require(TeachingDemos)
## Loading required package: TeachingDemos
require(ggplot2)
## Loading required package: ggplot2
require(scales)
## Loading required package: scales
require(plyr)
## Loading required package: plyr
nsize10<-sum(endfid$Tray_1=="2N"&10 %<=% endfid$Length.mm %<% 15)
nsize15<-sum(endfid$Tray_1=="2N"&15 %<=% endfid$Length.mm %<% 20)
nsize20<-sum(endfid$Tray_1=="2N"&20 %<=% endfid$Length.mm %<% 25)
nsize25<-sum(endfid$Tray_1=="2N"&25 %<=% endfid$Length.mm %<% 30)
nsize30<-sum(endfid$Tray_1=="2N"&30 %<=% endfid$Length.mm %<% 35)
nsize35<-sum(endfid$Tray_1=="2N"&35 %<=% endfid$Length.mm %<% 40)
nsize40<-sum(endfid$Tray_1=="2N"&40 %<=% endfid$Length.mm %<% 45)
nsize45<-sum(endfid$Tray_1=="2N"&45 %<=% endfid$Length.mm %<% 50)
nsize50<-sum(endfid$Tray_1=="2N"&50 %<=% endfid$Length.mm %<% 55)
nsize55<-sum(endfid$Tray_1=="2N"&55 %<=% endfid$Length.mm %<% 60)
sizeclassfidn<-matrix(c(nsize10,nsize15,nsize20,nsize25,nsize30,nsize35,nsize40,nsize45,nsize50,nsize55),ncol=10,byrow=F)
colnames(sizeclassfidn)<-c("S10-15","S15-20","S20-25","S25-30","S30-35","S35-40","S40-45","S45-50","S50-55","S55-60")
head(sizeclassfidn)
## S10-15 S15-20 S20-25 S25-30 S30-35 S35-40 S40-45 S45-50 S50-55 S55-60
## [1,] 8 34 102 129 83 22 1 0 0 0
hsize10<-sum(endfid$Tray_1=="2H"&10 %<=% endfid$Length.mm %<% 15)
hsize15<-sum(endfid$Tray_1=="2H"&15 %<=% endfid$Length.mm %<% 20)
hsize20<-sum(endfid$Tray_1=="2H"&20 %<=% endfid$Length.mm %<% 25)
hsize25<-sum(endfid$Tray_1=="2H"&25 %<=% endfid$Length.mm %<% 30)
hsize30<-sum(endfid$Tray_1=="2H"&30 %<=% endfid$Length.mm %<% 35)
hsize35<-sum(endfid$Tray_1=="2H"&35 %<=% endfid$Length.mm %<% 40)
hsize40<-sum(endfid$Tray_1=="2H"&40 %<=% endfid$Length.mm %<% 45)
hsize45<-sum(endfid$Tray_1=="2H"&45 %<=% endfid$Length.mm %<% 50)
hsize50<-sum(endfid$Tray_1=="2H"&50 %<=% endfid$Length.mm %<% 55)
hsize55<-sum(endfid$Tray_1=="2H"&55 %<=% endfid$Length.mm %<% 60)
sizeclassfidh<-matrix(c(hsize10,hsize15,hsize20,hsize25,hsize30,hsize35,hsize40,hsize45,hsize50,hsize55),ncol=10,byrow=F)
colnames(sizeclassfidh)<-c("S10-15","S15-20","S20-25","S25-30","S30-35","S35-40","S40-45","S45-50","S50-55","S55-60")
head(sizeclassfidh)
## S10-15 S15-20 S20-25 S25-30 S30-35 S35-40 S40-45 S45-50 S50-55 S55-60
## [1,] 6 68 117 97 20 6 0 0 0 0
ssize10<-sum(endfid$Tray_1=="2S"&10 %<=% endfid$Length.mm %<% 15)
ssize15<-sum(endfid$Tray_1=="2S"&15 %<=% endfid$Length.mm %<% 20)
ssize20<-sum(endfid$Tray_1=="2S"&20 %<=% endfid$Length.mm %<% 25)
ssize25<-sum(endfid$Tray_1=="2S"&25 %<=% endfid$Length.mm %<% 30)
ssize30<-sum(endfid$Tray_1=="2S"&30 %<=% endfid$Length.mm %<% 35)
ssize35<-sum(endfid$Tray_1=="2S"&35 %<=% endfid$Length.mm %<% 40)
ssize40<-sum(endfid$Tray_1=="2S"&40 %<=% endfid$Length.mm %<% 45)
ssize45<-sum(endfid$Tray_1=="2S"&45 %<=% endfid$Length.mm %<% 50)
ssize50<-sum(endfid$Tray_1=="2S"&50 %<=% endfid$Length.mm %<% 55)
ssize55<-sum(endfid$Tray_1=="2S"&55 %<=% endfid$Length.mm %<% 60)
sizeclassfids<-matrix(c(ssize10,ssize15,ssize20,ssize25,ssize30,ssize35,ssize40,ssize45,ssize50,ssize55),ncol=10,byrow=F)
colnames(sizeclassfids)<-c("S10-15","S15-20","S20-25","S25-30","S30-35","S35-40","S40-45","S45-50","S50-55","S55-60")
head(sizeclassfids)
## S10-15 S15-20 S20-25 S25-30 S30-35 S35-40 S40-45 S45-50 S50-55 S55-60
## [1,] 1 14 88 144 98 20 3 0 0 0
sizeclassfid<-rbind(sizeclassfidn,sizeclassfidh,sizeclassfids)
rownames(sizeclassfid)<-c("Fidalgo","Dabob","OysterBay")
broodersizes<-read.csv('Broodersizes.csv')
broodersizes$Date<-as.Date(broodersizes$Date,"%m/%d/%Y")
bnsize10<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="N"&10 %<=% broodersizes$Size %<% 15)
bnsize15<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="N"&15 %<=% broodersizes$Size %<% 20)
bnsize20<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="N"&20 %<=% broodersizes$Size %<% 25)
bnsize25<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="N"&25 %<=% broodersizes$Size %<% 30)
bnsize30<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="N"&30 %<=% broodersizes$Size %<% 35)
bnsize35<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="N"&35 %<=% broodersizes$Size %<% 40)
bnsize40<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="N"&40 %<=% broodersizes$Size %<% 45)
bnsize45<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="N"&45 %<=% broodersizes$Size %<% 50)
bnsize50<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="N"&50 %<=% broodersizes$Size %<% 55)
bnsize55<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="N"&55 %<=% broodersizes$Size %<% 60)
sizeclassbrfidn<-matrix(c(bnsize10,bnsize15,bnsize20,bnsize25,bnsize30,bnsize35,bnsize40,bnsize45,bnsize50,bnsize55),ncol=10,byrow=F)
colnames(sizeclassbrfidn)<-c("S10-15","S15-20","S20-25","S25-30","S30-35","S35-40","S40-45","S45-50","S50-55","S55-60")
head(sizeclassbrfidn)
## S10-15 S15-20 S20-25 S25-30 S30-35 S35-40 S40-45 S45-50 S50-55 S55-60
## [1,] 0 1 0 2 4 1 0 0 0 0
bhsize10<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="H"&10 %<=% broodersizes$Size %<% 15)
bhsize15<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="H"&15 %<=% broodersizes$Size %<% 20)
bhsize20<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="H"&20 %<=% broodersizes$Size %<% 25)
bhsize25<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="H"&25 %<=% broodersizes$Size %<% 30)
bhsize30<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="H"&30 %<=% broodersizes$Size %<% 35)
bhsize35<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="H"&35 %<=% broodersizes$Size %<% 40)
bhsize40<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="H"&40 %<=% broodersizes$Size %<% 45)
bhsize45<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="H"&45 %<=% broodersizes$Size %<% 50)
bhsize50<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="H"&50 %<=% broodersizes$Size %<% 55)
bhsize55<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="H"&55 %<=% broodersizes$Size %<% 60)
sizeclassbrfidh<-matrix(c(bhsize10,bhsize15,bhsize20,bhsize25,bhsize30,bhsize35,bhsize40,bhsize45,bhsize50,bhsize55),ncol=10,byrow=F)
colnames(sizeclassbrfidh)<-c("S10-15","S15-20","S20-25","S25-30","S30-35","S35-40","S40-45","S45-50","S50-55","S55-60")
head(sizeclassbrfidh)
## S10-15 S15-20 S20-25 S25-30 S30-35 S35-40 S40-45 S45-50 S50-55 S55-60
## [1,] 0 1 5 1 1 0 0 0 0 0
bssize10<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="S"&10 %<=% broodersizes$Size %<% 15)
