,1,CIN,NL,0,0,3,0,0,0,0,6,2,0,0,3,0,0.28,0,,0,0,0,10,2,0,,, +dicksmu01,1949,1,PIT,NL,12,14,44,20,11,2,0,673,216,82,17,80,89,0.25,3.29,,1,6,0,948,15,97,,, 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+hoodwa02,1949,1,NYA,AL,0,0,2,0,0,0,0,7,0,0,0,1,2,0,0,,1,0,0,7,1,0,,, +houttar01,1949,1,DET,AL,15,10,34,25,13,2,0,611,227,84,19,59,85,0.28,3.71,,2,5,0,877,8,101,,, +howeldi01,1949,1,CIN,NL,0,1,5,1,0,0,0,40,21,12,3,8,7,0.36,8.1,,1,0,0,66,3,12,,, +hudsosi01,1949,1,WS1,AL,8,17,40,27,11,2,1,627,234,98,11,91,54,0.28,4.22,,3,5,0,935,8,117,,, +hughste01,1949,1,BOS,AL,4,2,29,2,0,0,3,233,82,46,5,41,35,0.26,5.33,,3,1,0,349,17,49,,, +hutchfr01,1949,1,DET,AL,15,7,33,21,9,4,1,566,167,62,18,52,54,0.23,2.96,,5,1,0,762,5,70,,, +jansela01,1949,1,NY1,NL,15,16,37,35,17,3,0,779,271,111,36,62,113,0.26,3.85,,4,2,0,1101,0,130,,, +johnsea01,1949,1,BOS,AL,3,6,19,3,0,0,0,148,65,41,1,29,20,0.32,7.48,,1,4,1,237,8,45,,, +johnske01,1949,1,SLN,NL,0,1,14,2,0,0,0,101,29,24,1,35,18,0.25,6.42,,3,3,1,160,2,28,,, +jonessh01,1949,1,NY1,NL,15,12,42,27,11,1,0,622,198,77,19,88,79,0.24,3.34,,4,10,0,902,9,93,,, +judsoho01,1949,1,CHA,AL,1,14,26,12,3,0,1,324,114,55,13,70,36,0.27,4.58,,4,1,1,496,8,65,,, 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+kushem01,1949,1,CHN,NL,3,3,26,0,0,0,2,143,51,20,7,24,22,0.28,3.78,,2,2,2,209,17,21,,, +kuzavbo01,1949,1,CHA,AL,10,6,29,18,9,1,0,470,139,70,6,91,83,0.24,4.02,,2,1,0,685,3,76,,, +ladedo01,1949,1,CHN,NL,4,5,36,13,5,1,1,389,141,72,14,58,43,0.27,5,,2,2,0,581,12,73,,, +laniema01,1949,1,SLN,NL,5,4,15,15,4,1,0,276,92,39,5,35,37,0.26,3.82,,0,0,0,396,0,42,,, +lemonbo01,1949,1,CLE,AL,22,10,37,33,22,2,1,839,211,93,19,137,138,0.21,2.99,,1,6,0,1159,4,101,,, +leonadu02,1949,1,CHN,NL,7,16,33,28,10,1,0,540,198,83,4,43,83,0.27,4.15,,7,7,0,788,1,94,,, +livelbu01,1949,1,CIN,NL,4,6,31,10,3,1,1,310,91,45,11,53,30,0.24,3.92,,2,0,0,430,6,47,,, +lombavi01,1949,1,PIT,NL,5,5,34,12,4,0,1,402,149,68,14,68,64,0.28,4.57,,4,3,0,599,11,74,,, +lopated01,1949,1,NYA,AL,15,10,31,30,14,4,1,646,222,78,19,69,70,0.26,3.26,,4,5,0,907,1,93,,, +mallobo01,1949,1,SLA,AL,1,1,5,0,0,0,0,29,6,3,0,7,2,0.2,2.79,,0,0,0,39,3,3,,, +marchph01,1949,1,PHA,AL,0,3,7,6,0,0,0,48,24,21,3,19,2,0.35,11.81,,2,1,0,89,1,23,,, +marshcu01,1949,1,NYA,AL,3,0,21,2,0,0,3,148,48,28,3,48,13,0.25,5.11,,3,2,0,238,11,31,,, +martifr02,1949,1,SLN,NL,6,0,21,5,3,0,0,210,65,19,3,20,30,0.24,2.44,,1,0,0,292,8,24,,, +martimo01,1949,1,BRO,NL,1,3,10,4,0,0,0,92,39,24,5,15,15,0.32,7.04,,1,2,0,143,2,25,,, +mastewa02,1949,1,WS1,AL,3,2,10,7,3,0,0,159,42,19,4,21,17,0.21,3.23,,1,3,0,219,1,22,,, +mastewa02,1949,2,BOS,AL,3,4,18,5,1,0,4,165,58,26,2,35,19,0.28,4.25,,0,0,0,244,7,30,,, +mccahbi01,1949,1,PHA,AL,1,1,7,4,0,0,0,62,23,6,0,9,3,0.29,2.61,,1,0,0,91,1,9,,, +mccalwi01,1949,1,BOS,AL,0,0,5,0,0,0,0,28,13,12,2,10,8,0.33,11.57,,2,0,0,49,1,12,,, +mcdermi03,1949,1,BOS,AL,5,4,12,12,6,2,0,240,63,36,5,52,50,0.22,4.05,,2,3,0,349,0,37,,, +mcglopa01,1949,1,BRO,NL,1,1,7,0,0,0,0,47,13,8,2,5,11,0.22,4.6,,0,0,0,63,2,8,,, +mclisca01,1949,1,CHN,NL,1,1,8,2,0,0,0,69,31,15,5,12,6,0.34,5.87,,0,0,0,107,5,21,,, +medliir01,1949,1,SLA,AL,0,0,3,0,0,0,0,12,11,12,1,3,4,0.47,27,,1,0,0,26,0,13,,, +meyerru01,1949,1,PHI,NL,17,8,37,28,14,2,1,639,199,73,14,70,78,0.25,3.08,,4,1,2,877,5,84,,, +millebo02,1949,1,PHI,NL,0,0,3,0,0,0,0,8,2,0,0,2,0,0.2,0,,0,0,0,12,2,0,,, +minnepa01,1949,1,BRO,NL,3,1,27,1,0,0,2,142,49,20,7,18,17,0.27,3.8,,1,1,0,203,15,22,,, +muncrbo01,1949,1,PIT,NL,1,5,13,4,1,0,3,107,44,25,8,13,11,0.31,6.31,,0,0,0,159,5,27,,, +muncrbo01,1949,2,CHN,NL,5,6,34,3,1,0,2,225,80,38,9,31,36,0.27,4.56,,3,1,0,330,17,42,,, +mungere01,1949,1,SLN,NL,15,8,35,28,12,2,2,565,179,81,13,87,82,0.25,3.87,,3,2,0,802,4,86,,, +newcodo01,1949,1,BRO,NL,17,8,38,31,19,5,1,733,223,86,17,73,149,0.24,3.17,,4,3,2,1005,5,89,,, +newhoha01,1949,1,DET,AL,18,11,38,35,22,3,1,876,277,109,19,111,144,0.25,3.36,,3,0,0,1228,3,118,,, +ostrojo02,1949,1,SLA,AL,8,8,40,13,4,0,2,423,185,75,16,27,34,0.3,4.79,,1,0,0,634,12,94,,, +overmst01,1949,1,DET,AL,1,3,14,1,0,0,0,52,29,19,2,9,3,0.37,9.87,,0,1,0,89,6,21,,, +pagejo01,1949,1,NYA,AL,13,8,60,0,0,0,27,406,103,39,8,75,99,0.21,2.59,,4,5,1,571,48,44,,, +paigesa01,1949,1,CLE,AL,4,7,31,5,1,0,5,249,70,28,4,33,54,0.23,3.04,,0,1,1,348,19,29,,, +palicer01,1949,1,BRO,NL,8,9,49,1,0,0,6,291,93,39,6,49,44,0.26,3.62,,2,1,0,413,27,43,,, +papaial01,1949,1,SLA,AL,4,11,42,15,6,0,2,427,175,80,8,81,31,0.29,5.06,,4,1,0,677,13,103,,, +papisfr01,1949,1,CLE,AL,1,0,25,3,1,0,1,186,54,22,2,39,23,0.24,3.19,,0,0,1,271,12,24,,, +parneme01,1949,1,BOS,AL,25,7,39,33,27,4,2,886,258,91,8,134,122,0.23,2.77,,9,5,0,1240,5,102,,, +pearcji01,1949,1,WS1,AL,0,1,2,1,0,0,0,16,9,5,1,5,1,0.37,8.44,,0,0,0,30,0,10,,, +perkoha01,1949,1,CIN,NL,1,1,5,3,2,0,0,71,21,12,2,14,3,0.23,4.56,,0,0,0,105,0,14,,, +peterke01,1949,1,CIN,NL,4,5,30,7,2,0,0,199,66,46,8,46,28,0.26,6.24,,3,4,0,309,5,54,,, +piercbi02,1949,1,CHA,AL,7,15,32,26,8,0,0,515,145,74,11,112,95,0.22,3.88,,5,0,2,761,4,89,,, +pierema01,1949,1,CHA,AL,4,6,39,9,0,0,4,348,131,71,10,54,25,0.28,5.51,,4,0,0,517,15,77,,, +pilledu01,1949,1,NYA,AL,2,4,12,3,2,0,0,112,43,18,6,19,9,0.29,4.34,,0,0,0,166,2,20,,, 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+scarbra01,1949,1,WS1,AL,13,11,34,27,11,1,0,599,204,102,10,88,81,0.26,4.6,,2,7,0,882,6,115,,, +scheica01,1949,1,PHA,AL,9,12,38,23,11,2,0,548,191,104,16,118,43,0.27,5.12,,3,2,0,821,13,117,,, +schmijo01,1949,1,CHN,NL,11,13,36,31,9,3,3,621,227,100,11,92,75,0.28,4.35,,5,2,1,898,4,117,,, +sewelri01,1949,1,PIT,NL,6,1,28,6,2,1,1,228,82,33,8,32,26,0.28,3.91,,0,0,0,330,9,35,,, +shantbo01,1949,1,PHA,AL,6,8,33,7,4,1,2,381,100,48,9,74,58,0.22,3.4,,3,3,0,537,16,50,,, +sheasp01,1949,1,NYA,AL,1,1,20,3,0,0,1,157,48,31,5,43,22,0.25,5.33,,1,0,0,239,10,36,,, +shorera01,1949,1,SLA,AL,0,1,13,0,0,0,0,70,27,28,3,31,13,0.29,10.8,,4,2,0,125,4,30,,, +shouncl01,1949,1,BSN,NL,0,0,1,0,0,0,0,3,1,0,0,0,0,0.25,0,,0,0,0,4,1,0,,, +shouncl01,1949,2,CHA,AL,1,1,16,0,0,0,0,70,37,15,1,13,8,0.37,5.79,,0,0,0,116,10,17,,, +simmocu01,1949,1,PHI,NL,4,10,38,14,2,0,1,394,133,67,7,55,83,0.27,4.59,,1,1,0,551,13,72,,, +sloatle01,1949,1,CHN,NL,0,0,5,1,0,0,0,27,14,7,0,3,3,0.4,7,,0,0,0,42,2,7,,, +spahnwa01,1949,1,BSN,NL,21,14,38,38,25,4,0,907,283,103,27,86,151,0.24,3.07,,4,3,0,1258,0,125,,, +stalege01,1949,1,SLN,NL,10,10,45,17,5,2,6,514,154,52,7,41,55,0.23,2.73,,2,3,0,699,14,65,,, +starrdi01,1949,1,SLA,AL,1,7,30,8,1,1,0,250,96,40,6,48,44,0.29,4.32,,1,1,0,380,12,46,,, +stobbch01,1949,1,BOS,AL,11,6,26,19,10,0,0,456,145,68,10,75,70,0.25,4.03,,1,2,1,653,2,72,,, +stuarma01,1949,1,DET,AL,0,2,14,2,0,0,0,89,39,30,3,35,14,0.34,9.1,,2,0,0,147,4,33,,, +surkoma01,1949,1,CHA,AL,3,5,44,2,0,0,4,288,92,51,9,60,38,0.25,4.78,,3,3,0,435,23,61,,, +suthedi01,1949,1,WS1,AL,0,1,1,1,0,0,0,3,2,5,0,6,0,0.4,45,,0,0,0,11,0,5,,, +thompfo01,1949,1,WS1,AL,1,3,9,1,1,0,0,49,22,8,1,9,8,0.32,4.41,,0,1,0,80,2,11,,, +thompjo01,1949,1,PHI,NL,1,3,8,5,1,0,0,94,38,24,6,11,12,0.31,6.89,,1,0,0,134,1,24,,, +tomasan01,1949,1,NY1,NL,0,1,2,0,0,0,0,15,9,10,2,5,2,0.37,18,,1,0,0,29,1,10,,, +trinkke01,1949,1,PHI,NL,1,1,42,0,0,0,2,223,79,33,3,30,14,0.29,4,,0,3,1,308,15,37,,, +troutdi01,1949,1,DET,AL,3,6,33,0,0,0,3,178,68,29,2,21,19,0.29,4.4,,1,0,0,261,24,35,,, +truckvi01,1949,1,DET,AL,19,11,41,32,17,6,4,825,209,86,16,124,153,0.21,2.81,,3,4,0,1133,8,95,,, +vancujo01,1949,1,BRO,NL,0,0,2,0,0,0,0,6,3,2,0,1,0,0.42,9,,0,0,0,9,0,2,,, +vandejo01,1949,1,CIN,NL,5,10,28,24,7,3,0,479,172,87,12,85,76,0.28,4.9,,5,2,0,712,2,92,,, +voisebi01,1949,1,BSN,NL,7,8,30,22,5,4,1,508,170,76,14,78,63,0.26,4.04,,6,1,0,737,4,84,,, +walshju01,1949,1,PIT,NL,1,4,9,7,1,1,0,128,40,24,5,16,24,0.24,5.06,,1,0,0,180,0,27,,, +webbre01,1949,1,NY1,NL,1,1,20,0,0,0,0,134,41,20,3,21,9,0.24,4.03,,0,0,0,190,10,23,,, +wehmehe01,1949,1,CIN,NL,11,12,33,29,11,1,0,640,202,111,20,117,80,0.25,4.68,,7,7,0,937,1,119,,, +weikdi01,1949,1,WS1,AL,3,12,27,14,2,2,1,286,78,57,5,103,58,0.23,5.38,,10,0,1,448,8,61,,, +weltedi01,1949,1,WS1,AL,2,5,52,2,0,0,2,286,107,78,6,89,37,0.29,7.36,,3,1,0,464,25,83,,, +werlebi01,1949,1,PIT,NL,12,13,35,29,10,2,0,663,243,104,22,51,106,0.27,4.24,,4,8,0,945,2,117,,, +whiteha01,1949,1,DET,AL,1,0,9,0,0,0,2,36,5,0,0,4,4,0.12,0,,0,0,0,44,8,0,,, +wightbi01,1949,1,CHA,AL,15,13,35,33,14,3,1,735,254,90,9,96,78,0.27,3.31,,3,0,1,1036,1,106,,, +wilkste01,1949,1,SLN,NL,10,3,59,0,0,0,9,355,105,49,7,38,71,0.24,3.73,,0,0,0,484,30,52,,, +wilsoji02,1949,1,PHA,AL,0,0,2,0,0,0,0,15,7,8,2,5,2,0.35,14.4,,0,0,0,25,1,8,,, +winegra01,1949,1,SLA,AL,0,0,9,0,0,0,0,50,24,14,2,2,8,0.32,7.56,,0,0,0,75,6,16,,, +wittijo01,1949,1,BOS,AL,0,0,1,0,0,0,0,6,2,2,0,2,0,0.28,9,,0,0,0,9,1,2,,, +wynnea01,1949,1,CLE,AL,11,7,26,23,6,0,0,494,186,76,8,57,62,0.28,4.15,,0,1,0,724,2,84,,, +yochira01,1949,1,SLN,NL,0,0,3,0,0,0,0,7,3,4,1,4,3,0.27,15.43,,1,0,0,15,3,4,,, +zabalad01,1949,1,NY1,NL,2,3,15,4,2,1,1,123,44,24,5,10,13,0.27,5.27,,0,1,3,173,5,28,,, +zoldasa01,1949,1,CLE,AL,1,2,27,0,0,0,0,159,60,25,4,18,11,0.29,4.25,,1,0,0,235,13,30,,, +aberal01,1950,1,CLE,AL,1,0,1,1,1,0,0,27,5,2,0,4,4,0.16,2,,0,0,0,34,0,2,,, +albreed01,1950,1,SLA,AL,0,1,2,1,0,0,0,20,6,4,0,7,1,0.25,5.4,,0,0,0,32,1,7,,, +alomalu01,1950,1,CHA,AL,7,2,42,0,0,0,4,263,77,37,6,53,49,0.23,3.8,,3,1,1,388,21,44,,, +antonjo02,1950,1,BSN,NL,2,3,20,6,2,1,0,173,81,38,2,22,33,0.33,5.93,,0,4,1,275,6,46,,, +atkinja01,1950,1,BOS,AL,0,0,1,0,0,0,0,14,4,2,1,4,0,0.23,3.86,,1,1,0,22,0,2,,, +avreaja01,1950,1,CIN,NL,0,0,2,0,0,0,0,16,6,2,0,3,2,0.27,3.38,,0,0,0,25,0,2,,, +bankhda01,1950,1,BRO,NL,9,4,41,12,2,1,3,388,119,79,16,88,96,0.25,5.5,,1,2,2,572,16,84,,, +bantaja01,1950,1,BRO,NL,4,4,16,5,1,0,2,124,39,20,2,36,15,0.25,4.35,,5,3,0,195,7,22,,, +barnere02,1950,1,BRO,NL,2,1,20,1,0,0,0,101,25,24,6,48,23,0.21,6.42,,7,2,1,167,5,26,,, +barrefr01,1950,1,PIT,NL,1,2,5,0,0,0,0,13,5,2,1,1,0,0.35,4.15,,0,0,0,16,3,3,,, +bauerru01,1950,1,SLA,AL,0,0,1,0,0,0,0,6,6,1,0,1,0,0.6,4.5,,0,0,0,11,1,4,,, +beardge01,1950,1,CLE,AL,1,3,14,3,0,0,0,136,57,31,5,32,10,0.32,6.15,,3,0,0,211,6,32,,, +beardge01,1950,2,WS1,AL,3,5,12,9,4,0,0,205,81,32,1,33,20,0.29,4.21,,2,2,1,310,2,35,,, +bentoal01,1950,1,CLE,AL,4,2,36,0,0,0,4,189,57,25,7,30,26,0.24,3.57,,3,1,1,269,13,32,,, +bickfve01,1950,1,BSN,NL,19,14,40,39,27,2,0,935,293,120,25,122,126,0.24,3.47,,5,6,1,1325,1,135,,, +blackew01,1950,1,CIN,NL,17,15,40,32,18,1,4,783,203,86,12,112,188,0.21,2.97,,11,13,0,1102,7,105,,, +borowha01,1950,1,PHI,NL,0,0,3,0,0,0,0,19,5,4,0,4,3,0.25,5.68,,0,0,0,25,0,4,,, +borowha01,1950,2,PIT,NL,1,3,11,3,0,0,0,76,32,18,6,9,9,0.31,6.39,,1,1,0,115,4,19,,, +borowha01,1950,3,DET,AL,1,1,13,2,1,0,0,98,23,12,3,16,12,0.2,3.31,,2,0,0,134,3,15,,, +boyercl01,1950,1,SLN,NL,7,7,36,14,6,1,1,361,105,47,15,49,82,0.23,3.52,,4,3,1,508,9,52,,, +brancra01,1950,1,BRO,NL,7,9,43,15,5,0,7,426,152,74,24,55,100,0.27,4.69,,1,0,4,621,16,80,,, +brazlal01,1950,1,SLN,NL,11,9,46,12,3,0,6,494,188,75,12,80,47,0.29,4.1,,1,4,0,732,20,81,,, +brechha01,1950,1,SLN,NL,8,11,27,23,12,2,1,490,151,69,18,45,80,0.24,3.8,,2,3,0,675,3,77,,, +brisslo01,1950,1,PHA,AL,7,19,46,31,15,2,8,738,237,110,22,117,101,0.25,4.02,,7,4,0,1083,13,127,,, +brittja01,1950,1,PHI,NL,0,0,3,0,0,0,0,12,2,2,0,3,3,0.14,4.5,,0,0,0,17,0,2,,, +bruneja01,1950,1,CHA,AL,0,0,9,0,0,0,0,37,7,5,0,14,8,0.18,3.65,,1,1,0,55,5,6,,, +bruneja01,1950,2,SLA,AL,1,2,13,1,0,0,1,105,36,18,4,23,16,0.26,4.63,,0,2,0,162,7,21,,, +burdele01,1950,1,NYA,AL,0,0,2,0,0,0,0,4,3,1,0,0,0,0.5,6.75,,0,0,0,6,0,1,,, +burtsmo01,1950,1,PHA,AL,0,1,9,1,0,0,0,57,22,15,2,21,12,0.28,7.11,,3,1,0,100,5,16,,, +byerlbu01,1950,1,CIN,NL,0,1,4,1,0,0,0,44,12,4,1,4,5,0.21,2.45,,0,0,0,59,1,4,,, +byrdha01,1950,1,PHA,AL,0,0,6,0,0,0,0,32,25,20,3,9,2,0.48,16.88,,3,2,0,65,2,20,,, +byrneto01,1950,1,NYA,AL,15,9,31,31,10,2,0,610,188,107,23,160,118,0.24,4.74,,3,17,2,951,0,115,,, +cainbo01,1950,1,CHA,AL,9,12,34,23,11,1,2,515,153,75,12,109,77,0.24,3.93,,3,5,0,752,9,80,,, +calvepa01,1950,1,DET,AL,2,2,32,0,0,0,4,154,71,36,7,25,14,0.32,6.31,,3,2,0,250,19,42,,, +candimi01,1950,1,PHI,NL,1,0,18,0,0,0,0,90,32,9,2,15,10,0.28,2.7,,1,0,2,130,6,11,,, +chambcl01,1950,1,PIT,NL,12,15,37,33,11,2,0,748,262,119,18,92,93,0.26,4.3,,4,6,1,1097,2,138,,, +chesnbo01,1950,1,PIT,NL,3,3,9,7,2,0,0,117,44,24,7,17,12,0.29,5.54,,1,3,0,174,0,26,,, +chipmbo01,1950,1,BSN,NL,7,7,27,12,4,0,1,372,127,61,10,37,40,0.26,4.43,,0,4,3,534,9,75,,, +churcbu01,1950,1,PHI,NL,8,6,31,18,8,2,1,426,113,43,12,56,50,0.22,2.73,,4,0,0,562,3,50,,, +coleda01,1950,1,BSN,NL,0,1,4,0,0,0,0,24,7,1,0,3,8,0.25,1.13,,0,1,0,32,2,2,,, +colemjo04,1950,1,PHA,AL,0,5,15,6,2,0,0,162,74,51,9,50,12,0.33,8.5,,2,0,0,278,7,54,,, +connebi01,1950,1,CHA,AL,0,0,2,0,0,0,0,7,5,3,1,1,0,0.45,11.57,,0,0,0,13,0,3,,, +connebi01,1950,2,DET,AL,0,0,2,0,0,0,0,12,4,3,1,2,1,0.25,6.75,,0,0,0,18,2,3,,, +consusa01,1950,1,WS1,AL,7,8,21,18,8,2,2,374,132,61,6,57,38,0.27,4.4,,0,1,0,553,2,71,,, +cuellch01,1950,1,CHA,AL,0,0,2,0,0,0,0,4,6,5,0,3,1,0.6,33.75,,0,0,0,13,0,6,,, +dealco01,1950,1,SLN,NL,0,0,3,0,0,0,0,3,3,2,0,2,1,0.5,18,,0,0,0,9,1,5,,, +dicksmu01,1950,1,PIT,NL,10,15,51,22,8,0,3,675,227,95,20,83,76,0.26,3.8,,4,2,1,963,18,104,,, +dobsojo01,1950,1,BOS,AL,15,10,39,27,12,1,4,620,217,96,15,81,81,0.27,4.18,,4,0,0,882,9,103,,, +donnebl01,1950,1,PHI,NL,2,4,14,1,0,0,0,63,30,10,5,10,10,0.33,4.29,,1,0,0,102,6,13,,, +donovdi01,1950,1,BSN,NL,0,2,10,3,0,0,0,89,28,27,4,34,9,0.25,8.19,,1,2,1,147,4,28,,, +dorisha01,1950,1,SLA,AL,4,9,29,13,4,0,0,327,162,78,13,36,36,0.33,6.44,,4,9,2,529,8,90,,, +dubiemo01,1950,1,CHN,NL,6,10,39,12,4,2,2,428,152,66,12,67,51,0.27,4.16,,1,1,0,636,8,79,,, +dusaker01,1950,1,SLN,NL,0,2,14,2,0,0,1,109,27,15,2,27,16,0.21,3.72,,0,1,0,161,9,17,,, +epperal01,1950,1,BRO,NL,0,0,5,0,0,0,0,27,14,5,1,5,3,0.37,5,,0,0,0,44,2,8,,, +erauted01,1950,1,CIN,NL,4,2,33,2,1,0,1,196,82,41,9,22,35,0.3,5.65,,2,6,2,298,17,48,,, +erskica01,1950,1,BRO,NL,7,6,22,13,3,0,1,309,109,54,15,35,50,0.27,4.72,,0,1,0,441,4,56,,, +fannicl01,1950,1,SLA,AL,5,9,25,16,3,0,1,306,116,74,18,58,42,0.28,6.53,,1,0,0,475,8,82,,, 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+johnser02,1950,1,BSN,NL,2,0,16,1,0,0,0,62,37,16,1,13,15,0.39,6.97,,1,0,0,110,7,21,,, +johnske01,1950,1,SLN,NL,0,0,2,0,0,0,0,6,1,0,0,3,1,0.16,0,,0,0,0,10,1,1,,, +johnske01,1950,2,PHI,NL,4,1,14,8,3,1,0,182,61,27,3,43,32,0.26,4.01,,5,1,0,282,6,32,,, +jonessh01,1950,1,NY1,NL,13,16,40,28,11,2,2,597,188,102,26,90,97,0.24,4.61,,2,7,0,858,4,114,,, +judsoho01,1950,1,CHA,AL,2,3,46,3,1,0,0,336,105,49,10,63,34,0.25,3.94,,0,2,0,487,18,53,,, +kellnal01,1950,1,PHA,AL,8,20,36,29,15,0,2,676,253,137,28,112,85,0.28,5.47,,8,2,0,1023,5,157,,, +kennebi02,1950,1,SLA,AL,0,0,1,0,0,0,0,6,1,0,0,2,1,0.14,0,,1,0,0,9,1,1,,, +kennemo01,1950,1,NY1,NL,5,4,36,17,5,0,2,343,120,60,14,53,41,0.26,4.72,,3,3,1,513,10,65,,, +keriagu01,1950,1,CHA,AL,0,1,1,1,0,0,0,7,7,5,0,5,1,0.5,19.29,,0,0,0,19,0,5,,, +kindeel01,1950,1,BOS,AL,14,12,48,23,11,1,9,621,212,98,23,78,95,0.26,4.26,,1,1,0,903,15,105,,, +kliemed01,1950,1,PHA,AL,0,0,5,0,0,0,0,17,10,6,0,2,0,0.35,9.53,,1,2,0,32,4,6,,, 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+stuffpa01,1950,1,PHI,NL,0,0,3,0,0,0,0,15,4,1,0,1,3,0.21,1.8,,0,1,0,21,1,1,,, +sucheji01,1950,1,BOS,AL,0,0,4,0,0,0,0,12,3,2,0,4,3,0.23,4.5,,0,0,0,18,3,2,,, +surkoma01,1950,1,BSN,NL,5,2,9,6,2,0,0,167,63,20,5,20,21,0.28,3.23,,0,2,0,246,3,29,,, +tayloha03,1950,1,BOS,AL,2,0,3,2,2,1,0,57,13,3,0,8,8,0.19,1.42,,1,0,0,74,1,3,,, +thompjo01,1950,1,PHI,NL,0,0,2,0,0,0,0,12,1,0,0,4,2,0.07,0,,0,0,0,17,0,1,,, +troutdi01,1950,1,DET,AL,13,5,34,20,11,1,4,554,190,77,13,64,88,0.26,3.75,,0,5,0,791,6,84,,, +truckvi01,1950,1,DET,AL,3,1,7,7,2,1,0,145,45,19,6,21,25,0.24,3.54,,0,1,0,209,0,20,,, +vancuch01,1950,1,BRO,NL,1,3,12,4,1,0,0,100,33,18,3,12,21,0.26,4.86,,1,1,1,140,1,19,,, +vandejo01,1950,1,CHN,NL,3,4,32,6,0,0,1,221,60,31,10,59,41,0.22,3.79,,3,2,0,338,17,46,,, +vargaan01,1950,1,CHN,NL,0,0,1,0,0,0,0,3,0,0,0,1,0,0,0,,0,0,0,3,1,0,,, +voisebi01,1950,1,CHN,NL,0,4,19,7,0,0,0,154,64,33,7,29,25,0.3,5.79,,0,1,0,243,2,39,,, +wallmu01,1950,1,BSN,NL,0,0,1,0,0,0,0,12,6,4,0,2,2,0.33,9,,0,0,0,20,1,5,,, 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+crimija01,1951,1,SLN,NL,1,0,11,0,0,0,1,51,24,17,3,8,5,0.33,9,,0,0,0,80,5,17,,, +cristle01,1951,1,PHI,NL,1,1,10,1,0,0,0,66,28,12,3,9,6,0.31,4.91,,0,1,0,100,2,13,,, +dempsco01,1951,1,PIT,NL,0,2,3,2,0,0,0,21,11,7,2,4,3,0.39,9,,0,0,0,33,1,7,,, +dicksmu01,1951,1,PIT,NL,20,16,45,35,19,3,2,866,294,129,32,101,112,0.26,4.02,,1,6,1,1241,9,151,,, +dobsojo01,1951,1,CHA,AL,7,6,28,21,6,0,3,440,136,59,17,51,67,0.24,3.62,,1,0,0,609,3,68,,, +donnebl01,1951,1,BSN,NL,0,1,6,0,0,0,0,22,8,6,1,6,3,0.28,7.36,,1,1,1,38,1,6,,, +donovdi01,1951,1,BSN,NL,0,0,8,2,0,0,0,41,17,8,0,11,4,0.29,5.27,,0,0,0,69,3,11,,, +dorisha01,1951,1,CHA,AL,5,6,32,4,2,1,0,290,101,38,6,31,29,0.27,3.54,,4,0,0,406,20,50,,, +drewska01,1951,1,PHI,NL,1,0,5,3,1,0,0,69,29,16,2,7,13,0.29,6.26,,3,4,0,110,0,16,,, +dubiemo01,1951,1,CHN,NL,2,2,22,0,0,0,1,164,46,14,3,22,19,0.23,2.3,,0,0,2,221,12,17,,, +dusaker01,1951,1,SLN,NL,0,0,5,0,0,0,0,30,14,8,0,7,8,0.33,7.2,,0,0,0,49,4,8,,, +dusaker01,1951,2,PIT,NL,0,1,3,1,0,0,0,20,10,9,2,9,2,0.35,12.15,,0,1,0,38,2,10,,, +erauted01,1951,1,CIN,NL,0,0,30,0,0,0,0,118,50,25,4,23,20,0.31,5.72,,2,3,0,188,10,31,,, +erskica01,1951,1,BRO,NL,16,12,46,19,7,0,4,569,206,94,23,78,95,0.28,4.46,,0,2,0,829,18,105,,, +estocge01,1951,1,BSN,NL,0,1,37,1,0,0,3,181,56,29,2,37,11,0.25,4.33,,2,0,0,263,16,33,,, +evansbi02,1951,1,BOS,AL,0,0,9,0,0,0,0,46,15,7,0,8,3,0.26,4.11,,2,0,0,66,4,8,,, +fahrje01,1951,1,CLE,AL,0,0,5,0,0,0,0,17,11,3,0,2,0,0.5,4.76,,0,0,0,25,2,3,,, +fannicl01,1951,1,SLA,AL,0,2,7,1,0,0,0,46,20,11,6,5,11,0.31,6.46,,0,0,0,70,5,16,,, +fellebo01,1951,1,CLE,AL,22,8,33,32,16,4,0,749,239,97,22,95,111,0.25,3.5,,2,7,0,1061,1,105,,, +ferrito01,1951,1,NYA,AL,1,1,9,0,0,0,1,36,21,10,4,7,3,0.38,7.5,,0,0,0,63,5,12,,, +ferrito01,1951,2,WS1,AL,2,0,22,0,0,0,2,125,36,11,3,7,17,0.23,2.38,,1,0,0,163,15,16,,, +flowebe01,1951,1,BOS,AL,0,0,1,0,0,0,0,9,2,0,0,1,2,0.2,0,,0,0,0,11,0,0,,, +fowledi01,1951,1,PHA,AL,5,11,22,22,4,0,0,375,141,78,11,72,29,0.29,5.62,,0,1,0,568,0,89,,, +foxho01,1951,1,CIN,NL,9,14,40,30,9,4,2,684,239,97,15,69,57,0.27,3.83,,2,2,0,958,5,105,,, +frienbo01,1951,1,PIT,NL,6,10,34,22,3,1,0,449,173,71,12,68,41,0.29,4.27,,1,0,0,666,5,94,,, +garcimi01,1951,1,CLE,AL,20,13,47,30,15,1,6,762,239,89,10,82,118,0.24,3.15,,6,3,0,1066,14,101,,, +garvene01,1951,1,SLA,AL,20,12,33,30,24,1,0,738,237,102,17,96,84,0.25,3.73,,6,5,0,1044,3,114,,, +getteal01,1951,1,NY1,NL,1,2,30,1,0,0,0,172,52,31,12,25,36,0.24,4.87,,1,0,0,246,11,37,,, +grayte01,1951,1,DET,AL,7,14,34,28,9,1,1,592,194,89,17,95,131,0.25,4.06,,4,6,0,876,3,103,,, +grimsro01,1951,1,CHA,AL,0,0,7,0,0,0,0,42,12,6,1,10,8,0.23,3.86,,1,0,0,61,3,8,,, +gromest01,1951,1,CLE,AL,7,4,27,8,4,0,1,322,98,33,6,29,40,0.23,2.77,,1,4,0,450,8,41,,, +gumpera01,1951,1,CHA,AL,9,8,33,16,7,1,2,425,156,68,20,34,45,0.27,4.32,,0,1,1,616,10,74,,, +habenbo01,1951,1,SLN,NL,0,0,3,0,0,0,0,15,5,4,0,9,1,0.27,7.2,,0,0,0,27,0,4,,, +hackewa02,1951,1,CHN,NL,0,0,2,0,0,0,0,4,3,2,0,0,2,0.5,13.5,,0,1,0,8,1,2,,, +hansean01,1951,1,PHI,NL,3,1,24,0,0,0,0,117,34,11,4,7,11,0.22,2.54,,1,1,0,157,8,14,,, +hardyre01,1951,1,NY1,NL,0,0,2,0,0,0,0,4,4,1,0,1,0,0.57,6.75,,0,1,0,10,0,1,,, +harribu02,1951,1,PHA,AL,0,0,3,0,0,0,0,12,4,4,0,5,2,0.25,9,,0,1,0,22,1,4,,, +harribu02,1951,2,CLE,AL,0,0,2,0,0,0,0,12,5,2,0,4,1,0.33,4.5,,0,0,0,20,1,2,,, +harrimi01,1951,1,WS1,AL,6,8,41,0,0,0,4,262,87,37,6,43,47,0.26,3.81,,2,1,0,385,29,45,,, +hattejo01,1951,1,BRO,NL,1,0,11,6,0,0,0,148,55,25,3,21,22,0.28,4.56,,2,0,0,218,0,25,,, +hattejo01,1951,2,CHN,NL,2,6,23,6,1,0,0,226,82,43,8,37,23,0.28,5.14,,4,1,0,334,11,48,,, +haugsph01,1951,1,BRO,NL,0,1,21,1,0,0,0,92,28,22,4,24,22,0.23,6.46,,1,3,0,148,6,25,,, +haynejo01,1951,1,WS1,AL,1,4,26,3,1,0,2,219,85,37,9,37,18,0.29,4.56,,1,1,0,334,14,46,,, +hearnji01,1951,1,NY1,NL,17,9,34,34,11,0,0,634,204,85,21,82,66,0.25,3.62,,6,2,1,902,0,102,,, +heintke01,1951,1,PHI,NL,6,12,35,12,3,0,2,355,119,55,13,53,55,0.26,4.18,,1,4,0,516,13,61,,, +herbera01,1951,1,DET,AL,4,0,5,0,0,0,0,38,8,2,0,9,9,0.19,1.42,,3,0,0,51,4,2,,, +herrebo01,1951,1,SLA,AL,0,0,3,0,0,0,0,7,6,7,2,4,1,0.46,27,,0,1,0,18,2,7,,, +hillefr01,1951,1,CHN,NL,6,12,24,21,6,2,1,424,147,76,17,31,50,0.26,4.84,,3,9,1,594,1,83,,, +hinripa01,1951,1,BOS,AL,0,0,4,0,0,0,0,10,7,8,1,4,1,0.41,21.6,,0,0,0,21,1,8,,, +hisneha01,1951,1,BOS,AL,0,1,1,1,0,0,0,18,7,3,0,4,3,0.29,4.5,,0,0,0,28,0,3,,, +hoguebo01,1951,1,BSN,NL,0,0,3,0,0,0,0,15,4,3,1,3,0,0.23,5.4,,0,0,0,20,1,3,,, +hoguebo01,1951,2,SLA,AL,1,1,18,0,0,0,1,89,31,17,1,23,11,0.27,5.16,,0,0,0,137,9,17,,, +hoguebo01,1951,3,NYA,AL,1,0,7,0,0,0,0,22,4,0,0,3,2,0.17,0,,1,0,0,26,3,0,,, +holcoke01,1951,1,CHA,AL,11,12,28,23,12,2,0,478,142,67,9,68,39,0.24,3.78,,2,1,0,670,3,69,,, +hoopebo01,1951,1,PHA,AL,12,10,38,23,9,0,5,567,192,92,13,61,64,0.26,4.38,,4,3,0,790,8,98,,, 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+byerlbu01,1952,1,CIN,NL,0,1,12,2,0,0,1,74,29,14,0,7,14,0.3,5.11,,0,0,0,103,4,15,,, +byrdha01,1952,1,PHA,AL,15,15,37,28,15,3,2,685,244,84,12,98,116,0.27,3.31,,4,7,0,1008,7,100,,, +byrneto01,1952,1,SLA,AL,7,14,29,24,14,0,0,588,182,102,16,112,91,0.24,4.68,,5,10,1,876,5,117,,, +cainbo01,1952,1,SLA,AL,12,10,29,27,8,1,2,510,169,78,15,62,70,0.26,4.13,,1,2,0,718,2,79,,, +carlsdo01,1952,1,PIT,NL,0,1,5,1,0,0,0,30,20,12,1,5,2,0.41,10.8,,1,0,0,53,1,13,,, +chakabo01,1952,1,CLE,AL,1,2,5,1,0,0,0,36,19,13,2,8,7,0.38,9.75,,0,0,0,58,2,13,,, +chambcl01,1952,1,SLN,NL,4,4,26,13,2,1,1,295,110,45,8,33,47,0.28,4.12,,1,2,1,428,4,51,,, +chipmbo01,1952,1,BSN,NL,1,1,29,0,0,0,0,125,28,13,5,20,16,0.18,2.81,,1,0,0,172,12,15,,, +churcbu01,1952,1,PHI,NL,0,0,2,1,0,0,0,15,11,6,0,1,3,0.44,10.8,,0,1,0,28,0,6,,, +churcbu01,1952,2,CIN,NL,5,9,29,22,5,1,0,460,173,74,21,48,47,0.3,4.34,,1,3,0,633,2,85,,, +clarkmi01,1952,1,SLN,NL,2,0,12,4,0,0,0,76,32,17,2,14,10,0.31,6.04,,1,0,0,121,5,18,,, +coleda01,1952,1,BSN,NL,1,1,22,3,0,0,0,134,38,20,2,42,22,0.24,4.03,,3,3,0,207,10,21,,, +colluja01,1952,1,SLN,NL,0,0,2,0,0,0,0,9,2,0,0,1,0,0.2,0,,0,0,0,12,2,1,,, +conlege01,1952,1,BSN,NL,0,3,4,3,0,0,0,38,23,11,4,9,6,0.39,7.82,,0,2,0,72,1,16,,, +connebi01,1952,1,NY1,NL,5,0,11,4,0,0,0,95,22,16,4,25,22,0.2,4.55,,2,0,0,134,2,18,,, +consusa01,1952,1,WS1,AL,6,0,30,2,0,0,5,221,80,25,2,27,19,0.27,3.05,,1,0,1,320,14,30,,, +corwial01,1952,1,NY1,NL,6,1,21,7,1,0,2,203,58,20,5,36,36,0.23,2.66,,1,0,0,288,5,23,,, +crimija01,1952,1,SLN,NL,0,0,5,0,0,0,0,25,15,9,4,4,4,0.41,9.72,,0,0,0,41,2,9,,, +delocik01,1952,1,BOS,AL,4,9,39,7,1,1,5,285,88,45,9,50,46,0.24,4.26,,3,2,1,418,18,50,,, +dicksmu01,1952,1,PIT,NL,14,21,43,34,21,2,2,833,278,110,26,76,112,0.26,3.57,,0,1,0,1157,8,128,,, +dobsojo01,1952,1,CHA,AL,14,10,29,25,11,3,1,602,164,56,11,60,101,0.22,2.51,,4,0,0,805,3,66,,, +donovdi01,1952,1,BSN,NL,0,2,7,2,0,0,1,39,18,8,1,12,6,0.34,5.54,,0,2,0,70,3,10,,, +dorisha01,1952,1,CHA,AL,8,4,39,1,1,0,11,273,66,25,4,42,47,0.2,2.47,,3,1,1,372,27,28,,, +drewska01,1952,1,PHI,NL,14,15,33,30,15,5,0,686,213,69,13,52,96,0.25,2.72,,4,4,0,917,1,79,,, +dubiemo01,1952,1,CHN,NL,0,0,1,0,0,0,0,2,1,0,0,0,1,0.33,0,,0,0,0,3,0,0,,, +dunnji01,1952,1,PIT,NL,0,0,3,0,0,0,0,16,4,2,0,3,2,0.19,3.38,,1,0,0,24,1,2,,, +erskica01,1952,1,BRO,NL,14,6,33,26,10,4,2,620,167,62,17,71,131,0.22,2.7,,6,2,1,836,3,72,,, +fannicl01,1952,1,SLA,AL,0,2,10,2,0,0,0,49,34,23,5,9,6,0.45,12.67,,2,0,0,85,2,25,,, +fearve01,1952,1,CHN,NL,0,0,4,0,0,0,0,24,9,7,1,3,4,0.29,7.88,,2,1,0,36,1,7,,, +fellebo01,1952,1,CLE,AL,9,13,30,30,11,0,0,575,219,101,13,83,81,0.28,4.74,,1,3,1,869,0,124,,, +ferrito01,1952,1,WS1,AL,4,3,27,0,0,0,1,152,53,17,2,11,28,0.27,3.02,,4,0,0,207,17,19,,, +fisheha01,1952,1,PIT,NL,1,2,8,3,0,0,0,55,17,14,4,13,5,0.26,6.87,,1,2,0,83,4,14,,, +fornimi01,1952,1,WS1,AL,2,2,4,2,2,1,0,79,13,4,1,11,12,0.14,1.37,,0,0,0,104,2,4,,, 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+hudsosi01,1952,1,WS1,AL,3,4,7,7,6,0,0,188,59,19,4,29,24,0.25,2.73,,0,0,0,265,0,22,,, +hudsosi01,1952,2,BOS,AL,7,9,21,18,7,0,0,403,145,54,9,36,50,0.27,3.62,,0,7,0,578,2,64,,, +hugheji02,1952,1,BRO,NL,2,1,6,0,0,0,0,56,16,3,0,11,8,0.23,1.45,,1,0,0,79,2,4,,, +hutchfr01,1952,1,DET,AL,2,1,12,1,0,0,0,112,40,14,4,9,12,0.27,3.38,,0,1,0,157,6,16,,, +jansela01,1952,1,NY1,NL,11,11,34,27,8,1,2,502,183,76,16,47,74,0.28,4.09,,0,6,0,716,5,91,,, +jestevi01,1952,1,BSN,NL,3,5,19,8,4,1,0,219,80,27,5,23,25,0.28,3.33,,0,1,1,312,5,31,,, +johnsdo03,1952,1,WS1,AL,0,5,29,6,0,0,2,207,80,34,4,33,37,0.28,4.43,,2,4,0,319,11,41,,, +johnser02,1952,1,BSN,NL,6,3,29,10,2,1,1,276,100,42,7,31,45,0.27,4.11,,2,2,1,405,10,53,,, +johnske01,1952,1,DET,AL,0,0,9,1,0,0,0,34,12,8,1,11,10,0.27,6.35,,1,0,0,55,6,11,,, +jonessa02,1952,1,CLE,AL,2,3,14,4,0,0,1,108,38,29,6,37,28,0.27,7.25,,4,4,0,184,2,30,,, +jonessh01,1952,1,BSN,NL,1,4,39,1,0,0,1,210,81,37,8,31,40,0.28,4.76,,3,1,0,321,18,45,,, +jordani01,1952,1,CIN,NL,0,1,3,1,0,0,0,19,14,7,1,3,2,0.45,9.95,,0,0,0,34,0,7,,, +judsoho01,1952,1,CHA,AL,0,1,21,0,0,0,1,102,30,16,4,22,15,0.24,4.24,,1,0,0,153,8,17,,, +kellnal01,1952,1,PHA,AL,12,14,34,33,14,2,0,694,223,112,21,86,105,0.25,4.36,,3,4,0,990,1,124,,, +kellnwa01,1952,1,PHA,AL,0,0,1,0,0,0,1,12,4,3,0,3,2,0.25,6.75,,0,0,0,19,1,3,,, +kellybo01,1952,1,CHN,NL,4,9,31,15,3,2,0,376,114,50,7,46,50,0.23,3.59,,7,3,0,544,10,62,,, +kennebi02,1952,1,CHA,AL,2,2,47,1,0,0,5,212,54,22,4,38,46,0.21,2.8,,1,1,0,294,24,27,,, +kennemo01,1952,1,NY1,NL,3,4,31,6,2,1,0,250,73,28,6,31,48,0.23,3.02,,0,2,0,355,9,37,,, +kindeel01,1952,1,BOS,AL,5,6,23,10,4,0,4,293,85,28,11,28,50,0.23,2.58,,0,1,0,400,8,33,,, +kingcl01,1952,1,BRO,NL,2,0,23,0,0,0,0,128,56,24,5,12,17,0.31,5.06,,0,1,0,191,9,25,,, +klinero01,1952,1,PIT,NL,0,7,27,11,0,0,0,236,74,48,3,66,27,0.25,5.49,,4,6,3,368,5,55,,, +klippjo01,1952,1,CHN,NL,9,14,41,25,7,2,3,608,208,100,17,89,110,0.26,4.44,,12,6,0,885,10,110,,, 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+leonadu02,1952,1,CHN,NL,2,2,45,0,0,0,11,200,56,16,3,24,37,0.23,2.16,,2,3,0,282,36,18,,, +littldi01,1952,1,DET,AL,0,3,28,1,0,0,1,143,46,23,4,25,32,0.25,4.34,,2,0,0,212,17,24,,, +littldi01,1952,2,SLA,AL,2,3,7,5,3,0,0,139,35,14,4,17,34,0.2,2.72,,0,0,0,190,2,18,,, +loesbi01,1952,1,BRO,NL,13,8,39,21,8,4,1,562,154,56,12,71,115,0.22,2.69,,3,3,1,770,11,62,,, +lopated01,1952,1,NYA,AL,10,5,20,19,10,2,0,448,127,42,11,53,56,0.23,2.53,,1,4,0,608,1,47,,, +lowntu01,1952,1,CHN,NL,4,11,33,19,5,0,0,470,154,76,13,93,73,0.25,4.37,,5,3,0,704,4,87,,, +madisda01,1952,1,SLA,AL,4,2,31,4,0,0,0,234,78,38,7,48,35,0.26,4.38,,2,4,0,357,12,46,,, +madisda01,1952,2,DET,AL,1,1,10,1,0,0,0,45,16,13,1,10,7,0.29,7.8,,1,1,0,66,5,14,,, +maglisa01,1952,1,NY1,NL,18,8,35,31,12,5,1,648,199,70,16,75,112,0.24,2.92,,2,6,1,904,3,80,,, +mahonbo01,1952,1,SLA,AL,0,0,3,0,0,0,0,9,8,6,0,4,1,0.5,18,,0,0,0,21,2,6,,, +mainwo01,1952,1,PIT,NL,2,12,48,11,2,0,2,460,149,76,14,52,79,0.25,4.46,,2,0,1,652,19,78,,, +manvidi01,1952,1,CHN,NL,0,0,11,0,0,0,0,51,25,15,2,12,6,0.36,7.94,,3,0,0,83,6,17,,, +marlodi01,1952,1,DET,AL,0,2,4,1,0,0,0,33,21,9,1,3,3,0.42,7.36,,0,0,0,53,1,10,,, +marreco01,1952,1,WS1,AL,11,8,22,22,16,2,0,553,175,59,9,53,77,0.24,2.88,,2,4,0,771,0,68,,, +martimo01,1952,1,PHA,AL,0,2,5,5,0,0,0,76,32,18,1,15,13,0.3,6.39,,1,2,0,123,0,19,,, +mastewa02,1952,1,BOS,AL,1,1,5,1,0,0,0,28,18,12,1,11,3,0.4,11.57,,0,0,0,57,0,12,,, +mastewa02,1952,2,WS1,AL,9,8,24,21,11,0,2,482,153,66,11,72,89,0.25,3.7,,2,3,0,686,2,71,,, +matarle01,1952,1,PHA,AL,0,0,1,0,0,0,0,3,1,0,0,1,0,0.25,0,,0,0,0,5,1,0,,, +mcdermi03,1952,1,BOS,AL,10,9,30,21,7,2,0,486,139,67,14,92,117,0.23,3.72,,2,3,0,693,4,70,,, +mcdonji04,1952,1,NYA,AL,3,4,26,5,1,0,0,208,71,27,1,40,20,0.26,3.5,,1,2,0,312,9,31,,, +mclelwa01,1952,1,DET,AL,0,0,4,0,0,0,0,8,4,3,0,6,0,0.44,10.13,,1,0,0,18,0,3,,, +meyerru01,1952,1,PHI,NL,13,14,37,32,14,1,1,697,235,81,10,65,92,0.26,3.14,,2,2,0,982,3,99,,, 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+whiteha01,1952,1,DET,AL,1,8,41,0,0,0,5,190,53,26,1,39,18,0.23,3.69,,2,0,0,270,31,29,,, +widmaal01,1952,1,CHA,AL,0,0,1,0,0,0,0,6,4,1,1,0,2,0.44,4.5,,0,0,0,9,0,1,,, +wightbi01,1952,1,BOS,AL,2,1,10,2,0,0,0,73,14,8,3,14,5,0.16,2.96,,3,1,1,99,3,11,,, +wightbi01,1952,2,DET,AL,5,9,23,19,8,3,0,431,167,62,7,55,65,0.29,3.88,,5,0,3,637,0,71,,, +wilheho01,1952,1,NY1,NL,15,3,71,0,0,0,11,478,127,43,12,57,108,0.22,2.43,,3,5,0,646,32,60,,, +wilkste01,1952,1,PIT,NL,5,5,44,0,0,0,4,217,65,29,9,31,24,0.24,3.61,,1,2,0,305,34,32,,, +wilkste01,1952,2,CLE,AL,0,0,7,0,0,0,1,35,8,5,0,7,6,0.18,3.86,,1,0,0,52,2,6,,, +wilsoji02,1952,1,BSN,NL,12,14,33,33,14,0,0,702,234,110,19,90,104,0.26,4.23,,1,4,0,998,0,114,,, +wolfeed01,1952,1,PIT,NL,0,0,3,0,0,0,0,11,7,3,1,5,1,0.46,7.36,,0,1,0,21,1,3,,, +wrighed01,1952,1,PHA,AL,2,1,24,0,0,0,1,124,55,30,6,20,9,0.32,6.53,,1,3,0,199,11,36,,, +wynnea01,1952,1,CLE,AL,23,12,42,33,19,4,3,857,239,92,23,132,153,0.23,2.9,,5,1,0,1190,9,103,,, 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+rushbo01,1954,1,CHN,NL,13,15,33,32,11,0,0,709,213,99,12,103,124,0.24,3.77,,8,5,0,1007,0,102,,, +sainjo01,1954,1,NYA,AL,6,6,45,0,0,0,22,231,66,27,11,15,33,0.22,3.16,,0,0,1,310,39,27,,, +santijo01,1954,1,CLE,AL,0,0,1,0,0,0,0,5,0,0,0,2,1,0,0,,0,0,0,8,0,1,,, +savramo01,1954,1,CIN,NL,0,2,16,0,0,0,0,72,23,13,6,8,7,0.24,4.88,,0,2,0,105,9,13,,, +schalar01,1954,1,NYA,AL,0,1,6,1,1,0,0,52,20,8,3,11,9,0.28,4.15,,0,1,0,83,4,10,,, +scheica01,1954,1,PHA,AL,0,1,1,1,0,0,0,6,5,5,0,1,1,0.5,22.5,,0,1,0,12,0,5,,, +scheica01,1954,2,SLN,NL,0,1,3,1,0,0,0,14,6,6,3,5,5,0.3,11.57,,1,0,0,25,1,6,,, +schmijo01,1954,1,WS1,AL,11,8,29,23,12,2,1,556,176,60,6,64,56,0.25,2.91,,3,3,1,772,5,66,,, +shantbo01,1954,1,PHA,AL,1,0,2,1,0,0,0,24,12,7,2,3,3,0.36,7.88,,0,1,0,38,0,7,,, +sheasp01,1954,1,WS1,AL,2,9,23,11,1,0,0,214,97,49,9,34,22,0.34,6.18,,3,2,0,334,9,54,,, +simaal01,1954,1,CHA,AL,0,1,5,1,0,0,1,21,11,4,1,2,1,0.39,5.14,,0,0,0,33,2,5,,, +simaal01,1954,2,PHA,AL,2,5,29,7,1,0,2,238,101,46,9,32,36,0.3,5.22,,2,0,0,362,11,51,,, +simmocu01,1954,1,PHI,NL,14,15,34,33,21,3,1,759,226,79,14,98,125,0.23,2.81,,2,5,0,1074,1,101,,, +smithfr06,1954,1,CIN,NL,5,8,50,0,0,0,20,243,60,24,15,29,51,0.21,2.67,,0,3,0,328,43,29,,, +spahnwa01,1954,1,ML1,NL,21,12,39,34,23,1,3,850,262,99,24,86,136,0.24,3.14,,3,1,1,1175,3,107,,, +spencge01,1954,1,NY1,NL,1,0,6,0,0,0,0,37,9,5,1,8,4,0.2,3.65,,0,0,0,52,1,5,,, +spoonka01,1954,1,BRO,NL,2,0,2,2,2,2,0,54,7,0,0,6,27,0.11,0,,0,0,0,68,0,0,,, +stalege01,1954,1,SLN,NL,7,13,48,20,3,1,2,467,198,91,21,47,50,0.3,5.26,,2,6,0,711,15,107,,, +stewabu02,1954,1,WS1,AL,0,2,29,2,0,0,1,152,67,43,3,27,27,0.32,7.64,,2,4,0,251,8,52,,, +stobbch01,1954,1,WS1,AL,11,11,31,24,10,3,0,546,189,83,6,67,67,0.27,4.1,,1,1,1,789,4,87,,, +stonede01,1954,1,WS1,AL,12,10,31,23,10,2,0,536,161,64,7,69,87,0.24,3.22,,5,1,1,752,6,76,,, +strahdi01,1954,1,CHA,AL,0,0,9,0,0,0,1,43,16,9,0,8,8,0.27,5.65,,0,0,0,68,7,10,,, 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+conlege01,1955,1,ML1,NL,11,7,22,21,10,0,0,474,152,73,23,52,107,0.25,4.16,9,2,1,0,669,1,81,,, +consusa01,1955,1,CHA,AL,6,5,44,7,3,0,7,379,120,37,4,18,35,0.25,2.64,7,2,2,0,501,21,42,,, +corwial01,1955,1,NY1,NL,0,1,13,0,0,0,0,74,25,11,3,17,13,0.26,4.01,2,0,0,0,116,4,11,,, +coxgl01,1955,1,KC1,AL,0,2,2,2,0,0,0,7,11,8,0,1,2,0.61,30.86,0,0,0,0,19,0,8,,, +craddwa01,1955,1,KC1,AL,0,2,4,2,0,0,0,45,18,13,3,10,9,0.3,7.8,0,0,0,0,73,0,14,,, +craigro01,1955,1,BRO,NL,5,3,21,10,3,0,2,272,81,28,8,43,48,0.23,2.78,6,3,1,0,388,4,37,,, +cristle01,1955,1,DET,AL,0,1,20,1,0,0,0,110,37,13,1,14,9,0.26,3.19,2,0,0,0,159,1,15,,, +cronera01,1955,1,ML1,NL,10,9,33,15,6,1,0,421,117,54,11,42,76,0.22,3.46,7,2,0,0,568,9,63,,, +curribi01,1955,1,WS1,AL,0,0,3,0,0,0,0,13,7,6,3,2,2,0.35,12.46,0,1,1,0,24,1,7,,, +daleybu01,1955,1,CLE,AL,0,1,2,1,0,0,0,21,10,5,1,1,2,0.33,6.43,0,0,0,0,32,0,5,,, +davisji01,1955,1,CHN,NL,7,11,42,16,0,0,3,401,122,66,16,58,62,0.24,4.44,5,7,2,0,568,17,79,,, +delocik01,1955,1,BOS,AL,9,7,29,18,6,0,3,431,136,60,16,61,88,0.24,3.76,2,4,4,0,622,5,67,,, +dicksmu01,1955,1,PHI,NL,12,11,36,28,12,4,0,648,190,84,27,82,92,0.23,3.5,8,2,4,0,905,2,98,,, +ditmaar01,1955,1,KC1,AL,12,12,35,22,7,1,1,526,180,98,23,86,79,0.27,5.03,5,7,7,1,773,8,109,,, +dixonso01,1955,1,KC1,AL,0,0,2,0,0,0,0,5,6,3,1,0,0,0.54,16.2,0,0,0,0,11,1,3,,, +donosli01,1955,1,PIT,NL,4,6,25,9,3,0,1,285,106,56,16,35,38,0.28,5.31,3,1,1,0,418,10,58,,, +donovdi01,1955,1,CHA,AL,15,9,29,24,11,5,0,561,186,69,17,48,88,0.26,3.32,5,0,3,1,774,3,77,,, +dorisha01,1955,1,CHA,AL,2,0,13,0,0,0,1,51,16,3,0,9,6,0.25,1.59,1,1,0,0,73,5,4,,, +dorisha01,1955,2,BAL,AL,3,3,35,1,0,0,6,197,58,23,4,28,22,0.23,3.15,2,1,1,0,280,18,25,,, +edelmjo01,1955,1,ML1,NL,0,0,5,0,0,0,0,17,7,7,0,8,3,0.3,11.12,0,0,0,0,31,2,7,,, +erskica01,1955,1,BRO,NL,11,8,31,29,7,2,1,584,185,82,29,64,84,0.25,3.79,3,4,0,0,808,1,89,,, +facero01,1955,1,PIT,NL,5,7,42,10,4,0,5,377,128,50,10,40,84,0.26,3.58,4,3,0,0,524,21,58,,, 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+portebo01,1955,1,WS1,AL,10,17,30,27,8,2,0,534,197,88,14,54,74,0.28,4.45,5,0,2,0,770,2,103,,, +portoar01,1955,1,KC1,AL,5,9,24,20,4,1,0,334,109,59,12,67,34,0.25,4.77,4,0,4,1,506,1,66,,, +purkebo01,1955,1,PIT,NL,2,7,14,10,2,0,1,203,77,40,5,25,24,0.28,5.32,1,1,2,0,298,3,47,,, +ramospe01,1955,1,WS1,AL,5,11,45,9,3,1,5,390,121,56,13,39,34,0.25,3.88,3,2,11,1,548,22,62,,, +raschvi01,1955,1,SLN,NL,0,1,1,1,0,0,0,5,5,4,0,1,1,0.55,21.6,1,0,0,0,11,0,4,,, +raschvi01,1955,2,KC1,AL,4,6,20,18,1,0,0,304,132,61,10,35,38,0.31,5.42,4,2,1,0,466,0,66,,, +ridzist01,1955,1,PHI,NL,0,1,3,1,0,0,0,33,7,3,1,8,6,0.17,2.45,0,1,3,0,51,0,9,,, +ridzist01,1955,2,CIN,NL,0,3,13,2,0,0,0,90,35,15,4,14,6,0.29,4.5,1,2,1,0,134,3,16,,, +roberro01,1955,1,PHI,NL,23,14,41,38,26,1,3,915,292,111,41,53,160,0.24,3.28,3,3,2,0,1256,3,137,,, +robinhu01,1955,1,ML1,NL,3,1,13,2,1,0,2,114,31,13,1,25,19,0.23,3.08,2,1,4,0,164,6,13,,, +roebued01,1955,1,BRO,NL,5,6,47,0,0,0,12,252,96,44,14,24,33,0.28,4.71,5,3,3,0,368,27,51,,, +rogovsa01,1955,1,BAL,AL,1,8,14,12,1,0,0,213,79,36,5,27,35,0.28,4.56,1,2,2,0,311,1,42,,, +rogovsa01,1955,2,PHI,NL,5,3,12,11,5,2,0,219,60,25,3,17,27,0.23,3.08,2,2,0,0,282,0,25,,, +rushbo01,1955,1,CHN,NL,13,11,33,33,14,3,0,702,204,91,19,73,130,0.23,3.5,4,4,2,0,959,0,95,,, +sainjo01,1955,1,NYA,AL,0,0,3,0,0,0,0,16,6,4,4,1,5,0.3,6.75,0,0,0,0,22,1,4,,, +sainjo01,1955,2,KC1,AL,2,5,25,0,0,0,1,134,54,27,10,10,12,0.29,5.44,4,0,0,0,197,13,28,,, +santijo01,1955,1,CLE,AL,2,0,17,0,0,0,0,98,31,9,1,14,19,0.25,2.48,1,2,5,0,146,6,11,,, +schalar01,1955,1,NYA,AL,0,0,2,0,0,0,0,9,4,2,1,1,2,0.33,6,0,0,0,1,13,1,2,,, +schalar01,1955,2,BAL,AL,3,5,30,6,1,0,0,241,92,37,2,42,33,0.29,4.15,3,1,2,0,364,8,52,,, +schmijo01,1955,1,WS1,AL,7,10,32,21,6,1,1,495,187,68,8,54,49,0.29,3.71,4,2,7,0,723,6,76,,, +schmiwi01,1955,1,SLN,NL,7,6,20,15,8,1,0,389,89,40,7,57,86,0.19,2.78,7,3,3,0,522,3,40,,, +schulba01,1955,1,SLN,NL,1,2,19,0,0,0,4,89,28,26,5,15,19,0.25,7.89,3,0,4,0,133,9,27,,, +schulbo01,1955,1,DET,AL,0,0,1,0,0,0,0,4,2,3,0,2,0,0.33,20.25,0,1,0,0,8,0,3,,, +scorehe01,1955,1,CLE,AL,16,10,33,32,11,2,0,682,158,72,18,154,245,0.19,2.85,1,12,1,0,978,0,85,,, +shantbo01,1955,1,KC1,AL,5,10,23,17,4,1,0,375,124,63,8,66,58,0.26,4.54,11,2,1,0,550,2,70,,, +sheasp01,1955,1,WS1,AL,2,2,27,4,1,1,2,169,53,25,4,27,16,0.25,3.99,0,2,1,0,246,16,31,,, +simmocu01,1955,1,PHI,NL,8,8,25,22,3,0,0,390,148,71,15,50,58,0.29,4.92,2,1,3,0,572,2,76,,, +sleatlo01,1955,1,KC1,AL,1,1,16,1,0,0,0,77,33,22,3,21,11,0.32,7.71,0,0,0,0,127,7,22,,, +smithbo03,1955,1,BOS,AL,0,0,1,0,0,0,0,5,1,0,0,1,1,0.2,0,0,0,0,0,6,0,0,,, +smithfr06,1955,1,SLN,NL,3,1,28,0,0,0,1,117,27,14,3,23,17,0.2,3.23,3,2,5,0,168,14,18,,, +spahnwa01,1955,1,ML1,NL,17,14,39,32,16,1,1,737,249,89,25,65,110,0.26,3.26,4,4,2,0,1025,7,99,,, +spencge01,1955,1,NY1,NL,0,0,1,0,0,0,0,5,1,1,1,3,0,0.16,5.4,0,0,0,0,9,0,1,,, +spicebo01,1955,1,KC1,AL,0,0,2,0,0,0,0,8,9,10,2,4,2,0.52,33.75,0,0,1,0,22,0,10,,, +spoonka01,1955,1,BRO,NL,8,6,29,14,2,1,2,296,79,40,8,41,78,0.21,3.65,1,1,5,0,420,10,50,,, +sprinja01,1955,1,PHI,NL,0,1,2,0,0,0,0,8,2,2,2,1,2,0.2,6.75,0,0,0,0,11,1,2,,, +stalege01,1955,1,CIN,NL,5,8,30,18,2,0,0,359,146,62,22,28,40,0.3,4.66,3,1,3,0,519,3,72,,, +stalege01,1955,2,NYA,AL,0,0,2,0,0,0,0,6,5,3,1,1,0,0.41,13.5,0,0,0,0,13,0,5,,, +stewabu02,1955,1,WS1,AL,0,0,7,1,0,0,0,46,18,7,0,6,10,0.29,4.11,1,0,0,0,69,2,7,,, +stobbch01,1955,1,WS1,AL,4,14,41,16,2,0,3,421,169,78,13,57,60,0.3,5,8,2,1,0,634,13,90,,, +stonede01,1955,1,WS1,AL,6,13,43,24,5,1,1,540,180,83,14,114,84,0.26,4.15,5,9,3,0,814,5,98,,, +sturdto01,1955,1,NYA,AL,1,3,33,1,0,0,0,205,48,24,6,42,48,0.2,3.16,1,1,2,0,285,12,24,,, +sullifr01,1955,1,BOS,AL,18,13,35,35,16,3,0,780,235,84,23,100,129,0.24,2.91,5,1,2,1,1100,0,103,,, +surkoma01,1955,1,PIT,NL,7,14,35,22,5,0,2,499,194,103,23,78,84,0.29,5.57,4,6,3,0,745,7,109,,, +suscege02,1955,1,BOS,AL,9,7,29,15,6,1,1,433,123,49,12,49,60,0.23,3.06,4,1,8,1,599,10,54,,, +swansre01,1955,1,PIT,NL,0,0,1,0,0,0,0,6,2,4,1,3,0,0.28,18,0,0,0,0,10,1,4,,, +templch01,1955,1,BRO,NL,0,1,4,0,0,0,0,14,5,6,2,5,3,0.29,11.57,0,0,1,0,24,2,7,,, +thiesja01,1955,1,PIT,NL,0,1,1,1,0,0,0,11,5,2,0,3,0,0.35,4.91,1,0,1,0,20,0,5,,, +thorpbo02,1955,1,CHN,NL,0,0,2,0,0,0,0,9,4,1,0,0,0,0.33,3,0,0,0,0,12,2,2,,, +tiefebo01,1955,1,SLN,NL,1,4,18,0,0,0,0,98,31,16,6,10,16,0.26,4.41,4,1,4,0,139,6,19,,, +tremebi01,1955,1,CHN,NL,3,0,23,0,0,0,2,116,33,16,2,18,13,0.23,3.72,2,1,0,0,163,12,18,,, +tricebo01,1955,1,KC1,AL,0,0,4,0,0,0,0,30,14,10,4,6,2,0.32,9,0,0,0,0,51,2,13,,, +trimbjo01,1955,1,BOS,AL,0,0,2,0,0,0,0,6,0,0,0,3,1,0,0,0,0,0,0,8,2,0,,, +truckvi01,1955,1,CHA,AL,13,8,32,26,7,3,0,525,176,77,19,61,91,0.26,3.96,5,1,2,0,749,2,78,,, +turlebo01,1955,1,NYA,AL,17,13,36,34,13,6,1,740,168,84,16,177,210,0.19,3.06,4,7,7,0,1075,2,92,,, +valenco01,1955,1,CIN,NL,2,1,10,5,0,0,0,80,29,22,5,16,14,0.27,7.43,2,0,1,1,127,2,23,,, +vanbroz01,1955,1,KC1,AL,0,0,2,0,0,0,0,6,4,4,1,2,1,0.4,18,1,0,0,0,13,1,4,,, 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+urbanja01,1957,1,KC1,AL,7,4,31,13,3,0,0,388,111,48,7,45,55,0.23,3.34,3,4,1,2,530,9,55,,, +valdere01,1957,1,BRO,NL,1,1,5,1,0,0,0,39,13,8,1,7,10,0.26,5.54,1,1,0,0,59,0,8,,, +valenvi01,1957,1,CHN,NL,0,0,9,0,0,0,0,36,12,3,1,7,8,0.25,2.25,1,0,0,0,55,4,5,,, +valenvi01,1957,2,CLE,AL,2,2,11,2,1,0,0,71,26,13,3,13,9,0.28,4.94,0,2,1,0,109,4,14,,, +walkeje01,1957,1,BAL,AL,1,0,13,3,1,1,1,83,24,9,1,14,13,0.24,2.93,1,1,0,0,114,6,9,,, +wallmu01,1957,1,BOS,AL,3,0,11,0,0,0,1,73,21,9,3,2,13,0.23,3.33,0,0,0,0,96,5,11,,, +wehmehe01,1957,1,SLN,NL,10,7,36,18,5,0,0,495,165,79,25,54,91,0.25,4.31,7,4,2,0,716,5,91,,, +wieslbo01,1957,1,WS1,AL,1,1,3,2,1,0,0,49,15,8,2,11,9,0.25,4.41,0,1,1,0,72,1,8,,, +wightbi01,1957,1,BAL,AL,6,6,27,17,2,0,0,363,122,49,4,54,50,0.27,3.64,1,0,4,1,523,3,53,,, +wilheho01,1957,1,SLN,NL,1,4,40,0,0,0,11,165,52,26,7,21,29,0.25,4.25,1,0,3,0,231,30,28,,, +wilheho01,1957,2,CLE,AL,1,0,2,0,0,0,1,11,2,1,1,1,0,0.15,2.45,0,0,1,0,15,2,1,,, +wilsoji02,1957,1,CHA,AL,15,8,30,29,12,5,0,605,189,78,22,65,100,0.24,3.48,8,3,3,0,841,0,85,,, +wittge01,1957,1,PIT,NL,0,1,1,1,0,0,0,4,4,6,1,5,1,0.5,40.5,0,0,0,0,13,0,6,,, +wojeype01,1957,1,DET,AL,0,0,2,0,0,0,0,4,1,0,0,0,0,0.2,0,0,0,0,0,6,2,0,,, +worthal01,1957,1,NY1,NL,8,11,55,12,1,1,4,473,140,74,19,56,90,0.23,4.22,7,5,5,0,662,23,75,,, +wynnea01,1957,1,CLE,AL,14,17,40,37,13,1,1,789,270,126,32,104,184,0.26,4.31,7,0,5,0,1146,2,139,,, +zuverge01,1957,1,BAL,AL,10,6,56,0,0,0,9,338,105,31,9,39,36,0.25,2.48,13,1,4,0,475,37,37,,, +ackerto01,1958,1,CIN,NL,4,3,38,10,3,0,1,374,126,63,10,43,90,0.26,4.55,6,2,3,0,528,4,64,,, +aguirha01,1958,1,DET,AL,3,4,44,3,0,0,5,209,67,29,5,27,38,0.25,3.75,2,1,1,0,299,16,31,,, +albanjo01,1958,1,WS1,AL,0,0,6,0,0,0,0,18,8,3,1,2,3,0.34,4.5,0,0,0,0,27,6,3,,, +anderbo01,1958,1,CHN,NL,3,3,17,8,2,0,0,197,61,29,3,29,51,0.25,3.97,1,1,1,0,275,4,29,,, +anderjo02,1958,1,PHI,NL,0,0,5,1,0,0,0,48,26,14,5,4,9,0.36,7.88,0,0,1,0,78,3,17,,, +antonjo02,1958,1,SFN,NL,16,13,41,34,13,0,3,725,216,88,31,87,143,0.23,3.28,7,4,3,0,1014,5,101,,, +barclcu01,1958,1,SFN,NL,1,0,6,1,0,0,0,48,16,5,3,5,6,0.25,2.81,3,0,3,0,70,2,5,,, +barnefr02,1958,1,SLN,NL,1,1,8,1,0,0,0,57,19,16,3,16,17,0.26,7.58,0,0,2,0,93,0,16,,, +baumafr01,1958,1,BOS,AL,2,2,10,7,2,0,0,157,56,26,4,27,31,0.27,4.47,2,1,4,0,236,0,27,,, +beamoch01,1958,1,BAL,AL,1,3,21,3,0,0,0,149,47,24,3,21,26,0.26,4.35,1,5,6,1,209,9,27,,, +bellga01,1958,1,CLE,AL,12,10,33,23,10,0,1,546,141,67,17,73,110,0.21,3.31,0,4,5,0,750,5,70,,, +bessedo01,1958,1,LAN,NL,1,0,19,0,0,0,0,73,24,9,3,17,13,0.27,3.33,0,0,1,0,107,0,14,,, +birreba01,1958,1,LAN,NL,0,0,16,0,0,0,1,102,43,17,4,7,16,0.3,4.5,3,1,1,0,149,6,20,,, +blackro01,1958,1,PIT,NL,2,1,38,2,0,0,3,191,61,24,7,27,31,0.26,3.39,5,1,3,0,278,15,33,,, +bowsfte01,1958,1,BOS,AL,4,2,16,10,2,0,0,197,58,28,3,36,38,0.23,3.84,1,1,1,0,290,3,32,,, +breweto01,1958,1,BOS,AL,12,12,33,32,10,1,0,682,227,94,21,93,124,0.25,3.72,2,4,8,0,992,1,122,,, +briggjo01,1958,1,CHN,NL,5,5,20,17,3,1,0,287,99,48,12,45,46,0.27,4.52,1,2,1,2,418,2,52,,, +brododi01,1958,1,CLE,AL,1,0,5,0,0,0,0,30,3,0,0,6,12,0.1,0,0,0,0,0,37,1,0,,, +brosnji01,1958,1,CHN,NL,3,4,8,8,2,0,0,155,41,18,3,29,24,0.22,3.14,3,0,0,0,215,0,20,,, +brosnji01,1958,2,SLN,NL,8,4,33,12,2,0,7,345,107,44,10,50,65,0.25,3.44,3,1,1,0,487,14,46,,, +brownha01,1958,1,BAL,AL,7,5,19,17,4,2,1,290,96,33,9,20,44,0.25,3.07,0,1,0,0,396,1,35,,, +buhlbo01,1958,1,ML1,NL,5,2,11,10,3,0,1,219,74,28,5,30,27,0.26,3.45,0,3,1,0,319,1,33,,, +bunniji01,1958,1,DET,AL,14,12,35,34,10,3,0,659,188,86,28,79,177,0.22,3.52,4,5,10,3,923,0,96,,, +burdele01,1958,1,ML1,NL,20,10,40,36,19,3,0,826,279,89,18,50,113,0.26,2.91,7,1,5,0,1122,2,102,,, +burnewa01,1958,1,KC1,AL,1,1,12,4,0,0,0,85,29,11,2,14,11,0.26,3.49,0,1,0,0,125,4,14,,, +burnspe01,1958,1,SFN,NL,0,0,6,1,0,0,0,32,20,8,3,5,4,0.4,6.75,0,0,0,0,55,3,10,,, +buzhajo01,1958,1,CHN,NL,3,0,6,2,1,0,0,73,16,5,2,7,9,0.18,1.85,1,0,0,1,95,1,5,,, +byerlbu01,1958,1,WS1,AL,2,0,17,0,0,0,1,72,34,18,4,11,13,0.34,6.75,2,0,1,0,114,12,20,,, +byerlbu01,1958,2,BOS,AL,1,2,18,0,0,0,0,91,31,6,1,7,16,0.27,1.78,3,0,1,0,128,10,12,,, +cardwdo01,1958,1,PHI,NL,3,6,16,14,3,0,0,323,99,54,16,37,77,0.24,4.51,4,5,2,1,452,1,55,,, +casalje01,1958,1,BOS,AL,0,0,2,0,0,0,0,9,1,0,0,2,3,0.11,0,0,0,0,1,11,2,0,,, +chittne01,1958,1,SLN,NL,0,1,13,2,0,0,0,88,31,21,5,7,13,0.26,6.44,0,2,1,1,127,3,21,,, +churnch01,1958,1,CLE,AL,0,0,6,0,0,0,0,26,12,6,1,5,4,0.34,6.23,0,0,0,1,41,2,7,,, +cicotal01,1958,1,WS1,AL,0,3,8,4,0,0,0,84,36,15,3,14,14,0.31,4.82,0,2,0,0,130,3,18,,, +cicotal01,1958,2,DET,AL,3,1,14,2,0,0,0,129,50,17,1,15,21,0.3,3.56,1,3,0,0,185,3,19,,, +clarkph01,1958,1,SLN,NL,0,1,7,0,0,0,1,23,11,3,2,3,1,0.35,3.52,0,0,0,0,35,3,5,,, +clevete01,1958,1,WS1,AL,9,9,55,4,0,0,6,372,119,60,12,50,70,0.25,4.35,6,2,1,0,535,29,65,,, +colavro01,1958,1,CLE,AL,0,0,1,0,0,0,0,9,0,0,0,3,1,0,0,0,0,0,1,12,1,0,,, 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+grossdo01,1958,1,PIT,NL,5,7,40,3,0,0,7,224,67,33,5,38,59,0.24,3.98,7,3,1,0,324,23,37,,, +hackewa02,1958,1,PHI,NL,0,1,9,1,0,0,0,51,24,14,2,8,4,0.32,7.41,1,0,0,0,81,2,17,,, +haddiha01,1958,1,CIN,NL,8,7,29,26,8,1,0,552,191,72,28,43,110,0.26,3.52,7,1,7,0,773,2,79,,, +harshja01,1958,1,BAL,AL,12,15,34,29,17,3,4,709,204,76,20,75,161,0.23,2.89,1,8,3,0,975,5,89,,, +haydele01,1958,1,CIN,NL,0,0,3,0,0,0,0,11,5,2,0,1,3,0.31,4.91,0,0,0,0,17,1,2,,, +hearnji01,1958,1,PHI,NL,5,3,39,1,0,0,0,220,88,34,6,27,33,0.29,4.17,6,2,0,0,335,11,45,,, +henrybi01,1958,1,CHN,NL,5,4,44,0,0,0,6,244,63,26,8,17,58,0.21,2.88,6,0,1,0,314,18,27,,, +herbera01,1958,1,KC1,AL,8,8,42,16,5,0,3,525,161,68,20,55,108,0.24,3.5,3,4,4,0,720,8,76,,, +hillmda01,1958,1,CHN,NL,4,8,31,16,3,0,1,377,132,44,12,31,65,0.26,3.15,4,1,0,0,540,4,57,,, +hobbigl01,1958,1,CHN,NL,10,6,55,16,2,1,2,505,163,70,13,93,91,0.25,3.74,8,7,7,0,755,21,80,,, +hoeftbi01,1958,1,DET,AL,10,9,36,21,6,0,3,429,148,66,15,49,94,0.26,4.15,3,3,1,0,615,9,70,,, +hookja01,1958,1,CIN,NL,0,1,1,1,0,0,0,9,3,4,2,2,5,0.25,12,0,0,0,0,14,0,4,,, +howeldi01,1958,1,CHA,AL,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,,, +hydedi01,1958,1,WS1,AL,10,3,53,0,0,0,18,309,82,20,1,35,49,0.22,1.75,7,2,2,0,416,44,26,,, +jacksla01,1958,1,SLN,NL,13,13,49,23,11,1,8,594,211,81,21,51,124,0.27,3.68,11,7,10,1,846,16,93,,, +jamesjo01,1958,1,NYA,AL,0,0,1,0,0,0,0,9,2,0,0,4,1,0.25,0,0,0,0,0,12,1,0,,, +jayjo01,1958,1,ML1,NL,7,5,18,12,6,3,0,290,60,23,8,43,74,0.17,2.14,3,2,1,0,387,2,25,,, +jeffcha01,1958,1,CIN,NL,6,8,49,0,0,0,9,225,76,31,8,26,35,0.26,3.72,8,0,2,0,324,34,34,,, +johnsco01,1958,1,BAL,AL,6,9,26,17,4,0,1,355,116,51,13,32,68,0.26,3.88,3,2,0,0,487,2,58,,, +johnsdo03,1958,1,SFN,NL,0,1,17,0,0,0,1,69,31,16,2,8,14,0.32,6.26,1,2,2,0,108,6,19,,, +johnser02,1958,1,ML1,NL,3,1,15,0,0,0,1,70,35,21,4,10,13,0.35,8.1,3,1,1,0,114,6,21,,, +johnske02,1958,1,KC1,AL,0,0,2,0,0,0,0,7,6,7,1,3,1,0.42,27,0,0,0,0,17,0,7,,, +jonesgo01,1958,1,SFN,NL,3,1,11,1,0,0,1,91,33,8,2,5,8,0.28,2.37,1,0,1,1,125,4,11,,, +jonessa02,1958,1,SLN,NL,14,13,35,35,14,2,0,750,204,80,23,107,225,0.22,2.88,5,7,6,0,1041,0,95,,, +keegabo01,1958,1,CHA,AL,0,2,14,2,0,0,0,89,44,20,9,18,8,0.35,6.07,4,1,0,0,145,4,25,,, +kellnal01,1958,1,KC1,AL,0,2,7,6,0,0,0,101,40,22,5,8,22,0.31,5.88,0,0,0,0,136,0,24,,, +kellnal01,1958,2,CIN,NL,7,3,18,7,4,0,0,246,74,21,8,20,42,0.24,2.3,1,1,3,0,331,3,24,,, +kellybo01,1958,1,CIN,NL,0,0,2,1,0,0,0,6,3,1,0,3,1,0.5,4.5,1,0,0,0,11,1,1,,, +kellybo01,1958,2,CLE,AL,0,2,13,3,0,0,0,83,29,16,4,13,12,0.28,5.2,0,1,1,0,119,2,18,,, +kemmeru01,1958,1,WS1,AL,6,15,40,30,6,0,0,673,234,115,25,74,111,0.27,4.61,5,7,4,1,968,2,122,,, +kielyle01,1958,1,BOS,AL,5,2,47,0,0,0,12,243,77,27,3,18,26,0.25,3,3,0,2,0,332,35,31,,, +kippfr01,1958,1,LAN,NL,6,6,40,9,0,0,0,307,107,57,16,45,58,0.27,5.01,7,4,1,0,443,8,60,,, +klinero01,1958,1,PIT,NL,13,16,32,32,11,2,0,712,220,93,25,92,109,0.25,3.53,14,3,0,0,986,0,96,,, +klippjo01,1958,1,CIN,NL,3,2,12,4,0,0,1,99,37,18,5,14,22,0.28,4.91,1,4,1,0,147,5,20,,, +klippjo01,1958,2,LAN,NL,3,5,45,0,0,0,9,270,81,38,12,44,73,0.24,3.8,8,5,2,0,381,22,40,,, +koufasa01,1958,1,LAN,NL,11,11,40,26,5,0,1,476,132,79,19,105,131,0.22,4.48,6,17,1,0,714,7,89,,, +kucksjo01,1958,1,NYA,AL,8,8,34,15,4,1,4,378,132,55,14,39,46,0.26,3.93,2,1,6,0,544,8,67,,, +labincl01,1958,1,LAN,NL,6,6,52,2,0,0,14,312,112,48,8,33,43,0.28,4.15,8,3,1,0,440,35,55,,, +larsedo01,1958,1,NYA,AL,9,6,19,19,5,3,0,343,100,39,4,52,55,0.23,3.07,3,0,4,0,491,0,43,,, +laryfr01,1958,1,DET,AL,16,15,39,34,19,3,1,781,249,84,20,68,131,0.25,2.9,4,4,12,0,1085,4,91,,, +latmaba01,1958,1,CHA,AL,3,0,13,3,1,1,0,143,27,4,1,17,28,0.16,0.76,0,1,1,0,188,3,7,,, +lawrebr01,1958,1,CIN,NL,8,13,46,23,6,2,5,543,194,83,12,55,74,0.27,4.13,7,1,4,0,773,13,89,,, +lawve01,1958,1,PIT,NL,14,12,35,29,6,1,3,607,235,89,16,39,56,0.29,3.96,5,1,1,0,854,5,103,,, +leedo01,1958,1,DET,AL,0,0,1,0,0,0,0,6,1,2,1,1,0,0.14,9,0,0,1,0,9,1,2,,, 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+mcdermi03,1958,1,DET,AL,0,0,2,0,0,0,0,6,6,2,0,2,0,0.5,9,0,1,0,0,14,0,4,,, +mcdevda01,1958,1,LAN,NL,2,6,13,10,2,0,0,145,71,40,6,31,26,0.35,7.45,1,7,0,0,238,3,43,,, +mcdonji04,1958,1,CHA,AL,0,0,3,0,0,0,0,7,6,5,1,4,0,0.42,19.29,0,0,0,0,18,0,8,,, +mcilwst01,1958,1,CHA,AL,0,0,1,1,0,0,0,12,4,1,1,0,4,0.25,2.25,0,0,0,0,16,0,1,,, +mclisca01,1958,1,CLE,AL,16,8,39,30,13,0,1,677,214,75,25,70,97,0.25,2.99,4,4,1,0,941,1,92,,, +mcmahdo02,1958,1,ML1,NL,7,2,38,0,0,0,8,176,50,24,4,29,37,0.23,3.68,5,1,2,0,250,28,25,,, +meyerja01,1958,1,PHI,NL,3,6,37,5,1,0,2,271,77,36,8,33,87,0.23,3.59,3,7,1,0,374,16,38,,, +millebo02,1958,1,PHI,NL,1,1,17,0,0,0,0,67,36,29,7,9,9,0.36,11.69,2,1,0,0,113,4,30,,, +millest01,1958,1,SFN,NL,6,9,41,20,4,1,0,546,160,50,16,49,119,0.23,2.47,3,2,2,1,744,7,60,,, +mizelvi01,1958,1,SLN,NL,10,14,30,29,8,2,0,569,178,72,17,91,80,0.25,3.42,9,1,2,0,820,0,81,,, +moellro01,1958,1,BAL,AL,0,0,4,0,0,0,0,13,6,2,0,3,3,0.33,4.15,0,0,0,0,21,1,2,,, +moforhe01,1958,1,DET,AL,4,9,25,11,6,0,1,329,83,44,10,42,58,0.21,3.61,6,2,9,0,448,4,45,,, +monbobi01,1958,1,BOS,AL,3,4,10,8,3,0,0,163,52,20,4,20,30,0.25,3.31,4,1,0,0,231,1,25,,, +monroza01,1958,1,NYA,AL,4,2,21,6,1,0,1,174,57,21,8,27,18,0.26,3.26,1,0,0,0,250,10,29,,, +monzara01,1958,1,SFN,NL,8,11,43,16,4,1,1,452,160,79,21,57,93,0.27,4.72,5,4,6,0,663,10,89,,, +moorera02,1958,1,CHA,AL,9,7,32,20,4,2,2,410,107,58,10,70,73,0.22,3.82,2,6,0,3,570,9,63,,, +morehse01,1958,1,PHI,NL,1,6,27,11,0,0,0,277,121,60,8,26,54,0.31,5.85,3,2,1,1,416,8,67,,, +morgato01,1958,1,DET,AL,2,5,39,1,0,0,1,188,70,22,7,4,32,0.28,3.16,0,0,1,0,256,11,28,,, +mossido01,1958,1,CLE,AL,7,8,43,5,0,0,3,305,106,44,6,30,55,0.26,3.9,6,1,4,0,441,24,49,,, +muffebi01,1958,1,SLN,NL,4,6,35,6,1,0,5,252,107,46,11,42,41,0.31,4.93,9,0,5,0,396,17,52,,, +narlera01,1958,1,CLE,AL,13,10,44,24,7,0,1,550,179,83,21,91,102,0.25,4.07,10,6,3,2,806,11,87,,, +negraro01,1958,1,LAN,NL,0,0,4,0,0,0,0,34,12,9,4,7,2,0.27,7.15,0,1,0,1,51,1,9,,, +newcodo01,1958,1,LAN,NL,0,6,11,8,1,0,0,103,53,30,11,8,16,0.34,7.86,1,0,0,0,163,2,37,,, +newcodo01,1958,2,CIN,NL,7,7,20,18,7,0,1,400,159,57,20,28,53,0.29,3.85,4,2,1,0,571,2,61,,, +nichodo02,1958,1,CHN,NL,0,4,24,0,0,0,1,124,46,23,1,16,9,0.29,5.01,2,1,1,0,179,7,27,,, +nixonwi01,1958,1,BOS,AL,1,7,10,8,2,0,0,130,48,29,7,11,15,0.28,6.02,0,1,0,0,187,0,30,,, +nuxhajo01,1958,1,CIN,NL,12,11,36,26,5,0,0,527,169,74,15,63,111,0.25,3.79,5,3,1,0,735,5,78,,, +obrieed01,1958,1,PIT,NL,0,0,1,0,0,0,0,6,4,3,1,1,1,0.44,13.5,0,0,0,0,10,1,3,,, +obriejo03,1958,2,SLN,NL,0,0,1,0,0,0,0,6,7,5,0,2,2,0.53,22.5,0,0,0,0,15,1,5,,, +odellbi01,1958,1,BAL,AL,14,11,41,25,12,3,8,664,201,73,13,51,137,0.24,2.97,6,2,4,1,913,12,83,,, +otoolji01,1958,1,CIN,NL,0,1,1,1,0,0,0,21,4,1,0,5,4,0.15,1.29,0,0,0,0,31,0,2,,, +owensji01,1958,1,PHI,NL,1,0,1,1,0,0,0,21,4,2,1,5,3,0.15,2.57,0,2,0,0,31,0,4,,, +paineph01,1958,1,SLN,NL,5,1,46,0,0,0,1,220,70,29,7,31,45,0.25,3.56,4,0,5,1,314,22,33,,, +pappami01,1958,1,BAL,AL,10,10,31,21,3,0,0,406,135,61,8,48,72,0.26,4.06,1,6,2,1,574,4,67,,, +pascuca02,1958,1,WS1,AL,8,12,31,27,6,2,0,532,166,62,14,60,146,0.24,3.15,2,3,3,2,742,3,66,,, +penaor01,1958,1,CIN,NL,1,0,9,0,0,0,3,45,10,1,0,4,11,0.18,0.6,1,0,0,0,60,7,1,,, +perezge01,1958,1,PIT,NL,0,1,4,0,0,0,1,25,9,5,1,4,2,0.3,5.4,0,3,0,0,35,1,5,,, +phillta01,1958,1,CHN,NL,7,10,39,27,5,1,1,511,178,90,22,79,102,0.26,4.76,5,8,6,3,761,4,102,,, +piercbi02,1958,1,CHA,AL,17,11,35,32,19,3,2,735,204,73,33,66,144,0.22,2.68,2,1,1,0,983,3,83,,, +pizarju01,1958,1,ML1,NL,6,4,16,10,7,1,1,290,75,29,12,47,84,0.21,2.7,0,6,4,1,408,4,36,,, +podrejo01,1958,1,LAN,NL,13,15,39,31,10,2,1,631,208,87,27,78,143,0.26,3.72,8,4,2,3,890,4,96,,, +portebo01,1958,1,BOS,AL,0,0,2,0,0,0,0,12,3,2,1,0,1,0.21,4.5,0,0,0,0,15,1,2,,, +portebo01,1958,2,PIT,NL,4,6,37,6,2,1,5,263,78,32,7,19,39,0.24,3.29,3,2,1,0,352,10,33,,, +portoar01,1958,1,BAL,AL,15,11,32,27,10,3,2,614,173,74,17,57,90,0.22,3.25,3,0,3,1,826,4,81,,, +preskjo01,1958,1,DET,AL,0,0,7,0,0,0,2,32,13,4,0,1,6,0.31,3.38,0,0,0,0,43,6,4,,, +purkebo01,1958,1,CIN,NL,17,11,37,34,17,3,0,750,259,100,25,49,70,0.26,3.6,3,1,4,0,1040,2,106,,, +qualtto01,1958,1,PHI,NL,0,0,1,0,0,0,0,6,2,1,0,1,0,0.22,4.5,0,0,0,0,10,0,1,,, +qualtto01,1958,2,CHA,AL,0,0,26,0,0,0,0,129,45,20,1,20,14,0.28,4.19,4,4,0,0,184,10,22,,, +rabech01,1958,1,CIN,NL,0,3,9,1,0,0,0,56,25,9,3,9,10,0.32,4.34,0,0,0,0,87,1,10,,, +ramospe01,1958,1,WS1,AL,14,18,43,37,10,4,3,778,277,122,38,77,132,0.27,4.23,9,4,5,1,1112,5,133,,, +raydocu01,1958,1,PIT,NL,8,4,31,20,2,1,1,403,118,54,18,61,85,0.23,3.62,4,3,5,1,573,5,64,,, +reedho01,1958,1,KC1,AL,1,0,3,1,1,0,0,31,5,1,0,4,5,0.13,0.87,0,0,0,0,42,1,1,,, +ridzist01,1958,1,CLE,AL,0,2,6,0,0,0,0,26,9,2,1,5,6,0.25,2.08,2,1,0,0,41,2,7,,, +roberro01,1958,1,PHI,NL,17,14,35,34,21,1,0,809,270,97,30,51,130,0.25,3.24,5,2,2,0,1116,0,112,,, +robinhu01,1958,1,ML1,NL,2,4,19,0,0,0,1,125,30,14,4,13,26,0.2,3.02,2,0,2,2,166,11,15,,, +rodrifr01,1958,1,CHN,NL,0,0,7,0,0,0,2,22,8,6,2,5,5,0.26,7.36,0,0,1,0,36,3,6,,, +roebued01,1958,1,LAN,NL,0,1,32,0,0,0,5,132,45,17,9,15,26,0.27,3.48,3,1,2,1,188,13,22,,, +romonjo01,1958,1,WS1,AL,2,4,18,5,1,0,0,166,52,40,6,28,38,0.24,6.51,4,1,0,0,247,4,42,,, +rudoldo01,1958,1,CHA,AL,1,0,7,0,0,0,1,21,4,2,0,5,2,0.19,2.57,1,0,0,0,26,6,2,,, +rushbo01,1958,1,ML1,NL,10,6,28,20,5,2,0,442,142,56,13,31,84,0.25,3.42,7,0,1,0,600,1,59,,, +sanfoja02,1958,1,PHI,NL,10,13,38,27,7,2,0,559,197,92,15,81,106,0.27,4.44,6,3,3,0,819,4,103,,, +schmiwi01,1958,1,CIN,NL,3,5,41,2,0,0,0,208,60,22,8,33,41,0.23,2.86,7,1,1,0,302,18,29,,, +schroal01,1958,1,BOS,AL,0,0,5,0,0,0,0,30,6,5,1,4,7,0.17,4.5,0,0,0,0,39,1,5,,, +scorehe01,1958,1,CLE,AL,2,3,12,5,2,1,3,123,29,18,1,34,48,0.19,3.95,1,1,0,0,182,4,19,,, +semprra01,1958,1,PHI,NL,13,11,36,30,12,2,0,613,211,89,25,58,92,0.26,3.92,1,5,6,0,872,3,105,,, +shantbo01,1958,1,NYA,AL,7,6,33,13,3,0,0,378,127,47,8,35,80,0.26,3.36,7,3,2,1,531,7,52,,, +shawbo01,1958,1,DET,AL,1,2,11,2,0,0,0,80,32,15,2,13,17,0.3,5.06,1,1,0,0,124,2,16,,, +shawbo01,1958,2,CHA,AL,4,2,29,3,0,0,1,192,67,33,8,28,18,0.27,4.64,3,3,2,0,280,8,33,,, +sherrla01,1958,1,LAN,NL,0,0,5,0,0,0,0,13,10,6,0,7,2,0.47,12.46,1,1,1,0,29,1,7,,, +shipljo01,1958,1,SFN,NL,0,0,1,0,0,0,0,4,3,5,0,3,0,0.42,33.75,0,1,2,0,12,0,5,,, +simmocu01,1958,1,PHI,NL,7,14,29,27,7,1,1,505,196,82,11,40,78,0.29,4.38,4,4,3,1,727,2,92,,, +singlel01,1958,1,CHN,NL,1,0,2,0,0,0,0,14,1,0,0,1,2,0.07,0,1,0,0,0,15,2,0,,, +sisleda01,1958,1,BOS,AL,8,9,30,25,4,1,0,448,157,82,22,79,71,0.27,4.94,2,6,1,2,666,2,94,,, +sleatlo01,1958,1,DET,AL,0,0,4,0,0,0,0,16,3,4,2,6,4,0.15,6.75,0,0,0,0,25,2,4,,, +sleatlo01,1958,2,BAL,AL,1,0,6,0,0,0,0,21,14,10,0,2,5,0.43,12.86,0,0,0,0,35,4,10,,, +smithbi04,1958,1,SLN,NL,0,1,2,1,0,0,0,29,12,7,0,4,4,0.32,6.52,0,0,0,0,42,1,7,,, +smithbo03,1958,1,PIT,NL,2,2,35,4,0,0,1,183,61,30,6,31,24,0.26,4.43,7,3,2,0,274,9,39,,, +smithbo05,1958,1,BOS,AL,4,3,17,7,1,0,0,200,61,28,4,45,43,0.24,3.78,2,4,0,0,297,4,32,,, +solisma01,1958,1,CHN,NL,3,3,15,4,0,0,0,156,74,35,5,20,15,0.33,6.06,1,2,4,0,244,3,41,,, +spahnwa01,1958,1,ML1,NL,22,11,38,36,23,2,1,870,257,99,29,76,150,0.23,3.07,7,2,2,1,1176,1,106,,, +spencge01,1958,1,DET,AL,1,0,7,0,0,0,0,30,11,3,0,4,5,0.28,2.7,0,0,0,0,44,2,4,,, +sprinja01,1958,1,WS1,AL,0,0,3,1,0,0,0,21,16,11,1,7,1,0.45,14.14,3,0,0,0,43,2,11,,, +stalege01,1958,1,CHA,AL,4,5,50,0,0,0,8,256,81,30,10,24,27,0.25,3.16,3,3,0,0,349,28,36,,, +stobbch01,1958,1,WS1,AL,2,6,19,8,0,0,0,170,87,38,7,16,23,0.36,6.04,0,3,2,0,268,4,44,,, +stobbch01,1958,2,SLN,NL,1,3,17,0,0,0,1,119,40,16,4,14,25,0.26,3.63,1,1,0,0,171,7,16,,, +sturdto01,1958,1,NYA,AL,3,6,15,10,0,0,0,212,77,33,6,38,41,0.27,4.2,0,2,3,0,325,2,37,,, +sullifr01,1958,1,BOS,AL,13,9,32,29,10,2,3,598,216,79,12,49,103,0.27,3.57,0,2,3,1,836,3,91,,, +suscege02,1958,1,BOS,AL,0,0,2,0,0,0,0,6,6,4,1,1,0,0.6,18,0,0,0,0,12,1,4,,, +suscege02,1958,2,DET,AL,4,3,27,10,2,0,1,272,90,37,7,26,42,0.25,3.67,1,2,3,1,385,7,45,,, +terryra01,1958,1,KC1,AL,11,13,40,33,8,3,2,650,217,102,29,61,134,0.26,4.24,4,3,2,0,906,3,111,,, +tomandi01,1958,1,CLE,AL,2,3,18,6,2,0,0,173,61,36,8,28,42,0.27,5.62,2,3,2,0,254,3,37,,, +tomandi01,1958,2,KC1,AL,5,5,36,2,1,0,5,217,69,29,5,28,50,0.25,3.61,3,6,0,0,305,22,34,,, +troskha02,1958,1,CHA,AL,1,0,2,0,0,0,0,9,5,2,0,2,1,0.38,6,0,0,0,0,15,0,3,,, +trowbbo01,1958,1,ML1,NL,1,3,27,4,0,0,1,165,53,24,4,26,31,0.25,3.93,3,0,1,0,242,12,26,,, +truckvi01,1958,1,KC1,AL,0,1,16,0,0,0,3,66,18,5,2,15,15,0.22,2.05,1,0,0,0,98,12,7,,, +truckvi01,1958,2,NYA,AL,2,1,25,0,0,0,1,119,40,20,1,24,26,0.26,4.54,0,2,2,0,185,13,24,,, +tsitojo01,1958,1,KC1,AL,0,0,1,1,0,0,0,9,2,1,0,2,1,0.18,3,0,1,0,0,13,0,1,,, +turlebo01,1958,1,NYA,AL,21,7,33,31,19,6,1,736,178,81,24,128,168,0.2,2.97,2,6,8,0,1013,1,82,,, +urbanja01,1958,1,KC1,AL,8,11,30,24,5,1,1,396,150,87,17,51,54,0.28,5.93,1,5,2,0,589,3,92,,, 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+zuverge01,1958,1,BAL,AL,2,2,45,0,0,0,7,207,74,26,4,17,22,0.28,3.39,3,2,6,0,294,23,29,,, +ackerto01,1959,1,CIN,NL,1,2,37,0,0,0,2,190,57,29,10,37,45,0.24,4.12,5,1,4,0,277,11,31,,, +aguirha01,1959,1,DET,AL,0,0,3,0,0,0,0,8,4,1,0,3,3,0.36,3.38,0,0,0,0,14,2,1,,, +anderbo01,1959,1,CHN,NL,12,13,37,36,7,1,0,706,245,108,21,77,113,0.27,4.13,3,4,5,3,1004,1,117,,, +antonjo02,1959,1,SFN,NL,19,10,40,38,17,4,1,846,247,97,29,76,165,0.23,3.1,6,1,3,0,1150,1,107,,, +ariasru01,1959,1,CHA,AL,2,0,34,0,0,0,2,132,49,20,7,20,28,0.27,4.09,7,0,1,0,200,13,23,,, +arroylu01,1959,1,CIN,NL,1,0,10,0,0,0,0,41,17,6,0,11,8,0.32,3.95,3,2,0,0,72,5,11,,, +baileji01,1959,1,CIN,NL,0,1,3,1,0,0,0,35,17,8,1,6,7,0.33,6.17,1,0,1,0,58,0,8,,, +bambege01,1959,1,BAL,AL,0,0,3,1,0,0,1,25,15,7,1,2,2,0.4,7.56,0,1,0,0,39,1,7,,, +barclcu01,1959,1,SFN,NL,0,0,1,0,0,0,0,1,2,2,0,2,0,0.5,54,0,0,0,0,6,0,5,,, +baumafr01,1959,1,BOS,AL,6,4,26,10,2,0,1,287,96,43,11,55,48,0.25,4.05,3,2,1,0,432,8,47,,, +bellga01,1959,1,CLE,AL,16,11,44,28,12,1,5,702,208,105,28,105,136,0.23,4.04,4,8,5,1,1005,11,107,,, +blackro01,1959,1,PIT,NL,1,1,26,0,0,0,1,133,50,18,5,15,19,0.28,3.65,4,0,2,0,199,9,21,,, +blaylbo01,1959,1,SLN,NL,0,1,3,1,0,0,0,27,8,4,1,3,3,0.22,4,0,0,0,0,38,0,5,,, +blaylga01,1959,1,SLN,NL,4,5,26,12,3,0,0,300,117,57,14,43,61,0.29,5.13,3,3,2,0,449,1,61,,, +blaylga01,1959,2,NYA,AL,0,1,15,1,0,0,0,77,30,10,0,15,20,0.3,3.51,0,1,1,1,118,7,13,,, +bowmabo02,1959,1,PHI,NL,0,1,5,0,0,0,0,18,5,4,1,5,0,0.22,6,0,1,0,0,27,4,5,,, +bowsfte01,1959,1,BOS,AL,0,1,5,2,0,0,0,27,16,15,2,9,4,0.39,15,0,0,0,0,50,1,15,,, +breweto01,1959,1,BOS,AL,10,12,36,32,11,3,2,646,219,90,14,88,121,0.26,3.76,5,11,2,0,927,2,96,,, +bridgma01,1959,1,SLN,NL,6,3,27,4,1,0,1,228,67,36,10,37,76,0.24,4.26,8,3,0,0,328,11,38,,, +briggjo01,1959,1,CLE,AL,0,1,4,1,0,0,0,38,12,3,1,3,5,0.24,2.13,0,1,0,0,53,1,5,,, +brododi01,1959,1,CLE,AL,2,2,18,0,0,0,5,90,19,6,3,21,9,0.18,1.8,1,2,3,0,133,14,13,,, +brogler01,1959,1,SLN,NL,7,12,35,25,6,3,0,544,174,95,20,89,133,0.25,4.72,10,8,0,0,793,4,104,,, +bronsji01,1959,1,NYA,AL,0,3,16,3,0,0,2,88,34,17,2,13,14,0.28,5.22,0,1,1,0,133,8,19,,, +brosnji01,1959,1,SLN,NL,1,3,20,1,0,0,2,99,34,18,5,15,18,0.27,4.91,5,1,1,0,142,10,18,,, +brosnji01,1959,2,CIN,NL,8,3,26,9,1,1,2,250,79,31,7,26,56,0.24,3.35,2,0,5,0,358,10,35,,, +brownha01,1959,1,BAL,AL,11,9,31,21,2,0,3,492,158,69,16,32,81,0.25,3.79,5,2,1,0,674,7,73,,, +brucebo01,1959,1,DET,AL,0,1,2,1,0,0,0,6,2,2,1,3,1,0.25,9,0,1,0,0,12,1,5,,, +brunege01,1959,1,KC1,AL,0,0,2,0,0,0,0,14,10,6,2,7,7,0.43,11.57,1,0,1,1,32,1,9,,, +buhlbo01,1959,1,ML1,NL,15,9,31,25,12,4,0,594,181,63,19,74,105,0.24,2.86,3,4,2,0,832,2,76,,, +bunniji01,1959,1,DET,AL,17,13,40,35,14,1,1,749,220,108,37,75,201,0.23,3.89,0,1,11,1,1037,4,111,,, +burdele01,1959,1,ML1,NL,21,15,41,39,20,4,1,869,312,131,38,38,105,0.27,4.07,3,2,1,0,1193,2,144,,, +burnspe01,1959,1,DET,AL,1,3,30,0,0,0,1,186,55,26,7,25,49,0.23,3.77,1,2,2,0,264,11,31,,, 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+coateji01,1959,1,NYA,AL,6,1,37,4,2,0,0,301,89,32,10,36,64,0.23,2.87,4,1,3,2,425,17,39,,, +colemri01,1959,1,KC1,AL,2,10,29,11,2,0,2,243,85,41,8,34,54,0.27,4.56,0,3,1,0,357,6,46,,, +colemri01,1959,2,BAL,AL,0,0,3,0,0,0,0,12,4,0,0,2,4,0.26,0,0,0,0,0,17,2,0,,, +conlege01,1959,1,PHI,NL,12,7,25,22,12,3,1,540,159,60,13,42,102,0.23,3,6,4,2,0,729,2,68,,, +craigro01,1959,1,LAN,NL,11,5,29,17,7,4,0,458,122,35,13,45,76,0.21,2.06,3,2,1,0,612,3,49,,, +cuellmi01,1959,1,CIN,NL,0,0,2,0,0,0,0,12,7,7,1,4,5,0.36,15.75,1,1,0,0,24,0,8,,, +daleybu01,1959,1,KC1,AL,16,13,39,29,12,2,1,649,212,76,24,62,125,0.25,3.16,2,7,11,0,911,5,90,,, +daniebe02,1959,1,PIT,NL,7,9,34,12,0,0,1,302,115,61,9,39,67,0.28,5.45,6,5,2,0,452,8,69,,, +davieje01,1959,1,DET,AL,2,2,11,5,1,0,0,110,40,17,8,17,20,0.26,4.17,1,1,4,0,174,1,25,,, +delocik01,1959,1,BOS,AL,11,6,28,17,4,0,0,403,120,44,12,62,55,0.23,2.95,1,2,2,1,585,5,53,,, +dicksmu01,1959,1,KC1,AL,2,1,38,0,0,0,0,213,85,39,9,27,36,0.29,4.94,1,1,0,0,326,12,46,,, 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+bunniji01,1964,1,PHI,NL,19,8,41,39,13,5,2,853,248,83,23,46,219,0.23,2.63,3,2,14,0,1145,2,99,,, +burdefr01,1964,1,CHN,NL,1,0,18,0,0,0,0,60,17,7,2,10,4,0.24,3.15,2,0,1,0,86,7,7,,, +burdele01,1964,1,SLN,NL,1,0,8,0,0,0,0,30,10,2,1,3,3,0.25,1.8,0,1,0,0,43,2,3,,, +burdele01,1964,2,CHN,NL,9,9,28,17,8,2,0,393,152,71,15,19,40,0.29,4.88,2,0,1,0,549,3,74,,, +butlece01,1964,1,ML1,NL,0,0,2,0,0,0,0,13,7,4,2,0,2,0.36,8.31,0,0,0,0,19,1,4,,, +butteto01,1964,1,PIT,NL,2,2,28,4,0,0,0,193,52,17,3,37,58,0.22,2.38,1,6,0,0,278,7,21,,, +buzhajo01,1964,1,CHA,AL,10,8,31,25,8,3,0,480,150,53,13,35,97,0.25,2.98,4,4,4,1,652,2,60,,, +cardwdo01,1964,1,PIT,NL,1,2,4,4,1,1,0,58,15,6,1,7,10,0.21,2.79,1,2,3,0,82,0,9,,, +carrocl02,1964,1,ML1,NL,2,0,11,1,0,0,1,61,15,4,1,3,17,0.2,1.77,1,0,0,0,79,2,4,,, +chancde01,1964,1,LAA,AL,20,9,46,35,15,11,4,835,194,51,7,86,207,0.19,1.65,9,9,2,0,1093,7,56,,, +chartpe01,1964,1,BOS,AL,0,2,25,5,0,0,0,195,67,38,12,24,37,0.27,5.26,1,0,1,0,272,14,39,,, +cheneto01,1964,1,WS2,AL,1,3,15,6,1,0,1,146,45,20,10,13,25,0.24,3.7,1,2,0,0,199,4,26,,, +ciscoga01,1964,1,NYN,NL,6,19,36,25,5,2,0,575,182,77,17,54,78,0.25,3.62,4,5,6,0,787,5,85,,, +clonito01,1964,1,ML1,NL,19,14,38,34,15,3,2,728,206,96,20,82,163,0.23,3.56,9,13,3,0,997,2,112,,, +connoed02,1964,1,BOS,AL,4,11,27,15,1,1,0,242,80,44,3,64,73,0.26,4.91,2,11,6,0,386,6,50,,, +coombda01,1964,1,HOU,NL,1,1,7,1,0,0,0,54,21,10,1,10,14,0.3,5,0,0,1,0,82,3,10,,, +craigpe01,1964,1,WS2,AL,0,0,2,1,0,0,0,5,8,9,1,4,0,0.66,48.6,1,0,0,0,16,0,9,,, +craigro01,1964,1,SLN,NL,7,9,39,19,3,0,5,498,180,60,16,35,84,0.27,3.25,4,2,4,0,706,12,76,,, +cuellmi01,1964,1,SLN,NL,5,5,32,7,1,0,4,216,80,36,8,33,56,0.28,4.5,4,1,1,0,320,6,43,,, +culpra01,1964,1,PHI,NL,8,7,30,19,3,1,0,405,139,62,15,56,96,0.26,4.13,4,8,5,1,601,4,77,,, +dailebi01,1964,1,MIN,AL,1,2,14,0,0,0,0,46,23,14,3,17,6,0.37,8.22,2,3,4,0,84,6,16,,, +daleybu01,1964,1,NYA,AL,3,2,13,3,0,0,1,105,37,18,3,25,16,0.27,4.63,1,1,4,0,167,5,19,,, +daniebe02,1964,1,WS2,AL,8,10,33,24,3,2,0,489,147,67,20,64,73,0.24,3.7,8,4,0,0,679,3,75,,, +dicksji01,1964,1,CIN,NL,1,0,4,0,0,0,0,15,8,4,0,5,6,0.44,7.2,1,0,0,0,25,3,4,,, +dierkla01,1964,1,HOU,NL,0,1,3,1,0,0,0,27,7,2,1,3,5,0.21,2,0,2,0,0,36,2,4,,, +dillost01,1964,1,NYN,NL,0,0,2,0,0,0,0,9,4,3,1,2,2,0.33,9,0,0,0,0,15,2,3,,, +donovdi01,1964,1,CLE,AL,7,9,30,23,5,0,1,475,181,80,19,29,83,0.29,4.55,6,1,2,0,671,4,86,,, +dottega01,1964,1,MIN,AL,0,0,3,0,0,0,0,13,3,1,1,3,6,0.18,2.08,0,0,0,0,20,2,2,,, +dowlida01,1964,1,SLN,NL,0,0,1,0,0,0,0,3,2,0,0,0,0,0.4,0,0,0,0,0,5,1,0,,, +downial01,1964,1,NYA,AL,13,8,37,35,11,1,2,732,201,94,18,120,217,0.22,3.47,5,14,0,3,1041,2,104,,, +drabomo01,1964,1,KC1,AL,5,13,53,21,1,0,1,505,176,99,24,72,119,0.27,5.29,5,9,8,0,739,11,103,,, +drysddo01,1964,1,LAN,NL,18,16,40,40,21,5,0,964,242,78,15,68,237,0.2,2.18,9,11,10,1,1264,0,91,,, +duckwji01,1964,1,WS2,AL,1,6,30,2,0,0,3,168,52,27,9,25,56,0.24,4.34,4,4,3,0,245,15,37,,, +duffaji01,1964,1,SFN,NL,5,1,35,3,1,0,3,222,57,24,9,31,55,0.2,2.92,4,4,2,0,311,19,25,,, +dulibbo01,1964,1,LAA,AL,6,4,58,0,0,0,9,218,80,29,5,22,33,0.28,3.59,3,3,1,0,307,35,35,,, +durenry01,1964,1,PHI,NL,0,0,2,0,0,0,0,9,5,2,0,1,5,0.35,6,0,0,1,0,17,1,3,,, +durenry01,1964,2,CIN,NL,0,2,26,0,0,0,1,131,41,14,1,15,39,0.24,2.89,1,4,3,1,188,10,17,,, +earlear01,1964,1,BOS,AL,1,1,25,3,1,0,1,151,51,15,3,18,45,0.26,2.68,0,1,1,0,218,7,17,,, +egandi02,1964,1,DET,AL,0,0,23,0,0,0,2,103,33,17,4,17,21,0.24,4.46,1,2,1,0,157,11,22,,, +eilerda01,1964,1,ML1,NL,0,0,6,0,0,0,0,23,11,4,1,1,1,0.33,4.7,1,0,1,0,37,3,5,,, +ellissa01,1964,1,CIN,NL,10,3,52,5,2,0,14,367,101,35,9,28,125,0.22,2.57,2,2,1,0,488,32,38,,, +ellswdi01,1964,1,CHN,NL,14,18,37,36,16,1,0,770,267,107,34,71,148,0.26,3.75,9,4,3,0,1093,0,129,,, +elstodo01,1964,1,CHN,NL,2,5,48,0,0,0,1,163,68,32,4,34,26,0.33,5.3,12,3,3,0,260,15,38,,, +esteldi01,1964,1,SFN,NL,1,2,6,6,0,0,0,125,39,14,3,23,23,0.24,3.02,0,0,0,0,185,0,15,,, 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+jayjo01,1964,1,CIN,NL,11,11,34,23,10,0,2,549,167,69,17,36,134,0.24,3.39,6,3,3,0,737,7,75,,, +johnske02,1964,1,HOU,NL,11,16,35,35,7,1,0,654,209,88,15,44,117,0.25,3.63,6,3,7,0,909,0,100,,, +johnto01,1964,1,CLE,AL,2,9,25,14,2,1,0,283,97,41,10,35,65,0.26,3.91,4,5,0,1,412,0,53,,, +jonesgo01,1964,1,HOU,NL,0,1,34,0,0,0,0,150,58,23,3,14,28,0.29,4.14,2,4,0,0,220,11,24,,, +jonessa02,1964,1,BAL,AL,0,0,7,0,0,0,0,31,5,3,1,5,6,0.15,2.61,0,0,0,0,38,4,3,,, +kaatji01,1964,1,MIN,AL,17,11,36,34,13,0,1,729,231,87,23,60,171,0.25,3.22,5,13,9,1,1009,1,100,,, +kelledi01,1964,1,ML1,NL,0,0,2,0,0,0,0,6,2,4,0,3,2,0.25,18,0,2,0,0,11,0,4,,, +kelleto01,1964,1,CLE,AL,0,0,6,0,0,0,0,29,9,6,1,9,7,0.23,5.59,2,0,1,0,48,2,9,,, +kelsobi01,1964,1,LAA,AL,2,0,10,1,1,1,0,71,19,6,3,9,21,0.21,2.28,0,1,1,0,97,2,6,,, +klinero01,1964,1,WS2,AL,10,7,61,0,0,0,14,244,81,21,4,21,40,0.26,2.32,9,7,2,0,344,52,29,,, +klippjo01,1964,1,PHI,NL,2,1,11,0,0,0,1,67,22,10,6,8,13,0.25,4.03,1,3,2,0,98,5,10,,, 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+horlejo01,1965,1,CHA,AL,13,13,34,34,7,4,0,657,203,70,16,39,125,0.24,2.88,9,8,3,0,890,0,88,,, +howarbr01,1965,1,CHA,AL,9,8,30,22,1,1,0,444,123,57,13,72,120,0.22,3.47,5,9,1,0,627,2,61,,, +humphbo01,1965,1,CHN,NL,2,0,41,0,0,0,0,197,59,23,6,27,38,0.24,3.15,6,0,2,0,275,11,25,,, +hunteca01,1965,1,KC1,AL,8,8,32,20,3,2,0,399,124,63,21,46,82,0.24,4.26,5,2,2,1,567,2,68,,, +jacksal01,1965,1,NYN,NL,8,20,37,31,7,3,1,616,217,99,17,61,120,0.27,4.34,4,6,8,2,884,2,111,,, +jacksgr01,1965,1,PHI,NL,1,1,6,2,0,0,0,41,17,11,4,5,15,0.3,7.24,0,0,0,0,61,2,11,,, +jacksla01,1965,1,CHN,NL,14,21,39,39,12,4,0,772,268,110,28,57,131,0.26,3.85,8,5,5,0,1079,0,126,,, +jastela01,1965,1,SLN,NL,3,0,4,3,3,0,0,84,21,5,1,7,10,0.2,1.61,0,0,0,0,110,0,5,,, +jayjo01,1965,1,CIN,NL,9,8,37,24,4,1,1,467,150,73,21,63,102,0.25,4.22,0,8,4,0,673,8,83,,, +jenkife01,1965,1,PHI,NL,2,1,7,0,0,0,1,37,7,3,2,2,10,0.15,2.19,0,0,0,0,46,6,3,,, +johnske02,1965,1,HOU,NL,3,2,8,8,1,0,0,155,52,24,4,11,28,0.26,4.18,1,2,4,0,215,0,25,,, +johnske02,1965,2,ML1,NL,13,8,29,26,8,1,2,539,165,64,15,37,123,0.24,3.21,7,3,3,0,735,2,75,,, +johnto01,1965,1,CHA,AL,14,7,39,27,6,1,3,551,162,63,12,58,126,0.23,3.09,6,8,2,0,752,4,67,,, +jonesgo01,1965,1,HOU,NL,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,3,1,0,,, +joycedi01,1965,1,KC1,AL,0,1,5,3,0,0,0,39,12,4,0,4,7,0.24,2.77,0,2,0,1,55,2,7,,, +jurewmi01,1965,1,NYA,AL,0,0,2,0,0,0,0,7,5,2,0,1,2,0.41,7.71,0,0,0,0,13,0,2,,, +kaatji01,1965,1,MIN,AL,18,11,45,42,7,2,2,793,267,83,25,63,154,0.25,2.83,6,3,5,0,1115,2,121,,, +kekicmi01,1965,1,LAN,NL,0,1,5,1,0,0,0,31,10,11,2,13,9,0.26,9.58,0,1,0,0,51,1,12,,, +kelledi01,1965,1,ML1,NL,1,1,21,4,0,0,0,135,37,15,5,20,31,0.22,3,2,3,0,0,186,5,15,,, +kelleto01,1965,1,CLE,AL,2,1,4,4,1,0,0,90,19,8,3,13,31,0.18,2.4,0,2,0,0,115,0,8,,, +klinero01,1965,1,WS2,AL,7,6,74,0,0,0,29,298,106,29,7,32,52,0.27,2.63,14,1,2,0,434,58,36,,, +klippjo01,1965,1,MIN,AL,9,3,56,0,0,0,5,229,59,19,8,31,59,0.21,2.24,4,4,3,0,314,24,22,,, 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+mossido01,1965,1,KC1,AL,5,8,51,0,0,0,7,166,59,23,0,20,41,0.27,3.74,6,2,0,0,242,19,30,,, +murakma01,1965,1,SFN,NL,4,1,45,1,0,0,8,223,57,31,9,22,85,0.2,3.75,5,3,3,0,304,16,31,,, +musgrde01,1965,1,NYN,NL,0,0,5,1,0,0,0,48,11,1,0,7,11,0.2,0.56,0,2,2,0,65,0,2,,, +narumbu01,1965,1,WS2,AL,4,12,46,24,2,0,0,521,176,86,16,91,86,0.26,4.46,11,4,7,0,782,1,98,,, +navarju01,1965,1,DET,AL,0,2,15,1,0,0,1,90,25,14,5,12,22,0.23,4.2,1,1,0,0,123,3,16,,, +nelsome01,1965,1,MIN,AL,0,4,28,3,0,0,3,164,57,25,7,23,31,0.26,4.12,3,4,2,0,247,13,29,,, +newmafr01,1965,1,CAL,AL,14,16,36,36,10,2,0,782,225,85,15,64,109,0.23,2.93,10,6,1,1,1044,0,94,,, +niekrph01,1965,1,ML1,NL,2,3,41,1,0,0,6,224,73,24,5,26,49,0.25,2.89,3,9,3,0,324,21,32,,, +nischro01,1965,1,DET,AL,1,0,20,0,0,0,1,68,21,7,2,6,12,0.25,2.78,1,2,0,0,90,7,10,,, +nottedo01,1965,1,HOU,NL,4,15,29,25,3,0,0,474,166,82,14,55,77,0.27,4.67,11,3,5,1,684,1,99,,, +nuxhajo01,1965,1,CIN,NL,11,4,32,16,5,1,2,446,142,57,18,31,117,0.25,3.45,2,3,3,1,601,6,57,,, +odellbi01,1965,1,ML1,NL,10,6,62,1,0,0,18,334,87,27,10,30,78,0.21,2.18,8,6,2,0,448,42,35,,, +odombl01,1965,1,KC1,AL,0,0,1,0,0,0,0,3,2,1,0,2,0,0.4,9,0,0,0,0,7,0,1,,, +odonojo01,1965,1,KC1,AL,9,18,34,30,4,1,0,533,183,78,15,66,82,0.26,3.95,8,9,1,0,764,0,92,,, +olivoch01,1965,1,ML1,NL,0,1,8,0,0,0,0,39,12,2,1,5,11,0.26,1.38,2,1,0,0,54,3,2,,, +ortegph01,1965,1,WS2,AL,12,15,35,29,4,2,0,539,176,102,33,97,88,0.26,5.11,7,7,5,3,785,2,107,,, +osinsda01,1965,1,ML1,NL,0,3,61,0,0,0,6,249,81,26,4,40,54,0.26,2.82,9,2,1,0,360,26,28,,, +osteecl01,1965,1,LAN,NL,15,15,40,40,9,1,0,861,253,89,19,78,162,0.23,2.79,10,4,3,1,1169,0,95,,, +osteeda01,1965,1,CIN,NL,0,0,3,0,0,0,0,9,2,0,0,4,1,0.2,0,0,0,0,0,14,0,0,,, +otoolji01,1965,1,CIN,NL,3,10,29,22,2,0,1,383,154,84,14,47,71,0.29,5.92,4,4,3,1,585,2,98,,, +owensji01,1965,1,HOU,NL,6,5,50,0,0,0,8,214,64,26,4,29,53,0.23,3.28,8,3,0,0,306,26,28,,, +paigesa01,1965,1,KC1,AL,0,0,1,1,0,0,0,9,1,0,0,0,1,0.1,0,0,0,0,0,10,0,0,,, +palmeji01,1965,1,BAL,AL,5,4,27,6,0,0,1,276,75,38,6,56,75,0.22,3.72,1,4,2,0,394,7,49,,, +pappami01,1965,1,BAL,AL,13,9,34,34,9,3,0,664,192,64,22,52,127,0.23,2.6,3,5,3,0,891,0,81,,, +parsoto01,1965,1,NYN,NL,1,10,35,11,1,1,1,272,108,47,17,17,58,0.29,4.67,2,2,0,0,391,6,53,,, +pascuca02,1965,1,MIN,AL,9,3,27,27,5,1,0,468,126,58,12,63,96,0.21,3.35,0,7,5,1,659,0,67,,, +penaor01,1965,1,KC1,AL,0,6,12,5,0,0,0,106,42,27,4,13,24,0.3,6.88,0,0,2,0,158,2,30,,, +penaor01,1965,2,DET,AL,4,6,30,0,0,0,4,172,54,16,5,20,55,0.25,2.51,5,3,1,0,239,19,18,,, +perraro01,1965,1,LAN,NL,6,6,59,0,0,0,17,314,85,26,2,40,53,0.22,2.24,7,3,3,0,431,36,28,,, +perryga01,1965,1,SFN,NL,8,12,47,26,6,0,1,587,194,91,21,70,170,0.25,4.19,16,12,6,0,848,8,105,,, +perryji01,1965,1,MIN,AL,12,7,36,19,4,2,0,503,142,49,18,47,88,0.23,2.63,2,4,3,0,679,3,57,,, +peterga01,1965,1,CHA,AL,10,12,33,30,1,0,0,529,181,71,19,63,95,0.26,3.62,8,8,4,0,764,1,76,,, +pichero01,1965,1,CAL,AL,0,3,14,1,0,0,0,59,20,15,5,12,14,0.26,6.86,2,1,0,0,89,11,15,,, +pizarju01,1965,1,CHA,AL,6,3,18,18,2,1,0,291,96,37,9,37,65,0.25,3.43,3,4,1,0,419,0,42,,, +pleisbi01,1965,1,MIN,AL,4,4,41,2,0,0,4,154,49,17,3,27,33,0.25,2.98,5,2,0,1,229,16,20,,, +podrejo01,1965,1,LAN,NL,7,6,27,22,2,1,1,402,126,51,17,39,63,0.24,3.43,4,6,2,0,560,4,57,,, +priddbo01,1965,1,SFN,NL,1,0,8,0,0,0,0,31,6,2,1,2,7,0.17,1.74,0,1,0,0,38,4,2,,, +purdijo01,1965,1,LAN,NL,2,1,11,2,0,0,0,68,26,17,8,13,16,0.28,6.75,3,2,0,0,106,1,19,,, +purkebo01,1965,1,SLN,NL,10,9,32,17,3,1,2,373,148,80,20,33,39,0.29,5.79,4,2,7,1,549,10,83,,, +radatdi01,1965,1,BOS,AL,9,11,63,0,0,0,22,373,104,54,11,53,121,0.22,3.91,11,1,5,1,526,56,57,,, +rakowed01,1965,1,DET,AL,0,0,6,0,0,0,0,40,14,9,2,11,10,0.28,6.08,0,1,0,0,64,0,11,,, +ramospe01,1965,1,NYA,AL,5,5,65,0,0,0,19,277,80,30,7,27,68,0.23,2.92,9,0,1,0,374,42,34,,, +rayji01,1965,1,HOU,NL,0,2,3,2,0,0,0,23,11,9,1,6,7,0.35,10.57,0,0,0,0,38,1,9,,, +raymocl01,1965,1,HOU,NL,7,4,33,7,2,0,5,289,87,31,6,16,79,0.24,2.9,2,4,5,0,387,16,35,,, 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+morrijo06,1968,1,BAL,AL,2,0,19,0,0,0,0,95,19,9,4,17,22,0.17,2.56,2,2,4,0,133,2,11,,, +murphto02,1968,1,CAL,AL,5,6,15,15,3,0,0,298,67,24,5,28,56,0.19,2.17,0,2,5,0,391,0,30,,, +nashji01,1968,1,OAK,AL,13,13,34,33,12,6,0,686,185,58,18,55,169,0.21,2.28,12,5,3,0,910,0,63,,, +nelsome01,1968,1,SLN,NL,2,1,18,4,1,0,1,158,49,17,3,9,16,0.25,2.91,3,0,0,0,212,8,20,,, +nelsoro02,1968,1,BAL,AL,4,3,19,6,0,0,1,213,49,19,3,26,70,0.19,2.41,3,6,1,0,283,5,21,,, +niekrjo01,1968,1,CHN,NL,14,10,34,29,2,1,2,531,204,85,18,59,65,0.29,4.32,8,3,3,0,769,4,93,,, +niekrph01,1968,1,ATL,NL,14,12,37,34,15,5,2,771,228,74,16,45,140,0.23,2.59,3,16,5,3,1019,3,83,,, +nolanga01,1968,1,CIN,NL,9,4,23,22,4,2,0,450,105,40,10,49,111,0.19,2.4,1,3,3,1,595,0,48,,, +nyeri01,1968,1,CHN,NL,7,12,27,20,6,1,1,398,145,56,16,34,74,0.27,3.8,4,1,1,1,570,3,65,,, +nymanje01,1968,1,CHA,AL,2,1,8,7,1,1,0,121,38,9,1,16,27,0.24,2.01,1,2,0,1,175,0,13,,, +odombl01,1968,1,OAK,AL,16,10,32,31,9,4,0,694,179,63,9,98,143,0.21,2.45,9,17,7,0,950,0,74,,, 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+stangle01,1968,1,BOS,AL,5,5,50,2,1,0,12,309,89,45,10,25,53,0.23,3.93,5,3,1,0,418,30,54,,, +stephbu01,1968,1,MIN,AL,1,1,2,2,0,0,0,34,11,6,0,7,4,0.27,4.76,0,1,1,0,49,0,7,,, +stephje01,1968,1,BOS,AL,2,8,23,7,2,0,0,206,81,43,4,42,51,0.29,5.64,3,7,2,1,328,8,51,,, +stonebi01,1968,1,CHN,NL,0,1,18,0,0,0,0,88,35,18,6,14,18,0.31,5.52,1,1,1,0,131,3,19,,, +stonege02,1968,1,ATL,NL,7,4,17,10,2,0,0,225,63,23,9,19,52,0.22,2.76,4,1,0,0,305,5,27,,, +stottme01,1968,1,NYA,AL,21,12,36,36,19,6,0,836,243,76,21,65,140,0.23,2.45,7,3,3,1,1123,0,86,,, +sutheda01,1968,1,CLE,AL,0,0,3,0,0,0,0,10,6,3,0,4,2,0.37,8.1,1,3,0,0,20,1,3,,, +suttodo01,1968,1,LAN,NL,11,15,35,27,7,2,1,623,179,60,6,59,162,0.23,2.6,14,4,2,3,847,6,64,,, +talbofr01,1968,1,NYA,AL,1,9,29,11,1,0,0,297,89,37,6,42,67,0.24,3.36,6,3,2,0,421,7,47,,, +tayloro01,1968,1,NYN,NL,1,5,58,0,0,0,13,230,64,23,4,18,49,0.22,2.7,9,0,1,0,307,44,24,,, +tiantlu01,1968,1,CLE,AL,21,9,34,32,19,9,0,775,152,46,16,73,264,0.16,1.6,4,3,4,0,987,0,53,,, +tiefebo01,1968,1,CHN,NL,0,1,9,0,0,0,1,40,20,9,2,2,9,0.35,6.08,1,0,0,0,64,5,12,,, +tilloth01,1968,1,NYA,AL,1,0,7,0,0,0,0,31,11,5,0,7,1,0.28,4.35,2,0,0,0,49,3,6,,, +torremi01,1968,1,SLN,NL,2,1,5,2,0,0,0,58,20,6,1,12,6,0.28,2.79,0,2,1,0,87,0,7,,, +tovarce01,1968,1,MIN,AL,0,0,1,1,0,0,0,3,0,0,0,1,1,0,0,0,0,0,1,4,0,0,,, +tsitojo01,1968,1,CIN,NL,0,3,3,3,0,0,0,38,16,10,6,8,6,0.3,7.11,0,0,1,0,62,0,10,,, +uphamjo01,1968,1,CHN,NL,0,0,2,0,0,0,0,21,2,0,0,3,2,0.08,0,0,1,1,0,27,1,0,,, +upshace01,1968,1,ATL,NL,8,7,52,0,0,0,13,350,98,32,6,24,74,0.22,2.47,7,2,4,0,467,35,41,,, +vealebo01,1968,1,PIT,NL,13,14,36,33,13,4,0,736,187,56,13,94,171,0.21,2.05,5,9,2,1,997,2,67,,, +verbajo01,1968,1,NYA,AL,6,7,40,11,2,1,4,291,104,34,6,41,40,0.28,3.15,8,1,6,0,425,16,36,,, +wagnega01,1968,1,PHI,NL,4,4,44,0,0,0,8,234,69,26,0,31,43,0.24,3,6,1,5,0,328,20,27,,, +walkelu01,1968,1,PIT,NL,0,3,39,2,0,0,3,185,42,14,1,39,66,0.19,2.04,0,5,1,0,264,14,18,,, +wardejo01,1968,1,DET,AL,4,1,28,0,0,0,3,112,30,15,5,15,25,0.21,3.62,2,0,0,0,155,11,15,,, +washbra01,1968,1,SLN,NL,14,8,31,30,8,4,0,645,191,54,9,47,124,0.23,2.26,7,3,1,0,861,1,67,,, +waslega01,1968,1,BOS,AL,4,7,34,11,2,0,2,316,108,43,9,40,59,0.26,3.67,9,9,6,0,456,5,50,,, +watted01,1968,1,BAL,AL,5,5,59,0,0,0,11,250,63,21,1,35,72,0.2,2.27,7,4,2,0,346,39,32,,, +weaveji02,1968,1,CAL,AL,0,1,14,0,0,0,0,68,22,6,4,10,8,0.25,2.38,2,0,0,0,97,4,7,,, +wenzfr01,1968,1,BOS,AL,0,0,1,0,0,0,0,3,0,0,0,2,3,0,0,0,0,0,0,5,1,0,,, +wickeda01,1968,1,PIT,NL,1,0,11,0,0,0,1,62,21,8,0,13,9,0.27,3.48,3,0,0,0,95,4,12,,, +wilheho01,1968,1,CHA,AL,4,4,72,0,0,0,12,281,69,18,4,24,72,0.2,1.73,5,1,2,0,368,39,20,,, +williro01,1968,1,SLN,NL,2,3,48,0,0,0,4,191,50,24,4,28,39,0.21,3.39,11,1,1,0,270,15,25,,, +willist02,1968,1,CLE,AL,13,11,44,24,6,2,9,583,163,54,14,51,147,0.22,2.5,7,1,10,0,796,16,64,,, +wilsodo01,1968,1,HOU,NL,13,16,33,30,9,3,0,626,187,76,9,70,175,0.23,3.28,5,9,4,1,884,3,85,,, +wilsoea01,1968,1,DET,AL,13,12,34,33,10,3,0,673,192,71,20,65,168,0.23,2.85,4,7,0,2,909,0,77,,, +wiseri01,1968,1,PHI,NL,9,15,30,30,7,1,0,546,210,92,12,37,97,0.29,4.55,3,4,6,0,776,0,100,,, +womacdo01,1968,1,NYA,AL,3,7,45,0,0,0,2,185,53,22,6,29,27,0.24,3.21,9,4,1,0,254,36,23,,, +woodwi01,1968,1,CHA,AL,13,12,88,2,0,0,16,477,127,33,8,33,74,0.22,1.87,12,3,3,0,626,46,39,,, +worthal01,1968,1,MIN,AL,4,5,54,0,0,0,18,229,67,23,1,32,57,0.23,2.71,9,6,0,1,323,34,26,,, +wrighcl01,1968,1,CAL,AL,10,6,41,13,2,1,3,377,123,55,13,44,71,0.25,3.94,6,7,2,0,534,14,58,,, +wyattjo02,1968,1,BOS,AL,1,2,8,0,0,0,0,32,9,5,2,6,11,0.23,4.22,0,2,1,0,47,4,7,,, +wyattjo02,1968,2,NYA,AL,0,2,7,0,0,0,0,25,7,2,1,9,6,0.21,2.16,0,1,0,0,42,2,3,,, +wyattjo02,1968,3,DET,AL,1,0,22,0,0,0,2,91,26,8,2,11,25,0.23,2.37,2,4,1,0,125,10,9,,, +wynnebi02,1968,1,CHA,AL,0,0,1,0,0,0,0,6,2,1,0,2,1,0.25,4.5,0,0,0,0,10,0,2,,, +abernte02,1969,1,CHN,NL,4,3,56,0,0,0,3,256,75,30,8,42,55,0.23,3.16,11,5,1,1,374,20,38,,, +adamsmi01,1969,1,BAL,AL,0,1,6,0,0,0,0,24,10,4,0,6,2,0.35,4.5,2,1,0,0,38,1,4,,, +aguirha01,1969,1,CHN,NL,1,0,41,0,0,0,1,135,45,13,2,12,19,0.26,2.6,1,1,2,0,186,9,13,,, +akerja01,1969,1,SE1,AL,0,2,15,0,0,0,3,50,25,14,4,13,7,0.35,7.56,4,0,1,0,85,9,15,,, +akerja01,1969,2,NYA,AL,8,4,38,0,0,0,11,197,51,15,4,22,40,0.21,2.06,5,0,4,0,263,23,17,,, +allenll01,1969,1,CAL,AL,0,1,4,1,0,0,0,30,5,6,1,10,5,0.14,5.4,0,0,0,0,44,0,7,,, +arlinst01,1969,1,SDN,NL,0,1,4,1,0,0,0,32,13,11,2,9,9,0.28,9.28,1,5,0,0,54,2,11,,, +arrigge01,1969,1,CIN,NL,4,7,20,16,1,0,0,273,89,42,9,61,35,0.25,4.15,3,11,8,0,422,0,50,,, +bahnsst01,1969,1,NYA,AL,9,16,40,33,5,2,1,662,222,94,28,90,130,0.26,3.83,9,10,0,0,956,4,102,,, +baldsja01,1969,1,SDN,NL,7,2,61,0,0,0,1,231,80,41,7,29,67,0.26,4.79,6,7,2,0,340,24,45,,, +baldwda01,1969,1,WS2,AL,2,4,43,0,0,0,4,200,57,30,4,34,51,0.23,4.05,2,0,5,1,287,12,31,,, +baneydi01,1969,1,SE1,AL,1,0,9,1,0,0,0,56,21,8,2,7,9,0.29,3.86,1,0,0,0,81,3,8,,, 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+deckejo01,1969,1,CHN,NL,1,0,4,1,0,0,0,37,10,4,0,6,13,0.22,2.92,0,1,0,0,51,1,4,,, +dierkla01,1969,1,HOU,NL,20,13,39,37,20,4,0,916,240,79,18,72,232,0.21,2.33,6,9,1,0,1207,0,97,,, +dilauja01,1969,1,NYN,NL,1,4,23,4,0,0,1,191,50,17,4,18,27,0.21,2.4,5,2,0,0,256,8,19,,, +distaal01,1969,1,CHN,NL,0,0,2,0,0,0,0,14,6,2,0,1,1,0.31,3.86,0,0,0,0,21,0,2,,, +dobsoch01,1969,1,OAK,AL,15,13,35,35,11,1,0,706,244,101,16,80,137,0.27,3.86,2,9,1,1,998,0,111,,, +dobsopa01,1969,1,DET,AL,5,10,49,9,1,0,9,315,100,42,10,39,64,0.25,3.6,5,3,1,0,451,22,48,,, +downial01,1969,1,NYA,AL,7,5,30,15,5,1,0,392,117,49,12,49,85,0.24,3.38,6,5,0,0,550,5,57,,, +doylepa01,1969,1,ATL,NL,2,0,36,0,0,0,4,117,31,9,4,16,25,0.23,2.08,3,3,0,0,154,16,9,,, +drabomo01,1969,1,KCA,AL,11,9,52,0,0,0,11,294,68,32,10,30,76,0.19,2.94,2,1,2,0,398,37,33,,, +dragodi01,1969,1,KCA,AL,11,13,41,26,10,2,1,602,190,84,19,65,108,0.24,3.77,6,0,2,0,848,10,95,,, +drysddo01,1969,1,LAN,NL,5,4,12,12,1,1,0,188,71,31,9,13,24,0.29,4.45,0,1,2,0,267,0,34,,, 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+farretu01,1969,1,PHI,NL,3,4,46,0,0,0,3,223,92,33,8,27,40,0.3,4,6,2,1,0,333,27,33,,, +fingero01,1969,1,OAK,AL,6,7,60,8,1,1,12,357,116,49,13,41,61,0.25,3.71,5,1,4,1,503,30,60,,, +fisheed02,1969,1,CAL,AL,3,2,52,1,0,0,2,290,100,39,9,28,47,0.27,3.63,3,0,1,0,403,19,46,,, +fisheja01,1969,1,CIN,NL,4,4,34,15,0,0,1,339,137,69,15,30,55,0.29,5.5,3,3,5,0,502,7,77,,, +fitzmal01,1969,1,KCA,AL,1,1,7,0,0,0,2,32,9,5,1,4,3,0.23,4.22,0,1,0,0,45,5,5,,, +fosteal01,1969,1,LAN,NL,3,9,24,15,2,2,0,308,119,50,11,29,59,0.29,4.38,4,6,4,0,451,4,55,,, +friseda01,1969,1,NYN,NL,0,0,3,0,0,0,0,14,8,4,1,3,5,0.38,7.71,0,1,0,0,24,0,4,,, +frymawo01,1969,1,PHI,NL,12,15,36,35,10,1,0,685,243,112,15,89,150,0.27,4.41,3,12,11,1,1010,0,123,,, +fuentmi01,1969,1,SE1,AL,1,3,8,4,1,0,0,78,29,15,1,16,14,0.28,5.19,1,0,0,0,118,3,15,,, +garbege01,1969,1,PIT,NL,0,0,2,1,0,0,0,15,6,3,3,1,3,0.33,5.4,0,0,0,0,20,0,3,,, +garibbo01,1969,1,SFN,NL,0,1,1,1,0,0,0,15,6,1,0,2,1,0.31,1.8,0,0,0,0,22,0,4,,, 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+borbope01,1975,1,CIN,NL,9,5,67,0,0,0,5,375,145,41,6,21,29,0.300,2.95,6,6,3,1,523,25,47,16,1,19 +bosmadi01,1975,1,CLE,AL,0,2,6,3,0,0,0,86,33,13,3,8,11,0.292,4.08,1,0,3,0,127,3,17,1,2,2 +bosmadi01,1975,2,OAK,AL,11,4,22,21,2,0,0,368,112,48,12,24,42,0.240,3.52,3,1,3,0,500,0,50,3,3,4 +bradlto01,1975,1,SFN,NL,2,3,13,6,0,0,0,126,57,29,6,18,13,0.326,6.21,4,0,1,0,199,0,33,4,1,3 +brettke01,1975,1,PIT,NL,9,5,23,16,4,1,0,354,110,44,10,43,47,0.250,3.36,5,5,2,1,492,4,47,4,3,9 +breweji01,1975,1,LAN,NL,3,1,21,0,0,0,2,99,44,19,2,12,21,0.333,5.18,2,1,1,0,152,14,20,2,5,2 +breweji01,1975,2,CAL,AL,1,0,21,0,0,0,5,104,38,7,2,11,22,0.279,1.82,1,2,0,0,151,16,9,0,4,4 +brilene01,1975,1,KCA,AL,6,6,24,16,3,0,2,336,127,53,19,25,73,0.285,4.26,1,1,5,0,478,4,60,1,1,13 +brobepe01,1975,1,ML4,AL,14,16,38,32,7,2,0,661,219,101,18,106,100,0.263,4.13,2,10,16,0,967,3,114,8,5,19 +brookik01,1975,1,DET,AL,0,0,3,0,0,0,0,31,11,6,3,5,8,0.282,5.23,0,1,1,0,46,2,6,1,0,1 +brownja01,1975,1,TEX,AL,5,5,17,7,2,1,0,211,70,33,7,35,35,0.266,4.22,2,4,2,0,305,4,37,4,1,6 +brownja01,1975,2,CLE,AL,1,2,25,3,1,0,1,208,72,33,9,29,41,0.276,4.28,6,2,0,0,298,14,40,3,5,8 +bryanro01,1975,1,SLN,NL,0,1,10,1,0,0,0,26,20,16,2,7,7,0.444,16.62,1,1,0,0,55,3,17,3,0,0 +burgmto01,1975,1,MIN,AL,5,8,46,0,0,0,11,227,76,26,7,23,41,0.265,3.09,2,1,1,0,318,37,32,4,2,9 +burrira01,1975,1,CHN,NL,15,10,36,35,8,2,0,715,259,109,25,73,108,0.281,4.12,6,2,4,2,1020,1,121,13,9,19 +burtoji01,1975,1,BOS,AL,1,2,29,4,0,0,1,159,58,17,6,19,39,0.276,2.89,2,1,0,0,232,8,30,1,2,3 +busbyst01,1975,1,KCA,AL,18,12,34,34,18,3,0,781,233,89,18,81,160,0.242,3.08,4,8,3,0,1064,0,96,13,4,22 +busketo01,1975,1,CLE,AL,5,3,50,0,0,0,7,231,69,22,7,29,29,0.252,2.57,10,4,1,0,316,34,27,10,2,8 +butlebi02,1975,1,MIN,AL,5,4,23,8,1,0,0,245,100,54,12,35,55,0.301,5.95,0,0,0,0,377,5,61,5,5,3 +caldwmi01,1975,1,SFN,NL,7,13,38,21,4,0,1,490,194,87,16,48,57,0.296,4.79,7,1,5,0,723,4,102,10,4,19 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+birddo01,1980,1,NYA,AL,3,0,22,1,0,0,1,152,47,15,3,14,17,0.257,2.66,1,1,1,0,205,7,16,3,4,5 +blairde01,1980,1,SDN,NL,0,1,5,1,0,0,0,42,18,10,3,3,11,0.310,6.43,0,1,0,0,61,0,10,0,0,1 +bluevi01,1980,1,SFN,NL,14,10,31,31,10,3,0,672,202,74,14,61,129,0.242,2.97,8,3,0,2,914,0,79,13,6,15 +blylebe01,1980,1,PIT,NL,8,13,34,32,5,2,0,650,219,92,20,59,168,0.262,3.82,5,2,0,1,907,1,102,10,2,12 +boddimi01,1980,1,BAL,AL,0,1,1,1,0,0,0,22,6,5,1,5,4,0.207,6.14,0,0,0,0,34,0,6,0,0,0 +boggsto01,1980,1,ATL,NL,12,9,32,26,4,3,0,577,180,73,14,46,84,0.249,3.42,0,4,4,2,792,1,80,13,7,18 +boitada01,1980,1,ML4,AL,0,1,11,0,0,0,0,53,26,16,7,6,11,0.342,8.15,0,4,1,0,85,1,17,1,1,2 +bombama01,1980,1,NYN,NL,10,8,36,25,2,1,0,488,191,74,17,49,68,0.297,4.09,3,4,4,0,710,2,80,7,6,14 +bonhabi01,1980,1,CIN,NL,2,1,4,4,0,0,0,57,21,10,1,5,13,0.276,4.74,0,0,0,0,81,0,10,0,0,1 +borbope01,1980,1,SLN,NL,1,0,10,0,0,0,1,57,17,8,3,10,4,0.250,3.79,2,1,0,0,84,4,10,2,4,0 +bordiri01,1980,1,OAK,AL,0,0,1,0,0,0,0,6,4,1,0,0,0,0.400,4.50,0,0,0,0,10,0,1,0,0,0 +bordlbi01,1980,1,SFN,NL,2,3,8,6,0,0,0,92,34,16,3,21,11,0.288,4.70,1,1,0,0,141,0,19,2,0,3 +bottira01,1980,1,CAL,AL,0,3,6,6,0,0,0,79,40,17,1,13,12,0.348,5.81,0,2,0,0,130,0,20,1,1,1 +bradfla01,1980,1,ATL,NL,3,4,56,0,0,0,4,166,49,15,3,22,32,0.243,2.44,8,3,1,0,232,24,20,5,2,4 +breinfr01,1980,1,SFN,NL,0,0,5,0,0,0,0,20,8,4,0,4,3,0.333,5.40,1,0,1,0,30,1,4,1,0,1 +brettke01,1980,1,KCA,AL,0,0,8,0,0,0,1,40,8,0,0,5,4,0.174,0.00,0,0,1,0,54,3,0,2,0,2 +brusswa01,1980,1,PHI,NL,2,2,26,0,0,0,0,116,42,16,3,13,21,0.286,3.72,2,0,0,0,165,12,16,5,0,6 +burgmto01,1980,1,BOS,AL,5,4,62,0,0,0,24,297,87,22,3,20,54,0.241,2.00,3,1,2,0,391,38,30,6,2,19 +burnsbr01,1980,1,CHA,AL,15,13,34,32,11,1,0,714,213,75,17,63,133,0.241,2.84,2,1,4,4,970,1,83,15,5,14 +burnssh01,1980,1,CIN,NL,1,0,7,0,0,0,0,14,6,1,1,1,2,0.333,1.93,0,0,0,0,19,3,1,0,0,2 +burrira01,1980,1,NYN,NL,7,13,29,29,1,0,0,511,181,76,20,54,83,0.277,4.02,5,5,4,2,726,0,86,8,7,12 +busbyst01,1980,1,KCA,AL,1,3,11,6,0,0,0,127,59,29,3,19,12,0.335,6.17,0,4,2,0,199,2,30,0,2,4 +busketo01,1980,1,TOR,AL,3,1,33,0,0,0,0,200,68,33,11,26,34,0.278,4.46,6,1,0,0,279,9,35,3,5,10 +butchjo01,1980,1,TEX,AL,3,3,6,6,1,0,0,106,34,16,2,13,27,0.248,4.08,0,1,0,0,152,0,19,2,0,1 +bystrma01,1980,1,PHI,NL,5,0,6,5,1,1,0,108,26,6,1,9,21,0.195,1.50,0,1,0,0,142,1,6,0,0,2 +caldwmi01,1980,1,ML4,AL,13,11,34,33,11,2,1,676,248,101,29,56,74,0.285,4.03,2,3,2,2,940,1,112,6,7,33 +camacer01,1980,1,OAK,AL,0,0,5,0,0,0,0,35,20,9,2,5,9,0.364,6.94,0,2,1,0,61,2,9,0,0,0 +campbbi02,1980,1,BOS,AL,4,0,23,0,0,0,0,124,44,22,1,22,17,0.284,4.79,0,3,0,0,180,11,26,0,3,6 +campri01,1980,1,ATL,NL,6,4,77,0,0,0,22,325,92,23,3,29,33,0.235,1.91,8,2,4,0,440,44,26,10,5,16 +candejo01,1980,1,PIT,NL,11,14,35,34,7,0,1,700,246,104,14,50,97,0.276,4.01,4,0,3,2,969,1,114,14,12,25 +capildo01,1980,1,CHN,NL,2,8,39,11,0,0,0,269,82,41,7,51,51,0.253,4.12,5,6,3,4,387,6,46,4,5,7 +carltst01,1980,1,PHI,NL,24,9,38,38,13,3,0,912,243,79,15,90,286,0.218,2.34,12,17,2,7,1228,0,87,14,8,14 +castibo01,1980,1,LAN,NL,8,6,61,0,0,0,5,295,70,30,4,45,60,0.206,2.75,5,3,1,0,395,29,31,4,5,6 +castrbi01,1980,1,ML4,AL,2,4,56,0,0,0,8,253,89,26,2,17,32,0.274,2.77,6,5,2,0,350,36,35,3,3,16 +caudibi01,1980,1,CHN,NL,4,6,72,2,0,0,1,383,100,31,10,59,112,0.223,2.19,12,2,1,4,528,27,37,10,9,6 +chambcr01,1980,1,KCA,AL,0,1,5,0,0,0,0,28,10,7,3,5,3,0.270,6.75,2,1,0,0,43,2,8,0,1,0 +chrisga01,1980,1,KCA,AL,3,0,24,0,0,0,1,94,35,18,4,18,16,0.278,5.17,1,1,2,0,148,8,23,0,2,0 +chrisla01,1980,1,PHI,NL,5,1,14,14,0,0,0,221,62,33,4,27,49,0.227,4.03,4,2,3,4,308,0,35,4,1,3 +clancji01,1980,1,TOR,AL,13,16,34,34,15,2,0,752,217,92,19,128,152,0.233,3.30,4,10,2,0,1075,0,108,9,4,18 +clayke01,1980,1,TEX,AL,2,3,8,8,0,0,0,129,43,22,4,29,17,0.256,4.60,2,7,3,0,202,0,24,1,1,2 +clearma01,1980,1,CAL,AL,11,11,58,0,0,0,9,319,82,39,2,65,105,0.216,3.30,9,4,5,0,461,40,51,8,3,5 +clevere01,1980,1,ML4,AL,11,9,45,13,5,2,4,463,150,64,9,49,54,0.254,3.73,2,5,5,0,657,16,73,4,9,14 +collido01,1980,1,CLE,AL,0,0,4,0,0,0,0,18,9,5,0,7,0,0.346,7.50,1,1,0,0,33,2,5,0,0,2 +combege01,1980,1,CIN,NL,0,0,4,0,0,0,0,20,9,8,0,4,10,0.346,10.80,1,1,0,0,31,0,8,1,0,2 +comerst01,1980,1,TEX,AL,2,4,12,11,0,0,0,125,65,37,5,22,9,0.367,7.99,0,0,2,0,205,0,41,3,1,7 +contrna01,1980,1,CHA,AL,0,0,8,0,0,0,0,41,18,9,1,7,8,0.333,5.93,2,0,2,0,65,6,10,1,1,3 +corbedo01,1980,1,MIN,AL,8,6,73,0,0,0,23,409,102,30,7,42,89,0.213,1.98,8,3,1,0,531,63,31,7,2,17 +crawfst01,1980,1,BOS,AL,2,0,6,4,2,0,0,97,41,13,3,8,10,0.306,3.62,2,0,0,0,142,1,14,0,0,8 +cruzvi01,1980,1,CLE,AL,6,7,55,0,0,0,12,258,71,33,10,27,88,0.229,3.45,8,4,3,0,355,40,36,5,10,5 +curtijo01,1980,1,SDN,NL,10,8,30,27,6,0,0,561,184,73,9,67,71,0.262,3.51,13,10,2,2,787,0,84,15,2,19 +dacqujo01,1980,1,SDN,NL,2,3,39,0,0,0,1,201,67,28,2,36,44,0.270,3.76,9,4,1,0,294,12,29,4,5,6 +dacqujo01,1980,2,MON,NL,0,2,11,0,0,0,2,62,14,5,0,9,15,0.206,2.18,0,5,0,0,79,2,7,1,1,4 +darwida01,1980,1,TEX,AL,13,4,53,2,0,0,8,329,98,32,4,50,104,0.243,2.63,7,3,2,0,468,35,37,5,7,8 +davisma01,1980,1,PHI,NL,0,0,2,1,0,0,0,21,4,2,0,5,5,0.160,2.57,0,0,0,0,30,0,2,0,0,0 +davisro02,1980,1,NYA,AL,9,3,53,0,0,0,7,393,121,43,9,32,65,0.246,2.95,3,5,5,1,544,29,50,10,6,8 +dennyjo01,1980,1,CLE,AL,8,6,16,16,4,1,0,326,116,53,4,47,59,0.284,4.39,2,4,5,1,464,0,54,1,3,18 +devinad01,1980,1,TEX,AL,1,1,13,0,0,0,0,84,49,15,4,9,8,0.377,4.82,1,0,1,0,142,3,22,0,2,4 +dorseji01,1980,1,CAL,AL,1,2,4,4,0,0,0,47,25,16,2,8,8,0.368,9.19,0,0,1,0,78,0,16,0,1,2 +dotsori01,1980,1,CHA,AL,12,10,33,32,8,0,0,594,185,94,20,87,109,0.247,4.27,2,6,6,2,863,1,105,15,7,14 +dragodi01,1980,1,BOS,AL,7,7,43,7,1,0,3,398,127,61,17,44,63,0.251,4.14,7,0,5,0,564,18,67,7,2,11 +dressro01,1980,1,SEA,AL,4,10,30,14,3,0,0,448,161,66,14,33,50,0.280,3.98,7,1,3,1,624,9,75,4,8,18 +duesha01,1980,1,MON,NL,0,1,6,1,0,0,0,37,17,9,1,4,2,0.333,6.57,1,0,0,0,57,1,9,2,0,1 +eastwra01,1980,1,KCA,AL,0,1,14,0,0,0,0,66,37,13,2,8,5,0.363,5.32,2,1,2,0,114,8,14,1,1,0 +eckerde01,1980,1,BOS,AL,12,14,30,30,8,0,0,593,188,94,25,44,121,0.248,4.28,7,0,2,0,818,0,101,7,8,9 +eicheju01,1980,1,SDN,NL,4,2,15,13,0,0,0,266,73,36,8,55,43,0.233,3.65,4,3,1,3,377,0,41,6,2,10 +erickro01,1980,1,MIN,AL,7,13,32,27,7,0,0,574,198,69,13,56,97,0.268,3.25,0,5,4,2,811,1,83,5,6,13 +espinni01,1980,1,PHI,NL,3,5,12,12,1,0,0,229,73,32,9,19,13,0.250,3.77,2,0,2,0,317,0,36,2,2,6 +falcope01,1980,1,NYN,NL,7,10,37,23,1,0,1,472,163,79,16,58,109,0.269,4.52,9,9,2,1,684,5,89,12,7,7 +farmeed01,1980,1,CHA,AL,7,9,64,0,0,0,30,299,92,37,6,56,54,0.244,3.34,11,7,1,0,438,55,37,3,1,15 +feltote01,1980,1,MIN,AL,0,3,5,4,0,0,0,53,20,14,2,9,14,0.286,7.13,1,0,1,0,83,0,18,2,1,0 +ferribo01,1980,1,CAL,AL,0,2,5,3,0,0,0,46,23,10,2,9,4,0.354,5.87,0,1,0,0,74,0,13,0,0,2 +fidryma01,1980,1,DET,AL,2,3,9,9,1,0,0,133,58,28,5,20,16,0.309,5.68,2,3,1,0,215,0,35,5,1,0 +figueed01,1980,1,NYA,AL,3,3,15,9,0,0,1,174,90,45,3,24,16,0.363,6.98,2,1,1,0,276,4,47,0,3,10 +figueed01,1980,2,TEX,AL,0,7,8,8,0,0,0,119,62,26,9,12,9,0.365,5.90,0,3,0,1,186,0,29,1,3,2 +fingero01,1980,1,SDN,NL,11,9,66,0,0,0,23,309,101,32,3,32,69,0.264,2.80,13,5,0,1,428,46,35,6,6,5 +flanami01,1980,1,BAL,AL,16,13,37,37,12,2,0,754,278,115,27,71,128,0.287,4.12,3,12,2,1,1065,0,121,10,12,29 +flinnjo01,1980,1,ML4,AL,2,1,20,1,0,0,2,111,31,16,3,20,15,0.220,3.89,2,2,0,0,164,8,20,1,2,1 +fordda02,1980,1,BAL,AL,1,3,25,3,1,0,1,209,66,33,11,13,22,0.251,4.26,2,4,2,0,281,9,34,3,0,6 +forscbo01,1980,1,SLN,NL,11,10,31,31,8,0,0,644,225,90,12,33,87,0.274,3.77,6,7,4,0,878,0,102,8,9,30 +forscke01,1980,1,HOU,NL,12,13,32,32,6,3,0,667,230,79,15,41,84,0.266,3.20,1,1,7,3,926,0,90,9,4,17 +forstte01,1980,1,LAN,NL,0,0,9,0,0,0,0,35,10,4,0,4,2,0.222,3.09,0,0,0,0,49,7,4,0,0,1 +frazige01,1980,1,SLN,NL,1,4,22,0,0,0,3,69,24,7,2,7,11,0.273,2.74,3,0,0,0,96,12,10,1,0,5 +frostda01,1980,1,CAL,AL,4,8,15,15,2,0,0,235,97,46,8,21,28,0.308,5.29,1,3,2,0,343,0,53,2,3,8 +frymawo01,1980,1,MON,NL,7,4,61,0,0,0,17,240,61,20,1,30,59,0.209,2.25,9,1,2,1,331,40,23,5,2,5 +fulghjo01,1980,1,SLN,NL,4,6,15,14,4,1,0,256,66,32,7,32,48,0.219,3.38,1,1,1,2,338,0,33,2,1,7 +galeri01,1980,1,KCA,AL,13,9,32,28,6,1,1,572,169,83,16,78,97,0.239,3.92,2,6,2,0,799,1,90,8,3,15 +garbege01,1980,1,ATL,NL,5,5,68,0,0,0,7,247,95,35,6,24,51,0.288,3.83,5,0,0,0,359,31,42,4,1,11 +garlawa01,1980,1,CLE,AL,6,9,25,20,4,1,0,451,163,77,18,48,55,0.276,4.61,2,5,6,0,657,1,85,7,6,12 +garvije01,1980,1,TOR,AL,4,7,61,0,0,0,8,248,70,21,6,27,52,0.233,2.29,7,0,0,0,336,24,23,8,0,5 +gleatje01,1980,1,TEX,AL,0,0,5,0,0,0,0,21,5,2,0,4,2,0.208,2.57,0,0,0,0,30,2,2,0,2,2 +glynned01,1980,1,NYN,NL,3,3,38,0,0,0,1,157,49,24,5,23,32,0.246,4.13,4,1,0,0,228,11,26,4,2,3 +goltzda01,1980,1,LAN,NL,7,11,35,27,2,2,1,514,198,82,12,59,91,0.299,4.31,3,3,0,1,739,2,91,10,8,23 +gossari01,1980,1,NYA,AL,6,2,64,0,0,0,33,297,74,25,5,37,103,0.211,2.27,3,4,1,0,401,58,29,8,4,6 +griffmi02,1980,1,NYA,AL,2,4,13,9,0,0,0,162,64,29,6,23,25,0.287,4.83,2,3,1,0,250,3,36,1,2,2 +griffto02,1980,1,SFN,NL,5,1,42,4,0,0,0,323,80,33,8,49,79,0.212,2.76,6,4,8,3,447,8,35,8,4,7 +grimsro02,1980,1,MON,NL,2,4,11,7,0,0,0,124,61,29,5,12,11,0.351,6.31,1,1,1,0,196,2,31,4,5,4 +grimsro02,1980,2,CLE,AL,4,5,14,11,2,0,0,224,103,56,11,24,18,0.331,6.75,1,2,1,0,342,0,63,2,4,9 +guidrro01,1980,1,NYA,AL,17,10,37,29,5,3,1,659,215,87,19,80,166,0.260,3.56,1,5,1,0,929,4,97,9,12,18 +gullibi01,1980,1,MON,NL,10,5,24,19,5,2,0,423,127,47,6,50,120,0.238,3.00,2,5,2,0,593,1,53,3,4,8 +gurala01,1980,1,KCA,AL,18,10,36,36,16,4,0,850,272,93,20,76,113,0.255,2.95,6,2,5,0,1175,0,107,14,12,20 +haasmo01,1980,1,ML4,AL,16,15,33,33,14,3,0,757,246,87,25,56,146,0.258,3.10,6,5,1,1,1023,0,96,3,9,25 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+hernawi01,1980,1,CHN,NL,1,9,53,7,0,0,0,325,115,53,8,45,75,0.276,4.40,4,6,2,1,473,13,58,6,3,9 +hillejo01,1980,1,DET,AL,1,0,11,0,0,0,0,92,38,15,3,14,18,0.309,4.40,1,1,0,1,140,2,15,1,2,4 +hoffmgu01,1980,1,CHA,AL,1,0,23,1,0,0,1,113,38,11,1,17,24,0.268,2.63,2,1,0,0,161,8,12,1,1,3 +holdsfr01,1980,1,ML4,AL,0,0,9,0,0,0,0,59,24,10,2,9,12,0.286,4.58,0,0,0,0,93,3,12,0,0,0 +hollaal01,1980,1,SFN,NL,5,3,54,0,0,0,7,247,71,16,2,34,65,0.233,1.75,8,1,1,3,349,31,21,5,4,3 +holmasc01,1980,1,NYN,NL,0,0,4,0,0,0,0,21,6,1,0,1,3,0.250,1.29,1,1,0,1,26,1,2,0,1,1 +honeyri01,1980,1,SEA,AL,10,17,30,30,9,1,0,610,221,89,22,60,79,0.280,3.94,7,4,3,0,871,0,99,11,7,25 +hooddo01,1980,1,SLN,NL,4,6,33,8,1,0,0,247,90,31,2,34,35,0.288,3.39,5,5,2,2,359,4,39,8,3,9 +hootobu01,1980,1,LAN,NL,14,8,34,33,4,2,1,620,194,84,22,64,118,0.249,3.66,1,4,0,1,858,1,90,11,5,12 +houghch01,1980,1,LAN,NL,1,3,19,1,0,0,1,97,37,20,4,21,25,0.291,5.57,0,3,2,0,156,5,21,3,3,2 +houghch01,1980,2,TEX,AL,2,2,16,2,2,1,0,184,54,27,2,37,47,0.240,3.96,2,8,3,0,270,7,30,4,1,4 +howelja01,1980,1,CIN,NL,0,0,5,0,0,0,0,10,8,5,0,0,1,0.471,13.50,0,0,1,0,19,1,5,0,1,1 +howest01,1980,1,LAN,NL,7,9,59,0,0,0,17,254,83,25,1,22,39,0.256,2.66,10,1,2,0,359,36,33,8,3,4 +hoytla01,1980,1,CHA,AL,9,3,24,13,3,1,0,337,123,57,8,41,55,0.281,4.57,3,3,2,0,496,5,66,8,7,10 +hraboal01,1980,1,ATL,NL,4,2,45,0,0,0,3,179,50,24,8,31,31,0.223,3.62,5,2,0,0,261,25,27,2,4,2 +humeto01,1980,1,CIN,NL,9,10,78,0,0,0,25,411,121,39,6,38,68,0.240,2.56,14,2,3,0,554,62,44,7,1,10 +hurstbr01,1980,1,BOS,AL,2,2,12,7,0,0,0,92,39,31,4,16,16,0.307,9.10,0,4,2,2,147,2,33,0,2,2 +huttoto01,1980,1,MON,NL,0,0,1,0,0,0,0,3,3,3,1,1,1,0.500,27.00,0,0,0,0,7,1,3,0,0,0 +jacksda01,1980,1,MIN,AL,9,9,32,25,1,0,1,516,161,74,15,69,90,0.250,3.87,2,3,2,0,724,4,81,6,4,23 +jacksgr01,1980,1,PIT,NL,8,4,61,0,0,0,9,213,71,23,4,20,31,0.275,2.92,3,3,0,0,289,27,24,6,5,4 +jacksro03,1980,1,NYN,NL,1,7,24,8,1,0,1,212,78,33,4,20,58,0.288,4.20,4,4,0,1,299,4,37,3,4,6 +jeffeje01,1980,1,TOR,AL,4,13,29,18,2,2,0,365,130,74,12,52,53,0.281,5.47,2,0,2,2,535,4,78,14,5,14 +jeffeje01,1980,2,PIT,NL,1,0,1,1,0,0,0,20,3,1,0,2,4,0.143,1.35,0,1,0,0,23,0,1,0,0,1 +jenkife01,1980,1,TEX,AL,12,12,29,29,12,0,0,594,190,83,22,52,129,0.250,3.77,8,5,4,0,827,0,90,5,5,17 +johnsjo04,1980,1,TEX,AL,2,2,33,0,0,0,4,116,27,10,2,15,44,0.199,2.33,4,2,1,0,154,17,12,1,1,3 +johnto01,1980,1,NYA,AL,22,9,36,36,16,6,0,796,270,101,13,56,78,0.268,3.43,1,5,6,0,1089,0,115,15,5,33 +jonesje01,1980,1,OAK,AL,1,3,35,0,0,0,5,133,32,14,2,26,34,0.204,2.84,2,3,1,0,192,13,21,5,3,0 +jonesmi02,1980,1,KCA,AL,0,1,3,1,0,0,0,14,6,6,0,5,2,0.333,11.57,1,0,0,0,24,1,7,1,0,0 +jonesra01,1980,1,SDN,NL,5,13,24,24,4,3,0,463,165,67,14,29,53,0.276,3.91,5,1,0,0,638,0,71,11,1,16 +kaatji01,1980,1,NYA,AL,0,1,4,0,0,0,0,15,8,4,0,4,1,0.381,7.20,2,1,0,0,27,3,5,1,1,1 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+lacosmi01,1980,1,CIN,NL,10,12,34,29,4,2,0,508,207,87,9,68,59,0.303,4.62,8,3,1,2,762,1,101,7,3,22 +lagrole01,1980,1,PHI,NL,0,2,25,0,0,0,3,117,42,18,5,17,21,0.276,4.15,2,3,0,2,173,8,22,1,3,3 +lampde01,1980,1,CHN,NL,10,14,41,37,2,1,0,608,259,117,16,82,83,0.317,5.20,7,10,1,0,921,3,123,17,4,17 +langfri01,1980,1,OAK,AL,19,12,35,33,28,2,0,870,276,105,29,64,102,0.255,3.26,6,7,1,1,1166,2,119,5,12,23 +lapoida01,1980,1,ML4,AL,1,0,5,3,0,0,1,45,17,10,2,13,5,0.293,6.00,1,0,0,1,75,1,14,2,2,1 +larocda01,1980,1,CAL,AL,3,5,52,9,1,0,4,384,122,58,14,39,89,0.256,4.08,3,0,3,1,530,17,62,7,5,6 +larsoda01,1980,1,PHI,NL,0,5,12,7,0,0,0,137,46,16,4,24,17,0.271,3.15,6,1,0,2,201,2,24,6,1,4 +lavelga01,1980,1,SFN,NL,6,8,62,0,0,0,9,300,106,38,4,36,66,0.275,3.42,11,1,0,3,434,28,43,8,4,7 +leach01,1980,1,MON,NL,7,5,21,19,0,0,0,312,103,43,5,55,56,0.262,3.72,4,2,2,0,458,1,51,5,3,5 +leallu01,1980,1,TOR,AL,3,4,13,10,1,0,0,179,72,30,6,31,26,0.314,4.53,2,0,1,0,270,1,35,6,3,7 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+littljo01,1980,1,SLN,NL,5,5,52,0,0,0,9,198,71,23,2,20,22,0.282,3.14,9,1,1,1,280,31,31,1,6,7 +lockwsk01,1980,1,BOS,AL,3,1,24,1,0,0,2,137,61,27,4,17,11,0.321,5.32,3,0,0,0,211,16,31,1,3,5 +lollati01,1980,1,NYA,AL,1,0,14,1,0,0,2,97,33,12,3,20,13,0.280,3.34,2,2,0,1,144,5,14,4,2,4 +lopezau01,1980,1,DET,AL,13,6,67,1,0,0,21,372,125,52,15,45,97,0.263,3.77,5,6,3,1,534,59,56,7,3,6 +lucasga01,1980,1,SDN,NL,5,8,46,18,0,0,3,450,138,54,8,43,85,0.251,3.24,14,6,1,2,614,14,59,12,7,15 +lylesp01,1980,1,TEX,AL,3,2,49,0,0,0,8,242,97,42,9,28,43,0.306,4.69,6,1,0,2,353,32,47,5,3,10 +lylesp01,1980,2,PHI,NL,0,0,10,0,0,0,2,42,11,3,0,6,6,0.220,1.93,1,1,0,0,59,5,5,1,2,0 +lynched01,1980,1,NYN,NL,1,1,5,4,0,0,0,58,24,11,0,5,9,0.304,5.12,0,0,1,0,86,0,12,0,1,1 +lysanri01,1980,1,OAK,AL,0,0,5,0,0,0,0,41,24,12,3,4,5,0.381,7.90,0,0,0,0,67,3,13,0,0,3 +macwhke01,1980,1,BOS,AL,0,3,14,2,0,0,0,127,46,26,3,18,21,0.282,5.53,3,1,2,0,189,5,27,4,1,6 +mahlemi01,1980,1,PIT,NL,0,0,2,0,0,0,0,3,4,7,1,3,1,0.571,63.00,0,0,0,0,10,1,7,0,0,0 +mahleri01,1980,1,ATL,NL,0,0,2,0,0,0,0,11,2,1,0,0,1,0.154,2.45,0,0,0,0,13,0,1,0,0,0 +marshmi01,1980,1,MIN,AL,1,3,18,0,0,0,1,97,42,22,2,12,13,0.323,6.12,1,4,2,0,150,12,23,3,3,2 +martial02,1980,1,CAL,AL,7,9,30,23,4,1,0,448,150,75,14,59,57,0.259,4.52,2,5,1,0,649,3,81,5,4,9 +martide01,1980,1,BAL,AL,6,4,25,12,2,0,1,299,103,44,12,44,42,0.272,3.97,6,0,2,1,428,8,44,1,3,15 +martijo05,1980,1,SLN,NL,2,3,9,5,1,0,0,126,39,20,1,9,23,0.247,4.29,1,1,0,0,169,2,20,0,2,3 +martire01,1980,1,KCA,AL,10,10,32,20,2,0,2,412,133,67,18,70,68,0.255,4.39,2,4,1,0,599,8,84,3,4,16 +martisi01,1980,1,SLN,NL,5,10,25,20,2,0,0,359,127,64,8,48,39,0.273,4.81,3,3,2,3,525,0,75,4,5,8 +martiti01,1980,1,BAL,AL,4,4,53,0,0,0,10,242,69,27,5,34,68,0.240,3.01,5,6,1,0,329,26,30,4,2,13 +martzra01,1980,1,CHN,NL,1,2,6,6,0,0,0,91,28,7,1,11,5,0.241,2.08,1,0,0,0,130,0,14,1,2,2 +matlajo01,1980,1,TEX,AL,10,10,35,34,8,1,1,704,265,96,17,48,142,0.287,3.68,1,9,0,0,985,1,111,9,6,24 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+pricejo02,1980,1,CIN,NL,7,3,24,13,2,0,0,334,95,44,10,37,44,0.236,3.56,0,1,1,1,448,3,45,8,0,13 +prolymi01,1980,1,CHA,AL,5,10,62,3,0,0,8,440,136,50,7,58,56,0.253,3.07,9,2,3,1,618,31,67,9,11,17 +quiseda01,1980,1,KCA,AL,12,7,75,0,0,0,33,385,129,44,5,27,37,0.265,3.09,15,1,1,0,528,68,47,8,5,18 +rainech01,1980,1,BOS,AL,8,3,16,13,2,1,0,261,92,47,7,41,43,0.273,4.86,3,4,2,0,383,1,49,1,2,20 +rajsida01,1980,1,TEX,AL,2,1,24,1,0,0,2,145,56,32,7,22,35,0.295,5.96,0,3,3,0,221,5,34,2,4,3 +rasmuer01,1980,1,SDN,NL,4,11,40,14,0,0,1,334,130,54,9,33,50,0.295,4.37,6,1,3,1,488,12,60,9,3,9 +ratzest01,1980,1,MON,NL,0,0,1,1,0,0,0,12,9,5,0,2,0,0.450,11.25,0,0,0,0,22,0,5,0,0,0 +rawlesh01,1980,1,SEA,AL,7,7,59,0,0,0,13,341,103,42,3,63,68,0.258,3.33,16,2,3,1,484,39,44,15,3,16 +reardje01,1980,1,NYN,NL,8,7,61,0,0,0,6,331,96,32,10,47,101,0.231,2.61,15,2,0,0,475,35,36,8,5,1 +redfepe01,1980,1,MIN,AL,7,7,23,16,2,0,2,314,117,53,11,33,73,0.283,4.56,3,2,0,0,455,3,58,5,4,8 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+robbibr01,1980,1,DET,AL,4,2,15,6,0,0,0,155,60,38,12,28,23,0.287,6.62,0,4,0,0,241,0,40,2,2,2 +roberbe01,1980,1,HOU,NL,2,0,14,0,0,0,0,73,24,16,2,10,9,0.261,5.92,1,3,2,0,106,2,16,1,1,0 +roberda05,1980,1,PIT,NL,0,1,2,0,0,0,0,7,2,1,0,1,1,0.250,3.86,0,0,0,0,9,0,1,0,0,1 +roberda05,1980,2,SEA,AL,2,3,37,4,0,0,3,241,86,39,7,27,47,0.270,4.37,7,4,1,0,355,13,46,3,6,10 +robinde01,1980,1,CHA,AL,1,1,15,0,0,0,0,105,26,12,2,16,28,0.215,3.09,0,0,0,0,141,5,13,2,2,0 +robindo01,1980,1,PIT,NL,7,10,29,24,3,2,1,481,157,71,14,45,103,0.258,3.99,5,7,5,2,671,1,74,8,3,9 +rogerst01,1980,1,MON,NL,16,11,37,37,14,4,0,843,247,93,16,85,147,0.238,2.98,7,7,3,0,1151,0,101,19,7,16 +romoen01,1980,1,PIT,NL,5,5,74,0,0,0,11,371,117,45,10,28,82,0.252,3.27,4,0,1,2,507,38,53,9,5,4 +rookeji01,1980,1,PIT,NL,2,2,4,4,0,0,0,54,16,7,0,12,8,0.262,3.50,0,0,0,0,75,0,7,1,1,7 +rowlami01,1980,1,SFN,NL,1,1,19,0,0,0,0,81,20,7,2,8,8,0.206,2.33,2,0,1,0,109,14,8,2,1,0 +rozemda01,1980,1,DET,AL,6,9,42,13,2,1,4,434,152,63,11,49,49,0.277,3.92,14,9,5,0,620,17,68,13,4,15 +ruhleve01,1980,1,HOU,NL,12,4,28,22,6,2,0,478,148,42,7,29,55,0.251,2.37,1,0,3,2,638,1,51,11,5,19 +ruthvdi01,1980,1,PHI,NL,17,10,33,33,6,1,0,670,241,88,9,74,86,0.283,3.55,9,4,3,3,944,0,99,8,6,29 +ryanno01,1980,1,HOU,NL,11,10,35,35,4,2,0,701,205,87,10,98,200,0.236,3.35,1,10,3,1,982,0,100,7,7,17 +sambijo01,1980,1,HOU,NL,8,4,64,0,0,0,17,271,65,22,3,22,75,0.201,2.19,3,3,2,0,354,40,26,4,1,11 +sandesc01,1980,1,MON,NL,16,11,33,33,7,3,0,634,206,73,18,56,125,0.258,3.11,3,6,3,0,875,0,76,11,5,13 +sarmima01,1980,1,SEA,AL,0,1,9,0,0,0,1,44,14,6,2,6,15,0.255,3.68,1,0,0,0,63,6,7,0,2,0 +saucike01,1980,1,PHI,NL,7,3,40,0,0,0,0,150,50,19,2,20,25,0.281,3.42,8,1,4,0,207,9,21,1,4,4 +scarbra02,1980,1,CHA,AL,1,2,15,0,0,0,2,86,24,13,1,7,18,0.238,4.08,0,4,2,0,116,7,14,5,1,4 +schatda01,1980,1,DET,AL,11,13,32,26,9,2,0,578,178,86,23,58,94,0.246,4.02,9,8,3,0,794,2,88,6,3,16 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+urreajo01,1980,1,SLN,NL,4,1,30,1,0,0,3,194,57,25,2,41,36,0.239,3.48,9,3,2,2,287,8,28,3,3,7 +valenfe01,1980,1,LAN,NL,2,0,10,0,0,0,1,53,8,0,0,5,16,0.136,0.00,0,0,0,1,66,4,2,1,1,1 +verhojo01,1980,1,MIN,AL,3,4,44,0,0,0,0,299,109,44,10,29,42,0.289,3.97,5,1,3,2,425,13,53,7,9,13 +veselbo01,1980,1,MIN,AL,0,0,1,0,0,0,0,12,3,2,1,1,2,0.214,4.50,0,0,0,0,15,0,2,0,0,0 +vuckope01,1980,1,SLN,NL,12,9,32,30,7,3,1,667,203,84,18,68,132,0.247,3.40,5,5,2,2,907,1,96,8,6,17 +waitsri01,1980,1,CLE,AL,13,14,33,33,9,2,0,673,231,111,18,82,109,0.270,4.45,6,5,1,0,955,0,118,10,7,23 +walkbo01,1980,1,PHI,NL,11,7,27,27,2,0,0,455,163,77,8,71,94,0.276,4.57,2,6,2,3,673,0,82,5,5,11 +weavero01,1980,1,DET,AL,3,4,19,6,0,0,0,191,56,29,5,34,42,0.247,4.10,3,2,1,0,268,6,32,2,4,9 +welchbo01,1980,1,LAN,NL,14,9,32,32,3,2,0,641,190,78,15,79,141,0.242,3.29,6,7,3,5,889,0,85,12,10,11 +wheelga01,1980,1,SEA,AL,0,0,1,1,0,0,0,9,4,2,0,1,1,0.333,6.00,0,1,0,1,13,0,2,0,0,1 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+aasedo01,1981,1,CAL,AL,4,4,39,0,0,0,11,196,56,17,4,24,38,0.234,2.34,2,2,0,0,265,32,17,1,1,5 +abbotgl01,1981,1,SEA,AL,4,9,22,20,1,0,0,391,127,57,14,28,35,0.258,3.94,1,1,0,0,530,1,64,6,4,13 +agostju01,1981,1,CHA,AL,0,0,2,0,0,0,0,17,5,3,1,0,3,0.238,4.76,0,0,1,0,22,1,3,0,0,1 +alexado01,1981,1,SFN,NL,11,7,24,24,1,1,0,457,156,49,11,44,77,0.263,2.89,2,4,2,1,646,0,51,5,2,8 +allarbr01,1981,1,SEA,AL,3,2,7,7,1,0,0,144,48,20,5,8,20,0.265,3.75,0,0,0,0,191,0,22,2,0,9 +allenne01,1981,1,NYN,NL,7,6,43,0,0,0,18,200,64,22,4,26,50,0.259,2.97,8,3,0,0,286,35,26,10,3,5 +alvarjo01,1981,1,ATL,NL,0,0,1,0,0,0,0,6,0,0,0,0,2,0.000,0.00,0,0,0,0,6,1,0,0,0,0 +anderla02,1981,1,SEA,AL,3,3,41,0,0,0,5,203,57,20,4,18,40,0.228,2.66,2,0,2,0,273,23,27,0,3,5 +andujjo01,1981,1,HOU,NL,2,3,9,3,0,0,0,71,29,13,2,12,18,0.296,4.94,0,0,0,0,113,0,17,1,2,2 +andujjo01,1981,2,SLN,NL,6,1,11,8,1,0,0,166,56,23,4,11,19,0.265,3.74,1,2,0,1,223,0,24,1,0,5 +apontlu01,1981,1,BOS,AL,1,0,7,0,0,0,1,47,11,1,0,3,11,0.208,0.57,0,0,0,0,57,3,1,1,0,4 +armstmi01,1981,1,SDN,NL,0,2,10,0,0,0,0,36,14,8,1,11,9,0.311,6.00,3,0,0,1,58,3,9,2,0,0 +arroyfe01,1981,1,MIN,AL,7,10,23,19,2,0,0,385,144,56,11,34,39,0.290,3.93,1,3,5,0,542,1,66,5,2,20 +augusje01,1981,1,ML4,AL,2,2,27,2,0,0,2,184,75,29,4,18,26,0.300,4.26,3,3,1,0,272,9,30,2,1,7 +babcobo01,1981,1,TEX,AL,1,1,16,0,0,0,0,86,21,7,2,16,18,0.219,2.20,1,0,1,0,117,7,7,3,1,4 +bahnsst01,1981,1,MON,NL,2,1,25,3,0,0,1,147,45,27,7,24,28,0.247,4.96,0,1,1,0,208,4,27,1,0,3 +baileho01,1981,1,DET,AL,1,4,9,5,0,0,0,110,45,30,4,13,17,0.308,7.36,2,3,3,0,165,2,31,1,2,1 +bairdo01,1981,1,CIN,NL,2,2,24,0,0,0,0,117,42,25,5,17,16,0.271,5.77,4,3,0,0,174,12,28,2,0,4 +bairdo01,1981,2,SLN,NL,2,0,11,0,0,0,1,47,13,6,0,2,14,0.228,3.45,0,0,0,0,60,8,6,0,0,1 +bannifl01,1981,1,SEA,AL,9,9,21,20,5,2,0,364,128,60,14,39,85,0.268,4.45,0,7,3,1,522,0,62,2,0,9 +barkele01,1981,1,CLE,AL,8,7,22,22,9,3,0,463,150,67,7,46,127,0.249,3.91,0,5,1,0,660,0,72,6,4,2 +barlomi01,1981,1,TOR,AL,0,0,12,0,0,0,0,45,22,7,1,6,5,0.338,4.20,1,0,4,0,78,7,11,1,2,1 +barrifr01,1981,1,CHA,AL,1,3,8,7,1,0,0,109,45,16,3,14,12,0.292,3.96,1,1,1,0,170,0,23,1,0,2 +baumgro01,1981,1,CHA,AL,5,9,19,19,2,1,0,305,101,46,9,40,52,0.260,4.07,3,4,1,2,435,0,56,4,2,8 +beardda01,1981,1,OAK,AL,1,1,8,0,0,0,3,39,9,4,1,4,15,0.191,2.77,1,0,1,0,53,7,5,0,1,0 +beattji01,1981,1,SEA,AL,3,2,13,9,0,0,1,200,59,22,2,18,36,0.232,2.97,0,2,2,1,275,3,24,1,0,5 +bedrost01,1981,1,ATL,NL,1,2,15,1,0,0,0,73,15,12,2,15,9,0.169,4.44,2,0,1,0,106,5,14,0,1,2 +berenbr01,1981,1,CIN,NL,9,6,21,20,5,3,0,378,97,49,3,77,106,0.211,3.50,0,7,0,2,544,0,55,3,4,12 +berenju01,1981,1,KCA,AL,0,4,8,3,0,0,0,59,22,19,4,16,20,0.289,8.69,0,1,2,0,97,4,21,0,3,0 +berenju01,1981,2,TOR,AL,2,9,12,11,1,0,0,213,62,34,7,35,29,0.235,4.31,1,1,3,0,308,0,41,2,4,4 +bernadw01,1981,1,ML4,AL,0,0,6,0,0,0,0,15,5,2,0,6,1,0.263,3.60,1,0,0,0,27,1,3,0,2,0 +bibbyji01,1981,1,PIT,NL,6,3,14,14,2,2,0,281,79,26,4,26,48,0.225,2.50,1,1,2,0,385,0,30,2,4,7 +birddo01,1981,1,NYA,AL,5,1,17,4,0,0,0,160,58,16,5,16,28,0.280,2.70,3,0,0,0,229,3,19,2,4,5 +birddo01,1981,2,CHN,NL,4,5,12,12,2,1,0,226,72,30,5,16,34,0.254,3.58,3,2,1,0,305,0,34,1,4,6 +blackbu02,1981,1,SEA,AL,0,0,2,0,0,0,0,3,2,0,0,3,0,0.500,0.00,1,1,0,0,7,0,0,0,0,0 +bluevi01,1981,1,SFN,NL,8,6,18,18,1,0,0,374,97,34,7,54,63,0.217,2.45,3,7,1,0,513,0,40,9,3,7 +blylebe01,1981,1,CLE,AL,11,7,20,20,9,1,0,478,145,51,9,40,107,0.245,2.88,1,3,5,1,644,0,52,3,3,14 +boddimi01,1981,1,BAL,AL,0,0,2,0,0,0,0,17,6,3,1,2,2,0.261,4.76,0,2,0,0,25,1,4,0,0,1 +boggsto01,1981,1,ATL,NL,3,13,25,24,2,0,0,428,140,65,11,54,81,0.265,4.10,4,4,3,0,606,1,72,13,7,10 +boitada01,1981,1,NYN,NL,2,1,15,0,0,0,0,49,21,10,2,5,8,0.309,5.51,0,0,2,0,76,4,10,0,1,1 +bombama01,1981,1,TOR,AL,5,5,20,11,0,0,0,271,84,39,6,35,33,0.251,3.89,2,6,1,1,377,3,42,3,3,5 +booneda02,1981,1,SDN,NL,1,0,37,0,0,0,2,190,63,20,2,21,43,0.267,2.84,7,1,1,5,262,8,23,2,2,7 +bordiri01,1981,1,OAK,AL,0,0,2,0,0,0,0,6,1,0,0,1,0,0.143,0.00,0,0,0,0,8,2,0,0,0,0 +bradfla01,1981,1,ATL,NL,2,0,25,0,0,0,1,80,26,11,1,12,14,0.268,3.71,0,0,0,0,113,11,13,2,2,3 +breinfr01,1981,1,SFN,NL,5,2,45,1,0,0,1,233,66,22,4,38,37,0.243,2.55,8,3,2,3,326,15,28,9,5,8 +brennto01,1981,1,CLE,AL,2,2,7,6,1,0,0,145,49,17,5,14,15,0.259,3.17,1,0,0,0,205,0,20,2,0,4 +brettke01,1981,1,KCA,AL,1,1,22,0,0,0,2,97,35,15,2,14,7,0.282,4.18,4,0,1,0,144,14,16,3,2,6 +brownsc01,1981,1,CIN,NL,1,0,10,0,0,0,0,39,16,4,0,1,7,0.314,2.77,1,0,0,0,53,3,4,0,1,1 +brusswa01,1981,1,PHI,NL,0,1,14,0,0,0,0,37,12,6,0,10,8,0.250,4.38,4,0,1,0,60,1,6,0,1,0 +burgmto01,1981,1,BOS,AL,4,5,32,0,0,0,6,179,61,19,5,17,35,0.268,2.87,4,0,4,0,255,21,23,6,0,7 +burnsbr01,1981,1,CHA,AL,10,6,24,23,5,1,0,470,139,46,14,49,108,0.238,2.64,1,1,6,1,651,0,52,5,6,9 +burrira01,1981,1,MON,NL,9,7,22,21,4,0,0,407,117,46,9,41,52,0.235,3.05,3,6,3,1,554,0,56,6,6,7 +butchjo01,1981,1,TEX,AL,1,2,5,3,1,1,0,83,18,5,0,8,19,0.186,1.63,1,0,0,0,106,1,6,1,0,2 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+wittmi01,1981,1,CAL,AL,8,9,22,21,7,1,0,387,123,47,9,47,75,0.251,3.28,4,2,11,0,555,1,60,3,4,8 +wrighji03,1981,1,KCA,AL,2,3,17,4,0,0,0,156,57,20,5,21,27,0.277,3.46,7,0,2,0,230,7,21,0,1,4 +zachrpa01,1981,1,NYN,NL,7,14,24,24,3,0,0,417,151,64,13,56,76,0.282,4.14,1,1,4,1,616,0,78,11,9,6 +zahnge01,1981,1,CAL,AL,10,11,25,25,9,0,0,484,181,79,18,43,52,0.285,4.41,2,2,0,2,689,0,93,8,2,18 +aasedo01,1982,1,CAL,AL,3,3,24,0,0,0,4,156,45,20,5,23,40,0.243,3.46,2,2,0,0,212,18,20,4,0,5 +agostju01,1982,1,CHA,AL,0,0,1,0,0,0,0,6,7,4,0,0,1,0.538,18.00,0,0,0,0,13,1,4,0,0,0 +alexado01,1982,1,NYA,AL,1,7,16,11,0,0,0,200,81,45,14,14,26,0.298,6.08,2,1,0,4,293,3,52,4,3,3 +allenne01,1982,1,NYN,NL,3,7,50,0,0,0,19,194,65,22,5,30,59,0.266,3.06,5,4,1,0,279,42,22,3,1,5 +altampo01,1982,1,PHI,NL,5,1,29,0,0,0,2,117,41,18,2,14,26,0.281,4.15,3,1,1,0,169,14,19,4,4,3 +alvarjo01,1982,1,ATL,NL,0,0,7,0,0,0,0,23,8,4,1,2,6,0.308,4.70,1,1,0,0,29,2,4,1,0,1 +anderbu01,1982,1,CLE,AL,3,4,25,5,1,0,0,242,84,30,4,30,44,0.268,3.35,2,4,1,0,353,12,37,5,4,9 +anderla02,1982,1,SEA,AL,0,0,40,1,0,0,1,239,100,53,16,23,32,0.311,5.99,1,2,4,0,354,14,56,2,3,9 +andujjo01,1982,1,SLN,NL,15,10,38,37,9,5,0,797,237,73,11,50,137,0.240,2.47,7,3,7,2,1056,1,85,8,4,17 +apontlu01,1982,1,BOS,AL,2,2,40,0,0,0,3,255,78,30,5,25,44,0.246,3.18,3,1,0,2,349,28,31,5,2,9 +armstmi01,1982,1,KCA,AL,5,5,52,0,0,0,6,338,88,40,9,43,75,0.215,3.20,4,7,3,1,466,25,45,4,7,6 +arroyfe01,1982,1,MIN,AL,0,1,6,0,0,0,0,41,17,8,2,6,4,0.321,5.27,1,1,0,0,61,2,8,1,1,3 +arroyfe01,1982,2,OAK,AL,0,0,10,0,0,0,0,67,23,13,4,7,9,0.271,5.24,0,3,1,0,94,6,14,0,1,6 +augusje01,1982,1,ML4,AL,1,3,20,2,1,0,0,186,63,35,13,26,22,0.267,5.08,2,3,2,0,270,9,43,2,4,7 +bahnsst01,1982,1,CAL,AL,0,1,7,0,0,0,0,29,13,5,0,8,5,0.310,4.66,1,0,0,0,50,0,6,0,0,0 +bahnsst01,1982,2,PHI,NL,0,0,8,0,0,0,0,40,8,2,0,3,9,0.182,1.35,1,0,0,0,48,0,2,0,1,1 +baileho01,1982,1,DET,AL,0,0,8,0,0,0,1,30,6,0,0,2,3,0.182,0.00,1,0,0,0,37,5,0,1,1,1 +bairdo01,1982,1,SLN,NL,5,3,63,0,0,0,8,275,69,26,7,36,68,0.211,2.55,13,2,1,0,372,33,27,4,4,6 +bakerst01,1982,1,OAK,AL,1,1,5,3,0,0,0,77,30,13,3,4,14,0.288,4.56,0,0,0,0,110,2,14,1,1,2 +balleja01,1982,1,PHI,NL,0,0,4,1,0,0,0,24,7,3,1,2,7,0.226,3.38,0,0,1,1,35,1,4,1,0,0 +bannifl01,1982,1,SEA,AL,12,13,35,35,5,3,0,741,225,94,32,77,209,0.243,3.43,0,6,3,0,1022,0,112,10,5,17 +barkele01,1982,1,CLE,AL,15,11,33,33,10,1,0,734,211,106,17,88,187,0.232,3.90,2,10,3,0,1021,0,117,10,9,16 +barneri01,1982,1,CHA,AL,0,2,6,2,0,0,1,51,21,9,1,4,6,0.292,4.76,0,0,2,0,79,4,15,0,1,0 +barojsa01,1982,1,CHA,AL,6,6,61,0,0,0,21,320,96,42,9,46,56,0.244,3.54,6,4,1,5,446,42,43,2,3,13 +barrji01,1982,1,SFN,NL,4,3,53,9,1,1,2,386,125,47,9,20,36,0.262,3.29,5,1,4,1,520,9,54,8,10,10 +baumgro01,1982,1,PIT,NL,0,5,12,10,0,0,0,132,60,32,3,27,17,0.347,6.55,1,4,0,1,208,1,33,5,3,6 +beardda01,1982,1,OAK,AL,10,9,54,2,0,0,11,275,85,35,9,35,73,0.244,3.44,6,4,1,0,397,39,41,4,9,1 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+ujdurje01,1982,1,DET,AL,10,10,25,25,7,0,0,534,150,73,29,69,86,0.230,3.69,4,5,3,1,732,0,76,2,5,14 +underpa01,1982,1,DET,AL,4,8,33,12,2,0,3,297,108,52,17,22,43,0.269,4.73,2,5,0,0,428,6,66,2,2,3 +underto01,1982,1,OAK,AL,10,6,56,10,2,0,7,459,136,56,11,68,79,0.241,3.29,4,6,1,0,651,20,66,12,6,16 +valenfe01,1982,1,LAN,NL,19,13,37,37,18,4,0,855,247,91,13,83,199,0.236,2.87,12,4,2,0,1156,0,105,19,6,21 +vandeed01,1982,1,SEA,AL,9,4,78,0,0,0,5,228,54,20,5,32,60,0.207,2.37,7,3,2,2,303,27,21,6,2,12 +violafr01,1982,1,MIN,AL,4,10,22,22,3,1,0,378,152,73,22,38,84,0.302,5.21,2,4,0,1,543,0,77,2,0,12 +vuckope01,1982,1,ML4,AL,18,6,30,30,9,1,0,671,234,83,14,102,105,0.275,3.34,1,6,5,0,971,0,96,9,4,23 +waitsri01,1982,1,CLE,AL,2,13,25,21,2,0,0,345,128,69,13,57,44,0.290,5.40,2,4,1,0,510,0,74,7,3,9 +walkbo01,1982,1,ATL,NL,11,9,32,27,3,1,0,493,179,89,19,59,84,0.280,4.87,2,7,6,0,717,1,101,8,5,12 +welchbo01,1982,1,LAN,NL,16,11,36,36,9,3,0,707,199,88,19,81,176,0.229,3.36,5,5,5,1,965,0,94,7,4,11 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+zachrpa01,1982,1,NYN,NL,6,9,36,16,2,0,1,413,149,62,10,57,69,0.279,4.05,5,4,0,0,600,6,69,4,5,8 +zahnge01,1982,1,CAL,AL,18,8,34,34,12,4,0,688,225,95,18,65,81,0.259,3.73,5,2,4,0,944,0,100,4,3,32 +abbotgl01,1983,1,SEA,AL,5,3,14,14,2,0,0,247,103,42,9,15,38,0.311,4.59,2,0,4,2,355,0,46,3,2,8 +abbotgl01,1983,2,DET,AL,2,1,7,7,1,1,0,140,43,10,5,7,11,0.244,1.93,1,2,0,0,186,0,12,2,1,3 +ackerji01,1983,1,TOR,AL,5,1,38,5,0,0,1,293,103,47,7,38,44,0.273,4.33,1,1,8,0,426,8,52,1,2,17 +agostju01,1983,1,CHA,AL,2,2,39,0,0,0,7,125,41,19,2,11,29,0.283,4.10,1,2,1,0,166,13,20,5,4,8 +alexado01,1983,1,NYA,AL,0,2,8,5,0,0,0,85,31,20,6,7,17,0.277,6.35,0,0,0,0,121,2,21,1,1,1 +alexado01,1983,2,TOR,AL,7,6,17,15,5,0,0,350,126,51,14,26,46,0.279,3.93,1,4,1,0,482,1,55,0,3,12 +allenne01,1983,1,NYN,NL,2,7,21,4,1,1,2,162,57,27,6,36,32,0.278,4.50,5,2,0,0,246,9,29,4,1,7 +allenne01,1983,2,SLN,NL,10,6,25,18,4,2,0,365,122,50,6,48,74,0.265,3.70,4,6,1,1,516,3,55,5,1,14 +altampo01,1983,1,PHI,NL,2,3,31,0,0,0,0,124,38,17,9,15,24,0.255,3.70,3,2,2,1,173,11,18,6,1,5 +anderbu01,1983,1,CLE,AL,1,6,39,1,0,0,7,205,64,31,8,32,32,0.255,4.08,6,4,0,0,290,27,34,5,2,10 +anderla02,1983,1,PHI,NL,1,0,17,0,0,0,0,79,19,7,0,9,14,0.200,2.39,1,1,0,1,106,4,7,1,1,1 +andujjo01,1983,1,SLN,NL,6,16,39,34,5,2,1,675,215,104,23,75,125,0.253,4.16,7,5,3,6,943,3,112,12,3,10 +apontlu01,1983,1,BOS,AL,5,4,34,0,0,0,3,186,74,25,7,23,32,0.301,3.63,3,1,2,0,276,18,28,4,1,9 +armstmi01,1983,1,KCA,AL,10,7,58,0,0,0,3,308,86,44,11,45,52,0.228,3.86,3,5,3,0,432,33,53,3,4,3 +atherke01,1983,1,OAK,AL,2,5,29,0,0,0,4,205,53,21,7,23,40,0.215,2.77,4,1,1,1,277,21,22,2,5,4 +augusje01,1983,1,ML4,AL,3,3,34,7,1,0,2,193,89,41,11,25,40,0.328,5.74,4,1,1,0,304,9,45,4,3,6 +baileho01,1983,1,DET,AL,5,5,33,3,0,0,0,216,69,39,11,25,21,0.255,4.88,3,1,2,0,302,15,45,3,1,10 +bairdo01,1983,1,SLN,NL,1,1,26,0,0,0,1,89,24,10,4,13,21,0.224,3.03,3,1,0,0,122,9,11,1,1,3 +bairdo01,1983,2,DET,AL,7,3,27,1,0,0,4,167,51,24,8,19,39,0.242,3.88,4,0,1,0,233,10,27,2,0,3 +bakerst01,1983,1,OAK,AL,3,3,35,1,0,0,5,162,59,26,4,26,23,0.282,4.33,4,3,2,0,244,18,32,4,3,8 +bakerst01,1983,2,SLN,NL,0,1,8,0,0,0,0,30,10,2,0,4,1,0.286,1.80,1,3,1,0,43,3,4,2,1,1 +bannifl01,1983,1,CHA,AL,16,10,34,34,5,2,0,652,191,81,19,71,193,0.233,3.35,3,8,2,1,902,0,88,4,4,12 +bargagr01,1983,1,MON,NL,2,0,8,3,0,0,0,60,23,15,6,8,9,0.271,6.75,0,0,1,0,94,1,15,0,0,1 +barkele01,1983,1,CLE,AL,8,13,24,24,4,1,0,449,150,85,16,52,105,0.266,5.11,3,6,2,0,633,0,92,10,5,16 +barkele01,1983,2,ATL,NL,1,3,6,6,0,0,0,99,31,14,0,14,21,0.248,3.82,2,2,0,0,143,0,17,3,1,2 +barneri01,1983,1,CLE,AL,1,1,4,2,0,0,0,35,18,9,0,10,2,0.375,6.94,2,1,0,0,59,1,10,0,1,2 +barojsa01,1983,1,CHA,AL,3,3,52,0,0,0,12,262,70,24,2,32,38,0.224,2.47,2,1,5,0,355,29,24,3,2,12 +barrji01,1983,1,SFN,NL,5,3,53,0,0,0,2,278,106,41,7,20,47,0.294,3.98,9,1,1,0,395,23,47,4,9,3 +beardda01,1983,1,OAK,AL,5,5,43,0,0,0,10,183,55,38,8,36,40,0.246,5.61,4,3,2,0,268,26,39,3,3,3 +beattji01,1983,1,SEA,AL,10,15,30,29,8,2,0,590,197,84,12,66,132,0.259,3.84,6,6,3,1,834,0,89,3,2,15 +beckwjo01,1983,1,LAN,NL,3,4,42,3,0,0,1,213,73,28,5,35,50,0.264,3.55,11,1,1,0,321,15,40,7,1,3 +bedrost01,1983,1,ATL,NL,9,10,70,1,0,0,19,360,100,48,11,51,114,0.229,3.60,8,2,4,0,504,52,50,8,4,12 +beenean01,1983,1,ML4,AL,0,0,1,0,0,0,0,6,3,1,0,1,0,0.333,4.50,1,1,0,0,10,1,3,0,0,0 +behenri01,1983,1,ATL,NL,3,3,14,6,0,0,0,112,37,19,7,12,17,0.257,4.58,2,0,1,3,160,1,20,1,1,2 +behenri01,1983,2,CLE,AL,0,2,5,4,0,0,0,78,22,12,0,14,9,0.232,4.15,1,1,1,0,111,0,13,1,0,4 +berenbr01,1983,1,CIN,NL,9,14,32,31,4,1,0,559,173,80,9,102,151,0.247,3.86,3,8,2,0,816,1,92,8,4,20 +berenju01,1983,1,DET,AL,9,5,37,19,2,1,1,473,110,55,19,71,129,0.193,3.14,3,3,6,1,650,7,58,1,2,8 +bestka01,1983,1,SEA,AL,0,1,4,0,0,0,0,16,14,8,2,5,3,0.483,13.50,0,1,2,0,36,0,9,0,0,1 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+bradlbe01,1983,1,OAK,AL,0,0,6,0,0,0,0,25,14,6,1,4,3,0.400,6.48,1,1,0,0,40,2,7,0,1,3 +breinfr01,1983,1,SFN,NL,11,12,32,32,6,0,0,608,202,86,15,60,117,0.259,3.82,11,4,5,1,868,0,97,13,11,5 +brennto01,1983,1,CLE,AL,2,2,11,5,1,1,0,119,45,17,3,8,21,0.288,3.86,1,0,1,0,167,2,22,0,2,4 +brizzto01,1983,1,ATL,NL,1,0,14,0,0,0,1,61,22,8,2,6,17,0.278,3.54,0,0,0,0,88,2,8,3,0,0 +browncu03,1983,1,CAL,AL,1,1,10,0,0,0,0,48,25,13,1,4,7,0.368,7.31,1,1,0,0,73,7,13,0,1,3 +brownmi01,1983,1,BOS,AL,6,6,19,18,3,1,0,312,110,54,12,43,35,0.276,4.67,1,4,2,4,454,0,62,5,6,11 +brownst02,1983,1,CAL,AL,2,3,12,4,2,1,0,138,45,18,4,16,23,0.256,3.52,1,0,0,0,195,4,19,1,2,4 +brusswa01,1983,1,CHN,NL,3,1,59,0,0,0,1,241,67,21,1,37,46,0.234,2.35,10,4,2,1,335,21,21,5,5,11 +burgmto01,1983,1,OAK,AL,6,7,49,0,0,0,4,288,89,30,2,32,39,0.244,2.81,8,2,0,2,409,22,33,10,2,10 +burnsbr01,1983,1,CHA,AL,10,11,29,26,8,4,0,521,165,69,14,55,115,0.249,3.58,2,6,5,0,732,0,79,6,3,10 +burrira01,1983,1,MON,NL,4,7,40,17,2,1,0,462,139,63,13,56,100,0.244,3.68,4,2,2,1,641,6,68,9,5,11 +butchjo01,1983,1,TEX,AL,6,6,38,6,1,1,5,369,128,48,8,41,58,0.270,3.51,4,4,1,0,522,14,50,4,2,11 +bystrma01,1983,1,PHI,NL,6,9,24,23,1,1,0,358,136,61,6,44,87,0.285,4.60,2,2,7,1,540,0,75,3,8,5 +caldwmi01,1983,1,ML4,AL,12,11,32,32,10,2,0,685,269,115,35,51,58,0.296,4.53,1,3,1,1,970,0,125,4,4,28 +callabe01,1983,1,OAK,AL,1,2,4,2,0,0,0,28,18,13,0,5,2,0.400,12.54,1,0,0,0,52,1,16,1,1,0 +calvema01,1983,1,SFN,NL,1,4,18,4,0,0,0,112,46,26,2,34,14,0.307,6.27,4,3,3,1,190,5,33,2,1,5 +camacer01,1983,1,CLE,AL,0,1,4,0,0,0,0,16,5,3,1,2,2,0.250,5.06,0,0,1,0,23,1,3,0,0,1 +campbbi02,1983,1,CHN,NL,6,8,82,0,0,0,8,367,128,61,4,49,97,0.275,4.49,18,6,1,1,530,46,65,10,5,9 +campri01,1983,1,ATL,NL,10,9,40,16,1,0,0,420,146,59,16,38,61,0.270,3.79,2,3,4,0,590,4,64,5,3,22 +candejo01,1983,1,PIT,NL,15,8,33,32,2,0,0,593,191,71,15,45,157,0.257,3.23,3,3,2,2,797,0,73,4,4,17 +candito01,1983,1,ML4,AL,4,4,10,8,2,1,0,167,62,20,4,16,21,0.291,3.23,0,0,2,0,233,1,21,0,2,9 +carltst01,1983,1,PHI,NL,15,16,37,37,8,3,0,851,277,98,20,84,275,0.258,3.11,10,13,3,9,1183,0,117,20,4,18 +carmado01,1983,1,PHI,NL,0,0,1,0,0,0,1,3,0,0,0,0,0,0.000,0.00,0,0,0,0,3,1,0,0,0,0 +castibo01,1983,1,MIN,AL,8,12,27,25,3,0,0,475,170,84,17,65,90,0.278,4.77,4,3,1,1,686,0,91,6,2,12 +castima01,1983,1,SEA,AL,0,0,1,0,0,0,0,8,8,7,3,3,2,0.533,23.63,0,1,1,0,19,0,7,0,0,0 +castrbi01,1983,1,KCA,AL,2,0,18,0,0,0,0,122,51,30,4,12,17,0.300,6.64,1,1,5,0,190,6,34,0,3,3 +catoke01,1983,1,CIN,NL,1,0,4,0,0,0,0,11,2,1,0,1,3,0.154,2.45,0,0,0,0,14,1,1,0,0,0 +caudibi01,1983,1,SEA,AL,2,8,63,0,0,0,26,218,70,38,10,38,73,0.257,4.71,6,4,2,1,317,54,39,1,4,7 +chifffl01,1983,1,SDN,NL,0,2,15,0,0,0,1,68,17,8,0,10,15,0.213,3.18,1,0,0,1,94,3,10,3,0,2 +chrisla01,1983,1,PHI,NL,2,4,9,9,0,0,0,145,42,21,2,17,44,0.233,3.91,1,2,1,0,203,0,25,1,4,1 +chrismi01,1983,1,SFN,NL,0,0,7,0,0,0,0,40,16,12,1,16,5,0.308,8.10,2,1,2,0,72,0,14,1,1,1 +citarra01,1983,1,SLN,NL,0,0,6,0,0,0,0,33,8,2,0,3,4,0.205,1.64,1,1,0,0,42,2,2,0,0,1 +clancji01,1983,1,TOR,AL,15,11,34,34,11,1,0,669,238,97,23,61,99,0.271,3.91,0,3,1,0,955,0,115,4,12,14 +clarkbr01,1983,1,SEA,AL,7,10,41,17,2,0,0,487,160,71,14,72,76,0.261,3.94,6,10,3,0,697,7,82,6,3,22 +clarkst02,1983,1,TOR,AL,1,1,10,0,0,0,0,33,10,4,2,5,7,0.256,3.27,0,0,0,1,46,5,4,1,1,1 +clearma01,1983,1,BOS,AL,4,5,48,0,0,0,4,288,101,67,10,68,81,0.273,6.28,5,2,3,0,448,33,71,1,6,9 +cocanja01,1983,1,ML4,AL,2,0,5,3,1,0,0,90,21,6,1,12,8,0.200,1.80,0,1,1,0,118,0,8,0,0,8 +codirch01,1983,1,OAK,AL,12,12,37,31,7,2,1,617,208,102,17,72,85,0.264,4.46,4,2,7,2,884,3,115,10,6,18 +comerst01,1983,1,PHI,NL,1,0,3,1,0,0,0,26,11,5,0,3,1,0.314,5.19,0,0,0,0,38,0,6,0,0,1 +conroti01,1983,1,OAK,AL,7,10,39,18,3,1,0,487,141,71,17,98,112,0.232,3.94,2,8,2,1,715,6,89,3,4,11 +coopedo01,1983,1,TOR,AL,0,0,4,0,0,0,0,16,8,4,3,0,5,0.348,6.75,0,0,0,0,23,1,4,0,0,1 +corbedo01,1983,1,CAL,AL,1,1,11,0,0,0,0,52,26,7,1,4,18,0.351,3.63,2,0,1,0,80,7,10,1,0,3 +couchmi01,1983,1,SDN,NL,0,1,8,0,0,0,0,42,12,8,1,6,5,0.214,5.14,1,0,0,1,63,2,8,0,1,0 +coxda01,1983,1,SLN,NL,3,6,12,12,0,0,0,249,92,30,6,23,36,0.286,3.25,2,2,0,0,352,0,38,6,1,11 +creelke01,1983,1,KCA,AL,2,5,25,10,1,0,0,268,116,63,17,35,31,0.320,6.35,0,1,2,0,404,9,66,1,3,13 +cruzvi01,1983,1,TEX,AL,1,3,17,0,0,0,5,75,16,4,2,10,18,0.184,1.44,3,0,1,0,103,13,7,3,2,2 +curtijo01,1983,1,CAL,AL,1,2,37,3,0,0,5,270,89,38,5,40,36,0.258,3.80,11,8,2,0,394,14,44,2,5,13 +darliro01,1983,1,NYN,NL,1,3,5,5,1,0,0,106,31,11,0,17,23,0.250,2.80,1,3,3,2,148,0,11,3,0,6 +darwida01,1983,1,TEX,AL,8,13,28,26,9,2,0,549,175,71,9,62,92,0.250,3.49,3,2,3,0,780,0,86,7,7,8 +davisma01,1983,1,SFN,NL,6,4,20,20,2,2,0,333,93,43,14,50,83,0.227,3.49,4,8,3,1,469,0,51,2,4,5 +davisro02,1983,1,MIN,AL,5,8,66,0,0,0,30,267,89,33,6,33,84,0.266,3.34,3,4,3,1,382,61,34,6,6,6 +davisst02,1983,1,BAL,AL,13,7,34,29,6,1,0,601,180,80,14,64,125,0.238,3.59,4,7,2,2,831,0,90,5,4,15 +dawlebi01,1983,1,HOU,NL,6,6,48,0,0,0,14,239,51,25,9,22,60,0.185,2.82,4,1,1,0,304,37,26,5,0,5 +dayleke01,1983,1,ATL,NL,5,8,24,16,0,0,0,314,100,50,12,39,70,0.257,4.30,2,3,2,0,436,3,59,3,3,10 +deckema01,1983,1,SDN,NL,0,0,4,0,0,0,0,26,5,2,1,3,9,0.167,2.08,0,1,1,0,34,1,2,0,0,1 +dedmoje01,1983,1,ATL,NL,0,0,5,0,0,0,0,12,10,6,1,0,3,0.455,13.50,0,0,0,0,23,0,6,1,0,0 +deleojo01,1983,1,PIT,NL,7,3,15,15,3,2,0,324,75,34,5,47,118,0.196,2.83,2,5,1,2,438,0,36,4,3,5 +deleolu01,1983,1,SDN,NL,6,6,63,0,0,0,13,333,89,33,8,27,90,0.224,2.68,7,1,1,0,442,34,34,7,9,7 +dennyjo01,1983,1,PHI,NL,19,6,36,36,7,1,0,728,229,64,9,53,139,0.250,2.37,5,6,4,1,983,0,77,8,3,27 +diazca01,1983,1,NYN,NL,3,1,54,0,0,0,2,250,62,19,1,35,64,0.211,2.05,13,2,1,0,339,20,22,6,3,8 +dipinfr01,1983,1,HOU,NL,3,4,53,0,0,0,20,214,52,21,2,20,67,0.205,2.65,5,3,1,0,279,32,21,1,3,9 +dixonto01,1983,1,MON,NL,0,1,4,0,0,0,0,11,6,4,1,1,4,0.375,9.82,0,1,1,0,19,3,4,0,1,0 +dotsori01,1983,1,CHA,AL,22,7,35,35,8,1,0,720,209,86,19,106,137,0.240,3.23,1,7,8,0,997,0,92,4,7,33 +draveda01,1983,1,SDN,NL,14,10,28,28,9,1,0,551,181,73,18,44,74,0.262,3.58,4,2,3,1,756,0,78,13,4,17 +easteja01,1983,1,ML4,AL,0,1,12,0,0,0,1,35,14,5,0,10,6,0.350,3.86,1,0,2,0,56,5,7,3,1,3 +easteja01,1983,2,CLE,AL,4,2,41,0,0,0,3,171,69,23,4,22,39,0.309,3.63,4,1,2,0,250,20,25,2,1,12 +eckerde01,1983,1,BOS,AL,9,13,28,28,2,0,0,529,223,110,27,39,77,0.303,5.61,4,1,6,0,787,0,119,1,5,5 +eicheju01,1983,1,CLE,AL,4,11,28,15,2,0,0,402,132,73,10,59,56,0.259,4.90,3,9,2,0,577,5,80,2,4,16 +erickro01,1983,1,NYA,AL,0,1,5,0,0,0,0,50,13,8,1,8,7,0.213,4.32,1,2,0,0,69,2,8,0,0,1 +falcope01,1983,1,ATL,NL,9,4,33,15,2,0,0,320,102,43,14,60,59,0.256,3.63,2,5,1,0,467,6,47,4,4,8 +farmeed01,1983,1,PHI,NL,0,6,12,3,0,0,0,80,35,18,2,20,16,0.307,6.08,8,3,1,0,137,3,22,1,1,0 +farmeed01,1983,2,OAK,AL,0,0,5,1,0,0,0,31,15,4,1,0,7,0.366,3.48,0,0,0,0,42,1,4,0,1,2 +fernasi01,1983,1,LAN,NL,0,1,2,1,0,0,0,18,7,4,0,7,9,0.280,6.00,0,0,1,0,33,0,4,0,0,0 +filsope01,1983,1,MIN,AL,4,1,26,8,0,0,1,270,87,34,9,29,49,0.252,3.40,0,1,1,2,378,10,34,1,2,7 +fireost01,1983,1,SDN,NL,0,0,3,0,0,0,0,15,4,1,0,2,1,0.235,1.80,0,1,0,0,20,1,2,1,0,0 +flanami01,1983,1,BAL,AL,12,4,20,20,3,1,0,376,135,46,10,31,50,0.278,3.30,2,1,2,0,528,0,53,4,6,16 +fontera01,1983,1,NYA,AL,8,2,15,15,3,1,0,292,101,36,3,25,27,0.266,3.33,0,0,1,0,408,0,41,2,1,12 +forscbo01,1983,1,SLN,NL,10,12,34,30,6,2,0,561,190,89,23,54,56,0.266,4.28,2,4,3,1,790,3,104,11,8,12 +forscke01,1983,1,CAL,AL,11,12,31,31,11,1,0,658,226,99,21,61,81,0.266,4.06,6,6,4,0,921,0,107,4,3,23 +forstte01,1983,1,ATL,NL,3,2,56,0,0,0,13,238,60,19,3,31,54,0.217,2.16,9,2,2,0,316,29,19,7,0,9 +frazige01,1983,1,NYA,AL,4,4,61,0,0,0,8,346,94,44,5,45,78,0.227,3.43,4,5,3,0,479,30,44,6,10,18 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+gossari01,1983,1,NYA,AL,13,5,57,0,0,0,22,262,82,22,5,25,90,0.248,2.27,5,0,1,0,367,47,27,5,6,5 +gottji01,1983,1,TOR,AL,9,14,34,30,6,1,0,530,195,93,15,68,121,0.280,4.74,5,2,5,0,776,2,103,4,3,16 +graperi01,1983,1,MON,NL,0,1,1,0,0,0,0,12,4,4,2,1,3,0.267,9.00,0,0,0,0,16,0,4,0,0,0 +grosske01,1983,1,PHI,NL,4,6,17,17,1,1,0,288,100,38,13,35,66,0.265,3.56,3,4,3,1,418,0,46,2,1,11 +grosswa01,1983,1,OAK,AL,0,0,1,0,0,0,0,7,2,0,0,1,0,0.222,0.00,0,0,1,0,11,1,0,0,0,0 +guantce01,1983,1,PIT,NL,2,6,49,0,0,0,9,301,90,37,5,46,82,0.241,3.32,6,3,2,0,431,19,45,7,2,6 +guidrro01,1983,1,NYA,AL,21,9,31,31,21,3,0,751,232,95,26,60,156,0.244,3.42,3,4,2,2,1024,0,99,4,9,18 +gullibi01,1983,1,MON,NL,17,12,34,34,10,1,0,727,230,101,19,59,120,0.251,3.75,4,4,4,1,990,0,108,4,7,11 +gumpeda01,1983,1,DET,AL,0,2,26,0,0,0,2,133,43,13,1,7,14,0.257,2.64,3,2,0,0,179,17,16,1,4,6 +gurala01,1983,1,KCA,AL,11,18,34,31,5,0,0,601,220,109,23,76,57,0.284,4.90,6,0,8,0,881,1,119,5,17,24 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+lynched01,1983,1,NYN,NL,10,10,30,27,1,0,0,524,208,83,17,41,44,0.302,4.28,10,3,3,2,749,1,94,9,7,18 +lysanri01,1983,1,MIN,AL,5,12,61,4,1,1,3,375,132,47,8,43,58,0.276,3.38,12,6,2,1,543,19,63,10,8,11 +maddemi01,1983,1,HOU,NL,9,5,28,13,0,0,0,284,76,33,4,45,44,0.231,3.14,3,2,1,3,387,7,37,9,3,17 +mahleri01,1983,1,ATL,NL,0,0,10,0,0,0,0,43,16,8,0,9,7,0.296,5.02,1,0,0,0,66,1,8,1,2,3 +martide01,1983,1,BAL,AL,7,16,32,25,4,0,0,459,209,94,21,45,71,0.330,5.53,0,2,2,0,688,3,108,3,5,25 +martijo05,1983,1,SLN,NL,3,1,26,5,0,0,0,199,60,26,6,26,29,0.242,3.53,4,3,2,0,284,8,31,5,3,5 +martijo05,1983,2,DET,AL,0,0,15,0,0,0,1,40,15,11,2,4,11,0.294,7.43,1,0,0,0,56,5,11,0,1,1 +martire01,1983,1,SFN,NL,2,4,37,6,0,0,1,283,95,44,11,51,43,0.269,4.20,9,2,3,2,414,6,50,3,3,6 +martiti01,1983,1,BAL,AL,9,3,65,0,0,0,21,310,76,27,10,37,81,0.211,2.35,3,6,0,0,406,51,30,3,5,9 +martzra01,1983,1,CHA,AL,0,0,1,1,0,0,0,15,4,2,0,4,1,0.211,3.60,0,0,0,0,23,0,2,0,0,0 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+mcmurcr01,1983,1,ATL,NL,15,9,36,35,6,3,0,674,204,77,13,88,105,0.243,3.08,1,1,1,2,943,0,86,9,5,21 +mcwilla01,1983,1,PIT,NL,15,8,35,35,8,4,0,714,205,86,19,87,199,0.230,3.25,7,9,3,4,1002,0,99,13,6,14 +mintogr01,1983,1,SFN,NL,7,11,73,0,0,0,22,320,117,42,6,47,38,0.283,3.54,13,5,0,0,476,52,51,10,5,16 +mirabpa01,1983,1,BAL,AL,0,0,3,2,0,0,0,29,9,6,1,7,4,0.243,5.59,0,0,0,0,45,1,6,1,0,0 +moffira01,1983,1,TOR,AL,6,2,45,0,0,0,10,172,52,24,5,24,38,0.243,3.77,6,0,1,0,247,33,27,5,3,4 +mongesi01,1983,1,PHI,NL,3,0,14,0,0,0,0,35,20,9,4,6,7,0.377,6.94,1,0,0,0,61,4,10,1,1,0 +mongesi01,1983,2,SDN,NL,7,3,47,0,0,0,7,206,65,24,4,31,32,0.257,3.15,6,3,1,1,293,28,24,6,2,2 +montejo01,1983,1,SDN,NL,9,4,31,10,1,0,4,286,94,35,6,32,52,0.265,3.30,6,1,1,1,398,9,38,9,1,8 +montejo01,1983,2,NYA,AL,5,0,6,6,0,0,0,114,39,14,3,10,15,0.271,3.32,0,0,1,0,157,0,14,0,2,3 +mooredo01,1983,1,ATL,NL,2,3,43,0,0,0,6,206,72,28,6,10,41,0.279,3.67,3,0,0,1,276,16,30,3,5,8 +mooremi01,1983,1,SEA,AL,6,8,22,21,3,2,0,384,130,67,10,60,108,0.267,4.71,4,7,3,0,556,1,75,1,6,9 +morgami01,1983,1,TOR,AL,0,3,16,4,0,0,0,136,48,26,6,21,22,0.273,5.16,0,3,0,0,198,2,26,0,1,5 +morogda01,1983,1,BAL,AL,0,1,22,0,0,0,1,113,39,10,1,10,15,0.265,2.39,3,1,1,0,160,8,10,1,1,2 +morrija02,1983,1,DET,AL,20,13,37,37,20,1,0,881,257,109,30,83,232,0.233,3.34,5,18,3,0,1204,0,117,8,9,14 +moskapa01,1983,1,CHN,NL,3,2,8,8,0,0,0,96,44,24,7,14,16,0.331,6.75,1,2,0,0,149,0,25,2,0,3 +murast01,1983,1,CHA,AL,0,0,6,0,0,0,0,37,13,6,1,6,4,0.260,4.38,0,5,0,0,57,3,11,0,1,2 +murrada01,1983,1,NYA,AL,2,4,40,0,0,0,1,283,113,47,5,22,45,0.297,4.48,4,4,1,1,415,21,56,6,6,9 +nelsoge01,1983,1,SEA,AL,0,3,10,5,1,0,0,96,38,28,6,21,11,0.295,7.88,2,1,1,0,153,2,29,2,0,3 +niedeto01,1983,1,LAN,NL,8,3,66,0,0,0,11,284,55,20,6,29,66,0.170,1.90,11,1,1,0,366,38,22,7,5,7 +niekrjo01,1983,1,HOU,NL,15,14,38,38,9,1,0,791,238,102,15,101,152,0.241,3.48,5,14,3,1,1113,0,115,13,9,15 +niekrph01,1983,1,ATL,NL,11,10,34,33,2,0,0,605,212,89,18,105,128,0.276,3.97,3,6,2,3,888,0,94,7,5,20 +niemara01,1983,1,PIT,NL,0,1,8,1,0,0,0,41,20,14,2,7,8,0.357,9.22,1,3,1,0,66,4,14,1,1,3 +nippeal01,1983,1,BOS,AL,1,1,3,2,1,0,0,48,17,4,0,7,5,0.293,2.25,0,0,1,0,67,1,4,0,1,3 +nolesdi01,1983,1,CHN,NL,5,10,24,18,1,1,0,349,133,61,9,37,59,0.287,4.72,3,3,1,1,506,2,69,2,2,11 +norrimi01,1983,1,OAK,AL,4,5,16,16,2,0,0,266,68,37,11,36,63,0.213,3.76,0,1,3,1,365,0,42,5,2,10 +nunezed01,1983,1,SEA,AL,0,4,14,5,0,0,0,111,40,18,3,22,35,0.278,4.38,1,0,3,2,170,4,21,1,0,4 +oconnja02,1983,1,MIN,AL,2,3,27,8,0,0,0,249,107,54,13,36,56,0.315,5.86,1,3,0,0,383,9,59,3,4,5 +oelkebr01,1983,1,MIN,AL,0,5,10,8,0,0,0,103,56,33,7,17,13,0.376,8.65,0,0,0,0,167,1,34,0,1,5 +ojedabo01,1983,1,BOS,AL,12,7,29,28,5,0,0,521,173,78,15,73,94,0.265,4.04,2,2,3,0,746,0,85,6,11,13 +oroscje01,1983,1,NYN,NL,13,7,62,0,0,0,17,330,76,18,3,38,84,0.197,1.47,7,1,1,2,432,42,27,4,3,7 +owchibo01,1983,1,PIT,NL,0,0,1,0,0,0,0,0,2,1,1,0,0,1.000,,0,0,0,0,2,0,1,0,0,0 +ownberi01,1983,1,NYN,NL,1,3,10,4,0,0,0,104,31,18,4,21,19,0.240,4.67,0,1,1,2,152,3,19,1,0,3 +palmeji01,1983,1,BAL,AL,5,4,14,11,0,0,0,230,86,36,11,19,34,0.281,4.23,0,1,0,1,332,1,42,4,3,7 +pashnla01,1983,1,DET,AL,1,3,12,6,0,0,0,113,48,22,5,18,17,0.308,5.26,1,0,3,0,181,4,27,1,3,1 +pastofr01,1983,1,CIN,NL,9,12,36,29,4,1,0,553,207,100,20,64,93,0.290,4.88,3,6,1,3,791,2,104,6,6,12 +pattere01,1983,1,CHN,NL,1,2,5,2,0,0,0,56,17,10,3,6,10,0.246,4.82,0,1,2,0,78,0,12,0,1,4 +penaal01,1983,1,LAN,NL,12,9,34,26,4,3,1,531,152,54,7,51,120,0.229,2.75,7,2,1,1,730,4,67,8,5,12 +perezpa01,1983,1,ATL,NL,15,8,33,33,7,1,0,646,213,82,20,51,144,0.261,3.43,5,7,4,0,889,0,88,12,4,24 +perryga01,1983,1,SEA,AL,3,10,16,16,2,0,0,306,116,56,18,23,42,0.286,4.94,3,1,3,0,437,0,60,3,3,12 +perryga01,1983,2,KCA,AL,4,4,14,14,1,1,0,253,98,40,6,26,40,0.292,4.27,1,4,1,0,374,0,48,8,3,10 +petryda01,1983,1,DET,AL,19,11,38,38,9,2,0,799,256,116,37,99,122,0.256,3.92,7,12,6,0,1115,0,126,5,5,36 +pettija01,1983,1,MIN,AL,0,4,4,4,1,0,0,81,28,16,8,8,10,0.280,5.33,0,0,2,1,111,0,16,1,0,2 +portech01,1983,1,ML4,AL,7,9,25,21,6,1,0,402,162,67,9,38,76,0.298,4.50,2,0,2,0,595,1,72,4,7,8 +powerte01,1983,1,CIN,NL,5,6,49,6,1,0,2,333,120,56,10,49,57,0.286,4.54,3,1,1,0,480,14,62,4,6,8 +pricejo02,1983,1,CIN,NL,10,6,21,21,5,0,0,432,118,46,12,46,83,0.225,2.88,2,0,0,0,581,0,46,6,4,8 +prolymi01,1983,1,CHN,NL,1,5,60,0,0,0,1,249,79,33,5,38,31,0.259,3.58,13,2,1,1,351,8,35,3,4,12 +puleoch01,1983,1,CIN,NL,6,12,27,24,0,0,0,431,145,78,18,91,71,0.269,4.89,9,4,5,1,649,0,86,7,6,8 +pulidal01,1983,1,PIT,NL,0,0,1,1,0,0,0,6,4,2,2,1,1,0.400,9.00,0,0,0,0,11,0,3,0,0,0 +quiseda01,1983,1,KCA,AL,5,3,69,0,0,0,45,417,118,30,6,11,48,0.229,1.94,2,0,0,0,536,62,35,6,4,19 +rainech01,1983,1,CHN,NL,14,13,34,34,1,1,0,573,219,95,17,74,84,0.295,4.48,3,7,3,1,836,0,109,10,7,23 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+reussje01,1983,1,LAN,NL,12,11,32,31,7,0,0,670,233,73,12,50,143,0.271,2.94,5,3,2,2,935,0,94,18,6,23 +rhoderi01,1983,1,PIT,NL,13,13,36,35,7,2,1,733,256,84,13,68,153,0.276,3.09,15,6,2,5,1012,1,95,8,6,25 +righeda01,1983,1,NYA,AL,14,8,31,31,7,2,0,651,194,83,12,67,169,0.237,3.44,2,10,2,1,900,0,96,10,4,20 +robindo01,1983,1,PIT,NL,2,2,9,6,0,0,0,109,43,18,5,21,28,0.297,4.46,3,2,0,0,168,2,21,2,0,3 +rodasri01,1983,1,LAN,NL,0,0,7,0,0,0,0,14,4,1,0,3,5,0.222,1.93,1,5,0,1,21,1,1,0,0,1 +rogerst01,1983,1,MON,NL,17,12,36,36,13,5,0,819,258,98,14,78,146,0.252,3.23,12,6,5,0,1125,0,108,12,7,18 +rozemda01,1983,1,DET,AL,8,3,29,16,1,0,2,315,100,40,10,29,63,0.248,3.43,6,1,1,3,441,7,50,3,5,7 +ruckeda01,1983,1,DET,AL,1,2,4,3,0,0,0,27,18,17,2,8,6,0.419,17.00,0,0,1,0,52,0,17,0,0,1 +ruckeda01,1983,2,SLN,NL,5,3,34,0,0,0,0,111,36,10,1,18,22,0.263,2.43,0,1,1,0,158,9,14,2,0,4 +ruhleve01,1983,1,HOU,NL,8,5,41,9,0,0,3,344,107,47,13,36,43,0.249,3.69,7,1,3,0,480,11,49,6,6,7 +russeje01,1983,1,CIN,NL,4,5,10,10,2,0,0,205,58,23,7,22,40,0.233,3.03,3,1,0,1,282,0,30,6,5,5 +ruthvdi01,1983,1,PHI,NL,1,3,7,7,0,0,0,101,46,21,5,10,26,0.333,5.61,0,0,0,0,152,0,23,0,4,5 +ruthvdi01,1983,2,CHN,NL,12,9,25,25,5,2,0,448,156,68,17,28,73,0.269,4.10,3,1,3,0,623,0,78,8,3,12 +ryanno01,1983,1,HOU,NL,14,9,29,29,5,2,0,589,134,65,9,101,183,0.195,2.98,3,5,4,1,804,0,74,7,5,20 +sanchlu01,1983,1,CAL,AL,10,8,56,1,0,0,7,295,92,40,6,40,49,0.254,3.66,14,2,3,0,416,34,42,9,2,11 +sandesc01,1983,1,MON,NL,6,7,18,16,0,0,1,244,98,42,12,20,55,0.303,4.65,0,0,0,0,346,1,50,2,1,7 +sarmima01,1983,1,PIT,NL,3,5,52,0,0,0,4,253,74,28,8,36,49,0.243,2.99,8,0,0,1,356,17,35,10,6,8 +schatda01,1983,1,MON,NL,5,2,58,2,0,0,2,261,88,31,3,25,48,0.265,3.21,6,5,5,0,369,23,34,5,2,6 +scherbi01,1983,1,CIN,NL,2,3,73,0,0,0,10,276,73,28,6,33,57,0.225,2.74,4,0,0,0,371,32,31,7,6,12 +schmida01,1983,1,TEX,AL,3,3,31,0,0,0,2,139,42,20,3,14,29,0.241,3.88,1,2,1,0,191,20,20,1,1,4 +schroke01,1983,1,MIN,AL,15,8,33,28,6,1,0,589,196,81,14,80,80,0.266,3.71,3,2,9,1,843,2,92,7,9,18 +schuldo01,1983,1,CHN,NL,0,1,4,3,0,0,0,42,19,11,1,7,8,0.322,7.07,0,1,1,0,67,0,11,0,0,0 +scottmi03,1983,1,HOU,NL,10,6,24,24,2,2,0,435,143,60,8,46,73,0.258,3.72,0,4,5,4,612,0,67,1,5,13 +scurrro01,1983,1,PIT,NL,4,9,61,0,0,0,7,204,63,42,6,53,67,0.249,5.56,7,6,4,0,317,25,45,3,4,4 +seaveto01,1983,1,NYN,NL,9,14,34,34,5,2,0,693,201,91,18,86,135,0.235,3.55,5,10,4,0,962,0,104,9,7,14 +shirlbo01,1983,1,NYA,AL,5,8,25,17,1,1,0,324,122,61,10,36,53,0.293,5.08,3,1,0,1,467,3,71,9,6,11 +shower01,1983,1,SDN,NL,15,12,35,33,4,2,0,602,201,93,25,74,120,0.263,4.17,3,4,6,2,857,0,97,9,4,15 +simpsjo01,1983,1,KCA,AL,0,0,2,0,0,0,0,9,4,1,0,2,1,0.308,3.00,0,0,0,0,15,2,1,0,0,0 +siskdo01,1983,1,NYN,NL,5,4,67,0,0,0,11,313,88,26,1,59,33,0.235,2.24,7,5,4,1,447,39,38,6,4,22 +slatoji01,1983,1,ML4,AL,14,6,46,0,0,0,5,337,112,54,12,56,38,0.272,4.33,5,3,3,0,490,26,57,9,10,10 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+tewksbo01,1988,1,CHN,NL,0,0,1,1,0,0,0,10,6,3,1,2,1,0.400,8.10,0,0,0,0,18,0,5,0,1,1 +thigpbo01,1988,1,CHA,AL,5,8,68,0,0,0,34,270,96,33,6,33,62,0.273,3.30,3,6,4,2,398,59,38,4,5,6 +thurmma01,1988,1,BAL,AL,1,8,43,6,0,0,3,224,80,38,10,27,29,0.277,4.58,3,0,2,0,322,15,43,0,4,5 +tibbsja01,1988,1,BAL,AL,4,15,30,24,1,0,0,476,184,95,18,63,82,0.293,5.39,2,8,3,3,708,1,103,6,7,15 +tolivfr01,1988,1,MIN,AL,7,6,21,19,0,0,0,344,116,54,8,52,69,0.270,4.24,1,10,1,1,491,0,57,7,1,12 +trautjo01,1988,1,BOS,AL,0,1,9,0,0,0,0,48,26,16,2,9,8,0.382,9.00,0,1,1,0,78,4,17,0,0,4 +troutst01,1988,1,SEA,AL,4,7,15,13,0,0,0,169,86,49,6,31,14,0.361,7.83,2,5,5,7,278,1,53,1,3,11 +trujimi01,1988,1,DET,AL,0,0,6,0,0,0,0,37,11,7,2,5,5,0.234,5.11,2,1,0,0,52,2,7,0,0,1 +tudorjo01,1988,1,SLN,NL,6,5,21,21,4,1,0,436,131,37,5,31,55,0.247,2.29,7,0,1,1,578,0,44,10,5,18 +tudorjo01,1988,2,LAN,NL,4,3,9,9,1,0,0,157,58,14,5,10,32,0.284,2.41,0,0,0,2,216,0,16,2,0,5 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+assenpa01,1989,1,ATL,NL,1,3,49,0,0,0,0,173,55,23,2,16,64,0.249,3.59,7,3,1,1,247,14,26,7,2,3 +assenpa01,1989,2,CHN,NL,2,1,14,0,0,0,0,57,19,11,1,12,15,0.275,5.21,1,0,0,0,84,3,11,2,1,3 +atherke01,1989,1,CLE,AL,0,3,32,0,0,0,2,117,48,18,7,13,13,0.293,4.15,4,0,0,0,178,14,22,1,0,0 +augusdo01,1989,1,ML4,AL,12,12,31,25,2,1,0,427,175,84,17,58,51,0.302,5.31,2,3,2,1,648,2,93,2,7,15 +bailesc01,1989,1,CLE,AL,5,9,34,11,0,0,0,341,116,54,7,29,47,0.269,4.28,4,3,3,0,473,9,57,5,5,17 +bairdo01,1989,1,PIT,NL,2,3,44,0,0,0,1,202,52,17,4,28,56,0.211,2.27,10,7,0,0,276,17,19,1,1,4 +ballaje01,1989,1,BAL,AL,18,8,35,35,4,1,0,646,240,82,16,57,62,0.287,3.43,5,3,4,0,912,0,95,10,5,25 +bankhsc01,1989,1,SEA,AL,14,6,33,33,3,2,0,631,187,78,19,63,140,0.239,3.34,1,2,3,0,862,0,84,4,8,15 +bannifl01,1989,1,KCA,AL,4,1,14,14,0,0,0,226,87,39,8,18,35,0.290,4.66,1,1,1,0,323,0,40,2,2,8 +barfijo01,1989,1,TEX,AL,0,1,4,2,0,0,0,35,15,8,0,4,9,0.319,6.17,0,1,0,0,52,1,10,1,0,2 +bautijo01,1989,1,BAL,AL,3,4,15,10,0,0,0,234,84,46,17,15,30,0.274,5.31,0,0,1,0,325,4,46,1,1,6 +beardda01,1989,1,DET,AL,0,2,2,1,0,0,0,16,9,3,2,2,1,0.375,5.06,0,0,1,0,27,1,7,0,0,0 +beattbl01,1989,1,NYN,NL,0,0,2,1,0,0,0,18,5,1,1,2,3,0.217,1.50,0,0,0,0,25,0,1,0,0,0 +bedrost01,1989,1,PHI,NL,2,3,28,0,0,0,6,101,21,12,7,17,24,0.183,3.21,1,0,1,0,135,27,13,0,2,3 +bedrost01,1989,2,SFN,NL,1,4,40,0,0,0,17,153,35,15,5,22,34,0.192,2.65,4,2,0,0,207,33,18,1,2,2 +belchti01,1989,1,LAN,NL,15,12,39,30,10,8,1,690,182,72,20,80,200,0.217,2.82,5,7,7,2,937,6,81,6,6,9 +belinst01,1989,1,PIT,NL,0,1,8,0,0,0,0,31,13,7,0,2,10,0.295,6.10,0,1,0,0,46,2,8,0,0,2 +benesan01,1989,1,SDN,NL,6,3,10,10,0,0,0,200,51,26,7,31,66,0.213,3.51,0,0,1,3,280,0,28,6,2,4 +berenju01,1989,1,MIN,AL,9,3,56,0,0,0,3,318,96,41,11,47,93,0.246,3.48,0,5,2,3,452,17,44,7,5,8 +bielemi01,1989,1,CHN,NL,18,7,33,33,4,3,0,637,187,74,16,81,147,0.237,3.14,8,9,0,4,882,0,82,9,3,18 +birkbmi01,1989,1,ML4,AL,0,4,9,9,1,0,0,134,57,27,4,22,31,0.310,5.44,2,1,3,0,214,0,32,2,3,4 +birtsti01,1989,1,CIN,NL,2,2,42,1,0,0,1,209,68,29,5,27,57,0.261,3.75,8,3,3,3,300,12,33,3,6,5 +bittije01,1989,1,CHA,AL,0,1,2,1,0,0,0,29,9,7,2,6,7,0.257,6.52,0,1,0,0,41,1,7,0,0,3 +blackbu02,1989,1,CLE,AL,12,11,33,32,6,3,0,667,213,83,14,52,88,0.252,3.36,0,13,1,5,912,0,95,9,5,19 +blankke01,1989,1,CHN,NL,0,0,2,0,0,0,0,16,4,1,0,2,2,0.200,1.69,0,0,0,0,22,1,1,0,0,0 +blockte01,1989,1,ATL,NL,0,0,1,0,0,0,0,3,0,0,0,2,0,0.000,0.00,0,0,0,0,4,1,0,0,0,0 +blylebe01,1989,1,CAL,AL,17,5,33,33,8,5,0,723,225,73,14,44,131,0.248,2.73,2,2,8,0,973,0,76,7,7,25 +bockura01,1989,1,DET,AL,0,0,2,0,0,0,0,16,7,3,0,2,2,0.333,5.06,1,0,0,0,23,0,3,0,0,2 +boddimi01,1989,1,BOS,AL,15,11,34,34,3,2,0,635,217,94,19,71,145,0.267,4.00,4,4,10,1,912,0,101,8,10,19 +boevejo01,1989,1,ATL,NL,4,11,66,0,0,0,21,247,78,36,6,34,68,0.252,3.94,5,5,1,0,349,53,37,5,0,6 +boltoto01,1989,1,BOS,AL,0,4,4,4,0,0,0,52,21,16,1,10,9,0.292,8.31,1,1,0,0,83,0,18,0,1,1 +bookegr01,1989,1,SDN,NL,0,1,11,0,0,0,0,57,15,9,2,10,8,0.224,4.26,1,1,0,0,79,4,10,2,0,3 +bookegr01,1989,2,MIN,AL,0,0,6,0,0,0,0,26,11,4,1,2,3,0.306,4.15,0,2,0,0,38,2,4,0,0,1 +bosioch01,1989,1,ML4,AL,15,10,33,33,8,2,0,704,225,77,16,48,173,0.249,2.95,1,4,6,2,969,0,90,5,5,14 +boydoi01,1989,1,BOS,AL,3,2,10,10,0,0,0,177,57,29,8,19,26,0.253,4.42,0,2,0,0,246,0,31,0,2,3 +brantje01,1989,1,SFN,NL,7,1,59,1,0,0,0,292,101,44,10,37,69,0.271,4.07,8,3,2,2,422,15,50,7,3,10 +brownke01,1989,1,TEX,AL,12,9,28,28,7,0,0,573,167,71,10,70,104,0.234,3.35,2,7,4,2,798,0,81,3,6,22 +brownto05,1989,1,CIN,NL,15,12,37,37,9,2,0,749,241,94,31,64,118,0.255,3.39,10,2,3,1,1031,0,109,12,6,18 +buchabo01,1989,1,KCA,AL,0,0,2,0,0,0,0,10,5,6,1,3,3,0.333,16.20,2,1,0,0,18,1,6,0,0,0 +buicede01,1989,1,TOR,AL,1,0,7,0,0,0,0,51,13,11,2,13,10,0.220,5.82,1,1,0,0,74,3,12,0,2,4 +burketi01,1989,1,MON,NL,9,3,68,0,0,0,28,254,68,24,6,22,54,0.225,2.55,7,1,0,0,333,52,24,4,5,6 +burnsto02,1989,1,OAK,AL,6,5,50,2,0,0,8,289,66,24,3,28,49,0.196,2.24,5,4,1,0,374,22,27,7,1,10 +cadargr01,1989,1,OAK,AL,0,0,26,0,0,0,0,83,21,7,0,19,14,0.214,2.28,3,0,0,0,119,6,9,0,2,3 +cadargr01,1989,2,NYA,AL,5,5,20,13,3,1,0,277,109,47,7,38,66,0.298,4.58,1,6,2,2,412,1,53,3,3,11 +camacer01,1989,1,SFN,NL,3,0,13,0,0,0,0,49,10,5,1,11,14,0.175,2.76,2,3,0,1,70,5,5,2,0,1 +campbmi01,1989,1,SEA,AL,1,2,5,5,0,0,0,63,28,17,4,10,6,0.301,7.29,0,0,0,0,103,0,22,0,0,1 +candejo01,1989,1,NYA,AL,3,3,10,6,1,0,0,147,49,28,8,12,37,0.258,5.14,1,2,0,1,206,1,28,2,2,1 +candejo01,1989,2,MON,NL,0,2,12,0,0,0,0,49,17,6,3,4,14,0.283,3.31,2,0,0,0,68,2,8,1,3,1 +candito01,1989,1,CLE,AL,13,10,31,31,4,0,0,618,188,71,10,55,124,0.242,3.10,5,4,4,8,847,0,80,6,4,11 +canojo01,1989,1,HOU,NL,1,1,6,3,1,0,0,69,24,13,2,7,8,0.267,5.09,2,0,0,0,99,1,13,1,1,1 +carmado01,1989,1,PHI,NL,5,15,49,20,0,0,0,448,152,87,21,86,81,0.260,5.24,6,7,3,3,683,5,98,5,5,6 +carpecr01,1989,1,SLN,NL,4,4,36,5,0,0,0,204,70,24,4,26,35,0.262,3.18,9,1,2,0,303,10,30,4,4,2 +carych01,1989,1,NYA,AL,4,4,22,11,2,0,0,298,78,36,13,29,79,0.209,3.26,6,6,0,1,404,4,42,1,1,3 +castito02,1989,1,TOR,AL,1,1,17,0,0,0,1,53,23,12,0,10,10,0.333,6.11,5,3,1,0,86,8,14,2,4,3 +castito02,1989,2,ATL,NL,0,1,12,0,0,0,0,28,8,5,0,4,5,0.222,4.82,1,0,0,0,41,1,5,1,0,0 +cerutjo01,1989,1,TOR,AL,11,11,33,31,3,1,0,616,214,70,19,53,69,0.273,3.07,2,4,6,2,856,1,90,7,5,22 +charlno01,1989,1,CIN,NL,8,3,69,0,0,0,0,286,67,31,5,40,98,0.197,2.93,7,2,2,4,393,27,38,9,2,2 +clancji01,1989,1,HOU,NL,7,14,33,26,1,0,0,441,155,83,13,66,91,0.269,5.08,15,6,0,3,655,3,100,9,4,13 +clarkst02,1989,1,KCA,AL,0,2,2,2,0,0,0,21,14,12,2,4,2,0.438,15.43,0,1,0,0,36,0,12,0,0,1 +clarkte01,1989,1,CAL,AL,0,2,4,2,0,0,0,33,13,6,0,3,7,0.310,4.91,0,2,0,1,48,2,8,2,1,3 +claryma01,1989,1,ATL,NL,4,3,18,17,2,1,0,326,103,38,6,31,30,0.250,3.15,3,5,1,0,452,0,47,4,3,8 +clemepa01,1989,1,SDN,NL,4,1,23,1,0,0,0,117,39,17,4,15,18,0.267,3.92,5,1,0,0,167,8,17,5,1,4 +clemero02,1989,1,BOS,AL,17,11,35,35,8,3,0,760,215,88,20,93,230,0.231,3.13,5,7,8,0,1044,0,101,9,5,23 +cluttbr01,1989,1,ML4,AL,2,5,14,11,1,0,0,202,73,31,11,16,29,0.269,4.14,1,0,0,0,291,1,39,0,4,4 +combspa01,1989,1,PHI,NL,4,0,6,6,1,1,0,116,36,9,2,6,30,0.248,2.09,1,5,0,0,153,0,10,2,0,3 +comstke01,1989,1,SEA,AL,1,2,31,0,0,0,0,77,26,8,2,10,22,0.268,2.81,2,2,0,0,111,7,8,2,2,2 +coneda01,1989,1,NYN,NL,14,8,34,33,7,2,0,659,183,86,20,74,190,0.223,3.52,6,14,4,4,910,0,92,6,4,6 +cookde01,1989,1,SFN,NL,1,0,2,2,1,0,0,45,13,3,1,5,9,0.245,1.80,0,1,0,0,58,0,3,0,0,2 +cookde01,1989,2,PHI,NL,6,8,21,16,1,1,0,318,97,47,17,33,58,0.243,3.99,6,3,2,2,441,1,56,5,2,4 +cookmi01,1989,1,MIN,AL,0,1,15,0,0,0,0,64,22,12,1,17,15,0.268,5.06,1,0,1,0,102,5,12,0,2,1 +corbesh01,1989,1,CAL,AL,0,0,4,0,0,0,0,16,3,2,1,1,3,0.158,3.38,0,0,0,0,20,2,2,0,0,0 +corsiji01,1989,1,OAK,AL,1,2,22,0,0,0,0,115,26,8,2,10,21,0.194,1.88,0,0,1,0,149,14,8,2,2,4 +costejo01,1989,1,SLN,NL,5,4,48,0,0,0,3,187,48,23,5,20,40,0.213,3.32,7,0,2,0,252,11,24,0,5,2 +crawfst01,1989,1,KCA,AL,3,1,25,0,0,0,0,162,48,17,2,19,33,0.242,2.83,3,0,3,0,224,5,19,3,1,4 +crewsti01,1989,1,LAN,NL,0,1,44,0,0,0,1,185,69,22,7,23,56,0.284,3.21,9,1,2,0,275,16,27,7,0,1 +crimch01,1989,1,ML4,AL,9,7,76,0,0,0,7,353,114,37,7,36,59,0.259,2.83,9,5,2,0,487,31,42,3,6,20 +cummist01,1989,1,TOR,AL,2,0,5,2,0,0,0,63,18,7,1,11,8,0.231,3.00,0,1,1,0,90,2,9,0,0,5 +darliro01,1989,1,NYN,NL,14,14,33,33,4,0,0,652,214,85,19,70,153,0.258,3.52,7,12,3,4,922,0,100,7,13,10 +darwida01,1989,1,HOU,NL,11,4,68,0,0,0,7,366,92,32,8,33,104,0.212,2.36,9,2,2,3,482,26,34,8,5,4 +davidbo01,1989,1,NYA,AL,0,0,1,0,0,0,0,3,1,2,1,1,0,0.250,18.00,0,0,0,0,5,1,2,0,0,0 +davisjo04,1989,1,CHA,AL,0,1,4,0,0,0,1,18,5,3,2,2,5,0.217,4.50,0,0,0,0,25,3,4,0,0,0 +davisma01,1989,1,SDN,NL,4,3,70,0,0,0,44,278,66,19,6,31,92,0.200,1.85,1,8,2,0,370,65,21,3,4,11 +davisst02,1989,1,OAK,AL,19,7,31,31,1,0,0,508,187,82,19,68,91,0.288,4.36,1,8,3,1,733,0,91,5,7,24 +davisst03,1989,1,CLE,AL,1,1,12,2,0,0,0,77,34,23,2,14,12,0.318,8.06,1,2,0,0,121,2,24,0,0,4 +dawlebi01,1989,1,OAK,AL,0,0,4,0,0,0,0,27,11,4,0,2,3,0.297,4.00,0,0,1,0,41,2,5,0,1,1 +dayleke01,1989,1,SLN,NL,4,3,71,0,0,0,12,226,63,24,3,30,40,0.228,2.87,10,2,0,1,310,28,26,3,1,5 +dejesjo01,1989,1,KCA,AL,0,0,3,1,0,0,0,24,7,4,1,8,2,0.241,4.50,0,0,0,0,37,1,4,0,0,2 +deleojo01,1989,1,SLN,NL,16,12,36,36,5,3,0,734,173,83,16,80,201,0.197,3.05,5,2,6,0,972,0,96,5,3,12 +deleolu01,1989,1,SEA,AL,0,0,1,1,0,0,0,12,5,1,1,1,2,0.313,2.25,0,0,1,0,18,0,1,0,0,1 +deshaji01,1989,1,HOU,NL,15,10,34,34,6,3,0,677,180,73,15,79,153,0.217,2.91,8,8,4,1,928,0,80,11,5,12 +dibblro01,1989,1,CIN,NL,10,5,74,0,0,0,2,297,62,23,4,39,141,0.176,2.09,11,7,3,0,401,18,23,3,4,3 +dillago01,1989,1,PHI,NL,0,0,5,0,0,0,0,12,7,3,0,0,2,0.368,6.75,0,1,0,0,19,1,3,0,0,0 +dipinfr01,1989,1,SLN,NL,9,0,67,0,0,0,0,265,73,24,6,20,44,0.227,2.45,7,2,0,0,347,8,26,1,5,11 +dopsojo01,1989,1,BOS,AL,12,8,29,28,2,0,0,508,166,75,14,69,95,0.257,3.99,0,7,2,15,727,0,84,5,4,19 +dotsori01,1989,1,NYA,AL,2,5,11,9,1,0,0,155,69,32,8,17,14,0.317,5.57,0,0,1,3,239,2,33,1,2,5 +dotsori01,1989,2,CHA,AL,3,7,17,17,1,0,0,299,112,43,8,41,55,0.282,3.88,3,2,0,0,446,0,51,6,2,8 +downske01,1989,1,SFN,NL,4,8,18,15,0,0,0,248,82,44,7,26,49,0.261,4.79,4,3,1,3,349,1,47,4,4,7 +drabedo01,1989,1,PIT,NL,14,12,35,34,8,5,0,733,215,76,21,69,123,0.238,2.80,3,3,3,0,994,1,83,13,7,13 +draveda01,1989,1,SFN,NL,2,0,2,2,0,0,0,39,8,5,2,4,5,0.182,3.46,0,1,1,0,50,0,5,1,0,1 +drummti01,1989,1,MIN,AL,0,0,8,0,0,0,1,49,16,7,0,8,9,0.246,3.86,1,0,2,1,75,2,7,0,0,1 +duboibr01,1989,1,DET,AL,0,4,6,5,0,0,1,108,29,7,2,17,13,0.218,1.75,3,0,2,1,153,1,14,0,1,3 +dunnemi01,1989,1,PIT,NL,1,1,3,3,0,0,0,43,21,12,1,9,4,0.328,7.53,1,1,1,0,75,0,12,1,0,0 +dunnemi01,1989,2,SEA,AL,2,9,15,15,1,0,0,256,104,50,7,37,38,0.307,5.27,1,7,2,1,386,0,61,3,5,11 +dyermi01,1989,1,MIN,AL,4,7,16,12,1,0,0,213,74,38,2,37,37,0.273,4.82,0,1,2,1,317,0,43,5,2,4 +easlelo01,1989,1,PIT,NL,1,0,10,0,0,0,1,37,8,6,1,7,6,0.190,4.38,1,1,1,1,51,4,6,1,0,0 +eavega01,1989,1,ATL,NL,2,0,3,3,0,0,0,62,15,3,0,12,9,0.200,1.31,0,1,1,1,88,0,3,0,0,0 +eckerde01,1989,1,OAK,AL,4,0,51,0,0,0,33,173,32,10,5,3,55,0.162,1.56,0,0,1,0,206,46,10,0,4,2 +edwarwa01,1989,1,CHA,AL,0,0,7,0,0,0,0,22,7,3,1,3,9,0.269,3.68,0,0,0,0,30,2,3,0,1,2 +eichhma01,1989,1,ATL,NL,5,5,45,0,0,0,0,205,70,33,6,19,49,0.275,4.35,8,0,1,1,286,13,36,7,4,5 +eilanda01,1989,1,NYA,AL,1,3,6,6,0,0,0,103,44,22,5,13,11,0.328,5.77,3,0,2,0,152,0,25,1,2,4 +farrejo03,1989,1,CLE,AL,9,14,31,31,7,2,0,624,196,84,14,71,132,0.244,3.63,4,4,7,0,895,0,97,8,6,5 +farrst01,1989,1,KCA,AL,2,5,51,2,0,0,18,190,75,29,5,22,56,0.296,4.12,5,2,1,0,279,40,35,0,3,6 +fernasi01,1989,1,NYN,NL,14,5,35,32,6,2,0,658,157,69,21,75,198,0.198,2.83,3,1,6,3,883,0,73,4,4,8 +fettemi01,1989,1,CAL,AL,0,0,1,0,0,0,0,10,5,3,1,1,4,0.333,8.10,0,2,0,0,16,0,4,0,0,0 +filerto01,1989,1,ML4,AL,7,3,13,13,0,0,0,217,74,29,6,23,20,0.271,3.61,1,1,4,0,302,0,30,2,0,13 +finlech01,1989,1,CAL,AL,16,9,29,29,9,1,0,599,171,57,13,82,156,0.233,2.57,0,4,2,2,827,0,64,7,3,14 +fischje01,1989,1,LAN,NL,0,0,2,0,0,0,0,10,7,5,1,0,2,0.438,13.50,0,0,0,0,17,0,5,1,0,0 +fishebr01,1989,1,PIT,NL,0,3,9,3,0,0,1,51,25,15,2,10,8,0.329,7.94,3,1,0,0,88,3,17,1,1,0 +flanami01,1989,1,TOR,AL,8,10,30,30,1,1,0,515,186,75,10,47,47,0.283,3.93,0,4,5,0,726,0,82,8,8,23 +foleyto02,1989,1,MON,NL,0,0,1,0,0,0,0,1,1,1,1,0,0,0.500,27.00,0,0,0,0,2,1,1,0,0,0 +forscbo01,1989,1,HOU,NL,4,5,37,15,0,0,0,325,133,64,10,46,40,0.303,5.32,6,4,1,5,494,5,68,4,4,8 +fossato01,1989,1,ML4,AL,2,2,51,0,0,0,1,183,57,24,3,22,42,0.256,3.54,7,1,1,3,256,16,27,7,3,9 +francjo01,1989,1,CIN,NL,4,8,60,0,0,0,32,242,77,28,3,36,60,0.258,3.12,8,3,0,2,345,50,35,7,3,8 +francte01,1989,1,ML4,AL,0,0,1,0,0,0,0,3,0,0,0,0,1,0.000,0.00,0,0,0,0,3,1,0,0,0,0 +frasewi01,1989,1,CAL,AL,4,7,44,0,0,0,2,275,80,33,6,23,46,0.235,3.24,4,5,5,0,375,21,33,4,3,4 +freemma02,1989,1,PHI,NL,0,0,1,1,0,0,0,9,2,2,0,5,0,0.182,6.00,0,0,0,1,16,0,2,0,0,0 +freyst01,1989,1,MON,NL,3,2,20,0,0,0,0,64,29,13,4,11,15,0.330,5.48,1,1,1,1,103,11,15,0,2,2 +frohwto01,1989,1,PHI,NL,1,0,45,0,0,0,0,188,56,25,4,18,39,0.241,3.59,0,1,3,1,258,11,26,3,1,3 +garcimi02,1989,1,PIT,NL,0,2,11,0,0,0,0,48,25,15,2,7,9,0.357,8.44,3,1,0,0,78,6,15,1,0,1 +gardnma01,1989,1,MON,NL,0,3,7,4,0,0,0,79,26,15,2,11,21,0.250,5.13,1,0,2,0,117,1,16,0,0,0 +gardnwe01,1989,1,BOS,AL,3,7,22,16,0,0,0,258,97,57,10,47,81,0.287,5.97,7,3,1,0,393,2,64,3,4,7 +garresc01,1989,1,SFN,NL,14,5,30,29,2,1,0,580,149,49,11,46,119,0.212,2.28,3,7,0,2,766,0,58,9,7,9 +gibsopa01,1989,1,DET,AL,4,8,45,13,0,0,0,396,129,68,11,57,77,0.259,4.64,12,4,6,1,573,16,71,7,5,7 +gideobr01,1989,1,MON,NL,0,0,4,0,0,0,0,14,5,1,1,5,2,0.294,1.93,1,0,0,0,22,1,1,0,0,1 +gladdda01,1989,1,MIN,AL,0,0,1,0,0,0,0,3,2,1,0,1,0,0.400,9.00,0,1,0,0,6,1,1,0,0,0 +glavito02,1989,1,ATL,NL,14,8,29,29,6,4,0,558,172,76,20,40,90,0.243,3.68,3,2,2,0,766,0,88,11,4,14 +gleatje01,1989,1,KCA,AL,0,0,15,0,0,0,0,43,20,9,0,6,9,0.345,5.65,0,0,0,1,66,5,10,0,2,2 +gonzage01,1989,1,MIN,AL,3,2,22,0,0,0,0,87,32,15,2,11,25,0.274,4.66,1,2,4,3,134,9,17,1,1,2 +goodedw01,1989,1,NYN,NL,9,4,19,17,0,0,1,355,93,38,9,47,101,0.211,2.89,2,7,2,5,497,1,42,4,3,5 +gordoto01,1989,1,KCA,AL,17,9,49,16,1,1,1,489,122,66,10,86,153,0.210,3.64,4,12,1,0,677,16,67,4,4,20 +gossari01,1989,1,SFN,NL,2,1,31,0,0,0,4,131,32,13,2,27,24,0.212,2.68,3,2,0,0,182,22,16,2,2,4 +gossari01,1989,2,NYA,AL,1,0,11,0,0,0,1,43,14,6,0,3,6,0.275,3.77,1,1,1,0,56,6,6,1,0,3 +gottji01,1989,1,PIT,NL,0,0,1,0,0,0,0,2,1,0,0,1,1,0.333,0.00,0,0,0,0,4,0,0,0,0,0 +gozzoma01,1989,1,TOR,AL,4,1,9,3,0,0,0,95,35,17,1,9,10,0.289,4.83,1,0,1,0,133,2,19,0,2,6 +grantma01,1989,1,SDN,NL,8,2,50,0,0,0,2,349,105,43,11,32,69,0.248,3.33,6,2,3,0,466,19,45,5,2,14 +greento01,1989,1,ATL,NL,1,2,4,4,1,1,0,79,22,12,5,6,17,0.234,4.10,1,1,0,0,103,0,12,1,2,1 +griffmi02,1989,1,CIN,NL,0,0,3,0,0,0,0,13,10,6,0,3,1,0.500,12.46,2,0,0,0,26,0,6,1,2,0 +grimsja01,1989,1,PHI,NL,1,3,4,4,0,0,0,55,19,12,2,19,7,0.268,5.89,1,2,0,0,91,0,13,1,0,2 +grossgr01,1989,1,HOU,NL,0,0,1,0,0,0,0,3,3,2,0,1,1,0.500,18.00,0,0,0,0,7,1,2,0,0,0 +grosske01,1989,1,MON,NL,11,12,31,31,4,3,0,604,188,98,20,88,158,0.247,4.38,6,5,6,5,867,0,105,10,3,5 +guantce01,1989,1,TEX,AL,6,6,50,0,0,0,2,207,66,30,7,36,69,0.249,3.91,10,1,4,0,311,19,35,2,4,2 +gubicma01,1989,1,KCA,AL,15,11,36,36,8,2,0,765,252,86,10,63,173,0.259,3.04,8,9,5,0,1060,0,100,11,8,20 +guettle01,1989,1,NYA,AL,5,5,70,0,0,0,13,309,98,28,6,26,51,0.258,2.45,9,4,0,0,412,38,31,4,2,18 +guthrma01,1989,1,MIN,AL,2,4,13,8,0,0,0,172,66,29,7,21,38,0.292,4.55,1,1,1,0,254,2,32,1,5,3 +halldr01,1989,1,TEX,AL,2,1,38,0,0,0,0,175,42,24,3,33,45,0.207,3.70,1,1,3,0,242,6,24,2,1,7 +hamilje01,1989,1,LAN,NL,0,1,1,0,0,0,0,5,2,1,0,1,2,0.286,5.40,1,0,0,0,8,1,1,0,0,0 +hammaat01,1989,1,SFN,NL,6,6,28,9,0,0,0,230,78,32,5,23,30,0.271,3.76,2,1,1,2,322,5,34,6,4,13 +hansoer01,1989,1,SEA,AL,9,5,17,17,1,0,0,340,103,40,7,32,75,0.243,3.18,1,3,5,0,465,0,44,4,1,11 +hardyja02,1989,1,CHA,AL,0,0,5,0,0,0,0,37,14,9,1,5,4,0.286,6.57,0,1,1,0,56,4,9,0,1,1 +harnipe01,1989,1,BAL,AL,5,9,18,17,2,0,0,310,97,53,10,64,70,0.249,4.62,3,5,5,1,468,1,55,4,5,9 +harrige01,1989,1,MON,NL,1,1,11,0,0,0,0,60,16,11,1,10,11,0.242,4.95,0,3,0,0,84,7,11,7,1,3 +harrige01,1989,2,SEA,AL,1,4,10,6,0,0,1,100,47,24,3,15,14,0.353,6.48,1,0,1,0,152,2,27,0,3,7 +harrigr01,1989,1,PHI,NL,2,2,44,0,0,0,1,226,64,30,7,43,51,0.234,3.58,7,10,2,0,324,17,34,3,2,4 +harrigr01,1989,2,BOS,AL,2,2,15,0,0,0,0,84,21,8,1,15,25,0.208,2.57,2,2,0,0,118,7,12,1,1,2 +harrigr02,1989,1,SDN,NL,8,9,56,8,0,0,6,405,106,39,8,52,106,0.215,2.60,9,3,2,3,554,25,43,5,2,10 +hartlmi01,1989,1,LAN,NL,0,1,5,0,0,0,0,18,2,1,0,0,4,0.100,1.50,0,0,0,0,20,3,1,0,0,0 +harvebr01,1989,1,CAL,AL,3,3,51,0,0,0,25,165,36,21,6,41,78,0.183,3.44,1,5,0,0,245,42,21,5,2,0 +hatchmi01,1989,1,LAN,NL,0,0,1,0,0,0,0,3,0,1,0,3,0,0.000,9.00,0,0,1,1,6,1,1,0,0,1 +havenbr01,1989,1,CLE,AL,0,0,7,0,0,0,0,40,18,6,3,7,6,0.353,4.05,2,1,0,0,60,4,6,0,2,3 +havenbr01,1989,2,DET,AL,1,2,13,1,0,0,0,68,28,14,3,14,15,0.308,5.56,2,1,3,0,110,7,14,0,2,3 +hawkian01,1989,1,NYA,AL,15,15,34,34,5,2,0,625,238,111,23,76,98,0.290,4.80,6,1,6,2,908,0,127,3,3,25 +heatone01,1989,1,PIT,NL,6,7,42,18,1,0,0,442,127,50,12,55,67,0.233,3.05,12,4,6,5,620,5,55,12,3,9 +heinkdo01,1989,1,SLN,NL,1,1,7,5,0,0,0,79,40,17,2,7,16,0.348,5.81,0,1,0,2,125,1,19,1,2,1 +henketo01,1989,1,TOR,AL,8,3,64,0,0,0,20,267,66,19,5,25,116,0.205,1.92,4,2,2,0,356,56,20,4,3,3 +hennemi01,1989,1,DET,AL,11,4,60,0,0,0,8,270,84,37,4,51,69,0.251,3.70,15,0,5,1,401,35,46,7,3,10 +henrydw01,1989,1,ATL,NL,0,2,12,0,0,0,1,38,12,6,2,5,16,0.250,4.26,1,1,0,0,55,6,6,2,0,0 +hernama01,1989,1,NYN,NL,0,0,1,0,0,0,0,3,0,0,0,0,1,0.000,0.00,0,0,0,0,3,0,0,0,0,0 +hernawi01,1989,1,DET,AL,2,2,32,0,0,0,15,94,36,20,4,16,30,0.293,5.74,2,0,1,1,141,25,21,1,0,2 +hernaxa01,1989,1,TOR,AL,1,0,7,0,0,0,0,68,25,12,2,8,7,0.278,4.76,0,1,1,0,101,2,15,0,2,2 +hershor01,1989,1,LAN,NL,15,15,35,33,8,4,0,770,226,66,9,77,178,0.240,2.31,14,8,3,4,1047,0,75,19,6,29 +heskejo01,1989,1,MON,NL,6,4,43,0,0,0,3,145,54,31,5,26,44,0.292,5.77,6,1,0,3,219,17,34,6,2,5 +hetzeer01,1989,1,BOS,AL,2,3,12,11,0,0,0,151,61,35,7,28,33,0.296,6.26,1,2,2,1,239,0,39,1,2,0 +hibbagr01,1989,1,CHA,AL,6,7,23,23,2,0,0,412,142,49,5,41,55,0.268,3.21,0,4,2,0,581,0,58,5,4,16 +hickeke01,1989,1,BAL,AL,2,3,51,0,0,0,2,148,38,16,3,23,28,0.220,2.92,4,3,1,2,199,17,16,2,0,9 +higuete01,1989,1,ML4,AL,9,6,22,22,2,1,0,406,125,52,9,48,91,0.248,3.46,2,0,4,1,567,0,56,6,5,10 +hillesh01,1989,1,CHA,AL,7,11,50,13,0,0,3,359,132,63,12,51,76,0.279,4.74,4,4,3,1,533,12,67,4,2,7 +hillke01,1989,1,SLN,NL,7,15,33,33,2,1,0,590,186,83,9,99,112,0.252,3.80,6,11,5,2,862,0,92,14,5,13 +holmabr01,1989,1,MON,NL,1,2,10,3,0,0,0,95,34,17,2,15,23,0.270,4.83,0,3,1,1,145,0,18,2,1,1 +holmabr01,1989,2,SEA,AL,8,10,23,22,6,2,0,479,160,61,9,62,82,0.261,3.44,6,5,6,0,688,1,68,4,3,18 +holmash01,1989,1,DET,AL,0,0,5,0,0,0,0,30,8,2,0,11,9,0.211,1.80,1,0,0,0,50,3,2,0,1,0 +holtobr01,1989,1,BAL,AL,5,7,39,12,0,0,0,349,140,52,11,39,51,0.300,4.02,1,5,1,1,514,8,63,2,5,11 +honeyri01,1989,1,OAK,AL,2,2,64,0,0,0,12,230,56,20,5,26,52,0.207,2.35,3,6,1,1,305,24,26,5,2,10 +hortori01,1989,1,LAN,NL,0,0,23,0,0,0,0,80,35,15,1,11,12,0.343,5.06,2,0,1,0,120,6,15,4,2,5 +hortori01,1989,2,SLN,NL,0,3,11,8,0,0,0,137,50,24,2,10,14,0.282,4.73,2,0,3,0,194,1,24,2,2,6 +houghch01,1989,1,TEX,AL,10,13,30,30,5,1,0,546,168,88,28,95,94,0.245,4.35,2,7,6,5,795,0,97,3,6,10 +howelja01,1989,1,LAN,NL,5,3,56,0,0,0,28,239,60,14,3,22,55,0.211,1.58,6,1,0,0,312,41,15,4,2,9 +howelke01,1989,1,PHI,NL,12,12,33,32,1,1,0,612,155,78,11,86,164,0.215,3.44,6,21,2,1,827,0,84,8,9,17 +hudsoch02,1989,1,DET,AL,1,5,18,7,0,0,0,200,75,47,14,31,23,0.288,6.35,3,3,2,0,297,4,49,3,1,9 +huismma01,1989,1,BAL,AL,0,0,8,0,0,0,1,34,13,8,0,0,13,0.277,6.35,0,1,0,0,48,1,8,0,1,0 +hurstbr01,1989,1,SDN,NL,15,11,33,33,10,2,0,734,214,73,16,66,179,0.237,2.69,7,8,0,0,990,0,84,18,3,13 +innisje01,1989,1,NYN,NL,0,1,29,0,0,0,0,119,38,14,2,8,16,0.255,3.18,0,0,1,0,160,12,16,1,1,6 +jacksda02,1989,1,CIN,NL,6,11,20,20,1,0,0,347,122,72,10,57,70,0.271,5.60,7,3,1,2,519,0,78,6,4,9 +jacksmi02,1989,1,SEA,AL,4,6,65,0,0,0,7,298,81,35,8,54,94,0.223,3.17,6,1,6,2,431,27,43,6,2,7 +jeffcmi01,1989,1,TEX,AL,9,6,22,22,2,2,0,392,139,52,7,33,64,0.270,3.58,0,0,4,1,559,0,65,3,5,11 +johnsda04,1989,1,BAL,AL,4,7,14,14,4,0,0,268,90,42,11,28,26,0.265,4.23,1,0,4,2,378,0,44,3,3,6 +johnsra05,1989,1,MON,NL,0,4,7,6,0,0,0,89,29,22,2,26,26,0.264,6.67,1,2,0,2,143,1,25,3,4,1 +johnsra05,1989,2,SEA,AL,7,9,22,22,2,0,0,393,118,64,11,70,104,0.244,4.40,1,5,3,5,572,0,75,7,9,9 +johnto01,1989,1,NYA,AL,2,7,10,10,0,0,0,191,87,41,6,22,18,0.336,5.80,2,3,3,0,290,0,45,4,2,8 +jonesba01,1989,1,CHA,AL,3,2,22,0,0,0,1,91,22,8,2,8,17,0.208,2.37,0,1,1,0,121,8,12,4,2,4 +jonesdo01,1989,1,CLE,AL,7,10,59,0,0,0,32,242,76,21,4,13,65,0.251,2.34,4,1,1,1,331,53,25,8,6,5 +jonesji02,1989,1,NYA,AL,2,1,11,6,0,0,0,144,56,28,7,16,25,0.293,5.25,1,1,2,0,211,3,29,1,1,7 +kaiseje01,1989,1,CLE,AL,0,1,6,0,0,0,0,11,5,3,1,5,4,0.313,7.36,0,1,0,0,22,1,5,0,1,0 +keyji01,1989,1,TOR,AL,13,14,33,33,5,1,0,648,226,93,18,27,118,0.270,3.88,2,4,3,1,886,0,99,9,9,16 +kilgupa01,1989,1,CHN,NL,6,10,35,23,0,0,2,437,164,71,9,49,61,0.283,4.39,6,3,5,2,642,5,90,5,4,16 +kinger01,1989,1,CHA,AL,9,10,25,25,1,1,0,478,144,60,13,64,72,0.244,3.39,1,5,4,4,666,0,69,3,4,25 +kinzema01,1989,1,SLN,NL,0,2,8,1,0,0,0,40,25,19,3,4,8,0.403,12.83,2,1,0,0,67,3,20,0,1,1 +kippebo01,1989,1,PIT,NL,3,4,52,0,0,0,4,249,55,27,5,33,58,0.188,2.93,6,5,0,2,334,15,29,5,3,2 +kneppbo01,1989,1,HOU,NL,4,10,22,20,0,0,0,339,135,74,12,60,45,0.303,5.89,4,4,2,3,520,0,78,9,3,14 +kneppbo01,1989,2,SFN,NL,3,2,13,6,1,1,0,156,55,20,4,15,19,0.270,3.46,2,1,1,1,226,2,20,3,3,0 +knudsma01,1989,1,ML4,AL,8,5,40,7,1,0,0,371,110,46,15,29,47,0.237,3.35,2,2,3,0,499,16,50,2,1,13 +kraemjo01,1989,1,CHN,NL,0,1,1,1,0,0,0,11,7,2,0,2,5,0.368,4.91,1,0,0,0,21,0,6,0,0,0 +kramera01,1989,1,PIT,NL,5,9,35,15,1,1,2,334,90,49,10,61,52,0.224,3.96,4,1,7,0,482,7,53,9,4,10 +krawcra01,1989,1,ML4,AL,0,0,1,0,0,0,0,6,4,3,0,1,6,0.400,13.50,0,0,0,0,11,1,3,0,0,0 +kruegbi01,1989,1,ML4,AL,3,2,34,5,0,0,3,281,96,40,9,33,72,0.264,3.84,3,10,0,1,403,8,43,5,1,6 +krukomi01,1989,1,SFN,NL,4,3,8,8,0,0,0,129,37,19,5,18,18,0.236,3.98,3,0,1,2,177,0,20,0,1,1 +kunkeje01,1989,1,TEX,AL,0,0,1,0,0,0,0,5,4,4,1,3,0,0.444,21.60,0,0,0,0,12,1,4,0,0,0 +lacosmi01,1989,1,SFN,NL,10,10,45,18,1,0,6,451,143,53,3,65,78,0.255,3.17,4,1,7,5,647,16,62,8,7,14 +lampde01,1989,1,BOS,AL,4,2,42,0,0,0,2,337,96,29,4,27,61,0.235,2.32,6,1,0,1,445,14,37,5,5,12 +lancale01,1989,1,CHN,NL,4,2,42,0,0,0,8,218,60,11,2,15,56,0.226,1.36,1,2,0,1,288,15,12,3,4,6 +landrbi01,1989,1,PIT,NL,2,3,56,0,0,0,26,243,60,15,2,28,51,0.205,1.67,8,2,0,0,325,40,18,3,2,6 +langsma01,1989,1,SEA,AL,4,5,10,10,2,1,0,220,60,29,3,19,60,0.221,3.56,0,1,4,2,297,0,30,0,3,6 +langsma01,1989,2,MON,NL,12,9,24,24,6,4,0,530,138,47,13,93,175,0.218,2.39,6,5,0,2,740,0,57,9,4,11 +lapoida01,1989,1,NYA,AL,6,9,20,20,0,0,0,341,146,71,12,45,51,0.311,5.62,4,1,2,2,524,0,73,2,4,12 +leachte01,1989,1,NYN,NL,0,0,10,0,0,0,0,64,19,10,1,4,2,0.244,4.22,0,0,1,0,85,4,11,0,2,2 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+lovelva01,1989,1,CAL,AL,0,0,1,0,0,0,0,3,0,0,0,1,1,0.000,0.00,1,0,0,0,4,1,0,0,0,0 +lueckri01,1989,1,KCA,AL,2,1,19,0,0,0,1,71,23,9,3,13,16,0.258,3.42,4,0,0,1,104,12,9,2,0,1 +lugour01,1989,1,MON,NL,0,0,3,0,0,0,0,12,4,3,1,0,3,0.250,6.75,0,1,0,0,16,2,3,0,0,0 +machaju01,1989,1,NYN,NL,0,1,10,0,0,0,0,33,9,4,0,3,14,0.214,3.27,0,0,0,0,45,9,4,0,0,1 +maddemo01,1989,1,PIT,NL,2,2,9,3,0,0,0,42,17,11,0,13,6,0.327,7.07,0,0,0,0,67,2,14,1,1,4 +maddugr01,1989,1,CHN,NL,19,12,35,35,7,1,0,715,222,78,13,82,135,0.249,2.95,13,5,6,3,1002,0,90,18,6,17 +maddumi01,1989,1,PHI,NL,1,3,16,4,2,1,1,131,52,25,3,14,26,0.304,5.15,3,3,2,1,191,1,29,3,1,5 +madrial01,1989,1,PHI,NL,1,2,6,3,0,0,0,74,32,15,3,14,13,0.314,5.47,4,4,1,0,118,2,16,1,0,2 +magrajo01,1989,1,SLN,NL,18,9,34,33,9,3,0,704,219,76,5,72,127,0.251,2.91,7,14,6,5,971,1,81,14,8,24 +mahleri01,1989,1,CIN,NL,9,13,40,31,5,2,0,662,242,94,15,51,102,0.282,3.83,13,4,10,4,940,1,113,15,5,14 +martide01,1989,1,MON,NL,16,7,34,33,5,2,0,696,227,82,21,49,142,0.257,3.18,4,5,7,2,950,1,88,8,2,21 +martira02,1989,1,LAN,NL,6,4,15,15,2,2,0,296,79,35,11,41,89,0.219,3.19,1,1,5,0,410,0,39,4,0,5 +masonro01,1989,1,HOU,NL,0,0,2,0,0,0,0,4,2,3,0,2,3,0.333,20.25,0,0,0,0,8,1,3,0,0,0 +mccamra01,1989,1,SFN,NL,1,1,25,0,0,0,0,110,32,16,4,23,12,0.241,3.93,2,1,1,1,159,10,22,1,1,7 +mccarto03,1989,1,CHA,AL,1,2,31,0,0,0,0,200,72,26,8,20,27,0.280,3.51,0,5,2,0,285,14,32,3,3,17 +mccaski01,1989,1,CAL,AL,15,10,32,32,6,4,0,636,202,69,16,59,107,0.254,2.93,1,7,3,2,864,0,73,3,4,32 +mcclubo01,1989,1,CAL,AL,6,1,48,0,0,0,3,157,39,9,2,15,36,0.212,1.55,1,2,1,2,205,27,14,1,4,5 +mcculla01,1989,1,NYA,AL,4,3,52,1,0,0,3,254,83,43,9,37,82,0.255,4.57,4,2,3,0,373,20,46,3,5,0 +mcdonbe01,1989,1,BAL,AL,1,0,6,0,0,0,0,22,8,7,2,4,3,0.286,8.59,0,1,0,1,33,2,7,0,1,1 +mcdowro01,1989,1,NYN,NL,1,5,25,0,0,0,4,106,34,13,1,16,15,0.254,3.31,3,3,2,1,156,15,21,3,1,4 +mcdowro01,1989,2,PHI,NL,3,3,44,0,0,0,19,170,45,7,2,22,32,0.220,1.11,5,0,1,0,231,41,15,3,0,9 +mcelrch01,1989,1,PHI,NL,0,0,11,0,0,0,0,31,12,2,1,4,8,0.286,1.74,1,0,0,0,46,4,2,0,0,1 +mcgafan01,1989,1,MON,NL,3,5,57,0,0,0,2,225,85,39,3,30,40,0.293,4.68,4,3,3,0,333,23,40,6,4,5 +mcmurcr01,1989,1,TEX,AL,0,0,19,0,0,0,0,69,29,19,3,13,14,0.312,7.43,1,1,2,1,111,4,21,1,2,1 +mcwilla01,1989,1,PHI,NL,2,11,40,16,2,1,0,362,123,55,3,49,54,0.265,4.10,4,6,4,3,531,6,67,9,4,7 +mcwilla01,1989,2,KCA,AL,2,2,8,5,1,0,0,98,31,15,2,8,24,0.254,4.13,1,2,3,0,136,0,15,2,1,3 +medvisc01,1989,1,PIT,NL,0,1,6,0,0,0,0,19,6,4,0,5,4,0.240,5.68,2,1,0,1,30,2,5,0,0,0 +merckke01,1989,1,ATL,NL,0,0,2,1,0,0,0,13,8,6,0,6,4,0.400,12.46,0,0,0,0,26,1,6,0,0,0 +meyerbr01,1989,1,HOU,NL,0,1,12,0,0,0,1,54,16,9,0,13,13,0.239,4.50,3,0,1,1,82,2,13,0,1,3 +mielkga01,1989,1,TEX,AL,1,0,43,0,0,0,1,149,52,18,4,25,26,0.280,3.26,3,1,2,0,217,8,18,3,1,6 +milacbo01,1989,1,BAL,AL,14,12,37,36,3,2,0,729,233,101,21,88,113,0.254,3.74,4,1,2,1,1022,1,105,7,6,29 +mintogr01,1989,1,CAL,AL,4,3,62,0,0,0,8,270,76,22,4,37,42,0.230,2.20,4,3,2,0,373,24,22,2,1,11 +mirabpa01,1989,1,ML4,AL,0,0,13,0,0,0,0,46,18,13,1,7,6,0.290,7.63,3,0,1,0,74,7,14,1,3,1 +mitchjo02,1989,1,NYN,NL,0,1,2,0,0,0,0,9,3,2,0,4,4,0.231,6.00,1,1,0,0,17,0,7,0,0,1 +mmahake01,1989,1,NYA,AL,0,2,4,2,0,0,0,23,13,11,2,8,3,0.406,12.91,0,0,1,0,44,1,12,0,3,0 +mohorda01,1989,1,NYA,AL,2,1,32,0,0,0,2,173,65,32,8,18,24,0.286,4.99,3,4,6,1,254,10,41,1,2,9 +monteri01,1989,1,CAL,AL,2,2,24,0,0,0,0,119,39,14,3,13,27,0.255,3.18,1,2,1,0,170,8,15,1,2,4 +montgje01,1989,1,KCA,AL,7,3,63,0,0,0,18,276,66,14,3,25,94,0.198,1.37,4,6,2,1,363,39,16,1,1,5 +mooremi01,1989,1,OAK,AL,19,11,35,35,6,3,0,725,193,70,14,83,172,0.219,2.61,1,17,2,0,976,0,82,5,6,19 +morgami01,1989,1,LAN,NL,8,11,40,19,0,0,0,458,130,43,6,33,72,0.234,2.53,8,6,2,0,604,7,51,8,6,17 +morrija02,1989,1,DET,AL,6,14,24,24,10,0,0,511,189,92,23,59,115,0.283,4.86,3,12,2,1,743,0,102,6,7,15 +mosesjo01,1989,1,MIN,AL,0,0,1,0,0,0,0,3,0,0,0,1,0,0.000,0.00,0,0,0,0,3,1,0,0,0,1 +moyerja01,1989,1,TEX,AL,4,9,15,15,1,0,0,228,84,41,10,33,44,0.283,4.86,0,1,2,0,337,0,51,1,4,10 +mulhote01,1989,1,SFN,NL,0,0,5,1,0,0,0,33,15,5,0,4,6,0.319,4.09,0,0,0,0,51,2,5,0,0,2 +mulhote01,1989,2,PHI,NL,4,7,20,17,2,1,0,313,122,58,8,32,60,0.292,5.00,3,3,4,0,462,2,61,7,1,9 +munozmi01,1989,1,LAN,NL,0,0,3,0,0,0,0,8,5,5,1,2,3,0.417,16.88,0,0,0,0,14,1,5,0,0,0 +murphda06,1989,1,SDN,NL,0,0,7,0,0,0,0,19,6,4,1,4,1,0.231,5.68,1,0,0,0,30,2,6,0,0,0 +murphro01,1989,1,BOS,AL,5,7,74,0,0,0,9,315,97,32,7,41,107,0.251,2.74,8,6,1,0,438,27,38,7,3,12 +musseje01,1989,1,TOR,AL,0,1,5,3,0,0,0,33,19,13,2,9,3,0.404,10.64,0,3,0,0,58,2,15,1,1,1 +musseje01,1989,2,NYN,NL,3,2,20,0,0,0,0,79,27,9,1,14,11,0.267,3.08,3,1,0,0,119,4,11,4,0,2 +myersra01,1989,1,NYN,NL,7,4,65,0,0,0,24,253,62,22,4,40,88,0.206,2.35,4,3,0,0,349,47,23,6,2,2 +navarja01,1989,1,ML4,AL,7,8,19,17,1,0,0,329,119,38,6,32,56,0.277,3.12,3,3,1,0,470,1,47,5,2,10 +nelsoge01,1989,1,OAK,AL,3,5,50,0,0,0,3,240,60,29,5,30,70,0.203,3.26,3,5,2,0,335,15,33,3,4,5 +nichoro02,1989,1,CLE,AL,4,6,15,11,0,0,0,215,81,35,9,24,42,0.285,4.40,0,0,2,0,315,2,42,3,2,5 +niedeto01,1989,1,SEA,AL,0,3,25,0,0,0,0,109,46,27,7,15,15,0.309,6.69,5,3,1,0,171,14,29,4,2,3 +nielssc01,1989,1,NYA,AL,1,0,2,0,0,0,0,2,2,1,0,1,0,0.500,13.50,0,0,0,0,6,0,1,1,0,0 +nolteer01,1989,1,SDN,NL,0,0,3,1,0,0,0,27,15,11,1,7,8,0.375,11.00,1,3,0,0,49,1,12,1,1,0 +nosekra01,1989,1,DET,AL,0,2,2,2,0,0,0,16,7,8,2,10,4,0.333,13.50,0,0,0,0,31,0,8,0,0,1 +nunezed01,1989,1,DET,AL,3,4,27,0,0,0,1,162,49,25,6,36,41,0.254,4.17,13,2,0,1,238,12,33,6,3,7 +nunezjo01,1989,1,TOR,AL,0,0,6,1,0,0,0,32,8,3,0,2,14,0.200,2.53,0,2,0,0,42,3,3,0,0,0 +ojedabo01,1989,1,NYN,NL,13,11,31,31,5,2,0,576,179,74,16,78,95,0.245,3.47,5,0,2,2,824,0,83,6,7,14 +olinst01,1989,1,CLE,AL,1,4,25,0,0,0,1,108,35,15,1,14,24,0.255,3.75,2,2,0,0,152,8,16,1,0,4 +olivefr01,1989,1,MIN,AL,3,4,12,8,1,0,0,167,64,28,8,15,24,0.288,4.53,0,0,1,2,239,1,28,0,1,4 +olsongr01,1989,1,BAL,AL,5,2,64,0,0,0,27,255,57,16,1,46,90,0.188,1.69,10,9,1,3,356,52,17,4,1,4 +onealra01,1989,1,PHI,NL,0,1,20,1,0,0,0,117,46,27,5,9,29,0.301,6.23,2,5,0,1,167,1,28,2,3,3 +ontivst02,1989,1,PHI,NL,2,1,6,5,0,0,0,92,34,13,2,15,12,0.288,3.82,1,2,0,0,134,0,15,1,0,5 +oroscje01,1989,1,CLE,AL,3,4,69,0,0,0,3,234,54,18,7,26,79,0.198,2.08,4,0,2,0,312,29,20,8,3,5 +ottoda01,1989,1,OAK,AL,0,0,1,1,0,0,0,20,6,2,0,2,4,0.261,2.70,0,0,0,0,26,0,2,1,0,0 +palldo01,1989,1,CHA,AL,4,5,53,0,0,0,6,261,90,32,9,19,58,0.270,3.31,3,4,8,1,370,27,35,8,2,12 +palmeda01,1989,1,DET,AL,0,3,5,5,0,0,0,52,25,15,1,11,12,0.342,7.79,0,0,0,0,85,0,19,0,1,4 +parkecl01,1989,1,NYA,AL,4,5,22,17,2,0,0,360,123,49,12,31,53,0.264,3.68,3,2,2,2,507,1,53,6,2,13 +parreje01,1989,1,PHI,NL,12,6,72,0,0,0,6,317,90,35,6,44,98,0.232,2.98,13,7,0,3,444,34,43,7,5,7 +pattebo01,1989,1,PIT,NL,4,3,12,3,0,0,1,80,23,12,3,8,20,0.235,4.05,2,0,0,0,109,2,13,1,1,2 +patteke01,1989,1,CHA,AL,6,1,50,1,0,0,0,197,64,33,11,28,43,0.257,4.52,3,3,2,1,284,18,37,1,4,6 +penaal01,1989,1,LAN,NL,4,3,53,0,0,0,5,228,62,18,6,18,75,0.220,2.13,4,1,2,1,306,28,20,3,1,4 +penara01,1989,1,DET,AL,0,0,8,0,0,0,0,54,26,12,0,8,12,0.338,6.00,3,1,2,0,88,6,13,0,1,1 +perezme01,1989,1,CHA,AL,11,14,31,31,2,0,0,550,187,102,23,90,141,0.264,5.01,3,12,3,5,810,0,106,5,4,10 +perezpa01,1989,1,MON,NL,9,13,33,28,2,0,0,595,178,73,15,45,152,0.237,3.31,13,6,4,1,811,3,85,6,4,13 +perrypa02,1989,1,CHN,NL,0,1,19,0,0,0,1,107,23,7,2,16,20,0.187,1.77,3,1,0,0,141,6,8,1,1,3 +peterad01,1989,1,CHA,AL,0,1,3,2,0,0,0,16,13,9,1,2,3,0.464,15.19,0,0,0,0,31,0,9,1,0,0 +peterje01,1989,1,ML4,AL,0,2,7,4,0,0,0,94,31,14,3,14,16,0.252,4.02,1,1,0,0,137,1,14,0,0,2 +petryda01,1989,1,CAL,AL,3,2,19,4,0,0,0,153,53,31,8,23,21,0.275,5.47,0,2,1,0,223,3,32,1,5,6 +picoje01,1989,1,CHN,NL,3,1,53,5,0,0,2,272,99,38,8,31,38,0.278,3.77,10,2,0,0,394,17,43,5,2,6 +plesada01,1989,1,ML4,AL,3,4,52,0,0,0,33,184,47,16,6,17,52,0.213,2.35,1,0,0,0,242,51,16,0,4,5 +plunker01,1989,1,OAK,AL,1,1,23,0,0,0,1,86,17,7,1,12,24,0.172,2.20,0,4,1,0,113,12,7,1,0,2 +plunker01,1989,2,NYA,AL,7,5,27,7,0,0,0,227,65,31,9,52,61,0.237,3.69,2,6,0,3,332,5,36,2,4,8 +portuma01,1989,1,HOU,NL,7,1,20,15,2,1,0,324,91,33,7,37,86,0.232,2.75,0,3,2,0,440,1,34,8,1,6 +powelde01,1989,1,SEA,AL,2,2,43,1,0,0,2,135,49,25,6,21,27,0.285,5.00,0,1,2,0,201,9,25,3,3,4 +powerte01,1989,1,SLN,NL,7,7,23,15,0,0,0,291,96,40,7,21,43,0.255,3.71,3,1,1,0,407,2,47,5,3,5 +pricejo02,1989,1,SFN,NL,1,1,7,1,0,0,0,42,16,9,3,4,10,0.314,5.79,2,1,0,1,59,3,9,3,1,3 +pricejo02,1989,2,BOS,AL,2,5,31,5,0,0,0,211,71,34,8,30,52,0.262,4.35,3,1,0,0,305,10,35,1,3,5 +puleoch01,1989,1,ATL,NL,1,1,15,1,0,0,0,87,26,15,2,16,17,0.245,4.66,1,4,0,0,128,1,15,2,4,0 +quiseda01,1989,1,SLN,NL,3,1,63,0,0,0,6,235,78,23,2,14,37,0.261,2.64,9,0,0,1,317,35,25,3,1,8 +rasmude01,1989,1,SDN,NL,10,10,33,33,1,0,0,551,190,87,18,72,87,0.270,4.26,6,4,3,2,799,0,100,9,11,19 +rawlesh01,1989,1,MIN,AL,5,12,27,25,1,0,0,435,167,84,19,60,68,0.293,5.21,1,6,0,0,638,2,89,5,4,17 +reardje01,1989,1,MIN,AL,5,4,65,0,0,0,31,219,68,33,8,12,46,0.246,4.07,3,1,3,1,297,61,33,1,5,2 +reedje01,1989,1,SEA,AL,7,7,52,1,0,0,0,305,89,36,10,43,50,0.235,3.19,10,5,1,1,432,14,44,7,2,6 +reedri01,1989,1,PIT,NL,1,4,15,7,0,0,0,164,62,34,5,11,34,0.290,5.60,3,0,2,3,232,2,35,2,3,4 +reuscri01,1989,1,SFN,NL,17,8,32,32,2,0,0,625,195,68,18,54,111,0.247,2.94,4,1,2,0,860,0,75,7,7,10 +reussje01,1989,1,CHA,AL,8,5,23,19,1,1,0,320,135,60,12,21,27,0.308,5.06,1,1,3,0,470,0,65,2,6,13 +reussje01,1989,2,ML4,AL,1,4,7,7,0,0,0,101,36,20,7,13,13,0.273,5.35,1,0,1,0,147,0,23,1,0,0 +reynocr01,1989,1,HOU,NL,0,0,1,0,0,0,0,3,3,3,0,1,0,0.500,27.00,0,0,1,0,9,0,4,0,1,0 +rhoderi01,1989,1,HOU,NL,2,6,20,17,0,0,0,290,108,46,7,41,41,0.289,4.28,8,1,3,2,432,2,49,11,3,5 +richaru01,1989,1,ATL,NL,0,0,2,2,0,0,0,28,10,5,2,6,4,0.278,4.82,0,1,1,0,43,0,5,0,0,2 +righeda01,1989,1,NYA,AL,2,6,55,0,0,0,25,207,73,23,3,26,51,0.277,3.00,6,0,1,0,300,53,32,7,2,7 +rijojo01,1989,1,CIN,NL,7,6,19,19,1,1,0,333,101,35,6,48,86,0.249,2.84,3,4,2,3,464,0,39,3,6,10 +ritzke01,1989,1,DET,AL,4,6,12,12,1,0,0,222,75,36,2,44,56,0.265,4.38,5,6,1,0,334,0,41,1,5,4 +robindo01,1989,1,SFN,NL,12,11,34,32,5,1,0,591,184,75,22,37,96,0.248,3.43,6,4,2,4,793,2,80,6,5,11 +robinje01,1989,1,PIT,NL,7,13,50,19,0,0,4,424,161,72,14,59,95,0.283,4.58,11,14,1,2,643,18,92,7,7,6 +robinje02,1989,1,DET,AL,4,5,16,16,1,1,0,234,76,41,10,46,40,0.259,4.73,1,5,1,0,347,0,47,3,3,7 +robinro01,1989,1,CIN,NL,5,3,15,15,0,0,0,250,80,31,8,28,36,0.252,3.35,2,2,2,0,353,0,36,5,1,4 +rochfmi01,1989,1,BOS,AL,0,0,4,0,0,0,0,12,4,3,1,4,1,0.267,6.75,1,2,0,0,20,4,7,0,1,0 +rodriro02,1989,1,CIN,NL,1,1,7,0,0,0,0,13,3,2,0,3,0,0.188,4.15,1,1,0,0,19,4,2,0,0,0 +roeslmi01,1989,1,CIN,NL,0,1,17,0,0,0,0,75,22,11,4,9,14,0.239,3.96,1,0,0,0,102,6,11,1,0,2 +rogerke01,1989,1,TEX,AL,3,4,73,0,0,0,2,221,60,24,2,42,63,0.232,2.93,9,6,4,0,314,24,28,6,3,6 +rosenst01,1989,1,CHA,AL,4,13,38,21,2,0,0,426,148,78,14,58,77,0.274,4.94,1,7,1,2,617,2,92,7,9,14 +ruffibr01,1989,1,PHI,NL,6,10,24,23,1,0,0,377,152,62,10,62,70,0.301,4.44,6,8,0,0,576,0,69,8,1,13 +russeje01,1989,1,TEX,AL,6,4,71,0,0,0,38,218,45,16,4,24,77,0.182,1.98,5,6,3,0,278,66,21,1,3,13 +russejo02,1989,1,ATL,NL,0,0,1,0,0,0,0,1,0,0,0,0,0,0.000,0.00,0,0,0,0,1,1,0,0,0,0 +ryanno01,1989,1,TEX,AL,16,10,32,32,6,2,0,718,162,85,17,98,301,0.187,3.20,3,19,9,1,988,0,96,9,5,4 +saberbr01,1989,1,KCA,AL,23,6,36,35,12,4,0,787,209,63,13,43,193,0.217,2.16,6,8,2,1,1021,0,74,9,6,13 +samuero01,1989,1,PIT,NL,0,0,5,0,0,0,0,11,9,4,1,4,2,0.474,9.82,0,0,0,0,23,4,4,0,0,0 +sanchal02,1989,1,TOR,AL,0,1,4,3,0,0,0,35,16,13,1,14,4,0.356,10.03,0,1,0,0,61,0,13,0,2,3 +sandesc01,1989,1,CHN,NL,11,9,37,23,2,0,0,439,155,64,16,31,86,0.274,3.94,6,1,2,3,611,2,69,8,3,9 +schatda01,1989,1,HOU,NL,4,1,36,0,0,0,1,170,64,28,2,28,46,0.287,4.45,6,7,3,1,259,7,33,5,0,4 +schilcu01,1989,1,BAL,AL,0,1,5,1,0,0,0,26,10,6,2,3,6,0.286,6.23,0,1,0,0,38,0,6,0,0,1 +schirca01,1989,1,CHN,NL,3,6,54,0,0,0,4,236,60,33,7,50,54,0.209,3.78,2,3,1,0,342,24,34,2,2,3 +schirca01,1989,2,SDN,NL,3,1,5,4,0,0,0,64,12,6,1,13,17,0.162,2.53,0,1,0,1,87,1,6,0,0,1 +schmida01,1989,1,BAL,AL,10,13,38,26,2,0,0,470,196,99,24,36,46,0.310,5.69,2,3,2,1,686,5,102,9,7,9 +schoomi01,1989,1,SEA,AL,1,7,67,0,0,0,33,231,81,24,2,19,69,0.266,2.81,3,6,2,1,329,60,27,3,1,3 +schuldo01,1989,1,NYA,AL,1,1,2,2,0,0,0,33,12,5,1,5,5,0.300,4.09,0,0,1,0,49,0,5,1,2,1 +schuldo01,1989,2,SDN,NL,2,1,7,4,0,0,0,73,38,15,6,6,15,0.352,5.55,0,1,0,0,118,2,20,2,2,3 +schwami01,1989,1,DET,AL,2,4,13,4,0,0,0,134,58,30,6,16,13,0.309,6.04,5,1,1,0,209,6,33,0,3,2 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+cuetojo01,2014,1,CIN,NL,20,9,34,34,4,2,0,731,169,61,22,65,242,0.194,2.25,2,1,15,1,961,0,69,7,1,13 +cumptbr01,2014,1,PIT,NL,3,4,16,10,0,0,0,210,82,38,2,18,46,0.295,4.89,2,2,6,0,309,4,41,3,4,8 +daleyma01,2014,1,NYA,AL,0,1,13,0,0,0,0,43,12,8,4,6,10,0.222,5.02,1,0,1,0,63,5,11,1,1,0 +danksjo01,2014,1,CHA,AL,11,11,32,32,0,0,0,581,205,102,25,74,129,0.269,4.74,1,7,9,0,855,0,106,4,7,12 +darnelo01,2014,1,MIN,AL,0,2,7,4,0,0,0,72,31,19,5,8,22,0.307,7.13,0,0,1,0,112,2,20,0,2,0 +darviyu01,2014,1,TEX,AL,10,7,22,22,2,1,0,433,133,49,13,49,182,0.241,3.06,1,14,2,1,605,0,54,1,2,7 +daviswa01,2014,1,KCA,AL,9,2,71,0,0,0,3,216,38,8,0,23,109,0.151,1.00,0,1,3,0,279,11,8,0,1,3 +dedunsa01,2014,1,MIN,AL,2,5,30,8,0,0,0,276,92,47,9,41,74,0.261,4.60,1,8,10,3,408,8,49,1,4,11 +dedunsa01,2014,2,HOU,AL,0,1,5,1,0,0,0,26,5,3,0,5,9,0.172,3.12,1,0,0,1,35,2,4,0,1,0 +defraju01,2014,1,PHI,NL,3,1,54,0,0,0,0,158,45,14,4,12,49,0.223,2.39,4,2,3,0,219,17,19,1,1,4 +degroja01,2014,1,NYN,NL,9,6,22,22,0,0,0,421,117,42,7,43,144,0.228,2.69,2,1,1,0,565,0,44,5,3,13 +delabst01,2014,1,TOR,AL,3,0,30,0,0,0,0,77,19,14,3,19,21,0.211,4.91,0,2,3,0,114,0,14,1,1,2 +delarda01,2014,1,LAA,AL,0,0,3,0,0,0,0,7,3,3,0,3,0,0.375,11.57,1,1,0,0,13,2,3,1,1,0 +delareu01,2014,1,ARI,NL,2,0,25,0,0,0,0,110,37,12,2,14,32,0.262,2.95,4,1,3,1,158,12,12,0,0,4 +delarjo01,2014,1,COL,NL,14,11,32,32,0,0,0,553,161,84,21,67,139,0.238,4.10,2,9,9,0,768,0,90,9,5,21 +delarru01,2014,1,BOS,AL,4,8,19,18,0,0,0,305,116,50,12,35,74,0.293,4.43,0,3,2,1,441,1,51,3,5,9 +deleojo02,2014,1,HOU,AL,0,0,8,0,0,0,0,22,9,4,2,3,4,0.310,4.91,0,1,0,0,33,3,4,0,1,0 +delgara01,2014,1,ARI,NL,4,4,47,4,0,0,0,233,71,42,6,35,86,0.239,4.87,2,5,3,0,339,6,44,2,2,2 +denniry01,2014,1,CIN,NL,0,0,8,0,0,0,0,14,7,6,2,4,3,0.350,11.57,0,2,0,0,25,2,7,0,1,1 +descada01,2014,1,SLN,NL,0,0,1,0,0,0,0,1,0,0,0,0,0,0.000,0.00,0,0,0,0,1,1,0,0,0,0 +desclan01,2014,1,MIA,NL,2,2,13,5,0,0,0,99,40,23,4,5,26,0.303,6.27,0,2,2,0,146,4,23,4,3,0 +despaod01,2014,1,SDN,NL,4,7,16,16,0,0,0,289,85,36,6,32,65,0.237,3.36,0,0,5,0,404,0,44,8,1,7 +detwiro01,2014,1,WAS,NL,2,3,47,0,0,0,1,189,68,28,5,21,39,0.282,4.00,4,3,5,0,274,15,34,4,3,8 +diazja01,2014,1,LAA,AL,0,0,5,0,0,0,0,17,4,2,0,3,8,0.200,3.18,0,0,0,0,24,2,2,0,1,0 +diazju03,2014,1,CIN,NL,0,1,36,0,0,0,0,104,29,13,3,14,37,0.230,3.38,4,1,0,0,142,12,13,0,2,3 +dickera01,2014,1,TOR,AL,14,13,34,34,1,0,0,647,191,89,26,74,173,0.233,3.71,2,5,14,0,914,0,101,2,4,16 +diekmja01,2014,1,PHI,NL,5,5,73,0,0,0,0,213,66,30,4,35,100,0.248,3.80,5,7,3,0,313,19,36,2,7,4 +dominjo01,2014,1,LAN,NL,0,0,5,0,0,0,0,19,7,8,2,3,8,0.269,11.37,0,0,1,0,30,2,8,0,0,0 +doolise01,2014,1,OAK,AL,2,4,61,0,0,0,22,188,38,19,5,8,89,0.169,2.73,1,0,0,0,236,40,19,2,1,0 +doubrfe01,2014,1,BOS,AL,2,4,17,10,0,0,0,178,69,40,10,26,43,0.282,6.07,0,4,2,0,277,2,45,2,2,4 +doubrfe01,2014,2,CHN,NL,2,1,4,4,0,0,0,61,22,9,2,7,8,0.278,3.98,0,1,0,1,87,0,9,1,0,3 +downsda02,2014,1,HOU,AL,2,1,45,0,0,0,0,104,28,21,2,19,27,0.226,5.45,2,2,3,2,148,7,22,1,1,5 +downssc01,2014,1,CHA,AL,0,2,38,0,0,0,1,71,24,16,1,15,22,0.258,6.08,2,0,0,0,108,7,17,0,0,0 +downssc01,2014,2,KCA,AL,0,2,17,0,0,0,0,43,12,5,1,5,3,0.231,3.14,0,1,0,0,57,2,7,0,0,2 +drabeky01,2014,1,TOR,AL,0,0,2,0,0,0,0,9,2,0,0,2,5,0.182,0.00,0,0,0,0,13,1,0,0,0,0 +duensbr01,2014,1,MIN,AL,3,3,62,0,0,0,0,163,52,20,6,20,33,0.254,3.31,2,2,1,0,229,10,20,1,2,5 +duffyda01,2014,1,KCA,AL,9,12,31,25,0,0,0,448,113,42,12,53,113,0.209,2.53,2,5,5,0,606,1,52,3,4,7 +dukeza01,2014,1,MIL,NL,5,1,74,0,0,0,0,176,49,16,3,17,74,0.223,2.45,1,3,0,0,238,13,19,0,0,7 +dunnad01,2014,1,CHA,AL,0,0,1,0,0,0,0,3,2,1,0,1,0,0.400,9.00,0,0,0,0,6,1,1,0,0,0 +dunnija01,2014,1,SFN,NL,0,0,1,0,0,0,0,2,0,0,0,1,0,,0.00,0,2,0,0,2,0,0,0,1,0 +dunnmi01,2014,1,MIA,NL,10,6,75,0,0,0,1,171,47,20,4,22,67,0.220,3.16,1,2,4,0,245,15,25,4,1,1 +dysonsa01,2014,1,MIA,NL,3,1,31,0,0,0,0,126,41,10,1,15,33,0.255,2.14,4,1,3,0,181,12,14,2,0,4 +edginjo01,2014,1,NYN,NL,1,0,47,0,0,0,0,82,19,4,2,6,28,0.196,1.32,0,2,0,0,104,5,6,0,1,3 +edwarjo02,2014,1,TEX,AL,0,0,9,0,0,0,0,25,13,4,0,5,9,0.351,4.32,0,2,1,0,43,3,5,0,0,1 +elbersc01,2014,1,LAN,NL,1,0,7,0,0,0,0,13,4,1,0,1,2,0.235,2.08,0,0,0,0,18,0,1,0,0,0 +eliasro01,2014,1,SEA,AL,10,12,29,29,1,1,0,491,151,70,16,64,143,0.248,3.85,3,6,11,4,693,0,77,4,4,23 +eovalna01,2014,1,MIA,NL,6,14,33,33,0,0,0,599,223,97,14,43,142,0.282,4.37,5,6,7,0,854,0,107,9,5,17 +erlinro01,2014,1,SDN,NL,4,5,13,11,0,0,0,184,71,34,6,15,46,0.293,4.99,1,4,1,0,264,1,34,2,4,7 +escobed02,2014,1,BOS,AL,0,0,2,0,0,0,0,6,1,1,0,0,2,0.143,4.50,0,0,1,0,8,2,1,0,0,0 +estrama01,2014,1,MIL,NL,7,6,39,18,0,0,0,452,137,73,29,44,127,0.241,4.36,0,2,3,1,624,3,77,4,4,4 +evelada01,2014,1,NYN,NL,1,1,30,0,0,0,1,82,24,8,2,6,27,0.229,2.63,1,0,4,0,115,10,8,0,0,1 +familje01,2014,1,NYN,NL,2,5,76,0,0,0,5,232,59,19,3,32,73,0.209,2.21,5,9,2,0,322,16,26,4,2,7 +farmebu01,2014,1,DET,AL,0,1,4,2,0,0,0,28,12,12,2,5,11,0.308,11.57,0,0,2,0,46,1,12,0,0,1 +farnsky01,2014,1,NYN,NL,0,3,19,0,0,0,3,51,18,6,2,6,10,0.281,3.18,1,0,0,0,71,10,6,0,1,2 +farnsky01,2014,2,HOU,AL,0,0,16,0,0,0,0,35,14,8,0,9,8,0.311,6.17,1,1,0,0,57,6,8,0,3,1 +farquda01,2014,1,SEA,AL,3,1,66,0,0,0,1,213,58,21,5,22,81,0.221,2.66,1,6,4,2,290,22,23,1,1,4 +feierry01,2014,1,TEX,AL,0,0,6,0,0,0,0,22,12,5,0,2,4,0.375,6.14,1,1,0,0,36,0,5,1,1,0 +feldmsc01,2014,1,HOU,AL,8,12,29,29,2,1,0,541,185,75,16,50,107,0.266,3.74,5,6,11,1,765,0,86,2,7,17 +felizne01,2014,1,TEX,AL,2,1,30,0,0,0,13,95,20,7,5,11,21,0.183,1.99,0,1,0,0,122,22,7,1,1,3 +fernajo02,2014,1,MIA,NL,4,2,8,8,0,0,0,155,36,14,4,13,70,0.188,2.44,1,2,0,1,205,0,19,0,0,2 +fieldjo03,2014,1,HOU,AL,4,6,54,0,0,0,4,164,50,27,2,17,70,0.242,4.45,3,0,2,0,231,16,29,0,5,1 +fienca01,2014,1,MIN,AL,5,6,73,0,0,0,1,190,64,28,7,10,51,0.262,3.98,0,2,0,0,260,15,29,2,4,5 +fiersmi01,2014,1,MIL,NL,6,5,14,10,0,0,0,215,46,17,7,17,76,0.181,2.13,1,1,0,0,274,1,19,2,1,2 +fifest01,2014,1,LAN,NL,0,0,1,1,0,0,0,18,7,4,3,1,5,0.280,6.00,0,0,1,0,27,0,4,0,0,0 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+walljo02,2014,1,LAA,AL,0,0,2,0,0,0,0,3,5,6,0,3,0,0.714,54.00,0,0,0,0,11,1,6,0,1,0 +wangwe01,2014,1,MIL,NL,0,0,14,0,0,0,0,52,30,21,6,8,13,0.361,10.90,1,1,1,0,92,10,23,0,0,0 +warread01,2014,1,NYA,AL,3,6,69,0,0,0,3,236,63,26,4,24,76,0.219,2.97,1,4,3,0,324,11,27,5,4,7 +watsoto01,2014,1,PIT,NL,10,2,78,0,0,0,2,232,64,14,5,15,81,0.232,1.63,0,0,6,0,305,3,16,5,3,8 +weaveje02,2014,1,LAA,AL,18,9,34,34,1,0,0,640,193,85,27,65,169,0.239,3.59,1,3,6,0,888,0,87,5,4,9 +webbda01,2014,1,CHA,AL,6,5,57,0,0,0,0,203,59,30,6,42,58,0.238,3.99,5,13,2,0,296,26,31,1,3,9 +webbry01,2014,1,BAL,AL,3,3,51,0,0,0,0,148,50,21,2,12,37,0.259,3.83,2,1,1,1,207,13,21,1,0,4 +webstal01,2014,1,BOS,AL,5,3,11,11,0,0,0,177,58,33,3,28,36,0.264,5.03,0,2,7,0,259,0,35,0,4,5 +westma03,2014,1,TEX,AL,0,0,3,0,0,0,0,12,6,3,0,1,3,0.353,6.75,0,0,0,0,18,1,3,0,0,1 +wheelza01,2014,1,NYN,NL,11,11,32,32,1,1,0,556,167,73,14,79,187,0.240,3.54,3,9,11,0,794,0,84,5,3,20 +whelake01,2014,1,DET,AL,0,0,1,0,0,0,0,4,3,2,2,2,1,0.429,13.50,0,1,0,0,9,0,2,0,0,0 +whitlch01,2014,1,NYA,AL,4,3,24,12,0,0,0,227,94,44,10,18,60,0.308,5.23,0,2,4,0,330,3,44,1,2,5 +wielajo01,2014,1,SDN,NL,1,0,4,2,0,0,0,34,16,9,3,5,8,0.333,7.15,0,0,0,1,54,0,9,0,1,1 +wilheto01,2014,1,SEA,AL,3,2,57,2,0,0,1,238,47,20,6,36,72,0.171,2.27,6,4,2,0,317,18,22,1,3,7 +willije02,2014,1,HOU,AL,1,4,26,0,0,0,0,143,59,32,7,16,38,0.301,6.04,1,2,3,0,219,14,33,3,1,3 +willije02,2014,2,TEX,AL,1,1,2,2,0,0,0,30,18,11,0,3,6,0.400,9.90,0,0,0,0,48,0,11,0,0,1 +willije02,2014,3,PHI,NL,4,2,9,9,0,0,0,172,48,18,5,17,38,0.230,2.83,1,0,3,0,230,0,20,0,1,8 +wilsoal01,2014,1,BOS,AL,1,0,18,0,0,0,0,85,20,6,3,5,19,0.198,1.91,0,1,2,0,109,3,8,0,1,1 +wilsobr01,2014,1,LAN,NL,2,4,61,0,0,0,1,145,49,25,5,29,54,0.259,4.66,3,1,4,0,223,6,26,1,0,5 +wilsocj01,2014,1,LAA,AL,13,10,31,31,1,1,0,527,169,88,17,85,151,0.258,4.51,5,9,11,0,761,0,95,3,7,23 +wilsoju10,2014,1,PIT,NL,3,4,70,0,0,0,0,180,49,28,4,30,61,0.220,4.20,5,4,3,0,256,15,30,0,0,4 +withrch01,2014,1,LAN,NL,0,0,20,0,0,0,0,64,10,7,1,18,28,0.143,2.95,0,4,1,0,90,0,8,0,1,3 +wolfra02,2014,1,MIA,NL,1,3,6,4,0,0,1,77,33,15,4,6,19,0.311,5.26,0,1,0,0,113,1,17,1,0,2 +woodal02,2014,1,ATL,NL,11,11,35,24,1,0,0,515,151,53,16,45,170,0.239,2.78,1,5,6,0,694,2,58,7,3,18 +woodbl01,2014,1,CLE,AL,0,1,7,0,0,0,0,19,4,5,0,7,7,0.182,7.11,2,0,1,0,30,3,5,0,0,0 +woodtr01,2014,1,CHN,NL,8,13,31,31,0,0,0,521,190,97,20,76,146,0.277,5.03,1,2,7,0,781,0,110,8,4,11 +wootero01,2014,1,MIL,NL,1,4,40,0,0,0,0,103,42,18,1,8,29,0.307,4.72,0,0,1,0,147,15,18,0,1,5 +workmbr01,2014,1,BOS,AL,1,10,19,15,0,0,0,261,88,50,11,36,70,0.263,5.17,0,2,1,0,378,2,57,3,3,4 +worleva01,2014,1,PIT,NL,8,4,18,17,1,1,0,332,112,35,9,22,79,0.265,2.85,1,4,3,0,458,0,43,6,4,13 +worthda01,2014,1,DET,AL,0,0,2,0,0,0,0,6,4,1,0,0,2,0.400,4.50,0,0,0,0,10,2,1,0,0,0 +wrighja01,2014,1,LAN,NL,5,4,61,1,0,0,1,211,72,34,3,27,54,0.271,4.35,4,5,4,0,308,22,35,6,5,7 +wrighst01,2014,1,BOS,AL,0,1,6,1,0,0,0,63,21,6,2,4,22,0.256,2.57,0,1,0,0,86,3,8,0,0,2 +wrighwe01,2014,1,CHN,NL,0,3,58,0,0,0,0,145,48,17,2,19,37,0.262,3.17,2,2,2,0,209,15,19,2,3,5 +yateski01,2014,1,TBA,AL,0,2,37,0,0,0,1,108,33,15,4,15,42,0.241,3.75,3,2,3,0,156,12,16,0,1,2 +youngch03,2014,1,SEA,AL,12,9,30,29,0,0,0,495,143,67,26,60,108,0.234,3.65,3,5,3,1,688,0,70,4,9,6 +zeidjo01,2014,1,HOU,AL,0,0,23,0,0,0,0,62,30,16,6,7,18,0.345,6.97,1,1,1,0,98,6,18,1,2,1 +zieglbr01,2014,1,ARI,NL,5,3,68,0,0,0,1,201,60,26,5,24,54,0.243,3.49,6,0,3,0,281,11,29,2,4,9 +zimmejo02,2014,1,WAS,NL,14,5,32,32,3,2,0,599,185,59,13,29,182,0.244,2.66,0,4,6,0,800,0,67,5,3,11 +aardsda01,2015,1,ATL,NL,1,1,33,0,0,0,0,92,25,16,6,14,35,0.223,4.70,3,1,1,0,129,9,17,0,1,4 +abadfe01,2015,1,OAK,AL,2,2,62,0,0,0,0,143,45,22,11,19,45,0.251,4.15,3,4,1,0,205,17,23,3,3,1 +achteaj01,2015,1,MIN,AL,0,1,11,0,0,0,0,40,12,10,4,6,14,0.231,6.75,1,0,0,0,58,4,10,0,0,1 +adamsau01,2015,1,CLE,AL,2,0,28,0,0,0,1,100,37,14,2,13,23,0.276,3.78,0,1,0,0,149,9,15,2,0,3 +adcocna01,2015,1,CIN,NL,1,2,13,0,0,0,0,54,15,12,3,12,13,0.221,6.00,0,1,1,0,83,4,12,1,1,0 +affelje01,2015,1,SFN,NL,2,2,52,0,0,0,0,106,43,23,6,14,21,0.293,5.86,2,1,2,0,163,7,24,0,0,4 +alberan01,2015,1,TOR,AL,0,0,1,0,0,0,0,8,1,1,1,2,1,0.111,3.38,0,0,0,0,11,0,1,0,0,0 +alberma01,2015,1,CHA,AL,2,0,30,0,0,0,0,112,31,5,3,9,28,0.228,1.21,2,0,1,0,149,5,6,0,3,4 +albural01,2015,1,DET,AL,4,1,67,0,0,0,0,186,63,29,4,33,58,0.269,4.21,3,5,1,4,271,12,29,1,2,5 +alexasc01,2015,1,KCA,AL,0,0,4,0,0,0,0,18,5,3,0,3,3,0.238,4.50,0,1,1,0,25,3,3,0,0,2 +allenco01,2015,1,CLE,AL,2,5,70,0,0,0,34,208,56,23,2,25,99,0.219,2.99,2,9,2,0,286,58,26,1,2,5 +almonmi01,2015,1,KCA,AL,0,2,9,0,0,0,0,26,7,6,4,7,10,0.212,6.23,0,0,1,0,41,3,6,0,0,0 +alvarda01,2015,1,NYN,NL,1,0,6,0,0,0,0,11,5,5,2,1,2,0.333,12.27,0,0,2,1,19,0,5,1,0,0 +alvarhe01,2015,1,MIA,NL,0,4,4,4,0,0,0,67,28,16,1,7,9,0.311,6.45,1,3,0,1,102,0,18,4,1,1 +alvarjo02,2015,1,LAA,AL,4,3,64,0,0,0,0,201,58,26,5,23,59,0.228,3.49,4,1,5,0,283,18,29,0,1,7 +alvarrj01,2015,1,OAK,AL,0,0,21,0,0,0,0,60,27,22,7,13,23,0.318,9.90,1,5,0,0,100,11,23,0,2,0 +amarial01,2015,1,SDN,NL,0,0,1,0,0,0,0,1,0,0,0,0,0,0.000,0.00,0,0,0,0,1,1,0,0,0,0 +anderbr04,2015,1,LAN,NL,10,9,31,31,1,0,0,541,194,74,18,46,116,0.278,3.69,2,4,2,2,750,0,82,3,2,23 +anderch01,2015,1,ARI,NL,6,6,27,27,0,0,0,458,158,73,18,40,111,0.272,4.30,2,3,7,0,640,0,75,3,9,15 +anderco01,2015,1,CLE,AL,7,3,15,15,1,0,0,274,77,31,9,24,44,0.231,3.05,1,0,1,1,365,0,32,3,3,8 +andrima01,2015,1,TBA,AL,3,5,25,8,0,0,2,197,69,30,8,18,49,0.267,4.11,1,2,2,2,282,8,32,1,3,2 +araujel01,2015,1,PHI,NL,2,1,40,0,0,0,0,104,29,13,1,19,34,0.225,3.38,1,2,0,0,151,5,17,2,0,5 +archech01,2015,1,TBA,AL,12,13,34,34,1,1,0,636,175,76,19,66,252,0.220,3.23,0,13,3,0,868,0,85,2,2,10 +armstsh01,2015,1,CLE,AL,0,0,8,0,0,0,0,24,5,2,1,2,11,0.179,2.25,0,0,0,0,30,5,2,0,0,1 +arojo01,2015,1,BOS,AL,0,1,6,0,0,0,0,31,15,8,2,4,8,0.341,6.97,0,0,0,0,49,2,8,0,1,0 +arrieja01,2015,1,CHN,NL,22,6,33,33,4,3,0,687,150,45,10,48,236,0.185,1.77,2,6,6,0,870,0,52,4,1,15 +asheral01,2015,1,PHI,NL,0,6,7,7,0,0,0,87,42,30,8,10,16,0.339,9.31,0,2,1,2,138,0,30,2,1,0 +atchisc01,2015,1,CLE,AL,1,1,23,0,0,0,0,59,23,15,6,4,12,0.288,6.86,0,3,1,0,87,7,15,0,2,0 +aumonph01,2015,1,PHI,NL,0,1,1,1,0,0,0,12,5,6,2,7,3,0.313,13.50,0,1,0,0,23,0,6,0,0,1 +avilalu01,2015,1,ATL,NL,2,4,50,0,0,0,0,113,35,15,4,10,31,0.245,3.58,2,1,0,1,154,7,15,0,1,7 +avilalu01,2015,2,LAN,NL,0,1,23,0,0,0,0,47,13,9,2,5,18,0.224,5.17,0,1,1,0,66,2,9,1,1,1 +axelrdy01,2015,1,CIN,NL,0,1,6,0,0,0,0,37,11,10,5,8,12,0.220,7.30,2,0,0,0,59,3,13,1,0,0 +axforjo01,2015,1,COL,NL,4,5,60,0,0,0,25,167,56,26,4,32,62,0.259,4.20,4,1,0,0,250,43,27,0,2,3 +badenbu01,2015,1,CIN,NL,2,4,68,0,0,0,0,199,71,29,4,20,36,0.273,3.93,4,0,0,0,285,17,30,2,3,6 +baezpe01,2015,1,LAN,NL,4,2,52,0,0,0,0,153,47,19,4,11,60,0.247,3.35,1,1,1,1,208,8,22,3,3,2 +bailean01,2015,1,NYA,AL,0,1,10,0,0,0,0,26,9,5,2,5,6,0.265,5.19,1,0,0,0,41,3,8,0,2,0 +baileho02,2015,1,CIN,NL,0,1,2,2,0,0,0,34,16,7,3,4,3,0.340,5.56,2,0,0,0,51,0,7,0,0,3 +bakersc02,2015,1,LAN,NL,0,1,2,2,0,0,0,33,11,7,4,3,8,0.262,5.73,0,1,0,0,45,0,7,0,0,0 +balesco01,2015,1,CIN,NL,1,1,15,0,0,0,0,47,17,13,3,13,13,0.274,7.47,0,1,1,0,77,5,13,0,1,1 +balfogr01,2015,1,TBA,AL,0,0,6,0,0,0,0,13,3,3,1,4,0,0.200,6.23,0,1,1,0,21,1,3,0,1,0 +banuema01,2015,1,ATL,NL,1,4,7,6,0,0,0,79,30,15,4,12,19,0.283,5.13,0,1,3,0,121,0,17,0,0,2 +barnema01,2015,1,BOS,AL,3,4,32,2,0,0,0,129,56,26,9,15,39,0.311,5.44,0,4,2,0,199,7,28,2,0,4 +barraky01,2015,1,MIA,NL,2,1,25,0,0,0,0,73,12,7,1,18,30,0.154,2.59,2,1,0,0,98,5,8,0,2,1 +barreaa01,2015,1,WAS,NL,3,3,40,0,0,0,0,88,28,15,1,7,35,0.248,4.60,0,1,3,0,123,8,15,0,0,4 +barriyh01,2015,1,MIL,NL,0,0,5,0,0,0,0,20,3,0,0,0,7,0.136,0.00,0,0,0,0,22,2,0,0,0,0 +bassan01,2015,1,TEX,AL,0,0,33,0,0,0,0,192,66,32,5,20,45,0.269,4.50,1,1,1,0,272,9,33,3,3,7 +bassich01,2015,1,OAK,AL,1,8,18,13,0,0,0,258,78,34,5,30,64,0.244,3.56,0,5,9,0,361,3,36,1,1,12 +bastaan01,2015,1,PIT,NL,4,1,66,0,0,0,1,172,39,19,4,26,64,0.188,2.98,2,8,3,0,239,18,19,2,0,0 +bauertr01,2015,1,CLE,AL,11,12,31,30,1,0,0,528,152,89,23,79,170,0.232,4.55,1,7,5,1,744,1,90,4,1,14 +beachbr01,2015,1,LAN,NL,0,1,2,2,0,0,0,24,10,7,1,6,5,0.313,7.88,2,0,0,0,39,0,7,1,0,1 +beckch02,2015,1,CHA,AL,0,1,1,1,0,0,0,18,10,4,0,4,3,0.385,6.00,0,0,0,0,31,0,5,1,0,1 +bedroca01,2015,1,LAA,AL,1,0,34,0,0,0,0,100,40,20,3,19,34,0.303,5.40,2,2,2,0,156,10,21,1,2,2 +beeleda01,2015,1,CHN,NL,0,1,3,3,0,0,0,25,14,9,0,7,7,0.359,9.72,2,0,0,0,46,0,11,0,0,1 +beimejo01,2015,1,SEA,AL,2,1,53,0,0,0,1,142,49,21,8,16,22,0.283,3.99,3,1,1,0,196,14,25,3,3,6 +belisma01,2015,1,SLN,NL,1,1,34,0,0,0,0,101,34,10,1,15,25,0.266,2.67,2,0,3,0,149,10,10,2,1,3 +belisro01,2015,1,TBA,AL,0,0,6,0,0,0,0,24,8,7,0,4,6,0.250,7.88,1,3,0,0,36,3,7,0,0,0 +belivje01,2015,1,TBA,AL,0,0,5,0,0,0,0,8,6,4,1,1,2,0.429,13.50,0,0,0,0,15,0,4,0,0,0 +bellaan01,2015,1,TBA,AL,3,1,17,0,0,0,0,70,16,6,4,10,18,0.198,2.31,0,0,1,0,95,6,7,2,1,2 +benoijo01,2015,1,SDN,NL,6,5,67,0,0,0,2,196,36,17,7,23,63,0.159,2.34,1,2,2,0,254,11,17,2,1,1 +bergmch01,2015,1,COL,NL,3,1,30,4,0,0,0,205,82,36,8,15,37,0.306,4.74,1,4,0,0,286,6,36,2,1,12 +betande01,2015,1,NYA,AL,6,4,74,0,0,0,9,252,45,14,6,40,131,0.157,1.50,2,9,3,0,332,17,17,1,1,4 +betanra01,2015,1,COL,NL,2,4,45,0,0,0,1,118,43,27,4,12,40,0.267,6.18,4,0,0,0,175,9,29,0,2,0 +bettich01,2015,1,COL,NL,8,6,20,20,0,0,0,345,120,54,11,42,98,0.268,4.23,2,6,3,0,502,0,56,7,2,9 +billich01,2015,1,PHI,NL,2,3,7,7,0,0,0,111,53,24,5,8,15,0.346,5.84,0,2,1,0,165,0,26,2,1,7 +blantjo01,2015,1,KCA,AL,2,2,15,4,0,0,2,125,43,18,6,7,40,0.265,3.89,1,0,0,0,172,6,19,1,2,2 +blantjo01,2015,2,PIT,NL,5,0,21,0,0,0,0,103,26,6,1,9,39,0.206,1.57,4,2,1,0,137,7,7,0,1,2 +blazemi01,2015,1,MIL,NL,5,3,45,0,0,0,0,167,40,15,3,18,47,0.200,2.43,1,3,1,0,222,17,17,1,2,2 +blevije01,2015,1,NYN,NL,1,0,7,0,0,0,0,15,0,0,0,0,4,0.000,0.00,0,0,0,0,15,1,0,0,0,1 +bochybr02,2015,1,SFN,NL,0,0,4,0,0,0,0,9,1,0,0,1,3,0.100,0.00,0,1,1,0,12,1,0,0,0,0 +bolsimi01,2015,1,LAN,NL,6,6,21,21,0,0,0,328,104,44,11,45,98,0.251,3.62,3,6,1,1,466,0,49,3,2,13 +boxbebr01,2015,1,TBA,AL,4,10,69,0,0,0,41,189,54,26,9,32,74,0.231,3.71,5,5,2,1,271,53,29,2,1,4 +boydma01,2015,1,TOR,AL,0,2,2,2,0,0,0,20,15,11,5,1,7,0.441,14.85,0,2,0,0,36,0,11,0,1,0 +boydma01,2015,2,DET,AL,1,4,11,10,0,0,0,152,56,37,12,19,36,0.290,6.57,0,2,1,0,216,0,39,1,2,8 +boyerbl01,2015,1,MIN,AL,3,6,68,0,0,0,1,195,62,18,5,19,33,0.254,2.49,4,5,0,0,268,12,24,3,2,5 +brachbr01,2015,1,BAL,AL,5,3,62,0,0,0,1,238,57,24,7,38,89,0.203,2.72,3,1,0,0,324,12,25,3,2,6 +brachsi01,2015,1,ARI,NL,0,0,13,0,0,0,1,37,9,2,2,4,17,0.200,1.46,1,1,1,0,50,3,2,0,0,0 +bradlar01,2015,1,ARI,NL,2,3,8,8,0,0,0,107,36,23,3,22,23,0.267,5.80,1,0,2,0,161,0,23,1,1,6 +breslcr01,2015,1,BOS,AL,0,4,45,2,0,0,1,195,69,30,12,23,46,0.279,4.15,5,2,2,0,280,18,33,3,5,7 +brighja01,2015,1,ATL,NL,0,1,12,0,0,0,0,50,28,16,1,8,12,0.384,8.64,1,2,1,0,84,4,16,0,2,4 +brittza01,2015,1,BAL,AL,4,1,64,0,0,0,36,197,51,14,3,14,79,0.214,1.92,1,5,1,0,253,58,16,0,0,8 +broadmi01,2015,1,SFN,NL,0,2,21,0,0,0,0,52,20,10,1,7,13,0.294,5.19,1,0,1,1,77,5,10,1,0,1 +brookaa01,2015,1,KCA,AL,0,0,2,0,0,0,0,13,6,3,0,0,3,0.333,6.23,0,0,0,0,18,2,3,0,0,1 +brookaa01,2015,2,OAK,AL,3,4,11,9,0,0,0,153,67,38,9,14,35,0.321,6.71,0,0,4,0,232,1,38,3,2,6 +brothre01,2015,1,COL,NL,1,0,17,0,0,0,0,31,9,2,0,8,5,0.243,1.74,0,1,0,0,46,1,2,0,1,2 +brownbr02,2015,1,COL,NL,1,3,36,0,0,0,0,99,32,18,2,16,20,0.254,4.91,0,2,1,0,147,7,18,4,0,3 +broxtjo01,2015,1,MIL,NL,1,2,40,0,0,0,0,110,41,24,5,10,37,0.287,5.89,1,0,0,0,156,10,24,2,1,5 +broxtjo01,2015,2,SLN,NL,3,3,26,0,0,0,0,71,20,7,2,12,26,0.233,2.66,0,0,0,0,101,5,8,3,0,2 +buchada01,2015,1,PHI,NL,2,9,15,15,0,0,0,224,109,58,12,29,44,0.346,6.99,1,2,3,0,351,0,60,4,0,13 +buchaja01,2015,1,HOU,AL,0,0,5,0,0,0,0,27,5,2,1,4,5,0.161,2.00,0,2,1,0,37,1,2,1,0,0 +buchhcl01,2015,1,BOS,AL,7,7,18,18,1,0,0,340,114,41,6,23,107,0.260,3.26,0,3,5,0,469,0,48,1,2,13 +buehrma01,2015,1,TOR,AL,15,8,32,32,4,1,0,596,214,84,22,33,91,0.279,3.81,4,0,7,0,827,0,100,10,9,28 +bumgama01,2015,1,SFN,NL,18,9,32,32,4,2,0,655,181,71,21,39,234,0.222,2.93,2,1,7,0,869,0,73,5,4,11 +burawda01,2015,1,NYA,AL,0,0,1,0,0,0,0,2,3,4,1,1,1,0.600,54.00,0,0,0,0,6,0,4,0,0,0 +burawda01,2015,2,ATL,NL,0,0,12,0,0,0,0,37,8,5,1,4,10,0.195,3.65,0,0,1,0,48,4,5,0,2,3 +burgoen02,2015,1,ARI,NL,2,2,30,0,0,0,2,81,27,14,2,15,39,0.257,4.67,0,6,0,0,121,8,15,0,1,1 +burneaj01,2015,1,PIT,NL,9,7,26,26,0,0,0,492,174,58,11,49,143,0.275,3.18,2,6,11,1,699,0,64,2,4,18 +butleed01,2015,1,COL,NL,3,10,16,16,1,0,0,238,102,52,13,42,44,0.322,5.90,4,0,4,0,370,0,57,6,1,13 +cabrace01,2015,1,BAL,AL,0,0,2,0,0,0,0,3,0,0,0,1,1,0.000,0.00,0,0,0,0,3,2,0,0,0,1 +cahiltr01,2015,1,ATL,NL,0,3,15,3,0,0,0,79,36,22,2,11,14,0.330,7.52,1,1,1,0,124,6,23,2,1,4 +cahiltr01,2015,2,CHN,NL,1,0,11,0,0,0,0,51,8,4,2,5,22,0.143,2.12,0,1,1,0,63,0,4,1,0,1 +cainma01,2015,1,SFN,NL,2,4,13,11,0,0,0,182,71,39,12,20,41,0.293,5.79,0,1,4,0,271,0,39,1,4,6 +caminar01,2015,1,PIT,NL,5,1,73,0,0,0,0,224,63,30,7,29,73,0.224,3.62,2,6,6,1,318,19,31,1,1,5 +campole01,2015,1,SDN,NL,0,0,1,0,0,0,0,3,1,1,0,1,1,0.250,9.00,0,0,0,0,5,0,1,0,0,0 +cappsca01,2015,1,MIA,NL,1,0,30,0,0,0,0,93,18,4,2,7,58,0.168,1.16,0,2,2,0,118,8,5,1,1,2 +capuach01,2015,1,NYA,AL,0,4,22,4,0,0,0,122,52,36,6,22,38,0.310,7.97,1,0,3,0,196,7,38,2,1,2 +carlybu01,2015,1,NYN,NL,1,0,11,0,0,0,1,24,8,5,0,1,6,0.276,5.63,0,1,0,0,32,1,5,0,2,1 +carmofa01,2015,1,HOU,AL,3,5,20,11,0,0,0,254,90,41,9,26,42,0.275,4.36,2,1,1,0,357,6,48,0,3,9 +carpeda01,2015,1,NYA,AL,0,1,22,0,0,0,0,56,20,10,3,7,11,0.286,4.82,1,0,1,0,82,2,11,3,1,2 +carpeda01,2015,2,WAS,NL,0,0,8,0,0,0,0,18,5,1,1,2,4,0.217,1.50,0,1,0,0,25,3,1,0,0,0 +carpeda02,2015,1,ATL,NL,0,0,4,0,0,0,0,11,6,3,2,0,5,0.353,7.36,0,0,0,0,17,1,3,0,0,0 +carraca01,2015,1,CLE,AL,14,12,30,30,3,1,0,551,154,74,18,43,216,0.228,3.63,2,5,5,0,730,0,75,1,6,17 +carrosc02,2015,1,CHA,AL,1,1,18,0,0,0,0,110,40,14,2,13,27,0.280,3.44,2,4,3,0,162,8,19,1,2,3 +cashnan01,2015,1,SDN,NL,6,16,31,31,0,0,0,554,200,89,19,66,165,0.279,4.34,2,3,6,0,804,0,111,8,6,17 +castran01,2015,1,OAK,AL,0,1,5,0,0,0,0,12,8,1,1,3,4,0.444,2.25,0,0,0,0,22,3,1,1,0,0 +castrmi01,2015,1,TOR,AL,0,2,13,0,0,0,4,37,15,6,2,6,12,0.306,4.38,2,2,0,1,57,9,7,0,2,1 +castrmi01,2015,2,COL,NL,0,1,5,0,0,0,0,16,6,6,2,4,6,0.273,10.13,0,0,0,0,26,3,6,0,0,0 +castrsi01,2015,1,COL,NL,2,0,11,0,0,0,0,31,11,7,0,5,9,0.282,6.10,0,0,1,0,47,2,7,2,0,1 +cecilbr01,2015,1,TOR,AL,5,5,63,0,0,0,5,163,39,15,4,13,70,0.197,2.48,3,4,2,0,214,24,17,1,0,3 +cedenxa01,2015,1,WAS,NL,0,0,5,0,0,0,0,9,3,2,1,2,4,0.250,6.00,0,2,1,0,15,1,2,0,0,0 +cedenxa01,2015,2,TBA,AL,4,1,61,0,0,0,1,129,37,10,3,12,43,0.230,2.09,2,4,1,0,174,9,11,0,0,5 +chacijh01,2015,1,ARI,NL,2,1,5,4,0,0,0,80,24,10,4,10,21,0.240,3.38,0,0,0,0,111,0,11,1,0,2 +chafian01,2015,1,ARI,NL,5,1,66,0,0,0,2,225,56,23,3,30,58,0.207,2.76,6,2,1,0,306,6,23,3,2,5 +chambjo03,2015,1,DET,AL,0,2,30,0,0,0,0,66,32,10,5,5,15,0.340,4.09,0,1,1,0,101,10,15,1,0,2 +chambjo03,2015,2,KCA,AL,0,0,6,0,0,0,0,17,6,5,1,4,8,0.261,7.94,0,1,0,0,27,5,5,0,0,0 +chapmar01,2015,1,CIN,NL,4,4,65,0,0,0,33,199,43,12,3,33,116,0.181,1.63,1,7,5,0,278,54,13,0,2,0 +chapmke02,2015,1,HOU,AL,0,0,3,0,0,0,0,16,4,2,1,3,8,0.211,3.38,0,0,0,0,22,0,2,0,0,0 +chaveje01,2015,1,OAK,AL,7,15,30,26,0,0,1,471,164,73,18,48,136,0.268,4.18,2,3,2,0,672,3,78,4,6,12 +chenbr01,2015,1,CLE,AL,0,1,2,2,0,0,0,19,17,9,3,1,4,0.500,12.79,1,0,0,0,35,0,9,0,0,1 +chenwe02,2015,1,BAL,AL,11,8,31,31,0,0,0,574,192,71,28,41,153,0.262,3.34,0,3,5,0,792,0,78,5,8,15 +choatra01,2015,1,SLN,NL,1,0,71,0,0,0,1,82,29,12,2,5,22,0.279,3.95,0,1,6,0,117,4,14,1,1,4 +cingrto01,2015,1,CIN,NL,0,3,35,1,0,0,0,100,31,21,3,25,39,0.250,5.67,3,2,3,1,155,7,21,1,2,3 +cishest01,2015,1,MIA,NL,2,6,32,0,0,0,3,96,37,16,2,14,28,0.291,4.50,3,0,0,0,144,15,19,1,2,3 +cishest01,2015,2,SLN,NL,0,0,27,0,0,0,1,70,18,6,2,13,20,0.212,2.31,0,1,1,0,99,8,7,0,0,4 +claudal01,2015,1,TEX,AL,1,1,18,0,0,0,0,47,12,5,4,6,13,0.211,2.87,2,1,1,0,66,6,6,0,2,1 +clippty01,2015,1,OAK,AL,1,3,37,0,0,0,17,116,25,12,3,21,38,0.175,2.79,1,1,2,0,167,30,12,0,1,0 +clippty01,2015,2,NYN,NL,4,1,32,0,0,0,2,97,24,11,5,10,26,0.200,3.06,1,5,2,0,134,6,13,1,1,2 +cokeph01,2015,1,CHN,NL,0,0,16,0,0,0,0,30,14,7,1,3,9,0.341,6.30,2,0,0,0,45,2,7,0,1,2 +cokeph01,2015,2,TOR,AL,0,0,2,0,0,0,0,8,1,1,1,2,3,0.111,3.38,0,0,0,0,11,1,1,0,0,0 +coleaj01,2015,1,WAS,NL,0,0,3,1,0,0,1,28,14,6,1,1,9,0.341,5.79,1,1,0,0,44,1,11,1,1,0 +colege01,2015,1,PIT,NL,19,8,32,32,0,0,0,624,183,60,11,44,202,0.239,2.60,1,7,10,0,832,0,71,7,6,22 +colemlo01,2015,1,KCA,AL,1,0,4,0,0,0,0,9,1,0,0,2,1,0.111,0.00,0,0,0,0,11,2,0,0,0,1 +collmjo01,2015,1,ARI,NL,4,6,44,12,1,1,1,363,129,51,18,24,63,0.277,3.79,2,1,1,0,499,19,53,2,6,8 +colomal01,2015,1,TBA,AL,8,5,43,13,0,0,0,329,112,48,9,31,88,0.271,3.94,4,8,4,0,457,5,50,2,7,8 +colonba01,2015,1,NYN,NL,14,13,33,31,1,1,0,584,217,90,25,24,136,0.281,4.16,5,0,4,0,815,1,94,9,7,16 +conlead01,2015,1,MIA,NL,4,1,15,11,0,0,0,201,65,28,7,21,59,0.258,3.76,1,0,3,0,281,1,28,1,4,7 +contrca01,2015,1,CIN,NL,0,0,22,0,0,0,0,84,22,15,3,20,19,0.214,4.82,0,3,1,0,125,8,16,1,0,3 +cookry01,2015,1,OAK,AL,0,2,4,0,0,0,0,13,7,5,0,3,3,0.350,10.38,0,0,0,0,23,0,5,0,0,0 +cookry01,2015,2,BOS,AL,0,0,5,0,0,0,0,13,13,13,4,4,3,0.481,27.00,0,1,0,0,31,1,14,0,0,0 +cooneti01,2015,1,SLN,NL,1,0,6,6,0,0,0,94,28,11,3,10,29,0.241,3.16,2,1,1,0,130,0,12,3,0,1 +copelsc01,2015,1,TOR,AL,1,1,5,3,0,0,0,46,24,11,1,2,6,0.369,6.46,0,0,0,0,69,2,11,1,1,2 +corbipa01,2015,1,ARI,NL,6,5,16,16,0,0,0,255,91,34,9,17,78,0.272,3.60,0,4,2,0,357,0,34,2,1,7 +cordier01,2015,1,MIA,NL,0,0,8,0,0,0,0,37,13,8,1,6,7,0.271,5.84,1,0,1,1,56,0,8,0,1,1 +cornejo01,2015,1,ATL,NL,0,0,1,0,0,0,0,3,3,4,1,1,1,0.500,36.00,0,0,0,0,7,1,4,0,0,0 +correke01,2015,1,PHI,NL,0,3,5,5,0,0,0,70,37,17,4,8,14,0.363,6.56,0,0,0,0,114,0,23,2,2,1 +cosarja01,2015,1,MIA,NL,2,5,14,13,0,0,0,209,63,35,10,33,47,0.243,4.52,1,7,1,0,296,0,35,2,1,6 +cothaca01,2015,1,NYA,AL,1,0,12,0,0,0,0,29,14,7,4,1,11,0.326,6.52,0,0,0,0,45,4,7,1,0,0 +cottsne01,2015,1,MIL,NL,1,0,51,0,0,0,0,149,44,18,9,17,49,0.239,3.26,2,2,3,0,209,9,18,3,1,5 +cottsne01,2015,2,MIN,AL,0,0,17,0,0,0,0,41,14,6,3,5,9,0.264,3.95,0,0,1,0,60,1,8,0,1,0 +couloda01,2015,1,LAN,NL,0,0,5,0,0,0,0,25,9,7,0,6,7,0.265,7.56,0,1,0,0,40,3,7,0,0,0 +couloda01,2015,2,OAK,AL,0,0,9,0,0,0,0,23,8,3,0,3,4,0.276,3.52,0,1,0,0,32,1,3,0,0,2 +cravyty01,2015,1,MIL,NL,0,8,14,7,0,0,0,128,47,27,5,22,35,0.281,5.70,1,0,2,0,193,1,30,2,0,5 +crockky01,2015,1,CLE,AL,0,0,31,0,0,0,0,53,17,8,1,7,15,0.266,4.08,0,0,1,0,74,4,9,0,2,4 +cuetojo01,2015,1,CIN,NL,7,6,19,19,1,1,0,392,93,38,11,29,120,0.196,2.62,1,0,6,4,516,0,42,4,3,10 +cuetojo01,2015,2,KCA,AL,4,7,13,13,1,1,0,244,101,43,10,17,56,0.307,4.76,0,0,2,0,350,0,45,1,1,8 +cunnibr02,2015,1,ATL,NL,2,2,39,0,0,0,0,105,27,18,4,22,37,0.213,4.63,2,1,0,0,151,12,20,1,1,4 +danksjo01,2015,1,CHA,AL,7,15,30,30,2,1,0,533,195,93,24,56,124,0.281,4.71,1,8,3,1,768,0,104,8,6,15 +davieky01,2015,1,NYA,AL,0,0,1,0,0,0,0,7,3,0,0,0,2,0.300,0.00,0,0,0,0,10,1,0,0,0,1 +davieza01,2015,1,MIL,NL,3,2,6,6,0,0,0,102,26,14,2,15,24,0.211,3.71,0,0,0,0,139,0,14,1,0,2 +davisik02,2015,1,OAK,AL,0,0,2,0,0,0,0,6,1,0,0,1,1,0.143,0.00,0,0,0,0,8,2,0,0,0,0 +daviswa01,2015,1,KCA,AL,8,1,69,0,0,0,17,202,33,7,3,20,78,0.144,0.94,1,1,0,0,251,24,8,0,2,4 +deckeja01,2015,1,PIT,NL,0,0,1,0,0,0,0,3,2,0,0,0,0,0.400,0.00,0,0,0,0,5,1,0,0,0,0 +dedunsa01,2015,1,HOU,AL,0,1,9,2,0,0,1,63,24,16,3,9,17,0.293,6.86,0,3,3,0,96,4,16,1,1,3 +defraju01,2015,1,PHI,NL,0,2,61,0,0,0,0,240,92,49,9,32,68,0.291,5.51,2,7,5,0,362,16,52,4,5,5 +degroja01,2015,1,NYN,NL,14,8,30,30,0,0,0,573,149,54,16,38,205,0.215,2.54,2,6,2,0,751,0,59,10,7,8 +delabst01,2015,1,TOR,AL,2,0,31,0,0,0,1,88,28,17,5,14,30,0.250,5.22,1,6,1,0,129,12,19,1,1,1 +delarjo01,2015,1,COL,NL,9,7,26,26,1,0,0,447,137,69,17,65,134,0.247,4.17,3,6,3,2,635,0,73,4,8,18 +delarru01,2015,1,ARI,NL,14,9,32,32,0,0,0,566,193,98,32,63,150,0.265,4.67,3,2,4,2,809,0,103,8,5,18 +delgara01,2015,1,ARI,NL,8,4,64,1,0,0,1,216,63,26,7,33,73,0.234,3.25,2,7,1,0,308,13,28,2,2,7 +delosab01,2015,1,WAS,NL,0,0,2,0,0,0,0,5,2,1,1,1,3,0.286,5.40,0,0,0,0,8,2,1,0,0,0 +denorch01,2015,1,CHN,NL,0,0,1,0,0,0,0,1,0,0,0,0,0,0.000,0.00,0,0,0,0,1,1,0,0,0,0 +depaujo02,2015,1,NYA,AL,0,0,1,0,0,0,0,10,2,1,1,4,2,0.182,2.70,0,0,0,0,15,1,1,0,0,1 +desclan01,2015,1,CIN,NL,9,13,31,31,0,0,0,554,194,83,17,55,151,0.273,4.05,5,6,5,0,785,0,93,10,5,22 +despaod01,2015,1,SDN,NL,5,9,34,18,0,0,0,377,142,81,17,32,69,0.287,5.80,3,7,9,0,547,5,82,8,3,9 +detwiro01,2015,1,TEX,AL,0,5,17,7,0,0,0,129,62,34,9,20,28,0.343,7.12,0,3,3,0,208,4,37,1,3,2 +detwiro01,2015,2,ATL,NL,1,0,24,0,0,0,0,46,20,13,1,16,13,0.333,7.63,1,0,3,0,80,3,14,0,1,4 +diazja01,2015,1,COL,NL,0,1,21,0,0,0,0,57,16,5,2,6,18,0.222,2.37,0,0,0,0,78,5,6,0,0,0 +diazju03,2015,1,CIN,NL,2,1,61,0,0,0,1,181,58,28,9,18,70,0.253,4.18,3,5,3,1,255,16,29,3,2,3 +dickera01,2015,1,TOR,AL,11,11,33,33,2,0,0,643,195,93,25,61,126,0.244,3.91,1,9,11,2,884,0,97,3,11,18 +diekmja01,2015,1,PHI,NL,2,1,41,0,0,0,0,110,40,21,3,24,49,0.268,5.15,0,1,2,0,175,6,23,0,0,2 +diekmja01,2015,2,TEX,AL,0,0,26,0,0,0,0,65,13,5,2,7,20,0.169,2.08,0,1,1,0,85,1,5,0,0,3 +dominjo01,2015,1,TBA,AL,1,0,4,0,0,0,0,17,2,0,0,2,5,0.125,0.00,0,0,0,0,19,2,0,1,0,2 +doolise01,2015,1,OAK,AL,1,0,12,0,0,0,4,41,12,6,1,5,15,0.235,3.95,0,0,0,0,57,7,6,0,1,1 +doubrfe01,2015,1,TOR,AL,1,1,5,4,0,0,0,68,32,12,1,5,13,0.340,4.76,1,1,1,0,101,0,15,0,1,5 +doubrfe01,2015,2,OAK,AL,2,2,11,8,0,0,1,158,55,34,9,21,43,0.278,5.81,0,2,0,0,227,2,35,4,4,2 +drabeky01,2015,1,CHA,AL,0,0,3,0,0,0,0,16,9,3,1,2,3,0.375,5.06,0,0,0,0,26,3,3,0,0,1 +drakeol01,2015,1,BAL,AL,0,0,13,0,0,0,0,47,16,5,1,9,17,0.262,2.87,0,3,0,0,72,5,7,0,2,0 +duensbr01,2015,1,MIN,AL,4,1,55,0,0,0,1,146,46,23,5,21,24,0.254,4.25,4,3,4,0,209,9,24,2,1,7 +duffety01,2015,1,MIN,AL,5,1,10,10,0,0,0,174,56,20,4,20,53,0.256,3.10,0,1,0,0,242,0,20,3,0,10 +duffyda01,2015,1,KCA,AL,7,8,30,24,0,0,1,410,137,62,15,53,102,0.264,4.08,0,11,9,0,588,1,64,3,5,17 +dukeza01,2015,1,CHA,AL,3,6,71,0,0,0,1,182,47,23,9,32,66,0.218,3.41,4,0,3,0,255,14,26,2,1,10 +dullry01,2015,1,OAK,AL,1,2,13,0,0,0,1,51,12,8,4,6,16,0.203,4.24,1,0,0,0,66,3,8,0,1,2 +dunnmi01,2015,1,MIA,NL,2,5,72,0,0,0,0,162,46,27,6,29,65,0.227,4.50,1,2,2,0,235,9,27,0,1,3 +dysonsa01,2015,1,MIA,NL,3,3,44,0,0,0,0,132,41,18,3,17,41,0.247,3.68,1,6,3,0,190,10,21,3,1,3 +dysonsa01,2015,2,TEX,AL,2,1,31,0,0,0,2,94,24,4,1,4,30,0.212,1.15,0,2,1,0,119,6,5,1,0,3 +edwarca01,2015,1,CHN,NL,0,0,5,0,0,0,0,14,3,2,0,3,4,0.188,3.86,0,0,0,0,19,3,3,0,0,1 +edwarjo02,2015,1,TEX,AL,0,0,11,0,0,0,0,18,6,4,1,8,6,0.273,6.00,0,1,0,0,31,2,4,0,1,1 +edwarjo02,2015,2,SDN,NL,0,0,11,0,0,0,0,32,6,4,3,8,16,0.171,3.38,0,1,0,0,44,5,4,0,1,0 +eickhje01,2015,1,PHI,NL,3,3,8,8,0,0,0,153,40,15,5,13,49,0.212,2.65,0,1,0,0,203,0,16,0,1,2 +eliasro01,2015,1,SEA,AL,5,8,22,20,0,0,0,346,106,53,15,44,97,0.245,4.14,1,1,9,1,490,0,57,1,4,11 +ellinbr02,2015,1,MIA,NL,2,1,23,0,0,0,0,75,17,8,1,13,18,0.193,2.88,2,1,2,0,105,9,10,0,2,3 +elmorja01,2015,1,TBA,AL,0,0,1,0,0,0,0,3,3,1,1,0,0,0.500,9.00,0,0,0,0,6,0,1,0,0,0 +eovalna01,2015,1,NYA,AL,14,3,27,27,0,0,0,463,175,72,10,49,121,0.285,4.20,0,8,3,0,673,0,72,3,3,14 +erlinro01,2015,1,SDN,NL,1,2,3,3,0,0,0,51,16,9,1,2,10,0.258,4.76,0,1,1,0,65,0,9,0,0,6 +estrama01,2015,1,TOR,AL,13,8,34,28,0,0,0,543,134,63,24,55,131,0.203,3.13,2,2,5,0,725,3,67,2,3,6 +evelada01,2015,1,ATL,NL,0,1,10,0,0,0,0,10,5,2,1,3,4,0.357,5.40,1,1,0,0,18,1,2,0,1,0 +familje01,2015,1,NYN,NL,2,2,76,0,0,0,43,234,59,16,6,19,86,0.207,1.85,1,4,2,0,308,65,16,1,1,6 +farmebu01,2015,1,DET,AL,0,4,14,5,0,0,0,121,53,33,10,17,24,0.323,7.36,2,1,3,0,186,0,35,1,1,6 +farquda01,2015,1,SEA,AL,1,8,43,0,0,0,1,153,53,29,9,17,48,0.266,5.12,2,1,1,1,219,10,33,1,1,6 +faulkan01,2015,1,TEX,AL,0,0,11,0,0,0,0,29,8,3,2,3,10,0.216,2.79,1,0,0,0,40,1,3,0,0,1 +feldmsc01,2015,1,HOU,AL,5,5,18,18,0,0,0,325,115,47,13,27,61,0.275,3.90,1,8,0,0,451,0,49,1,5,15 +felizmi01,2015,1,HOU,AL,0,0,5,0,0,0,0,24,9,7,2,4,7,0.273,7.88,0,0,1,1,38,5,7,0,0,0 +felizne01,2015,1,TEX,AL,1,2,18,0,0,0,6,59,24,10,2,9,16,0.293,4.58,3,2,0,0,91,12,10,0,0,1 +felizne01,2015,2,DET,AL,2,2,30,0,0,0,4,85,33,24,3,9,23,0.303,7.62,3,2,1,0,121,12,24,1,1,4 +fernajo02,2015,1,MIA,NL,6,1,11,11,0,0,0,194,61,21,4,14,79,0.249,2.92,0,2,2,0,265,0,21,0,4,6 +ferreje01,2015,1,DET,AL,0,0,9,0,0,0,0,34,12,8,3,4,6,0.267,6.35,0,0,0,0,50,2,8,0,1,1 +fieldjo03,2015,1,HOU,AL,4,1,54,0,0,0,0,152,39,20,2,19,67,0.210,3.55,3,1,1,0,209,19,20,2,1,0 +fienca01,2015,1,MIN,AL,4,6,62,0,0,0,0,190,61,25,6,8,41,0.250,3.55,0,0,0,0,257,6,26,2,3,1 +fiersmi01,2015,1,MIL,NL,5,9,21,21,0,0,0,354,117,51,14,43,121,0.259,3.89,5,6,5,0,509,0,57,3,6,7 +fiersmi01,2015,2,HOU,AL,2,1,10,9,1,1,0,187,45,23,10,21,59,0.197,3.32,0,2,1,0,252,0,26,0,2,1 +finnebr01,2015,1,KCA,AL,3,0,14,0,0,0,0,73,16,8,3,13,21,0.193,2.96,0,0,1,0,99,3,8,1,1,5 +finnebr01,2015,2,CIN,NL,2,2,6,4,0,0,0,71,21,11,5,8,24,0.239,4.18,0,0,0,0,98,0,11,2,0,1 +fistedo01,2015,1,WAS,NL,5,7,25,15,0,0,1,309,120,48,14,24,63,0.295,4.19,3,1,6,0,449,2,56,7,5,9 +flandyo01,2015,1,COL,NL,3,3,19,10,0,0,0,205,73,36,14,25,43,0.277,4.74,3,1,1,1,296,2,36,5,1,11 +floreke01,2015,1,MIA,NL,1,2,7,1,0,0,0,38,16,7,0,4,9,0.314,4.97,1,0,1,0,57,3,8,0,1,2 +floydga01,2015,1,CLE,AL,0,0,7,0,0,0,0,40,11,4,0,4,7,0.220,2.70,0,1,1,0,55,2,4,0,0,1 +foltymi01,2015,1,ATL,NL,4,6,18,15,0,0,0,260,112,55,17,29,77,0.313,5.71,0,3,4,1,399,1,63,2,6,6 +francje01,2015,1,TOR,AL,1,2,14,0,0,0,0,66,27,15,3,9,21,0.300,6.14,0,2,1,0,100,5,16,0,0,3 +francje02,2015,1,PHI,NL,0,0,1,0,0,0,0,6,1,2,1,3,1,0.167,9.00,0,0,1,0,11,1,2,0,1,0 +frankni01,2015,1,TBA,AL,0,0,1,0,0,0,0,3,3,2,1,0,0,0.500,18.00,0,0,1,0,7,1,2,0,0,0 +frasoja01,2015,1,KCA,AL,1,0,26,0,0,0,0,70,24,4,1,15,18,0.276,1.54,2,2,0,0,104,10,5,1,1,4 +frasoja01,2015,2,ATL,NL,0,0,6,0,0,0,0,14,3,0,0,3,4,0.176,0.00,1,0,0,0,20,0,0,0,0,0 +freemsa01,2015,1,TEX,AL,0,0,54,0,0,0,0,115,31,13,4,25,40,0.218,3.05,0,0,3,0,171,10,13,0,1,2 +friasca01,2015,1,LAN,NL,5,5,17,13,0,0,0,233,88,35,7,26,43,0.297,4.06,1,3,3,0,333,1,38,4,4,11 +friedch01,2015,1,COL,NL,0,4,68,0,0,0,0,175,75,34,5,25,45,0.321,5.25,2,3,1,0,270,13,37,4,6,8 +frierer01,2015,1,TBA,AL,1,0,22,0,0,0,2,70,20,12,6,11,19,0.233,4.63,0,1,1,0,100,6,12,0,2,2 +fujikky01,2015,1,TEX,AL,0,0,2,0,0,0,0,5,2,3,1,0,1,0.286,16.20,0,0,1,0,8,0,3,0,0,0 +furbuch01,2015,1,SEA,AL,1,1,33,0,0,0,0,65,9,5,2,5,17,0.122,2.08,2,5,2,0,82,4,6,1,0,0 +gallayo01,2015,1,TEX,AL,13,11,33,33,0,0,0,553,193,70,15,68,121,0.268,3.42,0,10,1,0,793,0,76,1,3,18 +garcefr01,2015,1,SDN,NL,0,1,40,1,0,0,0,114,41,22,9,22,30,0.281,5.21,1,3,1,0,173,11,23,1,2,4 +garcija01,2015,1,SFN,NL,4,2,67,0,0,0,38,174,51,18,6,23,62,0.236,2.79,2,1,2,0,244,55,19,2,1,5 +garcija02,2015,1,SLN,NL,10,6,20,20,0,0,0,389,106,35,6,30,97,0.225,2.43,0,4,3,0,510,0,37,3,3,17 +garcija03,2015,1,BAL,AL,1,0,21,0,0,0,0,89,25,14,3,17,22,0.223,4.25,1,2,2,0,132,9,19,0,1,1 +garcile02,2015,1,CHA,AL,0,0,1,0,0,0,0,3,1,0,0,0,1,0.250,0.00,0,1,1,0,5,0,0,0,0,0 +garcilu03,2015,1,PHI,NL,4,6,72,0,0,0,2,200,72,26,4,37,63,0.275,3.51,8,6,0,1,304,14,28,3,2,9 +garciyi01,2015,1,LAN,NL,3,5,59,1,0,0,1,170,44,21,8,10,68,0.209,3.34,1,1,2,0,225,15,23,0,2,0 +garzama01,2015,1,MIL,NL,6,14,26,25,0,0,0,446,176,93,23,57,104,0.294,5.63,3,7,2,0,666,1,102,7,2,14 +gausmke01,2015,1,BAL,AL,4,7,25,17,0,0,0,337,109,53,17,29,103,0.251,4.25,1,7,2,0,470,1,56,2,3,2 +gearrco01,2015,1,SFN,NL,0,0,7,0,0,0,0,11,1,2,0,1,5,0.083,4.91,0,0,0,0,13,0,2,0,0,0 +geedi01,2015,1,NYN,NL,0,3,8,7,0,0,0,119,55,26,5,11,25,0.329,5.90,3,0,1,0,183,0,29,2,2,4 +geltzst01,2015,1,TBA,AL,2,6,70,2,0,0,2,202,45,28,8,26,61,0.189,3.74,3,4,2,0,269,12,31,2,0,5 +germego01,2015,1,CHN,NL,0,0,6,0,0,0,0,18,8,5,0,5,8,0.348,7.50,2,0,0,0,29,2,5,1,0,2 +germego01,2015,2,COL,NL,0,0,29,1,0,0,1,98,33,14,4,21,25,0.262,3.86,1,3,0,0,147,3,14,0,0,4 +gibsoky01,2015,1,MIN,AL,11,11,32,32,1,0,0,584,186,83,18,65,145,0.252,3.84,6,7,7,0,821,0,88,6,6,27 +gileske01,2015,1,PHI,NL,6,3,69,0,0,0,15,210,59,14,2,25,87,0.219,1.80,2,1,1,0,298,28,23,1,2,3 +gilmase01,2015,1,NYN,NL,3,2,50,1,0,0,0,172,50,17,2,18,54,0.236,2.67,5,1,2,0,235,13,17,2,1,6 +givenmy01,2015,1,BAL,AL,2,0,22,0,0,0,0,90,20,6,1,6,38,0.185,1.80,0,0,1,0,117,5,7,1,1,1 +godleza01,2015,1,ARI,NL,5,1,9,6,0,0,0,110,29,13,4,17,34,0.227,3.19,1,2,3,0,150,1,13,1,1,7 +goedder01,2015,1,NYN,NL,1,1,35,0,0,0,0,100,24,9,1,9,34,0.203,2.43,2,2,2,0,132,9,9,0,3,2 +goforda01,2015,1,MIL,NL,1,0,20,0,0,0,0,74,32,11,4,8,24,0.323,4.01,2,2,0,0,111,9,13,2,2,2 +gomesbr01,2015,1,TBA,AL,2,6,63,0,0,0,1,177,55,28,10,15,44,0.248,4.27,2,4,3,1,245,13,28,3,2,3 +gomesjo01,2015,1,ATL,NL,0,0,1,0,0,0,0,3,3,2,1,0,1,0.600,18.00,0,0,0,0,6,1,2,0,1,0 +gomezje01,2015,1,PHI,NL,2,3,65,0,0,0,0,224,82,25,4,17,50,0.278,3.01,4,3,2,0,319,21,28,1,4,6 +gonzach01,2015,1,TEX,AL,4,6,14,10,1,1,0,201,49,29,6,32,30,0.202,3.90,1,2,3,1,280,1,33,1,2,5 +gonzagi01,2015,1,WAS,NL,11,8,31,31,0,0,0,527,181,74,8,69,169,0.269,3.79,3,4,4,0,758,0,79,3,9,16 +gonzama02,2015,1,SLN,NL,0,0,1,1,0,0,0,8,7,4,1,1,1,0.500,13.50,0,0,0,0,16,0,4,0,1,0 +gonzami03,2015,1,BAL,AL,9,12,26,26,0,0,0,434,151,79,24,51,109,0.270,4.91,2,4,8,0,622,0,81,2,2,15 +gonzase01,2015,1,PHI,NL,3,3,7,7,0,0,0,92,44,27,5,7,28,0.346,7.92,0,0,4,1,143,0,27,1,4,1 +goodyni01,2015,1,NYA,AL,0,0,7,0,0,0,0,17,6,3,0,3,3,0.273,4.76,0,0,1,0,26,5,3,0,0,1 +gorzeto01,2015,1,DET,AL,2,2,48,0,0,0,0,118,45,26,4,23,36,0.292,5.95,2,1,2,0,181,14,28,2,0,3 +gotttr01,2015,1,LAA,AL,4,2,48,0,0,0,0,143,43,16,2,16,27,0.242,3.02,3,1,3,0,202,7,18,2,3,3 +gracema02,2015,1,WAS,NL,2,1,26,0,0,0,0,51,26,8,0,8,14,0.356,4.24,2,1,1,0,84,5,11,0,2,2 +grahajr01,2015,1,MIN,AL,1,1,39,1,0,0,0,191,73,35,10,21,53,0.287,4.95,0,5,4,1,283,19,41,2,2,7 +graveke01,2015,1,OAK,AL,6,9,21,21,1,0,0,347,126,52,15,38,77,0.276,4.05,0,4,5,0,502,0,57,1,2,9 +grayjo02,2015,1,COL,NL,0,2,9,9,0,0,0,122,52,25,4,14,40,0.319,5.53,2,3,2,0,185,0,26,2,4,3 +grayso01,2015,1,OAK,AL,14,7,31,31,3,2,0,624,166,63,17,59,169,0.217,2.73,0,13,2,0,831,0,71,1,4,19 +greenni02,2015,1,SLN,NL,0,1,1,0,0,0,0,0,2,2,1,0,0,1.000,,0,0,0,0,2,1,2,0,0,0 +greensh02,2015,1,DET,AL,4,8,18,16,0,0,0,251,103,64,13,27,50,0.308,6.88,4,1,6,0,373,1,67,2,4,10 +gregelu01,2015,1,HOU,AL,7,3,64,0,0,0,31,183,48,21,5,10,59,0.213,3.10,2,1,2,0,239,53,24,2,0,6 +greggke01,2015,1,CIN,NL,0,2,11,0,0,0,0,32,13,12,3,5,14,0.289,10.13,0,1,0,0,51,2,12,1,0,0 +greinza01,2015,1,LAN,NL,19,3,32,32,1,0,0,668,148,41,14,40,200,0.187,1.66,1,7,5,0,843,0,43,6,2,15 +grillja01,2015,1,ATL,NL,3,4,36,0,0,0,24,101,28,11,2,10,45,0.217,2.94,1,2,0,0,140,29,13,0,1,0 +grimmju01,2015,1,CHN,NL,3,5,62,0,0,0,3,149,31,11,4,26,67,0.178,1.99,1,8,1,0,204,11,18,0,3,4 +guaipma01,2015,1,SEA,AL,0,3,21,0,0,0,0,80,34,16,5,13,22,0.330,5.40,1,1,3,0,121,2,19,1,1,4 +guerrde01,2015,1,PIT,NL,2,0,10,0,0,0,0,50,26,12,5,3,17,0.371,6.48,0,2,1,0,74,4,12,0,0,2 +guerrja01,2015,1,CHA,AL,0,0,3,0,0,0,0,5,2,0,0,1,0,0.333,0.00,0,0,0,0,7,0,0,0,0,1 +guerrju02,2015,1,CHA,AL,0,0,3,0,0,0,0,12,7,3,1,1,3,0.412,6.75,1,1,0,0,18,3,3,0,0,0 +guilmpr01,2015,1,TBA,AL,0,0,3,0,0,0,0,16,5,3,1,2,5,0.250,5.06,0,0,0,0,23,2,3,0,1,0 +guilmpr01,2015,2,MIL,NL,0,0,2,0,0,0,0,6,4,6,1,2,1,0.444,27.00,0,0,0,0,12,1,6,0,1,0 +gurkaja01,2015,1,COL,NL,0,0,9,0,0,0,0,23,16,8,1,2,7,0.432,9.39,0,0,0,0,39,4,8,0,0,0 +guthrje01,2015,1,KCA,AL,8,8,30,24,0,0,0,445,186,98,29,44,84,0.310,5.95,1,4,9,0,664,4,101,5,6,7 +hagadni01,2015,1,CLE,AL,0,1,36,0,0,0,0,82,30,13,3,12,28,0.273,4.28,1,2,0,0,124,6,16,1,1,2 +hahnje01,2015,1,OAK,AL,6,6,16,16,1,1,0,290,88,36,5,25,64,0.238,3.35,1,7,8,0,406,0,46,1,2,15 +haleda02,2015,1,COL,NL,5,5,17,12,0,0,0,235,95,53,14,20,61,0.298,6.09,2,11,2,0,346,0,56,3,2,6 +hallco01,2015,1,SFN,NL,0,0,7,0,0,0,0,25,10,6,1,4,7,0.278,6.48,0,0,1,0,41,1,6,0,0,0 +hamelco01,2015,1,PHI,NL,6,7,20,20,1,1,0,386,113,52,12,39,137,0.233,3.64,3,7,6,2,537,0,53,5,1,6 +hamelco01,2015,2,TEX,AL,7,1,12,12,1,0,0,251,77,34,10,23,78,0.245,3.66,0,2,4,2,343,0,35,1,1,13 +hammeja01,2015,1,CHN,NL,10,7,31,31,0,0,0,512,158,71,23,40,172,0.242,3.74,4,10,6,0,710,0,79,4,6,13 +handbr01,2015,1,MIA,NL,4,7,38,12,0,0,0,280,107,55,9,32,67,0.292,5.30,1,2,3,0,408,7,55,5,2,12 +handdo01,2015,1,CIN,NL,0,0,1,0,0,0,0,9,2,0,0,1,3,0.182,0.00,0,0,1,0,13,0,0,0,0,0 +happja01,2015,1,SEA,AL,4,6,21,20,0,0,0,326,121,56,13,32,82,0.279,4.64,3,4,2,0,468,0,58,1,0,9 +happja01,2015,2,PIT,NL,7,2,11,11,0,0,0,190,52,13,3,13,69,0.221,1.85,1,2,0,0,249,0,13,1,0,5 +haranaa01,2015,1,PHI,NL,6,15,29,29,0,0,0,517,189,93,26,51,108,0.278,4.86,3,4,6,0,748,0,100,8,2,16 +hardybl01,2015,1,DET,AL,5,3,70,0,0,0,0,184,61,21,2,22,55,0.260,3.08,2,5,1,0,265,11,23,3,4,3 +harenda01,2015,1,MIA,NL,7,7,21,21,0,0,0,387,116,49,21,25,88,0.241,3.42,1,2,7,0,524,0,50,6,4,10 +harenda01,2015,2,CHN,NL,4,2,11,11,0,0,0,175,58,26,10,13,44,0.256,4.01,1,3,1,0,244,0,29,2,1,4 +harrima01,2015,1,TEX,AL,1,2,3,3,0,0,0,48,19,12,3,6,5,0.306,6.75,0,0,0,1,69,0,12,0,1,4 +harrimi03,2015,1,SLN,NL,2,1,26,0,0,0,0,81,30,11,4,13,15,0.278,3.67,0,2,0,0,122,7,14,1,0,2 +harriwi02,2015,1,HOU,AL,5,5,68,0,0,0,2,213,42,15,8,22,68,0.168,1.90,1,2,1,0,276,18,18,2,1,4 +harvema01,2015,1,NYN,NL,13,8,29,29,0,0,0,568,156,57,18,37,188,0.222,2.71,2,4,5,0,755,0,62,7,2,13 +hatchch02,2015,1,LAN,NL,3,5,49,0,0,0,4,117,35,16,4,13,45,0.238,3.69,2,3,3,0,166,12,19,2,1,4 +hatlema01,2015,1,SLN,NL,0,0,2,0,0,0,0,4,1,0,0,2,2,0.200,0.00,0,0,0,0,7,1,0,0,0,0 +hawkila01,2015,1,COL,NL,2,1,24,0,0,0,2,67,22,9,3,4,20,0.259,3.63,1,1,0,0,89,11,9,0,0,2 +hawkila01,2015,2,TOR,AL,1,0,18,0,0,0,1,49,22,5,1,3,14,0.319,2.76,0,0,0,0,73,5,7,1,0,2 +heanean01,2015,1,LAA,AL,6,4,18,18,0,0,0,317,99,41,9,28,78,0.248,3.49,1,4,6,0,438,0,41,1,3,10 +hellije01,2015,1,ARI,NL,9,12,27,27,0,0,0,438,151,75,22,43,121,0.264,4.62,3,5,6,0,636,0,79,8,6,9 +hembrhe01,2015,1,BOS,AL,2,0,22,0,0,0,0,76,25,10,5,9,15,0.258,3.55,2,1,0,0,106,9,10,0,0,3 +hendrky01,2015,1,CHN,NL,8,7,32,32,1,1,0,540,166,79,17,43,167,0.244,3.95,1,3,8,1,739,0,82,6,0,15 +hendrli01,2015,1,TOR,AL,5,0,58,0,0,0,0,194,59,21,3,11,71,0.240,2.92,1,4,2,0,261,14,23,0,2,3 +hernada01,2015,1,ARI,NL,1,5,40,0,0,0,0,101,33,16,6,11,33,0.256,4.28,0,1,3,0,144,7,18,1,0,3 +hernafe02,2015,1,SEA,AL,18,9,31,31,2,2,0,605,180,79,23,58,191,0.240,3.53,0,10,9,0,826,0,80,4,4,18 +herreke01,2015,1,KCA,AL,4,3,72,0,0,0,0,209,52,21,5,26,64,0.206,2.71,1,4,2,0,286,8,23,1,5,3 +hesslke01,2015,1,ARI,NL,0,1,18,0,0,0,0,37,16,11,4,4,12,0.302,8.03,1,1,0,0,57,1,11,0,0,0 +hestoch01,2015,1,SFN,NL,12,11,31,31,2,1,0,533,169,78,16,64,141,0.253,3.95,3,5,13,1,746,0,82,2,0,26 +hillri01,2015,1,BOS,AL,2,1,4,4,1,1,0,87,14,5,2,5,36,0.141,1.55,0,0,2,0,106,0,5,0,0,3 +hillta01,2015,1,WAS,NL,0,0,6,0,0,0,0,36,14,5,2,4,9,0.304,3.75,0,0,1,0,51,4,5,0,0,4 +hinojda01,2015,1,BOS,AL,0,0,1,0,0,0,0,5,0,0,0,3,2,0.000,0.00,0,0,1,0,9,1,0,0,0,0 +hinojda01,2015,2,PHI,NL,2,0,18,0,0,0,0,69,15,2,1,8,21,0.176,0.78,1,1,0,0,93,4,3,0,0,1 +hochelu01,2015,1,KCA,AL,1,1,49,0,0,0,1,152,49,21,7,16,49,0.250,3.73,0,3,1,0,214,16,23,0,1,2 +hollade01,2015,1,TEX,AL,4,3,10,10,1,1,0,176,59,32,11,17,41,0.269,4.91,2,1,5,0,245,0,32,3,1,9 +hollagr01,2015,1,KCA,AL,3,2,48,0,0,0,32,134,39,19,2,26,49,0.239,3.83,1,7,0,0,193,40,20,3,1,4 +holmbda01,2015,1,CIN,NL,1,4,6,6,0,0,0,85,36,24,10,16,15,0.321,7.62,0,1,2,0,136,0,24,4,2,3 +hoovejj01,2015,1,CIN,NL,8,2,67,0,0,0,1,193,44,21,7,31,52,0.196,2.94,1,3,2,0,264,12,24,5,2,6 +housead01,2015,1,MIL,NL,0,0,2,0,0,0,0,6,1,0,0,2,0,0.167,0.00,0,0,0,0,8,2,0,0,0,1 +housetj01,2015,1,CLE,AL,0,4,4,4,0,0,0,39,21,19,1,12,7,0.362,13.15,1,0,2,0,73,0,19,0,1,1 +howeljp01,2015,1,LAN,NL,6,1,65,0,0,0,1,132,47,7,3,14,39,0.272,1.43,1,3,2,1,190,18,9,0,1,6 +hudsoda01,2015,1,ARI,NL,4,3,64,1,0,0,4,203,64,29,7,25,71,0.245,3.86,2,5,0,0,290,13,34,1,3,2 +hudsoti01,2015,1,SFN,NL,8,9,24,22,0,0,0,371,134,61,13,37,64,0.282,4.44,1,4,7,0,525,0,62,2,3,19 +huffda01,2015,1,LAN,NL,0,0,3,1,0,0,0,18,11,6,2,1,4,0.407,9.00,0,0,1,0,30,1,6,1,0,0 +hugheja02,2015,1,PIT,NL,3,1,76,0,0,0,0,201,70,17,3,19,36,0.282,2.28,2,3,7,0,284,11,21,6,4,11 +hugheph01,2015,1,MIN,AL,11,9,27,25,1,0,0,466,184,76,29,16,94,0.293,4.40,0,1,2,0,651,1,76,1,3,11 +hunteto02,2015,1,BAL,AL,2,2,39,0,0,0,0,134,41,18,3,11,32,0.250,3.63,2,2,1,0,180,12,19,1,3,4 +hunteto02,2015,2,CHN,NL,2,0,19,0,0,0,1,47,20,10,4,3,15,0.303,5.74,0,0,0,0,69,5,10,0,0,1 +hutchdr01,2015,1,TOR,AL,13,5,30,28,1,1,0,451,179,93,22,44,129,0.297,5.57,0,7,11,0,664,0,103,0,6,15 +hynesco01,2015,1,TOR,AL,0,0,5,0,0,0,0,9,8,2,0,2,4,0.500,6.00,1,0,0,0,18,1,2,0,0,1 +ibarred01,2015,1,LAA,AL,0,0,2,0,0,0,0,12,4,1,0,3,3,0.250,2.25,0,1,0,0,19,1,1,0,0,0 +iglesra01,2015,1,CIN,NL,3,7,18,16,0,0,0,286,81,44,11,28,104,0.228,4.15,0,2,7,2,395,1,45,4,0,8 +iwakuhi01,2015,1,SEA,AL,9,5,20,20,1,1,0,389,117,51,18,21,111,0.240,3.54,1,1,1,0,516,0,53,4,3,9 +jacksed01,2015,1,CHN,NL,2,1,23,0,0,0,0,93,30,11,0,12,23,0.254,3.19,1,3,1,1,134,11,14,1,2,2 +jacksed01,2015,2,ATL,NL,2,2,24,0,0,0,1,74,14,8,4,9,17,0.167,2.92,0,2,0,0,94,7,11,0,1,3 +jacksja01,2015,1,SDN,NL,0,0,6,0,0,0,0,13,7,3,0,1,4,0.368,6.23,0,0,0,0,20,1,3,0,0,1 +jackslu01,2015,1,TEX,AL,0,0,7,0,0,0,0,19,5,3,1,2,6,0.200,4.26,0,1,0,0,27,4,3,0,0,0 +jaimeju01,2015,1,ATL,NL,0,1,2,0,0,0,0,4,0,1,0,4,1,0.000,6.75,1,0,0,0,8,1,1,1,0,0 +janseke01,2015,1,LAN,NL,2,1,54,0,0,0,36,157,33,14,6,8,80,0.176,2.41,0,0,2,0,200,50,14,0,2,0 +janssca01,2015,1,WAS,NL,2,5,48,0,0,0,0,120,38,22,5,8,27,0.248,4.95,3,1,1,0,166,12,22,3,1,2 +jeffrje01,2015,1,MIL,NL,5,0,72,0,0,0,0,204,64,20,5,22,67,0.249,2.65,5,4,3,2,285,8,22,3,0,12 +jenkich01,2015,1,TOR,AL,0,0,2,0,0,0,0,11,3,2,1,3,2,0.214,4.91,0,0,0,0,17,1,2,0,0,0 +jennida01,2015,1,CHA,AL,2,3,53,0,0,0,0,169,55,25,3,24,46,0.256,3.99,6,4,0,0,244,17,28,4,1,6 +jepseke01,2015,1,TBA,AL,2,5,46,0,0,0,5,125,34,13,4,20,34,0.227,2.81,1,2,0,0,176,6,15,1,5,3 +jepseke01,2015,2,MIN,AL,1,1,29,0,0,0,10,84,18,5,1,7,25,0.176,1.61,0,3,0,0,109,17,5,0,0,1 +jimence01,2015,1,PHI,NL,0,0,3,0,0,0,0,10,1,0,0,0,4,0.100,0.00,0,0,0,0,10,2,0,0,0,1 +jimence01,2015,2,MIL,NL,0,0,16,0,0,0,0,59,16,8,2,8,21,0.219,3.66,0,0,0,0,81,2,8,0,0,2 +jimenub01,2015,1,BAL,AL,12,10,32,32,0,0,0,552,182,84,20,68,168,0.257,4.11,1,6,11,0,791,0,89,1,4,16 +johnsbr02,2015,1,BOS,AL,0,1,1,1,0,0,0,13,3,4,0,4,3,0.214,8.31,0,0,0,0,19,0,4,0,1,1 +johnser04,2015,1,CHA,AL,3,1,6,6,0,0,0,105,32,13,8,17,30,0.248,3.34,0,3,1,0,151,0,14,1,3,1 +johnsji04,2015,1,ATL,NL,2,3,49,0,0,0,9,144,45,12,2,14,33,0.256,2.25,2,2,1,0,196,13,14,3,2,9 +johnsji04,2015,2,LAN,NL,0,3,23,0,0,0,1,56,32,21,3,6,17,0.381,10.13,0,1,4,0,95,2,22,0,1,4 +johnsst02,2015,1,BAL,AL,0,0,6,0,0,0,0,16,8,6,2,5,3,0.333,10.13,0,1,0,0,29,2,6,0,0,1 +jonesga02,2015,1,NYA,AL,0,0,1,0,0,0,0,2,0,0,0,1,0,0.000,0.00,0,0,1,0,4,1,0,0,0,0 +jonesna01,2015,1,CHA,AL,2,2,19,0,0,0,0,57,12,7,5,6,27,0.188,3.32,0,0,0,0,72,3,7,2,0,2 +jordata01,2015,1,WAS,NL,0,2,4,1,0,0,0,51,20,10,0,6,11,0.290,5.29,0,1,0,0,77,0,10,1,1,1 +jungmta01,2015,1,MIL,NL,9,8,21,21,1,0,0,358,106,50,11,47,107,0.241,3.77,1,8,8,0,501,0,55,2,5,9 +kahnlto01,2015,1,COL,NL,0,1,36,0,0,0,2,100,31,18,3,28,39,0.250,4.86,1,3,0,0,155,8,22,1,2,4 +karnsna01,2015,1,TBA,AL,7,5,27,26,0,0,0,441,132,60,19,56,145,0.239,3.67,1,15,5,0,621,0,62,3,4,8 +kazmisc01,2015,1,OAK,AL,5,5,18,18,0,0,0,329,84,29,7,35,101,0.213,2.38,0,2,3,2,440,0,35,3,4,13 +kazmisc01,2015,2,HOU,AL,2,6,13,13,0,0,0,220,78,34,13,24,54,0.270,4.17,0,3,6,0,323,0,42,2,2,4 +kelake01,2015,1,TEX,AL,7,5,68,0,0,0,1,181,52,16,4,18,68,0.232,2.39,0,6,0,1,243,11,18,1,0,8 +kellesh01,2015,1,SDN,NL,2,2,53,0,0,0,0,154,41,14,4,15,63,0.220,2.45,4,0,0,0,205,14,18,0,4,1 +kellyca01,2015,1,SDN,NL,0,2,3,2,0,0,0,34,19,10,1,3,7,0.365,7.94,0,0,1,0,56,1,13,0,0,1 +kellyjo05,2015,1,BOS,AL,10,6,25,25,0,0,0,403,145,72,15,49,110,0.276,4.82,0,9,6,0,587,0,76,0,5,12 +kellyry01,2015,1,ATL,NL,0,0,17,0,0,0,0,50,21,13,5,6,10,0.313,7.02,0,0,0,0,74,8,14,1,0,3 +kendrky01,2015,1,COL,NL,7,13,27,27,0,0,0,427,172,100,33,45,80,0.302,6.32,2,4,7,0,629,0,102,5,2,13 +kenneia01,2015,1,SDN,NL,9,15,30,30,0,0,0,505,166,80,31,52,174,0.258,4.28,4,5,7,1,713,0,95,8,2,10 +kensilo01,2015,1,SEA,AL,2,1,19,0,0,0,0,46,12,10,2,7,13,0.214,5.87,0,2,0,0,64,4,10,0,1,1 +kershcl01,2015,1,LAN,NL,16,7,33,33,4,3,0,698,163,55,15,42,301,0.194,2.13,1,9,5,3,890,0,62,4,0,8 +keuchda01,2015,1,HOU,AL,20,8,33,33,3,2,0,696,185,64,17,51,216,0.217,2.48,0,9,2,0,911,0,68,1,3,21 +kimbrcr01,2015,1,SDN,NL,4,2,61,0,0,0,39,178,40,17,6,22,87,0.185,2.58,1,4,1,0,239,53,19,0,0,4 +kintzbr01,2015,1,MIL,NL,0,1,7,0,0,0,0,21,12,5,1,5,7,0.387,6.43,0,1,0,0,36,4,6,0,0,2 +kleinph01,2015,1,TEX,AL,1,0,11,2,0,0,0,52,23,13,4,10,12,0.303,6.75,0,1,0,0,86,3,15,0,0,1 +klubeco01,2015,1,CLE,AL,9,16,32,32,4,0,0,666,189,86,22,45,245,0.231,3.49,3,6,11,1,886,0,92,7,4,18 +knebeco01,2015,1,MIL,NL,0,0,48,0,0,0,0,151,44,18,8,17,58,0.232,3.22,1,1,2,0,209,15,18,0,0,3 +knudsgu01,2015,1,DET,AL,0,0,4,0,0,0,0,15,13,10,5,3,6,0.464,18.00,1,1,0,0,31,2,10,0,0,0 +koehlto01,2015,1,MIA,NL,11,14,32,31,0,0,0,562,180,85,22,77,137,0.255,4.08,3,2,6,0,800,0,96,6,5,24 +kohnmi01,2015,1,ATL,NL,0,0,6,0,0,0,0,14,0,0,0,6,4,0.000,0.00,0,0,0,0,19,1,0,0,0,1 +kontoge01,2015,1,SFN,NL,4,4,73,0,0,0,0,220,57,19,9,12,44,0.213,2.33,3,3,0,0,284,12,20,1,3,5 +krolia01,2015,1,DET,AL,2,3,33,0,0,0,0,84,31,18,4,17,26,0.287,5.79,1,0,2,0,129,6,19,2,0,5 +lackejo01,2015,1,SLN,NL,13,10,33,33,1,0,0,654,211,67,21,53,175,0.256,2.77,5,5,4,3,896,0,71,11,4,29 +laffeaa01,2015,1,COL,NL,1,0,3,0,0,0,0,22,8,3,1,3,3,0.296,3.68,1,0,1,0,33,0,3,2,0,0 +lafrobo01,2015,1,PIT,NL,0,0,11,0,0,0,0,24,5,1,1,1,8,0.179,1.13,0,0,0,0,29,4,1,0,0,1 +lambjo02,2015,1,CIN,NL,1,5,10,10,0,0,0,149,58,32,8,19,58,0.296,5.80,0,0,2,0,220,0,32,2,1,5 +larocad01,2015,1,CHA,AL,0,0,1,0,0,0,0,3,0,0,0,0,1,0.000,0.00,0,0,0,0,3,1,0,0,0,0 +latosma01,2015,1,MIA,NL,4,7,16,16,0,0,0,265,85,44,8,25,79,0.251,4.48,0,9,1,0,372,0,46,4,3,3 +latosma01,2015,2,LAN,NL,0,3,6,5,0,0,0,73,31,18,3,6,18,0.323,6.66,1,1,0,1,106,1,19,2,2,2 +latosma01,2015,3,LAA,AL,0,0,2,0,0,0,0,11,4,2,2,1,3,0.267,4.91,0,0,0,0,16,1,2,0,0,0 +layneto01,2015,1,BOS,AL,2,1,64,0,0,0,1,143,41,21,3,27,45,0.234,3.97,2,1,2,0,207,9,22,2,1,3 +lazora01,2015,1,MIA,NL,0,0,7,0,0,0,0,17,5,2,1,2,5,0.238,3.18,0,0,0,0,24,1,2,0,1,0 +leakemi01,2015,1,CIN,NL,9,5,21,21,1,0,0,410,123,54,14,34,90,0.240,3.56,4,3,2,1,556,0,55,6,2,13 +leakemi01,2015,2,SFN,NL,2,5,9,9,1,1,0,166,51,25,8,15,29,0.249,4.07,1,3,1,1,222,0,25,0,1,9 +leathja01,2015,1,NYN,NL,0,1,17,0,0,0,0,35,12,3,0,7,14,0.273,2.31,0,0,1,0,52,6,3,0,0,1 +lecursa01,2015,1,CIN,NL,0,2,19,0,0,0,0,60,16,7,2,7,15,0.216,3.15,0,0,0,0,83,1,9,1,1,1 +leech01,2015,1,CLE,AL,0,0,2,0,0,0,0,5,4,1,0,1,3,0.444,5.40,0,0,0,0,10,0,1,0,0,0 +leeza01,2015,1,LAN,NL,0,1,1,1,0,0,0,14,11,7,1,1,3,0.478,13.50,0,0,0,0,24,0,7,0,0,1 +leonar01,2015,1,OAK,AL,0,2,19,0,0,0,0,80,30,13,3,9,19,0.291,4.39,3,0,0,0,115,9,14,1,2,3 +leonedo01,2015,1,SEA,AL,0,4,10,0,0,0,0,34,11,8,1,9,7,0.244,6.35,2,2,0,0,54,5,9,0,0,0 +leonedo01,2015,2,ARI,NL,0,1,3,0,0,0,0,11,8,6,1,0,2,0.444,14.73,0,0,1,0,20,1,6,0,1,0 +lestejo01,2015,1,CHN,NL,11,12,32,32,1,0,0,615,183,76,16,47,207,0.240,3.34,0,8,7,0,828,0,83,5,4,13 +lewisco01,2015,1,TEX,AL,17,9,33,33,2,1,0,614,211,106,26,42,142,0.266,4.66,2,5,11,0,861,0,114,2,11,14 +liberad01,2015,1,LAN,NL,2,2,39,0,0,0,0,89,26,14,3,9,29,0.232,4.25,4,1,0,0,122,5,14,0,1,2 +linceti01,2015,1,SFN,NL,7,4,15,15,0,0,0,229,75,35,7,38,60,0.260,4.13,2,5,1,0,333,0,37,3,2,8 +lindgja01,2015,1,NYA,AL,0,0,7,0,0,0,0,21,5,4,3,4,8,0.208,5.14,0,0,0,0,29,2,4,0,1,1 +liriafr01,2015,1,PIT,NL,12,7,31,31,0,0,0,560,155,70,15,70,205,0.223,3.38,1,10,5,1,773,0,75,2,1,22 +lizra01,2015,1,PIT,NL,1,4,14,0,0,0,0,70,26,11,4,12,27,0.286,4.24,2,1,3,0,106,7,11,0,0,4 +lobstky01,2015,1,DET,AL,3,8,13,11,0,0,0,191,78,42,7,23,32,0.305,5.94,1,2,0,1,280,2,43,0,1,8 +lockeje01,2015,1,PIT,NL,8,11,30,30,0,0,0,505,179,84,15,60,129,0.274,4.49,4,5,7,0,736,0,95,7,8,13 +loewead01,2015,1,PHI,NL,1,0,20,0,0,0,0,58,20,15,3,17,22,0.282,6.98,1,5,3,0,93,7,15,1,1,3 +loganbo02,2015,1,COL,NL,0,3,60,0,0,0,0,106,40,17,3,17,44,0.280,4.33,1,3,5,0,168,12,17,1,2,1 +lohseky01,2015,1,MIL,NL,5,13,37,22,0,0,2,457,180,99,29,43,108,0.297,5.85,3,5,4,0,665,5,99,7,4,12 +lopezja02,2015,1,SFN,NL,1,0,77,0,0,0,0,118,19,7,1,16,26,0.145,1.60,3,1,0,0,147,14,8,0,0,6 +lopezjo02,2015,1,MIL,NL,1,1,2,2,0,0,0,30,14,6,0,5,10,0.350,5.40,0,1,1,0,46,0,6,0,0,4 +lorenmi01,2015,1,CIN,NL,4,9,27,21,0,0,0,340,131,68,18,57,83,0.292,5.40,6,4,6,0,515,1,70,2,1,7 +loupaa01,2015,1,TOR,AL,2,5,60,0,0,0,0,127,47,21,6,7,46,0.275,4.46,0,0,6,0,186,6,24,2,0,3 +lowema01,2015,1,SEA,AL,0,1,34,0,0,0,0,108,31,4,1,11,47,0.235,1.00,1,2,1,0,144,8,6,0,0,3 +lowema01,2015,2,TOR,AL,1,2,23,0,0,0,1,57,15,8,3,1,14,0.224,3.79,0,0,0,0,71,5,9,0,2,2 +luetglu01,2015,1,SEA,AL,0,0,1,0,0,0,0,7,0,0,0,2,2,0.000,0.00,0,0,0,0,8,1,0,0,0,0 +lylesjo01,2015,1,COL,NL,2,5,10,10,0,0,0,147,54,28,2,19,30,0.290,5.14,1,2,3,0,212,0,32,3,1,10 +lynnla01,2015,1,SLN,NL,12,11,31,31,0,0,0,526,172,59,13,68,167,0.258,3.03,5,2,5,0,751,0,66,9,2,16 +lyonsty01,2015,1,SLN,NL,3,1,17,8,0,0,0,180,59,25,12,15,60,0.252,3.75,0,4,1,0,255,1,29,3,2,4 +machije01,2015,1,SFN,NL,1,0,33,0,0,0,0,105,38,20,3,14,22,0.271,5.14,0,1,1,0,159,8,21,2,2,2 +machije01,2015,2,BOS,AL,1,0,26,0,0,0,4,69,21,13,5,8,20,0.236,5.09,0,2,0,0,98,9,14,0,1,1 +madsory01,2015,1,KCA,AL,1,2,68,0,0,0,3,190,47,15,5,14,58,0.205,2.13,1,1,2,0,248,12,17,0,3,4 +manesse01,2015,1,SLN,NL,4,2,76,0,0,0,3,190,77,30,7,13,46,0.307,4.26,4,2,1,0,270,13,35,4,1,11 +manshje01,2015,1,CLE,AL,1,0,32,0,0,0,0,118,20,4,1,10,33,0.155,0.92,0,0,1,0,144,7,4,0,4,5 +marcush01,2015,1,CLE,AL,3,2,7,6,0,0,0,105,32,21,9,11,30,0.246,5.40,3,2,1,0,142,1,21,0,0,2 +marimsu01,2015,1,ATL,NL,0,1,16,0,0,0,0,77,30,21,3,14,14,0.300,7.36,0,0,0,0,117,5,21,1,2,5 +mariomi01,2015,1,KCA,AL,0,0,2,0,0,0,0,9,2,1,1,2,1,0.200,3.00,0,0,0,0,12,1,1,0,0,0 +marksma01,2015,1,ATL,NL,0,3,31,0,0,0,0,70,22,13,2,16,21,0.247,5.01,2,3,1,0,108,4,16,0,2,2 +marquja01,2015,1,CIN,NL,3,4,9,9,0,0,0,142,64,34,10,14,37,0.330,6.46,2,3,1,0,216,0,37,3,4,3 +marshev01,2015,1,ARI,NL,0,2,13,0,0,0,0,40,20,9,3,5,7,0.357,6.08,1,1,0,0,61,4,9,0,0,3 +martica04,2015,1,SLN,NL,14,7,31,29,0,0,0,539,168,60,13,63,184,0.250,3.01,5,8,8,1,755,1,65,9,4,19 +martich02,2015,1,NYA,AL,0,2,24,0,0,0,1,62,28,13,2,6,18,0.304,5.66,1,3,1,0,99,8,13,0,0,0 +martico01,2015,1,ATL,NL,2,3,21,0,0,0,0,65,24,13,4,7,24,0.296,5.40,0,1,1,0,92,2,13,2,1,1 +martico01,2015,2,OAK,AL,0,2,4,2,0,0,0,27,16,14,4,5,3,0.381,14.00,0,1,1,0,49,2,14,0,1,0 +martini01,2015,1,TEX,AL,7,7,24,21,0,0,0,375,135,55,16,46,77,0.274,3.96,2,4,13,0,558,1,66,1,5,15 +martira04,2015,1,WAS,NL,2,0,13,0,0,0,0,37,12,7,4,5,25,0.245,5.11,0,0,1,0,56,5,9,0,1,0 +masseni01,2015,1,MIA,NL,0,0,8,0,0,0,0,29,12,2,0,1,6,0.324,1.86,0,1,1,0,41,2,3,0,2,2 +masseni01,2015,2,ATL,NL,2,2,20,0,0,0,0,46,18,11,3,8,12,0.286,6.46,3,3,1,0,72,3,12,0,0,2 +masteju01,2015,1,BOS,AL,4,2,18,9,0,0,0,178,68,37,7,27,49,0.289,5.61,0,7,10,1,273,4,38,0,1,5 +mateoma01,2015,1,SDN,NL,1,1,26,0,0,0,0,81,22,12,5,9,33,0.222,4.00,1,1,3,0,113,6,16,1,1,2 +matthry01,2015,1,LAA,AL,0,0,1,0,0,0,0,3,0,0,0,1,2,0.000,0.00,0,0,0,0,4,1,0,0,0,0 +matthry01,2015,2,CIN,NL,2,4,57,0,0,0,0,165,67,25,3,17,35,0.300,4.09,2,6,2,1,249,14,31,4,3,6 +matusbr01,2015,1,BAL,AL,1,4,58,0,0,0,0,147,38,16,5,20,56,0.211,2.94,3,2,3,0,206,16,18,2,1,4 +matzety01,2015,1,COL,NL,2,1,5,5,0,0,0,66,21,10,2,19,15,0.269,4.09,0,2,3,0,102,0,10,1,1,5 +matzst01,2015,1,NYN,NL,4,0,6,6,0,0,0,107,34,9,4,10,34,0.250,2.27,0,0,1,0,149,0,9,1,1,4 +maurebr01,2015,1,SDN,NL,7,4,53,0,0,0,0,153,39,17,3,15,39,0.209,3.00,1,1,1,0,206,10,19,1,2,3 +maytr01,2015,1,MIN,AL,8,9,48,16,0,0,0,344,127,51,11,26,110,0.279,4.00,2,4,4,0,492,9,53,3,4,5 +mazzavi01,2015,1,MIA,NL,0,0,10,0,0,0,0,36,15,5,0,6,6,0.319,3.75,1,0,0,0,55,5,6,0,2,0 +mazzoco01,2015,1,SDN,NL,0,0,8,0,0,0,0,26,23,20,2,5,8,0.489,20.77,0,0,0,0,53,4,22,0,1,1 +mcallza01,2015,1,CLE,AL,4,4,61,1,0,0,1,207,70,23,7,23,84,0.258,3.00,4,3,3,0,299,9,28,1,1,3 +mccarbr01,2015,1,LAN,NL,3,0,4,4,0,0,0,69,24,15,9,4,29,0.267,5.87,0,0,0,0,94,0,15,0,0,1 +mcculla02,2015,1,HOU,AL,6,7,22,22,1,0,0,377,106,45,10,43,129,0.226,3.22,2,8,5,1,520,0,49,0,3,11 +mcfartj01,2015,1,BAL,AL,2,2,30,0,0,0,0,121,52,22,4,18,26,0.306,4.91,5,3,0,0,188,7,26,0,0,5 +mcgeeja01,2015,1,TBA,AL,1,2,39,0,0,0,6,112,27,10,3,8,48,0.197,2.41,1,1,1,0,147,6,11,0,1,1 +mcgousc01,2015,1,MIA,NL,0,0,6,0,0,0,0,20,12,7,0,4,4,0.387,9.45,0,0,0,0,35,3,7,0,0,1 +mcgowdu01,2015,1,PHI,NL,1,2,14,1,0,0,0,70,29,18,7,20,21,0.296,6.94,1,1,0,0,118,3,21,0,0,3 +mchugco01,2015,1,HOU,AL,19,7,32,32,0,0,0,611,207,88,19,53,171,0.263,3.89,2,5,9,0,859,0,89,5,4,12 +mckiran01,2015,1,ATL,NL,1,0,27,0,0,0,0,82,40,18,2,10,22,0.345,5.93,2,0,2,0,129,4,18,0,1,4 +medinyo01,2015,1,SEA,AL,1,0,12,0,0,0,1,36,11,4,1,7,9,0.234,3.00,0,1,0,0,54,3,5,0,0,1 +medinyo01,2015,2,CHN,NL,0,0,5,0,0,0,0,27,12,7,1,4,7,0.308,7.00,0,0,0,0,43,2,7,0,0,0 +medlekr01,2015,1,KCA,AL,6,2,15,8,0,0,0,175,56,26,6,18,40,0.252,4.01,0,3,2,0,243,2,30,0,1,4 +mejiaje01,2015,1,NYN,NL,1,0,7,0,0,0,0,22,4,0,0,2,7,0.167,0.00,1,0,0,0,27,1,0,1,0,1 +melanma01,2015,1,PIT,NL,3,2,78,0,0,0,51,230,57,19,4,14,62,0.207,2.23,2,3,2,0,293,63,22,1,1,10 +mendero01,2015,1,TEX,AL,0,1,12,0,0,0,0,35,11,7,1,7,9,0.268,5.40,1,0,1,0,49,5,7,0,0,2 +mendero01,2015,2,BOS,AL,0,0,3,0,0,0,0,6,3,1,1,1,1,0.375,4.50,0,0,0,0,9,2,1,0,0,1 +meyeral01,2015,1,MIN,AL,0,0,2,0,0,0,0,8,4,5,2,3,3,0.364,16.88,0,0,0,0,15,0,5,1,0,0 +mileywa01,2015,1,BOS,AL,11,11,32,32,1,0,0,581,201,96,17,64,147,0.265,4.46,0,10,4,1,831,0,98,3,2,13 +millean01,2015,1,NYA,AL,3,2,60,0,0,0,36,185,33,14,5,20,100,0.151,2.04,1,2,5,0,246,53,16,1,2,1 +milleju02,2015,1,COL,NL,3,3,34,0,0,0,1,100,21,15,2,11,38,0.178,4.05,0,2,0,0,129,9,15,0,0,1 +millesh01,2015,1,ATL,NL,6,17,33,33,2,2,0,616,183,69,13,73,171,0.238,3.02,8,5,6,2,860,0,82,8,4,17 +millsbr02,2015,1,OAK,AL,0,0,1,1,0,0,0,15,7,3,1,1,1,0.333,5.40,0,0,1,0,23,0,3,0,0,1 +milonto01,2015,1,MIN,AL,9,5,24,23,0,0,1,386,128,56,17,36,91,0.260,3.92,1,3,1,0,543,1,64,6,7,7 +mitchbr01,2015,1,NYA,AL,0,2,20,2,0,0,1,89,37,21,4,16,29,0.296,6.37,1,6,2,0,143,8,24,0,0,4 +montafr01,2015,1,CHA,AL,0,2,7,2,0,0,0,45,14,8,1,9,20,0.246,4.80,1,0,0,0,66,2,8,0,0,1 +montera01,2015,1,NYN,NL,0,1,5,1,0,0,0,30,9,5,0,5,13,0.225,4.50,3,0,0,0,46,1,6,1,0,0 +montgmi01,2015,1,SEA,AL,4,6,16,16,2,2,0,270,92,46,11,37,64,0.261,4.60,1,10,4,0,395,0,49,0,0,12 +moorema02,2015,1,TBA,AL,3,4,12,12,0,0,0,189,74,38,9,23,46,0.298,5.43,1,6,4,0,278,0,40,0,3,8 +moorety01,2015,1,WAS,NL,0,0,1,0,0,0,0,2,0,0,0,0,0,0.000,0.00,0,0,1,0,3,1,0,0,0,0 +moralfr01,2015,1,KCA,AL,4,2,67,0,0,0,0,187,58,22,4,14,41,0.246,3.18,0,6,4,2,258,12,24,2,2,6 +morendi01,2015,1,NYA,AL,1,0,4,0,0,0,0,31,9,6,1,3,8,0.225,5.23,0,1,2,0,45,1,6,0,0,0 +morgaad01,2015,1,PHI,NL,5,7,15,15,0,0,0,253,88,42,14,17,49,0.269,4.48,0,2,4,1,352,0,45,1,3,6 +morinmi01,2015,1,LAA,AL,4,2,47,0,0,0,1,106,36,25,3,9,41,0.265,6.37,2,0,2,0,151,10,28,2,2,1 +morriak01,2015,1,NYN,NL,0,0,1,0,0,0,0,2,3,5,1,3,0,0.600,67.50,0,0,0,0,8,0,5,0,0,0 +morribr01,2015,1,MIA,NL,5,4,67,0,0,0,0,189,67,22,3,26,47,0.275,3.14,0,2,3,0,277,18,26,4,0,11 +morrobr01,2015,1,SDN,NL,2,0,5,5,0,0,0,99,29,10,3,7,23,0.248,2.73,0,0,0,0,126,0,10,1,1,5 +mortoch02,2015,1,PIT,NL,9,9,23,23,0,0,0,387,137,69,13,41,96,0.271,4.81,6,2,12,1,563,0,77,4,0,19 +moscojo01,2015,1,CIN,NL,1,1,3,3,0,0,0,35,11,6,2,5,6,0.250,4.63,0,1,0,0,50,0,6,0,1,0 +motteja01,2015,1,CHN,NL,8,1,57,0,0,0,6,145,48,21,4,11,34,0.255,3.91,5,2,2,0,206,18,21,3,2,0 +moylape01,2015,1,ATL,NL,1,0,22,0,0,0,0,31,12,4,1,0,8,0.273,3.48,0,0,0,1,44,2,5,0,0,2 +mujiced01,2015,1,BOS,AL,1,1,11,0,0,0,0,41,15,7,3,3,8,0.294,4.61,0,0,1,2,56,4,7,1,0,4 +mujiced01,2015,2,OAK,AL,2,4,38,0,0,0,1,101,37,18,7,4,22,0.280,4.81,2,1,0,1,138,9,21,1,1,6 +muratto01,2015,1,CLE,AL,0,1,1,1,0,0,0,10,4,3,2,1,2,0.267,8.10,0,0,0,0,16,0,5,0,0,0 +murphda07,2015,1,CLE,AL,0,0,1,0,0,0,0,1,2,0,1,1,0,0.500,0.00,0,0,1,0,6,1,5,0,0,0 +murraco01,2015,1,PHI,NL,0,1,8,0,0,0,0,23,11,5,2,2,9,0.314,5.87,0,1,0,0,37,6,5,0,0,0 +narvech01,2015,1,MIA,NL,3,1,15,2,0,0,0,91,24,15,7,9,32,0.214,4.45,0,0,0,0,121,3,15,0,0,2 +nathajo01,2015,1,DET,AL,0,0,1,0,0,0,1,1,0,0,0,0,1,0.000,0.00,0,0,0,0,1,1,0,0,0,0 +nelsoji02,2015,1,MIL,NL,11,13,30,30,0,0,0,532,163,81,18,65,148,0.246,4.11,4,11,13,1,752,0,89,4,7,18 +nerishe01,2015,1,PHI,NL,2,2,32,0,0,0,0,121,38,17,8,10,41,0.245,3.79,0,3,4,0,170,8,19,1,0,3 +nesbian01,2015,1,DET,AL,1,1,24,0,0,0,0,65,22,13,2,8,14,0.265,5.40,2,3,4,0,98,6,14,2,1,0 +neshepa01,2015,1,HOU,AL,3,6,66,0,0,0,1,164,49,22,8,12,51,0.240,3.62,1,1,2,0,223,8,25,4,1,3 +nicasju01,2015,1,LAN,NL,1,3,53,1,0,0,1,175,59,25,1,32,65,0.263,3.86,6,2,1,0,260,12,25,3,0,5 +nicolju01,2015,1,MIA,NL,5,4,12,12,0,0,0,222,72,33,8,20,23,0.267,4.01,2,2,3,0,301,0,33,5,3,12 +niesejo01,2015,1,NYN,NL,9,10,33,29,0,0,0,530,192,81,20,55,113,0.280,4.13,2,2,9,0,770,1,93,16,4,17 +noesihe01,2015,1,CHA,AL,0,4,10,5,0,0,0,98,41,25,7,17,22,0.301,6.89,1,3,1,1,154,4,26,0,0,4 +nolaaa01,2015,1,PHI,NL,6,2,13,13,0,0,0,233,74,31,11,19,68,0.251,3.59,1,0,2,0,318,0,31,1,1,5 +nolasri01,2015,1,MIN,AL,5,2,9,8,0,0,0,112,50,28,3,14,35,0.321,6.75,2,1,1,1,173,1,31,0,2,3 +nolinse01,2015,1,OAK,AL,1,2,6,6,0,0,0,87,35,17,4,12,15,0.302,5.28,0,3,0,1,134,0,19,1,5,2 +norribu01,2015,1,BAL,AL,2,9,18,11,0,0,0,199,84,52,14,25,50,0.309,7.06,0,0,3,0,305,2,57,3,2,5 +norribu01,2015,2,SDN,NL,1,2,20,0,0,0,0,50,16,10,1,6,21,0.246,5.40,0,2,1,0,72,5,11,0,0,0 +norrida01,2015,1,TOR,AL,1,1,5,5,0,0,0,70,23,10,3,12,18,0.267,3.86,0,2,2,0,103,0,11,1,2,0 +norrida01,2015,2,DET,AL,2,1,8,8,0,0,0,110,30,15,6,7,27,0.216,3.68,0,1,0,0,148,0,20,0,2,3 +novaiv01,2015,1,NYA,AL,6,11,17,17,0,0,0,282,99,53,13,33,63,0.269,5.07,0,5,7,0,413,0,54,3,2,7 +nunovi01,2015,1,ARI,NL,0,1,3,0,0,0,0,43,10,3,1,5,19,0.189,1.88,1,0,0,1,58,1,3,0,0,0 +nunovi01,2015,2,SEA,AL,1,4,32,10,0,0,0,224,80,34,14,17,62,0.278,4.10,1,3,5,1,318,7,35,1,7,7 +obergsc01,2015,1,COL,NL,3,4,64,0,0,0,1,175,58,33,10,31,44,0.266,5.09,2,6,6,1,259,11,35,3,1,8 +oberhbr01,2015,1,HOU,AL,2,2,8,8,0,0,0,115,44,19,4,17,27,0.293,4.46,0,2,1,0,171,0,21,1,2,5 +odayda01,2015,1,BAL,AL,6,2,68,0,0,0,6,196,47,11,5,14,82,0.198,1.52,1,0,5,0,257,19,13,0,1,5 +odorija01,2015,1,TBA,AL,9,9,28,28,0,0,0,508,149,63,18,46,150,0.231,3.35,0,5,3,1,700,0,65,4,3,8 +oflaher01,2015,1,OAK,AL,1,2,25,0,0,0,0,64,29,14,1,13,15,0.312,5.91,1,0,0,0,108,5,17,2,0,2 +oflaher01,2015,2,NYN,NL,0,0,16,0,0,0,0,26,18,13,1,5,6,0.409,13.50,1,1,2,0,51,3,13,0,0,0 +ogandal01,2015,1,BOS,AL,3,1,64,0,0,0,0,196,59,29,12,28,53,0.242,3.99,2,1,3,2,277,16,29,0,2,5 +ogandne01,2015,1,PHI,NL,0,0,4,0,0,0,0,12,7,4,0,2,2,0.368,9.00,0,0,0,0,21,1,5,0,0,0 +ohlenro01,2015,1,TEX,AL,3,1,21,0,0,0,1,58,21,8,4,7,19,0.273,3.72,2,0,0,0,85,6,8,0,1,0 +olmosed01,2015,1,SEA,AL,1,0,6,2,0,0,0,42,16,7,1,8,4,0.281,4.50,1,1,1,0,67,1,8,0,1,3 +olsonty01,2015,1,SEA,AL,1,1,11,0,0,0,0,40,18,8,2,10,8,0.346,5.40,7,0,1,1,65,4,8,2,0,3 +orourry01,2015,1,MIN,AL,0,0,28,0,0,0,0,66,16,15,3,15,24,0.200,6.14,2,2,0,0,97,7,15,1,1,0 +osichjo01,2015,1,SFN,NL,2,0,35,0,0,0,0,86,24,7,4,8,27,0.218,2.20,0,2,0,0,120,6,12,1,1,1 +osullse01,2015,1,PHI,NL,1,6,13,13,0,0,0,213,94,48,16,20,35,0.322,6.08,5,3,6,0,328,0,49,4,5,7 +osunaro01,2015,1,TOR,AL,1,6,68,0,0,0,20,209,48,20,7,16,75,0.191,2.58,2,5,1,0,271,39,21,1,2,3 +oteroda01,2015,1,OAK,AL,2,4,41,0,0,0,0,140,64,35,7,6,28,0.333,6.75,2,1,2,0,204,6,35,1,3,5 +ottavad01,2015,1,COL,NL,1,0,10,0,0,0,3,31,3,0,0,2,13,0.094,0.00,0,0,1,0,35,5,0,0,0,2 +owenshe02,2015,1,BOS,AL,4,4,11,11,0,0,0,189,62,32,7,24,50,0.255,4.57,0,4,3,1,272,0,35,0,2,4 +papeljo01,2015,1,PHI,NL,2,1,37,0,0,0,17,119,31,7,3,8,40,0.211,1.59,1,0,4,0,161,34,9,1,0,3 +papeljo01,2015,2,WAS,NL,2,2,22,0,0,0,7,71,22,8,4,4,16,0.247,3.04,1,0,3,1,99,17,13,1,2,3 +parnebo01,2015,1,NYN,NL,2,4,30,0,0,0,1,72,30,17,0,17,13,0.323,6.38,1,4,0,0,112,8,20,1,1,7 +parrama01,2015,1,CIN,NL,1,2,40,0,0,0,0,97,32,14,2,6,23,0.264,3.90,1,1,0,0,130,5,15,1,2,3 +pattosp01,2015,1,TEX,AL,1,1,27,0,0,0,0,72,24,24,5,12,28,0.261,9.00,0,1,4,0,109,6,24,1,0,2 +paxtoja01,2015,1,SEA,AL,3,4,13,13,0,0,0,201,67,29,8,29,56,0.253,3.90,1,5,0,0,297,0,34,0,3,4 +pazosja01,2015,1,NYA,AL,0,0,11,0,0,0,0,15,3,0,0,3,3,0.176,0.00,0,1,0,0,21,1,0,1,0,0 +peacobr01,2015,1,HOU,AL,0,1,1,1,0,0,0,15,5,3,0,2,3,0.278,5.40,0,0,1,0,22,0,3,0,1,1 +peavyja01,2015,1,SFN,NL,8,6,19,19,0,0,0,332,99,44,12,25,78,0.239,3.58,1,2,2,1,448,0,45,5,1,7 +pelfrmi01,2015,1,MIN,AL,6,11,30,30,0,0,0,494,198,78,11,45,86,0.304,4.26,1,5,12,0,714,0,86,3,3,29 +penaar01,2015,1,MIL,NL,2,1,6,5,0,0,0,82,24,13,2,14,27,0.238,4.28,2,2,2,0,120,0,14,2,1,2 +peraljo01,2015,1,LAN,NL,3,1,33,0,0,0,3,87,28,14,6,8,24,0.248,4.34,1,1,0,0,121,7,14,0,0,1 +peralwi01,2015,1,MIL,NL,5,10,20,20,0,0,0,326,130,57,14,37,60,0.302,4.72,2,5,4,1,478,0,60,4,3,13 +perezma02,2015,1,TEX,AL,3,6,14,14,0,0,0,236,88,39,3,24,48,0.285,4.46,1,1,2,0,339,0,45,0,3,12 +perezol01,2015,1,ARI,NL,2,1,48,0,0,0,0,87,25,10,2,11,37,0.223,3.10,1,2,4,0,128,11,12,1,0,2 +perezol01,2015,2,HOU,AL,0,3,22,0,0,0,0,36,14,9,2,4,14,0.275,6.75,1,1,0,0,55,4,12,0,0,0 +perezwi01,2015,1,ATL,NL,7,6,23,20,1,0,1,350,130,62,13,51,73,0.291,4.78,2,4,9,1,514,1,66,3,5,21 +perkigl01,2015,1,MIN,AL,3,5,60,0,0,0,32,171,58,21,9,10,54,0.256,3.32,2,4,0,0,238,45,21,1,0,1 +pestavi01,2015,1,LAA,AL,1,0,19,0,0,0,0,35,15,7,3,8,13,0.306,5.40,2,1,1,0,60,3,9,0,2,0 +petityu01,2015,1,SFN,NL,1,1,42,1,0,0,1,228,75,31,11,15,59,0.256,3.67,2,3,1,0,316,15,32,1,6,2 +petrija01,2015,1,CHA,AL,4,3,62,0,0,0,2,156,56,21,2,18,33,0.284,3.63,4,2,1,0,220,18,21,3,1,12 +phelpda01,2015,1,MIA,NL,4,8,23,19,0,0,0,336,119,56,11,33,77,0.272,4.50,0,2,4,0,482,1,59,2,5,10 +pimenst01,2015,1,TEX,AL,0,1,8,0,0,0,0,34,11,5,1,3,7,0.268,3.97,0,1,0,0,46,5,5,1,1,0 +pindebr01,2015,1,NYA,AL,0,2,25,0,0,0,0,83,28,9,4,14,25,0.262,2.93,1,0,0,1,122,10,9,0,1,2 +pinedmi01,2015,1,NYA,AL,12,10,27,27,1,0,0,482,176,78,21,21,156,0.278,4.37,0,4,3,0,668,0,83,4,6,9 +pinoyo01,2015,1,KCA,AL,0,2,7,1,0,0,0,58,23,7,2,3,13,0.291,3.26,0,0,0,0,82,3,8,0,0,2 +pomerdr01,2015,1,OAK,AL,5,6,53,9,0,0,3,258,71,35,8,31,82,0.226,3.66,1,2,3,0,357,9,44,4,5,8 +porceri01,2015,1,BOS,AL,9,15,28,28,0,0,0,516,196,94,25,38,149,0.287,4.92,0,12,10,1,737,0,103,2,5,21 +pressry01,2015,1,MIN,AL,3,2,27,0,0,0,0,83,27,9,0,12,22,0.257,2.93,1,2,0,0,119,6,9,1,1,2 +priceda01,2015,1,DET,AL,9,4,21,21,3,1,0,438,133,41,13,29,138,0.241,2.53,2,3,3,0,592,0,50,4,5,13 +priceda01,2015,2,TOR,AL,9,1,11,11,0,0,0,223,57,19,4,18,87,0.207,2.30,0,1,0,0,296,0,20,0,3,4 +putnaza01,2015,1,CHA,AL,3,3,49,0,0,0,0,146,42,22,7,24,64,0.239,4.07,5,5,4,0,212,16,24,4,4,3 +quackke01,2015,1,SDN,NL,3,2,57,0,0,0,0,175,52,26,6,20,58,0.240,4.01,3,0,1,1,243,19,28,1,4,1 +quallch01,2015,1,HOU,AL,3,5,60,0,0,0,4,148,46,24,6,9,46,0.247,4.38,1,3,2,0,202,17,24,1,4,2 +quintjo01,2015,1,CHA,AL,9,10,32,32,1,1,0,619,218,77,16,44,177,0.272,3.36,4,5,8,0,862,0,81,4,4,18 +raburry01,2015,1,CLE,AL,0,0,1,0,0,0,0,2,1,0,0,1,0,0.333,0.00,0,0,0,0,4,0,2,0,0,0 +ramiral03,2015,1,CHA,AL,0,0,1,0,0,0,0,3,1,0,0,0,0,0.250,0.00,0,0,1,0,5,1,0,0,0,0 +ramirer02,2015,1,TBA,AL,11,6,34,27,0,0,0,490,145,68,16,40,126,0.236,3.75,0,3,9,0,666,5,73,1,1,13 +ramirjc01,2015,1,ARI,NL,1,1,12,0,0,0,0,46,15,7,1,4,11,0.254,4.11,2,1,0,0,63,4,7,0,0,1 +ramirjc01,2015,2,SEA,AL,0,1,8,0,0,0,0,25,10,7,2,7,5,0.286,7.56,1,0,1,0,43,2,7,0,0,0 +ramirjo02,2015,1,NYA,AL,0,0,3,0,0,0,0,9,6,5,0,4,2,0.400,15.00,0,3,1,0,20,1,5,0,0,0 +ramirjo02,2015,2,SEA,AL,1,0,5,0,0,0,0,14,9,6,0,6,3,0.375,11.57,1,0,2,0,32,1,9,0,0,0 +ramirne01,2015,1,CHN,NL,1,0,19,0,0,0,0,42,12,5,1,6,15,0.231,3.21,0,2,0,0,60,4,5,0,2,0 +ramirno01,2015,1,BOS,AL,0,1,17,0,0,0,0,39,13,6,3,7,13,0.250,4.15,0,1,2,0,61,3,12,0,0,2 +ramosaj01,2015,1,MIA,NL,2,4,71,0,0,0,32,211,45,18,6,26,87,0.184,2.30,0,2,3,0,277,51,18,1,2,7 +ramosce01,2015,1,LAA,AL,2,1,65,0,0,0,0,157,55,16,2,15,43,0.279,2.75,0,5,3,0,221,21,17,3,3,8 +ranauan01,2015,1,TEX,AL,0,1,4,2,0,0,0,46,18,13,2,8,11,0.295,7.63,0,0,1,0,73,0,13,2,1,0 +rasmuco02,2015,1,LAA,AL,0,0,16,1,0,0,0,62,15,12,3,11,27,0.200,5.23,2,0,1,0,88,5,12,1,0,0 +rasmuro01,2015,1,TOR,AL,0,0,1,0,0,0,0,3,1,0,0,0,1,0.250,0.00,0,0,0,0,4,1,0,0,0,0 +rasmuro01,2015,2,SEA,AL,2,1,19,0,0,0,0,43,25,17,2,8,16,0.373,10.67,0,4,0,0,75,5,18,0,0,0 +ravinjo01,2015,1,LAN,NL,2,1,9,0,0,0,0,28,13,7,3,4,12,0.310,6.75,0,1,1,0,47,4,7,0,0,0 +rayro02,2015,1,ARI,NL,5,12,23,23,0,0,0,383,121,50,9,49,119,0.255,3.52,3,2,8,0,545,0,56,7,6,13 +reaco01,2015,1,SDN,NL,2,2,6,6,0,0,0,95,29,15,2,11,26,0.246,4.26,0,0,1,0,133,0,16,1,2,2 +rearich01,2015,1,SDN,NL,0,0,5,0,0,0,0,9,6,4,2,2,4,0.462,12.00,0,0,0,0,16,1,4,1,0,1 +redmoto01,2015,1,TOR,AL,0,0,7,1,0,0,0,48,17,13,3,7,13,0.266,7.31,0,1,1,0,72,1,13,0,0,0 +reedad01,2015,1,ARI,NL,2,2,38,0,0,0,3,122,47,19,2,14,34,0.283,4.20,5,2,0,0,181,11,19,1,0,1 +reedad01,2015,2,NYN,NL,1,1,17,0,0,0,1,46,11,2,1,5,17,0.200,1.17,1,0,0,0,60,3,2,0,0,3 +reedch01,2015,1,MIA,NL,0,0,2,0,0,0,0,12,6,2,0,1,1,0.375,4.50,0,0,0,0,18,1,2,1,0,1 +reyesjo03,2015,1,LAA,AL,1,0,1,0,0,0,0,1,0,0,0,0,0,0.000,0.00,0,0,0,0,1,0,0,0,0,0 +reynoma02,2015,1,ARI,NL,0,0,18,0,0,0,0,41,14,7,6,7,18,0.259,4.61,2,0,1,0,62,3,7,0,0,1 +richacl01,2015,1,CHN,NL,4,2,23,3,0,0,0,127,47,18,3,7,22,0.272,3.83,1,4,1,0,181,0,18,0,0,3 +richaga01,2015,1,LAA,AL,15,12,32,32,1,1,0,622,181,84,20,76,176,0.236,3.65,2,17,5,0,865,0,94,6,10,19 +riefecj01,2015,1,TBA,AL,1,0,17,0,0,0,0,44,15,9,3,7,7,0.283,5.52,0,0,0,0,62,4,10,1,1,3 +rienzan01,2015,1,MIA,NL,0,1,14,0,0,0,0,59,17,13,2,13,15,0.230,5.95,1,3,1,0,89,5,14,1,0,2 +riverfe01,2015,1,WAS,NL,2,1,49,0,0,0,2,145,35,15,2,11,43,0.199,2.79,2,2,1,1,189,17,15,0,1,2 +roachdo01,2015,1,CHN,NL,0,1,1,1,0,0,0,10,8,4,0,1,1,0.471,10.80,0,0,0,0,18,0,4,0,0,1 +roarkta01,2015,1,WAS,NL,4,7,40,12,0,0,1,333,119,54,17,26,70,0.279,4.38,3,0,5,0,467,8,55,4,4,10 +roberda08,2015,1,CHA,AL,6,5,60,0,0,0,34,190,46,24,7,13,86,0.196,3.41,2,4,1,0,250,53,27,0,0,1 +roberke02,2015,1,COL,NL,0,1,9,0,0,0,0,28,13,6,0,2,5,0.333,5.79,0,0,1,0,43,3,6,0,1,0 +roberke02,2015,2,PHI,NL,1,0,6,0,0,0,0,13,9,5,0,1,1,0.474,10.38,0,0,0,0,22,1,5,1,1,1 +robincl01,2015,1,WAS,NL,0,0,1,0,0,0,0,3,1,0,0,0,1,0.250,0.00,0,0,0,0,4,1,0,0,0,0 +robinsh01,2015,1,MIN,AL,0,0,1,0,0,0,0,3,0,0,0,1,1,0.000,0.00,0,0,0,0,4,1,0,0,0,0 +robleha01,2015,1,NYN,NL,4,3,57,0,0,0,0,162,37,22,8,18,61,0.190,3.67,1,2,2,1,217,7,27,1,1,3 +rodnefe01,2015,1,SEA,AL,5,5,54,0,0,0,16,152,51,32,8,25,43,0.262,5.68,3,5,5,0,227,28,32,1,1,5 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+staufti01,2015,2,NYN,NL,0,0,5,0,0,0,0,17,8,5,2,2,8,0.333,7.94,0,0,1,0,27,2,5,0,0,1 +stitema01,2015,1,ARI,NL,0,0,11,0,0,0,0,26,14,12,1,5,6,0.368,12.46,0,0,0,0,44,6,14,0,1,1 +storedr01,2015,1,WAS,NL,2,2,58,0,0,0,29,165,45,21,4,16,67,0.220,3.44,2,2,5,0,228,35,23,1,1,2 +straida01,2015,1,HOU,AL,0,1,4,3,0,0,0,50,16,10,2,8,14,0.239,5.40,0,1,1,0,76,0,11,0,0,0 +strasst01,2015,1,WAS,NL,11,7,23,23,0,0,0,382,115,49,14,26,155,0.236,3.46,0,4,3,0,523,0,56,5,1,8 +streehu01,2015,1,LAA,AL,3,3,62,0,0,0,40,187,52,22,7,20,57,0.226,3.18,5,6,0,0,255,51,22,2,3,2 +strichu01,2015,1,SFN,NL,3,3,55,0,0,0,0,154,34,14,4,10,50,0.190,2.45,1,1,2,0,191,11,14,0,0,2 +stromma01,2015,1,TOR,AL,4,0,4,4,0,0,0,81,20,5,2,6,18,0.208,1.67,0,2,1,1,103,0,5,0,0,4 +stroppe01,2015,1,CHN,NL,2,6,76,0,0,0,3,204,39,22,5,29,81,0.167,2.91,6,6,4,0,270,12,24,1,3,4 +stulter01,2015,1,ATL,NL,1,5,9,8,0,0,0,143,48,31,10,13,31,0.268,5.85,1,1,0,0,196,1,31,3,1,6 +sucreje01,2015,1,SEA,AL,0,0,2,0,0,0,0,6,6,3,1,0,0,0.545,13.50,0,0,0,0,11,2,3,0,0,1 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+thielca01,2015,1,MIN,AL,0,0,6,0,0,0,0,15,5,3,0,0,5,0.263,5.40,0,1,0,0,20,1,3,1,0,0 +thomaia01,2015,1,ATL,NL,0,0,5,0,0,0,0,16,4,2,1,5,5,0.222,3.38,0,1,0,0,25,1,3,1,0,1 +thomaia01,2015,2,LAN,NL,1,1,9,1,0,0,0,54,16,8,1,6,18,0.239,4.00,0,0,0,0,73,3,8,0,0,3 +thompaa01,2015,1,MIN,AL,1,3,41,0,0,0,0,97,32,18,2,11,17,0.264,5.01,2,1,0,0,137,5,19,2,3,3 +thornma01,2015,1,WAS,NL,2,1,60,0,0,0,0,124,33,10,2,11,23,0.212,2.18,3,1,1,0,171,12,12,2,1,3 +thornty01,2015,1,MIL,NL,0,2,24,0,0,0,0,103,31,14,7,12,34,0.231,3.67,1,3,3,1,151,9,22,0,2,1 +tillmch01,2015,1,BAL,AL,11,11,31,31,0,0,0,519,176,96,20,64,120,0.267,4.99,1,4,5,0,741,0,97,3,8,16 +tollesh01,2015,1,TEX,AL,6,4,73,0,0,0,35,217,66,24,9,17,76,0.239,2.99,5,0,2,0,298,53,25,2,1,6 +tomlijo01,2015,1,CLE,AL,7,2,10,10,2,0,0,197,47,22,13,8,57,0.195,3.02,0,1,2,0,251,0,22,0,0,3 +tonkimi01,2015,1,MIN,AL,0,0,26,0,0,0,0,70,21,9,4,9,19,0.239,3.47,2,1,1,0,99,10,9,0,1,0 +torreal01,2015,1,NYN,NL,0,0,39,0,0,0,1,103,26,12,6,26,35,0.206,3.15,1,3,1,0,154,11,16,1,0,2 +torreca01,2015,1,NYN,NL,5,6,59,0,0,0,0,173,61,30,5,18,48,0.276,4.68,6,5,0,0,243,19,32,1,3,5 +tracyma01,2015,1,NYA,AL,0,0,1,0,0,0,0,6,2,0,0,2,1,0.222,0.00,0,0,0,0,11,1,3,0,0,0 +treinbl01,2015,1,WAS,NL,2,5,60,0,0,0,0,203,62,29,4,32,65,0.254,3.86,6,4,2,0,280,17,32,1,1,14 +tropeni01,2015,1,LAA,AL,3,2,8,7,0,0,0,113,40,16,2,10,38,0.270,3.82,0,0,0,0,161,0,18,2,1,1 +tsaoch01,2015,1,LAN,NL,1,1,5,0,0,0,0,21,15,8,3,3,7,0.441,10.29,1,0,0,0,37,0,9,0,0,0 +tuivasa01,2015,1,SLN,NL,0,1,14,0,0,0,0,44,13,5,2,8,20,0.228,3.07,1,3,0,0,65,5,5,0,0,1 +ueharko01,2015,1,BOS,AL,2,4,43,0,0,0,25,121,28,10,3,9,47,0.188,2.23,1,4,0,0,160,38,14,1,1,1 +urenajo01,2015,1,MIA,NL,1,5,20,9,0,0,0,185,73,36,5,25,28,0.307,5.25,2,2,3,1,274,4,37,3,5,10 +valdejo03,2015,1,DET,AL,0,1,7,0,0,0,0,27,10,4,2,4,4,0.286,4.00,0,2,0,0,39,3,4,0,0,1 +vargaja01,2015,1,KCA,AL,5,2,9,9,0,0,0,129,46,19,5,12,27,0.271,3.98,0,0,1,0,183,0,20,0,0,6 +varvaan01,2015,1,BOS,AL,0,1,9,0,0,0,0,33,14,5,0,6,8,0.311,4.09,1,1,0,0,51,2,5,0,0,0 +vealdo01,2015,1,ATL,NL,0,0,5,0,0,0,0,13,8,7,3,2,3,0.400,14.54,0,2,1,0,24,2,7,1,0,0 +velasvi01,2015,1,HOU,AL,1,1,19,7,0,0,0,167,50,27,5,21,58,0.240,4.37,0,3,2,0,231,5,28,0,0,6 +vendipa01,2015,1,OAK,AL,2,2,26,0,0,0,0,86,22,14,3,12,23,0.210,4.40,0,0,0,0,119,7,14,1,1,1 +ventuyo01,2015,1,KCA,AL,13,8,28,28,0,0,0,490,154,74,14,58,156,0.248,4.08,1,10,9,0,693,0,75,1,4,11 +verhadr01,2015,1,DET,AL,2,0,20,0,0,0,0,79,18,6,1,14,13,0.200,2.05,2,1,1,0,106,2,6,1,0,8 +verlaju01,2015,1,DET,AL,5,8,20,20,1,1,0,400,113,50,13,32,113,0.229,3.38,1,2,3,0,535,0,56,1,6,7 +verrelo01,2015,1,TEX,AL,0,1,4,0,0,0,0,27,11,6,1,4,3,0.306,6.00,1,1,0,0,42,1,7,0,2,1 +verrelo01,2015,2,NYN,NL,1,1,14,4,0,0,1,116,23,13,5,11,36,0.174,3.03,3,0,2,0,148,3,13,2,1,2 +villaca01,2015,1,SLN,NL,4,3,35,0,0,0,2,183,50,20,6,21,55,0.223,2.95,2,1,2,0,250,20,21,1,2,3 +villape01,2015,1,CIN,NL,1,3,29,0,0,0,0,150,57,19,6,12,29,0.294,3.42,2,0,3,1,214,10,24,2,3,4 +vinceni01,2015,1,SDN,NL,0,1,26,0,0,0,0,69,25,6,0,10,22,0.281,2.35,1,0,0,0,100,8,8,0,1,4 +vizcaar01,2015,1,ATL,NL,3,1,36,0,0,0,9,101,27,6,1,13,37,0.218,1.60,2,7,0,0,139,25,7,2,0,4 +vogelry01,2015,1,SFN,NL,9,11,33,22,0,0,0,405,140,70,17,58,108,0.265,4.67,2,1,3,0,598,6,76,3,6,14 +volqued01,2015,1,KCA,AL,13,9,34,33,1,0,0,601,190,79,16,72,155,0.251,3.55,1,3,8,0,850,0,89,5,7,20 +volstch01,2015,1,PIT,NL,0,0,1,0,0,0,0,6,2,0,0,0,0,0.286,0.00,0,0,0,0,7,0,0,0,0,1 +wachami01,2015,1,SLN,NL,17,7,30,30,0,0,0,544,162,68,19,58,153,0.236,3.38,4,4,6,1,762,0,74,8,3,9 +wadats01,2015,1,CHN,NL,1,1,8,7,0,0,0,97,30,13,5,11,31,0.246,3.62,1,1,1,0,136,0,14,1,1,2 +wagnety01,2015,1,MIL,NL,0,2,3,3,0,0,0,41,22,11,1,7,5,0.386,7.24,0,1,0,0,67,0,11,2,1,4 +wainwad01,2015,1,SLN,NL,2,1,7,4,0,0,0,84,25,5,0,4,20,0.238,1.61,0,0,0,0,111,2,7,2,0,2 +waldejo01,2015,1,SLN,NL,0,1,12,0,0,0,1,31,7,1,0,4,12,0.189,0.87,1,2,0,0,42,2,1,0,1,1 +walketa01,2015,1,SEA,AL,11,8,29,29,1,0,0,509,163,86,25,40,157,0.252,4.56,1,4,9,1,706,0,92,4,5,8 +warread01,2015,1,NYA,AL,7,7,43,17,0,0,1,394,114,48,10,39,104,0.236,3.29,1,7,7,0,534,5,51,2,2,14 +watsoto01,2015,1,PIT,NL,4,1,77,0,0,0,1,226,55,16,3,17,62,0.205,1.91,1,1,4,0,293,4,17,1,3,8 +weaveje02,2015,1,LAA,AL,7,12,26,26,1,1,0,477,163,82,24,33,90,0.264,4.64,3,2,12,0,669,0,84,5,2,8 +webbda01,2015,1,CHA,AL,1,0,27,0,0,0,0,90,41,21,3,22,22,0.325,6.30,1,2,1,0,150,6,26,0,1,3 +webbry01,2015,1,CLE,AL,1,0,40,0,0,0,0,152,46,18,4,12,31,0.245,3.20,2,1,2,0,204,11,21,1,1,10 +weberry01,2015,1,ATL,NL,0,3,5,5,0,0,0,85,25,15,3,6,19,0.248,4.76,0,0,2,0,109,0,15,0,0,5 +webstal01,2015,1,ARI,NL,1,1,9,5,0,0,0,93,32,20,10,20,17,0.271,5.81,1,1,2,0,142,1,28,1,1,4 +westma03,2015,1,LAN,NL,0,0,2,0,0,0,0,9,2,0,0,1,2,0.200,0.00,0,0,0,0,11,2,0,0,0,1 +whitlch01,2015,1,NYA,AL,1,2,4,4,0,0,0,58,20,9,3,5,16,0.260,4.19,0,2,2,0,84,0,9,0,0,0 +wielajo01,2015,1,LAN,NL,0,1,2,2,0,0,0,26,10,8,2,5,4,0.294,8.31,1,0,0,0,40,0,8,0,1,0 +wilheto01,2015,1,SEA,AL,2,2,53,0,0,0,13,186,56,22,3,29,60,0.246,3.19,3,2,2,0,267,20,24,4,3,9 +wilkad01,2015,1,LAA,AL,0,0,1,0,0,0,0,6,2,1,1,1,2,0.250,4.50,0,0,1,0,10,1,1,0,0,0 +willije02,2015,1,PHI,NL,4,12,33,21,0,0,1,363,161,78,22,34,74,0.317,5.80,3,4,5,0,553,3,83,2,4,13 +wilsoal01,2015,1,DET,AL,3,3,59,1,0,0,2,210,61,17,5,11,38,0.238,2.19,1,2,2,0,273,16,19,2,2,10 +wilsocj01,2015,1,LAA,AL,8,8,21,21,0,0,0,396,118,57,13,46,110,0.240,3.89,2,11,10,1,553,0,59,2,4,11 +wilsojo03,2015,1,DET,AL,0,0,1,0,0,0,0,3,2,1,1,0,0,0.500,9.00,0,0,0,0,4,1,1,0,0,1 +wilsoju10,2015,1,NYA,AL,5,0,74,0,0,0,0,183,49,21,3,20,66,0.223,3.10,0,4,2,0,244,3,21,2,0,7 +wilsoty01,2015,1,BAL,AL,2,2,9,5,0,0,0,108,39,14,1,11,13,0.289,3.50,1,0,1,0,149,2,14,0,2,6 +winklda01,2015,1,ATL,NL,0,0,2,0,0,0,0,5,2,2,2,1,2,0.286,10.80,0,0,0,0,8,0,2,0,0,0 +wislema01,2015,1,ATL,NL,8,8,20,19,0,0,0,327,119,57,16,40,72,0.280,4.71,4,2,4,3,478,0,59,4,5,10 +wojcias01,2015,1,HOU,AL,0,1,5,3,0,0,0,49,23,13,2,7,16,0.329,7.16,0,1,0,0,79,2,13,0,2,0 +wolfra02,2015,1,DET,AL,0,5,8,7,0,0,0,104,46,24,5,15,28,0.319,6.23,1,3,1,0,161,0,28,0,1,4 +woodal02,2015,1,ATL,NL,7,6,20,20,0,0,0,358,132,47,8,36,90,0.288,3.54,2,5,2,0,509,0,50,11,1,16 +woodal02,2015,2,LAN,NL,5,6,12,12,0,0,0,211,66,34,7,23,49,0.253,4.35,2,1,2,1,292,0,36,4,2,9 +woodtr01,2015,1,CHN,NL,5,4,54,9,0,0,4,302,86,43,11,39,118,0.229,3.84,5,2,1,0,419,12,48,1,2,5 +wootero01,2015,1,MIL,NL,0,0,4,0,0,0,0,18,5,8,1,6,6,0.227,12.00,0,1,1,0,30,0,8,1,0,0 +worleva01,2015,1,PIT,NL,4,6,23,8,0,0,0,215,81,32,6,21,49,0.287,4.02,3,3,2,0,310,6,36,3,2,8 +wrighmi01,2015,1,BAL,AL,3,5,12,9,0,0,0,134,52,30,9,18,26,0.291,6.04,3,2,5,0,204,0,30,0,2,2 +wrighst01,2015,1,BOS,AL,5,4,16,9,0,0,0,218,67,33,12,27,52,0.239,4.09,0,2,1,0,310,3,38,1,1,2 +wrighwe01,2015,1,BAL,AL,0,0,2,0,0,0,0,5,2,1,0,0,0,0.333,5.40,0,0,0,0,7,2,1,0,1,0 +wrighwe01,2015,2,LAA,AL,0,0,9,0,0,0,0,17,4,2,1,3,5,0.200,3.18,1,0,0,0,24,2,3,0,1,1 +yateski01,2015,1,TBA,AL,1,0,20,0,0,0,0,61,23,18,10,7,21,0.274,7.97,0,0,1,0,92,10,18,0,0,0 +youngch03,2015,1,KCA,AL,11,6,34,18,0,0,0,370,91,42,16,43,83,0.202,3.06,0,5,0,0,500,3,44,4,2,1 +zieglbr01,2015,1,ARI,NL,0,3,66,0,0,0,30,204,48,14,3,17,36,0.198,1.85,3,2,1,0,263,46,17,1,0,8 +zimmejo02,2015,1,WAS,NL,13,10,33,33,0,0,0,605,204,82,24,39,164,0.264,3.66,3,2,8,1,831,0,89,8,2,15 +zitoba01,2015,1,OAK,AL,0,0,3,2,0,0,0,21,12,8,4,6,2,0.387,10.29,0,0,0,0,37,1,8,0,0,2 +zychto01,2015,1,SEA,AL,0,0,13,1,0,0,0,55,17,5,1,3,24,0.239,2.45,0,1,2,0,76,4,6,0,0,0 +abadfe01,2016,1,MIN,AL,1,4,39,0,0,0,1,102,27,10,2,14,29,0.220,2.65,2,0,0,1,138,8,11,0,1,6 +abadfe01,2016,2,BOS,AL,0,2,18,0,0,0,0,38,13,9,2,8,12,0.255,6.39,0,1,1,0,60,7,9,0,0,1 +achteaj01,2016,1,LAA,AL,1,0,27,0,0,0,0,113,43,13,7,12,14,0.295,3.11,1,0,1,0,160,17,13,0,1,7 +adamsau01,2016,1,CLE,AL,0,0,19,0,0,0,0,55,27,20,5,7,17,0.333,9.82,1,0,0,0,88,11,22,0,0,2 +adlemti01,2016,1,CIN,NL,4,4,13,13,0,0,0,209,64,31,13,20,47,0.251,4.00,1,0,5,0,287,0,32,6,1,8 +alberan01,2016,1,MIN,AL,0,0,6,2,0,0,0,51,27,11,5,6,16,0.342,5.82,0,1,0,0,85,3,16,0,0,3 +alberma01,2016,1,CHA,AL,2,6,58,1,0,0,0,154,67,36,10,19,30,0.321,6.31,1,4,3,0,237,11,44,3,2,4 +albural01,2016,1,LAA,AL,0,0,2,0,0,0,0,6,2,1,1,2,1,0.200,4.50,0,0,0,0,12,1,3,0,0,0 +alcanra01,2016,1,OAK,AL,1,3,5,5,0,0,0,67,31,18,9,4,14,0.333,7.25,0,1,4,1,103,0,18,0,2,2 +alexasc01,2016,1,KCA,AL,0,0,17,0,0,0,0,57,24,7,1,7,16,0.316,3.32,0,0,0,0,84,4,7,0,1,4 +allenco01,2016,1,CLE,AL,3,5,67,0,0,0,32,204,41,19,8,27,87,0.177,2.51,2,3,0,0,264,55,23,3,2,7 +altavda01,2016,1,SEA,AL,0,0,15,0,0,0,0,37,11,1,0,1,10,0.244,0.73,0,1,1,0,48,7,1,0,1,1 +alvarda01,2016,1,ATL,NL,3,1,16,0,0,0,0,45,11,5,3,5,28,0.200,3.00,2,4,1,0,61,4,5,0,0,1 +alvarda01,2016,2,TEX,AL,0,0,10,0,0,0,0,35,17,10,3,2,13,0.362,7.71,0,1,2,0,52,5,11,0,1,3 +alvarjo02,2016,1,LAA,AL,1,3,64,0,0,0,0,172,71,22,4,15,51,0.298,3.45,4,2,1,0,256,12,29,1,1,5 +amarial01,2016,1,SDN,NL,0,0,1,0,0,0,0,1,0,0,0,0,0,0.000,0.00,0,0,0,0,1,1,0,0,0,0 +anderbr04,2016,1,LAN,NL,1,2,4,3,0,0,0,34,25,15,4,4,5,0.446,11.91,0,2,0,0,62,0,15,1,1,0 +anderch01,2016,1,MIL,NL,9,11,31,30,0,0,0,455,155,74,28,53,120,0.266,4.39,0,4,4,0,647,1,83,4,3,12 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+wislema01,2017,1,ATL,NL,0,1,20,1,0,0,0,97,43,30,5,13,22,0.323,8.35,0,0,2,0,153,8,31,2,3,1 +wittgni01,2017,1,MIA,NL,3,1,38,0,0,0,0,127,46,22,5,13,43,0.277,4.68,1,2,0,0,182,3,22,0,3,1 +wojcias01,2017,1,CIN,NL,4,3,25,8,0,0,0,187,71,45,14,19,64,0.287,6.50,1,1,5,0,279,2,48,5,2,3 +woodal02,2017,1,LAN,NL,16,3,27,25,0,0,0,457,123,46,15,38,151,0.217,2.72,6,2,6,0,614,0,50,4,0,10 +woodbl01,2017,1,CIN,NL,1,4,55,0,0,0,0,172,64,36,5,29,62,0.284,5.65,1,6,1,2,259,17,40,0,4,5 +woodbl01,2017,2,LAA,AL,2,0,17,0,0,0,0,51,20,9,3,4,22,0.290,4.76,1,1,0,0,73,2,9,0,0,0 +woodhu01,2017,1,TBA,AL,0,0,1,0,0,0,0,1,0,0,0,0,0,0.000,0.00,0,0,0,0,1,1,0,0,0,0 +woodrbr01,2017,1,MIL,NL,2,3,8,8,0,0,0,129,43,23,5,14,32,0.259,4.81,1,0,3,0,184,0,23,1,0,2 +woodtr01,2017,1,KCA,AL,1,3,28,3,0,0,0,125,56,32,4,20,29,0.329,6.91,0,1,1,0,195,10,33,0,4,5 +woodtr01,2017,2,SDN,NL,3,4,11,11,0,0,0,157,62,39,15,25,36,0.295,6.71,0,0,1,0,241,0,44,4,1,6 +workmbr01,2017,1,BOS,AL,1,1,33,0,0,0,0,119,37,14,7,11,37,0.252,3.18,2,1,1,0,162,8,17,2,1,5 +worleva01,2017,1,MIA,NL,2,6,24,12,0,0,1,215,99,55,9,30,50,0.339,6.91,5,1,6,0,333,8,56,2,3,15 +wrighda04,2017,1,LAA,AL,0,1,5,2,0,0,0,59,21,10,1,8,11,0.284,4.58,0,1,2,0,85,3,12,1,0,3 +wrighmi01,2017,1,BAL,AL,0,0,13,0,0,0,0,75,26,16,5,7,28,0.265,5.76,0,1,3,0,109,4,16,0,1,2 +wrighst01,2017,1,BOS,AL,1,3,5,5,0,0,0,72,40,22,9,5,13,0.377,8.25,1,1,2,0,114,0,24,0,1,5 +yacabji01,2017,1,BAL,AL,2,0,14,0,0,0,0,62,18,10,2,14,8,0.247,4.35,1,1,0,0,90,7,10,0,3,4 +yateski01,2017,1,LAA,AL,0,0,1,0,0,0,0,3,2,2,2,0,1,0.400,18.00,0,0,0,0,5,0,2,0,0,0 +yateski01,2017,2,SDN,NL,4,5,61,0,0,0,1,167,42,23,10,19,87,0.206,3.72,2,0,2,1,226,12,26,0,1,2 +ynoaga01,2017,1,BAL,AL,2,3,9,4,0,0,0,104,39,16,5,8,26,0.289,4.15,0,0,1,0,147,2,17,1,2,5 +ynoami01,2017,1,CHA,AL,1,0,22,0,0,0,0,87,28,19,4,22,23,0.246,5.90,0,3,5,0,141,10,22,0,0,2 +youngch03,2017,1,KCA,AL,0,0,14,2,0,0,0,90,47,25,7,14,22,0.353,7.50,2,1,1,0,148,5,27,0,0,3 +zastrro01,2017,1,CHN,NL,0,0,4,0,0,0,0,39,19,12,2,7,11,0.352,8.31,0,0,1,0,62,0,13,0,0,3 +zieglbr01,2017,1,MIA,NL,1,4,53,0,0,0,10,141,57,25,1,16,26,0.306,4.79,6,1,6,0,211,21,29,3,0,10 +zimmejo02,2017,1,DET,AL,8,13,29,29,0,0,0,480,204,108,29,44,103,0.313,6.08,2,3,7,0,713,0,111,3,8,19 +zychto01,2017,1,SEA,AL,6,3,45,0,0,0,1,122,30,12,2,21,35,0.208,2.66,3,1,5,1,173,7,12,1,2,5 +adamja01,2018,1,KCA,AL,0,3,31,0,0,0,0,97,30,22,9,15,37,0.246,6.12,1,4,3,1,142,14,22,0,2,1 +adamsau02,2018,1,WAS,NL,0,0,2,0,0,0,0,3,1,0,0,3,0,0.250,0.00,0,0,0,0,7,0,0,0,0,0 +adamsch01,2018,1,NYA,AL,0,1,3,1,0,0,0,23,8,6,3,4,4,0.267,7.04,0,0,0,0,34,1,7,0,0,2 +alberma01,2018,1,MIL,NL,3,3,34,0,0,0,1,103,45,28,10,12,32,0.321,7.34,2,1,2,0,157,10,29,0,3,6 +alcansa01,2018,1,MIA,NL,2,3,6,6,0,0,0,102,25,13,3,23,30,0.214,3.44,0,0,2,0,146,0,13,2,2,2 +alcanvi01,2018,1,DET,AL,1,1,27,0,0,0,0,90,25,8,5,6,21,0.223,2.40,0,2,0,0,119,8,8,0,1,1 +alexasc01,2018,1,LAN,NL,2,1,73,1,0,0,3,198,57,27,4,27,56,0.239,3.68,2,2,2,0,268,8,28,1,0,15 +allarko01,2018,1,ATL,NL,1,1,3,1,0,0,0,24,19,11,3,4,3,0.463,12.38,0,0,1,1,47,0,12,1,0,1 +allenco01,2018,1,CLE,AL,4,6,70,0,0,0,27,201,58,35,11,33,80,0.231,4.70,2,3,4,0,289,45,35,1,0,5 +almonmi01,2018,1,LAA,AL,0,0,8,0,0,0,0,21,9,8,1,3,7,0.300,10.29,0,1,1,0,34,4,8,0,0,0 +almonye01,2018,1,COL,NL,0,0,14,0,0,0,0,44,15,3,1,4,14,0.273,1.84,0,1,0,0,60,3,5,0,1,4 +altavda01,2018,1,SEA,AL,3,2,22,0,0,0,0,62,11,6,2,15,23,0.162,2.61,0,4,2,0,85,3,7,0,0,3 +alvarjo02,2018,1,LAA,AL,6,4,76,0,0,0,1,189,51,19,3,22,59,0.217,2.71,2,1,2,0,261,5,20,2,0,6 +alvarjo03,2018,1,TBA,AL,1,6,70,0,0,0,8,192,42,17,1,29,80,0.183,2.39,4,2,1,0,263,17,21,2,2,3 +anderbr04,2018,1,OAK,AL,4,5,17,17,0,0,0,241,90,40,10,13,47,0.285,4.48,0,3,2,0,333,0,42,2,0,9 +anderch01,2018,1,MIL,NL,9,8,30,30,0,0,0,474,131,69,30,57,128,0.228,3.93,0,1,7,0,644,0,71,4,1,13 +anderdr02,2018,1,PHI,NL,0,1,5,1,0,0,0,38,17,7,0,2,11,0.309,4.97,0,0,2,0,59,1,7,0,0,1 +anderju01,2018,1,LAA,AL,3,3,57,0,0,0,4,166,42,25,3,40,67,0.214,4.07,3,9,4,1,241,10,25,0,0,9 +anderta01,2018,1,PIT,NL,1,0,6,0,0,0,0,34,15,8,1,8,6,0.333,6.35,2,2,1,0,56,1,10,2,0,3 +anderty01,2018,1,COL,NL,7,9,32,32,0,0,0,528,165,89,30,59,164,0.248,4.55,1,9,3,2,737,0,94,3,7,13 +andrima01,2018,1,TBA,AL,3,4,27,4,0,0,0,179,55,27,7,18,59,0.240,4.07,3,3,2,0,251,6,32,1,1,6 +andrima01,2018,2,ARI,NL,0,3,14,1,0,0,0,57,29,19,8,7,19,0.354,9.00,2,0,0,0,89,7,19,0,0,3 +aranovi01,2018,1,PHI,NL,1,2,60,0,0,0,3,178,54,18,6,17,60,0.239,2.73,4,1,1,0,246,14,19,0,2,4 +araujpe01,2018,1,BAL,AL,1,3,20,0,0,0,0,84,29,24,9,18,29,0.264,7.71,0,1,2,0,130,8,24,0,0,2 +archech01,2018,1,TBA,AL,3,5,17,17,0,0,0,288,102,46,11,31,102,0.271,4.31,0,3,4,0,413,0,50,0,1,10 +archech01,2018,2,PIT,NL,3,3,10,10,0,0,0,157,53,25,8,18,60,0.262,4.30,3,3,2,0,225,0,27,1,2,4 +arciafr01,2018,1,LAA,AL,0,0,2,0,0,0,0,9,5,3,2,0,0,0.385,9.00,0,0,0,0,13,2,3,0,0,1 +armstsh01,2018,1,SEA,AL,0,1,14,0,0,0,1,44,9,2,1,3,15,0.184,1.23,1,0,3,0,57,3,2,0,2,0 +arrieja01,2018,1,PHI,NL,10,11,31,31,0,0,0,518,165,76,21,57,138,0.254,3.96,0,11,7,1,724,0,93,1,8,22 +asheral01,2018,1,MIL,NL,0,0,2,0,0,0,0,9,2,0,0,1,2,0.167,0.00,0,0,0,0,13,1,0,0,0,0 +astudwi01,2018,1,MIN,AL,0,0,1,0,0,0,0,3,5,5,2,0,0,0.625,45.00,0,0,0,0,8,1,5,0,0,0 +avilaal01,2018,1,ARI,NL,0,0,1,0,0,0,0,6,1,0,0,0,0,0.143,0.00,0,0,0,0,7,1,0,0,0,0 +avilalu01,2018,1,CHA,AL,2,1,58,0,0,0,2,119,40,17,2,14,46,0.260,3.86,2,2,2,0,172,7,20,1,1,1 +avilalu01,2018,2,PHI,NL,0,0,12,0,0,0,0,17,4,2,1,4,5,0.190,3.18,1,0,0,0,25,1,2,0,0,0 +axforjo01,2018,1,TOR,AL,4,1,45,1,0,0,0,153,44,25,6,20,50,0.234,4.41,1,6,2,0,211,11,27,0,1,8 +axforjo01,2018,2,LAN,NL,0,0,5,0,0,0,0,11,8,7,0,2,4,0.444,17.18,0,0,1,0,22,1,8,0,1,0 +baezpe01,2018,1,LAN,NL,4,3,55,0,0,0,0,169,46,18,4,23,62,0.220,2.88,2,0,1,1,237,8,19,2,2,3 +baezsa01,2018,1,DET,AL,0,0,9,0,0,0,0,43,12,8,2,9,10,0.222,5.02,1,1,2,0,66,0,12,0,1,1 +baileho02,2018,1,CIN,NL,1,14,20,20,1,0,0,319,141,72,23,33,75,0.313,6.09,9,2,2,1,494,0,82,5,3,4 +bandaan01,2018,1,TBA,AL,1,0,3,1,0,0,0,44,12,6,1,3,10,0.235,3.68,0,0,0,0,56,1,6,1,1,2 +barbajo01,2018,1,DET,AL,0,0,7,0,0,0,0,20,11,9,3,5,2,0.379,12.15,1,2,1,0,36,4,9,1,0,0 +bardlu01,2018,1,LAA,AL,0,0,8,0,0,0,0,35,10,7,4,5,13,0.222,5.40,1,1,3,0,53,3,7,0,0,0 +barlosc01,2018,1,KCA,AL,1,1,6,0,0,0,0,45,16,6,2,3,15,0.258,3.60,0,0,0,0,65,3,7,0,0,0 +barneda02,2018,1,TOR,AL,3,3,47,0,0,0,0,123,47,26,6,22,38,0.285,5.71,1,0,1,0,191,6,28,0,2,3 +barneja01,2018,1,MIL,NL,0,1,49,0,0,0,2,146,51,18,4,23,47,0.266,3.33,2,4,0,0,217,19,24,1,1,5 +barnema01,2018,1,BOS,AL,6,4,62,0,0,0,0,185,47,25,5,31,96,0.204,3.65,1,8,2,0,265,8,25,0,2,2 +barneto01,2018,1,TEX,AL,2,0,22,0,0,0,0,79,19,7,2,5,26,0.196,2.39,0,0,2,0,104,5,11,0,0,2 +barraky01,2018,1,MIA,NL,1,6,61,0,0,0,10,167,40,26,8,34,60,0.197,4.20,3,4,5,0,245,25,27,2,1,4 +barreja01,2018,1,ARI,NL,0,1,7,0,0,0,0,21,8,4,1,2,6,0.286,5.14,0,1,0,0,30,5,4,0,0,1 +barrija01,2018,1,LAA,AL,10,9,26,26,0,0,0,388,117,49,17,47,98,0.242,3.41,0,3,6,1,537,0,50,0,0,13 +bashlty01,2018,1,NYN,NL,0,3,24,0,0,0,0,96,26,15,6,12,25,0.218,4.22,0,1,3,0,135,6,16,1,0,1 +bassan01,2018,1,CHN,NL,0,0,16,0,0,0,0,46,18,5,1,3,14,0.305,2.93,0,2,0,0,62,3,6,0,0,2 +bassich01,2018,1,OAK,AL,2,3,11,7,0,0,0,143,40,16,4,19,41,0.221,3.02,0,2,4,0,204,0,21,0,0,1 +bauertr01,2018,1,CLE,AL,12,6,28,27,0,0,1,526,134,43,9,57,221,0.208,2.21,2,12,9,0,717,1,51,3,3,7 +baumabu01,2018,1,SDN,NL,0,1,1,0,0,0,0,1,2,2,0,2,0,0.667,54.00,0,0,0,0,6,0,5,1,0,0 +baumabu01,2018,2,NYN,NL,0,1,3,0,0,0,0,9,7,8,1,5,4,0.438,24.00,2,0,0,0,21,1,8,0,0,0 +bautige01,2018,1,NYN,NL,0,1,5,0,0,0,0,13,8,6,2,5,3,0.444,12.46,0,1,0,0,25,5,6,0,2,0 +beckch02,2018,1,CHA,AL,0,0,14,0,0,0,1,71,24,11,5,11,16,0.270,4.18,0,1,1,0,102,8,11,0,1,1 +beckch02,2018,2,NYN,NL,0,0,6,0,0,0,0,31,10,6,3,9,5,0.270,5.23,0,0,0,0,46,1,6,0,0,1 +bedroca01,2018,1,LAA,AL,5,4,71,0,0,0,1,192,63,27,7,26,57,0.263,3.80,0,1,2,0,271,7,30,1,1,12 +beedety01,2018,1,SFN,NL,0,1,2,2,0,0,0,23,9,7,0,8,9,0.290,8.22,0,0,1,0,40,0,7,0,0,1 +beeksja01,2018,1,BOS,AL,0,1,2,1,0,0,0,19,11,9,1,4,5,0.379,12.79,0,0,1,0,34,0,9,0,0,1 +beeksja01,2018,2,TBA,AL,5,0,12,0,0,0,0,133,41,22,5,20,37,0.248,4.47,0,0,2,0,189,0,22,1,1,7 +belisma01,2018,1,CLE,AL,0,0,8,0,0,0,0,32,9,6,1,1,4,0.220,5.06,0,1,2,0,44,3,6,0,0,0 +belisma01,2018,2,MIN,AL,1,1,25,0,0,0,0,71,40,24,5,10,21,0.381,9.13,1,0,1,0,118,11,26,1,1,3 +belivje01,2018,1,CLE,AL,0,0,9,0,0,0,1,14,7,6,2,5,2,0.350,11.57,0,0,1,0,26,2,6,0,0,1 +bellch02,2018,1,DET,AL,0,1,3,0,0,0,0,22,14,7,1,2,7,0.424,8.59,1,0,0,0,36,0,7,0,1,2 +bergmch01,2018,1,SEA,AL,0,1,3,2,0,0,0,42,12,9,4,3,7,0.231,5.79,0,0,0,0,57,0,9,1,1,1 +berrijo01,2018,1,MIN,AL,12,11,32,32,2,1,0,577,159,82,25,61,202,0.222,3.84,1,2,13,0,797,0,83,2,4,11 +betande01,2018,1,NYA,AL,4,6,66,0,0,0,4,200,44,20,7,26,115,0.186,2.70,2,4,5,1,272,15,22,3,1,3 +bettich01,2018,1,COL,NL,5,2,27,20,0,0,0,361,121,67,18,47,80,0.265,5.01,3,3,5,1,518,2,72,5,5,12 +biagijo01,2018,1,TOR,AL,4,7,50,4,0,0,0,216,96,48,14,24,53,0.323,6.00,0,8,6,0,328,7,50,0,1,11 +bibenau01,2018,1,TEX,AL,2,3,13,6,0,0,0,135,56,31,9,14,33,0.308,6.20,0,2,5,0,203,4,33,2,0,4 +biddlje01,2018,1,ATL,NL,6,1,60,0,0,0,1,191,50,22,6,31,67,0.218,3.11,5,2,3,0,266,14,26,2,1,8 +biebesh01,2018,1,CLE,AL,11,5,20,19,0,0,0,344,130,58,13,23,118,0.285,4.55,0,5,2,0,485,0,60,0,4,9 +blachty01,2018,1,SFN,NL,6,7,47,13,0,0,0,356,133,56,8,41,75,0.289,4.25,4,1,1,1,512,13,62,8,1,18 +blackpa01,2018,1,OAK,AL,2,3,6,6,0,0,0,83,33,22,2,6,19,0.303,7.16,0,1,2,0,119,0,23,0,2,2 +blackra01,2018,1,SFN,NL,2,2,26,0,0,0,0,70,17,16,4,10,33,0.207,6.17,0,1,1,0,95,4,16,2,0,2 +blandal01,2018,1,CIN,NL,0,0,1,0,0,0,0,3,1,0,0,0,2,0.250,0.00,0,1,0,0,4,1,0,0,0,0 +bleicje01,2018,1,OAK,AL,0,0,2,0,0,0,0,1,2,2,0,0,1,0.667,54.00,0,0,1,0,4,0,2,0,0,0 +bleieri01,2018,1,BAL,AL,3,0,31,0,0,0,0,98,36,7,0,4,15,0.286,1.93,1,1,1,0,133,4,7,0,2,5 +blevije01,2018,1,NYN,NL,3,2,64,1,0,0,1,128,36,23,6,22,41,0.225,4.85,1,1,5,0,188,7,24,0,1,1 +borucry01,2018,1,TOR,AL,4,6,17,17,0,0,0,293,96,42,7,33,67,0.256,3.87,3,2,2,0,415,0,48,3,2,12 +bowmama01,2018,1,SLN,NL,0,2,22,0,0,0,0,69,29,16,4,11,26,0.309,6.26,3,1,1,0,109,5,16,2,1,2 +boxbebr01,2018,1,ARI,NL,3,7,60,0,0,0,32,160,44,26,9,32,71,0.221,4.39,4,3,1,0,235,45,30,2,1,4 +boydma01,2018,1,DET,AL,9,13,31,31,0,0,0,511,146,83,27,51,159,0.228,4.39,0,6,11,2,709,0,87,2,6,8 +boyerbl01,2018,1,KCA,AL,2,1,21,0,0,0,1,65,32,29,7,13,9,0.344,12.05,3,0,0,0,109,8,33,0,3,1 +brachbr01,2018,1,BAL,AL,1,2,42,0,0,0,11,117,50,21,4,19,38,0.303,4.85,1,1,0,0,185,24,24,1,0,4 +brachbr01,2018,2,ATL,NL,1,2,27,0,0,0,1,71,22,4,1,9,22,0.237,1.52,1,2,1,1,104,8,8,0,1,0 +brachsi01,2018,1,ARI,NL,2,0,31,0,0,0,0,93,25,11,2,12,34,0.223,3.19,2,2,3,0,129,7,12,1,1,2 +bradfch02,2018,1,SEA,AL,5,0,46,0,0,0,0,161,55,22,9,14,38,0.262,3.69,1,1,5,0,231,15,23,0,2,3 +bradlar01,2018,1,ARI,NL,4,5,76,0,0,0,3,215,62,29,9,20,75,0.229,3.64,1,2,4,1,296,8,30,1,0,6 +brasiry01,2018,1,BOS,AL,2,0,34,0,0,0,0,101,19,6,2,7,29,0.171,1.60,0,1,0,0,124,5,6,1,5,3 +braulst01,2018,1,PIT,NL,6,3,45,5,0,0,0,275,84,47,10,57,82,0.244,4.61,4,9,8,0,413,6,51,3,1,9 +brebbjo01,2018,1,SLN,NL,3,3,45,0,0,0,2,152,43,18,5,16,60,0.226,3.20,2,1,0,0,209,17,18,1,2,3 +breweco01,2018,1,SDN,NL,1,0,11,0,0,0,0,29,15,6,0,7,10,0.357,5.59,0,1,0,0,49,5,10,0,0,2 +briceau01,2018,1,CIN,NL,2,3,33,0,0,0,0,112,39,24,9,13,32,0.271,5.79,6,1,3,0,162,8,26,1,1,3 +bridwpa01,2018,1,LAA,AL,1,0,5,1,0,0,0,20,14,13,5,2,3,0.438,17.55,0,0,0,0,36,2,13,1,1,1 +brighje01,2018,1,MIA,NL,0,4,4,4,0,0,0,49,16,11,2,13,12,0.271,6.06,0,0,2,0,77,0,11,0,3,2 +brittza01,2018,1,BAL,AL,1,0,16,0,0,0,4,47,11,6,1,10,13,0.212,3.45,0,2,1,0,63,11,6,0,0,2 +brittza01,2018,2,NYA,AL,1,0,25,0,0,0,3,75,18,8,2,11,21,0.196,2.88,0,5,2,0,106,10,10,0,1,3 +brookaa01,2018,1,OAK,AL,0,0,3,0,0,0,0,8,1,0,0,2,1,0.125,0.00,0,0,0,0,10,2,0,0,0,1 +brothre01,2018,1,ATL,NL,0,0,1,0,0,0,0,0,0,1,0,2,0,,,0,0,0,0,2,0,1,0,0,0 +buchhcl01,2018,1,ARI,NL,7,2,16,16,1,0,0,295,80,22,9,22,81,0.220,2.01,1,1,3,0,393,0,25,0,4,9 +buchtry01,2018,1,OAK,AL,6,0,54,0,0,0,0,118,32,12,4,15,41,0.219,2.75,1,1,0,0,163,4,17,1,1,0 +buehlwa01,2018,1,LAN,NL,8,5,24,23,0,0,0,412,95,40,12,37,151,0.193,2.62,1,4,6,0,541,0,43,2,3,5 +bumgama01,2018,1,SFN,NL,6,7,21,21,0,0,0,389,118,47,14,43,109,0.238,3.26,3,3,5,1,551,0,51,5,3,9 +bummeaa01,2018,1,CHA,AL,0,1,37,0,0,0,0,95,40,15,1,10,35,0.301,4.26,0,7,1,1,144,9,19,0,0,5 +bundydy01,2018,1,BAL,AL,8,16,31,31,1,0,0,515,188,104,41,54,184,0.274,5.45,1,6,6,1,750,0,116,3,2,13 +burdini01,2018,1,PIT,NL,0,0,2,0,0,0,0,4,3,3,1,2,2,0.375,20.25,0,0,0,0,10,1,4,0,0,0 +burneco01,2018,1,MIL,NL,7,0,30,0,0,0,1,114,27,11,4,11,35,0.199,2.61,2,2,3,1,152,6,11,1,1,3 +burrry01,2018,1,CHA,AL,0,0,8,0,0,0,0,29,12,8,3,6,6,0.333,7.45,1,0,1,0,44,1,8,1,0,2 +busenal01,2018,1,MIN,AL,4,1,23,0,0,0,0,76,37,22,8,14,26,0.339,7.82,0,2,2,0,126,7,25,0,1,2 +bushma01,2018,1,TEX,AL,0,0,21,0,0,0,0,69,23,12,3,14,19,0.256,4.70,0,1,2,0,108,6,13,0,2,0 +buterdr01,2018,1,KCA,AL,0,0,1,0,0,0,0,1,2,2,0,3,0,0.667,54.00,0,0,0,0,6,1,2,0,0,0 +butleed01,2018,1,CHN,NL,1,1,8,0,0,0,0,53,16,8,1,7,11,0.242,4.08,0,1,0,0,75,3,10,1,1,1 +butleed01,2018,2,TEX,AL,1,2,22,0,0,0,2,96,43,23,10,12,18,0.316,6.47,1,5,2,0,153,6,24,0,3,2 +buttrty01,2018,1,LAA,AL,0,1,16,0,0,0,4,49,15,6,0,5,20,0.238,3.31,0,0,0,1,70,4,7,1,1,0 +cahiltr01,2018,1,OAK,AL,7,4,21,20,0,0,0,330,90,46,8,41,100,0.227,3.76,0,8,5,0,450,0,52,3,3,9 +caratvi01,2018,1,CHN,NL,0,0,2,0,0,0,0,6,3,2,1,0,0,0.375,9.00,0,0,0,0,8,0,2,0,0,1 +carlesh01,2018,1,ATL,NL,4,1,53,0,0,0,1,189,50,20,2,27,43,0.221,2.86,7,2,1,0,259,8,22,1,4,9 +carpery01,2018,1,DET,AL,1,2,6,5,0,0,0,67,34,18,8,4,15,0.347,7.25,0,0,3,0,107,0,19,0,2,1 +carraca01,2018,1,CLE,AL,17,10,32,30,2,0,0,576,173,72,21,43,231,0.238,3.38,4,9,6,0,784,1,78,4,5,11 +carroco01,2018,1,BAL,AL,0,2,15,0,0,0,0,51,21,17,6,13,16,0.296,9.00,0,3,0,1,84,3,17,0,0,2 +cashnan01,2018,1,BAL,AL,4,15,28,28,0,0,0,459,177,90,25,65,99,0.291,5.29,3,6,3,0,681,0,97,1,3,13 +castidi01,2018,1,TBA,AL,4,2,43,11,0,0,0,170,36,20,6,18,65,0.178,3.18,0,5,2,1,222,5,21,0,0,1 +castijo03,2018,1,SDN,NL,3,3,37,0,0,0,0,115,23,14,3,12,52,0.170,3.29,1,1,3,0,150,5,14,0,0,0 +castilu02,2018,1,CIN,NL,10,12,31,31,0,0,0,509,158,81,28,49,165,0.245,4.30,1,4,5,2,708,0,89,3,6,13 +castrmi01,2018,1,BAL,AL,2,7,63,1,0,0,0,259,75,38,9,50,57,0.236,3.96,7,9,5,3,376,16,41,0,3,13 +cecilbr01,2018,1,SLN,NL,1,1,40,0,0,0,0,98,39,25,5,25,19,0.302,6.89,5,1,0,0,157,7,27,0,3,3 +cedenxa01,2018,1,CHA,AL,2,0,33,0,0,0,1,76,19,8,1,13,28,0.204,2.84,1,3,0,0,106,4,9,0,0,3 +cedenxa01,2018,2,MIL,NL,0,0,15,0,0,0,0,24,7,1,0,3,6,0.226,1.13,0,1,0,0,34,0,1,0,0,0 +cessalu01,2018,1,NYA,AL,1,4,16,5,0,0,2,134,51,26,5,13,39,0.282,5.24,0,7,0,0,195,6,27,1,0,3 +chacijh01,2018,1,MIL,NL,15,8,35,35,0,0,0,578,153,75,18,71,156,0.220,3.50,3,5,11,0,796,0,83,8,9,12 +chafian01,2018,1,ARI,NL,1,6,77,0,0,0,0,148,41,17,0,25,53,0.227,3.10,1,3,2,0,211,13,18,0,3,3 +chapmar01,2018,1,NYA,AL,3,0,55,0,0,0,32,154,24,14,2,30,93,0.136,2.45,0,9,5,0,212,43,15,0,0,0 +chargjt01,2018,1,LAN,NL,2,4,39,0,0,0,0,97,26,12,4,15,40,0.222,3.34,3,3,2,0,135,4,13,1,0,2 +chatwty01,2018,1,CHN,NL,4,6,24,20,0,0,0,311,92,61,9,95,85,0.245,5.30,1,8,7,0,486,4,62,5,4,15 +chaveje01,2018,1,TEX,AL,3,1,30,0,0,0,1,169,58,22,10,12,50,0.262,3.51,1,1,0,0,234,15,23,0,1,5 +chaveje01,2018,2,CHN,NL,2,1,32,0,0,0,4,117,26,5,3,5,42,0.190,1.15,0,0,0,0,143,11,5,0,1,4 +chenwe02,2018,1,MIA,NL,6,12,26,26,0,0,0,400,131,71,19,47,111,0.256,4.79,6,4,1,1,572,0,75,5,7,6 +chiriyo01,2018,1,TBA,AL,5,5,18,7,0,0,0,269,84,35,7,25,75,0.254,3.51,2,5,5,0,370,2,40,2,7,12 +cimbead01,2018,1,SDN,NL,3,5,42,0,0,0,0,145,42,17,2,10,51,0.237,3.17,3,0,2,0,192,10,19,1,2,6 +cimbead01,2018,2,CLE,AL,0,3,28,0,0,0,0,60,26,9,3,7,7,0.325,4.05,6,1,4,0,92,6,9,1,0,6 +cingrto01,2018,1,LAN,NL,1,2,30,0,0,0,0,68,19,12,2,6,36,0.221,4.76,1,1,3,2,95,2,12,0,0,0 +cishest01,2018,1,CHN,NL,4,3,80,0,0,0,4,211,45,17,5,28,78,0.181,2.18,4,2,9,0,288,10,19,2,1,4 +claudal01,2018,1,TEX,AL,4,2,66,1,0,0,1,205,91,34,4,13,41,0.329,4.48,3,0,3,1,299,20,35,3,3,11 +clevimi01,2018,1,CLE,AL,13,8,32,32,1,1,0,600,164,67,21,67,207,0.223,3.02,0,4,4,0,810,0,71,0,1,15 +clippty01,2018,1,TOR,AL,4,3,73,1,0,0,7,206,57,28,13,23,85,0.223,3.67,0,7,2,0,285,22,29,2,2,1 +cloydty01,2018,1,MIA,NL,0,0,7,0,0,0,0,53,25,17,3,10,13,0.329,8.66,2,1,1,0,87,4,17,0,0,1 +cobbal01,2018,1,BAL,AL,5,15,28,28,1,0,0,457,172,83,24,43,102,0.284,4.90,5,4,4,1,661,0,93,2,6,16 +coleaj01,2018,1,WAS,NL,1,1,4,2,0,0,0,31,16,15,6,6,10,0.356,13.06,0,0,0,0,53,1,15,2,0,0 +coleaj01,2018,2,NYA,AL,3,1,28,0,0,0,0,114,39,18,9,16,49,0.258,4.26,1,2,0,0,168,13,23,0,1,2 +colege01,2018,1,HOU,AL,15,5,32,32,1,1,0,601,143,64,19,64,276,0.198,2.88,0,9,7,0,799,0,68,2,3,11 +colemlo01,2018,1,DET,AL,4,1,51,0,0,0,0,154,43,20,5,24,41,0.235,3.51,2,0,6,0,217,9,21,1,3,9 +coleta01,2018,1,LAA,AL,4,2,18,2,0,0,0,108,20,11,3,12,39,0.169,2.75,0,1,3,0,135,4,11,1,1,6 +colliti01,2018,1,WAS,NL,0,0,38,0,0,0,0,68,23,11,5,12,21,0.264,4.37,0,1,0,0,99,5,11,0,0,3 +colomal01,2018,1,TBA,AL,2,5,23,0,0,0,11,65,24,10,1,8,23,0.273,4.15,1,4,0,0,97,21,12,0,1,2 +colomal01,2018,2,SEA,AL,5,0,47,0,0,0,1,139,35,13,6,13,49,0.207,2.53,1,6,3,1,185,3,14,0,0,4 +colonba01,2018,1,TEX,AL,7,12,28,24,1,0,0,439,172,94,32,25,81,0.291,5.78,1,2,3,0,628,1,97,1,8,6 +conlead01,2018,1,MIA,NL,3,4,52,0,0,0,3,152,37,23,5,18,50,0.207,4.09,1,3,1,0,202,5,25,1,3,7 +conlopj01,2018,1,NYN,NL,0,0,3,2,0,0,0,23,15,7,2,2,5,0.417,8.22,0,0,0,0,38,1,7,0,0,1 +cookry01,2018,1,SEA,AL,2,1,19,0,0,0,0,51,15,10,4,7,23,0.234,5.29,1,2,3,0,76,5,10,1,1,0 +copelsc01,2018,1,NYN,NL,0,0,1,0,0,0,0,4,1,0,0,0,2,0.200,0.00,0,0,0,0,5,0,0,0,0,0 +corbipa01,2018,1,ARI,NL,11,7,33,33,1,1,0,600,162,70,15,48,246,0.218,3.15,3,8,5,0,800,0,70,3,2,11 +corcida01,2018,1,LAN,NL,0,0,2,0,0,0,1,12,2,1,0,3,1,0.143,2.25,0,1,1,0,18,2,2,0,0,0 +cordeji01,2018,1,WAS,NL,1,2,22,0,0,0,0,57,23,12,2,12,12,0.288,5.68,2,0,2,0,94,3,13,0,0,2 +cortene01,2018,1,BAL,AL,0,0,4,0,0,0,0,14,10,4,2,4,3,0.455,7.71,0,0,0,0,26,3,4,0,0,1 +couloda01,2018,1,OAK,AL,1,1,27,0,0,0,0,71,24,12,5,11,26,0.279,4.56,0,2,0,0,98,3,13,0,1,6 +coveydy01,2018,1,CHA,AL,5,14,27,21,0,0,0,365,129,70,13,52,91,0.269,5.18,4,5,2,0,542,2,81,5,3,9 +crickky01,2018,1,PIT,NL,3,2,64,0,0,0,2,181,45,16,3,23,65,0.202,2.39,3,9,7,2,255,13,18,1,1,1 +crockky01,2018,1,CIN,NL,1,0,15,0,0,0,0,28,16,6,1,2,11,0.364,5.79,1,1,0,0,46,2,8,0,0,1 +cruzrh01,2018,1,TOR,AL,0,0,2,0,0,0,0,10,3,1,0,2,4,0.231,2.70,0,1,0,0,15,1,1,0,0,0 +cuetojo01,2018,1,SFN,NL,3,2,9,9,0,0,0,159,46,19,8,13,38,0.238,3.23,0,2,5,0,214,0,19,3,0,6 +cuevawi01,2018,1,BOS,AL,0,2,9,1,0,0,0,51,20,14,3,11,20,0.294,7.41,0,2,4,0,84,3,14,1,0,1 +culbech01,2018,1,ATL,NL,0,0,1,0,0,0,0,3,2,1,0,0,0,0.400,9.00,0,0,0,0,5,1,1,0,0,0 +cumptbr01,2018,1,TOR,AL,0,0,1,0,0,0,0,5,3,1,0,2,2,0.375,5.40,0,0,0,0,10,0,1,0,0,0 +curtijo02,2018,1,MIN,AL,0,1,8,0,0,0,0,19,8,4,0,4,7,0.308,5.68,1,3,0,0,30,2,4,0,0,0 +curtiza01,2018,1,PHI,NL,0,0,7,0,0,0,0,29,6,2,0,10,10,0.188,1.86,1,0,1,0,44,4,2,0,1,1 +curtiza01,2018,2,TEX,AL,0,1,8,0,0,0,0,20,6,7,1,9,8,0.250,9.45,1,2,0,0,35,1,8,0,2,0 +danisty01,2018,1,CHA,AL,1,0,7,0,0,0,0,19,8,5,2,4,5,0.320,7.11,0,0,1,0,31,0,5,0,1,0 +darnach01,2018,1,SFN,NL,0,0,1,0,0,0,0,3,1,0,0,0,0,0.250,0.00,0,0,0,0,4,1,0,0,0,0 +darviyu01,2018,1,CHN,NL,1,3,8,8,0,0,0,120,36,22,7,21,49,0.235,4.95,0,2,4,1,180,0,24,1,1,1 +davidma02,2018,1,CHA,AL,0,0,3,0,0,0,0,9,1,0,0,1,2,0.100,0.00,0,0,0,0,11,3,0,0,0,0 +davieza01,2018,1,MIL,NL,2,7,13,13,0,0,0,198,67,35,8,21,49,0.269,4.77,3,1,4,0,280,0,36,0,5,8 +davisau01,2018,1,PHI,NL,1,2,32,0,0,0,0,104,35,16,4,12,38,0.265,4.15,1,4,2,1,151,10,20,1,4,3 +davisjd01,2018,1,HOU,AL,0,0,1,0,0,0,0,3,1,1,1,0,1,0.250,9.00,0,0,0,0,4,1,1,0,0,0 +daviswa01,2018,1,COL,NL,3,6,69,0,0,0,43,196,43,30,8,26,78,0.185,4.13,0,6,2,0,261,63,31,0,0,4 +deetzde01,2018,1,HOU,AL,0,0,4,0,0,0,0,10,4,2,1,1,3,0.308,5.40,0,0,1,0,15,3,2,0,0,0 +degroja01,2018,1,NYN,NL,10,9,32,32,1,0,0,651,152,41,10,46,269,0.196,1.70,3,2,5,0,835,0,48,3,5,13 +dejonch01,2018,1,MIN,AL,1,1,4,4,0,0,0,53,18,7,3,6,13,0.265,3.57,0,2,0,0,74,0,9,0,0,3 +delarjo01,2018,1,ARI,NL,0,2,42,0,0,0,0,105,37,18,6,19,27,0.278,4.63,3,2,1,2,158,7,19,3,2,8 +delarjo01,2018,2,CHN,NL,0,0,17,0,0,0,1,63,14,3,0,8,20,0.182,1.29,1,0,0,0,85,7,6,0,0,1 +delgara01,2018,1,ARI,NL,2,0,10,0,0,0,0,34,11,6,3,6,5,0.244,4.76,2,2,0,0,51,1,6,0,0,1 +delosen01,2018,1,PHI,NL,1,0,7,2,0,0,0,57,19,10,2,8,15,0.271,4.74,0,1,1,0,81,2,10,1,1,2 +descada01,2018,1,ARI,NL,0,0,2,0,0,0,0,10,4,3,2,1,2,0.286,8.10,0,0,0,0,15,1,3,0,0,0 +desclan01,2018,1,CIN,NL,7,8,21,21,0,0,0,345,118,63,24,30,108,0.266,4.93,2,4,2,0,484,0,68,5,4,7 +despaod01,2018,1,MIA,NL,2,0,11,1,0,0,0,61,22,12,1,8,18,0.278,5.31,1,0,1,0,91,2,16,1,2,2 +despaod01,2018,2,LAA,AL,0,3,8,4,0,0,0,56,30,17,3,11,17,0.361,8.20,0,0,1,0,97,0,18,0,2,2 +detwiro01,2018,1,SEA,AL,0,1,1,0,0,0,0,18,8,3,1,2,2,0.400,4.50,0,0,0,0,23,0,3,0,1,5 +devench02,2018,1,HOU,AL,2,3,50,1,0,0,2,142,42,22,9,13,51,0.236,4.18,1,1,3,1,196,8,23,1,1,3 +diazed04,2018,1,SEA,AL,0,4,73,0,0,0,57,220,41,16,5,17,124,0.160,1.96,0,3,6,1,280,65,17,0,0,2 +diazmi02,2018,1,SDN,NL,1,0,11,0,0,0,0,56,16,10,2,12,30,0.225,4.82,2,1,0,0,85,4,11,0,2,0 +diekmja01,2018,1,TEX,AL,1,1,47,0,0,0,2,117,31,16,2,23,48,0.215,3.69,1,0,3,0,172,10,18,0,2,6 +diekmja01,2018,2,ARI,NL,0,1,24,0,0,0,0,43,18,12,2,8,18,0.300,7.53,1,3,3,0,71,5,15,0,0,1 +dixonbr01,2018,1,CIN,NL,0,0,2,0,0,0,0,4,0,0,0,0,1,0.000,0.00,0,0,0,0,4,2,0,0,0,0 +dominse01,2018,1,PHI,NL,2,5,53,0,0,0,16,174,32,19,4,22,74,0.157,2.95,2,10,4,0,231,24,19,0,1,3 +doolise01,2018,1,WAS,NL,3,3,43,0,0,0,25,135,21,8,3,6,60,0.135,1.60,1,1,2,0,163,35,8,0,0,1 +drakeol01,2018,1,MIL,NL,1,0,11,0,0,0,0,38,14,9,0,8,15,0.286,6.39,1,2,0,0,58,3,9,1,0,2 +drakeol01,2018,2,CLE,AL,0,0,4,0,0,0,0,13,7,6,0,1,4,0.350,12.46,0,0,1,0,22,0,6,0,0,0 +drakeol01,2018,3,LAA,AL,0,1,8,0,0,0,0,26,15,5,2,1,8,0.385,5.19,0,0,0,0,40,6,5,0,0,1 +drakeol01,2018,4,TOR,AL,0,0,2,0,0,0,0,5,4,3,0,0,2,0.444,16.20,0,1,0,0,9,2,3,0,0,0 +drakeol01,2018,5,MIN,AL,0,0,19,0,0,0,0,61,12,5,2,7,22,0.164,2.21,0,2,0,0,80,8,6,0,0,1 +duensbr01,2018,1,CHN,NL,3,0,48,0,0,0,1,113,42,32,6,29,24,0.286,7.65,2,0,1,0,181,7,33,1,3,5 +duffety01,2018,1,MIN,AL,2,2,19,1,0,0,0,75,26,20,6,4,19,0.260,7.20,0,2,1,0,107,4,22,0,2,2 +duffyda01,2018,1,KCA,AL,8,12,28,28,0,0,0,465,161,84,23,70,141,0.264,4.88,1,14,4,0,692,0,86,2,6,6 +dukeza01,2018,1,MIN,AL,3,4,45,0,0,0,0,112,44,15,0,15,39,0.282,3.62,4,3,5,0,178,8,19,1,1,2 +dukeza01,2018,2,SEA,AL,2,1,27,0,0,0,0,44,13,9,1,6,12,0.241,5.52,0,0,1,0,62,3,9,1,0,2 +dullry01,2018,1,OAK,AL,0,0,28,0,0,0,0,76,22,12,3,7,21,0.242,4.26,0,3,0,0,100,3,12,1,1,3 +dunnmi01,2018,1,COL,NL,0,0,25,0,0,0,0,51,22,17,1,18,12,0.344,9.00,0,1,0,0,83,6,17,0,1,4 +dysonsa01,2018,1,SFN,NL,4,3,74,0,0,0,3,211,56,21,5,20,56,0.228,2.69,1,2,3,0,273,16,23,1,3,13 +edwarca01,2018,1,CHN,NL,3,2,58,0,0,0,0,156,36,15,2,32,67,0.190,2.60,1,4,0,0,222,2,17,1,0,2 +edwarjo02,2018,1,CLE,AL,0,0,9,0,0,0,0,26,6,3,2,4,10,0.182,3.12,0,0,0,0,37,0,4,0,0,0 +eflinza01,2018,1,PHI,NL,11,8,24,24,0,0,0,384,130,62,16,37,123,0.261,4.36,4,4,3,0,548,0,69,5,4,5 +eickhje01,2018,1,PHI,NL,0,1,3,1,0,0,0,16,10,4,1,0,11,0.385,6.75,0,0,0,0,26,1,4,0,0,0 +eliasro01,2018,1,SEA,AL,3,1,23,4,0,0,0,153,46,15,1,16,34,0.242,2.65,1,3,1,0,210,13,17,0,3,4 +eovalna01,2018,1,TBA,AL,3,4,10,10,0,0,0,171,48,27,11,8,53,0.225,4.26,1,1,1,0,224,0,27,0,2,5 +eovalna01,2018,2,BOS,AL,3,3,12,11,0,0,0,162,57,20,3,12,48,0.266,3.33,0,3,2,0,231,0,28,1,2,5 +erlinro01,2018,1,SDN,NL,4,7,39,12,0,0,0,327,112,51,12,12,88,0.267,4.21,0,2,0,1,439,8,57,3,5,11 +ervinph01,2018,1,CIN,NL,0,0,1,0,0,0,0,1,0,0,0,0,0,0.000,0.00,0,0,0,0,1,1,0,0,0,0 +estrama01,2018,1,TOR,AL,7,14,28,28,0,0,0,431,155,90,29,50,103,0.274,5.64,0,3,2,0,627,0,91,1,7,5 +familje01,2018,1,NYN,NL,4,4,40,0,0,0,17,122,36,13,1,14,43,0.234,2.88,1,1,2,0,171,29,13,0,1,2 +familje01,2018,2,OAK,AL,4,2,30,0,0,0,1,94,24,12,2,14,40,0.205,3.45,0,1,0,0,131,7,13,0,0,1 +fariaja01,2018,1,TBA,AL,4,4,17,12,0,0,0,195,60,39,9,33,50,0.248,5.40,1,2,3,0,281,1,39,0,3,6 +farmebu01,2018,1,DET,AL,3,4,66,1,0,0,0,208,67,32,6,41,57,0.255,4.15,1,2,1,0,308,12,34,1,2,8 +farquda01,2018,1,CHA,AL,1,1,8,0,0,0,0,24,6,5,3,0,9,0.214,5.63,0,1,0,0,29,0,5,0,1,1 +farrelu01,2018,1,CHN,NL,3,4,20,2,0,0,0,94,30,18,7,16,39,0.244,5.17,2,1,1,0,141,8,22,0,1,1 +feddeer01,2018,1,WAS,NL,2,4,11,11,0,0,0,151,55,31,8,22,46,0.286,5.54,1,0,0,1,217,0,31,1,2,7 +felizmi01,2018,1,PIT,NL,1,2,47,0,0,0,0,143,49,30,6,23,55,0.261,5.66,0,3,3,1,217,7,33,0,3,1 +ferguca01,2018,1,LAN,NL,7,2,29,3,0,0,2,147,43,19,8,12,59,0.230,3.49,1,1,3,0,202,7,21,0,0,4 +fernajo04,2018,1,TOR,AL,0,0,13,0,0,0,0,31,10,7,2,4,6,0.244,6.10,0,0,0,0,45,1,7,0,0,0 +festama01,2018,1,SEA,AL,0,0,8,1,0,0,0,25,13,2,0,2,4,0.351,2.16,0,2,1,0,40,0,2,0,0,0 +fieldjo03,2018,1,LAN,NL,2,2,45,0,0,0,2,123,28,10,4,11,33,0.196,2.20,1,0,0,0,154,10,10,0,0,3 +fieldjo04,2018,1,TBA,AL,0,0,1,0,0,0,0,3,0,0,0,1,0,0.000,0.00,0,0,0,0,4,1,0,0,0,0 +fiersmi01,2018,1,DET,AL,7,6,21,21,0,0,0,357,121,46,20,26,87,0.258,3.48,1,2,5,0,502,0,49,1,1,5 +fiersmi01,2018,2,OAK,AL,5,2,10,9,0,0,0,159,45,22,12,11,52,0.228,3.74,0,2,3,0,212,0,22,0,1,4 +fillmhe01,2018,1,KCA,AL,4,2,17,13,0,0,0,247,78,39,11,32,57,0.255,4.26,0,3,2,1,344,2,41,0,4,12 +finnebr01,2018,1,CIN,NL,0,3,5,5,0,0,0,62,27,17,5,15,14,0.318,7.40,2,1,0,0,103,0,20,0,3,2 +fistedo01,2018,1,TEX,AL,1,7,12,12,0,0,0,198,73,33,11,19,40,0.278,4.50,0,1,6,0,289,0,40,0,1,8 +flaheja01,2018,1,SLN,NL,8,9,28,28,0,0,0,453,108,56,20,59,182,0.199,3.34,3,6,11,0,615,0,59,2,1,14 +flexech01,2018,1,NYN,NL,0,2,4,1,0,0,0,19,14,9,2,6,3,0.424,12.79,1,0,1,0,40,2,13,0,0,1 +floripe01,2018,1,PHI,NL,0,0,2,0,0,0,0,6,1,2,1,1,0,0.143,9.00,0,0,0,0,8,2,2,0,0,0 +florody01,2018,1,CIN,NL,3,2,25,0,0,0,0,109,39,11,2,12,27,0.275,2.72,3,0,0,1,159,13,12,2,3,3 +florody01,2018,2,LAN,NL,3,1,29,0,0,0,0,83,18,5,1,11,31,0.182,1.63,3,1,1,1,112,7,5,1,0,2 +flynnbr01,2018,1,KCA,AL,3,5,48,0,0,0,1,227,87,34,5,35,47,0.295,4.04,3,5,3,0,336,10,37,1,2,16 +foltymi01,2018,1,ATL,NL,13,10,31,31,2,1,0,549,130,58,17,68,202,0.195,2.85,3,7,6,2,744,0,65,2,1,11 +fontwi01,2018,1,LAN,NL,0,2,6,0,0,0,0,31,18,13,5,1,7,0.383,11.32,1,0,0,0,48,6,13,0,0,0 +fontwi01,2018,2,OAK,AL,0,0,4,0,0,0,0,20,13,11,5,4,9,0.394,14.85,0,1,0,0,37,2,11,0,0,0 +fontwi01,2018,3,TBA,AL,2,1,9,5,0,0,0,81,15,5,2,11,20,0.158,1.67,0,0,1,0,107,0,5,0,0,2 +frareca01,2018,1,CHA,AL,0,1,11,0,0,0,0,21,6,4,0,4,9,0.231,5.14,0,1,1,0,31,1,4,0,0,0 +freelky01,2018,1,COL,NL,17,7,33,33,0,0,0,607,182,64,17,70,173,0.240,2.85,2,2,6,0,844,0,64,5,6,24 +freemsa01,2018,1,ATL,NL,3,5,63,0,0,0,0,151,41,24,3,32,58,0.229,4.29,2,4,2,0,216,4,26,1,2,8 +friedma01,2018,1,ATL,NL,1,4,14,5,0,0,0,101,26,11,3,20,44,0.224,2.94,0,2,2,0,142,5,12,2,2,4 +fryja01,2018,1,CHA,AL,2,3,59,1,0,0,4,154,37,25,4,20,70,0.194,4.38,0,5,1,0,214,12,28,2,0,1 +frypa01,2018,1,BAL,AL,1,2,35,0,0,0,2,113,33,14,1,15,36,0.236,3.35,0,3,4,0,159,11,20,0,0,6 +fulmeca01,2018,1,CHA,AL,2,4,9,8,0,0,0,97,37,29,8,24,29,0.280,8.07,0,2,5,1,164,1,32,1,2,1 +fulmemi01,2018,1,DET,AL,3,12,24,24,0,0,0,397,128,69,19,46,110,0.254,4.69,1,1,5,0,558,0,75,1,2,14 +gagnodr01,2018,1,NYN,NL,2,1,5,1,0,0,0,36,15,7,2,5,8,0.319,5.25,0,2,2,0,56,0,11,2,0,2 +gallayo01,2018,1,CIN,NL,0,0,3,0,0,0,0,7,8,8,1,4,2,0.533,30.86,0,0,0,0,19,1,8,0,0,0 +gallayo01,2018,2,TEX,AL,8,8,18,18,0,0,0,276,99,59,13,43,56,0.279,5.77,2,6,2,0,403,0,60,1,1,19 +gallegi01,2018,1,NYA,AL,0,0,4,0,0,0,1,30,10,5,2,3,10,0.278,4.50,0,0,0,0,40,2,5,1,0,1 +gallegi01,2018,2,SLN,NL,0,0,2,0,0,0,0,4,1,0,0,0,2,0.200,0.00,0,0,0,0,5,2,0,0,0,0 +gantjo01,2018,1,SLN,NL,7,6,26,19,0,0,0,342,91,44,9,57,95,0.216,3.47,3,5,2,0,487,1,54,2,4,7 +garcigr01,2018,1,SLN,NL,0,0,1,0,0,0,0,3,1,0,0,0,0,0.333,0.00,0,0,0,0,3,1,0,0,0,1 +garcija01,2018,1,OAK,AL,0,0,26,0,0,0,1,94,18,11,0,20,22,0.170,3.16,0,4,4,0,132,12,11,0,2,3 +garcija02,2018,1,TOR,AL,3,6,25,13,0,0,0,223,76,49,13,38,69,0.261,5.93,1,6,3,1,335,3,53,1,2,6 +garcija02,2018,2,CHN,NL,0,1,8,1,0,0,0,23,6,4,0,6,4,0.222,4.70,1,1,0,0,34,1,4,1,0,1 +garcija04,2018,1,MIA,NL,3,3,29,7,0,0,0,198,59,36,16,28,40,0.239,4.91,3,0,0,2,278,3,37,1,2,8 +garcilu03,2018,1,PHI,NL,3,1,59,0,0,0,1,138,49,31,4,18,51,0.271,6.07,1,7,4,0,204,7,31,0,1,5 +garciyi01,2018,1,LAN,NL,1,2,25,0,0,0,0,67,29,14,7,4,19,0.305,5.64,1,0,2,0,101,3,18,0,0,0 +gardeni01,2018,1,TEX,AL,0,0,3,0,0,0,0,15,7,2,0,0,4,0.333,3.60,0,0,1,0,23,1,2,0,1,0 +garream01,2018,1,CIN,NL,1,2,66,0,0,0,0,189,56,30,8,25,71,0.239,4.29,3,3,3,0,264,7,30,1,1,7 +garvemi01,2018,1,MIN,AL,0,0,1,0,0,0,0,3,1,0,0,0,0,0.250,0.00,0,0,0,0,4,1,0,0,0,0 +gausmke01,2018,1,BAL,AL,5,8,21,21,0,0,0,372,139,61,21,32,104,0.281,4.43,0,6,5,0,534,0,62,0,2,11 +gausmke01,2018,2,ATL,NL,5,3,10,10,0,0,0,179,50,19,5,18,44,0.227,2.87,1,0,2,0,242,0,23,0,2,4 +gavigsa01,2018,1,TOR,AL,3,10,26,24,0,0,0,371,140,73,21,38,105,0.281,5.31,1,6,4,2,548,1,77,4,4,3 +gearrco01,2018,1,SFN,NL,1,1,35,0,0,0,1,90,33,14,5,13,31,0.275,4.20,1,0,3,0,137,8,14,1,0,2 +gearrco01,2018,2,TEX,AL,1,0,21,0,0,0,0,64,13,6,2,6,20,0.176,2.53,0,1,2,0,82,3,6,0,0,2 +gearrco01,2018,3,OAK,AL,0,0,6,0,0,0,0,18,10,4,0,2,2,0.385,6.00,0,3,1,0,29,1,4,0,0,0 +germado01,2018,1,NYA,AL,2,6,21,14,0,0,0,257,81,53,15,33,102,0.242,5.57,0,7,5,1,375,2,55,0,2,2 +gibsoky01,2018,1,MIN,AL,10,13,32,32,0,0,0,590,177,79,23,79,179,0.241,3.62,2,8,4,0,826,0,88,3,7,23 +gileske01,2018,1,HOU,AL,0,2,34,0,0,0,12,92,36,17,2,3,31,0.286,4.99,0,1,0,0,129,24,17,0,0,2 +gileske01,2018,2,TOR,AL,0,1,21,0,0,0,14,59,18,9,4,4,22,0.234,4.12,0,1,1,0,83,18,11,1,0,1 +gilmase01,2018,1,BAL,AL,1,1,12,0,0,0,0,81,23,9,4,11,15,0.230,3.00,0,0,2,0,113,2,9,0,0,4 +gimench01,2018,1,CHN,NL,0,0,1,0,0,0,0,3,2,3,1,1,1,0.400,27.00,0,0,0,0,6,1,3,0,0,0 +gimench01,2018,2,MIN,AL,0,0,1,0,0,0,0,3,6,5,2,0,1,0.667,45.00,0,0,0,0,9,1,5,0,0,0 +giolilu01,2018,1,CHA,AL,10,13,32,32,0,0,0,520,166,118,27,90,125,0.250,6.13,2,13,15,0,775,0,123,1,5,15 +givenmy01,2018,1,BAL,AL,0,7,69,0,0,0,9,230,61,34,4,30,79,0.218,3.99,4,4,3,0,317,32,37,1,3,4 +glasnty01,2018,1,PIT,NL,1,2,34,0,0,0,0,168,47,27,5,34,72,0.226,4.34,2,7,1,1,243,9,28,0,0,5 +glasnty01,2018,2,TBA,AL,1,5,11,11,0,0,0,167,42,26,10,19,64,0.208,4.20,1,5,3,1,225,0,27,0,1,2 +gloveko01,2018,1,WAS,NL,1,3,21,0,0,0,1,49,13,6,1,10,9,0.217,3.31,1,0,0,0,71,6,6,0,1,1 +godleza01,2018,1,ARI,NL,15,11,33,32,0,0,0,535,177,94,16,81,185,0.260,4.74,2,17,12,1,791,0,103,8,8,14 +goedder01,2018,1,SEA,AL,2,0,5,0,0,0,0,22,4,1,0,5,9,0.167,1.23,1,2,0,0,29,4,1,0,0,2 +goedder01,2018,2,LAN,NL,1,0,26,0,0,0,0,88,22,11,4,15,35,0.202,3.38,2,1,1,0,126,4,11,0,1,0 +goharlu01,2018,1,ATL,NL,0,1,9,1,0,0,1,59,16,13,3,8,18,0.222,5.95,0,0,0,0,82,3,13,1,1,0 +gombeau01,2018,1,SLN,NL,6,2,29,11,0,0,0,225,81,37,7,32,67,0.277,4.44,4,3,4,1,334,1,40,4,2,8 +gomezca01,2018,1,TBA,AL,0,0,1,0,0,0,0,1,0,3,0,4,0,0.000,81.00,0,0,0,2,5,0,3,0,0,0 +gomezje01,2018,1,CHA,AL,0,2,26,0,0,0,0,75,29,13,3,10,27,0.293,4.68,1,0,1,0,114,5,15,1,3,0 +gomezro01,2018,1,SFN,NL,1,0,5,0,0,0,0,28,20,8,0,1,8,0.426,7.71,0,0,1,0,49,2,11,0,0,0 +gonsast01,2018,1,MIN,AL,2,2,7,4,0,0,0,74,28,18,2,22,16,0.283,6.57,0,2,1,0,122,0,22,0,0,2 +gonzagi01,2018,1,WAS,NL,7,11,27,27,0,0,0,437,153,74,15,70,126,0.272,4.57,2,4,2,0,646,0,77,3,8,11 +gonzagi01,2018,2,MIL,NL,3,0,5,5,0,0,0,76,14,6,2,10,22,0.157,2.13,0,1,0,0,100,0,7,0,1,1 +gonzama02,2018,1,SEA,AL,13,9,29,29,1,0,0,500,172,74,17,32,145,0.267,4.00,0,2,6,0,686,0,76,1,4,23 +gonzame01,2018,1,MIA,NL,2,1,8,1,0,0,0,66,31,14,4,8,19,0.326,5.73,1,0,0,0,103,3,14,0,0,2 +gonzami03,2018,1,CHA,AL,0,3,3,3,0,0,0,37,24,17,4,6,5,0.400,12.41,0,1,0,0,66,0,18,0,0,0 +goodyni01,2018,1,CLE,AL,0,2,12,0,0,0,0,35,15,9,4,5,12,0.288,6.94,1,1,1,0,58,1,9,0,0,0 +gosseda01,2018,1,OAK,AL,0,3,5,5,0,0,0,73,25,14,5,8,12,0.269,5.18,0,1,1,0,102,0,14,0,0,3 +gotttr01,2018,1,WAS,NL,0,2,20,0,0,0,0,57,19,12,4,10,15,0.271,5.68,1,2,2,0,84,5,13,0,2,3 +gracema02,2018,1,WAS,NL,1,1,56,0,0,0,0,179,55,19,5,13,48,0.240,2.87,2,1,2,0,247,12,22,1,2,4 +gravebr01,2018,1,MIA,NL,1,1,21,0,0,0,1,100,41,20,3,12,21,0.311,5.40,2,1,4,0,148,16,22,0,0,7 +graveke01,2018,1,OAK,AL,1,5,7,7,0,0,0,103,44,29,9,13,27,0.306,7.60,0,2,1,0,158,0,32,0,0,3 +grayjo02,2018,1,COL,NL,12,9,31,31,0,0,0,517,180,98,27,52,183,0.266,5.12,1,6,6,2,743,0,102,4,3,12 +grayso01,2018,1,NYA,AL,11,9,30,23,0,0,0,391,138,71,14,57,123,0.270,4.90,0,9,8,1,582,2,73,1,5,6 +greench03,2018,1,NYA,AL,8,3,63,0,0,0,0,227,64,21,9,15,94,0.229,2.50,2,3,1,0,298,3,22,0,3,2 +greensh02,2018,1,DET,AL,4,6,66,0,0,0,32,190,68,36,12,19,65,0.268,5.12,1,3,3,0,279,58,39,0,3,3 +gregelu01,2018,1,SLN,NL,0,0,17,0,0,0,0,38,14,10,2,6,12,0.275,7.11,0,2,0,1,57,4,10,0,0,2 +greinza01,2018,1,ARI,NL,15,11,33,33,0,0,0,623,181,74,28,43,199,0.231,3.21,3,4,6,0,839,0,77,3,3,7 +grimmju01,2018,1,KCA,AL,1,3,16,0,0,0,0,38,17,19,2,14,8,0.347,13.50,1,1,0,0,67,3,19,2,2,2 +grimmju01,2018,2,SEA,AL,0,0,5,0,0,0,0,14,2,1,1,0,3,0.133,1.93,0,0,0,0,16,2,1,0,1,0 +gsellro01,2018,1,NYN,NL,6,3,68,0,0,0,13,240,76,38,8,28,70,0.250,4.28,6,1,5,0,345,24,44,3,5,7 +guduare01,2018,1,HOU,AL,0,0,3,0,0,0,0,10,1,1,1,0,4,0.091,2.70,0,0,0,0,11,2,1,0,0,0 +guerrja01,2018,1,MIA,NL,1,1,32,0,0,0,1,107,42,22,4,12,30,0.292,5.55,2,3,3,0,162,12,27,2,1,3 +guerrju02,2018,1,MIL,NL,6,9,31,26,0,0,0,423,143,64,19,55,136,0.264,4.09,0,11,4,0,611,1,74,5,4,10 +guerrta01,2018,1,MIA,NL,1,3,60,0,0,0,0,174,64,35,8,30,68,0.275,5.43,1,8,3,0,268,11,40,0,1,3 +guerrta02,2018,1,TOR,AL,0,0,9,0,0,0,0,29,9,5,1,4,8,0.243,4.66,0,2,3,0,44,5,5,0,0,1 +guilmpr01,2018,1,SLN,NL,0,1,2,0,0,0,0,6,7,5,2,0,3,0.583,22.50,0,1,0,0,13,0,5,0,1,0 +guilmpr01,2018,2,TOR,AL,0,0,6,0,0,0,0,24,11,8,4,4,5,0.324,9.00,1,0,0,0,39,2,8,1,0,0 +guyerbr01,2018,1,CLE,AL,0,0,1,0,0,0,0,3,0,0,0,0,0,0.000,0.00,0,0,0,0,3,1,0,0,0,0 +gyorkje01,2018,1,SLN,NL,0,0,1,0,0,0,0,3,2,1,1,0,0,0.400,9.00,0,0,1,0,6,1,1,0,0,0 +haderjo01,2018,1,MIL,NL,6,1,55,0,0,0,12,244,36,22,9,30,143,0.132,2.43,0,0,1,1,306,14,23,1,2,1 +haleda02,2018,1,NYA,AL,0,0,1,0,0,0,0,6,3,0,0,0,3,0.333,0.00,0,0,0,0,9,1,0,0,0,0 +haleda02,2018,2,MIN,AL,0,0,1,0,0,0,0,9,4,4,1,4,2,0.333,12.00,0,0,0,0,16,0,4,0,0,1 +haleda02,2018,3,NYA,AL,0,0,2,0,0,0,0,26,9,3,2,1,3,0.257,3.12,0,0,1,0,37,0,3,0,0,0 +haleyju01,2018,1,BOS,AL,0,0,4,0,0,0,0,23,10,4,2,3,0,0.323,4.70,0,0,0,0,35,3,4,0,1,0 +hallma02,2018,1,DET,AL,0,0,5,0,0,0,0,24,19,13,1,3,5,0.452,14.63,0,2,1,0,48,2,16,0,2,0 +hamelco01,2018,1,TEX,AL,5,9,20,20,0,0,0,343,115,60,23,42,114,0.258,4.72,0,6,12,2,502,0,70,0,3,14 +hamelco01,2018,2,CHN,NL,4,3,12,12,1,0,0,229,61,20,6,23,74,0.226,2.36,0,1,7,1,304,0,23,1,1,12 +hamilia01,2018,1,CHA,AL,1,2,10,0,0,0,0,24,6,4,2,2,5,0.207,4.50,0,0,1,0,33,3,5,0,1,1 +hammeja01,2018,1,KCA,AL,4,14,39,18,0,0,0,381,168,85,18,39,92,0.315,6.02,4,3,3,0,583,14,91,2,5,6 +hancoju01,2018,1,CHN,NL,0,0,10,0,0,0,0,37,5,2,1,9,11,0.125,1.46,0,0,0,0,49,6,2,0,0,1 +handbr01,2018,1,SDN,NL,2,4,41,0,0,0,24,133,33,15,5,15,65,0.202,3.05,1,1,7,0,186,31,21,1,0,4 +handbr01,2018,2,CLE,AL,0,1,28,0,0,0,8,83,19,7,3,13,41,0.192,2.28,1,1,2,0,115,11,7,1,0,2 +hanhoer01,2018,1,NYN,NL,0,0,3,0,0,0,0,7,4,2,0,1,2,0.364,7.71,0,0,1,0,13,0,2,0,0,0 +happia01,2018,1,CHN,NL,0,0,1,0,0,0,0,3,1,0,0,0,0,0.250,0.00,0,0,0,0,4,1,0,0,0,0 +happja01,2018,1,TOR,AL,10,6,20,20,0,0,0,342,99,53,17,35,130,0.229,4.18,0,1,4,0,475,0,61,1,1,9 +happja01,2018,2,NYA,AL,7,0,11,11,0,0,0,191,51,19,10,16,63,0.217,2.69,0,3,5,1,258,0,20,1,1,5 +hardybl01,2018,1,DET,AL,4,5,30,13,0,0,1,258,79,34,10,22,66,0.245,3.56,1,2,2,0,351,5,37,1,4,7 +harriwi02,2018,1,HOU,AL,5,3,61,0,0,0,0,170,48,22,3,14,64,0.226,3.49,1,6,2,0,230,11,22,1,1,4 +hartdo01,2018,1,BAL,AL,0,0,20,0,0,0,0,58,31,12,2,12,13,0.360,5.59,0,0,0,0,99,3,13,0,1,1 +harvema01,2018,1,NYN,NL,0,2,8,4,0,0,0,81,33,21,6,9,20,0.303,7.00,0,1,1,0,123,2,21,2,2,2 +harvema01,2018,2,CIN,NL,7,7,24,24,0,0,0,384,132,64,21,28,111,0.264,4.50,2,1,5,1,540,0,66,4,3,8 +hatchch02,2018,1,OAK,AL,3,3,34,0,0,0,0,109,43,20,7,17,30,0.289,4.95,1,0,1,0,167,14,23,0,0,3 +hauscmi01,2018,1,TOR,AL,1,1,2,1,0,0,0,25,7,4,0,4,5,0.241,4.32,0,0,2,0,36,0,4,0,1,2 +heanean01,2018,1,LAA,AL,9,10,30,30,1,1,0,540,171,83,27,45,180,0.248,4.15,0,9,8,0,749,0,91,2,5,17 +hellije01,2018,1,WAS,NL,5,3,19,19,0,0,0,274,78,35,11,20,65,0.230,3.45,1,2,8,1,370,0,41,2,1,9 +hembrhe01,2018,1,BOS,AL,4,1,67,0,0,0,0,180,53,28,10,27,76,0.233,4.20,1,4,1,1,260,10,30,0,5,2 +hendrky01,2018,1,CHN,NL,14,11,33,33,0,0,0,597,184,76,22,44,161,0.247,3.44,4,0,9,0,812,0,82,7,7,18 +hendrli01,2018,1,OAK,AL,0,1,25,8,0,0,0,72,25,11,3,10,22,0.272,4.13,0,1,1,0,104,1,11,0,1,2 +hernada01,2018,1,CIN,NL,5,2,57,0,0,0,0,192,46,18,6,17,65,0.200,2.53,1,3,5,2,254,6,20,2,0,5 +hernael01,2018,1,MIA,NL,2,7,32,6,0,0,0,197,68,38,11,27,45,0.272,5.21,3,1,2,1,284,6,38,3,2,5 +hernaen02,2018,1,LAN,NL,0,1,1,0,0,0,0,1,1,3,1,2,0,0.500,81.00,0,0,0,0,4,1,3,0,0,0 +hernafe02,2018,1,SEA,AL,8,14,29,28,0,0,0,467,159,96,27,59,125,0.262,5.55,0,11,12,0,685,0,107,3,4,16 +herreke01,2018,1,KCA,AL,1,1,27,0,0,0,14,77,19,3,2,2,22,0.207,1.05,0,0,0,0,95,21,3,1,0,3 +herreke01,2018,2,WAS,NL,1,2,21,0,0,0,3,56,24,9,4,8,16,0.304,4.34,0,0,1,1,89,11,9,0,0,1 +hessda01,2018,1,BAL,AL,3,10,21,19,0,0,0,310,106,56,22,37,74,0.262,4.88,2,3,8,1,454,1,64,1,4,6 +hicksjo03,2018,1,SLN,NL,3,4,73,0,0,0,6,233,59,31,2,45,70,0.208,3.59,2,9,8,0,339,20,33,0,2,12 +hildetr01,2018,1,MIN,AL,4,6,73,0,0,0,7,219,75,44,12,26,70,0.263,5.42,9,2,2,0,319,20,46,3,3,4 +hillri01,2018,1,LAN,NL,11,5,25,24,0,0,0,398,108,54,20,41,150,0.219,3.66,3,2,8,1,547,0,57,4,1,4 +hillti01,2018,1,KCA,AL,1,4,70,0,0,0,2,137,46,23,4,14,42,0.257,4.53,0,0,2,0,198,9,28,2,1,4 +hiranyo01,2018,1,ARI,NL,4,3,75,0,0,0,3,199,49,18,6,23,59,0.209,2.44,4,6,2,1,262,10,22,1,1,14 +hoffmje02,2018,1,COL,NL,0,0,6,1,0,0,0,26,15,9,0,7,5,0.405,9.35,1,1,0,0,44,1,9,0,0,3 +holadbr01,2018,1,MIA,NL,0,0,2,0,0,0,0,6,4,3,1,0,1,0.400,13.50,0,0,0,0,10,2,3,0,0,0 +holdejo02,2018,1,NYA,AL,1,3,60,1,0,0,0,198,53,23,4,19,60,0.214,3.14,3,1,1,0,272,14,27,2,2,2 +hollade01,2018,1,SFN,NL,7,9,36,30,0,0,0,514,154,68,19,67,169,0.241,3.57,6,2,4,1,727,1,74,9,7,12 +hollagr01,2018,1,SLN,NL,0,2,32,0,0,0,0,75,34,22,1,22,22,0.312,7.92,2,0,0,0,132,7,28,1,0,1 +hollagr01,2018,2,WAS,NL,2,0,24,0,0,0,3,64,9,2,1,10,25,0.130,0.84,0,0,1,0,80,6,2,0,0,3 +holmecl01,2018,1,PIT,NL,1,3,11,4,0,0,0,79,30,20,2,23,21,0.291,6.84,1,4,2,0,129,6,21,0,1,4 +hoovejj01,2018,1,MIL,NL,0,1,2,0,0,0,0,4,4,3,1,2,0,0.500,20.25,0,0,0,0,10,2,3,0,0,0 +housead01,2018,1,MIL,NL,0,0,7,0,0,0,0,41,13,5,0,7,8,0.255,3.29,0,1,1,0,59,5,5,0,0,3 +howarsa01,2018,1,COL,NL,0,0,4,0,0,0,0,12,5,1,0,3,1,0.313,2.25,0,0,1,0,20,4,1,0,0,1 +hoytja01,2018,1,HOU,AL,0,0,1,0,0,0,0,1,1,0,0,1,0,0.500,0.00,0,0,0,0,3,0,0,0,0,0 +huch02,2018,1,TBA,AL,0,0,5,0,0,0,0,39,7,6,2,3,12,0.149,4.15,0,0,0,0,50,3,6,0,0,0 +hudsoda01,2018,1,LAN,NL,3,2,40,1,0,0,0,138,38,21,6,18,44,0.217,4.11,1,3,4,0,197,11,25,0,0,3 +hudsoda02,2018,1,SLN,NL,4,1,26,0,0,0,0,82,19,8,0,18,19,0.196,2.63,0,2,1,0,118,2,9,0,2,3 +hugheja02,2018,1,CIN,NL,4,3,72,0,0,0,7,236,57,17,4,23,59,0.212,1.94,2,5,2,0,298,23,17,1,3,13 +hugheph01,2018,1,MIN,AL,0,0,7,2,0,0,0,36,14,9,4,5,8,0.292,6.75,1,0,1,0,55,4,9,0,1,0 +hugheph01,2018,2,SDN,NL,0,0,16,0,0,0,0,62,30,14,7,5,24,0.326,6.10,0,0,0,0,97,10,14,0,0,0 +hunteto02,2018,1,PHI,NL,5,4,65,0,0,0,4,192,65,27,6,15,51,0.260,3.80,1,1,3,0,270,10,28,1,1,5 +hutchdr01,2018,1,PHI,NL,1,1,11,0,0,0,0,64,21,11,4,13,19,0.273,4.64,0,4,1,0,94,6,11,1,2,4 +hutchdr01,2018,2,TEX,AL,1,1,5,5,0,0,0,64,29,21,5,13,12,0.333,8.86,0,1,1,0,105,0,21,2,2,1 +iglesra01,2018,1,CIN,NL,2,5,66,0,0,0,30,216,52,19,12,25,80,0.199,2.38,2,2,2,0,291,57,22,1,2,4 +infangr01,2018,1,CHA,AL,1,1,10,0,0,0,0,27,12,8,0,8,6,0.364,8.00,2,1,0,0,42,1,8,0,1,2 +jacksed01,2018,1,OAK,AL,6,3,17,17,0,0,0,276,75,34,12,37,68,0.221,3.33,1,5,2,1,381,0,37,2,1,5 +jackslu01,2018,1,ATL,NL,1,2,35,0,0,0,1,122,41,20,3,21,46,0.259,4.43,3,6,2,1,184,11,22,1,2,1 +jamesjo02,2018,1,HOU,AL,2,0,6,3,0,0,0,69,15,6,3,7,29,0.183,2.35,0,0,2,0,91,0,6,0,0,2 +janseke01,2018,1,LAN,NL,1,5,69,0,0,0,38,215,54,24,13,17,82,0.201,3.01,1,0,2,2,289,59,28,0,1,2 +jeffrje01,2018,1,MIL,NL,8,1,73,0,0,0,15,230,49,11,5,27,89,0.182,1.29,4,4,1,0,299,24,12,1,1,7 +jennida01,2018,1,MIL,NL,4,5,72,1,0,0,1,193,66,23,6,23,45,0.275,3.22,1,2,1,1,271,11,27,4,3,10 +jepseke01,2018,1,TEX,AL,0,3,21,0,0,0,0,50,15,11,4,11,8,0.238,5.94,1,0,1,0,77,3,13,1,1,5 +jerezwi01,2018,1,LAA,AL,0,0,17,0,0,0,0,45,17,10,3,8,15,0.270,6.00,0,2,0,0,71,1,14,0,0,0 +jewelja01,2018,1,LAA,AL,0,1,3,0,0,0,0,6,2,2,0,1,1,0.250,9.00,0,1,2,0,11,1,2,0,0,0 +jimenjo02,2018,1,DET,AL,5,4,68,0,0,0,3,188,53,30,5,22,78,0.222,4.31,3,3,3,0,267,17,34,0,2,2 +johnsbr02,2018,1,BOS,AL,4,5,38,13,0,0,0,298,104,46,16,38,87,0.263,4.17,0,1,1,0,434,10,49,0,0,6 +johnsdj01,2018,1,COL,NL,1,0,7,0,0,0,0,19,6,3,0,2,9,0.240,4.26,0,0,0,0,27,3,3,0,0,0 +johnsji04,2018,1,LAA,AL,5,3,62,1,0,0,2,190,64,27,9,22,45,0.268,3.84,1,5,1,0,268,14,38,2,4,8 +johnspi01,2018,1,SFN,NL,3,2,37,0,0,0,0,131,38,27,5,22,36,0.236,5.56,1,1,0,0,186,7,27,1,2,3 +jonesna01,2018,1,CHA,AL,2,2,33,0,0,0,5,90,28,10,4,15,32,0.239,3.00,0,2,3,0,137,15,14,1,1,2 +junisja01,2018,1,KCA,AL,9,12,30,30,1,0,0,531,182,86,32,43,164,0.265,4.37,1,9,15,0,758,0,94,4,8,11 +juradar01,2018,1,TEX,AL,5,5,12,8,0,0,0,164,66,36,7,18,22,0.297,5.93,1,0,0,0,241,0,36,1,0,6 +kahnlto01,2018,1,NYA,AL,2,0,24,0,0,0,1,70,23,17,3,15,30,0.256,6.56,0,2,0,0,107,7,22,0,2,2 +kelake01,2018,1,TEX,AL,3,3,38,0,0,0,24,110,28,14,3,14,44,0.206,3.44,1,4,0,2,152,31,14,1,1,1 +kelake01,2018,2,PIT,NL,0,1,16,0,0,0,0,46,10,5,2,5,22,0.182,2.93,1,2,0,0,60,5,5,0,0,0 +kellebr01,2018,1,KCA,AL,9,6,41,20,1,0,0,421,133,48,7,50,96,0.252,3.08,1,8,2,0,583,2,50,0,3,18 +kellesh01,2018,1,WAS,NL,1,0,35,0,0,0,0,97,26,12,7,5,32,0.215,3.34,1,0,1,0,128,12,12,0,1,0 +kellesh01,2018,2,OAK,AL,1,0,19,0,0,0,0,50,7,4,0,6,18,0.127,2.16,2,2,0,0,62,3,4,0,1,0 +kellyca01,2018,1,SFN,NL,0,3,7,3,0,0,0,71,28,8,3,5,16,0.292,3.04,1,0,1,1,104,4,10,0,2,1 +kellyjo05,2018,1,BOS,AL,4,2,73,0,0,0,2,197,57,32,4,32,68,0.234,4.39,0,4,5,0,285,9,34,0,4,6 +kennebr02,2018,1,SDN,NL,1,2,6,6,0,0,0,80,36,20,6,12,18,0.330,6.75,1,1,2,0,128,0,20,4,1,1 +kenneia01,2018,1,KCA,AL,3,9,22,22,0,0,0,359,125,62,20,40,105,0.265,4.66,2,4,1,0,518,0,66,4,2,6 +kershcl01,2018,1,LAN,NL,9,5,26,26,0,0,0,484,139,49,17,29,155,0.227,2.73,0,10,2,0,650,0,55,3,2,8 +keuchda01,2018,1,HOU,AL,12,11,34,34,1,0,0,614,211,85,18,58,153,0.263,3.74,0,9,2,0,874,0,92,3,9,18 +kiekhde01,2018,1,OAK,AL,0,0,4,0,0,0,0,6,7,4,1,1,1,0.583,18.00,1,1,0,0,14,0,4,1,0,0 +kimbrcr01,2018,1,BOS,AL,5,1,63,0,0,0,42,187,31,19,7,31,96,0.146,2.74,0,7,2,0,247,57,19,1,0,2 +kingesc01,2018,1,PHI,NL,0,0,1,0,0,0,0,4,4,2,0,0,0,0.500,13.50,0,0,0,0,8,1,2,0,0,0 +kinghni01,2018,1,PIT,NL,5,7,18,15,0,0,0,228,79,44,18,26,69,0.259,5.21,3,3,5,1,342,1,50,4,2,2 +kinlety01,2018,1,MIN,AL,0,0,4,0,0,0,0,10,9,9,2,4,4,0.474,24.30,0,2,0,0,23,3,9,0,0,0 +kinlety01,2018,2,MIA,NL,0,0,9,0,0,0,0,23,6,6,0,4,9,0.207,7.04,2,1,1,0,34,1,6,0,0,0 +kintzbr01,2018,1,WAS,NL,1,2,45,0,0,0,2,128,40,17,2,13,31,0.253,3.59,3,1,3,0,175,8,17,1,0,5 +kintzbr01,2018,2,CHN,NL,2,1,25,0,0,0,0,54,27,14,3,9,12,0.355,7.00,2,0,1,0,88,7,14,0,2,2 +kittran01,2018,1,TBA,AL,3,2,33,3,0,0,0,115,54,33,7,17,30,0.338,7.75,5,1,1,0,181,4,34,1,2,5 +klubeco01,2018,1,CLE,AL,20,7,33,33,2,1,0,645,179,69,25,34,222,0.223,2.89,0,2,3,0,842,0,75,2,2,16 +knebeco01,2018,1,MIL,NL,4,3,57,0,0,0,16,166,38,22,7,22,88,0.194,3.58,0,0,4,0,223,29,23,0,1,3 +kochma01,2018,1,ARI,NL,5,5,19,14,0,0,0,260,88,40,19,22,50,0.267,4.15,1,3,6,0,360,4,43,2,1,18 +kolarad01,2018,1,TBA,AL,1,0,31,0,0,0,2,103,38,15,0,5,19,0.284,3.93,1,0,1,0,141,5,15,0,1,5 +kontoge01,2018,1,PIT,NL,2,3,21,0,0,0,1,59,23,11,4,5,9,0.295,5.03,0,0,3,0,89,6,12,1,2,0 +kontoge01,2018,2,CLE,AL,0,0,6,0,0,0,0,16,3,2,1,2,4,0.158,3.38,0,1,0,0,21,4,2,0,0,0 +kontoge01,2018,3,NYA,AL,0,0,1,0,0,0,0,5,1,0,0,0,2,0.167,0.00,0,0,0,0,6,0,0,0,0,0 +kopecmi01,2018,1,CHA,AL,1,1,4,4,0,0,0,43,20,8,4,2,15,0.328,5.02,0,1,5,0,68,0,8,0,0,1 +kratzer01,2018,1,MIL,NL,0,0,3,0,0,0,0,9,3,1,0,1,2,0.300,3.00,0,0,0,0,12,3,2,0,1,1 +krehbjo01,2018,1,ARI,NL,0,0,2,0,0,0,0,9,1,0,0,2,0,0.100,0.00,0,0,0,0,12,1,0,0,0,0 +krolia01,2018,1,LAA,AL,0,0,1,0,0,0,0,6,1,0,0,1,2,0.143,0.00,0,0,0,0,8,0,0,0,0,0 +kuhlch01,2018,1,PIT,NL,5,5,16,16,0,0,0,255,89,43,14,33,81,0.275,4.55,1,7,4,0,373,0,47,6,6,5 +lamarry01,2018,1,MIN,AL,0,0,1,0,0,0,0,2,1,1,1,0,0,0.333,13.50,0,0,0,0,3,1,1,0,0,0 +lambjo02,2018,1,LAA,AL,0,1,3,3,0,0,0,30,15,8,4,4,11,0.326,7.20,1,0,0,0,50,0,10,0,0,0 +lasteto01,2018,1,CHN,NL,0,0,1,0,0,0,0,4,3,1,1,0,0,0.500,6.75,0,0,0,0,6,0,1,0,0,1 +lauerer01,2018,1,SDN,NL,6,7,23,23,0,0,0,336,127,54,15,46,100,0.285,4.34,2,2,6,1,504,0,61,4,2,7 +lawde01,2018,1,SFN,NL,1,0,7,0,0,0,0,40,16,11,2,8,12,0.286,7.43,0,1,1,0,66,4,13,0,1,1 +lawreca01,2018,1,SEA,AL,1,0,11,0,0,0,0,70,28,19,2,10,14,0.298,7.33,1,1,2,0,106,5,19,0,0,5 +leakemi01,2018,1,SEA,AL,10,10,31,31,0,0,0,557,207,90,23,34,119,0.280,4.36,3,2,6,0,784,0,98,1,3,24 +leblawa01,2018,1,SEA,AL,9,5,32,27,0,0,0,486,151,67,24,40,130,0.246,3.72,1,1,3,0,662,5,74,1,4,8 +leclejo01,2018,1,TEX,AL,2,3,59,0,0,0,12,173,24,10,1,25,85,0.126,1.56,1,2,3,0,223,21,16,4,0,2 +leitema02,2018,1,PHI,NL,0,1,12,0,0,0,0,50,22,10,5,8,13,0.297,5.40,2,0,1,0,84,2,17,0,1,0 +leitema02,2018,2,TOR,AL,0,0,8,0,0,0,0,20,13,10,2,4,9,0.394,13.50,1,2,1,0,39,2,11,0,1,0 +leonedo01,2018,1,SLN,NL,1,2,29,0,0,0,0,72,27,12,3,8,26,0.287,4.50,3,0,0,0,106,8,12,1,3,1 +lestejo01,2018,1,CHN,NL,18,6,32,32,0,0,0,545,174,67,24,64,149,0.256,3.32,1,4,6,0,761,0,75,7,3,15 +lewicar01,2018,1,DET,AL,0,2,13,3,0,0,0,116,48,21,4,14,30,0.320,4.89,1,0,1,0,170,6,24,0,5,4 +liberad01,2018,1,LAN,NL,2,1,17,0,0,0,0,39,10,4,1,8,12,0.227,2.77,0,0,1,0,54,4,4,1,0,2 +liriafr01,2018,1,DET,AL,5,12,27,26,0,0,0,401,127,68,19,73,110,0.252,4.58,0,9,5,0,586,0,84,0,5,16 +litteza01,2018,1,MIN,AL,0,2,8,2,0,0,0,61,25,14,3,11,14,0.298,6.20,0,0,4,0,101,1,17,1,1,2 +livelbe01,2018,1,PHI,NL,0,2,5,5,0,0,0,71,34,18,4,10,22,0.337,6.85,1,0,3,0,115,0,18,0,1,2 +livelbe01,2018,2,KCA,AL,0,1,5,0,0,0,0,20,7,1,0,5,5,0.280,1.35,0,0,0,0,30,1,1,0,0,1 +loaisjo01,2018,1,NYA,AL,2,0,9,4,0,0,0,74,26,14,3,12,33,0.271,5.11,0,0,0,0,108,2,17,0,0,4 +lockean01,2018,1,SDN,NL,0,3,4,3,0,0,0,45,22,16,4,10,12,0.333,9.60,1,0,0,0,76,1,16,0,0,0 +loganbo02,2018,1,MIL,NL,1,0,16,0,0,0,0,32,15,7,3,10,14,0.326,5.91,3,1,1,0,58,4,7,1,0,0 +lopezjo02,2018,1,MIL,NL,0,1,10,0,0,0,0,59,16,6,1,13,15,0.225,2.75,1,1,0,0,85,8,6,0,1,2 +lopezjo02,2018,2,KCA,AL,2,4,7,7,0,0,0,102,41,24,5,9,23,0.297,6.35,0,2,1,0,149,0,24,0,1,1 +lopezpa01,2018,1,MIA,NL,2,4,10,10,0,0,0,176,56,27,8,18,46,0.252,4.14,5,2,4,0,247,0,28,1,2,4 +lopezre01,2018,1,CHA,AL,7,10,32,32,0,0,0,566,165,82,25,75,151,0.234,3.91,1,7,10,3,799,0,88,0,9,11 +lopezyo01,2018,1,ARI,NL,0,0,10,0,0,0,0,27,7,3,2,1,11,0.212,3.00,0,0,0,0,35,5,3,1,0,0 +lorenmi01,2018,1,CIN,NL,4,2,45,3,0,0,1,243,78,28,6,34,54,0.258,3.11,2,2,3,0,344,10,32,2,3,8 +loupaa01,2018,1,TOR,AL,0,0,50,0,0,0,0,107,44,18,4,13,42,0.299,4.54,0,0,3,0,166,7,21,0,3,3 +loupaa01,2018,2,PHI,NL,0,0,9,0,0,0,0,12,4,2,0,1,2,0.267,4.50,0,0,1,0,17,1,2,0,0,1 +lucasjo02,2018,1,OAK,AL,0,0,8,1,0,0,0,43,16,10,1,9,14,0.286,6.28,0,0,1,0,66,4,11,0,0,4 +lucchjo01,2018,1,SDN,NL,8,9,26,26,0,0,0,390,125,59,23,43,145,0.254,4.08,2,4,4,0,548,0,63,3,4,12 +lugose01,2018,1,NYN,NL,3,4,54,5,0,0,3,304,81,30,9,28,103,0.217,2.66,4,2,2,1,410,13,36,1,5,6 +lylesjo01,2018,1,SDN,NL,2,4,24,8,0,0,0,214,71,34,12,19,62,0.259,4.29,0,4,1,0,300,5,35,3,3,4 +lylesjo01,2018,2,MIL,NL,1,0,11,0,0,0,0,49,12,6,0,9,22,0.203,3.31,3,1,2,0,71,5,7,0,1,0 +lynnla01,2018,1,MIN,AL,7,8,20,20,0,0,0,307,105,58,12,62,100,0.263,5.10,3,3,6,0,469,0,61,0,2,10 +lynnla01,2018,2,NYA,AL,3,2,11,9,0,0,0,163,58,25,2,14,61,0.267,4.14,0,2,0,0,231,0,26,0,0,3 +lyonsty01,2018,1,SLN,NL,1,0,27,0,0,0,0,50,24,16,3,8,19,0.343,8.64,2,2,2,0,83,2,16,1,2,1 +madsory01,2018,1,WAS,NL,2,5,49,0,0,0,4,133,48,26,6,15,41,0.279,5.28,2,2,4,0,195,10,28,1,3,5 +madsory01,2018,2,LAN,NL,0,0,9,0,0,0,0,25,10,6,1,1,13,0.294,6.48,0,1,0,0,36,1,6,0,1,0 +maedake01,2018,1,LAN,NL,8,10,39,20,0,0,2,376,115,53,13,43,153,0.240,3.81,4,2,5,2,532,4,58,2,3,7 +magilma01,2018,1,MIN,AL,3,3,40,0,0,0,0,170,58,24,11,23,56,0.262,3.81,0,0,3,0,249,9,24,1,1,3 +mahlety01,2018,1,CIN,NL,7,9,23,23,0,0,0,336,125,62,22,53,110,0.283,4.98,7,1,3,0,507,0,68,5,3,15 +makitka01,2018,1,SDN,NL,0,1,27,0,0,0,0,105,32,21,7,12,37,0.241,5.40,2,0,2,0,149,9,23,2,0,1 +manaese01,2018,1,OAK,AL,12,9,27,27,1,1,0,482,141,64,21,32,108,0.232,3.59,1,9,8,0,654,0,67,4,2,11 +mannbr01,2018,1,TEX,AL,0,0,7,0,0,0,0,25,7,5,1,4,3,0.250,5.40,0,1,2,0,36,4,5,0,2,2 +mapledi01,2018,1,CHN,NL,1,0,9,0,0,0,0,16,7,7,2,5,9,0.318,11.81,0,2,2,0,29,1,7,0,0,1 +marinjh01,2018,1,BAL,AL,0,0,8,0,0,0,0,24,9,5,1,9,3,0.310,5.63,0,0,0,0,40,2,6,1,1,1 +marquge01,2018,1,COL,NL,14,11,33,33,0,0,0,588,179,82,24,57,230,0.241,3.77,5,8,8,0,817,0,90,2,6,15 +marshev01,2018,1,CLE,AL,0,0,10,0,0,0,0,21,12,6,0,4,9,0.375,7.71,0,1,1,0,37,1,6,0,0,1 +martica04,2018,1,SLN,NL,8,6,33,18,0,0,5,356,100,41,5,60,117,0.228,3.11,4,5,11,0,521,9,48,7,4,14 +martich02,2018,1,TEX,AL,1,5,46,0,0,0,0,125,46,21,5,5,37,0.274,4.54,2,4,3,0,177,8,21,0,1,3 +mathije01,2018,1,ARI,NL,0,1,1,0,0,0,0,3,1,1,1,0,1,0.250,9.00,0,0,0,0,4,1,1,0,0,0 +matonph01,2018,1,SDN,NL,0,2,45,0,0,0,0,142,50,23,3,23,55,0.269,4.37,1,4,2,0,214,12,25,2,1,3 +matzst01,2018,1,NYN,NL,5,11,30,30,0,0,0,462,134,68,25,58,152,0.232,3.97,2,0,10,0,654,0,77,6,2,14 +maurebr01,2018,1,KCA,AL,0,4,37,0,0,0,1,94,42,27,7,25,31,0.323,7.76,2,2,1,0,160,9,29,1,3,1 +mayermi01,2018,1,SLN,NL,2,1,50,0,0,0,1,155,59,27,7,15,49,0.289,4.70,1,4,1,0,226,15,28,3,3,3 +maytr01,2018,1,MIN,AL,4,1,24,1,0,0,3,76,21,9,4,5,36,0.221,3.20,0,1,1,0,103,6,9,2,0,1 +mayzati01,2018,1,TOR,AL,2,0,37,0,0,0,0,107,33,13,3,14,40,0.244,3.28,4,2,2,0,151,9,13,0,0,2 +mazzoco01,2018,1,CHN,NL,1,0,8,0,0,0,0,26,5,1,0,5,7,0.161,1.04,1,0,0,0,36,4,1,0,0,0 +mcallza01,2018,1,CLE,AL,1,2,41,0,0,0,0,125,47,23,7,10,34,0.285,4.97,1,2,1,0,180,14,25,2,1,3 +mcallza01,2018,2,DET,AL,0,0,3,0,0,0,0,10,10,8,1,0,5,0.526,21.60,0,1,0,0,19,0,8,0,0,0 +mccarbr01,2018,1,ATL,NL,6,3,15,15,0,0,0,236,94,43,15,21,65,0.300,4.92,0,2,0,0,339,0,45,4,1,11 +mccarke01,2018,1,KCA,AL,5,4,65,0,0,0,0,216,70,26,7,20,46,0.259,3.25,2,2,2,0,293,10,28,0,1,10 +mccread01,2018,1,ATL,NL,0,0,1,0,0,0,0,3,4,2,0,0,2,0.571,18.00,0,0,0,0,7,1,2,0,0,0 +mcculla02,2018,1,HOU,AL,10,6,25,22,0,0,0,385,100,55,12,50,142,0.215,3.86,0,14,7,1,527,0,60,1,4,13 +mcfartj01,2018,1,ARI,NL,2,2,47,0,0,0,1,216,64,16,4,22,42,0.239,2.00,3,2,0,0,292,21,18,1,1,13 +mcgeeja01,2018,1,COL,NL,2,4,61,0,0,0,1,154,59,37,10,16,47,0.285,6.49,1,5,2,0,227,9,39,2,0,1 +mcgowky01,2018,1,WAS,NL,0,0,5,1,0,0,0,23,6,5,2,5,8,0.214,5.87,0,2,0,0,34,2,5,0,1,0 +mcgraky01,2018,1,SDN,NL,0,0,4,0,0,0,0,12,3,2,2,3,4,0.200,4.50,0,0,0,0,18,1,2,0,0,0 +mcguide02,2018,1,TOR,AL,0,0,4,0,0,0,0,25,9,6,2,5,7,0.265,6.48,0,0,0,0,39,3,6,0,0,1 +mcguide02,2018,2,LAA,AL,0,2,17,4,0,0,0,89,29,20,7,21,26,0.266,6.07,0,2,5,0,138,6,22,0,3,6 +mchugco01,2018,1,HOU,AL,6,2,58,0,0,0,0,217,45,16,6,21,94,0.176,1.99,0,0,5,0,283,18,18,1,1,1 +mcraeal01,2018,1,PIT,NL,0,1,2,0,0,0,0,19,8,4,0,5,5,0.308,5.68,0,2,1,0,32,0,4,0,0,0 +meansjo01,2018,1,BAL,AL,0,0,1,0,0,0,0,10,6,5,1,0,4,0.375,13.50,0,0,0,0,16,0,5,0,0,0 +medlekr01,2018,1,ARI,NL,0,1,1,1,0,0,0,12,9,7,0,4,4,0.429,15.75,0,2,0,0,25,0,7,0,0,0 +meisiry01,2018,1,BAL,AL,2,1,18,1,0,0,0,63,18,15,6,10,21,0.240,6.43,0,4,0,0,87,2,15,1,1,5 +mejiaad01,2018,1,MIN,AL,2,0,5,4,0,0,0,67,17,5,1,9,13,0.215,2.01,0,2,2,0,92,1,5,1,1,1 +melanma01,2018,1,SFN,NL,1,4,41,0,0,0,3,117,48,14,2,14,31,0.302,3.23,2,4,1,0,174,8,18,0,0,6 +mellake01,2018,1,CIN,NL,0,0,4,0,0,0,0,28,13,9,4,8,8,0.351,8.68,0,1,1,0,48,1,9,2,0,2 +mendeyo01,2018,1,TEX,AL,2,2,8,5,0,0,0,83,28,17,4,15,18,0.264,5.53,0,0,0,0,121,1,18,0,0,4 +mengdda01,2018,1,OAK,AL,7,6,22,17,1,1,0,347,103,52,18,26,72,0.234,4.05,0,6,3,0,476,0,58,4,2,6 +meyerbe02,2018,1,MIA,NL,0,0,13,0,0,0,0,57,26,22,2,14,9,0.338,10.42,2,1,4,0,97,7,23,1,1,4 +middlke01,2018,1,LAA,AL,0,0,16,0,0,0,6,53,14,4,1,9,16,0.233,2.04,1,1,1,0,71,9,4,0,1,2 +mikolmi01,2018,1,SLN,NL,18,4,32,32,1,1,0,602,186,63,16,29,146,0.245,2.83,4,2,7,1,808,0,70,8,4,18 +mileywa01,2018,1,MIL,NL,5,2,16,16,0,0,0,242,71,23,3,27,50,0.237,2.57,1,1,5,0,338,0,28,5,1,8 +millean01,2018,1,CLE,AL,2,4,37,0,0,0,2,102,31,16,3,16,45,0.233,4.24,1,1,5,0,154,5,16,0,0,2 +milleju02,2018,1,WAS,NL,7,1,51,0,0,0,2,157,42,21,10,17,60,0.218,3.61,1,1,2,0,215,10,22,1,2,2 +millesh01,2018,1,ARI,NL,0,4,5,4,0,0,0,48,24,19,5,8,19,0.343,10.69,0,1,0,0,79,0,21,0,1,2 +millsal02,2018,1,CHN,NL,0,1,7,2,0,0,0,54,11,8,1,7,23,0.172,4.00,0,1,0,0,71,1,8,0,0,1 +milneho01,2018,1,PHI,NL,0,0,10,0,0,0,0,14,6,4,1,3,4,0.286,7.71,1,0,1,1,25,2,4,0,0,0 +milneho01,2018,2,TBA,AL,0,0,4,0,0,0,0,8,3,2,2,2,4,0.273,6.75,0,0,0,0,13,1,4,0,0,0 +milonto01,2018,1,WAS,NL,1,1,5,4,0,0,0,79,37,17,7,1,23,0.330,5.81,0,0,1,0,118,1,17,2,2,1 +minayju01,2018,1,CHA,AL,2,2,52,0,0,0,1,140,39,17,3,29,58,0.220,3.28,3,9,3,0,209,9,19,0,0,2 +minormi01,2018,1,TEX,AL,12,8,28,28,0,0,0,471,138,73,25,38,132,0.235,4.18,1,3,8,1,640,0,76,1,6,12 +minteaj01,2018,1,ATL,NL,4,3,65,0,0,0,15,184,57,22,3,22,69,0.244,3.23,1,5,2,0,260,31,23,1,1,3 +miranar01,2018,1,SEA,AL,0,0,1,1,0,0,0,15,6,1,0,4,5,0.300,1.80,0,0,0,0,24,0,1,0,0,0 +mitchbr01,2018,1,SDN,NL,2,4,16,11,0,0,0,219,85,44,12,43,38,0.294,5.42,1,1,2,0,337,2,45,1,2,10 +montafr01,2018,1,OAK,AL,5,4,13,11,0,0,0,195,74,28,5,21,43,0.290,3.88,0,5,2,0,283,1,34,2,3,9 +montgjo01,2018,1,NYA,AL,2,0,6,6,0,0,0,82,25,11,3,12,23,0.240,3.62,0,0,0,0,116,0,11,0,0,3 +montgmi01,2018,1,CHN,NL,5,6,38,19,0,0,0,372,131,55,10,39,86,0.272,3.99,4,4,7,0,534,6,58,3,4,13 +moorema02,2018,1,TEX,AL,3,8,39,12,0,0,0,306,128,77,19,41,86,0.305,6.79,1,6,5,0,471,10,82,1,4,8 +moralke01,2018,1,TOR,AL,0,0,1,0,0,0,0,3,0,0,0,1,0,0.000,0.00,0,0,0,0,4,1,0,0,0,0 +moralos01,2018,1,LAA,AL,0,0,1,0,0,0,0,1,1,0,0,0,0,0.500,0.00,0,1,0,0,2,1,0,0,0,0 +morgaad01,2018,1,PHI,NL,0,2,67,0,0,0,1,148,49,21,5,22,50,0.259,3.83,3,3,0,1,214,9,25,0,2,3 +morinmi01,2018,1,SEA,AL,0,0,3,0,0,0,0,12,6,3,0,1,6,0.375,6.75,1,0,0,0,18,1,3,0,1,0 +moronre01,2018,1,SFN,NL,5,2,69,0,0,0,1,195,34,18,4,37,79,0.154,2.49,4,5,0,0,262,9,20,1,3,5 +morriak01,2018,1,LAA,AL,0,0,9,0,0,0,0,42,18,9,3,8,7,0.316,5.79,0,1,0,0,65,7,9,0,0,2 +morrobr01,2018,1,CHN,NL,0,0,35,0,0,0,22,92,24,5,2,9,31,0.214,1.47,0,1,1,0,122,30,5,0,0,2 +mortoch02,2018,1,HOU,AL,15,3,30,30,0,0,0,501,130,58,18,64,201,0.213,3.13,0,4,16,0,695,0,63,1,4,12 +motteta01,2018,1,SEA,AL,0,0,1,0,0,0,0,3,1,1,1,1,1,0.250,9.00,0,0,1,0,6,1,1,0,0,0 +moyaga01,2018,1,MIN,AL,3,1,35,6,0,0,0,109,35,19,6,13,31,0.245,4.71,0,1,1,0,159,5,19,0,2,2 +moylape01,2018,1,ATL,NL,0,1,39,0,0,0,0,85,32,14,4,18,23,0.299,4.45,3,3,2,0,129,6,14,2,0,6 +musgrha01,2018,1,COL,NL,2,3,35,0,0,0,0,134,36,23,7,22,32,0.224,4.63,2,2,1,1,185,8,23,0,1,5 +musgrjo01,2018,1,PIT,NL,6,9,19,19,0,0,0,346,113,52,12,23,100,0.253,4.06,3,5,8,0,486,0,56,3,5,5 +nealza01,2018,1,LAN,NL,0,0,1,0,0,0,0,3,2,1,1,0,0,0.400,9.00,0,0,0,0,5,1,1,0,0,0 +nerishe01,2018,1,PHI,NL,1,3,53,0,0,0,11,143,46,27,11,16,76,0.250,5.10,1,5,1,0,203,28,27,2,0,1 +neshepa01,2018,1,PHI,NL,3,2,30,0,0,0,5,73,23,7,2,5,15,0.245,2.59,1,1,0,0,101,8,9,1,1,2 +neverdo01,2018,1,PIT,NL,0,0,25,0,0,0,0,81,30,24,9,10,27,0.280,8.00,0,2,1,0,119,8,25,0,1,2 +newbeja01,2018,1,KCA,AL,2,0,14,0,0,0,0,40,13,7,3,9,11,0.255,4.73,0,1,0,0,60,3,8,0,0,0 +newcose01,2018,1,ATL,NL,12,9,31,30,0,0,0,492,137,71,18,81,160,0.226,3.90,1,4,1,0,696,0,74,4,3,13 +nicasju01,2018,1,SEA,AL,1,6,46,0,0,0,1,126,53,28,6,5,53,0.308,6.00,0,0,2,0,183,6,30,0,4,1 +nixja02,2018,1,SDN,NL,2,5,9,9,0,0,0,127,52,33,8,13,21,0.304,7.02,0,4,4,0,190,0,33,0,2,4 +nolaaa01,2018,1,PHI,NL,17,6,33,33,0,0,0,637,149,56,17,58,224,0.197,2.37,3,4,7,1,831,0,57,6,4,16 +norribu01,2018,1,SLN,NL,3,6,64,0,0,0,28,173,51,23,8,21,67,0.237,3.59,3,9,5,0,245,42,27,2,2,5 +norrida01,2018,1,DET,AL,0,5,11,8,0,0,0,133,46,28,8,19,51,0.261,5.68,0,2,2,1,200,0,28,1,2,1 +norwoja01,2018,1,CHN,NL,0,1,11,0,0,0,0,33,14,5,0,5,10,0.298,4.09,0,1,0,0,54,5,7,0,2,0 +novaiv01,2018,1,PIT,NL,9,9,29,29,0,0,0,483,171,75,26,35,114,0.268,4.19,4,9,4,1,683,0,82,4,2,13 +nunovi01,2018,1,TBA,AL,3,0,17,0,0,0,0,99,24,6,5,10,29,0.198,1.64,3,0,2,1,134,8,7,0,1,1 +oakstr01,2018,1,KCA,AL,0,2,4,2,0,0,0,41,21,11,1,6,10,0.375,7.24,0,0,0,0,65,1,11,1,2,2 +obergsc01,2018,1,COL,NL,8,1,56,0,0,0,0,176,45,16,4,12,57,0.213,2.45,0,3,3,0,228,7,17,0,2,9 +odayda01,2018,1,BAL,AL,0,2,20,0,0,0,2,60,18,8,3,4,27,0.237,3.60,1,0,3,1,83,10,9,0,0,0 +odorija01,2018,1,MIN,AL,7,10,32,32,0,0,0,493,151,82,20,70,162,0.242,4.49,3,1,8,0,711,0,89,4,4,9 +ogandal01,2018,1,CLE,AL,0,1,1,0,0,0,0,3,2,2,0,3,1,0.400,18.00,0,0,0,0,8,0,2,0,0,0 +ogradch01,2018,1,MIA,NL,0,1,8,0,0,0,0,21,7,5,2,4,8,0.250,6.43,0,0,0,0,32,1,5,0,0,0 +ohse01,2018,1,TOR,AL,4,3,48,0,0,0,2,141,37,14,5,10,55,0.214,2.68,1,1,3,0,189,7,14,0,3,1 +ohse01,2018,2,COL,NL,2,0,25,0,0,0,1,64,15,6,3,7,24,0.197,2.53,0,2,1,0,85,3,7,0,1,1 +ohtansh01,2018,1,LAA,AL,4,2,10,10,0,0,0,155,38,19,6,22,63,0.203,3.31,0,5,1,0,211,0,19,0,1,2 +okertst01,2018,1,SFN,NL,0,0,10,0,0,0,0,22,4,1,1,0,8,0.148,1.23,0,0,0,0,27,4,1,0,0,0 +olsonty01,2018,1,CLE,AL,2,1,43,0,0,0,0,82,26,15,4,12,40,0.245,4.94,2,1,0,0,118,5,16,0,0,2 +ortizlu02,2018,1,BAL,AL,0,1,2,1,0,0,0,7,7,4,0,3,0,0.500,15.43,0,1,0,0,18,0,6,0,1,0 +osichjo01,2018,1,SFN,NL,0,0,12,0,0,0,0,36,20,11,2,7,10,0.385,8.25,0,3,2,0,61,2,11,0,0,3 +osunaro01,2018,1,TOR,AL,0,0,15,0,0,0,9,46,16,5,0,1,13,0.267,2.93,0,1,2,0,63,13,5,0,0,1 +osunaro01,2018,2,HOU,AL,2,2,23,0,0,0,12,68,17,5,1,3,19,0.210,1.99,0,2,2,0,87,18,5,0,1,2 +oswalco01,2018,1,NYN,NL,3,3,17,12,0,0,0,194,69,42,14,20,45,0.268,5.85,1,4,4,0,282,2,43,1,0,4 +oteroda01,2018,1,CLE,AL,2,1,61,0,0,0,1,176,69,34,12,5,43,0.289,5.22,3,0,3,1,247,19,36,0,0,9 +ottavad01,2018,1,COL,NL,6,4,75,0,0,0,6,233,41,21,5,36,112,0.158,2.43,5,7,6,0,309,16,25,1,5,6 +paganem01,2018,1,OAK,AL,3,1,55,0,0,0,0,186,55,30,13,19,63,0.230,4.35,1,3,3,0,262,17,30,0,1,1 +pannoth01,2018,1,TOR,AL,4,1,12,6,0,0,0,129,37,20,7,15,29,0.231,4.19,0,0,2,0,181,2,20,1,3,1 +pareded01,2018,1,LAA,AL,0,0,14,0,0,0,0,55,25,14,5,7,15,0.325,6.87,0,0,1,0,86,7,14,0,1,2 +pareded02,2018,1,LAN,NL,2,0,15,0,0,0,0,23,7,5,2,2,8,0.233,5.87,0,0,1,0,33,1,5,0,0,0 +parkebl01,2018,1,LAA,AL,2,1,67,0,0,0,14,199,63,24,12,19,70,0.249,3.26,1,8,3,0,276,41,24,0,1,4 +parsowe01,2018,1,ATL,NL,0,1,1,0,0,0,0,15,6,4,1,3,3,0.316,7.20,1,0,0,0,23,0,4,0,1,0 +paulida01,2018,1,TOR,AL,1,0,7,0,0,0,0,20,6,1,1,2,6,0.240,1.35,0,0,0,0,28,4,2,0,1,0 +paxtoja01,2018,1,SEA,AL,11,6,28,28,2,1,0,481,134,67,23,42,208,0.224,3.76,0,8,1,0,645,0,67,2,2,11 +pazosja01,2018,1,SEA,AL,4,1,60,0,0,0,0,150,47,16,4,15,45,0.249,2.88,1,2,5,0,211,4,19,0,2,7 +peacobr01,2018,1,HOU,AL,3,5,61,1,0,0,3,195,56,25,11,20,96,0.226,3.46,0,5,3,0,272,20,26,0,1,3 +pelhacd01,2018,1,TEX,AL,0,0,10,0,0,0,0,23,12,6,0,4,7,0.353,7.04,1,0,0,0,38,2,6,0,0,1 +penafe01,2018,1,LAA,AL,3,5,19,17,0,0,0,278,87,43,12,28,85,0.247,4.18,0,7,4,0,389,1,45,1,4,7 +pennicl01,2018,1,CIN,NL,0,0,1,0,0,0,0,3,1,1,0,2,1,0.250,9.00,0,0,0,0,6,1,1,0,0,0 +peralfr01,2018,1,MIL,NL,6,4,16,14,0,0,0,235,49,37,8,40,96,0.178,4.25,1,3,4,1,321,1,37,1,1,7 +peralwa01,2018,1,CIN,NL,2,2,59,0,0,0,0,136,58,27,2,31,31,0.302,5.36,2,0,2,0,227,6,32,1,1,4 +peralwi01,2018,1,KCA,AL,1,0,37,0,0,0,14,103,28,14,4,23,35,0.230,3.67,1,4,1,0,149,30,14,1,2,5 +perdolu02,2018,1,SDN,NL,1,6,12,10,0,0,0,134,62,35,4,22,39,0.328,7.05,2,4,3,0,217,2,37,1,2,5 +perezci01,2018,1,HOU,AL,0,0,8,0,0,0,0,34,6,5,3,7,12,0.158,3.97,0,0,0,0,45,3,5,0,0,2 +perezhe01,2018,1,MIL,NL,0,0,3,0,0,0,0,10,7,5,1,1,2,0.412,13.50,0,0,1,0,19,1,5,0,0,0 +perezma02,2018,1,TEX,AL,2,7,22,15,0,0,0,256,116,59,16,36,52,0.329,6.22,1,3,2,1,397,3,68,1,5,13 +perezol01,2018,1,CLE,AL,1,1,51,0,0,0,0,97,17,5,1,7,43,0.155,1.39,3,1,2,0,120,1,6,0,1,1 +peterdi01,2018,1,MIA,NL,2,2,7,5,0,0,0,83,34,22,4,15,17,0.306,7.16,0,3,1,0,129,2,22,1,1,3 +peterja01,2018,2,BAL,AL,0,0,1,0,0,0,0,3,6,4,1,0,1,0.750,36.00,0,0,0,0,8,1,4,0,0,1 +peterti01,2018,1,NYN,NL,2,2,22,0,0,0,0,83,29,19,8,5,25,0.261,6.18,2,0,1,0,118,9,19,1,0,1 +petityu01,2018,1,OAK,AL,7,3,74,0,0,0,0,279,76,31,13,18,76,0.221,3.00,4,1,0,0,368,12,32,1,5,6 +petrija01,2018,1,TOR,AL,3,1,41,0,0,0,0,137,59,23,6,16,41,0.317,4.53,0,5,5,0,208,5,28,1,0,10 +phillev01,2018,1,ATL,NL,0,0,4,0,0,0,0,19,6,6,3,4,3,0.240,8.53,0,0,0,0,29,4,6,0,0,0 +phillev01,2018,2,BAL,AL,0,1,5,1,0,0,0,16,7,11,2,6,5,0.304,18.56,0,1,1,0,30,0,13,0,0,1 +pivetni01,2018,1,PHI,NL,7,14,33,32,0,0,0,492,163,87,24,51,188,0.261,4.77,0,8,5,0,694,1,91,8,5,16 +plutkad01,2018,1,CLE,AL,4,5,17,12,0,0,1,230,78,45,21,23,60,0.261,5.28,1,3,1,1,326,4,45,1,2,2 +pomerdr01,2018,1,BOS,AL,2,6,26,11,0,0,0,222,87,50,12,44,66,0.297,6.08,1,4,4,1,344,5,53,0,3,9 +ponceda01,2018,1,SLN,NL,0,2,11,4,0,0,1,99,24,10,2,13,31,0.205,2.73,0,0,1,0,132,2,10,0,1,4 +porceri01,2018,1,BOS,AL,17,7,33,33,1,0,0,574,177,91,27,48,190,0.241,4.28,0,3,16,1,808,0,97,3,6,8 +poundbr01,2018,1,COL,NL,0,1,14,0,0,0,0,46,25,13,3,2,17,0.368,7.63,0,1,2,0,72,11,13,0,0,3 +poynebo01,2018,1,BOS,AL,1,0,20,0,0,0,0,67,22,8,4,3,24,0.256,3.22,0,0,2,0,93,4,8,1,1,0 +pressry01,2018,1,MIN,AL,1,1,51,0,0,0,0,143,46,18,5,19,69,0.250,3.40,1,6,2,0,208,7,19,1,2,2 +pressry01,2018,2,HOU,AL,1,0,26,0,0,0,2,70,11,2,1,3,32,0.138,0.77,0,2,1,0,84,4,2,0,0,0 +priceda01,2018,1,BOS,AL,16,7,30,30,1,0,0,528,151,70,25,50,177,0.230,3.58,0,1,10,0,722,0,75,1,4,10 +pruitau01,2018,1,TBA,AL,2,3,23,0,0,0,4,209,72,36,7,16,42,0.268,4.65,0,1,1,0,291,11,40,1,4,3 +quackke01,2018,1,CIN,NL,0,1,10,0,0,0,0,27,13,11,3,6,7,0.333,11.00,1,0,0,0,45,1,11,0,0,0 +quinnro01,2018,1,PHI,NL,0,0,1,0,0,0,0,5,6,7,1,2,0,0.545,37.80,0,1,1,0,14,0,7,0,0,0 +quintjo01,2018,1,CHN,NL,13,11,32,32,0,0,0,523,162,78,25,68,158,0.246,4.03,3,3,3,0,739,0,81,8,2,13 +raineta01,2018,1,CIN,NL,0,0,8,0,0,0,0,21,13,19,4,12,7,0.406,24.43,1,0,0,0,45,2,19,0,1,1 +ramirca01,2018,1,TOR,AL,0,0,2,0,0,0,0,7,1,1,0,5,3,0.125,3.86,0,1,0,0,13,2,1,0,0,0 +ramirca01,2018,2,OAK,AL,0,0,3,0,0,0,0,18,2,2,0,4,2,0.111,3.00,0,1,1,0,24,1,2,0,1,2 +ramirer02,2018,1,SEA,AL,2,4,10,10,0,0,0,137,52,33,14,12,33,0.284,6.50,0,0,3,0,202,0,35,0,4,5 +ramirjc01,2018,1,LAA,AL,0,2,2,2,0,0,0,20,7,7,3,7,4,0.269,9.45,0,0,0,0,33,0,8,0,0,1 +ramirjo02,2018,1,ATL,NL,0,2,7,0,0,0,0,19,9,12,0,8,7,0.321,17.05,2,0,2,0,38,1,12,0,0,0 +ramirne01,2018,1,CLE,AL,0,3,47,0,0,0,0,125,36,21,9,18,51,0.228,4.54,1,0,3,0,180,9,21,1,0,2 +ramirno01,2018,1,LAA,AL,7,5,69,1,0,0,1,250,75,42,15,30,95,0.237,4.54,3,4,6,0,353,12,43,0,0,6 +ramirye01,2018,1,BAL,AL,1,8,17,12,0,0,0,196,64,43,11,36,62,0.257,5.92,1,5,4,0,294,2,44,2,3,4 +ramosaj01,2018,1,NYN,NL,2,2,28,0,0,0,0,59,17,14,3,15,22,0.236,6.41,0,1,0,0,88,5,14,0,1,2 +ramosed02,2018,1,PHI,NL,3,1,52,0,0,0,1,128,34,11,4,15,42,0.218,2.32,2,4,1,0,174,10,14,1,1,5 +ravinjo01,2018,1,ATL,NL,0,1,2,0,0,0,0,9,2,2,0,2,1,0.200,6.00,0,0,1,0,14,2,2,1,0,0 +rayro02,2018,1,ARI,NL,6,2,24,24,0,0,0,371,97,54,19,70,165,0.216,3.93,3,5,5,0,526,0,55,1,1,10 +reedad01,2018,1,MIN,AL,1,6,55,0,0,0,0,168,65,28,11,15,44,0.298,4.50,3,2,3,0,242,14,30,2,4,5 +reedco01,2018,1,CIN,NL,1,3,17,7,0,0,0,129,45,19,5,15,42,0.269,3.98,0,2,2,0,188,2,21,2,2,5 +reidfse01,2018,1,TOR,AL,2,4,7,7,0,0,0,100,31,19,6,21,42,0.244,5.13,0,3,1,0,150,0,23,0,1,3 +reiniza01,2018,1,DET,AL,1,0,18,0,0,0,0,64,28,18,5,9,18,0.311,7.59,2,2,0,0,100,6,18,0,1,1 +reyesal02,2018,1,SLN,NL,0,0,1,1,0,0,0,12,3,0,0,2,2,0.250,0.00,0,0,1,0,15,0,0,0,0,1 +reyesje01,2018,1,CIN,NL,0,0,5,0,0,0,0,17,4,2,1,2,2,0.190,3.18,0,1,1,0,24,1,4,0,0,1 +reyesjo01,2018,1,NYN,NL,0,0,1,0,0,0,0,3,5,6,2,2,0,0.625,54.00,0,0,1,0,11,1,6,0,0,0 +reynoma01,2018,1,WAS,NL,0,0,1,0,0,0,0,1,0,0,0,0,0,0.000,0.00,0,0,0,0,1,1,0,0,0,0 +rhameja01,2018,1,NYN,NL,1,2,30,0,0,0,1,97,38,21,8,8,28,0.295,5.85,2,0,1,0,140,15,21,2,0,2 +richacl01,2018,1,SDN,NL,7,11,27,27,0,0,0,476,159,94,19,60,108,0.263,5.33,1,7,10,1,682,0,98,5,3,22 +richaga01,2018,1,LAA,AL,5,4,16,16,0,0,0,229,64,31,11,34,87,0.222,3.66,0,15,1,0,324,0,43,1,0,9 +richatr01,2018,1,MIA,NL,4,9,25,25,0,0,0,379,121,62,15,54,130,0.253,4.42,5,8,5,1,547,0,65,5,4,8 +riosya01,2018,1,PHI,NL,3,2,36,0,0,0,0,108,43,27,6,15,36,0.293,6.75,2,2,2,1,165,10,28,0,1,4 +riverfe01,2018,1,PIT,NL,4,2,70,0,0,0,37,210,63,21,4,24,89,0.232,2.70,2,3,1,0,296,60,24,0,0,4 +rizzoan01,2018,1,CHN,NL,0,0,1,0,0,0,0,1,0,0,0,0,0,0.000,0.00,0,0,0,0,1,1,0,0,0,0 +roarkta01,2018,1,WAS,NL,9,15,31,30,0,0,0,541,181,87,24,50,146,0.262,4.34,3,5,10,0,760,1,90,4,6,18 +roberda08,2018,1,NYA,AL,8,3,69,0,0,0,5,209,46,25,7,26,91,0.183,3.23,1,1,0,0,283,11,30,4,1,2 +roberda10,2018,1,TBA,AL,0,0,1,0,0,0,0,3,0,0,0,0,0,0.000,0.00,0,0,0,0,3,1,0,0,0,0 +robleha01,2018,1,NYN,NL,2,2,16,0,0,0,0,59,21,11,7,10,23,0.280,5.03,1,1,1,0,88,3,11,1,1,2 +robleha01,2018,2,LAA,AL,0,1,37,0,0,0,2,109,32,12,2,15,36,0.237,2.97,0,1,1,0,154,11,15,1,2,4 +rodnefe01,2018,1,MIN,AL,3,2,46,0,0,0,25,131,42,15,5,19,50,0.249,3.09,1,2,2,0,192,39,18,0,2,3 +rodnefe01,2018,2,OAK,AL,1,1,22,0,0,0,0,62,20,9,2,13,20,0.253,3.92,0,4,1,0,93,1,9,0,0,1 +rodonca01,2018,1,CHA,AL,6,8,20,20,0,0,0,362,97,56,15,55,90,0.220,4.18,1,4,12,0,511,0,61,0,2,12 +rodride01,2018,1,SFN,NL,6,4,21,19,0,0,0,355,98,37,9,36,89,0.223,2.81,2,1,7,0,487,1,43,2,3,11 +rodried05,2018,1,BOS,AL,13,5,27,23,0,0,0,389,119,55,16,45,146,0.238,3.82,1,1,4,0,553,0,56,0,3,5 +rodrije01,2018,1,WAS,NL,3,3,14,8,0,0,0,156,43,33,8,37,39,0.228,5.71,5,2,3,0,233,2,35,3,1,1 +rodriri04,2018,1,TEX,AL,0,0,4,0,0,0,0,20,11,3,0,1,3,0.367,4.05,1,0,0,0,31,0,3,0,0,1 +rodriri05,2018,1,PIT,NL,4,3,63,0,0,0,0,208,55,19,5,19,88,0.217,2.47,3,11,5,0,279,20,19,1,1,6 +roech01,2018,1,TBA,AL,1,3,61,0,0,0,1,151,35,20,6,16,53,0.197,3.58,1,1,8,0,203,6,21,1,0,8 +rogerjo01,2018,1,BAL,AL,1,2,3,3,0,0,0,35,17,11,2,5,6,0.340,8.49,0,0,0,0,56,0,11,0,1,0 +rogerta01,2018,1,MIN,AL,1,2,72,0,0,0,2,205,49,20,3,16,75,0.206,2.63,3,0,2,0,260,6,20,1,3,6 +romansa01,2018,1,CIN,NL,8,11,39,25,0,0,0,437,155,86,23,53,105,0.267,5.31,6,7,4,0,644,1,92,3,4,8 +romeren01,2018,1,WAS,NL,0,0,2,0,0,0,0,6,5,3,1,1,2,0.455,13.50,0,0,1,0,13,0,3,0,0,0 +romeren01,2018,2,PIT,NL,0,0,2,0,0,0,0,12,7,2,0,3,5,0.350,4.50,2,2,0,0,24,2,5,0,1,0 +romeren01,2018,3,KCA,AL,0,0,4,0,0,0,0,12,11,9,3,2,3,0.524,20.25,1,0,0,0,24,0,9,0,1,1 +romerfe01,2018,1,MIN,AL,3,3,11,11,0,0,0,167,60,29,6,19,45,0.279,4.69,1,7,7,0,247,0,31,2,4,4 +rominan01,2018,1,SEA,AL,0,0,3,0,0,0,0,9,5,5,1,2,0,0.357,15.00,0,1,1,0,17,3,5,0,0,0 +romose01,2018,1,TBA,AL,3,4,73,5,0,0,25,202,65,31,11,20,75,0.253,4.14,0,2,2,0,284,39,31,2,2,6 +rondobr01,2018,1,CHA,AL,2,3,35,0,0,0,1,89,37,28,1,27,40,0.298,8.49,2,6,1,0,154,5,30,0,2,2 +rondohe01,2018,1,HOU,AL,2,5,63,0,0,0,15,177,58,21,4,20,67,0.252,3.20,2,1,0,0,250,26,22,0,0,5 +rosarra01,2018,1,CHN,NL,4,0,44,0,0,0,1,140,47,19,5,22,30,0.264,3.66,2,1,0,0,200,8,22,0,0,7 +rosscza01,2018,1,LAN,NL,0,1,17,0,0,0,0,34,9,6,3,4,20,0.209,4.76,0,0,0,0,47,5,6,0,0,0 +rossjo01,2018,1,WAS,NL,0,2,3,3,0,0,0,48,17,9,3,4,7,0.274,5.06,0,0,2,0,68,0,10,0,0,1 +rossty01,2018,1,SDN,NL,6,9,22,22,0,0,0,370,112,61,16,52,107,0.240,4.45,3,4,7,0,530,0,64,1,4,7 +rossty01,2018,2,SLN,NL,2,0,9,1,0,0,0,79,20,8,1,10,15,0.222,2.73,1,0,0,0,104,0,9,2,2,4 +rowlech01,2018,1,TOR,AL,0,1,2,0,0,0,0,2,2,3,1,1,0,0.400,40.50,1,0,0,0,6,2,4,0,0,0 +ruary01,2018,1,TEX,AL,0,0,1,0,0,0,0,3,0,0,0,0,1,0.000,0.00,0,0,0,0,3,1,0,0,0,0 +rucindr01,2018,1,MIA,NL,4,2,32,0,0,0,0,106,34,17,2,13,27,0.254,4.33,4,1,4,0,154,5,21,1,2,2 +ruizjo01,2018,1,CHA,AL,0,0,6,0,0,0,0,13,5,2,1,3,6,0.278,4.15,0,2,0,0,21,2,2,0,0,0 +rumbeni01,2018,1,SEA,AL,0,0,13,0,0,0,0,53,19,12,6,6,16,0.275,6.11,1,2,1,0,77,6,12,0,1,1 +rusinch01,2018,1,COL,NL,2,3,49,0,0,0,0,164,56,37,7,26,47,0.268,6.09,1,4,2,2,241,11,42,1,3,6 +ryuhy01,2018,1,LAN,NL,7,3,15,15,0,0,0,247,68,18,9,15,89,0.221,1.97,1,0,1,0,324,0,23,1,0,8 +rzepcma01,2018,1,SEA,AL,0,1,18,0,0,0,0,23,13,8,2,9,10,0.342,9.39,2,1,0,0,47,0,11,0,0,1 +rzepcma01,2018,2,CLE,AL,0,0,5,0,0,0,0,8,3,0,0,1,1,0.333,0.00,0,0,0,0,10,1,0,0,0,2 +sabatcc01,2018,1,NYA,AL,9,7,29,29,0,0,0,459,150,62,19,51,140,0.250,3.65,0,3,11,0,665,0,72,0,3,9 +sadleca02,2018,1,PIT,NL,0,0,2,0,0,0,0,13,9,4,0,3,3,0.409,8.31,0,1,0,0,25,1,7,0,0,2 +sadzeco01,2018,1,TEX,AL,0,0,13,2,0,0,0,28,6,1,0,11,7,0.182,0.96,1,1,1,0,45,1,2,0,0,2 +salasfe01,2018,1,ARI,NL,4,4,41,0,0,0,0,120,40,20,5,13,30,0.265,4.50,4,2,1,0,170,12,20,2,2,5 +salech01,2018,1,BOS,AL,12,4,27,27,0,0,0,474,102,37,11,34,237,0.181,2.11,0,4,14,0,617,0,39,0,4,4 +samarje01,2018,1,SFN,NL,1,5,10,10,0,0,0,134,47,31,6,26,30,0.270,6.25,1,2,2,0,207,0,32,1,4,1 +sampsad01,2018,1,TEX,AL,0,3,5,4,0,0,0,69,24,11,6,4,15,0.270,4.30,0,2,2,1,96,0,13,0,1,3 +sanchaa01,2018,1,TOR,AL,4,6,20,20,0,0,0,315,106,57,11,58,86,0.262,4.89,2,4,7,0,474,0,62,0,4,11 +sanchan01,2018,1,ATL,NL,7,6,25,24,0,0,0,410,106,43,15,42,135,0.213,2.83,0,4,4,0,553,0,48,7,3,7 +sandopa01,2018,1,SFN,NL,0,0,1,0,0,0,0,3,0,0,0,0,0,0.000,0.00,0,0,0,0,3,1,0,0,0,0 +santade01,2018,1,LAN,NL,1,0,1,0,0,0,0,11,6,5,0,1,4,0.375,12.27,0,1,1,0,19,0,5,1,0,0 +santaed01,2018,1,PIT,NL,3,4,69,0,0,0,0,199,61,24,7,12,54,0.240,3.26,2,3,3,0,271,11,25,1,1,5 +santaer01,2018,1,MIN,AL,0,1,5,5,0,0,0,74,31,22,9,9,16,0.310,8.03,0,0,2,0,114,0,22,0,3,1 +santihe01,2018,1,CHA,AL,6,3,49,7,0,0,2,306,101,50,16,60,103,0.258,4.41,3,1,5,2,460,27,54,1,3,9 +santolu01,2018,1,TOR,AL,1,1,15,1,0,0,0,60,26,16,4,10,24,0.333,7.20,1,0,1,0,91,4,16,0,2,1 +saupowa01,2018,1,DET,AL,4,1,31,0,0,0,1,103,41,17,6,13,16,0.293,4.46,1,2,2,0,158,13,17,2,1,2 +scahiro01,2018,1,CHA,AL,0,0,6,0,0,0,0,15,5,3,0,3,3,0.250,5.40,0,1,2,0,25,3,4,0,0,1 +scherma01,2018,1,WAS,NL,18,7,33,33,2,1,0,662,150,62,23,51,300,0.188,2.53,4,4,12,1,866,0,66,4,2,10 +schulja02,2018,1,TBA,AL,2,2,22,1,0,0,0,91,18,19,6,17,35,0.175,5.64,0,1,3,0,125,7,19,0,2,2 +scottro02,2018,1,BOS,AL,0,1,9,0,0,0,0,20,10,6,2,5,8,0.333,8.10,2,0,5,0,40,4,6,0,0,0 +scottta01,2018,1,BAL,AL,3,3,53,0,0,0,0,160,55,32,6,28,76,0.263,5.40,1,7,1,0,240,8,33,1,1,3 +scribtr01,2018,1,ARI,NL,0,1,1,1,0,0,0,11,4,2,0,6,4,0.267,4.91,0,0,0,0,21,0,2,0,0,0 +senzaan01,2018,1,COL,NL,6,6,23,13,0,0,0,271,94,44,10,30,69,0.266,4.38,1,1,3,0,390,2,45,1,3,7 +severlu01,2018,1,NYA,AL,19,8,32,32,1,1,0,574,173,72,19,46,220,0.238,3.39,0,8,5,0,780,0,76,1,2,13 +sewalpa01,2018,1,NYN,NL,0,7,46,0,0,0,2,169,62,38,8,23,58,0.277,6.07,2,1,1,0,253,9,39,2,3,4 +shackke01,2018,1,CIN,NL,0,1,5,0,0,0,0,24,13,7,0,4,7,0.382,7.88,1,1,1,0,41,1,8,0,2,1 +shafeju01,2018,1,TOR,AL,0,0,6,0,0,0,0,25,6,3,1,7,2,0.194,3.24,0,0,1,0,39,1,4,0,0,0 +shawbr01,2018,1,COL,NL,4,6,61,0,0,0,0,164,70,36,9,28,54,0.313,5.93,1,8,1,0,257,14,43,1,3,7 +sheffju01,2018,1,NYA,AL,0,0,3,0,0,0,0,8,4,3,1,3,0,0.364,10.13,0,1,0,0,14,2,3,0,0,1 +sherfji01,2018,1,ARI,NL,0,0,15,0,0,0,0,49,8,3,1,10,17,0.145,1.65,1,0,2,0,69,3,3,0,2,0 +sherrry01,2018,1,SLN,NL,0,0,5,0,0,0,0,17,8,4,1,2,3,0.333,6.35,0,0,1,0,27,0,4,0,0,0 +shielja02,2018,1,CHA,AL,7,16,34,33,0,0,0,614,190,103,34,78,154,0.245,4.53,0,11,9,1,871,1,115,2,7,10 +shiplbr01,2018,1,ARI,NL,0,0,3,0,0,0,0,15,4,4,0,2,3,0.222,7.20,0,0,1,0,21,2,4,0,0,0 +shoemma01,2018,1,LAA,AL,2,2,7,7,0,0,0,93,29,17,3,10,33,0.244,4.94,0,3,1,0,130,0,17,0,0,4 +shrevch01,2018,1,NYA,AL,2,2,40,0,0,0,1,114,39,18,8,18,46,0.262,4.26,0,2,1,0,170,17,23,0,2,1 +shrevch01,2018,2,SLN,NL,1,2,20,0,0,0,0,44,14,5,3,9,16,0.259,3.07,0,0,0,0,65,3,5,0,2,2 +simslu01,2018,1,ATL,NL,0,0,6,0,0,0,0,31,12,9,2,8,10,0.279,7.84,1,0,1,0,52,2,9,0,0,0 +simslu01,2018,2,CIN,NL,0,0,3,0,0,0,0,16,3,4,1,5,6,0.158,6.75,0,0,1,0,25,0,4,0,0,0 +sippto01,2018,1,HOU,AL,3,1,54,0,0,0,0,116,27,8,1,13,42,0.200,1.86,0,2,1,0,151,8,8,0,2,3 +skaggty01,2018,1,LAA,AL,8,10,24,24,0,0,0,376,127,56,14,40,129,0.262,4.02,0,2,5,0,533,0,60,0,4,13 +skogler01,2018,1,KCA,AL,1,6,14,13,0,0,0,210,66,40,12,19,49,0.246,5.14,0,3,6,0,293,0,41,0,0,7 +slegeaa01,2018,1,MIN,AL,1,1,4,2,0,0,0,41,17,8,3,2,6,0.298,5.27,0,0,1,0,60,1,8,0,0,1 +smithbu03,2018,1,KCA,AL,1,6,38,6,0,0,0,234,90,60,15,40,77,0.292,6.92,2,0,4,1,358,16,60,2,4,6 +smithca02,2018,1,BOS,AL,1,1,18,0,0,0,0,43,14,6,2,6,18,0.241,3.77,0,0,0,0,64,5,6,0,0,0 +smithca03,2018,1,MIA,NL,5,6,16,16,0,0,0,232,63,36,10,33,88,0.220,4.19,2,0,3,0,326,0,36,2,2,3 +smithdr01,2018,1,NYN,NL,1,1,27,0,0,0,0,84,34,11,2,6,18,0.309,3.54,0,1,2,0,120,8,11,0,2,3 +smithjo05,2018,1,HOU,AL,5,1,56,0,0,0,0,137,34,19,7,12,46,0.207,3.74,0,1,2,0,180,13,20,0,2,4 +smithmu01,2018,1,TOR,AL,0,0,3,0,0,0,0,10,5,3,0,1,0,0.385,8.10,0,0,1,0,16,2,3,0,1,1 +smithwi04,2018,1,SFN,NL,2,3,54,0,0,0,14,159,37,15,3,15,71,0.194,2.55,4,2,0,0,210,27,18,2,2,1 +smokejo01,2018,1,PIT,NL,0,0,7,0,0,0,0,17,11,7,2,5,2,0.423,11.12,0,2,0,0,31,1,7,0,0,1 +smokejo01,2018,2,DET,AL,0,0,1,0,0,0,0,5,0,0,0,2,2,0.000,0.00,0,0,0,0,7,0,0,0,0,0 +smolija01,2018,1,OAK,AL,0,0,1,0,0,0,0,3,2,2,1,0,0,0.400,18.00,0,0,0,0,5,1,2,0,0,0 +snellbl01,2018,1,TBA,AL,21,5,31,31,0,0,0,542,112,38,16,64,221,0.178,1.89,2,13,1,0,700,0,41,2,3,15 +sneltdj01,2018,1,SFN,NL,0,0,4,0,0,0,0,13,9,5,2,3,4,0.391,10.38,0,0,0,0,26,0,6,0,0,0 +sobotch01,2018,1,ATL,NL,1,0,14,0,0,0,0,43,5,3,2,9,21,0.104,1.88,2,3,0,0,58,5,3,1,0,0 +socolmi01,2018,1,ATL,NL,0,1,4,0,0,0,0,15,8,6,0,2,4,0.348,10.80,0,0,0,0,25,3,6,0,0,0 +solissa01,2018,1,WAS,NL,1,2,56,0,0,0,0,118,43,28,7,18,44,0.277,6.41,1,4,4,0,177,5,28,0,0,3 +soriajo01,2018,1,CHA,AL,0,3,40,0,0,0,16,116,35,11,2,10,49,0.230,2.56,1,1,1,0,164,29,13,1,0,1 +soriajo01,2018,2,MIL,NL,3,1,26,0,0,0,0,66,18,10,2,6,26,0.214,4.09,0,1,1,1,91,0,11,0,0,3 +sorokmi01,2018,1,ATL,NL,2,1,5,5,0,0,0,77,30,10,1,7,21,0.288,3.51,0,2,0,0,113,0,14,1,1,3 +spangco01,2018,1,SDN,NL,0,0,2,0,0,0,0,6,4,3,0,3,0,0.444,13.50,0,0,0,0,12,2,3,0,0,1 +sparkgl01,2018,1,KCA,AL,0,3,15,3,0,0,0,115,47,19,3,15,27,0.303,4.46,3,3,1,1,176,3,20,0,5,1 +sprinje01,2018,1,TEX,AL,1,1,18,2,0,0,0,96,32,12,4,14,31,0.256,3.38,1,3,1,0,141,4,14,1,0,3 +stammcr01,2018,1,SDN,NL,8,3,73,0,0,0,0,237,65,24,3,17,88,0.221,2.73,3,3,3,0,317,7,25,2,1,6 +stanery01,2018,1,TBA,AL,2,3,59,29,0,0,0,199,45,22,8,27,81,0.191,2.98,1,5,1,0,263,10,23,0,0,5 +steckdr01,2018,1,MIA,NL,4,4,71,0,0,0,5,194,55,28,7,27,74,0.227,3.90,5,0,2,0,272,17,29,0,1,3 +stephja01,2018,1,CIN,NL,2,3,29,0,0,0,0,115,50,21,7,15,33,0.307,4.93,3,3,3,0,183,12,30,2,0,3 +stephro01,2018,1,CIN,NL,0,2,4,3,0,0,0,35,17,12,2,12,11,0.340,9.26,3,2,0,1,63,0,12,0,1,0 +stewabr01,2018,1,LAN,NL,0,1,9,2,0,0,0,53,23,12,4,9,14,0.303,6.11,3,1,2,0,90,3,15,1,1,1 +stewako01,2018,1,MIN,AL,2,1,8,4,0,0,0,110,34,15,1,18,24,0.246,3.68,0,4,3,0,159,0,16,0,0,6 +stockro01,2018,1,SDN,NL,1,1,32,0,0,0,0,119,37,11,1,13,38,0.247,2.50,1,3,2,1,166,6,13,1,0,3 +stouter01,2018,1,KCA,AL,0,0,3,0,0,0,0,7,7,6,2,2,2,0.500,23.14,0,0,0,0,16,0,7,0,0,0 +strahma01,2018,1,SDN,NL,3,4,41,5,0,0,0,184,39,14,6,21,69,0.178,2.05,1,2,3,0,245,5,16,1,1,2 +straida01,2018,1,MIA,NL,5,6,23,23,0,0,0,367,107,56,20,52,99,0.237,4.12,5,1,7,0,518,0,62,4,3,8 +strasst01,2018,1,WAS,NL,10,7,22,22,0,0,0,390,118,54,18,38,156,0.240,3.74,2,5,8,1,544,0,59,1,5,6 +stratch01,2018,1,SFN,NL,10,10,28,26,1,1,0,435,153,82,19,54,112,0.273,5.09,1,6,2,0,625,1,87,3,6,14 +strichu01,2018,1,SFN,NL,3,5,49,0,0,0,14,136,43,20,5,21,37,0.247,3.97,2,4,1,0,201,35,25,2,3,3 +stripro01,2018,1,LAN,NL,8,6,33,21,0,0,0,366,123,41,18,22,136,0.257,3.02,2,3,1,0,503,2,42,2,0,6 +stromma01,2018,1,TOR,AL,4,9,19,19,0,0,0,307,115,63,9,36,77,0.283,5.54,0,3,2,1,449,0,68,2,3,14 +stroppe01,2018,1,CHN,NL,6,1,60,0,0,0,13,179,38,15,4,21,57,0.179,2.26,3,3,5,0,240,20,15,1,1,7 +stumpda01,2018,1,DET,AL,1,5,56,0,0,0,0,115,44,21,5,16,37,0.286,4.93,1,2,0,0,173,5,23,1,2,2 +suarean01,2018,1,SFN,NL,7,13,29,29,0,0,0,481,163,80,23,45,130,0.269,4.49,1,4,3,0,665,0,85,5,6,22 +suarera01,2018,1,PHI,NL,1,1,4,3,0,0,0,45,21,9,3,6,11,0.339,5.40,1,0,0,1,69,0,14,1,0,4 +sucreje01,2018,1,TBA,AL,0,0,2,0,0,0,0,3,4,2,0,0,0,0.667,18.00,0,0,0,0,7,1,2,0,1,0 +suerowa01,2018,1,WAS,NL,4,1,40,0,0,0,0,143,43,19,4,15,47,0.240,3.59,2,3,4,1,200,11,20,1,1,1 +suterbr01,2018,1,MIL,NL,8,7,20,18,0,0,0,304,102,50,18,19,84,0.258,4.44,2,1,4,0,424,0,55,5,1,2 +swarzan01,2018,1,NYN,NL,0,2,29,0,0,0,4,79,28,18,6,14,31,0.280,6.15,0,2,1,0,116,12,18,0,1,2 +syndeno01,2018,1,NYN,NL,13,4,25,25,2,1,0,463,148,52,9,39,155,0.250,3.03,2,2,7,0,644,0,55,3,3,11 +taillja01,2018,1,PIT,NL,14,10,32,32,2,1,0,573,179,68,20,46,179,0.246,3.20,4,2,6,0,785,0,69,4,2,14 +tanakma01,2018,1,NYA,AL,12,6,27,27,1,1,0,468,141,65,25,35,159,0.240,3.75,0,3,7,0,635,0,68,3,2,13 +tarplst01,2018,1,NYA,AL,0,0,10,0,0,0,0,27,6,3,0,6,13,0.176,3.00,0,0,0,0,40,3,3,0,0,0 +taylobe03,2018,1,CLE,AL,0,0,6,0,0,0,0,18,6,4,2,1,8,0.250,6.00,0,0,0,0,25,4,4,0,0,0 +tazawju01,2018,1,MIA,NL,1,1,22,0,0,0,0,60,28,20,6,13,24,0.329,9.00,3,0,0,0,99,8,21,0,1,0 +tazawju01,2018,2,LAA,AL,0,0,9,0,0,0,0,24,7,2,1,3,4,0.226,2.25,0,1,0,0,35,2,3,1,0,0 +teherju01,2018,1,ATL,NL,9,9,31,31,0,0,0,527,122,77,26,84,162,0.196,3.94,3,2,9,3,724,0,80,3,5,8 +teperry01,2018,1,TOR,AL,5,5,68,0,0,0,7,194,55,26,9,24,68,0.237,3.62,1,5,4,1,263,19,27,0,3,6 +thompja03,2018,1,PHI,NL,1,0,9,0,0,0,2,49,14,9,1,11,14,0.230,4.96,2,1,0,0,76,6,10,3,1,1 +thornty01,2018,1,BOS,AL,2,0,25,0,0,0,0,72,28,15,6,10,21,0.295,5.63,0,2,1,0,107,9,15,0,1,3 +tillmch01,2018,1,BAL,AL,1,5,7,7,0,0,0,80,42,31,6,17,13,0.365,10.46,0,6,2,0,137,0,32,1,2,3 +toccica01,2018,1,TEX,AL,0,0,1,0,0,0,0,2,0,0,0,0,0,0.000,0.00,0,0,0,0,2,0,0,0,0,0 +tomlijo01,2018,1,CLE,AL,2,5,32,9,0,0,0,211,92,48,25,12,46,0.304,6.14,1,5,4,0,321,19,52,0,2,2 +torreca01,2018,1,WAS,NL,0,0,10,0,0,0,0,29,9,7,3,3,9,0.243,6.52,0,0,1,0,41,9,7,0,0,0 +toussto01,2018,1,ATL,NL,2,1,7,5,0,0,0,87,18,13,1,21,32,0.182,4.03,1,1,2,0,123,1,13,1,0,4 +treinbl01,2018,1,OAK,AL,9,2,68,0,0,0,38,241,46,7,2,21,100,0.158,0.78,3,6,1,0,315,58,12,1,0,4 +triggan01,2018,1,OAK,AL,3,1,9,9,0,0,0,124,37,24,7,18,43,0.231,5.23,0,3,3,0,182,0,24,0,1,2 +trivilo01,2018,1,OAK,AL,8,3,69,1,0,0,4,222,53,24,8,31,82,0.201,2.92,4,4,2,1,299,10,24,1,1,8 +tropeni01,2018,1,LAA,AL,5,6,14,14,0,0,0,228,68,40,16,31,64,0.242,4.74,2,2,2,0,316,0,41,0,2,7 +tsengje01,2018,1,CHN,NL,0,0,1,1,0,0,0,6,4,3,1,0,3,0.400,13.50,0,0,0,0,10,0,3,0,0,0 +tuivasa01,2018,1,SLN,NL,3,3,31,0,0,0,0,95,35,13,3,11,26,0.273,3.69,2,4,2,0,144,5,14,2,1,4 +tuivasa01,2018,2,SEA,AL,1,0,5,0,0,0,0,16,6,1,0,1,4,0.300,1.69,0,1,0,0,21,0,1,0,0,1 +turnbsp01,2018,1,DET,AL,0,2,4,3,0,0,0,49,17,11,1,4,15,0.262,6.06,0,1,0,1,69,0,11,0,0,1 +turneja01,2018,1,MIA,NL,0,0,4,0,0,0,0,17,13,10,1,5,2,0.448,15.88,0,0,0,0,35,1,10,0,1,0 +turneja01,2018,2,DET,AL,0,1,1,1,0,0,0,3,6,5,1,1,1,0.600,45.00,0,0,0,0,11,0,7,0,0,0 +underdu01,2018,1,CHN,NL,0,1,1,1,0,0,0,12,2,1,1,3,3,0.154,2.25,0,0,0,0,16,0,1,0,0,1 +urenajo01,2018,1,MIA,NL,9,12,31,31,1,0,0,522,155,77,19,51,130,0.242,3.98,8,2,12,1,712,0,78,5,4,16 +uriasju01,2018,1,LAN,NL,0,0,3,0,0,0,0,12,1,0,0,0,7,0.077,0.00,0,0,0,0,13,3,0,0,0,0 +valdefr01,2018,1,HOU,AL,4,1,8,5,0,0,0,111,22,9,3,24,34,0.175,2.19,0,5,4,0,154,0,10,0,0,7 +valdejo03,2018,1,SFN,NL,0,0,4,0,0,0,0,15,8,7,3,1,4,0.348,12.60,0,0,0,0,24,3,7,0,0,0 +valenda01,2018,1,BAL,AL,0,0,1,0,0,0,0,1,0,0,0,0,1,0.000,0.00,0,0,0,0,1,1,0,0,0,0 +valenje01,2018,1,PHI,NL,0,0,1,0,0,0,0,3,2,3,1,2,1,0.400,27.00,0,0,0,0,7,1,3,0,0,0 +vargaja01,2018,1,NYN,NL,7,9,20,20,0,0,0,276,100,59,18,30,84,0.276,5.77,1,0,5,0,404,0,60,5,2,7 +vasquan02,2018,1,MIN,AL,1,0,9,0,0,0,0,15,5,3,0,2,7,0.238,5.40,0,0,3,0,26,0,4,0,0,0 +vastoje01,2018,1,COL,NL,0,0,1,0,0,0,0,2,3,3,0,1,1,0.600,40.50,0,0,0,0,6,0,3,0,0,0 +vastoje01,2018,2,KCA,AL,0,1,5,0,0,0,0,11,3,1,1,1,3,0.214,2.45,0,0,0,0,15,0,2,0,0,0 +velasvi01,2018,1,PHI,NL,9,12,31,30,0,0,0,440,138,79,16,59,161,0.250,4.85,1,9,8,0,630,1,83,7,3,9 +velazhe01,2018,1,BOS,AL,7,2,47,8,0,0,0,255,97,30,7,26,53,0.290,3.18,1,4,7,0,370,15,35,0,2,12 +vendipa01,2018,1,LAN,NL,0,0,15,0,0,0,0,42,11,4,1,3,9,0.212,2.57,1,0,2,0,57,6,4,0,0,2 +ventejo01,2018,1,TBA,AL,1,1,22,1,0,0,1,42,11,6,1,6,11,0.212,3.86,1,0,0,0,58,6,6,0,0,1 +ventejo01,2018,2,ATL,NL,4,1,28,0,0,0,2,61,15,8,0,10,16,0.205,3.54,2,3,1,0,87,4,9,2,1,2 +verhadr01,2018,1,DET,AL,3,3,41,1,0,0,0,169,46,29,6,19,53,0.222,4.63,0,6,3,0,233,4,29,0,4,4 +verlaju01,2018,1,HOU,AL,16,9,34,34,1,1,0,642,156,60,28,37,290,0.200,2.52,0,5,8,2,833,0,63,2,5,3 +vieirth01,2018,1,CHA,AL,1,1,16,0,0,0,1,53,21,14,4,9,15,0.292,7.13,0,6,3,1,85,9,14,0,1,0 +vinceni01,2018,1,SEA,AL,4,4,62,1,0,0,0,169,50,25,7,15,56,0.229,3.99,4,0,3,1,239,8,28,1,2,1 +vizcaar01,2018,1,ATL,NL,2,2,39,0,0,0,16,115,30,9,4,15,40,0.213,2.11,1,4,2,0,158,31,9,0,0,3 +volstch01,2018,1,CHA,AL,1,5,33,1,0,0,0,142,65,33,9,12,29,0.332,6.27,1,1,3,0,215,5,35,1,3,7 +vothau01,2018,1,WAS,NL,1,1,4,2,0,0,0,37,12,9,3,6,11,0.255,6.57,0,1,0,0,55,0,9,1,1,0 +wachami01,2018,1,SLN,NL,8,2,15,15,0,0,0,253,68,30,9,36,71,0.221,3.20,0,2,2,1,355,0,36,4,5,4 +wahlbo01,2018,1,NYN,NL,0,1,7,0,0,0,0,16,9,6,2,4,7,0.360,10.13,0,0,0,0,29,0,6,0,0,0 +wainwad01,2018,1,SLN,NL,2,4,8,8,0,0,0,121,41,20,5,18,40,0.263,4.46,1,1,2,0,181,0,21,3,2,2 +waldema01,2018,1,BOS,AL,0,0,8,0,0,0,1,44,14,6,0,3,14,0.255,3.68,0,2,1,0,59,5,7,0,0,3 +walketa01,2018,1,ARI,NL,0,0,3,3,0,0,0,39,15,5,1,5,9,0.294,3.46,0,0,0,0,56,0,5,0,0,1 +warread01,2018,1,NYA,AL,0,1,24,0,0,0,0,90,26,9,3,12,37,0.228,2.70,0,1,1,0,128,6,9,0,1,0 +warread01,2018,2,SEA,AL,3,1,23,0,0,0,0,65,22,9,3,8,15,0.256,3.74,1,2,1,0,95,4,9,0,0,1 +watsoto01,2018,1,SFN,NL,4,6,72,0,0,0,0,198,54,19,4,14,72,0.224,2.59,3,0,1,0,261,10,19,3,2,5 +weavelu01,2018,1,SLN,NL,7,11,30,25,0,0,0,409,150,75,19,54,121,0.277,4.95,2,3,3,0,609,3,83,9,1,9 +webbty01,2018,1,SDN,NL,0,1,4,0,0,0,0,15,6,7,2,3,4,0.286,12.60,0,0,0,0,24,1,7,0,0,0 +webbty01,2018,2,SLN,NL,0,0,18,0,0,0,0,46,16,3,1,6,11,0.276,1.76,2,0,1,0,66,4,8,1,0,1 +weberry01,2018,1,TBA,AL,0,1,2,0,0,0,0,16,5,3,0,2,1,0.238,5.06,0,0,1,0,25,0,5,0,1,0 +webstal01,2018,1,CHN,NL,1,0,3,0,0,0,0,9,2,2,1,1,3,0.167,6.00,0,0,2,0,15,1,2,0,0,0 +weissza01,2018,1,CIN,NL,0,0,1,0,0,0,0,0,2,4,2,2,0,1.000,,0,0,0,0,4,0,4,0,0,0 +wendejb01,2018,1,OAK,AL,0,0,13,0,0,0,0,50,8,1,1,5,14,0.140,0.54,0,1,0,0,62,3,1,0,0,1 +whalero01,2018,1,SEA,AL,0,0,1,0,0,0,0,12,1,0,0,1,0,0.077,0.00,0,0,0,0,14,0,0,0,0,0 +wheelza01,2018,1,NYN,NL,12,7,29,29,0,0,0,547,150,67,14,55,179,0.225,3.31,0,2,9,1,744,0,69,8,4,14 +whitlch01,2018,1,ATL,NL,0,0,1,0,0,0,0,3,2,2,1,1,1,0.400,18.00,0,0,0,0,6,0,2,0,0,0 +wickro01,2018,1,SDN,NL,0,1,10,0,0,0,0,25,13,6,1,1,7,0.351,6.48,0,0,0,0,38,3,6,0,0,1 +wieckbr01,2018,1,SDN,NL,0,0,5,0,0,0,0,21,3,1,1,0,10,0.125,1.29,0,0,0,0,24,0,1,0,0,0 +wilkeaa01,2018,1,MIL,NL,0,1,3,1,0,0,0,27,12,10,4,3,10,0.308,10.00,0,0,1,0,43,2,10,0,0,0 +williau01,2018,1,WAS,NL,0,1,10,0,0,0,0,29,10,6,5,6,8,0.270,5.59,2,1,0,0,44,4,6,1,0,0 +willita01,2018,1,MIL,NL,1,3,56,0,0,0,0,159,53,25,6,25,57,0.260,4.25,4,4,1,0,236,10,28,4,2,3 +willitr01,2018,1,PIT,NL,14,10,31,31,1,1,0,512,146,59,15,55,126,0.231,3.11,3,4,4,0,701,0,64,6,4,12 +wilsoal01,2018,1,DET,AL,2,4,59,0,0,0,0,185,50,23,8,15,43,0.223,3.36,2,2,2,0,245,13,24,2,2,4 +wilsobr02,2018,1,ATL,NL,1,0,3,1,0,0,0,21,8,5,0,6,6,0.308,6.43,2,0,0,0,33,2,5,0,1,2 +wilsoju10,2018,1,CHN,NL,4,5,71,0,0,0,0,164,45,21,5,33,69,0.223,3.46,1,4,0,1,236,12,22,0,1,5 +wingetr01,2018,1,SDN,NL,0,0,22,0,0,0,0,57,13,8,3,11,27,0.191,3.79,0,1,1,0,81,5,8,0,1,0 +winklda01,2018,1,ATL,NL,4,0,69,0,0,0,2,181,52,23,3,20,69,0.228,3.43,1,2,5,0,255,11,27,1,1,3 +wislema01,2018,1,ATL,NL,1,1,7,3,0,0,0,80,30,16,6,5,21,0.283,5.40,0,1,0,0,112,1,16,0,1,2 +wislema01,2018,2,CIN,NL,0,0,11,0,0,0,0,40,11,3,2,2,11,0.224,2.03,0,0,0,0,54,2,4,1,2,0 +wittgni01,2018,1,MIA,NL,2,1,32,0,0,0,0,101,29,11,1,15,31,0.223,2.94,3,2,1,0,148,6,13,1,1,1 +woodal02,2018,1,LAN,NL,9,7,33,27,0,0,0,455,143,62,14,40,135,0.247,3.68,5,2,8,1,637,0,70,3,7,14 +woodbl01,2018,1,LAA,AL,1,0,13,0,0,0,0,35,8,3,1,7,10,0.211,2.31,2,0,1,0,47,1,3,0,1,2 +woodhu01,2018,1,TBA,AL,1,1,29,8,0,0,0,123,42,17,4,18,42,0.264,3.73,1,0,0,1,179,3,17,1,1,2 +woodrbr01,2018,1,MIL,NL,3,0,19,4,0,0,1,127,36,17,4,14,47,0.226,3.61,0,1,2,0,176,4,18,0,1,3 +workmbr01,2018,1,BOS,AL,6,1,43,0,0,0,0,124,34,15,6,16,37,0.230,3.27,0,0,0,0,167,4,15,0,3,1 +wrighky01,2018,1,ATL,NL,0,0,4,0,0,0,0,18,4,3,2,6,5,0.182,4.50,0,0,0,0,28,1,3,0,0,0 +wrighmi01,2018,1,BAL,AL,4,2,48,2,0,0,0,253,101,52,12,36,74,0.299,5.55,1,4,7,0,388,16,55,1,6,6 +wrighst01,2018,1,BOS,AL,3,1,20,4,0,0,1,161,41,16,5,26,42,0.210,2.68,1,0,2,0,223,4,17,0,0,5 +yacabji01,2018,1,BAL,AL,0,2,12,7,0,0,0,120,40,24,8,18,33,0.261,5.40,1,4,5,0,177,0,25,1,0,3 +yarbrry01,2018,1,TBA,AL,16,6,38,6,0,0,0,442,140,64,18,50,128,0.246,3.91,6,1,8,0,628,3,70,1,1,10 +yateski01,2018,1,SDN,NL,5,3,65,0,0,0,12,189,41,15,6,17,90,0.181,2.14,0,2,4,0,250,28,15,0,3,2 +zagurmi01,2018,1,MIL,NL,0,1,2,0,0,0,0,3,5,7,0,2,2,0.625,63.00,1,0,0,0,10,0,7,0,0,0 +zamorda01,2018,1,NYN,NL,1,0,16,0,0,0,0,27,6,3,1,3,16,0.194,3.00,1,0,1,0,36,4,3,1,0,1 +zastrro01,2018,1,CHN,NL,1,0,6,0,0,0,0,17,6,3,0,4,3,0.286,4.76,0,0,1,0,26,2,3,0,0,0 +zieglbr01,2018,1,MIA,NL,1,5,53,0,0,0,10,156,49,23,7,17,37,0.254,3.98,4,1,2,0,213,23,25,0,1,11 +zieglbr01,2018,2,ARI,NL,1,1,29,0,0,0,0,65,22,9,1,8,13,0.265,3.74,2,0,0,0,92,1,9,0,1,3 +zimmejo02,2018,1,DET,AL,7,8,25,25,0,0,0,394,140,66,28,26,111,0.269,4.52,0,1,2,0,556,0,76,2,5,4 +abadfe01,2019,1,SFN,NL,0,2,21,0,0,0,0,39,9,6,2,3,9,0.196,4.15,0,1,0,0,49,1,6,0,0,3 +abreubr01,2019,1,HOU,AL,0,0,7,0,0,0,0,26,4,1,0,3,13,0.138,1.04,0,0,0,0,32,2,1,0,0,1 +adamja01,2019,1,TOR,AL,3,0,23,0,0,0,0,65,15,7,1,10,18,0.200,2.91,1,1,3,2,91,2,8,0,3,2 +adamsau01,2019,1,MIN,AL,0,0,2,0,0,0,0,8,4,5,2,3,5,0.333,16.88,0,0,0,0,15,1,5,0,0,0 +adamsau01,2019,2,DET,AL,0,0,13,0,0,0,0,42,14,8,2,10,9,0.259,5.14,1,0,0,0,65,8,8,0,1,1 +adamsau02,2019,1,WAS,NL,0,0,1,0,0,0,0,3,0,1,0,2,2,0.000,9.00,0,2,1,0,6,0,1,0,0,0 +adamsau02,2019,2,SEA,AL,2,2,29,2,0,0,0,93,20,13,4,14,51,0.183,3.77,0,2,0,0,124,3,13,0,1,3 +adamsch01,2019,1,NYA,AL,1,1,13,0,0,0,1,76,39,24,7,11,23,0.351,8.53,0,1,2,1,124,5,25,0,0,2 +adriaeh01,2019,1,MIN,AL,0,0,1,0,0,0,0,3,5,3,0,0,1,0.625,27.00,0,0,0,0,8,1,3,0,0,0 +agrazda01,2019,1,PIT,NL,4,5,15,14,0,0,0,220,82,40,15,18,41,0.283,4.91,0,0,10,0,321,0,43,2,1,5 +alanirj01,2019,1,SEA,AL,0,0,4,0,0,0,0,12,11,9,3,3,6,0.500,20.25,0,0,0,0,25,1,10,0,0,1 +alanirj01,2019,2,CIN,NL,1,0,8,0,0,0,0,35,8,7,0,4,7,0.195,5.40,0,0,0,1,46,3,7,0,1,1 +alberha01,2019,1,BAL,AL,0,0,1,0,0,0,0,3,1,2,1,2,0,0.250,18.00,0,0,1,0,7,1,2,0,0,0 +alberma01,2019,1,MIL,NL,8,6,67,0,0,0,4,179,53,34,8,29,57,0.239,5.13,3,2,3,1,258,13,34,2,2,8 +alcaljo01,2019,1,MIN,AL,0,0,2,0,0,0,0,5,1,0,0,1,1,0.167,0.00,0,0,0,0,7,2,0,0,0,0 +alcansa01,2019,1,MIA,NL,6,14,32,32,2,2,0,592,179,85,23,81,151,0.241,3.88,5,4,8,1,838,0,94,5,1,23 +alcanvi01,2019,1,DET,AL,3,2,46,0,0,0,0,128,45,23,8,15,24,0.276,4.85,0,3,2,0,182,9,25,1,1,7 +alexasc01,2019,1,LAN,NL,3,2,28,0,0,0,0,52,17,7,2,7,9,0.250,3.63,2,0,1,0,76,4,7,0,0,3 +alexaty01,2019,1,DET,AL,1,4,13,8,0,0,0,161,68,29,9,7,47,0.302,4.86,0,1,2,0,235,1,30,0,1,5 +allarko01,2019,1,TEX,AL,4,2,9,9,0,0,0,136,52,25,3,19,33,0.281,4.96,0,1,2,0,208,0,26,0,1,4 +allenco01,2019,1,LAA,AL,0,2,25,0,0,0,4,69,24,16,9,20,29,0.255,6.26,0,3,0,0,116,13,16,0,1,0 +allenlo01,2019,1,SDN,NL,2,3,8,4,0,0,0,76,33,19,4,13,14,0.330,6.75,0,0,2,0,118,2,20,2,1,4 +allenlo01,2019,2,CLE,AL,0,0,1,0,0,0,0,7,3,0,0,0,3,0.333,0.00,0,0,0,0,9,1,0,0,0,0 +almonye01,2019,1,COL,NL,0,1,28,0,0,0,0,102,39,21,7,14,29,0.279,5.56,0,1,1,0,157,6,22,1,1,1 +altavda01,2019,1,SEA,AL,2,1,17,0,0,0,0,44,9,9,1,12,18,0.180,5.52,1,2,0,0,64,3,9,1,1,1 +altheaa01,2019,1,PHI,NL,0,0,1,0,0,0,0,3,2,1,0,0,2,0.400,9.00,0,0,0,0,5,1,1,0,0,0 +alvarjo02,2019,1,PHI,NL,3,4,67,1,0,0,1,177,66,22,8,18,51,0.284,3.36,4,3,1,0,255,11,25,1,3,5 +alvarjo03,2019,1,TBA,AL,1,6,35,1,0,0,7,90,29,16,2,27,39,0.254,4.80,3,8,0,0,146,16,18,3,2,3 +alzolad01,2019,1,CHN,NL,1,1,4,2,0,0,0,37,13,10,4,9,13,0.260,7.30,0,0,1,0,60,1,10,0,0,0 +anderbr04,2019,1,OAK,AL,13,9,31,31,0,0,0,528,181,76,20,49,90,0.265,3.89,2,4,4,0,743,0,80,4,4,17 +anderch01,2019,1,MIL,NL,8,4,32,27,0,0,0,417,126,65,23,50,124,0.240,4.21,2,1,8,1,592,1,67,6,2,6 +anderco01,2019,1,CLE,AL,0,1,5,2,0,0,0,26,12,9,1,8,9,0.333,9.35,0,3,1,0,46,2,9,1,0,0 +anderdr02,2019,1,PHI,NL,0,0,2,0,0,0,0,18,6,5,1,6,6,0.250,7.50,2,0,0,0,30,1,5,0,0,0 +anderju01,2019,1,LAA,AL,3,0,54,0,0,0,1,141,42,29,6,32,60,0.232,5.55,0,4,1,0,217,7,32,1,2,1 +anderni01,2019,1,MIA,NL,2,4,45,0,0,0,1,131,40,19,5,16,69,0.238,3.92,3,2,1,0,186,3,19,0,1,3 +anderni01,2019,2,TBA,AL,3,0,23,0,0,0,0,64,12,5,3,2,41,0.160,2.11,1,2,1,0,78,0,5,0,0,0 +andersh01,2019,1,SFN,NL,3,5,28,16,0,0,2,288,111,58,13,38,70,0.291,5.44,3,6,2,0,427,4,61,4,1,11 +anderta01,2019,1,OAK,AL,0,3,5,5,0,0,0,67,30,15,4,7,18,0.309,6.04,1,3,0,0,105,0,16,0,0,0 +anderty01,2019,1,COL,NL,0,3,5,5,0,0,0,62,33,27,8,11,23,0.363,11.76,0,0,0,1,106,0,27,2,2,1 +andrima01,2019,1,ARI,NL,5,5,54,0,0,0,1,212,72,37,8,27,79,0.262,4.71,4,9,3,2,310,13,37,1,3,2 +aranovi01,2019,1,PHI,NL,1,0,3,0,0,0,0,14,2,2,1,2,7,0.154,3.86,0,0,0,0,16,1,2,0,1,1 +araujpe01,2019,1,BAL,AL,0,0,1,0,0,0,0,2,2,2,1,1,0,0.667,27.00,0,0,0,0,4,0,2,0,0,1 +archech01,2019,1,PIT,NL,3,9,23,23,0,0,0,359,114,69,25,55,143,0.249,5.19,2,6,4,0,526,0,73,6,3,5 +armenro01,2019,1,HOU,AL,1,1,5,2,0,0,1,54,17,8,1,5,18,0.243,4.00,0,0,0,0,75,2,9,0,0,1 +armstsh01,2019,1,SEA,AL,0,1,4,0,0,0,0,11,8,6,1,3,3,0.444,14.73,1,0,1,0,23,1,6,1,0,0 +armstsh01,2019,2,BAL,AL,1,0,51,0,0,0,4,163,58,31,7,26,60,0.269,5.13,0,4,3,1,248,9,32,0,3,4 +arrieja01,2019,1,PHI,NL,8,8,24,24,0,0,0,407,149,70,21,51,110,0.283,4.64,3,7,7,0,594,0,76,5,4,12 +arteahu01,2019,1,KCA,AL,0,0,1,0,0,0,0,5,2,1,1,1,0,0.286,5.40,0,0,0,0,8,1,1,0,0,0 +avilaal01,2019,1,ARI,NL,0,0,2,0,0,0,0,6,2,1,0,1,1,0.286,4.50,0,0,0,0,8,2,1,0,0,1 +avilalu01,2019,1,NYN,NL,4,0,45,0,0,0,0,96,33,18,5,14,30,0.266,5.06,0,2,3,0,141,8,18,0,0,4 +avilape01,2019,1,SDN,NL,0,0,1,1,0,0,0,16,4,1,0,2,5,0.200,1.69,0,0,1,0,23,0,1,0,0,0 +baezmi01,2019,1,SDN,NL,1,1,24,1,0,0,0,89,25,10,3,14,28,0.223,3.03,2,1,3,1,131,8,10,1,1,0 +baezpe01,2019,1,LAN,NL,7,2,71,0,0,0,1,209,43,24,6,23,69,0.174,3.10,1,0,4,0,276,9,30,2,0,7 +baezsa01,2019,1,DET,AL,0,0,1,0,0,0,0,3,2,1,0,0,0,0.400,9.00,0,0,0,0,5,0,1,0,0,0 +baileho02,2019,1,KCA,AL,7,6,18,18,0,0,0,270,89,48,12,38,81,0.258,4.80,0,2,2,0,389,0,49,1,3,6 +baileho02,2019,2,OAK,AL,6,3,13,13,0,0,0,220,73,35,9,15,68,0.254,4.30,1,2,2,1,307,0,35,1,2,5 +bandaan01,2019,1,TBA,AL,0,0,3,0,0,0,0,12,6,3,0,0,2,0.333,6.75,0,0,0,0,18,1,3,0,0,0 +banuema01,2019,1,CHA,AL,3,4,16,8,0,0,0,152,60,39,12,33,44,0.303,6.93,3,2,1,1,235,4,39,1,2,5 +bardlu01,2019,1,LAA,AL,3,3,32,3,0,0,0,147,41,26,8,13,40,0.228,4.78,1,5,5,1,199,5,27,0,1,6 +barlosc01,2019,1,KCA,AL,3,3,61,0,0,0,1,211,64,33,6,37,92,0.240,4.22,3,5,3,1,310,7,33,2,1,2 +barneja01,2019,1,MIL,NL,1,1,18,1,0,0,0,59,22,15,3,11,22,0.265,6.86,1,1,0,0,95,3,17,0,1,1 +barneja01,2019,2,KCA,AL,0,4,15,0,0,0,0,39,14,12,4,11,10,0.264,8.31,0,2,0,0,65,3,13,0,1,1 +barnema01,2019,1,BOS,AL,5,4,70,0,0,0,4,193,51,27,8,38,110,0.210,3.78,2,13,2,1,285,14,29,0,1,3 +barneto01,2019,1,CHN,NL,0,0,2,0,0,0,0,4,2,1,1,0,0,0.400,6.75,0,0,0,0,5,0,1,0,0,1 +barraky01,2019,1,WAS,NL,1,2,33,0,0,0,0,77,33,19,8,12,30,0.303,6.66,2,2,2,1,124,6,21,0,1,0 +barraky01,2019,2,SFN,NL,0,0,10,0,0,0,0,24,5,2,1,9,10,0.167,2.25,0,1,0,0,40,3,3,1,0,0 +barreaa01,2019,1,WAS,NL,0,0,3,0,0,0,0,7,5,4,1,4,1,0.417,15.43,0,1,0,0,16,0,4,0,0,0 +barreja01,2019,1,NYA,AL,0,0,2,0,0,0,0,11,6,6,2,2,4,0.353,14.73,0,0,0,0,19,2,6,0,0,0 +barrija01,2019,1,LAA,AL,4,10,19,13,0,0,0,248,92,59,24,27,75,0.275,6.42,0,1,2,0,365,1,61,0,2,2 +bashlty01,2019,1,NYN,NL,0,3,24,0,0,0,0,66,21,17,6,17,20,0.250,6.95,2,0,0,0,103,7,17,1,1,1 +bassan01,2019,1,SEA,AL,2,4,44,0,0,0,5,144,30,19,5,17,43,0.179,3.56,2,6,1,0,189,14,20,2,1,2 +bassich01,2019,1,OAK,AL,10,5,28,25,0,0,0,432,125,61,21,47,141,0.229,3.81,0,3,13,0,612,2,66,2,5,9 +bauertr01,2019,1,CLE,AL,9,8,24,24,1,1,0,470,127,66,22,63,185,0.218,3.79,0,8,14,0,664,0,76,2,3,7 +bauertr01,2019,2,CIN,NL,2,5,10,10,0,0,0,169,57,40,12,19,68,0.261,6.39,0,2,5,0,247,0,42,3,2,2 +bautige01,2019,1,SEA,AL,0,1,8,2,0,0,0,27,13,11,2,9,7,0.333,11.00,0,2,0,1,49,3,11,0,1,0 +bednada01,2019,1,SDN,NL,0,2,13,0,0,0,0,33,10,8,3,5,14,0.250,6.55,0,0,0,0,48,4,8,2,1,1 +bedroca01,2019,1,LAA,AL,3,3,59,7,0,0,1,184,48,22,7,22,64,0.207,3.23,0,9,3,0,258,5,30,0,1,5 +beedety01,2019,1,SFN,NL,5,10,24,22,0,0,0,351,127,66,22,46,113,0.271,5.08,1,9,5,0,523,0,70,1,3,7 +beeksja01,2019,1,TBA,AL,6,3,33,3,0,0,1,313,115,50,12,40,89,0.281,4.31,1,3,9,0,464,5,56,1,5,9 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+blachty01,2019,1,SFN,NL,0,0,2,0,0,0,0,19,14,10,2,4,3,0.438,14.21,0,0,0,0,36,1,10,0,0,1 +blachty01,2019,2,BAL,AL,1,3,5,5,0,0,0,62,32,26,6,13,17,0.356,11.32,0,2,0,0,103,0,27,0,0,3 +blackpa01,2019,1,OAK,AL,0,2,4,1,0,0,0,33,19,13,3,4,8,0.380,10.64,0,1,1,0,57,1,14,1,1,1 +blackra01,2019,1,SFN,NL,0,0,2,0,0,0,0,6,4,1,1,1,5,0.400,4.50,0,0,0,0,11,1,1,0,0,0 +blackra01,2019,2,MIL,NL,0,1,15,0,0,0,0,42,10,8,4,8,13,0.200,5.14,0,0,1,0,59,2,8,0,0,1 +blazemi01,2019,1,WAS,NL,0,0,4,0,0,0,0,15,6,4,1,5,0,0.286,7.20,0,0,0,0,26,3,4,0,0,0 +bleieri01,2019,1,BAL,AL,3,0,53,1,0,0,4,166,65,33,6,8,30,0.295,5.37,2,1,4,0,235,13,34,1,2,9 +blevije01,2019,1,ATL,NL,1,0,45,0,0,0,1,97,25,14,5,16,37,0.207,3.90,2,2,2,0,141,12,15,0,2,0 +bolanro01,2019,1,SDN,NL,0,2,5,3,0,0,0,59,17,13,3,12,19,0.230,5.95,0,1,1,0,88,0,13,0,1,1 +borucry01,2019,1,TOR,AL,0,1,2,2,0,0,0,20,15,8,2,6,6,0.441,10.80,0,0,0,0,40,0,10,0,0,2 +boshebu01,2019,1,TOR,AL,0,3,28,1,0,0,0,60,20,9,3,10,26,0.253,4.05,0,2,1,0,91,6,10,1,0,2 +bourqja01,2019,1,WAS,NL,0,0,1,0,0,0,0,2,3,4,0,2,0,0.750,54.00,0,0,0,0,6,0,4,0,0,1 +bowmama01,2019,1,CIN,NL,2,0,27,0,0,0,0,96,27,13,2,13,25,0.221,3.66,3,0,0,0,137,10,15,0,2,0 +boxbebr01,2019,1,KCA,AL,1,3,29,0,0,0,1,80,25,16,3,17,27,0.243,5.40,0,1,1,0,122,9,16,0,1,0 +boydma01,2019,1,DET,AL,9,12,32,32,0,0,0,556,178,94,39,50,238,0.247,4.56,1,6,8,4,788,0,101,4,4,9 +brachbr01,2019,1,CHN,NL,4,3,42,0,0,0,0,119,42,27,3,28,45,0.284,6.13,1,3,1,0,181,6,27,0,4,7 +brachbr01,2019,2,NYN,NL,1,1,16,0,0,0,0,44,15,6,1,3,15,0.263,3.68,1,0,0,0,61,0,6,1,0,0 +bradfch02,2019,1,SEA,AL,0,0,12,0,0,0,1,50,17,9,6,4,11,0.266,4.86,0,1,0,0,69,6,9,0,1,3 +bradlar01,2019,1,ARI,NL,4,5,66,1,0,0,18,215,67,28,5,36,87,0.246,3.52,2,0,5,0,317,32,30,2,2,6 +brasiry01,2019,1,BOS,AL,2,4,62,0,0,0,7,167,51,30,9,21,61,0.238,4.85,1,3,3,0,241,15,33,0,3,2 +braulst01,2019,1,PIT,NL,4,6,25,19,0,0,0,340,117,65,15,53,100,0.265,5.16,2,7,7,1,505,1,69,3,0,18 +brebbjo01,2019,1,SLN,NL,3,4,66,0,0,0,0,218,59,29,6,27,87,0.216,3.59,2,0,3,0,304,22,31,0,1,1 +brennbr01,2019,1,SEA,AL,3,6,44,0,0,0,0,142,34,24,6,24,47,0.201,4.56,4,6,0,0,196,7,25,0,3,7 +breweco01,2019,1,BOS,AL,1,2,58,0,0,0,0,164,59,25,6,34,52,0.277,4.12,1,9,2,0,253,8,26,1,3,7 +briceau01,2019,1,MIA,NL,1,0,36,0,0,0,0,134,37,17,7,18,46,0.214,3.43,2,2,7,0,199,10,21,0,1,1 +brighje01,2019,1,MIA,NL,3,2,32,0,0,0,1,115,36,19,8,14,39,0.248,4.46,2,6,1,0,161,10,20,0,1,4 +brittza01,2019,1,NYA,AL,3,1,66,0,0,0,3,184,38,13,3,32,53,0.182,1.91,1,3,1,0,245,15,13,1,2,11 +brookaa01,2019,1,OAK,AL,2,3,15,6,0,0,0,151,49,28,12,14,43,0.249,5.01,0,1,4,0,215,7,29,0,0,3 +brookaa01,2019,2,BAL,AL,4,5,14,12,0,0,0,179,69,41,9,20,39,0.289,6.18,0,3,6,1,267,1,43,1,1,8 +brossmi01,2019,1,TBA,AL,0,0,3,0,0,0,0,12,5,2,1,0,0,0.294,4.50,0,0,1,0,18,3,2,0,0,0 +buchhcl01,2019,1,TOR,AL,2,5,12,12,0,0,0,177,72,43,13,16,39,0.306,6.56,1,1,1,0,254,0,44,0,1,8 +buchtry01,2019,1,OAK,AL,1,1,64,0,0,0,0,136,42,15,8,23,50,0.251,2.98,2,1,2,0,198,7,16,3,3,2 +buehlwa01,2019,1,LAN,NL,14,4,30,30,2,0,0,547,153,66,20,37,215,0.223,3.26,0,4,7,0,737,0,77,2,6,8 +bumgama01,2019,1,SFN,NL,9,9,34,34,0,0,0,623,191,90,30,43,203,0.245,3.90,3,3,10,0,844,0,99,5,5,18 +bummeaa01,2019,1,CHA,AL,0,0,58,0,0,0,1,203,43,16,4,24,60,0.184,2.13,2,4,3,0,262,5,17,1,0,15 +bundydy01,2019,1,BAL,AL,7,14,30,30,0,0,0,485,161,86,29,58,162,0.256,4.79,0,7,6,1,700,0,95,1,7,15 +burdini01,2019,1,PIT,NL,2,1,11,0,0,0,0,26,11,9,1,3,17,0.297,9.35,0,1,0,0,40,3,9,0,0,0 +burkebr01,2019,1,TEX,AL,0,2,6,6,0,0,0,80,30,22,6,11,14,0.286,7.43,0,0,2,0,120,0,22,1,1,2 +burneco01,2019,1,MIL,NL,1,5,32,4,0,0,1,147,70,48,17,20,70,0.330,8.82,0,2,0,0,235,8,52,3,0,4 +burrry01,2019,1,CHA,AL,1,1,16,1,0,0,0,59,17,10,3,8,20,0.227,4.58,0,0,0,0,86,2,13,1,2,0 +buttrty01,2019,1,LAA,AL,6,7,72,0,0,0,2,217,69,32,8,23,84,0.247,3.98,0,6,5,0,309,6,34,0,2,5 +cabrege01,2019,1,SLN,NL,0,2,13,2,0,0,1,61,23,11,2,11,19,0.274,4.87,0,1,2,0,99,5,16,1,1,1 +cahiltr01,2019,1,LAA,AL,4,9,37,11,0,0,0,307,111,68,25,39,81,0.273,5.98,0,14,6,0,455,12,71,0,4,5 +cannigr01,2019,1,LAA,AL,5,6,18,17,0,0,0,271,80,46,14,30,96,0.235,4.58,0,9,8,1,384,1,46,1,4,2 +carasma01,2019,1,SEA,AL,0,1,11,5,0,0,0,29,11,5,2,5,10,0.297,4.66,0,4,0,0,43,2,6,0,1,1 +caratvi01,2019,1,CHN,NL,0,0,2,0,0,0,0,6,2,2,1,1,0,0.250,9.00,0,0,0,0,9,2,2,0,0,0 +carlesh01,2019,1,ATL,NL,0,0,6,0,0,0,0,28,11,10,3,9,6,0.297,9.64,2,0,3,0,49,0,10,0,0,1 +carpeda01,2019,1,TEX,AL,0,0,4,0,0,0,0,10,4,2,0,4,2,0.267,5.40,0,0,0,0,19,2,4,0,0,0 +carpery01,2019,1,DET,AL,1,6,9,9,0,0,0,122,61,42,12,13,25,0.339,9.30,0,0,2,2,197,0,46,0,2,4 +carraca01,2019,1,CLE,AL,6,7,23,12,1,1,1,240,92,47,18,16,96,0.288,5.29,1,2,2,0,341,3,48,2,2,7 +cashnan01,2019,1,BAL,AL,9,3,17,17,0,0,0,289,86,41,11,29,66,0.234,3.83,0,4,1,0,400,0,45,0,2,8 +cashnan01,2019,2,BOS,AL,2,5,25,6,0,0,1,161,58,37,8,29,42,0.287,6.20,0,4,3,0,236,3,39,1,1,9 +castidi01,2019,1,TBA,AL,5,8,65,6,0,0,8,206,59,26,8,26,81,0.230,3.41,4,5,5,0,290,18,32,1,1,7 +castijo03,2019,1,SDN,NL,0,0,1,0,0,0,0,2,0,0,0,1,2,0.000,0.00,0,1,1,0,4,0,0,0,0,0 +castilu02,2019,1,CIN,NL,15,8,32,32,0,0,0,572,139,72,22,79,226,0.202,3.40,0,5,7,3,781,0,76,6,1,17 +castrmi01,2019,1,BAL,AL,1,3,65,0,0,0,2,220,63,38,10,41,71,0.232,4.66,3,11,0,2,319,28,42,0,6,5 +ceasedy01,2019,1,CHA,AL,4,7,14,14,0,0,0,219,78,47,15,35,81,0.271,5.79,1,4,2,0,326,0,51,0,1,8 +cedenxa01,2019,1,CHN,NL,0,0,5,0,0,0,0,6,4,0,0,3,1,0.400,0.00,1,0,0,0,13,1,0,0,0,0 +cessalu01,2019,1,NYA,AL,2,1,43,0,0,0,1,243,75,37,14,31,75,0.246,4.11,1,1,3,0,343,14,42,0,4,9 +chacijh01,2019,1,MIL,NL,3,10,19,19,0,0,0,266,99,57,19,39,80,0.282,5.79,1,3,5,0,403,0,61,4,4,6 +chacijh01,2019,2,BOS,AL,0,2,6,5,0,0,0,44,16,12,6,7,21,0.267,7.36,0,0,0,0,67,0,12,0,0,0 +chafian01,2019,1,ARI,NL,2,2,77,0,0,0,0,158,52,22,6,18,68,0.257,3.76,0,2,2,0,225,6,23,3,0,5 +chapmar01,2019,1,NYA,AL,3,2,60,0,0,0,37,171,38,14,3,25,85,0.185,2.21,0,6,1,0,235,53,18,0,3,4 +chargjt01,2019,1,LAN,NL,1,0,21,0,0,0,0,64,21,15,4,5,28,0.273,6.33,2,0,2,0,88,9,16,0,4,3 +chatwty01,2019,1,CHN,NL,5,3,38,5,0,0,2,230,65,32,8,37,74,0.232,3.76,0,8,5,0,324,9,33,1,1,17 +chaveje01,2019,1,TEX,AL,3,5,48,9,0,0,1,234,82,42,12,22,72,0.267,4.85,0,1,5,0,337,5,48,1,2,5 +chenwe02,2019,1,MIA,NL,0,1,45,0,0,0,0,205,87,50,15,18,63,0.311,6.59,2,2,5,0,307,14,54,2,2,7 +chiriyo01,2019,1,TBA,AL,9,5,26,18,0,0,0,400,112,57,23,28,114,0.225,3.85,1,4,3,3,530,0,61,0,1,10 +cimbead01,2019,1,CLE,AL,6,3,68,0,0,0,1,170,56,28,6,19,41,0.257,4.45,2,0,4,0,244,12,29,1,2,8 +cishest01,2019,1,CHN,NL,4,6,70,0,0,0,7,192,48,21,7,29,57,0.210,2.95,1,4,7,0,267,23,22,0,2,8 +cisnejo01,2019,1,DET,AL,0,4,35,0,0,0,0,106,35,17,5,19,40,0.257,4.33,3,1,3,0,162,10,21,2,2,2 +civalaa01,2019,1,CLE,AL,3,4,10,10,0,0,0,173,44,15,4,16,46,0.216,2.34,0,2,1,0,227,0,18,1,5,5 +clarkta01,2019,1,ARI,NL,5,5,23,15,0,0,1,254,86,50,23,30,68,0.263,5.31,0,3,6,0,369,3,55,1,5,3 +claseem01,2019,1,TEX,AL,2,3,21,1,0,0,1,70,20,6,2,6,21,0.230,2.31,0,1,1,0,94,7,8,0,0,3 +claudal01,2019,1,MIL,NL,2,2,83,0,0,0,0,186,57,28,8,24,44,0.246,4.06,2,1,6,0,267,9,29,1,3,7 +clevimi01,2019,1,CLE,AL,13,4,21,21,0,0,0,378,96,38,10,37,169,0.209,2.71,0,0,2,0,499,0,38,0,1,4 +clippty01,2019,1,CLE,AL,1,0,53,3,0,0,0,186,38,20,8,15,64,0.176,2.90,0,3,7,0,241,7,20,2,1,3 +cobbal01,2019,1,BAL,AL,0,2,3,3,0,0,0,37,21,15,9,2,8,0.362,10.95,0,0,0,0,60,0,16,0,0,0 +coleaj01,2019,1,CLE,AL,3,1,25,0,0,0,1,78,31,11,4,8,30,0.290,3.81,0,0,0,0,118,9,16,1,2,2 +colege01,2019,1,HOU,AL,20,5,33,33,0,0,0,637,142,59,29,48,326,0.186,2.50,0,4,3,3,817,0,66,1,3,7 +coleta01,2019,1,LAA,AL,3,4,38,6,0,0,0,155,58,34,2,24,50,0.286,5.92,4,3,2,0,231,9,35,0,2,8 +colliti01,2019,1,CHN,NL,0,0,9,0,0,0,0,26,9,3,1,3,4,0.265,3.12,0,0,0,0,38,3,3,0,1,0 +colomal01,2019,1,CHA,AL,4,5,62,0,0,0,30,183,42,19,7,23,55,0.191,2.80,2,4,1,0,249,54,28,2,3,5 +conlead01,2019,1,MIA,NL,2,11,60,0,0,0,2,182,76,44,10,29,53,0.308,6.53,6,2,3,0,283,18,45,2,1,4 +coonrsa01,2019,1,SFN,NL,5,1,33,0,0,0,0,83,19,11,3,15,20,0.202,3.58,1,2,4,1,114,9,11,1,0,6 +corbipa01,2019,1,WAS,NL,14,7,33,33,1,1,0,606,169,73,24,70,238,0.227,3.25,2,4,3,0,835,0,81,8,8,14 +cordeji01,2019,1,TOR,AL,0,1,1,0,0,0,0,4,2,1,1,0,0,0.400,6.75,0,0,0,0,5,1,1,0,0,1 +cordeji01,2019,2,CHA,AL,1,0,30,0,0,0,0,108,24,11,3,11,31,0.189,2.75,0,1,3,0,141,6,11,0,0,5 +cortene01,2019,1,NYA,AL,5,1,33,1,0,0,0,200,75,42,16,28,69,0.281,5.67,1,1,1,0,298,7,44,0,2,5 +coveydy01,2019,1,CHA,AL,1,8,18,12,0,0,0,176,75,52,12,28,41,0.306,7.98,2,3,3,0,280,2,54,3,1,4 +crichst01,2019,1,ARI,NL,1,0,28,0,0,0,0,91,23,12,3,8,33,0.204,3.56,1,1,2,0,123,9,12,0,0,2 +crickky01,2019,1,PIT,NL,3,7,52,0,0,0,0,147,41,27,10,35,61,0.224,4.96,1,1,7,0,226,9,30,0,1,6 +cuetojo01,2019,1,SFN,NL,1,2,4,4,0,0,0,48,11,9,3,9,13,0.196,5.06,0,1,0,0,67,0,9,2,0,1 +culbech01,2019,1,ATL,NL,0,0,2,0,0,0,0,6,2,0,0,2,1,0.250,0.00,0,1,0,0,10,2,0,0,0,0 +curtijo02,2019,1,LAA,AL,0,0,1,0,0,0,0,7,2,1,0,3,1,0.222,3.86,0,0,0,0,12,1,1,0,0,0 +darviyu01,2019,1,CHN,NL,6,8,31,31,0,0,0,536,140,79,33,56,229,0.213,3.98,1,11,11,0,731,0,82,3,4,11 +davieza01,2019,1,MIL,NL,10,7,31,31,0,0,0,479,155,63,20,51,102,0.256,3.55,0,4,2,0,672,0,73,7,5,14 +davisau01,2019,1,PHI,NL,0,0,14,0,0,0,0,62,22,15,6,14,24,0.272,6.53,1,0,3,0,98,7,15,0,0,3 +davisch02,2019,1,BAL,AL,0,0,1,0,0,0,0,3,2,1,1,0,1,0.400,9.00,0,0,0,0,5,1,1,0,0,0 +davisro03,2019,1,PIT,NL,0,1,5,1,0,0,0,32,12,8,3,8,10,0.300,6.75,0,1,0,0,51,2,8,2,1,0 +davista01,2019,1,CHN,NL,0,0,1,0,0,0,0,3,3,0,0,0,0,0.500,0.00,0,0,0,0,6,1,0,0,0,0 +daviswa01,2019,1,COL,NL,1,6,50,0,0,0,15,128,51,41,7,29,42,0.291,8.65,0,1,2,0,206,32,42,0,0,5 +daytogr01,2019,1,ATL,NL,0,1,14,0,0,0,0,36,12,4,4,4,14,0.255,3.00,0,0,0,0,51,4,5,0,0,1 +degroja01,2019,1,NYN,NL,11,8,32,32,0,0,0,612,154,55,19,44,255,0.207,2.43,1,2,7,0,804,0,59,5,3,10 +dejonch01,2019,1,MIN,AL,0,0,1,0,0,0,0,3,3,4,1,3,0,0.500,36.00,0,0,0,0,9,1,4,0,0,0 +deleojo03,2019,1,TBA,AL,1,0,3,0,0,0,0,12,3,1,0,3,7,0.188,2.25,1,1,2,0,21,1,2,0,0,0 +delosen01,2019,1,PHI,NL,0,1,5,1,0,0,0,33,13,9,4,5,9,0.317,7.36,0,1,0,1,46,3,9,0,0,4 +delpomi01,2019,1,LAA,AL,1,1,17,0,0,0,0,28,10,11,3,8,11,0.286,10.61,0,0,0,0,45,1,11,1,1,0 +descada01,2019,1,CHN,NL,0,0,1,0,0,0,0,3,2,2,1,0,0,0.400,18.00,0,0,0,0,5,0,2,0,0,0 +desclan01,2019,1,CIN,NL,9,9,31,31,0,0,0,500,151,72,29,49,167,0.238,3.89,5,2,4,1,696,0,77,5,3,9 +desmoia01,2019,1,COL,NL,0,0,1,0,0,0,0,3,1,0,0,0,0,0.250,0.00,0,0,0,0,4,1,0,0,0,0 +despaod01,2019,1,CHA,AL,0,2,3,3,0,0,0,40,24,14,3,7,7,0.407,9.45,0,0,1,0,68,0,14,0,1,0 +detwiro01,2019,1,CHA,AL,3,5,18,12,0,0,0,209,86,51,20,27,46,0.305,6.59,3,1,3,0,315,2,54,2,1,9 +devench02,2019,1,HOU,AL,2,3,61,1,0,0,0,207,69,37,13,21,72,0.256,4.83,0,2,3,0,298,19,39,1,3,1 +diazed04,2019,1,NYN,NL,2,7,66,0,0,0,26,174,58,36,15,22,99,0.258,5.59,3,3,4,0,254,48,36,1,2,2 +diazja01,2019,1,COL,NL,6,4,56,0,0,0,5,173,56,29,7,19,63,0.251,4.53,0,4,2,0,245,20,34,0,1,9 +diazmi02,2019,1,SDN,NL,0,0,5,0,0,0,0,19,9,5,1,1,4,0.346,7.11,0,1,2,0,29,2,5,0,0,2 +diazye01,2019,1,TOR,AL,0,0,1,0,0,0,0,2,1,2,0,4,0,0.333,27.00,0,0,0,0,7,0,2,0,0,0 +diehlph01,2019,1,COL,NL,0,0,10,0,0,0,0,22,10,6,1,2,8,0.313,7.36,0,1,1,0,35,2,6,0,0,0 +diekmja01,2019,1,KCA,AL,0,6,48,0,0,0,0,125,33,22,3,23,63,0.212,4.75,0,4,8,0,188,4,23,0,1,2 +diekmja01,2019,2,OAK,AL,1,1,28,0,0,0,0,61,16,10,0,16,21,0.222,4.43,1,2,3,0,94,1,11,1,2,2 +dixonbr01,2019,1,DET,AL,0,0,2,0,0,0,0,6,1,2,1,0,1,0.143,9.00,0,0,2,0,9,2,2,0,0,0 +dobnara01,2019,1,MIN,AL,2,1,9,5,0,0,1,85,27,5,1,5,23,0.245,1.59,0,0,3,1,118,4,9,0,0,4 +dominse01,2019,1,PHI,NL,3,0,27,0,0,0,0,74,24,11,3,12,29,0.250,4.01,0,1,1,0,110,2,13,1,0,1 +doolise01,2019,1,WAS,NL,6,5,63,0,0,0,29,180,63,27,11,15,66,0.260,4.05,2,0,2,0,260,55,27,1,0,0 +dowdyky01,2019,1,TEX,AL,2,1,13,1,0,0,0,67,26,18,4,18,17,0.295,7.25,1,3,1,0,110,2,20,1,2,4 +doziebr01,2019,1,WAS,NL,0,0,1,0,0,0,0,3,2,2,1,0,0,0.400,18.00,0,0,0,0,5,1,2,0,0,0 +drakeol01,2019,1,TBA,AL,5,2,50,0,0,0,2,168,36,20,9,19,70,0.181,3.21,1,1,1,0,219,9,20,0,0,2 +duffety01,2019,1,MIN,AL,5,1,58,0,0,0,0,173,44,16,8,14,82,0.201,2.50,1,3,3,1,238,12,23,1,1,0 +duffyda01,2019,1,KCA,AL,7,6,23,23,0,0,0,392,125,63,21,46,115,0.251,4.34,0,4,8,0,555,0,69,0,3,18 +duggero01,2019,1,MIA,NL,0,4,7,7,0,0,0,103,33,22,6,17,25,0.254,5.77,1,2,5,0,156,0,26,1,3,3 +dukeza01,2019,1,CIN,NL,3,1,30,0,0,0,0,70,21,13,4,18,18,0.250,5.01,1,0,2,0,106,4,13,1,1,5 +dullry01,2019,1,OAK,AL,0,0,7,0,0,0,0,27,19,12,4,4,8,0.432,12.00,0,1,1,0,51,4,13,1,1,0 +dullry01,2019,2,NYA,AL,0,0,3,0,0,0,0,7,5,5,0,3,4,0.417,19.29,0,0,0,0,15,1,5,0,0,0 +dullry01,2019,3,TOR,AL,0,0,1,0,0,0,0,4,1,1,1,0,3,0.200,6.75,0,0,0,0,5,1,1,0,0,0 +dunnju01,2019,1,SEA,AL,0,0,4,4,0,0,0,20,2,2,0,9,5,0.105,2.70,0,0,0,0,30,0,2,0,2,1 +dunnmi01,2019,1,COL,NL,1,0,28,0,0,0,0,53,17,14,4,6,15,0.262,7.13,0,2,0,0,75,2,14,0,4,2 +duplajo01,2019,1,ARI,NL,1,1,15,3,0,0,1,110,39,18,2,18,34,0.283,4.42,1,2,5,0,163,5,18,1,1,6 +durapmo01,2019,1,PIT,NL,0,1,14,2,0,0,0,52,27,18,4,9,22,0.342,9.35,0,0,2,0,90,6,24,0,0,1 +dysonsa01,2019,1,SFN,NL,4,1,49,0,0,0,2,153,39,14,3,7,47,0.213,2.47,0,0,3,0,196,8,17,3,0,4 +dysonsa01,2019,2,MIN,AL,1,0,12,0,0,0,0,34,14,9,3,6,8,0.298,7.15,0,1,0,0,53,0,9,0,0,1 +eadesry01,2019,1,MIN,AL,0,0,2,0,0,0,0,11,4,0,0,2,5,0.286,0.00,0,0,0,0,16,0,0,0,0,0 +eadesry01,2019,2,BAL,AL,0,1,6,0,0,0,0,23,7,3,2,4,5,0.241,3.52,0,0,1,0,34,4,3,0,0,1 +edwarca01,2019,1,CHN,NL,1,1,20,0,0,0,0,46,8,10,3,9,17,0.151,5.87,0,2,1,1,64,3,11,0,1,0 +edwarca01,2019,2,SDN,NL,0,0,2,0,0,0,0,5,4,6,0,4,2,0.400,32.40,0,2,0,0,14,1,6,0,0,0 +edwarjo02,2019,1,CLE,AL,2,0,9,0,0,0,0,24,5,2,2,6,5,0.179,2.25,0,0,2,0,36,3,2,0,0,1 +eflinza01,2019,1,PHI,NL,10,13,32,28,2,1,0,490,172,75,28,48,129,0.268,4.13,5,1,6,0,705,3,88,6,3,15 +eickhje01,2019,1,PHI,NL,3,4,12,10,0,0,1,175,58,37,18,18,51,0.261,5.71,0,0,2,0,245,1,37,3,0,3 +eliasro01,2019,1,SEA,AL,4,2,44,0,0,0,14,141,41,19,8,17,45,0.224,3.64,1,1,1,1,203,28,28,1,1,4 +eliasro01,2019,2,WAS,NL,0,0,4,0,0,0,0,9,5,3,2,1,2,0.417,9.00,0,0,0,0,13,0,4,0,0,2 +ellisch01,2019,1,KCA,AL,0,0,1,0,0,0,0,3,1,0,0,1,0,0.250,0.00,0,0,0,0,5,1,0,0,0,0 +eovalna01,2019,1,BOS,AL,2,1,23,12,0,0,0,203,72,45,16,35,70,0.276,5.99,0,6,3,0,302,2,46,1,2,10 +erlinro01,2019,1,SDN,NL,0,1,37,1,0,0,0,166,72,33,6,15,52,0.312,5.37,0,3,1,0,251,10,36,1,3,1 +escoblu01,2019,1,PIT,NL,0,0,4,0,0,0,0,17,10,5,1,4,2,0.455,7.94,0,1,1,0,29,2,5,1,1,1 +eshelto01,2019,1,BAL,AL,1,2,10,4,0,0,0,108,47,26,12,11,22,0.313,6.50,1,0,1,0,164,2,31,0,2,3 +estevca01,2019,1,COL,NL,2,2,71,0,0,0,0,216,70,30,12,23,81,0.250,3.75,1,1,1,1,308,13,34,1,3,4 +estrama01,2019,1,OAK,AL,0,2,5,5,0,0,0,71,23,18,7,8,11,0.242,6.85,0,2,3,0,106,0,19,0,0,1 +fairbpe01,2019,1,TEX,AL,0,2,8,0,0,0,0,26,8,9,4,7,15,0.235,9.35,0,1,0,0,41,0,10,0,0,1 +fairbpe01,2019,2,TBA,AL,2,1,13,0,0,0,2,37,17,7,1,3,13,0.309,5.11,0,1,0,0,58,3,10,0,0,1 +familje01,2019,1,NYN,NL,4,2,66,0,0,0,0,180,62,38,7,42,63,0.274,5.70,4,3,3,0,274,14,39,2,1,12 +fariaja01,2019,1,TBA,AL,0,0,7,0,0,0,0,30,10,3,2,7,11,0.250,2.70,0,0,0,0,47,1,3,0,0,1 +fariaja01,2019,2,MIL,NL,0,1,9,0,0,0,0,26,18,11,3,5,8,0.439,11.42,0,3,1,0,48,3,12,0,1,1 +farmebu01,2019,1,DET,AL,6,6,73,1,0,0,0,203,62,28,8,24,73,0.247,3.72,2,4,5,0,288,8,32,4,4,8 +farmeky01,2019,1,CIN,NL,0,0,1,0,0,0,0,4,1,0,0,0,0,0.200,0.00,0,0,0,0,5,1,0,0,0,0 +farrelu01,2019,1,TEX,AL,1,0,9,1,0,0,0,40,6,4,3,3,12,0.136,2.70,0,0,0,0,48,3,4,0,1,0 +feddeer01,2019,1,WAS,NL,4,2,21,12,0,0,0,234,81,39,11,33,41,0.276,4.50,2,1,2,0,334,3,39,4,2,12 +federti01,2019,1,TEX,AL,0,0,1,0,0,0,0,3,3,1,1,0,0,0.500,9.00,0,0,0,0,6,1,1,0,0,0 +feierry01,2019,1,TOR,AL,0,1,2,1,1,0,0,17,11,7,2,1,4,0.393,11.12,0,1,0,0,29,1,7,0,0,0 +felizmi01,2019,1,PIT,NL,4,4,58,1,0,0,0,169,44,25,11,27,73,0.212,3.99,1,0,2,1,239,5,27,1,1,6 +ferguca01,2019,1,LAN,NL,1,2,46,2,0,0,0,134,39,24,7,27,54,0.234,4.84,2,1,6,1,204,5,26,1,3,2 +fernajo04,2019,1,DET,AL,0,0,4,0,0,0,0,11,6,7,1,5,2,0.375,17.18,1,0,1,0,23,0,8,0,1,0 +fernaju01,2019,1,SLN,NL,0,1,13,0,0,0,0,35,9,7,2,6,16,0.205,5.40,0,2,4,0,54,5,7,0,0,1 +festama01,2019,1,SEA,AL,0,2,20,0,0,0,0,67,20,14,5,12,21,0.241,5.64,1,1,2,0,101,10,15,1,2,1 +fiersmi01,2019,1,OAK,AL,15,4,33,33,1,1,0,554,166,80,30,53,126,0.243,3.90,0,13,9,1,754,0,82,4,5,17 +fillmhe01,2019,1,KCA,AL,0,2,12,3,0,0,0,67,28,20,6,12,15,0.308,8.06,0,1,3,0,109,2,20,0,3,0 +flaheja01,2019,1,SLN,NL,11,8,33,33,0,0,0,589,135,60,25,55,231,0.192,2.75,2,6,7,0,772,0,62,3,3,14 +flexech01,2019,1,NYN,NL,0,3,9,1,0,0,0,41,15,10,1,13,10,0.268,6.59,2,1,0,0,70,4,12,0,1,0 +florody01,2019,1,LAN,NL,5,3,50,0,0,0,0,140,46,22,4,14,42,0.253,4.24,5,1,2,0,201,4,25,2,1,3 +flynnbr01,2019,1,KCA,AL,2,2,11,1,0,0,0,88,38,17,2,17,22,0.333,5.22,2,2,3,1,139,3,18,3,2,6 +foltymi01,2019,1,ATL,NL,8,6,21,21,0,0,0,351,109,59,23,37,105,0.244,4.54,2,5,2,0,491,0,65,5,1,6 +fontwi01,2019,1,TBA,AL,1,0,10,0,0,0,0,42,15,9,2,5,18,0.273,5.79,0,1,0,0,60,3,9,0,0,2 +fontwi01,2019,2,NYN,NL,1,2,15,3,0,0,0,93,29,17,8,13,24,0.244,4.94,1,1,2,1,134,1,17,0,0,2 +fontwi01,2019,3,TOR,AL,2,3,23,14,0,0,0,118,34,16,7,11,53,0.230,3.66,1,0,1,0,162,2,16,0,2,2 +fordmi01,2019,1,NYA,AL,0,0,1,0,0,0,0,6,6,5,2,0,1,0.500,22.50,0,0,0,0,12,1,5,0,0,0 +francen01,2019,1,SFN,NL,0,0,5,0,0,0,0,16,4,2,1,1,4,0.211,3.38,0,0,0,0,20,2,2,0,0,1 +francty01,2019,1,SDN,NL,0,0,2,0,0,0,0,6,2,1,1,0,0,0.250,4.50,0,0,0,0,8,2,1,0,0,0 +frareca01,2019,1,CHA,AL,0,0,5,0,0,0,0,8,2,3,1,4,3,0.200,10.13,0,2,0,0,14,1,3,0,0,0 +freelky01,2019,1,COL,NL,3,11,22,22,0,0,0,313,126,78,25,39,79,0.296,6.73,3,4,2,0,473,0,85,2,4,12 +freemmi01,2019,1,CLE,AL,0,0,1,0,0,0,0,6,2,2,1,0,0,0.250,9.00,0,0,0,0,8,1,2,0,0,0 +freemsa01,2019,1,LAA,AL,0,0,1,0,0,0,0,6,3,1,1,2,0,0.333,4.50,0,0,0,0,11,0,1,0,0,0 +friedma01,2019,1,ATL,NL,17,6,33,30,1,0,0,497,174,74,21,47,173,0.270,4.02,3,11,5,1,702,1,80,3,2,15 +fryja01,2019,1,CHA,AL,3,4,68,0,0,0,0,165,44,29,7,43,68,0.218,4.75,3,4,3,0,251,6,33,2,1,8 +frypa01,2019,1,BAL,AL,1,9,66,0,0,0,3,172,54,34,7,29,55,0.247,5.34,1,2,6,0,255,8,39,1,0,8 +fulmeca01,2019,1,CHA,AL,1,2,20,2,0,0,0,82,26,19,5,20,25,0.243,6.26,0,1,3,0,133,3,22,0,3,0 +gagnodr01,2019,1,NYN,NL,3,1,18,0,0,0,0,71,34,22,11,7,17,0.315,8.37,0,2,2,0,117,8,26,0,0,1 +gallegi01,2019,1,SLN,NL,3,2,66,0,0,0,1,222,44,19,9,16,93,0.170,2.31,2,3,3,1,279,10,19,0,1,4 +galleza01,2019,1,MIA,NL,1,3,7,7,0,0,0,109,25,11,3,18,43,0.191,2.72,1,1,2,0,151,0,12,0,0,0 +galleza01,2019,2,ARI,NL,2,3,8,8,0,0,0,131,37,14,5,18,53,0.228,2.89,0,2,2,0,183,0,14,0,1,3 +gantjo01,2019,1,SLN,NL,11,1,64,0,0,0,3,199,51,27,4,34,60,0.217,3.66,1,1,0,0,269,13,29,0,0,13 +garcibr01,2019,1,DET,AL,0,0,7,0,0,0,0,20,9,9,1,5,7,0.321,12.15,1,1,0,0,33,1,9,0,0,1 +garcied01,2019,1,PHI,NL,2,0,37,0,0,0,0,117,38,25,11,26,45,0.266,5.77,2,4,0,0,172,9,25,1,2,6 +garcija04,2019,1,MIA,NL,4,2,53,0,0,0,0,152,40,17,4,16,39,0.216,3.02,2,4,2,0,206,11,17,1,2,4 +garcilu03,2019,1,LAA,AL,2,1,64,2,0,0,1,186,61,30,13,33,57,0.257,4.35,1,7,5,0,278,18,35,1,2,9 +garciri01,2019,1,COL,NL,0,1,2,1,0,0,0,18,9,7,3,5,2,0.360,10.50,0,0,0,0,30,1,7,0,0,2 +garciyi01,2019,1,LAN,NL,1,4,64,0,0,0,0,187,40,25,15,14,66,0.178,3.61,2,1,6,1,247,22,28,1,1,1 +garream01,2019,1,CIN,NL,5,3,69,0,0,0,0,168,44,20,7,35,78,0.213,3.21,1,5,4,1,246,4,22,0,0,4 +garrere01,2019,1,DET,AL,0,0,13,0,0,0,0,46,24,14,3,13,10,0.381,8.22,0,0,1,0,77,5,15,0,0,5 +gartory01,2019,1,SEA,AL,0,0,2,0,0,0,0,9,4,4,2,1,1,0.308,12.00,0,0,0,0,14,0,4,0,0,0 +gausmke01,2019,1,ATL,NL,3,7,16,16,0,0,0,240,92,55,12,27,85,0.290,6.19,2,2,4,0,360,0,60,6,6,5 +gausmke01,2019,2,CIN,NL,0,2,15,1,0,0,0,67,21,10,3,5,29,0.250,4.03,1,0,1,0,91,6,11,1,0,0 +gavigsa01,2019,1,TOR,AL,4,2,52,0,0,0,0,287,85,49,18,22,88,0.235,4.61,0,0,4,1,392,9,51,1,3,4 +gearrco01,2019,1,SEA,AL,0,2,48,2,0,0,0,124,38,18,3,21,39,0.247,3.92,1,4,4,0,180,3,18,0,1,9 +gearrco01,2019,2,NYA,AL,1,1,18,0,0,0,0,42,17,7,2,4,8,0.298,4.50,0,0,0,1,61,5,7,0,0,2 +germado01,2019,1,NYA,AL,18,4,27,24,0,0,0,429,125,64,30,39,153,0.228,4.03,0,5,5,0,594,0,69,1,1,6 +gibauia01,2019,1,TBA,AL,0,0,1,0,0,0,0,6,1,2,0,2,2,0.167,9.00,0,0,0,0,9,1,2,0,1,0 +gibauia01,2019,2,TEX,AL,1,1,9,0,0,0,0,37,11,7,1,8,14,0.244,5.11,1,1,1,0,55,0,7,0,1,0 +gibsoky01,2019,1,MIN,AL,13,7,34,29,0,0,0,480,175,86,23,56,160,0.275,4.84,0,8,7,0,706,0,99,3,2,17 +gileske01,2019,1,TOR,AL,2,3,53,0,0,0,23,159,36,11,5,17,83,0.188,1.87,1,2,0,0,208,44,11,0,0,1 +gilmase01,2019,1,BAL,AL,0,1,1,1,0,0,0,7,7,5,2,2,1,0.500,19.29,0,0,0,0,16,0,5,0,0,0 +ginkeke01,2019,1,ARI,NL,3,0,25,0,0,0,2,73,15,4,2,9,28,0.174,1.48,0,2,0,0,96,4,7,0,0,1 +giolilu01,2019,1,CHA,AL,14,9,29,29,3,2,0,530,131,67,24,57,228,0.205,3.41,1,6,4,0,705,0,69,1,3,13 +givenmy01,2019,1,BAL,AL,2,6,58,0,0,0,11,189,49,32,13,26,86,0.213,4.57,1,5,2,0,260,33,35,0,2,3 +glasnty01,2019,1,TBA,AL,6,1,12,12,0,0,0,182,40,12,4,14,76,0.186,1.78,0,2,0,0,230,0,13,0,1,5 +godleza01,2019,1,ARI,NL,3,5,27,9,0,0,2,228,81,54,12,35,58,0.276,6.39,2,3,4,1,338,9,55,3,2,9 +godleza01,2019,2,TOR,AL,1,0,6,0,0,0,0,48,15,7,2,7,12,0.246,3.94,0,0,1,0,69,1,7,0,0,1 +gomezje01,2019,1,TEX,AL,1,0,16,0,0,0,0,46,23,14,2,6,10,0.359,8.22,0,2,1,0,73,6,15,0,2,4 +gonsoto01,2019,1,LAN,NL,4,2,11,6,0,0,1,120,26,13,4,15,37,0.178,2.93,0,2,1,0,163,1,15,0,1,0 +gonzach01,2019,1,COL,NL,2,6,14,12,0,0,0,189,59,37,11,33,46,0.246,5.29,0,1,1,0,278,0,39,3,1,6 +gonzagi01,2019,1,MIL,NL,3,2,19,17,0,0,0,262,76,34,9,37,78,0.234,3.50,0,5,0,0,366,0,36,4,0,7 +gonzama02,2019,1,SEA,AL,16,13,34,34,0,0,0,609,210,90,23,56,147,0.264,3.99,1,2,6,1,866,0,106,1,9,22 +goodyni01,2019,1,CLE,AL,3,2,39,0,0,0,0,122,30,16,7,22,50,0.201,3.54,1,0,0,0,173,6,18,0,2,1 +gordoal01,2019,1,KCA,AL,0,0,2,0,0,0,0,7,8,5,1,2,0,0.571,19.29,0,0,0,0,17,1,5,0,1,0 +gotttr01,2019,1,SFN,NL,7,0,50,0,0,0,1,158,41,26,4,17,57,0.216,4.44,0,1,2,0,214,6,26,1,4,1 +gracema02,2019,1,WAS,NL,1,2,51,1,0,0,0,140,61,33,11,10,35,0.319,6.36,0,1,2,0,206,12,34,1,2,8 +gratebr01,2019,1,MIN,AL,1,1,10,0,0,0,0,29,10,5,1,2,10,0.278,4.66,1,2,1,0,40,4,5,0,1,1 +grayjo02,2019,1,COL,NL,11,8,26,25,0,0,0,450,147,64,19,56,150,0.259,3.84,4,7,4,0,637,1,70,7,3,16 +grayso01,2019,1,CIN,NL,11,8,31,31,0,0,0,526,122,56,17,68,205,0.196,2.87,1,7,7,1,708,0,59,6,5,13 +greench03,2019,1,NYA,AL,4,4,54,15,0,0,2,207,66,32,10,19,98,0.247,4.17,0,2,6,0,295,10,35,0,3,2 +greensh02,2019,1,DET,AL,0,2,38,0,0,0,22,114,21,5,5,12,43,0.153,1.18,1,0,1,0,151,32,11,1,0,1 +greensh02,2019,2,ATL,NL,0,1,27,0,0,0,1,74,25,11,3,5,21,0.269,4.01,0,0,2,0,101,5,11,0,1,1 +gregelu01,2019,1,SLN,NL,0,0,6,0,0,0,0,17,11,5,0,1,2,0.423,7.94,0,0,0,0,27,3,5,0,0,1 +greinza01,2019,1,ARI,NL,10,4,23,23,0,0,0,438,117,47,15,21,135,0.220,2.90,2,1,3,0,562,0,48,4,3,12 +greinza01,2019,2,HOU,AL,8,1,10,10,0,0,0,188,58,21,6,9,52,0.246,3.02,0,1,1,1,248,0,25,1,1,10 +grotzza01,2019,1,SEA,AL,1,0,14,0,0,0,0,52,14,8,0,8,18,0.222,4.15,1,6,1,0,73,4,9,0,1,1 +gsellro01,2019,1,NYN,NL,2,3,52,0,0,0,1,191,64,33,7,23,60,0.261,4.66,2,4,6,0,277,9,36,1,2,7 +guduare01,2019,1,HOU,AL,1,0,7,0,0,0,0,16,8,7,3,4,6,0.364,11.81,0,0,0,0,27,3,7,0,1,0 +guerrde01,2019,1,MIL,NL,0,0,1,0,0,0,0,2,4,4,1,0,0,0.800,54.00,0,0,0,0,6,0,4,0,1,0 +guerrja01,2019,1,TOR,AL,0,0,11,0,0,0,1,42,12,6,1,5,15,0.231,3.86,1,0,1,0,59,6,6,1,0,1 +guerrja01,2019,2,WAS,NL,3,1,40,0,0,0,1,161,55,29,9,12,42,0.256,4.86,2,3,0,0,228,15,30,0,1,1 +guerrja02,2019,1,SDN,NL,0,0,8,0,0,0,0,26,7,5,3,3,6,0.219,5.19,0,0,0,0,36,1,5,0,1,0 +guerrju02,2019,1,MIL,NL,9,5,72,0,0,0,3,251,58,33,11,36,77,0.194,3.55,2,5,4,0,344,11,35,3,2,5 +guerrta01,2019,1,MIA,NL,1,2,52,0,0,0,0,138,42,32,7,36,43,0.246,6.26,0,9,6,0,216,10,34,0,3,6 +guerrta02,2019,1,TEX,AL,0,0,20,0,0,0,0,79,26,17,3,22,27,0.263,5.81,0,2,2,0,123,5,19,0,0,5 +guilbta01,2019,1,SEA,AL,0,0,17,0,0,0,0,37,10,5,2,3,7,0.213,3.65,1,0,1,0,51,1,6,0,0,2 +gustaja01,2019,1,SFN,NL,0,0,23,0,0,0,1,73,18,8,1,9,14,0.209,2.96,0,0,0,0,99,4,11,1,3,3 +gyorkje01,2019,1,SLN,NL,0,0,1,0,0,0,0,1,0,0,0,0,1,0.000,0.00,0,0,0,0,1,1,0,0,0,0 +haderjo01,2019,1,MIL,NL,3,5,61,0,0,0,37,227,41,22,15,20,138,0.155,2.62,2,0,4,0,289,46,24,0,0,2 +hahnje01,2019,1,KCA,AL,0,1,6,0,0,0,0,14,7,7,1,6,7,0.333,13.50,1,0,0,0,27,2,7,0,0,0 +haleda02,2019,1,NYA,AL,3,0,20,0,0,0,2,113,39,13,2,7,23,0.264,3.11,1,2,1,0,157,10,13,0,1,3 +hallma02,2019,1,DET,AL,0,1,16,0,0,0,0,70,28,20,4,15,27,0.289,7.71,1,1,1,0,113,7,20,0,0,1 +hamelco01,2019,1,CHN,NL,7,7,27,27,0,0,0,425,141,60,17,56,143,0.260,3.81,1,1,7,0,617,0,64,4,6,13 +hammejd01,2019,1,PHI,NL,1,0,20,0,0,0,0,57,15,8,2,12,13,0.217,3.79,0,1,0,0,81,7,8,0,0,2 +handbr01,2019,1,CLE,AL,6,4,60,0,0,0,34,172,53,21,6,18,84,0.242,3.30,5,0,4,0,242,54,21,1,0,4 +happja01,2019,1,NYA,AL,12,8,31,30,0,0,0,484,160,88,34,49,140,0.258,4.91,1,3,5,0,678,0,88,0,2,17 +hardybl01,2019,1,DET,AL,1,1,39,0,0,0,0,133,38,22,10,13,29,0.232,4.47,0,3,0,0,182,3,24,1,4,1 +harpery01,2019,1,MIN,AL,4,2,61,0,0,0,1,163,54,23,7,10,50,0.257,3.81,0,5,1,0,225,7,25,0,3,2 +harriwi02,2019,1,HOU,AL,4,1,68,0,0,0,4,180,42,10,6,14,62,0.196,1.50,0,3,0,0,229,10,14,1,0,7 +hartdo01,2019,1,MIL,NL,0,0,4,0,0,0,0,20,4,0,0,4,3,0.182,0.00,1,0,0,0,26,2,0,0,0,1 +hartdo01,2019,2,NYN,NL,0,0,1,0,0,0,0,3,0,0,0,0,0,0.000,0.00,0,0,0,0,3,0,0,0,0,0 +hartlge01,2019,1,PIT,NL,0,1,29,0,0,0,0,105,52,35,8,18,38,0.351,9.00,0,5,0,1,171,10,35,1,4,0 +harvehu01,2019,1,BAL,AL,1,0,7,0,0,0,0,19,3,1,1,4,11,0.136,1.42,0,0,0,0,26,1,1,0,0,0 +harvejo01,2019,1,NYA,AL,1,0,9,0,0,0,0,30,11,5,1,7,11,0.282,4.50,0,0,1,0,48,4,6,0,1,2 +harvejo01,2019,2,COL,NL,0,0,9,0,0,0,0,24,7,5,2,6,6,0.241,5.63,0,0,1,0,36,3,5,0,0,1 +harvema01,2019,1,LAA,AL,3,5,12,12,0,0,0,179,63,47,13,29,39,0.275,7.09,0,3,3,0,266,0,48,1,4,5 +heanean01,2019,1,LAA,AL,4,6,18,18,0,0,0,286,93,52,20,30,118,0.251,4.91,1,4,7,0,409,0,53,0,2,5 +hearnta01,2019,1,TEX,AL,0,1,1,1,0,0,0,1,3,4,0,4,0,0.750,108.00,0,0,0,0,8,0,5,0,0,0 +hellebe01,2019,1,NYA,AL,0,0,6,0,0,0,0,22,6,1,1,3,9,0.250,1.23,1,0,0,0,28,1,1,1,0,2 +hellije01,2019,1,WAS,NL,2,3,9,8,0,0,0,117,47,27,9,20,30,0.294,6.23,1,1,1,0,183,1,31,1,1,4 +helslry01,2019,1,SLN,NL,2,0,24,0,0,0,0,110,34,12,5,12,32,0.245,2.95,2,2,0,1,153,4,13,1,1,2 +hembrhe01,2019,1,BOS,AL,1,0,45,0,0,0,2,119,34,17,7,18,46,0.224,3.86,2,2,3,0,173,14,20,0,0,0 +hendrky01,2019,1,CHN,NL,11,10,30,30,1,1,0,531,168,68,19,32,150,0.249,3.46,1,1,9,0,730,0,78,8,5,6 +hendrli01,2019,1,OAK,AL,4,4,75,2,0,0,25,255,61,17,5,21,124,0.201,1.80,5,7,2,0,332,41,18,2,3,5 +hergeji01,2019,1,CIN,NL,0,0,5,0,0,0,0,19,8,3,2,3,0,0.348,4.26,0,0,0,0,26,4,3,0,0,1 +hernada01,2019,1,CIN,NL,2,5,47,0,0,0,2,128,53,38,7,20,53,0.306,8.02,3,1,1,0,198,8,39,1,3,3 +hernada02,2019,1,BOS,AL,0,1,29,1,0,0,0,91,27,15,1,26,57,0.231,4.45,1,4,3,0,147,2,18,1,0,1 +hernael01,2019,1,MIA,NL,3,5,21,15,0,0,0,247,76,46,20,26,85,0.242,5.03,1,2,9,1,353,1,49,3,1,4 +hernafe02,2019,1,SEA,AL,1,8,15,15,0,0,0,215,85,51,17,25,57,0.291,6.40,0,5,6,0,325,0,58,1,1,9 +hernajo02,2019,1,TEX,AL,2,1,9,2,0,0,0,50,14,8,3,13,19,0.219,4.32,0,1,0,0,78,1,10,0,1,1 +herreke01,2019,1,CHA,AL,3,3,57,0,0,0,1,154,60,35,8,23,53,0.288,6.14,6,8,1,2,235,16,36,1,2,1 +hessda01,2019,1,BAL,AL,1,10,23,14,0,0,0,240,94,63,28,30,68,0.287,7.09,0,1,2,0,365,5,73,3,3,5 +hicksjo03,2019,1,SLN,NL,2,2,29,0,0,0,14,86,16,10,2,11,31,0.163,3.14,0,2,1,0,110,21,10,0,0,3 +hildetr01,2019,1,MIN,AL,2,2,22,0,0,0,1,49,30,19,2,7,15,0.395,10.47,0,1,3,0,88,5,19,0,2,0 +hillri01,2019,1,LAN,NL,4,1,13,13,0,0,0,176,48,16,10,18,72,0.223,2.45,2,0,4,0,242,0,20,4,1,5 +hillti01,2019,1,KCA,AL,2,0,46,0,0,0,1,119,31,16,4,13,39,0.217,3.63,2,0,4,0,161,4,17,1,0,6 +hiranyo01,2019,1,ARI,NL,5,5,62,0,0,0,1,159,51,28,7,22,61,0.249,4.75,2,2,3,0,233,11,31,1,2,4 +hoffmje02,2019,1,COL,NL,2,6,15,15,0,0,0,210,77,51,21,34,68,0.283,6.56,3,2,4,0,315,0,51,3,2,6 +holadbr01,2019,1,MIA,NL,0,0,1,0,0,0,0,1,0,0,0,0,0,0.000,0.00,0,0,0,0,1,1,0,0,0,0 +holdejo02,2019,1,NYA,AL,5,2,34,1,0,0,0,124,43,29,8,11,46,0.256,6.31,1,0,2,0,181,8,32,0,0,1 +hollade01,2019,1,SFN,NL,2,4,31,7,1,0,0,206,68,45,17,35,71,0.261,5.90,2,0,4,0,308,10,49,6,2,4 +hollade01,2019,2,CHN,NL,0,1,20,1,0,0,0,47,14,12,3,10,11,0.246,6.89,0,1,0,0,68,9,12,1,0,2 +hollagr01,2019,1,ARI,NL,1,2,40,0,0,0,17,107,25,18,5,24,41,0.198,4.54,2,6,0,0,152,27,18,0,2,3 +holmecl01,2019,1,PIT,NL,1,2,35,0,0,0,0,150,45,31,5,36,56,0.232,5.58,1,4,9,0,240,10,36,0,0,4 +housead01,2019,1,MIL,NL,6,7,35,18,0,0,0,334,101,46,14,37,117,0.244,3.72,2,2,5,0,462,7,49,3,3,10 +howarsa01,2019,1,COL,NL,2,0,20,0,0,0,0,57,21,14,5,10,23,0.276,6.63,0,2,3,0,91,3,16,2,0,1 +hoytja01,2019,1,CLE,AL,0,0,8,0,0,0,0,25,6,2,2,2,10,0.200,2.16,0,0,0,0,32,2,2,0,0,0 +huangwe01,2019,1,TEX,AL,0,0,4,0,0,0,0,17,8,2,0,5,2,0.308,3.18,0,3,1,0,32,1,5,0,0,0 +hudsoda01,2019,1,TOR,AL,6,3,45,1,0,0,2,144,38,16,5,23,48,0.215,3.00,0,1,3,0,207,11,18,1,3,2 +hudsoda01,2019,2,WAS,NL,3,0,24,0,0,0,6,75,18,4,3,4,23,0.200,1.44,2,1,1,0,97,14,7,0,2,1 +hudsoda02,2019,1,SLN,NL,16,7,33,32,0,0,1,524,160,65,22,86,136,0.245,3.35,8,5,9,0,757,1,80,3,7,20 +hugheja02,2019,1,CIN,NL,3,4,47,0,0,0,1,145,41,22,6,19,34,0.236,4.10,0,0,2,0,199,12,27,3,1,7 +hugheja02,2019,2,PHI,NL,2,1,25,0,0,0,0,69,16,10,7,8,20,0.198,3.91,1,1,0,0,90,1,10,1,0,3 +hultzda01,2019,1,CHN,NL,0,0,6,0,0,0,0,10,4,0,0,2,5,0.267,0.00,0,0,1,0,18,0,0,0,0,0 +hunteto02,2019,1,PHI,NL,0,0,5,0,0,0,0,16,2,0,0,0,5,0.111,0.00,0,0,0,0,18,1,0,0,0,0 +iglesra01,2019,1,CIN,NL,3,12,68,0,0,0,34,201,61,31,12,21,89,0.240,4.16,4,3,2,0,279,55,31,1,1,3 +irvinco01,2019,1,PHI,NL,2,1,16,3,0,0,1,125,45,27,7,13,31,0.278,5.83,1,1,3,0,181,1,28,1,2,5 +jacksed01,2019,1,TOR,AL,1,5,8,5,0,0,0,85,49,35,12,13,19,0.380,11.12,0,3,2,0,144,0,41,0,0,5 +jacksed01,2019,2,DET,AL,2,5,10,8,0,0,0,118,56,37,11,19,33,0.329,8.47,0,3,1,0,192,0,40,0,2,2 +jacksja01,2019,1,MIL,NL,1,0,28,0,0,0,0,91,22,15,6,18,47,0.202,4.45,1,0,2,0,132,9,15,1,2,1 +jackslu01,2019,1,ATL,NL,9,2,70,0,0,0,18,218,76,31,10,26,106,0.266,3.84,4,3,2,0,315,35,34,1,0,6 +jamesjo02,2019,1,HOU,AL,5,1,49,1,0,0,1,184,46,32,10,35,100,0.203,4.70,0,6,4,0,266,18,34,0,0,1 +janseke01,2019,1,LAN,NL,5,3,62,0,0,0,33,189,51,26,9,16,80,0.213,3.71,0,2,4,3,263,51,28,0,3,3 +jeffrje01,2019,1,MIL,NL,3,4,48,0,0,0,1,156,54,29,5,17,46,0.269,5.02,0,1,5,0,225,12,32,2,0,5 +jennida01,2019,1,WAS,NL,1,2,8,0,0,0,0,14,8,7,1,7,9,0.333,13.50,2,1,1,0,32,0,8,0,0,0 +jerezwi01,2019,1,SFN,NL,1,0,6,0,0,0,0,20,7,2,1,6,4,0.304,2.70,0,2,0,0,29,3,2,0,0,3 +jerezwi01,2019,2,PIT,NL,0,0,6,0,0,0,0,11,5,3,1,3,5,0.313,7.36,0,0,0,0,19,1,3,0,0,0 +jewelja01,2019,1,LAA,AL,0,0,18,0,0,0,0,79,28,20,8,8,23,0.275,6.84,0,0,3,0,114,7,20,0,1,3 +jimened01,2019,1,DET,AL,0,0,8,0,0,0,0,32,12,7,1,5,8,0.273,5.91,0,1,0,0,49,2,7,0,0,0 +jimenjo02,2019,1,DET,AL,4,7,66,0,0,0,9,179,56,29,13,23,82,0.243,4.37,1,1,4,1,257,29,33,0,0,2 +johnsbr02,2019,1,BOS,AL,1,3,21,7,0,0,0,121,53,27,6,23,31,0.315,6.02,3,5,0,0,193,3,29,2,0,1 +johnsdj01,2019,1,COL,NL,0,2,28,0,0,0,0,75,23,14,1,19,24,0.247,5.04,2,4,3,0,116,7,14,0,1,4 +jonesna01,2019,1,CHA,AL,0,1,13,0,0,0,1,31,10,4,2,7,10,0.256,3.48,1,2,1,0,47,5,4,0,0,2 +josepca01,2019,1,ARI,NL,0,0,2,0,0,0,0,4,0,0,0,0,0,0.000,0.00,0,0,0,0,4,2,0,0,0,0 +junisja01,2019,1,KCA,AL,9,14,31,31,0,0,0,526,192,102,31,58,164,0.276,5.24,1,4,11,0,771,0,108,0,5,15 +juradar01,2019,1,TEX,AL,7,11,32,18,1,0,0,367,148,79,21,36,81,0.304,5.81,1,3,4,0,541,3,94,5,9,14 +kahnlto01,2019,1,NYA,AL,3,2,72,0,0,0,0,184,45,25,9,20,88,0.200,3.67,0,8,2,0,248,5,27,0,1,4 +karinja01,2019,1,CLE,AL,0,0,5,0,0,0,0,16,3,1,0,1,8,0.150,1.69,0,2,0,0,22,4,1,0,1,0 +karnsna01,2019,1,BAL,AL,0,1,4,2,0,0,0,16,7,0,0,3,5,0.333,0.00,0,1,1,0,25,1,1,0,0,1 +kayan01,2019,1,TOR,AL,1,0,3,2,0,0,0,42,15,9,0,5,13,0.263,5.79,0,1,1,0,63,0,9,0,0,2 +kelake01,2019,1,PIT,NL,2,0,32,0,0,0,1,89,19,7,3,11,33,0.178,2.12,0,1,1,0,119,8,7,0,0,1 +kellebr01,2019,1,KCA,AL,7,14,28,28,0,0,0,496,154,77,15,70,122,0.247,4.19,2,9,9,1,709,0,80,1,5,22 +kelleky01,2019,1,MIA,NL,0,0,10,0,0,0,0,32,5,4,3,8,11,0.139,3.38,0,0,2,0,46,4,4,0,0,1 +kellemi03,2019,1,PIT,NL,1,5,11,11,0,0,0,144,72,38,6,16,65,0.348,7.13,0,2,1,0,227,0,41,1,2,4 +kellesh01,2019,1,TEX,AL,5,2,50,0,0,0,11,142,55,26,12,11,43,0.291,4.94,2,2,2,0,205,23,27,2,1,5 +kelletr01,2019,1,BOS,AL,0,3,10,0,0,0,0,25,9,8,2,5,6,0.290,8.64,0,2,0,0,40,3,8,0,4,0 +kellyjo05,2019,1,LAN,NL,5,4,55,0,0,0,1,154,49,26,6,22,62,0.244,4.56,2,10,3,0,226,13,31,0,0,4 +kellyme01,2019,1,ARI,NL,13,14,32,32,0,0,0,550,184,90,29,57,158,0.260,4.42,4,4,2,0,777,0,95,2,5,14 +kenneia01,2019,1,KCA,AL,3,2,63,0,0,0,30,190,64,24,6,17,73,0.261,3.41,1,3,1,0,266,51,24,3,0,4 +kershcl01,2019,1,LAN,NL,16,5,29,28,0,0,0,535,145,60,28,41,189,0.222,3.03,0,7,2,1,706,0,63,6,1,17 +keuchda01,2019,1,ATL,NL,8,8,19,19,0,0,0,338,115,47,16,39,91,0.265,3.75,1,6,9,0,487,0,50,5,0,16 +kikucyu01,2019,1,SEA,AL,6,11,32,32,1,1,0,485,195,98,36,50,116,0.295,5.46,0,5,6,1,721,0,109,0,5,15 +kimbrcr01,2019,1,CHN,NL,0,4,23,0,0,0,13,62,21,15,9,12,30,0.259,6.53,0,0,2,0,96,17,15,0,1,0 +kinghni01,2019,1,PIT,NL,1,1,14,4,0,0,1,104,54,38,7,17,32,0.365,9.87,0,1,0,0,170,3,38,5,0,0 +kinghni01,2019,2,TOR,AL,3,1,11,0,0,0,0,63,24,7,4,8,14,0.296,3.00,1,2,0,0,89,2,7,0,0,4 +kingmi01,2019,1,NYA,AL,0,0,1,0,0,0,0,6,2,0,0,0,1,0.222,0.00,0,0,0,0,9,0,1,0,0,0 +kinlety01,2019,1,MIA,NL,3,1,52,0,0,0,1,148,43,20,5,36,46,0.238,3.65,2,3,1,0,221,13,20,1,2,4 +kinslia01,2019,1,SDN,NL,0,0,1,0,0,0,0,3,1,0,0,2,0,0.333,0.00,0,0,1,0,6,1,0,0,0,1 +kintzbr01,2019,1,CHN,NL,3,3,62,0,0,0,1,171,45,17,5,13,48,0.215,2.68,3,0,4,0,227,10,18,0,1,11 +kittran01,2019,1,TBA,AL,1,0,37,7,0,0,0,149,51,23,7,12,58,0.263,4.17,0,2,2,0,210,10,25,0,2,2 +klinebr01,2019,1,BAL,AL,1,4,34,0,0,0,0,123,44,27,9,19,34,0.275,5.93,0,3,1,0,183,8,28,1,1,6 +klubeco01,2019,1,CLE,AL,2,3,7,7,0,0,0,107,44,23,4,15,38,0.297,5.80,0,1,3,0,168,0,26,1,1,0 +kochma01,2019,1,ARI,NL,0,0,9,0,0,0,0,62,29,21,8,4,9,0.333,9.15,0,1,5,0,96,5,21,0,0,1 +kolarad01,2019,1,TBA,AL,4,3,54,0,0,0,1,130,39,19,6,14,36,0.234,3.95,4,1,3,0,184,15,19,0,0,3 +kolarad01,2019,2,LAN,NL,2,0,26,0,0,0,0,35,9,1,1,2,9,0.209,0.77,0,0,0,0,45,3,3,0,0,2 +kuhnejo01,2019,1,CIN,NL,1,0,11,0,0,0,0,29,8,5,1,5,9,0.216,4.66,0,0,0,0,42,2,5,0,0,1 +lailbr01,2019,1,NYA,AL,0,0,1,0,0,0,0,8,2,3,1,1,2,0.222,10.13,0,0,0,0,10,0,3,0,0,1 +lakintr01,2019,1,BOS,AL,0,1,16,3,0,0,0,70,23,10,1,10,18,0.258,3.86,1,1,1,0,102,4,11,0,2,1 +lambepe01,2019,1,COL,NL,3,7,19,19,0,0,0,268,119,72,18,36,57,0.321,7.25,3,5,6,0,420,0,74,4,3,7 +lametdi01,2019,1,SDN,NL,3,5,14,14,0,0,0,219,62,33,12,30,105,0.226,4.07,0,6,5,0,313,0,38,2,2,3 +lauerer01,2019,1,SDN,NL,8,10,30,29,0,0,0,449,158,74,20,51,138,0.268,4.45,4,4,5,1,651,0,82,3,3,11 +lawde01,2019,1,TOR,AL,1,2,58,4,0,0,5,182,61,33,8,40,67,0.256,4.90,3,7,3,0,285,18,36,2,2,4 +leakemi01,2019,1,SEA,AL,9,8,22,22,2,1,0,411,153,65,26,19,100,0.278,4.27,1,2,4,0,576,0,78,1,1,10 +leakemi01,2019,2,ARI,NL,3,3,10,10,0,0,0,180,74,29,15,8,27,0.306,4.35,1,0,6,0,259,0,36,1,2,8 +leblawa01,2019,1,SEA,AL,6,7,26,8,0,0,0,364,145,77,28,31,92,0.291,5.71,1,0,1,0,532,4,80,1,1,13 +leclejo01,2019,1,TEX,AL,2,4,70,3,0,0,14,206,52,33,7,39,100,0.209,4.33,1,7,6,0,299,40,34,3,2,4 +leonedo01,2019,1,SLN,NL,1,0,40,0,0,0,1,122,39,25,9,22,46,0.250,5.53,2,1,0,0,180,11,28,1,1,5 +lestejo01,2019,1,CHN,NL,13,10,31,31,0,0,0,515,205,85,26,52,165,0.294,4.46,0,3,5,0,764,0,101,5,4,18 +liriafr01,2019,1,PIT,NL,5,3,69,0,0,0,0,210,60,27,8,35,63,0.232,3.47,1,1,3,0,301,8,32,2,2,10 +litteza01,2019,1,MIN,AL,6,0,29,0,0,0,0,111,34,11,4,9,32,0.250,2.68,1,0,0,0,146,7,12,1,0,4 +livelbe01,2019,1,KCA,AL,0,0,1,0,0,0,0,3,3,3,1,0,1,0.500,27.00,0,0,0,0,6,1,3,0,0,0 +loaisjo01,2019,1,NYA,AL,2,2,15,4,0,0,0,95,31,16,6,16,37,0.263,4.55,0,1,1,0,139,3,16,0,4,4 +lockean01,2019,1,NYN,NL,1,1,9,4,0,0,0,68,33,21,6,6,16,0.347,8.34,0,1,1,0,103,1,21,1,0,3 +lopezjo02,2019,1,KCA,AL,4,9,39,18,0,0,1,371,140,87,27,42,109,0.286,6.33,0,6,10,0,548,8,94,0,7,11 +lopezpa01,2019,1,MIA,NL,5,8,21,21,0,0,0,334,111,63,15,27,95,0.261,5.09,3,6,11,2,469,0,64,4,2,10 +lopezre01,2019,1,CHA,AL,10,15,33,33,1,0,0,552,203,110,35,65,169,0.278,5.38,0,5,8,2,809,0,119,2,5,18 +lopezyo01,2019,1,ARI,NL,2,7,70,0,0,0,1,182,52,23,11,17,42,0.232,3.41,2,1,0,0,246,13,27,1,4,5 +lorenmi01,2019,1,CIN,NL,1,4,73,0,0,0,7,250,68,27,9,28,85,0.221,2.92,1,2,2,0,343,16,29,4,2,3 +loupaa01,2019,1,SDN,NL,0,0,4,0,0,0,0,10,2,0,0,1,5,0.182,0.00,0,0,1,0,14,1,0,0,1,0 +lovelri01,2019,1,KCA,AL,0,3,25,0,0,0,0,60,30,17,2,8,17,0.353,7.65,2,1,1,0,96,5,17,1,1,3 +lucasjo02,2019,1,BAL,AL,0,0,9,0,0,0,1,47,14,10,2,7,16,0.237,5.74,0,0,0,0,69,4,12,0,3,0 +lucchjo01,2019,1,SDN,NL,10,10,30,30,0,0,0,491,144,76,23,56,158,0.233,4.18,0,8,2,0,686,0,78,5,4,13 +luciael01,2019,1,TOR,AL,1,0,25,0,0,0,0,101,36,20,4,24,27,0.275,5.35,1,4,3,0,159,9,20,0,1,3 +lugose01,2019,1,NYN,NL,7,4,61,0,0,0,6,240,56,24,8,16,104,0.192,2.70,4,2,5,0,314,14,28,1,1,2 +luzarje01,2019,1,OAK,AL,0,0,6,0,0,0,2,36,5,2,1,3,16,0.119,1.50,0,2,1,0,46,2,2,0,0,0 +lylesjo01,2019,1,PIT,NL,5,7,17,17,0,0,0,247,88,49,16,33,90,0.271,5.36,1,4,1,0,361,0,53,1,1,6 +lylesjo01,2019,2,MIL,NL,7,1,11,11,0,0,0,176,43,16,9,22,56,0.202,2.45,1,0,0,0,238,0,19,1,2,2 +lynnla01,2019,1,TEX,AL,16,11,33,33,0,0,0,625,195,85,21,59,246,0.243,3.67,0,18,8,0,875,0,89,1,6,12 +lyonsty01,2019,1,PIT,NL,1,1,3,0,0,0,0,12,6,5,1,3,5,0.353,11.25,1,0,0,0,20,2,5,0,0,1 +lyonsty01,2019,2,NYA,AL,0,1,11,0,0,0,0,26,7,4,3,2,12,0.226,4.15,1,0,1,0,34,5,4,0,0,1 +maedake01,2019,1,LAN,NL,10,8,37,26,0,0,3,461,114,69,22,51,169,0.202,4.04,1,3,4,0,624,3,70,2,3,7 +magilma01,2019,1,MIN,AL,2,0,28,0,0,0,0,85,30,14,4,15,36,0.263,4.45,1,2,2,0,133,8,21,1,1,0 +magilma01,2019,2,SEA,AL,3,2,22,0,0,0,5,67,21,9,3,5,28,0.236,3.63,2,3,0,0,96,12,10,1,1,0 +mahlety01,2019,1,CIN,NL,3,12,25,25,0,0,0,389,136,74,25,34,129,0.266,5.14,0,2,6,1,556,0,82,2,2,9 +mailelu01,2019,1,TOR,AL,0,0,2,0,0,0,0,6,1,0,0,0,3,0.143,0.00,0,0,1,0,8,2,0,0,0,0 +manaese01,2019,1,OAK,AL,4,0,5,5,0,0,0,89,16,4,3,7,30,0.160,1.21,0,1,2,0,109,0,4,0,0,4 +mantijo01,2019,1,NYA,AL,1,0,1,0,0,0,0,9,3,3,1,2,2,0.250,9.00,0,0,0,0,14,0,3,0,0,0 +mapledi01,2019,1,CHN,NL,1,0,14,0,0,0,0,35,6,7,2,10,18,0.150,5.40,0,0,4,0,54,3,7,0,0,0 +margeni01,2019,1,SDN,NL,2,6,17,12,0,0,0,171,73,43,12,19,42,0.307,6.79,1,1,3,0,263,1,46,3,0,4 +markepa01,2019,1,SEA,AL,0,0,5,0,0,0,0,14,10,8,3,4,3,0.435,15.43,0,1,1,0,28,0,9,0,0,0 +markepa01,2019,2,PIT,NL,0,1,15,0,0,0,0,52,16,11,3,13,21,0.239,5.71,1,1,2,1,82,8,12,0,0,1 +marquge01,2019,1,COL,NL,12,5,28,28,1,1,0,522,174,92,29,35,175,0.259,4.76,0,14,5,1,721,0,96,6,4,13 +marshev01,2019,1,CHA,AL,4,2,55,0,0,0,0,152,42,14,5,24,41,0.228,2.49,2,1,0,0,209,2,16,0,1,8 +martibr01,2019,1,TEX,AL,2,3,51,2,0,0,0,187,72,33,7,18,62,0.280,4.76,2,3,2,0,280,7,38,0,3,5 +martica04,2019,1,SLN,NL,4,2,48,0,0,0,24,145,39,17,2,18,53,0.220,3.17,1,2,3,0,200,38,18,1,1,6 +martich02,2019,1,TEX,AL,0,2,38,0,0,0,4,114,35,13,8,4,43,0.246,3.08,0,0,0,0,147,15,13,0,1,4 +martich02,2019,2,ATL,NL,1,1,20,0,0,0,0,53,17,8,1,1,22,0.254,4.08,0,0,0,0,69,5,10,0,1,3 +martico02,2019,1,HOU,AL,1,1,5,5,0,0,0,58,23,12,8,12,19,0.288,5.59,0,1,0,0,92,0,14,0,0,0 +martini02,2019,1,OAK,AL,0,0,1,0,0,0,0,3,0,0,0,2,1,0.000,0.00,0,0,0,0,5,1,0,0,0,0 +martiru01,2019,1,LAN,NL,0,0,4,0,0,0,0,12,2,0,0,0,2,0.154,0.00,0,0,0,0,13,4,0,0,0,1 +marveja01,2019,1,PIT,NL,0,3,4,4,0,0,0,52,25,16,4,6,9,0.338,8.31,0,0,2,0,84,0,16,2,0,0 +mathije01,2019,1,TEX,AL,0,0,2,0,0,0,0,6,3,2,1,0,1,0.333,9.00,0,0,0,0,9,2,2,0,0,0 +matonph01,2019,1,SDN,NL,0,0,21,0,0,0,0,73,34,21,6,6,20,0.321,7.77,0,1,1,0,115,5,22,2,0,1 +matonph01,2019,2,CLE,AL,0,0,9,0,0,0,0,37,4,4,1,6,13,0.098,2.92,0,2,1,0,48,8,5,0,0,0 +matzst01,2019,1,NYN,NL,11,10,32,30,1,1,0,481,163,75,27,52,153,0.260,4.21,7,3,7,0,691,0,83,5,1,16 +maydu01,2019,1,LAN,NL,2,3,14,4,0,0,0,104,33,14,2,5,32,0.250,3.63,0,0,4,0,141,0,17,0,0,4 +mayermi01,2019,1,SLN,NL,0,1,16,0,0,0,0,57,21,14,3,11,16,0.276,6.63,2,1,1,0,88,4,14,0,0,1 +maytr01,2019,1,MIN,AL,5,3,65,0,0,0,2,193,43,21,8,26,79,0.184,2.94,1,3,3,0,266,13,24,0,3,2 +mayzati01,2019,1,TOR,AL,1,3,68,0,0,0,0,154,45,28,8,27,55,0.230,4.91,2,4,1,0,227,5,29,1,2,3 +mazzach01,2019,1,NYN,NL,1,1,9,0,0,0,0,49,21,10,0,5,11,0.328,5.51,0,0,4,0,74,6,10,0,1,4 +mccarke01,2019,1,KCA,AL,4,2,56,0,0,0,1,181,68,30,4,21,38,0.285,4.48,6,2,2,0,268,14,31,2,4,5 +mcclare01,2019,1,SEA,AL,1,1,14,2,0,0,0,63,22,14,2,13,11,0.268,6.00,0,1,0,0,95,6,14,0,0,1 +mcfartj01,2019,1,ARI,NL,0,0,51,0,0,0,0,168,71,30,6,20,35,0.316,4.82,5,1,1,0,250,13,35,2,2,5 +mcgeeja01,2019,1,COL,NL,0,2,45,0,0,0,0,124,47,20,11,11,35,0.288,4.35,1,0,3,1,180,10,25,0,3,5 +mcgowky01,2019,1,WAS,NL,0,0,7,1,0,0,1,48,22,18,7,4,18,0.319,10.13,0,4,1,0,76,4,19,1,1,2 +mchugco01,2019,1,HOU,AL,4,5,35,8,0,0,0,224,62,39,12,30,82,0.221,4.70,0,0,3,0,317,8,41,0,3,3 +mckaybr01,2019,1,TBA,AL,2,4,13,11,0,0,0,147,53,28,8,16,56,0.268,5.14,0,0,0,0,216,0,32,1,1,1 +mckayda02,2019,1,SEA,AL,0,0,7,0,0,0,0,21,5,4,1,8,5,0.192,5.14,0,0,0,0,34,5,5,0,0,1 +mckayda02,2019,2,DET,AL,0,0,18,0,0,0,0,58,15,12,2,9,29,0.211,5.59,1,2,1,0,81,6,12,0,0,1 +mcraeal01,2019,1,PIT,NL,0,4,11,2,0,0,0,80,36,26,9,16,19,0.319,8.78,1,2,3,0,132,3,30,0,0,3 +meansjo01,2019,1,BAL,AL,12,11,31,27,0,0,0,465,138,62,23,38,121,0.234,3.60,0,5,5,0,637,1,68,0,3,12 +mejiaad01,2019,1,MIN,AL,0,2,13,0,0,0,0,46,16,15,3,12,15,0.276,8.80,0,1,0,0,71,2,16,0,1,0 +mejiaad01,2019,2,LAA,AL,0,0,4,0,0,0,0,13,4,1,0,1,6,0.235,2.08,0,0,0,0,18,0,1,0,0,0 +mejiaad01,2019,3,SLN,NL,0,0,2,0,0,0,0,9,8,3,0,1,2,0.500,9.00,0,2,0,0,17,0,3,0,0,0 +mejiaad01,2019,4,LAA,AL,0,0,16,0,0,0,0,26,5,4,1,7,7,0.156,4.15,0,0,0,0,39,2,5,0,0,0 +melanma01,2019,1,SFN,NL,4,2,43,0,0,0,1,139,49,18,3,16,44,0.278,3.50,2,3,2,0,195,16,19,0,1,8 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+woodrbr01,2019,1,MIL,NL,11,3,22,22,0,0,0,365,109,49,12,30,143,0.240,3.62,0,1,5,1,493,0,49,2,2,12 +workmbr01,2019,1,BOS,AL,10,1,73,0,0,0,16,215,29,15,1,45,104,0.123,1.88,4,4,2,1,286,30,18,1,3,4 +wothema01,2019,1,BAL,AL,0,0,2,0,0,0,0,14,10,8,2,2,2,0.435,15.43,0,0,1,0,26,1,8,0,0,0 +wrighky01,2019,1,ATL,NL,0,3,7,4,0,0,0,59,24,19,4,13,18,0.304,8.69,1,2,1,0,93,2,19,0,0,3 +wrighmi01,2019,1,BAL,AL,0,1,10,0,0,0,1,40,20,14,5,7,14,0.351,9.45,0,1,1,0,66,4,14,0,1,1 +wrighmi01,2019,2,SEA,AL,0,0,9,0,0,0,0,48,24,12,1,5,16,0.324,6.75,1,1,1,0,81,5,16,0,1,1 +wrighst01,2019,1,BOS,AL,0,1,6,0,0,0,0,19,11,6,3,4,5,0.407,8.53,0,0,1,0,33,3,6,0,1,2 +yacabji01,2019,1,BAL,AL,1,2,29,4,0,0,0,123,51,31,9,24,33,0.307,6.80,0,4,2,0,193,6,32,0,1,5 +yamamjo01,2019,1,MIA,NL,4,5,15,15,0,0,0,236,54,39,11,36,82,0.191,4.46,1,5,5,0,325,0,42,0,2,5 +yarbrry01,2019,1,TBA,AL,11,6,28,14,0,0,0,425,121,65,15,20,117,0.228,4.13,2,0,9,1,563,1,69,0,3,14 +yardler01,2019,1,SDN,NL,0,1,10,0,0,0,0,35,12,3,1,3,7,0.261,2.31,0,0,1,0,52,2,5,2,0,2 +yateski01,2019,1,SDN,NL,0,5,60,0,0,0,41,182,41,8,2,13,101,0.186,1.19,1,2,7,0,243,51,14,1,1,4 +ynoaga01,2019,1,BAL,AL,1,10,36,13,0,0,0,332,126,69,29,26,67,0.280,5.61,1,4,3,1,480,3,77,0,1,15 +ynoahu01,2019,1,ATL,NL,0,0,2,0,0,0,0,9,6,6,1,1,3,0.400,18.00,0,0,0,0,16,1,6,0,0,0 +youngal01,2019,1,ARI,NL,7,5,17,15,0,0,0,250,72,33,14,27,71,0.229,3.56,4,2,4,0,349,0,40,3,0,5 +zamorda01,2019,1,NYN,NL,0,1,17,0,0,0,0,26,10,5,1,5,8,0.294,5.19,1,0,1,0,41,3,5,0,1,0 +zeuchtj01,2019,1,TOR,AL,1,2,5,3,0,0,0,68,22,12,2,11,20,0.250,4.76,0,2,0,0,99,0,13,0,0,1 +zimmejo02,2019,1,DET,AL,1,13,23,23,0,0,0,336,145,86,19,25,82,0.311,6.91,2,3,6,0,504,0,89,3,4,5 +zimmeky01,2019,1,KCA,AL,0,1,15,0,0,0,0,55,28,22,2,19,18,0.337,10.80,0,2,0,0,102,3,22,0,0,1 +zobribe01,2019,1,CHN,NL,0,0,1,0,0,0,0,3,0,0,0,2,1,0.000,0.00,0,0,0,0,5,1,0,0,0,0 +abreual01,2020,1,NYA,AL,0,1,2,0,0,0,0,4,4,3,1,2,2,0.500,20.25,0,0,1,0,11,1,4,0,0,0 +abreubr01,2020,1,HOU,AL,0,0,4,0,0,0,0,10,1,1,0,7,3,0.091,2.70,0,0,2,0,20,1,2,0,0,0 +adamja01,2020,1,CHN,NL,2,1,13,0,0,0,0,41,9,5,2,8,21,0.180,3.29,0,0,0,0,58,5,7,0,0,0 +adamsau02,2020,1,SDN,NL,0,0,3,0,0,0,0,12,3,2,1,2,7,0.200,4.50,0,1,0,0,17,1,2,0,0,0 +adamsch01,2020,1,KCA,AL,0,0,6,0,0,0,0,26,15,9,1,0,6,0.375,9.35,0,2,0,0,40,1,9,0,0,0 +adriaeh01,2020,1,MIN,AL,0,0,1,0,0,0,0,3,1,1,1,0,0,0.250,9.00,0,0,1,0,5,1,1,0,0,0 +akinke01,2020,1,BAL,AL,1,2,8,6,0,0,0,77,27,13,3,10,35,0.262,4.56,0,0,1,1,116,0,17,0,2,0 +alcaljo01,2020,1,MIN,AL,2,1,16,0,0,0,0,72,21,7,3,8,27,0.244,2.63,0,1,0,0,94,8,8,0,0,2 +alcansa01,2020,1,MIA,NL,3,2,7,7,0,0,0,126,35,14,4,15,39,0.226,3.00,0,1,1,1,172,0,22,1,0,6 +alexasc01,2020,1,LAN,NL,2,0,13,0,0,0,0,37,9,4,2,9,9,0.214,2.92,1,0,0,0,52,1,6,1,0,4 +alexaty01,2020,1,DET,AL,2,3,14,2,0,0,0,109,39,16,8,9,34,0.283,3.96,0,0,4,0,152,0,16,0,1,4 +allarko01,2020,1,TEX,AL,0,6,11,8,0,0,0,101,31,29,4,20,32,0.238,7.75,0,1,1,0,152,2,29,0,1,1 +allenlo01,2020,1,CLE,AL,0,0,3,0,0,0,0,32,12,4,1,7,7,0.293,3.38,0,1,1,0,49,2,4,0,0,3 +almonye01,2020,1,COL,NL,3,0,24,0,0,0,1,83,25,9,2,6,23,0.243,2.93,0,0,3,0,113,4,13,0,1,4 +altavda01,2020,1,SEA,AL,1,2,13,0,0,0,1,35,12,10,3,7,14,0.255,7.71,0,0,0,0,54,5,11,0,0,0 +altavda01,2020,2,SDN,NL,1,1,9,0,0,0,0,26,6,3,0,5,10,0.200,3.12,0,2,0,0,35,2,3,0,0,2 +alvarjo02,2020,1,PHI,NL,0,0,8,0,0,0,0,19,7,1,0,3,6,0.292,1.42,0,0,0,0,27,1,1,0,0,0 +alvarjo03,2020,1,TBA,AL,0,0,9,0,0,0,0,27,9,6,2,6,13,0.250,6.00,0,3,2,0,45,3,7,0,1,1 +alzolad01,2020,1,CHN,NL,1,1,6,4,0,0,0,64,12,7,1,13,29,0.169,2.95,0,1,1,0,87,0,8,0,2,2 +anderbr04,2020,1,MIL,NL,4,4,10,10,0,0,0,141,50,22,6,10,32,0.270,4.21,0,0,4,0,202,0,24,0,3,5 +anderch01,2020,1,TOR,AL,1,2,10,7,0,0,0,101,45,27,11,10,38,0.315,7.22,0,1,1,0,154,0,29,0,0,2 +anderdr02,2020,1,CHA,AL,0,1,1,0,0,0,0,4,4,6,2,2,2,0.500,40.50,0,1,0,0,10,0,6,0,0,0 +anderia01,2020,1,ATL,NL,3,2,6,6,0,0,0,97,21,7,1,14,41,0.172,1.95,0,4,2,0,138,0,11,0,0,1 +anderni01,2020,1,TBA,AL,2,1,19,0,0,0,6,49,5,1,1,3,26,0.091,0.55,0,0,0,0,58,9,2,0,0,0 +andersh01,2020,1,SFN,NL,0,0,18,0,0,0,0,46,10,6,3,12,18,0.182,3.52,0,2,0,0,67,4,6,0,0,1 +anderty01,2020,1,SFN,NL,4,3,13,11,1,0,0,179,58,29,5,25,41,0.256,4.37,0,1,4,0,260,0,32,1,3,4 +andrima01,2020,1,LAA,AL,2,4,16,1,0,0,2,96,21,16,5,11,33,0.184,4.50,0,0,1,0,126,3,17,0,0,5 +antonte01,2020,1,CIN,NL,0,3,13,4,0,0,0,106,20,11,4,16,45,0.165,2.80,0,2,2,0,141,1,11,0,2,4 +arciaor01,2020,1,MIL,NL,0,0,2,0,0,0,0,6,4,4,1,1,0,0.444,18.00,0,0,0,0,11,2,4,0,1,0 +armstsh01,2020,1,BAL,AL,2,0,14,0,0,0,0,45,9,3,1,3,14,0.170,1.80,0,2,1,0,57,2,6,0,0,3 +arrieja01,2020,1,PHI,NL,4,4,9,9,0,0,0,133,51,25,6,16,32,0.298,5.08,0,4,1,0,190,0,25,0,2,9 +avilalu01,2020,1,NYA,AL,0,0,10,0,0,0,0,25,9,4,2,5,9,0.265,4.32,0,0,0,0,39,1,4,0,0,1 +bacusda01,2020,1,WAS,NL,0,0,11,0,0,0,0,34,14,10,1,9,7,0.298,7.94,0,0,0,0,57,3,10,0,1,1 +baezmi01,2020,1,SDN,NL,0,0,3,1,0,0,0,14,7,4,0,2,7,0.333,7.71,0,0,0,1,23,1,4,0,0,0 +baezpe01,2020,1,LAN,NL,0,0,18,0,0,0,2,51,10,6,2,7,13,0.159,3.18,0,0,0,0,70,2,8,0,0,0 +bailebr01,2020,1,HOU,AL,0,0,5,0,0,0,0,22,6,2,1,3,4,0.231,2.45,0,1,1,0,30,1,2,0,0,0 +baileho02,2020,1,MIN,AL,1,0,2,2,0,0,0,24,6,3,1,3,7,0.207,3.38,0,0,1,0,33,0,3,0,0,1 +bandaan01,2020,1,TBA,AL,1,0,4,0,0,0,1,21,10,8,1,5,4,0.345,10.29,0,0,2,0,36,3,9,0,0,3 +baragca01,2020,1,SFN,NL,5,1,24,1,0,0,0,67,17,10,3,5,19,0.210,4.03,0,1,1,0,88,2,10,0,1,1 +bardda01,2020,1,COL,NL,4,2,23,0,0,0,6,74,22,10,2,10,27,0.237,3.65,2,1,3,0,106,10,10,0,0,3 +bardlu01,2020,1,LAA,AL,0,0,6,0,0,0,0,16,7,4,2,0,7,0.304,6.75,0,0,0,1,23,3,4,0,0,0 +barlosc01,2020,1,KCA,AL,2,1,32,0,0,0,2,90,27,14,4,9,39,0.239,4.20,2,2,2,0,125,7,14,0,1,2 +barneja01,2020,1,LAA,AL,0,2,18,0,0,0,0,54,19,11,1,4,24,0.275,5.50,0,0,2,0,78,5,13,1,2,1 +barnema01,2020,1,BOS,AL,1,3,24,0,0,0,9,69,18,11,4,14,31,0.214,4.30,1,4,2,0,102,14,13,1,1,4 +barreaa01,2020,1,WAS,NL,0,0,2,0,0,0,0,5,2,2,0,2,1,0.333,10.80,1,0,1,0,9,0,2,0,0,1 +barrija01,2020,1,LAA,AL,1,0,7,5,0,0,0,97,27,13,3,9,27,0.223,3.62,1,0,1,0,132,2,13,0,1,0 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M273K,0.003194073,1.305298,26/08/2020,-7.44974E+17,0.000869216,1,761.2399 +538,A112T,0.022397926,1.479735,26/08/2020,-7.44974E+17,0.02260287,9,70.0291 +539,A112T; A116E; T179N; L253Q,0.002240005,0.78989583,26/08/2020,-7.44974E+17,0.000217601,1,768.76666 +540,A112T; D147N; L198P,0.003049524,1.3130994,26/08/2020,-7.44974E+17,0.000971061,1,737.05896 +541,A112T; F166Y; L182P; S220T; A260S; H266L; S267T,0.002972185,1.2814984,26/08/2020,-7.44974E+17,0.000884022,1,767.9378 +542,A112T; R148P; S205C; S220T; H230Y; L243M,0.023565758,4.115746,26/08/2020,-7.44974E+17,0.02131677,1,217.80414 +543,A112V,0.010200102,2.728107,26/08/2020,-7.44974E+17,0.009943116,10,276.29672 +544,A112V; P132R; S145T; S267A; E275V; A280V,0.003026052,1.2499893,26/08/2020,-7.44974E+17,0.000833075,1,780.3954 +545,A112V; R137S; F142S,0.003617611,1.6267983,26/08/2020,-7.44974E+17,0.001495496,1,725.5873 +546,A112V; R148P; S220T; H230Y,0.09035887,4.961121,26/08/2020,-7.44974E+17,0.091095873,1,59.330082 +547,A112V; T188I; T196M; S220T,0.004077055,1.7305039,26/08/2020,-7.44974E+17,0.00166136,1,719.7093 +548,A112W,0.012229389,0.66991013,26/08/2020,-7.44974E+17,0.011614994,6,68.61832 +549,A116E; D152V; S220T,0.002766062,1.1977365,26/08/2020,-7.44974E+17,0.00076793,1,815.67236 +550,A116F,0.12198899,0.73024565,26/08/2020,-7.44974E+17,0.132215524,5,6.505382 +551,A116G,0.11638377,2.1513145,26/08/2020,-7.44974E+17,0.118918574,3,18.921444 +552,A116G; Y177C,0.003094766,1.4385456,26/08/2020,-7.44974E+17,0.001291259,1,764.5058 +553,A116H,0.08142118,0.6107023,26/08/2020,-7.44974E+17,0.082930506,9,8.134505 +554,A116I,0.0424257,1.3589897,26/08/2020,-7.44974E+17,0.042844398,6,34.98034 +555,A116K,0.004364513,1.7817955,26/08/2020,-7.44974E+17,0.004036273,9,437.9104 +556,A116L,0.054501697,0.9966023,26/08/2020,-7.44974E+17,0.056013909,3,23.10569 +557,A116M,0.11176019,0.607558,26/08/2020,-7.44974E+17,0.118013767,8,5.6310515 +558,A116N,0.044629663,0.9599469,26/08/2020,-7.44974E+17,0.045349787,3,27.42564 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S226T,0.004401034,1.8585376,26/08/2020,-7.44974E+17,0.002224812,1,657.2265 +592,A127D; S220T; V221E; A282T,0.003991303,1.6951027,26/08/2020,-7.44974E+17,0.001914643,1,695.78827 +593,A127D; T188A; S220T,0.002744002,1.1571785,26/08/2020,-7.44974E+17,0.00069679,1,788.93945 +594,A127E,0.002807024,1.9955505,26/08/2020,-7.44974E+17,0.001914784,3,845.54895 +595,A127F,0.022055004,3.654885,26/08/2020,-7.44974E+17,0.021581277,3,177.33624 +596,A127G,0.007946501,3.42784,26/08/2020,-7.44974E+17,0.007077255,3,479.5125 +597,A127I,0.025388826,1.7226421,26/08/2020,-7.44974E+17,0.024531087,5,74.86387 +598,A127K,0.090936214,4.7344985,26/08/2020,-7.44974E+17,0.090697609,6,48.969162 +599,A127L,0.001511525,1.0587207,26/08/2020,-7.44974E+17,0.001306974,12,760.0942 +600,A127M,0.005910812,3.6203218,26/08/2020,-7.44974E+17,0.00516362,3,671.6161 +601,A127N,0.008773418,4.587471,26/08/2020,-7.44974E+17,0.008316569,5,547.146 +602,A127P,0.000895473,1.2019277,26/08/2020,-7.44974E+17,0.000494637,6,1581.1384 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M273V,0.002350389,0.82510763,26/08/2020,-7.44974E+17,0.000283394,1,774.5502 +614,A127V,0.3334155,1.5808095,26/08/2020,-7.44974E+17,0.317621612,5,3.3686535 +615,A127V; G284A,0.002717457,1.098672,26/08/2020,-7.44974E+17,0.000678177,1,760.525 +616,A127V; L253Q,0.003361408,1.4574617,26/08/2020,-7.44974E+17,0.001274367,1,740.2867 +617,A127V; M129I; R148P; S220T; H230Y,0.13009378,5.2760262,26/08/2020,-7.44974E+17,0.126184568,1,42.34306 +618,A127V; R148P; S220T; H230Y,0.30801845,0.6731411,26/08/2020,-7.44974E+17,0.349753402,10,1.6042314 +619,A127V; V135I; S220T; H230Y,0.6278677,5.940077,26/08/2020,-7.44974E+17,0.749377911,1,3.2078078 +620,A127W,0.00247883,0.9737162,26/08/2020,-7.44974E+17,0.000411367,1,780.3396 +621,A127Y,0.0187036,4.7469144,26/08/2020,-7.44974E+17,0.017293441,2,280.46677 +622,A127Y; A202V,0.006962602,2.6319556,26/08/2020,-7.44974E+17,0.004681256,1,534.5032 +623,A13C,0.051793087,5.4858437,26/08/2020,-7.44974E+17,0.050761454,2,111.11664 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C75R; T196S; L253Q,0.003099392,1.3019668,26/08/2020,-7.44974E+17,0.000866567,1,741.1964 +657,A13T; D147E; S220T,0.160641,5.18314,26/08/2020,-7.44974E+17,0.1694624,1,31.647236 +658,A13T; G24R; S220T; H230Y,0.004601502,1.8829805,26/08/2020,-7.44974E+17,0.002086762,1,671.10626 +659,A13T; L161Q; S220T; N251K; P270L; Q299L,0.003055989,1.2770433,26/08/2020,-7.44974E+17,0.000851909,1,742.6772 +660,A13T; L98R; T157S; S220T; M291V; K298M,0.003106904,1.3047667,26/08/2020,-7.44974E+17,0.000928555,1,771.04877 +661,A13T; M185R; L189F; Q283R,0.005246333,2.1380277,26/08/2020,-7.44974E+17,0.003435664,1,619.4608 +662,A13T; P45S; V66E; S103T; A159T; P171T; A187T; G191V; M273T; G286W; Y294F,0.003078407,1.355475,26/08/2020,-7.44974E+17,0.001106338,1,764.97205 +663,A13T; P88L; N178S; D287G,0.003072355,1.2358764,26/08/2020,-7.44974E+17,0.000729801,1,745.71246 +664,A13T; Q107R; F297L,0.2356721,5.3374424,26/08/2020,-7.44974E+17,0.247890821,1,19.66326 +665,A13T; R148P; P164L; H183L; S220T; 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S160P,0.002928544,1.2149171,26/08/2020,-7.44974E+17,0.00082651,1,752.95654 +710,A140T; S220T; A260V; A280S,0.004515624,1.9373109,26/08/2020,-7.44974E+17,0.002443749,1,665.38574 +711,A140T; S220T; K236I; V281A,0.017854452,3.8419154,26/08/2020,-7.44974E+17,0.015480422,1,277.37967 +712,A140V,0.010690515,4.2937374,26/08/2020,-7.44974E+17,0.01003439,3,433.12296 +713,A140V; L181I; S220T,0.010224465,3.1873863,26/08/2020,-7.44974E+17,0.007865661,1,413.24304 +714,A140V; R148P; S220T; H230Y,0.18182902,5.1833634,26/08/2020,-7.44974E+17,0.189382706,1,26.8864 +715,A140V; R148P; S220T; H230Y; E268D,0.01335397,3.5193105,26/08/2020,-7.44974E+17,0.011334836,1,349.27936 +716,A140V; S220T; G286E,0.013660146,3.5335214,26/08/2020,-7.44974E+17,0.011038779,1,338.40115 +717,A140W,0.001767638,1.0980797,26/08/2020,-7.44974E+17,0.000695717,2,931.6082 +718,A140Y,0.002360771,0.9494123,26/08/2020,-7.44974E+17,0.000382459,1,782.7018 +719,A149C,0.068767846,5.982729,26/08/2020,-7.44974E+17,0.068661578,2,87.89258 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A159S,0.3490504,1.2209514,26/08/2020,-7.44974E+17,0.362203924,4,2.4511602 +766,A156N; A159S; A296I,0.63771933,5.0121636,26/08/2020,-7.44974E+17,0.656914076,6,2.772441 +767,A156N; A159S; L189M,0.052713115,5.3428154,26/08/2020,-7.44974E+17,0.052088341,2,105.90142 +768,A156N; A159S; N238Y,0.36593392,1.432328,26/08/2020,-7.44974E+17,0.370727207,10,2.54928 +769,A156N; A159S; S160C,0.13966209,1.5398436,26/08/2020,-7.44974E+17,0.140413305,8,10.191972 +770,A156N; A159S; T188G,0.2554009,1.1493144,26/08/2020,-7.44974E+17,0.265993653,4,3.729948 +771,A156N; A159S; T188S,0.34316993,1.1359026,26/08/2020,-7.44974E+17,0.354735541,10,2.2532358 +772,A156N; A159T,0.33464056,1.0166639,26/08/2020,-7.44974E+17,0.349167317,6,2.1415427 +773,A156N; A159T; A296I,0.58232254,3.3377695,26/08/2020,-7.44974E+17,0.604549505,4,2.3265266 +774,A156N; A159T; L189M,0.18355727,1.0876393,26/08/2020,-7.44974E+17,0.185156336,8,5.2524123 +775,A156N; A159T; N238Y,0.24904083,1.2023954,26/08/2020,-7.44974E+17,0.255496034,6,3.9832258 +776,A156N; A159T; S160C,0.08194004,1.1005948,26/08/2020,-7.44974E+17,0.079626183,6,14.320837 +777,A156N; A159T; T188G,0.22885388,1.0719864,26/08/2020,-7.44974E+17,0.22858604,8,3.8853495 +778,A156N; A159T; Y177W,0.38450867,0.7604475,26/08/2020,-7.44974E+17,0.408591646,6,1.2975068 +779,A156N; A218L; T242A,0.73701847,9.620304,26/08/2020,-7.44974E+17,0.748763683,14,3.3779972 +780,A156N; A218M; A296T,0.68563825,5.6279583,26/08/2020,-7.44974E+17,0.688784226,15,2.54618 +781,A156N; A218M; N289S,0.6488239,8.980434,26/08/2020,-7.44974E+17,0.658352267,18,4.8311405 +782,A156N; A218M; T242A,0.68139994,11.87013,26/08/2020,-7.44974E+17,0.695897468,14,5.507583 +783,A156N; A296I,0.6265184,4.869675,26/08/2020,-7.44974E+17,0.634785843,8,2.8553543 +784,A156N; L189M; A218L,0.7649474,9.867989,26/08/2020,-7.44974E+17,0.787792316,10,2.9618914 +785,A156N; T188G,0.055576555,5.2833,26/08/2020,-7.44974E+17,0.05395825,2,99.09526 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H230Y,0.00161418,0.46827027,26/08/2020,-7.44974E+17,2.76E-05,1,722.58636 +809,A156T; T196S; E278G; M291K,0.005881102,2.2628856,26/08/2020,-7.44974E+17,0.003591072,1,576.3317 +810,A156T; T242A; A259T,0.5612482,2.1100457,26/08/2020,-7.44974E+17,0.581853905,18,1.637223 +811,A156T; V175K,0.73527986,11.591464,26/08/2020,-7.44974E+17,0.746180454,15,4.1202097 +812,A156T; V175K; A259T,0.8141453,23.147667,26/08/2020,-7.44974E+17,0.8206333,32,5.256273 +813,A156T; V175K; N289S,0.6269022,4.860811,26/08/2020,-7.44974E+17,0.649248233,6,2.8269556 +814,A156T; V175K; T242A,0.6681695,7.6933703,26/08/2020,-7.44974E+17,0.678675981,16,3.7824383 +815,A156T; V175K; W216R,0.75316393,11.276486,26/08/2020,-7.44974E+17,0.77256666,9,3.6090543 +816,A156T; V175K; Y177W,0.77530104,17.070381,26/08/2020,-7.44974E+17,0.784597102,26,4.9150987 +817,A156T; W216R; A218L,0.43053463,3.8518696,26/08/2020,-7.44974E+17,0.439573572,7,5.1305656 +818,A156T; W216R; A218M,0.7056479,9.524236,26/08/2020,-7.44974E+17,0.718938957,13,3.9209683 +819,A156T; W216R; A259T,0.6909825,6.7885523,26/08/2020,-7.44974E+17,0.70265964,10,2.977616 +820,A156T; Y177W; A187S,0.62060004,7.641008,26/08/2020,-7.44974E+17,0.636887364,8,4.6141686 +821,A156T; Y177W; A228V,0.18626112,2.9477274,26/08/2020,-7.44974E+17,0.187877421,22,13.055687 +822,A156T; Y177W; A259T,0.7848139,19.786201,26/08/2020,-7.44974E+17,0.793636763,20,5.383476 +823,A156T; Y177W; A296I,0.7165809,9.132586,26/08/2020,-7.44974E+17,0.728729372,22,3.5827417 +824,A156T; Y177W; N289S,0.53023326,2.5945454,26/08/2020,-7.44974E+17,0.546041836,11,2.2850385 +825,A156T; Y177W; T242A,0.41551962,2.6865888,26/08/2020,-7.44974E+17,0.414850156,14,3.8068447 +826,A156T; Y177W; W216R,0.31659833,3.4785345,26/08/2020,-7.44974E+17,0.312732419,26,7.5546174 +827,A156V,0.19877611,1.961109,26/08/2020,-7.44974E+17,0.202490673,10,8.230572 +828,A156V; H183L; L193H; S220T,0.003972201,1.7068332,26/08/2020,-7.44974E+17,0.001849509,1,674.26636 +829,A156V; L161P,0.005587658,2.207483,26/08/2020,-7.44974E+17,0.0030999,1,609.6396 +830,A156V; M184T; A197D; L253P; D287E,0.002007926,0.6993218,26/08/2020,-7.44974E+17,0.000163701,1,772.36365 +831,A156V; P164T; S220T; K236N; D287V,0.030640654,4.486474,26/08/2020,-7.44974E+17,0.027400908,1,177.14633 +832,A156V; S205N; L243P,0.004193347,1.7972537,26/08/2020,-7.44974E+17,0.001890657,1,687.0295 +833,A156V; V175A; N178I; G237C,0.002547025,0.96345687,26/08/2020,-7.44974E+17,0.000449159,1,755.86743 +834,A156V; Y192H; A210D; M248R,0.003794692,1.5975283,26/08/2020,-7.44974E+17,0.00146865,1,736.89435 +835,A156W,0.15402226,3.4898484,26/08/2020,-7.44974E+17,0.15345305,3,20.82421 +836,A156Y,0.099844225,5.828127,26/08/2020,-7.44974E+17,0.10000628,3,55.161873 +837,A159C,0.22883537,5.320227,26/08/2020,-7.44974E+17,0.24337897,1,20.401426 +838,A159D; L272Q; E278D; A303T,0.003207334,1.3858004,26/08/2020,-7.44974E+17,0.001119965,1,750.83044 +839,A159D; S220T; H255L; A282T,0.00725719,2.6761422,26/08/2020,-7.44974E+17,0.004813262,1,525.5413 +840,A159D; T179S; S267T; Q283L,0.061039984,0.49356356,26/08/2020,-7.44974E+17,0.052711125,10,9.102471 +841,A159E,0.42657357,6.898949,26/08/2020,-7.44974E+17,0.428409462,7,9.323028 +842,A159F,0.10402451,5.358543,26/08/2020,-7.44974E+17,0.10384793,2,50.522045 +843,A159G,0.23671241,1.1714389,26/08/2020,-7.44974E+17,0.237098402,7,4.121391 +844,A159G; L167I; S220T,0.564664,5.632406,26/08/2020,-7.44974E+17,0.674011406,1,4.1839542 +845,A159H,0.17577416,5.522496,26/08/2020,-7.44974E+17,0.178643691,3,27.10835 +846,A159I,0.2188824,4.4918814,26/08/2020,-7.44974E+17,0.220925489,4,16.845707 +847,A159K,0.28020906,6.3263426,26/08/2020,-7.44974E+17,0.289013972,2,17.064514 +848,A159L,0.21984021,5.782957,26/08/2020,-7.44974E+17,0.223136428,2,22.015804 +849,A159M,0.35108164,4.814491,26/08/2020,-7.44974E+17,0.358098638,8,9.018849 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S160T,0.22785954,5.3181076,26/08/2020,-7.44974E+17,0.247886599,1,20.17217 +862,A159T; S220T,0.562733,5.592763,26/08/2020,-7.44974E+17,0.677374881,1,4.1148844 +863,A159T; S220T; A229V,0.62490714,5.8631325,26/08/2020,-7.44974E+17,0.74561925,1,3.2063625 +864,A159T; S220T; H230R,0.634049,5.9080973,26/08/2020,-7.44974E+17,0.750751074,1,3.0930192 +865,A159T; Y186F,0.099198155,5.0481906,26/08/2020,-7.44974E+17,0.098450468,1,54.723583 +866,A159T; Y294C,0.006963113,2.5946321,26/08/2020,-7.44974E+17,0.004579709,1,532.74445 +867,A159V,0.20157173,7.0380054,26/08/2020,-7.44974E+17,0.206370536,3,29.03484 +868,A159V; S220T,0.47270733,4.696998,26/08/2020,-7.44974E+17,0.502381887,2,5.320316 +869,A159V; S220T; P300L,0.061615042,4.8735003,26/08/2020,-7.44974E+17,0.058390142,1,89.88476 +870,A159V; S220T; V221A,0.10730274,5.109267,26/08/2020,-7.44974E+17,0.106889643,1,50.692883 +871,A169C,0.06505966,5.939669,26/08/2020,-7.44974E+17,0.064494263,2,93.530334 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+884,A169P,0.107713275,7.5979214,26/08/2020,-7.44974E+17,0.105735176,3,65.91481 +885,A169P; S220T; D235E; L253Q,0.013655824,3.6178696,26/08/2020,-7.44974E+17,0.011135718,1,341.03128 +886,A169Q,0.11688239,6.6315007,26/08/2020,-7.44974E+17,0.118877836,3,52.65026 +887,A169R,0.082974866,4.94864,26/08/2020,-7.44974E+17,0.081821069,1,66.45275 +888,A169S; S220T,0.6637209,6.4428034,26/08/2020,-7.44974E+17,0.73985736,2,3.0209513 +889,A169T,0.06707607,4.7306447,26/08/2020,-7.44974E+17,0.067810953,1,80.996124 +890,A169T; S220T,0.70969754,8.041759,26/08/2020,-7.44974E+17,0.772378888,3,3.0832713 +891,A169T; S220T; H230Y; G264D,0.6116795,5.817334,26/08/2020,-7.44974E+17,0.74516749,1,3.38849 +892,A169V,0.07111594,4.808773,26/08/2020,-7.44974E+17,0.071826604,1,76.792885 +893,A169V; S220T; A257V; G264C,0.1210896,5.1554937,26/08/2020,-7.44974E+17,0.122592791,1,44.28765 +894,A169W,0.071892284,5.9891095,26/08/2020,-7.44974E+17,0.070192621,2,84.39285 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+906,A174H,0.00129717,1.4743875,26/08/2020,-7.44974E+17,0.000884301,6,1283.0696 +907,A174I,0.001851191,1.1070931,26/08/2020,-7.44974E+17,0.000661929,2,912.5057 +908,A174L,0.001381469,1.1058105,26/08/2020,-7.44974E+17,0.000596158,3,1086.0817 +909,A174N,0.002095237,1.7483053,26/08/2020,-7.44974E+17,0.00150395,4,982.5491 +910,A174P,0.004953643,3.2598038,26/08/2020,-7.44974E+17,0.004292426,4,719.7823 +911,A174P; H271N,0.005774428,2.308932,26/08/2020,-7.44974E+17,0.003509069,1,596.2926 +912,A174P; S220T; N251K; K298N,0.002982347,1.290468,26/08/2020,-7.44974E+17,0.000919794,1,783.09564 +913,A174Q,0.003470164,1.4924959,26/08/2020,-7.44974E+17,0.001308122,1,729.36053 +914,A174S; N190I; S220T; D233E; Y239H; E268G; A282T; P302Q,0.002927406,1.1946725,26/08/2020,-7.44974E+17,0.000782475,1,768.0452 +915,A174T,0.009781151,3.5174563,26/08/2020,-7.44974E+17,0.007985897,2,417.9212 +916,A174T; H255Y; M273V,0.002271419,0.8948497,26/08/2020,-7.44974E+17,0.000348239,1,796.4456 +917,A174V; L214H; S220T,0.003395609,1.3629242,26/08/2020,-7.44974E+17,0.000976064,1,752.7825 +918,A174V; S220T,0.014251522,1.9685321,26/08/2020,-7.44974E+17,0.013005251,2,172.90172 +919,A174W,0.001289665,1.5183891,26/08/2020,-7.44974E+17,0.000809162,5,1364.0549 +920,A174Y,0.002143578,1.6669482,26/08/2020,-7.44974E+17,0.001437145,3,960.33154 +921,A180D,0.00315354,1.3052384,26/08/2020,-7.44974E+17,0.00095024,1,740.3365 +922,A180D; S201F; S220T; H230Y; E295K,0.005966323,2.3457773,26/08/2020,-7.44974E+17,0.003931108,1,559.85583 +923,A180F,0.0021996,1.2626041,26/08/2020,-7.44974E+17,0.001042413,2,846.32294 +924,A180G,0.044407587,5.011878,26/08/2020,-7.44974E+17,0.042647781,2,119.46116 +925,A180G; S220T,0.6505395,5.776367,26/08/2020,-7.44974E+17,0.791680909,1,2.7241936 +926,A180H,0.001698859,1.020071,26/08/2020,-7.44974E+17,0.000613211,2,937.0968 +927,A180I,0.002371745,1.4869251,26/08/2020,-7.44974E+17,0.001282478,2,868.04016 +928,A180L,0.002241052,0.974323,26/08/2020,-7.44974E+17,0.001174145,2,692.0294 +929,A180M,0.002209505,0.84386396,26/08/2020,-7.44974E+17,0.000319923,1,793.7339 +930,A180P,0.002390338,1.3320904,26/08/2020,-7.44974E+17,0.001027,2,811.31036 +931,A180P; L193P; A277G,0.002070856,0.72774166,26/08/2020,-7.44974E+17,0.000163896,1,759.23615 +932,A180R,0.002690092,1.0919181,26/08/2020,-7.44974E+17,0.000593209,1,779.2052 +933,A180S,0.06773803,5.991806,26/08/2020,-7.44974E+17,0.066240889,2,90.00735 +934,A180S; S220T,0.48122224,2.6395192,26/08/2020,-7.44974E+17,0.524295368,2,2.8741946 +935,A180T,0.003220526,1.3651692,26/08/2020,-7.44974E+17,0.001015592,1,767.83966 +936,A180T; A187E; A195S; S220T; L253Q,0.004506061,1.8123713,26/08/2020,-7.44974E+17,0.001962821,1,659.5717 +937,A180T; S220T; F297L,0.001902,1.1937368,26/08/2020,-7.44974E+17,0.000741164,2,929.042 +938,A180T; V232G,0.004011058,1.7567437,26/08/2020,-7.44974E+17,0.00202336,1,714.85645 +939,A180V,0.003595813,1.5949491,26/08/2020,-7.44974E+17,0.001603641,1,719.2194 +940,A180V; H183R; A206V; V276A; V281I,0.002627191,1.0659395,26/08/2020,-7.44974E+17,0.000567821,1,781.44495 +941,A180V; S220T,0.002993079,1.2177702,26/08/2020,-7.44974E+17,0.00075483,1,777.66174 +942,A180W,0.002000982,1.5454215,26/08/2020,-7.44974E+17,0.001227654,3,950.5395 +943,A180Y,0.001673085,0.9725391,26/08/2020,-7.44974E+17,0.000508253,2,915.2212 +944,A180Y; N249S,0.003272933,1.366035,26/08/2020,-7.44974E+17,0.000967244,1,748.66486 +945,A187D,0.004977841,1.1419115,26/08/2020,-7.44974E+17,0.00421097,3,308.68964 +946,A187E,0.005281302,1.7610862,26/08/2020,-7.44974E+17,0.004425221,3,399.15097 +947,A187E; S220T; S226Y,0.009570478,3.0434003,26/08/2020,-7.44974E+17,0.007097144,1,430.2164 +948,A187F,0.004107571,1.8050746,26/08/2020,-7.44974E+17,0.001968499,1,691.57776 +949,A187G,0.06855559,5.7622037,26/08/2020,-7.44974E+17,0.066377389,2,86.08328 +950,A187G; S226F,0.06008231,4.8476734,26/08/2020,-7.44974E+17,0.059017353,1,91.78212 +951,A187H,0.004901019,2.0683692,26/08/2020,-7.44974E+17,0.002962361,1,633.3731 +952,A187I,0.002064448,1.1908597,26/08/2020,-7.44974E+17,0.000926282,2,862.116 +953,A187K,0.001706498,1.2570163,26/08/2020,-7.44974E+17,0.000913257,3,966.86145 +954,A187K; V250A,0.003030864,1.3254924,26/08/2020,-7.44974E+17,0.000944065,1,753.3268 +955,A187L,0.001361521,0.75895166,26/08/2020,-7.44974E+17,0.000299972,2,947.15405 +956,A187M,0.020991772,5.1023293,26/08/2020,-7.44974E+17,0.019136379,2,264.0753 +957,A187N,0.08283435,6.4728184,26/08/2020,-7.44974E+17,0.083605724,3,75.740204 +958,A187P; A228G; V250M; R279C; H288Q,0.00299856,1.3029282,26/08/2020,-7.44974E+17,0.001022336,1,784.7529 +959,A187S,0.30365738,0.79996276,26/08/2020,-7.44974E+17,0.330894277,8,1.9898069 +960,A187S; K236T,0.16691986,5.1208053,26/08/2020,-7.44974E+17,0.176197997,1,29.659145 +961,A187S; Q194R,0.003325322,1.4412163,26/08/2020,-7.44974E+17,0.001061931,1,732.44714 +962,A187S; S220T,0.71416247,6.8073826,26/08/2020,-7.44974E+17,0.796808023,2,2.4263537 +963,A187T,0.040815804,5.7113895,26/08/2020,-7.44974E+17,0.039746179,2,147.9243 +964,A187T; A195W,0.00285797,1.1378148,26/08/2020,-7.44974E+17,0.000659942,1,776.97906 +965,A187T; A202T; S220T,0.44822448,5.5462055,26/08/2020,-7.44974E+17,0.519497866,1,6.998455 +966,A187T; M291K,0.004822627,2.0210135,26/08/2020,-7.44974E+17,0.00268553,1,646.4847 +967,A187V,0.008004997,2.8052616,26/08/2020,-7.44974E+17,0.005488219,1,485.26202 +968,A187V; F217L; P240L; G264V; L272P,0.002964831,1.2542428,26/08/2020,-7.44974E+17,0.000834234,1,752.7903 +969,A187V; I212F; S220T; A260P,0.002469434,1.0316927,26/08/2020,-7.44974E+17,0.000545916,1,787.2342 +970,A187V; Q194L; V199A; A296D,0.002892128,1.3235863,26/08/2020,-7.44974E+17,0.001038914,1,763.7399 +971,A187V; S220T,0.016962996,3.8831313,26/08/2020,-7.44974E+17,0.014288521,1,290.6345 +972,A187V; S220T; H230Y,0.54102427,5.743999,26/08/2020,-7.44974E+17,0.631413217,1,4.713945 +973,A187V; S220T; R247L,0.007343224,2.6799448,26/08/2020,-7.44974E+17,0.004844,1,509.89423 +974,A187Y,0.001857709,1.4798881,26/08/2020,-7.44974E+17,0.001095379,3,1005.01056 +975,A195C,0.005922017,2.8963912,26/08/2020,-7.44974E+17,0.004633165,2,587.9517 +976,A195D,0.001546287,1.106124,26/08/2020,-7.44974E+17,0.000780048,3,960.65295 +977,A195D; G200V; G203D; E208V,0.003084351,1.3531809,26/08/2020,-7.44974E+17,0.001002321,1,762.46405 +978,A195D; S220T,0.003293469,1.443811,26/08/2020,-7.44974E+17,0.001214493,1,732.5268 +979,A195E,0.001407231,1.3976537,26/08/2020,-7.44974E+17,0.000781778,4,1203.0173 +980,A195F,0.002617618,1.0365272,26/08/2020,-7.44974E+17,0.000562985,1,785.6063 +981,A195G,0.18180425,6.4969697,26/08/2020,-7.44974E+17,0.18368355,2,31.139845 +982,A195H,0.001975922,1.2298832,26/08/2020,-7.44974E+17,0.000824845,2,905.97705 +983,A195I,0.002819392,1.2601786,26/08/2020,-7.44974E+17,0.000851979,1,777.88275 +984,A195K,0.001260264,1.5575665,26/08/2020,-7.44974E+17,0.000894944,7,1370.9152 +985,A195L,0.002424942,0.970941,26/08/2020,-7.44974E+17,0.000438156,1,786.4823 +986,A195M,0.003541739,1.4970589,26/08/2020,-7.44974E+17,0.001218782,1,745.00903 +987,A195N,0.002826748,1.2229548,26/08/2020,-7.44974E+17,0.000769381,1,818.0018 +988,A195P,0.001816219,1.1053162,26/08/2020,-7.44974E+17,0.000643512,2,901.26666 +989,A195R,0.001902731,1.1391526,26/08/2020,-7.44974E+17,0.000683215,2,876.38116 +990,A195S; P300Q,0.018188436,3.913231,26/08/2020,-7.44974E+17,0.015338406,1,274.49698 +991,A195S; S205R,0.061393224,4.864028,26/08/2020,-7.44974E+17,0.059733648,1,91.99773 +992,A195S; S220T,0.1358102,5.237169,26/08/2020,-7.44974E+17,0.137944406,1,39.329517 +993,A195T,0.001894092,1.1694605,26/08/2020,-7.44974E+17,0.000749795,2,906.0151 +994,A195T; T242S,0.003397138,1.3805904,26/08/2020,-7.44974E+17,0.000989474,1,746.69696 +995,A195V,0.001406211,1.0914521,26/08/2020,-7.44974E+17,0.00062751,3,1051.9595 +996,A195V; S220T,0.002401466,0.96934,26/08/2020,-7.44974E+17,0.000428732,1,803.91437 +997,A195W,0.001849522,1.1537613,26/08/2020,-7.44974E+17,0.000714136,2,898.0055 +998,A195Y,0.003294658,1.4103557,26/08/2020,-7.44974E+17,0.001114065,1,756.7684 +999,A197C,0.03360492,4.493321,26/08/2020,-7.44974E+17,0.030306182,1,163.5151 +1000,A197D,0.008113303,1.1606294,26/08/2020,-7.44974E+17,0.007148996,2,207.32124 +1001,A197D; A231T,0.002929537,1.2375116,26/08/2020,-7.44974E+17,0.000880963,1,759.63367 +1002,A197E,0.001210744,0.9541481,26/08/2020,-7.44974E+17,0.000422214,3,1136.757 +1003,A197F,0.002417086,0.97506446,26/08/2020,-7.44974E+17,0.000495453,1,802.244 +1004,A197G,0.01327976,4.314597,26/08/2020,-7.44974E+17,0.011951801,2,367.48087 +1005,A197G; S220T,0.050518963,4.7837453,26/08/2020,-7.44974E+17,0.048427066,1,109.40629 +1006,A197H,0.001986152,1.1958749,26/08/2020,-7.44974E+17,0.000762261,2,906.188 +1007,A197I,0.001839653,1.138238,26/08/2020,-7.44974E+17,0.000716526,2,913.3624 +1008,A197K,0.002830607,1.063804,26/08/2020,-7.44974E+17,0.000538206,1,738.7332 +1009,A197L,0.001721816,0.89400786,26/08/2020,-7.44974E+17,0.000457669,2,858.35266 +1010,A197M,0.001642256,0.9816664,26/08/2020,-7.44974E+17,0.00049116,2,897.2219 +1011,A197N,0.002274242,0.8217065,26/08/2020,-7.44974E+17,0.000272215,1,782.998 +1012,A197P,0.002619518,1.1138719,26/08/2020,-7.44974E+17,0.000634408,1,792.7109 +1013,A197P; S220T,0.003002897,1.2726967,26/08/2020,-7.44974E+17,0.000952722,1,775.4268 +1014,A197P; V199M,0.002315957,0.93149495,26/08/2020,-7.44974E+17,0.00038829,1,818.9496 +1015,A197Q,0.001460459,0.7979425,26/08/2020,-7.44974E+17,0.00031776,2,932.0693 +1016,A197R,0.00131961,1.5464851,26/08/2020,-7.44974E+17,0.000818086,5,1348.2242 +1017,A197S,0.033749115,4.479344,26/08/2020,-7.44974E+17,0.030677333,1,158.31052 +1018,A197S; S220T; H230Y; N249I,0.002801884,1.1447486,26/08/2020,-7.44974E+17,0.000567944,1,802.2427 +1019,A197T,0.006744158,2.535728,26/08/2020,-7.44974E+17,0.004467518,1,542.6901 +1020,A197V,0.023304425,5.0964265,26/08/2020,-7.44974E+17,0.0223278,2,237.97408 +1021,A197V; S220T,0.20264174,1.931312,26/08/2020,-7.44974E+17,0.211875283,2,9.038684 +1022,A197W,0.001190533,0.94188887,26/08/2020,-7.44974E+17,0.000443181,3,1134.0918 +1023,A202C,0.08322183,5.0307865,26/08/2020,-7.44974E+17,0.081246752,1,66.55422 +1024,A202D,0.048246555,5.2305593,26/08/2020,-7.44974E+17,0.046320513,3,110.64671 +1025,A202E,0.03340408,4.404031,26/08/2020,-7.44974E+17,0.029814831,1,163.9443 +1026,A202E; A223W,0.029953774,4.3621044,26/08/2020,-7.44974E+17,0.027260603,1,178.05608 +1027,A202F,0.024649996,4.321343,26/08/2020,-7.44974E+17,0.021028499,1,220.56471 +1028,A202G,0.32981512,5.3918695,26/08/2020,-7.44974E+17,0.369731549,1,11.95836 +1029,A202H,0.047284562,5.831054,26/08/2020,-7.44974E+17,0.046426196,2,129.50952 +1030,A202K,0.070261285,7.886839,26/08/2020,-7.44974E+17,0.069285214,4,108.01572 +1031,A202L,0.04170531,5.6210866,26/08/2020,-7.44974E+17,0.040384508,2,141.40149 +1032,A202N,0.04462386,7.0229516,26/08/2020,-7.44974E+17,0.043393096,4,156.75487 +1033,A202P,0.0037482,1.277999,26/08/2020,-7.44974E+17,0.002937231,3,437.82574 +1034,A202P; A223V; V250E; L272Q,0.002565188,1.0851657,26/08/2020,-7.44974E+17,0.000607274,1,800.4798 +1035,A202P; S220T; N249S,0.002692183,1.1313524,26/08/2020,-7.44974E+17,0.000654154,1,779.96545 +1036,A202Q,0.03446963,5.3817315,26/08/2020,-7.44974E+17,0.034651193,2,166.63528 +1037,A202R,0.046990898,6.36222,26/08/2020,-7.44974E+17,0.04538333,3,136.32803 +1038,A202S,0.05665868,6.626106,26/08/2020,-7.44974E+17,0.056237336,3,116.04991 +1039,A202S; F209S; H230R,0.003580854,1.4018809,26/08/2020,-7.44974E+17,0.002288741,2,548.63904 +1040,A202S; S220T; D287V,0.22244845,4.7655134,26/08/2020,-7.44974E+17,0.229869042,2,18.047798 +1041,A202T,0.072273195,6.1734867,26/08/2020,-7.44974E+17,0.070262698,2,85.87133 +1042,A202T; S220T; L227P; V232I,0.004622662,1.911269,26/08/2020,-7.44974E+17,0.002295911,1,655.7437 +1043,A202T; V204Q,0.08328401,5.074377,26/08/2020,-7.44974E+17,0.08092335,1,67.496544 +1044,A202V,0.16933385,1.8300526,26/08/2020,-7.44974E+17,0.168165039,4,9.986194 +1045,A202V; R247Y,0.09982036,5.1536393,26/08/2020,-7.44974E+17,0.096436968,1,56.0378 +1046,A202V; S220T; H230Y,0.8064239,15.045507,26/08/2020,-7.44974E+17,0.827918529,10,3.524126 +1047,A202V; T245A; H288Q,0.005909009,2.3484426,26/08/2020,-7.44974E+17,0.003342654,1,578.23 +1048,A202W,0.016875416,3.8628247,26/08/2020,-7.44974E+17,0.014070924,1,293.8925 +1049,A202Y,0.053235903,6.0733657,26/08/2020,-7.44974E+17,0.053265772,5,111.988205 +1050,A206C,0.044858444,4.735087,26/08/2020,-7.44974E+17,0.041676992,1,125.11967 +1051,A206D,0.001726858,1.4178494,26/08/2020,-7.44974E+17,0.001260925,5,960.81274 +1052,A206D; S220T,0.004630819,1.9930575,26/08/2020,-7.44974E+17,0.002455782,1,657.7973 +1053,A206E,0.020830605,4.993863,26/08/2020,-7.44974E+17,0.019570372,2,263.60803 +1054,A206F,0.005610843,0.8938527,26/08/2020,-7.44974E+17,0.004937058,4,213.21451 +1055,A206G,0.039575923,4.6911206,26/08/2020,-7.44974E+17,0.036559899,1,139.71382 +1056,A206H,0.007485546,2.0872803,26/08/2020,-7.44974E+17,0.006034104,2,356.4587 +1057,A206I,0.04282624,4.687701,26/08/2020,-7.44974E+17,0.041876781,3,112.9154 +1058,A206K,0.009014206,2.9208016,26/08/2020,-7.44974E+17,0.006393854,1,451.3034 +1059,A206L,0.019144163,3.8884454,26/08/2020,-7.44974E+17,0.017379246,1,251.62126 +1060,A206M,0.004473247,2.912295,26/08/2020,-7.44974E+17,0.003821996,5,701.38275 +1061,A206N,0.05292844,4.7819266,26/08/2020,-7.44974E+17,0.050637226,1,106.15402 +1062,A206P,0.08032277,5.0467505,26/08/2020,-7.44974E+17,0.079625353,1,67.68997 +1063,A206Q,0.01738604,3.2911627,26/08/2020,-7.44974E+17,0.016323993,3,207.10822 +1064,A206R,0.00309986,1.8922763,26/08/2020,-7.44974E+17,0.001863511,2,789.38715 +1065,A206S,0.07231116,5.634592,26/08/2020,-7.44974E+17,0.06956738,2,79.09907 +1066,A206S; S220T; N238K,0.17023593,5.170208,26/08/2020,-7.44974E+17,0.178296707,1,28.978846 +1067,A206T,0.0472099,5.747119,26/08/2020,-7.44974E+17,0.045197535,2,126.78228 +1068,A206T; S220T,0.6653148,5.9622817,26/08/2020,-7.44974E+17,0.792794088,1,2.605961 +1069,A206V,0.027361808,5.227115,26/08/2020,-7.44974E+17,0.026572406,2,205.47502 +1070,A206V; D211C,0.04766373,4.7002563,26/08/2020,-7.44974E+17,0.044456665,1,117.33513 +1071,A206V; L253P,0.002375853,0.7693365,26/08/2020,-7.44974E+17,0.000186709,1,774.92523 +1072,A206V; S220T; V250A,0.047198568,4.633408,26/08/2020,-7.44974E+17,0.045731292,1,114.791245 +1073,A206V; T207I; N249I,0.004585765,1.9250908,26/08/2020,-7.44974E+17,0.002302007,1,682.4374 +1074,A206W,0.002676693,1.7984104,26/08/2020,-7.44974E+17,0.001994849,4,784.1298 +1075,A206Y,0.005831065,2.3235009,26/08/2020,-7.44974E+17,0.003598667,1,573.98395 +1076,A210D,0.062153004,4.358541,26/08/2020,-7.44974E+17,0.062288439,3,71.52827 +1077,A210E,0.061877854,6.0466685,26/08/2020,-7.44974E+17,0.061199642,2,100.25163 +1078,A210F,0.2345353,5.33493,26/08/2020,-7.44974E+17,0.250537414,1,19.584467 +1079,A210G,0.12260616,5.6491776,26/08/2020,-7.44974E+17,0.122539257,3,42.66587 +1080,A210G; S220T,0.4636472,5.7426987,26/08/2020,-7.44974E+17,0.526362369,1,6.7360525 +1081,A210G; W216L,0.051881805,4.79166,26/08/2020,-7.44974E+17,0.050874213,1,105.47893 +1082,A210H,0.18120244,4.607945,26/08/2020,-7.44974E+17,0.183637002,3,22.125162 +1083,A210I,0.07723588,9.628218,26/08/2020,-7.44974E+17,0.07644477,5,117.636696 +1084,A210K,0.08785553,4.991304,26/08/2020,-7.44974E+17,0.087278482,1,62.789806 +1085,A210L,0.060574487,4.90641,26/08/2020,-7.44974E+17,0.058969634,1,92.08875 +1086,A210M,0.08282246,6.142078,26/08/2020,-7.44974E+17,0.082984201,2,73.622 +1087,A210N,0.10168835,5.1471014,26/08/2020,-7.44974E+17,0.098974202,1,54.7703 +1088,A210P,0.002715964,2.9598765,26/08/2020,-7.44974E+17,0.00224352,5,1185.875 +1089,A210P; S220T; K298E,0.002612047,1.0453721,26/08/2020,-7.44974E+17,0.000502588,1,812.976 +1090,A210Q,0.08421988,4.8339353,26/08/2020,-7.44974E+17,0.088223197,1,63.153625 +1091,A210R,0.07940856,5.762029,26/08/2020,-7.44974E+17,0.079713567,3,71.24797 +1092,A210S,0.32262233,7.3802366,26/08/2020,-7.44974E+17,0.340264687,3,15.866481 +1093,A210T,0.2015328,5.3505893,26/08/2020,-7.44974E+17,0.20359496,2,22.850927 +1094,A210T; A213S,0.05944663,4.8278604,26/08/2020,-7.44974E+17,0.057924582,1,92.895485 +1095,A210T; D211G; D287E,0.024715917,4.306128,26/08/2020,-7.44974E+17,0.021397143,1,218.14302 +1096,A210T; L214H; S220T,0.044471655,4.6984067,26/08/2020,-7.44974E+17,0.042525332,1,125.56395 +1097,A210T; S220T; A223T; E262D,0.023377812,4.164081,26/08/2020,-7.44974E+17,0.020075202,1,224.06175 +1098,A210V,0.21808924,5.2759366,26/08/2020,-7.44974E+17,0.231690928,1,21.582672 +1099,A210W,0.17352824,6.5805316,26/08/2020,-7.44974E+17,0.172892226,3,32.68936 +1100,A210Y,0.3089597,5.0888553,26/08/2020,-7.44974E+17,0.321558376,3,11.8345995 +1101,A213C,0.15298836,3.6042044,26/08/2020,-7.44974E+17,0.157355202,2,22.339249 +1102,A213D,0.006285003,4.0114307,26/08/2020,-7.44974E+17,0.005721061,4,682.45056 +1103,A213D; S220T,0.003240037,1.3459184,26/08/2020,-7.44974E+17,0.000960613,1,747.8017 +1104,A213E,0.01296343,3.5338488,26/08/2020,-7.44974E+17,0.010167122,1,359.09848 +1105,A213F,0.06807151,4.9367986,26/08/2020,-7.44974E+17,0.065573042,1,82.93191 +1106,A213G,0.23379743,6.5257626,26/08/2020,-7.44974E+17,0.237335027,2,22.633305 +1107,A213H,0.030957153,7.1022663,26/08/2020,-7.44974E+17,0.030564088,4,231.18134 +1108,A213I,0.096050665,7.034509,26/08/2020,-7.44974E+17,0.096506611,3,69.33641 +1109,A213K,0.004811887,1.9818213,26/08/2020,-7.44974E+17,0.002293548,1,628.0743 +1110,A213L,0.1625165,5.2655067,26/08/2020,-7.44974E+17,0.165621652,1,31.381054 +1111,A213M,0.14289182,6.4157276,26/08/2020,-7.44974E+17,0.143113504,2,41.584435 +1112,A213N,0.012714976,3.4792948,26/08/2020,-7.44974E+17,0.01028969,1,356.03485 +1113,A213P,0.009078482,2.9553392,26/08/2020,-7.44974E+17,0.00619967,1,448.8639 +1114,A213P; L222P; A277V,0.002559681,1.0855873,26/08/2020,-7.44974E+17,0.000582263,1,781.9168 +1115,A213Q,0.039688908,5.367936,26/08/2020,-7.44974E+17,0.037922689,2,143.91167 +1116,A213R,0.020097258,0.52122515,26/08/2020,-7.44974E+17,0.020668973,4,37.35366 +1117,A213S,0.08125422,5.0099134,26/08/2020,-7.44974E+17,0.079231202,1,68.24784 +1118,A213S; S220T; A223V; H230Y,0.15860163,5.1550984,26/08/2020,-7.44974E+17,0.16506307,1,31.58406 +1119,A213T,0.09105656,5.005263,26/08/2020,-7.44974E+17,0.090394421,1,59.946976 +1120,A213T; H266N,0.07952722,4.8101473,26/08/2020,-7.44974E+17,0.08194665,1,67.31203 +1121,A213T; S220T,0.09376007,5.016696,26/08/2020,-7.44974E+17,0.094769067,1,57.731907 +1122,A213V,0.076994665,0.9866707,26/08/2020,-7.44974E+17,0.081899723,4,14.76899 +1123,A213W,0.06648105,5.296062,26/08/2020,-7.44974E+17,0.064777212,2,82.17082 +1124,A213Y,0.058688566,6.7917886,26/08/2020,-7.44974E+17,0.057898716,5,112.48989 +1125,A218C,0.13317856,5.9212847,26/08/2020,-7.44974E+17,0.133242834,2,41.401867 +1126,A218D,0.06712881,6.0859895,26/08/2020,-7.44974E+17,0.065591522,2,93.30992 +1127,A218E,0.086699784,5.722531,26/08/2020,-7.44974E+17,0.087556586,5,62.611103 +1128,A218F,0.12392502,5.1739774,26/08/2020,-7.44974E+17,0.125955654,5,38.052746 +1129,A218G,0.2031591,6.4577026,26/08/2020,-7.44974E+17,0.208311133,4,26.171406 +1130,A218H,0.14694184,5.1764126,26/08/2020,-7.44974E+17,0.152544885,1,35.37379 +1131,A218I,0.41444612,7.674556,26/08/2020,-7.44974E+17,0.43227353,3,10.963892 +1132,A218K,0.07784644,5.1443863,26/08/2020,-7.44974E+17,0.075284488,1,74.19324 +1133,A218L,0.51894844,6.1256175,26/08/2020,-7.44974E+17,0.549031171,3,5.680867 +1134,A218L; A228V,0.7821051,11.935513,26/08/2020,-7.44974E+17,0.80095514,9,3.2331133 +1135,A218L; A228V; N289S,0.7223463,8.861545,26/08/2020,-7.44974E+17,0.742821544,11,3.3467321 +1136,A218L; T242A,0.7244481,9.817881,26/08/2020,-7.44974E+17,0.729866842,22,3.7000327 +1137,A218M,0.43422598,5.8088937,26/08/2020,-7.44974E+17,0.470313155,2,7.72722 +1138,A218M; A259T; A303K,0.8171794,13.396622,26/08/2020,-7.44974E+17,0.832784954,15,2.9394042 +1139,A218M; A296T,0.5169928,0.7256967,26/08/2020,-7.44974E+17,0.635747807,12,0.67265826 +1140,A218M; A296T; A303G,0.51116127,0.7309479,26/08/2020,-7.44974E+17,0.622773023,12,0.6960315 +1141,A218M; A296T; A303I,0.6864824,4.9121532,26/08/2020,-7.44974E+17,0.687097608,15,2.2050037 +1142,A218M; A296T; A303K,0.7031063,6.189109,26/08/2020,-7.44974E+17,0.775788466,2,2.3447044 +1143,A218M; A296T; A303N,0.64523375,4.0415382,26/08/2020,-7.44974E+17,0.656562044,15,2.1915753 +1144,A218M; A296T; A303P,0.61391056,4.7707653,26/08/2020,-7.44974E+17,0.671309273,2,2.8145256 +1145,A218M; A296T; A303R,0.73709387,6.3380966,26/08/2020,-7.44974E+17,0.764887762,6,2.155024 +1146,A218M; A296T; A303T,0.7102245,5.579718,26/08/2020,-7.44974E+17,0.74601375,4,2.1026425 +1147,A218M; A296T; P302K,0.5282433,0.72346187,26/08/2020,-7.44974E+17,0.663246378,12,0.6419588 +1148,A218M; A296T; P302R,0.32587305,0.42625383,26/08/2020,-7.44974E+17,0.387009661,4,1.0178987 +1149,A218M; A296T; S304C,0.6965142,5.740653,26/08/2020,-7.44974E+17,0.701318904,15,2.463501 +1150,A218M; A296T; S304G,0.69986355,5.802986,26/08/2020,-7.44974E+17,0.705964253,15,2.4483871 +1151,A218M; A303K; S304F,0.6632836,4.8449183,26/08/2020,-7.44974E+17,0.726555991,2,2.2451248 +1152,A218M; A303K; S304I,0.6603911,4.731097,26/08/2020,-7.44974E+17,0.664414994,15,2.3959746 +1153,A218M; A303K; S304L,0.6804384,4.430799,26/08/2020,-7.44974E+17,0.692089721,10,2.024946 +1154,A218M; A303P; S304I,0.56818295,2.494004,26/08/2020,-7.44974E+17,0.565770044,15,1.8839533 +1155,A218M; A303P; S304L,0.6302591,3.6207445,26/08/2020,-7.44974E+17,0.630540125,15,2.0955749 +1156,A218M; A303R; S304I,0.71716744,6.011889,26/08/2020,-7.44974E+17,0.732786926,10,2.3101122 +1157,A218M; A303R; S304L,0.6954882,4.6486826,26/08/2020,-7.44974E+17,0.710713724,10,1.9769366 +1158,A218M; P302K; A303K,0.7190982,6.254965,26/08/2020,-7.44974E+17,0.731767786,11,2.390275 +1159,A218M; P302K; S304I,0.65226674,3.9881773,26/08/2020,-7.44974E+17,0.653067576,15,2.0932233 +1160,A218M; P302K; S304L,0.6256251,3.297612,26/08/2020,-7.44974E+17,0.623925238,15,1.9484628 +1161,A218M; P302R; A303I,0.6689046,4.1850924,26/08/2020,-7.44974E+17,0.684064669,10,2.0131092 +1162,A218M; P302R; A303K,0.7027557,4.4137726,26/08/2020,-7.44974E+17,0.718791121,8,1.7937394 +1163,A218M; P302R; A303P,0.4021622,0.49415728,26/08/2020,-7.44974E+17,0.480810565,14,0.752587 +1164,A218M; P302R; A303R,0.70892286,5.603817,26/08/2020,-7.44974E+17,0.7178138,10,2.23635 +1165,A218M; P302R; A303T,0.6597085,4.2905736,26/08/2020,-7.44974E+17,0.661498938,15,2.1781821 +1166,A218M; P302R; S304G,0.5933086,2.535996,26/08/2020,-7.44974E+17,0.585288414,15,1.7189307 +1167,A218M; P302R; S304I,0.64324933,4.016716,26/08/2020,-7.44974E+17,0.685150977,2,2.0540435 +1168,A218M; P302R; S304K,0.65225536,4.30623,26/08/2020,-7.44974E+17,0.659391692,15,2.2596672 +1169,A218M; P302R; S304L,0.6010283,3.018233,26/08/2020,-7.44974E+17,0.600169621,15,1.9734997 +1170,A218M; P302R; S304P,0.62385106,3.4516892,26/08/2020,-7.44974E+17,0.628569348,15,2.048236 +1171,A218M; P302R; S304T,0.47245082,0.6434592,26/08/2020,-7.44974E+17,0.549300346,12,0.72523004 +1172,A218N,0.08754348,5.8989553,26/08/2020,-7.44974E+17,0.08630217,3,65.13096 +1173,A218P,0.16187352,5.1102996,26/08/2020,-7.44974E+17,0.171399599,1,30.541513 +1174,A218R,0.052142993,5.8341904,26/08/2020,-7.44974E+17,0.052215236,2,116.354485 +1175,A218S,0.13219601,5.1369896,26/08/2020,-7.44974E+17,0.136990301,1,39.770584 +1176,A218T,0.103925325,7.250952,26/08/2020,-7.44974E+17,0.102816082,3,65.160866 +1177,A218T; L222E,0.05686446,4.9096165,26/08/2020,-7.44974E+17,0.051946099,1,99.026886 +1178,A218T; L234P; V250E; A259G,0.002744742,1.0545801,26/08/2020,-7.44974E+17,0.000518945,1,816.9217 +1179,A218T; S220T; H230Y,0.6017848,5.8765497,26/08/2020,-7.44974E+17,0.700536587,1,3.6391745 +1180,A218T; S220T; H230Y; A296T,0.63456136,5.862151,26/08/2020,-7.44974E+17,0.760714779,1,2.9740536 +1181,A218V,0.3369285,5.428565,26/08/2020,-7.44974E+17,0.375184409,1,11.69795 +1182,A218V; D235V; E254D,0.11129894,5.1110787,26/08/2020,-7.44974E+17,0.113028135,1,48.455017 +1183,A218V; L222S,0.06414223,4.9553666,26/08/2020,-7.44974E+17,0.061053852,1,87.60913 +1184,A218V; S220T; L269F,0.33675238,5.4774256,26/08/2020,-7.44974E+17,0.366065608,1,11.696964 +1185,A218Y,0.16225117,4.735593,26/08/2020,-7.44974E+17,0.161602893,2,26.871382 +1186,A223C,0.05847815,6.5337586,26/08/2020,-7.44974E+17,0.058543354,3,110.7192 +1187,A223D,0.11404356,5.1910167,26/08/2020,-7.44974E+17,0.112324164,1,47.566883 +1188,A223D; G286V,0.054000657,4.708244,26/08/2020,-7.44974E+17,0.053502251,1,102.06507 +1189,A223E,0.06285365,6.107027,26/08/2020,-7.44974E+17,0.060697281,2,99.05952 +1190,A223E; L253T,0.002886348,1.2271457,26/08/2020,-7.44974E+17,0.000808358,1,789.7227 +1191,A223G,0.074279755,4.0365176,26/08/2020,-7.44974E+17,0.073703846,3,54.7231 +1192,A223H,0.11942007,6.380412,26/08/2020,-7.44974E+17,0.117993757,2,50.709812 +1193,A223I,0.049732465,5.800337,26/08/2020,-7.44974E+17,0.049129423,2,121.4486 +1194,A223K,0.047540043,4.791797,26/08/2020,-7.44974E+17,0.044708215,1,117.25462 +1195,A223L,0.08680065,5.712973,26/08/2020,-7.44974E+17,0.084615859,3,64.09696 +1196,A223M,0.12033489,5.0711255,26/08/2020,-7.44974E+17,0.119481906,1,44.284454 +1197,A223N,0.20771025,5.262853,26/08/2020,-7.44974E+17,0.218477811,1,22.81224 +1198,A223Q,0.09965459,6.9719563,26/08/2020,-7.44974E+17,0.0981214,3,66.20621 +1199,A223R,0.12352507,6.221118,26/08/2020,-7.44974E+17,0.126823623,2,47.312134 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P224H,0.018298045,3.9077091,26/08/2020,-7.44974E+17,0.015809087,1,274.6583 +1527,A33T; V52M; S220T,0.6022681,5.761553,26/08/2020,-7.44974E+17,0.726618227,1,3.5327604 +1528,A33T; Y144N; S220T; G237C,0.002469449,0.9999249,26/08/2020,-7.44974E+17,0.000458459,1,770.767 +1529,A33V,0.026375828,4.181656,26/08/2020,-7.44974E+17,0.025345,1,191.2944 +1530,A33V; A60T; A68T; D125E; G237C; M273T,0.002784054,1.0439358,26/08/2020,-7.44974E+17,0.000521243,1,773.85175 +1531,A33V; A80T; A89G; L124H; K298M,0.002822279,1.1016783,26/08/2020,-7.44974E+17,0.000593322,1,761.2935 +1532,A33V; D64Y; A111V; G215S; A252G,0.001861589,0.62960756,26/08/2020,-7.44974E+17,0.000108425,1,790.587 +1533,A33V; G126C; S220T; L272P,0.002604437,1.0847507,26/08/2020,-7.44974E+17,0.000593002,1,777.7054 +1534,A33V; M291Y,0.038140975,4.6268115,26/08/2020,-7.44974E+17,0.035715212,1,146.16609 +1535,A33V; P37K,0.060450867,6.0449104,26/08/2020,-7.44974E+17,0.058291511,2,102.52436 +1536,A33V; P45H; S67N; 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S292F,0.004521418,1.9683123,26/08/2020,-7.44974E+17,0.002437636,1,687.9578 +1881,A65V; V66M; R94H; V135I; L161P; S220T; E261G,0.003999932,1.7027938,26/08/2020,-7.44974E+17,0.001705967,1,706.04785 +1882,A65V; V99I; A140T; V141L; T245I,0.004622826,1.9262989,26/08/2020,-7.44974E+17,0.002366868,1,652.8667 +1883,A65W,0.002524342,0.92780435,26/08/2020,-7.44974E+17,0.000363771,1,781.1781 +1884,A65Y,0.003297695,1.3959277,26/08/2020,-7.44974E+17,0.001090517,1,743.2915 +1885,A68C,0.041550886,8.346399,26/08/2020,-7.44974E+17,0.041890221,6,196.99287 +1886,A68D,0.013063221,6.257101,26/08/2020,-7.44974E+17,0.012524565,5,490.72433 +1887,A68E,0.027215904,4.138207,26/08/2020,-7.44974E+17,0.025682627,4,158.73564 +1888,A68E; A80Y,0.015367414,3.7454255,26/08/2020,-7.44974E+17,0.012154726,1,318.97256 +1889,A68E; C75R; E91D; A282V,0.003537585,1.510057,26/08/2020,-7.44974E+17,0.001352669,1,740.86816 +1890,A68E; C75Y,0.001875997,0.61929613,26/08/2020,-7.44974E+17,8.72E-05,1,756.0761 +1891,A68E; 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S220T,0.004893084,2.0636222,26/08/2020,-7.44974E+17,0.002692643,1,636.8723 +1903,A68S; N102S; V113E; D163G,0.025000652,4.1807947,26/08/2020,-7.44974E+17,0.022381817,1,207.51674 +1904,A68S; R155C; A180V; S220T,0.003007565,1.2775744,26/08/2020,-7.44974E+17,0.000886297,1,719.8512 +1905,A68S; S220T,0.55505466,6.679746,26/08/2020,-7.44974E+17,0.590004706,3,5.300287 +1906,A68T,0.02337925,5.0816727,26/08/2020,-7.44974E+17,0.022544116,2,234.78085 +1907,A68T; A213T; S220T; K236N; A259V,0.004431487,1.9140635,26/08/2020,-7.44974E+17,0.002323273,1,674.82806 +1908,A68T; E261G,0.011284153,3.2294059,26/08/2020,-7.44974E+17,0.008812428,1,380.43286 +1909,A68T; E84Q; A116V; Y177H; M291I,0.002194322,0.8211857,26/08/2020,-7.44974E+17,0.00028714,1,787.4645 +1910,A68T; H154Q; L214H; S220T,0.00534523,2.1800005,26/08/2020,-7.44974E+17,0.003030603,1,619.49115 +1911,A68T; L119P; L134M; S220T,0.002730894,1.1295109,26/08/2020,-7.44974E+17,0.000669466,1,796.5445 +1912,A68T; L173Q; A252T; 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S220T,0.15763049,5.1374593,26/08/2020,-7.44974E+17,0.166130558,1,32.018497 +2131,C75S; A197S; S220T; V281A,0.00413558,1.8373094,26/08/2020,-7.44974E+17,0.002109735,1,682.99414 +2132,C75S; A89D; E278V,0.003948936,1.6885427,26/08/2020,-7.44974E+17,0.001710922,1,701.30176 +2133,C75S; A89V; R148P; V150M; S220T; H230Y,0.007322176,2.6579325,26/08/2020,-7.44974E+17,0.004885129,1,500.1713 +2134,C75S; D118V; S220T,0.014366065,3.6275005,26/08/2020,-7.44974E+17,0.011768895,1,328.29785 +2135,C75S; G104D; S117P; S220T; R247C,0.00286721,1.1805227,26/08/2020,-7.44974E+17,0.000750128,1,738.8629 +2136,C75S; I128F; N190D,0.003167893,1.2790569,26/08/2020,-7.44974E+17,0.000928678,1,750.2919 +2137,C75S; L198P; L214H; S220T,0.00310764,1.2721988,26/08/2020,-7.44974E+17,0.000812891,1,761.62006 +2138,C75S; N77S; N100Y; T157A; R165C; T188S; A277T,0.002360795,0.8412075,26/08/2020,-7.44974E+17,0.000276611,1,809.2293 +2139,C75S; R122P; I128F; Q299H,0.002793415,1.2727942,26/08/2020,-7.44974E+17,0.000897965,1,784.6605 +2140,C75S; R165H,0.009472522,3.093144,26/08/2020,-7.44974E+17,0.006662186,1,449.2279 +2141,C75S; T81S; D118E; S145P; A149T; V265E,0.002459676,0.9160171,26/08/2020,-7.44974E+17,0.000374927,1,738.28455 +2142,C75T,0.01079519,2.7410781,26/08/2020,-7.44974E+17,0.010260154,6,268.58917 +2143,C75V,0.02598824,4.9364834,26/08/2020,-7.44974E+17,0.025363392,2,206.58223 +2144,C75W,0.002008242,1.2722026,26/08/2020,-7.44974E+17,0.00116721,3,837.42236 +2145,C75W; D95Y; L193H; L269S; D287V,0.002221384,0.7904662,26/08/2020,-7.44974E+17,0.000223345,1,776.4053 +2146,C75W; L182Q; L227M; Y290F,0.002775003,1.1646076,26/08/2020,-7.44974E+17,0.000715384,1,766.38495 +2147,C75Y,0.00410733,1.725964,26/08/2020,-7.44974E+17,0.001596763,1,705.80585 +2148,C75Y; E91K; S220T,0.003010658,1.3018544,26/08/2020,-7.44974E+17,0.000907453,1,792.9376 +2149,C75Y; H255L,0.003045776,1.2913734,26/08/2020,-7.44974E+17,0.00090907,1,757.0484 +2150,C75Y; I128V; A140T; S220T; A303V,0.002140497,0.8285901,26/08/2020,-7.44974E+17,0.000303876,1,793.9448 +2151,C75Y; L101P; L134P; M184K; G215S; K236I; D287N,0.002486861,0.97605807,26/08/2020,-7.44974E+17,0.0004266,1,836.5635 +2152,C75Y; L176S; D233V,0.003679468,1.6201208,26/08/2020,-7.44974E+17,0.001581418,1,698.9151 +2153,C75Y; P171H,0.002054613,0.6705234,26/08/2020,-7.44974E+17,0.000126081,1,782.0427 +2154,C75Y; R148P; S220T; H230Y,0.003166801,1.3956128,26/08/2020,-7.44974E+17,0.001128878,1,768.98413 +2155,C75Y; R94C; L158P; S220T,0.002082116,1.1905472,26/08/2020,-7.44974E+17,0.000848273,2,849.84436 +2156,C75Y; S220T,0.001770175,1.1346474,26/08/2020,-7.44974E+17,0.000642922,2,944.6887 +2157,D118A,0.1263264,0.68253845,26/08/2020,-7.44974E+17,0.148599441,2,6.988189 +2158,D118C,0.20774116,0.61634153,26/08/2020,-7.44974E+17,0.249334566,7,2.686111 +2159,D118E,0.24208829,0.57436913,26/08/2020,-7.44974E+17,0.287394502,10,1.9771863 +2160,D118E; D139V; L181S; I212F; V281D; H288L,0.002842434,1.2675956,26/08/2020,-7.44974E+17,0.000865451,1,762.36035 +2161,D118E; D211V; S220T,0.34913737,5.3295355,26/08/2020,-7.44974E+17,0.392277849,1,10.692691 +2162,D118E; F166I; S220T; V250L,0.023308296,3.8808372,26/08/2020,-7.44974E+17,0.021672208,2,189.10341 +2163,D118E; I128T; S220T; H230Q; L243R,0.009287905,3.0188177,26/08/2020,-7.44974E+17,0.007098116,1,443.28622 +2164,D118E; L181I; F209L; S220T,0.05779794,4.9328055,26/08/2020,-7.44974E+17,0.053568851,1,98.64011 +2165,D118E; M185L; F217L; S220T,0.054527704,5.186674,26/08/2020,-7.44974E+17,0.05308984,2,99.41377 +2166,D118E; R148P; S220T; H230Y,0.60178584,5.814064,26/08/2020,-7.44974E+17,0.712992526,1,3.5654783 +2167,D118E; S220T,0.67013955,6.0559688,26/08/2020,-7.44974E+17,0.793592886,1,2.5995715 +2168,D118E; S220T; G244C,0.005923587,2.338324,26/08/2020,-7.44974E+17,0.003458774,1,566.6976 +2169,D118E; V130M; R137L; L173P; L246P,0.003349603,1.3720092,26/08/2020,-7.44974E+17,0.001023468,1,735.5715 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R122Y,0.12722667,5.0765553,26/08/2020,-7.44974E+17,0.132428552,1,41.189346 +2192,D118N; R148P; S220T; H230Y,0.5981619,5.7651777,26/08/2020,-7.44974E+17,0.715949187,1,3.594968 +2193,D118N; S220T,0.5505353,5.8007884,26/08/2020,-7.44974E+17,0.655741665,1,4.5767736 +2194,D118N; S220T; H230Y; N251I,0.59765315,5.671915,26/08/2020,-7.44974E+17,0.725055725,1,3.4923835 +2195,D118N; T121A; R148H; S220T; D287Y,0.12005109,5.178473,26/08/2020,-7.44974E+17,0.11902589,1,44.713192 +2196,D118P,0.036739357,0.928454,26/08/2020,-7.44974E+17,0.036941622,4,31.571203 +2197,D118Q,0.14579955,0.6046882,26/08/2020,-7.44974E+17,0.163208798,4,4.6369033 +2198,D118R,0.2009698,0.67814136,26/08/2020,-7.44974E+17,0.225504995,9,2.9813569 +2199,D118S,0.24141887,0.6900218,26/08/2020,-7.44974E+17,0.29904136,4,2.6076658 +2200,D118T,0.20037031,0.61856014,26/08/2020,-7.44974E+17,0.237480947,5,2.962363 +2201,D118V,0.11662755,0.47661176,26/08/2020,-7.44974E+17,0.126004199,8,4.3616977 +2202,D118V; G126D; L167R; L173M; Y177N; G191E; S220T; S267T; H285R; Y294C; K298E,0.003065226,1.3927028,26/08/2020,-7.44974E+17,0.001105397,1,782.0206 +2203,D118V; H154Q,0.04439315,4.555656,26/08/2020,-7.44974E+17,0.04298595,1,121.42369 +2204,D118V; L124H; K236I,0.002120997,0.7125501,26/08/2020,-7.44974E+17,0.000145134,1,756.10016 +2205,D118V; L151P; P171S,0.001949963,0.6386304,26/08/2020,-7.44974E+17,0.000117949,1,755.2462 +2206,D118V; L176V; S220T,0.3048911,5.2921658,26/08/2020,-7.44974E+17,0.340084586,1,13.459035 +2207,D118W,0.12056961,0.59785146,26/08/2020,-7.44974E+17,0.131671653,9,5.112646 +2208,D118Y,0.23931712,5.2010794,26/08/2020,-7.44974E+17,0.263491136,1,18.411211 +2209,D118Y; A140S; T245A,0.006569844,2.5330524,26/08/2020,-7.44974E+17,0.003985439,1,543.9879 +2210,D118Y; R122G,0.037152275,4.611716,26/08/2020,-7.44974E+17,0.033954537,1,149.69507 +2211,D118Y; R247S,0.04583477,4.6908126,26/08/2020,-7.44974E+17,0.04294395,1,121.815605 +2212,D118Y; T172A; S220T,0.3054159,5.3906283,26/08/2020,-7.44974E+17,0.335114882,1,13.403044 +2213,D125A,0.005147614,3.7187648,26/08/2020,-7.44974E+17,0.004581028,4,776.88947 +2214,D125A; A218V,0.017787226,3.8655791,26/08/2020,-7.44974E+17,0.015603063,1,273.09302 +2215,D125A; M129I,0.00301379,1.3188263,26/08/2020,-7.44974E+17,0.00103831,1,767.60144 +2216,D125C; G126S,0.00319177,1.3255191,26/08/2020,-7.44974E+17,0.001006246,1,728.7478 +2217,D125C; R137L,0.00509398,2.1057773,26/08/2020,-7.44974E+17,0.00281551,1,629.659 +2218,D125E,0.22406322,2.5243912,26/08/2020,-7.44974E+17,0.229969079,2,10.022265 +2219,D125E; D147V; F209Y; D287V,0.074307576,4.9214892,26/08/2020,-7.44974E+17,0.07611124,1,72.968216 +2220,D125E; D211G,0.13931936,5.093219,26/08/2020,-7.44974E+17,0.146892729,1,37.02827 +2221,D125E; H271R; Y290F,0.16719614,5.1885147,26/08/2020,-7.44974E+17,0.175645423,1,30.17476 +2222,D125E; P164S; M185V; V199E; S220T,0.00271912,1.0923032,26/08/2020,-7.44974E+17,0.000577391,1,780.2664 +2223,D125E; R155G; S220T,0.6501058,5.9588976,26/08/2020,-7.44974E+17,0.778452592,1,2.8094156 +2224,D125E; S205G; S220T; H255P,0.003378955,1.4327253,26/08/2020,-7.44974E+17,0.001176722,1,738.872 +2225,D125E; S220T; L243P,0.08691256,5.171611,26/08/2020,-7.44974E+17,0.084984516,1,63.769085 +2226,D125E; V130M; P138T; H271Y; Q283H; V293L,0.005021302,2.1070323,26/08/2020,-7.44974E+17,0.002869817,1,622.1473 +2227,D125E; V141I; V250M,0.060207408,4.827626,26/08/2020,-7.44974E+17,0.058949139,1,91.83212 +2228,D125F,0.004590728,3.0465598,26/08/2020,-7.44974E+17,0.003742378,3,743.8998 +2229,D125G,0.013371334,3.090641,26/08/2020,-7.44974E+17,0.011851676,2,271.73334 +2230,D125G; F142Y; R148H; D170E; S220T,0.003659247,1.6268098,26/08/2020,-7.44974E+17,0.001565109,1,707.74335 +2231,D125G; Q263H,0.004275744,1.8038446,26/08/2020,-7.44974E+17,0.00216763,1,678.5244 +2232,D125G; S145P; H183L; S220T; L246F; A296S,0.003078437,1.3595645,26/08/2020,-7.44974E+17,0.00109743,1,789.7201 +2233,D125G; S145T; D163E; S220T; D233E; K298E,0.002580276,1.0077852,26/08/2020,-7.44974E+17,0.000456583,1,766.3692 +2234,D125G; S220T,0.30178362,5.267505,26/08/2020,-7.44974E+17,0.33183766,1,13.472328 +2235,D125G; S220T; H288R,0.020658962,4.012088,26/08/2020,-7.44974E+17,0.018700688,1,239.56268 +2236,D125G; S220T; V221A,0.006725829,2.5073564,26/08/2020,-7.44974E+17,0.004181713,1,547.32874 +2237,D125H,0.001105428,1.114111,26/08/2020,-7.44974E+17,0.000516879,4,1257.1194 +2238,D125H; V150A; H271Y,0.001913517,0.6622924,26/08/2020,-7.44974E+17,0.000149485,1,787.4782 +2239,D125I,0.006267959,3.3303976,26/08/2020,-7.44974E+17,0.005788951,5,562.9765 +2240,D125K,0.00300252,1.2717259,26/08/2020,-7.44974E+17,0.000843162,1,749.73254 +2241,D125L,0.001358445,1.6882354,26/08/2020,-7.44974E+17,0.00097109,6,1386.7793 +2242,D125M,0.002252073,1.7406697,26/08/2020,-7.44974E+17,0.001431458,3,942.6113 +2243,D125N,0.013329234,1.6341677,26/08/2020,-7.44974E+17,0.012710495,8,131.62302 +2244,D125N; A187V; A206T; S220T,0.003652289,1.5555844,26/08/2020,-7.44974E+17,0.001390209,1,708.84686 +2245,D125N; A206T; S220T; V221A,0.002445644,1.0021755,26/08/2020,-7.44974E+17,0.000511505,1,787.23737 +2246,D125N; D147V; Y186C; I212T,0.003009927,1.3428439,26/08/2020,-7.44974E+17,0.001061331,1,800.90045 +2247,D125N; G136C; L189F,0.003339273,1.4676108,26/08/2020,-7.44974E+17,0.001273235,1,752.6863 +2248,D125N; G146D,0.002135551,0.74293417,26/08/2020,-7.44974E+17,0.00019399,1,819.5022 +2249,D125N; P133S; R148P; L151Q; S220T; H230Y; P302Q,0.002894091,1.2236078,26/08/2020,-7.44974E+17,0.000806139,1,786.9568 +2250,D125N; P164R,0.002478274,0.9388275,26/08/2020,-7.44974E+17,0.000384512,1,795.15485 +2251,D125N; R148P; A213T; S220T; H230Y; P240S,0.003555434,1.5052531,26/08/2020,-7.44974E+17,0.001312118,1,728.8669 +2252,D125N; R148P; S220T; H230Y,0.051247682,4.704313,26/08/2020,-7.44974E+17,0.049983901,1,106.810905 +2253,D125N; R155H; S220T,0.11555977,5.178227,26/08/2020,-7.44974E+17,0.115632552,1,47.373894 +2254,D125N; T196S; A223V,0.002836385,1.2491527,26/08/2020,-7.44974E+17,0.000859066,1,761.24493 +2255,D125P,0.006064727,3.0400972,26/08/2020,-7.44974E+17,0.005533272,4,546.1473 +2256,D125Q,0.002347066,2.4173684,26/08/2020,-7.44974E+17,0.001831538,5,1134.1658 +2257,D125R,0.002679814,1.0926043,26/08/2020,-7.44974E+17,0.000503698,1,788.57947 +2258,D125S,0.026141059,3.1109061,26/08/2020,-7.44974E+17,0.025839258,6,123.083374 +2259,D125T,0.20665452,6.6524673,26/08/2020,-7.44974E+17,0.206306602,2,27.168245 +2260,D125V,0.008818913,2.1574175,26/08/2020,-7.44974E+17,0.007409603,2,310.63196 +2261,D125V; A206V,0.007278266,2.6685412,26/08/2020,-7.44974E+17,0.004757907,1,527.406 +2262,D125V; D152V,0.004437886,1.8827752,26/08/2020,-7.44974E+17,0.002219911,1,655.5536 +2263,D125V; D170E; L214I,0.003136565,1.3018202,26/08/2020,-7.44974E+17,0.000941906,1,756.2136 +2264,D125V; L189S; S220T,0.002540697,1.0698216,26/08/2020,-7.44974E+17,0.000572325,1,773.19946 +2265,D125V; M129T; Y177N; N190I; N251Y,0.002647305,1.1064621,26/08/2020,-7.44974E+17,0.000618233,1,766.395 +2266,D125W,0.002802398,0.61264205,26/08/2020,-7.44974E+17,0.002467572,6,280.30408 +2267,D125W; A127T,0.002461888,0.9494308,26/08/2020,-7.44974E+17,0.000406736,1,796.63293 +2268,D125Y,0.001252383,1.5778099,26/08/2020,-7.44974E+17,0.000885441,6,1414.6283 +2269,D125Y; G146S; A174E; F209S; P219R; S220T,0.003322131,1.3950347,26/08/2020,-7.44974E+17,0.001168491,1,718.21564 +2270,D125Y; R148P; A169V; S220T; H230Y; H266Y,0.009279563,2.983527,26/08/2020,-7.44974E+17,0.007110292,1,445.399 +2271,D125Y; S205C; S220T; N238Y,0.00447583,1.9408654,26/08/2020,-7.44974E+17,0.002250665,1,665.00507 +2272,D139A,0.018126216,1.5994453,26/08/2020,-7.44974E+17,0.017713782,6,97.33644 +2273,D139A; S145L; D163G; M185L; S220T; H266L,0.00283364,1.0745881,26/08/2020,-7.44974E+17,0.000575105,1,747.4008 +2274,D139C,0.008850417,4.0273924,26/08/2020,-7.44974E+17,0.008063807,3,498.20813 +2275,D139E,0.056050416,4.8458104,26/08/2020,-7.44974E+17,0.05297839,1,101.13027 +2276,D139E; A149E; L161P,0.003717072,1.5760182,26/08/2020,-7.44974E+17,0.001461506,1,725.53485 +2277,D139E; G191R; S220T,0.002219758,0.84168684,26/08/2020,-7.44974E+17,0.000305394,1,780.6918 +2278,D139E; L143M; F209I; S220T; E268G,0.002494782,0.9669129,26/08/2020,-7.44974E+17,0.000399066,1,812.37634 +2279,D139E; R148P; S220T; H230Y,0.7607662,15.110641,26/08/2020,-7.44974E+17,0.780025276,10,4.6636605 +2280,D139E; T172A; S220T; G237C; Y290C; S292T,0.003295106,1.3649935,26/08/2020,-7.44974E+17,0.000984807,1,754.95996 +2281,D139F,0.009825776,1.7627378,26/08/2020,-7.44974E+17,0.008974346,3,214.70631 +2282,D139G,0.045009956,5.3281245,26/08/2020,-7.44974E+17,0.044380896,6,117.19337 +2283,D139G; R148G; A218T; S220T,0.08324939,5.035993,26/08/2020,-7.44974E+17,0.08188713,1,67.173096 +2284,D139G; R148P; L176S; S220T; H230Y,0.091432564,5.032155,26/08/2020,-7.44974E+17,0.087526504,1,60.1792 +2285,D139G; V141I; H154Y; R165H; T196S; P240L,0.006810137,2.5325208,26/08/2020,-7.44974E+17,0.004380619,1,536.14386 +2286,D139G; V175E; S220T; A252V,0.003204471,1.3311713,26/08/2020,-7.44974E+17,0.001036812,1,729.57635 +2287,D139G; V199E; S220T,0.002849069,1.1603663,26/08/2020,-7.44974E+17,0.000711588,1,768.2269 +2288,D139H,0.035820425,4.3550963,26/08/2020,-7.44974E+17,0.03475734,2,133.09393 +2289,D139I,0.009197338,3.7081354,26/08/2020,-7.44974E+17,0.008576228,4,432.6261 +2290,D139K,0.014272591,1.40536,26/08/2020,-7.44974E+17,0.013775608,3,120.55145 +2291,D139L,0.009565339,1.854607,26/08/2020,-7.44974E+17,0.008895682,5,219.16608 +2292,D139M,0.015782058,2.3940885,26/08/2020,-7.44974E+17,0.014758029,3,171.5113 +2293,D139N,0.048175823,5.093742,26/08/2020,-7.44974E+17,0.046865686,2,110.677864 +2294,D139N; L143M; G146C; A149E; S220T,0.002893494,1.2483755,26/08/2020,-7.44974E+17,0.000788465,1,783.6597 +2295,D139N; V141A,0.020786671,4.0559454,26/08/2020,-7.44974E+17,0.018440112,1,241.04526 +2296,D139N; Y192N; S220T; M291V,0.002490175,1.0287842,26/08/2020,-7.44974E+17,0.000540238,1,772.8411 +2297,D139P,0.007298897,2.327973,26/08/2020,-7.44974E+17,0.00691819,7,339.75742 +2298,D139Q,0.026262805,3.4106083,26/08/2020,-7.44974E+17,0.024572344,3,140.10379 +2299,D139R,0.023180628,7.198556,26/08/2020,-7.44974E+17,0.022786368,6,311.58328 +2300,D139S,0.013247303,3.5045438,26/08/2020,-7.44974E+17,0.010982655,1,342.5122 +2301,D139T,0.034619644,5.25548,26/08/2020,-7.44974E+17,0.033787191,4,154.62668 +2302,D139V,0.007423345,2.0056136,26/08/2020,-7.44974E+17,0.007027452,6,294.69528 +2303,D139V; A180V; S220T,0.004105227,1.7560471,26/08/2020,-7.44974E+17,0.001841297,1,669.88855 +2304,D139V; F166L; S220T,0.003456047,1.5399181,26/08/2020,-7.44974E+17,0.00125902,1,769.24133 +2305,D139V; L151P; L176F; H183L; S220T; G244S; M248R,0.002449854,0.9848758,26/08/2020,-7.44974E+17,0.000537001,1,781.5764 +2306,D139W,0.042688414,5.5730433,26/08/2020,-7.44974E+17,0.041732822,3,133.85876 +2307,D139Y,0.03217657,1.23685,26/08/2020,-7.44974E+17,0.030872228,4,45.842297 +2308,D139Y; N238I; G244D,0.003608315,1.564519,26/08/2020,-7.44974E+17,0.001500177,1,708.2484 +2309,D147A,0.023076586,5.0624304,26/08/2020,-7.44974E+17,0.022034676,2,238.88489 +2310,D147A; S160L; M273I,0.003713407,1.5458809,26/08/2020,-7.44974E+17,0.001388336,1,746.24524 +2311,D147A; S220T,0.12096303,5.1069164,26/08/2020,-7.44974E+17,0.122891942,1,44.027714 +2312,D147C,0.027582517,4.287572,26/08/2020,-7.44974E+17,0.025147592,1,192.92125 +2313,D147E,0.05914386,7.11248,26/08/2020,-7.44974E+17,0.058333303,3,119.00695 +2314,D147E; E153G; S220T; N251S; R274H,0.01831206,3.8904536,26/08/2020,-7.44974E+17,0.015691866,1,272.92432 +2315,D147E; M184K,0.002652666,1.1214366,26/08/2020,-7.44974E+17,0.000620658,1,813.28613 +2316,D147E; S220T,0.64125717,5.837289,26/08/2020,-7.44974E+17,0.767372746,1,2.9058828 +2317,D147E; S220T; A229S,0.42533532,5.6398797,26/08/2020,-7.44974E+17,0.477896561,1,7.935957 +2318,D147E; S220T; A277T,0.007393511,3.0772421,26/08/2020,-7.44974E+17,0.005990691,2,494.3641 +2319,D147E; V150A; L193P; S220T,0.3651338,5.4591694,26/08/2020,-7.44974E+17,0.409386884,1,10.181077 +2320,D147F,0.002161954,1.4914935,26/08/2020,-7.44974E+17,0.001540669,4,832.34174 +2321,D147G,0.005529155,2.9292943,26/08/2020,-7.44974E+17,0.004218313,2,623.4091 +2322,D147G; A149E; D170Y; S220T; P224S,0.003191317,1.3885005,26/08/2020,-7.44974E+17,0.001175773,1,717.87994 +2323,D147G; G162V; M273L,0.001430406,0.40665925,26/08/2020,-7.44974E+17,1.68E-05,1,697.1957 +2324,D147G; T188A; R247C,0.002784293,1.1497154,26/08/2020,-7.44974E+17,0.000709028,1,760.16394 +2325,D147K,0.014679672,3.6962683,26/08/2020,-7.44974E+17,0.012219313,1,326.8273 +2326,D147L,0.00420241,1.778987,26/08/2020,-7.44974E+17,0.001810253,1,694.48346 +2327,D147N,0.27198586,5.3496294,26/08/2020,-7.44974E+17,0.294581109,1,16.132774 +2328,D147N; S220T,0.5601019,5.558433,26/08/2020,-7.44974E+17,0.673743619,1,4.234327 +2329,D147N; S220T; M248T; V265E; S301C,0.002505428,1.011037,26/08/2020,-7.44974E+17,0.000506667,1,781.5054 +2330,D147P,0.032452688,6.1277227,26/08/2020,-7.44974E+17,0.031312413,3,194.8986 +2331,D147Q,0.022884816,6.1348925,26/08/2020,-7.44974E+17,0.022498829,3,277.6843 +2332,D147R,0.006547856,3.7530248,26/08/2020,-7.44974E+17,0.00585429,4,617.82336 +2333,D147T,0.023683446,7.3396597,26/08/2020,-7.44974E+17,0.022967266,4,314.09753 +2334,D147V,0.020031422,3.9518812,26/08/2020,-7.44974E+17,0.018123736,1,243.04321 +2335,D147V; A174E,0.00304893,1.2924061,26/08/2020,-7.44974E+17,0.000874933,1,765.67163 +2336,D147V; R148P; S220T; H230Y; A259V,0.003479037,1.5158787,26/08/2020,-7.44974E+17,0.001221174,1,766.40356 +2337,D147V; S160L; G215C,0.002596752,1.097826,26/08/2020,-7.44974E+17,0.000600243,1,785.2636 +2338,D147V; S220P; S225A; V293L,0.003062925,1.8892597,26/08/2020,-7.44974E+17,0.001816626,2,805.79846 +2339,D147V; S220T,0.0307783,5.304567,26/08/2020,-7.44974E+17,0.028631055,2,186.22253 +2340,D147V; S220T; A296G,0.008699721,2.9428189,26/08/2020,-7.44974E+17,0.005762878,1,484.71613 +2341,D147V; S220T; H230Y,0.29323235,5.3883862,26/08/2020,-7.44974E+17,0.31161223,1,14.508714 +2342,D147V; S220T; L253Q,0.003086712,1.3329848,26/08/2020,-7.44974E+17,0.001015763,1,760.6607 +2343,D147V; S220T; S267L,0.004222982,1.7873224,26/08/2020,-7.44974E+17,0.00186764,1,688.1252 +2344,D147W,0.001290426,1.2676263,26/08/2020,-7.44974E+17,0.0006784,4,1220.7413 +2345,D147Y; A197T; S220T,0.003096354,1.2901005,26/08/2020,-7.44974E+17,0.00091184,1,735.38654 +2346,D147Y; L243Q; M291I,0.002841385,1.1655974,26/08/2020,-7.44974E+17,0.000633888,1,738.8417 +2347,D147Y; S220P; Y239F; Q263H,0.002490363,0.91537553,26/08/2020,-7.44974E+17,0.000358631,1,778.7979 +2348,D152A,0.037334133,5.7326,26/08/2020,-7.44974E+17,0.036011648,2,162.88132 +2349,D152C,0.12200532,5.118526,26/08/2020,-7.44974E+17,0.125214338,1,44.07801 +2350,D152F,0.024696743,5.858451,26/08/2020,-7.44974E+17,0.024508912,3,247.5678 +2351,D152G,0.031259406,6.213113,26/08/2020,-7.44974E+17,0.030830816,3,204.20703 +2352,D152G; P164L,0.006484733,2.529781,26/08/2020,-7.44974E+17,0.004241781,1,558.5697 +2353,D152G; S220T,0.23256557,5.3025293,26/08/2020,-7.44974E+17,0.244311478,1,19.834173 +2354,D152H,0.07373787,4.850679,26/08/2020,-7.44974E+17,0.071475006,1,73.67739 +2355,D152H; V250A,0.03440944,4.499354,26/08/2020,-7.44974E+17,0.032076116,1,158.26245 +2356,D152I,0.04235416,5.553329,26/08/2020,-7.44974E+17,0.041077811,2,137.63434 +2357,D152K,0.033287175,6.45447,26/08/2020,-7.44974E+17,0.032188273,3,198.36707 +2358,D152L,0.02847986,5.2773814,26/08/2020,-7.44974E+17,0.027259383,2,198.76312 +2359,D152M,0.054160524,4.3208294,26/08/2020,-7.44974E+17,0.054146675,9,77.75192 +2360,D152N,0.035320677,4.5858436,26/08/2020,-7.44974E+17,0.031247737,1,156.92311 +2361,D152N; M185I; S220T,0.022042837,4.1709843,26/08/2020,-7.44974E+17,0.01932636,1,232.79613 +2362,D152N; S220T,0.34489357,5.4374065,26/08/2020,-7.44974E+17,0.382093545,1,11.358085 +2363,D152N; S220T; G244D; V293A,0.003296398,1.4362136,26/08/2020,-7.44974E+17,0.001160588,1,752.96014 +2364,D152P,0.003481987,1.4944305,26/08/2020,-7.44974E+17,0.001259714,1,724.73676 +2365,D152Q,0.062441763,7.1269035,26/08/2020,-7.44974E+17,0.061543428,3,112.6628 +2366,D152R,0.04078827,4.5490336,26/08/2020,-7.44974E+17,0.040222741,2,119.34867 +2367,D152S,0.040333893,6.828458,26/08/2020,-7.44974E+17,0.040177053,3,170.97835 +2368,D152T,0.055051133,5.513623,26/08/2020,-7.44974E+17,0.05385957,2,103.99044 +2369,D152V,0.19028635,0.77848345,26/08/2020,-7.44974E+17,0.172643144,5,3.8751512 +2370,D152V; E208D,0.26959994,5.398,26/08/2020,-7.44974E+17,0.289473987,1,16.322151 +2371,D152V; F166I; T179S; S220T; K236R,0.00291529,1.2321104,26/08/2020,-7.44974E+17,0.000834736,1,750.5061 +2372,D152V; V175A; A257P,0.00270561,1.1340114,26/08/2020,-7.44974E+17,0.00070819,1,784.84235 +2373,D152W,0.040935885,3.8562155,26/08/2020,-7.44974E+17,0.039794127,2,103.59986 +2374,D152Y,0.06987667,3.8330138,26/08/2020,-7.44974E+17,0.067109179,2,58.88222 +2375,D152Y; A174V; S220T; Q299K,0.002928021,1.274304,26/08/2020,-7.44974E+17,0.00098583,1,780.5454 +2376,D152Y; S220T; H230Y,0.5682027,5.727453,26/08/2020,-7.44974E+17,0.673909635,1,4.126452 +2377,D152Y; S220T; T242S; E268G,0.012446947,3.3950217,26/08/2020,-7.44974E+17,0.010638784,1,359.44635 +2378,D163A,0.11792788,5.2292776,26/08/2020,-7.44974E+17,0.115606257,1,46.639618 +2379,D163C,0.04509487,4.737146,26/08/2020,-7.44974E+17,0.04164117,1,124.866936 +2380,D163E,0.07463683,7.2535844,26/08/2020,-7.44974E+17,0.073860539,3,94.32292 +2381,D163E; L173P; M291V,0.002954287,1.2216398,26/08/2020,-7.44974E+17,0.000831674,1,763.64746 +2382,D163E; L173Q; L189S; S220T; L269S,0.003056664,1.3785392,26/08/2020,-7.44974E+17,0.001165838,1,769.04694 +2383,D163E; N190D; F209Y; A231V,0.002471522,1.0196679,26/08/2020,-7.44974E+17,0.000502608,1,820.10345 +2384,D163E; S220T,0.6650089,6.8744397,26/08/2020,-7.44974E+17,0.740147611,2,3.255001 +2385,D163E; S220T; D233N,0.35044235,5.569249,26/08/2020,-7.44974E+17,0.38657105,1,11.241224 +2386,D163F,0.042986628,5.565167,26/08/2020,-7.44974E+17,0.04186752,5,129.26999 +2387,D163G,0.045207027,3.5216844,26/08/2020,-7.44974E+17,0.044121611,4,80.360634 +2388,D163G; F166L; D211V; L234H,0.002156079,0.7882921,26/08/2020,-7.44974E+17,0.000212415,1,784.3959 +2389,D163G; L181I; S220T; T242A,0.029355526,4.3116837,26/08/2020,-7.44974E+17,0.027916304,1,182.33974 +2390,D163G; N251K,0.007259612,2.6513486,26/08/2020,-7.44974E+17,0.004784115,1,517.7117 +2391,D163G; P270L,0.006396392,2.4152565,26/08/2020,-7.44974E+17,0.003976299,1,557.15857 +2392,D163G; T179S; V281D,0.004763418,1.9750723,26/08/2020,-7.44974E+17,0.002339949,1,648.59753 +2393,D163H,0.032143243,5.615423,26/08/2020,-7.44974E+17,0.030678435,2,185.57318 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R274C,0.016121302,3.8365517,26/08/2020,-7.44974E+17,0.013611612,1,303.12854 +2406,D163W,0.03995139,4.785982,26/08/2020,-7.44974E+17,0.040119653,2,127.3841 +2407,D163Y,0.058506086,6.233156,26/08/2020,-7.44974E+17,0.056183516,3,106.645 +2408,D170A,0.013989615,3.2668417,26/08/2020,-7.44974E+17,0.012930743,3,255.8345 +2409,D170C,0.025174707,5.786028,26/08/2020,-7.44974E+17,0.024550358,4,234.90744 +2410,D170E,0.097893134,4.4870543,26/08/2020,-7.44974E+17,0.096117989,2,46.18876 +2411,D170E; E208V; S220T; H271L,0.4556674,5.8780923,26/08/2020,-7.44974E+17,0.510962204,1,7.0047927 +2412,D170E; E268Q,0.20917287,5.288498,26/08/2020,-7.44974E+17,0.222297271,1,23.039852 +2413,D170E; F209Y; S220T; A228S; V232A; E262K,0.00286964,1.2685328,26/08/2020,-7.44974E+17,0.000890708,1,779.64703 +2414,D170E; L176I; S220T; L234V,0.052773505,4.7204514,26/08/2020,-7.44974E+17,0.052068413,1,102.80315 +2415,D170E; S220T,0.51977473,5.5801334,26/08/2020,-7.44974E+17,0.619618,1,5.005711 +2416,D170E; S220T; H230Y,0.7749399,13.034755,26/08/2020,-7.44974E+17,0.794621907,10,3.7128315 +2417,D170E; S220T; V265A; V293G,0.002560767,0.98531866,26/08/2020,-7.44974E+17,0.000467619,1,773.11566 +2418,D170E; T207S; S220T; A260G,0.006694112,2.5216372,26/08/2020,-7.44974E+17,0.004393105,1,548.8667 +2419,D170E; Y186C; V204E; F217Y,0.002418746,0.92522836,26/08/2020,-7.44974E+17,0.000363393,1,797.54407 +2420,D170F,0.003532941,1.4827307,26/08/2020,-7.44974E+17,0.001340002,1,730.23987 +2421,D170G,0.005842568,2.3265479,26/08/2020,-7.44974E+17,0.003369984,1,574.8322 +2422,D170G; L243V; G264D,0.002362186,0.83498853,26/08/2020,-7.44974E+17,0.000264595,1,752.9455 +2423,D170G; S220T,0.02030687,3.8876724,26/08/2020,-7.44974E+17,0.017991274,1,243.40721 +2424,D170G; S220T; Y290F; F297Y; A303P,0.003042365,1.3036948,26/08/2020,-7.44974E+17,0.000931137,1,775.427 +2425,D170H,0.007079121,3.6854773,26/08/2020,-7.44974E+17,0.00654261,6,546.7866 +2426,D170I,0.006841241,3.1959898,26/08/2020,-7.44974E+17,0.005704315,2,547.1832 +2427,D170K,0.012861032,3.3962395,26/08/2020,-7.44974E+17,0.01084502,1,344.42517 +2428,D170L,0.005936196,2.7246227,26/08/2020,-7.44974E+17,0.004900439,3,528.65015 +2429,D170M,0.011618851,3.364397,26/08/2020,-7.44974E+17,0.008726837,1,389.72504 +2430,D170N,0.08577789,5.002237,26/08/2020,-7.44974E+17,0.086132086,1,63.88707 +2431,D170N; L182V; S220T,0.004653116,1.9574208,26/08/2020,-7.44974E+17,0.002323926,1,646.0843 +2432,D170N; L193H; S220T; A303S,0.002824778,1.159281,26/08/2020,-7.44974E+17,0.000696802,1,797.27814 +2433,D170N; P219S; S220T,0.04367526,4.740875,26/08/2020,-7.44974E+17,0.040307594,1,128.41283 +2434,D170N; S201F; S220T,0.003685329,1.5309192,26/08/2020,-7.44974E+17,0.001385751,1,713.84174 +2435,D170N; S205C; V265E,0.007355472,2.6072364,26/08/2020,-7.44974E+17,0.005195308,1,503.128 +2436,D170N; V175C,0.02285113,4.1617174,26/08/2020,-7.44974E+17,0.02034176,1,225.3631 +2437,D170P,0.005431532,2.8756068,26/08/2020,-7.44974E+17,0.004213871,2,633.2043 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L243P,0.012056123,3.4016166,26/08/2020,-7.44974E+17,0.009732302,1,382.58783 +2583,D95E; S220T,0.5798537,4.6424723,26/08/2020,-7.44974E+17,0.611730029,3,3.2860982 +2584,V281P,0.002564776,1.0795327,26/08/2020,-7.44974E+17,0.000615239,1,777.54663 +2585,D95E; V141E; L151P; H288R,0.002548561,1.0349306,26/08/2020,-7.44974E+17,0.000566432,1,785.49725 +2586,D95E; V221E,0.051459383,4.7486496,26/08/2020,-7.44974E+17,0.050674385,1,106.26859 +2587,D95E; W115C,0.007033977,2.6410973,26/08/2020,-7.44974E+17,0.004857383,1,521.4032 +2588,D95F,0.013242052,3.820248,26/08/2020,-7.44974E+17,0.011936644,2,328.83786 +2589,D95G,0.13965082,3.7697244,26/08/2020,-7.44974E+17,0.138056469,2,26.163486 +2590,D95G; A111T; L227P,0.016270554,3.817211,26/08/2020,-7.44974E+17,0.013518877,1,302.01382 +2591,D95G; A112V; P171H; S220T; P302T,0.015040525,3.6673405,26/08/2020,-7.44974E+17,0.012981621,1,314.39862 +2592,D95G; E108D; F142Y; G146A; G162V; H285Y,0.001942248,0.603913,26/08/2020,-7.44974E+17,8.96E-05,1,724.164 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S220T; V232D,0.002651467,1.1086279,26/08/2020,-7.44974E+17,0.000541119,1,784.0838 +2624,D95V; N190I; L198R,0.001978235,0.6955432,26/08/2020,-7.44974E+17,0.000141082,1,784.6519 +2625,D95V; P106Q,0.002194363,0.7296718,26/08/2020,-7.44974E+17,0.000187844,1,758.33234 +2626,D95V; P138L; R148P; D152E; A197T; S220T; H230Y,0.003659087,1.5823293,26/08/2020,-7.44974E+17,0.001558829,1,724.6052 +2627,D95V; S220T; A229G; A259S; E261G,0.002479603,0.9663363,26/08/2020,-7.44974E+17,0.000435306,1,784.6092 +2628,D95V; S220T; A259T,0.019965263,3.9644423,26/08/2020,-7.44974E+17,0.017711092,1,249.14407 +2629,D95V; S220T; H230Y,0.021201447,4.042082,26/08/2020,-7.44974E+17,0.019073408,1,244.31245 +2630,D95V; S220T; L246F; A259V; Q299R,0.003072508,1.3177931,26/08/2020,-7.44974E+17,0.000934091,1,762.2548 +2631,D95V; S220T; Q283E; P302R,0.021332314,4.0694513,26/08/2020,-7.44974E+17,0.018624392,1,238.7617 +2632,D95V; T172S; S220T; F297S,0.001741806,0.9876261,26/08/2020,-7.44974E+17,0.000617691,2,860.2058 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H230Y,0.002901165,1.2798141,26/08/2020,-7.44974E+17,0.000920024,1,751.7586 +2654,E108D; S205C,0.03596046,4.526683,26/08/2020,-7.44974E+17,0.033209223,1,152.91559 +2655,E108D; S220T; R247L,0.15758151,4.6157193,26/08/2020,-7.44974E+17,0.160370409,2,27.102406 +2656,E108D; S220T; S267P,0.009314325,3.095781,26/08/2020,-7.44974E+17,0.007304178,1,446.70038 +2657,E108F,0.015684646,0.91274375,26/08/2020,-7.44974E+17,0.015428778,7,66.35421 +2658,E108G; D118E; S220T; H230Y,0.14539678,5.2524166,26/08/2020,-7.44974E+17,0.146616028,1,36.467182 +2659,E108G; D163E; D170V; G215D,0.001204195,0.6121864,26/08/2020,-7.44974E+17,0.000159357,2,915.1536 +2660,E108G; V150G; M185T; S220T,0.003118596,1.3464143,26/08/2020,-7.44974E+17,0.000946047,1,789.8758 +2661,E108H,0.021006573,4.1037173,26/08/2020,-7.44974E+17,0.018137534,1,242.99428 +2662,E108I,0.007255711,0.9582932,26/08/2020,-7.44974E+17,0.006483361,4,167.84544 +2663,E108K,0.00659342,2.9068336,26/08/2020,-7.44974E+17,0.00609639,3,495.00726 +2664,E108K; 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S220T,0.004989377,2.0332546,26/08/2020,-7.44974E+17,0.002406203,1,633.10724 +3084,E91G; F297S,0.00211136,0.7189054,26/08/2020,-7.44974E+17,0.000191407,1,772.46136 +3085,E91G; L182P; L214R; S220T; L272Q,0.002613307,1.0300058,26/08/2020,-7.44974E+17,0.00045329,1,799.66565 +3086,E91G; P240F; G241S,0.011620736,3.333389,26/08/2020,-7.44974E+17,0.009246263,1,386.5705 +3087,E91G; V135A; Y177H; T179I; M185I,0.003796604,1.6007278,26/08/2020,-7.44974E+17,0.001491093,1,716.91595 +3088,E91G; V99D; L101Q; H154Q,0.00922269,3.0610673,26/08/2020,-7.44974E+17,0.006768283,1,458.4104 +3089,E91G; W115R; D152G; S220T,0.003126594,1.4003991,26/08/2020,-7.44974E+17,0.001160246,1,783.8295 +3090,E91H,0.080546826,5.041924,26/08/2020,-7.44974E+17,0.077654104,1,69.77216 +3091,E91H; V97F,0.020588573,4.0988193,26/08/2020,-7.44974E+17,0.018515541,1,250.24117 +3092,E91I,0.13839497,5.157112,26/08/2020,-7.44974E+17,0.140240893,1,37.67278 +3093,E91K; A112T,0.011882268,3.3854818,26/08/2020,-7.44974E+17,0.009790336,1,368.0139 +3094,E91K; F209S,0.003602553,1.5510843,26/08/2020,-7.44974E+17,0.001325369,1,740.2753 +3095,E91L,0.065932654,5.2653832,26/08/2020,-7.44974E+17,0.064439525,2,82.202545 +3096,E91M,0.07975902,5.025493,26/08/2020,-7.44974E+17,0.079349413,1,69.22866 +3097,E91P; A92T,0.028594425,4.401932,26/08/2020,-7.44974E+17,0.02588511,1,189.55026 +3098,E91Q; R137C; E153K,0.044937756,4.6613836,26/08/2020,-7.44974E+17,0.042532826,1,121.96099 +3099,E91Q; R96L,0.013044107,3.4275677,26/08/2020,-7.44974E+17,0.01100818,1,342.7766 +3100,E91R,0.060858794,7.0006385,26/08/2020,-7.44974E+17,0.059718558,3,114.014206 +3101,E91S,0.06683649,6.1578403,26/08/2020,-7.44974E+17,0.064548717,2,94.08325 +3102,E91T,0.08841622,5.0043926,26/08/2020,-7.44974E+17,0.08705548,1,61.673546 +3103,E91V,0.13144621,0.43091962,26/08/2020,-7.44974E+17,0.135980654,7,3.575187 +3104,E91V; A156N; A159S,0.23511322,1.0277209,26/08/2020,-7.44974E+17,0.236788058,6,3.6653326 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P300L,0.003231941,1.2988727,26/08/2020,-7.44974E+17,0.000905935,1,767.61194 +3116,E91V; R122S; Y144N,0.001963762,0.6271639,26/08/2020,-7.44974E+17,0.000106819,1,787.0032 +3117,E91V; S160D; A296I,0.6536434,8.042942,26/08/2020,-7.44974E+17,0.672658129,8,4.189372 +3118,E91V; S160D; T188G,0.35849515,1.154376,26/08/2020,-7.44974E+17,0.36752966,10,2.1400206 +3119,E91V; S220T,0.5439361,5.7364964,26/08/2020,-7.44974E+17,0.632955678,1,4.641608 +3120,E91V; T105S; S220T; A282V,0.16299726,5.2392387,26/08/2020,-7.44974E+17,0.165490899,1,31.284231 +3121,E91V; T121S,0.11439065,6.7996273,26/08/2020,-7.44974E+17,0.114580212,3,55.2709 +3122,E91V; T207A; T242P; M248K,0.002877296,1.188348,26/08/2020,-7.44974E+17,0.000693857,1,769.85626 +3123,E91V; Y177H; H183P; A202E; S220T; A228E; A282T,0.003745993,1.6172175,26/08/2020,-7.44974E+17,0.001568033,1,714.536 +3124,E91W,0.10794062,5.0748644,26/08/2020,-7.44974E+17,0.110459739,1,50.070705 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M273K,0.005231099,2.1540422,26/08/2020,-7.44974E+17,0.003005445,1,613.37897 +3146,F142L; R165S; L176V; S220T; L234H; Q263H; S301C,0.003015253,1.3443536,26/08/2020,-7.44974E+17,0.001003327,1,757.07043 +3147,F142L; S220T,0.24531154,5.4015665,26/08/2020,-7.44974E+17,0.259718787,1,18.5198 +3148,F142L; Y177F; D235Y,0.046721037,4.748648,26/08/2020,-7.44974E+17,0.043316042,1,123.11667 +3149,F142M,0.037621718,5.5873694,26/08/2020,-7.44974E+17,0.036827236,2,157.0659 +3150,F142N,0.004253665,2.835359,26/08/2020,-7.44974E+17,0.003444698,3,757.25476 +3151,F142P,0.002338294,0.93279934,26/08/2020,-7.44974E+17,0.001266348,2,633.604 +3152,F142Q,0.006937462,2.8818762,26/08/2020,-7.44974E+17,0.00614713,3,467.25235 +3153,F142R,0.001649016,1.2256511,26/08/2020,-7.44974E+17,0.000802518,3,972.1679 +3154,F142S,0.009251169,3.770481,26/08/2020,-7.44974E+17,0.007961814,2,469.10617 +3155,F142S; G284C,0.002421738,0.9905414,26/08/2020,-7.44974E+17,0.00046993,1,800.4916 +3156,F142S; L158I; G284S,0.002171082,0.7182957,26/08/2020,-7.44974E+17,0.000165042,1,771.2493 +3157,F142S; P164A; V175A; N178D; H183Y,0.003024459,1.1918646,26/08/2020,-7.44974E+17,0.000668559,1,807.9461 +3158,F142S; R148P; S220T; H230Y,0.44393885,5.610885,26/08/2020,-7.44974E+17,0.506621139,1,7.2787027 +3159,F142S; S160T,0.00566802,2.2682958,26/08/2020,-7.44974E+17,0.003374268,1,585.05066 +3160,F142S; S220T,0.01744578,3.8806868,26/08/2020,-7.44974E+17,0.014533072,1,287.05716 +3161,F142S; S220T; H255R,0.003330418,1.3796961,26/08/2020,-7.44974E+17,0.001066261,1,735.97845 +3162,F142S; Y144D; Y177H; S220T; S226F; Q283H,0.002720791,1.1234246,26/08/2020,-7.44974E+17,0.000597565,1,766.0481 +3163,F142T,0.08464948,3.1087742,26/08/2020,-7.44974E+17,0.082517827,3,37.347897 +3164,F142V,0.052027453,4.707671,26/08/2020,-7.44974E+17,0.050453638,1,105.08181 +3165,F142Y; L158H; A180T; L222P,0.002959412,1.2296203,26/08/2020,-7.44974E+17,0.000826324,1,773.6113 +3166,F142Y; L189I; S220T,0.47870594,5.727204,26/08/2020,-7.44974E+17,0.56061304,1,6.2640047 +3167,F142Y; S226T,0.06906207,4.9717216,26/08/2020,-7.44974E+17,0.067978385,1,80.49004 +3168,F142Y; Y186C; L198Q; S220T; V221L; L222P,0.003661732,1.5219007,26/08/2020,-7.44974E+17,0.001456716,1,705.8647 +3169,F166A,0.002839688,1.2460874,26/08/2020,-7.44974E+17,0.00087671,1,782.0286 +3170,F166C,0.00701934,2.541673,26/08/2020,-7.44974E+17,0.004656623,1,501.8464 +3171,F166C; G215C; S220T; H230Y; Q263K; V281I,0.003350735,1.4626453,26/08/2020,-7.44974E+17,0.001222322,1,732.96027 +3172,F166C; S220T,0.010319104,3.1318417,26/08/2020,-7.44974E+17,0.007689103,1,420.31183 +3173,F166D,0.010696797,3.314154,26/08/2020,-7.44974E+17,0.007544859,1,426.6262 +3174,F166E,0.00316239,2.2530696,26/08/2020,-7.44974E+17,0.002319846,3,839.5571 +3175,F166G,0.001199441,1.1846381,26/08/2020,-7.44974E+17,0.000602892,4,1250.6265 +3176,F166H,0.06834508,4.928484,26/08/2020,-7.44974E+17,0.066939474,1,81.225845 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S301R,0.002827111,1.6396819,26/08/2020,-7.44974E+17,0.001562054,2,781.26575 +3188,F166L; S220T; L246H; V250E; A282V; N289Y,0.003025278,1.3525146,26/08/2020,-7.44974E+17,0.001108852,1,766.6501 +3189,F166L; V232D; T245A; E275D; Q283H,0.003437481,1.4736035,26/08/2020,-7.44974E+17,0.001207601,1,748.74805 +3190,F166N,0.006434359,3.1967654,26/08/2020,-7.44974E+17,0.005154398,2,585.7302 +3191,F166P,0.002882361,1.2407151,26/08/2020,-7.44974E+17,0.000827521,1,777.67334 +3192,F166R,0.001116005,1.2431989,26/08/2020,-7.44974E+17,0.000674681,5,1299.9836 +3193,F166S,0.003075159,1.3390155,26/08/2020,-7.44974E+17,0.00103807,1,752.1034 +3194,F166S; S220T,0.004991683,2.0520651,26/08/2020,-7.44974E+17,0.002662044,1,642.1015 +3195,F166T,0.00119343,1.1596859,26/08/2020,-7.44974E+17,0.000583865,4,1225.5717 +3196,F166V,0.002197371,2.1829016,26/08/2020,-7.44974E+17,0.001619571,4,1125.4227 +3197,F166V; G237D; P302Q,0.003013698,1.3455867,26/08/2020,-7.44974E+17,0.001057892,1,756.0905 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Q299R,0.002553276,0.97578716,26/08/2020,-7.44974E+17,0.000420512,1,776.754 +3221,F217L; Q299L,0.052735593,4.7203646,26/08/2020,-7.44974E+17,0.049902613,1,104.8732 +3222,F217L; S220T; E268A,0.011835209,3.3516734,26/08/2020,-7.44974E+17,0.009368053,1,373.24435 +3223,F217N,0.052722935,4.7229557,26/08/2020,-7.44974E+17,0.052264339,1,103.91597 +3224,F217S,0.024629362,4.1704564,26/08/2020,-7.44974E+17,0.021599181,1,208.89453 +3225,F217S; P300L,0.003033996,1.2251633,26/08/2020,-7.44974E+17,0.000806162,1,746.87115 +3226,F217S; S220T,0.03084243,5.5285044,26/08/2020,-7.44974E+17,0.029412289,3,185.54921 +3227,F217T,0.051269118,4.7237062,26/08/2020,-7.44974E+17,0.050199671,1,105.87606 +3228,F217V; S220T,0.06639745,5.009564,26/08/2020,-7.44974E+17,0.064088604,1,86.862114 +3229,F217Y,0.36429828,5.475514,26/08/2020,-7.44974E+17,0.40745692,1,10.245256 +3230,F217Y; N289S,0.3343389,5.5293493,26/08/2020,-7.44974E+17,0.338645218,4,11.286717 +3231,F217Y; T242A,0.3465995,5.8117595,26/08/2020,-7.44974E+17,0.348062868,23,10.998512 +3232,F217Y; T242A; N289S,0.3475153,3.7187676,26/08/2020,-7.44974E+17,0.355902441,9,7.0911264 +3233,F217Y; V293F,0.002453913,0.91294366,26/08/2020,-7.44974E+17,0.000362325,1,752.4321 +3234,F297C,0.003434742,1.1062424,26/08/2020,-7.44974E+17,0.002682963,3,429.9036 +3235,F297D,0.000725657,0.5757975,26/08/2020,-7.44974E+17,0.000156944,4,1208.6024 +3236,F297E,0.000804533,0.40542963,26/08/2020,-7.44974E+17,8.96E-05,3,924.96796 +3237,F297G,0.00186585,0.58857703,26/08/2020,-7.44974E+17,7.74E-05,1,784.527 +3238,F297H,0.002261081,0.77795994,26/08/2020,-7.44974E+17,0.000239212,1,777.47144 +3239,F297K; K298Q; Q299P; P300S; S301L; P302A; A303S; S304*; *305-,0.018800814,3.9291816,26/08/2020,-7.44974E+17,0.01624559,1,267.04443 +3240,F297L,0.112447746,2.4171968,26/08/2020,-7.44974E+17,0.11296975,4,21.007206 +3241,F297M,0.03740578,5.295915,26/08/2020,-7.44974E+17,0.036809071,2,151.89569 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S103P; L119P,0.002980472,1.2224226,26/08/2020,-7.44974E+17,0.000747343,1,791.978 +3255,F86D,0.001378445,0.8966114,26/08/2020,-7.44974E+17,0.00082631,4,861.45013 +3256,F86E,0.002612289,1.1633914,26/08/2020,-7.44974E+17,0.000724138,1,810.7841 +3257,F86G,0.004453674,2.8256068,26/08/2020,-7.44974E+17,0.003540192,3,726.21075 +3258,F86H,0.00278175,1.7639384,26/08/2020,-7.44974E+17,0.001687722,2,840.2391 +3259,F86I,0.08640392,5.2415094,26/08/2020,-7.44974E+17,0.086609195,2,60.83281 +3260,F86I; A127S,0.009105755,3.0528865,26/08/2020,-7.44974E+17,0.006838288,1,450.25565 +3261,F86I; E91V,0.047156643,4.677842,26/08/2020,-7.44974E+17,0.044972006,1,116.51784 +3262,F86I; M129K; K236R; V265I,0.003254494,1.3985476,26/08/2020,-7.44974E+17,0.001116315,1,760.64667 +3263,F86I; M185K; S220T; D287V; M291T,0.002269955,0.9279866,26/08/2020,-7.44974E+17,0.000395794,1,795.6394 +3264,F86I; Q263H,0.005293584,2.192345,26/08/2020,-7.44974E+17,0.002978622,1,633.2958 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V204E,0.002215053,0.82197154,26/08/2020,-7.44974E+17,0.000243769,1,813.4462 +3276,F86L; M185T; S220T; M273V,0.002700946,1.149467,26/08/2020,-7.44974E+17,0.000731887,1,782.468 +3277,F86L; N102S; G191E; A218T; E261G,0.004214706,1.7883384,26/08/2020,-7.44974E+17,0.001908267,1,674.3712 +3278,F86L; R148P; P171S; S220T; H230Y,0.6202619,5.6847477,26/08/2020,-7.44974E+17,0.75498482,1,3.1886973 +3279,F86L; S103P; Q263H,0.004647358,1.9901888,26/08/2020,-7.44974E+17,0.002553746,1,650.37634 +3280,F86L; S220T; A260G,0.059248067,4.8206086,26/08/2020,-7.44974E+17,0.057287094,1,93.5994 +3281,F86L; S220T; M273L; H288R,0.011405339,3.3352747,26/08/2020,-7.44974E+17,0.008950217,1,383.64673 +3282,F86L; S220T; S292Y,0.035688933,4.528112,26/08/2020,-7.44974E+17,0.033289832,1,154.10062 +3283,F86N,0.003594221,1.5853688,26/08/2020,-7.44974E+17,0.001576425,1,708.19495 +3284,F86P,0.002329536,0.9061898,26/08/2020,-7.44974E+17,0.000383491,1,766.4302 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N238S,0.030448733,4.5195265,26/08/2020,-7.44974E+17,0.027162445,1,179.44327 +3296,F86S; T172S; V265A; Q283H,0.002289133,0.87066334,26/08/2020,-7.44974E+17,0.000308808,1,768.13293 +3297,F86S; T245H,0.002962794,1.3022645,26/08/2020,-7.44974E+17,0.000965042,1,792.4366 +3298,F86S; W216R; S220T; K298R,0.003583141,1.5632045,26/08/2020,-7.44974E+17,0.001406411,1,756.05475 +3299,F86T,0.021695647,4.0344567,26/08/2020,-7.44974E+17,0.019564675,1,235.52132 +3300,F86T; V199A,0.010938013,3.3254437,26/08/2020,-7.44974E+17,0.008045094,1,414.75226 +3301,F86W,0.008411408,3.9332898,26/08/2020,-7.44974E+17,0.007640963,3,510.51468 +3302,F86Y,0.018184364,4.4149566,26/08/2020,-7.44974E+17,0.01655451,2,271.91888 +3303,F86Y; D170V; S205G; S220T,0.004187037,1.7555354,26/08/2020,-7.44974E+17,0.001874261,1,679.7254 +3304,F86Y; E108V; S220T; G244C,0.00263064,1.0569487,26/08/2020,-7.44974E+17,0.000569875,1,764.2549 +3305,F86Y; H154Q; H183L; D233E,0.002118525,0.7670607,26/08/2020,-7.44974E+17,0.000215464,1,758.41693 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G203S; S220T; L272Q,0.003070902,1.2538487,26/08/2020,-7.44974E+17,0.000834981,1,762.1885 +3327,G104C; T121S; T207P; S220T,0.002947112,1.2220987,26/08/2020,-7.44974E+17,0.000747237,1,784.89386 +3328,G104D,0.001648573,1.1290303,26/08/2020,-7.44974E+17,0.000778997,3,936.1377 +3329,G104D; A112D; P164Q; V250A,0.003039931,1.2801869,26/08/2020,-7.44974E+17,0.000896182,1,760.94586 +3330,G104D; D125V; E153D; R165S; M185I; L193H,0.005692554,2.2764611,26/08/2020,-7.44974E+17,0.003431213,1,589.20483 +3331,G104D; D147E; R148P; L182Q; S220T; H230Y; G264A; S304P,0.00264785,1.1121434,26/08/2020,-7.44974E+17,0.000621908,1,813.26605 +3332,G104D; D163V; A174V; T188S; A213P; K236R; G244D; H271Y,0.002926784,1.1844726,26/08/2020,-7.44974E+17,0.000777793,1,777.0935 +3333,G104D; G126S; S226Y; L269M; Y294C,0.00241623,1.0269841,26/08/2020,-7.44974E+17,0.000525167,1,784.9807 +3334,G104D; H183R; L214H,0.002331042,0.81860906,26/08/2020,-7.44974E+17,0.000271418,1,767.38586 +3335,G104D; I128T; T157S; S220T; H266L,0.005012338,2.0449007,26/08/2020,-7.44974E+17,0.002577012,1,619.9028 +3336,G104D; L189I; L193H; S220T,0.003086577,1.375432,26/08/2020,-7.44974E+17,0.00105858,1,743.8305 +3337,G104D; R148P; A156V; S220T; H230Y,0.00180619,0.54494226,26/08/2020,-7.44974E+17,7.00E-05,1,722.1238 +3338,G104D; R148P; R155H; S220T; H230Y,0.004148,1.7698306,26/08/2020,-7.44974E+17,0.001950249,1,697.4006 +3339,G104D; S220T; G237S; R279P; V293F,0.002501642,0.9963685,26/08/2020,-7.44974E+17,0.00050839,1,786.5984 +3340,G104D; S220T; H230Y,0.001740912,0.53395665,26/08/2020,-7.44974E+17,5.25E-05,1,781.0373 +3341,G104D; V141E; R155H; L181I; Q194H; E268V,0.002700101,1.0750692,26/08/2020,-7.44974E+17,0.000627183,1,793.6049 +3342,G104D; V150A,0.0025018,0.93280965,26/08/2020,-7.44974E+17,0.000353786,1,779.23737 +3343,G104F,0.002868586,1.2381971,26/08/2020,-7.44974E+17,0.000739974,1,787.0369 +3344,G104H,0.002412272,1.0206786,26/08/2020,-7.44974E+17,0.000483137,1,787.70306 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L272P,0.001930199,0.61257935,26/08/2020,-7.44974E+17,8.59E-05,1,710.4161 +3357,G104S; A156V; T196A; L214H,0.00424356,1.7926993,26/08/2020,-7.44974E+17,0.001913654,1,662.36334 +3358,G104S; G244C; T245R,0.003634852,1.5276881,26/08/2020,-7.44974E+17,0.001330247,1,713.79834 +3359,G104S; M129T; M185L; L227Q; A260S,0.004684383,1.9875828,26/08/2020,-7.44974E+17,0.002528366,1,649.0888 +3360,G104S; R148P; R165H; S220T; H230Y,0.010811963,3.2164361,26/08/2020,-7.44974E+17,0.008209133,1,396.5122 +3361,G104S; S220T; H230Q; G284S,0.004127347,1.7454613,26/08/2020,-7.44974E+17,0.001748035,1,686.32025 +3362,G104T,0.001112701,0.96036613,26/08/2020,-7.44974E+17,0.000538235,4,1106.862 +3363,G104V,0.00116152,1.1764289,26/08/2020,-7.44974E+17,0.00061623,4,1260.574 +3364,G104W,0.001166599,0.84033346,26/08/2020,-7.44974E+17,0.000383474,3,1056.7759 +3365,G104Y,0.010634928,0.4969066,26/08/2020,-7.44974E+17,0.010279355,5,66.23523 +3366,G120A,0.092972815,0.75346243,26/08/2020,-7.44974E+17,0.097418098,5,9.241507 +3367,G120C,0.09178721,0.6220452,26/08/2020,-7.44974E+17,0.095914526,7,7.505441 +3368,G120C; A252E,0.003458031,1.5237155,26/08/2020,-7.44974E+17,0.001352603,1,742.5531 +3369,G120C; I212F; P270L,0.005313058,2.0995755,26/08/2020,-7.44974E+17,0.002764798,1,607.974 +3370,G120C; I212F; S220T,0.23436001,5.270392,26/08/2020,-7.44974E+17,0.253825427,1,19.620127 +3371,G120C; L158P; N178S; T179I; S205R; A218E; S292T,0.003164321,1.3636394,26/08/2020,-7.44974E+17,0.001046379,1,762.8751 +3372,G120C; P131S; G136D; T172A; S220T,0.002400698,0.8944782,26/08/2020,-7.44974E+17,0.000306974,1,776.6256 +3373,G120C; S220T,0.20928022,5.33425,26/08/2020,-7.44974E+17,0.21609152,1,23.027248 +3374,G120C; S220T; P224L,0.008144602,2.8364897,26/08/2020,-7.44974E+17,0.005459982,1,497.58295 +3375,G120D,0.119864464,0.5235152,26/08/2020,-7.44974E+17,0.134961265,8,4.5999713 +3376,G120D; A180T; S220T,0.002696392,1.0367062,26/08/2020,-7.44974E+17,0.00053184,1,758.46643 +3377,G120D; A218T,0.092584126,5.004869,26/08/2020,-7.44974E+17,0.092336635,1,59.525906 +3378,G120D; D163E; E254V,0.06654206,4.9596267,26/08/2020,-7.44974E+17,0.063203479,1,83.57677 +3379,G120D; R148P; S220T; H230Y,0.5269109,5.6765776,26/08/2020,-7.44974E+17,0.613674669,1,5.0226364 +3380,G120D; R155H; H288Y,0.016498521,3.8156157,26/08/2020,-7.44974E+17,0.014283653,1,290.60153 +3381,G120D; S220T; H271Q; V293I,0.65318406,5.9178123,26/08/2020,-7.44974E+17,0.790813815,1,2.7882693 +3382,G120D; S220T; P270T,0.011692119,3.3749063,26/08/2020,-7.44974E+17,0.009330769,1,382.76166 +3383,G120E,0.22138986,6.0096908,26/08/2020,-7.44974E+17,0.230198888,2,22.45703 +3384,G120F,0.031001663,0.77563184,26/08/2020,-7.44974E+17,0.031301287,5,30.468662 +3385,G120H,0.19650678,0.82526153,26/08/2020,-7.44974E+17,0.222457536,6,3.856144 +3386,G120I,0.004345642,3.2228603,26/08/2020,-7.44974E+17,0.003837535,5,795.45557 +3387,G120K,0.19202906,0.66470236,26/08/2020,-7.44974E+17,0.222613778,6,3.2479033 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Y294F,0.00265621,1.0079176,26/08/2020,-7.44974E+17,0.000531313,1,792.80176 +3399,G120S; S205R; S220T; E268V,0.016498473,3.7687016,26/08/2020,-7.44974E+17,0.014024902,1,292.7162 +3400,G120S; S220T,0.51804,6.7698298,26/08/2020,-7.44974E+17,0.561436274,2,6.274894 +3401,G120S; S220T; N289Y,0.004266907,1.7809266,26/08/2020,-7.44974E+17,0.001903263,1,670.2002 +3402,G120S; V150A; D233E; D287E,0.01357054,3.5677106,26/08/2020,-7.44974E+17,0.011241924,1,340.13278 +3403,G120T,0.06804541,1.030876,26/08/2020,-7.44974E+17,0.068655918,8,15.949554 +3404,G120T; A202V,0.06676012,4.8391333,26/08/2020,-7.44974E+17,0.06618307,1,82.653145 +3405,G120V,0.014316842,3.5699985,26/08/2020,-7.44974E+17,0.013854574,6,259.08505 +3406,G120V; S225A,0.015537517,3.7270548,26/08/2020,-7.44974E+17,0.013006471,1,303.3588 +3407,G120V; V141G; P171T; S220T; P270L,0.00222905,0.8627771,26/08/2020,-7.44974E+17,0.000297858,1,783.6698 +3408,G120W,0.013426665,1.3854718,26/08/2020,-7.44974E+17,0.012518495,4,124.4236 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A127C,0.0025603,0.9770886,26/08/2020,-7.44974E+17,0.000445917,1,783.14874 +3421,G126D; S220T; V281D,0.003731235,1.6444559,26/08/2020,-7.44974E+17,0.001632446,1,695.5994 +3422,G126E,0.001160711,1.4682133,26/08/2020,-7.44974E+17,0.000843141,7,1407.4803 +3423,G126F,0.001496565,1.1980072,26/08/2020,-7.44974E+17,0.000702802,3,1059.889 +3424,G126F; R279C,0.002367783,0.98263043,26/08/2020,-7.44974E+17,0.000462613,1,778.1015 +3425,G126H,0.001161528,1.650153,26/08/2020,-7.44974E+17,0.000869648,8,1552.4541 +3426,G126I,0.001155177,1.6486773,26/08/2020,-7.44974E+17,0.000896139,9,1533.7135 +3427,G126K,0.00202218,1.2607162,26/08/2020,-7.44974E+17,0.000841302,2,947.4967 +3428,G126L,0.002416574,0.9195131,26/08/2020,-7.44974E+17,0.000375081,1,782.9935 +3429,G126P,0.000932637,1.2603388,26/08/2020,-7.44974E+17,0.000595538,7,1534.7612 +3430,G126Q,0.001888485,1.1765556,26/08/2020,-7.44974E+17,0.000812468,2,904.2127 +3431,G126R,0.001575155,1.4206166,26/08/2020,-7.44974E+17,0.000968781,4,1089.6349 +3432,G126R; F142Y; T172I,0.002045606,0.7110846,26/08/2020,-7.44974E+17,0.000172693,1,794.77057 +3433,G126R; H154L; P224H; D235G; A282P,0.002222932,0.8462536,26/08/2020,-7.44974E+17,0.000318894,1,780.3009 +3434,G126R; S220T; A231T,0.003433308,1.4874326,26/08/2020,-7.44974E+17,0.001365074,1,751.0518 +3435,G126S,0.001609505,1.2649047,26/08/2020,-7.44974E+17,0.00081411,3,1022.3069 +3436,G126S; F209I; S220T,0.003186027,1.5035444,26/08/2020,-7.44974E+17,0.001278402,1,774.3527 +3437,G126S; I128F; P138S; R148P; S220T; H230Y,0.5874486,5.880144,26/08/2020,-7.44974E+17,0.68374653,1,3.879587 +3438,G126T,0.002969791,1.2773918,26/08/2020,-7.44974E+17,0.000897095,1,767.0262 +3439,G126T; A260E,0.002516341,1.0079666,26/08/2020,-7.44974E+17,0.000495823,1,791.5395 +3440,G126V,0.001640449,0.9649726,26/08/2020,-7.44974E+17,0.000473961,2,914.3508 +3441,G126V; A228T; R274H,0.003268185,1.4255221,26/08/2020,-7.44974E+17,0.001070709,1,746.4425 +3442,G126V; G191R,0.002128859,0.75100285,26/08/2020,-7.44974E+17,0.000217103,1,746.1057 +3443,G126V; P131L,0.003201258,1.4513423,26/08/2020,-7.44974E+17,0.001145748,1,770.9514 +3444,G126W,0.001044182,1.3770771,26/08/2020,-7.44974E+17,0.000653763,6,1500.0212 +3445,G126Y,0.001125813,1.4256858,26/08/2020,-7.44974E+17,0.000758471,8,1411.4215 +3446,G136A,0.009593321,3.0380054,26/08/2020,-7.44974E+17,0.006957968,1,436.18753 +3447,G136A; F209L; D287Y,0.010628029,3.2610576,26/08/2020,-7.44974E+17,0.008386272,1,411.54797 +3448,G136A; S220T,0.16211775,5.0714946,26/08/2020,-7.44974E+17,0.171011876,1,30.661837 +3449,G136C,0.002493779,1.7606865,26/08/2020,-7.44974E+17,0.001675998,3,870.4569 +3450,G136C; F217C; D287V; V293I; Q299H,0.004090576,1.8018411,26/08/2020,-7.44974E+17,0.0019543,1,686.6813 +3451,G136C; L143S; G168R; S220T,0.002232478,0.8818559,26/08/2020,-7.44974E+17,0.000328509,1,812.97894 +3452,G136C; R148S; S220T; M248V,0.003000407,1.254216,26/08/2020,-7.44974E+17,0.000844996,1,761.0784 +3453,G136C; S220T,0.010643218,1.5973988,26/08/2020,-7.44974E+17,0.009570099,2,199.23425 +3454,G136C; T242S,0.00457579,1.9490178,26/08/2020,-7.44974E+17,0.002274406,1,667.2374 +3455,G136C; Y192C; W216C; P240L; E278D,0.002852638,1.168852,26/08/2020,-7.44974E+17,0.000656631,1,749.438 +3456,G136D,0.005150968,2.1544957,26/08/2020,-7.44974E+17,0.003121059,1,614.27844 +3457,G136D; A156T; K236N,0.004242865,1.821391,26/08/2020,-7.44974E+17,0.002007435,1,684.6965 +3458,G136D; D147E; E153V; L193P; E261A,0.004296118,1.7600567,26/08/2020,-7.44974E+17,0.001883056,1,697.53076 +3459,G136D; S220T; H230Y,0.0491822,4.747558,26/08/2020,-7.44974E+17,0.047834396,1,112.92094 +3460,G136D; T157P; A202V; S220T,0.002776681,1.1241919,26/08/2020,-7.44974E+17,0.000669848,1,736.44 +3461,G136E,0.002805285,1.7325642,26/08/2020,-7.44974E+17,0.001541584,2,839.44 +3462,G136F,0.001479674,1.5393682,26/08/2020,-7.44974E+17,0.000954587,4,1234.2642 +3463,G136H,0.003796474,2.6426911,26/08/2020,-7.44974E+17,0.003317145,5,750.991 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S220T; H230Y; E295V,0.006935519,2.5941033,26/08/2020,-7.44974E+17,0.004889976,1,527.4724 +3476,G136S; S220T; M248T; R258H; V293A,0.002954386,1.199902,26/08/2020,-7.44974E+17,0.000765837,1,742.63367 +3477,G136S; V150A,0.003021446,1.1938823,26/08/2020,-7.44974E+17,0.000784631,1,750.62836 +3478,G136V,0.002583229,1.6148635,26/08/2020,-7.44974E+17,0.001444285,2,826.5458 +3479,G136V; D139G,0.003518903,1.4484797,26/08/2020,-7.44974E+17,0.001168884,1,734.5429 +3480,G136V; N238S; H266Y,0.003041179,1.308187,26/08/2020,-7.44974E+17,0.00097225,1,761.8528 +3481,G136V; S160P; S220T,0.003402753,1.422305,26/08/2020,-7.44974E+17,0.001100047,1,763.1802 +3482,G136V; S220T,0.007204699,2.6052282,26/08/2020,-7.44974E+17,0.004586149,1,516.3713 +3483,G136V; S220T; D235N; N289S,0.002128457,0.7271906,26/08/2020,-7.44974E+17,0.000171466,1,824.18726 +3484,G136V; S220T; K236R,0.003521659,1.5509095,26/08/2020,-7.44974E+17,0.001487868,1,712.39557 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H230Y,0.00341305,1.428414,26/08/2020,-7.44974E+17,0.001214089,1,716.1763 +3496,G146D; S220T,0.0030221,1.6423937,26/08/2020,-7.44974E+17,0.001790524,2,750.24963 +3497,G146E,0.001704089,1.0619892,26/08/2020,-7.44974E+17,0.00063116,2,934.59875 +3498,G146I,0.001169479,0.95814043,26/08/2020,-7.44974E+17,0.000452662,3,1126.66 +3499,G146K,0.001548359,0.8826584,26/08/2020,-7.44974E+17,0.000472482,2,920.32434 +3500,G146L,0.00152009,0.89167356,26/08/2020,-7.44974E+17,0.000397767,2,935.08826 +3501,G146N,0.002089747,0.69184464,26/08/2020,-7.44974E+17,0.001738143,7,408.45682 +3502,G146Q,0.001610012,0.97218096,26/08/2020,-7.44974E+17,0.000527469,2,956.2151 +3503,G146R,0.001757301,0.97001123,26/08/2020,-7.44974E+17,0.000597077,2,853.0254 +3504,G146R; H154L; S220T,0.002252523,1.4206464,26/08/2020,-7.44974E+17,0.001045588,2,882.8775 +3505,G146S,0.001237017,0.9303258,26/08/2020,-7.44974E+17,0.000406227,3,1067.2202 +3506,G146S; R148P; S220T; H230Y,0.006863125,2.616368,26/08/2020,-7.44974E+17,0.004371078,1,548.83405 +3507,G146S; S220T; H230Y,0.002783625,1.2189593,26/08/2020,-7.44974E+17,0.000783621,1,779.32666 +3508,G146T,0.001056649,0.788104,26/08/2020,-7.44974E+17,0.000301254,3,1078.1705 +3509,G146V,0.001309311,1.0100733,26/08/2020,-7.44974E+17,0.000521852,3,1061.6339 +3510,G146V; Q299H,0.002970078,1.160858,26/08/2020,-7.44974E+17,0.000638045,1,795.3459 +3511,G146V; R148Y,0.00295021,1.2661259,26/08/2020,-7.44974E+17,0.000820264,1,791.9116 +3512,G146W,0.00222885,0.8633856,26/08/2020,-7.44974E+17,0.000293624,1,747.9303 +3513,G146Y,0.00152862,1.2895828,26/08/2020,-7.44974E+17,0.000770642,3,1089.2607 +3514,G162A,0.011822433,4.1374273,26/08/2020,-7.44974E+17,0.010574459,2,392.78845 +3515,G162C,0.003861556,2.0118735,26/08/2020,-7.44974E+17,0.002550749,2,680.7158 +3516,G162D,0.000809548,0.88231516,26/08/2020,-7.44974E+17,0.000336067,5,1394.3689 +3517,G162E,0.002270871,0.8155439,26/08/2020,-7.44974E+17,0.000259556,1,775.93036 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L234F,0.009265239,3.0425289,26/08/2020,-7.44974E+17,0.006638575,1,458.13632 +3530,G162V; L198Q,0.002930644,1.2126551,26/08/2020,-7.44974E+17,0.000765552,1,767.9108 +3531,G162V; S220T,0.003336378,1.4584811,26/08/2020,-7.44974E+17,0.001161658,1,748.056 +3532,G162Y,0.000768121,0.7334457,26/08/2020,-7.44974E+17,0.00030544,5,1259.2233 +3533,G168A,0.017435709,3.8306859,26/08/2020,-7.44974E+17,0.014836959,1,283.80118 +3534,G168A; N249D,0.007401413,2.7183814,26/08/2020,-7.44974E+17,0.004758465,1,550.85626 +3535,G168C,0.004923244,2.4142578,26/08/2020,-7.44974E+17,0.003545459,2,604.6399 +3536,G168D,0.018280692,5.541836,26/08/2020,-7.44974E+17,0.017409681,3,320.33362 +3537,G168E,0.004952601,3.2362728,26/08/2020,-7.44974E+17,0.004091601,3,724.66547 +3538,G168E; A195V; F217I; S220T,0.001695514,1.0486739,26/08/2020,-7.44974E+17,0.000583388,2,942.6243 +3539,G168F,0.001436759,0.848899,26/08/2020,-7.44974E+17,0.000373556,2,952.53906 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+3552,G168R; S220T; H230Y,0.020253036,4.0945954,26/08/2020,-7.44974E+17,0.017074565,1,249.94966 +3553,G168R; S220T; Q283L,0.003992233,1.749155,26/08/2020,-7.44974E+17,0.00181094,1,708.7397 +3554,G168T,0.00768956,4.168505,26/08/2020,-7.44974E+17,0.006903189,3,588.49695 +3555,G168V,0.001458819,0.8714855,26/08/2020,-7.44974E+17,0.00040936,2,962.7526 +3556,G168V; G244D; T245S; A259G,0.002200418,0.8079639,26/08/2020,-7.44974E+17,0.000258852,1,798.31067 +3557,G168V; S220T,0.003271161,1.3584753,26/08/2020,-7.44974E+17,0.001063096,1,783.5574 +3558,G168V; S220T; V232A,0.004852065,2.022006,26/08/2020,-7.44974E+17,0.002588761,1,613.90076 +3559,G168W; S225M,0.003559891,1.4959214,26/08/2020,-7.44974E+17,0.001248216,1,710.63184 +3560,G168Y,0.000873595,0.95813686,26/08/2020,-7.44974E+17,0.000425676,5,1391.1412 +3561,G191A,0.040530168,5.536297,26/08/2020,-7.44974E+17,0.039778812,3,139.61417 +3562,G191C,0.01119202,3.2655382,26/08/2020,-7.44974E+17,0.009014176,1,380.24896 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S220T,0.003753017,1.662198,26/08/2020,-7.44974E+17,0.001595324,1,729.6079 +3597,G19F,0.001441227,0.76533747,26/08/2020,-7.44974E+17,0.000309096,2,907.7112 +3598,G19H,0.001019312,0.65860546,26/08/2020,-7.44974E+17,0.000268227,3,977.3206 +3599,G19I,0.000684159,0.68568903,26/08/2020,-7.44974E+17,0.000217884,5,1352.7117 +3600,G19K,0.001034711,0.65243244,26/08/2020,-7.44974E+17,0.000244338,3,1024.8451 +3601,G19L,0.002490314,0.98993224,26/08/2020,-7.44974E+17,0.000433987,1,794.00494 +3602,G19M,0.001114379,0.79503995,26/08/2020,-7.44974E+17,0.000301269,3,1061.7307 +3603,G19P,0.001417934,0.766935,26/08/2020,-7.44974E+17,0.000327681,2,924.831 +3604,G19Q,0.002628658,1.000002,26/08/2020,-7.44974E+17,0.000423948,1,747.4674 +3605,G19R,0.001963917,0.65797275,26/08/2020,-7.44974E+17,0.000143131,1,776.15594 +3606,G19R; A28T; L134Q; L143M; V204M; E254V; Q283H,0.004858726,1.9646252,26/08/2020,-7.44974E+17,0.002231222,1,635.445 +3607,G19R; D125G; T172S; A197S; F217I,0.002447611,0.9993052,26/08/2020,-7.44974E+17,0.000464865,1,788.54706 +3608,G19R; D235N,0.14422584,5.147622,26/08/2020,-7.44974E+17,0.148074928,1,36.099045 +3609,G19R; E46G; L119P; F142C; V150E; S220T,0.002771539,1.1581447,26/08/2020,-7.44974E+17,0.000688421,1,792.2581 +3610,G19R; G55S; S114L; S220T,0.003779789,1.6453693,26/08/2020,-7.44974E+17,0.001729373,1,712.9275 +3611,G19R; K34E,0.002944381,1.2566931,26/08/2020,-7.44974E+17,0.000868636,1,751.98486 +3612,G19R; L27V; S220T,0.002642447,1.0799342,26/08/2020,-7.44974E+17,0.000620594,1,774.10065 +3613,G19R; L32P; L93H; G191E; S220T,0.003628952,1.5884827,26/08/2020,-7.44974E+17,0.001449855,1,708.5924 +3614,G19R; L98Q; H154R,0.008712982,2.8887374,26/08/2020,-7.44974E+17,0.006321666,1,466.8586 +3615,G19R; N42D,0.00278019,1.1794574,26/08/2020,-7.44974E+17,0.000681065,1,779.48413 +3616,G19R; P45T; S220T,0.005105763,2.0820682,26/08/2020,-7.44974E+17,0.002684333,1,615.2977 +3617,G19R; R155H; Y239D,0.002847098,1.0845681,26/08/2020,-7.44974E+17,0.000581219,1,735.67706 +3618,G19R; S220T; H230Y,0.004221954,1.7791041,26/08/2020,-7.44974E+17,0.002041063,1,656.9176 +3619,G19R; S49G; A92E; S220T; H285R,0.003212296,1.2562777,26/08/2020,-7.44974E+17,0.00081672,1,751.723 +3620,G19R; T38A; V52M; R165C; Y177N; L198Q; S220T,0.004624922,1.9590607,26/08/2020,-7.44974E+17,0.002364619,1,665.612 +3621,G19R; V52E; A202V; T207S,0.002455432,1.035128,26/08/2020,-7.44974E+17,0.000560627,1,790.35657 +3622,G19R; V66M; I73T; P88H; D95E; G120D; N178Y; G244D; E295V,0.00282094,1.1488116,26/08/2020,-7.44974E+17,0.000613206,1,775.6382 +3623,G19R; V99I; L134P; R148C,0.00355479,1.5417135,26/08/2020,-7.44974E+17,0.001342444,1,719.9648 +3624,G19S,0.001177633,0.5242249,26/08/2020,-7.44974E+17,0.000130548,2,854.54565 +3625,G19T,0.002421225,0.8960306,26/08/2020,-7.44974E+17,0.000350367,1,807.5177 +3626,G19V,0.000813764,0.70175356,26/08/2020,-7.44974E+17,0.000271302,4,1225.2223 +3627,G19V; G24R,0.003807706,1.6141329,26/08/2020,-7.44974E+17,0.001443111,1,691.3739 +3628,G19V; L161M; P171L; G200V; N251Y; G284R,0.002394341,0.9212091,26/08/2020,-7.44974E+17,0.000358447,1,785.1873 +3629,G19V; L193H; A228V; H230Q,0.006854541,2.569782,26/08/2020,-7.44974E+17,0.004350956,1,541.5999 +3630,G19V; L198Q; S220T; N238D; R274C,0.002595662,0.994004,26/08/2020,-7.44974E+17,0.000434045,1,779.1588 +3631,G19V; R43H; V66M; D211G; H255Y; S267T,0.003598113,1.6084071,26/08/2020,-7.44974E+17,0.001566678,1,735.3815 +3632,G19V; S220T; N238I,0.002379793,0.9570242,26/08/2020,-7.44974E+17,0.000426415,1,792.1492 +3633,G19V; S69T; P131L; L193I,0.003672885,1.5449747,26/08/2020,-7.44974E+17,0.001407534,1,707.6361 +3634,G19V; W115C; S220T,0.004549466,1.8795459,26/08/2020,-7.44974E+17,0.002276272,1,663.34656 +3635,G19W,0.002078189,0.72345823,26/08/2020,-7.44974E+17,0.00015843,1,792.1649 +3636,G19Y,0.001275346,0.6104855,26/08/2020,-7.44974E+17,0.000158372,2,894.8747 +3637,G200A,0.08167595,5.027658,26/08/2020,-7.44974E+17,0.079867309,1,67.58656 +3638,G200A; H230Y; L269S,0.027489403,4.2711225,26/08/2020,-7.44974E+17,0.025353721,1,189.74861 +3639,G200E,0.043474503,4.5902424,26/08/2020,-7.44974E+17,0.042991646,1,123.40814 +3640,G200E; S220T; H230Y; A282T,0.6305031,5.723818,26/08/2020,-7.44974E+17,0.752486234,1,3.0480845 +3641,G200F,0.04082602,5.445021,26/08/2020,-7.44974E+17,0.040234271,2,141.5366 +3642,G200K,0.06465765,4.8268714,26/08/2020,-7.44974E+17,0.062474887,1,84.04362 +3643,G200L,0.013399919,3.5138676,26/08/2020,-7.44974E+17,0.011127901,1,334.78467 +3644,G200M; S205N,0.013943787,3.6386504,26/08/2020,-7.44974E+17,0.011333193,1,333.2927 +3645,G200N,0.14876974,6.0230246,26/08/2020,-7.44974E+17,0.150966024,2,37.1283 +3646,G200Q,0.07118501,5.0013084,26/08/2020,-7.44974E+17,0.068437519,1,78.94687 +3647,G200R,0.21158512,5.425273,26/08/2020,-7.44974E+17,0.215669371,1,22.866795 +3648,G200R; G203C; S220T,0.055503115,4.744134,26/08/2020,-7.44974E+17,0.054092189,1,99.05164 +3649,G200R; H255R,0.007719537,2.7481763,26/08/2020,-7.44974E+17,0.005465479,1,493.7859 +3650,G200R; S220T,0.24999437,5.3414865,26/08/2020,-7.44974E+17,0.264973143,1,18.229742 +3651,G200S,0.12655012,5.105253,26/08/2020,-7.44974E+17,0.131744135,1,41.18218 +3652,G200T,0.08784499,5.204455,26/08/2020,-7.44974E+17,0.086864238,2,59.394375 +3653,G200V,0.02778459,4.8918443,26/08/2020,-7.44974E+17,0.026292836,2,191.7437 +3654,G200Y,0.027586734,2.7207801,26/08/2020,-7.44974E+17,0.025212615,2,116.10538 +3655,G203A,0.032575734,4.417003,26/08/2020,-7.44974E+17,0.030837491,1,163.51054 +3656,G203A; D211V,0.005059292,2.1519232,26/08/2020,-7.44974E+17,0.003021613,1,626.2279 +3657,G203C,0.05071875,4.972962,26/08/2020,-7.44974E+17,0.049447805,3,100.0793 +3658,G203C; S225P; Q299K,0.013273553,3.555916,26/08/2020,-7.44974E+17,0.010723721,1,350.89926 +3659,G203C; T207A; S220T; H230R,0.030035704,4.379411,26/08/2020,-7.44974E+17,0.02687768,1,181.58517 +3660,G203D,0.062825136,4.9545336,26/08/2020,-7.44974E+17,0.060667971,1,90.81838 +3661,G203D; S220T,0.23498176,5.0626416,26/08/2020,-7.44974E+17,0.261108915,1,18.61872 +3662,G203E,0.029829772,1.6421038,26/08/2020,-7.44974E+17,0.028678531,4,62.552486 +3663,G203F,0.02505909,6.1887207,26/08/2020,-7.44974E+17,0.024815914,5,248.52966 +3664,G203H,0.036401734,5.6025734,26/08/2020,-7.44974E+17,0.035686543,2,162.67143 +3665,G203I,0.003342471,1.3923692,26/08/2020,-7.44974E+17,0.001112617,1,739.8825 +3666,G203L; V204M,0.001991418,1.2360618,26/08/2020,-7.44974E+17,0.000799055,2,890.15137 +3667,G203M,0.034798045,6.4937677,26/08/2020,-7.44974E+17,0.033413576,3,190.69202 +3668,G203N,0.0623,5.329604,26/08/2020,-7.44974E+17,0.060609223,2,88.648674 +3669,G203P,0.002145366,1.6824708,26/08/2020,-7.44974E+17,0.001428165,3,952.7479 +3670,G203Q,0.03987917,6.399723,26/08/2020,-7.44974E+17,0.039267948,3,162.36015 +3671,G203R,0.03683609,4.5609064,26/08/2020,-7.44974E+17,0.034314786,1,148.03008 +3672,G203S,0.03724357,4.5030913,26/08/2020,-7.44974E+17,0.034517209,1,145.5734 +3673,G203T,0.021435982,6.00604,26/08/2020,-7.44974E+17,0.020238477,3,291.7169 +3674,G203V,0.003509454,2.0695064,26/08/2020,-7.44974E+17,0.002120387,2,765.77106 +3675,G203V; A213V; D233V; L243Q,0.002661657,1.074929,26/08/2020,-7.44974E+17,0.000601738,1,776.11096 +3676,G203Y,0.022580573,7.2702703,26/08/2020,-7.44974E+17,0.021951925,4,327.4773 +3677,G215A,0.08476914,7.153054,26/08/2020,-7.44974E+17,0.08517904,3,80.92476 +3678,G215A; W216C,0.07761309,4.9607835,26/08/2020,-7.44974E+17,0.076553612,1,71.26863 +3679,G215C; L243V; E262G,0.003943946,1.6893603,26/08/2020,-7.44974E+17,0.001879086,1,704.43964 +3680,G215C; S220T,0.07370973,4.9440894,26/08/2020,-7.44974E+17,0.072879406,1,74.36227 +3681,G215D; N251K,0.040829528,4.7965236,26/08/2020,-7.44974E+17,0.037406273,1,138.60153 +3682,G215E,0.248336,3.2258532,26/08/2020,-7.44974E+17,0.266684315,2,10.717244 +3683,G215H,0.10688771,5.110514,26/08/2020,-7.44974E+17,0.108237288,1,50.861797 +3684,G215K,0.21584561,5.140939,26/08/2020,-7.44974E+17,0.220254335,5,19.289537 +3685,G215M,0.14056738,6.1746407,26/08/2020,-7.44974E+17,0.145000836,2,40.469296 +3686,G215P,0.22969188,6.269203,26/08/2020,-7.44974E+17,0.239279736,2,22.356447 +3687,G215Q,0.12817588,7.6170006,26/08/2020,-7.44974E+17,0.125414594,4,53.445942 +3688,G215S,0.31099403,5.2541585,26/08/2020,-7.44974E+17,0.322477273,2,12.283202 +3689,G215S; S220T,0.3263201,5.3539634,26/08/2020,-7.44974E+17,0.357671238,1,12.160065 +3690,G215S; S220T; G241C,0.015816316,3.703084,26/08/2020,-7.44974E+17,0.012902943,1,298.9441 +3691,G215T,0.23692936,5.757919,26/08/2020,-7.44974E+17,0.246619061,2,19.753977 +3692,G215V,0.07855175,4.6477923,26/08/2020,-7.44974E+17,0.074096529,2,61.612644 +3693,G215W,0.06552491,7.32613,26/08/2020,-7.44974E+17,0.065506727,3,109.89556 +3694,G21A,0.001599724,0.9289737,26/08/2020,-7.44974E+17,0.000426403,2,946.3341 +3695,G21A; G55S; D118V; D235E; A257D,0.00209074,0.8091153,26/08/2020,-7.44974E+17,0.000245521,1,805.43744 +3696,G21A; T81S; R110H; L214P; A229V,0.006780354,2.5590332,26/08/2020,-7.44974E+17,0.00453654,1,530.13794 +3697,G21C,0.001027339,0.777656,26/08/2020,-7.44974E+17,0.000288173,3,1124.4423 +3698,G21D,0.002516194,0.96997106,26/08/2020,-7.44974E+17,0.000434795,1,785.1594 +3699,G21E,0.001819709,0.8754098,26/08/2020,-7.44974E+17,0.000836576,2,741.2882 +3700,G21E; D64E; C75Y; F86L; S114T; F166L; P171H,0.002719388,1.0688556,26/08/2020,-7.44974E+17,0.000536731,1,831.80853 +3701,G21E; F142L; D287G,0.002047082,0.5938022,26/08/2020,-7.44974E+17,7.39E-05,1,720.5257 +3702,G21E; H154Q; H183Q; S220T,0.003760066,1.5124613,26/08/2020,-7.44974E+17,0.001371124,1,701.63763 +3703,G21E; I62S; M248T; N251Y,0.002837881,1.1486169,26/08/2020,-7.44974E+17,0.000716057,1,795.86316 +3704,G21E; N100K; S220T,0.003854623,1.6752349,26/08/2020,-7.44974E+17,0.001773429,1,688.137 +3705,G21E; R148P; S220T; H230Y,0.002539006,0.97462475,26/08/2020,-7.44974E+17,0.000395422,1,776.3151 +3706,G21E; S220T; S226F,0.004090709,1.730194,26/08/2020,-7.44974E+17,0.001815235,1,683.3761 +3707,G21E; S57T; S220T; L272R,0.003370108,1.4672914,26/08/2020,-7.44974E+17,0.001235791,1,709.58295 +3708,G21E; V85A; D118G; F217L; S267L,0.003166335,1.3112268,26/08/2020,-7.44974E+17,0.000947037,1,754.44434 +3709,G21F,0.001609135,0.87784,26/08/2020,-7.44974E+17,0.000374607,2,919.97186 +3710,G21H,0.00274434,1.0897297,26/08/2020,-7.44974E+17,0.000584915,1,749.70795 +3711,G21K,0.001592036,0.924868,26/08/2020,-7.44974E+17,0.000472711,2,920.49176 +3712,G21L,0.002231122,0.8060912,26/08/2020,-7.44974E+17,0.000248494,1,750.1882 +3713,G21Q,0.001897791,0.6443594,26/08/2020,-7.44974E+17,0.000123645,1,779.46985 +3714,G21R,0.001446066,0.8329095,26/08/2020,-7.44974E+17,0.000391947,2,922.35297 +3715,G21R; A33T; R148P; S220T; H230Y,0.002096185,0.75277406,26/08/2020,-7.44974E+17,0.000215771,1,787.58636 +3716,G21R; A65T; S220T; R258H,0.002455001,1.0305792,26/08/2020,-7.44974E+17,0.000559322,1,810.82965 +3717,G21R; A68V; R94P; A111G; R122C; H154R,0.006468173,2.5106034,26/08/2020,-7.44974E+17,0.004543782,1,552.7818 +3718,G21R; D163V; S220T,0.002527727,1.0168616,26/08/2020,-7.44974E+17,0.000507025,1,772.0614 +3719,G21R; E29D; V135E; S220T; E254D; Q263H,0.002859402,1.1809058,26/08/2020,-7.44974E+17,0.000670056,1,778.8674 +3720,G21R; G126S,0.002480116,1.0334274,26/08/2020,-7.44974E+17,0.000588247,1,821.4051 +3721,G21R; H183L; S220T,0.002615047,1.4407183,26/08/2020,-7.44974E+17,0.001595309,2,769.3245 +3722,G21R; L32H; L76R,0.002535083,0.9477785,26/08/2020,-7.44974E+17,0.000399008,1,751.4069 +3723,G21R; L51Q; S57T; V99D; S117P; R155L; F297Y,0.004198127,1.7920712,26/08/2020,-7.44974E+17,0.0020413,1,666.62354 +3724,G21R; N178I; S220T,0.00397056,1.7382016,26/08/2020,-7.44974E+17,0.001803163,1,702.08276 +3725,G21R; P302L,0.005117624,2.0828223,26/08/2020,-7.44974E+17,0.002676135,1,609.6586 +3726,G21R; S220T,0.003713163,1.625125,26/08/2020,-7.44974E+17,0.001619716,1,724.5596 +3727,G21R; T121A; T172S; N178H; S220T; A223V; K236R; N238K,0.00246129,1.5398366,26/08/2020,-7.44974E+17,0.001251816,2,852.0669 +3728,G21T,0.001414047,0.7860004,26/08/2020,-7.44974E+17,0.000306938,2,930.70306 +3729,G21V,0.002149573,0.74883366,26/08/2020,-7.44974E+17,0.000215479,1,762.3212 +3730,G21V; E84V; D118V; S220T,0.002228829,0.83045423,26/08/2020,-7.44974E+17,0.000297124,1,780.9539 +3731,G21V; G168V,0.002038388,0.7523903,26/08/2020,-7.44974E+17,0.00021196,1,766.69794 +3732,G21V; G55D; L76H; G191E; I212L; S220T; M248K,0.002599825,1.0285581,26/08/2020,-7.44974E+17,0.000493683,1,789.11633 +3733,G21V; N102K; V175G,0.00183798,0.5585765,26/08/2020,-7.44974E+17,5.64E-05,1,754.5571 +3734,G21V; R43P; R148P; S220T; H230Y,0.007595983,2.7134683,26/08/2020,-7.44974E+17,0.005230976,1,505.96048 +3735,G21V; R96L; T157S; S220T; N251T,0.003944411,1.6956594,26/08/2020,-7.44974E+17,0.001785,1,693.38635 +3736,G21V; S220T; A231G; M248L,0.00488883,2.0300505,26/08/2020,-7.44974E+17,0.002831461,1,640.7942 +3737,G21V; S49T,0.002276943,0.8947064,26/08/2020,-7.44974E+17,0.000354824,1,765.7391 +3738,G21V; V30A; L119I; G203R,0.005392628,2.2088134,26/08/2020,-7.44974E+17,0.002963729,1,596.1693 +3739,G237A,0.112501256,5.6079855,26/08/2020,-7.44974E+17,0.114063244,2,48.571735 +3740,G237C,0.26663172,5.435539,26/08/2020,-7.44974E+17,0.289041611,1,16.68315 +3741,G237C; A257V,0.027490953,4.27174,26/08/2020,-7.44974E+17,0.025634216,1,191.44104 +3742,G237C; P240E,0.029075587,4.2276716,26/08/2020,-7.44974E+17,0.027961,1,177.1089 +3743,G237C; P240Q,0.02576509,4.2916584,26/08/2020,-7.44974E+17,0.023293486,1,205.93777 +3744,G237E,0.062489364,4.864224,26/08/2020,-7.44974E+17,0.061238439,1,89.11666 +3745,G237H,0.13859552,6.1604867,26/08/2020,-7.44974E+17,0.136979561,2,41.467484 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D287E,0.003332063,1.4502218,26/08/2020,-7.44974E+17,0.001203474,1,748.63184 +3783,G244T,0.002824947,1.1500807,26/08/2020,-7.44974E+17,0.000714155,1,760.11346 +3784,G244V,0.001557483,0.91827714,26/08/2020,-7.44974E+17,0.000453201,2,961.63336 +3785,G244W,0.001636792,0.9604914,26/08/2020,-7.44974E+17,0.000534975,2,918.8221 +3786,G244Y,0.001695969,1.2163484,26/08/2020,-7.44974E+17,0.001137462,4,899.92694 +3787,G24A; A65V,0.002410328,0.94499964,26/08/2020,-7.44974E+17,0.000383199,1,784.0891 +3788,G24C,0.000929375,0.6265032,26/08/2020,-7.44974E+17,0.000192769,3,1032.721 +3789,G24D,0.001983111,0.7163484,26/08/2020,-7.44974E+17,0.000196856,1,804.6292 +3790,G24E; A280P,0.002467763,0.9670751,26/08/2020,-7.44974E+17,0.000416795,1,771.9374 +3791,G24E; A33T; A59S; L98Q; W115R; P133T; A159T; Y177H; N190Y; A202E; M273V; D287E; P302T,0.012141264,3.3761327,26/08/2020,-7.44974E+17,0.009593343,1,363.88306 +3792,G24E; D170G; I212V; S220T; R258P; H285R,0.003325992,1.4385788,26/08/2020,-7.44974E+17,0.001133726,1,738.62305 +3793,G24E; H36R; G104D; G162E; L181F; S205G; S220T,0.002445423,0.9929548,26/08/2020,-7.44974E+17,0.000439088,1,791.0018 +3794,G24E; H36R; S220T,0.003206467,1.3493527,26/08/2020,-7.44974E+17,0.001048472,1,765.19977 +3795,G24E; M82V; A92V; S220T; H271Q,0.002277522,0.93620646,26/08/2020,-7.44974E+17,0.00045788,1,794.93384 +3796,G24E; R148P; S220T; H230Y,0.004507429,1.9605811,26/08/2020,-7.44974E+17,0.002441233,1,682.32275 +3797,G24E; R96S; H183R,0.002240172,0.78570056,26/08/2020,-7.44974E+17,0.000225624,1,796.0307 +3798,G24E; S220T; H285R,0.002938689,1.2945341,26/08/2020,-7.44974E+17,0.000884476,1,776.2555 +3799,G24E; S67C; N178S; S220T,0.003995764,1.7534,26/08/2020,-7.44974E+17,0.001770107,1,716.0609 +3800,G24E; V113E; S220T,0.002996235,1.2109115,26/08/2020,-7.44974E+17,0.000806299,1,760.7287 +3801,G24E; V52M; A112S; S220T,0.00274995,1.1785836,26/08/2020,-7.44974E+17,0.000732437,1,796.60614 +3802,G24H,0.000952673,0.58959216,26/08/2020,-7.44974E+17,0.000192652,3,1002.7676 +3803,G24M,0.000951374,0.6385098,26/08/2020,-7.44974E+17,0.000223578,3,1043.4412 +3804,G24N,0.002164036,0.74486625,26/08/2020,-7.44974E+17,0.000192525,1,757.1663 +3805,G24P,0.00095587,0.6872911,26/08/2020,-7.44974E+17,0.0002548,3,1091.8267 +3806,G24R; A112T; S205I; S220T; H230Y,0.002947782,1.1428813,26/08/2020,-7.44974E+17,0.000610893,1,756.37256 +3807,G24R; A149E; D211V; S220T; P224H,0.002967846,1.194689,26/08/2020,-7.44974E+17,0.000711295,1,738.05023 +3808,G24R; A56T; S61T; M129T; V141E; V175A; S220T,0.003818085,1.6728954,26/08/2020,-7.44974E+17,0.001641068,1,726.7378 +3809,G24R; G35D; D139E; S220T; A282E; F297Y; P302S,0.002480747,0.9679401,26/08/2020,-7.44974E+17,0.000411071,1,760.03644 +3810,G24R; K34E; D95G; F166S; T172A; M184T; G200E; S220T,0.00321168,1.3962562,26/08/2020,-7.44974E+17,0.001025149,1,767.1446 +3811,G24R; M82I; V204M; I212V; S220T,0.003289366,1.3774987,26/08/2020,-7.44974E+17,0.000983991,1,760.1181 +3812,G24R; Q54L; E91D; S220T,0.002613724,1.0575602,26/08/2020,-7.44974E+17,0.000497295,1,825.81586 +3813,G24R; S49C; N77I; S220T,0.002300762,0.8807568,26/08/2020,-7.44974E+17,0.000295653,1,755.6337 +3814,G24R; S67R; M74K; S226P; T245S; E254V; E268G,0.008014264,1.9227928,26/08/2020,-7.44974E+17,0.006422472,2,310.9481 +3815,G24S,0.001507181,1.2176855,26/08/2020,-7.44974E+17,0.000741942,3,1050.3109 +3816,G24T,0.001541909,0.7604221,26/08/2020,-7.44974E+17,0.000390522,2,864.9216 +3817,G24V,0.000692958,0.67303663,26/08/2020,-7.44974E+17,0.000301439,6,1260.7816 +3818,G24V; A116V; T157A; A174T; S220T; H266Y,0.003197585,1.3447962,26/08/2020,-7.44974E+17,0.000910823,1,742.68243 +3819,G24V; A48V; N190Y; G203C; S220T,0.00252751,0.9998241,26/08/2020,-7.44974E+17,0.000494142,1,809.37115 +3820,G24V; A80T; R137S; A180P; S220T,0.002787798,1.1444236,26/08/2020,-7.44974E+17,0.000696605,1,769.8211 +3821,G24V; D139G; D287V,0.006553941,2.4627204,26/08/2020,-7.44974E+17,0.00411741,1,547.70337 +3822,G24V; D235M,0.002823275,1.1034361,26/08/2020,-7.44974E+17,0.000619592,1,733.736 +3823,G24V; P88H,0.002244521,0.85702705,26/08/2020,-7.44974E+17,0.000302336,1,785.166 +3824,G24V; Q54L; D152V; H154Y,0.004353447,1.8297395,26/08/2020,-7.44974E+17,0.001967587,1,676.87067 +3825,G24V; R155H; Y177F; S220T; A231S; S292T,0.002639857,1.1156703,26/08/2020,-7.44974E+17,0.000650895,1,803.7237 +3826,G24V; R94C; H255P; M291L,0.002885405,1.172217,26/08/2020,-7.44974E+17,0.000687979,1,744.12115 +3827,G24V; T39S; R137H; A280T,0.002502736,0.9430111,26/08/2020,-7.44974E+17,0.00041292,1,778.9763 +3828,G24V; T44A; P132L; S226T,0.004204342,1.7498397,26/08/2020,-7.44974E+17,0.001891472,1,691.47534 +3829,G24W; A111V; L189F; S220T; H230Q,0.003442812,1.3749547,26/08/2020,-7.44974E+17,0.000968351,1,746.69147 +3830,G24W; L119C,0.003549898,2.14065,26/08/2020,-7.44974E+17,0.002310845,2,765.0038 +3831,G24W; L31M; E84V; V113I; L143S; R274C,0.003568959,1.5320598,26/08/2020,-7.44974E+17,0.001422274,1,720.1403 +3832,G24W; M185T,0.002807838,1.145456,26/08/2020,-7.44974E+17,0.000696217,1,765.7017 +3833,G24W; M82V; N100D; E108G; D118G; F209S; S220T,0.002663929,1.0055957,26/08/2020,-7.44974E+17,0.000453339,1,766.163 +3834,G24W; N78D; L98Q; S220T; I256N; S301G,0.00284361,1.0619301,26/08/2020,-7.44974E+17,0.000556361,1,759.9305 +3835,G264A,0.08456175,6.4808335,26/08/2020,-7.44974E+17,0.08216181,3,73.90032 +3836,G264C,0.09136451,5.6744823,26/08/2020,-7.44974E+17,0.089617262,2,61.60516 +3837,G264C; K298N,0.003370818,1.4532895,26/08/2020,-7.44974E+17,0.001124729,1,764.2404 +3838,G264F,0.054366995,5.137823,26/08/2020,-7.44974E+17,0.053028557,2,98.766 +3839,G264H,0.14388858,10.080176,26/08/2020,-7.44974E+17,0.146742526,5,61.186554 +3840,G264I,0.005969902,2.373227,26/08/2020,-7.44974E+17,0.003641905,1,575.1353 +3841,G264K,0.10376276,5.0681148,26/08/2020,-7.44974E+17,0.103658803,1,52.200306 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V199M,0.002076558,0.7988314,26/08/2020,-7.44974E+17,0.000262654,1,815.4005 +3984,G55D; G104C; D163E; L181S,0.00292526,1.2779945,26/08/2020,-7.44974E+17,0.00093313,1,788.2883 +3985,G55D; K236R; E278G,0.006659988,2.5082803,26/08/2020,-7.44974E+17,0.004222584,1,543.10626 +3986,G55D; N77Y; S103T; L143S; D147V,0.002269255,0.83551323,26/08/2020,-7.44974E+17,0.000267109,1,787.28394 +3987,G55D; S220T; H255R; Q283K,0.003684524,1.583316,26/08/2020,-7.44974E+17,0.001464219,1,738.8447 +3988,G55D; T172I; M185K; S220T; E262D,0.003181702,1.3274139,26/08/2020,-7.44974E+17,0.000945889,1,780.8224 +3989,G55D; V66M; R155H; N178D; S220T,0.004042231,1.7159545,26/08/2020,-7.44974E+17,0.00174653,1,718.61096 +3990,G55E,0.003981927,2.1928911,26/08/2020,-7.44974E+17,0.002686553,2,708.56494 +3991,G55F,0.002138792,1.818448,26/08/2020,-7.44974E+17,0.001570393,4,983.84827 +3992,G55H,0.008555621,1.6786436,26/08/2020,-7.44974E+17,0.007115016,2,267.25916 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+4004,G55S; V113I; E153K; S220T; N238D; R247C; A280T; Y290F,0.004244752,1.8316039,26/08/2020,-7.44974E+17,0.002014039,1,699.56976 +4005,G55V,0.005171929,2.3546455,26/08/2020,-7.44974E+17,0.003864365,2,571.07764 +4006,G55V; A116T; S220T,0.002564558,1.0316074,26/08/2020,-7.44974E+17,0.00049813,1,840.096 +4007,G55V; A60V; S61P; V97F; H230R; Q263P; Q299L,0.003281756,1.4047596,26/08/2020,-7.44974E+17,0.001167192,1,751.26697 +4008,G55V; D170N; N249K,0.004007056,1.7312734,26/08/2020,-7.44974E+17,0.001832194,1,679.0369 +4009,G55V; R137L; S220T; L269S,0.003034621,1.3338253,26/08/2020,-7.44974E+17,0.001003355,1,795.5315 +4010,G55V; V71I; P132S; S220T; A223T; D235G; Y239C; D287H,0.004135116,1.7681285,26/08/2020,-7.44974E+17,0.001787964,1,692.646 +4011,G55W,0.002718479,1.0626106,26/08/2020,-7.44974E+17,0.000547911,1,775.07733 +4012,G55Y,0.00222347,1.8168551,26/08/2020,-7.44974E+17,0.001456265,3,982.5818 +4013,G87A,0.060124315,1.0008808,26/08/2020,-7.44974E+17,0.062597361,3,20.277407 +4014,G87A; A140V; V199E; L234F; L243Q,0.002241791,0.79582816,26/08/2020,-7.44974E+17,0.000223592,1,761.5819 +4015,G87A; A89T,0.037152156,4.5236034,26/08/2020,-7.44974E+17,0.033693401,1,145.80588 +4016,G87C,0.10398231,4.4521313,26/08/2020,-7.44974E+17,0.103135602,3,41.044582 +4017,G87C; L158H; R258S,0.003940493,1.6697415,26/08/2020,-7.44974E+17,0.001700728,1,702.6217 +4018,G87C; M184L; S220T,0.20662,5.16823,26/08/2020,-7.44974E+17,0.223892608,1,22.50436 +4019,G87C; R155G; G237V,0.007380329,2.678316,26/08/2020,-7.44974E+17,0.005167204,1,512.5663 +4020,G87C; V135E; E153D; M185L; F297L; K298M,0.005932012,2.3152359,26/08/2020,-7.44974E+17,0.003378193,1,590.455 +4021,G87D,0.16497314,5.31284,26/08/2020,-7.44974E+17,0.165781036,4,28.19476 +4022,G87D; A112T; S220T,0.058063634,4.8821454,26/08/2020,-7.44974E+17,0.056287317,1,96.030525 +4023,G87D; L161M; D211N; S220T; E262D; E278V; Q283H,0.00384559,1.6710564,26/08/2020,-7.44974E+17,0.001713185,1,713.3986 +4024,G87D; L173Q; L176I,0.025085319,4.1700253,26/08/2020,-7.44974E+17,0.023492321,1,203.32417 +4025,G87D; M129L; V276I,0.28280497,0.5179596,26/08/2020,-7.44974E+17,0.335833361,7,1.4838316 +4026,G87D; R148P; S220T; H230Y,0.5907256,5.6192718,26/08/2020,-7.44974E+17,0.705429423,1,3.6360648 +4027,G87D; R165S,0.03544554,4.5676312,26/08/2020,-7.44974E+17,0.03211071,1,154.99417 +4028,G87D; S220T,0.6464252,5.829658,26/08/2020,-7.44974E+17,0.712644049,2,2.9762864 +4029,G87D; V99I; I212N; S220T; V221A; E262D; E268G,0.010865067,3.2299643,26/08/2020,-7.44974E+17,0.008386371,1,403.5206 +4030,G87E,0.11226775,5.0318475,26/08/2020,-7.44974E+17,0.113554146,1,47.22132 +4031,G87F,0.080539435,4.7564435,26/08/2020,-7.44974E+17,0.079279076,2,60.107098 +4032,G87H,0.08558324,6.1314754,26/08/2020,-7.44974E+17,0.085366284,2,71.01856 +4033,G87I,0.08088642,6.1515203,26/08/2020,-7.44974E+17,0.0798506,3,74.424995 +4034,G87K,0.07703166,5.0130553,26/08/2020,-7.44974E+17,0.076108699,1,72.586655 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S220T,0.047267158,4.6568656,26/08/2020,-7.44974E+17,0.047267529,1,113.35444 +4047,G87S; R90H; L181S; T207I; R247P,0.00233407,1.010186,26/08/2020,-7.44974E+17,0.000511183,1,790.6682 +4048,G87S; S220T,0.3563354,5.624289,26/08/2020,-7.44974E+17,0.388509581,1,11.001595 +4049,G87S; V113L; L173V; S220T,0.01772391,3.830956,26/08/2020,-7.44974E+17,0.015274292,1,276.8977 +4050,G87V; A116V; L124P; V250E,0.002362243,0.8753126,26/08/2020,-7.44974E+17,0.000329427,1,767.95215 +4051,G87V; M184K,0.003406185,1.4269319,26/08/2020,-7.44974E+17,0.00114426,1,724.90204 +4052,G87Y,0.101116955,4.9871526,26/08/2020,-7.44974E+17,0.10421752,1,52.562634 +4053,H154C,0.22830318,5.188983,26/08/2020,-7.44974E+17,0.249437828,1,20.199419 +4054,H154D,0.003429024,2.5607705,26/08/2020,-7.44974E+17,0.002505335,3,854.82745 +4055,H154D; S201Y; I212S; S226P; G264V,0.002625264,1.0401449,26/08/2020,-7.44974E+17,0.000499756,1,755.6708 +4056,H154D; V204E; S220T; V232G,0.003268423,1.4317963,26/08/2020,-7.44974E+17,0.001152834,1,778.3353 +4057,H154E,0.027654763,5.5561447,26/08/2020,-7.44974E+17,0.026684493,5,202.99539 +4058,H154F,0.1730291,5.5285325,26/08/2020,-7.44974E+17,0.172287373,5,27.240181 +4059,H154F; A202V,0.22727406,5.293759,26/08/2020,-7.44974E+17,0.243219059,1,20.569025 +4060,H154I,0.35585096,7.5246778,26/08/2020,-7.44974E+17,0.357753177,8,13.7559395 +4061,H154L,0.08053337,3.5325243,26/08/2020,-7.44974E+17,0.080502117,4,43.346043 +4062,H154L; A210D; H255R,0.003545439,1.5712185,26/08/2020,-7.44974E+17,0.001475698,1,731.10645 +4063,H154L; F209I,0.00199872,0.6497912,26/08/2020,-7.44974E+17,0.00011615,1,775.3791 +4064,H154L; S220T; D233V,0.003859491,1.6193511,26/08/2020,-7.44974E+17,0.001581319,1,723.01886 +4065,H154M,0.15632083,7.5770397,26/08/2020,-7.44974E+17,0.159023998,3,42.6089 +4066,H154N,0.28100893,1.0554357,26/08/2020,-7.44974E+17,0.30519128,7,2.9296484 +4067,H154N; P219H; A296S,0.06569462,4.827489,26/08/2020,-7.44974E+17,0.064606717,1,83.61463 +4068,H154N; S220T,0.6760073,6.3893447,26/08/2020,-7.44974E+17,0.748222322,2,2.8493211 +4069,H154N; S220T; H230Y; M248T,0.013448156,3.5268285,26/08/2020,-7.44974E+17,0.01128498,1,339.87497 +4070,H154N; Y192R,0.002800786,1.1350164,26/08/2020,-7.44974E+17,0.000638618,1,732.78265 +4071,H154P,0.003034695,1.2685467,26/08/2020,-7.44974E+17,0.000803005,1,769.0463 +4072,H154Q,0.2063538,5.2280164,26/08/2020,-7.44974E+17,0.219780409,1,23.058577 +4073,H154Q; K298E,0.00275091,1.1032844,26/08/2020,-7.44974E+17,0.000577752,1,756.7889 +4074,H154Q; S220T; R247S; V281D,0.004536588,1.8561072,26/08/2020,-7.44974E+17,0.002122952,1,654.32715 +4075,H154R,0.003955758,2.3413253,26/08/2020,-7.44974E+17,0.002802751,2,737.0216 +4076,H154R; A213D,0.010829859,3.286118,26/08/2020,-7.44974E+17,0.008319737,1,403.857 +4077,H154R; H266Q,0.002508662,0.99834836,26/08/2020,-7.44974E+17,0.00044064,1,805.72546 +4078,H154R; S220T,0.009456641,3.4440713,26/08/2020,-7.44974E+17,0.008212915,2,419.51312 +4079,H154S,0.33384532,1.8351886,26/08/2020,-7.44974E+17,0.32598442,9,3.782028 +4080,H154S; A218M; A296T,0.6908828,5.3110104,26/08/2020,-7.44974E+17,0.724514447,4,2.225566 +4081,H154T,0.38183752,3.8911924,26/08/2020,-7.44974E+17,0.38062444,8,6.3925123 +4082,H154V,0.5833205,6.5281816,26/08/2020,-7.44974E+17,0.592798895,8,4.629039 +4083,H154V; A218M; A296T,0.39115828,0.4007124,26/08/2020,-7.44974E+17,0.479671641,16,0.64135426 +4084,H154W,0.14325981,5.2408943,26/08/2020,-7.44974E+17,0.14670952,1,36.473118 +4085,H154Y,0.060660034,4.902947,26/08/2020,-7.44974E+17,0.057748785,1,92.150116 +4086,H154Y; A156D,0.15361108,5.2270594,26/08/2020,-7.44974E+17,0.157183522,1,33.424496 +4087,H154Y; N190D; S220T,0.21044165,5.1816015,26/08/2020,-7.44974E+17,0.226966092,1,22.309172 +4088,H183D; S220T; D287V,0.002423995,0.94535595,26/08/2020,-7.44974E+17,0.000409402,1,766.6915 +4089,H183D; S220T; N238Y,0.005135515,2.1027718,26/08/2020,-7.44974E+17,0.002872201,1,623.285 +4090,H183E,0.020090617,4.0736203,26/08/2020,-7.44974E+17,0.016822505,1,256.50003 +4091,H183F,0.085103765,5.0952744,26/08/2020,-7.44974E+17,0.081852645,1,65.481995 +4092,H183G,0.045867067,5.6296163,26/08/2020,-7.44974E+17,0.042819567,2,128.32393 +4093,H183I,0.031450838,4.407044,26/08/2020,-7.44974E+17,0.028916904,1,168.13467 +4094,H183L; Y192H,0.00391419,1.671265,26/08/2020,-7.44974E+17,0.00165612,1,702.93866 +4095,H183N; M184K,0.003663323,1.585205,26/08/2020,-7.44974E+17,0.001392315,1,735.67163 +4096,H183Q,0.052352633,4.7254395,26/08/2020,-7.44974E+17,0.05182338,1,104.34827 +4097,H183Q; S220T,0.5930185,5.631094,26/08/2020,-7.44974E+17,0.707648399,1,3.622137 +4098,H183R; A202S; L253M,0.002107682,0.7283638,26/08/2020,-7.44974E+17,0.000187875,1,772.5889 +4099,H183R; S220T,0.003449433,1.433205,26/08/2020,-7.44974E+17,0.00118817,1,720.1055 +4100,H183R; S220T; E275A,0.003101955,1.3786643,26/08/2020,-7.44974E+17,0.0011034,1,771.0634 +4101,H183S,0.11650162,6.047146,26/08/2020,-7.44974E+17,0.115531669,3,48.3958 +4102,H183V,0.038191475,4.5380325,26/08/2020,-7.44974E+17,0.036710001,1,140.79898 +4103,H183Y; F297L,0.030970965,4.34569,26/08/2020,-7.44974E+17,0.028250351,1,172.21646 +4104,H183Y; S220T,0.3120456,5.497848,26/08/2020,-7.44974E+17,0.338403839,1,13.271722 +4105,H183Y; S220T; E254G; R279H,0.017878463,3.9323938,26/08/2020,-7.44974E+17,0.014931761,1,281.0187 +4106,H230D,0.09392782,6.154221,26/08/2020,-7.44974E+17,0.093836317,2,64.11491 +4107,H230D; Q263L,0.041320503,4.548828,26/08/2020,-7.44974E+17,0.040354501,1,131.51978 +4108,H230E,0.062136672,4.5068207,26/08/2020,-7.44974E+17,0.059848324,3,73.60655 +4109,H230F,0.13871334,1.9551568,26/08/2020,-7.44974E+17,0.140445104,3,14.018708 +4110,H230G,0.06564756,7.0200286,26/08/2020,-7.44974E+17,0.064785013,3,105.57059 +4111,H230I,0.09429223,6.7993836,26/08/2020,-7.44974E+17,0.092421387,3,69.0329 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A296T,0.001949368,0.59852207,26/08/2020,-7.44974E+17,8.34E-05,1,743.7435 +4238,H36N; V52L; A149T,0.01250009,3.3559434,26/08/2020,-7.44974E+17,0.01033678,1,357.50995 +4239,H36P,0.001791575,1.0669398,26/08/2020,-7.44974E+17,0.000628299,2,923.7002 +4240,H36P; L134M; P219H,0.004516657,1.8849059,26/08/2020,-7.44974E+17,0.002163849,1,653.24066 +4241,H36P; T44A; M74K; S220T,0.002843646,1.2311554,26/08/2020,-7.44974E+17,0.000804165,1,775.23773 +4242,H36P; W41R; T44A; L58P; V150E; V281D; A282V; Y290N,0.002215836,0.7404155,26/08/2020,-7.44974E+17,0.000179844,1,741.95197 +4243,H36Q,0.003664534,2.6780198,26/08/2020,-7.44974E+17,0.002883417,3,831.58435 +4244,H36Q; A231D; Q263H,0.002732248,1.1301168,26/08/2020,-7.44974E+17,0.000641773,1,796.1331 +4245,H36Q; A89T; R96H; D118V; S220T; D233N; V293D,0.002983274,1.2532524,26/08/2020,-7.44974E+17,0.000851761,1,769.19385 +4246,H36Q; F142I; S220T,0.044008374,5.4309616,26/08/2020,-7.44974E+17,0.04300012,2,129.44197 +4247,H36Q; G136S; M248K; 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A213T,0.001823269,0.6014341,26/08/2020,-7.44974E+17,0.000114456,1,781.90533 +4258,H36Q; S220T,0.07564993,1.8757049,26/08/2020,-7.44974E+17,0.071883814,2,29.285507 +4259,H36Q; T81A; V113E; P164L; S220T; R274C,0.003547215,1.5166649,26/08/2020,-7.44974E+17,0.001392954,1,720.103 +4260,H36Q; V130E; L134M; Y144C; S145T; T157A; V204E; L243Q; E254G,0.001780972,0.58174443,26/08/2020,-7.44974E+17,8.58E-05,1,762.0074 +4261,H36Q; V52E; N77K; V130M; G168E; L176F; G200R; E261G,0.00241695,0.9360793,26/08/2020,-7.44974E+17,0.000424895,1,783.36273 +4262,H36Q; V85I; A159S; P171H,0.005477534,2.1523535,26/08/2020,-7.44974E+17,0.00313173,1,608.0606 +4263,H36Q; W115R; D118E; S220T; I256F; D287N,0.003377206,1.3768394,26/08/2020,-7.44974E+17,0.001017644,1,747.7646 +4264,H36R,0.004699284,2.541725,26/08/2020,-7.44974E+17,0.004231337,6,581.0034 +4265,H36R; A60T; S220T,0.009528619,3.0211594,26/08/2020,-7.44974E+17,0.007117234,1,426.8031 +4266,H36R; G50D; T172A; 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W216F; F217A; A218P; P219S; S220V; V221L; L222A; A223P; P224S; S226L; L227A; A229H; H230A; A231V; V232D; D233L; L234D; D235K; K236G; G237N; N238Y; Y239P; P240G; G241T; T242L; L243G; G244T; T245L; L246R; R247M; M248N; N249V; V250N; N251A; A252L; L253E; E254H; H255I; I256A; A257R; R258A; A260E; E262Q; Q263G; G264V; V265H; H266S; S267E; E268L; L269P; P270H; H271L; L272M; M273R; R274E; E275V; V276A; A277E; E278R; R279A; A280V; V281A; A282Q; Q283G; G284H; H285G; G286D; D287H; H288N; N289Y; Y290M; M291S; S292V; V293Y; Y294E; E295A; A296F; F297K; K298Q; Q299P; P300S; S301P; P302A; A303S; S304*; *305-,0.002205818,0.83889717,26/08/2020,-7.44974E+17,0.000236651,1,781.53827 +4325,I128M; S220T; E254G,0.18677275,5.3104405,26/08/2020,-7.44974E+17,0.191567214,1,26.412716 +4326,I128N,0.006267722,2.6126716,26/08/2020,-7.44974E+17,0.005036798,2,508.29257 +4327,I128N; F142S; G215V; G244C; N289Y; Y290F,0.002923785,1.2215525,26/08/2020,-7.44974E+17,0.000792199,1,748.4805 +4328,I128N; L181S; S220T; K236R,0.003030387,1.2287655,26/08/2020,-7.44974E+17,0.00075697,1,755.58 +4329,I128N; R148P; L214H; S220T; H230Y,0.006423854,2.5046725,26/08/2020,-7.44974E+17,0.00405195,1,563.77966 +4330,I128N; R148P; S220T; H230Y,0.09220333,5.0389633,26/08/2020,-7.44974E+17,0.091727798,1,59.669926 +4331,I128N; S220T,0.053160213,4.853798,26/08/2020,-7.44974E+17,0.051288391,1,105.453964 +4332,I128P,0.005483017,3.1465697,26/08/2020,-7.44974E+17,0.004850686,4,625.83685 +4333,I128P; M129I,0.005655108,2.2479575,26/08/2020,-7.44974E+17,0.003091485,1,603.4127 +4334,I128Q,0.001789216,1.3542025,26/08/2020,-7.44974E+17,0.001373774,5,889.18036 +4335,I128R,0.001569427,0.9309919,26/08/2020,-7.44974E+17,0.00046459,2,926.0008 +4336,I128S,0.008720372,3.558092,26/08/2020,-7.44974E+17,0.007560554,2,473.61334 +4337,I128T,0.016929558,6.3161216,26/08/2020,-7.44974E+17,0.016394979,6,379.38025 +4338,I128T; A140V; R148P; S220T; H230Y; N249S,0.001956639,0.6672883,26/08/2020,-7.44974E+17,0.000130556,1,746.95605 +4339,I128T; A206D; S220T,0.008037748,2.831228,26/08/2020,-7.44974E+17,0.005744975,1,476.12256 +4340,I128T; E153G; L158H; S220T,0.00286688,1.2062348,26/08/2020,-7.44974E+17,0.000862743,1,756.8826 +4341,I128T; L158H; S220T,0.003907962,1.6660979,26/08/2020,-7.44974E+17,0.001703722,1,686.88464 +4342,I128T; S220T,0.023988564,4.221096,26/08/2020,-7.44974E+17,0.021447986,1,218.50166 +4343,I128T; V135G; A156T; Q194H; H285N,0.003044828,1.3322151,26/08/2020,-7.44974E+17,0.000938034,1,761.9494 +4344,I128V,0.12655787,5.2132053,26/08/2020,-7.44974E+17,0.126813561,5,37.368534 +4345,I128V; H154Q; N178I; S220T,0.003570879,1.5577949,26/08/2020,-7.44974E+17,0.001344431,1,729.5662 +4346,I128V; H183N; N251D; E268D; A280V,0.002093586,0.82162076,26/08/2020,-7.44974E+17,0.000299882,1,794.5564 +4347,I128V; R148P; L161Q; S220T; H230Y; A259V; Q263H,0.003426634,1.4370847,26/08/2020,-7.44974E+17,0.001049011,1,754.41113 +4348,I128V; R148P; S220T; H230Y; D233E,0.5146816,5.545946,26/08/2020,-7.44974E+17,0.610771655,1,5.1588774 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E295A,0.003683877,1.5663466,26/08/2020,-7.44974E+17,0.001408846,1,722.82764 +4541,K34E; Q107R; S220T,0.46659693,5.637879,26/08/2020,-7.44974E+17,0.524546569,1,6.649216 +4542,K34E; Q54R; D147Y; V232D; H255Q; L272P,0.002184955,0.7860334,26/08/2020,-7.44974E+17,0.000225999,1,805.18494 +4543,K34E; R47P; V71I; M185V; V232D,0.01016075,3.206515,26/08/2020,-7.44974E+17,0.007960057,1,410.9216 +4544,K34E; S220T; E254D,0.5726079,5.7238383,26/08/2020,-7.44974E+17,0.673328308,1,4.002219 +4545,K34E; V175M; F217I,0.003743,1.6327417,26/08/2020,-7.44974E+17,0.001583743,1,725.84314 +4546,K34E; V40F; D118N; S201P; Y294C,0.002563775,1.0408382,26/08/2020,-7.44974E+17,0.000559226,1,782.10034 +4547,K34F,0.006403635,3.9626124,26/08/2020,-7.44974E+17,0.00591693,4,662.18805 +4548,K34G,0.057067882,4.314866,26/08/2020,-7.44974E+17,0.055315999,2,80.73845 +4549,K34H,0.04873391,4.8796105,26/08/2020,-7.44974E+17,0.047689574,2,106.35069 +4550,K34M; F142Y; S220T,0.004161832,1.7455727,26/08/2020,-7.44974E+17,0.001905098,1,666.4731 +4551,K34M; G35C; S220T,0.002810587,1.1061535,26/08/2020,-7.44974E+17,0.000601921,1,825.4708 +4552,K34M; G35C; V52M; N78Y; E84D; V135E; D152G; H183Y; P224S,0.002551499,1.0035689,26/08/2020,-7.44974E+17,0.000485057,1,788.5063 +4553,K34M; I73V; N190Y; S220T; A282T,0.002809215,1.1571678,26/08/2020,-7.44974E+17,0.000697061,1,776.6681 +4554,K34M; L124F; L143M; L243P; D287N,0.001903455,1.1884545,26/08/2020,-7.44974E+17,0.000792419,2,919.444 +4555,K34M; L151Q; F166Y; S220T,0.003428927,1.4616762,26/08/2020,-7.44974E+17,0.001172408,1,742.4185 +4556,K34M; M129K; N178K,0.002640534,1.0437001,26/08/2020,-7.44974E+17,0.00051029,1,768.54987 +4557,K34M; N42S; G50C; V71A; P132Q; N178D; S220T,0.00381797,1.6326584,26/08/2020,-7.44974E+17,0.001574643,1,707.2029 +4558,K34M; P138L; F142S; S220T,0.002764076,1.0900961,26/08/2020,-7.44974E+17,0.000576127,1,777.46344 +4559,K34M; R122H; I212M; S220T; V232D; E254D,0.038491536,4.559507,26/08/2020,-7.44974E+17,0.035954023,1,142.58145 +4560,K34M; R137C; D170V; S220T; L234I,0.002409837,0.91614825,26/08/2020,-7.44974E+17,0.000433525,1,768.59015 +4561,K34M; S220T,0.54096496,5.835336,26/08/2020,-7.44974E+17,0.636073817,1,4.7516527 +4562,K34M; V71D; E84D; V250M,0.00273522,1.1269615,26/08/2020,-7.44974E+17,0.00065965,1,806.2896 +4563,K34N,0.20376408,5.2037063,26/08/2020,-7.44974E+17,0.221292038,1,23.387218 +4564,K34N; G104D; R148P; S220T; H230Y,0.00493636,2.0489953,26/08/2020,-7.44974E+17,0.002519379,1,638.9742 +4565,K34N; G35K,0.017094187,3.8323565,26/08/2020,-7.44974E+17,0.014724845,1,284.00592 +4566,K34N; T157S; S160T; S220T,0.09217965,5.0858965,26/08/2020,-7.44974E+17,0.092936742,1,60.00901 +4567,K34P,0.00088423,1.0780923,26/08/2020,-7.44974E+17,0.000432839,5,1477.468 +4568,K34Q,0.04291151,5.6945,26/08/2020,-7.44974E+17,0.041202618,2,139.53932 +4569,K34Q; N42K; A127V; V150M; S220T,0.00283578,1.1696637,26/08/2020,-7.44974E+17,0.000788669,1,760.0813 +4570,K34Q; S49C; N78S; Q194R; S220T; Q299L,0.002620857,1.0356702,26/08/2020,-7.44974E+17,0.000524079,1,756.86456 +4571,K34R,0.124910206,5.0640926,26/08/2020,-7.44974E+17,0.128276581,1,42.04141 +4572,K34R; A159V; S220T; H285R,0.027267246,4.2815304,26/08/2020,-7.44974E+17,0.025297435,1,192.04518 +4573,K34R; N102D; V130M; T179S; S201A,0.002563459,0.971444,26/08/2020,-7.44974E+17,0.000422589,1,760.229 +4574,K34R; Q194R; K236I,0.002061535,0.6968762,26/08/2020,-7.44974E+17,0.000148874,1,781.4949 +4575,K34R; Q54R; P88T; Y144F; S220T; E268K,0.044118464,4.0522556,26/08/2020,-7.44974E+17,0.042600273,2,99.45091 +4576,K34R; R148P; S220T; H230Y,0.61837524,5.8333964,26/08/2020,-7.44974E+17,0.733716253,1,3.2631674 +4577,K34R; R96H; V109M; A149V,0.003547305,1.4484389,26/08/2020,-7.44974E+17,0.001195944,1,703.31213 +4578,K34R; S61P; G126A; A169E; S220T,0.003723475,1.5733848,26/08/2020,-7.44974E+17,0.001379298,1,734.32104 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D211G,0.00206345,0.6122936,26/08/2020,-7.44974E+17,8.46E-05,1,715.189 +4622,L101Q; A174V; S220T,0.004503406,1.923804,26/08/2020,-7.44974E+17,0.002243992,1,677.3149 +4623,L101Q; A187V,0.003916134,1.7394938,26/08/2020,-7.44974E+17,0.001726485,1,714.8486 +4624,L101Q; I128T; S220T,0.00255463,1.1098347,26/08/2020,-7.44974E+17,0.000615753,1,776.5906 +4625,L101Q; N289D,0.002646771,1.1046703,26/08/2020,-7.44974E+17,0.000631865,1,768.1199 +4626,L101Q; P131S; R148P; S220T; P224S; H230Y,0.004816896,1.9068282,26/08/2020,-7.44974E+17,0.002312387,1,643.0652 +4627,L101Q; R148P; L176F; G203S; S220T; H230Y; H266D; R274C,0.005130546,2.1035385,26/08/2020,-7.44974E+17,0.002961051,1,614.0889 +4628,L101Q; S220T,0.003173809,1.3395127,26/08/2020,-7.44974E+17,0.000936631,1,767.62103 +4629,L101Q; S220T; G237C,0.002890588,1.170724,26/08/2020,-7.44974E+17,0.000782302,1,745.60815 +4630,L101Q; T157I; L198Q; H255Y,0.004204928,1.7891586,26/08/2020,-7.44974E+17,0.001869473,1,683.2408 +4631,L101Q; V130A; D147E; E268V; 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S220T,0.001943564,1.2166947,26/08/2020,-7.44974E+17,0.000799204,2,914.42584 +4705,L124H; S220T; T245I,0.002851546,1.201168,26/08/2020,-7.44974E+17,0.000771392,1,783.02686 +4706,L124I,0.10461816,2.8006191,26/08/2020,-7.44974E+17,0.103822598,10,24.835192 +4707,L124I; A187T; S220T; H266Y,0.005901598,2.3058596,26/08/2020,-7.44974E+17,0.003813641,1,564.95984 +4708,L124I; A197V; G215D; S220T; P224S; S301R,0.003372693,1.4110544,26/08/2020,-7.44974E+17,0.001165394,1,711.8113 +4709,L124I; L222P; V293D,0.003859485,1.6307435,26/08/2020,-7.44974E+17,0.001538812,1,700.34015 +4710,L124I; R148P; A156V; S220T; H230Y,0.5624538,5.5454655,26/08/2020,-7.44974E+17,0.676758028,1,4.1031795 +4711,L124I; S220T,0.4030398,5.4264393,26/08/2020,-7.44974E+17,0.45425321,1,8.615266 +4712,L124I; S220T; L234H,0.00298843,1.309174,26/08/2020,-7.44974E+17,0.000888998,1,774.7764 +4713,L124I; V130M; D152V; S220T; P300L,0.003226009,1.3952516,26/08/2020,-7.44974E+17,0.001135094,1,734.2879 +4714,L124I; V276A,0.001948815,1.2163036,26/08/2020,-7.44974E+17,0.000817832,2,898.4415 +4715,L124K,0.002178482,2.0337408,26/08/2020,-7.44974E+17,0.001536317,4,1072.674 +4716,L124M,0.059463274,3.9020188,26/08/2020,-7.44974E+17,0.059034627,5,65.096344 +4717,L124N,0.002338491,0.9352124,26/08/2020,-7.44974E+17,0.000454027,1,797.3239 +4718,L124P,0.001210954,1.3250997,26/08/2020,-7.44974E+17,0.00073019,5,1288.6929 +4719,L124P; A174E; L181F; F217S; S220T,0.046878614,4.775459,26/08/2020,-7.44974E+17,0.044447761,1,120.033264 +4720,L124P; E262R,0.003000887,1.3029929,26/08/2020,-7.44974E+17,0.000883183,1,772.671 +4721,L124P; F142L; L167H; V204E; H271R; R279C,0.003064576,1.2713137,26/08/2020,-7.44974E+17,0.000814502,1,789.1925 +4722,L124P; M184I,0.003050894,1.3391211,26/08/2020,-7.44974E+17,0.000964039,1,763.2627 +4723,L124P; N249Y,0.006376887,2.4454184,26/08/2020,-7.44974E+17,0.003958734,1,552.622 +4724,L124P; P133S; D147V; S220T; R247C,0.003362202,1.465722,26/08/2020,-7.44974E+17,0.001141724,1,726.99927 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N190I; A202V; E208G; S220T; G284A,0.002167614,0.79340816,26/08/2020,-7.44974E+17,0.000233481,1,804.8743 +4758,L134Q; R148P; S220T; H230Y,0.503176,5.6807528,26/08/2020,-7.44974E+17,0.580542833,1,5.523883 +4759,L134Q; R148P; S220T; H230Y; A252T,0.039811365,4.6638136,26/08/2020,-7.44974E+17,0.036120525,1,141.07178 +4760,L134Q; S145T; S205C; H271Q; Q299L,0.003533302,1.5539422,26/08/2020,-7.44974E+17,0.001430801,1,726.2712 +4761,L134Q; S220T,0.54103756,5.5911164,26/08/2020,-7.44974E+17,0.64054091,1,4.614458 +4762,L134Q; V135I; S220T; S267T,0.08819576,4.8680353,26/08/2020,-7.44974E+17,0.092725826,1,59.973473 +4763,L134Q; Y177H; G215S; L253P,0.002533146,1.103595,26/08/2020,-7.44974E+17,0.00062169,1,799.5259 +4764,L134R,0.106464535,4.9703016,26/08/2020,-7.44974E+17,0.106916851,1,49.80779 +4765,L134S,0.19899435,5.2436934,26/08/2020,-7.44974E+17,0.209579908,1,24.652525 +4766,L134V,0.11064276,5.1565523,26/08/2020,-7.44974E+17,0.110311308,1,49.312153 +4767,L134V; G168E; A197T; S201P; G215S; A228T; T242S; N251H,0.004383071,1.814732,26/08/2020,-7.44974E+17,0.002020511,1,676.7674 +4768,L134W,0.17146957,5.1837125,26/08/2020,-7.44974E+17,0.178525775,1,29.230633 +4769,L134W; G136C,0.003860863,1.6181021,26/08/2020,-7.44974E+17,0.001540051,1,738.8056 +4770,L134Y,0.13407804,7.745632,26/08/2020,-7.44974E+17,0.135779759,4,51.563263 +4771,L143A,0.011499154,4.0697575,26/08/2020,-7.44974E+17,0.010126321,2,404.79205 +4772,L143D,0.001924808,1.1879354,26/08/2020,-7.44974E+17,0.000765826,2,900.5633 +4773,L143E,0.002461776,0.94910324,26/08/2020,-7.44974E+17,0.000432008,1,752.431 +4774,L143F,0.042746283,5.675083,26/08/2020,-7.44974E+17,0.041084019,2,139.6451 +4775,L143F; M184T; L214H; P300Q,0.002668203,1.0371759,26/08/2020,-7.44974E+17,0.000488229,1,786.55707 +4776,L143G,0.003649525,2.0676787,26/08/2020,-7.44974E+17,0.002436091,2,728.02124 +4777,L143H,0.011708058,3.366666,26/08/2020,-7.44974E+17,0.009433567,1,374.27127 +4778,L143I,0.0532195,6.0652814,26/08/2020,-7.44974E+17,0.052476276,3,114.26767 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S220T,0.3795153,5.546426,26/08/2020,-7.44974E+17,0.424188644,1,9.613987 +4790,L143M; S220T; H230Y,0.44274235,5.570231,26/08/2020,-7.44974E+17,0.504670749,1,7.2597194 +4791,L143M; S220T; V221A,0.109864235,5.1032486,26/08/2020,-7.44974E+17,0.108240713,1,49.264053 +4792,L143M; Y186F; S220T; M291V,0.003217182,1.394518,26/08/2020,-7.44974E+17,0.000999051,1,782.03705 +4793,L143N,0.007553023,2.7396238,26/08/2020,-7.44974E+17,0.005262125,1,503.1204 +4794,L143P,0.001080827,0.9444743,26/08/2020,-7.44974E+17,0.000519825,4,1128.8627 +4795,L143Q,0.002272717,0.937192,26/08/2020,-7.44974E+17,0.0004406,1,793.4138 +4796,L143S; D170E; H230Y,0.005132445,2.1504126,26/08/2020,-7.44974E+17,0.003310301,1,615.5003 +4797,L143S; E153D; E208D; S220T,0.005707165,2.3164077,26/08/2020,-7.44974E+17,0.003527407,1,583.1799 +4798,L143S; H154L; T172I; S220T; I256T; L272Q; E295G,0.002353882,0.91328704,26/08/2020,-7.44974E+17,0.000366615,1,793.9259 +4799,L143S; H183R,0.002311439,0.89046127,26/08/2020,-7.44974E+17,0.000318889,1,811.8437 +4800,L143S; I212T,0.003041778,1.2806146,26/08/2020,-7.44974E+17,0.000878961,1,763.7807 +4801,L143S; R148P; S220T; H230Y,0.001899667,0.60083604,26/08/2020,-7.44974E+17,9.67E-05,1,793.5802 +4802,L143S; S201F,0.002282048,0.8286587,26/08/2020,-7.44974E+17,0.000273009,1,771.7344 +4803,L143S; S220T; A296T,0.003267061,1.3968389,26/08/2020,-7.44974E+17,0.000980691,1,775.59875 +4804,L143S; S220T; H230Y,0.010985668,3.2837968,26/08/2020,-7.44974E+17,0.00896806,1,395.5361 +4805,L143S; T157N; S205N; L234H; R258C,0.008780244,2.9432976,26/08/2020,-7.44974E+17,0.005971097,1,473.13623 +4806,L143S; T179A; S220T,0.005896051,2.303609,26/08/2020,-7.44974E+17,0.003494258,1,572.2027 +4807,L143S; T207I; A218S; S220T; I256F,0.002843277,1.1776373,26/08/2020,-7.44974E+17,0.000750893,1,774.39886 +4808,L143S; Y186N; S220T,0.002916914,1.2970368,26/08/2020,-7.44974E+17,0.000966387,1,774.33887 +4809,L143S; Y290N,0.003075176,1.3233215,26/08/2020,-7.44974E+17,0.000958087,1,758.76373 +4810,L143T,0.015039081,4.534761,26/08/2020,-7.44974E+17,0.013186388,2,341.58374 +4811,L143V,0.06497121,6.0682945,26/08/2020,-7.44974E+17,0.064135505,2,95.3015 +4812,L143V; S220T; Q283L,0.37602577,5.6149163,26/08/2020,-7.44974E+17,0.416084371,1,9.909507 +4813,L143V; V175E; N178I; E208G; D287E; Q299L,0.001950545,0.6723696,26/08/2020,-7.44974E+17,0.000130241,1,812.8534 +4814,L143W,0.023261445,5.2700577,26/08/2020,-7.44974E+17,0.022644917,3,237.37685 +4815,L143Y,0.031153806,4.460405,26/08/2020,-7.44974E+17,0.02919223,1,172.50912 +4816,L151C,0.058347177,5.621239,26/08/2020,-7.44974E+17,0.056045982,3,96.99377 +4817,L151D,0.001384181,1.604049,26/08/2020,-7.44974E+17,0.000906952,5,1324.4133 +4818,L151E,0.006513975,1.9228797,26/08/2020,-7.44974E+17,0.00567188,4,337.57986 +4819,L151F,0.17578821,2.5779974,26/08/2020,-7.44974E+17,0.174598895,4,13.219911 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P302T,0.00235327,0.8775624,26/08/2020,-7.44974E+17,0.000357296,1,801.59937 +4831,L151P; A282T; Q283H,0.007432658,2.6923153,26/08/2020,-7.44974E+17,0.005423651,1,503.43832 +4832,L151P; D287V,0.002644165,1.0802243,26/08/2020,-7.44974E+17,0.00065625,1,793.95667 +4833,L151P; E153D; G203D; V221L; G244S; D287Y,0.002120415,0.64201534,26/08/2020,-7.44974E+17,0.000105428,1,735.16394 +4834,L151P; S220T; P270S,0.002736558,1.1176308,26/08/2020,-7.44974E+17,0.000631826,1,796.04364 +4835,L151P; V276G,0.002115312,0.7295405,26/08/2020,-7.44974E+17,0.000173713,1,742.6103 +4836,L151Q,0.007242082,6.667721,26/08/2020,-7.44974E+17,0.006967353,8,935.60345 +4837,L151Q; H183Y; S220T; G284D,0.002783686,1.1514983,26/08/2020,-7.44974E+17,0.000678575,1,790.3107 +4838,L151Q; P164L; S220T,0.004687956,2.038761,26/08/2020,-7.44974E+17,0.002692564,1,648.8386 +4839,L151Q; S205N; D233V,0.001971179,0.6537744,26/08/2020,-7.44974E+17,0.0001226,1,740.0987 +4840,L151Q; S205R; F217I; S220T; P240Q,0.003333324,1.482709,26/08/2020,-7.44974E+17,0.001202893,1,742.17236 +4841,L151Q; V221E; A277V,0.003101576,1.356081,26/08/2020,-7.44974E+17,0.000941742,1,736.8545 +4842,L151R,0.008944792,3.7733345,26/08/2020,-7.44974E+17,0.007555455,2,488.36533 +4843,L151S,0.014036004,3.561008,26/08/2020,-7.44974E+17,0.010746004,1,337.9148 +4844,L151S; D152N,0.004567155,1.9122447,26/08/2020,-7.44974E+17,0.002419845,1,657.5933 +4845,L151S; E153K,0.003419009,1.4060155,26/08/2020,-7.44974E+17,0.001115334,1,736.1362 +4846,L151T,0.008981965,2.9270248,26/08/2020,-7.44974E+17,0.00645495,1,457.8365 +4847,L151V,0.050431773,4.151216,26/08/2020,-7.44974E+17,0.048529182,3,84.99497 +4848,L151W,0.08344053,5.280148,26/08/2020,-7.44974E+17,0.082433285,3,62.033875 +4849,L151Y,0.07440444,5.245226,26/08/2020,-7.44974E+17,0.07253648,3,69.896385 +4850,L158A,0.002972846,1.3154769,26/08/2020,-7.44974E+17,0.000889529,1,755.45044 +4851,L158D,0.003268937,1.8518877,26/08/2020,-7.44974E+17,0.002030474,2,757.5358 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E295D,0.002534149,0.90968454,26/08/2020,-7.44974E+17,0.000328004,1,793.24316 +4864,L158H; T196R; V232A,0.004507326,1.8867055,26/08/2020,-7.44974E+17,0.002317849,1,646.7654 +4865,L158H; Y294F,0.003020785,1.2149271,26/08/2020,-7.44974E+17,0.000723828,1,754.751 +4866,L158I; P164S,0.013532565,3.5385287,26/08/2020,-7.44974E+17,0.010779774,1,341.58582 +4867,L158K,0.006591515,2.708263,26/08/2020,-7.44974E+17,0.005241458,2,504.03506 +4868,L158M,0.08360647,7.6354084,26/08/2020,-7.44974E+17,0.082249661,3,87.49839 +4869,L158N,0.002112695,1.3317904,26/08/2020,-7.44974E+17,0.001039286,2,889.2882 +4870,L158P,0.001984126,1.1268139,26/08/2020,-7.44974E+17,0.000845813,2,852.32837 +4871,L158P; G168R; Y177N; S225T; D233G; P240T,0.002626326,1.0716501,26/08/2020,-7.44974E+17,0.000553139,1,762.9199 +4872,L158P; L161P; S220T,0.003661826,1.5663595,26/08/2020,-7.44974E+17,0.001414539,1,736.60974 +4873,L158P; L173Q; Y239C; K298R,0.003109526,1.2274134,26/08/2020,-7.44974E+17,0.000710112,1,789.05554 +4874,L158P; S220T; P270S; D287N,0.002569037,1.0239322,26/08/2020,-7.44974E+17,0.000494989,1,791.67065 +4875,L158Q,0.003752657,1.9874644,26/08/2020,-7.44974E+17,0.002533232,2,674.6592 +4876,L158R,0.002739066,1.6369507,26/08/2020,-7.44974E+17,0.00195533,3,741.48663 +4877,L158S,0.002458795,1.8023108,26/08/2020,-7.44974E+17,0.001709294,3,893.4116 +4878,L158T,0.002452577,1.6985236,26/08/2020,-7.44974E+17,0.001902579,4,801.0477 +4879,L158Y,0.002322978,1.8508135,26/08/2020,-7.44974E+17,0.00161755,3,957.4308 +4880,L161A,0.003869666,1.7473255,26/08/2020,-7.44974E+17,0.003390354,4,521.197 +4881,L161C,0.007468406,4.1282616,26/08/2020,-7.44974E+17,0.006576562,3,601.89685 +4882,L161D,0.002135489,1.4035786,26/08/2020,-7.44974E+17,0.001013271,2,923.4386 +4883,L161E,0.003451063,1.5084447,26/08/2020,-7.44974E+17,0.001273353,1,760.769 +4884,L161F,0.07298753,2.376088,26/08/2020,-7.44974E+17,0.071276744,5,32.75109 +4885,L161G,0.003375707,1.961043,26/08/2020,-7.44974E+17,0.002528128,3,692.7375 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M184L; G203A; A213T,0.003076923,1.2821401,26/08/2020,-7.44974E+17,0.000825238,1,774.73193 +4898,L161Q; S220T; H230Y,0.004474314,1.912107,26/08/2020,-7.44974E+17,0.002085165,1,671.51685 +4899,L161Q; S220T; H285Q; E295A,0.00285643,1.1535844,26/08/2020,-7.44974E+17,0.000662888,1,755.9746 +4900,L161Q; T172A,0.002067624,0.66693705,26/08/2020,-7.44974E+17,0.000119315,1,729.8189 +4901,L161Q; T188S; N249Y,0.002347596,0.98011833,26/08/2020,-7.44974E+17,0.000450992,1,815.57983 +4902,L161Q; Y186F; A187E; S220T,0.002548103,1.0030844,26/08/2020,-7.44974E+17,0.000445584,1,784.2849 +4903,L161Q; Y192N; S220T; S226P; E268D; R274H,0.002485992,0.99285644,26/08/2020,-7.44974E+17,0.000496782,1,787.3224 +4904,L161R,0.002752266,1.2477987,26/08/2020,-7.44974E+17,0.001497722,2,653.81604 +4905,L161R; E208D; S220T; P300S,0.003146802,1.3048078,26/08/2020,-7.44974E+17,0.00089904,1,758.0268 +4906,L161R; S220T; A231D,0.003225696,1.3724787,26/08/2020,-7.44974E+17,0.000984756,1,751.0366 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+4918,L167I,0.13049066,1.6555215,26/08/2020,-7.44974E+17,0.129386349,6,12.015364 +4919,L167I; S220T; N249Y; G284D; Y294H,0.00255123,0.97227895,26/08/2020,-7.44974E+17,0.000434961,1,787.2447 +4920,L167K,0.004322686,1.8550138,26/08/2020,-7.44974E+17,0.002099743,1,664.7444 +4921,L167M,0.10346037,2.378343,26/08/2020,-7.44974E+17,0.108180443,2,24.618021 +4922,L167N,0.003167117,1.3436986,26/08/2020,-7.44974E+17,0.000991442,1,741.7051 +4923,L167P,0.001222408,1.273204,26/08/2020,-7.44974E+17,0.000670463,4,1273.0002 +4924,L167P; A231V,0.002951792,1.2744831,26/08/2020,-7.44974E+17,0.000907394,1,767.82666 +4925,L167P; F217S; S220T; L246P; P270Q,0.004164786,1.8165454,26/08/2020,-7.44974E+17,0.001970032,1,699.7551 +4926,L167P; L227Q,0.00646675,2.4178855,26/08/2020,-7.44974E+17,0.004052315,1,538.4078 +4927,L167P; P171T; S220T,0.00277358,1.1779058,26/08/2020,-7.44974E+17,0.000814927,1,762.6011 +4928,L167P; S205C; S220T,0.003487751,1.432379,26/08/2020,-7.44974E+17,0.001089822,1,745.8948 +4929,L167P; S220T,0.002690181,1.0923294,26/08/2020,-7.44974E+17,0.000611074,1,778.6698 +4930,L167P; S220T; M291T,0.003746352,1.626514,26/08/2020,-7.44974E+17,0.001498424,1,712.9507 +4931,L167P; T172I; Y239C,0.003415894,1.3686842,26/08/2020,-7.44974E+17,0.001084177,1,738.8775 +4932,L167Q,0.004979479,2.6620662,26/08/2020,-7.44974E+17,0.003794169,2,639.0753 +4933,L167R,0.00193392,1.2133576,26/08/2020,-7.44974E+17,0.000793985,2,908.2711 +4934,L167T,0.009155674,2.763268,26/08/2020,-7.44974E+17,0.00816919,2,357.91864 +4935,L167V,0.034494624,6.6644206,26/08/2020,-7.44974E+17,0.033766203,3,197.62222 +4936,L167Y,0.003349077,1.4564252,26/08/2020,-7.44974E+17,0.001290567,1,751.46173 +4937,L173M; Y186F; T188S; S220T; L227M; Y290F,0.004905062,2.0132558,26/08/2020,-7.44974E+17,0.002359614,1,651.58496 +4938,L173P; E261V; E295K,0.004897313,2.0469174,26/08/2020,-7.44974E+17,0.002787106,1,653.6724 +4939,L173Q,0.07701498,5.0110617,26/08/2020,-7.44974E+17,0.075162343,1,72.902084 +4940,L173Q; M248I; A252T; A257T,0.003584776,1.5031962,26/08/2020,-7.44974E+17,0.001256165,1,737.8655 +4941,L173Q; S201T; S225T; A228V,0.008145147,2.8536227,26/08/2020,-7.44974E+17,0.005823611,1,490.85132 +4942,L173Q; S220T,0.2293869,5.2993646,26/08/2020,-7.44974E+17,0.24635964,1,20.25803 +4943,L173Q; S220T; A303D,0.17592329,5.248372,26/08/2020,-7.44974E+17,0.183235062,1,27.89394 +4944,L173Q; T207I; S220T; H230Y,0.2668663,5.3942866,26/08/2020,-7.44974E+17,0.290151834,1,16.577969 +4945,L173R; G215C; H230R; R258H,0.00660729,2.494497,26/08/2020,-7.44974E+17,0.004142093,1,553.1588 +4946,L173R; V175M,0.021215312,4.0714526,26/08/2020,-7.44974E+17,0.018918517,1,239.76476 +4947,L173V; A218V; L246R; M248L; P302L,0.002192629,0.69793636,26/08/2020,-7.44974E+17,0.000173497,1,747.3473 +4948,L176A,0.015899874,4.6578364,26/08/2020,-7.44974E+17,0.014452452,2,326.26035 +4949,L176C,0.042193186,4.7325587,26/08/2020,-7.44974E+17,0.038523519,1,133.66821 +4950,L176D,0.00208207,1.2185507,26/08/2020,-7.44974E+17,0.000898546,2,850.9858 +4951,L176E,0.003591661,1.5467752,26/08/2020,-7.44974E+17,0.001258442,1,737.258 +4952,L176F; S220T; V276A; D287V,0.003319966,1.4320503,26/08/2020,-7.44974E+17,0.001088122,1,753.4893 +4953,L176G,0.004743721,2.0258958,26/08/2020,-7.44974E+17,0.00252829,1,669.423 +4954,L176I,0.23228575,2.6846952,26/08/2020,-7.44974E+17,0.236894784,2,10.035097 +4955,L176I; F217S; S220T; L227P; M291T,0.002592217,1.0354663,26/08/2020,-7.44974E+17,0.000548869,1,788.7404 +4956,L176I; S220T; G244D,0.027636614,4.3317227,26/08/2020,-7.44974E+17,0.025202276,1,187.98851 +4957,L176I; S220T; S267A; A296D,0.025232786,4.2548175,26/08/2020,-7.44974E+17,0.022740141,1,207.96284 +4958,L176I; S220T; T245P; Q283E,0.003521413,1.5271533,26/08/2020,-7.44974E+17,0.001341951,1,733.0474 +4959,L176I; S301C,0.046093803,4.7815332,26/08/2020,-7.44974E+17,0.043483582,1,120.59399 +4960,L176K,0.008462602,3.694527,26/08/2020,-7.44974E+17,0.006866523,2,509.9805 +4961,L176N,0.013059164,3.72779,26/08/2020,-7.44974E+17,0.012137292,2,322.50836 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S220T,0.02494117,4.168617,26/08/2020,-7.44974E+17,0.022467006,1,207.8475 +4974,L181I; M184T; G237S,0.006207168,2.398483,26/08/2020,-7.44974E+17,0.003615282,1,562.94824 +4975,L181I; N238D; P240S; A257G,0.002713157,1.133564,26/08/2020,-7.44974E+17,0.000677771,1,759.5037 +4976,L181I; S220T; H230Y,0.66257983,5.980406,26/08/2020,-7.44974E+17,0.788258694,1,2.6866646 +4977,L181I; S220T; I256T; H285L,0.003769159,1.6735337,26/08/2020,-7.44974E+17,0.001702931,1,704.00726 +4978,L181I; V276A,0.002828835,1.1337304,26/08/2020,-7.44974E+17,0.000609102,1,811.11176 +4979,L181I; Y192H; S220T; P270H,0.0029777,1.2350264,26/08/2020,-7.44974E+17,0.000825705,1,753.3966 +4980,L182N,0.00264329,1.5963871,26/08/2020,-7.44974E+17,0.001430385,2,807.6599 +4981,L182P,0.006246159,1.1697569,26/08/2020,-7.44974E+17,0.005013399,2,276.56406 +4982,L182P; S220T,0.002240761,1.4502445,26/08/2020,-7.44974E+17,0.001109807,2,883.40295 +4983,L182Q; A218V; P270S; A277D,0.006989443,2.6551766,26/08/2020,-7.44974E+17,0.004663272,1,526.5839 +4984,L182Q; A260G; H288Q,0.002043868,0.722751,26/08/2020,-7.44974E+17,0.00017945,1,810.4641 +4985,L182Q; S220T,0.002616075,2.0864587,26/08/2020,-7.44974E+17,0.001775605,3,944.01624 +4986,L182Q; S220T; L253P; F297S,0.002311097,1.3635058,26/08/2020,-7.44974E+17,0.001022597,2,869.45624 +4987,L182R; E254G,0.003651712,1.5588198,26/08/2020,-7.44974E+17,0.001430341,1,715.91254 +4988,L182R; M185W,0.002687473,1.095138,26/08/2020,-7.44974E+17,0.00056157,1,791.98834 +4989,L182T,0.004821171,2.0189357,26/08/2020,-7.44974E+17,0.002556117,1,663.4208 +4990,L182V; M184I; S220T,0.002463229,0.95964223,26/08/2020,-7.44974E+17,0.000459938,1,761.00757 +4991,L182W,0.001507906,1.669871,26/08/2020,-7.44974E+17,0.001056634,6,1252.3527 +4992,L189C,0.09355554,5.6053863,26/08/2020,-7.44974E+17,0.091487897,4,56.91094 +4993,L189D,0.02173541,3.4883292,26/08/2020,-7.44974E+17,0.020710404,2,181.26038 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A228V,0.7033226,10.898229,26/08/2020,-7.44974E+17,0.718726161,12,4.547798 +5006,L189M; A218L; T242A,0.70538324,5.9713573,26/08/2020,-7.44974E+17,0.716524449,18,2.4588468 +5007,L189M; F217Y; N289S,0.32063648,3.567031,26/08/2020,-7.44974E+17,0.323561144,14,7.656616 +5008,L189M; N289S,0.36938092,4.304913,26/08/2020,-7.44974E+17,0.370596539,11,7.4154897 +5009,L189M; T242A,0.2866563,5.796596,26/08/2020,-7.44974E+17,0.288206867,29,14.486672 +5010,L189M; T242A; N289S,0.23726463,2.3094797,26/08/2020,-7.44974E+17,0.236142683,11,7.6206446 +5011,L189M; W216R,0.15794459,4.0714912,26/08/2020,-7.44974E+17,0.155192475,20,21.969826 +5012,L189M; W216R; A218L,0.3376715,3.7763326,26/08/2020,-7.44974E+17,0.340747809,28,7.445249 +5013,L189M; W216R; N289S,0.17201458,5.675514,26/08/2020,-7.44974E+17,0.17240406,14,27.65143 +5014,L189M; W216R; T242A,0.0726899,5.9414134,26/08/2020,-7.44974E+17,0.072901091,28,76.30113 +5015,L189N,0.028195934,5.3399367,26/08/2020,-7.44974E+17,0.026718109,2,203.68997 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Y294C,0.00347286,1.54464,26/08/2020,-7.44974E+17,0.001378371,1,742.31384 +5084,L20M; H36Q; S69T; L246H; R258H; H271L; V276L,0.0835689,5.0184174,26/08/2020,-7.44974E+17,0.081502027,1,66.3717 +5085,L20M; M74L; S220T,0.3140345,5.423208,26/08/2020,-7.44974E+17,0.339086992,1,13.153448 +5086,L20M; S220T,0.20733093,5.2578115,26/08/2020,-7.44974E+17,0.225650681,1,23.119843 +5087,L20M; V199M; S220T; S226P,0.00705893,2.599092,26/08/2020,-7.44974E+17,0.004567221,1,521.5775 +5088,L20M; W41R; R110S; M248T; L272Q,0.003012362,1.25207,26/08/2020,-7.44974E+17,0.000766937,1,763.4317 +5089,L20P,0.001940705,1.2218173,26/08/2020,-7.44974E+17,0.000793805,2,933.00793 +5090,L20P; A112T; R165S; H183R; S220T; G244V; V281I,0.003226191,1.2823193,26/08/2020,-7.44974E+17,0.000879446,1,750.1783 +5091,L20P; A156V; S220T; Y239H,0.004228081,1.7394357,26/08/2020,-7.44974E+17,0.001790756,1,673.7715 +5092,L20P; A218E,0.002248655,0.85699135,26/08/2020,-7.44974E+17,0.000334596,1,770.3765 +5093,L20P; A53V; D95G; A228V; E254G,0.009369304,2.9932048,26/08/2020,-7.44974E+17,0.006963239,1,445.70694 +5094,L20P; C75R; G87C; L119I,0.003631554,1.5062493,26/08/2020,-7.44974E+17,0.001249508,1,718.29065 +5095,L20P; D118R; P171H,0.003470969,1.4653524,26/08/2020,-7.44974E+17,0.001378712,1,732.6475 +5096,L20P; E208K; S220T; T245A; A259V,0.004450796,1.8751674,26/08/2020,-7.44974E+17,0.002039026,1,685.844 +5097,L20P; E91V; R110C; A206T; S220T; Y290F; F297I,0.003173226,1.4092029,26/08/2020,-7.44974E+17,0.001200549,1,762.32104 +5098,L20P; G21R; P88S,0.002871786,1.1257464,26/08/2020,-7.44974E+17,0.000617943,1,740.2137 +5099,L20P; G55S; N77Y; R90S; S220T,0.003073802,1.3010799,26/08/2020,-7.44974E+17,0.000837722,1,746.74475 +5100,L20P; H154Y; D170Y; S220T; P302S,0.002495351,1.0383247,26/08/2020,-7.44974E+17,0.000556709,1,757.547 +5101,L20P; N42K; T63P; L76H; S220T; N238Y,0.00418911,1.8110086,26/08/2020,-7.44974E+17,0.001969512,1,676.4315 +5102,L20P; P22L; A229V,0.002849623,1.1695393,26/08/2020,-7.44974E+17,0.000742975,1,734.21906 +5103,L20P; P22L; D170V; V204M,0.002752206,1.1275847,26/08/2020,-7.44974E+17,0.000604627,1,767.41614 +5104,L20P; Q107R; S220T; G284S,0.003356055,1.4323289,26/08/2020,-7.44974E+17,0.001071664,1,753.49554 +5105,L20P; S220T; N251D,0.003888949,1.6430051,26/08/2020,-7.44974E+17,0.001527069,1,713.3622 +5106,L20P; S67C; V204E; S220T; R279G,0.00296456,1.2356156,26/08/2020,-7.44974E+17,0.000849264,1,726.9181 +5107,L20P; Y83H; N251D,0.001926876,0.736411,26/08/2020,-7.44974E+17,0.000190017,1,798.8593 +5108,L20Q; A116E; Y144N; P219S; S220T; V265E,0.003542234,1.505149,26/08/2020,-7.44974E+17,0.001298624,1,735.5798 +5109,L20Q; A180S; S220T; L246H,0.002795593,1.1552725,26/08/2020,-7.44974E+17,0.000636488,1,782.62994 +5110,L20Q; A33E; R43C; G50D; L198M; S220T,0.002656373,1.0537204,26/08/2020,-7.44974E+17,0.000545733,1,790.3381 +5111,L20Q; A68T; T72S; L98P; D233G; L234H,0.004406435,1.916831,26/08/2020,-7.44974E+17,0.002327347,1,661.82745 +5112,L20Q; G24A; K34N; L134P; S220T,0.003108525,1.4153494,26/08/2020,-7.44974E+17,0.001111539,1,764.3864 +5113,L20Q; K34N; S220T; V250E; E268V,0.00436813,1.8539101,26/08/2020,-7.44974E+17,0.002042639,1,666.85516 +5114,L20Q; L182Q; M185K; S220T,0.002543523,1.0089608,26/08/2020,-7.44974E+17,0.000511046,1,785.64606 +5115,L20Q; L198Q; S220T; E254G,0.002987456,1.483661,26/08/2020,-7.44974E+17,0.001789906,2,681.093 +5116,L20Q; L214P; S225T; N238I,0.002731496,1.1163515,26/08/2020,-7.44974E+17,0.000692668,1,778.9225 +5117,L20Q; L31M; K34Q; S220T,0.007024384,2.5711968,26/08/2020,-7.44974E+17,0.004660009,1,530.31287 +5118,L20Q; M129K; G136C; R137C; D139Y; S220T; P240R,0.003649742,1.4726571,26/08/2020,-7.44974E+17,0.001057807,1,737.28174 +5119,L20Q; M23I; A28E; S160P; L173P; S220T,0.0044032,1.8836355,26/08/2020,-7.44974E+17,0.002074872,1,694.82245 +5120,L20Q; N42T; 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E275G,0.003719195,1.6581388,26/08/2020,-7.44974E+17,0.001568651,1,740.2093 +5130,L20Q; S49C; F86S; D163E; L272Q; V293D,0.007180397,2.6706386,26/08/2020,-7.44974E+17,0.004549025,1,532.3304 +5131,L20Q; T38A; P133H; L161P; L222S,0.003047312,1.2862995,26/08/2020,-7.44974E+17,0.000863594,1,735.15375 +5132,L20Q; T81S; T121M,0.011293122,3.2363727,26/08/2020,-7.44974E+17,0.009379532,1,382.29086 +5133,L20Q; V66M; A112G; V276L,0.00344123,1.4976332,26/08/2020,-7.44974E+17,0.001304464,1,752.9106 +5134,L20R; E91D; S220T; H255Q,0.003035847,1.3131764,26/08/2020,-7.44974E+17,0.000915344,1,784.5944 +5135,L20R; L189I; Y192F; S220T,0.002985467,1.1756817,26/08/2020,-7.44974E+17,0.000667449,1,782.75085 +5136,L20S,0.016557913,5.5966396,26/08/2020,-7.44974E+17,0.015563054,3,355.53732 +5137,L20V,0.030524049,6.0133815,26/08/2020,-7.44974E+17,0.029636743,3,202.35405 +5138,L20V; A149V; K298R; Q299K,0.005357042,2.2040672,26/08/2020,-7.44974E+17,0.003271239,1,592.1431 +5139,L20V; H36Q; V135A; S220T; V221A; 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V30Y,0.02239958,4.1909347,26/08/2020,-7.44974E+17,0.01992159,1,234.23741 +5268,L25G,0.14492975,4.2083216,26/08/2020,-7.44974E+17,0.1425057,3,26.739733 +5269,L25H,0.2672729,5.360731,26/08/2020,-7.44974E+17,0.287093636,1,16.442799 +5270,L25H; A92S; S220T; Y294F; Q299R,0.51718396,5.8486876,26/08/2020,-7.44974E+17,0.594480163,1,5.3601046 +5271,L25H; D64N; G87D; L101P; A112G; T172I; S220T; N251Y; F297L,0.002812094,1.1516517,26/08/2020,-7.44974E+17,0.000668884,1,789.8707 +5272,L25H; E91V; S220T; S267T,0.5062015,2.4529939,26/08/2020,-7.44974E+17,0.539689097,2,2.385317 +5273,L25H; G35C; L124P; L143W; A169S; S220T; P270L; L272P,0.004816328,1.9110241,26/08/2020,-7.44974E+17,0.002161954,1,652.3804 +5274,L25H; K34M; E84G; N190I,0.003090699,1.3779987,26/08/2020,-7.44974E+17,0.001035545,1,766.79395 +5275,L25H; K34N; L58H; I128V; L167P; S220T,0.002445032,0.9592197,26/08/2020,-7.44974E+17,0.00042034,1,770.17377 +5276,L25H; L101M; E108D; V130A; 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S220T,0.002493142,0.94638133,26/08/2020,-7.44974E+17,0.000387948,1,787.91473 +5286,L25H; T72S; N78I; S220T,0.014732759,3.725635,26/08/2020,-7.44974E+17,0.011631584,1,324.96042 +5287,L25H; V109E; A174P; S220T,0.002409652,0.94026375,26/08/2020,-7.44974E+17,0.000410183,1,784.31213 +5288,L25H; V52M; Q54R; A59T; D95E; M129L; L193R; S220T; T245S,0.002662165,1.220149,26/08/2020,-7.44974E+17,0.000855711,1,800.04517 +5289,L25I,0.1754347,2.7006521,26/08/2020,-7.44974E+17,0.173068838,3,14.050943 +5290,L25I; A111T; G244C,0.003719989,1.5690454,26/08/2020,-7.44974E+17,0.001452675,1,737.8513 +5291,L25I; A252W,0.005256377,2.8084266,26/08/2020,-7.44974E+17,0.004044885,2,639.82214 +5292,L25I; A56T; P138Q; L158P; S220T; H230Y,0.011080792,3.281359,26/08/2020,-7.44974E+17,0.009001439,1,393.92572 +5293,L25I; E46Q; V66M; R90C; S267L,0.002738488,1.225559,26/08/2020,-7.44974E+17,0.000723837,1,787.7256 +5294,L25I; H255Y,0.008568474,2.8944,26/08/2020,-7.44974E+17,0.006169387,1,471.1368 +5295,L25I; K34N; P45H; 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E268Q,0.003350095,1.4429348,26/08/2020,-7.44974E+17,0.00124361,1,731.7112 +5362,L27H; A197V; F217L; S220T; M248I,0.00284903,1.1640917,26/08/2020,-7.44974E+17,0.000615871,1,776.4876 +5363,L27H; A65P; N77S; L119I; S220T; A260V,0.002604054,1.0357473,26/08/2020,-7.44974E+17,0.000481031,1,781.88696 +5364,L27H; E29D; G50D; Y79H; V113A; H154Y; T207A,0.002740149,1.07669,26/08/2020,-7.44974E+17,0.000590997,1,795.3332 +5365,L27H; F166S; S220A,0.002441168,1.0021278,26/08/2020,-7.44974E+17,0.000454638,1,795.65814 +5366,L27H; K34E; N78I; A116E; I256N; Q283K; F297Y,0.003628696,1.5214541,26/08/2020,-7.44974E+17,0.001446658,1,713.2985 +5367,L27H; K34M; F86L; P131S; D147V; E153D; L269F; G284S,0.002802969,1.1463207,26/08/2020,-7.44974E+17,0.000691419,1,751.6216 +5368,L27H; L193H; S220T; H255Y; A282T,0.002552319,1.0604751,26/08/2020,-7.44974E+17,0.000551312,1,796.4255 +5369,L27H; M129L,0.003568464,1.564757,26/08/2020,-7.44974E+17,0.001460921,1,729.5924 +5370,L27H; M185T; M248L,0.003039514,1.3401778,26/08/2020,-7.44974E+17,0.001059871,1,758.099 +5371,L27H; R122C; S220T; L222P; D235G; M248K; V250E; Q263H,0.002600187,1.003641,26/08/2020,-7.44974E+17,0.000435494,1,777.54553 +5372,L27H; R148P; S220T; H230Y; E261K,0.004040646,1.7618641,26/08/2020,-7.44974E+17,0.002030206,1,683.906 +5373,L27H; S220T; R258C; R274H; V276D,0.003166735,1.3289337,26/08/2020,-7.44974E+17,0.000999019,1,793.31586 +5374,L27H; S49R; R165C; P270A,0.00432041,1.9223757,26/08/2020,-7.44974E+17,0.002168821,1,674.1455 +5375,L27H; S61A; L182Q,0.003606327,1.5616809,26/08/2020,-7.44974E+17,0.001407288,1,737.4367 +5376,L27H; T39I; A60T; N102I; S160T; T188P,0.003369385,1.4250176,26/08/2020,-7.44974E+17,0.001066377,1,733.6886 +5377,L27H; Y83F; R258H,0.0025601,1.006897,26/08/2020,-7.44974E+17,0.00046836,1,761.2374 +5378,L27I,0.06311261,7.796138,26/08/2020,-7.44974E+17,0.062844194,6,118.64896 +5379,L27I; A33V; P37H; V66M; S220T; M291V; 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S220T; D233H,0.5057826,5.779093,26/08/2020,-7.44974E+17,0.574103233,1,5.634474 +5631,L58I; E108L,0.026759818,1.8022231,26/08/2020,-7.44974E+17,0.025866713,2,85.42055 +5632,L58I; F86L; G104V; V150M; S220T; M248R,0.002752379,1.214949,26/08/2020,-7.44974E+17,0.000812752,1,772.0272 +5633,L58I; S220T,0.6610098,5.8195205,26/08/2020,-7.44974E+17,0.798328839,1,2.5453322 +5634,L58I; S69A,0.093791574,4.9098363,26/08/2020,-7.44974E+17,0.094588751,1,57.26557 +5635,L58K,0.10374838,3.1835885,26/08/2020,-7.44974E+17,0.103823652,3,30.666126 +5636,L58M,0.11015675,5.1137733,26/08/2020,-7.44974E+17,0.109757447,1,49.097256 +5637,L58P,0.0524045,6.0009685,26/08/2020,-7.44974E+17,0.052406329,3,115.64602 +5638,L58P; N77Y; W115C; A206T; S220T,0.002744665,1.1282924,26/08/2020,-7.44974E+17,0.000625342,1,765.68115 +5639,L58P; N78S; V113I; G120S; S220T; L227R,0.004990378,2.133427,26/08/2020,-7.44974E+17,0.002754695,1,644.0608 +5640,L58P; P70L; L76P; A213T; S220T; E295D,0.00632212,2.4260402,26/08/2020,-7.44974E+17,0.004025154,1,560.0667 +5641,L58P; R110H; S220T; H230F,0.05165091,4.805451,26/08/2020,-7.44974E+17,0.048924426,1,108.18834 +5642,L58P; R148C; S220T; L272P,0.002043707,1.5206503,26/08/2020,-7.44974E+17,0.001229452,3,930.89655 +5643,L58P; T172K,0.007327083,2.7622502,26/08/2020,-7.44974E+17,0.004818329,1,518.75684 +5644,L58P; V99A; A210V; E261D,0.002746859,1.1461235,26/08/2020,-7.44974E+17,0.000616233,1,795.2659 +5645,L58P; Y123H; S220T,0.002785491,1.1401415,26/08/2020,-7.44974E+17,0.000575368,1,763.0185 +5646,L58R,0.10679026,1.9415894,26/08/2020,-7.44974E+17,0.105480074,3,18.861513 +5647,L58S,0.16276713,2.6666658,26/08/2020,-7.44974E+17,0.168129023,2,15.872989 +5648,L58T,0.08214697,3.940032,26/08/2020,-7.44974E+17,0.080169062,4,47.036938 +5649,L58V; A60S,0.08012963,4.956061,26/08/2020,-7.44974E+17,0.078472437,1,69.01417 +5650,L58V; A65G; A156S; L158H; D170E; M185V; E261D,0.002185235,0.76985687,26/08/2020,-7.44974E+17,0.00021067,1,782.4719 +5651,L58W,0.11007554,5.563046,26/08/2020,-7.44974E+17,0.107718953,3,47.62177 +5652,L58Y,0.14937246,2.087631,26/08/2020,-7.44974E+17,0.14829346,8,12.54645 +5653,L76A,0.014601851,4.2806287,26/08/2020,-7.44974E+17,0.013864037,3,317.0121 +5654,L76C,0.008545336,2.5304883,26/08/2020,-7.44974E+17,0.007288876,2,364.36618 +5655,L76D,0.001622468,1.4631313,26/08/2020,-7.44974E+17,0.001041383,4,1075.6282 +5656,L76E,0.004516791,1.8912829,26/08/2020,-7.44974E+17,0.00211486,1,665.87775 +5657,L76F,0.00599738,2.3367712,26/08/2020,-7.44974E+17,0.003823632,1,574.64935 +5658,L76F; A127S; V150E,0.002935683,1.218226,26/08/2020,-7.44974E+17,0.000848261,1,767.2414 +5659,L76F; L193P; A252D,0.00417843,1.8553332,26/08/2020,-7.44974E+17,0.002085021,1,690.1664 +5660,L76F; M185L; S220T; K236N; E262V,0.004685299,1.9741409,26/08/2020,-7.44974E+17,0.002712703,1,629.79767 +5661,L76F; S220T; S267T,0.07898362,4.890947,26/08/2020,-7.44974E+17,0.081204981,1,69.22199 +5662,L76G,0.003114405,1.5740871,26/08/2020,-7.44974E+17,0.002313542,3,632.92645 +5663,L76H,0.002273439,1.4632508,26/08/2020,-7.44974E+17,0.001197789,2,870.5331 +5664,L76H; G286R,0.007465239,2.695005,26/08/2020,-7.44974E+17,0.005052886,1,503.79123 +5665,L76H; L134Q; H230Q; E295V,0.005095963,2.0976536,26/08/2020,-7.44974E+17,0.002898031,1,621.66156 +5666,L76H; L151Q; S220T,0.002962464,1.2490526,26/08/2020,-7.44974E+17,0.000850472,1,750.1269 +5667,L76H; L176I; S220T; G237S; G284D,0.002615701,1.1007293,26/08/2020,-7.44974E+17,0.000612559,1,787.0821 +5668,L76H; P164L; S220T,0.003643934,1.561103,26/08/2020,-7.44974E+17,0.001503034,1,720.6617 +5669,L76H; R148C; A252V; M273I; D287N; V293I,0.003079626,1.3163987,26/08/2020,-7.44974E+17,0.000872739,1,739.2269 +5670,L76H; R148P; S220T; H230Y,0.011157161,3.295102,26/08/2020,-7.44974E+17,0.009528968,1,391.0198 +5671,L76H; R148P; S220T; H230Y; A277T,0.002703028,1.0420983,26/08/2020,-7.44974E+17,0.000519516,1,795.62787 +5672,L76H; R148P; S220T; H230Y; L269M,0.003343618,1.3437431,26/08/2020,-7.44974E+17,0.000963592,1,714.5473 +5673,L76H; S220T,0.013676603,3.3935337,26/08/2020,-7.44974E+17,0.011836989,2,284.49716 +5674,L76H; S220T; H255P; V293D,0.003892422,1.6927521,26/08/2020,-7.44974E+17,0.001768452,1,723.33234 +5675,L76H; S220T; N238I,0.006249364,2.375748,26/08/2020,-7.44974E+17,0.003965407,1,554.83936 +5676,L76H; T105A; W216R; S220T; A259E,0.002722765,1.1719159,26/08/2020,-7.44974E+17,0.000684488,1,820.0391 +5677,L76H; V109A; L134Q,0.002619356,1.0687855,26/08/2020,-7.44974E+17,0.000566189,1,816.62115 +5678,L76H; V130E; S145T; G146C; P171H; F217S; E268K,0.002560874,0.99710613,26/08/2020,-7.44974E+17,0.000471227,1,786.9167 +5679,L76H; V150E; A174E; M184V; W216R; A231G,0.003251622,1.422335,26/08/2020,-7.44974E+17,0.00116027,1,769.52985 +5680,L76I,0.05598893,5.2455683,26/08/2020,-7.44974E+17,0.054144041,2,97.87151 +5681,L76I; D147G; V281I,0.002615425,1.0787861,26/08/2020,-7.44974E+17,0.000602459,1,771.94696 +5682,L76I; E91D; N100I; Y144F; H183L; T188S; L189I; S205G; L227Q; A282E,0.003461339,1.5096807,26/08/2020,-7.44974E+17,0.001280708,1,729.3605 +5683,L76I; F166V; S220T,0.003812801,1.5814322,26/08/2020,-7.44974E+17,0.001347047,1,723.3955 +5684,L76I; G200E; S220T; H255R; G284D,0.003409973,1.48398,26/08/2020,-7.44974E+17,0.001281716,1,744.0617 +5685,L76I; L176F,0.015110546,3.7308269,26/08/2020,-7.44974E+17,0.012415007,1,313.07013 +5686,L76I; M82I; Y186C; S220T,0.003687275,1.559415,26/08/2020,-7.44974E+17,0.001331181,1,751.2212 +5687,L76I; S160T; S220T,0.27771083,5.36001,26/08/2020,-7.44974E+17,0.300995188,1,15.665324 +5688,L76I; S220T; E262D,0.4842102,5.6255765,26/08/2020,-7.44974E+17,0.556463462,1,6.009467 +5689,L76I; T179I; E254V,0.012758299,3.503939,26/08/2020,-7.44974E+17,0.010459508,1,360.3677 +5690,L76I; V85G,0.00313099,1.318844,26/08/2020,-7.44974E+17,0.000988916,1,756.50684 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R148P; D163E; N190S; A197P,0.005458718,2.2450051,26/08/2020,-7.44974E+17,0.003016598,1,616.9881 +5703,L76R; S220T,0.002483478,1.005712,26/08/2020,-7.44974E+17,0.000483058,1,789.0792 +5704,L76R; T188I; S220T,0.002631606,1.0289732,26/08/2020,-7.44974E+17,0.000508737,1,799.6692 +5705,L76S,0.008853325,1.4596835,26/08/2020,-7.44974E+17,0.008083772,4,198.58557 +5706,L76S; V85I,0.02099164,5.0726547,26/08/2020,-7.44974E+17,0.020153744,2,262.2015 +5707,L76T,0.05382446,5.469694,26/08/2020,-7.44974E+17,0.051877552,3,102.29227 +5708,L76V,0.0565228,5.780947,26/08/2020,-7.44974E+17,0.05591381,2,105.53855 +5709,L76W,0.001942836,2.0617256,26/08/2020,-7.44974E+17,0.001482685,6,1155.5062 +5710,L76Y,0.002184251,1.3276676,26/08/2020,-7.44974E+17,0.001019536,2,866.0803 +5711,L93A,0.018269615,3.929368,26/08/2020,-7.44974E+17,0.01540867,1,276.68347 +5712,L93C,0.03903977,4.6590734,26/08/2020,-7.44974E+17,0.037737536,2,128.67268 +5713,L93D,0.001019728,0.79311126,26/08/2020,-7.44974E+17,0.000557923,5,1006.0032 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S220T,0.003361642,1.4659101,26/08/2020,-7.44974E+17,0.001272851,1,734.16626 +5725,L93H; N100Y; A116T; M185K; S220T,0.00300225,1.2228926,26/08/2020,-7.44974E+17,0.000871089,1,761.5724 +5726,L93H; N178I; G203C; A228E; Y294C,0.003835103,1.5697726,26/08/2020,-7.44974E+17,0.001343689,1,713.7347 +5727,L93H; S220T,0.003506067,1.5477246,26/08/2020,-7.44974E+17,0.001376924,1,729.3792 +5728,L93H; S220T; H288Q; K298M,0.002461235,0.9788763,26/08/2020,-7.44974E+17,0.000412925,1,806.7865 +5729,L93H; V150L; S220T; L243M,0.003461701,1.524335,26/08/2020,-7.44974E+17,0.001312675,1,747.4314 +5730,L93H; V97D; D163G,0.002179303,0.83411247,26/08/2020,-7.44974E+17,0.000292889,1,780.07733 +5731,L93I,0.062440656,4.8862906,26/08/2020,-7.44974E+17,0.059806191,1,89.869316 +5732,L93I; A213V; S220T,0.01381423,3.643684,26/08/2020,-7.44974E+17,0.010415299,1,349.41687 +5733,L93I; N100R,0.016086256,0.7328035,26/08/2020,-7.44974E+17,0.015694376,4,61.087128 +5734,L93I; P106Q; I128T; R137H; M185L; S220T; V281A,0.002815546,1.1740851,26/08/2020,-7.44974E+17,0.000700418,1,789.8854 +5735,L93I; V97I,0.012620545,3.4936666,26/08/2020,-7.44974E+17,0.010184905,1,362.7079 +5736,L93K,0.00133129,0.8384812,26/08/2020,-7.44974E+17,0.000605932,3,917.9703 +5737,L93M,0.055800613,4.8423305,26/08/2020,-7.44974E+17,0.053510214,1,100.06044 +5738,L93N,0.001475572,1.1607615,26/08/2020,-7.44974E+17,0.000676853,3,1057.9729 +5739,L93P,0.007125379,0.9709692,26/08/2020,-7.44974E+17,0.006569191,4,175.42236 +5740,L93P; A210P; A229V,0.002320646,0.9118105,26/08/2020,-7.44974E+17,0.000402202,1,807.3722 +5741,L93P; A213T,0.005321408,2.156668,26/08/2020,-7.44974E+17,0.002878617,1,615.04767 +5742,L93P; D95V; A112D; H154L; L161R,0.002948688,1.2140498,26/08/2020,-7.44974E+17,0.00085566,1,762.69867 +5743,L93P; L193H; S220T; A277T,0.003529391,1.5130608,26/08/2020,-7.44974E+17,0.001341496,1,716.49286 +5744,L93P; N102Y; S225A,0.002980191,1.3201436,26/08/2020,-7.44974E+17,0.000934797,1,728.2527 +5745,L93P; T157I,0.003368462,1.4226824,26/08/2020,-7.44974E+17,0.001112035,1,711.90295 +5746,L93P; W115R; S145T; L158H,0.00258885,1.0926952,26/08/2020,-7.44974E+17,0.000643209,1,820.3371 +5747,L93Q,0.017489752,3.8765972,26/08/2020,-7.44974E+17,0.01510486,1,284.68512 +5748,L93R,0.001921077,1.2385103,26/08/2020,-7.44974E+17,0.000848946,2,920.2367 +5749,L93R; R148P; Y177F; S220T; H230Y,0.004565918,1.9259522,26/08/2020,-7.44974E+17,0.002397224,1,644.83734 +5750,L93T,0.009513796,4.268296,26/08/2020,-7.44974E+17,0.00865666,3,484.24194 +5751,L93V; D118N; S145P; S220T; M248T; A252V,0.006233912,2.4122176,26/08/2020,-7.44974E+17,0.003842461,1,569.8023 +5752,L93V; L124H; L182Q; S201T; E278D,0.002202002,0.7259299,26/08/2020,-7.44974E+17,0.000177099,1,751.0389 +5753,L93W,0.00561623,3.5694168,26/08/2020,-7.44974E+17,0.004846768,3,706.9721 +5754,L93Y,0.03048163,1.6229192,26/08/2020,-7.44974E+17,0.029475038,3,63.523426 +5755,L98A,0.01316298,2.355638,26/08/2020,-7.44974E+17,0.01307284,9,186.16083 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H230Y,0.5748589,5.550418,26/08/2020,-7.44974E+17,0.699862949,1,3.8759265 +5768,L98M; M185T,0.007266628,2.7148647,26/08/2020,-7.44974E+17,0.005298014,1,516.4577 +5769,L98M; Q107K; S220T,0.41620567,5.428085,26/08/2020,-7.44974E+17,0.473938402,1,8.04925 +5770,L98M; R122H; R137C; T157P; W216S; P219S; S220I,0.003373255,1.4221517,26/08/2020,-7.44974E+17,0.001135433,1,732.97406 +5771,L98M; S220T; K298R,0.101559415,5.107882,26/08/2020,-7.44974E+17,0.10232872,1,53.504055 +5772,L98N,0.001487607,1.8001629,26/08/2020,-7.44974E+17,0.001252148,9,1294.7069 +5773,L98P,0.001960705,1.5022544,26/08/2020,-7.44974E+17,0.001179901,3,957.5821 +5774,L98P; G136C; H230L,0.002550511,1.0045403,26/08/2020,-7.44974E+17,0.000471644,1,796.0412 +5775,L98P; G168R; S220T,0.00255157,0.9971951,26/08/2020,-7.44974E+17,0.00044085,1,795.2254 +5776,L98P; G241D,0.007122963,0.8899634,26/08/2020,-7.44974E+17,0.006490258,2,188.78828 +5777,L98P; N102D; G203C; S220T,0.00291576,1.2487062,26/08/2020,-7.44974E+17,0.00085538,1,789.90796 +5778,L98P; P131A; F142Y; E208K; E295D,0.001804269,0.56184053,26/08/2020,-7.44974E+17,7.60E-05,1,757.65924 +5779,L98P; S220T; A260E,0.026921911,4.4426956,26/08/2020,-7.44974E+17,0.023680897,1,204.4393 +5780,L98P; S225P,0.003230714,1.4344126,26/08/2020,-7.44974E+17,0.001280649,1,751.81506 +5781,L98P; T157A; K236R,0.003395219,1.4529768,26/08/2020,-7.44974E+17,0.00121714,1,745.69336 +5782,L98P; T157S; S220T; P240Q,0.00341204,1.477642,26/08/2020,-7.44974E+17,0.001180218,1,755.37445 +5783,L98Q,0.001559185,0.89495504,26/08/2020,-7.44974E+17,0.000784097,3,829.55414 +5784,L98Q; D152V; A218T; V250A; D287V,0.002248694,0.85601234,26/08/2020,-7.44974E+17,0.000330125,1,803.727 +5785,L98Q; E108D; M129K; P133H; S220T; Y294C,0.003222849,1.3753691,26/08/2020,-7.44974E+17,0.00110351,1,738.1749 +5786,L98Q; G104D; Y177N; S220T; P240L,0.002574344,1.0802984,26/08/2020,-7.44974E+17,0.000638991,1,750.66614 +5787,L98Q; G136C; L143M; R148P; S220T; H230Y,0.003363439,1.4876705,26/08/2020,-7.44974E+17,0.001250814,1,758.0074 +5788,L98Q; L101P; L161P,0.002897818,1.278936,26/08/2020,-7.44974E+17,0.000929483,1,742.826 +5789,L98Q; L101Q; D118E; R155C; M291R; P300S,0.002759873,1.1278816,26/08/2020,-7.44974E+17,0.000650127,1,800.33344 +5790,L98Q; L134Q; K298E,0.003740696,1.6016504,26/08/2020,-7.44974E+17,0.001466947,1,731.0864 +5791,L98Q; L161P; S220T; N249S,0.003323351,1.473023,26/08/2020,-7.44974E+17,0.001305949,1,743.17065 +5792,L98Q; L173R; M248V; H255Q,0.005566481,2.2285378,26/08/2020,-7.44974E+17,0.003468114,1,582.85583 +5793,L98Q; N102D; S225P,0.003060586,1.2677703,26/08/2020,-7.44974E+17,0.000917191,1,738.083 +5794,L98Q; P106A; S220T,0.003278335,1.4312564,26/08/2020,-7.44974E+17,0.001118468,1,766.2686 +5795,L98Q; P131S; V141E; T179S; M248I,0.004503106,1.8762608,26/08/2020,-7.44974E+17,0.002145069,1,651.1112 +5796,L98Q; P138Q; S220T; S267A; L269M; N289D,0.002955589,1.3231714,26/08/2020,-7.44974E+17,0.000938228,1,821.662 +5797,L98Q; Q107H; S220T; E275G,0.002274633,0.8711262,26/08/2020,-7.44974E+17,0.000329718,1,802.5012 +5798,L98Q; R110H; W216R; S220T; L234P; P240Q,0.003319159,1.3960668,26/08/2020,-7.44974E+17,0.001102551,1,734.92236 +5799,L98Q; S220T; V232A; P240L,0.00344428,1.4188601,26/08/2020,-7.44974E+17,0.001073786,1,746.72943 +5800,L98Q; T207S; S220T,0.003752318,1.6460539,26/08/2020,-7.44974E+17,0.00156155,1,729.04193 +5801,L98Q; V150E; H154Y; A180T; Y186N; S220T; S267T,0.005042174,2.0463917,26/08/2020,-7.44974E+17,0.002756244,1,649.7499 +5802,L98Q; V150E; S220T,0.003133889,1.284665,26/08/2020,-7.44974E+17,0.000843928,1,765.3896 +5803,L98Q; V204A; S220T; S301I,0.002180678,0.7971512,26/08/2020,-7.44974E+17,0.000233516,1,776.46466 +5804,L98Q; W115R; I212T; S220T,0.002855074,1.1942973,26/08/2020,-7.44974E+17,0.000738994,1,759.00146 +5805,L98Q; W216C; K236N,0.002837706,1.1622449,26/08/2020,-7.44974E+17,0.000699276,1,777.1564 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H230R,0.020789405,4.1361704,26/08/2020,-7.44974E+17,0.017853764,1,252.692 +5830,M129I; L198Q,0.004105825,1.6978011,26/08/2020,-7.44974E+17,0.001806247,1,682.45215 +5831,M129I; S205N; S220T; A229V; Q263H,0.002398223,0.93443066,26/08/2020,-7.44974E+17,0.000397384,1,791.576 +5832,M129I; S220T,0.17920472,5.167042,26/08/2020,-7.44974E+17,0.187258664,1,27.689016 +5833,M129I; S220T; S226F,0.025284547,4.232346,26/08/2020,-7.44974E+17,0.022884258,1,206.56656 +5834,M129K,0.001160377,0.9068464,26/08/2020,-7.44974E+17,0.000419742,3,1088.6299 +5835,M129K; A159S; V250L,0.002963545,1.2117869,26/08/2020,-7.44974E+17,0.000729269,1,759.69073 +5836,M129K; D139G; E262V; Y290N,0.002905626,1.2518982,26/08/2020,-7.44974E+17,0.000797686,1,796.0413 +5837,M129K; L173P; S201Y; S220T; A260S,0.003485904,1.5074658,26/08/2020,-7.44974E+17,0.001311757,1,724.7258 +5838,M129K; N251K; V276I,0.003537244,1.5323871,26/08/2020,-7.44974E+17,0.001368118,1,740.92194 +5839,M129K; S220T; L269S; Y290F,0.004809466,1.9840649,26/08/2020,-7.44974E+17,0.002339508,1,646.9875 +5840,M129K; S220T; T242I,0.003245774,1.4007705,26/08/2020,-7.44974E+17,0.001083598,1,770.45575 +5841,M129K; W216R; M273V,0.003746479,1.5824544,26/08/2020,-7.44974E+17,0.001366336,1,739.15607 +5842,M129L,0.244608,0.522616,26/08/2020,-7.44974E+17,0.26795806,10,1.7919257 +5843,M129L; A156E; A159G,0.5596352,6.676499,26/08/2020,-7.44974E+17,0.57009203,13,5.2328587 +5844,M129L; A156K; Y177W,0.38572207,3.3053625,26/08/2020,-7.44974E+17,0.383298508,16,5.3050756 +5845,M129L; A156N; A228V,0.17362082,4.252481,26/08/2020,-7.44974E+17,0.173685937,15,20.5008 +5846,M129L; A156N; A296I,0.66268885,17.854124,26/08/2020,-7.44974E+17,0.667302011,32,9.068974 +5847,M129L; A156Q; A228V,0.12967257,5.7498164,26/08/2020,-7.44974E+17,0.129696125,17,38.982094 +5848,M129L; A156Q; A296I,0.741194,16.501513,26/08/2020,-7.44974E+17,0.756660508,11,5.690755 +5849,M129L; A156Q; S160D,0.6294832,7.7433968,26/08/2020,-7.44974E+17,0.638107576,24,4.5366573 +5850,M129L; A156Q; V250A,0.3015502,2.822642,26/08/2020,-7.44974E+17,0.29999444,11,6.67097 +5851,M129L; A156Q; W216R,0.23291345,2.9766421,26/08/2020,-7.44974E+17,0.231793618,25,9.906645 +5852,M129L; A156S; Y177W,0.57930994,6.9578633,26/08/2020,-7.44974E+17,0.587690558,21,5.0424795 +5853,M129L; A156T; V175K,0.6585566,10.507336,26/08/2020,-7.44974E+17,0.670027243,12,5.407067 +5854,M129L; A174T; A187T; T207I; E254K; E275G,0.003306242,1.3915112,26/08/2020,-7.44974E+17,0.001149916,1,727.33795 +5855,M129L; F217Y,0.23475963,4.5905185,26/08/2020,-7.44974E+17,0.234477139,21,15.07954 +5856,M129L; F217Y; N289S,0.33333236,4.5527973,26/08/2020,-7.44974E+17,0.330657939,20,9.158087 +5857,M129L; F217Y; T242A,0.1770353,3.8975508,26/08/2020,-7.44974E+17,0.176401901,16,18.391273 +5858,M129L; L189M,0.25346735,3.324545,26/08/2020,-7.44974E+17,0.25704759,30,9.865086 +5859,M129L; L189M; N289S,0.25329545,2.416587,26/08/2020,-7.44974E+17,0.254861841,19,7.2440467 +5860,M129L; L189M; W216R,0.111162506,1.50935,26/08/2020,-7.44974E+17,0.106691505,16,12.587545 +5861,M129L; N289S,0.499751,6.5528784,26/08/2020,-7.44974E+17,0.502308549,18,6.5560956 +5862,M129L; S220T; G237C; M248I; H266Y,0.003924672,1.7005706,26/08/2020,-7.44974E+17,0.001696135,1,719.85315 +5863,M129L; S220T; V265E,0.003083792,1.2800207,26/08/2020,-7.44974E+17,0.000922489,1,758.4032 +5864,M129L; T242A,0.3811071,3.0335631,26/08/2020,-7.44974E+17,0.379496919,33,4.946114 +5865,M129L; T242A; N289S,0.38035926,3.0286894,26/08/2020,-7.44974E+17,0.37652363,32,4.9569187 +5866,M129L; V175K,0.7012933,11.606162,26/08/2020,-7.44974E+17,0.708847131,24,4.914764 +5867,M129L; V175K; A259T,0.7770628,17.2114,26/08/2020,-7.44974E+17,0.787398903,17,4.8865128 +5868,M129L; V175K; N289S,0.5799025,7.732261,26/08/2020,-7.44974E+17,0.615277131,3,5.5054 +5869,M129L; V175K; T242A,0.62501246,6.1910734,26/08/2020,-7.44974E+17,0.63443943,22,3.6924236 +5870,M129L; V175K; W216R,0.57041407,9.837874,26/08/2020,-7.44974E+17,0.572127025,21,7.3910027 +5871,M129L; W216R,0.27096194,6.397213,26/08/2020,-7.44974E+17,0.27533184,12,17.391392 +5872,M129L; W216R; F217Y,0.06954863,2.0144432,26/08/2020,-7.44974E+17,0.067675852,13,28.166834 +5873,M129L; W216R; N289S,0.24982323,5.0627427,26/08/2020,-7.44974E+17,0.250108894,18,15.337433 +5874,M129L; W216R; T242A,0.12309082,3.0618753,26/08/2020,-7.44974E+17,0.122614992,8,22.68175 +5875,M129L; Y177W; A228V,0.20011602,3.3908496,26/08/2020,-7.44974E+17,0.203291463,17,13.757966 +5876,M129L; Y177W; N289S,0.6327855,6.08726,26/08/2020,-7.44974E+17,0.65006494,11,3.4911017 +5877,M129N,0.002199299,1.6764338,26/08/2020,-7.44974E+17,0.001351673,3,939.2903 +5878,M129P,0.12740622,5.8485665,26/08/2020,-7.44974E+17,0.128591171,6,41.365173 +5879,M129Q,0.007502968,5.2833867,26/08/2020,-7.44974E+17,0.007216472,10,715.0317 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H285R,0.002758178,1.1789708,26/08/2020,-7.44974E+17,0.000763226,1,806.701 +5891,M129V; N178K; G203S; E262D,0.002125901,0.769986,26/08/2020,-7.44974E+17,0.000239005,1,781.37604 +5892,M129V; S220T,0.1979067,5.477322,26/08/2020,-7.44974E+17,0.200843813,1,25.680813 +5893,M129V; V130E; S220T,0.002907016,1.1661755,26/08/2020,-7.44974E+17,0.000743264,1,790.92224 +5894,M129W,0.002290295,1.4808004,26/08/2020,-7.44974E+17,0.001170798,2,888.0333 +5895,M129Y,0.004569622,1.0415975,26/08/2020,-7.44974E+17,0.004067258,4,294.44803 +5896,M184A,0.00922783,3.7974763,26/08/2020,-7.44974E+17,0.008143937,2,469.708 +5897,M184C,0.035580438,7.873287,26/08/2020,-7.44974E+17,0.035607183,4,221.74934 +5898,M184E,0.00304321,1.2843369,26/08/2020,-7.44974E+17,0.000823968,1,760.0941 +5899,M184F,0.29121554,1.2310265,26/08/2020,-7.44974E+17,0.289386349,8,3.154844 +5900,M184G,0.005941866,3.411229,26/08/2020,-7.44974E+17,0.004968307,3,640.7592 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T188A,0.003735725,1.5953966,26/08/2020,-7.44974E+17,0.001538241,1,716.77313 +5913,M184K; V281D,0.002848074,1.194278,26/08/2020,-7.44974E+17,0.000736697,1,781.9039 +5914,M184L,0.08041574,4.9713635,26/08/2020,-7.44974E+17,0.077649135,1,68.67615 +5915,M184L; L227M,0.10389843,5.0571437,26/08/2020,-7.44974E+17,0.105176346,1,51.138695 +5916,M184L; N249I; L272Q; Q283R,0.002681305,1.1398968,26/08/2020,-7.44974E+17,0.000690306,1,753.27295 +5917,M184L; S220T; H230Y,0.6990512,5.832568,26/08/2020,-7.44974E+17,0.84594069,1,2.1479108 +5918,M184N,0.006472453,2.8163693,26/08/2020,-7.44974E+17,0.005124228,2,526.0025 +5919,M184N; G191V,0.0026093,0.9920293,26/08/2020,-7.44974E+17,0.000463455,1,817.94196 +5920,M184P,0.00275641,1.137602,26/08/2020,-7.44974E+17,0.000652974,1,780.2569 +5921,M184Q,0.058015082,5.4857454,26/08/2020,-7.44974E+17,0.05513247,2,98.074974 +5922,M184R,0.003827181,0.96580964,26/08/2020,-7.44974E+17,0.003134905,4,330.74393 +5923,M184R; L253Q,0.003728191,1.5737983,26/08/2020,-7.44974E+17,0.001485565,1,722.6757 +5924,M184T,0.008826875,3.079606,26/08/2020,-7.44974E+17,0.007705092,2,413.15555 +5925,M184T; E208G; K236E; M248V,0.002147257,0.7878952,26/08/2020,-7.44974E+17,0.000210077,1,794.355 +5926,M184T; E278G,0.003258079,1.4350498,26/08/2020,-7.44974E+17,0.001194138,1,741.0103 +5927,M184T; M185K; S220T; G241C,0.003357949,1.4892918,26/08/2020,-7.44974E+17,0.001275784,1,713.13837 +5928,M184T; V204A; S220T,0.003965322,1.6825604,26/08/2020,-7.44974E+17,0.001881245,1,672.8966 +5929,M184V,0.074189685,4.97048,26/08/2020,-7.44974E+17,0.07068374,1,74.999565 +5930,M184W,0.08660098,4.1627035,26/08/2020,-7.44974E+17,0.084234201,2,49.63538 +5931,M185C,0.01755564,5.621391,26/08/2020,-7.44974E+17,0.016864736,4,331.93515 +5932,M185D,0.011801747,3.350214,26/08/2020,-7.44974E+17,0.009438248,1,374.62836 +5933,M185E,0.006825567,3.740858,26/08/2020,-7.44974E+17,0.005889063,3,599.75305 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+5956,M185V; S220T; Q263H,0.003012803,1.328507,26/08/2020,-7.44974E+17,0.001003848,1,770.13055 +5957,M185W,0.02849461,4.341069,26/08/2020,-7.44974E+17,0.027464436,2,166.77216 +5958,M185Y,0.045721564,4.9116125,26/08/2020,-7.44974E+17,0.044549597,6,106.4924 +5959,M23A,0.007447605,3.5013068,26/08/2020,-7.44974E+17,0.006072911,2,549.34326 +5960,M23C,0.012402799,2.139829,26/08/2020,-7.44974E+17,0.011972902,7,183.72026 +5961,M23D,0.00224927,0.86695564,26/08/2020,-7.44974E+17,0.000305391,1,806.62744 +5962,M23E,0.00249422,2.3835957,26/08/2020,-7.44974E+17,0.001845059,4,1076.0824 +5963,M23F,0.003346968,1.4327664,26/08/2020,-7.44974E+17,0.001089539,1,747.6553 +5964,M23H,0.002310939,1.4569042,26/08/2020,-7.44974E+17,0.001163214,2,883.9327 +5965,M23I,0.015868098,1.205653,26/08/2020,-7.44974E+17,0.015618878,3,97.54813 +5966,M23I; A111V; T196S; S220T; G244C; V276D,0.003978256,1.7540783,26/08/2020,-7.44974E+17,0.001894705,1,717.8102 +5967,M23I; A149S; L272Q; G284C,0.003494546,1.4929905,26/08/2020,-7.44974E+17,0.001335988,1,707.6085 +5968,M23I; A187T; G203D,0.003175882,1.3160286,26/08/2020,-7.44974E+17,0.000943999,1,728.02936 +5969,M23I; C75R; R148L; V199E; Q283L; H288Q,0.002251683,0.79831344,26/08/2020,-7.44974E+17,0.000227968,1,808.727 +5970,M23I; C75S; P106Q; T179I,0.00524258,2.1264336,26/08/2020,-7.44974E+17,0.00285822,1,607.1348 +5971,M23I; E29K; V113E; L161P; S220T; L227P,0.014855492,3.6843536,26/08/2020,-7.44974E+17,0.012073911,1,324.19174 +5972,M23I; H36R; W41R; G203A; W216R; H230Q,0.003206792,1.3100934,26/08/2020,-7.44974E+17,0.000925596,1,745.3425 +5973,M23I; L51Q; V130E; T179I,0.010395917,3.1477416,26/08/2020,-7.44974E+17,0.007632033,1,419.4623 +5974,M23I; Q54H; G200E; M273V,0.002414036,0.93057775,26/08/2020,-7.44974E+17,0.000403386,1,778.3349 +5975,M23I; R148P; D170V; S220T; H230Y; R274G,0.003377389,1.3637832,26/08/2020,-7.44974E+17,0.001023032,1,740.40106 +5976,M23I; R148P; S220T; H230Y,0.14848754,5.0757875,26/08/2020,-7.44974E+17,0.158392318,1,33.86 +5977,M23I; R96H; W115C; A169T; M185T; L227M; N238I,0.003688712,1.5658752,26/08/2020,-7.44974E+17,0.001364172,1,742.1903 +5978,M23I; S301N,0.004295967,1.8151244,26/08/2020,-7.44974E+17,0.002180861,1,671.5241 +5979,M23I; T39S; M74K; M129I; S220T,0.003028054,1.237235,26/08/2020,-7.44974E+17,0.000811823,1,786.36237 +5980,M23I; T63S; N102S,0.033118393,4.5847554,26/08/2020,-7.44974E+17,0.030047702,1,168.34012 +5981,M23K,0.001392593,0.97945815,26/08/2020,-7.44974E+17,0.000581832,3,977.82355 +5982,M23K; A169E; S220T,0.003367913,1.441416,26/08/2020,-7.44974E+17,0.001224629,1,722.54626 +5983,M23K; A187T; N190Y,0.002539541,1.0173316,26/08/2020,-7.44974E+17,0.000500325,1,780.189 +5984,M23K; G120D; S220T; L227Q,0.003543227,1.5587347,26/08/2020,-7.44974E+17,0.00130581,1,736.0305 +5985,M23K; H183R; S220T,0.002921684,1.2043333,26/08/2020,-7.44974E+17,0.000792596,1,781.65857 +5986,M23K; K34N; P133S; V199E; S220T,0.004273871,1.8474882,26/08/2020,-7.44974E+17,0.002181587,1,694.3954 +5987,M23K; L101Q; P132L; S205G; S220T; N249I,0.005410811,2.1948824,26/08/2020,-7.44974E+17,0.003185512,1,609.19244 +5988,M23K; L32H,0.002675785,1.0744228,26/08/2020,-7.44974E+17,0.000593355,1,762.48975 +5989,M23K; L98P; G215V; F217Y; S220T; D235E,0.003220276,1.3485832,26/08/2020,-7.44974E+17,0.000978542,1,741.8041 +5990,M23K; M82V; S220T,0.002909772,1.2363745,26/08/2020,-7.44974E+17,0.000835256,1,757.013 +5991,M23K; N42K; E84G; S220T; F297L,0.00364244,1.5233343,26/08/2020,-7.44974E+17,0.001308658,1,722.196 +5992,M23K; P132A; L134Q; T196R; V204E; P219L; F297Y,0.001795608,0.5527563,26/08/2020,-7.44974E+17,7.25E-05,1,755.01886 +5993,M23K; P70L; P132Q; S220T; H230Y; E268G,0.005751288,2.2709646,26/08/2020,-7.44974E+17,0.003476389,1,584.0068 +5994,M23K; R148P; S220T; H230Y; N249I; E261Q,0.004577428,1.8362799,26/08/2020,-7.44974E+17,0.002067673,1,683.7761 +5995,M23K; S160P; K236I,0.006069437,2.357644,26/08/2020,-7.44974E+17,0.003906376,1,555.7667 +5996,M23K; S61P; V130A; S220T; V281D,0.002445686,0.97779393,26/08/2020,-7.44974E+17,0.000422986,1,782.1968 +5997,M23K; S69T; R96S; S103T; S220T; A231T; D287A; N289I,0.002503536,0.9688621,26/08/2020,-7.44974E+17,0.000406522,1,780.49023 +5998,M23K; V232I,0.002554013,1.020074,26/08/2020,-7.44974E+17,0.000515941,1,778.9038 +5999,M23K; Y144N; H230L,0.002177558,0.7196337,26/08/2020,-7.44974E+17,0.000173523,1,805.19745 +6000,M23L,0.005791721,3.6239016,26/08/2020,-7.44974E+17,0.004938281,3,689.48553 +6001,M23L; A53T; L98R; S220T,0.002541316,1.0543535,26/08/2020,-7.44974E+17,0.000524462,1,787.4805 +6002,M23L; E29V; S220T,0.002581068,0.9718062,26/08/2020,-7.44974E+17,0.000432245,1,767.70935 +6003,M23L; G146D; S220T; R247P,0.002856363,1.14586,26/08/2020,-7.44974E+17,0.000614502,1,802.39087 +6004,M23L; G24W; V130M; S220T,0.00298493,1.2089791,26/08/2020,-7.44974E+17,0.000793647,1,765.1832 +6005,M23L; H36Y; D64V; G126C,0.002625231,1.1099585,26/08/2020,-7.44974E+17,0.000716031,1,798.3313 +6006,M23L; I62T; S103L; D152G; P300L,0.002802109,1.1777129,26/08/2020,-7.44974E+17,0.000705619,1,781.9651 +6007,M23L; L119H; V130E; S220T; L246P; R274C,0.002263423,1.5106256,26/08/2020,-7.44974E+17,0.001187375,2,901.0465 +6008,M23L; L151R; S220T,0.003591802,1.4763426,26/08/2020,-7.44974E+17,0.001242508,1,727.7199 +6009,M23L; L27I,0.003730175,1.6214336,26/08/2020,-7.44974E+17,0.00146671,1,739.39136 +6010,M23L; L31M; I73V; F142Y; P171S; G191E; V221A; Y290F,0.002479278,0.9598087,26/08/2020,-7.44974E+17,0.00043308,1,788.2121 +6011,M23L; L93H; Y123N; Q194H; V281A,0.00241614,0.9423079,26/08/2020,-7.44974E+17,0.000372534,1,766.44116 +6012,M23L; M74I; T105S; S220T; A229T; H230L; Q263R,0.002695367,1.125722,26/08/2020,-7.44974E+17,0.000693844,1,763.36017 +6013,M23L; N102D; V276G,0.003851047,1.635639,26/08/2020,-7.44974E+17,0.001555014,1,699.5293 +6014,M23L; N78T; T121K; A180T; S205R; 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M248K,0.6146584,5.7574716,26/08/2020,-7.44974E+17,0.732018121,1,3.3363574 +6035,M23T; G24E; I73N; S205I; S220T,0.002957507,1.2531624,26/08/2020,-7.44974E+17,0.000907166,1,782.3514 +6036,M23T; L27I; A56S; D118E; S220T,0.003585892,1.5605758,26/08/2020,-7.44974E+17,0.001462553,1,717.19885 +6037,M23T; Q263R,0.003305663,1.3433865,26/08/2020,-7.44974E+17,0.00088875,1,742.4528 +6038,M23T; R110L; S220T; K236R,0.004013948,1.7567921,26/08/2020,-7.44974E+17,0.001666397,1,706.95337 +6039,M23T; R122H; L193H; A231V; R258S,0.003064952,1.3115953,26/08/2020,-7.44974E+17,0.00099424,1,752.5222 +6040,M23T; R247P,0.002731739,1.1518695,26/08/2020,-7.44974E+17,0.000659096,1,801.7699 +6041,M23T; S220T,0.009667086,3.1095426,26/08/2020,-7.44974E+17,0.007790089,1,429.17053 +6042,M23T; S292Y,0.004733925,1.981364,26/08/2020,-7.44974E+17,0.00253307,1,641.8078 +6043,M23T; T39S; A197D; S205N; A218V; Q299E,0.004237192,1.8740537,26/08/2020,-7.44974E+17,0.00216855,1,683.78815 +6044,M23T; T81S; D95E; D170N; S220T; 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P270R,0.002456344,0.9870687,26/08/2020,-7.44974E+17,0.000410863,1,795.4271 +6055,M23V; L167P; S220T; H255Y,0.002861426,1.1698006,26/08/2020,-7.44974E+17,0.000720341,1,775.9783 +6056,M23V; L167R,0.002102619,0.6425203,26/08/2020,-7.44974E+17,0.000106404,1,735.59735 +6057,M23V; L214I; S220T; Y239C,0.002806679,1.1431926,26/08/2020,-7.44974E+17,0.000646698,1,774.4242 +6058,M23V; L58I; T63I; Y177N; S220T; E268G; S301R,0.002969548,1.2461342,26/08/2020,-7.44974E+17,0.000845293,1,772.88776 +6059,M23V; L98Q; R110H; M248T; P270L,0.004204484,1.7432588,26/08/2020,-7.44974E+17,0.001785332,1,682.63824 +6060,M23V; M82L; A112T; S117T; S220T,0.002680626,1.1429225,26/08/2020,-7.44974E+17,0.000699609,1,783.0619 +6061,M23V; N42D; L134M; M185V; S220T; L222P,0.003795651,1.6456174,26/08/2020,-7.44974E+17,0.00159849,1,719.9872 +6062,M23V; P70H; V130L; S220T,0.003846083,1.6382738,26/08/2020,-7.44974E+17,0.001542781,1,725.2473 +6063,M23V; Q107L; H230Y; 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S301G,0.011038197,3.2616668,26/08/2020,-7.44974E+17,0.008558402,1,395.89755 +6133,M74I; S220T; P270T,0.063522935,4.787749,26/08/2020,-7.44974E+17,0.062861414,1,86.47165 +6134,M74I; T105V,0.04973665,4.7672696,26/08/2020,-7.44974E+17,0.046675762,1,112.17319 +6135,M74I; Y186H; S220T; H230Y; A257S,0.005262548,2.1302998,26/08/2020,-7.44974E+17,0.002761445,1,628.5258 +6136,M74K; D170E; S220T; N238S,0.004263923,1.8232367,26/08/2020,-7.44974E+17,0.002137692,1,658.6803 +6137,M74K; G136C; Q194R; S220T,0.00258606,1.1054186,26/08/2020,-7.44974E+17,0.00067171,1,806.02435 +6138,M74K; R96H; V150E; A210D; S220T; N238T; N251S,0.004552732,1.9174298,26/08/2020,-7.44974E+17,0.002029859,1,691.7462 +6139,M74K; S103T; I128V; V141E; L167P; A210V; N251S; H271D,0.002695539,1.1066898,26/08/2020,-7.44974E+17,0.000651673,1,768.9319 +6140,M74K; S220T,0.002299634,0.9822674,26/08/2020,-7.44974E+17,0.000496669,1,825.0934 +6141,M74K; T81S; G87F; P219S,0.002170453,0.76155823,26/08/2020,-7.44974E+17,0.000150723,1,770.36566 +6142,M74L,0.045345437,3.9924862,26/08/2020,-7.44974E+17,0.043882096,3,91.495964 +6143,M74L; L234P; F297L,0.002394166,0.8783598,26/08/2020,-7.44974E+17,0.000318461,1,777.5918 +6144,M74L; L76P; P171T; S220T; G244D; Q263R,0.002990132,1.2475863,26/08/2020,-7.44974E+17,0.000771773,1,765.8374 +6145,M74L; S205C; S220T,0.037470397,4.586357,26/08/2020,-7.44974E+17,0.034237534,1,145.21379 +6146,M74L; S220T; L222M,0.4536104,5.573381,26/08/2020,-7.44974E+17,0.526829363,1,6.9106846 +6147,M74L; V232G,0.18897538,5.1566753,26/08/2020,-7.44974E+17,0.205748667,1,25.37284 +6148,M74N,0.007873029,4.3190227,26/08/2020,-7.44974E+17,0.007180073,3,591.9735 +6149,M74Q,0.046112012,4.630048,26/08/2020,-7.44974E+17,0.042473449,1,117.79624 +6150,M74R,0.001005079,1.2468747,26/08/2020,-7.44974E+17,0.000619077,6,1447.3657 +6151,M74S,0.07046974,5.1658816,26/08/2020,-7.44974E+17,0.068660642,2,75.42729 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A89T,0.002335818,0.96787095,26/08/2020,-7.44974E+17,0.000438906,1,761.6806 +6173,M82E,0.002484448,1.0070424,26/08/2020,-7.44974E+17,0.000489617,1,771.6732 +6174,M82F,0.02693404,5.3252635,26/08/2020,-7.44974E+17,0.025675031,2,215.1021 +6175,M82G,0.021258602,4.6738667,26/08/2020,-7.44974E+17,0.019903762,2,242.25064 +6176,M82H,0.002595062,1.6154277,26/08/2020,-7.44974E+17,0.001384495,2,864.68964 +6177,M82I,0.038059436,4.537063,26/08/2020,-7.44974E+17,0.0358504,1,141.31334 +6178,M82I; A92V; A140T; A156T,0.004855867,2.0246608,26/08/2020,-7.44974E+17,0.002494468,1,645.32404 +6179,M82I; L98Q; S220T; Y239C; D287V,0.002441688,1.5541704,26/08/2020,-7.44974E+17,0.001318367,2,840.06445 +6180,M82I; M129T; L134Q; M185I; E208V; S220T; L253Q,0.002969164,1.2094194,26/08/2020,-7.44974E+17,0.000848954,1,751.5046 +6181,M82I; N100I; D163V; S220T; A228T; R258S; E261G,0.003519701,1.5285445,26/08/2020,-7.44974E+17,0.001384177,1,712.07336 +6182,M82I; N190I; Q263L,0.002411566,0.9869955,26/08/2020,-7.44974E+17,0.000479237,1,803.1512 +6183,M82I; P131Q; V141A; G191R; S220T; V232D; G286E,0.005107734,2.1172483,26/08/2020,-7.44974E+17,0.002940747,1,640.94836 +6184,M82I; R148P; L161P; S220T; H230Y; A277D,0.005044842,2.0927248,26/08/2020,-7.44974E+17,0.002794366,1,629.88007 +6185,M82I; R148P; S220T; H230Y,0.23679738,5.482311,26/08/2020,-7.44974E+17,0.245785506,1,19.939915 +6186,M82I; R148S; Y177H; Q194R; A296V,0.002098035,0.6936418,26/08/2020,-7.44974E+17,0.000157893,1,770.0618 +6187,M82I; S220T; A277T,0.004406773,1.8971454,26/08/2020,-7.44974E+17,0.002312995,1,667.782 +6188,M82I; S220T; V250A,0.043867026,4.6537876,26/08/2020,-7.44974E+17,0.042073038,1,124.37051 +6189,M82I; V113E; G146D; P164L; N238D; E261V; E275K,0.002555466,1.0584841,26/08/2020,-7.44974E+17,0.000514605,1,769.31464 +6190,M82I; Y239I,0.002630506,1.1153544,26/08/2020,-7.44974E+17,0.0006359,1,781.42267 +6191,M82K,0.002457023,1.5344783,26/08/2020,-7.44974E+17,0.001278178,2,872.27185 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V281A,0.14581819,5.0337253,26/08/2020,-7.44974E+17,0.151534982,1,34.543713 +6213,M82L; V221M; D287G,0.046054553,4.6603875,26/08/2020,-7.44974E+17,0.045071116,1,117.24239 +6214,M82N,0.022120556,2.624128,26/08/2020,-7.44974E+17,0.021251042,4,128.45375 +6215,M82P,0.004138567,1.7268304,26/08/2020,-7.44974E+17,0.001741723,1,700.9461 +6216,M82Q,0.013456713,5.9321923,26/08/2020,-7.44974E+17,0.012783094,5,453.588 +6217,M82R,0.001848828,0.6157129,26/08/2020,-7.44974E+17,0.000104792,1,743.71466 +6218,M82S,0.013953397,3.59485,26/08/2020,-7.44974E+17,0.011624932,1,334.1371 +6219,M82T,0.024811072,4.6545224,26/08/2020,-7.44974E+17,0.023286845,2,204.74368 +6220,M82T; D118E; G120C; S201Y,0.009670519,3.0070972,26/08/2020,-7.44974E+17,0.007157219,1,429.39114 +6221,M82T; E153D; Y177F,0.008803184,2.913703,26/08/2020,-7.44974E+17,0.006004005,1,459.537 +6222,M82T; E84V; S220T,0.00299951,1.8332334,26/08/2020,-7.44974E+17,0.001754219,2,793.9684 +6223,M82T; F209Y; S220T,0.005184942,2.0964472,26/08/2020,-7.44974E+17,0.00287205,1,611.8289 +6224,M82T; G104C; A282V,0.003099344,1.2954162,26/08/2020,-7.44974E+17,0.000892154,1,767.9811 +6225,M82T; G120C; G162R; P164L; L182Q; N249I; H255Y; Y294E; E295A; A296F; F297K; K298Q; Q299P; P300S; S301P; P302A; A303S; S304*; *305-,0.002045206,0.7021241,26/08/2020,-7.44974E+17,0.000145378,1,769.59607 +6226,M82T; G126S; P133H; V250M; A259V,0.002032613,0.6861506,26/08/2020,-7.44974E+17,0.000146939,1,762.2413 +6227,M82T; H183L; S220T; P300S,0.014271771,3.6189866,26/08/2020,-7.44974E+17,0.01185476,1,327.5769 +6228,M82T; H183L; W216R; S220T; H285R,0.003229734,1.4171308,26/08/2020,-7.44974E+17,0.001103265,1,753.9231 +6229,M82T; P133S; S160P,0.007530352,2.7111032,26/08/2020,-7.44974E+17,0.005061057,1,507.67517 +6230,M82T; P171R; S220T; H230Y; Q283H,0.005774494,2.2828677,26/08/2020,-7.44974E+17,0.003436598,1,585.91156 +6231,M82T; R148P; L182Q; S220T; H230Y,0.004435717,1.9028181,26/08/2020,-7.44974E+17,0.002251995,1,688.6125 +6232,M82T; S220T; M273K; A280T,0.002836782,1.0984312,26/08/2020,-7.44974E+17,0.000556483,1,770.9836 +6233,M82V,0.026417708,4.2867603,26/08/2020,-7.44974E+17,0.025705979,2,177.7108 +6234,M82V; E84G; T188A; D211G,0.002835057,1.2333705,26/08/2020,-7.44974E+17,0.000820772,1,784.682 +6235,M82V; F142Y; L158P; T172S; T179S; L181I; G191V,0.00298428,1.2426131,26/08/2020,-7.44974E+17,0.000740498,1,756.71075 +6236,M82V; H154Q; L253Q; E254D,0.002737157,1.1268268,26/08/2020,-7.44974E+17,0.0005947,1,792.7323 +6237,M82V; L101Q; W115G; S220T,0.002979642,1.2124869,26/08/2020,-7.44974E+17,0.000700969,1,768.2209 +6238,M82V; L158F; F209I; S220T,0.003143879,1.3947564,26/08/2020,-7.44974E+17,0.001107781,1,756.2758 +6239,M82V; N100K; S220T; A252V,0.002877117,1.3260474,26/08/2020,-7.44974E+17,0.000927038,1,772.7263 +6240,M82V; R122P; L161M,0.003634816,1.4585347,26/08/2020,-7.44974E+17,0.001113812,1,725.88654 +6241,M82V; R122S,0.011636107,3.3593655,26/08/2020,-7.44974E+17,0.009083926,1,378.917 +6242,M82V; W115R; G126C; P131S; R148P; S220T; H230Y,0.07979608,4.917806,26/08/2020,-7.44974E+17,0.080408299,1,67.51401 +6243,M82W,0.002032619,1.9412454,26/08/2020,-7.44974E+17,0.00143615,4,1084.6342 +6244,N100A,0.022491504,1.9397885,26/08/2020,-7.44974E+17,0.021932692,12,88.570045 +6245,N100C,0.022023037,0.81228924,26/08/2020,-7.44974E+17,0.021397073,12,40.433823 +6246,N100D,0.001970447,1.8792859,26/08/2020,-7.44974E+17,0.001739857,10,1015.18585 +6247,N100D; A112D; S220T,0.001837466,0.5797508,26/08/2020,-7.44974E+17,6.51E-05,1,759.0563 +6248,N100D; A174G,0.002286302,0.9164095,26/08/2020,-7.44974E+17,0.000368917,1,806.5572 +6249,N100D; E208K; S220T,0.003329366,1.4720924,26/08/2020,-7.44974E+17,0.001234971,1,756.65326 +6250,N100D; M129I; R137S; A206V; S220T,0.004894253,2.012059,26/08/2020,-7.44974E+17,0.002693036,1,630.3663 +6251,N100D; M185V; S220T,0.004453148,1.8812867,26/08/2020,-7.44974E+17,0.002071103,1,671.91974 +6252,N100D; P171R; D211E; D235G,0.002738248,1.1830741,26/08/2020,-7.44974E+17,0.000774329,1,795.19836 +6253,N100D; R165C; S201T; S225P,0.005663954,2.2940335,26/08/2020,-7.44974E+17,0.003363447,1,595.41174 +6254,N100D; S220T,0.005594546,2.7826772,26/08/2020,-7.44974E+17,0.004243112,2,604.37103 +6255,N100D; S220T; V221M; H230Y,0.005467617,2.2297375,26/08/2020,-7.44974E+17,0.003271819,1,618.3133 +6256,N100E,0.001444595,1.6585062,26/08/2020,-7.44974E+17,0.000924402,5,1318.0001 +6257,N100F,0.001692292,1.0634615,26/08/2020,-7.44974E+17,0.000610191,2,928.9152 +6258,N100G,0.013664564,3.9500945,26/08/2020,-7.44974E+17,0.012995493,4,306.0876 +6259,N100H,0.001208563,0.8554677,26/08/2020,-7.44974E+17,0.000478252,3,996.83014 +6260,N100H; Y186C; M248V; A280V,0.0071355,2.5989032,26/08/2020,-7.44974E+17,0.004531191,1,525.60394 +6261,N100I,0.015253694,0.506264,26/08/2020,-7.44974E+17,0.014399077,8,42.195244 +6262,N100I; A195V; F209L; S220T; T245A; H255Y,0.003043901,1.3148787,26/08/2020,-7.44974E+17,0.000982324,1,730.124 +6263,N100I; L167P; F209I,0.00294488,1.3181218,26/08/2020,-7.44974E+17,0.00101084,1,769.2147 +6264,N100I; L173P; Y192N,0.006607776,2.5342617,26/08/2020,-7.44974E+17,0.00447311,1,537.5443 +6265,N100I; N190D; A210V,0.00217164,0.8051219,26/08/2020,-7.44974E+17,0.000251712,1,796.01965 +6266,N100I; T196M,0.002924502,1.2491696,26/08/2020,-7.44974E+17,0.000877874,1,755.2141 +6267,N100K,0.001134533,1.6153822,26/08/2020,-7.44974E+17,0.00085535,8,1556.765 +6268,N100K; S220T,0.002619803,1.0548683,26/08/2020,-7.44974E+17,0.000553807,1,823.6168 +6269,N100L,0.001681331,1.8197058,26/08/2020,-7.44974E+17,0.001356898,7,1176.5867 +6270,N100M,0.017453294,1.6546977,26/08/2020,-7.44974E+17,0.015877939,2,126.8812 +6271,N100P,0.006893616,2.5516233,26/08/2020,-7.44974E+17,0.004529139,1,522.1597 +6272,N100Q,0.003599921,1.8453373,26/08/2020,-7.44974E+17,0.002369545,2,665.31323 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G215S,0.001902437,0.7150963,26/08/2020,-7.44974E+17,0.000188951,1,832.667 +6294,N102D; M129I; T242I,0.002522531,1.0609412,26/08/2020,-7.44974E+17,0.000591351,1,792.165 +6295,N102D; M129T; S220T; R279H; A303T,0.002364356,0.87016344,26/08/2020,-7.44974E+17,0.000309434,1,800.3811 +6296,N102D; S117P; L161Q,0.002217593,0.7746093,26/08/2020,-7.44974E+17,0.000243746,1,757.1145 +6297,N102D; S220T,0.028120887,4.311921,26/08/2020,-7.44974E+17,0.026822081,1,185.50916 +6298,N102D; W115C; D152V; D170N; S220T,0.003393557,1.4478117,26/08/2020,-7.44974E+17,0.001221497,1,736.08246 +6299,N102E,0.012944963,3.2764487,26/08/2020,-7.44974E+17,0.012096355,4,273.17142 +6300,N102F,0.003309528,2.7844644,26/08/2020,-7.44974E+17,0.002714909,5,907.674 +6301,N102G,0.06122088,2.2400312,26/08/2020,-7.44974E+17,0.061048625,5,37.60866 +6302,N102H,0.008145013,4.241274,26/08/2020,-7.44974E+17,0.007550031,3,564.4624 +6303,N102H; G191R; E278V,0.004621255,1.9712863,26/08/2020,-7.44974E+17,0.002206778,1,677.51 +6304,N102I,0.07453787,1.3922327,26/08/2020,-7.44974E+17,0.075273443,8,18.78362 +6305,N102I; L176F; H271D,0.007577328,2.7160978,26/08/2020,-7.44974E+17,0.005041466,1,504.42676 +6306,N102I; R137C; D139G; H183L; M185T; A303P,0.0033011,1.4021701,26/08/2020,-7.44974E+17,0.001195808,1,729.38446 +6307,N102I; R148P; S220T; H230Y,0.3550311,5.5976334,26/08/2020,-7.44974E+17,0.386578112,1,10.9408 +6308,N102I; R155H; S220T,0.12953468,5.038786,26/08/2020,-7.44974E+17,0.131724532,1,39.712772 +6309,N102I; S220T,0.45589218,5.6273713,26/08/2020,-7.44974E+17,0.514834361,1,6.9202037 +6310,N102I; T172I; S220T; L234H; A296V,0.022513501,4.103304,26/08/2020,-7.44974E+17,0.019416768,1,224.34499 +6311,N102K,0.001841029,1.7103544,26/08/2020,-7.44974E+17,0.001251681,4,1096.3462 +6312,N102K; R165S; S205R; S220T,0.002274056,0.968265,26/08/2020,-7.44974E+17,0.000436577,1,802.9 +6313,N102K; S220T; L269M,0.004256417,1.794628,26/08/2020,-7.44974E+17,0.001796346,1,665.2153 +6314,N102L,0.024695592,1.7427969,26/08/2020,-7.44974E+17,0.023565524,5,78.166595 +6315,N102M,0.112413995,0.70753616,26/08/2020,-7.44974E+17,0.116645648,12,6.177238 +6316,N102P,0.03198941,4.5568347,26/08/2020,-7.44974E+17,0.027754683,1,175.10545 +6317,N102Q,0.067424186,0.73493034,26/08/2020,-7.44974E+17,0.067627835,11,11.396954 +6318,N102R,0.001582096,1.7653884,26/08/2020,-7.44974E+17,0.001174848,6,1242.5103 +6319,N102S; D139V; S160P; L161M; S220T,0.005378312,2.155053,26/08/2020,-7.44974E+17,0.003185888,1,604.91705 +6320,N102S; G191R,0.003295017,1.3916781,26/08/2020,-7.44974E+17,0.001094898,1,747.2138 +6321,N102S; L167P; A174V,0.001985233,0.64442855,26/08/2020,-7.44974E+17,0.000144122,1,752.2799 +6322,N102S; R122H; P138Q,0.24170113,5.271406,26/08/2020,-7.44974E+17,0.260024328,1,18.65135 +6323,N102S; S220T; E295D,0.21334825,5.31588,26/08/2020,-7.44974E+17,0.227596471,1,22.431269 +6324,N102S; S220T; H230Y,0.6594695,5.730682,26/08/2020,-7.44974E+17,0.794162948,1,2.6082888 +6325,N102S; S220T; V232A; P240Q,0.18790078,5.234931,26/08/2020,-7.44974E+17,0.198209301,1,25.918175 +6326,N102S; V130M; A169G; A174G; V175G; L176V; Y177I; N178Q; T179H; A180R; L182T; H183A; M184H; M185D; Y186I; A187C; T188N; N190K; G191R; Y192I; L193S; Q194P; A195S; T196Y,0.003497281,1.5338203,26/08/2020,-7.44974E+17,0.001359539,1,776.3549 +6327,N102T,0.21474274,7.519462,26/08/2020,-7.44974E+17,0.215185335,3,28.566097 +6328,N102T; S220T; L227P,0.03577852,4.6017985,26/08/2020,-7.44974E+17,0.033675829,1,153.99652 +6329,N102T; V130E; L143M; Y177F,0.006262716,2.4253976,26/08/2020,-7.44974E+17,0.003929309,1,562.5751 +6330,N102V,0.08586599,1.8616949,26/08/2020,-7.44974E+17,0.085853095,7,21.538666 +6331,N102W,0.013944202,0.5805025,26/08/2020,-7.44974E+17,0.013641687,4,60.902397 +6332,N102Y; R148P; S220T; H230Y,0.005009471,2.0572782,26/08/2020,-7.44974E+17,0.002710284,1,639.46216 +6333,N102Y; S103P; V135A; S220T; I256F,0.002576753,1.0573789,26/08/2020,-7.44974E+17,0.000563164,1,756.89594 +6334,N102Y; T121S; Y144H; H154Y; S220T; Q263P,0.002805984,1.2014269,26/08/2020,-7.44974E+17,0.000788368,1,808.5865 +6335,N102Y; T179A; S220T; Q283P; D287N; P302Q,0.003077432,1.2617121,26/08/2020,-7.44974E+17,0.000877733,1,748.9148 +6336,N102Y; V113G; D118G; E153V; S220T,0.002921375,1.2607484,26/08/2020,-7.44974E+17,0.000871513,1,783.5969 +6337,N102Y; V130A; R148P; S220T; H230Y; G244S; Y290N,0.00499507,2.086275,26/08/2020,-7.44974E+17,0.002591437,1,644.35406 +6338,N178A,0.005111147,2.11293,26/08/2020,-7.44974E+17,0.002889451,1,610.43115 +6339,N178C,0.007422435,2.6964967,26/08/2020,-7.44974E+17,0.005071564,1,510.12802 +6340,N178D,0.020744732,5.6326675,26/08/2020,-7.44974E+17,0.019606519,3,283.53894 +6341,N178D; F217L; S220T; A282T,0.006190929,2.3808355,26/08/2020,-7.44974E+17,0.003830052,1,569.50397 +6342,N178D; S220T,0.13938598,5.3169937,26/08/2020,-7.44974E+17,0.13991706,1,38.826534 +6343,N178D; S220T; L253M,0.025571985,4.3124056,26/08/2020,-7.44974E+17,0.022917788,1,204.80127 +6344,N178D; S220T; N251D; P270L,0.007096975,2.696131,26/08/2020,-7.44974E+17,0.004690695,1,534.79047 +6345,N178E,0.04476198,5.7755384,26/08/2020,-7.44974E+17,0.043248448,2,135.3226 +6346,N178F,0.001622393,1.0236228,26/08/2020,-7.44974E+17,0.000563074,2,949.1594 +6347,N178G,0.006704367,3.0946488,26/08/2020,-7.44974E+17,0.005484357,2,542.8765 +6348,N178I,0.002460207,0.90402794,26/08/2020,-7.44974E+17,0.000321368,1,777.7731 +6349,N178I; S220T,0.002955434,1.164907,26/08/2020,-7.44974E+17,0.000670195,1,765.3327 +6350,N178I; S220T; H230Y,0.005647838,2.2828255,26/08/2020,-7.44974E+17,0.003511438,1,585.3731 +6351,N178K,0.0016366,1.3098251,26/08/2020,-7.44974E+17,0.000816556,3,1035.4806 +6352,N178K; S220T,0.001659812,1.0576614,26/08/2020,-7.44974E+17,0.000582099,2,941.3381 +6353,N178L,0.001501335,0.8900604,26/08/2020,-7.44974E+17,0.000426738,2,948.1172 +6354,N178M,0.003793772,2.3716366,26/08/2020,-7.44974E+17,0.003140732,4,701.78174 +6355,N178P,0.001909472,1.2080873,26/08/2020,-7.44974E+17,0.000801885,2,916.8849 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L246P; V265L,0.003153547,1.2950546,26/08/2020,-7.44974E+17,0.000929688,1,772.6929 +6368,N178Y; S220T,0.002133453,1.3251224,26/08/2020,-7.44974E+17,0.000956339,2,881.09576 +6369,N178Y; S220T; R258H,0.002725625,1.123661,26/08/2020,-7.44974E+17,0.000628692,1,781.78076 +6370,N178Y; T179A; G215D; S220T,0.003942367,1.666898,26/08/2020,-7.44974E+17,0.00165327,1,694.5891 +6371,N190D,0.16224056,4.7133684,26/08/2020,-7.44974E+17,0.166745053,2,26.664848 +6372,N190F,0.05608627,4.851655,26/08/2020,-7.44974E+17,0.053472492,1,98.83051 +6373,N190I,0.00267671,1.0858638,26/08/2020,-7.44974E+17,0.000605715,1,778.02795 +6374,N190I; H230Y,0.003153296,1.3847646,26/08/2020,-7.44974E+17,0.001088458,1,756.4586 +6375,N190I; M248K,0.003217908,1.4232534,26/08/2020,-7.44974E+17,0.001179867,1,751.00104 +6376,N190I; S220T,0.002207593,1.6837075,26/08/2020,-7.44974E+17,0.001376282,3,943.63635 +6377,N190I; S220T; P224H,0.003407677,1.4195387,26/08/2020,-7.44974E+17,0.001102849,1,769.0043 +6378,N190I; S220T; Y294H; F297S,0.003806456,1.5884008,26/08/2020,-7.44974E+17,0.001381526,1,708.27264 +6379,N190K,0.003193489,1.3820096,26/08/2020,-7.44974E+17,0.001167896,1,744.10333 +6380,N190K; T196R,0.002663591,1.1237504,26/08/2020,-7.44974E+17,0.000599843,1,761.178 +6381,N190L,0.00186797,1.9538494,26/08/2020,-7.44974E+17,0.001398701,6,1148.816 +6382,N190M,0.02231112,4.025204,26/08/2020,-7.44974E+17,0.020631311,1,224.17363 +6383,N190P,0.00152172,1.3938978,26/08/2020,-7.44974E+17,0.000908971,4,1102.2323 +6384,N190R,0.001418059,1.0837941,26/08/2020,-7.44974E+17,0.000680708,3,1011.1027 +6385,N190S,0.030100383,5.460919,26/08/2020,-7.44974E+17,0.029643843,3,187.99539 +6386,N190S; A252T; S267A,0.003248926,1.4098173,26/08/2020,-7.44974E+17,0.001111818,1,740.0811 +6387,N190V,0.002104046,1.3643941,26/08/2020,-7.44974E+17,0.00096611,2,924.20605 +6388,N190W,0.005770679,2.309212,26/08/2020,-7.44974E+17,0.003835854,1,577.0045 +6389,N190Y,0.008827677,3.2918231,26/08/2020,-7.44974E+17,0.007768158,2,436.05234 +6390,N190Y; A282E,0.005003538,2.0131652,26/08/2020,-7.44974E+17,0.002537984,1,632.6079 +6391,N190Y; A296T; S304T,0.007556402,2.7022414,26/08/2020,-7.44974E+17,0.00513856,1,515.0527 +6392,N190Y; L246P; S267L,0.00324944,1.4619561,26/08/2020,-7.44974E+17,0.00125267,1,737.70844 +6393,N190Y; S220T,0.018678328,4.0406313,26/08/2020,-7.44974E+17,0.015573529,1,272.566 +6394,N190Y; T245S; A277T,0.002787928,1.1174579,26/08/2020,-7.44974E+17,0.000573352,1,750.2006 +6395,N238A,0.07459505,7.203358,26/08/2020,-7.44974E+17,0.072906843,4,92.830215 +6396,N238C,0.122800276,5.049995,26/08/2020,-7.44974E+17,0.123618814,1,42.34348 +6397,N238D; H255R,0.003479864,1.4669952,26/08/2020,-7.44974E+17,0.001263697,1,725.36816 +6398,N238D; R247C; M291K,0.002772832,1.0885404,26/08/2020,-7.44974E+17,0.000589528,1,787.011 +6399,N238E,0.12887406,6.860981,26/08/2020,-7.44974E+17,0.127545157,3,48.667553 +6400,N238F,0.14593981,6.605516,26/08/2020,-7.44974E+17,0.146366815,3,40.5823 +6401,N238H; 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V26L; L27A; A28E; E29T; V30L; L32A; A33Q; K34G; G35H; H36P; P37L; T39V; V40W; W41N; N42R; R43T; T44P; P45D; E46K; R47A; A48D; S49G; G50L; L51V; V52A; A53R; Q54G; G55A; A56H; S57R; L58A; A60T; S61V; I62A; T63D; D64A; A65V; V66S; S67A; A68S; S69P; P70L; V71T; T72V; I73A; M74C; C75L; L76K; N77D; N78Y; Y79D; A80T; T81L; M82Y; Y83G; E84V; V85F; F86E; G87P; P88A; A89G; R90D; E91A; A92L; L93A; R94G; D95R; R96T; V97L; L98V; V99N; N100L; L101N; N102S; S103G; G104T; T105P; P106K; Q107E; E108A; V109R; R110A; A112V; V113L; S114W; W115A; A116E; S117E; D118R; L119G; G120A; T121E; R122Y; Y123L; L124D; D125G; G126A; A127I; I128M; M129V; V130P; P133L; L134V; V135G; G136H; R137P; P138D; D139S; A140V; V141F; F142L; L143Y; Y144S; S145G; G146P; D147R; R148E; A149L; V150F; L151D; E153H; H154R; R155E; A156V; T157L; L158S; S160L; L161G; G162T; D163L; P164T; R165W; F166L; L167G; G168E; A169D; D170V; P171G; T172R; L173A; A174E; V175A; L176F; Y177D; N178V; T179A; A180L; L182D; H183L; M184F; M185W; Y186T; A187S; T188V; L189S; N190G; G191F; Y192V; L193H; Q194A; A195L; T196S; A197L; L198V; V199G; G200S; S201A; A202G; G203V; V204S; S205A; A206T; T207E; E208F; F209A; A210D; D211I; I212A; A213L; L214G; G215W; W216F; F217A; A218P; P219S; S220V; V221L; L222A; A223P; P224S; S226L; L227A; A229H; H230A; A231V; V232D; D233V; L234D; D235K; K236G; G237N; N238Y; Y239P; P240G; G241T; T242L; L243G; G244T; T245L; L246R; R247M; M248N; N249V; V250N; N251A; A252L; L253E; E254H; H255I; I256A; A257R; R258A; A260E; E262Q; Q263G; G264V; V265H; H266S; S267E; E268L; L269P; P270H; H271L; L272M; M273R; R274E; E275V; V276A; A277E; E278R; R279A; A280V; V281A; A282Q; Q283G; G284H; H285G; G286D; D287H; H288N; N289Y; Y290M; M291S; S292V; V293Y; Y294E; E295A; A296F; F297K; K298Q; Q299P; P300S; S301P; P302A; A303S; S304*; *305-,0.47910717,5.662786,26/08/2020,-7.44974E+17,0.548887558,1,6.172861 +6425,N3V,0.09808718,3.2758102,26/08/2020,-7.44974E+17,0.097736766,5,31.964977 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L119H; D233E; A282T; Y290N,0.002873977,1.1708431,26/08/2020,-7.44974E+17,0.000686293,1,752.27966 +6437,N42D; N102K; R148P; S160P; S220T; H230Y,0.002841028,1.1900806,26/08/2020,-7.44974E+17,0.000771301,1,784.93964 +6438,N42D; N78K; S220T; H230Y,0.049611844,4.7081356,26/08/2020,-7.44974E+17,0.047298698,1,110.209946 +6439,N42D; N78Y; Q107H; S220T,0.003375231,1.6464965,26/08/2020,-7.44974E+17,0.002130489,2,661.4184 +6440,N42D; P106L; A112V; L198Q; A296D,0.002175404,0.79837984,26/08/2020,-7.44974E+17,0.000207124,1,840.847 +6441,N42D; P219A,0.004384837,1.8943733,26/08/2020,-7.44974E+17,0.002099782,1,678.0781 +6442,N42D; Q54R; D287G,0.003550685,1.5037819,26/08/2020,-7.44974E+17,0.001129538,1,725.21063 +6443,N42D; Q54R; G136C; Y144N,0.003368244,1.4415488,26/08/2020,-7.44974E+17,0.001117738,1,734.5349 +6444,N42D; R137S; P164T; S220T; K236N; Q283E,0.016296554,3.7639358,26/08/2020,-7.44974E+17,0.013412381,1,301.49802 +6445,N42D; R148P; S220T; H230Y,0.012572225,3.4134367,26/08/2020,-7.44974E+17,0.010234862,1,359.02728 +6446,N42D; R43H; A112T; D118V; H154N; F217L; H271L,0.00289843,1.2451696,26/08/2020,-7.44974E+17,0.000819655,1,775.1658 +6447,N42D; S57T; A159T; A202S; N238Y; P240S; T242I; H255R; L269S; H271Q,0.001770355,0.5081073,26/08/2020,-7.44974E+17,4.59E-05,1,725.02826 +6448,N42D; T72S; E208G; S267T,0.002975947,1.2870926,26/08/2020,-7.44974E+17,0.000931508,1,743.42413 +6449,N42D; V130A; N190I; S220T,0.003401429,1.4952784,26/08/2020,-7.44974E+17,0.001328314,1,739.2484 +6450,N42D; V135E; L176F; L181I; P219A; V250M; S267A,0.002055117,0.7426935,26/08/2020,-7.44974E+17,0.000209222,1,756.97705 +6451,N42D; V66A; S220T; K298E,0.003587141,1.5779519,26/08/2020,-7.44974E+17,0.001418583,1,749.329 +6452,N42D; V99F; V141I; D287E,0.003326488,1.3960545,26/08/2020,-7.44974E+17,0.001128769,1,734.30286 +6453,N42E,0.002389289,0.83151,26/08/2020,-7.44974E+17,0.000292844,1,771.88696 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D287N,0.003401678,1.4026821,26/08/2020,-7.44974E+17,0.001171439,1,733.09216 +6465,N42I; S220T; V221M; P240Q; L253M,0.002354382,1.0480318,26/08/2020,-7.44974E+17,0.000607117,1,820.4351 +6466,N42I; T196A; V281I,0.004147316,1.7898659,26/08/2020,-7.44974E+17,0.001935862,1,699.35175 +6467,N42K; A112T,0.006971657,2.571506,26/08/2020,-7.44974E+17,0.004597644,1,526.82935 +6468,N42K; A112V; D118N; G162E; N289I,0.002112184,0.8080717,26/08/2020,-7.44974E+17,0.000237719,1,786.0188 +6469,N42K; A127D; M129K; L182P; S220T; V232D,0.003449406,1.5446713,26/08/2020,-7.44974E+17,0.001362007,1,725.5606 +6470,N42K; A59T; T63S; V199E; S220T; H230Y,0.001603251,0.43260288,26/08/2020,-7.44974E+17,1.80E-05,1,664.3584 +6471,N42K; E91G; P164L; D211Y; Q263L; S292T,0.00216729,0.7931178,26/08/2020,-7.44974E+17,0.000226597,1,775.23975 +6472,N42K; M74V; G126C; V130L; A159T; N178D; H183Y; S205C,0.009499069,3.148692,26/08/2020,-7.44974E+17,0.006939501,1,457.63504 +6473,N42K; P138T; 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R279C,0.003404199,1.4486903,26/08/2020,-7.44974E+17,0.001236858,1,722.01215 +6563,N77T,0.028599784,4.2716923,26/08/2020,-7.44974E+17,0.027634802,1,175.23653 +6564,N77T; A223E,0.06284902,4.960851,26/08/2020,-7.44974E+17,0.05806865,1,90.15562 +6565,N77T; R155H; S220T,0.14367883,5.1577234,26/08/2020,-7.44974E+17,0.148351139,1,35.658287 +6566,N77V,0.03153561,4.4197464,26/08/2020,-7.44974E+17,0.029635901,1,168.78261 +6567,N77W,0.007777811,4.2780733,26/08/2020,-7.44974E+17,0.006934148,3,595.115 +6568,N77Y,0.008272965,2.8586075,26/08/2020,-7.44974E+17,0.006032666,1,478.04953 +6569,N77Y; A228G,0.004563553,1.925185,26/08/2020,-7.44974E+17,0.002188602,1,656.4553 +6570,N77Y; D95V; A169V; S220T; H271Y,0.002642678,1.1424491,26/08/2020,-7.44974E+17,0.000679513,1,802.74567 +6571,N77Y; E84V; S160P; F166L; F209L,0.002783667,1.1495808,26/08/2020,-7.44974E+17,0.000695241,1,769.1091 +6572,N77Y; F166S; S220T,0.002645158,1.107464,26/08/2020,-7.44974E+17,0.000618267,1,774.796 +6573,N77Y; S220T; Q263H; D287V; 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M248R,0.003166576,1.3436902,26/08/2020,-7.44974E+17,0.001002072,1,761.9803 +6647,P106L; R155C; M184K; S220T,0.004334544,1.7956412,26/08/2020,-7.44974E+17,0.001837292,1,698.35333 +6648,P106L; S220T; S292F,0.002947256,1.1310815,26/08/2020,-7.44974E+17,0.000658883,1,728.6376 +6649,P106M,0.051805656,4.766079,26/08/2020,-7.44974E+17,0.05008321,1,105.6961 +6650,P106M; E108K,0.004965321,1.6630133,26/08/2020,-7.44974E+17,0.003615665,2,449.24826 +6651,P106N,0.002614383,1.5703763,26/08/2020,-7.44974E+17,0.002329382,8,656.7676 +6652,P106Q,0.009766338,2.0599616,26/08/2020,-7.44974E+17,0.008890756,4,236.80615 +6653,P106Q; F209Y; D211V; S225P,0.003165044,1.3187037,26/08/2020,-7.44974E+17,0.000966858,1,745.8046 +6654,P106Q; P133L; S220T; D287N,0.003512081,1.5409642,26/08/2020,-7.44974E+17,0.001370477,1,750.54346 +6655,P106Q; Q107P; E275D,0.003648767,1.5222542,26/08/2020,-7.44974E+17,0.001471038,1,712.87195 +6656,P106R,0.024663383,0.9320197,26/08/2020,-7.44974E+17,0.024133182,8,42.579002 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R247C,0.002271047,0.8124767,26/08/2020,-7.44974E+17,0.000236828,1,745.3335 +6697,P12R,0.116189286,4.758519,26/08/2020,-7.44974E+17,0.11662329,2,39.960583 +6698,P12R; A169S; D211G; S220T,0.28189653,5.2757983,26/08/2020,-7.44974E+17,0.315351319,1,14.94906 +6699,P12R; H271P,0.003059614,1.2346864,26/08/2020,-7.44974E+17,0.000750466,1,774.276 +6700,P12R; Q194R; V199L,0.003203263,1.3136219,26/08/2020,-7.44974E+17,0.001007646,1,745.25006 +6701,P12R; R148P; S220T; H230Y; M291R,0.004956643,2.024438,26/08/2020,-7.44974E+17,0.002596126,1,627.1517 +6702,P12S,0.05958685,4.929649,26/08/2020,-7.44974E+17,0.057662983,1,93.820885 +6703,P12S; D64G; F86S; L119F; G203D; S220T; V250E,0.002701977,1.1472259,26/08/2020,-7.44974E+17,0.000661529,1,769.6663 +6704,P12S; E254D; F297Y; S301G,0.005116522,2.176346,26/08/2020,-7.44974E+17,0.002742143,1,642.2447 +6705,P12S; G35D; P106A; P131S; V175A; A260T,0.003712762,1.4483485,26/08/2020,-7.44974E+17,0.001177051,1,702.9406 +6706,P12S; P131Q; S220T,0.5722049,5.7962155,26/08/2020,-7.44974E+17,0.67199821,1,4.1529775 +6707,P12S; S201P; N251Y,0.003572498,1.5520889,26/08/2020,-7.44974E+17,0.001513576,1,719.313 +6708,P12S; T16M; A80V; T196A; S220T,0.003170921,1.3552508,26/08/2020,-7.44974E+17,0.001015976,1,747.7879 +6709,P12S; V17A; T39A; G104C; P106S; T172I; V204E; S220A,0.003210633,1.4200573,26/08/2020,-7.44974E+17,0.001161172,1,747.1898 +6710,P12T,0.09700528,6.0078564,26/08/2020,-7.44974E+17,0.097949635,3,59.02545 +6711,P12T; A33T; L182Q; T207I; S220T; M248T,0.002624053,1.0642017,26/08/2020,-7.44974E+17,0.000606234,1,748.4025 +6712,P12T; D64G; M129K; R247S,0.008077036,2.8443263,26/08/2020,-7.44974E+17,0.005858951,1,507.06903 +6713,P12T; F86L; L134P; V150E; R155H; F166S; S220T,0.004428036,1.861811,26/08/2020,-7.44974E+17,0.002093594,1,672.19666 +6714,P12T; L167H; S205R; L227Q; E254V,0.00342297,1.4833766,26/08/2020,-7.44974E+17,0.001212104,1,749.158 +6715,P12T; L173M; S220T; T242N,0.4312248,5.5336957,26/08/2020,-7.44974E+17,0.493712671,1,7.5265737 +6716,P12T; L51Q; S103P,0.002596749,0.9696265,26/08/2020,-7.44974E+17,0.000410725,1,776.61926 +6717,P12T; S220T,0.305398,5.398235,26/08/2020,-7.44974E+17,0.33447194,1,13.562033 +6718,P12T; V71I; M185I; S220T,0.020562194,4.043436,26/08/2020,-7.44974E+17,0.018246694,1,248.73193 +6719,P12V,0.05153588,4.6392717,26/08/2020,-7.44974E+17,0.048884431,2,95.80482 +6720,P12W,0.022376481,4.1468854,26/08/2020,-7.44974E+17,0.01983949,1,229.72061 +6721,P131A,0.046387002,4.6941934,26/08/2020,-7.44974E+17,0.043750888,1,118.760414 +6722,P131A; A169T; A206D; S220T; V276G,0.002740066,1.0994493,26/08/2020,-7.44974E+17,0.00061397,1,783.2141 +6723,P131A; G136D; R165L; E262D; Q299H,0.008754237,2.926747,26/08/2020,-7.44974E+17,0.006142986,1,462.52826 +6724,P131C,0.020598883,4.1020417,26/08/2020,-7.44974E+17,0.018117484,1,245.17804 +6725,P131D,0.18079376,5.258609,26/08/2020,-7.44974E+17,0.189754548,1,27.633804 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+6737,P131Q,0.15575546,5.1838474,26/08/2020,-7.44974E+17,0.159070344,1,32.713615 +6738,P131Q; R148P; S220T; H230Y,0.55926675,5.9691668,26/08/2020,-7.44974E+17,0.611006017,2,4.642295 +6739,P131R,0.025606489,5.8999376,26/08/2020,-7.44974E+17,0.025195888,3,239.05125 +6740,P131S,0.051914025,5.7963758,26/08/2020,-7.44974E+17,0.050905096,4,111.35958 +6741,P131S; A257V; S301R,0.004684051,1.9160295,26/08/2020,-7.44974E+17,0.002428104,1,632.71094 +6742,P131S; D139G; W216G; S220T,0.011404311,3.2810173,26/08/2020,-7.44974E+17,0.008766049,1,385.36273 +6743,P131S; S220T; H230Y,0.62421167,5.9194756,26/08/2020,-7.44974E+17,0.753536858,1,3.1413238 +6744,P131T,0.030501053,5.493198,26/08/2020,-7.44974E+17,0.029754732,4,183.83955 +6745,P131T; M185I; N238S; M248V,0.002073084,0.8135825,26/08/2020,-7.44974E+17,0.00026573,1,762.2387 +6746,P131T; N178I; S220T; P270A,0.003212649,1.3689992,26/08/2020,-7.44974E+17,0.001044394,1,747.8792 +6747,P131T; V135L; E275G; D287G,0.002919161,1.2191639,26/08/2020,-7.44974E+17,0.000750056,1,764.72504 +6748,P131V,0.016556365,3.3297615,26/08/2020,-7.44974E+17,0.015138989,2,233.26984 +6749,P131W,0.007849178,3.821795,26/08/2020,-7.44974E+17,0.006731559,3,538.211 +6750,P131Y,0.010508856,3.2547603,26/08/2020,-7.44974E+17,0.00792031,1,418.9645 +6751,P132A,0.008570266,2.881292,26/08/2020,-7.44974E+17,0.00729925,2,405.33276 +6752,P132C,0.010727923,4.311812,26/08/2020,-7.44974E+17,0.010133766,4,424.77267 +6753,P132F,0.001450324,1.177126,26/08/2020,-7.44974E+17,0.00062538,3,1070.0292 +6754,P132G,0.008786356,5.142151,26/08/2020,-7.44974E+17,0.008254399,4,612.1815 +6755,P132H,0.001804517,1.1255543,26/08/2020,-7.44974E+17,0.000668176,2,914.34717 +6756,P132I,0.016661245,3.8239708,26/08/2020,-7.44974E+17,0.014058603,1,291.47626 +6757,P132K,0.001646375,1.3336562,26/08/2020,-7.44974E+17,0.000838278,3,1034.594 +6758,P132L,0.005708256,1.5970936,26/08/2020,-7.44974E+17,0.004390396,2,384.331 +6759,P132L; D170E,0.004430455,1.8793832,26/08/2020,-7.44974E+17,0.002325232,1,643.0779 +6760,P132L; F217C; S220T,0.003033088,1.2122308,26/08/2020,-7.44974E+17,0.000724654,1,765.2721 +6761,P132L; P133T; S220T; H230Y,0.01751031,3.8352242,26/08/2020,-7.44974E+17,0.014996658,1,272.00778 +6762,P132N,0.001382502,0.9684742,26/08/2020,-7.44974E+17,0.000571646,3,996.9418 +6763,P132Q,0.001808333,2.0561862,26/08/2020,-7.44974E+17,0.001302151,5,1274.476 +6764,P132Q; D163G; S267P; A280D,0.002867401,1.1172365,26/08/2020,-7.44974E+17,0.000619858,1,783.37805 +6765,P132Q; T172A; S220T,0.007106154,2.6270835,26/08/2020,-7.44974E+17,0.004892023,1,515.4873 +6766,P132R,0.001925864,1.5740123,26/08/2020,-7.44974E+17,0.001098221,3,1020.66266 +6767,P132R; R148P; S220T; H230Y,0.005341973,2.1670492,26/08/2020,-7.44974E+17,0.002857288,1,643.16907 +6768,P132S; A218T; S220T; T242N; K298E,0.003274367,1.4327861,26/08/2020,-7.44974E+17,0.001168002,1,724.79517 +6769,P132S; R148H; S220T; P240S,0.004597907,1.9293649,26/08/2020,-7.44974E+17,0.002295132,1,643.2874 +6770,P132S; V135I; D147G; M248V,0.003805307,1.5954119,26/08/2020,-7.44974E+17,0.001548486,1,688.3564 +6771,P132S; V150M; V175E; Y177N; W216R,0.002657726,1.1660242,26/08/2020,-7.44974E+17,0.000757103,1,806.8069 +6772,P132S; Y186F; A218T,0.005128961,2.0890532,26/08/2020,-7.44974E+17,0.002603686,1,619.9642 +6773,P132T,0.019409236,5.6624074,26/08/2020,-7.44974E+17,0.018557415,3,305.30087 +6774,P132V,0.025123721,4.1826434,26/08/2020,-7.44974E+17,0.023714502,1,205.25603 +6775,P132W,0.001405799,1.0600133,26/08/2020,-7.44974E+17,0.000596627,3,1055.4923 +6776,P132Y,0.00155511,0.9479348,26/08/2020,-7.44974E+17,0.000476743,2,934.12366 +6777,P133A,0.0660515,4.8063316,26/08/2020,-7.44974E+17,0.066549798,1,82.68907 +6778,P133D,0.25372493,5.1888947,26/08/2020,-7.44974E+17,0.272624209,1,17.437902 +6779,P133E,0.06583545,4.9725714,26/08/2020,-7.44974E+17,0.063431528,1,84.71389 +6780,P133F,0.028686369,8.800159,26/08/2020,-7.44974E+17,0.028736411,8,302.79166 +6781,P133F; V135L,0.008207995,2.9366362,26/08/2020,-7.44974E+17,0.005871599,1,480.6667 +6782,P133G,0.07386453,4.256458,26/08/2020,-7.44974E+17,0.073931999,3,58.21677 +6783,P133H,0.0627003,5.1839604,26/08/2020,-7.44974E+17,0.061473602,2,85.90129 +6784,P133H; G168V; S220T,0.003742148,1.5456442,26/08/2020,-7.44974E+17,0.001293052,1,714.288 +6785,P133H; S220T,0.4401448,5.464728,26/08/2020,-7.44974E+17,0.506954295,1,7.129921 +6786,P133I,0.046039928,7.6309943,26/08/2020,-7.44974E+17,0.045724589,5,162.9111 +6787,P133K,0.060453687,4.8768396,26/08/2020,-7.44974E+17,0.057940797,1,91.640526 +6788,P133L,0.05155309,6.628557,26/08/2020,-7.44974E+17,0.050740071,5,126.489624 +6789,P133L; A149V; D233V,0.006127013,2.436937,26/08/2020,-7.44974E+17,0.003424671,1,589.39105 +6790,P133L; G136D; S220T; D233V; L234R,0.002487776,0.98248905,26/08/2020,-7.44974E+17,0.000432081,1,749.8596 +6791,P133L; L193F; S220T; G284A; K298E,0.00236637,0.97278595,26/08/2020,-7.44974E+17,0.0004686,1,789.37537 +6792,P133M,0.040225383,5.493972,26/08/2020,-7.44974E+17,0.039585606,3,139.74142 +6793,P133N,0.26704082,5.354181,26/08/2020,-7.44974E+17,0.288986271,1,16.488686 +6794,P133Q,0.18066101,5.337806,26/08/2020,-7.44974E+17,0.185583162,1,27.877285 +6795,P133R,0.023142558,4.184082,26/08/2020,-7.44974E+17,0.020572565,1,224.24779 +6796,P133S; N178D; Q299K,0.012801742,3.4624908,26/08/2020,-7.44974E+17,0.009922011,1,361.88574 +6797,P133T,0.092916116,5.1430006,26/08/2020,-7.44974E+17,0.094295854,2,55.494934 +6798,P133V,0.048994433,5.453918,26/08/2020,-7.44974E+17,0.048857236,2,116.42813 +6799,P133W,0.03913736,3.6090567,26/08/2020,-7.44974E+17,0.03787389,3,98.18433 +6800,P133Y,0.04623308,5.717551,26/08/2020,-7.44974E+17,0.04548628,2,129.19174 +6801,P138A,0.06358181,5.2958207,26/08/2020,-7.44974E+17,0.062991399,4,82.08372 +6802,P138C,0.04485441,4.464635,26/08/2020,-7.44974E+17,0.044457697,2,105.40064 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G203V; S220T; P300R,0.00379642,1.6597494,26/08/2020,-7.44974E+17,0.001676271,1,698.7531 +6815,P138L; H255R,0.002634167,1.060159,26/08/2020,-7.44974E+17,0.000604957,1,784.6183 +6816,P138L; L189S; L193P; S220T; Q283L,0.002960375,1.2954009,26/08/2020,-7.44974E+17,0.00089702,1,807.73206 +6817,P138L; M185T; G191V,0.008000023,2.74952,26/08/2020,-7.44974E+17,0.005581844,1,487.26566 +6818,P138L; P240Q,0.02230476,4.1518292,26/08/2020,-7.44974E+17,0.01923328,1,231.82436 +6819,P138L; S220T; A282T,0.4757563,5.7172947,26/08/2020,-7.44974E+17,0.53991306,1,6.387098 +6820,P138M,0.040645733,6.1847887,26/08/2020,-7.44974E+17,0.040966098,3,155.21638 +6821,P138M; A140V,0.002885146,1.1914337,26/08/2020,-7.44974E+17,0.000714328,1,791.1278 +6822,P138N,0.1307551,4.661244,26/08/2020,-7.44974E+17,0.131996786,3,33.136604 +6823,P138Q,0.063565485,5.9794474,26/08/2020,-7.44974E+17,0.06373551,2,96.21493 +6824,P138Q; D152N; D170Y; A195T,0.002313005,0.89634055,26/08/2020,-7.44974E+17,0.000333552,1,804.3541 +6825,P138Q; P171H; D235G; E275G,0.004325748,1.8402687,26/08/2020,-7.44974E+17,0.002038736,1,696.0406 +6826,P138Q; S201F,0.003062188,1.3794824,26/08/2020,-7.44974E+17,0.000999956,1,781.02747 +6827,P138Q; S201P; P240S; A280D,0.004360583,1.8251611,26/08/2020,-7.44974E+17,0.001886462,1,682.9358 +6828,P138Q; S220T; H230Y; N289Y; P302Q,0.005038656,2.1195178,26/08/2020,-7.44974E+17,0.002793219,1,620.5273 +6829,P138R,0.055675752,4.8833413,26/08/2020,-7.44974E+17,0.053019331,1,101.14882 +6830,P138S,0.082613006,5.101221,26/08/2020,-7.44974E+17,0.079662256,3,59.980373 +6831,P138S; A257T,0.022461684,4.07056,26/08/2020,-7.44974E+17,0.020677143,1,225.45013 +6832,P138S; D152G; T188S; S220T,0.07314042,4.904087,26/08/2020,-7.44974E+17,0.072386326,1,75.51562 +6833,P138S; S220T,0.5974282,3.5866864,26/08/2020,-7.44974E+17,0.641103939,2,2.2771118 +6834,P138T,0.042678386,3.525727,26/08/2020,-7.44974E+17,0.041522025,2,92.34936 +6835,P138T; M273R,0.002873907,1.19518,26/08/2020,-7.44974E+17,0.000772441,1,750.4088 +6836,P138T; S220T; E262G,0.13645574,5.23304,26/08/2020,-7.44974E+17,0.136938796,1,38.75267 +6837,P138Y,0.03798825,6.4184823,26/08/2020,-7.44974E+17,0.037747662,3,171.42943 +6838,P14A,0.13114554,5.1292763,26/08/2020,-7.44974E+17,0.135305834,1,40.420277 +6839,P14A; N42I; A80V; Y83H; T157N; L246P,0.002004421,0.70353895,26/08/2020,-7.44974E+17,0.000150161,1,767.2779 +6840,P14A; T38S; G126C; P132Q; P240Q; R274C; M291I,0.002207861,0.7598945,26/08/2020,-7.44974E+17,0.00020412,1,772.8442 +6841,P14C,0.04196976,3.4531956,26/08/2020,-7.44974E+17,0.04178818,3,87.08025 +6842,P14E,0.0325938,4.463155,26/08/2020,-7.44974E+17,0.030164738,1,167.48929 +6843,P14F,0.006664767,3.3512137,26/08/2020,-7.44974E+17,0.005831234,3,557.901 +6844,P14G,0.031025369,4.421118,26/08/2020,-7.44974E+17,0.028443185,1,170.17715 +6845,P14G; V18F,0.005358237,2.2025168,26/08/2020,-7.44974E+17,0.003115245,1,604.59485 +6846,P14H,0.032077465,5.41225,26/08/2020,-7.44974E+17,0.030806218,2,181.2101 +6847,P14I,0.004596601,1.9851043,26/08/2020,-7.44974E+17,0.002559572,1,668.8281 +6848,P14K,0.052105945,6.2206244,26/08/2020,-7.44974E+17,0.051662997,3,120.26448 +6849,P14L,0.006641113,3.5915911,26/08/2020,-7.44974E+17,0.005544687,3,597.621 +6850,P14L; L119H; I212T; S220T,0.009071099,2.9828804,26/08/2020,-7.44974E+17,0.006791778,1,456.4724 +6851,P14L; L27P; D95V; V97D; S103L; F142L; L158H; V232A,0.003520873,1.4981987,26/08/2020,-7.44974E+17,0.001203747,1,751.09656 +6852,P14L; L27R; A68S; L189S; D211V; A280D,0.002482271,0.94335866,26/08/2020,-7.44974E+17,0.000415585,1,803.89044 +6853,P14L; L58I; N78Y; E84K; S145P; S220T; N251I,0.001693623,0.96206826,26/08/2020,-7.44974E+17,0.000514922,2,913.82 +6854,P14L; P138Q; D287V,0.002820348,1.1802454,26/08/2020,-7.44974E+17,0.000651606,1,822.041 +6855,P14L; S220T,0.03142673,5.0256867,26/08/2020,-7.44974E+17,0.02985493,2,173.20834 +6856,P14L; S57L; N77I; N102T; T121M,0.002239302,0.76279354,26/08/2020,-7.44974E+17,0.000212468,1,750.5061 +6857,P14L; V40I; E153V; D233E; G286V,0.00227944,0.816129,26/08/2020,-7.44974E+17,0.000260652,1,772.42346 +6858,P14M,0.01605398,3.6515071,26/08/2020,-7.44974E+17,0.015130784,3,248.2613 +6859,P14N,0.04650795,4.3010197,26/08/2020,-7.44974E+17,0.045458042,3,95.75671 +6860,P14Q,0.036101997,4.2414117,26/08/2020,-7.44974E+17,0.035743121,3,123.79003 +6861,P14Q; D125G; S220T,0.01138329,3.2052598,26/08/2020,-7.44974E+17,0.008474579,1,379.35248 +6862,P14Q; L51M; D147N; L158H; P240L,0.001823553,0.6272917,26/08/2020,-7.44974E+17,0.000129288,1,759.3986 +6863,P14Q; R148P; G168R; S220T; H230Y,0.004590328,1.909794,26/08/2020,-7.44974E+17,0.002217783,1,670.6558 +6864,P14Q; S160W; S220T; H230Y,0.5897459,5.7512665,26/08/2020,-7.44974E+17,0.710580954,1,3.7554178 +6865,P14Q; S220T; D287N,0.27682516,5.405139,26/08/2020,-7.44974E+17,0.30088544,1,15.735948 +6866,P14Q; T44N; A206V; A223S; R247S,0.003841742,1.651216,26/08/2020,-7.44974E+17,0.001677302,1,705.37555 +6867,P14Q; V66A; N190Y; S220T; E278V,0.003941217,1.6580585,26/08/2020,-7.44974E+17,0.001628966,1,713.3277 +6868,P14R,0.025058983,5.0922055,26/08/2020,-7.44974E+17,0.023330882,2,221.35841 +6869,P14S,0.062065996,3.0473814,26/08/2020,-7.44974E+17,0.05984863,3,52.1624 +6870,P14S; A174V; V204M,0.006078625,2.4287443,26/08/2020,-7.44974E+17,0.004273734,1,563.15186 +6871,P14S; A56D; L101P; G104S; S220T; L227M; R274L,0.002458014,0.9530705,26/08/2020,-7.44974E+17,0.000406715,1,802.07965 +6872,P14S; K34M; Q263H; G284C; F297L,0.001917231,0.53758705,26/08/2020,-7.44974E+17,6.15E-05,1,747.6668 +6873,P14S; L76P; R148P; A156T; S220T; H230Y,0.003203294,1.3292274,26/08/2020,-7.44974E+17,0.001011061,1,767.65466 +6874,P14S; N178I; S220T,0.002799144,1.2054847,26/08/2020,-7.44974E+17,0.000754882,1,806.2848 +6875,P14S; P37A; G104C; L246P; M248K; R258C,0.001820201,0.6078153,26/08/2020,-7.44974E+17,8.99E-05,1,784.7004 +6876,P14S; S220T,0.304289,5.554271,26/08/2020,-7.44974E+17,0.322599335,1,13.912962 +6877,P14S; T44S; S61L; E91K; I128T; V281D,0.002129305,0.733345,26/08/2020,-7.44974E+17,0.000153706,1,748.72217 +6878,P14S; V17E; R148P; G215D; S220T; H230Y,0.00530712,2.1374557,26/08/2020,-7.44974E+17,0.002912923,1,610.32465 +6879,P14T; A60V; H154R; L173R; A252G,0.003064476,1.307082,26/08/2020,-7.44974E+17,0.000921071,1,753.0714 +6880,P14T; A80T,0.011256833,3.25011,26/08/2020,-7.44974E+17,0.009058302,1,389.0968 +6881,P14T; E84V; F86L; S220T; F297I,0.002900009,1.1637481,26/08/2020,-7.44974E+17,0.000654949,1,759.92 +6882,P14T; F86I,0.00850346,2.9234667,26/08/2020,-7.44974E+17,0.006542158,1,466.2115 +6883,P14T; M82V; V113E; L167H; S220T; M248V; E262D,0.012521596,3.4608352,26/08/2020,-7.44974E+17,0.010048963,1,366.07495 +6884,P14T; R155H; L158P,0.002248389,0.9109051,26/08/2020,-7.44974E+17,0.000365239,1,827.302 +6885,P14V,0.003874922,3.2773416,26/08/2020,-7.44974E+17,0.003360768,5,907.9564 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N238K,0.34368512,5.515533,26/08/2020,-7.44974E+17,0.383314157,1,11.329876 +6907,P164T; T179N; N251T,0.20198157,5.2252345,26/08/2020,-7.44974E+17,0.219568789,1,23.669193 +6908,P164V,0.13304253,5.109312,26/08/2020,-7.44974E+17,0.13631713,1,39.362625 +6909,P164W,0.04447267,5.692272,26/08/2020,-7.44974E+17,0.043186034,2,134.4164 +6910,P164Y,0.030759497,2.7593627,26/08/2020,-7.44974E+17,0.029196887,2,104.58532 +6911,P171A,0.06408725,3.9718258,26/08/2020,-7.44974E+17,0.064475565,2,65.19817 +6912,P171D,0.020928372,4.091203,26/08/2020,-7.44974E+17,0.018258597,1,241.08588 +6913,P171E,0.045686383,4.7586656,26/08/2020,-7.44974E+17,0.042504622,1,121.07391 +6914,P171F,0.013392248,6.714165,26/08/2020,-7.44974E+17,0.013220241,6,509.2602 +6915,P171G,0.010139257,3.9381924,26/08/2020,-7.44974E+17,0.008899863,2,440.20486 +6916,P171H,0.07439768,1.7281222,26/08/2020,-7.44974E+17,0.074111054,4,24.833727 +6917,P171H; A197T,0.003646215,1.5787116,26/08/2020,-7.44974E+17,0.001419227,1,717.7495 +6918,P171H; L173V; H183L; L214H,0.002287697,0.86450803,26/08/2020,-7.44974E+17,0.000306264,1,770.9154 +6919,P171H; S220T,0.31185386,6.2687554,26/08/2020,-7.44974E+17,0.320710006,2,14.601718 +6920,P171H; S220T; L227P,0.003424998,1.4565215,26/08/2020,-7.44974E+17,0.001242638,1,740.8343 +6921,P171I,0.012747146,4.768612,26/08/2020,-7.44974E+17,0.011760709,3,403.99713 +6922,P171L,0.002257691,1.4489607,26/08/2020,-7.44974E+17,0.001125261,2,885.306 +6923,P171L; S205R; S220T; N249T,0.003064492,1.274347,26/08/2020,-7.44974E+17,0.000900837,1,771.13513 +6924,P171L; S220T; H230Y; F297V,0.001358737,0.30754042,26/08/2020,-7.44974E+17,3.20E-06,1,676.8562 +6925,P171L; V175E; S220T,0.002826067,1.1964208,26/08/2020,-7.44974E+17,0.000751699,1,787.90076 +6926,P171M,0.005709499,3.6249957,26/08/2020,-7.44974E+17,0.004901054,3,703.2925 +6927,P171N,0.016852053,4.4239273,26/08/2020,-7.44974E+17,0.015331077,2,292.20688 +6928,P171Q,0.013550935,3.566691,26/08/2020,-7.44974E+17,0.010858664,1,344.8678 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S220T,0.1501211,5.2818794,26/08/2020,-7.44974E+17,0.149890503,1,34.617905 +6953,P219T,0.07790615,4.9446526,26/08/2020,-7.44974E+17,0.077854461,1,70.552536 +6954,P219V,0.05801536,4.905531,26/08/2020,-7.44974E+17,0.058775127,2,88.13195 +6955,P219W,0.083297916,5.1536174,26/08/2020,-7.44974E+17,0.083098583,4,59.75759 +6956,P224A,0.18834957,1.5941781,26/08/2020,-7.44974E+17,0.193260926,2,8.456756 +6957,P224C,0.06098884,4.9029307,26/08/2020,-7.44974E+17,0.057714984,1,92.00889 +6958,P224F,0.065525144,4.982822,26/08/2020,-7.44974E+17,0.06365804,1,85.91165 +6959,P224I,0.09647286,3.5512023,26/08/2020,-7.44974E+17,0.095593578,2,38.127876 +6960,P224K,0.059433542,4.872656,26/08/2020,-7.44974E+17,0.056735627,1,95.27387 +6961,P224M,0.098234385,5.0093126,26/08/2020,-7.44974E+17,0.098376751,1,55.65277 +6962,P224R; I256V,0.005180067,2.190205,26/08/2020,-7.44974E+17,0.002936332,1,628.20233 +6963,P224S; L227Q; N251K,0.014084983,3.5879357,26/08/2020,-7.44974E+17,0.011876081,1,324.61136 +6964,P224T,0.089541845,8.140466,26/08/2020,-7.44974E+17,0.089405371,5,85.05886 +6965,P37H; V141I; R148P; S220T; H230Y; I256T,0.019993387,4.049204,26/08/2020,-7.44974E+17,0.017574156,1,253.43944 +6966,P37H; V40D; T105S; W115R; L134Q; L193H; S220T,0.002471096,0.9750561,26/08/2020,-7.44974E+17,0.000407999,1,809.6038 +6967,P37H; V52M; I128T; D170G,0.003037142,1.2471296,26/08/2020,-7.44974E+17,0.000820098,1,749.0079 +6968,P37H; V71D; N77Y; N102D; F142I; S220T; M273K,0.002676116,1.100811,26/08/2020,-7.44974E+17,0.000658317,1,750.3195 +6969,P37H; Y79H; E108K,0.002481762,0.83025634,26/08/2020,-7.44974E+17,0.000247625,1,755.92194 +6970,P37I,0.020423478,4.01345,26/08/2020,-7.44974E+17,0.018844056,1,246.73148 +6971,P37K,0.12501687,3.9928315,26/08/2020,-7.44974E+17,0.125409936,4,29.461779 +6972,P37L,0.022041203,4.7335634,26/08/2020,-7.44974E+17,0.02048312,2,236.91838 +6973,P37L; R148P; F209I; S220T; A228T; H230Y; K236R,0.003201759,1.3648304,26/08/2020,-7.44974E+17,0.001131,1,731.8609 +6974,P37L; R43L; N102Y; T157S; S220T,0.003323396,1.4709204,26/08/2020,-7.44974E+17,0.001257638,1,755.52985 +6975,P37L; S69P; S301R,0.003450872,1.4647807,26/08/2020,-7.44974E+17,0.001237522,1,738.6696 +6976,P37L; T44N; L58I; V109M; L167P; P240Q; V281I,0.002720461,1.0918304,26/08/2020,-7.44974E+17,0.000600308,1,813.20215 +6977,P37M,0.04427867,6.428114,26/08/2020,-7.44974E+17,0.044308553,4,145.0986 +6978,P37N,0.080455184,5.388123,26/08/2020,-7.44974E+17,0.079848042,2,67.66065 +6979,P37Q; R43L,0.003196864,1.3978713,26/08/2020,-7.44974E+17,0.00116518,1,755.0478 +6980,P37R; P88H; E91D,0.093893595,5.1518493,26/08/2020,-7.44974E+17,0.089889732,1,59.99213 +6981,P37R; S67I; S220T; H288Q,0.6272447,5.7434072,26/08/2020,-7.44974E+17,0.759818413,1,3.0535543 +6982,P37R; V113E; G126C; Y177N,0.002085738,0.7045808,26/08/2020,-7.44974E+17,0.000160146,1,808.6609 +6983,P37S,0.063507065,5.471412,26/08/2020,-7.44974E+17,0.062250535,2,88.63144 +6984,P37S; E46G; G55D; V97A; S220T; N289Y; P302Q,0.002820064,1.2288299,26/08/2020,-7.44974E+17,0.000780463,1,775.6056 +6985,P37S; N100K; P164T,0.002689811,1.0944346,26/08/2020,-7.44974E+17,0.000598481,1,757.87976 +6986,P37S; R148P; A149V; L176F; A195S; S220T; H230Y; E254D; R258S,0.004116333,1.7789475,26/08/2020,-7.44974E+17,0.0020893,1,683.5909 +6987,P37S; R148P; S220T; H230Y,0.52222943,5.574635,26/08/2020,-7.44974E+17,0.612446877,1,4.989588 +6988,P37S; R43L,0.00208178,1.3285267,26/08/2020,-7.44974E+17,0.000951683,2,906.3281 +6989,P37S; S114T; G146D; S220T; A231V; H288L,0.003220308,1.3126242,26/08/2020,-7.44974E+17,0.000960131,1,748.1791 +6990,P37S; V130M; S220T; L227M; T245R,0.003086751,1.3246677,26/08/2020,-7.44974E+17,0.001019129,1,773.69836 +6991,P37S; W115C; F297L,0.005568069,2.2471154,26/08/2020,-7.44974E+17,0.003743658,1,578.2678 +6992,P37T,0.036664527,5.575376,26/08/2020,-7.44974E+17,0.035803982,2,160.84776 +6993,P37T; A303V,0.12985222,0.5250266,26/08/2020,-7.44974E+17,0.098667966,10,4.0398073 +6994,P37T; D64N; V135E; P219S; S220T,0.003032177,1.2830309,26/08/2020,-7.44974E+17,0.000809581,1,767.7604 +6995,P37T; N178K; S220T; V221M,0.003012808,1.2909994,26/08/2020,-7.44974E+17,0.000957029,1,758.1542 +6996,P37T; N77S; M129V; E153D; S220T,0.005877597,2.277794,26/08/2020,-7.44974E+17,0.003190343,1,590.0896 +6997,P37T; S49C,0.13538389,5.1809053,26/08/2020,-7.44974E+17,0.135930484,1,39.04645 +6998,P37V,0.03537278,6.14693,26/08/2020,-7.44974E+17,0.034614735,5,174.16876 +6999,P37W,0.008347251,3.6221056,26/08/2020,-7.44974E+17,0.007130864,2,500.7157 +7000,P37Y,0.06651456,2.5465887,26/08/2020,-7.44974E+17,0.066359906,4,39.568897 +7001,P45A,0.062685,5.9796247,26/08/2020,-7.44974E+17,0.062746371,2,97.56256 +7002,P45C,0.044369794,5.7753615,26/08/2020,-7.44974E+17,0.042397586,2,137.73265 +7003,P45D,0.030156167,0.96506506,26/08/2020,-7.44974E+17,0.02900411,3,42.824368 +7004,P45E,0.07540799,5.1251473,26/08/2020,-7.44974E+17,0.075321766,6,64.96944 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S220T,0.53254706,5.939665,26/08/2020,-7.44974E+17,0.56333196,3,5.1649866 +7016,P45H; S220T; H230Y,0.6852099,10.074655,26/08/2020,-7.44974E+17,0.695734498,10,4.5660877 +7017,P45H; S220T; V221E,0.04016887,4.7269335,26/08/2020,-7.44974E+17,0.036678506,1,139.30397 +7018,P45H; S49C; N102D; H154Q; S160T; S220T,0.004111345,1.8087468,26/08/2020,-7.44974E+17,0.001884135,1,689.72186 +7019,P45H; T179A; S220T,0.36757973,5.438186,26/08/2020,-7.44974E+17,0.407809617,1,10.020467 +7020,P45H; Y123N; T179S; G203D,0.003011267,1.3039199,26/08/2020,-7.44974E+17,0.000964179,1,757.5652 +7021,P45H; Y83D; A92V; S220T,0.008448492,2.8717613,26/08/2020,-7.44974E+17,0.006203065,1,465.816 +7022,P45I,0.06614825,4.5480723,26/08/2020,-7.44974E+17,0.064275431,4,68.055916 +7023,P45K,0.038089447,0.6510421,26/08/2020,-7.44974E+17,0.042799971,2,29.363161 +7024,P45L,0.04790992,4.7059584,26/08/2020,-7.44974E+17,0.046264128,1,113.32161 +7025,P45L; D163N; S220T; N251S; P270L; V281F,0.003931645,1.6956531,26/08/2020,-7.44974E+17,0.001685671,1,728.19147 +7026,P45L; L51V; P70S; D152V,0.009559692,3.0557787,26/08/2020,-7.44974E+17,0.006981818,1,445.04773 +7027,P45L; P70H; D139A; D147V; R165H,0.002616084,1.1429218,26/08/2020,-7.44974E+17,0.000702145,1,808.5251 +7028,P45L; R137G; L143F; T179S,0.06589613,4.9260263,26/08/2020,-7.44974E+17,0.063269037,1,84.537025 +7029,P45L; Y123C; T157S; P270L,0.006497198,2.4745512,26/08/2020,-7.44974E+17,0.004128613,1,545.55194 +7030,P45M,0.078580916,6.941308,26/08/2020,-7.44974E+17,0.078875837,5,83.82117 +7031,P45N,0.042643994,4.4667873,26/08/2020,-7.44974E+17,0.041445726,1,125.29822 +7032,P45Q,0.12821114,3.656698,26/08/2020,-7.44974E+17,0.125120839,3,27.133123 +7033,P45Q; L58H,0.0557397,4.788495,26/08/2020,-7.44974E+17,0.054803455,1,98.66577 +7034,P45R; R94S; A116S; L222P; D233A,0.003892777,1.6292611,26/08/2020,-7.44974E+17,0.001539661,1,719.0663 +7035,P45R; T105S; V150G; S220T; A228T,0.002878163,1.1812396,26/08/2020,-7.44974E+17,0.000788092,1,799.45306 +7036,P45S,0.16576128,0.5420496,26/08/2020,-7.44974E+17,0.188344051,3,3.7768466 +7037,P45S; N77D; D139V; T245I,0.003821264,1.7201318,26/08/2020,-7.44974E+17,0.001801489,1,724.7385 +7038,P45S; Q54H; L243P; Y294N,0.0043795,1.8866225,26/08/2020,-7.44974E+17,0.002233724,1,684.03503 +7039,P45T,0.217672,1.7629523,26/08/2020,-7.44974E+17,0.21985031,3,7.326489 +7040,P45T; A80D; R90P; E91G; W216L; S220T; L246P; V276I,0.002949471,1.2761562,26/08/2020,-7.44974E+17,0.000871224,1,766.0045 +7041,P45T; R137H; Q194H; S220T,0.38075775,5.373521,26/08/2020,-7.44974E+17,0.42954558,1,9.27027 +7042,P45T; V130M; P133H; R137H; E261G,0.006952876,2.6540422,26/08/2020,-7.44974E+17,0.004954501,1,526.7171 +7043,P45V,0.07127536,5.0307884,26/08/2020,-7.44974E+17,0.069878547,2,72.51683 +7044,P45W,0.02692497,4.29872,26/08/2020,-7.44974E+17,0.024656435,1,194.98007 +7045,P45Y,0.037748456,4.5568843,26/08/2020,-7.44974E+17,0.035503221,1,144.30496 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H271Q,0.008751834,2.9251332,26/08/2020,-7.44974E+17,0.006409,1,459.21484 +7058,P70H; N102I; M129L; R148P; T196R; S220T; H230Y,0.005367732,2.15989,26/08/2020,-7.44974E+17,0.002773067,1,610.07214 +7059,P70H; P106S; V130E; R148P; S220T; H230Y,0.004337989,1.8263172,26/08/2020,-7.44974E+17,0.002057681,1,672.5677 +7060,P70H; S220T; E254G; M273S,0.007725711,2.7687616,26/08/2020,-7.44974E+17,0.00511913,1,523.7379 +7061,P70H; S220T; R247S,0.4688062,5.5998006,26/08/2020,-7.44974E+17,0.53727448,1,6.4873934 +7062,P70H; S220T; V293A,0.05014876,4.806819,26/08/2020,-7.44974E+17,0.045435447,1,111.48513 +7063,P70H; S301R,0.021560282,4.0205646,26/08/2020,-7.44974E+17,0.018865328,1,237.64925 +7064,P70H; T105I; R148P; F166C; S220T; H230Y,0.004291899,1.8546472,26/08/2020,-7.44974E+17,0.002060856,1,664.2361 +7065,P70H; T121A; S220T; D287Y,0.063464545,4.877606,26/08/2020,-7.44974E+17,0.060171744,1,88.8032 +7066,P70H; T72S; S220T; A257T; R274H,0.022604661,4.14463,26/08/2020,-7.44974E+17,0.019734371,1,229.31723 +7067,P70H; T81S; F142L; F166I; L181F; S220T,0.002735036,1.1549307,26/08/2020,-7.44974E+17,0.00068171,1,791.0453 +7068,P70H; Y186N; Q194R; V204E; S220T,0.003023699,1.2853773,26/08/2020,-7.44974E+17,0.000929679,1,754.1178 +7069,P70I,0.05779522,4.213798,26/08/2020,-7.44974E+17,0.058285327,2,77.62283 +7070,P70L,0.037028167,4.4446287,26/08/2020,-7.44974E+17,0.036075181,4,123.78999 +7071,P70L; A210T; S220T; M273V,0.004297884,1.8065523,26/08/2020,-7.44974E+17,0.001889694,1,677.56696 +7072,P70L; D95V; R148P; S220T; H230Y,0.00435503,1.8227023,26/08/2020,-7.44974E+17,0.001894733,1,682.4218 +7073,P70L; P138R; S220T; E275K,0.22658257,5.243026,26/08/2020,-7.44974E+17,0.246702624,1,20.298262 +7074,P70L; R258H; G286V,0.002887277,1.2499436,26/08/2020,-7.44974E+17,0.0008392,1,775.98914 +7075,P70N,0.12871733,4.7665076,26/08/2020,-7.44974E+17,0.133555296,2,35.419724 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H230Y,0.49380502,5.5860033,26/08/2020,-7.44974E+17,0.573369136,1,5.765612 +7087,P70V,0.07248091,7.0627856,26/08/2020,-7.44974E+17,0.072061931,3,95.23573 +7088,P70W,0.014199355,4.9239883,26/08/2020,-7.44974E+17,0.013473335,3,369.76688 +7089,P70Y,0.108860776,5.144896,26/08/2020,-7.44974E+17,0.105840889,1,50.14793 +7090,P88A; L101P; R148P; S220T; H230Y,0.004425819,1.8860698,26/08/2020,-7.44974E+17,0.002043781,1,666.9764 +7091,P88A; R148P; S220T; H230Y,0.21792485,5.286565,26/08/2020,-7.44974E+17,0.228981319,1,21.775671 +7092,P88C,0.10483953,3.4625769,26/08/2020,-7.44974E+17,0.107298219,3,32.364777 +7093,P88D,0.08191305,5.833378,26/08/2020,-7.44974E+17,0.079300114,2,71.423485 +7094,P88E,0.1514199,5.181176,26/08/2020,-7.44974E+17,0.158121283,1,33.770424 +7095,P88F,0.1036814,3.8454924,26/08/2020,-7.44974E+17,0.102026451,2,37.909172 +7096,P88G; A89T,0.02793996,4.36,26/08/2020,-7.44974E+17,0.025549007,1,188.83867 +7097,P88H,0.08863865,0.8754344,26/08/2020,-7.44974E+17,0.093446752,3,12.060625 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A206T,0.002951671,1.2480967,26/08/2020,-7.44974E+17,0.000810129,1,750.34863 +7262,Q54S,0.05027961,4.771958,26/08/2020,-7.44974E+17,0.04752453,1,111.75322 +7263,Q54T,0.03914648,7.7053227,26/08/2020,-7.44974E+17,0.038196308,4,196.74928 +7264,Q54V,0.048116725,4.697217,26/08/2020,-7.44974E+17,0.045612435,1,115.264275 +7265,Q54W,0.030108206,4.04605,26/08/2020,-7.44974E+17,0.029311859,6,136.92336 +7266,Q54Y,0.05768169,4.9148345,26/08/2020,-7.44974E+17,0.054722742,1,97.92087 +7267,R110A,0.0701027,1.0367188,26/08/2020,-7.44974E+17,0.072114166,3,17.825027 +7268,R110C,0.069953255,0.8354176,26/08/2020,-7.44974E+17,0.071499695,10,12.435782 +7269,R110C; E153V; D170G; A206T; R258L,0.007756741,2.7618575,26/08/2020,-7.44974E+17,0.005124291,1,509.9408 +7270,R110C; P131S; P219A; S220T; P300L,0.0025226,1.0241631,26/08/2020,-7.44974E+17,0.000527865,1,788.72485 +7271,R110C; R148P; S220T; H230Y,0.6719696,5.985639,26/08/2020,-7.44974E+17,0.808597448,1,2.5056546 +7272,R110C; S220T; H230Y; L253P,0.004778648,1.9636903,26/08/2020,-7.44974E+17,0.002323886,1,642.01697 +7273,R110D,0.0555882,1.1146935,26/08/2020,-7.44974E+17,0.056499137,5,22.250282 +7274,R110E,0.08152895,2.395851,26/08/2020,-7.44974E+17,0.080711064,4,29.861572 +7275,R110F,0.05531871,2.3328998,26/08/2020,-7.44974E+17,0.054541163,4,44.686954 +7276,R110G,0.045627825,4.1558204,26/08/2020,-7.44974E+17,0.044834628,7,90.524704 +7277,R110G; A127D; V130A; A213T; S220T,0.002513479,1.0432483,26/08/2020,-7.44974E+17,0.000520301,1,781.554 +7278,R110G; S117F; L193I; S220T; A282T,0.008528749,2.8637724,26/08/2020,-7.44974E+17,0.005836954,1,478.6699 +7279,R110G; S220T; I256F,0.003465108,1.4793518,26/08/2020,-7.44974E+17,0.001237926,1,737.54645 +7280,R110H,0.18884762,0.8760383,26/08/2020,-7.44974E+17,0.191655445,9,4.124754 +7281,R110H; A112T; M248L; E261V,0.004851834,2.0814445,26/08/2020,-7.44974E+17,0.002756274,1,657.937 +7282,R110H; D235V,0.016009124,3.7995508,26/08/2020,-7.44974E+17,0.013846432,1,305.34824 +7283,R110H; G162V; L173Q,0.004265762,1.8690075,26/08/2020,-7.44974E+17,0.002106319,1,704.835 +7284,R110H; L227Q,0.005221125,2.1086416,26/08/2020,-7.44974E+17,0.002934365,1,632.11017 +7285,R110H; R148H; A218T; S220T; G237S,0.3001479,5.2635164,26/08/2020,-7.44974E+17,0.330226722,1,13.604904 +7286,R110H; R148P; P171S; P219S; S220T; L227Q; H230Y; R247H,0.003976821,1.6828567,26/08/2020,-7.44974E+17,0.001724108,1,736.8587 +7287,R110H; S220T,0.5525041,5.626805,26/08/2020,-7.44974E+17,0.66035934,1,4.394548 +7288,R110H; S220T; H230Y,0.70128894,9.495914,26/08/2020,-7.44974E+17,0.71529821,11,3.9958732 +7289,R110H; V141E,0.003432356,1.4748791,26/08/2020,-7.44974E+17,0.001408614,1,718.0493 +7290,R110I,0.096471354,0.9301819,26/08/2020,-7.44974E+17,0.098028663,10,9.592282 +7291,R110I; A210D,0.024542509,3.5149608,26/08/2020,-7.44974E+17,0.023031043,2,162.72046 +7292,R110K,0.09632365,0.6945311,26/08/2020,-7.44974E+17,0.100354284,11,7.3326917 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A257V,0.008585156,2.8712087,26/08/2020,-7.44974E+17,0.006147353,1,476.5318 +7316,R122C; S220T; G241S; H255Y; G264D,0.002763838,1.1110271,26/08/2020,-7.44974E+17,0.000577631,1,758.73126 +7317,R122C; V141I; S220T; H230Y,0.58484715,5.6948504,26/08/2020,-7.44974E+17,0.70167746,1,3.7144938 +7318,R122C; V204A,0.008931009,2.9625208,26/08/2020,-7.44974E+17,0.006272605,1,451.8381 +7319,R122D,0.23496175,6.2758217,26/08/2020,-7.44974E+17,0.241718649,2,21.63288 +7320,R122E,0.14349757,5.0693226,26/08/2020,-7.44974E+17,0.148997045,1,35.853485 +7321,R122F,0.10509149,3.69911,26/08/2020,-7.44974E+17,0.105666149,7,32.690674 +7322,R122G,0.09678238,3.8661127,26/08/2020,-7.44974E+17,0.097096926,6,37.742077 +7323,R122H,0.17873634,3.4210284,26/08/2020,-7.44974E+17,0.180880242,3,17.150345 +7324,R122H; A218I; S220T; E254A,0.3199545,5.353049,26/08/2020,-7.44974E+17,0.360109718,1,12.413103 +7325,R122H; A259V; P300Q,0.012514113,3.4930525,26/08/2020,-7.44974E+17,0.009760199,1,359.5239 +7326,R122H; D147N; S220T; R279H; H285Q,0.004612838,1.9551437,26/08/2020,-7.44974E+17,0.002359896,1,662.2665 +7327,R122H; E295G; F297L,0.002606331,1.1748494,26/08/2020,-7.44974E+17,0.000722169,1,783.9966 +7328,R122H; G126W,0.003145656,1.2864234,26/08/2020,-7.44974E+17,0.000968049,1,772.7893 +7329,R122H; G203C; S220A; L243M,0.008430799,2.8375144,26/08/2020,-7.44974E+17,0.006062101,1,475.58554 +7330,R122H; I128M; T172A; A202T; F217S; A229P,0.002539414,1.0145661,26/08/2020,-7.44974E+17,0.000485226,1,811.6404 +7331,R122H; S220T; H230Y,0.6117196,5.855001,26/08/2020,-7.44974E+17,0.728593933,1,3.4492927 +7332,R122H; S220T; L253Q; R279P,0.002649246,1.0999151,26/08/2020,-7.44974E+17,0.000622871,1,811.8268 +7333,R122I,0.06551856,2.7695885,26/08/2020,-7.44974E+17,0.065772377,7,41.63518 +7334,R122I; A169V,0.039560504,4.504443,26/08/2020,-7.44974E+17,0.038475665,1,136.23045 +7335,R122K,0.25116217,5.4446454,26/08/2020,-7.44974E+17,0.2617864,1,18.283281 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+7358,R137H; G284V,0.002677441,1.0702922,26/08/2020,-7.44974E+17,0.000526066,1,811.4812 +7359,R137H; I256N,0.002769475,1.1724024,26/08/2020,-7.44974E+17,0.000720892,1,763.2323 +7360,R137H; L143I,0.0721466,4.903653,26/08/2020,-7.44974E+17,0.071566985,1,76.28176 +7361,R137H; S220T,0.64830416,6.023605,26/08/2020,-7.44974E+17,0.713477663,2,3.0790024 +7362,R137H; S220T; A259T; E261V; E268D,0.6798285,6.6634784,26/08/2020,-7.44974E+17,0.74677982,2,2.883891 +7363,R137H; T172K; D211G; S220T,0.023742327,4.2115393,26/08/2020,-7.44974E+17,0.020875317,1,220.0619 +7364,R137H; T245I,0.00625686,2.4056458,26/08/2020,-7.44974E+17,0.003755409,1,558.7298 +7365,R137I,0.0651635,7.1891456,26/08/2020,-7.44974E+17,0.065153671,3,108.7337 +7366,R137K,0.2785033,5.9599695,26/08/2020,-7.44974E+17,0.284056623,3,16.043829 +7367,R137K; H266Y,0.030364003,4.514488,26/08/2020,-7.44974E+17,0.027723896,1,181.22177 +7368,R137L,0.15807256,5.561819,26/08/2020,-7.44974E+17,0.15856882,5,30.536217 +7369,R137L; R148P; T157A; S220T; H230Y,0.14152387,5.17354,26/08/2020,-7.44974E+17,0.1472376,1,36.842663 +7370,R137M,0.18170285,6.04385,26/08/2020,-7.44974E+17,0.182983596,2,29.276316 +7371,R137P; I212F; S220T; E254G,0.002749087,1.1829609,26/08/2020,-7.44974E+17,0.000772213,1,772.9358 +7372,R137P; L253M; H288R,0.002984843,1.1485066,26/08/2020,-7.44974E+17,0.00070035,1,754.7186 +7373,R137P; M185I; L193P; S220T,0.003311231,1.5146488,26/08/2020,-7.44974E+17,0.00141505,1,759.9988 +7374,R137S,0.25805438,5.3552,26/08/2020,-7.44974E+17,0.280173226,1,17.154613 +7375,R137S; D170E,0.20299166,1.1679603,26/08/2020,-7.44974E+17,0.215815725,7,4.9894915 +7376,R137S; S220T; N249K,0.003502081,1.5083814,26/08/2020,-7.44974E+17,0.001318111,1,727.17834 +7377,R137T,0.14586669,6.0965614,26/08/2020,-7.44974E+17,0.14911561,2,38.250786 +7378,R137V,0.055088468,4.05043,26/08/2020,-7.44974E+17,0.054813871,3,75.0431 +7379,R137W,0.059779868,5.8373356,26/08/2020,-7.44974E+17,0.059057275,3,97.12807 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+7389,R148H,0.08309909,5.780034,26/08/2020,-7.44974E+17,0.082948335,3,68.134674 +7390,R148H; F166S; A210G; S220T,0.003084311,1.3339987,26/08/2020,-7.44974E+17,0.000938918,1,772.8769 +7391,R148H; F209Y; L214V; S220T,0.004112877,1.6877456,26/08/2020,-7.44974E+17,0.001798006,1,695.9821 +7392,R148H; Q299K,0.07567988,0.7387457,26/08/2020,-7.44974E+17,0.068395804,10,10.162794 +7393,R148H; T179I,0.02272208,4.070346,26/08/2020,-7.44974E+17,0.021049255,1,223.48819 +7394,R148I,0.048702076,6.380875,26/08/2020,-7.44974E+17,0.047687528,3,131.7971 +7395,R148K,0.08578483,5.032271,26/08/2020,-7.44974E+17,0.085775204,1,64.89799 +7396,R148L,0.15324917,1.2916694,26/08/2020,-7.44974E+17,0.152178254,4,8.326359 +7397,R148L; V150M; G168R; T172I; S220T; V250M,0.00332148,1.362342,26/08/2020,-7.44974E+17,0.00099822,1,754.1795 +7398,R148N,0.07034993,4.9859805,26/08/2020,-7.44974E+17,0.068648995,1,79.53991 +7399,R148P; A149E; S220T; H230Y,0.73243463,15.14265,26/08/2020,-7.44974E+17,0.748289163,10,5.4647055 +7400,R148P; A156V; S220T; H230Y,0.49761164,2.243657,26/08/2020,-7.44974E+17,0.534818877,12,2.2598937 +7401,R148P; A159D; S220T; H230Y; T242I; R258C,0.06699047,4.886727,26/08/2020,-7.44974E+17,0.065364848,1,83.129265 +7402,R148P; A169V; S220T; H230Y,0.6490905,7.084745,26/08/2020,-7.44974E+17,0.714044478,2,3.5977445 +7403,R148P; A174T; A206V; S220T; H230Y,0.006690722,2.54325,26/08/2020,-7.44974E+17,0.004434464,1,542.94135 +7404,R148P; A174V; S220T; H230Y,0.024269136,4.144383,26/08/2020,-7.44974E+17,0.021805296,1,215.02704 +7405,R148P; A195T; S220T; H230Y; L234H,0.00341619,1.4943156,26/08/2020,-7.44974E+17,0.001259438,1,728.5096 +7406,R148P; A195T; T207I; S220T; H230Y,0.006343522,2.4781492,26/08/2020,-7.44974E+17,0.00389731,1,580.4082 +7407,R148P; A195V; S220T; H230Y,0.005379049,2.1929164,26/08/2020,-7.44974E+17,0.003037763,1,600.8314 +7408,R148P; A197D; S220T; L222R; H230Y,0.004717805,2.0715213,26/08/2020,-7.44974E+17,0.002629908,1,636.91235 +7409,R148P; A197T; S220T; H230Y,0.09298527,5.023378,26/08/2020,-7.44974E+17,0.094113606,1,58.725494 +7410,R148P; A202E; S220T; H230Y; S267P; F297I,0.004208693,1.7958286,26/08/2020,-7.44974E+17,0.002083089,1,685.25 +7411,R148P; A206S; S220T; H230Y,0.58261436,5.8046236,26/08/2020,-7.44974E+17,0.674243659,1,3.966787 +7412,R148P; A206T; D211G; S220T; H230Y,0.14534147,5.2849584,26/08/2020,-7.44974E+17,0.144189423,1,36.079327 +7413,R148P; A206T; S220T; H230Y,0.30409047,5.361093,26/08/2020,-7.44974E+17,0.33423923,1,13.641064 +7414,R148P; A206T; S220T; H230Y; K298E,0.006651478,2.494049,26/08/2020,-7.44974E+17,0.004372208,1,549.29504 +7415,R148P; A210P; S220T; H230Y,0.003956755,1.680491,26/08/2020,-7.44974E+17,0.001708138,1,697.6809 +7416,R148P; A210T; S220T; H230Y; H255Y; L269S,0.003823469,1.6881318,26/08/2020,-7.44974E+17,0.001757926,1,706.4138 +7417,R148P; A210V; G215D; S220T; H230Y,0.47822195,5.5300035,26/08/2020,-7.44974E+17,0.566875409,1,6.0896697 +7418,R148P; A210V; S220T; H230Y,0.44158292,5.7495904,26/08/2020,-7.44974E+17,0.502085157,1,7.4447813 +7419,R148P; A218T; S220T; H230Y,0.6324302,5.8337016,26/08/2020,-7.44974E+17,0.747160871,1,3.0311239 +7420,R148P; A218V; S220T; H230Y,0.7928773,17.688837,26/08/2020,-7.44974E+17,0.815847941,11,4.5423746 +7421,R148P; D152H; S220T; H230Y,0.68532395,11.685172,26/08/2020,-7.44974E+17,0.704613498,10,5.299372 +7422,R148P; D163G; S220T; H230Y,0.5224415,5.723091,26/08/2020,-7.44974E+17,0.609973843,1,5.1400733 +7423,R148P; D170Y; S220T; H230Y,0.009035456,2.9435244,26/08/2020,-7.44974E+17,0.006646502,1,452.8858 +7424,R148P; D211H; S220T; H230Y,0.5868392,5.6026654,26/08/2020,-7.44974E+17,0.698294129,1,3.728658 +7425,R148P; D211V; S220T; H230Y,0.44319007,5.684823,26/08/2020,-7.44974E+17,0.505534027,1,7.385113 +7426,R148P; E153D; S220T; H230Y,0.61717975,5.884636,26/08/2020,-7.44974E+17,0.739097638,1,3.258484 +7427,R148P; E153K; S220T; H230Y,0.68925345,6.101595,26/08/2020,-7.44974E+17,0.832885738,1,2.2494268 +7428,R148P; F217S; S220T; H230Y,0.01985976,4.092419,26/08/2020,-7.44974E+17,0.016426414,1,258.03442 +7429,R148P; G168R; S220T; H230Y,0.007687147,2.6985002,26/08/2020,-7.44974E+17,0.005356189,1,499.14435 +7430,R148P; G191A; S220T; H230Y,0.64305574,5.712162,26/08/2020,-7.44974E+17,0.786352376,1,2.8519707 +7431,R148P; G191R; S201P; S220T; H230Y; H288L,0.003687495,1.5681815,26/08/2020,-7.44974E+17,0.001405346,1,722.8033 +7432,R148P; G191R; S220T; H230Y,0.004098231,1.6767979,26/08/2020,-7.44974E+17,0.001646336,1,692.1055 +7433,R148P; G203A; S220T; H230Y; G264S,0.14265798,5.2044477,26/08/2020,-7.44974E+17,0.142856992,1,37.05519 +7434,R148P; G203V; S220T; H230Y,0.19412369,5.2466908,26/08/2020,-7.44974E+17,0.202202955,1,25.363478 +7435,R148P; G215D; S220T; H230Y,0.5204481,5.568809,26/08/2020,-7.44974E+17,0.610632727,1,5.019692 +7436,R148P; G215D; S220T; H230Y; R258C,0.286198,5.521334,26/08/2020,-7.44974E+17,0.308074241,1,15.5093 +7437,R148P; H154Q; S220T; H230Y,0.30773398,5.459365,26/08/2020,-7.44974E+17,0.336976996,1,13.599814 +7438,R148P; H183D; S220T; H230Y; A296T,0.016331363,3.745744,26/08/2020,-7.44974E+17,0.01387017,1,296.33038 +7439,R148P; H183L; S220T; H230Y,0.5765864,5.6800146,26/08/2020,-7.44974E+17,0.681590447,1,3.9727707 +7440,R148P; H183L; Y186N; S220T; H230Y,0.004908486,2.0351672,26/08/2020,-7.44974E+17,0.002615888,1,636.0741 +7441,R148P; I212F; S220T; H230Y; G286E,0.22977656,5.3053975,26/08/2020,-7.44974E+17,0.241071258,1,20.22462 +7442,R148P; I212N; S220T; H230Y; A280T,0.044664655,4.68578,26/08/2020,-7.44974E+17,0.042255331,1,125.648384 +7443,R148P; L158F; N178T; S220T; H230Y,0.005683906,2.2934606,26/08/2020,-7.44974E+17,0.003401043,1,598.74713 +7444,R148P; L158P; S220T; H230Y,0.002186255,0.80133635,26/08/2020,-7.44974E+17,0.00025181,1,806.5519 +7445,R148P; L161Q; S220T; H230Y,0.49428645,5.6009603,26/08/2020,-7.44974E+17,0.571535973,1,5.8441615 +7446,R148P; L176I; S220T; H230Y,0.20141424,5.304415,26/08/2020,-7.44974E+17,0.216214924,1,24.379007 +7447,R148P; L181F; S220T; H230Y,0.058168847,4.8350635,26/08/2020,-7.44974E+17,0.05530727,1,95.661316 +7448,R148P; L182P; S220T; H230Y; G244C,0.003435754,1.4597648,26/08/2020,-7.44974E+17,0.001151272,1,718.763 +7449,R148P; L182V; S220T; H230Y,0.019813387,3.9703584,26/08/2020,-7.44974E+17,0.017400521,1,252.82945 +7450,R148P; L189S; S220T; H230Y,0.090297185,4.9739223,26/08/2020,-7.44974E+17,0.088800421,1,60.4949 +7451,R148P; L198M; S220T; H230Y; V276I,0.589728,1.2252524,26/08/2020,-7.44974E+17,0.719084213,10,0.8344325 +7452,R148P; M184L; S220T; H230Y; M248T,0.008051881,2.8079088,26/08/2020,-7.44974E+17,0.00581636,1,478.77966 +7453,R148P; M184T; S220T; H230Y,0.0550621,4.8318896,26/08/2020,-7.44974E+17,0.051578704,1,102.30151 +7454,R148P; M184V; S220T; H230Y,0.15361924,5.137609,26/08/2020,-7.44974E+17,0.155348747,1,32.966835 +7455,R148P; N178D; S220T; H230Y,0.2978763,5.2513366,26/08/2020,-7.44974E+17,0.327732738,1,13.671652 +7456,R148P; N178K; L181I; S220T; H230Y,0.00374111,1.5807625,26/08/2020,-7.44974E+17,0.001474076,1,710.2905 +7457,R148P; N178K; S220T; H230Y,0.002253431,0.8182688,26/08/2020,-7.44974E+17,0.0002679,1,768.8427 +7458,R148P; N190H; S220T; H230Y,0.017408783,3.7767575,26/08/2020,-7.44974E+17,0.0150488,1,279.8746 +7459,R148P; P164L; S220T; H230Y,0.64514506,7.925957,26/08/2020,-7.44974E+17,0.690381175,3,4.1794267 +7460,R148P; P171S; S220T; H230Y; S267P,0.004085647,1.7597975,26/08/2020,-7.44974E+17,0.001893653,1,702.05316 +7461,R148P; Q194H; S220T; H230Y; R274C,0.08459835,5.0437446,26/08/2020,-7.44974E+17,0.084309309,1,65.417946 +7462,R148P; Q194P; S220T; H230Y; V265I,0.009348577,3.0474188,26/08/2020,-7.44974E+17,0.006809515,1,446.02716 +7463,R148P; R155H; G162R; S220T; H230Y,0.003151653,1.3649025,26/08/2020,-7.44974E+17,0.001040585,1,723.467 +7464,R148P; S160P; S220T; H230Y,0.24564607,5.3716354,26/08/2020,-7.44974E+17,0.264812933,1,18.567482 +7465,R148P; S205N; S220T; H230Y,0.61923784,5.8576684,26/08/2020,-7.44974E+17,0.734247685,1,3.2760231 +7466,R148P; S205N; S220T; H230Y; H271P,0.003367988,1.4206742,26/08/2020,-7.44974E+17,0.001022237,1,770.6707 +7467,R148P; S205R; S220T; H230Y; M273L,0.03569017,4.454337,26/08/2020,-7.44974E+17,0.033271259,1,149.05719 +7468,R148P; S205R; S220T; H230Y; N249S,0.043041434,4.656324,26/08/2020,-7.44974E+17,0.0402122,1,129.61227 +7469,R148P; S220T; A228V; H230Y,0.5839915,5.945717,26/08/2020,-7.44974E+17,0.678948259,1,3.9670775 +7470,R148P; S220T; H230F; L234P,0.00484124,1.9356985,26/08/2020,-7.44974E+17,0.002313518,1,649.4719 +7471,R148P; S220T; H230Y,0.58388764,1.2084414,26/08/2020,-7.44974E+17,0.670352105,1209,0.86100465 +7472,R148P; S220T; H230Y; A231T; L246P,0.006035995,2.3922298,26/08/2020,-7.44974E+17,0.003805715,1,574.86444 +7473,R148P; S220T; H230Y; A252G,0.27934197,5.240072,26/08/2020,-7.44974E+17,0.304160015,1,15.0341 +7474,R148P; S220T; H230Y; A257V,0.6911234,5.8311887,26/08/2020,-7.44974E+17,0.832996993,1,2.1932342 +7475,R148P; S220T; H230Y; A259V,0.5338343,5.70606,26/08/2020,-7.44974E+17,0.620560341,1,4.8510747 +7476,R148P; S220T; H230Y; A277T,0.023110632,4.2092104,26/08/2020,-7.44974E+17,0.02077282,1,224.00365 +7477,R148P; S220T; H230Y; A303T,0.5682393,5.5384555,26/08/2020,-7.44974E+17,0.58344081,4,4.138872 +7478,R148P; S220T; H230Y; D233V,0.014209872,3.605594,26/08/2020,-7.44974E+17,0.012210248,1,324.80774 +7479,R148P; S220T; H230Y; E254V,0.5414187,5.7759337,26/08/2020,-7.44974E+17,0.627156756,1,4.763347 +7480,R148P; S220T; H230Y; E262D,0.4031737,3.8016057,26/08/2020,-7.44974E+17,0.393513755,10,5.6844816 +7481,R148P; S220T; H230Y; E268G,0.3706115,5.4963646,26/08/2020,-7.44974E+17,0.413803905,1,10.158444 +7482,R148P; S220T; H230Y; E268K,0.53922576,5.6438313,26/08/2020,-7.44974E+17,0.636697009,1,4.691782 +7483,R148P; S220T; H230Y; E268K; L272Q; Y290C,0.002743674,1.1883548,26/08/2020,-7.44974E+17,0.000737396,1,765.8644 +7484,R148P; S220T; H230Y; F297L,0.5772441,5.829016,26/08/2020,-7.44974E+17,0.683388294,1,4.0505266 +7485,R148P; S220T; H230Y; F297V,0.37862378,5.4982557,26/08/2020,-7.44974E+17,0.419792604,1,9.656526 +7486,R148P; S220T; H230Y; F297Y,0.0633701,4.911742,26/08/2020,-7.44974E+17,0.059246491,1,88.24064 +7487,R148P; S220T; H230Y; G237C,0.5617762,7.483943,26/08/2020,-7.44974E+17,0.564732644,10,5.808594 +7488,R148P; S220T; H230Y; G237C; Q299R,0.14132768,5.207867,26/08/2020,-7.44974E+17,0.142545451,1,36.89175 +7489,R148P; S220T; H230Y; G244D,0.18179767,5.175673,26/08/2020,-7.44974E+17,0.196131694,1,26.908842 +7490,R148P; S220T; H230Y; G284C,0.101282425,5.0882955,26/08/2020,-7.44974E+17,0.09938772,1,54.5109 +7491,R148P; S220T; H230Y; H255P,0.004192277,1.7653764,26/08/2020,-7.44974E+17,0.001680534,1,686.8395 +7492,R148P; S220T; H230Y; H255Y,0.030244432,3.16033,26/08/2020,-7.44974E+17,0.029641257,4,110.39839 +7493,R148P; S220T; H230Y; H266Y; M291K,0.002479963,0.95513934,26/08/2020,-7.44974E+17,0.000439846,1,803.64276 +7494,R148P; S220T; H230Y; H271R,0.4355068,5.4788175,26/08/2020,-7.44974E+17,0.499251094,1,7.3800807 +7495,R148P; S220T; H230Y; I256N,0.57077605,5.2713065,26/08/2020,-7.44974E+17,0.612747951,2,3.8749006 +7496,R148P; S220T; H230Y; I256V,0.46995753,5.423082,26/08/2020,-7.44974E+17,0.540096412,1,6.196 +7497,R148P; S220T; H230Y; K236E,0.5849086,2.0188985,26/08/2020,-7.44974E+17,0.649931251,10,1.4034667 +7498,R148P; S220T; H230Y; K236I; E268D,0.22344235,5.3695116,26/08/2020,-7.44974E+17,0.228412409,1,21.215944 +7499,R148P; S220T; H230Y; K298R,0.5604819,5.665523,26/08/2020,-7.44974E+17,0.664658496,1,4.2736073 +7500,R148P; S220T; H230Y; L243M; Y290F,0.38321635,5.4396405,26/08/2020,-7.44974E+17,0.427396763,1,9.365235 +7501,R148P; S220T; H230Y; L243P,0.13346882,5.170752,26/08/2020,-7.44974E+17,0.135016069,1,39.42097 +7502,R148P; S220T; H230Y; L246H,0.004743842,2.0293348,26/08/2020,-7.44974E+17,0.002361783,1,643.5476 +7503,R148P; S220T; H230Y; L253P; Q283E,0.004737724,1.9299322,26/08/2020,-7.44974E+17,0.002014401,1,687.1511 +7504,R148P; S220T; H230Y; L253R,0.005602713,2.2505581,26/08/2020,-7.44974E+17,0.003228471,1,615.3971 +7505,R148P; S220T; H230Y; L269M,0.5927201,6.6075,26/08/2020,-7.44974E+17,0.651577484,2,4.3981605 +7506,R148P; S220T; H230Y; L272M,0.6559601,9.401467,26/08/2020,-7.44974E+17,0.669176007,10,4.875438 +7507,R148P; S220T; H230Y; M273L,0.08154959,4.96008,26/08/2020,-7.44974E+17,0.080629166,1,68.08029 +7508,R148P; S220T; H230Y; N238I,0.5965021,5.7255983,26/08/2020,-7.44974E+17,0.714524891,1,3.5741704 +7509,R148P; S220T; H230Y; N238S; A260V,0.18884982,5.0880237,26/08/2020,-7.44974E+17,0.205589151,1,25.019163 +7510,R148P; S220T; H230Y; N238Y,0.5943729,5.814488,26/08/2020,-7.44974E+17,0.70306493,1,3.7547948 +7511,R148P; S220T; H230Y; N249I,0.00278665,1.1240848,26/08/2020,-7.44974E+17,0.000630119,1,758.74994 +7512,R148P; S220T; H230Y; N249S,0.11267035,5.101674,26/08/2020,-7.44974E+17,0.111606935,1,47.52778 +7513,R148P; S220T; H230Y; P240R; L246V,0.006203953,2.3955858,26/08/2020,-7.44974E+17,0.003635944,1,575.8756 +7514,R148P; S220T; H230Y; P240S,0.106702074,5.119548,26/08/2020,-7.44974E+17,0.103367637,1,51.66021 +7515,R148P; S220T; H230Y; P300L,0.48472905,5.71024,26/08/2020,-7.44974E+17,0.556343054,1,6.2299323 +7516,R148P; S220T; H230Y; Q263P,0.002591696,0.9770903,26/08/2020,-7.44974E+17,0.000447957,1,785.27966 +7517,R148P; S220T; H230Y; Q283L,0.6023016,5.697891,26/08/2020,-7.44974E+17,0.71855839,1,3.4685345 +7518,R148P; S220T; H230Y; Q283R,0.061267942,4.822126,26/08/2020,-7.44974E+17,0.059438029,1,90.445465 +7519,R148P; S220T; H230Y; R258S,0.6386419,5.778663,26/08/2020,-7.44974E+17,0.775647736,1,2.9705086 +7520,R148P; S220T; H230Y; R279H,0.6258632,5.8054094,26/08/2020,-7.44974E+17,0.749266123,1,3.113664 +7521,R148P; S220T; H230Y; S267P; P300S,0.014030126,3.588444,26/08/2020,-7.44974E+17,0.011908143,1,335.08978 +7522,R148P; S220T; H230Y; S301G,0.25248906,5.3114223,26/08/2020,-7.44974E+17,0.269046205,1,17.648613 +7523,R148P; S220T; H230Y; T242I,0.56535953,5.6405144,26/08/2020,-7.44974E+17,0.668170383,1,4.122378 +7524,R148P; S220T; H230Y; T245I,0.024130058,4.1529336,26/08/2020,-7.44974E+17,0.021435082,1,211.05217 +7525,R148P; S220T; H230Y; V232A,0.57781845,5.818728,26/08/2020,-7.44974E+17,0.69400665,1,4.009755 +7526,R148P; S220T; H230Y; V232I,0.5900135,5.7438846,26/08/2020,-7.44974E+17,0.701663293,1,3.6972942 +7527,V293C,0.027047018,4.307267,26/08/2020,-7.44974E+17,0.024827837,1,195.03499 +7528,R148P; S220T; H230Y; V250A,0.34683427,5.4442277,26/08/2020,-7.44974E+17,0.38174737,1,11.092756 +7529,R148P; S220T; H230Y; V250E,0.08487767,5.032631,26/08/2020,-7.44974E+17,0.084292412,1,65.48316 +7530,R148P; S220T; H230Y; V293A,0.05697379,4.817208,26/08/2020,-7.44974E+17,0.054153561,1,97.936676 +7531,R148P; S220T; H230Y; V293I,0.56416416,1.2005287,26/08/2020,-7.44974E+17,0.688955936,10,0.9092228 +7532,R148P; S220T; H230Y; Y239H; V281D; A282T,0.004213869,1.7536529,26/08/2020,-7.44974E+17,0.00175416,1,680.52625 +7533,R148P; S220T; L227R; H230Y,0.009003115,2.9175324,26/08/2020,-7.44974E+17,0.00667427,1,443.52032 +7534,R148P; S220T; P224H,0.33750293,5.525748,26/08/2020,-7.44974E+17,0.36962002,1,11.695837 +7535,R148P; S220T; P224S; H230Y,0.35233167,5.5401144,26/08/2020,-7.44974E+17,0.38796955,1,11.134157 +7536,R148P; S220T; S225A; H230Y,0.6713054,5.6869645,26/08/2020,-7.44974E+17,0.821129716,1,2.39661 +7537,R148P; S220T; S225T; H230Y; P302L,0.48767683,5.4973726,26/08/2020,-7.44974E+17,0.561658491,1,5.935933 +7538,R148P; S220T; S226F; H230Y; N238Y; L246H; R279H,0.003320158,1.3492824,26/08/2020,-7.44974E+17,0.00098406,1,738.4482 +7539,R148P; S220T; S226P; H230Y; N249K,0.001969271,0.6577984,26/08/2020,-7.44974E+17,0.000145851,1,766.89655 +7540,R148P; S220T; V221M; H230Y,0.6080856,5.7060246,26/08/2020,-7.44974E+17,0.731173105,1,3.3874226 +7541,R148P; S225L; H230Y,0.042061154,4.551672,26/08/2020,-7.44974E+17,0.040766471,1,129.98334 +7542,R148P; T172R; M185T; L193P; S220T; H230Y,0.002483946,0.9092241,26/08/2020,-7.44974E+17,0.000377261,1,774.5285 +7543,R148P; T179A; S220T; H230Y,0.5807123,5.8326435,26/08/2020,-7.44974E+17,0.693909854,1,3.9856815 +7544,R148P; T179I; S220T; H230Y,0.500615,5.494957,26/08/2020,-7.44974E+17,0.580859647,1,5.5397143 +7545,R148P; T188A; S220T; H230Y,0.65991825,6.0052056,26/08/2020,-7.44974E+17,0.801352949,1,2.6637452 +7546,R148P; T207I; S220T; H230Y,0.60377145,5.5412154,26/08/2020,-7.44974E+17,0.731261722,1,3.4150007 +7547,R148P; T207I; S220T; H230Y; P300L,0.3497178,5.4709907,26/08/2020,-7.44974E+17,0.38789405,1,10.936231 +7548,R148P; V150A; S220T; H230Y,0.24249834,5.2735662,26/08/2020,-7.44974E+17,0.261078989,1,18.540247 +7549,R148P; V175G; S220T; H230Y; F297L,0.04739467,4.7346897,26/08/2020,-7.44974E+17,0.044759583,1,117.59474 +7550,R148P; V175M; S220T; H230Y; E254A,0.52022284,5.6323133,26/08/2020,-7.44974E+17,0.613552037,1,5.088523 +7551,R148P; V175M; S220T; H230Y; Y294H,0.2895999,5.4429197,26/08/2020,-7.44974E+17,0.321291433,1,14.90567 +7552,R148P; W216L; S220T; H230Y; P300A,0.116994865,5.023367,26/08/2020,-7.44974E+17,0.119631482,1,45.112965 +7553,R148P; Y177C; A202V; S220T; H230Y,0.006034819,2.4028425,26/08/2020,-7.44974E+17,0.003630947,1,588.7183 +7554,R148P; Y177N; S220T; H230Y,0.355155,4.946434,26/08/2020,-7.44974E+17,0.37431442,2,9.366762 +7555,R148P; Y186F; S220T; H230Y,0.34248522,5.3629885,26/08/2020,-7.44974E+17,0.386819781,1,11.126749 +7556,R148Q,0.09552277,6.2671156,26/08/2020,-7.44974E+17,0.095691569,2,64.14612 +7557,R148S,0.08107682,6.2422047,26/08/2020,-7.44974E+17,0.081377252,4,74.040665 +7558,R148S; D170N; L214H; V221E; A223T,0.015236792,3.7602863,26/08/2020,-7.44974E+17,0.011947905,1,321.1687 +7559,R148S; R155H; A174T,0.003013485,1.2506188,26/08/2020,-7.44974E+17,0.000891862,1,744.8307 +7560,R148S; S220T; Q299L,0.30679086,5.3787284,26/08/2020,-7.44974E+17,0.340787363,1,13.199202 +7561,R148S; T172I; S220T,0.2365181,5.3539786,26/08/2020,-7.44974E+17,0.25001564,1,19.440454 +7562,R148V,0.06605176,4.365016,26/08/2020,-7.44974E+17,0.065441002,5,64.85503 +7563,R148W,0.083685525,7.1944175,26/08/2020,-7.44974E+17,0.084270101,3,82.26426 +7564,R155A,0.20934828,5.2397046,26/08/2020,-7.44974E+17,0.218822121,1,22.660843 +7565,R155C,0.24444848,4.523388,26/08/2020,-7.44974E+17,0.250070637,3,14.706374 +7566,R155C; N190I; S220T; S225T; V250E; A259E,0.003372153,1.4114931,26/08/2020,-7.44974E+17,0.00105295,1,730.5966 +7567,R155C; P219H; S220T,0.18523556,5.122732,26/08/2020,-7.44974E+17,0.197718806,1,26.042143 +7568,R155D,0.042978153,6.022061,26/08/2020,-7.44974E+17,0.041843181,3,142.36397 +7569,R155E,0.3074104,6.3283916,26/08/2020,-7.44974E+17,0.307879372,3,14.813651 +7570,R155F,0.32900786,1.9965965,26/08/2020,-7.44974E+17,0.346752859,8,4.2045584 +7571,R155F; A156N; A159S,0.2974055,2.4238474,26/08/2020,-7.44974E+17,0.310722679,4,6.051125 +7572,R155F; A156N; A159T,0.28509024,0.9791606,26/08/2020,-7.44974E+17,0.297064544,4,2.7653239 +7573,R155F; A218M; A296T,0.7287736,7.2397265,26/08/2020,-7.44974E+17,0.735932581,15,2.6513164 +7574,R155G,0.15974805,4.347656,26/08/2020,-7.44974E+17,0.157115845,3,24.658823 +7575,R155H,0.25821015,0.77971673,26/08/2020,-7.44974E+17,0.281975679,8,2.455377 +7576,R155H; A156R,0.16172217,5.336319,26/08/2020,-7.44974E+17,0.166479512,1,31.967281 +7577,R155H; P270Q,0.011717177,3.405589,26/08/2020,-7.44974E+17,0.008968071,1,382.9229 +7578,R155H; S220T; H230Y,0.6320593,5.702611,26/08/2020,-7.44974E+17,0.765541531,1,3.0141327 +7579,R155H; S220T; H230Y; P302Q,0.56685907,5.711332,26/08/2020,-7.44974E+17,0.678014964,1,4.163573 +7580,R155H; S220T; V265I,0.62591547,5.7987432,26/08/2020,-7.44974E+17,0.74169318,1,3.1612916 +7581,R155I,0.1509744,6.529546,26/08/2020,-7.44974E+17,0.151216925,2,39.26616 +7582,R155K,0.29683438,4.1520576,26/08/2020,-7.44974E+17,0.302191459,12,9.948371 +7583,R155K; A156T; Y177W,0.5030534,4.0825887,26/08/2020,-7.44974E+17,0.504588303,32,4.0322046 +7584,R155L,0.13400002,5.191745,26/08/2020,-7.44974E+17,0.134268479,1,38.89699 +7585,R155L; S220T; G237S; D287E; A296T,0.6516623,5.446337,26/08/2020,-7.44974E+17,0.718844641,2,2.7233825 +7586,R155M,0.34059453,4.663451,26/08/2020,-7.44974E+17,0.346333315,9,9.156814 +7587,R155N,0.10620063,5.0424385,26/08/2020,-7.44974E+17,0.10779951,1,50.789135 +7588,R155P,0.004570527,1.930268,26/08/2020,-7.44974E+17,0.002200897,1,643.9642 +7589,R155P; A210S; D287N,0.002914416,1.2805681,26/08/2020,-7.44974E+17,0.00084802,1,789.1009 +7590,R155P; S220T,0.018003166,3.8718681,26/08/2020,-7.44974E+17,0.015668835,1,273.11563 +7591,R155S; L158H; A210T; L253P; P300A,0.006629528,2.5448427,26/08/2020,-7.44974E+17,0.00422022,1,540.4747 +7592,R155S; L176F; Y177F; A218T; S220T,0.014253706,3.663682,26/08/2020,-7.44974E+17,0.011713981,1,330.16168 +7593,R155T,0.1724604,6.0138764,26/08/2020,-7.44974E+17,0.177256435,2,30.82198 +7594,R155V,0.19462533,2.8092299,26/08/2020,-7.44974E+17,0.193074114,4,12.427279 +7595,R165A,0.03358321,4.39927,26/08/2020,-7.44974E+17,0.032386858,1,156.52187 +7596,R165C,0.056099173,4.567309,26/08/2020,-7.44974E+17,0.054835392,3,82.95308 +7597,R165C; Q263E,0.01866827,3.9594352,26/08/2020,-7.44974E+17,0.015970493,1,261.86676 +7598,R165C; S220T,0.5723223,5.862009,26/08/2020,-7.44974E+17,0.670650225,1,4.1606064 +7599,R165D,0.010793173,3.2722023,26/08/2020,-7.44974E+17,0.009408848,2,350.41165 +7600,R165E,0.033283982,4.513904,26/08/2020,-7.44974E+17,0.029872044,1,166.40706 +7601,R165F,0.028213706,4.883054,26/08/2020,-7.44974E+17,0.026760978,2,188.7559 +7602,R165H,0.08740165,4.9349847,26/08/2020,-7.44974E+17,0.087834212,1,62.686916 +7603,R165H; D170G,0.005523884,2.3026462,26/08/2020,-7.44974E+17,0.003571504,1,596.50385 +7604,R165H; D170Y; A174T; S220T,0.002286573,0.84446806,26/08/2020,-7.44974E+17,0.000309497,1,795.93896 +7605,R165H; P270S; S292F,0.003913326,1.6472186,26/08/2020,-7.44974E+17,0.001619478,1,713.9902 +7606,R165H; S220T,0.6392593,6.528052,26/08/2020,-7.44974E+17,0.670300775,4,3.6027522 +7607,R165H; S220T; G284V,0.004659815,2.0044613,26/08/2020,-7.44974E+17,0.002378393,1,670.873 +7608,R165I,0.11145624,4.084061,26/08/2020,-7.44974E+17,0.111520465,2,36.48279 +7609,R165K,0.12187789,4.9971275,26/08/2020,-7.44974E+17,0.123193672,2,39.473534 +7610,R165L,0.070083864,5.3406506,26/08/2020,-7.44974E+17,0.069282853,3,75.82445 +7611,R165M,0.06289787,5.43304,26/08/2020,-7.44974E+17,0.062073081,8,83.066475 +7612,R165M; G168R,0.003770096,1.6292461,26/08/2020,-7.44974E+17,0.001546461,1,693.5869 +7613,R165N,0.035734504,5.54715,26/08/2020,-7.44974E+17,0.035092728,2,164.45847 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S220T,0.003146061,1.3576297,26/08/2020,-7.44974E+17,0.001066822,1,738.31635 +7684,R43S; V135E; S145L,0.002315546,0.8846661,26/08/2020,-7.44974E+17,0.000357073,1,775.9007 +7685,R43T,0.002372498,1.461951,26/08/2020,-7.44974E+17,0.001121892,2,861.5546 +7686,R43V,0.00308462,1.2658311,26/08/2020,-7.44974E+17,0.000868062,1,772.58966 +7687,R43W,0.001214517,0.927637,26/08/2020,-7.44974E+17,0.00039308,3,1086.7476 +7688,R47A,0.038437676,4.4635606,26/08/2020,-7.44974E+17,0.037430972,3,121.3273 +7689,R47C,0.031922106,6.36879,26/08/2020,-7.44974E+17,0.031056967,3,204.62045 +7690,R47C; A111V,0.043411545,4.684134,26/08/2020,-7.44974E+17,0.041594544,1,127.57361 +7691,R47C; A156D; S220T; E254V,0.03376336,4.537218,26/08/2020,-7.44974E+17,0.030751453,1,160.72353 +7692,R47C; A59V,0.02620992,4.2066092,26/08/2020,-7.44974E+17,0.02402618,1,196.74481 +7693,R47C; C75Y,0.006581334,2.5295525,26/08/2020,-7.44974E+17,0.004310968,1,540.98114 +7694,R47C; D125G; M129L; A159D; A218T; G244S; L246F; 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T172S,0.00420202,1.8745618,26/08/2020,-7.44974E+17,0.002031992,1,671.63074 +7714,R47H; S220T; H230Y,0.6755646,5.7511377,26/08/2020,-7.44974E+17,0.821167219,1,2.3671546 +7715,R47H; S67C; N77S; V199A,0.004061142,1.7639917,26/08/2020,-7.44974E+17,0.002078087,1,715.92145 +7716,R47H; S67R; L93P; D125A; A169V; S220T; G237S,0.003508138,1.4639877,26/08/2020,-7.44974E+17,0.001162473,1,702.9457 +7717,R47L,0.01682909,4.0766664,26/08/2020,-7.44974E+17,0.016482464,5,251.972 +7718,R47L; A48E,0.003818097,1.6855592,26/08/2020,-7.44974E+17,0.001582749,1,701.71497 +7719,R47L; D139V; V141A; A149G; S220T,0.003815336,1.6287723,26/08/2020,-7.44974E+17,0.001548009,1,698.6785 +7720,R47L; F142L; S220T; A280V,0.044179842,4.61978,26/08/2020,-7.44974E+17,0.042784646,1,123.85053 +7721,R47L; G50D; M74I; D118G; A127D; R137H,0.003178873,1.3636947,26/08/2020,-7.44974E+17,0.001094617,1,771.1466 +7722,R47L; I62S; M82I; S220T; H271Q,0.002830055,1.1626763,26/08/2020,-7.44974E+17,0.000672863,1,764.52014 +7723,R47L; L176I; S220T; L246P; A257D,0.004985842,2.0123658,26/08/2020,-7.44974E+17,0.002516688,1,644.1105 +7724,R47L; L93F; L124I; A127T; S220T,0.002084954,0.8076182,26/08/2020,-7.44974E+17,0.000274859,1,833.83826 +7725,R47L; S220T; E254Q,0.20552671,5.1686687,26/08/2020,-7.44974E+17,0.220875563,1,23.106297 +7726,R47M,0.022202566,5.1070113,26/08/2020,-7.44974E+17,0.021158141,2,251.5968 +7727,R47P,0.01324199,3.895549,26/08/2020,-7.44974E+17,0.012133336,2,333.98703 +7728,R47P; A92N,0.005042148,2.1415546,26/08/2020,-7.44974E+17,0.002941207,1,639.4655 +7729,R47P; E84V; D163Y; A174T; L189S; S220T,0.003859414,1.6413462,26/08/2020,-7.44974E+17,0.001630753,1,691.8033 +7730,R47P; P171A; S201Y; S220T,0.002819087,1.2045391,26/08/2020,-7.44974E+17,0.000804047,1,786.19977 +7731,R47Q,0.031092966,6.215071,26/08/2020,-7.44974E+17,0.030418481,3,205.39912 +7732,R47S,0.014061217,3.532125,26/08/2020,-7.44974E+17,0.011387139,1,339.841 +7733,R47S; L51Q; Y79N; V199E; L227M; G286E,0.003345976,1.4570293,26/08/2020,-7.44974E+17,0.001161993,1,762.43024 +7734,R47S; Q54L; V99A; L198P; S220T; Q263R,0.002081614,0.77064025,26/08/2020,-7.44974E+17,0.00024553,1,756.20654 +7735,R47S; S103T; T188S,0.009110673,3.029618,26/08/2020,-7.44974E+17,0.006612834,1,447.9813 +7736,R47S; V71A; E84K; N178S; K298N,0.002145071,0.82651335,26/08/2020,-7.44974E+17,0.000287657,1,804.9283 +7737,R47V,0.072566055,4.2048798,26/08/2020,-7.44974E+17,0.071287698,2,60.141994 +7738,R47W,0.010519259,3.1459305,26/08/2020,-7.44974E+17,0.008134892,1,397.32846 +7739,R47Y,0.012717973,5.4092703,26/08/2020,-7.44974E+17,0.011820233,4,443.59735 +7740,R90A,0.046481997,4.7207294,26/08/2020,-7.44974E+17,0.042837014,1,120.17339 +7741,R90C,0.20744957,2.1398904,26/08/2020,-7.44974E+17,0.205676571,3,9.167167 +7742,R90C; R110C; T121M; A127T; A156V; L161P,0.004450016,1.9096831,26/08/2020,-7.44974E+17,0.002257372,1,660.00323 +7743,R90C; T196R; A213D; S220T; A229V; G284C,0.002868121,1.1964552,26/08/2020,-7.44974E+17,0.000798603,1,780.04156 +7744,R90C; W115R; G120C; L173Q; H285L,0.005615177,2.2889566,26/08/2020,-7.44974E+17,0.003310471,1,605.61835 +7745,R90D,0.04408714,4.6333513,26/08/2020,-7.44974E+17,0.04304136,1,123.479996 +7746,R90H; D152G; S220T,0.16071208,5.1390085,26/08/2020,-7.44974E+17,0.168102626,1,31.148571 +7747,R90H; E108D; A149T; S220T; K236R,0.33877876,5.4383097,26/08/2020,-7.44974E+17,0.377103796,1,11.600228 +7748,R90H; E208D,0.12083511,0.7551772,26/08/2020,-7.44974E+17,0.103518395,10,6.196793 +7749,R90H; G203D; S220T,0.53883123,5.5736876,26/08/2020,-7.44974E+17,0.636126118,1,4.6897745 +7750,R90H; R96S,0.28836945,5.420592,26/08/2020,-7.44974E+17,0.307239066,1,14.86628 +7751,R90H; S220T,0.70034534,6.8682604,26/08/2020,-7.44974E+17,0.784531743,2,2.6606078 +7752,R90I,0.0751556,3.1412535,26/08/2020,-7.44974E+17,0.074906992,4,41.856613 +7753,R90K,0.19093755,5.1537485,26/08/2020,-7.44974E+17,0.202580128,1,24.785677 +7754,R90K; S114L,0.11430317,5.2075043,26/08/2020,-7.44974E+17,0.112848734,1,47.64237 +7755,R90L,0.049107086,7.9968033,26/08/2020,-7.44974E+17,0.048897291,4,159.99962 +7756,R90L; D152Y; S220T,0.05383414,4.860461,26/08/2020,-7.44974E+17,0.05057659,1,106.47402 +7757,R90L; S220T,0.4469482,5.5703545,26/08/2020,-7.44974E+17,0.512439065,1,7.0400124 +7758,R90N,0.16925786,3.0861387,26/08/2020,-7.44974E+17,0.177145079,2,17.254044 +7759,R90P,0.055782493,4.4388504,26/08/2020,-7.44974E+17,0.056276418,7,77.83544 +7760,R90P; A174V,0.003294928,1.3864107,26/08/2020,-7.44974E+17,0.001034828,1,752.25714 +7761,R90P; S220T,0.5372233,5.636963,26/08/2020,-7.44974E+17,0.631606748,1,4.6949334 +7762,R90P; V109E; L189S; P300A,0.002246543,0.9093318,26/08/2020,-7.44974E+17,0.000352936,1,768.7826 +7763,R90Q,0.12109208,5.0570035,26/08/2020,-7.44974E+17,0.122582158,1,43.38607 +7764,R90S,0.361062,3.6336243,26/08/2020,-7.44974E+17,0.375100189,2,6.747487 +7765,R90S; A174V; A206V; S220T; S225P; E275V; E278G,0.005676757,2.2638338,26/08/2020,-7.44974E+17,0.002918456,1,617.21106 +7766,R90S; R148P; S220T; H230Y; A252T; G264C,0.011920163,3.4001262,26/08/2020,-7.44974E+17,0.009415021,1,375.98328 +7767,R90T,0.11677098,4.979233,26/08/2020,-7.44974E+17,0.115242187,3,40.428608 +7768,R90V,0.061421324,4.768693,26/08/2020,-7.44974E+17,0.061235786,2,80.95076 +7769,R90W,0.038611714,6.738756,26/08/2020,-7.44974E+17,0.038481519,3,177.04932 +7770,R90Y,0.045218576,5.436494,26/08/2020,-7.44974E+17,0.044757639,2,126.80902 +7771,R94A,0.087630406,5.106509,26/08/2020,-7.44974E+17,0.084754434,1,63.589096 +7772,R94C,0.035195604,1.5520821,26/08/2020,-7.44974E+17,0.035306555,2,57.64223 +7773,R94C; A116T,0.03467044,4.4438167,26/08/2020,-7.44974E+17,0.032932465,1,152.78957 +7774,R94C; E108G; W115C; S220T; Y290N,0.003186774,1.3487449,26/08/2020,-7.44974E+17,0.001044145,1,740.3165 +7775,R94C; L214H; S220T; A252G; H271Y,0.007426679,2.672766,26/08/2020,-7.44974E+17,0.005125992,1,506.2899 +7776,R94C; M184L; S220T; L222P; E295V,0.002788324,1.2164211,26/08/2020,-7.44974E+17,0.000838549,1,773.807 +7777,R94C; N102D; G162E; L176F; S220T,0.003600708,1.5443022,26/08/2020,-7.44974E+17,0.001343988,1,729.4302 +7778,R94C; P131E,0.037717417,4.5357213,26/08/2020,-7.44974E+17,0.036172099,1,141.88208 +7779,R94C; W115R; L119P; N249K; S301N,0.002380161,0.9209547,26/08/2020,-7.44974E+17,0.000356228,1,805.3807 +7780,R94C; Y123C; S220T; R247C,0.002670432,1.1281834,26/08/2020,-7.44974E+17,0.000656189,1,845.539 +7781,R94D,0.030850423,5.2843466,26/08/2020,-7.44974E+17,0.02996601,2,184.95972 +7782,R94D; I128N; L143S; L227Q; H266L,0.015006144,3.6662803,26/08/2020,-7.44974E+17,0.012203756,1,316.8564 +7783,R94E,0.043270726,5.8328714,26/08/2020,-7.44974E+17,0.04244912,3,137.11574 +7784,R94F,0.06589563,2.974999,26/08/2020,-7.44974E+17,0.065101088,3,47.25763 +7785,R94G,0.046246536,5.0293245,26/08/2020,-7.44974E+17,0.045797557,4,109.62318 +7786,R94H,0.051878322,4.7614174,26/08/2020,-7.44974E+17,0.051576949,1,107.18717 +7787,R94H; G146V; L158H; N190T; A213T; Y294F,0.004475979,1.8752089,26/08/2020,-7.44974E+17,0.002226517,1,658.26984 +7788,R94H; L119H,0.04917337,4.756164,26/08/2020,-7.44974E+17,0.047238853,1,112.540794 +7789,R94H; M129T; L167H,0.004495931,1.891706,26/08/2020,-7.44974E+17,0.002261227,1,671.50665 +7790,R94H; R148P; S220T; H230Y,0.44078514,1.1831063,26/08/2020,-7.44974E+17,0.505082408,10,1.5256 +7791,R94H; S220I; H285R,0.013130901,3.5069997,26/08/2020,-7.44974E+17,0.010559771,1,346.17566 +7792,R94H; S220T; H230Y; H271Q,0.7639941,13.939537,26/08/2020,-7.44974E+17,0.782098642,10,4.223772 +7793,R94H; T121M,0.1134391,1.0920882,26/08/2020,-7.44974E+17,0.101654719,10,9.225772 +7794,R94H; V141G; A156T; L158F; S160L; M184T,0.002753186,1.1306169,26/08/2020,-7.44974E+17,0.000635828,1,758.81866 +7795,R94I,0.056290258,5.4243956,26/08/2020,-7.44974E+17,0.055650414,3,97.07883 +7796,R94K,0.10480752,5.783109,26/08/2020,-7.44974E+17,0.10289286,2,53.86166 +7797,R94L,0.050978366,5.4984593,26/08/2020,-7.44974E+17,0.049772648,2,112.67272 +7798,R94L; F297S,0.002735802,1.1236618,26/08/2020,-7.44974E+17,0.000655658,1,790.9017 +7799,R94L; L193I; S220T; P240Q,0.016475039,3.579794,26/08/2020,-7.44974E+17,0.014902931,2,250.93562 +7800,R94L; R148P; S220T; H230Y; L234R,0.024846504,4.2018256,26/08/2020,-7.44974E+17,0.02285418,1,207.03178 +7801,R94L; S220T,0.23029733,5.405638,26/08/2020,-7.44974E+17,0.245452984,1,20.441572 +7802,R94P,0.049614202,5.5878935,26/08/2020,-7.44974E+17,0.048175289,2,118.56226 +7803,R94P; F297Y,0.006435453,2.4912186,26/08/2020,-7.44974E+17,0.00439844,1,558.2726 +7804,R94Q,0.11261586,2.8448129,26/08/2020,-7.44974E+17,0.111135335,5,24.051853 +7805,R94S,0.075539336,4.355855,26/08/2020,-7.44974E+17,0.074821854,5,55.88779 +7806,R94S; F166L; A169E; A259E; S301G,0.002247502,0.78449273,26/08/2020,-7.44974E+17,0.000194712,1,755.91583 +7807,R94S; R148P; E153D; S220T; H230Y; N249S; R258C,0.012902358,3.4857461,26/08/2020,-7.44974E+17,0.010295363,1,355.17233 +7808,R94S; R96S; G162R; V232A; A259S,0.002504188,0.8700182,26/08/2020,-7.44974E+17,0.000287141,1,773.1113 +7809,R94S; V109F,0.033715006,1.1304114,26/08/2020,-7.44974E+17,0.033141993,2,47.293526 +7810,R94T,0.07816261,4.5447235,26/08/2020,-7.44974E+17,0.078750943,3,57.92405 +7811,R94T; R110H,0.022802385,4.135935,26/08/2020,-7.44974E+17,0.020555099,1,227.8851 +7812,R94V,0.14824602,5.1702166,26/08/2020,-7.44974E+17,0.15222118,1,35.12496 +7813,R94W,0.030749872,5.1375585,26/08/2020,-7.44974E+17,0.029273359,2,180.89163 +7814,R94Y,0.10737879,5.219794,26/08/2020,-7.44974E+17,0.104949797,3,46.183582 +7815,R96C,0.07394357,0.85387677,26/08/2020,-7.44974E+17,0.075067326,3,14.348571 +7816,R96C; A116T; V204A; S220T; E261V,0.009878824,3.1455925,26/08/2020,-7.44974E+17,0.007637704,1,435.34195 +7817,R96C; A127S; H183L; D211G; S220T,0.003966318,1.6892931,26/08/2020,-7.44974E+17,0.001637073,1,695.05817 +7818,R96C; G120V,0.00379514,1.6195871,26/08/2020,-7.44974E+17,0.001482381,1,727.0312 +7819,R96C; P106S; T245A; H271N,0.002479201,1.0374362,26/08/2020,-7.44974E+17,0.000526496,1,783.7067 +7820,R96C; S103L,0.002940272,1.1757457,26/08/2020,-7.44974E+17,0.000634887,1,749.4123 +7821,R96C; S205I; R247S,0.007365024,2.676188,26/08/2020,-7.44974E+17,0.004859556,1,517.19086 +7822,R96C; V150L; S220T; H230Y,0.26103997,5.276361,26/08/2020,-7.44974E+17,0.2825622,1,16.748878 +7823,R96C; W115S; G203D,0.00233971,0.8595259,26/08/2020,-7.44974E+17,0.000277981,1,810.9131 +7824,R96D,0.002029316,2.0782082,26/08/2020,-7.44974E+17,0.001445974,4,1167.7621 +7825,R96E,0.003657632,1.5790944,26/08/2020,-7.44974E+17,0.003159517,4,511.49933 +7826,R96G,0.052841347,0.91547066,26/08/2020,-7.44974E+17,0.052780891,3,22.417742 +7827,R96G; M184V,0.007386537,2.704595,26/08/2020,-7.44974E+17,0.005075329,1,525.7973 +7828,R96H; G191V; S220T; T245S; G286V,0.003212603,1.4336126,26/08/2020,-7.44974E+17,0.00126835,1,744.99207 +7829,R96H; L176F; G203D; Y290C,0.003297266,1.4077094,26/08/2020,-7.44974E+17,0.001081664,1,737.9371 +7830,R96H; N100F,0.002217212,1.4220895,26/08/2020,-7.44974E+17,0.001163418,2,891.73364 +7831,R96H; R148P; A202V; S220T; H230Y,0.15554526,5.2336006,26/08/2020,-7.44974E+17,0.158705218,1,32.650482 +7832,R96H; R148P; S220T; H230Y; A252V,0.15372102,5.100661,26/08/2020,-7.44974E+17,0.159765431,1,32.737255 +7833,R96H; V199A; L243Q; H271R,0.095397554,4.990458,26/08/2020,-7.44974E+17,0.098453239,1,56.202602 +7834,R96H; V99F,0.003629689,1.9383262,26/08/2020,-7.44974E+17,0.002513553,2,694.39636 +7835,R96I,0.038504872,5.417682,26/08/2020,-7.44974E+17,0.037936067,2,150.11642 +7836,R96L,0.017411616,1.4464942,26/08/2020,-7.44974E+17,0.017073606,3,102.78642 +7837,R96L; N178Y; S205N,0.002278102,0.8929785,26/08/2020,-7.44974E+17,0.000309144,1,792.7038 +7838,R96N,0.049680438,0.8058873,26/08/2020,-7.44974E+17,0.052584657,2,23.96051 +7839,R96P,0.003804185,1.6301253,26/08/2020,-7.44974E+17,0.001529543,1,719.91907 +7840,R96Q,0.046124816,1.1675003,26/08/2020,-7.44974E+17,0.046724949,4,29.90477 +7841,R96S,0.07394338,0.7415725,26/08/2020,-7.44974E+17,0.066646982,10,10.521117 +7842,R96S; A112D; S145T,0.002270561,0.92537206,26/08/2020,-7.44974E+17,0.000385658,1,811.0355 +7843,R96S; D118N; R137S; H285L,0.029736433,4.4238377,26/08/2020,-7.44974E+17,0.027188445,1,178.93016 +7844,R96S; N178Y; S220T; A252T,0.005147475,2.1127207,26/08/2020,-7.44974E+17,0.002764798,1,623.26526 +7845,R96S; R137L; A257T,0.006892111,2.6106603,26/08/2020,-7.44974E+17,0.00431615,1,534.1478 +7846,R96S; S220T; H230Y,0.7215138,14.021151,26/08/2020,-7.44974E+17,0.738943333,10,5.340257 +7847,R96T,0.024287557,0.825025,26/08/2020,-7.44974E+17,0.025028062,2,53.010265 +7848,R96V,0.04972907,4.704475,26/08/2020,-7.44974E+17,0.048658519,1,109.4423 +7849,R96W,0.002914712,1.1848887,26/08/2020,-7.44974E+17,0.0006667,1,753.50195 +7850,S103A; L176S; S220T; K298N,0.003558092,1.4838183,26/08/2020,-7.44974E+17,0.001282064,1,725.81464 +7851,S103A; V150A; S220T,0.006762842,2.5615335,26/08/2020,-7.44974E+17,0.00457549,1,530.5396 +7852,S103C,0.014080341,1.3793354,26/08/2020,-7.44974E+17,0.013584298,4,115.56673 +7853,S103D,0.008277395,1.5801154,26/08/2020,-7.44974E+17,0.006859407,2,266.5845 +7854,S103E,0.0013324,1.4194697,26/08/2020,-7.44974E+17,0.000879553,5,1242.5837 +7855,S103G,0.06283192,5.290128,26/08/2020,-7.44974E+17,0.061486152,2,86.73495 +7856,S103H,0.001834164,2.2582366,26/08/2020,-7.44974E+17,0.001492591,7,1322.7086 +7857,S103I,0.018007623,4.681764,26/08/2020,-7.44974E+17,0.016629475,2,284.68082 +7858,S103K,0.000983181,1.0323775,26/08/2020,-7.44974E+17,0.000534761,5,1262.6522 +7859,S103L,0.001872101,1.7292084,26/08/2020,-7.44974E+17,0.001378541,5,1043.6067 +7860,S103L; A111E; H183Y; L193H; Q263H,0.028423361,4.2754683,26/08/2020,-7.44974E+17,0.026120841,1,184.93584 +7861,S103L; A116T; A187V; S220T,0.003115967,1.2576847,26/08/2020,-7.44974E+17,0.000846372,1,769.9225 +7862,S103L; A140G; D147E; S220T; S226P,0.004529859,1.9132622,26/08/2020,-7.44974E+17,0.002209659,1,649.10425 +7863,S103L; A156D; G191R; A195V; S220T; H255L,0.003740657,1.5886759,26/08/2020,-7.44974E+17,0.001497175,1,672.86316 +7864,S103L; D163E; S220T; G241C,0.004826577,2.029402,26/08/2020,-7.44974E+17,0.002515721,1,651.8406 +7865,S103L; D170V; T179S; H183R; M185I; Q194K; D235G; T242I,0.002454076,0.9788429,26/08/2020,-7.44974E+17,0.000430422,1,768.0344 +7866,S103L; E108D,0.009931264,2.9651809,26/08/2020,-7.44974E+17,0.008887437,2,347.1967 +7867,S103L; I128F; T157S; M185K; S220T; R258C,0.002977155,1.2881002,26/08/2020,-7.44974E+17,0.000925982,1,770.81415 +7868,S103L; M129T; D170G; T245A,0.002561616,0.9617024,26/08/2020,-7.44974E+17,0.000378428,1,779.18976 +7869,S103L; M129V,0.002550298,0.9605991,26/08/2020,-7.44974E+17,0.000373088,1,788.91907 +7870,S103L; S220T,0.005399518,2.2060392,26/08/2020,-7.44974E+17,0.002985616,1,620.6592 +7871,S103L; S220T; H230Y; A277D,0.00240696,0.86817735,26/08/2020,-7.44974E+17,0.00031092,1,762.60364 +7872,S103L; S220T; H271L,0.004255338,1.7793586,26/08/2020,-7.44974E+17,0.00184767,1,691.1178 +7873,S103L; V109L; A127S; M129T,0.001750186,0.5627597,26/08/2020,-7.44974E+17,6.28E-05,1,740.9626 +7874,S103M,0.006730852,1.5870887,26/08/2020,-7.44974E+17,0.006323063,5,269.50882 +7875,S103N,0.006472422,2.0287995,26/08/2020,-7.44974E+17,0.005962524,5,352.82913 +7876,S103P,0.001061891,1.1371008,26/08/2020,-7.44974E+17,0.000580102,5,1282.6298 +7877,S103P; E108K; G146C; S205R; A260E; N289D,0.003352949,1.4359543,26/08/2020,-7.44974E+17,0.00124526,1,720.4567 +7878,S103P; L134P,0.006056434,2.3263085,26/08/2020,-7.44974E+17,0.003484106,1,577.9701 +7879,S103P; Q107H; F142S; R148H; L151P; T157I; L193P; S220T,0.003249172,1.3553292,26/08/2020,-7.44974E+17,0.000969537,1,763.5294 +7880,S103P; R148P; S220T; H230Y,0.004044395,1.711393,26/08/2020,-7.44974E+17,0.001656448,1,711.2875 +7881,S103P; R155H; S220T; G244S; M291V,0.003821657,1.6137322,26/08/2020,-7.44974E+17,0.00158784,1,696.50055 +7882,S103P; V109L,0.002298856,0.9264284,26/08/2020,-7.44974E+17,0.000408121,1,800.78186 +7883,S103Q,0.002424088,1.718088,26/08/2020,-7.44974E+17,0.001669059,3,867.37085 +7884,S103R,0.001700738,0.9854902,26/08/2020,-7.44974E+17,0.000489345,2,908.3528 +7885,S103T,0.049810458,2.9038424,26/08/2020,-7.44974E+17,0.048938859,2,65.69839 +7886,S103T; A112S; D152V; T157A; S220T,0.002868462,1.1464759,26/08/2020,-7.44974E+17,0.000702026,1,762.2611 +7887,S103T; A116T; D118G; D163N; N178T; L189I; F209I; V250E; V293I,0.003147157,1.3715197,26/08/2020,-7.44974E+17,0.00108427,1,772.99994 +7888,S103T; D211G; S220T,0.051387366,4.717494,26/08/2020,-7.44974E+17,0.048417942,1,107.99576 +7889,S103T; G146C; H154R; S220T; A231V; N289I; Q299K,0.004804834,2.0769293,26/08/2020,-7.44974E+17,0.002545508,1,650.90924 +7890,S103T; P164S; H288L; Y294F; K298N,0.002142838,0.7434207,26/08/2020,-7.44974E+17,0.000177677,1,753.78735 +7891,S103V,0.010435894,1.2351865,26/08/2020,-7.44974E+17,0.009777214,3,152.37779 +7892,S103W,0.001511264,1.1840663,26/08/2020,-7.44974E+17,0.001067792,6,922.0331 +7893,S103Y,0.001163513,1.1948366,26/08/2020,-7.44974E+17,0.000915362,8,1147.5887 +7894,S114A,0.27097315,1.3831269,26/08/2020,-7.44974E+17,0.293304829,3,4.2040553 +7895,S114D,0.22083859,0.6108328,26/08/2020,-7.44974E+17,0.283917233,3,2.850724 +7896,S114E,0.19188301,0.4845621,26/08/2020,-7.44974E+17,0.226973659,7,2.41911 +7897,S114F,0.115683235,0.5381945,26/08/2020,-7.44974E+17,0.135326339,5,5.2106442 +7898,S114G,0.21132474,0.5129387,26/08/2020,-7.44974E+17,0.250608609,9,2.1489234 +7899,S114H,0.17355962,0.5177683,26/08/2020,-7.44974E+17,0.206420232,5,3.0427876 +7900,S114I,0.14867783,0.7204921,26/08/2020,-7.44974E+17,0.166465826,3,5.470319 +7901,S114I; G120V,0.02742285,1.0519636,26/08/2020,-7.44974E+17,0.027397999,2,53.74864 +7902,S114L,0.1662634,0.65553385,26/08/2020,-7.44974E+17,0.186195918,5,3.9545662 +7903,S114L; D139E; G191A; W216R,0.004189435,1.832859,26/08/2020,-7.44974E+17,0.002155717,1,695.70294 +7904,S114L; E153D; L158H; S220T,0.004098007,1.7826895,26/08/2020,-7.44974E+17,0.001877559,1,704.47906 +7905,S114L; L143M; V276F,0.013875635,3.429238,26/08/2020,-7.44974E+17,0.012623178,2,284.44232 +7906,S114L; L181S; D287E,0.007899601,2.798107,26/08/2020,-7.44974E+17,0.005711297,1,491.85526 +7907,S114L; R148P; L173V; S220T; H230Y,0.3202923,5.401661,26/08/2020,-7.44974E+17,0.349226949,1,12.650481 +7908,S114L; S220T,0.655958,6.66546,26/08/2020,-7.44974E+17,0.731648012,2,3.2835119 +7909,S114L; S220T; H230Y; M273I; M291K,0.004146934,1.7350295,26/08/2020,-7.44974E+17,0.001685782,1,701.90076 +7910,S114M,0.14055628,0.43327126,26/08/2020,-7.44974E+17,0.163372074,6,3.3723218 +7911,S114N,0.2186233,0.66581595,26/08/2020,-7.44974E+17,0.271385772,4,2.9324598 +7912,S114N; D118N,0.16208372,0.46401802,26/08/2020,-7.44974E+17,0.21937191,2,3.6323547 +7913,S114P,0.044037923,0.98399276,26/08/2020,-7.44974E+17,0.044377889,8,24.114761 +7914,S114P; L143F; M248V; Q299L,0.009595094,3.114,26/08/2020,-7.44974E+17,0.007135925,1,437.3653 +7915,S114P; T179S,0.079334825,4.9340787,26/08/2020,-7.44974E+17,0.077091067,1,69.542625 +7916,S114Q,0.22542073,0.46053657,26/08/2020,-7.44974E+17,0.277298873,8,1.7990898 +7917,S114R,0.19443917,0.5205421,26/08/2020,-7.44974E+17,0.238462121,5,2.6114726 +7918,S114T,0.26170877,1.086266,26/08/2020,-7.44974E+17,0.294325495,9,3.2467167 +7919,S114T; A197T,0.00694639,2.6211462,26/08/2020,-7.44974E+17,0.004680431,1,536.93994 +7920,S114T; A197V; I212V; D287E,0.016653374,3.7925534,26/08/2020,-7.44974E+17,0.014338147,1,289.53946 +7921,S114T; D170E,0.085261226,0.70719266,26/08/2020,-7.44974E+17,0.074412204,10,8.687257 +7922,S114T; H154N; S220T,0.6736092,6.027746,26/08/2020,-7.44974E+17,0.803678211,1,2.4986136 +7923,S114T; M185K; P240L; E254Q; A296S,0.004864989,2.043027,26/08/2020,-7.44974E+17,0.002605816,1,659.5826 +7924,S114T; P131Q; F166L; G168V; A180T; D211G; F297V,0.002304501,0.86655253,26/08/2020,-7.44974E+17,0.000275311,1,786.6028 +7925,S114T; P240T; M273L; V281D,0.002444555,0.8787609,26/08/2020,-7.44974E+17,0.000331649,1,736.7265 +7926,S114T; S220T,0.5591019,5.8393598,26/08/2020,-7.44974E+17,0.655539549,1,4.440633 +7927,S114V,0.21825287,0.77045876,26/08/2020,-7.44974E+17,0.262780048,4,3.3093956 +7928,S114W,0.15527236,6.5926623,26/08/2020,-7.44974E+17,0.158414027,3,37.456745 +7929,S114W; A116T,0.045882225,4.699037,26/08/2020,-7.44974E+17,0.043852654,1,121.928314 +7930,S114Y,0.12547165,0.48291266,26/08/2020,-7.44974E+17,0.13525917,13,3.8132586 +7931,S117A,0.24678664,0.5744151,26/08/2020,-7.44974E+17,0.309001397,3,2.2099593 +7932,S117A; S220T; Y239C,0.006780365,2.5727475,26/08/2020,-7.44974E+17,0.004391895,1,546.3681 +7933,S117C,0.2973392,5.251487,26/08/2020,-7.44974E+17,0.334591508,1,13.663151 +7934,S117D,0.2515022,0.83797824,26/08/2020,-7.44974E+17,0.288033732,10,2.6686234 +7935,S117E,0.3239692,1.8780835,26/08/2020,-7.44974E+17,0.344144316,7,4.083385 +7936,S117F,0.19049615,1.0948434,26/08/2020,-7.44974E+17,0.207673954,5,5.2816706 +7937,S117F; L181S; V204M; A218E; H266Q,0.002348962,0.8111798,26/08/2020,-7.44974E+17,0.000237264,1,802.7857 +7938,S117G,0.18527858,0.53486794,26/08/2020,-7.44974E+17,0.227727283,6,2.7822835 +7939,S117H,0.29335657,1.4388661,26/08/2020,-7.44974E+17,0.321963235,8,3.6305077 +7940,S117H; A156N; A159S,0.46146977,2.0841215,26/08/2020,-7.44974E+17,0.457515056,8,2.4581184 +7941,S117H; A156N; A159T,0.36854875,0.83977467,26/08/2020,-7.44974E+17,0.384639818,8,1.5038038 +7942,S117H; A156N; A296I,0.4265486,0.652707,26/08/2020,-7.44974E+17,0.545539477,10,0.9009365 +7943,S117H; A218M; A296T,0.725848,8.034426,26/08/2020,-7.44974E+17,0.736442047,15,2.984966 +7944,S117I,0.1220951,0.44139254,26/08/2020,-7.44974E+17,0.133005438,9,3.7822587 +7945,S117K,0.1671211,0.4311059,26/08/2020,-7.44974E+17,0.197830352,6,2.622483 +7946,S117K; G120D,0.27336332,5.358687,26/08/2020,-7.44974E+17,0.29621608,1,15.680311 +7947,S117L,0.15017793,0.6897186,26/08/2020,-7.44974E+17,0.162444391,8,4.4840817 +7948,S117L; A218E,0.17940055,5.193417,26/08/2020,-7.44974E+17,0.191096935,1,27.451708 +7949,S117M,0.18729965,1.3813921,26/08/2020,-7.44974E+17,0.197548763,5,6.6997604 +7950,S117N,0.16299568,0.55633944,26/08/2020,-7.44974E+17,0.1822072,10,3.21263 +7951,S117P,0.007800723,1.337612,26/08/2020,-7.44974E+17,0.007356679,6,195.55403 +7952,S117P; G136D,0.002554643,1.0017369,26/08/2020,-7.44974E+17,0.000481645,1,786.8814 +7953,S117P; G241C; V276D,0.001775568,0.5998784,26/08/2020,-7.44974E+17,0.000107976,1,745.47656 +7954,S117P; M129I; P224L; A231V,0.002808277,1.1803174,26/08/2020,-7.44974E+17,0.000698767,1,789.16437 +7955,S117P; S220T,0.004172628,1.7722365,26/08/2020,-7.44974E+17,0.001909866,1,670.62946 +7956,S117P; S220T; L272Q,0.002937531,1.1541953,26/08/2020,-7.44974E+17,0.000638496,1,757.99774 +7957,S117P; S220T; N238S,0.004176817,1.8027973,26/08/2020,-7.44974E+17,0.001868163,1,680.23065 +7958,S117P; T121M; S220T,0.014938357,3.6519256,26/08/2020,-7.44974E+17,0.01235361,1,317.31226 +7959,S117P; W216R; S220T,0.002987744,1.2748965,26/08/2020,-7.44974E+17,0.000897796,1,784.99756 +7960,S117Q,0.2547423,1.21861,26/08/2020,-7.44974E+17,0.279491878,3,4.1542406 +7961,S117R,0.18117215,0.49821496,26/08/2020,-7.44974E+17,0.215981267,4,2.9302804 +7962,S117T,0.29199955,5.340265,26/08/2020,-7.44974E+17,0.316557599,1,14.249482 +7963,S117T; D170Y; H183L,0.003484104,1.5592175,26/08/2020,-7.44974E+17,0.001405425,1,744.0345 +7964,S117T; V204A; F217L; H271L,0.002846563,1.1697642,26/08/2020,-7.44974E+17,0.000760282,1,758.1591 +7965,S117V,0.14999868,0.581362,26/08/2020,-7.44974E+17,0.163873838,10,3.7579105 +7966,S117W,0.116516046,0.656705,26/08/2020,-7.44974E+17,0.124811939,8,5.811686 +7967,S117Y,0.14753176,0.863801,26/08/2020,-7.44974E+17,0.175214394,2,6.8230667 +7968,S117Y; F142C,0.027863685,4.3077526,26/08/2020,-7.44974E+17,0.025409758,1,189.34389 +7969,S11A; A13T; G19R; L76I; F86Y; L182R; S220T,0.002312259,0.862713,26/08/2020,-7.44974E+17,0.000317251,1,733.1666 +7970,S11A; L31M; T39I; V113I; G120S; R148H; L189I; A210V; D233E; H266L,0.001863225,0.5950326,26/08/2020,-7.44974E+17,8.82E-05,1,767.8208 +7971,S11A; R148P; S220T; H230Y,0.6428746,5.8450108,26/08/2020,-7.44974E+17,0.778337021,1,2.9045067 +7972,S11C,0.09982317,5.4935474,26/08/2020,-7.44974E+17,0.097648558,3,52.69493 +7973,S11D,0.092838116,5.6941643,26/08/2020,-7.44974E+17,0.094927543,3,59.040974 +7974,S11E,0.069569156,6.766483,26/08/2020,-7.44974E+17,0.06867763,3,95.40846 +7975,S11G,0.12668607,6.410311,26/08/2020,-7.44974E+17,0.127096867,2,47.569794 +7976,S11L,0.11512253,6.081045,26/08/2020,-7.44974E+17,0.115826691,2,50.766296 +7977,S11L; A197T; D211Y; S220T; A257V,0.002775641,1.1028802,26/08/2020,-7.44974E+17,0.000639338,1,755.9549 +7978,S11L; A48E; V265E,0.001458357,0.32785034,26/08/2020,-7.44974E+17,3.80E-06,1,669.4434 +7979,S11L; L25H; A59E; C75S; G104S; N251K; S292C; P302A,0.003014322,1.3110846,26/08/2020,-7.44974E+17,0.000944655,1,752.1702 +7980,S11L; L32P; P270T; N289I,0.002276619,0.92793417,26/08/2020,-7.44974E+17,0.000411245,1,823.917 +7981,S11L; S220T,0.59731466,6.9302173,26/08/2020,-7.44974E+17,0.655393448,2,4.4927197 +7982,S11L; T38P; A140S; E153G; H154N; P171H; T172A; D211H; A260G; H285L; E295D,0.004991382,2.0875523,26/08/2020,-7.44974E+17,0.002756203,1,640.4081 +7983,S11L; V30E; S220T,0.39014557,5.3295884,26/08/2020,-7.44974E+17,0.453339152,1,8.849717 +7984,S11L; Y79F; S220T; A229V; A252T,0.002768483,1.1632313,26/08/2020,-7.44974E+17,0.000711817,1,791.0694 +7985,S11M,0.07899564,6.0921593,26/08/2020,-7.44974E+17,0.079082363,2,77.62393 +7986,S11N,0.12121831,5.876205,26/08/2020,-7.44974E+17,0.121142273,2,46.330544 +7987,S11P,0.11576388,5.109282,26/08/2020,-7.44974E+17,0.117343674,1,46.109596 +7988,S11P; A13V; Q54P; I62F; A228V; H230L,0.001792489,0.5927809,26/08/2020,-7.44974E+17,8.60E-05,1,744.4004 +7989,S11P; D95E,0.16343689,5.299823,26/08/2020,-7.44974E+17,0.166709948,1,31.463018 +7990,S11P; E108D; S220T,0.23435004,5.3035083,26/08/2020,-7.44974E+17,0.254941828,1,19.652235 +7991,S11P; E91V; T172A; M291T,0.005472689,2.2075183,26/08/2020,-7.44974E+17,0.003142849,1,606.28955 +7992,S11P; H154L; V204L; L269F; Q299R,0.003633308,1.592835,26/08/2020,-7.44974E+17,0.001422394,1,722.14764 +7993,S11P; H36L; T38S; T39N; S160A; Q194R,0.004512595,1.9139267,26/08/2020,-7.44974E+17,0.002300295,1,692.1846 +7994,S11P; I128T; L134Q; R165G; S220T,0.002750822,1.1687065,26/08/2020,-7.44974E+17,0.000708371,1,773.9526 +7995,S11P; L93H; F166S; P240Q,0.010826148,3.2564921,26/08/2020,-7.44974E+17,0.008870457,1,393.63235 +7996,S11P; P14L; Y83H; G136S; L189I; A195G; S220T,0.003061667,1.3046234,26/08/2020,-7.44974E+17,0.000925773,1,737.7308 +7997,S11P; R165C; S220T; R247C,0.6464743,5.964764,26/08/2020,-7.44974E+17,0.768686657,1,2.940191 +7998,S11P; V15F; V40G; N102S; W115L; S220T; S225P; E262V,0.0024738,0.9739202,26/08/2020,-7.44974E+17,0.000436537,1,784.86224 +7999,S11P; V97I; S220T; L222Q,0.009197465,3.038692,26/08/2020,-7.44974E+17,0.006830346,1,449.21204 +8000,S11P; W41R; M185T; Q194L,0.002320368,0.8209619,26/08/2020,-7.44974E+17,0.000257101,1,776.83514 +8001,S11R,0.10842267,5.708962,26/08/2020,-7.44974E+17,0.106278106,2,51.353184 +8002,S11T,0.25953397,2.9284687,26/08/2020,-7.44974E+17,0.277852158,2,9.359886 +8003,S11T; A112G; P133H; S220T; H230L; T245S,0.011364754,3.3346045,26/08/2020,-7.44974E+17,0.008901167,1,387.312 +8004,S11T; A33T; A60D; Y79C; V199A; T245A,0.002556153,1.1004839,26/08/2020,-7.44974E+17,0.000649962,1,798.98914 +8005,S11T; A48V; V52A; A80V; Y83N; R122H; D163G; L181V; G264C,0.002668031,1.101485,26/08/2020,-7.44974E+17,0.000591172,1,795.09937 +8006,S11T; A56D; E84V; S220T; L246F,0.002661158,1.1142514,26/08/2020,-7.44974E+17,0.000599515,1,796.6112 +8007,S11T; E29D; G136C; L227Q; A229V; M291V,0.00270664,1.118211,26/08/2020,-7.44974E+17,0.000582361,1,790.7346 +8008,S11T; F86Y; V232D; F297I,0.006730974,2.5233166,26/08/2020,-7.44974E+17,0.004183515,1,549.6608 +8009,S11T; G55D; G104C; S220T,0.002337064,1.3039309,26/08/2020,-7.44974E+17,0.00114321,2,809.1494 +8010,S11T; I128T; M129T; L151P; S220T; P300Q,0.002717294,1.1460035,26/08/2020,-7.44974E+17,0.000691265,1,811.3506 +8011,S11T; L246H,0.003134419,1.313737,26/08/2020,-7.44974E+17,0.001003337,1,754.2169 +8012,S11T; L58V; S220T; V232D,0.34005758,5.420508,26/08/2020,-7.44974E+17,0.376971977,1,11.549224 +8013,S11T; M23I; S220T; K236I,0.049276378,4.734482,26/08/2020,-7.44974E+17,0.047849054,1,110.82771 +8014,S11T; N42D; S57P; F86L; S117P; A127D; M129I; D170G; L272Q,0.00327561,1.3947787,26/08/2020,-7.44974E+17,0.001027431,1,733.75476 +8015,S11T; Q54H; V66E; Y83F; L134P; L167F; M185I,0.00222828,0.801952,26/08/2020,-7.44974E+17,0.000278325,1,775.27936 +8016,S11T; R122Q; D211V,0.06942923,4.99352,26/08/2020,-7.44974E+17,0.066912495,1,80.419876 +8017,S11T; R137P; S220T; N289Y; Y290F; M291T,0.003915064,1.6859846,26/08/2020,-7.44974E+17,0.001656828,1,714.18854 +8018,S11T; S220T,0.49700883,5.50444,26/08/2020,-7.44974E+17,0.591235512,1,5.5509405 +8019,S11T; S57P; G87D; P88S; A174V; S220T,0.002381115,0.9090884,26/08/2020,-7.44974E+17,0.000341414,1,818.40247 +8020,S11T; V15A; P45L; V250A; L269M,0.004908043,1.9953067,26/08/2020,-7.44974E+17,0.002480463,1,632.3968 +8021,S11T; V66A; A140E; A169G; S220T; V293D,0.00289915,1.2266947,26/08/2020,-7.44974E+17,0.00083476,1,735.5567 +8022,S11T; V66M; E108G; Y186H; S220T; V276D,0.003226016,1.3281628,26/08/2020,-7.44974E+17,0.000963474,1,734.5584 +8023,S11V,0.07816209,5.319874,26/08/2020,-7.44974E+17,0.075731851,2,68.816635 +8024,S11W,0.069763675,6.8854027,26/08/2020,-7.44974E+17,0.069837456,4,95.51814 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S220T,0.003854501,1.7193255,26/08/2020,-7.44974E+17,0.001752739,1,692.2573 +8047,S145T; D170V; M185L,0.003092364,1.2476836,26/08/2020,-7.44974E+17,0.000806906,1,759.284 +8048,S145T; L151Q; D152G; L182Q; Q194R,0.00768084,2.6564975,26/08/2020,-7.44974E+17,0.004944806,1,496.1107 +8049,S145T; L161P; T196S; A213T; F217S,0.002412674,1.0202874,26/08/2020,-7.44974E+17,0.000515273,1,818.4486 +8050,S145T; N251S,0.003010945,1.2519547,26/08/2020,-7.44974E+17,0.000828431,1,753.31433 +8051,S145T; P171L; N178I; S205C; S220T; H271L; V281D,0.004233548,1.7895535,26/08/2020,-7.44974E+17,0.00200965,1,679.74677 +8052,S145T; S220T; A277S,0.003669958,1.5524919,26/08/2020,-7.44974E+17,0.001430501,1,723.8817 +8053,S145T; S220T; M273K; V276D; H285R,0.002922492,1.2109603,26/08/2020,-7.44974E+17,0.000780078,1,769.19275 +8054,S145T; S226T,0.003078244,1.3098856,26/08/2020,-7.44974E+17,0.000996161,1,747.90094 +8055,S145T; T179I; V199M; W216L; S220T; V281F,0.003459863,1.4280771,26/08/2020,-7.44974E+17,0.001172658,1,725.2314 +8056,S145V,0.009171522,2.9865284,26/08/2020,-7.44974E+17,0.00653081,1,443.08563 +8057,S145W,0.001460289,0.79686475,26/08/2020,-7.44974E+17,0.000318204,2,906.11115 +8058,S145W; A149T,0.002352775,0.87247527,26/08/2020,-7.44974E+17,0.000359478,1,770.1993 +8059,S145Y,0.001672745,1.0374715,26/08/2020,-7.44974E+17,0.000596614,2,937.0377 +8060,S160C,0.22364321,1.451728,26/08/2020,-7.44974E+17,0.230861443,8,5.3348 +8061,S160D,0.41996416,1.1795644,26/08/2020,-7.44974E+17,0.492392526,7,1.6748272 +8062,S160D; A296I,0.7037708,6.8756247,26/08/2020,-7.44974E+17,0.743010032,4,2.7346518 +8063,S160D; L189M; A218M,0.5360728,5.368534,26/08/2020,-7.44974E+17,0.583827508,2,4.5871105 +8064,S160D; L189M; A296I,0.5930024,4.4452195,26/08/2020,-7.44974E+17,0.608327792,6,2.9975133 +8065,S160D; T188G,0.46745458,1.609834,26/08/2020,-7.44974E+17,0.470630302,4,1.8522283 +8066,S160D; T188G; A296I,0.6964097,6.6275687,26/08/2020,-7.44974E+17,0.733858202,4,2.7465827 +8067,S160D; T188G; L189M,0.32475817,1.1243593,26/08/2020,-7.44974E+17,0.337967774,8,2.4634802 +8068,S160D; T188G; N238Y,0.4589705,2.01362,26/08/2020,-7.44974E+17,0.453129456,10,2.391747 +8069,S160D; T188S; A296I,0.7229024,9.713719,26/08/2020,-7.44974E+17,0.737568911,10,3.6489334 +8070,S160D; W216R; A218L,0.73130625,8.042348,26/08/2020,-7.44974E+17,0.746411546,14,2.900555 +8071,S160D; Y177W; A218L,0.7586915,6.148848,26/08/2020,-7.44974E+17,0.837659294,2,1.6469738 +8072,S160D; Y177W; T188G,0.4438236,1.3564954,26/08/2020,-7.44974E+17,0.456128447,4,1.7277539 +8073,S160E,0.3190135,2.7305398,26/08/2020,-7.44974E+17,0.315265668,8,5.9793515 +8074,S160F,0.003897333,2.452892,26/08/2020,-7.44974E+17,0.00328706,4,702.2408 +8075,S160G,0.19308919,5.219693,26/08/2020,-7.44974E+17,0.192743613,2,23.535864 +8076,S160H,0.30519417,8.313034,26/08/2020,-7.44974E+17,0.308231873,4,19.29013 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+8098,S160V,0.21577929,6.5374784,26/08/2020,-7.44974E+17,0.222890177,2,25.357073 +8099,S160W; G203C; S220T; R274L,0.003443799,1.5060946,26/08/2020,-7.44974E+17,0.001303981,1,747.9771 +8100,S160Y,0.004117559,1.6979308,26/08/2020,-7.44974E+17,0.001739453,1,687.5927 +8101,S201A,0.035432003,4.5808697,26/08/2020,-7.44974E+17,0.03291948,1,156.71031 +8102,S201F; D211G; S220T,0.003230477,1.3917642,26/08/2020,-7.44974E+17,0.001102352,1,763.50507 +8103,S201F; S220T; D287G; P300Q,0.003623708,1.4755025,26/08/2020,-7.44974E+17,0.00118222,1,705.75165 +8104,S201T; E261D; E295V,0.014671733,3.741926,26/08/2020,-7.44974E+17,0.01203411,1,330.21738 +8105,S201T; E261Q,0.43556988,2.6481209,26/08/2020,-7.44974E+17,0.436972545,42,3.4409125 +8106,S201Y; S220T,0.002812093,1.5033501,26/08/2020,-7.44974E+17,0.001657207,2,728.38513 +8107,S201Y; S220T; V293A,0.002746199,1.093871,26/08/2020,-7.44974E+17,0.000585066,1,803.6317 +8108,S205A,0.12690316,5.170568,26/08/2020,-7.44974E+17,0.128250738,1,42.12835 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+8119,S205E,0.086641714,7.5987005,26/08/2020,-7.44974E+17,0.086482654,4,82.96528 +8120,S205F,0.0358248,7.234003,26/08/2020,-7.44974E+17,0.035194628,5,200.82866 +8121,S205G,0.12703182,3.286673,26/08/2020,-7.44974E+17,0.124534627,3,24.810163 +8122,S205G; S220T; K236R,0.23035827,5.3312755,26/08/2020,-7.44974E+17,0.241085272,1,20.289547 +8123,S205H,0.0446478,1.1892731,26/08/2020,-7.44974E+17,0.045452379,4,30.749973 +8124,S205I,0.044019546,6.333557,26/08/2020,-7.44974E+17,0.042530196,4,144.00972 +8125,S205I; D211E; R247S; P270L; A282S,0.003589936,1.5924115,26/08/2020,-7.44974E+17,0.001442633,1,710.46594 +8126,S205K,0.14942956,4.136873,26/08/2020,-7.44974E+17,0.154913993,2,26.059687 +8127,S205L,0.09372806,5.596282,26/08/2020,-7.44974E+17,0.093003552,5,56.081112 +8128,S205M,0.09403791,4.983099,26/08/2020,-7.44974E+17,0.094521691,1,56.407776 +8129,S205N,0.050857503,5.104888,26/08/2020,-7.44974E+17,0.048171016,3,102.2711 +8130,S205N; S220T,0.6096328,5.717055,26/08/2020,-7.44974E+17,0.726861199,1,3.374489 +8131,S205N; Y239N; A260V,0.002937763,1.212692,26/08/2020,-7.44974E+17,0.000901426,1,777.4411 +8132,S205P,0.15506779,5.1830306,26/08/2020,-7.44974E+17,0.155993954,1,33.29501 +8133,S205Q,0.03975486,4.536967,26/08/2020,-7.44974E+17,0.037205723,1,138.44531 +8134,S205R,0.10104367,4.9981537,26/08/2020,-7.44974E+17,0.102004614,1,52.998608 +8135,S205R; H230Y,0.18242477,0.7122831,26/08/2020,-7.44974E+17,0.140239266,10,3.496725 +8136,S205R; S220T; A223V; M248I; E254V,0.003451422,1.4871118,26/08/2020,-7.44974E+17,0.001351994,1,737.21234 +8137,S205R; S220T; G237C; A252G; Q263H; F297I,0.002689633,1.0804217,26/08/2020,-7.44974E+17,0.00058306,1,775.4512 +8138,S205T; A206D; S220T; S225T,0.004071402,1.7231965,26/08/2020,-7.44974E+17,0.00158951,1,700.5108 +8139,S205T; E261Q,0.41939935,3.0488224,26/08/2020,-7.44974E+17,0.412971757,36,4.233518 +8140,S205T; S220T,0.6115054,5.24878,26/08/2020,-7.44974E+17,0.667590251,2,3.1503992 +8141,S205V,0.07137169,5.030735,26/08/2020,-7.44974E+17,0.070566295,2,72.6534 +8142,S205W,0.006303296,4.23315,26/08/2020,-7.44974E+17,0.005891849,4,710.8583 +8143,S205Y,0.06356994,4.9681516,26/08/2020,-7.44974E+17,0.060504276,1,90.98317 +8144,S220A,0.14536193,4.9648504,26/08/2020,-7.44974E+17,0.156450368,1,33.65132 +8145,S220A; A229S,0.21356903,2.7272966,26/08/2020,-7.44974E+17,0.221218926,2,11.392527 +8146,S220C,0.194195,6.3160996,26/08/2020,-7.44974E+17,0.199052338,2,27.99333 +8147,S220D,0.09695105,5.104921,26/08/2020,-7.44974E+17,0.096977946,1,57.325676 +8148,S220E,0.12574609,5.0233374,26/08/2020,-7.44974E+17,0.133830977,1,40.85148 +8149,S220F,0.073058546,4.9036484,26/08/2020,-7.44974E+17,0.072140725,2,69.06596 +8150,S220G,0.13130184,4.7837763,26/08/2020,-7.44974E+17,0.129026226,2,34.9161 +8151,S220H,0.21278082,5.6589484,26/08/2020,-7.44974E+17,0.2148337,5,21.55914 +8152,S220I,0.11355615,5.1899724,26/08/2020,-7.44974E+17,0.11103642,1,47.992954 +8153,S220K,0.106143594,4.5281467,26/08/2020,-7.44974E+17,0.106280556,2,42.005573 +8154,S220L,0.1686326,6.550505,26/08/2020,-7.44974E+17,0.171690035,2,34.543633 +8155,S220M,0.1868492,7.587732,26/08/2020,-7.44974E+17,0.189107655,5,33.89109 +8156,S220N,0.32600343,5.795597,26/08/2020,-7.44974E+17,0.339104511,2,12.648225 +8157,S220P,0.094749324,5.0030737,26/08/2020,-7.44974E+17,0.095025779,1,57.382782 +8158,S220P; A228V,0.050778985,4.884208,26/08/2020,-7.44974E+17,0.047074819,1,111.68928 +8159,S220P; L227M; V250A; L253P,0.002269638,0.82838005,26/08/2020,-7.44974E+17,0.000274793,1,757.7481 +8160,S220Q,0.13603318,4.7730856,26/08/2020,-7.44974E+17,0.138550915,5,31.624947 +8161,S220R,0.053098995,5.365135,26/08/2020,-7.44974E+17,0.051977495,2,105.148155 +8162,S220T,0.6777918,5.3288846,26/08/2020,-7.44974E+17,0.697401341,685,2.5324886 +8163,S220T; A223E; A280D,0.003497571,1.5390314,26/08/2020,-7.44974E+17,0.001391972,1,741.70233 +8164,S220T; A223T; F297I,0.0427873,4.6648083,26/08/2020,-7.44974E+17,0.041789072,1,127.962654 +8165,S220T; A223V; A252V; E268K; V276L; E278D,0.003820621,1.6383101,26/08/2020,-7.44974E+17,0.001691697,1,710.8313 +8166,S220T; A228T,0.18548562,5.2968893,26/08/2020,-7.44974E+17,0.193171235,1,26.6478 +8167,S220T; A228V; H230Y,0.6183131,6.953012,26/08/2020,-7.44974E+17,0.686266789,2,4.0699153 +8168,S220T; A228V; Q283K,0.03642335,4.581447,26/08/2020,-7.44974E+17,0.033727002,1,153.00212 +8169,S220T; A229S; M248L,0.002413255,0.9020289,26/08/2020,-7.44974E+17,0.000336768,1,781.2293 +8170,S220T; A229V,0.43158665,4.1875725,26/08/2020,-7.44974E+17,0.45297567,2,5.6329646 +8171,S220T; A231D; E254G; A282V,0.010660195,3.1575487,26/08/2020,-7.44974E+17,0.008471374,1,396.6481 +8172,S220T; A231D; L234P,0.002559528,1.0514627,26/08/2020,-7.44974E+17,0.000544089,1,798.7648 +8173,S220T; A231G; P240L; L243M,0.019460527,3.9930663,26/08/2020,-7.44974E+17,0.0169376,1,261.0255 +8174,S220T; A231V; G237C; P300L,0.004516693,1.9649314,26/08/2020,-7.44974E+17,0.002289622,1,661.5327 +8175,S220T; A252S,0.15156454,5.153825,26/08/2020,-7.44974E+17,0.157683712,1,33.609875 +8176,S220T; A252V,0.09292915,6.3812733,26/08/2020,-7.44974E+17,0.090448811,2,67.312645 +8177,S220T; A257T,0.43484786,5.567489,26/08/2020,-7.44974E+17,0.499080431,1,7.4645863 +8178,S220T; A259V; L272P,0.00276751,1.2090442,26/08/2020,-7.44974E+17,0.00071931,1,797.5108 +8179,S220T; A260P,0.002720872,1.1114904,26/08/2020,-7.44974E+17,0.000594649,1,764.44617 +8180,S220T; A260T,0.5375409,5.76865,26/08/2020,-7.44974E+17,0.632352096,1,4.7840123 +8181,S220T; A260V,0.0683431,5.4004703,26/08/2020,-7.44974E+17,0.066487888,2,81.411766 +8182,S220T; A277D,0.002952749,1.3169028,26/08/2020,-7.44974E+17,0.001077575,1,795.0569 +8183,S220T; A277S,0.16563742,5.263683,26/08/2020,-7.44974E+17,0.17248531,1,30.88003 +8184,S220T; A280D,0.002686889,1.0931224,26/08/2020,-7.44974E+17,0.000541878,1,789.7828 +8185,S220T; A280V,0.5869432,5.704308,26/08/2020,-7.44974E+17,0.711141989,1,3.7297401 +8186,S220T; A282T; G286E,0.2209325,5.245122,26/08/2020,-7.44974E+17,0.237520784,1,20.867088 +8187,S220T; A303S,0.62730294,4.473914,26/08/2020,-7.44974E+17,0.683195932,2,2.4555602 +8188,S220T; A303T,0.64747715,5.8951783,26/08/2020,-7.44974E+17,0.765624138,1,2.8532944 +8189,S220T; D233A,0.46508583,5.4454513,26/08/2020,-7.44974E+17,0.536985217,1,6.400378 +8190,S220T; D233N,0.63386256,5.314456,26/08/2020,-7.44974E+17,0.690148896,2,2.8220444 +8191,S220T; D233V,0.068163,5.8453636,26/08/2020,-7.44974E+17,0.066213311,2,87.59991 +8192,S220T; D233V; N289T,0.003888333,1.5728414,26/08/2020,-7.44974E+17,0.001474887,1,706.98254 +8193,S220T; D235G; Y239C,0.003349186,1.4252634,26/08/2020,-7.44974E+17,0.001193323,1,724.0745 +8194,S220T; D235N,0.20794943,5.2533593,26/08/2020,-7.44974E+17,0.219139985,1,22.778887 +8195,S220T; D235V,0.09586597,5.1061873,26/08/2020,-7.44974E+17,0.094328158,1,57.86098 +8196,S220T; D235V; E254V,0.25867474,5.2491426,26/08/2020,-7.44974E+17,0.280050891,1,16.736965 +8197,S220T; D287E,0.6049292,5.475516,26/08/2020,-7.44974E+17,0.661350383,2,3.4102466 +8198,S220T; D287G,0.5810035,5.6240516,26/08/2020,-7.44974E+17,0.712613322,1,3.8212736 +8199,S220T; D287V,0.49910915,5.6442714,26/08/2020,-7.44974E+17,0.577982138,1,5.688745 +8200,S220T; D287V; S292T,0.003331313,1.4509155,26/08/2020,-7.44974E+17,0.001197518,1,750.0415 +8201,S220T; E254D; S267P,0.008388883,2.857632,26/08/2020,-7.44974E+17,0.005547759,1,490.99753 +8202,S220T; E254G,0.4803829,5.5450215,26/08/2020,-7.44974E+17,0.563735401,1,6.1474323 +8203,S220T; E254G; V293D,0.001922939,0.68601805,26/08/2020,-7.44974E+17,0.000180555,1,786.81647 +8204,S220T; E254K,0.6433905,5.819048,26/08/2020,-7.44974E+17,0.778262983,1,2.8304346 +8205,S220T; E254V,0.5320711,5.7033896,26/08/2020,-7.44974E+17,0.621801432,1,4.8568416 +8206,S220T; E261D,0.60490465,5.83297,26/08/2020,-7.44974E+17,0.72070317,1,3.504794 +8207,S220T; E261G,0.49921054,5.642752,26/08/2020,-7.44974E+17,0.537191177,2,5.6182675 +8208,S220T; E261G; H266R; N289I; S292T,0.002593461,1.0262214,26/08/2020,-7.44974E+17,0.000483949,1,801.8519 +8209,S220T; E261K,0.5401052,5.8947525,26/08/2020,-7.44974E+17,0.624361593,1,4.872439 +8210,S220T; E262D; A303T,0.36817637,5.5292106,26/08/2020,-7.44974E+17,0.41129281,1,10.154832 +8211,S220T; E262D; M291K,0.003920605,1.658586,26/08/2020,-7.44974E+17,0.00154387,1,701.72205 +8212,S220T; E262G; V281A,0.09444182,5.0755353,26/08/2020,-7.44974E+17,0.092776599,1,58.089783 +8213,S220T; E262K,0.026019802,4.148086,26/08/2020,-7.44974E+17,0.024215715,1,199.11287 +8214,S220T; E262V,0.14071928,5.09914,26/08/2020,-7.44974E+17,0.150631441,1,36.35127 +8215,S220T; E268D,0.29537266,5.4288607,26/08/2020,-7.44974E+17,0.320890836,1,14.338271 +8216,S220T; E268D; G286E,0.25184637,5.224322,26/08/2020,-7.44974E+17,0.275169962,1,17.658808 +8217,S220T; E275G,0.5141948,5.650785,26/08/2020,-7.44974E+17,0.597766304,1,5.31733 +8218,S220T; E278D,0.6090159,5.8755946,26/08/2020,-7.44974E+17,0.728973563,1,3.4612534 +8219,S220T; E278G,0.47776088,5.8409657,26/08/2020,-7.44974E+17,0.519979275,2,6.4386344 +8220,S220T; E295V,0.04719012,4.746758,26/08/2020,-7.44974E+17,0.04403423,1,117.76152 +8221,S220T; F297C,0.014060945,3.5425556,26/08/2020,-7.44974E+17,0.011305013,1,331.2937 +8222,S220T; F297L,0.4934644,5.7212834,26/08/2020,-7.44974E+17,0.572677069,1,5.984543 +8223,S220T; F297S,0.003846679,1.6537714,26/08/2020,-7.44974E+17,0.001677009,1,727.7754 +8224,S220T; F297Y,0.048831735,4.6556935,26/08/2020,-7.44974E+17,0.046446288,1,112.44523 +8225,S220T; G237C,0.52280426,3.4270175,26/08/2020,-7.44974E+17,0.560510841,2,3.0572798 +8226,S220T; G237D; R258P; V293D,0.005396616,2.1384852,26/08/2020,-7.44974E+17,0.002842,1,621.4084 +8227,S220T; G237S; H266D,0.073783636,4.913982,26/08/2020,-7.44974E+17,0.072166279,1,74.91101 +8228,S220T; G237S; H271Q,0.40032816,5.5968723,26/08/2020,-7.44974E+17,0.450526146,1,8.771936 +8229,S220T; G241R,0.012042249,3.403108,26/08/2020,-7.44974E+17,0.009649771,1,364.57648 +8230,S220T; G244D,0.06664364,4.830291,26/08/2020,-7.44974E+17,0.066183018,1,81.95971 +8231,S220T; G244D; R258C,0.027476834,4.252817,26/08/2020,-7.44974E+17,0.025207596,1,192.12544 +8232,S220T; G244S,0.024839558,4.1177607,26/08/2020,-7.44974E+17,0.023463075,1,203.62315 +8233,S220T; G244V; L246V; L253P,0.003234947,1.389274,26/08/2020,-7.44974E+17,0.000986195,1,737.63074 +8234,S220T; G264V,0.3086318,5.398519,26/08/2020,-7.44974E+17,0.337232257,1,13.352096 +8235,S220T; G284A,0.057143264,4.8637056,26/08/2020,-7.44974E+17,0.054455522,1,97.53443 +8236,S220T; G284R,0.046361033,4.7319965,26/08/2020,-7.44974E+17,0.043475653,1,119.85094 +8237,S220T; G284S,0.045116846,4.676868,26/08/2020,-7.44974E+17,0.04308092,1,122.503426 +8238,S220T; G284V; Q299H,0.002658229,1.1773895,26/08/2020,-7.44974E+17,0.000674225,1,782.4498 +8239,S220T; G286E,0.46368906,1.3852855,26/08/2020,-7.44974E+17,0.488404234,2,1.6307836 +8240,S220T; H230L; E262G; V265A; H285R; P300A,0.002802344,1.2296737,26/08/2020,-7.44974E+17,0.000869182,1,767.43427 +8241,S220T; H230L; G286E; S292P,0.002984053,1.240472,26/08/2020,-7.44974E+17,0.000839307,1,779.2965 +8242,S220T; H230N,0.5423091,5.7955303,26/08/2020,-7.44974E+17,0.628316107,1,4.7829165 +8243,S220T; H230Q,0.59089446,4.37384,26/08/2020,-7.44974E+17,0.65228541,2,2.8904486 +8244,S220T; H230R,0.51372933,5.7600226,26/08/2020,-7.44974E+17,0.59549505,1,5.305817 +8245,S220T; H230Y,0.6497537,1.5233736,26/08/2020,-7.44974E+17,0.740998816,593,0.8204924 +8246,S220T; H230Y; A280V,0.7284436,14.747415,26/08/2020,-7.44974E+17,0.744930888,11,5.4246564 +8247,S220T; H230Y; D287V,0.4159942,2.4393115,26/08/2020,-7.44974E+17,0.408976392,10,3.4728904 +8248,S220T; H230Y; E261G,0.5885525,5.9485617,26/08/2020,-7.44974E+17,0.692570866,1,3.8063354 +8249,S220T; H230Y; G237D,0.519814,5.685492,26/08/2020,-7.44974E+17,0.614545439,1,5.1874804 +8250,S220T; H230Y; G244S,0.07158101,4.9785476,26/08/2020,-7.44974E+17,0.068355941,1,77.07818 +8251,S220T; H230Y; G286R,0.58910114,5.632348,26/08/2020,-7.44974E+17,0.705638691,1,3.6316266 +8252,S220T; H230Y; H271P,0.005922935,2.3213122,26/08/2020,-7.44974E+17,0.003453373,1,577.67487 +8253,S220T; H230Y; K236R,0.6160739,5.617572,26/08/2020,-7.44974E+17,0.759832367,1,3.1723773 +8254,S220T; H230Y; L243M,0.6213234,5.699168,26/08/2020,-7.44974E+17,0.751201846,1,3.1883302 +8255,S220T; H230Y; L253Q,0.11560484,5.09599,26/08/2020,-7.44974E+17,0.116263555,1,46.04749 +8256,S220T; H230Y; L269M,0.627537,5.922759,26/08/2020,-7.44974E+17,0.748100773,1,3.1572049 +8257,S220T; H230Y; L272Q; K298R,0.081883945,4.9687753,26/08/2020,-7.44974E+17,0.079739699,1,67.97392 +8258,S220T; H230Y; M273K,0.002631309,1.0469704,26/08/2020,-7.44974E+17,0.000495493,1,768.0679 +8259,S220T; H230Y; N289K,0.2966381,5.2543707,26/08/2020,-7.44974E+17,0.331477035,1,13.83756 +8260,S220T; H230Y; P300L,0.67648023,6.058688,26/08/2020,-7.44974E+17,0.8159806,1,2.4973998 +8261,S220T; H230Y; P302L,0.6352266,5.835229,26/08/2020,-7.44974E+17,0.767157141,1,2.9625032 +8262,S220T; H230Y; P302Q,0.63926196,5.7977023,26/08/2020,-7.44974E+17,0.761025196,1,2.9329445 +8263,S220T; H230Y; Q283L,0.63641864,5.875068,26/08/2020,-7.44974E+17,0.767406514,1,3.0869899 +8264,S220T; H230Y; Q299K,0.6046488,1.3406447,26/08/2020,-7.44974E+17,0.726078013,10,0.8577869 +8265,S220T; H230Y; S267T,0.6401012,6.003465,26/08/2020,-7.44974E+17,0.762586066,1,2.9636264 +8266,S220T; H230Y; S301N,0.69038314,10.993806,26/08/2020,-7.44974E+17,0.704351859,10,4.8585634 +8267,S220T; H230Y; T245I; R279L,0.006998142,2.6601863,26/08/2020,-7.44974E+17,0.004775268,1,535.113 +8268,S220T; H230Y; V250A,0.62653756,5.7422867,26/08/2020,-7.44974E+17,0.756363418,1,3.0826986 +8269,S220T; H230Y; V250E,0.24690507,5.4956756,26/08/2020,-7.44974E+17,0.260091121,1,18.85869 +8270,S220T; H230Y; V293F,0.61222583,5.9126835,26/08/2020,-7.44974E+17,0.71913984,1,3.4591947 +8271,S220T; H255D,0.39734855,5.3600698,26/08/2020,-7.44974E+17,0.455277658,1,8.523825 +8272,S220T; H255R; V293F,0.002685883,1.0614567,26/08/2020,-7.44974E+17,0.000527657,1,760.29156 +8273,S220T; H266F,0.07336778,4.996064,26/08/2020,-7.44974E+17,0.071491132,1,77.444405 +8274,S220T; H266P,0.10989224,5.073032,26/08/2020,-7.44974E+17,0.113295526,1,48.848152 +8275,S220T; H266R,0.036885925,5.0992646,26/08/2020,-7.44974E+17,0.034746291,2,148.84952 +8276,S220T; H266Y; S267T; G286E,0.003489888,1.4524784,26/08/2020,-7.44974E+17,0.001093241,1,766.5452 +8277,S220T; H271Y,0.5446731,5.6021347,26/08/2020,-7.44974E+17,0.646659453,1,4.5475383 +8278,S220T; H285R,0.47518104,5.557747,26/08/2020,-7.44974E+17,0.549273611,1,6.2419624 +8279,S220T; H285Y; Q299H,0.4109892,5.681268,26/08/2020,-7.44974E+17,0.442825205,1,8.496332 +8280,S220T; H288L,0.115171134,5.213492,26/08/2020,-7.44974E+17,0.114874638,1,47.388035 +8281,S220T; H288R,0.2853071,5.401811,26/08/2020,-7.44974E+17,0.306714955,1,14.939031 +8282,S220T; I256N,0.003403954,2.1162522,26/08/2020,-7.44974E+17,0.002487117,3,740.80316 +8283,S220T; I256T; Y290C,0.002629179,1.1955855,26/08/2020,-7.44974E+17,0.000683447,1,783.6856 +8284,S220T; I256V,0.3395077,5.4729223,26/08/2020,-7.44974E+17,0.374487284,1,11.705634 +8285,S220T; K236E,0.6400399,5.585634,26/08/2020,-7.44974E+17,0.777626194,1,2.8488185 +8286,S220T; K236E; M291T,0.024239765,4.191016,26/08/2020,-7.44974E+17,0.020878446,1,213.34572 +8287,S220T; K236I,0.54281634,3.1144202,26/08/2020,-7.44974E+17,0.575684696,2,2.547913 +8288,S220T; K236I; A252T; R279H; N289K,0.003013022,1.2627082,26/08/2020,-7.44974E+17,0.000795893,1,769.3079 +8289,S220T; K236I; S292F,0.008986698,2.9341896,26/08/2020,-7.44974E+17,0.00663022,1,456.2282 +8290,S220T; K236N,0.4763612,5.5852985,26/08/2020,-7.44974E+17,0.552051117,1,6.2300096 +8291,S220T; K236R,0.578761,5.9768453,26/08/2020,-7.44974E+17,0.682869785,1,3.9711154 +8292,S220T; K236T; A252D; H271Y,0.003159987,1.3541727,26/08/2020,-7.44974E+17,0.001085655,1,784.6608 +8293,S220T; K298M,0.08541552,5.1125846,26/08/2020,-7.44974E+17,0.083572157,1,66.37073 +8294,S220T; K298R,0.25863048,5.2625046,26/08/2020,-7.44974E+17,0.281384776,1,17.088953 +8295,S220T; L222P,0.027096855,5.1889834,26/08/2020,-7.44974E+17,0.025652976,2,205.17848 +8296,S220T; L222P; A228T,0.007350251,2.6597357,26/08/2020,-7.44974E+17,0.005133672,1,521.0511 +8297,S220T; L222Q; K298R,0.01271166,3.4616902,26/08/2020,-7.44974E+17,0.009940473,1,367.111 +8298,S220T; L222Q; Q263L; S301G; A303S,0.00287552,1.203338,26/08/2020,-7.44974E+17,0.000706804,1,746.0989 +8299,S220T; L227M,0.66781217,6.8175807,26/08/2020,-7.44974E+17,0.742593096,2,3.1488369 +8300,S220T; L227M; A280G,0.17819084,5.180972,26/08/2020,-7.44974E+17,0.185742466,1,27.618818 +8301,S220T; L227M; N238S,0.32356167,5.40061,26/08/2020,-7.44974E+17,0.356959937,1,12.40506 +8302,S220T; L227M; Q263L,0.18589377,5.2608886,26/08/2020,-7.44974E+17,0.1920296,1,26.836763 +8303,S220T; L227P,0.012652672,4.21777,26/08/2020,-7.44974E+17,0.011353893,2,374.78928 +8304,S220T; L227P; Q283H,0.00400141,1.7145219,26/08/2020,-7.44974E+17,0.001775766,1,697.6432 +8305,S220T; L227Q,0.10727127,5.7949166,26/08/2020,-7.44974E+17,0.106853156,2,52.364517 +8306,S220T; L227Q; P302S,0.033407006,4.511209,26/08/2020,-7.44974E+17,0.030211962,1,167.1144 +8307,S220T; L234H,0.003267442,1.9421455,26/08/2020,-7.44974E+17,0.00190625,2,800.65 +8308,S220T; L234H; E268V,0.004980474,2.0205665,26/08/2020,-7.44974E+17,0.002555076,1,625.2819 +8309,S220T; L234H; I256T,0.002522718,0.9683102,26/08/2020,-7.44974E+17,0.000402916,1,771.66565 +8310,S220T; L234R,0.003588035,1.4611298,26/08/2020,-7.44974E+17,0.001156276,1,705.9175 +8311,S220T; L234V,0.05966575,4.8255897,26/08/2020,-7.44974E+17,0.057389997,1,92.56699 +8312,S220T; L243M,0.54152894,5.111938,26/08/2020,-7.44974E+17,0.585966755,2,4.223217 +8313,S220T; L243P; E262G,0.010967303,3.2409062,26/08/2020,-7.44974E+17,0.009131095,1,383.77405 +8314,S220T; L243Q,0.65077686,7.1136003,26/08/2020,-7.44974E+17,0.713615577,2,3.5898106 +8315,S220T; L243Q; L253P,0.002829019,1.1979266,26/08/2020,-7.44974E+17,0.000723776,1,797.37573 +8316,S220T; L246H,0.003370551,1.5064718,26/08/2020,-7.44974E+17,0.001311657,1,755.5197 +8317,S220T; L246H; M248K; E261G,0.002828368,1.1950457,26/08/2020,-7.44974E+17,0.000715229,1,790.628 +8318,S220T; L246P; E254D,0.003178525,1.3325609,26/08/2020,-7.44974E+17,0.000917748,1,747.95154 +8319,S220T; L246P; R247P,0.00316027,1.3802408,26/08/2020,-7.44974E+17,0.001020819,1,758.05035 +8320,S220T; L253P,0.002970471,1.2853954,26/08/2020,-7.44974E+17,0.000885745,1,748.60846 +8321,S220T; L253R,0.002344028,0.9761827,26/08/2020,-7.44974E+17,0.000446805,1,790.3991 +8322,S220T; L269M,0.5036501,6.638358,26/08/2020,-7.44974E+17,0.541212485,2,6.522886 +8323,S220T; L269S,0.3501194,5.4326215,26/08/2020,-7.44974E+17,0.393021666,1,10.922557 +8324,S220T; L272Q,0.07347245,4.939344,26/08/2020,-7.44974E+17,0.072025982,1,74.56464 +8325,S220T; L272R; A282G,0.048129402,4.716918,26/08/2020,-7.44974E+17,0.044990651,1,114.88554 +8326,S220T; M248I; A260T,0.004106111,1.7387629,26/08/2020,-7.44974E+17,0.001696342,1,727.153 +8327,S220T; M273I; A277P; A280V; K298R,0.003421943,1.5143924,26/08/2020,-7.44974E+17,0.001152098,1,760.20483 +8328,S220T; M273K,0.003461494,1.4614086,26/08/2020,-7.44974E+17,0.001184781,1,775.1156 +8329,S220T; M273K; P300L,0.004057331,1.7303212,26/08/2020,-7.44974E+17,0.001745087,1,686.56573 +8330,S220T; M273V; A280V,0.002901998,1.2280902,26/08/2020,-7.44974E+17,0.000857922,1,759.179 +8331,S220T; M273V; H285Q; F297L,0.002564189,1.0673765,26/08/2020,-7.44974E+17,0.000612016,1,794.8114 +8332,S220T; M291I,0.2986937,5.355439,26/08/2020,-7.44974E+17,0.330537546,1,13.766602 +8333,S220T; M291L; S292F,0.008272848,3.6180093,26/08/2020,-7.44974E+17,0.007078151,2,507.13583 +8334,S220T; M291T,0.057500605,4.9007187,26/08/2020,-7.44974E+17,0.054371581,1,97.795876 +8335,S220T; N238D,0.64787686,5.9342785,26/08/2020,-7.44974E+17,0.77322656,1,2.857764 +8336,S220T; N238D; G241C,0.025129905,4.2551713,26/08/2020,-7.44974E+17,0.022208883,1,210.6195 +8337,S220T; N238D; V250M; A257S,0.024332235,4.302858,26/08/2020,-7.44974E+17,0.021292637,1,219.49112 +8338,S220T; N238I,0.5468659,7.7890887,26/08/2020,-7.44974E+17,0.57462603,3,6.388677 +8339,S220T; N249H,0.31185198,5.245282,26/08/2020,-7.44974E+17,0.349801248,1,12.778255 +8340,S220T; N249I,0.016147437,3.3210738,26/08/2020,-7.44974E+17,0.014619771,2,235.87398 +8341,S220T; N249K,0.002792833,1.2347169,26/08/2020,-7.44974E+17,0.000770459,1,783.51733 +8342,S220T; N249S; P270Q; A282V,0.007473683,2.734235,26/08/2020,-7.44974E+17,0.005288446,1,515.0626 +8343,S220T; N249Y,0.007911561,2.7422037,26/08/2020,-7.44974E+17,0.005349718,1,481.29214 +8344,S220T; N251I; F297Y,0.015338332,3.696112,26/08/2020,-7.44974E+17,0.012485936,1,316.7083 +8345,S220T; N289D,0.1086244,5.13415,26/08/2020,-7.44974E+17,0.105735839,1,50.414326 +8346,S220T; N289D; A296V,0.41291597,5.4732275,26/08/2020,-7.44974E+17,0.475824931,1,8.132462 +8347,S220T; P224H,0.16978897,5.211853,26/08/2020,-7.44974E+17,0.176153323,1,29.912676 +8348,S220T; P224L,0.048777726,4.686101,26/08/2020,-7.44974E+17,0.047305299,1,110.99371 +8349,S220T; P240L,0.3210492,2.3907325,26/08/2020,-7.44974E+17,0.332571445,2,5.542687 +8350,S220T; P240Q,0.4822268,5.4915957,26/08/2020,-7.44974E+17,0.563198899,1,5.892978 +8351,S220T; P240Q; V276I,0.1706496,5.2755113,26/08/2020,-7.44974E+17,0.177805212,1,29.188412 +8352,S220T; P240R; R274L,0.09380355,4.9183383,26/08/2020,-7.44974E+17,0.094144088,1,57.256947 +8353,S220T; P270L,0.2656716,5.208993,26/08/2020,-7.44974E+17,0.293792212,1,16.135586 +8354,S220T; P300R,0.6553991,5.775476,26/08/2020,-7.44974E+17,0.799192846,1,2.6167045 +8355,S220T; P302Q,0.5544027,5.7882648,26/08/2020,-7.44974E+17,0.661986533,1,4.46663 +8356,S220T; P302R,0.61148906,5.766094,26/08/2020,-7.44974E+17,0.724217251,1,3.3795683 +8357,S220T; Q263H,0.07408449,4.8952427,26/08/2020,-7.44974E+17,0.073657623,1,73.44423 +8358,S220T; Q263P,0.002534109,0.97348607,26/08/2020,-7.44974E+17,0.000413437,1,766.18024 +8359,S220T; Q283E,0.619393,5.88907,26/08/2020,-7.44974E+17,0.733133441,1,3.2636843 +8360,S220T; Q283E; D287G; F297L,0.08447513,5.05946,26/08/2020,-7.44974E+17,0.08162929,1,66.4206 +8361,S220T; Q283H,0.4870073,5.7312093,26/08/2020,-7.44974E+17,0.567154338,1,6.068839 +8362,S220T; Q299E,0.56346035,5.6673045,26/08/2020,-7.44974E+17,0.683990936,1,4.1457396 +8363,S220T; Q299H,0.5139618,5.7978344,26/08/2020,-7.44974E+17,0.59049176,1,5.3980856 +8364,S220T; Q299L,0.6179498,5.860931,26/08/2020,-7.44974E+17,0.731523023,1,3.3107564 +8365,S220T; R247C,0.6007374,5.7933893,26/08/2020,-7.44974E+17,0.718900361,1,3.6107955 +8366,S220T; R247H; S292T; Q299R,0.007037901,2.6231813,26/08/2020,-7.44974E+17,0.004831137,1,513.49976 +8367,S220T; R247P; E262K,0.003256244,1.3817858,26/08/2020,-7.44974E+17,0.001123897,1,739.176 +8368,S220T; R247P; F297S,0.003142059,1.3355339,26/08/2020,-7.44974E+17,0.000982491,1,762.01715 +8369,S220T; R258C,0.516964,1.8095835,26/08/2020,-7.44974E+17,0.559467907,4,1.6587089 +8370,S220T; R258H,0.23888221,5.401589,26/08/2020,-7.44974E+17,0.254101079,1,19.495659 +8371,S220T; R258H; A280S; D287E,0.11958656,5.0197687,26/08/2020,-7.44974E+17,0.125263975,1,43.90043 +8372,S220T; R258S; E295G,0.005347563,2.1924145,26/08/2020,-7.44974E+17,0.003432048,1,602.4441 +8373,S220T; R274C,0.5643223,5.8184023,26/08/2020,-7.44974E+17,0.658777839,1,4.2797995 +8374,S220T; R274C; V281D,0.016718118,3.7903457,26/08/2020,-7.44974E+17,0.014065027,1,293.95804 +8375,S220T; R274G,0.06708448,5.041972,26/08/2020,-7.44974E+17,0.064252793,1,83.68779 +8376,S220T; R279C,0.6478883,6.9337897,26/08/2020,-7.44974E+17,0.719456637,2,3.518054 +8377,S220T; R279H; H285R,0.010068836,3.091877,26/08/2020,-7.44974E+17,0.00820772,1,411.03677 +8378,S220T; S226C,0.58608794,5.6148677,26/08/2020,-7.44974E+17,0.703202143,1,3.7496724 +8379,S220T; S226F,0.49231324,2.6154451,26/08/2020,-7.44974E+17,0.510798196,3,2.6909466 +8380,S220T; S226F; Q283H,0.078378856,5.0129824,26/08/2020,-7.44974E+17,0.076272734,1,71.13647 +8381,S220T; S226P,0.36519292,5.481441,26/08/2020,-7.44974E+17,0.408900002,1,10.3183155 +8382,S220T; S226P; L253P,0.003006364,1.2685847,26/08/2020,-7.44974E+17,0.000924964,1,763.3318 +8383,S220T; S226T,0.67150015,6.1907926,26/08/2020,-7.44974E+17,0.730842525,3,2.8703196 +8384,S220T; S226T; V265E,0.002661349,1.0033196,26/08/2020,-7.44974E+17,0.000480905,1,766.0311 +8385,S220T; S226Y; L234P,0.003255885,1.3654417,26/08/2020,-7.44974E+17,0.001012906,1,783.6803 +8386,S220T; S267L,0.007900762,2.810247,26/08/2020,-7.44974E+17,0.005498532,1,492.38452 +8387,S220T; S267P,0.006734499,2.5836463,26/08/2020,-7.44974E+17,0.004336808,1,540.90546 +8388,S220T; S267T,0.5371327,5.5732327,26/08/2020,-7.44974E+17,0.645026461,1,4.6861777 +8389,S220T; S267T; Q283R,0.3489174,5.357162,26/08/2020,-7.44974E+17,0.393725065,1,10.771062 +8390,S220T; S292A,0.53268975,5.524016,26/08/2020,-7.44974E+17,0.629936432,1,4.7125607 +8391,S220T; S292C,0.22837542,5.1984,26/08/2020,-7.44974E+17,0.245305048,1,20.096127 +8392,V293D,0.001008975,0.8161957,26/08/2020,-7.44974E+17,0.000450522,4,1060.5492 +8393,S220T; S292F; Q299P,0.002755697,1.191999,26/08/2020,-7.44974E+17,0.000793218,1,773.5911 +8394,S220T; S304P,0.66133904,5.84609,26/08/2020,-7.44974E+17,0.802162631,1,2.6385586 +8395,S220T; T242A; N249S; R274L,0.04210267,4.699578,26/08/2020,-7.44974E+17,0.039344544,1,133.69579 +8396,S220T; T242I,0.61764884,5.668945,26/08/2020,-7.44974E+17,0.752506806,1,3.1751776 +8397,S220T; T242I; E262G; A282V; G284R,0.002663428,1.1182759,26/08/2020,-7.44974E+17,0.00068342,1,780.30066 +8398,S220T; T245R,0.23136316,5.2520328,26/08/2020,-7.44974E+17,0.249030476,1,19.472801 +8399,S220T; T245R; E261D; H271R,0.13690348,5.2693405,26/08/2020,-7.44974E+17,0.134067928,1,39.169098 +8400,S220T; V221A,0.1583704,4.6696014,26/08/2020,-7.44974E+17,0.163651872,3,26.430878 +8401,S220T; V221A; H255L,0.004905777,2.0431166,26/08/2020,-7.44974E+17,0.002645407,1,613.418 +8402,S220T; V221L; H230Y,0.46684644,5.437479,26/08/2020,-7.44974E+17,0.548131889,1,6.3134265 +8403,S220T; V221M,0.29068038,5.1296406,26/08/2020,-7.44974E+17,0.297828844,2,13.227358 +8404,S220T; V221M; S225P; L227M,0.35554573,5.5643315,26/08/2020,-7.44974E+17,0.396786914,1,10.997555 +8405,S220T; V232A,0.61826444,6.7274942,26/08/2020,-7.44974E+17,0.686152602,2,3.9390144 +8406,S220T; V232I,0.6177831,5.8585176,26/08/2020,-7.44974E+17,0.730365299,1,3.3333204 +8407,S220T; V250A,0.5945272,4.495465,26/08/2020,-7.44974E+17,0.650709566,2,2.8937435 +8408,S220T; V250E,0.03388599,4.448381,26/08/2020,-7.44974E+17,0.032277165,1,156.81584 +8409,S220T; V265A; E278V,0.003854322,1.7388117,26/08/2020,-7.44974E+17,0.001890389,1,689.7375 +8410,S220T; V265G,0.002779824,1.1314279,26/08/2020,-7.44974E+17,0.000655285,1,772.32904 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+8434,S225T,0.15975207,5.991432,26/08/2020,-7.44974E+17,0.164006874,2,33.95972 +8435,S225T; L227P; A228V,0.012107863,3.384039,26/08/2020,-7.44974E+17,0.009887385,1,368.04715 +8436,S225T; M291R,0.003203695,1.3990071,26/08/2020,-7.44974E+17,0.0010354,1,779.9551 +8437,S225V,0.0878977,6.77045,26/08/2020,-7.44974E+17,0.086206771,3,74.124855 +8438,S225W,0.057997838,4.7351055,26/08/2020,-7.44974E+17,0.054932983,2,86.60527 +8439,S225W; A231S,0.029362228,4.406128,26/08/2020,-7.44974E+17,0.027158732,1,186.06337 +8440,S226A,0.06134164,5.283073,26/08/2020,-7.44974E+17,0.059240186,2,89.42162 +8441,S226C,0.10285683,2.9621363,26/08/2020,-7.44974E+17,0.103408529,5,27.627842 +8442,S226D,0.21228018,5.3931713,26/08/2020,-7.44974E+17,0.217692082,1,22.92601 +8443,S226E,0.23450014,5.6880155,26/08/2020,-7.44974E+17,0.238331993,4,19.200876 +8444,S226F,0.15458243,4.097047,26/08/2020,-7.44974E+17,0.15605087,2,24.954 +8445,S226F; H230I,0.046104286,4.6643963,26/08/2020,-7.44974E+17,0.044247883,1,119.41157 +8446,S226F; H230S,0.08364914,5.0041046,26/08/2020,-7.44974E+17,0.083567651,1,65.426926 +8447,S226G,0.10119229,5.8158436,26/08/2020,-7.44974E+17,0.101008058,3,54.764534 +8448,S226H,0.107928045,6.3274293,26/08/2020,-7.44974E+17,0.106197734,2,56.88382 +8449,S226I,0.07607146,4.9445443,26/08/2020,-7.44974E+17,0.074143132,1,72.955475 +8450,S226K,0.1538137,5.1336484,26/08/2020,-7.44974E+17,0.157334683,1,33.035675 +8451,S226L,0.20795669,2.6500468,26/08/2020,-7.44974E+17,0.20649068,3,11.124707 +8452,S226M,0.17764902,5.1500216,26/08/2020,-7.44974E+17,0.190998057,1,27.521988 +8453,S226N,0.1825221,3.6093159,26/08/2020,-7.44974E+17,0.181650949,3,17.457275 +8454,S226N; N251D,0.01927792,3.927289,26/08/2020,-7.44974E+17,0.016847557,1,256.40485 +8455,S226P,0.033792228,4.345716,26/08/2020,-7.44974E+17,0.031984402,4,132.68932 +8456,S226P; A252V; P270R; E295G,0.004887695,2.0520372,26/08/2020,-7.44974E+17,0.002776178,1,656.79565 +8457,S226R,0.022339836,4.1569257,26/08/2020,-7.44974E+17,0.019831137,1,236.59042 +8458,S226T,0.17074841,4.397123,26/08/2020,-7.44974E+17,0.171611643,2,23.23877 +8459,S226V,0.10441696,6.091154,26/08/2020,-7.44974E+17,0.105042649,3,55.15651 +8460,S226W,0.07112701,4.315951,26/08/2020,-7.44974E+17,0.070986804,2,63.64271 +8461,S226Y,0.1780929,4.4658866,26/08/2020,-7.44974E+17,0.180417974,2,22.607912 +8462,S267A,0.049964197,7.0856752,26/08/2020,-7.44974E+17,0.049500229,3,142.28598 +8463,S267C,0.04299023,5.741,26/08/2020,-7.44974E+17,0.041521003,2,141.5626 +8464,S267D,0.039832607,4.3913198,26/08/2020,-7.44974E+17,0.03866336,2,118.42802 +8465,S267E,0.00377812,2.1547737,26/08/2020,-7.44974E+17,0.002478122,2,738.1396 +8466,S267F,0.023090977,5.0955453,26/08/2020,-7.44974E+17,0.021970643,2,240.22638 +8467,S267G,0.0073808,4.090835,26/08/2020,-7.44974E+17,0.006462315,3,601.9759 +8468,S267H,0.064537786,4.9226093,26/08/2020,-7.44974E+17,0.062321502,1,86.67952 +8469,S267I,0.001980629,1.2185297,26/08/2020,-7.44974E+17,0.000941801,2,910.7211 +8470,S267L; R274H,0.004102044,1.7459323,26/08/2020,-7.44974E+17,0.00182428,1,665.9376 +8471,S267M,0.007376221,2.9154277,26/08/2020,-7.44974E+17,0.006066323,2,468.103 +8472,S267N,0.043112554,5.002151,26/08/2020,-7.44974E+17,0.041520408,2,124.05001 +8473,S267P,0.003034816,1.2823765,26/08/2020,-7.44974E+17,0.000844728,1,773.83527 +8474,S267P; F297Y,0.005513587,2.18709,26/08/2020,-7.44974E+17,0.002984898,1,605.248 +8475,S267Q,0.01809987,4.6859727,26/08/2020,-7.44974E+17,0.017027335,2,286.53952 +8476,S267T,0.058481183,4.77025,26/08/2020,-7.44974E+17,0.057908974,4,81.26868 +8477,S267V,0.007951911,3.1850064,26/08/2020,-7.44974E+17,0.007418389,4,432.70987 +8478,S267W,0.031269345,5.547937,26/08/2020,-7.44974E+17,0.030801152,3,183.65158 +8479,S292F,0.007919274,2.7928333,26/08/2020,-7.44974E+17,0.005721806,1,480.8499 +8480,S292G,0.008424319,2.9402895,26/08/2020,-7.44974E+17,0.00603914,1,467.39243 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Y239F,0.010245592,3.1655645,26/08/2020,-7.44974E+17,0.008083813,1,411.877 +8538,S49C; V150M,0.08120869,5.0394235,26/08/2020,-7.44974E+17,0.078701994,1,69.2285 +8539,S49E,0.11180939,2.5930219,26/08/2020,-7.44974E+17,0.11115859,3,23.358892 +8540,S49F,0.06072483,8.531772,26/08/2020,-7.44974E+17,0.060600653,4,136.2505 +8541,S49G,0.025271432,1.6061295,26/08/2020,-7.44974E+17,0.024925679,5,70.68715 +8542,S49G; A187G; L227P,0.002830388,1.2336652,26/08/2020,-7.44974E+17,0.000878747,1,762.08417 +8543,S49G; C75G; N100K; T121M; D163N; L167H; Y186F; N190D; H255Y; K298R,0.003630148,1.5094925,26/08/2020,-7.44974E+17,0.001388552,1,718.90515 +8544,S49G; D163E; S220T,0.50309837,5.5899563,26/08/2020,-7.44974E+17,0.584112284,1,5.54472 +8545,S49G; E84D; F297L,0.041422505,4.664043,26/08/2020,-7.44974E+17,0.038464652,1,133.22044 +8546,S49G; R148P; A169E; S220T; H230Y,0.76594704,15.6996765,26/08/2020,-7.44974E+17,0.782415565,10,4.7145967 +8547,S49G; S220T,0.5089316,5.677304,26/08/2020,-7.44974E+17,0.590140519,1,5.4284925 +8548,S49G; S69Y; E108G; L198M; T207S; S220T; H266Q,0.00245694,0.97405607,26/08/2020,-7.44974E+17,0.000433106,1,765.4432 +8549,S49H,0.0737788,0.66432905,26/08/2020,-7.44974E+17,0.076862716,6,10.327427 +8550,S49I,0.07807573,6.069074,26/08/2020,-7.44974E+17,0.076622443,2,77.82196 +8551,S49I; A218Q,0.058636416,4.8807545,26/08/2020,-7.44974E+17,0.055849786,1,95.0342 +8552,S49I; G104V; F142S,0.002751836,1.1087018,26/08/2020,-7.44974E+17,0.000581299,1,768.89746 +8553,S49I; G146D; S220T,0.00258624,1.0253391,26/08/2020,-7.44974E+17,0.000484623,1,759.4734 +8554,S49I; L143S; V199L,0.004332576,1.8177791,26/08/2020,-7.44974E+17,0.0020922,1,678.5084 +8555,S49I; R148P; S220T; H230Y,0.48157996,1.1644938,26/08/2020,-7.44974E+17,0.575735409,10,1.2594403 +8556,S49I; S220T; H230Y; H255Q,0.067632884,4.988939,26/08/2020,-7.44974E+17,0.064733873,1,82.185135 +8557,S49K,0.054487485,5.593154,26/08/2020,-7.44974E+17,0.052116517,2,106.42443 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S220T,0.003062753,1.2223765,26/08/2020,-7.44974E+17,0.000753591,1,784.27167 +8569,S49R; A156T; V175K,0.695938,7.8620977,26/08/2020,-7.44974E+17,0.702879562,20,3.4005384 +8570,S49R; A169V; E261G,0.029662786,4.3843503,26/08/2020,-7.44974E+17,0.028054039,1,180.16005 +8571,S49R; A187V; S220T,0.05432149,4.816022,26/08/2020,-7.44974E+17,0.052403339,1,101.76675 +8572,S49R; A53S,0.5006736,4.58055,26/08/2020,-7.44974E+17,0.506870047,21,4.565695 +8573,S49R; A53S; A156T,0.425398,3.8386662,26/08/2020,-7.44974E+17,0.430702531,19,5.2038693 +8574,S49R; A53S; A228V,0.33272523,3.6837986,26/08/2020,-7.44974E+17,0.333182109,6,7.564754 +8575,S49R; A53S; A80T,0.5526743,7.187286,26/08/2020,-7.44974E+17,0.556887781,31,5.810245 +8576,S49R; A53S; E91D,0.51328045,6.359839,26/08/2020,-7.44974E+17,0.52831488,5,6.0246572 +8577,S49R; A53S; E91V,0.3090682,4.6233087,26/08/2020,-7.44974E+17,0.312562535,23,10.39307 +8578,S49R; A53S; T242A,0.31789628,4.2428694,26/08/2020,-7.44974E+17,0.32162312,11,9.2191925 +8579,S49R; 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A282V,0.004735674,2.0433729,26/08/2020,-7.44974E+17,0.002504687,1,662.79315 +8622,S57P; Q107K; V175A; Y192F; S220T,0.020792231,3.9609063,26/08/2020,-7.44974E+17,0.018243433,1,238.69626 +8623,S57P; R165H; S220T,0.030574353,4.417615,26/08/2020,-7.44974E+17,0.027288713,1,180.14629 +8624,S57P; R94H; P219H; D233V; H266P,0.00377883,1.6393191,26/08/2020,-7.44974E+17,0.001589869,1,706.71344 +8625,S57P; T157P; D170V; S220T,0.0027136,1.2069284,26/08/2020,-7.44974E+17,0.00075474,1,806.7153 +8626,S57P; V130E; N249Y; N289I,0.00202318,0.63568777,26/08/2020,-7.44974E+17,0.000134466,1,748.73596 +8627,S57P; V135G; D139E; V141I,0.002765619,1.145713,26/08/2020,-7.44974E+17,0.000622965,1,791.13354 +8628,S57P; Y186C; S220T,0.002412889,0.9703581,26/08/2020,-7.44974E+17,0.000438486,1,759.30035 +8629,S57Q,0.15797983,5.110316,26/08/2020,-7.44974E+17,0.163451966,1,32.255585 +8630,S57R,0.113639176,3.5763485,26/08/2020,-7.44974E+17,0.115463319,2,31.830101 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D233V,0.003184352,1.2948071,26/08/2020,-7.44974E+17,0.000933559,1,765.8334 +8642,S57T; R96H; P131T; G215V; L222R,0.002365259,0.89558357,26/08/2020,-7.44974E+17,0.000317886,1,785.4916 +8643,S57T; S103T; V113E; S220P; M248V; D287N; E295G,0.003113556,1.3737797,26/08/2020,-7.44974E+17,0.001023249,1,753.59937 +8644,S57T; S220T,0.5704104,5.6802435,26/08/2020,-7.44974E+17,0.68440385,1,4.0348964 +8645,S57T; S67R; Y177D; S225P,0.00307142,1.3223625,26/08/2020,-7.44974E+17,0.0009645,1,745.37573 +8646,S57T; T157S; S220T; S267P; Y290N,0.002014261,0.68168217,26/08/2020,-7.44974E+17,0.000142317,1,735.06757 +8647,S57T; T245A,0.015022676,3.684186,26/08/2020,-7.44974E+17,0.01255072,1,316.1827 +8648,S57T; V175K; Y177W,0.80380243,16.012072,26/08/2020,-7.44974E+17,0.822858697,12,3.8327403 +8649,S57T; Y83N; V204L; M248K,0.002460719,0.9474604,26/08/2020,-7.44974E+17,0.000396992,1,757.9247 +8650,S57V,0.2892947,6.0383153,26/08/2020,-7.44974E+17,0.303945527,2,15.631485 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N289I,0.002501683,0.95745796,26/08/2020,-7.44974E+17,0.000449442,1,765.90344 +8664,S61L; A89P; P106L; E254S,0.009309198,3.0359268,26/08/2020,-7.44974E+17,0.006807232,1,440.16714 +8665,S61L; G87C; E153V; S220T,0.005006963,2.101217,26/08/2020,-7.44974E+17,0.002577879,1,662.53485 +8666,S61L; M82I; T121A; L182P,0.002033341,0.7642056,26/08/2020,-7.44974E+17,0.000221951,1,820.2387 +8667,S61M,0.04565261,3.451547,26/08/2020,-7.44974E+17,0.043912447,2,83.22578 +8668,S61N,0.24927606,4.089841,26/08/2020,-7.44974E+17,0.258036997,2,13.383255 +8669,S61P,0.007243391,1.8006986,26/08/2020,-7.44974E+17,0.006437344,5,278.91592 +8670,S61P; A174T; S220T,0.003052967,1.3243246,26/08/2020,-7.44974E+17,0.000940875,1,759.244 +8671,S61P; D125V; D163V,0.002239585,0.81358254,26/08/2020,-7.44974E+17,0.000254991,1,805.9531 +8672,S61P; F142Y; S220T; A223V,0.002338083,0.9878556,26/08/2020,-7.44974E+17,0.000498649,1,815.361 +8673,S61P; Q107H; L124H; L151P; Y177F; A282P; E295K,0.002195892,0.7965378,26/08/2020,-7.44974E+17,0.00025384,1,773.75116 +8674,S61P; R137H; S220T; P240S; N251Y,0.002726905,1.0835273,26/08/2020,-7.44974E+17,0.000568044,1,763.427 +8675,S61P; S220T; Y239C,0.002748952,1.1767938,26/08/2020,-7.44974E+17,0.000759439,1,768.20074 +8676,S61Q,0.093944155,3.5147412,26/08/2020,-7.44974E+17,0.093795707,3,37.08272 +8677,S61R,0.12622237,6.35042,26/08/2020,-7.44974E+17,0.12807091,2,47.426357 +8678,S61T,0.2541692,6.4688883,26/08/2020,-7.44974E+17,0.2658854,2,19.998718 +8679,S61T; A68V; T121S; D287E; A303T,0.0213418,4.0718126,26/08/2020,-7.44974E+17,0.019201033,1,232.77924 +8680,S61T; C75S; S220T,0.06515175,4.77807,26/08/2020,-7.44974E+17,0.065252371,1,83.727486 +8681,S61T; E261G,0.041320514,4.552861,26/08/2020,-7.44974E+17,0.039643448,1,129.89952 +8682,S61T; E84D; F86Y; S220T; H288L,0.010520584,3.219912,26/08/2020,-7.44974E+17,0.008026997,1,415.21475 +8683,S61T; L158H; Q194R,0.002021571,0.65376234,26/08/2020,-7.44974E+17,0.00013638,1,742.67725 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S220T,0.002561543,1.1327177,26/08/2020,-7.44974E+17,0.000624981,1,776.8481 +8695,S67C; N190D; S220T; T245A,0.004688643,1.9671704,26/08/2020,-7.44974E+17,0.002477089,1,630.6218 +8696,S67C; R148S; F166V; S220T; E262G,0.003032205,1.2779547,26/08/2020,-7.44974E+17,0.000958105,1,738.73663 +8697,S67C; V71L; T207A; S220T; I256N,0.004646922,1.9351195,26/08/2020,-7.44974E+17,0.002458754,1,638.4928 +8698,S67C; Y290N,0.003070088,1.3166599,26/08/2020,-7.44974E+17,0.000952999,1,745.14624 +8699,S67D,0.119423166,3.8284914,26/08/2020,-7.44974E+17,0.11930732,2,31.891233 +8700,S67E,0.18714839,5.2265143,26/08/2020,-7.44974E+17,0.196534673,1,26.505068 +8701,S67F,0.055720456,5.952731,26/08/2020,-7.44974E+17,0.054807979,2,110.35118 +8702,S67G,0.09422952,5.6229644,26/08/2020,-7.44974E+17,0.09107401,2,59.132545 +8703,S67G; F86C,0.03538718,4.4781804,26/08/2020,-7.44974E+17,0.033691049,1,150.96323 +8704,S67G; M82I; V135I; S220T; H230R; A282T,0.008483997,3.6452875,26/08/2020,-7.44974E+17,0.007431841,2,491.28665 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S220T,0.64836943,6.8313737,26/08/2020,-7.44974E+17,0.712706499,2,3.4313338 +8726,S67N; T81A; S103P; R137H; L214H; A252P,0.002280301,0.8793073,26/08/2020,-7.44974E+17,0.000326424,1,762.69934 +8727,S67P,0.002693808,2.3944502,26/08/2020,-7.44974E+17,0.002136969,4,993.6069 +8728,S67Q,0.20284986,3.5483587,26/08/2020,-7.44974E+17,0.205068058,4,14.817346 +8729,S67R,0.32710034,2.500922,26/08/2020,-7.44974E+17,0.332205478,2,5.6894145 +8730,S67R; P138R; N178Y; H230Q; K236I,0.002288007,0.8215339,26/08/2020,-7.44974E+17,0.000268749,1,778.22687 +8731,S67R; S103P; R110H; V150G; T188S; S220T; H266R,0.002918398,1.2353432,26/08/2020,-7.44974E+17,0.000769266,1,762.9174 +8732,S67R; Y79H; P138Q,0.010997231,3.2995636,26/08/2020,-7.44974E+17,0.008050377,1,403.7004 +8733,S67T,0.16265123,6.468426,26/08/2020,-7.44974E+17,0.163406752,2,35.456768 +8734,S67V,0.10013048,3.3748348,26/08/2020,-7.44974E+17,0.097783836,3,33.2779 +8735,S69A,0.118025616,2.7108781,26/08/2020,-7.44974E+17,0.117567503,3,22.549747 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D287E,0.002938044,1.1141657,26/08/2020,-7.44974E+17,0.00060712,1,757.9378 +8872,T105I; S220T; S292Y,0.003779887,1.607993,26/08/2020,-7.44974E+17,0.001553848,1,686.33124 +8873,T105K,0.003790973,1.6238649,26/08/2020,-7.44974E+17,0.001390038,1,732.17615 +8874,T105L,0.008666489,2.878471,26/08/2020,-7.44974E+17,0.007346328,2,393.77365 +8875,T105M,0.0448493,1.1107161,26/08/2020,-7.44974E+17,0.045373294,2,34.763847 +8876,T105N,0.003307464,1.3853712,26/08/2020,-7.44974E+17,0.00101859,1,710.7785 +8877,T105P,0.001585771,0.8721157,26/08/2020,-7.44974E+17,0.000534042,2,889.58386 +8878,T105P; F142Y; A180V; N251D; A252V,0.002897853,1.2329661,26/08/2020,-7.44974E+17,0.000846926,1,742.2209 +8879,T105Q,0.014506887,3.6083684,26/08/2020,-7.44974E+17,0.012350983,1,322.8125 +8880,T105Q; E108K,0.002483081,1.0129755,26/08/2020,-7.44974E+17,0.00051536,1,776.08813 +8881,T105R,0.011101795,3.3004434,26/08/2020,-7.44974E+17,0.008749032,1,393.9469 +8882,T105R; A169E,0.022280049,4.1221004,26/08/2020,-7.44974E+17,0.020218802,1,229.32384 +8883,T105R; S220T; S225P; L234I; K236Q; T245A; Q263R,0.003036627,1.2525977,26/08/2020,-7.44974E+17,0.000872986,1,778.32166 +8884,T105S,0.047459245,5.8020124,26/08/2020,-7.44974E+17,0.045263423,2,127.45157 +8885,T105S; A156T; N249K; V276G,0.003256649,1.3898277,26/08/2020,-7.44974E+17,0.001172388,1,733.40356 +8886,T105S; N238S,0.033646848,4.508788,26/08/2020,-7.44974E+17,0.030644824,1,161.33943 +8887,T105S; R148H; H183L; S220T,0.11142988,4.9919763,26/08/2020,-7.44974E+17,0.115867666,1,47.349457 +8888,T105S; W115C; S220T; M273T,0.003139991,1.3577046,26/08/2020,-7.44974E+17,0.000961172,1,759.3865 +8889,T105S; Y144N; S220T,0.003498257,1.5135282,26/08/2020,-7.44974E+17,0.0013355,1,741.566 +8890,T105V,0.027483871,5.3474293,26/08/2020,-7.44974E+17,0.026444983,2,209.34904 +8891,T105W,0.005016625,2.692897,26/08/2020,-7.44974E+17,0.003864582,2,653.93353 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N289Y,0.001896789,1.126035,26/08/2020,-7.44974E+17,0.00082382,2,874.9978 +8902,T10A; W41R; P164Q; S220T,0.003503684,1.468831,26/08/2020,-7.44974E+17,0.001094655,1,720.69366 +8903,T10C,0.11668192,5.1066318,26/08/2020,-7.44974E+17,0.118304808,1,45.55554 +8904,T10D,0.17100066,5.3375597,26/08/2020,-7.44974E+17,0.172651319,1,29.559353 +8905,T10E,0.13163665,6.121929,26/08/2020,-7.44974E+17,0.13526553,2,43.385914 +8906,T10F,0.11738611,6.0540347,26/08/2020,-7.44974E+17,0.116775265,2,49.26869 +8907,T10G,0.2565295,5.934696,26/08/2020,-7.44974E+17,0.265071183,2,18.260063 +8908,T10H,0.15388681,5.070659,26/08/2020,-7.44974E+17,0.156221931,2,30.649906 +8909,T10I,0.082443185,6.082949,26/08/2020,-7.44974E+17,0.082068452,2,73.77346 +8910,T10I; A116E; P131S; P164Q; V199A; S220T; V221M,0.002459548,1.0106694,26/08/2020,-7.44974E+17,0.00052255,1,797.3889 +8911,T10I; A80T; S114T; V130A; P240L; G241D; A277T,0.003594898,1.5080518,26/08/2020,-7.44974E+17,0.001269444,1,705.2088 +8912,T10I; E84V; S160P; S220T,0.003660021,1.5688373,26/08/2020,-7.44974E+17,0.001367233,1,718.6242 +8913,T10I; L253P,0.005329002,2.0701573,26/08/2020,-7.44974E+17,0.002645855,1,611.76764 +8914,T10I; L27H; A127V; S220T,0.012811375,3.4849777,26/08/2020,-7.44974E+17,0.010191551,1,354.01624 +8915,T10I; P22L; S69Y; N178I; S201T; S220T; K298E,0.002671227,1.1030967,26/08/2020,-7.44974E+17,0.000638575,1,778.9216 +8916,T10I; S49C; V109A; S220T,0.14058116,5.113762,26/08/2020,-7.44974E+17,0.143368133,1,37.134396 +8917,T10I; T38S; Y79C; D152E; H154L; T188I; T207N; V281F,0.007348985,2.6699045,26/08/2020,-7.44974E+17,0.004799456,1,523.17145 +8918,T10I; V26A; G146C; H266Y,0.003353345,1.4718711,26/08/2020,-7.44974E+17,0.001229844,1,741.07855 +8919,T10K,0.18491277,4.6471133,26/08/2020,-7.44974E+17,0.188607828,4,21.491713 +8920,T10K; A231T,0.06313157,4.8785753,26/08/2020,-7.44974E+17,0.060442768,1,88.15187 +8921,T10L,0.27918336,6.0701003,26/08/2020,-7.44974E+17,0.285584594,3,16.239025 +8922,T10M,0.12789746,1.2938559,26/08/2020,-7.44974E+17,0.132874368,3,10.905119 +8923,T10N,0.14842807,3.3386984,26/08/2020,-7.44974E+17,0.147569297,3,20.977884 +8924,T10N; K34N; T72S; T172R; L246F,0.002152372,0.69906116,26/08/2020,-7.44974E+17,0.00015119,1,788.77026 +8925,T10N; M23T; N42K; L101Q; D147V; P270R,0.011816383,3.3911562,26/08/2020,-7.44974E+17,0.00896314,1,382.80612 +8926,T10N; R110C; H266L; N289K,0.002546556,0.9849684,26/08/2020,-7.44974E+17,0.000408735,1,803.8094 +8927,T10N; R137C; A174V; V175E; S220T,0.003656075,1.581143,26/08/2020,-7.44974E+17,0.001648274,1,721.9096 +8928,T10N; S220T; G244V; A260P,0.002684624,1.0663822,26/08/2020,-7.44974E+17,0.000549388,1,769.07214 +8929,T10N; S61T; S220T; Y294N,0.005472504,2.1783366,26/08/2020,-7.44974E+17,0.002961769,1,616.6582 +8930,T10N; T44S; S57T; T121S; A280V; H288R,0.01981232,3.981239,26/08/2020,-7.44974E+17,0.017033106,1,256.31653 +8931,T10N; V113I; L158H; L167F; Q194E; P300L,0.004638386,2.0027165,26/08/2020,-7.44974E+17,0.002568146,1,646.5615 +8932,T10P,0.14085904,3.697836,26/08/2020,-7.44974E+17,0.14061061,2,25.751041 +8933,T10Q,0.22747613,3.8506067,26/08/2020,-7.44974E+17,0.232019578,2,14.348571 +8934,T10R,0.14413884,5.026905,26/08/2020,-7.44974E+17,0.144555313,4,31.308277 +8935,T10S; G24E; L51Q; S61T; M129K; P164R; N190Y; A202V,0.005439792,2.2186882,26/08/2020,-7.44974E+17,0.003198778,1,604.9413 +8936,T10S; P138S,0.13793899,0.437667,26/08/2020,-7.44974E+17,0.10047109,10,3.1888921 +8937,T10S; Q54L; A56T; S67T; D95V; T157S; G241V,0.002151198,0.74755317,26/08/2020,-7.44974E+17,0.000237486,1,743.8614 +8938,T10S; R137C; H154Q; S220T,0.16935776,5.326518,26/08/2020,-7.44974E+17,0.169897971,1,30.50827 +8939,T10S; V15D,0.003801617,1.6367472,26/08/2020,-7.44974E+17,0.001445062,1,704.901 +8940,T10S; V40D; N78Y; N178Y; A180D,0.00242929,0.9631874,26/08/2020,-7.44974E+17,0.00045397,1,804.5145 +8941,T10S; W41R; M74T; V99A; A218V; S220T,0.002644938,1.0497953,26/08/2020,-7.44974E+17,0.00054759,1,767.7368 +8942,T10S; Y83C; L193H,0.005474465,2.2238452,26/08/2020,-7.44974E+17,0.00328176,1,613.3424 +8943,T10V,0.13482873,3.9328501,26/08/2020,-7.44974E+17,0.133424121,3,26.9406 +8944,T10W,0.100989126,3.4096844,26/08/2020,-7.44974E+17,0.102455147,4,32.57848 +8945,T10Y,0.12963204,6.245306,26/08/2020,-7.44974E+17,0.129603341,2,44.95955 +8946,T121A,0.28101447,0.95493364,26/08/2020,-7.44974E+17,0.321512558,4,2.72675 +8947,T121A; A149T; S220T; G241S; P300T,0.067106605,4.961547,26/08/2020,-7.44974E+17,0.063699533,1,83.116264 +8948,T121A; A180T; G200V; A277D,0.002493346,0.9934459,26/08/2020,-7.44974E+17,0.000472165,1,785.09235 +8949,T121A; A259V,0.05158012,4.726224,26/08/2020,-7.44974E+17,0.049106222,1,107.84121 +8950,T121A; L181V; S220T; L243R; D287V,0.003049858,1.3625712,26/08/2020,-7.44974E+17,0.000995996,1,786.257 +8951,T121A; M185I; G203R; S220T; A229V,0.004003339,1.7483044,26/08/2020,-7.44974E+17,0.001705804,1,697.1512 +8952,T121A; P131L; S220T; L246P,0.002670581,1.1349163,26/08/2020,-7.44974E+17,0.00069094,1,765.48 +8953,T121A; R148P; S220T; H230Y,0.75721174,16.168652,26/08/2020,-7.44974E+17,0.775181659,10,5.107785 +8954,T121A; S220T,0.6188799,4.170511,26/08/2020,-7.44974E+17,0.666722956,2,2.4234283 +8955,T121C,0.3144441,1.3166891,26/08/2020,-7.44974E+17,0.355002498,3,3.2057621 +8956,T121D,0.010268872,0.7047969,26/08/2020,-7.44974E+17,0.009519725,7,84.43192 +8957,T121E,0.002804617,2.688093,26/08/2020,-7.44974E+17,0.00238719,6,1028.146 +8958,T121F,0.18771619,0.4830071,26/08/2020,-7.44974E+17,0.213525399,10,2.3753126 +8959,T121G,0.1613628,1.0150515,26/08/2020,-7.44974E+17,0.175898492,6,5.9696245 +8960,T121H,0.027484048,0.9151698,26/08/2020,-7.44974E+17,0.02768499,11,35.710026 +8961,T121I,0.21363337,0.4919721,26/08/2020,-7.44974E+17,0.255886413,8,2.0884295 +8962,T121K,0.003821203,2.176115,26/08/2020,-7.44974E+17,0.002978707,3,674.24567 +8963,T121K; D163V; L181S; N249Y; Q283L,0.002362622,0.8313865,26/08/2020,-7.44974E+17,0.000294824,1,783.31525 +8964,T121L,0.28429744,0.502716,26/08/2020,-7.44974E+17,0.359399038,7,1.4259006 +8965,T121M,0.23419216,0.49708372,26/08/2020,-7.44974E+17,0.270535663,8,1.8489366 +8966,T121M; A228T; D287V,0.019900482,3.9736843,26/08/2020,-7.44974E+17,0.01773521,1,245.66524 +8967,T121M; D125T,0.10646509,5.075724,26/08/2020,-7.44974E+17,0.106286973,1,51.412483 +8968,T121M; D170Y; V175L; S220T,0.002788328,1.139865,26/08/2020,-7.44974E+17,0.000671253,1,784.5661 +8969,T121M; L167P; L193P; S220T,0.006312152,2.4127302,26/08/2020,-7.44974E+17,0.003973495,1,576.78784 +8970,T121M; S220T,0.61396796,5.784251,26/08/2020,-7.44974E+17,0.736267725,1,3.3485732 +8971,T121M; S220T; L272Q,0.23132698,5.302573,26/08/2020,-7.44974E+17,0.251263179,1,19.912611 +8972,V97M,0.024734545,4.283319,26/08/2020,-7.44974E+17,0.021834727,1,213.86224 +8973,T121M; T188S; S220T; G244C; A280D,0.003675176,1.6079552,26/08/2020,-7.44974E+17,0.001551842,1,715.70197 +8974,T121N,0.22231501,0.7878676,26/08/2020,-7.44974E+17,0.254544396,5,3.2231052 +8975,T121P,0.012200014,0.7275604,26/08/2020,-7.44974E+17,0.011933387,4,82.1574 +8976,T121P; N190S; S220T,0.004816056,2.022805,26/08/2020,-7.44974E+17,0.002517821,1,657.0113 +8977,T121Q,0.026168749,4.2560663,26/08/2020,-7.44974E+17,0.025837775,5,166.6886 +8978,T121R,0.017980196,0.9434366,26/08/2020,-7.44974E+17,0.017207247,7,60.120438 +8979,T121R; N178Y; S220T; M291I,0.003093219,1.3535619,26/08/2020,-7.44974E+17,0.000955766,1,761.09174 +8980,T121S,0.25120997,0.8520859,26/08/2020,-7.44974E+17,0.289530131,6,2.8359654 +8981,T121S; A174E; L234P,0.003184088,1.3934934,26/08/2020,-7.44974E+17,0.001109584,1,756.87726 +8982,T121S; D147E; S220T; M273I,0.00542541,2.172113,26/08/2020,-7.44974E+17,0.003254382,1,604.84607 +8983,T121S; F142I; A231T; H288Q,0.006603579,2.484078,26/08/2020,-7.44974E+17,0.004206721,1,539.0875 +8984,T121S; G191R; S220T; H266Y,0.003286794,1.4348924,26/08/2020,-7.44974E+17,0.001112791,1,749.4129 +8985,T121S; G215S; S220T; M273K; V281G,0.003339591,1.3483258,26/08/2020,-7.44974E+17,0.000895424,1,764.5296 +8986,T121S; L134P; Q194H; T245S,0.002942995,1.3220711,26/08/2020,-7.44974E+17,0.000923584,1,757.4656 +8987,T121S; M129K; D170E; V293L,0.003471922,1.5140496,26/08/2020,-7.44974E+17,0.001239387,1,745.064 +8988,T121S; P131L; H183Y; V199E,0.002997072,1.2742392,26/08/2020,-7.44974E+17,0.00087397,1,759.9988 +8989,T121V,0.20610878,0.47305435,26/08/2020,-7.44974E+17,0.252868975,5,2.1972141 +8990,T121W,0.13539468,0.7162207,26/08/2020,-7.44974E+17,0.143493356,6,5.4608207 +8991,T121Y,0.08374977,0.6454212,26/08/2020,-7.44974E+17,0.087763516,10,8.041827 +8992,T157A,0.03864301,4.54848,26/08/2020,-7.44974E+17,0.036246588,1,141.44426 +8993,T157A; A169V; H183L; S220T,0.05302688,4.8718886,26/08/2020,-7.44974E+17,0.049258578,1,106.393074 +8994,T157A; A174G; Q194R; S220T,0.003402534,1.4859365,26/08/2020,-7.44974E+17,0.001331977,1,735.2181 +8995,T157A; D211Y; A223E,0.013424617,3.4632688,26/08/2020,-7.44974E+17,0.011185737,1,340.193 +8996,T157A; P171H; S220T,0.092630826,5.059421,26/08/2020,-7.44974E+17,0.091711898,1,58.80453 +8997,T157A; R274L,0.073173076,0.45903602,26/08/2020,-7.44974E+17,0.064384396,10,6.9477334 +8998,T157C,0.16578242,3.4148085,26/08/2020,-7.44974E+17,0.165762693,4,18.413733 +8999,T157D,0.00547332,2.9046137,26/08/2020,-7.44974E+17,0.004206929,2,643.0564 +9000,T157E,0.0829853,5.002773,26/08/2020,-7.44974E+17,0.080891127,1,66.86146 +9001,T157F,0.003130787,1.8885677,26/08/2020,-7.44974E+17,0.002003784,2,790.42 +9002,T157G,0.003634044,1.5214559,26/08/2020,-7.44974E+17,0.001409266,1,714.11993 +9003,T157H,0.002927418,2.2993348,26/08/2020,-7.44974E+17,0.002130632,3,909.2287 +9004,T157I; D163A,0.02362246,4.1826935,26/08/2020,-7.44974E+17,0.021310347,1,216.99164 +9005,T157I; L161Q; A282P,0.003056156,1.3599569,26/08/2020,-7.44974E+17,0.001052096,1,751.845 +9006,T157I; S220T,0.5468027,5.799685,26/08/2020,-7.44974E+17,0.63923492,1,4.6870008 +9007,T157I; S220T; H230Y,0.6189864,5.9298444,26/08/2020,-7.44974E+17,0.743116489,1,3.288272 +9008,T157I; S220T; S225P; L253Q,0.003787988,1.6497118,26/08/2020,-7.44974E+17,0.001756555,1,707.0935 +9009,T157I; T242A,0.19104558,5.31523,26/08/2020,-7.44974E+17,0.198619479,1,25.941645 +9010,T157I; V175E; M248I,0.002577464,1.0144928,26/08/2020,-7.44974E+17,0.000491206,1,792.95575 +9011,T157K,0.002709929,1.6482003,26/08/2020,-7.44974E+17,0.001394182,2,832.4327 +9012,T157M,0.23376144,3.3487265,26/08/2020,-7.44974E+17,0.234179922,10,11.235809 +9013,T157N,0.003808533,2.276293,26/08/2020,-7.44974E+17,0.002595786,2,747.70447 +9014,T157N; S220T,0.008523456,2.8395371,26/08/2020,-7.44974E+17,0.006058156,1,458.12885 +9015,T157P,0.06964032,4.731055,26/08/2020,-7.44974E+17,0.072664824,1,75.16682 +9016,T157Q,0.009603757,3.7242951,26/08/2020,-7.44974E+17,0.008447917,2,446.41522 +9017,T157R,0.001549052,1.421081,26/08/2020,-7.44974E+17,0.001065944,5,1063.9667 +9018,T157S,0.108702585,6.2345595,26/08/2020,-7.44974E+17,0.109858731,2,55.2254 +9019,T157S; G162E; L227P,0.002900042,1.223261,26/08/2020,-7.44974E+17,0.000742258,1,770.6012 +9020,T157S; G200R; S220T,0.5447727,5.793701,26/08/2020,-7.44974E+17,0.639319111,1,4.717438 +9021,T157S; G200V,0.008764258,2.9498975,26/08/2020,-7.44974E+17,0.006010769,1,475.17502 +9022,T157S; P219S; S220T; L246H; M248K,0.001986787,1.0282499,26/08/2020,-7.44974E+17,0.000891692,2,803.3417 +9023,T157S; S220T,0.23344953,6.327965,26/08/2020,-7.44974E+17,0.239428195,3,21.667969 +9024,T157S; S220T; M248T,0.004793454,1.9521856,26/08/2020,-7.44974E+17,0.002447906,1,654.4412 +9025,T157V,0.2090775,3.478011,26/08/2020,-7.44974E+17,0.213318817,7,13.60883 +9026,T157W,0.00563781,2.9364793,26/08/2020,-7.44974E+17,0.004480072,2,623.3707 +9027,T157Y,0.003741196,3.8231409,26/08/2020,-7.44974E+17,0.003235872,5,1081.4028 +9028,T16A,0.016912675,2.3430715,26/08/2020,-7.44974E+17,0.015971843,3,159.64091 +9029,T16A; A60D; D211N; S220T; P240T; L253P; D287V,0.00270378,1.0822443,26/08/2020,-7.44974E+17,0.000596685,1,746.2668 +9030,T16A; A65V; L181S,0.003185017,1.332332,26/08/2020,-7.44974E+17,0.000939768,1,769.9869 +9031,T16A; E262G; A296V,0.009393861,3.0189605,26/08/2020,-7.44974E+17,0.007007967,1,436.82758 +9032,T16A; E91V; A202V; A210T; K236N; E295D; P302T,0.00231429,0.85635626,26/08/2020,-7.44974E+17,0.000303341,1,785.7871 +9033,T16A; F142I; A257T,0.003269407,1.4500176,26/08/2020,-7.44974E+17,0.001191429,1,798.88654 +9034,T16A; L20H; A259V,0.00272477,1.1017592,26/08/2020,-7.44974E+17,0.000659341,1,778.50323 +9035,T16A; L51Q; S220T,0.003129778,1.3389031,26/08/2020,-7.44974E+17,0.001042835,1,749.97595 +9036,T16A; L58P,0.001840403,0.56882757,26/08/2020,-7.44974E+17,8.38E-05,1,761.772 +9037,T16A; P45H; F86S; S114T; R148P; S220T; H230Y; Q283L,0.002994695,1.2961133,26/08/2020,-7.44974E+17,0.000890879,1,759.57574 +9038,T16A; R165H; S220T; N289I,0.002752766,1.0962844,26/08/2020,-7.44974E+17,0.000615318,1,783.29565 +9039,T16A; T121M; S220T; T242I; E268V; P302R,0.004514171,1.9131163,26/08/2020,-7.44974E+17,0.002239694,1,666.79175 +9040,T16A; T63S,0.006809214,2.6327698,26/08/2020,-7.44974E+17,0.004610515,1,541.8811 +9041,T16C,0.019070508,4.8618093,26/08/2020,-7.44974E+17,0.018043461,2,279.83142 +9042,T16C; G21E,0.003451458,1.4722394,26/08/2020,-7.44974E+17,0.00124627,1,732.61285 +9043,T16D,0.001352483,1.3858117,26/08/2020,-7.44974E+17,0.000934952,6,1173.4744 +9044,T16F,0.00260654,0.97277623,26/08/2020,-7.44974E+17,0.000376219,1,771.325 +9045,T16G,0.00389421,2.2766888,26/08/2020,-7.44974E+17,0.002692068,2,750.0846 +9046,T16K; E153D; S220T; M248L,0.001990579,1.233718,26/08/2020,-7.44974E+17,0.000823666,2,922.62366 +9047,T16K; L161P; A202G; S220T; L246T,0.002880448,1.2779216,26/08/2020,-7.44974E+17,0.000935871,1,802.3386 +9048,T16K; Q54H; L98Q; A111G; P132Q; M185T,0.002778967,1.1611841,26/08/2020,-7.44974E+17,0.000678615,1,771.826 +9049,T16K; S220T,0.002959223,1.2682498,26/08/2020,-7.44974E+17,0.000905212,1,769.26904 +9050,T16K; S220T; S225T,0.001827318,1.1446596,26/08/2020,-7.44974E+17,0.000712474,2,911.6262 +9051,T16L,0.001854547,1.1580619,26/08/2020,-7.44974E+17,0.00072658,2,908.901 +9052,T16M; A174E; S220T; H230Y; K298E,0.002478044,1.0385331,26/08/2020,-7.44974E+17,0.00050071,1,790.4561 +9053,T16M; G87D; Q107H; S220T; A231T; L243M; L246P; Y294C,0.002756533,1.0887033,26/08/2020,-7.44974E+17,0.000606304,1,759.18726 +9054,T16M; L25P; S69P; S220T,0.003185015,1.4027506,26/08/2020,-7.44974E+17,0.001107732,1,804.4494 +9055,T16M; P70H; T121A; V232D; D233N; D287G,0.001794359,0.6809279,26/08/2020,-7.44974E+17,0.0001666,1,808.8987 +9056,T16M; R96C; G120S; R148P; S220T; H230Y,0.005040899,2.1189075,26/08/2020,-7.44974E+17,0.00280719,1,640.6547 +9057,T16M; S114L; R148P; A149T; S220T; L227M; H230Y; D233E,0.003239004,1.4170842,26/08/2020,-7.44974E+17,0.001229693,1,743.8613 +9058,T16M; S220T,0.002469996,0.9936025,26/08/2020,-7.44974E+17,0.000441528,1,754.95856 +9059,T16M; S220T; A277V,0.002801647,1.1628604,26/08/2020,-7.44974E+17,0.000716812,1,755.22797 +9060,T16M; S220T; L269S,0.003043139,1.3100343,26/08/2020,-7.44974E+17,0.000927867,1,765.51263 +9061,T16M; S220T; Q283L,0.002749957,1.1360488,26/08/2020,-7.44974E+17,0.000640981,1,786.29926 +9062,T16M; V26D; T105A; V150G; Q194H; L227Q; L246F,0.002155536,0.7463909,26/08/2020,-7.44974E+17,0.000186351,1,773.26294 +9063,T16M; V26D; T72N; S220T; N251K; L253P,0.003133253,1.2926136,26/08/2020,-7.44974E+17,0.000846301,1,733.7768 +9064,T16M; V30M; A60T; S220T,0.001793559,1.0497534,26/08/2020,-7.44974E+17,0.000664164,2,896.5292 +9065,T16N,0.006342591,2.4054682,26/08/2020,-7.44974E+17,0.003994042,1,566.6791 +9066,T16P,0.003386786,2.032387,26/08/2020,-7.44974E+17,0.00216634,2,767.3414 +9067,T16P; S117T; P138Q,0.00346844,1.5311946,26/08/2020,-7.44974E+17,0.001394855,1,755.9248 +9068,T16Q,0.001747949,1.4041889,26/08/2020,-7.44974E+17,0.000925322,3,1040.0045 +9069,T16R; E29D; Y123H; V141E; M291I,0.00293922,1.2170463,26/08/2020,-7.44974E+17,0.000723192,1,746.11676 +9070,T16R; L32H; L161Q; S220T; K236E; R247G; N249T; P300L,0.00424187,1.7756449,26/08/2020,-7.44974E+17,0.001832121,1,694.29224 +9071,T16R; L98M; S117A; D170E; T179I; S220T; D233V; A259V,0.002692479,1.1386012,26/08/2020,-7.44974E+17,0.000704138,1,803.0414 +9072,T16S,0.13242428,7.0157332,26/08/2020,-7.44974E+17,0.132749965,3,48.123775 +9073,T16S; A159T; S220T,0.46503955,5.5037637,26/08/2020,-7.44974E+17,0.530897537,1,6.593921 +9074,T16S; I62N; G146A; S220T,0.003551999,1.5102229,26/08/2020,-7.44974E+17,0.001218705,1,745.23553 +9075,T16S; L32F; R43H; S220T; A260T,0.003064143,1.2918873,26/08/2020,-7.44974E+17,0.000791092,1,751.54834 +9076,T16S; N42K; L58P; M82V; D163N; W216R; S220T; F297I,0.004741913,1.936508,26/08/2020,-7.44974E+17,0.00231119,1,660.3511 +9077,T16S; S114P; Q194L; A202V,0.006121287,2.3852737,26/08/2020,-7.44974E+17,0.003724006,1,557.65906 +9078,T16S; S220T; F297Y,0.052466627,4.609307,26/08/2020,-7.44974E+17,0.052989809,1,101.9699 +9079,T16S; V71L; Y239H,0.004591232,1.9473461,26/08/2020,-7.44974E+17,0.002360885,1,683.5754 +9080,T16S; Y79F; Y186F,0.002628801,1.1807495,26/08/2020,-7.44974E+17,0.000744736,1,785.05035 +9081,T16V,0.002609462,1.9451272,26/08/2020,-7.44974E+17,0.00181133,3,898.0835 +9082,T16W,0.002037891,1.0023754,26/08/2020,-7.44974E+17,0.00083658,2,761.89905 +9083,T16Y,0.001629033,0.94463956,26/08/2020,-7.44974E+17,0.000483493,2,898.80396 +9084,T172A; N190K; S220T,0.003158498,1.3898563,26/08/2020,-7.44974E+17,0.001089249,1,786.4143 +9085,T172I; A197T; S220T,0.029207718,4.4396987,26/08/2020,-7.44974E+17,0.026273822,1,185.18636 +9086,T172I; A206T; S220T,0.42379665,5.421081,26/08/2020,-7.44974E+17,0.488719834,1,7.762231 +9087,T172I; D233V,0.010477294,3.166874,26/08/2020,-7.44974E+17,0.00845676,1,404.07593 +9088,T172K,0.02828445,3.3746698,26/08/2020,-7.44974E+17,0.028576658,2,136.99576 +9089,T172P,0.051363096,4.804678,26/08/2020,-7.44974E+17,0.049327739,1,107.95447 +9090,T172Q,0.025126027,5.3461676,26/08/2020,-7.44974E+17,0.024091392,2,230.14072 +9091,T172R; L173Q,0.008870442,2.9363601,26/08/2020,-7.44974E+17,0.0063387,1,451.1966 +9092,T172S; L189I; G284S,0.007803441,2.7593906,26/08/2020,-7.44974E+17,0.005379104,1,487.2914 +9093,T172S; L198Q; P300S,0.007911467,2.8164546,26/08/2020,-7.44974E+17,0.005604567,1,475.02774 +9094,T172S; M185L,0.050586686,4.7830677,26/08/2020,-7.44974E+17,0.047761165,1,112.08604 +9095,T172S; S220T,0.47323596,5.588621,26/08/2020,-7.44974E+17,0.550388787,1,6.370877 +9096,T172S; S220T; H266R,0.016431725,3.8501675,26/08/2020,-7.44974E+17,0.013200988,1,302.64536 +9097,T172S; Y294C,0.005745798,2.2592216,26/08/2020,-7.44974E+17,0.003417131,1,591.7129 +9098,T172V,0.22151531,5.364764,26/08/2020,-7.44974E+17,0.228455507,1,21.690992 +9099,T172W,0.06278048,4.9218206,26/08/2020,-7.44974E+17,0.059369848,1,90.37274 +9100,T172Y,0.037648834,4.7200537,26/08/2020,-7.44974E+17,0.036199283,2,134.3996 +9101,T172Y; A180T,0.001687188,1.0128882,26/08/2020,-7.44974E+17,0.000551522,2,934.1352 +9102,T179A,0.08342757,6.9972873,26/08/2020,-7.44974E+17,0.083316643,4,79.85834 +9103,T179A; S220T,0.6073855,5.6693234,26/08/2020,-7.44974E+17,0.742579716,1,3.3966477 +9104,T179A; S220T; A223S,0.5793327,5.740201,26/08/2020,-7.44974E+17,0.682533047,1,3.9024315 +9105,T179C,0.19037572,6.16743,26/08/2020,-7.44974E+17,0.196821131,2,27.994135 +9106,T179C; M185I,0.033828735,4.51842,26/08/2020,-7.44974E+17,0.031305604,1,161.94125 +9107,T179E,0.006009417,2.400814,26/08/2020,-7.44974E+17,0.003934685,1,573.2524 +9108,T179F,0.002247954,1.5536033,26/08/2020,-7.44974E+17,0.001470864,3,875.20557 +9109,T179G,0.055125408,5.1053643,26/08/2020,-7.44974E+17,0.055020758,2,97.20186 +9110,T179H,0.006146367,2.4139261,26/08/2020,-7.44974E+17,0.003837612,1,557.58307 +9111,T179I,0.0798748,1.3325347,26/08/2020,-7.44974E+17,0.078855187,5,17.721352 +9112,T179I; H183R; S220T,0.002704845,1.1538138,26/08/2020,-7.44974E+17,0.000686385,1,802.4983 +9113,T179I; S220I,0.13471052,5.2384887,26/08/2020,-7.44974E+17,0.134643808,1,39.128185 +9114,T179I; S220T,0.46626925,5.589181,26/08/2020,-7.44974E+17,0.535525677,1,6.596128 +9115,T179L,0.03988896,4.556954,26/08/2020,-7.44974E+17,0.03830777,1,138.05473 +9116,T179M,0.017834425,3.9239647,26/08/2020,-7.44974E+17,0.015538315,1,279.6711 +9117,T179S; E208G; S220T,0.25091383,5.2342563,26/08/2020,-7.44974E+17,0.27722811,1,17.779367 +9118,T179S; H255L,0.007512229,2.6699905,26/08/2020,-7.44974E+17,0.005014057,1,497.09082 +9119,T179S; H255Q,0.034707803,4.4989395,26/08/2020,-7.44974E+17,0.032557657,1,154.95775 +9120,T179S; L181F; A187E; S205C; K236R,0.004681369,1.9981555,26/08/2020,-7.44974E+17,0.002458648,1,659.66925 +9121,T179W,0.001759628,1.0622104,26/08/2020,-7.44974E+17,0.000626075,2,914.5695 +9122,T179Y,0.00200337,1.2197262,26/08/2020,-7.44974E+17,0.000853142,2,894.90735 +9123,T188A,0.09745813,6.255289,26/08/2020,-7.44974E+17,0.098576059,2,62.75496 +9124,T188A; S220T,0.41502956,5.5677443,26/08/2020,-7.44974E+17,0.470622798,1,8.279497 +9125,T188C,0.19413567,5.372702,26/08/2020,-7.44974E+17,0.198539997,1,25.514389 +9126,T188D,0.06957492,4.830156,26/08/2020,-7.44974E+17,0.069217714,1,78.81956 +9127,T188E,0.09618624,5.0100675,26/08/2020,-7.44974E+17,0.095243633,1,56.55896 +9128,T188F,0.22219016,3.8868563,26/08/2020,-7.44974E+17,0.230057952,2,14.964651 +9129,T188G,0.16324233,0.3602743,26/08/2020,-7.44974E+17,0.172826477,7,2.23906 +9130,T188G; A218I; A228V,0.17635193,2.6307878,26/08/2020,-7.44974E+17,0.175659421,40,12.397955 +9131,T188G; A218L,0.481457,2.5480015,26/08/2020,-7.44974E+17,0.485835372,16,2.74696 +9132,T188G; A218L; A228V,0.37069237,2.9880893,26/08/2020,-7.44974E+17,0.3651188,21,5.1144 +9133,T188G; A218L; A296I,0.8143049,20.95061,26/08/2020,-7.44974E+17,0.825630223,18,4.7292027 +9134,T188G; A218L; N289S,0.53496164,4.014378,26/08/2020,-7.44974E+17,0.540660125,24,3.4829688 +9135,T188G; A218L; T242A,0.33137435,2.1108706,26/08/2020,-7.44974E+17,0.326353858,20,4.319399 +9136,T188G; A218L; V250A,0.19222823,3.499216,26/08/2020,-7.44974E+17,0.190589694,6,15.311667 +9137,T188G; A218M,0.57060003,4.6164193,26/08/2020,-7.44974E+17,0.58144274,19,3.458154 +9138,T188G; A218M; A228V,0.3683991,3.1879618,26/08/2020,-7.44974E+17,0.371550672,14,5.5195966 +9139,T188G; A218M; A296I,0.72715974,15.761711,26/08/2020,-7.44974E+17,0.740402079,16,5.866324 +9140,T188G; L189M; A218I,0.15255666,3.4454818,26/08/2020,-7.44974E+17,0.151386546,17,19.472466 +9141,T188G; L189M; A218L,0.3807945,2.5820885,26/08/2020,-7.44974E+17,0.37832327,25,4.2313166 +9142,T188G; L189M; A218M,0.37457246,2.387401,26/08/2020,-7.44974E+17,0.375650692,27,4.0141783 +9143,T188G; W216R; A218L,0.15956509,5.18881,26/08/2020,-7.44974E+17,0.157439354,13,27.776215 +9144,T188H,0.27194712,5.2282915,26/08/2020,-7.44974E+17,0.299676083,1,15.754753 +9145,T188I,0.13333325,4.5406656,26/08/2020,-7.44974E+17,0.133243794,3,31.607132 +9146,T188I; R279H,0.015807327,3.7163813,26/08/2020,-7.44974E+17,0.01371855,1,294.65652 +9147,T188K,0.07677364,5.013346,26/08/2020,-7.44974E+17,0.073871783,1,73.34749 +9148,T188L,0.15396112,4.107753,26/08/2020,-7.44974E+17,0.155130984,4,23.945585 +9149,T188M,0.28515828,3.8113923,26/08/2020,-7.44974E+17,0.28747336,3,10.145432 +9150,T188N,0.24932426,4.7096157,26/08/2020,-7.44974E+17,0.252041822,3,14.918195 +9151,T188P,0.001749087,1.5423226,26/08/2020,-7.44974E+17,0.001255091,5,1012.7839 +9152,T188P; F217Y; S220T,0.00274307,1.0677162,26/08/2020,-7.44974E+17,0.000571016,1,763.82544 +9153,T188R,0.02933823,5.032403,26/08/2020,-7.44974E+17,0.028425235,2,183.94916 +9154,T188S,0.34603292,2.5063767,26/08/2020,-7.44974E+17,0.339969389,10,4.8347845 +9155,T188S; S220T; H266L,0.2376502,5.28527,26/08/2020,-7.44974E+17,0.254886774,1,19.217464 +9156,T188S; S220T; H266R; E278G,0.003780981,1.6300298,26/08/2020,-7.44974E+17,0.001492606,1,715.5908 +9157,T188S; V265A,0.003687503,1.5902058,26/08/2020,-7.44974E+17,0.001503437,1,725.3672 +9158,T188S; W216R; A218L,0.38386697,6.719509,26/08/2020,-7.44974E+17,0.3932852,5,10.948419 +9159,T188V,0.18502006,4.9591713,26/08/2020,-7.44974E+17,0.179608204,2,23.918682 +9160,T188W,0.21726876,3.6667857,26/08/2020,-7.44974E+17,0.221618071,3,14.24514 +9161,T196A,0.17135288,0.907516,26/08/2020,-7.44974E+17,0.156119106,5,5.139636 +9162,T196A; G203S; S220T; R258S,0.027624303,5.3199887,26/08/2020,-7.44974E+17,0.025233083,2,209.26938 +9163,T196A; M273L,0.003561759,1.4858091,26/08/2020,-7.44974E+17,0.001293251,1,739.40375 +9164,T196A; P219L,0.02794465,4.363733,26/08/2020,-7.44974E+17,0.025493679,1,189.95695 +9165,T196A; S220T; M291R,0.003209022,1.3517482,26/08/2020,-7.44974E+17,0.001030668,1,733.8553 +9166,T196D,0.005219351,0.8817307,26/08/2020,-7.44974E+17,0.004663263,4,217.93805 +9167,T196E,0.004722798,0.89563155,26/08/2020,-7.44974E+17,0.004026071,3,269.36673 +9168,T196F,0.03727362,5.369634,26/08/2020,-7.44974E+17,0.036210807,2,154.50876 +9169,T196G,0.018565029,4.8832,26/08/2020,-7.44974E+17,0.017399752,2,287.03928 +9170,T196H,0.024476588,5.102707,26/08/2020,-7.44974E+17,0.022887901,2,228.56982 +9171,T196K,0.003087182,2.0625205,26/08/2020,-7.44974E+17,0.002302168,3,797.1908 +9172,T196L,0.044935882,4.77868,26/08/2020,-7.44974E+17,0.042516244,1,125.167305 +9173,T196M,0.07629524,4.259484,26/08/2020,-7.44974E+17,0.074761288,2,57.997166 +9174,T196M; A277I,0.002573134,1.0721794,26/08/2020,-7.44974E+17,0.00057761,1,782.9577 +9175,T196M; Y239N; H288Y,0.003767024,1.6171148,26/08/2020,-7.44974E+17,0.001536666,1,708.01465 +9176,T196N,0.11941211,4.9634533,26/08/2020,-7.44974E+17,0.121440006,1,43.36916 +9177,T196P,0.003439164,1.3991401,26/08/2020,-7.44974E+17,0.001095693,1,775.07904 +9178,T196P; G200R,0.002090867,0.62358195,26/08/2020,-7.44974E+17,0.000104691,1,769.28644 +9179,T196Q,0.05479209,7.808954,26/08/2020,-7.44974E+17,0.054316342,4,139.46947 +9180,T196R,0.018218594,4.677248,26/08/2020,-7.44974E+17,0.016731774,2,284.59018 +9181,T196R; H255R; N289K,0.00226678,0.8768768,26/08/2020,-7.44974E+17,0.000340401,1,764.506 +9182,T196S,0.28392646,1.1129999,26/08/2020,-7.44974E+17,0.261686467,4,3.1488101 +9183,T196W,0.02155724,4.1137843,26/08/2020,-7.44974E+17,0.018948661,1,238.00566 +9184,T196Y,0.007748844,2.7935805,26/08/2020,-7.44974E+17,0.005510488,1,500.7504 +9185,T207A; I212M; S220T; N238D; A252V; I256V,0.00459262,1.9569156,26/08/2020,-7.44974E+17,0.002290175,1,671.687 +9186,T207C,0.075819656,4.9891686,26/08/2020,-7.44974E+17,0.075267081,1,72.37029 +9187,T207D,0.035461333,5.606192,26/08/2020,-7.44974E+17,0.034655984,2,168.72635 +9188,T207E,0.08724842,4.7496834,26/08/2020,-7.44974E+17,0.086562037,2,54.94681 +9189,T207F,0.04681268,5.456874,26/08/2020,-7.44974E+17,0.045763721,2,122.758835 +9190,T207G,0.056177832,5.940869,26/08/2020,-7.44974E+17,0.053385203,2,109.64819 +9191,T207I,0.05273924,6.593676,26/08/2020,-7.44974E+17,0.051879432,4,123.489426 +9192,T207K,0.07281524,4.939967,26/08/2020,-7.44974E+17,0.071406667,1,75.59027 +9193,T207N; K236N; A277V; K298T,0.002732622,1.134988,26/08/2020,-7.44974E+17,0.000670046,1,775.3407 +9194,T207P,0.08218085,4.9671264,26/08/2020,-7.44974E+17,0.082198652,4,58.267178 +9195,T207S,0.06876355,4.8132935,26/08/2020,-7.44974E+17,0.067475017,1,78.99533 +9196,T207S; S220T,0.611278,4.0511107,26/08/2020,-7.44974E+17,0.65803003,2,2.4567835 +9197,T207S; S220T; H266R,0.01080867,3.2873795,26/08/2020,-7.44974E+17,0.008685785,1,402.71747 +9198,T207S; S220T; N289S; E295G,0.005082956,2.734203,26/08/2020,-7.44974E+17,0.003778611,2,661.9941 +9199,T207S; S226P; P240S; H288R,0.006970604,2.5989716,26/08/2020,-7.44974E+17,0.004812947,1,531.8136 +9200,T207S; T242N; H288N,0.31990936,5.471759,26/08/2020,-7.44974E+17,0.346170728,1,12.758776 +9201,T207W,0.0364623,5.615286,26/08/2020,-7.44974E+17,0.034581784,2,164.6142 +9202,T207Y,0.046864323,5.785457,26/08/2020,-7.44974E+17,0.046354473,2,128.33856 +9203,T242A,0.31700888,8.607009,26/08/2020,-7.44974E+17,0.322723067,6,18.765614 +9204,T242A; H285R,0.01593365,3.8052547,26/08/2020,-7.44974E+17,0.012814467,1,308.02725 +9205,T242A; N289S,0.43685302,5.5933766,26/08/2020,-7.44974E+17,0.440504971,18,7.2295413 +9206,T242C,0.282593,5.314976,26/08/2020,-7.44974E+17,0.311272545,1,15.010481 +9207,T242D,0.30673596,5.1650915,26/08/2020,-7.44974E+17,0.312555457,3,12.072556 +9208,T242E,0.41883308,5.5650587,26/08/2020,-7.44974E+17,0.477719851,1,8.057094 +9209,T242F,0.10139468,3.989306,26/08/2020,-7.44974E+17,0.101055642,4,37.68992 +9210,T242G,0.27844888,6.329689,26/08/2020,-7.44974E+17,0.281076655,4,16.79686 +9211,T242H,0.35272595,6.1661053,26/08/2020,-7.44974E+17,0.363069323,2,11.84306 +9212,T242I,0.125683,5.1930046,26/08/2020,-7.44974E+17,0.125525119,1,42.33707 +9213,T242I; M248K; E254K,0.002624427,1.0367075,26/08/2020,-7.44974E+17,0.000531354,1,737.3679 +9214,T242K,0.1173066,5.1670866,26/08/2020,-7.44974E+17,0.117468458,1,45.912086 +9215,T242L,0.07104206,5.7465806,26/08/2020,-7.44974E+17,0.069030303,2,82.92949 +9216,T242N,0.33525693,5.3814626,26/08/2020,-7.44974E+17,0.367431581,1,11.620872 +9217,T242P,0.039008774,4.759154,26/08/2020,-7.44974E+17,0.03839274,2,131.23232 +9218,T242Q,0.23884396,5.2850304,26/08/2020,-7.44974E+17,0.255488559,1,19.134731 +9219,T242R,0.19204706,4.897729,26/08/2020,-7.44974E+17,0.191877626,2,22.420336 +9220,T242S,0.15405546,4.73861,26/08/2020,-7.44974E+17,0.155862549,2,28.743244 +9221,T242V,0.2689487,5.9264083,26/08/2020,-7.44974E+17,0.279935974,2,17.12686 +9222,T242W,0.087440975,4.9895854,26/08/2020,-7.44974E+17,0.087143004,1,62.85854 +9223,T242Y,0.12557073,3.631486,26/08/2020,-7.44974E+17,0.124478156,3,27.607758 +9224,T245A,0.008939654,4.470424,26/08/2020,-7.44974E+17,0.008240897,4,528.23364 +9225,T245C,0.009303499,4.1286807,26/08/2020,-7.44974E+17,0.008256036,3,481.96387 +9226,T245E,0.007548073,3.3970048,26/08/2020,-7.44974E+17,0.006582603,2,516.6795 +9227,T245F,0.001835868,1.8913554,26/08/2020,-7.44974E+17,0.001358225,5,1162.1962 +9228,T245G,0.006393088,3.0529132,26/08/2020,-7.44974E+17,0.005124282,2,568.5431 +9229,T245H,0.010576425,3.9903343,26/08/2020,-7.44974E+17,0.009583983,2,424.63434 +9230,T245I,0.004762124,1.9825068,26/08/2020,-7.44974E+17,0.002322014,1,640.0441 +9231,T245K,0.03445882,5.209945,26/08/2020,-7.44974E+17,0.03309948,2,161.12956 +9232,T245L,0.006014045,2.3403313,26/08/2020,-7.44974E+17,0.003727341,1,584.9822 +9233,T245M,0.007137311,2.516321,26/08/2020,-7.44974E+17,0.005748886,2,434.52747 +9234,T245N,0.025594464,4.291889,26/08/2020,-7.44974E+17,0.02304118,1,206.19424 +9235,T245P,0.002270057,1.741529,26/08/2020,-7.44974E+17,0.00168388,4,893.7345 +9236,T245Q,0.011353176,2.8314602,26/08/2020,-7.44974E+17,0.009850628,2,298.54846 +9237,T245S,0.15193331,1.3612325,26/08/2020,-7.44974E+17,0.149831214,7,8.319832 +9238,T245S; H266L,0.004818834,2.0311577,26/08/2020,-7.44974E+17,0.002716098,1,647.3782 +9239,T245V,0.010356939,3.18715,26/08/2020,-7.44974E+17,0.007833314,1,414.29153 +9240,T245W,0.003149767,1.3863412,26/08/2020,-7.44974E+17,0.001150709,1,741.2144 +9241,T245Y,0.004087735,3.000212,26/08/2020,-7.44974E+17,0.003311427,3,820.5376 +9242,T2A,0.10352043,5.4609537,26/08/2020,-7.44974E+17,0.105610589,3,50.282284 +9243,T2C,0.20617425,5.159235,26/08/2020,-7.44974E+17,0.217790852,1,22.81379 +9244,T2D,0.11638199,5.2349825,26/08/2020,-7.44974E+17,0.118478182,3,42.297375 +9245,T2E,0.15748008,5.2639837,26/08/2020,-7.44974E+17,0.15841113,1,32.879993 +9246,T2F,0.15850101,5.274316,26/08/2020,-7.44974E+17,0.162028291,1,32.41929 +9247,T2G,0.043703154,4.538616,26/08/2020,-7.44974E+17,0.040890763,1,124.90554 +9248,T2H,0.15985781,6.0573745,26/08/2020,-7.44974E+17,0.162350326,2,34.100067 +9249,T2K,0.12507533,3.4420185,26/08/2020,-7.44974E+17,0.125389687,5,25.439135 +9250,T2L,0.29507396,6.594174,26/08/2020,-7.44974E+17,0.305489914,2,16.513517 +9251,T2N,0.13914149,4.182401,26/08/2020,-7.44974E+17,0.14195616,2,28.64516 +9252,T2N; N3H; H4A; A5S; S6T; T7A; A8T; T10S; S11P; P12A; A13P; P14V; V15T; T16V; V18G; G19L; L20G; G21P; P22M; M23G; G24L; L25V; V26L; L27A; A28E; E29V; V30L; L32A; A33K; K34G; G35H; H36P; P37T; T39V; V40W; W41N; N42R; R43T; T44P; P45E; E46R; R47A; A48S; S49G; G50L; L51V; V52A; A53Q; Q54G; G55A; A56S; S57L; L58A; A60S; S61I; I62T; T63D; D64A; A65V; V66S; S67A; A68S; S69P; P70V; V71T; T72I; I73M; M74C; C75L; L76N; N78Y; Y79A; A80T; T81M; M82Y; Y83E; E84V; V85F; F86G; G87P; P88A; A89R; R90E; E91A; A92L; L93R; R94D; D95R; R96V; V97L; L98V; V99N; N100L; L101N; N102S; S103G; G104T; T105P; P106Q; Q107E; E108V; V109R; R110A; A112V; V113S; S114W; W115A; A116S; S117D; D118L; L119G; G120T; T121R; R122Y; Y123L; L124D; D125G; G126A; A127I; I128M; M129V; V130P; P133L; L134V; V135G; G136R; R137P; P138D; D139A; A140V; V141F; F142L; L143Y; Y144S; S145G; G146D; D147R; R148A; A149V; V150L; L151D; D152E; E153H; H154R; R155A; A156T; T157L; L158A; A159S; S160L; L161G; G162D; D163P; P164R; R165F; F166L; L167G; G168A; A169D; D170P; P171T; T172L; L173A; A174V; V175L; L176Y; Y177N; N178T; T179A; A180L; L182H; H183M; M185Y; Y186A; A187T; T188L; L189N; N190G; G191Y; Y192L; L193Q; Q194A; A195T; T196A; A197L; L198V; V199G; G200S; S201A; A202G; G203V; V204S; S205A; A206T; T207E; E208F; F209A; A210D; D211I; I212A; A213L; L214G; G215W; W216F; F217A; A218P; P219S; S220V; V221L; L222A; A223P; P224S; S226L; L227A; A229H; H230A; A231V; V232D; D233L; L234D; D235K; K236G; G237N; N238Y; Y239P; P240G; G241T; T242L; L243G; G244T; T245L; L246R; R247M; M248N; N249V; V250N; N251A; A252L; L253E; E254H; H255I; I256A; A257R; R258A; A260E; E262Q; Q263G; G264V; V265H; H266S; S267E; E268L; L269P; P270H; H271L; L272M; M273R; R274E; E275V; V276A; A277E; E278R; R279A; A280V; V281A; A282Q; Q283G; G284H; H285G; G286D; D287H; H288N; N289Y; Y290M; M291S; S292V; V293Y; Y294E; E295A; A296F; F297K; K298Q; Q299P; P300S; S301P; P302A; A303S; S304*; *305-,0.15091239,5.1258354,26/08/2020,-7.44974E+17,0.15611374,1,34.408962 +9253,T2P; V85I; Y144F; E254V; R274C; A277T,0.003343314,1.444495,26/08/2020,-7.44974E+17,0.001084819,1,743.91925 +9254,T2R,0.16301951,5.339261,26/08/2020,-7.44974E+17,0.166273079,1,31.321024 +9255,T2S,0.16632266,5.100654,26/08/2020,-7.44974E+17,0.172588112,2,27.79238 +9256,T2V,0.12926757,2.7927604,26/08/2020,-7.44974E+17,0.13264327,3,20.831125 +9257,T2W,0.06614781,5.7595425,26/08/2020,-7.44974E+17,0.064563123,4,85.32384 +9258,T38A,0.012428461,4.180306,26/08/2020,-7.44974E+17,0.011973055,4,353.95212 +9259,T38A; A223V; N289I,0.004629885,1.9362279,26/08/2020,-7.44974E+17,0.002385105,1,648.65643 +9260,T38A; E46D; T63P; L124H; V130A; D152N; A218T,0.00393926,1.788895,26/08/2020,-7.44974E+17,0.001929009,1,711.14526 +9261,T38A; F217I; H271Y,0.004303813,1.8181018,26/08/2020,-7.44974E+17,0.002017921,1,671.8615 +9262,T38A; G104D; S220T,0.002287504,0.9413506,26/08/2020,-7.44974E+17,0.00040968,1,822.8887 +9263,T38A; M185K; G191V; S220T; V232I,0.003295133,1.3760905,26/08/2020,-7.44974E+17,0.001053182,1,775.7708 +9264,T38A; N42S; P138S,0.003204925,1.4834642,26/08/2020,-7.44974E+17,0.001243566,1,775.6088 +9265,T38A; R122S; M129L; S220T,0.008467695,2.8850074,26/08/2020,-7.44974E+17,0.005985338,1,472.63538 +9266,T38A; R94H; S220T; M248L; H255L,0.002889619,1.6152859,26/08/2020,-7.44974E+17,0.001672142,2,744.28265 +9267,T38A; S220T; T245S; F297S,0.003358115,1.4643742,26/08/2020,-7.44974E+17,0.001173388,1,747.79944 +9268,T38A; S220T; V250A,0.024435839,4.259065,26/08/2020,-7.44974E+17,0.021252976,1,215.82535 +9269,T38A; S57L; P138L; P270S,0.002036327,0.74981546,26/08/2020,-7.44974E+17,0.000193661,1,815.74207 +9270,T38A; T242A; H255L,0.004677643,1.9316682,26/08/2020,-7.44974E+17,0.00239618,1,665.3398 +9271,T38A; V150M; S220T,0.003706354,1.5509764,26/08/2020,-7.44974E+17,0.001373778,1,713.5292 +9272,T38A; W41C,0.00247188,0.91151744,26/08/2020,-7.44974E+17,0.000371971,1,756.9243 +9273,T38A; Y79N; Y144C; V175E; S220T; A228G; D235Y,0.002828817,1.15617,26/08/2020,-7.44974E+17,0.000666368,1,769.34155 +9274,T38C,0.08809048,4.055252,26/08/2020,-7.44974E+17,0.088161715,2,47.275818 +9275,T38D,0.001155349,0.82048154,26/08/2020,-7.44974E+17,0.000564358,4,960.59845 +9276,T38G,0.002404174,0.94734544,26/08/2020,-7.44974E+17,0.000420167,1,778.97516 +9277,T38H,0.002310435,0.8357821,26/08/2020,-7.44974E+17,0.000262929,1,792.8638 +9278,T38I,0.102175124,4.2928886,26/08/2020,-7.44974E+17,0.101247421,2,42.03305 +9279,T38K,0.001721419,1.0415887,26/08/2020,-7.44974E+17,0.000609432,2,898.0153 +9280,T38K; V40F; V52M; V71D; V175E; A213D; F217L,0.003073035,1.3870689,26/08/2020,-7.44974E+17,0.001067077,1,786.35 +9281,T38L,0.08214646,5.883443,26/08/2020,-7.44974E+17,0.083000881,2,71.59852 +9282,T38M,0.110017985,2.9513848,26/08/2020,-7.44974E+17,0.109891223,2,27.430304 +9283,T38M; A187V; V204M; S220T,0.00270644,1.1134555,26/08/2020,-7.44974E+17,0.000632093,1,771.4605 +9284,T38M; A210T,0.0632684,4.840664,26/08/2020,-7.44974E+17,0.060684432,1,86.54716 +9285,T38M; D95V; S220T; P224H; E278V,0.003218974,1.3393687,26/08/2020,-7.44974E+17,0.001073478,1,761.09344 +9286,T38M; E108G; S160L; S220T,0.004873827,1.9600134,26/08/2020,-7.44974E+17,0.002440326,1,635.70807 +9287,T38M; E46V; D152V; R155H; S220T; L234H,0.002543671,0.9813185,26/08/2020,-7.44974E+17,0.000399759,1,789.86926 +9288,T38M; F86Y; S117P; R148C; S160A,0.00500123,2.0765855,26/08/2020,-7.44974E+17,0.002563565,1,664.8006 +9289,T38M; L119H; M129T; S220T,0.015861554,3.7324495,26/08/2020,-7.44974E+17,0.013905957,1,301.9708 +9290,T38M; L124F,0.003266794,1.389037,26/08/2020,-7.44974E+17,0.001081126,1,758.7512 +9291,T38M; L51P; H230Q; A257S,0.002687556,1.0905153,26/08/2020,-7.44974E+17,0.000582324,1,808.4363 +9292,T38M; N249K,0.00402345,1.7222488,26/08/2020,-7.44974E+17,0.001648536,1,709.24927 +9293,T38M; S220T,0.61359334,6.7608852,26/08/2020,-7.44974E+17,0.654834589,3,4.1059723 +9294,T38M; S220T; H230Y; T242I,0.5757363,5.7645016,26/08/2020,-7.44974E+17,0.684705558,1,4.0572653 +9295,T38M; T121A; S145L; H230L; S292T,0.008265744,2.83128,26/08/2020,-7.44974E+17,0.005459159,1,500.79047 +9296,T38M; Y144F; A202V; S220T,0.589201,5.499399,26/08/2020,-7.44974E+17,0.722913237,1,3.5611274 +9297,T38M; Y83N; A89G; R110G; T121M; Y177F; Y186C; Q283R; G284C,0.002402403,0.98663193,26/08/2020,-7.44974E+17,0.000451248,1,805.18427 +9298,T38N,0.002892318,0.65786946,26/08/2020,-7.44974E+17,0.002457562,6,288.35623 +9299,T38P,0.001911158,1.0700893,26/08/2020,-7.44974E+17,0.000668439,2,850.15564 +9300,T38Q,0.001683545,1.0021703,26/08/2020,-7.44974E+17,0.000569017,2,910.1274 +9301,T38R,0.001483618,0.9202014,26/08/2020,-7.44974E+17,0.000464707,2,952.2151 +9302,T38R; A89V; S103L,0.002516192,1.0456045,26/08/2020,-7.44974E+17,0.000582174,1,748.7762 +9303,T38R; S220T; T245S,0.003312896,1.4209274,26/08/2020,-7.44974E+17,0.00118006,1,743.06104 +9304,T38R; V71L; N100I; A257P; K298R,0.002470922,0.9318935,26/08/2020,-7.44974E+17,0.000373648,1,770.6723 +9305,T38S,0.006744196,2.5201113,26/08/2020,-7.44974E+17,0.004023435,1,555.928 +9306,T38S; A187V; F217I; S220T; H288R,0.00265777,1.6044159,26/08/2020,-7.44974E+17,0.00138573,2,811.54443 +9307,T38S; A59V,0.003471292,1.4791869,26/08/2020,-7.44974E+17,0.001199732,1,749.174 +9308,T38S; H154L; S220T,0.004735383,1.9499702,26/08/2020,-7.44974E+17,0.002180225,1,652.0683 +9309,T38S; Q107L; S220T; E268G,0.003214883,1.3893273,26/08/2020,-7.44974E+17,0.001026425,1,772.48895 +9310,T38S; Q107R; F209S; L272P,0.003478983,1.603523,26/08/2020,-7.44974E+17,0.00155872,1,727.11487 +9311,T38S; Q194K; S220T,0.00271692,1.1654543,26/08/2020,-7.44974E+17,0.00066761,1,800.5989 +9312,T38S; R110S; L181S; N289D,0.003112722,1.2499926,26/08/2020,-7.44974E+17,0.000819582,1,721.41174 +9313,T38S; R137H; S220T; P302T,0.017676601,3.9144664,26/08/2020,-7.44974E+17,0.01539695,1,277.3274 +9314,T38S; R137S; A259V,0.00592343,2.3872354,26/08/2020,-7.44974E+17,0.003639435,1,587.5842 +9315,T38S; S57P; M129T; F217S; S220T; H230R,0.004230202,1.7577779,26/08/2020,-7.44974E+17,0.0019721,1,670.3621 +9316,T38S; V66A; H183R,0.001465064,0.77356166,26/08/2020,-7.44974E+17,0.000342523,2,896.65106 +9317,T38V,0.10635275,6.3272514,26/08/2020,-7.44974E+17,0.105997374,2,57.571762 +9318,T38W,0.000846718,0.6191325,26/08/2020,-7.44974E+17,0.000252149,4,1062.8282 +9319,T38Y,0.002279058,0.8794847,26/08/2020,-7.44974E+17,0.000332558,1,796.11 +9320,T39A,0.009756378,4.4810076,26/08/2020,-7.44974E+17,0.008871695,3,494.42703 +9321,T39A; D147E; L176I; I212N; R247S; P300S,0.004704632,1.9859438,26/08/2020,-7.44974E+17,0.002493837,1,644.97406 +9322,T39A; D170E; H266L,0.002423784,1.0117018,26/08/2020,-7.44974E+17,0.000499411,1,768.707 +9323,T39A; D170G; S220T; D287E,0.002925538,1.2214172,26/08/2020,-7.44974E+17,0.000714074,1,771.469 +9324,T39A; E84D; G237C; A282E,0.004270298,1.8398598,26/08/2020,-7.44974E+17,0.002150147,1,677.9535 +9325,T39A; G50T,0.008212559,2.856312,26/08/2020,-7.44974E+17,0.006205484,1,466.79773 +9326,T39A; G55C; M248T; E268G,0.002087404,0.7504914,26/08/2020,-7.44974E+17,0.000179921,1,793.3203 +9327,T39A; L173Q; K236I; M248V,0.002385767,0.885152,26/08/2020,-7.44974E+17,0.000331242,1,792.24005 +9328,T39A; L51P; N77Y; A210V; R274H; H288L; M291K,0.004010717,1.7177073,26/08/2020,-7.44974E+17,0.001770742,1,700.0727 +9329,T39A; S220T,0.052530576,4.678986,26/08/2020,-7.44974E+17,0.050817175,1,103.36602 +9330,T39A; V52M; A68V; S220T; S267T; L272P,0.002768008,1.0932825,26/08/2020,-7.44974E+17,0.000582445,1,758.6401 +9331,T39A; W216C; S220T; V221G,0.020888075,4.0753303,26/08/2020,-7.44974E+17,0.018320121,1,245.72935 +9332,T39C,0.049577724,4.089178,26/08/2020,-7.44974E+17,0.049076679,3,85.37623 +9333,T39D,0.000774119,0.9918803,26/08/2020,-7.44974E+17,0.000389064,6,1543.9121 +9334,T39E,0.004142364,1.83922,26/08/2020,-7.44974E+17,0.00197833,1,699.6551 +9335,T39F,0.015343293,3.6440024,26/08/2020,-7.44974E+17,0.012513866,1,307.58612 +9336,T39G,0.001751255,1.0632699,26/08/2020,-7.44974E+17,0.000635154,2,936.73535 +9337,T39H,0.019895036,3.962577,26/08/2020,-7.44974E+17,0.017559116,1,252.11699 +9338,T39I,0.022868175,4.7571597,26/08/2020,-7.44974E+17,0.021850749,2,228.47287 +9339,T39I; E84V; A156S; S220T,0.005956611,2.3338475,26/08/2020,-7.44974E+17,0.003649189,1,568.42865 +9340,T39I; L143M; L158H; P224L; Y294C,0.003629768,1.5599201,26/08/2020,-7.44974E+17,0.001315979,1,733.31354 +9341,T39I; L93F; S220T,0.03967162,4.574469,26/08/2020,-7.44974E+17,0.037155358,1,138.4773 +9342,T39I; M82V; S220T; L246H,0.00289445,1.1966548,26/08/2020,-7.44974E+17,0.00082785,1,745.32715 +9343,T39I; P138L; S220T; L234I; N238S; V276D,0.003389354,1.4404081,26/08/2020,-7.44974E+17,0.001193487,1,706.9364 +9344,T39I; V130E; S160T; S220T; A277S; G286W,0.00278515,1.1918545,26/08/2020,-7.44974E+17,0.0008033,1,764.108 +9345,T39I; V52E; D64G; T72S; L158H,0.008901035,2.9725175,26/08/2020,-7.44974E+17,0.006219876,1,467.14847 +9346,T39K,0.018444711,4.070343,26/08/2020,-7.44974E+17,0.01733487,2,244.92801 +9347,T39L,0.00543087,2.2026653,26/08/2020,-7.44974E+17,0.003116468,1,607.2909 +9348,T39M,0.022067465,5.104491,26/08/2020,-7.44974E+17,0.020812081,2,251.06354 +9349,T39N,0.012801821,4.4016037,26/08/2020,-7.44974E+17,0.011429352,2,384.13586 +9350,T39N; L134Q; R258C; V293F,0.00201292,0.77361906,26/08/2020,-7.44974E+17,0.000257586,1,824.9635 +9351,T39N; Q283H,0.00633138,2.4788098,26/08/2020,-7.44974E+17,0.004027245,1,570.32526 +9352,T39N; R148P; S220T; H230Y,0.047036238,4.726801,26/08/2020,-7.44974E+17,0.044838058,1,117.48822 +9353,T39N; R47N,0.003557848,1.5189931,26/08/2020,-7.44974E+17,0.001264982,1,704.6479 +9354,T39P,0.000732799,0.7941102,26/08/2020,-7.44974E+17,0.000288722,5,1389.3278 +9355,T39P; A127V; S220T,0.002511147,0.91363233,26/08/2020,-7.44974E+17,0.000405672,1,781.97394 +9356,T39P; A218V; S225L; P300L,0.003961196,1.6833892,26/08/2020,-7.44974E+17,0.001702687,1,705.83307 +9357,T39P; R43H; P164S; V276A,0.001881394,0.56918216,26/08/2020,-7.44974E+17,7.03E-05,1,782.19775 +9358,T39P; V113I; L173Q; E261D; M273L,0.0033,1.3785287,26/08/2020,-7.44974E+17,0.001048777,1,752.07855 +9359,T39Q,0.010453568,3.2123418,26/08/2020,-7.44974E+17,0.008167021,1,403.19843 +9360,T39R,0.006368283,3.8423784,26/08/2020,-7.44974E+17,0.005528185,3,667.1311 +9361,T39S; A48E; L51Q; R165H; A169E; F209Y,0.002559112,1.0108738,26/08/2020,-7.44974E+17,0.000439606,1,766.5227 +9362,T39S; D125G; S220T,0.006921456,2.6159713,26/08/2020,-7.44974E+17,0.004605221,1,544.06305 +9363,T39S; D163G; H271D,0.002733977,1.0345681,26/08/2020,-7.44974E+17,0.000511063,1,790.6588 +9364,T39S; D95N; L176F; S220T; V221L,0.00346867,1.4824065,26/08/2020,-7.44974E+17,0.001250499,1,762.4026 +9365,T39S; D95V; E108D; F209S; G237C; T242A,0.011309112,3.3380854,26/08/2020,-7.44974E+17,0.008857165,1,385.83868 +9366,T39S; E84V; F86Y; A213V; D233V; M291I,0.003189626,1.3628244,26/08/2020,-7.44974E+17,0.0009779,1,787.0368 +9367,T39S; N100S; S117P; V141M; S220T; E262V,0.006319564,2.441162,26/08/2020,-7.44974E+17,0.004042514,1,559.1745 +9368,T39S; P88T; A127V; R247S; E295V,0.003296551,1.4192929,26/08/2020,-7.44974E+17,0.00112263,1,743.32135 +9369,T39S; Q54L; T81S; L243P; A257S,0.002109607,0.7436445,26/08/2020,-7.44974E+17,0.000176118,1,761.5194 +9370,T39S; R148P; S220T; H230Y,0.6104971,6.0553493,26/08/2020,-7.44974E+17,0.726892793,1,3.5677724 +9371,T39S; S103L; S220T,0.001766188,1.0537887,26/08/2020,-7.44974E+17,0.000612795,2,910.589 +9372,T39S; S61L; G120D; L124F; D170G; S220T; G264D,0.003217124,1.2948034,26/08/2020,-7.44974E+17,0.00088755,1,745.76117 +9373,T39S; V40D; L143M; S205R,0.004224977,1.8151499,26/08/2020,-7.44974E+17,0.001969994,1,674.4867 +9374,T39S; W115G; L143M; Y186F; S220T,0.003328912,1.336819,26/08/2020,-7.44974E+17,0.00086511,1,752.56714 +9375,T39S; W41C; A174V; S220T,0.002375216,0.9073256,26/08/2020,-7.44974E+17,0.000355502,1,779.03827 +9376,T39V,0.035451736,5.1699657,26/08/2020,-7.44974E+17,0.034328489,2,156.2087 +9377,T39Y,0.018903501,3.9451475,26/08/2020,-7.44974E+17,0.016640857,1,260.47598 +9378,T44A; A174G; Q194R; S220T,0.002717672,1.1400115,26/08/2020,-7.44974E+17,0.000702642,1,755.3141 +9379,T44A; F142L; S220T,0.10167701,5.1229353,26/08/2020,-7.44974E+17,0.101290949,1,54.36682 +9380,T44A; G87S; R258H,0.006601133,2.501075,26/08/2020,-7.44974E+17,0.004138302,1,551.6502 +9381,T44A; P88L; S220T,0.024676478,4.132341,26/08/2020,-7.44974E+17,0.023267527,1,207.66077 +9382,T44A; R155C; R165S; H230Y,0.003282519,1.4147152,26/08/2020,-7.44974E+17,0.001275145,1,749.47797 +9383,T44A; R165H; S220T,0.2420834,5.2512393,26/08/2020,-7.44974E+17,0.256867942,1,18.25849 +9384,T44A; S220T; A296D,0.007005318,2.6365075,26/08/2020,-7.44974E+17,0.004653233,1,525.697 +9385,T44A; V276A,0.001928858,0.52345675,26/08/2020,-7.44974E+17,5.12E-05,1,697.0736 +9386,T44C,0.011276873,3.2521265,26/08/2020,-7.44974E+17,0.009475206,1,380.65454 +9387,T44D,0.059655856,4.855657,26/08/2020,-7.44974E+17,0.056771883,1,94.00137 +9388,T44E,0.006230461,3.0657928,26/08/2020,-7.44974E+17,0.005715566,5,528.53094 +9389,T44F,0.004385043,2.2443895,26/08/2020,-7.44974E+17,0.00386173,5,564.3297 +9390,T44G,0.009975546,3.1800852,26/08/2020,-7.44974E+17,0.00760749,1,428.91736 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F142Y; A149V; A156D; G215S; L246H; Y294F,0.002718355,1.1214184,26/08/2020,-7.44974E+17,0.000604723,1,795.48975 +9402,T44K,0.019520964,4.2611556,26/08/2020,-7.44974E+17,0.018490764,3,233.48064 +9403,T44L,0.004612489,2.5734236,26/08/2020,-7.44974E+17,0.003329338,2,685.2384 +9404,T44M,0.011823949,4.0356507,26/08/2020,-7.44974E+17,0.011120562,3,372.17093 +9405,T44M; P138L,0.003278525,1.3829323,26/08/2020,-7.44974E+17,0.001033461,1,780.911 +9406,T44N,0.08650413,3.191671,26/08/2020,-7.44974E+17,0.085996973,3,37.23585 +9407,T44N; A60V; S220T,0.625873,5.888213,26/08/2020,-7.44974E+17,0.737827547,1,3.2301486 +9408,T44N; G191V,0.002674297,1.0588439,26/08/2020,-7.44974E+17,0.000600383,1,774.6052 +9409,T44N; G50S,0.04030939,4.603138,26/08/2020,-7.44974E+17,0.03931852,1,136.61618 +9410,T44N; L182P,0.003681328,1.5568084,26/08/2020,-7.44974E+17,0.001408498,1,719.62775 +9411,T44N; M129I; G203S; F217L; S220T,0.002870306,1.1855829,26/08/2020,-7.44974E+17,0.000735925,1,795.90485 +9412,T44N; S205G,0.06840198,4.918568,26/08/2020,-7.44974E+17,0.066112925,1,81.866066 +9413,T44P,0.001733635,1.0254184,26/08/2020,-7.44974E+17,0.000589406,2,943.6203 +9414,T44P; D64E; A68E; A89V; D95Y; D118V,0.003462347,1.5422343,26/08/2020,-7.44974E+17,0.001518679,1,728.04626 +9415,T44P; L124P; F217I; L222M; H285L; E295D,0.003265117,1.3780693,26/08/2020,-7.44974E+17,0.00105994,1,728.87305 +9416,T44P; S220T,0.003518133,1.5631218,26/08/2020,-7.44974E+17,0.001384241,1,731.7736 +9417,T44Q,0.02038263,4.03316,26/08/2020,-7.44974E+17,0.017700608,1,245.69887 +9418,T44Q; G50C,0.007635287,2.7316084,26/08/2020,-7.44974E+17,0.005363105,1,497.4338 +9419,T44R,0.024196107,4.7388973,26/08/2020,-7.44974E+17,0.023776811,2,214.62334 +9420,T44S,0.0686891,7.0992546,26/08/2020,-7.44974E+17,0.068297142,3,101.40464 +9421,T44S; A252D; N289Y,0.003026423,1.3124089,26/08/2020,-7.44974E+17,0.000950232,1,768.594 +9422,T44S; A92V; D125N; S220T,0.028224075,4.34862,26/08/2020,-7.44974E+17,0.025643981,1,188.22781 +9423,T44S; E46K; M74L; M82T; L176F; L272Q; H288L,0.002299292,0.8048393,26/08/2020,-7.44974E+17,0.000233601,1,762.27655 +9424,T44S; E84V; D170V; H230N,0.003364811,1.4484836,26/08/2020,-7.44974E+17,0.001180017,1,737.6541 +9425,T44S; I62N; F209V; S220T,0.002856425,1.2101749,26/08/2020,-7.44974E+17,0.000769838,1,795.8557 +9426,T44S; L124P; T188S; S220T,0.002994601,1.2849139,26/08/2020,-7.44974E+17,0.000893026,1,758.79 +9427,T44S; L151P; L214P; G241D; R247H,0.009147046,2.9646292,26/08/2020,-7.44974E+17,0.006904402,1,448.9058 +9428,T44S; L51M; I128V; D147V; F217Y; S220T; K236R; H266L,0.003605671,1.5185167,26/08/2020,-7.44974E+17,0.001273273,1,725.6597 +9429,T44S; L98M; V135I; H255L; S267T,0.005349153,2.1452982,26/08/2020,-7.44974E+17,0.002926002,1,605.169 +9430,T44S; L98P,0.005230554,2.1468565,26/08/2020,-7.44974E+17,0.002876582,1,624.7766 +9431,T44S; P88T; S220T,0.5056067,5.769105,26/08/2020,-7.44974E+17,0.577688973,1,5.530414 +9432,T44S; S114T; V150E; E153G; N251D; H255Y,0.00193601,0.7062794,26/08/2020,-7.44974E+17,0.00015474,1,770.4755 +9433,T44S; S220T; H271Y; P300T,0.16410387,3.8728352,26/08/2020,-7.44974E+17,0.166963464,2,21.982145 +9434,T44V,0.006336077,2.436486,26/08/2020,-7.44974E+17,0.00383137,1,558.7468 +9435,T44W,0.008995315,0.8607906,26/08/2020,-7.44974E+17,0.008070057,4,125.67262 +9436,T44Y,0.00624652,2.455274,26/08/2020,-7.44974E+17,0.003835836,1,572.86365 +9437,T63A,0.15312749,5.1249356,26/08/2020,-7.44974E+17,0.157992046,1,32.953037 +9438,T63A; A116V; L214P; L227R; H230Q,0.006407079,2.444179,26/08/2020,-7.44974E+17,0.004208507,1,552.6311 +9439,T63A; A65T; V150M; S220T; H230Y,0.004377531,1.8590938,26/08/2020,-7.44974E+17,0.002187202,1,693.3146 +9440,T63A; A80T; L214P; S220T,0.007461869,2.6838045,26/08/2020,-7.44974E+17,0.004959831,1,515.042 +9441,T63A; E91D; P132Q; S220T,0.003202485,1.3425536,26/08/2020,-7.44974E+17,0.000903483,1,783.5941 +9442,T63A; F86S; A92T; N100T; V113E; E153G; T179S; Q194R; L234R; R247S,0.002200039,0.7678825,26/08/2020,-7.44974E+17,0.000225203,1,760.79004 +9443,T63A; G120D; S220T; D235N; Q299L,0.03210026,4.539073,26/08/2020,-7.44974E+17,0.02841936,1,172.88947 +9444,T63A; G120S; L151P; A213D; M248T,0.003284739,1.3844303,26/08/2020,-7.44974E+17,0.000979219,1,749.10297 +9445,T63A; M185I; L198Q; S220T; I256V,0.002084353,1.1896427,26/08/2020,-7.44974E+17,0.000898307,2,842.49774 +9446,T63A; S220T; Y290F,0.056820523,4.8703027,26/08/2020,-7.44974E+17,0.054070755,1,98.77409 +9447,T63A; V71I; V281D,0.011319486,3.2634108,26/08/2020,-7.44974E+17,0.009098356,1,382.5452 +9448,T63C,0.06336866,4.9903255,26/08/2020,-7.44974E+17,0.060539106,1,90.25805 +9449,T63D,0.21843699,2.6971269,26/08/2020,-7.44974E+17,0.220167294,3,10.567638 +9450,T63E,0.22621107,5.3608794,26/08/2020,-7.44974E+17,0.236705784,1,21.039421 +9451,T63F,0.06373193,5.8194785,26/08/2020,-7.44974E+17,0.062530243,2,93.18455 +9452,T63F; M74I; R165H; T196K; A206G; S220T,0.003195936,1.372563,26/08/2020,-7.44974E+17,0.001088233,1,745.3907 +9453,T63G,0.14575765,6.9341936,26/08/2020,-7.44974E+17,0.149788757,4,42.062557 +9454,T63H,0.064726405,4.8689833,26/08/2020,-7.44974E+17,0.06325197,1,85.81788 +9455,T63I,0.09834392,2.1257446,26/08/2020,-7.44974E+17,0.101072832,2,23.572735 +9456,T63I; A280T; A303V,0.11264733,5.1447234,26/08/2020,-7.44974E+17,0.115385607,1,47.85465 +9457,T63I; G136D; Y177C; N190Y; T207I,0.016428946,3.782792,26/08/2020,-7.44974E+17,0.01426938,1,289.11285 +9458,T63I; S220T; A252T,0.07882647,4.9504485,26/08/2020,-7.44974E+17,0.076515487,1,69.82334 +9459,T63I; S220T; P300R,0.54552484,5.6222997,26/08/2020,-7.44974E+17,0.64458622,1,4.4765053 +9460,T63I; S67G; Y177N; S220T; H230Y; A277D,0.004577635,1.8897164,26/08/2020,-7.44974E+17,0.002086043,1,662.5116 +9461,T63I; T105R; R148C,0.003133404,1.3561964,26/08/2020,-7.44974E+17,0.001083001,1,732.5563 +9462,T63N; M184K; S220T,0.005354445,2.2361357,26/08/2020,-7.44974E+17,0.003204834,1,630.4432 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L193P,0.003900189,1.6810447,26/08/2020,-7.44974E+17,0.001687178,1,716.93164 +9527,T72S; R110P; H154L; E208V; L272Q,0.003015885,1.1781931,26/08/2020,-7.44974E+17,0.000675022,1,757.52356 +9528,T72S; S117P; L189F; G191R; S220T; Y290N,0.002981589,1.1552997,26/08/2020,-7.44974E+17,0.000635731,1,777.9215 +9529,T72S; S220T,0.5840728,5.6819506,26/08/2020,-7.44974E+17,0.693215389,1,3.9068542 +9530,T72S; T188A; Q283R; S301C,0.006832813,2.5543234,26/08/2020,-7.44974E+17,0.004465442,1,530.3455 +9531,T72S; V135E,0.002343386,0.9760535,26/08/2020,-7.44974E+17,0.000427779,1,797.5241 +9532,T72S; Y186C; S220T,0.003186373,1.2531984,26/08/2020,-7.44974E+17,0.000777832,1,769.4586 +9533,T7A; F209S; S220T,0.003521406,1.4769961,26/08/2020,-7.44974E+17,0.00124793,1,749.78326 +9534,T7A; H36R; V135L; R148P; S220T; H230Y; I256T; E268A,0.003950513,1.7735177,26/08/2020,-7.44974E+17,0.001826685,1,694.6499 +9535,T7A; R47H; S220T,0.17611794,5.259604,26/08/2020,-7.44974E+17,0.179784249,1,28.892519 +9536,T7A; R90P; 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S220T,0.002849002,1.171273,26/08/2020,-7.44974E+17,0.000683723,1,753.0425 +9577,T7S; L119H; M185I; L253V; E278V,0.003395525,1.4646277,26/08/2020,-7.44974E+17,0.001239376,1,750.1589 +9578,T7S; L58P; V71D; T81A; S220T,0.003719471,1.6134791,26/08/2020,-7.44974E+17,0.001489125,1,733.2261 +9579,T7S; P70S; Y144F; E153D,0.21618144,5.256686,26/08/2020,-7.44974E+17,0.235354171,1,21.85147 +9580,T7S; S160P; T196A,0.003968491,1.7066449,26/08/2020,-7.44974E+17,0.001739897,1,701.3379 +9581,T7S; T44I; E46D; S220T,0.031339273,4.4186106,26/08/2020,-7.44974E+17,0.030447977,1,166.79634 +9582,T7S; T72S; S220T; P270T,0.008350474,2.9032624,26/08/2020,-7.44974E+17,0.005748178,1,482.22916 +9583,T7S; T9S; T81I; N100I; S220T; L272P; A296T,0.006120683,2.435972,26/08/2020,-7.44974E+17,0.00382195,1,570.9454 +9584,T7S; V130M; F297S,0.004221617,1.7983584,26/08/2020,-7.44974E+17,0.002097578,1,691.6163 +9585,T7S; W115C; P164L,0.003805958,1.6666074,26/08/2020,-7.44974E+17,0.00163222,1,706.165 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S220T,0.00271865,1.1098837,26/08/2020,-7.44974E+17,0.000627464,1,766.5391 +9608,T81I; M129L; S220T; L222P,0.00387897,1.6716988,26/08/2020,-7.44974E+17,0.001677013,1,689.1778 +9609,T81I; M82T; D95E; A174V; R274H; M291L,0.002219391,1.2649599,26/08/2020,-7.44974E+17,0.00111173,2,821.9442 +9610,T81I; S145T; L222V; A277V; Y294C,0.002915088,1.2681482,26/08/2020,-7.44974E+17,0.000869291,1,787.1832 +9611,T81K,0.012216326,3.605296,26/08/2020,-7.44974E+17,0.01117941,2,339.22018 +9612,T81L,0.004663792,1.9585778,26/08/2020,-7.44974E+17,0.002544436,1,645.72943 +9613,T81M,0.011210199,4.3955264,26/08/2020,-7.44974E+17,0.010385616,3,420.56042 +9614,T81N,0.028610755,5.6472917,26/08/2020,-7.44974E+17,0.028459263,5,199.40326 +9615,T81N; M129I; F166L; S220T,0.00497058,2.0826564,26/08/2020,-7.44974E+17,0.002829717,1,631.7745 +9616,T81P,0.004873697,2.039807,26/08/2020,-7.44974E+17,0.002860152,1,632.4713 +9617,T81P; N100S; R137S; S220T; H271Y,0.003070852,1.3356243,26/08/2020,-7.44974E+17,0.001032046,1,754.1225 +9618,T81P; Y83F; A174S; M185L,0.002521027,0.9675298,26/08/2020,-7.44974E+17,0.000391837,1,765.78656 +9619,T81R,0.021442942,4.1661243,26/08/2020,-7.44974E+17,0.018451909,1,244.3747 +9620,T81S,0.28636017,1.0798955,26/08/2020,-7.44974E+17,0.269193458,5,2.9761946 +9621,T81S; A92V; S220T; H230Y,0.7888508,16.472132,26/08/2020,-7.44974E+17,0.809458463,10,4.3266306 +9622,T81S; D125E; S220T; S292P,0.002595042,1.1163012,26/08/2020,-7.44974E+17,0.000606879,1,813.205 +9623,T81S; D170Y; S225T,0.003466882,1.5368966,26/08/2020,-7.44974E+17,0.00125599,1,750.84863 +9624,T81S; E108D; T172A; L182Q; A228V; K236T,0.002634806,1.0540276,26/08/2020,-7.44974E+17,0.000560466,1,805.83997 +9625,T81S; E153K; T172R; L173Q; A229T; A259T,0.002564403,1.0053656,26/08/2020,-7.44974E+17,0.000434621,1,810.47363 +9626,T81S; G136C; S220T; V250M,0.002301942,0.89148694,26/08/2020,-7.44974E+17,0.000380036,1,766.06805 +9627,T81S; I128F; S220T; D235G; A260T,0.003017417,1.1995746,26/08/2020,-7.44974E+17,0.000748831,1,751.8509 +9628,T81S; L101P,0.002700669,1.0879399,26/08/2020,-7.44974E+17,0.000570914,1,802.20996 +9629,T81S; L167H; L182Q; S220T; A252V,0.003214621,1.420594,26/08/2020,-7.44974E+17,0.001156069,1,761.3672 +9630,T81S; N102S; S220T,0.25869957,5.2112246,26/08/2020,-7.44974E+17,0.28628076,1,16.716671 +9631,T81S; R148P; S160T; S220T; H230Y,0.45057052,5.6517315,26/08/2020,-7.44974E+17,0.515748841,1,7.10425 +9632,T81S; R148P; S220T; H230Y; L246P,0.002100224,0.7376743,26/08/2020,-7.44974E+17,0.000193579,1,777.93634 +9633,T81S; S220T,0.62962306,8.00613,26/08/2020,-7.44974E+17,0.678031024,3,4.548674 +9634,T81S; T245P; L253Q; V276A; F297I; Q299H,0.002897887,1.2056035,26/08/2020,-7.44974E+17,0.000709754,1,753.8239 +9635,T81S; V141L; R148P; S220T; H230Y; Q283H,0.6562336,5.665478,26/08/2020,-7.44974E+17,0.793792249,1,2.650912 +9636,T81V,0.032334432,4.482672,26/08/2020,-7.44974E+17,0.03027821,1,166.0866 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G168R,0.002306567,0.8086298,26/08/2020,-7.44974E+17,0.000230799,1,806.85516 +9647,T9A; G87S; E91K; A210V,0.14365126,5.1009154,26/08/2020,-7.44974E+17,0.149768496,1,35.692905 +9648,T9A; L25P,0.002437766,0.81678516,26/08/2020,-7.44974E+17,0.000262609,1,770.7289 +9649,T9A; N289S,0.131583,0.5701825,26/08/2020,-7.44974E+17,0.102167973,10,4.321897 +9650,T9A; N78K; L167R; T172S; L243Q,0.00198995,0.6694509,26/08/2020,-7.44974E+17,0.000144561,1,757.239 +9651,T9A; R43C; S220T,0.004491443,1.8993729,26/08/2020,-7.44974E+17,0.002071172,1,664.7883 +9652,T9A; R90C; T172P; V232A; L253R; H266Y; V281A,0.002908225,1.2088392,26/08/2020,-7.44974E+17,0.000728327,1,739.335 +9653,T9A; R96L; I212N; V265I,0.050077952,4.781068,26/08/2020,-7.44974E+17,0.046981995,1,111.493904 +9654,T9A; S11L; D64G; S220T; L272P; M291L,0.003532484,1.5284863,26/08/2020,-7.44974E+17,0.001291976,1,733.41003 +9655,T9A; S220T,0.6360322,5.294565,26/08/2020,-7.44974E+17,0.69229714,2,2.849548 +9656,T9A; V15D; D95G; L98Q; R110G; E268K,0.00293837,1.2762605,26/08/2020,-7.44974E+17,0.000868497,1,768.96356 +9657,T9A; V18A; Y186H,0.003172999,1.2834439,26/08/2020,-7.44974E+17,0.00085145,1,739.5931 +9658,T9D,0.20561744,6.374372,26/08/2020,-7.44974E+17,0.205028629,5,25.258348 +9659,T9E,0.10562594,6.304369,26/08/2020,-7.44974E+17,0.104149749,2,58.04009 +9660,T9F,0.05092595,5.873385,26/08/2020,-7.44974E+17,0.050035587,2,120.10224 +9661,T9H,0.14106187,6.289353,26/08/2020,-7.44974E+17,0.141301482,3,40.367878 +9662,T9I,0.10544871,2.9563367,26/08/2020,-7.44974E+17,0.10418216,2,29.65436 +9663,T9I; A169T; A303D,0.09129232,0.9487891,26/08/2020,-7.44974E+17,0.08376751,10,10.40828 +9664,T9I; A277I,0.002499115,1.0668232,26/08/2020,-7.44974E+17,0.000598333,1,798.9865 +9665,T9I; D64V; V113A; S220T; A223V; L234F,0.003661866,1.6013715,26/08/2020,-7.44974E+17,0.001432631,1,728.261 +9666,T9I; K34E; G104C; M184L; Y186C; D211Y; P219R,0.002230882,0.96741843,26/08/2020,-7.44974E+17,0.000417104,1,833.44904 +9667,T9I; K34M; V71D; C75Y; F86Y; W115L; I212N; K298M; S301N,0.00219163,0.7922207,26/08/2020,-7.44974E+17,0.000220954,1,787.1333 +9668,T9I; L25I; V52A; S61P; A218T,0.003418532,1.4934675,26/08/2020,-7.44974E+17,0.001272304,1,744.79877 +9669,T9I; Q194H; S220T,0.3827222,5.377066,26/08/2020,-7.44974E+17,0.432465861,1,9.183825 +9670,T9I; T81S; S220T,0.55987585,5.8179817,26/08/2020,-7.44974E+17,0.657906876,1,4.3813224 +9671,T9K,0.15171848,4.9887214,26/08/2020,-7.44974E+17,0.152167974,2,30.221556 +9672,T9N; A127V; L198R; A252T; V265E,0.00347843,1.5395738,26/08/2020,-7.44974E+17,0.00133839,1,714.23035 +9673,T9N; L32H; I73N; D95V; R96S,0.002861107,1.1692511,26/08/2020,-7.44974E+17,0.000738295,1,786.0062 +9674,T9N; T172P; L181S; S220T; A231V,0.003319834,1.3979032,26/08/2020,-7.44974E+17,0.001055165,1,771.4576 +9675,T9N; V15C,0.068085715,4.7731657,26/08/2020,-7.44974E+17,0.068512174,1,79.07852 +9676,T9N; Y79N; L119F; S220T,0.064352125,4.807755,26/08/2020,-7.44974E+17,0.064483202,1,85.759995 +9677,T9P; D64N; S220T; S225T; A252S,0.018179927,3.770611,26/08/2020,-7.44974E+17,0.016594928,1,259.2856 +9678,T9P; I62F; T72S; R90L; H285L,0.004624513,1.9481741,26/08/2020,-7.44974E+17,0.002175436,1,676.69366 +9679,T9P; R94H; K236I; M273L,0.004804858,2.0353622,26/08/2020,-7.44974E+17,0.002774067,1,645.11096 +9680,T9Q,0.071967855,4.9808345,26/08/2020,-7.44974E+17,0.069587834,1,77.28984 +9681,T9R,0.22589402,6.273574,26/08/2020,-7.44974E+17,0.235371565,2,22.901386 +9682,T9R; E261Q,0.46067137,3.164569,26/08/2020,-7.44974E+17,0.460267656,125,3.7060769 +9683,T9S,0.30599847,4.0369906,26/08/2020,-7.44974E+17,0.313132004,3,9.702175 +9684,T9S; A13T,0.08135886,0.6734579,26/08/2020,-7.44974E+17,0.073466817,10,8.692781 +9685,T9S; A149G; E295G,0.003043095,1.3292931,26/08/2020,-7.44974E+17,0.000885707,1,756.5299 +9686,T9S; A59T; S103T; A149V; T179A; L189F; N289S,0.004290198,1.8111134,26/08/2020,-7.44974E+17,0.002172461,1,662.61316 +9687,T9S; A65V; T105I; S220T,0.003108276,1.3722143,26/08/2020,-7.44974E+17,0.000954995,1,762.78986 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A277G,0.62227255,5.7558985,26/08/2020,-7.44974E+17,0.751026112,1,3.1605992 +9698,T9S; T105A; L227P; D235G; A260T; M291I,0.001881419,0.56353354,26/08/2020,-7.44974E+17,6.42E-05,1,735.3638 +9699,T9S; T72A; D287E,0.010905747,3.2466757,26/08/2020,-7.44974E+17,0.009134447,1,386.25684 +9700,T9S; T81I; R148P; S220T; H230Y,0.20357007,5.279716,26/08/2020,-7.44974E+17,0.216473865,1,23.71276 +9701,T9S; V85A; G120C; A257V,0.005042536,2.1811202,26/08/2020,-7.44974E+17,0.003007057,1,629.52277 +9702,T9S; V85D; T105I; L253R,0.002034361,0.72047013,26/08/2020,-7.44974E+17,0.000195776,1,768.4324 +9703,T9S; W41R; A228P,0.002330592,0.84558225,26/08/2020,-7.44974E+17,0.000295958,1,746.34094 +9704,T9V,0.15683106,5.193894,26/08/2020,-7.44974E+17,0.161764606,1,32.26361 +9705,T9W,0.05116425,5.3069706,26/08/2020,-7.44974E+17,0.051002476,2,109.43356 +9706,V109A,0.09775184,1.9978902,26/08/2020,-7.44974E+17,0.101388122,2,22.446016 +9707,V109A; A257C,0.041644596,4.6764317,26/08/2020,-7.44974E+17,0.038428386,1,133.54608 +9708,V109A; H154L; S220T,0.021654751,4.0459876,26/08/2020,-7.44974E+17,0.019071485,1,234.21289 +9709,V109A; I128F; S220T,0.003128162,1.4067274,26/08/2020,-7.44974E+17,0.001147464,1,763.1761 +9710,V109A; L134P; S220T,0.003932484,1.7566897,26/08/2020,-7.44974E+17,0.00205807,1,696.43964 +9711,V109A; P270I,0.003508958,1.4918905,26/08/2020,-7.44974E+17,0.001208857,1,733.08575 +9712,V109A; R148P; F166Y; L173Q; S220T; H230Y,0.088255264,5.1167235,26/08/2020,-7.44974E+17,0.087035842,1,63.285248 +9713,V109A; S220T; M248K,0.002858964,1.2376732,26/08/2020,-7.44974E+17,0.00083676,1,764.5125 +9714,V109A; V130A; S220T; V221L; A260G; D287E,0.003305213,1.4130375,26/08/2020,-7.44974E+17,0.001202368,1,722.9617 +9715,V109C,0.038159497,5.5933046,26/08/2020,-7.44974E+17,0.038499662,2,155.06122 +9716,V109D,0.001164114,1.1952155,26/08/2020,-7.44974E+17,0.000589416,4,1247.1863 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D163V,0.026046365,4.2360334,26/08/2020,-7.44974E+17,0.024098987,1,198.33807 +9750,V113E; D170N; A197V; S220T,0.004548183,1.936171,26/08/2020,-7.44974E+17,0.002402427,1,670.29663 +9751,V113E; G146S; H154Q; S220T,0.004173912,1.8049245,26/08/2020,-7.44974E+17,0.001862501,1,717.7458 +9752,V113E; R148P; S220T; A231D,0.003879803,1.6755247,26/08/2020,-7.44974E+17,0.001725667,1,712.75757 +9753,V113E; S220T; P240L; S292P,0.003390507,1.4722674,26/08/2020,-7.44974E+17,0.001358212,1,728.8834 +9754,V113F,0.025437087,5.3842807,26/08/2020,-7.44974E+17,0.024526741,3,221.77275 +9755,V113G,0.030036764,2.138898,26/08/2020,-7.44974E+17,0.029447431,6,75.46372 +9756,V113G; G146D; S220T,0.003041938,1.1559823,26/08/2020,-7.44974E+17,0.000642951,1,779.2156 +9757,V113H,0.03898407,4.6083684,26/08/2020,-7.44974E+17,0.035947558,1,139.83704 +9758,V113I,0.09592093,2.2904694,26/08/2020,-7.44974E+17,0.095989934,7,22.977373 +9759,V113I; G136C; M291L,0.006395655,2.469416,26/08/2020,-7.44974E+17,0.004083498,1,560.6634 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Q194L,0.26679796,5.34749,26/08/2020,-7.44974E+17,0.286721417,1,16.397964 +9783,V130A; R148P; F166L; S220T; H230Y,0.01565168,3.764298,26/08/2020,-7.44974E+17,0.012360776,1,309.75253 +9784,V130A; S220T,0.045159053,4.837622,26/08/2020,-7.44974E+17,0.042219526,2,114.35595 +9785,V130C,0.14693105,6.2725596,26/08/2020,-7.44974E+17,0.150114993,2,39.221672 +9786,V130D,0.004615303,2.8693774,26/08/2020,-7.44974E+17,0.003826619,3,700.91095 +9787,V130E,0.008966139,2.9695199,26/08/2020,-7.44974E+17,0.00642121,1,458.7868 +9788,V130E; E208V,0.00394733,1.7568427,26/08/2020,-7.44974E+17,0.001778192,1,700.64124 +9789,V130E; L134M; M185V,0.006736962,2.5373387,26/08/2020,-7.44974E+17,0.004299758,1,532.47174 +9790,V130E; L234H; N249K,0.001916185,0.6537245,26/08/2020,-7.44974E+17,0.000119293,1,771.6898 +9791,V130E; S145W; A149V; N178S; K298R,0.002429478,1.0825334,26/08/2020,-7.44974E+17,0.000590133,1,804.1136 +9792,V130F,0.019833492,4.472858,26/08/2020,-7.44974E+17,0.01843882,2,252.08038 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A259V,0.004349587,1.8148504,26/08/2020,-7.44974E+17,0.002161463,1,673.5947 +9826,V135A; S220T; Q263R; N289I,0.002590849,1.0357171,26/08/2020,-7.44974E+17,0.00054983,1,809.801 +9827,V135D,0.003578771,1.6452695,26/08/2020,-7.44974E+17,0.002269692,2,619.98834 +9828,V135E,0.001671879,1.4974227,26/08/2020,-7.44974E+17,0.001069509,4,1065.833 +9829,V135E; D163E; L198P; S220T,0.002755798,1.1335534,26/08/2020,-7.44974E+17,0.000653687,1,762.827 +9830,V135E; L151P; A156T; A296T,0.003247074,1.4003202,26/08/2020,-7.44974E+17,0.001085922,1,788.5261 +9831,V135E; P138T; P164T; S220T,0.002508957,0.9312276,26/08/2020,-7.44974E+17,0.000386167,1,780.68567 +9832,V135E; R148P; S220T; H230Y,0.002538535,0.98267066,26/08/2020,-7.44974E+17,0.000479832,1,791.47943 +9833,V135F,0.001440898,1.0093036,26/08/2020,-7.44974E+17,0.00064781,3,959.3652 +9834,V135G,0.001583257,1.6032121,26/08/2020,-7.44974E+17,0.000982771,4,1203.9475 +9835,V135I,0.14092028,5.2267694,26/08/2020,-7.44974E+17,0.14338586,1,37.74482 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V141L,0.011103938,3.2608232,26/08/2020,-7.44974E+17,0.008751032,1,389.55798 +9858,V135T,0.012895494,4.228184,26/08/2020,-7.44974E+17,0.011798955,2,372.87073 +9859,V135W,0.001959272,1.1250523,26/08/2020,-7.44974E+17,0.000865297,2,848.85443 +9860,V135Y,0.001377696,1.1012505,26/08/2020,-7.44974E+17,0.000620153,3,1077.7465 +9861,V141A,0.06653088,5.146689,26/08/2020,-7.44974E+17,0.065383032,2,79.60937 +9862,V141A; E153V,0.007150705,2.6110907,26/08/2020,-7.44974E+17,0.004915355,1,523.3624 +9863,V141A; L173P; S220T,0.003631497,1.617734,26/08/2020,-7.44974E+17,0.001546529,1,729.7666 +9864,V141A; S220T,0.5337654,5.576329,26/08/2020,-7.44974E+17,0.629179224,1,4.815462 +9865,V141A; V175E; T188A; V199M; G286R,0.004087248,1.6619934,26/08/2020,-7.44974E+17,0.001657694,1,723.04004 +9866,V141A; Y144F; V150A; Y239N; E254D,0.002816073,1.0248145,26/08/2020,-7.44974E+17,0.000479093,1,770.7206 +9867,V141A; Y239E,0.002502884,1.0178486,26/08/2020,-7.44974E+17,0.000438386,1,814.25494 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V232D; I256V,0.003592134,1.5970321,26/08/2020,-7.44974E+17,0.001531364,1,718.07916 +9880,V141I; M248T; A296V,0.003001836,1.3323255,26/08/2020,-7.44974E+17,0.00102495,1,774.60596 +9881,V141I; R148P; Q194H; S220T; H230Y; Q263R,0.5222899,5.6879354,26/08/2020,-7.44974E+17,0.609955231,1,5.120597 +9882,V141I; R148P; S220T; V221M; H230Y,0.43656835,5.2797155,26/08/2020,-7.44974E+17,0.47073216,2,6.9873185 +9883,V141I; S145H,0.003072021,1.2966771,26/08/2020,-7.44974E+17,0.000934018,1,750.8899 +9884,V141I; S220T; E261D,0.55479014,5.922442,26/08/2020,-7.44974E+17,0.647136577,1,4.5737023 +9885,V141I; S220T; M248T; A252P,0.002971941,1.296506,26/08/2020,-7.44974E+17,0.001023519,1,755.59265 +9886,V141L,0.10844587,6.996558,26/08/2020,-7.44974E+17,0.109554685,3,60.226654 +9887,V141L; L151P; Y177N; S220T; M291L; K298N,0.002887501,1.2205708,26/08/2020,-7.44974E+17,0.000728122,1,765.6287 +9888,V141L; R148P; S220T; H230Y,0.5605253,6.2851377,26/08/2020,-7.44974E+17,0.566647766,10,4.904979 +9889,V141L; S220T,0.5378062,5.7574573,26/08/2020,-7.44974E+17,0.627751005,1,4.7962594 +9890,V141M,0.07724085,4.616181,26/08/2020,-7.44974E+17,0.077722932,4,58.39426 +9891,V141N,0.01382146,4.419938,26/08/2020,-7.44974E+17,0.012552649,2,355.90466 +9892,V141P,0.062185947,4.8549423,26/08/2020,-7.44974E+17,0.061609352,1,90.23274 +9893,V141Q,0.038718496,4.5689135,26/08/2020,-7.44974E+17,0.036348124,1,139.49336 +9894,V141W,0.027513443,6.9290423,26/08/2020,-7.44974E+17,0.02715024,5,252.98839 +9895,V141W; G146S,0.00387872,1.608332,26/08/2020,-7.44974E+17,0.001578284,1,682.24756 +9896,V141Y,0.027134303,4.3179746,26/08/2020,-7.44974E+17,0.024435719,1,195.12453 +9897,V150A,0.03958837,3.081447,26/08/2020,-7.44974E+17,0.038425278,2,88.06509 +9898,V150A; A277V,0.003865613,1.700215,26/08/2020,-7.44974E+17,0.00164845,1,709.51227 +9899,V150A; S220T,0.56934726,5.8146234,26/08/2020,-7.44974E+17,0.672096688,1,4.1909804 +9900,V150C,0.040616456,5.7673597,26/08/2020,-7.44974E+17,0.039494204,3,145.40352 +9901,V150D,0.011062153,3.7412858,26/08/2020,-7.44974E+17,0.010045278,2,385.49683 +9902,V150E,0.004886175,3.39354,26/08/2020,-7.44974E+17,0.004323197,5,737.28235 +9903,V150E; A282T,0.005519487,2.2247446,26/08/2020,-7.44974E+17,0.003232272,1,618.272 +9904,V150E; L198P; S220T; I256N,0.0042045,1.7640703,26/08/2020,-7.44974E+17,0.001945,1,667.17706 +9905,V150E; R165S; E208D,0.002065981,0.78227854,26/08/2020,-7.44974E+17,0.00028358,1,812.0702 +9906,V150E; S220T,0.10620256,5.138176,26/08/2020,-7.44974E+17,0.10547305,1,51.32545 +9907,V150G,0.018573055,3.9734821,26/08/2020,-7.44974E+17,0.016852172,1,265.60776 +9908,V150H,0.019189512,4.3656845,26/08/2020,-7.44974E+17,0.017945488,2,250.74635 +9909,V150I,0.08412329,4.995116,26/08/2020,-7.44974E+17,0.0816071,1,65.61649 +9910,V150K,0.00319979,1.4240369,26/08/2020,-7.44974E+17,0.001238016,1,757.0014 +9911,V150L,0.115598135,5.122064,26/08/2020,-7.44974E+17,0.117438526,1,46.197918 +9912,V150L; S220T; G244D,0.116376854,5.062479,26/08/2020,-7.44974E+17,0.120630415,1,45.811344 +9913,V150L; S220T; N249Y,0.003952373,1.6146297,26/08/2020,-7.44974E+17,0.001684,1,681.51215 +9914,V150M,0.01998888,3.959134,26/08/2020,-7.44974E+17,0.018065732,1,250.90607 +9915,V150M; E261Q,0.066076934,1.6668278,26/08/2020,-7.44974E+17,0.065275008,44,23.902573 +9916,V150M; F209L,0.002855933,1.1943667,26/08/2020,-7.44974E+17,0.000702916,1,829.95135 +9917,V150M; S220T,0.07058227,4.9170856,26/08/2020,-7.44974E+17,0.068487611,1,79.72453 +9918,V150N,0.028169222,5.352044,26/08/2020,-7.44974E+17,0.026795146,2,204.09464 +9919,V150P,0.01936433,6.0611334,26/08/2020,-7.44974E+17,0.018951821,4,321.7575 +9920,V150Q,0.010805047,3.2027414,26/08/2020,-7.44974E+17,0.008438784,1,399.02344 +9921,V150R,0.002263835,1.43531,26/08/2020,-7.44974E+17,0.001162981,2,873.6857 +9922,V150S,0.019949796,3.962136,26/08/2020,-7.44974E+17,0.017748847,1,252.1591 +9923,V150T,0.07264208,5.2954345,26/08/2020,-7.44974E+17,0.07146277,5,70.51124 +9924,V150W,0.010452813,4.0121326,26/08/2020,-7.44974E+17,0.009080048,2,434.60526 +9925,V150Y,0.013860354,8.179351,26/08/2020,-7.44974E+17,0.013391225,6,596.52765 +9926,V15A,0.045003045,1.364523,26/08/2020,-7.44974E+17,0.04423163,6,32.75186 +9927,V15A; D118V,0.028862044,4.4913893,26/08/2020,-7.44974E+17,0.02627167,1,187.00491 +9928,V15A; G104C; I128N; Y144C; A218S; S220T; A260V,0.003684931,1.6290642,26/08/2020,-7.44974E+17,0.001446419,1,735.24524 +9929,V15A; G35D; M74V; H183S; S301G,0.009998128,3.187596,26/08/2020,-7.44974E+17,0.007550034,1,439.7323 +9930,V15A; K34E; A59E; H183Y; H285R,0.005274891,2.2137244,26/08/2020,-7.44974E+17,0.003047459,1,627.309 +9931,V15A; L119H; R148P; S220T; H230Y,0.06135393,4.8183584,26/08/2020,-7.44974E+17,0.059717117,1,90.32708 +9932,V15A; M23V; N42Y; Y79N; F217I; S220T,0.002942683,1.2307868,26/08/2020,-7.44974E+17,0.00077441,1,763.73224 +9933,V15A; M74I; S220T; L246P; A260T,0.002732649,1.0850769,26/08/2020,-7.44974E+17,0.000542595,1,770.7108 +9934,V15A; P88T; G162R; S220T,0.003012022,1.3210479,26/08/2020,-7.44974E+17,0.000913337,1,777.7858 +9935,V15A; R148P; S220T; H230Y,0.49832797,5.873167,26/08/2020,-7.44974E+17,0.558022347,1,5.8509173 +9936,V15A; S220T,0.20092973,5.251231,26/08/2020,-7.44974E+17,0.208271722,1,24.230297 +9937,V15A; S49G; P70S; A112T; L214F; K298Q,0.002634008,1.0909623,26/08/2020,-7.44974E+17,0.00060915,1,779.9277 +9938,V15A; T44A; T81I; S220T,0.003932034,1.6728605,26/08/2020,-7.44974E+17,0.001779176,1,686.6586 +9939,V15A; T81I; R148L; A174P; S220T,0.002874208,1.290281,26/08/2020,-7.44974E+17,0.000914899,1,786.72064 +9940,V15A; V18A; L32F; S220T; M273T,0.003980019,1.693922,26/08/2020,-7.44974E+17,0.001764139,1,725.2915 +9941,V15C,0.06603557,6.592206,26/08/2020,-7.44974E+17,0.066392883,3,98.60461 +9942,V15D; A33T; G50D; Y123H; R137H; Q194L; N238S; V281L,0.005181025,2.0882754,26/08/2020,-7.44974E+17,0.002647027,1,627.45306 +9943,V15D; C75R; A92E; R110H; L181S; L243Q; F297L,0.006021343,2.3822863,26/08/2020,-7.44974E+17,0.003774228,1,580.31836 +9944,V15D; E108D; A202P,0.002663632,1.4836751,26/08/2020,-7.44974E+17,0.001367611,2,782.7898 +9945,V15D; H36L; F86L; D152V; L227R; N251D,0.002915899,1.2192607,26/08/2020,-7.44974E+17,0.000790706,1,749.12634 +9946,V15D; L25F; V135I; D163V; D170V,0.002662061,1.1048831,26/08/2020,-7.44974E+17,0.000585704,1,765.25446 +9947,V15D; L93P; A260S,0.002367346,0.95879585,26/08/2020,-7.44974E+17,0.000421903,1,787.7874 +9948,V15D; P22S; R122Q; Y144N; A197T; Q263L; R279L; M291R,0.003105325,1.2753148,26/08/2020,-7.44974E+17,0.000870803,1,771.9495 +9949,V15D; P37H; Y79C; M82T; L151Q; T179A; M185V; A206S; G244D; A296V,0.003674738,1.6313684,26/08/2020,-7.44974E+17,0.001566801,1,714.15625 +9950,V15D; R90C; S220T,0.003374054,1.4494269,26/08/2020,-7.44974E+17,0.001194474,1,770.0513 +9951,V15D; V109E; S145T; A156D,0.002487877,0.9920462,26/08/2020,-7.44974E+17,0.000424129,1,802.4022 +9952,V15D; V30A; S57A; A116P; M185L; L193P; S220T; L246F,0.002940967,1.8259438,26/08/2020,-7.44974E+17,0.001782067,2,822.2003 +9953,V15D; V99D; P106L; T179A; H183R; S220T; F297L,0.003196328,1.3392534,26/08/2020,-7.44974E+17,0.000992967,1,759.69855 +9954,V15D; Y186H; S220T; G244C,0.002524156,0.99569505,26/08/2020,-7.44974E+17,0.000477971,1,788.4009 +9955,V15D; Y192N; S220T,0.002762464,1.2217115,26/08/2020,-7.44974E+17,0.000783223,1,768.6704 +9956,V15D; Y79H; S220T; V250M; P270Q; H271L,0.003448261,1.4257954,26/08/2020,-7.44974E+17,0.001215022,1,732.75635 +9957,V15F,0.002729382,1.1131749,26/08/2020,-7.44974E+17,0.000599738,1,764.512 +9958,V15F; G24E; G120S; P131I; Q194L; A303D,0.001967422,0.68220013,26/08/2020,-7.44974E+17,0.000131488,1,773.14746 +9959,V15F; L20Q; L58F; H183R; S220T,0.002707403,1.0750049,26/08/2020,-7.44974E+17,0.00057216,1,771.5003 +9960,V15F; R148P; A210T; S220T; H230Y; A277V,0.004337899,1.792147,26/08/2020,-7.44974E+17,0.002158234,1,675.94775 +9961,V15F; S201F; A229S; G264D; L269M; M273L; Y290N,0.002189785,0.8649091,26/08/2020,-7.44974E+17,0.000317269,1,812.37415 +9962,V15F; V250M,0.002416991,0.8970511,26/08/2020,-7.44974E+17,0.000322145,1,776.0335 +9963,V15G,0.001476184,1.1675837,26/08/2020,-7.44974E+17,0.001022164,5,937.71814 +9964,V15G; D64E; F142L; S220T,0.00306411,1.3651109,26/08/2020,-7.44974E+17,0.001095388,1,765.02716 +9965,V15G; L151M; L167F; S220T; A260V,0.002772901,1.1084512,26/08/2020,-7.44974E+17,0.000633655,1,773.46124 +9966,V15G; N42Y; D235V,0.00363269,1.6083258,26/08/2020,-7.44974E+17,0.001482497,1,727.5947 +9967,V15H,0.002592944,1.0887945,26/08/2020,-7.44974E+17,0.000597425,1,824.37335 +9968,V15I,0.07680376,5.0874267,26/08/2020,-7.44974E+17,0.078133793,2,67.444115 +9969,V15I; A28V; S220T,0.007366544,2.6942523,26/08/2020,-7.44974E+17,0.005016459,1,519.07166 +9970,V15I; A60V; P70H; L173P,0.004066638,1.7444824,26/08/2020,-7.44974E+17,0.001677634,1,696.3878 +9971,V15I; G126V; K298E,0.004328848,1.8325762,26/08/2020,-7.44974E+17,0.002087083,1,666.0648 +9972,V15I; L25I; N190T; G284C,0.002354633,0.9345359,26/08/2020,-7.44974E+17,0.000410403,1,806.4237 +9973,V15I; N42S; A111V; R148P; S220T; H230Y,0.005842592,2.252705,26/08/2020,-7.44974E+17,0.003567815,1,567.552 +9974,V15I; P132L; S267L,0.003102007,1.356399,26/08/2020,-7.44974E+17,0.001073589,1,751.9779 +9975,V15I; V17M,0.007150998,3.3850088,26/08/2020,-7.44974E+17,0.005937437,2,557.7913 +9976,V15I; Y123H,0.003179288,1.3943588,26/08/2020,-7.44974E+17,0.000971,1,789.51355 +9977,V15I; Y177H; S220T,0.47520858,5.592844,26/08/2020,-7.44974E+17,0.553952344,1,6.4552064 +9978,V15L,0.007602019,4.075144,26/08/2020,-7.44974E+17,0.006654536,3,588.6149 +9979,V15L; L58F; R148P; A174V; S220T; S225L; H230Y; N238S,0.005105332,2.2040987,26/08/2020,-7.44974E+17,0.002973059,1,637.2738 +9980,V15L; R155H; S220T; K236I,0.010711243,3.165151,26/08/2020,-7.44974E+17,0.008225763,1,399.64114 +9981,V15L; S220T; K236R,0.030009748,4.3994966,26/08/2020,-7.44974E+17,0.027808831,1,177.45526 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V232D,0.8312229,17.688986,26/08/2020,-7.44974E+17,0.854674968,10,3.4976525 +9994,V175C,0.042211436,4.5961742,26/08/2020,-7.44974E+17,0.040283555,1,131.0991 +9995,V175D,0.007069991,2.5691533,26/08/2020,-7.44974E+17,0.004779464,1,523.7813 +9996,V175E; S220T,0.022460606,4.167302,26/08/2020,-7.44974E+17,0.020127971,1,232.74516 +9997,V175E; S220T; K236R,0.009371384,2.98527,26/08/2020,-7.44974E+17,0.006737629,1,434.3538 +9998,V175F,0.001628111,1.8758519,26/08/2020,-7.44974E+17,0.001124056,5,1295.9753 +9999,V175G,0.020483399,3.7017648,26/08/2020,-7.44974E+17,0.019467015,3,195.84875 +10000,V175H,0.004731968,0.6452688,26/08/2020,-7.44974E+17,0.004187991,6,176.18773 +10001,V175I,0.21313821,2.034607,26/08/2020,-7.44974E+17,0.208979393,3,8.298324 +10002,V175K,0.5391436,2.3444242,26/08/2020,-7.44974E+17,0.557258505,12,1.98979 +10003,V175K; A218M; A296T,0.78836334,7.2959013,26/08/2020,-7.44974E+17,0.837938872,4,1.7772372 +10004,V175K; Y177W; A259T,0.81882745,22.40716,26/08/2020,-7.44974E+17,0.825496881,31,4.9269114 +10005,V175L,0.021052776,4.869636,26/08/2020,-7.44974E+17,0.018949296,2,254.98074 +10006,V175L; L181I; K298R,0.003695488,1.6625785,26/08/2020,-7.44974E+17,0.001642474,1,719.62524 +10007,V175L; S220T; I256T; P270L,0.002786629,1.1391131,26/08/2020,-7.44974E+17,0.000616837,1,772.05334 +10008,V175M,0.1639518,2.7297883,26/08/2020,-7.44974E+17,0.167081235,3,15.317051 +10009,V175M; A195S; E208K,0.012298716,3.423431,26/08/2020,-7.44974E+17,0.009490813,1,371.44876 +10010,V175M; A282G,0.018505817,3.8788936,26/08/2020,-7.44974E+17,0.016836087,1,260.12506 +10011,V175M; T188S; V232A; V293A,0.007202531,2.6933467,26/08/2020,-7.44974E+17,0.005181084,1,510.47562 +10012,V175N,0.08469605,1.6473986,26/08/2020,-7.44974E+17,0.084364157,4,20.54497 +10013,V175P,0.004057959,2.852716,26/08/2020,-7.44974E+17,0.003304432,3,803.78564 +10014,V175Q,0.021674,4.937985,26/08/2020,-7.44974E+17,0.020592756,2,249.33289 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K298M,0.001288615,0.6226762,26/08/2020,-7.44974E+17,0.000177388,2,918.6923 +10026,V17A; P106Q,0.002335528,0.869337,26/08/2020,-7.44974E+17,0.000322798,1,806.73083 +10027,V17A; P70H; S220T,0.003643685,1.5378585,26/08/2020,-7.44974E+17,0.001325111,1,733.5625 +10028,V17A; R122C; D152N; P219S; H230L,0.002732417,1.168393,26/08/2020,-7.44974E+17,0.000762502,1,767.0197 +10029,V17A; S220T,0.006839956,2.5953722,26/08/2020,-7.44974E+17,0.004808149,1,539.2065 +10030,V17A; S220T; A228V,0.003622222,1.5644181,26/08/2020,-7.44974E+17,0.001465885,1,720.155 +10031,V97Q,0.014409273,5.2761703,26/08/2020,-7.44974E+17,0.013835964,3,387.44193 +10032,V17A; V30E; E91D; S226T; Q263H; P300L,0.003891782,1.7274562,26/08/2020,-7.44974E+17,0.001790165,1,707.3805 +10033,V17D,0.001561937,1.0450742,26/08/2020,-7.44974E+17,0.000777789,3,898.274 +10034,V17E,0.002295009,1.501599,26/08/2020,-7.44974E+17,0.001653702,4,783.6749 +10035,V17E; A197D,0.002875201,1.1769295,26/08/2020,-7.44974E+17,0.000779477,1,760.94965 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K298M,0.002123021,0.83491963,26/08/2020,-7.44974E+17,0.000289653,1,782.3886 +10046,V17E; M82L; D170Y; E208K,0.003110184,1.327233,26/08/2020,-7.44974E+17,0.000945125,1,748.67474 +10047,V17E; N178K; V199M; P302Q,0.003186825,1.383581,26/08/2020,-7.44974E+17,0.001054885,1,765.025 +10048,V17E; P45H; A174V; G244D,0.005900025,2.286712,26/08/2020,-7.44974E+17,0.003367159,1,592.6458 +10049,V17E; R148P; S220T; H230Y,0.005908115,2.326605,26/08/2020,-7.44974E+17,0.003454733,1,586.6731 +10050,V17E; R43S; M129I; F142L; H271R,0.009493951,2.997815,26/08/2020,-7.44974E+17,0.006943663,1,446.64273 +10051,V17E; S220T,0.003940799,1.7422017,26/08/2020,-7.44974E+17,0.00173136,1,692.4932 +10052,V17E; S220T; V221L; A252T; E254D,0.003112461,1.3100067,26/08/2020,-7.44974E+17,0.000911923,1,739.9804 +10053,V17E; S67R; A116T; S220T,0.004078477,1.7023084,26/08/2020,-7.44974E+17,0.001637373,1,699.8823 +10054,V17E; T38K; C75R; E84D; L143M; A156T; D163G; R165S; A260G; H285N,0.003781288,1.6217717,26/08/2020,-7.44974E+17,0.001492015,1,744.48096 +10055,V17E; T38K; D95N; F142L; D211V; S226F; H288L,0.002692424,1.1280037,26/08/2020,-7.44974E+17,0.000587457,1,806.32733 +10056,V17E; V52E; S220T; V281F; F297Y,0.002307674,0.9442976,26/08/2020,-7.44974E+17,0.000388574,1,859.75146 +10057,V17E; V97D; L189I; S220T; E268A,0.004468606,1.9147027,26/08/2020,-7.44974E+17,0.002301042,1,656.29663 +10058,V17E; W41R; I62N; M129T; G168R,0.00210092,0.7204951,26/08/2020,-7.44974E+17,0.000173688,1,803.26355 +10059,V17G,0.015616897,0.5385488,26/08/2020,-7.44974E+17,0.015341699,3,55.676758 +10060,V17G; M184T; A202T; S220T,0.002532378,1.5686754,26/08/2020,-7.44974E+17,0.001383156,2,841.2282 +10061,V17G; N78S; E91V; V130E; F217Y,0.003346042,1.488082,26/08/2020,-7.44974E+17,0.001158012,1,747.5133 +10062,V17G; V66L; M74I; R165H,0.003008877,1.2782582,26/08/2020,-7.44974E+17,0.00074921,1,770.37335 +10063,V17H,0.000975099,1.2623824,26/08/2020,-7.44974E+17,0.000606419,6,1489.0775 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H255Y,0.003450013,1.4814613,26/08/2020,-7.44974E+17,0.001239129,1,748.2063 +10103,V18A; L32H; Q107R; A111T; V265A; R274C,0.002426539,0.9350233,26/08/2020,-7.44974E+17,0.000402248,1,778.62634 +10104,V18A; M23V; R148P; S220T; H230Y,0.00536311,2.1883419,26/08/2020,-7.44974E+17,0.00330848,1,608.6352 +10105,V18A; M74V; M82K; P106R,0.002221198,0.7953841,26/08/2020,-7.44974E+17,0.000228065,1,834.5858 +10106,V18A; P22L; S220T,0.002441764,1.506321,26/08/2020,-7.44974E+17,0.001158193,2,854.60187 +10107,V18A; P22Q; R110S; S201Y,0.002151673,0.7888345,26/08/2020,-7.44974E+17,0.000233987,1,790.93427 +10108,V18A; S160T; F209L; S220T; V281I,0.004292273,1.8267419,26/08/2020,-7.44974E+17,0.001904154,1,702.6143 +10109,V18A; S220T,0.120055474,5.0992203,26/08/2020,-7.44974E+17,0.121727039,1,43.44758 +10110,V18A; S220T; R247H,0.27130732,5.3427033,26/08/2020,-7.44974E+17,0.293171924,1,15.83157 +10111,V18A; T63A; T81I; G146D; E208G; H266Q,0.003714312,1.4830916,26/08/2020,-7.44974E+17,0.001150204,1,733.7879 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Y294F,0.002650285,1.0231013,26/08/2020,-7.44974E+17,0.000477642,1,772.65454 +10122,V18D; V66A; G203A; E254G; Q263H,0.005561369,1.2288965,26/08/2020,-7.44974E+17,0.00448163,2,318.1075 +10123,V18D; Y123N; L253P; P270S,0.00308659,1.2670623,26/08/2020,-7.44974E+17,0.000910984,1,741.43896 +10124,V18E,0.001716757,1.0415642,26/08/2020,-7.44974E+17,0.000600287,2,943.9645 +10125,V18F,0.003678009,2.4895175,26/08/2020,-7.44974E+17,0.002792603,3,788.746 +10126,V18F; L158H; T242I; V250E,0.005042313,2.138602,26/08/2020,-7.44974E+17,0.003045816,1,616.095 +10127,V18F; R122C; P132S; S220T; N251I,0.002539037,1.1052762,26/08/2020,-7.44974E+17,0.000644909,1,812.65375 +10128,V18G,0.001187548,1.1371365,26/08/2020,-7.44974E+17,0.000572548,4,1204.755 +10129,V18G; G203S; S220T; G264S,0.002694071,1.1080086,26/08/2020,-7.44974E+17,0.00054777,1,769.3145 +10130,V18G; L246H,0.002202876,0.82042736,26/08/2020,-7.44974E+17,0.000248635,1,812.7732 +10131,V18G; S220T; 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L143V,0.008419998,3.6476538,26/08/2020,-7.44974E+17,0.007043155,2,506.7596 +10603,V66A; L76P; A89P; D163G; P164L; A257S,0.003621939,1.5470273,26/08/2020,-7.44974E+17,0.001442073,1,719.2947 +10604,V66A; L98P; A127T; S220T,0.002423422,0.95827657,26/08/2020,-7.44974E+17,0.000423668,1,786.763 +10605,V66A; M184V; S220T,0.009383028,2.9639552,26/08/2020,-7.44974E+17,0.006827756,1,436.05627 +10606,V66A; M185I; E208K; F217L,0.002757443,1.4124649,26/08/2020,-7.44974E+17,0.001528835,2,741.21985 +10607,V66A; T121P; S220T,0.001774979,1.073919,26/08/2020,-7.44974E+17,0.000644487,2,902.2928 +10608,V66A; Y144S; L161M; P300L,0.002619295,1.0729666,26/08/2020,-7.44974E+17,0.000633827,1,773.5377 +10609,V66C,0.043041606,4.640614,26/08/2020,-7.44974E+17,0.041075599,1,125.688515 +10610,V66D,0.00203862,1.2999083,26/08/2020,-7.44974E+17,0.000916531,2,903.9514 +10611,V66E,0.001006717,1.4626985,26/08/2020,-7.44974E+17,0.000694572,7,1620.9614 +10612,V66E; E108D; P133H; N289S,0.001989327,0.730043,26/08/2020,-7.44974E+17,0.000196219,1,770.925 +10613,V66E; E153G; G200R; S220T,0.00298605,1.2747709,26/08/2020,-7.44974E+17,0.000911525,1,785.62695 +10614,V66E; H154Q; S220T; N238D,0.003115469,1.3452802,26/08/2020,-7.44974E+17,0.000955439,1,747.2376 +10615,V66E; L222M; V250L; R258H,0.002922034,1.2487906,26/08/2020,-7.44974E+17,0.00084621,1,755.2512 +10616,V66E; L98V; A116T; L227P,0.002408441,0.95757055,26/08/2020,-7.44974E+17,0.000413313,1,786.43994 +10617,V66E; M129I; S145T; R148P; S220T; H230Y,0.00321614,1.3307354,26/08/2020,-7.44974E+17,0.001027599,1,767.3374 +10618,V66E; N238K; Q263R,0.004131278,1.7948554,26/08/2020,-7.44974E+17,0.002029204,1,680.87305 +10619,V66E; V113E; A116S; D118N; V199A,0.005490263,2.2954023,26/08/2020,-7.44974E+17,0.003413462,1,611.7392 +10620,V66E; V130M; S220T,0.002824941,1.1823164,26/08/2020,-7.44974E+17,0.000776417,1,768.45844 +10621,V66E; V135E; A213T,0.002257986,0.8620249,26/08/2020,-7.44974E+17,0.000327347,1,793.30725 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F297Y,0.002747388,1.086675,26/08/2020,-7.44974E+17,0.000558394,1,735.29126 +10633,V66L; P70R; M129K; T196A; S220T; Y294F,0.00247652,1.0881644,26/08/2020,-7.44974E+17,0.000582439,1,787.3204 +10634,V66L; S220T; M273R,0.002990383,1.2456863,26/08/2020,-7.44974E+17,0.000785348,1,771.917 +10635,V66L; V113I; R137G; F142Y; T179I,0.002557649,1.0756152,26/08/2020,-7.44974E+17,0.000528951,1,831.2288 +10636,V66L; V99D; D211E,0.001922739,0.6076612,26/08/2020,-7.44974E+17,8.35E-05,1,736.8395 +10637,V66L; Y83C; R148P; S220T; H230Y; Q263H,0.003822684,1.6805004,26/08/2020,-7.44974E+17,0.00169141,1,700.28796 +10638,V66M,0.016966257,4.76917,26/08/2020,-7.44974E+17,0.016418396,3,300.73834 +10639,V66M; G168V; L214P; D233V; G241C,0.002354073,0.8715619,26/08/2020,-7.44974E+17,0.000339468,1,790.7792 +10640,V66M; L161P; S220T,0.002354891,0.8848512,26/08/2020,-7.44974E+17,0.000314695,1,770.20856 +10641,V66M; P131T; A180T; T188P; S220T,0.003151932,1.3789371,26/08/2020,-7.44974E+17,0.001155412,1,763.7955 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G241C,0.018141238,3.8705366,26/08/2020,-7.44974E+17,0.016243867,1,265.53732 +10663,V71A; S220T; V221G,0.037746128,4.5209703,26/08/2020,-7.44974E+17,0.036148852,1,144.35265 +10664,V71A; S220T; Y294F,0.21984693,5.3670006,26/08/2020,-7.44974E+17,0.233460558,1,21.530468 +10665,V71C,0.0665702,0.98700845,26/08/2020,-7.44974E+17,0.067813044,5,16.854948 +10666,V71D,0.001476686,0.8264531,26/08/2020,-7.44974E+17,0.000369634,2,918.3839 +10667,V71D; F86L; D152Y; S220T; L269S,0.002852154,1.1828693,26/08/2020,-7.44974E+17,0.000708309,1,738.57904 +10668,V71D; L101Q; D125V; L234R; V265I,0.004657568,2.0504732,26/08/2020,-7.44974E+17,0.002292379,1,670.99194 +10669,V71D; L134Q; L189S; S220T; A280S,0.004590671,1.9478137,26/08/2020,-7.44974E+17,0.002297848,1,678.89044 +10670,V71D; P164L; N178I; A197V; E208G,0.002472355,1.0054145,26/08/2020,-7.44974E+17,0.000562301,1,784.54193 +10671,V71D; S220T,0.003437328,1.4425509,26/08/2020,-7.44974E+17,0.001117547,1,729.3393 +10672,V71D; S220T; A223V,0.002740635,1.0937488,26/08/2020,-7.44974E+17,0.000644708,1,773.34576 +10673,V71D; S220T; A277T,0.00215563,0.8368423,26/08/2020,-7.44974E+17,0.000318903,1,765.2084 +10674,V71D; S220T; R247C,0.008636172,2.9057734,26/08/2020,-7.44974E+17,0.006306321,1,463.39404 +10675,V71D; T72S; N238K,0.005411305,2.2024844,26/08/2020,-7.44974E+17,0.003115655,1,599.91675 +10676,V71D; T81I; Q107H; D118N; T121S; Q194H; E262G,0.003164055,1.304311,26/08/2020,-7.44974E+17,0.000993287,1,759.80225 +10677,V71D; V109G; A174V; P300S,0.005604172,2.243385,26/08/2020,-7.44974E+17,0.003247047,1,593.1486 +10678,V71D; V99I; A169V; G284S,0.002513873,1.0095147,26/08/2020,-7.44974E+17,0.000451024,1,787.5435 +10679,V71E,0.003357859,1.4193214,26/08/2020,-7.44974E+17,0.00104204,1,734.22736 +10680,V71F,0.016871613,1.4359381,26/08/2020,-7.44974E+17,0.016229489,3,104.90775 +10681,V71F; A80D; S220T; H230Y; E275V; D287Y,0.005047809,2.023034,26/08/2020,-7.44974E+17,0.002548851,1,635.20935 +10682,V71F; M129V,0.004232033,1.8183751,26/08/2020,-7.44974E+17,0.001994103,1,686.46936 +10683,V71G,0.003077733,2.3024867,26/08/2020,-7.44974E+17,0.00229999,3,869.2013 +10684,V71G; D170Y,0.002566101,0.9558033,26/08/2020,-7.44974E+17,0.000391213,1,773.036 +10685,V71G; F166S; T172S; S220T,0.003486747,1.4167656,26/08/2020,-7.44974E+17,0.001129004,1,754.3108 +10686,V71G; G126D; A127T; L269S,0.002755732,1.2125955,26/08/2020,-7.44974E+17,0.000765633,1,798.6842 +10687,V71G; M82L; D125G; R148P; S220T; H230Y; G244V; H271Y,0.003046003,1.3671346,26/08/2020,-7.44974E+17,0.001042892,1,793.4632 +10688,V71G; P88H; P138L; R148C; A149T; P219A; S220T,0.002932999,1.1968039,26/08/2020,-7.44974E+17,0.000741342,1,744.0831 +10689,V71H,0.001927057,1.7441659,26/08/2020,-7.44974E+17,0.00141349,4,1047.6312 +10690,V71I,0.23850173,0.9375656,26/08/2020,-7.44974E+17,0.268953477,7,3.2833705 +10691,V71I; A218M; A303P,0.5775295,2.4437118,26/08/2020,-7.44974E+17,0.577514904,12,1.7595581 +10692,V71I; A218M; A303T,0.6315853,3.5106256,26/08/2020,-7.44974E+17,0.671657975,2,1.884581 +10693,V71I; A218M; P302K,0.68161094,5.0517225,26/08/2020,-7.44974E+17,0.690945663,15,2.322589 +10694,V71I; A218M; S304P,0.3682017,0.4339354,26/08/2020,-7.44974E+17,0.456639311,6,0.8086668 +10695,V71I; A259T; A303K,0.69786036,5.0493245,26/08/2020,-7.44974E+17,0.763775462,2,1.9628394 +10696,V71I; A296T; A303T,0.5956011,2.4093423,26/08/2020,-7.44974E+17,0.600349584,10,1.6082759 +10697,V71I; A80C,0.23492777,5.348251,26/08/2020,-7.44974E+17,0.248566499,1,20.042442 +10698,V71I; C75D,0.002619838,1.0489475,26/08/2020,-7.44974E+17,0.000567492,1,740.69385 +10699,V71I; D147Y; E278G,0.003187303,1.4078962,26/08/2020,-7.44974E+17,0.001059007,1,748.864 +10700,V71I; D95N; S220T,0.61100596,5.990991,26/08/2020,-7.44974E+17,0.728152947,1,3.505441 +10701,V71I; D95V; M184K; Y186F; L193P; E262D; G264A,0.004295527,1.8388908,26/08/2020,-7.44974E+17,0.002043809,1,672.8963 +10702,V71I; E108D; T121K; L143S; L151M; N178D; G203C; F217L; M248I,0.002048036,0.6791178,26/08/2020,-7.44974E+17,0.00013956,1,762.1389 +10703,V71I; E84G; S220T; N238D; A277T,0.003815978,1.5803738,26/08/2020,-7.44974E+17,0.001392001,1,705.64386 +10704,V71I; G146S; L161P; N178S; S220T; Y290S,0.002613838,1.0446384,26/08/2020,-7.44974E+17,0.000529161,1,743.75336 +10705,V71I; G87D; N102K; Q107H; S220T,0.003407491,1.4657825,26/08/2020,-7.44974E+17,0.00128764,1,747.3608 +10706,V71I; L119F; L167H; S220T; L222Q; L253P; M273L,0.002824819,1.2295423,26/08/2020,-7.44974E+17,0.000763139,1,748.415 +10707,V71I; L76H; D139G; A187T; A282E,0.003617395,1.542977,26/08/2020,-7.44974E+17,0.00139906,1,745.30426 +10708,V71I; P302R; A303K,0.5081152,1.3289591,26/08/2020,-7.44974E+17,0.519396348,10,1.2884047 +10709,V71I; P302R; A303P,0.46110013,0.8590266,26/08/2020,-7.44974E+17,0.47291716,10,1.0210956 +10710,V71I; P302R; S304I,0.42778826,0.83539504,26/08/2020,-7.44974E+17,0.444474215,10,1.1421143 +10711,V71I; P302R; S304L,0.4012087,0.8124536,26/08/2020,-7.44974E+17,0.40993415,10,1.2514743 +10712,V71I; S220T; G244S; E275V,0.016810685,3.8616788,26/08/2020,-7.44974E+17,0.013812784,1,293.16132 +10713,V71I; T196A,0.07170329,4.8710938,26/08/2020,-7.44974E+17,0.070668908,1,76.45951 +10714,V71K,0.002393492,0.481265,26/08/2020,-7.44974E+17,0.001797883,3,339.5011 +10715,V71L; D118N; V250L,0.17617139,0.5682469,26/08/2020,-7.44974E+17,0.188960487,7,3.1310456 +10716,V71L; D139H; D147V; E262G,0.002517176,1.0054479,26/08/2020,-7.44974E+17,0.000466891,1,780.40594 +10717,V71L; L124H; Y144F,0.002739834,1.1144438,26/08/2020,-7.44974E+17,0.000546202,1,777.1951 +10718,V71L; R148P; S220T; H230Y,0.6197454,5.9058948,26/08/2020,-7.44974E+17,0.727104061,1,3.2854044 +10719,V71L; V85G; A92V; A127T,0.002668062,1.099803,26/08/2020,-7.44974E+17,0.000612782,1,767.80054 +10720,V71M,0.36965162,5.5284395,26/08/2020,-7.44974E+17,0.403656893,1,10.0542555 +10721,V71M; A156T; A259T,0.7486301,11.087598,26/08/2020,-7.44974E+17,0.758456529,17,3.6822176 +10722,V71M; A156T; V175K,0.73463774,9.602908,26/08/2020,-7.44974E+17,0.748571404,10,3.3927264 +10723,V71M; A156T; Y177W,0.61874104,5.125886,26/08/2020,-7.44974E+17,0.629345724,23,3.1398122 +10724,V71M; E91D; A156T,0.4834415,5.224911,26/08/2020,-7.44974E+17,0.494150528,6,5.6024776 +10725,V71M; V175K; A259T,0.79530525,18.271738,26/08/2020,-7.44974E+17,0.809156334,17,4.652294 +10726,V71N,0.001297156,1.3552741,26/08/2020,-7.44974E+17,0.00083656,5,1233.5538 +10727,V71P,0.001947098,1.141293,26/08/2020,-7.44974E+17,0.001163822,3,776.28174 +10728,V71Q,0.005582932,2.216284,26/08/2020,-7.44974E+17,0.003196164,1,595.7017 +10729,V71Q; M74I,0.002601145,1.2273512,26/08/2020,-7.44974E+17,0.001577251,2,666.2492 +10730,V71R,0.004866509,2.025941,26/08/2020,-7.44974E+17,0.002353938,1,657.8963 +10731,V71S,0.003495229,2.137548,26/08/2020,-7.44974E+17,0.002368186,2,775.5286 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G284S,0.003256505,1.3847619,26/08/2020,-7.44974E+17,0.001015079,1,754.76025 +10744,V85A; S226P; G264S; N289S,0.002996418,1.3348483,26/08/2020,-7.44974E+17,0.001060178,1,742.37946 +10745,V85A; T172R; Y290F,0.003058613,1.2991223,26/08/2020,-7.44974E+17,0.000934036,1,785.5758 +10746,V85A; V109A; E208K,0.007663554,2.729426,26/08/2020,-7.44974E+17,0.005356088,1,506.3795 +10747,V85A; V99I; R148P; S220T; H230Y; D233E; G244D; A252G; P300S,0.00296366,1.2392331,26/08/2020,-7.44974E+17,0.000798563,1,783.03796 +10748,V85C,0.02867425,4.315432,26/08/2020,-7.44974E+17,0.026506397,1,185.26724 +10749,V85D; A156V; V265L; R279G; N289Y,0.001932633,0.6791411,26/08/2020,-7.44974E+17,0.000148234,1,779.178 +10750,V85D; A187E; S220T,0.003305719,1.3799034,26/08/2020,-7.44974E+17,0.001052258,1,715.65985 +10751,V85D; L101Q; N249D; M273V,0.001993849,0.6148163,26/08/2020,-7.44974E+17,0.000106755,1,733.53766 +10752,V85D; P106L; L246H; N289D,0.00368405,1.5565684,26/08/2020,-7.44974E+17,0.001504622,1,727.2982 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A260T,0.003384934,1.4322672,26/08/2020,-7.44974E+17,0.001212466,1,736.9714 +10764,V85L,0.05089441,6.72454,26/08/2020,-7.44974E+17,0.051208689,4,130.75848 +10765,V85M,0.018257711,4.930266,26/08/2020,-7.44974E+17,0.016219671,2,297.7889 +10766,V85N,0.006690136,4.4687147,26/08/2020,-7.44974E+17,0.005940009,4,709.7474 +10767,V85P,0.00097972,1.1245725,26/08/2020,-7.44974E+17,0.000526828,5,1386.3497 +10768,V85Q,0.015572717,3.7577307,26/08/2020,-7.44974E+17,0.012725917,1,312.83313 +10769,V85R,0.005110063,3.9349978,26/08/2020,-7.44974E+17,0.004625959,5,811.2088 +10770,V85S,0.012771967,4.0314064,26/08/2020,-7.44974E+17,0.011551593,2,353.93 +10771,V85T,0.040679965,6.39024,26/08/2020,-7.44974E+17,0.040128084,3,159.14105 +10772,V85W,0.006507418,3.9140718,26/08/2020,-7.44974E+17,0.00574943,4,646.5365 +10773,V85Y,0.00595342,2.2777464,26/08/2020,-7.44974E+17,0.003554035,1,563.364 +10774,V97A,0.068129174,5.965351,26/08/2020,-7.44974E+17,0.068766059,2,89.253265 +10775,V97A; A218T; K236N; M291K,0.001786714,0.5590962,26/08/2020,-7.44974E+17,6.78E-05,1,731.66223 +10776,V97A; E108D; A116T; D139V,0.005676782,2.26253,26/08/2020,-7.44974E+17,0.003694557,1,590.53687 +10777,V97A; L161M; S226P; S301C,0.003523326,1.4723375,26/08/2020,-7.44974E+17,0.001168016,1,729.3667 +10778,V97A; L98P; H154R; M184I; S220T; A280T,0.005444635,2.1902483,26/08/2020,-7.44974E+17,0.003115255,1,613.46844 +10779,V97A; R148C; R155P; S220T; P224H; R247S,0.002775751,1.0738903,26/08/2020,-7.44974E+17,0.000501135,1,797.7384 +10780,V97A; S220T,0.6249026,5.457365,26/08/2020,-7.44974E+17,0.681721138,2,3.1331775 +10781,V97A; S220T; H230Y,0.7888526,16.223394,26/08/2020,-7.44974E+17,0.81150111,10,4.2601047 +10782,V97A; Y239H,0.005518063,2.2130268,26/08/2020,-7.44974E+17,0.003198866,1,594.7278 +10783,V97C,0.11758266,5.4234934,26/08/2020,-7.44974E+17,0.117558588,2,44.116047 +10784,V97D,0.036470827,4.461055,26/08/2020,-7.44974E+17,0.034842018,1,145.46413 +10785,V97D; E108V; L243P,0.003076745,1.3728906,26/08/2020,-7.44974E+17,0.001060187,1,759.07666 +10786,V97D; E295D,0.010464277,3.1798227,26/08/2020,-7.44974E+17,0.008370251,1,403.6238 +10787,V97D; F166L; V199M; S220T,0.024850564,4.144499,26/08/2020,-7.44974E+17,0.022026112,1,210.46822 +10788,V97D; G104S; L198Q; S220T; E268D,0.002232546,0.8966745,26/08/2020,-7.44974E+17,0.000341554,1,782.06635 +10789,V97D; L151P; M185V; V281D,0.004504383,1.8103306,26/08/2020,-7.44974E+17,0.001836187,1,678.4896 +10790,V97D; L189I; S220T; V221E; M248K,0.004159019,1.7542543,26/08/2020,-7.44974E+17,0.001976349,1,676.0105 +10791,V97D; M129I; N249T; H266L; Q283H,0.002387716,0.9740818,26/08/2020,-7.44974E+17,0.000475714,1,813.0816 +10792,V97D; P171L; S201P; V204M,0.002110819,0.74104524,26/08/2020,-7.44974E+17,0.000187262,1,723.7736 +10793,V97D; S220T,0.32588172,5.36707,26/08/2020,-7.44974E+17,0.353313645,1,12.187763 +10794,V97D; S220T; H230Y,0.5799034,5.6783557,26/08/2020,-7.44974E+17,0.692399443,1,3.8504965 +10795,V97D; T105I; D170E; Y177F; S220T; M273L,0.00321909,1.3626904,26/08/2020,-7.44974E+17,0.001074582,1,759.2551 +10796,V97D; T105I; P138Q; S220T; E254G; H266R,0.0033163,1.4338179,26/08/2020,-7.44974E+17,0.001162526,1,733.5518 +10797,V97E,0.015160188,4.9765835,26/08/2020,-7.44974E+17,0.014728416,6,336.22836 +10798,V97G; A127T; L173M,0.012310985,3.4655356,26/08/2020,-7.44974E+17,0.009779953,1,366.40854 +10799,V97G; L124I; S220T,0.19487172,5.4137325,26/08/2020,-7.44974E+17,0.196962069,1,25.942791 +10800,V97G; Y144N; R148P; S220T; H230Y,0.18791184,5.139655,26/08/2020,-7.44974E+17,0.201984551,1,25.628752 +10801,V97H,0.025550403,4.9865947,26/08/2020,-7.44974E+17,0.024083365,2,210.92955 +10802,V97I; A112V; H154R; M185L; M273V,0.005990028,2.3581195,26/08/2020,-7.44974E+17,0.003738898,1,576.1139 +10803,V97I; A156T; A187T; S220T; H230Y; D235G; E254V,0.006816716,2.5443177,26/08/2020,-7.44974E+17,0.004400496,1,545.7786 +10804,V97I; D152G; S220T; T245R,0.002585382,1.0597098,26/08/2020,-7.44974E+17,0.000582781,1,813.0267 +10805,V97I; D287A,0.039542876,5.671907,26/08/2020,-7.44974E+17,0.039146049,2,150.4426 +10806,V97I; G104C; G215V; S220T; E261D,0.004682826,1.9291046,26/08/2020,-7.44974E+17,0.002435044,1,627.4323 +10807,V97I; N100W,0.009713486,1.2426974,26/08/2020,-7.44974E+17,0.008960258,2,187.18658 +10808,V97I; S220T; E295V,0.01398949,3.6131084,26/08/2020,-7.44974E+17,0.010907879,1,330.59976 +10809,V97K,0.007607832,4.6831365,26/08/2020,-7.44974E+17,0.006962588,4,650.4151 +10810,V97L,0.03167527,5.5112586,26/08/2020,-7.44974E+17,0.030600175,2,185.51099 +10811,V97L; S220T; H230Y,0.43339205,3.5771117,26/08/2020,-7.44974E+17,0.456625705,2,4.8570414 +10812,V97Q; V99F,0.002444666,0.97843057,26/08/2020,-7.44974E+17,0.000377262,1,829.73157 +10813,V97R,0.008237099,2.7580383,26/08/2020,-7.44974E+17,0.005578851,1,471.92947 +10814,V97S,0.05704793,4.755787,26/08/2020,-7.44974E+17,0.056488656,1,96.28971 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S220T,0.003763107,1.6226405,26/08/2020,-7.44974E+17,0.001447915,1,696.8495 +10826,V99D,0.008994601,0.43860593,26/08/2020,-7.44974E+17,0.008691195,7,64.89794 +10827,V99D; G120S; S220T; L269F,0.005114778,2.0321336,26/08/2020,-7.44974E+17,0.00255665,1,638.05493 +10828,V99D; H154Q; L181I; G215C; P219L; R247S,0.004524463,1.9173903,26/08/2020,-7.44974E+17,0.002071057,1,687.103 +10829,V99D; L143S; Y177F,0.003077304,1.2388893,26/08/2020,-7.44974E+17,0.00078383,1,758.0616 +10830,V99D; L151Q; S220T,0.004221115,1.7901304,26/08/2020,-7.44974E+17,0.0019417,1,692.2656 +10831,V99D; L167R; D170E; A174S; L176F; S220T,0.003143824,1.3533832,26/08/2020,-7.44974E+17,0.001007471,1,743.5572 +10832,V99D; R122P; V150E; S220T,0.00296381,1.2850204,26/08/2020,-7.44974E+17,0.000973931,1,744.0276 +10833,V99D; S220T,0.002078328,1.1674625,26/08/2020,-7.44974E+17,0.000981799,2,827.2657 +10834,V99D; S220T; V250M; I256T,0.002096445,1.2733897,26/08/2020,-7.44974E+17,0.000880223,2,892.9794 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S220T,0.018002886,3.8099728,26/08/2020,-7.44974E+17,0.01550731,1,269.58154 +10869,W115C; S220T; V250M; L272P,0.003049936,1.2958149,26/08/2020,-7.44974E+17,0.000953251,1,726.93115 +10870,W115D,0.004473539,1.3689048,26/08/2020,-7.44974E+17,0.003954049,6,347.8144 +10871,W115E,0.00257751,1.6085317,26/08/2020,-7.44974E+17,0.001354896,2,842.0592 +10872,W115F,0.033632435,0.84655,26/08/2020,-7.44974E+17,0.033091093,5,30.448221 +10873,W115G,0.007945959,1.1269612,26/08/2020,-7.44974E+17,0.007231317,4,172.96185 +10874,W115H,0.023902718,4.3258862,26/08/2020,-7.44974E+17,0.022650964,2,200.113 +10875,W115I,0.009123237,2.9154034,26/08/2020,-7.44974E+17,0.009028715,9,330.80447 +10876,W115K,0.009007717,1.9834129,26/08/2020,-7.44974E+17,0.008557824,10,231.07034 +10877,W115L,0.007153307,2.845034,26/08/2020,-7.44974E+17,0.006800401,6,421.92166 +10878,W115L; R148P; S220T; H230Y; G241C,0.005796787,2.2879763,26/08/2020,-7.44974E+17,0.003318983,1,594.6692 +10879,W115L; V150E,0.004185531,1.765058,26/08/2020,-7.44974E+17,0.001880153,1,686.9752 +10880,W115M,0.011156436,1.1344537,26/08/2020,-7.44974E+17,0.010386977,4,124.865105 +10881,W115N,0.007221619,2.6738203,26/08/2020,-7.44974E+17,0.004906119,1,525.4663 +10882,W115P,0.003753897,1.447574,26/08/2020,-7.44974E+17,0.003515745,10,412.68317 +10883,W115Q,0.011926438,0.8858161,26/08/2020,-7.44974E+17,0.011399094,12,81.408875 +10884,W115R,0.008211681,1.0596426,26/08/2020,-7.44974E+17,0.008106682,14,137.85814 +10885,W115R; D125Y; T172S; S220T; V221E; L227M; E254D; Q299R,0.003370821,1.4718193,26/08/2020,-7.44974E+17,0.001345263,1,735.9064 +10886,W115R; D139G; A206D; S220T,0.00346523,1.4293559,26/08/2020,-7.44974E+17,0.001145703,1,715.00275 +10887,W115R; G191V; A213T; S220T,0.003213811,1.3988857,26/08/2020,-7.44974E+17,0.001061533,1,734.0346 +10888,W115R; L161M; K236T; V250A; E268D; A277G,0.002478803,1.0344988,26/08/2020,-7.44974E+17,0.000538633,1,785.5759 +10889,W115R; M129I; S220T; S292P,0.004062872,1.7298502,26/08/2020,-7.44974E+17,0.001758564,1,701.49365 +10890,W115R; N178H; S220T,0.004264435,1.0743158,26/08/2020,-7.44974E+17,0.00348517,3,346.7049 +10891,W115R; S220T,0.00626128,3.4672604,26/08/2020,-7.44974E+17,0.005809554,6,584.97205 +10892,W115R; S220T; E262G; H288Y,0.003189422,1.2985889,26/08/2020,-7.44974E+17,0.000916036,1,737.2552 +10893,W115S,0.006574791,1.0673339,26/08/2020,-7.44974E+17,0.006224984,9,179.88245 +10894,W115T,0.011118976,3.3461323,26/08/2020,-7.44974E+17,0.009031436,1,392.1749 +10895,W115V,0.013217906,2.4993422,26/08/2020,-7.44974E+17,0.012969737,6,202.55942 +10896,W115Y,0.10084052,4.9954195,26/08/2020,-7.44974E+17,0.10424881,1,52.7267 +10897,W216D,0.22540498,5.316994,26/08/2020,-7.44974E+17,0.241477355,1,20.816153 +10898,W216F,0.07954712,4.9463205,26/08/2020,-7.44974E+17,0.078003025,1,69.51494 +10899,W216G,0.13527878,6.0808043,26/08/2020,-7.44974E+17,0.135654263,2,41.909817 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L269W,0.002280415,0.8359909,26/08/2020,-7.44974E+17,0.000248753,1,789.16046 +10912,W216R; N289S,0.46821123,3.8920171,26/08/2020,-7.44974E+17,0.468631166,21,4.432339 +10913,W216R; S220T,0.05148887,4.7001505,26/08/2020,-7.44974E+17,0.050723693,1,106.94953 +10914,W216R; S220T; V232L; H271Q; N289I,0.002938932,1.2684832,26/08/2020,-7.44974E+17,0.000877229,1,768.5259 +10915,W216R; T242A,0.21520768,4.807283,26/08/2020,-7.44974E+17,0.215263399,41,17.602112 +10916,W216R; T242A; N289S,0.32679203,2.0668821,26/08/2020,-7.44974E+17,0.318644291,6,4.439887 +10917,W216S,0.16329212,7.6132855,26/08/2020,-7.44974E+17,0.163568254,3,40.68189 +10918,W216T,0.2019235,5.1872396,26/08/2020,-7.44974E+17,0.211778928,1,23.615717 +10919,W216Y,0.10467004,5.112098,26/08/2020,-7.44974E+17,0.103198689,1,52.188004 +10920,W41A,0.001666472,1.3205682,26/08/2020,-7.44974E+17,0.000860059,3,1035.3873 +10921,W41C,0.003591761,2.1507154,26/08/2020,-7.44974E+17,0.00243622,2,756.0175 +10922,W41C; D125E; S220T,0.06825115,4.908713,26/08/2020,-7.44974E+17,0.066831125,1,80.781204 +10923,W41C; M184L,0.006811448,2.5590003,26/08/2020,-7.44974E+17,0.004306889,1,545.97675 +10924,W41C; N42S; L93H; A111G; V175E; S220T; I256N; S301G,0.003818576,1.6475341,26/08/2020,-7.44974E+17,0.001481243,1,724.57166 +10925,W41C; Q194H; S220T; M273V,0.003316355,1.4465758,26/08/2020,-7.44974E+17,0.001307004,1,733.9617 +10926,W41C; R47C; D118G; L158F; S220T,0.002511648,1.0708846,26/08/2020,-7.44974E+17,0.000559592,1,791.08075 +10927,W41C; T157S; S220T,0.2817314,5.379025,26/08/2020,-7.44974E+17,0.315565633,1,15.1196785 +10928,W41C; T81S; A112V; D139E; R148H,0.003312635,1.369132,26/08/2020,-7.44974E+17,0.001009094,1,759.22614 +10929,W41D,0.001394971,0.7830268,26/08/2020,-7.44974E+17,0.000319393,2,948.3601 +10930,W41E,0.002699941,1.110011,26/08/2020,-7.44974E+17,0.000667735,1,806.388 +10931,W41F,0.052080654,5.178181,26/08/2020,-7.44974E+17,0.051266033,4,99.491844 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E254K,0.002326171,0.9132218,26/08/2020,-7.44974E+17,0.000350103,1,826.2144 +10943,W41L; S57P; E84D; M129T; E153G; S220T; H255Q; A259V; S292P,0.003686124,1.59492,26/08/2020,-7.44974E+17,0.001490601,1,720.93585 +10944,W41L; T44A; V66M; P106L; P132R; L143F; G146D; E295V,0.003025144,1.2566775,26/08/2020,-7.44974E+17,0.000897574,1,766.8327 +10945,W41M,0.003732878,2.6457298,26/08/2020,-7.44974E+17,0.002882306,3,806.3954 +10946,W41N,0.002465752,0.91964453,26/08/2020,-7.44974E+17,0.000311797,1,812.78784 +10947,W41P,0.001647215,0.9917645,26/08/2020,-7.44974E+17,0.000566458,2,942.954 +10948,W41Q,0.00179108,1.475418,26/08/2020,-7.44974E+17,0.000980152,3,1056.3715 +10949,W41R,0.002191878,0.51376116,26/08/2020,-7.44974E+17,0.001892217,7,305.5243 +10950,W41R; A156S; P171T; F217Y; A280D,0.007793761,2.670819,26/08/2020,-7.44974E+17,0.005581105,1,482.28568 +10951,W41R; A197T; M248K; I256T,0.002891972,1.1798825,26/08/2020,-7.44974E+17,0.000682229,1,760.9283 +10952,W41R; A60S; D118E; A169P; S220T,0.003081141,1.2873225,26/08/2020,-7.44974E+17,0.000839195,1,764.6535 +10953,W41R; A65D; S220T; A280S,0.003217545,1.4124935,26/08/2020,-7.44974E+17,0.001098388,1,784.47473 +10954,W41R; A65V; S304T,0.002344683,0.88709605,26/08/2020,-7.44974E+17,0.000338493,1,766.41235 +10955,W41R; A92T; L134M; L193H; H230L; P302Q,0.005246007,2.1599755,26/08/2020,-7.44974E+17,0.002933999,1,633.5828 +10956,W41R; A92V; D118E; L189S; G191R; P219S; S220T,0.001479507,0.87950444,26/08/2020,-7.44974E+17,0.000390066,2,940.8054 +10957,W41R; D118V; T245S; N289K; S301I,0.001689567,0.49291342,26/08/2020,-7.44974E+17,3.53E-05,1,715.6677 +10958,W41R; D147E; S220T,0.002703836,1.1324631,26/08/2020,-7.44974E+17,0.000653321,1,783.29486 +10959,W41R; D152G; S220T,0.003070189,1.3063614,26/08/2020,-7.44974E+17,0.000932605,1,766.14374 +10960,W41R; D170N; A174G,0.002665781,1.044381,26/08/2020,-7.44974E+17,0.000523478,1,770.24805 +10961,W41R; D64V; S220T; H271Q,0.002403976,0.9028605,26/08/2020,-7.44974E+17,0.000329921,1,781.3601 +10962,W41R; E295F,0.002002656,0.7048469,26/08/2020,-7.44974E+17,0.000150998,1,783.2675 +10963,W41R; G162R; S220T,0.002973676,1.1895787,26/08/2020,-7.44974E+17,0.00065204,1,768.7213 +10964,W41R; H183Y; V265G,0.003999474,1.6851485,26/08/2020,-7.44974E+17,0.001570189,1,704.0404 +10965,W41R; H288R,0.002686852,1.0652423,26/08/2020,-7.44974E+17,0.000523581,1,770.7412 +10966,W41R; L158I; F166L; A296T; F297C,0.002267088,0.86289525,26/08/2020,-7.44974E+17,0.00026776,1,791.4282 +10967,W41R; L227P,0.003393498,1.4799999,26/08/2020,-7.44974E+17,0.001346091,1,715.0821 +10968,W41R; M129K; A169P,0.004256607,1.7837104,26/08/2020,-7.44974E+17,0.00192991,1,670.73474 +10969,W41R; M184S; S220T; E295G,0.002542374,1.0144004,26/08/2020,-7.44974E+17,0.000442032,1,794.7906 +10970,W41R; N42I; A80V; S220T,0.004022372,1.7578237,26/08/2020,-7.44974E+17,0.001684164,1,711.20935 +10971,W41R; N42I; R148P; S220T; H230Y,0.003741424,1.6435382,26/08/2020,-7.44974E+17,0.001888261,1,687.3598 +10972,W41R; P106L; Q299L,0.0028494,1.1472439,26/08/2020,-7.44974E+17,0.000611581,1,778.7078 +10973,W41R; P106Q; Q107L; A223V; H271Y; D287G,0.003396324,1.4215125,26/08/2020,-7.44974E+17,0.001139553,1,733.19257 +10974,W41R; P45S; L98P; V141A; A174T; S205G; M248T,0.001909096,0.7467432,26/08/2020,-7.44974E+17,0.000204654,1,803.5932 +10975,W41R; P70L; Y186C; S205I; W216R,0.001990057,0.69494236,26/08/2020,-7.44974E+17,0.000160891,1,797.8408 +10976,W41R; Q299R,0.004844689,2.0322793,26/08/2020,-7.44974E+17,0.002558743,1,649.0739 +10977,W41R; R155L; N178D; S220L; T245S,0.001936106,0.6890336,26/08/2020,-7.44974E+17,0.000141947,1,778.2537 +10978,W41R; R43H; V99I; Q107H; M185T; P219A,0.001936427,0.72614795,26/08/2020,-7.44974E+17,0.000189888,1,839.0975 +10979,W41R; S103L; H271L; S292T,0.002746544,1.1368486,26/08/2020,-7.44974E+17,0.000667446,1,773.6214 +10980,W41R; S103T; D152G; G168E; S220T; E254V; E275K,0.003445992,1.4844801,26/08/2020,-7.44974E+17,0.001295282,1,696.28735 +10981,W41R; S220T,0.002562255,1.0062481,26/08/2020,-7.44974E+17,0.000444703,1,774.1961 +10982,W41R; S220T; A280T,0.002977389,1.2248486,26/08/2020,-7.44974E+17,0.000753895,1,742.782 +10983,W41R; S220T; E278K,0.003084248,1.3763012,26/08/2020,-7.44974E+17,0.001000989,1,782.18805 +10984,W41R; S220T; I256F,0.002984962,1.219783,26/08/2020,-7.44974E+17,0.000725416,1,768.2948 +10985,W41R; S49I; P88T,0.003204963,1.356909,26/08/2020,-7.44974E+17,0.00099402,1,746.16266 +10986,W41R; T105A; L161M,0.002056469,0.7329999,26/08/2020,-7.44974E+17,0.000210289,1,788.4762 +10987,W41R; T72I; M82V; P131T; F142C; S220T,0.001940856,1.1096854,26/08/2020,-7.44974E+17,0.000778678,2,838.38074 +10988,W41R; V109E; R137S,0.002234908,0.889361,26/08/2020,-7.44974E+17,0.000334584,1,783.9254 +10989,W41R; V204A; S220T,0.002684764,1.125075,26/08/2020,-7.44974E+17,0.000614369,1,764.17883 +10990,W41R; V52M; D118V; A210S; S220T; H266R,0.002699657,1.0828291,26/08/2020,-7.44974E+17,0.000574805,1,744.24 +10991,W41R; V52M; I212N; F217L; S220T; F297Y,0.002747498,1.1833324,26/08/2020,-7.44974E+17,0.000745077,1,782.31696 +10992,W41R; V85D; S220T,0.003815364,1.6748312,26/08/2020,-7.44974E+17,0.001599044,1,730.2239 +10993,W41R; V99I; S103T; S220T; M248K; S292P,0.003441586,1.5324781,26/08/2020,-7.44974E+17,0.001300139,1,741.38324 +10994,W41S,0.002970181,1.2106521,26/08/2020,-7.44974E+17,0.000720884,1,747.9108 +10995,W41S; D125E; Y239H; E261V,0.004576509,1.9735473,26/08/2020,-7.44974E+17,0.002308509,1,661.1028 +10996,W41S; L98M; S220T; A277V,0.002725025,1.1653825,26/08/2020,-7.44974E+17,0.000762512,1,771.3267 +10997,W41T,0.001226666,1.2251658,26/08/2020,-7.44974E+17,0.000614614,4,1257.8922 +10998,W41V,0.002211894,2.2554948,26/08/2020,-7.44974E+17,0.001662223,4,1135.4132 +10999,Y123A,0.00353857,1.485519,26/08/2020,-7.44974E+17,0.002311928,2,576.9436 +11000,Y123C,0.004600893,2.540999,26/08/2020,-7.44974E+17,0.003089051,2,684.1292 +11001,Y123C; A231L,0.002518427,0.98861897,26/08/2020,-7.44974E+17,0.00049179,1,782.636 +11002,Y123D,0.001757288,1.2968996,26/08/2020,-7.44974E+17,0.001140849,4,896.15607 +11003,Y123E,0.001199606,1.346335,26/08/2020,-7.44974E+17,0.000849484,7,1267.2454 +11004,Y123F,0.008027591,4.8246083,26/08/2020,-7.44974E+17,0.00736455,4,635.67224 +11005,Y123F; S220T,0.038169835,4.522761,26/08/2020,-7.44974E+17,0.03508008,1,142.79034 +11006,Y123G,0.001215948,1.3639588,26/08/2020,-7.44974E+17,0.00083228,6,1286.9446 +11007,Y123H,0.002475817,2.576501,26/08/2020,-7.44974E+17,0.001985088,5,1135.1434 +11008,Y123H; L124F; V130E; F217L,0.002425957,0.9947068,26/08/2020,-7.44974E+17,0.000509541,1,761.525 +11009,Y123H; N190D; S220T; H230Y,0.005659619,2.290318,26/08/2020,-7.44974E+17,0.003355341,1,592.9774 +11010,Y123H; T157S; L176I; F217L; S220T,0.003435951,1.42679,26/08/2020,-7.44974E+17,0.001237178,1,722.61914 +11011,Y123I,0.007686005,3.2991402,26/08/2020,-7.44974E+17,0.006651421,3,476.6228 +11012,Y123K,0.001433027,0.8762253,26/08/2020,-7.44974E+17,0.000963666,5,767.4775 +11013,Y123L,0.005434381,2.7044895,26/08/2020,-7.44974E+17,0.004315157,2,594.04443 +11014,Y123M,0.011989406,3.4899316,26/08/2020,-7.44974E+17,0.009081214,1,393.37814 +11015,Y123N,0.001686623,1.2907197,26/08/2020,-7.44974E+17,0.00093347,3,979.8156 +11016,Y123N; A169S,0.005160146,2.1037486,26/08/2020,-7.44974E+17,0.0027826,1,633.85986 +11017,Y123N; A210D; E268V,0.002710829,1.1149695,26/08/2020,-7.44974E+17,0.000689405,1,785.3805 +11018,Y123N; D125N,0.003143152,1.371469,26/08/2020,-7.44974E+17,0.001136603,1,753.95 +11019,Y123N; E295D,0.003019469,1.2707533,26/08/2020,-7.44974E+17,0.000900603,1,797.25574 +11020,Y123N; R148C; S220T,0.00253775,1.0771089,26/08/2020,-7.44974E+17,0.000582644,1,811.9601 +11021,Y123N; R258H,0.002881506,1.2748709,26/08/2020,-7.44974E+17,0.000867297,1,768.36774 +11022,Y123N; S220T,0.003330812,1.4303746,26/08/2020,-7.44974E+17,0.001209218,1,754.6895 +11023,Y123P,0.002106659,1.710794,26/08/2020,-7.44974E+17,0.001494972,4,943.1599 +11024,Y123Q,0.001421197,1.565816,26/08/2020,-7.44974E+17,0.001002132,6,1257.1569 +11025,Y123R,0.001391996,1.3734406,26/08/2020,-7.44974E+17,0.000848347,4,1200.1919 +11026,Y123S,0.002251311,2.863422,26/08/2020,-7.44974E+17,0.001842482,6,1362.0367 +11027,Y123S; D125N,0.003028682,1.3155497,26/08/2020,-7.44974E+17,0.001007514,1,772.4078 +11028,Y123T,0.005862318,4.262967,26/08/2020,-7.44974E+17,0.005343255,5,765.7081 +11029,Y123V,0.007084118,2.8583124,26/08/2020,-7.44974E+17,0.005900184,2,480.5249 +11030,Y123W,0.005429991,3.0505013,26/08/2020,-7.44974E+17,0.004606103,3,629.4606 +11031,Y144A,0.002810751,1.1360409,26/08/2020,-7.44974E+17,0.000666101,1,784.7154 +11032,Y144C,0.005441437,2.6323004,26/08/2020,-7.44974E+17,0.004183899,2,606.1895 +11033,Y144C; A156G; V175E; S220T,0.0039101,1.6053975,26/08/2020,-7.44974E+17,0.001358739,1,708.01764 +11034,Y144C; A202E; A228T; V232I,0.002706276,1.1308502,26/08/2020,-7.44974E+17,0.000661482,1,791.0869 +11035,Y144C; G146C; F166S; P171H; L214F; S220T,0.002910065,1.2091635,26/08/2020,-7.44974E+17,0.000773197,1,784.14197 +11036,Y144C; G215A; S220T; Q263H,0.00269155,1.0699949,26/08/2020,-7.44974E+17,0.000599506,1,787.4906 +11037,Y144D,0.002323725,1.1293344,26/08/2020,-7.44974E+17,0.001172907,2,733.06824 +11038,Y144E,0.000988061,0.95052147,26/08/2020,-7.44974E+17,0.000392065,4,1269.6887 +11039,Y144F,0.18998125,5.170674,26/08/2020,-7.44974E+17,0.204144275,1,25.518332 +11040,Y144F; A149V; L173Q; T179I; S220T,0.014621239,3.6509054,26/08/2020,-7.44974E+17,0.01239324,1,322.578 +11041,Y144F; S220T; M273I,0.015905883,3.7096963,26/08/2020,-7.44974E+17,0.013970243,1,292.8919 +11042,Y144G,0.000951689,0.65063006,26/08/2020,-7.44974E+17,0.000203225,3,1096.8326 +11043,Y144H,0.002563473,1.0207655,26/08/2020,-7.44974E+17,0.000489521,1,765.42255 +11044,Y144H; A180P; S220T; A223S; Q263H,0.004847455,2.060691,26/08/2020,-7.44974E+17,0.002633315,1,655.2459 +11045,Y144H; D152N; H271L,0.00265615,1.1188847,26/08/2020,-7.44974E+17,0.000542285,1,785.03015 +11046,Y144H; E153K,0.00292882,1.2832276,26/08/2020,-7.44974E+17,0.000938472,1,810.9356 +11047,Y144H; H154Y; A156T; L234F,0.002021382,0.6292876,26/08/2020,-7.44974E+17,0.000117385,1,740.40515 +11048,Y144H; S220T,0.002856491,1.2777299,26/08/2020,-7.44974E+17,0.000992551,1,797.43304 +11049,Y144H; S220T; E278D,0.003092316,1.3229169,26/08/2020,-7.44974E+17,0.000969419,1,768.9175 +11050,Y144H; S220T; V276D; D287V; P302Q,0.002901283,1.181336,26/08/2020,-7.44974E+17,0.000695644,1,759.20526 +11051,Y144I,0.022226496,4.007175,26/08/2020,-7.44974E+17,0.01982421,1,229.39449 +11052,Y144K,0.001044451,0.75624806,26/08/2020,-7.44974E+17,0.000303658,3,1091.8981 +11053,Y144L,0.02591675,6.7475786,26/08/2020,-7.44974E+17,0.024749127,4,264.77045 +11054,Y144M,0.010925491,3.2654018,26/08/2020,-7.44974E+17,0.008856157,1,401.86176 +11055,Y144N,0.002484445,0.992812,26/08/2020,-7.44974E+17,0.000459574,1,827.96436 +11056,Y144N; R155C; L176V; A187E; N190K; W216R; S220T; V276D,0.003838112,1.6997585,26/08/2020,-7.44974E+17,0.001767519,1,707.05725 +11057,Y144P,0.00117565,0.8711661,26/08/2020,-7.44974E+17,0.000399096,3,1057.8519 +11058,Y144Q,0.000800542,1.06188,26/08/2020,-7.44974E+17,0.000407327,6,1571.1838 +11059,Y144Q; G146C,0.002015852,0.71742535,26/08/2020,-7.44974E+17,0.000169092,1,773.1783 +11060,Y144R,0.000815773,0.92371404,26/08/2020,-7.44974E+17,0.000341748,5,1420.0431 +11061,Y144S,0.002034775,0.7514444,26/08/2020,-7.44974E+17,0.000200744,1,763.64703 +11062,Y144T,0.00315026,1.2916993,26/08/2020,-7.44974E+17,0.000905398,1,707.49713 +11063,Y144V,0.01790469,4.387639,26/08/2020,-7.44974E+17,0.016444489,2,272.3007 +11064,Y144W,0.011159923,3.3041368,26/08/2020,-7.44974E+17,0.008705306,1,396.6076 +11065,Y177C; A197D; A260G,0.001973786,0.7054188,26/08/2020,-7.44974E+17,0.000158194,1,779.0573 +11066,Y177C; L182P; S220T; E295D,0.00340736,1.4853748,26/08/2020,-7.44974E+17,0.001230249,1,752.33997 +11067,Y177C; S220T,0.034575894,3.358247,26/08/2020,-7.44974E+17,0.032777028,2,110.522385 +11068,Y177E,0.004205423,1.7897424,26/08/2020,-7.44974E+17,0.001937895,1,690.4607 +11069,Y177F,0.123499036,0.4720234,26/08/2020,-7.44974E+17,0.094491415,10,3.9067833 +11070,Y177F; D235G,0.008159611,2.8862367,26/08/2020,-7.44974E+17,0.005457991,1,496.3507 +11071,Y177F; I256N; Q283P,0.002018331,0.77041835,26/08/2020,-7.44974E+17,0.000215621,1,806.5126 +11072,Y177F; Q194E; A223V; K236I; E275D,0.005901767,2.397946,26/08/2020,-7.44974E+17,0.003766752,1,581.2025 +11073,Y177F; S220T; L272Q; E275D,0.00965785,3.0812688,26/08/2020,-7.44974E+17,0.007311091,1,428.36963 +11074,Y177H; S201P; S220T; N289K,0.002634987,1.0010787,26/08/2020,-7.44974E+17,0.00046187,1,779.09406 +11075,Y177H; S220T; R247S; H255R,0.003551947,2.1090183,26/08/2020,-7.44974E+17,0.00227011,2,761.4925 +11076,Y177H; V250S,0.015174747,3.6338058,26/08/2020,-7.44974E+17,0.013024976,1,311.35205 +11077,Y177N,0.016838469,3.7901373,26/08/2020,-7.44974E+17,0.014126749,1,291.53897 +11078,Y177N; P240T; T245I; M273T,0.003441122,1.4454594,26/08/2020,-7.44974E+17,0.001079374,1,731.2455 +11079,Y177N; S220T,0.15648162,5.272693,26/08/2020,-7.44974E+17,0.160188582,1,33.33891 +11080,Y177N; S220T; H230Y,0.52560055,5.7804604,26/08/2020,-7.44974E+17,0.611507568,1,4.9909153 +11081,Y177N; S220T; Q263H,0.003800955,1.6360538,26/08/2020,-7.44974E+17,0.00167356,1,720.26385 +11082,Y177N; W216L; D233V,0.003085944,1.2651293,26/08/2020,-7.44974E+17,0.000926008,1,767.1101 +11083,Y177S; S220T,0.014916713,3.6901429,26/08/2020,-7.44974E+17,0.012452454,1,320.16605 +11084,Y177W,0.42266598,3.927904,26/08/2020,-7.44974E+17,0.433887017,7,5.41387 +11085,Y177W; A187S; A218M,0.8325467,14.412063,26/08/2020,-7.44974E+17,0.851757143,12,2.8193326 +11086,Y177W; A187S; A228V,0.3647064,4.2611666,26/08/2020,-7.44974E+17,0.365124596,19,7.4721355 +11087,Y177W; A187S; A296I,0.807698,18.403486,26/08/2020,-7.44974E+17,0.823655049,16,4.327348 +11088,Y177W; A187S; D233E,0.704155,6.523705,26/08/2020,-7.44974E+17,0.72067019,18,2.7026594 +11089,Y177W; A218L; A228V,0.6502232,4.7750273,26/08/2020,-7.44974E+17,0.658799284,24,2.5493293 +11090,Y177W; A218L; T242A,0.77037483,9.150468,26/08/2020,-7.44974E+17,0.785476245,18,2.6836784 +11091,Y177W; A218M,0.8243735,13.721153,26/08/2020,-7.44974E+17,0.840423358,15,2.864893 +11092,Y177W; A218M; A296I,0.8372662,23.426094,26/08/2020,-7.44974E+17,0.849273017,18,4.4966674 +11093,Y177W; A218M; D233E,0.8465504,20.212927,26/08/2020,-7.44974E+17,0.869317971,13,3.590155 +11094,Y177W; A228V,0.2095631,5.202501,26/08/2020,-7.44974E+17,0.208525715,13,19.841694 +11095,Y177W; A228V; A296I,0.6916341,8.940367,26/08/2020,-7.44974E+17,0.703178793,13,3.9397678 +11096,Y177W; A228V; D233E,0.18554135,2.964923,26/08/2020,-7.44974E+17,0.18454613,15,13.280606 +11097,Y177W; A228V; N289S,0.24679027,2.5736463,26/08/2020,-7.44974E+17,0.247297965,18,7.988101 +11098,Y177W; A228V; T242A,0.13233101,3.6053174,26/08/2020,-7.44974E+17,0.131300613,25,23.90034 +11099,Y177W; A228V; V250A,0.09567581,1.3924994,26/08/2020,-7.44974E+17,0.089929316,16,13.806168 +11100,Y177W; A296I,0.7688196,14.908136,26/08/2020,-7.44974E+17,0.77575055,32,4.455904 +11101,Y177W; D233E,0.5949071,3.9500859,26/08/2020,-7.44974E+17,0.608355786,8,2.6458225 +11102,Y177W; D233E; A296I,0.73902345,9.998538,26/08/2020,-7.44974E+17,0.771483631,5,3.398766 +11103,Y177W; D233E; N289S,0.4189693,3.5016184,26/08/2020,-7.44974E+17,0.426550565,18,4.8766537 +11104,Y177W; D233E; V250A,0.17760906,3.2677932,26/08/2020,-7.44974E+17,0.177057404,22,15.322742 +11105,Y177W; N289S,0.56792855,6.348706,26/08/2020,-7.44974E+17,0.584196232,10,4.805359 +11106,Y177W; N289S; A296I,0.7646817,16.014688,26/08/2020,-7.44974E+17,0.776359211,21,4.8920603 +11107,Y177W; T188G; A218L,0.5622959,4.305193,26/08/2020,-7.44974E+17,0.570415161,16,3.3359091 +11108,Y177W; T188G; A218M,0.6095267,5.3185177,26/08/2020,-7.44974E+17,0.616186463,22,3.389177 +11109,Y177W; T242A,0.5630679,4.7652793,26/08/2020,-7.44974E+17,0.568278471,20,3.6866705 +11110,Y177W; T242A; A296I,0.7554879,14.916758,26/08/2020,-7.44974E+17,0.763941839,25,4.7944374 +11111,Y177W; T242A; N289S,0.46883643,3.0117104,26/08/2020,-7.44974E+17,0.473711825,31,3.415294 +11112,Y177W; V250A,0.39020526,4.0057096,26/08/2020,-7.44974E+17,0.389879688,15,6.303887 +11113,Y177W; V250A; A296I,0.7830277,16.106647,26/08/2020,-7.44974E+17,0.798899421,13,4.3936787 +11114,Y177W; V250A; N289S,0.39265603,3.364398,26/08/2020,-7.44974E+17,0.390888291,26,5.2288437 +11115,Y177W; W216R; A228V,0.0676349,11.981422,26/08/2020,-7.44974E+17,0.06750672,18,165.94131 +11116,Y177W; W216R; A296I,0.7359057,8.835208,26/08/2020,-7.44974E+17,0.749554659,17,3.1227531 +11117,Y177W; W216R; N289S,0.3981291,3.2302675,26/08/2020,-7.44974E+17,0.398745297,18,4.909433 +11118,Y177W; W216R; T242A,0.19771953,3.8158321,26/08/2020,-7.44974E+17,0.198119662,26,15.620775 +11119,Y186A,0.001090093,0.95729357,26/08/2020,-7.44974E+17,0.000551613,4,1130.0825 +11120,Y186C,0.002323398,1.7772152,26/08/2020,-7.44974E+17,0.001498887,3,928.2528 +11121,Y186C; S220T; V281A,0.003817536,1.5649805,26/08/2020,-7.44974E+17,0.001430668,1,693.49286 +11122,Y186C; T207S,0.003467264,1.5473734,26/08/2020,-7.44974E+17,0.001514176,1,724.869 +11123,Y186C; V232A; I256N,0.003620762,1.5137912,26/08/2020,-7.44974E+17,0.001245394,1,714.5077 +11124,Y186C; V250T,0.007586071,2.7412827,26/08/2020,-7.44974E+17,0.00495673,1,502.73962 +11125,Y186C; V276A; D287E; K298M,0.002013668,0.73272234,26/08/2020,-7.44974E+17,0.000194941,1,833.0537 +11126,Y186D,0.004516552,1.8704624,26/08/2020,-7.44974E+17,0.003587432,3,505.1476 +11127,Y186D; S220T; R247S,0.002834467,1.0921853,26/08/2020,-7.44974E+17,0.000535574,1,805.8589 +11128,Y186E,0.012296788,3.4440265,26/08/2020,-7.44974E+17,0.00949738,1,377.77237 +11129,Y186F; A197T,0.002752898,1.1173279,26/08/2020,-7.44974E+17,0.000565858,1,765.70013 +11130,Y186F; D233Y; V265E; H288L,0.002720117,1.0584062,26/08/2020,-7.44974E+17,0.000603435,1,775.16486 +11131,Y186F; V204G,0.002284655,0.88870895,26/08/2020,-7.44974E+17,0.000342142,1,770.33905 +11132,Y186G,0.002853213,1.781847,26/08/2020,-7.44974E+17,0.001672329,2,810.092 +11133,Y186H,0.003388323,1.4315954,26/08/2020,-7.44974E+17,0.001231077,1,742.8559 +11134,Y186H; D233V,0.003395312,1.4894447,26/08/2020,-7.44974E+17,0.001238372,1,774.87177 +11135,Y186I,0.003464688,1.4517184,26/08/2020,-7.44974E+17,0.00122758,1,743.48724 +11136,Y186K,0.002332659,0.88577116,26/08/2020,-7.44974E+17,0.000382519,1,805.44653 +11137,Y186N,0.001891672,1.0006487,26/08/2020,-7.44974E+17,0.00069049,2,820.6653 +11138,Y186N; A282T,0.00299905,1.2602602,26/08/2020,-7.44974E+17,0.000854317,1,720.4287 +11139,Y186N; N190Y; A195T,0.002074202,0.7021419,26/08/2020,-7.44974E+17,0.000161122,1,758.46875 +11140,Y186N; S220T; P224L; P300S,0.003713172,1.6658237,26/08/2020,-7.44974E+17,0.001604493,1,726.13446 +11141,Y186Q,0.004066686,1.6978266,26/08/2020,-7.44974E+17,0.00173701,1,706.41284 +11142,Y186R,0.003172711,1.3625911,26/08/2020,-7.44974E+17,0.001055286,1,755.56116 +11143,Y186S,0.003733676,0.9289026,26/08/2020,-7.44974E+17,0.0029231,3,341.95215 +11144,Y186T,0.001522901,0.8363117,26/08/2020,-7.44974E+17,0.000431525,2,900.5369 +11145,Y186V,0.010892761,0.79804486,26/08/2020,-7.44974E+17,0.010084394,3,104.30263 +11146,Y192A,0.007291769,2.6324768,26/08/2020,-7.44974E+17,0.005052655,1,524.0279 +11147,Y192C,0.010206362,3.9982762,26/08/2020,-7.44974E+17,0.008774432,2,443.67386 +11148,Y192C; A228V,0.004685103,1.9682469,26/08/2020,-7.44974E+17,0.002431904,1,650.5124 +11149,Y192C; S220T,0.016219433,4.3770785,26/08/2020,-7.44974E+17,0.01498532,2,298.67636 +11150,Y192C; S220T; L234I,0.008109081,2.874725,26/08/2020,-7.44974E+17,0.005806047,1,496.20728 +11151,Y192D,0.002331702,0.9022447,26/08/2020,-7.44974E+17,0.000357539,1,759.223 +11152,Y192E,0.005873761,3.004994,26/08/2020,-7.44974E+17,0.004531847,2,606.90594 +11153,Y192F,0.05147245,4.7874084,26/08/2020,-7.44974E+17,0.050399177,1,106.12244 +11154,Y192F; A213P; S220T,0.003724088,1.6498636,26/08/2020,-7.44974E+17,0.001630809,1,730.8881 +11155,Y192G,0.011640029,3.2937484,26/08/2020,-7.44974E+17,0.009031032,1,379.62894 +11156,Y192H; S220T,0.12899421,5.171704,26/08/2020,-7.44974E+17,0.128507844,1,41.33785 +11157,Y192I,0.014875958,4.8516564,26/08/2020,-7.44974E+17,0.014210985,3,346.55655 +11158,Y192K,0.002466136,1.4836115,26/08/2020,-7.44974E+17,0.001310264,2,826.104 +11159,Y192L,0.047155216,5.8013363,26/08/2020,-7.44974E+17,0.046109298,2,128.29938 +11160,Y192M,0.040827654,5.4131455,26/08/2020,-7.44974E+17,0.039797876,3,135.45073 +11161,Y192N,0.003725926,1.6325017,26/08/2020,-7.44974E+17,0.001647661,1,705.12537 +11162,Y192N; R274L; K298R,0.002204082,0.8312444,26/08/2020,-7.44974E+17,0.000248575,1,783.9178 +11163,Y192P,0.004466151,0.99996173,26/08/2020,-7.44974E+17,0.003839663,4,279.0659 +11164,Y192Q,0.032243956,5.562276,26/08/2020,-7.44974E+17,0.031337775,2,182.59749 +11165,Y192R,0.003365896,1.5041437,26/08/2020,-7.44974E+17,0.001281516,1,757.56226 +11166,Y192S,0.005170143,2.1159682,26/08/2020,-7.44974E+17,0.003003425,1,604.72107 +11167,Y192W,0.23854734,5.256915,26/08/2020,-7.44974E+17,0.253594582,1,18.90432 +11168,Y239C,0.002585847,2.0276518,26/08/2020,-7.44974E+17,0.001691223,3,941.8054 +11169,Y239D,0.002421909,0.97276086,26/08/2020,-7.44974E+17,0.000426892,1,819.2677 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+11182,Y290C,0.002019659,1.0146145,26/08/2020,-7.44974E+17,0.000804599,2,816.0163 +11183,Y290D,0.001076679,0.7638172,26/08/2020,-7.44974E+17,0.000287984,3,1066.6628 +11184,Y290D; M291I,0.001862145,0.56165326,26/08/2020,-7.44974E+17,7.43E-05,1,753.7319 +11185,Y290E,0.00076963,0.58932424,26/08/2020,-7.44974E+17,0.000202698,4,1138.2407 +11186,Y290F,0.023981946,3.936436,26/08/2020,-7.44974E+17,0.022963915,2,184.81407 +11187,Y290G,0.001175444,0.5925236,26/08/2020,-7.44974E+17,0.000212644,2,909.4103 +11188,Y290H,0.001211577,0.56867766,26/08/2020,-7.44974E+17,0.00017241,2,877.131 +11189,Y290I,0.001054345,0.65925914,26/08/2020,-7.44974E+17,0.000276298,3,1003.7558 +11190,Y290K,0.000947368,0.5096337,26/08/2020,-7.44974E+17,0.000182532,3,912.376 +11191,Y290L,0.001289285,1.0387356,26/08/2020,-7.44974E+17,0.000536094,3,1091.2363 +11192,Y290L; V293I,0.004114114,1.731229,26/08/2020,-7.44974E+17,0.001827061,1,671.8895 +11193,Y290M,0.001242047,0.81309235,26/08/2020,-7.44974E+17,0.000454239,3,945.6954 +11194,Y290N,0.002397688,1.1284525,26/08/2020,-7.44974E+17,0.001268151,2,705.7429 +11195,Y290N; Q299L,0.015254783,3.704075,26/08/2020,-7.44974E+17,0.012937353,1,314.5305 +11196,Y290P,0.001759056,0.57938445,26/08/2020,-7.44974E+17,8.20E-05,1,773.578 +11197,Y290Q,0.002272113,0.82664466,26/08/2020,-7.44974E+17,0.00026321,1,796.4542 +11198,Y290R,0.001950869,0.663773,26/08/2020,-7.44974E+17,0.000125432,1,753.68756 +11199,Y290T,0.000792795,0.6912564,26/08/2020,-7.44974E+17,0.000225326,4,1222.0189 +11200,Y290V,0.002285472,0.959224,26/08/2020,-7.44974E+17,0.000401729,1,762.9513 +11201,Y290W,0.001984478,0.7819685,26/08/2020,-7.44974E+17,0.000236908,1,803.66473 +11202,Y294A,0.007685375,2.9797304,26/08/2020,-7.44974E+17,0.006818939,3,433.54907 +11203,Y294E,0.002191779,0.96559143,26/08/2020,-7.44974E+17,0.001107439,2,680.92474 +11204,Y294F,0.16638003,1.9817477,26/08/2020,-7.44974E+17,0.161421754,6,10.669485 +11205,Y294G,0.00124437,0.57672846,26/08/2020,-7.44974E+17,0.000128141,2,888.3748 +11206,Y294H,0.018811375,3.978582,26/08/2020,-7.44974E+17,0.015712801,1,269.30368 +11207,Y294I,0.12684338,7.1791945,26/08/2020,-7.44974E+17,0.128251491,3,51.732357 +11208,Y294K,0.001788801,1.0914803,26/08/2020,-7.44974E+17,0.00067587,2,915.2324 +11209,Y294N,0.001250979,0.6294196,26/08/2020,-7.44974E+17,0.00020729,2,884.68634 +11210,Y294P,0.002777044,0.35285193,26/08/2020,-7.44974E+17,0.00247429,5,196.69197 +11211,Y294R,0.00155043,0.8612645,26/08/2020,-7.44974E+17,0.000474227,2,875.46436 +11212,Y294S,0.004266531,2.2147725,26/08/2020,-7.44974E+17,0.002911245,2,670.32745 +11213,Y294T,0.002525663,1.0497016,26/08/2020,-7.44974E+17,0.00058076,1,775.5136 +11214,Y294V,0.029623652,5.4800453,26/08/2020,-7.44974E+17,0.027604275,2,197.94623 +11215,Y79C,0.003405533,2.026014,26/08/2020,-7.44974E+17,0.002157353,2,765.84467 +11216,Y79C; E108D; S220T; N289D,0.002909651,1.188559,26/08/2020,-7.44974E+17,0.000736627,1,783.02936 +11217,Y79C; G146S; S220T; R274C,0.004560476,1.8915433,26/08/2020,-7.44974E+17,0.001941405,1,703.7003 +11218,Y79C; I212N; S220T,0.003675859,1.6802186,26/08/2020,-7.44974E+17,0.001715957,1,739.2256 +11219,Y79C; L134Q; S220T; S292C,0.002480873,1.0465976,26/08/2020,-7.44974E+17,0.000537455,1,804.5253 +11220,Y79C; S220T,0.009182435,3.0159683,26/08/2020,-7.44974E+17,0.007076991,1,449.29034 +11221,Y79C; V85G; A92V; G136C; L167P; N251Y,0.002702032,1.1007111,26/08/2020,-7.44974E+17,0.000588795,1,761.636 +11222,Y79D,0.002512362,0.97027004,26/08/2020,-7.44974E+17,0.000443169,1,799.2678 +11223,Y79F; H288Q,0.003932241,1.6756005,26/08/2020,-7.44974E+17,0.001719539,1,708.8631 +11224,Y79F; N249S; M273T,0.002823617,1.2103411,26/08/2020,-7.44974E+17,0.000778135,1,785.2506 +11225,Y79F; P219L; S292T,0.001946749,0.6056552,26/08/2020,-7.44974E+17,8.38E-05,1,752.5731 +11226,Y79F; T105R; Q194R; L198P; S220T; D235N; A260E,0.003420481,1.3981264,26/08/2020,-7.44974E+17,0.001016922,1,737.0301 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+Austria,Europe,2002,78.98,8148312,32417.60769 +Austria,Europe,2007,79.829,8199783,36126.4927 +Bahrain,Asia,1952,50.939,120447,9867.084765 +Bahrain,Asia,1957,53.832,138655,11635.79945 +Bahrain,Asia,1962,56.923,171863,12753.27514 +Bahrain,Asia,1967,59.923,202182,14804.6727 +Bahrain,Asia,1972,63.3,230800,18268.65839 +Bahrain,Asia,1977,65.593,297410,19340.10196 +Bahrain,Asia,1982,69.052,377967,19211.14731 +Bahrain,Asia,1987,70.75,454612,18524.02406 +Bahrain,Asia,1992,72.601,529491,19035.57917 +Bahrain,Asia,1997,73.925,598561,20292.01679 +Bahrain,Asia,2002,74.795,656397,23403.55927 +Bahrain,Asia,2007,75.635,708573,29796.04834 +Bangladesh,Asia,1952,37.484,46886859,684.2441716 +Bangladesh,Asia,1957,39.348,51365468,661.6374577 +Bangladesh,Asia,1962,41.216,56839289,686.3415538 +Bangladesh,Asia,1967,43.453,62821884,721.1860862 +Bangladesh,Asia,1972,45.252,70759295,630.2336265 +Bangladesh,Asia,1977,46.923,80428306,659.8772322 +Bangladesh,Asia,1982,50.009,93074406,676.9818656 +Bangladesh,Asia,1987,52.819,103764241,751.9794035 +Bangladesh,Asia,1992,56.018,113704579,837.8101643 +Bangladesh,Asia,1997,59.412,123315288,972.7700352 +Bangladesh,Asia,2002,62.013,135656790,1136.39043 +Bangladesh,Asia,2007,64.062,150448339,1391.253792 +Belgium,Europe,1952,68.0,8730405,8343.105127 +Belgium,Europe,1957,69.24,8989111,9714.960623 +Belgium,Europe,1962,70.25,9218400,10991.20676 +Belgium,Europe,1967,70.94,9556500,13149.04119 +Belgium,Europe,1972,71.44,9709100,16672.14356 +Belgium,Europe,1977,72.8,9821800,19117.97448 +Belgium,Europe,1982,73.93,9856303,20979.84589 +Belgium,Europe,1987,75.35,9870200,22525.56308 +Belgium,Europe,1992,76.46,10045622,25575.57069 +Belgium,Europe,1997,77.53,10199787,27561.19663 +Belgium,Europe,2002,78.32,10311970,30485.88375 +Belgium,Europe,2007,79.441,10392226,33692.60508 +Benin,Africa,1952,38.223,1738315,1062.7522 +Benin,Africa,1957,40.358,1925173,959.6010805 +Benin,Africa,1962,42.618,2151895,949.4990641 +Benin,Africa,1967,44.885,2427334,1035.831411 +Benin,Africa,1972,47.014,2761407,1085.796879 +Benin,Africa,1977,49.19,3168267,1029.161251 +Benin,Africa,1982,50.904,3641603,1277.897616 +Benin,Africa,1987,52.337,4243788,1225.85601 +Benin,Africa,1992,53.919,4981671,1191.207681 +Benin,Africa,1997,54.777,6066080,1232.975292 +Benin,Africa,2002,54.406,7026113,1372.877931 +Benin,Africa,2007,56.728,8078314,1441.284873 +Bolivia,Americas,1952,40.414,2883315,2677.326347 +Bolivia,Americas,1957,41.89,3211738,2127.686326 +Bolivia,Americas,1962,43.428,3593918,2180.972546 +Bolivia,Americas,1967,45.032,4040665,2586.886053 +Bolivia,Americas,1972,46.714,4565872,2980.331339 +Bolivia,Americas,1977,50.023,5079716,3548.097832 +Bolivia,Americas,1982,53.859,5642224,3156.510452 +Bolivia,Americas,1987,57.251,6156369,2753.69149 +Bolivia,Americas,1992,59.957,6893451,2961.699694 +Bolivia,Americas,1997,62.05,7693188,3326.143191 +Bolivia,Americas,2002,63.883,8445134,3413.26269 +Bolivia,Americas,2007,65.554,9119152,3822.137084 +Bosnia and Herzegovina,Europe,1952,53.82,2791000,973.5331948 +Bosnia and Herzegovina,Europe,1957,58.45,3076000,1353.989176 +Bosnia and Herzegovina,Europe,1962,61.93,3349000,1709.683679 +Bosnia and Herzegovina,Europe,1967,64.79,3585000,2172.352423 +Bosnia and Herzegovina,Europe,1972,67.45,3819000,2860.16975 +Bosnia and Herzegovina,Europe,1977,69.86,4086000,3528.481305 +Bosnia and Herzegovina,Europe,1982,70.69,4172693,4126.613157 +Bosnia and Herzegovina,Europe,1987,71.14,4338977,4314.114757 +Bosnia and Herzegovina,Europe,1992,72.178,4256013,2546.781445 +Bosnia and Herzegovina,Europe,1997,73.244,3607000,4766.355904 +Bosnia and Herzegovina,Europe,2002,74.09,4165416,6018.975239 +Bosnia and Herzegovina,Europe,2007,74.852,4552198,7446.298803 +Botswana,Africa,1952,47.622,442308,851.2411407 +Botswana,Africa,1957,49.618,474639,918.2325349 +Botswana,Africa,1962,51.52,512764,983.6539764 +Botswana,Africa,1967,53.298,553541,1214.709294 +Botswana,Africa,1972,56.024,619351,2263.611114 +Botswana,Africa,1977,59.319,781472,3214.857818 +Botswana,Africa,1982,61.484,970347,4551.14215 +Botswana,Africa,1987,63.622,1151184,6205.88385 +Botswana,Africa,1992,62.745,1342614,7954.111645 +Botswana,Africa,1997,52.556,1536536,8647.142313 +Botswana,Africa,2002,46.634,1630347,11003.60508 +Botswana,Africa,2007,50.728,1639131,12569.85177 +Brazil,Americas,1952,50.917,56602560,2108.944355 +Brazil,Americas,1957,53.285,65551171,2487.365989 +Brazil,Americas,1962,55.665,76039390,3336.585802 +Brazil,Americas,1967,57.632,88049823,3429.864357 +Brazil,Americas,1972,59.504,100840058,4985.711467 +Brazil,Americas,1977,61.489,114313951,6660.118654 +Brazil,Americas,1982,63.336,128962939,7030.835878 +Brazil,Americas,1987,65.205,142938076,7807.095818 +Brazil,Americas,1992,67.057,155975974,6950.283021 +Brazil,Americas,1997,69.388,168546719,7957.980824 +Brazil,Americas,2002,71.006,179914212,8131.212843 +Brazil,Americas,2007,72.39,190010647,9065.800825 +Bulgaria,Europe,1952,59.6,7274900,2444.286648 +Bulgaria,Europe,1957,66.61,7651254,3008.670727 +Bulgaria,Europe,1962,69.51,8012946,4254.337839 +Bulgaria,Europe,1967,70.42,8310226,5577.0028 +Bulgaria,Europe,1972,70.9,8576200,6597.494398 +Bulgaria,Europe,1977,70.81,8797022,7612.240438 +Bulgaria,Europe,1982,71.08,8892098,8224.191647 +Bulgaria,Europe,1987,71.34,8971958,8239.854824 +Bulgaria,Europe,1992,71.19,8658506,6302.623438 +Bulgaria,Europe,1997,70.32,8066057,5970.38876 +Bulgaria,Europe,2002,72.14,7661799,7696.777725 +Bulgaria,Europe,2007,73.005,7322858,10680.79282 +Burkina Faso,Africa,1952,31.975,4469979,543.2552413 +Burkina Faso,Africa,1957,34.906,4713416,617.1834648 +Burkina Faso,Africa,1962,37.814,4919632,722.5120206 +Burkina Faso,Africa,1967,40.697,5127935,794.8265597 +Burkina Faso,Africa,1972,43.591,5433886,854.7359763 +Burkina Faso,Africa,1977,46.137,5889574,743.3870368 +Burkina Faso,Africa,1982,48.122,6634596,807.1985855 +Burkina Faso,Africa,1987,49.557,7586551,912.0631417 +Burkina Faso,Africa,1992,50.26,8878303,931.7527731 +Burkina Faso,Africa,1997,50.324,10352843,946.2949618 +Burkina Faso,Africa,2002,50.65,12251209,1037.645221 +Burkina Faso,Africa,2007,52.295,14326203,1217.032994 +Burundi,Africa,1952,39.031,2445618,339.2964587 +Burundi,Africa,1957,40.533,2667518,379.5646281 +Burundi,Africa,1962,42.045,2961915,355.2032273 +Burundi,Africa,1967,43.548,3330989,412.9775136 +Burundi,Africa,1972,44.057,3529983,464.0995039 +Burundi,Africa,1977,45.91,3834415,556.1032651 +Burundi,Africa,1982,47.471,4580410,559.603231 +Burundi,Africa,1987,48.211,5126023,621.8188189 +Burundi,Africa,1992,44.736,5809236,631.6998778 +Burundi,Africa,1997,45.326,6121610,463.1151478 +Burundi,Africa,2002,47.36,7021078,446.4035126 +Burundi,Africa,2007,49.58,8390505,430.0706916 +Cambodia,Asia,1952,39.417,4693836,368.4692856 +Cambodia,Asia,1957,41.366,5322536,434.0383364 +Cambodia,Asia,1962,43.415,6083619,496.9136476 +Cambodia,Asia,1967,45.415,6960067,523.4323142 +Cambodia,Asia,1972,40.317,7450606,421.6240257 +Cambodia,Asia,1977,31.22,6978607,524.9721832 +Cambodia,Asia,1982,50.957,7272485,624.4754784 +Cambodia,Asia,1987,53.914,8371791,683.8955732 +Cambodia,Asia,1992,55.803,10150094,682.3031755 +Cambodia,Asia,1997,56.534,11782962,734.28517 +Cambodia,Asia,2002,56.752,12926707,896.2260153 +Cambodia,Asia,2007,59.723,14131858,1713.778686 +Cameroon,Africa,1952,38.523,5009067,1172.667655 +Cameroon,Africa,1957,40.428,5359923,1313.048099 +Cameroon,Africa,1962,42.643,5793633,1399.607441 +Cameroon,Africa,1967,44.799,6335506,1508.453148 +Cameroon,Africa,1972,47.049,7021028,1684.146528 +Cameroon,Africa,1977,49.355,7959865,1783.432873 +Cameroon,Africa,1982,52.961,9250831,2367.983282 +Cameroon,Africa,1987,54.985,10780667,2602.664206 +Cameroon,Africa,1992,54.314,12467171,1793.163278 +Cameroon,Africa,1997,52.199,14195809,1694.337469 +Cameroon,Africa,2002,49.856,15929988,1934.011449 +Cameroon,Africa,2007,50.43,17696293,2042.09524 +Canada,Americas,1952,68.75,14785584,11367.16112 +Canada,Americas,1957,69.96,17010154,12489.95006 +Canada,Americas,1962,71.3,18985849,13462.48555 +Canada,Americas,1967,72.13,20819767,16076.58803 +Canada,Americas,1972,72.88,22284500,18970.57086 +Canada,Americas,1977,74.21,23796400,22090.88306 +Canada,Americas,1982,75.76,25201900,22898.79214 +Canada,Americas,1987,76.86,26549700,26626.51503 +Canada,Americas,1992,77.95,28523502,26342.88426 +Canada,Americas,1997,78.61,30305843,28954.92589 +Canada,Americas,2002,79.77,31902268,33328.96507 +Canada,Americas,2007,80.653,33390141,36319.23501 +Central African Republic,Africa,1952,35.463,1291695,1071.310713 +Central African Republic,Africa,1957,37.464,1392284,1190.844328 +Central African Republic,Africa,1962,39.475,1523478,1193.068753 +Central African Republic,Africa,1967,41.478,1733638,1136.056615 +Central African Republic,Africa,1972,43.457,1927260,1070.013275 +Central African Republic,Africa,1977,46.775,2167533,1109.374338 +Central African Republic,Africa,1982,48.295,2476971,956.7529907 +Central African Republic,Africa,1987,50.485,2840009,844.8763504 +Central African Republic,Africa,1992,49.396,3265124,747.9055252 +Central African Republic,Africa,1997,46.066,3696513,740.5063317 +Central African Republic,Africa,2002,43.308,4048013,738.6906068 +Central African Republic,Africa,2007,44.741,4369038,706.016537 +Chad,Africa,1952,38.092,2682462,1178.665927 +Chad,Africa,1957,39.881,2894855,1308.495577 +Chad,Africa,1962,41.716,3150417,1389.817618 +Chad,Africa,1967,43.601,3495967,1196.810565 +Chad,Africa,1972,45.569,3899068,1104.103987 +Chad,Africa,1977,47.383,4388260,1133.98495 +Chad,Africa,1982,49.517,4875118,797.9081006 +Chad,Africa,1987,51.051,5498955,952.386129 +Chad,Africa,1992,51.724,6429417,1058.0643 +Chad,Africa,1997,51.573,7562011,1004.961353 +Chad,Africa,2002,50.525,8835739,1156.18186 +Chad,Africa,2007,50.651,10238807,1704.063724 +Chile,Americas,1952,54.745,6377619,3939.978789 +Chile,Americas,1957,56.074,7048426,4315.622723 +Chile,Americas,1962,57.924,7961258,4519.094331 +Chile,Americas,1967,60.523,8858908,5106.654313 +Chile,Americas,1972,63.441,9717524,5494.024437 +Chile,Americas,1977,67.052,10599793,4756.763836 +Chile,Americas,1982,70.565,11487112,5095.665738 +Chile,Americas,1987,72.492,12463354,5547.063754 +Chile,Americas,1992,74.126,13572994,7596.125964 +Chile,Americas,1997,75.816,14599929,10118.05318 +Chile,Americas,2002,77.86,15497046,10778.78385 +Chile,Americas,2007,78.553,16284741,13171.63885 +China,Asia,1952,44.0,556263527,400.448611 +China,Asia,1957,50.54896,637408000,575.9870009 +China,Asia,1962,44.50136,665770000,487.6740183 +China,Asia,1967,58.38112,754550000,612.7056934 +China,Asia,1972,63.11888,862030000,676.9000921 +China,Asia,1977,63.96736,943455000,741.2374699 +China,Asia,1982,65.525,1000281000,962.4213805 +China,Asia,1987,67.274,1084035000,1378.904018 +China,Asia,1992,68.69,1164970000,1655.784158 +China,Asia,1997,70.426,1230075000,2289.234136 +China,Asia,2002,72.028,1280400000,3119.280896 +China,Asia,2007,72.961,1318683096,4959.114854 +Colombia,Americas,1952,50.643,12350771,2144.115096 +Colombia,Americas,1957,55.118,14485993,2323.805581 +Colombia,Americas,1962,57.863,17009885,2492.351109 +Colombia,Americas,1967,59.963,19764027,2678.729839 +Colombia,Americas,1972,61.623,22542890,3264.660041 +Colombia,Americas,1977,63.837,25094412,3815.80787 +Colombia,Americas,1982,66.653,27764644,4397.575659 +Colombia,Americas,1987,67.768,30964245,4903.2191 +Colombia,Americas,1992,68.421,34202721,5444.648617 +Colombia,Americas,1997,70.313,37657830,6117.361746 +Colombia,Americas,2002,71.682,41008227,5755.259962 +Colombia,Americas,2007,72.889,44227550,7006.580419 +Comoros,Africa,1952,40.715,153936,1102.990936 +Comoros,Africa,1957,42.46,170928,1211.148548 +Comoros,Africa,1962,44.467,191689,1406.648278 +Comoros,Africa,1967,46.472,217378,1876.029643 +Comoros,Africa,1972,48.944,250027,1937.577675 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Rep.",Africa,1952,39.143,14100005,780.5423257 +"Congo, Dem. Rep.",Africa,1957,40.652,15577932,905.8602303 +"Congo, Dem. Rep.",Africa,1962,42.122,17486434,896.3146335 +"Congo, Dem. Rep.",Africa,1967,44.056,19941073,861.5932424 +"Congo, Dem. Rep.",Africa,1972,45.989,23007669,904.8960685 +"Congo, Dem. Rep.",Africa,1977,47.804,26480870,795.757282 +"Congo, Dem. Rep.",Africa,1982,47.784,30646495,673.7478181 +"Congo, Dem. Rep.",Africa,1987,47.412,35481645,672.774812 +"Congo, Dem. Rep.",Africa,1992,45.548,41672143,457.7191807 +"Congo, Dem. Rep.",Africa,1997,42.587,47798986,312.188423 +"Congo, Dem. Rep.",Africa,2002,44.966,55379852,241.1658765 +"Congo, Dem. Rep.",Africa,2007,46.462,64606759,277.5518587 +"Congo, Rep.",Africa,1952,42.111,854885,2125.621418 +"Congo, Rep.",Africa,1957,45.053,940458,2315.056572 +"Congo, Rep.",Africa,1962,48.435,1047924,2464.783157 +"Congo, Rep.",Africa,1967,52.04,1179760,2677.939642 +"Congo, Rep.",Africa,1972,54.907,1340458,3213.152683 +"Congo, Rep.",Africa,1977,55.625,1536769,3259.178978 +"Congo, Rep.",Africa,1982,56.695,1774735,4879.507522 +"Congo, Rep.",Africa,1987,57.47,2064095,4201.194937 +"Congo, Rep.",Africa,1992,56.433,2409073,4016.239529 +"Congo, Rep.",Africa,1997,52.962,2800947,3484.164376 +"Congo, Rep.",Africa,2002,52.97,3328795,3484.06197 +"Congo, Rep.",Africa,2007,55.322,3800610,3632.557798 +Costa Rica,Americas,1952,57.206,926317,2627.009471 +Costa Rica,Americas,1957,60.026,1112300,2990.010802 +Costa Rica,Americas,1962,62.842,1345187,3460.937025 +Costa Rica,Americas,1967,65.424,1588717,4161.727834 +Costa Rica,Americas,1972,67.849,1834796,5118.146939 +Costa Rica,Americas,1977,70.75,2108457,5926.876967 +Costa Rica,Americas,1982,73.45,2424367,5262.734751 +Costa Rica,Americas,1987,74.752,2799811,5629.915318 +Costa Rica,Americas,1992,75.713,3173216,6160.416317 +Costa Rica,Americas,1997,77.26,3518107,6677.045314 +Costa Rica,Americas,2002,78.123,3834934,7723.447195 +Costa Rica,Americas,2007,78.782,4133884,9645.06142 +Cote d'Ivoire,Africa,1952,40.477,2977019,1388.594732 +Cote d'Ivoire,Africa,1957,42.469,3300000,1500.895925 +Cote d'Ivoire,Africa,1962,44.93,3832408,1728.869428 +Cote d'Ivoire,Africa,1967,47.35,4744870,2052.050473 +Cote d'Ivoire,Africa,1972,49.801,6071696,2378.201111 +Cote d'Ivoire,Africa,1977,52.374,7459574,2517.736547 +Cote d'Ivoire,Africa,1982,53.983,9025951,2602.710169 +Cote d'Ivoire,Africa,1987,54.655,10761098,2156.956069 +Cote d'Ivoire,Africa,1992,52.044,12772596,1648.073791 +Cote d'Ivoire,Africa,1997,47.991,14625967,1786.265407 +Cote d'Ivoire,Africa,2002,46.832,16252726,1648.800823 +Cote d'Ivoire,Africa,2007,48.328,18013409,1544.750112 +Croatia,Europe,1952,61.21,3882229,3119.23652 +Croatia,Europe,1957,64.77,3991242,4338.231617 +Croatia,Europe,1962,67.13,4076557,5477.890018 +Croatia,Europe,1967,68.5,4174366,6960.297861 +Croatia,Europe,1972,69.61,4225310,9164.090127 +Croatia,Europe,1977,70.64,4318673,11305.38517 +Croatia,Europe,1982,70.46,4413368,13221.82184 +Croatia,Europe,1987,71.52,4484310,13822.58394 +Croatia,Europe,1992,72.527,4494013,8447.794873 +Croatia,Europe,1997,73.68,4444595,9875.604515 +Croatia,Europe,2002,74.876,4481020,11628.38895 +Croatia,Europe,2007,75.748,4493312,14619.22272 +Cuba,Americas,1952,59.421,6007797,5586.53878 +Cuba,Americas,1957,62.325,6640752,6092.174359 +Cuba,Americas,1962,65.246,7254373,5180.75591 +Cuba,Americas,1967,68.29,8139332,5690.268015 +Cuba,Americas,1972,70.723,8831348,5305.445256 +Cuba,Americas,1977,72.649,9537988,6380.494966 +Cuba,Americas,1982,73.717,9789224,7316.918107 +Cuba,Americas,1987,74.174,10239839,7532.924763 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+Denmark,Europe,1967,72.96,4838800,15937.21123 +Denmark,Europe,1972,73.47,4991596,18866.20721 +Denmark,Europe,1977,74.69,5088419,20422.9015 +Denmark,Europe,1982,74.63,5117810,21688.04048 +Denmark,Europe,1987,74.8,5127024,25116.17581 +Denmark,Europe,1992,75.33,5171393,26406.73985 +Denmark,Europe,1997,76.11,5283663,29804.34567 +Denmark,Europe,2002,77.18,5374693,32166.50006 +Denmark,Europe,2007,78.332,5468120,35278.41874 +Djibouti,Africa,1952,34.812,63149,2669.529475 +Djibouti,Africa,1957,37.328,71851,2864.969076 +Djibouti,Africa,1962,39.693,89898,3020.989263 +Djibouti,Africa,1967,42.074,127617,3020.050513 +Djibouti,Africa,1972,44.366,178848,3694.212352 +Djibouti,Africa,1977,46.519,228694,3081.761022 +Djibouti,Africa,1982,48.812,305991,2879.468067 +Djibouti,Africa,1987,50.04,311025,2880.102568 +Djibouti,Africa,1992,51.604,384156,2377.156192 +Djibouti,Africa,1997,53.157,417908,1895.016984 +Djibouti,Africa,2002,53.373,447416,1908.260867 +Djibouti,Africa,2007,54.791,496374,2082.481567 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Salvador,Americas,1957,48.57,2355805,3421.523218 +El Salvador,Americas,1962,52.307,2747687,3776.803627 +El Salvador,Americas,1967,55.855,3232927,4358.595393 +El Salvador,Americas,1972,58.207,3790903,4520.246008 +El Salvador,Americas,1977,56.696,4282586,5138.922374 +El Salvador,Americas,1982,56.604,4474873,4098.344175 +El Salvador,Americas,1987,63.154,4842194,4140.442097 +El Salvador,Americas,1992,66.798,5274649,4444.2317 +El Salvador,Americas,1997,69.535,5783439,5154.825496 +El Salvador,Americas,2002,70.734,6353681,5351.568666 +El Salvador,Americas,2007,71.878,6939688,5728.353514 +Equatorial Guinea,Africa,1952,34.482,216964,375.6431231 +Equatorial Guinea,Africa,1957,35.983,232922,426.0964081 +Equatorial Guinea,Africa,1962,37.485,249220,582.8419714 +Equatorial Guinea,Africa,1967,38.987,259864,915.5960025 +Equatorial Guinea,Africa,1972,40.516,277603,672.4122571 +Equatorial Guinea,Africa,1977,42.024,192675,958.5668124 +Equatorial Guinea,Africa,1982,43.662,285483,927.8253427 +Equatorial Guinea,Africa,1987,45.664,341244,966.8968149 +Equatorial Guinea,Africa,1992,47.545,387838,1132.055034 +Equatorial Guinea,Africa,1997,48.245,439971,2814.480755 +Equatorial Guinea,Africa,2002,49.348,495627,7703.4959 +Equatorial Guinea,Africa,2007,51.579,551201,12154.08975 +Eritrea,Africa,1952,35.928,1438760,328.9405571 +Eritrea,Africa,1957,38.047,1542611,344.1618859 +Eritrea,Africa,1962,40.158,1666618,380.9958433 +Eritrea,Africa,1967,42.189,1820319,468.7949699 +Eritrea,Africa,1972,44.142,2260187,514.3242082 +Eritrea,Africa,1977,44.535,2512642,505.7538077 +Eritrea,Africa,1982,43.89,2637297,524.8758493 +Eritrea,Africa,1987,46.453,2915959,521.1341333 +Eritrea,Africa,1992,49.991,3668440,582.8585102 +Eritrea,Africa,1997,53.378,4058319,913.47079 +Eritrea,Africa,2002,55.24,4414865,765.3500015 +Eritrea,Africa,2007,58.04,4906585,641.3695236 +Ethiopia,Africa,1952,34.078,20860941,362.1462796 +Ethiopia,Africa,1957,36.667,22815614,378.9041632 +Ethiopia,Africa,1962,40.059,25145372,419.4564161 +Ethiopia,Africa,1967,42.115,27860297,516.1186438 +Ethiopia,Africa,1972,43.515,30770372,566.2439442 +Ethiopia,Africa,1977,44.51,34617799,556.8083834 +Ethiopia,Africa,1982,44.916,38111756,577.8607471 +Ethiopia,Africa,1987,46.684,42999530,573.7413142 +Ethiopia,Africa,1992,48.091,52088559,421.3534653 +Ethiopia,Africa,1997,49.402,59861301,515.8894013 +Ethiopia,Africa,2002,50.725,67946797,530.0535319 +Ethiopia,Africa,2007,52.947,76511887,690.8055759 +Finland,Europe,1952,66.55,4090500,6424.519071 +Finland,Europe,1957,67.49,4324000,7545.415386 +Finland,Europe,1962,68.75,4491443,9371.842561 +Finland,Europe,1967,69.83,4605744,10921.63626 +Finland,Europe,1972,70.87,4639657,14358.8759 +Finland,Europe,1977,72.52,4738902,15605.42283 +Finland,Europe,1982,74.55,4826933,18533.15761 +Finland,Europe,1987,74.83,4931729,21141.01223 +Finland,Europe,1992,75.7,5041039,20647.16499 +Finland,Europe,1997,77.13,5134406,23723.9502 +Finland,Europe,2002,78.37,5193039,28204.59057 +Finland,Europe,2007,79.313,5238460,33207.0844 +France,Europe,1952,67.41,42459667,7029.809327 +France,Europe,1957,68.93,44310863,8662.834898 +France,Europe,1962,70.51,47124000,10560.48553 +France,Europe,1967,71.55,49569000,12999.91766 +France,Europe,1972,72.38,51732000,16107.19171 +France,Europe,1977,73.83,53165019,18292.63514 +France,Europe,1982,74.89,54433565,20293.89746 +France,Europe,1987,76.34,55630100,22066.44214 +France,Europe,1992,77.46,57374179,24703.79615 +France,Europe,1997,78.64,58623428,25889.78487 +France,Europe,2002,79.59,59925035,28926.03234 +France,Europe,2007,80.657,61083916,30470.0167 +Gabon,Africa,1952,37.003,420702,4293.476475 +Gabon,Africa,1957,38.999,434904,4976.198099 +Gabon,Africa,1962,40.489,455661,6631.459222 +Gabon,Africa,1967,44.598,489004,8358.761987 +Gabon,Africa,1972,48.69,537977,11401.94841 +Gabon,Africa,1977,52.79,706367,21745.57328 +Gabon,Africa,1982,56.564,753874,15113.36194 +Gabon,Africa,1987,60.19,880397,11864.40844 +Gabon,Africa,1992,61.366,985739,13522.15752 +Gabon,Africa,1997,60.461,1126189,14722.84188 +Gabon,Africa,2002,56.761,1299304,12521.71392 +Gabon,Africa,2007,56.735,1454867,13206.48452 +Gambia,Africa,1952,30.0,284320,485.2306591 +Gambia,Africa,1957,32.065,323150,520.9267111 +Gambia,Africa,1962,33.896,374020,599.650276 +Gambia,Africa,1967,35.857,439593,734.7829124 +Gambia,Africa,1972,38.308,517101,756.0868363 +Gambia,Africa,1977,41.842,608274,884.7552507 +Gambia,Africa,1982,45.58,715523,835.8096108 +Gambia,Africa,1987,49.265,848406,611.6588611 +Gambia,Africa,1992,52.644,1025384,665.6244126 +Gambia,Africa,1997,55.861,1235767,653.7301704 +Gambia,Africa,2002,58.041,1457766,660.5855997 +Gambia,Africa,2007,59.448,1688359,752.7497265 +Germany,Europe,1952,67.5,69145952,7144.114393 +Germany,Europe,1957,69.1,71019069,10187.82665 +Germany,Europe,1962,70.3,73739117,12902.46291 +Germany,Europe,1967,70.8,76368453,14745.62561 +Germany,Europe,1972,71.0,78717088,18016.18027 +Germany,Europe,1977,72.5,78160773,20512.92123 +Germany,Europe,1982,73.8,78335266,22031.53274 +Germany,Europe,1987,74.847,77718298,24639.18566 +Germany,Europe,1992,76.07,80597764,26505.30317 +Germany,Europe,1997,77.34,82011073,27788.88416 +Germany,Europe,2002,78.67,82350671,30035.80198 +Germany,Europe,2007,79.406,82400996,32170.37442 +Ghana,Africa,1952,43.149,5581001,911.2989371 +Ghana,Africa,1957,44.779,6391288,1043.561537 +Ghana,Africa,1962,46.452,7355248,1190.041118 +Ghana,Africa,1967,48.072,8490213,1125.69716 +Ghana,Africa,1972,49.875,9354120,1178.223708 +Ghana,Africa,1977,51.756,10538093,993.2239571 +Ghana,Africa,1982,53.744,11400338,876.032569 +Ghana,Africa,1987,55.729,14168101,847.0061135 +Ghana,Africa,1992,57.501,16278738,925.060154 +Ghana,Africa,1997,58.556,18418288,1005.245812 +Ghana,Africa,2002,58.453,20550751,1111.984578 +Ghana,Africa,2007,60.022,22873338,1327.60891 +Greece,Europe,1952,65.86,7733250,3530.690067 +Greece,Europe,1957,67.86,8096218,4916.299889 +Greece,Europe,1962,69.51,8448233,6017.190733 +Greece,Europe,1967,71.0,8716441,8513.097016 +Greece,Europe,1972,72.34,8888628,12724.82957 +Greece,Europe,1977,73.68,9308479,14195.52428 +Greece,Europe,1982,75.24,9786480,15268.42089 +Greece,Europe,1987,76.67,9974490,16120.52839 +Greece,Europe,1992,77.03,10325429,17541.49634 +Greece,Europe,1997,77.869,10502372,18747.69814 +Greece,Europe,2002,78.256,10603863,22514.2548 +Greece,Europe,2007,79.483,10706290,27538.41188 +Guatemala,Americas,1952,42.023,3146381,2428.237769 +Guatemala,Americas,1957,44.142,3640876,2617.155967 +Guatemala,Americas,1962,46.954,4208858,2750.364446 +Guatemala,Americas,1967,50.016,4690773,3242.531147 +Guatemala,Americas,1972,53.738,5149581,4031.408271 +Guatemala,Americas,1977,56.029,5703430,4879.992748 +Guatemala,Americas,1982,58.137,6395630,4820.49479 +Guatemala,Americas,1987,60.782,7326406,4246.485974 +Guatemala,Americas,1992,63.373,8486949,4439.45084 +Guatemala,Americas,1997,66.322,9803875,4684.313807 +Guatemala,Americas,2002,68.978,11178650,4858.347495 +Guatemala,Americas,2007,70.259,12572928,5186.050003 +Guinea,Africa,1952,33.609,2664249,510.1964923 +Guinea,Africa,1957,34.558,2876726,576.2670245 +Guinea,Africa,1962,35.753,3140003,686.3736739 +Guinea,Africa,1967,37.197,3451418,708.7595409 +Guinea,Africa,1972,38.842,3811387,741.6662307 +Guinea,Africa,1977,40.762,4227026,874.6858643 +Guinea,Africa,1982,42.891,4710497,857.2503577 +Guinea,Africa,1987,45.552,5650262,805.5724718 +Guinea,Africa,1992,48.576,6990574,794.3484384 +Guinea,Africa,1997,51.455,8048834,869.4497668 +Guinea,Africa,2002,53.676,8807818,945.5835837 +Guinea,Africa,2007,56.007,9947814,942.6542111 +Guinea-Bissau,Africa,1952,32.5,580653,299.850319 +Guinea-Bissau,Africa,1957,33.489,601095,431.7904566 +Guinea-Bissau,Africa,1962,34.488,627820,522.0343725 +Guinea-Bissau,Africa,1967,35.492,601287,715.5806402 +Guinea-Bissau,Africa,1972,36.486,625361,820.2245876 +Guinea-Bissau,Africa,1977,37.465,745228,764.7259628 +Guinea-Bissau,Africa,1982,39.327,825987,838.1239671 +Guinea-Bissau,Africa,1987,41.245,927524,736.4153921 +Guinea-Bissau,Africa,1992,43.266,1050938,745.5398706 +Guinea-Bissau,Africa,1997,44.873,1193708,796.6644681 +Guinea-Bissau,Africa,2002,45.504,1332459,575.7047176 +Guinea-Bissau,Africa,2007,46.388,1472041,579.231743 +Haiti,Americas,1952,37.579,3201488,1840.366939 +Haiti,Americas,1957,40.696,3507701,1726.887882 +Haiti,Americas,1962,43.59,3880130,1796.589032 +Haiti,Americas,1967,46.243,4318137,1452.057666 +Haiti,Americas,1972,48.042,4698301,1654.456946 +Haiti,Americas,1977,49.923,4908554,1874.298931 +Haiti,Americas,1982,51.461,5198399,2011.159549 +Haiti,Americas,1987,53.636,5756203,1823.015995 +Haiti,Americas,1992,55.089,6326682,1456.309517 +Haiti,Americas,1997,56.671,6913545,1341.726931 +Haiti,Americas,2002,58.137,7607651,1270.364932 +Haiti,Americas,2007,60.916,8502814,1201.637154 +Honduras,Americas,1952,41.912,1517453,2194.926204 +Honduras,Americas,1957,44.665,1770390,2220.487682 +Honduras,Americas,1962,48.041,2090162,2291.156835 +Honduras,Americas,1967,50.924,2500689,2538.269358 +Honduras,Americas,1972,53.884,2965146,2529.842345 +Honduras,Americas,1977,57.402,3055235,3203.208066 +Honduras,Americas,1982,60.909,3669448,3121.760794 +Honduras,Americas,1987,64.492,4372203,3023.096699 +Honduras,Americas,1992,66.399,5077347,3081.694603 +Honduras,Americas,1997,67.659,5867957,3160.454906 +Honduras,Americas,2002,68.565,6677328,3099.72866 +Honduras,Americas,2007,70.198,7483763,3548.330846 +"Hong Kong, China",Asia,1952,60.96,2125900,3054.421209 +"Hong Kong, China",Asia,1957,64.75,2736300,3629.076457 +"Hong Kong, China",Asia,1962,67.65,3305200,4692.648272 +"Hong Kong, China",Asia,1967,70.0,3722800,6197.962814 +"Hong Kong, China",Asia,1972,72.0,4115700,8315.928145 +"Hong Kong, China",Asia,1977,73.6,4583700,11186.14125 +"Hong Kong, China",Asia,1982,75.45,5264500,14560.53051 +"Hong Kong, China",Asia,1987,76.2,5584510,20038.47269 +"Hong Kong, China",Asia,1992,77.601,5829696,24757.60301 +"Hong Kong, China",Asia,1997,80.0,6495918,28377.63219 +"Hong Kong, China",Asia,2002,81.495,6762476,30209.01516 +"Hong Kong, China",Asia,2007,82.208,6980412,39724.97867 +Hungary,Europe,1952,64.03,9504000,5263.673816 +Hungary,Europe,1957,66.41,9839000,6040.180011 +Hungary,Europe,1962,67.96,10063000,7550.359877 +Hungary,Europe,1967,69.5,10223422,9326.64467 +Hungary,Europe,1972,69.76,10394091,10168.65611 +Hungary,Europe,1977,69.95,10637171,11674.83737 +Hungary,Europe,1982,69.39,10705535,12545.99066 +Hungary,Europe,1987,69.58,10612740,12986.47998 +Hungary,Europe,1992,69.17,10348684,10535.62855 +Hungary,Europe,1997,71.04,10244684,11712.7768 +Hungary,Europe,2002,72.59,10083313,14843.93556 +Hungary,Europe,2007,73.338,9956108,18008.94444 +Iceland,Europe,1952,72.49,147962,7267.688428 +Iceland,Europe,1957,73.47,165110,9244.001412 +Iceland,Europe,1962,73.68,182053,10350.15906 +Iceland,Europe,1967,73.73,198676,13319.89568 +Iceland,Europe,1972,74.46,209275,15798.06362 +Iceland,Europe,1977,76.11,221823,19654.96247 +Iceland,Europe,1982,76.99,233997,23269.6075 +Iceland,Europe,1987,77.23,244676,26923.20628 +Iceland,Europe,1992,78.77,259012,25144.39201 +Iceland,Europe,1997,78.95,271192,28061.09966 +Iceland,Europe,2002,80.5,288030,31163.20196 +Iceland,Europe,2007,81.757,301931,36180.78919 +India,Asia,1952,37.373,372000000,546.5657493 +India,Asia,1957,40.249,409000000,590.061996 +India,Asia,1962,43.605,454000000,658.3471509 +India,Asia,1967,47.193,506000000,700.7706107 +India,Asia,1972,50.651,567000000,724.032527 +India,Asia,1977,54.208,634000000,813.337323 +India,Asia,1982,56.596,708000000,855.7235377 +India,Asia,1987,58.553,788000000,976.5126756 +India,Asia,1992,60.223,872000000,1164.406809 +India,Asia,1997,61.765,959000000,1458.817442 +India,Asia,2002,62.879,1034172547,1746.769454 +India,Asia,2007,64.698,1110396331,2452.210407 +Indonesia,Asia,1952,37.468,82052000,749.6816546 +Indonesia,Asia,1957,39.918,90124000,858.9002707 +Indonesia,Asia,1962,42.518,99028000,849.2897701 +Indonesia,Asia,1967,45.964,109343000,762.4317721 +Indonesia,Asia,1972,49.203,121282000,1111.107907 +Indonesia,Asia,1977,52.702,136725000,1382.702056 +Indonesia,Asia,1982,56.159,153343000,1516.872988 +Indonesia,Asia,1987,60.137,169276000,1748.356961 +Indonesia,Asia,1992,62.681,184816000,2383.140898 +Indonesia,Asia,1997,66.041,199278000,3119.335603 +Indonesia,Asia,2002,68.588,211060000,2873.91287 +Indonesia,Asia,2007,70.65,223547000,3540.651564 +Iran,Asia,1952,44.869,17272000,3035.326002 +Iran,Asia,1957,47.181,19792000,3290.257643 +Iran,Asia,1962,49.325,22874000,4187.329802 +Iran,Asia,1967,52.469,26538000,5906.731805 +Iran,Asia,1972,55.234,30614000,9613.818607 +Iran,Asia,1977,57.702,35480679,11888.59508 +Iran,Asia,1982,59.62,43072751,7608.334602 +Iran,Asia,1987,63.04,51889696,6642.881371 +Iran,Asia,1992,65.742,60397973,7235.653188 +Iran,Asia,1997,68.042,63327987,8263.590301 +Iran,Asia,2002,69.451,66907826,9240.761975 +Iran,Asia,2007,70.964,69453570,11605.71449 +Iraq,Asia,1952,45.32,5441766,4129.766056 +Iraq,Asia,1957,48.437,6248643,6229.333562 +Iraq,Asia,1962,51.457,7240260,8341.737815 +Iraq,Asia,1967,54.459,8519282,8931.459811 +Iraq,Asia,1972,56.95,10061506,9576.037596 +Iraq,Asia,1977,60.413,11882916,14688.23507 +Iraq,Asia,1982,62.038,14173318,14517.90711 +Iraq,Asia,1987,65.044,16543189,11643.57268 +Iraq,Asia,1992,59.461,17861905,3745.640687 +Iraq,Asia,1997,58.811,20775703,3076.239795 +Iraq,Asia,2002,57.046,24001816,4390.717312 +Iraq,Asia,2007,59.545,27499638,4471.061906 +Ireland,Europe,1952,66.91,2952156,5210.280328 +Ireland,Europe,1957,68.9,2878220,5599.077872 +Ireland,Europe,1962,70.29,2830000,6631.597314 +Ireland,Europe,1967,71.08,2900100,7655.568963 +Ireland,Europe,1972,71.28,3024400,9530.772896 +Ireland,Europe,1977,72.03,3271900,11150.98113 +Ireland,Europe,1982,73.1,3480000,12618.32141 +Ireland,Europe,1987,74.36,3539900,13872.86652 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+Italy,Europe,1982,74.98,56535636,16537.4835 +Italy,Europe,1987,76.42,56729703,19207.23482 +Italy,Europe,1992,77.44,56840847,22013.64486 +Italy,Europe,1997,78.82,57479469,24675.02446 +Italy,Europe,2002,80.24,57926999,27968.09817 +Italy,Europe,2007,80.546,58147733,28569.7197 +Jamaica,Americas,1952,58.53,1426095,2898.530881 +Jamaica,Americas,1957,62.61,1535090,4756.525781 +Jamaica,Americas,1962,65.61,1665128,5246.107524 +Jamaica,Americas,1967,67.51,1861096,6124.703451 +Jamaica,Americas,1972,69.0,1997616,7433.889293 +Jamaica,Americas,1977,70.11,2156814,6650.195573 +Jamaica,Americas,1982,71.21,2298309,6068.05135 +Jamaica,Americas,1987,71.77,2326606,6351.237495 +Jamaica,Americas,1992,71.766,2378618,7404.923685 +Jamaica,Americas,1997,72.262,2531311,7121.924704 +Jamaica,Americas,2002,72.047,2664659,6994.774861 +Jamaica,Americas,2007,72.567,2780132,7320.880262 +Japan,Asia,1952,63.03,86459025,3216.956347 +Japan,Asia,1957,65.5,91563009,4317.694365 +Japan,Asia,1962,68.73,95831757,6576.649461 +Japan,Asia,1967,71.43,100825279,9847.788607 +Japan,Asia,1972,73.42,107188273,14778.78636 +Japan,Asia,1977,75.38,113872473,16610.37701 +Japan,Asia,1982,77.11,118454974,19384.10571 +Japan,Asia,1987,78.67,122091325,22375.94189 +Japan,Asia,1992,79.36,124329269,26824.89511 +Japan,Asia,1997,80.69,125956499,28816.58499 +Japan,Asia,2002,82.0,127065841,28604.5919 +Japan,Asia,2007,82.603,127467972,31656.06806 +Jordan,Asia,1952,43.158,607914,1546.907807 +Jordan,Asia,1957,45.669,746559,1886.080591 +Jordan,Asia,1962,48.126,933559,2348.009158 +Jordan,Asia,1967,51.629,1255058,2741.796252 +Jordan,Asia,1972,56.528,1613551,2110.856309 +Jordan,Asia,1977,61.134,1937652,2852.351568 +Jordan,Asia,1982,63.739,2347031,4161.415959 +Jordan,Asia,1987,65.869,2820042,4448.679912 +Jordan,Asia,1992,68.015,3867409,3431.593647 +Jordan,Asia,1997,69.772,4526235,3645.379572 +Jordan,Asia,2002,71.263,5307470,3844.917194 +Jordan,Asia,2007,72.535,6053193,4519.461171 +Kenya,Africa,1952,42.27,6464046,853.540919 +Kenya,Africa,1957,44.686,7454779,944.4383152 +Kenya,Africa,1962,47.949,8678557,896.9663732 +Kenya,Africa,1967,50.654,10191512,1056.736457 +Kenya,Africa,1972,53.559,12044785,1222.359968 +Kenya,Africa,1977,56.155,14500404,1267.613204 +Kenya,Africa,1982,58.766,17661452,1348.225791 +Kenya,Africa,1987,59.339,21198082,1361.936856 +Kenya,Africa,1992,59.285,25020539,1341.921721 +Kenya,Africa,1997,54.407,28263827,1360.485021 +Kenya,Africa,2002,50.992,31386842,1287.514732 +Kenya,Africa,2007,54.11,35610177,1463.249282 +"Korea, Dem. Rep.",Asia,1952,50.056,8865488,1088.277758 +"Korea, Dem. Rep.",Asia,1957,54.081,9411381,1571.134655 +"Korea, Dem. Rep.",Asia,1962,56.656,10917494,1621.693598 +"Korea, Dem. Rep.",Asia,1967,59.942,12617009,2143.540609 +"Korea, Dem. Rep.",Asia,1972,63.983,14781241,3701.621503 +"Korea, Dem. Rep.",Asia,1977,67.159,16325320,4106.301249 +"Korea, Dem. Rep.",Asia,1982,69.1,17647518,4106.525293 +"Korea, Dem. Rep.",Asia,1987,70.647,19067554,4106.492315 +"Korea, Dem. Rep.",Asia,1992,69.978,20711375,3726.063507 +"Korea, Dem. Rep.",Asia,1997,67.727,21585105,1690.756814 +"Korea, Dem. Rep.",Asia,2002,66.662,22215365,1646.758151 +"Korea, Dem. Rep.",Asia,2007,67.297,23301725,1593.06548 +"Korea, Rep.",Asia,1952,47.453,20947571,1030.592226 +"Korea, Rep.",Asia,1957,52.681,22611552,1487.593537 +"Korea, Rep.",Asia,1962,55.292,26420307,1536.344387 +"Korea, Rep.",Asia,1967,57.716,30131000,2029.228142 +"Korea, Rep.",Asia,1972,62.612,33505000,3030.87665 +"Korea, Rep.",Asia,1977,64.766,36436000,4657.22102 +"Korea, Rep.",Asia,1982,67.123,39326000,5622.942464 +"Korea, Rep.",Asia,1987,69.81,41622000,8533.088805 +"Korea, Rep.",Asia,1992,72.244,43805450,12104.27872 +"Korea, Rep.",Asia,1997,74.647,46173816,15993.52796 +"Korea, Rep.",Asia,2002,77.045,47969150,19233.98818 +"Korea, Rep.",Asia,2007,78.623,49044790,23348.13973 +Kuwait,Asia,1952,55.565,160000,108382.3529 +Kuwait,Asia,1957,58.033,212846,113523.1329 +Kuwait,Asia,1962,60.47,358266,95458.11176 +Kuwait,Asia,1967,64.624,575003,80894.88326 +Kuwait,Asia,1972,67.712,841934,109347.867 +Kuwait,Asia,1977,69.343,1140357,59265.47714 +Kuwait,Asia,1982,71.309,1497494,31354.03573 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+Lesotho,Africa,1972,49.767,1116779,496.5815922 +Lesotho,Africa,1977,52.208,1251524,745.3695408 +Lesotho,Africa,1982,55.078,1411807,797.2631074 +Lesotho,Africa,1987,57.18,1599200,773.9932141 +Lesotho,Africa,1992,59.685,1803195,977.4862725 +Lesotho,Africa,1997,55.558,1982823,1186.147994 +Lesotho,Africa,2002,44.593,2046772,1275.184575 +Lesotho,Africa,2007,42.592,2012649,1569.331442 +Liberia,Africa,1952,38.48,863308,575.5729961 +Liberia,Africa,1957,39.486,975950,620.9699901 +Liberia,Africa,1962,40.502,1112796,634.1951625 +Liberia,Africa,1967,41.536,1279406,713.6036483 +Liberia,Africa,1972,42.614,1482628,803.0054535 +Liberia,Africa,1977,43.764,1703617,640.3224383 +Liberia,Africa,1982,44.852,1956875,572.1995694 +Liberia,Africa,1987,46.027,2269414,506.1138573 +Liberia,Africa,1992,40.802,1912974,636.6229191 +Liberia,Africa,1997,42.221,2200725,609.1739508 +Liberia,Africa,2002,43.753,2814651,531.4823679 +Liberia,Africa,2007,45.678,3193942,414.5073415 +Libya,Africa,1952,42.723,1019729,2387.54806 +Libya,Africa,1957,45.289,1201578,3448.284395 +Libya,Africa,1962,47.808,1441863,6757.030816 +Libya,Africa,1967,50.227,1759224,18772.75169 +Libya,Africa,1972,52.773,2183877,21011.49721 +Libya,Africa,1977,57.442,2721783,21951.21176 +Libya,Africa,1982,62.155,3344074,17364.27538 +Libya,Africa,1987,66.234,3799845,11770.5898 +Libya,Africa,1992,68.755,4364501,9640.138501 +Libya,Africa,1997,71.555,4759670,9467.446056 +Libya,Africa,2002,72.737,5368585,9534.677467 +Libya,Africa,2007,73.952,6036914,12057.49928 +Madagascar,Africa,1952,36.681,4762912,1443.011715 +Madagascar,Africa,1957,38.865,5181679,1589.20275 +Madagascar,Africa,1962,40.848,5703324,1643.38711 +Madagascar,Africa,1967,42.881,6334556,1634.047282 +Madagascar,Africa,1972,44.851,7082430,1748.562982 +Madagascar,Africa,1977,46.881,8007166,1544.228586 +Madagascar,Africa,1982,48.969,9171477,1302.878658 +Madagascar,Africa,1987,49.35,10568642,1155.441948 +Madagascar,Africa,1992,52.214,12210395,1040.67619 +Madagascar,Africa,1997,54.978,14165114,986.2958956 +Madagascar,Africa,2002,57.286,16473477,894.6370822 +Madagascar,Africa,2007,59.443,19167654,1044.770126 +Malawi,Africa,1952,36.256,2917802,369.1650802 +Malawi,Africa,1957,37.207,3221238,416.3698064 +Malawi,Africa,1962,38.41,3628608,427.9010856 +Malawi,Africa,1967,39.487,4147252,495.5147806 +Malawi,Africa,1972,41.766,4730997,584.6219709 +Malawi,Africa,1977,43.767,5637246,663.2236766 +Malawi,Africa,1982,45.642,6502825,632.8039209 +Malawi,Africa,1987,47.457,7824747,635.5173634 +Malawi,Africa,1992,49.42,10014249,563.2000145 +Malawi,Africa,1997,47.495,10419991,692.2758103 +Malawi,Africa,2002,45.009,11824495,665.4231186 +Malawi,Africa,2007,48.303,13327079,759.3499101 +Malaysia,Asia,1952,48.463,6748378,1831.132894 +Malaysia,Asia,1957,52.102,7739235,1810.066992 +Malaysia,Asia,1962,55.737,8906385,2036.884944 +Malaysia,Asia,1967,59.371,10154878,2277.742396 +Malaysia,Asia,1972,63.01,11441462,2849.09478 +Malaysia,Asia,1977,65.256,12845381,3827.921571 +Malaysia,Asia,1982,68.0,14441916,4920.355951 +Malaysia,Asia,1987,69.5,16331785,5249.802653 +Malaysia,Asia,1992,70.693,18319502,7277.912802 +Malaysia,Asia,1997,71.938,20476091,10132.90964 +Malaysia,Asia,2002,73.044,22662365,10206.97794 +Malaysia,Asia,2007,74.241,24821286,12451.6558 +Mali,Africa,1952,33.685,3838168,452.3369807 +Mali,Africa,1957,35.307,4241884,490.3821867 +Mali,Africa,1962,36.936,4690372,496.1743428 +Mali,Africa,1967,38.487,5212416,545.0098873 +Mali,Africa,1972,39.977,5828158,581.3688761 +Mali,Africa,1977,41.714,6491649,686.3952693 +Mali,Africa,1982,43.916,6998256,618.0140641 +Mali,Africa,1987,46.364,7634008,684.1715576 +Mali,Africa,1992,48.388,8416215,739.014375 +Mali,Africa,1997,49.903,9384984,790.2579846 +Mali,Africa,2002,51.818,10580176,951.4097518 +Mali,Africa,2007,54.467,12031795,1042.581557 +Mauritania,Africa,1952,40.543,1022556,743.1159097 +Mauritania,Africa,1957,42.338,1076852,846.1202613 +Mauritania,Africa,1962,44.248,1146757,1055.896036 +Mauritania,Africa,1967,46.289,1230542,1421.145193 +Mauritania,Africa,1972,48.437,1332786,1586.851781 +Mauritania,Africa,1977,50.852,1456688,1497.492223 +Mauritania,Africa,1982,53.599,1622136,1481.150189 +Mauritania,Africa,1987,56.145,1841240,1421.603576 +Mauritania,Africa,1992,58.333,2119465,1361.369784 +Mauritania,Africa,1997,60.43,2444741,1483.136136 +Mauritania,Africa,2002,62.247,2828858,1579.019543 +Mauritania,Africa,2007,64.164,3270065,1803.151496 +Mauritius,Africa,1952,50.986,516556,1967.955707 +Mauritius,Africa,1957,58.089,609816,2034.037981 +Mauritius,Africa,1962,60.246,701016,2529.067487 +Mauritius,Africa,1967,61.557,789309,2475.387562 +Mauritius,Africa,1972,62.944,851334,2575.484158 +Mauritius,Africa,1977,64.93,913025,3710.982963 +Mauritius,Africa,1982,66.711,992040,3688.037739 +Mauritius,Africa,1987,68.74,1042663,4783.586903 +Mauritius,Africa,1992,69.745,1096202,6058.253846 +Mauritius,Africa,1997,70.736,1149818,7425.705295 +Mauritius,Africa,2002,71.954,1200206,9021.815894 +Mauritius,Africa,2007,72.801,1250882,10956.99112 +Mexico,Americas,1952,50.789,30144317,3478.125529 +Mexico,Americas,1957,55.19,35015548,4131.546641 +Mexico,Americas,1962,58.299,41121485,4581.609385 +Mexico,Americas,1967,60.11,47995559,5754.733883 +Mexico,Americas,1972,62.361,55984294,6809.40669 +Mexico,Americas,1977,65.032,63759976,7674.929108 +Mexico,Americas,1982,67.405,71640904,9611.147541 +Mexico,Americas,1987,69.498,80122492,8688.156003 +Mexico,Americas,1992,71.455,88111030,9472.384295 +Mexico,Americas,1997,73.67,95895146,9767.29753 +Mexico,Americas,2002,74.902,102479927,10742.44053 +Mexico,Americas,2007,76.195,108700891,11977.57496 +Mongolia,Asia,1952,42.244,800663,786.5668575 +Mongolia,Asia,1957,45.248,882134,912.6626085 +Mongolia,Asia,1962,48.251,1010280,1056.353958 +Mongolia,Asia,1967,51.253,1149500,1226.04113 +Mongolia,Asia,1972,53.754,1320500,1421.741975 +Mongolia,Asia,1977,55.491,1528000,1647.511665 +Mongolia,Asia,1982,57.489,1756032,2000.603139 +Mongolia,Asia,1987,60.222,2015133,2338.008304 +Mongolia,Asia,1992,61.271,2312802,1785.402016 +Mongolia,Asia,1997,63.625,2494803,1902.2521 +Mongolia,Asia,2002,65.033,2674234,2140.739323 +Mongolia,Asia,2007,66.803,2874127,3095.772271 +Montenegro,Europe,1952,59.164,413834,2647.585601 +Montenegro,Europe,1957,61.448,442829,3682.259903 +Montenegro,Europe,1962,63.728,474528,4649.593785 +Montenegro,Europe,1967,67.178,501035,5907.850937 +Montenegro,Europe,1972,70.636,527678,7778.414017 +Montenegro,Europe,1977,73.066,560073,9595.929905 +Montenegro,Europe,1982,74.101,562548,11222.58762 +Montenegro,Europe,1987,74.865,569473,11732.51017 +Montenegro,Europe,1992,75.435,621621,7003.339037 +Montenegro,Europe,1997,75.445,692651,6465.613349 +Montenegro,Europe,2002,73.981,720230,6557.194282 +Montenegro,Europe,2007,74.543,684736,9253.896111 +Morocco,Africa,1952,42.873,9939217,1688.20357 +Morocco,Africa,1957,45.423,11406350,1642.002314 +Morocco,Africa,1962,47.924,13056604,1566.353493 +Morocco,Africa,1967,50.335,14770296,1711.04477 +Morocco,Africa,1972,52.862,16660670,1930.194975 +Morocco,Africa,1977,55.73,18396941,2370.619976 +Morocco,Africa,1982,59.65,20198730,2702.620356 +Morocco,Africa,1987,62.677,22987397,2755.046991 +Morocco,Africa,1992,65.393,25798239,2948.047252 +Morocco,Africa,1997,67.66,28529501,2982.101858 +Morocco,Africa,2002,69.615,31167783,3258.495584 +Morocco,Africa,2007,71.164,33757175,3820.17523 +Mozambique,Africa,1952,31.286,6446316,468.5260381 +Mozambique,Africa,1957,33.779,7038035,495.5868333 +Mozambique,Africa,1962,36.161,7788944,556.6863539 +Mozambique,Africa,1967,38.113,8680909,566.6691539 +Mozambique,Africa,1972,40.328,9809596,724.9178037 +Mozambique,Africa,1977,42.495,11127868,502.3197334 +Mozambique,Africa,1982,42.795,12587223,462.2114149 +Mozambique,Africa,1987,42.861,12891952,389.8761846 +Mozambique,Africa,1992,44.284,13160731,410.8968239 +Mozambique,Africa,1997,46.344,16603334,472.3460771 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+Norway,Europe,1972,74.34,3933004,18965.05551 +Norway,Europe,1977,75.37,4043205,23311.34939 +Norway,Europe,1982,75.97,4114787,26298.63531 +Norway,Europe,1987,75.89,4186147,31540.9748 +Norway,Europe,1992,77.32,4286357,33965.66115 +Norway,Europe,1997,78.32,4405672,41283.16433 +Norway,Europe,2002,79.05,4535591,44683.97525 +Norway,Europe,2007,80.196,4627926,49357.19017 +Oman,Asia,1952,37.578,507833,1828.230307 +Oman,Asia,1957,40.08,561977,2242.746551 +Oman,Asia,1962,43.165,628164,2924.638113 +Oman,Asia,1967,46.988,714775,4720.942687 +Oman,Asia,1972,52.143,829050,10618.03855 +Oman,Asia,1977,57.367,1004533,11848.34392 +Oman,Asia,1982,62.728,1301048,12954.79101 +Oman,Asia,1987,67.734,1593882,18115.22313 +Oman,Asia,1992,71.197,1915208,18616.70691 +Oman,Asia,1997,72.499,2283635,19702.05581 +Oman,Asia,2002,74.193,2713462,19774.83687 +Oman,Asia,2007,75.64,3204897,22316.19287 +Pakistan,Asia,1952,43.436,41346560,684.5971438 +Pakistan,Asia,1957,45.557,46679944,747.0835292 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+Panama,Americas,2002,74.712,2990875,7356.031934 +Panama,Americas,2007,75.537,3242173,9809.185636 +Paraguay,Americas,1952,62.649,1555876,1952.308701 +Paraguay,Americas,1957,63.196,1770902,2046.154706 +Paraguay,Americas,1962,64.361,2009813,2148.027146 +Paraguay,Americas,1967,64.951,2287985,2299.376311 +Paraguay,Americas,1972,65.815,2614104,2523.337977 +Paraguay,Americas,1977,66.353,2984494,3248.373311 +Paraguay,Americas,1982,66.874,3366439,4258.503604 +Paraguay,Americas,1987,67.378,3886512,3998.875695 +Paraguay,Americas,1992,68.225,4483945,4196.411078 +Paraguay,Americas,1997,69.4,5154123,4247.400261 +Paraguay,Americas,2002,70.755,5884491,3783.674243 +Paraguay,Americas,2007,71.752,6667147,4172.838464 +Peru,Americas,1952,43.902,8025700,3758.523437 +Peru,Americas,1957,46.263,9146100,4245.256698 +Peru,Americas,1962,49.096,10516500,4957.037982 +Peru,Americas,1967,51.445,12132200,5788.09333 +Peru,Americas,1972,55.448,13954700,5937.827283 +Peru,Americas,1977,58.447,15990099,6281.290855 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Principe,Africa,1997,63.306,145608,1339.076036 +Sao Tome and Principe,Africa,2002,64.337,170372,1353.09239 +Sao Tome and Principe,Africa,2007,65.528,199579,1598.435089 +Saudi Arabia,Asia,1952,39.875,4005677,6459.554823 +Saudi Arabia,Asia,1957,42.868,4419650,8157.591248 +Saudi Arabia,Asia,1962,45.914,4943029,11626.41975 +Saudi Arabia,Asia,1967,49.901,5618198,16903.04886 +Saudi Arabia,Asia,1972,53.886,6472756,24837.42865 +Saudi Arabia,Asia,1977,58.69,8128505,34167.7626 +Saudi Arabia,Asia,1982,63.012,11254672,33693.17525 +Saudi Arabia,Asia,1987,66.295,14619745,21198.26136 +Saudi Arabia,Asia,1992,68.768,16945857,24841.61777 +Saudi Arabia,Asia,1997,70.533,21229759,20586.69019 +Saudi Arabia,Asia,2002,71.626,24501530,19014.54118 +Saudi Arabia,Asia,2007,72.777,27601038,21654.83194 +Senegal,Africa,1952,37.278,2755589,1450.356983 +Senegal,Africa,1957,39.329,3054547,1567.653006 +Senegal,Africa,1962,41.454,3430243,1654.988723 +Senegal,Africa,1967,43.563,3965841,1612.404632 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+Sierra Leone,Africa,1957,31.57,2295678,1004.484437 +Sierra Leone,Africa,1962,32.767,2467895,1116.639877 +Sierra Leone,Africa,1967,34.113,2662190,1206.043465 +Sierra Leone,Africa,1972,35.4,2879013,1353.759762 +Sierra Leone,Africa,1977,36.788,3140897,1348.285159 +Sierra Leone,Africa,1982,38.445,3464522,1465.010784 +Sierra Leone,Africa,1987,40.006,3868905,1294.447788 +Sierra Leone,Africa,1992,38.333,4260884,1068.696278 +Sierra Leone,Africa,1997,39.897,4578212,574.6481576 +Sierra Leone,Africa,2002,41.012,5359092,699.489713 +Sierra Leone,Africa,2007,42.568,6144562,862.5407561 +Singapore,Asia,1952,60.396,1127000,2315.138227 +Singapore,Asia,1957,63.179,1445929,2843.104409 +Singapore,Asia,1962,65.798,1750200,3674.735572 +Singapore,Asia,1967,67.946,1977600,4977.41854 +Singapore,Asia,1972,69.521,2152400,8597.756202 +Singapore,Asia,1977,70.795,2325300,11210.08948 +Singapore,Asia,1982,71.76,2651869,15169.16112 +Singapore,Asia,1987,73.56,2794552,18861.53081 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+Somalia,Africa,2007,48.159,9118773,926.1410683 +South Africa,Africa,1952,45.009,14264935,4725.295531 +South Africa,Africa,1957,47.985,16151549,5487.104219 +South Africa,Africa,1962,49.951,18356657,5768.729717 +South Africa,Africa,1967,51.927,20997321,7114.477971 +South Africa,Africa,1972,53.696,23935810,7765.962636 +South Africa,Africa,1977,55.527,27129932,8028.651439 +South Africa,Africa,1982,58.161,31140029,8568.266228 +South Africa,Africa,1987,60.834,35933379,7825.823398 +South Africa,Africa,1992,61.888,39964159,7225.069258 +South Africa,Africa,1997,60.236,42835005,7479.188244 +South Africa,Africa,2002,53.365,44433622,7710.946444 +South Africa,Africa,2007,49.339,43997828,9269.657808 +Spain,Europe,1952,64.94,28549870,3834.034742 +Spain,Europe,1957,66.66,29841614,4564.80241 +Spain,Europe,1962,69.69,31158061,5693.843879 +Spain,Europe,1967,71.44,32850275,7993.512294 +Spain,Europe,1972,73.06,34513161,10638.75131 +Spain,Europe,1977,74.39,36439000,13236.92117 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+Sudan,Africa,1967,42.858,12716129,1687.997641 +Sudan,Africa,1972,45.083,14597019,1659.652775 +Sudan,Africa,1977,47.8,17104986,2202.988423 +Sudan,Africa,1982,50.338,20367053,1895.544073 +Sudan,Africa,1987,51.744,24725960,1507.819159 +Sudan,Africa,1992,53.556,28227588,1492.197043 +Sudan,Africa,1997,55.373,32160729,1632.210764 +Sudan,Africa,2002,56.369,37090298,1993.398314 +Sudan,Africa,2007,58.556,42292929,2602.394995 +Swaziland,Africa,1952,41.407,290243,1148.376626 +Swaziland,Africa,1957,43.424,326741,1244.708364 +Swaziland,Africa,1962,44.992,370006,1856.182125 +Swaziland,Africa,1967,46.633,420690,2613.101665 +Swaziland,Africa,1972,49.552,480105,3364.836625 +Swaziland,Africa,1977,52.537,551425,3781.410618 +Swaziland,Africa,1982,55.561,649901,3895.384018 +Swaziland,Africa,1987,57.678,779348,3984.839812 +Swaziland,Africa,1992,58.474,962344,3553.0224 +Swaziland,Africa,1997,54.289,1054486,3876.76846 +Swaziland,Africa,2002,43.869,1130269,4128.116943 +Swaziland,Africa,2007,39.613,1133066,4513.480643 +Sweden,Europe,1952,71.86,7124673,8527.844662 +Sweden,Europe,1957,72.49,7363802,9911.878226 +Sweden,Europe,1962,73.37,7561588,12329.44192 +Sweden,Europe,1967,74.16,7867931,15258.29697 +Sweden,Europe,1972,74.72,8122293,17832.02464 +Sweden,Europe,1977,75.44,8251648,18855.72521 +Sweden,Europe,1982,76.42,8325260,20667.38125 +Sweden,Europe,1987,77.19,8421403,23586.92927 +Sweden,Europe,1992,78.16,8718867,23880.01683 +Sweden,Europe,1997,79.39,8897619,25266.59499 +Sweden,Europe,2002,80.04,8954175,29341.63093 +Sweden,Europe,2007,80.884,9031088,33859.74835 +Switzerland,Europe,1952,69.62,4815000,14734.23275 +Switzerland,Europe,1957,70.56,5126000,17909.48973 +Switzerland,Europe,1962,71.32,5666000,20431.0927 +Switzerland,Europe,1967,72.77,6063000,22966.14432 +Switzerland,Europe,1972,73.78,6401400,27195.11304 +Switzerland,Europe,1977,75.39,6316424,26982.29052 +Switzerland,Europe,1982,76.21,6468126,28397.71512 +Switzerland,Europe,1987,77.41,6649942,30281.70459 +Switzerland,Europe,1992,78.03,6995447,31871.5303 +Switzerland,Europe,1997,79.37,7193761,32135.32301 +Switzerland,Europe,2002,80.62,7361757,34480.95771 +Switzerland,Europe,2007,81.701,7554661,37506.41907 +Syria,Asia,1952,45.883,3661549,1643.485354 +Syria,Asia,1957,48.284,4149908,2117.234893 +Syria,Asia,1962,50.305,4834621,2193.037133 +Syria,Asia,1967,53.655,5680812,1881.923632 +Syria,Asia,1972,57.296,6701172,2571.423014 +Syria,Asia,1977,61.195,7932503,3195.484582 +Syria,Asia,1982,64.59,9410494,3761.837715 +Syria,Asia,1987,66.974,11242847,3116.774285 +Syria,Asia,1992,69.249,13219062,3340.542768 +Syria,Asia,1997,71.527,15081016,4014.238972 +Syria,Asia,2002,73.053,17155814,4090.925331 +Syria,Asia,2007,74.143,19314747,4184.548089 +Taiwan,Asia,1952,58.5,8550362,1206.947913 +Taiwan,Asia,1957,62.4,10164215,1507.86129 +Taiwan,Asia,1962,65.2,11918938,1822.879028 +Taiwan,Asia,1967,67.5,13648692,2643.858681 +Taiwan,Asia,1972,69.39,15226039,4062.523897 +Taiwan,Asia,1977,70.59,16785196,5596.519826 +Taiwan,Asia,1982,72.16,18501390,7426.354774 +Taiwan,Asia,1987,73.4,19757799,11054.56175 +Taiwan,Asia,1992,74.26,20686918,15215.6579 +Taiwan,Asia,1997,75.25,21628605,20206.82098 +Taiwan,Asia,2002,76.99,22454239,23235.42329 +Taiwan,Asia,2007,78.4,23174294,28718.27684 +Tanzania,Africa,1952,41.215,8322925,716.6500721 +Tanzania,Africa,1957,42.974,9452826,698.5356073 +Tanzania,Africa,1962,44.246,10863958,722.0038073 +Tanzania,Africa,1967,45.757,12607312,848.2186575 +Tanzania,Africa,1972,47.62,14706593,915.9850592 +Tanzania,Africa,1977,49.919,17129565,962.4922932 +Tanzania,Africa,1982,50.608,19844382,874.2426069 +Tanzania,Africa,1987,51.535,23040630,831.8220794 +Tanzania,Africa,1992,50.44,26605473,825.682454 +Tanzania,Africa,1997,48.466,30686889,789.1862231 +Tanzania,Africa,2002,49.651,34593779,899.0742111 +Tanzania,Africa,2007,52.517,38139640,1107.482182 +Thailand,Asia,1952,50.848,21289402,757.7974177 +Thailand,Asia,1957,53.63,25041917,793.5774148 +Thailand,Asia,1962,56.061,29263397,1002.199172 +Thailand,Asia,1967,58.285,34024249,1295.46066 +Thailand,Asia,1972,60.405,39276153,1524.358936 +Thailand,Asia,1977,62.494,44148285,1961.224635 +Thailand,Asia,1982,64.597,48827160,2393.219781 +Thailand,Asia,1987,66.084,52910342,2982.653773 +Thailand,Asia,1992,67.298,56667095,4616.896545 +Thailand,Asia,1997,67.521,60216677,5852.625497 +Thailand,Asia,2002,68.564,62806748,5913.187529 +Thailand,Asia,2007,70.616,65068149,7458.396327 +Togo,Africa,1952,38.596,1219113,859.8086567 +Togo,Africa,1957,41.208,1357445,925.9083202 +Togo,Africa,1962,43.922,1528098,1067.53481 +Togo,Africa,1967,46.769,1735550,1477.59676 +Togo,Africa,1972,49.759,2056351,1649.660188 +Togo,Africa,1977,52.887,2308582,1532.776998 +Togo,Africa,1982,55.471,2644765,1344.577953 +Togo,Africa,1987,56.941,3154264,1202.201361 +Togo,Africa,1992,58.061,3747553,1034.298904 +Togo,Africa,1997,58.39,4320890,982.2869243 +Togo,Africa,2002,57.561,4977378,886.2205765 +Togo,Africa,2007,58.42,5701579,882.9699438 +Trinidad and Tobago,Americas,1952,59.1,662850,3023.271928 +Trinidad and Tobago,Americas,1957,61.8,764900,4100.3934 +Trinidad and Tobago,Americas,1962,64.9,887498,4997.523971 +Trinidad and Tobago,Americas,1967,65.4,960155,5621.368472 +Trinidad and Tobago,Americas,1972,65.9,975199,6619.551419 +Trinidad and Tobago,Americas,1977,68.3,1039009,7899.554209 +Trinidad and Tobago,Americas,1982,68.832,1116479,9119.528607 +Trinidad and Tobago,Americas,1987,69.582,1191336,7388.597823 +Trinidad and Tobago,Americas,1992,69.862,1183669,7370.990932 +Trinidad and Tobago,Americas,1997,69.465,1138101,8792.573126 +Trinidad and Tobago,Americas,2002,68.976,1101832,11460.60023 +Trinidad and Tobago,Americas,2007,69.819,1056608,18008.50924 +Tunisia,Africa,1952,44.6,3647735,1468.475631 +Tunisia,Africa,1957,47.1,3950849,1395.232468 +Tunisia,Africa,1962,49.579,4286552,1660.30321 +Tunisia,Africa,1967,52.053,4786986,1932.360167 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Kingdom,Europe,1987,75.007,56981620,21664.78767 +United Kingdom,Europe,1992,76.42,57866349,22705.09254 +United Kingdom,Europe,1997,77.218,58808266,26074.53136 +United Kingdom,Europe,2002,78.471,59912431,29478.99919 +United Kingdom,Europe,2007,79.425,60776238,33203.26128 +United States,Americas,1952,68.44,157553000,13990.48208 +United States,Americas,1957,69.49,171984000,14847.12712 +United States,Americas,1962,70.21,186538000,16173.14586 +United States,Americas,1967,70.76,198712000,19530.36557 +United States,Americas,1972,71.34,209896000,21806.03594 +United States,Americas,1977,73.38,220239000,24072.63213 +United States,Americas,1982,74.65,232187835,25009.55914 +United States,Americas,1987,75.02,242803533,29884.35041 +United States,Americas,1992,76.09,256894189,32003.93224 +United States,Americas,1997,76.81,272911760,35767.43303 +United States,Americas,2002,77.31,287675526,39097.09955 +United States,Americas,2007,78.242,301139947,42951.65309 +Uruguay,Americas,1952,66.071,2252965,5716.766744 +Uruguay,Americas,1957,67.044,2424959,6150.772969 +Uruguay,Americas,1962,68.253,2598466,5603.357717 +Uruguay,Americas,1967,68.468,2748579,5444.61962 +Uruguay,Americas,1972,68.673,2829526,5703.408898 +Uruguay,Americas,1977,69.481,2873520,6504.339663 +Uruguay,Americas,1982,70.805,2953997,6920.223051 +Uruguay,Americas,1987,71.918,3045153,7452.398969 +Uruguay,Americas,1992,72.752,3149262,8137.004775 +Uruguay,Americas,1997,74.223,3262838,9230.240708 +Uruguay,Americas,2002,75.307,3363085,7727.002004 +Uruguay,Americas,2007,76.384,3447496,10611.46299 +Venezuela,Americas,1952,55.088,5439568,7689.799761 +Venezuela,Americas,1957,57.907,6702668,9802.466526 +Venezuela,Americas,1962,60.77,8143375,8422.974165 +Venezuela,Americas,1967,63.479,9709552,9541.474188 +Venezuela,Americas,1972,65.712,11515649,10505.25966 +Venezuela,Americas,1977,67.456,13503563,13143.95095 +Venezuela,Americas,1982,68.557,15620766,11152.41011 +Venezuela,Americas,1987,70.19,17910182,9883.584648 +Venezuela,Americas,1992,71.15,20265563,10733.92631 +Venezuela,Americas,1997,72.146,22374398,10165.49518 +Venezuela,Americas,2002,72.766,24287670,8605.047831 +Venezuela,Americas,2007,73.747,26084662,11415.80569 +Vietnam,Asia,1952,40.412,26246839,605.0664917 +Vietnam,Asia,1957,42.887,28998543,676.2854478 +Vietnam,Asia,1962,45.363,33796140,772.0491602 +Vietnam,Asia,1967,47.838,39463910,637.1232887 +Vietnam,Asia,1972,50.254,44655014,699.5016441 +Vietnam,Asia,1977,55.764,50533506,713.5371196 +Vietnam,Asia,1982,58.816,56142181,707.2357863 +Vietnam,Asia,1987,62.82,62826491,820.7994449 +Vietnam,Asia,1992,67.662,69940728,989.0231487 +Vietnam,Asia,1997,70.672,76048996,1385.896769 +Vietnam,Asia,2002,73.017,80908147,1764.456677 +Vietnam,Asia,2007,74.249,85262356,2441.576404 +West Bank and Gaza,Asia,1952,43.16,1030585,1515.592329 +West Bank and Gaza,Asia,1957,45.671,1070439,1827.067742 +West Bank and Gaza,Asia,1962,48.127,1133134,2198.956312 +West Bank and Gaza,Asia,1967,51.631,1142636,2649.715007 +West Bank and Gaza,Asia,1972,56.532,1089572,3133.409277 +West Bank and Gaza,Asia,1977,60.765,1261091,3682.831494 +West Bank and Gaza,Asia,1982,64.406,1425876,4336.032082 +West Bank and Gaza,Asia,1987,67.046,1691210,5107.197384 +West Bank and Gaza,Asia,1992,69.718,2104779,6017.654756 +West Bank and Gaza,Asia,1997,71.096,2826046,7110.667619 +West Bank and Gaza,Asia,2002,72.37,3389578,4515.487575 +West Bank and Gaza,Asia,2007,73.422,4018332,3025.349798 +"Yemen, Rep.",Asia,1952,32.548,4963829,781.7175761 +"Yemen, Rep.",Asia,1957,33.97,5498090,804.8304547 +"Yemen, Rep.",Asia,1962,35.18,6120081,825.6232006 +"Yemen, Rep.",Asia,1967,36.984,6740785,862.4421463 +"Yemen, Rep.",Asia,1972,39.848,7407075,1265.047031 +"Yemen, Rep.",Asia,1977,44.175,8403990,1829.765177 +"Yemen, Rep.",Asia,1982,49.113,9657618,1977.55701 +"Yemen, Rep.",Asia,1987,52.922,11219340,1971.741538 +"Yemen, Rep.",Asia,1992,55.599,13367997,1879.496673 +"Yemen, Rep.",Asia,1997,58.02,15826497,2117.484526 +"Yemen, Rep.",Asia,2002,60.308,18701257,2234.820827 +"Yemen, Rep.",Asia,2007,62.698,22211743,2280.769906 +Zambia,Africa,1952,42.038,2672000,1147.388831 +Zambia,Africa,1957,44.077,3016000,1311.956766 +Zambia,Africa,1962,46.023,3421000,1452.725766 +Zambia,Africa,1967,47.768,3900000,1777.077318 +Zambia,Africa,1972,50.107,4506497,1773.498265 +Zambia,Africa,1977,51.386,5216550,1588.688299 +Zambia,Africa,1982,51.821,6100407,1408.678565 +Zambia,Africa,1987,50.821,7272406,1213.315116 +Zambia,Africa,1992,46.1,8381163,1210.884633 +Zambia,Africa,1997,40.238,9417789,1071.353818 +Zambia,Africa,2002,39.193,10595811,1071.613938 +Zambia,Africa,2007,42.384,11746035,1271.211593 +Zimbabwe,Africa,1952,48.451,3080907,406.8841148 +Zimbabwe,Africa,1957,50.469,3646340,518.7642681 +Zimbabwe,Africa,1962,52.358,4277736,527.2721818 +Zimbabwe,Africa,1967,53.995,4995432,569.7950712 +Zimbabwe,Africa,1972,55.635,5861135,799.3621758 +Zimbabwe,Africa,1977,57.674,6642107,685.5876821 +Zimbabwe,Africa,1982,60.363,7636524,788.8550411 +Zimbabwe,Africa,1987,62.351,9216418,706.1573059 +Zimbabwe,Africa,1992,60.377,10704340,693.4207856 +Zimbabwe,Africa,1997,46.809,11404948,792.4499603 +Zimbabwe,Africa,2002,39.989,11926563,672.0386227 +Zimbabwe,Africa,2007,43.487,12311143,469.7092981 diff --git a/datasets/manuscirpt_figures_larger_bins.xlsx b/datasets/manuscirpt_figures_larger_bins.xlsx new file mode 100644 index 0000000..9abf74f Binary files /dev/null and b/datasets/manuscirpt_figures_larger_bins.xlsx differ diff --git a/datasets/michelson_speed_of_light.csv b/datasets/michelson_speed_of_light.csv new file mode 100644 index 0000000..d97bf4e --- /dev/null +++ b/datasets/michelson_speed_of_light.csv @@ -0,0 +1,101 @@ +,date,distinctness of image,temperature (F),position of deflected image,position of slit,displacement of image in divisions,difference between greatest and least,B,Cor,revolutions per second,radius (ft),value of one turn of screw,velocity of light in air (km/s),remarks +0,June 5,3,76,114.85,0.3,114.55,0.17,1.423,-0.132,257.36,28.671999999999997,0.9961399999999999,299850,Electric light. +1, June 7,2,72,114.64,0.07400000000000001,114.56,0.10,1.5330000000000001,-0.084,257.52,28.655,0.9961399999999999,299740,P.M. Frame inclined at various angles +2, June 7,2,72,114.58,0.07400000000000001,114.50,0.08,1.5330000000000001,-0.084,257.52,28.647,0.9961399999999999,299900,P.M. Frame inclined at various angles +3, June 7,2,72,85.91,0.07400000000000001, 85.84,0.12,1.5330000000000001,-0.084,193.14,28.647,0.99598,300070,P.M. Frame inclined at various angles +4, June 7,2,72,85.97,0.07400000000000001, 85.89,O.07,1.5330000000000001,-0.084,193.14,28.65,0.99598,299930,P.M. Frame inclined at various angles +5, June 7,2,72,114.61,0.07400000000000001,114-53,0.07,1.5330000000000001,-0.084,257.42,28.65,0.9961399999999999,299850,P.M. Frame inclined at various angles +6, June 9,3,83,114.54,0.07400000000000001,114.47,0.07,1.5330000000000001,-0.21600000000000003,257.39,28.658,0.9961399999999999,299950,P.M. Frame inclined at various angles +7, June 9,3,83,114.54,0.07400000000000001,114.46,0.10,1.5330000000000001,-0.21600000000000003,257.39,28.658,0.9961399999999999,299980,P.M. Frame inclined at various angles +8, June 9,3,83,114.57,0.07400000000000001,114.47,0.08,1.5330000000000001,-0.21600000000000003,257.39,28.662,0.9961399999999999,299980,P.M. Frame inclined at various angles +9, June 9,3,83,114.57,0.07400000000000001,114.50,0.06,1.5330000000000001,-0.21600000000000003,257.39,28.66,0.9961399999999999,299880,P.M. Frame inclined at various angles +10, June 9,2,83,114.61,0.07400000000000001,114.53,0.13,1.5330000000000001,-0.21600000000000003,257.39,28.678,0.9961399999999999,300000,P.M. Frame inclined at various angles +11, June 10,2,90,114.6,0.07400000000000001,114.52,0.11,1.517,-0.3,257.29,28.685,0.9961399999999999,299980,P.M. +12, June 10,2,90,114.62,0.07400000000000001,114.54,0.08,1.517,-0.3,257.29,28.685,0.9961399999999999,299930,P.M. +13, June 12,2,71,114.81,0.07400000000000001,114.74,0.09,1.45,-0.07200000000000001,257.45,28.69,0.9961399999999999,299650,A.M. +14, June 12,2,71,114.78,0.07400000000000001,114.70,0.05,1.45,-0.07200000000000001,257.45,28.69,0.9961399999999999,299760,A.M. +15, June 12,1,71,114.76,0.07400000000000001,114.68,0.09,1.45,-0.07200000000000001,257.45,28.69,0.9961399999999999,299810,A.M. +16, June 13,3,72,112.64,0.07400000000000001,112.56,0.09,1.5,-0.084,257.49,28.171999999999997,0.9961399999999999,300000,A.M. +17, June 13,3,72,112.63,0.07400000000000001,112.56,0.10,1.5,-0.084,257.49,28.171999999999997,0.9961399999999999,300000,A.M. +18, June 13,2,72,112.65,0.07400000000000001,112.57,0.08,1.5,-0.084,257.49,28.171999999999997,0.9961399999999999,299960,A.M. +19, June 13,3,79,112.82,0.26,112.56,0.06,1.517,-0.168,257.42,28.178,0.9961399999999999,299960,P.M. +20, June 13,3,79,112.82,0.26,112.56,0.13,1.517,-0.168,257.42,28.178,0.9961399999999999,299960,P.M. +21, June 13,3,79,112.83,0.26,112.57,0.07,1.517,-0.168,257.42,28.178,0.9961399999999999,299940,P.M. +22, June 13,3,79,112.82,0.26,112.56,0.06,1.517,-0.168,257.42,28.178,0.9961399999999999,299960,P.M. +23, June 13,3,79,112.83,0.26,112.57,0.11,1.517,-0.168,257.42,28.178,0.9961399999999999,299940,P.M. +24, June 13,3,79,113.41,0.26,113.15,11 ,1.517,-0.168,258.70,28.151999999999997,0.9961399999999999,299880,P.M. Set micrometer and counted oscillations. +25, June 13,3,79,112.14,0.26,111.88,6 ,1.517,-0.168,255.69,28.151999999999997,0.9961399999999999,299800,Oscillations of image of revolving mirror. +26, June 14,1,64,112.83,0.26,112.57,0.12,1.5,0.012,257.58,28.151999999999997,0.9961399999999999,299850,A.M. +27, June 14,1,64,112.83,0.26,112.57,0.05,1.517,0.012,257.60,28.151999999999997,0.9961399999999999,299880,A.M. +28, June 14,1,65,112.81,0.26,112.55,0.11,1.517,0.0,257.59,28.151999999999997,0.9961399999999999,299900,A.M. +29, June 14,1,66,112.83,0.26,112.57,0.09,1.517,-0.012,257.57,28.151999999999997,0.9961399999999999,299840,A.M. +30, June 14,1,67,112.83,0.26,112.57,0.12,1.517,-0.024,257.56,28.151999999999997,0.9961399999999999,299830,A.M. +31, June 14,1,84,112.78,0.26,112.52,0.06,1.517,-0.228,257.36,28.159000000000002,0.9961399999999999,299790,P.M. Readings taken by Lieut. Nazro. +32, June 14,1,85,112.76,0.26,112.50,0.08,1.5,-0.24,257.33,28.159000000000002,0.9961399999999999,299810,P.M. Readings taken by Lieut. Nazro. +33, June 14,1,84,112.72,0.26,112.46,0.08,1.483,-0.228,257.32,28.159000000000002,0.9961399999999999,299880,P.M. Readings taken by Lieut. Nazro. +34, June 14,1,84,112.73,0.26,112.47,0.09,1.483,-0.228,257.32,28.159000000000002,0.9961399999999999,299880,P.M. +35, June 14,1,84,112.75,0.26,112.49,0.09,1.483,-0.228,257.32,28.129,0.9961399999999999,299830,P.M. +36, June 17,2,62,112.85,0.26,112.59,0.09,1.517,0.036000000000000004,257.62,28.149,0.9961399999999999,299800,A.M. +37, June 17,2,63,112.84,0.26,112.58,0.06,1.5,0.024,257.59,28.149,0.9961399999999999,299790,A.M. +38, June 17,1,64,112.85,0.26,112.59,0.07,1.5,0.012,257.58,28.149,0.9961399999999999,299760,A.M. +39, June 17,3,77,112.8,0.26,112.54,0.07,1.5,-0.14400000000000002,257-43,28.156999999999996,0.9961399999999999,299800,P.M. Readings taken by Mr. Clason. +40, June 17,3,77,112.77,0.26,112.51,0.08,1.5,-0.14400000000000002,257.43,28.156999999999996,0.9961399999999999,299880,P.M. Readings taken by Mr. Clason. +41, June 17,3,77,112.77,0.26,112.51,0.11,1.5,-0.14400000000000002,257.43,28.156999999999996,0.9961399999999999,299880,P.M. Readings taken by Mr. Clason. +42, June 17,3,77,112.77,0.26,112.51,0.09,1.5,-0.14400000000000002,257.43,28.156999999999996,0.9961399999999999,299880,P.M. Readings taken by Mr. Clason. +43, June 17,3,77,112.78,0.26,112.52,0.08,1.5,-0.14400000000000002,257 43,28.156999999999996,0.9961399999999999,299860,P.M. Readings taken by Mr. Clason. +44, June 18,1,58,112.9,0.265,112.64,0.07,1.5,0.084,257.65,28.15,0.9961399999999999,299720,A.M. +45, June 18,1,58,112.9,0.265,112.64,0.10,1.5,0.084,257.65,28.15,0.9961399999999999,299720,A.M. +46, June 18,1,59,112.92,0.265,112.66,0.07,1.483,0.07200000000000001,257.62,28.15,0.9961399999999999,299620,A.M. +47, June 18,2,75,112.79,0.265,112.52,0.09,1.483,-0.12,257-43,28.158,0.9961399999999999,299860,P.M. +48, June 18,2,75,112.75,0.265,112.48,0.10,1.483,-0.12,257-43,28.158,0.9961399999999999,299970,P.M. +49, June 18,2,75,112.76,0.265,112.49,0.08,1.483,-0.12,257-43,28.158,0.9961399999999999,299950,P.M. +50, June 20,3,60,112.94,0.265,112.67,0.07,1.517,0.063,257.65,28.171999999999997,0.9961399999999999,299880,A.M. +51, June 20,3,61,112.92,0.265,112.65,0.09,1.517,0.048,257.63,28.171999999999997,0.9961399999999999,299910,A.M. +52, June 20,2,62,112.94,0.265,112.67,0.07,1.517,0.036000000000000004,257.62,28.171999999999997,0.9961399999999999,299850,A.M. +53, June 20,2,63,112.93,0.265,112.66,0.03,1.517,0.024,257.61,28.171999999999997,0.9961399999999999,299870,A.M. +54, June 20,2,78,133.48,0.265,133.21,0.13,1.45,-0.156,257.36,33.345,0.9962700000000001,299840,P.M. +55, June 20,2,79,133.49,0.265,133.23,0.09,1.5,-0.168,257.40,33.345,0.9962700000000001,299840,P.M. +56, June 20,2,80,133.49,0.265,133.22,0.07,1.5,-0.18,257.39,33.345,0.9962700000000001,299850,P.M. +57, June 20,2,79,133.5,0.265,133.24,0.13,1.483,-0.168,257.39,33.345,0.9962700000000001,299840,P.M. +58, June 20,2,79,133.49,0.265,133.22,0.06,1.483,-0.168,257.38,33.345,0.9962700000000001,299840,P.M. +59, June 20,2,79,133.49,0.265,133.22,0.10,1.483,-0.168,257.38,33.345,0.9962700000000001,299840,P.M. +60, June 21,2,61,133.56,0.265,133.29,0.12,1.5330000000000001,0.048,257.65,33.332,0.9962700000000001,299890,A.M. +61, June 21,2,62,133.58,0.265,133.31,0.08,1.5330000000000001,0.036000000000000004,257.64,33.332,0.9962700000000001,299810,A.M. +62, June 21,2,63,133.57,0.265,133.31,0.09,1.5330000000000001,0.024,257.63,33.332,0.9962700000000001,299810,A.M. +63, June 21,2,64,133.57,0.265,133.30,0.11,1.5330000000000001,0.012,257.61,33.332,0.9962700000000001,299820,A.M. +64, June 21,2,65,133.56,0.265,133.30,0.13,1.5330000000000001,0.0,257.60,33.332,0.9962700000000001,299800,A.M. +65, June 21,3,80,133.48,0.265,133.21,0.06,1.5330000000000001,-0.18,257.42,33.33,0.9962700000000001,299770,P.M. +66, June 21,3,81,133.46,0.265,133.19,0.10,1.5,-0.192,257.38,33.33,0.9962700000000001,299760,P.M. +67, June 21,3,82,133.46,0.265,133.20,0.05,1.5,-0.204,257.37,33.33,0.9962700000000001,299740,P.M. +68, June 21,3,82,133.46,0.265,133.20,0.08,1.517,-0.204,257.38,33.33,0.9962700000000001,299750,P.M. +69, June 21,3,81,133.46,0.265,133.19,0.08,1.5,-0.192,257.38,33.33,0.9962700000000001,299760,P.M. +70, June 23,3,89,133.43,0.265,133.16,0.08,1.5419999999999998,-0.28800000000000003,257.32,33.345,0.9962700000000001,299910,P.M. +71, June 23,3,89,133.42,0.265,133.15,0.06,1.55,-0.28800000000000003,257.33,33.345,0.9962700000000001,299920,P.M. +72, June 23,3,90,133.43,0.265,133.17,0.09,1.55,-0.3,257.32,33.345,0.9962700000000001,299890,P.M. +73, June 23,3,90,133.43,0.265,133.16,0.07,1.5330000000000001,-0.3,257.30,33.345,0.9962700000000001,299860,P.M. +74, June 23,3,90,133.42,0.265,133.16,0.07,1.517,-0.3,257.29,33.345,0.9962700000000001,299880,P.M. +75, June 24,3,72,133.47,0.265,133.20,0.15,1.517,-0.084,257.50,33.319,0.9962700000000001,299720,A.M. +76, June 24,3,73,133.44,0.265,133.17,0.04,1.517,-0.096,257.49,33.319,0.9962700000000001,299840,A.M. +77, June 24,3,74,133.42,0.265,133.16,0.11,1.517,-0.10800000000000001,257.48,33.319,0.9962700000000001,299850,A.M. +78, June 24,3,75,133.42,0.265,133.16,0.06,1.517,-0.12,257.47,33.319,0.9962700000000001,299850,A.M. +79, June 24,3,76,133.44,0.265,133.18,0.10,1.517,-0.132,257.45,33.319,0.9962700000000001,299780,A.M. +80, June 26,2,86,133.42,0.265,133.15,0.05,1.508,-0.252,257.33,33.339,0.9962700000000001,299890,P.M. +81, June 26,2,86,133.44,0.265,133.17,0.08,1.508,-0.252,257.33,33.339,0.9962700000000001,299840,P.M. +82, June 27,3,73,133.49,0.265,133.22,0.11,1.483,-0.096,257.46,33.328,0.9962700000000001,299780,A.M. +83, June 27,3,74,133.47,0.265,133.20,0.06,1.483,-0.10800000000000001,257.44,33.328,0.9962700000000001,299810,A.M. +84, June 27,3,75,133.47,0.265,133.21,0.09,1.483,-0.12,257.43,33.328,0.9962700000000001,299760,A.M. +85, June 27,3,75,133.45,0.265,133.19,0.09,1.4669999999999999,-0.12,257.42,33.328,0.9962700000000001,299810,A.M. +86, June 27,3,76,133.47,0.265,133.20,0.08,1.483,-0.132,257.42,33.328,0.9962700000000001,299790,A.M. +87, June 27,3,76,133.45,0.265,133.19,0.10,1.483,-0.132,257.42,33.328,0.9962700000000001,299810,A.M. +88, June 30,2,85,35.32,135.0, 99.68,0.05,1.5,-0.24,193.00,33.274,0.99645,299820,P.M. Mirror inverted. +89, June 30,2,86,35.34,135.0, 99.67,0.06,1.508,-0.252,193.00,33.274,0.99645,299850,P.M. Mirror inverted. +90, June 30,2,86,35.34,135.0, 99.66,0.10,1.508,-0.252,193.00,33.274,0.99645,299870,P.M. Mirror inverted. +91, June 30,2,86,35.34,135.0, 99.66,0.09,1.517,-0.252,193.00,33.274,0.99645,299870,P.M. Mirror inverted. +92, July 1,2,83,2.17,135.145,132.98,0.07,1.5,-0.21600000000000003,257.35,33.282,0.9962700000000001,299810,P.M. Mirror inverted. +93, July 1,2,84,2.15,135.145,133.00,0.09,1.5,-0.228,257.34,33.282,0.9962700000000001,299740,P.M. Mirror inverted. +94, July 1,2,86,2.14,135.145,133.01,0.06,1.4669999999999999,-0.252,257.28,33.311,0.9962700000000001,299810,P.M. Mirror inverted. +95, July 1,2,86,2.14,135.145,133.00,0.08,1.4669999999999999,-0.252,257.28,33.311,0.9962700000000001,299940,P.M. Mirror inverted. +96, July 2,3,86,99.85,0.4, 99.45,0.05,1.45,-0.252,192.95,33.205,0.9960600000000001,299950,P.M. Mirror erect. +97, July 2,3,86,66.74,0.4, 66.34,0.03,1.45,-0.252,128.63,33.205,0.9958600000000001,299800,P.M. Mirror erect. +98, July 2,3,86,50.16,0.4, 47.96,0.07,1.4669999999999999,-0.252, 96.48,33.205,0.9958,299810,P.M. Mirror erect. +99, July 2,3,85,33.57,0.4, 33.17,0.06,1.45,-0.24, 64.32,33.205,0.99574,299870,P.M. Mirror erect. diff --git a/datasets/microtubules.csv b/datasets/microtubules.csv new file mode 100644 index 0000000..40ebfed --- /dev/null +++ b/datasets/microtubules.csv @@ -0,0 +1,692 @@ +25 +40 +40 +45.429 +50 +55 +56.386 +60 +60 +60 +60 +64.075 +65 +65 +65 +65 +70 +71.764 +75 +75 +78.903 +80 +80 +80 +85 +85 +85 +85 +85 +90 +90 +90 +92.268 +94.831 +95 +95 +100 +105 +105 +105 +110 +110 +112.772 +115 +115 +115 +115 +117.159 +117.159 +117.898 +120 +120 +125 +125 +125 +125 +125 +125.587 +130 +130 +130 +130 +130 +130 +130 +130 +130 +131.505 +133.276 +135 +135 +135 +135 +135 +138.402 +140 +140 +140 +140 +140.965 +143.528 +145 +145 +145 +145 +145.851 +150 +150.633 +153.78 +155 +155 +155.415 +160 +160 +160 +160 +160 +160 +164.979 +164.979 +165 +165 +165 +165 +165 +165 +166.595 +170 +170 +170 +170 +170 +170 +170 +174.284 +175 +175 +175 +175 +175 +175 +175 +176.847 +179.325 +180 +180 +180 +181.716 +181.973 +181.973 +181.973 +184.536 +184.536 +185 +185 +186.498 +189.662 +189.662 +190 +190 +190 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+420 +420 +420 +420.332 +420.332 +422.895 +425 +425.458 +425.458 +428.021 +428.021 +428.021 +430 +430 +430 +433.147 +435 +435 +435 +435 +438.273 +440 +440 +440 +440 +440 +440 +440 +440 +440.836 +440.836 +443.399 +445 +445 +445 +448.525 +448.525 +449.508 +450 +450 +450 +451.088 +451.088 +453.651 +453.651 +455 +456.214 +456.214 +460 +460 +461.463 +463.854 +463.854 +463.903 +465 +465 +466.466 +469.029 +470 +471.592 +471.592 +475 +478.2 +479.281 +480 +480 +480 +480 +481.844 +482.982 +485 +485 +486.97 +486.97 +486.97 +490 +490.155 +494.659 +494.659 +495 +497.222 +497.222 +500 +500 +500 +504.911 +504.911 +505 +505 +505 +505 +506.892 +507.474 +510 +510 +511.674 +512.6 +515 +515 +515.163 +515.163 +515.163 +517.726 +520 +520 +520 +525.415 +530 +530 +530 +530 +530 +533.104 +535 +535 +535.667 +538.23 +543.356 +543.356 +545 +545 +545 +545 +548.482 +550 +550 +555 +555 +555 +555 +556.171 +556.171 +556.171 +558.734 +560 +560 +560 +565 +565 +565 +565 +566.423 +570 +571.549 +574.112 +574.112 +574.112 +575 +575 +580 +585 +588.186 +589.49 +590 +594.616 +595 +600 +602.532 +605 +605 +605 +610 +610 +615 +615 +615.12 +620 +620 +625.372 +628.833 +630.498 +633.061 +635 +635.624 +640 +640 +643.313 +648.439 +650 +650.352 +660 +665 +670 +674.069 +675 +675 +684.321 +685 +685 +690 +692.01 +699.699 +700 +705 +712.514 +725.329 +730 +730.455 +730.455 +745 +765 +766.337 +770 +780 +780 +785 +785 +805 +805 +810 +820 +835 +855 +865 +875 +875 +885 +885 +910 +940 +945 +950 +950 +950 +955 +960 +960 +965 +965 +980 +1002.133 +1055 +1095 +1165 +1230 +1240 +1285 +1320 +1335 +1485 +1505 +1520 +1785 \ No newline at end of file diff --git a/datasets/microtubules_14.csv b/datasets/microtubules_14.csv new file mode 100644 index 0000000..f38d80d --- /dev/null +++ b/datasets/microtubules_14.csv @@ -0,0 +1,141 @@ +60 +75 +75 +85 +115 +115 +135 +140 +145 +150 +155 +165 +180 +190 +210 +210 +215 +220 +225 +235 +235 +240 +240 +245 +260 +260 +265 +265 +265 +275 +275 +275 +280 +280 +280 +280 +290 +295 +295 +300 +310 +310 +315 +320 +325 +325 +325 +325 +325 +330 +330 +335 +345 +345 +345 +355 +355 +365 +365 +365 +370 +370 +385 +385 +395 +405 +410 +415 +425 +430 +435 +435 +435 +435 +440 +445 +445 +450 +450 +450 +455 +460 +460 +465 +465 +470 +470 +470 +480 +490 +500 +505 +505 +515 +525 +525 +545 +545 +555 +560 +570 +570 +585 +590 +590 +615 +625 +630 +640 +640 +650 +655 +680 +685 +685 +690 +695 +695 +705 +730 +755 +760 +765 +770 +770 +790 +795 +795 +820 +830 +840 +850 +875 +890 +975 +1000 +1005 +1135 +1305 +1400 +1420 \ No newline at end of file diff --git a/datasets/nuclear_power_accidents.csv b/datasets/nuclear_power_accidents.csv new file mode 100644 index 0000000..4cdb992 --- /dev/null +++ b/datasets/nuclear_power_accidents.csv @@ -0,0 +1,300 @@ +Date,Location,Cost (millions 2013US$),INES,Smyth Magnitude,Region,Description,Fatalities +4/26/1986,"Kiev, Ukraine",259336,7,8,EE,Mishandled reactor safety test at Chernobyl nuclear reactor caused steam explosion and meltdown and required massive evacuation.,4056 +3/11/2011,"Fukushima Prefecture, Japan",166089,7,7.5,A,An earthquake and tsunami caused emergency backup generators to fail at the Fukushima Daiichi nuclear power plant and reactors cracked as a result causing fire and meltdown.,573 +12/8/1995,"Tsuruga, Japan",15500,,,A,"Japan's only fast breeder reactor Monju suffers serious accident and fire caused by leaking sodium, reactor shutdown for 6 years while extensive repairs are carried out",0 +3/28/1979,"Middletown, Pennsylvania, United States",10910,5,7.9,NA,Equipment failure and operator error contributed to loss of coolant and partial core meltdown at Three Mile Island nuclear reactor.,0 +9/11/1957,"Rocky Flats, USA",8189,5,5.2,NA,Fire starts and destroys plutonium processing facility,0 +3/25/1955,"Sellafield, UK",4400,4,4.3,WE,Fire releases up to up to a few tenths of a terabecquerel of radioactivity into the atmosphere,0 +1/1/1977,"Beloyarsk, USSR",3500,5,,EE,"Unit 2 suffers a half core meltdown, repairs take more than a year",0 +7/14/1955,"Sellafield, UK",2900,3,-2.4,WE,Another radioactive leak and remediation operation,0 +10/12/1969,"Sellafield, UK",2500,4,2.3,WE,Enhanced discharge from the Building B204 stack of about 1.8 TBq beta radioactivity,0 +3/9/1985,"Athens, Alabama, United States",2114.3,,,NA,Instrumentation systems malfunctioned during startup and the Tennessee Valley Authority suspended operations at all three Browns Ferry Units.,0 +2/22/1977,"Jaslovske Bohunice, Czechoslovakia",1964.5,4,,EE,Mechanical failure during fuel loading at the KS 150 reactor caused severe corrosion and released radioactivity into the plant area; requiring total decommission.,0 +5/1/1968,"Sellafield, UK",1900,4,4,WE,A month long discharge of radioactivity occurs from Building B230 stack as a result of a failed filter,0 +9/29/1957,"Kyshtym, Chelyabinsk, Soviet Union",1733.4,6,7.3,EE,Heat exchangers failed at nitrate storage tank at the Mayak Scientific-Production Association spent fuel rod storage facility and caused a massive chemical explosion.,103 +11/29/1955,"Idaho Falls, Idaho, United States",1500,4,,NA,Suffered a partial meltdown during a coolant flow test,0 +3/19/1971,"Sellafield, UK",1330,3,3.2,WE,"Sparks from an electric arc ignited radioactive waste in a basement, leading to 0.48 GBq released",0 +12/8/1955,"Sellafield, UK",1300,3,-2.7,WE,A fire occurred in a radioactive waste silo in Building B247,0 +4/11/1986,"Plymouth, Massachusetts, United States",1156.7,,,NA,"Recurring equipment problems with instrumentation, vacuum breakers, instrument air system, and main transformer forced emergency shutdown of Boston Edison’s Pilgrim nuclear facility.",0 +12/2/1949,"Hanford, WA, USA",1100,4,3.8,NA,"Operators release 8,000 curies of iodine-131 over two days known as the ""Green Run""",0 +5/1/1967,"Chapelcross, UK",1100,4,,WE,"Fuel element melt and fire in part of core, 2 years of repairs",0 +9/9/1982,"Chernobyl, Ukraine",1100,5,,EE,"Partial core melt – fuel assembly channel 62-44 destroyed, irradiation of staff involved in repairing the core",0 +08/01/1983,"Pickering, Canada",1000,,,NA,"On August 1, 1983, Pickering reactor 2 had a loss of coolant accident after a pressure tube +suffered a metre-long rupture. The station was shut down and the four reactors at Pickering +A were eventually retubed at a cost of about $1 billion",0 +9/26/1973,"Sellafield, UK",990,4,2,WE,"An exothermic reaction occurred involving accumulated zirconium fines and solvent in a vessel in the Head End Treatment, exposing 35 workers",0 +12/19/1989,"Vandellòs, Spain",930.9,3,,WE,A fire at one of its turbines caused the malfunction of safety systems at the Vandellòs Nuclear Power Plant and forced one of its 508 MWe units to permanently shut down.,0 +2/1/2010,"Vernon, Vermont, United States",808.9,,,NA,Underground pipes from the Vermont Yankee nuclear power plant deteriorated and leaked radioactive tritium into groundwater supplies in Vermont and resulted in the shutdown of the plant.,0 +6/19/1961,"Sellafield, UK",800,3,4,NA,There was a release of plutonium bearing liquid into process cooling water from a leak in an evaporator,0 +10/5/1966,"Newport, Michigan, United States",793.9,4,,NA,"Fermi 1, a prototype fast breeder reactor, suffered a partial fuel meltdown",0 +12/21/1972,"Pawling, NY, USA",788.4,4,,NA,"Tube repairs lead to a loss of coolant accident, causing a year long outage",0 +12/26/1985,"Clay Station, California, United States",776.8,,,NA,Safety and control systems failed at Rancho Seco nuclear reactor and led to the premature closure of the plant.,0 +4/19/2005,"Seascale, United Kingdom",599.2,3,,WE,20 metric tons of uranium and 160 kilograms of plutonium leaked for more than a year from a cracked pipe at the Thermal Oxide Reprocessing (Thorp) plant in Sellafield.,0 +10/17/1969,"Saint-Laurent, France",541.4,4,,WE,More than 50 kilograms of uranium fuel at the Saint-Laurent Nuclear Power Plant began to melt after cooling systems failed and forced shutdown and repairs.,0 +12/7/1975,"Greifswald, East Germany",511.5,3,,EE,Electrical error caused fire in the main trough that destroyed control lines and 5 main coolant pumps.,0 +5/22/1986,"Delta, Pennsylvania, United States",462.2,,,NA,Philadelphia Electric Company shut down Peach Bottom units 2 and 3 due to cooling malfunctions and unexplained equipment problems.,0 +9/2/1996,"Crystal River, Florida, United States",444.1,,,NA,Balance-of-plant equipment malfunction forced Florida Power Corporation to shut down Crystal River Unit 3 and make extensive repairs.,0 +5/11/1969,"Rocky Flats, USA",425.2,4,2.3,NA,"Plutonium fire broke out in the processing section of Building 776, gave 41 firefighters high radiation doses",0 +5/4/1987,"Kalpakkam, India",346.7,,,A,"Fast Breeder Test Reactor at Kalpakkam shut down due to pump failures, faulty instrument signals, and turbine malfunctions that led to rupture of reactor core.",0 +2/2/1995,"Kota, Rajasthan, India",323.1,,,A,The Rajasthan Atomic Power Station leaked radioactive helium and heavy water into the Rana Pratap Sagar River requiring a two year shutdown.,0 +5/4/1986,"Hamm-Uentrop, Germany",308.2,,,WE,Operator actions to dislodge damaged fuel rod at Experimental High Temperature Gas Reactor released excessive radiation to 4 square kilometers around facility.,0 +2/20/1996,"Waterford, Connecticut, United States",293.2,,,NA,Leaking valve forced Northeast Utilities Company to shut down Millstone Units 1 and 2.,0 +3/22/1975,"Browns Ferry, Alabama, United States",277.1,,,NA,"Fire burned for seven hours and damaged more than 1,600 control cables for three nuclear reactors, disabling core cooling systems.",0 +3/31/1993,"Bulandshahr, Uttar Pradesh, India",254.7,,,A,The Narora Atomic Power Station suffered a fire at two of its steam turbine blades that damaged two heavy water reactors and almost led to a meltdown.,0 +7/4/2009,"Hamburg, Germany",242.9,,,WE,A short circuit in a transformer caused fuel rod damage and an extended shutdown at Vattenfall’s Krümmel nuclear plant.,0 +12/15/1971,"Sellafield, UK",180,3,3.2,WE,An abnormal discharge took place from the Building B203 plutonium recovery plant of about 7.4 GBq Pu,0 +12/19/1987,"Lycoming, New York, United States",173.3,,,NA,"Fuel rod, waste storage, and water pumping malfunctions forced Niagara Mohawk Power Corporation to shut down Nine Mile Point Unit 1.",0 +2/16/2002,"Oak Harbor, Ohio, United States",164.8,,,NA,Severe corrosion of control rod forced 24 month outage of Davis-Besse reactor.,0 +3/10/1970,"Sellafield, UK",150,3,2.6,WE,Abnormal discharge of about 1.8 GBq Pu via the Building B230 stack,0 +3/17/1989,"Lusby, Maryland, United States",139.1,,,NA,Baltimore Gas & Electric’s Calvert Cliff Units 1 and 2 inspections revealed cracks at pressurized heater sleeves and forced extended shutdown.,0 +3/13/1980,"Saint-Laurent, France",133.4,4,,WE,"Partial core meltdown involving 20 kg of uranium, repairs take years",0 +9/15/1984,"Athens, Alabama, United States",127.3,,,NA,"Safety violations, operator error, and design problems forced 6 year outage at Browns Ferry Unit 2.",0 +8/27/2009,"St. Petersburg, Russia",127.3,,,EE,A cracked discharge accumulator and malfunctioning feed pump forced the Leningrad Nuclear Power Plant reactor number 3 to close for extended repairs.,0 +1/21/2002,"Manche, France",117.7,1,,WE,Control systems and safety valves failed after improper installation of condensers and forced a two month shutdown.,0 +3/6/2006,"Erwin, Tennessee, United States",113.4,2,,NA,Nuclear fuel services plant spilled 35 litres of highly enriched uranium that required 7 month shutdown.,0 +1/21/2002,"Flamanville, France",104,,,WE,"Control systems and safety valves fail after improper installation of condensers, forcing a two month shutdown",0 +2/10/1970,"Sellafield, UK",100,3,2,WE,Abnormal discharge of Pu via the Building B230 stack,0 +11/29/1970,"Sellafield, UK",100,3,2.5,WE,An abnormal discharge of about 1.6 GBq Pu took place via the Building B230 stack,0 +11/30/1975,"Leningrad, Russia",99.5,4,,EE,"A fuel channel in Unit 1 suffered a loss of coolant, resulting in the degradation of a nuclear fuel assembly that led to a significant release of radiation lasting for one month",0 +12/6/1972,"Sellafield, UK",98,3,1.6,WE,Inadvertent processing of fuel elements from Chapelcross had a high iodine content and release 2.2 TBq,0 +10/8/1957,"Windscale, United Kingdom",89.9,5,4.6,WE,"Fire ignited plutonium piles, created radioactive dust which destroyed surrounding dairy farms.",33 +9/10/1989,"Tarapur, Maharashtra, India",89.9,,,A,Operators at the Tarapur nuclear power plant discovered the reactor had been leaking radioactive iodine through its cooling structures and discovered radiation levels of iodine-129 more than 700 times normal levels.,0 +5/2/1967,"Dumfries and Galloway, Scotland",87.7,4,,WE,Fuel rod caught fire and caused partial meltdown at the Chaplecross Magnox nuclear power station.,0 +9/11/1979,"Sellafield, UK",87,4,3.4,WE,There was a release of plutonium to atmosphere during transfer of low radioactive liquid effluent to Building B242,0 +3/5/1969,"Sellafield, UK",84.5,3,1.6,WE,"A spillage of 370 MBq Pu in Building B229 laboratory, leads to substantial discharge",0 +9/20/1996,"Senaca, Illinois, United States",82.4,,,NA,Service water system failed and prompted Commonwealth Edison to close LaSalle Units 1 and 2 for more than 2 years.,0 +5/24/1958,"Chalk River, Ontario, Canada",77,5,,NA,Fuel rod caught fire and contaminated half of facility.,0 +12/25/1993,"Newport, Michigan, United States",77,,,NA,Detroit Edison Company forced to shut down Fermi Unit 2 after main turbine experienced catastrophic failure due to improper maintenance.,0 +9/23/1983,"Buenos Aires, Argentina",74.9,4,,SA,Operator error during fuel plate reconfiguration caused meltdown in an experimental test reactor.,1 +1/14/1995,"Wiscasset, Maine, United States",71.7,,,NA,Steam generator tubes cracked at Maine Yankee nuclear reactor and forced the Maine Yankee Atomic Power Company to shutdown for 1 year.,0 +3/25/1982,"Buchanan, New York, United States",64.2,,,NA,Water and coolant leaks caused damage to steam generator tubes and main generator and forced the New York Power Authority to shut down Indian Point Unit 3.,0 +12/27/1999,"Blayais, France",63.1,2,,WE,A storm flooded the Blayais-2 nuclear reactor and forced emergency shutdown after injection pumps and containment safety systems failed from water damage.,0 +2/26/1983,"Pierce, Florida, United States",62.1,,,NA,Workers discovered damaged thermal shield and core barrel support at St. Lucie Unit 1 which required a 13 month shutdown.,0 +9/30/1999,"Ibaraki Prefecture, Japan",62.1,4,,A,"Workers at the Tokaimura uranium processing facility tried to save time by mixing uranium in buckets which killed two and injuring 1,200.",2 +8/6/1970,"Lawrence Livermore, CA, USA",60.1,4,3.6,NA,"Accident released about 650,000 curies of tritium as far as 200 miles away",0 +4/12/1987,"Triscastin, France",57.8,,,WE,"Areva’s Tricastin fast breeder reactor leaked coolant, sodium, and uranium hexachloride and injured workers and contaminated water supplies.",0 +9/22/1980,"Sellafield, UK",55,3,1.6,WE,Enhanced corrosion in the Building B38 magnox storage silo caused discharges to atmosphere of 35–70 GBq,0 +12/12/1952,"Chalk River, Ontario, Canada",52.4,,,NA,Hydrogen explosion damaged reactor interior and released 30 kilograms of uranium oxide particles.,0 +3/20/1982,"Lycoming, New York, United States",52.4,,,NA,Recirculation system piping failed at Nine Mile Point Unit 1 and forced a 2 year shutdown.,0 +7/4/2008,"Fukushima Province, Japan",52.4,,,A,Tokyo Electric Power Company’s No. 2 Kurihara Power Plant damaged by 7.2 magnitude earthquake resulted in cracked reactor cooling towers and spent fuel storage facilities and spilled 19 litres of radioactive wastewater.,0 +4/6/1993,"Tomsk, Russia",51.4,4,4.8,EE,Ice condenser containment systems failed at Indiana Michigan Power Company’s D.C. Cook Units 1 and 2.,0 +8/23/2011,"Mineral, Virginia, United States",48.2,,,NA,A magnitude 5.8 earthquake damaged Dominion Energy's North Anna Power Station and cracked spent fuel containers and collapsed administrative buildings.,0 +6/16/2005,"Braidwood, Illinois, United States",47.1,,,NA,Exelon’s Braidwood nuclear station leaked tritium and contaminated local water supplies.,0 +11/1/1983,"Sellafield, UK",44,3,,WE,Inadvertent transfer of 59 TBq activity with solvent and crud took place from Building B205 to a sea tank at Building B242,0 +9/5/1996,"Clinton, Illinois, United States",43.9,,,NA,Reactor recirculation pump failed and required Illinois Power Company to shut down Clinton boiling water reactor.,0 +4/10/2003,"Paks, Hungary",42.8,3,3.9,EE,Damaged fuel rods hemorrhaged spent fuel pellets and corroded heavy water reactor.,0 +8/22/1985,"Soddy-Daisy, Tennessee, United States",40.7,,,NA,Tennessee Valley Authority Sequoyah Units 1 and 2 failed and forced 3 year shutdown; workers also exposed to excessive levels of radiation.,0 +9/7/1983,"Athens, Alabama, United States",39.6,,,NA,Tennessee Valley Authority discovered extensive damage to recirculation system pipeline.,0 +5/16/1995,"Salem, New Jersey, United States",39.6,,,NA,Ventilation systems failed at Public Service Electric & Gas Company’s Salem Units 1 and 2.,0 +6/18/1999,"Shika, Ishikawa, Japan",39.6,2,,A,Control rod malfunction set off uncontrolled nuclear reaction at Shika Nuclear Power Station’s Unit-1.,0 +3/4/1960,"Waltz Mill, USA",38,4,,NA,Core melt accident,0 +7/26/1959,"Simi Valley, California, United States",37.5,4,,NA,Partial core meltdown at Santa Susana Field Laboratory’s Sodium Reactor Experiment.,0 +2/12/1983,"Fork River, New Jersey, United States",37.5,,,NA,Oyster Creek nuclear plant failed safety inspection and was forced to shut down.,0 +10/22/2002,"Kalpakkam, India",34.2,,,A,Almost 100 kilograms of radioactive sodium at a fast breeder reactor leaked into a purification cabin and ruined a number of valves and operating systems.,0 +8/4/2005,"Indian Point, New York, United States",34.2,,,NA,"Entergy’s Indian Point Nuclear Plant, located on the Hudson River, leaked tritium and strontium into underground lakes from 1974 to 2005.",0 +7/17/1984,"Sellafield, UK",33.4,3,1.8,WE,A solvent fire in Building B241 effluent treatment plant sludge tank was caused by hot metal from burning operations,0 +7/16/1979,"Sellafield, UK",30,3,1.9,WE,A fire of magnox swarf and uranium took place in a remote decanning cave in Building B30,0 +12/24/2006,"Jadugoda, India",28.9,,,A,One of the pipes carrying radioactive waste from the Jadugoda uranium mill  ruptured and distributed radioactive materials more than 100 square kilometers.,0 +6/9/1985,"Oak Harbor, Ohio, United States",26.8,,,NA,"Loss of feedwater provoked Toledo Edison Company to inspect Davis-Besse facility, where inspectors discovered corroded reactor coolant pumps and shafts .",0 +1/3/1961,"Idaho Falls, Idaho, United States",25.7,4,2.9,NA,Explosion at National Reactor Testing Station,3 +1/21/1969,"Lucens, Canton of Vaud, Switzerland",25.7,5,,WE,Coolant system malfunctioned at underground experimental reactor.,0 +3/28/1979,"Loir-et-Cher, France",25.7,4,,WE,A malfunctioning cooling system fused fuel elements together at the Saint Laurent A2 reactor and ruined the fuel assembly and forced an extended shutdown.,0 +6/13/1984,"Platteville, Colorado, United States",25.7,,,NA,Moisture intrusion caused 6 fuel rods to fail at Fort St. Vrain nuclear plant and required emergency shutdown from Public Service Company of Colorado.,0 +7/15/2013,"Shimen, Taiwan",23.5,,,A,Typhoon Soulik damaged the Jinshan Nuclear Power Plant by tripping the generator and destroying transmission lines which lead to shutdown and detection of radiation leaks.,0 +4/18/1984,"Delta, Pennsylvania, United States",20.3,,,NA,Philadelphia Electric Company shut down Peach Bottom Unit 2 to due to extensive recirculation system and equipment damage.,0 +6/10/1977,"Waterford, Connecticut, United States",17.1,,,NA,Hydrogen gas explosion damaged three buildings and forced shutdown of Millstone-1 Pressurized Water Reactor.,0 +8/11/2014,"Lancashire, United Kingdom",17,,,WE,Corrosion and defects found in the boiler units of Heysham 1 and Hartlepool forced four reactors offline for 8 weeks for repairs.,0 +5/1/1969,"Stockholm, Sweden",16.1,,,WE,Malfunction of valve caused flooding in Agesta pressurized heavy water nuclear reactor.,0 +3/5/1989,"Tonopah, Arizona, United States",16.1,,,NA,Atmospheric dump valves failed at Arizona Public Service Company’s Palo Verde Unit 1 and led to main transformer fire and emergency shut down.,0 +11/24/1989,"Greifswald, Germany (Lubmin)",15.5,,,WE,Three out of six cooling water pumps were switched off for a test. A fourth pump broke down and control of the reactor was lost; ten fuel elements were damaged,0 +11/5/1975,"Brownsville, Nebraska, United States",15,,,NA,Hydrogen gas explosion damaged the Cooper Nuclear Facility’s Boiling Water Reactor and an auxiliary building.,0 +12/17/1987,"Hesse, Germany",15,1,,WE,Stop valve failed at Biblis Nuclear Power plant and contaminated local area.,0 +2/4/1979,"Surry, Virginia, United States",13.9,,,NA,Virginia Electric Power Company manually shut down Surry Unit 2 in response to failed tube bundles in steam generators.,0 +5/16/2004,"Lorraine, France",13.9,1,,WE,Sub-standard electrical cables at the Cattenon-2 nuclear reactor caused a fire in an electricity funnel that damaged safety systems.,0 +10/9/1989,"Hadden Neck, Connecticut, USA",12.9,,,NA,480 fuel rods were damaged during the operating cycle by metal chips present in the coolant system,0 +9/12/1979,"Mihama, Japan",12.8,,,A,Fuel rods at the Mihama Nuclear Power Plant unexpectedly bowed and damaged the fuel supply system.,0 +9/9/1997,"Bridgman, Michigan, United States",12.8,,,NA,Ice condenser containment systems failed at Indiana Michigan Power Company’s D.C. Cook Units 1 and 2.,0 +9/12/2011,"Marcoule, France",12.8,,,WE,Marcoule Centraco nuclear waste site's oven used to melt radioactive waste exploded and killed one person and burned four others.,1 +4/5/2012,"Dieppe, France",12.8,1,,WE,Leaking oil from a reactor pump caught fire and forced an emergency shutdown of the number 2 reactor of EDF's Penly Nuclear Power Plant.,0 +8/11/1973,"Palisades, Michigan, United States",11.8,,,NA,Steam generator leak caused manual shutdown of pressurized water reactor operated by the Consumers Power Company.,0 +6/18/1982,"Senaca, South Carolina, United States",11.8,,,NA,Feedwater heat extraction line failed at Oconee 2 Pressurized Water Reactor and damaged the thermal cooling system.,0 +1/15/2003,"Bridgman, Michigan, United States",11.8,,,NA,A fault in the main transformer at the Donald C. Cook nuclear power plant caused a fire that damaged the main generator and backup turbines.,0 +6/28/2005,"Forsmark, Sweden",11.8,2,,WE,Repair work on electric arc shorted out a circuit and caused the Forsmark-1 reactor to shutdown and disconnected from the grid and forced a three-week outage.,0 +6/4/2008,"Ljubljana, Slovenia",11.8,,,EE,Regulators shut down the Krsko nuclear power plant after the primary cooling system malfunctioned and coolant spilled into the reactor core.,0 +7/4/2008,"Ayrshire and Suffolk, United Kingdom",11.8,,,WE,British Energy nuclear reactors (the Largs and the Sizewell B facilities) shutdown after cooling units malfunctioned and damaged emergency systems and triggered blackouts.,0 +2/6/1975,"Leningrad, Russia",11.4,4,,EE,"The secondary cooling circuit of Unit 1 ruptured, releasing contaminated water into the environment",3 +6/14/1985,"Lima, Buenos Aires, Argentina",11.2,,,SA,"Excessive local power in the core caused 46 fuel rods to fail, releasing fuel pellets into the reactor coolant system",0 +9/10/1988,"Surry, Virginia, United States",10.7,,,NA,Refueling cavity seal failed and destroyed internal pipe system at Virginia Electric Power Company’s Surry Unit 2 and forced a 12 month outage.,0 +8/9/2004,"Fukui Prefecture, Japan",10.7,1,,A,Steam explosion at Mihama Nuclear Power Plant killed 5 workers and injures dozens.,5 +10/1/1981,"Sellafield, UK",9,3,1.3,WE,Inadvertent reprocessing of fuel from Oldbury Power Station had only been cooled for 27 days leads to iodine release,0 +5/25/1999,"Waterford, Connecticut, United States",8.6,,,NA,Steam leak in feedwater heater caused manual shutdown and damaged control board annunicator at the Millstone Nuclear Power Plant.,0 +7/13/2008,"Tricastin, France",8.6,1,,WE,The nuclear power operator Areva reported that dozens of liters of wastewater contaminated with uranium were accidentally poured on the ground and ran into river.,0 +2/14/2014,"Waste Isolation Pilot Plant, New Mexico",8.2,,,NA,"Mine fire occured at salt hauling truck and required evacuation of the entire facility, extensive cleanup, and hospitalization of six workers for smoke inhalation and radiation exposure.",0 +11/22/1988,"Pickering, Ontario, Canada",6.6,,,NA,Xenon buildup causes damage to 191 fuel rods,0 +1/20/1965,"Lawrence Livermore, CA, USA",6.1,4,3.7,NA,"Roughly 350,000 Ci were released from a stack in building 331",0 +3/25/1988,"Pickering, Ontario, Canada",5.5,,,NA,"Fuel rod failures caused reactor power to be limited to 50 percent for the remaining six months of the fuel cycle.On November 22, 1988, an operator error damaged 36 fuel bundles. The cooling system was +contaminated by radioactive iodine that was vented into the environment over several weeks +following the accident.",0 +7/25/1979,"Saclay, France",5.4,,,WE,"Radioactive fluids escaped into drains designed for ordinary wastes, seeping into the local watershed at the Saclay BL3 Reactor.",0 +5/22/1986,"Normandy, France",5.4,,,WE,A reprocessing plant at Le Hague malfunctioned and exposed workers to unsafe levels of radiation.,0 +11/17/1991,"Scriba, New York, United States",5.4,,,NA,Safety and fire problems forced New York Power Authority to shut down the FitzPatrick nuclear reactor for 13 months.,0 +10/31/1974,"Morris, Grundy County, Illinois, USA",4.4,,,NA,Dresden nuclear power plant suffers a xenon transient and minor release of radioactivity,0 +12/10/1983,"Plymouth, Massachusetts, United States",4.3,,,NA,Recirculation system piping cracked and forced Pilgrim nuclear reactor to shutdown.,0 +6/21/2013,"Wanli, Taiwan",4.3,,,A,Air dampener at the Kuosheng Nuclear Power Plant fell into the busbar and tripped the reactor and caused a shutdown.,0 +9/26/1983,"Russellville, Pope County, Arkansas, USA",4.1,,,NA,"Control rod misalignment results in xenon oscillations, damaging the fuel and increasing gaseous activity",0 +12/1/2009,"Cruas-Meysse 4, France",4,2,,WE,"Vegetation blocked the intake of the cooling system, forcing shutdown and repairs",0 +12/18/1985,"Lacey and Ocean Townships, Ocean County, New Jersey, USA",3.8,,,NA,Malfunctioning computer causes fuel rod failures and increased levels of gaseous activity,0 +3/8/1981,"Tsuruga, Japan",3.2,2,,A,278 workers were exposed to excessive levels of radiation during repair of Tsuruga nuclear plant.,0 +2/3/1993,"Bay City, Texas, United States",3.2,,,NA,Auxiliary feedwater pumps failed at South Texas Project Units 1 and 2 and required rapid shutdown of both reactors.,0 +3/2/1993,"Soddy-Daisy, Tennessee, United States",3.2,,,NA,Equipment failures and broken pipes caused Tennessee Valley Authority to shut down Sequoyah Unit 1.,0 +2/24/2006,"Rock River, Illinois",3.2,,,NA,Small fire spreads at Byron Nuclear Generating Station and destroys Refueling Water Storage Tank,0 +3/12/2012,"Wanli, Taiwan",3.2,,,A,Maintenance workers at the Kuosheng Nuclear Power Plant discovered cracked anchor bolts that forced a 3 day shutdown.,0 +12/9/1986,"Surry, Virginia, United States",2.8,,,NA,Steam explosion in Unit 2 cases deadliest accident (fatalities) in US history,4 +3/9/1983,"Gravel Neck, Surry County, Virginia, USA",2.2,,,NA,Fuel rods were discovered damaged due to metal chips that had been left in the reactor coolant system,0 +2/11/1981,"Florida City, Florida, United States",2.1,,,NA,Florida Light & Power manually shut down Turkey Point Unit 3 after steam generator tubes degraded and failed.,0 +4/14/1984,"Bugey, France",2.1,,,WE,Electrical cables failed at the command centre of the Bugey nuclear power plant and forced a complete shutdown of one reactor.,0 +4/21/1992,"Southport, North Carolina, United States",2.1,,,NA,NRC forced Carolina Power & Light Company to shut down Brunswick Units 1 and 2 after emergency diesel generators failed.,0 +5/13/1992,"Tarapur, Maharashtra, India",2.1,,,A,A malfunctioning tube caused the Tarapur nuclear reactor to release 12 curies of radioactivity.,0 +2/27/1993,"Buchanan, New York, United States",2.1,,,NA,New York Power Authority shut down Indian Point Unit 3 after AMSAC system fails.,0 +9/29/1999,"Lower Alloways Creek, New Jersey, United States",2.1,,,NA,Freon leak at Hope Creek Nuclear Facility caused ventilation train chiller to trip that released toxic gas and damaged the cooling system.,0 +7/18/2007,"Kashiwazaki, Japan",2.1,,,A,"The Tokyo Electric Power Company's Kariwa nuclear plant damaged by a 6.8 magnitude earthquake leaked 1,192 litres of radioactive water into the Sea of Japan.",0 +8/12/2009,"Gravelines, France",2.1,1,,WE,Assembly system failed to properly eject spent fuel rods from the Gravelines Nuclear Power Plant and caused the fuel rods to jam and the reactor to shutdown.,0 +1/30/2012,"Rock River, Illinois",2.1,,,NA,Switchyard component failure and transformer fire at Exelon's Byron Generating Station's Unit 2 caused shutdown and tritium steam release to prevent a meltdown.,0 +7/27/1972,"Surry, Virginia, United States",1.2,,,NA,Two deaths in steam line rupture and accident,2 +7/16/1971,"Cordova, Illinois, United States",1.1,,,NA,An electrician was electrocuted by a live cable at the Quad Cities Unit 1 reactor on the Mississippi River.,1 +9/20/1981,"Baxley, Appling County, Georgia, USA",1.1,,,NA,Fuel rod failures caused air ejector activity to rise to 0.3 Ci per second,0 +2/26/1982,"San Clemente, California, United States",1.1,,,NA,Southern California Company shut down San Onofre Unit 1 because of earthquake concerns.,0 +5/21/1946,"Los Alamos, New Mexico",0,,,NA,Death due to citicality accident at Los Alamos National Laboratory.,1 +4/13/1958,"Chelyabinsk, Soviet Union",0,,,EE,Three died and one was injured in a super criticality accident at USSR Mayak Complex.,3 +10/15/1958,"Vinca, Yugoslavia",0,4,,EE,Death due to citicality accident at Boris Kidrich Institute.,1 +12/30/1958,"Los Alamos, New Mexico",0,,,NA,Death due to citicality accident at Los Alamos National Laboratory.,1 +7/23/1964,"Rhode Island, United States",0,4,,NA,Operator death due to super critical accident at Wood River Junction Chemical Process Plant,1 +4/5/1968,"Chelyabinsk, Soviet Union",0,5,5,EE,Technician died from exposure after initiating test at Russian Federal Nuclear Centre.,1 +2/11/1971,"Moscow, Russia",0,4,,EE,Supervisor died after criticality accident at the Kurchatov Institute,1 +1/5/1976,"Jaslovske Bohunice, Czechoslovakia",0,,,EE,Leaked carbon dioxide from a KS 150’s reactor coolant system at the Bohunice Nuclear Power Plant fatally suffocated two workers.,2 +9/1/1980,"Leningrad, Russia",0,,,EE,Operator at gamma irradiation facility died from exposure.,1 +11/22/1980,"San Onofre, California, United States",0,,,NA,Worker cleaning breaker cubicles at San Onofre Pressurized Water Reactor contacted an energized line and was killed by electrocution.,1 +9/23/1983,"Buenos Aires, Argentina",0,,,SA,Operator death due to criticality accident at the Constituyentes Atomic Centre,1 +3/29/1988,"Burlington, Kansas, United States",0,,,NA,Safety inspector died from electrocution after contacting a mislabeled wire at the Wolf Creek Nuclear Power Plant.,1 +6/17/1997,"Nizhny Novgorod, Russia",0,,,EE,Technican died from criticality incident at Arzamas-16 Nuclear Centre,1 +3/15/2009,"Oskarshamn, Sweden",0,,,WE,A maintenance worker repairing a shutdown reactor at the Oskarshamn died after falling from the top of the turbine hall.,1 +6/18/1988,Belgium,,,,WE,"Tihange-1 (Belgium) (see 9.2.2.1) +On 18 June 1988, while the pressurized water reactor was operating, a sudden leak +occurred in a short, unisolable section of emergency core cooling system (ECCS) piping. The leak +rate was in the order of 1,300 liters per hour. The source of leakage was a crack – 9 cm long on the +inside surface of the pipe and 4.5 cm long on the outside surface – extending through the wall of +the piping. The risk of a pipe rupture in the emergency core cooling system is considerable if the +emergency safety injection system is activated as large quantities of cooling water are injected in +case of a loss of coolant accident in an already degraded safety situation.",0 +2/9/1991,Japan,,,,A,"Mihama-2 (Japan) (see 9.2.2.3) +A steam generator tube rupture occurred at Mihama-2 pressurized water reactor. This was +the first such incident in Japan where the emergency core cooling system was actuated. The utility +investigated the rupture and found that it was a complete circumferential tube failure. The utility +found that the failure due to high cycle fatigue caused by vibration. By design, all tubes in specific +locations in the steam generator are supposed to be supported by anti-vibration bars. However, the +subject tube was found not to be supported appropriately because of a reported ""incorrect +insertion"" of the adjacent anti-vibration bars.",0 +4/3/1991,USA,,,,NA,"3 April 1991 Shearon Harris (USA) (see 9.2.1.1) +On 3 April 1991 workers at the Shearon Harris pressurized water reactor in New Hill, +North Carolina discovered damaged piping and valves within the alternate minimum flow system +provided for the pumps in the emergency core cooling system. The piping and valve damage was +serious, had an accident occurred the water needed to cool the reactor core would have instead +poured out onto the floor through the ends of broken components. The NRC calculated the severe +core damage risk from this event to be 6 x 10-3 or 0.6% per reactor year. The event was not rated +on the IAEA INES scale.",0 +6/28/1992,Sweden,,2,,WE,"Barseback-2 (Sweden) (see 9.2.7.1) +A leaking pilot valve in the boiling water reactor in Barseback initiated automatically +safety functions like reactor scram, high-pressure safety injection, core spray and containment +spray systems. The steam jet from an open safety valve was impinging on thermally insulated +equipment. Insulating material was washed into the suppression pool and affected the emergency +core cooling system, which is essential for heat removal in case of a leak the reactor coolant. +Similar incidents occurred in several countries and the problem turned out to apply to many, if not +most, of the light water reactors in the world",0 +5/12/1988,France,,2,,WE,"The Civaux-1 pressurized water reactor was shut down for five days, when, during startup +tests, a 25 cm diameter pipe of the main residual heat removal system cracked open and a large +leak (30,000 liters per hour) occurred in the primary cooling circuit. The reactor core needs to be +cooled permanently, even when it is shut down, in order to evacuate the significant amount of +residual heat of the fuel. It took nine hours to isolate the leak and reach a stable situation. An +18 cm long crack on a weld was identified and 300 m3 of primary coolant had leaked into the +reactor building. The unit had been operating for only six months at 50% power level maximum +prior to the event. The operator, EDF, suggested rating this event at level 1 on the INES scale, but +the safety authorities decided on level 2.",0 +3/18/2001,Tawian,,,,A,"The pressurized water reactor was affected by a total loss of external and internal power +supply. Power supply is crucial to evacuate residual heat from the reactor core. The plant is +situated near the sea. Salt deposit on insulators due to foggy weather caused instability of the high +voltage grid. During a switch to the grid a short circuit in a power switch of the emergency power +line occurred and caused a cable fire. A breaker and switchgear was totally destroyed by the fire +and the diesel generators could not be started up manually because of heavy smoke. It took about +two hours to restore power supply.",0 +8/12/2001,Germany,,2,,WE,"Philippsburg (Germany) (see 9.2.3.1) +A deviation from the specified boron concentration – a neutron absorber needed to slow +down or stop the nuclear reaction – in several flooding storage tanks during the restart of the plant +was reported to the authorities. In addition, the liquid level had not reached the required value +fixed in the operational instructions for the start-up and was only implemented with a delay. The +emergency core cooling system will only work effectively if it is operated according to the design +basis conditions. Subsequent investigations revealed that significant deviations from start-up +requirements and violations from related instructions seemed to be common probably for several +years and took place in other German nuclear plants.",0 +12/14/2001,Germany,,1,,WE,"Brunsbüttel (Germany) (see 9.2.5.1) +A hydrogen explosion caused a high degree of damage to the spray system piping of the +boiling water reactor. The head spray line is used for cooling the inner surface of the reactor +pressure vessel head and the flange area upon plant shutdown. Some parts of the 5.6 mm diameter +pipes were ruptured. An approximately 2.7 m long piping section had burst and was completely +destroyed. Some sections of the piping were missing. Prior to this event the possibility of severe +explosions caused by radiolysis gas during normal operation was nearly excluded",0 +5/1/2005,Bulgaria,,2,,EE,"Kozloduy-5 (Bulgaria) (see 9.2.3.2) +In the process of power reduction at the Russian designed pressurized water reactor +(WWER) the operators identified that three control rod assemblies remained in the upper end +position. The follow-up movement tests of the remaining control rod assemblies identified that 22 +out of 61 could not be moved with the driving mechanisms. The exact number of control rod +assemblies unable to scram (to drop due to the gravity only) remains unknown but it is thought to +be between 22 and 55. The WWER-1000 scram system is designed to put the reactor in safe +shutdown if one control rod assembly at the most is jammed in the upper position. The operator +had originally rated the incident INES level 0, but the safety authorities finally admitted to a level +2 rating.",0 +6/25/2006,Sweden,,,,WE,"Forsmark, Sweden (see 9.2.6.2) +A short circuit in an outdoor switching station of the grid nearby the boiling water reactors +caused the emergency shutdown (scram) of unit 1 and, in a complex scenario, led to a number of +subsequent failures at the plant. Due to a design error, the disconnection of the plant from the grid +and the switch to house load operation – where the power plant uses its own power to operate +essential auxiliaries – did not function as planned. An inappropriate converter adjustment led to +the failure of the attempt to connect safety related equipment to the emergency power supply. The +start up of two of the four emergency diesel generators was aborted, which lead to a partial +blackout even in the main control room. Due to the lack of information about the important +parameters for a period of time the exact state of the plant and the consequences of potential +actions to perform were unclear. The shift team decided nevertheless to try to reconnect the plant +to the grid, which was performed successfully.",0 +08/18/2015,"Blayais, France",,2,,WE,Accidental overexposure of a worker to a dose of ionising radiation exceeding regulation limits,0 +05/28/2015,"Cattenom, France",,1,,WE,Unexpected opening of a secondary system valve which resulted in a steam release,0 +11/28/2014,"Zaporizhia, Ukrain",,0,,EE,Disconnection of the unit 3 of the plant from the grid,0 +05/17/2014,"Tarapur, Maharashtra, India",,1,,A,Overexpossure of a worker during a transfer operation of irradiated neutron detector,0 +10/08/2013,"Krsko, Slovenia",,0,,EE,Discovery of damaged fuel rods during unloading and fuel inspections,0 +02/12/2013,"Puspokszilagy, Hungary",,1,,EE,Three workers were expossed to radiation while handling radioactive waste,0 +09/26/2013,"Petten, Netherlands",,2,,WE,"Three out of six control rods were found to show deficiences, the plant had to shut down for 5 months",0 +05/30/2013,"Forsmark, Sweden",,1,,WE,Emergency generator failed to start after loss of two phases in the incoming supply,0 +04/24/2013,"Blayais, France",,2,,WE,Accidental overexposure of a worker beyond regulatory limit,0 +10/24/2012,"Flamanville, France",,1,,WE,Leak on the auxialliary line of the reactor's coolant system,0 +09/26/2012,"Drôme, France",,2,,WE,"Non-complience to regulations applicable to labelling, storage and transfer of wet fissible materials",0 +05/09/2012,"Paks, Hungary",,1,,EE,Deviation from specifications on a maintenance task due to lack of written instructions,0 +06/23/2012,"Rajasthan, India",,1,,A,Exposure of workers to tritium,0 +05/04/2012,"Penly, France",,1,,WE,As a concequence of fire radioactive water leaked into the collection tanks inside the reactor buldings,0 +09/01/2012,"Busan, South Korea",,2,,A,Station blackout occurred when back up generator failed to start while primary generator was out of service for maintenance,0 +12/22/2011,"Bosco Marengo, Italy",,1,,WE,Fire in the decontamination machine of a former plant,0 +12/21/2011,"Cattenom, France",,2,,WE,Absence of siphon-breakers in two out of the four reactors of the facility,0 +10/19/2011,"Karachi, Pakistan",,1,,A,The nuclear power plant imposed a seven-hour emergency after heavy water leaked from a feeder pipe to the reactor,0 +09/21/2011,"Codolet, France",,1,,WE,One worker died and four were injured when an explosion in the nuclear facility triggered a fire,1 +03/11/2011,"Onagawa, Japan",,2,,A,"The cooling system of the reactor failed due to an earthquake, however the reactor had allready been shut down before the earthquake",0 +02/16/2011,"Tricastin, France",,2,,WE,Failed back-up diesel generator was discovered during a periodic test,0 +11/19/2003,"La Hague, France",,2,,WE,Worker was contaminated by plutonium during a dust remova opperation,0 +06/10/2009,"Cadarache, France",,2,,WE,"Underestimation of plutonium deposits in the facility glove boxes, operations suspended for a month",0 +08/27/2008,"Trhange, Belgium",,1,,WE,Maintenance operations revealed last Thursday that a fan for the management of air between,0 +11/22/2002,"Trhange, Belgium",,2,,WE,"On November 22, 2002, an incident occurred in Block 2 (INES 2). The reactor was shut down at this time and no longer critical. However it remained necessary for decay heat to be removed from the reactor by circulating coolant. The November 22 incident involved accidental opening of a pressure relief valve. This caused the high pressure primary coolant circuit to rapidly depressurize, therefore reducing the ability of the circuit to remove heat from the reactor (the high pressure prevents the coolant - water - from boiling), leading to possible reactor overheating and eventual meltdown. The pressure relief valve was closed again after three minutes.",0 +05/12/1988,"Civaux, France",,2,,NA,A large leak of coolant into the reactor building occurred when a pipe cracked open,0 +11/03/1997,"Ibaraki Prefecture, Japan",,3,,A,At least 37 workers were expossed to elevated levels of radiation after a small explosion in the plant. A week after high levels of Calcium were detected 25 miles south-west of the plant,0 +05/15/1996,"Morris, Grundy County, Illinois, USA",,,,NA,"Plunging water levels around the nuclear fuel in the reactor's core prompt shut down at Dresden Generating Station,",0 +03/11/1978,"Pinawa, Manitoba, Canada",,,,NA,"The reactor which was cooled by a type of oil, terphenyl isomer, experienced a major coolant leak as one of the pipes developed a hole and 2,739 litres of oil escaped. It took several weeks for workers to find and repair the leak. Much of the leaked oil was then discharged into the Winnipeg River. According to Dr. Agnes Bishop of the Atomic Energy Control Board, (later the CNSC), the fuel reached ""high temperatures"".",0 +09/25/1990,"Pickering, Canada",,,,NA,"Pickering reactor 2 experienced large power shifts in the reactor +core. Staff spent two days trying to stabilize it before shutting it down. The AECB later +criticized the utility for not shutting down immediately.",0 +08/02/1992,"Pickering, Canada",,,,NA,"Pickering reactor 4 had a heavy water leak from a heat exchanger that +resulted in a release of 50 trillion becquerels of tritium into Lake Ontario",0 diff --git a/datasets/qsar_aquatic_toxicity.csv b/datasets/qsar_aquatic_toxicity.csv new file mode 100644 index 0000000..ed6043f --- /dev/null +++ b/datasets/qsar_aquatic_toxicity.csv @@ -0,0 +1,546 @@ +0;0;0;2.419;1.225;0.667;0;0;3.740 +0;0;0;2.638;1.401;0.632;0;0;4.330 +9.23;11;0;5.799;2.93;0.486;0;0;7.019 +9.23;11;0;5.453;2.887;0.495;0;0;6.723 +9.23;11;0;4.068;2.758;0.695;0;0;5.979 +215.34;327.629;3;0.189;4.677;1.333;0;4;6.064 +9.23;11;0;2.723;2.321;1.165;0;0;7.337 +0;0;0;3.267;2.318;0.963;0;0;4.100 +0;0;0;2.067;1.8;1.25;0;0;3.941 +0;0;0;2.746;1.667;1.4;0;0;3.809 +0;0;0;3.124;1.8;1.417;0;0;4.340 +12.03;18.011;1;0.817;1.8;1.488;1;0;3.930 +69.67;97.435;0;3.122;3.723;1.26;0;2;7.371 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The idea seems intuitive to many of us, but it is difficult to pin down with a specific definition. When forced to think and speak about it, we say things like ‘what are the odds’ or ‘what are the chances’ because we have an intuition that it will involve thinking about how \"likely'' certain possibilities are to occur but even here, we're essentially substituting the term “likely” for “probable.