bssize15<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="S"&15 %<=% broodersizes$Size %<% 20)
bssize20<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="S"&20 %<=% broodersizes$Size %<% 25)
bssize25<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="S"&25 %<=% broodersizes$Size %<% 30)
bssize30<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="S"&30 %<=% broodersizes$Size %<% 35)
bssize35<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="S"&35 %<=% broodersizes$Size %<% 40)
bssize40<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="S"&40 %<=% broodersizes$Size %<% 45)
bssize45<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="S"&45 %<=% broodersizes$Size %<% 50)
bssize50<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="S"&50 %<=% broodersizes$Size %<% 55)
bssize55<-sum(broodersizes$Site=="Fidalgo"&broodersizes$Population=="S"&55 %<=% broodersizes$Size %<% 60)
sizeclassbrfids<-matrix(c(bssize10,bssize15,bssize20,bssize25,bssize30,bssize35,bssize40,bssize45,bssize50,bssize55),ncol=10,byrow=F)
colnames(sizeclassbrfids)<-c("S10-15","S15-20","S20-25","S25-30","S30-35","S35-40","S40-45","S45-50","S50-55","S55-60")
head(sizeclassbrfids)
## S10-15 S15-20 S20-25 S25-30 S30-35 S35-40 S40-45 S45-50 S50-55 S55-60
## [1,] 0 0 1 18 18 1 1 0 0 0
sizeclassbrfid<-rbind(sizeclassbrfidn,sizeclassbrfidh,sizeclassbrfids)
rownames(sizeclassbrfid)<-c("BrooderFidalgo","BrooderDabob","BrooderOysterBay")
head(sizeclassbrfid)
## S10-15 S15-20 S20-25 S25-30 S30-35 S35-40 S40-45 S45-50
## BrooderFidalgo 0 1 0 2 4 1 0 0
## BrooderDabob 0 1 5 1 1 0 0 0
## BrooderOysterBay 0 0 1 18 18 1 1 0
## S50-55 S55-60
## BrooderFidalgo 0 0
## BrooderDabob 0 0
## BrooderOysterBay 0 0
brvpopsize<-t(rbind(sizeclassfid,sizeclassbrfid))
brvpopsizedf<-as.data.frame(brvpopsize)
brvpopsizedf$PercBroodFid<-(brvpopsizedf$BrooderFidalgo/brvpopsizedf$Fidalgo)
brvpopsizedf$PercBroodDab<-(brvpopsizedf$BrooderDabob/brvpopsizedf$Dabob)
brvpopsizedf$PercBroodOys<-(brvpopsizedf$BrooderOysterBay/brvpopsizedf$OysterBay)
brvpszfx<-rapply(brvpopsizedf, f=function(x)ifelse(is.nan(x),0,x),how="replace")
brvp<-data.frame(brvpszfx)
brvp$SizeClass=c("10-15mm","15-20mm","20-25mm","25-30mm","30-35mm","35-40mm","40-45mm","45-50mm","50-55mm","55-60mm")
ggplot()+geom_bar(data=brvp, aes(x=SizeClass,y=PercBroodFid),color="purple", fill="purple",stat="identity")+geom_bar(data=brvp,aes(x=SizeClass, y=PercBroodDab),color="blue",fill="blue",stat="identity")+geom_bar(data=brvp, aes(x=SizeClass,y=PercBroodOys),color="orange",fill="orange",stat="identity")
![plot of chunk unnamed-chunk-1 plot of chunk 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o0WTBkjz+wDtSyKQNWgCDRaMGWMPLMP1LIoAlWDItBowZQx8sw+UMuiCFQNikCjBVPGyDP7QC2LIlA1KAKNFkwZI8/sA7UsikDVoAg0WjBljDyzD9SyKAJVgyLQaMGUMfLMPlDLoghUDYpAowVTxsgz+0AtiyJQNSgCjRZMGSPP7AO1LIpA1aAINFowZYw8sw/UsigCVYMi0GjBlDHyzD5Qy6IIVA2KQKMFU8bIM/tALYsiUDUoAo0WTBkjz+wDtSyKQNWgCDRaMGWMPLMP1LIoAlWDItBowZQx8sw+UMuiCFQNikCjBVPGyDP7QC2LIlA1KAKNFkwZI8/sA7UsikDVoAg0WjBljDyzD9SyKAJVgyLQaMGUMfLMPlDLoghUDYpAowVTxsgz+0AtiyJQNSgCjRZMGSPP7AO1LIpA1aANBPrPjCSNkWf2gVoWvaFAa4vaQNP8EgpnoOrMPlDLojcUaG1RG2iDM9Ckgilj5Jl9oJZFEagaFIFGC6aMkWf2gVoWRaBqUAQaLZgyRp7ZB2pZFIGqQRFotGDKGHlmH6hlUQSqBkWg0YIpY+SZfaCWRRGoGhSBRgumjJFn9oFaFkWgalAEGi2YMkae2QdqWRSBqkERaLRgyhh5Zh+oZVEEqgZFoNGCKWPkmX2glkURqBoUgUYLpoyRZ/aBWhZFoGpQBBotmDJGntkHalkUgapBEWi0YMoYeWYfqGVRBKoGRaDRgilj5Jl9oJZFEagaFIFGC6aMkWf2gVoWRaBqUAQaLZgyRp7ZB2pZFIGqQRFotGDKGHlmH6hlUQSqBkWg0YIpY+SZfaCWRRGoGhSBRgumjJFn9oFaFkWgalAEGi2YMkae2QdqWRSBqkERaLRgyhh5Zh+oZVEEqgZFoNGCKWPkmX2glkURqBoUgUYLpoyRZ/aBWhZFoGpQBBotmDJGntkHalkUgapBEWi0YMoYeWYfqGVRBKoGRaDRgilj5Jl9oJZFEagaFIFGC6aMkWf2gVoWRaBqUAQaLZgyRp7ZB2pZFIGqQRFotGDKGHlmH6hlUQSqBkWg0YIpY+SZfaCWRRGoGhSBRgumjJFn9oFaFkWgalAEGi2YMkae2QdqWRSBqkERaLRgyhh5Zh+oZVEEqgZFoNGCKWPkmX2glkURqBoUgUYLpoyRZ/aBWhZFoGpQBBotmDJGntkHalkUgapBEWi0YMoYeWYfqGVRBKoGRaDRgilj5Jl9oJZFEagaFIFGC6aMkWf2gVoWRaBqUAQaLZgyRp7ZB2pZFIGqQRFotGDKGHlmH6hlUQSqBkWg0YIpY+SZfaCWRRGoGhSBRgumjJFn9oFaFkWgalAEGi2YMkae2QdqWRSBqkERaLRgyhh5Zh+oZVEEqgZFoNGCKWPkmX2glkURqBoUgUYLpoyRZ/aBWhZFoGpQBBotmDJGntkHalkUgapBEWi0YMoYeWYfqGVRBKoGRaDRgilj5Jl9oJZFEagaFIFGC6aMkWf2gVoWRaBqUAQaLZgyRp7ZB2pZFIGqQRFotGDKGHlmH6hlUQSqBkWg0YIpY+SZfaCWRRGoGhSBRgumjJFn9oFaFkWgalAEGi2YMkae2QdqWRSBqkERaLRgyhh5Zh+oZVEEqgZFoNGCKWPkmX2glkURqBoUgUYLpoyRZ/aBWhZFoGpQBBotmDJGntkHalkUgapBEWi0YMoYeWYfqGVRBKoGRaDRgilj5Jl9oJZFEagaFIFGC6aMkWf2gVoWRaBqUAQaLZgyRp7ZB2pZFIGqQRFotGDKGHlmH6hlUQSqBkWg0YIpY+SZfaCWRRGoGhSBRgumjJFn9oFaFkWgalAEGi2YMkae2QdqWRSBqkERaLRgyhh5Zh+oZVEEqgZFoNGCKWPkmX2glkURqBoUgUYLpoyRZ/aBWhZFoGpQBBotmDJGntkHalkUgapBEWi0YMoYeWYfqGVRBKoGRaDRgilj5Jl9oJZFEagaFIFGC6aMkWf2gVoWRaBqUAQaLZgyRp7ZB2pZFIGqQRFotGDKGHlmH6hlUQSqBkWg0YIpY+SZfaCWRRGoGhSBRgumjJFn9oFaFkWgalAEGi2YMkae2QdqWRSBqkERaLRgyhh5Zh+oZVEEqgZFoNGCKWPkmX2glkURqBoUgUYLpoyRZ/aBWhZFoGpQBBotmDJGntkHalkUgapBEWi0YMoYeWYfqGVRBKoGRaDRgilj5Jl9oJZFEagaFIFGC6aMkWf2gVoWRaBqUAQaLZgyRp7ZB2pZFIGqQRFotGDKGHlmH6hlUQSqBkWg0YIpY+SZfaCWRRGoGlRJoL98cXcpv/8bAgX6KpgIdNEY34wQ6AJpcyNkdaNBoGpQBBotmDJGntkHalkUgapBlQS6z+Pd3e/+uvnXWql/nujNQ5IKpoyRZ/aBWhZFoGpQQYH+9Nnd54cvHvcunZ6kgilj5Jl9oJZFEagaVFCg7+7+NPrVlCQVTBkjz+wDtSyKQNWgegJd3W0f3mt/5DFQoK+FiUAXjfHNKOgyBKrO7AO1LIpA1aB6Av3X12d34f/4jwnWHCSpYMoYeWYfqGVRBKoG1RPoSpmDU9CHOx4DBfpamAh00RjfjIIuiz4Lv7/bvv4nz8IDfS1MBLpojG9GQZfFXgf6cPI6el4HCvS1MBHoojG+GQVdFvwwkcejPp+cf368X+XN2YUfLl2IQIGKMRHoojG+GeUK9PCuzqdPwL+/3+TLHwaX/frt9sK//B2BAlVmItBFY3wzyhboWFanmm8/ffr5m/uvfjxc9tv3G3WeXohAgeoxEeiiMb4Z3Uigq3PNt+v/rmT53eHCj7tTz5MLEShQPSYCXTTGN6MbCfTj/r77+8Ejnu93Z56rM9GLD4MmFUwZI8/sA7UsikDVoEoC3X720pMPtRs8ELp35equ/PFR0INM3yNQoNJMBLpojG9GtxHo8Rzz44VnjPb37xEoUFEmAl00xjejmwl0p8ifv3kq0KFU7wcJ/myEyOeGAq2uSi5k5mOgx3PMCwJdffP4LDwCJa8xCLR3EgV6/oz76nsnLw49JukUO2WMPLMP1LLoDQVaW9QGqnQX/tlcOQNdfWvkdfQIFKgKE4EuGuObUdCAzz4Gev2Tl8YfA12/Genii0ARKFAdJgJdNMY3o5sJ9PAs/Mn99dU9+rHzTwQKVIaJQBeN8c0oWaAnf1dukMHrQIdv27zqTwQKVIWJQBeN8c3oRgK9+E6ktT9Hnj9CoECVmAh00RjfjG4k0IvvhR9//h2BApViItBFY3wzupFAL34a04dr998RKFAdJgJdNMY3o1sJ9OTzQH/7fu3R/aeBjn6mclLBlDHyzD5Qy6IIVA3qJtDhJ9JvBbo6HUWgQC2YCHTRGN+MbifQlyepYMoYeWYfqGVRBKoGRaDRgilj5Jl9oJZFEagaFIFGC6aMkWf2gVoWRaBqUAQaLZgyRp7ZB2pZFIGqQRFotGDKGHlmH6hlUQSqBhUT6KU8/dvGCBSoJROBLhrjmxECXSBtboSsbjQIVA2KQKMFU8bIM/tALYsiUDWokkAXSlLBlDHyzD5Qy6IIVA2KQKMFU8bIM/tALYsiUDUoAo0WTBkjz+wDtSyKQNWgCDRaMGWMPLMP1LIoAlWDItBowZQx8sw+UMuiCFQNikCjBVPGyDP7QC2LIlA1qJJAeRlTPbMP1LIoAlWDItBowZQx8sw+UMuiCFQNikCjBVPGyDP7QC2LIlA1qJJA93m8u/vdXzf/Wiv1zxO9eUhSwZQx8sw+UMuiCFQNKijQk49iety7dHqSCqaMkWf2gVoWRaBqUEGBvtt/qN2Fr6YkqWDKGHlmH6hlUQSqBtUT6Opu+/Be+yOPgQJ9LUwEumiMb0ZBlyFQdWYfqGVRBKoG1RPov74+uwv/x39MsOYgSQVTxsgz+0AtiyJQNaieQFfKHJyCPtzxGCjQ18JEoIvG+GYUdFn0Wfj93fb1P3kWHuhrYSLQRWN8Mwq6LPY60IeT19HzOlCgz8ZEKwh00Rhv0qDLgh8m8njU5+zzTwTaANrGK22K2kAlBXp4V+fcJ+A3SSqYMkae6Qlt45U2RW2gogLNTFLBlDHyTE9oG6+0KWoDRaDRgilj5Jme0DZeaVPUBioqUO7CFzE9oW280qaoDVRSoIPPtUtQaFLBlDHyTE9oG6+0KWoDVRToyeeCzn8aPqlgyhh5pie0jVfaFLWBCgr0X18fXv25fj3T3HdyItDXD23jlTZFbaCCAn0cSHMt07mvpE8qmDJGnukJbeOVNkVtoIICfTd84HN1d573wgN9Jm280qaoDVRPoKuTzs8HXz7waUxAn0sbr7QpagPVEyifB1rJ9IS28UqbojZQBBotmDJGnukJbeOVNkVtoHoC5S58JdMT2sYrbYraQPUEypNIlUxPaBuvtClqAxUUKC9jKmR6Qtt4pU1RG6igQHkhfSHTE9rGK22K2kAFBXr6Vs75b4ZPKpgyRp7pCW3jlTZFbaCKAuXDROqYntA2XmlT1AYqKVA+zq6M6Qlt45U2RW2gogLNTFLBlDHyTE9oG6+0KWoDRaDRgilj5Jme0DZeaVPUBioqUO7CFzE9oW280qaoDVRSoDyJVMb0hLbxSpuiNlBFgfKJ9HVMT2gbr7QpagMVFCgvpC9kekLbeKVNURuooEB5K2ch0xPaxittitpABQXKh4kUMj2hbbzSpqgNVE+gfJxdJdMT2sYrbYraQPUEygcqVzI9oW280qaoDRSBRgumjJFnekLbeKVNURuonkC5C1/J9IS28UqbojZQPYHyJFIl0xPaxittitpABQXKy5gKmZ7QNl5pU9QGKihQXkhfyPSEtvFKm6I2UEGB8on0hUxPaBuvtClqA1UUKB8mUsf0hLbxSpuiNlBJgfJxdmVMT2gbr7QpagMVFWhmkgqmjJFnekLbeKVNURuooEDfpZx3HpNUMGWMPNMT2sYrbYraQPUE+q+v5z/xfpKkgilj5Jme0DZeaVPUBqon0ISXzp8mqWDKGHmmJ7SNV9oUtYHqCZQz0EqmJ7SNV9oUtYHqCXT96vm5bz46SVLBlDHyTE9oG6+0KWoDFRTop58+Sz0HTSqYMkae6Qlt45U2RW2gegI9eR9SxotBkwqmjJFnekLbeKVNURsoAo0WTBkjz/SEtvFKm6I2UAQaLZgyRp7pCW3jlTZFbaB6Ak1PUsGUMfJMT2gbr7QpagNFoNGCKWPkmZ7QNl5pU9QGikCjBVPGyDM9oW280qaoDVRXoD99lvRxTEkFU8bIMz2hbbzSpqgNVE2gDytr/u6vn9Z/GWmb+W/rTCqYMkae6Qlt45U2RW2gWgLdPwP/+UakSQZNKpgyRp7pCW3jlTZFbaBSAt38NaRN/vcX2/dzvtudjyJQoFfSxittitpApQT6uP1rcluPbt8P/3B38lfipySpYMoYeaYntI1X2hS1gUoJ9N1Om2uD7t4Nv7pTP/d98UkFU8bIMz2hbbzSpqgNVEmgxw+yO553Jny4XVLBlDHyTE9oG6+0KWoDVRLo8Wxz8Il273grJ9Dn0sYrbYraQMUEunvKHYFWMT2hbbzSpqgNFIFGC6aMkWd6Qtt4pU1RGygCjRZMGSPP9IS28UqbojZQBBotmDJGnukJbeOVNkVtoAg0WjBljDzTE9rGK22K2kARaLRgyhh5pie0jVfaFLWBign0UhAo0GfSxittitpAEWi0YMoYeaYntI1X2hS1gSLQaMGUMfJMT2gbr7QpagNVEuhCSSqYMkae6Qlt45U2RW2gCDRaMGWMPNMT2sYrbYraQBFotGDKGHmmJ7SNV9oUtYEi0GjBlDHyTE9oG6+0KWoD1RTow/ZloL98wR+VAxpIG6+0KWoDVRToL7u/57H5w5z8UTmgz6aNV9oUtYEKCnTzFz02n6f8yB+VAxpJG6+0KWoDFRTow+APyT3cHd/UOTX/zEjSGHmmJ/SGXqHoTYraQNP8EkpEoKsT0MEjn6t78XNPQZP+D5EyRp7pCb2hVyh6k6I2UL0z0F++OPk7nA/8TSSgz6WNV9oUtYFKCnR4p/2Rt3ICfS5tvNKmqA0UgUYLpoyRZ3pC23ilTVEbqJ5Az/6Q8TvuwgN9Lm280qaoDVRPoIM/Cv9p80qmz68cG0lSwZQx8kxPaBuvtClqAxUU6Pp19LvXMa3/OfvNSEkFU8bIMz2hbbzSpqgNVFCg29fPHzL3ZaAI9PVD23ilTVEbqKJAN2/h3OXwkvrpSSqYMkae6Qlt45U2RW2gkgI9fDh9gj4RaANoG6+0KWoDFRVoZpIKpoyRZ3pC23ilTVEbqAsCn74AACAASURBVKBA32V8ht0gSQVTxsgzPaFtvNKmqA1UT6BnrwOdn6SCKWPkmZ7QNl5pU9QGqifQX75I+AzQYZIKpoyRZ3pC23ilTVEbqJ5AOQOtZHpC23ilTVEbqJ5A1y8Dnf3az2GSCqaMkWd6Qtt4pU1RG6igQNcvA808B00qmDJGnukJbeOVNkVtoHoC3b0E9Bg+jQnoM2njlTZFbaAINFowZYw80xPaxittitpAEWi0YMoYeaYntI1X2hS1geoJND1JBVPGyDM9oW280qaoDRSBRgumjJFnekLbeKVNURsoAo0WTBkjz/SEtvFKm6I2UFGB7j5U+SHj5UxJBVPGyDM9oW280qaoDVRSoNvnkdYCzXhNfVLBlDHyTE9oG6+0KWoDVRTo8cNA3/GJ9EADaeOVNkVtoIIC/dfXd3d//MdPn60/TfkdfxMJ6PNp45U2RW2gggLd/iHOrUBP/0TntCQVTBkjz/SEtvFKm6I2UEGBbv8S/E6gCZ/NlFQwZYw80xPaxittitpA9QS6Uub6nHMn0NUpKO9EAvpM2nilTVEbqJ5Af/li87zRXqCPCBToc2njlTZFbaAINFowZYw80xPaxittitpA9QR6dhf+HY+BAn0ubbzSpqgNVE+gK2Wuzzl3Ak34C0lJBVPGyDM9oW280qaoDVRQoI93a2duBbp+TejcV9InFUwZI8/0hLbxSpuiNlBBgW5fPb8R6MPd3fy/7pFUMGWMPNMT2sYrbYraQBUFevKRyrPfiIRAXz+0jVfaFLWBKgp0c8c9zZ8I9PVD23ilTVEbqKRADwpN0CcCbQBt45U2RW2gogLNTFLBlDHyTE9oG6+0KWoDRaDRgilj5Jme0DZeaVPUBqom0J8+W991n/sBTCdJKpgyRp7pCW3jlTZFbaBaAj08e7R9F1JOkgqmjJFnekLbeKVNURuolkDfHZ59TzRoUsGUMfJMT2gbr7QpagOVEujj3eZNSJsXgmb8ObltkgqmjJFnekLbeKVNURuolEDf7d+3uborn3cKmlQwZYw80xPaxittitpAlQQ6+PT5lD/HuUtSwZQx8kxPaBuvtClqA1US6OCTl376LO+Z+KSCKWPkmZ7QNl5pU9QGKirQhE+xOySpYMoYeaYntI1X2hS1gSLQaMGUMfJMT2gbr7QpagNFoNGCKWPkmZ7QNl5pU9QGikCjBVPGyDM9oW280qaoDRSBRgumjJFnekLbeKVNURsoAo0WTBkjz/SEtvFKm6I2UAQaLZgyRp45H1qxw2/GrPZKm6I2UDGBXgp/F94JWrLFS6BtVreiqA0UgUYLpoyRZ3pu8RJom9WtKGoDRaDRgilj5JmeW7wE2mZ1K4raQJUEulCSCqaMkWd6bvESaJvVrShqA0Wg0YIpY+SZnlu8BNpmdSuK2kARaLRgyhh5pucWL4G2Wd2KojZQBBotmDJGnum5xUugbVa3oqgNFIFGC6aMkWd6bvESaJvVrShqA0Wg0YIpY+SZnlu8BNpmdSuK2kARaLRgyhh5pucWL4G2Wd2KojZQBBotmDJGnum5xUugbVa3oqgNFIFGC6aMkWd6bvESaJvVrShqA0Wg0YIpY+SZnlu8BNpmdSuK2kARaLRgyhh5pucWL4G2Wd2KojZQBBotmDJGnum5xUugbVa3oqgNFIFGC6aMkWd6bvESaJvVrShqA0Wg0YIpY+SZnlu8BNpmdSuK2kARaLRgyhh5pucWL4G2Wd2KojZQBBotmDJGnum5xUugbVa3oqgNFIFGC6aMkWd6bvESaJvVrShqA0Wg0YIpY+SZnlu8BNpmdSuK2kARaLRgyhh5pucWL4G2Wd2KojZQBBotmDJGnum5xUugbVa3oqgNFIFGC6aMkWd6bvESaJvVrShqA0Wg0YIpY+SZnlu8BNpmdSuK2kARaLRgyhh5pucWL4G2Wd2KojZQBBotmDJGnum5xUugbVa3oqgNFIFGC6aMkWd6bvESaJvVrShqA0Wg0YIpY+SZnlu8BNpmdSuK2kARaLRgyhh5pucWL4G2Wd2KojZQBBotmDJGnum5xUugbVa3oqgNFIFGC6aMkWd6bvESaJvVrShqA0Wg0YIpY+SZnlu8BNpmdSuK2kARaLRgyhh5pucWL4G2Wd2KojZQBBotmDJGnum5xUugbVa3oqgNFIFGC6aMkWd6bvESaJvVrShqA0Wg0YIpY+SZnlu8BNpmdSuK2kARaLRgyhh5pucWL4G2Wd2KojZQBBotmDJGnum5xUugbVa3oqgNFIFGC6aMkWd6bvESaJvVrShqA0Wg0YIpY+SZnlu8BNpmdSuK2kARaLRgyhh5pucWL4G2Wd2KojZQBBotmDJGnum5xUugbVa3oqgNFIFGC6aMkWd6bvESaJvVrShqA0Wg0YIpY+SZnlu8BNpmdSuK2kARaLRgyhh5pucWL4G2Wd2KojZQBBotmDJGnum5xUugbVa3oqgNFIFGC6aMkWd6bvESaJvVrShqA7UT6Mf7Vd5cuvztyDWSCqaMkWd6bvESaJvVrShqA3UT6Pv7Tb784ezyX79FoBLQki1eAm2zuhVFbaBmAv1wv/bkz9/cf/XjyeUrfyJQCWjJFi+BtlndiqI2UC+B7s8zVwb9bnj56msEqgEt2eIl0DarW1HUBuol0I/7++7vTx4GXd2v/7f/RKAS0JItXgJts7oVRW2gXgJ9v7/r/mH4KOjq/PPtb98jUAloyRYvgbZZ3YqiNlArga4suTvx/Hj/l78fLv7533/8hEBFoCVbvATaZnUritpA3QS6s+TP3wwEevqtTe4HmcckL8sNt3gxtCJtipKLmSnQ42uVEKhqSrZ4CbQibYqSi0kU6OnT8NyFV4HecIsXQ9usbkVRG6jVXfj4GegwSQVTxsgzPbd4CbTN6lYUtYFaCTT+GOgwSQVTxsgzPbd4CbTN6lYUtYG6CfTwLPzZmzkRqAi0ZIuXQNusbkVRG6iVQIevAz17LycCFYGWbPESaJvVrShqA/US6Mg7kT4hUBloyRYvgbZZ3YqiNlAvgY69Fx6BykBLtngJtM3qVhS1gXoJdPTTmBCoCrRki5dA26xuRVEbqJlATz4PdCXNg0cRqAi0ZIuXQNusbkVRG6ibQIefSI9ABaElW7wE2mZ1K4raQO0E+vIkFUwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OIl0DarW1HUBopAowVTxsgzPbd4CbTN6lYUtYEi0GjBlDHyTM8tXgJts7oVRW2gCDRaMGWMPNNzi5dA26xuRVEbKAKNFkwZI8/03OKvG1q9ulNiedstZAZdViLQf2YkaYw8czb0hlu8FlrBrIFOieVtt5AZdBlnoOpMzkCj0AomZ6By0AZnoEkFU8bIMxFoFFrBRKByUAQaLZgyRp6JQKPQCiYClYMi0GjBlDHyTAQahVYwEagcFIFGC6aMkWci0Ci0golA5aAINFowZYw8E4FGoRVMBCoHRaDRgilj5JkINAqtYCJQOSgCjRZMGSPPRKBRaAUTgcpBEWi0YMoYeSYCjUIrmAhUDopAowVTxsgzEWgUWsFEoHJQBBotmDJGnolAo9AKJgKVgyLQaMGUMfJMBBqFVjARqBwUgUYLpoyRZyLQKLSCiUDloAg0WjBljDwTgUahFUwEKgdFoNGCKWNuy/zDrTJglmzxCmgFE4HKQRFotGDKmJsyb+bPoUFLtngFtIKJQOWgCDRaMGXMTZkIdEloBROBykERaLRgypibMhHoktAKJgKVgyLQaMGUMTdlItAloRVMBCoHRaDRgiljbspEoEtCK5gIVA6KQKMFU8bclIlAl4RWMBGoHBSBRgumjLkpE4EuCa1gIlA5KAKNFkwZc1MmAl0SWsFEoHJQBBotmDLmpkwEuiS0golA5aAINFowZcxNmQh0SWgFE4HKQRFotGDKmJsyEeiS0AomApWDItBowZQxN2Ui0CWhFUwEKgdFoNGCKWNuykSgS0IrmAhUDopAowVTxtyUiUCXhFYwEagcFIFGC6aMuSkTgS4JrWAiUDkoAo0WTBlzUyYCXRJawUSgclAEGi2YMuamTAS6JLSCiUDloAg0WjBlzE2ZCHRJaAUTgcpBEWi0YMqYmzIR6JLQCiYClYMi0GjBlDE3ZSLQJaEVTAQqB0Wg0YIpY27KRKBLQiuYCFQOikCjBVPG3JSJQJeEVjARqBwUgUYLpoy5KROBLgmtYCJQOSgCjRZMGXNTJgJdElrBRKByUAQaLZgy5qZMBLoktIKJQOWgCDRaMGXMTZkIdEloBROBykERaLRgypibMhHoktAKJgKVgyLQaMGUMTdlItAloRVMBCoHRaDRgiljbspEoEtCK5gIVA6KQKMFZw74j1vliESgS0IrmAhUDopAowXnXf9m/hwYFIEuCa1gIlA5KAKNFpx3fQS65BavgFYwEagcFIFGC867PgJdcotXQCuYCFQOikCjBeddH4EuucUroBVMBCoHRaDRgvOuj0CX3OIV0AomApWDItBowXnXR6BLbvEKaAUTgcpBEWi04LzrI9Alt3gFtIKJQOWgCDRacN71EeiSW7wCWsFEoHJQBBotOO/6CHTJLV4BrWAiUDkoAo0WnHd9BLrkFq+AVjARqBwUgUYLzrs+Al1yi1dAK5gIVA6KQKMF510fgS65xSugFUwEKgdFoNGC866PQJfc4hXQCiYClYMi0GjBeddHoEtu8QpoBROBykERaLTgvOsj0CW3eAW0golA5aAINFpw3vUR6JJbvAJawUSgclAEGi047/oIdMktXgGtYCJQOSgCjRacd30EuuQWr4BWMBGoHBSBRgvOuz4CXXKLV0ArmAhUDopAowXnXR+BLrnFK6AVTAQqB0Wg0YLzro9Al9ziFdAKJgKVgyLQaMF510egS27xCmgFE4HKQRFotOC86yPQJbd4BbSCiUDloAg0WnDe9RHoklu8AlrBRKByUAQaLTjv+gh0yS1eAa1gIlA5KAKNFpx3fQS65BavgFYwEagcFIFGC867PgJdcotXQCuYCFQOikCjBeddH4EuucUroBVMBCoHRaDRgvOuj0CX3OIV0AomApWDItBowXnXR6BLbvEKaAUTgcpBEWi04LzrI9Alt3gFtIKJQOWgCDRacN71EeiSW7wCWsFEoHJQBBotOO/6/3EzlyHQ20ArmAhUDopAowXnXb9CZhXMPl6pYCJQOSgCjRacd/0KmVUw+3ilgolA5aAINFpw3vUrZFbB7OOVCiYClYMi0GjBedevkFkFs49XKpgIVA6KQKMF512/QmYVzD5eqWAiUDkoAo0WnHf9CplVMPt4pYKJQOWgCDRacN71K2T2h5ttNgR6GyYClYMi0GjBedevEGjJbmsDrWAiUDkoAo0WnHd9BPraoBVMBCoHRaDRgvOuj0BfG7SCiUDloAg0WnDe9RHoa4NWMBGoHBSBRgvOuz4CfW3QCiYClYMi0GjBeddHoK8NWsFEoHJQBBotOO/6CPS1QSuYCFQOikCjBeddH4G+NmgFE4HKQRFotOC86yPQ1watYCJQOSgCjRacd30E+tqgFUwEKgdFoNGC866PQF8btIKJQOWgCDRacN71Eehrg1YwEagcFIFGC867PgJ9bdAKJgKVgyLQaMF510egrw1awUSgclAEGi047/oI9LVBK5gIVA6KQKMF510fgb42aAUTgcpBEWi04LzrI9DXBq1gIlA5KAKNFpx3fQT62qAVTAQqB7UT6Mf7Vd5ELtwnqeC86yPQ1watYCJQOaibQN/fb/LlD89eeEhSwXnXR6CvDVrBRKByUDOBfri/f/vp08/f3H/14zMXItBGW7wCWsFEoHJQL4H++u1alRtZfnf9QgTaaYtXQCuYCFQO6iXQj/u76e8Hj3hevHCQpILzro9AXxu0golA5aBeAn2/v5f+YfCA58ULEWinLV4BrWAiUDmolUB/+35/jvnx/i9/v3YhAv3vTlu8AlrBRKByUDeBvt3+6+dvhgJ9euH9IE/G3Ow2OGD+4WbQP9y+57BpG2gF85VDqzdpDfQlmSnQ/dNFJ668eKGGQP+nGFrB7AOtYFL09UFfkkSBHp5xv3jhMEmn2Clj5Jl9oBQFqsO8vUCvn4EiUKCCTIq+OqiVQOOPgSJQoIJMir46qJtAD0+4H16xdPFCBApUkEnRVwe1EujwJZ/Ht21evBCBAtVjUvTVQb0E6vtOJBdmHyhFgeowgwLs+154F2YfKEWB6jBvJFDfT2NyYfaBUhSoDvNWAj356M/fvt8p0+DzQF2YfaAUBarDvJlAhx8+fxCowSfSuzD7QCkKVId5O4G+PEkFU8bIM/tAKQpUhxl0GQJVZ/aBUhSoDjPoMgSqzuwDpShQHWbQZQhUndkHSlGgOsygyxCoOrMPlKJAdZhBlyFQdWYfKEWB6jCDLkOg6sw+UIoC1WEGXYZA1Zl9oBQFqsMMugyBqjP7QCkKVIcZdBkCVWf2gVIUqA4z6DIEqs7sA6UoUB1m0GUIVJ3ZB0pRoDrMoMsQqDqzD5SiQHWYQZchUHVmHyhFgeowgy5DoOrMPlCKAtVhBl2GQNWZfaAUBarDDLoMgaoz+0ApClSHGXQZAlVn9oFSFKgOM+gyBKrO7AOlKFAdZtBlCFSd2QdKUaA6zKDLEKg6sw+UokB1mEGXIVB1Zh8oRYHqMIMuQ6DqzD5QigLVYQZdhkDVmX2gFAWqwwy6DIGqM/tAKQpUhxl0GQJVZ/aBUhSoDjPoshKBpuT+vgezD5SiQO2YCFSd2QdKUaB2TASqzuwDpShQOyYCVWf2gVIUqB0Tgaoz+0ApCtSOiUDVmX2gFAVqx0Sg6sw+UIoCtWMiUHVmHyhFgdoxEag6sw+UokDtmAhUndkHSlGgdkwEqs7sA6UoUDsmAlVn9oFSFKgd01eghBBSHARKCCETg0AJIWRiECghhEwMAiWEkIlBoIQQMjEIlBBCJgaBEkLIxCBQQgiZGCGBfrx/e/znKm+uHvTb9/fbfPVjBnN03IeTH2X8B4smNG94UEbR0Lz3q8u+/OGZnyye0LjhQRlFw/M+HH6Y+b/S6Lj9QTk33WfnnV88t2ho3ulBs5sGx/38Te4mfUF0BPrrtweBvt8u03H7XTho9a/5t8Jnx+0v/svfn/nB4sDn550eNL9oaN7+4t16zG0aGnd6UF7R5+etdtybaz/ZCxMadzgo5ab7/Lyzi9Nuu1fnnR40u2ls3OmPkvIbDUdGoOvF2e22D5t/rW4fT5Z9cNDP38z8/3dk3Op/eWvr7H+U8R8smNC8s4NmFw3NWx20vsWtfqLvPo3+ZC9iPj/u7KCMosF561OZrXxm/0rD444HZdx0A/NOL55fNDTv9KDZTUPj3udu0pdFRaCbk/DDmcPb3UXfjR60Ok+ffZL+/LiPu7O27Y8y/oNFE5p3etD8oqF5+4Peb747u2lo3OlBeUWfn7d+SGPzzfm/0vC4w0EZN93IvJOLE4qG5p3+LAm/0ufH7X+E7a8/5zcaj4hAV6fd//afO5l9PJ6Lvxk9aHX7eftpXgLj3u/+N7b6v/2bKz/YC5CBeacHzS8amvf+cAq1Pnh209C404Myigbn/frtl/9nL9m5v9LouONBCTfd0LyTixOKhuad/iyzm0bGfTjevt8m/UZfEA2Brv538Xbb/9Nxw69W5uRxjJODPr3fPYy4+9aXP2weIPlu90DJ2/FLA+MOOfwSNv84/cGmMK/NGzloftFr855ke2uc3/TKuJGD5he9Nu8k7++/2y1KStGxcSMH5RQdmzc4YnhxQtEr80YOSrjtjo/b57iHs4q+KCIC/fcfDwuxO1H6dLxLduGg9f+A/9+398dHgb78r93zBx+PzyNcvjQw7kk29wvOfrApzGvzLh+UUPTavPNsf6a0phfHXT4or+jFecOs7wlu5ZNSdHTc5YNyio7OO+Tk4oSi1+ZdPmh+02vjjscM76snbtJYNAS6zkCghyd2nmy3wzc3j1+us/n/3/qrN7uXOXz14/7ZhMuXBsY9yeaXcfaDTWWOzbt8UFLRsXlPl3d9cVbTy+MuH5RW9PK8QX79dvVDHIw3u+j4uMsHpRQdn3fIycUJRa/Nu3zQ/KbXxh2P+cvfN9femz1rk8aiJ9DjS4uuCfTj7rUM2zP2/Ss69pduH0G+fGlg3HlWP9Lq4rMfbCpzbN7lg5KKjs07ycf748tFEpqOjbt8UFLRsXmDvF//NKfPbs0pOj7u8kEpRcfnDVfieHFC0WvzLh80v+m1ccdjvvy/W7MOn0Oav0mD0Rbok14H4+0felld8N3xyP1/t6f1ly8NjDvL6nrr7579YFOZY/MuH5RUdGzeST5euhVObzo27vJBSUXH5g2P2D4se268iUWvjLt8UEbRK/MOObk4oei1eZcPmt/02rjjWmx/4dvzyLRNGo22QMfPQA/Z3Ir2R+5XYr9Oly4NjDvN6mqbOU/+5zaNOTbv8kFJRcfmPcnmNXVZTS+Pu3xQVtGReYf89v3mmyNnoC+GXht3+aCEotfmXVqON4m/0YvzLh+U0PTKuH0+7u/Sb36mzKKh6An09FGM/eMe350edMzmKdfjigxX4/k66gAABzlJREFU5vKll5in406Z66fpvnv6g01mjswbgSYVHZl3trqfdg/CJzW9PG6EmVR0ZN4R+mH/fPnFx0BfDL0yboSZUPTKvKeru7k47zd6ad4INKHp+LgD8+PgDTi5RUORFOjhebQvfygX6OqL3f9iT3+wqcyxeSPQnKJj88a3W0LTkXHL77Zr2239Cpb1hU+fNp9U9Nq4Eeb8otfmLf8bLfmVXv2N7q+QXTQUPYEOX8n15HmOmwh0+PVQPac/2DTm+LyRgzKKjs97ms3FGU1Hx40clFF0fN7xi2MSil4dN3LQ7KJX542sRt5vtORXeuU3erxCdtFQBAV67b0ET+/nb56PnCvQs3HHrNQz/ESh4Q82iXll3uWDMopemXe+ELu7h3ObXh13+aD5Ra/O2+dMPnOLXh13+aD5Ra/Oe7oam4tn33avzrt80OymV8cNDnqzvzjhpvvSCAp0/zjwpeemB0+bb/9Hs12D2Wegp+MO+fXb4SvETn+wKcxr80YOml/02rzBxYNH4uc3vTZu5KD5Ra/NO8/hRehzf6VXxo0clHPTHZs3WI3hxfOLXps3ctDsptfGHbJ/Kf/2mlm/0WgEBfrp+kfaHF75PnjUabZAT8cd8uH+7DY5+MGm3QjH540cNL/otXmHrK6yu/irHxOaXhs3ctD8otfmnef4vvmZv9Ir40YOyrnpjs075Ozi2UWvzRs5aHbTa+MO+e3wdo2Um+4LoyjQT+Of6Hc8aPcerP1taKZAT8ft8+u3x/tKb85/sGlaGZ83dtDcolfnHbN/UP7ChypO3G5j48YOmv8bvTLvPIMPCJhX9Mq4sYNSbrpj8445u3h20Svzxg6a3fTKuGP2t/Ckm+7LIinQT6OfKT04aPNGrF3xhCeRhuP22W/Is1/hm6nMq/NGD5pZ9Oq8Yd6fXDyv6bVxowcl/EZH5z0F73+Y2UVHx40elHHTHZs3yNnFs4uOzxs9aHbT8XHDbH6US/boJFBCCDELAiWEkIlBoIQQMjEIlBBCJgaBEkLIxCBQQgiZGARKCCETg0AJIWRiECghhEwMAiWEkIlBoIQQMjEIlAjkp8/u1vnjP3Zf/+vru9//7aXX/tP+65ddm5DpQaCkPI93h+zE9xIFPhyvvVMoAiW3CgIl1Rn48+7ud39dXxRX4OrIQbansAiU3CoIlBTnly/22tycTB7uxoey8ef22us523NQBEpuFQRKivMwePRy/Wjmn19y5XcraX6+/2J9Kru+NgIltwoCJcV5tz//XGdo00DWwv38+OXD9gQWgZJbBYGS4pwIdGXEtUC3Cjx5fHN31377gOmfD9c9ucu/uhe/njUQ6LvBA6vHqx8dff41IS8KAiXFuXTSOSbQ3QuW9s/Wrw+4cI//INCH02sfr747bT3/mpAXBoGS4qwtNjgH3WREoEfhbQ25+vrSffW9QAcvcNoYcvM00y5/vvA1IS8NAiXVeTd8AdM2Z49iPmyFuRbq5qjH3d3ux8tP2u+uvfbj5sxy/Y/1cbsxmzmXvibkpUGgpDzHM8W9NU8F+nj05s5zuwc7rwv08fDYwO5M9d3+RHN3wPnXhLw0CJQI5N21l8LvX500fLrpcXNy+Xj5+Z8nPlz5dn3Bw5lvz78m5KVBoEQjj8OT0KECDy9VWmnw4Lvts/XXz0AHX97tT0lPHu48/5qQlwaBEp087F9TNFDg+gHMPx3+dfqs0kqj1wV6fJfo5oLDie7uSffzrwl5YRAoUcrD3dkrOQdP8Ayeg98JdPdQ6Hn21x5e4fx1TZ9/uvQ1IS8LAiXF+emz4X3o7RM7R4G+Oz6z9PR888nrQLcPig6ehd+6c/cY6DqH09jPL39NyEuCQEltHk7d9XAq0IfBy5sGGhwcfeLUd5thu2s/HNx7ds2NNAfXO/+akGgQKKnN492J3N6d3IU/PAG/zoX3HZ29F/7x8HrR7cvw9+7dXn76wOrv/3b+9QLlyGsPAiW12b/KfZsTBV74rJC95h5333i4O/s0pvUX5wJdI3av+zzecV9dcP41IS8NAiXFedw/9b67L31U4OGtRLtsvr17Rejxk5f3lx3viB9fJ398R+fudfdb0O6Z/fOvCXlpECipzvAt6ycfifzu5BsrNw4/u/78SaDBc+3HdyIds/Htu7Mjz78m5IVBoKQ8T704ItCLH580OOrsbyIdvvO/vj580vLQp0++JuSFQaBEIPvzyP1zRGMC3Z+unt7ffrg7vfDw7NDWt8P3fO5Ah+eizr8m5EVBoIQQMjEIlBBCJgaBEkLIxCBQQgiZGARKCCETg0AJIWRiECghhEwMAiWEkIlBoIQQMjEIlBBCJgaBEkLIxCBQQgiZGARKCCETg0AJIWRiECghhEwMAiWEkIlBoIQQMjEIlBBCJgaBEkLIxCBQQgiZGARKCCETg0AJIWRiECghhEwMAiWEkIlBoIQQMjEIlBBCJgaBEkLIxCBQQgiZmP8P8Pdk+2vFJbYAAAAASUVORK5CYII=)
brvp$PercSizeFidalgo<-(brvp$Fidalgo/(sum(brvp$Fidalgo)))
brvp$PercSizeOysBay<-(brvp$OysterBay/(sum(brvp$OysterBay)))
brvp$PercSizeDabob<-(brvp$Dabob/(sum(brvp$Dabob)))
brvp$BrSzPercFidalgo<-(brvp$PercBroodFid*brvp$PercSizeFidalgo)
brvp$BrSzPercDabob<-(brvp$PercBroodDab*brvp$PercSizeDabob)
brvp$BrSzPercOysBay<-(brvp$PercBroodOys*brvp$PercSizeOysBay)
BRvP<-read.csv('BRvP.csv')
View(BRvP)
ggplot(BRvP, aes(x=SizeClass, y=BrSzPercPop, group=Pop, color=Pop, fill=Pop))+geom_bar(binwidth=10, stat="identity", position=position_dodge())+scale_colour_manual(values=c("blue","purple","orange"))+scale_fill_manual(values=c("blue","purple","orange"))+labs(x="Size Class", y="Percent of Pop. Brooding at Size Class")
![plot of chunk unnamed-chunk-1 plot of chunk 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)
ggplot(BRvP, aes(x=SizeClass, y=NoBrood, group=Pop, color=Pop, fill=Pop))+geom_bar(binwidth=10, stat="identity", position=position_dodge())+scale_colour_manual(values=c("blue","purple","orange"))+scale_fill_manual(values=c("blue","purple","orange"))+labs(x="Size Class", y="Number of Pop. Brooding at Size Class")
![plot of chunk unnamed-chunk-1 plot of chunk 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lNUGt3hjw1HoKHgJovLZ3o2WWRB5eToDn9sOAINBTdZXFUOPoNAvcIRaCi4yeKqcvAZBOoVjkBDwU0WV5WDz5wXaHPW6DBcSG8AjkBDwU0WF07znIJAvcIRaCi4yeLCaZ5TEKhXOAINBTdZXDjNcwrHQL3CEWgouMniqnLwGQTqFY5AQ8FNFleVg88gUK9wBBoKbrK4qhx8ZppAN+Kjn3V0hz82HIGGgpssPmuy55EEgX58/qT76Ea+lF0Z3eGPDUegoeAmi8+Y6rnkskC3e2lW7w9f3xQvi+7wx4Yj0FBwk8XlMz2bXBToZuDMzZyLmBDoemwE6hxusrh4oueTSwLdHhizvDKU5ewswBFoKLjJ4uKJnk8uCLT05XD5umZ1ell0hz82HIGGgpssLp3nGeWCQLfH25vbGSuC6g5/bDgCDQU3WVw6zzPKBYFujjc3i41S3lTOAByBhoKbLC6c5jnlvEA/Pj+WZfEY98IbgCPQUHCTxYXTPKdcXEzk+IzRBoFagCPQUHCTxYXTPKcgUK9wBBoKbrK4cJrnlIsCZRfeKByBhoKbLC6c5jnl4jHQI1lyEskGHIGGgpssLpzmOeXyZUy3Bw/tZlxJrzv8seEINBTcZHHpPM8oFwT69unh5ma5oMihU5OjO/yx4Qg0FNxkcek8zygXBFrqcnjZ/HbOgky6wx8bjkBDwU0Wl87zjHLpXvhyAab+Hnu5moh4AxSBrsZGoM7hJouLJ3o+ubgaU7maSLvNWb/HnPhGTgS6HhuBOoebLC6f6dnk8nqgu4O35JRvfyLQ9dgI1DncZPEZUz2XJKxIP3hv41kL0iPQ1dgI1DncZPE5cz2TpL0n0ra255zF6KvoDn9sOAINBTdZfOZ0zyG8K6dXOAINBTdZXFUOPoNAvcIRaCi4yeKqcvAZBOoVjkBDwU0WV5WDzyBQr3AEGgpusriqHHwGgXqFI9BQcJPFVeXgMwjUKxyBhoKbLK4qB59BoF7hCDQU3GRxVTn4DAL1CkegoeAmi6vKwWcQqFc4Ag0FN1lcVQ4+g0C9whFoKLjJ4qpy8BkE6hWOQEPBTRZXlYPPJAu0XFLkky8ft7NWE9Ed/thwBBoKbrL4jKmeSxIFWq/IVAp01np2usMfG45AQ8FNFpfP9GySJtBmRbtCoKxIbwWOQEPBTRYXT/R8kiTQ8p2Rvvfrt0/L5ewKg4rfFh6BrsZGoM7hJotL53lGSRLornofj1qg5U68+E09dIc/NhyBhoKbLC6d5xklSaCbaiH6RqDF5qj4RJLu8MeGI9BQcJPFhdM8p6QItFBmuc3ZCLTYBBXvw+sOf2w4Ag0FN1lcOM1zSopA339anTdqBbpDoBbgCDQU3GRx4TTPKQjUKxyBhoKbLC6c5jlFsAu/4RioBTgCDQU3WVw4zXNK4kmkcpuzEWixPfpE+t10hz82HIGGgpssLp3nGSX1MqYnrUDLa0LFV9LrDn9sOAINBTdZXDrPM0ranUjV1fOVQMt3iJffDq87/LHhCDQU3GRx6TzPKJNu5awjvxEJga7GRqDO4SaLiyd6PklcTKTccZ/vTwS6GhuBOoebLC6f6dkkeTm7RqFz9IlA12MjUOdwk8XnzPVMwoLKXuEINBTcZHFVOfgMAvUKR6Ch4CaLq8rBZxCoVzgCDQU3WVxVDj6TeCtnfwE7FhMxAUegoeAmiwuneU5JFGj/5iMEagKOQEPBTRYXTvOckirQ3tXzCNQEHIGGgpssLpzmOSVZoDf1SiKPCNQIHIGGgpssLpzmOSVNoJ/8g+f7N5NDoCbgCDQU3GRx4TTPKcnrgZZvx1kfCEWgJuAINBTcZHHhNM8p6Qsq79oDoQjUBByBhoKbLC6c5jll0or09Z2cCNQEHIGGgpssLpzmOWWCQOuTSbe8pYcNOAINBTdZXDjNc8oUgdYLijxDoCbgCDQU3GRx4TTPKZME+litpzxjSSbd4Y8NR6Ch4CaLC6d5Tpko0OpUEgK1AEegoeAmiwuneU6ZKtDuVJIousMfG45AQ8FNFhdO85wyfTWmj88RqAU4Ag0FN1lcOM1zCsvZeYUj0FBwk8VV5eAzCNQrHIGGgpssrioHnzkv0PLKz9vhe3JyEskIHIGGgpssLpzmOQWBeoUj0FBwk8WF0zynIFCvcAQaCm6yuHCa5xSOgXqFI9BQcJPFVeXgMwjUKxyBhoKbLK4qB59BoF7hCDQU3GRxVTn4DAL1CkegoeAmi6vKwWemCLQ6mfTk8vNOR3f4Y8MRaCi4yeJz5nomSRDopnlHzl1zDv720gtOR3f4Y8MRaCi4yeLymZ5NLgp01162VK4iMtegusMfG45AQ8FNFhdP9HxySaDd8nXlYsrF/8v/7d8ifmp+I4r0dbHhQ/Z5ga4Jn8leOG7hJotL53lGuSDQ8rBnfdl8sQFavTN8aVDxJqjuv5+x4WyBhoKbLC6d5xnlgkB33VmjbftRYVLxiSTd4Y8NR6Ch4CaLS+d5Rrkg0E292VlteO4/Eu/D6w5/bDgCDQU3WVw4zXPKeYHuZVlsd7Z3wG+4F94CHIGGgpssLpzmOeXiYiLN7vp+Xx6B2oAj0FBwk8WF0zynpAp0sz91hEBNwBFoKLjJ4sJpnlMuCrTehd8fAt0/Joju8MeGI9BQcJPFhdM8p1w8BtpdxNRak7PwNuAINBTcZHHpPM8oF87Cb5s992IP/lnzUO/DydEd/thwBBoKbrK4dJ5nlAsCLe/fvK1OIbV78NsZC9Ij0NXYCNQ53GRx6TzPKJdu5dy0N8DXW53VgkziDVAEuhobgTqHmywunuj55OJiIo1BqyOg9XoiMxa00x3+2HAEGgpusrh8pmeTy8vZVda83X84Z0FQ3eGPDUegoeAmi8+Y6rlk0or0c07AV9Ed/thwBBoKbrL4rMmeR3hLD69wBBoKbrK4qhx8BoF6hSPQUHCTxVXl4DMI1CscgYaCmyyuKgefQaBe4Qg0FNxkcVU5+AwC9QpHoKHgJourysFnEKhXOAINBTdZXFUOPoNAvcIRaCi4yeKqcvAZBOoVjkBDwU0WV5WDzyBQr3AEGgpusriqHHwGgXqFI9BQcJPFVeXgMwjUKxyBhoKbLK4qB59JEej7T9vFQKtseEsPC3AEGgpusrhwmucUkUB5UzkDcAQaCm6yuHCa55TpAn3/KQK1AEegoeAmiwuneU65/J5II2EX3gAcgYaCmywunOY55YJAq7fwOApvKmcAjkBDwU0Wl87zjHJpF363pD8R6GpsBOocbrK4eKLnE8FJpBnRHf7YcAQaCm6y+DKzPnQQqFc4Ag0FN1l8mVkfOlxI7xWOQEPBTRZXlYPPSAT6/re4jOn6cAQaCm6yuHCa55REgQ7PJXEdqAE4Ag0FN1lcOM1zSppANzcI1BocgYaCmywunOY5JUmgb58OBXor/W66wx8bjkBDwU0Wl87zjJIk0GID9El5V9KT+mOxPxHoamwE6hxusrh4oueTFIF+fF7dvLmrd9238j14BLoaG4E6h5ssLp3nGSXtOtDq5qO3T6vLQQudsgtvAI5AQ8FNFpfO84ySKNDb/f+aXXlRdIc/NhyBhoKbLC6d5xllgkCLTc/qLvi3T1mNyQAcgYaCmywunOY5ZYJAHzf1pufbp1zGZACOQEPBTRYXTvOckngSqdr03NYLge64DtQCHIGGgpssLpzmOSXpMqbmxPvupjqLtGVBZQtwBBoKbrK4cJrnlNQL6Ut1Fv8r1Lm74SSSBTgCDQU3WVw6zzNK+q2cz/Z3dHIZkwE4Ag0FN1lcOs8zSppAPz6vrNm8wYd4AxSBrsZGoM7hJouLJ3o+SV3OblevqbyZ9Y4eCHQ1NgJ1DjdZXD7TswkLKnuFI9BQcJPFVeXgMwjUKxyBhoKbLK4qB59BoF7hCDQU3GRxVTn4DAL1CkegoeAmi6vKwWcQqFc4Ag0FN1lcVQ4+g0C9whFoKLjJ4qpy8BkE6hWOQEPBTRZXlYPPIFCvcAQaCm6yuKocfAaBeoUj0FBwk8VV5eAzCNQrHIGGgpssrioHn0GgXuEINBTcZHFVOfgMAvUKR6Ch4CaLJ07rcq2hNjNWynAZBOoVjkBDwU0WT5zWfYHWi67nk8T3RDqMcE163eGPDUegoeAmiydO64FA5W/44zJCgQoXBdUd/thwBBoKbrJ44rQeClS+3rrHiAUqMqju8MeGI9BQcJPFE6d1KdB6n7RShXzBdYdJOwbavCnSY/0betZ/YFJ0hz82HIGGgpssnjit9wItxdB8WG+WNg+X2rgNadfUN5Xbn1yr35uz+GUIfhW6wx8bjkBDwU0WT5zWYwLdDk7Ll+788yEPkSYJdDOQZf2Z6M2NdYc/NhyBhoKbLJ44rfcCLf3Z6GFwYVP/KKD4TdFNJvEYaP+48K76R2Qn+adEd/hjwxFoKLjJ4onT+vgkUinS5p3Q2x3WypzV/0OdZEKgXuEINBTcZPHEaT0UaLmFuWmvBy0N+qQWaOWL5vM4SRFo8fsZ7sKXvyIEemU4Ag0FN1k8cVoPBFrKYb9P33xUCvRJ7/M4STwGOjiJVP0qNhwDvS4cgYaCmyyeOK17Aq23OztfVu4otrT2D2yCnUZKPQvf/tTlb6L8JW1Fd73qDn9sOAINBTdZPHFaH21WItCDbPvHOG5uu2PDU6M7/LHhCDQU3GTxxGl9JNDRXfjqgTx34ZvLE/Yb6bsb2bIrusMfG45AQ8FNFk+c1sdWHJxEerbfcc3zJFKV5kquevN7J1xzRXf4Y8MRaCi4yeKJ0/pYoKOXMRXuqI6WZncZ03LRHf7YcAQaCm6yeOK0Htkv33Ah/RrRHf7YcAQaCm6yeOK0Hjuwuen7sxLon3qe7a2ci0V3+GPDEWgouMniidN6/MzQ5mAxkSfVI6EOgD5KTiLN+UdEd/hjwxFoKLjJ4sJpfpzedU3BIrmMCYFagCPQUHCTxYXT/DiZC3R3g0DNwRFoKLjJ4sJpfpzMBbrY3QO6wx8bjkBDwU0WX2LOV8lboB+fL3XqTHf4Y8MRaCi4yeKLTPoyeQu0+OkXerdn3eGPDUegoeAmiy8z60MHgXqFI9BQcJPFl5n1oZO4C49AzcERaCi4yeLLzPrQSTqJJHr/o7HoDn9sOAINBTdZfJFJHztJAl1sH153+GPDEWgouMniidP6u9OyiEqsJPl94RfZBtUd/thwBBoKbrJ40qSeqM8MBdpfSoUL6a3AEWgouMniSZMagV4KArUIR6Ch4CaLJ01qBHopCNQiHIGGgpssnjSpEahedIc/NhyBhoKbLJ40qRHo/Ly5e7n/sMgXJ56nO/yx4Qg0FNxk8aTJj0Bn58OrTqD3d1U++3r0ibrDHxuOQEPBTRZPmv0IdG4Kf7YCfag+evfi7vNvxp6pO/yx4Qg0FNxk8aTpj0BPpzx9dHvpJFKhy1ag7aZo8dDrMZ7u8MeGI9BQcJPFkxSCQE8nRaDFTvvv/GEj0Dftvvv9+GFQ3eGPDUegoeAmiycpBIGeToJAi43Nl99+1Qj0vt11fxg/Cqo7/LHhCDQU3GTxJIUg0Fl593vfPLYCLf7fbHi+ufvBL0eerDv8seEINBTcZPEkAyDQ2ekJtDmZ9O5FJ9C7Xpb4ZmQs5wW6JnyR+sRvEOjstOLcX86EQJWDQMmVgkBnZ0ygo6fhdXdAYsPZhQ8FN1k8afYj0Nk5uwXaj+7wx4Yj0FBwk8WTZr9MoB+fd+ekx99t7u3TT748+T3PflE1l8/Cj+RwMZGzx0D70R3+2HAEGgpusniSQmYL9GZ0ufZMBdqdhecyppXhCDQU3GTxJIWIBdpos1TpiEFzFGj/OtDRezl1hz82HIGGgpssnqSQuQIt33NtRIchBNpmd9P+iPWl9QfpBMqdSHpwBBoKbrJ4khzmC7T4+Pg4aCSBvn3a28beHf9z0QmUe+H14Ag0FNxk8SSFzBdo55T6wGj9cenIQjy97bXt/nDp8RevlSSBbgb/QGyO/rnYnzxiNSY1OAINBTdZPEkhCwi02K8tTbjtn5cvHPnn+kcM28OJ1WeHX7xeEt/Soy/63eljoKwHqgdHoKHgJosnKWQBgdafNDu6hW7KbdByA7MUze6mekfg4inNo+VnB1+8YpYWKCvSa8ERaCi4yeJJCllAoPVe7baRYWHH2+q/tWd21Y76rtuzH/niFZMi0INjvBu59HWHPzYcgYaCmyyeNKkXE2ibeoOtNmWnn+4Z1QcHX7xiEo+B9jS/PXXrQEJ0hz82HIGGgpssnjSpFxZodazztn+ivdwy3T+92k4dflGqoyWSeha+3W0vP5RfQaA7/LHhCDQU3GTxpEm9gECbT/Z3J1UCbY8U7gpH7g8jVkcQh1+U6miJpF0H2p0ea386YXSHPzYcgYaCmyyeNKkXEGi3015dxNQe5mwduT0nUBdboNWx2jZzrmDVHf7YcAQaCm6yeNKkXkCg9XnpTbOb2wo0zi58meYyrHmXXekOf2w4Ag0FN1k8aVLPF2h9JqjbyNzdHJ5EenbuJNLYSiR6WWQ5u+ToDkdILQEAACAASURBVH9sOAINBTdZPGlSzxdofS98K9DySOhtd8VnewHT0WVM/S9eMQjUKxyBhoKbLJ40qecKtNy3bU4hlfu3+7Pw9UXyzVU/xxfS9754xbAL7xWOQEPBTRZPmtRigR6uB1qfRLq5+X5zX9J3/v7+xs6jWzmHX7xeEgXaW9dujkJ1hz82HIGGgpssnjSpZwu025WvLFNsWG7q80Tf+cWZxUQOv3itpAl0sC7ojIMOusMfG45AQ8FNFk+a1DKBRkmSQKt/LbozZDOuG9Ad/thwBBoKbrJ40qRGoBfTX/OkOUcmi+7wx4Yj0FBwk8WTJjUCvZhN/8BnsTvPvfAG4Ag0FNxk8aRJjUAv5eBi1RnX/usOf2w4Ag0FN1k8aVIj0Eu5uB5ocnSHPzYcgYaCmyyeNKkR6KUgUItwBBoKbrJ40qRGoJfCLrxFOAINBTdZPGlSI9CL4SSSQTgCDQU3WTxpUiPQi+EyJoNwBBoKbrJ40qRGoBfDhfQG4Qg0FNxk8aRJjUAvZ3Ar54yb4XWHPzYcgYaCmyyeNKm/+92fTotYHxbDYiJe4Qg0FNxk8aRJPVGfeQqU5ezMwRFoKLjJ4kmTGoHqRXf4Y8MRaCi4yeJJkxqB6kV3+GPDEWgouMniSZMagSaFXXhjcAQaCm6yeNKkRqAJ4SSSOTgCDQU3WTxpUiPQy2FFentwBBoKbrJ40qRGoBfDhfQG4Qg0FNxk8aRJjUAvhls5DcIRaCi4yeJJkxqBXgyLiRiEI9BQcJPFkyY1Ar0UlrOzCEegoeAmiydNagR6KSyobBGOQEPBTRZPmtQI9FIQqEU4Ag0FN1k8aVIj0EthF94iHIGGgpssnjSpEejFcBLJIByBhoKbLJ40qRHoxXAZk0E4Ag0FN1k8aVLLBFpdW967u/H9p4MbdN4+Pb5f5+ApNsKF9F7hCDQU3GTxpEmNQC+HFentwRFoKLjJ4kmTWizQZ2egwQTKYiL24Ag0FNxk8aRJjUCTwnJ2xuAINBTcZPGkSY1A9aI7/LHhCDQU3GTxpEm9jEA7O26rBd9agdaHSuuPD54y+PSsjddM2mVM87Y799Ed/thwBBoKbrJ40qReVKDtscI/XQt02x44fLJ/Snv66c8MX7GUoyYm8UJ6+Yn3QXSHPzYcgYaCmyyeNKmXFGhzkWQpxfLTt0+rJxWflp81At0MnlK8onnmQo6amMRbOcWXzg+jO/yx4Qg0FNxk8aRJLRZol1qSpQd33d569UF702Ohx9v2KfXH3VPaV7QPa4ctUK9wBBoKbrJ40qReUqCbdlNtO3jji3o1jvop2+FTuldsltrKm5bUO5GWsbvu8MeGI9BQcJPFkya1WKDHu/D7B/tn4aujnLfjT9l/OmOJjjlJOwu/1BEG3eGPDUegoeAmiydN6mUFetv7tL+dejv+lP1KcTMWiZuTxGOgw7CcnQE4Ag0FN1k8aVKvKNBiw625qAmBVtEd/thwBBoKbrJ40qReVqCDXfh2GbihQN3twiNQi3AEGgpusnjSpF5QoIUxGwtWJ9e7zcvdfhd+/xRHJ5EWi+7wx4Yj0FBwk8WTJvWSAm0vRqqv7mwF2l0dWj9lcKWTh8uYlovu8MeGI9BQcJPFkyb1kgIdXiVfPKfc2R2chfd4If1y0R3+2HAEGgpusnjSpF5UoO1p97/wdH8S6ebm+9WTD57S3O3p4FbONtWPM6un7vDHhiPQUHCTxZMm9aICrZds7xYTqexYbFpWBz5PrDdifTGRbbsayqZ3X78wusMfG45AQ8FNFk+a1DKBzs/bp1fa6BzkkkDbDeRn+4VRZhhUd/hjwxFoKLjJ4kmTWlmg3eVKmysd9RzmgkD3dwL8xU/r+zk3N4O7VKdFd/hjwxFoKLjJ4kmTWlmgu2YLbrPU/eXzckGgu/r8V+3Ruu92xvEG3eGPDUegoeAmiydNamWBdocSr3bYc5ALAm01Xxq02WAudurFm866wx8bjkBDwU0WT5rU2gJtzs1b2H9/vCTQ/UJ2++3OOYvb6Q5/bDgCDQU3WTxpUqsL1FTOC3S/tdlb0W7DrZwW4Ag0FNxk8aRJjUBPZ78WPQK1BkegoeAmiydNagR6OgjULhyBhoKbLJ40qRHo6SBQu3AEGgpusnjSpEagp4NA7cIRaCi4yeJJkxqBng4CtQtHoKHgJosnTeqf/vT8H99RhPKwGQTqFY5AQ8FNFk+a1BP1mZtAx4JADcARaCi4yeJJkxqBng4CtQtHoKHgJosnTWoEejoI1C4cgYaCmyyeNKkRqF50hz82HIGGgpssnjSpEahedIc/NhyBhoKbLJ40qRGoXnSHPzYcgYaCmyyeNKkRqF50hz82HIGGgpssnjSpEahedIc/NhyBhoKbLJ40qRGoXnSHPzYcgYaCmyyeNKkRqF50hz82HIGGgpssnjSpEahedIc/NhyBhoKbLJ40qRGoXnSHPzYcgYaCmyyeNKkRqF50hz82HIGGgpssnjSpEahedIc/NhyBhoKbLJ40qRGoXnSHPzYcgYaCmyyeNKlnCLRea+OTLy9/k/f/7szXuhU7UkgLB4F6hSPQUHCTxZMmtVigvaWKbs99g8dyDeIzz+gvefQkqfKCQaBe4Qg0FNxk8aRJLRXo26c3N88GH5xMocjzAm2+ur2s4qWDQL3CEWgouMniSZNaKNBCe9/7dYPYXBBfqkALUMdUynSBFv9esB6oATgCDQU3WTxpUgsFOnDdBfElC3Qnd5MwCNQrHIGGgpssnjSpZQItNNJzYvXZtrXotjqUue1OC232hzerB+v9/feffuffPy+Pn44I9OPz7sUH1KWDQL3CEWgouMniSZNaJtCDbcVNYbe3T+uz6IX9bhsF1t7sBNqeLqpeWgj0z1YfH+/Cb3vnlAbU5cMxUK9wBBoKbrJ40qSWCXQz3B6sthM37UmlQpDbevtxV/2vUWThwOazUpOlTYvn/4/jk0jFNl4JKh4vn96nLh8E6hWOQEPBTRZPmtRSgQ5OvFcbpLt2+/HJgWAbRe6aCz3rT7v3XO9fxlSZtN1rrw8T9KnLR1egvxFF+rrY8CH7/J/smvCZ7IXjFm6yeNKkFgm02Jg8Fuj7T0tBfnxebzf2NhhrYxavaRy4bfbn24OhJ64DbUW7py4ftkC9wtkCDQU3WTxpUi8n0Hqz9O3Tcotxtz9b1NvkvG2fXTyl+3T/eLHJuTdo5dXyCz3q8kGgXuEINBTcZPGkSS0S6Ogx0FqM29qb294mZSfQ/jurjwi0PbffnYFq9vz31MWTLNDydoEi8zSuO/yx4Qg0FNxk8aRJLRPobqiS2qfl3vZ+X3vX3d+eLNCaU7vqky8bnQ6pCydRoLtBdXF0hz82HIGGgpssnjSpZQIdXhvfflbsbQ/2tUtndnvrB5fTnxZoe/y0vdb0kLpk0gS6vellhsp1hz82HIGGgpssnjSpZQIdniVqPym2S//t6Nml5iRS/0snBdp9vmvOyh9Tl0uSQMtN4uan7X0oiO7wx4Yj0FBwk8WTJrVQoKP3wpcXx1cbaJ0sewIttuMa9WxPnESqtznbz8sjobcD6gpJEujgXv/tpaVTzkR3+GPDEWgouMniSZNaKNDx1Zg27WmWwTXxzRVLzZXxjY/Gz8IXry7EWYq2Owvfoy6fFIF2F1w9Nm3EZXSHPzYcgYaCmyyeNKmlAh1dD3TXurQ7kV7Kpvqkdytnq9PbQ04t2OaE983395uxa+3BJwp0sP07Y8UT3eGPDUegoeAmiydNarFAx1ak77lm11NrczKpORtT+2dMoM963Ore0Hpb78BgSwaBeoUj0FBwk8WTJvUMgR5nnbPlq52DTz4G+uTkZ5OiO/yx4Qg0FNxk8aRJvahA17nefa2r6B/Tz8LvC+xmLJuvO/yx4Qg0FNxk8aRJvaRA11kyaaWFmKqkXQdaGLTZi2/WkBJGd/hjwxFoKLjJ4kmTejmBbm7WONmzDrVN4ln4sQisrjv8seEINBTcZPGkSb2cQOdcIXk661DbIFCvcAQaCm6yeNKkXk6gHoNAvcIRaCi4yeJJkxqB6kV3+GPDEWgouMniSZMagepFd/hjwxFoKLjJ4kmTGoHqRXf4Y8MRaCi4yeJJkxqBJqU5EDrvgird4Y8NR6Ch4CaLJ01qBJqQ3nkkFlS2AUegoeAmiydNagR6OYPz8CyobAKOQEPBTRZPmtQI9GKq1aS6