\" Thinking a bit harder, we'd like to say \"perhaps it involves mapping possibilities to numbers such that we have higher numbers for more likely possibilities.\" That would be a start. To think more completely about the nature of probability, it will be helpful to consider how our predecessors have thought about it historically, particularly the two main schools of modern probability and statistics: frequentists and Bayesians." + ] + }, + { + "cell_type": "markdown", + "id": "8f0b3054-9b26-424b-be3f-42dc69f33307", + "metadata": {}, + "source": [ + "## Interpretations and definitions in brief\n", + "\n", + "The pioneers of probability, such as Bernoulli, Bayes, and Laplace, considered _probability_ to represent a degree-of-belief in a statement or hypothesis. Thus, for example, when a Bayesian is talking about the probability $P(D)$ of the earth being a certain distance from the sun, they are talking about the state of their knowledge of the relevant distance, given all the information that they have. \n", + "\n", + "For frequentists, probability is defined as the long-run relative frequency of the event, given many repeated experiments. As we'll see, in some cases, this solves an apparent challenge of the Bayesian framework (which we'll deal with), that the Bayesian definition _seems_ subjective: if we define probability as the the long-run relative frequency of the event, it doesn't matter from whose perspective we derive the results, as relative frequencies are objective. This goal is admirable. However, it introduces additional challenges, such as \"What does it now mean to say that 'The probability of the true distance between the earth and the sun is so and so'?\" In a frequentist framework, there is only one true distance and so it doesn't make sense to even talk about a probability here.\n", + "\n", + "To be clear, when done correctly, frequentism solves the challenge of subjectivity, but we also raise it now to give a hint of its limitations. In this chapter, we will start first by stating our desired outcomes, show how history has influenced our ways of thinking, and finally show how the issues with frequentism are solved by Bayesianism.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "cfea8c11", + "metadata": {}, + "source": [ + "## What we're looking for: towards a principled statistical workflow\n", + "\n", + "Most conversations and works about interpretations and definitions of probability are not really actually about interpretations and definitions of probability. More often than not, they're driven by worldviews about the best way to do statistical inference and often the conversation about definitions is a proxy for a real conversation around the methods we want to use. To this end, we want to expose our motivations clearly and succinctly.\n", + "\n", + "This book is part of a movement working towards principled statistical workflows that allow humans to understand the world better and to make better decisions. We believe that such a movement requires a lot of moving parts and our opinionated take is that working data scientists require the following:\n", + "\n", + "- The ability to build models that capture our current state of knowledge of the world;\n", + "- Principled and consistent workflows: this includes necessitating being explicit around assumptions and ideally that someone else performing the analysis with the exact same information would get the same results;\n", + "- The ability to model the data-generating process(es);\n", + "- An ergonomic and intuitive abstraction layer, such as those provided by probabilistic programming languages (which are also in active development!).\n", + "\n", + "\n", + "There are also key questions around how to build organizations in which such work can be seamlessly intergrated into the decision function, among other cultural concerns, such as how do we think, talk about, and model uncertaintly, but our main project in this book is to equip individual data practitioners with the key concepts and tools to do the above.\n", + "\n", + "\n", + "We believe that Bayesian inference, when combined with probabilistic programming and modern hardware, is well-poised to meet all of these needs. Through a discussion of the different historical interpretations and definitions of probability, we'll also meet the practical considerations that impact this question, including the affordances of both frequentist and Bayesian statistics, what they're capable of, and what it looks and feels like to work with them. To this end, our intention is not to cast shade on frequentism by any means, but to show how principled Bayesian workflows better meet the needs we have outlined above. In this spirit, let's jump into a brief historical tour of how humans have thought about probability over time." + ] + }, + { + "cell_type": "markdown", + "id": "f1e14dc1-d5b1-4937-8e1f-3cac7c8de805", + "metadata": {}, + "source": [ + "## Frequentism\n", + "\n", + "### Playing games and the doctrine of chances\n", + "\n", + "In 1654, the Chevalier de Méré posed a question to Blaise Pascal that became known as the _problem of points,_ involving a question around a game of chance. This question was first posed in the 15th century, but Méré's posing it to Pascal is central to any historical discussion of probability as it played a key role in Pascal's correspondence with Fermat, which laid the foundations for modern probability theory. In fact, the problem of points resulted in many developments, such as Pascal working through the first explicit discussions of what we now call _expected value._ These were the first inklings of probability taking on a quantitative nature. Probability had of course been thrown about, but is was essentially qualitative up to this point.\n", + "\n", + "What is important to recognize is that these pioneers were _explicitly_ thinking about games of chance: playing cards, rolling dice, flipping coins, and developing the first explicit quantitative techniques to think about probabilities in this setting. To be clear, in this setting, we can enumerate a finite set of explicit outcomes, perform experiments (either explicitly or using thought experiments), and calculate the fraction of times a particular event occurs, out of all possible occurences, and define probability as such:\n", + "\n", + "$$P(E) = \\frac{\\text{Number of ways E can occur}}{\\text{Number of total outcomes}}$$\n", + "\n", + "The reason for being so explicit about this is that we shouldn't necessarily expect such a theory to be applicable for probabilistic questions that occur ouside games of chance. To expect this is akin to expecting that machine learning agents, trained to play games such as chess, Go, and Starcraft, will somehow perform well in the real-world. Or expecting that self-driving cars trained on real-world car data will perform well when confronted with streets full of other self-driving cars.\n", + "\n", + "### Frequencies\n", + "\n", + "In games of chance, we can map out explicitly all the possible options, assign probabilities, and theoretically calculate the probability of any given outcome. But the question still remained: how does this relate to empirically measured frequencies? It was the genius of Jacob Bernoulli and what he called his \"golden theorem\", now called the \"law of large numbers\" (LLN), that allowed us to answer this question. The LLN states that:\n", + "\n", + "> the average of the results obtained from a large number of trials should be close to the expected value and tends to become closer to the expected value as more trials are performed.\n", + "\n", + "Let's do an experiment. In fact, let's do Bernoulli's urn experiment. Bernoulli's idea was to have an urn filled with 10s of 1,000s of balls, some red and others white, with the fraction of red balls being some unknown quantity $F$. He would then draw balls from the urn and calculate the sample ratio $S$, in an effort to estimate $F$. \n", + "\n", + "Let’s simulate it and check out $S$ as we draw more and more balls ($n$). The law of large numbers says that with increasing $n$, $S$ will get closer to $F$. (If any of the following code doesn't yet make sense to you, it will in the following chapters.)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67218eca", + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:38.576321Z", + "iopub.status.busy": "2022-04-30T07:01:38.575909Z", + "iopub.status.idle": "2022-04-30T07:01:39.586714Z", + "shell.execute_reply": "2022-04-30T07:01:39.586398Z" + } + }, + "outputs": [], + "source": [ + "#| output: false\n", + "#Import packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "sns.set()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f8e638f-de0b-4c54-8e09-07f5c3cba1b5", + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:39.588768Z", + "iopub.status.busy": "2022-04-30T07:01:39.588661Z", + "iopub.status.idle": "2022-04-30T07:01:39.837680Z", + "shell.execute_reply": "2022-04-30T07:01:39.837232Z" + } + }, + "outputs": [], + "source": [ + "#| fig-cap: Simulating the Law of Large Numbers\n", + "rng = np.random.default_rng(42) # Set seed for PRNG\n", + "k = 10**3 # largest number of draws\n", + "F = 0.6 # True urn fraction of red balls\n", + "data = rng.binomial(1, F, k) # draw balls from urn\n", + "\n", + "x = np.arange(k) # number of draws\n", + "S = np.zeros(k) # array to store calculations of S \n", + "\n", + "# Loop over number of draws and calculate S\n", + "for i in x:\n", + " S[i] = np.mean(data[:i+1])\n", + "\n", + "plt.plot(x, S)\n", + "plt.xlabel('sample size $n$')\n", + "plt.ylabel('Sample fraction $S$');\n" + ] + }, + { + "cell_type": "markdown", + "id": "f98a1a95-4fd9-4be5-97ee-42f515f47a80", + "metadata": {}, + "source": [ + "By eye, we can see that $S$ indeed seems to converge towards $F$: pretty good! But did this answer Bernoulli's question? Bernoulli was interested in finding out what the true fraction $F$ was and showed that $S$, which we can calculate, gets closer to $F$ as $n$ gets larger. So it tells us about the probability of $S$, rather than that of $F$!\n", + "\n", + " _If this feels counter-intuitive to your prior classical statistics training, you’re not alone._ Aubrey Clayton, to whom the authors are much indebted, has dubbed this [Bernoulli's Fallacy](https://cup.columbia.edu/book/bernoullis-fallacy/9780231199940). Let's now dive a bit deeper into this fallacy as it sheds light on the types of questions frequentism and Bayesian are adept at handling, along with the types of questions that scientists are usually interested in." + ] + }, + { + "cell_type": "markdown", + "id": "dd49af80-ece2-4624-9ae3-96170756caa6", + "metadata": {}, + "source": [ + "### Bernoulli's fallacy\n", + "\n", + "Recall that the law of large numbers (LLN) states that:\n", + "\n", + "> the average of the results obtained from a large number of trials should be close to the expected value and tends to become closer to the expected value as more trials are performed.\n", + "\n", + "So what Bernoulli was saying with the law of large numbers, and what we demonstrated with the above simulation, is that, the more balls we draw, the sample urn ratio $S$ gets closer to the true urn fraction $F=f$. Using the notation of probabilty, \n", + "\n", + "> $P(S \\text{ is close to} f | F = f)$ is high. \n", + "\n", + "Now Bernoulli's interest was in finding out things about the true urn fraction $F$ and it seems intuitive (if not obvious) that the statements \"the sample urn ratio is close to the true urn fraction\" and \"the true urn fraction is close to the sample urn ratio\" should be interchangeable. Although the property of closeness is indeed symmetric in many cases, it is definitely NOT when considering probabilities. In our case, the resulting statement would be that \n", + "\n", + "> $P(F \\text{ is close to} s | S = s)$ is high,\n", + "\n", + "where $s$ is the observed sample fraction.\n", + "\n", + "Bernoulli interpreted it to mean he could make statements about the true urn fraction and he did *not* have the notation to recognize that he was confusing $P(S | F)$ for $P(F | S).$\n", + "\n", + "Essentially, he confused the following two statements:\n", + " \n", + "- We conclude that, with high probability, the observed sample fraction is close to the true urn fraction.\n", + "- We conclude that, with high probability, the true urn fraction is close to the observed sample fraction.\n", + "\n", + "The confusion is understandable as the difference is challenging to parse: one way to think about it is that the first statement says that, if you sample many times, then 95% of the time (for example) the observed sample fraction will be close to the true urn fraction. But the latter makes a claim about the true urn fraction, given a single sample fraction (which could actually be quite far from the true urn fraction). To summarize:\n", + "\n", + "> $P(S | F)$ is not logically the same as $P(F | S)$, even if in some cases, they may be identical quantities.\n", + "\n", + "In Bernoulli's case, the difference did not particularly matter but in many cases it does. As we'll soon see, Bernoulli's fallacy is the same mistake as asumming the following are the same:\n", + "\n", + "- the probability of person X having an infectious disease, given a positive test result;\n", + "- the probability of person X getting a positive test result, knowing that they have the infectious disease.\n", + "\n", + "Later, we'll see how the former can be around 30%, while the latter is close to 100%.\n", + "\n", + "In another statistical anecdote, popular statistics outlet FiveThirtyEight had an article that described how, by focusing on the wrong probability statement, English football fan Charles Reep may have contributed to English football’s fascination with the “no more than three passes” style of playing - and possibly helping to “ruin decades of English soccer”. Here, Reep was fixated on “the percentage of goals generated by passing sequences of a certain length” (i.e. $P(sequence length | goal)$), which focuses on sequence lengths that produce a goal. Yet, the more valuable question that should have been answered is $P(goal | sequence length)$, or “the probability of a sequence length generating a goal”, which requires looking at sequences that _didn’t_ produce a goal.\n", + "\n", + "How does this all relate to frequentist and Bayesian ways of approaching statistics? Using both examples above to illustrate, frequentist methods start with a given hypothesis (having a disease; producing a goal) and ask about the probability of observed data (positive test; sequence length). Bayesian methods start with data (positive test; sequence length) and ask the probability of a range of hypotheses (having a disease; producing a goal). In a sense, frequentist and Bayesian methods answer _mirror_ questions. To reinforce the point, frequentist methods do *not* tell us about the probability of a specific value or hypothesis, given observed data. In fact, they *cannot* tell us about the former, as the probability of a specific value doesn't even make sense for a frequentist! What they can and do tell us about is the probability of seeing certain data, assuming specific values and/or hypotheses, which can be valuable.\n", + "\n", + "Yet, the vast majority of scientific questions are of the former form: we want to know the probability of the actual rate of infection (or more generally, the probability of a particular hypothesis) given the observed data, and *not* the probability of seeing the observed data, were the hypothesis true (which is what frequentist hypothesis testing gives us).\n", + "\n", + "Put another way, when doing science, we are interested in learning about the _inferential_ probability, rather than the _sampling_ probability.\n", + "\n", + "This issue does not merely apply to estimates, as above, but to the underlying questions these methods are able to answer. For another example, let's look at confidence intervals. As Jake Vanderplas wrote in [Frequentism and Bayesianism: A Python-driven\n", + "Primer](https://arxiv.org/pdf/1411.5018.pdf), given a 95% confidence interval,\n", + "\n", + "> - A frequentist would say: “If this experiment is repeated\n", + "many times, in 95% of these cases the computed confidence interval will contain the true $f$.”\n", + "> - A Bayesian would say: “Given our observed data, there is a 95% probability that the true value of $f$ lies within the credible region”.\n", + "\n", + "To be fair to Bernoulli, for many of the questions he was interested in, including games of chance, $P(S | F)$ and $P(F | S)$ are numerically identical, even if logically opposite. However, even in games of chance, the frequentist interpretation of probability presents other issues, such as in hypothesis testing and in estimating the probability of extreme events.\n", + "\n", + "\n", + "### Hypothesis testing\n", + "\n", + "Null hypthesis significance testing (NHST) is the gold standard for hypothesis testing in a freqentist framework. Let's say that we wanted to estimate the difference in means between two groups, a control and an experiment. We would set up: \n", + "\n", + "* a null hypothesis $H_0$ (that there is no difference), \n", + "* an alternative hypothesis (that there is a difference), and \n", + "* a test statistic (related to the sample difference in means).\n", + "\n", + "We then calculate the p-value, defined as the probability of observing a test statistic as extreme or more extreme than the one observed, assuming that the null hypothesis is true. Generally, if $p \\leq 0.05$, we reject the null hypothesis $H_0$ at the 95% significance level in favour of the alternative hypothesis. Note that we're essentially calculating $P(T | H_0)$, the probability of the test statistic, given the null hypothesis, rather than the probability of $H_0$, given the test statistic, and hence committing Bernoulli's fallacy! Having said that, when used and interpreted correctly, NHST is valid.\n", + "\n", + "The major challenges with NHST are the arbirtariness of the 5% significance level and the choice of the test statistic. Many treatises have been written on these so we will say merely a few more words, in the spirit of our purpose, that of looking for principled statistical workflows and methodologies. So let us be clear: NHST is anything but principled! It is motley collection of dozens (if not more) statistical tests, the choice of which to use in any given situation is often unclear. Your authors have been hearing the same types of questions for years now: can I use Student's t-test here? Or is Mann-Whitney more appropriate? Is it a paired test or a one sample test? Scrap that, I just realised what I'm looking for is the Chi-squared test!\n", + "\n", + "It is, of course, important to choose the right test when doing NHST, however, a principled workflow shouldn't require anybody to choose between a set of dozens of bespoke tests, all built for slightly different purposes, and all containg relatively opaque assumptions (\"wait, does my data need to be normally distributed for this test? If so, do I need to test for that before doing the hypothesis test I actually want to do?\"). As we'll see in this book, a principled Bayesian workflow includes a single protocol that can be used for most types of scientific questions.\n", + "\n", + "In addition to (1) their propensity for answering the wrong questions, (2) the arbitrariness of what we call significant and not, and (3) the practical difficulties involved in choosing between an array of tests, the other major concern we (and many others) have is the unintuitive nature of the p-value. The p-value is _the probability of observing a test statistic as extreme or more extreme than the one observed, given that the null hypothesis is true_. This definition is so challenging to parse that even research scientists are prone to make the mistake of thinking the p-value is the probability that the null hypothesis is true -- which is the wrong definition! Or that it's the probability of observing your data if the null hypothesis is true -- which it isn’t! For more on this, check out [Not Even Scientists Can Easily Explain P-values](https://fivethirtyeight.com/features/not-even-scientists-can-easily-explain-p-values/)! Once again, we are not claiming the NHST is wrong. When used correctly, it is correct! What we are saying is that it is often used incorrectly and that it is easy to use incorrectly.\n", + "\n", + "To summarize: \n", + "\n", + "* The key definition of p-value is unintuitive and, as a result, often misinterpreted by experts;\n", + "* The collection of tests to use in NHST is difficult to navigate, and lay practitioners incorrectly assume that they just have to pick a test to answer their statistical inference question,\n", + "* In the vast majority of scientific settings, NHST answers the wrong statistical inference question ($P(data | hypothesis)$ rather than $P(hypothesis | data)$), and \n", + "* Frequentism can be considered unnecessarily complicated, and this will become clear once you learn the logic of lay Bayesian statistical practice.\n", + "\n", + "\n", + "\n", + "### Becoming a definition\n", + "\n", + "\n", + "For Bernoulli, using frequencies was a method of calculating probabilities, rather than definitional. It took the 19th and 20th centuries and such statistical and mathematical powerhouses as Francis Galton, Karl Pearson, Ronald Fischer, and John Venn, to make frequencies the defintion of probability. \n", + "\n", + "A major issue here is that this definition doesn't even make sense for games of chance! To see this, consider the following: as Aubrey Clayton points out in _Bernoulli's Fallacy_, the likelihood of getting 13 spades in a hand of bridge is 1 in 635 billion, which is many orders of magnitude larger than the amount of bridge hands dealt throughout history. A frequentist method could potentially require us to deal bridge hands for millions of years, in order find the correct frequency. But what if we still saw it zero times, or twice, by chance? But a frequentist would then say \"you need to do the experiment more times\" or \"we need to perform this experiment as a thought experiment due to the limitations of the physical world.\" Note how this interpretation results in a circularity: if I flip a coin enough times and don't get close to 50% heads, a frequentist would tell me that there is something wrong with the coin or I haven't flipped it enough times.\n", + "\n", + "\n", + "This means that, from the frequentist POV, the logic of probability is:\n", + "\n", + "> We somehow \"know\" the frequency of an outcome --> this allows us to compute one-time probabilities\n", + "\n", + "Whereas the logic really shoud be:\n", + "\n", + "> we know things about the physics and symmetry of coins that leads to a degree-of-belief around $P(H)$, say, $P(H)=0.5.$ --> We then predict the long-run frequency of the theoretical result of flipping the coin lots of times --> THIS is the frequency\n", + "\n", + "The real damage was done when the frequentist interpretation of probability became definitional, rather than an approach for calculating it. This resulted in _the definition_ of probability becoming objective, which was part of a broader scientitic project. Bernoulli had always interpreted _probability_ as representing a degree-of-belief and used the LLN as a method of calculation, not as definition." + ] + }, + { + "cell_type": "markdown", + "id": "fb4e9f91-5a82-4a39-8b05-acff37a33787", + "metadata": {}, + "source": [ + "## Bayesianism, degrees of belief, and probability as logic\n", + "\n", + "\n", + "### Solving Bernoulli's fallacy\n", + "\n", + "Reverend Thomas Bayes was explicitly interested in answering inverse questions such as \"given a sample rate of infection, what can we say about the infection rate in the entire population?\" Thus, in a strong sense, his aim was to find a solution to Bernoulli's fallacy, although he did not frame it as such. In order to do this, he needed to be able to switch between conditional probabilities $P(A | B)$ and $P(B | A)$ and, to this end, he proved an equation we now know as Bayes' Theorem:\n", + "\n", + "\n", + "$$P(B | A) = \\frac{P(A | B) P(B)}{P(A)}$$\n", + "\n", + "$B$ can be a hypothesis or some variable we're attempting to estimate, while $A$ is classically the data we've observed. Notice that the space of hypotheses is large, as is the space of ways in which the data could have been generated and collected, meaning that we’re able to assign probabilities to them.\n", + "\n", + "Let's spend a little more time with this form of the equation, $P(H | D) = \\frac{P(D | H) P(H)}{P(D)},$ for data $D$ and hypothesis $H.$ For reasons we'll go into more deeply later, much of the time we are interested in what $P(H | D)$ is proportional to, in which case the denominator is irrelevant:\n", + "\n", + "$$P(H | D) \\propto P(D | H)P(H).$$\n", + "\n", + "\n", + "### Solving the inverse problem: temporal and causal order are unimportant\n", + "\n", + "One of Bayes' keen insights in answering the _inverse problem_ was to decouple probability and causation. Until Bayes can on the scene, it made sense to talk about $P(E_1 | E_2)$, the probability of an event $E_1$ occurring, knowing that $E_2$ occurred, only if $E_1$ occurred after $E_2$. But Bayes' solution to the inverse problem now meant that it made sense to think and talk about $P(E_1 | E_2),$ independent of temporal or causal order!\n", + "\n", + "Let's consider a variation on a simplified example that Edwin Jaynes constructed in [Clearing up Mysteries — The Original Goal](https://link.springer.com/chapter/10.1007/978-94-015-7860-8_1):\n", + "\n", + "> You have an urn containing 5 red balls and 5 white balls, randomly mixed. You take out one ball $B_1$ and it is white. You take out a second ball $B_2$. What is the probability that $B_2$ is white?\n", + "\n", + "We know that there are 4 white balls and 5 red balls in the urn before you draw $B_2$, from which we conclude the probability $B_2$ being white is $4/9$. Now let's consider a second related, yet distinct question:\n", + "\n", + "> You have an urn containing 5 red balls and 5 white balls, randomly mixed. You take out one ball $B_1$ and do not look at it. You take out a second ball $B_2$ and it is white. What is the probability that $B_1$ is white?\n", + "\n", + "This can seem counter-intuitive, even for those of us who have seen this example many times! The psychological paradox-of-sorts lays in reconciling the following two observations:\n", + "\n", + "* The colour of $B_2$ CANNOT causally impact the colour of $B_1$;\n", + "* The colour of $B_2$ DOES provide information about the distribution of colours across the other balls and thus does tell us something about the colour of $B_1$.\n", + "\n", + "To make this totally clear, consider the case in which our urn contains 1 ball of each colour. In this case, it is clear that, if the second ball is white, then the first must have been red, and vice versa. What this reveals is that we are dealing with logical statements and connections here, rather than causal ones. What Bayes had done was framed probability as logic, rather than merely in causal terms, and it was this _probability as logic_ that allowed him to move back and forth between $P(E_1 | E_2)$ and $P(E_2 | E_1)$ to solve inverse problems. This idea of _probability as logic_ allows us to incorporate all forms of information into our state of knowledge and probabilities. We will have more to say about the utility of thinking about _probability as logic_ as we go on, but now it's time see how Bayes' Theorem can lead to a principled workflow.\n", + "\n", + "### The principled inferential workflow\n", + "\n", + "Let's now name the terms in the above equation as they are key players in the world of Bayesian inference:\n", + "\n", + "* $P(H | D)$ is the **posterior distribution** and tells us about the probabilty of the hypothesis $H$, in light of the data;\n", + "* $P(D | H)$ is the **likelihood** of the data, given the hypothesis _or_ the probability of seeing the data, asssuming that the hypothesis is true;\n", + "* $P(H)$ is the **prior distribution**, which encodes what we know about the hypothesis before seeing the data.\n", + "\n", + "We can think about these terms as follows:\n", + "\n", + "> The posterior is what we want, the data is what we have, and the prior encodes our knowledge about the world.\n", + "\n", + "\n", + "So what does the workflow look like to calculate our posterior distribution? In the basics of Bayesian model building, the steps are\n", + "\n", + "1. To completely specify the model in terms of _probability distributions_. This includes specifying \n", + " - what the form of the sampling distribution of the data is, given by the **likelihood** _and_ \n", + " - what form describes our _uncertainty_ in the unknown parameters, given by the **prior**.\n", + "2. Calculate the _posterior distribution_.\n", + "\n", + "This is it. We will see this play out time and time again throughout this book. We'll also see how probabilistic programming languages, such as PyMC, make these steps relatively straight, including calculating the posterior for us! \n", + "\n", + "Note that, in completely specifying the model in terms of _probability distributions_, we have to make explicit what we consider the data-generating process(es) to be, which is one of our requirements for our workflow.\n", + "\n", + "It is now worth saying a few words about the prior, as it is one of the less well understood aspects of Bayesian inference.\n", + "\n", + "### On the subjectivity of the prior\n", + "\n", + "The main concern voiced about the prior is the perspective that it is subjective. First we'd like to note that this concern is understandable, if overblown. The truth of the matter is that the prior is intended to encode certain assumptions about our data and the data-generating process(es) that any analysis will necessarily contain, explicit or otherwise, and that all Bayesian inference does for us in this regard is to force us to be explicit about our assumptions.\n", + "\n", + "Perhaps more importantly, the true nature of the project of Bayesian inference requires that, if two people have exactly the same information, then their priors and analyses should be the same. This underlies not only the underpinnings of Bayesian inference, but of probabilistic programming languages, such as PyMC, and principled (Bayesian) workflows, which we are teaching in this book.\n", + "\n", + "Both Bayes and Laplace knew these things and used them well. Laplace, for example, used this method to estimate the mass of Saturn, given orbital data and knowledge of celestial mechanics:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6f2374f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:39.840134Z", + "iopub.status.busy": "2022-04-30T07:01:39.839986Z", + "iopub.status.idle": "2022-04-30T07:01:39.843970Z", + "shell.execute_reply": "2022-04-30T07:01:39.843602Z" + } + }, + "outputs": [], + "source": [ + "#| echo: false\n", + "from IPython.display import Image\n", + "Image(\"../../img/saturn-laplace.png\")" + ] + }, + { + "cell_type": "markdown", + "id": "22e04e2b", + "metadata": {}, + "source": [ + "We'll have a lot more to say about the choice of priors in the coming chapters, but we wanted to mention it here as we're aware it's top of mind for many people. But it also begs the question: how do we go about incorporating known information into our probability distributions? This raised a broader point worth mentioning about Bayesian: that _all_ probability incorporates information, in other words, that _all probability is conditional_. Let’s address this last point now." + ] + }, + { + "cell_type": "markdown", + "id": "43a27605-7806-4767-8b3b-c8bacede7a7e", + "metadata": {}, + "source": [ + "### All probability is conditional\n", + "\n", + "Notice that, in the above figure, we are interested in $P(M | D, I),$ the probability distribution of the mass of Saturn $M$, given data $D$ and something $I.$ This $I$ is the set of all other information used to calculate $P.$ In Laplace's case, that distribution included, and therefore was _conditioned on_, what people knew about physics, celestial mechanics, and the telescopic telemetry of the time. Once again, Bayesian inference requires us to be explicit about our assumptions, along with what we're conditioning on. In the Bayesian interpretation of _probability_, then, **all probability** is conditional.\n", + "\n", + "An obvious question would then be: \"What are we conditioning on when we say, for a fair coin, the probability of heads $P(H)=0.5$?\" And the answer is we're conditioning on all the information that led us to presume the coin is fair, from the physics of wind resistance and gravity, to trust in the Mint that they are making unweighted coins.\n", + "\n", + "**The point is**: There is ALWAYS information we're considering, and that information is used to condition our probabilities.\n", + "\n", + "**The question is**: do you want to be explicit about it?\n", + "\n", + "Having said that, it can become cumbersome to write $P(X | I)$ all the time so, for brevity's sake, it is standard to write $P(X)$, as long as we remember, when we write $P(H)=0.5$ in the case of a coin flip, what we really mean is $P(H | I)=0.5,$ where $I$ is all the relevant information that led us to assume the coin is far. Now let's move back to thinking about _probability as logic_ and how it can help us.\n", + "\n", + "\n", + "### Probability as logic\n", + "\n", + "The 20th century saw key developments in the formalization of _probability as logic_, from Andrey Kolmogorov's work on Measure Theory to Richard Cox's axiomatic approach to probability and Edwin Jaynes' work on principled Bayesian workflows (such as robust approaches for assigning priors), the principle of _maximum entropy_, and his work on transformation groups.\n", + "\n", + "Cox, a professor of physics at Johns Hopkins University, stepped back and considered what quantitative rules might exist for reasoning that is both logical and consistent and he began by thinking about how we can express relative beliefs in the truth value of certain propositions. He discovered that _transitivity_ was a minimal requirement for expressing beliefs consistently, that is, \n", + "\n", + "* if I believe proposition $A$ more than I believe proposition $B$ **and**\n", + "* I believe proposition $B$ more than I believe proposition $C$, **then**\n", + "* I also necessarily believe proposition $A$ more than I believe proposition $C$.\n", + "\n", + "He then made two other assumptions, which seem reasonable:\n", + "\n", + "* if I state how much I believe $X$ to be true, I've also (implicitly) stated something about how much I believe $X$ to be false (although Cox didn't assume anything about the nature of this relationship);\n", + "* if I state (1) how much I believe in $X$ AND (2) much I believe $X$, given $Y$, then I have also specified how much I believe that both $X$ and $Y$ are true.\n", + "\n", + "Under these assumptions, assigning real numbers to degrees of belief, and using the techniques of Boolean logic, Cox was able to show that\n", + "\n", + "$$P (X | I) + P (\\bar{X} | I) = 1$$\n", + "\n", + "and\n", + "\n", + "$$P (X, Y | I) = P(X, Y, I) \\times P(Y | I)$$\n", + "\n", + "Not only was Cox able to derive these building blocks of probability theory from his minimal assumptions but also two important corollaries, _Bayes' theorem_ and _marginaliztion_ (see below).\n", + "\n", + "\n", + "Edwin Jaynes continued this project of _probability-as-logic_ and made it clear that Bayesian inference is not subjective, rather it is about the information you have and the degree of certainty given that information. Any two people with the same information should form the same priors and analysis. Moreover, Jaynes envisaged a future in which you could program these analysis into a robot.\n", + "\n", + "### A word on marginalization\n", + "\n", + "The second corollary to Cox's theorem was the principle of _marginalization_:\n", + "\n", + "$$ P(X | I) = \\sum_i P(X , Y_i | I),$$\n", + "\n", + "where the $Y_i$ s are all the possibilities for another variable $Y$. As we'll see time and time again, it turns out that marginalization is key for statistical inference, in particular when dealing with what are known as _nuisance parameters_, and is something that Bayesian inference is better-equipped to deal with than frequentism is.\n", + "\n", + "In [Frequentism and Bayesianism: A Python-driven Primer](https://arxiv.org/pdf/1411.5018.pdf), Jake Vanderplas gives a nice treatment of an example that harks back to Reverend Bayes himself:\n", + "\n", + "> Alice and Bob enter a room. Behind a curtain there is a\n", + "billiard table, which they cannot see. Their friend Carol rolls\n", + "a ball down the table, and marks where it lands. Once this mark\n", + "is in place, Carol begins rolling new balls down the table. If\n", + "the ball lands to the left of the mark, Alice gets a point; if\n", + "it lands to the right of the mark, Bob gets a point. We can\n", + "assume that Carol’s rolls are unbiased: that is, the balls have\n", + "an equal chance of ending up anywhere on the table. The first\n", + "person to reach six points wins the game.\n", + "Here the location of the mark (determined by the first roll)\n", + "can be considered a nuisance parameter: it is unknown and\n", + "not of immediate interest, but it clearly must be accounted for\n", + "when predicting the outcome of subsequent rolls. If this first\n", + "roll settles far to the right, then subsequent rolls will favor\n", + "Alice. If it settles far to the left, Bob will be favored instead.\n", + "Given this setup, we seek to answer this question: _In a\n", + "particular game, after eight rolls, Alice has five points and\n", + "Bob has three points. What is the probability that Bob will get\n", + "six points and win the game?_\n", + "\n", + "The naive frequentist approach uses maximum likelihood to estimate the location of the marker and then estimates the probability using what's called a binomial likelihood (to be defined in Chapter 3). The Bayesian approach recognizes that we need to incorporate our uncertainty around the location of the marker into our analysis and uses marginalization to do by setting $Y$ above to the location of the marker. As Vanderplas demonstrates via simulation, the Bayesian approach gives the correct answer and the naive frequentist approach does not. He also points out the issue is not necessarily with frequentism, but with applying it naively. However, he also correctly notes that Bayesianism provides a more natural framework for these types of questions." + ] + }, + { + "cell_type": "markdown", + "id": "eef079b7-b7f4-41f4-a681-e5e7933da025", + "metadata": {}, + "source": [ + "## Towards a principled statistical workflow" + ] + }, + { + "cell_type": "markdown", + "id": "33b81b71-3e21-453e-9a58-301e2bc2be31", + "metadata": {}, + "source": [ + "As stated earlier, this book is part of a movement of working towards principled statistical workflows that allow humans to understand the world better and to make better decisions. We believe that such a movement requires a lot of moving parts and our opionated take is that working data scientists require the following:\n", + "\n", + "- The ability to build information-inclusive models;\n", + "- Principled and consistent workflows: this includes necessitating being explicit around assumptions and ideally that someone else performing the same analysis with exactly the same information would get the same results;\n", + "- The ability to model the data-generating process(es);\n", + "- An ergonomic and intuitive abstraction layer, such as those provided by probabilistic programming languages (which are also in active development!).\n", + "\n", + "\n", + "There are also key questions around how to build organizations in which such work can be seamlessly intergrated into the decision function, among other cultural concerns, such as how do we think, talk about, and model uncertaintly but our main project in this book is to equip individual data practitioners with the key concepts and tools to do this.\n", + "\n", + "What's clear to us is that the above is achieved far more readily by Bayesian inference than by frequentism because \n", + "\n", + "- it requires us to be explicit about our assumptions and mdoeling choices;\n", + "- the workflow is principled, in the sense that the steps are similar each time, and there is not a different test, statistic, or approach for each type of question, as opposed to frequentism;\n", + "- modeling the data-generating process is baked specifically into the workflow through our priors and likelihoods.\n", + "- PPLs provide good, ergonomic abstraction layers for such modeling.\n", + "\n", + "We look forward to going on this Bayesian journey with you!" + ] + }, + { + "cell_type": "markdown", + "id": "9a2d1eec", + "metadata": {}, + "source": [ + "___" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/test/02-What-are-data-GM.ipynb b/notebooks/test/02-What-are-data-GM.ipynb new file mode 100644 index 0000000..1a4dd34 --- /dev/null +++ b/notebooks/test/02-What-are-data-GM.ipynb @@ -0,0 +1,1060 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# What are data?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quick and dirty definitions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Data\" is Latin for \"what is given\" but this couldn't be further from the truth. Data is collected, in contrast to its etymological origins: It is something that is gathered, collected, synthesized, cleaned, and transformed and the most robust (data) science accounts for all of these aspects in the modeling processes.\n", + "\n", + "For example, consider COVID-19 case count numbers. At many different periods of the pandemic, case count numbers was considered a proxy for prevalence in the population. But even just thinking about the data collection process for a second ot two, we realize that the case count number could, and often would, fluctuate as a function of the number of tests administered on any given day.\n", + "\n", + "To quote [Nate Silver](https://twitter.com/NateSilver538/status/1241113755016138755?ref_src=twsrc%5Etfw),\n", + "\n", + "> Washington State is a good example of the importance of accounting for the number of tests when reporting COVID-19 case counts. Remember I mentioned a couple of days ago how their number of cases in WA had begun to stabilize? Well, guess what happened… Today, they reported 189 positives, along with 175 yesterday, as compared with an average of 106 positives per day in the 7 days before that. So, not great on the surface… new cases increased by 70%! But you also have to look at the number of tests. Washington conducted 3,607 tests today and 2,976 yesterday. By comparison, they’d conducted an average of 1,670 tests in the 7 days before that. So they’ve increased testing capacity by 97% over their baseline. Meanwhile, detected cases have increased, but by “only” 70%. Looked at another way: Today, 5.2% of Washington’s tests came up with a positive result. Yesterday, 5.9% did. In the 7 days before that, 6.4% of them did. So, there *is* a bit of progress after all. Their number of new positives *as a share of new tests* is slightly declining. For the time being, 1) the large (perhaps very large) majority of coronavirus positives are undetected and 2) test capacity is ramping up at extremely fast rates, far faster than coronavirus itself would spread even under worst-case assumptions. So long as those two things hold, the rate of increase in the number of *detected* cases is primarily a function of the rate of increase in the number of *tests* and does not tell us that much about how fast the actual *infection* is spreading.\n", + "\n", + "Silver then wrote [an article for FiveThirtyEight](https://fivethirtyeight.com/features/coronavirus-case-counts-are-meaningless/) called “Coronavirus Case Counts Are Meaningless” with a subtitle “Unless you know something about testing. And even then, it gets complicated.”\n", + "\n", + "This is one of many examples when considering the data-generating process is key for real-world inferences and robust decision-making. \n", + "\n", + "Before statistical modeling, inference, and decision-making can occur, we need to have good tools for loading, exploring and analyzing our data. In this book, we'll be using the PyData stack to do so. There are many reasons for this, not least of all that Python is in many ways a Swiss Army knife for data science, in that you can do nearly anything with it. This doesn't mean that it will be the absolutely best tool for any particular job, but it will allow you to do pretty much anything you need to do, from loading data in many file formats to visualization, statistical inference to machine learning, scalable computing to model deployment and much more. In this chapter, we'll introduce you to the main tools in the PyData stack that we plan to use. We'll do so by exploring a dataset and introducing the tools, as they become necessary." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The PyData stack" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The PyData stack is a loosely connected and interoperable set of Python packages that began to be developed at the turn of the 21st century. Core developers in the ecosystem were and still are, for the most part, research scientists (_not_ software engineers) who decided to build the tools they needed to solve problems in physics, biology, finance, geology, and astronomy, among many other disciplines.\n", + "\n", + "In his Pycon 2017 [keynote](https://www.youtube.com/watch?v=ZyjCqQEUa8o), \"The Unexpected Effectiveness of Python in Science\", Jake Vanderplas presented the following figure, which depicts many of the main players in the PyData ecosystem, in particular, many that we will be using in this book: \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:41.776955Z", + "iopub.status.busy": "2022-04-30T07:01:41.776572Z", + "iopub.status.idle": "2022-04-30T07:01:41.817851Z", + "shell.execute_reply": "2022-04-30T07:01:41.817377Z" + } + }, + "outputs": [], + "source": [ + "#| echo: false\n", + "from IPython.display import Image\n", + "Image(\"../../img/pydata-jake.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chapter, as stated, we'll take a brief tour of the parts of the ecosystem we'll be using. In particular, we'll be making most use of:\n", + "\n", + "* pandas for analysis and manipulation of tabular data;\n", + "* Matplotlib and Seaborn for data visualization;\n", + "* Numpy for numerical computing and simulation;\n", + "* SciPy for an assortment of statistical tools;\n", + "* IPython and the Jupyter ecosystem for interactive computing;\n", + "* PyMC for statistical modeling;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now load the packages that we'll introduce in this chapter:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:41.855097Z", + "iopub.status.busy": "2022-04-30T07:01:41.854867Z", + "iopub.status.idle": "2022-04-30T07:01:45.122472Z", + "shell.execute_reply": "2022-04-30T07:01:45.122068Z" + } + }, + "outputs": [], + "source": [ + "#| output: false\n", + "#Import packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import pymc as pm\n", + "from ipywidgets import interact\n", + "import arviz as az\n", + "import scipy.stats as st\n", + "%matplotlib inline\n", + "sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll introduce the packages as they often appear in data science workflow, tools for\n", + "\n", + "* importing,\n", + "* visualization,\n", + "* numerical computing, and\n", + "* inference." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's also worth noting that there are many Pythonic tools for data science that aren't in the PyData stack per se. For example, the PyData ecosystem doesn't aim to solve the natural language processing, deep learning, or machine learning deployment stories!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing data with pandas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the most common forms we'll find data in is a CSV file, which stands for comma-separated values, and pandas is a useful tool for loading such files. In [the project's own words](https://pandas.pydata.org/),\n", + "\n", + "> pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,\n", + "built on top of the Python programming language. \n", + "\n", + "Arguably, one reason for the mass adoption of pandas and, as a result, the mass adoption of Python for data analysis, is how it allowed us all to import so many different types and forms of datasets with signification flexibility. Let's now use pandas to import the Gapminder dataset, which contains economic indicators for many countries since the 1950s." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:45.124845Z", + "iopub.status.busy": "2022-04-30T07:01:45.124711Z", + "iopub.status.idle": "2022-04-30T07:01:45.190102Z", + "shell.execute_reply": "2022-04-30T07:01:45.189770Z" + } + }, + "outputs": [], + "source": [ + "# Import data\n", + "gm = pd.read_csv('../../datasets/gapminder.csv')\n", + "# Visualize first 5 rows of data\n", + "gm.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code loads the dataset into an object called a `DataFrame`, which is well-suited for analysis of tabular data. There's a handy `DataFrame` method called `.head()` that displays the first several rows of the `DataFrame` `gm`. We can immediately see that there are columns for country, continent, year, life expectancy, population, and GDP. \n", + "\n", + "To discover a bit more about `gm`, we can use the `.info()` method, which tells us what datatypes each column contains, how many non-null elements there are in each column, the memory usage, and more:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:45.192058Z", + "iopub.status.busy": "2022-04-30T07:01:45.191949Z", + "iopub.status.idle": "2022-04-30T07:01:45.199406Z", + "shell.execute_reply": "2022-04-30T07:01:45.199118Z" + } + }, + "outputs": [], + "source": [ + "gm.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that there are 1704 total rows and that each column has 1704 non-null entries, meaning that `gm` has no null entries. This is atypical! Very often we will encounter null entries or NAs and we do not, in this case, because the dataset has already been processed and cleaned. Also note that year and population are integers (int64s, to be precise), life expectancy and GDP are floats, and country and continent objects (the most general type, often corresponding to strings, which we have in this case).\n", + "\n", + "We can also use the `.describe()` method to find out more about the statistics of the numerical columns:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:45.201272Z", + "iopub.status.busy": "2022-04-30T07:01:45.201158Z", + "iopub.status.idle": "2022-04-30T07:01:45.211014Z", + "shell.execute_reply": "2022-04-30T07:01:45.210744Z" + } + }, + "outputs": [], + "source": [ + "gm.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the minimum year is 1952 and the maximum is 2007, which is useful to know. We can see minimum and maximum for other columns, such as life expectancy and population, along with other statistics, such mean and standard deviation, but it isn't quite clear how useful these are, as they are essentially over all countries for all the years in the dataset. We can see that the minimum maximum life expectancies we 23.6 and 82.6, respectively, but it would make sense to look at countries across the globe in a specific years or a time series of a specific country over the years.\n", + "\n", + "To this end, we subset the `DataFrame` to include rows for 1952 alone, view the `.head()`, and then use the `.describe()` method:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:45.212791Z", + "iopub.status.busy": "2022-04-30T07:01:45.212688Z", + "iopub.status.idle": "2022-04-30T07:01:45.218427Z", + "shell.execute_reply": "2022-04-30T07:01:45.218135Z" + } + }, + "outputs": [], + "source": [ + "gm_52 = gm[gm['year'] == 1952]\n", + "gm_52.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:45.220240Z", + "iopub.status.busy": "2022-04-30T07:01:45.220135Z", + "iopub.status.idle": "2022-04-30T07:01:45.229748Z", + "shell.execute_reply": "2022-04-30T07:01:45.229448Z" + } + }, + "outputs": [], + "source": [ + "gm_52.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now read off indicator statistics of countries around the globe for 1952, such as\n", + "\n", + "* the mean life expectancy across all countries was 49,\n", + "* the minimum population was 60,000, and\n", + "* the median GDP was 1968.\n", + "\n", + "Note that we haven't yet specified the units of GDP! According to [the documentation underlying the dataset](https://github.com/jennybc/gapminder),\n", + "\n", + "> Per-capita GDP (Gross domestic product) is given in units of international dollars, “a hypothetical unit of currency that has the same purchasing power parity that the U.S. dollar had in the United States at a given point in time” – 2005, in this case." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are a huge number of other common data analytic and data exploration patterns that pandas allows us to work with. We'll introduce them on an as-needed basis herein, but it is worth mentioning one of the most common (and often most useful!), that of the `.groupby()` and split-apply-combine. Let's say that we want to figure out the mean values of some of our indicators over continents. \n", + "\n", + "Then we can\n", + "\n", + "- **split** our dataset of countries into groups, given the continent that they're on,\n", + "- **apply** the `.mean()` across each of these groups,\n", + "- **combine** the results back into a single `DataFrame`.\n", + "\n", + "This has been a common pattern in data analytics for a long time, but it was only formally given the name \"split-apply-combine\" in 2011 in [a seminal paper by Hadley Wickham, \"The Split-Apply-Combine Strategy for Data Analysis\"](https://www.jstatsoft.org/article/view/v040i01), which also introduced the R package allowing you to do so, which in turn inspired `pandas`! \n", + "\n", + "To do this, we use the pandas `groupby()` method, as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:45.231540Z", + "iopub.status.busy": "2022-04-30T07:01:45.231441Z", + "iopub.status.idle": "2022-04-30T07:01:45.237617Z", + "shell.execute_reply": "2022-04-30T07:01:45.237331Z" + } + }, + "outputs": [], + "source": [ + "gm_52.groupby(['continent']).mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualizing data with matplotlib, pandas, and seaborn\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We've already managed to do more exploratory data analysis (EDA) than we usually would without any data visualization. Normally, while performing EDA with such pandas methods as above, we would also be performing a variety of visualizations. In the spirit of not introducing all our tools at the same time, we've split apart some of this process, but know that we are often using many of them in concert. First, let's plot the histogram of life expectancy for `gm_52`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:45.239493Z", + "iopub.status.busy": "2022-04-30T07:01:45.239291Z", + "iopub.status.idle": "2022-04-30T07:01:45.332413Z", + "shell.execute_reply": "2022-04-30T07:01:45.332087Z" + } + }, + "outputs": [], + "source": [ + "gm_52.lifeExp.hist();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that life expectancy ranges from 30-something to 70-something with more occurring in the 40s and 50s than in other decades. These are the types of things we want to start noticing in EDA: we're not making strong claims or robust inferences, but attempting to get a sense of what information is in our data.\n", + "\n", + "Although we have seemingly used `pandas` to plot the above histogram, it is actually the visualization package `matplotlib` that is creating the visualization! pandas methods such as `.hist()` and `.plot()` provide convenient wrappers around `matplotlib` functions. We can also use `matplotlib` directly to plot the histogram:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:45.334541Z", + "iopub.status.busy": "2022-04-30T07:01:45.334306Z", + "iopub.status.idle": "2022-04-30T07:01:45.417193Z", + "shell.execute_reply": "2022-04-30T07:01:45.416875Z" + } + }, + "outputs": [], + "source": [ + "plt.hist(gm_52.lifeExp);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Often, we will want to see the relationship between different variables and so will need to plot them on the same figure. Let's plot a scatter plot of population against life expectancy to see how we can achieve this with `pandas`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:45.419256Z", + "iopub.status.busy": "2022-04-30T07:01:45.419133Z", + "iopub.status.idle": "2022-04-30T07:01:45.516174Z", + "shell.execute_reply": "2022-04-30T07:01:45.515522Z" + } + }, + "outputs": [], + "source": [ + "gm_52.plot.scatter(x='lifeExp', y='pop', c='black', alpha=0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the first things to catch our eye is that we have populations occuring over several orders of magnitude. When this happens, it can be useful to use a logarithmic axis for the variable in question, so that the data is not all packed into a small region of the visualization:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:45.519207Z", + "iopub.status.busy": "2022-04-30T07:01:45.519001Z", + "iopub.status.idle": "2022-04-30T07:01:45.832152Z", + "shell.execute_reply": "2022-04-30T07:01:45.831842Z" + } + }, + "outputs": [], + "source": [ + "gm_52.plot.scatter(x='lifeExp', y='pop', c='black');\n", + "plt.yscale('log');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By eye, we can perhaps see a negative correlation between log(population) and life expectancy, but we would need to do more work to figure out if this is actually the case or not." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We may also want to plot a numerical variable, such as life expectancy, when grouped by another, such as continent. We can do this using Seaborn, \"a Python data visualization library based on matplotlib\" that \"provides a high-level interface for drawing attractive and informative statistical graphics.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:45.834168Z", + "iopub.status.busy": "2022-04-30T07:01:45.834051Z", + "iopub.status.idle": "2022-04-30T07:01:45.912538Z", + "shell.execute_reply": "2022-04-30T07:01:45.912177Z" + } + }, + "outputs": [], + "source": [ + "sns.swarmplot(x='continent', y='lifeExp', data=gm_52);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above swarm-plot, the y-axis is life expectancy and the x-axis is the continent, while also being used to spread out the data so that it is all visible." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we may wish to plot as many numerical variables against one another as possible, in order to get a stronger sense of dataset as a whole:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:45.914846Z", + "iopub.status.busy": "2022-04-30T07:01:45.914718Z", + "iopub.status.idle": "2022-04-30T07:01:47.710507Z", + "shell.execute_reply": "2022-04-30T07:01:47.710128Z" + } + }, + "outputs": [], + "source": [ + "sns.pairplot(gm);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generating data with numpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just as both pandas and Seaborn use Matplotlib under the hood for plotting, pandas is built on top of [NumPy](https://numpy.org/), \"[t]he fundamental package for scientific computing with Python.\" It is probably impossible to overstate the importance of NumPy but let us merely states that it contains a vast array of functionality for numerical computing in Python, including array computing, \"mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.\"\n", + "\n", + "We shall use NumPy for many purposes, including random sampling and simulating data, which will be a workhorse for us. Let's provide an example of generating samples from a uniform distribution and plot the resulting histogram. In the next chapter, we'll explain more about these terms and code but for now, let's give a sense:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:47.713285Z", + "iopub.status.busy": "2022-04-30T07:01:47.713175Z", + "iopub.status.idle": "2022-04-30T07:01:47.805171Z", + "shell.execute_reply": "2022-04-30T07:01:47.804676Z" + } + }, + "outputs": [], + "source": [ + "# Draw 1,000 samples from uniform & plot results\n", + "rng = np.random.default_rng(42)\n", + "x = rng.random(1000)\n", + "plt.hist(x);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the cool aspects of NumPy's random sampling is that we're able to generate samples from many types of distributions, including the Normal distribution, or Bell curve: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:47.807796Z", + "iopub.status.busy": "2022-04-30T07:01:47.807630Z", + "iopub.status.idle": "2022-04-30T07:01:47.892727Z", + "shell.execute_reply": "2022-04-30T07:01:47.892185Z" + } + }, + "outputs": [], + "source": [ + "samples = rng.normal(0, 1, size=10000)\n", + "plt.hist(samples);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing we'll be doing time and time again is matching our real-world data to distributions that we can simulate. To do this, we'll often want to plot the real-world data on the same figure as the simulated model. As it's difficult (at best) to see what's happening when plotting multiple histograms together, among other issues with histograms, which we'll get to, we'll introduce the empirical cumulative distribution function (ECDF), which allows us to plot all of our data and to compare several datasets, simulated or otherwise. We'll be working with ECDFs at length in Chapter 4, but are introducing them now in order to demonstrate the power of NumPy's random sampling.\n", + "\n", + "**Definition:** In an ECDF, the x-axis is the range of possible values for the data & for any given x-value, the corresponding y-value is the proportion of data points less than or equal to that x-value.\n", + "\n", + "We now define a utility function that will allow us to compute ECDFs from our data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:47.895299Z", + "iopub.status.busy": "2022-04-30T07:01:47.895178Z", + "iopub.status.idle": "2022-04-30T07:01:47.899310Z", + "shell.execute_reply": "2022-04-30T07:01:47.898406Z" + } + }, + "outputs": [], + "source": [ + "def ecdf(data):\n", + " \"\"\"Compute ECDF for a one-dimensional array of measurements.\"\"\"\n", + " # Number of data points\n", + " n = len(data)\n", + "\n", + " # x-data for the ECDF\n", + " x = np.sort(data)\n", + "\n", + " # y-data for the ECDF\n", + " y = np.arange(1, n+1) / n\n", + "\n", + " return x, y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now plot the ECDF of the normally distributed data we generated above:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:47.902220Z", + "iopub.status.busy": "2022-04-30T07:01:47.902066Z", + "iopub.status.idle": "2022-04-30T07:01:48.007937Z", + "shell.execute_reply": "2022-04-30T07:01:48.007369Z" + } + }, + "outputs": [], + "source": [ + "x_s, y_s = ecdf(samples)\n", + "_ = plt.plot(x_s, y_s, marker='.', linestyle='none')\n", + "_ = plt.ylabel('CDF')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given a dataset that you think may be normally distributed, you can \n", + "\n", + "* compute its mean and standard deviation,\n", + "* use NumPy to randomly sample a normal distribution with the relevant mean and standard deviation, and\n", + "* plot the ECDFs of both to see if they match up.\n", + "\n", + "\n", + "In Chapter 5, we'll see why we may think certain data is normally distributed, such as speed of light in the famous Michelson–Morley experiment! In the meantime, let's plot the ECDF of both the data and the samples generated from a normal distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:48.010516Z", + "iopub.status.busy": "2022-04-30T07:01:48.010364Z", + "iopub.status.idle": "2022-04-30T07:01:48.174917Z", + "shell.execute_reply": "2022-04-30T07:01:48.174186Z" + } + }, + "outputs": [], + "source": [ + "# Load data, plot histogram \n", + "df = pd.read_csv('../../datasets/michelson_speed_of_light.csv')\n", + "df = df.rename(columns={'velocity of light in air (km/s)': 'c'})\n", + "\n", + "\n", + "# Get speed of light measurement + mean & standard deviation\n", + "michelson_speed_of_light = df.c.values\n", + "mean = np.mean(michelson_speed_of_light)\n", + "std = np.std(michelson_speed_of_light, ddof=1)\n", + "\n", + "# Generate normal samples w/ mean, std of data\n", + "samples = np.random.normal(mean, std, size=10000)\n", + "\n", + "# Generate data ECDF & model CDF\n", + "x, y = ecdf(michelson_speed_of_light)\n", + "x_theor, y_theor = ecdf(samples)\n", + "\n", + "# Plot data & model (E)CDFs\n", + "_ = plt.plot(x_theor, y_theor)\n", + "_ = plt.plot(x, y, marker='.', linestyle='none')\n", + "_ = plt.xlabel('speed of light (km/s)')\n", + "_ = plt.ylabel('CDF')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### IPython and Jupyter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At first glance, the above are the key basic tools needed for data exploration and iteration. But we would be remiss to not state the absolute and fundamental importance of both IPython and Project Jupyter as foundational infrastructure for a great deal of this work. This book, for example, was written in Jupyter notebooks and Jupyter lab using the IPython kernel. It was inspired by [workshops we have taught at SciPy, PyCon, and ODSC](https://github.com/ericmjl/bayesian-stats-modelling-tutorial) using these tools. Were you to try to reproduce some of the methods you learn in this book (which we hope you do!), the easiest approach would be to use the Jupyter and IPython ecosystem.\n", + "\n", + "IPython, \"a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language,\" was first released by Fernando Perez in 2001, and provided a more useful interface to execute in the REPL style of \"Read-Evaluate-Print-Loop\", which turned out be incredibly practical for exploratory and iterative scientific research. Fast forward to today and we have both Jupyter notebooks (formerly IPython notebooks), which are web applications for building data and model-centric narratives and documents (not dissimilar from research scientists' journals!), and Jupyter lab, which is \"the latest web-based interactive development environment for notebooks, code, and data.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is statistics?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are many ways to slice the discipline of statistics. One way is into exploratory data analysis (EDA) and inference. The former is essentially about beginning to notice potential patterns in your data, the latter about drawing inferences, that is, about generalizing from the data you've seen to data that you haven't yet seen. We have already seen a variety of EDA techniques above and we will continue to develop a zoo of such techniques as this book goes on. For a deep dive into EDA, we recommend \"Exploratory Data Analysis\" by John Tukey, the modern parent of exploratory data analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### EDA and the bootstrap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We've seen how computing summary statistics, such as the mean and standard deviation, is an important aspect of EDA. Once we've computed such a statistic, however, a key question arises: how certain can we be of our estimate? Let's make this concrete by looking at a dataset containing measurements of Galapagos finch beaks and computing the mean beak lengths for 2 species:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:48.177575Z", + "iopub.status.busy": "2022-04-30T07:01:48.177480Z", + "iopub.status.idle": "2022-04-30T07:01:48.184630Z", + "shell.execute_reply": "2022-04-30T07:01:48.183997Z" + } + }, + "outputs": [], + "source": [ + "# Import and view head of data\n", + "df_12 = pd.read_csv('../../datasets/finch_beaks_2012.csv')\n", + "df_fortis = df_12.loc[df_12['species'] == 'fortis']\n", + "df_scandens = df_12.loc[df_12['species'] == 'scandens']\n", + "blf = df_fortis['blength']\n", + "bls = df_scandens['blength']\n", + "np.mean(blf), np.mean(bls)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So what is our uncertainty around these estimates? In later chapters, we'll see how Bayesian modeling and inference can help us to answer such questions. Here, we'll introduce a technique known as the _bootstrap_, which allows us to estimate the uncertainty without making any modeling assumptions (that is, the bootstrap is _non-parametric_). The key insight involved in the boostrap is that we have calculated the mean once already, and if we can sample the population again, we can calculate the mean again, to get a slightly different result, based on variation in the sample population. We can then do this as many times as desired, to get a distribution of means, which will allow us to express uncertainty around our sample mean.\n", + "\n", + "The way we do this in the bootstrap is to sample the original dataset with replacement, each time we calculate the new mean. Let's do this now for the Fortis population of finches. We'll also plot a histogram of resulting mean beak lengths:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:48.187058Z", + "iopub.status.busy": "2022-04-30T07:01:48.186869Z", + "iopub.status.idle": "2022-04-30T07:01:48.298203Z", + "shell.execute_reply": "2022-04-30T07:01:48.297653Z" + } + }, + "outputs": [], + "source": [ + "n = 10**3\n", + "blf_means = np.zeros(n)\n", + "\n", + "for i in range(n):\n", + " samples = rng.choice(blf, len(blf))\n", + " mean = np.mean(samples)\n", + " blf_means[i] = mean\n", + " \n", + "plt.hist(blf_means);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see immediately that the mean beak length ranges from around 10.3 to around 10.7. We can also compute percentiles to see get a 95% confidence interval on our estimate:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:48.301112Z", + "iopub.status.busy": "2022-04-30T07:01:48.300940Z", + "iopub.status.idle": "2022-04-30T07:01:48.304609Z", + "shell.execute_reply": "2022-04-30T07:01:48.304318Z" + } + }, + "outputs": [], + "source": [ + "# 95% CIs\n", + "np.percentile(blf_means, [2.5, 97.5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We interpret this as saying 95% of the time, our computed mean lay between 10.37 and 10.66. There's a lot more we can already do with this simulation which we'll leave for future chapters. For example, if the histogram above looks like a Bell curve to you, this is actually due to the Central Limit Theorem!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's also worth mentioning that the bootstrap here is essentially a frequentist method of estimation. We do not necessarily advocate the use of such methods for inference but, as we're discovering, it can be an incredibly useful tool for EDA! This is because it allows us to get a sense of our data, while making minimal assumptions. This is actually also why we don't consider it the best tool for inference! As we'll see, we already know much about our data (such as how it was measured and collected), the details of which we'll want to include in any inferential project." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Inference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inference is all about inferring unseen stuff from the stuff we **can** see, that is, from the data we have. To perform robust inference, we build statistical models and use them to make predictions.\n", + "\n", + "As we have seen in Chapter 1, science is deeply interested in questions of the form:\n", + "- Given data collected in the world, what can we say about the data-generating process(es)? That is, what is $P(H|D)$, the probability of any hypothesis $H$, given the observed/collected data $D$?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll be using Bayesian inference and probabilistic programming, in the form of the package PyMC, to answer such questions. To round out this chapter and to give a sense of where we are going, let's look at a couple of examples. Neither the details of the statistical modeling or the code currently matter and we will cover all of this in later chapters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, the dataset we'll be looking at is from measurements of Galapagos finch beaks and we'll be able to ask (and answer!) questions such as \"do different species from different islands have different beak lengths?\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What is the mean beak length of the Fortis population?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To answer this question, we first specify a statistical model (once again, all the detailed will be explained through the course of this book):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:48.307243Z", + "iopub.status.busy": "2022-04-30T07:01:48.307074Z", + "iopub.status.idle": "2022-04-30T07:01:49.648253Z", + "shell.execute_reply": "2022-04-30T07:01:49.646865Z" + } + }, + "outputs": [], + "source": [ + "with pm.Model() as model:\n", + " # Prior for mean & standard deviation\n", + " μ_1 = pm.Normal('μ_1', mu=10, sigma=5)\n", + " σ_1 = pm.Lognormal('σ_1', 0, 10)\n", + " # Gaussian Likelihood\n", + " y_1 = pm.Normal('y_1', mu=μ_1, sigma=σ_1, observed=df_fortis['blength'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that, in the above, in accordance with our protocol outlined in Chapter 1, we are completely specifying the model in terms of _probability distributions_. This includes specifying\n", + "\n", + " - what the form of the sampling distribution of the observed data is, which is given by the **likelihood** _and_\n", + " - what form describes our _uncertainty_ in the unknown parameters, given by the **prior**.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next step is to calculate the _posterior distribution_, which we do by hitting the \"inference button\":" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:01:49.654457Z", + "iopub.status.busy": "2022-04-30T07:01:49.654118Z", + "iopub.status.idle": "2022-04-30T07:02:06.312414Z", + "shell.execute_reply": "2022-04-30T07:02:06.311863Z" + } + }, + "outputs": [], + "source": [ + "#| output: false\n", + "# bust it out & sample\n", + "with model:\n", + " samples = pm.sample(2000, return_inferencedata=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now plot the mean beak length. In fact, we plot the distribution of the mean beak length, capturing uncertainty around our estimate:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:06.314781Z", + "iopub.status.busy": "2022-04-30T07:02:06.314652Z", + "iopub.status.idle": "2022-04-30T07:02:06.470214Z", + "shell.execute_reply": "2022-04-30T07:02:06.469864Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " az.plot_posterior(samples);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are 94% certain that the mean beak length is between 10.38 and 10.66." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bayesian hypothesis testing " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use a similar process to determine whether the mean beak length of the Galapagos finches differs between species. As before, we first define our model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:06.472469Z", + "iopub.status.busy": "2022-04-30T07:02:06.472343Z", + "iopub.status.idle": "2022-04-30T07:02:06.498444Z", + "shell.execute_reply": "2022-04-30T07:02:06.498006Z" + } + }, + "outputs": [], + "source": [ + "with pm.Model() as model:\n", + " # Priors for means and variances\n", + " μ_1 = pm.Normal('μ_1', mu=10, sigma=5)\n", + " σ_1 = pm.Uniform('σ_1', 0, 10)\n", + " μ_2 = pm.Normal('μ_2', mu=10, sigma=5)\n", + " σ_2 = pm.Uniform('σ_2', 0, 10)\n", + " # Gaussian Likelihoods\n", + " y_1 = pm.Normal('y_1', mu=μ_1, sigma=σ_1, observed=df_fortis['blength'])\n", + " y_2 = pm.Normal('y_2', mu=μ_2, sigma=σ_2, observed=df_scandens['blength'])\n", + " # Calculate the effect size and its uncertainty.\n", + " diff_means = pm.Deterministic('diff_means', μ_1 - μ_2)\n", + " pooled_sd = pm.Deterministic('pooled_sd', \n", + " np.sqrt(np.power(σ_1, 2) + \n", + " np.power(σ_2, 2)) / 2)\n", + " effect_size = pm.Deterministic('effect_size', \n", + " diff_means / pooled_sd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then hit the \"inference button\":" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:06.500738Z", + "iopub.status.busy": "2022-04-30T07:02:06.500411Z", + "iopub.status.idle": "2022-04-30T07:02:23.284940Z", + "shell.execute_reply": "2022-04-30T07:02:23.284573Z" + } + }, + "outputs": [], + "source": [ + "#| output: false\n", + "# bust it out & sample\n", + "with model:\n", + " samples = pm.sample(2000, return_inferencedata=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now plot the distributions of mean beak lengths. Most importantly, we can plot the distribution of the difference of means!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:23.287033Z", + "iopub.status.busy": "2022-04-30T07:02:23.286915Z", + "iopub.status.idle": "2022-04-30T07:02:23.624343Z", + "shell.execute_reply": "2022-04-30T07:02:23.623420Z" + } + }, + "outputs": [], + "source": [ + "az.plot_posterior(samples, var_names=['μ_1', 'μ_2', 'diff_means', 'effect_size'], kind='hist');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping Up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chapter, we introduced the key tools in the PyData stack that we'll use in the rest of this book and that we hope you'll use some of for your own work. To recap, we introduced\n", + "\n", + "* pandas for analysis and manipulation of tabular data;\n", + "* Matplotlib and Seaborn for data visualization;\n", + "* Numpy for numerical computing and simulation;\n", + "* SciPy for an assortment of statistical tools;\n", + "* IPython and the Jupyter ecosystem for interactive computing;\n", + "* PyMC for statistical modeling.\n", + "\n", + "We did all of this looking at real-world datasets to give a feel for how these tools are used in practice. Now it's time to discover more about data, statistics, and probability using `NumPy`'s pseudorandom number generator. We'll do this in the next chapter." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/test/03a-hacker-stats.ipynb b/notebooks/test/03a-hacker-stats.ipynb new file mode 100644 index 0000000..827fa52 --- /dev/null +++ b/notebooks/test/03a-hacker-stats.ipynb @@ -0,0 +1,768 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulating Data and Hacker Statistics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Chapters 1 & 2, we have been introduced to both probability and data. In this chapter, we'll learn how to simulate data and delve into how doing so can help us think both probabilistically and statistically. We'll simulate data using `numpy` but all the techniques are library-agnostic." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we have seen, science is deeply interested in questions of the form:\n", + "\n", + "- Given data collected in the world, what can we say about the data-generating process(es)? That is, what is $P(M|D)$, the probability of any model $M$, given the observed/collected data $D$?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On the other hand, statistics and probability are better at answering questions such as:\n", + "\n", + "- Given the model $M$, what is the probability $P(D|M)$ of observing the data $D$?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall that it will be Bayesian inference that eventually allows us to move between $P(D|M)$ and $P(M|D)$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before jumping into this, it will be instructive to delve into probability distributions and the data-generating processes that correspond to them.\n", + "\n", + "To this end, we'll begin by exploring how simulation and hacker statistics can help us answer questions of the second kind above: knowing the model, what is the probability of seeing certain data? This will then allow to think about how to answer questions of the first kind, those of deep interest to science." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulating our first probabilities: click-through rates and (biased) coin flips" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a first example, let's consider a common concept in data science, that of website click-through rates.\n", + "\n", + "**The Question:** Let's say that a website has a click-through rate (CTR) of 50%, that is, we expect 50% of people from the general population to click through. If we drew 1,000 people at random from the general population, can we calculate the likelihood of $x$ number of people clicking through?\n", + "\n", + "We can simulate this using `numpy`'s random number generator. To do so, first note we can use `np.random.default_rng.random()` to randomly select floats between 0 and 1 (known as the _uniform distribution_). Below, we do so and plot a histogram:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:28.532144Z", + "iopub.status.busy": "2022-04-30T07:02:28.530722Z", + "iopub.status.idle": "2022-04-30T07:02:29.575370Z", + "shell.execute_reply": "2022-04-30T07:02:29.574875Z" + } + }, + "outputs": [], + "source": [ + "#Import packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "sns.set()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:29.577691Z", + "iopub.status.busy": "2022-04-30T07:02:29.577556Z", + "iopub.status.idle": "2022-04-30T07:02:29.721572Z", + "shell.execute_reply": "2022-04-30T07:02:29.721188Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Draw 1,000 samples from uniform & plot results\n", + "rng = np.random.default_rng()\n", + "x = rng.random(1000)\n", + "plt.hist(x);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To then simulate the sampling from the population, we check whether each float was greater or less than 0.5. For each float, if it is less than or equal to 0.5, we say the person clicked. If it is greater than 0.5, we say that they didn't click. We also print out the computed number of clicks:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:29.740173Z", + "iopub.status.busy": "2022-04-30T07:02:29.739849Z", + "iopub.status.idle": "2022-04-30T07:02:29.745671Z", + "shell.execute_reply": "2022-04-30T07:02:29.745296Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Number of clicks = 508'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Computed how many people click\n", + "clicks = x <= 0.5\n", + "n_clicks = sum(clicks)\n", + "f\"Number of clicks = {n_clicks}\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the total number of clicks but we're often more interested in the proportion of clicks, as it doesn't depend on the sample size. So let's compute that now:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:29.747649Z", + "iopub.status.busy": "2022-04-30T07:02:29.747527Z", + "iopub.status.idle": "2022-04-30T07:02:29.751496Z", + "shell.execute_reply": "2022-04-30T07:02:29.751148Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Proportion who clicked = 0.508'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Computed proportion of people who clicked\n", + "f\"Proportion who clicked = {n_clicks/len(clicks)}\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we've just computed the number and proportion of people who clicked, given a particular CTR. Essentially, we've performed a mini (computational) experiment! However, as we've mentioned, scientific questions are often the reverse of this. In this case, the question would be: given the data above and the measured proportion of people who clicked, what can you say about the click-through rate?\n", + "\n", + "This question contains several sub-questions, such as\n", + "\n", + "* What would your \"best\" estimate of the CTR be? and\n", + "* How confident would you be of your estimate?\n", + "\n", + "Perhaps your answer to the first question is \"the proportion of people who clicked\" and to the second \"I'm not certain but I'd be more confident the more samples there were.\" If so, you're on the right track and we'll see why soon. Part of the power of Bayes, as we'll see, is that it allows us to recover not merely the best estimate and confidence intervals around it, but also the entire distribution of possible values and their likelihoods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Different click-through rates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One cool aspect of simulation techniques is that we can plug in different values and see the differences in results immediately. Let's see this in action by tweaking the above code to perform the same calculation for a CTR of 0.7. Once again, we'll compute the number of people who click, along with the proportion:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:29.753466Z", + "iopub.status.busy": "2022-04-30T07:02:29.753373Z", + "iopub.status.idle": "2022-04-30T07:02:29.758281Z", + "shell.execute_reply": "2022-04-30T07:02:29.757948Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of clicks = 691\n", + "Proportion who clicked = 0.691\n" + ] + } + ], + "source": [ + "x = rng.random(1000)\n", + "clicks = x <= 0.7\n", + "n_clicks = sum(clicks)\n", + "print(f\"Number of clicks = {n_clicks}\")\n", + "print(f\"Proportion who clicked = {n_clicks/len(clicks)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This model, in which the outcome is binary (that is, there are two options) and each outcome occurs with a certain probability, is known as the _biased coin flip_ and is used to model lots of real-world phenomena, such as click-through or not, affected by disease or not, and bought a product or not, among many others. It's known as the _biased coin flip_ because it can be used to model the result of flipping a coin (heads or tails) when the probability of heads is not necessarily 0.5 (hence the \"biased\")." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pseudo-random number generators and setting the seed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reproducibility is fundamental to the scientific. Now what does reproducibility mean in the context of simulation and hacker statistics? Firstly, it would require that, were we to send you our code, that you could run it and get the same results. It also means that were we to run the same code twice, we'd get the same results. Let's run the most recent cell and see if we do:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:29.760279Z", + "iopub.status.busy": "2022-04-30T07:02:29.760164Z", + "iopub.status.idle": "2022-04-30T07:02:29.764685Z", + "shell.execute_reply": "2022-04-30T07:02:29.764365Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of clicks = 700\n", + "Proportion who clicked = 0.7\n" + ] + } + ], + "source": [ + "x = rng.random(1000)\n", + "clicks = x <= 0.7\n", + "n_clicks = sum(clicks)\n", + "print(f\"Number of clicks = {n_clicks}\")\n", + "print(f\"Proportion who clicked = {n_clicks/len(clicks)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We do not! This is unacceptable for science. So how do we ensure that we're able to reproduce our results?\n", + "\n", + "To answer this question, let's spend a minute understanding a bit more about how, `numpy`, Python, and our computer generate random numbers. In truth, they don't quite. Rather, they generate pseudorandom numbers using what's known as a PRNG (pseudorandom number generator). These numbers are actually determined deterministically _but_ they have many statistical properties of randomly generated numbers that are suitable for our purposes. In fact, the sequence of numbers generated by any PRNG is uniquely determined by an initial value that we can set, known as the _seed_, for obvious reasons.\n", + "\n", + "All this is to say that, once we set the seed with `numpy`, our results will then be reproducible! So let's now see this in action by setting the seed, performing the same CTR experiment as above, then setting the seed to the same value and re-performing the experiment:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:29.766406Z", + "iopub.status.busy": "2022-04-30T07:02:29.766297Z", + "iopub.status.idle": "2022-04-30T07:02:29.771426Z", + "shell.execute_reply": "2022-04-30T07:02:29.771114Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of clicks = 696\n", + "Proportion who clicked = 0.696\n" + ] + } + ], + "source": [ + "# Set seed\n", + "rng = np.random.default_rng(42)\n", + "x = rng.random(1000)\n", + "clicks = x <= 0.7\n", + "n_clicks = sum(clicks)\n", + "print(f\"Number of clicks = {n_clicks}\")\n", + "print(f\"Proportion who clicked = {n_clicks/len(clicks)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's once. Let's try it again and see if we get the same result. 🤞" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:29.773377Z", + "iopub.status.busy": "2022-04-30T07:02:29.773165Z", + "iopub.status.idle": "2022-04-30T07:02:29.778030Z", + "shell.execute_reply": "2022-04-30T07:02:29.777706Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of clicks = 696\n", + "Proportion who clicked = 0.696\n" + ] + } + ], + "source": [ + "# Set seed\n", + "rng = np.random.default_rng(42)\n", + "x = rng.random(1000)\n", + "clicks = x <= 0.7\n", + "n_clicks = sum(clicks)\n", + "print(f\"Number of clicks = {n_clicks}\")\n", + "print(f\"Proportion who clicked = {n_clicks/len(clicks)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we do! Now all of our results are reproducible, as are yours." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## In which we introduce the binomial distribution to simulate coin flips" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above, we have used the uniform distribution to sample from a series of biased coin flips. We now want to introduce you to another distribution that you can also use to do the same and with greater flexibility: the **binomial distribution**.\n", + "\n", + "The **binomial distribution** with parameters $n$ and $p$ is defined as the probability distribution of\n", + "\n", + "> the number of heads seen when flipping a coin $n$ times when with $p(heads)=p$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note** that this distribution essentially tells the **story** of a general model in the following sense: if we believe that they underlying process generating the observed data has a binary outcome (affected by disease or not, head or not, 0 or 1, clicked through or not), and that one the of the two outcomes occurs with probability $p$, then the probability of seeing a particular outcome is given by the **binomial distribution** with parameters $n$ and $p$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Any process that matches the coin flip story is a Binomial process. Note that you'll see such coin flips also referred to as Bernoulli trials in the literature. So we can also formulate the story of the Binomial distribution as\n", + "\n", + "> the number $r$ of successes in $n$ Bernoulli trials with probability $p$ of success, is Binomially distributed. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now use the binomial distribution to answer the same question as above: If P(heads) = 0.7 and we flip the coin ten times, how many heads will come up? We'll also set the seed to ensure reproducible results." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:29.780203Z", + "iopub.status.busy": "2022-04-30T07:02:29.780083Z", + "iopub.status.idle": "2022-04-30T07:02:29.783392Z", + "shell.execute_reply": "2022-04-30T07:02:29.783032Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set seed\n", + "rng = np.random.default_rng(42)\n", + "\n", + "# Simulate one run of flipping the biased coin 10 times\n", + "rng.binomial(10, 0.7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulating many times to get the binomial distribution\n", + "\n", + "In the above, we have simulated the scenario once. But this only tells us one potential outcome. To see how likely it is to get $n$ heads, for example, we need to simulate it a lot of times and check what proportion of simulations ended up with $n$ heads. To do this, we can handily use the 3rd positional argument of `np.random.binomial()` as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:29.785134Z", + "iopub.status.busy": "2022-04-30T07:02:29.785026Z", + "iopub.status.idle": "2022-04-30T07:02:29.863767Z", + "shell.execute_reply": "2022-04-30T07:02:29.863403Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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2drbq6+tVXV0tv9+vyspKzZ07V7W1tVq3bp3y8/P1s5/9TFu3btXw8LAef/xxVVdXKykpSXv27FFTU5NGR0c1c+ZM7dy5czKeEwDgLqIGX5KcTqecTmfEYwcPHgy/PWfOHP3+97+/47gFCxaora3tIUcEAMQCv2kLAIYg+ABgCIIPAIYg+ABgCIIPAIYg+ABgiDH9WCYQCx/5A/yWK2Ajgo9Jk5qSJOf6DrvHkGeXy+4RAFtwSwcADEHwAcAQBB8ADEHwAcAQBB8ADEHwAcAQBB8ADEHwAcAQBB8ADEHwAcAQBB8ADEHwAcAQBB8ADEHwAcAQBB8ADEHwAcAQBB8ADEHwAcAQ/IlDA0z9vyl6JI1/asB0VMAAj6Ql87dkAXBLBwBMQfABwBBjCr7H41FZWZmKi4vV0tJyx/a3335bFRUVKikp0ZYtW3T79m1J0rVr17Ry5UotW7ZMq1ev1q1bt2I7PQBgzKIG3+v1yu126+jRo2pvb9exY8d05cqViH02btyoF154QadOnVIoFNLx48clSdu2bVNVVZW6urqUl5en/fv3T8yzAABEFfU/bXt6elRQUKDMzExJUklJibq6uvT9739fknT16lWNjo5q/vz5kqSKigq98soreuaZZ3TmzBnt27cv/Pi3v/1tbdy4cczDJSYmjPPpxJeP/AFZ1lS7x5AkOaZNsXsESczxSfEwRzzMIDHHJz1I/6IdEzX4AwMDsiwrvHY4HDp//vw9t1uWJa/Xqxs3bigjI0PJyckRj4/HtGmPjmt/3NurW4vtHkESc3xSPMwRDzNIzPFJWVkZMX+fUW/pBINBJST897tGKBSKWN9r+yf3k3THGgAweaIGPycnRz6fL7z2+XxyOBz33P7+++/L4XBo+vTpGhoaUiAQuOtxAIDJFTX4hYWF6u3t1eDgoEZGRtTd3a2ioqLw9hkzZigtLU1nz56VJHV0dKioqEgpKSlasGCBOjs7JUnt7e0RxwEAJldCKBQKRdvJ4/HoV7/6lfx+vyorK1VbW6va2lqtW7dO+fn5unz5srZu3arh4WE9/vjj+slPfqLU1FRdvXpVDQ0Nun79unJzc7V792499thjk/G8AACfMKbgAwA+/fhNWwAwBMEHAEMQfAAwBMEHAEMQ/Anwi1/8Qk899ZSeeuop7dy50+5x4sbLL7+shoYGu8ew3enTp1VRUaHS0lK9+OKLdo9ju46OjvDXy8svv2z3OLYYHh5WeXm5/vnPf0r6+CVtnE6niouL5Xa7Y/ZxCH6M9fT06M0331RbW5va29v117/+Va+99prdY9mut7dXbW1tdo9hu3/84x9qbGzU/v37dfLkSV26dEl//vOf7R7LNiMjI2pubtbhw4fV0dGhvr4+9fT02D3WpDp37pyeffZZvfPOO5Kk0dFRbd68Wfv371dnZ6cuXrwYs88Rgh9jlmWpoaFBqampSklJ0Ze+9CVdu3bN7rFs9a9//Utut1t1dXV2j2K71157TWVlZcrJyVFKSorcbrfmzZtn91i2CQQCCgaDGhkZ0e3bt3X79m2lpaXZPdakOn78uBobG8OvRHD+/Hl98Ytf1Be+8AUlJyfL6XSqq6srJh+LP3EYY1/+8pfDb7/zzjv64x//qN/+9rc2TmS/F154QfX19erv77d7FNu9++67SklJUV1dnfr7+/X1r39dzz//vN1j2SYjI0M//OEPVVpaqilTpugrX/mKnnjiCbvHmlTNzc0R67u9YOV4X3jyXrjCnyB/+9vf9Nxzz2nTpk2aOXOm3ePY5ne/+51yc3O1aNEiu0eJC4FAQL29vXrppZd07NgxnT9/3uhbXZcvX9aJEyf0pz/9SW+88YYSExP16quv2j2WraK9YOXDIPgT4OzZs1q1apXWr1+v5cuX2z2OrTo7O/WXv/xFLpdLr7zyik6fPq2XXnrJ7rFs87nPfU6LFi3S9OnT9cgjj+jJJ5+MeLlx07z55ptatGiRsrKylJqaqoqKCr311lt2j2WraC9Y+TC4pRNj/f39Wrt2rdxuN1e1kn7961+H325tbdVbb72lzZs32ziRvb7xjW/oRz/6kT744AM9+uijeuONN7RkyRK7x7LNnDlz9NOf/lT//ve/NWXKFJ0+fVr5+fl2j2WrefPm6e9//7veffddff7zn9cf/vAHfetb34rJ+yb4Mfbqq6/qww8/1I4dO8KPrVixQs8++6yNUyFezJs3TzU1NaqqqpLf79dXv/rVmH0xfxotXrxYly5dUkVFhVJSUpSfn6/vfe97do9lq7S0NO3YsUM/+MEP9OGHH+prX/uali1bFpP3zYunAYAhuIcPAIYg+ABgCIIPAIYg+ABgCIIPAIYg+ABgCIIPAIYg+ABgiP8HY+0ZbqewOIYAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Simulate 10,000 run of flipping the biased coin 10 times\n", + "x = rng.binomial(10, 0.7, 10000)\n", + "\n", + "# Plot normalized histogram of results\n", + "plt.hist(x, density=True, bins=9);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is always important to take a look at your plots and extract information from them. So what can we see here? We can see that the number of heads ranges from 2 to 10 and that just over 25% of the runs have 7 heads, which accords with our intuition, knowing that P(heads) = 0.7." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Answering more questions about the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using such techniques, we can ask (and answer!) very many more questions that may be of interest to us. For example, we may not want the entire distribution, as above. We may want to know the probability of getting a certain number of heads. For example, we can ask the question: If I flip a biased coin ($P(H)=0.3$) 20 times, what is the probability of 5 or more heads? And we can answer it by simulating the experiment, as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:29.866030Z", + "iopub.status.busy": "2022-04-30T07:02:29.865906Z", + "iopub.status.idle": "2022-04-30T07:02:29.887646Z", + "shell.execute_reply": "2022-04-30T07:02:29.887335Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7628" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(rng.binomial(20, 0.3, 10000) >= 5)/10000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Perhaps you discover that your coin is actually fair (P(heads) = 0.5) and your question becomes:\n", + "If I flip a fair coin 20 times, what is the probability of 5 or more heads? You can then simulate the answer as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:29.889670Z", + "iopub.status.busy": "2022-04-30T07:02:29.889550Z", + "iopub.status.idle": "2022-04-30T07:02:29.911343Z", + "shell.execute_reply": "2022-04-30T07:02:29.910638Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9937" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(rng.binomial(20,0.5,10000) >= 5)/10000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's say that you wanted the entire distribution of possible number of heads when flipping a fair coin 10 times. You can simulate the experiment and plot the histogram in this manner:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:29.913712Z", + "iopub.status.busy": "2022-04-30T07:02:29.913559Z", + "iopub.status.idle": "2022-04-30T07:02:29.997380Z", + "shell.execute_reply": "2022-04-30T07:02:29.997048Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot histogram \n", + "x = rng.binomial(10, 0.5, 10000)\n", + "plt.hist(x, density=True, bins=10);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** you may have noticed that the _binomial distribution_ can take on only a finite number of values, whereas the _uniform distribution_ above can take on any number between $0$ and $1$. These are different enough cases to warrant special mention of this & two different names: the former is called a _probability mass function_ (PMF) and the latter a _probability distribution function_ (PDF). We will discuss more about this point later but wanted to draw your attention to it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although this histogram is instructive, there are certain questions which we cannot trivially answer by reading off the histogram. For example, if you wanted to know the probability of seeing 4 or more heads, you'd have to squint, approximate the height of several boxes, and add up those values. In the next chapter, we'll introduce you to a figure known as the empirical cumulative distribution function (ECDF), which makes it far easier to answer questions such as this, and also has many other benefits." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Practical example: Test for infectious disease" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose that a test for an infectious disease X has 99% sensitivity (true positive rate) and 99% specificity (true negative rate), that is, a 1% false positive rate and 1% false negative rate. Suppose that 0.5% (5 in 1,000) of people have X. What is the probability that a randomly selected individual with a positive test has X?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now harness our new computational powers and the intuition of simulation to solve this infectious disease testing example. Before doing so, however, what do you think the answer to the question \"What is the probability that a randomly selected individual with a positive test has X?\" would be?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:29.999742Z", + "iopub.status.busy": "2022-04-30T07:02:29.999615Z", + "iopub.status.idle": "2022-04-30T07:02:30.002247Z", + "shell.execute_reply": "2022-04-30T07:02:30.001897Z" + } + }, + "outputs": [], + "source": [ + "# Take 10,000 subjects\n", + "n = 100000\n", + "# Sample for number with X and without X\n", + "X = rng.binomial(n, 0.005, 1) \n", + "not_X = n - X" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:30.004279Z", + "iopub.status.busy": "2022-04-30T07:02:30.004168Z", + "iopub.status.idle": "2022-04-30T07:02:30.006482Z", + "shell.execute_reply": "2022-04-30T07:02:30.006082Z" + } + }, + "outputs": [], + "source": [ + "# How many of those with X tested +ve ?\n", + "X_pos = rng.binomial(X, 0.99)\n", + "# How many of those without X tested +ve ?\n", + "non_pos = rng.binomial(not_X, 0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:30.008319Z", + "iopub.status.busy": "2022-04-30T07:02:30.008198Z", + "iopub.status.idle": "2022-04-30T07:02:30.011170Z", + "shell.execute_reply": "2022-04-30T07:02:30.010921Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.32035737])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# how many of those +ve tests were for people with X?\n", + "X_pos/(X_pos+non_pos)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you were surprised at the answer, that's not too surprising: you've just experienced the [base rate fallacy](https://en.wikipedia.org/wiki/Base_rate_fallacy). The base rate of 99% true positive may lead one to think that most positive tests will be of people who have X, however the vast majority of the overall population don't have X, which means that there will be more that test positive incorrectly than one would otherwise expect. You may also have noticed that mistaking $P(+ve|disease)$ for $P(disease|+ve)$ is an example of Bernoulli's fallacy!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chapter, we've introduced you to the foundations of random sampling and hacker statistics using `numpy`'s random number generator. We've simulated data-generating processes using both the uniform distribution and the binomial distribution. We've introduced the story corresponding to the binomial distribution and many real-world phenomena that it is used to model.\n", + "\n", + "In the next chapter, we'll use Monte Carlo simulation to see how wildly applicable these techniques and distributions are to many concepts and problems in statistics and basic science, from diffusion and drift, to approximations of the Normal distribution, to the cognitive bias considerations of the Law of Small Numbers and selection bias, not to mention approximating $\\pi$.\n" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "98b0a9b7b4eaaa670588a142fd0a9b87eaafe866f1db4228be72b4211d12040f" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/test/03b-hacker-stats-MC.ipynb b/notebooks/test/03b-hacker-stats-MC.ipynb new file mode 100644 index 0000000..35d204e --- /dev/null +++ b/notebooks/test/03b-hacker-stats-MC.ipynb @@ -0,0 +1,535 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulating Data and with Monte Carlo Methods" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous chapter, we introduced you to the foundations of random sampling and hacker statistics using `numpy`'s random number generator. \n", + "\n", + "In this chapter, we'll use Monte Carlo simulation to see how wildly applicable these techniques and distributions are to many concepts and problems in statistics and basic science, from diffusion and drift, to approximations of the Normal distribution, to the cognitive bias considerations of the Law of Small Numbers and selection bias, not to mention approximating $\\pi$.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Monte Carlo simulations to model all types of phemonema" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next chapter, we'll be meeting a large variety of distributions, along with their data-generating processes and stories. We'll also learn to simulate them using analogous techniques to those we learnt above. Before that, we want to demonstrate just how powerful the techniques you've just learnt really are by showing an array of other questions you answer using the power of simulation. To begin, let's look at the well-known field of Monte Carlo simulations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. -- [Wikipedia](https://en.wikipedia.org/wiki/Monte_Carlo_method)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimating $\\pi$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a first example, let's use our sampling techniques to approximate $\\pi$, a question that has occupied our cultural consciousness since the ancients were thinking about it! To do this, consider two shapes: (1) a circle with radius 1 and centre the origin and (2) a square with the same diameter around the circle. Notice two things:\n", + "\n", + "- The area of the circle is $\\pi$,\n", + "- The area of the square is $4$.\n", + "\n", + "This means that the $\\pi$ is equal to 4 times the area of the circle divided by the area of the circle.\n", + "\n", + "So: if we randomly populate the square with points and then count what fraction of them are in the circle (and multiply the result by 4), we'll be able to approximate $\\pi$. So let's do it!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:32.124705Z", + "iopub.status.busy": "2022-04-30T07:02:32.124071Z", + "iopub.status.idle": "2022-04-30T07:02:33.103376Z", + "shell.execute_reply": "2022-04-30T07:02:33.102950Z" + } + }, + "outputs": [], + "source": [ + "#Import packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "sns.set()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:33.105655Z", + "iopub.status.busy": "2022-04-30T07:02:33.105543Z", + "iopub.status.idle": "2022-04-30T07:02:33.292875Z", + "shell.execute_reply": "2022-04-30T07:02:33.292559Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3.14308" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng = np.random.default_rng(42)\n", + "N = 10**5 #number of points\n", + "\n", + "# Generate x, y coordinates\n", + "x = 2*rng.random(N) - 1\n", + "y = 2*rng.random(N) - 1\n", + "\n", + "# Calculate pi\n", + "4*sum(x**2 + y**2 <= 1)/N" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voila! We have calculated $\\pi$ correctly to 3 significant figures. You can play around with the number of points yourself for increased precision." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random walks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A random walk is a common model across many disciplines, most notably in physics, biology, and finance. It is used to model the motion of bacteria, heat diffusion, and share prices. With the techniques already learnt, we're able to simulate random walks!\n", + "\n", + "The model is defined as follows: we start at position 0 and, at discrete uniform time intervals, we either move left or right 1 unit, each with probability 0.5. First note that each choice of left or right is given by a Bernoulli trial. Then notice that, in order to find the location at any time point $t$, you need to take the sum of all previous coin flips. Let's now simulate it and plot the random walk:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:33.313144Z", + "iopub.status.busy": "2022-04-30T07:02:33.312930Z", + "iopub.status.idle": "2022-04-30T07:02:33.433296Z", + "shell.execute_reply": "2022-04-30T07:02:33.432910Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = rng.binomial(1, 0.5, 1000) - 0.5\n", + "t = np.cumsum(x)\n", + "plt.plot(t);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### A brief aside on the quincunx or the Galton board" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next chapter, we'll spend more time with the Gaussian (or Normal) distribution but, since we're having so much fun sampling, it's worth using our new tools to show how the it's related to the binomial distribution. When our sample size $N$ is large enough, the binomial distribution in fact approximates the normal distribution.\n", + "\n", + "19th century statistician Francis Galton, in fact, built a [mechanical device](https://en.wikipedia.org/wiki/Bean_machine) that demonstrated this and we'll simulate it here. In brief, one would drop beads into the board and each bead would hit a series of pegs. Each time a bead would hit a peg it would bounce either right or left and after a certain number of pegs, it would land at the bottom. if this sounds like the random walk model we just met above, that's because that's exactly what it is. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:33.435642Z", + "iopub.status.busy": "2022-04-30T07:02:33.435506Z", + "iopub.status.idle": "2022-04-30T07:02:33.446264Z", + "shell.execute_reply": "2022-04-30T07:02:33.445335Z" + } + }, + "outputs": [ + { + "data": { + "image/jpeg": 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NkebPpjnGDjOR6HVLaKIqjCKUj5lNys2c5UnJHBPHHYDmvOdaul1j+7PVWGwr/wD5r7FOvw06IZAx6D6PUk840uIjHpj9axb4b9CMoJ6A6PXcMqfkYQG57DnuOfSr+OUKWMVy45yo3BlU59CcYx7VmkkcY4KgAkSgxBecD09D+uapziv+9/dk8zw37F9ik/7MOh9xMfw+6Ufaw4/ZUeduO+AcfrW+L4adEuiKfh50kwyCR+xYgQcEEZznFXvDPjKBiQzKYTxj2xyO3rUhcuCyyxSHPGVGftnH1qOc4j//AEf3Zm8Jh7bi+xzg+F/Q5Ck/DrpbcVIyNEj45747V7F8OOgCx2/D3pJyPxD9jR8H149OPSul8MnLFYCCAWIO0c9hwRzW1N6rtKXCc7S6uwUHGOeTx9fWrLFYj/8A0l92ZvCYb9iOWHw3+H5wD8POkmU48y6PH+n09aD4bfDtgT/2fdI57Y/ZUYGfpXVO3lkQvcoCMEE4K8e3v60gmW6gWRZpJQyfjXkYBxwAPf7dqjnOI/8A9H92V5nh7X5NHM/9mfw7LDd8Pek8H/8AJSDP8uOBWxPhj8ORnZ0B0kTnzA6VG2K6Vo+DuNxk+uCQRjHf3r0iYBXDyZPbOf0z78/zq3OsQv1v7spzTDvSKKBPhd8O2jCp8P8ApQ4//I0WTz3zmsx8MPh6uMfDzpIYProkXB9vrVyk8shCs7CQYwN2Dj3x6it+8+Quzuo7nxQQMjHHNWWLr/vf3ZR4Kiv0ooh8MOgOfD+HXSWRnH/mKI5Gfr3rYnwx+HgP/wDjzpE5GP8A5hRkE9/birpWy492Q+bcuPr2PB+lZIWfIR22yeRSXHB/u/i4I9jUrGYj97+7KPCUexFSvw0+HTZx8POjiB3I0OL+lZp8Nfh3zt+HvRm7PY6FGP8ACrUMSGzI5I5KkHK8enPb/GsvFfzjxpCx7MQSG7euef8ArV1jMR+9/d+5TmdLsK5Phn8PlfK/DvovBPH/AJkj5/lW1fhj8PGO4/Dzos8Y40aIcfT61aRzvGMrIV2gdmZT/T71uSaRkAEy7gTyDn07jjj1rRYyt+9/cwlhYrRFN/2W/DtSN3w26QdfRk0eI/41uj+Fnw8OAPhr0iSeTjRI+36Vaw3ThikjxKQ2ARlu+f8Ahrczvg+RQxzypx29eBWixtb97+5lLDJZfllF/wBm3w1CnPw66QBPbdoSL/Ue1bh8Kvhs+W/7OekceuNGiq/iupXXKmRRjGCTjn1INaxcOspyXDcErs5IHf8ApV1jay1mzN4dcP5KUfCr4anzD4d9Hn6fseL/AF6Vl/2T/DbuPhv0lgnn/wA0w1ffNmQMCGKnv+7x9e/vXhvNoyV4IwCVGR/yq3PKn7mU5u/jZTr8JvhmT5vhx0quOw/ZEWf6Vt/7JPhquR/2ddJYHr+xYj/hVtFfY/EFk2gZK7QDWa36xuB4e5GOAwKn9efyrWONlxkZvDy7/uVS/Cf4cDA/7OOkODz/AOYoDj+VbP8Asn+HGAP+zno8D0J0GA//AHNWpvi6kJbyu3bhBz+efpWs6iduQky57EQFvXvjNX59JfqZnzWTIH/ZL8Ouw+HPR7f/ALAgH+FP+yf4cgAn4b9I5J4/8wQf/U1Yi6WMIJJsN6M1qVyfrzxxXp1Apkq554A8Agf1FW5/JcfVEc1l2/yVw+E/w2IP/wDLnpJQP/yFCf8A7mvG+EXwzUZb4edJZHp+xIv/AKmp7azb28iRzOFkZcgAE5/Pnmt0WqRSsDDJkAfwyEZ/Xj3qV9Qf7g8LUSvnYpx8JPhaxUHoDpBWPGDo8Q5Pp2rZ/wBjnwxfGPh90j39NIi/yq9TUWABWUk4GB43+f3716mpSYG8xlif/eZ/nWixva/QzdGpwb+5Sp8HPhor5X4edIgj1OjxH/7mtn/Y78NAefh70kPqNGh/+pq0fW4YseNcRAA4/teCR35rxNfjkX93c2znPb5gewq6+oRX6ivNq7zz+7K8fCH4aoAf+z/pIe2dFh/+pqRF8JfhwWwvQPSOef8A7yQf/U1LXXYWIKTROO2PGwPvmi6/ZlnC3ablHIF0e3v2q6+ow4yRDw+I7WRG+E/w3CqG6D6Rwc4A0SD/AOpr1fhF8ODj/wCsLpIc8Z0SD/6mp4123jxvvUyRkH5lsHntWadSWOG/3+22x53H5o8e+7NXWPpN5yRm6GJtltepB/7H/hy2cdBdJf8A7kg7/wDw1tHwc+Hh5HQXSn/7kg/+pqcvUNo4wLmJ8A5AnB578nIrYmvwBUIlQ5H8MgbH04Nbxx2HerRk6OKXFld/2OfD0D/1C6UwORnRYP8A6mto+Dnw+yP/AKwulCf/ANCwf/U1YDXRIp8Pz4Gc5yP5Gs16gQsQTIO5OUOK2WNwnEzdPF9r+7K9Pgz8Ph26C6VH30WD/wCpr0/Bn4eEYPQnSvfP/wAxrf8A+pqyTXsrvfcqHnJyKy/b5KjEcvP/AM7I++c10LH4K2hm6eLvvP7lZ/2MfDzOf9g+lSf/ANC2/wD9TXh+C3w6bv0H0r+Wiwf/AFNWjdQKNuHwe5BU8DHr7V4uubjxnceT9vftTn2Bf6SOSxeu0/Uqj8GPh1j/ANROk8f/AKFt/wD6msT8E/hySCehOlR/+xoP/qat26hGCI9hI7jaR/PFYy68iqSzIo7DknP5AVR43A9hZUsZ+5/cqx8GPh2M46F6UAx/+BIP/qaN8Gfh2eD0L0mfb/zHB/8AU1YL1Rbkqviwk/xABhj+XNYydU2y7Qbi3z6gKx/nVOfYC3D7luQxt9X6lYfgn8NwTjoTpQn1/wDMkP8A9TWv/sR+HLc/7C9LDPb/AM0RD/7mrR+q7cAE3EC5XdyxGRRep4HPluoiSMALKGqjx2BfBF1Rxyzu/UrP+xb4eqRt6G6V9f8A7zw//U1gPgv8PVB29C9KnPvpEJ/wq6OvEsV3RMQBuAYEivDr/kLKYmA7+YHPHpisnicH2EqOM/c/uUp+Dnw+DDPQfSuQR20eD/Ktf/Yz8O/LnoTpbg8Y0iH/ACq8bWiUz5fN2IIrUNY3bcpGHPqWxkVk8Thi6hiv3P7lKfg18PQBs6F6VbHf/wAzwn/CtR+DnQGM/wCwXSvBz/8AMeL/ACq7OtA87F3Y5AfJH0rH9sg7W2jHP/tPT7Vg8Rh3p+TVQxX7n9/7KAfB34ekOf8As/6ZXaSBu0iHzfUcduayPwb+HgxjoHpX/wDdER5/SrqTWoIhufDDsSr5GeePtWhNYink3KpTB8qs5Bzz9KyeJorj8+xoqWJed388ypb4QfDxSP8A6wOlSTk//MaI/rxWJ+D/AMPgDn4fdJ5B/wDwRDz/API1bHXIfcqCcAgk9zj/ABrFtciXP9ueeB4bfXNZvFUe0uqGI7/Uql+Enw/HboDpLy9v/MsJx/8AI16fhT0CDn/YDpMEf/kWE/8A3NWJ1fxXkVLlk2YBBQDHf0NejUmyVFyX458o496pzuHf6FuQrcX/ACVL/CzoEZA6B6TwcD/5hw//AFNYH4U9Bjk9B9Kc986JCP8ACrr9rbv494PrnH/StT6wVx5kJPADMw/+5qHiYcZF1RrcLlOPhX0GGOOg+lQcYJ/YsXYgj2rA/CboE4/+sDpRsD/8DRD/AAq7bWCCPPG3qMTf/wDNahrE34TGme/E68j9KpzmC4s0VGu//Skf4R/D/IP/AGf9J9vXSI/8q0v8J/h6zMo6A6VJHoNJj5/lXQvqzMSPDdSOMeU1rGrI7lkhaMnjBYZb6/yqvOIv9ReNKrxv9/7Oeb4T/D0jP+wHS+PppMX+VYf9kfw8kBI6A6Z9xjSIv/qa6OXVQGIiQxKFJKsRn+XpUY6wrOVcEcbuB3Hv3rKWISe8axo1X/6Uf/ZF8PRnb0F0xk986RDx/wDI1ifhL8Phgf7BdLHb/wDkWD6f8NXx1X+EAAfXv/Wo51YliDtPsSwwf0NZPE9jZdYeoyjb4S/D8/i6C6W+v/mSD/6msf8Asl+Hw/D0F0p3PfRYB/8Ac1ayauypnaDj1Pb+tZ/tUY/dGEuRgck/fgVnzqX7n9zbmkuwqP8Asr+HwOG6C6RySSP/ADND2/SsR8L/AIeqR/8AWH0j/wDueH/KrQ6k4YrtiwRyWWTP8j3rSdRcEBofTJGZGJB7dh2qOdT/AHP7s0WDb4EV/ht8P1BH+wPSHlHP/mWHP9K0t8OegVznoPpAcemiQ/8A1NTWv5mRpDDwp5DK5x/Lua1HUpEbbFFtzlt20g498YzVXi6n7n9zSOC7iJ/2fdB4x/sF0hj/APQkH/1NYHoLoJj/AOoPR5AAyBokP/1NSP2pmRsvsc8gO/I7c4PGe1G1CTYg3AhWbgOxb05ORj8zUc7q/uZqsCuwhD4e9B4yehOkT/8AsWEf/c81rk6A6GC5ToPpDOckDRoDj/5DirFb+Qom89xlWZsfYDPc1ohv2kdvFVQFGcHkgdhz2OfoMcVnzmp+5/cusFDjEgN8PuhVbP8AsJ0gcYLY0S3xn7la0P0F0OEOehOkOBn/AOYcH/1PNWq3YfbsiBbJPBzj/M/QVpe+LyFSgbI4wu4/X2/17VHOKz/U/uaxwdLjFFZL0N0QCCeheksAc40S3B/+V4rRJ0Z0VuCxdD9JrnkZ0O3IA/8AhqeLz5jaYZoySNzZs2YDvzkkZ+nArCG/k3lWlLy5OALcNkcdz79/t+lRy1b9z+7N1hKC/SivbovoscHovpXd9NCt+/5JWpujOjVUk9IdJrx5c6Hb5x6/wVYvqMiFQ6Bc8dkBGPQBckn+nvUZpyWV3Rc8jYWBJA/vHOAB396cpVf6n9y6w1D9i+xF/wBkOkQ7BOjOlQMZwNDtyQPf+zrRJ0v0fGQ0nR/SoORk/sK1OP8A+3irGGWQOUijQzOwASNCTwO5ye5PJJ9/QVLtoZSjKvjlzuUS+GirxgHH+f6U5Sov1P7snm2HX6F9kUadN9JDlOlOmVLHhV6dtDj8yn8uTWDdK9LNux0p02/uV6ftu+e39nVnNfSwlpHmC+Gv8T/hGMZGB3Oe/wClQXvrSythd6heR2tmm1JmETudxOEjRF5kkY8LGoLMcAe9XU6smkpP7siVDCwTlKEUl3L2KLqTSej9A0ya7vOlOnYIgVSWWPp+2klBfhIoo/D880h4VR7+gBI/EnxoYt8QL8nStO0IGOLbp+npGqW67BhG8MBDJj8e0Y3ZHOM1+xOor1rK6i6g1dNl7ZSu9nY+JvXTVZSuCR5XumJXxJB5UCiOPgMzfij4jKF6lkIyQ0SkMRgtyea+0+gw2K2bu7Pj6L3+2Wb+I+tSUqe1GKirqytb7+337uSpSlfbnx4pSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUAr7t8GlQ6POZSdpfac9sbc/5V8Jr7z8F5Gj0eVwM7HZj9tnH+VeL9Y/5X4ntfSP8Ap8j9MaNa215ptzb6hCs9tdyPHcRFThl2sCp7c/b1xXx+w1K4+A/WlxoWtzu3Rd5KxEscPiNprOwIuI177DtAdB7NgZAz9l6cmHyxQSYxJuO8DgFTx9e2QTV31P0Lo3W9i9prMSeMFmjt7kcvGRsbBH8S9+D7nGDzX53QxKoVJRqK8Jar+Gu9H32IwzrwUoZSWj+cCDcdRqHs0jusWTwl4Jkj8W3li2ZEqurYK8bg39OarV6maWaSSbVZ3zA/jAR7VKZ8wB3e+QD6dq+LdTdCdafBy3mSIz6l0UWLR3NtmRbEseSp7qpJG5Dwfqe9BJ1JrW0KJHlWXEissnDjkj15Xntx9RXUvp8ZLahJOL0fzic/PZRynGzXz7H6Qtuo2ldVuLtRCIYzcRvGB5Mr+PDd8YzWvT+pZpJCDIjQJNtdJiMbSo5PfjGK/O69TaygwUkCFt0ifwtx6jPP58VsTrLXIozGHlI7FcjGMDPH5fTsKj/Hy7UTz9djP0euuyParJJD4kyN5CpLEruP4wO3lx9uKzuuoAMtBbxu3iERNtYkj0xg8/evzget9YZtzpmQY8wcgnvnJBye/wBK3Q9f6zAQYg8ZznCuQP0B+nvVf8dMv/kI95+jzr4VpFjtt0KlSMRMcjJwc5wfWtsPUVokSmaAxJ4rKow6EnAxwW+p+9fm9PiRrYUK7y7eBgSsPz79/wBe5rbH8UNajLk3F2CygELIPN9ye/aqP6bU+MlfUI95+g26qi8RDBKyg7Cc8EHdxjnGO/8AOpkvUgeOQzyx+NsRVbAJDZOeB7jOfT1NfnE/FPWiRm5vCMg+ZiwGP+EEVpHxM1YEEeKx5HJPGe/Oc89s98U/xtQf5CB+nZ+oHLr4MkFuTCxfzrz5uCNucY4rCPqERQOUe2Vtq/8A2QrgNjzDv/e/rX5pi+J2sh0A8faWG0RyOpHOc4HevW+KWs7seZlVyQPORnPDc5z24yaj/G1Sf8hTP1AnU03h7mMav4kbIfEXntx+Lj1rE9SMHkWK4TiRhGUdcE45HHtzX5nHxY1lWLYZ3Yd3LHBPc89yaD4ra0yGPGIj2AXKr74BB7+tV/xtXuH+QpH6bk6haJx4l0kUoj3EGMHDFsJyGA3ZzxjtWybqNI5UjM8LFlPjbT2HGM+3mzX5iT4raopb/cLYqzbihgBUkHg89vyx9a2D4tat4ZX5K1Zc5/8ARVGTx3+nA4+lR/japP8AkKR+n4upwVQvjeAQpDckZH8q8HUkeXAaAlcfZuMnjPvX5j/7XtbLBzCoYAqCIwABkcD9K8T4uayrZNurDOSDGCCeOT6+nbgVH+MrfGOf0T9NjqpRI2JIEUqQ22ZQBx9T75xW5Oq1DEyyWxOMklkXH4u3P25FfmRfjHq6BwsTJvwCV7YHbj9a3x/GzV0TlGLAkjyJgfqp4+9Q/ptbs9SefUHr/B+lD1hbKzhZrRFHZTOoGCPvWxep0YnakbHwFLn5jjJz/wAXbNfmz/t11oAAGUkYA3+GRjPb8HArH/tv1Y7srEC3Y7MkDPbtUf42v2epHPcOfp/9urDNsSPEQ25/fA844HevbjqF/wB8sisfDZNm19wyeewPFfmOP41aiImBSFyQRkxNn9cfWsH+NOoSIVltY3UjnYpViffOPSo/x1fsHPMPe5+qP2+x82ze2cHfIVHJOcDNeft+chMW8jjIGVmB44yv4vrX5fPxsvX8zWm6TJJIUj0I/Pv2Nep8bb0bs2q7T/eiLYx2xzUf4/Edg51hj9TR63LszsZWVVwofGCT9TzUga45lQbGyqF1QuQSeM+vGPp71+Vl+Nt2Cd1tE3HGbfGO3seQea2j45XXBkgQYPA+XOR9M5wO1RzDE9hV4jCs/UiazI8UTvHO8jkFgt04AOO3J5x9axGuv4pJFymZAvFwzbu3H+vavzB/25EYxAVPGDmXKn1PDYzj0xWB+OEpYAQAJuGMLISuCSO7cnmnMcT2Dl8KfqmLqNyzK4vV85HhK7Nn6/41Hfqh/BYsl4ju5yzM3l5wftX5mb49Xfih44ocjne0LEk+mee3HNeL8drkIVa3huBISzqiyBT9gT/j61PMsV2EKrhLn6fk6muoQotDeRMeCHRvrn9cDmtw16WRYwZrgluSCN2CDj1/1zX5dX48SiQMtrIMDBG3O7v7tnPNbG+OjMihbGRCpzkZUjkeuTntn+lRzLFDlcJ2H6ej1gKWPzNy+6TlDEoyBjgGtq69sYRqJYo9+A/h5LDAOMYx61+W1+OWCWGmeYn8WW3HAGOckfTtW2P45oBibSIpsYG5o2JH1B3d/wCtOZ4paIcphHqz9SL1CqDyiVBsDEKqj0OT+H8sV7B1IHkDETneg3BVAzzjHbtX5dHxxti6N+yrcMDwRakHaPTg49TXi/HFMgrYxwH08OyBweOck571PNsWuBT/APGz9QWnVUkzSLt8LwzjwlXJHkyAxIxj6D0reOrZD4yhpPJIIE8pxz/EeO/8q/MTfHpptniK2EXaStkUZvvhuawb49um4RxzEFMeGtowzyec7jz9e4qVh8WtLkNYJu+R+qX6iBf+0lCqe6oMkD07e9Yt1LE2FlaVl27mJhXGST9Ow+tfl1/j8/ieWO5IjPGQ2G+h8vPp+lej/wAoRkj/APQpgwbPmiZ8/c96tyGM7DPYwfb8+x+opuqsztHEIVCtlnkgBcnAPt65P8q2Sa7F4LSNh5RGCFMCBAx9T5c4r8st8f8Ax8NcQssi8qVtJck57EBx7Ac1j/8AxBTZZSGkhK7TGtlIhA/7+/uO1TyWMd739RyeDsrM/Vq65ZrkPI5GN2FiUZPft7eg9q1rr1oHt4mLu0qFzKYVCrg9h9cnNflkf+UZcoAHgLsDhj8s+GHpjng+9Iv/ACiyNh+RZjHgKzmQNjvg8cnP0FORxf7fn3Kcnhf3fPsfqg61YRpLPKzttwx4GWHrge35Uj1qCa6urcsY0hfh2AcsdoOcHgd/5V+WIv8AyjSUJl0/fnho9rnj0Ge2Oa2J/wCUQiyyTDTmSWUHcUiPJwBlgeOw9KjkMXxiTyeFz6XzI/UUmr2Fo9vDjxpJpc+K8aAKcHzHH2I/OvH1WzlurSGd1dZ958qABNq5GR65yK/MCf8AlFQsYzNpbu6MWSRQVwSP7v8Aoc15F/5RMYkglltZZJIt2xihTvweAT9P0pyGJ/YTyeG/dmfpwa3btEk0bRjO0IknYA+hwP0Araup2ojV45Vj8vmAgB9iVAPpzX5eb/yiI2TwxZyopZSfLvIAGMc98+pP5V6f/KKhQxlLFgEGCGjJDcjnOe3FV5DF/tJdPCv9Xz7H6mGq22QVuljONgUqo2r7VimtW8u+GO6VDGu4lkXB5I/qM1+Wz/5RluzZGkxnvlnjOSfpxx/WvG/8oWzwoj0decbmMZA/Iffnn3q/JYv9vz7leSwvGX8H6cuNcgtFSQsJgFyQkak9ifX7fzrb+0ICqPDcRruLceChOR25Jr8rv/5QEExKtpkkMeORFL4e7uM9vrmvW/8AKAt/Jt0OIkd2Dkk/riq8hi/2l9jCW3j9QtcWkhgUQwN4mcloV4IA47/eo/iWf7lVt7VpcgPiLAxyMKM8dq/Mw+P9p+6P7EuYzGSVKzZHIxkjPP8AL1NZH4/2jPEz6RexlcbmjZdzY9/MPU+/tVebYn9hZcgv1s/S/wAxa43NCkbHdtCk4ADHj8XtXrTWKnItLcknaD5hgd+PN/rNfmNfjvbIHC2N7CMDHJb1y38QHOf+tZj/AMoC3DsRa3uclhuIx6cY7n19vyqObYn9ha9D97P0kbm3JzEkaBZNrYnk9+cYPvSO+RoomiwoleRSzyltpXPbPPpX5qHx/gyhFleRbW3YGCGbnlsffNeH49W5UBbS7QKhChUxtJPJyTzn+VRzTE/tLbWG/f8AyfpX5x8symFjznM2Aefavf2nckBJY1diMZV1bH57q/NR+PkJJAtLplJ7kBSP8BzWK/HWFTzb3koC4CsigZ47nvnv+v0qOaYr9pO1hu1H6PGourlgmxCe5Tj054P17Vg2px4JPhAo+1vckd/XivzYvx186t8lKoA5XZwWz3P04Ax9KH464C7LGTA5wV4Oc/4805lif2l+UwvafpAatArHc0ZOcHzc857c0GpQtIIwEHm42se59eDX5uPx4kwc28zckncuOT7+/FYj48urbvkpWJ7FieP505lif2jlcL2n6QN8uGZ9jbQOMtg/enziqG2mMgsAcuw/SvzQ3xzcZEdk2DjO8ZPGPyHah+Os7NzZZPfAXjtx3IpzHE9hPL4btP0o+qRR5/deIM8lSw/xrwXdtJ5WjlYA5wtwVJ+mefavzQ3xzlOcaaWySSQwH8s0HxwnUkLp8ozjlQOPtk1PMMR2fwTzjDLR/wAn6Qk1Sx3lm+aAznaLhSMZ98ZrN9StfPmeQ5JOWdW96/NTfG66JObCUA9soCQOfrWX/bhdhiRYzyAnPmjRffHbt/OnMMR2E85w3b/J+jvn7ZQdq+Vf4to5/wBe1Pno1Lfu3OMEcjnjsec4+lfmxvjhe+YnTWyRwfb61r/7br0k50/aD6eg/wCdP8fiOwnneG7T9LnUIA6ho1ORkkjIH0NaxqFuYspbQtnO0NEOR79uBxX5sPxtvmbIsBt77fr7/Wtf/bVqQj2izJP04wD37VP+Or9g53h+3+T9LHUbaNs4so3DY5gJI/QU/aUJHNyGZzuwsTDdgev2r8uyfGLXCw8GJo/3m4+TOB/dH6d/rQ/GHW9ysbYPtDKNz8gH04HNT/jq/wAZHPMP3n6dXU4IWQrclB3JViDk5xyaLqsONqzylcnOScfUk+tfmb/tl1kNuS1ZDweJiBmtMnxh1mTiS1LD/wCjGp/x1b4yee0PiP04NStSpxJIQOQoGcZ9c1h8/aphC8m0jsjtjv6nGf8AOvzJ/wBr2q+IW+SG32WcjtWDfFvVn5a2YduFuPL39sc0/wAbW+NE8/ofLn6ca+ikKv4kqiNsR78rgc9s8+9amvLSNVCyp2woMpIz6/n9f6V+Y/8Atb1vBHgKAfZ24HP1+tP+1vWeMwFyBhd07AD3OB/nVv8AG1vjHP6KP06L+1UiUTKRjapILMR+Z4rFr+1J8OQx7B2VpW5/L0r8wP8AFPWmUBYwoGMYds8en861f9p+vF95C5A4/F3qf8ZVI/yFLvP0385p7JsWUxqMBv8AeHOcjsAO3bNYz3dluUB1jXHqxBIHYcHt9K/Mh+Juu4IyT5cclsVi3xM6hbGXHHYhW4q3+Nq9o/yNPvP06uo27SPm5R3kyCZT/m3A47e2K1Q31hFKzS3luu+IgqvlJzxgEEkD+Z4r8xj4i6+WzuQ4OV3IzY/U1gPiBr4cMJEUg54jP9Pzqf8AG1O0j/I0+w/TD3+ibkYXO0IcFVkIDHjgjGSO3rWEd9o8sqva+CWCncwYkKBnkZ/rX5qT4g9QxoscXySIPT5BMkffvXjdd9Syd7iIfRLYLn74qf8AHVO31/oj/IQ7Gfpu31rS48l7mMErhIpJCc5/vHgZOc01DX9GiiCma2RVTBKLkn6AY7fzr8vDqPqG4Zd1xll/D5O1T473WEtvm9X1A21qZBHGqQeJPPKxAWOGMcySE8BR+eBk0/xzTV5E8+Tzsfe5+qtHsonCiSGOAgMfAZizv5VREA3SSucKiDkn25I6iz0q6iiTVdfskh1aCOX5K2a6Mv7ODoFYswOx7plLKzrkRqTGhI3u8f4ddAXWi2djqPWUUI6gjLm1sF2SLpW/hmklHM12yAK0n4Y1LJGBlieo1ySMxyNDGBnPmx3JXHb6e1ZThHDvZi7y4vs7l+X5LjfJVZYqV3u8F297/H3PhnxDfMEwKqMHJAOBgY5OO2MV+MviM/idRbickwjzc8+Zsfy4/Kv2F8R3SKGTcCMKx5HY4OPWvx58Rl29QKD38BSf/iavqfoPWeR859b6vzOSpSlfbnxopSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUAr7n8GxnRbtmQFEWVt24ZB2AYx9j/WvhlfcvgxtOkaiCCzrBJJtHqBjvz9Dx9a8b6x/y+aPZ+kf9PkfqPpBmM0UbqkkSlQ2c8nLD0PccelfSYYyssiyB2UyP2yMkhc98fT9a+V9OOnhiVD/7YBiSORgMPc4zn9K+kxE7oWc5VJHVwFPPAwf5V+WVl0j9Np5xOu6Zht5DLaalbi5sru3WKe3kjyrqwIZWB4PrX4TuLCbQm1O0MhZdKvZ7MbucqjsE49fKo/UV+2NHYx3lqYnwwQbin8XJ5/Ovzj8Z9LaykupWt3mjnnkkhljVlEbyQJM4bAw2MFstzhsDA7er9OquS5PgeTjaWzUdS/Z+T4Db9baoYFmew0qba2G/cumTj1IbH+jU9OtDxu06BTjIDx7hx35BHrn0rjLdvG8GOELuYZbdnn2OPSrB4JG/eMu4ZDKpGWI9Sc8etfWzoUr5o+fhWqW1OjPW4HK6dasvu8RH5cEmsE67ztQ6RZKzDs4k47j2qiiij8N3cyRoQRnIAYjnB96xIyFICqzjbz68ZI2n0wKz5Cl2F+Vqdp08fWcEgKtY6enP4j4gz7c4rCXreGMfvtJs1CnDFFkYjjHbb71yzJFMXEiNEnlO5PNj3HP+uKxRmMbiKJ1J7NnB7+vuOPyqVh6XZ6h16nb6HVjrSzYgPp0Krg8hWxj/AF61k/WNmAwbTF2qBlhEeecY/F/OuU8BJRvt2WQA8eGu0gY549SKxeQEblCsp4JznyjnP096c3p9hHLTOtTrGzkkO3SmAxksFUAD3/FWhuq9PUYNhOxXIwGU4wec+auZKGbyxnCk4BJCjn79vsfesPBkusy+GqqMFg7hTnBJGDg+n+FSsPT+Mry0zqx1xpausf7OYkjjdEP1yGr1estMkO5dNYD08if/AFVciyr4aqRK21iR5sBeOMDH8qjxxpIY3kJCLywXAb/u89gavzakV5eodg3Vums206XMgyQCIwAT39GrL/azTHyVsC/GcFMY/nXJXKylYy6ELKpaPCnbtJI4+2P5VpXYxkCABduPw7c/XNObU32/ccvM7AdYaSw4sSWDYx4ajP6mtr9ZaWoJOlydx/7FfX281caVRBubzIikjIyOKfu0gSSTa0DYwWyvftU82p95HLzOyTrLTSCRp0wI94FwD7Z381s/2y0tZAjWE4ccECGM5yP+/XBvbySDfETFgkEgZ3evY16EWMBl3Hgt5xnn6e1Oa0hziZ3bdZ6apULp07Ejt4UY+x5asW6204gLDo10Nq5JLxAOf54riI5GAPkCyKQ21jnGAfes2iWVv3owiISdrBh7DFOa0lqhzipwOlPW9pLM4OkyoYyAV+aT+f7ut0vWMSlwmjg44PiXAX8xhPWuSe3iijEk7OiZ4AP4+fWvCysSjgFCuORgH6Grc2o8F6v3I5ep2/wdjH1nbFR4+jvHxlhHNG+P1UVsHWtgBuGjSP5cqGmWPP6ZP6VxnhokSeH4YQKOApyPzr12QoqsSJASoj291xwc5+pGPpUc2pPh6v3J5ep2/wAHWDra5EOYtE0zzNjDI7nH5nNer1pdvKoOlaGisOGeJxgfUZ71yBnk8H90SSByeD2/wra3imNGVeJSQrk8MQRkfTuKc2p9n8kctPtO0/2rlkRNlv087k8qquuOcfi7cVWDry5Ryq6Ro8nJOZEOf681z91KsKQtArTOFVZQdq5bJBwQO2CO/P8AKvCnhbvGKbgORjgfQ49fpULDU1qiXXn2nQv19cDJk0TSVwfN+5fH8jWS9c3TyY/Y+klSM5WOQf41yzxugKxBSG5dwWyBW1WK5j2o3l444Pp/zqzw9L9pHLVO06NetriVyn7J0xR2DDxOT6jOe9YnrWVgxOn2Ax/DvlVgfY88VzpgieMeGrRyLy2G8pPbOKya0kklLFyTnBJIpyFLs/kctU7ToIut5nYr+y7PxO+1XlA/mazTrO4L/v8ASNPjQAknfKf0wa5sb0LYcyZbBAXn6VgEwm7HiBwfMBz9+/FOb0uz+SOWn2/wdlZ9XzkTtqOm2NtDEgwviS+JIxHCrz9sk9hUZOuXfKjRLdwOPJdSKCPpu5/nXMxRRsSV/hIUq5zjtWbKkYCAjaFDFhUc3pX0/n3J5afb/B0s3XXh4I0FGGMnF436dq8TrmIjc+iDBI4+db1/KuXMTBdjYkWRcthcYB+vtWrMcWWeRSw7lu3tx9anm1H9vq/ccvU7fRex2Q62gDbG0UFi2OLsmi9bW7B9ujDK8AC6P+dcoYzKXEON6cHHp/r/ABrTErRPhpAuVKtkE7fcfyx+dRzal2erHL1O30R2bdaWwiLLpEmF75uWX6d/WtB66jdZPC0RCVHl3XjkHtjsB71zgGYQsfsQR359q1DcYW3NyQSABk5A+tFh6XZ6scvU7fRHU/7bup50S1OMEbrqUj/rROuXy4OiWWBwMXEpH3ya5pbdwypINjlSCpO3AwO/15rW0RcDBP4ThvRsY71PN6XZ6v3HLVO30Xsda/W5U/vdDtmO0n93cvj7ViOuInyRo0OADk/NOO1c3DGvhuAynaBtYD2yT9awS3GW3tlCh4JyCx9ajm9Ls9X7jl6nb6L2Oqk6xjBQ/sXYDnJ+Zbg4z+fevP8AbGE7f/NY25wT454/nXKuX8ZQGdWAKkY4I9h7fetrwBYQ6FsMAAxP1Apzal2erHLz7fRHUp1hFjJ0mAZ7fvnP5V5/tlCFydKh3EjAE0nIxzXNSx4KgFWwMDA/D7mkyq3mTY4PmzluG7H/AAqObUuz1ZPL1O30R0v+2VuozJpUOCONsjE4z9eKf7Z2wUZ0jB/u+Ic455781yr2hGAsiqy48Q45GecCkgLuoX8eOCG7/nU82pdnqyOXqdvojrf9srPGf2WQPXEv+PpWJ6xt2YqulO6hsblnAFcrJmRkEmN/ZuCcn0NPDVDtLLGTjvwAf+dRzal2erJ5ep2+iOqHWds4B/Ys2CMn/eVH/Otbda2qvtGjTnPGPmV/y5qgSNjwu0gk7cYI+taUQyNxgYPKBsfr/lTm1Ls9X7jl6nb6I6j/AG0swxB0a54Ix++TBz+VbL3rLTbdl8CwmuXChnTxFjCMTjaDzu+9cmYlZGIyV57c15CPDVfCVdoGFXOc8c8++Kjm1Ls9WTy9Tt9DoW67tlRtmjyEr+LdcD1BPAx9K1T9dSklbbRbKM+pnu5HP6LtH+NUAIEku9wfUY7du2RWnCJIi7lL8AheGxWiw9Ffp9X7lXWqdvojpf8AbHUQ8aLpejMZDtGPGxuz/wB7t61ietL4M/i6bpEW04HM3P1xu7Vzvy435juGEbEkB18wH19uaxeJlXBVWBGcKeRj6+wFTyFLsI5WfadDJ11dxt59L0wjGco0pJ+w314nXNz5f/NOnTEjPEswBz6Dzf1qiCA+Ltj37QCD2Kkn/lWvxCoaVoywJ/Dj8I9DTkKX7RytTtOqTrJjD4kuiWmA20ql1Jn9M1vt+ropmA/ZEMSZ5aS5Yce4BNcT4YDoyLiN0zk985NbFhIdDIC6eoHZQfeoeGpdn8+5Krz7fRex2UnWcEZCfsVWIwWZJ2Kjj33VgeuLTbn9kqFPIJmbP9eK5OdPlQqLvO1tyqWGMetYR+Isb+FlM5KvjJA9MfX0qObUraerJ5ep2+iOxj63swYxNpRhDnBb5htvf05rKTrK0SQpJpXuUK3JYNj8+K4t4rmdT4oaSYLjc7bjj2+32rMWrRFzHG8hRQzbMgZ9c59qjm1L42OXqfEjrW62tAwR9KwxAOFujkCg61tWYg6Wcg44mYn+tciVli/CxGATkD07/nzWTwyMy75EyBuyOMjHAP8AhTm1Ls9WTy0/iR1b9b2sbSq2lgMvC5uWAb+dZP1rDGhMmlxA7sBVuJCf5VyItzLNnch7Abh2XjOaQhQfMu4g+RkbHvk5NObUuz1ZHLT7fRHXTdbQRnB0tBj3uZBn6D61n/traYw2mggrkmOd+P1xmuQFtHMQ1uJJGD7sE5x6dqyltkABmkUEk7QrbiPTkVHN6PZ/JPLVPiR1bdc2IBMekSSdt2b3GP5H+tejre0C7n0VgO+BfHge54rkBBDErGMqDwTn1Hfn27VnJA8KlJVRBtypJ7Z5OfT8qc3o9nq/ccvU7fRex1Q66tMKBopZiCwxd4+3cUTryycOTpEwwQAPmRz79xXJEKfwqF3nOfv/ANKyVgvO0Bc45GQB7U5tR7PVjl6nb6I7I9Z2Kld+lOVYcf7wK8frWxDFW0x0ZVL5NxgHH+PsK4zyMqOMHGQqgdz9vzrx4FU5LBWfyvyM49qjm1Liv5HL1Pljrh11ZsTv0eVQc4xP6/eidcWrqWXR5dqDJzd4wM45GK5ExIhdVVMl8bccY9zWz5aMAqzGPxOCF5z9PtUvD0ez1fuTy9Tt9F7HWHrWAgMujSOc8n5rjH3xWt+trMAA6U4wM4S7OefeuVnhXOZHHkUJ5ckHGe3vXkMIOAcqqkkg5yKc2o2vb1fuOXqdvovY6tetbdjgaU4zyc3p4Pt2r2LrOHPOkyplsri83Zrm44g0WEyXJyFwAAn9c5rZDYwJbTTtfxR3QaMRW/hszyA7tz7gNq7fqcnIwDyRXkKPZ6snlqnb6I6aPrK2IYNpsgIXdzcck+3/ACrMda2xIxp0ypj0kUk5+neuaaMEyMBkomSWfB/KiSxucojrLFgKSAcnHf8ALPeqchT7PUvy01x9DqI+sLOYurWtzCw77+f6YrwdU28hCRW8iHuGlBCn77SSB+Vcy0EszZdgyjnYo4GPWsAWSQmIKHBxt38Y+p7VHIU+A5afE6K76lQjZIYoEZSRIokb09Bn7frX6B/8mDpOyuupdY6k1FGvdR0mKCHS3fOy3aXd4kqKf4yoADegJ9SCPzNCgkG69kIRYd24MCAnsB3yTz3Hav2n8A9Jk0jStRSU/vp44JHJxnJUccHvXm/UXzeg9jK+R04ePLztM+zZCzBcZVTlgvGDu9zXNa7I00YjVQE8wGOykcf4V0FzJtMpY52rxjt7+n1FcxqsgaKQr7ll4I4/P718q5Z2PdpRyufEfiCitZ5bnybjufIwA3884P5Yr8h/FFY06jgED+InyEB34OGJXJIz9Sa/WvxIythcBSFMNtuA7HdzzX5R+LsfhdVQRghgumWgyP8A6EtfZfQOt8n+D5n631XmvycFSlK+4PjBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBX3f4EAsJFVlVnBTL9jlX4OR6nAr4RX3P4GfgnYZ3LGSgGOT5sA59M98c+1eN9Y/45Hs/SP+tH6a6RMk2nxpKMO82EJY5Xg+ufX8q+j2Z8RopFfO2UkMT6FDz3r5/wBHj93pMaF8Fye/DHa23Az6YHb2rvdLLSQRFAxVdjEY4zsbI/Wvy2tvM/TaW6XeljM9uCDnZ/DwM/07fyr5H8ebMy9LaDLtAVHlj2BiAzMjZyBxwBx9PvX13TyZJoyisAHAyw4xxXDfGO0a56IsmjxiCdWYHngq6f1ZeK6sA9mdzkxa2vnifiWy2tbxQxW/hQvGDI2MuQRkc+gJqRHAQFiaTwyGDbGUgk554xjsD61cWFu09tZG3j8QpaxqWaQc+XjOOQB2yealwaZcMNhWMxKg8r5DD0YKccAY+1fZSmrny8YOyOeSyeR7VX3op9DHt3Djkc9uf5Vl+yXdJHOAUXIQhcKAeM+uc8Z9K6K4sTHEqwu8u4DiIAq+0kAt7exz71r+S3M5ZY0aWFo5TvPk7HI57+XgfXnNRtltk5s2SrMiqoEo4aNvWTB/x9DUeO0kkuEVRICuXkjIwRj0zn6108ltvQPbyloE5Ru57HkggjPOM+leWWno9pKx2mcjKhMhjwePY9vT6+1XU8ijjdnKW1ituiQAtKx/CTktnJ5z+or0WU0MiGMNkoxVgQSeOxGPY+ldK9jDvhiDeE0kah2Q8M2M4yewHK59wc1lHbeCi+RY0kIwDkbDnnOeO/GM+tX275lNg5z5dniMcmXdxyIwCVCsDz/yrFrSW72KXaVlJWEy8kDOdoJ7f4k1eRQzbJAy5MZwrfhAyeOTwc88HNbZbGRIy7IVd1V0yu1GYMOeMg8HjHv9KttWK7Nzmfk28NtwwHYfvMklQF/Tt6VhHaRLIphyhyWyoJHf2P64rqPkop5RIoVZpHzthchVxkngjPPoB+VarrRHlETSbXnmiZoy744HbcccZ5Pof1qVURDgc0unEK8oGRlSGGewPf2x6/lWxrOQCRVPD5ckngj2wPrnHtXU3OlSW9vIm4Nsn2l0G08DjAxjHGcHGAK1CxWKdWyAqjkEfxbuCR7ex+lSqlyHA50aaZUkjkZN8ibsjse3kJ9M+3rWCWTxSRRTqPCDjZGDnPYZroorD5kyyW9rclJJMRo4Bcg9gMAAkep4HbFZQxCZysckCzRPsaPBDp2GMfXJb14BqdsjZOcTSmaOV3jfbggSKSFPPcA8k4BBX/Ksf2cuxCDtAfEYMZG7I7nP07Gum/ZxZGadEDqm4HwmLKfUk9ufTtmvVt98ABdmn3ZMZZiVGD+QULxjOKcoNg5k6dFBHI5QtIe2/J/Me/2rJtMZYFd2bYFKhwMBTnj7jvz+VdFbWYmkkRXUtAcRlmyit9x9M/Ssp9LjuY4Ejt/EXcFaZ5MDxTyBxwMYJH8z6VHKZjYOamsY41XdbkkjbvVyc4BJAHIz2+gFYpaeNDGyKkbyKrgZ5XjPP1rpjCl2A8LpCsjBmkDgGQcgEKxGTgEVZrpFrNpbqBHbLBeeKHRzISroVGQASRuX+uKcpbUbFziZNPeYqYXTAOx3XJX0OTge2K2DSVnLrAwklQZGP4wTwVDYP1PeulOkRvbD5YNNI+0bNhTgnhQCMljz9MDI9cbJNHhjDW0apOIWwS0ijy5OFBOD7ZP0qHUJ5M5G2sUyRMpkzlWUOQQwBAOcdgcHHrj61sj0gpLHtAbcuUGwAk45JB4AznGa6ifSZbyZYo08jn925UBig4OT3PJIx64GK2LbLMXNmIoYAcvJKrkR7c7CxPc/iOPyqeUHJnL21mstwqTIWD7kJCYbJBxnsM5ArUNJN3vWRT5TnysVZfTcQOccGuoawj+XiJmuBNcS5jXA2yDJAyMEKGycD3zW606eMqbTPBaEqrNNKniSY3cKqgghuOwPrzUcpbMbDeRzAtpFjEUVuxy5EfidiQcHnP6/ejaYqpH4YR2OQmG/Fgct/PiututGIWdLO9ikE3hgRREmRCePNnkEqfTvWEGmLaTtHKHdVVopIZVKGE5Pf/iHfH3qOULcmzj/ANnx/LswJQKwLnOCB69x7n+tZW9g5bDllhbl3/HkZ5b6/wCOa6e40f8AdPd3D5hDBbePaXLyMCVUn+8QpPb0rc9narPFJexTpEiDbHt3blOOxGOTjvzjH3qeUK7ByFvpbys6NxtZgGUbst3wfT6YrXHpKxRZEnDEKADnnvk/nXWvazAhcXCQI6oEcbwCew44J+g+9b2sy05uZIgrPu8sjkFCADuIHfysOMVPKMcnc49baJ4ztlXMbkBWUcnsccZ781sfR/EhWRlV4y2HlbKlfbj2P54rso7KKZIks4pAAcXSuuShxjIBB7jOfaosGmhVfeA+VkIjg/GoHAOT2zkDH86jlRyZyiacAJHuF3qMgpk7iMckH17jsa2xaCgGZAwXgBmOFxjsPfvXTmwhi3hCyqqGNZVjLLlyOM/wnIwM+v0o2lwm3EjJKpBLMshwQF4Yn8yo+tOUHJnJrpaRyELuPHcc5z9fUVk2kB23O5LIOR/Efb7+vP0rrT0+GtDPavNkBkKmMKCw9A2e2PbGKwTSjuOYU8DjLrneQRlRnPLHinK95PJnK/seJUkLGOMJklC+1z6jGeD6Vsh0KMwvK6MBE/m8vdfTg89yOK682JjlW2KxsFQF4ud8hJOTuHbAHHufetdvZAXMhG8iL8I8UuFGG27jjknnAIGaco2OTOaOmwyOSsTSygcbR5efr6nj8q0Pphk3tGuEX+BfMRx/P6/auwS2uIIo45UYxyYONhBXnG7PowIbj6V41isuE8JkBJwgADFjyeSB6+3oaKpYjkzlW05jAY8GOaTCiTA4x7frg/SsJdJ+WJUtnbtGQx4fHfHsf8a7q3sjHKtvaRJccf2si48RFBPGeQp25ycHANL3S/l7aUC8aW4nLeKvgEAZYLuBzj04+4pyuZPJ5HBJoYlmj3sVK/wN249/0P51vOjQLDJJslLAEOQuFBz7/Suo/Zj2hUXSPhjtGxsleMqW4yM89h2+9eRWczQzqLd/JtweThf4pH/4e+eParcpcrydjl4dLhKMivmUNjjsQe3Gf55rdJpD26iOSBvx5UNGNxGPQ+oGM4ro7DTW1JVngUpbyjckmFCHPAyMZ5J4J5GRWC6VCXcmZIpLeRYzJjOG4yuFJ7Zz6d6cpmFA55NBItQgVzNzJlRgsDwPqfXjvWtdKjL4eNyijzCQ5KPjuAPTIPJrsY9PSdp40LxT7gUYqUJG4+pP4jx9PesE0yGNvGlV83cKu25WUhUOAP8AhHJAPrzVeULcmceNPtiWUeOssY/DIMjkeY/TnOPesDpiCTGct2GR39/1NdxHpTxwieCKPZKrPukyTgHCgjIyTzgfX8qjpp8lsJHlh2Ss25/OGbA7gqOR6HJxzTlByZysWkTRoSisgcnc271I4X3rS+iQ27ESSiVtwLBeCcDnNd6bKORnt/Eht7UD9+6jc6o3PB9XPIH9BVW2m27Rho7Z1RHaPL4Y7gSR+RBGfY0VQOmcqumiZcxiRXbnwzk+XjBX09/tXkWjpEPDcjKt5FAwMkYPOeOMe+a6qHTmt0Z4XKsoztZQCOQCe/uc/wDWs7rTL17jY0e5FjLSSIVJV8nbu78HANTyneNg5BdNJAtXlOXYYVVHGOM8ff8ArXvyyJcrJEBPKRkhk5yBj9BXVDTnnvI1Us8rBi8a5BMn95uPTOe/O4VtmsZt0EEBErSy7ASRGGlORg+ucjseDmo5QnkzjG03YGdwd2fMQn4/bnse1av2cuxQhVi3q38P0x6/T3r6HHoz2hke7hSZYmAQiUMFkx2IHDDPp2JGK1x2S3MzLEht763y/wAxGMeJkfiKkZBGSM8VHLDkjhBpxG9mYHfjcC/m4Pt9wa2/sgZiMbFzIC20DjOcY+/2rq4tAYGWOe3aV1cKrOxAGQTyvrmsxpr3kaRWdvM9pCzOzJFudAPxNj+EAY5PuKnlSOTZyI0N2t1upEKQsSieJx4rLnO1e+0YwWHGeO9ao9MJmbc+xSMspOMAew9TXVwaU+xm3FI4kUpI7bAqEkDk84zn6dzUu30ZJLaSOKBZHMhEkjMSiZHOM8kf8R/OjqWCp3OLfTkfbv8ALK65MJXBHfuT68Dt715+zAWjaKJ/DkPK4xzjBwP866qHT3aOSExvJ4eFMgG9iVH4+OMZ9vevESYyTyqmTbhywAwMMcbh7nHGPzqdtjY7Tk20shR4Skg8EbwBjNSJtPEcBTxHaRHG6JpCDgnjA7V0V5o1pCu2HCNkKUfCujnJAJyQSc8j0xzWcunyeBEptDDNIg2LJMGO7Hmzx6gjA9wajlByZyc+mmP97L+5CgZDegIOD+dZPpB3cqykucsGDbgB3xXYfsKS48HNvJcxzDw0EbBtxwD5kByOx57cfSq5rWWN1VERGZQVaLkFT3J+3qe1FUuNixzEels0cjrgL2OOwHbGPXOa3w6eqbkEQYjJ27sle3+Yrq00qSWIhYowIZA80xXynAIPtuGcYxzmtKacngSTwwnaxbOUYEYOQPZsYPA596Opccmcq1g2XU7yGO04XjArGKyjTYISyOJNo5yAODjHfPPc12dvpIVSu8SLMCVAUsxIGcA9hyQD6ZP0NQl06UR7ZHKbXZmwAHznADZ9R7jjtTlBsM5xtKlfEhk27znjzYP1PpW75N4ofDkSORsgd9rMSeePzrqprIOHFs3hRujEGNTJyoJwP55Pp9aiLYsxjVbd3mc5IxjgLncTz5eO+fUVXlLk7FjlTaKs7pGm1lwuQ3B5xUh9PcRyHY4ONygjIJBxnntxXT/sxdjxyurklRJtIxkLyoH8PJHJ4J7Vsu7EomxYywMitIcKceqqT6n7VPKEbByy6aqzbnKhADmUdiPXH15xj3pLpxhMTSIoJUMkOOVXsoP35NdLaWUNwrsiy3MruTGkkYdYmGMkgHjt3NZzadcmSdrrcw37neUFcE4J5GewwSfqB61DnmSoZHGm3cpsMiCdD+FxgN9gPb0qwnt0kmjebZCyAkxrxg+gz68ffmrabT3V5PDHjPKVKoEBYKPxZY+vb/lWaxi0UsZNvly2wkEjH4RwPt+RNS53Cic6+mwliAWTKh5M+dlH1wMD7VkLQvMwjV2Gcs/ZlIHH610NzpTqk1tPvgnAAdI4yeDjbn68+/cipa2TFjFFBuvZe6j0YEc5OMggHtzVXULKBys0YILC2txvbBSIncoC43Ent9s9zXsFpbvE5iBkLsBu7bee2D7fX3rpZbGGbfbRSEwtL4rKwACHtnPcn6elatO0Sa5+WgiVXM8m0JuwWHJxk9uATzVXPIsoO5TnS5IYxLPblBIQoBYHbzxnHfOKz+XbZL8tBE0YBHiDAPH1P58etXL2TyWiyR74lyDlXDRsVzyuf6ipFtZrLE8IdJJYosxDBBXPfOe/pj+lUcy6gUQsBt2Ry+FIyjdlTluPUe31+3FeS6WsdsI9/iKT+8LKob8JyQuc+grobSCZAbqRi0TYKEDz4HYnA7A4yOM/lWaaDF4oS8CiJYFlyWKmR25ABIwAe5b0FV5SzJ2Lo5yKyEun3EEUYjlWIyLHkM0n91QO2715z7V+4fhTaS23TemvdweBdXFgzzqI9uCswVRj0AAwBX5Gn0v5KwMJMcsed0zRyrIrkA7UUjuMZ5H61+x/hsrf7KdOHaFJ0c+U+YgeN25/0a8v6pLaw/mduDjs1DqrhpJVYIAW2g5x6Z/1zXP6sqrAxfkhWJx+HkD6fSr2QliSuSQo+nY1R6qXktJEIYsxIz9Oa+Ui7yR7kconwb4jObizuREVKvCo3YOcbWzx9/6V+XPjSR/trhQFVNPtEUD2ESj/AAr9Udf5+UlSMDyIiHYfXznnn0z/AEr8q/GJCvVNodpUPpNmwz3IMQ5/OvuPoHW+T/B8p9c6vzX5Pn1KUr7g+MFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFfbvgm7R21wYxlxgjIzg5YdvXgn9a+I19v+CuPkp2ZWI/DwD3L14/1f8A5H4o9f6T/wBS8D9S9JoscOmuMndP/eJORvAAH5/zrvdMKeEhABxGpJAGOx+lfPekoxHDpyl1VlvQx5AYHIHbPbDEfYV2+kFfk0cb2R4hjIzjjGDk1+WVt5n6fS3UdRp7qlxEoB4Xg4PJyDjt9BzVF19Yi76IvogGLR2izgDg5SRWPp7Zq20sbr5BjgADt6Z7Zqfe2Qv9PW0ZsrdWkkJz6BgwFbYRtSbObE2T+d5+KNLhabS7QRSL4Qt0DGM7nkTbjJJA4zjGPYetT00xPGka1dWkRAxUSkrln53A8buM8jt2zW7pz5OTQNNli8tykcS3Mc0e14pQmcd87ceYY7571cTSWgZ7cI8dz4DEQWyrGwOBkEsd7e/OQK+plJ7TR4UYrZTOfnto2Ee8qJ1lZxINgDKQCCB6kkHy+neoVwscV0/iFUiuEHl27djHOQPcg49OP6Wdq6SQ5d1gBkdY/ERYkeHPDqqkhy3PPBIAwO9Y23h3t1FFZRxCRpDCDEGVUAB3cHjPHPpnNXV0VdmUsysbF5ozIAW8MIX4ZABnao4yDjuBnmvBbvHHOFSMSK+2No13yEAEFcjI28j9DU+9tFkaRtOt7e5lMeFiFztd1A5C7gOxZe/ccD0rZqLw29rPKscsdyqhFSNFZTJ/AQCc4zzx+ea0uZNcSkisTdRJM0cc8qsfJ4YjMjcttBzjt/Os7azMzRxS7FvijMqBfHDNn8PBGM4wx5PYetXV9IJZtTt7eWAr4ohlljyzSHA3HGDgbv5YzWwA2lkNRt2jh2SF5LPdh7e5IIyrYOYj6EfUHjmrKTKtI5+NIJREH8cQmUkwmUKUOcYGRgf1x654rJbWYPul2xSkGZZEw4MYYqzYXsTwDjn3q+mW2YLNZu0cqI7XCvLvDFcgqSeQcge/eotxaRySTXlxMkFntWbcsaqsjMCFwgHkAJz5u3c4qb3KtWKySJmG6V5sRxeKHiO0oFOFIyOc5ByO3Na5rVhIxdJUWQqJBPlsjy9znJIH9QBVvZW9tJBCbWR0QqRMwmClpCeE45VsKcfTP0rcbcb7lE8K0dmiISNfFVXycJuzuZsjlv4TS9ha5StYfvJoEcSyhmBba2CA2N+3k8cDH19qlR6ckEVz46nbAI5IkaIyYDylMAg8HcQecjiptyi2+nXGoarMsVmGXx45Ew7YYKRE2STjIUtjB5qRaMLVNRsRNaXk91GIFFsjRsQpDZ75z5SATnvnGaXdhZXKuG1jsLxhJOlvHAQ2WcSg7M8FVPockLjOPtUSDZZmTfKs1stwBEpRlxndlc4zt9h3GcDOKvZLeK7d2jKrcrs3hnI3uBw+Dw2Rxkcgc85NZvbxTLMsFvJb+EDvFxMrMmHA2gAHhtwOQex54ptdo2ewppICp/cKLaSY/uDL5tuM5/eAdiPTgHg+lbY7WSZIwbcsFcbWiG6VhjLIy/QAndnj7Vf2cDW11dGa2tIflAWCrGwljlTI27/wsCzKoBBGSSBxmoUVncWjW8momWHDPK0k8HiZ4IYKRy49OBhj2PFRtEpFX8hbX0CrFE73E0YdlyrxMOyh8jKnbzu+lbtP06W+vlWK5VJpSzq88BVEVI/JnaCGJK/qfSs4LZC2LKWO1iFu28yKFZSMZGGOG4PHOQSDzUq/im3xC3kVI2kVXEkW4+EM7iCgAjIJALe5z6mpvwFuJSWFva3yadBceHcHT1JSUR+GtqW8q5I82xvOpDZwcMBjOfV0xbYSFHYwzPJuaecZCliVAA77V/mcgc10lpZXf7LvkhgDrLclblIm3lQGIWQnsSMjykqQWUlsVEubUSTCOINJM+x5IyVSGONh+JMHkjGSPwnmp28yNjIiRxvBex3yo06RzEqEK7X8pwx7ZUbhn27d6i3MBt0WVTIqLksxcBWLDJTwx6ccZznvjNXslnHZ39o9xObnZtkVcnPhiRcxhRjcSpdz6cVgttIvhy2atG6M2VgugrFWOQ2OQcDAyRzjtxmoTJsQ7PTJGdI5I4U8OESlHjPBI8m4d1Abvkc/nWuO1t4rQOzIt1G7tISg8qswAJA42g5x698kVbX0DwTy23maWEPC8iThgzhifOyenOM8+1bdM065vL1DpYkvJCwhZSitgN3kUvgEAZ83dcGovxJt2FGE+WvRFaXB/ex7XQMA24EgjIHB7gc9ifvWBtDGgkuYjcSrCwiKsTJEoPtkgDGcnv8AhGas7TVodaW8tIrN7NNNu/CWWWHcZ+PPjABTjDDOTgj3qde6ZYyQ3W+S3a7McsaeEBG8a8KckDB3ZP0zijbi7MJKSujn1sM7sxtIk2NwIBAOwYQKDuDBjnDf3c8CpEmn3F7nCSuysqsTGZQ3mwxbJIzzzjkgfSrS0gZYIpJJvBR44gziMOBvPm5HYghdwJBzxzUoyyWH+5JZrePPKY1eO3jkgLjB2y58yuRjbjvxg8Go2nwJ2UiklhiXT2+YEkt3NPCCvi+GFdCQTkjPKP34Ix27Gs9PUWsERjl8d3VZG8U5jVgNwh38lfp96uYvAm19WmtEnsZEmgkQR5mtywIDKpJYsj4GcZOT3AzUWW1ure6uNI2RNJa7XmnDlXZ84Y7VALBiMkZIxnb6UvfIJWzKbwkvb6cRM1yM74iZMEbcBsLnIwePsBU0RpH4l3eySHxk3+CyBXYHdg5PPAycfT9bfUIbi4jNrKY8oWA8KMAkRrjy9y3lPPGGAHAxWOmWvzaLNaTmO1hCRqhRnaODOA2T5cDPPfbkcVG1dDZsznDpUc8lmizm+keEtbpGGYxqP4uB3Ug/iwM8ZqVLZyRR2w+XCRlFeJ5G2YVi2N3ByMlc/wBKu3tooU0qaWeTELtMYXQgllG9iCTkj0xg4OKxmiLQqitG8buYgJJC0hXBJyo5DAhefTjPrUuTCiVdlbyS2pEkW2aHuEOzb2U717nk/XvnilxZxwy29ndRNHceGF+WtAXJ3gspznluFB7ZJ+5q3XwrfwIbzwoEuo2kLqTvWNVYAFcbiSQH9T6c4qf+wbqW5je9sbjTluG2R+JJJG0XlBikOQcknzZ9+DxxVdqzzJ2b6HL3UQkt0i+aSaKVAGBd3YHOcvxgMWyMAnsQTxWUlu0tojOiXEfcJjBfn8JUngAr9B981c6ibrUL2AaVa3LTSM5a3kTLG4DBVLAeVVADEjPJPA4NaYrOJ0NrLcwPJdyDw57Z3jeGTnk9x3UA54ptZE7JVva2ySRh1MabvNJ4O1j/AMTEHA53KAO+K13GgpaRltRintraQsrQGUI64wyyFADwCwHOCSRjiuiktlNz4ps3kVUjjAjYgo4OS6McY4AxnJ961T2q3CeMlxdTvP4okleMsgZv4XKndIWxznBPvRTDgioME6QXMKiZIJ2RTEVXz4ONwGeMcevP61h+z5UgSWxjeSExqd2xvDXOMYY5OOd3fnir2a1S58K4EkRDlNzKTIhZlwY1J4yPYZIziltplxcqtrCXjXxi6pDuhiU4255/C3ZeOMd6naI2SmGlyXVtdwwJLJPPCInvFhKMjEAK2SeGJIHOe3oa9tNMSZ91w4Mk1oIpUjRgJdpX8J78tg+34h7V0C27R26wzRGymiPhRwztxIp3F8LjHiY2uOecHkYqPHbpDbtqUzSssk8cJtxMQoKjblV5JH/CDjjPJqNtk7CIngfLXYhjk3bwR4ZjeMsWGQAMjefKeMkD1qngtXeCaVpo42gdnARsqEJyPoRnOe/rXSzaXa280kjJJAltO0ssk7eIhyp8ozxjJJOcEYz9K9uoUlnaQSRXWI5C7SAHxFyQqA4GOcZGOBzk1KkQ43OfuFWI2rTw284ARyiTFSSrHdGSOc85HrgflUk2aywo8IltkkiYyeBDs3k4LJhQOSCDuPuT7CraCwRPFhiV3mEIlUjbz3JPYbhwSM88g+lez2ZuZjFcGSzvbjurOdiKihecHAABXGAck8+1NobJTtFcXDQxzwpAiojI8R3qnoSWPIBAGBnGR61jp9jFqNsssNwJhMWKwy5Ade5G4jJIJ9h6DvzVmvhL4BWdpYCEKKNyl2Tg7g3bn6fqKny2Muo3SpaWrvLdKjwwRNhYiFzj0JJK5Jz3qHKxOyUF1YGCfDxQwyoQoVuRk8D3yeDgHGMVvg04RWsxeIqFYRMAxDZ5y324Bx2ORVtqqXMibFDfMkC1mSVVDRz5K4DkEgjg55xyOaqvAdWkRJpxe26LCYQhZi3ruzg4IKke/f7QpNomyRFOjxLcRqs4hjdVEYkjErK7Ad/Qduw759KTssuoxzSq0At0DpKYQkeC21g4IwuTtII9sVMjuI1MkLQzJ5YlCod/fJPYDJC4HHue+KtFhQzPbvNnZG2I57dkkwwzt3E4UYIPA7+vepcmtSuynoc+kMV5d4F28pmdUuYfCBXb3J3d+4GAQPtXslhfwLDJJHBaNNguyRq/yysSCpLeXIAzk5xkCrSPpoXsUsUNnFDLaSRGa4uVxJM3GQpY+iY5GMkipfy8qwFJo3RvGQjfISjxlv8A3QOM4yMD2qHO2hZQvqcqulb4biG1mmuzuI3LIJDuAOXLj1IAAXHc8VsitRDOGSMRWkm2OMPMS6eXLYP8WNvauj1CDwbYo1vEYIpW8HwkIhWIKMlRx5yM5/E3cfdF4kMcsenyqI2AlRHj3FSTnAGMsCM9iTxzTbbQ2LHPpZqyl2WQ20qgq7yCMBMefcM7QVyMe2eM1pmgiSa5WGWORUaFGk3spl9WAJ9fQfka6HULV3S+gtyjSzSRxGK2XIRW/CoH94ZyfTt7cYxq1vPGi3R5YraSJISsaZAZixHckgnn+oNSpEOJz9wHSNxDOtnHEdsbGIyICHBwWByWwfQ85+lb30+KWzM9zb20UE0oGLa9duBk7TkAHJABBJA3e/a0vJdRFrs1aaSa7LNII55QCVDfjOfKq52j05OK3WNlbWds8OpbzaFI1eSFseArn028bCc7lIwCM5o5ZEJZnNQaRDBLcLaTw3kcTgGRkaPIGDkYJbYCSAfTFeS6dGgjmmj8aES5lY7mUqcY8o77f0P8QrpTobXauUm8Mw72R9yBXibIVipz5WB7Hnd7YFYRPBp0yW8Jhs2uIXWZZJFkjfb/AAswwoBHp34BHam3cnYsimGj+NdhbRDDO0ruYwGXIOcYPrjvgccjmsI7VzHbw3CQ7olJ8JJsYPdmAxh89wef0roV0+Z7yZNPEpmmO23aOfcYSI8kZI820ZAGR2APatdtYS3bSLNe2omkgM7CYFDgExqD5doY/iAHI9eTTaGyc/BYC4aP5WIMZFMkTyqd0598LnJHmJ45xWm1h+ZuLp7eIzKYw43HZtyo9ccAgn610V1pfyJgkFwltNGFaOAODLG4GFOGGcYyB/3j9K8VLiaV9Q/eWjMSRv4CBXXyoEPnOSe+OAARU7RGyc/PYwB0UQgQC1kiYDC5B8hXnJI75Ock+uOKxs7SN7a2s4ZoI7WEJKsckO9gu3hUAIJDYHBOAB7muis9PhkMEz3N0hSbcZo4mfMiAsVkbGD3A44BODUGCSJzLbwLBGzSSB4GhQPHggKquRkZBz3A4GTxUqVyHGxGu7GB7RLWK2unuNyC2S7l8IiEE7ptg4yWyvBOAvfmq421svh7mErDLpG2chdpBCj3PLE/YV0scCwHxStxqEO5i3zDFVILAghgeOdxyD5iCPtFXS5W1L5GzjF0UyLdjIwAiPIkeTGcAADOPpz6xtE7JTmzlmkjnhIxt8IHKlTwScD0HYbvzrY1pBbRRxOiRSwABpJslNnBLA985Jx6Y9TxVvc2dpYQfKgmYAFfHnJZ5ZBwzFAAEjGSBgljgZx2rC6043HhxwsZA0cbgsDGJFO5vKx7DCrkd+RUqVyNkqprSFoWSR1V5ZElLDOWTnueNnBB28cH1Oa1C08C1ZfBbajptWJCS4PZS2BkkchScc1eSTi6YGVjLKSSixwklQQzFSBwUGeC2TnH1rGa1M6xRSCRViZVUm6wCxPG1SRkjBG7+EA9+KXa1Fkynv7BLIl2htSqOI2SPftjYnO5ickkEEA9uR6VjJaCYpapE3gxxhYwMhpUU8Abu3rgjGSKvI7eWeSOCKKB7iXzCRJRJG2FJxuBOcep9a1PFCbf97O08Xji3EzJiMHjcgBALcA9snBptDZRUsj/ALL8lnGFuFG5niw+UOfCOeAMDPGOe9SriwlMb/MyBJ3VBLtj74bBJJIHsCFz25zU2G3ikYJCIDJt8NFxuPIJOWHAQrgZ9MY71shs7WS8C7VuAxz3AMYC7gq59dwHIHAGM1VyJSKSAR7IQiCJChaJZDsMhB86FgcEj64x9a2DTS73Burxkt5d24urAZClvITxnsOff0qUka/LmARvIFk8QxspVSM/iOO4yeOeea2yaYUhlgJ2sqLLsj2kKC2VGM9z3xzxjP1Nk2KTF3by+HhHuNvjhfEOXJCBpGHbILAcn14rKaFLdnjgzbyMhUs2Q8frlcdjxjJ9M1eT2KiWSVruO6dhIXO4ZTzcHOPMT+g+9R0t2S6ttztEwwQhRQWKA8NjjOM8E+nvTaGyQLKwiEqypalmQKjSoM7FKjO4n8Jwfqc1ujtobKNoNzRylyJUdDtUEYxwMncDk/b34qe1rK8FsqtGjq6SOsZKszPkDaOxAPdvatkOkb7seIpkuVwR4bAq2Od2M8f4Zqrl2llHsKr5BGmQlg0g2qsKxOzYxtAVQB2GPt96ws7KFLnwvARpVyQXVshN2DuXO3Izjk/zq5sbSM2Ul3IkSrPciFHe4O4tyCqsSDgYyT2z78CrCHTpXuVU2wtLTc25y6pGyDgBSTls5GMccH7VVysWUblReQJfSsI99qlsh4MeySdiMYUAbVUAHjtz3rCDTZJfJEWhlkyAzQ5ZU9VQH0IGBirYPBM0DSiPxI2RTGYy6hidoww98+uAMg1vkgktZLZZQyRh5BLJCSdxHPh5JyO4HHpk/SstprI1snmUL2azW9wLe3zLPbmKNBkqWKHyqT3Jx2xz71+seiJJToHT/wA2GWZtGQvu7ht449O3bH0r82pawWmk3SXN5Pb3aWpijiQqipIyMQ3Pm2k7BlfTIxmv030pAbPR9DhBy8WkRxk4I8w8PP175rgx7vQaN6CtIs5wMTBRnahYe32rn9TUR20m1Rt3MFGeOO5/WuhKsWO/zAKSR7n0rnNSQ/JuQNwG4gZxz2FfOQXSPVW6z4j1/jwpguSoRSQPqD9K/Lnxmikj6g0V59okm0CwlKrjy5iGBx9MV+neu42EBV+AQg5P4RyT+Zr8u/F1QutaJgHnQbI5J7+Tv/h+VfdfQes8mfJfW+r+x8+pSlfbnxopSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUAr7b8Fv8A5nXzKGPhhWYr/CviKD/Jq+JV9n+DCqbS+V3ZEkifJDEAEMpGcdxkdjx2zXkfVv8Alfij1vpX/SvBn6h6aCNaRiV8Kk4wcYB7FeMcfh/n9a+g2PFvIGeabYsgaRuM4PJIHrwa+bdKO6afdYcncufKcY4OPuOP+tfRLRWMs2CP7SVwxAGBnJHb0DEV+W1l0mfp9HdR01s2y/IGctjAIyDx74/MH3q+hGZNOzk42gfbcK520wblHY55j9foDx9e1dPD/aWuMNhu3/i4rTB5tnLitEfB+mNHt4+mdH/aWlCK3jXYRKu922u6BhIi5AIXggZHI9M1YNp1rPf3dhbWcNkLa08RIba1Z5Qd4CsCyk5yeeeAM4q8sLl7DRokXUVtn+UURTvGvAfBG8chiG4UnAPGeRzrewlnEtlbSrZTkRzySzIsqyypIGZSzMCqYBQFcldx4r2lK7bZyOGyrFOmmCGV76GBYZUtRHIs+2RIo8kgrxhtpyOPzAOMa3gs7VEgu7eI2N3Osi3by5DylGDCMenlwQWOeSe4ArqF/crBb3ngTId4kdwJVJwD5lOGG7IAb6ZFc1dG4hmmla7ghtIo2iWRYwxIDg7htIJGc+UgAg4z3NWWbK5WJU3TularYRWvEqTy4ijaETpAjE+G7Skg7RgkBcnAFVcHS0Z1dNOeOJrwxyTeFvVtqgnO1WGe+PMODnHfNdDbPbzQ/L3sTWsl1MZZWjkO4wlvM428IQSPLgDAxyDUvUrieGfVhpsx+cvJ2ZYWXanj+bI2/wAAJ43ZP8QwCRVllkVaKa56Jtru3a8SxiKAs+1Iv3kaHAO9B5mAZSSv90cckitUWgaRpNjIuyxF+xkaKWG3eURskfOV7JgEZViAe2DV9Nara6oqwK9yYrVZIHmZRgScqyKQwGFDr5mGO/OTSO1khAZY7uWeSO4B2xDjevm3A8Nx2JznvyOalSehVxWpzv7A0i6S3imt7FRbqYRKlum0y7QW7kYPAA4wO3vWx+mdKu7WSeSF0gL4SAQrulZhkKy+YKOBnHGQQe1dHJJPb6aI4ZkAht0LPcBX3kp+FY2IUuWU5Azzk8k1hKnhRyNETFJ4m3fnchYMvlKx/wAI3E4JqNqS4jZi+BTS9P6Sy31sNLsbmaSIeIkSxoVmC8K0o55H8XpjtyMwpOkNGihMC4t2mUkTzW4bwcqCyx+hLcjsp5HHNdBOIvlJL+8Sa0uPnI1/eRKVD4ZlyVwWXapODggfepVm893apFaSSrEmQLnaQwZW8rleOCA3O3zAqcjvU3aQsuwo9T6O0jWkXVPA/wDNrySJNHJa7pYCy9sEZER2AAY8uTUJui9LfVysVjDcsLfakcVs0Uku4hmKSHCAAEcjgduDweotlkguZLma0XUdTlDRRSXFoxCL4oWTeowuPKCqnPrjAOa06jczOssepzQeIqhYrdo2UiFXDEM0YbZHwCQuN2B+UbT4MKCvmjnLPoDpuFrBP3UVlFIkEOR4k80oAKvEy88klTuHI7YGDUiboPQUSzOrR2EJaJ4gbm1GGTyh97Lk5AUdySMYz3roGkazElwzQ+PchZ52MOzJfkMAeBLyAwHcbexzWqVpbpobgzPcWm9/FWWJUUg4KbfMABljuwctwe2abUnxGxHsOeuvhyNStYxfwWaSSSFbWfTpXkVGOD4wOB5wF24O5cE/WraDoqyvPlbaOx06XUAg8ATRkpJCFBkUHn94fORk8845FS7u4mMEssEckQjXw0hkiBkSQtw67T+BhwA3mJJB4OKzVo7pNRaHdBHFciS5Z1bEjyqC3hDO2TafxAgbWPGcEVN5PwGykU03Rmj4uluNMaJZmFyXSJSI07Nt3n8AyOB5hwPYVutfh3pl/ZYjsrWYMh/dYkjkkG7hs7ssRkA55HPHaugL/wC8xw3BRYEZlWNo5JFKRpt34XjfkbmyOMjkEit+kwLdSw3FrL4pkkQOEBSJI/xN4BIDEnP4jySPrVby7SzS7Dhj0HoPhGW3tJWQyOB8yWYNg4K7MDjKkE+owMA81OPw90yd90gt4dMj3yRmGVlOxlALoxOCMgDBB7mumuriW21N11GM6vPJMEhdDtRwvJLAA+dj5gOM+bPHNRw6PJCnhtZoJGL7H4fHmO0rk/xr5QASCe1TtS7Rsx7Dl7r4a2F5ZC3itLGZEg2ySeETgq34g2RtUgKOOM/esZ/hpp7avZtdwi8tPlJhcKtsz5kygjdps5QKAyqoGCPUcCu5tRdjS4Xe1FleSvtlaeAMEUcqJMnP1BbgY57GsrNZ4CNP1K0FzNGfGjeeYwvncxYF1O3aOSAcjkd+KnbmuJVwg+Bwtt8ONKhAhgtnjk8ZZN/jMzyybsgkZwDzx+XI9ZV58NtJsoljuNNeFbUsWgiZkeLd+JQ4wW9V5OACccGuxm065l+UsrWL986LJHqU6Ai0CEFycHdIWDN+I8KcZzisJpn1Q2x1SWAwjaLeO6nkEc0K5y6jaSxAPlyfX0NNqds2NmDeSOHsfhto8Pb5pLi6XasgupljAB8zMqkbshUXAzgdu1ZP8OtJiuri3uFuYmI32jfMOsW/YVCZ3EuSGLbeMbfTNdzEJGNzazXULsYMqjb2JZVB5bIIIB/CCSQc8c1jFYQx3dvJaHY00aHOo3BKTSyMAGCYz27EHgHNQpzfElwguBxMvwo0UrI18t5NM7MGkiuSomAwAufT8ORj3xyRz5e/DfS7KK2uHlEJkVDAPFxHBIjZVRjhtuSSWIPOc9hXbJOLeCPZHMqSAxSQQxMjKQWyirkFWJ43e3HHJqPY2wFxHvtA1xPHFFP4cpaIhANm5CMeVPL2DnGOe9TtS7SNiPYcq3w+0q91Ka5WK5ubiC5lk2NK0jOGfIyw4G1uQR7/AEqRqXw9sr68Jv7a4gWdBMR84VkjkVcZBUknHHkwc4z3rurqzfTYZJzPFqDO5ldba6PnWKMr4bRMMqBjJxzye+KgWoiae6t4raK0iyWmuCqwtLL/AAruIJUE+uOdp7ZpeS4kJRa0OKn+GmmOLVlt74Ixa43x6gTHIzDaWI/hkUr69vrmlx8PLa4gkhvru8WS6bxpGkudxOFIKnjgYIPGAdnPNd7YfMyXBKRRAsxx4k0aRLuJ3klscYO7IyP1r1HttStZ0k1GG2aFJPwYWUwyAbhGcBWO5TtHPD9qbU+0nZgtUcPd/DsCaHwETdDCy2YknYRxqBxGRz+IjzN3J71hbfDTT5HGyO8ZpWCh0lwsCZ4xkcEZILDGc9ua+hZjWCC2t90kCxtEgYFm2gAklhhmI/vds4+lZyXV+9tJbzyyeJLNEquJB5F2KZGG4NwOD3+5pty7Rsx7D5ifhrp4uY7hpNSuCwGYDcbWCrlAZHHYgcADkZ+9b7n4dWzyxCOe8kNxHGgD3IYd94DsRjavHbk5HHau8vtngq/gncXR5nE3n3ov7phjjksTt4GalWOmte3V8ZLeG8twFczwKQ8bAbyWJwgPAwBn0yR6TtyZGzBK7R84X4RwzRyTXj3iyLDKFmEgQAcebdtJZ92Bu4IzxW24+GsEsbRXkk0s0f8AYRRqrMoxtzkDLH7kn19a7q0vRLMyTJLcQRtuCrhotoCkOCBtyxJwrAjOc8GpQaYTqlsbWORHVEaUjLfjKPtQ4XIBAHck/Wo2pPiS4pPQ+dj4YadqCxC8luLiKMfvDMVUPkBiHQAbiVGM4/nUCH4YRJe3l5czXMcCQRLFawXOxd2S6y7nGRkNjyjBxn0r7BFbTTwF9Ft4rYTsnzaTKRmbbgedgWVQAo2nHp71WXeoLcRWsK3ix+CptJJYgCyqrcxSHbnaG7ehHc1ZSaWpS0ZPQ+bf9mdg81pDFcXFuS/+7GQLOsWAobbkfxbQd4Oee54qUPhzDaXD+KLiR3PhJE08W3coADPs7914H/DnnNfQJSk7zQPNLBI9u80ixzb225xl3xhVO1vw88DFR7VYYNTlfUNhMgGH3DEW5gRIMHBJXaAMnOcHkVDlJ5XLKMVwOFm+HiSusQlvogY28NGmU+ZRlwAQdoJGPTvx2xWy8+F9vEZnna8WKLeyzQ3KSKAy+VYyM4H1x3HABr6jDb3U91Jd3TWtnClsXs3ZkiiuTvHkO382wB/F9KgXF41nqFnb3dx8uojcQCOA+ICYyyh8AjaeSSTjyDjNSnJEdFnzWT4a/MTkW11PEJLdkKB+GIXIfY6YHBBOeeTXlr8O7W40yb/fLq7eeySSR/GQiPBIBBK/3lIOPQZ+tfTUgC3tnMYp1uBhh8rDvVlB84IJ4XaNuT7k4OajXhtUgMMIitTbZ8JZWyYxIxHBU4KnjBBHft7Q5StqSoxvax8wf4X2V1aNDNdtBFsiSR/F2u2F5LkDdlueB6cV6/wxgEsNzZarewgRmNHNtHmFTgvIC/BOQMAjGfSvrdtdRXSh4pJIbU3KLPJagefy8lsZDKTn/CommwtKjDVQ8CqC++aPdlBjDORk5UdgAACST605SfaOThnkcRd9CLqqeJPe6jLC4VD4pXzhTuGXIwBn6cY4qrm6DFtHOYriW4OA5EKr5ju7hgAcZIHYivo0dwgiaO4kEMFw/iZa3ISSNmVlKoSSzHOeO4FSLS8vfAuDLnx7ra3zKRyLHEoJwVXO4nyr5eMMMe9VTl2lrJcD5VJ8NYjdwWUtxeWzgmRvDjWMNMTxlcZc+bG48ebIHIx5N8JrCZbq9SW7tWFp4QSN18MN5VVi2MsOCQD6sQK+nWqW6W9wLy6EV3K/jOXmTbJnjcqgZY4Uce/vip0E0BtbN3RZt8jShlK+Uqu4yBezEZOM8DP0FWU5riQ4Q7D5cPhxHLJMbiRpdksfy3hRqjKxVzKoQEj8BXj0z6ittn8NIEtCrXN2z/JmCOYRDKIoO7GSNx8vJP196+j3Mcd3thkxbBJt6Ns3bYwoTB92bBX7E5xxWVlK8XjXNzFOzyr+6m+TZDGjkrwrZTscY7cZ4yKjalfUhwjbQ+aD4XQK8ks9zd3MJmjuFYoudpl4X2VcY7YBPPGKj3fwqLuf98u5PCYsoDqGQAYL4wOQAOBz3PvX1K7hjvYGuttyihkWJl2YZE7H6Z2nGB3OAK2yqupJHLFLFfm6JWBF8ocbMlyw8yjJI+h4zmm3K5OxGx8oPwyZ7jxbW8upojGgeZokz6eXb6k99w98HvSL4QTyW63GnQXLtEsi/MqQNxwB+DO3H04449a+mRxJqaSQeHLJbWgiFvO27aOAsaLyOOCC2D29TnEoyL4VzFpN5PYP4hEbRMxCZJXeAylXHAP+NTtyXEbKeSWZ8sn+G9rBZ25uJrowpCwgj8NHEjMM57AFhljzz6Got18K/k40mt9RvrS1urOEeA9svkA5YBFwMu+dx+g74Br6jJp8wUyTTuq37qsfiOJd4AIkCnIILYJ4zt9OwrTDLJc6WZJblp1t0K28yueATgHJztyTySfrxTlJIcnB2Z8o1L4WXjwNZWWoz2olJiktAESPco8voD6HOT7+9TJPhqFsoxba5dHTpbnF0EgTlPQSY/Gvpntgg44zX1rWzFqtjdtawW5tLxjMFbBWRm8rYdvY85/TuKj28cNrFJMpEblYwiopCl0zu2xg4J4JyOCB6YqXUlpcqqcXnY+P3Xw4gm1IT2l9cK0s8ckqFfIwjBy2ScAEgAse2TwcVnafD/5Y2j2N/K8oZ0EptI1jnlz+JFYsWAA74HBXnNfV47q2YbtQu7eGC7WRVRSkskmzD52AkeIACWGCFB75ODCtzFfXUrJFG0ZaOSGcYLLCV3E+ZcJjzcgc5Ge4FOUmlZl+Thd5Hzf/ALNXtGt4ILuazv2kaFLY8yrleGOSC/Oc47Bhnscef7B3T2cy3l7eXKxxbmsZlA8MbymAVALMc5ycYGBX1S9gur+OLxjNqdzA7G32xjfGrYyH5XecDO324FZH521jEt/YXQ8WBo4lnmja4n/i8Tw0OEUHI5OeMDk1G3J5kbEFZM+Sp8Noba7srmS9nnkLeZmt4kijBXvkct+IkAZPOR2zUqH4cJbK9zfzzzXFrbGNEk2hQNw2queFVgg5J/xr6f4ZeVrqVp5EWWSaaW6QRmB8LthIHABDKFK7jhc8VGubO0ivG/aLxw4Mcs7RRSSAfuMqPffwMH0yB3o6knxCpw7D5tN8PV06UzxXmoNISHkLuIxwMgAIPKDxk9sehrVJ8MpLl7iOS6e4eOVRFG6p4vmXOeVGcEsM84zmvqyWltaS3dyLiKzaMxrDCsbSGIgDcWODtUdiOeSM5qun1K0s7pY9ULjDzOJjJvJAXgfiBx+IHsBkCiqSQ5OL0Rwcfwli8CzFrJKJx4vzEcUm5BEN3uSc5w3iA98fWog+FcKRxB7vUYWc75o2jypQA7Ny8tww3cnB4wOOfqIeeZrGSBjCk067jbOJDHhc7M7sBXbGCM+w96qrS9VdOa8sJbm1S5h2u0sTbgFl8sgzyD+LgggD3zTlJ9o5KGljiougI7i7S1uLlrcyWUbyPG+xwykhiQVAC4wcA5JfBFQ/+z/wJTBb3N05t1Cqk6w9ye7DnaDnCgexyeCK+sgX4M7uIZkWQMHinUIsIUNtkXBZiWIJwBgAc9hUPbH46qZFae7bZAdjuJQGy3HbIJ3YOPoO+Z5SQ2Inz1fhQkUbNK85lL4YM5dohsJDkdj5s/8AhHevG+GcpibUn1G7j8MLGrB1YRxs2CpJDL3wcj0Jrv2nvMxw3nydjJJKFj8S7IzjJUM3G9gqsO3GOcZ53S2VncFjaQ3VpJ+CSLxVlWQjB5wBhj25GO3pUcpPtHJx7D5ZD8OLG8V3SG68CLl58rHHu3Z3ZO3zEDIC5Azjnitknw+tBqrw3MmqzXkKNexrchoVRXZSfOQUD8D17D64r6G91FaLDHcQ2kdxdySeF4UvjvCqLv3bTxu7gEDPHtisYzJd3IngL3N5DcBZ3eB5EL7csTnCg4OOM4yatykiOTj2HBSfDq0iiu5Gnu7mGWF4oCr8KANybCAM+YsD98YwKk2nw1sIktpLOWcRxhpGja5824qNqt5TgDkH7cnJrvGvmiWcXtxOfAxHMYm2qC+c7RjBYYJ3c7ePWq+2X5K0tILeb5jakUEEazMz+HnAiUlQWye5OSTyfaq7cnxHJxXA4+8+GWlSxOswcclN4lJ8TA7Lzg4C9uwBBOaJ0Tpd5JGbwAW2fFikVnDyMO+DgEqcgBQBkc5HYd6LWbwVtGhwkxVGUjCx4kGccnOQG49eOarx4d6sbKZYlWJy++NSXbOP7Tvg5zxjHqOBVduT4l+Th2HIJ0HpCokyxSXF3KhEWZCETk4MnPfg4GRUO3+Gkd5HdSTahFP8wzmBFidVFu/Cn8ZLsRklsgZGB6mvo6rc3sT26LbQXMUUUdxA7bSsQJ2vtABZsnBPoBWqTUPnL35aysfCt4IVSKVJAIyQWBGwYKhFUcnjk/nO3NaMrsR7DjG6C6fSztIJLRdPuoHVZY54TuvVx5YwUOFPIzt9e/IqP/2f2LaeWlAsY8nxDZZy52jtjk+TKj2Gfc12b2yWryfKsBeRx+C1027EcDEcBuTyeNwHOce9ZpMI7d98UDxKFaDxI3icDIUkAfhyD37n2A4pyku0lU49hxkfQsd899Df6amlWNgmyzurecO1wr4LFgQShH0A4AAA5NSx0LbPCZLe8+e1O5bwbczZEwgVAhdQcsoXJ83APIHfm5mvnM0tuxhTTDtWFR5ZBuYly443dhxjPBBwKtp4reeaC9jjjMUaiCeJpHEYJwTvZTwFJGAvfsM5xUOcidiKOdk6CsbO3SKe5Amt7QlZWn8PxZMfjfkdzkj0yRxWifoCyaEhI4YgokgeB0JErKMhFZiS33BFdBLJdXVuFe622ts7RzSNgPIScRoIiNwZsblzyFGT7VJkmM1pDYW0U6I8Kywxi4xuYYG0HaT34J+/BPbNykuJdQTRzum9JWFpGkMqYjZ/mY027jlCMAnuBjsD3wT2r6howYpEzEE/LsW3D3dTn71yEVo+8yT25S4RiWcKSdwyuC/8QAY9u2K7XRYyS5A8xteD/wCIfzrjxN5QZpZRR5ceUyN+FckcDOBXOaoVazuCOQoc+2Tk/nXS3MLMXaPbtGG7n9P61zWsgR27BTvU5yRz6mvGgumdia2D4X1vMCZF5U9wp/hx6j+lfmD4vSGTWdCyQdugWKcD2Qiv071xJG2/92qyDP4Rx2Y9h9vevzD8XpN+taINu3ZoVmvfOcKea+5+g9Z5HyX1vqz59SlK+3PjRSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBX2j4Ksvy96rMw3RsoVf4skcHjtx9K+L19m+CeCl6Dw2zyk9icng15P1X/kker9L/6kfprpMYspPM3nhGSo7jawHqPufbNfR7FnG47CTtnII5OCqE89sdjXzjopX+TjhADjDKAWyDhM47jPvmvo2luktuuG87CXjIPBiU1+W195n6dQ3UX8Lfv4jwF2qeD6bRXVQZZ7bk/jG33HIP8AWuTTPjRkbv7NMc/QY/wrrrYlpoASSRIAePqO9XwW8/I58Xoj5dYW2m6fpcZjAllvrcOLSV90ssChTIkTZwyru37RwCwGBVhDPGNXubQtA1yoN38mZCbie3XA8Xw88KWJVSo7j2qlsrqfVNLtYrSyGr3ECgwMipCc7nXwFlc47JuK8HK8elXawrbODbw24IgWLPh7j4QZmMe4EsNpU4TH8YAGc17C7zmle5GsobWH/etQtooYHZomiUDxJCRkgegJDZwTwfYmqtYsXUBtEIuJo4tsBiw3fd5mHO7avOewBxVkz3WnTC4EghuIldREkSBJg/LLKCcMo3Nn1OOecAx3hYSWUemzSSkgi3t4IxGjSPgYK+m0YVcnOcHsTV13FH3kW4ZgjGwE5jS4ZC5ugfEIYEeGo5ZST6kcDB7VN06XN4bzUcrcXHiPNcOFzuLYbeq5xwB37BTwCak29kb+1ubeeBnLfvDL8uFRJUwdq5wexAPHt24rRBG3ghROjJcTFZbfZzkjL+I+ck8AEAYLE5OTS5GQnhnt76CK4mmMngb4o3nSL92isyeXJzkuG59Bx3NRJNRhW+tzCqWbvmVn8VlfcQTtRu6E7Ow49McimmxB7maSNY42YRuzCXwzKqN+6AJwO4ClfYgetJGktYflr23udSPzIURQw+JtYZJ3LnBVM7jxx6E5xU8QZzPeW1jLdLBcahcys6nYQWfIJbepyEbbhcjLZ71jJDOuqxA3GoaXYJbx2UMUkLIuRLvyEABZvIoKnDbQQuKyuYZ4FSFraZkuHeYv2QDPl27ju9c5woBJx7VvgM8kB1PVL9oZEZkWO8QI87EhC5IXByCMHjvnPpVloV1JV8yPELa3eKa7sZ452iD4Sd9hDRscZB4Q85PYepqLcSMVNyxSYSeIiRRS4lbajHYh7GTaGOMd/UcmrCxmklMdvOlxaQxHbI0cW5HDqCnOSQAxAJ7gkdhUKPUrKG7MNtMjIHCwHwz4TShizBcA4wBjg87T7VAPYdUkuodPDreyz+JHtjurlmZAMAqsox4qdvxErnODWdwttqF1qcF0ryxs0csf7lXVTsGOV/hDbiCDwScfSTLPOdNNpqrH5bxGa3R4NqA43FFTk7B64Y9/TGKpzajVbS1vtL1vU9HkikNxDHpxYxTBDjaItw3/AIcKMjALDac4pqxoizv7W4vYtN8KF47MeETbECOUJncgXOWxlC3cHAwTVeJPl7+6ErfL3kblZUeIIDMcsACx8gwdqpkcNnJqztnuL2a2miRrZnWTwVkk8T5cEkLvXgKWwWKjseCa1W73ungJkzQOxm3xujhpRwokVh5icfiBOOBwDUJ2yZPgZPcW9h8vKIDbT4KwlFZ5GfIG3cMqmCBjB3FRxnFTRcyqtok7i4sWSWOVRhwrDzlmx5kAYd1BIPcGoDpI+oxQyae7lZEkW1+YBWMYy25CSfEA3EKvGWzxWy/tRcW8WoaTKZfmnJjQQFVUht34Vw27nsTyAAcVOgE2qJ4l1cW1s/zECSyZ8VJBjGMKcgE4xnAPBHqQK2xOsUMaqsaXJAS5m8XcrrtHlC43KQQCSQTwCMDNV03hwz5aCJvkfGGnT74zIqqWJ3eXCnAKkDsOD2qXOPBxBbSvOJyuJI2AZot24eIQAFzzk5PYYqCSyaSGSNnvpFaeBi0pjlVFYA4CSxkEFlyDkHjGR3rRAbRoLOe0uFVGkMxWG0bLIzYVgjlcOQRycc9s5xXk9jbODFZ3amO4w1w01uVhYEZC7fMz9sFiOeM4rC7hW3MJivhI1q6TQvbTh4uAceIeDkkKATgZP0qSD2SSa/ubOW2tvm2mjPzW+AwpkllRpARmNSBggcEsMjkmpM8QvPEtLue4e3jdoRLDEkeY1wVeRhhAOTgg5KgZ5OKp1uVkZLGZ/mYYoy00MSkyMGVtyMzHuAckjPepul6haaiYJY7qb5Axm4Uy2vhlYG2qilf4RwRnjtz6VHAnRkm3MUmpKs8kTzW1r+7mhV0jkXcednHILdj/AHc9sVrl1pJJXn33YlixF8sV8KKKNRtZyrjceVJJU+3oRUbUXhtLJnmvrm7vc+JAijxJI4m3qSucAHAwMnaAec4qRHcX93pwltoIEupogJJPmAyMpQEgykEscYOc8ccVC1LOxKW3uobmFCBKsnhm4kZPEMbhSzHaAAB5gO/IPINYtdotu5aKF4B+7kt1kRGiG3y4l4I3cZC8D2OOdySQwX8F2zQ+E85KTOMtJL51DMoYecDAyODnt61DmgSFLT5p5t6yvKYzCqHCEYBi3cj8XBOeARjbipK+JJvL1DMtvC01zc3bNMkxYFkiIQRkBRk8h+MDOPNjNSpYLZGtVm1DVLa0juopYJ4rPw9kmMK3fyAAqe5Oc5GOKr9N1MxJJMY3u59zIYEHESZDjbKxGVKkrnI5yDUq8ka3jW2imitJL2Zl+Yt87AFXguozjGRznAxzVr5lHHgexwW97doEv8vdxbnupUCuANy7X9T3KkE5IwazeIWttbXmopLL4LFxFMwkEQUg78Z5TDKcezDj0rRKly+p2EQlt0060aeZoFikW4mBj8piw2M7yWY8lvQjBrOXO6O9Mmy5HLiRFdN68qHGPMDjA+px3wagEgOlwySyfLyrHK7pGE5YAYXacbjzztAHtgntFmlknuvmI0a9tYgvy6hdm9HYL242kFB3GTuOKsrSNtRtrCWOdmeW3OyVodpR2jLNHICTtdc4APYdjUddRE9nB80jS5bzPKrBpXzhgxXHlygIJ5ye3rU6DwCy3sF5f7j8r8uvmaJ90jB2OxDI+4hM58o28DBNLaKVLGNVWaISRMiLHGyqZSdwQ85Ck8c+p78VHS0tNNhmW3klnl+akuZp5LmW4d2kzt3O5CgYyuF/CBxz39aUTrGls/y9zYxZIiBWVYyx8yKeDgnaQMnBBGKPWw4X0DahFJJcfuUZlVZJC8m5tpTcqMpGc+YZyM+oxkGspLuBmZopo55AFSG2uWG2VlQnJ4AJXjgkZ4Hfg6JA8ngyxzyWUSRC4dQqIOGCbmyC5LBRjnOAO9S7a5e0iWAxJHPKRyTs5Zhk7uA2X5x7L96E8DKSLG+e1a5O6QqLx2ypXapXcgwFXzEYAyuyvZJ7eyhlhtrTdCGUNCeFU8eEdx5JyQQ2cgCsyJGdmYyOYrrxY1lmZXjYKSBjPmzkkqcHBzislJuGlaffbxiQGYlGILHJKkgY2jGfzXHalyCvhudNM8V5BA0mrvPvdrXUGikkfO1jOd2HjPAHlLYGcdjUqRvm76We6dI5iu7xXEakrsxw3YHgdwew5qJJdSC5a6WZY3VV8CS4Ys8o4JUk9yQMYGOOc1ss5VljW0uY7UwQgrKot2mDSEblJ3HgMu3sccc/Q5XJ2bZk8bbRrQ2sc8ccWUSOS4Ee5MZZWRQcqRznIPPZajRW8UMcohe1nnijdY1kjdYmRk8rbS3LYXAUcZyQM1p3bUla5nebb5Y2yoAYON3iDOMgg7RnH3FZQpc2FtdPYIrandXEUrNLGHjAAAKmQewZRkcZY1FxYjtdxX3Uq2cvT+sxqzW10LqBEhsolkzlC7Nu3jZ5gMjLZIAqzhlFzJFLHMLqVJt0ixOA4wSyDthV44AJzn0rHUHkWb/7IS7tHMcg2u0Mb8ErnaCx83fJwPXFZTQS2/gzXdy6SGcu8ES4jkDDD7nP4QPxBiM8EdjV3a5mr2NN/by+Ks6i3a4/HJK8XnJYcbW5HJCYGO4ya1yW873NvazxgF4H3s8o8NWVN4PfLFhkBeAMnNSr2FEljVJImulmRIriO2KPtEYJJAIHB9fKACPWsfDSe5l+b0/ZJbypLtguVVIiMEDt5S2C5yccgdsVW2eZdPIxghlmEL/KyyQwGRylq6xggkKGdR3G3I7cAKR3zUq2VlLR2k6290skghk8QPtAZS2ATjYFwPTt68VVEi1jkjnidrRplbwJmC7UbntjBA3J24zmrRBdTNLczus9nDnfHOSpdQBtUHgYDsePp61AasQbTR4LjVo0g0+3YqjMEm5WMRofDcrkHADHgENhsccVamAWV5uiikvbSQIxW6kbaGGVAVM8Lt4wDxjnJOa0OPltLnisxDNPNC0sqpN5XY4Vo45SBgEeUtxnk1qgL6PKkFxaiMyMhgijzmPAGGzg7QAvrjtk1a5XNs2Xt5cLbor29uq20sZVVtDD4Kn/AN2i98A+5+prLH+8rDb2Hy9m+He18AhnVx/f/iYepHoSKhy6i+yeUNvLuommQsRKy42BSf4R5jn1PNSLz5S6mea6jN9LOzM8ELM6tGGHn45U8ZwO5z74qL34llGy0I/7PkjjuGkWGBmjWOdmVmliRO7Fslh9+5BIqZdzI8QjtruYWvy6fLxxykF3Pl2yIeBng8Zxg1HEUtm4Qw3FvFMix3COXjxx+LBXGM4wM+pzWixsJ7gvdriIWks0bMNpBAyRzgcknkj14ppkRrmzZcNbyXC5R0LiN0QhnkIXJEj/AN0g8lVzgfniTOYZXmlggnNu0YXaWLHzHOcZ8oAye5OTnHFRzFLdLpxtRPIsUTrFEpGIwy+Z/cuVD5Y+544Bp8o0i2txHB8xD8sJJHcmEjDEk45Yg5GSDjH8hOmrNtvDJbi1mjZbZFCrI9wCqzqF7v3I24GCAPwt71sWeO3lggDR37xESASYjjCAbt4Gee/HJ5zwK9SOORYv2lfOzzSKkrCJ5WkHmH8BwM8YBGB61HlhKS3bp4FsIImLM8Oc8gR9jgnt3xjPYVBGoHja01gk0qTBI3zNG58u78T9sZxsRVz+H15Na72RHu54bOFlE6gsFi8RTtLbUyPLgMNpAPJ3d8CsNStI9YvAklraXSxMgkEwfaz7AFI5OMDPlwf4QPXOy11ORGljQMIln/dxNAEWMv5diLgZADE8j1PrUthKyyI90qzRzhsweDGLcl7cYRXZQzqAT5uAMkeUZHvWy801bR47UTGUz70ZWRnZAWy6BgexwQTwFDHHpW8pIcwaikyR23htGkYWF4skhmZzwwx3wO+Rzio94sO0RTxxhpHZseYrGnYSYHBJ2qo5OMZquhdZkK6t4bS8C2MNvHKrlo0hVp1UEKpiPmHIIb+L157VlBpV7+6M6TAyTyRCG1c+GipGHLkKMruZvQ/bvxtjVLHR7RNIjFvYIiiJY2DR7dp4L482d5J3Ywe2M1Ht3LIskPhgNK7wxuWKZIAYtzlt2ODg9h6UeROdjbpcd05uL3VHtoiLh2eKKRomEG0DAUpkA5CnJB9vet9obG2nS6stjeO4WO5WVZosgfhViNzEEds7fyFRjNZ3Dvaw3aT3STSfMKsrBZi3ALbs+H5j2HHb2pqVvaQFM2iStbw+O1oluW8OLb2iB4YnBHOCQee9Lixm+2OVLWSQxzT3M0k852F5XzgkY8q4VVRcep96TXbjVrmKORlJeYzyRuFMXkC7QGGWcbFAxznOPes0tkkSBUgma4gY+E80gJQhdwcAj8WWyAPUrzxSPS7qykNp8lHcx6cWG+DEYgVgCAwOWkYnIaQe3GM1ZJkXRCjKXslmsvMxjYR4BUwMgy+xe+3hc7s88c44kmy0e4mhvdS092eKNPDd/wCxnAyxbYT+IgYwOckAjJqO9tPaC5uJVBmSNJZvBZUkO6QhSCfyXCj6/fLRrsW18z6mskc8KPbrCT4x2s+5BGRllUgZJOCfNjvmoDzWRhBNOslrNLbm3eIH+0ZgZFKgpGy9iFZgDg87Tjtmo91CLO2XT9OSSJtyRTyjJlJZsttBBwuCSqjG0k5x2FvJby2MTvHe2wtUlVw4XKiQ5DIysdwJ4Vc8HJ9eaqVvriQuLyJknj1GRZGSUGIefksx7p5toGM5yajMnJu546QPdSWccTC5vGKEWROdoKgJvGAQMDJ75zyajz2i3U91LCpM0WVnkC4aS5LEhTHjPAZhjgEkH0zUuG0n2T6dbzxQasszhle4b97HLIwV3IyFLFX2IMgbecVjHAnjW87R29ybhjI+xWickMSN5OCdxX8ZHIAwKXJyI0LxK9hJHA8jm6W3DTRABVdDuwCOSACQfTA4rIwSRWxK3mkzIriR45D/AG0pBzuPJwM85Pp9TUlbWaVjcyW8jbLZPFMTDcZvFAYIuCEIBJGe+M1DmCT3cznw4rWNnkhVwFUEnzDcAc4/DtH15yagG144Wvlv52kmurR2SDClV3yAKzmMEAZJbHAIXmvL3Rv25HHpssd7FO22OOWImApIASzKWfBfvkHJYHnNafmYJxNHHFd2kzmdlW4iwZIwBiTynbtYAsCx4HfFeFJJtNSaX5a72uux3jVURnxgl8+YHCj/ABycVa7vmVS7D3w0n0+GG4tZJJrfxIbHdM8pUxce2WHIznOT9jWuC0/ZIgDeHE1xMj+LmUTl23Fwyt3U+hHoOKkWxuBHbxQIdu4KTFJuV2GWwFAyCwyB9O/11ymLR9O1OeFbrVJkQiMyMWE0kjedFP4SFXIUL7Hk4pdsmxL3WS3UbxwkW0QaT95/AuOCw5zzjAGCffFRoNIuJooY9ShmsYVmI/fjCtkFl5DB2JXJ424AGeK3NdyRTzWukvDG9syyK/js8zZbAdsgKM7SMDsDkAAVUsl3Fb41AyzKkLTY8USSM8jKW3ZJDDJUEHkAd/SoRBb3EiXePD1AXsqtsZyuyKEE5JTk5cDvgkYOSar9UzFbzTSXK/L2q+I375IkMfZ85HChSCM9yO9aYobk6gLmZrVbh4/7WO3dii53YJJK4z9V4AArd8mVeBJbf5mNkkaG3kZSryFjhmPr2Ge+MDAzio0ZJFkjuvEFy8QuGtzJBGpmAzHt2hlb+NvMeecZJx2qXbvL87N4Bmigh2EQM6+RVwwJLHPIHI7cj6CoMSG2exuppZIIkLNJPtaKUyuoDMyk5A4UgY4DZxxmpeptbpqgluib6KMpbiJAvhjLeVQigGQ5bJOT7elSDTLbTXNy6JMZPGytrH4QDtuydxk/FjBPJ7jn7YSOzNBNerKl6bYeJK8WIZQrgAhcju2CCfb0BzW61sZb+xjs7u6lubh4XMsjWRjhZl8pUjuq4b1PbP2rO2IYmFL+WVQMzqbdJFARBhCxztDMwIIGcJx3qpKyNUdukfgto1lI9zDIzyyX96CJpPxMqBB5iNwOeAM4FTbOE3NndNNbuk0KFXj3CLxeMgDByq7s8d/K33qHBAy2asYlSeSNLaa4SMKxXBymBjBPfjJwOanWFhPNbmK0jdJ2G4ruIZQrEIPKoAX8Tdzkk1VmiNmlW0mwySt5iIYkVBlthUsDj+AcduSeK7LSGGIwSFb5dz5voy/864Wwvri5mjsgUK2XjTXzAl8y4CqAVG36D1AUjnOa7fRQzTRKc8QlRx7kf65rmrroh6ZmVy7b5BkjJHYehB+lcnr2ViAj5G9VOO/JJ4FdfdLucsx38JgZyAec4ya5DW1jR9jAsvDEADnB75+1eSk1Js6YtbJ8Q68YxpdwoMfvPMAx5AB5P5mvyt8U3L67p+d/l0u3Ubk29gRx9OO/rX6h65lzbXLgfjLFSB2GCP61+Xvitx1JbIzbnj022V/odnb+dfa/Qes8mfK/W+r80cNSlK+3PjRSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBX2T4JOM3kbAFZI2Hbng54/p9jXxuvsvwMYfOOrjKGRQ3OMjPIz6CvJ+q/wDJI9X6X/1RP090fIBFahz/ABebPopQjPI78favoGk5SNGd2bEgDAA5G6ID/DPHHNfNuj5MXEUYwqLIM+QsD3A7jzd8d8GvpOkBgq74yN8sTHy45WMLj3/h9Pavy2vlJn6ZQd4ourSQb4ogSVaNPw+pxjHfFdnaEu1qCThtnJP0HNcXANj24I2qcZOCM8jJ45967OzOWt8EEgr/AFH/ACq+D3mZYzdR8j0xILm2t7uzsFhkeSd5IJbEoIZ45mVnJz2YlpM7clmBBINWuqz3N3qkkyy28sLbGXdaMsu5V2BmKuAz5ycgDJwQK2aTaRPpdmIXSdDLOJo7dFRcNI5yzKwZiOA3H0OaxvDJaRzIbVluIiZ96eZTvIzjjKlWB49QSc+leqnm7HPll2laYJJZVhuZCDMdxMiuE8RvM27J/AzEABcgYINI0sYfmppLGN4EaGMRyIrZyNrOzk9sDupJ8wyeayvFt5Y1e4E8sSR7nEsmUdGJYsdvooOCD3IHFbLW0lt/lbm3gjgt5Y/GSOyiAcFTiRCD6jgk+o2YHetEUZpMvyUltPcyx2kxiZY1iuPE3jcM7dwOMbhwSCQoHPetkbXd4jeLGIUWTxWlMqOkgPG5MeXv6Ag+aosupNZC3+XvpLu7dibhrnyyrCMOoIXHoxG7vhcHitNxJJ8nDFdwXktvNyUtn2qPUMVDAbc4bcOQA3OKsiuhX61a63fdLSWnSl3a2E2pTRCCe7R2WKIS75Nq5y25RjaASAWGckGujaPxJY20qxnisYg6wKik71faS2BnawAzzyfpzVZGI9Ql33srCFZEDNGJCPFLEZG7AVOO6gYwR9auLfc9gka2kzND4t18zFGsoRw23b+IMMeZiefxDB4qdp2sVsk9oiySyhTZw2zvZSlFhYqG8XuN+6TkknIIGCAcVu0/StJ0fRLd9XgErqd1qkqmf5WVlcKynuCeQGOM5wMZo4dI5r+OF7sSRrbMFuVlffnbgqq4Cue5GW3DJxxWE0E1rfsl4fES4jFrb7nDCKRJMliAuCoTkNkkYwMd6ZskzluL+6d0udOt7oSxLFIwn+X3bny0YUZYtt7EceUc+o9iSS6WQzRG7kdGVJoVEUjL4h4Jk2jAw2R6bgPUE6dStbaWRIJdSuJPl5ldpAcIY0zgtGNw9R5CMkJk4zzYX12mo6bBdEy3QYl40aULtI8oc4GeCCcnGc8UbsQlcipBdpp8AsFEawQpEZJyNqkrzhiQAAM9geAe5ry3+Ys1h8OaS2ihxBK7wglCcjsTuLM54PfAHsa33OtWySzzG2skmjijjaAXPgK+DgZXkP685xtrGLUreCEz6TNBFLFO6eHcRePK0bkHdC4HnGcgA4GM4yRU2F2SIrWG1hHiSRQzjw44CRl15Y5GADufecjBBwPXk+Sx28TWxtPCjkt7dkt3iiV5ERkAIxjO3OSAecse1aI47WW6uLaRkazUjw7gBowd4IUKxAGQCAVBB3YzweY+l6jDesg1FZbO6gAACkOJGXLbQVH42xlxyQV4quZJZCILeLfQsrTRSLuMx2lJAqqA43YZW2qctgDv6itrWbXLstvLEyNciUqE3BVMXnHmwD5kLbgcgDmoNjFbahbSSsZZrqLgSgmGOI5Vi5YeU5xkqQSQBwMVjJqEtyL1baOZIzDKIkcGMsFUOyBslQzENtwex5xUhEt4I9Vn8O2EJuBIuyNYlkSeZnOGLMQdg8TOCNwz7VptJ7qaO6msYZm2qHSQ2gkBHIKmXIztyzYI/u8nPHlif2rdsLaG3UWymRrh2beMgBQrE4LEEA9+D9BW/ULG0ttUW51ALcz2yLPDFJI6xEeHsAAQ4YAjJ7nJ9ai/BjwPbNXWymv4GurqRVdI457YRsJWOMMwbKnBPoeD9qmWt1dySlvAiuIIYPFmup/7N02YfMeSVVM5OTwduDzWq5u4lslu5Df3Ei7ZnvJYjE6bgcxqpwueQVYDBHHetX7TVNQhkSbc6llCNIHKjYcRuQcZ5PBz7ZAxVtGRmyLqkUt5M3yF0IQ5jCvawBnMR/Ex/hI4UZGMDJ7jFZWbR3Nw+I3RpE8FPGkZsYBVf3nJJwjEeo9M1umiku4oYv2HEYT5byV9Q8OSCNjkYjXJk5UMWBCruzg5NSb+9eeN5Va3niaYRiF1Vt27GQuSDtA9Tz68YqOBKIE6aVJN4l7Yz301sAVeS380gxlgcdwcll+x3Y7VOt1s2jgtxcHwE3TbLeIeI6SedSVOACFHOB6AehrUiS2OnyRrvFs08luwnKwGRB/7vYrbVwe7DJKn3rZc3BuZFuFghto/JuSJjtbKruZgqKx74+xOeTTQnNk9mS6uJ9yuYreDCw+JGXjBBbJG07d23k5zgHB99aFYGFmq3SIuZVR3VlMXHmBHDEAhcn0IA9ajalIxMkXyqz2YbxGEUAjVIsYBIOC0YHBUE582BjNSltp8T24MUNoAq2kg3SZKr5VEaEZUckknue3aoI0RrvNRlto4/krm7NvJO4mdoSUgHIjVz3wSG7jgbfQ1lMikzi5uY5pFPhTpE4ErKV2B8AnaccDI9yOWrOG4uPl4zo72sep52yzRsXjcIgwsqsMANg8DnH1NZb0Ey+FZJazyuF8VGj4dfxbUI3N2/wClSyFkzVqkccxnih32zMZY9sMK70XADOr7s8bcEkjlsAZOK029s9tY3paS3gkiSKMAzMyNG2za7Z5LY5+hOMVYu1rNL+5i3wl12h0C8tgyYGDgMDnB9jjFY3NtazSxfKyxQo0Rtt0bNI5RXbYVbDDcNygDBOM84xUEpu1jVbXIV4Xsgsl3FslWPzFX5xtlZclM7c5xwMY74rfqBis9SiSxWYu9tuVthkDOHHK45Uncx55YYzitCs899L8nK1xDHvkKQxhmbG3Cg8Fec5BGCSRnivPm2tmaON3cAu3y2eHUKpXPoAF5PbIxgnIFWuRbMtERraOBCJDCmfFvZd4A7lQYs4fnHmwMc96qlW6vYpLeWSe9upCEIkAJjYtnxRtXuAykc5zn7VqvriSDUCHlilvfBEczxYBVhhWc852jjn0PA7mpEdnE98guJWimjISNMNIiEqFHI9BwTk8547UuQlbMk2Fqral4MO3aWG2ME/vWAzz2wGA7kZz+dZKDI4S5e3aFEjkSFB4i+F2LswJB7HjPpk1S2Ueo2eiabNqktk+tpBIlzdWiultJKSRmP/2hPbC4OcZ7cVY2V5aWK3V00swt5LZmdlTKja3ckZwEYcgDsDwc1DyCu8yPMsSh710lMK7be3aJmDRq5JZxuJK5IIyTyMgdxU/5S6khSe2RbmWR9ltiRckhQduTwc4BxjOO5zisJLe6lW7kj3Tfvzuk2KQSBkFVHBUZwMEevvWq4hlsn2WDSRtFN4+NqIASANjKq48w5AHYkebg0y4k3ZqfUrRo0DzXMc1xGIY7cwoJIyoyz5ztYnsG7BR2qc7WsltOsIntVS4GI7ZBGrsMbiSTnB4BUDk++cVT3EME1pdpJELqNiSVabO4FjhkOBz7ngjA75q8sp4nEcMtswhZAD4KBN6ZyzDeSPxD1+p7nFEHkaprSa4nu7SaS0hvAEeO3udqLlSSUkJOCcYwQDjHY9qzh1Wa2d7xwoEysqyI4K44wGUcen09CRW+1jkuj83aWzXUsF5IFL2wbABzsAxnd+I7uOO1YXbo8k9xaQyzC4iKW8Eq4VvOquu7GcbT5c8gkj0q3DIpq7MgQwzanDM9l/vPm8d2ALhF3DJ2hhkHkc5/DnmrBYpYhNGxjZXRHi3Dcroc7j5j9B6+vGa2fOR6g80lhbTOzuFBjmY5IbzLtAA3YLAkE9/pSDTzcXCOySSTFidqwgIYwCOecHytgc9wxqLLgS5duRV3d1aaVq1pe21raWsaqRzGdoCkkueMEZOAMnvzWmNA43MIGitlAbwDlXOcAhfw/hJ45/DnFTLO2MN8ySDmzld/H2jzZA8jAnaTk5z9OQa23MEDSXVxb3pmhmlingVysbJuBZu2cptBbAH9aNXRKaTNKWq2LxRWjfMXQd5JN0YJ3NnzEk8jOOMAHjAyRW9H/fTQPbvPNbOVTxH8XDM4JJGORnPJPqKhX9zZw6RFNPCWaOOWVhvZVkAJ2FWbvhhjtg9xzkVYw28NxFas8EZ8oZI4g0uVYnkoSDuxu45JwKJEt5XZE1BLe+tZAlgh02ZHCN46qybTkhAcbWYcZBO3J9xXqu0N5JZ6dstII5l2Kkm5XhC7yHJySvJGAckjn1rI2r3arbiWW3QgGVlTeSi44VcnaQNp5/TnNapYb43t8rXEdxaSRLPaF1PiW86g7lyqjCnuvJz5h6CneR3GvddXTzNCFtkaYqHkOMBWBwvI5YAd/wDrJecpPcKzxSsrPcxBN0RkXHdiR5TngckNjPFTrjx5TJcXs0fiSqkvhiNlaVxlfKMeVWbHcjPfAFanlgnmmiVm8Z2cuIYSQi4BCgHPsfM3B45xUaC9zSpgJklgE6ySyPMPGuXMkrFe20Eg/iJGOFwPQCvdNvISITIkOnNON01oIxH+9HCszY8+ARj3J5zWObaD5WSKK5aJ7cGNnx5ly0bo0g43cHnPYg+1SZG/fXEvzYhdMsk+QCE8uNpYE8Yz+lLu5GTRHurWGx8RUa3OoTW4BdJHUiNm3Mq5AUjynzDB5xUeaK1nkW9gEDM2wJbugUhdynCtnO3jJA4OTyc1rsp5bqdTqADW0EKyGRJCDE+fMGZuMZ27SBkh/pWyK8ihuHmWASQTq8jiM5Twwpy5OMjJJULn04zzhctZoRXMtvJMLeaFFO642uSw2IfMFIG5hknB/wCWZFvFcW9rNe3N1HMzJuS3SLYDHnaBkZ9c+b+IknHtrle2bUQblHSBvDZ/FlIBUqCWUbSeCBhe/asbaSRbHUre3t7aCaE4ICsHBCrhCc4c4bOfQZ7VJFmWEls0l/c29qkaxSlG3btzSStxtAbAA4XkZOfasreQ2kMdvNc28KzYFsiyBgXJ/ExHOQc/UkgDgZqsMM8I8SRIBItsnzATvHuAYrG3fJzuwo4zxUWC3a2Oy4uILeQSqYZGcoQrAgDBA8wQn7E8c0uRa6sezJGGYQ4uZRjbCimMLCrDLljjC8HK9yfSt9/8vbzo0lzpFjPdxqbWxV42nK7QCY0GWPJzlsYBPrzUK9M1v8xNeTxR7IhIomnDMeMBSRlVOcEjOefU8V5d6engz3UVhYTy2tmxkfw98zKWbyjjlclsjOSQc+1Re3A0td6nt9eW+iW0g1mzgg0+TxDqNzdxH/d1woVEER3sGYoP7wyCTknAXqvap5RFbGGZLkxEy+MAm0geuVOSNvHGDisLZbOW704zwG1trwAYkB2ncA6MAfZvL9yScnFb5YXkvYrWZomiaMiUhRgrE6jcgU/xbiMEEHb71FybW1+fPYjafC0dndGO7tZpVZ/CSRJIycjerM2CCCTzjA4NeBLm4s3tLm+k1EPLDHJHYs0cR2glzySdu4nnI4wD7VrEgbUbhry2uJb3cqy3DSBFORhUxngAjO/GSTj0xVjpstw5+Z+ZOm2Lylna62p4Ua4RDvXaTuJOFwSdw3dqcReyIcsd3PqiRG6ubqG3Z2m8FPEZU8PGwkAbVynrg898YqdHfJZxWsEBlltpVbw5pjlzlgTgk84A2qOTxjnNYyXz/syWGa6f9jySC5gENuIGuIeDgnPmYnLDJ7KMjFaVEoCTRiRmcsW2TKjbCNqIu38KknOc84PPNTcjxNps5L9numhmvZYdogS4kKKrAKF8o4xtOTk+nOSa8uXt5L14YCFs40IW4kiwszqo8yAjJJ8y47BVI9qjXMk+mWrGEfNLN/6PudjnYxBLEHaMgg447Dk1Juby60edba5s4sRLGHnTDyMSAxkLE8IFOOBjIbvUXyI45EDwFs4FnuvAI8QvODh2aTuoUKcYLAkAEkBfSsIwbeKxgika6lf96Q4KkBMAsCpIyz8cnIGfc1ru9YhSW2t76SK3lSeZZFijZnUnBxvXG0gZUkHvn2qVdTRXt+Nok8KImSQsSQW2kMGVcbyBgcjjPJqC9jHTt1nIBFBKIo90jMGXargtlY14JIyTnHPPPqYgRtscNo9818kviRXM0qGPf4f4ZFGCVK+3K4z95F0fEliuEKwzSorXK28qxpFGpceGjgEO244zkfi57AVourxphbJZadZQxCBlgjUgsjg5L9gNx3Elm9RzQjiWWn6tJFK1zHaiOd4kRY0dQsk4B/eA/QZ5OOBzyMVzerafdCO9gS0h1GEKTZ20rmNDImcmXeDtVcqynGGY9j3roJ5Qlta3lvGs8yoI2C24dIXPMR+mAeGwcliayM5ju760S63z3duJBD4W93QMA3ryxO3zE4HbBxSMrEPMqpZJGUNNbtGtzKWllhkVIvMAzISe4GQvbnsOamxNf2qGw0ZIJkkvGjVxE/iY8MkZGPL6Y9ah2fieLFEqxx2QdwIY5A0qg4w3by+YFsZ/pzqvbiUwpKm0GeMgs4y0ZHqxBBzgHkgHle1TfMWNqwQSRozvdxyPcYkkhBlfBBQZwMLkf3u/HoKkG9tpbizWZpkureKWSFIvKQqKA0jRcqMDAxn17ZNYrPDFBeWMUogtbZHMtuuXMsowctn1Jx+HI575GKjxMw8W0ggBnMRfbNcBFR9pKYTGSSMttOdu/PehGputpIZYlaKSMKhDGMLg+OBlmKAeoIyzHGc4HFaZmae/aWO9jF0k2+2FypYRtjLIF7/hHftuYDnmtvjxPHazETxF90oiCkZiPlLD17nHOM8YIFQodXgk+Xn8U3Eq/vjHK6lp0XO8wpjeFTDfi/FtP2pmD21T5SzmAii2MgOwefaSGVPEQEgYPPbnB5rS1pqccblwZ43VBGzuUl4bO/IxwCclcDt2PGJMKQSXgaymty7MN9yQieMIwfLuAydu7H2zjmtBWO4jS4G4RLMkas0rhXKgkqOcgeZjyc5OKXJJ1pZwXCpM95KiAG48ZvMcAgKrFhkFj3ycgCoaxEeBNFa4Xa5jmEYZWAwdzvgHvuwTkk8DNbUgit7K1W2gm8OULJM6N+FW5UIH7k8lmPsMA1nNtup9sWWgO6V2jcn8AJAx6ckD19eMVANN81vLKrXd/wCDHJKI1h3Pi4buCQMqd5JJzxwB6VnaWSJJeRG3jW+a5E0MRVQeO7bhhdxPlHOAAeQTxBCukKtLNdS+M6hjCv8AYqjYIBbAIK7SR9eO1SoLq2N/Hy7WlqkxitghQ3BBwPOwwFz68lvzzUMseW9qttA8/hyTfLhpTI8WzxjIcPMoGQzcnntwcZrd486s7LcmKfCRZcFldeV2svq2AcY5ya1Wpkltle7iWWeRkeJNxZRgE4AVuwA2gcgc55GK3SrK7SXez5iXB8KAyKULkj8S4HAA4OeOfeqtlkjCwmkkvYWLTxfKK0dx+7HJGDsyBwoBBweTuz7V2eivtubhNy4a28RW24LcjP8AhXEaBeW+mxw2WnRajbXE7tI0ELAr5jzK4HLAn+I+gAzxiu80weVRjPiQlNpYjcMjI/SuTEbrLm27dkn2KwVUVST7g5rlNUUPGWlVx4jOAWJPHm/yzXWXkcUbTuF3sAiKCewxXLa5L4FrJIRlkUovPbJxkfr7eteWrqTN42cUfEOtLaSWQ28YO6NlDbv4VKsR6/QmvyV8SZWm6iV34zaxgcegyBX7N6vhIgFtIys5G+dsgbTjGPqeTX4z+JTbupN3vbpj7c4/lX2v0B3q+R8r9c6rzOPpSlfcHxgpSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUAr6/8EiBdy5GfOueAcAsBnn7ivkFfW/gxgTTlh/ECvmwMhga8v6p/ySPU+mf9UT9NdLrIkuMrzOApVlHO44yAOP6cd6+l6UhYHePOrAkK34fK24duQfv6fWvmPTbeHO8jsvlkjb8IB/F6AnPrgivqGllELlcKPFG454x5hke/evyzEbzP02hultEy7rZgAXLA7T6c4x+grtrLINtk8AoO/wBfSuDjYhrZZQdo4JPvnP8AnXc2BO+AOQQNmSO+avg99lMYuij5rpD2iWcClrKAJJN8wgmCon+8SEkr2yxCnhhznI5rOW4gvPmJdOvET5NjI0YZgGiPCswHJTnHbkn6VD0m+tzYXT2dtcPdW99dW06BCzFku50LNnIEI5IP37cCpbRtZxtdTyrMJ7icTb3MkR/s18o+vG1FAAI+5r1bWZyJ3SI8UFvdXNsng3GByJIl57jOVK8AjGOTnjNa5EWQ5muU04XcnzLiJ4/GVdpUt50I3cMudvH55qTqN+LW4tYtSkg+WeaQDYPOVXcu/Kd8Yx2PcZ7Col5qa2TNYeNK891MY4S0BC5IJ2eIFyvCsdxIUcDjIBurtlXpmRrOG2tpIIobmazEMwktnV1LyKrgmJiQvfd3OcHPftU2C0iWP5lWaI3EMkZCfhikIKngHKhiSNnYbuMcgR5LjdeTfNyiD92qh7NFYAMmRgcA4wpxnOC2fSmp6nB09bTTJZ6lqeLpIf8Ac7fxUBkwC7oDuVFBJ9T+Hj1q1m8kUbSV2e2yXCn5vUnOoSPaiBRsIMTbfNye+5hkDA/CR71LmVibQxxeBezPgvaxFnYbM+UA5UE4APoScZFa7hY5YbvT7iVk2hh4N0GTO1jhgw7cDDZwPOpz3rKXT2t9SeCK1gtv3iuYpcCNonXa0qqGwyZbGBS1xckXyXlzpypcibE0HhzGCYRyxgdxmMbtwb1Ge2BUKGyi1UCG1tRsDlUlVwVyMuQ5zjy5yccjA+1TLeQQxw2/y8M1nGGIYhnMjHnaqMMkBgCGyMZx99E1tEbS53l7iHKxlEeNC+DnaBt57fi5OSQfTEkEfwTeLMLp2uHuJlk3ugyWYjBbyjBIycepYHByak6Y1releINnjR2s91cFYtsiTfu02gHcePLngkYycmpYjW8eRVlYwXL+JuKBnYAc+V2KkJtJHY5+2KgwTJBsMBs7UkQhbnwg27DbjvAO0vkk9sjOQc4qCTVPcwamskmnwRy2tyZWy8REzEHDYD7ShKAj0XvxnmrNI4HCoj6fdR7GRnh/doXI5iV2OBxgYPOQOxqueBoyILpi7wwXG64ViUC7yVAZcAhQQCxxyx9uZdnq9sLGTw1lhu7yLdGY7dGTKqNyruGV7blUgjzHnjiQT5rmS10aFW3pG7xR+FKhiU4GwEYG0cD1IyfSqi5W5N1FaXUFraS2waNsRdlTDEgLhQy+VuMg5IHpWd5PPcLMBtM7wxybXkaOGNwpUNt5YBgR5eM444Jrc88ttNm20177YwQQ3C7GiUvsk2zA5QInmCscttwB7FcjJE/SoG1XVvFaSJ7SCYvdsxwXjfdtkIJwD5AGPHf2NVttZTzR71jjiRFF2sLNtKqyb9p44HA574J75qdpsdqdZeNfk7qKNQkUcQC7G2AMwZWKkFCBjB7kenCa32W1tJKsbbIUiE6HcxVQVWNgoJBGR34bbn3o3kFqZ266fcxC6lS+aR48wmJvCEynKjCYZsZVsk+xYZ71ufWJ5bfO2OaTT7gncm1lkiQ8gCQZVByN20ZIwAajNI81xC0YYmMeHKwmaNUjAIZo2H94k5Rl45KsM1rguUu9TtUgit2nhZQY18RVuWRMQk4P4c5OTwOQM1FybG+W9uYZEhETtex2+1cqDKqmTzZZchSUIOdowAR617qt0ZiyzxFpoiQDGFTMYUbQxAy7hgO3PlAGc1otoZ5p/mGuZZri9kLr4QVgMH95JlcnnAw2CBg4PIqRayLdTxwalNJFcGWQxBFVpE9VVwqKSuCvGWPY0J7yPfG6tZ4itsjNMggWVS6BGYnM2QGVRtBUKRk47ipMc4S3Fgl1FfCB/K9vCiyCMHlA5Y7TkKuSTkZ5540Kx1VLQS3dsk8Mc8Z3EyFhv4QBjs9z58/SpDwwJJp7hvEurclkRNiJMVQqm4qBuOOADkY7+lLixttzujlt7e4cvqEa3FvGQgMQAJYLkd2UgeZiSV42+vkNv881jNpF2JG+XhKO0hiDAsQMIoxnC7ipIzg1pjsY4rXZBMyXEqFCng7lVS3mUswP8fG36LjHetcEdutpJHo9nd/PXHjqsKRosSBWZiVwTyOcAkEAj0pqNCZPfCb586tZPLGLbxNoVgxcMcKyqB5uGOzByQo4HervLSzsikU0yB7tvLdbMOS7eZlXIEjgrnbjO0juKnSak0tnE95JdRw20qRxyPDmSQbMZ3DupO7BPPoKkQxrey2xeSKazjkWIQqjIhTcu1xv7FU3DsCCD3omNCGJkvRLHY2P/myDaIpllLlQuV3uwIIYnI4znIANS0a22PK1mIDHdJ8msK+E8jA853jK8HjvzgjNemCHTHn0m2u1R1IYKsgQqittWVkIy3mOdueMg9iK1OUEkcwSaO2glW3AaaNtzYyzBQCwJLLgEgAKp47VFySTcSzRjfZ2FhZ2pQGFvEZJZFVss271HONuc5LfQVqlaC3uR4dxHGl1lmhIMIZwvKso5yFRTk4I2j07SH2z3UjStEJ9xe5wu8g7Hx4nuBj045OO1Q7gCQbJbmWFnhCmQRbWIb+HJG4geihgFJ/Kl7kWsIrZUjuZERreWdvDdZpSQVwTksDll5P97G7HPFSl0+4MKraTK1mkZtURplMaZC+Z5ACcZUcY9s4FQBHNdvcRwSSae0pnQXITG51KhdrMANuzcpCnBb+LNWdrJFbfNvbaTcXMMkZcySARIqDymOTDeowVB54qxDeR4Lc4ZZReNcwTlZ5LsBti+XLnZ5VOQFHvu47V6gD3M8cjhlfDJJE5XwzwjJyvfBHJJB55HFYQXMiXk0kxIsw5eNI4fFyd2cDJ3cdz9+KmR20Etz5ts8EsROWcrEy+oVefw4Gf19RUMaHl4VgurlPDikkTw2nleDxHjSQ+VgwJCjjA45xycVAhluRdXCuUs5TEY38FfIu0kiNSQNzAevpknmtaWyi1jVpo4kvIAjPDLsLgufMCOGOQuFPHJBwKlXl+8U0fhGSVI5S0lthSH9AxIztyxJ5z3OaDuMpZhFbO1lbXFhdRRHwxaXbGFdwXkqRkHk+mDu9yDUVJWSVZrgBwirIZrVTJHIoBwUHO5Qd2EPPGSTXiGzh1FLuTUIzfhHs7WNA7MGY7iGZV2DDbGBBznjI7VstoIZp1BuFa4uAzOEjLBghcsyuOBzuUYxg5zUtBE55pXgt0iNu8iMN9s2ZpAW/9o5xgthhx2XOPrUWzjgtbqSWN3iihYRIPxBWbnCgHy9282T3rTKEkuj8pE5ZZBJPi3EcLYQkhiTwcPk+h+hFbbSF7zwDFvMxhEj7QdrFBkKAByMEY3E+oAqBZJC3xNEEfxZIrNyjRxEq02SR58YwNpwc5xjNS5Zp3ZUV9VaKOERgkxkDaxJc7hlSAckjhuOfSosV5BfJLfWl2sjo8kbhE2gNuYFMg8hWcDjgFfoRUxjFOs4SJo5hHskb5revdQygjB7jjv2PvTQM1xO1tMY7hmiW5URSTRqp3bs7zxgLjtkHJGSK8ljnub5UjeVkecRRvKBC4JPlXeM7VBBx6njPtUZxbNcRC6Ro7WaXxHdj+8RR2Uqe3GO/f8qs7dUkdIbgSxMh/deEo3MRyO/dh/LnNL8A8syp1ZrmGZbi7I+aEkiOyRAR53YwpbnPByTxgCp8NqyLE5W3gMJ3N47cOCm0kAHzBSQNvHOAM4OU6i+gZiZCIAkjy5AVxuGd0rdmGe3YitNvYSNqEt7c+FaW8kQ2RXUoZcofKVPcE8ngE9yT6UIvlY9hju2EczqCUYMscPKW2BhDluMZG7BIxn61JtJ7lZhJJcyy3gZNksZCyoE4IyCF/v8n1b2r2S7hjspreK9eTxJPHEgGGYE8pszl1IBAY9iFrXdZWMtD48EUsu9GedXLuMY3DGQWJ5HPGPel7aDXJmqS4hu7R3uXnaaSX98xnLO0jOCCMY7kbeSe3asra/aS3hma2uJV8NhIFQyDcMBVG7BI25G4jsOPesnu55LdXzi5USLCCyIiEkeY4A4O1m3dxt7Zr2CaO3WSETTWmoJau0KhTJH5AScMRuz+Z7jmmo0NJS3kLfO6dcR+JbwwyOZOASfxY759jnjA98VPj1C5uJ3gaWWT906KItirsO0BS7cnB5z6c8dqqrlLzUrd5zd/MTRRpdEFt+5mBDLyeMcBeODk1ttY7m5zaQziVRBu3xBWEjqchsnIHPBB9B707g0rXZvwJryaOKNIZ5XwjwASxpCpIOd34w2ATkDnjsK03EF3E83jWpEvipiMncrtn+zC5A2hdrHHf86lQxNaiKW3tIJobhFkKCYh2Icqozg5AyCeAKhPJbT3c6XsbWyXAEMxacqZgCcgNnOcHgjHA+uKXJRkby51O7XT0jEZjO4RSwsXI8yl8jsSd3PPPb0rfICbOKYzYjhYjxAwSGIKxB2/3WO7bz7YqLDKBiPTi7XE4zw/mVEI5GRgqOAfqeeayu7AfJRu0czpIHEtnKw86bgAChGCc5O7PYDHOMRcmx5C/zV1DdG4NzIiNbyCNgwDK7AqCCQW42jt2xmvN9pavZieG4uoGuXaQJOoWXaMxI4ByV5KkA48pzkHnci2en+Da2k62gZMvMLQIzSSfhVVRfTc2WOOSc1HmMNtZB57eFnkkzbCR/DC4Ylwy8jKgnj+LAFSQSdSt3d3uYQNSUu+XeYR7kYqDg5AG4EjjkKOMZFafEgsL24FzCbV8+I8FyqyLEZCXTcQTtUgheTwMcV5b3MDQIr+KkAVXWNIxgjf3c5GAfLx37jisikJ1W6vkj8aQ3Dm7eWESqkgGNykHJK7SF4O0miHcGa6m2R3zpLc5JWzihjRkRSMmPGOB2yBjPJOaiRMq3kNtDIy273GxgcR55Do28nA4cqAe2M96lu5a9vpJ3aOe4UOLqV90rjYHCMuPw4U8HjkfnHM6TQm8s4PFiimMYW4iVEHAznjlg7c47EDtzg2SkSzc2lrKxkuBeSW6hVRpyscbEsGLIvL+4UMMYB5qsFjcX8E0ZlSCC5dhdLFAts2zchQrtxtCIrcDJOQePXda37Pd3cZa3iRgqu1wQoYlid4YfQfYcd+9R5/nbhbPdLK9zcXAgtthSRbcOcCQHbuxjHf1U1CfAnZtmeaaYrmW2jknuWi8XxWDAg+CsilUYkE5AOBnvjNZy7jcy/PIILCaHxIrbhinIJZgODgdvYn68TWuH8KSSSaTwUaQRo2EK58hdj3YnHAA459aiPFE0kkEF0lr8pGLafc25guC2Qq84bn0znnNQwiMyTBphfWts8dtcoqTLcZXw5BtBCkcAn1AY+UYxUqK9h+Tk/cbLZQ0irASEuGOAMDhl2gnAx3fNa5NPt4xZSQwvctaCRDM4VQTtyrjnzkKcDOfc+tbWggi8N7xRLNE4RYw8YCh+SDx5SAp4HHBHOKkami6n06w0820tqYVIjd9wYBWOOGPY4wc57E/aotxa28tq0t5bK5gjjhil3+dVHlJA5YgdwDjt2NWVo9xdwXTqJp5YYRDIxyCxaUqGAPO0ZznPJU4PPEScSLc234ry4ecQyiMlh4e3aNzk4UAjJb0zgdziLEp2K+yudUi+dtmvpTeXOqrFFKgWRUtmt4iN6gHYq7jnPJK9+asHmhslmD+FAyxtbJO0TMUVjvKxxrk72ynJ+pzwah28UJtobSUTwx3t0njLE2xJYzuMhLeq7Vzkd+D35qSbk+I8gKmxuk2LtbCxyBcjlhjyq23j1A9atxHCx7MkCC3tTbl0UeIsdqxILhmIJPrjknbnBPfmsQ1vlW0ovbpJMjTu8beE7nygKORnOckkDyjPetvydtc2ltBaz/MzRbk+YaJpNxRlUxqpIBxu82cD3FRLu3MniWltHvMkZVlaIbZGyPNtU8A477Rjbniq2IMkkFzcwyz3UdxazSrOwS3EURRRgMewPO7sOT2wKgXUKzloG8OcTeF4iSygCNWbgjb+GMhG75Izx3qwvrK1tl8I3Ms9tHt3ucyPkjCrgYKjPf6faot3ZperHf3ht2m2oklxv2ylZX3GJcD8Csm4cgjHc8mrIgnXMbzWcN/OwU3vMaWAZEB/wDetuwcllIKgEev0qEZrS8hNxIm6FLhi5Yj8RJO/wAuMdxkseRnHpWJcR2CSBUFs7lCCzPKseD5gP4iOwGOd1bZbeSSazN1aqHaQs9rIAxlChRhm4KnHB4xnA7Cg0MCEu55YY7X5aJykUt5BGSF3DbIUGRkE8BvcfnWiaCazaSEbZzD4cny6NGrROFC+EGUcnnBbnGTntgeNHb/ALRuJgl1JcyRwh/BnJVSvA4z5UXPtjnOCatba8FtdpdQRxiLbJvCoVbvkKAe4ZOf73HPfFRohZkOW3ghvI545oLS3RGZWY53FG2nCDzEHLNu4HC1hNpMVlO+pyyk3hUeE0WCw2jCc43BAWzwOfrUq6trcWyNBK7RN4lwvBYlCBhDk87dp4HuOOK3Rww3lymGCXFsI1Em4RbpCoyrkdyOQFPPFENCJJcXEiSyW4jkVRvCtF5TKfMxPAyCqkeh5+gFZFIVsZrd5HcSRnKCQvH5sHam08E+gHPNR4tMhv76S2eVkQXEZ8SJyWlYKSxGQAByOf8AKlyUthZvFDBILNTbgKCscS78lFVhln4A9sc9qlkGnxHtrvZGtzFCFRXe4nWMx5GPMDyOAR69wMkZrZeT20Anngit0MhaXd4ZkbYv4sqcZOMjAHOcAc16JrfTLiCTUH+USYGRtrBVRSdwOTkn8JAJ9Dn1BqvguortLW6VjLb3EKmNJlYMNzhgobvjcQc/QjmoJRJlh+XnaK0tZbbwnDM0ZyJNwGCqktt9Djk4J+1Z/PGWSWBreCylwF8R33siZLeIFPYs2Qv0wfWo9vxZyTRyJb2+2Vp52G5XQEDIdsE5HHHcnj6zba3s3a2kt40W1O92kuHRZpSchmTJJbg4zzjPbiql7kSO4TTr62ub8nwjtPiIoCIRuB3cZYYwcAcd8Cso7+2ttUg0W3iIvDBviaOFpwuCVDuSCvmY5CY9Dmsk+XZhLEWCvIJFKgxL4inO0Eg8AKMn1xnipN2ZoJJrSK6vJbl8ORCoCvI+RhQcbUVSSWY+mfUA1yLGmDVb2OxQXLXqzzeWVD+88Hbntjj+EnHYH9a7rS5N8EDqHTdbbsMfwg7e59T71xFlMI4I3+ZW5GHjC2k/nxsUsEdhjAPdgBx2z69XoACaHoUSncU02PGG3Z8qDv8Al3rjxHVtmiWaRKupwS+18NI2QBgcYFczfyFIneV8l3DqP7uFH+NdBIC5VVGR4gGTxx61zWrShYzuC72LbAOeQnt+teTB3Z1tJRsfLuqJEYSQmRlZ5Q7O3JyzEsAPbAAr8d/Ez/1nf/6Cvpj1Nfr/AKtkb5R2z+/SMADgeb3/AJmvyD8TQR1Q+7GfBXOO2cmvufoHW+TPjvrnV+ZxtKUr7g+NFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFfX/glGJbuRGydzlcKcdxx/PFfIK+vfBIj5t9yq6iZCyscAgEd68r6p/wAkj1Ppn/VE/SXS7l0jL4LyRqSQCeRKcen5favo2nsD4pGAY8MFxjblj3x27kflXzLpiJYYogyFfDV1ycEZFyCCOc4I9a+mWZIWZGJXKOMEYxiRW49u/wDOvy/ELpM/S8O+ijoBHGjxKrnKlsEccb84B+v1rtbPAMRblsgE4xkcV8/8RzKytwPFIC4JOCex9fr+dd1YHc6HORiPGftUYV2mTi10EfNo7q4ENwZ/DiWO7vRiSSNBODdTbMjJOQoGG4P5cVsgaFbOAyyOZJDMqoqM6ysWLZGBgHOOSft7VohvPk0vkmu0T/zjqDxnxCzEG9nXAUhQCp44yMDk8Gpyyv4jlSm2TEiCFsGdGK4j3t5SuTu9TkD2Ar1rZ2OO94pkBJ7aKCC2EUjgW7TC3QSJ5Yl2yK7gcN5wDnlhyKh21wDbG1t2mjhmwyCSR1Qoq4ClSBuAIXzdgRzVzdTzAxNPNJHEruvnjWXeAdu1QVJbJGT68Dgdq0RqpgnEy/MBw0ckoZ42CZ4RsjhT6Y9QOOTWiyKPMjXbsssE1nAA8U7lh52WMZXIUcAnaPUgebI5FSi3hwSvctLNBHIYWe2VjLb8nDmPJyDuXJ9PfkV5DaxLfXl5eNcy2cqpObbxhNgAZ3BW/AW4bHI9gM1jFYy/I4lupJrVXZ7ZWcFgMMSPEYAY/wCBeRwwPermZGu7aGBxCdt6ZHKJIt0VmEqghlIC845yG+mR61jawSWoieK+klglaRxDvUEAk7l2uxPKlf8Ah4HYmt0s/gBjcBriN2jESRcSyFR28Q5GV83mXvg5Oa14uorwfNWOmwyKySNvnLkkjaWX044BC5BBBz6UBsMml214Hjihvbm5K26TXEkh2oFPlDY3Al/NkeoXPpSG9uEBErx4DxuZXbxViQgqc5UZySCFPP1GKiRzZlkaLUNtxC/G9gkUaZyCwJLAZ7Y5OBxzmt0+fmcBWkljYyG3ZSfIwXd4iAcr+IgLyoPpzQG7UCkWtSQSo0s0JlCxysWCoB5m2hhtBAA78g5rbczRxWOnlXSS3YSq4UYh3huRCBhSQFzknLc4PtF069iu5LO8tpRdNJESk0ibZCEBVS7tycqFAHfgAnmsViuZreO3h8O6t7a3cRXEq7BNIyEgkjkKPUZz3xUlUWjTm0SVpV8GeJGkjaMY3LtOUAPOdp3DI5KkehqK9zImo3AuJnDwqUlkdFfaP4XYrnHt5cH3ByK139u5aManK0cqwRiE2/nBVUXcrceYkodpyDlcHg1LtbKJ7j5S4aOz3SeJDGySqMlcBX2nILYBLfcexqMiwu9MmW4hie5RRdKm7yZl8VTwCvA2nChcsPxY5JFeX0MA1RpxC9zqckR8O4klMSyBmQqxAIJxvAwMglRnAqrsFsbKdZUhtWmeR01LxLaSZgFTgsRwQWRMM2AU5GCCKsZSjP4chW3eIEWkMV0LnwSRnYB3C7cDPpkD0xU6EJtmVveZs/nY7CWCWKMRRziYbxvGRHsxjgAscZ281KsrW5e7sDpomtLmdBKJfnAqyBTvYEdmLhipU4UgE49aj2SfKzG6nDJptvMJ7aJCW2gEBsbuWY4/TgVtvtNsZ9Pa3in+ZiSKNkPiDEq7xmNs9sqSOT2HHrUaMnhYsDbtc3N1aW9rDb3JaV/lonVzMykeJHChPLd8DcA3Ye1QJJrwWq29t4cF/cW2yON4ivJOTGysQ3mO7KnJGWJ/DWu6kjtppYraBri4jjysEa4QAvhQFHCgHPY5I98VJ8CKXT4ZVfw5mupFhJcBoiztyr4PfYck+hIqLJZi7PbeznNw4sRbwyRIWiXwRgk+UMMkZ2jOABhvLnGOJyRQWxjNkWENx4RdtmWRkRVVQwGdu7255+lVKbbC0ijjt5zHDCdyvhwDuOPDO7cWOeB7gYOeKzdbj5ieOJlmu5o1mjRt8LJGwA8IuzYHmyQRyec1BZHkMttJoam3vjeSiDbD4ELMrEFiqnnJy2dxOQQM+gqVIstpD8zMiremOO13RlVt95G7C4OFz3BwMnvVJY6WTqLTPeX76vbxyx2lul0saSb0PMagBQwXygOWPcipEAt47F4r60ihQRtIsLxlISuc4A5YMR7ge1S0SmXdxNet41pYQQ7F2SyztF5WTBIAwxOW48wXk5NVz2gWytpmWIPtkYRLIXDiTHmY98E+vAyOMDNboZoYreVbaBbIeGGkEjtGXLZKBjuKqu3aNvuD7YrN5JYtcaK1DTQxrvmtgzPJ22q6Buy5DY9OPT1WK3NWLmOGPYltbttMUphXxJRnBIXJI5AbBGT6AAVKEOnR6i/jL4Phf2UM8ilZGZckpgbjtweOeMH6VHhuY7ZIlaWdo44SgnDcCZcbmYHk4y2TwMZ/Kxgtb6OzMqWPyz2sh2Ja4lXYVPc8gH1yOO3PBzVotexps7eKG8QmKBtRMJjdUP8AvEaFizMzdjgbRzyARnFarb5eFUaOZLC2Yb4/DncvE/JZmHHJOeQSe2T6VhNFLZ2U8UWoLEstw0KCGLxPGY7TGzhRu8yhs917HGKSCO7jBNvHp1zBKwlhEmXkbjIePapXsTtGM5xkjNSlYXubE1KZI3Us0qPDuHjBFxuBDEgE4XapJHOBn7VuuLq5+Y+cu4lOS0KQWqPMq+LIBkAYYlQo5GMAE4Oajl/2ckkzPM1ltiVUWbLlJCC7MM4UKNvl7kHmsluY4rSO9kLxwJGsrjDIVVXIOfRNqg5Pc9qlFXqSlhuGMsU1y0dnMA3hTXJfxow5wcds+hAIPlGea1xMRumtSrwQTFHDSBfDlXtvyBuBBTyn129+1boYYL6wWJLmNnCCWO3Vd4jjcqTJtAB4OMAjv61uYJc3EawRtdwPANsCKQURGJJAByxGScjk57cVN7kaDM8OksjPZXVw8xiQQyMBuYnbjIzJ/LOPpWNoXgeKOSB4nCPL4O9gkgzgjcx8wA4z7c+lex3pQSQ3duJrqEoX2hkjbbk7i4OAMBeMk9+1abydY2gn8Fbe5hkaSKeIGQFTGQ6nngjBO7tjdk0YWppmk2zzxJeSAl9pQqSoDezKp3DK4JOAQAQakafsSxCzTuIJZJEhRIADtLDKndyxXkgtk8VXb5bK1ulOmPC7TFlt1kLGNmk2EsSeFbllC5wMHbitt/MkVu0zXN23jXAmilhiGYpMF/DfGDtJRgSc5yB60z0JJ19HKl/JFdX95cwCVEiEj4AQKGzgD0JG3PJyME968meMWcws0WWAnCM4aNbVx5RuIGPLk+gAIHBNeNcQz3McS3L2Es0ilY5G2QyMQMh2J8x25wDt2gjPtUe3tvDEgmtYEhkd0mKzNtIBG7w1yQy444GD9+aXISR7MIg8hecSJIFWQSSB0JPkLqnGNvBzjnFRHD2upzfs64VbdN5WFifO6yEKSc5wCAVUYGXGe1WUkkKp4pilhkBdxMjLLukCDJZtuSOMDbwduKguqw3cXipEmxi5QqjM+doLtk5wdwBAA5b071BZEv8AZ/jTrd6hNgIGdFLFXUv5iykYHIYjcePbvmq64vb/AE7UruO8s4IdBCrLFfQyeZZTj908BySCG8rLnJJ4xipViInttjSgpFCDIssZG4nuAMHbtwQTk4HvmtrxWylpDHDcQK2II/O4jITk5VgXPAOT33YGKlMhkgkpbHeLea1mjQKkQHhyp6Me5C4PB+vPqK1QxRwWxSMyGMRp8wS/h5Q5JwO/0JODUKFLZLWWCVbg2svhCWOKzC7WYekbsdqYU9vU5HtU+4kktb42iof95cII5rfxFd8DEbFeFOAMDvx6VDBlBaK7BZJ/DghZ5xvjVgu2MAjJGQCMHBOSD71ojtJ45p9SVGkAO26cK2SmCMkY/Ec4xjcB3rXI8jyyF98rGQrO8kvhuQFOCSMZGN2T24IxxWiO4meOK5a8QWceRbx3T/ulwxy7AAlueSecgj05ohmTpflbgXb6escl3AUSSMwMpXG3CknzHg/kDWvwk8Vprs+IkLBy5GxY2APB7ksAOD7elbYIoI3VrW8W8uPGaWd94VZJNgVynG04JXPb0xUNZLhoIJTJbyRElvmBbu0UaspOVOeTj+fvTiFobpI8WayzO6BvNCpxIVUqMsABuA/Fz9z2qRIpkkubcTqtxBbAO5k273bOVPmDZwo54yDxmohuLKWS0uZBIhiZ1BiyH8N0ztl7byCBwOxPsM1utYIUBR4I/mJYUE0sxkmlXYuEyTww4wCf731zUZEq5ue6m06VDcQWsTPGG3IgYh8/hReVVdoJ4BJ7A1lb2NxeM1nJq9vbxTKssniAoYwwDISAMZyDhRnvz25gPdTwRsRG5i8u/DZeEF+Cw77NvBAwRjFbZGXZmYR386lVkReDljjBJxjCnOeOwGalOxDRKj2RQySt48eyMiCTYJZImA245xkHO7njvUU3N0ZF1CyjjEkqRqdyquWLkPI3qC2eeMEZzW6SF7q4iSe2WXUobUbWnYSgEO6hyy8HyhO3Y4rFZ3jaBZ7yKGF0dsGDzsSyr5Tk8AnkLk9s45FRoSszTLdm2urZDP4Uaqw8cPzHvUeREXlg2PcDtuxits5aJyw+aS6mWMyqsqbCFY7QZTxxypX1z9Kn20zJPbftSKCNkDM84RUREBIBUqO+CcAc++MGsZrSW1tv92dIrIpMRLFh1EOcgFiDw3ckc8d8UI4kH9ozPtTTb1HCmIgkZOwlycNzklifUHy+3NZ7IrZrUXUcCOY+RIPDiyTy8jNnOQMepJNefN28UNpPtdbyGX5mUBwI1TlcKo4x67uSSPQVHsZruOecSWN4yXUoWK4eOOZlkPmLHdkIgGcn1IX0NSCes0M9jGI5GWO4bM7SbN23k5yo3HtxnByRU09LXZhlcXNvaQGPiCGdP3eckDcRwQWzjOC2Sa1mRLyZHhaWXbtcoXjh8MKOM7VA285OQeKq5I4I5I5/nLVS1xumD4SJlMn9n/xHsv51KsV6XDIzuHv0tDY2trBbSoA4UlvDR2OGJfB3yH0Vc457ACpjpFpGnwRamjLh3nhtN2xFXPJye+RjC/meTxEF7HFE7Qyx6eiptWVgY1jHfAJzjtjPt681hLJJJcW++KEieSMSTCQTptI/vEHIyQADjP2qlzSw1RVS4gQvY3UMal2jWVxGdwPlaUjG/OBgcADPGBW6F7qw1C7uSxufDQyBCfDghQFUjj8uC5G3IUn09axe7gubDwre2niSSTfcF3BLDHA8NRtVScfU5J+labmSKNJEN3b3Mk7LG29S6eIjNyB2JA34BH19Km/YQllZk2SyjjsEv5IjCIyEMlxIIWdiTwhOSTtJJwMDPfPFVckRkF00Edu238RiEc0UqiRYsCVTnLNkYPmwCABzidaai7ajDcT3aXs1ttiVHOY48jAzwScA7sDsR74qpSdESKzjsh8tb3JNvFnDSM2cSOq8KR6KPwjk8k0ysFe+fz5+TZdT2sEryWOy2uDK2+RAYwPOBHlWOcZzjA5A+lbowJVTfayS2ZhkjAKA72Z87iMnBAbt7EVtlu7WS6Y6jJ4rLMitEixysMBSi+bAKkseOSOexzUOS5Wd5FilngLwtPdRyxqqqEiyWJTlVVT6c5BHNTYlMnXFsmq2VgXlS0Eqxi6jWLDKqADO/d5/wr5QOWNR7V0eykE0UUMdyyxRi6TKWwA2kEAgglQePfsAOa8uYZbaFLqxheSRgciPAaEMuSUUcqMf+LLA1BE6Ighu5VKSTCS3CQZDbSdwGRyMYBzk5NRe5K01NaQQx+MWtHkht4meCN18Nmy2ScD08qjvyvAz2qVPG7TNf38gWWWNj54DJGhUZCjzYLBiw/Tk4rx55EXw0t5J7CO9WTxXO6WbGRti3cbOWUE85yfpW22lOol7iJJZtNlLDx4w0qYBxsBOP4lGcYO44HapZBhNFeWt2kV187blHkMTTuFCqUGfKDwcEkE8ZT7VjeYurNI47eRrq2ff4caBlVSTwHyM9wQCe/FarC1XwYZIAFVhDsubhxDtwArnJzt9Se54AzyK2vJH4itbs629oCI5Ym3o5Ib8bhvXhh2xx2zQgwFp85Agv7twkKRmRZbpYoCCTvLLkDeScFvY4H12JapCL4NLLf3iQyMbweRHaQhUQNt8yKMAED1I5yTUK1txZ20ls8BWJBHIYmtgYmZV2nzc5PbPfv8ASoV/BdrFe29ibaO9nkCq9xLtVi+CXYjuQG4HfPYAii7A0XyyyPuiNuV1S3lbarrjYsahlkUnIVskggZPb1qBpFtJJPHBaySWbrG8phWUucjdlmJzkkY7n8QrJpLe4ubh5ZxKJyIVlhkbKgsG3HceGAAwMGtM1zHa2l5cRiG+8S6KQRYeHEJYk75PXAx6c5PuKEG6we4WzM8csTyNhJjb4eUzDsFJJ7dyQPY5rGNnuLS7guriWIJcgQneGmlwcLhe24sGX2XGTwOfY45bWxtZmEUN4kA8OWZ2AG/IZ2Qe+M42557VmLc6X4UsKPqMUEJjtYdx2sQ3AAc5GBuLepJ49ahkmqHVZiEV5FeBCzM4iEAIJHBdsZYZJIPfOa0P80+ns/yUM0cUkUjySFpFl4ZGbkDBYDAx28xPFbzDEbC3smtna5kzF4rcAlz5SqEcoBnPHfPNaLy0s+oPAt59QgudKhtT48SjDN5QuyN1PkyS271O7HGc1KsQ+4wAt1e1ktZ/GkkgZLMy3HBy/EibQMDAyvuteXgKXFlaQyqskW8yOkQWGUgcPzwACOcnzFqmQ7pJp5YyYlilWSxgCgFAAAD9VGDtAJGO9axeSxNeW6MCY3H7ySMKodcbiq8FyQygjOBjHaovmTYwuZJXVLizaK5dZmk3b2yWK/jXHfbjIU8ZI9QK1QIt7Dc2kzfNN4scrNPdFncK21seXgZLYOOdvqa0ePJcmFiAA03y6iT8MYbzNkr3Jx+QI+lbrVJitxJp6tYyyLEkRjbftRSf3jIcnHLHA/iPJFGWFtb3EhEtlNcWMCKEKXECIxAbyLhzwuQT6EhSTjitElnb6hqFjqNxALXULaMkSqzMY4+RhR22lyxywyTyBjBrI3T3emfLGNVgQK0axSqsuQeF2nJDHkHIz7YxWMkiwXEM8CrbozlN4TxZjKpwDvGckMfw4wOO5NQLFpPZPcmLw2t7WODDpG9w00oClfOxyFUnnO7tkYBrTeXjz3d5K0jyC4lWSNIcI0pL+aPcwyy7QB9eScYqHfXLx+JcxHPhyNIQG2tJzyQM4zu9iRnAFR5ZVtpluJJjdrNbld8qcIFOQAEGMYLbvU+oNZamiViwuUZYZZrODxVdsbduAOwAQnBbAPBz2X612VnELWwsIjt2QWoiGfUKFGcd64+HUpkgRknmkhjiRntIQWePlQuxj5SMEnzYOOR6V11ru+Rsx+EmD0z/AMP5+1cWIb5Jm8F0kYzkQI5IDEEAjgY5H9K5nVmMVo7yANiJu2BgY5x7fer++80bRK2XdiOBz3ArntZCESxsBsEeT6478fyrzKep0z0PlvVwVbWaaRi7uX2pg4XOwAf1r8jfE/P+1MmRj9yuPtk1+uutJALRlxg+KGK+3ORX5G+KDb+qpTkH9yvb86+6+g9b5M+L+t9X5nGUpSvtz44UpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAV9Y+DDEXVwqnG4NznGDgYNfJ6+qfBqURX8uQCHOxuM5Ulc8YNeZ9TV8LI9P6bliYn6R6VIjjkSTc48N3I/EQCynOO+Qcj6V9OtCEZxw6yLMGG4YGVQjufcfyr5d0quJrVAAgNvdqWPZs44PH86+lW8hlZJTGCcyHAOAp2L6DGM4z+Zr8vxG+fpeG3S3hPnADOzCQ9ySeQD6/n2r6DYAlLdjnLbf5DFfPlwzxsSzllUncPUDGPXIwBz9a7zT3AiiPcBUIOfp3quGyqZl8VnBHzd49/wC0ISskEct/qUUjEls5vJXGOOckkgc4O3B9akz3Qiijt1s1+WnkzAYCF85B5DMRhlAGSM+X3qHod3M2o9S28LW8zQ67qh/dyMroq3JOD6K4BJ7cj1OTjMQPPdeJcpH4vjSREbCvhoTkmP8AhfPI4Gc4A4zXrWs3c8+LUoJozsPAtbiOWV4PIVadSu0LFtPCy+uWGT3Jwc+mdep3Mk04vLi4czw8rYpMZSAV4ZkGAcA9ucZzU+Blihc2TKgiMnjBokKlWVQCB3z5QMe3pVVsZb1bSKPEwIVDEiiMqyk4Z+Qd/fHATaqjNWWbIkiwuEtJ7Z47OG4Mgk2eD23BcHy8ebPod2f1qHc28bXUsizqvEBiXYWaKdi2d5GQCCo5J4APpWlJbWC4uYL3KKXiw28uIwWYg49SwBO78GeCOBU3U5r7TblztRrWMv4bF90TmRTukOThUIIGD22kcVcqRBbzTPL4FzMxS43TI0StLFMFIAbjKRsAAOwydvGa2XF1a3NisFvGrODJI7yReAsS5Bxxl9uFPP2zXrtbMqrGrXE0YVLfDDed2MowPJbJx/EONwJ9NtvDGt23j7WgiZwk80aSSBCxXYre4A5BBIYccGpIK/U4I547yy0+d7S5l2+A8sPj7lEwIYqx5V1B82SRv47YK1+XvIpI9TgigcMZJo5kEvlBxGSSOSSvGBuOM8VjHeWfiuLbbBfRnmWOPY0LLuyp7shGMr2XPOBxU3Wbu7vI4JLjT0e8XbLHcFgrXLK2FDkAbnGM71GGBGRxU8LEaMS6bbm7u5tPv7+b95GqNLgFmkTcTuAzwAo9BkDPfNQLERm6RLgFG3B5CAVWAFkB8RchRuJGAeSAcYHIubNLS2udTjd45RauDJF44ICLHwMttAbLk84yo+lRNNd4/k7l0t0tlYlECEs58IHBGMYUsDu/4scZoPAlNM6RJaarPFbvFC8Ut8ihw74I3kZyASMgL6n1qK9xeRWkMNpD8jbsdoYsJBI574bG7cAdu7Hc+uBnbaWd0+nL8zAiTRbcWUqAqz72DP5T2x5u4/GCO2KiXM3gC78Fbe4KSXBcyTFAh4yMAnfgR58uNxwPelkERbd7pbiVsxW+PxIreeSHkMj7jtkUkhtu0AHsDmrHR4TJcSQXMsNxY2ztPMou8+U4KAtuGcEkDgdiAMDJ0eNDDJKVjzcWGbi5AulAbkbY8YLAMD5VAK5DetbkmRSyGF55JhL4sKqIzNjKqzORhvK5C/bgA5qW8hYlWscXzCybzc3hCrEsK7FQN+DvjOQwJYdq32cdmrQWt5e/NXk8huUtndGzEg2mViMFsuePYEHtUbTpPlXjhhhmCQwuBHMCVWNjjChiAXB35HoufpWMEOnRPazQR6cjeH4cTpCo5yA+0LkhMkbl3cEDHBzVCSRc3C39lKbm2soWEX7hzCGVpU3+Vj/FgcgjnsccVPhjnuopIprmC3YgRxhpgVEajIKADGTg+3JqktbhLmWGA27W6y3rzR70jU2ynsWUOcsWHp6AZ9qRSRmZmkkMEXMOEAeOYlt0nK8o3Gc8AgketLWJLbUHWOK8yslszTyNFGs6qoSPJC4/hDc4J55GMFgaj2cqSWELQwm3mkt0kS2k88pRBgIx5A4YjJJOCecitWnXsT6la2eq2k9vHGr3E1xOP3WyRTkpJkgyjwQpT2cHkcjzTJc29qbu5M72sSRbEk8Vl2nOTjBLNtw3OGy3pRhE59q2kFxZx3QMsg3Ws6M8byEAgkdyFUncuQB7+laZpPGFslrc3YcRkM8jlo+G3IxcNycqW555I5rdBLHLdRpb3F5HbpchpBcoA8yEkYDZxgFyDjIIwfSo9paajYW3i3Rj8WORmkWSZWjUAEIMhfMCG4HJBGec1BJqvVS0Y3i2PiztcQbFsJTIBkkFwhyoIZySP/Ee1WxYP8ravJHIdzQyRMSWeNg5VhjkYKNxz3BqtiNnpcUBTx7i3ZSBPFuPkZQiI47jzEFVGDzzgCtRFvdWlwbvF4YHLb1f96jEEEqqkY2kH8XBIPtU6gu7GL/fAZ4zbCTxWllbynwkQZGcZOME5OScgYNV+lWiTNHZW1vtMMA8OK8QgnD5PmbBGFYHPYk4xVVeSfsu1sy7SzRRWz74pLXc9zuO4RKob8WNqk5A5IqYs4EFxLHCv4jKskkpjY7juYAu3PYdl7ADtUWJuaNO12JNa1bT7u1Z4NFvdPVLyS5W3kefwnklJXg4jjMYwueXPPBAvrq5vr9o766nsV+ZIUzMixTSqhBXzHmTn1A9zVdpkTJf200lql7KEMpliuDcBWwE8hI29m57ZAyBUudRFdwiax8SCGVg0CD99b+RiGZwckO21cDC4zk8GrNq9kUS4s9Dtstptkgbx9zxkKyvkhzJxgnGBgDP54qU7iGd5hL4NxG8jyxhN7FuQCMkAEr3z9arzbLI6sHgiuLhFd4XyFYsdweNl4Me1SCBzlfrUhjG1sZbeWbbKWaLZpwVSrHlg+RnheAOcZ9ckVJZOka4eO3m3SGUxOIvKoXBGSeO+Pb2+1aZriC9umZPDcwjbG8SeII3BwBgds5OSewxjPrDs7/xTMqopYskjzBthlAB2gLnghONjAHBHcVKjvDMfCtYblpld4YVdFKfbjzc55xx3x2oNALn5l7kOt0sgiJlc8Ky8HaOQc5BxkYz96zVnieW3CzQPMGZkZw3kxk7iDg9wQOO1Rri6geOYW1n49ta7GV5f3GJXYYAIy2FAzjHmzwR3qTLMlvFAt8iNKhZFLZ8SVE29x3Ks2Rx6D61LZBK8KGA3yXzm3aGBY2UxgKAhVl9TljvbgYxkitMdu37Wht9MjluzKu0qAScAt5+3ceUjnHeq3SNEMNmllcNDezxZjSZ0Am5Yt3JwTg8cdscnvUqMiS4muXuZnmY7i06FHJGR29xgD6LjHvS6FjZqzrbStZrBOTaofHZcvkZyFU8ktn15wWGa3xXsEsTeL4VlEgYwuxfw3BHl5UgYw34gRkkAj0rxLS0aRYbqBJ7ePyEeJskBK7jlQCM4wKqbaaZIGk1BI5pEVJPA+V/CAGLFCxI74O0j+Adu9QW4E+7GxTPBaOsUFsjQx8jfg4A2/lxyO59Ky+XlRWu4Y/AEo8YooQpuwF3jccL+EZHAIGfTNDbCPxnlMsyhz4c3hqu1iiAAnkBsk5BB9MCo62MpeKS8MAC2zwyJPIMyZbhXQ8ElSQPpjIGaAmw4KW1jC7SzzyvKY1mys4cZI2gkDABIPHHpWqBFuIofl1BMJ2RrDbLEAM92UgHIyCeMkNxWiLTo7IrYwuttFuV7O3t0xHHGmAEAXlQhIIAA4APIrKaABH+eWaG7u5TJchlbxSAR+I57vgcrg4+vNSyEJ1ndLSzmnkfZI8u4EqzQAA+dcfhJx7nP0qRPqNmTJ8ughkm8SVDbKTGZAMFy2c7gMEDjG3itLxlRbXEwVhdBgqIWDbMZb1PI3ZOM+g4waw+YtrswRNs8OVSYwVGSgBxgcjk87iP0qCTf8hbNBc28kjCdGEEMhDTFNyhvUEFSHcMSSQfzqNcXSpC8LW7xZkKtcSW5jEkm0Aq2PcAYyOePatlzJFHap8pHI17EEmlafkyIowWODjABAPc1HjFzdzSoIpLuUThc72kEkMYI3FOwBYsB6+XvQITwRKIL2KZreXwI4Y/EXMZjBLlM4IALcnnnaAO1SBNItsUkkuI7fbBM7Q7ZVXjaytg8dkYe2QPetGrWi21i8klz4pikeSIRzb0cFcBQgHZC3v65+lez21w80ctx8zmaRYiPIscD/32OeMAAADJ3H0xQG2x1AmJzBGulzWzqjA3G2QqPMrKQpI3A+YNn1FbykE2lP4k7yBw8m0OSWZsec8AnA9OcMa1IdRkSz8IFbmVnkkeZXXcyE5IIBIUqwAJxncKzvbuG3aNrQM12rZjPj5WVBgO5AGE5wDtyTkY9TUsghWsdusvjwqLW6uLZxJOY2Zs5AYEk+Zu5zjua2SPNDCkly8O6ci1jmky8jPIfJGq4ALEAAszADnj1qTcYkvSkVwkd+jsiYXYpY+YEgDJCqMAdu5Oeaj29/MxjjCrNJIDKVdMYYNxs54wXOTgEAj3xQlk/KTwW5jhgJmuEWCN4HjbaQCQm04PA7sQMn6VWtd3FjJJbSSm1aaWSJVRA6q2MsgkYjGMZznuQPXNWcVwttArQxCSS0nj8IlS4mQ8hzt/CdwYN7gDPBxWt7qG5j1Tx7hTZhReRO7KrGU9yUOQM7iMDjgZ70yIzNhtp5RbnNtdW4AdnEqcndncRu/unGDntxnNaA0d4WI3gtHKyMu5VIZhhF9gqZ44yeftGiVtUtp5Lu18Ga0iTE8CRoDMzA45H4So5A7AtyOKyWZrmP5hPLhCPEnIyzswZ3AzjzEIMegUAUdiczyXUo57mJkiWEXDvBG8ynJj25bAAzsIXLDK4OTWuO9WOGZrhYZVVhiZnY5O3nbjjPPfsoHPpW3wrq9vFEcExgkyfDM4j3KCQ0oJ529uPTdmpjQ3E0iyQ26M6S5ty021C2MYZQSSuFY424JHBBqLXF0iEs84kgjukhdcqz7W8JcqGV8Eglhj0OCfpUmD5aW/sY9SgmuDdxHYwiASzEattZz387KwU471XQNNarNaW1vbXLWsJMbbQHBzlGZ+2cqe4JxU++vby2tLaZZIbZ2lSCKMHhol5dmJ/AueMebtxyaLJkPMgx3UQfUFSWZp3kBW3nuUG1ickx994J2gqRx3qbM6zyAJFIsrMRvhwqohUDzZH4QSVHbs1amluzJLezRwpaoZZLgSIS7ekaLySM+nORmtX++ySTobayJNuxNzI22NIxtYq/I/CTnGe4GeDimpbvPZ3Um5jiWIEW5MkpvFKqzYYjjjYoUDA5AAHc1t2SmdJLO6Q28kESK0aLAiKWzvUNjK4O3ceTkgdzWGr3FwlnfywLHZQQRsibj5QQC27djgtgZb0LKPSspWczIbiGKSLw2wgn2CdgfIBnnbk9gD6nNQDVFCZJ4JlS5W2k27pVkCMQWOWBzzhsn05x3rZa28llNapcqVtUmj2tsRSi4JIKgAEsoA3d8mtxubS3+Zu7SbT47y5miSWQwrPO2CpCY9MHHJ43NxyDjOS0up7mGU3EcwlcyKspLYkIG04A3kHC9zjv7UIuyujk03Ubue1nSCzksDH4MxVpUZmyGKMAcsuACWA78e9bnQPbT28sybtSiPzU0TEFV3IBtyF3KQBuA9wPXNbri7neW2llvkXaGfzRnYu3jzKgGSSchSMc1K+ZmdbfwBbX0ohcpLHE0aqfLu4fsD2wOc4wKm/YO8qZp9RE0TrcyJcS7lXxFXlSMBT9fTvgAYrdb3L2ySOskEm4MlsttICz/XB7sME4rUwsv3tvO6T7IGaMvMVWJc7ixY4/ujkjivJtOVrjxkhUfvHQND5o3UKSH9MEsx47cZPahLtaxFMiqqRabKz35MYxs/eJlslnLDggLkDHJ5xWy31bddQXWom/1UCQqIJJguzahKl14HLAkg45A79qwjVLhlcSz3EpuDMqoyncpXPijcB5SCQGHoPyrHUYoNQtndrt4bFiglnkQQJ4K7S0yycA459fXPrViDKfVpL5rgzXMDfOyosSMixoUB2mLzZGSxPBHoPbjI27W73B05YtQikJEsLHapcBTkoGAIUKPNz7/Sozagllf6kWjewjSfwtpdXSUMoVcNnCrtbnPY8jmtsEke02yxSW8I22kzQDe2w9gG9j7j6VGgIEOjyWWoXF7qF7O6x2kS2iTwO9rCsUniPOEJ5dywAUHICknnAFj8qZdLnlXNtbzxG4e7+W2KMgtlC53sSCuNgG0ZpbP+0IIpNQjubSaMsiqkm1Y2TcOV5BBUclRg8e+K0s08d1B4kobLqzl5CQseMna2eFUA+XgkgAD2ltsqjZaaZJeX8ZjMatABJZrcTrubYoVnT0LE8jd/OsLaK4givoLaB5WtpsRPGH8bcSpO8/3zuY8fwrml6i6abVrpmRbmOO5dFkIUYY7SQAeRxxkDmosjBBKjBlbiFzHM0I2SOAWLbtqja3JPou32FCSZBMHWeTwo2DTRgJKFlRAwILyPnOcHOc4HpzmtGp3cNw8QiCxXE/hxuNzN8pF3k2sPwlh3bueAMDk67iKFHkS0kRtOjkBVVjznP7uNlOQoXzNwdxJZiAOa2taQTajbW7CGBobphcCRGZY/J5Yw6+20MSc4/PNQD20vd8s721000DTyRpGBgKqELsRgTnBJ79+R6V7D48etsNWaFQAxEAiJ2qI9qpxgYDMMkgAYbaPWtNybOKKCztRJss4S0RiK/u0JI4UYJLckgDJJ5r17wie2kgtpcAqJGlGCAq8szHgLuIYknPlApoxwI0On+Bb3EFg8tskkIjQtlgzADyDPO0gnA5wPoKnsGPgQ3VnPZiN1V1IADHgeX6EnGewA9O9VgnhuBIlhdw3crFlnuYZeRkghA58uDjk57E+1SJtUgt9RlV710WRxHHCN7NKzJt5YjG3OMfQA4FMybmuO527xbzMs9uGlX5jLJsPOWx2DH0GM454rfPLdRfLy6gItPjCvcqYABKy8cGPuAc8DO0Yzk1nEoE18s+xJrhGSK2KEtgIwIQ4GcsQCxHrx61Hkt/HM7FjKjSNJcg7nZwFDtg4yxwRgZwcjsKqSmR3mN/NLe3EVztnjfyW7LlY12jze2MqQPq2e9b3LPFKq/NwGJolE6wgu77gQcHAJIxnGBk4Gak3U0f7Qf5yV7W3ZCWR3AymVG4AcYyw54rbcQ3QimgSEWSJjwm2bVBGSBuUnIPdjjvj2qrLFY0suJZ5k8eLYvyhj3GRJMFSgwcYHCjk/xH143XUEt2bpkZYQviTljDsC7iBww/HnkE4/TmtksS3sqyard3CxzJvthh02gABWPr5idwzjjngd5DukgS2mjS/W3h3b5izB1duAETlzx29TxxzijLpnsCyv4N3NE8ZnhChcFUlTamO3B244/wBE9NaoWsrFlBy0XBHfuuTz9K5OzknTUrsXKGN4gsIIkVyXI3YIHYgMCQMgEkZ4rq7QEafYLncyW5Lf/I1wYnq384nRDeRpnbgMVfGeSfQZrltXYRQTyOOQgLYyfKFP+f8AKuqndVyHG4FuMk8j1/KuS1rPy13k5JiOMe2K8+kszeo8j5d1dnY0algGZtw5JI3Hn79v0r8nfE0g9USEf+6XP6mv1d1Qd73TcgKcYxjvn1/Svyf8SeeppCRj92OP/E1fd/Qut8j4v611fmchSlK+1PjxSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBX1L4PH/AHqfvuzlQMdwVPr9q+W19T+DQ/8AOishKushIx34AI/pXm/Uv+WR6P07/pifo7p6Mb428gcO0W7IzhoN49eOa+jWm1niOxQRGvkXgZZcZHfv/jXzvRg0l7GyMfCWS3die4DW5HftyAPT1rv7AlhbYkZSYIOCOxIA96/L8RvH6bh90ubSX94r+Uljn68D+tfQrInwIzkkbF2jHbj3r5vZnckTTKVYSspX1xtGB+oP86+i2LiSHJHJhiYg/Vcf4VSh1hfEbiPnUUZTVNbS40xg8usajJ4kkKgyqZn2PHIuWzz2IGPX0rW+LK3ltl3XUYjDRG4cli20ZO9jnDbSDk+mR25kQzxw6h1G7meMNr15FGWjKMH8m4J/w53ndnBAIxkU8GJZreNZzA0t0CpyHeZjhVfLg5HPA4BxwK9W+ZwQtsKxGivpRHszA9uJEZJplyZFZwqozAEgj6k8evFZySsxtoIvlmlinTxLmSQ7GjRm52KNrSAjG3jPBPbmLc3EcFzE147eFIWWKYIUSQA7YyyLwHJXjPB963QQ3C3tssptRbSQyytcY3NkAlm8Lbgk7SMkr24DZq6IkbboSzM1qY4bllcSeBHIYwY+5YAKSBnuc8gng1DhRIZGtdTWG7LQOwJfKPG/l8rjIIYBcDAzg+5r21kluxcC3kt5VOdiwttaNsDJJ/CMMTxzkgisU8KzvIEaT52FLchVSLw/Ew2HARgPMCQQORgnGDzV12FCytZWW4jeKNUmk2LCvhrs/wC8pIUhsZzk/WqPUJIItWeS6tbSOC5g8aWQROiqwbcAVyrc55PIPpzgVPMkjrAlxG0BkdSwuJQ0recHO5Tgk5BI7L7nmtfhZufGmEUkDOdyGHa78gbmKnGCQTgDP4eD6Wi7FWrkWS7LXdlLcwxwBliCSyyNIGQfwbCGlb04LEHj0qXCvhanImn2k1xMWwscDlBu3A+YHueB3OGx61ISwRfAuZJXgW22sEi8sbLhTkH8WeCF5zyO54rZqMFxLd28gtY1lkR5pIRDKZSikfxdsgqMA5OCcAUbQsRLGaVbSUa1HJdRsoGHjQEM5Y+Rgu0sChHm4yMcHNbNOu54Lg3VpdZjljkk8SVFJAwu5VVRncQM5AGCexzUdtQuoNTDi4imsnVVWNIFdkALZZuWDKu7bgYOW7ZJqV89dRyrFc3ZmWRFYSQxRHBY7U37U3RAjsWH8PqDwFjOa+DxXFxYaQplQxzSXEl8RkvjOw723YUny4UbcfapD3m5bpTE7Q2GUt5jF5uW3FDtBL99w459MntX3d/HZz28AtomRYEmQvDuD4dUwqrydvBPYnIPpitirdXql7Rkt5A0njQPdRx7zkhShcFThQ3fzYyajgSYwzG3sPmLe2O+CRfHWQM6uRnDowwduACAcMpweRnE++uZp7dLrUrr/eZ0CrhFCs+7cp5JyOwHrkE5FVklxNNaB7dpooLrZHtIjzNt3eRG4z+Ju2Dg+b3r2FJrO0l8SOSWylumkSJ1Iht4t2CrkHgAnjGeVyOMUIJtxLHG0c88kESW6MrF5seGcBSEJJIBU5Dfi4xzWyO3aG/tJbS0WKC5geMXKpuZAycAD0XIByRnlgTmo+nTI7WqCGKe8MoMshLEqwUBucjkgHCDK8Z+2TX9xekTNdsYZsOflk2JGu7nzt6sOCOfXBFQSSZWjgsbZJmnVrchrZnQTFCPwFiOMEq2OQefY1EttPliW7E0H+83cKuWi8j+KQWL5BBVMMFKqOARgnHGUNv4ss8ETQ3V2zALEsQDqJEGASuBwMsPsAagGfUbpybeykae0P7yK5uI4nLKVDL4rDCKyEHbgsxx2qNSdC1u9at3fT7e3vbvSdPZVdWZNkICKqhjuOQvOBu5OM8eu03AiVwbmd2jZVKtGcx5LDzNtOWCrle49vatCXU8m6TXbrUbOweQrIHsnYIzk+HlgRu5ZOwAyDjFVws5Xt5ZrW3fULUyYSeK2a23Sxlm2qhIHZuA3B5yQRRLIHR6c1vNZS28s8l1GkW5VZBI0anJ3YYdyOcHH4TjFa5mRZljiu3htVU+fITK4JY8ZJOF4GAcn1xVQ8izaiWspB83bR+KIYfK8jMWCKVHKgDdk4/i9uakva3Ufh/KJaQWzyTHdKS48PaAijcfxNzv4Oz+H1pYm5tjkurY6isdz4jPbLDHbSIF2ZwVBzzy5ZcDk7RwOa36ZaRTajdJBBHIqcXHiTSLEV2klQSSSymQn29s4412MMiJFZhZGhWVi9zKsm0OHyS5JJAD4PcAEjsDWnXJrextbu00+G4S3aM+PceH4iRupG9SqlSfKT5gQU3Hn0MkG+GZLLUbtYmSO9jeOW5VEeUr+ENg9ztO0hCAcntWQu5LeG5m+ZFtJ8vJMq3EAj8IA7n3ZztO3AyewbbzyKjXN80EMsF1GNQgMYkWRCRPFCx7bn4fyqm1mPIU9yK9trc6jbW80dxDeQCZhBG0ODgFiZGI+oPYn8IOMVBJnHqMUKtOtlDPcwwkT/KxmQxQ8BCgGAQGA4B7HJAHFTUa8nFruh8RpVyskPlCBo13NljlgWy2GOOMemajQ7ziSxghMIhKxyPck+Dlg7E/w+qk85/hHHeOl7cJBdKY2EjKggl2hHBGUdI2PDNkgjgjII5HYCxlSfxEN1AZr23UNZwoP3gYKpOE4AG84J7+nI7x7ZLjw4hchnmNuBCVGBbqxyw2gYU7DgMBk5I9M1nHaXdiommceE1oDDI7El5N5w3IG48Hd2x9eDWy1uLohfERXhGA4DM7xuVzvPHfdkgHjzAHNHkEeywC1h0ux2QuyXQke2kiAM525nWMswOdhILehU+gxWjTbe8a2tbeJDBdwhThyN8xYEqSO7LhiPQkjjJzW6cJBbvctZxnUoGn8PUFt1eRZMBQRu4XPGTjA3HvzWGkCeGytYdTsjZy/KxvdW0d340NvKoXcys3mYhmIznHsKtwIvnYkvFbWfhSwKYGnJht1gRkKYypAHqBnHPI4rNoZPno4yvgbAVSSQjeFHIz/wAOCWK5xyO/FbZZv90svlJ2sfEnYPJlhIpwMYUdiwUHP2xyeK8S25t57C8mlIikMVp/u4aNpE2HzFm3eU5xxzjuaqSidIHeGOe3DLCxEFvmMq5UkbnAxyOTzyRj0rf4clw8NxA7yzO2PmI0aIRrnCttOSQSu4lePfFQpoLy01NLi9W7kt7F38NLaMgMxIzIpP4gBkFRjB+nNeKJdRu7i71RCzRzSxqUJgVlIXzEEkDByDjAzjg45ZAyjuNLjluILO8tY7xUYXIdmdo8PmJgPx8lyCpHcgE1iYpLuVSFhufBLLEYgVJIbBzuAG4jC4x/LNQ00rS9Lv72e1BDtIpa+W5kLzoJAWLMR5PPn+7kE1YaeJ0ijt7axZrh5GWG4e6PhwliBKwJHl4HPfjOB5hVuORHC5hNE1rMbW7kJuAyHFkcsqjIAB7KM43Me5OeMCpUaWlurTvbFbs+IsSl/wC0dgPMVPJY474yMVDikVppkSeK9jRHEkfARAEYqcHBYFgp3duwA4rIrc24t2d5dsse15ldgxOFZkCrzjHfnsCBVSTfPbPFJLGkrJEtwsjqN+5zx5cdg2cZOT2PatuYJ1ggsrv5qeBXlXxRte4KMQwbA/Cu8fcAD3qMl0+bV3RflwvjR+C/E5L7VB47jvxxxg+9ar23kk1KP5VUM6SMyWwfw5myRv2ElckZxwcY96IM2CyQTxNbrc3y/NMWiNwEdpMjaACQFTvwMDsec1oN7ZWgTDRw2tvH4ss7vtjTw2yyA4/ApAwcnOcYqXJNcXGp2tvdxRsmnuPFLgujybDkZb+JQSpx37jtUXWbefVZNNfTNfm0qTTtQMkyRwxTQXsGxB4bK4OODgYxjO7B4qyzdmVbaWSubbl1gnmufHef5d9100UOZLeNj+8QHklcFfLx6H+GtWpR3DKE0kzWEskXgw3AgMsUXiZ2nA/EcZx2PmNTxqDfI3MrXEgDyJ4cMIfZ4jZ3rtwCWIHA7k/So9pqM2nQyi1+dgs0jUtNBEP3Kc4XgckDjv7YqCVclW7/APpCMXePxII0aS4UMzbSHJJ7ucgAegPPekptIrpv2hq8RnM/hx22TiRt/Y4GOM5OMDPJ7VDup9PVm8OykmTaRGfAEWxSy5+jemScHk55FZQySyahcwIPEe2JQyCHw45G/HgAnJKr3xxk85wahZgkrJK0cLRtIRLjGXKOFKkqwYYI3cgZ7d88CtKWVtNOxurmGFNo2kb23kZ8vHqFLZIwD71jpeszXV3GjC5di0jSyMwz4JYBQxwBgYI9yCMd6NfJZ2qW0GoSGwt5Wd5JyqrDDtJYhm/u8ZPHGc1NgZ3sstnbxtpt9aeCJN91c3MbsrRjCMR/dPmGG9c7cZ5rDWbVrSWDxJEedmKBt2zD7SdvmOWJCA7RzhckGoMV9Y61ZfONqtnqOmTgwQzwE+G212TCejEujE5zg8jFTb5rG81n5m5K3F+00cttEYzIiyLuHiRsT5WKO6+nBOc4qdMmRrmj21vJEtrGWRWRrUsGeJ3Iui2PM27gDPAAGAewqXNdwXEMaXYs3ubkNHa+JwuVALq/ckce3GBjOaq7C3t2+asbhpEt7iKbDyFtyLgsF3cAMSvHY8L6GpclzDJGJdVFlY28U4f5i8jWRmZShPIHckDLDH6Cq6l3ZMjCNbaC7lGnoYJD5hAwTBGSWAzj8IUAfT61ttJZ7kw2xhuZI45h4phhRIQvDAMncbRznPvnHNbEdJUci0jaRo0eeVQEN0shAG1OcsAB3ICjHHNS2N1tVI4pJiybWt5IdkoOARn0IPIbkVJDZpig+SCJdlIsrvQtJtEsmQytsGSyYHbsfsKrVjvluEH7m5Ewaa52yESyxsPKoK4y2ATkcDjNTrq3YpL80jwrc3Dw21zHCZDEVXnzZG4DHHGCOM1jexTR211dXV5GpjPgQzl2V1yQWdWOSM9jjgYAGCagGRSzWBrcxJforgBxKCyqo5xjG0kYGR9fYmoV3DLLGrSNOzIgiYyFHXBbI2qoyMcHd7KKmXEyW0t/ZwXc0WJWEMJ4cr4bZ2g5PfBB9QfyMeOK/kitvCubz5Z7cxyz20iq2SxDAknH8PGOwJqHkSmtTGSK4tiYIra5ma12SCOV42DnHkfdtBO4lVGSAcZ9K3WUVtKj3K7YbK7Vbd7eeMrG0nO8BQTuBIHI74PFaZ9L2W0ib45dlwrzQxMB4IAwy7/4gFGOTnJrVp90GKNfmdJvHMwhH4UU+XCjuDknnOGyaIkmJbtZrKlnHExTMkhjkZCQQocKW9dinOc8J6VHSAfITW4mYS2rfuJVLOyIuPOMkHaeT3PY1ruo7sGyu7q9spgzCFkhj3+HleWLr5eO7cEZ9eDS7tYobLZBcFru+lVp5otzRpEGy2WIADHO3HYKScE0XeGWMWmFFhkeOG2ivFV3cY8aRFAIZRnODkgD0LdxioUipb2kNtqETRrAVaSSeUyM5ViCdy+4bnB5xzWFlOE+VJSIRRRvGsTSmSNoyeCrdz5v6N2xxJsAJ9TtY1VpUiSQBZMCPjhS2RkcegHf3xSw7zV83arBJJFc7LFEKmdSCNviA7gc7SNu7GPU/QVaMxtdOuryaJYpZN0NmYwXJO5clu54VRk9hmoVrNd2vzNr8sZfBgYTzxiIxRqdzA89xu8uMfXFQksrppY5FlkXbHiHwpT4rKVyyAY8q55PuPvxOhXU2SzzSyXF5dpawHwCk6CMeU5PcEnzAHBz34OaiW9u9hcrb/Km2s40jZYlbZEZSpA5HmkIDKNoAGWzng1Jb5jAuIre2t5EufBlcpuZ0x+PdxnjA4X19KziDadcOthHP8xcgxuA42xRqyhssx3MG5wqg9iCcHmUQyQ0cfzqrqPzJl8Jj4hjyYEwByQO2R9Tio0kpkuZLOS0t5laIFV8ENCqKcGNifKFPbA575HFQdNv7C+hivbY3tvBJGT8y6CN5QGZM7WUAcqQDwMYOORUmSW1t45bWIXPjHfJGpKqCSBuYtxwSFB/vHj1pZp2Y1MtR1C58W6F0bdYLBBKieCMR5BUeQDzHOCBk+UVhI0yRWpghSGSZjIWJywfGWYEDl/4Rnt6d8jO/RILRxG91bXs4UpCgDDcoGWOc7QcAAYJzzxgVBjMMqSSqsiPcyPliQNzcHaSxOBj2Hp2qdCBKskF3H8uEg3EkxTRNJjaMsjMuCXyCAPsAPWpO2AfKh4YobIx+LtkUpiUYbYrdgd2TyTgjHFRbCE6ra3FwkchBZ0WXe8SxzAgHucjHBz9z7YlXkCytN495LfwySZxOAYuRtCqSeV/F3wOQMGjyBjBJPYySeGfFiFtmVcje7CTIZjyylQMgfU57itSmJ9jon7R8kySEjIZmPlGw/3V4598k1n4sVq8kt6qRQOqRW8G4OxUlsrj+7gAnOScgeleWc6+DhIztPgPGywL++DjGwMQMkdyT7AetQSeQWsMkccnzCTMk5huLdVCF+MkAKDyO24ntk+uK2RXzB1jvFYL5wUzth2kFt7Z8zZwnfvx3qOAsksszW8y/MTFIx4u2OPPlJCZB3kDJPbmtkhlto4Uv5GKtcBdkOweRSNpAKhiVAzgnBPfNAY3cRdYpUaaEwkubmeFWErKwI8NcZA2jufT3PbYZ4GszCrOkckybkLBXeTGQNoJzwMgc/iz3xj2O8njhmg0+VniuEYR4b8UJ3CQhscZ7E9++PeoN1MYVnEUaNBIomiij2hywG5Q5OTlmAUbT2545oDETWejacNTmu9Pjs7eR33oMMpkJHIGBjcQuO+AakPHFF4L3E7fNMjCK4iXyrMoDKCAMEdsAEmvLVLq50/Trm+mi06eexF1qKzNuW1L5xGMDax2uAMY9BzzWyLTGaR52a4+TuBEMyTLhI41IBjXPLkkEngAAZPahBHR38RZLiYXcQVYiiqgUzKm05x+EgsdoPHripdlZG5ktjAqW7Sw5SMuzSxoMIWbgDJBGPXA+tQLe4EF1P8ALTW9pBEWiWEqqxFlGd+GHLHON33xXpmjuLotcMfmFHiQSQoczOOVIb8PB483qex9ILEsFbOVli0+CSeFmIvVzuVVcYDAggKNobB74zitA8O6mhtmeSIi2aQom5mdFYDblv4dz9gPMe54AGdmFuDHPZQGcOPFCzzszSSPlfEbspJOT29B7itqQwwT3YhT5e9lwq3G/cxjjbam5jyRuZiFGASSSe1QSjLUoJHlifw3b5GJFbZNho8YypBOzO0A5xkYrKS9W23xQt4UrfvEtoDu2Q88ZA5bkkkEge+TitUz/LzXUq28bSJl1M6+WaQ8klccg8DvxW6GbUp7WGBbw2jzxuTGPNPgAjYuRgEndg49M81m9DRGoW3gQXEFtK5tpkLujNsklx2zkggcEYP3rrrE77SBkKyo1tuR1Xhs7cYx6EVy+mIzxx2srkC5txsLRhgQByCGPnA9TnGSck+vT9PwCDp/QoQ2RHp0SEoeCFjQcY+1cddf62bJ2aI9yolV2bAjZlBx9frXNXtr8xvJHJX74GOPX+VdhPCCqQzA+Ql8ZOODmud1ICCJdv7xsiPaQRjIJLZ/MV5tJWN5u6PjvVkPkuNgJDSkLkglgMcnH1zX5I+JoC9UyhOVES4/U1+weqoGjtmZSdilkj3tjygnLnHua/H3xNx/tTIV7GJcfbmvufoXW+TPjvrXVeZx1KUr7U+PFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFfV/ggU/beJdwjDEuQMnGB2H8vzr5RX1n4DyCPqXcyiRVBJQjIYDBxj14BrzfqX/JM9H6d/1RP0TpSx/tCLZh99tZGTw+NuI2UkgEYGFx29D35rv7J9sseWDFQgwGPowx644H9K+baUwa5tI8l91tbqCTj8MsoGR7EY/WvpMDGHwGlIBCZwuDjD9v0FfmOI3j9Lwz6JaQIDEBl02z8EE+uf5jHv6+tfRLBcM+eD8vGCg5xjPevnqFke7jfGPGAcbu3mYHg598fevoumneMlQCYYz2/4axodYbYh9A+bQ2Zj1Dqu5tp5GDa/fRSRi2JdG8TJzkfg7HK8HbkHIIqZaFR4bXStLcQSIVKgSrKxXbwOPDOPMPbsSc1Eks5peoNeAu5Bs1i/mhiA/CHwOCDnuh8p+4x66Ibid5ZJvGS4aEtGvzMRUGNhgFcNz2IBOQcngEGvXbuzzoRtBIjz2862sCaK82kyxzxs8UmnxsHjU5Kr4h27SOC5I2nJwanSRzxWr3awyTwLCZIp1JxCxwGjOOx7kErjAwMVpS0unBUO8DSRkG1BVsnJDOx3ZXIwPMD2PPNR4vm9PnE9vOXudqAGDxPBaMnAhfdnJYdmA7n1qe4nTMlTw2l1bKizRvJdRp4cXmwYxnzMxwASexHfseawWCYSLDZwlkjQSCSc7I3QsFZUDZIyTztAznsck1rS6uJobt5J4Ejd9iia2eTb7LIBtJUZ4HYk5zU7wZrK4vQ6xOLeGScq9wJEiGMEggEMQBvXOCQCPxc1ZFGV15boJhfT6sYZIom+WV9qxIvJOWXJOcDuAOAB71JMDTXGFDIwCz5ECl2bgqTgh0D4IV8Hkc44zptLq2kWydLO3jkRlx4EewyqUJHBJbAAJx9+2KjXcX7QhnCSuzzq4SRZdp4UZRXzkcc8bgwY/XFu4jgZObwi7jOiXkqTQrHkQvMrReVmdd7hjuztIx/Ce2ONuk6g9tp80Wn3DzNaOEhW4iCyiFyxRGbkttOBu7ds5xmsEmhuJLa4vCgt1jDNEcHawzuQkbSpHJ5H8QGcUgN5bXtu0U1xHm1fMgVWPmO7DqcEcEFdoYcenOZdmrMrme3G6XciyrfuiLKXG5B4aAHHH94Mp3++DU5vE0qODVLKSSy8oiIVl3SRLkoqyZIkAYsDjBOeDwaj6eZkuyy6jc3A8JVeaCFF3Kp2qN/4TuwM+X0A5FeyTxC0Nx81Pp86xgQQrCJUjTBDIRwASSeVPPlqCSVPNbTarBNdOiyQ+MYHEfhM5lTASUcAjKHaQM5xk8YrRcwLZywS2RtnjmVJYlbzTAE4YMigqXy5PGc53Duarrt79byKaG2ubgRCMK0UQWJ5xnCsSCSF3AkkDufUCt1y9zALpRc20xwrmSOHwDErFuHYE8ZOCPv2q1sit8zKeytIZbGeGFSkkjQYMh2KMlmUFm2R78KT23DjnJFSdRuts8sNxpsNlcGEbWRTvMmV2sSzAMh8pUAY4I5xUczRada74JVsYmiMcrhTJtjDBRtwucA5X2AK9uaTRzCa5+cu4iTLHaxQeCu6SVmwmWxkKCucAjBb9RJIae6MD6lcrcxxQybbaSUvEiIoBIyRyS2QBxjOe1RljiWLOl77uB3t7rEEKoIixO7Of7VCC2QMk/oKlJN8zcESSeIIgTC8kHCqwKjw1BAPIGSecqecURPHaQyXdrPJ+Ce3wGBO1gu+XIC+uO5GcZyQahBnuqXt/Eq3FqxtUiTxGK3B2oM8ALxkgk5JOQqY21rnvI01XwbJT80kschUsA52cN64ZAW9Bx2rVZQXiSTQaS+nXOn28cTwxwOYlYjOUw2TgMvmPIxgc8itVrcXOlSeK8b3V7JCh8JogYyoyd4IxsUvhRjvsGcYqLFlkW8WmQrePLpo8K7UCKE3gyQ+0bX4GEA8uPXscYrZYtqEaLYNcrCBuLzs5QFAfIzMuQJPEDgHj8Y9BmobSGSfZLLMWikEpcmMrNER+7JLZyCzHaMlvKATxW6KznNw8l4JLMrMTIU83iIAUVQF5B5A2g45OcUGotHnu7GKO4thDE8ytxKq4BJQgsD33bWBzhu3pispGlWG1muizLLLGgdRuV8qVJKkYOCAfpnIB5qN40dzNHK19dwW1mCnhMsaKJgpIWQKBu2qTheyDk8jNbrWMSN82s4a4mtDHLKsYQgkHYXj4yVBAbBO7GQeaAyUy2ct1LPIzC3iSN5pDnem4d1OFcsdoPYH+VSpUW3vLBkR4RuVY+SVQjAdDGPMeMDJzwQc81DhZbMWc8TLJdyoUjVNjwS7jkoMZ25wSQMdq3W/yj3Yuj405RyscryMGhO0Njn8O0nAxwfUDHMAgxTXhs4be7mlmj+XiMgtsJITuZlIxgnZ6qOMEe3OiL5eW1tTDbmBZL6JbeLaqC3i3CRyT33eUgnjOSOcc74bCS0jSeOLe8L+K8iTFXeXG4gFmyr5OMjHfB4GKlw2l09+1rI0moSxKfEnnCIsTMFLeUAFuQeScZzUg9imknsYYSgigvZ92/wBmBg+QQp8oXkksQRjJyTWmG7a2to4dT1W1KLMQt06Mqb8llPbuQVG5e+RxwTXO9TafrV9Np2odPThYYoTGILomH9w+dqA5Kkrkt6AgjHNdLazzWVlcTzSrcbZAt9JBbFmWSMDPhxjAbiQKVPAxng81NsiLmU1sGJhurwLLErFISOBI2TIOfMuBhSQD6D0q08ePdA9l5JrlHnP77DsyAKE2kEhM5wOBznioCwER2lpEXW6mid5rZDuLRrk7Ty7Z3DGBwCR9qkm5uY54RpcUkDnbkRReEVY7d8cm7+EMfxgEYzjHpUsI3u1tnKzBxOpuXRGUOZQd2Tt4ZiBjy+uOCM15p7XOo28ywSxyTxytDNaynaI32B3RxjIIyDu5+nAqBMLiKG7WAASWuS+Bu8x24R5VU7c5POOc5GcVtijvNNuLc6fdxxSyXPiyFICjSDj1PdmzsweTgZ4FT4jwLi6smtnNzJMbj5hwibD5ZrnaOGHdWTbkAnn0OKlXN1c6TLHaie1ubO8uBJ84kK+I5Y4OWzkZ5BA7YAA71Vpcza1ErXk6ymU7JPGuBsLBSfOPXjgE/qM1osFS7ikhmtr1EuQjhTIVh7EGVSMgBQTkjjI4zTgVt2mUN+7Xuq2MMK3t/InjywxqUCAs7CTPqmQTJ9wM44MszvIC0kNvdQlGEMICqpQDA3FSN4AySG4O7HODWGotHcGOVLif5Sc+JLtO0zOCNoZlPAUbiewOORzW+GVFuZ7xrqV9RmClbW4t/EhUBD54sqNu4HGDn8WKEmy4+ctALTUUuAjum2Tkho2YhU3A5LAKpPlxwOTUC4Ed1ZqlrK8cklwruoQnPmyXBUZC8AMVzxjPevIbpBb24vX8VUmQvLDHsw+WCgMWB25LKBz3wB3FSpJPGkhlurtBaRhtqQJtIiDec7s5BwB2wM471BJ4tibW2u5bnUILyY3P9pGEyig58IbsE7cAL6nknHpp/a0t9BJdXkreDDIEuCbZjub8RXgbRhFBG04yefapd4k0cU8MF2zrqEYj+TaJzhUB2scscc4Vm4Y+prYL+W1uV+VuLdoZAd0HheVVJCmRsYA2hQO+STjtUkETcmpReHAjO8oMKW5jDnawCgqeF3YXccfhBHBJqaszQStPcTwCSFT4i3IijELErvdWP8AxYUZOeffAqPqN7NPCltP4NwJIykihCY15wQFHY8e/I9sVidQGmQyNlJbd5FjkiEKMeGHlDZJx5v4R6GgJM7rZ2Tx3d5bwRxyIxwzgP6A7jhj/Icj05rROZorUQZ8JopgyoqlXRig8wwe528Lg8HA5NeGGefT74zwlGRTKsoZdsochc992BuAYAd2Hf00rIdMe3+bZgYDu3DIdGix5yc4C+Y8DvuHelrBZnt6sstvd3s0csLSZkkWRdksMb43AbsgsNoGMZ7ipDFo0UtGCrCNoo1QqHUgFW55yMY7evYUvYp2nguZJDNCkRLqs3D5dWLCQ559OefvgVJs9O1NXQ20toD47Ms9xqCMN4OQSwz29RxTXQhtLUpLxZD4qzwPNaqUBAkLC4zuOR2P4gATznv61nFqEEk8McNlGlxYP+8e4KnDMDjjjkDPtgDA71Y61d6heNJFM8N3LkGJ/mw5ZvUqwyBjy+/fkiq3VTAPHNmryiePazRx4VkXJIII85GOwPOeM0JTubbj5XMMCIZEG6Y7ZSqxn8JKkctk5wD6VJtnVzg3LRyMrMVuoRvVgAcZwcqApz6ZrTps8brHLYQLIlwr3UEQbxnJRPMCxPdsjgE/h5IojWy+BuBt7sTG3t97h4hgbpCCO6sfLjJ5BAJFLC9zdpc2mW0ixwqsE0aHbCI9yK6MxdzjHdnLZx3JIOa2ah1JNcxwWsUcFnCtyX8K3h2qzbQe7cucn09Tjk8VDtC3zCy3028gjxpO6IFOT3OQpzkkccc+lSpNOjtE+btiotfD8RZjJ47khySwY8A+wAAGeam5Wyujcii8lIWQIyFvFS5AQMxGGwQSoOVO5u+FwPWvBZwLJFFa2lr40DpJeRgBgsW0g5UnlTjOe3ryaiXPjq9rE2zY9k0hmTMCIfEI5IBAJ8xOc+vbNabtfmdGmtJZHtpWYMXudrDzYDAAYwvlYjd6+h5qCxaxeOsNujzW51EKxnWM4XeuWyMnKjkYGCe3FQ7a4SawknSaYwnZgPaEKgQFi3PqfL5ucHjviotvJJAkfyXhSiGJZnWMcuGTsqnglR65BPJwe1bYZyk9xClxI8DRNtm2cZyCqsqnAOPUenfnioJLKadlhtLi7jVpkdIrYkg7QFHljVj3BHdhgc1F1NbuO8iMS+HcCTe8ccuMAEnD9uwOQDjkZ9qjNK8KSyFxHcSykKkj/hgVirvgeVAGySzc+YZyTU4WOlpbFrDWLyR2SQW6AgQ3BOM5LJluw47DjtUpFb2NM9q0F3PPIyywSSqC/iqWGMgvswcc7QSfb1rQjTf+1kMULSSA7E2+MwztJz+HLZ8o4wvsefIF8K8C3N1DCsirGG2ktKxBwqMTg988+30rGFYJLG4v02QuGVliiVnYoDliy9sdgB6k+woSS10+KSAgwyxKCwy7NNGGZQCytzjOSQMd8mqvWLGKOdzLL4tskUUYEa+IsSkZDse4IPYHgY9zWWoLcrbyC4Zo4C+NsAaKSVc8ZjGcFsE5J9u1ZbJJP3diFg1CSFUZIZS7ybVXCueyhTgDgkgZ4pYXMLPTtWstRna3SKXT5ISYVDbCsm5Twh7KFyT9cVK8aO18O1RrkRFwPA8bxIVdDuVSTwcFRkc5754rHEktxdCUW8RmYHG3CyOqt5V9Tklfc9/aotzPJ+yiJ7e0GVxI68HwwdpVR6ZHLKvOMc8ZoTftJUd1vu7qBCYnmVgsaOsqsMnBLkcYJBHB9qzklNjbsqtdxxR26tLJCAybs+dl3eZiScegG30qLa6rLGgAaPwRbwyq6Rt5Ywd5Lg8kEAgke1QtPvpZbeFNPhNxa28MizpGpIkd8kIF5JwGAJA9DSwLWG9KuZVnES+GGm3PtI8ufDYLnJ5x+Q7VsDyRkIVDs4LeBHBvaTGAAHzwoAxz23VFeKVdMmjdm/dJ5ZFQx4YPgoSPLxxjPJOa3LOGjnNvbhornCzC3d4wzeq54ABBy239RQgkah+8iCzwwRSsxMsqvG7hAuRu5znPA4PCk1qF0ILaHxEjmLKYzKJQWgZSQpQ/Ujk4wcGon7yaAFHjt5YQGU7ljkVTuXeCRjsTkHP4RWmKwWP5cvEIpANsoKlvF2crgt2wDkcc5+wokkDeEkEtul1ZJDFKH3CNyrKQ3lkOeCGD9uBwc5xWfhG3FxDM9qd7mMPC3iFhnIBJ+xOMdu2KxgjuHjnillDGEeYBM5cNyDknAXy9uO/aoV8tzDZQeL4a3Er7A/OASFVsLye3dj2yBUi5JvJLi7zHZxQoqiICB5NryspyXO3kKMAFR6cVnEJ4UtZfHTmSWMzMwXczAuoIAzjBPc9sDvWeovdWupLBpMCzi7TCP4a7UIHn5Y8Dn0xjA717J4NkY2ulF7HG4ddjl13YI3ADk42nk8Y7d6i+QKhXj2X8E01qpmXYbiMNuk3c8jHJC+n2qSb62u5lgsvHBWf/AHgwb8K6+p9zgAEjgcY5pPeT2ySyfLo8plZGKruBBx+LjKjjGfb6VotB4viSSxm4jUkXDLC6ocYyAOAV5PbOeanXMi5JIFzE1he28Nq5AktXWUIHVjloueVbaMcnuc/fbHdtdBH+aEM0MiK628TFl253+U8YGRk/h8p7k1BSZYzNJPDD847PFHKpMhKIwYKqv6cDzDucj0rMSyXNx40UsQYggqh8rA8oVJ9QewBAOSeQaMLMk2ixw3cd5HPPcs67iVn3xRIPxFEI8pZhtB7/AIsdqi3V1Dby2/7QaO2Mg3F5W2+IoBAC5xtP1z3NeW2pXN1fq0W8AxOtzG7MxVsHw0XACkDJJck/wgVvMM+ntAJLlJ45bdBIIzuVdp84DEcAHJC4yQATUeJJu2O9taKLK1uZUTc/zBykYyV3MvO4cgADuc8nHEHThEBLdTpdxmGaR5plIjCOrbUxgDHYDGAcemO8aGS8vYleNxEwDmYLKm3YqeTDHPbB4GOW5qUrXF3OLa1gMSbw1xaqgaMOAG8zMPxAEnOcZ5PtQGU8dvdrbm6jk4YNExOPwqS2fXHPfBzntWNxcTXF0sEkbuiq0ksqKyp2GE3YOG5ztHbHpWHg3hlkW5WCOSNlmdg7DwiV4x7k8D28pxUNbS6kbxVgggcbMkbgUG78LEZBBwDgZqATiULhHlmLW7AoZYyzMcHO7jacLgYOckd81naSJiOa6t5EiFyy7JFB4zkZ5BBKn1PH1FeLKLfAmCgplJMh1WU8nJIIPCjIP51ttJDJc26vbxSxXBSOGEyyZ2KDgkMDnvjHf74yI4EpGl9R8SHbbSRXckCxxPHIMAOo3EFcjA28g9u2Kl2BnhJlXy+FIim4Y4aSVvNtRXHIAPcYAznI4rICW4uJDNbW1tbw4ntkAXG0nl24z354yWHbvUPTJo7e0RoFihiw2+RZOIk3Env/AMQ5+pBOarcvsm9vlZAz3E0viW0gjeNbffwWPkVOxGDnPJ+vas4GCsj27bZ3hcQbgp4BOBg9u5yOw7Ek1E2XNzFGq3g0y5lY/LMIlmkSQkYIZhguRuPOQBzjkCpVxjTQZ4vCkKk7FZtzFc4OM8sxbJP1J9BWbNESYtWMd7BDIqLcXagpJISRgLuPlUElR2AGM/06HSZlfp/RZCVdX01G3cAEbEOR+tc8uoXDJcRo8jxA5ad/JHtxhigPLcfTgY57Cr6wwug9Prs2N+zV3cY7pHgYrmrdVJ+H8mkd5fOB7PMjLKAyq7DIJXJGT6foKoNUud0cqIwKlQOCAWc9+30A/M1bXEjiBlmU8lSVJzk88d/pXMalIwjZ1TftQiMf8WOK8ylmzeaSR8460uhIkkMBcC3bJyQFYcjA98Ec/Wvx/wDExdnU7D18CMnJ9SK/WPWErBSinxZgFiBAAAAbnH0yfavyd8SyD1Q+3sIIx/KvufoS/wB3kfH/AFrqvM4+lKV9qfHClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCvp3wXlMOvhsArvQnJ7YdTn8sV8xr6P8I+dVmyPKqlieMjj0z6815/1D/lmeh9P/AOmJ+lLBzDc25bjy7SvYN/vD+bHOO+P0rv4WUwibAw0D7ickqyvg+h9h+tfOdOkWaKKSUKf7UEhs5bxOMk49ST+VfRbEyNbKoVgVWUjKkAgqp59O+fTjivzCusz9Lw+hex58a9BLAdyR77wRj69z+VfSNM80SSdy0EeSf+6Of5mvmdtMpupAVLrxkHPOR6nH3H9K62LqBtEi0lJLVL1ZrXZMjSMrFkUYK4/PNY0Mp5m1dNwyKe3ie21Xqo3EcrBtau5ooDuAZSE2sd3bs5XHBznHNRks3VolRhNPt2hmAjkEYccMOxGW4PHpx79Nc3Wh69l7TUtR6e1FMlRITNDvPbdjJ2YyMFSMHtVJN0/rdvJbY0C06k05YSZr/RXiuJEbft80I2O4KDBAX2znbXqRjyjvHM4NuNKKUsvH5b7Mpr+OCWeUlcXrRjwJLTCOGyeHxkMARyGHcD849zEGMRn2zygBVWeVkRdq5yxGQTzyWxkngelWkTWLmUWVwtheBiwjvLcgqqttCsCM5wT7emc1YSQw20chvZoZLOWQ3CzKpMEmBmMDGSMlRnnHGMDtUJs0aKpktgjNcpJczIQGiln2pkf+7OM+nB4zzxitXyk4JguV8Qeabwn2sWG0eHg52kjIBYnB9geKtFiV41kS3jSIkssU0kh5b8Q8pAHf6gZxj0rnotNigjt0sfCj0+7IiHgASI6AnypwcAsQSMA5GPSrpmbRJgt901pc3stzOFZxEzzYMDgFvDXONhwAPU4BpDHueS6t43kEcagIkkaPKVXcYwGPcZ4KckjGaTXEVjBdAmB5rcK8TxuJH2L+IMh4YqyngHOM/llOsBMc5haJI4kYx7ViCqDv8oBxzwxGe/p2q1yLEm1iuA1irkTCF0R9sjRtGnfyD+8QwznO7kHPpAVLmO6Q3l1tjkcxlJZgV5J8p8xLLtGVYDI9uDUyBJ0E0DPuZiBOzgJJGuMqxAPIIbBDHIGDUdns5JIreKB7kMMSRyQRs8bZDBX58yngjgGiZFiHDfRWN2LMYkuVlMUMXiBmTdgtuAztUMvBz+VbLi6EilEV0vWkeISXJEpibZuIDrkbNoxyM5x2yansXh3qS8fjkNEpCjcWJADoBuLc5yeRkHIrF/l/FintLmMXMTbD4qAGJSOVKYwCcHk+n51ZMho0LfIsMqygYgYrbyvFiNFI4EnmCbtw8oweTznBNSbXT3kW4ufAYSrM6OzoT+IYYrjJ98ZODlvetLJctZ2y6jbWj6dctII5FYRJJICPL4f8AABO5iNw7VFfSmIKvDFCjXjGRTcHcULcowAHqob2IYfQ1ORGZJV7lIA1ip+YMWEZ4t7iRjguVVgecoV/mKySwja/uZtMkla0d0N3+8jeaKTeQxKhhhWxnCgEMMZoqxSxCRiI0ieXwbd2dGjQnIL4BJXAAOM8HPbmo9oY9J02PTobextJ41DRRRcW54y6qgUMyOcHGM5UdzmhGdySsUlvaJGl+kMccwfdLGolWNCxwGYcD0KkA8gd+aymlEbSQQW1qbp0WXDTDZExcDIwOxHPI4A54xW9bKyubmK1gtYSmTsdiCVXaADsLY/Fknvwo7Ht5Nb287LJOIZ3mjkUhF2l1k3FgV7r5RxzxjscVFySPH4x1iZrudvChl2hreVvLISeGLLk8MG44LemKwvbiVddgSSMQ3jx+Zm3AZVWwz4/BlcZXBByDya2gzLaOklot/ablP7iQl/DAy4wfw54B54A7CtchvohIqzi1efw9sEp+ZaVAcEynckedzKFIJxgjmpINy20FxJEsRNqYY1Cy9wu1QQAPYEkHv8Ai+pqRbywacFhuZBDAkKxGSyQboXI8xJY57EeYEcDHrzFjtIbqydIo50hjkJSDJjw7KGJyMkLj1U457CpCSzpcxmKCVJk3mZxGNyptBLEOACp9CM4OPU4qCXoV62tqLeOP91K6LNLYhoWiVozwS7ghgp2nh8kkbfvMu5Y7meGaaH5WxnZYUDytE7DYABtHoQuRz24PtVZZQyXw0ufRIriRIpnOyWU7nP4SrMQBwxB288cirK5QL8qLm48CWCTZMqeYTsrbokx3KgLggDBI9qlolMy3zzRyWyTTlobZZPE8aMiTLYG0sv4iDjJ54PtirGLUri4t5la2WMOqiJOE753cqAewOAQDkCoDyRMbi/WMxQWzSCBnuRsdW5Eb5AwefJzgZUd632F9FDaW2ltawQQRrJD4MVwXJKgNGMuCGZlyWOeD75oVK3UNWt7S2l8NpjHbtLHZoF5kZUKgEkcEYfOc5xn6Vf604muru3d9pjMRXdL4Me0RrsJc53clsD6Gq0Wtm0U0vzPzTFgVkDo0fhychcIfL6d+M+3rI1PUknnQ2sW68jUiAxR7gzBSpAHG4kHO30zkcioa0JRruTcXfhx2+0W6OCWaFl9fINzeUg8AkkYyDWWn3XhsDcP4MU8p/3YzCSaOTjw5VUDkkd2x75xwazSK4u41i1G5uGmlb91GzvJuDMuQFBHBAI9MDHFbFktodWcyQsL+1jkcP8AKAsYiVVVU/iBG4n1zg5oGY6res15HFf3CTyrBKsy8ZaMEIqBuMZJGASTy1aLm5g+VisRFaw3IdNkEyljKcBmUdy/BU4yfvxxvdb6SOSGJzLJAjK3iJ5VkZw+9vXw+CMgHzeo71osYbmS4uWcDUI7Ng1qFgWBYTycluwypGT7YzU+IRY2UDvcC3jt4pv3jSIYo3JkA4Yvz4eVYn8sYBwK1y393FE0xEbTwIUWVYihZlO7BwfKuSQMg5x9qgspmg+X1CO5S2k85tY7oBnZRz3IXk8cYJz9KmPM1ylleOJhNcRuskSyKCzsp2oUGCcBuxPGODzQEq4Ehuo7dC0j+Oq3Esf4FyM5wwyQchcfp2qMpFwkkaTXEdtHu8NEmaNYWQDAByPfOMEfTFarZ7ycRBFM3gxlRDJGY2wGzkZ84YgMCrZ5Ckcc1heae8ttZ3N26yzwXLTtFIWV1kaIxgNzhkKMwBwe4J5FRawFxdF7m6htnbw0idnLRm3V/wB4Ax3gBc42cZHYnj1y1LU/HjfZby/2gjdPHRfE2+Th+Tnue2QMY75rabea7nKW8LG0uZD4k0RJhLEEbSWUmThTk4wCFGKwureSPWIHt1t1W2JB8OIlnQrtU7m9QuWxjOFOMcYZEm2dpbqN4riOe5tIWVxA24NM+BtkXGTlfTB9D2OTUKy0y5ilhvdce6uJXtxvzG7eJtyTtGBnBbtgc8ntWy/FjJbpHbtcW72rOTBEZDFgBdsnisRt2k5PGMN35rKylluo4PCnuUnjVJmMyeKUcqMglTtCg7e2c5571KbSIN9sLeW2XMrlnjWGQlBIHIxuVi3mOSB/dHrWye2SaSaF7Tw45CXaJV3b2wpJCqBhsDOOM4z6Vlctc/LSmZmaaTjyY8Vn4UgcEEck5xjP17ane5vnXwLaaC4MwePbdLIofK7RIq5527gdvbHfFV4luBHhlt4tIl+XuN87IwY+FucFWbJLdl2q23g8ngZJNTp7yBg87jc8DHcikjw5RtC4AzjHOcD17d6gpczRwTz3JZyIWESxO0eDnA3DGCQqbh74OOTU9dN1Owvr2eAQXdv4EeyNTEvirjDOEzlcqCCCO4qxV2I06LYrHOJJESSab5iYhfF4TB+gUMV9iNv0NaxdQqYYFY3otY1SOXeCsiAkkHdwcYPP3GRW51l0mSO2uZZ50luRDNbFMmCP0ZmxkZILZznH0rYrvJbLLbXFvbSSxSxwyxqJAshDCNyrDzABlJXgEHI70ZFyNqGJ9MvJ72dbszeEzEJ4ahSysqsR2UADkdzyK0tZx2V3dXV/d2+nQRxGW5Te0hZF828gIVxlSM8sQamRQx20Vhby3NtPcfLLLessPhpNKCN7ovPlLD7c4HvUj9r3WnXUcbWRijjMaAyKXSTO4shwPQK2Se1Ro7FtVdFTYnTtUEN7pOrQXdpbmRGezKgMSvlJYAEEY4U9wM1Ja/kcpMi24FtDK86/NgsWBHCJ3Cktgkke3rUXT7DS+lra8WKygs5JI/EumigBEBU8OVGQpzITwMkE4Aq6uLO4igQRNJEiYIuI4VxIpZckMOwzg9vtipduGhVd5BWG2SWFLK2ERDNHFFAAyAMAQPxYXO39KznvZTEItXnjnXwACkEKyKgjfgqVGAQcdie/0rZMkskitb3QnRvPKIbfESqM4IySSwOQXAB7D2rXMbK0u7Wzku4p0iQNDOjyRqit+NVJGGXIyR2FCTG6MQmZrqLLozTG5C5lIUMMEZ2kKxHpzjPpS1gghWZrmVJLab9zeGS4xGF8u5wxzk5J++0fatUM0qXchaBbeQ4dGgRmdWYEEj8SkAebHAHrW2Ro/l/lmFuYzCZsmXeDs5ywQeUjAOPt9agk91S8F5p0l7Cu2Dwt0ivHkspUsxAOMZHJz654rbbJBJLLb3MiRPdXm4STuAFUnK43Dy85XdjjBwSTmvI42v440tJ3gnlmm8WeTPGFCliO45Pr22g1pPzDT3W+Hwmdot6TNtwAA+Aue5BBBODgk80GWhHjuIWvY1jWVoHidYZU2mIlmyEfdnHlBIA7/wARHAqx0+C2kigktLuC4nSN4lVJlXchONpJHocksO3YVXzWsKxRzI0dx4YJklS3XzdyR5jjH8OScYH6+Nal83sKSTQmEJaJaQN4W+RgRtA4YAdyOBTUMsCmn3trLbaeFcFjGgkOUnCnkbd34ARnJPJ5xVVZ6gx6gSK/WBbW30uGe3iSIb7aTeVdT6BecjBzkMe4qcZHXxJJ7lbEljGB+N2IbCRFl7BvMOPpjtW9LN4pFjmSTULBQ3iFJCoSTgk4fAbngd+B96XBCugkslwDGW2qqhmtgJElHKFWGdvC8DOeecZqelz+2JZHGopbu6KluF/9rkkEngc8cnsOAPWo11EY5o3eeaCHwd7eJAvPJUAKDg5BwASP17bWi02BEgmgmsVtiWLtKi+KucMFAwxOGOQBgDHvzYhmMiJp0MUl24tJDmSQrIFEhyo8pGfMCScg9ieM4xBh1FVuttpfLKJ5WgYWvkMh5Ylj3C4AyeMYPuKnQXTQnxVS1sLezgdljVHwCjDgnHA8w/D7YNatPIuVhe4jJeM5uZEjJAB5ePORlsDvzx3BNQCPbma3WGLS5JUKwszhgVMcGMgYHK858w759MGtkkFrcXUl5DYq8LNEZGZmERjDDIIOc7hkDHsfet1/HHcWsctnZXkElsynxQfHlfcuSjYGAOwIUcVtN+k1zDdWkMkEsiQtcKx3hWH4WRR+AEZyCAaEmnVcXXz8emTWEaG3l3jALQ25O7YMZGSSuc5OeBjNarC5N9dLpNxM+lGG3UxC3Do8ihQSMcEtwGOTjv7VL+dN2slqkUKxws6i3jiUSuRnLhF77e3tk55NYTSo9o1tJcK8YdiEkxtKhcqp5yGHGTnvj3pcrbKxHR4Le/t0haOFSVAiR2ii2EEg4U7Tkt37+nqTWLXDafNFFfXWm7CwSREmA8PIY5Cn0yqge/1zWam1MvhalNJFaGXiV03dlBHqCVz29ufatTW1qIraaCSRrCwd44iJPDDHkFimctkYHcngE8ChLZBCIVRrt08GUu7kRK0akMwKgHB5B7j2qWLmK+it5ZI5HbaboJIoZkBTB3HJCgAAfQnHesi9zJam2iC2UMMglS1hyTN5cnaPrlSR7Nn6V5NNI19GRJ8tbRqSMowYuwyBtwQwXJ4ODkA/aQabgR+E10ltG5Lu84aZkaPc/KlG4IJIPsORXuoT2qRXsiXdvElvbySXuHBaziVN5BYnAXOW7Ht+m2J2Ky2uo3E8xJCs/wAq0MaxqN4VCdxcjB3Ecbnx9RjeiLUYpry+8IxS+GzOkiuHBBDJsxzjvnnJIplcjMQSykzmNBZh1LLLLOA4U42qoxgFgDyASRwMd6xgVFmDWrx28krhXSRw5RguV35B5CkkjGM++KjWsk76kYtRsbjwoiNlyHyH5Iwqg8sFHPGM4PPNTViudNge3srdy9xLFvYThMh9xeV5D+IABVx6n0wKjQngarPUI2R3vrxoIYCbjEcTM8q5IU+pbuuMdzzxWuLTY7a+vL2OUJLIHuYxJNuYl9qjEeSqfg+wBHGSa3yzWSwzrBFvkeJ5JZmkBUBsLhc4GwZ7n1z6d43iJb3s0FvJBNIVSUkIBkEAOFbJyfMuD2BPH0m7IyNgs5Y79HByZl2M+5XyxO4Dk+pTkAY9+1bYr59ShNx5Gu2j8V4J4zEpQg7ic4CKAOOxwR9qykQ2MklrM7RXMiqVUw5dtvmY5x+LnAHbGTWMhNxEyXEW+4uA2VYDk8GINnsBj19xVbkojNNJNel7a4ULZGNygyyu5UnAxn09DnkjIqQbq6tJhNBORBcJJMI/C5kXAAZlcHb3wc/41ummEupIsrxLI5Ewhd8CJypBAB45K8A/iPsKxFxbxpbTos8waNZeR4Rnb0AY52gDA55JzgioJI8xkVFnhigtlWSOLw2C24fgM4Tb+Jjkk4wo3DnJqazxuh8VN4YErFJATEQp4JUYHftuOSQeCK1QPFiN2thfz20n7hluSMDcGbGByMgDnGce1ZRPdTzuY7qGEws8CKUKK/fzH2z2GT2zxzUNlkVxngv8wWsNzBNCzRM9zbFTO24MY0Lg7gvI3dvNhRgVKimNyl1C1tMlvHclysLfhLDGQSex3H6jaDwMV5BEYAEvCm2UvJDDDMpbDd2baSdvJOeB2r2a5uBbW7R6dMqxM3ESnYVYgK4Y45IHr7ZwRVWyUjBbJ7JXWOVp4xEAFyZCgIAU7m4PKnnJ4+2K2Jb3VyVZHiki3AyPKGLxoVw4G05zt7Z4OAPevYoZ4YnhjvGBYF2ikkyu7OAB3wB9MD7816Gtvk/mdL33tyqF408MlzIcZ3kgZB5wMAYHHaq7RfZMY9Sg8SK4gkRDI4BbaN7qoOEO8/3RzgVI1G2W5lkYiUwjDvAwHG3JK7SMYJwACaPp95PLC9xp1miOUE8AcIyAk4b1O098k84rB7WF7czzyyXELuXXaGPjgEkjP8JxgZ+/fFVJPYIHu7k3UkciSw5KKMAxYXjORxx6jvxWy008+RpHWVShO+byKVHGBty36YqSlhGnJ8CKyXZKd0hRu27A3fiAVl7gYBHrxUa+u7ayurjfMhEgQW0AYsSwGThRyRgg4Hqce9Vd3oWWpvhtYlSUi4WZlieMv4G2FCQcAsWySMEjj/OrKwAPTHTbbmmY6XFks3LDw4+Se/8A1rXpfTet3Nx87q9zciylhdmfVYIrGCNz+DZHjxGxnnI5wOTUu2tUstJ0jTkuob2bTLCOCaeFWEbOI1AI3AHHlJ/Osa8XClJPuFOop1FYrryRnBRcF/uK5bV2ZVwVMpX0BwCMgZzXS3UTJEWJDE4xnHvXMatuaO4YeY4IO4YA4Bz/ACrz6SzOmpofKOpcIGVFO4Z5PY8+mfTJJr8r/EbH+0jY/wDcJ/jX6j6kZWVt65/EcsO5Ixmvy18Q+Oojj/3Cf419v9C63yPjvrXVeZylKUr7Q+PFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFfRfhKN+o3q7d3+7yEc4wwRiD+tfOq+h/CUga4Q5wjpIrfUeG3H+vauD6h/yz8DvwH/TE/RumynbEhwQs8/mHOzcw8vHpwCB7Gu907DWxG/a4x6Ad0OO445GOf8AGvnGkTNJeSuwYsZjNlz5ix75wBlv8hivodhKqRSSK/kiCng5AAPOefvX5liFZn6VhnkX1qyYZyvJVc4YY4Ge+Pvx9KvOoJHEekYRRvWQ4bt+AHGfT3z9KobfCNJGF8ygDDe3OPX6e1XXUBDRaBvKplXI83ORGOB9TXJT3vI66m6iC0nKpI+xgM7JBuz69zg8496xinntd8tu5iljON8LbPX05z6c4rKQN4TKkglTklZMvn8+/wCtQ5AIy23fHj1XLJn39ga3gYSOnh691c/uNU+X1iLbgpfQiRgvYjfw/p3B9RUU2nTOpPNLYNrHR15KGHiadJ8za5PctCcNnIz39PY4rnCMgAxxXbc8xkKxAI7BjyeOwNZxSxuwSCR4pSAGWSMnDfXOD/e9eTXSq0073v4nM6ULZZeHtp90dhb9Oa9O7PYXOi9YwCMMywOttcOV5ACMuVbdzuw+SecVQ33VD6RezR69p+p9Nu+7edQVXTdIQvm2L3PbcAPzOajqrwSciRZFJwUbOM+vOD6jsa6C26z1aG1+SvJBqdiRhrW9gF1ER7bWGR6djWsasJbyt4fPco4TW67+Px/wiPNHa6q8cOm3UV1FF4asLeY5lJDEsyJzjlR9gc8ZqvvLiK81FGBmj3QsrTEqRCyB2VwoGTk7l4wR3PoKsorD4f6rdxz3nT7aJdiTxPmNLmZAGwQR4bnK5BP4D6CpkfSPUhYydI9V6Z1JGoUR2+oqbe6QKScb+G5yw4OOexrWMVJ9F3+ff0KOpsrpq3zt09Sq02KNLmZrUpNCkQhlaSdizbhkEj3B7NjP4gSeKwkt/HuJo1S3aBVCh0lLBxje7MijaD5QM8nJ9yKz1C9uend0eudLano9zKwaaaYePahWOGPiJhiQuccE9hnHNbtMk0zWL3Tm0+eyvpJoZ1LQXHhyoBG2VMRw2ByM44I71VqSeZopRcbpkW2MM8s1xf4jaRjsd7liqY55IJHJHOM8cHNQr+TTsXE+22g+Xt1ku3nA8FWEgUBnOchyV2qcdjnANW0dldo8M0YgDOd8ajKk4HO4sPXPfHp3Ppp8KOFktja/L2kviNGroGjBO1hlcduMD09/cFInZIf7OuIbm6aOOZrjO1bcRLvIHO7H8IxgADsOe1e3xbclxYCFnh2kJKxjWTCoSDg5YhWx9+K3fOaJMILQQyR2Pza5aCFifHIbKvwGJAXnAIxj0BFFCxw3K2siahp7XTqgCeK6o2CMxkdxjGPpkccC1+JW3AmXel24+SS7iRbjxGZnMJ3+HtYLExyCygbh5h6Dtiq0RiMyy6lb4gVjAgjmUyWzHD4DHHi8HJDc8gDB7ybe4ghsoYbmNFjRMqiw5cd9pYE+Tjtk8E+leyXEl01yiF7AO67RcRhm5AXBTHZiAcjJOCcjNQmxY0Xem/28DySI9s0dwjIGjj8LJ/edjgbvD4HfnPajancQJJNa72gWRoxPCPDldnJyFfnLHBPOcD0Het5luYreKe2lDw2KGeNopfNDt7ru9VbbkMQSCSO2a3icWFzNMjwzxyLGyWYTxGU7mxMGBySd+MjsOORVkyrRWySSXV2LpJ2utOjVA8LXAj3Epgbxs8z4KjAODjdgEGpOyGSa5+cuI0tZDF4xmcTON25QqMcKVJBI+oI7kVrjltrdbhLQi6guLNltoWti4B4LqGUhs5YDd6Ed/SvbS28009pcy4upyDM8xlwuIyUjjYHgsCoO3Kck881IsGuWNs8MztOqoslpLAohSVG4jbA5VuDuVs4Pr2zjcXqWvyRt1jkW3s1WVJchvMAQFJGCckDnIOGODxUaVhNdqLESlSwSdbgZwA+4oqHnAZeSeOxzk1aW4nWO5fxNkDttMQmA2yZ8p5zxlsAjGOD6UIMb2aCaeCCSKJFjkM0wLFNgyFAULgk/xFhgAFOcnFbInjk8RXMwjhRYkURFf3IbC55BxubAJwRkd6qppRpl7dXUEVxctFGrSJIhd4mdSGII7jllKqoydvHqLi8nS+v3gUma3YRjxJCS0keATtBA8w8wPfAGR6VLIRGgLvPsMkHhCQukM0yZY4BUPnuRjjI7gHvxWm8uh4zT2yKUihfyNKAIO3qoKjBztIyRznvxvkla1tljWX51Jg7uJyN+3LMGyp4CkLx+LGfrXs0UVvpiGK1luDdRKyQvPho8sQ5G3JOe/wCHngHjNE0TZnkN05haS1a2lTDGIGRUZGaMedsL7Djg4BJ5zUKIzDVLi3so2vr6V2mMMcYbw0T8eNpA2gMFOODuGMc1ncWLQpNDbTMkYBjt3edSzFchQAvYeYA/bsMVLa1tU1CSyjhdprSQwZCbi4yQzBsjB8rcevHB4oCALB/CVL+zH7N2Ce3iMmN4VThVxz+IqfyroJXa+nco7TS2sbN4pwcqneJnyMg4IyecZ5Heqvw7YSStcwROXBUTeGWZlYhVXZkMRtQEY7854rZDFCrrElqXd1+VcrHtRYlOQzrxjnH0JxjINQSNO0uyh1hVQW1jBOEEUK3LGCFg5AKruZcMxGQOOOeQDWSWouYYUeMyGPdcGGMYzG2VO05A57kfxYFb2hWS3nuIrpdLSBFjV3ZXXl1R1KgE5yvOMKM44zmtdnHMlm/7KiluZmcRGW3XKDdI0h2ou7kHPJyR5RyM1OpBqu47i1tZ0SwhWPwoWaaabZCyI+7Azzgru3Ywcsc4xg747Z3kkgubyEO0sYMMc/jNboSMBC/mc57n13D0qQ0S3F/d6e8qw3MEcck8bQskETMCVQuSQWJyzAZ2BuccCtMVmght5vAW4WM+DLJay7ZYsMAYyNwO3kge23mp7iL3RKEKxWpmlNrfXMcrOk7sytbqBhVC8HG3OSGIPpxxUC4un1Ca3u512Ism9YweXZQMFWwMDBGTweDnnNZ3FmFZGtDAy2tu2wMv/tB23sQR23ZwD2zkGoXUOmX+qxNBo+tWNkrgzysIxOZoEbhotuAhDHkE8/aoWbJ0J091Oke2KS3tmdmij/3gxRxxgeR1HO4nBzjPBHBJqRJK95dP4m2OaJYs2oTktnIYbfQ5yMdwDnOKyso7ffDHc3l1fRyxuyyyLGrSk8szeVtg/COOOR6mq+1SfU/lxPF4UrBtk4uI5lkjjfKhWXGGIwpUgcE+ookiGyxgw8txDFA8vjQzQTPuwioQSwJOdoO0cdqC4srl5JY1aOMqsasNzxbt/KjJA3bsj8vbAp849y05baFjVJSjRDeQfMRtJ7AEZI4JqMyPKbu6up1jQBVbcN2CWUBiQRlnCHgcDt6VBJIS4MyXXzmqeFD4kgUNbC42lSOWC4AGc7RjHHl9qhfMafaXMR0Y3xvI4V8W4UKbZQ5AKgEZZifQcjua9kdoLSDekcMMtuqQpErRyAclCvbKgsCR9u+axE8fhyrqsiS3UIaWWS2Xw4juAUkREnDbTkD1HrxUgkTB4ZS0ksCQtB4wedN/O4EPjGQMYP0A/OvbawiSV1McNrG8IaCVSFi8Q5/ixuKkkncuMZB55Jygl0e2u5YnnZ72AxyLbFVh8FZMD943mLbihxjy8AffTcveJLFOsdnEULeBbW4DsU9TjOAS2QCxycYwAMUBs8BFuIWvEVSGIVJrppnZl4Zs8Eg4xgdxkVvt47i1vjb28SgybYskbZFzjdyB5eAML/dPOMcRYpXmxNZSPcliWDRbHKkjODkeVfw4zjkDA5rGHxpIxcSiPx7mER2ruGTYWYnd5cbmGOM9/wCVCDOz1iC/0tS1q0SNchC8u4zQvEwzvydoHGQBwwHbivdTvGsL6S7htzcS20XiSSSxMyksQGLY447cj17VVTW05WR9R8QRbQ7l28JItvlBJzjbhBgZ9e1WSNNFBaCykubiSW1SVojE2yTeSWXAPbOcqfy7UJMp3NrHPbz5WCMFjNFJsOQMOpPckYP6gist0Elwy6dcC5cKu628ctExGRjAPDknOADkgZ+ta8cEs93LE0k9t4KrFbyEp4FxtY7d/GAchRkZyuOfSaIJLaWRUSae9SRSioMCA8Dg48+W3ZORjioJIkksyvKdNuHhkjupQQ5YhZcupOSPOc89sZOD+Hm0SJ7a0uZIbgLDafu51nJkdAAAw5yBzjy9zn6Vqu5WZoILSytZYJ4ZWnlnUbgBliu4HzEHHCnJOT2GaxWG6u9Ge6ubTZczRCeC3eRllbapbeUIIRsZGOfYc1JBtuEgudLW2gjklSeWRJgbg+IQGbLLg8qcYUYHfBqIl+ljcMLfT1a4XAblQIjhssyHknORkkgbfrW2dIFs7OG1v44IWmLzzOfCjUMM98ZySccY5ya1hpri+WS1mSO7ZdschO7fIwxkHs5wAPXAJIqAT7m8s5THMjm2lkZZds6uiMZFLDgZyoKkMD2GM96rFiia4mS8hWC5mdTKlyy73JiVG27QeGHqBnGMhcVNgU21qEhgeEhSoVCZFjdiSwfkcnLE59SPQGvUsLnULi2NtbXQuHfdG8BETvl1bcdwAQnHbHAX2qCdDG7s45B4VxE1ubZkW5tnjyvhkcMynPlOMbfTOTxitM0dxc3EWr6fJcRpM5tHhuCnhukIJ8ijG0jzfh745BrbDaXFrcx2AsLrTPlEkYIsyh0k4yCO+0hc+xyPeoyPa3SQXV5fQyRdrG4htGnn3EnxSrZCJ+E53ds+3FW7iNcyx0/U/wBlvudokkcqbbOcLvBITn2wTz2znOKqbksbRLi5MjSSNLGLhHaALuLDaVQY4Ud+ASvoDVhczCaa9jtbSa6htIw6xzy7WlQKQ538c9wG4GT7VoSWOCe0FravHEITIispIiVnUqAWbP8AC7ZOc5zUcBlc0WkE2lTXHjSvBJHHbwzhpyCqnc+04G0EnuT7CpyQCa8eSeNJ5oiUjkLZKrnluQM5Kjg8kms7hzA8l7p7CKIyeO0aOZFcKAVR+BgEbueMnk5rVLeq3yxS9uBLO4+ZEcZYmQeYJEGABCqM5I5HmNSDM3CyXXjwCOOZVEAAkwIix3EgrwVOdxGBjtzWwvNcRS27TQ3F0/mSZExG6htu4juvcnPGSAK1SajbxrNK7i1R+xUhFzjhiVBwuE5J49a0Tm1j3CGa5uTG4S5eBViVgFP8frnb3HAz61DzJR5PqDMqJeo7LHIwCNKsTEHPmx6HyjhuSO5rSbmOyxBaJ+yUkTczRsN0kjOCMDJyG9Wz2BxmsYXt3+XZrjwJC/j/AC9nGHChhhwzHJBwNnuTn3rdZui6faWMllpcdxC/7x87oolVmxuLZIbAHGDhh9KA9uZ9TW8Txn3vlo18LAh2kkEE7skkHsxHHNYs0F5bYiAtZFKLJeSgInIy0gQ/iHoM84ANTLqeOC9kS1Lv8srSTxIRIniE4PmKgFjt7dwPWtAdGmEUsc8YUwz/ACk3LnC7tzAZOBz64OR39CBseznsXmlB33TxxCNbsDBlIIYgDnGQBx7nB9tZhkure3tWaSGK3k2xo3hhedrtiMDkkA8kk9h71k0cX+6PeSP48UBEMElqFlXcwBOAxwO+CPTdzWqOWC2hNujbmKtKieDgJiNSzPzwPIQvsMD15m5FjOCWG4Zbi1mFuzOsaLLyHyxCqGU+Xsvb+6fXGJC6HdaQh3D5bwMgCJGfYSTzvJ5U+oznn1NRre4kikszcxLdRzbpoEkUukfJZGIBHmKkEBvQ/atD2NzFLCs8ZuZzbgxwh/KXG5Dn09G/TioJN4At47iJ7hrtbdfCkkSHYSo74ZmB27jgbfqT6CtAmjaeYzzeOIkXIZWaWJMDhRwGB4wW7dqymsEuQTe2qQQtE/7h280reb+LnjzDGOAB9azWYtELuWaW4aWaPwViCy7dnfDYIwzdjjGA2BxUgwgkdrmOWRJ7ksxaLxJdqEhGQhtvtn8I9u9aUhjSIXYkV4bsO8AhhjA8JDtXKg8gsDhmAz9gasJLa5u444FPy1wJCyqwYHDuQCQcN747ZBJrG2sDDdELEVtzEpUq8cYYhvKu38Q3Ht3JA+9RcEAxzzvLJcm3skTAY/jkGCPKqgY2qvc85JPAHNb5NPt7iVl8ZYLe3dG/3ef+zBfyB8kHJHcc/arEyJMJ7eMmO7hYqdqFiQhBKhiM8jkjgnjtVdFDlfHkiWzURKvy6AMSrEkl85IPbkk4JIzUJktEWaOW3RpAq3MR8OITLcOu1S2HBO0l/KMfiGc47Vk9ony88wMsm2LOVKvgLjy5482QAAcZGBwK2PepPfxxy3MMeqGJGaB3XfCoUbGKDheWxg8nI7nmrNLMPLFGsOwxIGVMlX8TP4s55AxwcfpiobsSiLezW9nI/g2tpagje6puXcq4z37EBs98Zz7mo8d7Ey2JuSwKyZKSuqsOT5toyDjDe+SeKsDFbwTgXEcbhF8jCNXDMcEDzZAPP5kAe9bLb5mylSF2SFmhEipNbJvxnH7zjv7LngcnuBVb5FrEFrHT7yaLxkeMIAyCPMQDEcElD3ORxwTgV5MlrHdI6TzzQDJVwzbQ2Ch4I3MeOCO+O/erG2iSztY47mKOJLhv3hUYjUkgKWQDOScj07ZJAqO9xMot44o1kMSMPBMA3TBiMbHztXGCcc4B71W5YzMktvdR212jeNEgebx8SkqfKFdEJ74B259q9/3nU21AXavdEhm8wwiryvbGQccD6Ctd/d6V0+8z6lc2tvDFG0s0s0yvICewWJTnHbn3IrDTdUOpxx2mg6Lc6xuKg+DAfDCHAztGeckjcSO5PpRRlLRFdqMdXobYoYZrhwsMywidQ+Yw28rxtb1yQO38I7gVtkZRqnjXl2Zp/KrwsI1XaDwSBztUfwn0Fbk6Z11CF1q70vpeD8Xh+Obi6U9/7OIYJyBks3JrckWgaTCEmk1PqKYsSzX0wtoXYkHPhRYZskdmY5HfikoKG80iqqbW6r/O3T1KKC+sru8nTS47jWdSuroBjbo5AxwSpHOMYABwBg+1XL9MdQ3MbHWp7PRbfdnGpzAttIHaGLnPGBnkD71lJ1de28HyWlLbaNaHP+7WEK24I98IMn7mqKW4nkcnknJDFmIJP+j6n3qjrU1uq/j89iVCpLV29fb+GdDNF05aGYXt1fdQySSK7qQLWE7ewJGZCo9tw7CtSdWz2CuvT1rZaNG2d3ycIRz95Dlz6dyK59oizIo/HzwTg4H09APU1gzIjAME5Pp37Y/0K55158Hbw+XNo0oPez8fbT0Jk+pSXT+Ldz+JK3O8uXY/nUjQJVnvtaGc7HgGJBwP3QPY/wBKrgeA8cbDPrt2jt78cflW/pqeN9U6jSNdrRz26zP4u4SE26nIwOOCB6+tcss4S+cUdF80i1u0WSMhwrIjbTgc8j71zGsw4gkWRSIVlkYYbGRj+nIH6109551Lnc5YDyEcDGduPyrmtZkJ8XxsiMQuuASxkJYc8cD196xg8y09D5H1NGqjaSS+1kUk8twCeK/KvxGG3qWQe0KD+tfrLqZYwJCqgMN4BYYIOAMCvyd8Rv8A1lfBJHgpyfzr7f6F1vkfHfW+q8zkqUpX2p8cKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAK734W860qkgAknnOD5SMfzNcFXd/C3J6gtxgtmZRgYySQRjmuHHf80/A7sD/wBMPE/QOhNH4sbK2zKq2cD8WBwcivolq223nw+xlgIzgEnGOP8An6V896fYKwO7csicbSc449j+X1xX0TT1IUq25ZDC/O7sNv8A3vp/WvzTEan6ThtC/iI2rgDPmBIIA/F6nIq+1+PdDoWPXxMjb6iPv9ea522X9zbyZ5LOhUdjwrDsO+D9av8AXVLt0+m792YpgUxnJERJ/lXDDe+521N1ENSWUNHnZzzAR3+oPrUE3GJCgdDMo4QnY3PYbT/h9anPARuZN2SM7kGSR9qiyeKfJJKJAp/C0YbHtgZB7+1bRMJGDCNmJeNVk55K7C3+B/1ms5o2uFCsYZFPcTA5K+2QDj9Kx2lSFjUbe+Y5SdvuSjj7cA15OxSNjjbyMjG0MfUDPH6e9aGZqihkhAXwWh2t2ZvFQDPuPTH0rd477cMA8YyBscEZPp78ce3pXquYQE3rjOVBO0Hv2PI/61IHmA8VAxzjJx+fm7e9SQeQXyNhWJ3HA/FtI/L37VKjkIwUBBHuMHPp27H61oW2R8/iVc/xeYD65H39698GSMfuX8MgeXdiVB9wew7UZKOp0/rnVtPRYzdu9u2B4V0omjbP6nH8vtW2WXovXHE+v9KWUdwMbbuzQExkdmUfwkfQCuVWWVN3iQsykcvCd2G7Hy+np79q9jKPk20vryVBXjHqO/8ArtV1WqR0ZlKjTlna3hl/H5Orm6NN5BKekdf07VWeIeHb61GxmVwc7llBz2yMH1qhuRrnTV+X6w6Rl0/S4ZZHW90x2uIZA2cuwGdhOfXt29eNKswOUjDMD2Vwrf1wau9L6y1bS5WittQmLL/7OZCDj254I+o963VeD3o/b5+TN0qkd2V/H3XsaIdYtdXurSOy1ZdURiF/3qQxng/gVMAgjbgg5HKke1QZrUCOa01GHUIJo5FXxYp2hmxu3RAiM/3CuQApIB55zXQXt90p1QpTq7pmzmmdgzXNuohlLDs2RjJH1z2rCTpE3MBj6L6+urJsJtstdt1uYxhgcbiA2CBtwDjnNaRUJbsvv8/i5V1HDei7ff8Aj82KG2/ajTPPqckdv48bS26zJgAbdkbPnuSTv2huOB3GaxtbYXASHU2mhe2VnuCzCMkZATzL5SCMjaOAfr3ktYda9M3ls3UPSlve2H4p9X0mV7pFADEfuMEgjgZyAcg8YxVVpvVWjajAdN/aUMNy0oWWC9YwyhQNwzvAGQeSc9gPpUypzi80WjUhNXTyJcCC7tEikklnWOeMhYnMcjFeCkjd5FPHc47ZyDioVlcRSw3N5e3CWWnb2gj8Av8Au2Ax2C5XJG3HcYHvV1cWzywGWY2rW10qvHLE3lkVjwCwA3DBH04PfGTHaKaSJ5bWGB4THktCPJP/AHsk/iOF9PXFUuaW7DLUbcWt9crFcILURrsUklQcAhQwznIAPPuMkZqJp8c93d28bSRm3ljLTTwyOoi/EpUop/DjPrkcnJPfMB3SYL4E/jyOzRQSn+yUqxcHBO4nntwFIzgVunkjg3XywxPaSfu2ATaY9h/sz/wkEtwB/F9cTF2KtGiSwFlqSR3JADSCR2VQd4LDw2RjkuPKOW57g+9eXVudYuNSgt4RcyzT7JhaMgimcKMu5Y7o2GB5Scgr61Z3F/dT6NHbJZSXd7aMscE0o8PwwVywYleVKtjOByM/eHGEklwkVqA7qhhdiGZmymFlXGedowVUktyc1NyEQprM29zdSXaSRNh5ZLfap3sO8bEgYAJBzkA7iR61hdSPqsrtctJa3coVraYyMizMTg+Gy+cbBkg9jmlvFbW7xw3axO94sngtjxo2cAZPlGTg/i74xzxzVlq/hr4XzccsdtZ+HA4MRMcMhK4dJAMjI4BHlIOD9JuRY0hDLPbvLBcPHeSCSUTxFDgKQxxghcBRgHHfzVhBdQiK3SWRVZt8TyFBKq+XyJnkMxABGPY8rXk6zzTLb6jbi3gMjPcKZtoSJkxh/TJ3Dt7Y4rYttLLBd/KzN4MoZECWm/M6eYmRWYAeYcbQSRRd4MYyLfU4ovAaSymsZbmCURFnDo8eQpGcZz+ox71KS4V7+7BNvBdXLrdIDzIF3KoYoOchuMge2e1RNUjmlkNxDHc2d3GFmEgIXxWD52gbsHIHPvkZ9TXvh26Wtl+0JxbwyGXCQ5CKfEcbCwyQGbJ7gZwcjFTkDJoUKzRXMEkBkuk8SGTcoZmICod20DOeWB7GjATT3aanosguEk8QghGYMxGGI7N5iTnjsDWFrPNIwileKd5JS2YzsEdryqAE57YTLc5wTgZFW8CC5a0Txzst2e3CKSTGmCAUdAd6kLjt5KtoVNUEb2Ue7wR80X8OcG1aQmXOCXA8qsFGCWJ75OawOqXsrT3GpOs8MLOJbSG5IUrgiJfDVQA5GNykYJPtitkj3JFlLegMZVFtP4QbYXQ5RmUZww8oy2M49c4qFYLM1wqh1muA6tdF4FMjPg4dXBwcqUXPbA9DmouRa+bNVxBaaLJaWlxAuk6dejw02DavjFlZVfvjt6nHYc5xWcwmT9/fQw2iKgVY0t2eTH4lJZOCWbLZ4AAH1ryWOLbbiW1N7um2zmT/AHiLPmIdwoBOPQ+mBWmGOZ2VEuH09iPE2lJGnY8oqruO1GCAtvPBBAx3zOpazRvFpJbxC3tnidlhWWKF1LoFcll24JC5Afae3tnkVNsmt3Nw2k3Fre+OpHixS4bw0ZkK4I4O7co4xnPfFamFztjknSR5A7xfN29wpMRBICH2LcgHHv6jNZwPa3UTwyy2pnZUTxUJwWXAKMcAj+7uIyCSR70KmLxWYnWCRUnt/DMuTIWdQNpwpzyS3mIOc7exrASz+NFMXbLRyNaxx+UPtUEMzf3jwcDAwvpXk2nwWVxOZYFtzbwuTiRm2MgBBxnvtBJxnJ/nDW5t4bTwbyRGltgJZGR3y8nO8HA8wHGFGRkeuKIm1zXcQfP2CtIHKOPGwIyo8SM+dMA5PmYkDOOM+5r23khjui99l7S32qYrlSrltxJTAfupIJYn+lT0m2Tmy+XzJIm5o1jJkVWGFKnPBwzHj3OSK020Ml5cyKdlrDagumYtiqQ4GCFyxBC54/xpcE0x2sDTPHbzG0VkKO7iWZBxl1bAYYX+E98ZByK1ie8W3JZJZLc+dYSSqgA+RtoDYcr3bJz7Vokf5ZZYCkM3hqYUDSu5lcDjcSASOCCT3/iraJ7i4aGMzSS+KGBma38MRkhhgJxg4xlQcY+9QDbJqM9jCxtbhIp50UqltGhd9zbjhCf4j6nH4SapbRpzqPz2mwb4fkZLS9dABJGYnVoiFPfAaRSFycuSc+m6Bo2sb2CF/EngRI0mhgjcHcRhgp/F+IE8nggDFalktvCWZDZSarpaorXHgvGhlIOSsZJRcqRwTjPc+lWWSI1ZKa4jlkdpo7d7oMDErhnAQMp3r2DMpHc9s/SjXUSR2zzzW7CPbLbkLmQYI2gL2HBznI7gAc1hdSEtcvZpFcKZkLBEKF1ICknHbbk5OAM8DtUyO5083k2+WO2iYkxO6FVcDkOxI4YkcDk/SoJ0RqtWuDfm3lSEyrM00pJV1UbcAjPEfqMHcwIznnjOK5kbT2u/mLkyzS72nmzy2f4UHJHfA7c5rBr4mzvLloLh1T8UJP8AGOcOPUgAAAELlhn1ryVYW8Zo40tjBBHEtwZCpWTYWZ9rDaQNygN6kHmhBsbUJnVReSRXqSArbM8e5SEk3KCzHPlIJKnnj61HBjVvGlRlmSXxRawzmLeQS3hbhw65G4gHAFRnt7aa08WaJpWit2lEZQ7Y90YyVZfwq3mJyMg4qxt5VukS9ha23Rxxyi33gTFShKOVwQvuMnsRQk0wzoU+eQzC4TxlWF3bw96NlZQuO+w9+xH8mnyTXVw101wY5BsNyVO0+GpJL85JOTwc8EfXFbrKNtEtLgSz/LXkIa5VtzsXAHbdjcfxcKMgngZpfzx3VpGLQC1u8bwGtwg/er5kIGADkA7DyPXHNAixEz25j1GNRPb3MgkWF4Vbwm2lSq4UcnbkbickkVDuDEUlhtV8NVk3OmMGGNlLqR7YBBHoM4qtMjXMlrCkt5NdS7nVjHhvLyYyvCKNxOC2SOMe9dFDB4sVzLJMLaYzFtk8pjRiBxj3XHrn+AHNQ0FkRppoITHFc2FveXjEzKHkDJEqjzSMgwAxJY8/86q7q0vLa7ik1iK5khu/GaJfmGxGR5skBTgkZOTx25PFWOoQW9oHt7bZJaEhtsTk7lUgs28DcxySCOc59qqZXjvVuHVpvC2F2lil3LHkthn58u3GMc4OaIlG211aCKTUbq+dvlFT97Lab3eKMINrbT5uRuyMkk88YFSJjaWY1J5LdPlZZoRdRhWCmTAZg2BwAGTyj+Z5pp9oJtqWkh2ThnS48NRsjxjOFPPmJIz2AyajT+JHBKgmka8giVLyCCZiyOXUedAeDt5z7EUJJJKmSWETyRNNJBHLLHI58z48pb+6WOCM4AzntUi8srezmvH0uVWkiumkuJJTswqxjLKoGW54GT+ua0SabbTbLfVLH52O4lijBaR0huSWzz9MdyPXv3rKzltvGulMU7RQyPCJkDEOoKHJyQuRk988AgcUINCpHPE0toyxWLCBoirvG0BDMJGJIzk7j5u2Rgd+JL2kECJOkN94V7J+5mvW3yXXHDLHx4eMHDYOc5Oa03d/HcWd09jdtqGnQxqdxiwfDb8XmB/eKSuSFwQG+orZfl2gtopo3traNYUtgrfvznuSOSCe30GM5oD1GkntRBeWcYLlYvBjbBjUMVDsTyckYJOcegrVbTRbI5r23knhQlIlOTtAI7AHB4JXH2JxW6e3W4uC7RQaJJM4VSFWVlY48iY/tHIXd9OaiyX1vLcGCe4t4Ly4WZoY9wADhgGAjJywJ7gnkg+lQsyRFbCWQJPaCI30O1VbAmcF22uxJBwTGQOOAxAHvG1KE6k0Vq48O1kuQywooVZGiG7YsZOSXIDFjyQo4AJJneFDaQttK3lxIGiZwrRy5lXzbR/CQxYAAjGeMYrU2jWyjS5ds+yxkWWGMQAvMphdGd2wMAbgoxznGcipTzGZYXthcgiO5KBGhMx3RpIPPgtzwCVGMZ+v3rXeyNdLKfHSdbgh/NKQzlGzvY9wpXIJAIzjvmvJLU389wtxJPNDH4MggK5fC5wDgjOWwMngbRj1rZdmeW2kQwI5uGZZJlQbwpyuzJA7ZJyfpxVbk20EVtthtxGbaPxW8WViTIAp4BIIBIJY4+ijvxWuCCKV5zHbvDcNuhaJGztj3HOXPJZwF5PI3cEYFe6hPqeyF3tSzMixQ20JwoiAChnx2xn8+a8e4hGm3JLRxXLKYRLySY+OwXvk55xnzelLiwtFS3jikvbkRx7GaRIY8Dcrgbd3J5HGR3Bz3rdNcSzWt1bSGSPxQzyldm6JCcFhg5Ud8ZHGTWYhs30wTXey6TwkLq8jBWUZfYuOcgBcAcevtW6WaF7SCE3yLhxGFXMkgZlJ2luQVAzntjgVDYSM4YBG14qy3CGBTD4mzkRMoyM87gVPp7mokdpFHFJC0qKJGb5WTcFG0jAiVO4xwMjv9q8Ki7meJmURMw2NMoBEX93g98cgH9DjnyDTJp71ZrYTT3UUjlUSFpUDAbgFIAwcYOTj9Ki/Am1syNawxxkQzxySSwuQUHlyMcEg+h9s/lWfy0JkL3EMw3Eb/N4eEBHmX1AzgDP171vay/aFuBALg7ArF0OByMlS2MAgkZHqSRng1LglMspEkKySEZgWI7wjA8byBjHJIx6UbJIt40MSo1yPCMU7SrcZYycgbiy92YjaMn9cVpnRJbmXbuabYsizIQkrSDcxzxtHJPvjg1ssrqXLRX7R31yyGaQ2yMEEa5zuJJx6DBPoam+C3zEtrH4jeGgdh+Exlsc4x2wQMemaroTYoU0+01GWWe70xReJMs/jKitKwDZU7xyMckZPp9RVxdWkk8K21yfEkln8kB87sr/wjBBJOCTzhR3rdJLpgsZZ5760sliGXWRRs34OzzDtkfXvyM1v0fS7++2XOlaNeXj7QEumiWGMKVOEMjn8AyGY9ySO/NSlKbskHKMFduxCWIFoxYQMEjUKngwhUQrxg44IIzz6DtWy4VdIiku9XubWwjNxsd3l8zhTmRmBPAAP4RVnL03dvE6dR9WxaVCwwNP0SPxig9vFYAZPPocVtN301paJHZaOl/OgAFxq7/MuSBgHZjbnHripcYx32vnzjYy5Ry3E387Xb0uc5ZmbVWeTpjSrzX2y8UZt4yY3V2yXLsQiDaMZJ9T3xVvH03rQeKfqnqTT+nkSNVNjpbtcyMVHBdhgZHGMHGQM5Fe3/V+o6mnhTXMghB2iJT4aAegCL6fTFUzyzPnOE988AfcDJrN14R3Vfx9l7luTqS3nbwz9X7FrFYdH6Xukt9EOtXDMrNc6uyvvcdjsUAE55wc1t1DrDUp7cwLOtpbLkiC2UW8XP0XH3qiEbE7yzM327f1J/l9qxUxwMcMoYtuY453Y9M/p9KxlWqTVr5fPmZeNGmne1335/wA6eRhJNI27mRyfxFRtX7ZPOfpXgBTcXbB74jBLE/lzz+XH3rIsWOFX/wATcZP2GT/SgQlmBZsDjAAXFYWNrkZm8FjsAGfxjbkj74/6VqdpWxJIrxpg98oSPoMFvzxUzG3Cg+bBYKp5H5Ctci7WYsBGu0cu3P5gf4mlgaQkpAQK8jID5eVVeex9TWIgSMgSuqFtzCKP8bfQZyST7/epSoWRwXlK4xtQeEPbkjn+dZwJDCHit0hhZsRsYV5xn8JPJP8AWqNFkyMYSvmkijRW5HjybmHscc1j0+znVOpmkcPF83AIRjAwLaPJx6c571Nk2pMCCPFcEKiDzY/LkDiq7QPEOrdXFnYK19bqokGCoFrF/Xk5+tUatTl4flF0+lH5wLu+IYqYiC4OAWOCBjniuV1gq2JVJBCybVByX/yGavrjK43kh/NkY/TiuX1aZWmbJJZVJGD3zkAfUfSsKeppU0PmnUD4MolcMwQqQiAA45OT9Sfrmvy18RMjqI5x/YJ2/Ov091CrJHKSy79z+Yjk9v0r8wfEQY6iIJziBP8AGvuvofW+R8Z9a6vzOUpSlfZnx4pSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUArt/hlP8rr9tOU8RYbiKQoSRuAbOMjtntmuIrsPh9j9pSZ5OFI/X/XFceM/wCefgdmDyxET9FaG22dVX+zG5VG4nAB9QfXA7j1FfR9PUqSVTJKMFG48+TPqAPy+1fNtFTcxJb8QOckcc/UccY/OvomnuiTW7EgKTjtk88HsP8Air8zxOp+lYbJHR2pV7KMtkpHcLgkgnBUjvn6fyq+1vzjpliyhvEnTdn/AOcP6diOMfr7Vz+nO5tplYN4sbR5XJHO4g8n7g8Vc61MAnTTHnbcyDPqf93m9Tx6Vww335/wdtXcMP3ix4ADjP8ACf57W4/Q1rYguePN3KEBSR9M+v2puXDeG+H7kAAMM/TP09hUeacFismGGRgHjP3B79h2NaxZjIy2DIULnH4eckH7H/XFamVPMqM0b5/s3UqePTGPt2zWed5KxkMQM+HuwePowH8vesvFIDI6kDjKunYfY8dq1MjCSMooC71VfRcbT+WP9ZrJV5JCAntgMV/pxnj2FZnaG4WVFAP4X3DA9NpyR/yrxCjgANHMw5O18H6HbnI7UAWRQ2WDwluCxHB/Mce/cVviZmwUfecZBAB5P2/5Vr8SLeQ7lCRyHUgEf6zWSxJnAAGefIeM/QfrzQk3AsWGQpPAypKt/P8ALsa9dBIF3NFI49JVKyAf94c/yNa1aVchpRKDwFcBSD96yeRDkFTGucqGwR+dVB642ALMgWMHyl8lQfo6/wCOKxcBWAcyIn8KSMZIx9QSTj8jW4O6qWQnaMLlDkc++Of9Gs1KgnaNrDnyeU/cjHPrUWJuRy8uwEGWME/iixKp+6N+R4NbYLmVCQssc0YGN0XlZT/xROTj/wALEfasvBQklCo7njj+lYyxggCdQ2OQcYP5HnFNCblxp3U19pkkZsLt43xkLHKV/LaeP0q3u+otM6jVI+tdD07V3XAWW5gEcq+vDj/OuLaNZGIcSkFfMHUSKewrKMYObdsDbt/dS5VgP+Bs4P1BrWFWcN1/PAynRp1HeSz7eP31Ol/2G6fmunvuj+oL7pjUJFbMV6vzdqSSCTtPA7DtjuftUZunettFif8AaWjQdWWxxnVtCuwl0UZzu/cMAOFPGMYGMVWR3QifzkIT2HhhT/lVtY6/eWRVrW6eLHqvY84roWJjLfjf587DF0JxzhL7/wBW9bmoato+qyFRq/yWsK+wWGqWrWM+0Aqu0OQCcZzjP07V7cWaSK8yRFJGn2uzvJhFC8DYPLtLe31Brpj1fFrUcUXUOn6frUcRyDcQq7R5BBw3dTg9+OKpP9gOjLyVZOmtV1jo68UHYkF001sM5JBickEZOSM4OKvFUp7srePy3qV26kN+P2z/AIV/QLBJe29tG0UFx+4dmQXG7gg5Cq2M+p82PTAyKiNbRbYWuD4cUDpcPGr5ZtvYkEAZ5BOAM4zVgvSfV+kTtdtZaF1nbwkzrJp7G2uXwMEeCTtLleAQQM9yKq9a1LQbjV57HWTe6VFqHma06gtVgUqUUeFvc7ckqByTnOKs6U46kxqwnlFkm2EdhaRJPu8SaVvllJIHmJIkdQQDyTwp+5xUG3sp7u7tLS3gkjjuJWCo2USSQocgsvp5O2ccAYBNWNvoOp6dAoaK3YBYxFKcOgVTgDKDBUjPlGBkAAY5o1ikJe6tURLaJRIWUckgtyoJwcA7ufUVndJmmquinFqbO5eZUlmhtGVbkxZPi7n2pDuxk5wTx6LzjvXiQX8kvz5muVtbaCVI7aGZ2TAk8kRTGO4/GuGIye1Xd/A7TLgfuFZmVJXJETthnQYOfMcYzwBxUC6tWvba/jvEWdHlV0aRwB3zxjkAdsH2HFTtDZujXE7shT5iKSGFY2ud1uWKE4yoySSeeCPasLeJbK5iMp8FEkAVZcP48YY7wwAABJGcHgDt61KubSQzeNFH86ZJGkWVY2VkOzJ8Q5xjA4IHBxUeW3ktWWC6uIVX5RZpCXZkwxLMox5WJwpJycA9ueZQsRHhljvJ7ISCS0dpIkyWy6sMnc4wRnbgfcetWIMk0FwLfwlsoo1KQEDwNvdiqkk4JXO7Iz+VQ9Xs4rywkkuohCZnil2QZZ4yXI8NjnaAoJIGADyT3qVqHjwWdvbFoL1Y08JJJIfFBReeD3wCfwt6Z9AKtfIrY03lzFBAsdo8SbJpIreNx+8lRGDOin0yCcEZJH259iuLpLkwSsWj+aWNdyqrkspyBjnAyo3EbfP7jNYS4AtzGsLIy/hCqUeLaAwBJyOTnIORnk1lDZJDbiHUWuLdJ4kWIDzIo8MOobOTjd5Tg4GM45qblbGa2qWNjJNLLNHqRmkW0USB1aMZG7nkjnbk5GR+dV6xX0FpcPNCEuYThDMjMWcKDgKTtGMt6cnAxViI1if5i+uVV7W0Rn+YkJ8MR8kbe5XJ/DkA15IxY6kI0VZbht8RJHg7VdvQfhPt69gTxUbRKRsl1EL4MRuoxC7jwFKtG8DK+dqgDJTGRk5zkgVGhlN5dRjUDPDPbq0rypaqVdM+dVHDBslSVJ/WvX8e2VpEhmczBQSo2RMoHKrndlsg5PHJIHesrcG+0u5Roi1tceI1xIJO0TLhghI9WO3H8+KkixHj6evrW+imgnu5IopVSYgh4mUqfxR8/vAx3b+OMDsKs7YXkeC0kMJjKLBMsalwMEFVxwu4DuDkk1SqlzaTWhmkFzcC0XxWnlCsYsqN4UgAkeTsc4GKmTGNJLeSWJrpg8qLtjB8SNTkk49MMMDOAfpzUkWMm1aWzIUtFJqUqyhLhYwwdiQSiuR3z/Dkf1rRbwGK3BQlpZHDyJHnOFGEG0jDEe7H13YrS7TanE0Nu09pbRhiJLZVEW7cu5WySNxyrDHIyc8VZWOmbpTZk+PGzfurVIsFlbPnyW5wd33AHajJ0MDBeSyOYLWZocoPCaYIJBkhgHZhmQjkBmB9eQK0mO8tnvYbpd1paRxZMVwVJLjkZI8zEfxHGNvFQ0JuEEMItYZVR99u9w8ZikYsuDngvheTwORg817cIGmghuInWQSsUdj4wSbaEEWGBDMVJweRtz25oHkb4WuX8XNxbRW21njCyKTHGI08zMOQSC32x3yalxiZWm8HaPMuIEQ7vC2jbhXIJzwcgnuM+tRbdhZfMreQQQFUw8it4kZdvMyAp2AG0HGTnPYVp1e81K3+WECahPBE0CTxQ3AE4ilKh5EJ5Bj4YHBJAZdo7iUruxVuyua4ZSkiabqVrHAqqku+QeG8j5yQ+0nJBIYL2O096uVEzxtJ81ixWEeGikeRh5maPlSx4xk8HNVEsjeDPd2jRXUUUrpZx+UCNuzjcfxbvxYOCORxipssE9lcxtezQ2kjztLaxSSIrMiBFZhjGVzxkk8HAxQkx1GGO3hlhjSIRrCjyOj72Ej5OHCAB+NpJB7gCvYpJ5L9ri5hURRgM8fiABPKuNwPHG4ngeoH1rW90y3MyzeIkjomxiNzCRmUKWHIwS2OxwPtWV1ZTQXAxaHJkPjqMyb02lSN3Prg54PAFRcG9n+el0+Ga6CPqZuIotsZR2j5YkKffgbj5cDjk8RYNMtbaz1F7orqRjl2RpaqI0jhUqoyxznJOT396k3+nzWlvcPHOpgNsIpJbs92C8BAOQONuCD2HeoEUv8AvWN91HCtqPAkWMEGJ2DFMgeZRtG0d8BjxnFTfIjibLma5i1Z3EkixklLdoiUIO38W5vwnv6egHFbhql4LGd7eFWvORAkG5yckMzMgyqgIRls55wDk0ulnsNNmmjvmjne7RllaA3CLC0ZVC0bsPEJYKxQYz9cVXXMccW2aK8s96wxS3LMzRbU2KXP4cDO9iA3YleKiyZJNKW4nIvZgkEG3MjyFTw2ThQCVwMHceMZ9udep6i6XInYO2mTyJBEyuJGiLBlDKDgAMDt9MHB+lbruCW8itbW+LWKx/iJWGVgVXd4aykZwcjJY5x2rTLZSXMyQRhFjuCA9qFHgGJWYksgxn+Hv+HIPFSmuIZaQXtzZRK6Xi+LJ+7MQcSFhk9iSSTwC3YcHmoVj4k2sSrqN3bKjDek4z/dyIiNoGdxycdwxxWGoeLaW7NtPjRCRdwgDHBGyMcA7ju544A+9bYbmNJEQ2rTPEHEFw4271C+Z8DlcrwC2cbTjtUEok21ubUXk0QM8wtw9vG++XZKPxkMDjBJGM4O1j32moiaerSSypI9v4ID3FyZMiWQx5aQA5O0sZOT7DHIrYuqWsUsiwqZUubVTE7KBK+CSFDEHAwpxnjOCOOa0+IpgtFtUuJ2uIFuEHhgYkZyQDggk5LDv6DjmozJ8TTLIbm3tLq7VbISRqsEhj2vEASRIM54xgjtyxwB3rZdW93bqRuaRY2RN8mGfBQ7N2fxHaCSfyNZixu+omllgsAY7cM0kryLbgM6nJZ2PAwFAHO05NI4bK2tPBnkhkuoZVMs6PI2+RTsbYCT5TkjPsfXtTgTxsSRDFYLYyR26S21kUjbOQBbHzMy4XaSu0YGPNn6ZrG+u50vkmudyGcGRzgKyqQNmAQfNjaOT6nIzwN0QRZUuLvZLII3IiMjGOJAc4JOMM3YDv8AhzUXUiIZvl7Of5a3iuGuJFltWKhdn7uMHI24J5Y+3APJqNdRbM0JJHaMpjMlvNJLuIRQvjBxycNlt3b8Pt6CtiBp2eO8khFu2EjWJ0ALZwquxO7A4GxeWPYjNS54vn1huHkaeKKEm0VUKrHM2FJAxypHbccduKzhEZstP3xXKzTSFZZrYLIqSxMCCSe3BUjHY5pcWK6znube3gnkllubeW4/fJPhWiVuAVTjIwPKz5PnPrU3w7pXFlE9zdW4uWaCVWJEq548w7BQMEH2znNbrWxtpY5LqeK7hmkgaJmj3h7hkJKgHkccDOMZJJPatRgaaN7O08KCC4J2pIGKgHBLM3BYLlsLwCfejaJSI0bQLbySyQhowPCVZlIcs3GUweVxjge5xUm332UMUtvagRENFFPIfOuGbaCmcr6nGM+55rxrV1t4H8EKcqwiV85iC+Vwe4DHGcYwPXmvL9LU2EjiLfLLtlJEYjbdwC+7udoXGfYVRyL7Nyc03zV5HY2lxKLiTCIZIA2NoGSCAFIGSx9uBWi3e4tpisjTXg3BPFlIJeTaQrt6E5ycH9OeJljpYkupUtbsSSyM8pueIigfDOF+42j17DNaY7QWqSpa3E0/ycqFDFGIo5CQcry2fxcFse3tTgVyMbeWSQRI0gQYCuEiDeY5Hb6AAEZyQc5raLeFlie88G5AMfiiCIlZHQkg9xkZ9OcVgEaGKSK6eOK4eTDI25wrkAgHb2bA7n0FTrTTJTFcveJDCtuivBOZAqEnazMAeF4J75yKrcs7IhW6LaxTyOpabDFXE7KBknKBANuV/njmtEs4WK5jvbjwnu1SS4it7okyFV2bmxjgKFB9CCfcV5eX2kCRoNVvI7y/jkEkK23lcoo5Uoud4OSRgd9tWljoGtXe+bRemhotntVRfa3N8o7jPYIMyEYAJyBnArSMJy0RlKcIZyZXQ2LSzwQ20Mfjg+IZDH5HfJ/Bn8PrxjjPrUu9EMVoi6nqUcdoZCUjgYRsz4PlJ7tkE8D9avP9ntPt/Bk17qC71G5jQL4OmRi2iPc8u2Wxz34NeR6/pmhyBOm9MsNLmOMzbfHuCACB53yR+VUkoRd5S+2f9eojUlLKEW/HL+c/siPpuk316RNo2lXEkLJ4guLhBBAHUnaMvjI7HAB5A9zWw9OQR4Ov9SOMbg1rpaCXKnhlMsi/QZwuc+tQtQ6ivtTcPdzTzEn8UrkKP/COwqA8jy4G7JPoDgY9yP8AXeqctFbqv4+3/pPJ1Jb0reHu/ZF5He9OaK4/YegWSTxsXW4uk+an3HHILZC8KPtjjFQtS6o1PUz/AL1cuyHjDyHb+QHFVnhkAhQqKDwB2zWgjYAZFVGI83m4z75P2rGdapLJsvClTi7pZ9ur+7DSs7FS0kx9Qgwv+vzrFQ0a8oq+vcf0GB+prIO0wGwSMmSFCDaCfufSsDFJuH7yNAR/BH4hH1yTj+X51ibGCywsrbH8YKSCIR6+3Hc/nivFnYZjjjDEd1znH5LkfzrctuApYhmz/FI27v8Ay7CvBLE2QjhwPSMbv6cCrWIuavDmk4kcxrglgTz/AC/zrYkCJzGCcDAIGP0rYpJOPDAGOx5J/Idq1vMowDIZD/dQbvsKEGagKBuIQ/f0rASIpAiTeQe/8Ofqe1Y43AMVWA+m8eI/5D8I/nXrybCANzMOAzNkkn8v6CgMiXQMTiMcZCjH/Oo0xC4ZiO+1VGM5/wA/1xW0K4ZVYqgXJL9uf6/QVplMdtCZZZEtoxwSW29/Qep7/wDSgRiGwf3ieUZzubbk+n1I+wqQqSSBYkL+o8OMbB9Rk8/c8AVAS7Rf/RLY5O3zMvhrnuMk84+gFS7cTsv7648rdxGNi/bceT61m2aK5K8lupG5EVx+DbwePYckc9zVF0xtm17raNC426tCkm7vkWkJHHoMEcVY3BigyuQQ3spB/Mn/ABqD0mVOudcZUoianA453cfKQjt37iqy6ufh+USt+Pj+GTr5izgL22keUfeuQ10KJ4gceXdukyMYB4H513F/GWJdY2VGBKjPcfb/AArltYtwys0ykBQd/wCuOB7Vz0nmjarofI9bDESZGSQ2Mc1+aPiMAvUZVRgCBP8AGv0/1DhfFJUBcuqnAGQOAa/L/wARTnqNjgjMEZwa+6+idb5Hxf1rq/M5OlKV9kfIClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCuw+H65v7licKkRYn7c4/PFcfXY/DyNH1KcM+GaJkCjgnKPzntwcceua48Z/wA8/A7MH/0RP0PopSSYFGOCxDHAz+JRj78j7V9AKAWcch5YMQFBGAcHP9D6+9cF0ywmnSWRhsba5JyQAXHoR2AP6V38cxbTtsYO2OWMSA8Z3JuB5xwSR981+Z4jeP0rD7p0GnSF5ZSJWGUGGYNjIcHBGO2frV1rkgEPS7MCx+c4wOR/u8/+GaobBSrSNySIjnPP8Q+/5c1d634fyvS7Nk41FPDAXIZmguPt6Z7c5rhhv/f+DsqdWeKqTJkOCowoZSAcfXv9ahPDLHJmK4Z1I88EoBX8vUfrj6VLyhwxUseeWG7H0zwf1JrQ8wVSZGeJT2ycgj3459+9aRMpEaSVYgRJG6RqOCE3Lz7g8jH2rdESDmHbt7YU9vpjkY7cfyrwNkbv4OctGcjmtWyN5GPhoSMjeOSP07D/ACrUyJIWMMP4TnBA/wAjxntWBgyyeMkEzKTscxhHA+mSf5GvCx2eXDAYYeIQ2B6jJ7Z4/SvVO3dtBCjkiPkn7A9/SpBIj3RgKvioo9PxD6+v3rMLnG0qw55U4B/L8vatKsu4heSP4T5G9uxH0rYWcnDKxb/icKSfUebAzxQGxdwxwCD6Hgn37d/U1nvTaFfMfPqMdvy+ta1lU/iyvqQ64+/+PapHnQESDyHhtw3A/r/KoJMPBXPk8km3AZePy7f4UPjqrmYpc7ckF1x/MDj1rMJtGVSSNe52Dcv5gdvX2orEqHgKXAU4zGcN9cg8H19agAzAswkSRWz3ZO49ww/rXoVoj5HceynzKfp/KsQ6NkEgMByGXwz9+e/5Zp4EUbHCmJjkkAlQe/oOPT2qCTYQowZE2LnhlXt+QrImM4JnQk8gse/Pv+nqK0eFKqgpsbnznkE/XK/4j0rJbjzESxSR8/jC5B/T7ew7VBKJWxh2d+TkYxID7fU1ireG21WiLn+6dhP/AISP9ZryNhJ54GjcZOdh28/l2P3reZmTPiEqh4y4G39eV9qC4JVv7eNW47yR4YfZh9vTFbA5UHEgwf4WcuB78E/evFjjC4jUqO4Ck4P07kfyr3lcqXwBxhx+mSP8qgEqDU7i0kVoZPB8PO3w3KY/XvXQRdazz2slhrkMGqafKm17a+gSRGU5BHOQwP8AhXK5YAHDH6gk4/L8v50UdwCDg/w8ff8A1j1rWFWdPOLsZTpQqbyudJBoXR94xbSxqPSly+CG067ZrdTjbnwGygGOOAP1qDdfDW+GsnXbPT+mOtL3wfBS42m0vEI/CcOzRMyqTzjPtVUsm1iQW4PIyFBP2x34qXDqVxZt+6YxgZ7OVP3GePX6fpXTHFfuX2y/r0MZUJfpl5PP+/UgXNzBYyTW3Uen6l01qJO2ObVLUmGQL3xKmVbKjOSQTiry1DyQPfxQxXtvFumMloA4RgFVFz6tjgnPpj0qzsuvr23jkt7qRLq3Iw0Uyh1I9c+/tRtN6K1iV547O46dvZZEla60e5Nvl1OVZlHkY/8AeU1ouRnpK3iVc60N6N/DP0yf8lRI1yFkksPDmS4QN4zou5WOC2Duzgg47Ht35qC8MtvbrF+4FpFJvtjtHLHzSbVBzsGNwLcdhgmum/2S1e0n36HqOj9R2bAk29/H8pdoMj8Ey7oz2OQyqD9KpNQjn0u4b9vWOpaBFHA5W8u4Dcwo5ODIHi3K3BHDEeoGO9WdOUVe2RMKsJuyefr9tfQrYo0W8vledIoYykSRgZ2sAcO5PDks3Y4G0+ncY+Hc2KQ380bzBldQkYJEi7OSAD+IH7gkdzxV/wCIt0JrloodTtF8ON5racObhPDJAfbkJH5iNpPrVffSte2syTeJePcBSVhjVWBVfKqDB49cDg4IyKzuaEPw1tr/AE63jt42eaAy53eEYlZvMqk/hbKg5+nPFaNQaawgnvonuZtOQ+KtvDCZ5nXGGwgI5yclQe2cZJAqdqEbu1mskivLu3u0yKxl2DmM44B5P5j2zW6507YEjnitomMuGjhmVpIkwct7Dk8jnAHtzUpkNXNNvcM1sqam/wAwq7A7oAP4dw28HJ9+wqvGnSQyTzzXZmQvGY4riLw1Qhslzx5t5AO45NSLHwV+UE80azwMr/vIyAGUYbyDGc5OAeOaxmt7axkULPMTOGaQyyESTyO27cRj9BgYwMUTLWsbppVuo7+V7yO4jt38RoHTYZPKcmMEAnkjjuee3FQF8d7eWWxkgjJ3yxpIrDdnJBVRgqCASc4OQMjnNTL1BITDFEA8Od/iIfxHvubGPXGft71jFCbidI08Ozi8wcBAzTE912N5c5zyOSAMdqlMixleaUrXV9Bp0MD25RJoYGXMiIr++QT3J3AkjA+lYurzwTafc3JbTLq1kgBSbbLGwA3LGCDyf0754pNdNbJayyzu7wRLho5CSRjaU9APopyRtrOAM0a/KQx/NYRJ7VMkSKQAHyR+IjOVOASO/pVkylrFfYaRPLBsu4gPLJFK8a+JIy5AVmU8EEAfXjk1vsEv760t5bqaTSrvfua4aNZSjJxuiVQCBgHuPXn1FGtfl7+7a0S2f5Ynw4i7QiM7iAXGMfgOeODtqJLNFIYLnzQs2WlcHLOFJI249dw9sD196m9ybF7cajpuoXdzFLbz6xPjwZ5rqAwx4A3LleAx4Hm+g9qpJ5jHvWBwlzEywG4AcReHkDnbwW82AfTk9jUj/fJfHSF7iVoik5klvGlckkrgHJyuck45GPrWrYflVsblJW8TfKGkjYElTtz38wye2e2e5qFkyLZGt2iMyxW/ii6laSO3jkCuTABt3MOByXKg98Z4NYIxtrXw5orGbwpAZmcMGQlyEXdnztlhzxg5yO1YyNax7JZ5bfwVaR3lEQERY4CntuRc7eByeBxUq1h0mQWNvMsjSWrkr4qKkattbDoq5OC2QCSKsQLm3E1xZSxYOxAzNMfDmEQPmIK5UKcPzjd3HNRyUjitN15NBdoqmB7mLyOreVA4913ZwMdxxmsRJcRW1tIS11fzyFbgyhiPC2uCSVO7d2Gc+57d5ZigjaG3niREuHXYjjduA7/i4A47jJNNCDy4hkttnjQXM1ugDS3csTKokGcHkkrjAIPPfnmop1mQ6rJBf3lpsSWRUeNFmi2t+CN8cqcjPf05Na2uFMKyiSLw41PiyeIrZVSVK4HKj8R+wqWbkJOyWTvDHOh8fwbwKsrlFACIR27gnJzx9qIkjTRhH/8ARzYLOVXMT5d5QuCowTsUZBz25POSK2w28V5BC1tdusdu7WLyQElZAjAmLnJUDC8jn2yK90+4s5/nrK3knt7u+hmkjRogIzCeMEd1YcYHIO30OK3IsOn7beWRlKiMOEVVkUsCoBZR5m4zwO4A4qG7ZBZjR3c3NuBfxXI8crHIV3GN8MQWOAFbBCgn1/Ool3b2+o2RtP8Aed8paJ5YWKXEKl9wyAMcEHggnDGt1tdwXd0lnoksV3fRz+HdxPeKuR2YsCQePKx74zgcgitlzd+GqTX4igto5ZYo7iIMN0qhsnxMruG0cL3+vFMybCHM1nFYW8C3VvZFflprpmVwYhtLZwCeAecDJTGaxv5DBJco96lvapK8qRwpsDsGVgmQB+InJOfwjt6VqjhlhuJ1jnYF2RWkjTC5RSVyx5Clm8yjH4iM5JrfbrcTrIl40dxbTnOZFKiSMZYoVGSPNjnnA4z3qSEjKe5njuGytx41q+QjRnJjCL5lJONvn5PfjFeJDFBqtxvaJbcQeKjFyju0nEm5TwMNs2/94/n5dCRLTDX6sqJlMtuYFn8qrlcY2k45J8orOGVZ4re2k+ZS2uIozGvjFyRtHmKEZLZ2+UY78kVFy1iPZuHWZbhZ5JvCLSGc4CCMAgebHJUHPpx397BXa8chtsyxpFCjBiojc4I3kZGdq8t6Hn3rJ2lupIYxI8hSQRzRMnC4Bzvk3Z5cqMDJ9O2TUafxDYsjlpYFkEQWBRmN1WQZyM7sErjHPH1qLixGvrSC9W4toLa28wjg8EnxsgsTtY9ztA4Y4wPN3qxlS8fULqQeHKvjFZU8MReQsCMMTl1XnsOMg81uubqf5WFLOEhRGUuJ2wGibgqAvG/lsEnJGaxa2mOpeLbPLMkD7Zc84LPgtjHlA5BB3cHJPYVG0TYrZXeK7lkjgt55EDvMsknnUt+Fs54UEHBx71Zw2F/ezrPexmZ0TeCHWTOVG0HP8OAeO/Nc71brN1030tcXcKxT6hbywxQE2uY0UlmZnyDujChm5PG7H0qfp/TlhFqI1rTVuLW5uDNclVHheJ46ErER2O0sXxgnccntipt0dpshvOyLJJLuZp5Z4MtGSWD7vDRsgDKjgH8Rx64Ge9LaGVWlFzMZLPxi6pDlC3CtySeAD2Vfz9q37JPk4zBFPb+CxkYSFE3yHII4/hyBjJ78+tYq8zTlGkZ5ViGbZGRyckkeYkAZBwWGSMe9ZmgurCa41VL27uJIjZxvEqI3kZnABYk5JOAAM5Hf17ZLazwXjSBWihCKuWwoMbepXk98D374HFepPFFulhHkZdpbeBsXKkYA985+2PfFbZoL7e5S0WBoUhDNNnew5ySBxnuOfvVSeBDkSS1uJbmSYG2hEiKVhLsYtygAAcksy5ycYHHrmpNxDFHFJbRW80Vy5/eMYgURA2G8555OP0IA5rRZpb6ndy2unTTatPu/DaxeJk5/iVRtUDaMZ44966iLQOpEZWvZtP6d0+Nsh9SkEszArjPgxtwT9X49ua0jCUs0jOdSNOyk8+z+tWc8LWfxTPe3n7vCJHGwBDZ/9oI14OAMck4PJ7YrJ7/RspbS2z6zd+GyCG2dmOGc58q89sdx6VaLpHTUMZ/aNxq3VMpTY4kc2lux4z+7TBI4zyx44qZJ1nPYRfK6QLLRIW5EdlCsbMPcnue1VbpwzlK/hn/RG1UnlGP3y936IrLLp3qnc072Nj01YMQJLrVrnErIBtGEGWyFJ77cnvW49N9NwxFdX1XWOpVaXxmhWU2lruwByRhmGF4Gaq7jUXuZWkInu5cnMkhyR9ct/hWorNNIDIViRwSzElnAHAA9Bnk+tZc5tuRt6/16MtyMpb8vtl7v7NHUw9TwaHE0PTWmWWiRn1t4BvPYcueSe3vVRdazfXsjPcTuzDnLuWP5e1ViwQxszxpufsXZiS309vyFbGIjZ97KrFvVsnjHGKylVnPeZaNKnB3is/X76nsimUkyO8oJ5GcD/nWtQFP93kkALg+wzWRZyWYq3HbdhQOP9GsDIijBl5Hog7f5fnWZoZkBfxdywOGGSKbywyAQOcFuM/l3rwO3AjhI995wfv7/AK0Lc5LZz6KMf86kgxK8gkn2x2AFeCFEbeEAfP4m7j8/b7Vt82DtyvH4s4FacpgkF7gjHCds+nPapAZ1AbAMrAdlGaxYTtkgx26DjLDc3+VZHxFBCeHCO+QAzfl6fnzWsQENlVDPj8cxMh++OAKgGoIjtk+Letkne3KgfnwKC4c42hF9Tht2MemBxWTW4lIE7NOTjhmOB+Q4/lmtUssFuZAzooUcogBI/wDCOfyoSYrb3t/fRwWt5aQxpC01y95C8i7d2FVdhGGLcAnjCucHgVtKeE3hM6ByOFjJIP8ALIGKjpdzssscEMmyQLmSYLGBjOAB3PcnJH51j4UzMWeYKG7iJAMj/vHJ/p9qu3dFbO5McxxoclVUjgscVoF6jA/LRyTAfxABF/8AiOMj61gkUayEyAyMv8TsSR9qzeQRFGuDsweGkIG4/TP+uKqSapBcz7Ua4Nku07khAZv/AIz6/UCsIbOJMTJGXlzgSyN4hGeSct2Pf+vsK2Nc+KrGFNw/vyqUUjPoTyR/X61rkHj5WeZ2ySfCtc/oWHI/UVVkoxmnjiZPEmUM2QmTg5x29z+VbEnZlYpFKSvd3OwfqeeftXnhtbjw7cJag/xB98n5emfrk16UiTb4rEjsPHfcB9l9T9KzZdGTyK/iKJt+3G5LVMjPfBY5yfzH5Vr6P3nXOrXkiAC3cKRqXDZ/3eM5yPqTW+S6MceCpjiAJDXB2ggn+FM5/pUHox5JNc62YllLavFycD/7DhxjHpVZdXPw/KJ/VH5wZf3hmjSSSbarLGwBA5Izjd349ePrXK6zuUhW8gj7Y/EcZ4/XGR9K6m/ZWbMjBfK4yBjHc4HufXPp7VyerQzbofBiQBImO9nLBB6D7nP3rnpamtTQ+UdQglV8crsjJITPJbBI/IV+XPiId3Ubk/8AuU/xr9QdSFSYY9w82T5D3znPGM1+YPiKc9Rtxj9wnGe3evu/ofWeR8Z9Z6rzOTpSlfZHyApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUArrvh42zXIzxw69+3qP8a5Guo6HbZqbMO6bWH/AMQrlxavQmu468I7V4vvP0h0jLtNuwUBFeHdwCAuf8/pzX0CH+wuFmAkdLa0JKrt5AwRwTnuOePQY4r5z07NsmUkBlCKAWGd3nU8jn1PevoKo9tERsKP4ADDY/O2V1LEjhuABkYxivzLEb5+k4fdL6zIw3cOgcBioHrkc9z6d/btV71EfDsulO5I1iLCYyW/cXGPr+Qqh06QtIIxlkKSBlUeuDggYH9M1ddRoJNL6WdiSy6vBJnOePBuP8xXFT6z7/wdlXq/t/JgJmPhsGRAMl1YEk/TPcc+4rW0pkj8QL4WTz59ynn3Hf8A51nDIVgUeI5RhnzksB+tRpWUuxx4TY/Gpwf9f5VrEzkekAtzg7uzKcH9Rz71rAkZN0Uqy4YYWU7CeBxuAxn7/WsN8sZyXilYeY5YxMOPQ8qf0FYWsjRh4542R1YgqyBiOM59Djua1sYkoTSLgSpLEpIAZhuRvs6/n3x9q3KTIpYjI7h1865/w9BWjxUCl4nZCeDt8wP9Gx396KihhKVCuB+OFyrj6Ht71VhEyMiTABDkDG1hu9D6H/XasxFsHkwV/iVnJqNHLvbw3lSZsfgnTY38vv8AyqWC6hd29cjjBzz9P1+lQWNReSMZlDDB8zKiyIB2yQeQP1xWyF0Q/wC7KCB3+XbcB+n+vpW5TuG7O4enuf8AX3rU8Cs4G1HIIwNzI+fXBH51BJsDyBtzqlwPRkAVgPX6e9JJo5AGklkRh/75c4/8Xf8AnWpd54inZmAziUeJ/P8AF29q2xzGReY94JwWjkEgH5cH0PBoDL9/jbtRwc9iJUYenHcH9a8VpNn7oFU7fuZA/wCRVufyFejADNbqS23PkwDj7cDv71ljzYJCHttyDnn09j3zz+VRYk9DnADtGHHYFTEx/wCdbsORlDkHn95GJVP5rgitKlgDgh1I5DrwPcEjk9+2DWSSLuXdGVIxkx4JH/h4Pv6UBk4h4aeDfjjKLuBOPT1FZQbAMxytFJtGVeQtj6ANyfyr1ZImYYkVnHBDHDfz/wBcVuwWTa3IHALJu/w71BJ7tkV2IJwe5B2/r3B+/wBKxE7r2PlC4PB/wH8/esPBXgxoEb08Ngv8s49qC5MeS8ysQc/vAY2P5j/XNRcG9HV8tgPjuyMDx78f64rYG3L5ZVGMYEinH69/ao5MZBd4zjHLAbwPuyYNEALlIL0tIeAjhZhwORjysO49TQEtkbad2dq8EYBUdvzA4rFl242ggeg3YH19s1rQuowUVpMAEWzlW++1sH+tZxSbnI3Et2IkQqf0I5oDU0pV1GRtY5O5dpBHqD2J5/lW0hkUeCGKlhjJPC+uCP6etbTxlJAyk8YbkHn1B4rULaIFfA/dH8QKYX+X+FQWJEGo3ELqYZCvPBA3D6evP6+tdFp3Xmo2Y2tulQ9/BfIH3VufyxXLkuB5xGwbnazFM/TPP5dqFCeZDKgzkK67lP09ff3q0as6bvF2M50qdRWmrnTyJ0lrN21zd6JFDesRmfTmaxmH32EB+efNWyfpKR1B6b1uC9l3lhb68X3c8sEmj98+oYDP0rjyjeGQ6QzRjADRsQQeOVDdsfepVvfywsPDlmjIGdrr6/rxnPcH+VdEcU3vq/z5rcxeGt1cmvX55WLKSwm0Oxe36k6TvtNTLH9o2hF1brlVUnxIjuCjnmRMYBz61UKum3WnxNFPb6j4ME1sbiFg5QHDZYL2JJxz3447Cuo03qy8sGQxs6DBOV8pI/5f41ZftrRdTnkm1PTbeS9eCSF7iNBDOUYYZS6gZB47+wPpXQp0amjsZf7qesb+D/D9zgorq3vhI1xPJAsjgSQxo6shUgHj+LB524+ucGrIFXKeDH4F2V80gBUdm5IzgcHkVdy9IA26x9LdTb0RGENlr0HzKDIH/t02y8bcqWLYyR68Ud9oeuaW73F9pV/cuXAebR5Pm4mO3ADbP3igdxuXHNXdKWsc13ExrU5ZPJ9+X86+pC8IRWB87AkJEN0PliIJyRnjHHBPGSPat6xPLdzxPEht2iKmKV12FwdylT3IyfQjjkYrUdSgv7yOGwuba7lV0SWMyqSCGOMxMAVJ+2MYyKmNHHFZyyz27yI0hlZh5/3hU5AXk45IHbsKyvY2tcqljaeN3aOzs1aQeF4juTKy+ZmGcjb+I+XPbI4rWLuEwGaO3ngnmkC3EokLMdyqUJA4Vv4Qw4HbHOayj6ftbjWdH6gL3rX+n28sEao7MhSQKMmIDCsFXAYYAB4B4qXZJdW0Rje7W4Vgq52A7kKkemAMMQc4yPUVZtLQqr53MNSdyWaWaWCBJVSeTxCSwY8Z9yO/Oc7gATUSOxaeSZLeSKNLXEkLRoz7wZCW2N27DsSck9vebJM1iXF1NFJcrBvHmJkYB8bsZ5YMP6VIj8J2ubi+illuJdg8RbvcXZgF/ADtjJPcqB/OieQsV6iOyuJZLi6KwzMogWFVURcbW2v65PmyQMZx6VH1GYQXZ+clW3nSJZEM2ZAYzJtydi5OGG3Hfzfapt3DbtFazXKQ2MwCzSPIcqQQqEZ7bsuF25/XitF3HJHFDYQz7rcR74CpdUt3zwWVlILbWOG9D3FWXaVZXxDx7lWaaO2tVeS5WLZIzzKUdVyoUBFHLbP+RqytbWRLbcfAj8WMu53bmZxtG4jA7gkYz78UOIZoL6N47mTxPDgRpM7kK4OGwFGMEn07UuJnuJotyxusEbGUrdskchwD3A7ADjJHY0vcWKuQrHZ2/iyEMs0qylYNuGJDOCVPHlUAc5PJxzgSptQiljAhvSJzvllgngeQiIJtTLpnDDH5A/SoVxp72kstvaWLysm2WZYpfmVcCRpdikuQ+CxIycjaBznFbmuJIbki3RvmLlS0UzzAIU2twMjJOQvf0Ug1cqYz2V1Z3EylFtYGuBtmhTACFcuclsLgZHuc+ua8WK7e9lRQnhkKXjEgd1jAwmxhy+cY25yM1reW3S3u7po7dp3aE2yxKoIlCklZGJwpbkFMYyMj2rdcqI7dVgshEyFs+JIN0ahiDnccgYK8jsD70IM7ckWFxZ3CxOYBtaG7Qo0aup3BzwTgjJx2ByBwK0Waz3ENr8yp+YB8Fk8TCyOoBGN5yFOchs5B7895U2nS3Q1MWEafOxObZWlRnkLYV9x3EDbg8c5bv6VHWYOBK0LQSsARDjxQoZj4m0Hkd8Dv7VBKPbQQTNHdrbW6lGliYrsUxhj52dy2WOQVGM+bHsatZtGtzfRS3abJVVprcNMdsr4wpMZyDsHYZ7sDUGU2hEkjwxkJEHUGMIFBcDYFPduSf4ccnnitslzJdIFm1B7i+8Pwo5Y0ZmRif4R+EkNgd+eAM81V3vkWsRTJG09vHLPK0rMkrLK7EN7gZGeGOcHGce1bbuwazMiII5bfB8djGXfwGyHRQpAMhB4zwNxPYc2tlAtxCJIAJFEhQxBsySiYhfEzkDykZ244BPsK0BGs7qKFGjB8J9sCtlvGDY3c4wAuQOcnJo5EpcDWZ45vEUPJbvE5khFqMBVPlX8eVOMkA9jknHGaxUwySwyIzW8QRlWWIAuANoz9CQpyPr3qQYlW4ZJRGrPHIoVCA6HdheWGAfzznvUxdNS3V2tIIrm6JQSPIwG9MZBXb5ck85zyBtGKo2uJZIpwiu8unfMxahcq6mIRpLCsIb+zBB3AsxzuCnHb1rKK5dRDdxwyxq1w6rFNamB/EXvIV3EYYk7fN2XtVtIUEaie2mllWEBImdxGyj3UcjnuAOeB25rFRNdXDT2t5G8gLDx0TJztVdoBBUHnHbGBxTaLOLINva25iDLK0O/aJHjiV5WkPl/tOcDJA/61vnmltriRHWeD5c+EZI8yhpGw2PL3JOwHA4yeM9vZbWFJnWPD3C7XiSLjx8ZIBHphuQB2PYVnPq9jol1ZNqM0GmrvQwiRyXkl3ZwpOPQHPHbFFmQ8jG/s7W+0+6ttRtRd2YiUXUExdVkGACxK9+csVGfNgHipUdrbWk8TXFtNOwjZI2EXiMgH8eecDAGV784PNbtPtNU1N1bROn9YvoPFIWUt8pEEAI3eLIRgHGTtU5Lcds1Jfo26km8Tqjqi20aNYRGtjoqm4mUYYbWnYBcgEDhR29TzWipT2byyXfkYyr01Kyzfdm/ssyst7c6TayPO0dnAwUgbRu3FvKQDkeUgttx9aj2Tt1DA9r09aajrbXE5ZxaRS26GENwTcFdqZ2gcHJw2O+a6e0tul9FniuNO0aXVb6HIivNXnNzIuPVVPkT8gKan1rfXqfLzXgSPsIYT5R9AF+/pWe3Rhq7vu/sm9ao+jG3j7K/8o0Do3VZJVm1280jpopKs3hK7ahcgg8KBlUUYxj8WCM4rett01ZSBruPUeqroPvE2q3A8JXAPmESBV/MgnnGa5+bULqfJWIIWJw07EE88HHc1Ge2NyoS7LyoBufDmND/4QdxH3rGWKz6Ebev8+xoqDe/J+WX8Z/dnVXXXUtvEtjaSxaZaHhLWzjWJcHJ4VRmudn1GSRzIyqrnnM7Zb9OT/SiQ+GCqsiBht2xoEGPy5P614Hjh4C7CTnjAJ+vPNYTnOo7yZrCFOmrQVjQyzXJBmmkwe4AMIP5DLf0rYLeG3OCyo5zkjEefuRlj+ZrINMQDGjKGPG8lPz5GSPypHGfV85JysY2gD79/51Wxe4aRYydqbV7jPGfy/FXrM4GFCrk8+I23j3xyTWyKFLcfu1SMtydmefue5/OtjKAeC2ceoA/l3NSkUbRo2uxZpHkYdsAbRj39/wClZLGkefDVAPXHJrb/ABYbIx6A4ArFiD6AH/XvU2IuYZ/AcHHpz/hQbyfIyRr3yFyf07U354UDOewpljgNjtnBJJz7YFAYHavmYyy555GePsMCvQ/GeIV7EMMH7YrwuTn+LjPl4/nWtY2HcRhvQjLf1x+uKA2DbISw3Sj0z+FfrzwK8Eu9iq+cg4/dZbBx6ueP0r0hSd0hMj9wWbOPbArx1LkeIWCgbdpbgfkKEGtplj2qzBWJ/CmXYn8q8MrEcR+GMEjxm9PfArMRqgIRdvGDxisAFRew5PfHc0zJNbRl+JJHkB7qp2L9e3P86wSCOFAkMSQRLyQi7AT+XJNSCCxJUbsc+Y8f860sC2Ap3NjgYwPvU2BiwC7s+o4wMkn3wOfyrTK7IuWXtx+8OAD9lyf8azEkb+VAZR+FgoyOO+T2/nWLAjzFgigZ8vOB754A/pQEY+Ow275ZM5J2IIB+RJyO33rxYI4WEiLHbZPJjTc7fdmyf5Vml5EXIhdpnI7Q+Y4+44/n61kWnA8iRxv/AMXm2c/8P+dQSZPAHJLI0pzy0zcD6/19K8XaBtafCjgLCg/oBmsWAB/eyyzMn484Cgn6DA/X9K8M27iMgj3C/wA/apsQZAkAkJtUnlnfBJ/mT/Ks4d4J2upbsWWPGFPYbieO3v8AlWkqI3G5VU/w723Ej3P1/Kt0aqzDftkwcjePKD7gVVoujJVTcdm1pQOCEDfz7D9KdKzxy6r1WuwK0WpIsrkgmQm1jIPHspC/lWZYnAUFgv8AD2/pUfpVz+0urzvLt+14+MnaubKE8H8+azkv9c/D8ot+qPzgT7x2BKgE8MD6YH+X+dctqoklRolcs77N5zjgnufsK6W8JAd2IYhDtJ/ED6gVyurkiO5MexWYpyeeB6fz4rno7xpV0PlfUmCyT+GI922OMBcEKDxx9cjNfmL4ijHUbc5/cp/jX6a6kdvFV1H4dqru9881+ZfiJx1E3/0Ff6mvuvonWeR8Z9Z6rzOTpSlfYnyIpSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUArpuiiP2hIpXO9VAI7jnPH6VzNdD0ewXVYy4yodcj6Z5rmxXUyOnC9dE/R/Tg3CViVjxBjOMgkLu/mUxX0S1VdyRlGYFrlVaR8s+X7HHYZc9vevnnTQXEwt33K25CzDIXDMAv1GPX6/Svolu0cLrEqMXSWViSTkghcjt7/yr8yxO8fpWH3blzoy/MC2yHj3NgZ45Jxjt9autbBl0LpliFDrfWzjHv4cwxj35qm0dPloYnJUMgcjbhtxU5HPoOMe1Z9ba02i9D2GppCtwtheWbtGzY3IxmQjPcY3fyrjpxcqqSOurJRpXfcSiqruYHZ5QWJDIfXjjjOPt3rTLkBt5yBjBblfuSucd6odI6x0XWYj8jfxxPH/7CeQOFHtvH4ef72D9KvSkybPFhMZb8LRuWVvbHbj8zW+w4uzVjHaUldM1q2TuhLDvnZJ4i598jJHYcEY5ryHehdExMQfwgBgD74A4/T1rEqJWG5BK/wCEngE/TOM+3H868XOcb9zA8LInI+xxn096uUuSdm9QoXOcgZOV/ng+nofyrYG2MFDEjOQBzjj/AIuf5+tRFlC7jNHIBxuIO8ffK/41JURyoNojkT+8PMPz7+mPrVbAkPJ3W4hOw5BJTIz6+3tRFJG21leNQBnadwGc87T9/cVpWUwjiQqAASeSCMenJxxn09az3bSHYDOMl3yo/Jh275I+lQTcnLNJG2Jo1OR+OM9/uvcH7bqkBtygodyEe/f8+1V/i7M4Yxep3+cHvjBB/rx9RWRySXmTauc745Mqf0O70qpNyYdsigtkAHOSoYD8+cdz7VrkiLedvOQ2Nyjcy4+/+BrTygyr7QBgknP9Pz5zW1XZyfKHJ4OxhnPqOf8AA/nUMsjDfJna43uOcMmSR+eGB/I1uW6APhl/DABJU52/rjj9B+delwxVZG2seyypjPsAKy2BlBUsueOMEZ+x+/bg1BJltLgkFkHGXjYYP144/TH1r073QqoiuGDY/ec/rjkH64qOERHBVNjDB/cyGMn38pIz3963eLvbbvDOv/s5F2mgMxIAQJi0JB42PvU5++P9YrISBmKxTwvz2J2n9ffv6U3NjDr5SOF78Z/Q1ijQXWQwWQ8gqQOcfQ0BJMksaEyKF5/9oPLjPuO38q2rJuTlmA9QrbgPy/12qGiCDIjk8Js4I8Qjg+4OVx39vzreDJnzxxyeuNu0/cEZHf1oCQsScsuASeWBKEH3yDXrxK7ZmjDk92PPrzyef5mo6SDI3rJE3Ayp9x/zrehkbmJxKDxggA/p2z/rmoJM/DYjAkfkco4Dj/P/AKV40aupWeBJoieV27x24yjf4ZrwTMuPGt3+uxsHP9P5+tZeNESAzeGW42zLhj+o/pmqknkUaINls8saqcmJXyuDn0bJ/Qis90ikh4w6nguoKnP27VsKbueSO43eYfrwayIHY4YHnytuxUC5glwj7v4QDjD+Uj1yPcVsCLgFPJnjKnhv8D6mtHgRs5XwxJnjbtwe/opx6Z/WsTDEj/u7jwXIGQzEH/4TUMk2tHIGDeHBcpkc/wBk+fvkg9/p2rXJNGcLLmA5x4dyBhue4P4fX0NbN7Rr+9Me05ySxUE+2Dkc888VsUjBDBgD7YwR6ccj1/lQkjohiGR40QByDC25fzU9u9bo5GlAVHjl5/CBg/of6Vm8S+GAreHu/CVbafy9PQ160ZLneiShu+RtIPoMkfemgvc2x3TqcqWiJ7DJxn/X9MVZ2fUV7YymW3kwzkbimAzkAKCex7YHPtVCQqhfCnli/wCCUbwfcZPPt2PrRldhhs4IyxEhHpyf5n9KtGcou6ZWUIyVmjqrzqDR+pNsfVuh2WoyId0bzxbZlxxuSTAZTz3B/Oo83SmkTIp6a1+60iSJgywakpvrcjIwpORIEB7Ybuec9q58q5QhzuXG7k/4gYz9xzisMsjAwmRU77fEDA8cgA5GfX0+9dSxdT9efj8uc3NYR3G4+Gn2eXoWzdMdR6U0Eh0VNWigQ/vtHuVuFZskZMchWQAD08x5wPSqjT9RspLmSze88CZIpJXtbj/dpVUg7cxviQE4IAAyc57VLt+obrTWVzcSWyrgiRxtBHvuB2/zq6fqiLVreGy6l0yw121hKmNL2FZdhH4WDnJB44Oc81qqtGet16lXGvFcH6fPujn7Zm8ITfKi3u2/cyvDIzEKOFJyuThVUnGcFiOa9EYcbcRxiMqW2H8UrA+diuN6gc/6xVuNC6cvZzPpmqav03M7FniM3z1m5JzykmSBnnysvJqLH0r1Lplqr2MFn1TFlVkj0e7NjIqYx/YTMwIBycK4PPrWqht7jTKOtGO+mvH309SDb3Au7q4tSyzJbvkeLAWjXBGDkjG7PpjjHPpjcJmZvlbeVmLziGdPFVX24BB2fxA8jaMHkEVV2+t2dhcRrrzy6Jrc1yI4ob2ze3LMwOd5c+Hlto9TnaPVsVJvbmRoZpdEWJrkKjLHcr4Qkl3gmMuvIyAcOMqCeRjtVxadmbJxayMkdbeBS9vaxsEK7pC2RlyWjTLYBBOSSADgVA2pMY/CmmubrYcDARC5OcSHsMgk7eT2zirYq7SXKXUMgjEYjA8ceJNIyZcABduFIxnkMR2xUa6cTrHLelpIDkNuGzbjIOMYCc4PbBz39KJjUr1aQ2Ng81xBFbzSeODbSK2Np8rE55HCn17jHFSLmK1nKu6S/OCQ7ZZk8Q3PlKhhjyquSWxjJOM5qFqYV9NuPkkmhkaIoLuNwNmANyKh4PZtucY759tiXDW17HcRyObZ4RC8UhOAzYy02TjYAq42jIOeTkGr95mzRc2niC3MEK3ilPDZ3DJ43YtlchUALBstycD0raGtVMk1pYQ3F9AykLIfEVWOfMyhi2wYU5yR9BivZZZZ5JbqKBcMqArIGlUct5s99pTDDJPP2FaI7W6zcPbSpZWsqBSJMCJiqngkHzAb245Ayc471IN15czW7X04SW9CI4DK37y4fA8qnsAAVB5y3PpWU1rdWMcEjEXMLSCKYxSCJ1JYhWV8Hgg4zxjFbVS5OmyNNC1tBskVLZ283hAEHAAO3hS3B7kc8VGktpA7Q3McLQFIgwKbvKBuBzyAoyvf0bIOagI2TWcLCGEXqTxRoEllMZUeJuzu3kDvtPm5HrTUrK4vLC60+1u3thcW7QwSptbwnDqysqngrlDuOcjjGM1YMtzerFFOzrICY1Dj/wBifMSv8I4OPpxUiN9PvIreTxIJYZ498Mh5UIRtKnHOAV5PoeD3rPbaNdkr9SFlLOilZkDoDalEAWZ+3hrtJLcFucYBIPccS3vms5bkXUs0MtlCHnMreFEGYjHm9PKcFhwTjnvUa28MaY8tsqNfxvGluXkfMjnKx7mRGOCcHaMAc8jvW/qTXLKS8ZbBzJdPCFksra0a9kaTtgxoG5G7GCMDv6Zok3ZIh2Wp5ZMLi1gtbWW6uIIyXR5LkrIjFiWDPtJOCeF74PckYFi1rLLawRo8PzDws20EqqoGO3AB9PYEZx71FtemeprmKO6+QTp1HVC1z1BdCFgoUAhLWLLnk5AJXndnGal3HS/T4ij/ANp9d1XqtkiMQs7ZRptmqbicEL+8YckcuePatJU9nrGo+Ov21MVWT3FteGn30+7RDk6k03T4Y4b3VIJJtrxvavOGuvEQqPMqAksc9hzn71I0fROq7+PxdE0IWMIAJn1qP5KFhgHKqcvySTnbmra06jt9Bg8DpDRtL6egUDm0tl8Q493IyT9frUG71O91B99/cyTscnzuW/lmsuVoQ0Tfovf0Re1eaztH1f2Vl6skN0nYpJC/VvVkl9PGwfwNEtvBG4Z7ysSfXGRj0qZZX+iaNIZemenbK3n4Pzt0DdXBIBGd7ZIOCf1qh8RUGSwTGcDPJ/19q8W5csfCt55gOCzYVcke5/yrGWKnpGy8PfN/axZYeL325eOn2Vl97l3fa9qOpMDf3ksnpgvgD8h6VXM2fw4IxngZx9TioK3Ody+PCGX8Sw/vGz6Z9KMYzISUaV29ZmJ/Rf8AoK5XJyd28zojFQVkrIzmRLgkMrTkHPLEgfcDArJYgiFR5FAwFXyqP0x/WmJNvmcIgzjPlA/TNebDtLMdyjnceB/Pk0SJuY+MkACp3bnIOM/f3rzxJ2c7EZAcnd+EE/nyf0rKPwkJ8ELz+JlXn/4q2bGY8YQnluDnGPerJFW0aVjWQl3kfaDjbkAH7nuftkVmmyIHwVUe/hLgn71ikkKNtVi8nsoLk/XA4rJjNjhUjQH8Upzge+AcfqasQz1Q5527fXk7j/KssgZUsSecL6D9KjZ3plpJJlzyyjwk/XgY/OkVzGygQHcv8Kwru/PPahFiVuHOFGDznsP0rx5NoG7yg8/f860HxSf/AHC4yf8A2kn8uBXqxhWygIkJxuk8zfpnipIsBIXDYbwgvBYd8e49BWIYEHYrOMcHPH6/5Vizxh8OxnkH8ONxH5DgVkXkY87YgeB6t+g7VBJmCcYYgH9awYg8HBOe3fFYEDAG0yZ/vcZ9+MZ/lXrHwlJmZIR2Cjy8f15qQZMze4AOcFyDgfasFBkBDMzd+Nu3j8vtWKyNkGGMKu38b9/px3/WvBD4isJpXkB9M7F4+g9PuagG7cFYBCA3sp5/lXucduMVq3Rxqdij7Lz/AMqxlmMQLyPHbxgcmRhn/L+tSDYcfxZB9s0OQWwp3DkqvIH3NRDcxlA2GmQ9nc4B+uT/AIA1pFzLIgKt5VOSsKZH23tgfpQEqWfwYnZ3RB+FSx7knHc8VGRozEZLdGugfxzOcKx9TuOBjjAwD2qGY43uY5G8Iup3syx+OwxwAGPlHc9vapjqzvuZN7jgNcPvI+yjgUINbS3NywLypGg4SOEFjj7ngfkK0y20G5jdTPLI3ASabeBj0CflW+42BQbqc7fUFwij6bRyf51rWfwlItYGRSOTtEYPsMnkk0sSetM5XYitswOD+7UL6Db359sf4UkISJjNN4ManIC+QD2BbufyqNNNKrRjxBFuJXMak8jnO4/pmiYR90KjcP8A2jMWP+u3tVrAzXaq4itiwODvkBX8/Nz/ACrKR2C7mY49l44+9anErEh5ip/E+FH8+5P51oIiRlDNnJGTIxZv0NLA3rcJjdEGkJ9QufT3/wCdbPGYt+FR75O4j8hUR5Wd9oU7i2CxySB9R6CvfEDIQ0mxic8nbxVWSizhPiqRIWkQAZDdv0rToJAvepmCBVfUoSpB4OLSIE/qO1ardGYBQrvuPGBjP2Hqa16LOTN1MAoDRayISq5OMWkJK/fnkDjJrOS/1y8Pyi6a24/OBOvZ/wB3sz5PE5BP1x6Vy2rzKLZigIUrlmycYHA/P/Or27LzpuyYtrKBggknPf7VyWtScF05ViRwPYHnvWFJZl6mh826iYJsHIPJbPcsTzX5v+IZz1ET7wJ/jX6H6gk3MNvALZ55P5mvz18RgB1NIB28FK+5+iq1TyPjPrHV+ZyVKUr68+TFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFXvSpYagCn4gVIHuc1RV0HSO06mAx2jjn2rnxHUyOnDddE/RmkTyCNpkdnzB+7kKkHaZW2k9/Tt9BivoSSrcwltiMFldWYHknw14OT7DPFfMNCnlMFmrA8oseS59CGPPp/Kvo1hOZ7QqhYqZ2wNxPLKoB4P1xmvzTExs7n6Th3eJ0unLJ8rFLKYxHJMyE7gQeGUg+Y5wfr9aw68Vp/hrqqsASIbRxvYj/wBqck+3BOeKw0mVVBGBIIp1zuYquRJj1J4I9/f8qn9SKv8AsN1DEqrIP2ZvwrZyArMMZH5/8q5KGVdHTiM6DPxVrUMya5cPbjw2WJJ0mgO1wGwM7hz6cfQ11XTvxN6r6aiQWl+lxERiRbxA8cgyDg57njGeCPeqfWESHqu4lhmikZoEMfk2q4Ge+OMEDGBgjtgVHaFXeM8SykAiPnjvwR6EDPHbtX3UlGpCKmrqyPjYuUJNxds2fbNB/wDKG0a+VF6q0m60ksoVriwHzEAP1jbzDHuCe/avpOja/ovUh3dMa3p+rENnw45PDmz3x4cmGHpwM9q/JDqZoyFXGU4KgAdzj6UuIIFhiFyizzjBDHv/ANe1efUwFGW5ePr8+52wxlWO9n8+cD9mzCa2ldZo2jfB4YFW++D2/p+tazNC8m1gYpDgZLkE+wHOG/wzX5j6f+JvVugQxQ2Gt3Fzp8eCLbUEF3GB7AN5l448rcV9A0v4+28qCPqjQZgHUZuNNkDr65BhlP5cMa86p9Pqx3c/nztO2GNpy3sj68GYOj7g498YI9cf9KyiYRy73YxnsXC449RkdvWue0PrDpjqKQR6Br9qs5IUWkkvy8o+hjmAHtwpPaukkhu7QbZombPK+QxnA+nAPp2rglCUXaSszujUjNXi7mYmCeRtuQOTkKQPpxz+npWSzgMJGZo2POZIih+2QO3ao2U2ruJjGSRnb2+mc+3pj61kGdOcj8IHsO/1xjv6E1k1Y0uTY5d6b1JxtGZM549sgZ/XPesgpdsEJPk98AHH1AxkfbFQd8faQKXPIySr8+3v963+IzkeHMdvcq5V/fnPoc/lxVGiyZJWUQDau5Ix6ZLIRkgryc9/c8VtWeRVcqjSDgEw5GR6ZHp+lR1lDDfgOqkAlGyv2yTwO1bAEypSMK+fLtPh4+h9h2/WoLEiO+iKhTKoHqrgjHp2NbjsMaLGAQW7DIx6Zx29CagNIy/idgowNtyu4Z9t3OO3r70EQTb4cUsP/HbOHGPqDx9KgE4SKrBTL4eeB4kZTP2PY96zljWbi6hf+8CAP1BH+f5VojlLxnaTMnJYABhj6jH9faslVEGY3aPBJBjAX0zjv/OliSREHjTEVyXReyzJkAewYen3BxWw+JGCXtyVxktA6sSftwf5VH/eh1LMk5AwGfET++Mjv/yrJJwF/eQzQgkc4EoH5jn9agkkJcRHIDBfcPGQfqOQPrW7wkkwSqjg5ZO4/LjPvWqGZZFIgmWUKMELJn+RxXvgRqR+7MZyBmORo+fyJH6VANyhlGVlbAwCCoO3t3z7fegLP+7aHOTyO+cfQ8f1qObhYgN8uHxgiUbfX++APr9K3LJlQxUIpPB4kTPvkEgcen1qoNitCMeG/wAtjsMFePt+H8hit6tIOX2yqBwRkfz59vpWgMqoOWKtgDbyhPbt2oo2sGQKrLgs0bbSR9VbHtQGwXESpH8wdhI/EU8pOfzPf61s/GMEMwDeUfi/ka1iVAWVmUMACy7ij5+x4o1qhzyyP/ePlznPYgYPIxiosDAJsz8l6HJVD2P/AHG5rFWLMVMbF05Bt5CGP3Ru/wDOtrxXCjLmOZB2Enp9Ny5I7itcjjhbhDFzwJf3qDj+9ncBVbF7mcdyHYrkkr2jdDGyjseDjJ/KtoniVl8RhCTwN+QAfufL7itOzeqlt8kY4/dkTgD6q3mHbuOa8jDMCLe4KE8EKe3/AIW/OpQyN4LugcqskJBG5RnnPbHt+vpWKbApKoIz2YAcD8v8vesEVwzMVjX0yg2n7cce9HUMwMigN6MD2P5d+cfpSxFzY6hWywAPcHk/69awzuUneW7DzAjkfUc5rWQ8Z/dOdp/hz3/Mfl3rFrh41USK7+mdmcfmM49O4pYG1n8ElhlSePL2I5zkjv8A9KjMQgaQK6KOzBNxb/4eR6ckVujcOMLIeDjv2Pv/AEr3w1JyqA45yuVbj7H7UsSaImuFAMEqyRx8n1JH0Ycg/Uiplvq80BAy0BXsd2ce/aorW8blQuJCpyGcdj9D3GeB+tGEgO6ZtyYypkXdgj/iHPb796lXGTOph6yvHgNpfmHVLIrzb3cYmQ8+zZ7VFn0npDV7hp4Yb7pW+mI8W50mcbZMNkB4nBUjPsPU1zLWscrJK1uTjJ3x4JB7EjHIz9RWpY3cf7pfJI2ceDIg3Y9/T/WPeumOJqQyvfxOaWGpt3WXhl/X3OuXpzqO1i3dO6ho/VCl+EUfKXCjnzeE7eG55K4BTjnOaptWnNvI9jr9vd6fc3OEuBdW7W/ilDuIikIKspBHPI4IGahJfXNluaaKYAZYuq7go9ckcr29uK6XSviDeQQmGS4+btWG0w3aiSM/bv8Azx9q2VenLeVvD2/szdOrDOL2vHJ/deyOR1NY5dMsXWcx2ly6ySIVaR1Bl8pV14YBAAV4xg5xyK13EbrG5t5VktY0ZGEbM7BTncAeyEKpO3zZJ4POK6TruDQbfpv9u9P29rYXsc1uJoYdxh8MzIsgEQICNhjtcAEHnBrgNT1caZd3vzFtd3tizrvntYXxERwxJQliuD+Lb3HeutQyyzMVUve6sXc2yeOMGZVVlhaIXRKGRQpLZORuXATjjkN3qNcyFp0ur2UzFkaS1zJv2qfRAvCoMj2Bxjmq+6v/AJqNPClkljhuY1EcaqzGJR/ACT3XJywzgdsGvCptJnvGktRZbt1ndNcYknkO4EugwVUFgoXPGRgYBNSoktnRwtD48vjRQ6hJK7BLYybmiwOMY8xUf3Qf7vtUtbGdpoyEnsZzEGX9yHQoow8YyByApxkEHiqbTF+alktlhkjnjgaRzE2VWQ+YnjgMRnuSc44q20qz0wwmS7knls45oza2dlO0B8kYz4s/MmNzP5Iyvpk84rKSS1di8ZPgbrWNJVgsrLT5tQlul3yxRq7zSKFAzsXOBwPxY+pNXUPSt+qQft200zpyONmKveXgmuOGyrLDEPxcDOWA7etRj1Rc2Vk1jpi2ugaeFLeDZgWqHHqT+JzjuSTmqJrmeYboWeRWXP7oBAwP/G3OPy5rmdWEdFfx9l7o2UKk9Xb1fsvszof2T01ZR/79JqvUkhDCQXE/yVsxbv8Au0O8jnsWPFbrfqy40+yFj0rb2OhWKDatvpVssaY9MsBnP1zXMx2bM2668AOcnjMrZz/ebAz9hUlbeLgMZJfLwHb/AA7fyrN4irLJOy7sv7+5PIUtZLa8c/TT7I9mvzcSFp7iSeUnzBGLNn7j/OsQ7hiRCkYJ5LElv5ZJrafLwwCrjgE4H5UMmCRGWb7ACuexs5HoEgwJG3k9w3kGPbHf271lgMCGbjPIBwD+Y5/nWjxhnaGyxGQqJkj8+wrX4hOApjQtn+1Bdj9lX/H6UGZNQrytumBzyiY/n7/etUksSYilkQM3Jjklad2P/dAwP5VHnIV1jnkXcfwpI5yw9cRrz+VZqkpieOBWCMv4SfAX8wvmP3NV1JRtMpVUGxYk9PHOz9I15rIGULvMsiIO5EYhUf8Aibmi2rs2fmJIwRhktcRg8ercsf1FZiG3XPlDkdyTux+bVZRZF0eI6tkRyAY5JTzE/wDibitggSQksm5hyC+JG/yzWYOR5lAx/f5rxpV9A8rEkYUZP+VXsUuYvK65JaKGMdy53f8AKtXknXDePek9w67UP/hHf9DWW9w4A8OPAwAzkt/8I/zrJkZ/7ZmKn++2xftgf41GZJ4ZpFITdsJIAjiXcw/wH5isWhLMNwVCeQ0q+I/5DsK2ZSNTucxR9sINi/r3NeCZWB8JN4Azu/Dk/wBakgG3RuXTxSP45/MPvgf8q9ZgFzJIz+yJ5R9gAM/1rHY78yNtAPIj9f8AxHk1msYU5Axk8nt/OpBiJHI/dxEDPBZtoP196xETEN4ru6tkAZKr9sD/ABraPKAfX6cCtZcd1BYgfp+dCD3YqYVQAvYKq7RWDHAO0YI9KMSR5yPpgcfasN5HlVSoHJJ8v8u9SAyucDf4an0jHJ/M/wCVYCKJDvWJdxH43BJP5n/CsnO0MJHAPuPT860G5V94i3SOPxEAnH0zSwubjJwARk4zkDP0rU8hJwFBOP8A2jYH6DJP6Vq8R3JwgQY55yf5cChG0bZCAp7gnAyffHr+dSQZuz5BZnwCT5V254/M1oVI4pGdYUEh53MAzf4kU8ReFjiaRiccYVR9T9PoM1g8hhQ+POsO7geQHP2z3/T0qCTMxvI2+R1LA8FUHlHtk5rUz225VmkE0nO0OTIR74HpWEs6MpISSY9suSAf17/kKwS5ZVKKYofTYq5/kOB+dAbjPIzoYodi+Gw3zEKAMj07/wBK173kU7p2diBlLdNgP5nOB+dYuC5BKNJtxkv3OR7en0oziJfMUAJ4wpz9sd6sVubTtRvIsaHbjKJ5u/OW7n71pa4SMplk3Hsu7c3t2/xrTKS6gksgPBB8o7+1akuUU9kCscBUyD9sDn86lIXNkjBmBMY3L6vyR/jWPiMSTEGc478AfU4/xJrXFY3Fzcu9pFNIWVVKgAqpGckDGRnIzk+g+tUOv9b9MdNFo+pOpLOC4Xk2lqxupu/bagPP3x+laRhKbtFXZSU4wV5Ox0DTkKzSsEiyPKj4DD2zivU8WaUpBAZXcjCheT9Md/Qc18Z1f/yhdPRmHSfTskxA8t7q8m1R9fDQlj/8Qr5rr/xU6w6gd4NR125gsjn/AHXSl+VjHHGdnmb82Nd0Pp9ae9l8+dhxzxtKO7mfprXuotD6YjH+1ev2WmMAf93aUGZvoIky+fyFfOdc/wDKL0PTQ0fSujXOqzdxNfN8nCPrsG6RvzK1+e1ttj/ugYmKnLoVB5Hm5PfjPJrydEnjiNvHsyBEI9xy3/Ec/wBe1ehT+nUYvp3l6L55nFUx1aW7kdn1H8cuuOoleI6udDsHQ74dJj+WyPYycyN/8VfdfgZI/wD2d2TySPLJPqF0zM2WZm8CDlicknk1+Sbq3khklglXYqIwkBxjdjsK/XPwSR/+zPRAXVvFlv3wGzgKkI59jxWf1WnTp4S0ElnwLfT5ynibydzt7qQx7uDgNlcDGeAfU1y2syJHGfCfA2HJOBgnv/WuhvJfIhYKSoyOD6gjJ/lXMaqzW8L87JEYEMB2P94A/avlaSzPoamh816iDDarbgQ2ApXaQMZ7en51+e/iMQeppCBgeCn9K/Qevs/js1wXklZjJLJK+9mZhncxPOTnPPPNfnz4i/8ArK594U/pX3P0brPI+N+r9X5nJ0pSvrT5QUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAVd9MNt1DIwTjjNUlXXTWf2gNoyTgYrDEdVI6MP10T9CaMEAsWiGA6BgAMeYIoPPvXfaaD4EAwxZ5oyu3sBtXI5B5xiuD0C38WPTkXdu2xwYCg8vKV7Dn0FdvC7QLFMIzHiTJRh+EAgMhGOPwj7V+b4jN2P0mhodNZSESyKScpdYVVPAHiduw/0K6e8jNzod/EUw02mbAAu4Z2TgA9s9vfmuLST9/clCGBuSwwvcbie+PXFd3Zxi5tZQAMGPDBBwQVm9Mf8Qrgpq1VM7KudJn4i1OG4n6mMohe4jNnGXMabWOCfYcHg5yPQ0XwhGj+JDDKnmKA7Dx7L7fpTXd9t1Y1xiUsLFfNG2187tpyV9+3Pf61MeOOBZYHt5I5Ij4bQS53xyAnhuBjHPHuPpX3N+hHwR8Wl0peLI1ukaWl1HcSR5kAMLZyQc9h+tYyxTMkjG3bwo24AH+OO9SflmAikli8QSuFMpHC4GSfofMP8Kwlzah2tnZJADmRecscAHb2xnNVvmW4EJkBDIEK8lSdvfnP5VHuX8GFRIqPg8hSMpkev6Vvd1jYhV3Z44wo78gf1rTI/BXLMDjBk4I+hwOfWtYmbNUkUbIyywkhvNzhgD/r86vdE6x6h6U8mh63qGnRK+fl/F8RDjvmJwVqlQoyru2K4xwOA/wBefrWWFJcSucKpI3HIxjsPfmrSSkrSV0VTad0fZtH/APKDv4nK9U6Bbagqjzz6a/yc2PcxtuQ/liu+0T4q9Ea2VSLW/wBkXEnl8DVYTbd/aUEof1HevytBGzSEois2CFDMMH19e/r2rJwGVVA3tjdyeBk/y71w1MBQnwt4HXDGVocb+J+3Ugee2W5til3aP5kkgcSxsMd8j7+/86wxvAwCeSfIeRn/AB+v1r8ZaJrepaBcCfRdTvdHm5y1nOUyQf4gOD+fpX0vSPj71Ja7R1Dp1j1DCxYAkG2nx77wCD+Y5rzKn0ypHcd/T+vU9Cn9Qg99W9T9CA7n3LJu2ggsT5se2cffj6Ctg3EsrBGAOMOm7H3xg+3YV840j459Eau8aXst/oVweD84ivGvH/vFP8sZr6Hps9trNv42i31vq9sBy9pMJsfcdxn7CvMqUKlLfi0ehTr06m67m9JRIGzhzjP7vLAfQq3Oe3evV8GRnw53jBfblGHscZrF/ELYKiVlB4Mvhv8Alv7frjisWuFIVLlsN6LPg5H0Pb27H3rmaOhMlBZVIHiiRkOA0yjPP1H+HvWfjzRZaeEhgcMeZBx2O7G7J59PXmtESEeaEui7s8MZFI/7pAOePetiyyImVEcuBkNG2w/oTx+vpzUWJNsd0O4Em0ZztUNz9hj69xUmK4jc4SZST2UnnHb0yPXsRUSR4CQLxSn8IM6Dg+wcdu/qa2GJTtVX2ggbQxD449ic/mD6UBMYJIT4salhwGMe0g/de/6ivVYBcLMdvqGORj+Xv9+1REeSPlS3OBlFPP3BHI/PNYPfgSDxo2jAUZLI/v6HGMY7g/rQFoG2r5lZc9yBkH7jFahaRyE/LnwpMcPFIYj2+nHr9RWgMWRTDNsYEHDOSrAHtkfb781k86FP97iKEn8SEsPqdyj+ozVbFkzMwzplmCSnaSXI8J/zKHaSMnuvpXpvI4VImZoV52maPCnjP4hkEfQ+1E8dl/3acTJ7ON+PzHP6iti3DqHE8EijHLW58RSPXK9/yqBckLJvjBz4sfG3PmBH58Yr2NRCALctEg/hUcfz4H2qHHbwM3j2ziPJ5eA7ef6fkRmsnSYbSzxyeo8WPac/QjigJYkmiY74omfPHhMY3I7cg8E9+3fitgmTA3uYlYdpV25Ht/SofzRQYdJIlH/jXI+351tinVwSjRsO7beRn6j096A2SW8XiJKYB4gHEkTAnGe2c8jt+lZNNE52tKq+yzrhgfpx/rNaDArOzIsiSt/FCxRsemV4z+eay8eTaUWdZAT5Q42n/Aflilgb1weQWbOMHhwfzrzysQTnk5IDf6/x/lUfb5t0oaFjwWAGP0/zz+VZJJIqt/ZzDP8ACMHHsQP9c0BkVGPLgA8EgYH5j9K1MDxtOB2H8X249Pb8qxknQEhwUJ48wIz9Mj8+9YKWcNtkL44LMN3H3HpQlGfIGZYcBezIckY9vWvA/iFzG3ijHC5B+nfOR61l4kncpvbH4gAp49M8/wCsVi0sbqPGxhv/AHvYn/veme1LC4NyyDbcIy4OM7N2Ce3b/XFbUO4h42HPJMbZOft+la/DBjPhyMFHdgdwBNYeBG7AiMMxHDghHB+/580sxc3O2cM4DEAkHHOPyx7CsSiu22TEhHIWSHn/AOI4wP1rBPFBUocjOCjjGfz9Dn+lZGbahMsTBAcsqrvH5e1LC5gYPCXGfKw9TkEexU8+3b3rRt3yLKyoWwTvjZgw7cYxz9/5VuW4ilLpFIiureYI2D9Dj37elev/AMYUYP8Acwc/XJOf5VKQuV3Uk8kHTuqtE8shNoWEUcY3cOvb1LdsZqp1CO7s9XdBqNr8yLQmIxySQKxZmHiSOsZw+ABtLZyeAR2mdbRxT9Ha7HqC7bOXSpo5zKCQYyyErnHA75b04qDcwXtvCTBdW+mMArsPNNEyGUkRLnjcQRjPbJAzivXo9UvF/g82p1r8iba3M0uqfKG5gihtpUSVBJG6w5hOW3jjGNnA7k+/Bom08SX2myr4cNpANvjX3iR3JXB8qlgw5GeACMD0xU0qYL9tNCWbJp+wGe4gkCOzIG8NSmAzRlVO88AsBgngZTqrRuBbIsNu3i+IY8ujYO0qecEbtu0H1PtitV0XkVfSRLsNQ+S/ZjadatapLMBcQyORvjY4wGGQCASxzgkZHBNXFmY5LQ/Lb2tHlkKBJCQBk5UvweMY9KpGuH0/T4o45ozA+JmjjlzgEH+0Prz6DBJzj0qdpYhGlQGOOaRCz+C1w5J2bsBtp9TjOMf51y110DalvE6MwBoxBHvbbglQGOP+8e3p61ILrGAWBUM2CM4+33qIPFkUB2dV9FztwPsOa3QweGDtIUqe4XaP5f4n2rzrHZc3pISNoV2YjAJ4GPtWxQ7dyBwSAq8fz/5VrUZA29/cDP5Adv508Iy58UAr6h23Z/8ACOP1zQGYEe7yAyN38o3HP3ryWcjKnbHg8KfOx+uF/wA6SiGEoLp1jTbjazY/kOcflXhn2oPlkzgAqSSifftk/pUAYa4B4kCHsJPKp+u1ef1rJoVjGbm4KIQQVU+EjfkOT+prFizlQ8krHGAiHb+eTzXsUIiIMESxt2LklmOPqcmlhcyhaOMBbSzbYc5l8sYb758xHtwaz8WXdh5YFIPZELv+ZPb9KwkCsoEjFifTOQfyr3xMZ8CFY0HHJAOP+7/nVrEXNwycg7mPfErHv74HFZ5Cgc49uQB+lRQzkHbhvfjAH+NYmVVJy5DE+nP9akglSS7FyBGig8vKdqivA5ZQHkmmOP4B4af1/wATWpFHiK+xd6nKs53EH/pW7O3h2/Xj+VQSeoXxhEESkcBT/iBXpjMg5kCrz+Hv+porHHl5Hbhe351s5yOMknvQBI0Q7kXLkfiJyfoOayOVUNK+BnjzbQDWtVkYkhkUeixruP3LHj9BWAaJZPJmWQHJJbeV49zwKEGXzAbPysU10R6ouF/N24oDNtBcQw4/FtO/+Zx/SsmaU4Y7YgexYbsj+g/nWssoHmOTngsc/wAuP6VIAPlO0GU+jE4X9T/QCjK2QJnC5/hjHJ/M8/0rxmbudy448/B/TvWqWRIEDSuscZGcsAOKkgzBGCyIWXtk8/z9a8JYhvOAv91MHd9Mg4x7mohuzOuYYJJwezFdi/8AyWM/lmtcrzPkSyhU7FYV2nPoC3+VBY3TFYQDM8akn/2jevsBWMcjuh3nLZx5U8o/KtAxApkjSC2XsZZfxH9Tz+f86jmcSMOJpjny4Xw0/wAP8akEyS4EZKGQ7s5CBdzf/CP8ajyzLCMtEsbA/iuJRGW+nqa98Jl8slx8uh7RW/G77tjJP6AVpjFvDGxhhRnVjvdvMw9Tlj/rNLEXPDO9wP3TXE+4f/Y8fhg/+NuwpFHKCjKqW/HIU+IwH1kI5/Lis5Xk3uZUVVVSzlzkD6Z9/vWr5rzlUJmZQNyxHnt/e/5+xqbC5sdFPmlBdSc5YZJ54H1+ta/FK+VUUZzje2cZ9MD7/Stk4kSA3d74VjaqBl5pAqL95GOMD718+1z4wdE6Duh/bMut3XIEOlQiZeOOZm2xj8ia2p0Z1HaKuZTqwp7zsd3vb+J329gqAKv+f8/SsoLa4mjLWsGIEXzPjAH3PYfcmvz9rv8A5QmsztKOl9EsNIRP/b3xN5Ooz3A4jX9DXzLqHrDXOrXaTqbWb7UwAWWKe5Kwrj0WJcIB7DFejT+nVJbzt6/16nBPHwW6r+h+nNb+JPR2gNi/6ghv7kZX5fTv97bcO4LKfDX25avm+t/+UVNukj6U6bitwh2i61aXxW5/+cxkKPzY18QmJjhLRKhgZfOhOCpznGBXpBhwHLKmMj0OPTg+/FejTwFGGbz8ThnjKs9HY6TX+vuqeqmaHX+oLy4iH/2NbsLeDb7hI8Dj1zmueiijW2lMSqiouAYl/Dn6etaxGzSKQXQBVDrt4A9u4+/Na8CN3J3FTnA7AjPqB6V2xgoq0ckcjk27s2P4ht1Lk3AiHhxx7wx2jnGO3ck1JUTRWybZG8JpGQZwNpKg5A/lUOOQPJGQjq7k/wAO3OPY1Li2xhi9t4spRlj8+3ax43Y+n+NJEpmqSYPIo2y88NyCCPUfyrOWRJEkaEOQSI1DEtlcjybvatrRJLMy28cg8PytGpB9MHAHbtnHP51qbyiRdwAD8D2I7k47d6jInM0XsMapISx44JIAJH0H9K/WPwejVPh90skfO970gOcd/DAz+navygLL5qLwwY45M92G0BQMkn1OPev1v8KYo5egulflE/ciO7KlvxBAExwOc+vHqTXk/Vn/APmt3/g9L6av/wBFy7umUQAM0YJJXdyRwTwP0Nc3qwZsbSBuh3kEgZ4NdNrJB8IJGtuOMowK4wMnK/wk57GuWvcEWchJZXjZQBnkYOB9TXy9LtPoKh821vEZYYQ88BTwpI/n/nXwL4i4/wBozj1t4/6V98108EdyO+e2cV8D+Ih3dRZ//F4/6V9x9G3/ACPjPq/V+ZydKUr6w+VFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFXfSwVtUQOTtxzjueDVJVz00QuooW7BlyPfmsK/VS8Dow/XRP0J0nKrwQqQpkhjSRGLHOQ+SR7Ebh9/vXfMhR4GzGXSJidwBzhmwSSe+Pt9eTXzbphyoikIfaBtBJ5ABUZ/LOPzr6bZYleA4IVreaTaCeQdwxn27Y/0a/N8TlO5+kYbONi0UJJptu6uuS7x4VVXau7I7HHrXcaSBHBAM8Nb2x57HO/PqfeuF0bEttKuAV8AunfjBBHvnAGOa7yxBWyt++5baDGe+FZxXBHKol4nbUzpH446gskPW2oxzKrKtrHHsGccyEZwMHjGePWszbzxyzxyORJuGMt+PA7sfccDHPrXQ9WxxW/xE6khLNCNiBFYEruE0pGfUcf1NaZYo/mCZwplEu5IUbKNGcEEEgZ5JyPbGRX1229mPgv4R8qoZy8X/ACzmpY08RXmymW8PAUjCoPxc/eoVzB4eJAimNiy7klyOTxn8v0xXRpCMiVJPmDKFLBiOeTjPcehGO+RWppS9wIsQrOyZHhJjc5IwBjHYenetVIzcTnJbCG3IRJzM6nAIcENntjB9uP8AGosgVUk3L42ztuO3cQSfTt6Vdz2vhMVuVIwpctHHlnIAwwb/AB+9Q5reFIWKgq8ZBZy/lIPOe3sa3jIwkrFZ4YeUmBwvlUsWXjcOSM+pz/KvJ4sRwS+Xa+do9O/pUmKOSKVWRijp5gQNu8DIz/hUUqHZSCwIwAQOB2Huee1bIzZjI7W6iNWxJ6Y48vfv6Vn5mkUPJ5VO0FU4yff+9/Ks/lALuYSIQFLozHJ4B4H6n0rAr4cxlRJJH8oXccBfQkfWmRBlOF8RV2+GoYh2HOOePyH86x+bfaisIt6KAyyHOccZ/wAfpWbYjjzLiUsG3KD/AAjHOfT71qiiWQHILN5sqMB+2cZPemXEXzPZQiorIisrHhuWBHPNb7Sd7Cb5nTbqa0mA8slvOY3Hbzce1YRx7A5lEMqRRbgZJQpbkDCgjBPJODjgGsN2yFxEXMa7cHcPvUdwPpmg/HTrDS4ljvp7XqC2CgmPUYysv5SLzyM8n3r6Do3x+0G9YQ69pV5o4dgokV/mIsn3DY45HqMV+epEngeaGXEMvKZLjch9ty8GsWjaKND4fh7ec787xjGM+vrXBUweHqaxt4Zf16HZDFVqej+5+2LKWxv7me20y4ha+tW2T20Lqs8LY/CyHn27EipTq8blXxIwIzvA3KMY9h6f1Nfh+F2siJIE8B2ThomEZU8YCkYIOf8AGvoOg/FzrXpxYozqUWr2XOyDVYFm2gD+FwQyjn0PpXmVfpkluS+56FP6gnvx+x+oFcJgDMRx+FCcMM9gPX7Gswiox24iJ/F4ZMJJ/Lhv0FfI9F/8oPQLsCHqrRrzR5uN0tifm7dj2yVJDqOM8Bq+j6P1BoXUKBumddsdSLdo4bhVkx9Ym2uP0Oa8yrhqtLfj7fc9CniKdTdZcxyAg/vMj1DN3H3x379sZqQC64Lh85xuOcH6+g9PSoDt4MnhszxuAdqPlCR9FcH9MeterIYzzCir/fh8uT9RnH5/yrmOkmeUBiFRXP4fTJ9iQfb71sUyRncu8AcblYsuPr2YH8scGoyttVskE482SUB+oI7Z5r0SohO5WgAGcgAD8yp79+4pYG/91MpkkRTznxFww7epHI9OCDWcSs8YNvMGTGEOA/14K88cd/StG/ePEQW90wyc/gcnnkMvb86x8VFbDh4GORiVBz/3XXn9cmoJJTSqG33NvJHuXO9V3g/8WQM49x6VlFcpIR4cwx2zu9eCME5z/Q1gsjRYJEi5Iy2NwB9/UY9ckChRJwWKRu7LgiM5OPofvmqkkkPuUFiGwMg4xx3Hbt6VgyxSHDkAkYyo5+3+s1pWFEf90ZbcnOAdwx9ucH14rb4kgTEpSVf+7twfY8j3GOPepsDwW8ijySskeOVfzgc+mef5+tbPFkBKlPETbzsORgd/Ief5GtaSANiFmQj+/iRRxn0x9MVkzbMCQBo8jDR5we3YH8vrzUEmSzQ78JIsbMOV3Mpx7Y/Ws8kgkmJ17AldmB9x/XHpWhpYJxtaQpkDucc/TIyPy96y8JlJeNsZOMq+f5H/AFzQHu8sVUKyqoP4GLY+23zfyP2rWsiNlUkjds4C47+/PBB9RxzXjyTRty6SHGQzHwzn9P8AWaxkAlGy4UygdvLvH5Z/LtUA9mlKDcV3HtnaWz9cgZGOO4/zrYrh0LhBIhxzjKkfcev3FaTbbVPykrW5B/8AZ5IH69u444rSHfxCHVJ2GNzQERsfrj1oCR4EUm9oswyHnejbST9xwf0r12nTOXU47bxtZR65YYzUVJVdj++TIGSLhSrj3GR6d+/tUlN+3IHkyMZww59sCpBm93FGQLoPBlsZbJQ+h8w/xrM+C4JV+e2VYcH34/x961+PtTnKj+Jt2QPyPNYNDGWYIy78+fb+7OKkg2Sxs7gyLHJs7ZwCPzHp7fao28xEAPPCV7BgHQ/TkZH61mVmiG2OV5FyPJP5sDj+P2rWZhE224R0TOQpw6nI759ffn3qSSF1Y6J0prc1w4wulXP9nCJGCjafKmdxzgDA4OecVULfZtnt7W3ErzQNBDc3DFSDtyEWFdqoF7Bmyc/U1M62ax/2W19nEKzxaNNtuELq8CbwW27eeSqklRldo+1V+pQ3t/ai4th4DoFk3TSbm83mY7j5mPAwduSeTzgV69FLkl4s8yo3yr8vybI5G+VNzfLFbQ+KSJZHMjeEmATE23gHcM4GWJ574qQhhuPHa0R9nhLIPJ4Ue0gbQoPIz5VOOw/FzivfmHntYr35tRdhS6RfLM+N5K7WB5U9ySORkHk81hcTG4vY2gCTbCQlzHcMZH2qpMbLgAnOAODk/WrEkeS61O8V312CMXEsviR2yyb/AA1L4VgUO2NP3gBBzgsBknNXfSsxbpnTnYEySI5fbk/xHuT9PeqpXks7yW2wjzTTgAxPulWIqD5iw8uG8wHPIx3qV0tO0nR+hx2fgrCLfCSS+ZiCx52jByTk847/AGrGv1fmvyTS6xLu9i7ifxlVoSrKwyNuTnPYj8qNcwxP4bSKJT/Ag3v+gGf1rRJGjw7ryaZotuA7sIkx7YUg0huItoW2UsjckwLhW49X7V5p3kuOed0DJayRqc4+YITsfYZP+deFQyE3ErtGDk7P3SgfVs5xn61oRZCpJePL+0Zc/mTgfoP1reqIzK8qCXaf/aN4oBz3x2/lUWuLmVv4cabtPtoyG5MkDAAj6se/3BNbmDsW3sq4HaLlvpz/AMqxkLgSSP8AvEQ4zux/L2+laTc4DbVaUepUYA/8RwP600GpuBCKAuD2BJBY/n6isZp1i5llCE/3mx+W0cn/AJ1hiRiMttwM7U/iP1b/ACrTDLbxsUtmTxT38JC7N9yMn9aA3eODxDAx3e3k5+o714Y7qZvJcQ26r3VIjI5x9zgV5LNK7qG8MMM8S5Z//hGa8w8mfE3yf8JfYv6L+vNAZtCi7VuppZyewkbOf/AtbojIQBBA0IHCiQ7M/wDhGT+tR4435zIkA44t4sk/dm5rY1rufznxM9hIxcD/AMPAoDb46eI6NcFz2IgQkD8xn+tZtdRQttIIbBwDyfzGePzqO8KoqC5Z9oH4XkKr/wDCMD+VYSTRQjYsGFPY7QgJzx35Pp2FATVuzNhbWPxAT+Jgf6ccfnWzdKP7WSKP04BJ/nxULfJIOGRMjsM5H9MVkECceU47cbRn7VIJBkVwoVnnXHLMdq/4A/bBrJ7oKoGN3GCoGQP8K1s3JG/tznv9zWreOSpOewI9P1oDa07scxptUEYLyYH86ji7fO6JZbiRj3VDHGufXc2Cfyzn0rLYn9ze3bgkn+fH8q9LmRgGYknGccED2oQYmGWQ5e4MeceSLAH6nJ/SvY7eCORWWPc3PnkYsR9cn1rJmRZQpygOMA5ZiPtWuScKTvDH057g/QD1/lSwMnbcRlmlJGCfEwD+vJ9McVrxJhy8kcMR4G1cn75b+ZxXks5BMavzs24U5A+gP2rS8xPkRBJcM34cEufYYHP6AVaxW5n+6A8QBZnVuWkbdj8z/hXjTFMFyRGSMgEgbc55H+fJqq17XdN6ahaTqnV7DQlwSFuZlWUj6RjLn9K+Za58femtPLJoGm6n1DOORNdEWNueO/my5/QZrop4erV3I3+duhhUr06e8z6u06h1EEUjKxOGRQoPvz2rTfznS7NZ9bvdO0GzHm33cqJx93Iz+QNfnDWfjj1nrLyw2WoWvT0GzzDS4Qsg+njPuc4HtivnF/Ib2aSfUJ59R1Fmy1xdSNK/f03EmvQp/TpPflbwz+epw1MeluL7n6R1n419EaaXWwe+6tuEH/sYvCgz3/tJABj/ALqGvn+sf+UD1VqUngdP2Om6DDz/AOjqLq4Ax3DyeVfyUdq+WKrOsbPIpEsRwW4Xb6fTNavCi8gZmRCpR5FbsccED1r0qeDoU+F/HP8Ar0OCpiq0+NvAl6lrF51DceP1RqOoatOxO2S+uHm2H0G3OOfoOKr5nKqoQF7deOcgAHvgdh29M9qkzRyARiJAzDA3s2RIx5yPyI+1RvmFaCFJI97RyFjIv4toAG3dnhe/p3Jrvj3HIzKV40uESN3Bzh8jj3HfmtE4JG4PhgTx3OPT/H9a2k+J4ojVVRskc8jP19ua1HciZRkMfdGyOwx69j6GrJFTF4pISuUIJ7lAP0z7/StjP4jFl8xc7jlRx25z61jMZjKfCKOW5Xb2I9/pWKqY4QgjUeITncv4ce3sakg2rG5jkkAbxA/nI4IyOCR60hUJOryKcAbgTzn6AVsgs5NrEiSONmwxyOCf8O1ZrAGkVVZUVRsEn4gxB5z7VVtFlc8mSXAIY+KyDdt8xAP1+navZA2wpt/e+GELuF3D3G7uRVhHYyExhvJEB/FwQO5P24NSVtHlaPEKIUV8St5sAnJOPsAOxrLaSNdlshwWMYEctsGEWOJdh4xnjB7c/nxW6TTp508VljR5Pwsh5I/vEdvp71YDTJBJGkUckpkI/dIC25gPQf4+2akxRNJKlsYzBIiFUJKbdx5PI4Gc49fbuawc3e5solZLGkSSNLGNqbo934RjbwSf9Gv0v8MIZJPh70nBBc/KeLaXGbk4/doQu4j3OBxj3r8+sBBZXj3QjJWKQxhlx5myMHj88/Sv0f8AD6Nl6H6RQKRiyY44wQY0968n6lL/APP5npYBf7/ItLyWKW9abT4Z0iswsYlYcS7gexP8Xcnv3rktRk2WyFmysKqUXdjHPPHc8Gut1m4gNiFRCjqCFVMcEDAGPQe57k/SuR1y3Zi0kSskLiRBtY4JwMg98fpXz1LVHuVdD51ryk5/GSHIy3PA9foK+A/EP/1h/wD9eP8ApX3/AFrPjXOe+wcYOR7DmvgHxD/9Yjng+BH/AEr7j6N1nkfF/WOr8zlKUpX1h8oKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKuOnOdQUHGDxzVPVv06Ab0jGWIG3ntyKxrdWzeh1sT7zoY2xITyRGGJBBHJzjtx9RX0mwnMYtwOWSJ1wvB4zx2/D2xXzqwOQrYXY8MO3ac/wL9uQR/Ou90hfEaZZMECHeq57ksFA7/8AF/WvzjE55s/RsM7F5ptwYYlkIVlMRU5weCe5/wBeld1p5JsI1GGHyzANg+jueP8AXrXCGBo9NtiyyKd0m5mVhzkcZ59/yr6BY5ltyFbK+Ep9uGB/P0/lXnx6xeZ6E+rPgHWEEVv8UuplnXcr2tsy8HDbnc8EevP6ZrmpIY5UnZ9sUBmd0GzAjxwVGB2AA/x9K6/4i2LN8T9SnVzGjaRYcgEeYtIQd2MKcIe/oTVKst1HPDKIYkQArneSxU5yABwB9T3x7V9JB9GL7l/B87JZtd7/AJKmZd0Vs4lieyLKH3IuApJAZl2/hyD5vf2qIlrGg8CJMq6ExGVSJCBncVJyMZGc5Oc1alDHciG4jQxOm5WMrEoq98kDGCQDntk4zUG5tvFQxCSTMz7hiRtp3dlxt4OPfuM1umZSRDubaSaENbxyyRlS3lIyq9l2njHI7H3qD8pJLCyyMBcIBGiqoClTncWJP24GeSat7uBkiRneMSkANvkdQGGCMg8EnsTUa7txIu4ZdT5S74VkAIUcgkY7c45FaxkYyRUzwJFAZCQjswGwndtH9cnjtkCoUqKZ2upU3qjhvx8cdhjt7AV0j6c9vPJ+7WZd37t2xhl+mBheR296gGBoY42aOKac8q7KSAORknHB59vTPFaqRlKJQeCkigqpR3JJUnhc5J59P+da57bw1HhO7luVTdlVOP19sVfRacoGxhEVRd3lkKlm47+hxUqazv55IVu1RpV3M0K7iQfXgeUDsfer8pmZ7Dsc7c2wimaKZS7A7cBCc5x7jjk/btWohzvLo4dWBA8QHBHGP51fyxLFAyhRCRExLI7kseDtYHuTjJ/X0qPaWiiCWW4jVtxRSyHnnljjtk4x64zmpU8hs5lPGiw/+mRGZXjY4VjGqkjynd64/Q5rddRJNLJcZgRpVBVEmDZydq5x6+XJzjjn1FbXj8K1EixASbdu5zkAZ7fl/WvXt7hIsxhBE6EnEahgDznFW2itiIHtwHECq27lQD3A79/zqZEgZHiIWRBk7y+RtA9R6GhR9irtbBChXyODjnd657cfbFaU3FpChAMiruQNlfDHf+Y59aq8y2hi0EMw2RlQpUM7j8LYxnHPJ5Fb5IGIKjE5Khl4I2ue4HHPoCexPIrKIJNIoaNf3/ZyceGQ3faO/Hp2wc+lI7WRtseUgjBErOcbe/p78ntVWyUaJ90UXgkqzqd5IbcBzn/OsY0ikjWcRGL935GYgMhDf3s/zraFngMx8JVBGAHGcEd/zzWueSaYsVjQnO5s5yBjjy1KB1uhfFbrjptI4LHX5tTsd/FpqaC5TA7gb/MPyNd7on/lFWryLD1PoEunsRuN1pc/kPv+5c8jtwGr4fczQhWyiLIzYXcCMn3+ntUCW78IhZBGHUDeQSdx98emKyng6NfOUc+7L+DWOJq0t2X5P2b038Q+meqiBoOt2VzcEEC2uD8tcDPH9m2AfyJrpmk8GURzCSB8eVJ1YA/QFhg+nbPrxX8/pWSRmxGZI+QhdOGI9s9vfNdT0f8AEDrfTL2HTekdQ1a8d+E0yMNerIccAQkN/Idq4an0Z60pff3XsdkPqtsqkft7f2ftryeIrEBHGcMqhWI/Pt6elZiZjhP3MynB2uQu4enbP09BXz7pLVfiRd6fHL1n0bpmiiRC6s+sJZSsOOTauJGB+4TuOa6s3Qijj+bvrSJZFIkhmR5CvHoVDA544rwatGVKWy2n4NP+D2aVaNaO1G/mmi4jKgA+GyfiVgHyPYr2GPSsjHExIdI3bPBXG8D6c+2Kr4rq0ZFd73wXwACgZl2nvwRw2QeCK2/PIcEzRyhsAxlDgfb+X6+lYWNySXlQoN3ixhs4lUhSo4xvI4OMd/atvj7MLMksGeVdCZAQckEZ5B/M1EfVLKNXZzKnm8wRCPvwSOD9u/Nahq1nGCsbXKZPG2EKrkd/KWHt3HelmCel0SQY5YZ2B8wRyjZ57q2ePqP8aySRFfBVouOCARx9MEgkE45qsl1zTAweQ3RYLyXjRhn25YkfcEV5/tJZZYiC8ZgeD4Spuz/3mwT9/emyLl9mVlzmOdcBsP3xn3AOOfcVqKoC7CGaDGQZIW3KfcHYTn8xVV/tHpKp++tL4MGz4kWEJB7jhvTPcYBzzW9Oo7IOhWO9AOWDSxKzjGBjaG3E8nt6U2WRcmrK86kW80cwxg55Kg9hhTn89tYeMSxTO04yUTBI+pHfP5D1rB9csZ4sXVpdbt3CsY3O31JxyPfB55rB9Us2CqYrry8iF0V059OTlfeosSmbz+8AZGKspzu3EbfXGfT19a9MoVD4jKV3ZG5sKPsf+eeKhDUbQpGUjukbB7SKefbDE5/I+tbxqcO5s5EbLkHtu+hUEj+fvSzJPXLYJM8oyMFnG9T9R+vv71hJKIZUZrSR92CZYGB2H6r35BPvWuRrZ8mH5mASfjaErHkf6/0KyM0ZVgWZ2CjDMqj8u/Pc8VFgb0uUmyokSZhgldoLD2JXv6/rWRYyA5RcL5QAAfy/oKjNcWsuVlBcc8MoYEepU/iHrxzWoyWquWiuLmNsAIBGJFI7HnPl55zn8qtYgliR1kwjNgDtjy/oR68+teCQ5yY3ULkEx8j7kHtz7VGF5AwUl/mbgZUGGPIYemCxGOM/oMVrknt5JS0NncT7FAAMioWPGPqv1wcfSpUXci5C62kSDorWpVDMf2beRqyKocZibHJ9AQCQO+AMjg1XX4g2myaKdFtGiURKxLyIVVhnPfzljgk5544BrZ8SnuIug9ZSODZG1pcgHx1YqfCPBVe/r/P6VhNaXGp2lsltJcQxq0KRzT5UFGPmZNozkccEg/4+vSVqS8X+DzJu9V+C/JjdpFZyTTwLmNtquN+WUbBg98Z7nBPHArdaTraSxTRT7X8KMwxfLsniDlm2Nklmy5JyASfU1DVIYYpxFZWWyG73TrbusRjaNtryHep3NtUZzjsT9aGUPN4trDE0ohHhzswebeeQQ0flKDvn6Acc5vbKwvmbo9k+35GOQXF9lnjjkclFUnPoDtP4sc1ZdKi4k6L0DxJSu6y5KjcxG493I/oK521lvjLZTXIN00Dp4kyytIIwFYEuXxjnJIPYj8qvOlLp7Xozp6GLTZ5WTTo3aUTqwyfNzgFvU/bFY11/rt3r8l6cv9i8H+C6gt4EdZEgjaYDO6ZPGf6AFvXgcDFSiWJkM25Yx6uwXdxknGfriqlNUcSmK8AkuckeHZTBAMngEyqHJx3wMexrM3wRgI9PiVs/iuLvxNp/+HAAxg8jn3rzdk7donRzHKmCJpCDww8oyP8AiOM+nbNeyOYR4k9wtuPYAbiT6ZI5/IVB/aMskyeLbTva7PPJFcLw+RhcKue2T39K9trnYxMUEUR97eRd32Mj4yeR6YqNlltpEp5F2eIsDnLYDXDFASPYH/AVsD3EhJWYAcDcE2Z/+LJ/kKiw3scbCWW1milK+aSW6R8Z9NwGP6CpJvtwJSESYONy3SNj24Azj8qjZY2kZGxRlXxHNwTk8kkK3pkHv7+lbpVSKIid9sWchSREgHtheT+ZNV8c19KUMlxp0QA/eCC5dX59Azx5z99tSY7aBDuWNGkAwrPOXI9ccg1OyxtGxLqAKPk2DL2HgjaB6cn/ADrIGUHBCRLnCg5JzjufT+Va7qecQn5dfGmA4Tx1Tk/UgD9eaj+MgDK8pWRsDDOqDPryQc47cd/emyyLkySVI+fxcd/xGtZklZvKNgPADHb9u3Na1eNATgMVPBSVfy9/8PzrJpHjlZZR5VHeNwT9iSBSwueRxqqnz4P/AAjbj8zlq3oBDkxrsfHm5JPbPfvUJrobfJE23ZkM77zk9+x4H61qAt5Tm98a7VTuXxHQxkAjsqHPv+IfnSxNyc1+itsRtzjgqi7h39T2rdC08ys6hEUYGfxufr6AfzrTFe226RLbeIhwApEY7cZXn1odTSNWA2FWUnJYuMY9gFz6celLMXJYjkGCfFJ9zgc/TA7fcVi4VCVnlWOTA3Z749u/r6VBuNXt45Nl58zGVADIke1Tn08vmAHuSawXWbTapgEkdugORKoUjPJACgluDj0JqNlkXJYleUL4Vm7qwwHuCVXHPOBk9h6d8U3OyP4rDL8bIl2qB/M/n9KqJNRnMhW2s4ni2MXM2oLE7MBkAIV2rndk7nGK+dfEH4gdc9M2puNP+Gupz2wQk6hd3IvLVP8AwWnkC/8Aefmt6WHqVpbMbfdL8mNXEU6KvK/2Z9YSRHlMVpF4kxAxHAu9s9uw5/M1QdQ9b9PdIsIep9cstOudpYWryNNcH7xRBiDnGAxBNfj3XvjN1v1XE9pqGvS2lgTh7KwAs4QPXcsYBP8A4ia5CBZbaQmIR5Y5BZsluO/P19a92l9Fks6svJe79jyKn1W/Vx+/t/Z+pdd/8o/S4EKdK6Jdak5OBcanJ8tF7f2SEufzYV871X4x9a9Q+JG+tPpVizYkj0yMWsag5wCU85/Nq+WWlxt2rcW4kjc5bZJtZufQkcVL8SIbSI9mDhstk4798fl967Y4GjSyUfN5nHLFVamrLJUVLqR3AnmkXLSNlmkz3O5ufatbW8c6MswaQ4I2OuQSOfxVGS0Zg8kQVsISRtxxn37flUhg5fCbcDAGOQvPfFbWs9TK9ySZY4vEKx/MOygqAwGD9+w+/wBK0LHHGDsG1VXICOxH2yfv68V7Dp9xOzQWZV/DUv4SLgZB78/lxWbwCFIyWaPaqjDKQSw5IwfTnFRki2bzMJBEEDu7sGywVlxj2x9MjvjtWU5Z0V5hHtlBVSFXc3/F37H2rA20zSk7jvGOAyHn6nHNZNaySMsl4qEEFWKAAg/+EfSpyK5mhFeVH8Jc7VL7UY+QDgk+xz/WsFto7dJkjAGJRuy5C5A9PbNSxYSNdRLCCgYZ2mRXIyeSf+dZJYyn5plzPglkRlwxUn2zz9qspLtK2ZDHiiPMa7mKBVQ4Ocntg8gd6fLFmn8XCOg4TZgY98e1TpkljtCskESh0UkQRkEgZ8zHk+2B9a2xxyT4edvHkJKyoDyy59TnPfAx371O0LFZDGZ/3SeVe+Ccnbk4/L1/KpUUDRwpuAVZFwOQTnJ/UfWpUVvdMyIscAldeFUDv/r2+lTLjSrvx4JWRPN+6Z0l3KTjgduCPocCqSkiyiyGLJYZCk58u07sHc3sBt/X3xVlDa2uwu96saSsN4hhYn244HHH51qGlbGiiiJiHhh1YDHr6N3qzs7aaU3EcAjkieHeRI2xFQdmDn1z+v51jKWWptGLvoQltpUXLRMrhmwJ+NwBPp9sfU5qXp+nxXCWqQIzzyiVGRO6qQoB9ffj6A1Y29o/gqskAlKNuyJS75IALHIAyfvwQa2GRxGYINOihXc6R+JMO24hiCPLu28ZP1x71i53NlEiafafuo7ZxaFEUeHNu7A8EueD+HjjvjipsNgpRVtrh1gkkJVXIAwvYlsHB9cDtmtsNqAFEAguCzLHGyy/gPb0Hb0FbI0uTZyi2/ePKhCIYiQz4OcYxgFuO4+tYuVzaMSH4q28F1JbwLMfAcP4oG1t3OSy9ifQY444r9DdJrs6J6dWNUiAsc7uTgeDGf6favgdzawQaJPEfl42iti8xjuWkUTFTu85/EAeMDjnvxX6G0EfL9OdOGRVcR2kQIKkqT8vFkYHpz2rzPqD/wBR34JWqmm7jkmfxJWVbOUJBkcnAOTj/E/bNcpfNvtlVQBsaTO0A4XaD7fQc1117ct8upmOVOGKgcHLk/b0xXFak4MKgEbGU55wOa8elmepVdjgNYd5nnkkZ5WZ23O7liSDySfWvgfxD/8AWNsf+5T+lfoHWwoM2wFUErhd2MgcH0r8/fET/wBY25z+4j9fpX230brPI+N+r9X5nKUpSvrD5QUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAVb9ONt1BScYGM5+4qoq00AkXwxg8Dv9xWVbq2bUesR+gNIQPZx7jgiFP4SMbH2+2D3H+Nd1p5eOa4OW4t2bzZPmxuA7cdq4HQSfAcISqrA24Ajkh1JrvbFkS/EaeEch4iN2Q3lIBHl+vv+tfnGI1Z+i4d5LyOpmOyHwniEciXU4we+C3B++P9Cu40dQ9lYM2GPyYI7cDn61wpYSzSCBuM7lJXAOA2ftyO1d1085fSrFjxmEqMewye9eZDeR6U9xnzLqzRZLz4ly3DS3ENiOm7KKcwS7XZ2mudpCHiQAIcjvjNUMnTkt6k5020vZkSdQyQ6exAODyrrkEYIPOMEn2GO71+0hl641ES2/js3TWmxeWTYyhr273MGwccDnAye2eTUC6s4B4yRxAJHMBGyEPuZsk+JjykgDgBiW+mAD7cZtJeC/hHkbCd/F/yz57cdPy2Bm+cgmXyIUQRsoaMDOzcScsG5IyQ3ofSquTx4PEmliuZTEoIbYzbsAZYLgEYB9CPYV9Nez0wWXjIZrS0kQq7JbOrOobcgyFJBGc5yMZHBIqLfaONUkWeSWSySVlaOAuYjHkjL7guQA3fuW9ea3jVvqZSpW0PnV6tssEclxPNO8jRkJFudkJH4iDjIxnOcgetaSs1rfePLLHHcKQrxz26OJQPN50bPlO1SV5xj2q5uNMeXxmtUmWSGUrhlK+Id3G0kZcljtK54JFap9Ju4Lea9jtbi9t1l+UaWKEynxmHY4JbsORzjack10pnM0ymmtkuGS2VQ1xLKwtmjAZZWcgqG3EKgUBh2OSRuxgGocXhXu4xQpbkqyeVSoYYyD3HHqMDirKQxDwo3K75QZFiljwGRT6E53E9yO/bvWaIoJW3kEyRnLGQHzAFcYBGcdwe2MVdSyKOOZWAMVQSyRYkbdvOCV7AHaOfQ8Dk/SspnF5PE58IyyK2X27fE4xkDkdhwBnuec1ZNPLbXBhmtHjPiRxwRw2YVpGjQhWkIwdw353ZweO5oLNo4URHLW8gUl3BbYG9HbHkBOQPrjNNorslJbWmYmWN1bYMmInO3y857c84xyOOK0RWEcaRqZIDArqxU7dxPOT2OSPMPoa6qWzm8VYoirxSIvmkRISjD+EnJz2A3Dk4zgVourWC2bw7WR7oqOZGbMQye5GQzL+nH3qOUJ5MpRaRrFK8TsEJZo42h3K4XGMZweT6fyqKbON42luNk0rfvcYHILYK/bt+h9K6G5Z7eOa5M1o7yP5TM7YVSAowUHJDE+Xb9u1bvCZ7pby4mE1wUMbvMm9SWHALMPMCuOSB3Htmm2xsJs46LSpGEwvb1ZrppS0aJBgFj2BxxkYzn2rY+mx7YWXw5fGX8RG/gc8Y9ODk+n510j2ZtUkm2PZxeCpj8JwCmQVC4J3DBAB7rg9+1Q7TTJ2WGys4YlaWQAzTMI1RWHLMT2A55/kattt53K8mlwOduNNSzhaUNGZGfH4dpOOdo+uPSo6wvZyZmJEKrntkBTnOT/hVl1BPa2lpCLC8FzNsPiX1tuCTAMCrrv57EjIUZqv0yx1frrUPkOiNB1HWrt8BUsYWlEbHI8zjyqvrluBiumEZSV2c05QgyALhEuCytFGqO7qWkLDce2PpwOPvUOe+8BBukdGySWHBCk8596+uaZ/5NPVF/tn6017SelUc+e1jY6jedsbjHD5AfTDSA96+gaH8G/hn0sieJpFz1ZfoWJudanKxlsAcWsLBQBjgO7fiOc4GKTxWFpb0rvsWfroWhQxFbchl2vI/L+j6VrvV97cW/S2j6hr10kiiWHTrWS5ADHAclR5QTgDPFfWOlv8AyYeoLi4mf4iahYdKwCGbbCt0l3dtLsbwsxRbgF3lSxZl8oOBkiv0SNduvkI9P02JNL01eFsLGBLaAA+giiCqfzB5qtzI/AO1McLjBH6DivPq/WJaUo28c37fyd9P6Vxqzv4HF6J8Dfhz01HE2o2t91dfqMyTardG2t2f/htYTu9OzSHvyMV9Dg1R7S1kstAgtdFsZDlrfTbZLCE9u4jUMfTvn71AjCeUKpPcDcvIGfr+VbThQTJtTsec55HufpXkVsTWrv8A2Sb+dmh6tLC0aG5EJvUkOFAP4hGh/wA8nt6+1bDIgJ3YQEZJAC8HvkD/ADrVyq/3h34Bx35+/r+te8RkEIikkAOx3H6Yz/Ue9crZ0me7cFxGnlH4jxt9e59frz9K1KCgKlmJ98gcn3z6/YeorI7WBwJJCG/iGftgZGf+tejPZhhs8qPN65/13qCxqO122h0cAncyrkt9z2H9eaJbwY3YMhyQASDsHsCSfpzj1reFyFCxg58w3en29T/yrYATjaMjnB2gY5+5x/jxQEdoY0cGONMnPmfLt+We2OO1eeGXJ8Ykt/CwAJHI4yfy/Q1JVCfMZChweNpPHPr+tGK8lVJB4BLbQP65OCagGmNI4XOYpShBHkbvxxyT74z68VsUycKAzEgeRQcA+wx/ifavD/EQVRCcYbIX8s845rLflvNggEAhGKgfy/pQk8FoVjVmSNVbjztknjvxwf1rERqhURIXj3Yzv2BcjPc+p45Ar1GQnEb+ETnLKOT7ksayVUUDcV2nByW5Pscj/XHahAEaA5LM6cnAULn7nk/Tj2rah7mMkDJJIJ5++ea8BHlbARsAFlXPPfufrTcxOeQRntyfUev2oAx3b3Bx7kjt9Bn8vtWo4lYKVPqQRk5z6D/Xp9ayCFySqqPQZOcfl2HpRkJ278txgEvtHsPv+lAYPKqYbKZ3A4PJPHHGeBRpMkljkbgCHYZLDsMfpXioAoO/yE+Xwztz/wCI8n05x+lFBTHh/jJPfjP2Pf1x6dqsgbGfexaRgqnuxOMAe30/KpUExj2YTILdyNvHfIz+nb3qtB9S772P8AwSP6+v/Oswdr7m2gFh5iw5/L/HtVkQzX8SiZfh3rhhCY+TmfefKF4ALE+gyV5NVqcIVjhWODw1kcrLhx4YARsZwoYnOznO9exFZ/EXD/DzWGLFF+VmJmI8oATtg5J9fTHFary+MMV4xu0s52SKZvAtC0kkZVTlF5IJyD34ySPevWo9SvF/g8mplWfgvyaZ4ALWCWOSXxptscsbBlww2hVC5HkVjnOccY9TWy2LmKOSEKzmN4mD3CRNG/AU+Y44HHHbdjk1C1NFjjvU2MiwSOxSa6UyMCMg+Jk7184PPbtjIrFULRIyx21o80ODv/eo7RkHJ3ELnHHpyeOa14EXzNUYubCKNZ5oohdQI5ZE/DIGwNwHdRtPnBxyT9K6rpKGF+jumI0jiSQaVFu2udw7nIPBbPv61zN9exySZgkjkA/fbVRUQOwKKqr/ABdsnk9icV1fTUUh6X6dUXEcyLpsQb5iIMSMHscAgZ5H0xXPiX/q8/c0or/Z5exb4MsYDJ+6GOCvJUDP4sZH6+tR44mhTwYs+Ec71Qgjv/xd+2e/pWb+JbqpaOcIp5Ma+Mq/kTvA7elYC4WQeQRytwMxHPH1U8/9a8q532PDKspjMv7vzADIKswAGMZ75z6VmELB1HlHZiVwSfXGOT9692CU4QrIFbcQwLADHIrSkXhophcIEG1RkOuOcf8Ad/LFMyT12G8NIw48xVhjcv39Pscc1iIlckygR5ypZEILcZGCPTvnP5VuPlALxeIB+LYC2B74zn/qa1KRJhuYCx/gbzMQeB2+n2qCD1ZmkQuH8VMhfOm8HnAHGP8AE1khjSXIjdHVjlYpOC3ttIwaSr5hJ4XiNgqc5Rgfby9+3t61rZZIVTMbHHBG3cOe3K/b15qQaNOW6tNOtrWW+N9cpGEknuE2tIc/i2jknH8x9alreHAExw2cFWXaCR9DxULaisTIAWK7DIxBx9AfT1/WtqtJ4eI5BtHJB8y59h/n2q21dhKxIlEYZOFVu4ZQVOfoV/Meta5EdmKpNMfUfhbc5PvwQcc9+axL5ZSABuIGEft9MHivEkAj3Y2k5OxgUPtz39TQGbcgC4CsOScNwv8ATvz+hrxsqzOjYYdwyg4BxgE+3c8e9YttGViRpDwrFD/MYPPb29a8afewMRBbBwr7lwfy/wAqgHskrbstEpK4CiInIXPO4HkkkmtsavJGAInmRQzyB03YG4DnOMdwM++PetaskZG5XHqT3Gc+/bHf271qDFCvihAADl43JGAfLkew496siDJgyAKXKq3ZRh89yVGffitiOJHiLpulbBLI/wDFtOOD9/51rYCbbuCkMQCGAIwex/6VkVCExsZliPIyuVBHoM8j7fSguevCu4RgMiBQDnI7cgYxyPbGa32kkunSCTTrt7STOWEblGJB/EMY74rT4kmzIxGgbgjtkjjg/wCuKzLnKO4VthGEA785wQe/+WaixJE1/p7QesYnHWfTOk627f8A2SIfl7sDsMTx7X/+LcK+Wa5/5MPS+pkTdG9S3/T07AFbXWbf5mIn28WNVdR2/Ehr64xMni+KS5Y/hjbBJJ5OewA/zrGWMbyJWIm90cjA989j9z9K7KOLr0NyTt2ar1OWrhKFbOUc+7I/MGu/+T18SumopLyDSI+pbJSS1xodwLw4xnd4ajxFHPqo5r5hLdSadeyW95HLaTo43Q3UZR19fMp5zznmv3es0umyCW3uJIGUjYQSGz9GH9eKk6lqUXUCG3600bSup4EGAuqWSXDqD7SY3r+Rr1aX1a+VWH2PMqfTWs6cvufhGK43jcq+LIR+Injg88j0q1guQEgknU+G4JXnG3+9n7e351+kdU+AHw016KV9Cn1no28ctjwJ/n7QMR2KNh9oIzwx9q+ea3/5MvXumvJL01JpvXGmx8odNuBHcBf+KCTDA8dl3d+DXfHE4etuyt45f0cUqFejvR/KPnlsEN1EJWDYBXxQ5wpOcYAHHtipkVujwyGZn8MMS4jUEgYwOD/Sqi6t7nQ75LDqXTrnQ7xH4j1C2eBlX1OGAzyc1aQXu6IrI5lXAB8NcknOAfqOe1TOLQhJMxdZfmIkg3PmPxUjRNxk9iccevapEsIdlVEaLxMszyLgA55GfTHH86vbOeNYXiuZWKTFQ8kke1YwoyCSBuwNvb61t06za6LT2vgznyFoLkElvKCG/LPPHtXNyljoVO5zf7NL7GEYXcSnl5B5HJyPvz2FSVsiQjgEEf2Z2ksDjBBOe31+tdBHDJcrIjK9qTJvYZLeXOcnA7fTPuazgt5IoWe6RhE9wyQiOMM2zO4ZzxkjnH1xkU5RkqkiggtXcSPazcx4R12Djg98Z9u/rWyGycSRi5L2LJ53fbkqynJ3duTn64zV+dLEUls18S7XjmWMW0gXBXPmKqAOMHBA9K2NptoqNJdzXLMY3RJI2UIUPI55Y9yO3vUcoSqZQC0k8QrKEQxDyCKQAyjJwA4yOwPP8q3PDtWRELo6oWl2w7j+LOAy+g4G7jnNWwsfl3ESSJFOMg7mwdhXIXA4zz+me9ZwLNarMwncPISCVuDFHI+R5HUckDGceuajbuTsFS9sLcgM3KDeStuyYZRtIBJ4wSMn+Vb5IkOxp1gxAT/aShASMfxDO/kgD2qeYZl8S4voGe3iQ+eVeRnJZkVwQc5x2zxx3rdPbyzhBEJjPNEGYtzKqkbuEbsNnOByO9RtFlE1xxz2ii4fw7WKSTZGyOzBz3KDPK8HngcCtk0EouxBaSQxR7iVjiRmTOMcZPJGfxZ4rZ+xrvUp5Ejj+cs5laWJ0lVY+69gSCW9Tj25q1n0fwJVtbdxHiUzSnaw2gDc24/xMFGdo7Dn2FZOSNVFkCC1ayJa4WVGjiIZ1lyYypzzwO+fr79hUjTrCNkkiSaMPMCUiY7jlgcNnHJGP5jFdBp2gWkEby+CssiRsyrOhQNlcr+LjBU55Aq1tbH5C3nuUtN7RqpSESRbMk7ty4OVABXHFc8qnA6I02cjNpF/qGj3gs7WK5nWPw1EgCuh53eGO3pjjtnHfNfa1iMOhaVGR+GMKQee0EYrk5tDvJelpb66FwPK8imKQxPLIx7qreYgKDx6j3JrsbtAmi6cRkhRwSP/AJzH6e9edip7ULfOB24eGzO5S6pKI4EKZOIFOQvrk8VyWph/l+A+NjMDuI47Z/rXU6m+Rh2LLHCqYOQMnJ71zd3gzQRnHEBUk/nn+tcNLI6auZ8+1Y77Xew2M0pyp5P4RXwT4i/+sr//AECP+lfetVDAPG3BSXB9Oce1fBfiL/6yv6/uY/6V9r9H6zyZ8b9W6vzOTpSlfVnywpSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUAqz0HnUEGMg8H9RVZVjonF8hAzjB/mKyq7jNaPWI+96CwkiZFIz4Ug2HjK+Hn+oP619ClbbeRyrsLuUJ7jdjB9SO+7+Z+hr53oKyR3CBQQVJ2gcgBk78fQ19AuGJhtnLqqOkZUe4MacjGOPIeO+RX5ziN9H6Lh9066KETTWUkIb94/hyMxAxkEbSMZJ9j2PvXYdKybtKtFZvNiQDPBGBz9hXBp5bi0kukCFRAxYnJIHc/UcdjyB2Nd70ygS0t0OAVe4UgcfyPb0rzI5SR6cs4s4rqS4hi+KYhuLk24uekI2XMalXMd9LuDMeUwH4P1+1ZKXu/HU2tuwJEiSvJmPCf2jbeM+XgcHJJyO+feq4om+IzSia4+Zh6Wt/DgSAFJA19OMmQkbWDbdo+pPpSSdli23TwJNcRMV8e5BkDpgpIWCbSNyk5PcDBGDkerfKPgebFb3ixpzSRWlm4gZ1kGHdnBeJef3bIcBVzjBZcdwCaiTWmoxa2iXy28L7wJprhiDFE3BYsBnxOFxwQR96mSxLqQisLySNprqDY8kL5V93GdxHHI4K++RwK0y+HYJ8ol1ehy6RWhZpJni2HOFlC4EYIxvYAZIBI4q0XmRJZFe1usKtDEqTQKAhu3cQm6x32qxUqqnGSvtms572BbqXU7iSGWZEjSO4S1UfMmMkHzeVi2TyOxxnHqby0tEnZdLu4o9TYxSeI74QQIOFdztJTkKOCCSc4948rFIbeS7UO3yeBEtwivKqgKpZCQNq5xvUebIJ78W2rldk5y80W3nsbTxAq20V1HM0UQVdkYGChbuCSR+EjHbnFQrnpF/AnutNuIJlAE0jNJui8EIu15WjHhg7jtMe7cpHKg4Y9NJa21zJFJGt2tusYWeAjd4u4sQpJHmDYHY574Jxzumu5tPubGxRbPM6N8vIG8YN5hvh/dttcsv4t4JypBxtrRTaWRnKCbzOBvdDvtKimuULl5rbxZJYHOxoj2fJ8/4geCcjiosthcxMrWlwohkLPI8RxuU/iYE5I5zyO3GcV9XispbMNE8FvH4kkniRi3ztGeHjQOfxdtzAntjGMiBb2kkyH5CAFGV3iWOPeuwsU3AD8S5DDv2GSanlWORR8vaTfNEL6OKKNJM753ZQWCjapwPbkk98+5rXcwwyLbKvycl1cXLrAIXMjRxK2GLsVUjcSSo77SO3r9I0np6xveodNghk0WyMkjMHuJEFuhALeFI3nVCShCBh3GM1yENpqvUN3rNp0No15r1pAWhVlggICnuGeEt5SynDB1BHc1tF3zRhJKLszHQ0ez1a2LWXiwFZ0Rmk2kFoG2gsVOQN3I9sAEE8UN7qmmfsnTir5u2tkeaFWS4eaU7gZI1HYcAYAJHfkd+1i+EWs3MjjrLqbTen0hJ8K309FvnyeSpjiKqByfxyk8dqvdJ+Hnw/wCmsSWGgfty5iCFZtan8YBlx5hbxhIgc/3t/tzVeVowzlK77iFTqz3Y5d+R8c019Z6vvpdI6P6cvtWvI1Uyi1t8hBwAWwMIB/xMuCfauq0/4C67Pe7+seotG6djEhBhtZDql4QcZHkJiAP/ABSflX2C41+/v7dLNbgRWUa4S0hUQwITyP3SBYx+me9Vgl5CmRcrxhWGV+n6/wCNYyx7jlTil45/0arBbWdSX2KbR/hb8PenjFJb9PTdR3kBA8fXrppIxj2toyseP+FywrsLjqK4azTTYZltdNQERWVlEltbIO+PBjAT19RVSsaz7XILgc5cH+h4/l6VIVMc7m4JBCKF5/7x+n9K4aterW35NnbSw9KluRSMCWZfPtRFI7rsyeMflWwW6CPaiMqcgBSFH2OPp9a9UKoBjG1vRiCXP1LHnPfmsy/oH5I4IIyc9/p65rnOkwdQu9lwI+/lAUHH0FbNyDkoAuM7n8q9/Y/lRApJwrO5PLOef+X5VuClclNgYj2BJ+nNUJMAwI/cxO4Kg5ZMAA/XGPT8q9RezKvmIG4gljnv9l9O2K9Ybv7TdIRkbt+fz74H/SvVCsCyjfgY/dknA57igMSADkjA/wCMkkng5xWaZGCxUEcZIxjOP09KyG+NiQAx9VLZJx6fSvNwA5DAk/xc8c/6/OlgYHkncByeDuGe31H+dZqoXG4MVHHOPT2rxYuDhVLdiRgAnH6/pWW0BgAwbHcY7/XJ9BzSxNz0qFIwZDxwDhVP6d/+deE5yFIIGT27ngZyfyrHYq/jKpkkkKSxzknt2HOKY3BmUKrbj5GJ4H5cdj278fagMgpZgQwwBgDGW9MfTNYMqKzEHYx5JZt5Gfr6f8qMQXUbnkY/hUNx99o7/wDOvWyv4QI04wrnHvxtH3qCTwso5HGDhw3mI78Z/WvdxKZk3EZOdw9fbPb3rASKVGzc3J5UYUevrn6dvesfEUSLvCxu5BBJJJOeMZ57EHmoFzINIXOck7vbGfyxz270EkSP4YYTNxgIC5BPvjnH3I/KsSQyFZyJ93bcmRg5yP6c49K2AblVCS6DgRjKqo99o/1xQGSuC4DLtIOXIcF8/YZH6t9K9aNtwJIaPHIGA30yecflWtXCYTl8DkLyc/5d++O9ZZQZZmRAvcsc8j144/wqQZFBn8TMFJBA5x9j39Kb9p4IjfPAVdxJ9ec4HqM1qSRJFV7ZS4OBuztB5znn/XtWQlyCEZXD42MARgAdsev3qQed2LbFDDADS8A/UfzrEP4mV3CUZ8xyQOPrj+Y4oQzZwCccA5B9Pp+VenezbS7BsADa3rngZPbGcVIMACSxkfaDx5UA4+5/rj9azhQ5Vowxc92ySW9eSfTOfpWrxcnK+ZgeCoJ9D+nFYSXDPK8bM8uCcRoScfmOPfgn244oQQfiKzL0BrOSxlNvKF25D5247+/P0FQpp1ubI29zaBSqRwIkcxPijaDgtlQCxb1wAAM5zW34gS//AMudXDIPDaF1VS6uWOAPTgj1Pf7+tYWsNyI1lglNxFFaRSGSAMEnLLgrtbkYXgL6cfevZo9SvF/g8ir178F+RfxDZeLA3jrHgiOFwqyZbLMXPG7I9uMcZ7HQLSRo5lkS4WKVvHMSRNLHb4fhZvQLtyN2R2PINRddLR6fjRJjNOpUo9wrbIrYMRJGUXJDgeZc+49e0i0uxPbJPLcXazpaxKpgwWXztz4eR4zYJAznjHbArWztcpfMxMVvcQXEN2FmazhMskcjndGyjcjIeWbPAHpt4IOa7DpwNN0poPmXzWETGRZPE++MgA+uPyrhLu4tru1h+Se9aJI3E5umhLZ2NjeydmxyFJbK4ruOlst0l06wKjOmwhty9zjHoOO3tXLiVamvH3NaDvU8vYuRIvlRGkjaPOQzc49Pp61plUOBu/eKCDhjkc/XuOPrWRmC7UO2I5A8wUgjvgHtn74PHatUgKqxK7k9ASDnPqvv6D6E15Z6Jr8N1BBVhEGJKuviKTwc54dce3Nbt4CscAKoABByB24yOR+deqGJPZm5ALZGfqB74yK8Uh9rtGUc5wzeQ/8APHAokDJo41bcERnX8Pm5xjGffsO1aWG2HLbSVOWbGCOeee/J/St204A3lyB2J7n0OfyrLPhjzBjxwO/9fyqbEEUR7ArPIzHbtHieYfYevvXhn2xkMjOvYnBZT9eO3rW/fscEAA54IbB+1YZDH8RBP0Ax7n/XvSwuasRmIOhjY4ySCAqj9K1PbhU3AbsHOAQD+nINbCVzuyqlTyy8nB9Sf9d6wZAw8uCMEhzhQP0/Olhc8NwzAbsMOMb1KnA9M9q2ho9pwGjjAB4BKn8xwaM7AOxIXKHG85x+nrn+laVbwgzbS/bLQMTgH6DB/rSwNqr4m4rslBOSUbkf5CsVwcEgp5ezHt/h7VgoScndNG20kNvGCT6c8HOK2FcJtUvt5HJyCPUevtVrC5jl4yTGAwXnh+eB3/wrJZsOcg5BznGOf+YPP2rAOELE4LZyQBj6fnXoXcpOHIXKnDDBHPH9TSwBHiDjayAnOMkKv1X1xketZR5G7wyCNoGd+HBxzwe45rQoUkbSnK7QVOOO/wBj2NegO0Y+ZT6ZWUqV+xHP6VDBviYyE+E2c/iAG1gfqvr3rNsjGV8FiCCcAHt2x29ajlGKiOQlkVsEzrznHuBz+VbB4ivuJKg9iPOvvj3/AFoDaFTGJCTgkDK4+339RRsFVBYx8ZKrxkfUVq+ZXJ4ODxheCfuPy/nWe9PLtO0Efh7c8VJAlV8AoOCeOSAfua1tuc/jDlzglY9uB6c/p6VkzAcbRn1GM7h9D+nf2rIKHUgMGxg4J3A/WgI0iocMxU7OC449Ofv9fzrJEuI3WWGQb+NhyQF49CORmtjRhSPIAg5yvIz+Va0CsuZRHI7EjByc/wChQm5bTdUXd7Ztp/UVrbdQacy7TBqcCXcZH/iG4fl9K4TWPg58L+pC0ljZ6l0VfN5hJpdz8xArccmCUkgfRSK6IlYj5C8YIDFXYED9fuK1SlcOuE3jJ/FjuPfGRnn3rpp16tLck0c1ShSqb0T5rqH/AJPfVdhNLedCatpPWcWSflI5hZXIXGAPDlIySM58xr5xqsWo9L6gbTrHT73prUBCQBeQPCM5C5VgMFQMcg81+kkdkIIkIKjIyPX7+n/OruHq3VYrY2NzIuo2GPPbXkSXMJH1WQHj7V2xxv7438MjklhGtyXkz8z2Gqy6lDsknkuiZGwOHDjaBgHucd6sjJcX9v4fzCuzMNokJKBF/hIUYA+/JzX13UegPhv1FIJX0q46OviWZLrQJQkW5vUwOGT9NtcpffAfqWzUSdFdS6V1RbOd6xXJ+RvMZ9QxMbHgD8QPtWqnSnuyt4lGqkN9HLTRqs9uWjhghiRFUpJtULliSGyRwTtKYzisIbpY4I1gtl2RufFjIHnXdjbt9wD74xgVjqtl1F0sV/2x0G/0qRVDfvrENHJGMDmTlSRjJP0B9RXSWsUc95NMt549m6rIt/AyDapjB8wPIzwuAvoT2FJXjqWjaWhzyaatvLcWzWqK8k21EK4fbjgqc4VcccDkn6Gph0iaOxuJ7i1Ie4aMR5XxAcvyNwJAOCB9uTXU26W8F4saajaESbpI7Sfck5Ow4Oed6kDPpz75q4+TK6T4V1DaJZPKCdoKPKMAfiLDaPQYy2M59KxdVo2VJHD2/TFzdTpgRrOECRrGwMgKZLPgDAA4zgngAUt+mJG043Vram4m+YjdpA7AqFJwAQPKCWIxyMjBHY13cD5VwTPHHaxgv5Gh8z534KndtzjH0PvW21ElrFZ3kdvi3Fqlu287MEdsg7cozkHgZ96rysi/JRNNvp8lu6G800SSbo3U+LsLueAdhzgDI7d8Z9Rmnh0kt4TPErvPI7sTvHityHbOCCozgAdyTntVtHGJNPhgdEVGlQS+PMNjqSWDEn3OBgHnIA5pI0UlmzzwtbJb2wsra38IqiYYsQFDcE5yT65x275qTNdm5W2scixx24MaImVijdhJgNzu348x4PlGe+KsNOu7i2t21CbTYFwZJAJ33G5ZG2Bm2ncOWHYc8gCrD5dWjZr6WO5hG2EXXhiJF7FRtJ5GcL9cYqAdPmtLpms7kRXa7g1uoSR2yDgEMBtbJDcccY5zVG1Iuk4nmr69NpunahrFxnUPBs3lYSgpuBxhCCu5F5XEarlicZNdxq6E6VYo/JLsGyNuf3aZ49PXj8q4ySKW2yIp7yBpIPCntvmvKnJbxCo7Eny7j7gD3HcaowFnZyMpP432hjkZiXjP3I5rkxFtjI3pJ7eZyjjxpLlEtgY3ULHK0mAWxngfb6euK5LUgyoNgOXQqCTgAnvjmuwvjbx2J8QFNRK7mm8VvPhQFCA8DAPHbmuV1m4DIH86RfL5Rk5A2jBHsDzXNR1NauhwOrMRPKQwchwOBxkDmvg3xE/9ZH5z+4j/AKV951XYu7YMIMZA759s18E+Ief9o3z38FP6V9r9H3/I+M+r9X5nK0pSvqj5YUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAVY6Jn50YGTgf8Aywquq06fIGpxZAIJHB+4rOruM1pdYj7lpJQyLsyr/uxg4GSFA/SvoNukb2ERTkstrJz77XX2HOMemDXz3QyzRIRklBEnH4tmTx/KvoljGx04eEu9RbWpPADbd7jsRz5sZA5r86xOp+h4bNHRgLLZ2MkWQ5ghEmAVJIbH2xjnj39MV9A6dO5YnIPM05OSTgkZJ/618zt3K6aBIxJKuuGwOzAAHI9B9/619O6ZxJDDjexW5mUbzycIO9eU1aSPV1icP160lr1rfTqBPDB0xZu9vHI3jyH5+5wY1XzYGTkg8eXvVhIJNLmlku7y9QKUYsXknCYYBjtxgOFOVQ8cHnGBUnqi1DddXc7QwsYunbI75ELgj5u7JUA4UHAyG/EPSvJtNljg/Z6WtqsLOu6ZVKOoUZAdNxV1Hfeee1ehtKyXgcMIvPxf8kab5fRhFpslyWQSyfLhwFedtwwzIFQI7KScOck5wDW+BDDNePZS2kMU8g8YrFGreLtDeaTkhdpJOMcjnOc1Fks7WxlvbnTUMY8LcMo5Lg5wMk5JOwevoe1TmjtbdWadBbyRyFAzSAxp5QzB8Elcqc5zjnmpuTssjCaa30+2+ZKQQLOGhEedyzEMSpYjDK2WIByRxwO9R7maOPw7qO2tTclFt0ign8eSRxBkxFVOVQgDLAY4OMkEVEuepOnL+WUeCuvXKfufAsoHuU8IxDcxlAAHkCBskYH5VC06HqnqeS5n0vSodNtLdd008UQvJUQNjbCMiJmx3Jcfi961Ue3Ixclwz+fYt9KaTUUjitmvLiaMCF5hYFkG/wDBgKp2sBkDA5GSuCTUE32ji1lt9JzdapMoQQ2cLySlT/FvXcqk8jBx25rfpvRjadqjX3UnWMkuoWl0ht59L1CaScJsZZNqxeHFG/4VB3PgbuxNTbMaDodlZQafYLqL2UBt47vVmWU+HuBx4EYSHhuQSrNzyT3qXsR43+fOwhObytl8+ZXKvQdRm6hkuLPo7RJIHtUBE92hvYh/A8fgw4UJnzeaQHPPrirq46F1HULO0l+IOt6faSW7T5gWVpPEidshdkPhrg+oLt2AIPNZX/W+r3sIjfULh4hyqL5F59BGoCj9OPSufmu5ZXLt4jy5ySAWb2/T8/Ws3Ud+irfyWVNfqd/nzgdDNY9JabYtp2k6LBqVk6wq6X8KvbEoc5FsMIGJyckNwSO1L/qK7vofl5pWNspOy3iRY4UPsI1wg59h61z4Bk5SQAfhJA9fXnsP51sESqN21tpAPfd+QJGB+Q96ycpS1ZooxTukb2vzI22Nxkdz3X9fwj7fWsQxcLyrAchi/lP5jj1NEmzImzOVx3Oce3rj9MVirs4yFLsynHOQfuaoWN4EewLIhlJXlQDhs57Z+lblOw5RVTPptyfUAe9Ro8g53/kF5IP+HatyKythUj2fxA9zxxgDt+dQy5s3+KRu3OxPGeAT9/8AXetitggNt3Y9fX/X+Ne7XKsHcEY27c9x6du9ZAr3Xz7VwMELnHbn8qqSNuBg8AcDgZPt5c/1963bYsgqjrjB/eSY3fcetYJndglFXIIKjGB/Ws1aJc7FAOCB6g/6xVbEmYkzjY31Bxx+lbeSDvcJzjB9f9cVpMkjA7c7Mc55z7f6+letG75Z8kjPmc5AP0wPr7VFhczJjxnAdh6sMff6dq8Mskh/vY7nBAAzzz+XtWICKzb2DH0xx7j/AF968Nw2CkIMrAZCryc/Yf671IubQuPLvHBzwO/+vr7U7/w8j17D/X+ValUuojcAeqqece/bj09/SvCyRkljtP0Ytj8u3vUEme7OeC4GAWx+HOe+Bkd6ZxKyvtj5BG1gR7+nbnjB5rWxcjEe5k9u4/lXrExjMhSArg+QknPvj8vQ+tLC5kFkIbYqKoPJchcH7foa9ZERv3jiYBh2XaB9Mnn07j9axyhKmNN3HleQ4yfy59v1pl87QwRgdoWMAnt3zyfQe3eosTczMpiXI3WvHlxlTx7HuePb+tYlcYCR7FXGS655+36VgrhdoIAYY53Z+2f888/SvXkITexZT3Aft2GD9P19qAFXctvmZxwu0ccY7f6x9uKMUUquxMfxDPfA5+9YB2Zu25fRxgLjnsSft9a8IJXaPKD6IQqjg5wxx9uxoDPttHCFs+UgDJ/L7Ece4rwBn2l42b05fAz9f5cAdjXiiKIMWLLkgsT35HbJ9P5cVsVmOSEyuQQWUsfz9D3FBc9bsRHkKBjy8HH1x+VexoE80iA44DMMk8Zxz+Xbim9V8oJLYztQZJP2H1rUsi+JIEULIvHl/eYzj24UZx3PpSwN/wCGQ7pMnjsCc+/0/pWIkLcFBu7He2cH/We5Fa2d9w3+VQRhpTuwPXAGB/0NapHV49wRpF3Y84Cj7gHA9v0oDexVjtd2LYztXGQPYAH8u+e1atxcbGWOJVByZWDYB7+Ufc96wdDJGYpGCw45w20Aff3rwKi4AQuSc/vDyM8/xc/n96ulci5j8wxQOwMgGCUJ2Juzxlvr9O2aFriVAoYQox5SM5Hr5dxA+nOOfWttwFtYDdX8sNlaqB+/upRGij6Fu/5c8Vx2r/Fro/p4tvvptXnGSFtEwjH23vjPJ9Aa1hRlUdoK5jUrQpq8nYtPiZNFp3Qd7CCBc3aSIspbGAoXPOC2OQOBjJGaqonuYdMjdbU3k0kCi1Y3HhHcqnKMckbh/dIAxjvXy/WOu7v4j9VSKE+U0mO0eJEhkDqsCjeyA8FiWwzHjJAHAFfTUmg+SyLkT3YIzciJCm0KDlWXO4dgB75+9e4qLoU4xlrqeLyyrVJTjpoWya6RJcael3ZeCIFbdfR+Mu/B2wsGBOCeAcqQe3aqO31GPT9LNxHaG5vLpQuyOEr8tEWOyJpecYILFT5iCPQVYTRMunSWgS6uROpedY2JA28gNtYjJIHJx+gzUeJLbV7eEG4gS4mVXMkUfhTnJIKyFBmVCcc5OPTtUJIu73MrrTZI9JvZXubGytjE7CS1vPFk8R8kM3BJTJ2jjhcegrtel5lbpPpssDGradEfCZiDyCTyTzx+f9BwV8bm0Wf5IW11HLJJFbNnYyMCVwxH4iGIHPYLWXw868gaOPpDXZNL0i+srVHsjPfqsd1G7ErHl8ASDOcEjj0zXNiKc6lJuKvZ+hrSqRp1VfK59RaQxttcs0eCHAQD6ZI/XkmsI0G5WXG7ZtJRvKF+36jj0FaLiG505I5LqyuIEAKq23dE3PdXB7d+awWbcCZdkjKSpZXyFH5c+p44xXjnqkvxXdX3ocAjCo2459tp7e/5V6khKLLC5CJjkckj2Pt/yqPA0rcRLIFD5yylVxgcDPfuf0qQojbaZSzsxAPhtg5OOD3OOTmoBg+MqI2GEB3CLnA9jjk/Ssj+E+FIQ2eSqjy/1NaWkSXa7eHcFWyrZwwx7EduMitbMLluHG4f+/8AO/5OCG/6VINnjFCvitGVxgYG0kfn/nXrMkigN5VOM7gSpNRH8XLsVlAJBJwJFIzzjJDA/Qe4oZ5ixCxBtmSBBICxHb8DAc4zxQhkkp3eNfDGchl4JGe/3/5V4FcufDlww7FlBOOPUc+/vWoXUQcK7mCU42pIvhscj+fr29q35wwJTDAcbxtOKki4kJbyuVYNw3OCRWAiG4lsb2HIQ4zj9DXu7OBxzgAA/wCvpWLFQDkEDg5HPY+/pxQXPFUkpzuX8J8Tz8dxz3/nWoxDb+7ynpt2nB59FzxWwMR5l2sqEjjgD8/y/nWpy2DvDgAdx5gQcfr/ANaki5lmTHnCkcnAxjOfTHIJFYNErsjqctnBcjH3Ge5/50BBG4MOAQCTgjt3/lXsm18B0UBcbSvDY+hz9R+lSDLY53sD+L+E84H0J+oHr61krtuG5SEAzhB6Z7YrU5K/gbd6gSrj/wCSFbkl3biVYBe2AW/PIzSwM45BK5Rdu5SCQwx/WvXbAPlBAGSe+f8AE1rIjlTkx3ClcjOcg/atLAKQMtE3oHUgfTBpYXJJlJGC/HoCMgffP2oZAcggKCeNrY/ke/61H/e+g3kEHIO709PWgkDDGxmBHA25P8/z4pYXNjSGMmQKWUnJKDnj1wayR1clUw2DwvY4/wBZrWNpXjCbvxALzn7dj3r0YYKPK2TkqBjB+3txUWFzYTtBKMYyTnJBJ+/+deMiOwcsW/uup7+n+NYg4KhHIGeefT/WKxYMyk72SXnzRHBP3Hb+VCT07kBUtuAOTnsTWssCMLviIO4YIJH+uK8d5U/jV9vKjAjOPv2P6VrjmVlIG6Nzkg5zjjv7VYqZ4AjVFBwAeQAufX/I5rBzjG1XUrzlvL+h969GNwGQVY4x2/MVizeHjdmPtywIFSSC7MwjHkDLwfQex+1BcTW6k2shVyPOFfA/P/Kte4ugyygDO1lxz+vHrWtwqgHcExzggp/yFSQX1l1hrNgphS7aS3YbTFPGJI2HsUYY9uKi32ndDdT/ALzXum4LC4YMTeaS4iy/o+zsWHoc8ZwPWqZChCgEqfxBS3IH+vUVoKk7wAqszEHadvJ57c57VaMpR0ZVxjLVEu4+G2quko6L61stYjnG17HWVMMzR7w2ze2eB2wDz2471Sala9RdM3S/7ZaVdafECWaVR8zBtwPOO+QDuIx6D14xZpcyMAyHcp4w2VOO3HuRzV1pfWmraVCqWd9cLADtMLEPEeP7jDHPtxW3K31RnsW3WcxpvUENzd/Kxhp5CpRkhjJlVs5K7SQcsOQpwBjAzVit/bzpe2cUj2hKlmeQpGys2AELYIGRnP2AHeru4l6Z1/jqHpmO3eYEyXWmSm2kLE5JKDKE85z3qsn+H8N1dPc9F9U2szSgk2muAxOJCAMpJynb37VdOEu4NzjqiYsHz0L28NyHidN5jhTEeEUqGzjceS3bjPbGBUXTNLW7vGeUfMTxwiV1hhI8Q8eRWJxjnk4GAPWqDXNL6g0JA3U/TOrW7oBGt9FN8zbngbn8QcYwOBkHjFe6Z1MLq0MGm3Et9cSTeaK6VUeIY43kYUEkkjucDiolCSV0WjUi2XTwRWLS5hDxMqtLNEGcDL5BxzxwO3P50a2mgQzTw28lzckoyTZWR2wS5VS2d5AJ2HkYwe1YWGu29vLbRyx+AiM2HWVXiyc4C4xkrjOPsPWpRigv5PG/3Z3jj2RHaqtgLlyoP4Thju9yxAPvg7rU6FZnqmC5tJ3ihi2Jb4WSZ8BHCqSNo5LZA55A4x611OuozadZIr7HbG5vp4aE+1UV7DeTxmKzeEO8BZY1twq7imArYGfc85x6muh6gBj0/TvDUEgALuOF/s0GSRjA+tcdZ5HRBZo4eWG81gIlvHHMU/h3hARwAoyR249a57Ui6LLt2oqsYpQWG3d67cDGOKtbvw5IpPCUeLDKpUAE8EZ355IXt3xnOKrdVWOSGwG2NXl3rIFfkEEENjPBwaimsytV5HAasdpmQebcN2VJ757818F+IQx1G/8A9Bj/AKV951dy2/AC7CFPrznJJNfB/iH/AOsj45Hgx/0r7L6Pv+XsfHfV+r8zlKUpX1R8sKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKsdDONRiPsQf5iq6rHRDjUIz2xzn8xWdTcZpS30fbtHPDAdi64GB/fI+9fQdP2iyERB2rZo+9V8x/ej6kY471wWjMNygsAA+SM5P4813tj4zWsMkTuu6ykXCD2dsjv6jv9K/PMRqfoeG0LyB1TTFbszNIDuTC8NyV7Dv3+5r6p0iS8dqrvuzPKVzgZPh8ge/8Ayr5lDhrCFRJIpZJXw+Qqg7hxzyDgE/WvpXRF3CtjaTTTRxhbt9zSNgBPCxkk9hnAryZanqp9Er9divG+IuoR6ZtWY9NWLF9wBRRe3QJAJAPcd/6ZrntV6j0yOC9SMPqOqIwhkstOjBwSQGdph+7BG7GzcDyPYV1XWeg9K6z1Auqar1H81BHbQ202nWdgs6SCOWSTzTOwTB8UDG1sYOM7qpLjqDRbWCGDQemLMpE7PDJfn5ho2OMsqMBEpOBwF9B3rsio3TeeS+fLHHFzUWtM38+XKqwXqLqq3UaLptsLawCLLeQn5l5eWxtLhYo8KV3EkkFSM1jL0XpUtzFP1Nr8cxiKGW0s3/aTSOuCHyAlvGcgA7d3AFa9Z6z1XWXB1bUbi8Y87HlJRfbC8KB9lqne+diFMhQ8gKG5H05H5dq3UmtMjJpS3s/nzgdhJrukWSRfsbRIJZreY3K3epkXUhmLZ8QINsSEkjICkcAelVmpdTajqojW+vJLmKPCpA7YijwOyRLhFxx2Fc6WkkbZ+Fj+EPk5OewA/qa2iVDPgSpI2/EhyHCn/wCVz9z61Vk3zJYvHk24JeMD0HlAH1PGAOfpRVZpBvPiHdz4K459Oeeee/51FLtMoVQAyjILElDz6KML7cVuUYK5kVkx6A9/Tvx/jx2qhJntXYC5JABOAxOP1OPX6/StsLbgHiIZOTknd6e5wP0BrBHG9cBS5xg/jP6ngD6ACpKh9xJypPcsf8h9uKqyxi/jM4SFVbHMpYb2x9P5/wAqziZf/a7VPoo8xB+uCQD69zUTUlkM0B33P7tD5Y0wD7A9zjj2zUi3cqFKkEcE7R6e38qgkkk7hgLvJGCScj9MY/Ss1LyMu5ySM7hnJXv7f517H58lmZhkZGcA8njH6/6xW0osZUccYOF4/kPX6/WoB4mFAAVgPTJz/r/lW4eJtLIVRO+B7fWsQ6j8BKenA9f9D+dYo4cHyl/ViSSfXse1C1zeiqrHAJb2H+voK2LIGz4YHByD3P6flWjaRne6oO/f8zj+fpWwOn8YLZxw54B4x9/09Kgk2L5nOGzxwO5+nH+ua3BNpCkhONp9c/kPvUZiy7d4aGJvfyfoO5/TiskcBk2b5efwgbFI5Hbv7+vrVSTY84QgBym44BOCcn2X9P503kkFnES8fjJJII9AOfpzj0rCKX0U+Ex5MceM4+rY5/P3rFWKBfDG0EZGOcDsSf8AXpUC5tUxgBolJOOTKfUeu324/mDWXmO8CNmiB/CkWFyRnsMD/RrQGQYKAsxAOV8/9PL/AFNZtkbS48Mdsb/Tn1/wFSkLm5wScSMMHOOBk4/l7Vio3AukYwB2z2x9/uOfpWBmKMCjFcDkHj8s9/6d61HPDSPL5f7zkL277f0pYXN7uTgsThjnC+UE+3PJHJ7A1izgMWdwhLZzgDPPf39O38q1NKMOHZyAfMEJB/I/y7CtO512+Gnh5zhxjy4xkbjgf8j64oLkrLtyECIRzuBGOOTjv71qZ9yDc7Pge2EGfYEgZ7/pUcReK28SuSMlgh5xkHAJyMfXGftWxUU+ZEKlhuDO5lYf+JuMfb+VBc2o7uu1MAA4xGuRj/vHgfoe1ZRIZcOIt7jHAO7B9i3/ACrHcr5Vm8aQcYdmYcf8I7/nWRw45l3gD8IXgfZRjAqNkm5sdI0JZ5wZAOFXLlfyBFa3dduJTIoAIJJCkZ9ePXv6n1rIDahXAQA8HH+Qz7Vj4uwnYPEOMkDyg/n+nv3ook3PFnIwIEC4PB28j3x6+hrGVTI5Ny7BBx+9fAHr27k/njgVpaZySIjtC9lVTn9f9frXkluH8wbwj282JGx7j0HpwKnZIuZC4UKVUK8XcgrsQY9D6H+fetuJixVSxBIIUMAufv8Ab6H3r258KwhFxqFxFZwj8M15KsQ++T3/AC5+lcVq/wAXeltIKwwXVxrEzkKkdmggTceB+8fzHn2X071pTozqO0FcynWhTV5Ox2QjmjIBeFDzuwSxHPGM9/5VInUWUD3V3JDZ22ctPdzCJAPoTjP5V+etb/8AKC1uZ2h0K3h0SJgf3kA8STPPBkfJBz3wAa+Yan1JrOv3TXOsX088hbzPJKzN/wDE2TXrUvpNaec+ieXV+q0o5QVz9Na78XOk9CVwt3NrUy4AFuvhRFvbe/J/IGvm+uf+UDrN34kfTtlb6TDjiRE8R+f+OQfzC5r44zKqlfMzfhPPfPPJ79xXu3BAwyMuCeOP5/lXr0vpmHp5tX8Tyqv1GvU0dl3FzrPUur63Mt3qupXF3cNnJlLOV9OGY8k98DAHaq+6tVtbkqkkF7INrLcWrM65ZQcbyMkjOD7EHB9a1bMHnBXGff8AOpNsiySjcTGqqMEnHrXoqKgrRVkee5Ob6WZ2fQenNcXRYNGGkhZRuAJJxgDnI5GcEg19u0u3tILG6tWMdqIInO6K0kZ5mxlgu3aMAMCAccEDHc18k6QWRr1naR8RWsgyy7sBuBwAMAZ759q+qaZCfnXtIIBMhiXxIRGAO+0HLOSGbLfhzgDgc4ryMQ7yPXwytEu7vqK6FrFZXlz8sQozHbJtK7AAfKgyWbjOTgDPeqiNZZVspNNuQxkjQW86eZYERwc7duQScAkc4BxxU5fH/Zf/AJvNnbRxynwx4qoDEpImZsMHztyBnI/vDk1rhsBp0MD28wzNbLMVhXc6Lnuj7ckc447Hd6c1xJJLI7DTeW1+6+FN4Vne3LK8j74xsYBg0i4OWGCRkjPv718A67tYzdxniQeEg7eVvcA48wyOG9a+9QpLeCfZcK0NwwWNPGCNLkEEBnUZYEjue5AH0+LdaQm4vXMXjMZARKhUqEKuR6+2D7fau7Cu0zhxSvAo+meuuqujEDdLa/fafEGGLdbgvC2eTmF8qRwPSvqGh/8AlM6pHGqdU9PafqZVvLPZE2cvqSSPMp+wAr4c6BZHClm2nDA8cfavChxlPMo7fX7131cJQr78U36/c8+niK1HclY/X/T/AMYOj+p3CprH7Hv5ApW21lhCWDDPlmyYz39WFdxLBKkaTyIVibJWdRuRvqrg4P5GvwNCzI2N3ht3O3OBx61e9OdXdQ9JO0nS2s3unhjkR28pWM/eM5Q/mteTW+jxedKVvH3/APT1KX1WSyqRv4H7UkZj3GN2TnYOAe+PyrEtuCCQK25duWUEfpX570X/AMo3UoF2dV6JYaupPMtufk7g8/iKgNETz6KvavpejfGTofqABHvrnR7juItRt/Dwee0ikof5d68mrgMTS1jdd2f9+h6lPHUKukreOR2ylUbZGpWPaDtLZBb2A5Pr7/SsNqsMum/+HIOSefQHnH61naJDf2kN1pk0d5bSr5JoHDq32xWuQuCVkVQmeTtz/L04rgaO1SuYGJJYvBVy0YXDxSjgEf8ACf8AX1pHA0bKYHkiwPwxklfzDbuPsRWJJEI55B9SXQfb2rMSYwzEEL3Gc59/rSxNzaJZXk2GLcNxA2Ovb3I7/TjNetKC5G8rIOShUj9RxXninzbiGVe4Yf0H51hOzOmx3ZVUcZG4DPHY8fU4weKmxUwlEbHewVWwGV1Oxs++Qf617ufkFcgDCkkZ/X+ece9RyzpJwhUY8sgbMZGM4wSSCc5x2rLefK0iKzbvKUcj9PXGP8agk2EjgDaxPdjjn86xXwww/sWc5IBJ3YA9Pf8ALNayuSSzGYAZw3AUHnsO49s571mcMnYLnACsMgeuf61axB6kufMWMTd2VsHI9vT9akqx2g7kIPY4x/Kow3hGPgeKvIyMMFHuecj147fWsTJHhtxRSOWBbA++PQc0BvfZMdrpHJkYwV5H5d/X+VeEuI8Ru8cTYBR/Ov5j07fyrV5ymJGLnthhwazSbDZePy4yNj+n59vWgNiMy7R4SqgP44lGDx7etYLKSx8ExTBeCHOxsk+bGRgj7VissbuT2fOCOxK+vHrWe7em3ZksD5sAj8wf9cUIPU2qoVj4YzghuTg/Q8cY/lXu0MvcDPHK4H2r07SuExgDaRyNuMfr/wA61yho3OCQvrHjv9j+ZoDF2lQrFEIlQ842+YewGf6VkAwHBYgHGH5x7HPqPTitPjK6+dg/5ZH6Dg/oKzADt5WK7SchGwD+X2z6UJNgkYMMK2B7HPPtj7VrE6Op2kPwTjIXC/X6fSvQG37ZCpOPYoT7AEd/b8sVpM0QRQS6DIIEv4Sc+hH+NAZ7NpLJszjOM7T7D+QrxWaIFMYUcEbODzxx9f8AGvCNh5LIvY7vMpz/AKFa2ZwNxBdMHBQbs/fHb07+9AG2vu2xiJSPxJ2JH0P0rVJ4rh42AMfHKNnJ+x596yaRc7UJC4OcHge3HPt6Vg7+U71yScZ5zjPfH8vzqUQY+IGyeRu7qcDaPYDvj/nXmQQwHlO3zD7f1/xzXr4LFeSzdsjOPfGfcfWvBHmQBSGPPAY/0P6/lViDwKgYqo9PMTyQc9/r7/SiAbyx8zOMt4Zz29xn7/pWoE/hyQwJADD09BSPuCVdWOCBkMB/r7+tSDJAAqMGH/eIKtj/AF/SjTyCUxKHZlXO78QP3Pvz9O1ZqQcuxDDAHqP69881rKquVYkN2G70Hscf5VILfTeqtV0iTbpt7cW27OVR/Kyj3H4SPyqXfX3TnURDdXdL6beSk8XVovysxJ9Tt8jH7rzXNKo3nG1gxJJx3Pr2/wBd62eG48No5QFXPGzduNSm1oGlLVFs/QWh6haLB0v1VeWRzthsdYx4YXGTGsiqVBLD8RAxmtB6R6j0MoeqtIkm0sRnffWNy1yqKp3bsg4GcAYGBnHB5NQxM6ehxj+HnP5D+tXOidR6hpjs+m38tsVOWMUhQD7j1+xo5ytZloxs8mVVpr631jeNam1mkvlaWazEfhugKMQrsO55B3Z3YBz3Ar6H1DCTpulqYWuD8upEIYDxG8NMLz3ye9V9z1DpnUJQdW6DpmsORgTeF4M3IxncvB/PisupOpLTXYZ4bBbmwS0ulhkeTG+MLGrngd8h1xXFWs1eJ1U3JNbRy95Haaba3Ftdt/vtwTJJNbTbo48LxFjABxgdhxgCuR1YGTBnib5u4cEgYzj1Ax9B69vzq/1a4vJrfZLJm38bhCo3ISCwB9frnnviqLVJI5beHKBGDMrFiMHjuMDP3z39KikmncVdDgtYVCzbhjIbyhuAc/5V8G6/O7qBiMAGCPAHoMV911iQM7yIFG8HsMKOO2K+FdfR+DryoZBKy20W5guBnHavs/pG/wCR8b9W3PM5elKV9SfLilKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCp2lDN2B3ypqDU3Sv8A0sf901SpuM0p76Pu2gygXVg25mLTMrIRkEFgBgY/4q+gaOqPFYxhmUgzISe20KWzzkdgRXznQXYJaFAVeO7LrIOCAFQ+n2zX0XScxzWxlyZI71hgqQBuB+nv/WvzvEr59z9Cwz+fY6NY/DsrNiD4iQqWOMKN2ec+vfHParxpTb9DTyqDvUDGFLA/v4hj8s1zFg+YbrlnCMJNpfIUnA7cD1xiugvAo+HV+N2I8MM7vTxozg4P1/nXmW6aT7UelJ2ptrsKkOUuJmfw48Nw0o3NgMfTsBUO7k3PnzyHdwSSP5VKfEc0rRmQOrllKDIJB9jnNQrkygsYv3ZCkMzdiCPUY5rojqZT0I4EnO+RYzklVC5yT7855/I/aiLBuxbodp5dABtJ454A/ma1j1bGAM7cpjPt3+1ZK7yKu4n2KrjH+vt61uc5JgwASn7ok524Hf7E49vQ9qyRAckqHZGG1gmCCPQdgPyH3rWjKARsGT3DNz/yrNGRjgBkG7O5SQD9znkd6WBvRGBAknLEZOMkDn/X863IdoKpGWIJyWPl/TmtO4qV3gR+nIA5/Pn+VbN5T8ZwRjJYnP6H8/Sq2Jubx47D90eAOR2x9/X3rfCqocyMGUYxsJ79vp/o1GR5JGx3OTgHj8uP9c1tKKnmbnPID8ZHbj39Ko0WTJTRxiIiIbSVx2yxz/196qIWuYbgRXRkkAwPEKlUz75x27HirW2nTiNZI0BGACMnH64Hp2r27slu45PM5UHLOMkg9uB298feq6Emm3kjI/du8vHp/mfXtVggcgFQF4JXB3Nn19ccD71TxQizfLkRknG7fg5A9Bn2z2qdDMpJLSAEnn1P/ShJJ/Ay+RSdvdju/l+XPH+dbVOB5nKJxgdgSR7/AG9awRsg7VO337ZPbj1I5HH0rCQ45kI7ZyxIGfz7+vH0qCTblVUbGJUn0TaeD68n2rMSNEuYykSnuwwD+veo/wAwpGxjxnG0gg59s/r714rTuQ0cSJxnc/mfOe+MDHcY7UFyQkW58ou/I5ZztD+/NabrULSxP/nC6WBmB8iyBcfckZI/KvGiaRCJZWlLdxnaPrkD6++a0R2VvG8jQweO0i4bIHr/AMfp2x34yaWXENk4TbgvgINjDcvmwG9R75/l6V77723cg+wPHqPy/rVZp+nXGlDwy220YeWDcG2HPYe32HFWW7IJAyeOQmBg+v50skRc2K42hVIAHZVGR/r9a8OEwXIQjGM8k/kOPbj61qAKou99i91UA59Pz9T+teq6IMqFVuMtIBn7j/XpQm5s3ghjGGHsW4GfT/DtWotg4cgY7ALyDg+vp60LySZz5mHcueMevbn/AEKxfysAw3McKATgD67R/n7UFzIPuB2qCOTwM59/oOf6V5teR8SOAWxlT3+x9fWvN25OTnHY9/8Al6/yrzfyBuMjKeVB4H3x6cn19RSwuZlFXO2RQU/hHIznjgcYFZKcEEgY55bGTj1A5/rWAkdl/ckJt7FMbh+eMfT6Vh2bzsPXAHnbv+nepsLm3egBKuJMA7ieFJ+/6f64rMz+UiLkgHkAemcd8Z4qLd3EFnE0928Vnb54muZQgA9O+Mn0wo9K4jW/jD0voyMsNzPq8q9ltVMUf/xuM/oprSFKdR2grmU60KavJ2O8AIVmfbF6OSe3r+L1P5elLx7axtzcahcQWlvnLSXEgijB+7Yz+We3avz5rfx41zUA0fT8EWkJyEaIZkA4/wDavkj/AMIGa+fXuu6jq1wbnVb2aaXBJaSVmY/mxJr1KX0qtLObt/J5tT6nSjlBXP0hrfxf6W0TKwST6rKDwlsNkZ44y7DJ/JTXzXWvj1rl4uzRootKi3EFrePdIRnnDtk+3YLXyWOMqgbILHPJ/i/OhycegA478V61L6bh6eq2n3+x5dX6jXqaOy7iy1bXdT1i7afUby4uGKktJNIxY5+pJI+1Va+CSTIHdyM+wx9Sa2lI9qhBgHjGQTnH869bb58AkqoAz/lXpRioqyVjz3Jyd2zwEqAYihUAd+cGsUZ5GyG8mcEt2+1ZrvK5CL+HGGX3H+vtWEqneVz5ccADv71ZFAq7FUKfN/F7VmWLEiRVbaeTnBxXiP8Au3PG/PHJz9v5V6Vkk2lmxkZxjtUg85cNtJAAzggf1qysPDJD3CDwgQADypzWiG2cNlxlQwIJHGPt61c6elr4YS5QuhfCsibiM8DK+o/Ks5tWNIK7O96TvJ4UnMhZi6xxIY8/3yBuAzwQSMgegr6bLcOUlSfmQKuZlTLop2sUJxncCGwOCN3vXzrpO0niuZYZEjkjMaiLdHywJwAuMHZkn0yDj3r6VpiO6tKgkzG5KZiLPnOzldpBI3YB29wB9a8OtbaPao32STbTJaQSx6fDGiTMss4kyh28bn8x43HOQTk8DmoFtC1tclxMjr4u6N13bnctgeCGO7PbK9gBgg1Z3SzuYopNQXU3t7c5mkPikSlvwLn1UZJJGck44FVl9bS6hHHd29nK13HFNHZyqojEQPLksuNwBUkADOOO4OedHQyDel3ga4vWkuLl3ViwkIFxJuILs5ztKnPA98Ejivl/WSpBezYlgKsodzAxwzAsu4FgCM88c+9fYrqyM9jdukky3EKtNGFVTDHHkLsYEDBwuQo5ywODzXx7q4eIltufxJVjJkaSRT2IyfpnPI9x9a66DvI5cQrRPm88U3nlhDP4WGY8DYNwGf1IFQ2LqWZlbL+YMMYI+3pVtfqsgDKOQGIGPQcZqAXfcy52q/BGOR+de3F3R4klZmO5ViZSduQOCpyeff29a8OE5GSD3z6fUV4sSENKWG5TtXOckHv/AE/nXquMcjI28AEf41Yg8yQoZGVlI3be+R7VugilkJM4McaguX5IxjI49fQVqjYouAduD9uO9bnMrHDHMYUsRn+WKhkmzTNUvNPuI7zSL2azuU/C1vIYio9+D3r6f058fOpdKgki6gFt1GgYENdbknVQDlVkTjBOPxKTn19K+SvgDAiBJ8ysPYivYkdmICtgDkk+tYVcPSrK04pm1OtUpPoOx+oenvjd0h1BI0V9Jc9M3QAKm/G6BmOODMg8vrgsMcV3Vr8tqELXWjXlpqUIHmmsblJVUf8AFtJx75IHpX4j8rnIXkj9efWpel6nc6HqFvf6VeTadqEGGintmMbKQcjJHf8APNeTV+k03nTlb1/v+T06X1OpHKav6H7UCkYUqr7i2GXjB/p/0NZrLE21opVIJ8+G3ZI9z6cjtX5y0f4+9U6cAusR6f1FET52uofAnAJzxLHj19wa+iaV8bei9aZIdXmven7psZW6Tx7csP8A55Hgj1/EAK8mr9PxFP8ATdd2f9+h6dPH0KnG3j8sfRyWRWDBkHIDK2cd8dzweawZjGoB2AgY5IXj7Gtdm0epwC60e5ttVt9obxrOZZQFPrgE47+teiZkDHO3cSDz6/WvPtZ2O5O+aNuVPDb899o7D/LuDXgQ7QBLsweAABx6Z9D6VqMpAZnDAkHlSVPrwB2IrHfIqnhpD6CNeAee4/nxmliTd4jAHcA7Z42jaSM+o/Ttxk0KqylXUYP4iB3OO/8AOsVnSYsYyj8nnsRxzn2xWbMowOxJ3biuCSAOc++B/KgNeViCvMXhJIAD5AB9MEZHqe9BIdxMrJ6EcHOfXnsfT/lXodkBdXMIPA3L5SfvnPofSsAnhKS29AV5A/CPrwO33GaA3jG3DEOoPKsMjP8ATvXhUlt0UhiII7LvU+/fsc47EVqiUN5kmAX/AIeRn64/1kVtA2pn8OM9jj8sUBl4kgZRtSUZG5l/F+h59fevUlV03RqysBnacgjvwQcVgcck4YFcAk44/wBY7VomdY93jONoz/aeb7Y/U0sCU6uypsPiA4wzAZz75GP+Wa1hhs842Z48/II+p9PvWK5KsVYqTyO7f15ArLM0ZOyVlUeivnd9gaAzi/dnMcgQDBAAyAMd/p3rzOSwcYZu4RsgHPcqfvWAYMd3CP3cgHHPuDx9eMdq2ht6jcCUbgDGcffNAaR5TuAXOQPIxU/of8zWKRKZC2BGx4Jxhgf9f0rMtnIZWjO4jBXOMemP8q15Cs7HKyY2hlc4GM8YP0z/AJ0BjJzzIN7E4ww3A/Y8fStHhyRZ8BZljwByPGVR+u4etbxI5UqdrjPdPt/d/wBc15tTcyq7Rsh7JwfzB/rQEcPvzhFHP4kOP/kcVgT2LLuU4HHIx7Y7+tbn3guxBk/u7Tg/64xWL4dguSMckeucf3h39fSrEGovvDZy/mGCBnJ7cf50KlWYh3jxkgjsB9v60ZeSz+UHOW9Pfv2A79x7V4mJACu1gOUOe3v29fX8qtYGEkqy7FePPYHK4JBHv/Ksy48uDtPB58/6Z9eawlPmQMzZ/hBHYEHFequ6JVRAcnzFCSM+xHBwPepIB3x72iwrLxuALd/p7f0rKO8J5eNdxOCyN3HufbvWkDasrx/upHAOQTtz/gf5V7LcvJIqOQ8jHIDjcBxzyORQXJKy5AbG9V9RwAPbNb0dSSpVSfqO/PbNQlZXz4bDKkBfMT9+e/vUhJPDYFg3mx51wf5j/GqPQvFkpIwkSxxBkVecb8Hk1A0X99B1ZEwaSNep5kCuMMQtrbHBB5Iz6nuPpU6BNzHny4AIHmDfTk8VU9FLA1t1fHqVxtA6x1KQmNwS37m32rnPtx+VZWtTm/D+UXb/ANkF4/wyyu51MoQSA3AUPIRjBJznucZGQMj7VzGtWU7BIoCjGVtybSXZ+OQMcZwPoKvLu5gbxHt18OSNU8PcSxVe3Htk+9UmrSb0O5FXw1C7PxHHIIP6nNZ0rpo1qZo4jV1A3KqgDIYkAHAPpkcfnnFfCfiA27qOTjAESAc5zx3r7trTv4pUnAIAPOBx249K+D9fAjqB8nJMSGvsvpG/5Hx/1bc8zmKUpX1B8uKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKmaWM3ij6H+lQ6maZ/wCmLjHY9/tVJ7rNKe+j7boUoiePYoQNKrEA+8XPB9+f1r6Ppl2JzCQzbTqMTBu3PIzn88V8t6cn/fQO4O2Nu4ycDZjP867nR7h44IMtsn8ZS2FYBgGyvoB2+vcdhX5/iYZn3+GlkdnA7GAMQ0jzR7H3e/iNzxnP4R+lXV/MH6B1jOZFjTcfVTl4+4HJ5Hbj8qoHuB4EEzHKMS0YDcD942PUjk+nPeuivUA+H/UJiUmUBydoznBjP+deUt+PiepLq34FTIXEkzPvkOWHBCDv+v0x9Kr5lZt8jABT+EAfTnzHOT+lWMj+HeT7gFdpCeTnPPt/rvVddllJA2oT2Zjg449BmtoGUyOT5iQx3fhJA7/rWzLcbjsPs3qfuTz+VYYORmYsvHZcAD/EVkhDFQzncQcxpkH3IroRzHtvHHCeFcuTlicjP0559qlLuIHkXBHcn8XGeT3/AExUaEldq4CsfXgE/T/QraviSclSQAc4YjB+pJ49aAlZEIBc4VvwgKQD3+5NZA+QiKNYwq+o5HJ/PtiowR41X96F2ngj6e7H688VvDHC4DKwHlZuP6/64qCTa2GG2abKd/XG3jH+FbEKoSVAdwx3lhj19f8AKojSFvMCcDncMnt75/1xW0MjEF9nP97lsenHfuKra5ZMlLK+fICeMABQuPp+Vb47iRTlG2kZ8pJJGe/J9eccVB8REJDglxyd39eCTjn6VtAOR+7KqDkgjAH2Gc/rVbFrnk2mQznx1ASYgoJicYHqBnJUY9AB3rGJfBPhmXd5QcJHz9855/SpSrlHEkoLqQAhYA5PqFHJ7fXHv6Vm5DfgQgHtvP39Bye9RmTkexsR/AZCBnzvyD9v8K8KpgB9pPBIQAcgcHv/AI/rWOzeApLMp4ALbVOO+APz75oUQKpzGysATz4e33VhxyMfnkH1zUWFzcH27iiyqWBZznGT6+Y8/pW5WJYtu4ycFVPPt5j2/Sq9bqETtbgu0oiErHwiFCliPxnjJIOB3xmpILMRz29d2e/rSwuSAy5DABj3DAZ+3J7ev6Vn8w0hIOCSRjnPNRgQoBc4ZhxvPc/QCs2J7ndjOecAf5e/P0pYXNwYQszrhctliefTn6dh/SvFlO0EZf8A428uTkfT+gqN4wYOIQZGHPlOf/kjxxWBulAKtIoYHsOSf9Y9vSlmRclcAnlQ245CAc++D/KgYZG0FgDyd2AD96iNc5KlRhWGf3jYxkegxn+VNzNln5XsA5wv22+v51ZRI2je06EhVYyH8J2NkD7kdvX1+leGbbgZjjA8oDKX/THB9fetM00NjCZL64hs4wMh5HWJMD1Gccf92uI1r4xdK6MzrZyzarOuRsg/dqT9XbJP5CtadKdR2grmU60KavN2O6IZjll7fxOf/lR+p5IrO6a3s7drjU7mOGA+YS3c6xJj/hzgH8ga+A618btdv/EXRYo9Cix5HiG6Ugf8Zyck+wFfO9T1HVdUu3m1m8u7iaRVfdOzbiCMg5PJGO3oRXp0/plWe+7ep51T6lTjuK5+i9c+MHTGkhkgln1a4VW/d2wMUa4/42GSO/4V9q+c6t8ddf1EtDocFvo8OfxwIGcA/wDG2W9fQLXyjw8B2beQx7seVNZAHytgKA34QMckYxXq0vp2Hp6q77zzKmPrz0dvAsbvXdY1G9W61HULl7tHDrKJ23hvfdnP86gYUkkEJIxwT7GnhsvnVUVBxwAPz/13r0D0PJAOcL3OfrXoKKjklY4XJyd2zxg4wI9oVucjjB96CTLuG3g5wpbkZ+lej+0YMMBl7ZHevZl25L7WXb+Entj1xVipjI6xsxypyDn0wPpXrFlU+IdrEjg9zWqPCgg4LD1HIx962gZAcqqjGAA2amxB5hSqo3nz3H4f0rLxQqFIeFXAwOcH/RrYkJGNu0hTgjB/KpCae6qCcKzHt6ZxmobSJSbIc2FJOAqEA59Bn/nWyK2VjibdvjUsTtJyfT/rVhZ6a82USGU5JLcAbP8AL863xwsSqZJkICF9uAB65qm2kWUWVogIIFwm3zZJwcgegqZHbyKgkbeEGQNowc9sH/nVrHaPI4YOJycAbR2GMAE/YVtitUaWXfG8RZwFDN5W8o7Hj7fcVk6hoqZDgtIyfFEZbOCmGJPHfPqfoBire0sxHdRjwmIdQSzvtIPbAx2BOf5VujsA+yOW4QxhN2WPkGBg5IHcnI/WpdpFGsTNcLGz5UsFcYHqN3fPP5c8+lYSmdEYHVdMhLe48ZhFbvcxyIimPeZWUZC5JAzl8AeuB3IruNLs7m7u547CWG41OHYZrMXq5jXarbpQ7KAcfTJ5ANcRoVrE2WuTHbkwCQK8RcAAFdx77e/cj0Ptx01xp+0XRkt4nu0t1ikmkgBeVQq/ulfB8xJBHfj1Arz6mbPQp5IuJrPUtM1aJdQjS28V1kSSJoSF8zBjIELYA3nBBJwpqP8As9ls7ee7Ycq3iKFYr+IFFAA5w2OMjvyRzU5rOGyghkscEGQOsYs/CeIL5SwIHdTjKgHGc88VGsJbK6CwxQTOxlijtnilUSZyRhlK4AJ/h/mM1z3ZuQry0e0tzJKYLi22KJWlyxLKFHlUHO8HaT33djwePm/U1p4jsr26TbkMhRDt3RKAMoAOBnkrz+lfT51Ci48K0jvbaEqwEkAWVnUkENjhQCV4wSM5Jr59r8AkklUQyEKSUVHJwpPl2IeQO+fTFdFF5nPWV0cHqNkjuFOZYggUl0AJOOeB2A/yqkktVZd4APJEmPcf8q7C8iE84wknDB23xhVHuNw7+h96rZLVEDYxmRsyq0ZAfJxx9MAnnvmvThOyPMnC5y0tuNzAJhv4SBkn24/xrXNA4OOWyeGwQM10Y0slI8wMoWTszbcn0BP+s1EmtS0qqy7WYcKD7DP+Hr3rdVDFwZTSkYCEHy+XgGtAIj2sDjB5BGcfnV69gg3neFKjOdpwR9/8KrooYcMxmUHHk5PB9TWikmUcWiOS3hjxNpG7Mbr6fT7ViC4dlQgbecdsVJdCu8kCU4HBzyPQmsGiIdg3Ct2Cjn+dWuVMUGSd2znHl7f4ViQAcZbP90Hn9K8C78eEwcA4LHjPNebTtG5lQFvfJNQSeToyBSHweMgnk1m0hCFdwUgkMy+oI7UAPkBTy8sxHOPY5rHagwrAbTjcM9v9YoCRZXtxpsyz6ddTWNyF3GWCRoXzn3Uj+dfSNH+PPVWmhE1RrbqS2Aw37Qj2yj7Sx4P5kGvl6lXJSQEKc9jW0luBHtRlAXyrgn8vesKlGnVVpxua06tSk7wdj9GaL8cukNSYLrMGodNXJ4benzNuD/308wH3WvoelyWWtW4uOnb211e3c532c6uQceoHmH5gV+MpC2QI03k53EDHP/KsYGezuRPZzSW90naSNzG+fXBXBry6n0ulLODt6/36npU/qVSO+r+h+0SjcjCHnLbhtI/yNYB3U43sq524xn/X8q/O2j/GzrLRkWLUruHXbYKpVNSiDvgH/wB6uH9++a+j6H8c+ndUiDdRadeaCzlkE2w3Fs5UZIDLhgcEcYPcV5lT6fXp52uu75c9Gnj6M9XbxPoCSgqQwL8kFVOQPyrZv2lo4C6sDwAxGT9Ae/r2qLp95pfUEBfQNTtdUjOS3yk6u2ft3B+4rc6yxNh87ARhZFB5A75HBz+VedKOy7M71JSV0bnBllJdMMBnglHz27Z4/MVsB2j8e3HlHiAH9SP8vaog4hXavhbTn8Jde3P+ga9V33L4gbYc5UEspB+/P5fzqLFrkpn2OvJjyCu4HI/M/nQeZMxsr+uFJZdv9M/p2rTEcqzxqrKgxlHxn6YNeARtcADw1mUnjOx+3b9PfNRYkzZj4mCzMME7NuQeeD2yB9j+le+MWLLgDtjbn6nsa93PGMsqgYzlgT6+4rUqK65wQR/dORz25+nFRYG3O0t5fEPJGOSAPf14rPcoClWAY5K7jkZ+4+9Y+G3n8wk2geX8ByM+n2rxzhgZNqMCAM8ffB9fX9KEmwqzR4VndQTwDk/zrzO1icEEEdvcVrJ3IMKzk9vXIz7j07/StjsQVyeM5by7sdu3t/jQGmVMqxUKRwcMCCT9SPzrXkCTwizKwOSuM9vv/rmsmxkn8JOVJHI9/wDX2rAEs583Zc8evpjP60B7vYAM2CN3AHGfTnH2/pWO9doZkkDZ/jxnj/p/WvSTEpU7yeDhh5hn1PtnArRcMisVMbZPcJ7Y55H/AF/WpTIMgu19qS4YDaQM5LegH6fnWseAGVNu18l9oTYT9wOD7ZrB1jI8NQVjxtKbcY7nsfzrH94rorAPGqkbX7/bJ59u9SD2UvEg8Ql0HO5ScHn+X0+9YTuoIco5XG4MrZK/8QI9KSTrCUDIEZmURtjnOfT+X5ZpvCAs7hMZwBtGB9Pfv96sVPTIjTP51Emf3gyM59c8c9jWKsxVVb98f42DYI784xya8idGjUuyDvngDcf1rwxsyKsTBSXG5l5+uQPyHP1oQbUKBy0bFMjHsA3v/PPbua2x72cIEBTbnzPyfp9PaoeSG3tGGI/Cx7hscjPfOf8AGpCNNJvBDlsEYBxx6EZ7/rVWXRZQyR4IkRkDOCAwyAO3B9BVL0Hc/wDm/qad977ertRKevm8OEZ9P+VXNrO8uyRSGAYbR+ffHeqb4e2q3+ldUQQjn/azUyQ2ACMQ5AzzkjsePp71R25Gd+7+S/8A/aHn/BdyCyjmdFQ20pdQs/lOxxzkj+Luf8uK5DXAXMZPmZlk3g+pBHOPT1rsb+xt3t1htrxHDscLFuyjlu5BY7ufLkY5H51w+p3EUEk0crqrb8KsjqrE5weBnPY/pXPQzd0bVslmchqu0kMncNk45wK+HfEH/wBYm/8AoEeP0r7hqrZZgBtDORxjJ5/0K+G9fkf7RSAekUY/lX2f0jf8j4/6tueZzFKUr6g+YFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFStP8A/SR9jUWpWnDN2gFUnus0p76Pr2gnFuXJHHA9RkjHrx6V9F097h7RA4f5aOQrDhmZExjjGAAexP8A3ua+fdPlVsGOQcypxjnbhv8AXf2rvNNjSS1E0YjErOoZvMTIdp7nkZ4x9gK+DxO8z7zC6I6qxULaQZdiyQIe2Mky8A89uc/lXUakq/7DdVROyqFjuQCxyFOFJOQc8GuW0WciCcbVdUt1Qbs7tu9WwDjuCO/Peuj1QE9C9Wqz4UQ3Tb9pwD4YPpwBXj58qvE9aT/1N9xSXmxpJx5QhY4KnGfTvx61GmKoqmTmVkyyrz9AeOOcVKugGubnAUlXbv7en/Kq+SFgMBXK44I5Hf0J4xW8DGZrweAgCs3Ze/8ANvr7CtmGJy0jNjnIY4/U4rSIguB4iM4yNi5Y4+p+/pXq5fcVyXC/wruPHbnsP+tdBzkpFI5Z40QcZPv/AI9vrXoWPduO9wfUHaPXPB49PatC7dwdyzNk8AlyT9D2FbosDJRAe+WJB4/McDNRYXNwMmcrFub1O0uwx9ScD+Xetm52DeLuU+wcOTx3Ppz7c1qbY5VT4h2jgp+EfT9KJK7AqSsRHB2Alj7ZAGff9KixNyQrbSmJFjK92J59uBkfzrwYIJVmc98HhCc+o/Tvn+taQAreZQDkD8QznHA+h/5VsDk7Qo3H0Y8ZPuR39RSwubkfZgLhAOBzx68Y9fTipCkspALMCMYz+HOP19KhiSPftQl3Csdoy2MY/n27+xrNg7HltqBO4OMflx6f0qLE3JfKRgL9eFXAwP8AX8qLcbTnlwPZe/5j86jqqgFncOqnDYOFxn34Ge+Mmsyw29pXcEkbSMAfXGBx74NVsTc2NJIoA2uARgL2z9ORnuPWixurtudQD2JGQjfQ5zjtWl5wT5pQmBnCYZh+fYdjWLXMZJDxSEFSoOcLu9NxJyBweQPUe+abLJ2kTd0SykAgsG4Rckgn1OOfr3rMSllTdmMgHAJAB479iePviq8SOQdoAQ5Xyqff3P8AlxXuyRMlEjVsYLPlmP2yAPbipsRtE1ZyykRhgMebYoXOe3Pf/X0qPLcupjaOFSzSKuSwd1BP4sE4wM88+vavZ7eO3hae/nMUC5JkupdkY/mB+VcbrHxc6V0XetlPLrc4OClggCA4xgysOfyB+9bU6U6jtBXMZ1Y010nY7YRtKDvMkuTlN2Aoz7KK8mRbCCS51C4isbTHLyyLEv8APv8AlmvhGt/G7qC98mh21po8D/8Auk8SUj6u3r9gK4C/1W91W48XV72a9lPJeaUuSPuew+gr0af0yrLObt6nn1PqNOO4rn6D1j4t9K6PuSzluNXujwvysfhx9zgF259ucY9K+e658cdfvRjQ4rXR4n5Vo13yj6F2yQftjFfMnKrENsmShPr6flWwl9w4JJX0HHNenTwFCnm1fxPOqY6tPR28CTqGq6jrExn1m+uL1yc7pJGb8uahbkjCLGig4PG0Z9OMjkjjjJ4raN0RYEsw3ZBXGMV6MbcxYLHg49RXekoqy0OFtt3Ziu4qHALM34gBtz6969eZpTmaSSVwqxqzSE4VRgKCfQDgDsBWDrK2FVtoPG3dj862eExYsx/CM896sVMAFby7EUn8XB4IPevEXdGoCfizk5yc/WtkLK8bpIclUyWzjFaS42FoVHrg+9AGh2YK5LbeORzzWxGDSkPhmbk+1ZxwhY4lY4dskH1+305rcLWYZ3IoUDvnjApclJmppNjZyF3YIyM5PbNa1zN2cvIScqEJNT3sTcKBCC0p54OcnHfH+Vb4rXxJYrQHmQEgDO0gem4VXaSLbLK+O1PiIABl8fhzx64/SpIsfMvhK2F+mM84H61bRWrnxC0TMLdwEKghQpGePp2zUi7b5SP98qs4kCtFtYs5x7ZwB35rJ1Hc0VNcSJp+npPcplZjGkal0YYx3Pb19c1riiw8uDjG0sj8cE43HPIxjiugg228Uc8O5XL4XLblzgjnjOP+VR57V0t2uEa4kk3NIJGXJWMt5fL34NY8pdmuxZERtOaaFUQLbbT5pZJtmR25B/M1uFlFtCpcwyhMZ8NsBQMdz6n/ACqfaLEqfuy80spyBsVlBHcknn3PFabOzZeLdUt5O7CLILMSe2c8dv1qu0y+yjfJDHbKkbqkpOSo27N3OCce2eOfvWyJ0cebbHIp2BEYMA3YBlPBXjv9KsIrcz2/hKs5t13RhApHOO5JHI/TJqH8l4ZeNJ0EgjDvHgZ28AkEHkn2H+NZ3L2NkSTvsZii+KMtHCVU7APqTwT2BqZJa2qW8kgSOaMjdBEjAEHvksMeoJweeKwtIxH+9iDxwgDnBJ2kKduM8+bv6ZqfbW0syxRQIGjmuC7+GC43E4AySoY53HH5DmqNl0i60SK6jtJYPGlNxNCERI1J8V8Mckg5PAPGePXGa6ywuTJGjyDxZIYx4sSTEZBCt5VAxIeCTnuPU1W6cs+mQ/LNFFeShXP7lzEzuFOAxb8BxwS2M59aurNXYM1nsaH5ciO1llOAuDtYhSuHGw4YZ/MGuObvmdkMkELWYthdNPZRCPxis95+8TcRgqGGcFWycHBwMVutrrAuAs8qRybXkiiPhSMucsjPk5Jxny+X0GK9lsktp/FtfmZEKK6i4jHhSlmGJSQ3cZ549R6Voh+UgtUF0GleBnV5In2qqDPmcnjYMkjnOfTtWWTRroea1DBEzR3SXT2bszsxchcKBsJwBz5eB5jjOa4fXIWmme6hiFvArtG20klhjO7cc8nI9B2xXfSQm4iuIVmF7cxyhZZ5EVT4Enm4UE7iMAfXPbiuT1yG/uofnHZvAE5O6eVY0dQc7Qo7/wAz9AK0puzMqiuji/BleKJXcyzADaHbAH2XtnHOP8qrER4541mfmPCLubd39Mev3Jq5keO+adpI0llcopXdtEhHbsBx27e1RboSSRww3cM5VTxCjFSwHG1SOx7kA57Cu5OxxNFc8CCATZZ3OPEXwwqggnAB/P7VEeFhBCzKhkRgsqse5z3z7VcXyxG5M1qtw1uBGYMzZB4A24GB3HII7kn61qktXmjWeHTVgZkLNwfMS3dgSeO+CO4q6kUcSsfS4lglndSFl2rHkcs/91QPpxn1I7cGqmW0ihkfxl4LZjHi4Pf+IkDIA4xXQX1q8Rt/molZTIPDfxiqg598444yBWpY7JpCwR5iWYhXyUcLgYwRkDnOfpjFXU2VcEznJ9Nk3Owt2KF1PCkkL7j2rBrIZIjZ3dScN4nmH0K49q6Yw/7xHNfzO5YMCBuwhBwxyPX+VR7q3u4lYfvTltqo7F2bGeDgYKkH8q0VR6Gbpo5c2m2WMMrKFG5vDHDY+npmo/gSgowRkBHYrnjNdIYX8m+JY1cbjEHxux3yf8P86hywblnEh8CQqudzcY5xzWqqGTplEY1QDeoUk+bLf5V6pYQuzeEqhgAWYBvXhR3/ADqzktZIGV12yNGcAMPp3I/nWuWAoySoytKwAGF5yRzwf61ptIz2Wit2kSAuSFY8hjjP0NehGRGXeF2gtuXzZ+mf8amfJFnKo+ZAw3ZOFz6Ak8frWqa3K3BVeSUB9hnHNTdMizRHhMiFVAZOM49881tjfaxCSlY+CeO/0rCUuqlthIGcYJHt/rFeyBufF3LyM8cf9anUg3Ykky6HJzgkjBIrUm9FZlOF3Eg7jhT68fbjNeSSM/MxAUfTis5BkFnZTJxxx2zz+dQTcwjXwCk1q7290o/tUkKMG9wRgj7V9D0L4w9VaAiRveJrdvEuNmoAyNj6SKQw7e5r58fDffJIdrSHcp75bt/r7VtjkAhRRvZwMDgEZ+g/zrKpShVVpq5rTqSpu8XY+/aP8dOnL1R/tBpt9oshwGkhIuocn18u1wB7bWrvtJ1PSeoI2bQNTs9S3c7babMgPqGjPmHH/DX48TcSJSisEG5udoB9O1blaGBxN4o8bcGVthV1YezDkV5tT6ZSluNr1/v1PQp/Uaq31f0P2BdWSidRcAiRVyGJMbYP9e3rislG5AGTaGHZm3j6AfyNfm7Rfiz1Zof7uPUJNVtB5TbaqTdIB28rE71/I19C0f47aZM5j1zQriwYctPZTfMRYHr4b4YfbJrzan0+vDRXXcehTx1GersfUVueZPBzFsO3kcZrFx40qs4ZW774W25P/EOxqr0HqfSOqUR9E1GG7kkGTApxImO+UOD+matWikRjG6urg9sHI/L07158oOLs1ZnfGakrp3NySSAqu4SAnPPlP5g1tNyyqFLFOc4/hz9u3vURhtyNxcIQdpGP8a8d3QssodQ5IAY44/XmqNF7knYrbmUshyMbfwgd+3+NeSByQwxLjJUg7cfUDtnv6+tRo5wCpO0kjIOducd62M4clmxngjdnjnnt3qtibiVtwbGEkIw+RyPv6H2rXIHTyplsjJZfQ54yvtxyRWTSbt3Cn059D9x2PesROoUbw0Z7AkjGfuP1/wAqWJuePKTDKjASLtwccfkD7/atLMdxx++wACJCVcZ9m9fTuKzlk2K7N4ZX1diB6di3p+deRBUdDcZY9yN3oef04qLC56SgBJjLjBXYxyccevrx/KtSPhNsbEBVIBLYJx6855/TNeeILiEM0ZRXGQH449CB9uK98Tw1Uv3YY8w4yPb/AC+tWSIbPHkeHLQo5ZR+AAd/Xj371okV1K7kwwJzgcBu/bv+Ves3hRlChADeXYTjBPr6j1rBEdAzsN3mO3w/Mdv1xz6n3qSD3PjgJHuycMABtIz3+v54xRGwHZQwiyQy4z6/hz6fn2rW6JdRr5EmjHIKuCc/Ru6+/NJRtRk8wfcApAOf1Pp371JB6kpBAbz5JBG70HPGMg+3B9amwSTBNoZSd/mj2+Ufnjk59arli8TKhlUB/KBk+Xg4wPXj2qTC21gwViVGCxO3Ge/bIqjLot4JInlQbEidThc9gPvVR0I0kFh1Q22Ha3WOoKoYkgHZbkyeb8+/5dqtbN9zIrllDEArwBz9M81TdIhJ9O6tWBZFR+u9VijVl8wURwDn8sk1W16M/L+SW7VYef8ABY38EQmMunXfzSwoy7xDyCRuLFueefy+9c5qMcUm2doY1u5E2zPs9j5Tn09e2K6zU5QdPkMJAtXuDgeFtY8EsXbGTnA7EjFcnqdqqNdGP/2jLJCEwAVyMjP0rnos3qrI4nWmLxSMh2twcFcDPIJNfDuvW39QMRkAwR4z9q+56mPxbTuyOPWvh/xDkaTqWVnbefBjGc54219l9I3/AC9j5D6tueaOVpSlfUHzApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUAqVp/8A6Un05qLUrT//AEpeM8Gqz3WaU99H2fpxP/N0yMOZJISq7cEgAtkcc12GlTSIyJvVka5icc5AyOff3FcX0yrTJbRgbi8qKB/ePhH3NdfplwtvKkczklRA4O4E7goP97Hoa+CxG8z7zD7qOtsXWI3W9Mh3IGVzgcgngDP+sV0upkf7C9WHCsrQ3G4ONuVMOc8CuVsyoyyOGEnnJB455Hr/AD+tdNf/AL34b9TnB2/KXhHm9off1+2TXk2/2R8UepJ/6peDK+YskkhOQ4OGOc5Jxj25/P3qsuVVLhvFcHPcO3cj2UcDtVgIgzkclMAqFGFGRzmokyqAFjYr6khv9H9K1hqZT0I8rxgOwjUITgiZt4H68Cs934VPO4+nA++P9dq8kkbABLbduOPX7eteldu0vtUEZ5I/p3rpRznqO+cEhPMRuxwB9j6/5Vt2MxBw0hBIzn/X1rSux8FT4inuMgA4H/OsxIQvD+Hk9z2/U/4VJBsVXbBC5OTuBOAD65/nWcY3KHR0SIAcIN2R9D6+vp3rWSijzopUgg7v3mc/Q8fmBWckjMPKCzHuePt9hVbEmwR8+UKCGGd/m4/w/LFZkEgv+Jc4DNwvvz/nmorMDhXZzIDwFYk9vU4Oe2ayQnJaZfBYr5nY5JH17mlhcl/MDbmFCUU4xGoPp/ebA9fQf5V4zEhxuAIwDlzIwYHIweAOfQD1571oVx3kDMwHZgMe/wDod/pWaOWJCLjPKkKc/p6VOyRc3NLGHDMQxB4Lnn8h+nA+tay7sMRozrjBIIT0+vf1r2dYrKD5i+uILO3XOJLmRIlx2xyR9frXGav8Xul9L3raTXGtXIPazXYn/wAbYB+4BrWnRnUdoK5nOtCnvux3ASWMZlZhkBsKvLDtnnuOO9ZSrHZwG5vZIbKFODLOygD/AMTHvj2FfCNS+MmvXm+HQLS10mBy2PDBnlBOW/HJkAdzgDFcLqOoXuuSx3GtX1xfXEnO64kMh/Idh9hivRp/Takt929WefU+owW4rn37Wfi50vpO5La5l1mdc+SzB2k+xkfg/lmuB1L446/qG5NDtrbRY/4X2+PMR2/G/GfsK+bJOjZ2R7iD/dA4r3LMpLsc98DnP+VejTwFCnqrvv8AbQ8+pja0+NvAl6lqepaxN4+t3txqEx/9/KWK+3HYVgPDCIibGAPnDrtUHHGD79+K0nZGQMAKMnJPIzWzACKS6MoYDeT6+nHpXdZJWRx3bd2eqjPFwPJs2kkcjJ9Pr/nXiRYk2Sj8Xpkc/TFbVBkRvAc4zjtjdzwPp6mtQ2k5RQrZ4+v1zQgN6yiMIzt3A4x6gfbtWCzeJNnaSoJDAc5AFZ7tzBpYySuSNpxz/wBa2W9sNwIkCEtu4U8fn2pkiDUkpbZhfcZH0+lZoUc+X8WPKDkAn6kVmUTaoaXzKMKMkEDPbFbIbVm9s5wQc5znAGKNolJmpI227iC/BA7kk+3FZLbyKrbwNxGAuQfzxmpkduI4yXciVgGxjCrzz9zUg2ymHY6xvIzDDMvY/wDT0qjkXUCEsRyEVdiMPKFGecYJP1xWcdoVSLbjxnjI2uCVbB47f6zVva20FwoVEKHdg54wCe4I59M1sTTmZXWSMjwiHIzswMnvnIOePbPvWbmaKBWQ2guGd5FG/wAXzBmPAwPfkev8sVNSxdldIHEkZGWJIWMe3PfH39eKsbazRrlQmVZVC4KBS2e+QTkn1+lbBaRzQSzCRvA3M5jzwjcgEjjPrj+VZOoaKBW2ttKHgZoZCsxEiSAhgR2GCD9cY9KsILdYGCSzJ4e8EQgA4I7EHBIHbtzWdsr2aJbWds8QmxuKKsajjJ7nt7nA5NTbRvEZIxGQG7KYxsTjgnjJz+Q4/Os5SNIxICQGSBon2bhhTsTbx6gevcYJqZdWBkminIR7hoozJIhBGwZ5LHkBfUHmszb48eZBHH4KkStG3CkHAIB/hycECvY0nchYT3I8UpGE3Ac7FHr3JzjnFV2i9jG501UYT3Ny8ccRwZkiwzZAPA9eDnIz6H1qZZSwymSJRLBFc7jHLbpiRe2cqTjb64HPfGaz8S4ljWd7PxoSrMFjTcRHn8PH4WAUfYVusNOu1sy625uRKA0myEylCpOQMA5kx/D34PGOao3lmXSzyI12diwpCQi7vKQcEsfwsVxnHrg4P3rZZTjYRNHItsTsJK+GPIQTyOzFse2fT1q8senf2hDPceKIpFKfLpGcmUh8NvZvwEAkcfp6VeW/S9taLPFcTbtkgUyPGWUIRkM5xhVU4YfxH9aydSKyNFTk8zi7K11C78XwIxMzTMiJGhIDE45OMIPTnjvV1pHRV3NA0khE4RiscEU6ZDg7WLB8e/cZ4FdnJcvHLAAkV7cSznfEbAPEp7iVmV+XyMgYHfNWOm2KSO99HHDNLJIoKmI5eRmJAxt5fnBwe/HvjKVZpZGsaKvmc3adJw6bYxzXgMi+GweWA7t+DuSMN+AKdpXsfWrLwpf96jlwLYeGrRzLGFjKkEkcDBy3oQAMHynvsukjurjAEnzFs6lHjkMYTKkbTgfhHfvxjnHarR7WaTw4orjxfmV5SOWORXycmPOcJ5xzk54AIwc1i5N5s2UUskR7y2Evhy3MMsgjX8ME6ssQ7qQhHbtkg8f1jxbIP30L3kSLtb95GF8IqCrKPxAbicgjPA59BUu6YwzhdOlUeEhLxqNhWYMQ6+G34cf8XkIHbJxUqzvYRPIUuZfFW2QqgLeGXZeFbLA78kk5I8rYGO1Vu0i1k2QbewDhYnjU4dSGDeZwM7VDEAYBHH/EQD2qLbtbtEpnvrWeeRlaWJk8Eg4yd24jzncp24PoeKsFa4eSVbwWa2qSZjZ3KqPNy27JwuckooyR68Vhaxwy6UZ5rq2CQqqm4itJXjYrjA24zwfUn0GKlMNGV8I7iVoWlRYo5A8c0LlO5xtAwWGAW5/CfQVVnS5orhVv5bq3eNw0glKn90yAJsJGGRvuTyckcVa+TdiaAyW5lZVvmgkVuI9xjBIGGG0A59ORWMkl6Waxh8FohESFuF/dbHIK8tyOFzk4IZfbNE7C1zlb3pG2O5pVNmko3wpBEEGGH/uskqePqOCc1zU+hTWkDzqVljBxK7Y8RCr7cDHJww7+vr619PuPkdP0uWVzBGYxsFxDdma4lGeBtUbzlmHbjk9hWm+SALcx7bfAkdYU8PahcHc4aRySCcgnuCQfw5rSNWRk6UWfIb21dbqTMEkEmOd5I3nPbsMDBxk/Y14yNCqLGOG8yGRAHjUHuXHJz2A5/pX1a40m2kD/AD0CNNIdq4i3Zx2O4D0POCeMD34o9R6Z04pBJNcXZuZHeVha/v8AYAQhYr+JgSDgZ9M8ZrVVk9TJ0Gj541kLi6uvGWW5ZFQRq8gjVTg/TgYGMj2+ta7YSxs3hJ8tI0zSZibKoq/iJ/iUd8d8+1dBe9MzD/dop451RjHCF3RHKknk/wAPHue5quvIZ9Pup3QNbSQuQ7NIBmMcY3Hg+p4rdTTyRi4NZsrL2L/dZQI4riFZQUG8IWJKgnHoTwdw459KLPIoLQTMNpw+7L4QYC5X+p4qza3LvHcgvyTKYWAUK+OD7bSR6/41Z3Et8mnM7XFm9scu6WsSr5SRkdu/uMj71O1wI2OJzfhS38e6SSAxIrSFIp+QOxznA+vviqySINExWGWOF4Qu12BLc4I9ec4x29DVlA1tcErDE0UYkO1tmVYHsTgZHoKxMAkjRC4SNSFXEeBuzk8gcknIOfXFaJ2KNXK64to5mVriZ4QoACR7ckLnylj2Oce/ao3goso8bL8K4ywODgH2q9S2uTsiKsfLlnaPDMM8qe5JGe1RZ7UKrHeEVwrqXwHdcnnthj9PQenNWUyjgUxtvDxJHu2Z8yg9iQe/07CtMloZXLRMck5k/uhse/t9K6FoFSBVtnJBI3eYKc8/qOOfSoL2rRSYiSR3I34PmjDD+H6++R7irqZm4HPz2XhyxzpIZd6kBTnAb1OPasAoVZURY52lB2REEAe2Dng10EsG3a0SuS+XlyAcAfxD6HtWk6bJC0zkeVWKlto3AAZxn2+1aqp2mbp9hz0lvIA6uuGRN52uu3Zn+f5Vpk3Z/eBgNowAABj61dvpTMBO6M4ABDFtoAA7ewNYXFqI1SSRJPDbBUyIcH6Y9a0U0ZuDKyOUFCG2MFB8pTP2rWpJJLcFuTu8u6pkkcfglYw5ZiTtGfTkj+dYJDLAFlZW2cAjhj9s+hq10VsalDLGfNtVWyPUZ9sViFLeIYlG3+FsY9a2x5cuqRlJTlgxYebnjHtWhZNpIYM3JGQuefUZoDLLEKiRkEnBZew+4rDBWNllkJ9cA9sf69a3ENEgEgAbYGKcnk9v+VaJNoffH5cg7hj8jRA9RTnLqwdQXVtxBUdwQe4rrtF+KHVegIqQ6o1/bDgW2oL8zGV+hPmH5MK5iGSGMO7sWcIypnnHHArAL4gWNyc8txjIGP5CqzhGatJXLRlKDvF2PtmifHSykjWLqXRbizYABp9PkE6N/wARjfDD8ia7/ROq+n+pUCaBq1ncyPwYFlMEv2MT4z9xmvykreI4ExJOGVCxHIHavMIeLuFGAUhSzfh+tedU+m0Z7uXzv9zvp4+rDezP2JJC8cjqyfLydtrgxMT9BjaR+VagWQHJRifUjaGHqMjP64r819O/EbqfQZYrfR9auprLco+WvcXMRXgHyuDj8iK7nTvjmhXZ1BoGGLkGfTbjYMZPPhyZAPHoR2ry6n02tDdz+d56NP6hSlvZH1jxcD96JOAR5hgHn3FZo6ybxEyMcYbIDKfrnv8Apmud0jr7pTXn26fq8EDjnwr1fl35+5wfvmuiktCm15AQrgMjqeG98EcH8jXBKnKDtJWO6FSM1eLuelmIy8Z3bSMxv4mMj2474rWwDKI4gSmdw2gY7Y5Hoe9Y+I8ZyOVOeef0r0ybhh1VjgHPc/5/9apY0ubXYj8QHAGeMkn1FeMAytlSM8HA7/cH04Ga1SbkLFo3Rk5xjB9+/f3rDLExsrrJg/x8kZHoRzxmlhc9aHarNGIzkeZAQpcYGAM8HsOD9a1gBm/duNqcMNuBkemB2xx2rKaeNSsbqyl1JBC5XH3Hes2YyAs+TjPKjB/x57VFibmlVG9ioI9WYHP2B+9HYsEUNHLuypJGDn2wKzBaEBRucg8FuGIx79vX9a0XDx5YOWPhjyEAkY9eR/rvQg3Y2ueCTwCx7D6Z4962wxzusrxJlBlePU4yPv8AbH581XMRHPu37t6YwWHqffOQfuOM1KW5MuMqwUZDAKrrn15HOOR3NUkXRdWbFJLVkJVwVJJGADn1rnuiLxpdJ6vku03SydY6vNIChOxmEOSMYAPl7Z4Bq8tz8vPC5bauQQ6nKH6/65qH0Vb2djp3V6Ok13Zx9WaqWEbDLBktmPKHHBbtngDB9are1Gfl/JZr/dDz/gm3t0Hkhu9TBuVijIS3xwx2gYGDzjPH2rlNSBjhRQHdooHkOecZYZ5P5fzrqmhs3WzhNywjg3uWkCSGdicmNcfX2zwOK4q/jdZrhlHkcFSqllAHpxgc/U/WsKKVzWroczqpJBLYU8E5xxn7V8L68G3XsEYPy8ef/hr7fqC+LbGLe6I+1S+M4+1fEviBj/aOXBziJBn8q+y+k7/l7HyX1Xq/M5elKV9OfMClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCpemjdeRj7/0qJU7SY1e8Uu4jVQTknufQAevOKpPdZpT30fYOk5FQQSFmUpIhBCkgeQjBwf0ruPCtLhGuIUkjLNDGyM44IXGc5Htn9BXzvp5i0A2YGMYwuDkA/wCFfStNZG0x3GCVuYsEccFG+38jivg8UrTbPvMK7wSLq1GLS2G7aEi2lsHaCCQc9zzx69/aumu1KdB9SK24sLe73YGTjweOcd+PeuWtnLQN7oXB5yfxEg/zP6V1dxIX6K6mOTKfCnwSoGT4JHsM15L314/k9N9W/D8FUhLIzHaSVXOT/wAI54FRJZ1jJ2qFP8Ts2B+Xb+pqQwKxkspiXapIc5YcDg5qruFUE+ZskfiLbj+nb+VdEVmYzeQlnD5HmdhwwBK8/bv+tI9wLLtVOewGc/c5zUd7jtgjAPOzvn0/pWoJuJXYAx4BC55H0rpSOZsm+MikEct9T6+59v8AnWD3O1iC2CRwgXDN7Yxk1GCuwEblCh54G3PpyB/nW5QiZVS232HAPvU2IueqJm3bIY4+QXLtk4raibiC5aQryVGQD/mK1XlzY6bEJtZuIbSEjO64nEeeedoPt9BXIav8W+nrAkabBdaw5PDIDDAT7Bm544/h9a1p0alXcVzKdWFPedjvYy2xvDiUepCjGfy/Os72S302EXGq3kFjCMeaeYRL9+e/5A18L1X4r9R6i7Q2UkGjRkZzbx5kxj/3jZP6Yrj7q4ub67e6v557i4bvJI5dz/4m5+vevQp/TpvfdvU4Z/UIrcVz7jf/ABZ6Zsysen+NqshYKCq+DChPG4yNlsYPOFrg9W+MHUepKDp/g6TF7Wy5cjGMM7d8984FcUZTLGmSxMY8NCWztHJ4+mSa14yckr2zgcEV6NPB0KfC/j8sefUxdafG3gZX1xNqdx42q3c9/PkeaWYuw/XsPtWDFdqlf3ZX1I3Nn6VmAsW0thWLYP27ZJrDaIx+5wSfUH19q7V2I5Lnhc7W3Z2gZryNVYqsKBm/EOc5/wCdZox5IjK8Ecn+XNZhQSYs7U/EvO76Zx65xzUkGpEcqpTEYYdgazhGAwJPiAZwT7YooBDnLKQcNjtzWSIHIG0MTwxzijBqc7yh3knBPbGOfWskdztwik7sMM+nvW2GzklkYRq8rYY5XzYAGS32A9fSpMcHho0oZtkXLuqZAJHHf6+tQ2hZmKGYupibxACXIUe3f7mtsdmWKuXZQ2OW4yOe1SPl3V1VZI1ZlGwAYIyO5I4rb8ts584YINspOc85z9fX7Vi5GqiaLRS0kSQfu3YsBlvxAf1H/StqWsjhVuWKKwIztyGAHpzVtFaSYhc2qyeLu2bhnjHJwRnP0ra8uC00VrA5kXazuC0igcY54QYwOO1ZOfYaKGRXR2MKAKk0YBIG0DGMnvnn+WcVvhhh+bVJpFWQ5WTLcBsYB9wPvj7VaR2UYa1uHCm1jDKX2gqwwM8D68Y7+tS5LR7i1juLgPKkC+FIZjuDAduQfxc/XtWTqGqgVfy0aQCaLw2ZDh8cjjgkZ+vt3zXm1ERWw7RyMIg7Lxu7jH8+TViLCWN/mH8oQb1XA3MCO+D+Rz6itgtZVRvGu3VnkQO0i7/DwCQDjsMAfriq7RfZIUMDhI41jSSQDcFZ/wAQJ9iPrz+tTIo1kghWNjdhlZWcBlVefwqfX29M4rOO3mt1+Zhi8MJcCPyHxTuB8rD1A798DFZtbAp4Y2qHyyP2jG4csW/vBs8HtVW7lkjyO0zIHtwAse5BMiqOdvDbsZJJOPpXq20QtZJEgaI7A25ocAL23kHgjJOD7j0zW62+XuJY7C1lkivsCRQzoE5IBYHPYns2Dzjirax0241PUZFgQMJsliJMJIAe+QAASQePqBVHK2pdRvoUsCXVzdwm2gLDf5TFEMMce2MjsODWdvBBJK1tJDNBdLKwLMcxL5Rv47s55yewFdQelwbeCS7ka0keRy0hYqrgFQABg8bGJz6cCrjT+k4J7T9xHvjZSrXM8XHHHAHBGRj1AI/KsZVopGsaMmzi7SxuLqTZao/hRRvJFLkLh1AHAPLAlsYHbPPtVhF05dxLKxCpJbskkyqdr+ZtuRngkMVHbkHPIr6B4729lYw3CRXGnpMzW882TJHLhuy48g8pyAoGADj3rlYvZ7d0U1zHvZxDIAszlwUC4G5WIHG7uS3C5rLlmzbkYoq4tEgQSKkngb1EYAL+Zw/IJyu1j3O3nt2q4htFnlilvrqaKHKyQ7hty7nawdCVyTgHO4Hn1zUln8eBprpVmVbgSKu0RzM8uQd7A+bKjB9frxWpIvlYVF3H4KROJLhJNytww2rFKOGyDgn0IFZOTZqoJFncTpzDutLeOOYCUovieICDhmcHJIIAxgDnjIFemCDUtN8lmILO5tXtpds5cPM55bjOWDYwCOATWvTYV+XcSSRNZGZZZdisSx5275y44Q4BBJPoD3rHUI7SQRDTi0NxbTLJbslwUBQhlK7HDqGyedrdu2MAHO+djSxIa0TTrcXd9GtrfiUyM0AkaQyIoxH4QyvIGSTtIyQdwxUe1mvGmguRBey21tK0cMMMRlEe7+JmGRuU9gfqPWtDT27315aQJfR3XlkeVwULIoKrhlyzIpJPGPqTmplnJqdhe3cqXHy8j7Qy2twolkCHGVj7njdkDk5zjtU8CES0XxVl+WnvBBgwsVt907hl3MmFwM4xkDBHY1oHjHFwZLeWAKLctED4UYU5KkgbmypBGQSPMOcUeKVNRkdtTIyDKLuRww8RsqST+IMSxAwMjBHatzTRRo5eSVkdsyQwgMVZOCS2PXcCV47nHrUIMyubeC3tbXxbmyuzGAIDGXiL2+7CqcqCQFIHKtjIweM1Dshb3kRghgtIruSMRxg5UMnBVmBOJAu0DcCD39s1ubU3hkRrWPxL4wSRj5qAXEhQkKAWX+E8kBsHyjvitEkJvcG5kiulNxI6bI8hf3eQHVsMDzu4BOT2xU8CDeY7dpoI55lwTmWcRcRDDSfgwwZThcc8Dk+lbrSX9oRm38+8bpZUt9hjkBRim5NvI798e9alUEJHatbQs6AW/ijc24rny4yrM2DnIwO3pxsaFro2gS8kg1RLdRLcGcIsjOgwrBgANv4dpyOeDQGDW08LSLC6X0eXklVB5pSApDL2B9QeMYNePDLM1ukEZ1JppWk8SZDMpbcTnKkDaDuUA9ioJztqLcQTwvZXSNJPGZpWkKyuxdniVRgcbcM2SFJHGCD3rO0jNra2lsZbXwlVRJH4LbgEBwANpzlm3FTnueSDS5NiOus/LpC1vcF3kmjgKvEqM6MDw7N+IFkYZC+UjGa1sbe5tkXT7RdO06eOVtxRJDtG55gowWVmdl54JycHAqdcQmWykjjidLmKSeSWSdWRUREOWQEbjsDED3yDnitVrBZ/KyzWtzHum2NJHFiTJCsoZ94CtlX2hVIJBbuQKjaROyS4kn1MQxWbRsZERt0kZjllPhD94xJ29g3uGJz9Kp7O3a5ilk8Fbv5eQhiodTEFIwUZWDEhiDtAxweSDgTLiaxn00SWts7IxCrevcjwwwO4RFCBkAexAOMj1olyZ7K2srC5Q24l+YujlI5nk2gxIFOQoaTJYjGcqSeMVVNos0meQpNYtAqXdzb3VwVtyhhi2s5YbRsHLMO7duSTk4xUa50pJ5nu9RvLW3Y27MiTE+HLjKqq4Xb/AHVIO0gn0HNTxYJIwgu74R3iqxTwYV80gAViN3dcgncec/WocUdxqFpIESGCSIhHHnhDk7VVXUsSzv3GADgt6nNSmRJHP3WgxTeItvvFyW8KRFUqFDf3eNoHAHbJJ9O1V7dMzvEVaOW3tJI3aaO4iMrbOx2BSxUnGdxxnnAPr3OowlLyTwGa0VJedkDpE7K38CuzEgYG4se+cY4qPayWdrenxMfKvdiWaBPMqMpbzMAVO0EYyTj1rdVGlkYumnqfLbjR2014lhtry2txGSjvyJScED2Ddv19PTQkTK0MtvKjXjyBV4yY9uD5t3cccHtzmvra2lpOl1Abi5VHZwzAs0NwnOA0THKEY/hyPwk8VQatpem3FlE0TmdJsO2IcbB6HOcE5XsABjvkc1qq19TF0baHBSW0qx7SJrmcqxiMRy2W5PA4Yenpye3FYG18WGdCszOCUCsCRESBuyc8AEknHv8AnXR3HTt3FDaGyglEBSQl1hCKGC7uSudvDYwe/pxzXtzpYtrmKcwX04mRXh8JELszHaRk+UHPPmHrjiteURnyb4ooJ7KUKFeCCMSbCm0gmQEZYEvyoO3uR2HrmtF2kcyTfKQhfIGj2sF5BwAoH/PJzVhPpnyy273QEcwXe6Z3s5Iyys3O3uB29MA8V5LZSQxl7WRZJBgTFu6D+IZGfKMjkfmalSKOJVSWq3Yi8YhSz4VliCh1AwQcAliT6nisbpY0vZts7uqyFlCNu2nhefrjjHp61NurOaxv4IpgZrlZQY4ICEV1UZDB/rz+X1rBbiW3XAkXw2mJHhIqKwbJbgckAg85PIrS5TZKQ27WcUnzcQUM/LsoGDjgdvY8e+K1waYZISiNgqisC/ITd2OTxg1eos1yzPbrASechTuAGcDPAYn68nio1tbGONJbUw8FREzqI8Z98nPfjHfvV9spsFLNZiTC+Ism7ABQ85+g9cCoUkaFHe33KG4DbQCefxDj1xXTtpy+BK221gt1fDNHxEWzzx3xn1Pv3qPPZn9nt+8Vo4xti2HP8WBjPYYz+lXUyjgclJEwKuFZvLtjwM855H3rVLYszLsBdpCSrI208cnI7f4mujFr4qmWDeq4C4UFx5RjP1796hfI+CkjM6ySs4kRQGLKQfQY7YHvW6mYOBVeBIGijUEvuCo2chicfhPY1H5jEhfCspKuH4IycYq6nUPIUjjkRWJbYi5YL9AfT1/OoTxBoiQGUN33EcD/ADzV1Io4lcWd5F8oGBjntwDxn3rJyGGASAFC5/nW5oDbBGUFl3ZyRgk/415JEUiHYncGAAwftitLopYxDrEEZkDtyf3h4IrSIJG3FAsij+6pyP8AKpDuTbp4iqGB2jHJAz6ZrV5gcb2bPA29smiBnbANd25BUuZUPPlBORXrTbTco0at+9Yg4yR5vQfyry2c/MQhPMomTjPA8wrWMtPIwJQs7DJORgsc/agPXh8OQeLEAGw8asBgj/u1f9OdY6902HGi6vc2kP45IAQ8R+nhvkVz8zSCXaBjAyCrdx9/evOQNp285xjg/X/rVJRU1aSuWjJxd0z67pXxyu1lVOo9JtZ4sjM2nsYZMAdxGTtOPbiu+0br/pTqFvC07XIILl9uIr9Tay59ACfKf1r8zs+Ewx8RCwdlIzgjj8+5rF5SYjE5VFfkIyb+O3r/AJ1wVPp9GemXgdtPHVoa5n68ltZI0WXLKJBuSQEOGGefMDz681HmEkjSg28NwpHLqQWPoAfUd/rX5h0LqbW+mUb9iapcWq8AwK+6Fh/xI2QeK7rSPjjdK4j6n0iG9GSPHsn8F144Ow5U/kRXm1PptaO50vT59z0KePpS3svU+vvdTKo2IyDbg5bgn2BGD7dwO1ZRlCq4RYwreYqQpBOPXBB7/SqDSviB03r7LHa6sizydrS/zbS/lvyjc/3Wq+ls/CYNLHJB6hm8oY+4P4fX6150qcoO0lY9CM4zV4u48ZkVgjl0ckYdNwz/AKPeoJdUYtaSFFOAUXBX71JJILEKy+XgjKj9ajOXUAqijJBJVgpHr6d6rYtc2IESQSC2aVE9EPBzxk++fapGVRwYT4Z25CjOAcfTntj9Kr3kkjjCQNvBXbtfy5Hc/Q8ZxUyO6UqrruMIBIIYZLfXH+J9KykjSLLWzkUFUwA4YZOSMjvWHScdpPY69ClxGbZuqNQYyvGMcrBnse24YBP3xWNpJI0ilz4gLBgG8pJx3IHGMn1qL0mAuidRgLJMv+1mo4WFSzScw57dv8Kykv8AVLy/k1T/ANkfP+C517XI9TEZt7QQPEFkSR4QJWQcL5gc+/cVx96wneXEpEQgLFGXgENxyVBPc11epxXFxcXh8FY47aNFyhBIwMquV/EQD9uOTzXI6hcJb3UKzSOHniLRqFJUqTg8H6+vFZUUtEXrPtOTvSVV8ZyOxHp/yr4j1/8A+scnsIY//la+3X5CJkn0BGRjOCR+dfEfiAc9RyH/AOcx/wDytfZ/St/yPkfqvV+Zy9KUr6U+aFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFSdPx83Fntnmo1SbD/ANJjz71We6y9PeR9X6eKiORsjhjuDDPAJ/wrv9Guw1nJAJMFyjD+HlSPzPBrgtDA+XkGcqpJDMMjOTXWW9pKltE0QmUDs20j1Pbtn7V8NiUnJ3PucM2oqx3FtlHmAO5UkY7iee/Oef8AWa6flulNfRlUs6XKhiM5xATn14FcZpUskolEnDqx5C7cZx39f9Guwhk8DpvWSQct8y20jvi3Ykj9OBXjyT2ketdODOct50extnTDgwhlKvjj0JH+fNVk7o773VmVu/GB98Dnn61jD1Z0+mlwPPrEMB8IbzcBl3H8XGB5q5fV/inoVuSNMhu9ckI3BsG2hBz7kbj+n516FPD1Zy6MWcFSvSjHOSOoARSFiQKO2WU+Y49vSs7l4NMiaXUbiDT4kXc3zEix4QHuB3P2GTXx7U/itr91EyWEy6XHKGjMVpbD8Bx/7RssSTntgj3rjnuBPLvuJZp5c+ZpELEn6k8/zr1qf06bV5u3qeXUx8FuK59hv/in0/Zs8VhDfalIFYhgBDFux5Rk+YrkDnviuQ1f4o9QX8aiymi0mF+6WakOOecyNlgT9MelciHijH9gyluSxGR9B/rtWvGxyQyMc8qw7gV6FPB0Yfpv45/0cM8VVnxt4ZHtzJJNcNcXlxJdT9mkmdnY/XcSTWAYtEcDa5fDNg49MfnmigFwoHODwQe30rbv8ICOMhUc+Ydziu3Q5LhUdnZ5ixx5AcZPHvXoYrhS3m7EA54rxSzSsYt2DlmyOMdzXqspc7QV7EFe5J+tCDLG2NDvYDO1gOcD0qOVYqDMjAMcA9iR71tBLFdzFQDhto+uO1bAgkYgEDYSAee49OaaA8VwpjaQ4I7BxndXjEGJmVOG4AHrj3rZ4Mu1JMlfMdo747etbBaxgK6I0u8kmIHOKi6QszQsUjgeF+7ZiDl+Sf8AXNb44CAXk4GCSw/wqV4LK5RkSBQPOpJ9uASfU8cfWpDWR2F7krDAzFgoGcnHbHYfzqjmXUCtWE53KSyHzfhzx+YqZJE7yPIIwV4OVXDZxjP8uaz8HKCOM7YWzkucHHYDGeB9fqKsY7CWNDLBE7sjeIUDd1x/PvmqOZdQIFvDG3isMmOYiJ13MpUEZGcDlSe/2qQulSTIGNzbxllIkAuQG2H/AAPuTzUuyt3uUuUzMZy2UZNrB8cnJzx+VbTYNax5dkJjZW2pIHXDZJBx7YHc+tZuZdQMLSBo0YSKCpIbwPF2hj2GDnA45zW9YLURrJbwRWqrJvbEucg5UnsB7c+tbxaQzMzbhtXywoG2gsewYkY7Adz61uSACFmN20viHawJHlC4Pcjj7fpWLkbKJ5LA0scMqo8jeHs8zGQFec4OefXsfvVgixlPlZHdriJBtlYZ3Nt8oZRncCCMMMn3qZoOgz6r8w25LK1spVae62ZyWxtiRRgFyAW25yOScCuh0pdA0cyNH09Z6tHKVJl1HULmXcck8pBJGAfqNwGe9QoSksiHUjDU5S2tI7i3LzpBeGJg87LlY1BHBYjgYI9cfiArdMk2oSpFGoklVlJVkA2dsDJJHOCAR3Ga7lI+neofC0oWF90vcMc2zafdy3lk7jkJLazN4nmPG+KQnt5G7GjuNEura/8Al7u0SOYoGSdG8S1mZWyriQHlSOxxnjGAcis5xlDNmlOcamSOejb/AHhGfap3ERhhlcd9uSQpIGMfQV6bm3luI1u9qEBS7qgyzAHtx5hyPeutj0spFJc6xPa7b1UEywt3BYKGVMELycA8e/asxoMVqjOZ447mG4bw0uHwQS34R/C5AwSVGP8AHHlEb8mzkrPTLnUHECwrBM3imFmUlDjDA8KcevB4xk8Vd2mgrqWjSi1lmlVJ1IaOEv5yBkg5xhlJG3Oc4+1dNBp/jSSOS4thGiXSbcoMAKecncMhRkADPr3qY/gG2t5pLy5lniJCG5PltmG3LIcYDep4yM/lWcqr4GsaS4mzQfhjPcaRaXMEa3bbDLCngRXAJxuwuDvR/wCE4PB7jINex200ZWFbaO32CNLjxsybt5wCI+2BjBwc7gMVIHyzu2otcWkcUl0olk3FgxG1mQKDlsAjdwO4593+1GjwxLbSWd/L8vIzIsNzHbxS7gCMxtGxYrztbfuGT3qsaVWrmkTKrSpZNmEFrbG+W4uC8MImjjnEE3ibWYkKnOcFsE9seTPFRtPX5W+OoTBPEhnEUgachAoJBTaMrt2sSfTgetdBY3dhrc0baXfyxxLEYfDvgoaLODhRH5XGck42EZzg7jVaxwsNxAFjhbM0eo2yFlzvC+EQWOS3J8o4wPQ1jOMoPZkjaE4zW1F3Ke9lRy0ttOLS1tpgksVuquqRZZljZSGOWBByuCQePSpQgkuLOa3uAskLbiMxoWZ1PIQrgrzyR3O4+oqw+SnRI/2pbxQmBgkrsQ0oXbuDKc4JwMnk4BGO1aMWzyXhghtpZFkEywEiQxk4wCw/CwxhePT61Fy9jOVbe8F1eJdmOSK4A8t4IhNG0YBMZ2ZwOdqg5JyDUq+hle13xRq0ZuI0e2hjdJ18PhgqsCD5eeScbjznBGhdUt7a4kuL2e4OmxmMqtnCjHIxhHIAZCQFyeMkVu0xbaC/jvbdEm8C3iaaacCFFLkbmMuMEgKQfT0xzVLMm6ML/Tw8uoEzsLGW3A8E/wALBRJkFRtJz/DuBzk89q3xpL81Dp9u+9C6/NW7wkiVVxli+CwwWGGUfWq666r6QsrdoILjULoNI0g/Z8UarksNpy7ZwNpOQvJ9cVP6Z6j6X1LU47iK/uLV3JEsWo24V2kKntKjbSQQuFO3AFbvD1lHacWYrE0HLZ2vniRUUtZ3IiuIzOsysjxQrIRHhopVLOpKAhl7jsM9yKm2kFxpbBRMUt7WIPOroF8GUJzIrSA5yQpzgj8zVlHA8GnxWzpM5V0S4VSgVj5VkcsQOMH8B4yDnluKazv1+U+bgsszbVklhuHZEcb+VJIww27iAuVycAnuOe7Z0WSJkXVNm9tarBpYmu7fa0M1tMY5m252xsCMsxyScex7dq0WV/FdaY9jaTQtJaQSeMsK7iWTBYMWBL+by8ZBOfTNbQ/zT28FrpdrqVxLOssbNegIgUDPYjJBTChT7k+pqsNjdWunQWks1xJL5EiRSYVRwP3iGN+CeCWzt3bsjNSkirbPYnkiKQ3NrLEHCRzBIl/eYI2gEEkjAzwNx9KsLSfd8x8pLaxTQ4KytZiV33SMzMS3KHPYAY/vEcVrNzPIitJes89hHAwuijSLaksozIy8eUE4B9xxzXmra5YafBs16SSBoHkAtLcmKWXACl9mDtjYjPrzzjjI0UZTdkijlGCvJm6e7GleCyXEmdSn8KYybYy6spII7gLjgjOCD6VF0iWO+njsbO/tATFGzfNRb4oo+AhBBBC4Q4UnsCe1U6fE+SZ0Gm2+iWUUjgqsthDL5QuFLSyK7NtLD0A+1TbfrAAySz6fpcmxEBn0yCO1uY3XsVaIbXK5/CysOw7ZNdjwlRR7zi55Tcu4t2uY5pN7tcR3LbYnMMpKqASN6s3l25BUlex5FYNEbidvmLi4+Xjhz4D3TrKH8uRu2tnBzlWB798Vgk0rW8d5FdrNMHeKyeyjJkmg8zsHDZ2SDJ3Z7EHBIINREx+zrh0juI9kqy48B2RcSMAzOzDcvHIJPP078LjZ2PQUlJXNzzRaWfC8JEkgjHy810uTbd9sjNv3KWznZjBwvHFSYZmInW5nnu7uKXxpTdiQBWZWUFTGdigNu8pAJ4bdxVZPefOTLHbRx+LcK5JmUMr7hwwBO5RkMcAlcc4JrZPcKjPPJP4dnbBrrfLK+zcBwSsfJJDAAP6nLADFVaLXJFtLHLcwS/NSpbSXBEKNHmVxnl1UABV/FgvleBwc4qetvdb5G0NpJHRJI44VmVSysnAOR6ADJIxkcc1HhiimkubG/Jtre6k2EQwNIIwBuix/Ec5AyAfU8VHvbzSOn9QvklmW+jGz91FCgCE53GaYkiQ5JIwMjODnAxMacqjtFETqxpK82TLaGOOCO3hs5LmyvLdhErkPKqI6LjdjgnsBkjkHvWq1i09of/ObvG7IZVJjZh4o4OZFBcEoN25U4Dbcd6rbTqA6fJKw066uFkUG3eTVRKlupIbMZVVCfhU55yO+KutP6hXUZn+UvW0+UmSa4dh4UowhJ2qvDsTx9e+ODWk8NVp5tZGVPE0quSeZ5eaP4OrJJ+zI5YfHjZIpJxKbk4Eg3SBQcFhgHA4UAjiq20ikl06a1aXTjezTtK002AqpFKS/kBydqjA7+uau0tPnL6eG8M7HxCgmkyXV2Usd2DiMNtUA+u7J74qHJdX13FBLJdyW9zcTtcWlq1sXR3G4hMoAAQGUEMcELmsUzaxquRJPCJpfFiPiSSJ46mNdgxsY4BCnjAGTuDDgGqiS2AhFqiPCpjUeCx+XV0ADKnAJHPJ49s9jVzDIiMI5ZNOiuTja11csIgUIUKApOAVXIyO4ODwKgz6dPf32oLqFzbX1tHIWjnhuzKbaRRuZFbnBIwckk4BDYqyyDzMLNkti1nGnyzRMuwRt/ZoSFZVK8kFhncAcAMcYBz5ZWBSae2ugEn8aa3hCXG6EZfKsG43txtOePMcc1eRwaZaql7fRSxQ29sEeSRFcbWOHCORvLlRtCoeA57ZLVz0fxD0HShPFpfT/AO1ba4/eMmqXrgSYyAUijwcL6KrH8I54Nb06M6y6KOWrWp0XaTK260WyWWW5fTI55onRZyJ3AkU+U8jll7E8fbHeq/qLSJLhJotJmuJXVClzBdTECNg29Y1Y5wpAJxxuwvvXS2Gs6Zq+nvd2OjmCGz8Rpri1VrxLZiuVzHIfEAHLZTJADHkZxIuLGG9sYZokt57CWKGHfHcfu9+WJxzzu2l8n0YDHpVpwnRacisJ06yeyfNbqN5r8RXiRyRLbqpS3fYEHr25KnOQBjke3FQZ/DkZks0lWIuvBjCblAA4JJ+nORz3r6FqWlGeWKGUeDbW8CEmAp5F5I8xwpYkcjOATg81US6ebqOO4u7dG8SZfDeSds7GbPIHBGAcEbuM9jirRqIiVNnJrbpFEiRQp4R4ikkJ3EYJyHBHqM9vSspdNuPHiItpXhd1Lv8AwOuc59Sp7gZ5OTV9edNRJEP3i3Vq0ZERkhcglm/GRwVHmHJzjNRbe0d5TG9vLJcKQrLbSLv3HBXJ9BwRx2xn3q6lfQzcLalethHdSSJAs0TyB2uWuIlwEGDwRnAAyoA+nqcVWXsrXMZkS68eJivgBSMnHowxkeufbkDNfRtO0q3t47lYlttTla5DT3N9CZbSNgvZIO0hGMF3yDxhe1Xmq6WunXim9vdMggaPxIWj0W2iiH2CqCVb0PHIOK6o03xOSVWPA+IJDdRtI8DeFExBRYVVRtfAbzZ5ycexFbYlMcoXx2t2kIQMrMqxjk4AUcnP8/pXb6xoiabB4irYXnixlyLOVj5s7yDuyynJ9QQTnB4xXGi6E081ysXhABgyeMxK8AYwAoJ9/v61Ek1ky0XGSuiDILZLlkZmkLcFpPxlxyckdvt9KqbiMOm2OVdok7Y27lGMn1/Xgn2q8eJnmRXDN+7JMqMC6hce/wCefak1gViW6li8NIkJLN/GeecYzzx/KpUrEONzl7m1dJFUKZYpeVYNnA75yeMcc+1YG0VmkkYnAIHqcjv3zzXQSW8khEUjFoxGWZMhW2tjPBPbIA9OBUCKCEwwhy6sfxAKxGc/n6D+dbqZi4FLJCAFDKhLL3Jx69+P0xWgxH95JFIvPBjzgY9f0roxY7pZGQCVdrBd6biQO5HP071Ge08RZJbeHapk3ruYYH+sjirqaKODKO1gdpI5GjwgnQo45B8wFJXWW6lG093wTg5O45NWK2i2syIyYJlQDCnAIYfyzisPlSt3IAgQK7scnPHOe3bNX2kU2WQYlWZlbxFVQPOzHBwO3+hUcBAD4YGcjY3Yn1qZ8m/i7P8A2m7yL7e2fbNa18ruzxgns8Jyef6jmrplLGjaYuHJ3jzbAc4Pv+eaEghRlj/dB7Y/0a2H8B8wUfxP3AHoDxWUiqEj3ASkR43K3Yk5yMd6m4Ir+hV+VyTk4+nGe9bhvBbC4BwD6kNjOea8WHBUMQQRkjbyPbn60EjPhVbuMIMAHAPr7+1AaX86ky8nGCAchv8ApVpo3VGt9PyKNC1KeyTPEKuWjb6GNsqQftVeQfCjMJXB439vTP8Ao14qqisuV3Hnnk5qGlJWauWTcXdan0/TfjPewAL1Fo0NwCQTNYnwJCfcryh/lXZ6X8Q+l9bZY4tTW0nIwItQj+XbOf73KH9a/PoaQ7yAVGMAfXFYSSgu/k/dM2SGGfTtiuCpgKM9FZ93sdtPG1oau/ifqtbd3RJAhmt3OQ6uSjfZh9h61jsjL7ZE8PnJY5wPqO+P1+lfmLR9e1LQ3L6JfXWnMxyVhmIUj2K/h/lXf6Z8adV0/wDc61pdtqikjbPExt5R79sox/IV5lX6bVjuO/o/b1PRp/UKb31b1+fY+120ecMHLAsMYTPGfr/h7VI6KvWh0zqVrfxLc/t/UQNlxj0hwASOM8E+2TXFaH8VejdRaL5i9k0eU4Yx30bKCPpImVP54q+6Ac3Gi62PFjiiueoNSkVpHMYEBMBRlY/3sj0yRXk1qM6dKXKRtpqelSrQqVI7DvqWhfxrgKZG+WlkJKxNn+LgY9ef5Yrn9QBeKR90jGN9kbnjaAS3bmukldpbWVFaNHggYRsq7Xdt2cls5J7n2Ax3zXMapzA8oYiF3QnGNwyMnueK5aWp01dDjr6XxffK7uc/U18c+IIx1G4/+cRf/Kivsl2MfMbV2LlsDtkY4/LivjfxC46kkB7iCIf/ACIr7H6Xv+XsfI/U+r8zlqUpX0h84KUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKkWWPmEz71HqRZnFwn3qst1l4byPsnTNrHcwzRyOkeC4G5jjGM/n/zrurDSbdoGLTSghAu1ZO4znvjvznP5V866fkZPE8PAUgnI4J8ozX03TiHaRRhyiLJzkgjA7+4+ma+CxV1N5n3eFs4rIn6fCsU0oXxJSJSPNgkHHftXYaRI8llLgskniTRqysVZd1q5zxz7f6FctAyyyRnC8OAVzgny55GTn79/vXT6BGDFEuQQb4jdtIHMLeuMZ59/wDCvMebPT/TY/H2qp57EDgiNhkc4GR2+nNYkvAgMrAgAgA+/wD1qx1xBFqSLLklTKOTySGHf61Gkc+J5JMhRgY5r72LvFHxDVpM1QxsBySzE5IC/T0NalQR7V2L5gMg+vc/0rczuSoI2KSe+ftikxDKPDTaMbVUjn7k+5/pViDW37gecF0JGQMBtv04xmteI2fwyxeFXBMix5OPfnB+wOKylJ3eZS6jyt7Y+9ZtEfBClv3fcFcDOfWraFWRpmPnSEuqnIBHBGfSthXHljOMnAUd/wAq2GNgzSEttHCbj39q3xQcBpFQRuufNnPelyLEXlM7QGCr5mPHl7dvWpItbgMk1wEgiAypkfDOe/C9z+lesqqGCt+7P4j6KP8AXtUq1hLKzEsP+Nxg/wDOquRKjc1tbmQKBIR2JwvfPoPWtkFssRnOwMoxhNuCTjnFbhaMkmJokkK91ONw9SfL34rakMRY5xEpUtjJ5XsO/b2/Ws3I0UTXDbLPEGO9FkkwGwMA45zk+1bFsfDZkYHePXgDtxirGyth+6le3WWJciSIqTjGecg9+TzmpT2cAjXwN/hK5aPx3O4qf4WI4JycZHBwKxc87Gqhlcrh4sbeG0YLuE2pnsWBIJ/Id/StqqZ3hE0jHA3vINzF8EccDAqaqGNZYl3XESlVCzDLBc5Kr/xegGfepD2k1rM5ki2sPKGUhgM8AZB+nbjtVNovsmiKFd5kazhlEgIaJSRkAnHOe/1H1GDWE9mqIMXBZCwVH34w4/EMY7beMj3FSpbd1jm8KFRHvMYlj4IH/Lv+fet3hPbswZEv4gUJEkZCB8bmOAew5yRgHn71W5bZIbQiH5eGSCVXwcKMksu4+3pjA49qlW1mEjMAghdHXMsaOSS3ocqfw+nufyqR52ODcIsKcQspCL5yXK5HPtjBqVZ2d5J4kscGZA0fADfunDAlMrnIHfJOefrVHIuokW1+Xmkb5dA6oZVcLlcjg+XGRkEHv9DWyQIkcssExdBEQUds7lHm4bPOPpyK6Ky0C6kaObUYZra1ussdgJTYoJLAngFvMefbHcirtdMt9bnggFtaJNLATFFbKAZDnKu+9tu4KDu/CBnnBFYuokbKm2SrXSrc6DpdkzqFtbRJbpVR08SdyZJZFJOC4GU3jHlAH303nTvhGYiS3TwhgRxzhzk9lBDeZtxVePU4PZjVto87XNvbAuiBRHDNGFKnykhlJI8p8vPft+ddTFbWdvYSTX0UnhwMYkRHV2LNjCqhAyzDjtyM+m416SdlkeU1d5nyqfSZ44WFxHH4KkjzuoBxyTzwVHbJwM+9dJ03rD22LbUXmhCwtPBJvCSLngtyffa2DjOPari70zZJNqGsTJJO5WNEPhxoH5IQSECNwCME/hXuAfKKgalp+p2slpDqdxFe299buiTIUQNtwWjV1/tAvlIZu+V2k84zrWnTaZejeNWNjVqFxFpEXl/3gq3zOUiGVLZZ1LcA4OSQM7iRgntWf+4m1gvLG4LwXTNmK6WRFkU57yA4y2QSOAMDipCW9jazJY2qxXVo7CI+JGwlyyksoOSCACTkgNnBqPZSLaQCzt5pTAkYUSgnxFjLAFH9nJPAAypOM14dz37EmKJ47gXEs4N9Im0NEyuHD8li/DKw29to3DBIGam3d1DJpd1PdSNEJI1O9W2rcFe24qCd49C3qSDniq1ryOC7nl1CMSQSQhY9xY7LceUxA9yVfaM/8WDntUDX7eVOndWG6KK5NrFLJCjZYpFKjPtyB5gqsPQj7GrRipSSfcRKThBtdjOM1HUrnqK8kuLiGSTTYz4aKuSqREklBt7ZPJbGcg84qbbWN1FZoFdrqSSaUP5TtwMYwT25bAPA9OTxWOk3On215497crZw3gANyE8VGwQFBReSTtC8ADzc8ZIiFrWzYzXdm8MrT7o7eQJuXcOxBzuUD0+or6RJJbK0PmG23dvMtYU8MpLESZDjHhkALzyufXHHb/Gu0s9U/atpBNPIPGiaOJnYHACscblHBbzFQTyCVPPFcLa2U0caxwbZs52iCdlTOTtUrztYLhcYA9ia6PRo5o5NQa1aJhaJARIDyhckKdnYAN5T+R5xXFjIqVO/Yd2Dm41Uu3/06NLSGRruNr+W0d5JoYhG2YllzuZZYyN5jP4S65OVBxjitF68FrEZNJtReBZo3nAgEUbcBg2zbvKrk5bHG7tzSwjaeyuluEgtmi2rIzzbEb+JQrA7hjavKgnse1b54na78DUBe2CQ3bbUunIQPkEvvzgDGMhvxA9jXiaHumEUtzcgxy+Fp9zFdYtZo3A3KyrmIjaq42N5m9Rj1qh6huPnyC97MtnbyOJQln40UjAhVaMNIMZAPLDCgk4yTV+sjNexiG0Wa6huVO+BVSzkdclWUKAxyFC91A9Pc/OpL2RtDt0ubtnMNz++j3jcvJUyquATjGDjJGec549DBQUpN9n5POx03GCj2/gqZNKa9ukHiIpg3bTHtAhhZS2Svdl2559+K2i1W8O+VmkbYwt1RzcS+hGSSCvcn3rNnis7SGXUY7m/hGYmNpGsbyMqkR4DA8BsZIxwvBzzWVnAlzPBchETcQ++aU54X8IPfPJ49/WvZPEO16T1y7MMunyzC+KupSVrZ3laJAcxIoYd8j8XABJ9OOj07VmmmsozGrhrNhIstoB4ssWRuTtsLA7ccDj6g1xOnQsOqNOiVwJC0hkaMrGykgsRuz3AUevBOB3rqrqz1PWdS+es4mj063fwLhpkQG5uCAqgY5YZyWUYzkE8DNeFi4RVTxVz38HOUqWfB2/gkRJ8vJZTNcItgZhbO0U7vu2sQGbLf8PlYceXnvmtk9jc3Rlge3ju1ghECzWc0kgkYk5kHJAX6Y3duO1bbqy2fLS6jBcIvlji8YBUEaZ8uDzjaTlvKAP0qBbwQPFew26PZWcspkRRIoMe4bQ/lUMyHHH8XqAa4k+J2tG3RntoLS8S4dheK5EsUUR8VRhV2pvbai7cHcQcHgetcT1HqMN3qVjewmRz4IM5GMSZ/CEAOQoUqCN3YHyqQQe7HiamNOtrq4Eg8UQxIkf714hlFIXOQRhgMgBgADzzXzK+Mlu1xp+owxrcnfGTLD+DwW24XngsysMEe3PYV6WCs5SfE8zHXUYrgR7l5kVPmLba+3fiOM+fBGOB24IPI7Ee4rXbxtIryWjFZIScqEyT/CSMjJB5yPrSCSFp1MjMbiXAMaqQzD0wxztOQO32zVsk1zLbWsFtcGGJwUlieMBAxxtJJbzfUtyuCB7162h5B0HRs4t9ai0p71bOKdN7u7bog5Q+VsYPmwVzxg7e/rfy3cyaLaTW4R0tEIEvjMIc5IEjOT58oAvIPIAwCTXF9LTSW2tWPiiO3NpA10wBEQPiA4Q4JG0lmI9ghH26zTgktnI0Fysmy8mP7/YDHE5Kn0yeGBBAOcDOPTxMZZVb+H5PdwTbpWff+P7Jfy8k9mY9k19YSqllAJLfa6Orqdp2sSpDIdp3bc8cese6Y6ddQyNC8M024Qm7tUt8kknDqW242g8kgjGD3rdbmK4SK4mn+ZM+EaK4lLxFxgLM65UbCBuAzySM+tZJq+5VieWaxuNwUO94xSQK5Bdw2VIIBbgDAx3JIriO4qta1OPTbS6m026X5iTbEi5U4yo83GNnHAA7c8ep+c3MpMbxpLCrb98kj4xHnJ82eCBjHY9xXaddSrJBYvJLNLC8sgWCfB8AMgKsP+IqMnPbjjNcQ/iTQqHmEUPmCKYBgtjP4/fkDv7eoxXuYKKVJPtPBx0m6tuwjTQROnjOJXcwhVijlJJccAFc9scfmPbFXFmLiCSOb5yFZoYwUPhGMHw1XzKM4yScdudrGoVqYRj/AHVIkkXmaSLkEqRuG1sBdwPcZHB96tLbVobWS3eEZdhtkMTfjBQqAAfqSR+fPPHazhR9Bjv5L3puK78FGsrjxZJHgKBIcA5OwYZWUrgDkHI7AVaW7tGUitPFls1mZWNxzDMqgHG5h5gNquMHAycnmud0a1bT9NsJp0lgF+XdZfC3RGIllTCHgn0PHYgnNXN+zz4W3kuTAsEJj/fgZDqMEygr5TtwwIPde3NfMVEozaWl2fVUm5U03rZGV1d3D6lp97p9zawWnhhnkiCnnBZAGPGMAnuRj3qGJ47e28NVvI7mV44WDxgM0a5bxGIUh/LtBHBPfHarubU/mL2Zpy0qR20kspRo2hjR0AMeMZ4Ct5xx5Thqq7xJFtVdy7rHvMr3AZvEBP8AaORkJkHaPYAYI5NZo0sc9rc3z+opE0knhaeQsMcxbdLKfxtngHzqVB4OB+VUN1awQhphD4pKkZaLw5UzknIzgp3x24/nts9SSwlvRKFilVWtnVlPhxOmcgMRkkDGP72R96w1JriW8EksV4zyxqXW6ZVMjlscZ/F+HHqOD7V9NTioRUVofLVJOcnJ6nV/CcLpeoT39pMrIlusUytGoZmHnTwyfwtg5AGSee+al6paW2ndQ2t1p3iGx1B2nnk8MACXzs4jB7btjZKgc57dqm/Dq2v4lvzNbn9n3UkcD21tbs0Rkxu8WVHOHQEoCNwwMtnCkVh1nczXmo6NC8E4hE73pgkhgWV4ApZvFZWKeIq5U4ySpByc5rCvnGV+w6KDcZRt2lJJGqv4kcRcW1sGiRJT4ZUAZ3K2QoB9+5PY4rWLTwMW9mI7mwkRJBH4oA24HDKDwexXAGQeQM8WMSLLbu013p+nSJhI5HtBLG0isCzu6Z2cAEHseeBnnXf6fLaWkEYuNLugIBI8lrP4quEA2Sc+bf5ckNkY9ea8S57tjxprZVieeH5mLwlguD8yQI2GWzjnAIXC5wueCeKrbu+8C1ZIlSSadNuyzRCIA3J84xuOMZwMDkfWrePS7tLFJ7EXF1p6q00hSRY1ZiCFbbkMTkHIGeT7VzesrJYx2N7qElxcJBeyJcyQyjxCrjDKiHBU91wf4iPQ1vh9l1FcwxF1SlY5/UkheKS4WR3hMjunjTsZZEbhc4wgIA9AO4zWEVskzBRbCJhsymGDKvvvPbPBOBgDJ57idqMlgl+1v8pb2u0RqkVs+8IgAKsh5PIIyGHPPPFb7RC8/h3FyFs1jk3wScAx4ztZiQzqMDHGOexr3r5Hz1syHb2yI3iSz2sbqY4FMj+GEJIVZCuOcdyWzwSTn0pTp0kepX9tZncPHIgIUNnK4JBPAXJOPfGa6q1s5bqwhggndTdRgNDMcMxGAQPMTlsYy3AHNRH0lLiDdZMFLNJJGiS+ZPMNoXaM8hV/F29q5MRJKJ14aLlJnLmwNud16JNwIVWEY2beMnA4bnb/AI1skl+VaMXFosyyE+EDHtwM8gMPwg5Iyc5q8bRjcXaC2tmkidV3RoznwsnJO8qSR3HYngnHJxGn0OVJ7ldMnW6tDgNLE5UMCpOFLD8QOQVOMe1camnqd7g1oc5JBvl+WbbHK5IKd/Jkkkn0Vd2P581nPaQwzSxxQ3HjpwMMkcWABnzHOTjHtUqaHDh5GYQgbjtwmCO4IA784H/WoNwptpZi42CODe7ltoyRlQMDnsB+X3rVMxasa7WEtvW93FpcRmLbsEeBkkNjk++fYe9QZbWKKZo45JAVBkcY/AmcBgvtgcj61NeUm3ZLdspJHulZpOz8ZYKO4+9ZRqUY3UOzcwV2O7aCx8oOQMbceh45NXvYpZGhLezFhAYiHundWk5OCpYHze3Azn7VXy2xieNmgMn7wrlTwxJz2+31ParW5bbLbkTsFL48NvxRYcAYPY5545wBWd7pTNdIHhuJ42AkaOSIxOqdsjPJ/wCVSpWDjfQp4NPEjvLNa8xsQ0YHlckckehIPOM9qrWtDJb5jglYk4Lt5QM+x/wrpx/Y4VQXTcIwG7ntntxwe+fpUN5mGTI4Iwqk5wUXcCe/9fvWkZszcEczdW2YSEQRrkFiGyQT/Fj61HSMiNmYosYfGGTzN37+3aurto0luZhcwLHb3ETrtxnDd183ryOSPSql4mndNwLhsFlTvj1IHoMVsp8DFwK/ItwsciqyyR5Az2yODmq7LhlVck7sKSc/lV1LAZEnC4YggLHnc+PbJ5xUSSz2thWCCQ5AbjAFaKSKOLIheIquXIlzz5OM5/p9qSq0hYx4OMEH+7ntW5dPlWVgwBxnGM5ArFUdpXiCt4UikeZQG+p/lVrrgVszXjOw87m47/lWpSpc5DYXvhfw/fHvXsSruZizMNx5XtipDRh0kMQjKJ5mBfaxOR6ep+n3qQkR5I8Z2Jle2SBx+VezByd6blVRg+gpKqYYRq+CRnnj9awy/iDKsQoxtbkD1zREkeRcgDHDL2GP1Br9MfB1IH6Rto7gN4Bu7tmCBiQR4GORj1AGa/OF2YpohlI7dkTYQo5P/E3ufc1+kvhQmzpbTxCYWbddSnepYeV4TwPc4xk+9eP9Yf8A+XzPU+lr/wDR5He62tha2bRaTLIBIfFuY5nL+G/cgAEgKOeSc81xWu3viIiEABIo1TY2Pc/h9z3rpLqWaIXN0n7hZt0koX8ZBOGAXAAHI9Oa5HVInkklwTgAZQ+p+nH+NfKUI55n0tZ5HM34eTxQCzStkFySeTnivjHxAUp1Eys24rbxAk+p2jNfa7kEDDADB9Gzj6V8V+IPHUso9oo//la+x+l9Z5ex8j9T6vzOWpSlfSHzgpSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUAqRZ/8ApCfeo9SLIhbiMsMgEZ+1VlusvDeR9V0FwxIkLZ8NsELkjCZHH5CvpuknbMpkZYka3XLFeB5B3OOORivmXT6Mbvwo9048yqY+7+Q4x+VfR9DA8TT2AXbLAigdt2V2/wCPevhMYukz7nCaIvLSVZ2kcKoHicAnIXIweciuq0M7AjKnkjvVxuOSD4R9NvGce9cnp4MTTmUgqCM+Zjjn+ddRpIbwJiyBT8yWyykE/ujznH1GPrXl/qPU/Sfl7ryJrLqiRIh5kuJxjGfUVTrJmXxpCEXGTsGMius+KNiX62uIomKhriZ8r6g7T/jVNDbQQxSfMzSCdQNqkY3ZPJyAQCAc88cH1xX29OS5KPgfGzj/ALJeJAnybVol3PNIRtGQTkn9cACsjbtjKuv7sjJAOMH+tWDW9rHtltblXMnlbejKwP1+h+latnjeH4KtkHLZ4GMe3p+dW2yHErkslQt4jE7lBJAG3/rW6OMeRJskNwAgB4P3/pVnaWXiybGUgFAceoGRyPv6frWxoYPGRsEbQNvHmYsODnnv7f50cxsFU9j5920hQAAMEiQ9+B7/AEqR8srBioCrI23O05BPB4/WpiRvb8DgyINqyIdvf0IPB+tTnVJHjnjneV05KOCDwPwg/wDSqObJUSJFFHOYwh8GCNRHh8ED0Oceuea22VpLeWtxcTbybZlAMnCuhJBx7elTUGITazSZLbCzgFRtyQFBGAxHrj25oqW7xTRxTzPgEYkAy64/CNuRnPPOO1Z7RpskaLT7eziZV8aIJhkKKHRMnG3n9fzqZHHMtkqoC0MbIzpswm70JJ4/1617EkdzaPt3KYfP55CQRkZyPbjOfpiul0Lp19QsbprzUY9O0mWQZuWTxp5ynlxbw5GU3cGQkKCMcniozkxlFXKa3VLUQtFCVjYnc8gxnPHm4x6etbI55Lgy+IssE5KltspfcFPAHAwDn713H7C6SgeJ7VdWmaMBWiu78L4ozzu2xYQ7lyMA1Z/7K6TqlxnpjWLhdVYKRputNErXSsdx+VuIwqPJgECKVUYjhWJOKiVOSV7Exqwva585Wy3ajY+GoA2KSVJYKNxzwDgkYIz7etejw1kmi/cJaLJJ4cnOWwcED37eo/wrrY+m7q51LTYfkUle/aQStKrQ7FjG/LEYGfx5DcAA5HFeWWh20TWt9c2k5jjRpUAhBLkEe/lIwQQTz9MjFcnKI61TbOKyss5bTnEVvlpVYBicMRjk8BcD0q60zQJbtds9yoQsN4fcm0HluW9MA8euK6g2VvHCt1LMthI0kziK3hO1lC7gu9WxjnGcYPII4qVbz6gGJuI0kkjjRpFkYDLYIETIRgA8+YdsA/WqyqO2ReNJJ5kD9lW2koIHR3ngeRR4p3LJCQCpjyAVLKNpHsO/pUs6akMCz3KCApJ4aSRSNvZQVwWHGSFzn8sE8VJkuLa5nnnlkghSBWmczTuWROQxU4A2ZK4HB9R35oZeqNPjuIBo2ntqOpqUAm1FP3h5/CvYY8mSG9xU06dSroRUq06Op1JuP2beSPEEIgxAJY8SxO/YMGxje304GMfUwZreS6CPfQ21r485iSGFSyxu6+bduJKYVjjPrkgccVGmfE2e0uVt77TLCe3mkLyPawtbq271bwyOeCOQwGO3FdpfawLtw0cFxG1uqyRQtJG0iSMxDF2CrkADh85JJUqDjMVMNUpZtCniadV2ucVc69Y9OXsIvHKvdFjM4YSwoyYEbMV/4cBiuT+En1FfQ9B12z1QvdW7/M+FGGtljlWRVUDa7FgSvisN3fOFwvqc/FOu5FfUYrexLRwxWx2JKFUklizHucg7iRzn7VwrJkHwCYhGNwlRinHbOAckn/Gu+i701c4Ky/2Ox+lNe6lsNNuWk1O7tFWI70s7i1AZkdAyAlSd582du0EEYzVPp91FfpcXU4SP58KHgVPCZLckBQ5QdmyeQO9fJNBWN7+JWsGvTK6lkfDccFseuTjmvuV1JaC9t30XS76x3TeaCdRL4inBHhoCSUHP4sAeXGa5cTN22TpwtNX2mZ6TIGN1JPcLA8boYRDDhZI2G3IBwSwwBk5wAOD3ry98TSG3xmSK72bbWVo8tGxOXZhxwCTxkjjnGahXut29pNJDZQR31zHPIs93N5lRl4ZQocbjhuSSFG0rk4qPZ9XdTzCxl0/XhEpdvDt4rW3cqu4rgKYyME44JPvzzWMMLUmtrQ3qYynB7OrOgfSL61HixxOr2UqMizhV4KJ5+PKvlwykEqT9ar9ajEOnmNrTxooRKjEFZXWPjLBh3Zw5GA2QD6AVPseqtK1O6bSfiUyaHeyxSQRarBZ+CIAeCl3CBhEJYYli2lDglSM491W0mW+vdKME0RTxmFoGEysFwA3kJBDbc5GBhlI71lOlOi1tGtOtTrRezqfMerIFgu5rWa4k1CRIkTT7iR1iLwgnyspGWcE4O0kHH1JqsmE9zdPba5HeG9EPnNzG8jAEAjL91bBHoQBj6CsOuZvm9YYzM6yQQJsEkWxW2rgK/PlZeOAOeCSa5Z9b1cReBb6tex28YwkXjlyM98Z4HHH2r2aNVuC2jxa1FKb2dDubZZtOjnupLWOxi8NYfPJteZiW/AnKlic5HpkY7GvodnpskNiNxIhMjCWaFHKNIdvlKk8FeQM4J3E/b4poMYk1GxeRE3TyhS0z43H15574GfrX2fTbC+iuQltfCynXawtJLsoWKIT4niFgGUDsF/vD2rixdRystDuwdJQvLUl20kVnNNJcxy7UZI/ENv4YK5JBZXYgt5QPDBA4znBAqb8nNc6lAY5lu5BiETSoY5GdiTl+SMhScsQBnj1FclqnxDs9IQDTrPUNdubksyTm6EEIkbOAjKpkZsuRuIHI4JFLL4r2drzcdHX63cSsGu9H6kkaQApxmK4Rw3YqQCpOO+SKwjhK01ex0PGUIO1zpbjQYwgnUyZKMVJKxpuzym8ksJAeeQATjBrhup7VbDVRqIsLfZ82xkj8Ql4nZRh+ByGIJIB4x2557WDWdD6itVvLZ2mtY4lhmF+WjvLY543IPIwZmA3KWBGPwkEVx/WtvBZLew3L5uXxJAI5Nw3Lt7qcFsqNuRwMCq0XOjUs9S1ZQrUrrQ4tNahjZoYJJWZpQEQptU4z6n2J7EAc1jcXTXd60lj8vIfDzHgHy47/AJ7c5z2P5Vzcs9xat4RkVmjDEB0DqMknHmB9/wCVWEV3dXkEFm8pks87kiJwIiDkuigdyRycc8A54r23VSzseEqLbtc+kdJskyTXUsNwqSN4EMgheKErwXwxxv3YxlT5e3Y4r6Bpp3XIX5SaR2iHy3hjc4l8uYowpAAKru5ySR39a5PpqZW0/SIrqV/AVirJDHICVZhmMngAdzlc4PHBq2TVmsSpMd583G0qNCviJIXVztdTwVZTxuGVwOBk14VXarVHZZnvUtijTV3kXlzHI4u457GWO7kiSXFrmF2idTkSJIrAlcnIxgcDJ5rG1ma0eW0tZIzE8RSSSH9yWAXbjcpDA5DD0AyfoK5PUOrNdu7qaV7j5IgBJYoYQr4x23NukYD/AIiPqKgp1RrUcszSi31SAB4ZRPaCTIY5ZA4w4yRnhsj07VssFUtqjF4+nfRnfWS3Ors2nBVmu1VJ0dZkid2jOPMzEM8rDZ6DIJx61w3xA05dtzdS2lxayW1y0U6RgF1kYZ3beMgDAyDnAAyxGavY9Si17T5nYWkcysvi2dyjHwmJLh1O3LLkBQe2DggZzXM/EGLwbV2ZrqXwn3JIYgFQu2SvcgDg8AnGBg96xpqdKquDNqjhVpO2aOUs2j1C62Q6xprRSGTD3M62saOuNqnewOCOOfWsodThs90d/dWtyUB2Wum3JuZXJwNrTD93Go75JOckY7Y4S+g2yyTPGkpZld1Ybsg/f/RqRYwCR28JiFjQKqFgmGwcjjjHpz+fNey6mR4qpq59z6Vt2l0oSW9v4FzdAXDW8RHhBYw5VE758vl77sgcDmungtDdWo+VSOS9IkkF1eyhmVsHYjNjKBSxcBeMnntXF9M2NxcaHpMMs8UhOz+xjClGTGXHAJZSw5+mOcipvVHXspu7WLS3jXUIoJYn1F33CVXYbtqsoDkY/HJkjkDjBrx1TnWqOx7LqwoU1fQ6VrbfdI9tBf8AhPEuZliR4/CVhvIOTyMZXlQwzistrrHFdX72s0ReVTb3OZCIl3bpEVeO7BVzwD2B718tl6mv7oNLda3qks6neZxdOGTGdilAQO6g4A7H6Yq603rq4geL9pK2u26KCQxHzCqTlXSYAEFfMQr7hu54raWBqJZO5jHH027SVjptbtoE0PULKKCSSG5AuUjSM4g2KTmGIc4DDLYPYnsK+bXunTaTqghlT9+x8REL7IpM9njb7c+/bivrE80l6Uv9KmkvdHaJhDKu5GlRlK8HPD5ADqOCQw5r4z8QNORb557olXmQxoqnaRtPlPbg4/hqMJVlCWy/jJxdKNSKmviN0U95bFF+V272R4QrFxsw3JZj3zknPAPfHFZJPBeXsWnWEUUhTcjTcuIznxCWkLZBAUjyY9STXz5iHgjheRZe+7cWxgclu+P5V03TXytvOi/LNc+NIyNt3bWjdTyeeCPQj3r051Go3R5lOknJJn6KsLmO20qCO1hgjjdzHJHfyK63IZRjYGZcJjyjCjg5znmpf7N0m3tGbUHltoUbdDLLbJPbowBRlMKFwR5lA4OcBic1Qgxvpcdze3kdrDa7o1d4mldoRtCqnG4lmAAJb6Dg1wmqfE3qGOTwNDuLvpyxhLRwLZuI7l0IwTJcL5geB+7QhVHHmwSfFpYadd5ZHtVsVDDpXzZ9Ml0eO0WSe8ykTxRxQvGrQTT7huD7iAMHc6kebgngE5qLJZ6hphlV41kWfde5gLmOV2RQGBb1VR5ew83OM18x07qzrGO6tzF1Z1DapOSxYatNKrrgggqzlGzjsQPUeua6npbr4X8zHXpFg1q6kUftGdd0U4EbRvEyZ8KOQptOQoDBcnJ77VcFOmtpO5jSx0Kktlqxn1Hpnz0s2pR7ReGNTdiK3KPIE4MhQcFwu0FV5YDPfNcxbKDO76VK8+lypuKheGIPA8vI+vI5Hr69rqUUY+XtppIdNtUWXmWRmVmGQp3kAnOc4HsO1fnrUrc2d0yuxEwl2OYd3BB75yTjjv74rowlVuOy+Bhi6MdraXE+5L1c/T8HzUsyIZTve5nkcbznzBo2Y+Ie+Djjcce1RtE1mTqKXXNStEhjmlYCFp4t5QuQGc44DMAAxwO2T7V8W09Ue8LurSmR9g3Lli3cAH1GPfHNfROgpLJbi58YSXEZCt4MMxMpCEN2OAQPZuPXNTiHeLSKYaKU02fUtOni02SM2wMtqkhWQ7nkSKLHfwvw7TtAJCkntxisIdNjigjubf8AsNRdhcskqGSJGZjt3DG4YfBwRtwM5xg4ahqWjafp8UkFrJPq15uItFfwhICuPFlPOMEjO3BOMLz25vWOqNfVpVu9QijRAGZI7KNTjjad7BnfICnLH74NcNLDVKqusl3nfVxVOi7PPwLi4tLOCymbU7aA2FnbsgN0cDcpycA/iTcefUMwHrUOa2intp5NUuI2G1muGlGEZZNqMN4GEw7gk/iyRyAM1SH4hahZTNFrJi1G1IyWeMRuS3LbSoGcnk5B55q5LiGwlubOO3Mk1s0lntkO6YYwVZ+F3BsZHGznjBqZ0KlHN/ciniKdfJfb5qcXr101reW0OpX4S3tgyWmoPa5ZXVQCjSrklQ23Jwcg8FeRVaNbhgWD/edPRVj2rdR3Z3F++5fPuAHOM4BwOKs+t9MebT5LhpJfl7eQrI9ydkrnIxweDk7+eM8V8wuAGUPBbokaIPE8xJbyqC3m5GTzgcDOBXp0KrlBXPMr0VCbsdoesTeYtdIsYYDKxWeVE8SQxtgEKZTk5A57HGcHnjv7Kc21pbXN5ax/LzOjpIrkHaxC71IOQo3j1x/DnmvjWn24nDCFljSMKzbhlgT2/wAeO/NfXdMkh1exV0t7axniRIN8BeKPCHcXc5JULgfhHb9a5sT0mjqw3QTOksbm0g1KS5mW1QWziOCNZ9jS7cbVAOFJJ52jnA57mtVxoi2WnNcLe23zD2pnmmkjxDAS545Vd+VGMA5J7nnNVUOpXeoF7fRzJDpFuGKEFoZ7pc5L7icr9FDA4GCSTWEWnW6yePYX19Bcxr4oMl9teRSAQR5iQ3fKsM+lRHCO127CWMjfoq5WXGm2epSrHDJ4UDvF+78TAHPaPg7l8ygZxzxk1Wx6TM9m5V5YyUaOSJmDFVBBwc4yRk/pn7XVzq138mbHVrmKaFsohjZYZYuM+dVOOO4PcEetb9Ss/FsYLm5SJbi+j3+MZ2DCNcKzEHI2HgDjJOTmqTjKk7M0hOFVXRx91o11E8n7t0l8zo2FzEkZycjJOCD7ZJJB9K0Szz28sCzXtxPcsQ6RbfKoyMbcjG7J7e1dbNp8ytbHSbfbO0jGbYyrvjwAMk8jynsePf6Rn0lb+X5uZ2fdlreGKIB2GBuZ84CEYJyBzxzzUKa4kum+BzT2MgvYYQWuUiffujcMEJycY/r9DW2aCTVbiG32SGSViXmeRnkVQM4JPp2wgwMe5qzk0eBbqyaVJIU8Mc7l3M27DALk5OfU4wMVPaGz0VITIiBmGQDcvumkBYMJcYKqN3IBG7PHGa0i9ppLUzkthXehzlppWpaikiaHbTau6Axt4FsZ1j5JwcDahwRjccmsNS6Q1XT/ABRq2iXenRzI4R7mN1DgnnzjKjtwOPXtWzUNc1XVY2gml3WdsCkVsf3cShfREXCrxzwB9ea90+X5WLNurafKwyos8AyeuWGeR9TuPpgA13Kg0tTz3iE3oUbWwntlS1hg8YriKRdyYwQfKS23kc/T2qNJpWzc3GzIcu7e4xtHvzjtXQXkkd5C7KohZSSVG3YCxwWHGFyODjGM1BMMgjkshO6XVwVbeu1vDGeF2n8S5B9seme1YyvB2N4tTV0U5to2UJIY1MbCQkDBK598c9uwrRd24kICSOUDhSgQlpCcYPHbHNWqWsc8x+SZ5VLqAwGGLeuAckj0GcVoliuDCkqqFS3j8/Koee+7JyxOO4FFLMlxKO7tgs7PvH4V2lWKgD6/UGoMpijdZJpghPlVpQSSO2AT2FXdrE0e4hVbhlSNuGPPYD3+v515c2sk21Z4hGpXK5jySQOzVqp2eZk43VykNr4oUgnG0gIgGB7feio7iNWUiYHltnfAzn/XtXRy6bCtsJ47NCqgKk4k4cD8WEzgc1UvFgHcXbd+FscKT6A+9WU7kbFipkZlTCOrKByoJXP+da7h47dssJGU4wuQcA9vrVxLp67EI5XuVKlsYwecVoNmRumjliYl8bCSpB749xV1NFXFlXcouZWdsZQgbuMH2Nfpb4WzzWXSujTLFFMqwXUjrKAVxvT0PHcLX5turdDDLl3iLZO0qSDgc84r9M/DyIjpjQ43yVa0uCfNjHmUk49+2B615P1dp4ZeP4PS+mL/APR5FzfTXDLLJdxW6W7q4gWOMMiE87QMjsRwfpXOX7+JIEkljAdN7rgEHHPHp34H510t/wDK2tnBY2rRSRiVzNKIdszyH8RYhfwgnjv2/KuZ1iQwRqoYBUk2KB5Qcd+O4zXzNLuPoavec1d8+KRjbvJGM4HP6etfFfiD/wCss3/0KP8A+VFfZ7nzwykAKCzHAHGcZxXxj4gkHqaYjsYo/wD5UV9h9M6zy9j5P6n1fn7nLUpSvoz5wUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAVttjiUGtVbYP7QVEtC8N5H1PpyVEuISTtRvUHGODnH5V9M0e6NsdO2DgxoEO85DCTn6e/+VfMelQPHtiVLAcbRnnORxXeaKVVrJto8rRnG3/iGR/Wvh8ZFObR9vhHaK+dh1Mc6C7un3bkJKo+cbgTwCcc/rXV6OdqPlWKNPjn+EmJuewzjvXHQIFu7hWIBDELjn19O9dZo0rpHNgZb5lNgxy37thmvIt0ker+lnwz4sia260hmt4VleSN+G9AY0yR9a5XT7E3MqLNKVVnIAdcp6gjC5/n3r6B8YIFTqPSWbAElu3JJC8KRzjn+EVzcMyx75v8A2QKptHlwMDkduM+tfV0ptUY27PyfL1If7ZX7SDJDawBLW38V8AGQyxGNuDzgdx75qQ1nEbTfJv8ACkJQvH+LOOAV7Gp80TK86vbvcBtocSvu2kY5yeRgZ96xjigt71pDBFNAAxVS5RjnAUswB59PzFTtDZKeNGieMgBWPBaQ5LZ4+mT/AJVLhE1q2bebBY4eMIDuGfX0P3NTFt7MyF40I2sqYKh/DJG4Y3dux5H50aPwm8R/DaVcgHBEYbglc49j+tW2rldmwexmnkM0GNnmikG0eVs4IPP1x6Vojt2twwe1PiQnZ4i5LIOQxPpz7HBqUIPmb0R3kkqQIyx5fsNwyWPqQOPp6VPt7aa6uBaQeGpllJzPMF+adcBV3dgfp2zVXKxZRuQNrRQW0RkMieJvGUBKAg+UevPHb/GtkMMxn+WVjKYgj7FGGQ55yDyTn0z6V0dtZ3ZtJvH3QRQy+HL41qojDEfhZW74OQSO/GM1Zx9NW0EXzVjNa3IORNHGcKhHAVckgoT6jjgis3USNeTb0OQtOnnv9VitJ5JlWWRYZXV8hF5LEDvlQpPt2r6NdS297Nn5ZYbdIUgtY4mH+6xpwiAf90HtjJJJOarPkLfTnluL13imilKtBMQpwcoGUqoVSM9vTODj1vF09YYgJJUWBUyRKrxnA+oyvPuM5rsoSUotnDiIuMkjSRHA67ZNoMRUgwkZYHIPOCDxiqjqm5+QigiSJ5HdRIEjYMcLyCuff7g5yRV/fanFp0ccs88aWrS+GJIJUfc23PYrn8J7fU1yWq3bXsBmhkNzYxsgmfaUhQMdoZ35Ix7D24BrqSzORs6rRuqbi6httSN7G13HI63Erx83bbPLuUHiQoRv92Qn1NItNmbTpJpWLqY2uEmMbLvlxt820jABXGcenPfNc/0n4mljVoWghnRmG6FVIDox3L/3l/iHY88gYxV/EJoL6U2cz3Npaf2sMEjEKSDk443IjEEDgcng14mIio1ZbJ7mGk5Uo3PPk5IRbWWrR3SWcyIzA4KSN3yr8t6gbexyay1CK31G6KyTm1SSQRRFmJhiGSc5IGJAPvwfvUrTrQRLFZPFdpPNbuIUgdC0h5yuwjLcE+UH1P3rG/vts0I+S8VNp3ToyrHbx42qAnccNjHJyGJ+nNxOrgc1flryBdPWK6WxRxdSWxPjeZxnLkcnjaRyQRiqJrD5KZJmhkgjUSNHLs2mbkgZPt6E+lT+rjdaRqsQjhNtZXFiAnjgo0pQFQwjzlDtwR2zgYqnhmEkM4kmV3clww/C/Ynn1OTjGR+tfQYdLkotHz2Ib5WVzy3/AN2lvFNst14sTW6NPOwEYBA8Xg+YgDaF/D5skHgV0ekTXcclrFOsSwj9wHkYedQxKx+2QDnOfQe3FAJkzIXdJiuFKScsQew79jg/41ZadNp0USXtwFinjuhIWgXCLGDlyR/4eF/4uO+a1mk1mYRbTujj+pykt3NKbeCKQ5ZlQcHk5x6EZ5zVJGviTS7CGiZVQgKMpjktweal3N1JfM0lyPDnly0gkOPxMSQMewrTbRFJHkt0dSrgR4PmODXJHoqx3PN3Oo6Ys/nNRijliE0SxMzRjy+MeB6kY4Az/wA6+kRXE2n6Dqd5Zw/JkotvHGluMCaVhtUYXK4HbkD75ArkukwFis7m3TddxY/d7hEIwmNzMGO05BPcjcQBzXYXkCT6BdWsEAtZLaMSWsUcrjcEZZcYbncyqyjgntXn1JJ1Enpdfyd9NNUm462f8FHqexIY7W2ihLW+4uoi2R+U52ecZCjB4B9fSqWK8mtNeQmSNC8mGi8RiCJB2DDDArk4IPoD3yal3/ylprkq21+t1ZSBLi1XxRdwuj52KjkA4KjJ3AEHyketV1tdCJo5NNtdjHf4YMoLoc9gRxwPbH07V7aR4TZa6reXtrbzX3zry3kJkikjVkcSW7MCHcFcMSox3POOQau4tQnWyTT4jJPLAVTT5EJLvaOizRRuW7gI+0d8BcA4Fc1DcQCBzHFLMsWFiCoDIWZjhMLlecZOccnGa6bUtUxrOvyQ3scxZFitp4rjKNGIwQFIGMgKqbcLjn655sSlydjqwzfKJo+e9SMLqVhC8ih8HxHfJJxyB7AccHnA7VQLbvMwDsJO/baN49cn8z/KrDUtSFwyxu6Tb8cxx4BYKM4J79uSABUFP3UsLzHa4JbaBgjzZ+2OB+tYwTUTok05XL7p9kOqQ2xeHxEhDSSu5UbmYAlV+gPAPHBJr6PrFxcm0jW2aK4jS2JLssbAhGCMHVF25ZwMZXJAwR78P0s03zLOrs7RndEfCZQVYEEBgQNvJBHqR6YFdVcrLp91HDK5uHgVBDGwETAAHu2MZBY49cEe1ZRSlWVzWTcaDsUOo21m11cz3enTfMvnxDEwjMRPAzxhgOSRxnvkcVXpbQNDLJFKvitzB4mHUqeNp24yTg+pwfWrl2leQSOGXY5cKWXkjv2AySB9a0DTvD8SSAqwkZshEXBycg4I4xuzxj2r1E7I8hoy0xfltYh1Oyke6klxE8m7lTycPlScHBADDLflmtXWNwFighijCKVLGVcBu5zEGxgKBjA9MnPpU21jkjAaSM7pQGzsOSCcBgcjGcEZwfbtXO9XX6SQ2cjIqhCVjgPuxc+IyDsGLd+T2rjrxTkmduHk1GS4HMXLJczBICZC7A4Xgq3Yn6jHf14qTpBkeMrPJCsb71VFGWQA+V/QgA4+vtVa8yZ2xMsT4y4DBQR9PX3q+0eBfHttluZJM/jkkC+IT+FVPIx2znNVllEvHOR9Z0REuLS2v7GSO0mJC3LkMxWRVbL7VGfPwcDA/F6kVBklW81CaVHe7jQlNrOUMa8jIDHyjOSR2Gay0a7jg0+2jgLR3xWMu8tuJYJ9hLs4J7yDAyQewzgbcVr3XEN/PBcHyRSMypLGqMI3JYcKORz+IcgDHocc+FSVRnRir8mjeA52N8wqK4XBLMAT7jPt9PpVdJBBDJFaI8Badti7ogrIcndlyuRkFjn3I9ubeMTxtGpZ45AdrR/L+XOewZt2crzkj0Jqj1KeMSW88r204DbJCYgSwJJzwccj1B5zXpI8tkjSr57F7KWGclnYwMrvJIwRwFCnOACME4GT25Ppp68vrkWhtLrd8vHwjt54vE54QkZBUc8nuTxVc121z8sqvh2bKjfgI55DA4IVgox9eOK29TTQX9ln5plllRZHaRWkIHOe4GGOB3/pXDiIrlYs9DDN8lJHz2W3dEDSYdnwpb+8cfiB7Ee/tWds6rEsQaNJHBKyFWfKqAcjHvzxWEmEQT2hddwLE7QNwyBgjn9K3x2bzwzqVWaZyVdA5Q7exIIIz7YHHvWj0zM1qfS9Ojjk0RZ0je5JjSOGTep8O4dvM2AcRj8ON2M45PGKqrjT9JAZTdTeK+FlEsA8IAHHdCSR9e2ftWOkjw9IWyjklIW2CRwthi6KQChwAc5z7nsMVvv2iVYIwBGYcIGYmRSpzysZHlGcZAzjJJAJqcNlfxIxV24vuI8kkkQjWAxyCEtIRbuZHlJGAnOcAbc/+I96tbHIhktJFNus5ZpU8QmPJAKgKM7m98nPGB61zssbQM0dpC8Ei5faJCFIJOCNp7Dv79uK88TY7p4n4l5jwZQ2T5uFGD+fsfpXY1kcNzstFmaOC/sopoYjaS77bMjEZcedgnctlO3plsc5rluuAzahCJQke20d1EuI/EUN5ScHAPnPH3PrXQdPpbw6PqU13tQXcvyol8JcLGFYu5duR5vIowOQeT2rmutCr3MazPlgW2sArbtuATj7nHHfAryHbnDt8yPZjfmyv8z9jhvA3sF8QsM87R+LnI9B7+tdPoJbxLR7eAy+G4jxHgEMxO1h9AeTntxXNt5JGjdsp4hCuE5wTkHGe30q1tQUu0jt1SZ43CE7x2IOOeMcH7iuqaujmg7M+s61Ctw+g6e0s7DDXcjRoiqzpkIFVsHAZWyG7k+nFUVzayy5ndQ06qGeUybycjkg4wVye47A9+KsLjUp4LfStQlR42WERq7IArJuGwtjPGUIHHORxzVRJfRXdy6GGCc790kaAr/BsCDHYhlGCM4zz6ir4bKmvP8AkyxXWO/d/BiJWmkmiEs0SEFdwJIDjODtA5xk4POM/St4svm0uLW4jR5xZtM52szDwwGLg4JyOMnAGAc4qBLPb2ttGsbrgSMiOc75AOGQg9yCVOB7mpSXu1TPE91BqFujsCJyTMzKRt3gcKScMhyGyQfxV0s5UdvpOtXFwYZri4gkmYCR1aR3kRwSPFyx28nIwvHbvmvjGuwmLUL4JcGSfxmlk3ByME98t5s88/X8q+tCF7S30+3vIVvGt7GPKypxbR53SHZH+FQXwOxOe/pXzTqS28PVL4TxLIhSMKQ5AnVhlZAe+CADj8vSvGpWVR20PcrXdON9f6KCGcWM8EMgXCBd6o3bceDn3z7dq7npOVW1NgWdJbqIRSNDtCKwbG4s5GM85I7ZNcAVeAqNiMsiElQSmOf+Ln07f1rounZIZdTgRnaOXZ+5fwdyIe53ZPb3AHvmuioro5qcrM+ydO/+cWW+uAkc8rlYXYlCqL5FVAq5PbBXPvxuIrmuuRaRPalQ1o8qhjbxt4zKSAdykHCqSfwEZBznvXmmXxt7i50+W7khMU5RJghdUcNg8hs7SADhR6g881j1UZSlijG8F1AW8WJ4giCRhwAxVG3DB55Uj1zmuyCStbQ4Ztu99Sks7wzSwJq5Wdg29GyfEIIIIViSud3PPqPat9lItpPNvVLlWaW2cFTjxcbg2eQCwGMA5ODVNBNGY7hEtzHtjb92UWRA23II3cgY3Zxn2FW2mX2pXOt2V1Iss89kqbIVXDBY1Kpw5K55BGBjJPrU1rcnLwFG/KRt2lr1NbwxaNc28ltI9zKqSNIzHw4VIDJ/3gwOQ5wByMZAr5HdjxY5RC3mcktIF/AQcZ5/pX1e5ngljLy26zSvGAsVyzkMCcB1yQQuSDjtwa+b3NvIrs8SM7b2IckbTx3GOMf8s15tDI9Sur2IEQ8JnG5/F3AbU9CO+D7d/wAq7vQ7lV0a5gFy7xTShQRHuIjJyVA9Txg8jg1xFlvZVe3k2Qnl/KBtQ44B7/X2710Gh3C2sLwt+6k8QOshbIKkkAgA8+mfofpXVltK5yu+w7HevNpNvaSLHbzqzq6JJPIpER58rLliQ3uePYetI7uadybK3nimZi2GnIJTudoQAHBB/LHfk1WW14lqqII492zBC7F8Uc9+GJz7/wBCKm2V4URbkWxVYmAWeW6wI3PYH8I47g+uBXVY4rmjVLe6uIlez06JYrnIYwRvmZsn3J5H09gfWrHp2aW/t9PllZp5IXkt0QMEZo1/gkYnLDsAMAAfcVhcawxh3PqFraxyKQN8u6RicZAByc+65A98CvOntRttO02W2SBU1O6Ls8viBLi5QNnZxz4IUITtOCe5IArixfVndhOsPB4LxLDPEbqOeTe0KwmXOW2t4YXJbADDbycfbncktpbsYrUo1qyK1vPcQGKWOQ52spC8KSCpDDjJ9+MR8utvCNPuprabjfcgiDxucAEEMMqB6c5we9W8Atr6/WWSdp5mZYUR5ASe5zuHZR6j7HvXlt2PVSuQTdJHZw6fodvazWVzL490GAQARE+TgYKsCWDDPIPrzXK6gJpb9I0TZDExSF2iLNHtwZfJ3PL+vtxXQo6lLaSWW5nfxCZyHICyklVwxGFwGPGSGPHaqy+sreHWLhro4S8tfC8SeN1lWUYi5BwVYZDAe3IHGR24RpVDixabp5fPjKBbXaIQzOlwNyIGl4Q53FpCRxhWPlxyW9MVlpFrDaXSHUrZLgoxMEHhsQVAJ3MeBtxxwfc81HtWW5F1aukEyxzAnflFkIPAGTwpxuxnHB55rbE8zSNJIZISjjc55BGQODn2UAD6mvYPGLrqa4sryO1m0q0tbWNh5vlkY+IWBHcryOB3PpxXNPp0gbwb22lhZU28gBoxu7kcnGc5qzTbf6hFC1zJNYiVpJGkcqJEGCVA5wTwvH97NdGLHxLkmR2aaaJYosKR4RyQItwAIPPY5z64zmvOxE1FpHpYaG1Fs4nw9wiKwC0mSU+GPD2hVC4BXJAH+P8AKol5bSwmJbsTzTod3IB3D055wT9vQ+ldm2lajcutrGYh4kal7pofEbw/UrkMdo5GcjBxVNcaSIlitVHhSSZKgymTIyQuWHAPv7ZrmU0dLgzmYLZjPElxuunklO1UAyVDHBPoMAAVumSJLmKSym8TJYZmhYtnBGRtOB3749q3i0kivZShlZJEUDMZbYgTacEZx2bzev3rBlZplYoUGAdjsEATBwoHp68Y+/Jra9zK1ivRIcurhAjMFCbiSOecE8dxWb24Zgot3Zz2Vo8BcnhSRjP5VNAiaQSoJN7FW8TAIQjAIKAntn29c1hcK8MYhWFU8RN2+Qk4POFx9ueKm4sUvytwjyxSPNLNnG4HyKORxnvWJtIiZyH3qO6yDBBHp9Tn1q4+VkeNZtqtb4dUVPOQVGfU9s/yryRxDIskCJ5TkxgjbnOVYDv6VO2yNhHMX9g+yVUMjIYS2M9zjO0mv0b0MjponT6Ix3JZPIyliBtyhOTz7V8L1GP5mzuVtGZQEYySocgg5z39x7V+gOlbb/zRpQaLxY1sNsiEfwnwx/U968z6lO9BX7Tu+nwtWdjfrd2mqvcyGBYXWMuwDjklsnnAJPPY1zWq+axymVO7JOdoUED74q6muVjt2XxIxKp8JUGS7Bs559MY79+a52/YyW7LGxO1cEY4yD2rw6Ss0j2Krujnn5gUAkrnjOf+lfFuvyT1HJn/ANxF/wDKCvtDE+ECCSo7kjuRXxfr4beopAPSGIf/ACIr6/6Z1j8D5T6l1S8TmKUpX0Z86KUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAK22/My5OORWqttvjxkznG4ZxUPQtDeR9Q6VJQ2LsMq02z8XIAGT+XPf6V3Gm+WKEuWAyAQ3phgPY9v8a4HQW8SxhtkUNIJC68ck7MYru9PjMdpCr+UyIrAngEEZ7nFfFYtdNn22F3UdduV3VkQu7RncM9m3HnG0+nH866jSfPDOCNgFxECcc8q/wBPp/OuOimWa3tQeWQODjzbQWJHbP19OK6/QS2JT5d6ywvuOAMYYY7D+tePa0kete8WfPPisYY9a6YnmiZkljuI1w3OQe3865C5tV3o1sWdFJfdLtAAX3A5zn0rtfi/bu7dHPEcSC7ulGWAyfDU4BbjJ7c1QC0mkjiuXMyYZ1dGkGCSOQR6AHH6Yr36TtSi/H+WeFUV6kl4fwipAkkmI3s+8bnAH4j34AwTXpIt3lRAcOhH7xTknHDbfTnn7VeWekTXVuXVmjEQd43IZmXBHBCnJz6Y7celSdPs7SWyjunKSzs6tmTjcXyOc/3fYg9vTFaOaRRQZzsVvKttN4qySgAFV2EEAZGft5u/P0qXa6Rc3qNOkEpKMFCNGxIOMnGO57nH3q/BifcZAlugcAzP++2r+EnjAJJYAdvzqys9QXSUktbe7iNndB/9zlR1a5K4yFQZO7zZ4Occ8cVR1HwRdU1xZWy6Pb6ZaiT9nPIsMgAuWCgH+6NpHIO71IH1JGKmWmm2+nz3CiRlbckQfb5AzZdpSM52NtIyPw4zjmtsOnNNd2psZf2Wk7eAtiPELF3XcEXeCR25yTgAnkCp93rejaLFLb2lybzqAKcXoWN4IvMCVgypGMLjd+LknODgXp051ckUqVIUc2Yala3LS25kufn3hCSyeHASqIq8bjtAxg4Hp2+tb7aaW5szci7U+HPwYyMojd8hfwqChz2GSee1VcfxFvIdhlguFwuSDdt+7JyT4b43jDEnksOcEY5q2s+rrTqK3Wz1CNRduymHUPAj3swQARzHAfbhVAYlgDgknFaTwk4xuszGGMhKVnkRmubhIJIr1vmbGVG7ImCW9g3cBSMng9z6VQ6d1TbM9xZRXEen38I2RvLIY/FVVJ3Bt20nH8Jwf7uc1bdQrFb6cs0TyPbWzMZm3gSpMxG8FAeFDqU+tfGbq8Nzj5nDRyeYKyAYzzx7VbC3TYxVpJH0fU7DXJHHzyvchysZlkt/F8pAKYcEgg4IzwBtwfaqy509r7U7MajDaP4r+JFbxybpJ1z/AGZKMdqAgEng8YrhY5pc7LSWaEcjbHI4HI9QOCa7Toi0SO7M0qLG1tEvhlpGGxz3ZiOScZ75H512zrOEbo4KdFTlZs+gQrbLDYWwjiF0JPmZDZSlVSZwyhItw5AARcY4xx2Jq2sm07R/C/2hvdO0WUsMPdXCIXUkbm2gl2OSedvJNc9Pdoxjj02E2moC2RDMkZkaOFC20xqxKrIxJ84A2jvzXD3FolpcmUxIUk5lWQnazgc7ifxjOfMfXkelcVPDOt0pu1zuqYrkOjBaH2XStOh1G4aLp3VND1a5VpFhgt9Rj8VGACrIYGImUDJyERnJPbFaGaCW8vGjt5JpbWb5W8EqzWzh2GRIqtztfHlfaeBg4Jr5OlhJevMsEdpCYIGlYzTqc7Np3KT6DIAJHv3xXYdN9XX097FDq7vI9rAJYr1pBIZLVWAZGI5wGOQcc855AqK2D2I7UHcmjjXOSjNWI/UVvY3OhXEIDzXFtI+/xmYOkm7yk+4CdgQDj2xXz60v4pHkEVxbrI7ZCzbcxsDkEBgFP5Zz6gV3fW/U95LZR2GpNDd3kcSTxySh/mFiOVA8Tsyjd+E5K8V8pmjUOHBxgEJt5H/UVrhZSjGzMsVGM5LtL0gIiNc6lbBnByobG3Bx6ZBLYOAo/wCWL6ustnLBaeJ8vHNsZ28hkfH4zz5QM4A757+gHNoiItw6kFyxZRwgHq3+dXVg7PYSRbYcFs48IkuT2J9/ftxXVUm2jkp00martC8jySAhGXAwwGTwB/jV30t05qnU00kPT+japrRjJWY2URdEOMjxJDiOMYB7t+VaenNEtdYv7ie8DvZ24JLRyBC8uDsU5I8pI823zY7YrsurdRedbO30R5rbSTbeWzaVHFqQxzH4ONqMCpbt5hhs81WMNovKeydXonQuv6Hps+oX3TeryQ2SSGZ4IPmFRtoKlmhD7ccYJ7Dcfavbae2s7SLVd8VyhJk3xyFFMTRjBDbWZiSQQwB789818qsr7UtN1C3u9I1G80y6hDeHdWURtpIyeWbehV8E88Ng84GO/wBN0brO46yt2fVFspNatxKxuTGqJdjGd7IoUrIp8zMow4/ENwy3JXwrS207nXQxik9hqxxvUGq6Vo3Uc/yenS21swWZ1MxJgufxF0ACjB9VxkHOKrLjVtNFrDNa39hdEMYQqRbJEiBLKXbapOckE8seMngGqvqC+SXVHL2yxMku2Xw8GIsowSCAM9v8+a52SaSYq0okZM7sMOM/bt/r1rqpSkoJM5a0YubaOrj6haa5gityDLJMsZuCn4ASRna3dQuRsPlJ5Pse51a7Wy0e706ARwws5CO65WQDcfxYzuJwcnjFfNulEd9Tjm2PP4XnjVTsBPbkHvyexrrdb1SGfTYoEKvKxkKxgq0hbIUA8DnnBB4x27Vz125zSOiglCDZxcUBkuYrSKGa4nlIjjW2iMkjE88RgEt+XNdxafBf4kXTolv0hqMr7jKseYEuCmPSJ5BIDx6pnmsorc9N2ctjolxJHfjyahcRuUkKE4KKeCBxyqk5HJ7nHN3PT1lKRJJZxyMoXeJIYwBk87iBu9hnJ7/Su2NO+pxSq20Ov07RdU0S/Fv1BpV709fBJJzpt/HLbzMx2qu2ORQDGTksygj27V0K2fzaxWxuba3iuVDWqyZRHATJViv4Scfl7dhXPdL9U6p0/YNol5dX1/pgkU2ui3Uwntg4LBjDKxDWcwBO1k8rdmBXINtd3dgdKnuGzdQmMz2bJmFiJOIkeIcK453MCQSrelefXpSpT2onoUKsasNmRWafc2mpR+LZCO4MLFZBFL54znsfU9uCMCt8dubiZ2s7G7eWNWlZYxnYg4HdfTOS3t9q+QXbvve4jcpJu3LJEcAAN/eBB9f1qLPd3tyqw6lc3k6Mhwkty0ob8t2B3r0E7o89xsz61+1bS8uHgLCaaAg3MwuFKxRlsLF28zE5zgnjIFcz1FdFpzHE8gjMkgJwCSf4hnGcDnBP29K0dAWzJ+0fC3KQEJUDBfbg4zu8oxnkA+uK3X1q+rXMRleRIJGkM0pBBgTJJ/d93PoF7kkDjOa5JdKrY64rZpHP2sLXN0sKQm4lkJ220aO8hHGSoUEnn0Fd3bdJ9Saaq3Nz0t1XZ2Q3Obk6NcJDGSCPxNHx29e1eWeoaho4ktOlRJomnrjeIpmhuLnjDPJOhVpSBklAwRRnC1tsZtX0yWW4/bFzZXEeJi9jq80cx4yCCDkk59+Peuh0tpZnOq2y8kdVGmnPYpI5tIYHcrI5i+YkkVh5sBSFVffJxkGqnVWjsrP55khQLbKqRk9xk5QspOCAc+oyRnipFn1vc6rOYeoQ9xqMiFYNSlijEjSknaswACuSduJWwS2A27uIfU9wYtPv0DPBFcpgIj7TNtX8bgHv344OB27V5jhOjUSZ6aqQrU20eWHXOnXUpeG+W0n2+ZJ5VjkY8ejYBOcY2nnmqzUepNIZi8l5DK5wDDb/AL49+xUcDH1PFfNLiQFXwEywy2wDaB9B6f1rVEWMmETmI4kIBJz9/tXqp5HlOOZ9g6auYtT0976RoUtzNICktwdqxpgJknAYsxycA5wBwBUTqxJAZImjkwVwPmozESG8wzg4HfjgdxzWPRu+LSFht90oBMjKEXdgKzFQCCAcKTnPY4+tTr29tr2Z2uVF3bRxiKNOAs24Z5cclVz6EZ4XIGa820p1meleMKKOO0DprWtej8PQ9B1LXEgxmSys3dIR6hpPwKPoW71ZXXw96r0iO4ueounNYtIoOZpRaeLEgYgLvdNyx9zyxAwKmatqt1qWyG9KSRwIsUXiyfuYxzgRwgBUXbg4RefXvWEHUWq214streSWzwee1ktz8syrtVRyhB5WNBgkgbRivQ5JtHncqky60nwP2e4uPEmggBhjRSFklcryA2QxIA3AAjkdsVTdQ3Ueiy2rTpP4M9tkFdo8ucBghwDuGdygjkV0Nhr8usQzpceGmoSoGL+CirdqDhmYcKrgZ3FeGGOM5zyHXUcoS38RZECsyITGEDo2SAoXjbngdhxXBBSp1rM75uFWjdEIavYyhJF1V4Dl9rtCyNzwSqhe/pnmoU2uWkMoTSRIJWO1pZ0MaDLfwoeSee7YHAOK56Xf4sCyN4O3y5K7gq57/b/Ot8Gx4pHZsHgEjIG0Hn19q9Fydjz1BXPtb2hjs9ETUbRPkmjkW0PzCXAztI8J+QzEY9iMNjOTXMdQ2kVpawSSRhbOZdzNHCfD5IyM+h4JxnjFXujWIudPt0LrKpXxnkYxuiREfiRjzgfiYHG0r2NRpOtNN0hIrbQtD0/Xp4maSXVdds1uWkbaFDR2hHgRKF7F1djnPHAHmUacpyyPTrVIwhnxPnEki38yR27qyvtCAMpc47HI9e30qZFaKo2tKyMQSsfhiUr6HcPT9a7W26q13UMW17FoVzbRxzzfLajpNm0CKIyWO7w1IOFGMMCDjbzVHe22nagtnqGk6eNFmureO6Fi8ryQzBlBb5YyFnGMElXY8Z2n0HdKLijhjNSZ2FjeRzaVeQWUsbWskoiMZjTft2qQoJy4GTgY7fSqXVOq9Ja5u9L6wsryCe3Vfk9QtSszqDg/vUyCckKCR6DsDW3QrdLKK9ewuLp7lN15Iyy+LvYfiXC+gU5UDt9c1xHVpS21FPChBIt0y20rl8ebGRweftzxXNQezNpHVXW1BNnRQax0mbOX9r315fXPgqqwW9q6bVyCQrcbWBGdxOMHFa7bq2PVuoraa1sXtbHcQ4Ehknc5/E2TtG3OQF4HcknmuBWQlgm1sHJ8wGexwOPT9e1WmhXPy2oWzsJCUfcAGwVPfIb07A59DXXUbcWjjppRkmfZbOSZJ7e3trxGuHDIiSxNGFOTlVycFSMBsnPtng1wvXckR1GAP4IjLeEMbyxI/hLufP6/zr6SmutbaekpDz3sgZRauvjrOSADI8jZCnJPn2t3HGearbTVtduXu0sLy3l2yeELGwVYCzbCAXkI3EjcTlnJ75Oa8+hSm3tcD0K9aEVscT40skc2SJN6xA8EglSPTg8D0q70u4MtwrTlRbRHc4YAYwM8epGe5roL3WNWuFFxrFrZamgUqgvoMyMB/dl4lSQDH4XIPHeq26trS2uLeSwlmnt2Kom908QOVBxvHlb8XDYG4DkKc12VKbSOOnUTdjptZu/Chgv4pbuS6S1R3kkILFQD5B3GQMAAHGCRniqUa9p2qQh5b6GRydzBpNjhfX8QBAOe3PcVbLeQXlj4V1avc3MVubfaxwsKsNvikE7ixI7D0B49/lV1CtnKYWXx/C4eQgHBHfA+/aqYebS2XwL4imm9pcTt9S6igkWQDUWv7q5bbJGq+IX8wOA54U8Yznt6HtV70fqInt5JJB4LEqoYxl/DQnCqB3Zhhj343E+tfKrd/CuJJLpgylWUNxkgj0+3p619F+HdwS01q2ZWkuYmWFtwjIyFUsfw5AOc8ff0qMS24WJwyUZ3O6s9AstUnt4LsiaGGDbLMQyNtRuG2ksAXXGOe2eK+T9Qy29nqktrbzjbHIWnBiYvBnOA4HY9vz719b1Hqiw6etINO0/TI77UWDTX0EjFLbc7HyyKhVyNoBVFZVIbLEjg8/ffEXrqExuvUesabEm5fA0+f9nRRgkcJHbhEORjLMCT65rPDUZvpS04F8TXiuitUfK47qC8WSW3eF2jjbaO4U/91cDOM9/erfTLnaot5EiWEoWdjKB2Hp/F6nHOOfTvV/d9RTa4zJ1fbxasxBK3rxql8gHLMs6gFvUbZNwOcYHcUbwW+lT28u83EYYPBPyC6EjsPceo9PtW9SDijGnUUmXF7q/7OVVPzUVldbwpViwVgBkNx3GR/So9t1Bp8Upd7hLpJVfcgiGWXGM4K4BBwc/TjHeoPUP+8WLXKkeWQyKq+XI4x27gj175FcwWwjMu4pJuYKeHLDkZPqO36CrU5vZK1Kcdo7XVutEu4Xs+mfnxYNhXadIopZ1ByUUJnYM54LEn1NdtpK6ayn5NpLVJ1ZpXkYGR0LbnBAywHAyvAJFfIdGESskU2+OZuXdQGKHH9098/T0r690zJDbaZb3Wv3nyNgkv+8TGFWdohjciRHzuzhVUAbcFgSwGSOXEXk0kdWGtBNstbYm4uZo7GCOaW4kVUiXL75D5Q6qRukXDDGO230q0uumtUtUVr3SNSgnkjZkiu9Oli2hRlc7lAEbeHzkYOfrXzfWPiDr9542ndM3T9MaTJIVWy065MUkqk8fMTphpGPGeQgxgKMVzq2k9o63VjfTLOZdvzSXMkTb9ue+dx7984OfrVo4FtXcrMrLHpOyjdH028Et6LX9mXD2zO4SeG1tTB4JGCT6xuoAOMkHLZxmoDukulCKRRHaOzsTJFuUqXwMPljuyCdwAYZ4Jqn0bqvVDc29vqN47CUFEuJJSSiNjerFeWzj79snFdA3jWkIhcKts1z4W1XVA0gX0zkkYAUnPGDtrkqU50ZWZ2U6sK0bo+dXlwmmyyG6guW0+OUmGaNBlhzghZMbhk9+R7Cq646qijwbaGW4ZRgiZgqkg8Zxyf5Vd9Y2T3WwrCZLa2VowVG3C7iQNvZQTnB/XmuAV5A5CMIz7EAffnvXqUqjlG7PLqUoxkdRpPUVzDqcF6YPmrvb4agv4aKjZBVV9DyP0+lfTLKWGFbcXQ+VmliZ5ooxumkLMN37wkhj2wPLjtzwK+N2KM1/AYCSinA92yOBnvyeK+sWmsjTunre7CWcN1qSGO3t2k3xiBCySTyRkABWlJVMnzMjHBVeeatTdSSsdNKpGnF3Ohi0jUpLqe4tbG8jsSymI20IRGkbIOXGBuwDkZ9AMDBrSXsLbwzcC5tIbgsk7bWYOjbiATnZxhSScjn61wV9Jd698jLrt014kUBUtdsXVcEgKqcqCP4cBRz7YrC1eTTrsPo5kgt0kWKQoC685JZlOQex8uMYAo8FlvFVjc93I7Ex2FxdC4s764EzSFEYLtUHaDgHgONo7EcY4rn9T0S2V1+XmnVQoMk8kQ35J8wX1YAfiyO5GO1Wmha8ZoZ7eCRbaS4CRyG3wAUG47lBHBJOD+XpWx43urdpIFtwkbCMSm5wzsUzuAYhmI7n08pyc1xtSpTsztThVjtI5NNPlFtJLb3BVCx848rblORjHOD+vfioE7XKSSQsXHjApI5Uk5BzyfTOQOK7qPT7m6gEscrPKyrHuE8ZId/wLnkHvuOOfcitwEepWcU0lzPCJEbKytvxtyGUDbuwSOM9sircrYjk7nCtFcQRKZI50g43GFhnA9MkDdnP9eajKjCOVVV1iH4cnChu+Twc+XmuluLGRFA3W5JCtva53kKcY4GPTnGAeKgzQ/L3kSvLCYwC7FVAY7Rxx3Oe2B796tt3KuFil1UxvaztbOoHgM7IQACcEZwOcYz9fU1900lhBY6auNy+CiFS21SD4ZA/XBr4xqcItbOeQogVoGLqyCQHcOCDnJ5IznsePSvt1tau1pGsal5IIA6Lg8sojHfIHA5715+OadJHbg01VbK/UT4cssIaQxeP+ExAc8g4Oc4HPqPSub1IkxvlyR6M75Pp6frVvNIXaRzsJJ3Bgcc5zz71V69E9ubgEbMbXBx3JUH/GvMpKzSPQq5ps5rnbh1wVJHJr418QBjqWYf8AzqP/AOVFfZbgFch+XzkknJ5r418Qf/WWb/6FH/8AKivrfpnWeR8r9S6teJy9KUr6I+dFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFbIDiVfuP61rrZD/AGi/cVD0LR3kfR9DcRrHOoDqCwAz39P8a+j6cqHTztbc6wwFTvxtIDZH8vevmWjbf2fLvJyqrsXI77smvpmjqIJBA7ExMAX25IIC5/Xnnivi8bvM+2wmiLOzlYE5YuDkgb8+/bmu60NCrairIMYjxuAAIzXDaervFM5G/A3EDt35744rvdCUeJfqm0lliO3g8Dn/AF7V5Et5HqLcZznxJEccHSEk0aTJ+1poWBbbxJbnnI9fLx7/AJ1CttOxo8tpBGsrqy7IGkYFSxHO0cnkjynOfoau+v8AT7rUtL6ci0+S1iuY9UMym55jwlrLkEc9x2xmueUrpcxlF1bXaWc6kSRRbA84Zd4H/EG4I75CntXpwfQil8zPOkunJv5kjQtkkSj9pwzRoYtyWsbbZADnJK8bVO3t3Jzjipz7SzuIt0zSFM8KSpIwSw7HlRj0yc1Kvov/ADnfXUckrWssxKXEQAMWScleBxyRnn8RqvyjPbQS2qrF4uFjJbAwg3ANkMNxIz2xz6Ve9ylrEy6ljiEPgRQSu8xjAAJYOD5jGRwwwcFc4HqDWPh3Pzck9kj+FFE4hlt0YiGVWCOzBcHJBxnONoP1rC233M8MUNvNf3UReWaBJGjJK42yEngMeR25G3itZvLlbW6S6At7i3Ym0s2lO1VGD+8AACtyTwCCfsKEkO61S/0OB/CFxcXpEphuXXeygqMsWOSpVXfBPoRzjiubWKZRCYifGAHjJbIZSA2RkY/F6E4zwRz6Ve9T2MptJr2ygkkhaXxIo5S7GTxQA7Lg5fBUd+3FcXCLux1D5i4uIlzIfEjDjxXIxzg9hyDjvx617eD2XSy1PDxu0quenAtPl5JZPBtVkluUUFl8PiPJxx647nOOOeKlQ6OIzGRJNHOMl3QbTGc8BQeST9qure7RoYyJlKu4JZ2U7iSCWGQDk85+/wCtbqOq3BhkiUh/7obEgUZwAM+nJ5wT6V13Zx2RB6r1Y3WnweMkfzjxo8siArnJIYFNoGcjkg5yTk9q4C6JnuCRG20jvnGSfXmr7WHia7jjCgRqgADyE845bvgd+w9hVBAyvId7qbfl97cZx6kf0HrXHFJN2O1ttK5LsrMDbJuQSofKjlsPnscAf1I9K+jaDbxafolzNe3tr4jSNGkUcu8M5XJO3AByMcZzkcVxGigl1K+K6ggNxubAycYH1PuPavpekmGeGzgTl45nkkGwKsA2kEbDyMjHPOMd65a8nodVCK1IGoXgtbqSRZbGKWWRUe2MhjllXJUEq38GzbkhvUE+9c5LeoyP5op5C5O4R+YAE8evBP8Ao10Ov6Jp11fR+MJdP1CeGX5dHcFHljyxjfIzkrwrA8nAweDXMSTtdMotlaAhN0IVgT4ZIOzcCORgd++B616OHkpU00ediIuNR3MrRHMfhoVKBAsauSuBuJO0fQnOD3qwtWmW7guvEdAD/aZwHUAh0LEZbyg43HiqtLy1juHRUkkLkJnBLZB7g4x3/oat52mlcQyhC0kIeaO2OQ6kjGATyxxj35YjtWlRpRdzGmnKSSKfqa7kubpo3DSTqMAlSpKcEAE8ny45rmTINxzjBGMSc857/pU3VZYH1Gd5oHt5CxKoAyqreo3Hv74/rVZGJYpt6njggliVBPbPtXHTjaJ3Tk3I2RxiKSNwNo3fhP8AF7/9KtordyqSwShkhUsBn0A9Md8/8qhuniqFB3zbhuAzj0JBx3z9PYVY2URWNpGIVH/dSGNNytxnjODgc+3NJMRR0ujTjT9EUFHFr828u+LCyOSoXaN2ccHGcH6isVlhtZXjiiuIbBzgws6tKdp3Bcldp49QByPwjNabRrUWk9kscM0quLmOUbwJwFO6PbnAGDu5Utkd8cHAyWbRrHcWsTIjbgIA28gZPK7ucA47DgDOa6qdnFM5KiakzM3FxNaG4G99p/8ASEjZMkfw8AKp7k7uT9qsNJhNtdwXcYiEvjwyBVhUhhuKtk5BAx6YYHntUSH5m5drbSrOa9ldmlSJXbyp3JwSBwMHPPY1l+1JbLT5CyGU7AI1Mm4BgDg9zk5ycD2PIq8nkZxV2cdqoCXV3BbMZbEzyLGrnAKBjtPrg4x2qviJMSxohjB85U/hXHsD3r0unyxVZjIduCsZ254xkn19/pWUMIbic7Ei/tGU7sDHf6/l71zJWR1t3Z0PTzLBptzPKx8ZmVI90JYqo7sG7Lj6k9+BmrywuWl1iLdK0AVHkZWQISwU45bkjI/kM1XWdux08OQ5jkYOqB9qrg54/r7GpcMotHtLi+RJYAxZXjckTJghgpB9Ax4z3rmutu50tPYsdFJLeObJZpFj05EIPy7rGAGc7mZwjbieO/2wKr5DA9ysEAa5jXuXbDnsOAwxgnjlfsea7TR5P2jarNYtZ3d0fPFcpIInwW2hZFAGeCBuwQCecHkbtR0aCK4dJ9LvbSSMp40VwsVyqEghlDLtYNkE7Tnn1Heu5SSPOcTidRsjtkFpDJczlQyww2+5g3mycjllxz2yByRWzqjUXktHubaYdjuxNhtuVGCOBjcCeAODwBzVje2ml2Mzx7ppvHTcxyS2eWD7X28jjncwHP4q5LXLrxLW4kdYzHIqyW9xPli231zk4zknPpx6VnWacUjagmpXOHuA7eIZHBU4xtbuu7n6CsOLdADkMDtCY9D9RXk6KASI1QlsMcbSDjGK8jBtmYKfO2Q2PNlfz7cVKD1Oi0GS6s7WbwVgZZDvPiAEryPXGcEe3biuw6Otori/vJL6d71bbwkZVQ4iTcxOCcZCsAR2zg4PFcNGWSMqkaSS+Lv2oN23+HH0ySP0rsuhriG11SW3nlWIahHtjmecxNHMuXTa/dVYb0b33r7Vit65tLcsXGvzjRot88brE8z3ZRSz75fViB5sDv2xnk5qrgktY4pIL1Jjui3eJbMhzJuBIUvGSfKQDnHvz2OxBFqd20FvFbyNFZu/75WMipjIiJbK7gxxgDBBzk5rUkEkotIPCml1C4bEPnxGQMAgjsMd/piutaHFmWdpo8E0l0NMYwRvCQ7zqJmiwM5yMAHOCO3NVWvSTanohunnaZ3YvIEHIlAwz/nk/Tk10NlANItxLOkkLW6p4iTA92d+FAO1skjH2PYmuH128kfSUt7jwpIADIJFiZQjYIKE4x2bdnuc9+K5K2bSOyhkmzlb0s+1duDgjw2H4T+fbt2pZgy3McSFTcEAu7DGBnGPY8Y9K0XGx/PCxnRSCAF3EA+lZpEWjkiD7F2htyZPY5PB57e1a8Cl8z6Xp9x+z+nbKWL5e3uGJCSpvD4xjPi8qOfTbn7DNeadBJdqsc/hQTyBseQDzkEgYLBcHnJ7A8/SoWjSmW0jtbdopEdlYtN5gygZ3H1Pb059ua1QTQ3cZWCWS4jKh0EQLFlJ8xIPOV747/pXNQymzor500boTayjxLlEYsdzKGMAKheAG3Mc57nGMYxWyOPxQh2LcFUJkNugXcVOCd305zj2qC18C7Ms5QBuCWOW44I9OcDj71veERW6yXAMcCM7I207m3AE49cAA/Tjj1r0DzjeskkRDzTMwA8oeXJznJAU54IJHGBzVX1bI7W1sZzIsrW8fhMRgSRsPKSw4zxx+VSpnhEMcXz0jBJNu12JRNwAzwT7j+XvVd1Hc3E0Vu4T/dY4Y1jJkDPJ4cjZ3Adh5sY+hNclWzmjrpZQkci0AMcCnIaVsbeTx7Gt8KyQXIQTPbISASi7vrnnj868k8JQDG43HkKdxIHufyr2NQxCBdwU8nAxn1ByeOK0ehRH0ZL2S/t7e2lMETXI8N0FkkA2ggkkoMknAPOT2+1V727CWUCMbzhny5x7H6DHpx2FWlpdwGzjuUnlvZ1Ro/E+XURhiy+Q+JyRt3fhAOcZ4pPqLSnFusYIiJCRLwztj8QJz2/Ic+5pRyjkRXznmQntorizmjkiSeHCiYNHyDzwOOOcjPHbisoNPjsreO200pEYQXTLAghQD5j7/wCVToGkLhEaRdygSjKgTYztLAn0JJ98mo5vXiJy6GNnY45UfUZAJAxntmtTFGyCaO2ile1ijVpByYW84J/hK8HABxlTz61S9btCLhvkbeeFnO4JdqUba3YrnuOO/wBPXvWGlzxpf+LcwrdwlCJYnHARshuce3OPeveq7cIVjPzLiMxx+NLIG5IJHfkA49PavOStUPTbvTsca7RCbbBGrMPbIx9am6a/gPC7IZth4VBgocgggEYPI/PtWm4tfEjJkDAjAIPHJHJ4qYwMamaMRwHerDw2yMegHqOxrrehyrU+p6RFFKXv5p0nAj8JQVK8kKdyjIxjjhvT0ozSaPcyo881tCyOQivIsyx7NoRSvAbDbgSMkcHOeYmhXlpHptxDqZFvHcpHIH8NpJGAJ2+UnHuuffHPattxfCYSrNGVKscozkESKoVSwHAOMjAzyM96vHNGM9WV0Vmot4reaWX/AHuBA5lCnIU5UdwsfY+bPv2rC+s2j0yW4gmYLK20sSVDMFYEqexGBgkZPbnFaWW3gQxKskUUisJUDj8YCnJJ5YHcSQajzXkZSN3P7rxP3SqSxx2AyRuGFB/yq8t1lI7yIelXN2LpTAoaRU5VHK7vTGe2CM/6Nc3qFuIruV9ixsBkR9ihPcEgkZByM/Suhljt1ltpPBmDNubdJ2lDE84A8oHbjklvTFVmowv8y7SSHe+SUjJAUZ4B3DJOOe1cUMpHoTziU6qsREZBdiMAbsEH3H1/yrsunLyCz1UFkeacw+ysoIHZk4yCcDv6dq5pIo90bNl3VsNKzAcdxx6j196k6Q8y6gGG2Rwu3bIQqyE8klvQf51pJKSzM4txeR9AtUa6ZrmRhLLPKxc3BUDf6rx2B7farFUUlSrI6vu25TIJBIKH2I5/0agaURBJcQXKsNrETJlSM+n0PI5Oc4x3q3mCujLGEm8XmSNztGcYDAEZGO4z9jxitzlObv444S8e9TFPkqSmGPOSq+uDjuR7iqjVJfCTdEhtthV0zlhH5RuOfcnB+/0rpr/TFaMyRBMsFC+Hjk55Y8AD8/euV1CNg0dvB4rnGWXBfJ58uBzjaME89qibWy7loJ7aKsCeZLiNGD74tyhX8oG7zd+w7fmaqZoEhykqCLaRkk5YDtge/wDzq6MLSSHwQzDcQixrk49sevI7VVXEZjRXTYsx8+A+/BB7N7H71zxOqR7FNNbLIybcBiMAeYYHt+ff6V02m3DXelrHGv8AEgO88sFUkFifTkn71xsfimVmgBjjVvPJnOecnj866HRJra2vBDNF+5dkJi3tg44xkepB/XGa0ikpIyldxZKSFEVpACCWJbJDFR9T6GraG9L38M7sjKU8OQyjarJ2Gdo4CjAyOe9e38dqUikF1GY3G1jb5wSOcdhnAwOe7ZxwM1oS3QeLJLI0uxSOcZJ7MDzx7cV16nHoTIoYZbuMTxySKrFFVJfCKtkAYL9uMgZx359qvLe/e70qyvZbi4mhhgKy8osnkbll24JG3ALnjOO+K5O2+ZuG8KzhnZt8UahBkO2QQGUfn6HgV2HyY082sMUkd+LeJdpTG7JJJ9CVOc4B4Nedi7WS4npYO92+BV9T6Y1xDOkG+a2Nr80s0kxkV1GCQd3ZgMZ55A+4r5dd26KyyS+YBuc+UyfYe3rX2G5dWN0ZYmkeKMRNJKzOV3ghiYwcDk8HnOAcV8w1i3WOQqpIidS0O/uVzjIz354zWWHb0NsQlqV6S/vHESgmPDr5u/ucnvX0KyitpLC2aOw8Rowr71wAVGMLjnjnv2Az96+b7nWUeIwVgmCV5AOeOw5FfS+mtct7vQY7NphbXUDpGSCy5gzgsxHLKOOB2Bz3rujkzgnmiBdWa+IquGViXy3iA8DGMYzjtxmojWwtBhi+XUkFT5exKtx37/qanrdQSXBSwiS4d/40kIHYnIXng+pOe3YVXtM0SHeCzsxTeuOSMcewxx+orc5ido8sMF/YywNuJmWF4mOGORjGe3rXfXHzC20T3Frm4iVPDtms42lYKowgZjhuxXzd+wHeuN0DTZz1Tp0M1vK19bTNJ4SedmcrhFCN32tzjPv6Cust5Lt7YPOkqSEvKN8v72RU8rDb6DIP64rx8Y06it2HtYJNU3ft9iNA8bJtgFoB4/iLBGjLFFvG0+UkYwVK8HB9AK2JCYJrjxpX3EeK8hk3NkKTnB4GQV7H6VPjDLciaRLnEeQGnhDsWBB8pGB+ENtABIPcnOarb6Ge6sYZ7W4a4VLsxJutyG8NnOAx53EceYYGcg4rhvc77WNV02YlS4ULNbKRJKbcL4bYXIbaS245AHbua33UKLdTQfNQpIqtDKBH+7UgrjzHgMSSAQTnODWE08yTD5O7CyRfiW2uGwrjy+ZlG0k+q5J7c1va7jWZP3oVFlCNFPkmVlQsCuF27iRuAOMkY5xUO5ORS6pCzWerxLDDPHc2zWyxyp4brk7gQx/hIJJr6nHMtvPGzhGTDJzyOQoGV9Qfavm95GYbC5lh064ZLraxVhl2Z9pVc5XBGc57DOCfSvody7xFjHtVk3nc4AAI24+5z+XFcWLzp2+cDpw+U7nOzhkmdyMASqpYoMg8g55xj6CqzW3SNFIR3J8pbABwEGCfbsKsZYnb5vcxJG2QsBjdkjkcduSc1SarIsa3SDbKUbCmQbuCMHHPc1y01eSN6j6LKCVSiNtIORuyM18d+IH/AKyzD2ijH/yIr6+zeICCQeMcHtXyD4gc9SznGP3Ufr/wCvrfpvWeR8t9R6rzOXpSlfQnzwpSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUArZApeVVUZJIAHvWutkH9oufcVD0LR3kd9pQ/dwqAfNkkfSvo2jbkhhnl5RlZCx5PHf+WOa+c6JhliI7F9p9+a+iaexbTyvmXYSTkH3Hb0r47Gb1j7PCaHU2OBZqihA/h+bjuDIOAR68j+ldX03IWW/IONxTad31P39sVxNjIGkIxhVBO49hnvyTXZ9MBvDvSGHHh7sHA53Hvnj/GvFkrSPXTvEy6skZYOmRHuJl1SWIbe6k2rjPvXLxCay1C4lsPHZA8gk8Fo5CjO67icnaDhMEZyP1rqOqJ0httABtY7lm1C4WNJEDBXNrLtbB7EEd/TNUNlJOoNrZgwypDk2m2ONVz/AGgAA2vgsSSOTk8mvQhur5xZwy1fzgiNewpeWU1y9uFu7eUNCJP3sciAjaHI4IZfYYHBFZSQEajdssTOzy+WVlBWNCu8k5xvHIXj+gxWVtMTfSG3iluXZhcXRTB8IqfxsVIPKgABcYH2qq1bWm0FRaQk3mq7S10zSBlRWztWENjJ2k7sj6e+eulSlVdkctWrCktpli2mQGOKQl/CLvKZ4ld2j4IYlsd8jII/lipt5bG40mWa8mifTY4/7O2Tyvgjad5OWKkEkD39RmqSXX9cmun+evpbcXJIK2qvbRtwPwiNtoGRnC4APYCrU9UXdxLFD1VqF7MrRNbpqNtt+bt1wVQMdpW4RfVXBYjOCGwa6pYKaV0zkWOi3nEpr/U59Ojke0il+Vlkfx18XajQ+GfOvdPx4GMHPvXy+XU1uGhXUJZpLdSPOH3EbRhQQe4AxwCO3rXZdUa7cfKvpd9tW9tHjhu/lVXwZ4mDPC8LDnwyoBCnODuzXAybbm8LNGQr/wB4gZPrgen/ACrXDxlSvcyxEo1bWOoteo9MtrOaOTUQyBjsiNq7B+/nOOFZj7cD1qMNfgmjvJUVo7aJgC7AlpGPYDHYADk5z/SuVZHQ4tYyWTyqSfKvPJNW8ES2trcQSsriRdzYyRlgBg+xGOK65zdjlhTSZCuLwzzbllaSSQHI9cZ/kKl6Xpdxrcxg0Syl1JlTe5t+Vj9AXY4CDPGWI+9arTSxPc2tvE6xiTl52XGxB3cgfiwB298V1l7YmK2is7M7LNiQtnswUUJkyzA4DTE885wOBlTSMU9CJT2dSx0D4falaXw+au9FsrkSBTEdesS7DAOGUzY7n+Ft2R24NdXquhXNgtrFqyajpryAbI7qNC7qx2iRMMQVyDyDjBz6V85axSNURJUgUbdyrMXLSKCC7LjB9+BwO2MVa6D1a+mbNPV2uNCnmUrbXbKWt3/CJIGPKNtJztxu5DZHbKrhnPNPM0pYpQ6MlkaOtv8Ad9MtIGaTZI4nRvGJ47cr3xjGD3rkYtaaF0NzbW98ITuPjg89wOVwfX2NdB1TO6XckUSxu8A8PKoGU/3WxweVIPPbOPQ1xUyt8yC0RlbcDkEccY/DSgnGNia7UpXLWbqq8Kr8lbpajIjWRCzyBScjBLAe+CR611a3TadpcKRxgXskeJ5JcSNK38S8/wAJAGMdtuK4zTrA3Wo28ckZeBXLShThgBydv19K6q/1K1nhRLUOE8qhVfiNcnJII98fpUVntNImjHZTZzPiM0qp4jASMAioC2cnkbRkk59BXWWPQEixl+oupdM0RVQstt8vcXtwqjbkusClIwN3O+QEdiKt+h9CW5ZtWYoMu8dsCASjKB4h9wDkc+oyPU1bapp1udPu7S5vJzBIwZ4o48KBk4U4IzyQOfTANbRRhOT4FKvwstnujadL9c9O6lqLABNOvfmNHuZWyMCJrpfAY9sfvRn07iqm50i70g6hZ6/Y3Gm32lsIbm2uEMcsEnBw4OCARyCMghsgkHNYppzujCZJltyy7lKFkkBJAAOSDgce3pXTXcUmsdL26b91zpkUk1hchCWMIB8W1bJOV2gvGDyhRgOGAqasMroilUd7M4y5uAsLRiaa3lDCW1kIJ2nJ4ODj7Yz35r1OspJnYa3atJLtws9pJ4EuO4LADbtxkHGDzzVNJPE6ur/vhh18RXwfTDZ9Pcd6r5DG5kVEGcDeynIPsGFUgrGlS0jpZOpdAKQt+y729ETHy3dykUSsBxgICcjPp3HtWF3eS6hpsFxfJF4kzbwIjtVE7BVUduw5JJJrmDGjqU3F8HI2qSAfXPt966MRwXMFnBvPisFjAjyCEJwWOfxHtx6Vao27IrTSV2Y6R03PqkXz91ex6Zpqko91LH4js3qkUK+aVh64wozywzXQWFj0PG0aGHrS/SIiOW5iuLK1DOckbYjHJtBwTguTipGuafDaPeShDGLcw20McbhNihckJgHyZLA+pOSTk1Czb3Cxx+PGPDVxvRSZQ20ZKqOW7Y9fXtWygrGEpu+R00egjXrWG36Fe/1VnLGLT7qyVryQbCx2iIlJcBT5VCtgZCHBrk4ZIZVSaCeHwlVMLEmVUEc/l29PWp8bNFbi406WK3lt5FmFzHKI2LA8Y24ZPQj6jIORWrqr5nU7yPUW3QtdtJLdCTGJJxgufLwSc7jj1JPrXNUpKOaOmnVbyZUwdSaloM4aB1MTZkER8pDZ5KkDKn3GMH2NWw+J29YwlvexOtv8usSyReHsHOzHA2g8/h78965C8EcyM+/ds3Kj4I3D8/Tt3qHDBF8rI7xvIy5OB6+3J7VeLsisldnXQ9Uahr2qraw25ht9jyOC5llkwMhd2O2ccD0+lWPWc+YCZJWR3AZYUiKKASfM2eRyD2545Fcz0pbs+tRFT8usfmJaQgDj+8Bwfr2rodQ02KXVGt7mNZLSONpyzTl/EVT+Bn/ulsZHoC2awn0qqRvDo0mUWndKy6tHDNf38Wi2U39lNdq8jzDGMxRRgu4PPmO1c/xV1MfQvR5jD/7e6nFNKdrNL0hI9urLxgtFdPJ7Z/dk89q3/s++uLiSbU3TLMHaWGIOrDGATgZCjjyjAAAA7CsT05b3Piyw+HcmH+1j/eo6kqQMKeOSue/qM4rsSSOJtsrtV6VvNA0y3milsNU02aTFtqWm3YntncDLRknEkUn/AM6kRGwCRnGap5pbeTyt8zFLjYQrBcL6cevNdhPHZ2gANsFku0HzMazsS67jhCD6rjhu4I4+vO6klsklzFu8Qx5J24QYbBUnA4OO/tXLONnc6qc9pWZY6F8TjoF3HJqmnLf3CqqfMRcMqbuS8X4GbAHPGcYI5zVtd/Ezpp5IruKa/a3CuHgTTVWQuzcjczbVG0lQQT3PHAr5TcR+AuzyvIso82TluPX6c4/KtSysisjIuVIdexwec4xWys8zF6n0iw63vur9TSyUJpmkRsXWz3mRXmxjxnP8UoUALxgAYAp1ZdwNZzJCNio588r43FguXCjIXJzx9K5rpcmCdvOJvHKblVtqqB5uSB9CP8a669RL5rqS+SGcW/h+HBNEBI5bkBAvDKowT24OOeRXJJbVZWOuDUaLOP03p3VtaU3GmabJNauxUXUrx2tuSBnb40jKhbAPlBNT7novXLeBGisrG7iQkudMvba/ZQAG3FYZHk24PfGKuZon1C6D31ysvlEWPE3FDyVKgABRzt29hj61XEz2BKQINzTEkCMBkdTwc9x3PAI7DvXbsHFyhKtYJdtu8citBjcxRctCWAOcZ58/HfGR2qHqNwLWOHw5n8YDxZJAQCsh9Rz+Lt79zV3aalcX8UkFxDFJcFdtvMyKbl1XgiQ9y3mG1iB2+mRzepXQmvGG35eNkIKxDceOO3A7jHf2NcThKM7M7VOM4XRjddQQudmoWaQ3KsT41vJ5ZCFOA0TcBjwDtI+wqHfdQW8iL4FrPbs7ZJncKuRj8IUkgDn15z6VWaiiPEzW8QmgRixkkbzZ4zjHA5qGvlWJH3u7ZYqF7g9uPtXZFuxySirnTWr3l7Jcz3DGJ4k9FUKVznyjOOAM47muh/Y2p9UOml9PWkms3HgLLKkBCiBRgfvJGIRBxkM20eYjJqj6St4Ly7givZXsLLPgz3AgMzxREjdKiZAZgmSATzgCvoHXnU9t1IINJ6U0eHp/ovT5CtjYt+O4dSR8zc4J3zMCByTtzjJ9MFBync2c1CFjjT8NNUZ5Gk1zoqG5bO60fqu035xk87vDz9N/eqrVeltS6ZvI7XqS0m0iacCW2+YXfFcgY8ySKWjkHI5Vjg98V0MVtNCypHIQvJKjBDjdjOD2xip8cnyMU+nXEK3miXThpbMODE2e0qf3JAezDB9DwSDvKORhGWeZo0+FrnT2e0ge8aNAWMYzIsh7lV9BnHfvn14qND1DaR31ul/NFYPbXHitHJbpLAzEKGIVgwzxyjeXy8YqxkknWRlieLjCW8sKbPEQjK5HYgg8jsCT7Vxetx5vFjtnjA25Zg3MeAOOe1ctKTUmmddWKcUzqor2wht0N3d2JGF2YvVlBJOeVXJwMdhg1Ek6gmuS1lpjSvBMALmXiJXA5ACjkDOTluT7CuPZAgMcHhb0APLlQx7Hv64qZaNJbyKkIVHdRzt/0MVvJto54xSeZe25EN3GzJ4cRHhyuwZgw5J8oror7pC81QTXdtPp2naUsgxd6ldG3iRgMhAzZaRwDwkauw47Zql07w7meAXm0RpmWcDAChE7c8ck8HnBP0qdqGoPrE63Erh41Xbbx/8As4os48NPpgDJ9TknJNYwg5O5tOpsqx5c9DaH8tEIviXor3b7neNtA1URIfQGXwTnPuFxxzVPqPTOodP7bvxdP1LT5Z0SHUNNuxc20hxkgkAFGz3jkVG+lXMUEaBlaZWRc4ygLA54znt+VYyQNbC4JEzyXcQWZFJKSAcKGHZx3OcZHGCDXRKN1kc0ZWZMiihn08N4sfzKktDIJBg7iPKVwMgeoU/UciqebXba4BF3ObOVN24Tbn3EN6SAHuc9wDVjGqWklxptyIoZIowFD5fc55GGAPm2n0xk+lcJqABedFBVIp27fiXPOfbGRXPScoto6ayjNJl7LqmkgH/fH1Fg+4wxDyYznGWA7/nRNSu9RYvOrMg2IkcZ2KiKOEBHqOPv61z0TYkUMMc5YBRkntj2HHNXdoqC0Ub/ACxs23LbW5I/CM5B/rWlSTeRSnFJ3LVYLibUrS305Lq81G4k2W0FshkYOF7IACTz349DzjmrDXemLtro/N6npFncnzGC91dXnR/4ldYhIQ3B4JGMVIdZbYS2FrIlxEqZ1W7tZGV5cgE2xdiD4UedpVBhnLbtwAxos4PlIozAjQbTsCo6ouM9sjt2+nOaiFLRsTq8EclqWhXWlQF7mGOW1U7FvLSYTQl/TzADHHoQOajWqMpSaQiO3kzHuOcMxHPHfHIz/Ku5t97TCOOf5eWVmRZXz+FuRuGTkZCqRyMGqHVNPt7OSNZo5EVy2LZsiOJsFsgE52kYOO4yRyAKvNWRWErs6a9v1tLW1vLeNjc2djHJMyRqdx5ByH4YjgZI9vpUK26/06YBjdOnq3jxklyP+EZHbjg5969ijGp2lwZJYZJWjDO05YZyMkhwDlQMcHBr5vLbJ52C4IOOAQAM+/pnvisaEsmmbV4ptSR9F1TrOylSVbLxZpuDEjJsVM4DBjnkY57Ht3FUzSyNBceIXl8cqjPAg3hc5fA5HoB9hXMRt4UZMe4GNQQg4ySQO5q/0oW+oXXy96nzEMe64nt8bA6qRhODyGbaM57ZPpWkk5ZGcbRzLLS+nLi+torwS2ej2k3Fte6hI4WWMEhjHFGrTS9iMouM9yMVlP0vocYAs9cuTGSTJ/5mkjTIH8JMzPyfUqPt6Vc3urrd3c93qMiXFxMoTDQKUjVQAkaIMBUUeUKAAAPvmIsxvI5DboHwoyIcqUG7AZgOMZ9PrWkaaSzM3VbeRxeq6V+yZEljg3IxKrcRzGVGyOR5lDK2PRgD7VrgD4VGRXZjwWbaG9hkf1+1daROgK3URlglTa6SDJbjsefzB/OqWDSPBkljglM6xEMkbcscjvn29PyqlRbKL03tOxnqOrrZyLBfWrmFgJBJG+CP+HJ4YfUVFk6ltjHsjjuT6YeNR6kjOD9T2rHVITPZwM3MnjOAikncDwSPpiqP5UgSGRTGFzgscFgPXFTCb2SJ01tHQ6ZrMyanbu7NGqSAqULcZbGSwwc8kj0xX0iwl/aGuwFo7ueLa7W8UCeIJcfhGeMMT6k/w84718s0ob5lty3yyyMAjqcKBjlSTzzmupn1h77TrG0SdW8a3MdxKo2eLEjsqjHoX/i/7v1Nc9Sm6sjphUVKJ2V/relRX0t5qGoLf3u0JDbaLbrcw2pyfK88mIpHGTnYG5ONwxiuE1KysrlE+UV7R1ULuu40kyCc/jTzKp542kA1NlZWkjUyLIxXYHwERlAxhUQAADj6+veokwRraZd6FMAPGsG0bsjaO+duT39+K6IUIQ0OWeInPU5Oe2ntJ5IbhI0jg8xzIDvz2Ix+L79h2rYkzxsk0IKuzYfPlBQ4/wAfeuhvrZHjeJYGZEy0HjLhgQMEZ+vPGaoHZkOUiZ4nA4JznOMcntx+lRNbLLQe0izl6muJprltRkku5h+8kcAMzqBlSexGM9vbIqHNq6XTObKzkcMNu2WQBckHgKPTHcduBUG6RFlVrYB2kHmYHcXJ4wfyqLEkI8RTGj5bGEyTn/lVlN2IcFc7bpjXJ7Xf+zpJhJIrxSXEZYMAQOEI5UA+2M19A0uwiuFurjU7hlj0qaCS4vJimy3JUbY1Zh5GI5IGTkr3JxXxW1uJoPD2KVH48KxUqMfiwfXPbjNd1qt5Ldrb27wxfLWMYRY4Y1jBmYZeRgoAdicjfywC8965JUHUmdUa/JQOvv8Arjp6GXw7bTLnVoo1At7iULbFUZhgIWyzfc4+mKwfXI7zZcMbu0aX92ou8SKwyxKhwdoByo5xjGeT346RDc6WEt2ZIkbBUA7V5O4Z9uxPetUVpCTcQFt2yIhxM5UsMgLgc4OcYx+fFaczpWsjJYyre7O7u7i3iVoXhEjrG0Bjjn8QQwH0UkDaN2CCQct2zWEMjRqwsIPGntCkiRl2jDgsFLHcBhhkEkd8e1UejTPGbi0kXZKF8WLxSvIBwd3OAF785yPQ1ZQ29zYX1uk81rJc+JneWLAMV5jzkgqORnA7njFeXUp8nJxZ61OrykVJE+86f1G503Um0sTQW8tu6T3k9yqxWyBm/wDaZ3SuQAdqg8E54xXXXI3zFd6KGDZLNwoGCCRx9sc1x99dW9razi5sGsr1w5xc7WAcDAcKO4xyNnJA9T27F90j7VlG0DcxyV3KGTJPrg+lebib7HzuO+hvHOyXJkF4zNGkjKXbkHJyAAPzqouvBlabxLpYFkcAySRO6he+7CAnOeNtWzufmpzEX8JmZVDMMbWzjPHBrnrghVlZgGwpHqQTj1rnpLM0qPIpA2UA75FfIOvxt6muAOwjjx/8Ir66mBGpHHBFfIviAMdT3OOxjjP/AMgK+s+nda/A+X+odUvE5ilKV9AfPilKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCtkP8AaL9611nF+MVD0LR1R9D0HHhQZJBMq5r6BasJRcSN3cbiVAJJJxx7ckVwGgH91BnhRIhyPz96+iaSBJaXu1gmyyeTl8DAZcgc8tz2r43Gb7PssJuotLSMrcMV2bi5HIA/lj867jpxEWz1JmKuQYeMsNy5cY/PI+2K5GNFF9IoIwGGQAT/AA5/xrqdIZ/ltSRQdzKhBzj+I/b/ABrx5O7R6yyTPerwTYaPKrrG0GoTy+bu2LWUYXjvz3+lc/DcRQPDDNeSB5WMqJK6boDgbSAMgMB3wefbmrrrOMT2WjJ5DjVZTh1yD/us39AfzqkgvYgkb2cME7HLtNJMMjDZBwMYx7HNehDdRxPV/Ow88Twpkur9Ekihw6llCgnORx2wTk/WuEWSS9VppJlaeWUtOpkUvNu8wfBBGAcg8/THFdPp/USavNqOyEQRwFWSRC0iu45bPtuCtzwORXz57V7eS3zEgjkLiGQsp2qSCAR+vfvXuYJWTT1PDxzzi1odlp03hGRLgRxBWzJ4Kcbhg5O0kH7bR3qxkJV4SH8NnbcCQi5G/GMKRg4PtnmuEhhU2yrEWlneRm8OLAdwfReCD+WKu9LvJrxYI5LZxEzlS5iJCknIG5dw9B3X716DR5yZA66baNJdlkt/DjmtkyBjYr+IoB9QDI364riGLLLKZMehGBgc4Pf6+9dD1dqsd1eRWtr4ZhtULMycq0r4LYIOCBgDsOQa5tJGZgud77SP0H8uKwazZ0p5IkRb2k8OGQR7yOWGPXuT2xV4ZYTLJHbpGQnAkct5x68Hj8zzVYtldylBDC53YBJYHAHJPtVxCYoigeN+ThpRLwfYYxgfzrCZtEk6FNBaaxPLdSTxhIf3DQ+ZvFBDLyzDAJxnHt2NXXzG6y8W4kAnd18cyAyHcMgluBycj+ma5mSQWlws3jCOQ8DBwTGfXj0wfvxwKudUnPzTxw3c0sUsaTPIF2xvGMr+7DE+IpbnccHAyBXTRd4nNWTUjZaeK7GC3EqwlleaMgYft5VcAhV47Ej8603dil3d29k00kNhbEgDfuY7s4Cn2JAAYZ/ECO+KkrNPYeH4jxbIynhHDbZs9i+0cnLYHqAM4qe2km3h8Z7m2i0l45DcXUcomjhVDjwzwMMTnAODyMZra9jC1zmerPB/bMUKF7fbZxxTBwc+Iu4c57+gycVzCzsHfIxztK4/CR2Ofet2qahLqWoXF5M0x8d9wEpLEL2UEk5PFRyrLJiNQCVyM87h649K5rZnUnkT9KDwuLrClYzlGVcHPcDNWMfka5ygOWSSJQdw5OcjAwv596h2wWG2KbA06sEUpKW78ncpGPQciptq8MNwrQSCRY+GXedpyM+3rWEs2bx0O5sZ57XSdGubGaNPIhAEAMaymV0cOWODuH4jgbfIRzUVpxIklxeTxwPP+7ZI1cuxUnyqcnA259QD+Qqu0PWn+UOmagqxQ3d35C21U5wGQ557gEHI5z34rXLZQfOrAWllidHUiNW8RdozvY5Axk4wcDJ+9ddNpo46iakbrC3gid2tby7uYXUnBi7LuyMqGxzhieTnHer9baSw0201KaSF7aNHWKPeHdOGxwxJKkMwBA45BwaobCK5gWzs7/T5IS6GDY7eDsCjAkKrknzAc+uD7VPutSjeJULbhGoSSMT7gshHHBBxnk8DgcHOampKyIpx2nY+bSWzwQGNsJKu0bC2AE9iKgCHwXmltmBAJOWYZ9sAcfT3qbqgkF1IoyUVsFzxuP0NRkt35WR0XDMTvP58VlHQ2lrYzW3UICGxwSSh7fU+/wB6s9JdYr63aUIgE0aiWT03cZ7/AFyKgRLGrqI3HPJfaVz9c/yz6VNjtNwkLzeH2yHJODjAwft+VQ3ZkpXR1N5fSte3spVh4RDq80SorEcF2U7lycc445rVLChKloImkflUhjYCTLA7SvBXBHG3H04rOPW7jWhEZ3jm1J9sd1BHkCcKuNwXgORgEg87iW5r2NxHcmLE6llIaK4GUORg7SwB4927EcGupO6ORpp5mJe4dpI237GcxeFKSoDjPDZ53AEge1edQ7obXTxBIjtHNI0nhoVBGADtGSOR6n2qbc+BpkO+/jeylMf7u3mB3sxPGwZyTnByPc1r1WSS6R2n2RwFhnw4QkaOAcLgDgdx2HIJ9a5600lY6KNNt3OHu54lnmEkRDq7FV3ZAPr27j1FRVm3TKE42AY2naSR/hUlnSYuuAChyCpyBz6Go8cblIk27gTuUtGCQASMk+vrgVC0LPU6XQLqXwZl8OHEzBmZmA3hfT2OCQceuDUvQtSS31iUsGEMiLGxtQI3ZTIuShZSN3bkj71Q6U58FnJUSbcAOmSox5iPY9voKmQRmKdhFBHKUCs4kGCVDDuB79+O4rJWjO7NHeULI665kjkdiyNLNOSXHCuuScB9pwWPc49eKl6ZqkGm7hPDJKssglJkkJACgjadp59Bge55HeqtLqC6ge4tszZZijQwjykH8DZ5HZcDHvWie6eOWNlRmmiTDmGNk2AYyWXPB7e3vXZqcWhcw3kzLIEjgtysZjLAJhyVLDORkkE8Ec8d81z3VEFp+17qO1u47uKKGLc0cbYc7MswD4Yc54YD+lWceoyQDE+ZIZMiOOZ/DO8IQJABuPlJViOQdpB4Na5oVm6fkSH/AHnKB22piQsOeZD3Gdxx9ex71y1pKNkdVCDldnC3LrNOgjmYqqebecevb+v61ETC3EqRvCydgoHIOcevf61ncK9vNkhLhO4ZmJHI4yfpWpRIhk8JFEixbm5GAQe3860SyKt5nUaKii0m+dkjshLtjWYusZ3f3QD3yMg5/Kugt5GuCHnCEiWQShFAUMMYY7Rgntn3qhscT2UQht2l8R9wHhbzIUQEhv7oHLe5rfpIKXFzZXMYiSYh7cyv3YAg4HPm/L0rKnbbzNal+TyLtEdyp2yO7tmRnk27o+FZdhIBPI9PSs0jWC4l3zQwKuPEMsoAI9Cu0HnHOOexquwYTiVkCgDPiMQ3HHcjjt6+9YxsqsPAk+XlwGCICuTnj88/511nEWvhvHZoq7c7NqsSCXbfknOML3Hf271X6nB8xG7TkTlyRGBCD/COWOPQ57ms3eCJ2jgPzO1iAQBu5zwO/wBfp2+1WN1aJHZq8Vx4MjRxkiVNzSDGCAT2wQTnHrz2FcNeSTR34eN4s+azkt4akAuZFXawyXY8EEexPP2r2MAoJgXYkEDawIb6H2Of0qVLEjxeKdgdXJ3O24ocdwajxlinhKobnHD8/U/fNbrQyep1/TdlFFZrPqDTxwNKgkMKJM2zOThWIBz7ZAwPer+eJEg8EOhgjV1lkC7HIL52rzg44IH35xXKaPII4v3Z2qnALuDkg4P1zjP51fSyyMA8cMaWzjKqoJA47jJPtSHErUWjEzCVWIkBkLnKsAGYAgZz2PHoOPb3q3t47dplW5VH2J50UMAykEYGT9jn+lVMm2OYKTFuJ8uGDLgngFsAZxjt9K2RXslmkksDrCyRkiUDgYOcZP8AF6/pWhkiOqNZXb+CGuUtx4QjaUjxIwxONvbPr78Cue6ijS31SUKm6BQCpBJXOMkfUA55+tS5tRa4uZ7mdg8chDMsi4BGcDA4weO3aqjWCWuWYEs8Z2HDZwB2+/f0rjSvUudrdqaREmjRp1RmHgDBXcARtIyDj1xW/TY2ndBEEilZwAXbgAcjHfioTnxJF/d5UADBAzgcZ/nVhYsjzI9xtCYCnfjgHPb68VtLQyjqdHa24soFZXy0znzfiKqBnafQnPIz7CrvUj8xeX1wkmWlmMjtt5DHt2AGT9OBmsLr5eW0xYEXC3AVy8EZYIycAE4Hr5c9uaiRXytCpA8gYFxsBC4ODz64yQfbA96rQltImvHZZlGiXIU+KqFiAcDb5c84B7/Y1LdrdZ32KFiJJAd84HPHsfbj9Kgq4/ehpDtkGC/DEgN6nvnt9a8tIrZpoZ+oJpbTS23vLOsRlLBe6RoCuXPAHmAG7PpXQzmRo1ueNNXWOFgiKiTRxcHG7vg8fT0+1c9qcRled/7JgQm1DyuCSRzz7Dmp9/qkWuXTTSwTRmdw1wVZWBU/wcjOQOBzjjPaqnVFikutyRxmWBSpAUkhf+8PxHHcmuZZyOvSNiAqHnztIhJ3AHAJXvjPeptpIqzLmR42jKNwMjkjjjkHH3zVYgSW9glwHY+ULIhIz3z34qxshFcy5nfcS4DEfwnnj6jsa0kUifSY9MS3g8eaKNfFfcFjlUjGCTxndnscevOe9QJJPDaddySKuQCYgoG31wPU+/rUe2uTb7YpAksuQLdpF80gAxgY547Y9O9Z+PHuj8ViDImSJgQDzjJ9cc8e9axd1cwkrOzLKNYrm2LC3KPEyAqFyj7vxAnO1APQAZ7e1VPUaW5htvEkaJjsHiHnLKDjOOTkVuW4WJWdNzsjchQDuBOM8fX+veoeqXNreXKJFOnysRYMztt85A3Htxg+gzjtmqVHaJemryRHtbu5svCNuFZX2fxeIB9R6Due4Nc/dRi4e4ZVeLEm7AAOWyARj+dW0KmONmwskDKVKGQKu0d/L3P2+lVt54lwTsO1wQQoUKMD2xxwMe/euaKszrk7qxDUxyBFVfMwAcHjPPpVrotyQ91DgM8keVjK7TkMoPHt2/SqSR1FwCpMicAuDjPGePXOR/KrG1eOOZJ45kYyuWZShHBHYk9/863vsu5g1dWOvi02Jbp8pNdW8ZQSNsKtk+59D9e1FmkWddiAFfID6bR3x27Y/X05ry1nVZSFDTbl8SVUQYUgYXLbjwc+n1xQwtFBbPGsYjvCzJK7YCkcEY9Acjn1AGO1bp3Oa1jagjSKJ18jciRd3IDMQCMj8P27EHtVLf2qsyzI4jdMojKSCdpPbHbg+tSbu+gsVN9LteEEqGkDbN54YKo/EQMdsgYzzUNLj9xIA0jSXLI27cQAckAYPuCc/Ssarysb0lnchXc5lkkgljPhnaSGXkZ/ix6elU62g8ZEVg5RiFX654H09O9Xc3goxhWLLOdqMCVVmyDkn19aqp4gblmhlkiUK+7cOxX2P61nE1ka45liuUzEwdWwWBGCePzJH2q+050MYdQ37qMIIn8rcMcgDHJ5B9uaqPmEaTLDLEkbVbO7PY/f3qVY3U8Usoml8RXCghlyV5xjPqOf6e1aRdnmZzTayLu6idmYyje27EjEY4PAIUVshurj5iKSFGiyzLBmQqVAUrlSMEfcYGeKi+OBKyhg6gAIx7kdsY9xzWsSs6sXQGMMGchwCAORju3oOPtXQcpNB3KkxZj4K7Mlex47/wB3JLY+1UL7IrgnMsgUEGNmLID/AAhRjOM/U1Pm6gluY49PhEQQDDyRjBYZ3HeQeT2PtwBUJBFtMlxLukiYEsjHgYz34Prx965Ksrux2UlZXIF5atFLgzLJL5RsTPlYDkH/ADqI0YgZBMQz44BbB5HbkfbipUxZXbIk8FwD4gCnLsOe3bgVC+X8xbzHnERkIP54znj6VEdMyXqSolAZZGjBVG8VlPDD8j3rsF3RsZldJLUxsZA2V3SM/wDCSOSAVyAT7Vw427HUbwQu3zMTk+o79vvV7pGqLeW0VncyJD4K/wC7ySplOTyCfyH9cE8VrDJmVRXV0Xm54TsgZiXB2yZKjGMheeO/t71ZReJGypqDJdoCJGtovOxBTjt24A78j1qph1GS2DZtfmJGJIWTEiE7SMhWHB53AkcccetSLK4sxBLLfBzPhW3zArxjk5Vu4PYH0zWkjJE6zdbfWLdra7aSSKDxBIsYQjykYGTyRn7ECulddRvUkg0uTxLlAHa5uIxb9vVtpK9wTgc49DmuY0DVYLzULtp4UkWWIRhvliW2ZOdi8YJ4Ge/augtIFe4jubaaWOGIO48aI24dMEOQCTu5AHPIIGOa8XEyvU8D28LHZp+JNFxOLQJfSqslrAJTM2GmXf3TOPwnkjtgfnXTTNukeKMhfFV1JJGNuQfXHt/KuLjltLiO4SCGC8kljkeJTdi28PykMRK5GDjgZxkg4zXXXzqhJX8Q3gYYkn8PqCK8nELonqUX0irmAd7lk/CUUg+vNc7fptkuEJf92xU4zjt3x2q8mMkK3MZTbIwKsPpjOCMevvXP6lKQs0a7sSyKcEE45ycelc9FdI0qvIpif3GB254JzXyT4g89Tz5/91F/8oK+sSybbceKdoOQOPU+lfJ/iB/6yzf/AEKL/wCUFfVfTutfh7HzP1Dql4nL0pSvoD58UpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAVlH+NfvWNZR/jX7ioZK1O/0LPy3/d2nk89z6V9Fs5D4c3iY2vAxTJP05H1r5toz/uIwGOCfT7/519C0skxEZ2gxOM4/w/xr5DGLpNn2OEfRsdaLfddt4Xoq8D18vP8ATNdJYqqWd+zZJ8EBmIG4+Y4IP6fT6CqA5AWRgFzHFjZ2GExjH9a6KxGyyvIzHIA6AKqqMtyCMfmTxXgt5o9paM09WOyWWj+dkP7WIOGALE20vlB9M4qm1KS1tLfwJ4/BtJ4AYi42C4MjBcKScbgc7l4Ix9auOp7zTrW20o61ape2cusrA6MMlS0E3nH1H+Nce9jaW4NqxS4Wxmj8JW3MA4QYkKqfXI7ZHvmvVgrpXPOk7N2Iny1h05BNFb/MvEFLyO2RlwPX0OCfU/SuCk1IB3WeU3YOWPhrtKMeeM8Hk5xXZdWagFtPDjaOUyIApjHl44Ix3wCMgc+navnc0Qxtjxt3bCeBg9816mGbXSPMxNpdEtv2rZTmOWSeVZ4+SREVeQbccsD3HuBzUW56hdIli0+SSFirLLMTtdgfZRxk8c+lQJIzIwyrKnY4Hrxx/KtZjBa3CpgebkJkk/69K7+UbODkoojPDGzAAjbnBZh5v+fNS9Ktpr69aO0ilkuJEPniBztBA/Iema0tavPOI4YiZmYAgkYJ+hrrbW0NsrWdoWCElp2T8UpB5zjuAew9PvzVoraKylsmu16RlkcJJfaXZDeMme52Y4wASiMB9TmrfUun9a0XTo5prZksd6RxX8M6z2pkP4QbiMsqsccK4UnnjvWgbvMGcIWYA7uAR9/Q/UVN03VDpTzSpI/gXChLpSx/eIByJEHDp9D68jBpKimRGs0zlNRlIV5WQlw21gD6n+IY49KrLXWLmxjkikWG7tx+GGcHMbcnfEw8yEZJ4ODzlTV31DpkOlXYjsJi2nXkXj2jORwAcNGT7qfU9wRXMSE8YYqWGcYzt54GapBbJab2jqrfq8WkFtc2+nMt+pHh3hmDSceu4jv+WcVSahrd9qx2Xs8gtBL4ghJO0vySx9zyftngDmoEcDXD4dcsWJyTgH2wKnQWgykTMduGO5hjJHf9Ks5EKJqQSLMhCELyqgd2Y9vz5q60/SliEQ1e8ls3VMNBbwieQ85IJZljX8yaxsxNA7SjY8oIWIpnI45I7c4/rU1rOC5hUOjMVJJjGfNyME54z3/1irxgmrsznNxdkWen6V07deEdP17UNOui3bUNNVoQ5wPxwyk+5wEY49M5qHqUF9pszjUIlZpgfl5rR90FwqnBMZKgHB75wQcZAqJbRb2JtipbG4o6ZHAznnjnPbArpra5nv7CbQruXbY3EizLC65SG6A2rKmclfRG2nlTg9hVZ0Vqi0KzvZnIX9ybhFjuvEu5Y8kSyTFgQfTH0+laYes9YhEdvLepeW8MQhQXcYkZY1OQgkGHKg+hJxUe4l2B45gIWUlhtX+IemR9qqVhYxmVCirkZGclc8/pxVIKyNJu7LbUOsNX1dp3aSG0+YJ8YWkIhL855bJbA54z+VdbpsklloNhbW/hx3Bt12zSDaq5JYhiQc8457cnvXEabZve30caoSZnwpbhVHuT2x9a7vXJIo0aBpY55YF8GRkBIDLyBzjPHbuOPrWVZ3aia0VZORyCWFzq161vbKkk24lpSQsaAHkk+i8+gOfQV2uk9FdMxPF+2tY1G6JQgNbeHZxoCADh3SQkZOc4BIU8AkVH0yxjsrMSQxrFcXiLK25st7KCPt/M1Od9mQysdqYz2x9v+ddUY5HLKeZJ1Tono82qL09qGu6dfRsTnUvBvbNs9lLRIkiDAJyFfuPKO447WtLvNHupNN12MWs+I5FVZhLG0BGVkjkTKyKwHBGeQQSCCK7JZCjGPaxDEnaWHH19x6etReoFa86YvIG/efIn5qxbxh+4Vj+/jC+quMMRxhkBH4mzScSYT4HzvUJQzEHDE/iJ8uDn6f1rKHXdZSFLeLVr0QKcIBcNgZ9iefQV5dOJeYYyxcAh+SwH/M/yqC6usZXeyOR5VAySufTHFRHQtLUn6Skl9q8T3DtNPu3PK8mWyD3LE/lXbaxcRSWMQjdFlijdI2SNgkw7klc/i7EH9Peua6Qtn/aiyeKXGCXXw1bg9vQ4Par3VZfnZrbTw4ha4cLkDbsHuoP4cAZrnqdKokdNPo02zndE0G41BjulSxtclDO6M5b1KoieZ2+nAGRkiuqj6N0SExHVf9pL11dkV7aS1hJGBk7CJOBkHJbj3q+h014IIRYiFQ8BRFQeJ5PREZWO7vlsYJJNRIoEs7eewQw3UW5QGj86KAQ27B8ykYHbk9j7V1pHG5EQ9JLLasnR9z+15GIUWE9oIr925JWPaXjkxjspVz6KaobhQ1uQ8g3NGMqNuVX2zjntjvmujDAyM9vDLBMH3llO1WO4kMCDxjGQcDFc/wBYqpnhvIw3iXxeOdShz4wIyze2QQSfcE+tZTp53RrCpwZQXviW5k+WlMAAAJRsbznOSfp6cVIuuqtUuGmFmLGBWH4oLSNHA7bd2Krrpst4EeWy2QM7Rn15PYfeoaSlTtLbo41ydrA4B4P3FXjdIrK1zoOnBJd6sz3t7ul2HDSs0hK+q5z25/rXQ6xcRWVvIshSCBVLYxjPkO3yn/vDkdwaqOl42he8uER1kgTPHA4Gd6kd/wDCuz0/T2upjFLKHEEZk2lt2VHIJGcsRzjGDmuWfSqWOqHRpXOMtekJJ7NLjV7uHRoiCUg8J57hlAzu8JMbFOeC7KT6CriLoLQLqyjiTraPTLstsA1zQZba2cHnm5t5Jwo+rKBjuRzjpClnbXNwt/JFPiUTGSKQO6nZzgFsBexKEHOO+TUWVC8QZrx7MOhltpZMxtKMEZA3cegwcc+hrsRwtnP6noWp9MzrZdR2QSKeH5q2vbO5S5tbiIeUyRTxko+MkZB9QDjtVXdi2MEcis5hmlLknB2r6nI43E/6FdXpklnHp11pEmombSr4sXtnsnSOGccfNxNkhJFAG5lADLwwPGOQupZBdyhytsYJzDtkYsMg4Y5xyM/1rnnBxkdMJ7UczWeqLy1YR3VvFc8bld28Nx9CwyGP3p/tpIUCQaZBFJkbWadnAPbIUAe/vVJLGJbgB5fEc7uG54J9M1tt7XMrI0ZZ1bAywBTHH9a6E8jBxuzotCupp53vb4tIV2MiBQiAhhlAo4XykkfX6111wspgtbGC2uFubhfCjVpGmkmcE7cJ2TPbvg9/Q1yegwSBljyQrPtAZu7+hwO+D/OuraZIrZbaygjRp1MdzcxIwk8IHlAxBIB4GccKMdia45R25nZGWxC5Ct+lOn7RY16t6iu5Z1JaS06esYrtlf8AuPdTyRw5/wDoYkHHc1suum+h70FtK6j6j0OdASg17RIZbZnB4DTWcjSR+vmMLDtnFaYbXYsficEKVVscfYfQds9u9TE0+3d2VZoAAG4eQ7v6cHP68cV2WSRwttsqI7GTSZxp+qmCPCs8c8O2eCQH8LLKPK6E58y+/IB4qjub+70q6lEBDhZCHiJLq3HqR+XINdpqNpDFaPZW9wl54bSTeUFozJgfvIsgDkDzn+LjIBQVwurKpnjAcltu7AG3ace9YJWmdDe1At26yR4Le2bSbWNIoQRi5lKlj2fDBiCQeRnBPPeqqfW5r9lhfZFGhJ8GNDsGe5JJyTx6n9KrAhZ1XcSzMBuPHHt/Kt1gfCWNLtcTsMtImFU4OFAOMDitHoZrUvreVGnDLHJKGUtJ+82kKcDhs++O9SLrp1b5muGv7e0smZnFxKpd3GOdsa8tyMZ8qZ/irTpKJPeZLtiEB2AByV7cNjAJJxir6WBnxczSxBtw/dMcMFA4wMY28kcZx+WapCGdzSc7KxDm6f6RCSpDr3U8sqnyu+lWioTgY/d/MswGf+Ltz34qu/YktkqSwzxX9qsoMjxoY3jycZdG/CM8ZyRn1q6W2txEFm2NJGW8YImNwLDgZ9B37e9bSWUmWNf3207mjI2KvABx69u3buDmtZRujGMmmWKvBBAJbkXywOhEvhEFouMKSGbAUsMY+471xN+ZdIu//N98YyJJCsgTbvBwSCpyCef5V2MU6w6dOvg2cYaWOIxzDd+8zyoDfhBwexx+dcv1T8ta3Fus0tubmQsxRZQW5PlOFJ57H9a4aacZ2O+o1KFytbqTUlQNK0KEOAHEC+UemADj0GRioyXdxeTi4vLjxWTcVK4Ur7nYOw+1QisgLgKUUEAuRmpdqkSzK0iO23hgIy2fp9K7HoccdS5tbKTUt8VokzX+5FtLeKBpJLqaRsJFHGvJdiQAADVtfdN2VvfXUev3lxHfwZt3stMWOXY6nDK8zNsyDkEIr4IwTUrpDWX0HTtSu9LSSDW7kmwt78Flaytdp8Yxn/3ku4JuxlFUkEFqtLd9Li02CBtNWG7gj2NJFJkyBu2F9CPoeQearGNsyZT4I5ubpbQdxEN5rmlv2D3EMN5Hkn18Lw3HIxwrd+1VUujXPT9/AuoCGSLxCsE1u5kjuGB58NiBgjsykAqTgjkV2syIsMkbxtMBJ/vFuz+G+4DsTjO3gHIz3x9qy9jknslkuHPiliAMBUXC5RkGABzuUnuferSV0UjKzMZVW/0m6khkUT28b7ZNgj8NkIOc8nBDY9ySOfbkF6gmEZ8W3iZsADzEEgen0rtLKTw9OzArwyEfvAikFtwwwY9x78d64CaCdWdBGQVLIQ7AcZ7jPrisaMmro6K0U7MmXWu3l5HFCwt4oIy7IIlwSGxuBfuewqTBIY402ATP+JSxClU9e36/nVWqHGCxEb+QKVLKPbn/ABFWmn2zSTQxLIqSyFeR/CoHJ59AAfzq88ykMi5bSbm6aJ/Gt7WR4tzzzysY4gwxjaFJZiMHaoJrdddM6dLbMJNVvWuD5YymjIqMNpwTmfeOT/d5xnjtVxax21wqytIw2DEcZXf4aA5xyefTJ7k5NLtgsBRWL7R5lxgAZ7/f/MVeMEjOVRsqNW+GGu6Z05H1dp1zp+u6DhRcT2dyjzWBLBF+Yti3iRgn+LGMnnGQTyIMxZHaNYpF4G1s8A+3pzg12a2MXzMVwkCw3KKNrNCDkEE5Z++MHBHbANc3d2CR6oEiUQwkFgF52jJDLn29voRSashCTbsTOpJ0k0yzmsAbeaAiNlV+SrnIPHGM849DUAdR678jsEsTwW7CTJijGw5wO55yT9f5VOj8W/0O7gRBsiYMqIc/csfT14rm5LeN1SKIqu1QfMOMdzzWdN2VjWoru5YRNdXd0k14yzZ3FneQttXBwB6YP09xU+K5M6G3t43efISCIR73ldsYCKMndngcZz2qmtwZI4iskaMcYVXBZhntj0H3ro+mZjYCXUbST5e6UNFFdRMVe3QjEjow5DkEICORubHPNS47TIUtlFlL03p2lqsXUOqzyX/iFrvSNJRJWt+e0tw/7pZM/wDs4xKV53FSMVGuNB6Zupmn0mz6l019mzxL3Ure7AOMnyrFFx24zW+xhhgtExC6FGIWPO6NR/CAAOCMc88+1erLLDcMYMSvJGArEZJB7jB7Hntxx61sopIwc22cvdabLpUwWdlIbhXiUruGO3PKt7g/lmtNuzxS23hRnwWTcJJHI8vqOeeD6116RjUoJrKZ9kJBf94nO/j+IAkdiQfpg1zCOypNGJmRYGJAbkbs8g+2cZ+tZTVjaDuY6pvgZLqyuGhW4UDbE+MH3IPfOP6VVSXt6wkSeaRsAE7jwB+Xv2ro726bVrIGZwLuIFodihMr6jAAz6c1znhRlmOd8Z7Agg/6z/Wog3azLTSvc3208aTqAEHisokQAj07Z9e1dHpOmz9TXbWGmhHkhDzytLKsFtbwAczzSscRoMjJ+oAyxAPLwokD4kBJYhtpAB49+f8AWa6+C8NtoMWnwW+xL0LdXYLsoeTkxqQPxhAchScBmY4JxhsbTI29mJYXOn9E2kLw/P8AUPVF6XG64tJY9Nsx6EI0sck8gx2JSIeuKpbjQ9HuopfkJNQ05iDt+buI72IoewLKiODn1AP2rFYlUEuC/mG8Y28HPI/p+Va0VFLEcIBntx/yroVOKRz8pI527sJrKeaC5gy0cQZQHOGGO6n19fvUiSJ7aOFvCkXyqx3J2Pc4H09fvV1qEXz9nJE74kiO4MD2XI3AfQj+YqoIeFJY1iSOMNsIR+W575PJ9MmsZqzsdEHdXNcGr3enXsht5vFQEhYpkEybWU87W7YByD7gH0rUHkuBGXff2Ubm4z2yfrW2aJUKOpDBgVCY4Qj0xgDnOa8HhSbOIo12k4GRu7+v+FUcroukrlvo1+dPvIWOHicKroFzuBBBwT2P1r6Xd3M88SzWqQ3jWsZmA3k71PmHl4ztAz+RGBXyuxkmgjj8JsuCFB29/cY/n+Vd1pt/FeWymV48ohkIDujK+fx5z2PIIGc/SvOrxzTPQoSysXVyJLKCSVEik/3WWQXAVJYmUoQQ6OpGee4xjv3xXZSbvmI87GTz8NwMgLyCPy/Suet/C1O0mAuEtcQMojEIJQAHazHt/D32kAjGavLxt0ys53lS7NleceXJP65ryMRnGx6lLeKe9uWmn8VnEknhebzcklMeprn9VZfDTuN4UlS+RnnOKvLpuYAdo8ignnI4++KoNRbMERIUHGCB9zWNHeRarusqsAwEOM4PrXyb4g/+s0//ANCj/wDlBX1pSPBYDHfP3r5L8Qf/AFnn/wDoUX/ygr6j6d1r8PY+b+odUvE5elKV9AfPilKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCso/wAa/cVjWSfjX7ijJWp3Oif2UJzkjBxj6mvoeiZ+XbkcISO/IJr57opxDA3AO0gj1zmu+0gsIgQQPL6Y47/Q18jjd5n1+E0R2kTJPDbkJvfwlLdhnA9OOc4rpLRmSwujld3ho6E577R39u1claOrwxBmQbfLgjkADP8AlXWacc2c6geZoEPc8tkj6ewrwJK0ke3HNMg9YM3yemqDGjNrcYPiHn+ymyFPbcRwCe2TVLcSRxRNHNsifYWjBmV1MYB/dnkdgeO/6VadU3K20OjyTRiRf2wgKqF/igmGRv44J9fSuT1Fo9LgjCS2HiAqzKrgy5IxgYBBH1zivWpq6R5k3Zs5XXLjxp5CF5UFxIcKcsc9hx2Arn5EmgdQjeZRywGDgcE/zqXcjx5XeVlkaRQVQoc4BPl9+exrRuEsbRuiW8x8jKqEDGORkmvVgtlWPMm9p3I0W7jYxbc23bnsPQk9vY1M+TkuY9tz4MUuSNzqRlvQZGcenJ4+taIY41LRoNy+GD7sv1H5VZQL48LyxbAWXDrG+CF7Atj61o3YzSuRtPjW3vbZ87Ocdh2P8hVnGfGlw5U+cBsA5GSeePWoQYrF4cbsWIxgjadwIIP2/wAjWJZGZ5LXessZBZUAOOeSPU/f0zXVQldNHJXjazOimsZDEJYkY4OzIwSx++cj71FFvLGEbYcshcng8DPFXGiapHLFbQxyxXcgBYOwKMoxgndkHOeCMn6VZXTJPzIs86JAfxyiUKee+/nH17DGa2vY57JnA9Qyl9Js4ypYpeSYVicBSgzgY4GQtcxCEjKv+FgxZif4s9gKudf1CO/upFtVxZQeVCDnJ7E5z9OP1qnAARvDHJUggr64PP3rJu7N4qyRYIjBgIdzBATuc5PHqR/hUiEiYOjRhvUrk5wR+g7VFj2lIzOybCBkbuSx+31q0W3PhpcEyMzHardsA/xVhJ2N4olQXbQxTGXbsmQASYJVSW5Qn0bBFT5pWhikmCkuwDZyCGf3BH4se1U16kkEctrcCQQ3Sr4iv2ZlPlIJHHJI/OtKTSC2MTs6wxwr4XqN2TkDHFdVJqUUclVNSLiGaIrumEguzIS6sAFX1Jzj6gVN0XzzRKWDfvCC2DgfhOc/nzj3qgtrl5ZEhiiZmJG0AFiT7gevPpU28mmSJobeQzTHC3LKwYx5H4Pz/ix24FXm1FFIJyeRzl/dfOXFw4U4kmlZZCTggscH6fetMIA3OYlOAM8ZwB3P+Ne3JAYHYxRcKNy8D/lWEMQkkdo0L4J3kcZz2B/pXOtDp4l7oqRjURdO8UQTlW3+UevbufTt71Ku7l7ku4IKyFgSikEOOABnvn61XWSrHln2u6gswB5C47j3wRVnaFrgEyeI5WQbY0UAZPbOfT+dc8lnc6I6WO7tNDjl0Ox1CS6gRLmIP4BdBIxXylcBiVz9QPpWUuhx29k0tyt2CxXw2TayOHwB6hlIIPqfriuZ0Pqd9IWS2vWWS1ll5mDhfljuJK7SuNpIHIrrbK5knu/90ZZBmIZjkS38QkcYJ8pxx6Y4+tdMZNq5ySikygudNls4YmkZlZx5wqnhz6bvwtxzwf0qNdzi3tb+XyKhtJIgjAgknyjv6ndmr6+tJ0sIpJ7S5sEhi/eXIjZUMca7MkqQMlgOcEcgmuK1a48bT3lh2/Lr51LPtZ1OQMjjI79+/eplNJERg2zmJElebxGm8HaAoJ8vH+vStWHdJTKcSqfxZ830x7CtS3Mis7wkyBjh1btj0HNZRoDMTsHhtkbQ/r/hUWL3Oh0e4WzspZIyiyTeWQqBvxtPIz2BIxkc8VMsJUm1O2kYqYfEVZF3ZMWR9PXmoNhHdRWmIolbawLEMCF+pLH+lbJJnQSKHzvID7FzkH249Me9czylc6dY2OzaQ3JXE7qY1UBYQsSFowP4F4Ukdzzk8+tabi8a4nCQXE8bkj90ECLGw5wwViSDjvjGTUfSry31KNY5JVtrpFZW/fGPeQOCDg5HPI/w5qSn+7zbnnuIJ45HjYK4kwuOCCRhl759cdq7E09DhaaeYup7e2uMwxz78KY5SNm1c5PlycE5yc55H1qg6puC1rZQySmNjNJKCfxEbQM/qcVaXt+Y1e51B1t9yrEJNoBfGCNvBJY4zkc1R6tpslwtxLcMsL+ZY42IJTA3D6g/n61SclFWZpTi5O6OXZJmAQKikc99w+5JpBDs3lSrq5CsB649D9Bn861ShQXWQ/iXg4x3r1eVQeMpQnnAxn659ftQk6zp6IR2ssrNDMgbcUBwxYjaF47j6Djiuk0TUnTT72VYoS3zCBt7l2AEeVGBjvh8f51z+jrFFYoMbJAoP7tuccZbHtVnomsLp+szStdKsUuY232/ibAuSCyDHl9CAc4PHOBXKn022dMlenYsLrUbaXdtSUW7SBgbcIUwOxAIDEg/UYx61piFqJ5E8srxt+7aVSCxJ5IO7ABHoeOxqDbQbcmG+iID+PtiXcGQ5zsxwME8A/TJqat1Z2SzXl6pvJW4eJpfLL6ZAx+LHGexPYYya7fA4SzktY3L3EfjQzwef5iIptOQQwYEDI2jkDOPUHOa4fU0uRM0lv4jwTlpAEyfN6nOM/WrvVBIL2KDxbc3lyCGtYn8Q28Y7KXJ2knGcj2x2rVqTr8hPLZRiO1lCxNcQnJYAdiOyDdj7+/Nc9SWaR1Uo5NnGvkkruJidhkcZyfr3rwcSANK8hdiRlcntnv3zW65dooolkTxAVycYxj2x6+9aEhDIG3gnBJJyDjOeBWiM2dP0vbSK9r4iOIGJK7SNrljjBHoM85PtXS+PFHeXRuWkluEfytIzeZMDHKkD/pwfSqPpq6e3U7yZY51KcPtZWIIXy4zgceox35rbJdQ2uoyIyD5aceExR9zxkfxD6Z9Bz7fXni/9jubyX+tWJ10zvLsDs68FCWYhQcnA54+3vUu1kDNMZJfDEgG92Tc47ZIBI74yfoa8isx81EogmKMo2kbZFmH94MDjsOx96w1crp8/hxpN4uzwyqAFtykAnHOAAQK6rnISGK+G3htEJIWcl3c+ZQjE8fUZr59qD+JDb7ZSxAIIZAGyeScE9jXQalqsGz5FGhF5sKYLCWOJDyUJHBduMnjbz6mqW8mSWIC5llLA8OzbgeMYGeR2rOW8jWK6LKVcOq5yCMYwO31yfzqbZRlpniEwdnOREezeh/zqI8lsihZJkhbnmRuf0PYVY2NuWuo2j2yrkMHUZAHNTLQR1Oq0q2lsYsMsptVY5burMBwPTA45z+VWs8wvmBjCpHtUReQse2cAn8/0Fe77eDTZLi4tgQ8am3EMjxIH2nnB4Yj0z7VpguGljTwy7jbt2RgblPcfXGT3H1FZ0Z7SsXrQ2XdGlZGgcNACF2lWPfIzx29MevetkV0IyrvIxQMyhcEYDfi+4I71HkuDENrBkU8IHIDHHsPb2rRMZLOJpbiQxqxXw427vx3x7DPfjtXTfI5kibDMZpDbgAwGf5gb+EJIAAY/TAOPWqPX7QSGOV7WOe5eT925XzgZP8AFjOOe3pW2yR7u6jB2qGkwXkO1AvZueO/b2HerLXwklsGskE0QRVybhCYnBPOM59CeMjn6VwN/wCy6PQSvTszin8Q+IzyANwoyo4AHHf0rKOIRgkMd0mV3ZyR+fp/Wo8t7boyx/MxMQwwpkQhfp3zUm3RQ27AYbQOG4H6V1NWRyp5nV2AeOygyCpjDAhnIKqTkdznH1q+NjLCYmRnt8yKRHsPryvpjj2PbPaqaG4/3SOZ3hlWJc52nPJ5P1Axx39e1XFpfiW2uHknBYphHyXBzgYGDxkcZ7iojK6E42dzG5iBm/3NySysJTIo8z7uy478kYx2x7VjNeFNOlUXZELDY0YbO/aQduMHjvkemKT3CQSyCKNlEEZEMOO3IAUtxnOSSfsK56/vlnJjRoZJWILkDDAdiuRxxjA9hV27IoldmWj3ETzhpSLiGdgJmkJXaDnABB9e1UWoL4d1dBQy/vM+ZNzc+uf6mpgkKzxs37lXlO4Fs8A4P9Qc1E1WOPcjRMXkYeGSDgyc5XjOQSK5orpHVJ3jYhp+CTwZlRVbcVIOA397JGKsNJbwdajEu+QzQyqJD2LbckD24B/lVSXJBQqpl3EtgegNWNnOiSwH5h4/PuVpYwSmOcAj1Pb8620zMdcj6HZ3jJGI921ly0b8Ec9weDwfqPetGs3bMV8FmjwV3MXzvyO2dg4z6H+dQrS8azuQzIzeCdxWKbaQPcEZ4PuPUVhqGoPdZ8V5pBzgu+5vpuPrx64rVWeZg8sjFJlUCSJRbuuRhRlSTyRgHj7DiqTVkLNBL/7OMSNId3KA4wf1H6E1YO8YtC9xHs86sJjKUVVGQV29jkkc+m3HrUGO6WWcLJNG8MgbYMYYAcDuO574BIx9eKrUdkaUld3ItrdGCFI4F/eCPG/fuDck+ncZz9aqrmMTgRvHGjAnhh3xjAz2/Wptw06iXLrDErYXsmeOxx/yqAL61eGVdnixsobfFGXEXp+IcAe+ayiuKNZPgz21h3RskdvtQLuGxcEn+7j1/wAq63otY7o3NtcxgwhWZ0LFfKSrYGOeQCMd++Oa5K3khfb4ZjkjAbBDZ83H9astMluYrx7lVaALtKGN8HK8cEdjnODV72eZVq6yOsvITI8wV0VhllkJyOe4yO/HA9v1rSqqYt8ckcM//s32HynODx6nFRra8Dylo4kVgNzQhj5c87gDkd85B71LNyViMkZUsQNyA8ndzwB6jmtLmLVtTaIltYvmIbhYZEcxqyPgPwQcY5yeBjtyfauWlZriVoQjRYcrGSNp75455XPIz2zU7U9aW2sXtIZnvbguybgxEECHHKL/AHiMZJ5GBUNkeW2CqyvtBRi/L7Dzxzjv+eKxqM3poheJJFA+550Cnakh9s8jPpzniq5otiqYwc5PHfj/AFmp00p8HwoUJQDAYN6juftk1E+TDhnkik8LOwlTg5PakciZHiQxO7Kki72XOAcEn3Gft9+K7nRrOHUdMhmRiJo4sP4jqqF/N25/DtUZzznIA5FcTMgEr/ucq4AXaPwEYyRVtoOrCwlk0+4ZmspPOhGGMD44fg8g9jjPBPGaunYzkrotIArHLOrlVGxc47HOMZ9yfyrFLeSSMq+FPIZgQBj05/Xmt09s8W15SvgoxQTwedORnhh6Y+1R3wrlo4URXUl3PCop5/Hn6Z5963uYGEzRxQzImQiISA2GzkYPPoM+lVI2s802/dwd8gj4XsWO08f64rde3qSjw7X95AnEkqkgs/pgd8Z4Ge/etrQQyWMVzGrGaKLBZBtDHd6884B71x1ZXZ2UotRIEwZpCghQKgxkk9vQ89j/AJ1ERBG0ZuWM6biQQ47+w/51PnjVnkKRoVDKVl3EYOfxZAJPOfaoLObUEHOWyWKjbk4yD9DVEaMl22ZTwwSRskqGxtAB4B9PrXR9NpLqEq2zsOCgeQp4gCn6KRz2Gfr61y9iRDJGZ8+JIMDcvr68/wCs1b6fcGO8SSJfGlz5grbSfUc/l+lYVFe6N6bs0ztL24kEd1BHJNLaz2zRIsSbmmU8FVVfMDnII/Piu5uCrSL5d0h8UEZzx5MDt964ewgeSzvt11YWEbwvhZw+8An0C4O7GfMD6c13CKZ5oUBjxtlY+IxVcAr9eT6V4uKsofO49bD5yKW+V1jhw3l4yw75yRzj/Rqi1TiJM8EZyCc45robyMtayMVdcEjcfN9lwa5vUGHywyvPikEHHH2FctHNo2q6MrMnY45wSMV8n6//APWWb/6FF/8AKCvquQFI9MD/AF9a+VdfnPU1x3/s4u//AHBX1P0/rX4ex839Q6pePucxSlK988AUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAV6n4l+9eV6v4h96ErU7nQwQiEdiOB719B0dQ9qpwCQ4UfmG++P5V8/0XmCIKeT/KvoWjAfJvubvMm3+6OGySfSvkcbqz6/CaI6PTyVhUKCFGcE9u3GRXX6QcBwCULW0ecj/55j8++fX7Vx2lEBA2Txjccjn711FjmKyvw5C/7snAIP8A7Rcj6HP9K8Oe8exF9EqetnHydingvMV1SJvDC5Lfu5gMD1/5VwHUV3Gk00UDmSKPYI/MA358DHc8frXZdcylDYpHGkh+ajcI3IYKJN3GRnyk8V85vZYpJ3jhQQRliYwhyW445r3MPHopnj1pZtFabgRQOjl1eVv7RCOQOMZ9vesbGyn1acwWSvNdN/Cq5LY9ee2Pc8V46KqEgfv0G4nduzxk8enrXSw2TaPZQWKGRXuEDXjx8bn7hGHqi5/XJ54r06cNo82pPZRknSCEWp1XXLW3Tg7Le3e5wCONzjaM/QE+1Wlp8PtS1qDHRWoaf1TfKHMmm2Qkg1BY0Xc7fKzBTIAP/dtJ+VeJKdCtj87lYnGRDIEkQqc/h5yvb+dUmqX0eqPAthJJaPGF2pIMhWHPkkHbHGM4Nb8imczrSRTPcnUZA8UsEaA4Z5UITIGcEdwT2xVbPezLetc24S2dnLhYQIwgOchVHCge32q+1zULnWhPrV20aarvSLU5UUL8ySCYp2A7SHayuf4iATyxrliwLYk3qgHk8gJx9/aqqOyy7ltovbPqK+sVk2QwShmBk8VeMkfix2z9sVDu9dutVEYuJTDEpZisceAR6cnJxmoNrteOQy8IpJZ2UnB9MCtlud0bBsEHADHgjng49u1WbZVRRkHKyMpkERbcc7QVIFSNO0O71ILIbi3tLblRc3MpEbHP8IALP29Bj6itum2K313bWrxneQd2DuLgc4x9SMVcJZFiskqvK7gxrEF4A2/hUAeXA7CpjG5WcrEyDo3Q2AWTrbT9MuHCrF81od74LvxwXRXI9Dkr2qdr3TWsdIrb3N2dJ1XT7kYs9b0i8W7srjHdBKuDG/fKOqNkfhrnWVkwYp5IS3kCrIRhuAcjjntV/pV/KEuwIVeKZWS+hdlSO7iUD3bDMvJVgMqex9KmVJNERqtSOQec3jr+0Hch0LAK44PfHPftVWt1LGfEt7h4Rnc6o2Aee+DxmrTqCJtOvp7VZzNFEqyxyOnmlR1BUkDscHB9iDVCZWmaPA8uM7W4/LI9arFWLydy3TXrufxVl1CbwZBtYq2zH1O0Ank17AGsbBYkUiUuWY58v6/aoEdm8gkVH2qRtZsd+ewq3nnE7JvxDEY1jCld2R7VEnctFEK5Qs3CN4YGGz3VvYGrzT+nIFtopdcuHtd5wLeBlMgDH8UrHPhjvxtZj7CoWkwRy30g8RSIQJESQ+VpOy8/Tk/lXSqry3dxLnLSOCxBA3EcDczZHAGM9x796vGN0ZylZlpFa9DzPHFZ9MaqX8JUZ7TrNGun/wCKOKa0VWz/AHQPpmqvX7CGGD5nQLmfVbKFgssV1arZ31sD6zRqzo6jIHiRtjkblTIFIbOHVjPG08KCViibpARtAG5jkE9yBn1OccVvvbc6ZdW1xp9slrIrKyOqeUgjGxhyGU4bPfIODUyhFlYzlE5K7lLeIviBgxDkFB6+304qvg1i+0z/ANAvZrZcEskE7KG9z7Cp2vwQ2l7PHYbkgIR4Yv7iMAQpJ54yR+VUvhzAqFCgnnDHAHp+dZxVjWTuSru6vbmMG9urybxh50aZmDfcdufb6Ve6upjs7eK2XdB4IRhkcN69+c/5iqPTIAt5AwCrh+5xgj0we2fv3q51m4+azGUZZopcLExJZjgAE/mBVJ5ySLwyiyBp+nTamN7zRw20fDTlcg8ZChf4m/THrXX6Rb6BaNG46ai6jjRiskep6ncxqxx+INatGF+gyfzqQdJTTtKzD+9itk8GUmMsAxJJOcYUnBIOfpUdnihWa3McTo77SAg7bSVPpyCMAnnBPFdKSOZyLTWLPprWtyaJoVx0fP4JHg2+rS6paM2ezJMomjyMkFHl99uK5UC4sbmW2vYi0kYSTHA/Qn8SkEEEd6slZ7m2giceOGTdEXJOxw3dcYOcDHmz/StV80UumkhzOLaXYodCGETE+UNnkBhuA7jLe9Y1IK1zSnU0RzerPNMikhkEnmUE8kZzn9R3+lQTrWoRxxhL262bezXBIzn+VSrt0L+YvImzBGeAPb6d/Sq4weMuwFE5zw2Bjng/Xiohki082bk/3i8t/F3Tt4gIaU7jj659K7Tqkyx2UQhjgJ2YZxGFbHABB/I/pXMdO2U19qiRW4zKykjKgkEDsPftXR62nzMgtS7LK0cce0IYSgJ5XzHzd/T71lUzmkbU8oNlX0/0wurqt1fXMlrpidxCitc3Cg4YxK7AbFPBkY4BOAGPFdnaXXQOlxRpF0BPqag4+Zv+sLhJWP2ghRFP2Bx9ao3tVmVI1jaLdFsGVCgRrkDaXGBgD88nnmpcKGBtttGGmI4KIN/A77foBweK7FGxxOVy7H+zlxGv+zUeq6BejI/Z2rXUd7aScEhYrtUSRCewWVCDnlxXHXExFxcR2sc6XKsyujoE2NnzDB/x9qt/k5ZfFZNoBHKrlWJGTvO7jGeCKrepi8cFtPPH4lyrGCQ7s9hleV4PH9K5501e6OinUdtlnN6hdzxzNJGQWbawCryT6A1jHrupIsyQTCBbgYlWOJQT34Gc47+mK03Jd2wuS4Azk/y+v/Osba3weQxXtgt3OPerrJFZZstdC8aNLiQy7ZXZXZs72OG4IJPfPvXXRWSXVshvLh7ezHldYkBnmbk7QDwG5BLHKgEHBOBXOaJAY/38KmdULRsiA/vFPHJwfpXUH98odXddq4iYnewBGcc/U98egxWWypzNXJwgYxS2Fpj9naFo8W4ja+oWwvZXUHuXuDtP2RFHfitt7FpmoGT57QLC1mwR87okQs3i9TlEYwyD1wVH3WgklitwttclMbUKFdqscY/Cf64+9SLjUZZGNztjNyreY28McS4wAVwqAenfPI4rexzXKqysPlpRHLI95GqE29zEpBfnIYr6N3BH/KoGpShnxM8viwRBVZY1UMmOEI/vDOfzq9VhJDO8btE0jsGXYoVEOSMH04BB/IAiud1Xw1Hi3AkmVFH70NyVxnORwT+vauSUbTO2MrwKGG7n03eunXUttPgFzHJgLx2I5BpcaxrV5GxvtSunEmA4DhQw9jtxn86xkhRXIkAkWQlgRk4BGM/lWnBKEqcxKBjPt7c10p5HK0bbJWhlVUCorKAWVT5Tnuf17V1tt0/44tL3XoneK7RriztVYxmaIEr4rsMERlgQuMFsHBAGTzllumeKPJUkLvwo55/w719EzGZYfDhZJ40YTSseNucINpOFCKMAYqLXdyW7Izsb640fKaVDpenR7Ny+FpltIHP90tJG7E477mPNaLmPT70/+dILaJxjdeWlqtvID6b1jCxuM4/Eo+/NbFeCYn5SAoZX8sW8nCkcfi7Hj78HNZR27AqpMG4jBR3yGHfB55HbBHbHvUtIqpMj6n45tR4kgjkRD5g2d7AZUhPYentXG36iO5M1mWiZpGbxEkOC+0Z82cj7V2+oSW9tblUTx4ijhAihcYyR5sZYDOCzd8c9hXIaxJbyzvAFB8NMkLw0ZIyeex9+a5orZnZHW3txuyGNd1LO1tSumUt/eBOPv3rKxkDXIuJneWTJyS2W2e2T9+1VwGZ9sXmZuQEXHOeBj61ZxRl1Ii3YJ8+5eR9MelbS0MIrMutNLxzRLcqchch0I3DJwRt9jwCPrXSTTaVpkKfMafadQap4bO/7Qh32lo+fwLGpHzDj1Lnw1JxtcgmuX0NjG8wiX96fwAHJBHbAwcnkEfUV1Xy1luJ3S7onwVS0ChkxhgFL5BB9DjJzkiqQgr3Zec3bZRKXrjqiWzaFruzttOO4LbQ6DpkcI5HlCC2wo7Y+351SX2hWerK50tINN1CRlC+FGsEUz5xhwMImf7y7QPUY5Fh4Vvu2pFO8eAu6XCjH1C5z6HANS7G3Mzlbh/NwTyDjjGQDwecVs8zBZHLWSRxRnxElhk8NoyLhgRG4/h7YJyCMZrmJC1hezLaTyRuq7neB9oyPT2b/AJV3epafb22oLCNx8XbISoBIK8YGfquM1w+qrtYOpVo2z5W9QOOfrXPDKTR1TzgmaLi+uLpGW8vbiZSwHmOzH/eUflUi2kZLTap3pCSdj87gMcrjt9vUVCggCCNI1dlRckMd20f696kwPEnmk3FUOdyjOD6j/nWsszKORZWNnNfyySLKbZY1Ek08udsKEgBj+ZwAMknsO+OktbXp6SW3VtDuNYmZwZJ9TvpIlkOdo2QW/h+GMd2aSQ/Y8VU2SllgiRfEQv4pR+N8jA8nn0HA9uferaNkTyOyBNw5Z8+X1Azz35z9asoWzKSm3kVL6LpWoRTLFZPos54T5W7kmiBBwQUl3Fuefxr2NVlwJoLiK2lSE4I8KaE5Ei4/FkjjnjnkV0sdvHvU24ikOclBk9uecZI9wf8ApXnUFos+nT3kabLiF1kMUaLGFAHmGFAGcYORwfoaSiISdyt6ku5TpVs9lIomtZFPzCMFMeQQUwOMHv7Vzy9Ram3iIblVCLkOsCk/yHeugk8W90WVS7eGiHAKqecAAE9/euUwI1/djcxUl891GMc/nWVJ2VjaqrtMze6u5ZlknmaZxg5fB25PoO2asIVkkfa6u7kFSq/jPp5fzwMfUVWhBGilixfIZ9y57e2atNLf/wA5QsobEEJePJyVYAkNj3zyKu1conso7PT3sej3xZWGmax1CRulutRtkvbeylOAUigf927p2aVw3m4UKFLNaX3xD681a6nGtdddRSiQEtCdWktYMewij2xqPZQuPSuejik+XIADQ788Mq7PTLd/rwTzR97yk+EwK5zxnxOe/tjtWyVlYwk7u5M1SyOvpI+sXkM11t3m6ZB82rbfdRiUHA4bn2I71xMkU9vfrb3AV2t35IkADt2yD9fSuyhlKSbQ7IoI3ZbcGPr6c/TFVHVEMV1d211C6xrJGUIQE7AvOCD271lNWRrB3dil1wyvb21wJW8pMZCAAZxn09ef0BqmFzeGNg0kzwL+P94cL7fWuoubMPo8ZjlRJQok2lCQ7Hgc5wBjIzj1rmFw/mc+EBjLEH8v196rTd0XqLO5tspI4VEiskO3C7idwP3H9avdPj+dura2Q+PkMDtlAG0DLF2b+EDBz34xycVzsapEufxd2ZjwMD15+tdfoMm/SpDbqqtczkN25RBkAHuFy2T7lR7CrOO0yqlsolQ22l2Xh+HYQarPGMNLfoTEec5S2TaABju5Yn1A7VsvFieLdd6PYxRFsg29mLRs89thH881e6dEj288NzdwaaWDSsXUpvyPKF2Kw7js20c9zXO3LRkCUYmkllOd75JGMZxj2z2rVRRi5PtKa+05IA81q7XFu5DMJiN0OeMEjAI+vHsar1mQBYooiqpKXilQjO3+IY9q6S4jtQN6zReAwEZifcXdCOSeMY7+uQfTtXOSJtNwVVUjUkEg+b2xn8s1lNWNqcnIji5nsbxmWeaNHBYrGzDJPft+IZqDcTXd0qLeXEssSFmCOd43E+3p2qffwOYo3COxAI5JGT3yCfv/AFqCIyvhqoH1Xvk/86ReRLWZIRtgXedykYyn4sk/Sur0e0ivA0hltra0hjLXE8sJYIjEhVVRy8pIwqDGSM5ABI5C2TwZH3gNG3Eqt298ffNdVZwmTTbIuMRsjyBB5gz47fkCOfqfeq7G0y224ou16h0bTmYdPdI6PIfW96ijfVLtwOf7LcttH9FCMRn8R71DvNRtNVA/aeh6G7FmJGm6cdOmX1yGhPh8eilT27Vrms7q2aAeGV8RMgHgMvcNz3HoDx2rBFjdACQnAK9/xDjn2wM81vsqxz7UrlZqdp8iY57aeWWyYkBnXayMO6yL23Y9Rwe/HIqLZGMkiGXCLy54wFxyCp75PGKtmtwS8MhVorlThewyOx/Ud6pjYWskjqm2FiRtPYADH4z69646kVFnbTk5I7Sxv5J7PwrZIFaFWMLzzNlB2IA3AD2x3r6NaTsxjIV5jJG2UU+m9efp7mvk1tCY7e5hntxdRFS3y8ZbEo9MHPb19BxX0+w2S2cIkVjL4AKlCAQxkUYPv/hXhY1JQPZwrbkar0/u7tdir5VwMZY+ma5rUyDC20E+dTn8jmuhvpM3M6LzlCvHBGD/AIYrm9UCKsgB9Vxx34/1zXFQ1R1VtGVowVIPIIHb9K+T9eEnqObPfwos/wDwCvq2SV3Hk4ByR9a+VdfjHU9yP+CP/wCUFfUfT+tfh7Hzf1Dql4nM0pSvfPAFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFer+IfevK9HcUCO40NmNugAyQf8AOvoeikm1kBJJ2JIcMewJGTz/AMVfO9Dx8ud3suOM87q+iaNkI24yDdEQNp4znjPPb3r5LHbzPr8Hojo9NJVSAAwBBzjtgduK6eyLS2eoqjDeLUFdp5z4i4xXK2LBXXcct/3e/H0zXSaYXWC/LBgptlG4ZGR4g9cf5V4cl0kezHdZyPxJnWFdJaSNpFabYVGcjvzj6Vw1wqhivMirKdioCNuewFd18TDmLRXlDjN+VJHcEg8/qa5JFVgPEZpcjnMQJBHc8fX7171DKmjw6udRlZgGXawMbDymRDnI3DuB+eavtSunhu3ntZot0h8o9Cue5+mfftVFKSAP3RcI6qzqNo257/bk/XmvTcNfQTJE25o1PiIMbpIxgnbnjOBkD39K9Kg87HnYhNq5qu3aSZpZkkl3KSZcYUccc/pW6MZZA0MRjbCkAZwO+c/l3/KodvcNEsQBkXw2BiBJJc9+fQnHoBzipn7SaR/lSu5T4j7F2rtYgDLNjIH0zj2rvPPGomW10e6hZpzHKke0yKAFZZQQFJ5xw3FcxITuQuRudcqY2zkH7fpg1YX93Hdt/uuDFG25svkFtoHf27nHpmqljtTIkDOSfKExgVhLNnTFNRJ9ohKsrMypGhPHJJ9BipUKG4CJCgLADIzyR3z715ZWt1qiSSaPZahf4wJGgs5HUL9SgPsf0rbbFoPESX93LGw8SJkKzZyO6nBUdh2rGVzWNnkSdLb5K4JdgXAGxh3B3dgatZL25guXuLWeaO4RwUlinMTK394FccgY7EEVWXs4iuPHMI5JJhceXb6jn29KyuvGW38fCi1mARJYmGxsluGwcqe/B5rek7owqq0iYqGKZw0DmaVyu3sxduQ2GBxwQeffNZxSS6eAYJZbeWJmUop2+TPmBIyMe9aILw5YSSJNHKqI8krCRsKABsJ5BCgAfT3rTd3SW6SW1lcF7SUYbEe0MByPqG5Pr681o+8xSuV/UN4t3e2k0ruXW3j8XZwXPJ/XkVTcSz+JvxGpYMrA8/UEetSrwrK7uzKWLHlRjGPQVpRQgRmKxAsN24gA/rWJ0EkKpZVidZQVXIyTwfTHrU6E/MWp8SRTIswUgLgnPqAO1Vq31st2Gjkh2gYCrIBk9s1aoJGtyZ/Mm7G50wCc8Y+prKWRrHMm6ZJHa3FxHbtJtmj/AHyLIR4gU5wcemcHnjir7Trq4kRDp/jwypBIs4jfaGDYDbj65HBFcs7R2qq6YMiAvgniQDtx2NX2m6yl/NJJZxx20vhkPF4QLcL2IGCx9se/5VrTd42MqsbO5Ou5IreUMn7mOfD7ozldgweMfX09Patlmwnu7lnZjMrbg+7btPb2xjn29QK0aZfXSW09vLZEQeUyTRx73QDAwVwQOc+b8qx1LqqdZbgQyrHdMNrDlnhAAXLOD3OAQBxxnAq7djNK5yXUkxfVbvbL4pifw9+3AYjO7+ZNVKEMo34Azwuf6+wrZPJ4m4MTySxZj3+pqy07pnVtciFxpGm3FxbK+35jKxQg/wD0SQhD+tZmpFtFDSqDhUGGKhfbt/Op0srLLwdnhhXBVcliDnB/zNXerfD7rHp7TUuta6avI7BAfFvbZkuYYwfwlpYWdR35DEVXQojSIgYTRzIGJLe/bH+VZSyZrHNHRkpAi3UBCl8PG6nDuD+IAYwNpzye+RWiOVi0UlqnhtLt3hXUq3c5Hqp7cc/4VURahb2UMdteYMSPlLkDeY8HlSf7tWUS+PiWzc3uRt3wMHP0DJkdh7cHFdMXdXOWUXFmbNGA+JHZWXa5hXb/ABc7hwQQT/SoOu3LCzbe6uZ5AFLMckA55z6duR7mtN7cR2Vx8zqEwV5CSkEWD5c5GVznJ9c4rZPHKbUzlUZhF4jwNEW2RNwME8D61SpJRVi9ODkzm7mTfGjK6pEONuPw1AIUu23dG54L+pqZNLlMmTc+78DLwB6c/p+lRJW8CUAsoLYAAOCT78+uaiKLNlxpcwXJMhhlBGx1IXKjuM+/bn71YPffMXkFw5DpCuMTE+UenOe/rVfpixSeQF1Q7izbc7OPX6VPBYxiZFiWU42smNu0DHbv+dYyspXNo5xsddc3t1c/K/tWeeJFto4TFPIWIVQAoQscYAwVGcenFYRq6Z8SHxmPkV1jMbZwQWbIwSPv3qNpnUcOkQvYalHbwX6qsVvdlQVdD+HxAe2PR8dhg1LbTLz9/wDI+LdlZRukjb90cDup7fiPbtzXSpJo5ZRaZrjj8fel2UQsQPKfIeOOR+fc+tV3V5+aXcoRlDCRkyEYAAru28Y5zx3wayv72HSmnl1V4o5XiwsELb5XYnsQDtA9yccelRr2G4ls3kn8GCOZCQEPmLekYHbg4P5/lWdSVjSnFtnKAhIlLhw+OR/h/SvBlTH4qjC9k5H3+33pI8MP7yUIivjaGOAD69/XNY28iybmhljkynZGDHP19vvUpC+Z1vTYkWCWSJ0RAGGBgkNkEf8AER9uPerawuxFAouvEcRSOncDtnaxHbIPB+mPvVJpRQWmFYxyRrhecYD8HJz7+1bJNVms735iYpKszgtgqvPIDD6+/wBKxhK02bTjeCLlTIAu4DeiAYYquRjj+RBrdC7JGiIArtzwxGeMDy44J7+veqm31LT5CpiulZApCIZArbuM5Bx6jt+hqTc6pZ6csUv7St7eTAIjtgJpjnO5gfwoeP4jk5/XpOVImXdylmlkkszRIrCVsrvCkgbWx3Ze/wBPUZqr1CykMLROs0DwkhIii8Y/4RxjBOO+c1Ct9SWQs005toWGAUIYpjsGPrkgZI/Tip8sp+TEgiYvldsisWDHPmJ59PLz9a4pu8ro7qatCzOVlB8XLSgqQWV38uRj2H1rC3PnUR4ZwGJOPwjHel3Iqlg3DYxhv6+xrCErIgUFFKnB3H0PoD2/WuhLI5m8y806C3e2lmKlpYl/dqm7cp7ZP5kCurikWS3iljfl1G5hGTyeceufv9O3euX0xS8cayzeCSrHcePz3D/RrOPVZLG42ujTQg+dRlTk8ZH/ABYP51SL6TLyV4po6YSJHGUh2ldsgUupJJPO7PoT2z6D05reDFGWEaZHhsWdk4Pvx/Cvf65x71zsOr2Ui7PnGjYAHYy7d3PGc+2fetsvVMEICwEXBiUhEiXy7yeSWPp7YrYxLXU7p9Qs7aDZErzyHaqjLrvO1Uc+2AxA+ua5rX7MWkscpDCSTmQcsMDjv6dvWt1heyu7Pt/eufK3GFzkcD078Y9z2qRq1rcX1xHaWcM91eOMrZ21u8kpx7AbmYevGcetct3yh1pJUzmFZXJVyMnhOwPHtVjbzyb4WLKWjYKT3bGeM+4x6+lWM3w76vt4zcz9GdSxxIpZ5G0a4KgAcnIQ4xx3qntZBcBSiqCCctwpHoR9D9K1eaMU8zpdJspowLtDmAMQjLkMF5IfI7DHOe/lIro4UAkHiySGRAZiVYKSW7EF87ue5Iycn6GoGlCNbHdshRJU8Nw/nDqCOT9uB2/iNafnvlZ1W+j2W7MSjopJXORgjkkcceoGO+KyhPNpmtSnkmi1jtmg8TxFOHQBs/wEH6H6Y/OtgJikj+WJhYKSxOMD3O7vxj19qgx6lawqCt7FMGBHMxyR27YHbHrUW66iRtz2UKSXSoVEr7go9uON2Dg47ZGTu7Vs2jBK5G1++Jvw+5s2aCNSkuGYFi4Ax3IJ+tc9qf7wK837u4DuX3Eg5POT9Ks5/DcuWInYLltxzuzyTn8z+lQuoEjt5SJ5FjEnbcwBYeh5/KsYu8jdq0bFGWK4EZDqxyQpIB/I85+9SHjZkHDqDjcABnt7HtWnx4/FxG8cnG78WQPrUlYzhIjIJGdMZlJAC59CK2ZkjpNIJlhiaJGLAhMgYO49vyqXMoR1aRSy5DNj8RX1/LNVkkT28bXEG5USJTNbsD+9GOdp9GGM9+RW79ox3WZLWWF1OGIA249ACPoKvCSkik4uLzLiGdFtwN7PMJCFQKPDUccgD1I9+1QdQu4hYXTxoChUDfk4DH0HvkDt+dRJNRNrGu6UQOGDhhIB/L3xxUG4vzd7QGi2hSw8IAb27ZYZzv8Av6VE3ZCCuzYl0sSbimMzNHIiL3xjke+QcYrn5SwnYoQkTSHaAM8emD2+lWaTeLqEXir4I5WMK2B2OGPvk96qbjwYjFI7rEcELuOBx7fnWUFY3m7oyRiwzcedsnDNnBPpUiyZ7edXVgVT+0bOMgqR689j2qFDKJBA8Z3uqBTJnIYj1P196m28TOx2OxOOUVcjjJzwe2au8jNZnZtLCpSTdiymjYRoswnkj5HDnavPr2wQeK2NYyQFi0zKSSiB9y7sc7gSMEY/hzmqKO5k0G1LsDdWquo8yjO4gkYHfAycj+dZR9T2g2k3dwhV96kQ+IeB7MfpV4yUldGcouLsy4lnlFtw24tIGy+D5hnJGeeRVJqrJNJGhw0sZOTjOCect6eo4rTqXVCXWRZh2nfGLmbaCvsAvIwM+/5VFhkjuIEHiMshcs8rAn05z7k1SbyNKazuaZcIs0bSeGJGXep8wBAzjGaqbpGjkZFwBuAyfUemfrmryy0TUdZvl03QLG81nVTGZRZaTavdS7F7koqkgcjzEcZqw1Hobq22Hjat0pq9mxTaf9z3MMDnKL5uO54FRHImWeRzCFWiLlshj2ZQfpnHY10mh3BjtpoJSVkt5mO0gZ2tj2+xrmotyyebaNhKk7cYBPbHp9qmWM5ivBKr7OCmT7e317CrXsyrV1Y7MTgqQJC0bggxYChSQMEEDOM4yCPSoFwhF0vchWIBZgcD8icD6cmtIvLaeaWK3ni3wth0kkVHbjn1wcEnHPavbiWONCZ7hQoXIbxBjHsK2TTMWmadxfd5MBQTuzkduTz+v55o+LKaFxbfvx5WaTGVY/THfGcCoL6k00yQ2MTPhe3Yk/c/41Imt4pcw3AV5c7XAlG0sRkYPYH61y1ZJux00Y2VyvvJZHlWVhkS4O/O5T6A5/PtVckCkzQLlbggtg857cVJv4UtJnS6cWszIFVGIy+TxjH4vvWqQQXW3fMiTpgHeQC49Dg+o7VMckS82aPDhiCpMzl2BOYz/Fntk98e9df03LbT6dBJdSrG0f7tkGCxGeBu9yO2f+VcczSeOCAvhsdq89sAcn6Gp+k3jWk2H3PbzAiRA20OM/hz3H39K0TsZtXWR1bCNdkefGUSPt8/4VHGWx649D+lRC1uqs0jXDyFfKMqgVs8bs8njnitfzVjcF306/8ACkyqyQTOEcnHJB9gcgd8gjPrWb6hBBaubq6GXAI5VmDZ57Zz6f8AKtLmVncTeRTJtQGJCx2uSWyMBc9vX0qHHdvaQlo32wuyo/7sHn2BYfriobamb6cLaW5S1XzYkOHJ7E4GBj/nVk8qzDzeYLsSNVwMZySwPvx9K4azuzuoppGvabO0I2iKVQ6iFJCxBbORk+/fFfYtFRBZ2olRXHy2FzjyncnIwck18gvo4rfT5hKZZYpYmYjZwQBwR+fcj719g0eUW8Fu742rb8c4GQUP29K8bH50k+89bB5VCDehkvgEG5xjO1/XHJya57VgAre5xls5/wAauJpC0niEBn4PuOTVXqwCpJtYZYjsOD9eK4aOUkddV3iynU/uR/3T618p6/8A/We6x/cj7f8AcFfVRxEp7blPc18p69/9ZrnjH7uP/wCUFfT/AE/rX4ex879Q6peJzVKUr3jwBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBQd6UHegO30Mn5Y/b/Gvo2hxkrtyOIWJAzyM5r5toZAhBzjAyfrzxX0fRF3hBhQPAce+cKf58V8njt5n12D0R09mpMEQdsYZjyDn0A/qa6HSxtW/SME/ulIBx2EqelUGnkMkK+YDxOdvHcD+fFdJp7h7bUGJG6SzfPoAxkUc/zrwZPpHtRzich8Sod1royH+HVCCQf+EmuDmKeIdn907AW2g+1d714hGj6OQi/u9Qi8pGRjwmzx69q4R5klTfgo5ALZHtxz9/pxXv0erR4dXfZouBJDAqyBQ7qyP5uAMDn2OT2qjmZjMpZztjGQ2dpz2Bz6HFWs6rBJJJcKDuJeJl5Mnp+QGc1UztubMJPhhcBe+ACf5120zlmbI7+bhMRSmcjc8sQyB9xyDxyajT3V3dN8u8iRW6tzEuFUjHOccnJ9zQxszIsYB9SScEen+VbEjeRpJtuS2F2scMQPQV07bOfYXYV5PyUKhVxGp2OQcgk9+PrV/omnW1hGL/VLQ3sQbPhufIoI8u4H8bt3C9gOTnIFQBBkCMoAC4XHJOD7/ka7u7kNzBGixOtuYVjEcrhlWTjzDnGSD2Hb7VpDMxqXQTqLWIJAdTvrtlaLdFDBdyxLD/wBYyqpgd9qj/CrXUfiJqmpWMVr1WP9sdJMQEcGpXQmlgA3bTHcsDcQt7KJNp9UYcVU3N0ljdj5OVlul2iRZYwyEeuCOcn35/Ksr1Y2tGRZZfBE5XZJEFZg2CG3HnByew9Kvsp8DG77TnOobM2F1FGkss1pKWlhmuCA+wnG1wON68crgHg9jXPreXOi3TSabdOjnvImCrD6qcg/nXX69bp/sxCkkkryQzxtDvCt4aZZNuc7iduPQDgD0rh3kRnHBBbOQ3+AqkVss2b2lmWL6/eTicf7viRg24WqoxI+vp+Va7N/Gt7iaW4Y3GfD82ThT7VDVRHInk3FjwO4z+VWWz/doxEpMpbcQDjB9qSYijC3tzeXMNtbEK7nALH8P/Ece1fTOnel205TJYhXv/7L5nwo55SSccbuEAGfwDPbk1yXSFtsllcAiVnVcqwLEYJx+uPrX0O2kxcxpuMQZlkCXigBiee+O3H+iaWyKt5kyK517wHf9pPfWzthrbVLGC8hfJYnMUiHg4PbH3FUOv6BpmuW0r6Jpo0HXLaJprrT7ed2sb+NfM0lskhL28qDJMGXVgDsKkbTZxzxxQ20khRDlmLRuoOQB/Cp9yf6mueu+oJJblLu2DmOOUPl2LFW3Z7EY/Tvk1GzcKWy7nGSsJI2EhG1Yw0aEeVR3OD7/wCdUs7IW/Fglgx9PT39qtdct0h1rUfBMaW0U7FI1OFRScgfYA4+wqrUv4rjCbDygBBJH/SqxVjSTuJGmeFc3U80bHCq0reQfrit2nQ+DZSSopTLbePpzUZ1ZwjMWyuML2z+Xap0DxG0CplT+Jtozk4/lVpPIiOpO6b0QXLPdMizxxP5EniLiRu5LgcFVHoeO2asprZ9Zu2mv2llki2nZOozgcH+HC9uFxjtUzRplOnx2zOIIPDcPIVPJYg8/wB7sePoalvEt5DBLYTzMBGFYFAAhJPcg7u49vrxWsckYSzNeiSpol6t3ohk0y5XLyXFrKYZMEEbQy4yOeRjnPY01lIbmNtQghtYLhRm9W3jAjdPSUKp4YE+YKACMEAc1hHNclgjZt0RkwE5yoGVJx3H34qe9xGDItyWkd45pN52hFOCCcDGMjHHbFUnFMtCTRwt1Kg3bm87FQg3eb6t9arZ28abcUTYinA4wv5+tSgoeMCWQo5jDAYwCDz+tQQ3h7yRt/h8wyQKzijaTNllaqZsZLpuBkA4yP8AE12WqvcWdosabyAo8jSHcmTwMexA4Nc9o6n5+EuXVg2cgDKjPpweQB6V2OsMJflLOJpJLbxFEjySDMxJz+mPtye1Y1HeaRtTyg2c9Z6TBLF85rMrNHIcw2wYqWHcFm7gH0UeY9+PW/sdTEDn5TTun7NWyhibR4Js+pVjKrt6DuakSxGRm8ZmwcPGgHlzztx6evr2rWGkGSiIviRhiFQcHsRjOBx/PvXWoq1jjcnclx2Gn6pCklpa2WkSTqUe8sx4MIc+k0G4qAO+5AvHODiuamtrjR55LW+i/fRuySDcp5x38vDKRyCO+c1fW0EYhfYvigsPKOGznnkDHv8ApVZ1LDHHHayzDCkeETnORjKnI7Y8w/PFZVILU1pzehz2sXCeHaOiYdIxGxI5KDs3NUhVoYmMLFY28pAYjufpVteSo0m6Jwv7vaRjIH29qrp0yyiMp5gSAeO/r9ftUwyRM82eWkazyp4pGzILEnv9/evo9xaw37QBWeCyiXdO8cY8Q7gOFzwWJ7N+H157HhtGtc3VsoPnVwOOSD9vzr6XJALezilMyMk00kZRJyzoFYZ3KMEA5yM8HHoarJbU0Wi9mDJWnahedORtcdOWGnaSs0Qi8c2sN9NIrKA2ZZ45CH4z5NuCeABXl3quq6zHImv2+kdSxLHnZdaTBE4BwCVmhWORSOBw/B9D2MQyW58KWxhjJUtzKoUMDxwAeB9M1jA8kCxbTCxTb+5uRvG8DA8rZB49c47VpYwvmQZ7Szto3l03xUs9yqLa5CvLaybcmNpNuHQgEq+AW7Ebhzyt6iyyyJaRbxu8hxtHl549PX9a6i6cLmKLesdzHLE53DaMYccdwQwz+dcrdSBvDEgbbjkemPbArFx2ZG6ltRzK2c7GXxUBduSCvAOeAPftWaL4kxYqAJCfQAHj2FYSRKwLKSAxGCx7YPvUq3XeqlGkPGW8u3GPr+nFaPQotS40uWS3lgW0c+UDjgc/UdiOavzGLjT0hRGt/MBNcK24A4JICn39DnA9apNM8OK4Mm9Qc4IkXC5+n+FdNZpFKkbDiTA2kqCTxxxwe2KwUdqRu5bMDy3tbewKta6bYyPggyX1ql45yMciUFB34woxUu5ktdXiEd9oGiSInPjabpaabcKMHjfBtH/xow4rNnfCR7QvicAOABuGeM5Axx71t065SO7JmhyZEwBGACPX8Q9cZ5+9dFjmvdnNxWJ09hLE8k1m2FQ7druw/wDZsBwGAOfQN3GOQKC/feqzLIMsxCjleAeOM5H0Nd3rFiY0v3ihZbZXbwyZd2R3BJ9QGyB9DXCXRUqfDXO/ACnkpgYxz/WsbWkbJ3jYrJEKMuTnYTgsScfT+ZNbbZVD+JKduCO3OSKxIZZGQ4IJ5K1IgwySyRcmEbjuOfp/WtOBmtS80gEPGqkGLcCwYZwM/iycdxx+ddXFfXFs81varJbQuSCLeWSMn/vkEM+PqcewrnemmVXeV9reH4f7tyDuBPYfX19uK6SZVaSSQzDxN2eSRk+9VUVqWlJ6GnTbdbeeO6tpJ4WDZ8S3laJ8ZyDvVg3oc/et2qwft+VpNVdzc7gsV7NjxN2O0rf+0QnuWyV4OcZFYANvUvywOR9Seeff14q2sUiuonVmiW5OQkcobazdgDj0xmpZVZFb0+EmQpdMtoVK+ZyMgjj+ox9xULXLBRIgAfG5icsd3r5sf67Ve2ZMF5Pi3ikbewJmHD48hAGe4zUHUTFKsCmMoE7M7+ZTjjzD0+n1rgWUz0cnA5ZYHVlbHmTIGQcqTya2i38RgUztU/x/lzViIP3sYTzMwIIB4x7/AGqK48Il0CBkOVGc81tc57WJVpYjcPHDxwptyyEb25yEAPAP37d+eBVzHqFxbAfsdYdJBXay2iZlJ5GZJnG9i2TwCAPYVos4B8lEXy4Kh3bvgkZyfbjAry5KqqEDaxU+Y8f6yK3jFcTCUnwML24mv4gL+SPUl/ihvEV8gezYDL+RGOK5DUtLjtHNzZM5snKrtkO5oZPQE/xA+h78YP16eZgxYjClxlsLndx3x3B98VX3tui2UxeUyJJ5HXdnAxlSBjPBHetGlYonmabAjwHDmNFVf3ysPNtOclcHnk5wea4mVG3sHOO6jy/0Nddp4iW3mZWKTIgMbB28zeueMfmPUfWucv1j8aZZG8R5DuZhnyk+n1rCm82dFRZIjKoQnxEzjLAk5J+nParKOdYyWlCqrDkYz9MD61XRuQdkcm+MHGccj69s1mWZEZYmycAxMw4z/wBa1auZRdjttB6eiu0TUtaFy9gGeK2srd0juL+VQCyKzAiONcjfKQcZAUM3bpP27caK+3RtF0HTLaSLMJs9EtrqXnsTPdpLK3qCxIz7DtWq3hEMEE9pcTQFIFg3eHvUQggDAHPmYsx7cuTVUEjmnukjbw2KkqZPwlWbhdo/CQfyqYxRScmy5vorHqiYftmw0qWW5UyrdW9lDpt/CMYDKYAsMvPBEic88+tcDLp507UIYUuPEiYh7acxlS6+vlHKsDwwycehIINdnGrxXMiSttkVY3DFR5FAI2kAck5x34+9Q+soFg0u1kLA3CN46qoAK5IU/UDn7Ej6Uki0HmVl0ovtLuEkZCbaPtGGISQEZJZs+h7DFcUp3yJv4GcqV9R7V3FpPA9vdlCjAbMxqcrj13AcBgff371xl5tWWZjIxx5csnIU8gY+n0rGlxRtV4M8jwjMrKQgDZGcDg9v51d6BCl3cFbx2gswjPePGBv8LIUiMNwZCSAueM89hVKhZ2IY7mZM5K+w+ldX0bam6a6hMiRy4BBfOBhDtPY5OScVq1cxTsdDP1XqUmn3OhaEZdD6bmJxpenTmJZmxgS3Uq4e5k4yd5Kn+FVAAqiOlR6fOPBiEErKrb1Ta6nGfKwwcc+lWg014rdBJNFCwUlldgpJ7Agev/OvLiAR3JWNwYyg2kEEY9vYdu1aoy4lfqdodRVvn5WN4ygpdsu5nxjCM3dwee/I4we4rlkA3K0CHuYxkZIPqSK7p4xGh3OvlB2ESYPuO3271yV+ySS3J8RV8IhzkhQ2UBweO/OBWU8jaDNXUsCRi1k2qTtCEL2IzwxHpnI/OqRF8BQWULnOTtyQPQ4rpZ2WfTnUvvKkcKuCM84BPt7e5rnGUrIrSkq2QeeM+nb9P1qsNLFqmtzfbn9yiq+zafO28jKntn+tdNpNhbXMb3N/PPDY2yqXMHEkrtnbEnszAHk8KoLHPAPOR2yAATT4fsVCgDPfkmus0mJX0m1kjLuviOVJkBZCcFnwOQcBFH54qbKTIu4ovbbrHWtFtDadLXo6WtWdd8WkqkM0nsHuivjy9+SXA9gKgXfVWuXniQaxqV7qkb/ji1WX55JMng4kDY9eQQfrWg+GkrsjhduWYSDv3GBjjJx2IrWIWxujXcqYA38khh2x6H/KtlFdhhd9pQ6rpkP/AKVY7LeIgB4VZmRM8ZU8nae2DnHviqi3jVy7mSBWXuCxAHoMADmuxmjSRyBuKTryGIGQRj0/61zfgIkkIuUDKSN6QybHGM7fMQcfcisqnRN6fSIGowAvG6RxqjKMuvKZ7HAP+s1qUCOGIW7AqvpgBx9RVg4lbSsSyNP4bcHb3z3yahKud/hDwOc7Wbv78H07VRO6NGrMk2kpI8/70FCQTnPfByPQVeWsq20MUEIimeRd/hvHll55GfQZ7VS2wIZzlRxg7WBOMdwD3qSjZ2sfDZmOYfEGHOMcfT0OKwmrs2i7FxIALCaO4lkMXhOREjdiVOO45575Ar6nBxYR8EnwseU4yQR6DmvlV/c77RUEhVIo3ACoSHYjLYz37j8zX1i3jaW2jjGT5GHuQQyj7f6NeRjOrR6eF32V11wV4yWx34zjPrVPqJ3W7ksc8ZOOe1Wl7MZJIixBBOSRgcZx6+nFUt+xUcELgk8H6VxUVmjqqvJkLAEa4wR718m67OepLjHP7uP/AOUFfVBkQjPp2r5X10AOpLjAx5Iz/wDIivpfp/WPwPnsf1S8Tm6UpXvHgilKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCgpSgOx0Y/7vtx3xj9a+k6NlJYAfK20gEDOQQR/jXzPRjiI8cjH9a+labuSSHOcqFwfptr5XHbzPrMHojp9KkXdCOCVkUZzxjHb1rpNEZnju/4CLc8A5x517j27/yrm9KO6RO+PHQ5Izg8+9dFpTBZr9VOH8KQHPOMMMds554xXz89T24aHK9fXKJ05p1zFHhRqNuyJj0MbgDP51xk1wx8hXx4WCGIbyuwEZ/h/P8A5V2XV6uemdNWPl1vLPAHPOGH+NcVDb/7qgDQfu87jIeFyfw49/XH1r36Ntg8SpfbK7UGIUSeaGFhgqz5C+5OMZqpW4QzM8UiyjG5tq/pUm4uAY3ZQezKS3uef6VDjb+zVAEJB4zznPrXfBWRxyd2b4lhDAb28UgYwue/fmt8sS3USlGbco2EY5J9jWWn6Te6nIn7Ksrq+YDDPbwGRVPYAsBgfmatZultc0SBW1HSLuwQ5L3Dws0I9fxpuAPPqR9asypWXokWdhC7EGMFQ5ACn2PGcg1fT352Wl6qLFbNGI5mBG1XU8Hbjy4yRgZ4NU1xtMwQIXO3PiNyrg9z34P3PrVfDq0+jPMsJLxvgSxMwGACOx9CfpWtJ2yMa0bnUIpkhjlt7eOZjIOSHGfL65xn39+B71daUPkvlpXkuFWFt6MU3LuYYKD8uSB6fauNh6gi86SSNEW8vKYaIbg2F7jA7Z71Im6uMMIi04z3UpXCT3J4i5Odoz6g9zj/AArobOZJkzrXUonlW0jkWaYuJJM4YbeduT3OSSRnsMVwau3jP5TjPlOO/wBM+9SDOzTzTPKzzSg+IXwxbnvz61oysQ8y7lDBAOck4+nrWaNOBIhOx9qHheQTzg+tWUkpMIZnTIGFY8ntwOPWpukdFdQazKsOkaDql7PIu6KOK3O9/bCnBPp2rG9sdQ0C6ks+otNutK1BCcWl/aPDKBgebY4B/ljtVJF4kzp+7RHki27HwZFUNjccYYD/ACroG6hmCgWty9vImByfMCBjsfp2wDXE3UrWEiT2snhTx4ZWVclWB8x9gf8ApUuPVtP1CXdc3baXdNtLM6mSB/qCMsp7cEY71pB3RnUTTyLIyiQLLPNEcEAuwJYkdmyBgGpsNwjWYDsvhsp3Pnk47kkDng59qpTLY+HtvNdtFtjypj3SyjuONo5Hrgkd/pWm3nh1FPAsY3jtdpR5ZuHkbGST7LnsOTxzV5SUVczhFydkRL65+au7iVSNzOWQM3dT6j6Y4+tVUgeQgAMwxwOM8VJ1JZIHbcNmRgemcfzqx0zp15I4pdV1CHR4QN22RHuJsd8iJBkf+IrWUdLm0sitjkAjB539uVwQalW7JtdCpJC8AEk7j2OK6W36C03UIx+yut9HF2y7kttWt7jTRMxH4UndWhBzx53QfWqfUNB1XQNQuNM1fTJ7G/txvkicquIzgq4YEhkPcMpIPoaiRMXcnadcxSS20LJFDILgFZnHKjHYjOMZ55Ge1b7YRxFbe2cb2CqrHBZTu7hh3Bz61zlxOY8uVVAxKqy/rnHrmtdvqo3Ml5aFuNoe3YKQffBHf+XNaQllmZzjnkdHfSRRXCwWxZwTsU7SDknny9zyMfpWepXtzbwSRmWUS3EbQqnK5jPD7iO4wMfnVDJ1PHbRRrY6bHHODu8eacsx+hRcL9P61ui/3/TZ7vU5XeW4YuwYsvic8AbRgDA7cUnKyFODbKu6mQyF0XaSoxvJb/XaofMzkryuNxU9gPcmpEVvdajcw2unW8tzcSn91DEmSRjn7Ae54Aq/sOiY42zrHUelabLnzxRW9xet98woV4+jGoSsizZF0CSOyuUvGlKKpwoIJLHHbHpUy81G7j1C3/3QyQsfElkaQ7QoPKgHsfb37CrSLo6e3UHp7WNJ6nj2NI66Y0qXMQHJZrWaOOYgDOSiuOM5xVRGxmYOJAyDD+Y4LA9sA9/eueStO5vF7ULI6kXIhkBmKSxkDZIyblOQMHnuMfpWCssjK0GyN8EBYyPOPXIPp9K5+0135KCO31Mp4Sk/vYgW2E+hB7rz2HarAXtm53wX1msbHnEiqQewIB5HvXUndXOVqzL63uYwq5byLKS/hkBt319K5/qMtcm3QpIUEjSOxB7kYAOPuT9q03Gv20OxLecahdMRECqkKMnAJbgfl61aXcMcMix3MrIyKWXaV3bhydw9s+oFY1Z7ORtShtO5xVy0LGQiIrsAD87S3tkf41DRWVlYBchdox7Y4q4tNNv+oNVksunrOTUpnYArAuUB92Y4VPuSK6aL4L9deHHJZdPyag8y5SCw1GzvZW+oiilZ88HjFSskRLU5vR0DShmO7b69juPY13GlPa3X+7sGicb98kl0AHAOVG0D3wSc5IHYVxi211Yajc6fPBNYXluNl1b3ELxzRPjkNGwDL+lZz3hsp0lt/BkxzIgU+b23E1XSRfWB1NxOzyMI0USJHjLZGMDOMcj1z9fzpDPKVa4lRJY3ycFSAQMDj0Hc49KpLbWbGYyNLOmnHdnBLD157cdvc8+1YTa1ZRY8S/F6FGUVQeRuBwcduQD371uc2Za3d2xtjDsZMykqSMEgggd/fn9K5/V45o1Msp3pKFjDkY7e+OxxittjqJ1K5eWZliZ3IWALjbz6MTjt29vSsdakSYFoCzrvClZSQxHYHGME49a5pO8zpirQKAgl9zKG5x5T3NSomwUMhwGHHrj349akaJoOsdSXCwdPaTe6tLECX+WhMgTHfLcKv5mulm+GHWemAtcaEbjZh2htdRtbqUZG7mOKVn4Hfjj17Vo7LUzVzzS7MIoVrpSp83kiLjPomMg/nVqXjSaRIZXkVWwpkTaSQOM57Go9hbRRQH5KYCRWIKufOuQDjB/iHYg+1VtzqPy92GMnjFlJbJJyM5Bye5HP5EVzU5dI6Zx6B0Miqqhi5IUEjxFPn5/hHuPrXkjRRoCW8I4C5HBz6tnPcZ/Q1V2+v2s5zFOpHYxzEA9+xz6cHtgdq1X+tWMETmSaJ5SAqxxgt+fB5NdZyFhqN1L8iyuu0BikYz+IH0I7jNchqcRicP4IjUheEjK4A4JrfHq8t7eRySqzQoMIp5YMTwfbNWkel33ULCx0fT7nUL9lbw7e1t3mmk9yFXOAPVjx7msZPpG0V0Tl3leNWUYJ27c7eMVnHMFwUyBwGBGSx+ldW/wp6uabbLFoEE4CMbebqvTI5gD2Bja4yDxyDyPXFUOvdN610nf/ACXUOl3Ok3bAlFnQFJB/ejkUlHGPVWNaZGaZZ6L+4jeSQlVY+bJ5x7gd8V1ceoMY3jtpmVJk2SAHapTg4wR7gGuUsJIpIrdw+GVOCwLFeOf8T+daI9bltHmSSNJreFNynxQjsuQNq5/F3zj2B9qzjLOxpONldHVmUxbMNg9gPce9brO9igkaW5eRQB5Ase4yc8gHsMH/ACrkm6rtUjeRLe63geUFlUnP61H/AGxdamFa5/dQoNiKhPlGc4zwSeT+tXehSKuzp7LU5wzo7sFMjzyBSG2sx44z9OcVZ6k/zIeSD5maKIgOZQi4YjdgKnpyfsK42C7tVd0kWbDcDcNzADkEEev+ddLDZX+uQONJsJ7iTdgzxoViOcBQXOEU59z6+lcbjnc7E+jY0G/WPCxuWVQypn0yOOf54+lQTdIyhix3bfK2MjI7ZrzVem+qdEj36lomoQ27LzKsPjRY9CZIi6j8zVE+oeLGoEoMb4/CAcgdjW2wY7Z3Gk3glskRX/eQjZjxAc45HPY5BrOe6UYVty8DAx7+mfvXNWs0qQRTAbhiRj5uCg/vE8Dk/etKdSadcqnjXPysgB3LIu0kH/iAIP8AI1rB3yMpq2ZdiQrIGzgMuExyVHfA/wBetRNRv2gs5lR5DDMmxxjAfzZA+vmGfpzUCbqDS0UmK5eb/gtyS557EkAD/lVRcamb91Vi0cY8sUIbIjHcn7nnmrydkUirsmQTPaSwiNX8YgIgL42qe4I7fmP8Kr9bRxdCSMKFZihT17+/8q3JsiQSNllJIO9/vg9+MV0Fp0Xq2vxJqMx0nSNPePm71nWYNMWRwuMokjeJIOR+FCDWMV0jok+icSHRjsXKsMc1u2sFl3YBxtI5AyR6munu/h7qcME50q/6b6kC58uja9bzzAjB4gYrI4OeNqnPOK5jwTHK0UsbrKrEOkikMrDurIex4OR3rVmKPpug6yg0q3lLxs8ybWt24ywHOSPtwOe4H1qDeS+LP42PCzvmUbQDhj9APeqiH/dI3mihxGrB3iUgjOM98+3P17elbodetbpJJIL6FSSZEVlC7WIxznnHHbtSElIicXEukkM+24ufFm8ILHHsHBUEHa2BycHPNRep7iNLKHw43RpGUI0oAJVSDtUdxxknNYXPV+n21vELacM43ZgiAk2g/wAIJOByOPYelcq9899Ory5R1BVVbnYmODu9T7/aknkIrMstMuvCm8BdvhyOJSfDYHPHmPvwB+lVWsEJfTMcyI2Gye49M/n7Vf6do+p62ZhploJPk0SS7uZ50trW23c4kmkKoAQMhc7jjgGpt/0ZZIZVh676RmYgkx2n7QuUXdjytMtrs7+oJHfmsYp3ubSkrWOFh3Y2gko3G4ehq76dvms7lTO0bRTKI/NyqY5Qng49eccHFadT6dvtGjWW5+VubEyFI9RsLpZ7dm77GYYKsfQOqn6VVI0G7azKQuFyVOB9cdq1Mj6VLezzW5juRGGQkPtRSR2xg+x/OqyFVgIdHywPlwpOGHvn0qgl1D9lPEVjM9o6Y278MpH90nPBz2reeqbfagTTLzKAZb5lFGR9QCcY+lWi01chxs7HTx754ru4SWMWEUZaRpF2NvBHl2j1OeBz7+9cuJJ71yy+PJbNlmjQA44PbvgDjn6GoT6vc38hTi3iUh1VCWy/bLN3c88ZwB6CpouHlhUySSMiPteEDZnn8t3OPesajZrTSWZWtcOkksSudyoWkOd3qD5TjHbj86jTMct5lB9cegPqR2HNdFP09qixLI9iNPJ4Vb27gtWKk5PkkYNk8elVWoabeaVGXuLdoojgGaFlnjz3OXQso71KDZXpv5aSLHl7gfi9+O9dl0vqCmzuopGhBh87b4ixKMR+H35rkACrOpZ2eQFdwXtg5/w/nUqxvLmwv4JtNuJYLuION8BwQCCGz9CCR/KrEWurHaXEyT3DhGCxs2VQYGQR+I8c9/1qG9v4bKspLZAxtHGzvnPfP3r2bX9GaGM30t1a6jbIi7tqyRvFnC7VHmLDLFg2MY7ntVXc9QaXIjnfNMXTdiODw9rf3TnjaMDnPr9K0TMmiZeSrAbl4VQR7xsUHOTjjHf7ZH51RiNktXaVU4fAG8Nn6H1FQnu5b2ZA0RRSBsUcAY7En1Jq907TrvWJFgsrf5iSMBpdzqqKoP45HJConOMsQPv2rCo7s3pqyKaaR4w+8tGXyXOeB27/AFNV1wJEVzFh225UE5+uM/Supuem4TcyCTXdGicuWJg+YuIwRxjcseCfqu4H3qquNHntDLKyR3VvyWmibcMehK4DKPXkUSazLXTyuQbeJ3jillKxlR+HAbk+59BVpFsnvLdEBeQhSJPD5AxxnPIOPXtVOZGVQnmUgkYAwfzqVA6tM3iNKwZtqkPjaBxx/lVJK5eLsXt5I8enTb1LrkACVxuIwQcEjGfy9BX1iHAtIipG0Bz+MZADD/L0r5kqMliHsrO41FIY3lkKSiNYQBgFxhi35e9fSonHyUIUsDtkwGA9XH+s4FeLjerXiethd9+BW3fEzrjyLIR6k4z2x2Iqm1I+QqGPLdtv0q4uipedlIOZOO5JHviqrUBiLyscB2AOPXHauKlqjpq6MrSA0aZ7nt6elfKeu/8A1kuMf+7j/wDlBX1YKBEh5IUZ7+tfKOuhjqW5Hpsjx/8AAK+k+n9Y/A+ex/VLxOcpSle8eEKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQHV6QxEDDP3r6VYtkxeUMAVwDweV/pXzXSVzCPr9M9jX0bTCxaEynkMuCB2XaR/Svl8dvH1eE0Or0cncDkZEkfI9OT7V1WlblmvN7nCRSZIIwCHHNchpmd5wOxTODjI9fX/WK6vTAGvLwbuNsmOM4Adfp/r3r56pvHuU9DmOqZVh0CzaUlUFzaFtgyeWxwPXvXDXIVUkSJo5DvZwAvYkAc+zce5Fdt1lKbfSbczMVIe2LEnHaVe/0wTXE6jJHcRmeV8Mc4KpwAOAQRgE/U17tHdPFq6nOF40JjJWT1Lbf4vv3NW2jaG11ZjVb22J07xjBEnbxphgsM+iLuXcfXcBnvVc1vI7GW4YAEgrNK3LZHt3zXbW8DafoegW6B7eSSzEyMj8vvZmbnv91PtXq00mzzajaRkZxJDuu42aKMgCNYyIVOGwNq+XHHt6etNPuTpd411oF2+k3UKgrLaXJhOfsAAQc85BHuKibFURM4IDsSSv4Tke3oa8aPaIzFM1yGGTiMHH0/lXVZaHHxIfUECLbT31vFHE8Df7zbxKFjZTwJQM+XzY3D8PmBHqK4meVZDub2DbWHJNd8Zn8FonGYnRg4bhcMDz9Mg/bivnIm3bWLBlVTzjuSP86z2UnkaKTazMmBV5DzggZGPrWcQOHYnagyQu7uP8eawV2dTu5Yvk7R61v2HaeMsWOSeKlkmlcsfDXiQAEA5O72Arr9K0ez06xuJ7lL1dXJV7WaIr4Lc4dGJw6cHyuuTlSCMEEc9pMQkvrJD5gXDbSB5iMkd67Oe8WSK1d/NLJCoLkBiCfpz9e1WijOT4FOltHPIo1S0imMj/ikXITHAwf+tWx6l1NdKfSZ7i41bRIwzJpt/I0sUDnjfBuO6FxxypA45BHFYCOGWZLKXUrEWs8qE3U0rxxRufVhtJX24FarW6FyWF3GjmRS80WwnDEd84wp/wAKu81mZrJlNqyQxM0jnfDLH41s4YZkDZB3Y/CVIwR7iudn/gWKRZDwxcDbg/aui1/dFpluqne0VxKql4wp8JlU/wDy3NcxCBuYMS+Mgc96zUbG17oGQeM4Vm3KOxPBru7BR+yLW2jWGPALM0jAE8HGGP2PBrkLWDM8CSIDuYEKOSBnt967XVpZLWN7ORY42cgIxYudo9COwArCtnZI3oq12Vuh2DXmoXF1shl+WcbYpASC3+Qq5SKRVk3W8F3NKgBkKkyc5PJA9Bnn8q09NailppMInuFgia4nyGBbzYGPKPyGTxUmO48WN5/Iy8eKzPsOPRu+AM+9dEVZHLJ3ZBFvIDHcTo8RUgNNGwzz2PJ744xjGKsbC5fVdFPTWqyGWC2WSXSZGAzZyt/Cuf8A2TkYZBwCwYAEVrSVnxbwptQvhU4ZpOPXuMZOc8VJtE+XupGe1lCRKshMUOfCG7uS3OPfFTJXVmVi7O6OBF8b6B/BRQ6Dzr2xyeO/HaorxJvB8RxgZK+Gecj71uuJCVmKIojaUscDGWJJGf1qLGXbcGXB4yC3cfUe9Zpdhu2ZREmQog/efwgjsfausuIvl9LiidQRHETnAXcD259xg/Wua0+B5biORAw2MCSSDiul1qQpFFCuSPDckAA4Ofcen3rKpnJI1p6Nlpo2nrplgixxl7m5iDzs6HD+uwFSDheB/wB7P0qVLazmGMGK9iJXsuWAHvyeOfStczyfNbCsex0BiDLwQ3uTx6VkPDTcWiRcADcpBGQex9vzrpSOW9yPLZXzFZ5lvEaFvEjZVbfG47MrqdwI9Paqu/zeW8V1KhWYqI522Z3OMkSYOMMeQccEjPqa6RooJQy3HhI7oWiUg5P0znGP154qr6htfA0mczSxmW5MaMgcOUKHjn14ziqySaJi2mcleq0ib1KHPlYHucc9v8ar2iAO8IQ5HlDNUqSWMDa2I5HICkEcnHpisFYkngkqvb61SOSNHmbYI08SDIURuwwWOAfcn+ld7EGa5hhheaKaVSzyMyhRHwT6e5wMnHauQ0nFxfW0Looy+PMwAHoAfYf5130NjJ41xDdr8q4jErlSHD544IOMeUAd/rWMulNJm0Lxg2jNzYPb2skkq2Vmrv4dsyBo5if4lA7sRnzMPXvWjULawukbwLG3I35gnNuqklcAjt7YwfoaytNSDQpIb26iEKBN5LDuM7fLkZ9M/StkFzG0rKLhZJGfITxWXcO4znj/AKV0pWOVu5tudRl121hsOp7h5ltVEenXkzCWSy54XefM0R7GMkgZ3DBHPz5xNHO1vPHtnjkZHhOR5lPb6jvX0NpmtUIaR8NkbUm25Vu/m7d+e3cc8VwGuHfq183KktH4jEZAOwfzzWbSNIyZR3IDytwFUggrnd+Xb+tFiRAF3kADaCfT6ZrKWTB4XLjg85/MVsiZMASoArvnzZ49v86twILLSUKGJHgMqM5cl8gKAD2+/wBK6FrFLm5jWXdHGgEshT+LB4Uf3STjn0GfWq7RrdtsrTzfu921m3A4BHp7Zz3/AJVfWO2aV2EUjxbgSgkK+RRyc4OPXmsNZm+7AvYpbZrGCFrrb4IIjhh4ghTH4VXIxz34OSSSTURILNo1VrSDjvuiGcn0z9DUZo1QfuklRGBYHxA3H3A+nY+lEuZI9wR5AGwGBIK/TgVvocxdNqE4kcttkE6FBI0gd0JUZRi341IGMH8PGDXBakVkmAh3IjNt/eYwR3B4xxj0rrkmDzCOUlhJwXwP1x/l7VyeqCNrqTaiyLHMQWA4I9D/AMqwlFKVzohJtWZzktuEafegK4HfkH/lXixERk4OQRtGeFH59q2uN21RKZnKnLE4J5PB9PtWqMbCGc71Zf4ff61qZFjYiP5iOJ0BmZ9qgnIz6cZ/nXXM8yWMlnFMUsDjxrdZCgnz/wC8HBbHHlOQMfnXNdNgPrlqhClMlipBxJgEgHHIz24rrrp5LicPKVBlkdyCowCTk+mPXA+2KJcQ27WKz9lRpHgRRNCeUCxKQOOxqbZXU9rZvp0arPpNw4M2mvIflpTx5wrHyP6h12kY744OeUJUmEN3wAuFY/bPpUkxQi3y11AkzkRyQtA+9V58ynBDDgZ5XuBg8mrvNZlEUfgy21rcwWc7zxcSJ+6HmVmAU7sYDYyPyNc7qyr4roiNERhRuXI/4hxXW3FwRfzwRzvIsisQ5jERJJycoM49ePpXH323fKdzhcYXcAMc+3+u9c8cpM6XnBEdLcPGxixLxwOQx+uakwDYY/EzEW7BsnaMgfpUOI7v4hkHKsrbTj1FTbWKWVo0Zl2ucKWyC3r+vFaMzReaRbLLdW87wq+xSdhbaszYI5xjC45OMe3Gc10s8M2pGNdQZ51ij2RRSBdkWP4FjHkQf90ff3qn6YkWS1kGwk+Iy4dyM85PHb/WKu/FUBQVIYgnAYAf04470ihJ3IcFm2m3CvaRiCaMt5oV8J+3fKkHI4wfQgVWdQWS6haTXpiZNSBLNcIvluFH4t4xxKOCGHDDIbnBN2l94Q2xEDtleOQe/fAz96zjSAEtcS+C8nONgYk/bPY+vHFTYomcRZKJ4ZgtxGjSq3Dq3HucD0IrnZVcNJGCgTnLLwD9sfnxXTaddxwiGC6jWdBcZWIcF8EjG4ciqnVBKL2bxjnMnKKAFH0x6AVnB9Jo2muimQIlYBTEDnBxlcen+VWELvIPEwC34No8vAx/LmoR8hKrIA4G7yMSP1qWtxlmjLHg43BeCPt+Qq8syiyOn0Cx3vJdyQKTZkpHHKNyKwPLkEYJU8KDkZ5IOMVavbLPNPc5UXMkn72a5lyx5zuZj5jgDH1rX06fm9LCZkHizscucFnJ2jPHOMnj0yTW2S3khO3ISTO7bxhuTyCPYipirIrJ3ZAntlluGmh8yEnasm0nI9QceuTjgYqLrcDX9p8xJJ4lxH/ZXJBMhVe8b/3iBypPPoc+lmZI3kcqgVBh9q8jb9TnuK13Nw8SXcRmdI5vDzHu4bBzk+4GcirvQotSChBt5oiHeNIlWMk5C8cAe3qa4o2zAuuVUbyHyvOf8q6nTmJht9k3jAqA0YbLcdlA4Gf6CqHU4fDu5oAAkisWPORkjGMjv2rnp5No6amaTIMWS6IWOQSAfTFWmm2xubq1RHZnZihwpYhe5JHuAD/Sq0ESSqruCwHAAxwB6+1XHT9wq6zHJKniIqsMBzhsfUcg1u1cx0OsHBSAxqdPRVMMed2zPIKk9pD6t3NeSublpm3TAMU2xNLu4Ax7/apSlFVLcSLti80cpyTjvyfQ/Qc4PetCyRhSZRmRiMHJwmDz29Oc8+1WRma7eQ2/jbY0mMqmIrJhsoT+EjOMcevb0rlNQsxZX0kduZGiIDxFu4Xvg/Y5H5V2jFIoS+dwjBOD5QcjGPft9q57WI1OoMGTd4asQoOBgH2Pf1qsi8c3Y13nh32g+JsRrhJ1cAAcjBGF9fuOx4PpXPIhaMZ2ggnOM5q4SceA6LJneDkqMeXsMYx2qu8EhnO3hcF9x4rKOWRrPPM8jeRpXMIfc38JY+b7D1NdvpFi9raRPA8gupFEm9c5gDDsvsTnkjkcDjnPDiNmZFOXUtwdwAxnnH5V9LlgW2aJyk0SEZyrAEnPY54OKvZXM28itW0TLErud+5Jy27I/M1sWylj3vBC8QAw+3t9C2fT3BrdKgZlVyCwAD7V3eb2BGeeRVtbbZLRpEmjzGSuGjZy6kHjIIBIHGDjFXuUSPnOq2S6dcLMrNaxSZXYFOUfGSB98ZH6VFs9skUjglAG87hxyc+uPf2rqeqIIxG+/wDforJtZEAfynHKhjg4OO9c5HCUDiISCNUymBgg5557Y7VlOxtA06lG8qo6FXdSTwMMVPY/b71XEeRIgQ4B8xySD+VdDCrT2EsU7SFQuAqgYJHfIP3/AJVz2Tb5QqN3cAjHr3qIdhM1xJkIkmuoYYIfFZmCRqrADk4HOfciuxkVobcWKlntw4aba4USzAf2jAdx32j0B4wSa4/Rpl/atkJFBRpRlSeOc4P64ru3EMQhG5R5QWddo+mQBzn681okZybsQWt1E26RVaIltvPH+vrSO0dZR4U6CUEgkdu3c59P61m5jibLIZJC2dzKQGP51ik21z4hXxFBbIOB3zyfvxVmURzer2sFhcx3FqixwybsKFIUSYwRj88j9PSoZIaLajSJNtUAnzDPOPT6/euh1CKe9spWhy0iv4rPvAAGdpJJIzyw7c1RxBYy++NZWCjJJ78/yrnlqdcM0dBYzxywkLIqyZ8MP4Z27sHO7H6c96+naaWfTrd3TKvbttJGBu8QHg/SvkAuru1t5XgQKcbo2YnIX3z7e1fWtHVf2ZaM+0g25434PDjvz9fz+teJjlaHmetg3ediLOWJZTt4YZb8vv2/KqzUcopJIJWTJznB4HNWl2i+LMo8rjGPUjj2/OqrU1BSQKOCdzE4B744+lefS3kdlTRlbnKrznK18p6746luh/wR/wDygr6rjyoOMNxwfpXynroY6luRjHkj4/8AAK+l+n9a/D2Pnsf1S8TnKUpXvHhClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUB1WkNthX6/519C02VAISx3cceX02884NfOdJYGFR7V9C0Zs24xkhfNjPbFfM45Zn1OEeSOr0yQGMsGbGBjg4OB/PmuwsMC81FcDYY5ScYOM7SCDj7Vx2m7Y3ZCMAc8d/WuwtP/S58Orl7X8WOx2Lnn7ivnam8e7T3Ti+vbhV0mxkOUSSWIeY7uA6964+WaFGlS2bfJjBCZOSOeM8fpXV/ENRFoVhtfKC8RN+MZUn2riniKP5nV5GA5DAkN9vt2r36KWwjw6re2yDcrJO0XijY3O5SCSCexq+0m/WfQ7e2ljEgtGdFZjyVJznPcYycVVKzKpLkEoxDOUHJqsEksFwxtZsO2DtReCffBrvpSsziqRuj6AIYpwuy5YpIeFZ1bA9COAfTnisF0q4t1ypV9w5jkypIJ/hP+Ncnp/UywtumVsHhwAGB+m0mrFuuLeFC1hayu4H4R+7Q9/4ckevoK69pHHssk69cfs+3ldiYJHiZEJH9png8fnz+tfPXjGwLjwxjG4eh/KpOoapcavc+NfsG3KAqIgVEA9AB2H+jUe2trm/mItgCv4mLMFRF9yx4AqdWTayPFjZ0Y4VkGBkH+dWJ3AIJZVKIuRtwT+eKsoOnbK3iWbVNUuY0mbj5fS2nTbnDEFpI8449B3q5t+i4b2Fj0jr1jr85jVn01oZLK/7keSGXyzYx2ikc8/hqsiYtXscuES2MVwhQBWDANn/ABroRqMfyiCDw0wNwIHOT6j3Ht7VRTEBVcjBfI7ZIx6EH1B9KiLdzwK8aMrwbt209lI9V9ifX0NIPtIqK+h1VxbSxSQStEyvNGJYiF2iRdxy4OMFcqRn6Gt+nzRvBcqC/wAwrBdnAUgnJAOclu+RjtXKx6zaogE1rMpznKsCCPbv/hXlz1LLIjQWEC2CNlZHBzIV9cHsPy5rZ6GKTuZdQXqTXSx25zFDldy8B3J5OPYcAVSYO8lW8wGQCMZo7ZEYAO4A+X/Kuq0TpFbkeJrV9+zoApZlhtDdTovplSyIpOexfI9QKpwNCHo67rsNtVtqjCHP4vXtVhfyx+E7tKgnM+GLDJwVxg/nXS6X0n0bdRbf9ttX0u9EefC1Xpgtbu3sZbWaRl9DkpiqPW9HvOn7gJevbXcdwp+XvbW4E9vcAHBZGwMMCOVYBl9RXPJdK5vCXRsQ7C6jiRrGa6eKJiAkjL5IpgMKzc/hOcEnOODV3cqIILRJFMVxCGWSNGU+OxbhlYD8LKR+YPvXM3pEcRMZK/vCWjYY9B3x71E0/XNU0plXTroJFu3bHjSVO3PlcEdq1g20ZTirnZOsdpAHneNJ2xEtuZSWHbAY9gvr3qkvdQjsLF7SJYZbuaQsHTIdUIAYE5xjjhccZJzzVJf67e3jSLJLGm/krHCEVf8AujsPyAqz0jT1m0/xgF37SztuG5c+p9v8amctlXIhDaZTs5bdvwFbntzmo7oDvOSXxnI5GfvWyRw+EiBcM2FI5Yt6AD37Ve2fSF5IM6rfWOibeGS63vLn0zHGrFfbzY5olYm5X6TdtBcfvFAJxt3DG361PMnikkbiHBAx2yfv96tLXoaWXddaXrui6jOvl+WMslrK5zjCGVRG5PoA+T6A1UTWs9sJ7e9imhkt5Ns0UyFJIXz+BlPII54x7VlJK9zSLyOh0+5N5BHZGfZcwLtT1DpjI+5rOea5tZAsi4GwYYAEj/WK4+aaRTH4YACLnI8uMdjmt8PVmp2+V8YMqjgyxqxzjjzYya1i8jKUc8jsrWZZVXYsTMBuUvEY1TafXGPqT74rnuo9QjvbqO2s9pt4WLl0By7n1B9hyM/pVPda1qOphYry6Hhu39moCIc/b0q91Kyt5G2ukmMY8WNSytgfwjA9v61ScrNIvCF1c56bwdhEys21fTv37/etUchkIAYkqoGCeMfX7VLstNudVnKWwjCR+aWS5mWKOMd8lzwPsMk+gNXFt05ps5/33qi2hkYcCHTLiZc98A+X147VdLIq2atBMJv4luCQgPJcYB44+33NX0GoWo1EJ488cSqYVkYjcrY8u4f3ScAn0BzWdn0rcaZBJcCS21O1kf8AtrR2jeMD8W6OVVdO+MkbfZjVDcLFH4rRq7RFjtk7Z9+O1cz37nQtyx10VxJbyOiJPHKuGZvG27l5zgHH17enrWTy2btiRQ0oOC2M8Y5OV+9cmNfnjaK1kiF3bwp+73nbKgI7BxyR9DmpL9WxJCwt7S5Y8Y33CgDHfGF966E7nM1Y6CSeCG1lmdgURmjITIeT0A7cZIH3rhtUgmFxJPNn96+5mbjntgD+VbodSudYukFwVRYzmOKNdqqfcH1Pbk1O6ilxYW8LpIsseA4zuJbJ7nvnJ7fXvWcpPaSNYxWy2ctGoCtGin3Bzg59vrUuG8cqCVfMY8vH4M8fpXV6F8PLrWIxNqep2uhw4LAOhmnAOckoCAnP94+tSrvoXRwrfsvX9SlmiZsC506ERS4J/C8czMMkDuvvV7opmQYbQolu7vFCcshYrtEhB5BJ7/8AKpNprAtrtJ7sGSCRwjNltvr3wRkY9q3Xdg2kKtsxQspYwzxXPEysPMoz3I7EccGqE4uGSXdLECvhMrvnsRgewNc0d650ys42OwgnEcuAIwEKkHgqx75PvxWuS5T5l28TYzDdgeUfbHt9K5iy6gOnA2t3ape2q5EZfCug9ADzxxW9uq7CGOL5HSpvH2kF57gDa+chvL3AHpxXRqc1rHRxXkQk2yCKWWXyRq5xk/T7Dv7ZrmtS8SWFQUUeDI2GBBVnHfJ/iwPr61t6e1NrnU5Jr6SRWbAWOOMBduScAk8En371M1BJL6C10+C3uLp8N4UUUW5pXI4Q7f68+tYSb27G8I9C5yTSSKEkZlTACqcYz9P51rhePaRvCKmAM8AevrXY2vw/jZn/ANouqtC0Sfs9qsN1qU6MD2Ito2RT/wCPioVx0XcRTMui6pp+uiNmMcNuk1vcsmPxCCdFLevClj9K3ysYcSBpEhEvjo7FrdjuweM+xxzyK615VlVLiAr4byH0DFH7spHp34Htz61Q6VIRC0kchTjEmU2spHpyPoMg1qnvZ9MYXMXhbZlBmhYFw+O24cc88EHis4y6VjWUejc6e0uovELqqOMjKNkD2z3B/StbXBG/Hn4wQcc45wTz65yOK56PrGKKZHOmZmVQoCXhVTxjk7SR9qrbnWrjUwUAEFsQVMcRPI+rHk1qzEuoL0SS3JaNXh35ZQ+3P0GR9e+arNYiVFg3jdHuYjdznOMYwBk/ypbungRjOAXyQF4HA7n8q9Fjd6pfR6foOn3OpX0/njtbSFp5S2cFgqZ8p49qwS6Vzob6NipjCuyr+I+nsPf7VtOGBAG9RjC54PNdTL8MOuo7AXUvSmqNCDtCxpHI6nJGDGrFxyDxtrl44ZRNJBdRPBMsgRkMZRo2z2KsMqfvWrszJHRdOtsYx3Y/dTAFGfJQSA42n2yO1XUtztDowG7/AIhgjHoPYVVWoNvHJFdQAqrEBWbCsD6/bjv9vaqy/wComtb6WPwHu7dWKp4suJEX2LY82PQnmqQltOxacLK50bTqyBlZFIJJwf8AXtUPUdQS2gaXPhZO1M8FuO5/X+XpXOv1QoV2hssH/jfIBPYeXn+dVwupb3xJp7hJiOdoG3A7YAxjH0rV5GSRLtR8vOjJF4sp7beM5Hrg54rXe3DyXUnkUMSFxjOw/akqrCFuGVX3LsJiO07yeMj8uMV2918Jr7RdMjvOvtd03oye6g8ez0m6EtzqdyuBhjbRDMKt6GZk/QHFEs7l5OysfPofO7BisRwXVcnzH7/4V4JVnQFMkNg5JwMV1Fv0to5SVYNelCmLcHutIOwHPGfCldkH12tVRf2F3pVysN3HE0cnEU0EgeKTHqrev2OCPUVLIRfdK6rBG1xZzswkkCGBt+ASSCcn+HOBz9CK6NNxmKsJJthYEHa3bI3/AMv9dq421t7aK3PzCuiyAqqk7SQO4A/TH51CHU1/ZJ4EqR3MauNomzvUA8LuXBIH1qIO7sWkrZnXuiwgCN9m0nvzz6n68YzmqrVrsNEFUIpKlF45OT3I/XH5VSXHVd3LCFhs7W0BIYMFJb9T+dRbWW4mk8Rt8kwGQxOMn0P61d5IzisyzSeKFo/GcwyA9lXd3I9B3NRtdiEN0siNzIGwQMEn7ehroOkehuqPiFfXFj0VoNzrV1bxl7pokWOO1zkhpZ3IjjB2tjc3ODjNeap0q8lpZqnUPS91O8fiLDBrHitEcgeGWEewMfbce3eskrO5tKStY4xSyly+wAHh+7HI7VItryezuYprd1LRuGBGDkj0zXtzZ3OlOY9QtzGWB2uzLJG5/wCF1JVh9jWgz7QCYkD5BBxgZ9/6VsZHc212t/D8xHKqKY8ZY4xj0P1wcc+1ZGUlSyMviqmHZDk9vft2PauNUXtntuLOYo0mA+08k+zKeK3SdTagylbpbeWUAASNDhgB9jjt64qU7lWmjsLiaMJcSTMSsSkrhsKOcbSfv7VzbJ83NLIA6iSPJJ5G1c98++TVRPqV1eyL85M0qIeEJ2IAfYCpkMi+Ge+zIQl2zx7ce/vWcrmkLI1kxKCRIqs3HGQSK1sRuwwbG4nDZAzjnvV1pXTl9qwS6Q22n6TI+z9o6hKYbUFcZVW2lpWUkZWMMwyMiprdG6eSPC6w0ueXccA6bfRxH/xtF7c8rUIM5i2BZdrKGGOyd1Hrj/Ou10zWZr7ToYpAHaLytz5lx/ga5O40+502RPnoVEcmfClhnEkUnuA49cc7Tg/StbTS208M0LbGwVOw+Uk9s+9W4kcDvJJQI1V/3qqCPMcBRxwfXPH8qi+I0bh7fYZU7ExrwfuR9jXMJ1HOijx7KOR1JLMjlM/kc15c9QXMyBYIki4AAAy+Ce2f8asUsWeo3QeZLaSUxjvKVJJOMYQemfXntxUJwWUSEvsAG0EkEnIz9O3NQbdo2wW5clgWznJP0/L+dW1pp9xe3kVvpUFxcX821Y7W2jMju55ACDktjufQc1hLU6IZIiXz4MhI8NV5wB9Pftn/ADqlmbLFlyDwSu7/AFmusm6N1K3MqX93pdnOpP8Au41AXLqe2G8BZFVs44JFUl9o11Z7/GEFzaqoLXFnMJkXIHcjBBB4OQMEGrxVisncrIHCS5jygUghjzg57/evoekavHq2mSKbxra4t87oI4iSx7ZB9jmvn8cqxksiFiBgMeeffFJLhxNDJAzwSR8qyttI+36VbiVtkd3M6nO6Ahk4LMWzj6jjNRmYyn92WQDJZgMbfz9q5uHX79sJI0c8jE4dogMj05GOc1sW/n1GQC5lxGoLlEUKn/Pn3pKViIxuTruRru9SNVZxEgSL3XI/Fj9MfStckLRbGWEo6uATIu4E444PJ/6VCs3V2jxFhlbIAONxzzz6CrPxZJY2V9+zeWaNEJ2qBzg9vauSd7nZC1iNcTSNBJM7N4iYyDyG9OfYc9q+t6dvXTLRRuIFqcAqD/GM18mvw9rYSyAlY4o9y4/i9i369u9fX7CFTpsHlGVg4IGTw4/l3ry8d1a8T0MH1j8CJcqXnJ8wUhTyfYehqn1Mgq+BkYbv6djnmriVj5P+4OQ2cYz/AKxVRqAJj8248EDI/wBc15lLeR31NGVKPvgRsDOfbj/XFfLevOOqLvH92P8A+UWvqUSYiA9Ccj+tfLevMnqe7zz5Y/8A5QV9PgOt8vY+dx/VLx9zm6UpXunhClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUB0+irvRF9WdQK7nS1mgYJJjbtIBA+vBridC/s05wRIpU+3Fd5aITFC8Z87Bs45xhjXzWNfSsfUYTdR1GlAybxIMFYiQdw4wfaut09/FvZlORttPwn18ozj8hXJaYQGkYEAeGwJx25H2rptIfddSHuPlnXhuAdvHrXz1TOR7sN05z4neHJoUXgFDF+0I/DOeCueOR9q4m0wE2yEYRcMCBjA9QcZ+1d38SvLoRbaF8O/tyVXHHOMCuAVDGjFNghcfvFZiXU7hx/r7V7tDql87Dxa3WMjXWzdMFBMg8q+4xxyPX1qpkZlZd0YLr2BJGP0/xq0vGV7ueXxmVpGLYKnnnjB9Kq22tGxJAlGRgqT/ADrugcczSV8dyPTb/AOR+XrWllYpkjwy3DsvqBW5ByqDCEHcXHNbUZYlkZ0EjseT6E/X6VtoYsrRkI5jQq24eHkZz6V1kGjwrbLHIzxQxtmUY3CR+TkAfmB9BVGIgkyb0fbDIpOG4xnOPoK7L5IyAwAiSMLiKUkDOc4Dd+3Nbwd8znqJ6FU2lwTSZGFw2OX4K+mAfXAFSoIo7V1EpbAPhqChHh5YEEf8XbBzxXrWExRFw3iIcfjHC8d+fSp/7MEhi8kKovLq0nmIxz5u+e3v24rW5jY53WZ31GKHU7hzJcu7RXcrnmRwMiQ+5bnJ9SMnkmuWmlLjKgFR2x2Nd11ZbxwaI0Ue5fFnBQMg3Acn8QADY/Lv2rgmZh/Z75E9yvNZ2V8jVXtme+baQxIQ84TBFZpGyFp/Kw7DK+p47VhG+AFU8nuvpUlJBJasHDu4Y7hjABxxRkovemNIe6luLuVTJHbDZgHnOOSB34BrprO3s1h87ymfLMdxDg47nBwOxxVJpSpH0/E/jBFgupHkUbvMxVcfyzwT6VZszeGm5nVzjcjKxJHp/KpRm9TCS1WWFJVaaGZHwoBDEDHB45xVpps6yWsuk67O0mnzDZHtjB8CY5KSqceXaxGfdSQKhlPB2SBZCgBwrE5LDuB24xzWBuHjnUlluIQ4KuFI4/kwqWrqzITs7nGXckgZw+3xYmaNwuTgjg4qvAQygIMxgDcB6e+asL90e4llOFZpHII5J3E84zxVYuVbEbt7HjHFZxNmeqgduxPOBj3rrZ7Z7XSfBUFcYyAeGx3yffn+Vc7ZRLPNAQSuHy/HpV3ezJci5iiLNtYMhY/U+nv9azqZtI0hkmZdP2HgR/PGHfPKzJADIECgd2yRwc4A/OrZbKOJomPg7o3ZJvEzyx5J59u35Z9a9W4kt9J051gWW3lhWNna4VG3qx3bR9yOa3xvCsLiVrjKguWhuYyCCTjJPse/rW60OZ6mi70+FZJYYlgkBdQREy4K47D0I55z7Vt1AzatbyvcRMb3TrfNs0khfdDGMGEsSSQq5KZyV/CDjAEiNbWeQlbnenr8wMKGzzgLn9akwxItvcTNcRSRpbyuGjYsy+Q8MDjHY/rUSzRMcmfOL12WA+DCpXO4ADOc/wDWoEhZiwkXIdR9Cv0/lW4zFo0L8YTgH1rUAd+TsXbyOfXHt61WKsjRsn6dHvubfcDIquDhRkke31NdNcKxjVnkjkMrgoQ7NtPOEbjAP2qh0VVkugwA8nmweMn0x7/aurEapdWQPguzzMVUYIQ7SQCOxBPJrGec0jaG42VxsliiWI8QQ+TeeMMe7ffPf1rKGGW4lhURrPIwPljG7ueMD7e3rVjKVmW2EdwjGYckxMoYg+ZWHYHP8Q47Vhe2ktt4bXNk8Cz26tFIy43Rkkgrj0O04P0rrONmyya50yZHVLjdGPMgfja3BVs8c+2Me9VutwvbXCBgI7eVDLHETgJngpx7EcfQirG1h3+HGWiYkbdzHdnIzj17D19KruoZAfkVxHIRvz9V4H+vtWc0nmaQbWRSSM0TkqzBsYyDnjH0+9R5JA+0A8eo7YrezRIULZ3MMBd2APYVoYoZ87jwSPLz27VRFyZpgVLgb4mYdig75AJzXZ6BYW9/qbXRVIhCQsQLYLMOS2DnkA4BPqRXIaeFSdHYFnGSMDjPv9a+l9ITW8lqYZJfBxIzMSiuoJAyCPXK/wBTWb3zRbh1ugagUuLm0u7KKTToyyrMAPGD98ncdpVgCpypH2PNSLmw0+5gS2+VRIrfd4UkKDBJPqVPHckfl9BU4Txu/iLNGxUIUKSDJDD/AI1OMZ7e59RWtU3+Jsid9rlsoNgCkZ47D17fXgHio4kdxwuraTLDCIpQEt7kkBxksp7AkZznn27E+hr56/7pdrMFfnfH2Ckdxz9R6V9t1qzMdlI/y8gjAAYqA2zBwCC2Nw9Md+PtXxK+2SXErQtEIxO+w42nljg57elHmWjkVUy+I25GZO+3d3z9qjBSXGSxO45A4H/Spdwr+KCjZZm4bcCv+u9a/E27iwG4HAOMebPetFoZvUs9ClKZU71dnGCnOcenJxXb/Ki3RkitprWJjicu21znzKn0THOBw3bPFcVokZuoGXLYSTeqluAQMrj65wa7uOeB7GxkSAIqwbmiJLLLLyC5Jycn1wABj86zstou3aCIkVusQOIgzIGDYGBg8EccVm0Uc8bR+QMEPHIXcDlQGyefb9a9MybplhjJjZhtUnd2HcjHNb7Q25VvFXO1Qc7+e4ySMjHHtg1qYkC5WSDxp50cyugVpcFhKuPX0P378D2zXFXu1pDs3CPnjdg4r6Dq0EQ0popRIGLkxBZcDnluDkAHjnvyK4PUbguqplQC3DKhKn0HmrK3SNr3gVpHLZJVCO5GSR9K2wkY/drnt6YPJrUQQ0YHm7DaD7f41mMnJYZIPAIIIP3rVmZeWVvaXFzJDv8A3ix+Z4z5s55IHbIrrGluY9Mn0u3mktbB2zPbRMIlnYdmmYYMrffyj+FRXO9IQpJqEnioCTENgGTyc/4gfbFdRdKscrBdyuCUdSy4H3Pvn24+tUSLN3KpNBsd48S2t4pt+4yiJQy4HcEDJPpitepQtqkK22qzTySqu22vXyZrc4yAXPLx+hUnjuuKnldxIZgEJwDg+vr+lb4rSO7hnWWdYWYBEaQMVOeMng8DHt7VdlCkh3Qodp8OTGyQSBShPYrn64zzjgVyOslPn5MhFHGCmcZxXUx7nuLiOINI0Z8+G4Y7cZAxlv0rl9W8k4Vccck4IJOee/0rnp5TOmpnBFavBO5SQMZ28Y/P71IgVGBkDeU8EBeawOTERjAbHp3P+FbIwwT0kC9+O4BroZzo6LpKN/nku45biOW1aN4JIjsaOVWBVt3upG4H0OD6V1gtru+uLi/k8aS7LtNfXbM0k8sjNlpZZGOWOcckknNVPSTLcwXYfZGpdQu4kHBHoPuKuZ4jN+GJppV3nBQ+XBHPHc/XsOPyhB6mLRpcyGSdVZgFKpjb5c4yu3t6mqvULN42nhug/gSFeG82MdmHsVzwfY4qzO0sFJZuMMe2M/UDmvZ5fCZ1VfDZWYqCox2wRj2oQUkcc80LyOqDw2bz+CGxsXgKPQ8f51yGrAT3sjGJoyxDAD+8R/nXXRASrIm1pAckquQSN3I47+9VGswLHLH8pGSj5Y4UZPOM59h2x9656btI6qivE5nLtksST2HripsFrJPMkEJK73wzjI2r3Jz24rWYtrbXXzehxzVx09Az6nHFGqgmJ2XPcgYJwPpiupnMdJLdXk2jDQ4b28h6fM/jnShdObUzEY8V0BAkchRlmz9MDisLe0a4aLZJlsqg52hT6D+VTZYCmAz5BUbQT+XFYEgthiBsUpgdsff3P0qSgNmL6CaKZI5YfMJ33YB9iAf4u/I5/KuAu7OWxvJ4vxrHkZPB2nsT/XivocE8kJymFXcC/A5Hbn8q5bqBIZtXQohI8HY6hhxhjgj8sGoeRZZkVo47nSJgFOYhlV7sSCM/14/5VTDGAB+E8nI4rodHjMzTRiJHURkAs4jwccE5x7fzrn5MZZcgNuxz2xWcHm0aSWSZlFtLESgMo/CTxjnHf2q+6ftrW41S1GrxC509Zg93BDJ4RmiB5jV/4d3C7vTJI5FUUa7iMsFUZ9eM9qvOm/7aYOWcKgH48eYHPf2q7KcDttf1jUep9S+b1N7ZFjjFvawWybLewtlOI7e3j7RxryPUtksxZiTVWIwh5KhhnBPcityurlC6eJjJlBJUkf8AX2rVjwxlmXO0lgSDz75/SrlD35VJYpYZcPBMuZAR3wCR+fse9cXNZvbyLCu2SRZCgAO0bcZ3f0rtfmXhCsqxluQCRux9f1rmtYkDanIyv+8IXjbjadnJx6Z71SWRaJTtGUDKFY48w5AwK1GTsuPEjIOM9/sDUu4cPGi+GyMIxvbHc9sewH0qLIFIiK8Z749B/wBc0RLJUUmBlF2FBkhcA8e1dpFb/J2klso8NpExdSK5zJk52k/3ABjb2JBJzxXGWZME0fiqrqkiMfLkYB/519GuYjGI5kBRGUFnCjnn0Hv3z3qLK4byKzDW0EccJEUcbs6+GMKmRjGO2Mf4V4kGy4Rg/hu3G9n5Hv8A9PWpcrgxbUDbW3BBnIVR347E8f1rRHIX8q7sYPccn/XNWKHKa1p8Fg4uoI2WGViqrtChZPXHsMc1VPzKqMCQTgAHdt9P8q6bqWNJLAAKSPFQqB3B5zVA9tldpUK23amPfPBqjyZrHNGqPdHJsjCl1ByXI2k+v51mRtcOVVsryOdo+tYum6PDq2QAGwP4s9/869V++1vU9u1QXRugl8SSRQwDRkEcHzD6fl71ZxlQreFvRSQOfMAvuf8AlUCMOrKsiKcA4K9jn3qWrIzOYXkiyxZNoztGf4sVhM2ibtTt4/2bIsrPco5CpGp3HaMMWwDwF+vJ496+sxHbYQhsEeE/JUYxvHrXzGSdYDLsikiZlMTsuAdhBA4Yc8+tfTYVYwKVBYiI5Hcfi59Oa8jGv/WvE9PCLpvwI285OME5GCRnvnn+VVN+ytCuPcjGM+tWIy5cqWO0DjPsPXP3qqvQyxrkNzk4Jz68muCkukjsqPokKPHgpjvz618p66bd1LcsPWOI/wD9ta+qphI1I9G9/evlfXf/AKzXQHYJGP8A5Ba+k+n9a/D2Pn8f1S8Tm6UpXunhClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUB0+hsBCMjJDA/yr6Hp8YIjRvIdspPPoP+nFfO9DOYsAgf8ASvoemOHWEDBzHLlfyr5nH7x9Pg30UdHZECRPJ6DAJ5yfY10misnjyYJY+C/OfYZ/xrnIAd8ZYHa0anGftiul0Rn8SVcEq8bh2A7cEivAn2nuw0KT4kKsmgXCkgob22JJO3jxP5d6+eSbliILkpvcL5s8D2NfQOvJGl6eupJkXd40JcMvBxKB29j3rgMTSNmIqzIpYgDCg55r3MP1a+cEeNW6z52shX0xneR1T8I/CWAGcAcfpVWJEEe4uEkX8QI4+wqTdJ4R3KVZsF8bj29a12MUmq3i29oivO6+SMEcfUk8Ad8k8ADNehBZHFJ5kWNk35AkAHoR359Km2xBfL20iorFQ7DALeo+tXFtY9P6YztrDahrM6MAgsbtLS3U/wAX7xkd3+6qBVnb33Q168MWp2vU3Ts28ql/bXcOqwqD2LQNHFJgeu1ySM4Ga1cZPRGG3FPNnKyRZKRR+dDgNg9v+gq60fUjNEYoizSqMMoPcD1x6+lYdT6Pc9OzWjGay1jTr3L6fqemlmgvEGN23cAysu4B43CupPI7E8fdSFEiZWZGRj+HjB+nrVqaaZWdpI+mEu6t++kjzjcM5rySURKzfMKQpwXJxtHuc8AY9cV8+Gu3tsVVZ1kCjId0BJP3/wA6jXGo32pMsc87SA5zHwF/StzCxZ9R62mpSRx202+3tywTjBdj3bHoMYA/OqBznbzsyMED71lu/dbMAEtyal2Gk3mpmV4/Cht4SBLdTtsjUnsCRklv+EAmiJ0NCKVGJECqPY55HpVhAxZZAMsp5BPck+tXFp0/oiMhvtW1ZsthntdNjKqMcY3ygk59wKm3PR7iCW56W1Fddht08V4GgMF0iDuTESQ4HqUZsd8VSRaJU6Tqa2TPbajs+WnK7mkRnWFxna5UckYJBA9OcHFdJfWDafLKlwsLxsokSeCUSxyKRkMrgZIIIwOMdjg1w5uEkiLxc49CefzH9K12WqX+nLINOvp7ZGwSkcmEJHOSp47/AEq0SsjtnkgaOFFSVXZiwTeduRxux6e5qr1TVxaxXUVjLJ4skZSQknyZ9ST3JxgfrVBc9Qape+ILq+km3nc+cAE+pyBx+VbrezMOnicpgspbdkZOT25pJ2REY3IMpV0ViV37TnavYViAwQMATxgEH0rK1gmvpBBAoXAyWwcAepP/ACrr7LT9F0oqZYItclKlSt4Jo419CwWMjsc4yT2GRSxLZz1kjFlCHdjG5N2GwDUwyvJPIdhUAgv4Z7f866YT6JeiOC+6bSMoWWK4srqW3mjQnIUBiyMF57rznv7Uuo2I0uNZobg3lpPzFOPKR7q65PPHp39KykmaRku0k6FewXM0NnemMypIflhOuVYM3Kk+h444x3q/vRbQBIkhtzJkmXCErxxgDvwDzXBXh8ngqqgsPEbj/H6V7B1RqlnEsBuUu4Y/wR3UKy4/8R5/nVo5orJZnaRtNdb1sIFmZ2C7oYsjHGR9h6/lUPqnV00+xuNHi2NeTN/vG0ltg9s57nsR6CuWu+qtYnQxi4FvD7W8YjyPbI5rNLCKHSY5WPiSyqSQ45BJ7/Wpk7ERjcrGkdRt8uMeoya2adp9xqk5i0y0vNQYDLC0gaRh9cKDxXQ9OaDBdQR6lqEDTwjIhgZSVbHdmx3Gew9fX69knVGtw7I7a91WythgBLS4e2UAfhwse1R9qnPgLnG6NDPY6hNHdxPbXaAbobiDw3Ckd8OOD9a3XF3+/SWzUh42DRs3ByP+npXfJ11ql/ZNZ6vqU2v2OSZrLVpDcqytjdh3zKhO0DKsMYGPauG6h02KxuI7jTGb9lzsRGjnc8D45hY+uByrcbh9QawcXtXZtGa2bBNTEkbOEWLbIWwAwdQR+EDJGPyz25rL5iEtlIzL4hMr7zyxIweD2I7/AKVy13KVdXSV/EB/GDgnH9a1ftW9CtllY+mYlyfucc10Rd0c7jZ5HbwXEZdyWiito2IebcMhfvj8hjnmqHVpnuL4yyHCkeGsfcoo/wA6pmvLrU7iFbyZpSHARcgKPsowBXRajFEsDbGREQbQ2O+OScn/AKVSbs0jSEcmymYjO7HAOAcdqZ2TEIDzjiuu0DpVZCJtWtnujgSLaGUwx7PQyOMNk+iKQfc+lXMOrW1usiL0z0uI08rwyaOsmAp/vsfEJOe+7NPAg5Gzlkt4VfcyNJwDgYwSBnmrGyvl09ib4lYJHAJUcxsOzY9av9Q03R9VtIYrKzGh6ggGyCKdns5mYg7P3hYwseMHcQDwQAc1xF1KbXd80kiSqWieMjLxsDghh6Ec1jnc2unGx9nsXltrWBoJFmVgCrxhSsi89ioAI45/KpdsGuZtvh4O7sF5f1H3I+3FfCtP6i1bQ90Wk3s0Fu/442VZIufUIwIBPuOamSdd9SXEIVdVeJiDuaGCOM9++4Dg/UYq+zczvY+v9S62uhWclta3cc+qXURSKDw13xo3eVmB4HORnuR7Zr45cxxxsqudiAgDK8HA9vUVnpEhmaVpmkkaU7nkbLO7fUnn9atLy3hltHnMzRkEoN8QXdj6A8Vi3aRuleJzUg8WUqw2E9mUelaHQbfxFpDyQBgKPv612en9LWdtEk3VlxfCRiSmm6bGqTAf3pZ5AUi99oV39wtXDR9FSR+AOh79F8PHi/7WTePkevNvs9Dxt9a3T7jnZx2gxSeFI0e0nsqlgMnPp9v1rpXu44HazVmCXLb4JGbgyfxKew5PY457V5daJptot1d9PS3s1oi+JLp94yG7t4xjzb0XZIBySQARxuUZzVDqc7Xe9VjWRWQLlBuDIO2Se+Kxu9u5vlsWO7TTLgSSrfxTRTxqOMMCHI/Dg8kevp9Kx8MtIW3tJvT+0Y7O31P5Z9a+d2fVGr2CRRxXm/Znb43mIGMYJ74wMY9K13vUGpasub2dTGPIFSMLkf1raxgddq+sx38wgiEfytvnxCH3GU+p7/pj2rndZhEJi8KTcrPhQcc8HOeftUTTwpcRRoqtjBZk5APPeunj063fbJqkYvplACwbWWIfVwmC5x6ZAGec9qzs9o1utmxwvzEKyulxPAHBJB8QD7D6GpWGmwcNJzwVIY/ma+tWPXGvaToj6TpqaNZ6aUaPYOnrJn2t3DFkO/6FwzLk4IzXKXmm2d7sdYbewuyuBNZ2wiQnkZkhHlYH+8oU/ftWhkiv6TkEN6zSoTA8bIxDcKfRjj2PP2zXUIQTtLRliN7Jye+ScfTHIrir27uNLnt7OMJNczgrPNCrY2ZxjH1Az9jWR6quNMYQRqlxalFKRtn92fYH8sYPvVFdsu7WOt2xEuNiyBjw+SAjZ9/Xj3rFr6CyjZ5djsisMGVh39x/rNcxedbJcTknTjuAxhpVyxPPm8v/AEqpuNbvdakCXZRYEBVUByqexJPJq+ZRFpaXiG5jlljUgk8hiMD0/Tiq7VR/vMZclgw7H0/yFbbUhpdpKsjLuOF9auYOm0vhFLq+qxaVZqUUGK1a7uHXOCUiUgYA/vugOOCayiulc2k+icmkRERdnUecKwJxt+tS13IdrKyBmByfXng11zdNdH7Wht9a6sHl8s0miWZVj9Y1utwGf+In6VRalo8mkJ8wLiHVdP3BFuYd6bCeyvGwDRk+meD6E1o8zJEnRdUl0vUDcTKGifCyhAMkDsV/4gef1HrXaRsNRhS4jkQwshjDKd+GGCQyghlJPuAB9a5C0hFqkctwsahjl9ozj19Tjv8Ariq29v7jTr5rrT7h7eSQcsj5Le4OeDzWcZXdjSUbK538V+9mZAmN5J8/h+bkYI55HtxVPqV/4CeGp8W45IU/wg+p9v8AGuUm6i1WdW33TDIyxjRVJH1IFRYZCGyZVEnfDEksf86u0ZrU6WymCNLGxPmx4Z3HIORzn6j/AArDXoZRhXXeiKGWSMnYysTyc+vp+Vb9BV/De/u1MVlFKI9yNh7hyMmNeDjA5LdgOPXFXkesaevgiXo/Q9TVD+8OpzXkzye4LJPGP0Ue1ZKD2rmzmtmx8zQiOT1PJwCM1Is7yezvIblVbcpwc/xAjkY+tdfrOm6Pq03j6fo8HTDKgG3Try4uLcNjJOydncDB5CyHHoK5G7he1uJYr4Qp4a94uzA9mB9QfQ10MwWZ3y38V7bJcjHhyD93KBwzAcDA7HnkGohkGQhxzwMjjHfn/OuP+amsLWGaxUguSHVhkMB2yPy78VuXq24ERDWVtuPCks/58A0WYZ0s12sdvI1zgw58uexwO5P24rkLudrh2nhjMkhbbEuecep+laJ9RudSZWuZMooyqDyqK3IiEMyIeB5iew+n+FRIRMhd4A8fcYVXhcFsE9z96rpRjc/4myd2K67RNB0yUx3fUkt7Hp8iFo7TTlj+alPG05k8kKHOd5DMQOEOc1sGlaQ0cWNIwyphn/aMzsx9z2A+wGKRJbOPTbHuBZizfgIGDUzSrqSynScAEgeYem08Hj3qxuun/FGNGaeWYgKLaUh2c+0bYGf+6R+ZqiiZHaEo7h8Av2GHB7jH+POQakg762u0mijkRg+7gMB9hj6f4UMu7CjhiTy3+vpXJX15cWMcF1ZTNFJlo5cchyPcHg1oHVl+4kVVtgcBSwgyfvyTg1KzVyJKzsdXPPFbxtJctlD+HHBLHBAGfzB9v0rn5phLunkePEkhXJP4iRk/9ap/mp7ubxLmaSV8YBYcfl7V1Ggafb3kYuNRknFoMqkMZVWnI9N5GFXPd8E+gBPaskWiykuI3iZVcKPLuTMm3dnkVoddzFiAFPsT+pr6j+1zDZpZ6Xo3T2l2qLs2Q6NBJM43biWuZhJKx7c7hx2AGapLrT7W/AE9lBDN28W1iWFvbOFAQj7j86Ii6OMjfygKcqV/Etdrpurve2aREB7iNQAMc/8Ae+xHp2FcbfWE2m3PgzsrOwyrY2h098f1HpWcTu92HhmlhmDDDK2MD2/MCpeROp3So+4fu9/qpJ/I4x2rb4U7gbLTdIR5SGwR78Y+nvXHxdZ6hEFEsMEzRkg5DIefoDjmtN71Rql9A0Kz/KW7jDJANoYfUnkj7VJQmanfLcXCWsAWRI2IdlfIZvYc84+nvVd4aRKkmVeQk7trd8ccfatMAjVVyCgU5XaSDn6D/D612Wn6PBp26S+tbe51Rl5W4AkjtOfwBcgM+O5OVB7AkZrJ5s2TSRx88lvLIqW0sTE+0mSMentUa0QsrugBjB459c44/Wvq0+rancQKk2pz+AFC+EuxIgAf7iYHfntzXPalpsM7sZFiguH3bbmKIR5P/Go4P34NTZpDaTZyUbGTKgZkQ8/86sYFMjv4TqG8MBwByy5+ntgVAEMsMrLcAq6S4cgcggeh9u1TbRpYZmcwySCMnjHKnHr9COKwmbwJyljZNLKY2eIjLlvwgnABXse+a+s2o/cbVbDKhCtnsS5HavnUcdqtrOIYI2DqoUN5gjNgFiPVh2x2HJr6JG2LNey714OcDIlJ5/n3rxca7wXiethFabKlXyxGctISuPfjPc1V3gV4EMZLEoSc/Q1Zs/hSbR28VuPTjNVNy29QTtJwRn865KS6R0VHkRFz4SnjHt+dfLuvRjqe6/7kZ/8AkBX0128O3Rs5DHke1fMuvv8A1ou8f3Iv/pa19HgOs8vY8DHdUvH3OapSle4eGKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQHS6AwUKSMgYrvdEcjYoAOFcDntkc/0r5/oTbFDGu70XHiKDkLlgPfscV87jlmz6XB6I62AhjbH8H7jHbgYNdP0+xZ7oHABiJx78Ee1cnYOSLdS5/dgH3Ayxz/hXVdPsolufL5lgcHPGODk/Svnamh71Mp+vEI6fvUBZ3Dw4ycEnxk4z/jXKXMzW9mtnJHGj7sPIIvKxHp+VdX1o/8A5nlWR1hEhQEgbQn75Dx7AZrkNdntdk6RTMZS4J3eUkfT6+ufWvYo7iXzgeVVyk384nK35aCQ7vOpyQFAOBVnpNuU0uS5WMeNqDFC44YQqcbfoGPJ+wqsa1CbJkcSY9+CDn2NXloBLo1tHGgMkMjrjtgZz/jXrUrHl1r2IbQr4oaSPBVQBtHK/T+VaxBDMDGq/jOS2RgcZzUmeCUFQ4KuCBsI8rD0GayVo2jMRiPmPfdjn6/4V2HARIIHht7jT3kZrG7bLAnJhuBnw5gPQ58hPqrc+lcc8zyBd5zJwGHrXevDHHbC6cqoTLOueePXFfP3nyGJYhid2BTVlloejDLukGD6n1qZY/uxPMxGAu0eX3NQd7FQM7cjPm9amoCtpIGK7nIBGM4x71DLojhDcuiRscucHPoCe9djZwwxKqLC0sVumEAYgJkZJJ/vHvn8q5qwIN/bIeE3bSPuDz+tdTZxh7gW+2QDfgufbv8Ap71ZaGctSXbQLLFvgUQoVUSRpJnLf3jgfn+lbkt1ieN4ZvDkicPHMOCG75BHbFTra1iikJWXzFvUEHg4zkfX0rZKFHiGXzbW9ATzz6VW4scP1VDHHOl9FGqm8BMoUAAyg+Y8e4IP3zXNblCgswbJ7f3a6vq6VVsrSLHm8ZmXj0CgHj9K5AvluFwvqfepRY3YCuqhgVJBBI7/AJV1epylLKNGSKLMYDiNuGbHt6faqDRoxPfxhvwqhbGfYZqxu7hnj/cucdhx39aynnJI1hlFk3RdO32LTkRfvGyuTzxx2/nUmFIQ5IRSyrtKdgR27k9/Wo+iTxxWmHZtgJjkJHPOaksm9SwCjeuQMADvgHPvWy0OeWpYRK7RfuHeEoA21Zu4GB2B7dua3O8t5YXNjJCwjlU7mMgK7hyCowMdvfnNVELuQySSI3OxgRnBAJxnFWE93Db2DSjH7pWYkDPONoGfvUMlHD3Eo8MGMnwyuGyvb6VFIYBX7lvy7VsmZMIRuJ7MD6/WtUKsZNj8An8J4xVUsjRm+EKSu/kMcAGuo1JfDsGjjYgKnk3gMewwAc1z+novzcRddzA7uTwB7mrjW/Bl8VopGByABjAPAHp2NZTzkkaQyi2dypW2ijtbX91DEiLEAGX8K+bnJG71z9a1l5sF8LlY/MxPrn3xk1H0HU7ee2hUr5iMfiwAeAQc/arme3iBLop8RjgbS4ycfQ+lXMivjuZg5kSZxtwXTcD3GOA3H0qP1NHnQruWb93KoRyoh2ZYMMEkYByC3pUieFznwncoM4UxnkEDnJHr/rFc71VfPHYmzYhZZ2A2sMNsXkn8yB96WuSjj55Mlm3MBnJGP9etRGOMqwALeYD3FbpyHVdygH6H61pUpNIw5HG0LV46EMm6XEZbuJVHZuR2H6121la+Pq9lBcsGgMxkbdnBVF3Yzg8ZA9DXJ6BGDejfK8QC5JVckfrXW6feLp+tQeI7PFKDB/aEKAy4HbBAzj9TWE982huNnQM8cc8kdzM4LkPs4YOATk+nPrW2K0WK4E7+EqDHhqSrH02tjI5zWU2nPP5beKGdePOkyo6ydsDcRnPsM+5xVebW9k3qLdgqKDtRS4x68DPqauZEm4D3DOs0m9hGI/BOWGc4ABx6k8n7j61yfUwkS+j8dM3bwlbjDcmSNtuSR38u3P2rrFF3HCWWJIWTlkIZSTx6N+RrkOrr43eqPKVO9MGTzZ8x5Iz+lQyUc9IwYudxXHYUgVFkQNygIzTw2ZmHmxgkDj3r1FL7UyVx2wO1SOJeaR+9aUglM7gozjP/AErqUgjS7bxClwgJMfhYLhgBtLZHb1rm9LaRrdVPlGCMBRlue3Pp9a6JfDins2i2rKY3eTvsJU4Ax+v51y//ANDq0geMolfLqwJYEJ4hwx9Cf8TWLxxPEEdAWzkMeACO/wB81tN2JuFWOPggqowc5zlvcn3/AFocs3CBVGeJDnH5en8xzXWchgfFsHintmdJITmNs8Kee3pjkgj1BNUWuuDdNtYbZFyi5wFBGdo9vbA9q6+xkXcS/GTwSRtz2AHrnJ9M964bWJA968ed6oAoQjOCP8Qazksy8XYpyoOO6jv+lZRExhcL25IBr1hhwrI7ZIwM4JNbzEhU43cDBPqARVgWGkBHvIVYkBmBw6Zx9a7Vl2eVGY7eC2PN+o/1zXB6Wz2UqOZy4DBkLDkY9OPevoC3e9ZNo8SElW8pIwB3w3bHOPyqq1JZ5JJJnL5DZ8zbMY9+BxWBiWTGYiwY5YAYJyO1YZVl3I2VzyOc815JcGHJhYHa2Tg8++c+lWKlVqUht7d45co6kjIGDnGPN749M1xWoAtcSMQVDuWCe3pXZ310WlOVVnY+dnYsM4JyD79uK4/UNiyZYiQn8PG0gfQetZR32ay3EQFGMgKHI5B7Y+lSICY3HYAd2zyc1GT+ASEhOcMf4ual48MhR/DznHtzW7MUXWgqk95GJ/MozI2DjJ9K61rUyvK0mSSeEDKRgnjkd/vVH0zKiXCpPEGhZS4VSecnuP5cfSupuJkLuIxuUybg24A7c4wQBgZ5rNFmRCgnjMLKJCv4WAAKjv8AlWCwYRnRF3sp3Ak4Kf3GHqD3x6HBrfKoSUnG0AgHHPB9K3W6O8sar5d2ASo3ADGeSD9OxxViqKWGKGOOUT4aJXzl8HKDkd+xAI9O9c3qlzJImI8mNcgrIAWH39qvfEWZrpt6EhPMjSBT2B7fpx61zGr8RptzvblgoPlY81zwXSOmb6JW+M3YMR/eA9azjuGQgoMSMfLk5+lQ5R4M+2NmyOH2tj71kjvHskVA4TlQ3pzmuuxyn06KQJDbwgmOGzXw4FU4xnu59dxbkn14HasX2qAoVRHyyuRww7cZ7VGtpYpolcedJUV1btjP+FbvE4AKhsHzMV2jdnJ7c1AN6TKFRdxeMHeyBSMfma5zqyHFvHJhd6yDaB6K5yB+uD+Zq5QYywMg3DyAc559ft/0qs6qdEttpY7WkCh182VU8kY9O1AjnTKzacVkLID5lCnyt3qCjMWIJOMAc9qnxLG9rtbexwSqehP/AC5NVfjMhADFj2BH+dRHiWkTIDiQcF/YDgfnVzpIFxPFBON8BkDMv95AOVx+WP1qiBVRkFVVVGPNgk+wq40S6C6lZBiQvmQcdtwOM/nRoI65meWR5JnaSVz5z6H/AF2rYYgCm5Q5Kg43g5/KtYBLDuWJx5Rn9KzlciQpt2svHfn9akqaxGzbRbKRIj70dASykHjn6H1rmepQ7au13OVE10vizYG0FxjceOMk8n6k11GRswewzzn/AArneovNPbJnJjiZn3jkbiMf0qhKIN2puLGbwt28BWVMlwR6nPYfb61RFvDOFI3Z9uBXTWU8MsbxOxRpIzgJgDPoP1FcxNxkdiCRg/Spp8UWnwZtEhaMhCAVXuPUk19IgiWKMQR8CICNB6YHuPbOTn618xDAR7l4K+/qa+k2N585ZwzA5Bw2T/LmrNGZObCuWDLKoPvgj78V54PjYDg8D1Oce329a1yuGVXiDAhRuy2cn3AxwM+n8zW5vIIyGO8qSw2YAbPGPfj1qCSo6q05G09Zkw5hcFGClcqeDgn8vzFcvayeHcqQuzLfYZz6/pXTdQOV01gcgTyqqqc4IXlvy7frXLTBky0ZK7VxHk5Ix7gf1qr7C6Gr26pezlYyiSfvBkEEZPoKrmznYC3HYEcCrjUY1eFJWw23AY5ySSM/nzVSSEY5kCtjKsVJzz247evf2q0XkRJZl30vEp1aFp/N8ujzorHgso8v3wSDj6V2cUqyEEPLHLklSrY3HPY4FcBpF6NP1CC4cAqrFZC3I2sMEn9c/lX0CMRRvuJl3kcshwPoc7sHI+lCpJTBVdkk5BGR+8Gf0x96hPJ8y+2R3Oz+NhzUtJUQMP3zeyiXI9jnJ+9Vl8pkdgimJW5XL/T1qCSp1pNrQXGTzGUYAZDbT5fzwagWkiRXBjmPttcnkYPuO45qXrDK00duCpkt4scjCls5wT9vWokCIu4yIdjjGYwMv/w5I+nNc0+J1w0R1cMI+Qe4WNfmAdpGfISSBjHbODwee9d5DtMEa7SFALcsqj+045Pt9fpXy+2v5ItLkjYhHSMBTtUl19QS3YAevft2r6PDIY7O3lUEkwgnyZP4zj+leLjItQ8z18LJOXkQpQ28lTlAzHn6j2HY1U3BKqcggBO5+5q2kBeac7VAySMHOOe386q7kJ4AGFz6ebPvXJS1NquhDjOYFBAzn7+lfL+uxjqW5B5IjiB/+1rX08Em3Ugng5Ga+Yde/wDrTe+gxHj7bFr6PAdY/D2PBx3Vefuc3SlK9s8QUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgOh0PlDg9hnn7V32hlTIoc4AbggfQ1wGhcJnkc44+1d1pUhR42T/AITxXz2OzbPpMHuo6qwfw1QMwbYO+f8AiJrqen8Jf3SqefAmIHfgCuUtQTEsoB7ZYd8c/wDOut0CX/zvcFS6LJa3PBXk5i3DP0r52rxPep8DmfiDIYenVKvhty4dvQ7gcn6cVxd3fNext4kKSnP40iHIJ55A+tdh8RolHTIjZdsfixgjvgMa4yO2lsColj2sp2lonBUqOMgj6GvboW5NPv8AY8etflGVbQpC0wRV7gFmByFHsfapdrcSWEzkss1qwAYryQ394ete3STrbs1uomJyqsox5fbnsaqd8qqJFILY9f4a7Yt6nLKKtZnbWd6GhCQyGZZBztwQw+mfueKgXtmJJN0cmwN+LIwMeh+n+FdVYR6G+jad8z0jotxOtpGTcB7mJpMIMltko8xP8XAPtWizt+ndQ1SG1vOk7AQNMI2Pz12+0McZAaXGcnvUc9iuD9Pcz5pJ6NevsfONd1UJHHZW7CQNlnlXsR/dH0+tcuTtBL4BPbn61aa3bG2vVgxtaINGfphiP8Kr2VWfD4K+g716MXdJnG1bI2qN20sG5xzxU+Ax+FHvRm7k8559qhKfbIYjnHoBU+JjNHHuK+U4BI/Fx2yPpUSJRruXx+8QbNvCcYx69q6nStTa/VdkgWRVy6Ajv7j6VzU5WKNi8ec8IAePt706csYtQ6i062uBJ4UspDiJyjYwTkMO1VvZNkuO07HbQSswLiUZw2foP6V5JuEB3yMUBHOcc1fnQtMt8h77XQ483FzD6jkcxZqFf9P6R8l81bNqd5dQyx+H85eBliGcEhQqgnkCubnVPRGvNppXPmOs6g9/fEgskcOY4wTzj3P1JqDEAWA/Ef5VunCfMTh+CJG9KwjjAOQQeOQDiu1PIwsSrONlbAdMnsFH681OUIwdkZXZR5e4/wBcVDgyikjKjOCT6ipbQbE8gGRyEzn8uKylqWQi1Q6bdBxFuiYeePuGH5+tXVs9tfjxLG+jTPm8Jl2sp9fKf8K5q5lOCE3bSfX0/Oun6e6ftL/QbW5nUu7NJnOOMMaidRUoXZKpupKyNskUVoFe4uFjTkk7goP175+9QdaufGs7N7d1NtcCWQLtwRtIAJz9yQPtXUWHTVjBIJIrO3ncKCDOm5c57ke1VGtQfLTiFcBLWW4wM443Dsv0rBYmNR2RrzdwV2cYY8suCvk4GfWtJRi7DdtJPPrW4sHY7pCQWOCB6/WsMbHJRsMPUds11IwZJtJDBLJIMd8475qZLKdhLEFjgkn+VV0Sb+CSBwSfse9TGlxkgqqnjZswKrJZlk8jOLVJdLZmVUmt3GJIy3f7H0NfYbHQuqpYA8XT93cDw9y7L2B8AjgnZIT2+35V8LugRHIvA3EHk19avgk8ha42MxYKN0fB8oFcuIrSpKOzxv8ANTooUY1W78C+vtD6pgtLq8TpfUHSCGWR0uprWJQAMk79+5sAfhAJPYV8c1qdptSuGMhmACbGI4AKBsD9a77TreJLrKRwqdj4CpgjKntXAXKEtNKFOxNgYg+uwVWhWlUb2vnqya1GNNLZ4lUyq68Dzjkk1rDEAEuAe4z6Vmp3N5Qc9ycdjXqnbzg88LxXoI4mW+iSyRpPMOJFwM9+Pt61JmmEpd5A7O+SvOPzNQrPKRqdzFmBKew/51sBJYsV3Jny5GDWLV5XNU8rHUaf1fElubXV5TFIyBFnYnDAHjd6qc+38qkLPbhfEtL2xETeXy3Azj3/AOVVPR15cWXUsd1Z+ErpDMjB40kzujIPkbOcDPpxXXTXscIc3WlaBfFG2759HiLOAf7wwf61z1cQqUtlo3p4flY7SZSS6+qwTx6fO11cpHuJWWRozg8ZycDv2HtXIyh2kczSMZWYknPJPqf1r6Dr11ZXukLLaaLpGmSQNIskmnWzwmVSmdrAswOMZBAHeuX1a08K2RkhaPdgktkMaQrcpnYidHk8igKNhlGDg5OD2rcqGMbovPnv5P6H0rS6lmIY+H68g1sUjIUnO78Te/2rqOc6XRIpntofDjeQbjlFIycD/nU3VLjBV/HAmi4VkQsHPfB+ncZxUSxBW23RSMk0YBj2r645BxzjHr6Gos8hRwZCJPMcBuM/9PeuTWdzr0hYu9MubTULhIUkRZXIHhu/mX3xnuPr+vNTks7khz4FwCEDErExGB6seyitfRVtp93bXq6jp1veq84QCWJXK+Ufhz25JORzS80eylbAtRDA+QYY5ZAo5PG3dj2qJYqMJOLQWFlKKkmRdRv47CZod8L3RPhskLrJ4RPPmYErng8A1yt6rrLuJ4Awp7EnPP581e63Z2kEGmC3gitYjJJkQptDEdj9Tg1H1BYpdPE6xRxsCDwcH6k/X8qsqu3ZlHS2LoolDJkq20Y59/1rdGjZDkoQRwR6V40XiZWOIrznkcY+3tWUIy2Rt9yPX6VtcysTtOC+LGCCzLkgYxjirCw1tbZ3hu4ytozHaUO8oAMkY9f61qgt1FpuVQspXntyc96kdNR20nWGjJe2vjWXzYNxCB/aLjzD7Gs9q132GuxeyJB1fTiC37Vtl4/jbw8enYjJrRe6pK9jdXenQXWowW8YD3iWzi3jXsMuQMnJAGcZx619QvtShtZNuhaRpekBWwrRWKSSnv5tz7sdvT3qh631G/1DpLWEvb+7niRP3cU0x2jlQTsB29/Ye1cyxe00lHXvN3hdlN39D5/DO1xDcRIRvkTIOMHj8R+5FU+oxDcWc5Ypjd7EH/pUzT/35Ko0pwmQyL5sev1rRrQJliyrqmwYDd8/X610xynYwlnApoiNzBUByOdwz+dSYCABnuMg+mD71HK4kO7y54AqTCMybQASTjn1/M10s5kdNpFu0TpcxygOSY0WQfiBUkgn07f4101rE1zHDJD+9iUjcqlSUOOzD07Z5+lc/YtJFFJM8qyRuNwDxbxGccbR6nPFdl0bZ6JcaRDNq3Tum6jfPcSAXc011DKUBGAVimQFQOO3HeuKVXk82dapcpktSskIEuwRlWVD/Dncfpjue9V+rXcdlFiTK3LeWK324aQEevPA/Lmu/aXQoPCiHTsMcW7DNBqV8pz/AA95j9Pavj3U621n1bqMenxGG2STEMZZnKKCcDLksfuTmppYhVXZJkVKEqau2bEnaW1uElMYbI8TA/U575qBqoG19sjL22HHoPU/elrOzSAIyKmS5yvd/SpGq754zcTPIxZcK7fc8YrRZSKN3ic2ygysxyCxz9MVt2gxKwGFbOUzj8xWBGAjYUZbnPIFex4kQCRwBg4DE5P2rpOcvdB1aKxxa3pfwOyOo3bD9R7V0C39jIwktrmG6UBtw8Ucex4O7jOeRXFJCSLUnd55V/H9/Svv2sapc2ZdRbabeIGAAvNLtpiO+SGdCfyz6Vy1q/JNK17nTSo8om72sfJrjU7G1z4t2k0inhI5Nzn17f51z9xdPeyySyghVG1UznYPb6n3r611Xr15N0TrNj8lpFvFLEm/5PSYbZiQ6kDcgGPfivkscDujNgsQAGA/mamlW5WLdrFZ0uSla9yPvKxmOKRgGH4T6n+tRZEKk+bByMgDAqdKxAOAMoQTx/jUJyZGIXG08gnj+ddETJmQKKFyviuCAnoB/r2qbERIyiMYmGfMHPpzUDwiAgZdrjnI54qVbkMfI/nHmPHf6fWokEdlpWt216VF5PFaSiTY3jy7FkIAJw3oeRV5dWO7EllLazKw5/fggfUHt6e9RvhmRa3GsMvgyMUUHfCso8xXIw6ken+VdbqV6YXj8CGxyzsGxp9uc84HGzjt+dcdTE7Etmx1Qw+3G9zgNS1SG3yJ5opHP4YopA7N7biMgVRTzeNKZ5wFmxhuO/HC/b2+ldR8Sbx7mfQ2EcFuzW8ryfLwLCrtuUZKqAOwFcfA2H2lmcBThmxjPtj1rWMuUgpGUobEnE0KGUl3LhieNhxUS4PiyHeCMHII7kVPkARywU7Rz37VFYACQHIds885raJkyGreHxyMjHft+dXei6zLpNwts6me0mzmPOCpxncvp9xVPEqbsSFs9iQMkevI/wBd6ysyWvrUn3IA/ujBq7Ko+hxXmnXMO+1u3AYeWOSJ1PHcbiNpxz2Peo0uoR21vLIrT3SxABhFExVckYy+Nq+3fNfQOnr27sektBS0vbi0VLMbhFK8fq55wee/86r+veotYvOitRtb3Wb65tjLat4M907LkSDHBOOK85YpupsW421/o7nhkobV+B8yN9Le3Imu1VFQ7VXH4RnkD/H3rUVXEjKCQAxT070tPxMmMB8nOO1b52KEQxjBOBwe3FdXE50siE0m8GLcijbzvYY7f6/Wq/w2B8irt24z7fn6VYy2nlVwVDMDj+8Vx3/OoLKQyhUBPp5u5rWJm7mIRo4+eVxw2eM19C6Sste1S1EOjaBqGvWlrbRyTPp0Bmkh3ZwGUckcHt2xXz9twL7CyHGeBwK+rdIWZi6XsZmXzXLeJCduCAoKntz61hXqOlHaRtRpqpKzNcy3FsfDvtOvraVW/wDb6bcRlf8AvZTiqPUdblswBBBcQ3MmRHNNbvGoGcEgMAW+2PavoVn1dqlmFh+fuGhHBjaQsh+4zXGfE7V5NavdIlui7SxWjrlzuym/OPsOf1rlp4mVSWy0dM8NGEdq5yUKqp8RmZ3Mh3M/AJ7H/WalEQLcSLISrDB3IOAfp2z+VQ7LBZI2YiNgTj0DZ4/Kp1uYzGzsnGDlfUKCQcH29a1lqVRpuLaREuZoZvI8ZVfLztI7H6HnFfWLdd1lGmRk26BSO6nf3r5jqGoG20KQRlCZ8Khxu5JwftwD+dfUEmeG3t2gYJMkSlSDyCGODXl467prxPQwllN+BEc7bmRVcMDuIIYMp+ufaqy5STw84JCk/WrKZn8ZpHw0hQu20nBP+HNVOoRB18XcVY/xKcHnuOK4KO8jrq6Mirj5dVI5Pbn0xXy3rnP+015kYO2PI/8AAtfUUBECZJLY5Pv9a+X9d5PU10T3KRZ/+1rX0eB6zy9jwMd1S8fc5ylKV7Z4gpSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAXujHEbE98jFdzpI8RUGM5Vc88VwWknKEfyzXe6WNjRDIPA49TzxXg47Vn0WD3Udbp05iRjtZt0RSRTkcEgZGPaun0SYrq0ZBI/3Z1OT/egIzXI6cFdZQy5AiO0fUEYrqtJGL22ITvCR9R5GH5185USue9TeRSfEeJ06dmUAxsrQFd/GM9u33rhIY2R3eJgHIBCxYYE+td/8RC0ug3ewK7AQABvXBFfOJWnXKxusZ4xsOVP0BPp9a9nD50vnceTXyqfO8yuXdreRpA0DFSDyQuR24qoujJbFghDhRkr3ByO1S57meeN/ExKo/8AZDGB+v2zVZ54nc7QSSDtB24HtXdCJyydz6jppCWFopG1xZgYPORgVEibwNTRhkMJ42/+SX/OplhlbCwcjww9nF2GATtGT9jiq7P++qRnmVBnt/EK8t7x2LQ4rryD5Xqq9hTsHcgH1yxNUaKC3I2dycEn+VdV8TIj/tlPtbb5BztzmubSBWUtHyec8ete7Sf+qPgeTUX+xmPgOAWRQSRwR6VNBJUlQRJ/wjAzXkasAXPlBH4SMnmtjt5ioUuMlhjjIqzdyiRDmBIDNkEktuz/AFqZ0jluqNNwCuJD5vbg1CnG3PintyMc4HoKndLZHVWn+gLkn/4T2pLcfgyY7yPrN6pckEAnH8/8KwggEtvqa8ZaDdwO+HQ/4VlM6iQgKGTOfL6ZHNbtEG/5pNpw8DHjt3B/wrwj1uB8MuY2+du8LuHitzntWUMYwO+VBIYcjP29K2zQu+oXywqWImJOPatsGUZuQN/II9696+R49szwL4auMbtxGQcAfp/jW7x+WZVPhhecDHP1pPEpLBiPLjt/EMVrk27eGDgHuB2/I1GpOhpvHZxuZVBPcjj8673o8AdMWnfmSYd+D5j2r5+XUloxj/iJ/rX0TpQBOl7EEcJJKvPr5yc/zrlxfVLx9zpw3WeXsX9l5JO4BzwQO1cn1eTa6neDzRscsowckMQQe3aussyS+1juy3tkCuT65iKa2v4l8W2DAA89+4/SuLD750130TipInZy7NuV2JJC4wf868VDIwAbPPAJ4xUzDKTuDMe+TXpjRpBk+EcjLZ/wr19o82xoZQqgruyMenf6VhLICUZGBJG0/SpDwldoWTyFst2wa0y4LuAWLjGNvGP86XuDRdlUjWIjBJydy+Yjivrd7u8YKXKsMcn7etfILpg7qzEnzj17jPrX1y+lHjMAjAfavPxv6fP8Hfg/1eX5NcDFJvMUyEYoAPTHvjmuBK7o7xWIBO1QMA84rvdPbxLvwsEFsnJyMCuBZpZLq9t4tq+E+4jaMnB9azwq1LYnh5lQYGR1A8xP9a8wCRnAOOee1SpFw4d2Ab7+9aREolO8OQRwQa9dM8xosbUtBEpCbN3lyTxzWasQVyThSRy2MV6ihbcs7AIvHlIBP+NR3ILqGGWbKqD6ff3rPUtoX3Rixy9TwK/lQ29xjAOc+GcH65NdTexYiAYZw7BRtIyePX8q5Do5iOpbUx7pGWKZQB7beT9OM12l+cRiSNnBZn3bvwDJxx659z+leVi+tXh7np4TOk/H2IN0xk6fv2lcbhNgbucLsf0HcVTamgENuqQiNGjXbKRjevvyTV9cMkXTGpmPc8YcOcMFJG1x3xxya5+9jxbp4TKU8FAjA8DHpn6VehoVralCzNIQzgMAMA85NeojbFVE3MvpjJzUuKHZJudc47AfQV6IvCktxty83IycjjuP+Vd+0cGyXdpvgtUljAQlg2/OSuT6Z4/KoV5GDJjOzksfLg/kParfw0+UZJRF4QJxlW2n125+veqieRYnymFkVBtJB5z7Y78VzxzZ0SyR0/QGyNL+NhvjS4SQvkYzt/Dzz6g+1S53GEywPqV75/1mo3Qs7rp19GhBPzaFmPBwUxUt43OFiV3Phbtv0Aya86v1rO+j1aKPXLdZdPsEHObmRdx4PYH/ADzUO8UGwKlG3u4MbADnAOSfrVh1DGRpdggUtm9kGAcZBjzj7VDmybNYmcuxQBY1AO09+SCfT9a6qW4jmqrpsoYx+J1ydwHJPY1IjgDtK25lbnJ789v0rYkKo7CQBz+LgcfnW5f3ZlYJh2AAZuAPrXZc5lEs4AEs45FieUBcIrjKgewPc+taOnl3dVaWpGD4jHB4AGParLYwtZXZg06RBlkc5BIxlc/nUHp1vH6n05lJKBpWx7EKcCsL9GXgza1pR8jvLlMtMg24UsQC3GMGqnq846W6kH8W0cjBIIdP+dXFzLi5YplS2Mk8ljt5/nVR1q+3pvqVGwf3akcHJ8wGftxXnU9+Piv5O6puS8H/AAfMrKQwldoMh2jKjsD9a26oTKqE4I4IIOTz3H/KtNntjkUuqeYDDA/4VaalGSgSRfBYYByQRkexr2NJHlLOJzsmEQjO3DYZWGO1SLUyK4CqMFvUd/zNavCDMefoozwTVhAoXa0i7gcDOMDjvWreRklmdK42WTfMKcKMKW4IfuSMfT1rqOk7gnptSqPHH80WO4dmDc8/Y9/XNcmZrcacd/DkBFAfGDjPGRx9a6vpby9NJGXVoPGchSTkBucg45yAK8ytueZ6NLf8iZLvMx8UHG4jBOQPTj1718y6tXb1NqL5ADSbTkcj86+lSFnkbzAnG4grk5z3r5n1qAepNUKNu3y88c4B4P51XB9Y/D2JxW4vEroFHlMGVIbJL5U59MY57VauviRzL4RVVPdvxAt6Y+1VKeJC5YZBJGW4/wCtWq2qrZhy7sJX4QA9vU59q9CWRwxOcaNSuHVic4xux296xRVEi4BTBAAra0YLOyhgpyF3jByDW6G23xPucIinO7Gf0re9jGxY2cCyLYeVFIuUIG4DOWHp7H6V9k1WNXe4ZgMr6jvyeT/r3r4xp8cUl3pqxxSbUuoxuxlmG4dvb7V9l1ZyfGw3LLyy85avKxWqPTw2j8jl+qVA6Z1hmAIMaev/ABr/AJ185G+LByFD9iT2NfQ+rMf7Iaq34f3UeT2Od6180WTdgvggJ5R/d/zrbCr/AFvx/CMcTvrw/LN7QnDxuschXnIG7HvUOWHjbnG/6Y5qYrgSKIwfqD68dqxOTucjcVHGR3zx2Fdidjktcr9oVgCCNv6ipdmu25UsShznd3x9cD+lZtFkMzKCeVwBz/0rK3VhJGwTKoQG3AA4z71Ld0EszvPh47M+qNKAr7lDY9SG/wBc10mq5M6sfMeO/HqeAe1cr8NWV49SYBipmUc88Y+n0rqr8BZBjcI1A25BA9c15FbrGerR6tHE/EAMbnSFG0n5eTGRnPnFc5A4OCcqVAxwPrnNdH8QCy3Ok4JI+Wkyc/8AGP8AKuYjbDg7ioxjJ9OK9Cj1S+cThrda/nAyMargqqq5bzbTyaiyxlG25dh7ngmpe4qW8o77gQOD+dZTedcRM6gHu3PHrWqdjGxXRxlNuyQGaQEj6fStVlEw1G2JBDMx9OexqxWEOGIAGDxn+Zryxh36zZAZIaQA9sY7Gr7WTK2zR9osoymkWUQZVKQ7Tjg42A85+9c91oH/ANlL4ErhZYCSR/8APB+tdHu2WsW7y42qM+g2Y/wrmutCH6Ruw2M+Jb4z6fvByK8Wn1kfFfyevPq34Hz1J2jVmDeCXGBtHP5Cs12ygb/OoPcAjdj1x9ajBdzBwMnBz9PbFbo3BcBC3dQBnnI7167R5RsAXw5MMd5AKBR5fvmq9w8obfuAXAzmrFFBV8hgCCQFbA+n5VoA8XliCVGHUcffFSnYNEB2BBOc8YOO4wOa+xdPxiHpDQFXH7y3kc8DuWIz3/4R7V8mnhPhnefQruA8o4/wr6/p9qLXpbp0rgo9nvEgzh+WBI449Dg1yYt3gjpwqtNkR+Z/KmCuSccelcn1qGN1poRtrfLMMn/v/wAq6oj/AHiPd5sgkYOfSuU60ZVv7BTnm0IJB5A3HP5cVx4frEdlbcZS2iRqyiUN5ck85DcH/EirJZlZY+Y0YeQMX285449cjjP0qntZMMWj7n0x7dufzqfFLIjl0VN3YBVUj7evvXfJXZxxZKuvLZyTSPHuP8JT0P6CvprOojhRxu2Iq7f72524z+VfOpwf2fMu7fmPHnwRkD8XPY+wFfRmXxYHK5/dLGGwPd2/y/lXlYzcR6WF3mZauQ91Iq20UJiHhlYhwSoAJ/ka5+8bbZKoyGPI4wMYq0kmDmQyMMsCSwx3PqKq7lVWAFlzKvGGPI4GK4KCtZHTVd7sixKGiRlbPqPX07V8u68Oep7v/uxf/S1r6jbsxjViQBt9/pXyzrg56kuec+SLn/8AVrX0eB6zy9jwcd1S8fc52lKV7Z4gpSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAXGk9q73T/K3sNo5z257iuB0rAQfc13NpIWkQFsEYxk9uf6V4eNV2fQYPdR1ukeHJK6SnG+FgCQThj68H6V0ujSZu7N+BiFhk8cYb6/lXL6QweUEMF2x5x/o/Wuk0MhZ7BgO8cgLHnkZ78896+bqanv09Cu66Zl6fu2WTBVUII9MMP8APFfNDlrd1iCtGsh538cDnHqO/wCtfT+uudAvASJAI1J3DOfOuf8AGvmssSEhltomCNuDn6HGMfevZw3Vnk4hdMr54IiFbxGWQoMrg+XPb7nio0wC8MAPxbiOSeODU278RbuQsqMM5GD2yKiSn975h5mPJYA4rvicrPp2lsw0WyBcHbBEdjP/AMPfB9P+VVZybtcBiPGXyk/Ue9WWleTSdPckhvllw4xk8D/WKgABrwIAGUOpGOD3BrzHvHWtCj+JsSydYsXIIMIzk45BP8+K5+KF5EZEKoeCA/4GH3FdL8SY93VqEglTbbj7/iNUKRLKnhh1TAzsHfBNenSf+uJwVF02R2hlwUcqj/xYBGPtWliqRlCArDsBkYGfWr6WB1iUqGCqo2eTAx/jzVNcxMZHbaUZh5TxhwPWtYyuZSjYr7liAAML9c8VYdInHU+mHv5z3/7pqtnLZAKsWI7nt9hVn0qpHU+nDaAPFOAD9DWs9x+DKR30fVZxy+MZEYPIyf8AWaldLIXuZECjzwMvLbR2rROoLMSOCo/MfX9K2dOcS85I2ZI/OvDZ6yPklxEP2rqBOVCTEHPA7+vvWyFEMm6NiMMB5vTmt19DIdc1bKqYluHLZB8vm/pWRSMFiADhQVIbge/5V6ieSPPtmeanYKSslspA2g7c8qPc/wBKqAiuMMN7ezcYPtXWrGJrEeZFdSV8TJ5DYO7H0+lU97aYgMjDtnbJj8R9+e9WhPgyJx4ooJm8NiQqnjnHp9q+j9JsD0rakErvebH/AMZr5uXVgzHPifQcCvovS2P9k7H6STZH/jrPF9WvH3L4bffh7HQ2S+cDBPm7gVy3xCCjWbMtwGtBkg8gbj2rqbAjfkcYPH0Nc78Q4kOtaVuVifkuzcc7jn8q4sPvnVX3DlGXew3LnaRtMjYJHsK9BL/uwoQEZYn0qRumfaGRmVV3BSOB/kO9YOqNhJIzuzv3IoJH+dehc4bHk1o4i3R5cADzADtjOKqpjIoV93uME5q7iRpZxsfgADHIyQOPoO386hXlputfEYeYcEKOQ3sf86tF2ZDRSOuZF7YLADA9c19cu3AuSdpOcg85/PmvkpXzouCNzKCc/UV9auJM3AD4bk5z6feuTG/p8/wdmD/V5fk16c+b6MnOAexOO1cDeQsmtawhbZtmYN5iCPN6139gd08RRNpJzj9K4bVt8HUWsHeoUXbghl3ZOe9UwurJxOiNAtg0ygRgoR6jPP0rWLYTTDw2ZQrHOPU++DUgbyMJklTy4Y4rTIvGDuZg27LcE13ps4WizgtfmrXw9vnjXJLHBx7mqpjtP7wcnO0/WrHTgzSLk+VSDIdpx696X9pv3zkZAO3jHH/KoUrOzJcbxujf0S2zqWHZ5n+UuAe/qmK6+8CZhVNpbcyvtHPB4ri+jRs6nhw+7FvOQSvrsrsZnAmyewY+vevPxnW+XuehhOr8/YynVZOl9V8XlfLkbeSPN6+tczG0QhaRCRFKobuQ32H0rrZFP+zOrZGMQKSFHYZb09a5LY88R2qY4QBh2/1x6frShusisukjyG3LRzHwS2ApB9Bz6Z9frWz5OTxS+JIirDwyYiAx+h+1bIFOza7sY1/tEL8fdf8AL6Vl4YjVDLudBn8XfGcjAycf8q6No57Ei1ie4j8ISBS2fK6kFiPb0/Wq/VrbwnxE2HUDOedv0zVjEsiJsJYR42kLITj17Z+vpUlreF9NMkh8TzFACM4GB3B5Bz61VSs7lnG6sSuhAY7W84cbrlFIVRgjb/X6VYXxZ5FdJFGVXgLjt9eeah9HR7rS9jKeX5xQQO5AQmrC9A8SFMbSIV47ntx+XeuCv1jO2j1aK7XY9mk6f+6Mub5lKZPJMfrnuPWqqRQZY7S1h+ZLoI0iiUuzkZ4UDnOK+idNaXa6xc6WmoW4vYobuSfwmO1HYQgAN7gbu3qa+q6X8OtC07raK81sLoMDWZFuNLdEack+Zc4wuzaM4GTuHOK0hUUY2KTg5Suj8xSWsy3QtTDcC8AC/LPCRJwORsxnGD7Vf9N9Gy9UvcG8vBpUNs2xzLES5fvtKnGMDHf3AxX6j6Su9P03q7VNE6fgh6k1W6h8Rr27hj8WO1IG2NpDgbUOcleCXGRmoV/8M7Sa5vbrXNTvNI1VyVjtbQRvFEuTtLsQTIQMA4I449M1flm+4pyR+a4ek9RbqRNA023S+u5iPBVRtVkxu8Qlj5MAHPOBirK56G1ro/qzT5taFnNDIk2JrK6E0aPsyVPClTjkZGDjg13mmW9toF1+10db/X/FbTrhbWQsXDNsKRRk+6jvz3yar+qYtTh12yiu7Ux286zEyiUPhlQkAgdjwamU3ZruIjFXXic9cAmQ4kxwBke1V3WKO2j6+SpHiRo67iMf2qjJ+tWcpHjAIcAyBTu9Bk1WdZKE6f1sLHtUxAfgx2lXkH9a5Ib8fFHTPcfgz53p8Jme4dmwAAVAZQw/I+n2qyvII4wfmU3yCMFdrYKnGTn61rtFis7El42kzw5VecDPOawljIdWulcA5Dc85I4zXqXuzzkrIim2ba4df3hwQD5c+tbIkEhBkz5m4Y4/TmspYQYwn4mzkAscjjGa1NB5lbapxyzZ7EDGcetaXK2sW8E0UlsbJgu44/FgqeDgfmPauv0IxpoixRyu0yyKZR4ZCqDkIAxOTwDngAelcLZK6/2RUqACCeCSPb2ru9EWL9hp4UkTytO5lCjLKTnByRgg845PY9q4q+UTqo5yM9xFygB8rls89+D61806wcL1Nfj8O1wPqePWvpX45kBGQV43cge9fMusTt6q1IKA37wZIHc80wfWPwGK3F4kK3VnfwUVWYn+05yPzq7uZAIYtyCMkYV5fOTweAMgKDitFlDFLZqF2l9uHbtlvr9PStl5LlnhSOSUjhuwAO0cY9RntXY3dnLFWRVyWzEsF86sM4xnB/61i8RPkO2Pcv4icD9KkoPECsikKmM4OMD/ADoYHhjzEhJflh3I9+T6/ar3KWM9DbwNb0RPGkhzeI3iRqxIAzjIBBPt9ia+u6nwHP8AEqDcp4wOw+/evkvT9vM/UOj/ACquj/PKGYeg5z/LIr65q6NGshKZxGOSecZ9segGc1wYreR2YbdZyfVf/qlq4GCPDi79vxg1823FNr5DJjlT3Ir6H1Tn/ZPWRgnKxj/5Na+Zw5diWYfhw2faujCL/W/H8IxxO+vD8smKnL+YlAvlKjOM+9TYgkOxQnflC3Yt7k+tbIbBBK6uxdg+3aD2TuM1peRbfK5kQcbWXs3vx/St73MbW1EaMssnjMw2ggkEE5/wrHbsmVZdoLAj24x2P3rdGk0y+LN4auBzlfStSpI5LFzjeAF2gfz9ai4sdb8LhizvyVYf7yQBxxhBx9MZrsNQYu6rnOzsDk/rXI/DA7rW93Dk3LDBXnOxQQfoK6rUM+MAGDbeCw9/+mK86v1rO+j1aOH6/AF1pJ7D5aX04I3/ANa5eEJLufHAI59AT/X1rqviAp8XSZApWPwZULemd+cfeuTKRoUiRsAY3nHIP2r0KHVL5xOKt1jN8LSMF8pPJOMdx9anJEI4mdvDDswAByeMd8/ljFZW9tACnhFi5wuGcA8/9KtDaTXMIdgpjWPcqNJgpnspHfJ5P50lIRic3MGXIdcFlzk9wP8AnipGhoD1Ho5CF0Nyuecc81suUZD5htYIFyy+mDx981u0FCep+nhOiBROCuOFKYJx9+9S30X4FUukj6pIHEJLgPtcMMEHK5xnOfTNc11SzDpC8C53CSLB/wD1o/wrochoioIXDMD6g5A/yx+Vcz1UvidI3wxko8IAz2zIteXT6yPij0p7j8GfPWODgELjn7/StsT7NxYZz3GO2a1h1BC79qnkD24/rW61ga4lwMx5LYPf09a9hnkokxltjIqp4W3ghPXv/nWa2M7xid41SNyQGZjjtzz6/kDUyztFAwx8RkGPDY47dx+We4962X8JgnHCFlRAEz2U/Xv/ADrHazsbqOV2VM9uYVkzOu5VO4MOORz+dfUYN8XS3T4LKyjTlKqDznnOf0H+jXy+9jz8w5yqGLcW784Pr6/lX1iZNugaCqseNLixk8r5fwkY45J/WubEvoo3w+8ymfDmMkFCysCcdq5nrBibuwckB0tzyV7kufWumYByoY4AYqB6muX6wOLuxAVyflcjGMY3mufD9YjorbjKWHkeGAc8EEt7VsgC5JJU47tg8fT/AJ1XgMMgozbyRuY8AVaWUsaIr3WOfKufxMSccfX2r0ZLI4o5kiTfJZ3O8jasQK/x+uMgH6H0r6kuY7a8dQwysJU9xw75/WvnupJHDZzsrbPKNhXkhsgbhjtn2rv2KmC4B/EI1I9cfvWz9q8fGO8F4+x6eFVpPw9zRcY2SuP7LdsBK8H1GD2PBqquQZLQnnhlOPyqWSwjyrMAQpKq2AefaoE4PyT7jyoJx7jmuSlkzeoR7Qb7dd3PGAe2a+Xdcf8ArLdf9yLP/wBrWvp8BLW0e0jJBzx9D/yr5h1x/wCs13n+7H/8otfQ4HrPL2PCxvVefuc7SlK9o8UUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgLfSuUP0Brt7Lb65znA2/euH0zhPyPrXbW7Mr4wCpPmPfOcV4mM1Pfwm6jqtEfE0aLjzYBz9/vXUaGx+bs/MRlpFI5/u965PRjsIbBIAyABwTkYzxXUaAALu1ODv8dg3GeNmfyr5yrqz36WiIvWBEmh3hClgYvwjucMuf6Vwd9a+AC+xE7keX1B713HWEgh0SQqxX90TuK/nmuEvngk2tG7Sbh+8kBx4n1Ir1cPuo8yvbaZTN4zs8hUnxOUYnt9q1NIHkcuuGBAIz/ritpZ5FGWO05TgdgD3FaI2jkeXxCHbj8Qxgg/1r0UcTPpWlYTSrMAji3VRz9OcVWoQLxGJ3fvxzj1yKsdH/AHmkWjZAYwAcNgEEEf6FQipN7HnA3SZGfX0/wrzJbzOxaED4ioW6rt25DC15wcep/wA651LcNKyI0YYnzMOCRj/nXUfELEfU1o7480X+INVNsi3YLRIXdW5DPgnPsa7qcmqaOWavNmyZQoWOJ2FsfKAHycgd8+vPpVbeIF4cpvAKqydhgeuat/BaSfwxLHFcqpfgDJ/4eO+eKptUJkhYngpliCeSwrSGpSayOemB2hmJ3KSPv9as+kif9pdNPC+c54/4T3qsc+RvKjgfXnmrLpXJ6k01sAOZcAZ7+U11y3H4HLHeR9YudxZ3XaAUH4lJBOOxHvya96Z/GxAGBAfy5HavZ0BSVhIreXGNpypB9T25/wAK96dyzyn18EnnHHavDZ6x851CSSHqHXBFuYi6cAdwBn1rCCBVmIGxTIo3BuFx9/QVu1hc9W9QLv2YuHbhM57Zryzt3Eni+GHiY44wTj1B9cV6KfRXgjia6TLCK3aPyXLoPBGCYhkcnggfSqnVZIWXxJS0pkQcYxjBP+dX0rRx+GIY/AMqBDg+Xv3z+n6Vz+sK7SncryDIG5hhe30qIZyJnlE55kzISzDDZ81fRelBu6WsBzjxpgPr56+dElpPJy39419F6TXPS1lyT++l7HnO+rYvq14+5XDb/l7F/ZJlgQBwxxz/ADrn/iGga90to92VtW3Z9Bu4wK6KwJO0Zxg44Oe9U/XCmS60pixCraS5xxgBhkD3+1cNHKaOurunJByqBA/LjJB55x2qN4kiSxssoIJwQQTjH0qYkqgyHwfKMMAw5P1zWnxpEbbENwc5y6DGB/Ou9HEyVZwplDC7NIrEjnCj1JKnBqPqgQWxBc4BKqe289zj6VN3sksMd6pV5d2wANkDHbPtUHVMqmY5VlAO7AHb8vWojnItLKJzRjzNGTnBkHP5jmvrN2VW7IVMYyO/vXywndPEBjHirxn/AIhwK+o3QCXbgKu4ZUhueaxxv6fP8G2E/V5fk26emLmHO8MCNof2P/SuIv7ctr+otIQkfzj7goweDyM+ldnpeWukDEnAUgA8dj/OuWv4F/ad/uVzuvJDkngHI/nWWGdmy+IV7ELY2yWRdqqcsADk8cn+VaGIkkcopVhjOBzmpzrGqxhcor9w34gf8RUVo3JdtzcHOMYwfr713JnG0WFnEsaFWUCfaCAhJZhnnFZ6wSI8oz7HO5kKj8X5VlDOyzRK6HCjkqeRnvz6YzXmr24ijZfFXcMgBWD/AKsOKp+pF3ukbooEdUKW8p+VnP8A8hxXWzA524VmJBYYz6965LoxlHUkWNzf7vOc4wT5R/KuvnQI7BsjBB7evtXHi+t8jrwvV+fsSoIHuemtbWFCzrakqB3ON3+Fc9b+G8aEIMFTtcclWI789/8ApXT2sXi9K9R8eaO2BxjPOTVVp1hGDHJLumyPEjhUZY8cH6VnSdkzSortEMWZMm0hjlz5tnmUYyOPbNZTtuZHRQqjAyoxg+uT9BVg9lMiwv50kZOcjDAZ/U/nj0rGK2PhRxyvhI2eVz6FzgD7nj+tbKRlskWGIK4BkV4NxK7TwBz3Pc9qsLqCO3RBGWITH7wqM8jknPfkCtttYC93yW0kDLEvMayh3kIHZFH1Hvk9q7TqT4cazo+g/O3ptlPhh3tBPvnjGMksNuCwByVB47elVcsyVHI4zpR4flNRZoZWT5hRHumbCts/EduM+2DxW64nHiruX+0iVB6bfL3rR0k7LpmpFWZg9ypGTnI2D0rKRsqWwP7NVG/6Vz1d9m9LcR9E+GGg23UGr6auoa9+w4LaaaaSRIw80/kjVUjDcDBO4k5wB2Oa+5aJd6B0nrd9DqOqQ69LceeK9u4U8QW/YQeqjBDE7QM7hntXxX4P9T6H0zqst1rcMcuopG50+SSMP4SsVErKDnDHaq7vQZ966b4l6hf/ABC1HTV6E0G41eWGOaa7jtLFTGmSoV2PAVmwQMnJ29uDUwg3Z8Cs5pXPpdpp0OodNXcfR+q2mkadpd5Ncxpcb2aSSRCzjxclggz2wQAAPQVDuP8AY26tZJLrUtXlne38K5vfmChtXxlXCqCq9/4gfTvXM9K/FC26U6etdIlsHgubeP8A84C6hClZv/aBweBj8OD6CoOq9fdV/BOGGyvdCWz0XV5ZryCeXe+8SSE+DkZ3OgKqsZIIGBipdJt5Ecqksz6c1hBc6TYWWr9NTaZpMDJNayNbJC25DwVYeZXJ82TgnJz3r5X8ZbDQba+0W60C6eKeV7qKaxeTxFdBbs3ijJyrKQAecEMPWvoV3Z9Qafb2Godb3NleaTPGzahaQ3ePlXB3RqwwC+XAVsHAOe4Oa4v4q9S9HdRaVaXNhZWSa9bShLK4t4QkhDKVkiYgDKshJwexUEVRR6LZZyu0fF//AGgBz5ph9u4qF1ZGH0XV4owqb9iqVYncDKOST2x7mrMKRPGVBGJF7cdv8arusFDdL37SsxDBVIHf8a84981EN9eKLz3X4HI2tgWhlXxoo41BDF3A38en04qDdktMZJQo8y7Tn6d8+vap2lJI8T7GdrfJQ7wpY/Qj0+9RLiMRylGLuAxjbxAOMcHHHb2rvTzOFrI0RxI8+DJtB7MOG+mfzqRLA8EkbSmOSIkllR+QfXPsakK7NEUJKS7gyHAwcfw47/nUaeQhxKTCXlTcTGSQp9Ac9jV7lbEWWOMzFoyzQ7fMHADH3/yr6B08zHQbdRbIiq+RLu5fJP4vTC/TH51xHhkSxneixM2/Z249cZ9PrXeaKhXRIEcl2Z5CSeARk5H/AD+9c9d3gjairSue4BmjILcYAA9Tnvn1r5n1cR/tLfknktnHtya+nxnNxCdxJKgc+/evmfVI39S3oXBJOQOMsOe/tUYTffgTitxeJu0lHufChU4ZwSfNjJ9h79q8vN0ZVAS4UgyM7dgTz+fIGTWekebarqIZ2GFdyMAfT+73717qIz4rQMz3BQ9xuXxAexI45wTXV+o5/wBBDt4ELQglgC7M3OARj3/12q9ZWkMSofFYrhVTGAPt7fWqGHdADI/JXG1sAffC/nW6TUiIljg4m7hl4I49fpj2q71KLIstHVH6i0tVYbFvkIGOTjJPPrivqPUDgWsnZtxG3acYHucntj245r4/ozEaxo2AxRL9EyDgZwfTvX1LVv496sGIXk+g5+lcWJ3kdWH3Wcj1Q4HS+qswJH7vPqfxDFfNIo0VVBIGSMkHmvpfV746T1XY2DiPuMfxj/CvmykB4iiKqn17AH/XrXTherfj+EYYnfXh7nRxXMUgRoyVO3ZIhGfsPTB9frUYhN7xqQ2G2sQ+Mj6+n6VKtvKwk8Ii4Xbt2ncJFPB+1a/BR5J/Lskj2tBEed/PPm+h9KusirzRqd0EYRgSDjcNx4I7CsrgBsld4kGOUPcY9Mdq1G3MijzeGzFhk52g+4z39alPFJJEHXgqNgVRj8PPPrk9/wAqXISOj+GIUWl95ACLqTcM8HATtXTX/ml3E53Lnt37VzPw4YmzvAGO1rli20kZIRCPvXSX6DxSwByueSmMfQf69a4K2dRnbS3EcX18AbnR/DXkW0nP/jrlYgCN6gbT3Y8fTmur68j33Wk7e620mfNg7d/pXKoilcBjweePSvQo9UjhrdYzpNMkmkhiXxASoDRquOT78+oxjmvLxyjlZAfFQEFmJVg/fBx684qLp0jwI6xqpwjJgjJIOB6duDUy7toopZTI22TO6JcHcM8qO/Ydz6+/NUeUjRZxIBKlQJGGQMsxJOT71J0BCnVWgOymU/M/hDYB8p4+ma0yukbK0aBS6jBkAIkHqeO32qRpClurNCMQEgM5AO0gnynBHsKPR+DIWq8UfRCuGkU5IViN3tk4+w7/AM65rqvjpPUlfOfFg7DjPir+tdLPkRTvvRlUsQWY5Jz9vpXP9UnHTWpYYAl4Bn/9YK4ae/HxX8nZPcfg/wCD5kGKYwGBxhif8qnWyDxU5Z1PcbeCMf8AKowfJQE72IAx3xmprqFLpGZAivjg8ivXZ5aR1FnFEiwMshBDKfDjY5b1HOOFPf3NL9lkit3AEY3435zhj6Ed8f51F0tRMQguM+UHKryMfU/p9KsbplKuZTDIcEqqna0mcEenIxg+nauJ5SO1ZxOcuU22VwuPEdY3y3JwuDwPavqepxqmnaaY1KI1jEygDygeGMhckkgHjJOeK+Wapxa3K8w+MrFFYlWchTnB9u9fVdayLPTUOf3djEpUgLjyZ4+/f86yr6LzL0dWc9Eu+bPJZT6exrl+r9q6hp4m4j+VLAgd/Mf8q6uAguwK+Ykn+Vcn1mP/ADpaOQuflcZPp5ieRWWH6w0rbhSKqXTeWNgqtv3MeeOefofapVvmNiyhSwwRuPlB78j0BqJC0cncmM+Xcp5J+tSI42WJ2G1scc8Ej0xzXoPsONF4rrb2s0kbxCY5Hibjx/w5wPft6ivoEQYpKiKRugyDnIYhn4A9uP5V85t7hVs7jZbiafYcuU3LCSB5tnrjA8x5FfQcqbW1kUYYwjJ4773I4/WvIxi6KPTwr6RXv/Y7+ykBct3GD9+Kr7pyY3DDJ2YOec8VMkwbZzH+A4O3IxnJ4FVV0ZTK+EABzge1c1LU0qPI9gwIEK9yD+fFfMOtznqa89OI/wD5Ra+oIP3ADdwTz29K+Xdbf+st5zniP/6Wte/ges8vY8XG9UvH3OfpSle0eKKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQFxpfZex8rcflXYWY/eEKQRs3H7cVx2kkjBAJ4bt9q7HTyrpuGTuibj2IFeLjNT38Juo6bSMgSMqj8Kk8Z/iFdXomTqMIyAgnOQffaa5XSVL4jUbjLGBjHfJNdRoTE6kmc48bHIwRw3P0r5yrqz3qWiKzrtj+wGJ3KTGM7eTyD/Ovn3jvYpslAmhCbeP7uc8n3rveuiB07I0QU4VCMrkfn7964AlVjhkLo5I2khv6V6+H6s8qvvkBnZd2zcgViy4GQM+/5VDkYo6hUySDkjjAqe0JhleSHYI+xUZyfcge1Q5VAZlJwpGeD6e1ehGxyM+naKQ2h6c21QBCm7j6d+/atCqXmVsAqsw/i+vNbNIJfp+xEgddtupXsQ3lBGPbt3rDaFuxgebxe/c+9eXLeZ2x3UR/iHFv12wdV3sfECBhwfw1TWASG5VEYAhsNtBw2OcZ/xxXSdc2S3eq6cfE8Pa82Mg9vJVfNbOUV0fxgqHc0oC+GM4ycZ4+/vXRCXQSMZR6bZolnmht3F1cpO5LFcwBdyenb0rmr68aTxAp8g52g5xVrfXXkjBlDRZ/drGewHG7J9D6VSTqrMXZAjFiPL3+/511U1xZz1JXyK25UKeNozzn61O6W2p1FpjNnd4hyc/Q1DnUncvZee5xUrpkiPqPTWBAAk8uBn0NdL3H4HOt5H12ff4DKQrAtgsPesen1yWYcIUKndSZh8udoPlAxle4P/SveniPHZ0bjZxnt657/AHrxXoeocJrUTf7Y624dtzXBKgHGRgdjW+1Xwt6IY1kLhMEmNgfU+x9qnamBH1drMhuEGydpEgdc7sKOcH6mvbvFzAkt6FikQiYSKjMCh9B3Hcfyrq2rpLuRjs5tkO7n2wNGikNIOA3IOf5Vzd8zIUDbmRTgDPAq2vZXeRnVt7yBsgHaH9sCqd3Lkruyq4Xj39a6Kasc9R3K5XYMSyEBgQM13/SgX/ZWyAI/t5QDnGfNXz64Z95w4CgnsO9fQ+lAf9lbMEZxNMP/AJKoxXVrx9ycPv8Al7F9p3nZdp53ioXWu+N9Fk8QiN4JAy5UqwDjuO49fvxUzTB503ngkHJPYflUXre3aRdLESwxubWXZyMsNw9PX1H3rgpbyOye6ca7LJlHyUwTsCkgr6HNYM0jzpFFEpBwG35wv596li4cYUbSAuMbMbB6flW+zs1VWnkZhIH2nzDg4zn6/wCjXanY5bXMpLprS1eCV2lKHdHmQ+XjsMj8q5i6Jkdhb7ypwSr+b/5KukvERII3c+N5eUKgAH7+9c9OzMjYVsueM8gCr0+0zqEDwVE0ITLfvFOD37ivpd5zezF2AUykheM9+3FfOFz8xFnB/fJknnOGFfRr0x/OXG0MuGOMAcc9vtXPjP0+f4OjCfq8vyZ6OD84u5Sd5AG3j0qg1ON01XVHjQEm6fKgFScnv9av9GEbagN0wiIYlT4W7cQPw/TPbPp9ah6zp/zV3dHcY1edgWVTjA5IPOcduRWNF2ZtVVyhkkRQPEQb0XhgPNg/Y1qQqTGSzeZcHA5B9Me1W8umrJHJLC/ikRZKbcZYnyioto0kHhpbmZcltxKn2/mc12KStkcri75m62gtY9PPixytKXK+VwVX3BPr+Rqs1CVrksqofDIJAXBOexJxVuw8KAFJVZ8cxD1BPJPtjtXP3KOknmUo38QXgE1aGbuVnkrEvo7fF1Ig4XFtOMMcD8NddcsEnmj2n8YClT3/AD/xxXKdJlv9o4G5LeBNnA/4fT611Nww+ddtp/tDyx/rXHis6nkdeFyp+fsdJoFnPfdO9VRQRmeU2SCNE7yOzkBQPqTiuy1P4BdV9N9NPqz/ALL1RoId0tjYzO06gfw8qBJj+6p+2a5Dp7U5tH6Q6w1S1JW7s7K3mjP/ABCXiv1VaQQzdJM1xrLTastuJWaEgQ79m4oD/dz5c9z34rjUnG/zgjpcVL54nwi++CHXsNnpd3dadB4k7rHL4F8jtaq+MvMgx5F5J2k4xj1rtNW+DXRPTljaa1LqeoagNMdbi/gvHjW3uolOGXaADGCcHG45Awe9XOndS6l1o8GhaZdS6Xc3to0vzOp6dNFHbRAZLkMBvGcLhM8t3FRumZOpLPXdVTqrpI/sbT4WU38e66sbibIPdxgKAcjIIz3IxWlzOxp6i03p34l3WmaZ0/HpXT19p88V3JdWlmilLdDhh4SYDksV27sAH19Ksesvh3q2uagLPQ9XtJILiGRpLy98vgv2A2JnfnJIxjGOTVtrP7M6j1ezms4NP6e1rYEmvxZ4eayyN8RCld/mCMhJ8pHsSK1f7FdSRatrC6Pr2kX+nJBFJZSyyNBMzEEPEyHcvsQ+R3xjuQTS4izfA/OF70XfdA3Wo6NqlzFeSqRJHNb5AePaPNj05BqllTZgMwB2ZyOe/IrotZ16/wBevb39rpLFLaXMlskTgB0YAeIGC+u7juewxxXNzsoJIAwEHY5z/rNUk23dl4WUcjo+k26CXV4z8TLXULplt2NgtldSQBSXG9iY/MW7ADtya6PTfihYdB9SanoPw2Ooz6ffzR3ax3YkublpmjCMgKAswUAYBGRuIrjdDu+lrXXrM9Z6U+rO0T/JBZzEqkMN/buTx+Wa+k33Ub6HdrrnwP6SurKdLfwdcn0rTnnEUIO6IyAHcrE7uQckLzkCu6mugviOKq+m/jOag6U1T4qda67e9QvddJsLiJHW8tWhmaRUUpIYnwcMduM+nNdB083VvxP1nUOl+qtdEFhYTR3N1Jcxq0ZmRjsACYZjuGd2Rge9XuiaRD8W+mYuq+oetjpevXMBgt7W4to3tkjjd1VpgGEisTu7NkLjgnivkDaP1zpGlQ9Uah09d2Wm3kvhSXTArAVBKq6jd4gQkcORznPbFaRtLLsMm7Z9p22o2+sDraw6P+InUtxJpmoMZJryG9yWji858I4GGYqBls4yeM1Y/ETp3oG3Ojaj0tqF/wDtO0uDH4NzqJuo543jZXO0jyuMghh3wQfp8ok0a86r13pwXurLpN5eXTR7z5vlIEBdnUE5ZjgjB7kj0r6N1d8OemtEt9N1vRerL2+vbK4AFpPFGscwdSrFQn8QzkHkYBFUqJKOvAvTd3pxRyxl/eqE4UZ7jngD9agdSAHpy/G6IDyAuqnI86+4z2qUv9vHjdySBgEnOB/yqL1IzDpzU9zgFdmWZsqcMvPvzmuCC6SO6W6zkLN2w6TmVVKbS6INwUdgT25/WvHmkijCPHIoKEZfD5JPbJ7Y4zisLBJ7qJEjR2hYn37Zwcff69quby28axR5ZUzbsyRiFRGScY9O5rsbSZzJXRTBZFjEj8xrjLBM4/0K2eHbuHVpV8NxvR0OMKp7EY5Y+9TZoLgajMp2QNJGrHBztxgAcZH/ACrBdJZWLBXlWJGaT0GcZyMcgH37VO0hss0taMbgs65hKk7j5vL3xk/Wus0aQtokBO7+NlXtxk44qntbGQQyBV8MuirsOWOzPB9u/GattJQJokS+Luw7qTtILENzkHt9PtWFR3iawVmZ5Iuc5zt2ggj0OK+cdSrnqXUCvDBxnC9h9K+kFQzjcAPODjGeMfz7V876jXPUV6W9wRj179/X9athd9+BTEbq8SHZTlZUwCY2BBLLknjsPrmrC4iRCLUTKAnn2jv9sfTFQLAIJYw58N1yysTz2yT/ACq+t4zfruLAvw/lfa2MZJUgeoP611zydzmjmrFLbQGY+TcXySQW7ccDHfitMkTQK4kTwkdyBg5IHf0+uK6NtLChbppgqhcHHmZiOwPp9x3qrZZVQPJI+4n8IOF79xjv+dFMlxsYdPBhr2kDb+7F3kbuTuAJ3e4r6bqbjwnQYRtwJwCPp618+6eiWLV7FXkVboS/gEBYOMNnDDgEDn6gGu+1F4yjKAGlDIVYScAYPcfljn2rkxDvJHRQVos5fq2QHpDVH75EXHp/aAVwFvCzzguwQDGSPX144rvOqWA6Q1AnzgGLA/8A1grgkHiO5ZyxIB8p57/6/SurDdW/H8I58Rvrw9y5tpMqm2R4yF2qRlQ3POPt71KnWUXDTKwk2gshz5icckn1/wCdU1oiTKsc7SrHJ/Eh2E4+vpV+qpJiByWijA2lmLNjHr79v61aeTIjmQhLiKSK5YqWAKpt8uQe4H1FeS+HOwcOrZbxE2nG1jwfX64xUz5PE/iROjPhs7s5IA9z3H2rXDY3F5MX2K3iDAYgA5Ayfy9Paq3RazL74eoI7e62jOL2QEkYydiVf3rgznyBTg9jVN8PoVSznUFSfnZg5jP/AAp+tXV8WaQ5IZdoK5B7ZNcdXObOmnlFHEfEB9kujqACPl3ySvI8/oa5tV3jdKTg/hI7muo68Aa70hmI2C2fA/8AEDXMQgN/Au4PxxwO3+dehR6pfOJw1esZY2m7KoqyzPhSyKPKatZg908jwQjCkORuxknvkn3+ntXPo6pIQGcZB2srHNXli6tZFrkuYNwBIOMt2wPXt3+9VmrZl4O+RXag500p4Ecs43KQwHAPqO3P9OKtOnW8LqHSJoHMcy3DkSK+CPI5OCPU4rKeFpokkkO2CTIMe/HmyQB29ua3aPYtaa1pQmc+I8rNIBkeXY2T27AYH61RyWy/Muo9JHcTqsu45RA+4gBsjkn3PJ+tcz1TLu6Zv3DbcmA4z7SL611M6SQPLBIGjZZWiZe5JBHHI9/zrluqRnpa94IUPEpB7/2q/wCVcdPfj4o6Z7j8GfP4EAljyFYZHuM8Z9KlhtjYBUEnLk/0zUSJgnl25OQBj6VNaRXR2Q+Xb3Zc+bFeqzzUWGmzpHN+JWK5z4mBn6CrqIfOKNgQzmRTGxJXy8DDL6kZJJFc3DukClNqlm/GB2Pckj8q6SzljR/EtHjdYgu1JCxIHfOccnOMVzVFxOmDKzqHT5bTT5QwhVWjddxcA8Z83PPPFfReoc7bVVyQLdOMg9kGf5+lcTrUCXWm6pdMDI0qOWaFgRCSOxz9u31ruOpDvmUrkYiUFS2ecc/qefzrkqu6Xn+Dopqzfl+TnLdl3gFcEAdjxiuU63bZqVssedxtBge/nNdXBlpD6eUFSTjtXJdaNjWIGAbmzVc+mfEamH6wVtwqIC65FuuH4/iqdAwbYco24g7cHaPb/pUFFlWNZRGQD5V2rwSPT785/Op0SqFUXO4AJgpjknOe/pXfI5Ymm4uHWK7IYgncFBBXnsc/kcV9XZgtlHszmOBCGUAEZZ+/rXy2/iLWbOJt0fhjLE8g57D1/OvqDSvHDGY2KsIRz3Pdu1ebjM4x+dh3YXKTKwr4kTgk4yBkknFRJotiSH8XuxqXtYpnBOMchfT8q1XiE24LghSCM9hzmuKnkzonoQEbxoopI2GxwTkfUd6+Ydbf+st5xjIj/wDpa19NgiFtawRBiwUbRn0r5j1px1Jd577Yv/pa172B61+H5R4uN6peP4ZQUpSvaPFFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoC10w4AP3rsdKx5dxAARucduK4zT/AOyB57muv0mQMSG7CI9+x4rx8YtT3sJuo6mxkCrbNKoYIORgcjNdVo7sdUgJyGacsM5OBtIH+NcnagfLpzgAZI+me1dLohHz1o0m3KyKMkf4+9fOVeJ7tN6Fd1i6jpl3KgqsaZVuQcNivnbDbEsSKHBOBgcDjvxX0brDxJOm7oTYGI9oyMeUNxXz51GcvujKeUEcADHb6/evXw3V+Z5eI3/I0GIkxhDIHIzuXnGPWoqLjbJkgNlXyMH/AKVsLLgk8ccFe/HvUa4y0W4SELxnB4JruijlPpGitjRLLHb5UKSfseBWtXK3URZjlnGccDIr3SCo6fs+NzCBcMPXvz/yrWmfGQk7R4g5rzJ7zOuOiLbq1RJqWluZioEs/wDGFHBXHPpxxxVPcJGqsYVWWRgxjBYfulzxyMbvtXQdRW0t1e6c0O0rG1wWZvwrkjGfX09K57U2mgRlnKXXyu7aUxnJ9R24xxV6eiRE9WzmbwzSsyx4De7Lkfl6VBZXZF3gDuA2Mce2Kmzbrsq6RyHYuwIDhiOcnA+9QXjIg4zEQdoXGcn6/WvRjocEiBIqN+HcykYGTgnFTOl1YdQ6cRwfFPHOOxqLeSBtkboAyptI7D/rUrpt9nUWm/w4lyc8ehraW4zNbyPq8+3EpVdqtjHOSOBx/WsunyEmKq2F2kFTzu/Otdy37mRh2YBR9cc1noLBZmzwFU8dsf6/wrxmeoVWpRRzdQ6u0ixMsVycuxCvHwpxn1B9qgXVw7QOBGiQlSY/Dypznt3Ixjk/yqZqVqh6m1uRFcXLT5Ro0zjEant6/wDKqq9ml2n5d5Ax/iB8y+bn8/8ACtocDOT1Kq5jKOETw2wwwyKQCOTnmqpkdWbdlQG3KPera6USSRbZ5HB5dmGBux29ar5HUEoygBdzeU8bj2rtickkVstuZJQhy2PNwPSu/wClh/8AWxaDniaYfzrgJGw+Ysp/ECG9T3rvOlnA6YtccYmnyT/3qpierXiWw+/5ex0GnYBhwAcY781n1XGsz6Ruj8N0tGKuJP4dwzkdhknOa16fjdHklVzz/nUjqxSg01pVkQfJs0beJxgsByvcgkflXnQ1O6WhzkKeLdiNMefmQFNwHtg+o47fWtUkDNIiSMYtxPibuwUZ5wfXAxW1LGXcsccB3yIpP7zO0kHjPp71mZ45FlPh4WFCjSbc7zznzep4/nXQmZNFVqU0PgiSGQsuwgFx25/Cff71z0twvyrLISZGO3Hcfcexq11GQXFuTKsm/duJY5AJ57VTFdy+c/vMZUAY/Suymsjjm8yJFH4c1uhcMzTKOQRjzCvpd3n5mZWblZGOO/8AOvnEY/f25GM+KvB9BuFfQbxStxcEgr52IyPXNc2L1j5/g6cLpLy/JJ0Z8agmVHDMFHrnHHpUq4IlvrgGFpm8ciJUCgjAwdxOeMnOKi6Qw+aiAYjL5/D9636ssYkGUjLZYhTzliSCT6ZI9PpXNA6JlcnzC7uCIQwKnZgttzk/kc8VkbgSuIol8w8u4ryR7DtivDIHJVG8NPE2/Lnylj7Z7kVjKrvPvizbSKzFixyucdgOQMD1NbmJBvIImikf5eRHDFPEEgKjnkbR9RnmuduHKsRHncDyFzxV/eIrKu64SJnXLL4Z5xwMketc7PIWlBbIJYLkHA/n3rppnNULHpTK69Hg8tDKoHryveuqulZblgcll4ORy1cr0i23XomdhhYpBkjP8NdTc5N0wL7sg7iQBk49q5MT1nkdeG6vzO4+HUWn3NvqVrrUixafdzWUcwPO5BNvKYPBLBcY+tfftX6s6O0zTILzRNL02xniuUWBkbw9zEnOecNx/ezjg18C+GGmaDrk9xZ9XzzwaTvt7hktpjDJK8e8qgkAyoJ5JHOF4x3r6zqXxA6S+GfVCTdOaVJfWt5YL4k9xMLs28gZgUXxOVDAgkg845rljG9/nBG8p2sdpF1BcfE9/wBl3FzNp6wp4wvo4vmWgwOMAHHm7ZJGfrVpZQahpOgypJ1Datp0LpbRB4i7TxnybyAdoJyx2nIHqea+adG/F2K71jVNN+H2jwq89w11NaadGWZ3KrvfbGCMA8ccAnjvWep/EzpDWNG12x1i1u9I1a43zeO6OHtpUB25BwoUEYZCB3OeamVOTkVjUSRf3Hw76K0yLSh00uoQ6jE8Nu89vcPdzXsJYCSKSOQleRyHUL4ZGRwMVI1rpq1sevVivJrvpvpZ9OjumLXDFzcl/DMSMSUIxhznkdgCDx+fdH6616wRdb094YPlNpY+Z9yuNpO3PmXn/Ku40TrjTvifqS2HxP1iSx0mwIug1o3hLdSH934ZaUHhVJY8Z/CAfWtZUpXuzJVIpWRy/WWhad0t1PqVn09rMmuWUxFykswBmiZ1wySFRtY5G4EY4IzgiuHuVVGKpkZRceue2a7brLS+ndH1+9i6J1Z9X0Ro0lBcpK1vIynMZdQqv+EMOOM4OcVxNwrC4fAw20ZA+wNc8smdMM0VWuRWsl1pTXjImVnETOcDdlP8K+pdE3vxL+GfRM/UHTuk3jdH3krX/wA1bxpO58u0yMiuJFUhPKzDaQM8Dv8AObvpderL/QbKS7NjDtu3aQLuY4EflAP/AHq+i/DrS/iLrME2k9N6nNBpdjG9kt7eXCx26xgFeeCxwDjauc16EJf6Yr+fE4KkXyrfzREnrP4cXN7omo9cWnWej3eoXls2p32mNaLEYXZN7JDMpKs3POQMtnFZ3Hx6fUOkrq1u4452msTFLaSKwlGU2kFCOAO+e3FfP9Ha/wDhd8Q9M07qY2cz6Ve+FOAikXACExMHPdD5W7Djg19c64+JXS/Vl1oMvWegx3l1FqUMhYqgaWNclkkI5eMnaDng8Vo1ayeaMk9Wsi6tfiR0fpnQrdN2WnQroCWISJY7YFkxHxL4pyS5bDbs9/0r4KOlOvLC80HUuq9JubPSVm8zfL7F3mNtpYeh/ln0r6V130/0h15YRQ9AaZJoPUr3MRs7eItDaPN4gPmQ+VQBuPHAxkCtHXfQfXXT76ZedQa9b6xoi3WbpYNQkmKuUYI2xwPLu+55FZt7MXbjfU0SvJX7tDloj+/i4AVWByOO521F1/fD01qxwyOoUguuCfOvcDj9PpUhGxIAcknng9sMMmtOvgQdO6sxWGIbFYmKVnziRe5PrXBHeR3y3Wc1GBLHJHbo8s743uXK4GecA4Hb9auF0sLsErokSB5FMZwG4yCcnHfP0q66C+G3VPV90x0nTxatHGsrXGqhrSMBs7NpZdzE4JwAcDvXead8EOsZ/wBoxyx6TYGyYiNZL/cLlj5sREAgqc4y+0A8e9aTlbQrFJ6nzb5dFkkkdI5phFjB9c44BA/P6YrKPQDCWW4jeWNwEBiZPFY8AlW3cAZzj9a+99PfC7pa86dtodYl1RNbuEQyPBcKvyspH4FTBU7MkZOc4PYVlonwc0Pp3S5T1682papclwGtrsxwom8lMcZZyuCc8ZOADjJzu7F8rnw5LRrdZ0lEV49yhiuEjbyEDG18nkHkH24xWoRrDZRpEfLlss2TkhjyT/rvX1rpL4V6FrQ1a76i126m0qDU7mzsRZOim4jiwu6RipAGfKFUd0J9hXAdWaHbdMavfaNY3cuo2tqUaGe4UByjKHAbHBYbsEjGcZwKq78Sbrgc8MrMvoCwxz7Hk/1r551GjNrl/wCEA43qSM/fufWvocbF7qMYCkkdzkc++e1fPtcjdtevvDdg7EbTvJG7147V04XeZzYjdRrgRZFlEahjwG53ORjP6Vb6QVluHUEeCUCsd2ctzhQD/wAsVTPmBFK4QgE7h3z7mrbTbeaWTKxBMASSAqCFP4efvn75NdctDnjqWuoWaW6ExxoIvCyVYlGY+xTPJA9RxVeIHkKypDHdCVgo2yACNgBlSvoVz396tIGkuXZUfcC5Vkj8+0+nmPcYz37YrRHYrYIbS3laYI0jF1hHJIJ598479+Oa572Rva7NVtbvJrOlTuRuSYCVMEYXa3APqc/rXR6s0ibF5VF82CMAnv6fiqvtrJF1WzVVIaKbfIc52ttbv/T71banHnbkqVLE5C49OTmsKju0awVkcl1UM9J3wXIO6Mj6+cVw0cbgkoMoeJFIwPfuK+g9Txn/AGXuiMjM0PYZ/jHYVxsUipvklXG1sDC8fcj9a7MO7U34+xy11014e5ptgUniEhwmMbcjJ+wroLcrII8XTRInlZJXwC5HI4HaqVPMVDxowIyhH4lx6j/XpVnaJL8uGKqqsdshbjcx/meK0nmZwyL1rKNLZ5LhricGX92iOuxcjj0JwDngcc81HtpBLEp2nbK4i+X3tlMk9j+fatcM6G1XL3KuuWLzOxDZPYp2z6VZWlybIxyHdl5MBFYZR8ZwRjg/bgVyu6R1KzJPRhjaG4ESrsF3MO/C8JgVNu1/fgZ42r/0rR03IhF7Iq+GGvZ90YOApAj4/n6VuvsmY5JydobjPO3tWMtS8dDkOvmCzaSXx/6M/YD+/XNg4XEC5ORh/wDXY810vXDf73pZPB+VcHOORu9K5uInwQJSW8+7HAwMetejS6tHDV6xmYjTEuxS3ON55x+dW1qsxjEbYm8NhgSttCjHoPpz96ro0cktFtXBy25xxx3NSrSJLiVPEkU+IMOc5Vsev61MtBHUvpIllVhFGnhxgF/CP4D64OeT68fXvVlpcanWbUBWWUXJEUSsxZ1SJvPz/lj3qFF4skUqTL8y8C7jGrDyewyPYe3FS7G5sbnVNLDrLFKbjynw8hgEc7Tg8EgnGfX1FcjvY6kX0753nlmEhYk5JLHk/wA6oOp8jpvU/EVnUSRcKQP/AGi10MzK88hVSsRnLKhXBC5OAQPp7VznVH/q1qRKAkPCSMcD94MVjDfXii8tx+DOAi2xhhlm3Y2nb2+hqS4H7sbwRgZAHcHv6/6zUVGwxwSgPckDb+lb43Bzv3E4AyR2FeqzzkS4UWTJjHnCnnbz2xjFWOnNJCSqs67RwhwxUA85z/jVZFcyrGqxMYwFPZjgZP8AOrC1IQi4mP7vIO7HBP6HJ+9Yz0N4Fpdho9IvIZE32/g78ODjkHjgYBycjNdn1DnxyDwoVccemO9ceZ1fTpol3qsDyy7SMsEGBnHA/X3rr9cJadQ3m2xL3OceorgqcDrgUUY2sSfUFeT65rker5iupwFZMH5Zcr/e8zf4110aAvn2I+veuN6wG/VoFKlR8sjA9iPMfWrYfrCK24VbSxT2MNuJJUZLh2yvAG7aM5z3yMYxUnwVgA8ZpW2chQ5APfvwc1CjhYRBwuOd+5hn9MGpcTu8OGbv+D3Uetd7ORGFzcy+CwDYBUBRgEe2PvX1d0WWMiU4AjXHH/E3/Xivll48ItMEPvKqFUMAqsSOc+uPb619PYr4EhBYYVcDggjc/wBePSvNxm7H52HbhdWRVRVzu2ZUr2PGMfWod6waNcKACeP59qkyyvGXVSSVYFQPbFVd1dyt5Xi4Hc5riprM6KjyPGBd0wfKOMfUGvl/Wef9o7vPfZF/9LWvp7ksiAEBiu44754r5j1v/wCs17/3Yv8A6Wte7gesfh7Hj43ql4+5z9KUr2jxRSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAs9P/sx9zXXaV5WHlOCme1chp/4Pzrr9NPKEc4Tgds15GL4nu4TdR1ViJJI0SMksYyOO2M5Oc10GjcX1qi7c/MDP25FUGm4Lxlcf2BPbknNX+jMy6lb+ThZl5A9/WvnKnE92nwInWTmTp28IEhC25UB274Pp9K+eKplSLxNyjAfdjPavo3VzKNBuSu4MIDuyc859PYV84OY4gkruy4AJIHb8q9XC9X5nm4jfI0nmbDL4eTkqVwD+VQP3eApc7uc+o+gqeXlkXzE4VTgnzcVEZkyxGQCMAY7ivQich9E0Vg2h2AXzH5dfz7/AF71goPjJgcbxgkd+1bdDYnQLDYQFNsMjA7c8Vr4FyoTdwVPNeZLefidcd1HWX5j8MiVpRneIzGwADE9zxyK4jUJHgjWMKU3Aszd+Mng+xrrda16fQpbP5O3+Zlv/Ft1DfhQkAlvrgc4rlLmSNneNmSRnXDqq/hwM5Ptn1zWlNWzKyd8ijunLErC7SSAAEk4AB+tVbALIxkzhe43YIb3q3niUbnXcqtyo75Hpz/jVRdcTMHHGBk5rugccyDdSpLM7kyEnk5963dPuw6g08yZyJTzj6Go8oAyYnYAnG4j+VT9ALw67p0hGcSZBU4I4PrW8t1+Bit5H1K6yY1G/duzkE8Z+grZ0/PJFeNLE4SaPLIQoPI9cYxWi4bMeQhV1Pc55GBz9K3aFxPcMcEqrfiH5V4r0PURCu7qez6i1sJ5o2uU8UbsY/drzkYIP596pwyzM/yzqF3OQmeUAPYA8n3z39KstQ8aPqLXGjYxlbgZQrkNiNef51XTRRnxZreAyd1M3hsBnuRkcBj7H2raPsUZTzxRKjCYlJixOc9gPXH+u9VVwF8R9xDFQCCDjA+3rVzdqLmMSKw7ESM+e/oAPWqKaOWJzkeVeOec12wOSZBuMRooTOwtkYHHNd30sxHTFsSFGZpxjH/FXETMgdsR+IF5IxjFdv00VHTFuABt8ebbnsOarier8yaG/wCR0Wn+WSIc8dj9c1s6oZmfTPDiacx27h1Y+TGV5z7+uKwsP7ZcA+prLqxEabSlkZGb5eQIG5GAV4GPvXnx1O56FKJ+JVUqWXyodpRlHbA5/wCdafmSodBGJIwec8t3xnHY962NcSeI7TJ4SkeZC6kLgfhIPqO+a1FRcJ+6KIgXaGyAWA5zg9/v9q2RkyovYPEsyLeKRPMfF3EEjHbIHI/pVRPGoTcpGCPLk+vtj0rp5Q6RtiIyGMDIVgMAjgk/WucvXaNB2R84YKvABrrpu5yzXErF4u7YsAT4qljnjuK+hXQHjyYOcE5OK+fJvN3b+INoaRceXv5hXeylPmJywz5yKwxesTfC6S8ifoyt85FlcAEeYfett5va5lHhLNFuaNSeNvmzgg/X1rVorKbuMqeSwJHtSVHmurwpmGISsJRG/mkG7g7f8eK5oHRLUjBTbpHNPEkLBGC48z5JOcn0NZi4lm8Lx8tIE3RqzDG0DuR7n9TWM9xbs7llifgFt64+mR7fnUTw3uGiQokIRgR4YOOfU1sszJ5Ee6ikktgqqyB8M4VO4/r/AJVysixrI5fzGM4C+g4wa6u7jZTBHEQZAPNyGGAfcduKodXhIn3bEEcreV0bOT7EeldVN8DmqIl9LN/56iI4YQScDGfw10l06+K/lyB2Peub6WVRrqPzt8KQN5fTbXQOx8Qh87D6j1rkxO/5HTh9zzPoHwx0LT+otUFtreqT6TZwXEN1I9s4SaYKsg8JGbIXJYZJBwAeOa774k2fR3Qms6P1PpVhFr8MjfLHS9WmE8MUg83jLkYzgYIbI/WvjOi6QmtXdpbvr1p06qzGRrq6yVbCEeHgdyc/yrD4i6XcaBqmlW56r0jXra8jYRvaZAiwwB/FyCc9zntSlDa4la0rcD6le/8AlB63a6ieo9D0K10zS7qOKwl1KGykWGeSIsVhM6IEym84AGOfXAqh6c+IuinrvWureuel7fWNR1ORJ7ITASQRAIEMngucM5K5y2ce1V+j/GHVLLpi1+HBawOmTw/syOGaImFA5PnY52ZBYvu/FkU6p+FZg6o6e0vozUbjryG6snSQQWqBraVCu5ztOPDbPBJB9O9bqEIuzVjByk1dO59H6Tj+GfWD61qWtve2tld6gXs9Gt735O3syiqC6hOX3PucAnYoOAtT9D6U+H/7X1y2127OqQWl4U0zwLowFIXhRyZQvlaTcxHbGEBAya4aHoqTV+prv/bnWh8PHs44bOPTl05C86RxgCdQ7bcEHG4Ek4Oap9O+G9pD1nrtvpvXkdxY+JATc3SqrSF493KDILDsMEcVSy0uXu+wx6m0bR+nOpOobDpm9mv9Nk8CdfmHEjQs0TZjLAYbBAIOM4NczeJiSUrkEBT+gWuh1vp7TOmdT1Gw0fVZdb+ZEd3NcyYG1ipTZx6ZGR9OK5y8ZWZyrBg3H3HH+VcdTeO2lunb9B9T9K9J69ZXfWOhrrVyglbTlkj8aOLdsEreHkbmwqgewJPfmpes9UavqfXWpxfBzpbWLnSWhS4ubbSbchLe5fJkBQAqm4ANjggk8VT9LdWdL9LapHJ1h0tZdTGaJ1tPnkWVIBuG/ajEAscDzdwBx3qZN8ZLm06hu7H4UaZD01osgSW5s9MiVo3uyNrNjOAxRUGBxxXVRi9lNL2OWtJbTV/fQutI6h+Get9K3UXxE6Z+Y6nvreRdU1G4gkM9rKm5QsbE/uvDCqMYHIO4HNUXQXwVsJfh3f8AUtz1jp8ep3+nNcW2mvCJIkXaSolm3ZDkL2UHaWA55q86SjtOkur9Y1j4o6Ot91ZqNwNRj8QCaJY5ACjFBwWyDnI7ioOs9Da/8Sus9U1roPRrRNFnkjaZ5njt4zIMCVUPYAnvx3JHFa5rJOxhk7Noygi+Hkmi2T/DDSdfk66jK/su6vL6VjNI2A6lSfDAMZf8IyPyqr6qsuvobnR26nsLm20RLhjLJJMHHieG/hg47Ln19TU3oz4O9R9VazLqumSWXSkWkXV1GJ9ULxmWdcpJDGkeSFU5G/37A4IrnNe1/qa26pt+lupJLi3e3upBc28tyswYrExUqw/EhyCG/wAarJpppO+XEvC6abVs0ZxktKCeW2uAAM+mf8KsraD5mWGOKS3ic3NsfGumZo0IlQksueccnAAycVWW6+dwxC7WUcDPBRv86uNAW9bV7FdGtre61GS9t/Bjlfw1LmRcuzfwhVBbOPT3rzj0OB+i+nOpNQ1pL3TbW3uL6azZlmMimM5BO1jvwV3AZA9R24qu1Tp/rNuoNHi01rS4tL0EXc8d0U+RCrv3yKV8552BVIy2OcduxMOtS6jfzy9RabcSQRRQxoquCWC/hLH23AZ5xnnFc309rlzJZXfU2t22oxaR54FQwNGgMT4abfnLKxYqOAP3ZOSKhaFnqaoOk/8AZSxXp3Q4NR1rXLm9NzDqWoTrHst/EBlG78CKN2AnJO71yMVd71RrPRvVGm2+rRrcXeZCtupE2YyhAkG3II3cehBHNW9nr+garqk8mpapqcUOALO1iPhHJ4LMSCXBbgZxjHrmskute+HNnPf30iXIu7vwoLxXBlMTNthicADaxOeFyCze/FTe7GiyKvpfq6yuLjW9bi6ckvgXIuZLO1aeGNlBDFgo2rI3BY9ztGfXPxPqjUdO1XXNQv8AQYlh067k3Qxg7lUgYfHfjerYHOO1fftU+KsvQ1yF620zUtOW83TQBoDtlIA3NheSc4zn86/PGtzabdXtzJ0zpsmj6Q83iW1my7WVG8xIUk4DFmYD2YVWz1Ivd2KNG2SR5BO1gc+neuH1Us+vX8ajLPLlj3PAHP8AOu0Rs3LqrHcFzgc8f4VxOvHZrN4RhGZwwZee4HHH+hXZh95+BzV30UYbWBfdKsWHbw2UZbPH69qu7cuizxO5RSV3yK/P1OO+ee3oaqrOKNnQPsymPM3lOT7E8f55xVtsdIGikbEnAO0rmInuckcH8/yreTMo9pbW0R8OCNCbaA4CeICc44P3osLqoDoAi8BlbLcHnH/PnvVS0oCoVZSN27coJK44wB6n1yalSXCLAqpLCzzqQVdcndkc/T/nWDRtcuoYrgX9qYiqeFIWuYZAAWXGA4J74LAYHPJPvUnUmBCnI3N2UNwfLyePbiuf06QQXVkiRK6T3aecxIwwqMfKx8yNnH4cZGc+lW+pHGzcD3xuwAOAO2Oe9YTVmawdyk6rcjpa7YJuSN4CxB27cvwf1rhFIdSRks55AOK7XqlVPS12ZHxGLmAHA/4vT61w0cmAoblgGXceQG9K7cP1fn7HJXfT8idbxFk7xl0bG0dxn0qZpEc9spguLl7kFt4lmzujxjgd69022hKeLPcpGvpv5JYe3FTijlu2dxwrMu4EE+bA98e9XlLgVjHiSflDCGhnfysdxLnh89mOf+nFTbYvKjp8zJbvsWQSeGCUz3UN68etabOaO0bdLAlwZP3KeLyygemOT2OcVnNEm9njdnj38kSBiP8Awn34H/Sudm6LXpSJUiuRbsMG9n2s/b8MWCWBrO/8s8g4GSMYP/DWHThikS6WD92gvJkwQRjCxk9+3rXl5cI93IiNltx4xz2/5VjLU0jocr1uVa70re2ALUkDvzv9qoXVkjUk71LA5PfGKu+uSfntM2HB+Wbze3nqkecvsD5cqOOMcevau+luI4qm+zZCFldvGKqoIBYjg1OsI2kuowC4hZiIyke4nngYP86iwpuZwrbVwACPNt/wzVmbVtOle2Z1Xw3VpV3kZPoR7cH0/wAKmT4CK4kq3tfCUCFzImGVl/AQCO3f/WKvbfTZn1rSZLFYXggMjyLGV5EcRLOMnkYJ7fzNUsaOhVzHI7ytkKuDtBwMnPHt/o1Z6XJDcXVj4yus/jF1CoDtcxsF7ngDn3Nc07nTG2heTqrOw86DxD3GSB/iao+qVI6Z1ELwC0OCT/8APVq/uGV3k2KY4/Eyq7s4Brnupzjp2/HbLQ8n6yCsYb68UWnuvwZ8/RSu1sFmXAUFcgipSkxM2dq85ZgOBnu1aUBUjucKTweR6fz+lblkkeNY5HcoFHD4P8/yr02eejfGGm8NQ8bKuSCMkt6cfU8VaR26m0XwocTR8OG/C/m5zntiq+2iWWWM58ONCT28o9OavBMrQqpVih/E6tvKjHAxxx6fTNYTZ0QRtmRfAnZsBIcvGH4IYDg4z6fn9q7HWij3j+GxdQFXJXaX7nJHv6fpXDSxC5t7hWMNuyKfPL5QD6k+/HsM+1d11Ebf5mQ2c5nhAGHa3MRLDv5dxx7d+a4qmqOqBz6HCkZwcL/WuQ6wkVtVSMgf+io24fduK6vP9p3wcHt9a4/rHDauiNkf7nEOODnLd60ob5WtuEadoBc5ssSReGAoUnPIyV+uDn9Kwx50ZA2FIOcZ59jWuGNmcRhtg3fj/LjB9q2iOS2UREYZn98Bx9/yrteRyo06q0lxGpl2owkQMx4LeYZ/w/SvqM0pCMFwSybTwBjzNgfXtnJr5xLBCLNyZWJWRQseOPxeufX7V9EmH7t8c5ROce+6vOxb6MfP8HbhlnL52kUmNiFGRh+dxHNQZ/Oy9hkHHPrViV3kBWP4sjg+wqunjOd/uvr+lclLU3qLIxHIjOByOa+Y9bDHUt4M58sX/wBKWvpsgOV2kcf518z63/8AWe9/7sX/ANKWvawPWPw9jyMb1S8fc56lKV7R4opSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAWOnnCH7112nBGEODhig5PauQsPwfnXW6ecLF6DYv9a8nFnt4XdR2Okr5owyjf4D5zxzz61caO4N/Zg7SHlXj6/wDWqjS/MBg5YRt2wMYOattIY/P2znBHiKc/mfSvm56s96GiMesmjfp+4A2IwtD4mN3fJ5OfXHtxXzTx2VkRAXGNyjPByO4Ir6b1kfE6dlBCgrZOg/X1r5v4cVpbQjaqy7f/AGblt2B6jt+lerhbcmediN80SEsoRf3fdsAkn8zUFQ+SCG2ryMHBzUt3wA2ApBztA4OfSobsryIdvhsWzw/H2rvicbPoOkBv2FpbAHeYtgJ7ck45rKby3JwWwGH09qx0htvT+nIqswIHcjjzH9a2TlTdOwGCX7ewFedLefidcd1Fl1htE2lOxOVkm5U8/gUd6rCrzMY40UKeAIxuyo45OM/erfqxd8ml58oSWU8e+wED7ZqnUBzPlB+8cMJASFGO42+v51MN1BrNldf5trgKx/eDI2rgqB6YrlrjdulB8wAxyOM+33rqrpXltHZ8FeSihxuZQcZ459K5u7QuG8PxSjMCQ454rtpHJVKziJQwJbOcKOw+tSNBmCdQ6eYwqskvO4bx2PJB71omOVBwCSPzFSOmomk6i01FDNI034QM8AGup7rOdbyPqdyNsKAPv3cjjAHevNI80sgG4lSu4Ed+axunUxISu07e270po8hJn4ydue3bGK8VnqEHWXaPqLVwCSGlQ/xHdlFzwK0z+E1sphRi0DYEbOVCkj2Hb789sVI1JinUGuxJEZA7qcBtuR4S8VDNxcRojvGY5pYl8SM4Zlx6ZHNbRWS8ij4kMCWOQBliGQfCB2gyDHO3/niuduLaWO5eJuHPmcE4Ofb/AJ10c7RvBNJMrAkgYbznjkfl/lXLzNlzuAcAYU5x+lddM5ahEuLdkXYAQWP4fp7V2vS2R03bDuwnmz+tcPM7KQNzE48pPA/Ku26UIPTtvn/30uOO3I4piOr8xQ3zptO806jt+db+qXiEujO0MRU20iBgpA3bl5P1Pv8AyrRp52TgcfXcOKz6mkBvNGhBSM/KzLEQvO7cnBPtgV58dTuehRfJPPdCOzg2v4uGMmPxDkrknkc5rCd7VJpJFFvuAAMZLEKuTnGDxk+xra0jwThjJbnepAeMbipHfcPT1rSfAnkLFvCgKjxJHh7KfRQOck85+tbIyZ48sQFz4alQ0TBIXO8cAc5PP2zXM3jhgybTGGbykHO4Y7N9a6SQ72MMkcdrHGiucjc+3OM57nOO1c3qjWwaRmciUYAVSCqj+ua6KephU0K1NpvbYs24tOgAJ/4hXeTkPPMAqf2p55rgrUh7y0VGUqs6sDtI53Dj6V3M4PiylSxwzHnms8VrE0w2jJ2hjF8mCCDj8/zqPdwuLm4cETobttqqCD+I+U/T6etStAG67QsFViR3GKxlXxrqd2VA4ldpv3u1mwTtb6+nHvXPA2lmaJIlxtRM7Q2d0e4Hn8Jxz7VlG5t1T5qHaHBYR78cAgZ75P8AyrLwIVVUnEqkbUVUkweBk7j3rG7it4wrzGUs6ZVgBGqjPoO5PatUVfaabnbO0r22EHik5UeUD3yeR+fvVRfv4kZQgHBIxIRwffP+VWLEySyLLI3hAb9sO0eg5IJ5FVepW7uJSAckB+PfsP5e3tWsNTGehF6a/wDm1gHdiGXJ9CdvpXQ7l8YnBwTn7cVzvTSFdbGQSTDL37Dir48NkEHLYK447d6yxO/5GuH3PM0a5K9roNrO2Cw1BSdwwCCjDH8q/QXw76l6Lh+Hf7K17TbW9S8tydRmvLdWJyDnD48gXPGCMYz3r5PofQmo/EiO20XRJLa3lF7FPNc3ZIit4VSQs7YGTztAUDJJxX0V+m9O+BvVK3/Wml6V1hbXkedLkDf7rHIozIXglO0vyMbs4HOKtBp00uJnUTU2+BF0T4ra98Q+mrroLpLRItXt0tDbW9rYaaZJIYVG1HLY2qVUA79w59yapPhz1pq3QKdQ3TWV+ulm6jt5dQmspI4I5owQ8BfbhGUtkqfU8+lXl58R9Z+G9xc6l090tJ0v0p1Vc/PWAFizRXbMoDCN4yPLuBKxnAw2RxVp1H8UfiJ8NbltM6wgsPC1WP5wW8MHixSCcljG4XymQMCHDfxep4NXs+CyfeZbS4vNdxo6w6O64+KXTlt13op0yWzS0Ig0+Vj83Nbh2Y3CRsNpHDEDIYgZxyBUDqdunOouldD0n4WdNalJ1bEyBLiK1Dy6xGf7RpfR8DzKRwg44BxXDad1Lq/SeuaXoXUeoXEGmterBdLaXTeHHASCUQA4J2kqMHA4719Q/ZvTVzqFo/8A5NfTd5pvVrqYLpYb9oozZHBkZmuW2ghhGMg85xUtONvTs8yF0r+v9Hyq20LWentT1u06r0q80zVI5IWaG8i2vtKEgjHBXv2z615fPvuJuAV3jsAB29q6Xqq362tNdv4PihHLDqYiiW0WS5jmU253ZKvH5SN2c47GuXuSPGkbHl3d+3pXFUd5t/wd9LKCRYWWuaLoGr2MnUXTll1EJ4JVgjvN+2JldGJwuQcjjnipnUXWo1PVF1zpPQtN0ywsrdYLhNNxADOpLBt23GQpxnHtzxWfTnxV1T4XahBNoqwI+owuskki4YKjK2AwGQM8nHfAr6VqGja/1HYWnxV6stdBuenRax3V5o0sZjubuKMNsmdSu0oSysQxyyqCRjit4PZim16nPU6Umk/Q4/pPrPo7XNFkn+IvScXUmqzyOG1BbxvEgh42IJA67Qoyc45JJqFZdeav8PrS30mPT7+y018yaLC0Qkea0ldmhbORyQe5+9X/AFZa9Pf+UP1Nosempp3SV5YwFtSuxFGAbQYWMCFDh5A5OM44J9AKvJvhfo1x1VHpPV3X7SdP2VvDLYXFqUW9mYMR4ZWQskSoVzwCG3KBjkDXajx+xjsy4fc5nrL/AG7+H+s6XbXGry6jJ1DieM6egdYJ3OXtSGP4oz/GOGznFQutfhL1Z091BpPWvUdxZX8E6tDdG3u/FmtWaIiJZQVA5xtyuQCcccZ53rmx1HpnrC5Oja/b6tpujNHe2tyGYFw6nyuCxCuoyDt47H6VBT4p6n1be6fpJkuPk5maW4EsqtnYjEL5fTOOT7VLi9hyj2MRa20pdqLSPKPJu5wAe/qKsYNP1fUpIrPpWKSTXJrqAWaFggZhKufPwAu3cS2cBQTVZGSshwQA0bDJOTxVjptrrOpXlvadIkrrUlyqWrC7FuOe5Mh4UBQScjntzXnw1R3y3WW3xF6c626G1rbrxQjWMSQ3Om6nLPazPtG9WOFIcDBwQMjnmrDRvjB1PqtzadN315apHdyLpruLdpPDVwIycbgpCqw44PGKtYtF1rR+oNHHxyihv9EhElzZodRju4ZbxQoAYR4wFR2bBHJ79sVp6x6RHWvUunyfBHQLd9QuZHlv7S0CW1uiLt/fBmIWNifJ/wARPAJFdiUXGzONtptos3+J/R1t0ZJo+iaFFDf2a+HFqDRnd8zG21Z5JxksN67vXg4x6VwMfxZ6xeWDw9WW6ltwZ/LGqqpQZAJOckEA+npyDXT3er9LfEbXIOjrfovQOj9bS5ZTfizWOezMR3SBo02lvIrLhyMkjnnNVnUPReifCPrvRtY1vW7fq/pi7My/JmEW5M6xZjSaPJWSPnPcAlcNx3rGME7NZvzLOU2rp5G3Sp9c/wDKKtJbDUNe07SNN0VluRez2ryhpWVkWJQG3EkZJI4AUd8jHLTaXN054uj3dxb3U+nO0DywzGRJSrfjQkZ2kH8J5Xt6VF+LHW/TV1c6fcdAdPwdMXcu83sFsghilXA2SGNGwrZ3c8ZB7cVX9LzPL0tprXDbppEmdmBxuYytzn7VWrBqCeivoWpSvNrVkq2G28y+ckAnA781xmrwtLr12ysqr4wPfIHlFdhbZW4ymcHAIXgntXF6zEX1+7yyqN6ksfNgbRzioobz8C9bdXiWVgWuF/eTRgxK2FCjcPYZPHc1MMYhiJlgcAhgruwILHjlQePU/XHNRbEokryQLBIwQqYpEznd/Fn3/pUyGJJGWMMbfxcR7+wxgZP14HtitXqUjoYxO8sQDlHmSPAxnGO2SB6/X6itrXNv400Aila3KhnkZsEFcDH19O31rSkYIDK3hM2VCrHnIzzgZ8wFSFUvmKVlmt4lDDxSIwxI749+1UZdHumxIdQsDIkizvLldwUcFGIOOTyPrV1fxBkG3g7hnPbsPT8qiWTyTX0Ms4XxJLlscZ2r4ZJwQcDPAqzvdqlCTk7ySmR7Dt64x6Vz1NTWGhzfVSt/stcIvrcQ55xwDn1rgzGZJA5G0Kff8R96+h9VKy9OybecXEOcc8A/5f0rgtzqGZFXaeDjnH15rtw+4clffLnSEgaGN7tHkY8rsJQAe2MZz98Vawwhbfx95LbwUjZuHPfk54wfQ1UWoPgP+8QvH5lBBQtn0OB7+pqbCvy6MJk/eNGuzz7lZSOcfY8c/SolqWjobIYBko+JMSkMHyRj3z6kZJqf4Lm23Wkm9BltsagBkyeSCefpURRLsIwI4lwCrPnuPb1HP6msjdIfDgMTuWBKKjqC30HBJ/pjNZu7Lou9GtpLWK7jYAsbiWTMTgghkjJIPPHJGPyqtvg8N88y4dw24HOQf86senmbxbvxCPENxJv3cc+FGeff0rG+jEk0oJC4JI29hx/zrCTszVZo5TraXE+l5fdG9sWKZ7eb8Xsfb8qo0CMAQS2QdnHOft3q760j8ObS0Bz4dqRtxwfN3NVEcrJJG6Algcggd/8ApXoUurRw1N9kiyV42zKp3jOEJBzzzwPpzV5HIDJFEzpIoGUL4JPGe/qPSqqzZRLOrKWi4Ab/AIO/f6n9anpiVVeMCPOWJAUdhyBnjGOeapPU0gSYrdEn8RQ4Dr/aA4OcZOPTgdvtVnZqTrWmB4lSBrpmZFwrITG5xn2yAfuDVbGAsDvdNugBwRjaxweAAe+cDtjvUrRZZzrll84qW0UtzkgHczZRsgtnOOOcd6xlozZHTKd8rSYBPzAVwpA7ZJIA9OeDXPdSKT03eAgKxEIOTnnetdACF8TeQxzkYPc+hB+9UHUxDdPX4UkKWi9efxisYb68UWluPwZwKgvK7KSxGCf+7n0rLYpYgJuwoO4sc4zxxRwCNp5I4BHtnkVkDGQqqmQFGcHk5r0zgLKzn8HOC8eSCrKBj8x7VbwolzG5QoZGfkJ3347D3/51SadA877YlC5baSTkDPv6dvar+Iw3FrN8rbmTa27KJ5AhOFHHIP0HcVzVNTphoeXoKRuVKRu6ARxsSzIQRgAg8HGfyrrNfwLiTbuY7TliMHJB4/oPyrl7ibyF4UUPIg2hXJyV4YH1CjkYrpOoV23EqKQQrsPKOOx/pXJPgdESjXJDEHI7H6cdv5VyvVxzq4+trFnj8I55BPrXWRLuTkk5lUdvdTXH9Vur60gJzutYQR7960ob5SruFenf962Tjn1yMdh/Ot8LPPhMZGMKM5B+5/P+VQXTaGGScnaW7bR71JSFpNoiBYFd2PcZ7V3NHKjbNDMbco8TK6uoOGDKO368GvpT5NouDzsXbnHHmk7180S2lS3cqBsRh+Jj/omvpxDLZ/hJVUUnjBB3SYHH3/lXmY3dR34XVkB+AxUEHjGfY5H+VRLrGdq+o/KpEpK5wAD/AIcVDmmBdRnyjgc9vauSkszapoYE5kTPcjtXzHrQ7upLs/8ABF/9KWvpjAhlPbDDHrXzTrcbep70D0WL/wClrXt4HrH4ex5GN6pePuc/SlK9o8UUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgLDT+w+9dXYcRqQeUQZz965Sw4GfY11mnkGJxx/Zn+orysWe1hd1HZaYwFtKxZUwcA44yRVjpDg6haZOP3qZ47c/aqewcNYSMwO4yqVO76EdverjSMi8tGB8plVcZ+vevnJrNnvQ4Gzq9i2hXS5JC20gGR9e1fNFtxKqhQQqAA4yAOPWvo/VT50XUNpKkQPjPcd6+bSh4jGzMxcqrMVyQw98+or1cL1Z5uI3zCQmLDn94vIyV4FRZGVpF3AAjkbRnH1NSXbwsbiWi4BP8OftUQA72AIjkU5GSRx/lXcjlZ9A0li/T9gV2FSO2P+I1nMw8clgHG45GcZ54rVoe06FpzE4VkAyDkAiQ1mxPj/iycnPp6150l0n4nVHdRddTWz3c2jokgRTeSCRsdlEeT/SqaebF880AZbdGAyByzYxwauuo1Rl0pZIUl3XT+Vn25JjPrXPeFNG5eSNFTsDuJPHpgjv/ACxUw0RL1MbmZzIAIw5wxO4qGJ9s+o9a5y7Nu0kxjZ2mTyl8kKp9AM96uPFMwJfxWIJViiZ/Me1UF7/aBH3FAcgA+tddNZnLUdyqmAG4tw3oAKlaHuGt6avhhv34PfBP0zUecBDlj+HgN61t0JnOuae55dpxgZxz/hXW91nMtUfU7vKpzw+09h7VrsGZmuwOSIiRn0wRxXt2MYDFhnJBP1rLSPNcTjB/sXI/SvGPTIeuLInUWtMscmwvGTMMKqeRePrgjtUSNyTL4ZG6JhsjUnJBGPYit2vvCuv6rM7ujK8YXcNylig/Qn7c1BjMazBxkkkGTBAwPt64Nbx0RR6mu6j2MVeLxCgGw+ICyj3Hv7VQ30iM4lK8gYVQuAPuKur8tLl41UbRhgTtLKT3x/UVRXjsJEVkG/hS27I/5V00znqFfO3mbCjb2PH5/pXadLqP9n7fIGPGm7e2RXETLiU+IwIIwAe+a7bpQZ6fgC5A8abg++RTEdWRQ3zprDideRjs3OOKw6qMbT6PH4TC6WCXwXLgDO5f4f05zXun4M68ZU5yK2dSQ+PcaU8gjKx2kpIYeb8S5x259cV58d47ZaFFFGWnRg5Vty7lfGeOD2HNSBewMv71VB8Y7NqcKueQR2PPpUbz71mVNqvlSQ34AB359a1SGJFK2wJ2IQgd8nPv7dia3SuZ3MLs26XMp2x7VTahkYYx6Aken2+tc9q0m4JLKkO3JJYDgD7f9avcyS7fEKPbjG5C5Bz7fpk1VapLEdrRWcUR7bj5j/Ot6eTMJ6FLbvvv7ZR5n+YQknsBu9K7i6RQZz+E7uAp+uK4mBi1/aGVtzi4TjG3HmGOK7ScZklUkct/TNUxOqL4fRln0+c3MeT59wPI7+neoc7l7ifCsUeaULwCDgng/TI4qX09IouoxIOx7E8d+efSobW0yyTPAqpGZ5Bljgnznkcc/bmueBtI2y3c0lqgcgtIoxuHG0+o44P1z6Vptp7iMMY/CMjkxvI6BgOf4c5Hp3FbY7qOBcBm2R52rJkevAPsMntUSIqPEkdkjVXYJvGQ+fQD+H/OtUUbNbL8u3gM2xl/GCvbI9SRjFV2o74mCTkO3H4GBwPQfpVyI41tZY5ZJo2JBWNotwZR9feq/UpMxmP5WGILh48wbHYeoznPrWsXmZS0KzpwKNbCqScxSkZXGRjir+TCMN2Ox5C45+9UfTqg62H5/sJM59OPX2q4lQQtJGeSOATzWOI3/I1w+55lhD1Bf9OxWs+lPcGaW68CRIBukcbCwwB3IIr6P07ounfG3SJ0676un6dFhdlLK0lto3dn2jMkquRtXkDAyTyT6CuK6N+Iuo/DS+t9W0aGKS4uJXgy0ZZkUrklSOVz2JHpWHxK+Imqdf8AXGj319s0K4kt4oJrqW1I3p4mBKBIPOVBIyBzjHpWlGMnFWy7zGtKKk759x18/W3xA+KBv+gNKhGsX1vKXIWOOKGIWzAiRJC2B+ABcAdwK0fD7Uun+qby9HxwudQSDSbpEh06S9kRnmwxZnlxuAXAwoxknJPAqw6qsOhOj9EOo/CXXNcm1szRw3KT3LTC4Qkln8ihkYYDEdiM/Q1u+JGidEal01Nqfw01bUT1s0q+OvzDzPqryMq7fB/gfJyoX7c96snF5JWuUaerd7FF1h0roC9WvrL2ur6r8P7a5jxM0TRxQmRP7I3QTbxIVG5ueQCar9S1PprSbmxn+GcGradrBDrdvLqays1sBkqNuQDvAPPfniuxv/if1Z0P0cfhz1ToR0yVtLax/Z14kitMJEIZ+AVfJYtvDHn6irW3+KB660DTvhqtho/Smn3rRxC4ihUrZbAD4wQBcsAmMludx75p0ks8148B0W8tfDifH213V9Sj1W/1qeS8ulltoYEmctiNsg9u3cmtszZkfHYMQPyq86n6Li6G1jUbK11636mt7lre5S6SPwmGNy7GQEgEEZ4PIPpVFO3mlJcuN58xPJ+tclRpyy+aHbSTUc/mbNV/Y6XqN1pMWttKQUnECxEZZspnv9K7LpHp/wCIfUuk3U2m6PqWtdG2i7buW41DwI7q0Q4kijU8uSqlcLnscDNR+kOhrj4gavpWm2ekSaqVS5d3DBY7UfusPIxIA3E7R3JPpU3Sl650fqmboTTFvdIudOuBb28FzeKsEUcilhISM4j2ktlTgj2Oa3g7wsuHac9RWnd8fYuYzo/xetbLQPhl0z0p0lf2jLdTXx2xILUZVhI0e53yzJgE5yueMGq4/CKx0bqa9i+JnXNjpMvy8QsDo8K3EM8WW3FhLgrhhjB5Oc5qJ0J8FRHp+vSah1tpuj9R2l9NaW9oq7re4VCMvJKrBk3k5UgHAHI5xXH6nD8zpl7eXN94fUtjdTW11KlwHhjgjXAUFiSTv3Hn3FbK17QlkY52vJZku9/ZvSHxRsLSC9PWOiePBcOJ7ZUSc7Cdki9sKQpIHlIxXedcfFHRusodJ01embGx1RJy8N0ttFC8SrG28AxjkEcc8dvUVP0z4g6f8SOjtM+HHT+n6bpalY/lNQmkEr20y4drhzjxC52vnOM7iO2K5vq34Raj0ZqunaydY0/VtO8WSKX5VNjo7xsFYg9xnj6ZrKaTXTVmkawbT6OabKhDiCUsB6//ACprDVLC71yzt7TTruK3vbi4g8Ca4nEaRefOSf4eOMH1rOKMNDNuIySB9vLVZ1NDNfdNz29rxdPcQCNsjaCZVAPP9a5ae+jqqbjLm86KlgvOnrr4odRi20eG6eIwaZf+LeNI0ZKFSS2ASuD7DsDmutuNas+m9Wt0+BEPUuqXepQE6lFcBr5ljiP7uQAqpALSMPbOMVzPwx6h0zpH9sWvX/T1t1Al6iRwRS2kbxrGN3iEJnO8nb5wcgDjGTV10Z0bJr/UHUesfCPqW26XsoUitRaX97I0lxMQXaISqG2qo24LgjccehI7nxUtF9jhXdq/ub7HR9E6z1nXNZ+LGs6n0v1adlpp9s1uIWVljBW4nQjLht20KCPKuck4qi+GvxE1/oS46h1DUtEGpaVNKNJS+lgMlk7qzb4/ElDKS+BwTyFxXRpB0be2t7a/E9NfHXkUsltqGpabqcjrEIyRGoIzGyqhVsnGSx7DtC0H42z6L0fa9K31jZx6TBbparZ3GV8ZV/CxhwSzsTvBUZy1UzkrWv8AOBOSad7fOJl0VYN1D1L1LrnQPw7i1DTp7aKGT5W3d7SKYeaRYWGMv+HyqCPauVtJd+mbzb/s6V2mJs3Xw2ixM4KlSMhgQcjg5710HR/xpuulelrfQbeAacNOaWOW1eGSOSN2kZzlSM7ju+9U41646ntLnW75Ss2pb7hkx+E+IyjPscDP1OayrRaV7G9GSfEhQApdMwJHoARj88VxOr4Ovagc4bIDDOTgoMH/AJ121qSZhznL8+5Ht9q4jVXij164cuVw4GSvGdo4+oquH3n4Fq2iJ+lQmeRSIjNDHxheOcc/f6+9WUzhbpprsRTAZOZXyHHbGRjPbtVXa3PhNFJkRQLxJyMsPcDtVlaxvDcxyiKWaNX8TcSqhXXJAP2HOBzzWktSsdCZFcfMXBMs1qu1AqgLtjYd+/OB/P8AnWN/KIrhRbpbtPGSwAmzGx+gIPOCcfUVCUNPsDKEUfwZ2jk8gH6/et7DwVEKpByrbnVCdwBwcHvxkc8ciqWzL3J+mTwC9tYYlaHJYqCAWbC85OAf8OKt9Rdt0Y8vB/Eq4z98/wCuKo7N3g123jKsqTEllABGVXtn0PrVxqJzszklXIPYYOO31+tYz1NYso+qXI6ZnYAAfMxZwccVxICuiOJQHBG4EgH6HHvzXZ9ThW6ZmDDduuYgBnHOe/6A1wsULSqCGyMHcBxz7V14fcOStvl1CkaxA79hDYfcSNw9yR9asEdJZHCSxpE42qWBGfftz9vtVVb74h+9GV4bLnGdvfFWAK3cXn3RP4eWEhynJ/CfqRSRMSeLebx3NvaiSQKHByeM5yWznA4qNCd3yzeLFtPn3FiWKn8OPpz6VnbqWBhilCRCTduZy20A4x9R39KzKyzwhFlJijLeFsIVsFh79uAOB6VQ0LzQJElWeRIWQmaVQg5DMqRjJA7fUnHNa7g4nk3EsM7jz3NSNHkZn1CUzTXJN1Id7nBwEj4wDjA7AewqLdYWSQDOM8AnHORXPLU1RznWaMNQ08Lji2POMn8Z7CuejB8Q4Bl3fwFsc4/lV71wh/aenjd51tSw5xnzn+dUMqh8OFCPjJI7E+v5V6FLcRw1N9ltZuVM6I+HeMEoMc++P9d6kxxvdQMEjEcbYJAb8I7EknvnFVmngRl5gVKsoACOV5zxVvbQIAJZG3BWJEYTJPOftjJNVkXjmidFbqmADIrou2ONhzt79z6Yrfo3hnWNNO6VVMwYoCGJyrfz7VokleeHEoCMNwwW8pAx5QT7cVcaRabNYhBMEUYPjSPJKqnaqEcKeXJLYVV5P2Ga55aM3WpObyjk7mVl3AcYPqKpOqcpoOornkNH29RvHGauxkkIM792Md8e/Fc/1SwPTt/2VcxHt/8APFrOG+vEmW6/A4ZHDqrTLtbH4V5GT7n0r2BUDOuQm4ctgn8q8Ee9FLBghX07H8/etsKPM6474x+EA/n716RwosbQRxEHfja+CGXOAR6YGKuRcRmZLSGLxLfdvUISHd8j94Tnj6ewqjt3lWSUy8LGoYnjjb6D+tW8eI3iW4XJC5G5WZhk5D8n6elc89TojoToUtpGWKSB0t43jacDOVTeMkEE8D19TV91C7LcTsyeF+9YkY7eY1QRoltAZ1Z7iMyKBtTlGDDG4HvyeRzV71EGFxcBgR+8Yebnsxwa5J6nREqo+IkAOSZycD7VxvUwZtVeLxdo8GEke5AJH6ZrsYiBHFk92Yk5Pt2ri+rFP7bc7QP3EOWBHovtWtDfM6u6V6s4EkhUlVXLk8bvb+db4Z5HjBaYRqSN54HI44qMAXDAhz2x3PI+lSIVmCMsTbpWH4CMblyM4z612M50SrgRm2YQkXD+IoDOu4YyCQPrj1r6G7BY7YMcIYscDn8b+tfPJ98cTMm+NxKijaO/IzyPyr6FIMW8DBl/sAeCSfxydxXmYzcR3YbeZDuMYRtrDcuT79/So067nJyuC2Q30qTIAY1EfbJz68A5qJMTuB5wSSK5aRvUNJOQrH35/WvmnW//AKz3v2i/+lrX0qQDaPXg4wa+bdcDHU95znyQn/8AtJXt4LrX4ex5GN6pePuc9SlK9k8UUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgJ9h2/Ouw00DY3OWCtlSO2CK4+x4XP1rqtP5VsgnueK8vFntYXdR1mnAPZPtO3bJvwfYd6utHJaaDzfhnRgfzH+dUOmSOlv5SVLOF/XI/Sr7RHMVzCrYINwoYHn/AFyK+cq6s92noj3qjK6VfA5J8FifXHevnRScxJM6EKyrjHf619G6p8umagR6QN+HueDXzVZvFCIVZsrgZcgYx/KvUw24ediN81yblLbTkMM89j9KioS0kYmIVQAFZm7j2rdJFJGoVFIRRgKW71El2s21iHGO2eK74nIzvdEZBodkUB4yMDOGXcazmJa4J/hLHj/pWvp/H+z2nsXUFt/AGCCGPY/6yDWyfl19QcEfzrzp778Trjuov+ofCaPTBdDCJdZ3g4ZR4ZyO+GB+tc/O+ZZYUHiRGIBi5IwuSc/bHFXnUMSzR6Ush5N2uc4AI8M8c+9VF5NFFHKUcqjgKWUjkDjAXHH3zSHASKyS+ItlitI5IoQcpsfAHHIx6++c1RXYzLI2xQx4QqBtIx3+9WdxCYwZXdgncHI5Of6VXGRZCAVjUFsccEf8q7YKxyTzKyZvKxeM+YDB7Z/5VnoTmPXLHaSo+ZU4xwD9K9uNhQoSSc9j6fStWkYfWbHPrMAV2+tdP6WYcUfUbrG1BwAAOPbmtujZ+cYHg+G4GPsa03GQyZYjgE5rfoQ/37k48jcj7V456SK/UAW6g1vwg7ANBIQCoAAQe5z+lVUkZkEzjw7dJceJGFAOAeCPcfTvV3PJaSdQ6nFMk3j7omR1UFA3hjH1B4759aq5pmABnPmUnJwM5z/IYraPsVZBv41hYqsqyrgZcnG05/CM9jXO3ZXxZWUjy5Bz34+vrXQXE0aiSZYFlXIzuXsxHpVLMqyYwSxxyFBJBJ7Y9TXTTOaoRGgkaBZZHBIUkbhk+4rr+lXL9PwnkHx5Qc/cVwxKMCrMSRyec/pXbdIknQYs4ANxLj19qYjq/MijvnT6Yf34zhQBnJH1p1P5rzRPPtj+VmVgmSfxLx9Kw07+1xkDIHrj19K3dRiMyablh4htJcLym0ZXzA9iPftXBHU7XoUsdu08kcl2PEgYHAJA3Lxxj3471jLcK3jxywW7ByTEwk2mLHYD05/nWdwn7P8A7O1VA+CHbKllAyQBj+dRJJ5pCBbhlEq5eNY9zNz69/14rdK5m8jXcNLuLGGFMsSh3eRzjg+nOfbNUN/NuBAG0B8NuH4T6jiryWZEU+LKSygEgDv/AOHsKpL3YyvI6qrj+4eCffFbwMJ6Fdaoov7Noz5DOnJPIORXZ3Ds8024d3Y4A59a4qzDDULPbjAuEAGf+IZ4rs5gHlYscZyQc+vrVMTqi+H0Za6BkXUPYgHGO5796hQTxs+oeJuLG7kwFODjcf1HHpU3p8E3a47hsDj7VXQXCZnLSENHdTMCy42gv6NXPE3kSV3tAWuAizTAI7NGBx2DMBxwPzrG5tVgaNY3juM87VHbn0FaZGecl8bipXOwgsWP24Br1rl4SI8Fyrh9yDg/Tn+tamZjuinUuW3KoZCDlguOcAjvjPrVdqg2KvhtKS4K75GywP8AUVlBEY0MpkCvPIWCRsW2j6nFYX7EIoR8ALycHOc/T3rSKszN5oidOgJrcayfvcQvna34hj+tW04AZ9mW54z61UdPkLriDlv3MnB4A4q1dx9hxyKxr75pQ3DquiV6tbVLEfDiznv9X3yb0hRMrDsyWJc7VGcdyPavpeodY6z0j1VFL8fOkbTU7yWyMOlnUGt7pflyx8ZUCuUB3YB53KO2Aa+SaHF1NNcafD0ItzJqckzK6W8gQmMJnJJ4xnHfivoGsat1X8M9T07Ufip09oGuR6hE0GnrftFfm3ZSGkXyBlRn3KcbTnGM5FTCKaSy/JWo2m9fwRvht8V9F+G/V3UeodLafPJbXkiJYNcXqyNawYy8KljgqX7nuQoB9an9J/D6/wCsdW1brz4f69Z6DZ2dxJPoukM5maecKRIqEeSJcswQnPPAAHNbta0pfh51B/tz8Qvh703+zOpRC2k2L7RFYsieYPAcIkjgByG9AexBFcF1HrH7X6nvNQ6ZsoemNPvIUa3ht4TDCh27XmhUbQysw7gEbu3etktrOPFa6mF7K0uD8Dq+nfifpPXOrWFr8XobjWdG0pjPDEYHiT5oeXwnZCTtClmKgjJA4IBrG20H4b3/AMYHc6zqendHppvzMdoZSgF0z4NuJnU4QKQ/m5xgfWoPVHxE1fpn4TH4e9X9JNoVqLZDaw3NjJby/NDkXQJXBkPJLbudxB9ql3/xC0G9+Etv0ha6MbbTbIC7tryN1L/N480xcHLM+SCD6HGOKbLWcVlp3eIvfKWuv9GnrnRul9J128/2B1W51DTbowyuLpxI1vIAw8MOoClcYYcZHrnvXG3IxJJnB8+CRzzjmqbohpp7XV5L+WR5RPAWEjHOME8D8hz9Ktbs5ZyCWBYkE+v61yVY7M2r3OylLagnYlD4g6v8Pv2dPpF5NbW974gnWPByU24ODwe5r6toFh0j8SenoeouserdRsuq9QtxHG0ckMkMEUbMIleEr5z3Y4IxuwO3PDdG9eaX0FqVrdavoWn6xLcpJHayXkaP4BDKW2bxtBbyjJ5wK6Cw1boL4gddavr3UuoTdPWCpDEdM0lkiW4n2nfJ4yqRGpG0ERgFjk5FbQX+tNK3eYTfTav5eRw+qaJp1loFtqPTOv3F3emBpdTee0zGtwGIZ4W24CkAADuCK+o9TfGPoy46Fs9Jg6WsHt7B4Z9NjlSEfv0ZWD7x5nLEHfn8W5g3eoXTsXwV0K76h0y70i76ot3vpDHJeSzEWkTKpWJNrKMr5sysMnj2qv6e6+0f4caRcaAnQljZS3UTxNNqtpi7voZNwRsumSHVgQVIX1q8unwbt5Ga6PFK/mOtPi1qXxLtbO1sNG0ttelu4DbX6RCJoVL4YySbVIQ7sHJOAc8Yqb1p8Ouo+nLPRtT1fqvQNVtVu/CubHTnmLBnjcKweQYcAjnbjuDz6cvpPQdzpet2/THUWv2+nWNzZSxzalbnLeRAUj29iC20bu2Ax74qBqnSes9O6zpEmpajY3dks8gT5SfcJG8JsPjJIH0471EtlRai8i0dpyTkuwmwMFgYnAIdRgjjGAPy71LsbbR7+4sbTqq9msdGe+jF1dQSFZAFfxAqMR5CSAM44yTUKPPykxXIYMTwM8DFeXnTcvVMI0W2vbXTJry/TN5dybYosHcWJAJzgcAckkCuSG8s7HXLdZ2fxEb4V6frmia5pdhN1BbqssE2ly37XFsr7VKzFsgjnKkE4O4diDnkPiD1yvU01nddH9PQadFplqUvY9KsiIIgz/uvGMACqQFb8RJI79q6XQ+ier+g9TfR9Lk07r9uqrc2RtrJTcMkUZEjBlbbsXJB3nABUZPaven/AIgdY/Au51bpO60V9Ake9a/lt5F3m4jkACtujYo/4CuMkDBGe4rtjFLTO2mZxSbeuV9cjm9b+Fmj6vc9NDobqk6h1HqcsUOsJePtt/mGyzyDaBiJVB8vJwvGc19I13S7v4G9V6f1ZZXuldVS3lutnbXrQvHJZ3CqCx2sWxuRcK45G0ggZyaHQl+Htj0vfQ9YWmo2fXEguZ3uJZZiukzsWZY7eJSIwiqUBzksCewwBVL8PIOofhiNU6g60jXqb5cXmm6XbMHh37crFNLI+7ewO0KuAhbJLektt5SeXz0IStoszTdT9XfGbrHWta0TpS816S3SCO5uLKNShkRDwzSFdzbcc8kgDjtVRp0U66FarfQSW18UmFzA0ZUxSrK2VZeCpGf1rvdQ6b6x+BPw+tZLbXtN1G2nvPGv7SwUF7OeUAcl8eIMqF8Re3sRg1wthql5rGkQ6nqk3iXd6skszs2CztI2R/xcAZrGq7xyta5vSylnrYxtVJCc/iI9c1w+swk61dGOJ5SZRuUAceUY/UV3NkckYOQCcsR7ewritXVTrl8jsCfEVl82f4RxVKG8zSruo2RwiaaNWhaIfhPOfUDAz6/U1Pl3IwWHehySCAoOOwzjvk55P0rRp6O0YMilO5RM8sffGO31NWbWscqCXUJjvIyojTBwPXcTWj1KxWRFYr8oXmPjgy5JR8yA4/CB2/wrB3uZ0EQhjFoXG5BncvqpXB59c1vjij85gG/cMxoWJZcgAAkYz2/WvUhV3tYbiRkhLd5PKdp5wB9ccZqpYm6NPFO9pIYQkzO4A2lSqDPB+p/0auLxCyoyoSFZjuyMYHcD7cfXmq/SI2a5DoJ/D3hP7A4jOCQCw4B28gc5xUy9y3hnaSBuOSRj09P8f8q556msSi6oAPT7gqCDdxYyBgcn3rixhomyTtztI4H+jXZdVqf9ngRwvzkOT7DnNcSnk2lcs5yN2eAD/U12UNw5K2+XenziCAmOTwlbjIXPJ7VOjRD4oKpuYFRIRvDHHfHqcVAtpBIRFg+AAvB9cd81Yl4fmCkYaIF9u9iMc+mPTnFRLUtHQztYz40IjWKQxssoZWAO4d88YAIHattv4cqYeEwxYE7oGHrnP1znGBUdFE8ptIosEvtwvJds9s+/t9O9bTchFObiYYwkruinw2zxtPc/nwMVVoui60LywOHAC/MvgDDYAWP+ff8AWtN0czO27nxBkAd63aBk22AylfGlKBB3GI/bgk/So05DyZ45k3Zz2x7VhLU1RzvXBAv7EuoYfKHGf+8f6VQQuAYnzH3wykZx9xV/1oV/aliM4kFlgHk585qiIjxHIrDeFyUCc5z3z/rvXfT3EcVTfZMtVd22ny7jnHHv9Kukimhc7FyNxxhdxHpjnv8ASq2yTwAzyQ5G4MuXI+hGR6VbRvI8LrFLstiQSI/fcOOfX07/AJ1SepeGhsSVPCRoSzbMRjeoPHJB+h75P5VaaRbl9VtL7ywoF2o7lcnyHO1T9Bwc96qo3WdJYzGwONrFWwMN3GAO329qnad4a6tZmTFybdHwFkKAYUgD7Dk7eM4FYS0NkW5cmQkARjO5RksQCPUjuaoOqgp0HUQwBBZF9hneMH7Zq9KAviRtxIHPYnt2P+dUXUr7tA1EqMgvH5SBz5xVIb68S0t1nCQmRJAUdo1BySpyPatryiMKJR4jhsADjIPao23DBcgDP17fWtwimzjejjICjsBznvXos4EW1jdGNiVRZYwuCGQbQD34P2/nVu7SSzKyBUkUYAZgq574A7/SqTT5Cd+xQzEKi5/hGck/arSNWikaZlIikY7ye5PocH+lc89Tog8iVAd3yzruikEkapufKtIsi+XC9u5BzXS9ROFvbkDJzM+TjGTuOff8q5mQCcrMDlI3jUrHkDdvADHjg8/zro+pIw11Mw7eMxB/OuWeqOiJAgIdLJSM8vkdsnmuG6px+2XByo8GLIz38vauyhbLwq2CF34BrjuqQP21OQVAMUIOfTy+la0N8zq7pUq7EsYmIZRnBPHHvUxHYxb1QE7z/wC05AOOwqKrxkAhRuHI8uc1migk7SFyMsuM7vv6AV2M5kT97QoqCTMhdcBWyFGRkYx9e1fRky0CqgPNt/D3P7x/f059K+dpK0tsjqUVi6KUPlBwRxzX0BELxxBtoHhDBz6bm/Q8HivLxm6j0MNqyISAispxhcggZ+laLjkIByAM81KkPlZgBjbgYx/lUS5HIxjsOMVy0tTWehpkPlP39a+bdc/+tF7/AN2L/wClJX0lgM5Hqor5r1wd3U96fdYv/pS17WC6x+HseTjeqXj7nPUpSvZPGFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoCfZfg/Ous0wrGZVbnytjB+nH5VyVl+A11OlkeKd5BG09h9K8zFaM9nC6I6fT9vy84Y4xhh34Pof610OlOjaooQn/0pCc8fxe1c5pqNJHIiEDcowW9vuO1XunBW1SBWAVTcKD+tfOVdWe7T0Ru6n/8AmXfgDBMDH69jXziAHbGdrA4BDf4V9M6m2tY3audq+EwPJwOCK+apEiW6hZRJHzjaTk8d69PC9WefiN8jXLb8b90hGRknkflUKWNo23BdvY8N2qezBMFiXwBnd3/WvCVIVsk4/hPv967lkcjOv6d3NoVoUyUViWXPbk81tkBDqoz3B9696fQLocD47M7fhzjB9/SvZFHiNjkeua8+e8zqjuotOo8LbaYXIUG5UEkZx5DzxXOTl5CE8bxISxBIyPt+eK6fqFC0OlFVB23AJyOMeGc1z7wgFktwAhYuvI3DHr7Hv2q0NCJEO43CfdIGmHopwwA+o/lVPdpLLcSLg+HuJAxtxVpPELd3EZYKRzjlSceufaqW55dgH3ZOcMPy4rrpnNMhXJeRc/iOBmvNJTOsaYzHjx1I9aykQsmSOe7V5prY1vTwrYHjrkmun9LOfij6bPlShGc4/LGa26Kc342k8Lj7gitNzw3H4q3aNhr5QcBTwTjPGDXkvQ9NFdqksX7a1XxDGhUQ7kdc7h4Y5OKq0/3aGS1t3iQLyPFlBABPYfSrDWY/E6g1JvF8OLfEC23kqIlqJc27hN0XNuhUuoXJ+hLeq/pWkdEUfEgeII3kaBxLGFGdi4EY7cn3qnnCF2l3bT3G3hs1d3UtsIUVZv8A2mZAuNv2PpjvXPyiNpGRHUk9wp9Oecmuqmc8yBL5mbKbG47dyK7XpPyaDGN3Anlz+oriXKLIROGztJGWzXbdLtnQRjAUTyYBHP8ADU4jqyKO+dPpzFZRg4yMMNoIA9+adU7VfSRIobdayLtyc4JXlf59612BPiDJwNucdweK36/skutHEwHhrbSsCE3YYMvfnkV58dTsehRW+0iS4E/huH2kyyZyD9Md/wBax8XMUm2UKdxUmMZ347EH/Ct5stqPLK8SS3EhYLtHLD179z9Kr41ESyLG7F0bcwZNu1vsf1rZZmbyI8ziMlDjex7yZGD9c9xVLeIRG+HClmICK+VwP8a6G6J2vPG7FguDkdvTGRmqG+tkiLGNtpHPPJNdNMwqEGyjLXllJlceMnY8k7u9ddcAiZ0J38nnHBrlLPa2p2ZyGzKmPT19q6m4YNISpOe59AayxOqNMPoy86aKPPCo/EZO4BJxkemeaoxske4VN02y8l8jLtQruPDfftxV102wa7t1wTg5bHtVRaQo5vmeZIALmZkTwwdoBx/iOKwhxNpG2RYn8Zo1MUWf4QT5wO5HtWH7sqs8kpnAxkFcZ+lWEMEbW8Qspo7ibGdrr4eM+o9Cfpmq+5cr545FZB5fPwp+w9DWiKMXMJmuHZfCl3ZIKsMgDkegqBc/LOn7t5RIP7TfyOO/1xWzPh3AlcgyFsggjBA+nbPYVXakoJ3iILMPxfvCBg/Q9z9q1iszKTyMOnogmuBgFCtHLjJ5B28VbyEAHOBgjOeKpenlCauGPBEEgIGcZxVxKM7+e+Rz6VliN/yNKG4Z3+s32iWFleaXJLFcR3vhjwn2kgoeP5V9CsuievfiH0VF1nbyQC00h5Lqys73Uf8AerkxcvLEgXaoG0gA4LEHGao+kR0gLyzPxIgvLvSBKXjhtJDGZJQhADMPMFAJOBgnjnGa6punV6k69PT/AMGdYfSOlp7FZrmTUbmYrpisSrqed0me6jGfNjsM1enLoZZd5nVV55/Y2zWnxI1roq16q6h0lbzpWwuIdVmtdSlh8S4jXs4gHndBv3EnGRnANVnxL+Lmo/EKz6fOrRWsT6TepcW13GGcwELhIwSowpIHlAx5RWrpzpj4i9Ta51F0Lo8+2XS1NteX93qRjskjxhTypJLqPKvP24ro+gdJ6O0BdW0H43aWZdahv/CS1jnkWOBAilJo2iPnZt5YMSRgDA71boxd9X3dhn0pZfyWs+p/Ea90Wx1X4m9Oy3vR+nXcd7dpqRhV2KkiMiEtuZS7Lu4AI4Oah65YdNfF7WtNtOlun9L6Z6i8TxZb6W3NnC9uo/etJGn425GNoHPrXOdG6jpvWuu9R9N9a9YXtt0pB4sTTxXI8a7HiYjCK6kKDjcxOe2B3yLTpb4N6VN1JqUEHXca6VZCIWF3HDvuJ965ZJAGG0JgDKnkkYquyovPJ9xfactM13lN1J0FedBa/qGm3t1Y6gtysM9pPp+4K8YDKdyt5lII9eOxziuRnw0mFIxvbsc55rr+qOlZOkeor+3m1yPqCK6ijkiuId6MsYDAo6sTjn6kHvXGspyiqexJ/wBfrXLO+1m7nVT3SDrunW+q3fS1nf3PylrPeSpLPs3iNPJk7fXHtX0Tr/pv4baf03pF98O7+9sLqwmii1V1vmaS/hZ8M57CNl9B2AP0rX0Pf9D2Wown4laT+2YgknycDF/DDMVEjnYQ24ADafQknGcY03MekdP9fw65ZaXqPUXw9tmDpBqsDm2W5dSFjklK7ZChIID89s+ueqk3spZ5fZnLVilJvLO3lkXPxI6v6Q6p6Y0VdP6UTT7zQXhj02TwlRZIlfcYZDnMwPJJOTkk+pqXqXxV1H4oWEemdSxRNp086RtqMVo9wdOVnBabJH7sKM8ZwTj0rWfhzZfG3VU1j4b9O6F01FprFdQSW88C3nkwrLDGibgHIBzjAAIzjNWcnxevrm0vek9K0+Nbq5aTTItIs4isrztlPCMYAwQe5JxxnmpSjayWa9CLtO7eT9Tb1H8NeiepNW6aX4edb3NmYZJBeC+aC4XwfDJaRERUDOSApU8ENnjbzUddfCq36Mu9I1bTNeh1iyJkt5YPkxbvDI8TFZFwx3/hKnPbORXDdV9LdW/Df9lv1Zomo6JPIwEc8rxyRMFGWAeNiCe3HcjmoS6/qGsa9pMN946WgMzKZA5V3EZ9T3wPSji9jJ3WYjJbeazujo4t0lhL+EkyYwH5GR7en3qr6qdoOn5Xifwpo7mDbLvIAbeAGJHbB71Y2ijweMAsTyD9cZxVV1cIn6QujKAkHjwl2yf/AHgBz71y0t+Pijqqbj8D6NqFp038POjJuoOkut73VeqrKILMyLFDbSws6+IEhALE8KQ0jchcYGa5vRtQ174x9a6PHc3kNvBHZSD5/UIWiijiRwzlNg87bnACKT35IGTXN2em2kS2k+nyXHUBgu4JLbTFg8YXjA5EZRfNJxk7f+E54r6Nq/X+u/Fv5PpKbUbHpuSxzfL84stvEqRjYIgu1drN4g4A4C5rratmvv2HJrl6dpw3xi6MveiuspFuuqotVstcQSxXccTRg7WCOGiZmO5QQc5O4c8dhv656R6W0zTLuXojqS81Czt0UGzvIR4oORhTKAAQTyBxjgc12XTXXup/BHWLy2vpdH1a+1GJZLe9gnEvhQLlXj3MrEZY7iPXj2p0R1Z8NvldY1DWulP2rf6jqFxKba9jeaCwiZ8rHAhO2Py5beRuG7ggAVO1NJPW3ZxK7MW2u30OO6K0IddOYOvOoJdD0aG3ErSpNvlaQ/gVA5xgHk49uK0aJELbpixht547mOOOWIuvBkVZnxIAewbiu/6a+E3Tl9ozXuq9U3UcV47y2EVvHE5tbZ2JjXxHyWYIRk9s9veuJFiuj2I0iG5jvYrJmhFxEuxZQrsAQM8k5GfqD7VjWldam9GNnn80MbBv36qMH6kZA4rjNTJ/bd4EDcTAcdvwjt712NiT46jB3ZxiuH1eUrrt8AASZAcgkFcAVWjvM0q6ItrN5Wik2hthbc8vJwR/CT7DvzUm2leLZEVDuGBC4yWVuRjHfPOf+dV9ncGLyEkxoGDlFxuJ45/WrKC5+TkcpIyuYjs8vY4wGHGe35VeSIiboIIryF4gDbPGEYSqGxjBzuHt3wR2rKBZVgdxDvTcFOw939Qe5xg/SosUTPaugH7qFkBx3Zm9cE57+g7ZrZLIySxZkZkjbJhZChBIxjf27459ao0WTLHTpZfnrZ3hwpWR3YplhwRhewA9KsdRJURDH4gTjjngc/69qpdLEkOoxLOkqyKjPGDjauUIbdzkHPYfmatdULLKisMHwyxBOT3xzj7VlNZl4lN1S6jp/cRx83Hg98cGuLZ1dF84VwMHHr9/auw6kIPT3c83aDA5/hPeuLj8WLOxdqnyoWTOPrXVR3Dmq7xdWU7RJmGXbMWVfEK5Cj6e9TRLb3DpFHICHdg08i+c8nBP0z2qnh8scfzEW7BGAJSCD9eMDmrGF4gXkZN6xgjw3kI2g559e57D3NJImLJdyGub3w5h4b5A2gHLDHB3djn6e9a5UK24QhLSOUlGZ3xnJ5Pv7cflXrKJx+7jVCu1N7Ockjnbis7m6+bBh2IZQSu/g5Hsf8KzNC86ezHbPvO/DS44P4dqDitEuVccbSz8nv61I6fjEdk+UWMFnO1V9CEx9zWEoJlU+m8Z4rCWppHQ5vrONv2lp7FSd9rhfN6hyTVErsWUxtgkFRkn+tX/AFj5tStMpk/JryBn+NuK5+NAQpJ2sAcE88/lXdS3EcdTfZLtlV2IXGWHmD9vf/An8qtCY3h3cx+cAIMjIOTkj64qoswnmF0xC5y231H0ParONkSSMwBgFR9zO2dqnsST9cc/SonqWhobQSHMIUpsz45Bxj1GPbvjHrmrbS2LXtgpiVY0dgojUDa5VjyByfX+dUkZLhoSgllJUOVKsD/ic+1X2jz+Lr1pbv4rOWKQKrqQDsJGf+EAMcDB44NYS0NolwQfEJVRIqYDlpdgXJxznn1/zrmdf/8AVu/AbJzGM9//AGmK6S4GZz4asseSy+J32nOM89yD71zXUJH+z93uyPNHkD1xIprOG+vFF57r8Dh4sxkEDDjkEEYrehMzL3LAnhBjGQPStfhIVd48BR+JCa3qrS+aEYU4BOcYwOcH2r0WcCJ9lJHbq2/bI+chDn/WatokNx51kUgtgjP4V+ueBVTZKojWWNmjbcccZC49verOOXMo3QbZBhFiyW38jBI9PX71zz1OiJMhhtJ0iF3czQzJcRMpKho8eInBxznuTjPcVe63IpuJFIGTM/GCCfMaporiUvgMkCGeISEAYfEqFfxDy9uw5PvVvqufm5CSThieRj1OT+tc09TdFVASsq7seZcge9cj1S2zXLjcgZWjjH28tdZHlZYyD3TuPzrjuqnKa9cgZ7Jwf+7WtBdPyM6u6RI2DBQOwXj0/kK2wL+6A3+Ugszk5Cj049/86iw5CseTxzgY4qb4QkVUTykAbhnIb25rrZzo2xeJIoEII2yDljg9xk19Hhb90jqEysS43evmk/wNfO4ZFhigwWQmQKVVQckkf6zXfsuLKMg5BVRwf+J68zGZpHfhsmyNg7kHbGew++Kj3Dfv+Rj8v8PyqYpG5TIx3cknHPvUKUjxFzjPcgehrlp6ms9DS2fDUE4Yow+1fNuuP/We9/7sX/0pK+lPzjv2Oa+a9cHPVF8fpH/9LWvZwXWPw9jysb1S8fc56lKV7J4wpSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlATrI4Q4966fSsA+YbjtPr9K5ix/Cfauk0p8twcEKcV5uK0Z7GF0R09rEzWxMaq4Qqx5wQKvtOZP2nbY8w+ZU9+3mqoQNHaIYTgtErMRxjB5/rVppZ23kBPI8Vc4+/avnZ5nuQyJ/VB/833p3EkxNnj6GvmtvMrxZj3METBP4TmvpfUoIsLzspMLdiMj8q+cLCyREj927DOcghsehFd+GdoHFiN81TSsGZSuM4+vFRjMQ2WI5wPuK3bWlIkZihHHHqPasBGyhsIHPYZPb8q7VJHI0dp024bRbcgHO+TkDII9a2SvmdgBycZ5rV06inR4AhJUPJ24Pf+dZMD80y4OSF7iuCb6TOqOiLfqJwLPTnJCoJ1LMT6bD61RTZdUYnaTGWCDuR/r0q46kOLXSthIJnA+2UIqhLvLM67gQqklnQLgD1yKtB5CRpllg8F1lRjIMFZEOAox2IPFc3dMqzICmMdz6EfWru8RtpZmUKMnaj5U+maoLorl9+CrHPPau6kjjqGuZWCZOduMjbxXulgHWdPAXBEy8H/OsfEllUlMKg47YwK80za2rWckbZ8Ode4PFdPBmHFH066B8VQpzkDBHOeaz0c41ERvgOd2PyFa5VG/Bzyo496y0jat8oIJwpAAryOB6XE0aqVbWb7xI4nzIm3f3P7sfh9DzVNIkjH92Wd1Uxk8qrDPb61b67EJdZv8Az+ZPDIwpJ/AODjiqGZ2hiE65EisA7byAP+YrSHArIxuyqxNAUVVI8x8LB2ntVBJMRvUAqpTD4QZxnOP5VfB1ZWlHiyHGTkfhP2/nmqK8cySEIcMzZLEAZzXVTOaoVch3AEgYB/PFdv0pgdPLg5PzEoz+lcbcIdx4xx+IV2PSgH7AAwD/ALzJ/QVOI6sUd86awLBs55UYJBrb1OJYZtGnVfw27ncT2O4Y/KtGnk7uDgEcAf1rb1Ku79l4B89s4cnkhQwPvx/0rz46nY9CkEcuxJhJGZWO4nuB+nrj0rCSRmEsnypRo/IdylSD/eY+vFJ3Z9rltyALGFxnA98+v2zWFxbPNhvB2KmBtPnGfp/o10IyZHuVbx1MUoeAjnHcn/LNUV/4kewyZRR6nuR7n1q8YxRW7A7nK8MB2z35wOao9RlSaQEMjAAZ8uB9gK3p6mE9CNp3m1OwLuGXxVxj3PrXVSZ3MGIbjgflXKaYryapZCHIzMuFPpXUS/20nY4H61niN5GtDdZedMg/NQnOc+UqDz9CR96ql8GVZAsrLumk3hSMqwcnirrppB83FsYE4Gaq7UybGWOFNqOzbyAASXPOT/T+Vc8TeRvxBLEu4sZCQ29iSrKPr6VWPI5QttA8fJ2uQN3J5HrjFWbSxykfMzSzSAAbyThQM9h2/Ko1wYcbI4pGRGx5sKAO/B7gVojNkVy3lKx+IgCqTG3OPXOagXgiTfmPAfzDJ4U/T3+9Wr2zwl3d9yoNyqikls/wgjtj3NVd4N0YjjRxGo8R1ZuU/P1rWOplLQh9Obf2ykRZiVhl5bjuM1dXPmmlDDsSfMPr61W6Own1kLACVWGQg4I7Lyf0q0vCPFmYFmGAM4rKvv8Aka0dzzI+qtCmlWLXh2It5gPnAVjGcf0rsfh50p031fqcxl6wi6XW0jRbiQw/NPeSNkjHnQAKAB3J57VA6f6ksOk7mx1LVtJtNahjn2xwXaho1lKnD7TwSOcZ4rr7zqDob4n9am/6k8XpKKzso4XTRbSFRdNvY7nIUoNowoO0ntzV6blsWS8/6Maqjt3f2/s1xfD/AOJHS951Hf8ARw1DqjQhdB/2lYSRxNchUGWEBLMQudhIzyDgmtusdB2up9JwdeX/AFpDe6t4Ud9q2hyKUBt1wTbrMG3FwnDAgZOQMY5uNC6b6kutf1C1+DOryx9I2bIsN5rGsJD4czIHkQnbubDHOVUd8VwHw6g0LSesNcsvi0H1safczQNpluSY7mbeQWaRMO0YPmABHOM+1XTbu75r7mbSWVsn9jrevOv9R+LvSqL0x01vm0F45pL7SrNZEsbUgqVkCLgR4xhW58mccE1u6x0/ojSOj7G++HmqXt31As8cepfMSvctqjNxv8If2TLxtCcFeDnGapNB+KVz8PLrVenfh5M66BFevJAs9vukdnGdjkMAxX8OSDwK5/4b9dah8O+p5762tAl7FA9uGnc7YSzBj4Z/hJHGcduKlQaXRVlw7/EjbTfS1/jwNGkyXc+o62dRaZbhWhR454WiZMIxC7GAI/P3zWpmKuNo45/Kuu6n+JF78T9Zu9SvookksraO3MysS0x8zcseWxnAz71yEjYYlcZ71x1LuburHbTtsK3zMtbD4f678QruwtOmRZq9qsrXE95dLbxxhtgU7jznOe3sa7Cf4hajDpsfwb6xkttA0/yabOkMZdLaMHc0y8hJCxXcGzliwNU3R/Qutdc6hbp07qml6QlmsjXM+o3MkSYbYEUCMFyc57cAA5NdVHf6L8LLrWNA+IuhaJ1Hrl9blrrV0m8VflXGIxC8uGi2gd1G7cO/atoO8Ute7iYVE1NvQour7bS/hbZ6JH8Kuqb/AFOw1G5MVzBc3MUbR3BAw+UUjYR3/iGO/NXvUnQ+idJWbdZ9JdZajqfVltILq4ikijMV3M5COIMecHDnbnJOMHvVJ8MdQ+H9v0xLF1do0nVGr3pceJewOWtIwxwsG0gK+ACznBzj0rhNM0zVdevPlOj/AB9TvxcvJYpY22+4uDGxYfh9gMk8DjNa7Lvrpq+0yurXtrp3H0jrHRPizrOhaPcax07eSWw1O38KCaSFJzK77IyLcMWwScZbAGRwBzU7rqD4j6Ra6NY9Z6INN0Ga4dkmiS3dPEWNyilonYgnnvjPOM1Q9JTfEbrvX59E0BtTtta04fM3n7Su1tBb7TgRuzrnLk7QD3P0BNVOt9cdUXevL0t1Qbq0uLaZnurO4jVZCVRsZ25UgZznPPGKzlGVmrLI1jJXTu87G6DaIzggrvIKs2BnynPP3qbaa/a9HXWn6zqunQX9tZ36MIJVBBck7HIPlO1sMM8ZFV9orOJU2llchvbjGDz+VaNUurKytUn1eEXWmx6nayXUBkOCm7lScnHb/pXNBXkkdEnaLZ2nWPx90+TqTQeobTRbO16jQzQ/tAqqTeC8e1t5j79+CckZbHc1svviGPjbqVn0/wBR6/b6QtuWvmvVjFzKEQbBEpfAJYsBgkjAyRwK09YfFTp9Om7VNL6b0RlivIJrWBLaJ1yjZ3kLyQBnucHsaq9b1vqj4m9HXE+h6Lp8ul9NBb26mt7RLfCEFRGuQC5IyxUei5zwK61TSs7W77nK5u7zv3WLLrzoLprpv9m3PSupav1TqN7dR6f8rcQwu5LAmMwxQgYywKlcnO4HPBqTdafF0B1W178X+g7K81TULeI6bHcXULwJbxRhDmNJPDLg4yHJIyDjmrToLoXpaTQLTU+pNfkg10YuLAWE3gx2chXiRzyZGwcYJwATjk1S29lq8VppnUvxo6M1TVul4l2aP8/OBDLcORlmjBV9rIo27gA3fJ4pfg3f5wDXFK3ziXNj0HL8R7a/6j6f6t0HpTTNRmY6bpS2LOIlXyN+8XCwozq5AAbAOfpXzXRoivTOmhwVlW2bIQrIrFZGDEnP4sg/cYPrXaS9E6t8TdXudX+GGjaJ07oEarbyW8t8lrCl0v41iQZ9GQsMEBifXNcna6dPpGk2dlfW/wAreRZhmRCSEkV2V1JHlJBHp6YPrWNR9G1zamulp8yNNqzJMpHJ39j9zXG6kqN1FdHKRlZADIcZA2iu3tV2su/hSc8+v51yOpR//XBclvC5lzlju2jaO4qKL6TLVVkjbYxxWpwlwoXICvIhIB7nIFTUVAVCXBWNhu3rb4Z8nB2jORzjv6fpUAHG3xi0ucopJAwD65+npW+SMBkVss8bsrPvyWUDgk/0rR6lUZRSQxyzSPI5l7FsjyE5yy+mcDitwjWcR+JPtRhsUhOcZHOPTHv9qyjgBjgSeOQhkAd2fAlX0+oAAxWMVu8sSqkjbiMthtqRr9/Xt9BVS6J2lCCK8PhlbkvERFKXKtHtPmGPUkHknt6etT9RVVnVVIIVSCQDz9cHt9qh6ShW+VWHiAIcuTgLyMj65xUy+/HGQXYFcZJz2J7fSsZal0UvUWP9nfPKIgbpOducnBOP9YrkI1G45HiFfwrnG4/eux6lAPTsalpBm8Qnb68GuSht3IAjQBQc4bkY966qO4c1XfJ0A2Kkk+yVDhSpYEg49R6VYxGSOBIreIS785dec47nb6CoUOFRyjKC2SNqjuPr61OBzthgCHJ52nBwD7fzpImJhjxXdwwbgRguQpAz7fw5I/PtU20MiW5DSRxksFfAJKrzx9OTXqqTaE+CHghG58KD4j54zj6cfnWOY5llZ90O7c24sMDgeb/DFZs0WRc6KwNo4AI23MqcjBONnOM/9BWibh07Dzjj8q3aOMaaV7sJpE3eGFyQEycVqnYC5wmdoYDg8cf9KwlqaLQ53rSQjULUq2AtoMIPXzH1+1c5EwaN2HK+revbv/Kr7rOX/wA5Ww3HeLQN+Lj8RHA9/wDKuZhjR12rtCg5IbtXfS3EcVR9Nk+AiZm2nbhc8KSSPoPWrMSxxLIUCK7kBElBbJwfQd+/b7VVwqiRMsysyb/KVxn6D+VW6r51bxlMmCPKuSc8Zz7VWZeGhlvhDhZbdEkJ3METjg4AX2GKtunoz+29NSJFuLhrk4RH2+XY394Dnv34+9VMEeZpFYsC+PDXIIDjuMn/AB7VZdObLnVreKRXuPmNyKEYBydp7nnt7etYy0ZqnmXxCmeQqOFBIGeQfX19O1c51IM9O3gJwWeEZzz+MVfv5pZgiKqebCgEgDuOfpXPdSnHTd7kZ/eRDn/vis4b68UXnuvwOKTcrFA5DHy8evNSYw58pUKR6Mp5FRlJUqAowPxBuFJ96zjfe/mLYIOxi3qPbNejY4EWVoN8p2JgKcgHzbceuKuonimIaVAY2JyWJ3k+3f71TWyDdsQuhZQxf+EH6YqfJH8qZA8qy9iCr7lYnsfy/lWEjeOhPjkbehUM6yXMQAxxkSjHHsMf0q/1h2+YuNwKsjsMH2yeP51z8AxDbSMkJja6t02BQWH75TkZ5B47j9KvdTYNdT7u+/B/UiuaZ0RITqiyxKpGQmSQO47/AONcR1Q4Gv3LcZVY8jHpsHeu2vCv7QBjddgIGfXt2P1rheqA46gulI2nbHk57HaK0w66fkUrbpCjBR23M3JxGff6VOEiRxqZ2APlBH90c8n6+wquy4RVLnC+g53e32re5YmTbgIcHaefvXW0cyZaQvkQPjIE4/edgRkYJ+nbj8q76SRpoI8spYoGOTjJzIftXz63GIYYo4lj2yZbd2HI7H3rvxuC8EnygYH/AHWNeXi9Ed+H4miRvMec5GB6cVBnO58D+6DyKlyuwc7CSCAe1QWODu4Jzjj+ea5qZrMyY8KRzkV8163GOprwHvtiz/8Aakr6Wedo98j/ABr5p1vn/ae+3d8Rf/S1r2MF1j8PY8vG9UvH3OepSleyeMKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQE2zGUP3rotMysgyO4IP6Vz1l+A475q/09sOv34HvXn4jRnsYbRHZhgthAxxll2qd31xn+Rqz0s77iJgRgSLwfv3qki89tFnkKme/H14q60YZliA4zIg+3NfOTVkz3IaosepCRZ3nY4t2OM/evmoE5hVpjHsKjaVbNfTepY2OnzME2q0MgVsYBwTmvmlrGDErbS5J28D6V2YfcOPEb55LIWVCIyuO21u/61G8d1bayj2NSnUKAAww38WOCP8Ki7mzlgGGfT1+tdcTlZ23TW0aLH5n/ALRtpXjjdzn+VeyNicLwQB71o0PI0hWGzKzNjHpzntUqeIC4yuACxHJ7V5895nTHRFl1CWFnpvmEbCVGU8ccHPfvXOyecsgBfJBdWxwPXNX3Ui/7hpzkD93LEeOT+VUdxD4RBjYyerZTG0k+h9a0hoRIr5QMuWwEZzgq44x6fnXOyZaUrIwRGbG7OcV0OoSlUcPKHXOSQpXt2GK5ycNI7CVl55DD0PtxXo0dDjqammVm3GNlAZfY5P61u01//OVr7mZSSPvWlwIuUQMMdzx+lZ6dv/atoUPAlXH610vQw4n06eWUOUOwRnBzjnjOa80r/wBOww7qce9LgqJcMzcg4H1rHSv/AE5fopwa8ngelxI+t3Krq+pQgkNtjO5X25Gwfi+lVLhjslmgEm1t23cMEejY5z+dTeoCRr2oSlVcOIwyhfMRsGB+R/WoitFCNrMSCu11RgM8+/04rSOiKPUrpZN0edrqjSd1OVPsD7Gqm4wLmRzGSh4BDcir2d5UE1qkRjhGfxKOCOeG+9c/cMwXbhcHByBz+ddVM55kK5XxCGC4J8owSRXZ9K//ADD78/MSc/kK4uZi67iFAB4BPI/Kuy6VdToTFiB/vT9u3ZaYjqyKO+dNYAjGACucNn3rf1GFkXTd25iLd8bVHuCefT/nUax7tgAkY4HepOvmeQ6XHFF4sbQvuGQQeRjg/wBa4I6na9DnAu3bJKdibTuGcfcdqjDYvhs8kolflo2QkA+hB+30q1S1AVo4FXxORtH4Xb1AJ9foajXF6qokJ2iRAVbKBnOB6N2+npW6Zk0QSx8X8crFmJjJUccdiK5++UeO4KBcHyk1frHM6747ffgHO3PB+lU+pESrvZSHzwNm3A/rXRT1MJ6EbSiG1iwB8jeOoODziulkAEzYzjPq2a5vTHP7UsYwoOZhnd6j710TN55GIHckEH0rLEbyNKG6zo+l5c3UI2rvA75798VTMp8Yq8TCJQ20uxK7ieSO5BHftVz0wmbyM4ZhsJxjv3zUV/L4jtGLgbmO+NcMDn1we1c8XmbtXRCjt5JzEyM8il8c8AkdufQV6099LIzRyeIisBlgM5545rfI5iJmt5Ny8kBMnBx68fyqH4yySFp2aUYJbauCGJ/EB961Rm8jN5XMnhybopCD4jB8c+gx6faqi6fypGSFRgd0QPqPf1qZPPdfMW+/Z8sU5kZCW3r7/Wo+oMzlWbaVYbidgH5Zxz/zrSJnLNGjREaHXFLAK6xSDAPA8tWEi7dyHPB55qt0As+sYCqFMMnGc48vrVk3LZPc81lW3zSju+Z0nS/R1915e2WlaW9lbslys81xfOVhhiVWyxxlickAADJJ/Ouv1X4e9M/Dbqhbb4kR/wC0CXUCtptxod9JCjBWIl3Lw2QdvqRggj6fPtO0zWNXudPtumJUg1BpyQ0lwIV2BDnLEjH0rttU06PojVLH/tdsNL6xN/bsNNWHVJZPlQjfvAT5QSSynv2FTTbta/ktSlW1728+Bx+pzWg62udL+EttfLHdPClrpxneaVrllw2DkFgTg+Y8c5NfQ+peuriy6ZPwz+JPTFlpuo2ipAwmjRZoLphtFyGQZbJbcWViGHqa36F0Nbavb3/xA+Fuoab0/dJCwstCjRneYINkzCViQjNglVxg47jNUXS3X3T/AFjrqaj8X9Mk16HT7U29hBNGyKwcnez+Gc+XsnJwST7Vs2pcL2+9zFJx42v9rFzqnw5+HXTH+z110913O0HzUMOrpdxxSGRMEtJAFHkbK4w2QA2ckrgzdU6A6M6n6hsoPh51JDYQX3izak2r3JvPCKhdskOwBmJJKlOwAB47Vw8nwp1zqi66mvfh066l05o8XjRftKQQzu5GTDHgZklVeTn0+pxUmS26Yl6Dis+k9O1nVuvTOk0eoiJ5ZJ17PBFGpx4eCTkDdkZJHaoto9p3+ai+q2Vb5obepfh+vw31i7sW1y312K/SK6huLe2MPC7kKtGxO05AIyeQc+4rjRzI+CR58fyFbNMg1S21TV16mhvbK+j8IPFdwPFJGuGIwrc+/vUdmw+RwC5OPWuaompO7udlJrYVlY6zpTpqLqXUIkbrDT+knt4ZCpu0kb5ksygKNhGMYJJz6jg1F+Vv+ifiSmu9QxaZ1ZY6XsS3uIpWktTIU3IcPkkocnkEBsZrXoWl9NavqVkOsNcvNEggjkaL5SBZGmJZdwO/ygAfQ96+m6f0D0b1TLqGnaN1zc6doawK+67Vbm7+bcsWO07EWLYqYwM5OBgCtoS2Y56eH5MKkdqTtqcj1F8QOn/iJ8SNM6g6w0xrmzhtiuox2UrBL+UHMMc7LgHjOQDkgAVZa71pbdJ9QS9c/DTpaTpzRbm0FhqN1aacfkY5vEDKnlOI2IVcqDk4Ukc84wdYL8NundZ+GMtvpWuaOu8XUkakLevIu5JcYJ3gFcc5UriqXQfjJqMvQUfQMNvZJbNafs1LF8qPEc4DuPwZLtuL5z61oo3WSy/BjtW1ef5Oi611X4lWGkW/xJ1WBrTTb6GK2cwui3ZXcTFLNCckRkuVUsc8jgAjPDalp3VN9q+hdW9WaTqsNpqcUkdpfXcY2yKsZ2qCO2eSMgZHbOK7Tq7oX4p6fpljbPd2HVkV1e20UkWmaj8z4jb/ACjw2VTglcknIA5OPTPr4fE63fR4viDpN/p+kTOcSSCIxM4iJjXMTuBwDjOO3FZtpRytxNUm5K9+BydqeImkBKMhOCPQOB/jUu26jToaey1trG11NbS7jYQXUYkjLNuCttIOSM5GQcHmoETqYoTgE7HViT+HzA1Otdd1Pp6XTtV0K1S81RLyMW8Qg8bdu3LkIfUDJz6VzRzkdMt1nUab1F8Ofif1JCes9HstDt7K2aWWDSMWfzxYhVSR0C4C8t5QCTjJ71nq3SOsNPdaf8BNN1nqPQ/lDJJGt0GS0cll8N5PKJcqMqM7scc15q9zJ1Tqem3/AMaNK1bQ9KtoZFtLrS7ZLaSed2XKOSrMVCKSPL3P0rZYXXUHTcmpf9kNl1JrnS8ri4mnubcTOtztCtFkbeyqmOOM9q6s+H9HLrr/AGcpP0NDD0y9nZdayftlbQCXTDYMkyS7fNADyfxeTHFdTq2p9Y6ldWnSHxdg1PpLTdZT/eru8slCqka7zsbcVZyUwo4wTk9q36VffDS80JtR16x1YdWTB7+7nk1G4QW99kuWES7YwEkxwR/DzXM9E9a6X13Pd6d8WNW1g6ZZRpcxxQ3Rhk+bJKhwdnlVQWPIOSVHbOZzaba07v4K5LR69/8AJq65h0P4fvp0Pw06j1LUtLulka4inuN7xzDB3AxbVKMMeXGQR35qu0id59B0+eeY75oBKFYHlix3E8nk+/0+ldloHww1/UtQ1q96EEnU2iQKiWl5fSwW0rvjcbdEcqCw8pJGB5h68Vytt8zHotuL6NbedIR48HgjfHIXOVYnBUg5H+FY1WnGxvSTUrm60G6YqBtOSDg8jmuQ1EK+uXwjTcVlG7PHO0cZ9q6y0J8YducNyeK5G+nCaxfLktGbliwA5IwMms6WrNamiJkckTPvIlVk/iKZDr2yOO2eK9OyFvEkg8SSVTmRpB3A4IHtwBWuCQLkRtgOxGAT274z2/PtU20Aa4DwhVODhiM4/Lse9aPIqsyMChlaQyZaYLuZ/KDkc5/LNbkhiG5iWkcL4ZQOAAAfb6DFaxO8hlaVzIvKf3d309OP+VeBkWMq7+HbZ3IqvuJIPJ/PtUMlFrpAZLtbfzhiHYrjg/hAA475Ppmpl6T4kIbGSGJA45z6/pUDRGjOoZJBHhsCJEIUcgkn1OeRirC7wHgxH4YKZyR+L6/69qxlqaIp+ohjp1NxAT5lSSRkevB/OuSWLxWTaWDIN2AcDHsfpXUdUsv+z9vlSSbpdpBxzg1yz7SY0RhlMlhg4YnvXXR3Dlq7xa2lq9xFG0ZG3eCGPbPHl/KpbOqzuEMciMxAMadvTd7+g49ai2E5ET7WWAKpOzc25we4GP8AWBWZkcSFpJt0hBIckvtQDOP6cfWoepZaG+CQRWynIWLkq7tt7eoHr/jWAaZVn8AnxOSxPPOd3AHHat3j+NgsxdmQbZmUN7cD0GP6VESaWO6czuEJQkMPKC39M+maqWOl0xbcWjmJBGhnmMkYyBvwmSAeVB44PPNRZXJuQM8sxY8dzzW7Q3BsAXBDCdwQvmIOU71pOfGz2JGe3bOa55amq0Ob6xJGqW24AhbNSGwMg7z61QySh5DkFixwcLgZx3NXXWZ261bFV3H5NMjOBjc3FVBmfdtWMyEqRgcZH1rvp7iOKe+zdbJKs7gbQoIBLN+I/bH2qwmmSONWNq+TwhZiMnPooHAqvtpJ03CCVo2zlgncfn3qwjZ7tmEpbLnO4sS3Hvnt+VRLUtHQkzKojjVCwk2gDceQpycD68HP5VL6cbwNbsW2FcO2wY2kgIe2O/34qts38QRpvEZZSN7jsBnJPv6D3q86bs1uupbDeplkzjhsbhgk4znn0+9YSyTNVm0WlwqxSXCqRMEfuv4WHYEduOapOo1VtAvBv2ruhy303iugvUa3urpDkNHOwIZNn8QI8pHHHpXOdTYOgXQAABliX9HrOG8vE0nus4lnKyMYnJCpgntmsm2sm6aRix5UpwVrDAVhzvDfyNN4OATlhzgHkD2r0jzyytLngLJK+9cckA7h7GrGEgSpu3SZ52qoPI9f5VU2DxNI4LByikNt5AJHGfrVlAryS/v3ZY5MkgEYzt4Gawmbw0J9ptlezaTcJDfW5Xcqsp/eDIznIJ/nV/qR3zOeNxck4XjG6ubsI7W3vtMikDBPnIQwhC7gokXGM8D0/SujvWIm857OeVP19P8AXrXNPU6IlRIjpfStHlVLZByMj0/xrleovL1BdM/KgoCO+PIDXZTY8aRju2rzkj0BH/OuL6lX/wA/3wXjLJlRyQNorTD5z8vYzq7pCRCx8vlLcj1H6VujkYE427cjyDtWqGRpCdsYU+rH1H0+lSJdqRqqvuJYcr2GT3rqZzonQkMkBYM6m4X1x6jnj2rumbEIDHGSCcDsCjd/z5riltlkCoXilm8eNlZUbcoHcDnBz65HGOK7N1JtwVJGApbzcng5IHfGK8vFPJHoUOJHuMkhs4OAD345qM6rvcKDgnPfOT9Kkk7sk5PAGa0lMMxYEMCPKO2MVyRZvJGCEYye4Oe9fNOt/wD1nvvXiP8A+lrX0sqec5C7c57eor5n1qCOp78N3BQf/ILXtYHrH4ex5ON6tePuUFKUr2TxhSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAm2n9mffNdDp6g7OO4H61z1mMow+tdFpw/dqcZBwK8/E6M9fDaI6iwwUXewXIcZPocCrvRTm6gHcF0yM/8AEKo9MLbgFGW3HHGf5Vb6TI3jx8HLMvGOPxCvnaq1Pbp8C46gYtBcruJUxPwScDvmvn+nyxIgQjyiP3/ET7ZrvdeyILnGRiNh/WvnFngxkZYNgHI7EV00M4HLXfTJEixA44TA7+hqHMVJAjzwPQYzUiVBs3bgQTjzDtUVsqeDjHFdUTmZ1/TeDo6K3BEr+n2rfO2ZRghvuK09Nr/5qPBwZGwCMZNbLj+05GMBQcH6VxVN5nRHRFprSLJZ2KOxQF4Tu4wMk5z9KobqR4onSCQBQMeGMtn6CrvWjt0+2buMQ/w5HdqobgPPbhGbLfhLlcHPoKvTIkU98ZiG8YYfGfMwyw+30qmmCPKPGXGRgbSc/ermdHE7s/4iPwk59PQ+nNUt1Ivj7thAVhkc8celenTOKZCd1dQqtkZ54/wrfprZ1OzOckTIMAeme9aJEKh9oOzJwQP6mtmmlv2hZ5B/tU7cetdL0MeJ9TnkXdjaXdlHOOBWGmD/AHxCASMHmt0oJdlRScqpODWjTcrex5IxtI59Rg9q8fgelxIOvIP29fnJAwmQy5VzsH6H2qsuE3yFMyuyjhcZI+vHpirLWJRHr1yrL4hOCAJCmDsUAj61XujgvL5gHGWDY83PcAelax0Rm+JCuTPHu8RtscqqQC3PPqfrVPfR/vlbYRGoG4ds+9W89usxjXcyMeFXJbIHc5qrv4lKjaQgQcAZyv5eua6qZzzKyUAtnuA3v6V2XSZI0RlXbuNxIDkZ7hc1xzDAVeX5yT2A+1dZ0uSdDYZHF0/58Cpr9WRR3zqbMnzdzggZzjAqR1CNwsFKghYZGfOclQR2xx7mo1rgnOAQxCg5/XipGuxpJJp+5yIzbuHBGDyRzk/1+teetTtehzMNv4dsHICtIS3HP7vPHb1OM1uEKovzDPiUHAiwCSfXnvisEjEpaBFmQs/lcEqFI/16ZrIJJJP4oLxybdzbQE2j/XvW5kRHlMizqJnL54XBx9MH0P3qlu3FwgfazHGMnJ5q/uL93CrcyHxMkcAK3/iwKpLmGMblaXYcZUlcqa3gYzI2mIV1nTuO757cCugYg7yCGz7DB7mub0s7tX0/CvM3iHdgZzwe35V0srrweTn+8vvWeI3kaUN1nUdLAtcDIQHaeeQc4J596qA7I/hoVkJyQS/4cE43Acf41Z9HnN2M+byEDn1qNMliwMlulyLkEkPNKBGyg+bbgd8+nrmuVPM6GsjQkkxErBXDOSX258nAAOfT71o3LKrPMzy7TkqzcffI5Nb5rfZCXaRy+xiFjKkBT6EY/XNYMqJbLLLcMhlUKnhjzHkHnI4GfpWqKMhMYlYsysC7EgD+NvYj0FQr0kB1aRmA4wuRj6+xq3jk2Hw5seKW84cd8DkH0IqsvifAkMeVck5wcgnPYVpF5mTWRX6G6/tQYRWHhSAMON3Hc/WrZwA+Pbj3qs0jC6vGQAu6N8j0BA5xVnKR4jY9z3qtbfL0twja3LLHpEMtvlpFvEGB65Vsj867j4d9W22jXMl/r+hC9ilcRWN1fW+6LKfjSNnyjEEjIznt7VXdMdG6p11d22k6C1nDcfMrNLPeSFYYYlVtzuQC3cgYUEknivo+r651npsX/YrrFhpuo+LEttp6Lk2jjnZdI/4QoyWL8MMEEZq9OS2Nn5YyqRe3tHCXHU3T+vfFmSbUrt9L0CZlkvItORQ5IXzJHjhcnu2DjkjmukvovhDpnVinT9E1fVNLktVeGyfUJdtvJuOSHG123DHBPHPvxkOqrf4f9P6j8L+rdB0m6NsWjup7YoWuJM70njkK7yeV2kH0x2qn6I6A6J1vp28tPiXr+s6Jr/zTrFDG0e23jwNm9WGZCck8MMDAFaO2uaXdx7zHuyb7+HcXc1pq1jqFnrHQXT+s9P8Aw2u7iJJL+OSSe2tnLbZ5O7Ejj1IG7jNbtZn6R+Eslt1V8H9Y1V9Rvn+Ruob2SOd9j/vDIpQKy8oAw+tUKfFTVrXS5+jH1CM6Lg6MLhbY7Ets+H4oUHG3Hmx3z+tTJun9O6SuNN1L4Xa/qPXGqyBraawuNKSZo4yNxmCgkDBUA5583eos1vfnPxLJp6f+eBR6z8Q9X+IuoXt3r7yE2FvHDAHj2Fg7MxJzkn1xk+p7ZrmzlpDjAwR+VdP1Bq/UWrapfP1npVxpFxHbRLaxy2gtg0XnydgHOW9c8Yrl+0h3difWuWpbayOunfZzMNR0i41ltLWzuEtpITM29pNh52Dg12HSvQ2giW7XrbrOXSNTR0MCQ2C3SzIVzl2dh5t3GB6CuH1eVYF0yV5fBAknRW27wWKKQCB39K+ra+PhvqXQ8N1ps13pXU1hYLI1/FI7NekJkpOn4QC3ClcbeBzXTBy2EvmpzzjHbb/PcT+nPgpBrWl6pqH/AGiab4sk8wsG/ZpbxkU7VeYhx4ZOPwgHaAO+cVstdY+Hz6E1ja9I6cxaPwkuUjMk7zY2gq2dzOXGQAMk4wK5nQugNL1DpW01XTuv9OstQurXx5dOuLQgQOQSYxIG5P1Ze+a39J9O9VfDyCw+J8XT1vHpsdsvy0txcI09v4o8s/hKSyAjs5GRuB4zUt3veV/QosrWX5Kb4e6V1z1L1A1n0ZaSw6voqtNexXLizKk5QRt4pA3tk4TjJz2ArZrHXfUWpazB031B8xbyQyO91BNAIWVkQ7QVBOefXtW7qb4v9Q9W9UWmoaPDM93cpHZvLaDLXUhYlAzMPORkgZ96vOtvhj1Ho97pfWHVGtabqVxJCbS7tIZTJNaMyNsDPgLIe4bbwpwORzUVO2aSyy7blqfZBt5+Ry9uDtjbyjExPIzwRirKwutZs7nT7jpWyub3WpLtI4ba0gLyyhgwYKo7nGTn2Bziq5cKFBDLiNW4Pfzd/wCVWemf7Sz3diPh/FNPrfjqIEgKoWQKwkyzHA8vcnArjjvHXLdL/W9P66+IV/b9Iatp9xod2U+fkm1+J7OGOOMgZU8lmLOBhff2yRom6t1z4FwW/SGr3VrI0URurafTS0sd0ruTlWOMPuG0hh6A5IqP1Xo/xl13VtF0240TU554I5p1zcxhD+FWO9H2r3X+LJJ47VhpsWndLTTWfxt6La61xjmFrq+xHHadhtIc58+7LZJzgegrsTVtU12I43e+V13suuo/h3P0hcW3X911BpnUfy9yuq6rpEkRRY5SQcKwJWZUYqTwCSpOK8u+orX/AMoXXITruv2XT97pcZll1Czt/FnnWQhBFuymcYLck44HrXBQz6l0D1Hp03WvT11a6b80ZY7PWLF4lu4efDzvG1gMqSMc4GR3r6Dq99qPxxji0zofQ+n7C/0si4uJy1vaxiAjaI3ZAFO5sFVznKk8c0aas39+wJp5L7dpU69q918Ip7bp/pvqh9Y0qSKS6huJUjSQSM+ZFYbjhs4O7J4P0rn7bUJdT0uG9un8e6vYfHnYjJYlyS327V1PS97050dp9zoXxW6Hs9V6kEzG7N5EGFtB3jWJgwCIVy24cksSTwMcvb3MB0K2XTN8OnMhFtEcsfDLtsycckDAz34rCppp59pvS118uw3WjDxAcgehOea5a+2LrGpbmO0zt/DgrxyPqK6GwbbIpYHAIP35rm9ZXwta1AySDLzZ4OTtIGBx2OP61SlqzWpoTrGKSW0MjyQRybfJ5iSSPQgf41sTxYmCOA08i5OMYyfYD1rQLgXGUUgtgDnj8h+oqRtntYV8YRwXABXMal9q5wTu9M/rVmQjYITHxIkhVCdhLZC4Hm+n/SsPE+XhMUJMeY2Q7Sf3oBGAw9PX71mIljDtFJ+7AXJ25DsR2wPXGPoK8uka3EjojEgMrA/3u/p3FVuWJ2hjF0rbh4Do5UFhxyCQV7/nW+4bLw/9zJ75z9f8q1aPGPHmmVmDeCU8MqD7eo4/L3rbdoRMg7Hwxkhg3P5VSWpKKbqyQDp6Pdxm5TAx34PHFclDIQFOTlTn8/bmuu6oB/YEeCA3zKjLHAxhuPv9q45VVsMFzxjPYZ/xNdVHcOWrvFrBNLNvPCh2DHBG/APOB9h/KpU8arIRAyo4fOOTyewz27VBs2VlVmDNKzebDAbc+pJ9P8Kto44ndAzR+VCM+Jv4J425/i/zpLJkxzR4ZmTxPCtgjlSqMxzyR+g/StxRoYrdjcK0pABSIcqw7gA9/v8AWtQkjQYZZGRMr52KruJ57d/yorxtCZAhVWYoFxggew5JAwP51maIv9GLLZsz53fMSnkY/u571FkfMhyxJ2989q2aQ8ZtmEcXgILmcGPGCp44/wClR5T+95zkhc/c1i9TVaHN9blk1i1z6WaHB9BuY9q51fE8RSGwwPHPJJ9TXQdZoZNYhjAOflUxgjvubuao497SkZ2lQMke3516FPcRw1N9m+IeLKHnkChgdxHb71bQIGDEBpdyEE5xkcc8+tVlpuI3hhtAPBA5P+uauI7eVkAaMuThhu5AX7VSZaBItk35aaIJCckMew9B69s1Z6FaJca5ZQB1t/FmZGEh2gNtOWJAJABxyAaqI2RSTJCJMsCFwRgfTHarfp95DrtoqyLbzF3w5baYm2nsw5yOMexrCWjNkWl8d1xcZd5i0uRLICDJzyxB5yTk88881z3Uxx0/d7c8zxf/AC9X+reClxefLSG5iEzeE5ySw9+fc59T7+tc51M4GgXOSB++iz9fNWdPfXkXnus4jHmyWwx9AewrbIRGybly5wMHnNaEG0hkPIz64AFbYot5IVsuM+fnj616jPPJ9m5RGCABcDIx65759T7VcCZoVUPGniNkHeMrjjntyeexqrtY4DuTbJDcLjODkN7Z/P1qdCU3NFb7lDsCV/gYE8/zrmmdENC3gjdr3RTb2cAzewoUgDMXYyAgHnnt9sVcX1w63RlR8lpCGOc5B781ztmyzanpviTLGwvoF3RjJVd/JA7mrzUMd9oUCT8POBz2/wAK5Z8DoiQ28rT9mBRhkjgZGf8ACuM6kYHqC95BIKYVT/wD1rskYv4g4wGwePTmuK6iOdbuuzHcmffhBWuH334exnW3TQJEaJiI23jlQre/0reFmlG8txng/i/6GosUQd13+VSRuI55+3tUueWPxXEZwEUIjBuB9vpXWznRKsp7cXkMsrERvOocLgMcjnGQQD2713pQrBIse5hgZYr2GCAP51wkG5XiErhQ0y5GBn8QAH15Fdsx/dPkAEKB37mvMxfA78PxMHJBLL/ZkAewzxWbKoQl2wz8Dt+n1rUxAcg4GTwB6ZArESEJtH/F39644o3kzU5IRdxz5eTnNfM+uP8A1p1Dt3Tt/wBxa+ltja7HzFY+cfrXzPrb/wBaNQyc+Zf/AJRa9nBdY/D2PKxvVrx9ygpSleyeMKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQE2y5BGM89q6HSiDGATzlTyPvXPWQyrenPer3SjkjnsR/WvPxGjPXw2iOq0ptk8RHJEhyPzq1sf3d1EcAZkAxz6mqnTWAuoiuNviE4xVpZAm7h3HBMgyO3rXz9TU9qGiL7qHaLa5UKc+G2Tn718x092+SjQgKAo5PJI/wAK+l65zDd4HBRgAB96+aWaCKGBY90gcYJCjyfc104fq/necuI3zOeXK8HKZPlz2PsPpWh/KqufwMOBjgVJWFT4p3Db6f8AeqM2/kAkqfbn1rqRzM6vpyQrpLBck72P2wQRzU27AEjbQcZwMGq7QQTpVxtDHEjev0H+sVaXZZiu/u3v9q4qm8zojuon6swGm2rMMjEOee/4qoLmaN3YtFBGFYKxTOW9sk8+tXuplW0eHdwuIwf0NUUl145Z3dWeIfhVRz9x71NMSIFzFyzNK9sHALAqTvX/AA5rnrtCpceJg7uDjk10l0skq+TBJA5JB/6VzN1t8RyqFSx25B716NI46hFmKiLmUlCeN3rWNmdmp2mMbjMv9aymSNS27ClT6djWm0AGo2nOT4qev1966+Bzn1eU5cFdy4OSM4DcetYacCl+hDEdwMH0weKNwcjIyvGTn9ay0xVbUIg7BVOck544PtXk8D0uJW9Qh/23dlF3MWjQluMeRefaobKTlZQXZU2xBVGSpPPFWmrM7a5fYaNY1CZO7G47B6f4mqudWc7Y5/BlYcOpzgfStI6Io9SDqcMplDWtw0J3EExjn7Z9Kp5FEXDsSffdz9zV1OuF3iTzyL6Dufc1Qzxttfa3n44Irqp6HPUIsg/EfEJXGBz/AI11XTB/8ySZG39+5454wtciIvIFwM+uWx/Kur6ZOzRplByBcMMnt2Wpr7hFHfOms8FzuHHBBPYVI6mYoul+cf2T48wIfkdqi2JHj45yDxit3Uj7V0rajO/gSn7YI8w9+9eet47HoUXiiQqCXVo5CXfxD+AjsR9x6VrJYwkYneMhtpC/iB5x3963rOHdl4RgAS8gBzx6/wBPpUaQQrausKrIAfLI2d4P055H3rdGTMGBmtJUdVkjyACwIOe5OTiqmeISAyfMRpz5U5z/ADqwlklVnSWaRgAQAWyfoKqrqfax8Nxt7hGGcH71vAxma9JTGr2hjwD4hOPbymujz5UDEHJB7VzWlknU4ACCMk89xwa6OdgWC5IAPpWOI30a0N1nWdGxE6lHt4LIxHHeq9UHiHJbKbvLjGOe4Pb8qsvh+PE1ZRyVCMxwOR9qiWQhmCGSeLf4jRtEXI45PBGdx/SuTizr4I1G1yNkrsjEExtgFV7H19DWl4JGeRZLi3V4ZFG91L7uMkAVLECCFyFwsvIEmcrn0U9sYH86125niljy8LxLgrGTkgYx3xgc1omUaNLKEXFsxlUjeeAV7cgHv/Kqa7UXTf7qFg5y0YPJwPf1q6hjuL+ZFKRZ8zvIAVcHPCgdvz+lVtzanYzW+SI2LAlec+3YVpF2ZnJZFdpiH9qRu+7JSQNjn0/rUqTl/TnP9ajaQ6jUD+6PiNvYyE59ORjtUiTAkIA4HY/nUVd4U90wvNUvdIt7S70mSSO5W5C7o3KEgqfWvr2g9MwdV9HDqrVuuHs+qILKU2GnYDxJGMttmm3BwzEfwjC5HB5r59050lfda3dlpmkT2trILgTS3F3kRRRKrbiQMlj2AA5JPp3rrH6C/wCyrrrTbjree06j6QmVnhexhxHdyAA/LyqfOBzllzhgMZIJrWDi4bKeZjUT272yIV90fE2kWXWUPWen3mvpaRTNpj5GwkqRGJGJ3OMjJPHB9K6D4lfDDVoNPbquw6vg129W3RdSsoo/AO4YUGF8/vBzjJCnA7YqVJpnwz6466vZXnl0Tp35FGudHsh4KfNMxClDjMcQUBvD4G7GMA4ql0zoHT9Q66uenNZ6pFl0ysBuDfW4DXE3mxGiK7bVbOC30U+9Xu8nfTu9DOys1b19Sxu/gzedIdOwaj1TrWi3+jw7BrEGmzypdpESAVRnTYTuIBYemSM1J0bp3Q9f1a0j+Dd/qWha4iMLqXWdYdYltMecghCxO/ZgAHOTntXBdaaZr9pqN/0u2r/tKyZcx3kc7FJ4c5GIznYTgZBJwcgVyOlzX6X072bLa3NgBHK0UjKz5Pvnjt2q0YSlG7l88CrnGMrKJ9Q+IHTvUvT2uXMXWWsWetNPbxGyubW4aZDGC4ZcsqsGDAZGB3GM189LEOMDnk81Khu9SurzUpNYuDcyiOARlpS5RfN7+pqGf7Q84PP09q45xcZWZ203eJ2vw+6/tvh3rlpqM1hHd3r+KtnOcM1sxCbygPG4gAZ74yB3NTJfib07onxdTq2z0eKOWS3NwLSR1EEN6W5nWM5RWwSQDwGOe+KoumunukOprqOD4haveaXZwCRrdbQIrSuSgOWfIAAH559K6K06u0v4L6lqmn9HyJ1B09qixyvd6nZK2CFYGBn2lWxndwB+L3ramo2yTb+cTCo5bWbSRv07qfoXrjr7XupOsobx7W48LGl2jssF1MVxLJI8Y3bScZRCATnJrTpWoL1X11qnS1nrd10/0Y6StFNO5bZZABRHEj48Rtx2AlscZOcc+ax8I73qbp5uutFu9Iht70C/k6cgjaBhbKPNtfONzBS2zjAI5zxWzrT4kdR/FXo6ys7TRVXTNLmgZLy1tGePT7YeXGNuFTBHl5zt+lXST3fDwM7tb3j4lJ1joJ6VvYemOktWOracZYrq3v5YliaJ+Vw4XPmAHGDggg1TvNq8+paYdTvPEtQ0hWIzmRiwjODgk8Cvouuaf8Opul9K0nRdZ1hbxLyF9X1lFJeWAH97iLhIlUY2Acg8knJrnuo+n+k9LvtMvelupNU1e7czRmC7hRVCGM5YMoHPbOe9Q59Bp9/AsodJNd3Er5VOY1b1h9fowNQtWuL+DSbefQ3mGovqMSQbDseRnJAXg+tTpHA+VH4sqR7dxnmpGl9PWfUzabpWpaxHoVrNdpLLfMgkMe1WYBVJALkjAyQOa5YNKSbOqabi0i36t6c6/wCienLbqzX9cso1hdbe40221JpbiNZDw7tjw8hlA2gnGfvXR6d0na6v07pPW+tdWSN1jp0Yv7LTZBFNbKiZlSORmwfEYgHK4VSVGDgmqmKLSvhh1bbjq+70nr3p+4t5PkImjLI1wCvmmgkYgFVJIAJBJz6CuA61vdN1bqfWJejI4tM0dlQ/LBFC27bf3kakHypxwO4BxxXWlKeS+9vQ421DX7X9T6NB8Tbz4i6fbXXxA6cv+pOi7Sfx7uYW86W8U4UiIPLkd2cZVTzkZ71yvWHU3S/S1/bS/DG3vumkvkLXlpBO7b2Q+RkV9zDuwwePWrS6+MGs6nokXT2qrFpem6mi2McMtjLFBEsmF3bCAoUDDEjk4FXOmaLofwdnstX+F2s3nWfUlz/uEsR05JSqnzmSFY9zrgoASecNRLZea8uHmS25aPz4nM9JdcdItb3F3190haa/q5lw1xqU7ExwgDYm0suMDJJxyTUGK4tbvS/H0yH5KwlUvbW4JYRxF2Krk57DgZ96kfEOK+696in1brqzu9E1eKCO2EVzYPC7xLuPikMATySu4/3cVX6XbkdNWAQPLGtqu2TaANvIH+uazqpbKfH0NKV9qxJtXAn3rGsfm4AJI9PfJrn9QjSfXtSQHJ+cbKgbc7QOSf8ACryzYi4Qlh+IYAOec1z+tyY6i1V5CDL8y6oNwHlQYAI/LvWNPVnRPQsrZGWJDcxxlWIwqtycHuB/mfSrN3hgYKsHjSsv4pJTIwB9QFGB/Oo1o4XM/wC9DyHaiJ5cnHqwGAO/A5pbWMs7vDBIqb1Z2kBwsYB7njPf9TVWy6XYbIY3mJV51JQ7mBKjcMdtv9awimdlEdv53ERy/GHO7g4+pz+VZzLFE8gTaXijaRjcMVQqPXPHqf1963iOPGyOWK6BSN3ORg/xbcE9vN9jzUXJsNOiktZ3t5I1XFuSSpywOQcN9OfyrbcArcqHAVtgGBjHasbAp4sreIhd0ZiiRELjdwc8Dj2ryUhbwZ/u5x69qqQVHVTgaDbg4Ja8UDI5HlPIPpXIAxdyC5BGBjGR75rqOp2C6JbM2OLnIBGcnaa5dFBTCj8Yy7HgKK7aO4clXeLG2iZmAidSTgbT2UZ9fbFTZLY/LoEMLMMgsFLZP9cfWq2E4jKuCAMYVIxlvXv3J71OiSRbmHDCOFUywZMebPbnv3/rSWoWhvGxQSVlKlDtHieYNjBPr+daoVS5hClFXaP3j7TnbngY/Kt+xlhdpWO0+U+H5efbI75zSz00rFmJTM6glUL8EA5Y5PJ/1is+BdFzpWWsnBPiubi4bcxOMeXHHpx/hWqXImbcDjcAOK2aK5bTgVOSZZhvz35HfJ9O35VhKAJACBxJnA9ucVg9TZaHNdXgjV4txwotEJPfnLVRwIrRsNyhhzz6j7/4Vd9XAvrMKkcG1j9cDu3NUWM+GFG7JPf+ua9CnuI4p77LG1YNFtVHII3cKTj3+1WEQh8MiZ2BZPIVbk4PHHsM1X2crKBLFuWUMqqwPfnvU6FfCZmMY8FRhyG5+2M8DOOKrItEkATArGkoZ3IUheWz74/rVj023h61Zi62GM7wZGOQq7W3Nnv6kj61WRGGMEwHxRLgYk52EZ7kc/kOKtemoFueqNIju4gtqdxlkzw0YU7+CRj3POfasJaM2WqJ98675xECEDHYo7BQMAZ4/pXPdSkDp24X/wCfwgD8+2a6DUpHuLy4mm2GR5yxC4AySPYkVzfUr+HoFw/tcRenfk1SnvxLT3Wcaruc+GhIwcsR61JGE8o5LDB4xxj1qMjqMhWcmTtt4x7cVIdVZV8TO0HHB9ua9JnAT9NmVH3s5VA2NoODx3H8/wCdW6SJ448NNgclQIWOFJ9QPU1UWhBGSqlA20sBzira2VoXjS3dnXADtnLnn1+2TXPM3hoWegLe3Wp6XZQEyRS3cSBI1UOw3ZOTj0AySTgAH2q1uwssjnJwXfbjn+frVLozTvqmmpab3ujdKsccW7fg53fhHbbnPpjPpU69OcCDyoQSgHArkmszoi8jXtDeMeMtk8nt+dcZrrBtbvBgsNy+bPptFdfYMzwnfg5X159PauM14iTX707lYFxjy/8ACK2oLpPwKVt1GmGEvu44TvwT60XL84Ma524Ycmtcim4iESSBNpyCCR5vepMEeCTK7EDaRuOSe5zXWznRKhGXjXDKUlQqGPOMiu6cb4yG2jAAAA7jH/OuFiKmVGkAWMSKF2DAzkY+9dyzPGoHKgqjEY9xXmYvgd2H4mpwXPcADGPyrW+M+Vs4zg+1bWxFJsU8bv5VHkBIJ7eua5IG0jYAMev4ec/avmXXH/rVqP8A3l7f9xa+mr5V98qd3296+ZdcnPVepf8AfX/5QV7GC6x+HseZjerXj7nPUpSvYPGFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoCfYfharnTSRIo/4h/WqWwPDfU1caevm/ofzrhr8T1sPuo6mxbbMpPDBiftVvYyb7mHd+IyDnPqTVNZt5wxBLd+K6CwTxGh2gPh92B68189VyZ7VPNFprQzBc8k7kfHvXzu2At4IlcAMqjHoR9a+iasuLeUjG1kkwM+x7V80s4VFrtXcHwDlvX/lXRQ3DmxG+bpZCzb1H2KnIb8q8lt2jjG9lEnBVfX86yg8O18sWGm7s7Ht9qjZMjkliWJ9fWulHMdX00gfSp1x3mI4+3b6VMuWKvGcbcjsR69qh9OnFjPvRc+NkjGB+lSr4gNFjIJB5OK5Z7zN46Is7zz6TAQwwQnJHbGa56VfDilluCu1toBDYGDwM49avtRcrotsNu/hRj6VSXNuzRNG8YTIG7cpGQD70pkyKmctEjI6PAecgk+b2OKoZCHnbAIxkKQDyfery/bxSqAFSq+Q5yftmqK6yHKqcOO+1uPvXp0zimR55PEwznz5IJP0rRZMP2hatjH75Mcema3l4xGxdSSwOPWtVorC4ssgY8VSMA9s10LQwPqb7QxZePLg/rWemH/zhERgnOe/0rW4VsMcEN6Y+tZ6ao+bQAYwCF/SvJeh6K1K3XFB6h1LztlXiJCjJyUGDz9aiwzp48gggkkTcu5iwK4xzxipmsYHUWqSzoqW6CJQ6t5mOwcAe3rmq2ST9629x4YYDxI+c+3bjFaJZFXqRbwo90WY+EhJKYGc/aqe73q3hyeIygkBc4z/zq8ukR2wh3tDydwPCnvnHrVDeOGPrhWOFOf0rqpnPMgTJu8u7bj8QJ7V1fTIP7FmUksTcHgfYVzEwaSTew7Lk5711HTRB0eQ7s5uM5/IVNfqyKW+dHY8TZI5zkDt/Os+psn9lB8+GbeVX57LlT6ela7L+0JXBJJBB+1beo2UnR47neIWhlLFVznBX19K8+O8dj0KOVkAxE7MQzBVAwcH+dRpYvEj8TG5RkY2ZA/Xip+WiV3ZyGlyFI5O3PHP+VRZiwdmmkCEjAMa8H8u1boyZCMbTzgkc5I3AY2/n61D1BQp5VQp5XOM/9KsnmaRmXxIUZeTwBj/w+uaqrrDoCDuYOQucYreOpjLQj6ZITqdrtBAycED6Hmr6Yhp1GeQTnAqi0hQ+qwDcwKNwOw7Gr+Rk3EpnO/v7fSsq++vA1o7p3Pwzhb/aFEP/ALhmBI4I/Orfo7o3UNVh0PWta0jWLfp6W5c3F/b2bRxtFzysnpu/CG7VW/DRGGtucKXSymce/AyM/pX3ro34gXfxE6VuoenbM6uo08x3EUcyQpBmMjY7uQEPBwM+mR71wO93b5qditZfOw4jrTpfoc6n07b9FSnQW1K7+XuUubqSdFQLuaZQ5J3gDGM4ORmpPWfwp0W3sIL74c3OrXmtGSNJbaR0kN0jttbZgKEYZDZB24zn3r6RBpkPR2h6LK2i2UsMNugkubxo725aU8bmkYE+48pAyaqbbpx+ruqrTXOn9Us+n7a0ikivoBDuWeRh5WUAgIQO/bPFQm+DLPvR8qsPhX1R+3v2P1VCOnIRCLg3TFbiNo92NsZjOGfJ5BYYHPtVvH8JumdJ1i/tOr9cutWs/BRrZLV/lSQc5aQAnJBGBtIGOT7V9FTQryfqwWXU2pXMtjCm+xXTnCm9LDDF8glAmMY9Sc5xXMat8P5L3rPU4NV1mez0WGGJ4IJ7FHuGZs5AckLhcdwDnI4zVlJvK9ijVuFz86apocGj9U3KaU1xcaUiuInlG7aSv4Cw4JHv7VXtyXIAGOe555ru+tdMt+l9Ul0qwvmvLWfdNG8oCuSO4IHBrg5PM0vODn0rZu9mZRVrlt0/oOv9Q31la9HR7tRWQyNuuVgVYwp3EuxAHOAPrVj8Q9C6z0q/0Ow6/spm04zGcbNRS4SbZjeoePhTg/fmuXkuri0t7OayuWt5RdhQwYgnKHjP3r6V8Ptf6P1FdQj+LFnJrF0D4Nl400x8CNl8zqyH+0JxgkcBeO5rppuUY7Vr/wAnLUtKWzf2NvVfV8/XPRNnp/QfR0enWfT1zGXu9Ms/Egson8r/ADEiochjhiXJby59K19d9DdM6PoGn6v0z1dfdSdQB4ra/haCMw3CuSMW8KDcu1gDgkkg54qv0T4n3fTd4/QnTGqiLp+W8ktLae4tgqtHKSpmkORkgMcnBJ28VaP0zqHwf1XSNb6M6qTrS4kL2cdvbWDrPEWTPiBFLFhhcFgcjd9avnHLTs7/ABKZSz17e7wLP4baZ0Ra219L8TRq8HUNwxt47WWaSwNnAMbCkYAJcglstkduO5OXQvQ/wsi0S/F/rd/e6xdyyldShvvBeJPEIRRGw2v5QCxYHLE4wK5jVOsU6s+JVtqfxU0jVY0s7BUktFWW3e+Kudok3EOUGSDjBIGMivNZuOkuo/ijbzWrydJdKTWyzXMNrZKBHMvDRwbgRHuG085Ckk4qrjJ6tq+eWngSpRXBOxyNnarp2q9U2cV9+0o4L1I4roDb4qru2tj049BUeN+SxBIO7jPPeu866tuj4NZlb4bi5j09raKS5hmna4EUg3gESNyS45PpkEjg4rgowPDUfT3rnm9qVzqgtmNjTrELTjS2idopElmK7W5/CtfQvh11P1Nd2afDvTdNh16O8lln+VuIQ4y2C0ryMQsYGBlyRgY79q4ea1trqfRRqLTLaRXcksyQMVkkjVVLqpHY7QefSvp3WHVvS11Y6dc/CPpp9D1tCIHOmRMGntPxHxBlg5BCHee/qTXRHOmo2v8Ag5p5VHK/yxA6e+E/VE3Uuq9Ja/q1l0pb6fCqXd+0zXStHICU+WRGAlyOM8AYOSDVpY/EV+gZ7nopJLS5ls5hZeFbggXbEBVIXsd4I7njOD2qp0nR7LrvSpdZ6h62m0HqsO1vHZX8AeNYU4TxDkPuLFjlQQBgYrXadcdIDow6XqvR+mXWqQwOrawABcy3QyBKrg7iS+MDHYAdqtm3nn89SisllkZ9Q/Bnqvozp6a8uL7TtbeZvlWsNKuZLi5iWV8FUUrhyO3lz6HsKkdUdWfD7VNO0rTek+k10bWbVnSG8Wdnkki2HxPGYsS+RggkDB7e1cPpfX3VnROvaNf3lrqVhNA5z82kqliylSArY45JwK2aj1DpXUXVVlJpmlWunXUnjSztC5CN5DwN3bPJNROEmntZ65omEoprZy01LeQBktJDyihM44IzxzVRr5SPpuN5mVVF1Cd2Pw+Zxk/zqfG6SWkAx5ii8g8jBHpnittprWnaClvqGtWEWr2lpfri2n5Rid20sO2FJzg5Ga5IXUkzsnnFnnQ3XVp0nPeNf6boevwX0aRBNQhSUQKpJO0FTt3EjJxk7RzxVvpukdCdW3erazdiLRTct/utlYQyBFKKMMFPGGcdj+ldDbadovxu1JB09+xOjX0mMG7uEtjKZEk4RPDj4JBVmJJ4/OpfTWh9DfDy913RuuRF1Zf3Mu6KW3muYoo4HQBcRow2SE7i2SfTB9+qUlna6l3fLHMoPLRr55lMnxe1b4kwx6J1x+0tX6e8Zf2s1nasTBGh3FhL5gnmVRkAd616VoOs2XUq3/wSvNb1q4S1kFzbyBIZYIHKgHzEByWGBjlsH2NSdM+LWtaNoOpaF0ZpN23Sln49pAItPd1mi8y7pWUbHJBO5m+tcnAI/hfpMF50v1LJevfFEv444DhCgyhBfG78TDHpUpapK3d7lb6Nu/f7HZ2/Uekz3OoD47dKXmqasqrFZftBpY3htTksyqjqvL55OcYrk1SBtKtZtOtJotNa2/3V5nyxQMQoGcZKrgHA9ck1a2WkaZ8XNGudc6x6qu9M1GDba6YrCMoYcbnaTnIyxAAXPY5zmqOyt4bLStPjWWK4dLHZuCNjK8Bg3qCAD7DPNc9VJK3H08jeldu/D1Ntjn91jAO7BGPrX0zoyw0CWfV11bS7HUZLm6lFw1xbB2OG24DHlcAYGCPevmult/vcO4BgXBBYcZz/ADr7L8Luj+nLM6l1DrM0mpXt7e3E0dlO3+6xAyEcx/xsQO5OBngZ5rnOoiQfAqC60OKbpzqKZtTnZpIbW7jEdt4ZbhDIuXBCnG/nJ/hrluqOjNe6Z1qy065gjubzUnjt7VbXDwzsThI1c47H+9j3PHNfcdL6cT5q+vLe/t9E0V1VbCwhjMjJjh2BJAVCeVUZPc5HArn9K6/bR+oOobO/ibUodK2C4keAm3hkByrlz5VO1sjJB5qrvqWVlkc7b/CHqDpbXOn9Q1XS7Dqa0R989tpMjXQiIRivixMoDx78HIyMgDtV1qOp9O/FjqXTNM6mMdpLZRNcyx2YjW4niBCCEnnYpcg8jPlIAGci76N1fV+sp9Y1DRp7bR9FWXwrS4kk8U3Lqo8QoqnhFbjJOCfw5wamT9MaVqdppNxqt8tlrelyOwutPRFMrkMGWViuWRtwJHByBg8ZrN65l1pkfLPih8PF6F1dJdGW7OkX1udi3cqyvbyBlOzcMeVkIYbu3Iya+azIRckqDtIJGR3H0+ldp1RqF1dRXdnqGoSSPZ3IjNuQCjjOC6sMevoR2NcM4xdnsSM8juf+lXg7ozlqUvVBxodsQf8A7IHfsMg9/wAv61y8LhwinJYYbn1rqOqCDoloCwYi5XC575VsnFcosgBOAwXHJzxmvRo7hw1d8tLJC5Vy+05OccEEDIq2QtcSZZ1ZtgBWOM4GBwCfp/WqiyTZC4kIIlYbBjOQOw+lWcTFWDsFQs22VR2x7YqstS0dDBf96ijYKUMYbCyR8E+5Hrz60R50mB2kSbslseZmP19MZrNJYJnZYZi0C9m/Cfrz3OPSvEePwlhiARQCWY8/nnPc+9VLI6HTZGkt3eURxlp5TiMAggEc1GlJDj6v/hWzSmb5FSxDDxJgDx2BUemK1Tg+Nk+j8/TisHqbLQ5PrFj+2IsAFTaRnPv3qttgWG0HIA2hvv6f1q26sZF1pQxwflIsevPmqkfyncm4OGyRjgfeu+nuI4Z77LG1UIiopDOx5Ochc1PFyIkAVgr4DAkDDc9sgfaqa2LHczYYehwSQferQSIyBJyIlGAD7Aj0J+vtVZF4kqIeMzSEoXLDykk59xxVroltDdaxZiaOSWJxIsqxpvK+UjOOx7Dj19ap4ZWXykLGmBEoOBn/ADBq06ee6/bFltkaeYZ2Q4Y7m9MADP8AXg1hLRmysT9SuHuprmZ5ZHaQ790mNzYb1xgZxjj0rnuqSo0G4DsAvjxc/ma6HVY1S8vETCqJJFAVGQZyM+U8j14PtXOdVZk0ScE5zcRZI9e9Up76LT3GcnuCKvhbjx5iQMA/b1rakrMwZUO8EAEAd/pUVfKhLthifKAeS3+VSYBhsAliDksvp9ea9JnATbZplUgKvJ5yMDH1FXqIyxkEEkHOYwOAO459O9UVsUwyzZl2ttTzYwv0q3DFi3hbkj4Hhht3cc5PHf8AlmueZvAtbSVl1Ox8CaSNmuAN0cpQsh4KgjsCM5Htn3qZccyMOAMHbwQKr9HEUmo6WzSAxyTgOikAqwHBGeME4+tWMiM8pyCPIxyW7Y9zXLLU6I6ES1YhTyVBbBIH0ridaY/tq9JO0CXBG4c8DtXaWnCyD138/bGcVxWvxyNrF7sP/tPyU4FbYfeM62hqVwY0lUANu4IH9ak7fMDI20uBhyc4H+hUUNHEkYgQuznLEkkgj6DgVLUnO3bGVXliRyPcV0swRmiqxgzKYo4pk4Azu8w4HP1ruZRsSHIA/dr6/wDDXCOQ00LKqOpljwG9PMOw9K75ycEA+gBI742e9edjNInbhuJqZt7eoLHI+hqPLyhweQf+tbBkbc+nOO/rWpztQnggniuOBvIkf2kLDHlCndjvivmXXf8A62al6edf/lFr6YeFbYOduCB9eK+ZddDHVmpD2df/AJRa9jBdY/D2PLxvV+fuc9SlK9g8cUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgJtj2Oe2auNOYByM8Z/nVNZ/gb/AL1WtjjPlxk9+a4qyvc9TD6I6u2yWUZ7jHauk0JlS8iLldpcAkny/biuc05s3Nv6eYAmrzTEZbxU43LJtzntg+9fO1uw9ukW2r4+XmHBIEg49snHNfPrQF7ZTHIjttB5ABFfQNYz4MxznIkORz/Ea+cWcv7hFkKKQuM4x9s10UF0DnxG+bXidn2quTjJA74+9a5DGhKAFSD2LZrbI34jwuPKTnkVBkzIcKECjnd6GupK5zHWdOgS2cqgru8Qkc8nGCePWplyCGQg8HufWoPTKA2UihRjxsj9Kn3AAEBA757f51yVN5m0dCzuw50e38M4bYO33rnxIf3iAMxChipbcE+o9OaudTVZNChU7cqykhiQDhu33qjdzMxWLJO048gUKCe3tU01kTIh3ACb5NpYKPMrAcfbH65qiuolSUlc7CM8nvV47Ay+A5SQnIPcEH61RTpGZC7hYyo43GvQpnJUIDxmJQhUomMjJ4rXAX+ct2JJ8649fWt7FGG1duWPGOPzqNanF7BknIlUd/rXWtDnPqjMR4eeMD29a3aWrPfRg8jn27YrRLlQuGYj2NbtLBN8oGT3zx7CvIeh6K1K/WVc9Q6gY98SL4TeNGpYqdg7+1VL3UgcSRSHxXbJIxtf9PWrbqDc2t6lGhdHYR5UD/gH9aroyZEZIoFkdf48ElR9O2PtWsdEUepouw0Ij3yRh+/HDH7/AJ1z1y53h3PlY48uO9XN3EJBJIZGmKttRnGQ1U9wAZDmPwxgbiRxu+nvXTTOeZCbBdWXGFOPrXU9PZTSZBx/6Ufw9uwrlnChyWbgjPFdT00xXSHBbg3Rz+gqa+4KW+dBaMQ5w3rW/qN/l20ZpFZRLDKFYMQDnHrUezJDHA5J4zUjX4xLLpAO1BHDKzOzZyOMjFcMdTrehVM8O1BPbqy4/EXOV/Qj9KimMu7+FBtiL8gPgfkO9boVZgdr+JCCAQgGTn2JrXMbZGjSJZTETgiRPU9x7VqjNkSRBDuEbbQ6/jXzAgnuc1V3QXEmxSVDld/BJHocDtVjcBRIFknVMvwEiK5HoKrLwSJLIQ4YL+Fsd/pXRAwkYaMd2swAsSF3cY7Daa6B33MQuAc8ZHeuf0hj+1rUsSzYbJI7+U1fFcuxBVufc1jX3/I2o7p9A+HMlvHr8w1B2htXtHjlZT5lVjg7T6HBPPpX6X0m00ZOjruw+HmjWek2iwOojt28MSybduXJOXYjALMc1+XPh3ZjVNTmsm1NdIWeJU+cIBMI3AsQp4LbQQM+prsviFY3nQMiy9F9T313o9xEFKSzo8okxg78KAPyH865FBzbSZ0uagk7HW6XN1PH1Jo+n6707fS2N3I37u/EbW7FU3AsQcbVwDlvbtXcdQdS/wCyvVNtBc6BZySXloZh8giusm04LFFA5GQM49a57Rtc6S+I1zp+o/P3PT502z+Tlt7XEfivsTDsGJ7bSAcc7uTxXNWvxeufh/1Dq2k3dz+2BBcusFzEVbdAQCmRnIPJyM8EevFV2JSdrZk8pGKvc+gaXpM/UqDqTQtTOn30qPbw2884WAQBt3CgE+Iz48xYBVGMck1WXGhaj1R05JrN/rbadrqRkQ21zGrBBu8wmTO4MdvBD8ZGQc4qo0XrXpWTUL276pF/pdxrESXVtatN8sgjZSvjxleC0hBJJ7EduTXJWPxlXRh/sfp0ltNo0kny1td3EokkZGJHiSccvlizcjmpVOb0RDqQWrM/jH0do2lwaXqmiCKWeffA7/MM8g4zk5OOQDnGOa+GyHBYjOM8cV9n+JvSeg6Nomjah0/1Rc6tKsvgXVvcxxjx2ZD+9TafLgjleRg9+K+MMVzISe2T3qYqxDdyJq0HzGhRxM21X1CNSxH4chhmrHqHo6DQrS0n6d1F7t54Qt4p3KUcsRsz6nHPFdD0Tc9NWmpWb9b2g1DTFmEi27qfCklCnbvAOWUbiceuBniu96pm+FHUfV/T11MsuladEk73Nhp00sMNzgKIyD/7LBLbioGRgcYzXZSqSikrO2Zx1aak27q+RRa18YLjVOkB0zdabpqW7262djG20mE4CqyDblCuAc59K1L0zq3w46YPWGm9Zwz6rbYims9OwoWBz5mEgwGbIXK47etTT8HelOuOuWh6Z6vl0rpyK0FxuuR81cW0jMQsS4K7wcbtxPAGOTUXQek9D0XrXXNC+K19Be6fZJF4aadL8st+JOVkaQAlQFHK8Hcee3Nk4JWj4tWKPbbvLwTuWnVnSHUXWvw7XrbXOpoLnWILMyRaPdR7pPkwdxfxgcLIeSEweO5ycVHuOu7TqD4e23Rui6W1yY4w2n21nGJJzNjJZVXzljzk+3fiqS7HTGg/Ei102a+l1XoaRPmFtfFZppoxnFuzAjIyMFgASAfWtlp1PB8MurtY1/oWxm07SdT2xw3E8UoW2OdzWyzYODwDjOcAZqUna3muHkL538nxOG0Sw1HSG1eHWY7qC8bwvEjukZJFYg9w307fStueO3Y8Vf8AUHX138QtXvL+/IZ7a3igWQZJcZZjyeSBXOg5bn3rCo5OTctTpppKKUdDr+jPiZcfDLUYr2ytraW4uo3ijklHmjHlLKDg4DcBuOQMHimjfFix0DrrV9T0HT7XRn1GCLxVgDLGJASW28DaCSDgDHtVdoHxD1L4cXVtfaT4Cteb4XaSASMoXaePUcnnH0ro7f4465qXU0si6fp2rXeqRxWwgbShLNPs3bV24LfxnFa04vZuo+pjUktq216FFr3Vtn1t11a6prLzxab4SR3Zszh7jaeQpPOSPLuqP1BPo8fxGXWPh5ZLpNlYvBex6c53LbzKcgDJJxwDgnvVr1l0td6H1tbX/wATunBpFheN4q6VDKkccm1QMExswRt2MpkEZHbNW3WHxA6MuoumIm6PsbK0sL9GNvbxLbiS3CnckgU5dC20ksckg88mtb6bKuZNa7Tsadd+OfUHU8lnZ3SQazLa3is0bwNcDcQVAUNksx34wtedZaF1Fo93oj9RdGSdNW84mdLlrAwLIwjPkJLHB9dpAP39N3UXWej6tc9Of9nPSFho/VNvqMctp+zLYJJMqq2V2Kx3EnByfUZFedWdU9f38FhpvV+kalp+mSSmR5LmxaBGdY2KLuOcnufSsZKyySRtF3ebvoc+pDQGMMSyb/KRyex96Jf6VpRt7nqCwTU9Mh1KNpbeUnY+Vbbu5GQDyfQ+tZR5BkjDHjnbuwOU5z6+nes9L6gs+l7+x1bU9Nt9ZtoLpG+XnjDx+KVYKxVgQ2084IxnBrnjqdEtCX8R+tdCvdH0dujNCsunr+3mKMbO0W3WeJhzvKHc2DzuJ9T71Z9S9MdG6ho2g/7G63I/WTzQ2t/dXMjLbXhkODlO0SxjsBkkAk5JrzUUj+LHWNlH8OeibO31aWzmk1GK0MNlCIsqFd8/u15OOOTkcEkVr0bS+nOirTWdL+JegXsnU5ikTF1cB49OBzskgw20twpEnIxwB3rrXRiksn2fOBxvNu+a7fnE63VdN1voC86c6c6d67e96S1N/lpLl3FsLVipabMIzlSNxTkZJw311dR6N8M+gdY0vV9Kjuep7Jka0i0vXLpbuKJsBhMo8oBwrLtPHORXyLpnQn6i17TrHqbWxoempHJK9ytqHYOFyoCk4JOfUgAZrv8AHSPwy12CTUbrSPiPp97byQ29tc2KxrbSBlLOU3FNzKNoOcgZ7ZqHCztfPu4llK6vb78C9tugk+LrXusdCHp3onTIVNrLayBttxddyYgisI8KyhiDjPpkGvn/AIEmmQWVncIYbqyg+XmXxQ4V18pAx9c++e9dlo/TZ6x+f1joHXNA6L0HxSkemT3s/FwAPEZVQHCEkdzjg44rjiklvZRxXM0ctxbxrDM0MgdZHGQXzjlSeQfWuep2X8jen2kjSgPnbUHuZUHlPH4hX0roTRPGtbe6PU1jaWVzqFwskQtZJp4h4js2xj+7LcYwfw57HtXy2xmLSL4RAcEbD3OfSvoEXx2uNe6SXpf5G0srN7c2caQkbYzjaWRAM5BG7J9cn1NYqEpbps5RjqdtqQsdC1rTdO0jX5tT0ma4CNNdMoltwWyQxUYYEE7exzwa+jW+orc9P3uj9BQqzTmSSW0aRTHcSSZDtKX7kg459gMYAr87/C/4oWfRmka9Y3dsL86ncLBOzFBGIFyoAU88lixYcnA9q6PrDxPhzoGk3/SPVdxq8rvImqPcxQBUCqDGyIig4OWGSzHgZ71MqTvskRqK20dzcXbRbOgelOn7XTtVsrLNpGkixKqAAgtKcgg+rnOSfU4q9j6Q0xYorW68aDXtkb3V6l+RGdv41QHKHswztz2PfFfKOh9K1r4jGbqS16wtdKmghfTpBc25LTFcSKqlOFXLZOc+nB9OakvuoNS1a90T9pXzdXW900Mfh6lClusaDzh8jaqc7g455xg9qq6W07X8Syq2Wh1fxY0HpbSriyvemNNudKu7xJI5ozcyTQXUYdS8gMhP7xXAzjnDDI9a+Uuf95kLEs3HOa7TqqTWNDh0vpHXtZ0zVFVJdTjksJWmbxHfbIDI4ByMjsMMCPauNnC/MNtXC4UZySPX39ahJpWbDaeayKTqogaHZsx73eAT2/Ac/wBRXKRBhuIUiMKcHHJP1rr+pQraNb4wf354JAx5TXMSMq7WZtoBHkB4PHrXdS3DkqLpFhYzJHBHGMmXJ5PbP3qSHd3mSQmF8gKvhgnIOSc/67VpilRosIVRkXLO7YG4+32Ge1YxRiRuWaQAgryc5PsO9QyyJEYErkHdvUDaExkr6kg96mi3iDKI33OeOQVJweePX2x9arYWjR5PmV/eFQVEeQcdifetv7RUfv5VeQIxKEDtkdmz6E4qjTLpov8ASiTaPiNYf30vkU7gPMB3/L+dR5pf3i9+5J/wrDSZZWtI/EjWJzLMzovYef0/171hMCsqZ/ug9qwlqaLQ5rq5t2tpuycWsWMY+tUqOC21n/4sZ+varTrJc62qqqnNvDksewwaqRE0kh8Jdq58hzjHuK9GnuI4Z77LOz3nDxt4a7SfKc9j2J9M1OtwS8jMFQgDdxjPHAGfv6e9QbQ7tpaQtgeqYVRg5yRxnvVlZD5uZA8qIjFfEyhfgewH0x2rORpEyMrBxvWMhORHjLAjHrVx0200fUGmm3SXx/OT4YIbcUIwvGc47CqyaO08SN7J55pFPBeEIM59Ocg4+lWHTjiDWYp5DNMIXeXbC+yRtozkPkkHjv8ASsZbrNVqTrxUhEiRKIwjFVUMGwvbGf0zXP8AU43aM6vgYuYwx/8AirotQdXuJHiAWOSUsFA7qefryc81zPURzpLsoBInibGM/wB6qU99Fp7rOUAZty4KqQBjHp6GpEfmcO5wcEgj39BWiJszjzZVh2Hf6CtzYVjvHJOVYHIPHrXos4SfYs6zI0KlpAfU4AyfarmGGJPE8SW3ilJH4nJwueBx65JqpsXSEsjmSTjBQHnP39DVkGwjqytKrDDMoxz6YxzXPPU3hoWOiWRvtX08W8ZhCSNIY5JFxsVSWILYBPHbufSp1xMAXVvRCce+RVXpSeLqtjFdiJdkwPnUswZVJHfgD3Hc8VZzruWVgAMetc09TaOhFtif3gP8RB/ka4zXPNrmoDIOJO302iu2gYqV/wCJHXBzXE62q/tm+ZmziXGwDnsK2obzKVd01QMS0Q2FY2YHA9fSttuSoABXlj3Ga1BiHI8w7Y57kVs2qVYqpCt65/rXQzBG0YNzaoWKn5hMAcFvMK792JjODjgZOcfw9q4KFBJPaNtDYmiGSfwncO3txXekL4JkzgpgED1G0fzrzsZ+k7sNxNDHB9snIAqM3IYrxyK3EkKp4JznvnFaWBw2CSe4rlgbSJSMG3gfxJn9K+ZddHPVmp//AEQf/KivpwHDAcA8D7mvmHXBz1Zqn/0Uf/KivXwXWeXseZjer8/c5+lKV6544pSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlATLT8B+9WtkMHI71VWv9mT9as7Q8jFcdXiepQ0R1WnnE0ZJ4DDn24rptN82qeYjBl5PcZrmdPJLrgdj3710dg4GpryeZO7dxXztbU9uloWWqYNvMDyCJMf/ABV80tbKTwldmGCOwGTX03VWKocE8RuP518+gG23QDkE5Pm5Nb4d2gc+I3zWYgACGDZ78YNRmRRMN2Qp/SpoUkqqIy7CDk9gK0uN+/c27ny/8q6UzmOh6ayLacdz45PHttHOKmznJTykKGYDd7f4VB6aINpOD6SkkZ4/CKnXR2yKHPOSfyPvXLNdJm0dCdqDB9DthIxC8E4Prn3rn5XjEEURaYxMSAoPl3ehq7vyDoEICqwGAQTgYB7/AHrnVuMuWiz4bfiC/ux960prIibI6g3CJ4sjF4j5ACAc/Xjmufvd6OxkGTkjvn86vbmV5HfP9nglWA2g5PIH1qkusAsOdvGORzXfT1OSZEJ2cquN2OT61ot8PdwE5DeMvH51IZOzMAVA5UtWm2P+/QZRFBkXnOcDNdSMGfUJXIKjcCuO3+NSNJx86pP91vXGeDWMkaFNzOMgYA9680tiLxeQNoORXjvQ9JakPW1P+0motHIUJWIs2702+mP+VVNzNsn2LM8yMR5j3+uavtcATV9Td4vFBWIHB2kHZxmqG58GLMspjjXYoYFMcnjv9a1hnYpI33tpIlr4szQtEOBKjLk+wI9PvXNyoQpaQ58Qn+Kre4hMitLHHHHEc+VHJ2ewwTn9apZYWTCshUsecj2/pXTTRhUK2Qgj92rEg4ORiuq6bIXSDtH/ANkt/QVzToqHjKnPY8/zrpOmht0lxnOLk/0FWrbhWlvHR2Q83bAJPb0qX1AxdtJVgpIil25Ge5Xg4qFYnzHnkip/UMhWLSjFLsk8KUMCOCPLx75wK4FqdnAorGXNqzXEZhUOVKKc49Bn1qNcKkzbixVmYKEZydw+9T9yiING6AuQd6Yb68/Wq542mcom6K4Zjs247Hv34raOpm9LGMzmSYqYgWjXBbAAHsTVTqUPBKHbuGWIfcD7c1LO6KUrKoSQDBJIX+tQpxwXIBJPq3+FbxyMJGnQxjVbfaeAGBHp+E1fFSDlffuDzVFpKhdYhwuR5t3sPLV3lMnnnccZrKvveRrS3SdGH+RkEbtETNGA4xxk1arp2pav+zbZLuCH5m5EQDjgcfiPvj/GteizaDDLC/WFrLf6MZ4/mIIZShkPJVSwIOM4zgg+xr6jBpnwk6p6y0q8t573pi0sbY+PZQ3bgXBcgIQZi+wYDbgPxcYxyarCWytBUV3qfK+r+mtR6F1u4tI5V1q0jdV/akdpLHAshGTHvxgkZHGa7LprX+lNI09tM6h6Os+oZixllv7q3BmZ3AJBIYYC/hGOMD71O1f4t3PSWpav0H8+ZOkHuXtIxkSObRzgSbvwMwDEg44NV/XXTXwy1K86b1Hp3VNTsbf5qO11S3a5a5eSIKcyIzDKuQoBHbJyAMYrTOSSmvNGWSbcX5EyP4VX3XVjLrvSfUGiaZpLGQ6do09zKHgiycoHIKplgxAJ9RzXNdH6l0PrvUZl670pzpNlaoqwWjy26NLwgd5E5BABIxwWIzkcVY9b6d0yda0x/hWLt9OviI5tJjWW6eJ1HmZGxuZD3IIO0k84IrPqPqTQeqNd0WyuI10u3ijZZkssRmYJyFIXg4IPcZzmpim1bP8AhoiTV/lmcT1LbaZY9cNb9NatLqmjxxtLbeOgEke5T5XwBuI/vYGfaoD+XJX8QPFfU/iJqvR1zoujWPTml2trqEMxMM0cf74rsO/e55bP19a+WSeXcSO3oPWsKmbVzopqyZMj6c1fq42endO2M2p37XfiCOLCkAKSXJOAoHucCu6uOpR8P+h9Z+HfXPTqaffzfv7ueeUPcvcDmKVJBkkKCAu1tuC2e5rjNL1HXdNubF+lEuZr6SUxtDAm9nTbntjn86+hyar1N091ToXU3xU6bkdILd4tJF2kRIlyCWaIE8qPwq33rSD6KTtb1uZz3m1r4ZHz3T9KGoWmjx9HRXutdVXbsLiOJy/iJldvrkY5yTjvXaaf0Dolhd3kXxavdZ6Z6ok2tClvLE6CAr5CRhg2TnjIPHvzXX6lqWo/FfqSDUOi5tG6d1iyt2i1C4u5BZNPDIwMaYRGzgqTkjgcdsVQT6Ho8HUV5H8Y9Q1R+oUSNIDo98rQfLbcxneqEkklu4GMVZ1HLJ5fz/4Z7CWaz/j/ANIPTHxV0T4fWOu9MXFjpvUdrLcSJe3l7AniX0X8KkOCwCjgBSMHOMd6y6O+IfUPUPTtl8OdI0i2vLS8ga3tdPJIjwSzB3Y+VcbtxkJBHJ5ryw+KMPw+mudF6esoItGjkY2U+p6eGnuo2YsHL7P3gLEgHgcVyej6d1DD1MZOi/mb68ngaSaO0kUGIO2SAOwG44x7VOzFptr7/MhtNNJP5+TZrPQWufD7UL/T+p47RZ3jikgms5xNDMnmBKuO5BBBBxj+dUCZGD35xXR9Qp1THqs69dQ3NvOLeI2qXEisTGS2Txx3Fc6SAMA4XcTXPO7ldnTDKNkXugddXPw/1Cy1GzhtZZZvEhD3EIcx/hOVJB25wORX0aH9t/FCeDrm11u10nV9Ogks7JY7fDXK5O4F/T8RAyOcntXyy26YuOrWsbWyuLW2MUkkjPcyBRjCgAZPJ+lWOn6b1j09rN309oGk3XUctsqzKdKDSoocZG7aCAP0NbQjFxutTGbkpZ6EXUOo7zqfWUl6tsbzVNE6fuNl0oicW4YuA4d0GIw20DJPvzXb6/1Z0ZrkWnS6X8PtAtI9KYXsqwWywmWEAoVLISZATIp8xx5c5qT038TJNJ6PuOirjR/BS9MkN/bMQsk90+VYOhG8vk4C/aqHS/hT1homiPdXOnJZ2ssi2M7POFaGNmGWdCMlFyMlc9qvk9craZlM+GfbkXTdV2+pWSaX8NuiNN03X3Ilt5reFFnVYwWkdWyT+HcM55zjnOK5HVuteqtevbDTeoJLv9nq0r7JF2q0ixnGQPUZrrNU+GOidAdN3vUmh9TXVxrNrEzyIsaxJKhI3BTknODkenoa+bJ1bcdT6/ZpLO08cMMzktjJOzH8NV2Y7MnFZE7TUkpPMuI2zc5x5WjyV/I4qt6kLQaIjxr4h+bh8g53E7uB9as1KpcxHlvIBirXSrDVtT1HR4embCO/1Y6nDJb27lFU7QxYuWwoCjJJbAFcsHaSZ1TV4tHJaL1NrfSN9Jc2/wA1ovjwhAsqD98qnPAbAOD6V33UPw96r6ustG61618GPQhHGlyhuIxqMVkH3NMYgPKp3HaMlgOcV78XV6re90vQuvbJdIktA17bOUiEczJhSVkRmDY4yOMccc1B6Evrv4mavdaBrHU0uk6TLbuLy/mZZlCHA2xxggyMx4AJAAyTnGD23ckpqyOOyTcHcuOqrD4RahedPrpEmtdNQLJIHW1uZJjLiPg/v84IYjJAOQSMcg1XTdE2l7rCH4arf9ZQPbslymoQxgWo3AptZSoJbnOB6Vv6e1fQvhanU3RurWVj1M1/iK61EuCZrbAaIIWy0WD5iFOdwHfArmOg+vv9mk1K2t/HXxJRIzzozNgAgKfXIFRGMuDfnx/JLlHikvAv9P6Q6Yvbe5bqTqK+6L6jMrRmwXTkeKBVACO2TuJb8WVPbFctZZHT9jtPlNtGC4Xvx9s896jdQT9R9b3Wo61p+jXV1Y8W63AUqG2DB+nr2+wqVbjGhadvK+KtrCu04Urx2xjPcfyrKsmo5s0ou8tCZpm5ryDYRkuAOfWoug2ul3cFvH4Iljv/APdII3OEluHO0AHHJ3nPB9z6VnZM/ibVJB7ofXI7V9A0348XXU/TundH3iW2kabdRx2CN4YaO0GAokWPgDZjcORyO+TWVLaz2Ua1HHK7POqfglfafd6Rpkk9rcafDbi61rWNKYlLC1iK+M2HAJfzeX+9z7HEjqWD4W9PXukalp19d9RaNITBNpN86SeLLsBSRpkCFlGGyh4J2n6Veap0ro0WuaXZdJdfzw2WrWs1lqv7RVCTEIy/iIIyCzFhtCHjzd8Zrlra6X4HaxqtrqVxpXWVtrdt4MN1IirGtsG/eRtFICELHaSe5CjBq8W5K18/sUklF3tl9zTr51TrS4N78Fuj5LWzsoCNUt9NIgiEhOYyVD5LbQcjuQATU/VPiBot98OF6Sm6d8DW4IQnipYqt42o5AYEjzFmfKleSQQPQV800/ri90XW72y6Mtbqw0rUro3NlbW6blZiAp2FhyMrgH6V0d98P+tdE+JOk3N5qOkx6xfBtUFwtx4gtpUABjd8fjGVyRkZzg8VtsJWUvFdvmZKbea8H2GwaZ1Z05a2mndUdP3+i2lzI95DJeWuwmZUVNobv+FslT7DvVO0v71sLjzDue/Hc10/Ut31kb60ter7qKewCSOjRTq6NKAAMAHJOCfv9K5R3Pj5YY83Yj6VyTTvn6HVB5ZEDqiUjRbTkFjdMFBH/CeSfpXIJK7FNw8IbsKo9AP866jqkGTR7Acj/ej/AEP/ACrl2ZQV483PmHOOf611UdwwqbxcQHdEwnBDM2MbgO9WNpevENhFvaxk5BKFTjH6n8zVPbKsispLGVh+HaCce1WDyCN1SdWESKMs0RO4/TPpx/OokWiYW6obqN9uFwymT1GeR5vfvUyG13XMUcRkDOwVCByD3JPv71FhGWLlAxTzOpb+XFZwTs2SHbJyQ/II5IwvtxmqMsi/s+IfIrogMuzxOCVDEZNaLxcXRBII44xj29K26cqGwhWNWWMBguc8AOfetF2f96YZzhyKwepqtDmOrT/5+dcA/wC7w8A9+DVOAQoZlxH7B+30q56qiMuvygBgzWkRJPAAAPc1UKzNGoUqu7257V309xHFPeZNtly0UZi3qSRsY+p9asbdySU/EHPAHcDOPT0FQrWP5cpJM+VdhhG7njHPPH3qckkLSNGquk6BTl5DsXnPfGSapItElXMbndM1usGCCWYHccZA47n7VZ9L/wC7ava3KSGAKHfcMKIgB3GQRn1HfPtVKt3GFZpRuOM5C7t2PX3NdJ00Te6vYR4uJo3/AIIHC4PBC88buPyxWE8kbxzZs1FiLmRjt5lZj/Dng9wTwfcd81zGvnGj8MRmeIce/mrp9Q3me58VlMhlkdvUlieTk9+/euW6jx+xgD2+aj/TzVWnvome6zm1URu7OzHPGMZ+4r0LsbcuRsHqOa1CRkXyqxDufD9ef61vdHjbZ5WjUgbV4yceteicRLtiyTBkwN5BODtwTXQ29qzWgubeaTxQNx49Aee3rXOQs6EsreRQBtOO/f8AWr6y1QRRNazF5WYcCNdwOR2J+lc1S/A2ptcSy08uNY0yJnORcDcFO4gbTwB/OpF1dC3hdQQVYEcj61V6LsXWdKMkRhj8UdmwQu08j69+Km6iBKrE8ZPPH51zSVmdEdDFduIWJyqvjn61xWtNjXL/ACCf3xGe/oOMV20Q2W+c9jxXGa0yHWL4MwQm4bnHftmtqG8zOroQl3AFggYngZPNTonG8PgtgDcB2qMrpGOJEbcfLj1H1PpWa7eWWQM4UZGRxn0FdLMEWFkH8WHAwpnVuRx9xXZyHZHn03YPH/CP8642wVp7qKUCdEi8oUjCHj+Zrsptqqy7hIoPdeRnavP+vavNxWqO7D6M0EjaF5GTzxmtaj96wU+ueayYHeuDjjnP2Fa0YNK2Bz2J+tc0DaRNGRI/GFAwc/rXy/rdt/VeqNjGZu35CvqMZ3AAjlh5vy/6V8t60bd1Vqp95z/QV6uC6x+HseXjOrXj7lDSlK9g8gUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgJdr+D86tLPvzVXa/g/OrSz7j71yVeJ6dDRHTWjmMcfiAGK6HS23XNuxOfOu4H39a5u1AOCc421f6W2JoSPRx3FfP1ke1SZdam37hyR/DJz7+Y186s3IVVyGweNxr6NqqhYZF3K3lkJKNuXnnGa+cxJGkEORuDDHfGOa3wqvBnPiX00ZylVZmSQPxjy8CtAutkRyN4LDAPpWbqEDBie3lFaAQ26MnbkdwODXaoI5HJnTdNSo1pMu1SGl9B28tTbpiZAW78DGciq7pZVFtPv8zC4U8/UYzirK6jwVYEAjH4eR+Vcs49Jm8X0Ubr+TbokJAP4yRj7mqKVHlhjnJbxmzkb93Hvir++Hh6LC2FYeJyr9jyeKq5BLiLiNNoOArkqR7DPFWgrIiRUXLSqVEI5Xko5IU8d8fWq+5fepLxiJvZW4NX1xGygEksi4Yjg/8AyVVU8aSSs6gr6bc8D611wOeZSFAE5zuzyc8GsIowLqEhRnxFwc9uatmtmQ7spuA9ecj7VHtYNlzE2ON69/vXQmYM72S3HnffiQgY9a3aX/6epweQefcVI2NgFFG3b5ifSsdOjU3ic4BB4P2rynoegiJrW063qSEO3iCLDdiv7sfrVLJEJ0uAyBosKWD42/ma6TUJRba9fSKyoxMfkChgBsHfNV11bfOQiO0ghheJixXxApb3O01ePAiWdyjnVvCQIUeNUCiNewH27/nVTLnIKyOu0cc8H610V2gc7kYCUZG1VwMD796pruEqOQZABnPp3rqgc0ypk4XlgS3uf6V0fTif+Z2Bzn5g8/kKqJYAy4BBOe1dL07CBpLjA/tzn6cClbcFLfLOx3BhjtxntzzU7qBVf9i+VSiq5c/iIPHH515YwgFQD2A9KndQQ7rbSlBUZjbaQOzccGuHidnA51bTEDSbEhjiPMjHJzngAff0qC8ZUl3cqXztVlxj3JzVr8hHtmDrh8l/EjUeYk8jn2rEaVEiMJJcggF1IHJ/rxWidijRzrWxwcyEkcrHj8859RVdeIZO6BF9MEHNdTPbL4TCV4wx4Uquc/TNVt3AsSbUUSIvG498+1dEZGEkVGlBv2nDGoDDzdh/w1eCNtwGD35BFaNKjK30acKvmJOxck49+9XLRAHJOR9RWVbORrR3TPT7zS9INrddRaXFrGmrcKZLWR2VHYggbiCDgH8q7y96n+G/V2o6Np11pSdGW1oJZZZNHto0kuUYKBE74YYyN2WDEc4xmvn+pLDFpIadGKpcw5AXPBJHA9TW24i0aSfSNNvzLZN45klkhjKTlMdgGAxnHGfWr04KUbszqyalY6qaLpjpv4iT2vRdlcdYQ65p5itre9iN1cRF1ZHVNiYJA8yttGPyzW/ozQ+kdJs9c0r4taXrFvr0rNFaR3Ymhk0uPAKSxrkDeTg5OQQMe9XWt9R9J9Pafo978LNNn0rXoXNvLfSu7z3EDL+88VmOGJIXgYAGQOK4vUOuDrvWumdRdUW0GpvpqCAwzqQkm0kqXHIYAnscipSlJcfzkUezF8PwPhl8T774ZnUpdKu4bo3koja9eVhKyLkKoYZIBznHqTWfxG6XttS0+0+Ir63btearei41TTIrYRG0DHaGDBiWJxluActXUan1X0V8VesrfV/iDp7tFaWgi26c4iS5ffkeI0a5wo4GB696yA03p/rE9SdPWIl6OhKw2tvdYYx3OznBfzMqkHBbsT37VN+le1nx9iLdG17rh7nzSf8AYUX7PW3+an6geR2aZ5g0aw7DwFBzz7mq9x9O57e9fYviX1/p/WKaXbSWFtBqSStJDOIkWVVCEMMqM7T9fWvmbWi55zgcVz1G28zpppJZGWlLryT2J6Otr+81IykeHYxl5PD2HJwOwHHevOtNd1y8v9K0PryPUtMtxcCWb5238KRR+EsinuQCastO1bVum5LW76duZreZ2KTbDgGPbnkeozXb6D8Wb+z1mS56s0i31bNr4VtNdwRyMI3P7wYfICtgAj1x3rSm2ldJP+TOok3a7X8HMdc3fQ2m9MaWfhxps+k6jbzmO5mnkl33yEZJdi34gRkbQAAxFbn+GehdRdHwa1J1k0PVctv4tzayY+VKgEiANneDjAyc5JParHpbqD4eWnWGvXsnS1lqdvNHEsNtchbmG14Jk8NC2ACcDIJ24x71U6TB03J8QXbUra7s+knleeC28JlEsPGIkkA3eHkkZU52jANXvJKyuuP9Gdk83bs/s86h+Mo6t0bSdK6t0u0vtNgkgWKPayPBAhUmNG42KQoGAa6zXuptH+Kd9pGm9LW2idHavF3vmhW2X5NV5jIh8zebaVH0PPeuf6o0rojT+trC/wCl7EXeiJG88+lXUjNDCSSq7S3mKc5GexFXuoWuk9Z6SmgfDjoqztdeZkuIntkjSTYv49zufwEHksQM4xzVWoZNK34LLbzTd/ycn1j0hP0r1E8Nx1JadSLe26Mk1tE6FAhYYKuSw5wRzzmuJfJ9M8+1d5N0RrfS2o38PV1lcWF9JHG0QJR0aMbgdroSODwQKp/2fHlVw+AVrCUulrc6IR6Oli1+HnS2kdU6hbx9SalLp9lZmSUpBtEtwWCqEBYEAdyTgntj3HTdddRxfCC5gX4e6rqCaLqjNJcQTSpOyzKAM8bcAqBgelc3p3Sdzrl1ZR6XeW9lLG0rO1zIEV1wo2jPfmuv0eDp/o7Vruw+Imi6R1Vdyxq9tJPIJIreA5DbEyAGLA5Y5PAq8ZK2efcZzi7vh3kWL4dX9xDH8SG6qt5upQp1M6T8pnBK/wBn42cGQJznH4uPrVffa9178Ruk79+ldPvb63WENNciPwkjjXzEAsfMx2/hH171edFdF23Xmp661lqkXTfTySvHA5L3EoRs7ESIMAcL3ZjgccGrr/bO46Udum0n3fs6RbOJY4WCv2CsMeUBgQSSeMnvVlNp21f8EbCavon6nzzQ+jes7/QbbqXXdJ0tumbYreXdpNeBJbyBPOR4RJbbwCRxnGK2dY/EHRer10qz0vp3TNOvIg7xz2cEUZji2cr+7HIbj8XtXeap0Bq3TFut5danDr2iRuJNQjWd1WSDOZE2H0I49eKjdXa/0r1BHpdtoXTWlaNepvaOSws47crFsO5Ts/EDx+Ks5z2s2r/gvCDjlc+Q7trxHHOBk4+orPVNX1LQYY7/AEmWS3uhepGGQ4JVgwI/PFdXc2USxZUkkDH39q1dSaHc6pZRWmjwT32oT6hCtvDCheR254AHesqcltK5pUi9l2J2katcXFrpvXfX0a6xaaPefK6fp13CsgmlmU7p9p8uIyq4BBBJB9Kj9RTdIfEHrBLm2mh6DmNqWu/BsVMd05dQuYwQqkDcSeM8VedL9DaRNZarpfxhOqdN3a7Y7WzybeQHGfHLPkMu44CLgcEkngVy+mfDywh6hnl1/UdaTpeykktk1mxsP3c0vGxNzDaCQTnvyMV1Jxu3f53dpzNSsjfd9Dw2fUNtJ8ObLWuvneLx72WPThcGKRG2jKphVQjsM91NXP7C+Hus6He3fUz6no/XV6zTXBDGIWsoyohNttwVAUZzzkk5HAravVMHw81eTS/h5qep6rZ6t4cz/NWzNMJQCgjUR4LDbyBj+I1Ifp/TrTqzUtR+NWk67Hq+putxFpkdyIkeHaFEkvhsSGJU5TIxjmm1Li/clJcP6OS+HNp8WLnpKY9KabcS6VFJMImjuYYldyfNtWQgk5PcD+lULR3EdjbC7DLd+EiThz5hKB5gR34Oa+oaB8N9V1TRrrVOkertMsrO5ubmbS7G6mn3xQF2CB5E8ueM89hjJOa475HwLCySUhnEKLKwbeGkUebzevOfvWNWak3a32NKUHHW5Q6cdt3GzKQpbGT61zx6a16x6eN1c6auzwvFUmQBlQ8hiv2weK7/AE+0i+ag8pwHAOV5q8+D/TsfW+l6pa9W9RzaZpdsot40t/3s75BOQH8iKAAM4JJPpjNWozcE34CrDaaXicx0DedEHobU/wDaqC5vtbvY2W1vFTaNMVecxgeUuxA3M2fLwO5qRY/DD/6yrfrTXuo9M1C4s4Rdv08sJCfL4zteUEfvDkNsAPAILZ4q+j034edE3V10/q+g2vVF1ZsGbUZpni8VG8ygoHCrhSF4zyDXBW1pDDfrb6juGmTyb/B3bWMG44TcfpgZPtXQm5NuN0c1rWTO41u76z+NHTltrui6Ktxp3TJcx3FuwhCjwwPl7dW2l8KMlEHAAyckCo8WhdLRdALqv+0lzedcC0+agjhYfKxhlBNv4WDJIWBwXLDDAbRxz7pX+3el9LoemLK+PTpSV9MeMrtEZZsnI5Xncfrj61zOo6t050/quk6z0RdSWcNrcwTQWc06y/vF/EeCSVDDdz2NVS/THTu/JZvjLXv/AAQ4JdcTqCOPqCw1HTFFhLJFDc2skROWQEgPgt7cdq2umJfKpADevNdVqnxN1b4g6jb2erXxuBZwyTq/IIYlVwGJJ5/wFVEsQ8Yn1JJ5OTXPVbvmrHRSS2cmcz1QjJoNo/7wx/NgMF/7rGuVEBZVcHdhd2HGP1rvOp4UbSbIgksJ/wAOO42HPPp6Vz8UEPlUrsYA5Ocgg+4rek+gZ1F0jXYRtBhmYRwLgEs3qfqOakyCN8/KK6gdlEm498ZBx2+9bLK3jjTcXON3H09OPvUxE3M4h8m+MjccE59s9qiTzJWhX7POjFXLcgoODn6n07d6zkR2aHKh2Ug5Lcj65PPr61Z2yIQYrh4vC7uWGCDjjzY/lWaKTvmiKIjuY2YdivtnFZ3L2JOnqfkYt8Ziddw2lt2CHPJz/Soky/vQAMeYg/WrXSI41sYkwGC7juz387c1HlRRMmAM7hgflWT1NFocV1aX/b0wJ2q9tEHOCccVUqsysNyoe2CPXiun6qjT9tyEryYY/XkeWqpIkeQKxAdh5fTNd8H0EcU99nlrbCYbGCcHcx2fzzn9KnCAgkOm4vzl3zx755pbrBHxlTnIYMSckemMVaJLAsJOBDOnYFsgj0GKpJl4ohQBkgKwOrlmJK9tpXGDn1q66faS+1e3M0YkRo5diySlVyR2z2UfpUGEuqNHsYRE7WCLgnI9T/rirjpYOdWgOwOkJIBIUqCRhQQ3DZb8hz2rGWjNYi+dd8phAMZdwhBz5c8H1zx71zPUsZbR+FJ/3qP+jV2GpktLNI6BGlldnAjCAMSSQFHA5H1rnOoVB0kccfMr/Q1WnvIme6ziViYlmwSofCkHvxz9akR8g+KcA88DsakxovDJjt7V6QAQSB5iMDH+u9d7Zxmy1gKjxGKkY8wDev1/0asIFMaR8NuJyx4A5Pb61rttpKhlV2HHEYU5x2z3NWCRYjAhOV5YPIOfr+XGM1hJm0US9It2n1K0MXy8cUTliskgVmIBAVB3Y57Ct94MjaVIzJk47ZNYWMSLrGniNopHLZMkZKhMA8du9S7+JAAeRIJOdvAA7cGuWep0R0NEKMsOffg/z71wmrKf2pfmRgAJiBn+lfQYo9kB7MRJgZ47jNcTrAUardsF/wDbscDv962o6mdXQq4rfdkjCsGyA3tW8xOBJwFlc+VsDg1IVF8PcRvOMHJ7D/OtgK+KF5bsRxyPpXQ2YI16RJKl0kV8u11dQGJ4O7tXb7iInJxhm7/QAVzFvGWurVgoJEo3euOef8K6Vi3gEMOd+Dn7CvOxebR24fJM1tnBOP1FawN0kuDy2PzNeE8ge55zWS8Pn13DA+ma54mzJsJCvj0K5/P/AEa+XdaDHVWrD2uDX1CHzbs+mR78H/rXy/rU56s1c/8A4y1ergusfh7HmYzq14+5Q0pSvXPIFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoCXafgP3q0teOcdqq7T8J+9WtuQUVs+wNclU9OhojorEnIxwCpFXumLmVOwG9ftVDZFQsRbIG49vbFXmnkqQ3YhkP88V4FY9mmX+pqfCkHA8knb718ztpT4Qwd/PtnnmvpupnKSjA5RxXzaJ08P92u0DnLetdOE3Gc2J30ZNl2DKOccg+lRWZQecHB4A4qQGLPuGJPTAHY1EZNu4ZwV9zmu5HGzqOlhiC7UnBEinBPJ47ZqynbAXsccfaqfpc7re5/Du8QZyMelWdxuWVRjy9wD/ADrlnvM6IPool6i7fsa22dzLjt35Pp2qneOTbySwHpjBH1+tXN4y/si3Eu7b4hxtNUj7JUO2V/KcMSeaiGhMj2QsF8MyyFcE4I4/SqSV2EgaMZBODkelXEuCiqm12x5fLjP3NUspwTGhOQOcHIP5V0wOeZpZ0GcDAzzk8itMRPjRHIbDr/Ws5ig3eU7iBj7fatSYE0JQjG8ZA+9dCMGfTiMiJmwB3HPBrywf/fExydrZ4zzQkeCM4yMfi4JBrXpxzeIOOxry2egjDWNx1e7dNqhwisG5Jwg9arvGUOjBSjA87QWJPv8A86s9UbfrM/jyu3iMuxQpIXCDLECqVHbcY2eUxnLBowBg+xPtWkdCr1NF1IwjLKCNxJySeCf8aqppMSYdXyF9OARVvNIgjZgJI3Yc7yPN9aprpicqmWbtuzx9q6YHPM1PLkZwwGO5Oa6Lp6UjS5BnOJzj9BXMzKc+VML/AHhmuh6f3DTJQckmc/0pV3CKW+dLavyRnuBipnUDMsOllcDyPyc8Hj2/xqusWG9hjPAwfarLXWC2+ncDLI+QwyDjH+ua4eJ2cDnRcRmOII7OzMQ5J4LHsufSpccsXjSNKd7owVIjwAfVuc/yGKjySCU5mgMjJ+FYyM8duPXFLuRWlB5XIHcHcD3OFrSxUPMw/d4hfK8ExgZH+dVEzmXcrKCASTtGDU55Igxf8OB+AefJ9/p9qrLlGixIjMDn1X+WPStomUhpzn9oQtkkkN6cdqt2lwSSPTGc1R6Wu2+i2+YKGJGfpVy5BJyOGIqlXeL0t06LQda1DQdQsb7Q7JtR1CO4RYIEtzMxYhvMqAEkjk5HIrpviX1PrXWl1Zw/EPR77TdRsLVnsWvrOSJpA7KG2mQAsowPTAJrjrHXdR6Zkh1HQbh7S7WZYw6nnawIP61t6z6q1jrafRH6ivmuBA0sVomDvMrhc4wSecAfeppRzTKVZWuiuPS+q3kqalZ2mqT6NEhjvtQghke3hbnCtJ+EfXnjIzXS6d0x0NLq9pb6xq+radp5s98/gKqGSYvg4dgQFA+nJNWtp8VeqPh9ox6V1HTjZvZQmyWxliYFt2eCBwd245OcGttx1tc/EHo3T+gdH6ajOpQKH0iCxZUe3lVcMyn+FSM7ixwe5JNXbqPw7nwM0oLx7zn7nono5usotM0rqe8sum/lxN8/NDulEmcNErjapbGDk4wDW34gt0v0tqeg6X05qV3rXT021rwXUwMobdggSKAvmHPuOe9e9YDqLpLpWw6J6r0xrG9tJW1KFlkErSPINjyCVcqw4wRnjGMVKh600bSvhbcdO3WnDUZJ71L6WWeMMJZwyhAG74CjG0e596lbWTvf5qRlmtPmhj1N1b0ZqumaVpXTvSWm6NqMMu9Lm3VTMUVG3b5ASzhsjv7D2rjHlbk/TPeur6i6/s+orPT9Ki6X0TS5kmMq3VjZJE6qEbKlgoPOfUmuQlztbOe3Nc8lZ5o6oNNanV9IaJadS6lZ2mqa/H07axs0klwYRK0g2geGqEgEnOeTxiseuNAtOmusVI1lOqNGjgjlAZPlwybiDHIAzAcj0PY1SWOkx61dWUFxdrZxpK7tIw7jaBjHr9q72/Xo3o660rU7TTo9fugslvMl6RJFIpX8Ww+UFcHH/ePrWkLpXRlUs3ZlN1z8Q7L4gDpi3i0Gylk0aX91a2UAYC3A5i2RL+DjJ/M1N6++MbdY9KppmrrEsEc0Py7JMC1sqtz4Y/gG3IxxUk/EOLp7UYerehumYenbcRtZXuoW0Ti3LMQ4h3r5Vby5wDnHeufk6xuP9sYuvZ9H042UbeG4aECNpmU/vShBGee5HfmrKKy6OmmfEo5PPPXu4Fr1J1v0OOkNP0yw6btbQx3cckd5JKrXMzchnklyWdcEcHCjAwKg9JaNrl9cT6l8O18doU8K+lguUwQfMqg9mPGceg7+lWlp8UOnYOr9N6hj6P0qK6ihmWOaK2XwWlbbyFKiMuFB7dsk96g678RrjUesnPRmhtpsd/GsksFjGsay3PZnCKNuSNo49RUxUkrJfdkNpu9/siHrE/Uz61dDq5JYpVgRbdZXz5Du3H6c1z5ncux5GTXQ6/pvUunatNL1tB8tc3MMbWyGdZCsYDZDY7Nn0471zBPLehHr+Vc8rN5HTC6R1fSekaT1Ff28PUvUUnT1vbrJIrRW6yvMTtG3zkKAOSfyx710s3VGnfD7V59C04af1lZXIW6jvL7SkMyhhjwsKG/DtyAMfiritF6Qu+sr2ztLDVLDSDb75ZLi9ZsbSFAVVUEsc+mPSuw/a2ufBnUI9Gg1nS9Xe+VruO8hhYF93lI3Y3KVxjHAwRVopNWvfu+IpJtO+neRdK6Wu9QiutVS5i0W9u7qWU6TLC0Atk3YQYzwSOcexFUNpfD5y40q8jiutTec2odmZd07EBTkeUAEjn0Aqy1mHqi/6v8A2h1QsvT9nqgjlS9u496sigAuFPIOOcHvxXX6Z8KtG6j1DUoukeoreHS47cMl9q5aW4e9bLMpRQuFOA24njdgBu9abSiukZqLk+iVOv8AT3VXw+isLvrC40XXumBcRxah8jdsd6sSAjKSCFZgASB2PpmqzqPqPpC9k0+PpfpeHQ9WJcmSGZuYgh3ArubIJx7VX6TqF/oPVNzadTaYb5dHLLfxGKSe3hcjCOznKopzlc45Iqb1ZrvTeoz2EOj6LbWGosZH8SIAER7Du4BPB4/OqyjlmWi88iknuZAGyScAHA59Knv1jqHSTxazpkypcRTxpuYcBXQg/wAv61TzuSzqv8SD0+lXei6vYaHqWlX+s2sV7aJLH+7mQMnieGwViDwSD2zWEUtrQ6JPIt7fVbb4u3Iv+qusP9l30pDawbLQPJOG85bzNwAeB68mo2sfEfUbPpe+6BM0Wp6LbgWMN14LKJRnKz98K2TuJ75zU7rPVX+KWt6HpnS1rZ6fqyB2kfMcEcduAPNJgYABIxxk54qn13oYfD7qHSLTqW9suqNDmPi3UtoskTSqreeFgxDYJ2+bjcpPaulKN7PyRyvaza+5LsumF02TQp/hdql/rPV4EvzoeRY41h8PzNGXxhlOBkkk7vSq3ruHqOx1bS5viXF8uHgeOxkedJd+GBkX91nHdTjBzXSXGidN9YataR/DK0suk9cj8SeaW5vJooXtyADEqkttO8qRjAHPesdN626g+DnVVxF1Klhe3k9qjWF2l2s+yPeQ5WRh5SxC8YXIAqE23pn36/clpJa5d2hr0Loc9V6ZLrGjdYaLoFre5a2sZYZmaEDyZYghQSVLYIOM/lXKuGt7SzieSOVooEjZ0dXVmAwSuPTj1qw6kt9b+JWpan1fpM9laWl4fDDeIhAeNNrvg88n1xyQTVLDCIdN05GKlhbxqzIcjIXkn3rOonbN+XYaU7XyRJsJmFxEQoJByKzuounH0Lpn/ZSBbS9ihZNS3Fma6kPm8Ykn0O4YGABxWnSPNdwBRuLH8PvVRD0Tc23StpqEGuRTW9xH489pFKQUXcSUI9eBzjilK1838zFW9slf4iHpS21/eNd68ZdjykRzNEFSRV4O0nA447dq+g33wo1220zUertH0+K46YniF3BbG/D3vgCMEuIyOVPLBc5xzipep/EfRupuirfpGSwsZbER+Dp6/Mxnw5nIAZADlWLHzHH3qFrzdXdChtJ1jU2vLSCKOIXVrKWt54hhSQcbsfwkcVu5Tb7O4wUYpdpdwfCrqCPQopdI600iN5rdXFkwuFaLem4oGGUbAO3ccck8cVC6o+KuldXdNQaBH03p0aCaKGwtbKzi8ZZQ4VViWNd5bjHPf6k1wMV1rt0kzaFaTT27ljbsZZNoXJ7J2IHpk+lXeg6dJca9pVz8OLOV9Zs5BdW8byIWUKuJJGPpyTjPqQBzTY4zd/Qna4RVjK96X1HpXqb5bWtG1HSZpbF5I/nLcxq43oMAn8WCecc9qjs3707iOM+narnX+q+p9V1e30vqu3lhW0iklAljKefKrgdznH+FUIfbISefKQdx9a5Z3b6R0wslkQ+qPFXTLNdjbmuAdu3J27M/5VzsLyA4RVJAO1m4HPAq/wCoV36ZZ4O0C5bOGAz5DXOIoyoBA8xOAc5z6fyrenuGc94nfNZeKGWcPOeWSJSB9yewz2qxkZpx4awJbhcjKt3+x9TzUS2kxs+bKqobOY/xEHsCfXk1Ktirx7UdhGWyAFGSce3f071WRKPY3UxLFGhVBj93gnP/ABH3/P8ASt12OI1huGKsxVFbChvcfTGefvWqBWQSS5ZmLYZyuAG9sD6UUAMHSIMHjH7snPfPDfT14qhpwLvTmJtY9rDyxH/5Zv8AX2qFKD4iDGAMH3zxViJriaINfXRu5hAEMhkDZUMQoXHGAABgdsVXXHFwAMH3PbHFZljmuqSf23NsyMQxZPf04qtZ3DEMcMg4INWPVBI1ufADHwYsE++KqQhIyzEZP4T7V3Q3UcU95ky1mLLgtubO7kZ+hqwj2EGMSbSRwCp4Prz6n/Oq22YMW3qzYwfIOc/pVlE853GRkZU5VWATA/zqJFosm2kQknVZYxhQSyfwk/Ud/arHp5Fk1iKSSPzqJGJ2lkQnnGDxzjt/WqjcAvkiCSoCwYyfiz9exq36YmeTW7QwRsEVHYpv2E+UZO4+wySfqPtWEtGbLVEnUWLF5N80haRzumAVm59ccZ+1UWunGlRDIz8ynGcehNXOp58Q9vxN2zj8R7VSdQEDTQMjm4QA/wDhNVhvImW6zlg+N2xxxzWzxCG2s2/ny4OTwPWtUagyY3E7uwxkDjtWRVRyFwSSRx2rvOIsbeQCU5YeZRx2zx2qyDP4igx7ljXGCM7Rj/P+lVdiMz5K7nxx5uw9/rVpGvmjkkRTGEYt5u5zwCQe/r+VYSN4aE/SRD+09PjRXYBmUsCCzZBzx2z9Kk6gxyOwKDsDUbR9pvrJhJKzNIQQFAVBg53N6tx/Ott/gu5AxuyQM9h6CuaWputDepOSCARhcgHg8VxWp5bVtQ4x+/bn6V2ET7hFyVzgdvyrjNUz+074nJJuGAGfrWtHVmdXQxiY4HiYCZ/D61sKCIsxfLcEEcZHtUdByCzNk8DHf9fSt8EpL5RpNxPl8mea3ZkiVaszT2XhjaqycsSeQfT6101xhIiADyQckY9BxXJWshi1C1hTKHxV3DOcjJPI9DyK625JMOVGCCBgnvlRnFcGK1R2YfRmqT8CewJAI/KvAMSeXkZH9aeUowIw3G38u4/StUYPJUkqTnn+lcsDaRNgYHcD/Euf0r5n1p/62ax/+dP/AFr6TAcdzjHvmvmnWLB+qdWZDlTcuQfzr1sFvvwPMxvVrxKSlKV655ApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlATLP8J+5/pVjbks5AwAcdqrbX8B/71WFscMD7c1yVeJ6dDRHSWoBiCnuM4+9dDYRhkYgYzGGBJxyDXPW77WOexLZzx3roNLVnRlBxmNhnP0JrwK+h7NLUvdSwVIHba2Oc+lfOrQoY0SRS4KnkDlfyr6JeHcgI/DtIH6V82tpkMQ8vC5Hbk/nXThdxnPid9fOw2vEArbD4hznPsPtUCRHGWfK+2e5qUs4ZMrlew71pnDCRcFTxzznFdyONl/0zuMdxlQ/nXOeSODVrcDzjHBHHfvVP0ygUXR3A5KnP+u1Ws2N5G0gk++a5qm8zaG6SNQO3SbbykkuRwcH9arCp8AGNMgN+H6fU1aXzhdLtt6Fv3jHGOKqJm2S+QsmBuJ34P6VECZGJt4JQAv8AaOT5W52kfeqi7kd3YSBcj1+lTXbd5yN+8/2gOB+lVt1u358gA/hJ5zXVBZnPNkeXLKrEdvXOa1R+aVORwwPbHrW1zuztVsHuN3rWnIEqAgk7h3PbmtkYM+mt/YAHtnjPc8Vq08ZuYwvfn+hrJiFhVTkK2MZNeaYf99Tccrkj8sV5jPQRo13cus3BjbYWVNnPc7Rmq0phd4kfcThhu7H7VYa1Gh1W6YsV27DJk98KP51ClkZGEiBfDPAJGG/I1pHREPUgyyktEDgo27cCvcYxj9ar7pmVyWOCCNg9D96t7ghVXejlDnI/u1WXj+ICGjQMABuXjI/pW8DCZUvuLNvJY5zjGMV0nTgP7Kctn+2bv9hXPeZhkMEz7iui0AN+zHDHcBOwz78CrVtwpS3i+sziUj0xg1b64GNvpKqnjK6NvjHBIwOx9KpLRvN+eBV11KjSWWjIg2rsfxGDEE9sDjv9q4eKO1aMoo0HjEoqQZHHiSbcfTPv+le+FsURP4RCgbQrDO76+uazkQmMyT5dHwBGWJ2+x+n51hOiM8j258RiwwBGc5x2+tXRUr5S5ZQy7W5IXxNvOfpUC4DNuVQctgnJ5/OreUMqKsrEv38vmP6+lVt1Bu8zkqmcAZ7fetosykiLpgZr1WJBOGB9PSrckbxxxu+9V1ipW8jUjaQjEkevHFWBck5LHJ96pU3i9LdLH5C31OGC1v7o2cD3abpkOTwCeMf4V9W0XoLpjVbqC16a6naO8+WIurrUd0ixHgxtFGMEZbO457AHvXzrQ9S0TS760uOq9Jj1zTUnBNqxIRpAp2lgCCw5PGa6y+0/pf4gdQtP0RPpPQZjgUXVvKswWeQsdrKAxK4Xg4IzxxVVe3FCVlIuNP6TuuoOpL3Rvin1J+z49GeIw3Fo0c8t2CCyGNmAxH2J3DdzjA5rkNN6O6zs+tOoh0UsWqHSWEDz2kyRtNHIA6lVZvM2MblB4rnNe6Y1jSeo5zfXMt9ZC4iin1i1spJ7REIHIZhjIBxgnv3rretJumrNNEk+EqaodSuHMF9Aksss17IB5ZsDs3cFVGMY7VsrrR3v3ZGLaeq078zpOn7bp3rLTrlfirqOr6d1HBLLaQRJdCFrRcjzGMqQ7E84PGAPfNfNo16Oh0W8sbW1Gp66XNvZ3TXir+/D4V1hBzkkZ5yOaj6tbywS3p6h/adj1HJEfnba4UrcLJjygKTv/DjBx25r6lF8TuneoeirHpRdL07SbaaOO3sbpLOJhbTYASUOAGyrDcfUnOTzUNOOazQTUsnkz5xrHQfU/T6aXqmvW9jFZzOUb5ecM4kKHAKk5HY1QyYPGB+dfTuv/h9B07pWmanadZrr2ybw7i1ayMGWdGG9D4j8AjtgcGvmDntyO9Yyltu9zeEdlWNOrfNG0sm052in+ZO119Bt5rvug+l+k+o+m9Rk676m1C26hldorHwJlWOzQdi0ZHn3HvjGABjmuMuGnS209rNTNI16IhGELl9yHgKvJPHoK6/pLpnp68mkn6v1/UdFutzB7CG3ETrjgZaQZU49hmtou1O38GMl/sLDSviRPrWhp8Nd2m22mzM1kpeJlhjYE5uCOxPG7d+I9qtOo/hhdaDH07b9AXkvXLCfxLyzezVW8ibvECBtpQdvNznA5zXz6+6d0jQptba1+cudMtbpltr+aJwMEgqC+3azc7Tz3q00nReq9D6Tk65t722sooHEa2MTEXDwseZcfh2g7RgnJ9Ks0tYu35ZVN6SV/DgiZ1v8S9Z16107SOpbRoE0678QWptWiaNtpTasZGR35AFWem3f/aRd6boei3endMz6RE0zXMsUq4Q4XYAo3li3PHbBroP2T1NJpNn8RtR1lV13S7ZzaWEluryeAw8zsw7Ng8Dk4zzzVTDfX/xuUalqWvWtm2luYY2a32mcsATlvxYHH5mqqyVkre/2JzvnxKDqjpfUOldfu4dW1uz155rWOSGe1aTai5YEESeYHgH6iuSbPmK9ue36V0GsdOS9O6xdRXGpwai01ujIYicRgFht5/WqDGZCMgcd8fXNYy1N4aGrV3uIFsJLMgSCWQHOcEbR7fWvoV/Y9HaL0/ovUVrrU2p9UWbILsMB8uyycGOOPGV2HncSWYg/SuPXRdc16fS7HpTTbjVtTkuJCLaFN2UCDcTnAVRxySBXYaJY9IaLpep6Z13oF/ddSFHhmW8Yquny4PEaA4Dg4wxyfbvWifQX4MpLpslal8QtH6uuNKtuplvL/TIrjx7k2UjDGFIUNKBlAzYHByewxW7qV9K6j6g0Wy+E+nWegzTRyG/iM8lvEQCMTO7MXJ5IOc/SqS4uuoOkPh7e6DqvT82mWOrxp48k9symUjGxmb8IAxkc8EnjNU1oul3+j6Zp2l2SafqU92Gk126WVkEQU5RvLgLgcAHJNW2Vqit+DO/n621v4VXE3SMt/Ya+mrL48slnbSOt60vk8MqeXIwEAbHGDXEar8M+q+irixvOo9Eg02xlEyB4LyOcxuVJWNwh8pxn6cVK6r6MTSdT0C/6P6oPUOooWe4VYFiEZRdwMYBJx3/Ec55qnverdb1rULGw1Sed7VRLKVdu7BDjgffuearZ7N4+ZZPpWl5GEpB8y8HaD9/epovdK0trObqPTI9Y02O4RXt5HKq7FWCk4IPB9M4qFFyArZ/Djj7Vp121m1LRYbazjMlxJdw7UAx2Dc57Y4rGCW0rm830XY7pdO6Y+IGqW8PSUWkfD++soXkklYSMtyjYVYyFY4ORuz3GKlaLbab0v1Bf2nxCg0zqZxbILNlmaaKOJiQ7YPO8lQMnkAcYya+a6f0vrMuqWFrMtvaJPu/fMwxwM4PoK6rr/ofTulNDtdbseoDeXr3CWtyYztVIyOCg7jBByD3zmurZSdr5fOJyXdr2Jlt0X0nqXU97DadUL0xai1SW3iktfFJd3I2nLDCBRxznJGK16R1zqfw21bW+nbe70vXIJjHMbhYt8bxlCoUAgnjJyD6nvXvVGr9F3PRNlYdNaU9jqdtOjftcjM94WIEhmck+IpHZey4GMVI6p/2RHRCw2kQtdQtkDHUVX987EjJYnuMcAfSottZSWROmcSx0fp/oTqTR7vWdW1+/0bV9RMkh0+yFvDDb912gMpYZxuOCPxcY71w5KC1tvD2KojjCBM4IC4yM5Pb6n71I+I/+xKdOQXHw/jv9Guoo1hmillaRLsnvLuY8OR3C8H6VECsmnWKhFjYW8a+Vs9kH/Wsaiyub03nY9sZDAySx8tFlgSPb6V3esat8ONa+HFv010po72MxWIQ3SvIsryEqXMrk4YON4I+owBgVwdpnzCIZIRiM++DUrTdU6J1KDT7WDQW0CS6aKE3cN20hhzgNII2yMjnucc1Wmr555E1HwyzPoN71k3xa049EdOdK6TDqd5Hs09BFBCLEx4IlWRQDGqBeWJ+nJODzPTNna6F1TqWlfGq1WRLKON106Od/BvWZsiRnUktEMAhVwCTz2xU7qbQNE6Dgm6h+G/UGoG5nVILq0u3jkZomf8SOAMgHbkEc1X6D8QemtT02/teu+noNX1K4mKDUpH2TwwqPKIpcrs2ksTt4y3I7Vql0egsvUyb6XSefoWmqDUhqq3nQGj6rZdDLLBBLdwQSNaWDMf3m4csVUNuwSMZAJAqf1LB0X0laR3/woudVudWldLe4t5Lv5lrhCxJcKB5WBG44wMZGBxXGaT8Tta6c6bs9BghutP0GcyxWc81m4SaGaRgZS5wG/GSW5zVsOiI9L1TSJPhV1lc9Q61DKYktblY7VdpRg8gk3bVwo7tzjimy473/AL4ja2tP/PAo7vq3Vtf1uK31WO4iFrZySI0sewsSyjj6D/Gq89zx6V03VOg9X6Tr9vd9avaXCy2MkVvJbX0d1596llYqAAcfT07muZfksfQVjNpu6NoJpZkTqJBLpNscgbbkvg9sbT61QRgA7juVd/GOeMf510WvAHSbZWdkU3BIwPUIcfpXPR2rM3lbkDkk5H5VrTfRKzXSJ6ZJRXIjITJ5yc/X61uj/fK3i70kVRl1UYA9T9v8vrW6wmZZQsUaryOwzg988/8ASs5D4qKlzLwCzHyhAzei9u3rzmqtlkj0hAVeQtNEUO1iNqDPHYHg49u9a2/dujJI+4AOOMgA/X6fX3rb5JG8CZ1Qso3NzwoGTn29O32r0RTJGdkqKEYZ2Dl+eG+o/wAqoWJulyh9NhcHYCr44xj963pWiY77jax5B5Oc+lSbAk6fbMQ3Ksf3nLf2r8n71BkGboDPOarxJ4HPdUKX1u4JHZIypz67aq2ZeAqh8HnHJJ+tT+qCH124aTcf3cWCD67RUDIjCrv2kdyeDXfDdRxT3mT7dGWItJtAcY29j37Z/rW2N9ykzygk4wSMnk+n6VDhCuoxKCynuRxVlGqRlSrSFypKkLhT+R/lVWWR7JbGI7XOAuX28beeO1XPTBkk160eR9koSTa4AIBxxngj8sHOKo2i3o7sxdeBnbhT7gf696uelGMuvWqQjw41imbEkixj8PHmPbJ/wrGW6zWOpN1AruxGWKhiMt+L8R78VQ9Sc6WoHGLtecd/Ie1dBqBDSnaMBmPBOfWuc6kyNNBblDOn6gNVKa6SLT3Wc5Eyq5A8Qh/bg8/415a25t/EMr+Kp7FuCB7Gkb5EgCjOBjB5rarKysTxzhvriu9nGibb8KGB27TwQRke9TopVEhFuvzOAc4GRyfb0A4/WquAgEE7iN2/A4/KrWBVJeQSneVyQq7dwA7Z9KxkbRLXS7hnv7RZC8ztuXG4gLhTzjt+Vbb45dfqPyqP0vIYtWs5vk4blnSQRRux2KSjDdgHJxyecDPPpUi8ADoB2xxk+lcs8pG8dDFCFijb2K9z9RXGam3/AJ21BcgYuHwDxxn3rrkOUG05Iwefoc1yGopu1HUJOMfMv37d61o6szq6GEaMoOxVyRySf9YrcJC2XAbAHq2DUUAKpPqf4SakiIRyksGBHAGMcgV0MxRPsRGJhIyv4pkQLsxg5PJYk+g+9dJL+CQ4Vfwgc8k4FcvaoJZbdpIxvSRXBBwO/fFdXLMJIUt22kb1IYnB/CBjNebitUd+H0ZFfIUEnJB7mtSMDIwJ49we1SZgAycqwIK+XtntUeNcykdiy88fSuaBrLUkwgEA+hB4r5l1Whj6k1VG7i5cfzr6hb42qR7Y4r5l1jj/AGp1jH/9XJ/WvWwW+/A83G9WvEpKUpXrnjilKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUBKtvwH/vVYWzecY9BVfaHH/xCrG2Qhyccc9q5avE9Ohoi/gOVGMjJ/Wun0NGa4RAuWJUYz78f41y1mDJtHbPrn6V1WjyATJuBYBBkAZyK8HEaWPYo6l5qMbRbkk/EgIbJ9QK+YRQKsSupyxY8elfUtUVFdgo2sWbAA4C+1fNrZB4YjKgkEkc59e9bYR9AwxS6fzuI8rNFGsXGAOT2xzWp1UEFRncOPrW2cgPtVsA+/etZJJAH14A7V6COJl90y2Uue7Dy5z2FWEzbmDYwRwarenDlLkADAKgVPlXbgHPeuWpvM2huknU2A020Zm2nxCRuPA7VVXCnBV1EhPrySQR39uKtdRCnSYN+VALY44zxVStyFXc4HmG3YMjNIaEy1NDq6nKAbMfjI25AFVroJJPMuCDzzirKctIcyecOMcLxx/Oq+RgGVcCMkYbcMkfauqBzyIkijLBvyOeK0qv7xApzhh/WtpYFmVwSvYHPBrfbWZkKOASNwPf61toYnfPkQjPlyQCo7fyrzSivz8WcAknBHpW+e2MMWAuARxkg8VH0gZ1SAMdi5OTjOOK8t6HoLU060y/ti7xk/gYhVGfwiq8hpEEpAZQT34b/AJVZ64Ej1i68CZrjKoXPhlNp2jjk5/pVRMMRAttkaRuMEll+57VpHRFZamtiqMxlbIceUZOCfXtVRcKSx3LtUnPBzVlcnYCqkM4PLsTj/pVbcHbuKY2uucjtiuiBzzIbbixVSTt9x3ro+nx/5oYAkkzNz+QrnMd2Y4LDgg+ldJoQCaUwHP79vX6Cpq7pFLeLe2GX7evFdB1EhFpojYLFopAqg+uBgke1UVqMMvbvXS9RQqNO6fLAAyQyjcXx/d5rgk80d0c0yo8B3i2ylYxKPxmMsxA+wqufMN4m0jzZ2FzglfUkf4Vd/JSppb6nIt1JYwnDyR2MrKD6gygbfzzXW3Hwg1qHpL9uRS6Qf3PzDWRmb5kx4yTkjbu284z+dFNLUlxb0PmN1IyxSIiFo2wwUDAx9c1WtGpXy8nGQo7D7/ar0z29zFMhaNSAPCjUgFs+3NUk5CzsBzKTgKeAM85reLMJI0WZPzwV8s6q2e2Bx2qWTkgD1yfvUaw8RbhDIH8NkbYxjIDfY45re/LDHJHp+dJ6k090kahY3eqWkNppNtcXF7NfRpFDbIZJWODwqjv2rqtG6B0+zmng+IOp610j1AGBjtrjT1ZTCQCrbTzknJz9KpdO6i1Tpe4ttQ6fd47zxmUsgyQpU5/LiukMs3xK6usLv4l6te9N2EFjsSeGLZPfIXJKoWOOOeTnvV4bWz3eplPZ2r8fQ8sfiX1B0joeo9N273GsaEBNbxvJbsiXUbk+YjOMEn17YqfadH6n8L+nLfrDROq4NV1l/DtZtPs4S5ijkYBfDkZsud2AQAPvVtZ9I9UazqOtdNfDXVluek7e3RIrzVdRSKMK6Z8A+Ql5O4wnHYnFch0L0YnVWpanY631D/sm2ky+GqNCHke8TsG5GI1I5Ye4xU3i03p29/iVzuvTuPoeoa/1r8KdQtOsuqLbS3vNYjjtY5YG8a5tAgOLeUqDsdsk7VJz2JyMVyPU/wAJepLNLzXrqx0bTrG/uxM6xXqtJYLKwy8kWOwJ5CkkZ+lZdLWFn1lc6to3xN6pvdLg0u6CQLZ3KStJcqCVky4x4a8HjzHPcYrlNQ6iu7+6k6Z1/XpvkfmxBdNbyeIzIGzuRTgnOAeTSMWnlrxy4Eyaaz04eJcdR9FWWiafp+pJ1pY63MJ9hsba1KbtyMN+8n09sCuRfnOBwCOavde6e6f006Ve6H1PPq8/jOnyctp4W0bDls57iueY8ce/esp5vW/obU9NDpulet7noDUrPU7BIpZ5GaFGZfMhK8lW7qSAQSPSnxA6h1Lrjqh9Xkin066ntUWV1QgTEcBwSOSBgZHsKi9PdUw9I31vez29vLvzEjzqCEOM8Z7Zxivol5JrHx8e10vp+O0ubnT28We6nvRDFboylVQyY7seyKDwCTgVeCUenbzM6l5PZv5FLF8cdQbom46XMMU9iLE2fyqF3DDbtyVxzz5i3vzVXqPw91ez6RmuodctrlI7dXdFZsuAAdoXtXS6F8QOo9I1Cbo7SNCjOt2260eysLHdcM6rjHkXzAjnfnBByag9D9BdQ9W6LONZ1SLprwXeNVljZ55HjfG0x5ATzDBJ9uBVotRu1kUfS1zI2mN1b8nbz9V9O61/stA6rqc7Bo4/BPG3PBG44BweAaseppOn+o7/AESw+Ful6b03dybxdQsDDA8SgYlbc2dwPGR3zjmue17r/qKWz1Xp66lu72Uq1tNbRxtI2zPm57Yx/hU573Q9c6VTQug+i4/2vGySi8t1E924X8Zd+WK4z5eAOMDNLO93/XmLrRf35EPXOkbzpLWriHU9c07XZL2CJklsshYwGIKbWAIx7+tcznDzktg7torzTJJn1HVVvVmintzEjxzIUKnzYGKyZSPqNxJ+9YzupZm9OzjkdDoPUeu9PX0R6Xtri7ludyTxwW7TEKuCCQvIGf8ACsOo9R13WOubXW9Z0uW0v9Pjjcrc2xhaYKeGw3Jx2z9Kh2vVWrdJS2t3oV1NbSTu8U3hNglQAR34PNfTNF0WL4kaQdc6v6ivoNSkhNvZR/u2VVz+JwR33HsD2q8eitpopPpPZuVd58R9d6t0XUrLS9IvNbjjj/3xI7KSaKKMHcxkPYDAP3qN1V8c77qPo+60a9miazuoxHAI5SUjJYEMiY8uMA/TFZdB9Vdcvb3HSfRNhc6i1n4scsVnagxswJVpXkyFAOe7e+K09O9Ma/1Dod9ptp0At3b2R+X1G6MQtygjbEipK7AFxtIAQE5o1BPpJZd5VOTWT17jZb6R0c2jwWOiajdXHVk7pDb6nqM7Q20bMw3J4SAYQrnnljgYxWjrX4at0rNpusQ9S2OtWxD27QwW7wMjMpIYbmJccY9MVyWr2PTWrTWEfSzXVjMbxFVrqbxorVScbmK5IHbvg+lXWvdGDRF0++HWGl60m5k+UtYZEbLIfP5icgY+neklZbz+3yxMXd6FEGwBt9zXQ6Ro+pa7d2Fj0+0Md/JdRkPcS+HEkYVi7M2DgAewJ7DBqhUoFBBI9eakXktyLC2OlyOt0LlBlT/Dhs1hFXZu8kd91h8OdcsLpLbW9esI4reA3NhcQXm9LifIUwkFV24UkkkHPHPeuCsuop+luqdJ1DW7a01SytxI8VtcIDGJtu0SEchiuTjPA71aJoeqdVXGlftHV47SFJTEXuz4m1SuWYD1PlAA4712ms6fpPwvt4up+lNZuL7VI0FrMt1HGY/Cc8lEA8vIHvnJrpXRWy8zmfSe0sji9R6r6M13rXR9W1LpuwhsVaQ39raOFhuXK/uwyLgY3eYhe+Oe5qdq3RFn8StbjtfhTpXyWYXmvLe8uzFaWa9lcF8lQTwF59cVhZ/FjSrnqC41LWOmtE1L5izFqzGzQRtufcwIYBdxwOeGwOOKstA6c1/VItV6i+FNlbadotw6o9lHqaJLPNGDu8IOMMoyB375AzijvDu88grS7/LMx6fv+kOi9GvtN1bQ7eW9gR4L+S7CyFGUEMi5/CM+wyeK45mjNhbGJWjQxoUU87EKjA/IYFTND6p6IuLG+u+uul11XXZpZTqE91dOCjAkBVAYYIAAzg81CaQHTrVbcbYDDGVDYLbQMqCfoDiqVF2o0ptPRmViVyDIdqbTyfQYNTrj4SdX6V0F/tBd6dpbolkLk2y6kvzKR7Q29osdwpDFQc/nkVW220h9w/hIz+Rrqbzp3rXpjRLO66lu7HVdCTwBfi11MS4t8ruVkwCePL3I71Wm2nky1RJnB6RoN319cW2gdNXFvLe3Y8be8wgjjVRlpJX9AP6kYFfR9U1PXr+wh+EFz07Z6hqlvB8rp9rAQ6h8czxykBVU8s0hYDuTVR1z1JoMVuuq9GaJLoWqRNhvlEMYeAct5eSAODu7DAqfqOnXegdOL1pB1lHd9RCyWO80+JQRFbswOxZmOZHGQWG0L6DOM1s25WbXh4+Rgls3Sfj4ELrD4pa3penXfSfV/h2r2sAsmsXQuY12hMp3TGBkMpweMVB1aPouw0eC56Nl6iTXIXQWs95eBoyG4cbQB3XPb6VZ9Odaav0zd2/X3VPTRdb6MWui6pqcasoVAQ+wEEZyTg47cLkgmoLdC3Nv1nFcfEHQrzp7pe/c3tlEFEW9WG7aO5Q85CsAQDyKlJR7vB6hty7/ABWhRWmqahqOsH9qXUlykNmxjEjljksMkk/pW3Iwx9TxU7VNA0jRupLiXQNRmvre5sydsuS6AOMDJ/SoRT8eBkDkYNc9VpyyOikmo5kXqEg6PaLgb2uic8f3OQfpVNG4bEcaN7gnuPbmrbqOQppFiEYiRrs7Rtzn932qLoOl3eva1YaZZiOO7uWK5lyFhXGSXHfAAJwBk8VaGUCJbxstA8sbDwmOzJGPLz749eal/Krbnzrtc4Yv4mcHGOPfNfRtf+EGo6LJpA0C/TqJ72ZLWS3itvCl3sCVYZYjZwdxJG0DJqL1N8POoelWtH1u0jYXOLe2aymFyGm7JCAozvbOe3pweKxc0bKJxrWYQeHjfGMK6KVJ78Enua1q0kcMcbtGJIztyxI2YerrVdAutKU2GpW97YXC7T4EyGKTZjuNwzjAxkVM0PpfWOqLi4PTmjalqT2ybpBbwh/CBOVyxIGeDgE5PPFU21a5pslLaBm0ywZlBZ43ZuMYPiv6VXHm4X1OeauhA9rpumJOrJIkUwk3KQQ4mcMCDyCDkHPrVMADKDn8qsjNnNdS/wDzduVK/wDs4sH7KKrQwfvtIb+EetWHU5H7fuRngLHnj12iqyNWEhVWfB4O4V6ENxHDLeZMhO0KUB3AYwPf7VNjiyqPnPHYHjg/yqDCVRWBlCP/AArgnNTSz7lJDsigDax4P3+nHaoZaJLmRRGPEZkZj5QfX3qf07DB+2bWS4dpE8KQybUzxgHjII/lUFzIdkwEEcZJ24JY+noSf8qtenpwdagQFlDJIchfOuMYwQDwfbHNYS0ZstSRdBdy7FCgZI54x6VSdRkrpMfHHzK7v/gNXl6d0hLBhjJ8/fOT3qj6lGdJj5A/3le//cNVp7yJnus5MBSFLYU9+TW/fEI8HOS2CQeAP8a1hYwn7xiC3YYyazVQWfA3DGckYz9q7jjLG2YZ2HnzYAU9x/oVNiZkRC4CKpLPkgYJPJJqvslCq0jDajMQHYZ+/FWkUKKWjmKsoBBUHue+c+n2rCRtEnaGoh1aEOskk0qukBV1O1sHDFcc+XI9ME59Kk32PETcRnbzg559qgaIA+rW5uC3hIH8qFeRtPAz7/rUq7Dtc7Bjvgc4Hfvk1zT1N46C2UEgHBArir9R+0LkOcAys3867yKIRTgAhsgHI9MH/nXG3Ecfz16TyDPJkfxd61o6spV0REx4mwtygIyAOSKlzQAHekrtGFxhxjaPt6CsQyK5EcjZPfjtW1pzsyoUbiGBxjkVuzFGyJytzbqCpDSp2P8Ar2FdPI22F1b1dRyMnsK5rT7aN5lkZHkkXDhYjynIwz8fhGe31rp7lwIY4yNm19w3DB5VfX24rzsVqjtw+jNcgC5I7ZOB9v8AlWjOJRjGPXHrW6Q8E9+ckZ96jKRvx3GDxXPA2kTIAMFOM4yM/wCvpXzLq4huqNXIOQbuTn/xGvp0RG7LewX9TXy7qrjqXVge4upB/wDJGvWwW+/A8zG7i8SopSleseQKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQEm27HHvVtZbSVUnAPcjuOKqbXsfvVhDxImP7tctVXuenQ3UdHpqAqM8Bjjv9K6fRE3XUauobIC8jiuZ08hUjyd3IP29K6XRwPmGXBXynkdwR7V4GI4nsUeBf6lxJjsVcjGMD+dfMbUsiP5gpJYDI5719N1WQyEPId7MxJJ7ntXzGK+RAy26qX3HOeTnNb4S+wYYqymYT+Y8Kwx71hkAY5JxzWc80jzKxOd2CMe9R3yknk3YA9K9BaHCy/wCnsbbvBJAC+lWE5wwP61WdOcRagH5PhjGODVnKdxT3I9K5p7zN4bqJ90SdPtcAEEOefuKp7iVo1Zoo2dsgiNSOT9atbx/D02A7toCPls8dxVVvMjbyoLseCOB/1qIEyI9zFKRloyu4cAcEE1VyABwWPI9/5VbzPtGZVJYdgCSM1V3BTxPwgjH8Pb+ddMDCZEly+8nA2/yNZWV6YpFViSpYZ9K1O6GJxEDknnI5+1RA58RQC2Mj8ua3tcwPrd1crJABlmG3jnJGagWal70Afiw2OM+le+K5tgM+UD378V7pjsNQiYMY2DEg45BrzGegjRrLY1e6O1fOE5Pr5RxVeYSsbuMbMYZQcDP61O1wBtXcZLY2HnjBKioXmaJlJCIjZXCZJP1rSOiKvU1geHC7hSR/CXXsapZ/EjK7j5TnAxgE/wCFXkkbHzCcAscfvcYNUd4+JgEUHBPmJyDW8NTCZFdf+JducDbyBXQ6CMaS4ByBOwH6CufLs2/Lg/3uMV0GhyqdLYhdv75hj8hU1d0rS3i6tsBl9u/Nfd+ho+nTa6dP1Lp9rqlxb2n+7R3UfiRxh/xNsPBJxxntXwa3IyMY9SK/QfQQ6eg0y0utfVbqVbWNIoHcqqcEluCCSc9jxx2rzqnA9CnxOtl+IlhpVpJa2rw2mmQoEFvlUiSM8FAv4cHPb6187Nl1LayWt3r9lZxaHJcss6JqJO23bO0GLHBwVBG7HNd5adW6RoceoWlhAFtZXEkwkQyRjI4XcwI+wJ4qPpXS7a7Gl9r9zYXmnyMZI9GkiLq6k/uxK+QD77cY7ZNUTSJabKDryR+rOmH0XStA+evYVQ6dbWdsqtEVYDKYxtXbkE5xXC/EjqqwtbSDQtZ0yCxW2aPFrJGEkiHHYDnHB57Hnk19MgsZbOfU+o+iOmbXSrWeNVu1juPAZzEWAMcJ4UfizjG7A4NcN1N0nd9bal071hrUlgdEgX97E0jG5lR8sh27cbc+57Zq0JR2lfQiSezlqcH1Hq8d5EI4JEdDhoypyCo9vpiuPY5cA8jPHof1rs+sLTSEl8XT7RIJ0JCtHxkHvkfauHdsOQTnmt/AyJmoXVxp1rbXNiwin+bjUAN2zkflUnXbjWtZvtGvOotWmW0O+CNnkErquefKfr2GKn9MWvTd9fwW/W008ekGcStHA5jaVlBwhfBKjnJI544x3rs+qtX+HHTPUlhcWGnx65p11asn7P1Gfx4oHVhmRXJBJYHHJOMHBranK2SWZz1I3zbyOT1u+s9H1mzt/hb+27szWokuYcySzSXA4MhVQdoYY4AGMcVh1PHoL6BDqWkG9s+o1Vjqsl2CwuJ2x58MMKQcrjv781L0v4qxdLdTagnwxsTpFpe+G8sFsWn3SgYIVgcgc/hyRya6LSW6XsNf6gk+M3Rslz1PqU5uJILiRhHbxSKCrKsbYVyMsSTnPtzV25Rd2vdlFaWSfsjleoOjND07pe21TpjqCeXWFG/VBOykTMcEtEuPKRyMH6EVddSdZ9A6p0dHpMHTFlpk0kkIjv4YlN0G3AtJ4mdzEjOc8c1z+jaN0pqmhaxc6v1NqSrmb9m21vEudiMdgmkIywIAGAfzqxk6p0DqPpd9PtugLQWaPA9/d6bZnxbWJXUs7TYOzIG3JPrRrS93Z+ATte1ldeJG1vTujre2sH0F9auta8U/vr2QCNY9p3AIBx6c5Ncs/pXbdX3Xw6bS9PXozp46PrK3AAl8adzIm1twbe5BHbkD0rhXfDAdsD2rCWvHzOiFrexeaF1Xb9HX1pqF1pthqSMWiVb2ASrGSO4BBAPpnBq/tOsdX6h6pnk+Hulwaet1GhuotNRVjaQZG8qRgcHHpXL6XrmlaJd2Vzr+l2ur229lWK5JCK+O/f29+KveovifaXOuWcnTGmWOiwta+HcLp6Igdg2VyF4OATWsI3/SZVJWepcx9T9cdD9WXltdwbtX14xtm3UTSXC4ChNy45GBkdvepuo6N17Y9eHS+pp9P0u51IfOm7lvQ8ESEgMN0eQzKRgoOc+vrUTRtHvev7dNb/bo0y8sBJHYwsnmmPrlgfIPQVF0i207ra1vY+uer7jQ9Y0uVrbTYdiSxHsWMuccHgAL2xnmptbP75Fbt5HSWfVNn8IOodTs4tVt9bub5fmf2sIRC8iOMGMqS20AgjGeQRXI9MfEaDp/W9an0eBbVb6Yyo4yMp6gEDgZ9KkdJa10zplhLY9Q6PY9R6lDdSxzXvhs2/DYUJjACgDgcdzVXp/RuqdbdY6lL0VpEK6MzjymZYIrfOMAMxOSSD5ealJK+19w28tkx1TqS86j17WNQnSW1t7uK2RScqLgR7lDH+9gk9+1UzPgNjPLelXfUeha/wBN6zLp/UcUcCxQxGzMc6yxshYliCvHccjvXOb95J/4q552vkdEL2zL/SNP0XU7+0j6ouri2sopJHCwcNK20DGfQDua6LV7fpbTOodDg0LWL3TdAuN3ziFhLKgUZIjcjgt2yQduc1z3Tc3Si6jAevLK9vrJS5t47W5aDEnGWLL5uB2x7+tReoZ+mbPqO4s9AOo3iXsMb20d2Wlmgct/Zpjk5GMZxnNa088szKpk+B1fWXU2g9J6lbRfDC5n0vRdRVpZbNrxn2yrgbvEcZYEc5984rR0nrXxFudPVtIF9eQKzvbSgAo2WJyGPYbs885qZ0/c638FIGvOtelLPdrkniWtzfCO6kSNRgQ9n2EZzt4JzW7qPUOsuhkhn1TSxp2m6jm4sYbF0aKGNzkRMAf3brnlT2zUq2iz7+0jPV5d3YaNI+InUSdLX3QWj9OmS5eGW3vbKwtWaWWU53yPtHmYE53E4FfLbS4U6xZoskjlI5chgVwQuDke9dx038QeqtEnntNOl8O41GZpnMMI3uWOQC/f6DOarOp+gdc6R6ht77Xri3vI75JmLwHPhysNxU49ee/0NWso7XeVTcnHuKk3Dt64qTdtO+nRC1kaOX5yPHrng5H1zUE8M/vXR9O2uj3uoWkPU7O2krdLK6I5QylVJVNw5UE9yOcDiuaOTOmWaOs6J+Ha9c6bcXurdVx6Dc2UrDTbOK1DhpQP7SdichTnAA9Mnj12dMdLaBrGhzt8RdQYzSM6QWmnzeGkLqxXe7d3PGQvb1qL1/a9NvcaZcdFeBo5mmSCdEJZMn+PBPfHvntUTrnp3QtB0tLnojVZsIF8WO4ZZWLk+Z1PpuPOPTNdCu9Xr6HO7LgbOmutLDofSdU6MjsLPWNOF3IbkzhNs5ONrNu7ELgAjtXAv1pfWl3qFv0irxaWsrGCOK5YRxseTj+8M/bNWWg/7PdHavaalr1jZ9SG6gEqRXcmY4p9xJ3qeHOMcHivokHQ1x8Tre66s6Xk6b0OO4XwbbTjG8fzEsQIMg2LsVScLn1Kk8d6luNN3ayfHvK2lNWTzXDuOfuPiL0/rPTNvFr3Ten3LWliIFn8scikKQc7cMSWycHPJrnnf/crcopX9xHtRucDaP8AlXXacPh7P0uIr7SIDObc/MzeI2/xQp8Qk5xndn9BXISMDCioG2rDHtDcHbjjPtxWM0lkkb02282bLMhkkVjkMjdvsasfhbY9L65o0k3xJ1qZbNlMEOl2sxSWUjHnklIO1B6IoyxHJA701rMU3EA5CMcD7Ht9auusequita6Tsv8AZLRF6e1S1hjgtjYYVrnsNrgEl37nfjdycmlO7ulxFSyzZK6A+Iw+FMmuaToiJMtzdOs19IcSXNsDhEYkHygZO0cEknFaumrLojqOPVr/AKo1LUEW/uZja6XYARJbDcSpdypLAdwnAHqeMVb3XSnUPwt0HSl1bTNH1eHU51Ihhh+emtrmThYXIwd524GCVzxn3574i9Aa50nKbnX9OiiXXJsRQaVKJwkrABbeRV4Ehx2BZfQEkGtFsSd083x7TJ7UVmslwNV91R1xq+g6bMdL1m506K8t3tJm05vlmZGAh2NtwckjHpzXa3v+0t1Pps3xm0zVrXphbozTkzrC8lxtYRpuQl1DEnLDkgYGM5qs1nrzrxRD0l1X8/01d3Kx2pN3C6LboMDdnONoC8bRzj0xXDazPreqT6dpEr3WreK7SCKHfPI3hnAZVH8PrmpUXLsXztIvbtZ2vVn+xT6sl10HZzWTzWzC6ia7lnVkDjaQZSWBzx3wa5wtkPjIz9Kp9Kia217UIJ4p4J1tkD+PEyN+PjAPNWTt5seuTmuepHZdrnTTd43LbTtRh0+fSzKrSSyTyLAFQswYRjJAHrj1rvemNN0vqnX01TUtQudPm0xZI0MCBZJiR+B9wJwPT+tfOYtbXRP2dM7bTLNJHvAyQNgP3A7V9V0jpe76l0aDqWHXLDT5JbcLHZXNm8viqGPneRXUqcegBxxmsZZLsNVm7Eu2XqhNeurrTvAm0LTGeN5zMTLdh042pjA255Azk+vYVN0Xq3Xeqb+C76NtZNSt7Fne5upIzCsWAUxGHwzSZJGQBgZAPNXVn0+miXlmst/eXuiyRTTahK8XhKsqhdiKVHkjYkj3wvfmrXU7rSuiILWbTOnflNJk3Dw40YRtIT325znucmqSkrF4p3Of0jVrPrW/XXOtdEu9U07TUJs7nULCWOzklZsPjeB4mzZ6jaCfU10ehaZbKupzdKapp+jaVcztOsCwtta4bCuQFOAoAXtnse3rRad1uvWl7caPNLdW2lm2k33CQl12cIQhIKHBOOCQCDxkVC0j4fafYXd5aWvXOpyaA6h7YPHHJcrKfxq0gwhTgEAAHJIOMZOT+xrF2z1PlnU93eX15DdawkaaizXUdyIyGXelw65BHcEAHP1rjlbEi8cEgV1/V2lSaHPbabPqEWpyQJP/AL1HH4YkVpSy5X+E7TyMnn1NcemBNtOQ2eK3hu5GEtczmupj/wDXBdYBI2pke42iq8FRIzyEYY8Ed6ndSJnXbl2wVIQfbCDiqsMjrwPD5GMcivThuo4JbzLK2kaAZEgCOTkEA8fY1MeVZFIRVwVJwhwR+VV8EsS5DNuJPOQCD/Op7LbDHkZmztHh8Dt2/KqPUvHQ3WwbaJFADLx252+n5VedNyCPV7eWJJJP3Up74yQOR34z6/QVQwMyohbLbwQXHbHqMepq16el2a3ayBXAMcpUNyXGVyMfXOKxlozWPAnXmBIcemRzjPc+1UnUDbdLhbAOLpMA8+jVb3jediAR+IYbBI5PeqDqRiNHiwASbken/C1VhvImb6LOd3+YMCQy9z7UGJXfYCCByR6Viyxl8xufBJOC3cj7e9ZBpHjKDAGRuXsT967ziLCCQyxxqjeQebc+BzVhbnAUlA+CcFjhT7k1X22CiAsu8n1wQoHvipso2xoDMxizuI9Se3Yc1hI2iW+i+HLcRqNy4DbdvO9iDwR7Yz+lZag58VsfXGPaoejCQXkAUx4AbfxjnB/PNSL1wZwTz6nP3rmlvG8dDdC5LwmQ44ODn/XtXE3jM1/dYZV3Tv3PPc12cZH7gYyFccA1wl2m69ufMGb5hzx7ZrajqylXRG5GGdrgqxH5Vti/D5t2c4IHeo6yEgE7trcDJ7D6VvijkGGO/wAM9jjGRW7MEXeiJD4N3JLHI0gQFPNjPIAB57A8n7Cr6fyRAsjGJ+NxGQSqrx9O9cxY6pLA4gaEP47eEGZclF4zjHHYdz710tw5IRFLlu+3HAwF/I5/wry8UndHoULbLNT+oYg4X+f0qOvLHA/1it0mQCR+HHA9xmtQILtk8+mKwhoay1JUB/Ep/pXzHqr/ANZtX/8AzyX/AOWNfTIDiYH3Kn8s4r5h1KSeotVz3+blz/8AEa9fBb78Dy8ZuLxKulKV6p5IpSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlASbX+LjNTof7RKg2vr96nQf2q1zVNWelR3UdLYKHVdwJ2xkjHoa6HSCPmV3nJIPfjNc5p8m2McAnkZroNK81zFzjDj1rwa61PYpcDpdTRtoLp4ZbcV+2O9fL4rpnVlYKVTO0gDjmvpupEbB+IeZshjnAr5gi7GKqVXa7bj+dbYNdBmGK3zw70cunoOeOa1M3AKAcn3r1nV2YDIHowrWk2HwQRj0PP516KRxF9026t87htuUUEkcd6sbhgkoVWBwvtVb0+7Mbva2QQucDGefapt1ua4QgH1AzXLUXSZtDdLO6IOnQ7sACNyCRnjI9KrY4WukcQhWj7cZyv1GKsJjnSlJ7CJs/qKgLcy20UgtRJEGwdxGCPqKrFO2RZkC6lc5EQDsgxhVI/WquRcKH27x22n0+9TpHeIuUf95I2S+4gmq24dQGyPN6n3NdUEc0yMZFYZxg47gYrEHLAFuxH0rwz43BlJH0rDxFLJhTwRnNdCRifSGdjbKoBIK5JFe6U5XUIcEg54OccYrW7OtmCCAuOx9ax0Z915bEk53d68xrI71qZ6+GXVp2wRGI0y2ACRt7ZqF4qTRQ/Kk7yMNwDgj098fepmtiNtTuPEkCoAnAB7barYYmLFQvhlW8h7Daefyq0VkHqaLoCJkkgHhuSd2DuJ+wNUt0/wDZsgc54/Ce9XTR+KxjOwuXPmkb/Gq66PhSGNnYxg4Bzwft710QOeZCkJUDOF+lXuhvnSScn+3Yf0rm3LIWwcofTvmui0PjST3yZmP8hU1V0SKe8XkDYcZxwp4r63018Pupeq5UutG1600DTY9LghMl5+8NxMFJJVQDjAI83H0zXx+A/vAAccV9O6J12TRNXbUepUu4umGjit7OQ5SCW62AsjN6YXJ49a4Jxk9Dti0tT6Jo/Xeradrlr0fJc2cd1aSKngmXPKKGMhXHKkebd6596s5undWQ3cun9Q6ffXCsZbncjwKjO2SoA3ZwD6Y/KuXgvvhbc9UHXmlvodYmuWUzjU38MRCMDwwpGMc/r6180661+40nrPV4en9Vmk066ZZYik2QysB+L2Ycg474qsKTk7JWJlVUVd5n12TpDrD9oXUGl6la6hpw2u2pC7WBtjfiAgYF9wyeM47c1k+p6n0jO2l6BpWq3egyzLZ2WpXsIK3JZR+JwuAMg8naOO9UWraf1fJHqXU3Q1u0XTS20U0Kpexl9gjHiMkLkuQCCe+TjgVwep/GPqC50O40v5gLA6FfFiLB2U4PAPAY8VEaUp6WEqsYdpC62+Geu9FLaaprU1rcWV2ZFje0uTMkLNkhCCARx2PbiuB3hpVHbB5+pqbqfWGt6z8rpuo3k01nG5cI7DzMFwM49s+tVjEiXz5IDZzk9q6diUcpMxUoyzRJ1CO5urW2j0+Bp5/mxhF74wc11PT19rXwx1oXfUXTlqW1G2U2x1KJJiY1bnZuBC8kelUFudUU2bdPbzfrc5Xwzzt2nJFdxayWL6olv8ctK1O6uliDacvzjJtibO5js5ySB37YraO5Zq69TGS6V1k/QtDoeu/EDUH656YfSdGdbcRQ2sCiCSYxk+dcKFB9BxziuS0DqnRD1JeX3xK0hOodViPgLHeXfkhVMjDpld/Pvx9KqJ+rRoet3+kdJy3MekI5ax8eY7lQ87W9yDnHbirvUfiZo2pdFNonUnT+m3M9vCyw3MaKk4lOcvuA3EknJ7g1fYaWmXqkZ7ab1z9Llr0x1b8MEu9enuui7G5W8uZWAmLPFboewiQMqoBzgkHvx2FUFr8U9W07pabQbVvl9BnRrZoo4lBMDHBZj/E231PrW+/+INn1f0pBpNx07YXs1jZiKzltLX9/CqgAZKrnaMZO7I9auLj4T9OQ9JyXd7r1zPd20YneJcCF9oyyd84PIqFGK31+SdqT3H+Cg1iz6As9PsP9l4tW/bnjBY5bucbTHtO4bAMdvUVzMjck44x610mrdW9H6npljp/T3Sem6VqLTKy3du7NKqhSSCSTwRxg1yrv5qxmnfO/mb02rZW8i70LqWbpjULO8tbC1v5CzoFuYFmVOB5trAj0xmu8t9X6T6u11dU+KXTUqxC2EVgLRDaQz+Yl3zGoLEEhR6fyr5zpnVM/R11aalbqrZkMbB4w/BGc4P2rode+I1l1pqGiv1HZm40fTpGLxWzm23Ej8GeCAT3xitIU7q9vPiZznZtX8ibF0xqNxrGrf9mYSbp5LjbaxXd2PFifaC65JDFQxxzVAkEWoSJZafBczdTv4kVzDbuJJLi53EeRRyAO2e2BnNdrbL0b8Quqom0u5suhNJsrRIZrWwtlkknlyxBQM23dgDczE8jsTVBdp/2Vdc6pP0pqA1Owlt40ivJwpkXd5mRynGc9yPTFWi3px+aszaWvD5oQB8Ltf/2mh6a1m2bpa6WNZria7l8oQn8QKE7+c8DJzV1qt9cfCrXm6d0XWptetp4Y7pJ4LYAgkFWRkB4wRwfqKy6uu+sOtelrLqk6VMentOlZJdSSL92NxAbhjudQcZZRhSe9QtEupNJvtHk+Hdxf6p1fqCPFfxxRo42ggr4Y9EA7nI96Xk10n5E2jF5FZe9Wan1RrF02rpNCLSFEjSdNjAEkk7fTNVUb8tngbj2rq+uNN6usOpZrn4hQrDfXNpEISLiKcmNWYYJjJAIz2zXIKecbgSSSc8Vzys3dHTC6WZG10GW308K0ik3UmDGpLHyjtivp131R00/ROnaP0P0tnXLSVLizuYLfxL2adTukkLglnG0HI/CoH0rmulut73oPWbbU9JgilnZZI0eQAtFnBJU44yBgmpln8WJ9D+It/wBRaJptvp+oX1qEnliXA3FtzlTjyljjOBzWsYSlFZad5jKUYybvqW/SVv1L8X9OuIo9Vt9NtNPnWZJNUOPFuE5WJFUE7vdmwBVVD1vPrmqN071je38dh8y0WpeFGN0QUnIDDI3EjaG9M5qBb3HUPxG67vV6OsoLaefbJcGSQ29vGcYMrHIxk/rjtXRav1xqfw90TWPh5rNrYO0kWy7ktgWW7d+RPv2gsMnIJ7Y7VZxzsln2dhRSyu3l29o61fpuy1/Qr34Vx3ZuLr/dP2eZ3nLOMbWRn53E8H0+1bOtumOuLCLS9Q6ttkttOJkXaL+KeRZSnlDqhO3POO/5VqXpTpBOnbT/AGc1i+1XrhZYja3lxeCGEPuAMYjP4VIJ8xbORUbqjp/rTSYtOn6naFtLMjqNl+kzeJsO3KjkD9aze7ZP76s1Wt7fbQ5LZ+8OfUD+tWllYafqD2lrrV3NZWhuwzyQ435VWwo9sn+lVRJ8RAfVgM01eP5m3tYDOtqsuqRo07gkRg5BY454rOC6SNJPos7PqXpDTmhs5Olb++13UTMsUdltBLBs5K44yMZ+1ctoWiya91V+yutb+bp/SIcyOAvnyvGBnHm+/aumGl/7NS2Go9H9TXfUeso5gFrbWLYVXBDOBnPAHP0NWOmdQ6Lq3Uzp8YNO1I/IQ7ooo1ks2mdjgl2XzlQB+ZPPaum7Sy/s57JvP+iELjpvpTV9Z08aaOs9JkEDQ3eoJ+8gUofJvUAA5OcjGRjvitWh9JdWdQafd6l8PlsbLp2aSQafDcasI24O1tqEgkbs4zjtV1H8UtO6Q1fUtK6HSS20VpRcFLuVpcyOgDAuwJONoHmycVEs+mdW62ml1/StW07TbbUSXih3NH4ePLu8McckFsYqEmld5ePsG03ZenuQdR6s6MvdKm/aHSFlFcQ25ieaCUxAuq7cnacFtwz2yTXPtJ/utuQSwMCYJ7sNo5NW1nP0L0bp+o6L1F0xZ65qlrvjutRkncl3wTuTzDb3GOCao2G2ztjhhujQJv742jj8uKyqRS0TNabb1seREhZAnJ8N+frtNXEnxJ6bbpG4g0/pTRNPuDbqsU8VunjpMAuGDctkMM+gqlhYokhGD5GOW7Hg8faun1zr+fU9Gsrbrjo+O0sJjAksn7LNs4iyuSsm0YBAwCCe9RTjd6X8yakrcTf8PZdU+Ict3FqPV0mi6fpxW4W4b940t2MmONUyu1c8s5JwBwMmvdF/bHUd9qMWu9RDSpOnJDdWbeKsqXV6hxGoPAC43HdgntUfrm76P1OLSv8AYjTLbpW7mvFhldUbwHjOcsysTkr3yPtUL4g6H0xolpp150Bqeo6tK9wqajbzsJWueNzyrsUeHnBG0dgRzxWyW09LX7tP/TFuy1vbv1J+kfEaHq2+0+3+Ikd5faBC5nmURmPcycrH4iHcAW7kEcDjvVvc/EHpXoLrF9T6HtJNOi1a1CSiSeSRYCjnCo75cKwPOeMrVZrnxd/a/TkujWltZiO8Aht7SCEId5IC+gxg45+lSrP4f33w50X/AGxv9V0vVNYs0wdL+VE6xRsQrSF24LjjhQcDPNQ4xjvZX4dpKcpaZ24lf1F8QR1zrKlIlMlpa+abBywZ+FycZA757VSM2SMepPp9KsNW+IN717qWdTW3aWztSfFS3Ee7cw4PGSBjNVZYkBgcHkjFYTjZ2tY3hK6ve5e6PpsGrX+lxXsjJbRSSM6qBuYkKAAfQdyfyr7MNDt+j9H1XWdMN7PplpbRSWxn/s4pBJ+8y38WV7cYXnJPGPlXQ+h2XUGvWKavr0mg2FnHNPK8MaPLOTsVUXfwPUliDjHAyeOjN11nrWva50Z0hOmsaZaxlPnJZI4oo7eRcK00pIRTgkYALEg4FZOLlxNdpR4HSf8Aa7YyyWf7WBWxknjinAYZkVjggepqzvns/ifLJbxa2umdP28iePbzRNK07o2/wxyuFKgZOfXj1x8d+HnUSfCjU9UTW9HjPUVtM9sssgjdLZABlo93ALf3++O2Mmuj1XoLWbjR9P6n0tLLS9M6g1A3Fw3zrqLOCVxsnaJcHYOTgHOGXtni06UVLs7GVjVbXb3H0PUvitLoWpXGi3EfjLbpHGgtItsFvGACixpwqADj9fvXLaBpWn9X9SarNPrC6Ppm5bi4t4VLOs0mRsUBgBnYWLHjkAA8kQ9bs+i+hdU0bqTRtSvNfvbXcJ4LwRskkhTCTJGOE2HsrFu4OcjNTH6x0v4u63Db69qk2i29lGZpmhiR5rgNhFj5OFx+LnJwDgc5rPk7K6Xn/RflLuzZxnVmmDQ9QuLOO/j1FFkkaO4QBS8ZK43Lk4YcjGT/ADrj42/eAklecj710XUukWug6tqFlpmqNq1sZfFjleNVdFZQPDYLxkEHkd8+lcyOJwMjj65q0VZEN5nOdTSldbuvVPJkH/uiq6eJo0VWVSc5X61YdQS7devCWCqoQj0J8oqvVy6hWGfMcKecV6MF0UcMn0mTLfEZQ+Ejgj19DU+EELGFRjwDgdhn/nxUG3EcSs8ksmVGFiijDF/tngVOQM8Qkg3odoG0ncyj2yKpJF4nsE5Z0cOF5Kkjy7SODV10uskfUcPizsrpaSkktjHC4Iz649KpE2OTkICGOeM4A74FXXT7B9ZgjnfdILSVSHiPlBIxxjzZ45x6VjNZM1jqibclGMhUblOShzz37mqDqMZ0uHIzm4A7/wDCa6SQtJcMrESl2wWxgcn2qj6jKDSo1bbj5lRkj6GqQ3kTPdZyYj4BVQNnbnsa9VDHHuIzjv8AevFk2s643gHgAfpWz5pnV1OVfPBB5Oe9d9jkubbbcsSeGQDnysCBznnPsKs0EZdS2UkPDYXuD7/69arrWMvuClgy8gEev51YNK3hoHZTOpy7AZG4j+dYyRrEttLwupW4jEfhqGUZzn8J/nk9zS/wJfrjsPTmteiqX1mB3mjiUBhvI44X+f8Amayu2JmORt4Ix7c1zSXSN08jbC373HbzLj9a4a4IGoXKhSd0z5x/3jXcwKfGjPYHJ/nXC3DD5q6JLczOAPpuNbUVmzOrojai+YYK9uMDsKls/wDYjejso58uMD71AA3A5YH2HrW9F3b2ZvKMEjdit2rmKZd6DZftO/aFbxbMRRtK5J/GoBJ3fTjGBXSTTFIfDUghysmcDIO0AD6CuGtBHJdW5MaHbMu2Q53D2Hsc+9dy8qrblYJADIyiVO+V2g9/oa8vGKzR6GGeTNEyHnH4mTcpBzx6/aoTcEkcAjIAqbKgMjovoMq45yO9Qy37x8qBkdsdq5qZtPUlxAO2OcnjI++R/SvmHUpJ6i1bPf5yX/5c19Ps/wAarnkMO/3r5h1L/wCsWr//AJ7N/wDLmvWwW+/A8vG7i8SrpSleseSKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQEq0IGd3bIqdCB4owD+tV9t6496sIGIcDjuK5qnE9KhuovdOQybB2zxXQaYx8dRyfMuRVFprATQE/3jk1fWCjfuB4GDjH1rw6+rPWpcDptVXAcEBSGYY9q+YsYvGnZjtJZiSOwOa+m35zBuIOS7Hg/avmU/Mtw4CELISuTitcFusxxe8jQpRgNue2Rj1Fa2OQQ3OeBmtjM789yRzgY/WtJTvg4xivTOEvungN1yAAoVVxkYzzVlcEYjbaCFbODxVToAUtcByGwq/1qzu1ISMDv9K5ZrpG8N0tJgW0ciIEERNjnBzuHr2qlMTQyYHiYIyFbPB9TV2W/wDMzN/dgJ5HH4hVCPMjKPJnncf8s1WHEtI1TYRnO9SVXA2jt+dVF07O2x0wuO5XmradpPMT4ZVh5jjGfyqpmB3Dbnyj7mumBzTIrAKQO5HoK1v5WUDsSMD6ZrdNlwQGywGfrUfcBtPc7hiuhGDPoUpHyqj0ZOOMZpovlvbQAZG/jNeSoPlEIxnbzg17pHlvrTaDxIM15r0PQQ12GSTUZsRZXw0J83OMe1V4CbYPOQ/OEb+L/pVrrCSftlzbIrSbAZDu5Ix6VWyQeZJJCUUpkMRkg/SrR0DWZo3xxqfEXO07tm7Ctn1Hv9qq75jK5bOxRxtxgVYyytGI1lXxYyN2HGARUG6QSlZYEZkXna3YH2+tbx1MJFWQobC8j2zXQ6Jk6Ycj/wBu3P6VQSQyqpI4GM8DtV9onl0oZO4eM3+FTV3StPeLW2wHUY9Kn3LTy3TJrPzw6bikiG+FDhXKDcEJ8pb+lV9u2Je3rV9cdU9R3mj6h0+lu17o0e3ZGYwwU4DZB9OfWueKbkbTdkTfiBcdNanbdPjoXSL3SrKx3JqVxBE8pMbbcSTM2cvkHBbA544rzrmw6L1nTNGHQ9nqVrfLcRxXrlnna5ix5pCp5Djj8Ixiqf4ffFS86C0W70dPCFnctJ87CQ377cMFWwPMMcYNS+iepdS+GOpQ9Q6SYpJdRZ4Yoky0kA3AhV2gnnt29MVtsSjpw0z1MNuMteOvcXvTmo/EK90W/TozT77VdK0+KSJZtsaqqKCCN7MNxC/wgE1U/Dyz6LuNDvY/iJc6ra6o3NmkLeEsAABWRl2nxCT/AAnAxTV5ettQ+JE2i/Iz9LXOuyLOkV2XhjiSUZaUqOynlu2fp6VgvQup6x1/P0nq2q2Fj8lse61SN/GSSIfhMS+Utu9jj61FlZ3duOWv9k3d1ZX4Z6HIXy6sJbM32ntaWLlzHIUwJDjjHtxziopOJa634l2d3oHV9pop1ZNY01LYz2sqAqdpBGGQk7SMehI7VyDg789gT7VWT2rM1grJok3109rpsM8UrW5S7UF19AQc10nSl50XqbznrmK/1W9hcrC8F2URI8cDjJ75yTVRbabLrEFraW1ncX0019EiQW0fiSMSG7L9MV2c17F1TYWvQdx0lYPr0Sm106QAW80bDJyz/wB3OS2449asmtmxSS6Vyi6r+H2oaTpWpdQWvTmqWvSczmWxuJGWfETYCliDuAz2LAcEV0PRnxC0npezha16Y0u2kWALJdSRIXI285L5Kn3xVjY6n8QNX1duhL6JbLVJoTHJ82AlvFEuAzM4yCmAANoOcjFcrrXwx6nvut16QtdHjuNUIN0JWuU+VeIH8YlOBtJ45wc8YomprZqNf0VacXtU0/7MemfjPedHaPPpehrb2NvNvULDbkPKGzjcw/FwfWqW5Xq3Qra3vNQ0XUbbTIXR3lu7FwkgBB2sTxjtmuv63+KN3e2B0TqTSdOstYsJkVHmtwk1s0TDgHA/u4xkj2rfY9W9afFLRtS0TQUGpCe3dZ8lkQL3YkscD8u5qyuulspX1K5Po7Ty0KXqj4ny9X2mnadPpukwsJ/FS4trZEcAK3AIA4OeRXHyEEkqDj61HbT10vWLayuoTbX8DtHJCy4ZCFPepDDJOeckVScYxaUTanKUk3Iu+ndT0XS7+CfqjR49bttxWOCVjtD4/ERkZ4rsbvQLfUml6/6V6IsE6X0+Iw3tsZQ8Rm3f2gh3EkKCAxGQM89jXzO5sru/jsYNMhnurqW7Ajgt4zJI/lPZRya6vpnVutdG1ZejNP07VI9WkVlj06aEREBxklt+Aq85J7VMY9G6efjwKzl0rNZeHE96i6xtOv8AVtBsoTpnT9vZxyCSa1tgIlUkEKQqgMRgn/GuvHw96Y6juYdM6Y6sS1jFuZb671GFpZJJiwCtHGmAqYzkc4wO+aptb6gvului7n4ba3oUWmywypNMUAaRJiR/vAccN5c4IbBHFZ67a9ByWXT9r0ZdXGj6q1yI7/VdRllnNxDtJLOvABUgbQgHfBPrU2yWzdLuz8ynF7Wfp5EbWOvte0GZ+j9Sd9Tt4x8n4FtBmOe37MFx5sMueMZ5q41Tq2167Gl6X8PdC0TQtZtdxWa1WGzxbAYZGkXBbJ2/i5yPvS61DQvhd1Vp3UnR3UE3UNzdW7QiS9VI5YJFwWdNvCqRwAeRzyc1z3W9xqHWVxH1zb6GdP0wM1re6tDGFhuLljkKzAAFsdyPcZOarsptO1u/v8CbtJq9+7u8TX1LonUWjatGerLy2uJJoU+X+XvRcbVDnIJ9K5diDOcZCjkc1jpl1Jd6hqLXExmMUcaodxIXk8A+tYs3nf1wMCqzTTszaDTjdHQaD0xB1TfWlrda1HokUJeRp3TezZAAVV9fXvVr1l0to3w/kt5dG1eTqOO/kKSNc26q8bKMhgqscKQf1FcTfoGFi3iLCRM4yxwD5RxXovXh1m3jMqXSmMbYYIvEIYn+6MljgVpTjKyd8uwyqSSbVs+0+k6P09a9S9Pt1BpXUaaTrxXwV0toxFG8SMR5pAcq55IyCMYHc1zH+zPWF509P1jrHTt3daUwdY7pWEisi+XJUtv2jH4sY9as9F6Dn6xSXVB1RpXTwuCy/KT27eLGQduWGPKTjPerLpLTPiD1lol7pemXdtb6ZbRSWQv7y5EVs6oSpEfBd844AH3ptbN7P78CNnatdPyOX0vpjpuTp6e4vtd1C61xITLY2lvE2xJ8ZUHOd3PGBiq+5g6rj1LTm6r0vU9NtyknhG7hkRWbZ6Fvp2FdtM3Q/wCzbf8A2K0HUrbqi2lT9nPDfSyeLdKQB5GJUksCeKreqNf65vv2fZ9YWOp2lkHZ913CyqzhDgA1DlLP8/glRSa/H5ObAYyJjCkuAP1q70a46fttStH62sv2jpS3JLW+9kWSTa2wMVIOO5wDzgVRxKWmiHuw+vrU2G+0fT5rW46o00atpvzG1rcytGN5VtpJBBwKwSu7G7dlc6a76t6X6G6osNU6I+etYru3kjkgubhpFgGQQUJ8wB7HPtU266q6a+JurrJ1tqmo2sdlERBNYFcyM7eYMzAkgAD8yar7L4laH0b1BJf9J9N22m213ai3uTNIZw3nyBlt2PTPvgVt1XTrTWtTt+q+sNB1DQ+ntQhRLCbTolto7yQMS7gEZxtIAYLg4JGcVvayzT8b5nPe+j8uBzep23T+l67Lo3T1xealCVWWJ3gZ5mLDLLhAc4/xqJp19fWTzRRiW3iV32xXAaMxY7hx3Hvg+9d63Wdj0TexXvwv0O5sBJblL+9zLI+A2ULv3BOTyMZwOOKmad01pnxZt7nqfqPXryy1G6AiighAEZEZwGlBOWyQfyFXVR2zWXqVcFfJ5+hwZsNU0/pddZ6j6TuStxG8i3slvJtlDZIfgYAxjvjgVquEYW8G/AbwkJUduVH54rq9Q+N2q6db6np9xIj3FuJLdoiPKSARxzjbj+VclcktBCxO4tGhJ9CdorGptZXRtS2eDNUO5Uba3mCsVI75wcfzrsn6i62+Qsx8UNNvtT6Zjkjl1GKeTCSIMFASCCRu255rj7VPFDqf4o24H/dNVXT9vd9TaNJbahq4sbMQsxeQsxcpyqYz6kDmlOKabZNSTTSR1eo9P9EdY9UaTadNSno+ObxJLl5pDOkMarkeGpbOWbgAnHf2rr+k+juqemOuFh6D1m26skntJlS8ykSWsWVD+J4jBU3ZAHJJ5HNcRe9e2tv8OZunNH0G105Lhon8SO3YzzSgjDMxXn1A5xzgCtcDdb9Cpa6pcaHq+k22oqsEby2wRH7soCn7E89+a1cZNWv3WfH8mKlFO9vsddHF0j0Emq6R1v0naT9TjfLd3ct14wjZsshhZWCheVIIBziuI0PU7KbWdLs+sbyV9Hu8td+DOfEdQMhE7hNxwC3fGcV09p1Jc9Pa5b9S/EPphbm9vYFGj3GpBGURJ3YIw25BbIyOB29636V8VLLQ7jqSK56Ys7c9RSPPcCe0CtcQSYxt3JkISMgJjJNQtpJ5X8/4JybWdvL+Sv6hs+jbTV/E6AS+t4pbTNxBczGUJhxtKs2Dzz3qlY4Rfov+Iqp0awu7HVdYhvrK6spjCmyC6jZHRS/lGG57VbyI6PhlYEKM8fWueorSte5vTd43tYlWehL1DrejWkt+umwrFcTSyFcllBQbQPXJNdR1Xa2vRVvbS9Darfu1zOBeK8yjxH28MABgYAxivn2t3KWsukyvN4DiOfw3zgY8uRXWdFXfRuodOXdx1hNd6nrEhcWjxO0Sacq/xqBhWc+pfIC8Ac5raKtBSMpPptGrXui+qLs6dEbCXqTWNahluRa2IaWS2UEDE3BxwVOc85x3qv1/qvqG4gk6dvpLy51JmS2fTWhYSKFxwUA3cADFedO9W9XdEyXMlz87p892iBoJ7N4mdASUYIcEg7s5+or6HcdQ9YfDe5/2x12wtLW76iRIUnLg3FtGqYEcoXPhsw8wXOcYB54qzbTtk+wqrSV1ddp8x6Q0vVuq+rING04WixzwyMTPIYo7dVHndztJJ9MYJycVfw/C8aP1dqdt11r1toH+6pJYzWCi5iuhu2nlipwMc9jkiqrUNO6nvI9S+I9vaNa6HczyBXt3EbnLBWbYOdpb3qbqvQ/U69OHrPqcRQ2cSKkdq9wXu1iJ/tWT0UkjgkHHIGKmTu95Lh5kJZaX4+Rol0uDSb7Uray1VdZhaSJkuFi2AAr+Hae2Kgbs3J5784A71D6ZlE9pqEiltjXn8fdsKP8AWKmR4a6YkHOOPpXNUVpNM6YO8U0c11Cm7X7r1JCkkn/gFQonKxHxU7jIzwan66jNrt63AQMo7eyiq9UIZiSVBJwc5xXXDdRzy3mTbZyzJ4qBwfUvtqXbOy+GHwMnyKOSR9v860WR2qyMcKBwGAP5c1OhdHiGcRAOCrYx+uKpJl4mpALaMlDneSzhjjknsau+mxJbdQxtHIFkS1kzjLbTx5c/yxVXPNvm2MqyxlvxhTvP0OfzNW/TKbtfjiEayKLOVjtyBnI5/L/DnisZPos1jqTnlOSxJJId+f659eTXMdUEnSbfGf8A0kDA9RtNdJMwKSOGByCAVGBya5vqUj9lxbzwbgYz/wB01WnvoT3Wc0is3Y+GB3wfyrdGCVkwAduDyeMVojIZXGOR+HPFMsvlPcH8q7zjLG2kZmVQeMjIHII9s1Yxx4mJlkZCVJHYlj9KrLI4UtuYADgcYyff6VZ2s8k0pjZ8mUDIzjsfXHp9KxkbRJuhlJNQgM0g2gvwFJZSF4GO2T/KpFwP94IIPf1+taNPYjWIUcskZV94UAj8J4AH5VIuW/3hhxnOSRXNPeN46G6IlJ4M+jDP864CaPF3cOT+KZ+3f8Rrv4W3SqzHkEYP518+lYi5nAUY8V9vGec1tR1ZnV4G2IKeMMGU5zxWxR+Mycn0z61rjAK7ed+Mk1sxkBGBGeMZ71uzFEmHC3NmCx5kUqffNdsIZhCk7xN8k0mwSFMgyBQdp9sg5FcXaBBPamVu8w2fU13MNxNbw7Yynh4JcB9pbKr6euK8rG36Njvwts7kV1QbwMkAcFT2PtUHBzIHzuHrip7OXJ5VWPBAHBFQ2U72zgn1Pfke9clM6JkmzYGZffcCOfrXzHqM7uodWPvezf8Ay5r6bacTxkjAYj1+tfMeof8A5v6r/wDnk3/y5r18FvPwPKxu4vEraUpXrHlClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUBItfXPvU6LG9fvUG27mrCMH8WeA1c9TU9GhuovdPG4JgZw1dJpb5lUNg+UjHpXPaSuHD5xg9/rV7YHbN3HB714WI1Z7FLgdLeuDAvAyzH1r5krh1kDRx5EjYbHPevpd3s8A8YO7AOeMY9q+Yr+ObIziQr9MZrbB7rMMVvI8CHOEGfQtisCjcE4JPcVskVdowW3A8EGtRyH2kcnvXoo4S10BQrXI2nhQDj71aTt+9A549DVVoB3XM2Vx5OfbvVjcn/eOAV8vqc1z1N42hulz/APeGQ7TgRHknv5qo7iRWRPDdGKLkkJ3H90/51d/j0GZTgjwyO31rn9x2K4X92Bg4wDj1qkOJeR7cES4+Y2JIyDDRn8IqnuQI23ASNjjOM5q0dUIByCASRgd/pVXcNglfMoPZa6YHPMhMfMC4IQjjPetO0nGOOfSpDvh2Dcge55FRyBnK/hz2BrpRzs7+YsLdASMBBwR2rbo4LahaAcnxF7fetVwCbdDjjYvYjFbdIGdStB2zKo/nXmvQ71qSdeVv2tc7SwQxpuGOfrjjFVrJHbXCxKxdAdwIUl/v7fzrrL+C5m175awsri+uAFMcUMLSMVOcgAAmqltLvY9XSxhtb5705xbQwv4pPsUxkYrOMjVxKe7hjkIiEzSSyDcvOF49B71S3WHlbymJVGNu7ivt3Q/w60m7tZb3q6a8gvWlZBZjMDRKvA35G7d9AAK+cdb6HZ9N9QXFnp8r3VoyiSNpCC4B/hJHt71vTmr2MZxyucZNlZVG4BccAirnSARp5DAKTKx49e1VEqswxEhO5tqr3JP0q5sbWWysRDdxvHIJCdrd8cVrU3TGG8ToSDJ6E5r6V0l0n11rFpqdx03Y28mmbgI/Gv4YGnOxchUfluTjPvxXzKA4fP1qw0frPXLLqU6RZajJaW7XCRq7zbY4gcElsjsOTxWUVJ32TSbStcvOm/inqvTekDTLzSrER+K8UkU9orsG3kNuOOfXv7Vc9SaL050p09pnUfQvUGq3+vafOqvHPNFIswkOMxIqgqyk8DniqT4m9D23TVpDe9Ja8/UdveSESpcRAXCO2WZhtOCvfn0zXPdH6FrvUmpB+jSkl9pYFyyyzrEQewALcM59B9K12YSW2nbt/sxvJPYav2f0W3W+s9X39rpWodUadqFkoLxWM09o0ZnkYA7Fx5j6H6+lTdVn6Mveg4rqzS5tOsrdI5Lm+maTE/o8TKTtUA4xjB45zmrvSupPidJrzPa6HqepT6APHug1p4jW7MCFcAc5xu5XJxn0rhNUj/2y12fqRtGaTRrS5+Z1ZI2ZFJLgMOQDye/HrSKvZPK3YxLK7Wd+0hXTaZLY6TISJdcklmaecS53Q7RtTGT2wf1qsJy4xnvX0f4jdc9N9V2eh2mi6JZ6XfWeVjktbNIcQhDlW28tzjv7V85Ztz7gMD0ArJtvVWN42Sydzpem+r5eiZ4L+HyCdvAZvYEH9Pap3WnxBTqDVrK/0wpDrEYPjTwtzsxjBPv2qisNT/Z72En7Li1jfKYxbSQtJk7TyAvOftXcaV0r0z1Frcs/WFtqfQckMCrbW8Nv4LXGckyYnHmUcDHqfWrx2Y5tGc1KTsmcXedVdTXWrWV0t3cSyR27RxlGG7aSCcnHbI7VfaaPiH1J1CH060uPnIbYKGluI4Q0W7PBbAJ3elYjS9S6c6h1N+n2udf063ZYjfxWmEUEBgpA4DYPOKpuoeq9b13UIJLJJc2QKOTuTzNzgEc1rZSXRSMc47zZ2WjdZQ9L9Ua0vxU6Ztb/AF14FgJv1jmEduV7gfhy398cgAVqtrnXPhnpRvW0W60vpfUZ3m064IEqvGx3KpKkkMAeA2OCDXPaYOm9Zso16usb86sGYXdzE47egIbOMDFVFx1dqd5o8miQarJNbySLDDDLMPMA3kGD29PWoULvJeP9Eudlr4f2dj1v0l1HHaaX1nrlraxW2qSKdgmzcQRlSI2lTGF3AdgSRkZr56zDceOK+j9X9J9caV09Z3nUWq6ddaYs6C4trfU/HkDYIXcu0DAP1NfNj+P7iufXjc6llwLvQOq9Q6OvLfUNGO2eQtEx9lIyee47eldNP1j1p1z1PDf9NWE11f2dl4FzLbLl/DZsgEtyeRXAz201zbWi2jhJBcAkntjac8V1/R3Q3UmqfPajoutaZZSwkRiza6aGWYAZyCAV7nAyea1Sgo7Ttcxk5uWyr2Oj0PRrXqXqW8b4zSalpxgt0t7ayjmEMjoQT4pb1AJ4Xt/j816p0Relb+5stPuY9TtIkDwXYfGEJ4BHoR7VH1XUb2XVdSm12XOp2zmCSOWUOF2duT3/ACr6gNL6Dtug11G51K7v+qLaNLueQzr8vIyYIgEHbZ6dsnv9KvnTaet+C0M8qia0txepSdPap0o/Rl1oWr6VBrur6mR4eorIrTwydo44TwU5PK482ec8Vd3svU+hWujdFfE21utA6dvIxCZIBFJEqAbjsdSybyR68gnJ7VEu+o2+MOp6XZ2Fto+l9S2yiS31J2MPhxx84fav5DgnJ9q5vra76l0/ULLQ+tHJjG6aErMJUdxxuBXvx696oltSs8nr/aLNqKuvncyy6v0bpLQ9WROgtTvr6zuoEaaK7kWR42DEcOoAII9McYrjJcbnwMLu5rzSzu1G7JVyB4fmJ78n3rNzhyDwobmqTWy7XubU3eN7WOj6V1/RNB1a3uOpNIh1eEl1gjuEEkaPgZOw8E47Z7V1P/arofTPWh13pbQbHSrl7IQTvCqwhDv3ZVRkKSMA/QAGvntpLpEU1meorSW6t2dwixyBDuwPWu9bqqxtrjTZ+hOiLd20pXluZFtDdNCj4UPIfNjJHBP1wKKEWrtX88ispNPUoNU6oteu/iY2o6zczaTpt0kb3k8a7pJCowxjLDgsBjdggd62db6raaPrsVp8KluU0y8hHymnLPLO8UhJG1T+KTJ57dyfSvOo/iBa/Efq+wl62F6be0tXRns/JIzE5VcsPwjn0q4tdctujtXGv/C7SNTjWOAQ31/O7ztCxOVO8DMZYA9vQVpnFLLhpwMspXz468Th7bRYOnoXvZ7i80/XrAB44XhZJfHPdCDyp5+/0qw1HW+rNWl0tepLXV7bTvO8cl7EypI+09iQBULqLqW96n6uj19Xji1iF4/DOMNmMZDtnkn6n6Vca38ReoeqGsNM1m6lntELTefsWVDgdz2zVpqTjdpfOwiGypWT4/LlTD/bxlf7x9asNPstB1CW1tusrq5tdJ8cuxtcb3cIdq7iDgc+x7VVwH9+vr3I++K06tL4WmRM0bTYvEAVRkkkHt9a5oq8kjqk7RZe9X6P0ppt/Yz9F395cWcCGeSDUmVxuVuOdoyMehGK6PTNevvjUZND1bqC30e3tAl093PaswDL5URFB3c5PbAAFVj9A9S9LrY9Va9pL6PHBsks1uJI5HkfPl8SLJKjjgMO4rf1VrPUXXuinqVun3/ZmjMYr3VYLYKpJAwjHgvtxk4HlyM962upWs795hZq91bu7S96Z6ii+F7ar0xfXlnfvNJ8xNcxPmOdGHlJDewHIPao9v0FqusWMuvdNa5odtBqEjT2mnTtLG0ak4BBxtOcbtufUVQ6brsuidKWEXV3Rkc1tqbvNY39/YtG10GIC+HIByoGABnH61v6r6O1b4e6TBcaZrVnr1iFXeIHeL5aQ/wFSTuUZwGU8jvinHWzfqNVpkvQyk6k6Os+kr3StX6TsLjU7eGWK6vvxTS3WCGfxN2c7uQAMVzUkn+526tgN4Ufc5/gFbdD6L6m1npuXXU6W1XUNOXe0txDGqrKATuZSTn0PIU9vWtd2yssUkUXgq6IVTdu2DHAz6496zqKKdk/U0pOTzZlbHaGbJG1CSQOQAOTXb3Z6A6i6Ys9I6J6cXTdcm8GGxuxcOhlmJXHjMzFXB5yMDvxiuGhJ2MAC3lbhe5GO1dZHqvRfU9npmldP9MaZ01qlzJHFa6mJJC0D5GXbzEvgA54FUj25l59hYP13191tf2/SlhbNf63NcZgt0iTCmFtzEu5woG3j8q5zqzrDq7U1n0zVXvrfULa6Vpbe4QRlJFbgHk+pxnOKk9T6J1F8PeqbS56f1WPqT56B4hLptu4lwMM2UbcfbntVzo970nfwahdfF/RdRuOpJozDCLsywm0gUeWRVXaC5OTluAABjvWy2YpSik13amL2pXTdn36GWsdN6v0ppdn1nruv6Vrup6f30xVe68DewG/xHwrsOPKoIGe5xUC/wCt+p/2pp3XfUelK9qyiz06/vEHEisXyo5I7naxGARxVYvRM990jLqDdY6UbtoxPDYQo0kk7DlY2YkKpPGcetdRqPTvUXT2nWesfEyHStZ6ftCDJpw1XxikhBWMsqAAKpJ4yajo8c39vJEra4Zev3OU1/r+7661WSa9kSZrW2VfEBJJLPnBY8tVW8smMNgAjHB71Za9rfT2s6kz9LaLaaN4duouBafgcl/LkZIz3qqk5Bx7VjJJPJWNottZu5PtkuY9Z0O/g0qPV7fSYbnULuGVVKLEhUb2B7gEjA5yccV02tdST/ErX9F1DQuk11XVtFPzN3FaQgg20YyDMqALgNgjPJ7VH6M0bqXXtdt7Lo9bcPNYTC9e5uFgjSEsoG5mBzk8AYJP2BNUtrd9VfDfXNdsBbHTWaWLxHiZZhIACUIcYDAjkVpC0sla6RnO6ed7M7q112y+OeuL/t51Dc6IdFPzEV3DGJJRO7AAfvMZC7dxA+gzXDarDNqGtX2h9Uay15bQHxUnt5Gk8Zv4Dj+Eck/l3q50rTukOoen77VNXvrqDrLUZWlDK48KADIEZjxh84yzcYJ4478X09Do/U+q2Gn6xqb2dvdSn5q5iGJFiQElVHYFsYGcgZzg1rFKN7ZJfMjKTbtfV/Mz6BZfD7qG7+HD3cXUelx6b4Ty29jJPJ4z2ykkNgLsViBuALeozgmue6WnPXOqPp2u9Sy2Ggqu67uvE3uVAyFRCcFmOO+cDNT+tNEttJ1rT9M+G91faxpOoDw7awBa5uIiACykLww9c44B+lVWmDpS5+IWrN1T87plpGgY2Ng4hYzbsMjMVJULzwBnmoTbi3+M0S7Jpfkg6bZDTZdZsorlb2O31KSOOdFwJFCjDY9CRWMX/pDYq71VOn49Rvx0alxBppkVzFcSmVlkKjI3Nyc4B596oYv/AEjk8f5muaTu7nTFWVii14A63elgF2lQv18oqCY2VI921VI7/wCFT9a5167UuVBIOB77RUVck+GD4ijjI/zrrjuo55bzJFpC8sTPnKAZGWxuxzUxEZoi+HCgZ3Md3b+gqJbqqgE4YrygAySfYVP8GRUVJmCqPxxtyCffH8qpLUvFHsbMsYCeGEwOUOSxPJOfyqy6bXxtfXGwM1rJkl8YGVJH8qgyTjfhSqRE+QKRtwBz+n0qy6VtyNdizbidTaSNswx3Akd+fbB/maxnus1jqiRduHSRgwYl8kj1JJNc/wBS/wDzKhPfM6//ACpq6uiFwFYHcM985wTzmqbqHnSYckri4B/kaU95ET3Wcwr57gBs88fzqS6l0DvhAOM9zitKRtJlVwTkbiO9bML5fbHkGc4+4rtOQl2kUS7RJJtLHG3b5j+dWG3A2w8RnIwRhh/gTVdbPvi/eYLg5AxxVijqjQkAhNudoJIHftWMjWJL0uIx6vEqgEDfk5zjyHvUm5OZ2yQcn07Vp0aVfnoFWN48lg8hOS3l54P0rZcndcOckjPFc8tTeOhKtchomwOWA9/WuBlK+JKMH8bnAH/FXdQtjGcYzmuGnkYzThWAJkbA9Bya2o6szq8D1SAiEAjn8JatwLN/CpbGMVGCEsozk5wATgfc1ItiGYo4zkcfU+lbsxRJtigvIFxtPir3GR+VdoJV2omPOOQwA9hgH8xXFWy/79bjccRzL6eldoy7NrOMqwC7jkHgA9vzry8b+k78NxMZXVmfavlzlUJ5+oqEMeI/Oc4YH6VKkUIecFs9iMjiowxvYEEcn+dclM3mzfbcuvoQ3+NfMNfbfrupsPW7lP8A8ma+pWv9vGATjcv9a+Wa4Ma1qQ//ABqX/wCXNevgt9nl4zcRApSleqeUKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQEi17nFTomJZFzxmoNr3P5VOh/dyxsO+fX3rnqas9Ghuo6bS2xET7Ef0NWtm48dW4PI7VWWWPlJCAFJBNWFo53oRjsvpivCq5tnr0+B1N3/ZZJ43f4CvmqBjLOIUB/esSMgHv3x619LvjlGxzznI+1fNUigN5cG6YZ8RtmPTmtsHusxxW8j07HmBLGNlGQMYGahMQWbJIbJ5FT5jtcKsviLj15xUKVh4mSvOcAV6MThZZ9PuHmuNrbnWLGCcY5qxmyr5JJOTg1C0EZnl8uAYu351Y3THftGNoGR6Vz1N41hulmh3aBcqB6VSzwBFxKdpyMqvfGO9XlscaBc4YYwe/wBqoWZZc+ENyn13dz9KpDiaS0IckYjfahyD6exqNPJuIjJXPrx/jVlL4awkq7YzyhHY1V3KNHICVyzA9q6YZnNLIiyKSBx3zj6VHZVRhtGTkA4NSPOQcFgpqKAN2cnvyK6EYM+gTY+VQLydq81npDY1OzHH9uv9a0TAm1i2kYKrn3rfo+Tqlj2P+8J/WvOeh3rU+4dC6lcWOu6/Hpen3dxdgQmSeG3aRVQKcIWAwDnnb3NbrLrzUNR1rUNa0fR726gs4hbzarDauVJVvMm4j+E96g9KfEfUrPUJen7EIgnvXiXgrvP97PYgY7/Suu1rVOptBFvDNNDq0t+7rFHpqPIWfltoQDOcetcNu0679jOD+Iun9dveP1Pa6dEmj3EUXz0jPHLJF2HjeFncAARnGftUnrDoGzfpaM2+iPLczKNl8ihp5ZMcHg5A+nbFdZomotq9hJZdbRappdzdo8S2s0bW0hXGD+IcmtN10pq1nGZbnXrOzggmQ2kZ3TNNGeBuxtCt7jntWinay7DNwvd63PgmgdODTLWS56is5I9VWRlWKby+CoHBAHqfequ8ZTJIVYkbjjJzivqPxB6f/ZGuW9xdaxHqFjcwtuW3/dt4gx5WHPGD3H2r5VdJBEZhbBhGZGIyxbn711qW2rnPs7LseW/OM+9XenR9FSxa2mvW9w2tS58K6EpC2zADacDv25z71QQHhMd81JiTQHv9VXWLS4uZEG6QxMRtygwTwcge1XgrspN5e5Z2nTvWuhdNz9Yy6Pcp02FaJJx4as0bHAfwid208Dfj1FW8nTOnaZ0eeqOmOpHbXigl1Cw3rHGUxkCIjzbl7HOc54xiqfp34ga28dv8/a3Wt6DalXvILnDxGAHADDH4M478VqtujtY65vr89DaTcahYQmS4uUjdEWJO4QMSNxHooNau6fSdvmjMFZro5/OB193N1z8N9Ms+obnVI76DVVWaVLWQyvZvjCxuWxztI8wJHcVzusaL1jHC2ra5LDZ6brdzvuXil3eCG5y8a+v+NTfh/wBOW/Wel3S9TdUS6HaK2y2tkXxJXkU/xq7AIgI5xycVJW31u606K8640HVG6H+YaCa9STw0ncEhNh/FtJGc4+lQmotp6lrNpPgcxrnS3S+g2+n3HTmv3WqX8pdZo5YFjQJtzuXBz3965wN5+OK6zrW36IivdNk6IbUkuZFlE8V3cmdAm0YILKCDmuT/AIz+lZtt5u/mawsllbyLjS+q9U6Pa01DQZWiuXlMbFFy20j0/wCorpzrkHxJ1mK8+IOt3ujyWUQigxa5aZSSSfOcd+351yKa9P00llf2hTeJyuXjDgAj2P2rteqtE6z6w06w6n1JIp4YLYxxwxOFnWMndnYPT2q8UrJ6d5Sbd2te4nQdZf8AZ5PdWfTa6pP08sviQahdQMRK7Ab8uowSDx9hUTTYNc+JPWRksbjT9MgmgEjy3LmJAFON2FBZic9sdh6VT9I9fdUz9Pz6Foum6jqunwRtut47Jp0VOcs3cDGSckZFWfWmv9G2/RGkr0Zp66RqmmuF8cBxJdIw8weTOSc+YEdu3aptJO1s+38srtJq98uwtktOnfhx1JqVl13omkdY6rcEXPz2HeLwWA2hUcjaeCCSCfrVT0t8TulNA6c1nTrLpyyhS7M6vLJGkszBidg3MCRjI2gY7Vr1XpHS9d0HTbnpnXLnWutbkRxT2T48GUseFiJ5XaCSWY4OCeK91ufUYzF0VrXRenw9TlFtbUC3EchbAw4kBCFcebcSRjmq7MXk83x4acbE3lF3WXrrwufN11nWb6SwttReUWokyQXYgsAcZzUjPJP1Ndj1d0d1j0zbWD9QafbxacZgrz299DcDdtOAdhyPzrj2449KTkpu6L04uKszG+8Q2Nt4MhikFypDD18p4r6HqXTGhJ05BqnSfU0r6wlqGvra8YFZpAMlowuNuO2w57ZzXELYpqS2ds8EtyZL2NFigQs7sQ2AAOT2rqujNM6a0/Ub6L4hwapKoJjg0wF7df8AvyOo3ED0UED39qm/R8CrXSz4mN/rsnVPw5i06Dpqa/j0VSxurS0d2jZjl2kdUwM9zuPFcvp2qSaXq2mXunkym3kE6xunkOO2ffv2r6Dovxwfp/S5dI0ZlTRLNnS2ieQlmjBOMgDDE57+taPh70v0j1Pos+odYX11He3BkFrHbTiJbPnytj+M/Q8YqylsJ7UcilttrZln7DU7vqX4maNd3XS3T1nJPorCe7urdFjfaRjwwfKXJ77VycDNW3Q03SMnTJ/2t0+21XV7uPDyTqxe3GfwR85UjuT3qk6P6c69tbrVbLoyb5zTbW8ZFvPm44I5HwO+49+R2+1cjDeWug9TRR9Tr8ysF451CySchZXBJZWI7qT3weRRJO8Y6d2obatJ+uhrs47dNa1n5Kbx7SNwLY5BIUE4BNaNpO8+uc12nWnWuj9aa2LvQtJs9JeC0WO4+UhSJXwTsG1MLwOM9/euLiAZTn1NYybbzVjohZLIttE1bRdIurdupdFg1u3lLpHHM7KEbjJGCOan3XxEg6V1ySb4f2sug2tzEpuooJPG865weT7H3NVOnB5byzhh0aTXJZmkVbeGJ5HHA8wVATXYWfw4tOoOoZF6t+e+H8UECRw28unnxbkkks4EpXyjAHvk+lXi4JdL55Gc9tvo/PMqOtOk+qZ+mo+udT05beyu3jdnEyLcpEcASvCo8qsT/ME96hdJ/Ea/6MsnsLd/k4bpzP8A7xC6+MGAG7t5hgYq2v8AXeobXULvRpb6PUYbKUMWlw0VxHHhl4x2OBwau7nVepPj41rFafs35jQXFx83fTGNVHOIFZhyWI4UDHHerXezaaVvliluleLd/lzK/wDh7rOowWnU+v28Gl6fqkkRaeMoLi1txx4zwHAUN2AJzyMgVVda9GdO6FJp+p9O9S3GrO4eJre6WIMMqTlfD44xzkc1X6t1lrGtrPoPUF49hFLMsF27RYMWDk5weTkelU2p6NZ6de2MltrK6i58RRFHGVAG3k8gc1TZml0n9i94t5IikgMuR61daT0xfdV6hYabpN3babci6S5W6upCscQRWO44BJOcAADuapX7D3Bq1sNFvNfurW00u/tdOnMviGe6mMaBQpyMjnJrJa6m0tDspumur+q9cHR3VurrpFuyNd3GpM4uI9iHA8MJgyEsRySMDPtXPdTaz1F0G03R1zqoubdbYw20tsMx3ET8ZCkbsn1B9areutB1vpG5069u9asdRkkVokjtbiRgoBGdxODzn+VZdPHpnVrS6v8Aqy5updWiBFsIG8sBXlMknklq6YxyUtV2W4nK5ZuKyfjwL7qHXuutS6Zk03qTSNUg0+NV8VpbRkWFEAG4H04wOKgfD8dM397ff7fXEl3pUICQ2UczRePKezPIOdq98DGTjJ4ryLqrqnVYotG1V9Rso9RdbaV7yNkhiDHBd88kDk44rPr74e2XSs2lXPSnWA1GC/nW1nNxbiEwkjIkwpOVHP1H1orJbDyv2B3b2lnbtNmsdf3+hSanpnSss40G1UwWEbNuKxbexY88ZP1qhuCzW1sXw5CKMjjPA5q7+JHSvT3S3Tmnt0h1DPqMrDw743hRjI5UsZI9v4Uzxg5PrnNUVwcxwhTlfDUgD/uisZKOymjaF9ppmULKiyswLgISwHcjHOPyq+vrf4eanosEPR+lXmka1K0Udndz3zMpkJA84PGMZziqK2wY5BwS0bYAPrg/pV7rvwmvun+m7fVLzqHQrqK1SOaeK3LrInb8LEAMQT6d8VFPZvm7E1E2slc6i86Yuvh3o0nUmjdVG71W3i8OSC2h2R+GzDdgk7mbIHsMVy/VidTdR2GndW9R20lxYSsLG3YYa4kJbjEYOSpY4Huc1j0XcaFrup3tv11qc37Fto8PDbSGOW5kP4VLkHagxuOBk8D3rZZHWtO6pv73pW+S/wBJ6eYXML3OVeQFcAYJxvXd6Y7ZrZdF9/zIx1Xd8zIFt0ymkazJafEW01TRLUZlbT2QQylMZTzH8K55JzxgiqS06nafUbG21CKTqCwa4Pi2W+RmnQZI8o5A4BOB6V2l18Uf27rui3vVOnWurLpTSm3SVAQsrAAZDfiAxnB4zjvV5fdaa51P1Zb9SdG6P4mv2dvL89dRRqJBC4ChmC+ueBjnGfSp2preXrkRaL3X6HB3epaFqOozjprRV0URQoJ0Ertvbccfi54FR5OMD7VadQdYav1brU0nUKFbi0gVA7RBGcliTk9z+dVJJOfyrmmmnn7nTDQkG+1SwurafR7p7dRAwnCHBYbxj7811fSfS+pdeXetahq3U9ppkUDxW8CTwtOZWCbtxwy7QN31yTgAYqH0np3St/q6v1tfXtpFb2j/AC0dpMITI7OASzkEYAHbHJI54waXqrQ5dEn1K96Kj1LWdDR0JvZxkLIwzscrjcQMeg4IrWLutlZPtsZTVntPNdhIGidOaT8/Z67dajc6lE0ovZrSRBbOxJIK5wQu3bVvP1P0npnwzbpqXpWPStUggBa8nhCXgujhg5c+fPP4QMbTjtVdrPTPRa9F2et2mq3Nx1hBGtze+IweG5kZh+7VNo2bchR3yRV7eaX1n05rul/EDrLQopIrVmnfxZoZZI5cBUkliUnbjjG7POM9qs2na/tn3FUmtPfLvOS6Y601Xo/UZbjprUB83dweFL4OGeKIHJxj8IJIyOM+vauvTquy0+11G0640Kw1K+6jb9oyTsFWZUxsj8/dCuwnj+8eKrOpOoP+0vqrRr/bFoYnL211q6Wm+JgfPtZlAV3wpwM+verjWvh5bXOr2EPQEmp9d3ksJFwgso0+WCEEMArYCsWx5jnIqJOOW0rP5xJipK+y7r5wPnmgtbeBrDaYji0OoP4W48hdo7+9bo/7btxuGasru1v7S81OHWdLk0W6W62m0lj2MqiNcE/fPp3qvUgzfUt3NYzd5XN4KysUWtyqutXfkZ5SwOP+HaM1pikgaGMvG+fUxt+H8jW7Vxt16/dGw24DH/hFYpH4hcmSOPADOu0kfmewrdbqMWryZNtrdI8urgqRkMoBNbkt40ZVLtvdQyheSB7tnjP0rDT7IsGO7wpD6J2I9vtUsQmSdIgm0SMFVPy96ybzNorISzzSECQrMpJWPyAEk+nvUzpKFTq/nhR4EspW8x3ZGQTkDmsI4oVeS3ZWXKlQ2MsCO4B9PvVj0/ZNBrchkgSJGsJSib9qtgr6r3JrGUui0aqLuiuvCVZQTzs7ff0qo6hJ/ZVvj0uF+34TVneMDL9SPT0qr6hYfsiHzYzcADPr5TWlPeRlPdZziM0YCoQu7lv8q3qhfDHayKeSPT6Vp8JgjErsQDP/ADrJDlUZlAcYPI7D7V2nITrBmhUZB29yfWpkLCTMx5QA84AGPatFuhZ9sjAqOQCOCalTM6BDLGI2CjcEAwPXgVjLU2joTdLYjUbMv5AQ5AJyPwnvWd15bpvv6V7ozeHqELO4kLJJlXU4AKHk47n1/Sl5lbhgCfw4JzXPLU2WhIttpIVuPX+dcH5Q87uhDFiI/b8RzXc2p/eoMfiBHH3FcPuJmkRAWJdiuT9e1a0eJnV4GRSQBd+4ljz7EVtXcCCMBwfxd8D7VrQuScblAJJ83f8A5VtgKiVCoCk5DZPODXQYo3W8QS+tf36SqJF4UYOeeK7YSMS65JHBAwSAcAZ59a4uyiPj2YWCQFJQGfZhcn/i9+a7RtyysxZk7Dj147fyrzMZm0d2G0ZqPAbJGAATk96ibSJJFYAEHkVJZPxBSDkYGfStT/27H1IB/pXNTNpM32v9qhznzAfzr5ZrX/zY1DH/APUyf/LGvqds371M+rivlesHOrX597mT/wCWNerg95nmYzcRCpSleoeWKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQEi17tip8RJYD71BtD5mqdDw2fvXPU1Z6NDdR0emDfEEblcsP5VZW2RswRxxzVVpID7s8girexQbwvavDrZNnr09EdRe5VCOw/5Cvm8saNczBs8uew5719JuzmIkZ/z4r5tJIUnmaVcgMeCcHOa2we6zHFbyNZB9QFHbHatI4OM5H3zW3dlC5HlJwVPOK1KmByeB2/616KOEt+nvPdTBDuJhParC7xiM85K4/MVC6ZKrdy7ScmPA4wO9Tb9QkqBTlQ39a5p75tDdLWyP/mW59cf5VSSXS+ZxGoLDjacY+uParmwAfR7tSMEgj+VUktqojTwWJG3Lj1P3qkNWWlexHyVAUqGL9yOSarZyS442k9sjmp5QRBn3bWB4GcEj3FQ5j4jbtzcnkgV1ROeRoALE7nx9TURwoBHse+KlPnkDJA7Dtio7c+V8jNbIxZ3c5U2kGGX8Knhec4rbo+F1WwJztFwhP23Co8wVbWDht23De3at+jnOp2YHfxl/rXnvQ7lqfo3SdD0u56am0+71l3vJ5WmSSGJFERLEhc43EDPJzk1x/SSdXPY6vq+n6nbwRWFvOiwyzNJNclPxGPAAA4PJ71yXQWtDRroaj1jC1zo00ksdn4kDLG+Gx5m43gD2/OvesuoNKh6j0y36GZNE0y4G25ETMIoWZjlgDnuOcA96xjTd9lF5VFvHadPfEGf4iwJoV7cx6MqxLMt3Ivi7QDg7BkHcckZJ4rfqGm6TpXUkVt1d1JLdaRHIktilvN4TS5Hm8XOSMdhg8+4r5P1z0xoXSUmj3nSOv3mqC6Dpdm4EabX7rt2dh9657U5mvF0mIl4Zo5HN7drliUYjaGycHHOPvXQqKlnF2TMHWaykrs+y69oHS3UGvata2evzwaa9pHNCQyvLDIGPkDH+E8H39K+KvaCwW5gSZp0WZirt3I4719cdfh/qmmaJpGm3lzpF1Hu+Z1ML4styuCWV1fC4zjAA8uPrXyW7s4rFryG2uJLqJbhtksgG4jPrjiqQussy8nfMQfijHpX0TpH43X3Rkd5o9tAq26OfFCKQZ8qM7sDzkjivncXdPpXa9GXvWlrf38nSHTdzqUcb+W5i08S84BwHPar2i77SKtyVrEvpPojq7qDo7WNU0F9K0y1v4pRDYT3uyWeIsSFCBT+QLD07VWWmk9dfDzpS31eOSCfT2UTSpaTBmtnOOJV7qe2SMj0qhsOpOoYpTDe6hNZSXc5W4yxCwbm8xK+454GO1Xmp6RpnSNxp9xJ1HB1P0tPOEubYs0Nw4wSFdd2AD6kGt2pXs+PcYJxauuHeYar070/rGkC66T6gu16ivCGntrlV2TTyHnY2BsySe/p6171trXXXT/TrdP8AVtjKbaYRwBsLJGwXGNsikg9uPWoeh9K6H1b1Pd2ui9QfsXT7aIzW5kXxMSk/u0xngDnJHIFTYdG641DWpdNh0266httIXx7n5Z2mj5yFYDjPYnAy2Ki6TSb0zzGbV0tcsj51Esg1SEyW8sO6J9rSqRnjsKlhsSgsNwxzV71D1k3VN/Z2Rt4LUWSSDw4kK7T2IIIBB49eaoVx42TkDnGKibbeaNKaSWTMtURnsLILC05N2PKilj2Paur6U6k6h1O/i0TQ/GN5I3hI0z7Y0yPxMTyABya09MQ6zNfWP+zOmSatqAmOYEjD+XYckg8Y+5qBqdtrWndSXivZT6VeRcTRSDY2/wD8J4H51am01slKialtF9oXUPVPRnUUvRtlqIhkF4YXljkAt2LcmQk87cHPari1Wb4Ca781bX+mdRpqyFIrtLdgUAOXj2MDtzn07iq5tV6Kk6Quxq2h+F1OLct+1/FdpPHHY43bcHgYI7VjHoenWHSdn1DF1N811VZfv1sVRfl1Q48gzljJ65PHGKh56rueWoWWj0zWehG0/pnqrU+tL1tA0iXRdQiDX0MF3KLULE/YRh+MnPA9qm3n/aHqurS3kmm3rz9Mwl53fww8aOMZUfxcAnAzxk1I60ses5tH07qzVtVSWc4hNk0hE0aMch2YcDJ9K3dUeL0Loema1o3VU+p61e7YtUtQylEcjyCHuWUDhs857Uu3bTsISSvr2nGa18R9S6hFtpM8++Mzb5v3e3lQcA1Tk4JzjmtDPDJfxXDPuvZJWZxtwVYg5yCOK2kYBx9PSq1EovJGtJtrNnUdJdXDpC+S+AGZSYVftsyDnB9MjIrqeoBqXxqv7Sz6Ys7e6v7ZP94mmukijSLBwryEgZJ4A71w+harpujXMEutaRBrNuWKiGfO1WI/Fx6967IaRq/VM8ms/Cbp6HTrGEeHeQ218IvFmHI2qzc4BGcZ71Csulp38BK7dte4mm61T4pLJ05p/R1vd61piLG728SwtaqmF87naoHGBknPpVL1B05oEekwT9K6xcL1F4yQ3VhqCI0RmLBT4ZA8mDngkjjNUOg9fdSdMXOppcLdwPLeM9yzQkgzLwQze4/OrDQNQ6Nn03V7zqWyn1LWr0yyi4ExEdrKSSGRQwG7OOSD9BV9mUM1p3Ge0p6695P17obVeh9W0ax0vqa11WDV+ZbsK0KW8/eRSobzKByDxn6Vd9Sw9OfCu/tNa6UuzqUuoRfKXq6mEn3PgM0oGDtDEducACvncvT8tz0n+2bjXoW1G2dfl9NjTLyAkAkj39AKuumXvOlrlbrrrpBp7W7jAsU1OyKIcHLMgONx7Z+lTJXWbv3aXIi7PJW9bFfqnWMHU1/dNYaXp9iqW6rI9pAI95yfYDPfviquJCYWfsikKTkdzV11Jf6Hq2rSv05pMWkFIB4yQjCMTnnHofoK58KTk/kPpWErJ5Kx0QvbN3LLQOudT6Pu47jTG8Np90TlQQSowRkjnFdPPrEfxL1SO56o106CtjHtiaS0eUTlzk4yecEd/c1zOjdRXHS97b3FrBFN4u6M70DFRgHIyOKu+rdd6q+IljHf6Xo99qNjoe5bi5s7ZzHbbx+FmAxnjOPStIpZO1u8yk2rq9+4s+ktO6B0fqXqKLqa9uOqI5FSO3y8lugDJy5VTksDwB2AFa9G6D1630/UdT6L1G2n0lLl/lbX5vw7uWNTjftxg47d88dqrda6003qjpDSdAt9PitJtO2x2t7bg+JCrsPFeUgbmBOSdx79q3dS6X0z09Lop6N1y8k0qR44L2Odg8yt/G6EYADc4BB2k+tT0r53u/NZfwV6PC1l5anDzz2Gq9QWVxrr3EtrLNuvWjbbIwHdQT9u/pXadRzdDuumjpDTr211PDh5J755w0e05JDcD8sCutbrPp3o/XY9e+G3SZ0+H5X5W6kVXkjiQkbdxfdhnwck43YIxivlWpajp+s9azahplnDp6XCOzQQcKG2+Y49Mn0qW3NaNJLtEUoPVNtm1QCT7VF12XwtNhkDMji7XaV+xqSDhk+tS4tX/YrW138nFfkXIURSJuHKnnH0rGDtJG891nRafB0Pe/Dq6bqI3Op9TsvjQ3CO6C0APliRfwnd/EzZJyAMY5l6t10eq+n7K10DpGCWTp547uFrGz8R7OKNtzM7IvCn1Lnk1A6T6f0v4h9Syz6pqI6X0a1JMyQIGmnlIyEjRjgDI/E3Az61e6b11rHRJ1HovpmJtRgtnYf7pbM7XZYZDuE4ZsNg57VrK17LN6+Bir2zyWhznU/xmvdX6dvLGa+Wf5mLw1UFnfvnPI8v9a0dLfDnq3qrpq51oyabbW0UJa0gujtku2xk7AvPYfibA9KlfD74h2Xw9tb+BNLs3vbgtHc3M0IaTZnmJdykqM8ELgnsas+penerejtNjvLjUbG502QpuSxvSflEc8IyEA5UHHBxxUt7HRirfkhdPpSdyn6Wn6QtenNRm6ngfW7y4tWWJZFIjtht5ZTn+03cZ9Mcd6qrndhBIT+Bd4I5PlGK7LqHTvh83S9zFbac1rLFAzJLHK2dwHDEHgnNcbc5FvbkKcGFdpPrwKyqWvc1p9hhC26KQkEgxuO/0NdXpfSXVvQ9pY9TXuoaLd2ulhbtrdbvxZGAGRxgglcg49xXKLGQJkk8jBGVgx7ZB71a6/0PPp3S7fszrHTNWRkQPCiukhU4GAG4PfvxwKU3fK+vcKi420LjqHqq3+JWrabret2N8un2W5NS1TT4sMxYDwonlwFySDjOTUm86ffq+/hj+EfTnydnaW7NqfiXZALk+R2aRiS5AP3AzgVS3nxD1O26THSYhtoLK5iaFLeKLyluMyHnGcgHPJPFaeletbnoKxk0+aTYtzKZy4jLM/AHO32xWyg0ujw0MXJN5+ZY9M/CvQ9R0rUr/q3XJtG6iWRo7KzjVGSJlON05PJ3egX0OahdPS9X9E6NJ1FLo01loGoSCEXajIeRc4O3IbbzwcYqdY9OW3X8V11BcdZW+jTX8j5tXtyWjUHaMt6EgZ49xUPpy+1Lq+8fpXUNc+V04RmOe8fzxrFGdoKpwWY4AAyAM59Kjaed3ft7vAnZWVlbs7/Eg3PVE/V2qXM0yqggjjXxGXaWyTk4qK34sDtu71aa50dbdG6lcQWOtLrUFzEjo4j8N48EjDKCRz6c1VkBWUHjBrnm4uV46HRBNRtLUtNMtulrq/jHWE2owssH+6NZyKpHm82cg59MCuw6f6Z6kkGpv8L1l1Dp0yZlF/qEEMktwVG5NrkbuNnOOM18+bSZ9Wv7SOxsLvULhbeR8Wtu8xjQMuWIUEgc4z/nUrQ4OprCyup+n1li0wXLqsTyeG6zADcWQ+ZQTj68VoleOT++hm3aen21N2nxdJWWm3cuvftOTWrcyPLbmdY4ba6UnK4HJ2t296qtT6+1/UOn7y2u1un+Zi2TIyADB5OT3IP+NdUOs+ldS6TFjq3SulSajFbtHNeeEscpuMEFiynczFuec0m1TrzqHpK5v4unNQurTwNovYdLwh4/EJPUDHpnt+daJ53kvu/4M3paL+y/ksTrmsahZaXofxK0fVtG6TUbgkVqbdIpFQ+Fs4xnd9s85zULSNF1y11+4PwrurvW4ntf963+HC0UYfy8MQDk57d8H2qPqkPVt5pkcHXgvpNAgmjl1ApcjzIp/wDZk5GScc8/SoHVeudNaHFZydAWd7pVxM+Lvxr93adAvlYkk4we3AHNVWeUeP2LN8Xw+5nqk2uy39+OrYJbfUklWMRzMCwTYMEkcc59Kql/tF7fi7itOj6nc6xaXN5eMWmkuio8xbACgAZPepCjzpz69qwllKxvB3imWVlY6RJfXxv7WGeaWXzmTzHOBgD2/KtMPQOpXj3zaYsfyccpMEc8gEjqOeAR79s98Vv03QUl1O71OO6EUwkLiJhlT5R3PpwK+pdNHVtOtrTqDXtGlttPdF+Xa5KhnZ/wnw8kgYHG4DuKq57KyZZRvqjm+lvhDd69oa6vJrNpY388QaGwkhLY/uq75GGOOwBxmp3Slh0Vb9HSXPU0TNrd80iyyNcsnysgcqIowDjggE5zk/Tivodt1dp9zqsz3Gm+PI9qzKqQsSu0jz4A4AB5J47c1Xrq+m9HaXo+p6V0xZ2lvcxzILl7cfvJWbO7e2dxK87s8+nFYSk5ZM3ikij1LQOg06dWy03T/B1aOMhL4zs0zSEDzMc+fJ7rgDHAAqu134dN0TrFvcQ6zDrMM1lJG7NbfLsku0HAXJBUjIGOR/OrvTtFstTcdYnXbXTt04urbTUtQysFyCWkJGAWUkADA479qp9c6r1/XIdGk1XTry00m8k/dXs9nIqZKnbtdlGCcnBHesnfgy6txR8lvcGU7e2cAVVdQ86PEP8A8YGD/wCE1Z3TkysewyeKrtdQHSolKhs3AwP/AAmu6D6SOOejKCFQyAksDjABPGK2KFUAtIFDHGAMk4rLaNuASSBz5f5VikO6QEB+OR5u1dlzlsWFuzJGERTKMg4IwD9KmQxoMggoFILZJ4Ge30qFDl0DneApORnGPzqTuDIuNu7fjCZAAx65rGTNolnpEhXUYEk3yM5YhPoAfbnsKXfNzJnuGI96l9N215Lr0P7OieW5hjlmDxJnaAhBYgdlGec8feoF0w+ZkAPG7iueTW0bJZEqzBSUHHPiYHHuK4Rsq8mW2As27H3NdxbHdKcEkbwc/TGK4TdiSQOMAO2D78mt6PEyqcDcASw2BsL7cCt6hkfjg4549T25qMj5XbjLEg9+3+sVJTZt3o2AOSpPf8q3ZkjbHbXEWo2DT78SS5IB4HHbHvXZyghiCSM5zn7DHb6VydpIJbq3DnlXG0HjJPsPtXXyP5yScfh4IzxtArzMW84ndQSszTINqqcYUjIz71G7zkDn0/QVKcARMrnJwdu39f51DzmYZHGeOK56ZpMlWp88fGeRmvleqnOqXp/+fyf/ACxr6pbH95Hx/EK+Vakc6jeH3nf/AOWNetg95nmYzdRFpSlemeYKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQEi0/EasEUqPQ+YevoRUCzGXNTEBD4+uK56mp6FDdR0Gknw45CD2X1+9Xdhj5hd2dp7kD0qj04ERt/wASEDmr2wDNPH6lgK8Ovqz16XA6W8INthDuBJIJxk8V82nU/OTYORvPBFfS9Ql8SE7VKIP4fbgV85mEiXFwzBdjMVx3JrTBu0WZ4pdJEQRYkeQcMB2HY1rneR+425OCO2K3QjwwEALDvgn1rG4VWDZYg/TtmvRTzOGxP6cY/OyiRsjZ2zxjNW+pYlVZFABJqt6XCjVJAxEQMWd2M1YztuhYNxg+tc9R9M2guiWelMy6Zd8kfb7VTSPG9v5GfJAyO+D9/arjR1LaZeeq7f8ACqkBGjSNxyozuP8AmKzi82XeiIs0BY+IgYIxGAAM1BuQFLFchz+LPv8AYVY3DBFHiHanchRxUFyDFvBON3Chf610xZzyRCljwpIDK+BznitBJGCWLY9PepskZMis4OG4UegqJhsAYA81bJmLR2cyj5eBs5yOTnk8V7YSmO7iljxuVwQPqOa8uAvy1sQhHl7+larNysoPqGznv6Vxao6+J2etfFa5676Sh0LUYEPys261aMlTAwGDtGOBjjHaqLouDp61lvYOutNvNThmBitZ/EdQjeskbdmYce+Pauh6c6E6Pl6ZlafU7tdUliL+LHKFUNjPb19qmN8T2606QHQ8djZXfzEXy1nHK4CQuBxIoxlCMZyK1eStBZfYy1d5PP7nN9FDodtV1SDrKK5122hn8OzHjNCgTHLME53fep2k9DXnUOqa9pnwv0/5zSIpBLEt1dpGQWHEe5/xHIOPp3rVda5p3VHTFt05bdNGXW9OtjDZyaXFunbA5BVBuZSctk/rUHUdSh0T9g2nSSfse5+VS1viZGiM0+7mSQnPPPtxip6V8tfTxK9FLu9fA6DpDVtD6W0nVNC1jpx7vqa8LWlw0yb54nPAihOfId2ORyePSvnRtJ9PS7tLyKe3ure6aOaO4Uq6uMZBB9a7jXLKL4bdRad1dY9UWfUVzcOMytCUkt59vcDJJUdg/wDKuR1bqGfqq9vdWvGV57q5LMy8g9uao1+pcf5Lp26L4GEP4k78Hmus6ak6/ae9XpOyvruxQlohG4VOBnueBzXIwsNw9jXa9IdI63r91fSWfU+n6NYM+EFzM5ccAE+Gvpn3xVU0r3LNN2sR7jpPpmXorUb266n1BupvDa4aEsiwrPnJQry2M5Gc1lb9JaDpfSUHUOtdQrrWrWMiXA0swp8q6cZRz+Jm5+3HrXOaRpKx6tE/UumanN0ta3TwXtzAjpDO4JAUSgcBiM8HNYdaWWhWuoQw9LXt3Pp91zHaTDdIjZ8qFsDdz2rdXva7+cDB2ttWXzidB8QPiLB1xDokUNjBY3NoCqtBEioYscR+VR68j1rT0t1trXS+qDRen7mTU57uRZVjhiLMZGAG3jGMdvaofUvSfUWgdH2dz1J03cadpVzMshu3MbNjOBnaxZQfqBUbV+oNMsLjTdR6TtF0i7jgEW21fzSD1kycnJ9T61KUHHZirohuSltSdmSuuum+oOnuuLiXq+GGDUdUje5kFvOsqHtkErkbge49/WuYziTB75710evRdST/ALJ1LqfTNStbe7gkFhcXkHhrIgAJ2/f7VzSEeJyeM96wbfE6Y2zsXVpc6rAbBenY7q4vp7nwlhtFZpHXaScBRk/pX1DRD0few3B+IttfwdVkNDK7zy20loB+ELH2yOCdwOftXzzp/rPUeiLq1utJGZrjdE2ASQO/BHIz64q7bWrDqLr/APbvxBsbqK3aGMtbfvI475l9XP4tuOCRycUSbXd3akNpP30OW6a0+1bVrk3OnXfUQTfJJ4ULyEpyFdiinAHfHFU9sLnUtRttM0wKr3M+228aUIBzxuz2x9e1fXE+IN1051PqMXwx04WdhflJ5bexRthmxgqoByFwB5e1Q7LW+lodB1u217pyGz6hvfEluL6eHdNbzElvIxOEAOOK25SSz2TDYjpch3+i6xpVxYQfEiGROnZ3InezvA5lYDKoGGdue/btWgdRaT8MeqLTVOltR/aMU0TiBLyIF7Q5HmDAckjgNjPevnc2patqNmJpgr2dvyMMx+m7knFd6H0HqfprTenekdBt26lNwlxDqE0wM0rLy+92P9nj+HgD71Mo23s+3sIjK+7rw7SV8RNN6n1drDrfXNGW00/VcJHcGVRKwCnbI8f4gG9GI5x9q4D0HBzXRdW9TdUyXNtovUyT26vJvdZVADbQcYx3ArnOwAJNc7TjZHVFp3LGx0dtentbRdQt9MHjbzPcHCjC9qtb5dS6G1WTT9L1+LUrVkEyTWjFUye4I98itHTunaTrNzDF1HqU2mWccm8SW8as7sFxt54A9STU34ldM9P2V9pj9FarqN7HPE4kS5dHkDAgDGxQCDnG0DvWlN6JmNRatFppnxD6x07piSyu9OurvTyjsHmsGeOQNk784xjnOaptQ6r0fWel0s4ulNNtr2OFYobyzhaJg3ADMQMY9cn3q06S6g64vL2DovT57yw1GWPwDHqP+7wwJjBkkLDIUD6VcXPWGsW2oP0L13eG0sHf5XUHtVQbogPxRvjBBwMHHrU2s8ku3Ii91m32ZkK/+HUnQfTlv1JYdYWurXlgyTtZR27IUJIHkkZiHIz6gdvSqHq7q3qHrey0/SpEm1Gb5kTRxxQtJKSBjgcnsftUvVNG0DTeotK0+5199T6ZvJHMhjOy42KMhWPIBzjJFXU02ndGa5baj8KG1O8We3aK6SfMwt1BBBDgA4J4/Kib1eb4ZWDXBZLjmfO4tJ1LQr+/t9e0+90+5MaMILqAxMV55APpRD6D1Iq/6q6t1XqrWHbWYniktbcKgkjZSwYkk88mufUjGe+Kym23d6m0ElGyOg6X6f0nqTU4bbX9al0WCLcyPDGrvIxAGBuIAA5zmuhk6j1L4Z6g+gdHa1Jq2nyDx4JRGEZnc4KuoON2R9eK+c3UIlNmxfw2WR8H34HFdTofR82vdQaXBpOrWsFzOhku7i83iOBFIAwF8zHngLWkUtm8nkZyb2uisyTafE3VOltGvulriK006Ro3hu4ktwplLZzvb17+vaqjp3Temrjpq+l6hub261IRH5Bo5PJCw/iPocnAx7V3kWpRfCHX9Q0+a207qC+m/fftZrbzTRN6KrhivbHHNUOidQdBW+g3Kan0hZNet4jy3EzsWRmLYA8wCgZGAB6Cp2srxjr2FbZ2k9O0pukeu9Z6OtZtNtyBHeyeLLGLcyCVtoGCPX6Zrd1P0V1B05rNrq/UWmwaemprI6xRSKWhJUEJIi/gbGDjnH3q51/pvS+l9L0vqHprqK41jVbdUW5jdUBZmxholXtg8c81VdQnq+9XT77qLTr+30rz+HNcpsDyMvt3qJO/Sjl2kxTXRlna1igBJIz2DYrbLrdzoNvDe2GDOtwqAFQcgq2e9aVHmXB7muj6RXVJNc0+Lp7Tl1TUZLgJFCyoQBtYs53EKAAOSSAKwTs7s3auiysukl6v0CHqSHqOxsdYmDBtNmgAjCqSFy4Iw5xnGMcgU6V1L4jdHdOpdW1ldLYXrPOktuqHxCSQNxHI7cZ4xipGrdF6xqXxDvrbrC9tuj4XVZbidY45jIGGMwKpxnjHcY9arbvqnU+ltWuND0rVbq+sreRLe2uFKhZUwNpY544PI5rdPaVlZ8fjMX0Xd3XzsJehdX6v1naWnSx6es9Y1SffHbxR2yq4cklm38BQCSSzEAck1X9KWdtbdWT6P8WYpU0/Tg3zFlHMwa5cHCqZVJwnrlcZwOa6fqLp7T+hNHbqDpHXbuS9KBNQSZ1O/ccsyccDd6e2KpdLvukOstKvbvrB789SS+S3voZ2X5WJB5QFGA2eck/l2qb5PZWXqVtmrvP0OL68tbTTtU1G36QkurvQ9qyRJKHk+WDd0LH29CfSp1y2IYB7RL3+1XvTXxcuOm+jZdEsY4vlgshkDZ3TyNkeIwI8xIPqe1UNzzGhCLETGCwz27cVnVcsk0aU1HVM1QNhZV4xsIPHHKmut0Xpjo79gf77rWpXUhh3x/LqI9jBcqxPJOD6dq5Oz2gu0h8gXn7YNTOjumOoumVsde1TRrptMtyJxvYmNTjKsy+nvz+dKejzFTVZHR9KydMaFpwTrrpS7veoHj3XEuoBozADkjYmVCcYOSCa1aNFL8PZILzq7RLTUZNVjFzp01y6ylIfRSp8obkHBGeastc1W1+Mut6dZanqFnpbjEs+osPFKRL6AfxMScAZ981R9aWdponXFjpMmqnqDQ4rcXEUjDbIM5UK4BIBBHp71e13Z8ddfQpeyuuGn9mu61O16v67SaUrpFk0KfMCFQqnbwduBjdjA4q76y6c6Rm1jS7jpy7tumBKkkUiqrSJIqqCMj3z3PrUPrzrrTtR0PT9NgtIrd7SRWgWBADCmOTx6H696o+mbXTLnVRqXXWl6pc6KkBS0YM8CNMSOA4HcDJwPz7Va1o30K3vK2pruNMTTtSuxDqqauHhjbfGhUIMtgYNYk5l5/v/AOFWOq2/TsOrXZ6QS8jhmhjedLqcylDk4wxAJ496rRhn3AY81csnd3OmKsi20br7VehL/OkZHzkGJXRWZgFfjhecZP61q1i5n6rtrzqaLWVg1WZmV7N4SrKiKAJB35OD37YFWHTfUXU2g38zdJWFzdrJar83JBaNN4Y3ttyQOAef0q86d6g6L1X9pz9d9OPd65dTFJkmaWNYkAULtiQqoJGTk57/AErSPRV0vtqUl0nZsrousek9R0KCK86U07bFbiJJowEZvLtG5gcliffJJNWeoN1r0U+i9NaxqcDWN2kVnFf2sm6GGMrtIJPIKLkbeMkd+a5XTuqehLDRLm0/2U0+ZoBIqXk0zNOxy21s7vxcjGBjgVI6l6J1e26Kk1HVOq9KuZbdUna0iaV2Pbs58uRnOB3xWmyk7PLxzKXbV1n4E/qPonT213StC6Q6mXUNL1WRoPF1AfvbZVXczPsOGGBxjBJwKjad03Y/BvWZ7zW/2N1ZaX8bW1nOwDIjA5fKP+FscZ++KiajpnSVr07DPp+r6ld9RRyxiG8lxHAgJ8yiMd9w9Sc8Vp6f6msen9SkPVOm2OswzW+2zivFDLEd3mcBj3PAz7Zx61NpONr3X8kdFSvaz/g13Gp6bqt9f3eg2MGnWL3HEUOTHvCDcR/yqID50xxUmXUNM1O6vrvQLKLT7BpyFhgzs3bBuIz9aj7gHUE1zSyZ0x0O06P6AvOtG12O11yDTAJAA08e6NCVGFOPMScEnHYV9AuLfWbPR4bvr+OwutC+YNvPZpfBmnZWAjYKMHYWAOCQe2R6VyfSPSMT6FrOsQ9V/JajeyvJa6coxGoXygvJnIZsHgDAyO+eHR/X110hayajrdjNdw6q4ayuZ4t0O1QQyRl+Gw3JasmnLTMvdR1yOxtddvprrV7n4adITpFcoi6obOLIVFP7tDuYYzknaO+M49a1dUdb6D1FpKaFqkt5bWbXMVtM24sz4kGVQEYDEKFyoJHf0qDb63r2ndK3XUOiaXFpXSc13JebrVA7zFiPElESsCRuAUNjHHHHNRzY6p8Nemr/AKn1fp21tNZ1C8Mj3UkqXFxaRySAqmBkQk9m285JBI7VXZV+/hmW2nYx+KLaDo3S7R6HpB0G5t1R4ohuj8QM20gmTPPPB75FX/Wp1KXpjTZLzqGzu7mOa2a9sow3hlQygrHITyQfpztPauctPidda8l1ruu9Ox6zYaNGA00kIkjtXkIUnLjbuK8YAJAJPHeuV6y1q9611G113pvRZotHtJ4ItTmiXdBAzyeQHHCnGe3PbOKclJ2T4BVIrNHF3mBdSE4OHbn86ha0w/Z8PofHG3PodpqXfEC6uB/dkPH2NQtdKHTId67lNwPLnnG01vDeRlLRlHBIZCHUbQv/ABYNSYZDHIrr3X8WBya1woMgbcDZwD9O2a3QxmSRvFPh5U49c/69q62zmRugZBERESCAWC45FToYyFQ3AWNHIw4bG7j+QrRbxoCNp8R88AjnNWsEQLAjEgCOS2Oxx2rCUjaMTPSkmtNQV4Ll0Rkbs+0nAJH3Gaj3mFvJPbcRwKsdIinTU4UNuGcRSM+cfhCE4JHp/Kq295vZcZPmNYXuzW1kb7IYcccngGuFAO+Xykjcc+gbniu6s32lT/e3Z/WuH8RXlcZ2srkAk/U5rpo8TGpwNqIzMojAIONxHvW2WLap2ZUYxk8/c1hGzjad2BtKjngZ/wAa228edysA0fse36Vq2ZpEmzUS31o6ugZHAKsOcc9jXWZZpC2QFPA2+mAOP51zFugN1asrAMJADx3H+FdSUAMgDDeGA2k8EYHI/P8AlXm4p5o7aCyZ5KApLlfweVuefpUEgrIVwcj3PepjuQpJy2R5yO2fSoBP73knJ75rCmaTJtt/axd87hj9RXyzUv8A5o3f/wBGf/5Y19QtmIuIM/hPf6nIr5dqH/p91/8ARn/qa9bB7zPLxm6iNSlK9Q8wUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgJVl+M1MBzKSfeoVl/aY7ZqaBiXB9DXPPeO+juovdPz3JGNpwKvdPy/K7cohPmJGapbFNskZIGO334q5007ZcehyCK8OvxPYp8DqL6TfaLwo49PtXzuaRGmn2kFgxH4jX0O6jY2wODjjJxx2r5wciSfygLuPfirYTdZTE7yNbgsQWGGIxkVHkUgZJwDwSe36VMfB2geUFce9RCCrAFg5HCk+tegmcTRb9LJt1RlzGu6MhWJx+tWN2jQzTKWGR6VWdPRsuquxOf3JJBHerG5YvIWwCxzmuepvm0d0udDXxNNu14ye/P0qkaIhisf4e2/bwfzq96cBbS78gcY9B9K2dP8ATWqdSXotNB0+W8ufDDPtdVVF/wCJjwKxUrNmlrpHPyxssQ8UYcjJ4GPvVXdR7Hy6EA8gqeK+kaf8OL+76hm0rVd2jNZKHmabz8N22hTgg++apeuekJOkrlLd7iPUbaUFoZkTHPqCvofzreE1exlODscTJ5VxvPc8d6r5ZCr4X8BXnPvV/f6NqtjbLcXunyQW78hmAGAfcdxVC7xxo643OcEEt+E5rqi7nLLI7KZ/3UHb8Pb8q125KOCFyDn+lJgPDhxjlR/SvLR/3oJPAP8AKuQ6bmywsTp2lw3y6rFcW9u6tLaO/mlXPK4xnBrubGbo/rfV7K2tLWy6Juov3kt1aRF96digTcBkkjnI7VXnRfh+vQs7aVJqUmvQx+JDLPeHw2kHO3wQMYPbvVHoOi6LdrMOrbu90LVgVa2EKCWLwyM+ceuT9eMVu7TTeaMM4tLL53l/peqax8LOsdR0rp2dNXuLoqYbuzgPiTRN+FcA7lOe65re2taBKuv2vXPSUdr1LcSyvc3TZ8e2mJyGA3YU9uMEcmougdG9U6TaXvU3TGr2Wpx7m8kE+y5MS9nCsMYx6A5FaunuhJfiGLjqXWOqNPsL24zJHbyQtKzbeAHcYC9u/NQ9jVvz43JW3ol5cLFf8NfiVN0HqV5JdWkM8kwXwJLmFDtQZ/CGUjn1qt6g12LqPW9W1O3tFsFursuYVj2DO0ZOMDGe/atml9VanpPUcWpwQwfMWyeHHIY1l2EHuPf71r6g6lvurdVvNW1aMJdzy7W8uMgKBnFTUir7VhTlls3IcD4cZGRkcVBvb2S313UYkldCdpXaucZUZqZbnLfXNXnT+l6nf9XaidF0S51aSFI3cRQGRVGzs3tVabUW2TNNpWJ0HxV1LU+jrrp67Pzdl4HhGFtwC7ezY7AjvmtVv051NpfREOvp09PDprTxy/tCVV3RgHhlBbcB9cVI6M6L6Z1calN1nqN/p1/KZEisoWELQMOAzhh5uf4axg6s6r6i0vUNA0yNr5zBJC3lGBEowxznGMDiru17QXjcortXk/AvNBu+ruudJ1KXToobyx0wLdXZukCrcCIh/CUH+0fjOB7VYah1h1D8aoLSz0m3sZNS0SUXayXDhNgGR4SkjGWP8J44r5foHxBuumbWK2sr2ZYI87UEW4ZPfv61aXOm630rbW+tW94kn7ckybazceJG2fKrDsSc1Dp2lw7iVUuuPeZdTdbdRa7qEOj9TCaN7IO7RzJhgwG3HrwOa5ZD+957Hua7frfpLqXSbjStb6pnsXN5bNDHDBciWWDABAlwMBiD2BOPWuFOSzZ5571k9n9JtHazuXWn69LoFzY3cMazPvdRuj37OBzirDqHqfVOvtc0+zjsL3ULuCJiny9uzuQT6KOcfWqiHqSfpZ7W9tCmWco26IPxj2NdPH8VeobbUV1eKeONHiEA8KEI5AOf4cHufetoJpXSMZtN2bIGh9Z3/REt5YyxXOmPBKd6TwFZMkc5B8wqX0/1tpVp1XedSdQ6RZavdXJHgfN7WjTAwW2N5S33ziuh1uPVNB1Ox676+0rTNUt9Whjihhkk8WS1Kjy+MgyA7DkA5+vNJ7bTdN6im1Dq7o19MbVES5tLeVPCWSHbgMIx2BIzjg88jmm1GS01+MjZceOhOtui7DXumtV6i0rVNPsIJZPnbfQ1tWZHjTlkZ9wA3YJAAwARXFdQ9Tw69o0eodLdI21jPazIbjUNPtpESJW42uwG1dxI+vFV/Ud5bS9QXlj00DbxXBVYbODJYM3HhqAeT/nV5fdd9SaJ08/Sl3p02nfMJ8tHYGzaIs7YAyOxP15JqFBp31+eock12Fb1D07qg0rTtf1zWLWWYuY4tP8AFaSVEIPnLdh9q5fdzu/Kr7X/AId9YdL2unaj1Rpy29k5KB0uopGDFTjeqEkdvWuedd5UcgDk1nJ3eTubwTSzVjzUDJ8lbi38TxDdeURDLZ2nsBX0+HqnpTS+k7P9j6IJ+pLSVJ4dVusm4aYd/NnAQDOEAA9TzXI9NdWX/Rl9BqGjoGuC3h52bioxnI9vyrpOnrfpTqzqLVtU67a70kvGUgt7Q/Ls8j5JmOQcqCeF9ass45rLuKPeyeZ5r/VfVfW9ivUUWm3tzBon7m81W3iYCMN2jL5yRxnA7VG6a6g6b1TTdUn6u046rq17CYY72XcWtEH4WjORhuPxHPepvRepdVTXN30n0WH1S1tXljSVtsUJQ588jsQq5zznOfSq89CdMWGgS22p6pqNr1ZYl/mIVCTWzTAkhAAO3YZz61boro/xqU6T6X8k7ovrC16W6d1HQ9c0J7ltbyrTXkP725hJ/dCIsNwAPI2dzUeKPqr4TsD1Np0mkQ6qN1u8sqylgv8AC+1iVOD2NedTa/1trOlaZqWp2MxbSpkkiuJI8NAP7qn2yew4FQJOt5ri+0+46rtYdX0y2kZxFdHyNMRgFgO4AzVlF6211K3Wl9NCNq/WY6rYySXTzTW6GNVMOAE75L55yeAMemc+lUgOFY8cDmrzqTqXTuqdbu7zSNNs9NjS2RJUtIkjRmycYCgCqBdo8UMGLNjbt7fnWEtdLHRDNa3MNTgW4htM3Hy+2YneTjHl9D719Q6gs+hLboiOXpd3HUsHhf8AnG4vJJHuQxAZHXO1FzyNuMY9a53onq+y6O1q1vr7TYNQl8y27TxrIImwNxCt5ckcZPpmpC6z0m/X8ur6hoIvdCdGk/ZZk22y3DE5O1CAFAJwoIAP2q8dppa2RSWym9Lsm9T9LWdppumXmgdQydXa5KVgudOtbY4DHOBDgs7Y9S3f0AqL0j1DB0veGxvejrS86hMxV47qyee63E8IIyDz6YxU7py51vpTUNU616NsZ26WjPykE804LQl+W2AHcyjG0uAcdiagXPxa1qLqC56ot7ZrSG8jW2TUktG/eMgwwWQ4OecHB+9WtKS2dfmhS6i9rT5qdBoGhdJWOi3i9YXGrW3Vys8q27S+BFp8wJZV8MDc5HH4jge1fNpustV6lvLODUr6SeKNHfa8pfLY711/VXR2sRXNj1f1Vd29xpmptFLfWVtckSRAgBVmHAywxnBOM81H6z1jprVL2xOgaBbaPekOT8smxdgXGNuf51VtWvrf0LJO9tLepy6nJ571IGv3nTiw32nTmCXxRGxX+JSDkfyqIDjB7jNWendSTdJ3dlqdrBFcyJN4YWSMPjcp8wBHf61lHXQ1k+jrYvtKvNO+IcUt91T1FcadeWTeBaII0ZWQ8szA49cYA+tT9D+Fc3Uj6otn1NpVvYxS7ILq4R3mnfAJ8g/AMnGSfyqq1bWZ+vOpNNv9ZibR9OSFojdwWuxZ2zkgcAMQD3rR1qug9Lz2UnS1xPHbT58YSyM7NJ6yHGO/H0rotLSLt62MG46tX9LkvRuiOq9YtZ76WLTTpOmzM00FxcBWvjExyqKOWHl+gNQutOsU62TTL67sbeBLFBEzQJsUQjsmAB29ucVs6M6T6j1y0imsdQtrHTbncyz3FyQY1JPJQck+vat/Qdj0foMuuaf13Z/7Q3sNy0EamSTw0QdnQKR5myTk9sUbSbbza7CEm0ksk+0w6q6M17rDRl6s0bpfwdEFvgPAQhkCjG9Y2Idhx+LHODjNUFx/ZRD/AOdqPb0HpVrf/ELV9O03ULXSJHTT41eC0y24pB+FfMTnhePyqpuiTb2x7qIlAI7dgayntWV9DWGy22tTy0JAJU7dqlgTz2Gf8K6vRfiJq+rRWmnKplu7/bbxwsMBmbjljwF9yewrlbU4J99hGPy7V0uv/C3VenejYtbh1/SLye0gWae3hV1kjGBkBzwxGfTGcGopuKyfEtNS1XAq+teh9U6Cn021vbvTb4XblUvLCUsi4wWUhgD68HFX17F0tH0KLW3lSTqdpUdtWZDlGLBfDHGBGFJ49Tk1Eu+mdMu+kRf3vUzXPUyMptLS3Aa3jUkBlcnLMxB7jAXHrUzpC36JtdDkXq60vtf1eRXB8aaRIbQ4IzGqkAsOPMxIHoK1bvHO7a7DJRtJpcSWfhx0zpWmz6pP1HPe6nEpnQKiCF2TlVYMSWBwc8CqnU/idqvxD0WPpmJ1ummx8tAu/wDcsDnIB8qY57d6q7DQ9SsdNg1u6v8ATZrS3K3MsLyJ4k8SkMUIHPI7j15rptZ6pt/ipqdjBpsWk6DqvMx1AII8RBTlW2Adzge9Razu8+/sJvlZZd3acHpGjXuiXGoxamf3zCNslskjnvU+Ieh55/wrBLC907UtYt9VuYbq4TwvPC+9SuCeDWy1Bdh6Dd3/ACrGo7yuaU1aKRc6Zd9WWNyz9HW1/cwtCvzPyv8ACQTjgd/WpNnfdO6qkrdbaHqL9SvKxu5lnZXAyAiMDxgIByR6mtWhWXV91qly/RqStbxwJ80Y7hYvNlsAbjzxn9a9b/Za/sdQbriwv4uq1aQXl0Lo4jI4RSD5cBQvPqatFrT+NfMiV9f508iNo3xEstIM+kaNoulNbLcPFbI9qs00m5uAXI5OTjJOKsG6Wuvh91Zpo6rvdP1TSX/eJJa+aOSUDd4TbgDweO2Dj1FYyap07J0np+g3HSy2GtWUKR285iEF0bhuxbOGYsxzgj1HpW2HRdW6AI1H4k6PpGs6e6eBFE1ysywzN3ZgCcNgEA5wM8VpdJ5ZX9SmbWfD0InU9rpvUPUemwaBpkk+o31wfDs7ZMnBGWYL/Cv1OPX0FbdEvLboC61vRurumUk1K5kXeupRLvMQGFKFjgLnJyp71M6f6d1bS/2n8QOjI7Oy0+SMxQ2JmL3Ekf8A7SVAedgK+pBI7VT/APahZaprZverNKs9XaOARQveg+XzFmwue2T60d5dFZr1uRlF3eT9LFaLrTrqXUZtCt1tbBrltkaNuXO1c4PqM1pLZnQdsHHepdzqthrdxeXujWUGnWbz4ENuu1NwUZYD61DQg3C/esJLM3joV7TpBLqix3Qh2vJnL4KE+2fWvoM3xjg1zohtE1W3hnsBZiIQyFSEZY9ibQOQwwMYqmtuo+k49HvdM1bRrV7yOedpbpiqyMzE45PJGMADHpxROr9Lvek7WwPTWlxyrbrBbTrFiXf2U5GCTnBJJ981vbaSvHQxvst2kWfUnUXXvRHT9tbdR6dc6UxtY4oG2qUCgAL5gSABj8PfNbulOp5uvNUi0brHVprfQvDM19JEyqXC/hRM92ZscngDJ5qmvNZ1W3u7Cx6zB1Owhu1N3AZcpK6chR9Djk57VlrfT8nUutP1To+kXWj9MzOVvr62tt9tBJ3YKBxxwOOBkc96bMbZpLvG1K+Tb7jdr2tanpCal0lpOqLe6SsJWJ1VQHjccs3/ABeh+1Y6f8U77SekYNB0y3htbJV2LHGn4nYgNIx7Envn2AqDrmgdN3uuaJadMX16hvcxahe6lggKMfvNowAMZAXt25r34m9K6T0y+j/7KaxNqWnTTJE63QTxVYYOQV42mloO0Wte4i81eSehUXRxczevmIP61jc6dJqtrbWySLEDMGZzzgAGvLg5uJyT2c/1qVaQzSNaxwSLH+9G93GcLtPp6mueOTTOllNq2iy6CyI7LLFMCyyg4yR3B9c1a9MdJan1ah/ZCwpEjf2k0mFZh+LaMZP1NdxY6altdpNfeHed1hdk4QEcjB9TXRaPouqRT6rc9Laakul2iI822dEbxnyxREPc4APp3rSVSyKRhmfL20O80+e9tdQtpnmsBuuVt4mlEYPKneowAfTNWPR2ip1Xq6aNJdfJQtE0txJGRvSJeSFB4JJbHPvk8DFfcemo9Wtunrqa51Kzs9RvJDP8i0ZIiDJhFlb1OANxxgE4GcGo63XS+ldNwWdtZ2sLQREo9soVgxGWbfjc2W5JY9voMVyuo3c6Ni1jjuqfh7pHT97pl507fXNxbl3inhvGV2H7tirqwA9R2I4r5Zfg/P3A/wCI9zX2TX+nRb6do+sL1FaanIdkz28duVTzoR5WLHcRkeg/wr43fg/tC545Ehzn05qIO7E1ZEiyUKkRbHc1w54cjMY8zc+veu5th+7izgccDNcEWbIwMYcnjn1xXbR4nNPgboMo20k7T2AGc1KR2Vd0cpk3twD7VGChQyhsbAcbfWs4QDhvxZ4GONuK1eZmi1tsreWgkGXeX1GM8c4FdTJazB2uN0cSKyptbgs20E7R7Y7/AJVyViF8a2eVlaRphgfxHg5/KuulZ4i6FAcEt5uQDgV5mKyaO6hozQJGTEigDHOMDg/4VXSKVkKkcnkEn86sXO5ew54PGP6VAmAz5C3lPrzisKRpMlW2RLCc5w1fLb05vLgnjMrf1r6hanzR9h+8GPpXy+8/9MuP/ojf1r2MHvSPKxm7E0UpSvTPMFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoCTZ/2lTB/aH71Csxlz9qnE5lPp2rnnvHfR3UdBat+8jxzgZ/lVzp/Fwqv23YI+9UdvjPIH4Mj9KutPAN0pI5BUivErLU9ilqdNMN1oMkny+/fivnLyjxZEJYsWPBHpmvo067bYjuP618+bl5Bw2XPfuKnCbrKYnVGhWL5XBOff0rwgoBlfLn0HNZkYUFQCoPGO9YNK7lgq5wfWu84y06cTOsoFPlZSAR3z7VLu/LdyqRgbjgVB6eLDVIAysGAJOO1T9S2pesFyeAT9zXNPrLdxtHc8y/6XXxNL1SONS8hwERT5iT9K7/pDpnq3oWxkvLixSC1nKNIVulaSMdsso5GM/WuF6a1+503prUQZV+VtbmO4RDGuQ54JD43cj0zivqiP1R1R0e9/p1qiafKvnkluUSQRfxOqH8XGfaueWTdzaOi7SVr8MGpTWV9o2ub7yRFglguMCJkznII82Rz75qBr/SOnwJ4+oXk2t6jCA9jHaIwAfuRsGSxrHVrfpO+0aztNEmGk6zG6LBdSys5c+odexyM+3ParS26f1Xp29gu9A1OfXkugsTBLURC2bvuLZPlPv6VEXsqxLzZ8s1+/k1ppdPjIa7KlXikyjJ77gQCK5LVdMhmtjaXEa23gADMYGSfoa+z6/q40nUdUtuqNEtjrV3Hy52ySvGB5GVhyR9vzr4tJpk2pWU+pT3MluWZkjjPPAPcj3rspyv3HJNW7zGYYWIegXFYWmS64HrWdwrBICc42gA+9Y2LDx4gP74qGDVpmgWSaZdXlzq3yz2sbTQxsxIkcc7cdh96lapo3WepWum65eaJdppU6oiXkqkoVPALEchR9RU/proXXLPSL3qfWen7q56bjZ3WSRsJg5AYoDux65xis9L691S8t10G61K403TbxfBe5dNyxRY7gDk8dhXVtNvo52OXZVrSyuTNU6V1Po2OC6/alnqnT8kyLPLZSMpCk8rtJ4zyM1WXd1oS9YxDTlbT9CuJfEa1jXLJH6oOcjPPP1rzqfR7Xp+OxVOo7XWLGdioESFGBAzlhUvRPhfrnVWly63082miJIyYYLifZLcgd9gxj9SM0TSjtSZLT2tmK/J1V1070/wBa9Xxf7DPp3SdkLYfOwypIyBs+RkQebdjOea4jqjQ5+m9c1HTbq+h1Nop8rcwqVWRSoIO08j7GrrVNP6Ts+ioLrQpprLqe0Ba9kmlZhde4I/hKngYPbvXBWF5LqFpLcXDmSWSYkknPoKyadsnku01utrTN9hNtjlgBiu56Y+KMnSlzeaeFEaI4kaQZG4lR3Pqa4e3GGGfpXUdO9fX2hXV/pdtYW93E0ittks1mLkgcds5qsVd6XLN2WpLtNV6e6360vNV6ovLyw01kUCGzwj3JxgneQduffGauulfh5Pqc+ur0j1XYaRowkaGze6aV55QRkqwQY4JxuJ+uK+daTEuo9TXSdRwvpMSzu0lrEhjePJztx6V2Go6LZXvVen6b0BcQ6PZXyZmaeR2W3A4aTvlj64962lG2SdvVGMXfNq+fmax1RLrHTb9O3ugC8srNflpLu308u1qFO1pd4GMDBOSar9Y6S003mk2/SvWEdzHO5IF/F4TQ7RkMWXIPt6V0JvOpen9Zf4eR65C9pMjRx3xXZbyQNx4rDOex/DgnNchL0Bq9lr/7DhurTULhIfGjvFnBTw87RweQc+lRGyeTsWldrNX4ErqfRdb0uWwk13WbPUISkiwx29y0hU4HmOR2rkj+PueDV/rvSet9M6haza5LbzQzQukXhOCFIxniud3Ayt7A1nLXU1h4G7VHiS2sDdD934zckZGcV3E/S3S0nQcd9p2v3g6jtU+ZeJ5FNvIe/hqn4lI7A5Oarunuibjrqexs4L2006K3lMs9zdZKouOAFHLEn0/nU+z0HSulup9S034kQG9giRhBFbykQS5Hkn3AglR3A7ZHNTGSaSTzRSUXtNtZM2avpPXGn9N2uuatNbRWVpcRXDW8t+ss3fyt4fpgn1Oasba01/4vaeZp9Z063/ZkjiFr7IeZ2HKrtzgduTgZrk9NkvbOSKbq3Rb6fpXeyiSYPFHMf4MseB7/AFxUzStK0XV+qJ00rVB03oO1WZp08Z9x/F4Q4DdvU4q7WXf22KJ3fd2XLfROn+itI6buL26ubkdaWiPMtyl1iKOdSfKqAcjjGc+taeq1641LpPTuoOpEFtplnPG8ZmuE+YDNwhWMebbk9zjv2rRoem9D6Hqus2uuLfdR2kU0i27SXBhiC+kjKp5YZz3xn3qf0vpcl709ea7qHUvy8tmTc6TZIBIZ2QkqZSxwgwOAAST7VDdntfz+OwlK62f4/PacVqvWOq6s1rpl/Jc/Lli5Eq7dxAOPTmqwkqPoOa6rqv4h6x1jBptrqhWWOFjL4jp5h5SAoPoOa5VsHOTxj0qku9WNoaa3LGx1S/0d7a50qQpP420njgFee9Wkuqnq3qE3fV19dW8iRLGjQoGdox6+g/yrm7xpIrKGSKQRFblfMcY/Ce9d3FpvQerdHpLqU13F1HDCZDfrKSpYZ8nhY27ewGMH75q8WoxTsZTu5NXN2j9K9QW1he6n0Depd6LLIzxWpvlW52rxl1xg5IOMc1k1h0beaMdWjudXk6p2LcNtl3Ry3Q5xs29t3A5zUDUOmYNH6dXUdD6qtb9VgWS4gkj8KSNsDdsAOCPbPNcg+oR6LLY39nLslglEsKo2Tn+9irJOWaf4+5RvZ1XzuO5u+qtX1q3h0bURc6Yt+wglnuYiqW6n8RPv9PrUnWNI6f8Ah+NP1BdUt+q4ZmMJt762UJGcA7+CcnAxzUK46z1jrXS/2de3F7LpLPGb67itS3y0W4Zck+voK91236L6Zn0jUtO1G76ixI0bWmqRp4WSv48IB2+uarnez+yLZWuvuyh1nqDTde1WebR9LstMjjgVJRZIER2JPOP8qp1I4x29eKuuoupbXqfWDNYaVpulRwWwVhYwCNXPOCcAZNUEbtxx68faspKzNoPLU6DQOlh1ZfQ2j6pa6RFGWd5rhC+eAAFUdzVzcdCdMdOaxcad1Nrk2rwzwpLa/IJ4e3khtyHPOQK4qS21C6nsk0q2lu7rxG/dxIWJGB7V3WjWNj09JPB8U+kriXUrnD2pmlaJo4QMAKFYYGSSc/nV43UdfLIzlZy088yil1e7s7rUdA6Pm1O902CIrGBG0hVCMsGVQRxzV8/xKim6EXQb+0t20aC3CxQ54V8cMF7h8+tW1hr3Ufw30ua96d0u403pHUHae0mTExlQ+jMpB7jgN6Vz190r1Z0t+y+peoNEt0sGuVuz+/SXYWO5fEjXO3kjvV+jLXyzzv7lelHTzyyt7HInUIrpdPsdYublrF5o/mo1yshQHn8XbHvXY9TjoKODTk6N0W503UiWDyS3UkviJtO4sGOPb8IxVr1J1knxKNqmr6ejQ2ribUL2zVfFhthwfNjHJIHqea5zqHQej9Iayn6Uu9Qku5C6ulwVI27e/AFRN3Wd14aCKs8rMoycMe2K3S6vLoiW17bqjMtwFw6Bxgg+hrS3cZxnHIq30KTQY7+0brC1N7pgl/sd7IGk2nbuKkHHfgd6546q5u9Mjoov9q/i3pU9hpb2zx6VtuC0zi2RW9I4ye7n2HGO5qvhuOlYemZ9C13TAOppJ9kuo3cmZbeXsqAZyEBOSAOe+asbyxa614RfB7TblbZ4/GvLZLlligcnC4ZzkAgevtW3Req7zou/v9B1fp/T7nqS8kZLqURi5uLhpfwojgMeQQAFIrfO2XjbR+Zlxz++qIXWPTNr0XZWVx0n1ENct5AI5Ul/cyRyEc8IcMhPbsa32N50bqfT1lBrGlyJqUcZ8e9t1KPK5OW3ehHoAewFauldO03pW+1Wx+K2g3VxqkDNFDp92CiWvvISpG5h2BJIHPrXN9O9IXPVPUOo2nRVmstjuMjtdXXhRQxknbudu+QOO+atZW6T04lc79Fa8Dp9O+HXT2r9Iz3TdZSW7yK8kFqlmpRIwTtR5N24nAHYetcldKuyErtAKDAXtjGBWOrdO32gNqGm6hdw29xDu8RIW8RUz2CtjsR/WsrlUjtrdApJESgnHbA7VlPtvc0p+Fjy3w6OPdG7H6VVjWC+ixQvdySRSlUdGYksM8rjsPvVpa92HY7T/Sta26ajpNpbrps1qL0wwx3EsLpDDuYDxC2MEAc5FXovUircuOkdN03W+orDTL7VDo2nIxuLmZSGYRrwEVTwWJIxntya3dU9P9O6Z8RLe0fWLu/6euds7sGAk8PnMTbPKCSMbsevvUzq/wCHui9GWlpqGhdUftsSusEqSQAPuIPmUAny8dj+tYdG/EtOndH1XSLiC2uYL6Qrdm4TBmXACpjGcD0Axyc1a7l0oZ+hSyXRnkWOsaZBrHWmmSfCfp64u7dVN1d6NFGZIkSMjzZbnYf7rHkj1qH+0+nertftbbWUXplY98ty+nwBZDjjZtYbQSTzxxjtVF09rmsdEST3OlyOkOqSDaiDe42klRjOeAa6qbpiK26qTqH4tWbNBqluDbQWl6I5Vk4yZdnKttycc9+e1Va2dfLtJTctPPsKrWNM0HTNWvU6Tv7y+tpERpGvGUujcgAFQAQR9KgwIFx2IDZP6VM1G20CDWNRHSZuxbyJE0i3MxkZW57MRkjHvWmFMEHGSe/3rB6m8dCn1PUtSstbgt9MkZRcRKCDIUB8xAJwa6vqzpfR+mYdL6l0zWDq+pWjxzarFc7THNNnvEO+xTgANycZ9cVn0x0rofUWrXcnVGrXmm21vbRpGLUKXd2LEszPkBRgDA759Mc1mhGw0frm807Xp7fWdPsxvgQRhVuCRlS4HfAOcVtHPTh6mMlbXjp3F5Gj/HHVRrWu67Do0mneU3zQF57iU8qoGRwuOWJ9eM1Rx9KdYdbw61p9pBFc2umSET3Ml3tSVx2WPd+Jj3AH5kVJ6nt9MutZ0yHpjwdGa7lY3BnXMSLj8WO/+dStc3dIalpmk9Ia9capaXwLma5cQhJicOzAD8B7jBzjjNWzWUfLuIye9595Q6RreqLNp+ii8NusxFuGmk2xwKOCWA5IXngd66i4bQ/hNfyfsS/g6pttWUJJJfWSbk2c8AFsAlv5Cout/DvozTOo9Ikg6nvpLGZX+dm2oZWmC53R44VWYng5IA9a0xalJ0Jrly/TF22t6dd2wSSSe1B8BQ/AIxjzH1HfFQ2p6fbQJOGv3KVtTtdYa+vbCzjsYJLlsRRLtUEKoJx3GTzio8R/ej2zW79pHWGv76SJLZpbp/3KpsC4AGcDjJ9q0RH96o7cjmsZZM3jmjs+h+vtM6civrVdFs5dRN1M0901vHJKdzZGGYEgAccY9a1adL0Nruu6tqfUtrfXEk92XhtIJTHAnAG4FRyScnuBVfH1N05L03LpOrdPWfzFvNLuvgoWd3ZieSOXzkDH0FauofhzeaF0ZpmoDWIbu5QeLfWCoF8FD+Fd/wDEQO/pWkYxvnk2ZOUrZZpFr0a/RU51KTq22uNaBup4tPtZiyRww9lk8mDI5GeTwMVD/wC0S9WzvOmNJnI0W2SS0toZFyDDyBk9j3OTjvmsLPpHqP4jdPWN5oUVvHZ6cDar4lysEtwicsiD+LlsZOOeB2q11u91b4q6VHoXSWgwi66et1VWgZYfk7eM7SjOdoyzEADJZmNWajtZ/wDhW8rZf+nO9HdI6/1ToOup01pq3C2sYikuZ5kiUuvIWMscs5x2A+5FSbTRumP9lpLnqDUrq611QslvFCPDitHDDIYY87k+U54HpVVpGrax0rpQtbK7mBUPK8O/aUl/iB/Mc1J1a86Lvum4JLOwuLbVtsRMq3RKy3GRuYoSeSc8D0q0tq/d3ER2bd/eVNxkyyluG8QnGO3NS7aGe4ntYrR1jczAsX/CF2nOaj3eRLJn++39an6Jp9/q2saRYaMYRcXN2Iy0zbUVdpySfQCuVHUfUdK0IWV1Yza+sF3p23fGquctJ/xKfYduTVvZaK97r0lj0xfRaBplxA91fSEF/DlZgiiNSeWYZ9QAEJ9hUN+itU1LXo9G1bVbW20ezEdyb+2uCDcnJHhICMqcBsk9gQRyQKstQ6m6R6d1u+so9DtYpHghTdErOzDGeWySTye5+tZybllHNl0rZvIsVtta6V6Za2iSHUtPjk8D57hZJxkAytHyQAWGeewqlvdH6T6Su4NRN/c6xaQAI2l3Tq9ushIUOe24euw5XzD2rij1vczdS2ljqs81noF9OVQkhVNvz2bnnjH35rrToHSvU+u28eg2NzqWlw2sj3emjdL4+0AIS/4hyc9+4HrVJQcd4lTUskVfV+ma3r+qaP1NYWC2/TpuYUlEfHickCRFHGwcAkcZFfK9S41G63cfvW/rX3K/6q0YdMPpGszXdtdwxIiwq5jMIQDaoT6BVHPcd+9fC7+YXN9cSqoCySlgPoTSCktUTPZejJdu+QCSDjygGuCiXzuVIGMjn15rurXsMk9zxXFRAeIwyCqknPb9K7aXE5p8DeoUqmdxJ7kVtClgWkXHqA7DP0rUm5ciI7hnHPt9K2Y2BA6gs54PrWjKE/T28SW0SVw22XyKMbRkHJPqT2x6V191lWlPBCsFLf8AhHArj7NfCvbV2TDFvKxOdv5V1dy6ljuJ/GNwJxnIHf8AOvNxWqO2hkmaZPKSGICnHGRwarpwFlbDZJOCcetWLk8qeQRyP8qrD7Z4HasaSLzZNtBmSIEZ86j7818tvObuf/6I39a+n27YeJuR+8X+tfL7k5uJj/xt/WvXwesjy8ZuxNVKUr0zzBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAkWf8AaH7VNUZkOPeoVn/a1NX8fPb3rnnqd1HdRfQABAw77RzmrnTsm5jOfaqS3x4KjI5QH+dXOnrmVMnHlFeLW0Z7FPVHWXa7LdgBtz6Z+lfOZc+PJnAQscj619Hv+YFIIJwATnucV85k8rymMKXMhyPcVGE3WRid5HiBkyMmPIwffFancocAsExwQO9bTMpdQdwxyTWbyiVWVTs49a7zkJOgSKL1HG4hQckn1qXqbD5+Vl5B+lRNBUR3CxgqTj2781IvlU3EuOfMa55db5Gi3PM6LoPVNN0syvr9gupWJnUmBxuQsO25fUZ9K+1aVrHTfVc92kE76HZNB4T2tiFQDcD5gMEL+Qr886VcQ2clpLdWq3cMd2rNC34XwOx9xX1TXviTa65b6bLBpFrCumyJLcLGFhzF2CYH4sn0+lVlT2ndImM9nVl30j1CvTl9e9H9MxJrgjkeNWuGTxpR3G8+gxxntWGm9W67Zaw/TNzZSJf79os9uH+mPQj2YHH1qt1LqG56wuLa86B6Xt/25bKVuprWKOJzCeysMjccjj1rjtV6t6kGuxahqAl03UbOM2nhyRbJAnfkexq0ad+GZWU7H0XTNU6k0rrRo+srIaG/y4W2u5VSZ1QtygdSQCT6Zrh/ijot3Z6rfXej3v7U0udfFeVbfaYJD3DY457iqjWestf6xtyJbe/u7G0kBkKW7FfEA7kehrZ01d9V3fRXUUmj6Xe3ektn5x44jt8np352+vBxVlTcOk7FHUUujmc3I7tFErSFl2AgegrG1H70beCGyKyZ/Ehhb1aME/TisbUefLHPmqQjorfrzq676cvoYLaa4Edu3iFY/KsY7sx7YxVfYdTjX9MGhabaWc19fFdrBNjqw9Sx7Yq/0PQupLHpO41WTWNLXTrf99NYtM0jzxg8oUAx29zUjUtZ074q3GnQacND6f1awTxBcx2wiEqYx4bFFH88kVqtlaIyak+Jnp3SmrfDySw1XUrmw6ju7+RbSGzjhNw0bnkADBBJx6Cq7RptdvutL7R3lbpUeOzz/NBohaqf/nWMnvwBjNazpvW3T/UMdxZwG+g02HxmmspDMqhvLuPOfftUfV9bm6/1xLnUbyGwurWERNJMjAy88Bu5OPTNSrvXP52B2WSuu7+yJaWR6B6yuINQjh6iis38SN5ELW8oY5DlDwD9D61XazrEGu6jqGoWVrFZQz3BIiiQKoOOcAYxXW9P9Z/7I3N9phvobwK+6WXbgSAjgcjkDtiuQ1PU4dZu7y9s4VhhlnYqqrtH3xSd3mxGyyRHtvxd8V2PSGg9XvqN5qPSotlVyBGZLyOGRiFAON3pXG2x84rfZ6q8PVItHvBa2skkYd+cxqQN2ADyfvVIpvQtJpWuX3Vb6Pe9PvNLaS2fVkMpN5cpIWM0ucNyDyR9KkXR6Zfoy1GhWN3N1VGylb4tJLLdZ7x7M42+2Bmuj1m/6R6E1bTOpfh4kkGobjHLHLcG5UHGTMNwyrHHOOOarYbHWRc33xK0OexspZmJaxhz4+xvxzAdlBI5GQeaunkuHzQq45vj7dvic3LFpS6BeXPUTajZdVQqFiEsWxYefwMjc+uea6zXJOkbPou2n6b3DqW12+NeSSNvu1OMqQPKgB5UAfrVZcaz+0eoNO6y6zhsdZt5F+W+TuCGZiB5WkXPPc4yfaoF3p3T971HbXiabqydIqx8eOJnMUcp/DEJBkD7ZzipebV/ncRpe3zvOcudWvdW1BPnJCUjhbYhYtjtzmoXZmNdf1hB0vJr9mehtObT1azcXMPjO65BGDlzkHvmuSXzEk+9Zyd3e1jWCaVmyyS6uLN7B7O4eFyWGVO0nAq5l6Q6t6ssLnqhdNl1LTLKLCTNcR+IAhyxWNjuYA+oFcxe2Bvk0+Pf4QDuxIOPQdjXX9Ax61qt9/szZat+z9PCsst7K5aOCM5ydgG5jzwoPJrSPRhdGculKzLObXuuNY6SGq6jps9703YyQ3F0b0IEZVYY2oTl8EgnArHq/q3UviDBoGo6vZ3lrpNnKbZtVht3WMLjiBWIC5HcL6ZrGw6e1LUNW1TpDqDqaPTNPs4inzxJl+YX/wBmEjJGN2Bkk+UZ71Sap11qBsJOl9Tbx7GB0juIRhYvDVgTtIPrjuADzUJXeSX9MhtpZt/2jLU4ui5+t9OexspxokcSvd2JnLtLIPVmznzdz2+lWlxL0dc9d7mtbmz6XeHxJdKtiyDef/Zq/LKnr3zUnWdRtPifpFh0v07pWi6ZcwkSW7R7IlhjX8RZgMnOR+LJJrlNc6RuejtY0zTuqLi2vLSYF1ksJf7QDuN3oasrPJuzId1mldFdr8Wi2nU72/TU07WK5MMU7ZaLK8rn1x7moZHA7/5V3/W3VWj6jomj6PpOjQ6WtsxaBViCsq7fMS34mJ7kk8muBcjGDmsW29UbxSWh1HR19pem6la3GuWkd7Gku6COVQyeKF4JU8EjnAPFdV1LosHXOs2Vv0NpQ/aly26a2Eghh2geaRi3CjP8zxXzGeG3mt7VLthHF8xnJOBnacAmuv6I0TqRtSun6Bh+d3IovWF1FGEPJVcyMAeMnFWSSjtXzKSzls2yNmgdP9PfK38HWM2qPqClol0+2IRIpASBubBLnOMDgVYWvw3sNO1GG3+LFjrHT9nLbB7VIzGjTOfXd5h5e+33NatL6B6k6k6g1bV9Y1Gy6f1G0m3x2t0d8k80YBH4fKM/3jxms9Ttuq/iB0yuuahIg0zSblt8ck4juZgOJDEBnKqBySR34FS2297x/ohJWzj4f2adAFzrN9qfQuja5Ba6OA4lvZUGwoMYLKvmZ84GPQ5rfpeo/wDZVcavonUEOn66dShAadm8RZbYfhC7gSvPOBg5xUfrDrbS9e0vSLbRtK07Tru1KrA1sibjAByrbeW98sc5qo0zWbrpzU4L6SG21C4uhiFGtxMYtvoFzwec+vNTstrNeRXaSeT8zlLQ27Xepm1h8CNpQQjE7lBzhcH2963RkDcMZAwAM10PVuna/b69eaj1bbLaX2qRpdBN6lgvIAZV/C2McVzUPIJHGWP6VST2nc1hHZVi40bqHUOnrqKbSHaNpWZJCvsBkflVjqGpv8QOodK03XtTXT7S3DvLeyw7tinuEUnk/cjmsOkemG6q1CO2TVLXR4YizyTXKGTdkABQg5Jz9sV1C9P9OdN3+qaX1aseu39yEe01GxLxm2iA8u1c8HOcg5zUxlFeJWUZPwJNnps971HN0fYdXtF0sYFI1SYAssHovgjgyEjGA2B3rXpli+va7q3TnW+vT2+k2q/vp7e4UtcH/wBmEyMY4BOc9sCvluoSDRtWuLhLl7qOVtsZkwxYDtxXY630NcaLoVn1BFrdpq2o3zRx3OmwQeaBm4RVJ4c84OBxWjja2ev8male+Wn8C66f1rQpr/Q9JttR1DSZoI2uNQt7RmRbVjxI+BxgAnuBxmq3VND6f0w6fPpOs3uoXj7lMc0BVQm3kgkmu0teqetvhnpsemdUxSQR35JRJJPHEqkYCeQkcAY2ntXz++6P17QNTt7rWrM21tMH8MA5CbhkL+hql207v+y1kmrL+jUoHORznFS4rjSrVreTqC0+fsvGA8PxTHltpwcj2qEAwY575zzU+x1yHp2aK/u7C11CNZdnh3UXiIpIPm2+4rGK6SNpaE3WeoNOh1C1HQWmDTD4RN6qXbvv/ubmyee/FaNAXq6fVrzW9M0i6uJtOG65uol3CIFeMFvXH513OkaI3xF0+bWdHuNE0EWv7iC3Fs8Xzz5ycMB5QvbLDufpUKx0TrjRunptbjljazmd5pbO0vAZ4VXIyykdiFzkE8YzW+3FLZ495jsNu/DuOS1PqT9qazpurdTwi+t7EeEtvcgAMc5wwX0z6V03UfxC1brnVl1PT2it1tolhuDARGXP8AwAOFHAwMAcCpdl0fqPR+mP8Qb250i7u7lQ82mNEZmtYZMYZmPBOMAgA4zXManrkXVmoi+0sWVgIk8N/lofDSRic5wR6e+BUrZk7paEdKKs3qc/q/TXVWsftTVo9Mv721WQi5uooGeNcAcbvXAxmp1yd0FuyAqojUeb1G0V2Whr8Q7Po2dtH0y9vdGjEpjukZCJASdz7SwY+vpziuNnLtbL7YB3bcEnFZVJuWWWXYaU4pZ55mNmdx5wPKR9uDX0yZOutT6S02LqvSrzS+n7yGKI3zQRsLeLA8/4ty8Y7gd6+YWeVJPOQpxxnnFdf1H/ANocPRsr6/pV/Z2U0KLLOQgjROMMSDkenHFUirvh5/gvJ2XH52myO4/7L9Ss9c0nqG01pCpt1VISjxMw/H65OARn6mpOoabH1Rr1t1J8S7HUNM0G+hUWzW8axSX7KeT23KMfxFefSqfT9P6ci6UnvtR1C61HXYIhPZbeIIHGDyAMOWxjJ4+9buo7LrTqHQbO+6jguLLSoLiJ5ZbmZUlGfKCiDDYGe9bZXvx0v7GXDTLW3uTrXpTp/X+qdQFh1LPoOmaekT2ULoJnWZlJO4kg7BgcnJ830rmLuNbzqO50zqm+lkh0+PdBPalpVaRiMMSTwNucCui6sHTWn9HQLoFqlteWTAtctHl7lmPO8nv9B6VjovxEOmdFzdO3umpHHeRtJdx3MWyS6c/hkOVy2Bjbg4AFT0krrMjot2eXE5vTrSzsr7U4tOleZCYy5k75wasg2Iu+MjB4xXN9JW72aXyd8ypn3HBrpkOI+SFFZVN9mlPdRja9FdRdT311N00bZktbdAyzXIjy3mIAB4zgZ/MVI1PqDSrboiTp690a1tNTicLNqDqpuvmdw3OZB5sD27YqnutSNjqrIlw9uWiV2XxiFYAkZ9O1WegdP6npt9Z9W9YaDNqPSsrtNbNcIvhzueEaRc52eozgHirpZJy8ij1aj5mzWOn+ibPRbE6ZqGrtqHzMQu70ybvEUk7tsePLxjHJPrUHqaw0j9nWx6YuNQm1Evh57vHMYByAvpz6iseo7mxGstc9J6VdSwCNjLbpmRLctxjjIUc9u4FSdX6NvtD0q51ltb0xydhkhRtxRCQvl+orRZWuyjzukih0qHSda1axh17UZ4LWNXbdakB1fHAx6c9/Xiu70boy5vdUu4egdWW4spYFa+k1e5FvtIJCDJBLZ5OAOMVzfT+j9LaTrUjdQWbapDNbssKeIylXyCZAQQS2O33q507pRL7VdTn6H1WOxtkWJZrfULkrJJIckAMPYY7+9RN30fsTBdq9yjvNNvtGe9stZWNL+O6cyCN96YONu0+oIqEgxIARj7+tSLm2u7R7yHV5op71btw7xvuXsMYPtjFaASZE5JAIrnlqdMdDp9N6V6L1XRJZ7/W9Tt9anllkcRMpijbcdqhCM8ADnPvVP01dW0ur2UXVf7/SGVmnjDOGucDyx5B8oJ5OOcDHrWzSNBsJ4LmU3h3zzS+LEBgd8EcfSrfRP9g5uiZ7C501rrUGikP7Sd3MsMgJwUO7aqjCjGOQPrW97R4v8HPrLgvybOptbsW6k0uw+H0MWi2t4jGSzMjxQwsD/ad84I7++KwutOvuj9Zh0rorWJeoo9TJuJHhQQv44wGyOfLk5BzkCqPTup9FHRK6Ja2lrHe3ciF72bBmE5wqsWxkKo9O2M+pqbqfReoWHyU/TmvDqHWJG8JrWCAoBHgliCCOBjkUtbJ5ePEa5r04EHqToyXRdSurHVdfhu5LuJbnZbHyKzE+IpJ5O04Gex71JvuorKLpu209ulrGBhFHFb3fyLK7YIAl3+ue+7nNdL0r1c3Ruj6jpfWGh23z18Sbj5yBMG3AwFUvzt/ET35P2qk13X+sbbRLa3uY3i0u5ERtQpVo47YkeGPoQuBjjFVvJuzLWis1/BzMzhpJduSFcjJ+9YtqFzpr2M9lJ4UwmIBHfG05FbrgEyMq5J3nGPvWWn6Pb9QanpNhqN2bCykuiZ50ALKgUk4HvWUbXzNZXtkd22lRXnSEGvR9Rueo5E8W4sjJiMQjOEDd/EHc+noBxk8HbquvWTatc3vhOGICiVshQueeeRxg5qXqVjpvSfW8GmpqE+pdLviUKwHiSR5yY2K9gexwexrsrjQLP4paqq9JRaT01Hp8Rku1IbwpdzDwoyiZyeD3PYeta32Fd6Pj2GTTnktewjdIWsui2djqnxL6SeTRZ4Nmjreg+Gxcku4UHcCQPKSOOSOea3aXq2o9I3PUOs9Eafdr0zPJBBLcSSNJ8vIFZvB77tvmz24yAa1Xdz1T8QW1HpmO0V57Ft94Z3EcUOw8fvSe57KB3zXKaBc9Q6Zod3aw5t7UzTx+A0g4k/iDDue1Rs7V2/sL2tb7lj1NoHU2vdP3PW6NBJaXzNKIhPtuBEmF8Qp6KcHAznArn7sf74yjI5GPtgVAk6ku16d+Ukv+0Ih2eIcqPbbUy6GLqQAkgcZ/KoqRccmWpyUtCXaHKoM98447VxauMoGySSe5rs7MkRrjgcjJrj41L7NoIfOSR379qmlxJnwN58MoZG271bvzyD2rIbhuIbcx4xjtWEeAXLDCrkcevt96kLC0yllLpGvdhV2VJNoSupWUYO8FgSWwAvc112oKPmJNvPmGMDHoOa4yyctqFkBhsybe2DnFdpfbklkZ13ZYfiIz2wa4MTqjro7rIk5LBjnzAc1Bc4ZiT29fzqbMMFsEgEDFQZcEvjkHHP8ASsqehaZtTAeP0PiqP518xuOZ5f8Avn+tfTYSrEEEH94AePXivmM39tJ/3j/WvVwesjzMZpEwpSlekeaKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQEi0/teamhsPkYP3FQbX+0/Kpq/irnqandR3S7t3B8LcMYXH3q8scxSQFlKqwOMjgiqG24eA5q3tW2hGI7A149ZHr02dneBvAQOFZyBznHP296+eFF8a5Yptw5HJ5zmvoc58W1Vm9QDn8q+f74fmp1ll3ESHgDPNUwmjJxOqNB284Pn+3pWrdhPw5Pr962XN0UmCxxptbuWJwK22tjJqUvh2yjeBukJOFFegu84fAk9P7Xvo0yQ7ZBGK23aNFcOjd8nitWnWMmnatHHIBucHkMcGpWqJjUJe/BxXPPrPI2jueZAuITPBbW6TC3M12i+KeyD3ruNa6Q0np/Q7e46f1mbXdQu5VS6hcLGgGMhk7k4PfNcksVq8K/PuqIsoZCz7RuxxXVdJ9MW3VmpbZ+pU0uCzTy7IfGBc/QEfmc5rZO0b3yMWulaxzVvear0bqreMr2Rul8QGN8Aj0zmr7RZtO1LX7jUeu7O91IXMSG3KysilRxkEfi7d/Sr6LRumV1G+tOu2m1y8t2McdxazyRxrGfw4ByQce5qk6t0XXbbTtOGo6Ne2OnkG1065dlZUizldzKcg455Aq23GWWj+aFNhxz1XzUuj1/d9O67qC9Gwaj+y32vyslxufHmBY8/rXOt8WdV06C98WZf8Aet58BUwvm7ggEAfWpWna/rHQ6Jpem6o08KsVheHGxyefWvNF6q0rpbT9RttS0vTbm7nWQzzTxh5mL57ZBx39Kqor9tyzk+2xzRcvbWzcZaMHArO2xvGe271ptVreFo8bCgxz6elZwABs4PesmzVLI6TT+hRddJ31xJ1ZbpMImkFnHAWaQDkxs+Rjj6Gq/Udb0rVOntHsNA0yLTZkuRuvkh3gKRyJGxz2yB+lbrHpSxuumrwJ1RKuoPGxitUsw29j2TeTkA9qmajJ1l0/0Yug6po9zb2NxsRQiI0Ik/hbepOG+9bJ34/gxastOHj/AOEvX7nT+i00jUOkdVv7ua6Xw75jICzSjsUQDCrjjHet2i2/TnUDPqnU6XUerzsRKyyeEwxwF24/Xioek6b1F0GLLqHUXtJzjYmR4jwH3I9M+9Vmra3P1T1u2qaldXFpa3ar4/y5Iacjgtg+pH51KX/pDf8A4RE0i3bq/UdL6c06/wBcSKQPHHDA9w5BHIbYM8H1qnuN++5FxaPp8guHDWzxlGjIPYqeR+dfQX6sselepnh+Hljd2EFzAjXEMIkmZnGR5jndk9/zrida1O71vUL2+1KCS1upZ2MkcqFWGMDkHsarKTZaMUiHaj95yOK6nS+q9I06y1fS7/StPme5TbLcyRhp84BUocEjH0xXMWq+fn+fauz6L6n1Oy1u40jQtJGpXsuNsUWnieSTI7bvT88CqammljjNCj0vUNatT1JPc3NngkRJLhiufw+4q56jew0zWLiy6XadNNuIy0MDb5JI1P8AAT3P3IroNG1600zT9T6W6n6ds4r03kr3MEqqs8UxOVZD3yBjG3IqR0T+0vh/qcsmuaS1rdXWHtXkwz+F6d/WtnK7uYKOViB09010brfRzWGq3F1DrbjxI7qaZgIG9EEXbb9+a06d1fqOodOzdA2LRzQITGIEjIRWU/2ufw5zznvUTqzUGuuqpLq2cQ2czq1xGiKTnPmwceUmpXVuqdMWVlbah0roy6NqoYKUSdikyevBJOfqKi19c7+hOiyyt6lJrHRN/wBM3ED6ldQXQniICocsrAjvVKgHAIzg5IFbv9odR1rUd9+r2sUUJMUe5sHkZ5PescAuSqgA+2cD7ZqlS6eZpTs45GeoWtxOmnmzfwpRI5z7DA9KuB0brmm9PDqaHULG+8T+0s0Yh4l9GOMfyq76G1XRNJ1e1m1/TItTkjUtbJMniIp4DMU/iOP0qbqXSes9X63rT/D7T4I9FMfiSrPKluI5CMtDHuPm+igZGaQqNKzyRE6aeerIuuaHoC9MW2rabrt2eoVMZvyxUxSISAVjQglSvoSTmvOppehLHTbG80bQ5INTiuUYS3czSC45829WOG/LFRjoXTEPR92bm81qTVoYj4G6QLBHN2Axg7ucjvUjQLnSn0CXpiy0DS5tY1RRElzdOrXBl7g7jyigjJxge+atfjmVt4ZmnqPXLzUNDs9ZtunYtP022mMT6jaWHy6MxHEe4AE888ZxWFrc6vp2iyat1boAOi6sDb2dzcKzkbe+08sCTj0GcYFOsNc6o0Wxh0TXoJiPERShKNDhSCNhHcHH0qXc9Va/1Rax6Fpf+/39yVaKLw9gjCebcWJwMY4xRaLJW/A/U83f8nKat0Pr/Tr2+p6vok2nabeFhbzTOu45HGVzuX8wKqyvmGf5V0fVGrdSTPa2HUNreW0Sszs068M4HoQBn8651eWOMt6c8cVSTb1NIJJZHS9K6tpuh6jbzazpsGqLvIhinQOivj8W08E47Zq26u6yhfqC3m6V0yHSi8Ja7hslW2Vmz5SQoK5x9K4zxFRLfdGZFM2047jynkV29j8PIuo+mZdYt+pbfStQEbeDp7wZDRrnl5M8MfbHHFIqK6UiJbT6MTik16fUNTKRXix6hfThHeQFxEexOTwcCuo6r6YHTmikaB1Gur6b4a+OJF8O4XcfOFUEgjnhu9XFx1Tol50SllNolhFHbW4W3u0RBIkgGAQ45Pm9DnNVsHS2vWej2mvQanp9xcQTRsml20W+ack4HAByec4rTb8jLYy7SJr+pdJt0rFaaDoVppeohleO++YLTeXuc57emMCrzVB0fD0jbXuiC8/2lsYVf5+S4dmuGOMoV/CgznG0cfWsra31Lo/qPTNW6l6ReziIka1+cij2tMw9FyRkDJAP6VW6ppOn9U9bafaaDNFoYuTJcXMVyS8NuuOWEanOSew/oKrdPjlrqXs1wz00ONm1jUdYnvJdTc/u0UJvcuQOfU96jwKRkkZxkZ+tdL1r0sOldYltU1W11SGRUMUkERiIHOdyEnH61z8OVRhg7c8ZPrVJSUs0aQTirMzghkeS28GXY6s52/3+BxVzZQ6Db6zP/tgZbyQxo0aJOsKontyKz6Vj6fbUrc9XWkt9Au4wwRytGGfjuV54HoKvdXuOnNC6zt9Z6W0S2tYo7YF4LnfMkUu7IkAcsc4HrmrxllYpOOdzHp/qvp3QDqtpcdP2b2t5K0iftBA8ggIwm0uAQvfBXvmo3Sdp01qGk6hea1fagZw0j6XBCPDS3KklXlbGXxgYGQBnJzXQXvVOu6nfp8QbrSYrzT0C2SapLB4h8Vct5VPmxg4DbcZ4FV970bqHXL32upqlrZPqgEvyxG3HGBuHucc/eit4EO/icxdz611RFpMS3SiWO5QQrPL5d7cbi3ooHNdB1v0fq+hDTbvUuoNM1i0YshS0klJRyuRkSAZH2qRbab0pZ9FTWJ0y7vOpohuiv1lkZluBwERAdm0njsSa4e6uNYOp2dtrdjcWCxo/kkVx5tv/ABf0qW9q9srBLZavxsYrnv25rG9MKW0JuxmP5hfTODg81nwWOP1xUyDT7HVXs7bVpWt7Px1MroSS3B4/6VjBpSRrJNxdiQNH6osdHTVNF0m6bRpFyty2cDOfMEBzjg9xzWxIb6BrPR9M1PxI71xALqZmCxqwy7bc4OMmpM2vXmmXsPT2l3jzR3cny1t4kxSNI2O0M/GcY/pWXxC6Gm6IOn3uh61B1HFcERBI7d0aN9uSFQkkrgHmuhPNJ8dDnaSV1w1JHVPS+oaHbR2L63BqGhvJHFLdxE+IYieRsJ8pqX1Ha9Jp0ebDovT57fWPFjSK5Vn8aeRm27CeBg54UDjvVT0Hf6Dr17cp1paGTToY/wD0K3eRGllPAZ25YBe+B3OM1O6d6E1TqHqC/t+ntShgsNGbxIb7UJm/tMbliUf3z2+lVeW89Cys1eK1yK/X/wDbL4e6V+zdWil8JYNkU8E4eEqeO4HJGcGqd1b5VMAuBGjFgPcDk/rVz1N1Zqt3pWo6PfSTmcApNDLFjYR3JOefyqklGYockKvhqOPUgCs532U3qaQtdpaGdvHJA5SWNo2xnawwe3erjResryBNNbXP/OelrIj3MM77o5VHZSp4ILY75qmts5JLlmKsdxyTnB5q/HTHSl507AvTtvqVxq5VDbBpAyzTnGBjgDJz+v0qIOPEmalwLfUtO6c676usI7SX/ZS1lzPqS24UoEGAGjiztDk9vTua5zrQ6hpmproK3cms2c8mVuIC0jyQBuSE5Ctjv3qbJ8Pdd0vULP8A2nhHS1q0bMLptlyJH4xGQp44yefar3QRq/SuvXl903He9SD5MLLe2mmyMltG2QY8KONwz9xmtLqOjv8AO0z2XLVW+dhQ66/SWi2Gnapotvqc11Z3UbxC+mEsUgzyNuOeOfyq5ur6++Nd/bQtqNtZNYgGfUZ4mHhR+kYyMsT6KBgYqo6f+F971eJ9dlu7TQ7Iu8lna3GWll5IxsB8gyO57fWuc0m/vW1ZrHp3Tb68mK5ljiyrZBxxjORmrWT0ea9Ct2nmsmXl30u3SGpahZPfDUxI6SxzhNhZdp4ZfQ/SiHLNuY7cDgGoEceqx31+Nfs7iwvPEQiKUHIXb7nk1ISTa7AcZxXPJu+budEUrZInab0foPVGsyHrDVLnTbW1tlCi2VTLIxJ/vkAAf41Onutb1+41Do3Tr43NsU+XW4lKrH4IUEMR3zjAx71z8VnYT9R2ZvtUubONoXDG1gEjNtPbDHvz7f5VO1eGx0nqC3g6Pa6YXoTxf2jcbX8ZjjczLjAIwcY9K1jnbP8AoykrXyLro7q5OiNGk0Z/AtZIXIvAWAZz9u7cdsVy/RfWEmhdZi51u2MaW7ySWlvcQhSJCfIXB74ByAeM4NdfqujWHw11Cz6ktdattcupCqyyy2eFtp+4aNMklQOATzxmtVl8R5r271rVH0yC6tdSVLWbUrm0OyYqvKBmBIxnPoT+lTdSTaV7kWs0m7WIfVus2/Vmu3l5Y6OGl+UVr64toS4iO47TIUBVSR/SqfprRtN1ie9mi1+DSo4di7Wt/EWdiCS3cduBXRdGfENumF1e10GKG0tb26M8mAwBUqAB2JxweO/J965/p1dF1vVtYutUOp6e3j/7ulkAoIOS2VwfpgemaK8U1okMm09WyLc2X7OkuLVb39pbLuQm5CFQ5IB7elR14lH0IqRNbWdpLLBpU1xcWi3UhV5yC5PGckcE5zWpV8+e53VhJ5m8VkdX07oXRN5pU76tqGrR6jdzyeOLe5CRjzkbQmMny4Hfufyqp0q36M06zlttQ0m4lEMkoa4muvDEqbjtYjt2wOB6VL0/RenJdEnkt9U1Wz1aRpXuSjjwd2SSBgeXA4zmqTShP14dK0vQIo5tQtYkVWuFEUUEaNkPI54xk+nvW6zvm7GEsmslc6hNQ1bpPTrHRdS6Yg0yW/gVLNJrRS8ySZ2HxPQkEEkkEetUcnR3WXTPVFhZ295DqUwEixTW90PCZQuW2tx6cZPfFSet9c1+O+g0TXkuZL1JvFVlIkTy+qEckH344q16ZstD1PQJr3qXULqDWsONPgtiUS2GDlpQcs7NjGMgKPqaJuK2u0NKTt2FPJoo6r1mSH4galNpEFjEgjUgSzShidx3HAAGP51S9T3q6brcGiaZqMl9pxmgMby53smRw3p6dwKvLrojq0aEeoLqG1Rig8OCOU+MkffcQc/fnmp9/f8ATY6R0+yTSYZb0TpIuquMXFxOzDkvnJAHAXsAPvRyt39xCjfuOQumy7A+rNn9a3WOmwarf6Zb3eoNp1v47F7lU3FRsPYHvWi4GZiDzyeaten4NGk13S/9qY5ZtMikeR4Y5TGZmC4C7l5H5VhF2Zu1dF5p/WLfDHUL+xt9Ut75dShObtrfBeAjaYSACQp54HfJ5qFedL6103pE3U2nXFnFaarOkjaVDK0csCOcIzYGB3/Dk4BrX1hpXSFxrckmlQXNkZbUSCGSZ5URQxBbc/mAxgc8cVTNcaleQTHZ8zpaKXV1VjJJGv8AFycY49K6YparzMH2PyOn1+1k6T022u9I1qTWdRvZEOo2cQMYSduAIyMl1AAXnknJrb0n0/pMUWoP8SoNa0/VLuQmGySU24WNgP3h8pZiTkYyO3rXG9O9U3EWqrqtldNELXd4Ub7f3YIwXHpu+vpXU219pvxFiu7/AFzWdUtNRixbWZhI5XuWkA55JwAD6fWolGUVZ/cRcZO6+xhN1R01pXSt5oh0S2mt4WmjS7ntx487biA+/GSc8cHAxXLXAKzuSfxdsc5q/vPiIYejx07cLDc21hC1pbxm23EkEjerdhk8k5qku0IuHDYYjAz2zWM1Y1g7ki0JiBCMM+oxx9q4hXKgqc4Hrmu0tQFUnJJ49KgnpuOeS2i0yYJI/wCPxW3AD3+9WpSSvcTTehRxgFe5TC8EDJzW9AAh3HaoUBcn9a7K06Ctr6eG1gvp4JO87Mob7Y9veug0j4PzXeu2scV/a3MEEiy3C3KbEdBzjPrkjt271aVSJChJnz2zjX5yxmcMzeIrxttI3DGMA+vvxXWamPDncMCygjOfr2rvevOord4LbSb6OE3EM8Xgnjyvnuv0xXB68yreBIVKpIinBbdzjvn6kE/nivOrSc2jthFRTK+cgszRkr2I+1QHyWO4gHGDxUxssrbWAY+v5VXux2/XOO/0qaZWZuhJ9u8gOa+aSf2j/wDeNfS4ch1xj8f6cV8zf8bfevVwesvI8zGaR8zylKV6J5opSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAbrXmUD3qcn4uR6VBtv7QVYR/iHBPesKmp3UN0srZ8eCRjAarqA+UA+5+lUUONq8cg+lXkS4XzD17flXk1j1aZ3g2jR7GRl4MJJ+tXHTqdLHQp7O+0dHu3DPIZIT4jE/xAkZx9qrbW+W20fSpCqv8smSp57tX0nQetTrGqxrcPDE8EAAZlBwp7YrghodU7XufJNO0NtNtpLa8tIohdFmjll2tuj9PtVFYaTe6Rqd2LRobmzK8uTtI9gBX2+5tNPv+r5o702s4NsHWV/MqgnHCe/1r5zregWug6zqUUOoNLHLh4REu1efpXdCpm0cUoWt3HNJp1183Dd3QWNQfwBgTg1F1lQNQXK4JQk8VcStfrdW0eoWM9oHYcyKOQOxqP1FGBqEBxk7MHisqk7VEu41hC9Ns09O6pbaNqNrdXdpBeKsmES4jDoGI4JB4q36n1pde6ohudJsrOxJtwsscMSJHK4PBAGBkD2qhi0jUNTntLXRo43uWuRzKdqAY5LH2rq73QE6W6q02LrC10m8tfB8S12XG6GZx3Dbscj2NdEHHXiYSUtOByVp05r+tarqsfTWmz6yYE8a6lhPCEDJHfBwPSuy0GHqX4jdM39rp93HYQJH+PUJAokkXske3JLZ9TjFSrPUruHV9SuOh9Euf2U7eIsemKph8UDzKPQD7VW6RpXUkWmXOpRvpqF2acwNdmOSEE5YbRwSOas57SM4w2Xx4kbS+ltE1HTmbqe+1C51JI2ULHxtkHopHB5riJdJk0q0MOuWl1ptxtJ23cLRtJ9Rkc19N1C36ettNs7/QtMvNN1KGZZQY7x3jnOeWZGJB98ipmvTa/wDErpq4isPlrxNNzK8lyxjJIH4I9w5b6Dj60jVafcJUrrv/AJPnMcebS2ZcYMYwfpWcIPictu83DYxmpUEJNnb5QofDXy49cVlDFtm5zwawc1c6FDJFhpWk9OQ9N3ck82qy3yBpIMT7LdJgcjgd+fStN/1Br0vTplSK5NpG6NcbnyM54A455rdZ/D/WW6a1HV01HSkdA88Vm5YyMuSfQbQfvUvTLa+bSbe/1K4tUgt2Rmt1jOZsj8RGcADvXRtJZ3uYbLeVrEfSdT/2rI0jV57jQbSRQ01xKRI2M9kAxz963XGmdO6L1h8lds/VGkLAr2guV8Ehv4gxQgMfsexqz6iupNXsbax6ZtUl1guGiNuAWAPcscYx9DUe36hkt9HvOkuqIUa4jLPLaXEZaQSHs6sBj8wRRyb0Gz2/O4h6r1HbdKa0170fpV1o2lXEY8V7VS8fjDuAzE+nfB4rkrjVp+oGn1O7LtLcTMSXOT+dfQulrzU9X0lOkNNgS5cxER20yBIIv+NpDwo+veuJ/Yt1pMcun6gqi7tbiSOURtuXIPcH1FZykku8vGLby0Itoh8Q7+B9quulbrqCy6i1X/Z20vZ4pvDWRrSPcchexz6fnUWztzvJAP6V0mhdPdQC4vb7ShFdWszri3S9EUoZVxkj2NVjNZlpQeRDj0vqrVOq9R1p+n7x7jTGDXpYLugULwcZ8xAweK96v6ku+q7CGLT38We2fcZWhYbf+Hn1qfpR6wsLe51q6s5LqKd3lEVpdKzxAcbXBBI7d6mr0eU6al1qTqOFr+Z/mLrTltPIkZIzGkuc7scZxitttJ58DLYusuJ81iDw6U8sV2kkcjqZ1G1yTnBA9j9q7S/vdG/Y9jpvT/SlpPqU13EYPlx41xIQcnnvjHocCuq6itbTrDR7LS9E0HStM1eJlaxlaNLdUVT5sso8wx964vX7XXOjdRsYrjSY4rh2Li5t7sOjKPxbCOx59alVFPxIcHT8DZ19J1CdR0+16n0W50kRWbG3E8CxZGRwApIrixH+8wvt+tdFrHUN11BfRRTpMLa2t3KPKRl2JGcAcVUpb7iQR3rBytqdEY7WaJmm6LpWqX1mOoLq5t4YYyw+XXLHJGefSrfV7+Po6+S16JF3NYTReKqT3RBSQ988eYHvxio1h07Hq+r2njaw+jLbREiVFJZiSOBireDRY9K6sh07Tri66ivrlVnhldV3BiSMOWbAAxnOcYq8Zq2vkZSg76eZTadD0/a6fd3Wu4uGkRisbXCoqyt64znIJ7YqT/2aromiPr9r1Ja3eo2MYka2VCN6keYb85ziu10a9X4eXmp2etaVoyanKXe8m/dXDzK3P7uQrnbg48nrXNnQtQ03TItU1a/0O60hJQ93bIMTGJjwjNjkcjOKnlHfJkcmrZr+iR0TN0z1rp0k3XBnlVGaOxt47x0ML45lLfxH2Xt+tRNK6G1WK11HX7LWrRxYyyR21moKz3UAOC+4eUNxnae+DWfVl/Dr13pVl0Y1npGqXIEMi6a6JH4X99tvAIHr3NQte0XVPhybXT1vbXUtNuWSNdURHQx7/wAWVJxkc96lSb0dr8CHFLVXtxKXX+qbvXHs4JLieWCNXP74AHOPYE1Som45Hpz712nVHTnTOi2ul/sO0u4tRcsGmuJyxmXbydvYflXMpCQCM4GO1ZSqR/Sbwpy4lt0rZaJd3sH+1Uc09mjmRYYnMfiNjsxHIH2q11iPSNK62tLLSsjp28Hjm0Kb5VQd4g582Dzz3xmuUvYLkQ2HyLvHKLoEundV28mu76h0/py56e0+K30mS11BZ0zqQmMl1MCfOzEscjHbgAVaMlk29SkovOy0Od6+0zpm2u7bU9FsHsI7qQqLNYjhTj+AHkj+lW/T+maWvTz315ruonVYojPaW8EYijjccgO2Cz8cY4Aq2ubbp/p2XSdV0nT7/wDattLk3V7cmc+HtOWK9gB6Y96rLzTNX1aafXLLSNSk6aZXnuZ40KI5JyWWIkFlGParbe1FK5TY2ZN2OW1vqefrHTbaxkF7ctHKJZLVk3PwMFuB2xnmr/XOrNEtOlrHS9M0zTNPlSYNHNFGDO394yPjcc9uTxxgVcaRpOudIu/VkVsmn6RfRpDG8sgEyD8QaRBkxq3p+WajPrOnw9WWd+dPspWkSTMjR74fEbHPmGM9+cYqXOLdkskFCVrt5nzk3x1e+u5TIH8MIAFBwo59+a2GP92CMZLetdn1RqEHUeuXD2VtawSRQoJpLZAiMeTjgAEj6VzIiPA9BjGaxlUTZtGm0jQ/hJJYLNHIzvKyxeFGXYNgdlHNdFbfDzVOo9blspVk6cjNqrmfVopIRMSeCqgZP3rHpzTdcl1mwvembV7mawMskmxlVlUgLldx5bngCum1XU9Z6lvVe5knE2nER/L3iFXnL92GDnAwB35rSNRpKxnKmm3dEIahrOiTydJ+HcandxRCOO0soPEjnUjgqx7A9+RmuY6b1PWEjntJ4limtpCjCRCrxHPY84OPrXadM6V1ZGuoa9a/Jyl5GiSC3vBDOUTjgEHy8e+agaFpk731n1N1h0zNLpt07GNrwjwJ5yf4hkFhgHG7ANWU0k7lXFtq3xHD9PjV7bqQ3Wlw3WpLYSSTyS20bS+F7yYHAx711GudZXXU4s7V7xLpYi0jF1KueMcZ5OPWupHVyaFqmpSdPQDSdPunDvFZQ70dyMFVCnGPpnHNcd1H8P8AU9D1ey1u+OniPUJGYQW0xd7csCQr8YDe4GcZqJTjPOWWQjCUMlnmUioe3scVsXTbrU2tbPTbKa/vLi7jSGGBcuW57e3bueAKl+AS3cDzH7dqm6ZqV3oN3Z3WnxTSyvL4WIYy7BWBycCsIz6WRvKGWZ09rFo2g6ZeaX1J0tBBrMCFLm5unDXEcvoQxbvyMYBzxitegN1j0BGnUOuQ2VjalPARi674yxzudRkjIGMn9K9urWx+IHUcM/VV5fadDYRDfOtp4c9wx/CF3/wrj8R/KozdP9RdUzarpunx2cmmadIrXFzcXTI12BgiONTxuI7gfqK2TVrPzMnFp5eRha3vTvV3Ul/ql3ayPMyogeyLQrIed7DgZ5xk1F1q+j6QvnTpqw1M6Y0YmmMLHEcp9GZlI5Az71ddQajf9aaVo+gaBbW0dzayf7mqZhS0jH49zcY+vJJNb9Hvdf6H2dJXwbULm6LTxxWMZuUvC5/vEgbsjGW7VKmrfgjYz/JS6ZYdJazod5rHUcF9eatfQs8rLcMkcAAOAMfiIAGSxxn0rk5lL28ITlAg2EgZIxxmuyj6R0TS9J1C16u028i1OPfJepFqQ8KNySVAVeDgYHrk1zd3blLePYGVQoG1u4GOM1lOavkzSEHbNEG1RsNt4ypGfXtXlh8r4WkP028smptMgsrUFgxkPB5IwPXmpUEYWORiAAFPcfSux1f4i6pedDWsJtbl2lSKKwhawkCq3AAVtoUD22nnNTTm+CIqQXFkLWbDW7+WwsupxfaPZgtNFOzhzJOB5UG30wSc8k4rTZdY3Pw9u5tLj1K+uPGIu3eJC7biMANkgjhRjPp6VbaPPdQ3Yl6+6RjawEe22j1CZzGZz3bczLghewGPXvWzSbfStI1K6u7DpqEWF9P4ljcXZ8SKUqu2QRO5w4Vs8885x2q+2rWl8/krsNu6+fwc9o0fUGuWdxqUElmYruSSSMzXGHTk5yuQD9OKu7TQYem+mB1HpPUEjay8Cm/solWNFj74Ru7sD3zgHmsPnLLqrra+h/aUenadDBGb1rJI3cnGCkLYKqccFhwPvVfqUWl6X1aug/NAdNyQieGOd1WVoyf7FpTjjjvxmpcm8tCFFLPyKNNfm6ouby/llkdVkSKNnOW2haZw7Fuw9vWpot9LW81RNAVRaJOobY4dd+znDDj9K2Q2TPmR0yhICeuT68VhOcVI3hCTiabXStS1vV4LfSdNk1FktmeQQhVaMAjsT3/rVr0toek3U+rXPUOmy2+oNL4ccbHzRYHlY47k96abqmo6ZrEr2kM72rW4+YlgRXKHdxkccfSrvR5dE6oOp6jrlprC6lKWtxLDeGEwRqOGYL5cnk88DjmtI1Gombp3lkcLe31p+07zT9QvYkhtCQCU2rux6n1YV09le6/0V0fbJrOm3dhoFxvlt90CyLMpOdxGfITkfiArPprXrL/Zl+ndDjsma9BttssWZrh2JCuxx+E53ZzjHNbuqpNd0u0/Y+sqt4ZQqyXKThrWVVwSV4yScdj2q7nd7NiijZbV/nzwK7pq71LpnRopNYsG0ayv5JLq0mu7fEcyMckhsenAx9qxj6a1PrS61i/0OTTorYFQBcSGFpZAvIQAeo98elWer9ZL1PpKabc3qTpessMKCNyYSTjdyMKB9O/GK3a5ouldIaELrQJ55/ATdeLcyt5yONwx2+1RtvXiNhacDgrXTLzS7X5LUIhBeQSyCRFIIBz7jiswnPHcGtmm3cmqaWmoTgeJPJK52jgebAH8q3i3I25U7SV/qKwnNqTudEIdFWJ8Fv0re9O3kK6ZKl+rSrdTpekb5dx4MZOPbAAqH8zFDaadadOWHi6yGFu8MRZZHPch8DjH51YaT8PtaW3vtfifTLi3ubia4jspJyk5UM2DnGBnHb7VcT9G3en9Lz9SprNq+vsvzh08QEJECB+6WQNktt9e2cj610bcVxuczjLssbtFuNV6T1NL/qjTYbG7uF8PT2mnD7VAy3JP4uc84+lV2paPdddaxNeaXqEOmWK/u52VyEnkByeB+Idh9TWy56V1XqTR21DULqLS3s1MtlbSsZDcvt5LEnyrjgepJrDTtC1HSujjq8GqWl3qk6m4k0l4iscKY42yA5Le61KmlnfMOLeVsid+1tevdVuentNt3udUVMCQsqW4BH9oz5yFA5/lXA6zpg0TWbXSryMSX0F5FHI8eDFgkHcreuftXZa4lt0v02dfttRudQ1icRftGJz4Vs24+RItvICg+pOeTW7WOndBOi2WpPNdt1NFPHcXNwshMDEsNsSoeyhcDcSSTk+tRyijnwLbDbtx/BwjrunYkYGTjH3qDq7TQDTmth4kpuDhACxPl+nNXPgnMhxxk1P0y4u9N17RptMtZrm6WVhGsEe90O38QHrWEJ2kazheJL+GvX8nTy63brBbw3l86C7NyeWjUYA5GSMk8Va6RoFx1P09eXf+0OnaVZ3UkwtLE2jtIIN5G0nIWPdg4HJAI/KFb6NqvVnVF/8AJ9PzSrbwxmZ5pI7Q5OcHzHHODx34JNSLDpfUEW7N/qUWhXMk7otg9sJ2t14ClZBwSwyeBWzlHVZPIyUXo81n3G7V+stO6oGkWEfT1lqV0l1H8rYWdrl9iZ3AqgB2gDkHuBVL1nqE3V9rHadLWNtFqVupeRdNTwikAHPibQPXHB5q36W6c6j6TLdUp8qkEds6RGGbfdLHkjxQn1A7Zzg1tgttZ1NdR6q0iK38CePatvLJ8vPcFeS+FzgZ4xn0peMX0eAtKatLic9caD0iPh/EGsLmDVre1zNe+M3M3qGU8AA8Ad6pbxMXMpIyc84/Kry4s+mdU6POoXNiY76S3MtzcC6IDXB7+Qn34AHpioF9bMty6kcggYHvgVnUn2vtNIQ7F2EeCIlHIGOfzrXJBBY2fzdnIzzxqMo0mdw/zqwhgJikDAg4JB/Ku/t7fQrroyDS59Nt7iIxq7NghmPB3bxz6e9RCaJnTZzWg6ZrV7dRNpMbajfTgbraNlURqPUsxA+5Pviu5h1G36f0qSz1rS2XXZE3XDbt8itk4AYHAUDGMcd/Wq3qbWNH6Zkgu+jbJbGWZlgKREksmM5wPUHnj0qfr+jdP9WPpMFpPfnUZ54Y7ibxiomi3ZdcdlyMgeoz61WUtq19CYxtpqU+o6D0hPo0F5dT3V11MpWT515+7j+EJ2C54x9M5zXG9QkNfKyjIEaZ9Occ13HxS0/RdDv9GbTbH9hkyBBHFOXgkAU+h7NnHP65rhdexHPCHG4mBOzfxY5rnk72NkrJlZvHI9BnPHvUJ+Qyr3zz96kSDeG2DOVB4OcHHaojsPMcgc5xW0DCRJj5kA7ebk/lXzQ9zX0qI5kUnuSDjNfNT3Neng9ZeX5PNxmkfP8AB5SlK9E84UpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgNtv/aCrGFm8VdpxVbB+OrCEkypjGfTPasKiO6hoWNqAcfQirq3IAkHrn1qktztyCMH6VeITkkjHl7V5VY9SmfSrOK0l0TRkuSY0kH71h3PPAq86rTR7TTNPn6ftLXTr4ypC+xfLJuOMtjkkVA0jo6/6i0rRzbaNf6napB4kkdujeZs+U7geK6O4+GOo6hbhLrpDXcKB4ZZwWjPuMtXirE04Ozl/HuepKhOayX8+xbahpOkJociKdl5FExF2xzhsckj2+lcjfx9MWen2WpyWxm1W3eNjeGQgMPUlc4xVpN8O+p57c219p3UN1atw0TRRpuHsWXkiq+6+EmoTWwtW6b19Il/DGbneo+wJ7fetFi6PGS+5nLDVOEfQqdWmttZvrVbKaN5C4disocBfyNQrrTLSe68a4jaTY+Mbj/SrnT/AIVdSaNO0mj9N6mGkGCHCNn8gRU8fDrrhnYt07eBmOTmAAD/AOTrCtXhKV4zX3NaVKcVaUX9jgNQtr3TdQ039hIl0bmfCrISoIxyCR2+9dxcyTdM6jp971HNpFyl1Ftt4zCZlt2H4kLOCCx+gxXk3wk6suxAZ9H1yKa1bfAbaKLAY+pyxzW7UPg31LrIhGu6T1LP4HKqkcEaD64z3rphiqKilKaMZ0Kjk2osqNMn1bWta1M9LWtlYWUzkQyFjBHGceY7F4IzzjFVnR/TfUPUlpeW0Wr2enwWrvG0k8G97iRScopHIUn1rrdO+EeuacjR6XpHVUEDnJi/3dufUhjyK2af8J+pdHzHpOm9SQW8jFnjmS3dtx7kNkEH8q055QV7SXoZ81qtq8WcZpnQur6vpdxrSalb6RqFmHWCwWPxPGA/FuY8DtwMVE0PU9dtNDupFtfmnkV5I337FAOe6gc19Bs/hN1HbW81tb6d1SbZy26M+AW83f8AedwDUT/sc12GwksLPSeq7a2YFTAZIXRR6gH8WKnntF5OSIWEqLNJ/PQ4izt3fSbGSRcu8IdsDjJrO3tCshk8Ij1PlxXYw/CfraC3igtdIv54IkwitaLuAx7h61v8J+upoXR+n9cETfjW3giQuPUBi52/fFc7xFJvKa+50KlNJXiyt6Q6XuNW0iaHWdcFnZzBtot4VM5BzhSx4AH617onRemal0hcWN/qt/d3ibnikFxtiBUnGFHccVb6d8LOsLGHwIener/k/wAPhK1qXA9g5Oak2Xwp6hsYnt7DpXraws5Ad0K3Nq6jPfDE5GfpXRzukr2mvujn5vLK8X6nL9NdL67a6LPr6anDeXSIR+ziTExjXswkHG7jsRUq36OteoJhruq3s8Oqzxgr4DeWIY4GT3/xq/t/hR1Ra2zWkPT3WA00qVEQa1EgX23k5x+VaYfhd1XYwC3t+nOtmswCqQiSz3KvtvJJA/Kp55Sbb2190iObSSS2X8+f0cx09onUenW91rUc8WriRmVrZJPCdEUnDZAxz6iqGyu7rqG0fUbuEpPcXUhKLyFAOMZ9a+hQ/C7qq2ie3suler7e0dSfBWWzO0H0D5JH9a2Q/CfqXT7cWukdG6ytrywFwY2ZWPfkP/1rOpiqLW8r+K9y9PD1ItZP19jiLCxfxAuxgc5/DmvOnLm8uOqtU0+KVY7aNlLlotzoCBnac/1zXcwfCbrSMK7aNrsTHsqWMTY+uTJisY/g1rNtcfNad031jaXrnLzfuZDJnvlScf5VSniaMb3ki86NR2sjndctrrRSdO6eb/d7lXVJZdzTKuOcc4J57kVF6d6N1zqbQb+wvdbh0ywjjKQg2gee5I7Izgghfdj/ADrtZvhBrF9dJc6p0z1fqE6KVRpZIoQo+gjIxXtp8IeqdPRotM0fqq0tXJJh8CGRhnvtkY5Fa88opZSV/L56GfNZt5p28/nqcX010ZfWnTba9JqSNqNohMOmLHhXiGQwaUchuMjgiuifSZNT6Zj6ku7todRtoWeLSVYPH4J5bfKRzIeCABgYx61YQfB/qm2tpLKDR+rk08qV8DwYC4Q9x4p59/1rBPhL1RFYmwuNI6nfTivhiHwoUbYP4TJ3I+1RLGUm77S+fP7EcNNJJRf9/P8Aw+cLrMnVOvC3tYNsFvYswXK9yR7cVuGhXETgsgYfxbWyV+4rvtP+FGuaPJIui9PanpfjRhZHaNWJx2IOeK2t0H1akUkA07VZS4x4q6chJPqQd2D+dc1TFUpS6Mkl4nTToVFHpJ3OFsumLjWtbWWx1n9izWSbchTlt35EY47VaaN8ONTj1zUtTXX4b/VLchUVi2boFclS/wDAQMYGMGr23+Emv21y9zb6Z1gl5KuJpVjt8Se3lIIGKk2fwr6lsJ55dP0vrWKSc77hpIreUu3bdk8KftXRHGUlGymvQwlhZOV3FlNDoGo9cbdSutUj6fNoJIILWWL5mSQg4LOQAAM9hVNofSnUOpaI99rurW8lpaEzpp/gIfmtpPlJABwAM8n8q7S0+EfU9nFJHZaZ1nHbyFi/iR27OWP4iHIyuftSH4PdT2tk9jDo/WcenPlfCX5YuqnuPEPOOac8pLJSXoRzabzafz5/RTQadD1r8odGlsdHk0r98sh0/YJyy48E7SCBjJLelUtxu1LXrPTep9NhOnWs5MlmlwzePJt8uWODjnsO9dhpnwT1/RHL6No3VdgWGJMrbzAj04YYBrC7+A99ftNNddN9VTXkjeJJcvNEWLfYYApzygnv/wAe45tUa3fE5Lqrp3QNO1DSm0ixubW+l3r4ck7MCgUk4DdgK1R6FaqP3od2P4sHt9K7Gz+B3UWmSvJYaNr3jOMePd26TuB/dXzcDmtz/CXrcnH7J1EDtu+SA+/G+uWriYTeUzppUXBZxPnFzps/+0Oh6bY3gsTcSO5nkjDqF2+q+v2q5vdGuugp2urm4j19dWc26vHiJwxH4cEcA/Suvb4N9TThWv8AQNca4g5huLeGKN0/NmIx9KXvwf6g1IRNrnT/AFVqRiBMIQxQiFv7wVcZP1Oa3hjKMYpOS79DGeGqSk2l4fP6OX0vpDW4Lgtfy6dbQarMyyFzua3TGeEBzJ24BI5q+istXsNVXpRb3fDJAQmt5xCkB43+B+LeM42ZwT64qTL8KOoZ3t5pdI6yFxbEmCQ+C+w/90ABs+pOa2H4UdUzXkd9JY9YfPKnhqfk4BHsPOPDPB++as8ZRf6l8+f0Rzaa4P58/s5zVJ9UWa86YVJp7kxbTe718N484EgX8QJAzg9jUDp74f3utPdT6p1H8pcWLmKxi+WEiP5eTJjHfIH866xvg9rTX76hdaV1o1667TKhhj8o/hAUYA+le2vwo6isnlbStM6utjO+6UNbJcbj/e3MOG/1iixtGKspIjms27uLPmukpdvr2swX0cQktSIpDCPJwD+H8+c1tGg3uyRjGAM5G5wMivolt8Ide08XDaX031CJLjzXEt5CsjOQc58uMc16nw162Z8/7L6tPtxhUsyv/wAsea5qmKhKo3CS4HRTouMEprtOL6d0bWzfhtLjhzbIfmYJbjwmfJ8u0+4wa221jqd91xNN1VJeW1siiI2lheMviKBlVL53HuSSOftXWj4T9bR3k2oJovUkd5IArpHbwGLb6KFbJyB65qLe/BTW7wNLd9I9Wy37y7nuVliLMffHCqPYAcV0wxlJLOS+eZhPDybyXz7EG+0iaPX49D6GitNFsJ4d0hukeVrHceWGWyc8kbsnNYavrd1DZXfS11Ibu5O20UuqiFsjCy9+BjnGM1Z6f8F+odIla4s+m+uLe5k/tJQttLvH1U5zWuf4JatdXzXU/THXFxduwdpnhiDbh2OcY49u1W51RvnNenv+SroTtkvnzuKLXehhoXTWkQ9La1dT3cc8cdxHdOgikdm4KeqqD6E81r6i0jqGK40h9UjsYbJbhgwt5vEeWQocHBJwPoK6i5+E3V18oTU9E62uYoXEkUa2kEW1x2YkfiI+tb5/hH1rctBNcdPdZXE1sS1usltbLGrEYywU5bj6ioeLptZyXH1CoOLyT4enzzOOn0gMFdUwdnAA4zUGTUZumJtOvIC6FrkxNsbadu05wfevoH/Zr8RNmw9H6qQoxn5cA/f8dQrv4M9T6qVXW+k+qJFjBMMcEEKKhPcks5LGualWhGS25K3idFSDlHorMha1pnUXWWhRdR6Vq82mzWKSfLWruDNcIeWdmHlA4wF5J5NVllYa7oGgpdrPBqU1zCbgmR2HgueSGAIz711UPwo6utLAaemkdZQ6cyGNk+XtfECnuofOQMfTNYyfCTWGsf2dNo/Wv7PK7RAzW+GA9CwG4j866eeUUrbSsc6w027pO/z5+D5/YaV1Mb/TFtrpp5PNIy3AVUcEZPbHvxzxXRXS690/r0V5qc1pIl3bmJILGXzQBTk+Y5znPvV4nwiu4pInttB6viuYOIpBIknh59Arcc+1eX3wj1fVJEbWdD6wmljG2JkihhRR64VRyT6k0eNoN7y9PclYWpFaP19jj9Z6D1fr2yutfOq2tgGQrBYSOzO0aZG53Axk8/bipcGnB9PhEilMou44B9AK6S2+GGv2tmdP0/TOrreyYEPH8pC74Pfa57foazg+HXWERENvoWrzQqoCeNaIjAD3O/H51z18XTqRSU1ZG9HDyhJtxd2cGbCSATrsLbEZskd8A12Wh9V6n17YWmiwapJYySQrI89wu+G1VMHgA8kkAAADvk9qmXHwz6svUaGTpvVYI5OJJIEiL49QCXxn684rRZ/BvV9Fl8fRtB6stJnXaAYoJ0A+qg/zJqaeKoqPSkr+KInQqOStF2KvVdF1jrU33Tt88emx2LJPNPdT+Mlw/wDAkaqBgHuWJ7VsuvmOv7e46LlmFh8kqfMPNEphtlX8IjwSWJ7DAAwSTV5H8Leporua7isOsDeXGPGeSwhaM7RgYTGFx6c/rWm3+DuvafeTX2n6d1hFe3GTJNLDC4kOe7KQPyrZYyiv1rLTxMubTf6Xnr4FD0x0Dp2h3Wo2WoPNLrMZVra7sZtvhQkceTscnOc+w7Vh0g1v0/1Bql7qRt9SuruXbb3motG+UUEFRvyv/Sry/wDglqeoztc6ho/WE19IAstwUjG4DjaFUYC/SsH+AsksSwDpXqlXThXLIf5EYA+1S8bRle89fD3IWFqK1oaePsc672GraprM2iW1tDbJKkbtaBRG0m3zEY4PtkcV54PhPZRMhXMuweYDJwTXU2nwk6k0eEwdP6Dr0cLsJJvmrVZdzdhjG0AfTmvW+F3WcsyXF3YayWhyYlFlGka54yctk8VzSxFJybU1bxOiNKailsu5xcmu3eg6pKkEAljaGMCEziNyxY459QftVlqvSMGj6pYaxrOoDULe4YtqNmW228UpxsUDOWQcAk9zmr+T4G6zeXHzWodM9R6jPJjLvPFb4Ufh2gLxj61Mufg31BqkSQat011VLCjApE2oQ7F/4iAg3Eema3ji6MUrSXfmvf2MJYepJu8fDX2KXV2/7Qep7fVNN1CyhudKjxcTxLhJWI8kOBgHAyOOBwKwtumT8RIrq06lmbQ9LsZyirbv401xMB3JbARADnAHJNW2nfBXXdKE0undOdUW0j8F01CD8vLsxWdj8JOqtMSQaXpnUkDyyeJO9w0E4Zz3cA9iannlFbs1lpmhzao9YvPXU5+51SxQXfTsUMNtdGcaeZ0tkGzOB4obOeBzgc5qH1x07cWvTssj9QyajZ2ihzC8expAOBux+I9u/rXUH4B3rzGZ+muqZrnd4jztdwqzued5OO+ak3Xwh6jvEEOtad1NeWIIZbfxLeIORyN7DlsUWNoJpqa9PQh4WrJNOP8AJ8w6WtnfpmzZYmQF5cD/AMVW6We14y6kp4i7ifYHJ/kK7eL4Ua/bR+Fo/Tur29uCSiSrHIFb1IJYfpUe7+DnVGoxmLUNM1yO1I80cCwxlvu24nH2rCWKozm3tpJ950RoVIwS2XkUFpoHUNv0i13p9/plw9zbyXEYl3CS3V8tsXJ2sQDwccE/Smo9JdSWXRcUVvqek3zvbRq5w6yqrbfLknBxn8QGcA10X/ZL1JHYjTILTX7fS/D2NDm38QJ6qJDyoP2zWUnwp6ku7YaebPqB9JCqPCVoElKjGFMp5xnHpnit1jaN99a93zyMHhalt16fP/fQptY6f1zQY7G0+f0y8t55ktp9TjRlliVuNwjY7d3sTn09aj9Z6AthZwWXR15Pa6bOfA8CcLJOAFyVEpGQD3PrzjtXQX/wq6i1SJLbV7LX7mzRw0cUctujFh+FmcZLY9O1RH+Cmr3FykupaZ1HfGLyReLexjwx7A5/pRYyirNzXoS8LUekX6lHY6LoB6Vn/bwn1SaGB1b5q8ASCYqRlUz+LOMf0qNd9OaVB0npM0Gr6pcG3lt2ijnuFMTT7hwF++cDnAq/ufgJO8wmj6Y1qBc5YLdQOSc98k5FexfAXUIb2G5tOnNahaFxIkb3MDAMO2ST2qee0Lb/APHuV5rV/Z/Psck1i+ZN0bfiJ7ema0X2p3HTlxpl7AuP3zg87SR4fYN6HvX1CT4f9aSgg9LXPn5yzR4Of/HjHeq27+DPUGrPCda0C/e3h3FIILiFNzHjJ8xrmp4qkpJzkreKOipQm4tRTv4Huo22padoZ6invDe3U0SNc6XDKAIIwPLtlb8b8+bgD0HbJoupunNYtdLXXtN1SePVtRKJdaXvwkcZISMB+7Ng5Y8d8DtXR3Xwe6rubT9nPY6umkkAPELq28TaOy+ITnHAr26+D/VmopHbX9rq8lhE6sqR3dsjMy/h3ODlsd66I4yinfaX9fP/AEwlhajVtl/38/8ACj1rRNb6W0jT9L0m8tNWe4CWfzbho/lncYPkzhwvO0n74rXf6XqmgXGjaAl3Z3tvdZtl1PLK8QVcsfCJwWxnBPrziuiufhD1ffGAana6pJDbv4kCx3dtEfEHZmYHkge9Y3HwX6pvp4ZtTstXubiAlrUpqFtEIjjlgAeSfc+lOeUOMkOa1OEX8+f+Hz34qdH6HommRXnR4ntLWNoontZJvE3knhyfcnkirMaYktxLNNHvRNh2n1Jxmupvvgx1RqwX9v6dqd1bR+aOD5y2iUHH4yAck4zya3J8KutreExWthdm3TbtMk1s7ADsuS/pWVbF06kEttXXf4GtLDShJvZdn3HI3NmTGssdu0amJl2kfhO1qiXfR93p/RdpHp3UV2Q8UbT2xwoySOFPqOex4rtbz4VdeXseye2uIomyCkVzapnjHJ3Z7E1pPwT6qkt7e0u7PVriwh2usJ1O2Xle3IbPHpnOKijiqVNZzXzxFXDzm8oP54EmG503oDUtPmsdSmmvLm0EDy30qFY3UBm2HA27ief+6K5hYrnWeoJNd0a8UWsExkjjWf8ActJghyvpzk9vWupvfgrq98g+f0LUJ2ClYzcavC2z7DOPb9K90z4K63ZQNDBo2phJD5xHrEEasCO2AePuMVbnlD96v5e5VYapfdy8/YqdOtU6osLrWtehWKNIyljBOfEcy55kzwFAAwOCST9K4fqlS13A+4MDGuCBjjHtX1V/gt1RFavbaZpV/a2hU5jOp282MjkAls18u6ttZdOvra1vo3hmhto1ljdSpUjIPHp2qIVoVanQdxOnKFPpI59XUxsreb+IY9vaozgKG45zj+VZsw3AxqfN2BqP+HK4HH1zXoxR58iVBgyxge+ftXzc96+j2/8AaIfUN296+cHvXpYPWXl+TzcZpHz/AAKUpXonnClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUBsg/HU+P8AEue1QIfx1PiIyue1Y1DtoaFlYKHB7nB7Cr5CXfYVYZGAuMkn0xVDplyYCwUeZiAvuTX2TpDR7TRpE1PWLqM6iRmNIZlxDn6+rf0rxMXU5PNns4eG3kj798MJLHpnofTrKcTyahIgaYMjDb7Dj2rqLjVo1D/L/gU/iljJX7Zr4yerXjZQL24IYfg+a3lj9e2B9Kxbq+RQrTuy+hVZA3J+5718fLDTnJyfE+mjiIwil2H179qx8M5jG/jyxNyfbPathv7WVioVAF7grgj8s18gHVQSQIWmEpIGxZVCnPv61tk6mtn80y/MMPL5gj7vy4HFV5rItzmJ9fS8VVTfayBT+Fo4lYn/AOSrI3ls+5PlJgeCQ0SgL9c7yOa+Ow9Q/LnK6TKuAWzGkK/0bj8hWwdVsPM1pdxpt5PhrhR6Y82PzNObMc4R9d+Zsdodo/DOOCNpOf8A4q2ie2Zm4PiIDnzonH+FfJIurpolULHdDfnYCoXcfqR2P1xWZ6ruEiG7dGGJwm4gufv3NObMc4R9UW6tB4ZLXIbPAVcE/nt7/SvBf220lVvlydu6SFhk+3+u9fK/9qZh5DPO7lfMYpSu32H4TXh6nuxgvNdTbvKAyu35ggr/ADpzeRPOIn1gXcBjQK80KdxI6lf5Vn4sRKBblsEdwA5P5Y/lXyKXqS5jcoRIzkYwssuCPqd55+2K2R687Y8IHycbluJiP138/nU83Y5dH1hgEbEl2EIPA+XJY/pxW5oQfO1y7AKGUPb+GMeg5Ofzr5Aerp/DJhQSW6EbSUyrHt5QPr71kerbgEqkdiWTIZg7sUI/hwcjcac3Y5wj6+JLU580JcjGC4zx3OO9ay8ahtkVlJgAqxnVfyFfK06tkYsXs03bcMGAZefUjOT+ZquuOqtDt4sy2digkIb99aBfEb6DPC96jm8iHiIo+0JcQxKwI0zeWwdt0pH55Gc1MWezDL5rVSzdljDZ+vPNfEo+srBY1+VSwmPqLeNAB/3jngfatx67SPHh3mnwCTAJaPxB/wB4Kp5/OnN5DnET7Y11ZFcrKuRjlIARge4H+da/2hZoGImu2QnH7uxOPyGP5818Vi66lkA3anZsAdoRLRhIQPszKoP2zRuton2qZgHduR4UzAfXJ5H5YqOQkOXifaje20gbZFqT44LPZFV/Nq2wtbMGAN2pHIJQAj75FfDH6psrgN4MCXbRON0ryyAn6ck/rj7VobW7ZvExpkJKt55DK7Yz/CAO/wCYqeQZHLrtPvgltCQknzBfYTtLA/zFZeFYyAKZLkMM4KkMT7cdsV8Et+pbVIClnZQorEB8yszc+pDDgepFexdWx26lI3trS3U7Q0iOV+pVV7Z9M1PIPsHLLtPvRtrPadrXTMfM26eJFX8ycD8zWgRWIZd1xcRgAEE3kDbh6tkKcD88mvhcfW9hjY93Z+LldkRHiE89/N5Qftkj3qxfrElnRJIAVdVy0uEJPcqpJxx6nJP0o6DXAKvHtPtSWdm7MsWp3YULkqs0X8/JxxWx4rBuPmrgn+Ivqipge5OzH5cV8MbqxZndF1IMQGcLLaRybQD+IDtj+dR4+rfEieWLU7l4oz66bG5Bzg7F4AHtxTkH2Dll2n3c3Wk2wHzN3FDIB3n1qLGfbtXsWq9OsQH1GEc5ITUlYfyr4c/WohRklvroukgQmTTlXEnpyDnOPTPP0renWEkqJJaTv8vy29bdVaT+8Qc4XkegJ+tW5J9nz7Ecsu0+6Je9PNkrfwuARuAnBOPtmt3j9PyHi8Q4OdokUH9Pevg0nWdxIY0muJBLO2Yk3uyrgd8g+Yge+BWuXrO8H7ofO3LBMl1WR0iH1Awc/mc05LuHKrtPv6NoJLqt8UYckLsyAfoQajSXvSsOTN1F4fmwAZIlx9M7K+BjriKF4FKanBIWIy1pIOw7j0B+uCakQdX3kqAW0xaI5EZKyGQfXLYUH67ank//AJK8qv3H3eG96WeUiPW7qVwOQr5JB/7sR/lUhJNBdT4ep6icEA7PEO3348MV8Bk6tN1lIGnfeTEWjuXjhidR5uAwLe3JNa5NegfHzW9ViOwbLyfG4ngKDJgd/wDlTk1+0nlP/o/Q3y+g5DrqGouFz5hK5Jx6YwMViYNEMuG1jUYfcmQk/Ybv8q/OKa0lmHggsI2MQDbprq4lzuJ/vOeff+lSX6jvI1YwwWLIWCbUMyjkc85AH17k1Owv2r55kbfez9DJDojSAjWdTkUe8sXf68cCs9uhbxv1S/UgHO+aMFvsQCMV+eJ+odTdHJ+QfbgosonkUHsAAx7fXin+0t+05jjFsdo2nc8wDZ/ERweB7Y5psL9qG33s/RIi6eyqjXrntuUC6jxz35xn8q9E/S5k2t1HIjY7NexqQf8A4a/Ox6va5jkjiZZkTJDNCWBIHK42Yxj1rKbqmSNAfAjeMqCYZ45GI47KNoC/Q/yqdi36ERtX/Uz9Cj/ZGZQG1oySk7cPfIN35hc/yr06d0qxj8R0Ck4djM8hA+hKAH9K/OEXWsXhAwWU3hkYVXbAdh6YC42/U5+1ZSdYSwg+Hbs5fG6PxHCqvfvtHv6VOx/8Ijb/APtn6Uk07o63iz4t2dhAASdcD6cr+dRjH0mfDBuJChbkSagsYA9/wE4/OvzdN1s9n4TshWPGI0gilaQeuC+Ofv8AWtZ69tgIt2p6kGJ8wj8RNn0KeHtP6e5qdm/6F9iNtcZv7n6X+U6UY+ee6VxwypPv/wDuB/KtL2XRcsxgmOsQv33PARGPpuKYP61+af8AbHxJUNprmp7V7QyoNoHqMmPt/jUmLrJ44VdJ5JjISz+PcOMgHgbVRVz+VNj/AOF9ht//AG/ufpNNF6EkUlrrUgAAdkcYz+Xk5/KtL6B0EjgPfalIj58zNGF+34O9fnl+p5WKGcwTs/BD5JCnsAQMj7nNLzruLTZViufmTK6lgYptmADjvjP6YzUqN9Ka9fcjatnyj9PY/Rh0DoKIeaTVIRjLM6JhfbOU/rWg6R0SwYpLqKYUnJFuuB6HlPX0r8/v1ayJlY5bfau/MUmxSo5IKkEZ+vetL9WyqqhopppBg77i4eTaG5BAxjt6Eenao2U//wCa+39kqVv1v55H6LXSekUiXF1ehiFOXngj8p9SfDwayNj0oTuXU7tGLYIM8QwvPJ/d8/lX50k6inRkY7ZCzM3iD8ZbAySSnr7YxWA6qmlErLBbF1GCs8ayAsOzHABPHp2+lRsL9iJ2/wD6Z+kvB6QUqJNVvCU5/GOx9/3eKz+T6MAVxqFxIz4C/vR5wf8Awdv5V+bj1Vsu0to0jt5JY/FWW3UIyhQNwJ5znP0wOK8k6pmRiFggMecPhn8Vl9QHDjA+nap2V/8A5oja/wDtn6QlXpGJSG1KeNQTnF1kE57D91/15rQ9t0o6tm9uGbGSWnUhR7f2eP8AGvzVL1mls0Qlt3VpsqAtyfLhsDPm57+tSv8AbN3lMEdrExjlMTOykIXUcjYH7fbmjgn+hBTt+tn6GNp0eVQpeyMu0sAtyoz7keQCtfyvSaKGF5KiuAVBuYgD/Kvz+/Ws0UbXEsAFvvVMefdIzDg53ZXGMYo3WTJGoRPCTwfmDhGbxE9UbJP/AMVV5NfsRblP/pn394ekQ7f747t3wlzHz+grcsPTCMP3l8pCjJN0pAP+sDFfn0dZX91brLZOIoeybrZ53+o3sDgZxjGK1SdY6mAu230+0KnO+KOdiT6+VjgfenJpfpRPKP8Acz9EmLpkSD/ebpCWwQ0v4fqeMD9celeJH02xxFeXpQjIBflj9Bjn9a/PcPVeol4llmgSRlY+JEJogBxxtOQT9cVrHWn+9LFPcpIsjNExklI27c+YkjkH2xxTk/8A5RHKW/Uz9EInTT5Pzd6/JBIkLBSPT15H+FYAdPCHxVmvmGMkjcT9+a/NsvV+m3EUbfNQwtJgiNr8s0WeAOMYHGTj3rbF1bp6xrNYvdkqxFwyancMcHjO0DsT2qeSf7Byq/d6n6Jkj0Fiu173EnAJZj+hI+ta5U6eyzCS7BXjb4vC+nqff0r8+N1MsviJHBcGaCTzpJqFxsZT7YIxjvn3r1+pZh4ngxXUiAlCsuqz4bHIbI5xVeS/+fn3J5XvP0JnQomCXF3dRMvAG9csf+H0I+1Yibp8Mga5uCpfBCyhVLDvl+y47fevgc3U81rF4kdxfWybQZFGoPKMeuN6nHGeR2rZD1bKJFjS+uRGyKywi7c5Qg7SxZck9j3pyX/yOVX7j74w0CRsmedY1U7nNyvJ9Tkn8I9+1ePHoBLf73Pt5PEwkbHYEjnH9OK+BT9U3Mlurrd7bhBvUyKpQMCcNgj2+1RG642jdJdwylwxUF0GTg8Mcdu/ep5HsiiOVX7j9DRRaA5dPnLhWAVSQRv9+ExmtW7poI8iaheDOAu07/N/xHH0PbPavzzL18BKUN9CYJE3I3iLIYnx3/Ccj0Are3X0SPhb8SupKL5CWTjGAAg4OR9PanIv9o5aP7j74X0VCB8/dbSAcMhPf6Adq1zSaOI5C9zf4BwvlIyB6nA45r4IvVk7RIJikKSLuZI1Keb7LjPFYx9XSFmjka4QMuG2RsNnqGz3wcVHIdxbll2n3lm0BdoiuLl8YBzvCqPctnGfpWwHQWKu894I88EqxGfX1r4SerJ8hlZZY2TyCQSHPqBk9gefvWiXq4y2xlZFbaCJVXKBvzHOOfQinIvsHLLtPu4udGZZQsl5sB7i3Ixzgdzkk+gNYO+mY3H9oZK5OLRyFJ7A4PHevgd11jHuL3VssIYgYjvSNo9MBSccfzr39uGKUn5KNl/9211t5P8AEee9VdDuCrrtPvgbTw4Mcl+VH4sWzcj8/wDGvBPpgYKZLsygHym3Knj0/F9vzr4QdYY3DKLaDft27muEKqTyDjHmP1rVH1BZ+UC3jSOVin48bCh52gDsfX1pyD7CeXXafoFrjT0DCcXKgc/vFZQAPY55NeePp27AWdeRhdx54+hz29BXwUdXI00i28NrCR+JuQvbsBtwRmsx1gny/iNLCIST+72nf+LBOfb744qebvsI5ePafdPnNKXh3dmBxsyST7/1qO97pxG4BiG4UEkn75r4ZL1owiw2oRYZTgs3m7/3e3Pv7VqHVLR+MqXaO6lcMCSWTAwMfrU83ZHOI9p92N5pUjsG8QPtyEyWI74/19awN7pYXfHIXPYeYsc/b/E18Qn6qljJiWXaoAOUYkEeuOO+DXqdVlokeGchSx/EGzg/UD04xinN32Dl12n3JdQ0+KGXxGYlpFwXYApwc496wGo6YYmWSeNOSfPuPHv2+tfFB1VcRNFHHOMpyjlDzxnzHHPrWSdYS3QDxzSBwDGGG7IHHIGOPoac3Y5dH2v5vS1CFVRg2Ch3nzHuT/SgvdLIbAjLDjynd+v+hXwyPq6dmPkVo5Fwys+Ax7Z/Dx29Kzk6ovGl8KMW7o4wN8mwAkf9wk8ZqvN2OXR9t/auk7dqOiSbWG9Z8Mp78ccY7559K/Mvx80OJeoY+obGU3Gm38UcM0zNkwTqNoD/AEYAYbjkHNdU/U9+QvltnGSGDLuH0IO3+tRrzXRewPaX9tYXVhP+7mjkj2BkI5GQue/rXRQi6E9pGNeUa0Nln5/MDuwj3YKn+I9j68VpkQxuVzx/Suj6i6cbp24hkt5TNps5xaXJbLIf/cucDzDsDjBFc9KxaVzJnuc/Q19JTkpK60Pn5xcXZ6mduxLpznB9q+d19FgbDLjbzx9a+dGvUwmsvL8nmYzSPn+BSlK9A84UpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgNkP46sbVJJp40gQvIzYVQO5qugyZAFGSeKt7ef5DiJj4z8GQeg9hWNQ6qTsjr9JmHSswmt5oZtQkQrI7RqyxZ7quc5Puf0qU3VEkowTCpDbh5Bya4F3Mjbmck596xLNnuf1rz5YWE3tS1O9YiaVlofSD1hcb1Z5ICVHGR2+3NeHrCTK73j7g5VM5P6183yRXoYjtmqcypluc1D6SvWxWXe0gVs55U1m3WPiYIu1Oe2R5e+e3oa+aCRvc17vY+pqOZUyedVD6YnWJGf8AeEUFcMNg/lin+12y38JZhjfuyQTzXzPJ+ooGNOZUxzqofT/9rxvMiyyNuwCcD+VeydXiWVZvHuS4G3GDwPoBXzDefvXu9vrTmdMc6qH0perVjBC3LkZPBLAivU6wVW3eJnPLBpM7v5V803n2NNzexqOZ0xzqofTB1fCCDtjxx5d/I/lWyPrG0CFGRZOcgk9vyr5eC3PBr3J9VP8AWp5nSI5zUPpn+19rtCEuoUgjbKwB+mKJ1haIHCE+dtw3yHj6fWvme845z+lN5+v6VHMqZPOqh9S/26iznKrxyVl5pB12IDujnck8MMjBr5aWP+hTefb+VOZUiedVUfUn65Iy1qqZblg0akMfrmtUfW0iPG8nhxugAXw4sce2QQcfSvmglYdsj8qF2PfJqOZUhzuqfUx1/wCWPeZN6MSCAQMn6V6evQXRjLISm4DaSMA9/WvlW5h703N7tTmVIc7qn0+LrQQRPFFPMsZOQqsMD6Hgk1i/WxByLyVQTkr4fOfftXzMM3/FXu98/wAVOZUhzuqfTI+tfCbfDOx3MGcsvJOMZ/SsoOungBAmcDsCOCP5c18x3ue+6m5vZjTmVIjnVU+pD4gSbWBk8zEE+UY4/KvT8QJWY+ZSviBwvA/wr5Zub2NMn0H8qcxo9hPO6vafTj1v+8d8biylM+Mowuc/3a0r1dbBGQq2D/8AjB3d8/3cV83y3sf0puPtU8ypEc6qH0tusLWRmYrJuZ9/9v64qOOrjEoSLxE2jareMw4zk8ZAr55lvQH9a9Dvz+IfnTmdMc5qH0RetHJQy3U5aPO3N12z7e1bR1wRIsgmMjgbf3k4PH5181yecg5rLcRwMinM6Q51UPpg67mZ4mSYho92CHUYB496jL1nqMGAl3lQfKC/b+dfOtzeuayM0hHOTRYOmhzqoz6HF1jPCAEumVQ7PhHU4LdzyDW09aGYH5nUJx5xJhUiOXHY/h/yr5vvY968DP7c05nTHOah9J/22kDMRqchLYzmEDOPYA4rCTq63l/tpw5Jzu+VUlT9DntXzrLetMmo5nTHOah9CbqmxZSFcgnGCYyO3qcNWX+1diDlZJlPc4X+X4hXzvJ9q9yf9GnM6ZHOqh9FPWFoFIheSIMu0qm5AP8AiGG71m3WkPhGNb66VMYAwxY/XO6vm4z7U83tTmdMnnVQ+i/7XWfmEdxcxKRjuTtPuOayfrKGUjffXClT+IKcn+dfOQG9jXnm7YJpzOmOc1D6RJ1ujoA17K4VdqHwyCv/ABZz3re3xFeRUR52KrnBAxnj7Zr5fgn+Gvef7v8AKo5lSZbndU+mL8QJId4tbo26vgEJGWzj+leHruV0RTfSnb7qvNfNMH+7/KvcH+7n8qcypEc6q9p9LPX0x4+ZcY9cA/41hN1u88iySSTOyjykSKuPtXzfn2/lTafYH8qnmdMc6qH0kddSgrsDKQCN2c4B7+v9aDruY5JldGON2Iwc47Zr5rtPsK9AYeg/SnM6Y5zUPpX+3T4AMkkYJJ4jI5Pc8nmvR126ghJ51D/i/c5NfNPPTMnvTmlMc5qH0puuXEscweYyqpRS0RAVT3AwfXFenry4Iybq5Oe4EI5+ma+aAyD/AKmmX/0TU80pjnNQ+iN1nvKs7yuy8qWX1zn/AAFe/wC2bB96ykyFi+WBwWPc4r53uYHOcfrTe3v6+9OaUyOc1D6E3Vs+ADdQumc7Dv4PpzjFYN1Vcvwske3bs4VuF9s+tcB4jf3j+te+JIf4mP505pAc4mfRE6wdBsE6DtncpB/pQdVqdxcxsT/E7dvyAr53lycn9cmm5/WnNIDnMz6G3VKMxZ3gkYAgFucD7YrQ2uQg+Ir2xZew2559TjHNcIGevRLIDw38qc0gRzmZ9Et+rJIkjjhuEiWI5GIl49e/Gazl6xDqEeeOQZLOScbz/wAWO/NfOC8jfiOfTtXmX9//AJGnNKZPOah9EHWcamQrIFdzl2UZZvpnPAoOsRnySvjOTtY9/pXzve/v+grzLep/lTmtMc4qH0hus2kUozkIRgrgkt+ea9/20O8PvG4DaMhuBjAFfNd7D1/Ra9EjZ7n9KjmlMc4qH0j/AG1fBXx+/BOM1rHWWFUDa20+U8DH8q+dmRj3NeiVj/G1TzSn2DnNQ+jjri5ZwySmNhnBEgAX+XNe/wC3ExJIc7iclzcFcn3Pua+b+If7xr3eT/EajmdLsHOqnafRF6vlA/tYDgY80zE/fOK8PVe7zStayeuCWPP145r56Xb3Oa8LN7n9anmlMjnMz6MesN3PiqD2wgIHb7Vj/tYoTwwVKdyDnLfnXzrxD33fzrwyEH8X86c0pk85qH0heswrDaFbBJGeeTWLdZb3JllyM/hxgcV848T1LfzNAw+n6U5pTHOah9GPWQJLeOoOc5Cng1kOs1G396PLwoCkYz3J+tfOAwHavd/1FRzSmOc1D6J/tZGxBPLLnB3HP+sVl/tcjFSx3BfwoV8o+p96+c7/AHIpuH96p5pTHOah9EHVSI29QfEK4z4YPHryeaf7ZcNycnHJXGB7cV8849x+tOPQj9ajmdIc6qH0FustzFi2MjBAJxj2r1+tAQAojTByOOP6V89yfQ/zrzJ9/wCdOaUxzmofQW6yXglYd3bI4A4xT/bVV4t47aHjnANfPt5Hdh+tPEP98H86c0pjnNQ79esVRlIigwo4ArJessMHaO2wPwqcjH8ua+fB2P8AF/Om5u26nNKY5zUPoa9Z5ChWt1UHJ4zWL9Zu4G0xBh3JFfPcn+83615uP9405pTHOKh9Dj6rnuY5rW4aKa3mGJY2Aw4Bzjnt9D3HeuWaWN7khJlYM7CNy+duMeQn1PPB9ce9Uu9l/CxBrdFdySJ4SSeDgg8oNpIq8cPGG6Q60pbxeW4IkAJyQfyr55XcC88Ha91JFjBw6sO4HrXD114WLW1fuOPFSTUfP8ClKV3HAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCmT70pQHuT703H3NeUoLnu4+5pk+5rylCbnuT7mmT715ShFz3cfc/rTc3uf1rylCbs93N7n9abm9z+teUoLs9yfc0yfc15ShFz3c3uf1pub3P615ShN2Zb2/vH9aeI/wDeb9axpSwuzLxH/vN+tPEf+8361jSlhdmXiP8A32/WniP/AH2/WsaVFkLsy8R/7zfrTxH/AL7frWNKWQuzLxH/ALzfrTxH/vN+tY0pYXZl4j/3m/WniP8A3m/WsaUshdmXiP8A3m/WniP/AHm/WsaUsLsy8R/77frTxH/vt+tY0pZC7MvFf++3608R/wC+361jSlkLsy8V/wC+3608R/77frWNKWQuzPxX/vt+teeI/wDfb9axpSyF2ZeI/wDfb9aeI/8Aeb9axpSwuzLe394/rXm4+5rylSLs93N/eP603N/eP615SguzLxH/AL7frTxH/vt+tY0pYXZl4j/32/WniP8A32/WsaUsLsy3t/eP608R/wC8361jSlhdmXiP/eb9aeI/95v1rGlLC7Mt7f3j+tN7f3j+tY0pYXZ7vb+8f1pub+8f1rylBc93N/eP603N/eP615Sgue72/vH9abm/vH9a8pQXPdze5/Wm5v7x/WvKUFz3e394/rXu9v7x/WsaUF2Zb2/vH9a83t/eP615Sguz3cT3Jpk+5rylCD3cfc0yfevKUB7k+9eZpSgGaUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCmaUoBmmaUoBmmT70pQHuT715SlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKy21jWY7CoZKPCKBcnis8cc15yR9Ki5ax5tA4zk0K179qEe9Lk2MdtNtZfen2pciyMdtNtZV5S4sjzbTbWWK8pcWR5ivMVlSpuLIx2021lSlxZHm2m3JxXvcjPH1oQASAcj3qLiyPCuCRxx7Gm2vf517gYGD9+O1LiyMdtelMeor2hpcWRjimK9p29c1NxZHm3jNNte969NRcWRjt4ptrINgEYHI9RQHGeAeMcigsjHbTbXte5xxS4sjEr9vyptxWQ5ND3pcWRjtJoVweayFeVNxZGOK9CE9q9pmlxZHmKYxg4zXtKXFgQN3lHB7V4VxWXbtXqtgEFQQf5VFxYwKmvNprP+dMetTcWMNppisyP0NMcZpcWMdtebTWVZDkY459falxZGvaa92n2rLGTTt3pcWRhtNNprMjFe9xjHI9aXFjXtptNZV6px6ZFLiyMQtNprIjFMevpUXFjHaabD9P1rLH8687UuLI82cdx+tNprKhGP8KXFjDaabazrcIiU3hfL27UuNkjYptra/ccY+1Y4pcWMNte7ayIxinvU3FjHbzQL71l/WneouLHm0V5t9qyx6UxS4sY7abaz28EjmvO581LixjtptrKmKm4sY7ftTbzWVMUuLGO2m2siMUpcWMdtNtZU49KXFjHbTaKypUCxjj6U21mMZ5P8q8x+lLk2MdtNte17xj61NyLIx2021lSlxZGO0021lXlLix5tptr2vaXFjHbTbWVKXFkY7abaypUXFkY7abazrylxZGO2m361njPavMUuLIx20xWX3pipuLIxxxWNZnsawoirFKUqSBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAVsUZUVrrdHytQy0dTyvOa2lcc8Vi3mOcYFVuXsYelMHj61ljnmn2oLHmNv1OOa8rIce36U4PpQWMaE5+le/lT8qkWPK8rKmKCx4Rj615WWK9UDPmzj6UFjEEqcj0+leVkR9MUxzQWMfvzT8qyxmvcZoLGFenHGM9uc17THqagWPKMQScAAe3tXuKcenNSLHg78V5WYHsM0qBYx57+leVlin+FSLHnpivK3lFwqrGd3fOd2ax2bmwBj71FxY1d69/rWxl25xk/XFYhckcUuLGOD60rYY9v/EfpzWNLixjyK8rPH0rzFSLGNKzxj0r1E3HA/rUXFjXXvbj1rYV25IB++KwxwKCx4e9B9ayXHY9v6VksLMCQDtHJNLixhtb2NeYravpzgjsc1kSp4fIP2BFLixp2EdwcGgXDD1HetqsFbJHHqPSvVCMuMbfzzS4sajGwGcce9Y4IOCMGtm3YdrAVnkKMNuI9xzS4saMd69x9RWbAdl7e9ebPqP1pcWPAvB5H615j6isimPWmzPqM0B4EYjIGQO+KxKkdwa2bSncfrWSnHAUkH0BpcWNOK9x9a2vH75A+orArj1pcWMMfUVkY2GDjg9jWXh57c/nTGOCP8KXFjXgjgjmlb9wIwQSffOa1beaXFjHNen0LVs2jvt+vesNtLixjTt3raE8obbx2B+tYlMGlxYBwFI2j8+awGT2rZtGCDyfQ5oVBXIGCO/NLgwAye+KcD61sK5HbB+prHb355H86XBh3r3FbMDcNoAz9ayMW0/vd0ZIyPL3pcWNSsF/hz9+a8PJzjFbNqhvdT+WKKOMbf59qi4Ne3jvz7V6Ac9qy2HcMD8hXuKm5JiVx/lQAVmACOV/OszHu5QYx6E5qARzyT9KYz2rcYgp/e7oz3A296x2gE7Tkd+1TcGAX3FCvt3rL8qyZSoBKbQRx9agGoA57VkBWWK8+1AYYIPFCMVnj3rwj3qbgwp2rPb9K824pcixjSsttAtLixiD+dO9ZbfrTbS4sY4oRiswMVkdrKP4T/KlxY1UrMpjvXm2lxYxpWW2vQBnnkUuLGFMYrPFe4pcGv34p962Y4+lNopcWNZ/lStm3BzivQqk49aXFjUfwnitdSJRtT86j1KKS1FKUqxUUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFboc4IHvWmtsJwe2TUPQtHUmRhSPMAB714IQ/YitZct3NWNmQygeXt2IrFux0I0Lpc0gBjQuPpW0aDfYybaXH/dq4+UtMJ4aSCfuXVtqj8qjXksqPhZpACMfjNZ7bL7KSIB0G9xn5eT/AOGvP2He+sDj/wANRzdXAJHzE3H/ABmvVu5wwJmlbHoXJz/Or9Ip0TcdEugDmJ857bax/Y9z/wC6f9K1fN3GctcS/YOa2wXlwSQZ5P8A4jTpDong0i5Of3Tf/DXv7HucZ8NvtivfnLkH/wBIl/8AjNZDULlCN1zMQfQORTpC0TD9lXOCDGcd/wAPNeHSrj0ic/8AhNZ/tG65xPKB/wB816NRvdu43cwHvvp0h0TA6RdAcxtz9KHSLkDPht+levqd0GG26nP13V6dVvu4u5f/AIqdIdE1/sy4xnYf0p+y7kjIjOPtW4atfgjF5N+tbhrOoDJ+ZduMebmnSFokIaVck48Mj717+yrn+GMt9hUo6xfgZ+bfP2FZNreopszcNGD2yAc0vMWgRP2TdesLfpWJ0y4Bx4Tk/app12+DYF2x+uBj+lbDr1+qYS6LMT32rj+lLzFokAaRd8EQv+lP2RedzC/6VNTX9TbdidiR2O0cfyr1OodRJGboZz28MUvMWgQTpF0P/Ysfyrz9l3X/ALhuPpVh/tFqQ7XCjPP9mte/7Sapg4uRu/8Aoa1F5i0CuGnXfcRv96fsq69IX/SrH/abUtgPzKZ9vCFbE6r1JR57iL/7QpqbzFoFV+y7vH9i+K8XS7pu0LH7Crf/AGs1XaSJov8A7Stal6m1M/iuR9hGtReYtArv2ZdZOIm+teHS7nH9k/P/AA1ZL1Lqewlrlcj08Ic1Ltes9Wtl2iW2bdyS1upx/Kl5i0CiGl3TEBYHJPbCnmsfkZ4XXdES2c4K5BrqV671gNgNZNxwTarWUXxC1jHMOnnHfNmuKXn2E2h2nJfJTMeEY/lWY025PAiY/lXYD4g6whDiDSmA42/KD/Ch+J+ur2ttKUdv/QgTS8+wWh2nIDTLsjiF8fVTWt7OSPyyLtb+7612n/adrRA3W2lcHsLUDP8AOsJfiZq10NsljoyjIORYjP6k1F6nYLQ7TjPlpGH4f0Fb1068lYbYZJD9s5FdonxQ1m2jJSz0QgH+KwUmto+K+rqyubDQnK5x/wCbhgn9abVTsJtDtOFOmXOcGB1P17V4tncBtrQPIDxtA5/Ku+X4wayp3fsvQAQc/wDzOAH9az/7ZNakUI2k6BJk5wbH1+nm4qNqp2IWp9p8/wD2dc5wLeVh6EIf5ism0q7VUKwSOW52hTkV9Ch+NGrJKGk0rR3ZWG1vlc7R6qcnzKfqfzrYvxc1MOWj0nQJQzFmf5MqxJ9xu9O3HpTaqdgtT7T5ydPuyuHtpiPQ7DXg028TGLaRvXGw4NfTZPjTqvhqf9n+nN6HIIs2DA+483/Kt0vxt1EkyRdPdOsQ2UZbRgw9ww3cio2qvYTan2+h8uXS7iUeS1nT7xkivV0S+f8As7ZpP+5gn9O9fVT8dr4sC3SXTPPdfl5AD/8AJVl/27XAQoei+mBnG4iCTn7+am1V7F9xs0u30Pkr6Tfx/wBpZ3CfeI1iljcZO62lI9th5r7Kn/lBzRDnobpd1AwqlJsA+583Ne//AMQs5Uhuh+mPNwdiyp/RqbVX9q+42aXa/sfGRpt4e1rN/wDazXh0+6X8VtLn/u19m/7f2kIMvRPTvlXAy83f6cmsU+PVsrMzdCaEWPORczjn8jTaq/t9Sdml+70Pjn7Pum7W0vHrsNeHT7oYBt5AT/wmvs4+PNmVRV+H+isVbJPzk/P071kPjvaI5YfDnQHZiTg3Vycf/JU2qv7fUbNL93ofFv2fcD8UEgP1Uivf2fdH/wCx5P8A4TX3KL/yjLVSB/2ddP7VGCvzE2T9cnNbD/5SFoiE/wDZt08c8Am4mJA/rUbVb9vqNml+70PhiaZeMfJbTEj2Q0GlXhPFtLn28M5r7o//AJR9lJG4k+GfTju2NrLcTgAfrzWKf+UTp8KgN8Mun9rDHN3cc/zptVv2r7jZpfu9D4mljqCKUWzlOfURHP61pfTrvOXtpR91NfcD/wCUTp8m4P8ADPpzIHlxc3Ix9uawX/ygrBmkA+HOgJuHIWWUED6FmptVV+n1GzS/d6HxJdNuxz8tIQfQA1j+z7kknwH/APhPFfY5Pj9bYYxfD3p8D03SykgfkRWmT45QSLh+guniSc58Wbtjt+Kp2qv7fUbNP93ofI/2bc9/C49yRWs2UwOGQ5r62fjRZ4JHw+6cLH13y9/1rS/xdsHTc3QXTxHt4s3J/Xmm1U/b6kbNPt9D5d+zrpfxQuM+uK8/Ztz/AO4c557V9Pf4u2inD9C6Bux2E0v5cbqxHxdtlU//AFj6CAfeSbJ/nU7VT9vqNmn2+h8zGnXI58FuPpXp0+XGXAXn1bOBX0iT4tWjruPQ2hqvb+2m/wA61N8T7GVklbonQgvI2CSUA/U8/lTaqdnqRaHafOv2dOeViZlPY4obC4GQYmHoRX0mP4swHCxdHaGi8DJeUAH75rxvixE2QOjtDHPGXlP+NNqp+31GzDt9D5sbKcnmJgPfFYfJzesb5+1fRf8AtSgzj/ZDRsg5J8SXn+dB8TLcuy/7I6SoJ4All7/rU7VT9vqNmHafPBZzdtjfn2rP5Kcclcg+3NfQn+IiCTH+yGk4GGIMkoGPX1rR/wBpcHhnHSejlmJ43y8fzqNqfYNmHacMbByOCM5/iUisDp02cgKfsa7w/EyIqQeldHUj0DSZ/rWofEqNsEdL6R+bSZ/rU7U+wbMO04cafcYJ8JiBWJtJAcurDHpXeD4io42/7M6QM/xBpM/1rWPiKiHJ6Z0plxgLuf8AzptT7Bsw7Th/lJWHlUkZp8lL/czXbP8AEKJl8vTmmKfQefGf1rSnW6rn/wCt7TTt/wCJxj+dTtT7CNmHacd8lMRkxmvBZy5/sz+ldh/t1Hk/+YdPVvbL4/rXh62LDA0TTVJzz5/86bU+wbMO05D5SbGdjY+1EtpecRkj7V1T9aMPINJ036kb/wDOtUfV8qk50zT24/ut/nTan2C0e05j5aX1jb9K8+Xk/uN+ldQvVsoXH7Osf/hP+dYDq+fkfs2wHp+Bv86bU+wi0e05v5aXAzG36U+Xk/uN+ldEerZeNtnY5PfEZ4/nXh6slJx8nZ49T4Z5/nU7UuwWj2nPfLSd9jfpQ27r3Uj7iugXqucqT8paLjt+7Jz/ADrW3Ul15d1ta49vD5xS8uwWj2lEIZB2Vv0p4Ln+E/pV2OorhmI+XtgPTyVj+3JieYYD/wCCl5dgtHtKbwX/ALp/SvPBf+6auX6huVwVitv/ALXWI6guMea3ts/3hHU3l2C0Sp8B/VT+le+C5/CjfpVsNduhysVoR6nwq2R9U3sY8kVoBnJ/cCovLsFolQLWUkgI2c4xisTA/qO3Hauwg6kujaI5t7F5Gzgtb5wf1rKeOS4hjkvXhjZ8kxRwrGFGfXBJ/pVdt8S2wjjTCQuSCK87cCp16wdn27Rt4AAxxUCrp3K2Ncx8gH1qPW6fsK01rHQwnqKUpVigpSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAK2Q/iPIHHqa11lH+KoehK1JC+uTVhYD98C/4cVAU4/51LhJHIOBWMjpR01tIzRs0SgptwpzzVXqGWSLJAIyMgf1PrVnpxX5YDA4Wq+9wY/LnhuwFYLU1ehROBvOTXmfRa2TAeI27j6eta8+wxXQjA8wPWt0BwxxWkn2rbAfPg98VLB7/ETXkmBznd7HtWTg5NeogdctnioJYaSLwgFiZZPUlsg1qVC55FZk5PPNCQfxAYHoKEGCbFyGPH2pyUbsF96zJj5Y4H0A5rUWVn/ur9eaAyCuUBz5Afes81odvNgEla259qA9PKmvGHkXysPqTT0PrxXilSBkNwKAyVTJKiA7s8D0/rWauwLeGBsX1IzWHmXD58NWOBXg2b+ckDnt3oDOWbx8sxZnwAOMcVrSUxp9fTA/xoWLMxLYBouTG/7vJz+I+lSAKyP4T7Yrz070/hOcdveoBgzb41Ab8sdq9VgwxI58o8oArxCFwQAfuKyVyy4J4ByOKA83PgLuO0+lZk7CNjMW7H0rHGe9eKOcBQT6UB6zbAQVBPuT2ooBUZyTQqY8ghT/ADoHLKu49hgcUBkvAPetazELgkkZ4GazXPp7VpHc/epJZLWbcn4iuOeB2rBXAYkkAd8kd61htxC7iEHqRWWQ7BWZio7cVBB7G+5mYqDjvk4oGGGLKD9cdqwCgbm25Ue5xWOGZSedvf6VINoIA5HGKyUxlBndWoHbGSy59iTWa8qCOagkzxHgks2PbHNebITGD4jcngbef61h3rBYyRkAkCgZsCpHyrsBj1jrZGY49+ZAxIwVKEhvvWolnKgFiwPr3rws4kyGw/bNCCTCqlGKurQj8Ubtgj/XvWr+yYhGyD7+tYq4Rs5yw9jnNeAjB8u0fTsKAyLbuQoOKzWbygFcAezZpGEVhuljjJ9Tk5HtjFe+EAcRlWA7Y7fzoDByMcIrfqP6VmJtwGQVwMDzZrBl2jJ9PbmscgjI7UBkwBbjvWJyeFXIoc5AUZJrHkZB70IN0ZwhIAXHtWXzMhxvdsH1Y9607mCDJBHoM15sGVZvKD2zzQm5tbBP0o4fw/LnGf51mQMD0oAduSw+2e1AYqgCglwzEcjnj71pIPJznHas5G27cYwfrWvA2nOc/SgM92wZjYhvU9sViQCPxKSeftXr8Rr9a8BB/ECaAE5TyDB9TnOa2gcDBGMetad2EIAI+npW8jCDtQIxYYztbOPXFa/NIAHl4UdmNZscjvxjFYqu7aCRz7UDPY1IZmjOMD0FeNuXseD71tAWMAHhs8gNyKNnGDz96A0OgGOxP0rP+HOBxxWTDLr4CkOSAAPeteByMbmz3HagNiEYx+dZAc4zitcRBB9azwMeVTn1Oe1AeoyL4jF23AcLjvXnibEO1Nrk8MPQVrZcFt/lPt616jup5LHIK9+49qAz2sF3ONxPqxzXkf4T+EY53Z5rAhlcDGG9vWkiO7ZJVj3I3AEflQGRwVO0A8968GRERnbnGVA7/nSNGyQYyT6c0yQhwwxn86AyGQAaSRnwdykMPUA8j8qZyBxWL8rQHrEgKd+444x/DWGFBDSebPsc1lJu2DcB+VeyO8xy+4jOTz6+poDErgksCfYA0V2fPiAn6+1eBckso8o/vc1iPw5ZvXt70BkpCgEHDg8DGaR7iz7zkmhZdyhQYz6knP50QYZiDn6+9AZZI71iGbY6rnaTnOeK9PetaxsxOASM/lQGQUR4LggEcY9ayBB3FztHceXua8wqeUjn3B7V7vVyd5IXHAAoDHdgYx3Pes2b94oZg7f8X9KwZSxDOTj0NebVLHBAX3NSDMfiY5H5V76+1YLjnbXvrUEowlOCKx+4rKXJYAc8VgKlEGW45+tbFAdsNgH37VqFZLgHmgLe0XbaqR+8XxOwrpJxG1vulU8RnbtbGG7Z+30+lc9p/KW4zgNJkc9qvroDwowPYnPrXPLU2Whyd6G3HJ3c96hmp16uWG3kZzgVCPc1qjMjz+laa2zHkVqrdaHPLUUpSpKilKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUArJPxCsa9X8Q+9CVqSlx6D8zUyEBmXdyKhDPpxUy3YBlOcn61hI6EdNpbIbcJhTxjJ5IqJfxhVlWIkgc9sVv0nBicsdp5IPbP51HuMtFJyTn196w4m/A56YYc88VhgnsKznHm5P/ACrDJxjnFdKOc9B25HOPpWUAPiDA5rA47Hn6V6jecZ4HtQEpyofAHiN9ew/L1rFDnJY7jR8kjHrWcIXZIWJ3+gxVSTSe/NYYP6VsYCsTUhmpqxNZsDjNYcCpIMc1IzwMCtHrW9cEDPJ9qMhHmODzXhc7SFc7B2FZd81gFUIWyc+gxUEnuFGDnP0PevXBJBC7c9gKwU7icnHHrWaDuArb6sVPMvnb/hXpRmUqgOf4hmvBhc991YhdxITmhKM1Bxgc1sTy859PSta8DBrID+lVJNSsW42jvyQOazG5iwiB2+tYgMAWXj65r0KFUlw3PbmpIPQRkeUsPUE16HZWIAGTxj2rBQP7wH3r0KWIx+H3Pb9aAyiyCwMYkx6Z5rFBgc4JBrxfM/lVgO3l5NepjBwMD0BoSjZHjcAxKjPJxnFaS+GdVwQTwSOa3Ac8nvWrYdzbRnH8qEsyGQVEYbf9a8LFNw828/WiqWy7bmUdyKAKAXI49BnmhUE+VcqQfc17sBICAt7jtXhXy79wHtzXiOxbzBnJ9jQHsmJGP4UC+9exnyfemADiVTn2r1Bhc+lCTL9a0ocfizgH0rcOfvWB/ARtHf8AFUBngxvyVOD255rwoc4xgn0ocZAJ3kDjB7VkSEYDlT67hViojVlJYMAR3JNebSc99tZKVJJKl/txWvBbPegNjBMA5O/3A4rLlTwcVrbIReAFz3962jGKqWDSHBJx9+1Y7yxyePTPaje+e3asWLSMpY5PuaA2OFbao2Dj0zWAIDnkFfqKN+P955vQba9XyEBkGW7bjjFSQeKAWJztH1rEjDqRzz6VkwAXkYbPoKyB8qKSME8BRzQG0kkZxXhOMmsvuTWt+EbsPpUEmtzuYYI4FeFuwwAftXqnkEAEn0xWYfYoyu45/hbGKkqYooJIfP5CshtPHIX3rwMivnLqD39TWKlWBycew7VIDN5SA3BPYVtwNgrUQAvBHNbvxKPU1DLI1vwuBjLGsfTaFGR3IrJx5DnHFeZPmwfDGOBUAy4Ozn6E44rP7CtIJIAycDtk8Vsz5tuRj70BjKAAeOT70wBgKNpxz5qPiQAqcv2INYmNlOCpz7YoQbIcbDinBNeQ428cAGsjjNCTVuXc2VOc8YrNDu/Ex3jtx3+lawcMSMhs8VlkqQ8h3E896kgzlbxGLAYA7HAz+eK14CjdIDg/zrN8nknaD3Uen1rEwlsMoJX3qAYDBJzkD0xWQB2HJAXPb61lhlyIt/bk4okZK5Kng/ioSZ4GK9YBlPp9q9xitbOFGT2NAYgAckZ+9eHO4n09hRZBg5GSex9qCgPR5+GCoBzn3rW7hioxtUccVmxCjntWLbWAKrs2jnLZyfepB6F2MCSAT3JFFyd5znn19axUgEtuGR7jNZIcluc81ARln6/lWvxWJIJ49hxWwN7gfXitGRk8Ec+lSSzYXBCgKF9/rWQK5wdxX9DWvAPZh+fFZKp9aEGzykDjkepNZMQx/eZx9BWK8tgA5PYU7Y+9QAw2rgetFHcnjAr1iD9DXgAHI49vrQk1SHzce3esMfXPGeayl/H7cVhj17j3FSQZCvfNgZwAfrXinHpkfWshhj5Qc0BfWEXmsx2yc8+lW93xGOw4JB9TVbpseJbYMM+UkirS+GEB75BrmbzN1ocvdtufnuKhMQfxH86l3Y/eNkAAHFQ24J9MCtkZEWb8Q5BFa6zl/HWFbrQ55ailKVJUUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFB3FKUBJz2x+lTbZtuGDY9OKhA8DAqVAe3GaxZ0ov8ASpWwy8EegNJ/KHUhRkenpTRx+/I7sfStt/GVnbPJPua53qbrQ5q4xmtHbtUm5GOD3z2qMRwTkcV0IwZ4STjPpXsZG4VhWSfiGPerEEts8elEHfPFC+MEUjI3ZkyeOw96qSeMPYV59O9HJBGR37Vjkntz7mgPCFJO5io+gzWDhdx2ZC+m7vXrPjt3961596lEHrBcAKTn1yOBW1RlQaj963rkKBRkIyxyc8isQhYHLYX2JrOvCMioJPMYA8MZI7kDtXi8lmfcffFMsvA5X1B7Uzj8bYB7hakix4HAzhQc+/pXqqAQMbiR2BoylDkr3GQCQeDRAGyWyD6Y4oD1RtyDwfasgO1YLxnHH55rMZPaoJNYQYfLYI9D614Du4wSfTmsgG3SbQMepNYbhtAAwffNSiGeuApAOeO9Z7lHlxuRu+TjB+lMgLhcpuGG57/SsGXzYTLflUkGwtsYrG5VSMHBrGMLnauB7FjivVURtubB4zhh3pDIyb9uFLdzjn/lUMlGYRuDgge5GK14XxG3ZA+lZliTySfuc1iTiQ8nGPvUFmYCNnDbAxUcnFPJt/Ed3tXm8BCF3c9xnihJKBasUPW8ijynJ969Ry7jcDnsAorFM7gWBb71mAocl8oRzgCgPFO0yEMFJGORk/lXsRPh1iAF3O5U/Q1nGQy54/IVDLI2Bl25IIb0rWkQJLYLKO+K94zxWoMcMB3zxUBmSkBixJUjtihBPnchmJ9TzTLKV3jP39azG0yEHapx29BUlTHaSchSqAc4NZhjghQfDHfNYAec8bh9KxKk54NSDNhH5SCCfasvStbEhF7Bc9/U1tGMVUsYscIawBUx+7H3NbH/AAnHNawm5VCg5PfNAz1ELA4O0Ad8V4uS+WJP1HJphgxDOFwMe9ZITj93nPqakqZbk5L8n6mtZAyp3DJPYV4ELNx69qyYnhQoGO5FASCeATWub8Jraoyv3rVMPKagsYgbXVT+8OOwNeZIYhs59aKi7hgtjHfGK9AUK5Az7EmpKgjClmXPtzWBxgbM59a8VSSf8ay4A9SakHhAI3EknPYVIHC8gg49a0tyhxwuffmt6k7RhuKhkowkwV5P6CsM7vOzbmJyRjFbHOUbjH2FawG2KS32AqCTzYSfw5JrMuFYFVC8Yx3rx+4JO4/TiioCMtx9jVipi7DggBR9KAhciPLMR35GK9fbuAySPtXgkAY9xnsfWoBshJCc4b7isjtJ5GK8gJKHjj61s5HGePtmoLGgrgPhxj29TWIG7ARTnHPrWZdSSMhcdxjvWAPON2wH1oDxG2Nkdx7it6BclSGYPyMehrSzkqEQkjPOTkH8q3qe5APH92gNBZgT3XPpXoKlR+InPbsKycZYk5JNeAUBuOw4wWH86jzjC+4PY4rd2zWuRT4ZHcdzQGhSa3IM9zitA9DWxST2qWDN1zkHHFYkDyrwPqazAJr0HjgA5qAahGN+C2B74rZsCLjv9a93HGD2rJvTFAax35qPkk59c1I7Kaj4HvUolntZJxWIPHHpWanI9M0INgbH0PvTOMV5Xp5+tQDI8jtXmePelBzwCD96Eml+XOKxGR2ODWUg85OK8HbvViD0nsSMH6dq9AJ/WvVLehB+lZBRuUEbST2PFQDp9LUC4hXPIiNTb9zt2tjA/SoGmjbeJxgiM9/Sp1+wePleftXK9TdaHMXZBc4x3NQW9ak3bYkINRM1vHQyI0n4zWFZSHLnNY1utDmeopSlSQKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUBvU+UCpVuc8VDTsKkwHnGM1lJHRE6PSwWu4wnH1qw1iWOZ12DDIMMfeqrSikVwCxPOKtdTidyrqu2Mdz9a5nqdEdDlLzA3YBHNQ/61Y3q4Z/QfWq7PsK3joYPU8P1/SvByeafyoKuQS1IIGK9QZPHArBeQM1nu8xwMA1UlGL8H3rE8jms35IrzHFAzU1ayOK3MOMYyawZGVirjaR3BqUVZr7fU1IUnaAB+daMgemTW5DkYbPFGEejj1rPbkcnmsK3KcqFIAAye3PP1qCTWCQfL3rEovJJO6svU/yp644zQGDKoGF4Hua8kbICq2VH0xXrtgggKT65rWxLEF23GpQMouxyf0rcrc8CtER4IrcvcDuagGJBLt5QRj1rFBHscsTuHbn1r10cSnjGR/FWICqMk5P8qkhnvLDILbq8zjkHLH2716VZlLAgKeODWSbI4jk7mYdlOCPvmpINbZGGQEAepojF3Zm71kWBwMEn1p2fGAOOwOaglGWK1zf2nrjFbM4FYvjxF35AxUFmYAbVJxn2JrNSGA38AeoHrXh3SHHmKjtWRA2gbiT/AHQKkoeZJJc7mA7HtWMbd2JIb04rPaGPoAO4zWO45ynlHbgVIMSd/oSx7mtkfAIx2rEuUIMefqaRNuDHtmoJRt9uc1goJ3BVBPv7VkK0YJdsZqCzM9obAUMW9a9IXcFUc+pY16RgqqgqfXmsXAVto5x34qSh7kZJLYx2wKyiY7TyCPbGc1hv34AUL/jWTEtgf0qQettYBhEVx3PpWxRWpsoqhtwOeAa244ByPtiqljx1AWsAcJncPsBzWxiQhHvWkDcoGNv1oDJF8RgDwPcCsGGCwUnB4rZswR5u3qKx2gliGwB7+tWKnqABQT3+vamw7M5G3PbNeEMw82SB61nwEGcZ9Oagk3r6VhOPIa2AeUVrk5QjOagk04yBls/cUKAdjn7V6QQgO4H6ViZCxBKrgDsOKkqPKG4GR9abiM44BrNXDMCVBB+tBnd5B/KpBj5imcA49T6VuzhR61qKDZuLevatuBsWoZKMWIKn0rVghBkACtr4CsDWhSVAwTkVBLMl/OhrZu8owoB9weaxVQWwTj61Yqe4Qkex9RTYrNgkgAe3NYldrkISQD/OvGzkjdz96AkQAbD969c4NYW5Kxle/Oa9YYbNU4lzSQN7Hjg9jQnJztH0ArF+ZDigPBHbPerFWZ4dFXdjaTkDNeM5J8vkU+i+1YEcAnt6V6GA5LFT6cd6EGaMuW3EgY/M16Gx3rRuzk4ySa2D2qCxIDbsVqmJVe1ZKD71hOcrke9CTSCOKyHesfvWS5FSQZrz3rPsBg968B47A17xUAY4z6UYn2r36cDHv61k4XI2bu3Ocd6AxYq24hdo9gc4qMqg9mH51IdQqthgRnH1/So6kgH2PfBqUDLw2GMDP2OaKMcHj70LnAHYj9ayVmbuQfuKMHvr3oO9ZHaRyMH6V6EBxtOagHmea9z9BXu33HasaA0PkOSCQfpTJPtXjHznn1r3PHYVYGXGO2Kzhb97GueNwrAEDHpWcO3xkxnOR3qoOssWjS8Eb/xJnd2Citl6eMDj6VhpsebiUuMgIOfrWeoY2kkEexrn4m/A5e7x4jZ9ahkcfepNx5pDk1GPHf2rdaGRFf8AEaxr09zXlbnM9RSlKEClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAbI+R9BUiP8XFR0PGPrW9Dhqzkbw0LizYLJEWOTmul1GUNbxYx9AK5a3OCmBk5rp51D2IkJxj0Fc0joicvqP9o2e5FVzRlFUsVO4ZADZI+9WmoKN4zwuOcVVs4Y8KF+gFax0MpamsjFM+3emfavOPXk+1aFTenK5rYp5rBDla9Hf61Uk9yCafXFe8Z8/PHbNYk5yO1Aebip8hKn6HFaWatrLjjvWs/wA6lFWYEkduK2Qdq1ntzWyHJG04qXoQjYO/tWwkkcc1rrcACowSWyc8cD25qpY1Yxya9O7aM5A5IFOxxXhx3zk0BgxxWsnn3+1bNxQh14IPBxmtRqUQzKE8mpKs2ByVH04qPFgHjmt4JNQyUa3j3yeXitb9toX881ufcrZUgcVqjyWPl3GpRDCgBeD5j6VkMRnzLk+xrPJZcFgAB2A5NYcnGRQHq7d2WTdnsAcV4qbTlgVYngemKAhm83lA/uiveA/H6k0CMsen868YbSrZXA9z/hTHNeSnbtOM1BZjIyWfdsbIBAxk/as4hkHMqBMc4Pm+2DWo4Y7iuF+le4UAlgwHGBipKgoh/s3ZvcY/xrzf5AuSSfSvFIycg4rJXKEbkDA9wfUVJB4QxZQ/I9BmskwGYAAD6UldFUYiKuR6nPPvWNv3bI71BKNxJIUYGB2wKisSHbFSiK0EgM2VyfTmiJYxuTIB+pzXiKP4ice4FZqBjzE0G5hsDeX2qSoUjcRGQB7tXqjdnaMn3pHBkkrjC+9AS2XK5QegOKA1NkketSU+taS7FfLgAn0qQq7lOPQZqGSjx+FPrWoHyA7snPC4rax8hHpWgHKhcDg96gkzLneGKhV9hXhZWbnO304rwkMFHAr0HzbQePcCrFQN208evcmsXycHFe7ck7TwPU8V6QMDk5oCUvYfasZeFPpis1GQOawnHkxnk1UsaQ25QqgDHrTBZSAFGO5rAqwA/wA6zYDyqqnce+asVMVTdnGOATya9HbjNZBdhwUDNWakgMpYqD3FAaWI29z9hW7PlA+nvWtgSpwnr+LFbNvkFQWRix8jcVgRgJkj7CsyPI2DWoNlQGHA74qAzIkc4A+lebvL65P6V72UZXGexr1nRmHlZQPrVipie6gD78969ZcOQuEx7nNAS5JIYqKxUDaxdTj0xUA225wvHv61maxtRvQgsFH1rayqM4OarxLkQlPPxlvSsdrbQWyFr0YEjbhuHtWZQJzIucjKgNVirMWOQMNuP1ouHBMhIOOOO/0r1GYgqxCqe/GaxYkkMew7cYoQeE4GAuD71kNoUEt6+1ZBjcMS52uf5/8AOtedzKCTgHihKJIwBg8/nWqf8GQTg+/pW7HqCK0zZCnGMeozUFuBpFZAYNeKhb8OCPqe1ejg896sQbAfzrIcn1rEfWsu/wBqqD0g5x3ow74oO4549qyPNCTW+QhqOMd+xqRLxGfSo+eBUohnoz75raPTNahWwd6MGwDPrXoHP+VYrntQZDH096gGe47sZyPrXpIIPpWO7nnmssjHtQkisMMTXgGe2K95J45+1egj1qxAAwK22y7pk+9a/bmt9oMzqcEgc1Vg63R875iTlfavNWIDHB4x6VJ0RVMExZfWoupKWRiRtJOMVzcTo4HL3PLH2qM2Mc1Jn4Y44qLJ2NdCMSGe9eUPelbnKKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAZIfat6d+3FaEremciqSNoFrbehPYGusVDLpWT7cAVyFs4HB5rtbDEulHeCMCuSZ0wOS1DhlzjtVOcEnPFXmpKoxt5A9ao2GXPrWsNDOWp4Txgce9eY/SvduO9YmtShvjPHHArMfi+la4u3NbPXiqgxfg8V6KOORnig+tCQeK1P9a2McVqLChBiTWUPc1gazhI3ECrFUbfWtw49MVqrMHj8qqWMO5JoMAjIz9PevQvGScD+v2rz04GBQliXb+JSeTyue1Rz/KtrCtZHPIqUVYiPmqUKix431KAzUEoykTdET61rCMFA2hSfXNb8nYeK1jJ9aIHgTbwT+leMvGcfassYPbmvDk896A1cpGQrcnvxWtMh+a3Ovv/ACrWr8gYAFSDdtU/xCsZVGF5GK9xg9s15LnaCDzUEhY1weCW9MtxWHguzY3Zx9a8OTksea9AyBt7+pqSh6yEJwpyP0rHbgAkktXoLYOScZ969YsUIZjn0AFSDx48bTI/J9jk1lGoBIXP51iItuGJCg/rW6JwshOCeP4u9QyUe7cfStOPOeAa3ck4ArXgrIRu2fXFQWZhkknPNZHlBkgCvOW4H4R+VCN34BhfvVihiDkkA4BrMELuUnK+ufWscA42jHua8DAKRtH3oDIHCHsBn863RjcRxwfpWrDbMgAL7+tbQWZR5jioZKBBVDkcio+9toAAHufWpEnmyfX14qOhLLtZgADntUEgEYzjNZMWXlSAT6D0p5n9PKPyrJY2bzRrhB6k1YqYRg5OV3VltYpuJG3PbNMqMgEtn8qyIVUHAz9+agEgHyisJcBTWY/CK1yDKGoLGk4wDhgD65r0quR3I9z3rwgBMqSTXhZ2IZiWxVip6cg5AIrJULA4A+tFly2fLz7+lAQ244J9sdqAxaQ7CuSefet3IQD6VqIIiPmAB9PU1sx5B61DLIxb8DZ/LiteM7QzLj6elbGBCGtfIVT5fpioDAGXAHmA96yLGLcu0EnjPtWIOeRkvn0ocjAK+Y9yasVBPlwCST35pjYNr5P0zWW3e2HZUxWMYGSfKcf3qgG2D8HsM1uAzxtzWqBht/yrd9jzVWXRFXCliwBHpzXnP4sgZrFjiQ1sX96e4WrEGKbnO3PBo0ZY7Fy2K3gZAGRgV5nAwAKgEc7lUAAYNbCpcAlcODyfet7P5SNqjtjA9KwHJ570FhjNaZhhakd/TP8AjWmfkDihJHx+VZKOax7d6yWpINoFe4JNB27Zr2oB4Ad2BWXp3rzswrLBwc0JNcvMZqMKkzfgNRRUoqzMVsjOM8Z4/StY+nes17Zzzn86Em1cj6islHPtWOe309RXueeRUAzwucN6eo9PypID68+mRXnORjmvCSAccUBE7c1mD+deDB+nNehcDjn3qwPQR9ql2IJnA/F9qiDnvU3TY/36lWBb29qo9CVqdjorHwZdpwA3BqDqrsPxdzU7SCBaFscsxBqv1UjHNc61N3oc1M2ScVEc+U1KlGDxUSThTXRExZFpSlbnKKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAZxgs4A7mt44OBUdDhh6VvHJx6VSRrAn2gJbOOO2fSu70Yo+lSKxA+9cFaEeIM9v6V23TmDbShgHHsRXLUOqGpz+qqq42kHn0qhZRlzuAYfw+9dFrAG9wi7R/zrnpRiT0GPWr09Ck9TXtyuSw742+v3rHcV4HbOcelZZLEKMDPHPavHRVOA4fjuvbP+vWtTMyi9a39sYrRF68d621DB7IPWsQfzrNhkc1hxjjP1oDFiOa1H9TW1jkDtxx2rUwqUQzEjNZQnDmsDzWcDbWOADkY5Gcf5VPAgk4/0a3KVTlBub+8w4H2HrWon0/lWS9smqljxmZie5J7k96wPH1Netnt2oeKEmpwR3FYe9bWGa1Ec81KKs9QjdxzUpeexqGn4xUwcUZKNrYCeprUCR9K2HlRjvWKgEnOR9frVUSY4wO3614SfWsxg/iyPsOawNSQanNax+IYrYwzzmsP4gKkEgY53H7cViwyBya971hKcJUEmtsDt3r3aeOcn2HoKAl8DAwPastwXlBg1YqeAebap3fX0r3xCF24BArwEhS3JJ78V4pZgBihBk5JK5G01mi7nPIPHpWo8EZ5PtWxc7znj6CoZKNv0H6VqYlpD5QcD9K3KxAHOK0tgyndwPZRVSzCKUG/Kn2yM0MpySwBz71rLkNwMAHis3KsRzn6kVcoBypy2PyrxwSg8oAr12yQgbI98UMfmAyWFAY+E5XODtHNb1UqBWll3A5bCj0rcnAGahlkGJwc1pB3IRkDB7Y5NSG/DzUVQFG7IzntUBmxAUG/y59ARmvCxdhvzg14CXdWzg+9ZEgNnuRVio25Y7VO2tb9+BgVmGY7vKSxoAQmSB370BJXsKxmHkNZJ2H2rCUZUiqlzUGLKFJ2qK9GXBBcAD3FYGMoBu969Zzwoxj1OKsUPEXL+UbhXjEsxAyo9hXuQrcE4PfissBmAVSBQAbhEcKMD+KtgPlWsWjAQ5Yn6VmBhBVSyMWOVYitQG5AQFUD9TW49j9q07iCvK8dsUDPTkHdnBHYVht43Mf581sIJIBYe9eMAx4AUD1qxB4wXcANw9808Lc3kOfqeKBh+IklvSvAWUkjnNQQSLdcJ2xzW3sa1Wx/dHIOc1tz71Vl0Q32ljyQfrQcfasXP7w16vfOKsQb1PtWY2ng1ioyBXoI/P7VAGP0rIKO9eAE8DlvavAe/Y/aoLHpOa03ByB7VuFaLjsPSiINNZL3xWGD+VbE24xjJ981Yg2KM0HBp27imfaoAyWOayH19K8HcelZD1+9CTCXlDj1qPt9+KkTNhKjli3JqUVYxWa/SsQR69q2jB+3pRhHtejucUxj04oBUEmZODwc/lWLnIrIZz714+NpoCLj2r0Er24rEDuKzBI4PNWB6SCO2D9KmabxPn1HrULAPbj6VYaYRvYY/WqS0JWp1mjCSSzb93sQE7T3Jqv1f8DA8Yq70+Lw9Niw58wJxVBrJGGyc1zrU3ehzshArQ/Kmt8uCRUeVtq10owehFpSlbHMKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAejuK3KQe1aKkJtCndndnjHaqs0gSoGIII9K7TpklkYLyx75riozkiuy6XZ8MFUcjjHeuWpodUNSNrMQLSMODk5rlLkjfxXYa1C0ZfccljnGa5C6/EM1NIipqR68rNtuF2huw3Z9/pWOK3MjZCfNW7vWiL8XepDuWYEnPAHYDgDioYM2xjnNaQAO1bD+HFYLxUEmLfatT1ubjmtTDNSiDWRXsX468bkUjGGGeKngVJeKyAxjPtT7dqyHHNVLGHY02nGWOBWTnDeXivDljk4/pQlmDcDjitBGRzwK3t/o1rP1qUVZgv4hxipQH51HUchiRjOOTUvcABgc+mf8qMlGwAlRngfX1rTkhvL+tbeduW5z9a17znvj7VUkE8AYAIzz6mvAM89v8a87elCT61JBrk559K0j8Q+9bn7Vp4zzxUoEkHgUkXyHdxWSthRgYryQbk4FQWNW3AAU5ptK+Vjj+dYbCoB7V6AWHBA+tSUY5wQxIHoK9wFUZLbj2GKBSPM2MfevHbcM55qSDMogUNnJJ7e1ert34T861kLtGCSf5VlFGVcE/1qGSiQPoK0TMUkIB/St4GfTNaZADJz5aqizMMDZkA7vf0rzczgZGayJLeRTlR+WaHJPkGMexq5QAEtlgMDuK9DBicYQewrBAzNz5qyRTliuOPegGPLwhJ96kIv7sE/ao4J2tnJzW+MeQetVZZGT4wfSoincNvAOeeKlsuQSfatCLtUvkZzgCoRLMk2qPKcnPt6VikhifJ9sjIr3BRcsuWPYmhc7ACdxxjPsKkoeMxMQPOQMZ9+aw2MVBPYVkSPKASwoyZGey+2asCVH+Ec1jN/ZkVmuAoz7Vrk/CcCqcS5EwxXO7tjg96zbAUBQc/WvWB2KcKvtRnwwI5+9XKHrg7UZmGSOAPSvYzl9zMO1eMpJDHGT2Ar1z5gGVQB6CgMzhicZP2r1MsnFasF87PKPYGtsP4APrUFrh/wGo21QO+alyDCk/lUVSTjy5H2qEGe7RkbfN6nFZEqy84GOw96xO5Rz/F6CgkKA7ABn1NWKmRRuDgKPTFPxOS4LD68ViH7YGD71kJM5ByR7Y71ANtu2U/Otpx9q1wEeHyuDn0P+FbdoPYj7HiqF0QJB5zivU70dSrksCK9QHP3q5BvXnt3rM+Q9w2e5FYAYxWR55HBqrJPePTtXgwTzQAnjH2rwAZOf51BJmB9Rn2qNcYwMn+VSQB3NRbnIwMVKIZqHes1Ga1jvxWxf9YqzIMwDWXbuKKP9CvT+oqAB3HrmvTWI79s1lgAe1CTVccDGa0gZ9O1brgYUfeo4JGKlFWZg+1bV4PBzWoc1tXtQk257elFHJB4rEEVkv6VAMtm5sBgp9MnisJdygrIMEVmvJOO9eyHMe1hwOy5/of8KAhcZ7Yr3GBz2oEPJXnHceo+9eg8AEAf41IPMYHBzVnpYGSTyKrsYGcVZaUBtctVJaErU7W1YNYxsJjI4XHbgfQVz2rk85AwfSuhsvJp0SkALtyMdzXOau+c5OawjqdEtCgbAbPY/XkVEmBVfpmpb5btz/Wocx44zXTHU5paGilKVsc4pSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUArcnqD61prcuMgDJ45qrLx1JcZziuu6VJM+0NtyK5GIEgY966vpZGW6QlThuxPrXNPQ64am/XeJnHr71x10vPHvXddQxt458o2kZFcTeAgkfWq0hUIWMUx71796xJz610mJnH+Kt+M1oQYbnv7VJ/xqCUejtxWvnNbV4GexFa/5CoJMWFanrd9a1N9aFTWfpRB5/c16f0rxT5gOKsVJvtWSjI5rFcjHr9azAJ5NULoxcY5rzB7Nx7CsnGMVgW9+TREhsAfX39qjsef733ra/ua1YyeTirIozAElhk/nU5F4yeOPWobbRjb6d81LQErzxn3qGEbSwYDaDwMc96x27TksAPcjn8hXqnaPJ396xKckyMR9O5NVLmJI4CKQfc8k14U2/jIB9vWsi+3hBtz6jvWvGPSrEGLnI44FaMc1uOMHdnt6Vq/iFSQShgKB3rwnOaKQM5GfbntRsenFQSapGGwDA/KtZQ7cntWxkJ79qSDYyjj9akqal4cby236DNZYbcCayHJ3OOPSvAuTuYkA1JBkwb17VmAd4G3Fa3GCPNn2FZqG3AsP1qGSiQM4+laZ22SKVw3HPHrW7HFaZiRIMECqoszSisznyk++K9C5PYhfWvfMrnzH64oVz+HOPrVyh5tBfyAgDvzXgyzEIPyFekAjCjA9TRQEbyt9zQGWSqlWOPpjmpCP5BgVHBUhjwfqa3RDyDNVZZHpyVPNaAcIe2c1vbt61qUxhCSpJzgVCJZi+cAtuIxxk0cNwSwz9K9LEYLYP0r1DlskhT7VJU9ViFwCF75PvWODsJVScevoKyBYuSBv+lYvKQuzPGewqSESVPkGPakmPDYnIrFPwikuShNV4lzQNpwIwW+9ZeQ53hi/sDxWsBo48k4BoSPLtJJ9auUPX3japAUCsl2rzk7vSsWfa/AP50WT8WKAy7sc5Yn+7W2A+Q7vetQm2AlfLxW2L8AIqrLI9flSaiKrhQc4HpUuTlTmoQJxzkiiDPRkZOabiQN54FZ4BXy53GmFJC9vcmpIselt4UcfYVjISrYjPYUzuO1QAB6471kMZ/djP3oQbbYHw8n3rf3HFa7bsN3PPNbmxzt4FULormYq5wTWxGyRkAH3rU/9oa9SrkEsLwMnvTt2rWpI57VnncOaqyUZAg4GMGveNqggl8988YrEfavQcn6j2qCTLFRbg/wt27/apZPuOfeotyxKquQQM4GPepQI/JJrYmQeK1gZragPrVmVNg7Zr2vMHFZYCjjOfeoJPQOeOftXvoO1Yg5b2NbMYAzUMkjXH4RWlRkgZx9633I4GOa09yM/rVkVZ7jntz61mnHNY+nuKzX6UYMxhu/l+tZrkDB7ehrXjFbEYr65B7g1BJ6Mbua8lOIyTyD64/rWW1WJ25FYzEhMn9RQGgD8OMn2IPP5H/CjAgbj2J/EP8RXinHbv7ehrPfnkcn1/wAj7irFUYYIXPpVrpalBIXA4HrVXn+7wPbNWumJmNj2GKzloXjqd1DhNJi8vJHLe30rj9VI3AHv611IUx2aMSTkdq5LVSA/HeueGpvLQqHBJwP5VEnYEcjn3qS7d8/0qLP2HNdUdTmnoaKUpWxzilKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCt0fIrTW2M8VD0Lx1JkPcY7V02gS7bxAvY4zxXMw+ma6LR323ERxzmuWeh1R1Om6khOVc/xLXz+/GCR9a+jdRZa1iIzjA5/Kvnl+uCxPvWdIvVK3HtzQgL2OT7+1ZenbisD/Suo5z2P8fFSu2NtRE/EKmghQPVv6VDCMlXAyfXsK1sCTn+lZqfzrx847cVUsaTWDcVtxnNa3wO3J96sQzSfrxXinzAdhWTH17Vj2YferFCcucLnnjtWxTitaHKjNZCqM0PJeQMVhg7uazcVhgmiBi49zWlsZ9BW8rgdua0MMn6VZFTEHnjv71MTkcnA/rUMnHbj3qZEvkDN5VPqf8ACoYRuBwoKkc+xyaxMZXmTyn0B7n8q2o2wfu/KT6n8X/KtJYknH6mqljzB9u/pWLcZrPaSO+PrWDYUngkCrEGBjOCScAVqcYIxW5yXHP+dR2GOBQgkJyuSKz257VhDworeMZ54qCxHlBxxWAQhd3BJ963uodsE8V6MKePSlyLEcI3G44HfmhYdjk47YqR3OWGce9eFuchQKm4sRh+MehrYoYyZOcehNZNgAnAJNa1YlxmhCJQ7VpmbY4YDOMVuAz9q0TtsZT3xUIlmJZlYvxknOBQSksTtBrAyl3zjFegMPMeAauVMgcqcnH5V44JQAAAfSs2OzAQ7j6/SsCdvCnJPegsYkeGpyMn05qTCfIM1oKhF8wJat0PMYqrJRm3aogXG4+n3qWR3xUbAw3B71CJZlkYBjU8dzWIBkyxP616QfDwAQPesVOyM9qsip7HuIODgevNZMwMe1EHHdvWsE5ByMmsgo8PJP5ZowSo/wAC8Uk4jNFHlWvZR+6OarxLFflnAHoK2ABF7HdXhAEYK4B/nRSduW5+9WKnobae4LH+VZeWNhyH4rDw+AcjJ9KzYHI3YP0FAYkB8kkDHpUmL8A9K0bSM/w/TvW+H8IqGSj1vwtmouQ2MDA9cVMYeVvtUQYJwhx75qESzasTMNwwtYMhUlSME+pFbgSMYNGj8VtzMc+1BYjBRtwAd2e9ehfCPm5z7VK2gDG0YrX4XJIbH0pcWMohhPzrYexrxRxgelZYwpqCSvZSS7egxnmiVi34zWSVcqSV7UwK8HAr3g96qyUe5weDivRjP1rEnB4rIAk1BJsJx3FQ7rG4CpgbaCQAeMc81EucZHt/SpWoNK4BG7kVuTGPf860AYrbFgZyu7j3xirMqSdmACCCPbPNY7ck4OCK93L7GmT7/Y1AMcEEk/rWfIUc5+teKSSQazJ249DVSxFuDyMcVrGe/b+hrZcelagx9e1XRVmQ5Psa2quBWsDKgjjPvW1MgdqMGSo2O1ZBCTg8fnQMSMg14AfTP2qCUChDA5/MV7PKrIe4P970P3FBnmtU3Ce1CTTke20/qK9+/wCtYKcY9foazXjtVip6D/1rorFMREDgba57g/Q10mlqTFluc/zrKpoXhqdLO4W0TjsorjdSwZB6feu4vodtmjccADAriNRXz5+tYQNplTICCcioc/4qnvwKgz4zxkfSuuGpzT0NNKUrU5xSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBW2I4rVWcZxUPQtHUmxcYzXSaMcbSff+Vc1Dnv6Cr/SCcgfUCuWZ1xO31uLfpscoIxgcf6+9fOdRXBJPHHAr6Xdwyy6OuDwFGa+dakVjZtgOcEZfk1lS1NahSnnkjA9hXjMX2rj8IwMCmCSST27k+leMR2Ucev1rrOUKcNx396lDsPX61EHcVKTsM1DJRsWjHyUWsmUbfrVSTT6cdq1tWw8VrftzzViGajWv1FZmsRjPmB7cc1ZFWToz5RxWa1qj4QVtWsy5njA3Y5rAqzDcSEU5IrI9jWtVBJyP50B4wyN27Oe5Pf8AOtDAseBwPWpDgDluPZRWiRiRtBwmc4HbPvViGYghOV5Pqx7fkKkxsWXOTn3NQzipUBO0f40YRJQcZz+ZrW2M8c/etgxgY9O9az39qqSMDGXJz6AVieftXp57Vjn0oDBvY960N3qQx4JP5VoJAByuT96sQzfEfIDitoOQBjH29a1wcqO9bgOaqSam/FivcYoR5jWXegABxnFYnvzWXJ7n6ViRxQGtvXitafjFbX/rWpP7QD0qxHEl9vpWicbiBUjFaLjy4J5qESauEOMZrL2L+b6ViP3jZxgCtmRkE8gdqsVPZRjlRjgYIOR/1rWij8TZrPcduJMhAcgVqkVgM9hQGecKSwyPrW6IkpmooDkfSpUKkR1DCMz2NQwwDEPnHtU3aQCahnaCT61CJZuXkA54B4FbWVDyVXnucd6jk8AsePQA16rbmzjAHuakoe7Mk7OBWshQOMk1t37sk5ArDfgHA5oSiUnKrnvXsw/dmkR8q1nMP3JPpVS5WBxwAM4rYCHPnO1R6CvFAxlffvXkiliNpzVipknc4OBTDcc49qKD4Z7AD1plTt7mpIHm2sFYY9TUiBcqKiyN/d4qVb8IM1DJRsdeDUSAZdj7VKc8GokX4jg1CJJOOeTWSEA5I3D2zisBWdQSe4HrWOc9uayxxWOcHigMwAPWh7HNZEqQMc+9YnsagFcRlzWS8c14xAYge9ZIx9KuVNi81mPagbcMHFZYx2qGWMWGO1ZocH61jnPeshyRUAyb1qJc8MMcVMA4P8qh3f4hUrUGkHnjtW2P61pxitsec1ZlSRjj3pnjtXgbjBAr3I9BUEhRkmsm5wPpWKms29KqSRbnuK1AgDnvnitk5IK4rUP5VdFTMd+OKkL2Ab8jUcc1vXIAqAZg4x/e96yDZ+n9K8UrjGK9A/OoZJ41arj8PNbR+I55rTc9hRA0cZ+lZrWtfcVmCfWrkG3bxzXTaIoMa/8AeFcwr8ds10uiMWVFUclxxWNTdNIbx1uqIBbEAnOBiuFvgfEx613usgi2Iwc4GTXAXrYcgj71hTNplbIO+Tj2qBP+Lk5+tTZPN+Hv7GoEv4vauyByVNDClKVqYClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCsk71jWSfiqGStSZAcHP1q+0pvOMe9c/Eea6LRo92W9AwX+Vc0zsifQZFL6IQMlgvbP0r5tqS/iyexOTX1GwRW0mTcPTj7V8y1ddssg7eY4FYU942qaFATnjsBWJFZMOTXhFdhysxHepUY8oNRKmRnyVDCM071sIyOTgep9qwXvWbfh5qpJoJ78/Stbdua2MOa1uMD3qxDNW3JAHrxitZHfA7VmQawYY7/lVkVZMi/ADW4fetMXmStyr2J4FUZcy2k8CtZO38PJ963g8EAce1aH/EfTmoBjtz2OffPvWiTAJwQfrW4859q1Px25qxDNRAAySc/wAqlQHy8VFxzzxmpdvgLxRkIkKCO4rA5JArND9ftWD8VUsY5oRgfWvR9ea8bzGgNTGtDD3qQwwK0OfftViGbrc5FSFPNaLfscVvHeoZJjnDGgNeEZNe4xxUA9UZIA/nXjDgUxQj3oDW3IrSv9oKkMoAPOa0qMSD3qxHEljgg4yfao9zgAZ7VIHatNwoKiqoGpTwNo49q85yWyAPSslwG4JIFBjJJ9Owq6IMc7+WPPoKyKkN3yvpXiAlvMP1r0uM8jJzQgyU5Y4IJPGfapVvt8LzEA/QVEODkyfkAa22/I+lQyUbWHtUNW5K4GT6mprHI+lQGjyzc4FQiWZNhfKCCfU16zAYHcV52UAYoSdw3DipID7iuVGErzGQWfIyfQVkQXOPwj2pjcvJPFSQS4+UT6Ctkn9mc1jGMIM98Vk/9meapxLlewAX1716GxwOBXpwU9Sc14BnG/7jFWKnjEEYUevevSCAN3A9K9wxGFGBXmfMMDOKkHqAHdgDHuakQjyio5AbJdsfapcAHhgiqslHjjg1ByVJI96sWXKnNVzevOaIMmdwPesvTmsYz5V+1ZE+g4qCTw9qx49Kz+9eds0BlGR6jNeseDWMank1k34TUAr5Tlj2rxO9eE4c4rJPpVypvA4zWXpXg5ArLFVLA4xxRaACvV/FQGzGBUO6/EM1LA4qHdfiFStQaMVtjBwfp/OtYzxWxKsyptA/OsgO9F7V6M1VljIDB5rJx7dvesQOea2OMYxUAg3H4hWod63XQBYelaRV1oVM1/nW5c8CtC963qDRg2DkYHas1OKwXtx3rapyPN39xVCxgCCa0XBz9a3jg5Wo9wO3pxUoGke4r0fyrwHPfmswParFTJB611ugxLugBJ5YE1y0akgYGSTxXddPWpWW2VxknnGawqvI1prMudcfNqy58p7AV89ve7Yr6Jr8YCPjnn9K+e3nO4jk+tZUzWoVEmahyMSeTnFT5QPtUCQYauyByT0MKUpWpgKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAK9X8QryvV/EM0JWpKj710WjHE2O3uPeuej710Gk8yjFcs9DrifTdHdW0+VcFjjBPtXznXV23MvGPNX0fplg1rKGxyK4HqVQLybHbPFc0N46J7pyL/iII9PStZH8q2yEhiBwM1qNdyOMxqXECUqIamQNlO+cDFQyUbVGDivSOK8Qc1m2D9KqSaCOMDtWthweK29q1Oc1KIZoNayK2kZPvWB4/DyferoqyVbjC4Pf2/wA639zyc1Htvw1JVckDtVGWWhmvrWt1OCwwRnB+lbQMHg1qYcmoJNXYVqYVuKn7CtUnbAzViGajgDPc1KtgQORjFRDx9ak2rZXmpehC1Ja8VqkOTmtq1g4+lULGGPShAwePbHNegccUzjJP5UBqYe9Rn/WpRGck1Hcc4qxDNlt6ipSjmo1sO9ScYqGSYOMNXox6164JxmirUAd6yCZBOVGPQnvTb9K9+tAa2UAZNRgPPyakyt+tRe7jParFSd6cVqn/AA5raBwK1XOdvHpVUWZo5ZhkYHtXgTz5AA+9EJ3DNZKTuOB+tXRQ9zuGQTn2FYkru5GAPavRxwOSa9YAAKOc96kg178knH2qVAPJz61qAAyIxz61vt+AcnJqrLIzx9aiMn4sfzqaeagszb2UDOahEswRBtyTWwKVAJHPpk15wMBjms8Z5ft6c1JU8GeWYjNY7MAknP0rNU7nAA9MmjDwmdDjKnFASolxEOe9euP3Z+1eRf2S17IPIeaqXIRAK8kn7V5uA5FedkI3flXu1dvf8qsUPVJYHAyTXhBC8nH0r1iEAC5/WvN5cgdsVIMCu7sMVNgOIhUYrkN3NSIB+7FVZZGb/gJqvUbmwO5qwk/CcVDh/tlz/eogySgwijPasxWuI5WtgwBUEmJPtWPc1nWOMnNAbI+1entyM14pBHHYVkeBUArJFHiNjP517GDXkhBdscc1nGx7ZrQqbgPevfzp3716RwKoWHfGa9jHn4rzFexf2lAZ+9Qro+cVOYZJ9qg3Q84+2alBmkDJ4rdGK0rj65qRE3bIyPSrMqbAAKyptz+HkfzFZBCw8oNVZY8Uev1rY3GOawT2NZP3qAQ7n8fFaQa2XORJzWtautCpmoqQo45rQorep45qAZc4rNfpWOc1sUYHIqpY1Ee3FaZ+MVJAz9a0XIwRUoMjYrNRWOc1mBkVYqS7dsJuJ5BwMV3XTUZlmgLHI7muAiOEX719I6PIaZCQMKtc9bQ2pakvX5sJMMAk8c186vX5x9a+g9RkASluxPevm94cyHA4BqlJF6mpFkYkYJ4qBJ+Kpj9uO1Q3OTXZA5J6GNKUrQxFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFB3FKUBMj9Kv9NJ/h71z8R9avdLbDCuWZ2RPpvSSb428Q+/Fcj1cg+emwOMV13R/rk4zXO9dQhb07f7p7etcsd86Zbh88l4kIHatWK3yjDkmtDGu5HGYGpUH4fr7VFNSrfJHvUsIkL3rJkZuFoK2K+zJNUJIsilOG71oZvapEzb2zUdhirIhmsn2/M1rY479/ath961n19asVZKtTwftUkVEtOSfapYqj1LI2fatZA824kccfetoxx3z61rc5PbioJNTvnA5xWiRsk4GK2y/pWg81ZFTW1SbXgHP51HPp6D3qRa9yO9S9CFqSwayO3BLk9+1eLzXj9sVQueySK5GxAuBjvnNaiK2CNvD348ucfX9KxKlfQj2yKA0scCo78n/Gt0hIGMVHNXRUkWxG4+1SR3qHbfiNTB3qjLISDJouRSTuK8B9DQGeT39q8JzRjngCsGPFAa5GAzWiNS8gCjJrOQZNYJw64/WrFSwHatVwCE471uUYArXMcL+dUWpd6EQ5HLDmvdu1e3J+tZHAIyM162fF25DjggirlDDlAB71l2AC9z614yhmJJxTGVHFSQefgOC45Hc1vtfMma07fE8iAfU1Jt12KVHpUMlG11IAPoRng1AyTKwPFT2HFQmXznK1VEswXvhOR71kikufXFe8KnfvXgYsQE4FWINgIIdWCgHH5VgQrAtyTWLq2eB3714oYgjOKAnw/2YrJxlDx6V5brlFFbGHlNU4liqEWQxJwM16TtUDH5mhyCfUA0fOQW4q5QMQpyOTjmm/Ckjua8ywXgcVlu8uAvepBgrFUO4E+1Trb+yB96hZxkMM1OgI8EVVlkZuPKc9qr4v7ZP+8KnvypquHDE/WoRLJET963elREPm4qUn4RUshHuPavORWWa8GO5qCTNNxXmhHHbFZIQVrxh7VUkq3I3n71khwcisH/ABtXo7d+a1KErOayxnFaUOTW4VVkoH2pEf3leikS7ZKgk2nvUC6Hn471YGq+6/tKlakM1evPetqMRx3HetYbcBurLt25FWZBIU7uTW0Mw4qPHzUgcVVko9Vf1o4OayXnGK9aoJK64H7ysAD6elbJ/wC0NYqxGcetXRUCtykY/wAq0gVmtQDfnFbhyK1IMjmtg/CaqWPM4rRcthlyK3r3qNdfiGOalahmoYPpWYHscZFawK24x7GrFTci4VNwzzX0Xogfvfw9h2FfPVztT15FfSuhxnIwcbfT1rnraG1LeMOpSW3lsAV87l5ZvXmvo/UiliwXtXzm5Xw2IPbNUpaF6mpDlC84yCar3/EanzEEc9qr2/EfWuyBxzPKUpWhkKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQEmLsPrV3pf4xiqOHkCrvTATIFX25rmqHZDQ+ndKFVcZ+9V/XUQM4bGO4H0qX0sf3vPPA71n13DxuHHPr/AFri0kdWsD5FdDDmtB4qXdRkynHcE1FZcV6COJmKFgzKhALAqftUi2PFRj6YFSLb+VS9CESuOCD3Pasv8qx9ayJxyaoWNLkZ9K0uueT2o5y+Qay74FWII7e1YGt7r9K048wFSirN1tgGpyD1PaolomPPJwD+Eep/5VK3ZPPaqvUstDId+KxmfJHpWXc8VpuQEwdwznsKhEmuQZrQwrcp3UkUY4qxUit7VItMkn6VobvxUizwGIOT9ql6ELUmrWEpwK3KnOMY+9aLgYHFULmkSkHI4P3rYZjJlnYs31qLXuSDViLm5zvzu7DniozccYwa3qC/AyT7CtMmSck5PvQgztfx1NGM81Ctv7U47VOx5qrLUsjCQ4xWG8Dgc1smJVSBjB9xUMHk4ogySZABTcD3qPuOc1mj4PfJ+1WKntwgVsK24e9aF4YYqUV3DuajlcEEN64xQFhGDgVhOuErZGCQMAmvJh+7PmxVFqX4EArkjGc+5pu29h/Ks0ZpeOw+9O+VBIA71coYZLfhXH1NekjgHlqyGWP4iAK82EckmhBi3kGE7n2rfa5A5rD8IJwcVItjuGfeoehZam1qr3c72FWjDOarpIwZWPtUIlmluQADms1BUccEVmpB74H0rwkD0PNWKmCZGWJ4IwfrXu7duEWSM8Z9q98rL7fevIlKg4IAIxyKAn2oxGBWbjKnHatcH9kMc1uIOwiqPUutCocAZOec8U4x/eJre8Y5xgVp25IHarlDJUJyeSor3OSN/lA7CvGx2Ukge1ebgvfn8qEAKrZLNip0GNgA7VHCo45Iz9anwRL4YxUMstTVJwnFVh7/AHq4kjyuB69qqJEIkPHrSJLMkGTUoeVcVqiQ96O5HrQqZsQPWsDKK0kmsKWJuWMBylZv24rC1GY62uvlNVZZFO/LmgryT+0bHbNZIMitChuQY71tLgVpLbax3c1AN3ijisomy9R8mt9qQWqCSQar7v8AtatcDGTzVVeY8WkQ9DUKzXvWte9SIl96sQbUxgE8H6VkZAO+PyrW5x2rXnmoBNiYNgitrAZrRakD04Pet7jmqMsVlwf3prDPA4/Os5x+8PvWA4FaIqZAZrakeTWMa5rfjYtQBjaAK3IV2ZIOfp61FYlyP6VLUeX8qqyxhgE8DAqHccyVOxUKfAkqVqQzAHHHetigN9K1g81tCkbdwwGGQfcVZkElVP7v1FfTujHEcewDLEDivmajzRivqnRkax27SNjcRxn2rmrPI6KKzIPUXLSbuK+fXr+YgcgV3nUL72YKe/cEV8/u0AY+nNVpk1CvmIyf4agt3NS5icHPNQz3rticdTUUpSrmQpSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlASIO1XGnN+88tUccgTuKn22oxQMCUc++MVjOLZ0wkksz6l0zL4cgLsFUDJJ9MVc9bwhrMPz2Br57pPW+n2MitPbXLqO4Xb/nV31B8T9H1ay8C3sr2NtuMuExn8jXE6U76HUqsNm1zhrgiK5ye3Ocfaq5hkAH25rdd6hFO5ZFcZ98VF+YX2NdqTscrku09PGTW22/Fio5mUngGsorhI2yQ35VNnYrtItOwFa5j5eK0ftCHA8r5/KsZL6Jhwrj8hUbLJ2kYMdv3NZqcDnvUczqSDz+lBOo77qmzI2kSGb615hIFDyDc55WP/ABP+ValuI1y2CX9MjgfWtTyhiTySfepsyLolQMzSlnOT2qZ3NVkU6o3OcfSpQv4h6P8AoKq0yVJE6LAOWQv9BWN3ASMtshX6nmtC6lCvYyL9lH+dR571JD5Qx+rUSYckZZUHCngcZHr9a2YLD059zURblMgsDj1xW176PaFjRhg5BNTZkbSDqEzuPPpitlpJh/KAPrUNpgxyc5rOCdI2y2f0qWnYJq5cA5/Ea03J8pA7VqGowD1f/wCGtU17C4wu79KzUWWuu0xZ2c5c5PasK1eOv1p4y/Wr2ZF0SUYgjHBFeyKWA2jNRxcIB6/pWLTg+ppZkbSJFtxLg1Y4PtVTDPEhy27dn29Km/tG392/+GqyTLqSNk4ytQv4jW2W+hYYDN/8NaFuY1J9cgjkfzqUmQ2jaB79q8B5NajcR8YJ7c5HrXguEHqf0qbEXRNDYXHpUdj5/wA6wa5Q9if0rDxkyMk/pSzF0XMXlUEGvJV3J757VHi1C2VAGdgf+7XrajbH8LH7bapZl9pW1MDGyJwMD3rBM4rKTUIXXbuOP+7QXltt8zk49NlWsyl0YByrccisxmU5etfzkDNnJUfRa9+bg9HI/wDCamzF0bMEsRjgelb7ZcD2qIt5Ap/GT+VbYr62X8Tkf+E1DTJTRYkeU4qvKgyncxUe4GcVuGpWoB/enJ/4DUCW6iaTKucfaqxTJbXabSpTdhgw9DWe0eHljz7VHN1CFwGyR24NBcxHG6TGP+E1ezK3RuYnONtePhlx2FYG6hJ/tP8A5E1j8zDk+f8A+RNLMXRY2w/djFbf4DUSC/tUTDSkH/ums21C12kCX/5E1Rp9hdSXaaAMyNuPrxRvKOBWg3ce8kNx9jWbXUB7P/I1ezKXRmD5TxisOOAfwg/rWk3Ef97+VPmEOMt2+lLMXRJ3iQ8jAqfbgCLANVSTwgH95z9jU6C/tVXDTAf+E/5VVplk0SZCQp2mqgyN4h9s1Pkv7UrhZgT/AN0/5VVvNHvyrevfFIpkNplgj+UZWsJCjZ9DWhLuMcM/8qxknjJyrj9DVrMi6Myo5wa1kc1h46/3q88VM/ipZi6LazH7qt0gwpqLZ31vHHh5QD9jW6W/tWUgTKePY1m076F00U0n42+9bY+BzWp5Iy7ENxQSIOz/AMq1sZ3RuY81jmsDKp/iFeeKv94VFibo2A9hUu2Pm5qCJUyMsKlQXEIOWkUVDRN0TCvY1W3f9qfWp/zNse8yj9arrmRGkJRwQaK4bVjBQQR7VJj7VGDjaV3rgcisllUfxCpZVNG5u5rDNeNMmBhxWBlX+8KWJuifajNSWHJxUK1niUeaVV+5re91CQcTJ+tUady6asQZv7RuMViB715NIjSEhgRWKuB/EP1rQzvmSoyAKyZ+9RxKnqwrwyqOd4NRYm6NufMMVPT8PNVQlUMPMKsY7iERjdKmfvVWiVYyI5qBcD96am+PDniZP/iqGTHJPzKqr75orkuxrHet6E7QCeAeB7VqkMavhJFYe+a9WRMfjX9asQWMB8SeMd6+sdHKvgyL2wO9fJtKmgW6Vppo0UHuzYr6h01rWkW8Ugn1WxiJHAedRXJWTOmi0UmvsRcyggjHc1w9zndk811vUGq2LzOba8tpR/wSg+lcdczxtyskZ+zVNNMibRBm57cfSolSpZN38StUWuyOhxz1FKUq5mKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQH/2Q==", + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "from IPython.display import Image\n", + "Image(\"../../img/Galton_box.jpg\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we simulate the Galton board and plot the results, let's think through what to expect. Because each bead at each peg has an equal probability of going left or right, most of the beads will go left and right around the same number of times. This means that a lot of beads will stay around 0, the centre, and there will be less and less the further we get from the centre. Also due to the equal probabilities of left and right, the distribution will be symmetric around the centre. With this in mind, let's now simulate the quincunx and plot the result:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:33.467297Z", + "iopub.status.busy": "2022-04-30T07:02:33.462964Z", + "iopub.status.idle": "2022-04-30T07:02:34.579104Z", + "shell.execute_reply": "2022-04-30T07:02:34.578483Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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tjaKiIjZv3szo0aOZP38+e/fuJT8/nyVLlrB69WomT57M8uXL2bJlC3PmzLlkhUiS+q7PYfDee+8BcM899/DRRx/xox/9iOuuu46xY8cyZswYAEpKSqirq+Paa6+lra2NyZMnA1BWVsa6desMg36SfcUIhmf6rktS7/X5iHHmzBmmTp3Kr371Kzo6OigvL+dnP/sZ4XA43icSidDY2MiJEycuaA+HwzQ2Nn65Ldd/NTwzNKjXm5eUfPocBjfeeCM33nhjfPnOO+9k3bp1fOc734m3BUFAWloa3d3dpKWlXdSeiFGjshLexsG6G1N/SsWapET0xz6QivtVosfMPofBG2+8QUdHB1OnTgXOH+Bzc3OJxWLxPrFYjEgkQk5OzgXtJ0+eJBKJJDReU1ML3d1Br/uHw9nEYql1T6Te1pSKL2zpU5d6v07VY0VTU0tCgdDnj5Y2Nzfz2GOP0d7eTktLC9u3b+cXv/gFhw8f5siRI3R1dbFz507y8vLIzc0lMzOT+vp6AKLRKHl5eX0dWpJ0ifX5ncG0adN48803ue222+ju7mbOnDnceOONVFdXs3DhQtrb28nPz6eoqAiAmpoaKisraWlpYeLEiZSXl1+yIiRJX86X+sjJgw8+yIMPPnhB29SpU3nhhRcu6jthwgS2bdv2ZYaTJPUTv4EsSTIMJEmGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJIkveQ9kSRpI5zq6CIezL/m/29O/2dbeSfOZs5d83KHEMJCUNDLSh1HyUHTAx93xeCnNAz7qwHKaSJJkGEiSDANJEoaBJAlPIPer7CtGMDzz0v6J++OTFJJkGPSj4ZmhQfvkgyQlwmkiSdLAhsGOHTu49dZbKSgo4JlnnhnIoSVJX2DApokaGxtZu3Ytzz//PBkZGcyaNYubbrqJa6+9dqA2QZL6pL+++dwbA/Xt5wELg3379nHzzTdz5ZVXAlBYWEhdXR33339/r9a/7LK0hMe87LI0srKGk3mJT+ImIvLVEf9T4w7m2Nac+uMO1tgZ6cP4v9V/HvBxAZ6sLKC1j8e/RKQFQRAkPEofbNq0iU8++YTFixcDsHXrVt566y1+85vfDMTwkqQvMGDnDLq7u0lL+/9JFQTBBcuSpMEzYGGQk5NDLBaLL8diMSKRyEANL0n6AgMWBt/97nd57bXXOHXqFGfPnuXPf/4zeXl5AzW8JOkLDNiZ1a9//essXryY8vJyOjo6uPPOO/n2t789UMNLkr7AgJ1AliQNXX4DWZJkGEiSDANJEoaBJIkUC4MTJ04wb948brvtNmbNmsWHH34IwJkzZ5g3bx4zZsxg7ty5F3zfIVm88847TJo0Kb6c7DXV19dz5513Ulpayt13383Ro0eB5K8rlS7GuH79eoqLiykuLuaxxx4Dzl9WpqSkhIKCAtauXTvIW9h3v/3tb6moqACSv6aXXnqJsrIyZsyYwerVq4E+1hSkkLvvvjt49tlngyAIgmeffTZ44IEHgiAIglWrVgWbNm0KgiAItm/fHm9PFp988kkwa9as4Lrrrou3JXtN06ZNCw4ePBgEQRBs3bo1uPfee4MgSO66/v3vfwfTpk0LTp8+HbS2tgYlJSXBoUOHBnuz+uQf//hH8OMf/zhob28Pzp07F5SXlwc7duwI8vPzg/fffz/o6OgI7rnnnuCVV14Z7E1N2L59+4Kbbrop+OUvfxmcPXs2qWt6//33g+9///vB8ePHg3PnzgWzZ88OXnnllT7VlDLvDE6dOsW//vUvZs2aBcAdd9zBgw8+CMArr7xCSUkJADNnzuRvf/sbHR0dg7WpCauurubuu+++oC2Zazp37hwPPPAAEyZMAOD666/n+PHjQHLX9dmLMV5++eXxizEmo3A4TEVFBRkZGaSnpzNu3DgaGhoYO3YsY8aMIRQKUVJSknT1ffTRR6xdu5Z7770XgLfeeiupa3rxxRe59dZbycnJIT09nbVr1zJixIg+1ZQyYfDBBx/wjW98g+rqau644w4WLVpEeno6cH76KBwOAxAKhcjKyuLUqVODubm99te//pW2tjaKioouaE/mmjIyMigtPX83tu7ubtavX88tt9wCJHddn912gEgkQmNj4yBuUd+NHz+eyZMnA9DQ0MDu3btJS0tL+vpWrlzJ4sWLueKKK4Dkf86OHDlCV1cX9957L6WlpTz77LN9rikpb3u5e/duHn300Qvaxo4dyzvvvMPChQtZtmwZW7dupaKigs2bN1+0fhAEXHbZ0MrBz6vpmmuuoaWlhaeeeqrH9YdiTfDf63rqqac4d+4cFRUVdHZ2Mn/+/M9df6jW9XlS8WKMhw4dYv78+SxdupRhw4bR0NAQfyzZ6tu6dSujR49m6tSpPP/880DyP2ddXV288cYbbN68mcsvv5wFCxYwfPjwPtWUlGEwY8YMZsyYcUHb+++/z+233860adOA81MMn55MiUQinDx5kpycHDo7O2ltbY3fV2Go+Lyatm7dyqZNm5g7d268rbS0lGeeeSYpaoLPrwugtbWVBQsWcOWVV7Jx48b4u7hkqevz5OTk8MYbb8SXk/1ijPX19SxatIjly5dTXFzM66+/ntQXm9y1axexWIzS0lI+/vhjPvnkE44ePcqwYcPifZKtpq997WtMnTqVkSNHAnDLLbdQV1fXp5qS479cvXDVVVeRk5PD3r17AXj55ZeZOHEiAPn5+dTW1gLnXxBTpkyJH3yGsh/+8If85S9/IRqNEo1GAYhGo2RlZSVtTZ9asmQJY8eO5Xe/+x0ZGRnx9mSuK5Uuxnj8+HHuu+8+ampqKC4uBuCGG27g8OHD8amJnTt3JlV9f/zjH9m5cyfRaJRFixbxgx/8gD/84Q9JXdO0adN49dVXOXPmDF1dXfz973+nqKioTzWl1LWJ3nvvPaqqqjh9+jRZWVlUV1dz9dVX89FHH1FRUcEHH3xAdnY2NTU1fPOb3xzszU3Y9ddfz7vvvguQ1DW988473H777Vx77bWEQuffnEYiEX7/+98ndV1w/qOlmzZtil+M8ec///lgb1KfrF69mj/96U9cddVV8bZZs2Zx9dVX8+ijj9Le3k5+fj7Lli1LqmmVTz3//PO8/vrrVFdX89prryV1Tdu2beOpp56io6OD733ve1RWVvLPf/4z4ZpSKgwkSX2TMtNEkqS+MwwkSYaBJMkwkCRhGEiSMAwkSRgGkiQMA0kS8P8AYYTedCPUmfgAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data = []\n", + "for _ in range(0, 10000):\n", + " x = rng.binomial(1, 0.5, 1000) - 0.5\n", + " t = sum(x)\n", + " data.append(t)\n", + "plt.hist(data);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Drift + diffusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned above, the random walk is used to model the process of diffusion. However, for many phenomena, diffusion isn't quite enough and we also need to account for drift, such as bacteria climbing a chemical gradient, the current in semiconductors, and stock prices that on average aren't stationary over time. What this essentially means is that we have a diffusion component, described by a random walk, and a drift component, which means we're tending in a particular direction. If we're drifting to the left (down), we can simulate this by making the probability less than 0.5:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:34.581608Z", + "iopub.status.busy": "2022-04-30T07:02:34.581464Z", + "iopub.status.idle": "2022-04-30T07:02:34.677341Z", + "shell.execute_reply": "2022-04-30T07:02:34.676946Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = rng.binomial(1, 0.45, 1000) - 0.5\n", + "t = np.cumsum(x)\n", + "plt.plot(t);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we're drifting to the right (up), we can simulate this by making the probability greater than 0.5:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:34.679610Z", + "iopub.status.busy": "2022-04-30T07:02:34.679448Z", + "iopub.status.idle": "2022-04-30T07:02:34.755294Z", + "shell.execute_reply": "2022-04-30T07:02:34.754916Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = rng.binomial(1, 0.55, 1000) - 0.5\n", + "t = np.cumsum(x)\n", + "plt.plot(t);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Investors, coincidences, and survivorship bias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we come to a particularly fun example, a variation on one described by Nassim Nicholas Taleb in his book [Fooled by Randomness](https://en.wikipedia.org/wiki/Fooled_by_Randomness). One of the reasons this example is fun is that it illustrates the very real problem of survivorship bias in a concrete way and we also get to simulate it!\n", + "\n", + "Our thought experiment, based on Taleb's, involves a population of 10,000 investment managers who are investing such that, with each investment, each manager has a 50% chance of winning 10K and a 50% chance of losing 10K. Each manager starts with 10K and once a manager has 0 USD, they are thrown out of the sample because they have no more money to invest with any more. What we'll see, when simulating this is that, even after thousands of bets, there will be some managers left and others quite wealthy, given that we started with enough managers. We may be tempted to believe that they were talented and may know some secret sauce about the investment world, but the truth is that they were just lucky. Once again, each investment manaager is going on a random walk and we'll plot only those who survive (hence the term **survivorship bias**)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:34.757686Z", + "iopub.status.busy": "2022-04-30T07:02:34.757507Z", + "iopub.status.idle": "2022-04-30T07:02:35.323023Z", + "shell.execute_reply": "2022-04-30T07:02:35.322637Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for _ in range(0, 10000):\n", + " x = rng.binomial(1, 0.5, 1000) - 0.5\n", + " t = np.cumsum(x)\n", + " if (t > 0).all():\n", + " plt.plot(t, alpha=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Law of Small Numbers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before moving on to the panoply of probability distributions and their stories, one more example is in order, as it demonstrates another statistical bias we have, _the Law of Small Numbers_. This is one of many that you can find ih Kahneman and Tversky's seminal work of behavioural economics [Thinking Fast and Slow](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow).\n", + "\n", + "The authors tell us of a study of kidney cancer incidence across over 3,000 counties in the US and they mention that those with a low incidence rate are 'sparsely populated, and located in traditionally Republican states in the Midwest, the South, and the West.' They then ask what the reader makes of this and propose a few suggestions, which we presume you can approximate.\n", + "\n", + "But then, they also tell us that those with a high incidence rate are also 'sparsely populated, and located in traditionally Republican states in the Midwest, the South, and the West!' They also propose some reasons why this may be the case.\n", + "\n", + "The punchline is that there is not necessarily any causal relation between (1) counties being sparsely populated and traditionally Republican and (2) kidney cancer incidence in those counties. What is, in fact, happeneing is that we are seeing more extreme values due to smaller populations in those counties. _This_ is the Law of Small Numbers: the less samples you have, the greater the chance of seeing extreme values.\n", + "\n", + "To make this quantitatively precise, Kahneman and Tversky provide a toy example, which we'll simulate in a minute. Consider an urn filled with marbles, half of which are red and half of which are black. Jack and Jill take turns in drawing marbles and replacing them. Each turn, Jack draws 4 marbles and Jill draws 7. To quote the authors, \n", + "\n", + "> If they go on long enough, Jack will observe such extreme outcomes more often than Jill—by\n", + "a factor of 8 (the expected percentages are 12.5% and 1.56%). Again, no\n", + "hammer, no causation, but a mathematical fact: samples of 4 marbles\n", + "yield extreme results more often than samples of 7 marbles do.\n", + "Now imagine the population of the United States as marbles in a giant\n", + "urn. Some marbles are marked KC, for kidney cancer. You draw samples\n", + "of marbles and populate each county in turn. Rural samples are smaller\n", + "than other samples. Just as in the game of Jack and Jill, extreme\n", + "outcomes (very high and/or very low cancer rates) are most likely to be\n", + "found in sparsely populated counties. This is all there is to the story.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now simulate the above to see if it checks out with our new random sampling skills!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:35.326590Z", + "iopub.status.busy": "2022-04-30T07:02:35.326399Z", + "iopub.status.idle": "2022-04-30T07:02:35.334758Z", + "shell.execute_reply": "2022-04-30T07:02:35.334022Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "134" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Simulate Jack\n", + "samples = 10**3\n", + "jack = rng.binomial(4,0.5,samples)\n", + "# How many times does Jack draw 0 or 4 white marbles?\n", + "sum(jack==0) + sum(jack==4)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:35.337066Z", + "iopub.status.busy": "2022-04-30T07:02:35.336921Z", + "iopub.status.idle": "2022-04-30T07:02:35.344804Z", + "shell.execute_reply": "2022-04-30T07:02:35.344462Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "27" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Simulate Jill\n", + "samples = 10**3\n", + "jill = rng.binomial(7,0.5,samples)\n", + "# How many times does Jack draw 0 or 7 white marbles?\n", + "sum(jill==0) + sum(jill==7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We indeed see that Jack draws all red or all black marbles approximately 6 times as often as Jill does and we've demonstrated the Law of Small Numbers using our random sampling skills." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chapter, we have used Monte Carlo simulation to see how wildly applicable these techniques and distributions are to many concepts and problems in statistics and basic science, from diffusion and drift, to approximations of the Normal distribution, to the cognitive bias considerations of the Law of Small Numbers and selection bias, not to mention approximating $\\pi$.\n", + "\n", + "In the next chapter, we'll make a brief excursion to the empirical cumulative distribution function (ECDF), the histogram's more sophisticated cousin, and then we'll jump straight into the zoo of probability distributions and their stories." + ] + } + ], + "metadata": { + "interpreter": { + "hash": "98b0a9b7b4eaaa670588a142fd0a9b87eaafe866f1db4228be72b4211d12040f" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/test/04-plotting-data.ipynb b/notebooks/test/04-plotting-data.ipynb new file mode 100644 index 0000000..e022fe7 --- /dev/null +++ b/notebooks/test/04-plotting-data.ipynb @@ -0,0 +1,922 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting your data and statistical thinking" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recall the histogram" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous chapter, we used the histogram to visualize our data. The histogram can be useful for getting an immediate sense of what you data looks like but it has certain pitfalls and there are other visualization that are more appropriate much of the time. Let's recall the histogram by generating some binomially-distributed data and plotting it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:37.475280Z", + "iopub.status.busy": "2022-04-30T07:02:37.474875Z", + "iopub.status.idle": "2022-04-30T07:02:38.463653Z", + "shell.execute_reply": "2022-04-30T07:02:38.463206Z" + } + }, + "outputs": [], + "source": [ + "#Import packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "sns.set()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:38.465994Z", + "iopub.status.busy": "2022-04-30T07:02:38.465861Z", + "iopub.status.idle": "2022-04-30T07:02:38.595164Z", + "shell.execute_reply": "2022-04-30T07:02:38.594798Z" + } + }, + "outputs": [], + "source": [ + "# Generate data & plot histogram\n", + "rng = np.random.default_rng(42)\n", + "x = rng.binomial(10, 0.5, 10000)\n", + "plt.hist(x, density=True, bins=10);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can immediately see the range of the data, the mode, and the shape, which are good things! However, there are two immediate problems\n", + "\n", + "* Looking at the histogram, we cannot easily answer intuitive and important questions, such as \"can you tell me the probability of seeing 4 or more heads?\"\n", + "* It isns't clear how much of what we can see is a result of the bin sizes we've chosen (known as _binning bias_).\n", + "\n", + "These problems occur when we have a single univariate dataset. Histograms are more problematic when we're trying to visualize several datasets, such as control plus experiment _or_ multiple populations, which we'll get to.\n", + "\n", + "Let's delve deeper into binning bias." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Binning Bias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following, we generate populations from two normal distributions (bell curves) with different means, combine the datasets, and plot the resulting histogram:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:38.612389Z", + "iopub.status.busy": "2022-04-30T07:02:38.612235Z", + "iopub.status.idle": "2022-04-30T07:02:38.730005Z", + "shell.execute_reply": "2022-04-30T07:02:38.729440Z" + } + }, + "outputs": [], + "source": [ + "# Generate data & plot histogram \n", + "rng = np.random.default_rng(42)\n", + "x1 = rng.normal(10, 1, 1000)\n", + "x2 = rng.normal(14, 1, 300)\n", + "x_all = np.concatenate((x1, x2))\n", + "plt.hist(x_all, density=True, bins=50);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that you can distinctly see two modes (peaks) here. However, were we to choose a different bin size, we'd only see one peak! We do see here:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:38.732427Z", + "iopub.status.busy": "2022-04-30T07:02:38.732268Z", + "iopub.status.idle": "2022-04-30T07:02:38.823887Z", + "shell.execute_reply": "2022-04-30T07:02:38.823532Z" + } + }, + "outputs": [], + "source": [ + "# Generate data & plot histogram \n", + "rng = np.random.default_rng(42)\n", + "x1 = rng.normal(10, 1, 1000)\n", + "x2 = rng.normal(14, 1, 300)\n", + "x_all = np.concatenate((x1, x2))\n", + "plt.hist(x_all, density=True, bins=5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On top of this, if we choose a lot more bins, the histogram begins to contain a lot more sampling noise and it isn't even clear how many actual peaks there are:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:38.827116Z", + "iopub.status.busy": "2022-04-30T07:02:38.826920Z", + "iopub.status.idle": "2022-04-30T07:02:39.002266Z", + "shell.execute_reply": "2022-04-30T07:02:39.001941Z" + } + }, + "outputs": [], + "source": [ + "# Generate data & plot histogram \n", + "rng = np.random.default_rng(42)\n", + "x1 = rng.normal(10, 1, 1000)\n", + "x2 = rng.normal(14, 1, 300)\n", + "x_all = np.concatenate((x1, x2))\n", + "plt.hist(x_all, density=True, bins=120);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Put another way, binning bias can lead to challenges with seeing multiples modes in your data, referred to as multimodality.\n", + "There are heuristics and rules-of-thumb for how many bins to use, however the point remains that the act of binning your data introduces bias into your visualizations and this bias is unnecessary as there are more appropriate tools. Before introducing such tools, let's look at plotting histograms with multiple datasets." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Histograms and Multiple Datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are many reasons to plot multiple datasets on one figure, for example, if you have\n", + "\n", + "* multiple populations;\n", + "* control plus experiment (or several experiments);\n", + "* data and a model.\n", + "\n", + "We'll see all of these in due course but let's consider the first case, of having multiple populations. We'll be working with the Gapminder dataset, which we encountered in Chapter 2." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:39.004361Z", + "iopub.status.busy": "2022-04-30T07:02:39.004247Z", + "iopub.status.idle": "2022-04-30T07:02:39.017039Z", + "shell.execute_reply": "2022-04-30T07:02:39.016723Z" + } + }, + "outputs": [], + "source": [ + "from gapminder import gapminder as df\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:39.018889Z", + "iopub.status.busy": "2022-04-30T07:02:39.018780Z", + "iopub.status.idle": "2022-04-30T07:02:39.023362Z", + "shell.execute_reply": "2022-04-30T07:02:39.022998Z" + } + }, + "outputs": [], + "source": [ + "## Create individual dataframes for specific years\n", + "df_52 = df[df[\"year\"]==1952]\n", + "df_62 = df[df[\"year\"]==1962]\n", + "df_72 = df[df[\"year\"]==1972]\n", + "df_82 = df[df[\"year\"]==1982]\n", + "df_92 = df[df[\"year\"]==1992]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now plot the histogram of life expectancies around the globe for the year 1952:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:39.025504Z", + "iopub.status.busy": "2022-04-30T07:02:39.025327Z", + "iopub.status.idle": "2022-04-30T07:02:39.104207Z", + "shell.execute_reply": "2022-04-30T07:02:39.103779Z" + } + }, + "outputs": [], + "source": [ + "plt.hist(df_52['lifeExp']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An obvious question to ask is if this distribution changes over time. In an effor to answer this question, let's plot the histograms for 1952 and 1962 on the same figure:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:39.106422Z", + "iopub.status.busy": "2022-04-30T07:02:39.106261Z", + "iopub.status.idle": "2022-04-30T07:02:39.192935Z", + "shell.execute_reply": "2022-04-30T07:02:39.192537Z" + } + }, + "outputs": [], + "source": [ + "plt.hist(df_52['lifeExp']);\n", + "plt.hist(df_62['lifeExp']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As the 1962 histogram covers much of the 1952 histogram, we can add transparency to the figures to counter this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:39.195045Z", + "iopub.status.busy": "2022-04-30T07:02:39.194879Z", + "iopub.status.idle": "2022-04-30T07:02:39.282406Z", + "shell.execute_reply": "2022-04-30T07:02:39.281967Z" + } + }, + "outputs": [], + "source": [ + "plt.hist(df_52['lifeExp'], alpha=0.5);\n", + "plt.hist(df_62['lifeExp'], alpha=0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although we can see the trend (which is good!), neither of these figures are particularly attractive and the situation gets worse as we plot the histograms for more years:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:39.284702Z", + "iopub.status.busy": "2022-04-30T07:02:39.284459Z", + "iopub.status.idle": "2022-04-30T07:02:39.407387Z", + "shell.execute_reply": "2022-04-30T07:02:39.407092Z" + } + }, + "outputs": [], + "source": [ + "plt.hist(df_52['lifeExp'], alpha=0.5);\n", + "plt.hist(df_62['lifeExp'], alpha=0.5);\n", + "plt.hist(df_72['lifeExp'], alpha=0.5);\n", + "plt.hist(df_82['lifeExp'], alpha=0.5);\n", + "plt.hist(df_92['lifeExp'], alpha=0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case, the histograms are almost useless!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, let's say that we had a control plus experiment and wanted to visualize them together. We'd end up with exactly the same issue when attempting to plot histograms: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:39.410248Z", + "iopub.status.busy": "2022-04-30T07:02:39.410100Z", + "iopub.status.idle": "2022-04-30T07:02:39.508582Z", + "shell.execute_reply": "2022-04-30T07:02:39.508234Z" + } + }, + "outputs": [], + "source": [ + "# generate control data\n", + "control = np.random.normal(0, 2, 10**4)\n", + "# generate experiment data\n", + "experiment = np.random.normal(1, 2, 10**4)\n", + "# plot both\n", + "plt.hist(control, alpha=0.5);\n", + "plt.hist(experiment, alpha = 0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have exactly the same issue as above and this is merely with a single experiment. Often, we will want to plot the results of several experiments together!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having recognized the limitations of the histogram, it's now time to introduce the empirical cumulative distribution function (ECDF), which solves for all the issues we've encountered with the histogram." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Empirical cumulative distribution functions (ECDFs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An ECDF is, as an alternative to a histogram, a way to visualize univariate data that is rich in information. It allows us to visualize all of our data and, by doing so, avoids the very real problem of binning bias.\n", + "\n", + "On top of this, you can plot\n", + "\n", + "- Control plus experiment, \n", + "- data plus model,\n", + "- many populations.\n", + "\n", + "You can also see multimodality and read off a great deal of important information that histograms do not easily provide, such as percentiles.\n", + "\n", + "We encountered the ECDF in passing in Chapter 2 and let's recall the definition:\n", + "\n", + "**Definition:** In an ECDF, the x-axis is the range of possible values for the data & for any given x-value, the corresponding y-value is the proportion of data points less than or equal to that x-value." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define a handy ECDF function that takes in data and outputs $x$ and $y$ data for the ECDF." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:39.511278Z", + "iopub.status.busy": "2022-04-30T07:02:39.511086Z", + "iopub.status.idle": "2022-04-30T07:02:39.514455Z", + "shell.execute_reply": "2022-04-30T07:02:39.513889Z" + } + }, + "outputs": [], + "source": [ + "def ecdf(data):\n", + " \"\"\"Compute ECDF for a one-dimensional array of measurements.\"\"\"\n", + " # Number of data points\n", + " n = len(data)\n", + "\n", + " # x-data for the ECDF\n", + " x = np.sort(data)\n", + "\n", + " # y-data for the ECDF\n", + " y = np.arange(1, n+1) / n\n", + "\n", + " return x, y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In future chapters, we'll import of ECDF function from a script of utility functions as follows:\n", + "```\n", + "from generative_thinking.utils import ecdf\n", + "```\n", + "Let's now see how the ECDF can help with the challenges described above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reading off percentiles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall the question we asked when looking at the following histogram: \"what is the probability of seeing 4 or more heads?\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:39.516876Z", + "iopub.status.busy": "2022-04-30T07:02:39.516664Z", + "iopub.status.idle": "2022-04-30T07:02:39.598383Z", + "shell.execute_reply": "2022-04-30T07:02:39.598002Z" + } + }, + "outputs": [], + "source": [ + "# Generate data & plot histogram \n", + "x = rng.binomial(10, 0.5, 10000)\n", + "plt.hist(x, density=True, bins=10);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To answer this question, we really need to squint, approximate the height of several boxes, and add up those values. Let's now plot the ECDF:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:39.600401Z", + "iopub.status.busy": "2022-04-30T07:02:39.600295Z", + "iopub.status.idle": "2022-04-30T07:02:39.718947Z", + "shell.execute_reply": "2022-04-30T07:02:39.718596Z" + } + }, + "outputs": [], + "source": [ + "# Generate x- and y-data for the ECDF\n", + "x_flips, y_flips = ecdf(x)\n", + "\n", + "# Plot the ECDF\n", + "plt.plot(x_flips, y_flips, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recalling that, in an ECDF, for any given x-value, the corresponding y-value is the proportion of data points less than or equal to that x-value, we can read off immediately that 80% of the data contains 4 or more heads, meaning that the probability of seeing 4 or more heads is approximately 80%. \n", + "\n", + "More generally, we can immediately read off percentiles from the ECDF! We do so by finding the value on the x-axis that corresponds to any given percentile on the y-axis. For example, the 50% percentile (or median) is 5, the 25th percentile 4, and the 75th percentile 6." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multimodality" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we saw above, histograms require binning and this can result in binning bias. The following two histograms are of the same data with a different number of bins:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:39.721130Z", + "iopub.status.busy": "2022-04-30T07:02:39.721007Z", + "iopub.status.idle": "2022-04-30T07:02:39.877641Z", + "shell.execute_reply": "2022-04-30T07:02:39.877301Z" + } + }, + "outputs": [], + "source": [ + "rng = np.random.default_rng(42)\n", + "\n", + "# Generate data & plot histogram \n", + "x1 = rng.normal(10, 1, 1000)\n", + "x2 = rng.normal(14, 1, 300)\n", + "x_all = np.concatenate((x1, x2))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(x_all, density=True, bins=50);\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(x_all, density=True, bins=5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is no binning bias with the ECDF, as it plots ALL of your data. Let's see it in action for the above dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:39.879699Z", + "iopub.status.busy": "2022-04-30T07:02:39.879573Z", + "iopub.status.idle": "2022-04-30T07:02:39.954208Z", + "shell.execute_reply": "2022-04-30T07:02:39.953850Z" + } + }, + "outputs": [], + "source": [ + "# Generate x- and y-data for the ECDF\n", + "x_flips, y_flips = ecdf(x_all)\n", + "\n", + "# Plot the ECDF\n", + "plt.plot(x_flips, y_flips, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now the question remains: can we see multiple peaks here? The answer is yes but we need to recognize that the peaks in histograms turn into points of inflection in ECDFs (recall that a point of inflection is when a curve changes from being concave up to concave down or vice versa). This is a result of the fact that the ECDF is the cumulative sum of the histogram.\n", + "\n", + "We can see points of inflection at around x=10 and x=14 in the above ECDF, which correspond to the modes of the data we generated! Note that the points of inflection are less pronounced than the peaks in the histogram (well, at least in the one in which they are visible) but this is the price we necessarily pay for being able to visualize all of our data free of binning bias." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Control + experiment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now see how we can plot multiple ECDFs together. Above, we tried to plot control and experiment histograms together, to limited success:\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:39.956461Z", + "iopub.status.busy": "2022-04-30T07:02:39.956337Z", + "iopub.status.idle": "2022-04-30T07:02:40.050278Z", + "shell.execute_reply": "2022-04-30T07:02:40.049740Z" + } + }, + "outputs": [], + "source": [ + "plt.hist(control, alpha=0.5);\n", + "plt.hist(experiment, alpha = 0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now plot the ECDFs on the same figure:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:40.053281Z", + "iopub.status.busy": "2022-04-30T07:02:40.053141Z", + "iopub.status.idle": "2022-04-30T07:02:40.138144Z", + "shell.execute_reply": "2022-04-30T07:02:40.137176Z" + } + }, + "outputs": [], + "source": [ + "x_c, y_c = ecdf(control)\n", + "x_e, y_e = ecdf(experiment)\n", + "plt.plot(x_c, y_c)\n", + "plt.plot(x_e, y_e);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can immediately see the differences between the control and the experiment, including the percentiles and the modes (remember, points of inflection). We can also see that the variances of both are around the same." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multiple populations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now try our hand at plotting the ECDFs for multiple populations. We'll use the Gapminder life expectancy example once again:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:40.148317Z", + "iopub.status.busy": "2022-04-30T07:02:40.148117Z", + "iopub.status.idle": "2022-04-30T07:02:40.255097Z", + "shell.execute_reply": "2022-04-30T07:02:40.254602Z" + } + }, + "outputs": [], + "source": [ + "# Create data for ECDFs\n", + "x_lifeExp52, y_lifeExp52 = ecdf(df_52[\"lifeExp\"])\n", + "x_lifeExp62, y_lifeExp62 = ecdf(df_62[\"lifeExp\"])\n", + "x_lifeExp72, y_lifeExp72 = ecdf(df_72[\"lifeExp\"])\n", + "x_lifeExp82, y_lifeExp82 = ecdf(df_82[\"lifeExp\"])\n", + "x_lifeExp92, y_lifeExp92 = ecdf(df_92[\"lifeExp\"])\n", + "# Plot ECDFs\n", + "plt.plot(x_lifeExp52, y_lifeExp52, label='1952');\n", + "plt.plot(x_lifeExp62, y_lifeExp62, label='1962');\n", + "plt.plot(x_lifeExp72, y_lifeExp72, label='1972');\n", + "plt.plot(x_lifeExp82, y_lifeExp82, label='1982');\n", + "plt.plot(x_lifeExp92, y_lifeExp92, label='1992');\n", + "#plt.xscale(\"log\");\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the data for each year distinctly, along with being able to see the trend of life expectancy increasing over time!\n", + "We also mentioned that ECDFs are good for plotting data alongside a model and we'll see how to do this in the next chapter. Before moving on to this, let's look at a selection of other statistical plots that are useful." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Other types of statistical plots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Swarmplots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Swarmplots (or beeswarm plots), which we briefly encountered earlier, are another way of plotting all of your data points: the y-axis is the variable of interest and the x-axis is used to spread out the data so that it is all visible. Let's see a swarmplot in action for our 1952 life expectancy data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:40.257322Z", + "iopub.status.busy": "2022-04-30T07:02:40.257224Z", + "iopub.status.idle": "2022-04-30T07:02:40.319690Z", + "shell.execute_reply": "2022-04-30T07:02:40.319291Z" + } + }, + "outputs": [], + "source": [ + "sns.swarmplot(y=\"lifeExp\", data=df_52);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use the x-axis to encode a categorical variable. Let's do this now with the above data and the variable 'Continent':" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:40.321950Z", + "iopub.status.busy": "2022-04-30T07:02:40.321854Z", + "iopub.status.idle": "2022-04-30T07:02:40.406589Z", + "shell.execute_reply": "2022-04-30T07:02:40.406101Z" + } + }, + "outputs": [], + "source": [ + "sns.swarmplot(x=\"continent\", y=\"lifeExp\", data=df_52);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Box plots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Box plots (or box and whisker plots) are in a similar vein to swarmplots, but they do not plot of your data. Rather, they encode summary statistics, such as percentiles and outliers. \n", + "\n", + "The line in the middle of each box corresponds to the median of the relevant data, the top and bottom of the box to the 75th and 25th percentiles respectively (also called the 3rd and 1st quartiles, $Q_3$ and $Q_1$, respectively), and the whiskers to the median plus or minus 1.5 times the Interquartile Range ($IQR=Q_3-Q_1$)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:40.409600Z", + "iopub.status.busy": "2022-04-30T07:02:40.409474Z", + "iopub.status.idle": "2022-04-30T07:02:40.497687Z", + "shell.execute_reply": "2022-04-30T07:02:40.497172Z" + } + }, + "outputs": [], + "source": [ + "sns.boxplot(x=\"continent\", y=\"lifeExp\", data=df_52);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Kernel density estimations\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Kernel density estimation (KDE) is a method used to create a \"smooth version of a histogram.\" The intention is often to get a sense of what the underlying probability distribution could look like. Consider the following histogram of life expectancies for countries in 1952:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:40.502397Z", + "iopub.status.busy": "2022-04-30T07:02:40.501991Z", + "iopub.status.idle": "2022-04-30T07:02:40.590369Z", + "shell.execute_reply": "2022-04-30T07:02:40.589943Z" + } + }, + "outputs": [], + "source": [ + "df_52[\"lifeExp\"].plot.hist();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A corresponding KDE would look like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:40.593065Z", + "iopub.status.busy": "2022-04-30T07:02:40.592889Z", + "iopub.status.idle": "2022-04-30T07:02:40.675732Z", + "shell.execute_reply": "2022-04-30T07:02:40.675000Z" + } + }, + "outputs": [], + "source": [ + "df_52[\"lifeExp\"].plot.kde();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having introduced box plots and KDEs, it is worth mentioning that we can combine them into what is know as a violin plot!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:40.677884Z", + "iopub.status.busy": "2022-04-30T07:02:40.677746Z", + "iopub.status.idle": "2022-04-30T07:02:40.776225Z", + "shell.execute_reply": "2022-04-30T07:02:40.775505Z" + } + }, + "outputs": [], + "source": [ + "sns.violinplot(y=\"lifeExp\", data=df_52);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chaper, we have introduced the empirical cumulative distribution function, a method of visualizing our data that avoids many of the pitfalls of histograms. The ECDF will be of great utility in plotting our data and models in the rest of this book and we hope that you'll find it useful in your work! We've also introduced several other visualization tools, such as the swarm plot, the box plot, and kernel density estimation." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/test/05a-probability-distributions-and-their-stories.ipynb b/notebooks/test/05a-probability-distributions-and-their-stories.ipynb new file mode 100644 index 0000000..d6d9211 --- /dev/null +++ b/notebooks/test/05a-probability-distributions-and-their-stories.ipynb @@ -0,0 +1,992 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Probability distributions and their stories (Part 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Chapter 3, we saw that we could match data-generating processes with binary outcomes to the story of the binomial distribution:\n", + "\n", + "> The Binomial distribution's story is as follows: the number $r$ of successes in $n$ Bernoulli trials with probability $p$ of success, is Binomially distributed. \n", + "\n", + "There are many other distributions with stories also! In this chapter, we'll introduce several key distributions and their stories." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For each distribution, we'll introduce\n", + "\n", + "* the story,\n", + "* the parameters, and\n", + "* how to simulate it (which will include plotting histograms and ECDFs of our simulated data).\n", + "\n", + "We'll then show real-world examples of each distribution, when possible, and demonstrate how to fit the simulated model the empirical data. We'll also occasionally use our simulation skills to give us more insight into the distributions we're introducing, such as to simulate the relationship between the Poisson and Binomial distributions, or to simulate an example of the Central Limit Theorem.\n", + "\n", + "We'll first introduce Poisson distributions and then Gaussian distributions. With the Binomial, Poisson, and Gaussian, you'll already be well-equipped to model a variety of real-world phenomena! We'll then introduce the beta distribution, which is inordinately useful when we need to assign probabilities on unknown probalities (such as when specifying priors).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Poisson processes and the Poisson distribution\n", + "\n", + "### The Poisson story\n", + "\n", + "In the book [Information Theory, Inference and Learning Algorithms](https://www.amazon.com/Information-Theory-Inference-Learning-Algorithms/dp/0521642981), David MacKay tells the tale of a town called Poissonville, in which the buses have an odd schedule. Standing at a bus stop in Poissonville, the amount of time you have to wait for a bus is totally independent of when the previous bus arrived. This means you could watch a bus drive off and another arrive almost instantaneously, or you could be waiting for hours.\n", + "\n", + "Arrival of buses in Poissonville is what we call a Poisson process. The timing of the next event is completely independent of when the previous event happened. Many real-life processes behave in this way. \n", + "\n", + "* natural births in a given hospital (there is a well-defined average number of natural births per year, and the timing of one birth is independent of the timing of the previous one);\n", + "* Landings on a website;\n", + "* Meteor strikes;\n", + "* Molecular collisions in a gas;\n", + "* Deaths fro mhorse kicks;\n", + "* Aviation incidents.\n", + "\n", + "Any process that matches the buses in Poissonville **story** is a Poisson process and the number of arrivals of a Poisson process in a given amount of time is Poisson distributed.\n", + "\n", + "### The parameter(s)\n", + "\n", + "The Poisson distribution has one parameter, the average number of arrivals in a given length of time. So, to match the story, we could consider the number of hits on a website in an hour with an average of six hits per hour. This is Poisson distributed.\n", + "\n", + "### Simulating the Poisson distribution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:43.087314Z", + "iopub.status.busy": "2022-04-30T07:02:43.086654Z", + "iopub.status.idle": "2022-04-30T07:02:45.842569Z", + "shell.execute_reply": "2022-04-30T07:02:45.842022Z" + } + }, + "outputs": [], + "source": [ + "#| output: false\n", + "#Import packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import scipy.optimize\n", + "from generative_thinking.utils import ecdf\n", + "%matplotlib inline\n", + "sns.set()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:45.861205Z", + "iopub.status.busy": "2022-04-30T07:02:45.861043Z", + "iopub.status.idle": "2022-04-30T07:02:46.052705Z", + "shell.execute_reply": "2022-04-30T07:02:46.052260Z" + } + }, + "outputs": [], + "source": [ + "# Generate Poisson-distributed data\n", + "rng = np.random.default_rng(42)\n", + "samples = rng.poisson(6, size=10**6)\n", + "\n", + "# Plot histogram\n", + "plt.hist(samples, bins=21);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If this distribution looks familiar, you've got a good eye. The Poisson distribution is related to both the Binomial distribution _and_ the Normal distribution!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In fact, the Poisson distribution is the limit of the Binomial distribution for low probability of success and large number of trials, that is, for rare events. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To see this, think about the stories. Picture this: you're doing a Bernoulli trial once a minute for an hour, each with a success probability of 0.05. We would do 60 trials, and the number of successes is Binomially distributed, and we would expect to get about 3 successes. This is just like the Poisson story of seeing 3 buses on average arrive in a given interval of time. Thus the Poisson distribution with arrival rate equal to $np$ approximates a Binomial distribution for $n$ Bernoulli trials with probability $p$ of success (with $n$ large and $p$ small). This is useful because the Poisson distribution can be simpler to work with as it has only one parameter instead of two for the Binomial distribution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The ECDF for the Poisson distribution\n", + "\n", + "We'll now plot the ECDF of the Poisson-distributed data that we generated above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:46.055113Z", + "iopub.status.busy": "2022-04-30T07:02:46.055010Z", + "iopub.status.idle": "2022-04-30T07:02:46.419848Z", + "shell.execute_reply": "2022-04-30T07:02:46.419538Z" + } + }, + "outputs": [], + "source": [ + "# Generate x- and y-data for the ECDF\n", + "x_p, y_p = ecdf(samples)\n", + "\n", + "# Plot the ECDF\n", + "plt.plot(x_p, y_p, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example: Field goal attempts per game\n", + "\n", + "Let's first remind ourselves of the story behind the Poisson distribution.\n", + "> The number of arrivals of a Poisson processes in a given set time interval is Poisson distributed.\n", + "\n", + "To quote Justin Bois, who developed this example [here](https://github.com/justinbois/dataframed-plot-examples/blob/master/lebron_field_goals.ipynb),\n", + "\n", + "> We could model field goal attempts in a basketball game using a Poisson distribution. When a player takes a shot is a largely stochastic process, being influenced by the myriad ebbs and flows of a basketball game. Some players shoot more than others, though, so there is a well-defined rate of shooting. Let's consider LeBron James's field goal attempts for the 2017-2018 NBA season.\n", + "\n", + "First thing's first, the data ([from here](https://www.basketball-reference.com/players/j/jamesle01/gamelog/2018)):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:46.421806Z", + "iopub.status.busy": "2022-04-30T07:02:46.421695Z", + "iopub.status.idle": "2022-04-30T07:02:46.425323Z", + "shell.execute_reply": "2022-04-30T07:02:46.424963Z" + } + }, + "outputs": [], + "source": [ + "fga = [19, 16, 15, 20, 20, 11, 15, 22, 34, 17, 20, 24, 14, 14, \n", + " 24, 26, 14, 17, 20, 23, 16, 11, 22, 15, 18, 22, 23, 13, \n", + " 18, 15, 23, 22, 23, 18, 17, 22, 17, 15, 23, 8, 16, 25, \n", + " 18, 16, 17, 23, 17, 15, 20, 21, 10, 17, 22, 20, 20, 23, \n", + " 17, 18, 16, 25, 25, 24, 19, 17, 25, 20, 20, 14, 25, 26, \n", + " 29, 19, 16, 19, 18, 26, 24, 21, 14, 20, 29, 16, 9]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To show that this LeBron's attempts are ~ Poisson distributed, you're now going to plot the ECDF and compare it with the the ECDF of the Poisson distribution that has the mean of the data (technically, this is the maximum likelihood estimate).\n", + "\n", + "#### LeBron's ECDF\n", + "\n", + "We first generate the x and y values for the ECDF of LeBron's field attempt goals." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:46.427110Z", + "iopub.status.busy": "2022-04-30T07:02:46.427004Z", + "iopub.status.idle": "2022-04-30T07:02:46.507417Z", + "shell.execute_reply": "2022-04-30T07:02:46.507021Z" + } + }, + "outputs": [], + "source": [ + "# Generate x & y data for ECDF\n", + "x_ecdf, y_ecdf = ecdf(np.array(fga))\n", + "# Plot the ECDF\n", + "plt.plot(x_ecdf, y_ecdf, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### The Poisson model for LeBron's field goal attempts\n", + "\n", + "Now we'll draw samples out of a Poisson distribution to get the theoretical ECDF (that is, simulating the model), plot it with the ECDF of the data and see how they look." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:46.510358Z", + "iopub.status.busy": "2022-04-30T07:02:46.510174Z", + "iopub.status.idle": "2022-04-30T07:02:47.295911Z", + "shell.execute_reply": "2022-04-30T07:02:47.295519Z" + } + }, + "outputs": [], + "source": [ + "# Number of times we simulate the model\n", + "n_reps = 1000\n", + "\n", + "# Plot ECDF of data\n", + "plt.plot(x_ecdf, y_ecdf, '.', color='black');\n", + "\n", + "# Plot ECDF of model\n", + "for _ in range(n_reps):\n", + " samples = rng.poisson(np.mean(fga), size=len(fga))\n", + " x_theor, y_theor = ecdf(samples)\n", + " plt.plot(x_theor, y_theor, '.', alpha=0.01, color='lightgray');\n", + "\n", + "\n", + "# Label your axes\n", + "plt.xlabel('field goal attempts')\n", + "plt.ylabel('ECDF');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see from the ECDF that LeBron's field goal attempts per game are approximately Poisson distributed. Note that we are not being super rigorous here and that this is intentional. The goal is not to state that the data is indeed Poisson distributed at the X% significance level, but to give a sense of how real-world data can be matched to probability distributions via their statistical stories. Also note that this is inherently a frequentist approach: we are approximating the Poisson distributions by looking at the long-run frequencies of outcomes, when we simulate. But this is fine, as we're merely using it as visual inspection tool, rather than to draw inferences, and thus Bernoulli's fallacy is irrelevant here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example: Death by horse kick\n", + "\n", + "To show off our new skills, let's look at one of the more infamous examples of a Poisson distribution! In 1898, Ladislaus Bortkiewicz, a Russian economist and statistician, published a book called _The Law of Small Numbers_. It was about Poisson distributions and in it Bortkiewicz showed the number of soldiers kicked to death by horses each year in the French cavalry corps is Poisson distributed. This dataset has stood the test of time and holds up well as an example of Poisson-distributed data. To see this, let's first import the data and check it out (data from [here](https://www.randomservices.org/random/data/HorseKicks.html)):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:47.298324Z", + "iopub.status.busy": "2022-04-30T07:02:47.298103Z", + "iopub.status.idle": "2022-04-30T07:02:47.309266Z", + "shell.execute_reply": "2022-04-30T07:02:47.308849Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "df = pd.read_csv('../../datasets/Prussion Horse-Kick Data.csv')\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now get it into a form of total number of kicks each year:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:47.311993Z", + "iopub.status.busy": "2022-04-30T07:02:47.311803Z", + "iopub.status.idle": "2022-04-30T07:02:47.317721Z", + "shell.execute_reply": "2022-04-30T07:02:47.317268Z" + } + }, + "outputs": [], + "source": [ + "df['kicks'] = df.drop(['Year'], axis=1).sum(axis=1)\n", + "kicks = df['kicks']\n", + "kicks.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We plot the ECDF of the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:47.319925Z", + "iopub.status.busy": "2022-04-30T07:02:47.319801Z", + "iopub.status.idle": "2022-04-30T07:02:47.404056Z", + "shell.execute_reply": "2022-04-30T07:02:47.403550Z" + } + }, + "outputs": [], + "source": [ + "# Generate x & y data for ECDF\n", + "x_ecdf, y_ecdf = ecdf(np.array(kicks))\n", + "# Plot the ECDF\n", + "plt.plot(x_ecdf, y_ecdf, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's simulate the Poisson model and see how our simulation looks, when plotted with the data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:47.406356Z", + "iopub.status.busy": "2022-04-30T07:02:47.406216Z", + "iopub.status.idle": "2022-04-30T07:02:48.201685Z", + "shell.execute_reply": "2022-04-30T07:02:48.201317Z" + } + }, + "outputs": [], + "source": [ + "# Number of times we simulate the model\n", + "n_reps = 1000\n", + "\n", + "# Plot ECDF of data\n", + "plt.plot(x_ecdf, y_ecdf, '.', color='black');\n", + "\n", + "# Plot ECDF of model\n", + "for _ in range(n_reps):\n", + " samples = rng.poisson(np.mean(kicks), size=len(kicks))\n", + " x_theor, y_theor = ecdf(samples)\n", + " plt.plot(x_theor, y_theor, '.', alpha=0.01, color='lightgray');\n", + "\n", + "\n", + "# Label your axes\n", + "plt.xlabel('Number of Deaths by Kick')\n", + "plt.ylabel('ECDF');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This looks good! The ECDFs are evidence for our data being Poisson distributed and for the process of being kicked to death by a horse being a Poisson process.\n", + "\n", + "### Simulating relationships between distributions: the Poisson and the Binomial\n", + "\n", + "We gave an argument above, along with an example, as to the nature of the relationship between the Poisson and Binomial distributions, in particular that \"the Poisson distribution is the limit of the Binomial distribution for low probability of success and large number of trials, that is, for rare events.\" It is possible to prove this relationship mathematically however such a proof, although rigorous, does not give an intution behind the relationship. To provide this intution, along with the argument and example presented above, it helps to simulate the data-generating process.\n", + "\n", + "Recalling the example,\n", + "\n", + "> [Y]ou're doing a Bernoulli trial once a minute for an hour, each with a success probability of 0.05. We would do 60 trials, and the number of successes is Binomially distributed, and we would expect to get about 3 successes. This is just like the Poisson story of seeing 3 buses on average arrive in a given interval of time,\n", + "\n", + "\n", + "We simulate it by generating 1,000 instances of doing 60 trials, each with $p=0.05$, and plot the ECDF:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:48.203909Z", + "iopub.status.busy": "2022-04-30T07:02:48.203783Z", + "iopub.status.idle": "2022-04-30T07:02:48.279573Z", + "shell.execute_reply": "2022-04-30T07:02:48.279175Z" + } + }, + "outputs": [], + "source": [ + "bts = rng.binomial(60, 0.05, 1000)\n", + "# Generate x & y data for ECDF\n", + "x_ecdf, y_ecdf = ecdf(bts)\n", + "# Plot the ECDF\n", + "plt.plot(x_ecdf, y_ecdf, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we'll draw samples out of a Poisson distribution to get the theoretical ECDF (that is, simulating the model), plot it with the ECDF of the data and see how they look." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:48.281705Z", + "iopub.status.busy": "2022-04-30T07:02:48.281552Z", + "iopub.status.idle": "2022-04-30T07:02:49.044965Z", + "shell.execute_reply": "2022-04-30T07:02:49.044645Z" + } + }, + "outputs": [], + "source": [ + "# Number of times we simulate the model\n", + "n_reps = 1000\n", + "\n", + "# Plot ECDF of data\n", + "plt.plot(x_ecdf, y_ecdf, '.', color='black');\n", + "\n", + "# Plot ECDF of model\n", + "for _ in range(n_reps):\n", + " samples = rng.poisson(np.mean(bts), size=len(fga))\n", + " x_theor, y_theor = ecdf(samples)\n", + " plt.plot(x_theor, y_theor, '.', alpha=0.01, color='lightgray');\n", + "\n", + "\n", + "# Label your axes\n", + "plt.xlabel('Bernoulli trials')\n", + "plt.ylabel('ECDF');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see from the ECDF that the results of the Bernoulli trials are approximately Poisson distributed. Now that we're familiar with both the Binomial and Poisson distributions, let's move on to the Gaussian!\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Gaussian distribution\n", + "\n", + "### The Gaussian story\n", + "\n", + "The normal distribution, also known as the Gaussian or Bell Curve, appears everywhere, and we will use these terms interchangeably. There are many reasons for it being so widespread. One is the following:\n", + "\n", + "> When doing repeated measurements, we expect them to be normally distributed, owing to the many subprocesses that contribute to a measurement. This is because (a formulation of the Central Limit Theorem) **any quantity that emerges as the sum of a large number of subprocesses tends to be Normally distributed** provided none of the subprocesses is very broadly distributed.\n", + "\n", + "\n", + "\n", + "\n", + "### The parameters\n", + "\n", + "The normal distribution has two parameters, mean $\\mu$ and variance $\\sigma^2$. The mean, as you may expect, is the expected value (or average) of the distribution, while the variance captures the spread of the distribution.\n", + "\n", + "One useful way to consider them is $\\mu$ as a _location_ parameter and $\\sigma$ as a _shape_ parameter. What we really mean by this is that changing $\\mu$ will essentially translate the distribution along the x-axis, while changing $\\sigma^2$ will make the distribution wider or more narrow.\n", + "\n", + "### Simulating the Gaussian\n", + "\n", + "\n", + "Let's now simulate a Gaussian distribution and plot the resulting histogram:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:49.047301Z", + "iopub.status.busy": "2022-04-30T07:02:49.047164Z", + "iopub.status.idle": "2022-04-30T07:02:49.206481Z", + "shell.execute_reply": "2022-04-30T07:02:49.206105Z" + } + }, + "outputs": [], + "source": [ + "samples = rng.normal(0, 1, size=10000)\n", + "plt.hist(samples);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The ECDF of the Gaussian\n", + "\n", + "Let's also plot the ECDF of the Gaussian data we just simulated:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:49.208504Z", + "iopub.status.busy": "2022-04-30T07:02:49.208392Z", + "iopub.status.idle": "2022-04-30T07:02:49.296016Z", + "shell.execute_reply": "2022-04-30T07:02:49.295625Z" + } + }, + "outputs": [], + "source": [ + "x, y = ecdf(samples)\n", + "\n", + "_ = plt.plot(x, y, marker='.', linestyle='none')\n", + "\n", + "_ = plt.ylabel('CDF')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### An example of a Gaussian distribution: the speed of light\n", + "\n", + "Now it's time to see if this holds for the measurements of the speed of light in the famous Michelson–Morley experiment.\n", + "\n", + "Below, we'll plot the histogram with a Gaussian curve fitted to it. Even if that looks good, though, that could be due to binning bias. So then we'll plot the ECDF of the data and the CDF of the model!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:49.298213Z", + "iopub.status.busy": "2022-04-30T07:02:49.298071Z", + "iopub.status.idle": "2022-04-30T07:02:49.391068Z", + "shell.execute_reply": "2022-04-30T07:02:49.390640Z" + } + }, + "outputs": [], + "source": [ + "# Load data, plot histogram \n", + "import scipy.stats as st\n", + "df = pd.read_csv('../../datasets/michelson_speed_of_light.csv')\n", + "df = df.rename(columns={'velocity of light in air (km/s)': 'c'})\n", + "c = df.c.values\n", + "x_s = np.linspace(299.6, 300.1, 400) * 1000\n", + "plt.plot(x_s, st.norm.pdf(x_s, c.mean(), c.std(ddof=1)))\n", + "plt.hist(c, bins=9, density=True)\n", + "plt.xlabel('speed of light (km/s)')\n", + "plt.ylabel('PDF');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Speed of light data and Gaussian model ECDF" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:49.393179Z", + "iopub.status.busy": "2022-04-30T07:02:49.393058Z", + "iopub.status.idle": "2022-04-30T07:02:49.478306Z", + "shell.execute_reply": "2022-04-30T07:02:49.477927Z" + } + }, + "outputs": [], + "source": [ + "# Get speed of light measurement + mean & standard deviation\n", + "michelson_speed_of_light = df.c.values\n", + "mean = np.mean(michelson_speed_of_light)\n", + "std = np.std(michelson_speed_of_light, ddof=1)\n", + "\n", + "# Generate normal samples w/ mean, std of data\n", + "samples = np.random.normal(mean, std, size=10000)\n", + "\n", + "# Generate data ECDF & model CDF\n", + "x, y = ecdf(michelson_speed_of_light)\n", + "x_theor, y_theor = ecdf(samples)\n", + "\n", + "# Plot data & model (E)CDFs\n", + "_ = plt.plot(x_theor, y_theor)\n", + "_ = plt.plot(x, y, marker='.', linestyle='none')\n", + "_ = plt.xlabel('speed of light (km/s)')\n", + "_ = plt.ylabel('CDF')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data looks pretty Gaussian to us! Some of you may ask but is the data really normal? I urge you to check out Allen Downey's post [_Are your data normal? Hint: no._ ](http://allendowney.blogspot.com/2013/08/are-my-data-normal.html)\n", + "\n", + "### The Central limit theorem\n", + "\n", + "Recall that, in Chapter 2, we used the bootstrap to estimate our uncertainty around the mean of the Fortis Finch beak length population:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:49.480732Z", + "iopub.status.busy": "2022-04-30T07:02:49.480601Z", + "iopub.status.idle": "2022-04-30T07:02:49.585742Z", + "shell.execute_reply": "2022-04-30T07:02:49.585330Z" + } + }, + "outputs": [], + "source": [ + "# Import and view head of data\n", + "df_12 = pd.read_csv('../../datasets/finch_beaks_2012.csv')\n", + "df_fortis = df_12.loc[df_12['species'] == 'fortis']\n", + "blf = df_fortis['blength']\n", + "\n", + "n = 10**3\n", + "blf_means = np.zeros(n)\n", + "\n", + "for i in range(n):\n", + " samples = rng.choice(blf, len(blf))\n", + " mean = np.mean(samples)\n", + " blf_means[i] = mean\n", + " \n", + "plt.hist(blf_means);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We noted that the distribution looks approximately normal so let's now plot the relevant ECDFs to confirm this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:49.587922Z", + "iopub.status.busy": "2022-04-30T07:02:49.587768Z", + "iopub.status.idle": "2022-04-30T07:02:49.667034Z", + "shell.execute_reply": "2022-04-30T07:02:49.666662Z" + } + }, + "outputs": [], + "source": [ + "mean = np.mean(blf_means)\n", + "std = np.std(blf_means, ddof=1)\n", + "\n", + "# Generate normal samples w/ mean, std of data\n", + "samples = rng.normal(mean, std, size=1000)\n", + "\n", + "# Generate data ECDF & model CDF\n", + "x_f, y_f = ecdf(blf_means)\n", + "x_theor, y_theor = ecdf(samples)\n", + "\n", + "# Plot data & model (E)CDFs\n", + "_ = plt.plot(x_theor, y_theor)\n", + "_ = plt.plot(x_f, y_f)\n", + "_ = plt.xlabel('blf means')\n", + "_ = plt.ylabel('CDF')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That looks pretty good! Knowing the story of the normal distribution, why would this distribution be normally distributed? It comes down to the Central limit theorem! Recall that the CLT states that:\n", + "\n", + "> **any quantity that emerges as the sum of a large number of subprocesses tends to be Normally distributed** provided none of the subprocesses is very broadly distributed.\n", + "\n", + "And the mean is proportional to the sum of a large number of subprocesses, each of which is an individual measurement of a beak length. Now we've added the Gaussian distribution to our toolkit, let's wrap up this chapter by diving into the beta distribution, which will help us quantify uncertainty around unknown probabilities." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Beta distribution\n", + "\n", + "### The beta story\n", + "\n", + "We'll start by telling you the story of the beta distribution, for completeness' sake, but then we'll delve into other ways to get intution for the beta distribution:\n", + "\n", + "> Say you wait for two multistep processes to happen. The individual steps of each process happen at the same rate, but the first multistep process requires α steps and the second requires β steps. The fraction of the total waiting time take by the first process is beta distributed.\n", + "\n", + "\n", + "This story doesn't really tell us some of the most salient aspects of the beta distribution. The most important aspect is that, using the beta distribution, we can assign probabilities on unknown probalities, such as what is the probability that $P(heads) = 0.5$ for a biased coin. The beta distribution is also defined only on $0≤x≤1$, and x can be thought of as the probability of a Bernoulli trial, for example.\n", + "\n", + "Consider the average CTR of two websites, **A** that was launched several minutes ago and **B** that has been live for some time. What we'd really like to do is to predict the CTR of both A and B but the thing is we have a lot more data on B and so can be more certain of the CTR we have calculated. Let's say that 10 people so far have landed on website A and nobody has clicked through. Would we assume that the CTR is likely zero, then? No, not at all! Why not?\n", + "\n", + "Because we're going in with prior information, namely that a CTR under 10% is quite common and that a CTR of 0% is very unlikely. And it is the beta distribution that allows us to encode this prior information.\n", + "\n", + "### The beta parameters\n", + "\n", + "$\\alpha$ and $\\beta$, which are both _shape_ parameters. These uniquely define the beta distribution, in accordance with the story of the distribution told above. As we'll see below, when $\\alpha=\\beta=1$, the beta distribution is equivalent to the uniform distribution and, due the to 2 degrees of freedom, the beta distribution is versatile and widely applicable.\n", + "\n", + "\n", + "### Simulating the beta distribution\n", + "\n", + "\n", + "To simulate the beta distribution, we'll need to pick some parameters so let's do it a couple of times.\n", + "\n", + "\n", + "Let's first simulate $\\beta(1, 1)$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:49.669320Z", + "iopub.status.busy": "2022-04-30T07:02:49.669173Z", + "iopub.status.idle": "2022-04-30T07:02:49.754985Z", + "shell.execute_reply": "2022-04-30T07:02:49.754566Z" + } + }, + "outputs": [], + "source": [ + "samples_1 = rng.beta(1, 1, 10**3)\n", + "plt.hist(samples_1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's no coincidence that it looks like the uniform distribution... because it is! This encodes no former knowledge about the CTR, except that it lays between 0 and 1 and all possible values therein are equally likely. One the really cool aspects of the beta distribution is that, as we begin to see results from our experiments, we can update the beta distribution to include these results in an intuitive manner. In particular, if we see $a_0$ Os and $a_1$ 1s, we can update our distribution to $\\beta(1+a_1, 1+a_0)$ to encode this additional knowledge. Thus, were we to see 10 people not click through and 2 click-throughs, we would get $\\beta(3, 11)$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:49.757326Z", + "iopub.status.busy": "2022-04-30T07:02:49.757172Z", + "iopub.status.idle": "2022-04-30T07:02:49.843780Z", + "shell.execute_reply": "2022-04-30T07:02:49.843300Z" + } + }, + "outputs": [], + "source": [ + "samples_2 = rng.beta(3, 11, 10**3)\n", + "plt.hist(samples_2);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The ECDF of the beta distribution\n", + "\n", + "Let's now plot the ECDFs of these two distributions to get a feel for them:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:49.846141Z", + "iopub.status.busy": "2022-04-30T07:02:49.846001Z", + "iopub.status.idle": "2022-04-30T07:02:49.924703Z", + "shell.execute_reply": "2022-04-30T07:02:49.924334Z" + } + }, + "outputs": [], + "source": [ + "x_1, y_1 = ecdf(samples_1)\n", + "x_2, y_2 = ecdf(samples_2)\n", + "\n", + "plt.plot(x_1, y_1);\n", + "plt.plot(x_2, y_2);\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example: baseball batting averages\n", + "\n", + "This is an example from sabermetrics (the mathematical and statistical analysis of baseball records). We'll be looking at average batting rate, which is analogous to the average CTR story above via matching both stories to the binomial distribution! This example is inspired by [several](http://varianceexplained.org/statistics/beta_distribution_and_baseball/) [blog posts](http://varianceexplained.org/r/empirical_bayes_baseball/) by David Robinson.\n", + "\n", + "Let's say we have a batter $A$ with 3 out of 10 hits and a batter $B$ with 250 out of $1000$ hits. $A$'s batting average is 0.3 and $B$'s is 0.25, but we have much less data to go on with $A$!\n", + "\n", + "But we also have a lot more data! In the end, we can use a technique known as _Empirical Bayes_ to get better estimates for the relevant batting averages, which involves two steps:\n", + "\n", + "1. Estimating a beta distribution for all the relevant data\n", + "2. Use that distribution to retrieve estimates for each individual (such as $A$ and $B$).\n", + "\n", + "Here, we'll do Step 1 and we'll return to Step 2 after introducing Bayes' Theorem and Bayesian inference. The attentive reader may notice that we're referring to prior and posterior distributions in Steps 1 and 2 without yet using that language.\n", + "\n", + "So let's now import our data, clean it up a bit, and model it as a beta distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:49.926847Z", + "iopub.status.busy": "2022-04-30T07:02:49.926690Z", + "iopub.status.idle": "2022-04-30T07:02:50.167248Z", + "shell.execute_reply": "2022-04-30T07:02:50.166937Z" + } + }, + "outputs": [], + "source": [ + "df_batting = pd.read_csv('../../datasets/Batting.csv')\n", + "df_p = pd.read_csv('../../datasets/Pitching.csv')\n", + "df_batting.head()\n", + "\n", + "dfg = df_batting[['playerID','AB','H']].groupby(['playerID']).sum()\n", + "dfg['Av'] = dfg['H']/dfg['AB']\n", + "dfg.reset_index(inplace=True)\n", + "dfg.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's clean up our data a bit by dropping pitchers, include only those with > 500 at bats, and dropping NaNs:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:50.169545Z", + "iopub.status.busy": "2022-04-30T07:02:50.169393Z", + "iopub.status.idle": "2022-04-30T07:02:50.304610Z", + "shell.execute_reply": "2022-04-30T07:02:50.304188Z" + } + }, + "outputs": [], + "source": [ + "dfg = dfg[~dfg.playerID.isin(df_p.playerID.unique())]\n", + "\n", + "#df = dfg[dfg['Av'] > 0.18]\n", + "dfg = dfg[dfg['AB'] > 500]\n", + "dfg.dropna(inplace=True)\n", + "plt.hist(dfg['Av'],bins=50);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now fit a beta distribution to the data, simulate from the beta, and plot the resulting histogram:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:50.306955Z", + "iopub.status.busy": "2022-04-30T07:02:50.306668Z", + "iopub.status.idle": "2022-04-30T07:02:50.434934Z", + "shell.execute_reply": "2022-04-30T07:02:50.434607Z" + } + }, + "outputs": [], + "source": [ + "data = dfg['Av']\n", + "m = np.mean(data)\n", + "v = np.var(data)\n", + "print(m, v)\n", + "a = m**2*((1-m)/v - 1/m)\n", + "b = a*(1/m - 1)\n", + "print(a, b)\n", + "samples = rng.beta(a, b, 10**3)\n", + "plt.hist(samples, bins=50);\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then compute and plot and ECDFs for the data and the simulated model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:50.437043Z", + "iopub.status.busy": "2022-04-30T07:02:50.436927Z", + "iopub.status.idle": "2022-04-30T07:02:50.524893Z", + "shell.execute_reply": "2022-04-30T07:02:50.524386Z" + } + }, + "outputs": [], + "source": [ + "# Compute ECDFs for sample & model\n", + "x, y = ecdf(np.array(data))\n", + "x_theor, y_theor = ecdf(np.array(samples))\n", + "# Plot sample & model ECDFs\n", + "plt.plot(x_theor, y_theor);\n", + "plt.plot(x, y, marker='.', linestyle='none', alpha=0.03);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that out data is approximately beta distributed, where $\\alpha\\approx 80$ and $\\beta\\approx 229.$\n", + "After we introduce Bayes' Theorem and Bayesian inference, we'll see how this can be updated to retrieve estimates for individual batters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chapter, we have introduced 4 key distributions that will provide a firm foundation for any future statistical modeling. In particular, we have focused on matching distributions to their stories, in order to give intuition for how we think about these distributions and how they correspond to the way we encode the data-generating processes in our modeling choices.\n", + "\n", + "We've seen how many of the datasets are related and used our simulation skills to demonstrate these relationships. We've also looked at a variety of real-world datasets, considered what data-generating processes could have resulted in them, fit the relevant models to the data, and used the ECDF to visualize the data and the model together, in order to get a visual sense of the goodness of fit.\n", + "\n", + "Now we have a stronger sense of our foundational family of distributions, it's time to move onto other distributions that will be useful to any statistical modeler." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/test/05b-probability-distributions-and-their-stories-part-two.ipynb b/notebooks/test/05b-probability-distributions-and-their-stories-part-two.ipynb new file mode 100644 index 0000000..4bd24b8 --- /dev/null +++ b/notebooks/test/05b-probability-distributions-and-their-stories-part-two.ipynb @@ -0,0 +1,1749 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# More probability distributions, more stories" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous chapter, we introduced 4 foundational distributions, along with their statistical stories.\n", + "\n", + "\n", + "We'll now dive into a variety of other distributions that you're likely to find useful in your statistical journeying. Do feel free to jump to the next chapter and come back to them, when necessary.\n", + "\n", + "Once again, for each distribution, we'll introduce\n", + "\n", + "* the story,\n", + "* the parameters, and\n", + "* how to simulate it (which will include plotting histograms and ECDFs of our simulated data).\n", + "\n", + "We'll then show real-world examples of each distribution, when possible, and demonstrate how to fit the simulated model the empirical data. We'll also occasionally use our simulation skills to give us more insight into the distributions we're introducing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Exponential distribution\n", + "\n", + "### The Exponential story\n", + "\n", + "We've encountered a variety of named _discrete distributions_. There are also named _continuous distributions_, such as the exponential distribution and the normal (or Gaussian) distribution. To see what the story of the exponential distribution is, let's return to Poissonville, in which the number of buses that will arrive per hour are Poisson distributed.\n", + "However, the **waiting time between arrivals of a Poisson process are exponentially distributed.**\n", + "\n", + "So the exponential distribution has the following story: the waiting time between arrivals of a Poisson process are exponentially distributed. \n", + "\n", + "### The parameter(s)\n", + "\n", + "The exponential distribution has a single parameter, the mean waiting time. This distribution is not peaked, as we can see from its PDF. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:55.066530Z", + "iopub.status.busy": "2022-04-30T07:02:55.065946Z", + "iopub.status.idle": "2022-04-30T07:02:55.089776Z", + "shell.execute_reply": "2022-04-30T07:02:55.089280Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "from IPython.display import Image\n", + "Image(\"../../img/Exponential_probability_density.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Simulating the exponential distribution\n", + "\n", + "\n", + "Let's now simulate some exponentially distributed data and plot the resulting histogram:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:55.118176Z", + "iopub.status.busy": "2022-04-30T07:02:55.117801Z", + "iopub.status.idle": "2022-04-30T07:02:57.630423Z", + "shell.execute_reply": "2022-04-30T07:02:57.630077Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hba/opt/anaconda3/envs/generative-thinking/lib/python3.9/site-packages/aesara/link/jax/dispatch.py:87: UserWarning: JAX omnistaging couldn't be disabled: Disabling of omnistaging is no longer supported in JAX version 0.2.12 and higher: see https://github.com/google/jax/blob/main/design_notes/omnistaging.md.\n", + " warnings.warn(f\"JAX omnistaging couldn't be disabled: {e}\")\n", + "/Users/hba/opt/anaconda3/envs/generative-thinking/lib/python3.9/site-packages/pymc/sampling_jax.py:36: UserWarning: This module is experimental.\n", + " warnings.warn(\"This module is experimental.\")\n" + ] + } + ], + "source": [ + "#| output: false\n", + "#Import packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import scipy.optimize\n", + "from generative_thinking.utils import ecdf\n", + "%matplotlib inline\n", + "sns.set()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:57.632524Z", + "iopub.status.busy": "2022-04-30T07:02:57.632423Z", + "iopub.status.idle": "2022-04-30T07:02:57.752834Z", + "shell.execute_reply": "2022-04-30T07:02:57.752287Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng(42)\n", + "samples = rng.exponential(1, size=10**6)\n", + "plt.hist(samples);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The ECDF for the Exponential distribution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also plot the ECDF:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:57.755168Z", + "iopub.status.busy": "2022-04-30T07:02:57.754950Z", + "iopub.status.idle": "2022-04-30T07:02:58.198387Z", + "shell.execute_reply": "2022-04-30T07:02:58.198068Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x, y = ecdf(samples)\n", + "\n", + "plt.plot(x, y, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### An example of the exponential distribution\n", + "\n", + "For an illustrative example, lets check out the time between all incidents involving nuclear power since 1974. It's a reasonable first approximation to expect incidents to be well-modeled by a Poisson process, which means the timing of one incident is independent of all others. If this is the case, the time between incidents should be exponentially distributed.\n", + "\n", + "\n", + "To see if this story is credible, we can plot the ECDF of the data with the CDF that we'd get from an exponential distribution with the sole parameter, the mean, given by the mean inter-incident time of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:58.200364Z", + "iopub.status.busy": "2022-04-30T07:02:58.200256Z", + "iopub.status.idle": "2022-04-30T07:02:58.288145Z", + "shell.execute_reply": "2022-04-30T07:02:58.287738Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load nuclear power accidents data & \n", + "# create array of inter-incident times\n", + "df = pd.read_csv('../../datasets/nuclear_power_accidents.csv')\n", + "df.Date = pd.to_datetime(df.Date)\n", + "df = df[df.Date >= pd.to_datetime('1974-01-01')]\n", + "inter_times = np.diff(np.sort(df.Date)).astype(float) / 1e9 / 3600 / 24\n", + "plt.hist(inter_times);" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:58.290404Z", + "iopub.status.busy": "2022-04-30T07:02:58.290266Z", + "iopub.status.idle": "2022-04-30T07:02:58.535202Z", + "shell.execute_reply": "2022-04-30T07:02:58.534893Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute mean and sample from exponential\n", + "mean = np.mean(inter_times)\n", + "samples = rng.exponential(mean, size=10**6)\n", + "\n", + "# Compute ECDFs for sample & model\n", + "x, y = ecdf(inter_times)\n", + "x_theor, y_theor = ecdf(samples)\n", + "\n", + "# Plot sample & model ECDFs\n", + "plt.plot(x_theor, y_theor);\n", + "plt.plot(x, y, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the data is close to being exponentially distributed, which means that we can model the nuclear incidents as a Poisson process.\n", + "\n", + "\n", + "### Simulating relationships between distributions: the exponential and the uniform \n", + "\n", + "We'll soon use our simulation skills to demonstrate the relationship between the exponential and Poisson distributions. Before this, however, let's show how the exponential and uniform distributions are related (this will be useful in demonstrating the former).\n", + "\n", + "For a uniformly distributed unknown quantity $X$, there is a corresponding exponentially distributed unknown quantity $Y \\sim - ln(X).$ We'll demonstrate this by simulating $X$, taking the log and scaling accordingly:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:58.537416Z", + "iopub.status.busy": "2022-04-30T07:02:58.537214Z", + "iopub.status.idle": "2022-04-30T07:02:58.540046Z", + "shell.execute_reply": "2022-04-30T07:02:58.539597Z" + } + }, + "outputs": [], + "source": [ + "# Simulate X\n", + "lam = 15\n", + "x = rng.random(100)\n", + "y = -(1./lam)*np.log(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then fit an exponential model to the data generated and plot the ECDFs of both the data and the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:58.542012Z", + "iopub.status.busy": "2022-04-30T07:02:58.541889Z", + "iopub.status.idle": "2022-04-30T07:02:58.771216Z", + "shell.execute_reply": "2022-04-30T07:02:58.770785Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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41tO5vk5EsYrBHiT4jlNfm4goVjDYR/HYWjHQ8jf/MgwgccZORDGH+9iHeWytGPjzKzB4hgAAAhIkYxx3xBBRzGGwD3O3NwMeFyQAsgTELVyDhA2buCOGiGIOl2KGBdxtCsC0bANDnYhiEoN9mHD2825TItIEBvsw/24Ywd0wRBTbQgr2o0ePoqioCAUFBaipqRnT39bWhsceewylpaXYunUrrly5EvZCI8lja4W77RP/oV/cDUNEsUwx2G02G/bt24f9+/fj0KFDOHDgAFpbW/39Qgj8+Mc/RkVFBY4cOYJVq1ahuro6okWHk283jPti4/AxvRLA3TBEFMMUg72+vh65ublISUmB2WxGYWEhamtr/f2NjY0wm83Iz88HAGzfvh2PPvpo5CoOI4+tFYOnD43shgEgMlbBXPws3zglopiluN2xs7MTFovF37ZarWhoaPC3L1y4gPnz52PXrl1oamrCsmXL8Itf/CIy1YaRb6aO4X3rMiTIkgFzb93MUCeimKYY7LIsQ/JtFYF36WV02+1249SpU/jDH/6A7OxsvPbaa3j55Zfx8ssvh1xEWtr036i0WGZP63WXW85iwDPyHNNvXNehd8VGPHTT+mnXEs2mO056wjEKDcdJmdpjpBjsGRkZOH36tL9tt9thtVr9bYvFgiVLliA7OxsAUFxcjMrKyikV0d3dD1kWU3qN93vPht3eN+XXAcCQJw7DD76DAPDp0PW4ddmqaX+9aHYt46QXHKPQcJyUzcQYGQzSpBNixTX2vLw8nDx5Ej09PXA4HKirq/OvpwPA+vXr0dPTg+bmZgDA8ePHsWbNmjCUHlnBu15mG4awYnGKOsUQEYWR4ow9PT0dO3fuRHl5OVwuFx588EHk5OSgoqIClZWVyM7Oxu9//3vs3r0bDocDGRkZeOWVV2ai9msSvE89cfZcJMbzhAUiin0hJVlJSQlKSkoCPvf666/7/37t2rV45513wltZhI2esQsBXJ+qYjFERGGk2ztPg89dnzd/vqr1EBGFi26DXe46DwD+/evWJI+q9RARhYsug91ja4Xr648AeGfsQjIiYfFqdYsiIgoTXQa7q+WvgOwG4F1fb5+7ljclEZFm6C7Yg2frHhgQt/w2dYsiIgoj3QW7u70ZkEfW0/82lInFa9aqWBERUXjpLth9T0rycZoXICmB+9eJSDt0F+y+3TAAIAtg8VwViyEiigBdBXvw+roMA8xLov/4AyKiqdBVsI9ZXx/MxKLVXF8nIm3RVbAHr69fSUjHnFnx6hVERBQBugr24PNhFs+RJrmaiCg26SrYg8+HmTOPJ38RkfboKtiDz4dJN8uq1kNEFAm6CfbxdsSkZuaoWxQRUQToJthH74gRAJpNqxF33XJ1iyIiigDdBPvoHTGSAETqYnULIiKKEN0Ee8COGADWWVxfJyJt0k2wB++ISZk3T9V6iIgiRTfB7puxS/CG+2zTkKr1EBFFin6CfWjA+xGAJAGGxNnqFkREFCG6CHaPrRWuhmMARmbso9fciYi0RBfB7m5vBsTwVkcBSJIBpgUrVa6KiCgydBHso984BYCh5T/iM06JSLN0EezBb5wmz52jaj1ERJGki2AP2OrIN06JSON0EezBM3a+cUpEWqaLYJcSk72BLrzh7pvBExFpkS6C3T9jlwLbRERapItg96+xc8ZORDqgi2D3P2BDAgCJM3Yi0rSQgv3o0aMoKipCQUEBampqJrzuxIkTuOuuu8JWXDh4H7DxISCG97EbjLw5iYg0zaR0gc1mw759+/Duu+8iPj4eDz/8MG699VZkZgbe4NPV1YVf//rXESt0utztzRCyDEnyBrsp6w7enEREmqY4Y6+vr0dubi5SUlJgNptRWFiI2traMdft3r0bTz/9dESKvBa+B2yI4dtOjfOXqFoPEVGkKc7YOzs7YbFY/G2r1YqGhoaAa95++22sXr0aa9eunVYRaWnTfzPTYpn8ZqPLLS44MbIjxmx0IVXhNVqkNE7EMQoVx0mZ2mOkGOyyLEPypSIAIURAu6WlBXV1dXjrrbfQ0dExrSK6u/shy0L5wiAWy2zY7X2TXjPkiQMw8oCNAU8c3Aqv0ZpQxknvOEah4Tgpm4kxMhikSSfEiksxGRkZsNvt/rbdbofVavW3a2trYbfbUVZWhm3btqGzsxNbtmy5xrLDRzj7/dscfW0iIi1TDPa8vDycPHkSPT09cDgcqKurQ35+vr+/srISx44dw+HDh1FdXQ2r1Yr9+/dHtOipGDIk+Y8SALiHnYi0TzHY09PTsXPnTpSXl2PTpk0oLi5GTk4OKioq8MUXX8xEjdek/+I//Msw3MNORHogCSGmvrgdZpFaY/fYWtF3+CUYhAeQAMlggrnked1td+S6qDKOUWg4TsqiYY1d8c3TWOZub4YBHv+OGO5hJyI90PSRAsFPTuIediLSA00Hu6fr3KgdMVxfJyJ90Gywe8+I+WhkRwzPiCEindBssLvbm4HhM2IArq8TkX5oNth5RgwR6ZVmd8XwDHYi0itNzth96+sA19eJSH80Geze9XWP/41Trq8TkZ5oMthHr69L4Po6EemLJoNdOPu958NII20iIr3QZLD77zgVgW0iIj3Q5K4Y3wydM3Yi0iNtz9iD2kREeqDJYPfvYR/+K2fsRKQnmgt27x72DwHBPexEpE+aC3Z3ezMEz4ghIh3TXLDzjBgi0jvN7Yrhjhgi0juNzti5h52I9Etzwc67TolI7zQX7FJiMiTBPexEpF+aC/ah/iveGftwmzN2ItIbzQX7FXfcyHNOwRk7EemP5oLd1Xlu1Iydd50Skf5oKtg9tlak2M74l2F41ykR6ZGmgt3d3gwJHt51SkS6pqlglxKTA9bXedcpEemRpoLd03XO/zg8rq8TkV5pJtg9tla4v/5oZMbO9XUi0qmQgv3o0aMoKipCQUEBampqxvS/9957uP/++1FaWoodO3bgypUrYS9UCU91JCLyUgx2m82Gffv2Yf/+/Th06BAOHDiA1tZWf39/fz9++ctforq6GkeOHEFWVhZ+97vfRbTo8fBURyIiL8Vgr6+vR25uLlJSUmA2m1FYWIja2lp/v8vlwp49e5Ceng4AyMrKwqVLlyJX8QRGn+oogXecEpF+KQZ7Z2cnLBaLv221WmGz2fzt1NRU3HPPPQAAp9OJ6upq3H333REodXI81ZGIyEvxPHZZliFJ/lt+IIQIaPv09fXhJz/5CVauXIkHHnhgSkWkpU0/hC2W2QCAyy0uOAAYhkszG11IHe6jkXGiiXGMQsNxUqb2GCkGe0ZGBk6fPu1v2+12WK3WgGs6OzuxdetW5ObmYteuXVMuoru7H7IslC8MYrHMht3eBwAY8sR5T3UcXooZ8MTBPdynd6PHicbHMQoNx0nZTIyRwSBNOiFWXIrJy8vDyZMn0dPTA4fDgbq6OuTn5/v7PR4Ptm/fjo0bN6Kqqmrc2fxMkB19PNWRiAghzNjT09Oxc+dOlJeXw+Vy4cEHH0ROTg4qKipQWVmJjo4OfPXVV/B4PDh27BgA4KabbsLevXsjXvxoA0iACfCHO9fYiUivQnrmaUlJCUpKSgI+9/rrrwMAsrOz0dzcHP7Kpmiw4xyM8P0vCO86JSL90sSdpx5bK8wX/8a7TomIEOKMPdq525shCd51SkQEaGTGzrtOiYhGaCLYhbPf+6apNNImItIrTQS7lJjs3cM+qk1EpFeaCHb3QC/3sBMRDdNEsPd7EgKenMQZOxHpmSaCfaD3e87YiYiGaSLYr7jiOGMnIhqmiWA3fP/dqBk77zolIn2L+WD32FqxqO9z/1ZH3nVKRHoX88HuavkrjPD4Z+u865SI9C6mg9353ddwff0RIEbOiIlfcZvaZRERqSqmg91xvhGQPTwjhoholJgOdmPSbAieEUNEFCCmg93j8D5+imfEEBGNiOlgNyZ5Hxjrm7Fz/zoRUYyfx84ZOxHRWJyxExFpTEwH+6CtDYBvxs47TomIgBheivHYWjHw+XHvHnYJkHjHKRERgBiesbvbmwEP97ATEQWL2WDnc06JiMYXs8HO55wSEY0vZoPdtwOGO2KIiALF7Junvhk6Z+xERIE4Yyci0piYDXa56zyAkRm7r01EpHcxG+xCoU1EpFcxG+zG+UtGHrABbnckIvKJ2WB3D/SOeoA13zwlIvIJKdiPHj2KoqIiFBQUoKamZkx/U1MTNm/ejMLCQlRVVcHtdoe90GADIhESRmbsfPOUiMhLMdhtNhv27duH/fv349ChQzhw4ABaW1sDrnnmmWfwwgsv4NixYxBC4ODBgxEr2MfZ+z1kwP8Qa87YiYi8FIO9vr4eubm5SElJgdlsRmFhIWpra/39Fy9ehNPpxLp16wAAmzdvDuiPFHvi9fDACAEDYIzjAWBERMMUb1Dq7OyExWLxt61WKxoaGibst1gssNlsUyoiLW3qyyjxi1fhzTMb8cw9s5G8NBuJi7Km/DX0xGKZrXYJUY9jFBqOkzK1x0gx2GVZhuTbLA5ACBHQVuoPRXd3P2R5ahsWVy2ag1urHkN/rwN9APrsfVN6vZ5YLLNh5/hMimMUGo6TspkYI4NBmnRCrLgUk5GRAbvd7m/b7XZYrdYJ+7u6ugL6I8UgSUhKiNkTEYiIIkYx2PPy8nDy5En09PTA4XCgrq4O+fn5/v6FCxciISEBZ86cAQAcPnw4oJ+IiGaWYrCnp6dj586dKC8vx6ZNm1BcXIycnBxUVFTgiy++AAC8+uqreOmll3DvvfdiYGAA5eXlES+ciIjGJwkhVL8bfzpr7ADX+0LFcVLGMQoNx0lZTKyxExFRbGGwExFpDIOdiEhjomK/oMEwtX3v4XqtnnCclHGMQsNxUhbpMVL6+lHx5ikREYUPl2KIiDSGwU5EpDEMdiIijWGwExFpDIOdiEhjGOxERBrDYCci0hgGOxGRxjDYiYg0JiaC/ejRoygqKkJBQQFqamrG9Dc1NWHz5s0oLCxEVVUV3G63ClWqT2mc3nvvPdx///0oLS3Fjh07cOXKFRWqVJfSGPmcOHECd9111wxWFl2UxqmtrQ2PPfYYSktLsXXrVv4sjTNGjY2NKCsrQ2lpKZ566in09vbOXHEiynV0dIg777xTXL58WVy9elWUlJSIb775JuCa++67T3z66adCCCF+/vOfi5qaGhUqVZfSOPX19YnbbrtNdHR0CCGEeO2118SvfvUrtcpVRSg/S0IIYbfbxb333ivuvPNOFapUn9I4ybIsCgoKxAcffCCEEOI3v/mNeOWVV9QqVxWh/Cw98sgj4sSJE0IIIV566SXx29/+dsbqi/oZe319PXJzc5GSkgKz2YzCwkLU1tb6+y9evAin04l169YBADZv3hzQrxdK4+RyubBnzx6kp6cDALKysnDp0iW1ylWF0hj57N69G08//bQKFUYHpXFqbGyE2Wz2PwJz+/btePTRR9UqVxWh/CzJsoyrV68CABwOBxITE2esvqgP9s7OTlgsFn/barXCZrNN2G+xWAL69UJpnFJTU3HPPfcAAJxOJ6qrq3H33XfPeJ1qUhojAHj77bexevVqrF27dqbLixpK43ThwgXMnz8fu3btwgMPPIA9e/bAbDarUapqQvlZev7557F7927cfvvtqK+vx8MPPzxj9UV9sMuyDEkaOaJSCBHQVurXi1DHoa+vD9u2bcPKlSvxwAMPzGSJqlMao5aWFtTV1WHHjh1qlBc1lMbJ7Xbj1KlTeOSRR/CnP/0Jixcvxssvv6xGqapRGiOn04mqqiq89dZb+Pjjj7FlyxY899xzM1Zf1Ad7RkYG7Ha7v22322G1Wifs7+rqCujXC6VxAryzjC1btiArKwt79+6d6RJVpzRGtbW1sNvtKCsrw7Zt2/zjpTdK42SxWLBkyRJkZ2cDAIqLi9HQ0DDjdapJaYxaWlqQkJCAnJwcAMBDDz2EU6dOzVh9UR/seXl5OHnyJHp6euBwOFBXV+df2wOAhQsXIiEhAWfOnAEAHD58OKBfL5TGyePxYPv27di4cSOqqqp0+X81SmNUWVmJY8eO4fDhw6iurobVasX+/ftVrFgdSuO0fv169PT0oLm5GQBw/PhxrFmzRq1yVaE0RkuWLEFHRwfa2toAAO+//77/P4QzYsbepr0GR44cEffdd58oKCgQ1dXVQgghnnzySdHQ0CCEEKKpqUmUlZWJwsJC8bOf/UwMDg6qWa5qJhunuro6kZWVJUpLS/1/du3apXLFM0/pZ8nn22+/1e2uGCGUx+mzzz4TZWVloqioSDzxxBOiq6tLzXJVoTRGJ06cECUlJaK4uFg8/vjj4sKFCzNWG5+gRESkMVG/FENERFPDYCci0hgGOxGRxjDYiYg0hsFORKQxDHYiIo1hsBMRaQyDnYhIY/4/U82e5Zw4ScUAAAAASUVORK5CYII=", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute mean and sample from exponential\n", + "mean = np.mean(y)\n", + "samples = rng.exponential(mean, size=10**6)\n", + "\n", + "# Compute ECDFs for sample & model\n", + "x, y = ecdf(y)\n", + "x_theor, y_theor = ecdf(samples)\n", + "\n", + "# Plot sample & model ECDFs\n", + "plt.plot(x_theor, y_theor);\n", + "plt.plot(x, y, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulating relationships between distributions: the Poisson and the exponential\n", + "\n", + "Having seen a concrete example and also the relationship between the exponential and uniform distribution, let's now dive in and simulate the relationship between the Poisson and exponential distributions. To do this, \n", + "\n", + "* we initialize the number of Poisson events to -1\n", + "* generate an exponential unknown quantity $E$\n", + "* increment the number of Poisson events by 1\n", + "* repeat until the cumulative sum of all $E$ s is greater than 1 (encoded in the `while` loop)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:58.773467Z", + "iopub.status.busy": "2022-04-30T07:02:58.773335Z", + "iopub.status.idle": "2022-04-30T07:02:58.776941Z", + "shell.execute_reply": "2022-04-30T07:02:58.776436Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18\n" + ] + } + ], + "source": [ + "lam = 15\n", + "T = 0\n", + "n = -1\n", + "while T < 1:\n", + " #generate exponential random variable\n", + " E=-(1./lam)*np.log(rng.random(1)); \n", + " T=T+E; #update sum of exponential variables\n", + " n=n+1; #update number of events\n", + "print(n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result should be a Poisson distributed variable. We've generated one instance of the unknown quantity in the above and now we'll generate many of them (using a `for` loop), fit a Poisson model, and plot the ECDFs of both the data and the model fit:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:58.779369Z", + "iopub.status.busy": "2022-04-30T07:02:58.779253Z", + "iopub.status.idle": "2022-04-30T07:02:58.876601Z", + "shell.execute_reply": "2022-04-30T07:02:58.876274Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "15.011" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "N = []\n", + "\n", + "for i in range(0,1000):\n", + " T = 0\n", + " n = -1\n", + " while T < 1:\n", + " E=-(1./lam)*np.log(rng.random(1)); #generate exponential random variable\n", + " T=T+E; #update sum of exponential variables\n", + " n=n+1; #update number of exponential variables\n", + " #print(n)\n", + " N.append(n) \n", + "\n", + "np.mean(N)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:02:58.878659Z", + "iopub.status.busy": "2022-04-30T07:02:58.878535Z", + "iopub.status.idle": "2022-04-30T07:03:00.036644Z", + "shell.execute_reply": "2022-04-30T07:03:00.035941Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate x & y data for ECDF\n", + "x_ecdf, y_ecdf = ecdf(np.array(N))\n", + "\n", + "# Number of times we simulate the model\n", + "n_reps = 1000\n", + "\n", + "# Plot ECDF of data\n", + "plt.plot(x_ecdf, y_ecdf, '.', color='black');\n", + "\n", + "# Plot ECDF of model\n", + "for _ in range(n_reps):\n", + " samples = rng.poisson(np.mean(N), size=len(N))\n", + " x_theor, y_theor = ecdf(samples)\n", + " plt.plot(x_theor, y_theor, '.', alpha=0.01, color='lightgray');\n", + "\n", + "\n", + "# Label your axes\n", + "plt.xlabel('N')\n", + "plt.ylabel('ECDF');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulating relationships between distributions: the exponential and the binomial\n", + "\n", + "As we've seen, the exponential distribution is related to the Poisson distribution, which in turn is related to the binomial distribution. In the spirit of fully utilizing our simulation skills, we'll now directly demonstrate the relationship between the exponential and binomial distributions. To do so, we'll \n", + "\n", + "* generate our Bernoulli trials\n", + "* identify at what times the Bernoulli events occur (the numpy array indexes that have value 1)\n", + "* compute the waiting times, that is, the differences between these times\n", + "* plot the histogram\n", + "\n", + "As $p$ is small, these Bernoulli trials will result in Poisson events and the waiting times should be exponentially distributed." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:00.038922Z", + "iopub.status.busy": "2022-04-30T07:03:00.038822Z", + "iopub.status.idle": "2022-04-30T07:03:00.120411Z", + "shell.execute_reply": "2022-04-30T07:03:00.120004Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute and plot the waiting times from Bernoulli trials\n", + "bts = np.random.binomial(1, 0.05, 10**4)\n", + "times = np.where(bts == 1)\n", + "wt = np.diff(times[0]).astype(float)\n", + "plt.hist(wt);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That looks possibly exponential but a more appropriate eye-test is to plot the ECDF of the data with the ECDF of a fitted exponential model:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:00.122703Z", + "iopub.status.busy": "2022-04-30T07:03:00.122578Z", + "iopub.status.idle": "2022-04-30T07:03:00.423308Z", + "shell.execute_reply": "2022-04-30T07:03:00.422901Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute mean and sample from exponential\n", + "mean = np.mean(wt)\n", + "samples = np.random.exponential(mean, size=10**6)\n", + "\n", + "# Compute ECDFs for sample & model\n", + "x, y = ecdf(wt)\n", + "x_theor, y_theor = ecdf(samples)\n", + "\n", + "# Plot sample & model ECDFs\n", + "plt.plot(x_theor, y_theor);\n", + "plt.plot(x, y, marker='.', linestyle='none', alpha=0.1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And our simulated data looks exponentially distributed!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Gamma Distribution\n", + "\n", + "### The story\n", + "\n", + "We have seen that Poisson distributions can be used to model single-step processes. However, there are many real-world processes that require multiple steps to occur, such as genetic expression, rainfall, and getting an insurance claim filled. Enter the Gamma distribution. They come up a lot in biology, for example, the time between spiking, or firing, of a neuron.\n", + "\n", + "The **story** of the Gamma distribution is the amount of time we have to wait for the arrival two Poisson processes is gamma distributed. More generally, the amount of time we have to wait for $k$ (or $\\gamma$) Poisson processes to arrive is gamma distributed.\n", + "\n", + "\n", + "Before jumping in to the Gamma distribution, note that\n", + "\n", + "* Gamma need not be an integer\n", + "* When $\\gamma = 1$, we retrieve the exponential distribution\n", + "* The Gamma is peaked.\n", + "\n", + "\n", + "### The parameters\n", + "\n", + "\n", + "The Gamma distribution is defined by a shape parameter $k$ and a scale parameter $\\theta.$ The shape $k$ can be thought of as the number of steps required and the scale $\\theta$ as the inverse of the rate parameter $\\beta.$\n", + "\n", + "\n", + "### Simulating the Gamma distribution\n", + "\n", + "We can simulate the gamma distribution using `rng.gamma()` and plot the resulting histogram to see the peak:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:00.425431Z", + "iopub.status.busy": "2022-04-30T07:03:00.425319Z", + "iopub.status.idle": "2022-04-30T07:03:00.582695Z", + "shell.execute_reply": "2022-04-30T07:03:00.582317Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g_data = rng.gamma(3, 2, 10**4)\n", + "plt.hist(g_data, bins=100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The ECDF for the Gamma distribution\n", + "\n", + "We also now plot the ECDF of our simulated data:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:00.585312Z", + "iopub.status.busy": "2022-04-30T07:03:00.585185Z", + "iopub.status.idle": "2022-04-30T07:03:00.664214Z", + "shell.execute_reply": "2022-04-30T07:03:00.663796Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x, y = ecdf(g_data)\n", + "\n", + "plt.plot(x, y, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use `scipy.stats` to fit a gamma model to the data we just simulated and plot both ECDFs. We do so in order to demonstrate the method and to double check that our simulation process worked as we think:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:00.666555Z", + "iopub.status.busy": "2022-04-30T07:03:00.666422Z", + "iopub.status.idle": "2022-04-30T07:03:00.780304Z", + "shell.execute_reply": "2022-04-30T07:03:00.779936Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.0174566424863496 -0.03591188164295 1.9885784405903286\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import scipy.stats as stats \n", + "fit_alpha, fit_loc, fit_beta=stats.gamma.fit(g_data)\n", + "print(fit_alpha, fit_loc, fit_beta)\n", + "\n", + "# Compute mean and sample from exponential\n", + "samples = np.random.gamma(fit_alpha, fit_beta, size=10**3)\n", + "\n", + "# Compute ECDFs for sample & model\n", + "x, y = ecdf(g_data)\n", + "x_theor, y_theor = ecdf(samples)\n", + "# Plot sample & model ECDFs\n", + "plt.plot(x_theor, y_theor);\n", + "plt.plot(x, y, marker='.', linestyle='none', alpha=0.01);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### An example of the Gamma distribution\n", + "\n", + "One of your authors used to work in biophysics and cell biology, thinking a lot about cytoskeletal dynamics (the cytoskeleton can be thought of as the dynamic railway track of cells!). In particular, he researched microtubules (MTs), which are cytoskeletal polymers, key to many cellular functions, such as the formation of the mitotic spindle. Microtubules switch between periods of steady growth and rapid collapse and the switch from the former to the latter is referred to as _microtubule catastrophe_.\n", + "\n", + "For a long time, it was considered dogma that microtubule catastrophe was a single-step (Poisson) process and so an exponenttial distribution was used to model the time it took for MTs to catastrophe. And then, in 2013, some of his colleagues realized that a Gamma distribution may be a more appropriate model and [wrote a paper demonstrating that MT catastrophe is in fact a multistep process](https://www.cell.com/fulltext/S0092-8674(11)01287-6)!\n", + "\n", + "Let's now import the dataset and see whether a Gamma distribution looks like a good fit." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:00.782359Z", + "iopub.status.busy": "2022-04-30T07:03:00.782235Z", + "iopub.status.idle": "2022-04-30T07:03:00.788032Z", + "shell.execute_reply": "2022-04-30T07:03:00.787730Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 60\n", + "1 75\n", + "2 75\n", + "3 85\n", + "4 115\n", + "Name: 0, dtype: int64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_14 = pd.read_csv('../../datasets/microtubules_14.csv',header=None)[0]\n", + "data_14.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:00.789744Z", + "iopub.status.busy": "2022-04-30T07:03:00.789648Z", + "iopub.status.idle": "2022-04-30T07:03:00.887870Z", + "shell.execute_reply": "2022-04-30T07:03:00.887420Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.342652806181448 1.0146015528754604 139.8577998935649\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import scipy.stats as stats \n", + "fit_alpha, fit_loc, fit_beta=stats.gamma.fit(data_14)\n", + "print(fit_alpha, fit_loc, fit_beta)\n", + "\n", + "# Compute mean and sample from exponential\n", + "samples = rng.gamma(fit_alpha, fit_beta, size=10**3)\n", + "\n", + "# Compute ECDFs for sample & model\n", + "x, y = ecdf(np.array(data_14))\n", + "x_theor, y_theor = ecdf(samples)\n", + "# Plot sample & model ECDFs\n", + "plt.plot(x_theor, y_theor);\n", + "plt.plot(x, y, marker='.', linestyle='none', alpha=0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This does look good! What's more is that this result then led to a flurry of research to decipher what the constituent parts of this multistep process could be." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulating relationships between distributions: Gamma from first principles\n", + "\n", + "We have simulated the gamma distribution using the out-of-the-box function `rng.gamma().` Once again, it will be instructive to simulate it from first principles using the story of the distribution itself:\n", + "\n", + "> the amount of time we have to wait for $k$ (or $\\gamma$) Poisson processes to arrive is gamma distributed.\n", + "\n", + "So what we do is we\n", + "\n", + "* Generate many Poisson events using `rng.binomial()`\n", + "* identify at what times the Bernoulli events occur (the numpy array indexes that have value 1)\n", + "* compute the waiting times between each 3rd event\n", + "\n", + "These waiting times should be gamma distributed.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:00.890243Z", + "iopub.status.busy": "2022-04-30T07:03:00.890150Z", + "iopub.status.idle": "2022-04-30T07:03:01.108775Z", + "shell.execute_reply": "2022-04-30T07:03:01.108238Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bts1 = rng.binomial(1, 0.05, 10**7)\n", + "events = np.where(bts1 == 1)\n", + "\n", + "times = np.diff(events[0]).astype(float)\n", + "t = times[:1500].reshape(500,3)\n", + "waiting_times = np.sum(t, axis=1)\n", + "#yoi = np.diff(times[0][2::3])\n", + "plt.hist(waiting_times);" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:01.111696Z", + "iopub.status.busy": "2022-04-30T07:03:01.111548Z", + "iopub.status.idle": "2022-04-30T07:03:01.217136Z", + "shell.execute_reply": "2022-04-30T07:03:01.216503Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.868961091573543 4.324370801765211 19.75405287669441\n" + ] + }, + { + "data": { + "image/png": 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fZDtQgtWougu9tBZXeJw8aCRGJfVEXrRixQquu+46rrnmGp5//vmj2jdt2sStt97KDTfcwJe+9CU6Ozv7vVAxskViXfxs5Rb++83tTKwu4ss3ziTgdx/3Z6xUAmPvx9ixBuy2/aB50Ior0Sun4KqYJJtfiFGrz2CPRCI8+eSTvPDCC7zyyissW7aMHTt29HjNI488wpIlS3jttdcYP348zzzzzIAVLEaerfvaeODp91i9sYlzpoRZcuts/N5jB7KVSmB1RDBa63OrMfoKwcigBMpxjZmCXlYns1/EqNZnd2b16tXMmTOHYDAIwPz581m5ciX33Xdf92ts2yaZTAKQSqUoLi4emGrFiLO/OcGTyz/G59H4l7+5gOLCY09ndCwTK9mOufdjwMFIxcEBxR/C0T24aqaiFQQHrXYh8lWfwd7c3Ew4HO4+Li8vZ/369T1e80//9E/cddddPProo/h8PpYvX35SRYRCx/9yKxwOnNT7DZZ8rQvyt7Yj67Jth//zzHsU+l08fO9cxo0pOubPWak4meYGjGQLmWwUd3gshtKFGhqH7vajeP0UTpqK6vb1S235ROo6efla22DV1Wew27bd4wk/x3F6HKfTaR544AF++tOfMnv2bJ599ln+8R//kaeeeuqEi2htTWDbTq9t4XCAaDR+wu81WPK1Lsjf2g7VZdk2sc4Mf9rUREM0yZ2fOYMCXTlmzXY2TWbH+9ht+7FtIN2J5nhRFBVtzCR0XyHoXjIdJnBqf+98v2b5Jl/rgvytrT/rUlXluB3iPoO9srKStWvXdh9Ho1HKyw/PC962bRsej4fZs2cD8NnPfpb/+I//OJ2axQjmOA7/9sKHbKvvAODsyWVccmbVMV9vZ9MY0d2YkW1gmOBkoKAKtaQ6t2Z6sFK+IBXiU/r88nTu3LmsWbOGWCxGKpVi1apVzJs3r7u9rq6OpqYmdu3aBcCbb77JrFmzBq5iMWw5jsMfNxxgW30H500t5x8WncVXbpqJ2suaL1YqgdGyF2PfeqzWBkh3gtcDihu9ogZ39TRcZeMk1IXoRZ+/FRUVFSxdupTFixdjGAa33XYbs2fP5p577mHJkiXMmjWL7373u/zd3/0djuMQCoV49NFHB6N2Mcz8f7/+hF+9vYNw0MvfXD8Nt0vr9XVmoh1z9/tYmSROJok2ZhoUhFDcftSiClxjpslDR0Ich+I4Tu+D24NIxtj7Vz7WtnlvG//3Fx9y3tRy7rru6FA/tGWdY2QwW/dhNW1F0f1YiSiuyingLUArqkQtLEXz9X+o5+M1A6nrVORrbXk1xi7E6bAdhxXv7uE37+1lTFkhX7h2aq+hbjTvwmreCQ5YlgEoYGdRfCWoZXXoJVUyN12IEyTBLgZM1rD40SsbWb+zlbqKAA/efQGabR/1OjudwKj/BCcRA5cbtSAIwTGoio5SWomrbKyMpQtxEuS3RQyY19fsYf3OVhbMHcfNl4ynPFTQ439FD+1JarQfwO5ogEwW7AxOoBxP7WxUt1fWehHiFMhvjBgQzW1d/OZP+5gzo4Jb5h29QqNjmRiRXTgdTZipTkCF4BjIJtArxqEXlQ1+0UKMEBLsot9Zts0Lv92Oqir81eWTen9NsgNj70c4RgrHccBThOb1oxSF0IuPPa9dCNE3CXbRr1a8u5vVmyJEYl187uopBHtZ+8XOpjHb6rE7IqC5wEqj1p2Du2ISiq9oQGa9CDGaSLCLfrNi9R5e/sNuSgIevnzjDM6fdvR2dIe2rzM6IpBN5LavU13opdXopdJTF6I/SLCLftHQkuTVP+xmxrgSvv6XZ6JrPR9qdiwTKxXHOLANq3kXqurG9haiFIVRvYXohaEhqlyIkUeCXZy2jbtb+cFLG1BVuPPaqb2HeqyBrvYkRsMWMFJAHPwh2b5OiAEgwS5Oy5+3NPPjVzYS8Lv4+m1nUlZ89LK5RlsEa996MgWe3PZ1BRUojok2ZhKuyikypVGIfia/TeKUvb85wlOvfUJdZYB7F05nTKigR7udTWPFWzB2r8OON2Ga/tz2dYXFqL4AesUU6akLMQAk2MUp6UqbPPs/W6go9fGNRWfh97p6tNvZdG5lxs4IdrwZ1R9CUTJopdW4amai+otliQAhBogEuzglv/r9TrKGxb0LZxwV6gBWPIbRuAXHsSDegu0rwV1WjRWaiV4sDx8JMZAk2MVJW7+zlbc/aODyc6qpq+y51ZedTYOZxoi34CRawO0HXUMrq6Vo9gWYiSEqWohRRIJdnJRdjZ384KUNVIcLuPmSnksFHBp+cTJJrI6W3MNHmgqFYVyl1Wi+ACTybzlVIUYaCXZxwkzL5r9e3UhxgZtv3n42hb6eQzBGrBGjfhPobki1Q3ENmktDKSxFC8jwixCDRYJdnLA1G5to6UjztZtnEvC7u88f2iTDaNyME4+CtwBQ0Usr0QMh1KCspS7EYJJgFyfEcRze+PN+qssKOHty+PD5gw8fGe2NOB0HQPdCOgllE3FVTkErKJY56kIMMvmNEyfkD+sP0NiS5C8vn4iqHt58OtdT34KV7IRkB4TqUKwsrglnohfJMgFCDAUJdtGndNbkxXd2MqGqiM+cP7b7vJ1NY8TqMZt358bVrQyK24OrdDyukpohrFiI0U3t+yVitHvt3T0kUgY3XDQORcn11g/NgDEPbId0HNVXAMExuCrG46qeIWPqQgwh6bGLY7Jth1/9bicr39vH+dPKmT3x8MyWQzNgHFUH28C2HPTSGvTQOAl1IYaYBLs4pmVv7eB/1+5nzowKvnjttO7zdjadmwHTGQFvAIrCuKqn4KqYIptkCJEHZChG9Gr3gU7+d+1+zp9Wzr0LZ+DSD98qVjyWmwHj2JBoBW+phLoQeUSCXfTqf/+8H49LY/H8M3qcdywTq6stNwPG7QFVR6uaLKEuRB6RYBdH2dsU50+fRJg7s7LHAl+H5qybnREw0uAo4HajeQuO825CiMEmY+yih86uLE8u/4hQkYf559f2aDu0YQa2BW4XFFWiaG40jwS7EPlEgl308Na6ejq7DL711+dQXuIHDvbUk+0Yu9ditzfm5qz7S9GLw6iFpaj+4NAWLYToQYJddNvV2Mnrq/dSVxFgck0QyIW6Gd2LGduPHW9G8fpx0knU8HhctbNlwwwh8pAEuwAgY1g8vWITxYVuvnrzzO7zVrIDY9/HWOkkxFtwysahFvpw1c5CD1YMYcVCiGM5oS9PV6xYwXXXXcc111zD888/f1T7rl27+PznP88NN9zA3XffTUdHR78XKgbW+5sjRNpSfH7+GYSDhzektlMdWG0HINsFKKhFZbgmnIMrWDl0xQohjqvPYI9EIjz55JO88MILvPLKKyxbtowdO3Z0tzuOw1e+8hXuueceXnvtNaZNm8ZTTz01oEWL/pVMGzy3ahuFPhczx5f2aDNScYhHIdkG2KjBKlxl42TFRiHyWJ/Bvnr1aubMmUMwGMTv9zN//nxWrlzZ3b5p0yb8fj/z5s0D4Mtf/jKf+9znBq5i0e/+5097MUybv1kwDV074kGkVAK7cRug5B5GKgyh+wol1IXIc30Ge3NzM+Hw4fW3y8vLiUQi3cf79u2jrKyM+++/n5tvvpmHHnoIv98/MNWKfnegNckb7+3n7MllPdaCsVIJjMg2SMXAXwC2Db4S1AJZileIfNdn18u27e4V/SA39HLksWmavP/++zz33HPMmjWLf//3f+exxx7jscceO+EiQqHjP7UYDgeO2z5U8rUuOLHaHMfhB69sRNcUvvqXZxEO5eajGx1R4jvXoiY7UMwEelk1TjZD8dlz8Y8dM+B1DZV8rU3qOnn5Wttg1dVnsFdWVrJ27dru42g0Snl5efdxOBymrq6OWbNmAbBgwQKWLFlyUkW0tiawbafXtnA4QDSafxsg52tdcGK1mZbNa+/u4YMtzXzm/LFotk00GsfOpklv+QNWdDeKpwjHXYSlF6KGJhLXykiext95uF+zoSB1nbx8ra0/61JV5bgd4j6HYubOncuaNWuIxWKkUilWrVrVPZ4OcPbZZxOLxdiyZQsAb731FjNmzOiH0sVA+vkbW3l99R7+Ymo5N148Hjg4Z72tEat1L6Q6cGJ7cgt81Z2Lu3a2zFcXYpjos8deUVHB0qVLWbx4MYZhcNtttzF79mzuuecelixZwqxZs/jhD3/Igw8+SCqVorKykscff3wwahenqD6a4I/rD3DVeTXccdUU4PCDSEbzztwCX4FSSCXQa6fiClUNccVCiJNxQtMbFi5cyMKFC3uce/rpp7v/fOaZZ/Liiy/2b2ViwLz8+114PRo3XDS++5zRFsHc8wG2aYGVAdWNWlKLq0geQhJiuJF5a6PMm+vq+XB7C7fMm0ChL7dyo51NY9RvwG5rAG8RFIXRS2vRymrQCoJDW7AQ4qRJsI8iW/e18Ys3tzNjXAnzj9iU2orHsNvqwUhBOgGV03DVzkIrKJY560IMQ7Ie+yiRSBn856824PPo3HX99O4dkexsGrOzKTeu7vGDlts4Qy8KSagLMUxJsI8CjuPwo5c30JUxWXTlJEoCHuBgqNdvwozV5zbOUFzgK0L3Fw9xxUKI0yFdslHgg20tbNnXzvzza5k78/ADRmZHM0bTdhRFxXG7UAIhVH8xmgS7EMOaBPsIt2lPjF+8uY0iv4vbLpvYfd7OpjEat2DH9uc2ztB9aMEa9LJqVK/sXyrEcCbBPoKZls0zr39CxrBYcutsNDU38malEpite7A7IuDygpmF0nG4xkxCC8jYuhDDnfwGj2BvfdBAeyLL0r86kzPGlgAHF/fa8R5GvAXizRCoADuNq3KchLoQI4T8Fo9QpmXz+uo91FUGutdYP7Rio9G6D81XjKU4KL4AenAirsozJNSFGCHkN3mEevuDBhIpg9sum4iiKId76h3N0NGE7fJCYRhX1RRc4fGyDowQI4hMdxyB4l1ZfvPeXsZVBrhkdm4WjNFaj9G8EwVA18DlxVU9TUJdiBFIeuwj0NOvbCDeZfDVm2Yd7q3v2wDtB3DcHVBYiqt6qoS6ECOU9NhHmG3723l7XT1X/0Utk2py89GtzigkmnLTGrNJKKqQUBdiBJNgH0Hqowm+t/wjfB6dq86tAQ5+YdrRCMk46C5QdbSyOgl1IUYwGYoZQVb9eT+KovDk0ktx43R/YWp3NOc2o/YFoVDDVVg61KUKIQaQ9NhHCNt22LQ7xvS6EqrDuSdH7UQMo3Ufim2ApqMUhnDVzEALlPXxbkKI4UyCfYRYs6mJtniGWRND3eeMVBzaDuB0RAAbNTxOtrgTYhSQoZhhznEcNu6O8cJvtzGhqogLp1cCubVg7JY9gAOKAkVhXIFSCXUhRgHpsQ9zqzc28eTyj1FQ+OJ10/C4NexsCiO6G6fjwMGZMGnwlqIWhPp+QyHEsCc99mGssyvLG+/vw+fR+d59F+F2adjZNMkd2zEaduQ2zyitgUwcV910NJ+s2ijEaCA99mHsjff2UR9N8qUbpuN2aUBum7vUvo04qfbc5hko6OHxuEpqh7RWIcTgkR77MLa9oYPxYwLMnnh4loudTWLEImCqoGpo5eNx18yQ3roQo4j02IepplgXO+o7GFsR6HHeti3MTFduU2ps1MJSCXUhRhnpsQ9TG3a1AnDlOTXd5xzLxO5oRlFUcGng9qJq2lCVKIQYItJjH4Ycx+GtDxooD/qoChd0n7eSHdgt+0ABsgb4SlG9RUNXqBBiSEiwD0PvfNRIJNbF9XPrUBUFyPXWrXgUutpQPQWggFpWi1YQHNpihRCDToJ9mFmzqYmXfreTcZUBLp6VW2vdsUysWANGeyOk4yiaAm4XWrBSdkUSYhSS3/phpCGa4JnXN1NR6uOOq6egHOytG20RrH3rwbbApeMursDwKOg+GYYRYjSSYB9G/rjhAIoC3/rrcyn0ubCzaax4C8buddjxJlBd4C/FFazARQGqPzjUJQshhoAE+zDRlTZZvbGJqWODFPpcWKkEZv0mrGQMO9mC6g/hZBOowWoKJp1DNqXLujBCjFIyxj5M/HbtfuJdBtddOA7HMsk2bCa772Oszgik4ziKhlZcgavuXNylYyTUhRjFTijYV6xYwXXXXcc111zD888/f8zXvfPOO1xxxRX9Vpw4bNOeGOVBH9PqSrCSHZiNmyHRAp1R8IdwVU3GPeUSXMWy1roQo12fQzGRSIQnn3ySl156CbfbzaJFi7jggguYNGlSj9e1tLTwr//6rwNW6Gj2yZ4Y2+s7+Mz5Yw9Pa0y2gaJCJoUSKMVVMUl66UII4AR67KtXr2bOnDkEg0H8fj/z589n5cqVR73uwQcf5L777huQIkcz23F45tebKS50M//cMYenNWaSuT1M3W608gkS6kKIbn322JubmwmHw93H5eXlrF+/vsdrfvaznzF9+nTOPPPMUyoiFDr+WibhcOC47UNlMOr6cGszbfEM/3DHOYyr9BDftAfN7CTt1fFWjAXVTXBsLe7SnrWM5mt2qvK1Nqnr5OVrbYNVV5/Bbtt293xpyD3OfuTxtm3bWLVqFT/96U9pamo6pSJaWxPYttNrWzgcIBqNn9L7DqTBquvlt7dTXOBmSlURkX31ZHZuBmxIGSRtL65gFW0pHfWIWkb7NTsV+Vqb1HXy8rW2/qxLVZXjdoj7HIqprKwkGo12H0ejUcrLy7uPV65cSTQa5dZbb+Xee++lubmZO+644zTLFgA7Gzr4eEcr554RxqWr2NkUpDrByIKuo5XW4KqeIcMwQoge+gz2uXPnsmbNGmKxGKlUilWrVjFv3rzu9iVLlvDGG2/w6quv8tRTT1FeXs4LL7wwoEWPFm9/2ICqKiy8aHzuS9NES27zjEwXaBqqv0hCXQhxlD6DvaKigqVLl7J48WJuuukmFixYwOzZs7nnnnvYsGHDYNQ4Kv3uowZWb2ziolmVFBe4sdMJ7NgB0NygaRCoRPMU9P1GQohR54SePF24cCELFy7sce7pp58+6nU1NTW89dZb/VPZKLZmYxM/W7mVGeNK+MvLJgJgdXVCZxNgg2GhFIZkyQAhRK/kydM8s3ZLM0+//gnBgId7b5iB33twTZhEC2RT4PXnVm4sGyvDMEKIXkmw55Ete9v48SsbKQl4+D93nkfA78bOpjHrN2HG6sHMguoGbxGaV7a7E0L0ThYByyPvbY7gdmv8y99cgM+T+2jsrg6M5l0ohomjaiiBEKq/GM1fPMTVCiHylQR7HtnV2Mnk6uLuUAewMknsWH3uwLZQiitwhcehSo9dCHEMMhSTJ3Yf6GR/c4LJtcHuc45l4mRTuSEY7Nzc9UAYvbRGdkYSQhyTBHsesB2HH728Aa9bY97sntvdmZ0RsExAAU1DcXsl1IUQxyUJkQf+8HEjrZ0ZvnjdVIoLPQDY6QRG4xZIdoKqQVEliuaWuetCiD5JsA8x23FYsXoP48cc3pwacvPWzebdgJ0bW/cFcJVWydx1IUSfZChmiP3+o0ZinRkuO7u6x+JqZrIdutrAMnLz1mVdGCHECZIe+xBqT2RY9vYO6ioDXDijsvu8lUpgNW6FbBpsB4Ll6EVhCXUhxAmRHvsQ+t8/78cwbBbPPwNdy30UjmXmHkZKNOWeMrVNKKpAC8iWd0KIEyM99iES78ry3uYIU+uCjB9TBBwM9ehejOhuSMahsAgUBb1CdkgSQpw46bEPkV+v2UtbZ4YbLhrffc5KdmDs+xinswUcG9wFKKEJuIoqhrBSIcRwIz32IdDYkuTtDxs4b2o5U454IMlob8Jq3QfeAHjcqKU1uMrHoxUEj/leQgjxaRLsg6wrbfB/l32EYdosnDuu+7yVSmA2bIGuztyXpkVluMon4CqrkweShBAnRYZiBtlv19XTFs9w9/XTqCk/vN6LnYhBKgZeH1gmSlElekm1hLoQ4qRJagyyj7a3MLG6iIuOeBgJwDIzEG8BB8BBLR8vX5gKIU6J9NgHUawzzZ6mOGdPDvc471jmwYW+FNA1cLnRdPfQFCmEGPYk2AfRjoYOAKbVlXSf67HYl22Bquf2NFXloxFCnBoZihkktuPw+uq9FHh1ao8cW5fFvoQQ/UyCfZC8/0mE+miCu6+f1v2UKchiX0KI/if/vz9IfvdRI2XFXi6cWdnjvJWOQ6oDrKws9iWE6BcS7IOgua2LbfvbuWB6BeoRKzja2TRWyz4wMpDqAt2HVlgqoS6EOC0S7IPgZ29sxe3SuOKcmh7n7a4OnHgLuL2gKFBUJZtUCyFOmwT7AIu2p/hkTxuXnV1FScDTo83KJCHeDJYFtolaWimbVAshTpsE+wD72Rtb0TWVuTM/9UBSKoEVb87tZ+r1g9uNVhiSJ02FEKdNUmQArd/ZyqbdMW69dEKPKY5WKkF2+2qs9iiYBhSVgxpEk966EKIfSLAPEMO0eG7VVipKfFx1bm2PNrP9AGbzdtBcubF1XxGuslrZTEMI0S9kKGaA/PebO2jpSPPZKyfjcWvd5x3LxGith44WSHaA24NeMQF37WyZDSOE6BcS7AMg2p7inQ8bmDO9gjMnhnq0WckOnFgDKCpkMhCowFVaK6EuhOg3JxTsK1as4LrrruOaa67h+eefP6r9t7/9LTfeeCM33HADX/3qV+no6Oj3QoeTzXvbALh+7jiUI+atA9ipDuhq657iqIbrZCMNIUS/6jPYI5EITz75JC+88AKvvPIKy5YtY8eOHd3tiUSCb3/72zz11FO89tprnHHGGfznf/7ngBad7zbsaqUk4KEq5D+qzbYtSMfBMAAHtahcZsIIIfpVn8G+evVq5syZQzAYxO/3M3/+fFauXNndbhgGDz30EBUVuX05zzjjDA4cODBwFee5+uYEH25r4axJZUf11q1UAjvZmhuG8fpz29+p2jHeSQghTk2fwd7c3Ew4fHj98PLyciKRSPdxSUkJV199NQDpdJqnnnqKq666agBKzX+O4/Dfb25HVRVuvGR8jzajowVj27tY0XrABl8xBCpQ3b6hKVYIMWL1OQZg23aPnqfjOEf1RAHi8Thf+9rXmDp1KjfffPNJFREKHX/+djgcOKn3GyyfruuZ1zayeW8bX75lNhPrDn9pamdTdO7+hFSiAY8/iFlWjSc0BleoisIJ4wYk3IfLNcsn+Vqb1HXy8rW2waqrz2CvrKxk7dq13cfRaJTy8vIer2lububuu+9mzpw53H///SddRGtrAtt2em0LhwNEo/GTfs+B9um6Im1dvPr7nUyqKea8yaEebUZrI+k92yDRAi1NUH4GhGdiBkrJdJhA//79hss1yyf5WpvUdfLytbb+rEtVleN2iPscipk7dy5r1qwhFouRSqVYtWoV8+bN6263LIsvf/nLXHvttTzwwAO99uZHOtt2eOq1T/B7dL60cEaPFRwB7GwyN2dd0cECrWoyrlCVTHEUQgyIPnvsFRUVLF26lMWLF2MYBrfddhuzZ8/mnnvuYcmSJTQ1NfHJJ59gWRZvvPEGADNnzuSRRx4Z8OLzxetr9rD7QCe3XzWZUPHhsHYsE8dIY2W7wMoAKmjIuLoQYkCd0Dy7hQsXsnDhwh7nnn76aQBmzZrFli1b+r+yYWJ/c4JX/7ibGeNLufKIZXkdy8SI7MLpaMJOdYIvAL4SsA00l2xULYQYODKB+jSt3ngAx4EvXjsVVT08BGMlOzD2foRjpHAcB3ylaP4AituHWhA6zjsKIcTpkWA/DbHONG990MC5U8KUFvUcgrHiUeyOCCiAaaBOOA93xSQUXxGaT1ZxFEIMHAn2U+Q4Dsvfzj2Be9vlE3PnDo6p2/FWzGQrZBPg8gIWWrASvbRqCCsWQowWEuyn6K21+3l/czML5o6josSPY5mY0b3YyRh2IobqLcD2BVD8YVA1dF/RUJcshBglJNhPwe4Dnfzglx9RWernxovHAWB2tmLs+xDbsnBS7ejVM1CLqlADJai+IlR/cEhrFkKMHhLsp+CXb+/A53Hxrb8+B01VD86A2YHVWg/eANg2iqLjGjcbzV+C4imQOetCiEEjwX6Smtu62LKvnduumEzAn5u2aCU7sJp3QlcC0l1QVocWqkUPVsjKjUKIQScbbZyEdNbke8s/RlUUFlycW+TLzqYxWvdCvBncHrBNlNJqCXUhxJCRYD8Jz/x6M81tKW69bAKhYl8u1Petx4zsPrgbUhACIVyhKgl1IcSQkfQ5QU2xLtZtjXL5OdVce0EdAFY8htG4BRwLcED1oVXUohfLtEYhxNCRYD8BtuPww5c2UODVuX5OHXY2jZkwMeItOIkWcOc2zdDGTMZTM0MeQBJCDCkJ9hOwdW8bDS1JFl0xiaAXzPpNJFvBOrAHNBdoKhSGcZVWS6gLIYacBPsJ2LSnDU1VuHh2FUZsH+aBLRglpWDEUUrHoqoKSmEpWqBsqEsVQggJ9r6Yls26bVHGVxXhcdKk9n6ME2sgY3SA4kcP1aD5AqhBWV9dCJEfZFZMH17+/S4isS4uP7saOxHDSbaApmGlEyiFZbgqJqKXT5AhGCFE3pBgP45UxuQP6w9w5sQQF86oxDIzEG+BbBbHNFHLx6MVBGVqoxAir0giHcfqjU0kUgbzzx+LlUpgJ1sBBTxeFJeNqsuGGUKI/CPBfgzvfNTAyvf2MibkZ1JIwdixGiveBtjgK8bt82HLFndCiDwkQzG9SGdNfr5yK6Bwx5UTMBq3YDTvQbFt8AVQC0vxT5gps2CEEHlJgr0Xb3/QgAN84dqpnFGmYTbtgEQLTmw/+Epx1c6icNJZMgtGCJGXJNg/JZEy+J8/7WVaXQlTK90YLbugqw38AcBGLavFVTYWVYZhhBB5SsbYD8oYFk+9tolt+9tJZSxuv7AEY9u7mF3x3BZ3xVXgLsRVNlZmwQgh8pok1EHL3trBh9tbmF4b4KrZJZR2bseI7Uf1FGEHytACpajFFejFlUNdqhBCHNeoD/ZUxuSP6w/w+48amVVXxN9eFcZob8Ss3wvZLHa6EaW0Dlfdublwl3F1IUSeG9XBvur9fbz0+11kTZvp40r4m8vLMeo/xjLSkElAcQ2KlcU1bhaukCzFK4QYHkZtsO9q7OTF3+2iuqyA2y+rZVzQwdi3AbNlH+g6KBqarxCtOIyrpGaoyxVCiBM26oLdtGx+/sZW/rD+AKEiL/deU0Woay9mYwq7bR/oGlg2SrAGV/V0tOJyGX4RQgwroyrYbdvhyeUfs3lvG585fyzXnluBXr8Oo70BR/WCoqG4/SiKgl41Bb1UtrgTQgw/oya1HMfhqRWb2Ly3jduvmsyVs8MY0d1ko7sgmwUzAiV16FWTUN1e9FCdhLoQYlgaFckVj3fx8u+2s2FzPVfNqOCyMwqxItux2iKQ7kQpHoOTceGqm44nPB5UXUJdCDFsjej0MkyLbXtbeeX1P0I6wTXlFldMrcRu2gKaGy1UhdXegKLpaKGxuEpqUVwyni6EGN5OKNhXrFjBj3/8Y0zT5M477+Rzn/tcj/bNmzfzwAMPkEwmOe+883j44YfR9cH/b4Zl2zTFUjTHuli7Ncq6rc0Um63Mcu9n7rnVVPgyuDxuHBMUy0SxLFzV09CKKlELS2WzDCHEiNBn+kYiEZ588kleeukl3G43ixYt4oILLmDSpEndr/nGN77Bv/zLv3DWWWdx//33s3z5cu64444BLfxIG3e3snFHlE92NXOgLYOFht8FF9S5uSRoUOH4QI9jmypOqhPVX4RSPhFVBU33yqwXIcSI0mewr169mjlz5hAMBgGYP38+K1eu5L777gOgoaGBdDrNWWedBcAtt9zC97///QEPdtOy+fOWZt77JMKmnc2Uu7sIF3m58NwSJk4aS02hhZ5uw2hsBsI4mU7U4Bi0ikmo/mIJcyHEiNVnsDc3NxMOh7uPy8vLWb9+/THbw+EwkUikn8s82kfbW3h6xScAXDqjnEVzw7i8Phwjg+L24mTT2Goxqq7jqDqq14dedy56sayhLoQY2foMdtu2URSl+9hxnB7HfbWfiFDo+GPb4XDgqHPXhgOcP7uKgN+NS7UxOqJgA6qOXliCmWgDG6zAX6C6feiFQTTf0e9zOnqrK1/ka235Whfkb21S18nL19oGq64+g72yspK1a9d2H0ejUcrLy3u0R6PR7uOWlpYe7SeitTWBbTu9toXDAaLR+DF/tiNrAuBYPrDN3FTFDvPwsRZGQYcEkDj2+5ysvuoaSvlaW77WBflbm9R18vK1tv6sS1WV43aI+9xoY+7cuaxZs4ZYLEYqlWLVqlXMmzevu726uhqPx8O6desAePXVV3u0DxZF01Fc3u75558+FkKI0aLPYK+oqGDp0qUsXryYm266iQULFjB79mzuueceNmzYAMATTzzBd7/7XT7zmc/Q1dXF4sWLB7xwIYQQvVMcx+l9DGQQnc5QzFDJ17ogf2vL17ogf2uTuk5evtaWV0MxQgghhhcJdiGEGGEk2IUQYoTJiykjqnr8ee99tQ+VfK0L8re2fK0L8rc2qevk5Wtt/VVXX++TF1+eCiGE6D8yFCOEECOMBLsQQowwEuxCCDHCSLALIcQII8EuhBAjjAS7EEKMMBLsQggxwkiwCyHECCPBLoQQI0xeLCnQmxUrVvDjH/8Y0zS58847+dznPjek9fzgBz/gN7/5DQCXXnop3/zmN/nWt77FunXr8Pl8ANx3331cffXVg1rX5z//eWKxGLqe+yi/853vkEwm+e53v0smk+Haa69l6dKlg1oTwC9/+Uuee+657uP6+npuvPFGUqnUkF2zRCLBokWL+K//+i9qampYvXp1r9dp8+bNPPDAAySTSc477zwefvjh7us7GHUtW7aMn//85yiKwsyZM3n44Ydxu9384Ac/4Fe/+hVFRUUA/NVf/dWA/l58uq5j3e+Dfb0+XdvOnTv53ve+190WiUQ488wz+clPfjKo16y3jBiye8zJQ01NTc7ll1/utLW1Oclk0lm4cKGzffv2Iavn3XffdT772c86mUzGyWazzuLFi51Vq1Y5CxYscCKRyJDVZdu2c/HFFzuGYXSfS6VSzqWXXurs27fPMQzDueuuu5x33nlnyGp0HMfZtm2bc/XVVzutra1Dds0++ugjZ8GCBc6MGTOc/fv3H/c6XX/99c6HH37oOI7jfOtb33Kef/75Qatr165dztVXX+3E43HHtm3nm9/8pvPss886juM4X/rSl5wPPvhgwGo5Xl2O4xzzsxvM63Ws2g5pbm52rrzySmf37t2O4wzeNestI1asWDFk91heDsWsXr2aOXPmEAwG8fv9zJ8/n5UrVw5ZPeFwmH/6p3/C7XbjcrmYOHEijY2NNDY2cv/997Nw4UK+//3vY9v2oNa1a9cuAO666y5uuOEGnnvuOdavX09dXR21tbXous7ChQuH9NoBfPvb32bp0qX4fL4hu2bLly/noYce6t6P91jXqaGhgXQ6zVlnnQXALbfcMqDX79N1ud1uHnroIQoLC1EUhSlTptDY2AjAxo0b+clPfsLChQv5zne+QyaTGbS6UqlUr5/dYF+v3mo70uOPP86iRYsYN24cMHjXrLeM2LNnz5DdY3kZ7M3NzYTD4e7j8vJyIpHIkNUzefLk7g9hz549/OY3v+GSSy5hzpw5PProoyxfvpy1a9fy4osvDmpdnZ2dXHjhhfzwhz/kpz/9Kb/4xS9obGzMq2u3evVq0uk01157LS0tLUN2zR555BHOO++87uNj3WOfPh8Ohwf0+n26rurqai666CIAYrEYzz//PFdeeSXJZJJp06bxjW98g5dffpnOzk5+9KMfDVpdx/rsBvt69VbbIXv27OH999/v3ppzMK9ZbxmhKMqQ3WN5Gey2baMoh5eldBynx/FQ2b59O3fddRff/OY3mTBhAj/84Q8pLy/H5/Px+c9/nt/97neDWs/ZZ5/N448/TiAQoLS0lNtuu43vf//7eXXtfvGLX/DFL34RgNra2iG/Zocc6x7Ll3svEolw5513cuutt3LBBRdQUFDA008/zcSJE9F1nbvuumtQr92xPrt8uV4Ay5Yt44477sDtdgMMyTU7MiNqa2uH7B7Ly2CvrKwkGo12H0ej0V7/t2swrVu3ji984Qv8wz/8AzfffDNbt27ljTfe6G53HGfAvzD6tLVr17JmzZoeNVRXV+fNtctms/z5z3/miiuuAMiLa3bIse6xT59vaWkZ9Ou3c+dOFi1axM0338zXvvY1ABobG3v8381gX7tjfXb5cL0OefPNN7nuuuu6jwf7mn06I4byHsvLYJ87dy5r1qwhFouRSqVYtWoV8+bNG7J6Dhw4wNe+9jWeeOIJrr/+eiB3kzz66KN0dHRgGAbLli0b9Bkx8Xicxx9/nEwmQyKR4OWXX+bv//7v2b17N3v37sWyLF5//fUhu3Zbt25l3Lhx+P1+ID+u2SFnnnlmr9epuroaj8fDunXrAHj11VcH9folEgnuvvtuvv71r3PXXXd1n/d6vfzbv/0b+/fvx3Ecnn/++UG9dsf67Ib6eh0Si8VIp9PU1tZ2nxvMa9ZbRgzlPZaX0x0rKipYunQpixcvxjAMbrvtNmbPnj1k9TzzzDNkMhkee+yx7nOLFi3i3nvv5fbbb8c0Ta655hoWLFgwqHVdfvnlfPzxx9x0003Yts0dd9zB2WefzWOPPcbf/u3fkslkuPTSS/nMZz4zqHUdsn//fiorK7uPp06dOuTX7BCPx3PM6/TEE0/w4IMPkkgkmDFjRveY7WB48cUXaWlp4dlnn+XZZ58F4IorruDrX/863/nOd/jKV76CYRicc8453UNcg+F4n91QXq9D6uvre9xrAKWlpYN2zY6VEUN1j8kOSkIIMcLk5VCMEEKIUyfBLoQQI4wEuxBCjDAS7EIIMcJIsAshxAgjwS6EECOMBLsQQowwEuxCCDHC/P9J9Tj5PgOZ1gAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import scipy.stats as stats \n", + "fit_alpha, fit_loc, fit_beta=stats.gamma.fit(waiting_times)\n", + "print(fit_alpha, fit_loc, fit_beta)\n", + "\n", + "# Compute mean and sample from exponential\n", + "samples = rng.gamma(fit_alpha, fit_beta, size=10**3)\n", + "\n", + "# Compute ECDFs for sample & model\n", + "x, y = ecdf(waiting_times)\n", + "x_theor, y_theor = ecdf(samples)\n", + "# Plot sample & model ECDFs\n", + "plt.plot(x_theor, y_theor);\n", + "plt.plot(x, y, marker='.', linestyle='none', alpha=0.1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Negative Binomial Distribution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Negative Binomial Story\n", + "\n", + "Often we may want to model the number of failures we encounter before a success in a series of Bernoulli trials, for example, if we want to know how many times we need to drill in a particular location before hitting oil, or how many times bursty gene expression will happen before it stops. The negative binomial distribution encodes this process by matching the following story:\n", + "\n", + "> We perform a series of Bernoulli trials with probability β/(1+β) of success. The number of failures, y, before we get α successes is Negative Binomially distributed. \n", + "\n", + "The negative binomial is related to many other distributions. For example,\n", + "\n", + "- it arises as as combination of gamma and Poisson distributions and hence is sometimes referred to as a Gamma-Poisson distribution;\n", + "- it can be modelled as a sum of geometrically distributed unknown quantities, among other things (we'll meet the geometric distribution next).\n", + "\n", + "The [Wikipedia page for the negative binomial](https://en.wikipedia.org/wiki/Negative_binomial_distribution) is an enlightening resource with respect to these relationships.\n", + "\n", + "\n", + "### The parameters\n", + "\n", + "There are two parameter, $\\beta$ and $\\alpha$, as described above. In the wild, you may also see the negative binomial parametrized by $r>0$ and $p\\in [0, 1],$ where $r$ is the number of successes $\\alpha$ and $p$ is the probability of failure in each trial.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulating the negative binomial distribution\n", + "\n", + "Let's now simulate the negative binomial distribution and plot the resulting histogram:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:01.220560Z", + "iopub.status.busy": "2022-04-30T07:03:01.220384Z", + "iopub.status.idle": "2022-04-30T07:03:01.314654Z", + "shell.execute_reply": "2022-04-30T07:03:01.314203Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "samples = rng.negative_binomial(3, 0.5, 10**3)\n", + "plt.hist(samples);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The ECDF of the negative binomial distribution\n", + "\n", + "Let's now plot the ECDF of our simulated data:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:01.317193Z", + "iopub.status.busy": "2022-04-30T07:03:01.316996Z", + "iopub.status.idle": "2022-04-30T07:03:01.400798Z", + "shell.execute_reply": "2022-04-30T07:03:01.400437Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x, y = ecdf(samples)\n", + "\n", + "plt.plot(x, y);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Relationships between distributions: the negative binomial from first principles\n", + "\n", + "\n", + "Let's now simulate the negative binomial for $\\alpha=3$ from first principles in order to see it in action. To do this, let's recall the negative binomial story:\n", + "\n", + "- we simulate a series of Bernoulli trails;\n", + "- we count the number of failures before 3 successes." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:01.402889Z", + "iopub.status.busy": "2022-04-30T07:03:01.402771Z", + "iopub.status.idle": "2022-04-30T07:03:01.485263Z", + "shell.execute_reply": "2022-04-30T07:03:01.484598Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bts = np.random.binomial(1, 0.1, 10**4)\n", + "events = np.where(bts == 1)\n", + "\n", + "# count the number of failures before 3 successes\n", + "data1 = events[0][2::3]\n", + "data = np.diff(data1).astype(float)\n", + "plt.hist(data);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's fit a negative binomial distribution to our data generated by first principles.\n", + "For previous examples, we've used `scipy.stats` to fit distributions but, at the time of writing, there was no such method for the negative binomial so we opted to hand code our own. If can you follow the code below, that's great but, if not, it's totally cool." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:01.488352Z", + "iopub.status.busy": "2022-04-30T07:03:01.488223Z", + "iopub.status.idle": "2022-04-30T07:03:01.516720Z", + "shell.execute_reply": "2022-04-30T07:03:01.516087Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MLEs\n", + " alpha: 4.027204119629421\n", + " beta: 0.1349167978498449\n" + ] + } + ], + "source": [ + "# To do the fit, define log likelihood function\n", + "def log_like(params, n):\n", + " \"\"\"Log likelihood for i.i.d. NBinom measurements.\"\"\"\n", + " alpha, beta = params\n", + "\n", + " if alpha <= 0 or beta <= 0:\n", + " return -np.inf\n", + "\n", + " return np.sum(stats.nbinom.logpmf(n, alpha, beta / (1 + beta)))\n", + "\n", + "# Get MLE by optimization, have to minimize NEGATIVE log likelihood\n", + "res = scipy.optimize.minimize(\n", + " fun=lambda params, n: -log_like(params, n),\n", + " x0=np.array([1, 1]),\n", + " args=(data,),\n", + " method='Powell'\n", + " )\n", + "\n", + "# Extract and print result\n", + "alpha_mle, beta_mle = res.x\n", + "print(f'MLEs\\n alpha: {alpha_mle}\\n beta: {beta_mle}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now sample from the negative binomial with these fitted parameters and we'll plot the histogram of the sampled data.\n", + "After this, we'll plot the ECDFs of the orignal data (generated from first principles) and the sampled data to make sure that it passes the ECDF eye-test!" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:01.520149Z", + "iopub.status.busy": "2022-04-30T07:03:01.519988Z", + "iopub.status.idle": "2022-04-30T07:03:01.599720Z", + "shell.execute_reply": "2022-04-30T07:03:01.599332Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "samples = rng.negative_binomial(alpha_mle, \n", + " beta_mle / (1 + beta_mle), 10**3)\n", + "plt.hist(samples);" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:01.602533Z", + "iopub.status.busy": "2022-04-30T07:03:01.602399Z", + "iopub.status.idle": "2022-04-30T07:03:01.680683Z", + "shell.execute_reply": "2022-04-30T07:03:01.680269Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute ECDFs for sample & model\n", + "x, y = ecdf(data)\n", + "x_theor, y_theor = ecdf(samples)\n", + "# Plot sample & model ECDFs\n", + "plt.plot(x, y, marker='.', linestyle='none');\n", + "plt.plot(x_theor, y_theor);\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Geometric Distribution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The geometric story\n", + "\n", + "\n", + "\n", + "A special case of the negative binomial distribution is the geometric distribution, which gives the number of failures before a single success. This is the negative binomial distribution with $\\alpha=1.$ To be precise, the story of the geometric distribution is as follows:\n", + "\n", + "> We perform a series of Bernoulli trials with probability of success θ until we get a success. The number of failures y before the success is geometrically distributed.\n", + "\n", + "\n", + "If this reminds you of the exponential distribution, well noticed, and in fact it is the discrete analogue of the exponential distribution: the exponential distribution models the waiting time before an event whereas the geometric distribution models the number of trials before an event!\n", + "\n", + "### The parameter\n", + "\n", + "There is a single parameter $\\theta$, the probability of the event in each Bernoulli trial.\n", + "\n", + "\n", + "### Simulating the geometric distribution\n", + "\n", + "Let's now simulate the geometric distribution and plot the resulting histogram:\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:01.682811Z", + "iopub.status.busy": "2022-04-30T07:03:01.682690Z", + "iopub.status.idle": "2022-04-30T07:03:01.851646Z", + "shell.execute_reply": "2022-04-30T07:03:01.851113Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gm_data = rng.geometric(0.35, 10**4)\n", + "plt.hist(gm_data, bins=100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The ECDF of the geometric distribution\n", + "\n", + "Let's also plot the ECDF of our generated data:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:01.854066Z", + "iopub.status.busy": "2022-04-30T07:03:01.853841Z", + "iopub.status.idle": "2022-04-30T07:03:01.938092Z", + "shell.execute_reply": "2022-04-30T07:03:01.937653Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute ECDFs for sample\n", + "x, y = ecdf(gm_data)\n", + "# Plot sample ECDF\n", + "plt.plot(x, y);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The relationship between distributions: simulating the geometric from first principles\n", + "\n", + "\n", + "Now let's simulate the geometric distribution from first principles by \n", + "\n", + "- simulating Bernoulli trails and\n", + "- counting the number of trials between successes." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:01.940403Z", + "iopub.status.busy": "2022-04-30T07:03:01.940279Z", + "iopub.status.idle": "2022-04-30T07:03:02.030859Z", + "shell.execute_reply": "2022-04-30T07:03:02.030434Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.random.binomial(1, 0.35, 10**3)\n", + "events = np.where(x == 1)\n", + "\n", + "# counting the number of trials between successes\n", + "data1 = events[0]\n", + "data = np.diff(data1).astype(float)\n", + "plt.hist(data);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now compute the mean and sample from a geometric distribution with that mean and then compute the ECDFs for the sample and the model, as we have done before:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:02.033225Z", + "iopub.status.busy": "2022-04-30T07:03:02.033059Z", + "iopub.status.idle": "2022-04-30T07:03:02.114414Z", + "shell.execute_reply": "2022-04-30T07:03:02.113994Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute mean and sample from geometric\n", + "p = 1/np.mean(data)\n", + "samples = rng.geometric(p,10**3)\n", + "\n", + "# Compute ECDFs for sample & model\n", + "x, y = ecdf(data)\n", + "x_theor, y_theor = ecdf(samples)\n", + "# Plot sample & model ECDFs\n", + "plt.plot(x_theor, y_theor);\n", + "plt.plot(x, y, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Log-normal distribution\n", + "\n", + "### The lognormal story\n", + "\n", + "We say that a distribution is Log-normally distributed when the log of the distribution is Normally distributed.\n", + "\n", + "\n", + "\n", + "### The Parameters\n", + "\n", + "The Log-normal distribution has two parameters: a location parameter $\\mu$ and a scale parameter $\\sigma,$ just like the Normal distribution. These are the mean and variance of the log distribution, respectively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulating the Log-normal distribution\n", + "\n", + "Let's now simulate the log-normal distribution and plot the resulting histogram:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:02.116452Z", + "iopub.status.busy": "2022-04-30T07:03:02.116336Z", + "iopub.status.idle": "2022-04-30T07:03:02.259125Z", + "shell.execute_reply": "2022-04-30T07:03:02.258811Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "samples = rng.lognormal(0 ,1 , 10**3)\n", + "plt.hist(samples);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The ECDF of the lognormal distribution\n", + "\n", + "\n", + "Let's also plot the ECDF, as we often do:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:02.261098Z", + "iopub.status.busy": "2022-04-30T07:03:02.260983Z", + "iopub.status.idle": "2022-04-30T07:03:02.337221Z", + "shell.execute_reply": "2022-04-30T07:03:02.336827Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_s, y_s = ecdf(samples)\n", + "plt.plot(x, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:02.339148Z", + "iopub.status.busy": "2022-04-30T07:03:02.339034Z", + "iopub.status.idle": "2022-04-30T07:03:02.669010Z", + "shell.execute_reply": "2022-04-30T07:03:02.668626Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_s, y_s = ecdf(samples)\n", + "plt.plot(x, y)\n", + "plt.xscale('log');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### An example of the log-normal distribution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As an example, we'll consider the distribution of adult weights. This example is from _Think Stats_ by Allen Downey. When introducing the data, Downey states\n", + "\n", + "> The National Center for Chronic Disease Prevention and Health Promotion conducts an annual survey as part of the Behavioral Risk Factor Surveillance System (BRFSS).3 In 2008, they interviewed 414,509 respondents and asked about their demographics, health, and health risks. Among the data they collected are the weights in kilograms of 398,484 respondents. \n", + "\n", + "Let's now import it and check it out:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:02.671397Z", + "iopub.status.busy": "2022-04-30T07:03:02.671262Z", + "iopub.status.idle": "2022-04-30T07:03:02.681545Z", + "shell.execute_reply": "2022-04-30T07:03:02.681246Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0agesexwtyragofinalwtwtkg2htm3
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" + ], + "text/plain": [ + " Unnamed: 0 age sex wtyrago finalwt wtkg2 htm3\n", + "0 0 82.0 2 76.363636 185.870345 70.91 157.0\n", + "1 1 65.0 2 72.727273 126.603027 72.73 163.0\n", + "2 2 48.0 2 NaN 181.063210 NaN 165.0\n", + "3 3 61.0 1 73.636364 517.926275 73.64 170.0\n", + "4 4 26.0 1 88.636364 1252.624630 88.64 185.0" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('../../datasets/weights.csv')\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now transform the data so that we have the logarithm of the weights and we'll fit a normal distribution to the logarithm:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:02.683419Z", + "iopub.status.busy": "2022-04-30T07:03:02.683314Z", + "iopub.status.idle": "2022-04-30T07:03:02.774533Z", + "shell.execute_reply": "2022-04-30T07:03:02.774068Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "weights = df.wtkg2.dropna()\n", + "log_weights = np.log10(weights)\n", + "\n", + "m = np.mean(log_weights)\n", + "sd = np.std(log_weights, ddof=1)\n", + "\n", + "samples = np.random.normal(m ,sd, 10**3)\n", + "\n", + "x, y = ecdf(np.array(log_weights))\n", + "x_s, y_s = ecdf(samples)\n", + "plt.plot(x, y)\n", + "plt.plot(x_s, y_s);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks like a pretty good fit to us!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chapter, we have introduced a panoply of distributions that will provide a firm foundation for any future statistical modeling. In particular, we have focused on matching distributions to their stories, in order to give intuition for how we think about these distributions and how they correspond to the way we encode the data-generating processes in our modeling choices.\n", + "\n", + "We've seen how many of the datasets are related and used our simulation skills to demonstrate these relationships. We've also looked at a variety of real-world datasets, considered what data-generating processes could have resulted in them, fit the relevant models to the data, and used the ECDF to visualize the data and the model together, in order to get a visual sense of the goodness of fit.\n", + "\n", + "Now we have a stronger sense of our foundational family of distributions, it's time to move onto Bayesian inference, through the wonderful world of joint probabilities, conditional probabilities, and Bayes' Theorem." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/test/06-Bayes-Theorem.ipynb b/notebooks/test/06-Bayes-Theorem.ipynb new file mode 100644 index 0000000..f6e4a72 --- /dev/null +++ b/notebooks/test/06-Bayes-Theorem.ipynb @@ -0,0 +1,989 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayes Theorem\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us recall the context and our goal:\n", + "\n", + "\n", + "* Science is deeply interested in questions of the form: given data collected in the world, what can we say about the data-generating process(es)? That is, what is $P(M|D)$, the probability of any model $M$, given the observed/collected data $D$?\n", + "* On the other hand, statistics and probability are better at answering questions such as: given the model $M$, what is the probability $P(D|M)$ of observing the data $D$?\n", + "\n", + "To get out of this slightly paradoxical situation, we will require tools that allow us to move between $P(D|M)$ and $P(M|D)$. It will eventually be Bayesian inference that allows us to do so, and we'll see precisely how in the next chapter. In this chapter, we'll lay the groundwork by introducing Bayes Theorem, they fundamental result of Bayesian inference, after reminding ourselves and the reader a bit about joint and conditional probabilities." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Joint probability" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We expect that the reader may be quite familiar with joint and conditional probabilities. If so, feel free to skip these sections, however they may be both interesting and informative.\n", + "\n", + "We have already encountered joint probabilities above, perhaps without knowing it: $P(A,B)$ is the probability two events $A$ and $B$ both occurring.\n", + "\n", + "- For example, getting two heads in a row.\n", + "\n", + "If $A$ and $B$ and are independent, then $P(A,B)=P(A)P(B)$ but be warned: this is not always (or often) the case.\n", + "\n", + "One way to think of this is considering \"AND\" as multiplication: the probability of A and B is the probability of A multiplied by the probability of B.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Example: Joint probability coin flipping" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a toy example, let's now verify that $P(A,B)=P(A)P(B)$ in the two fair coin-flip case (A=heads, B=heads). We do this by \n", + "- first simulating two coins being flipped together and calculating the proportion of occurences with two heads;\n", + "- then simulating one coin flip and calculating the proportion of heads and then doing that again and multiplying the two proportions.\n", + "\n", + "Your two calculations should give \"pretty close\" results and not the same results due to the (in)accuracy of simulation. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:07.513040Z", + "iopub.status.busy": "2022-04-30T07:03:07.512302Z", + "iopub.status.idle": "2022-04-30T07:03:10.046809Z", + "shell.execute_reply": "2022-04-30T07:03:10.046119Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hba/opt/anaconda3/envs/generative-thinking/lib/python3.9/site-packages/aesara/link/jax/dispatch.py:87: UserWarning: JAX omnistaging couldn't be disabled: Disabling of omnistaging is no longer supported in JAX version 0.2.12 and higher: see https://github.com/google/jax/blob/main/design_notes/omnistaging.md.\n", + " warnings.warn(f\"JAX omnistaging couldn't be disabled: {e}\")\n", + "/Users/hba/opt/anaconda3/envs/generative-thinking/lib/python3.9/site-packages/pymc/sampling_jax.py:36: UserWarning: This module is experimental.\n", + " warnings.warn(\"This module is experimental.\")\n" + ] + } + ], + "source": [ + "#| output: false\n", + "#Import packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from generative_thinking.utils import ecdf\n", + "%matplotlib inline\n", + "sns.set()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:10.075943Z", + "iopub.status.busy": "2022-04-30T07:03:10.075713Z", + "iopub.status.idle": "2022-04-30T07:03:10.210278Z", + "shell.execute_reply": "2022-04-30T07:03:10.209651Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2442\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Solution: Calculate P(A,B)\n", + "rng = np.random.default_rng(42)\n", + "x_0 = rng.binomial(2, 0.5, 10000)\n", + "p_ab = sum(x_0==2)/len(x_0)\n", + "plt.hist(x_0);\n", + "print(p_ab)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:10.213058Z", + "iopub.status.busy": "2022-04-30T07:03:10.212870Z", + "iopub.status.idle": "2022-04-30T07:03:10.254226Z", + "shell.execute_reply": "2022-04-30T07:03:10.253844Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.25471309000000003" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Solution: Calculate P(A)P(B)\n", + "x_1 = rng.binomial(1, 0.5, 10000)\n", + "x_2 = rng.binomial(1, 0.5, 10000)\n", + "p_a = sum(x_1 == 1)/len(x_1)\n", + "p_b = sum(x_2 == 1)/len(x_2)\n", + "p_a*p_b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** In order to use such simulation and _hacker statistics_ approaches to \"prove\" results such as the above, we're gliding over several coupled and deep technicalities. This is in the interests of the pedagogical nature of this introduction. For the sake of completeness, note that we're essentially\n", + "- Using the proportion in our simulations as a proxy for the probability (which, although frequentist, is useful to allow you to start getting your hands dirty with probability via simluation).\n", + "\n", + "Having stated this, for ease of instruction, we'll continue to do so when thinking about joint & conditional probabilities of both simulated and real data. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Example: joint probability for birds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can answer similar questions about empirical data, such as: what is the probability that two randomly selected birds from our Finch data have beak depths over 10 ?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:10.256486Z", + "iopub.status.busy": "2022-04-30T07:03:10.256337Z", + "iopub.status.idle": "2022-04-30T07:03:10.262176Z", + "shell.execute_reply": "2022-04-30T07:03:10.261793Z" + } + }, + "outputs": [], + "source": [ + "# Import and view head of data\n", + "df_12 = pd.read_csv('../../datasets/finch_beaks_2012.csv')\n", + "df_12.head()\n", + "# Store lengths in a pandas series\n", + "lengths = df_12['blength']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:10.264387Z", + "iopub.status.busy": "2022-04-30T07:03:10.264257Z", + "iopub.status.idle": "2022-04-30T07:03:10.268074Z", + "shell.execute_reply": "2022-04-30T07:03:10.267704Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.724891534007516" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate P(A)P(B) of two birds having beak lengths > 10\n", + "p_a = (sum(lengths > 10))/len(lengths)\n", + "p_b = (sum(lengths > 10))/len(lengths)\n", + "p_a*p_b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is sampling the dataset itself. We can also use the bootstrap over many random samples to compute these. \n", + "\n", + "In this spirit, we now calculate the joint probability using the resampling method, that is, by drawing random samples (with replacement) from the data. First we calculate $P(A)P(B)$:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:10.270101Z", + "iopub.status.busy": "2022-04-30T07:03:10.269953Z", + "iopub.status.idle": "2022-04-30T07:03:10.631669Z", + "shell.execute_reply": "2022-04-30T07:03:10.631354Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7255206086" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate P(A)P(B) using resampling methods\n", + "n_samples = 100000\n", + "p_a = sum(rng.choice(lengths, n_samples, replace=True) > 10)/n_samples\n", + "p_b = sum(rng.choice(lengths, n_samples, replace=True) > 10)/n_samples\n", + "p_a*p_b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we calculate $P(A,B)$:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:10.633526Z", + "iopub.status.busy": "2022-04-30T07:03:10.633416Z", + "iopub.status.idle": "2022-04-30T07:03:10.648626Z", + "shell.execute_reply": "2022-04-30T07:03:10.648324Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.72389" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate P(A,B) using resampling methods\n", + "n_samples = 100000\n", + "samples = rng.choice(lengths, (n_samples,2), replace=True)\n", + "_ = samples > (10, 10)\n", + "p_ab = sum(np.prod(_, axis=1))/n_samples\n", + "p_ab" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From these simulations, we see that $P(A)P(B)$ and $P(A,B)$ are approximately the same, as we would expect, given their independence." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Joint probabilities and multivariate data\n", + "\n", + "To think about joint probabilities, let's look at our Finch beak dataset and check out beak lengths and beak depths across the population. We'll be using Seaborn to produce some basic statistical figures to get a sense of this multivariate distribution:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:10.650713Z", + "iopub.status.busy": "2022-04-30T07:03:10.650538Z", + "iopub.status.idle": "2022-04-30T07:03:10.883246Z", + "shell.execute_reply": "2022-04-30T07:03:10.882721Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.jointplot(data=df_12, x=\"blength\", y=\"bdepth\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In a joint plot, we see a scatter plot of the two variables, along with histograms of each. Immediately we see that the variables are likely correlated (and hence not independent!). We also see an outlier in the bottom right, so let's just keep this in the back of our mind. Let's drill a bit deeper by plotting our join plot and colouring points with respect to their species:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:10.886322Z", + "iopub.status.busy": "2022-04-30T07:03:10.886097Z", + "iopub.status.idle": "2022-04-30T07:03:11.205509Z", + "shell.execute_reply": "2022-04-30T07:03:11.205179Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.jointplot(data=df_12, x=\"blength\", y=\"bdepth\", hue=\"species\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suddenly we see that the outlier is of species unknown! As we wish to concentrate on our two species Fortis and Scandens, we'll drop this data point for the time being:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:11.207649Z", + "iopub.status.busy": "2022-04-30T07:03:11.207528Z", + "iopub.status.idle": "2022-04-30T07:03:11.211431Z", + "shell.execute_reply": "2022-04-30T07:03:11.210703Z" + } + }, + "outputs": [], + "source": [ + "df = df_12[df_12['species'] != 'unknown']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also note that the two species look relatively distinct in the scatter plot. They also look quite separated in the beak length KDE but not so much in the beak depth KDE. To see the power of jointplots and the ease of the Seaborn API, we can plot a contour plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:11.213660Z", + "iopub.status.busy": "2022-04-30T07:03:11.213540Z", + "iopub.status.idle": "2022-04-30T07:03:11.831445Z", + "shell.execute_reply": "2022-04-30T07:03:11.831107Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.jointplot(data=df, x=\"blength\", y=\"bdepth\", hue=\"species\", kind=\"kde\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can also include a linear regression:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:11.833802Z", + "iopub.status.busy": "2022-04-30T07:03:11.833664Z", + "iopub.status.idle": "2022-04-30T07:03:12.225771Z", + "shell.execute_reply": "2022-04-30T07:03:12.225256Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.jointplot(data=df, x=\"blength\", y=\"bdepth\", kind=\"reg\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we noted earlier, the beak length and depth _look_ correlated but are they? To begin answering this question, let's first compute their Pearson correlation coefficient:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:12.228493Z", + "iopub.status.busy": "2022-04-30T07:03:12.228342Z", + "iopub.status.idle": "2022-04-30T07:03:12.231584Z", + "shell.execute_reply": "2022-04-30T07:03:12.231222Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6057479578552036\n" + ] + } + ], + "source": [ + "x = df.blength\n", + "y = df.bdepth\n", + "r = np.corrcoef(np.stack([x, y]))[0,1]\n", + "print(r) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A correlation of 0.6 is relatively strong correlation but, once again, it is important to ask how certain are we of this estimate?\n", + "To answer this, we can once again use the bootstrap method to resample from the dataset and compute correlation coefficients of the bootstrapped samples. We'll also plot the resulting histogram and compute the 95% confidence interval:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:12.233358Z", + "iopub.status.busy": "2022-04-30T07:03:12.233248Z", + "iopub.status.idle": "2022-04-30T07:03:12.356427Z", + "shell.execute_reply": "2022-04-30T07:03:12.355983Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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3u/fff98559zq1atdT0+Pc865jo6OyNc3b97sQqGQc86506dPuxUrVrinn37a1dXVub///jtr9yEdkl2Xo0ePusWLF7vKykq3bt06Nzg4mLX7kGqx1uTnn392zz77rFu4cKGrq6tz586dc86xV6Kty2TeKzeLF80CAOM4tQIAxhFyADCOkAOAcYQcAIwj5ABgHCEHAOMIOQAYR8gBwLj/A2mTflJ7KWofAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n = 10**2\n", + "\n", + "cc = np.empty(n)\n", + "\n", + "for i in range(n):\n", + " i_s = rng.choice(np.arange(len(x)), len(x))\n", + " x_s = x[i_s]\n", + " y_s = y[i_s]\n", + " yoi = np.stack([x_s, y_s], axis=0)\n", + " cc[i] = np.corrcoef(yoi)[0,1]\n", + " #print(cc)\n", + "#plt.hist(cc);\n", + "x_1, y_1 = ecdf(cc)\n", + "plt.hist(cc);" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:12.358760Z", + "iopub.status.busy": "2022-04-30T07:03:12.358602Z", + "iopub.status.idle": "2022-04-30T07:03:12.363095Z", + "shell.execute_reply": "2022-04-30T07:03:12.362736Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.52480273, 0.66971759])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 95% CIs\n", + "np.percentile(cc, [2.5, 97.5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that, 95% of the time, our correlation coefficient was between 0.54 and 0.68. Once again note that we're using the bootstrap for EDA to get a sense of what's in our data (but not to make inferences!) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conditional Probability" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a grasp on joint probabilities, lets consider conditional probabilities, that is, the probability of some $A$, knowing that some other $B$ is true. We use the notation $P(A|B)$ to denote this. \n", + "\n", + "Conditional and joint probabilites are related by the following:\n", + "$$ P(A,B) = P(A|B)P(B).$$\n", + "\n", + "\n", + "Why do we care about conditional probabilities? There are many reasons! One of the first is that they allow us to ask and answer questions such as \"What is the probability of a finch beak having length $>10$, knowing that the finch is of species 'fortis'?\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example: conditional probability for birds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. What is the probability of a finch beak having length > 10 ?\n", + "2. What if we know the finch is of species 'fortis'?\n", + "3. What if we know the finch is of species 'scandens'?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:12.365540Z", + "iopub.status.busy": "2022-04-30T07:03:12.365398Z", + "iopub.status.idle": "2022-04-30T07:03:12.369364Z", + "shell.execute_reply": "2022-04-30T07:03:12.368874Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8514056224899599" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(df_12.blength > 10)/len(df_12)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:12.371426Z", + "iopub.status.busy": "2022-04-30T07:03:12.371325Z", + "iopub.status.idle": "2022-04-30T07:03:12.375344Z", + "shell.execute_reply": "2022-04-30T07:03:12.374942Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6942148760330579" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_fortis = df_12.loc[df_12['species'] == 'fortis']\n", + "blf = df_fortis.blength\n", + "sum(df_fortis.blength > 10)/len(df_fortis)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:12.377835Z", + "iopub.status.busy": "2022-04-30T07:03:12.377573Z", + "iopub.status.idle": "2022-04-30T07:03:12.381408Z", + "shell.execute_reply": "2022-04-30T07:03:12.381067Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_scandens = df_12.loc[df_12['species'] == 'scandens']\n", + "sum(df_scandens.blength > 10)/len(df_scandens)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** These proportions between species are definitely different. We can't say much more currently but we'll soon see how to use hypothesis testing to see what else we can say about the differences between the species of finches." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bootstrapping for conditional probabilites" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we have developed our bootstrapping skills this far, it would be a shame to not leverage them to think about these questions. To this end, we now use the bootstrap to compute boostrapped probabilities of fortis finch beaks having length > 10:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:12.383450Z", + "iopub.status.busy": "2022-04-30T07:03:12.383358Z", + "iopub.status.idle": "2022-04-30T07:03:12.723889Z", + "shell.execute_reply": "2022-04-30T07:03:12.723538Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n = 10**3\n", + "cp = np.zeros(n)\n", + "\n", + "\n", + "for i in np.arange(n):\n", + " cp[i] = sum(rng.choice(blf, len(blf)) > 10)/len(blf)\n", + "\n", + "plt.hist(cp);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have developed a bit more intution behind both joint and conditional probabilities, it's time to arrive at Bayes' Theorem!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bayes' Theorem\n", + "\n", + "Let us first state Bayes' theorem. Then we shall prove it and demonstrate its utility. \n", + "\n", + "$$P(B|A) = \\frac{P(A|B)P(B)}{P(A)}$$\n", + "\n", + "As we have stated several times by now, it is Bayes' Theorem that will allow us to move back and forth between $P(data|model)$ and $P(model|data)$. As we have seen, $P(model|data)$ is usually what we're interested in as data scientists yet $P(data|model)$ is what we can easily compute, either by simulating our model or using analytic equations.\n", + "\n", + "One of the coolest things is that Bayes Theorem can be proved with a few lines of mathematics. Let's do it!\n", + "\n", + "Recall that conditional and joint probabilites are related by the following:\n", + "$$ P(A,B) = P(A|B)P(B).$$\n", + "\n", + "By symmetry, this implies that\n", + "\n", + "$$P(B,A) = P(B|A)P(A).$$\n", + "\n", + "Dividing both sides by $P(A)$ yields\n", + "\n", + "$$P(B|A) = \\frac{P(B,A)}{P(B)}.$$ \n", + "\n", + "But $P(B,A) = P(A, B)$ and thus, from the first equation above, $P(B,A) = P(A|B)P(B),$ implying that\n", + "\n", + "$$P(B|A) = \\frac{P(A|B)P(B)}{P(A)}$$\n", + "\n", + "and this is Bayes' Theorem!\n", + "\n", + "\n", + "Let's now show how it can be applied in a practical setting by returning to our 'test for infectious disease' example from Chapter 3.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Example: Test for infectious disease" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall our example from Chapter 3:\n", + "\n", + "> Suppose that a test for an infectious disease X has 99% sensitivity (true positive rate) and 99% specificity (true negative rate), that is, a 1% false positive rate and 1% false negative rate. Suppose that 0.5% (5 in 1,000) of people have X. What is the probability that a randomly selected individual with a positive test has X?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also recall that, **If we can answer this, it will be really cool as it shows how we can move from knowing $P(+|disease)$ to $P(disease|+)$, a MVP for being able to move from $P(data|model)$ to $P(model|data)$.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We solved this challenge using random sampling previously:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:12.726862Z", + "iopub.status.busy": "2022-04-30T07:03:12.726708Z", + "iopub.status.idle": "2022-04-30T07:03:12.731406Z", + "shell.execute_reply": "2022-04-30T07:03:12.731109Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.34147997])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Take 10,000 subjects\n", + "n = 100000\n", + "# Sample for number with X and without X\n", + "X = rng.binomial(n, 0.005, 1) \n", + "not_X = n - X\n", + "# How many of those with X tested +ve ?\n", + "X_pos = rng.binomial(X, 0.99)\n", + "# How many of those without X tested +ve ?\n", + "non_pos = rng.binomial(not_X, 0.01)\n", + "# how many of those +ve tests were for people with X?\n", + "X_pos/(X_pos+non_pos)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bayes Theorem solves the above infectious disease testing problem\n", + "\n", + "Bayes Theorem can be used to analytically derive the solution to the 'infectious disease' example above as follows." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From Bayes Theorem, \n", + "\n", + "$$P(disease|+) = \\frac{P(+|disease)P(disease)}{P(+)}$$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can expand the denominator here into \n", + "\n", + "$$P(+) = P(+,disease) + P(+,no-disease) $$\n", + "\n", + "so that\n", + "\n", + "$$ P(+)=P(+|disease)P(disease) + P(+|no-disease)P(no-disease)$$\n", + "\n", + "and \n", + "\n", + "$$P(disease|+) = \\frac{P(+|disease)P(disease)}{P(+|disease)P(disease) + P(+|no-disease)P(no-disease)}.$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculating this explicitly yields\n", + "\n", + "$$P(disease|+) = \\frac{0.99\\times 0.005}{0.99\\times 0.005 + 0.01\\times 0.995} = 0.332 $$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This means that if an individual tests positive, there is still only a 33.2% chance that they are have the disease! This is because the number of non-disease is so high compared to the number of disease." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this Chapter, we have introduced Bayes Theorem via reminders about joint and conditional probabilities. We have proved Bayes' Theorem and discussed how it lays the foundation to moving between $P(D|M)$ and $P(M|D)$, a basic concern of science and inference. We have also demonstrated its utility in the specific example of testing for infectious disease. In the next chapter, we'll see how Bayes Theorem gives rise to the basic building blocks of Bayesian inference." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/test/07-Bayesian-building-blocks.ipynb b/notebooks/test/07-Bayesian-building-blocks.ipynb new file mode 100644 index 0000000..ca8beef --- /dev/null +++ b/notebooks/test/07-Bayesian-building-blocks.ipynb @@ -0,0 +1,942 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Building Blocks of Bayesian Inference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## From Bayes Theorem to Bayesian Inference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have Bayes Theorem and have seen how it can be useful to move from the measurable $P(+, disease)$ to the desired $P(disease, +)$, let's see how it can help us move from $P(D|M)$ to $P(M|D),$ for data $D$ and a model $M.$ " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To do so, let's return to our example of a biased coin: let's say that we flip a biased coin several times and we want to estimate the probability of heads from the number of heads we saw. First, let's recall Bayes Theorem:\n", + "\n", + "$$P(B|A) = \\frac{P(A|B)P(B)}{P(A)}.$$\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then calculate the probability of a particular $p=p(H)$ given data $D$ by setting $A$ in Bayes Theorem equal to $p$ and $B$ equal to $D$:\n", + "\n", + "$$P(p|D) = \\frac{P(D|p)P(p)}{P(D)} $$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this equation, we call $P(p)$ the prior (distribution), $P(D|p)$ the likelihood and $P(p|D)$ the posterior (distribution). The intuition behind the nomenclature is as follows: the prior is the distribution containing our knowledge about $p$ prior to the introduction of the data $D$ & the posterior is the distribution containing our knowledge about $p$ after considering the data $D$.\n", + "\n", + "\n", + "**Essential remark:** we get the whole distribution of $P(p|D)$, not merely a point estimate plus errors bars, such as [95% confidence intervals](http://andrewgelman.com/2018/07/04/4th-july-lets-declare-independence-95/).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Note on the term probability:\n", + "\n", + "Note that we're _overloading_ the term _probability_ here. In fact, we have 3 distinct usages of the word:\n", + "\n", + "\n", + "- The probability $p$ of seeing a head when flipping a coin;\n", + "- The resulting binomial probability distribution $P(D|p)$ of seeing the data $D$, given $p$, also called the likelihood;\n", + "- The prior & posterior probability distributions of $p$, encoding our _uncertainty_ about the value of $p$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Key concept: the prior\n", + "\n", + "We only need to know the posterior distribution $P(p|D)$ up to multiplication by a constant at the moment: this is because we really only care about the values of $P(p|D)$ relative to each other – for example, what is the most likely value of $p$? To answer such questions, we only need to know what $P(p|D)$ is proportional to, as a function of $p$. Thus we don’t currently need to worry about the term $P(D)$. In fact,\n", + "\n", + "$$P(p|D) \\propto P(D|p)P(p) $$\n", + "\n", + "**Note:** What is the prior? Really, what do we know about $p$ before we see any data? Well, as it is a probability, we know that $0\\leq p \\leq1$. If we haven’t flipped any coins yet, we don’t know much else: so it seems logical that all values of $p$ within this interval are equally likely, i.e., $P(p)=1$, for $0\\leq p \\leq1$. This is known as an uninformative prior because it contains little information (there are other uninformative priors we may use in this situation, such as the Jeffreys prior, to be discussed later). People who like to hate on Bayesian inference tend to claim that the need to choose a prior makes Bayesian methods somewhat arbitrary, but as we’ll now see, if you have enough data, the likelihood dominates over the prior and the latter doesn’t matter so much." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## This is inference!\n", + "\n", + "We are now beginning to see how to perform _inference_, that is, infering our degree-of-belief around unobservable quantities from observable data. Let's make this concrete with an example we're familiar with:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example: Bayesian parameter estimation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to estimate $p(H),$ let's now generate some coin flips as our data.\n", + "We'll write a function that takes two arguments, one the probability $p$, the other the number of flips $N.$\n", + "\n", + "Before doing so, let's make two notes:\n", + "\n", + "- given data $D$ consisting of $n$ coin tosses & $k$ heads, the likelihood function is given by $L:=P(D|p) \\propto p^k(1-p)^{n-k}$ so we have included this in the computation. You don't necessarily need to know this and when using probabilistic programming, you will not need to hard code such likelihoods;\n", + "- given a uniform prior, which we use in this case, the posterior is proportional to the likelihood." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:17.693804Z", + "iopub.status.busy": "2022-04-30T07:03:17.693126Z", + "iopub.status.idle": "2022-04-30T07:03:20.171447Z", + "shell.execute_reply": "2022-04-30T07:03:20.171089Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hba/opt/anaconda3/envs/generative-thinking/lib/python3.9/site-packages/aesara/link/jax/dispatch.py:87: UserWarning: JAX omnistaging couldn't be disabled: Disabling of omnistaging is no longer supported in JAX version 0.2.12 and higher: see https://github.com/google/jax/blob/main/design_notes/omnistaging.md.\n", + " warnings.warn(f\"JAX omnistaging couldn't be disabled: {e}\")\n", + "/Users/hba/opt/anaconda3/envs/generative-thinking/lib/python3.9/site-packages/pymc/sampling_jax.py:36: UserWarning: This module is experimental.\n", + " warnings.warn(\"This module is experimental.\")\n" + ] + } + ], + "source": [ + "#| output: false\n", + "#Import packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from ipywidgets import interact\n", + "from generative_thinking.utils import ecdf\n", + "#import arviz as az\n", + "%matplotlib inline\n", + "sns.set()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:20.189651Z", + "iopub.status.busy": "2022-04-30T07:03:20.189499Z", + "iopub.status.idle": "2022-04-30T07:03:20.193056Z", + "shell.execute_reply": "2022-04-30T07:03:20.192544Z" + } + }, + "outputs": [], + "source": [ + "def plot_posterior_flip(p=0.6, N=0):\n", + " \"\"\"Plot the posterior given a uniform prior; Bernoulli trials\n", + " with probability p; sample size N\"\"\"\n", + " np.random.seed(42)\n", + " # Flip coins \n", + " n_successes = np.random.binomial(N, p)\n", + " # X-axis for PDF\n", + " x = np.linspace(0, 1, 100)\n", + " # Prior\n", + " prior = 1\n", + " # Compute posterior, given the likelihood (analytic form)\n", + " posterior = x**n_successes*(1-x)**(N-n_successes)*prior\n", + " posterior /= np.max(posterior) # so that peak always at 1\n", + " plt.plot(x, posterior)\n", + " plt.xlabel('p')\n", + " plt.ylabel('L')\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets now use this function to generate data and a posterior for $N=10$ and $p=0.6.$" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:20.194893Z", + "iopub.status.busy": "2022-04-30T07:03:20.194801Z", + "iopub.status.idle": "2022-04-30T07:03:20.289664Z", + "shell.execute_reply": "2022-04-30T07:03:20.289301Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_posterior_flip(N=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now generate data and their respective posteriors for combinations of $p=0.5$ and $n\\in\\{5,10,50,100\\}.$ The intention here is to show how the posterior narrows as we collect more and more data, thus providing more certainty around our estimate of $p.$" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:20.291836Z", + "iopub.status.busy": "2022-04-30T07:03:20.291729Z", + "iopub.status.idle": "2022-04-30T07:03:20.510168Z", + "shell.execute_reply": "2022-04-30T07:03:20.509871Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplot(2, 2, 1)\n", + "plot_posterior_flip(p=0.5, N=5);\n", + "plt.subplot(2, 2, 2)\n", + "plot_posterior_flip(p=0.5, N=20);\n", + "plt.subplot(2, 2, 3)\n", + "plot_posterior_flip(p=0.5, N=50);\n", + "plt.subplot(2, 2, 4)\n", + "plot_posterior_flip(p=0.5, N=100);\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We encourage the reader to check out the github repository and use this code to play around with different values of $p$ and $N.$ Think about what you discover! For example, can you see that the closer $p$ is to $0.5$, the more data you need in order to be certain of your estimate. Why would this be the case?\n", + "\n", + "If you're doing this in a Jupyter notebook, you can use the great ipywidget interact to check out the posterior as you generate more and more data (you can also vary $p$):" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:20.512128Z", + "iopub.status.busy": "2022-04-30T07:03:20.512002Z", + "iopub.status.idle": "2022-04-30T07:03:20.601937Z", + "shell.execute_reply": "2022-04-30T07:03:20.601638Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "484256761b424eda82e90481627d4844", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.6, description='p', max=1.0, step=0.01), IntSlider(value=0, descript…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| output: false\n", + "interact(plot_posterior_flip, p=(0, 1, 0.01), N=(0, 1500));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now move onto the real-world example we encountered in Chapter 5, that of estimating batting averages for baseball players." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: batting averages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Chapter 5, we saw that we could use the beta distribution to model what we know about the probability of batting averages in baseball. In particular, given data on many players, we began with the question:\n", + "\n", + "> Who is the better batter out of the following? Batter $A$ with 3 out of 10 hits or batter $B$ with 250 out of $1000$ hits. \n", + "\n", + "\n", + "The challenge is that $A$'s batting average is 0.3 and $B$'s is 0.25, but we have much less data to go on with $A$! So we use the Empirical Bayes method, in the following 2 steps:\n", + "\n", + "1. Estimate a prior from all of our data;\n", + "2. Use that distribution as a prior for each individual estimate.\n", + "\n", + "Note that the use of data to estimate our prior is not standard in Bayesian inference and we'll get to other methods soon, but it is a useful way to illustrate the important concepts here.\n", + "\n", + "Recall that fitting a beta distribution to data involves estimating two shape parameters, $\\alpha$ and $\\beta$, and that we did Step 1 in Chapter 5, as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:20.604494Z", + "iopub.status.busy": "2022-04-30T07:03:20.604408Z", + "iopub.status.idle": "2022-04-30T07:03:20.921048Z", + "shell.execute_reply": "2022-04-30T07:03:20.920681Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.25858687458828294 0.0006178976021790106\n", + "79.97509357607375 229.3023734392516\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_batting = pd.read_csv('../../datasets/Batting.csv')\n", + "df_p = pd.read_csv('../../datasets/Pitching.csv')\n", + "df_batting.head()\n", + "\n", + "dfg = df_batting[['playerID','AB','H']].groupby(['playerID']).sum()\n", + "dfg['Av'] = dfg['H']/dfg['AB']\n", + "dfg.reset_index(inplace=True)\n", + "\n", + "dfg = dfg[~dfg.playerID.isin(df_p.playerID.unique())]\n", + "\n", + "#df = dfg[dfg['Av'] > 0.18]\n", + "dfg = dfg[dfg['AB'] > 500]\n", + "dfg.dropna(inplace=True)\n", + "#plt.hist(dfg['Av'],bins=50);\n", + "\n", + "data = dfg['Av']\n", + "m = np.mean(data)\n", + "v = np.var(data)\n", + "print(m, v)\n", + "\n", + "a = m**2*((1-m)/v - 1/m)\n", + "b = a*(1/m - 1)\n", + "print(a, b)\n", + "samples = np.random.beta(a, b, 10**3)\n", + "#plt.hist(samples, bins=50);\n", + "\n", + "# Compute ECDFs for sample & model\n", + "x, y = ecdf(np.array(data))\n", + "x_theor, y_theor = ecdf(samples)\n", + "# Plot sample & model ECDFs\n", + "plt.plot(x_theor, y_theor);\n", + "plt.plot(x, y, marker='.', linestyle='none', alpha=0.03);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So $\\alpha \\approx 80$ and $\\beta \\approx 229.$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was Step 1, estimating a prior from all your data. Then Step 2 is to use that distribution as a prior for each individual estimate:\n", + "\n", + "- for any batter with $\\alpha_0$ hits from $\\beta_0$ at bats, we add $\\alpha_0$ to $\\alpha$ and $\\alpha_0 + \\beta_0$ to $\\beta$ (this is a particular and super handy characteristic of the beta distribution)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So the posterior for a batter with 3 out of 10 hits is $\\beta(83, 239),$ which has a mode of $82/319 \\simeq 0.26,$ and the posterior for a batter with 250 out of 1000 hits is $\\beta(330, 489),$ which has a mode of $329/817 \\simeq 0.40.$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we see that, using our Empirical Bayes prior, our batting average estimate for batter $B$ with 250 out of $1000$ hits is higher than that for Batter $A$ with 3 out of 10 hits!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now do this computation for all our players in the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:20.923574Z", + "iopub.status.busy": "2022-04-30T07:03:20.923439Z", + "iopub.status.idle": "2022-04-30T07:03:20.931202Z", + "shell.execute_reply": "2022-04-30T07:03:20.930819Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playerIDABHAveb_est
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" + ], + "text/plain": [ + " playerID AB H Av eb_est\n", + "1 aaronha01 12364 3771 0.304998 0.303866\n", + "2 aaronto01 944 216 0.228814 0.236161\n", + "7 abbated01 3044 772 0.253614 0.254072\n", + "11 abbotfr01 513 107 0.208577 0.227387\n", + "13 abbotje01 596 157 0.263423 0.261771" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfg['eb_est'] = (dfg['H'] + a)/(dfg['AB'] + a + b)\n", + "dfg.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chapter, we have built from Bayes Theorem to introduce the basic building blocks of Bayesian inference:\n", + "\n", + "* the prior distribution\n", + "* the likelihood function, and\n", + "* the posterior distribution.\n", + "\n", + "We have demonstrated how they allow us to move from $P(D|M)$ to $P(M|D),$ for data $D$ and a model $M$, and we have provided two examples:\n", + "\n", + "* estimating the probability of heads in biased coin flip,\n", + "* estimating the batting average of baseball players.\n", + "\n", + "In the process, we saw how generating more data can impact the posterior. We also disambiguated the overloaded term _probability_ and discussed certain nuances, such as the choice of the prior. At this point, it is worth saying more about the prior distribution and we will do so in the next chapter. Then we will be ready to use probabilistic programming to build our Bayesian inference machines." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "484256761b424eda82e90481627d4844": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "VBoxModel", + "state": { + "_dom_classes": [ + "widget-interact" + ], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + 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We also discuss seemingly paradoxical phenomena, such as the fact that the uniform prior is not uninformative, along with remedies. This is all important, as the prior distribution is one of the more contraversial aspects of Bayesian inference, which it need not be. One of the reasons it is perhaps contraversial is that it is not well-understood and we hope to make a small contribution in demystifying some of it here. Having said that, we do not necessarily expect anybody to have a good handle on the actual practice of choosing and using priors after merely reading this chapter. For that, you'll need to see many priors of a variety of forms used in different contexts, and there's plenty of that to come in the chapters that follow." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The choice of the prior" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the small to medium data limit, the choice of prior can affect the posterior. We'll now introduce several types of priors and then we'll use one of them for the coin flip example from the previous chapter to see the impact of the prior:\n", + "\n", + "- **Informative priors** express specific, definite information about a variable, for example, if we got a coin from the mint, we may use an informative prior with a peak at $p=0.5$ and small variance. \n", + "- **Weakly informative priors** express partial information about a variable, such as a peak at $p=0.5$ (if we have no reason to believe the coin is biased), with a larger variance.\n", + "- **Uninformative priors** express no information about a variable, except what we know for sure, such as knowing that $0\\leq p \\leq1$.\n", + "\n", + "Now you may think that the _uniform distribution_ is uninformative, however, what if I am thinking about this question in terms of the probability $p$ and you are thinking about it in terms of the _odds ratio_ $r=\\frac{p}{1-p}$? You rightly feel that you have no prior knowledge as to what this $r$ is and thus choose the uniform prior on $r$.\n", + "\n", + "With a bit of algebra (transformation of variables), we can show that choosing the uniform prior on $p$ amounts to choosing a decidedly non-uniform prior on $r$ and vice versa. So you and I have actually chosen different priors, using the same philosophy! How do we avoid this happening? Enter the **Jeffreys prior**, which is an uninformative prior that solves this problem. You can read more about the Jeffreys prior [here](https://en.wikipedia.org/wiki/Jeffreys_prior) & in your favourite Bayesian text book (Sivia gives a nice treatment). \n", + "\n", + "In the binomial (coin flip) case, the Jeffreys prior is given by $P(p) = \\frac{1}{\\sqrt{p(1-p)}}$.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing the uniform and Jeffreys priors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall that, in the previous chapter, we plotted the posterior distribution for $p(H)$ with a uniform prior, after simulating a number of coin flips. We'll now do this again, but we'll generate two posterior plots on the same figure, one using the uniform prior, the other using the Jeffreys prior. We'll do soon for differing sample sizes and our intention here is to demnstrate via simulated example that the choice of prior matters less as you collect more data. We'll start off by defining a function analogous to the one we defined in the previous chapter:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:25.665573Z", + "iopub.status.busy": "2022-04-30T07:03:25.665047Z", + "iopub.status.idle": "2022-04-30T07:03:28.202119Z", + "shell.execute_reply": "2022-04-30T07:03:28.201585Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hba/opt/anaconda3/envs/generative-thinking/lib/python3.9/site-packages/aesara/link/jax/dispatch.py:87: UserWarning: JAX omnistaging couldn't be disabled: Disabling of omnistaging is no longer supported in JAX version 0.2.12 and higher: see https://github.com/google/jax/blob/main/design_notes/omnistaging.md.\n", + " warnings.warn(f\"JAX omnistaging couldn't be disabled: {e}\")\n", + "/Users/hba/opt/anaconda3/envs/generative-thinking/lib/python3.9/site-packages/pymc/sampling_jax.py:36: UserWarning: This module is experimental.\n", + " warnings.warn(\"This module is experimental.\")\n" + ] + } + ], + "source": [ + "#| output: false\n", + "#Import packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from ipywidgets import interact\n", + "import arviz as az\n", + "from generative_thinking.utils import ecdf\n", + "%matplotlib inline\n", + "sns.set()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:28.222215Z", + "iopub.status.busy": "2022-04-30T07:03:28.222056Z", + "iopub.status.idle": "2022-04-30T07:03:28.226589Z", + "shell.execute_reply": "2022-04-30T07:03:28.225943Z" + } + }, + "outputs": [], + "source": [ + "\n", + "def plot_posteriors_flip(p=0.6, N=0):\n", + " np.random.seed(42)\n", + " n_successes = np.random.binomial(N, p)\n", + " x = np.linspace(0.01, 0.99, 100)\n", + " posterior1 = x**n_successes*(1-x)**(N-n_successes) # w/ uniform prior\n", + " posterior1 /= np.max(posterior1) # so that peak always at 1\n", + " plt.plot(x, posterior1, label='Uniform prior')\n", + " jp = np.sqrt(x*(1-x))**(-1) # Jeffreys prior\n", + " posterior2 = posterior1*jp # w/ Jeffreys prior\n", + " posterior2 /= np.max(posterior2) # so that peak always at 1 (not quite correct to do; see below)\n", + " plt.plot(x, posterior2, label='Jeffreys prior')\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now plot both posteriors for combinations of $p=0.5$ and $N\\in\\{0, 5, 10, 20\\}$:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:28.229130Z", + "iopub.status.busy": "2022-04-30T07:03:28.228991Z", + "iopub.status.idle": "2022-04-30T07:03:28.544548Z", + "shell.execute_reply": "2022-04-30T07:03:28.544206Z" + } + }, + "outputs": [ + { + 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\n", 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\n", 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uzmZgeCAlxdVjcWRZrrNR2tzUp2FcldfLD9lShq2sGHstffLtkWbZzDskJIT8/PyrbpJPcPAv1YScnBw2bNhQdSyEcFpMR0tiyziA5NWR06aOGM12hke03arQFVRqDXa1AZ0wYbfZXS2n1VKnCaKjo9m3bx+FhYWYTCZ27NhBTExMVbrBYODNN98kMzMTIQRr165lwoQJzSra2QiLEUdWGpoewzl4sgAPvZr+bahXaOvWTVWxN9ei8fJHhSDz7GnuvXc6Dz00m6SkxFYTQn2Fhx6a7bJ712mCTp06sWDBAubMmcNdd93F1KlTGTBgAPPnzyc1NZWOHTuyZMkSHnvsMSZNmoQQgrlz57aEdqdhP5cIsgNV9xEcOZnPoPAgtK1sBllj0egN2CQdx44eJCKiD598so6jR48QFzeZv/3to7ov0EJ88sk6l927XvWWadOmMW3atGq/Xd0OiIuLIy4uzrnKWhBbxgEknyBOlPtSYbnw37DptklWViZvvbWM0tIS9HoDCxYsxGY2sepf6zCZrcyaNZ2KisooU51OR05ONiUlJWRnZ/LYY08SEBDA+++/g8Vixs/Pn4ULn0OWZZ588rds2LAJlUrFkSOHWbv2M555ZjFLlryAyWRCpZL4/e8X0r9/VDU9M2dO47bbxnPo0AEAnn32RXr37sMTTzyCr68f585lsGTJMubOvY89ew5jNpt5440/cebMKVQqFbNm3c8dd0ytEXrtzDkQba/y7mRkUymOnHR0AydzID0fT72GyO71qwrZTu3FdnJ3s+jSRsSg7T26wectXfoSCxY8Te/efTh37izPPfdH1q37kgfvvZfktGO8sOTPrFq1EqgMoV669GX8/Pz485/fxWazMW/eHN54411CQkI4cGAfb7yxlPfeW05oaBhJSYkMHTqcbdu2MHnyVDZv/obo6DHMnj2H/fsTSEk5WsMEAAaDB6tXr2PPnt0sXfoyn376OQA9e4bz2mtvVsu7atUK/Pz8+Oc/11NcXMz8+Q/Sq1cEUD302pm4vQnsZw+BkBHdhpO05zzD+wSjUbu+G7QxVFRUkJ5+nNdeW1L1m8lkorS0BKE2oELGaqo5++xKqHRm5gVycrJYtOipqjTjf+cmTJlyJ9u3byUyMorExEP84Q+LCAoKZvHipzl16iTR0WOYMePXteq6887KeQtjxsSwdOnLFBcXV7vv1SQmHmbRohcA8Pf3Z+zYGJKSEvHy8qo19NoZKCY4sx9Vhy4cK/bAbHUwol/9V5fT9h7dqLe1M0hOTiIsrAuBgUEIIVCr1ciyjE6nr1a/zsvLxdfXD62HJwIJuaLmnIIrIc0Oh0xoaFjV+Q6Hg6KiQgBiY8ezcuVyfvjhO265ZTR6vZ4BAwaxZs16EhL2sHPnDrZu3cRf/rK8xvWvDqUWQkalUlW779UIIV9zDA6H/br5nUHbfOU5CbmsAEfuaTThIzmQnoevp7ZZNuRuDrZs2cju3T8CkJFxmtDQMLy9venSpSvbt28F4NCh/Tz++CNAZSiFLGnQCQuyo/bu0m7dbqa0tJTk5KSqe7z88mKgshdw1KhoVq5czh13VLYPly9/j+3bv+WOO6ayYMEznDp1stbr7ty5HYBdu36gW7fu+N4gjGPIkOFs2fINUDk/4qefflRCqZsTW0ZlY83RdRjJO08ydkBn1Kq28V64//6HePXVF/nyy38TFBTMkiWvA/DSS3/izTdfY926z9BotCxZ8lrV4KZKq0NGQraaUXnUHETS6XS8+urrvPfeW1itVjw9vXj++V9mdd1++8T/zjOorMbMmHEPr7zyPFu3VjaYr857NampyWzevBEPDwOLF798w3/X3LnzePvtN5gz5x5kWWbOnIeJiOhDRkbz7d/s1qHUxg3Pg9bA0R6/4eMt6Tz3wFDCw66/5HpbCaW+EcbLeehlI5J/GGqtrt7nORwOVq5cTocOHZg16/56nzdz5jT++tcVdO7c/FtbNWsodXvEcTkTuTALbfgt7D+eS6CfgZ6hrou2bCm03v6AhK2sqEHnzZv3ACdPpnP33TPrztzGcNvqkO10AkhqTCGDOL45mSm3dGtVa4w2Fzq9jnLJA4OjAtluQ6XR1uu81asbN5i1YcOmRp3Xkrjll0DIMvaM/ai7RnEgw4gQMKpfiKtltRiaRn4N2ituaQJHznGEsQhtr2gS0i7RvbMPoYFe9Tq3FTShmoxer8OEAbW98mvQHmjK/4tbmsB2ai/oPLnk2Yuf88qJ7l+/tYM0Gh1GY2mbN4IkSai9/AHaxdfgyv4EHh7KJh31QlhN2M8lou09mr3HC1CrpHrHCnXoEERRUT7l5cXNK/IGqFS/7FTTFIQAS3kZOgqRKsqR6rF+UGtGo9ERHt6d4uKG71HgdiawnT0IDiuq8Gj2fXGJQeGB+HjWr6tQrdYQGOi6rVvBuV3HSSkZdN23DHNgP7rMeKruE1o5Wq2Wa9eLrQ9uVx2yndiNqkMoKcW+lFXYGDvQtQ+1KxkY1YMjqkH4XU7BeinD1XJchluZwFGYhZyXgTZiHD+lXqKDj57+3Vvf6nIthUqS6DT2LsplPQU/rGnzbZ3G4lYmsJ3YBSoNpSFDSDt3mdFRIaha86YDLcCAiDAO6UfhU3YOc0aiq+W4BLcxgbBbsJ1OQHPzEHall4KAmIHNP5Tf2pEkiT633cklhx+le9YhHO2jy7QhuI0J7GcOgMWIqk8sPyXnMKBnAIF+7rEMSV307hZAiv/teFoLKT28xdVyWhy3MIEQAuvxnag6hHG0pAOlFTZih3RxtaxWxZi48aRYb0JO2YxcVuBqOS2KW5hAzstALriAtt9t7DicRXAHjza1mkRL0KmjJ4W943HIULhzlVs1kt3CBNbU7aDzJNMrinMXS5k4vCsqNwiWayjjxw3kB3kY+rzjWM/sc7WcFqPdm0Auzcd+7jC6vreyLSkXL4OG0fUMk3A3PPQaesTezXl7IMbda5Aril0tqUVo9yawpu0AVBR2jubIyXxih4TVuv+YQiVDIoJJCpgCdjMlOz92i2pRuzaBXFGCLf1HNL1uYXNyKVqtignDutZ9ohsjSRJ3Tolmh20YmoupWI5/72pJzU67NoE1ZRvIdsp63M6B47ncOiis3nFC7oy/t57usXdz3BqKOWEdjoILrpbUrLRbE8gVJdiOf4+mx0i+SjKi0UhMGnmTq2W1GUZGdub0TdMpc+go/fY9hLm87pPaKO3WBNYjG8FhI7/beA6m5zFxeFf8vd1jeXJn8atJg9momgQVxZRu/wAht8+VrdulCeSS3Mq2QJ9xrDtQireHlkkjXLtKRFtEr1MzY+YEvraORpV7AuOPq9tlQ7ndmUAIgTlhLWi0pHlFcyqzmBnjeuBpcLupE04hpKMnw6fcxTbTQMSZvZj3/7vdGaHdmcB+PhFHZgoMuJO1e3Pp3tmXsUqgXJOI6hFAyLhfs8fcG3vqNswHN7QrI7QrEwhzOZa9a1B17Mo/L4RhNNl4cFKEMjrsBMYOCkM9cjYJ5l7Yk7dg2vNPhBOmebYG2o0JhBCYd69GmMs4HhrP4VOXuTumBzd1arnN/9o7E0fejGrUA+w09cOR/j3l295DWE2ultVk2o0JbGk7sJ9PpKz3FFbuKSPy5g5MGqF0iTqbCSNuImTCHL40jUTOTKHkixdxFJx3tawm0S5MYP/5KJb9n2MLHcgbSYF09NXzaHx/t5811lyM6hdCzK/u41PHFMrKjJR/tQTToa8QdqurpTWKNm8C+88pmP7zAXbfMJadHYhKreKpewbh7VG/5QUVGkePUF8enhvPfzrNJdFyM/akjRStewbr6YQ211Zos6tSCyGwHfsOy77PqfDsxLKL49B6+vDUPQPpHFC/1eTaIi29v3N9SMm4zL7vvifGkUAXTREWQwCeAybg0WcMkqHuVaGdxbVlU99Vqetlgk2bNvHhhx9it9t58MEHue+++6qlp6ens3jxYoxGI8OGDeOVV15p0LY6DTWBozAby/5/4chK47y6Ox8WjKRbl2Aeu6s/vl7tOzaoNZoAwCHLJKTkcPbgbobYj3CzpgAHKio69MIrfCg+3aOQ/EKaddHjZjNBbm4u9957L1999RU6nY5Zs2bxzjvvEB4eXpVn6tSp/OlPf2LQoEE899xz9O/fn9mz678v7Y1MIISMMBsxF17ElH0ax4UkPIrOYEbHZuNAjqqiuHNMD2KHhLlFV2hrNcEVhBCc/LmY40nJGLIP01d1nkB1ZdyRRdJTZuiM7N0JlW8QWt9AtD7+6L190Xl6ozV4oNbqkTQaJKnhNfXGmqDO13VCQgKjRo3C398fqNyuddu2bTzxxBMAZGdnYzabGTRoEADTp0/n/fffb5AJamP3qg8IztmNBhmVVGkQHZDn8GGndTAXOw5l4NCbmR3VWZkf0IqQJIk+3TrQp9utyPI4LlwqJfFMBrbsExjKsuhQlkeQMRvP/OqrWjj+++cKspAqd9VBQvDLy+3qv1+hVPKl5yNvN1pznSbIy8sjKCio6jg4OJiUlJTrpgcFBZGbm9sgEbW5Na/vEC7ZLMgqLbLOC7wD0AbdTGCXrswP88ND775hEEFBbWfso1MnX0YM7AKMA0CWBYWlJooKCikvyMNcWoS9ohTZbELYzJU9TLIdSXaAkCv/QOXiqVVUrzVo/YKryqQxZVPnkyTLcrV6nBCi2nFd6fWhtupQ5C3R5IfX3BMXoLzURPsN7L0xrb06VF/8/fzw97v+1lgNJT+/rNHVoTorXiEhIeTn5191s3yCg4Ovm15QUFAtXUGhtVPnlyA6Opq//vWvFBYW4uHhwY4dO3j11Ver0sPCwtDr9SQmJjJ06FC++eYbYmJiGiTieoNaymBX7Sjlcn2uLpv6llO9u0hXrFiBzWZj5syZzJ8/n/nz5/Pkk08SFRXFiRMneP755ykvLycyMpJly5ah07XvrkqF9kOrGCxTUHAlbT5sQkGhqSgmUHB7FBMouD2KCRTcHsUECm6PYgIFt0cxgYLbo5hAwe1RTKDg9rjUBJs2bWLy5MlMnDiRtWvX1khPT09n+vTpxMXFsXjxYuz29rkWZm3UVTYffPABsbGxxMfHEx8fX2ue9kp5eTlTp04lKyurRlqjnhnhIi5duiRiY2NFUVGRMBqNYtq0aeL06dPV8kyZMkUkJSUJIYR49tlnxdq1a12gtOWpT9k8+uij4siRIy5S6DqOHj0qpk6dKiIjI0VmZmaN9MY8My77Elw9Y83T07NqxtoVapuxdnV6e6ausgFIS0tjxYoVTJs2jSVLlmCxWFyktmVZv349L730Uq3h+o19ZlxmgtpmrF09I80ZM9baKnWVjdFopG/fvixcuJCvv/6a0tJSli9f7gqpLc7SpUsZNmxYrWmNfWZcZoKWmLHWVqnr3+7l5cVHH31Ez5490Wg0PPzww+zatcsVUlsVjX1mXGYCZcba9amrbHJyctiwYUPVsRCiQUvctFca+8y4zATR0dHs27ePwsJCTCYTO3bsqDYj7eoZa0CjZqy1VeoqG4PBwJtvvklmZiZCCNauXcuECRNcqLh10Ohnxnnt9oazceNGMWXKFDFx4kSxcuVKIYQQ8+bNEykpKUIIIdLT08WMGTNEXFyceOqpp4TFYnGl3BalrrLZtm1bVfqiRYvcqmyEECI2Nraqd6ipz4wys0zB7VFGjBXcHsUECm6PYgIFt0cxgYLbo5hAwe1RTKDg9igmUHB7/h85+MOcAMqQDgAAAABJRU5ErkJggg==\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplot(2, 2, 1)\n", + "plot_posteriors_flip(p=0.5, N=0);\n", + "plt.subplot(2, 2, 2)\n", + "plot_posteriors_flip(p=0.5, N=5);\n", + "plt.subplot(2, 2, 3)\n", + "plot_posteriors_flip(p=0.5, N=10);\n", + "plt.subplot(2, 2, 4)\n", + "plot_posteriors_flip(p=0.5, N=20);\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even for merely 10 data points, the posteriors are quite close. For 20 data points, the posteriors are indistinguishable to the naked eye! We once again encourage the reader to play around with the code and what happens for different values of $p$ and $N$. If you're doing this in a Jupyter notebook, you can use the great ipywidget interact again!" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:28.546818Z", + "iopub.status.busy": "2022-04-30T07:03:28.546660Z", + "iopub.status.idle": "2022-04-30T07:03:28.668028Z", + "shell.execute_reply": "2022-04-30T07:03:28.667573Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5a0734682291499184ea6722ab5636b8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.6, description='p', max=1.0, step=0.01), IntSlider(value=0, descript…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "interact(plot_posteriors_flip, p=(0, 1, 0.01), N=(0, 100));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The hidden powers of the beta distribution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have seen how the beta distribution can be used as a prior in an Empirical Bayes setting. We have also seen how we can use the beta distribution to assign probabilities on unknown probalities, which makes it clear why it's useful for the prior distribution.\n", + "\n", + "And, in fact, several common priors, including two that we've already seen, are special cases of the beta distribution:\n", + "\n", + "- The uniform prior is $\\beta (1, 1)$;\n", + "- Jeffreys' prior is $\\beta(0.5, 0.5)$;\n", + "- A prior known as Haldane's prior is $\\beta(0, 0)$. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's simulate $\\beta (1, 1)$ and plot a histogram to see if it looks at all uniform:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:28.671089Z", + "iopub.status.busy": "2022-04-30T07:03:28.670974Z", + "iopub.status.idle": "2022-04-30T07:03:28.785925Z", + "shell.execute_reply": "2022-04-30T07:03:28.785581Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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A7tCUe/KLFy/WsWPH9L3vfU/RaFSNjY1auHChOjo6tH79ekUiEZWXl6u6utrLegEACbij6+SffvppPf300zdNKy0t1d69e+9kswAAj/CLVwCwGCEPABYj5AHAYoQ8AFiMkAcAixHyAGAxQh4ALEbIA4DFCHkAsBghDwAWI+QBwGKEPABYjJAHAIsR8gBgMUIeACxGyAOAxQh5ALAYIQ8AFiPkAcBihDwAWIyQBwCLEfIAYDFCHgAsRsgDgMUIeQCwGCEPABYj5AHAYoQ8AFiMkAcAixHyAGAxQh4ALEbIA4DFCHkAsBghDwAWS0rIv/3223rkkUdUWVmp3bt3J2MXAIA4ZHu9wUAgoO3bt+sPf/iDZs2apZUrV+ob3/iGHnzwQa93BQBw4XnI9/f36+GHH9YXvvAFSVJVVZV6e3u1bt26uNbPyvJNed/+++7xZLqX20r36TOxppk2fSbWxHMxc6dPNm8q+ee2js8YYxLe6iR+/etf61//+pc2bNggSfr973+v48eP64UXXvByNwCAOHg+Jh+NRuXz/fvIYoy56TEAYPp4HvJ5eXkKBoOxx8FgUH6/3+vdAADi4HnIf/Ob39Rf//pXXblyRdevX9eBAwdUVlbm9W4AAHHw/MTr/fffrw0bNqipqUljY2N69NFH9fWvf93r3QAA4uD5iVcAwMzBL14BwGKEPABYjJAHAIsR8gBgMUIeACyWdiHvdofLwcFB1dXVqaqqSq2trRofH09Bld5ya/Of/vQnLV++XMuWLdMTTzyhq1evpqBKb8V7J9NDhw7pO9/5zjRWljxubT516pRWr16tZcuW6Sc/+UlGvM7vvfee6uvrtWzZMjU3N+vatWspqNJboVBINTU1Onv27C3zkpJfJo1cvHjRLF682Hz88cdmdHTU1NbWmpMnT960zNKlS83f//53Y4wxW7ZsMbt3705Bpd5xa/PIyIj51re+ZS5evGiMMeaXv/yleeGFF1JVrifieZ2NMSYYDJrq6mqzePHiFFTpLbc2R6NRU1lZaQ4fPmyMMebnP/+5efnll1NVrifieZ0bGhrMoUOHjDHGbNu2zfziF79IRameeffdd01NTY0pLi42H3300S3zk5FfadWT//wdLmfPnh27w+Vnzp07p3A4rJKSEklSXV3dTfPTkVubx8bG1N7ervvvv1+SNH/+fF24cCFV5XrCrc2faWtri/vupjOdW5vfe+89zZ49O/br8ccff1yrVq1KVbmeiOd1jkajGh0dlSRdv35dd999dypK9UxXV5fa29tve6uXZOVXWoX8pUuX5DhO7LHf71cgEJhwvuM4N81PR25tvu+++1RRUSFJCofD2rVrl7773e9Oe51ecmuzJP3mN7/RV7/6VS1YsGC6y0sKtzZ/+OGH+uIXv6iWlhatWLFC7e3tmj17dipK9Uw8r/PmzZvV1tamb3/72+rv79fKlSunu0xPbd26VYsWLbrtvGTlV1qFvNsdLm28A2a8bRoZGdHatWtVVFSkFStWTGeJnnNr8wcffKADBw7oiSeeSEV5SeHW5vHxcR09elQNDQ3as2ePCgoK1NHRkYpSPePW5nA4rNbWVr322ms6cuSIGhsb9dOf/jQVpU6LZOVXWoW82x0u/3P+8PBw2t8BM567el66dEmNjY2aP3++tm7dOt0les6tzb29vQoGg6qvr9fatWtj7U9nbm12HEeFhYX62te+JkmqqanR8ePHp71OL7m1+YMPPlBOTk7s3lc/+MEPdPTo0Wmvc7okK7/SKuTd7nCZn5+vnJwcDQwMSJJ6enrS/g6Ybm2+ceOGHn/8cS1ZskStra1p/81Fcm/zU089pb6+PvX09GjXrl3y+/367W9/m8KK75xbmxcuXKgrV67oH//4hyTp4MGDKi4uTlW5nnBrc2FhoS5evKhTp05Jkv785z/HDnI2Slp+3fGp22m2d+9es3TpUlNZWWl27dpljDFmzZo15vjx48YYYwYHB019fb2pqqoyzzzzjIlEIqks1xOTtfnAgQNm/vz5ZtmyZbG/lpaWFFd859xe58989NFHVlxdY4x7m999911TX19vHnnkEfPYY4+Z4eHhVJbrCbc2Hzp0yNTW1pqamhrzox/9yHz44YepLNczixcvjl1dk+z84i6UAGCxtBquAQAkhpAHAIsR8gBgMUIeACxGyAOAxQh5ALAYIQ8AFvt/e4aAz1iNJ7wAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Uniform\n", + "a, b = 1, 1\n", + "rng = np.random.default_rng(42)\n", + "samples_1 = rng.beta(a, b, 10**4)\n", + "plt.hist(samples_1, bins=50);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also plot the ECDF of this data with that of data simulated from a uniform distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:28.788040Z", + "iopub.status.busy": "2022-04-30T07:03:28.787918Z", + "iopub.status.idle": "2022-04-30T07:03:28.921138Z", + "shell.execute_reply": "2022-04-30T07:03:28.920678Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "samples_2 = rng.random(10**4)\n", + "\n", + "x_1, y_1 = ecdf(samples_1)\n", + "x_2, y_2 = ecdf(samples_2)\n", + "\n", + "plt.plot(x_1, y_1)\n", + "plt.plot(x_2, y_2);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These ECDFs are extremely close, when inspected by eye, and they are both linear with a slope of 1, which is what we would expect of the uniform distribution. Let's now simulate $\\beta(0.5, 0.5)$ and plot the resulting histogram to see if it looks anything like the Jeffreys distribution, which indeed it does:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:28.923309Z", + "iopub.status.busy": "2022-04-30T07:03:28.923177Z", + "iopub.status.idle": "2022-04-30T07:03:29.033061Z", + "shell.execute_reply": "2022-04-30T07:03:29.032675Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Jeffrey's\n", + "a, b = 0.5, 0.5\n", + "samples = rng.beta(a, b, 10**3)\n", + "plt.hist(samples, bins=50);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chapter, we delved into the different types of prior distributions, to give a sense of what the possibilities look like. Using simulation, we showed an example in which generating more data leads to different common posterior distributions converging. We also showed how several common posteriors are special cases of the beta distribution. As stated earlier, we do not necessarily expect anybody to have a good handle on the actual practice of choosing and using priors after merely reading this chapter. For that, you'll need to see many priors of a variety of forms used in different contexts, and there's plenty of that to come in the chapters that follow. So let us begin building our inferential machines!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "035b4f30b8024cbb91b6c75eb4dbdc9c": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + 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\n", 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To this end, let's recap the general workflow. \n", + "\n", + "In the basics of Bayesian model building, the steps are\n", + "1. To completely specify the model in terms of _probability distributions_. This includes specifying \n", + " - what the form of the sampling distribution of the data is _and_ \n", + " - what form describes our _uncertainty_ in the unknown parameters.\n", + "2. Calculate the _posterior distribution_.\n", + "\n", + "In Chapter 5 and 6's biased coin flipping example, the form of the sampling distribution of the data was Binomial (described by the likelihood) and the uncertainty around the unknown parameter $p$ captured by the prior (we used both the uniform and the Jeffreys priors)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now it is time to do the same using the **probabilistic programming language** PyMC. There's _loads of cool stuff_ about PyMC and this paradigm, two of which are\n", + "- _probabililty distributions_ are first class citizens, in that we can assign them to variables and use them intuitively to mirror how we think about priors, likelihoods & posteriors.\n", + "- PyMC calculates the posterior for us!\n", + "\n", + "Under the hood, PyMC will compute the posterior using a sampling based approach called Markov Chain Monte Carlo (MCMC) or Variational Inference. Check the [PyMC3 docs](https://docs.pymc.io/) for more on these. \n", + "\n", + "But now, it's time to bust out some MCMC and get sampling!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter estimation I: click-through rate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A common experiment in tech data science is to test a product change and see how it affects a metric that you're interested in. Say that I don't think enough people are clicking a button on my website & I hypothesize that it's because the button is a similar color to the background of the page. Then I can set up two pages and send some people to each: the first the original page, the second a page that is identical, except that it has a button that is of higher contrast and see if more people click through. This is commonly referred to as an A/B test and the metric of interest is click-through rate (CTR), what proportion of people click through. Before even looking at two rates, let's use PyMC to estimate one.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First lets generate click-through data, given a CTR $p_a=0.15$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| output: false\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import pymc as pm\n", + "import arviz as az\n", + "from IPython.display import display_png\n", + "from generative_thinking.utils import ecdf \n", + "\n", + "%matplotlib inline\n", + "sns.set()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:36.261316Z", + "iopub.status.busy": "2022-04-30T07:03:36.261153Z", + "iopub.status.idle": "2022-04-30T07:03:36.263972Z", + "shell.execute_reply": "2022-04-30T07:03:36.263653Z" + } + }, + "outputs": [], + "source": [ + "# click-through rates\n", + "np.random.seed(42)\n", + "p_a = 0.15\n", + "N = 150\n", + "n_successes_a = np.sum(np.random.binomial(N, p_a))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now it's time to build our probability model. Noticing that our model of having a constant CTR resulting in click or not is a biased coin flip,\n", + "- the sampling distribution is binomial and we need to encode this in the likelihood;\n", + "- there is a single parameter $p$ that we need to describe the uncertainty around, using a prior and we'll use a uniform prior for this.\n", + "\n", + "These are the ingredients for the model so let's now build it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:36.265796Z", + "iopub.status.busy": "2022-04-30T07:03:36.265697Z", + "iopub.status.idle": "2022-04-30T07:03:36.278819Z", + "shell.execute_reply": "2022-04-30T07:03:36.278522Z" + } + }, + "outputs": [], + "source": [ + "# Build model of p_a\n", + "with pm.Model() as model:\n", + " # Prior on p\n", + " prob = pm.Uniform('p')\n", + " # Binomial Likelihood\n", + " y = pm.Binomial('y', n=N, p=prob, observed=n_successes_a)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we've done the hard work of building our model, \n", + "let's visualize the model graph using `graphviz`.\n", + "In our case, the graph will look simple\n", + "because our model doesn't have too many moving parts.\n", + "That said, being able to see the model graph is awesome\n", + "and will introduce you with a tool\n", + "that will help visualizing more complex models in the future." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:36.280658Z", + "iopub.status.busy": "2022-04-30T07:03:36.280555Z", + "iopub.status.idle": "2022-04-30T07:03:36.475266Z", + "shell.execute_reply": "2022-04-30T07:03:36.474820Z" + } + }, + "outputs": [], + "source": [ + "gv = pm.model_graph.model_to_graphviz(model)\n", + "display_png(gv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's now time to sample from the posterior using PyMC. You'll also then plot the posterior:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:03:36.477460Z", + "iopub.status.busy": "2022-04-30T07:03:36.477344Z", + "iopub.status.idle": "2022-04-30T07:04:07.910093Z", + "shell.execute_reply": "2022-04-30T07:04:07.909733Z" + } + }, + "outputs": [], + "source": [ + "#| output: false\n", + "with model:\n", + " samples = pm.sample(2000, return_inferencedata=True)\n", + " # We're also sampling the prior/posterior predictives,\n", + " # which we'll introduce and explain below\n", + " idata = pm.sample_prior_predictive(model=model)\n", + " idata.extend(pm.sample(progressbar=False))\n", + " idata.extend(pm.sample_posterior_predictive(idata, progressbar=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:07.912450Z", + "iopub.status.busy": "2022-04-30T07:04:07.912327Z", + "iopub.status.idle": "2022-04-30T07:04:08.024448Z", + "shell.execute_reply": "2022-04-30T07:04:08.024068Z" + } + }, + "outputs": [], + "source": [ + "#| fig-cap: Posterior samples of $p$, the click-through rate, having seen the data.\n", + "with model:\n", + " az.plot_posterior(samples, kind='hist');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Predictive checks and traceplots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now introduce 3 diagnostic tools, which will come in handy when building models:\n", + "\n", + "- prior predictive check,\n", + "- posterior predictive check, and\n", + "- traceplots." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Prior predictive checks** essentially generate samples of the data\n", + "based on your specified priors without looking at the actual data.\n", + "They help you check whether your priors are sensible distributions,\n", + "or if they have the potential to generate wildly unreasonable data.\n", + "Let's plot the prior predictive check ECDF for our model above:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:08.026666Z", + "iopub.status.busy": "2022-04-30T07:04:08.026552Z", + "iopub.status.idle": "2022-04-30T07:04:08.113013Z", + "shell.execute_reply": "2022-04-30T07:04:08.112677Z" + } + }, + "outputs": [], + "source": [ + "#| fig-cap: Prior predictive samples for observed data.\n", + "# Generate x & y data for ECDF\n", + "x_ecdf, y_ecdf = ecdf(idata.prior_predictive[\"y\"].data.flatten())\n", + "# Plot the ECDF\n", + "plt.plot(x_ecdf, y_ecdf, marker='.', linestyle='none');\n", + "plt.xlabel(\"Number of Clicks\")\n", + "plt.ylabel(\"Cumulative Fraction\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we have to ask the question: Does this prior predictive check look sensible?\n", + "This question is both qualitative and quantitative.\n", + "Qualitatively, it looks extremely similar to a uniform distribution's CDF.\n", + "Quantitatively, its lower bound $0$ and upper bound $150$ are what we expect.\n", + "We know that we are sampling the number of click throughs\n", + "out of a total number of 150 website landings\n", + "so this prior distribution looks pretty good!\n", + "\n", + "Counterfactually, we would be concerned if there negative values _or_ values over $150$,\n", + "as they would violate what we assume about our data-generating processes.\n", + "In the next example, we'll see how red flags in our prior predictive check\n", + "can lead us to alter our priors." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The *posterior* predictive check samples our posterior distribution of data so we would expect it to look something like our actual data. Let's check it out, in this example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:08.114981Z", + "iopub.status.busy": "2022-04-30T07:04:08.114862Z", + "iopub.status.idle": "2022-04-30T07:04:08.188994Z", + "shell.execute_reply": "2022-04-30T07:04:08.188599Z" + } + }, + "outputs": [], + "source": [ + "# Generate x & y data for ECDF\n", + "x_ecdf, y_ecdf = ecdf(idata.posterior_predictive[\"y\"].data.flatten())\n", + "# Plot the ECDF\n", + "plt.plot(x_ecdf, y_ecdf, marker='.', linestyle='none', color=\"black\")\n", + "plt.axvline(x=n_successes_a, color=\"black\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that this looks somewhat Gaussian, centered around 21. Now our data is a single number, the number of clicks, so we would expect it to be around $21\\pm5$. Let's see what it is:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:08.191459Z", + "iopub.status.busy": "2022-04-30T07:04:08.191067Z", + "iopub.status.idle": "2022-04-30T07:04:08.194182Z", + "shell.execute_reply": "2022-04-30T07:04:08.193796Z" + } + }, + "outputs": [], + "source": [ + "n_successes_a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This looks good." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's now plot the *traceplot*, which plots our parameters as a function of iteration number and tells us about whether our sampling has converged. Essentially we want to make sure that the sampling procedure doesn't \"shoot off\" in any weird way, such as in the following:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:08.195961Z", + "iopub.status.busy": "2022-04-30T07:04:08.195858Z", + "iopub.status.idle": "2022-04-30T07:04:08.211321Z", + "shell.execute_reply": "2022-04-30T07:04:08.211016Z" + } + }, + "outputs": [], + "source": [ + "#| echo: false\n", + "from IPython.display import Image\n", + "Image(\"../../img/badchains.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What we really want to see is a traceplot that doesn't exhibit drift but diffusion,\n", + "that is, noise around a certain value.\n", + "There are ways to frame this in terms of autocorrelation but,\n", + "for the time being, the most important way to think about it is\n", + "you want to see a traceplot that looks something like a \"hairy caterpillar\",\n", + "which we do on the right in this case\n", + "(on the left we have the samples of our distribution):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:08.224767Z", + "iopub.status.busy": "2022-04-30T07:04:08.224622Z", + "iopub.status.idle": "2022-04-30T07:04:08.350494Z", + "shell.execute_reply": "2022-04-30T07:04:08.350151Z" + } + }, + "outputs": [], + "source": [ + "az.plot_trace(samples);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter estimation II -- the mean of a population" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now calculate the posterior mean beak depth of Galapagos finches in a given species. First let's load the data and subset it with respect to species:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:08.352903Z", + "iopub.status.busy": "2022-04-30T07:04:08.352803Z", + "iopub.status.idle": "2022-04-30T07:04:08.359554Z", + "shell.execute_reply": "2022-04-30T07:04:08.359239Z" + } + }, + "outputs": [], + "source": [ + "# Import and view head of data\n", + "df_12 = pd.read_csv('../../datasets/finch_beaks_2012.csv')\n", + "df_fortis = df_12.loc[df_12['species'] == 'fortis']\n", + "df_scandens = df_12.loc[df_12['species'] == 'scandens']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To specify the full probability model, we need\n", + "- a likelihood function for the data &\n", + "- priors for all unknowns.\n", + "\n", + "What is the likelihood here? Let's plot the measurements below and see that they look approximately normal so we'll use a normal likelihood $y_i\\sim \\mathcal{N}(\\mu, \\sigma^2)$. The unknowns here are the mean $\\mu$ and standard deviation $\\sigma$ and we'll use weakly informative priors on both\n", + "\n", + "- a normal prior for $\\mu$ with mean $10$ and standard deviation $5$;\n", + "- a lognormal prior for $\\sigma$ with $\\mu = 0$ and $\\sigma = 10$.\n", + "\n", + "There are biological reasons for these priors \n", + "(**NOTE: not really now, with the lognormal prior? \n", + "Need to explain what the biological reasons are otherwise**)\n", + "also but we can also test that the posteriors are relatively robust to the choice of prior here due to the amount of data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:08.361612Z", + "iopub.status.busy": "2022-04-30T07:04:08.361479Z", + "iopub.status.idle": "2022-04-30T07:04:08.486076Z", + "shell.execute_reply": "2022-04-30T07:04:08.485746Z" + } + }, + "outputs": [], + "source": [ + "#| warnings:false\n", + "sns.distplot(df_fortis['blength']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now build our model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:08.488187Z", + "iopub.status.busy": "2022-04-30T07:04:08.488089Z", + "iopub.status.idle": "2022-04-30T07:04:08.505923Z", + "shell.execute_reply": "2022-04-30T07:04:08.505630Z" + } + }, + "outputs": [], + "source": [ + "with pm.Model() as model:\n", + " # Prior for mean & standard deviation\n", + " mu_1 = pm.Normal('mu_1', mu=10, sigma=5)\n", + " sigma_1 = pm.Lognormal('sigma_1', 0, 10)\n", + " # Gaussian Likelihood\n", + " y_1 = pm.Normal('y_1', mu=mu_1, sigma=sigma_1,\n", + " observed=df_fortis['blength'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once again, we can also visualize our model graph:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:08.507902Z", + "iopub.status.busy": "2022-04-30T07:04:08.507787Z", + "iopub.status.idle": "2022-04-30T07:04:08.724910Z", + "shell.execute_reply": "2022-04-30T07:04:08.724321Z" + } + }, + "outputs": [], + "source": [ + "gv = pm.model_graph.model_to_graphviz(model)\n", + "display_png(gv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now press our inference button and sample from the posterior:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:08.728025Z", + "iopub.status.busy": "2022-04-30T07:04:08.727782Z", + "iopub.status.idle": "2022-04-30T07:04:37.700011Z", + "shell.execute_reply": "2022-04-30T07:04:37.699677Z" + } + }, + "outputs": [], + "source": [ + "#| output: false\n", + "with model:\n", + " samples = pm.sample(2000, return_inferencedata=True)\n", + " idata = pm.sample_prior_predictive(model=model)\n", + " idata.extend(pm.sample(progressbar=False))\n", + " idata.extend(pm.sample_posterior_predictive(idata, progressbar=False))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now plot our *prior predictive check*, data, and *posterior predictive check*:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:37.702165Z", + "iopub.status.busy": "2022-04-30T07:04:37.702055Z", + "iopub.status.idle": "2022-04-30T07:04:37.796182Z", + "shell.execute_reply": "2022-04-30T07:04:37.795570Z" + } + }, + "outputs": [], + "source": [ + "x_ecdf, y_ecdf = ecdf(idata.prior_predictive[\"y_1\"].data.flatten())\n", + "# Plot the ECDF\n", + "plt.plot(x_ecdf, y_ecdf, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:37.798410Z", + "iopub.status.busy": "2022-04-30T07:04:37.798279Z", + "iopub.status.idle": "2022-04-30T07:04:37.884162Z", + "shell.execute_reply": "2022-04-30T07:04:37.883706Z" + } + }, + "outputs": [], + "source": [ + "x_ecdf, y_ecdf = ecdf(df_fortis['blength'].values)\n", + "# Plot the ECDF\n", + "plt.plot(x_ecdf, y_ecdf, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:37.886316Z", + "iopub.status.busy": "2022-04-30T07:04:37.886146Z", + "iopub.status.idle": "2022-04-30T07:04:38.147748Z", + "shell.execute_reply": "2022-04-30T07:04:38.147434Z" + } + }, + "outputs": [], + "source": [ + "x_ecdf, y_ecdf = ecdf(idata.posterior_predictive[\"y_1\"].data.flatten())\n", + "# Plot the ECDF\n", + "plt.plot(x_ecdf, y_ecdf, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although the *posterior predictive check* passes the eyeball test (when comapring with the data), the *prior predictive check* does not! Looking at the x-axis on it, we see that we end up with values orders of magnitude larger than what a beak length could possibly be!\n", + "\n", + "This means that our priors aren't quite right yet.\n", + "Looking at them, we can see that having a Lognormal prior for our variance\n", + "means that we could end up with some pretty large values\n", + "so let's try a prior that drops off more quickly (we say it has a _shorter tail_).\n", + "We'll try an Exponential prior here as we know that these have shorter tails.\n", + "We don't expect the reader to be aware of this\n", + "but we hope to help build your intuition around this here\n", + "and in the following case stories!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:38.149719Z", + "iopub.status.busy": "2022-04-30T07:04:38.149597Z", + "iopub.status.idle": "2022-04-30T07:04:38.162292Z", + "shell.execute_reply": "2022-04-30T07:04:38.162002Z" + } + }, + "outputs": [], + "source": [ + "with pm.Model() as model:\n", + " # Prior for mean & standard deviation\n", + " mu_1 = pm.Normal('mu_1', mu=10, sigma=5)\n", + " sigma_1 = pm.Exponential('sigma_1', lam=0.5)\n", + " # Gaussian Likelihood\n", + " y_1 = pm.Normal('y_1', mu=mu_1, sigma=sigma_1, \n", + " observed=df_fortis['blength'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:04:38.164071Z", + "iopub.status.busy": "2022-04-30T07:04:38.163989Z", + "iopub.status.idle": "2022-04-30T07:05:06.470933Z", + "shell.execute_reply": "2022-04-30T07:05:06.470292Z" + } + }, + "outputs": [], + "source": [ + "#| output: false\n", + "with model:\n", + " samples = pm.sample(2000, return_inferencedata=True)\n", + " idata = pm.sample_prior_predictive(model=model)\n", + " idata.extend(pm.sample(progressbar=False))\n", + " idata.extend(pm.sample_posterior_predictive(idata, \n", + " progressbar=False))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now plot our *prior* and *posterior* predictive checks:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:06.473133Z", + "iopub.status.busy": "2022-04-30T07:05:06.473001Z", + "iopub.status.idle": "2022-04-30T07:05:06.575678Z", + "shell.execute_reply": "2022-04-30T07:05:06.575268Z" + } + }, + "outputs": [], + "source": [ + "x_ecdf, y_ecdf = ecdf(idata.prior_predictive[\"y_1\"].data.flatten())\n", + "# Plot the ECDF\n", + "plt.plot(x_ecdf, y_ecdf, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:06.577750Z", + "iopub.status.busy": "2022-04-30T07:05:06.577650Z", + "iopub.status.idle": "2022-04-30T07:05:06.830394Z", + "shell.execute_reply": "2022-04-30T07:05:06.829984Z" + } + }, + "outputs": [], + "source": [ + "x_ecdf, y_ecdf = ecdf(idata.posterior_predictive[\"y_1\"].data.flatten())\n", + "# Plot the ECDF\n", + "plt.plot(x_ecdf, y_ecdf, marker='.', linestyle='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The *prior* predictive check is now looking a lot better!\n", + "There is still the possibility of negative values, so that may still be an issue.\n", + "Additionally, if it were still generating data that are too large,\n", + "we could increase the exponent `lam`, among other things." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's plot the posteriors of our two parameters, mean $\\mu$ and standard deviation $\\sigma$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:06.832600Z", + "iopub.status.busy": "2022-04-30T07:05:06.832464Z", + "iopub.status.idle": "2022-04-30T07:05:06.942120Z", + "shell.execute_reply": "2022-04-30T07:05:06.941667Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " az.plot_posterior(samples);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We once again plot the traceplot, which plots our parameters as a function of iteration number, showing that our model has converged:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:06.944271Z", + "iopub.status.busy": "2022-04-30T07:05:06.944167Z", + "iopub.status.idle": "2022-04-30T07:05:07.211815Z", + "shell.execute_reply": "2022-04-30T07:05:07.211264Z" + } + }, + "outputs": [], + "source": [ + "az.plot_trace(samples, figsize=(10, 7));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bayesian hypothesis testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After parameter estimation, a common scientific question involves hypothesis testing, so let's now see how Bayesian inference and probabilistic programming can be used for hypothesis testing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bayesian hypothesis testing I: A/B tests on click through rates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assume we have a website and want to redesign the layout (*A*) and test whether the new layout (*B*) results in a higher click through rate. When people come to our website we randomly show them layout *A* or *B* and see how many people click through for each. First let's generate the data we need:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:07.215919Z", + "iopub.status.busy": "2022-04-30T07:05:07.215769Z", + "iopub.status.idle": "2022-04-30T07:05:07.219078Z", + "shell.execute_reply": "2022-04-30T07:05:07.218599Z" + } + }, + "outputs": [], + "source": [ + "# click-through rates\n", + "p_a = 0.15\n", + "p_b = 0.20\n", + "N = 1000\n", + "n_successes_a = np.sum(np.random.uniform(size=N) <= p_a)\n", + "n_successes_b = np.sum(np.random.uniform(size=N) <= p_b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once again, we need to specify our models for $p_a$ and $p_b$. Each will be the same as the CTR example above\n", + "- Binomial likelihoods\n", + "- uniform priors on $p_a$ and $_p$.\n", + "\n", + "We also want to calculate the posterior of the difference $p_a-p_b$ and we do so using `pm.Deterministic()`, which specifies a deterministic random variable, i.e., one that is completely determined by the values it references, in the case $p_a$ & $p_b$.\n", + "\n", + "We'll now build the model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:07.221532Z", + "iopub.status.busy": "2022-04-30T07:05:07.221388Z", + "iopub.status.idle": "2022-04-30T07:05:07.232365Z", + "shell.execute_reply": "2022-04-30T07:05:07.231899Z" + } + }, + "outputs": [], + "source": [ + "with pm.Model() as model:\n", + " # Prior on p\n", + " prob_a = pm.Uniform('p_a')\n", + " prob_b = pm.Uniform('p_b')\n", + " # Binomial Likelihood\n", + " y_a = pm.Binomial('y_a', n=N, p=prob_a, observed=n_successes_a)\n", + " y_b = pm.Binomial('y_b', n=N, p=prob_b, observed=n_successes_b)\n", + " diff_clicks = pm.Deterministic('diff_clicks', prob_a-prob_b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now plot our model graph:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:07.234650Z", + "iopub.status.busy": "2022-04-30T07:05:07.234508Z", + "iopub.status.idle": "2022-04-30T07:05:07.445397Z", + "shell.execute_reply": "2022-04-30T07:05:07.444823Z" + } + }, + "outputs": [], + "source": [ + "gv = pm.model_graph.model_to_graphviz(model)\n", + "display_png(gv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sample from the posterior and plot them:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:07.448531Z", + "iopub.status.busy": "2022-04-30T07:05:07.448328Z", + "iopub.status.idle": "2022-04-30T07:05:23.321707Z", + "shell.execute_reply": "2022-04-30T07:05:23.321369Z" + } + }, + "outputs": [], + "source": [ + "#| output: false\n", + "with model:\n", + " samples = pm.sample(2000, return_inferencedata=True)\n", + " az.plot_posterior(samples, kind='hist');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We once again plot the traceplot, which plots our parameters as a function of iteration number, showing that our model has converged:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:23.324203Z", + "iopub.status.busy": "2022-04-30T07:05:23.324032Z", + "iopub.status.idle": "2022-04-30T07:05:23.692700Z", + "shell.execute_reply": "2022-04-30T07:05:23.692325Z" + } + }, + "outputs": [], + "source": [ + "az.plot_trace(samples, figsize=(10, 7));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that, for the sake of brevity, we did not perform our predictive checks, but this is something that the excited reader can do, in this case, and for those below!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bayesian hypothesis testing II -- beak lengths difference between species" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following, we seek to determine whether the mean beak length of the Galapagos finches differs between species. For the mean of each species, we use the same model as in previous section:\n", + "\n", + "- Gaussian likelihood;\n", + "- Normal prior for the means;\n", + "- Uniform prior for the variances.\n", + "\n", + "We also calculate the difference between the means and, the _effect size_, which is the difference between the means divided by the pooled standard deviations = $\\sqrt{(\\sigma_1^2+\\sigma_2^2)/2}$. \n", + "\n", + "We then sample from the posteriors and plot them!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:23.695827Z", + "iopub.status.busy": "2022-04-30T07:05:23.695685Z", + "iopub.status.idle": "2022-04-30T07:05:23.724423Z", + "shell.execute_reply": "2022-04-30T07:05:23.723798Z" + } + }, + "outputs": [], + "source": [ + "with pm.Model() as model:\n", + " # Priors for means and variances\n", + " mu_1 = pm.Normal('mu_1', mu=10, sigma=5)\n", + " sigma_1 = pm.Uniform('sigma_1', 0, 10)\n", + " mu_2 = pm.Normal('mu_2', mu=10, sigma=5)\n", + " sigma_2 = pm.Uniform('sigma_2', 0, 10)\n", + " # Gaussian Likelihoods\n", + " y_1 = pm.Normal('y_1', mu=mu_1, sigma=sigma_1, observed=df_fortis['blength'])\n", + " y_2 = pm.Normal('y_2', mu=mu_2, sigma=sigma_2, observed=df_scandens['blength'])\n", + " # Calculate the effect size and its uncertainty.\n", + " diff_means = pm.Deterministic('diff_means', mu_1 - mu_2)\n", + " pooled_sd = pm.Deterministic('pooled_sd', \n", + " np.sqrt(np.power(sigma_1, 2) + \n", + " np.power(sigma_2, 2)) / 2)\n", + " effect_size = pm.Deterministic('effect_size', \n", + " diff_means / pooled_sd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now plot our model graph:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:23.727140Z", + "iopub.status.busy": "2022-04-30T07:05:23.726984Z", + "iopub.status.idle": "2022-04-30T07:05:23.949966Z", + "shell.execute_reply": "2022-04-30T07:05:23.949552Z" + } + }, + "outputs": [], + "source": [ + "gv = pm.model_graph.model_to_graphviz(model)\n", + "display_png(gv)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:23.952269Z", + "iopub.status.busy": "2022-04-30T07:05:23.952106Z", + "iopub.status.idle": "2022-04-30T07:05:40.275112Z", + "shell.execute_reply": "2022-04-30T07:05:40.274744Z" + } + }, + "outputs": [], + "source": [ + "#| output: false\n", + "with model:\n", + " samples = pm.sample(2000, return_inferencedata=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:40.277255Z", + "iopub.status.busy": "2022-04-30T07:05:40.277126Z", + "iopub.status.idle": "2022-04-30T07:05:40.507192Z", + "shell.execute_reply": "2022-04-30T07:05:40.506776Z" + } + }, + "outputs": [], + "source": [ + "az.plot_posterior(samples, \n", + " var_names=['mu_1', 'mu_2', 'diff_means', 'effect_size'], kind='hist');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:40.509062Z", + "iopub.status.busy": "2022-04-30T07:05:40.508972Z", + "iopub.status.idle": "2022-04-30T07:05:41.253222Z", + "shell.execute_reply": "2022-04-30T07:05:41.252900Z" + } + }, + "outputs": [], + "source": [ + "az.plot_trace(samples, figsize=(10, 7));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bayesian regression\n", + "\n", + "In the above, we have seen how to use the Bayesian workflow and probabilistic programming for both parameter estimation and comparison between groups. To now round out our first few Bayesian modeling tools, let's see how to use the Bayesian workflow and probabilistic programming for linear regression!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To this end, let's first generate some bivariate data, consisting of a linear relationship and a noise/error term:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:41.256159Z", + "iopub.status.busy": "2022-04-30T07:05:41.256050Z", + "iopub.status.idle": "2022-04-30T07:05:41.437471Z", + "shell.execute_reply": "2022-04-30T07:05:41.437102Z" + } + }, + "outputs": [], + "source": [ + "np.random.seed(42)\n", + "(m, b) = (25, 0.5)\n", + "x = 100 * np.random.random(20)\n", + "y = m * x + b\n", + "\n", + "# add scatter to points\n", + "x = np.random.normal(x, 10)\n", + "y = np.random.normal(y, 10)\n", + "\n", + "plt.plot(x, y, 'ok');\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now recall that our Bayesian model building steps are\n", + "1. To completely specify the model in terms of _probability distributions_. This includes specifying \n", + " - what the form of the sampling distribution of the data is _and_ \n", + " - what form describes our _uncertainty_ in the unknown parameters.\n", + "2. Calculate the _posterior distribution_.\n", + "\n", + "The sampling distribution of the data is given by $y = mx + b + \\sigma$, where $m$ is the gradient, $b$ the $y$-intercept, and $\\sigma$ the error/noise term. These are the unknown parameters, which we need to describe our _uncertatinty_ around using priors and we choose:\n", + "\n", + "- Gaussian priors for $m$ and $b$,\n", + "- A Half-Cauchy prior for $\\sigma$ (this is a distribution that has desirable properties for us, but isn't worth getting into the details of)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:41.439730Z", + "iopub.status.busy": "2022-04-30T07:05:41.439535Z", + "iopub.status.idle": "2022-04-30T07:05:41.458095Z", + "shell.execute_reply": "2022-04-30T07:05:41.457740Z" + } + }, + "outputs": [], + "source": [ + "with pm.Model() as model:\n", + " # Define priors\n", + " sigma = pm.HalfCauchy(\"sigma\", beta=10)\n", + " b = pm.Normal(\"b\", 0, sigma=20)\n", + " m = pm.Normal(\"m\", 0, sigma=20)\n", + "\n", + " # Define likelihood\n", + " likelihood = pm.Normal(\"y\", mu=b + m * x, sigma=sigma, observed=y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now plot our model graph:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:41.460299Z", + "iopub.status.busy": "2022-04-30T07:05:41.460008Z", + "iopub.status.idle": "2022-04-30T07:05:41.653671Z", + "shell.execute_reply": "2022-04-30T07:05:41.653164Z" + } + }, + "outputs": [], + "source": [ + "gv = pm.model_graph.model_to_graphviz(model)\n", + "display_png(gv)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:41.655962Z", + "iopub.status.busy": "2022-04-30T07:05:41.655850Z", + "iopub.status.idle": "2022-04-30T07:05:56.736296Z", + "shell.execute_reply": "2022-04-30T07:05:56.735967Z" + } + }, + "outputs": [], + "source": [ + "#| output: false\n", + "with model:\n", + " samples = pm.sample(2000, return_inferencedata=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll see in following chapter how to plot and interpret predictive checks for regression tasks, such as this." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now plot the traceplot, which plots our parameters as a function of iteration number and here shows that our model has converged in a desirable way:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2022-04-30T07:05:56.738504Z", + "iopub.status.busy": "2022-04-30T07:05:56.738393Z", + "iopub.status.idle": "2022-04-30T07:05:57.106856Z", + "shell.execute_reply": "2022-04-30T07:05:57.106514Z" + } + }, + "outputs": [], + "source": [ + "az.plot_trace(samples, figsize=(10, 7));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chapter, we've introduced the fundamentals of the principled Bayesian workflow and demonstrated its utility in answering the common scientific questions of parameter estimation and hypothesis testing. We've done so for two different types of data, the first an example of data from A/B testing, the second a dataset from basic research of Galapagos finch beak measurements. We also introduced Bayesian linear regression." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}