RZ5nLK2nO/yx4Qg0FNxk8aRJjUAvZteTZrfMsyS6wx8bjkBDwU0Wl87zjJK4GtPwTe5ZjckAHIGGgpssLp3nGSVFoAdv5rRlRXoLcAQaCm6yuHCa55TEWzn7O+0sqGwCjkBDwU0WF07znIJAvcIRaCi4yeLCaZ5T2IX3CkegoeAmiwuneU7hJJJXOAINBTdZXDrPMwqXMXmFI9BQcJPFpfM8o3AhvVc4Ag0FN1lcOs8ziuBWzhk3w+sOf2w4Ag0FN1lcPNHzCYuJeIUj0FBwk8XlMz2bsJydVzgCDQU3WXzOXM8kLKjsFY5AQ8FNFleVg88gUK9wBBoKbrK4qhx85rJA3z69Weyd6XWHPzYcgYaCmyy+yKSPnUsCra5gmrmM8j66wx8bjkBDwU0WX2DKR88lgW6WWIi+i+7wx4Yj0FBwk8Xnz/jwuSDQ8rr58sbN8hy8/Pr5LrrDHxuOQEPBTRafPeHj54JAN+0dSMWu/AKboLrDHxuOQEPBTRafPeHj57xAC222253bGbfAd9Ed/thwBBoKbrL47AkfP+cF2lt56e3TBc7E6w5/bDgCDQU3WXz2hI+fZIHOWcWui+7wx4Yj0FBwk8VnT/j4QaBe4Qg0FNxk8dkTPn4QqFc4Ag0FN1l89oSPHwTqFY5AQ8FNFp894eMHgXqFI9BQcJPFZ0/4+EGgXuEINBTcZPHZEz5+EKhXOAINBTdZfPaEj5+LAh0L7wtvAI5AQ8FNFhdO85yCQL3CEWgouMniwmmeUxCoVzgCDQU3WVw4zXMKK9J7hSPQUHCTxVXl4DMI1CscgYaCmyyuKgefQaBe4Qg0FNxkcVU5+AwC9QpHoKHgJourysFnEKhXOAINBTdZXFUOPoNAvcIRaCi4yeKqcvAZBOoVjkBDwU0WV5WDzyBQr3AEGgpusriqHHwGgXqFI9BQcJPFVeXgMwjUKxyBhoKbLK4qB59BoF7hCDQU3GRxVTn4TPLbGi8S3eGPDUegoeAmiy849aMmZT3Q959+8uUy3013+GPDEWgouMniy8z60EGgXuEINBTcZPFlZn3oXNyFL9yJQC3CEWgouMniy8z60LlwEmnDeqBW4Qg0FNxkceE0zykXBPr2KQI1CkegoeAmiwuneU65dBnTqEERqAE4Ag0FN1lcOM1zSsp1oBwDtQhHoKHgJosvM+tDB4F6hSPQUHCTxZeZ9aHDnUhe4Qg0FNxkcVU5+AwC9QpHoKHgJourysFnkgXavMOx+PxRFd3hjw1HoKHgJovPmeuZJFGgvTeIn6NQ3eGPDUegoeAmi8tnejZJE2jPnzc3M84o6Q5/bDgCDQU3WVw80fNJkkA/Pi+8eVt9uCs+ki/QpDv8seEINBTcZHHpPM8oSQLd9aRZyvRW+t10hz82HIGGgpssLp3nGSVJoJv+gc96hSZZdIc/NhyBhoKbLC6d5xklRaDFRuez3qdb+T687vDHhiPQUHCTxYXTPKek3Yk02GnfcS+8BTgCDQU3WVw4zXMKAvUKR6Ch4CaLC6d5TmEX3iscgYaCmywunOY5hZNIXuEINBTcZHHpPM8oXMbkFY5AQ8FNFpfO84zChfRe4Qg0FNxkcek8zyiCWzln3AyvO/yx4Qg0FNxkcfFEzycsJuIVjkBDwU0Wl8/0bMJydl7hCDQU3GTxOXM9k7Cgslc4Ag0FN1lcVQ4+g0C9whFoKLjJ4qpy8BkE6hWOQEPBTRZXlYPPLCzQb7+6q/P5N2Nf1h3+2HAEGgpusviycgiZhQX64RUCVYIj0FBwk8WXlUPILCzQdy/GzdlEd/hjwxFoKLjJ4svKIWQWFuibuy/OfVl3+GPDEWgouMniy8ohZBYW6MPdy3Nf1h3+2HAEGgpusviycgiZhQV6/4Nfnvuy7vDHhiPQUHCTxZeVQ8gsK9APrz77VXka6dR+vO7wx4Yj0FBwk8UXlUPMpK0HmnoD57sXzUn4z77uHrvrRVTRXr57JleqdF6ga8IXqU+IzySuSJ+4gt2b5vql+95lTPEEes6f1zIoAiXkCkl8T6TENegfmk3Pb7+6ez32dd0dkLXg5wU6E54cduFDwU0WT9ZIvll2C7TL/fhhUN3hXwuOQBGoKzYCXS+pb+kx8V08HsZvRdId/rXgCBSBumIj0PWSdhb+7dOJ26AIVApPDgINBTdZfMqUzzSJx0CHuXxSHoFK4clBoKHgJotP90l2WVSg337V3oh0P35Hku7wrwVHoAjUFRuBrpdlt0DbDc8Pr8bvSNId/rXgCBSBumIj0PWy7J1I715UZ9+LLdHxe5F0h38tOAJFoK7YCHS9LL4aU50T93LqDv9acASKQF2xEeh6WfotPeol6Uevon9EoEsGgYaCmyy+sBwiZtLbGn/y5eN26iX1g+gO/1pwBIpAXbER6HpJFGh9HqkU6ORr6vvRHf614AgUgbpiI9D1kibQ5jx8IdDNzRyD6g7/WnAEikBdsRHoekkS6MfnNzff+/Xbp4VAS4OmLm53HN3hXwuOQBGoKzYCXS+p98I/K+/nLAVa7sQ/k3433eFfC45AEagrNgJdL2kLKld3wjcCFazN1EV3+NeCI1AE6oqNQNdL4nJ25TZnI9BiE1S8D687/GvBESgCdcVGoOsl8VbO8rxRK9AdAkWgCNQRG4GuFwSKQOfBZ7IXjlu4yeLCaZ5TBLvwG46BIlAE6oiNQNdL4kmkcpuzEWj6OyQdR3f414IjUATqio1A10vqZUxPWoGW14SKr6TXHf614AgUgbpiI9D1knYnUnX1fCXQ7c2NfA8egS4YBBoKbrK4dJ5nlEm3cqa+ocfJ6A7/WnAEikBdsRHoeklcTKTccZ/vTwS6YBBoKLjJ4vKZnk2Sl7NrFDpHnwh0ySDQUHCTxefM9Uyy9ILK56M7/GvBESgCdcVGoOsFgSLQefCZ7IXjFm6yuKocfAaBItB58JnsheMWbrK4qhx8Jlmgb59yDLQJAkWgrtgIdL0kCnS3v4qpvp9TFt3hXwuOQBGoKzYCXS9pAt3e9MNbeiBQBOqIjUDXS5JAy933Zt+996EgusO/FhyBIlBXbAS6XhIXE+ltdW5vWEwEgSJQR2wEul4Sl7PrK5Pl7BAoAvXERqDrZcKCym1YUBmBIlBPbAS6XhAoAp0Hn8leOG7hJosLp3lOSTwG+uTkZ5OiO/xrwREoAnXFRqDrJfUs/P6t4HczrgTVHf614AgUgbpiI9D1knYd6K67fr7YnZ9xJb3u8K8FjynQn54OAl0fbrK4eKLnk/MCHSykvMCSyrrDvxY8pEDP+BOBKsBNFhdO85yCQBFoFQR6XbjJ4sJpnlMQKAKtgkCvCzdZXDjNcwrL2SHQKgj0unCTxVXl4DMIFIFWQaDXhZssrioHn0GgCLQKAr0u3GRxVTn4DAJFoFUQ6HXhJourysFnpi+ozEkkBIpAXbER6HpJEmjvXeERKAJFoM7YCHS9JAl0uCA9AkWgCNQTG4Gul8T1QOXrhwyiO/xrwREoAnXFRqDrJXE5O/ESysPoDv9acASKQF2xEeh6SRTos8vPSonu8K8FR6AI1BUbga6XxF14BNoLAkWgrtgIdL0knkRa5hAoAl0wCDQU3GTxZWZ96CQJ9OA9PeTRHf614AgUgbpiI9D1knYh/WBJ+hnRHf614AgUgbpiI9D1kngnUmFQrgNtg0ARqCs2Al0v3ImEQKsg0OvCTRYXTvOcwp1ICLQKAr0u3GRx4TTPKdyJhECrINDrwk0WX2TSx07ihfRcxtQLAkWgrtgIdL1wJxICrYJArws3WXyZWR863ImEQKsg0OvCTRZfZtaHTuJJJBYT6QWBIlBXbAS6XlIvY+JOpH0QKAJ1xUag6yXtQvqlFrTTHf614AgUgbpiI9D1kngSietAe0GgCNQVG4GuFwSKQKsg0OvCTRYXTvOcgkARaBUEel24yeLCaZ5TeF94BFoFgV4XbrK4qhx8BoEi0CoI9Lpwk8VV5eAzCBSBVkGg14WbLK4qB59BoAi0CgK9LtxkcVU5+AwnkRBoFQR6XbjJ4sJpnlMQKAKtgkCvCzdZXDjNcwoCRaBVEOh14SaLC6d5Tpl6DPTj8zk3deoO/1pwBIpAXbER6HqZfhJpztJMusO/FhyBIlBXbAS6XqYLdM7STLrDvxYcgSJQV2wEul4ElzHN2ATVHf614AgUgbpiI9D1IhIoJ5EQKAL1w0ag6wWBItAqCPS6cJPFhdM8p0wX6JzFlXWHfy04AkWgrtgIdL1MFmh5Uaj4TY51h38tOAJFoK7YCHS9SC6k/+RL6XfTHf614AgUgbpiI9D1IhGo/D2OdYd/LTgCRaCu2Ah0vUwXqPgM0iMCXTIINBTcZHH5TM8mLGeHQKsg0OvCTRZXlYPPIFAEWgWBXhdusriqHHwGgSLQKgj0unCTxVXl4DMIFIFWQaDXhZssrioHn0GgCLQKAr0u3GRxVTn4zHmBHi2lzILKf4xAEagzNgJdLwgUgVZBoNeFmywunOY5BYEi0CoI9Lpwk8WF0zynTDwG+vbprFuRdId/LTgCRaCu2Ah0vUwS6MfnpT55TyQEikA9sRHoepki0O28lUQeEeiSQaCh4CaLz5jquSRdoPXxUPFKdlV0h38tOAJFoK7YCHS9JAt0M3chkTK6w78WHIEiUFdsBLpeEgW6q86+i9+Ns43u8K8FR6AI1BUbga6XJIEusfde5TeiSF+3Fvy8QGfCkzNknxdoAk4s0JnFF45buMni82d8+KQIdJG99yq6/36uBWcLlC1QV2y2QNfLZYHOvPRzEN3hXwuOQBGoKzYCXS+XBDr70s9BdId/LTgCRaCu2Ah0vVwQ6PxLPwfRHf614AgUgbpiI9D1wr3wCLQKAr0u3GRx4TTPKQgUgVZBoNeFmywunOY5BYEi0CoI9Lpwk8WF0zynsCI9Aq2CQK8LN1lcVQ4+g0ARaBUEel24yeKqcvAZBIpAqyDQ68JNFleVg88gUARaBYFeF26yuKocfAaBItAqCPS6cJPFVeXgMwgUgVZBoNeFmyyuKgefQaAItAoCvS7cZHFVOfgMAkWgVRDodeEmi6vKwWcQKAKtgkCvCzdZXFUOPoNAEWgVBHpduMniqnLwGQSKQKsg0OvCTRZXlYPPIFAEWgWBXhdusriqHHwGgToSqNRxCNQ+3GRxVTn4DAL1I1Cx4xCofbjJ4qpy8BkEikDnwSf/GNaG0wbcZHFVOfgMAkWg8+CTfwxrw2kDbrK4qhx8BoEi0HnwyT+GteG0ATdZXFUOPoNAEeg8+OQfw9pw2oCbLK4qB59BoAh0Hnzyj2FtOG3ATRZXlYPPIFAEOg8++cewNpw24CaLq8rBZxAoAp0Hn/xjWBtOG3CTxVXl4DMIFIHOg0/+MawNpw24yeKqcvAZBIpA58En/xjWhtMG3GRxVTn4DAJFoPPgk38Ma8NpA26yuKocfAaBItB58Mk/hrXhtAE3WVxVDj6DQBHoPPjkH8PacNqAmyyuKgefQaAIdB588o9hbThtwE0WV5WDzyBQBDoPPvnHsDacNuAmi6vKwWcQKAKdB5/8Y1gbThtwk8VV5eAzCBSBzoNP/jGsDacNuMniqnLwGQSKQOfBJ/8Y1obTBtxkcVU5+AwCRaDz4JN/DGvDaQNusriqHHwGgSLQefDJP4a14bQBN1lcVQ4+g0AR6Dz45B/D2nDagJssrioHn0GgCHQefPKPYW04bcBNFleVg88gUAQ6Dz75x7A2nDbgJourysFnECgCnQef/GNYG04bcJPFVeXgMwgUgc6DT/4xrA2nDbjJ4qpy8BkEikDnwSf/GNaG0wbcZHFVOfgMAkWg8+CTfwxrw2kDbrK4qhx8BoEi0HnwyT+GteG0ATdZXFUOPoNAEeg8+OQfw9pw2oCbLK4qB59BoAh0Hnzyj2FtOG3ATRZXlYPPIFAEOg8++cewNpw24CaLq8rBZxAoAp0Hn/xjWBtOG3CTxVXl4DMIFIHOg0/+MawNpw24yeKqcvAZBIpA58En/xjWhtMG3GRxVTn4DAJFoPPgk38Ma8NpA26yuKocfAaBItB58Mk/hrXhtAE3WVxVDj6DQBHoPPjkH8PacNqAmyyuKgefQaAIdB588o9hbThtwE0WV5WDzyBQBDoPPvnHsDacNuAmi6vKwWcQKAKdB5/8Y1gbThtwk8VV5eAzUQV6TnHnXDGXfU6gaRqSFU8TqPC3Ml+gQngSe5CDv5VV4YsGgfpMUIGed9xMgwoFOt9xZ4onwaW/laTi5yKV89zN21XhywaB+gwCRaAIVARfNgjUZxAoAkWgIviyQaA+g0ARKAIVwZcNAvUZBIpAEagIvmwQqM8gUASKQEXwZYNAfQaBIlAEKoIvGwTqMwgUgSJQEXzZIFCfQaAIFIGK4MsGgfoMAkWgCFQEXzYI1GcQKAJFoCL4skGgPoNAESgCFcGXDQL1GQSKQBGoCL5sEKjPIFAEikBF8GWDQH0GgSJQBCqCLxsE6jMIFIEiUBF82SBQn0GgCBSBiuDLBoH6DAJFoAhUBF82CNRnECgCRaAi+LJBoD6DQBEoAhXBlw0C9RkEikARqAi+bBCozyBQBIpARfBlg0B9BoEiUAQqgi8bBOozCBSBIlARfNkgUJ9BoAgUgYrgywaB+gwCRaAIVARfNgjUZxAoAkWgIviyQaA+g0ARKAIVwZcNAvUZBIpAEagIvmwQqM8gUASKQEXwZYNAfQaBIlAEKoIvGwTqMwgUgSJQEXzZIFCfQaAIFIGKgGB7SQAAEXVJREFU4MsGgfoMAkWgCFQEXzYI1GcWF+ibuyJfnPii2vAj0DE4Al0SvmwQqM8sLdD7uyqffT36VbXhR6BjcAS6JHzZIFCfWVigD3d3Lx8f3724+/ybsS+rDT8CHYMj0CXhywaB+syyAv3wqvRnZdDXY19XG34EOgZHoEvClw0C9ZllBfqm3Xe/Hz8Mqjb8CHQMjkCXhC8bBOozywr0vt11fxg/Cqo2/Ah0DI5Al4QvGwTqM4sK9Nuv2g3PN3c/+OXIE9SGH4GOwRHokvBlg0B9ZmmBvqw/eveiE+hdL8OnCxV3bk4koM/TV4Sfn81JcKmHZv5Wkoqfpc8sLmRfD67yRy4tfg4umfZZZ1GBtueQ5gtUPCtS2NeCJ7Hd/VaSis/9raxRnOE8W5wkZTWBjp6G190BiQ2neCi4yeJLyiFo1t8C7Ud3+GPDKR4KbrL4knIImvWPgfajO/yx4RQPBTdZfEk5BM1qZ+GvexlTBnCKh4KbLL6kHIJmtetAR+/l1B3+2HCKh4KbLL6oHGIm6J1IGcApHgpusviicoiZoPfCZwCneCi4yeKLyiFmgq7GlAGc4qHgJosvK4eQCboeaAZwioeCmyy+sBwiJuiK9BnAKR4KbrL40nIImKDviZQBnOKh4CaLq8rBZxCoVzjFQ8FNFleVg88gUK9wioeCmyyuKgefQaBe4RQPBTdZXFUOPoNAvcIpHgpusriqHHwGgXqFUzwU3GRxVTn4DAL1Cqd4KLjJ4qpy8BkE6hVO8VBwk8VV5eAzCNQrnOKh4CaLq8rBZxCoVzjFQ8FNFleVg88gUK9wioeCmyyuKgefQaBe4RQPBTdZXFUOPoNAvcIpHgpusriqHHwGgXqFUzwU3GRxVTn4DAL1Cqd4KLjJ4qpy8BkE6hVO8VBwk8VV5eAzCNQrnOKh4CaLq8rBZxCoVzjFQ8FNFleVg88gUK9wioeCmyyuKgefQaBe4RQPBTdZXFUOPoNAvcIpHgpusriqHHwGgXqFUzwU3GRxVTn4DAL1Cqd4KLjJ4qpy8BkE6hVO8VBwk8VV5eAzCNQrnOKh4CaLq8rBZxCoVzjFQ8FNFleVg8/oClSWuzvgumyKq8PdFs88CNQrnOKh4G6LZx4E6hVO8VBwt8UzDwL1Cqd4KLjb4pkHgXqFUzwU3G3xzINAvcIpHgrutnjmQaBe4RQPBXdbPPMgUK9wioeCuy2eeRCoVzjFQ8HdFs88CNQrnOKh4G6LZx4E6hVO8VBwt8UzDwL1Cqd4KLjb4pnHg0AJIcRkECghhAiDQAkhRBgESgghwiBQQggRBoESQogwCJQQQoRBoIQQIgwCJYQQYa4i0Dd3L/cfFvni7JO+/equzuffTIKffN3D4HuebnCYpNf1nzShedLL7ovHPvt6evGk1/WfNKF48sseuu+ZXjz5de2TJv6tXHrd4cOJzZNeNnxSevHE1717IfsjJ9NyDYF+eNUJ9L4e/f3sHnlS8dGESXHxde3DP/jlhQbH4MuvGz4puXnSy9qHmx9vcvGzrxs+aXLxyy8rJvQXE4unv6570rS/lYuvO3g4tXnSy4ZPSi+e9rrh95z2KycTcgWBlmPeTOaH6qPiz/joz6b3pHcvEjcnkl5X/JNdaqr9nqcbSF538KTU5kkvK55UToDiG79+nFj88usOnjSheOLLyi2l2lXJxdNft3/SpL+Vy68bPpzcPOllwyelF0963b3sj5xMjr5Aq32LbovlZfPQ65NPKnY/0vc9Lr/uTbOZV3/P0w1Erxs+Kbl50svaJ91XX51e/Ozrhk+aXPzyy8pDFNUX04unv6570qS/lYTXDR6e8itPeNnwW6YXT3ld+73q8Zn4KydToi7QYm/id/6wcdyb/S7GFyefVPyZv3xMTMLr7pt/houtjy/ONJC9bvik5OZJL7vvtsTKJ08pnvC64ZMmFE982YdXn/3LVrKpxZNft3/SlL+VlNcNHk5vnvSy4bdML57yuof9H9TLqb9yMinaAi3+FXxZD+vjXhzFgA8Ozwye9HjfHHVsvvTZ19Vxn9fN8Z+Xg0er1zWPDl7Xpfsjqj4YNrgAbx/9OyebD+HJzX8yfHS8eJt6coiK/6PTv/IBfNKvvHz0Dy4Vv7973fxuphW/v/vZTxr26eJ7+LTie/ip5oOHRb/ylyeLD7/llOL/pPfoaPH93JlTnKREXaC/9003vs0G1+N+V3DkSeV2wq9e3e0PVn32X5vzFm/25y96j/6f9tHh645S7dccNLgIbx892bwPn9q8ffR88fpbr1S8+fLixcsdzdpxE4vf/d3LxffwicU7+Km/lcHDU5s3+ccnig+/5bTiP7o7X7z48+vtq0v/Vo5GkYzkGmfhewLtzvcczebui9VhzTLVv9/lZ180V298/k17FqP/6F+vZttnXw9fd5Tqj+mgwWX459+Uf16f/fdzzVv45Oaf/8+7v/LP7j77b+eKNy+bXPxv/4e7v/avLxVvXif4lZfg08U/vCq+VyfQib/x/3z39746V7wHn/4br+Gn/lYGD6c337+saF381keLH1dNK949sxzOE8XL71S9uDX/1D/y7i+BXMo1Bbq/4uicQN80l2jUOyLthSfto/WB8d6jhSleVo8OX3eY4nsXDx80uAj//JviFX/zH9797FzzFj61+ed/Urzsf724+6PTxd/c7a9emVS8el3x+bniHXxq8erxd2eK39f74P2TWEnF/0v56H1lpNPFe/BJxXvwU38rg4fTmxcP/Y0aXg7nj8eLD79levH2931f/h2eKP6m+8esfw4p/Y+8e5RcjBWBHg1XJ9D20FHxwOv9M9v/13srvUd//lW1Z3P3evi6gxRPKL960OAi/HX5umKf6Wfnmrfwqc1/Vr6sePRfnC7+ZmxSpBSvXvcfzxfv4FOLVy87U/xNc9T2UKCXipeFXpev+3CmeB8+pXgffupvZfBwevOHyl+vy5cVtX88Xnz4LdOLP9z94Mfl/4tn/vhU8TfNQNbbkZP/yPePkouxItDTW6BdqknSPrMd4Hb4u0c7gQ5fN0zx9eoFR/84X4D/7FWtirNboC18avMfly/rNx8/aXrfPG1S8dfl6377/BZoB59avPRQaecTxb/9qnrmiS3Q0+xvv/rtF6MC7VccwCcUH8DP/q20D4t+5T98dWoL9MS3TP2N1xfHnyj+pt2lrzoLiiPQ5Ng5Btoeznk9fNI+D/WeYjvQ/QHvPfqfjgT6UO+99uDlacbXxw0uwos5V2+K/PxM8w4+sfldewb0df9lB7+Vx+acwMTizebI3dniHXzqr7w6lvzDk8Uf2tPvo8dAT7MfCmZ9ePPMb3wAn1B8AD/3t9I9LPiVP9z9rT+4+93zv/HukqNpv/H6ItYTxd/07lQRFUegybmyQLvTg/vj4esLtPik2RYYNkhwXLP7/PPTzffwSc3/74+6beILAm0nxZTi3Yw7U7xXdeKvvFXFePHyApnyweOz8OeLF6/71UCgY8WH8PTiQ3iqQKf+yhuBrvIbPyfQ/cEC6d8KAk3NNQXav0Dt6HzJsgLtc/qKGzZId9zJ5mP+TIAXL/tL/3z/6Hjx/sNTivdn3Llf+fHDMwTaY+wzpXj/dSeLH8CTix/AT/z4c3/l1W/ln67zGx8RaPfJ/o9I+reCQFNzVYGeu0XieD+/Ot2aLNCfD1+3T+Gq/spE/QaXHPeX/0376KnmffiE5uW2Q/fo744W39Me6pOxicWL1/1+b8aNFx/Ck4uXT2xV8cNTxQ8cl1z8wHHjxYfw9N/4EH7ib+UAl9p8/1u5L371d3904Td+X1+PnFh8P5z3dz86U7x/Q8eEP3IEOjVXFWh7eHvsVHbvLHz972c9tOlboMPXdfnwqn+F27DBOXi7A18/eqL5EJ7cvPjwr/6ke/RfjBd/6J8YmFD8oWEXj/3oZPEhPLn4w129J1w89PsnizfprnVPLd482uxln/tb6Z2Fn/S30sFP/K0MH05u/lBekVb9VvoXPhz9xvvs9OLlVm3dpjwLdqL4m7v+Cyf/yrtHycVcVaCP51fY6S6k7x08Sxfo8HVdHu4O5kivwfk/3N/un6gcbz6EJzd/KE9id4/+q/HixVeahz8/WmEnwfzF66q97PHiQ3hy8eLD8lxM+bI/OVm8yf6++cTiQ8ed+1vp4BP/Vjr4ib+Vg4dTmxfPaH4rjZ3Hih9WTS3e7Kw0p+1OFN/fbjHtbwWBTs51Bfp4eqHC/ZOaW8vaKZIq0OHr2nx4td9z++KwwXlVDA+ajTU/hCc277/sh9XxgZHi9dTpfcvE4r3X/erVqeKH8ORf+f5lH04Xb4e6261MLD4U6Jm/lT182t/KHn6i+cHDqc33L/tQX+Q1VnzITi/ePfNc8fZPaurfCgKdmisL9PRS2b0nVRfgNOM55SRS/3Vt2hl/8Kf7xSV4/3U1c6T5ETytef9l1dVAY8XL3A8eTitefvaT+nXNpDizIn0HT/+Vty/7cK54/cT2e6YXH54oP7Osegef9LfSg59ofvBwavPuZfWj48WH7PTi/+9FfzhP/cqr7zk2zRDoouE9kQghRBgESgghwiBQQggRBoESQogwCJQQQoRBoIQQIgwCJYQQYRAoIYQIg0AJIUQYBEoIIcIgUEIIEQaB5p63T2/KfO/Xzecfn9985xdizJP2cyGGEFdBoHlnd9Ol8Z3IfNs9plEoAiU5BIFmnZ4/b24++bJ8SGC+4iW91NuyCJTkEASac95/2mqz2obsduOnpfJnjSmB9TYoAiU5BIHmnG3voGV5EPNWRNkU0nzWflJu05YYBEpyCALNOZt2+7NM36ZTUpr32f7Tbb0li0BJDkGgOWcg0EKEpUBr8w0Oaza79vUB06Ot1M1w37/Yiy+hPYFuekdY95y9rA8/J8RNEGjOGdvoPCXQ5jqlm8MNy/KZI7v+nUC3Q8ye02y2Hn5OiKMg0JxTyqu3DVrlhED3njswaPGFsX31VqC9C5wqQ1anmZrcjnxOiKcg0Kyz6V/AVOfg4OW2FmYp1OpZu8O97d342fsGU/qx2rIsPyif1/Aq4NjnhHgKAs07+w3E1ppDge723mz01hzj7D/jjEB3nW6bLdVNu6HZPOHwc0I8BYHmns25K+Dbi5L6p5t2w6OVu/HzP0c+LMRbPrA98O3h54R4CgIl3f1I7b50Z77uCqXCfp3mmrP1vRef2QLtfXrTbpIODncefk6IpyBQUmXbXkrUM1953PJJ99HhafkmhU/PC3R/u2j1QLfF22zGHn5OiKMgUNJke3NwAWfvvE7vHPyhQA+PiTZpMf1XHl7X9Oxx7HNC/ASB5py3T/u7zvX5nL1AN/szSyc2M8scXQdaHxTtnYWv3dkcAy3Tbc8+G/+cEC9BoBlnO1TWdijQbe/ypp79xigDuW4qaoPZdhI+QFTS7L3u8HNCPASBZpzd8KL4zWAXvjsBX+bE7UZVDu6F33UXjtbX47cSrh8fHmH9zi8OP1/uhyNEIQg047QXt9cZmG9kiZDWbrvDXe3tzcFqTOUnhwItv1dz3ed+x7144PBzQjwFgeacXXvqvdmF3puvu4OoSfXl5orQw5NG9W2ft93TKifvr5Pf39FZvq67k6k5xX/4OSGegkCzTv9O9cFKyJvBFwol9teuPzzXM7zIqd6M3N+JtE8l3s3BMw8/J8RREGjeOfbiCYGeXzWp9/SD90TqvvL9591Ky32fHn1OiKMg0NzTbj6254hOCbTdXD2xm729GX61OztUi/dZ757P5jt2J6UOPyfETRAoIYQIg0AJIUQYBEoIIcIgUEIIEQaBEkKIMAiUEEKEQaCEECIMAiWEEGEQKCGECINACSFEGARKCCHCIFBCCBEGgRJCiDAIlBBChEGghBAiDAIlhBBhECghhAiDQAkhRBgESgghwiBQQggRBoESQogwCJQQQoRBoIQQIgwCJYQQYRAoIYQIg0AJIUQYBEoIIcIgUEIIEeb/Azhc021xv+ZqAAAAAElFTkSuQmCC)
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