From aceee5d2fb995b7b095de89d7118cc85f1e866dd Mon Sep 17 00:00:00 2001 From: milesmedina <44986194+milesmedina@users.noreply.github.com> Date: Tue, 27 Aug 2024 14:00:09 -0400 Subject: [PATCH] formatting changes in figures --- R/cumu_pyro-max_sal-mean.R | 98 +++++++++--------- R/cumu_pyro-max_temp-mean.R | 166 ++++++++++++++++--------------- R/loadnorm.R | 9 +- figs/cumu_pyro-max_sal-mean.png | Bin 594978 -> 261979 bytes figs/cumu_pyro-max_temp-mean.png | Bin 546899 -> 261524 bytes figs/loadnorm.png | Bin 309627 -> 311960 bytes 6 files changed, 143 insertions(+), 130 deletions(-) diff --git a/R/cumu_pyro-max_sal-mean.R b/R/cumu_pyro-max_sal-mean.R index 60777fc..10033fa 100644 --- a/R/cumu_pyro-max_sal-mean.R +++ b/R/cumu_pyro-max_sal-mean.R @@ -11,9 +11,9 @@ load( "../data/Pyro.Rdata") # Specify subsegment subsegs <- c("NW","NE","CW","CE","SW","SE") -# Loop over sub-segments to generate plots -png( "../figs/cumu_pyro-max_sal-mean.png", width = 14, height = 9, units = 'in', res = 500 ) -par( mfrow=c(3,4), mar=c(4,4,3,2) ) +# Loop over sub-segments to generate conditional dist plots +png( "../figs/cumu_pyro-max_sal-mean.png", width = 7, height = 9, units = 'in', res = 500 ) +par( mfrow=c(3,2), mar=c(4,4,3,2) ) for( subseg in subsegs ){ # Assemble data for analysis @@ -22,13 +22,13 @@ for( subseg in subsegs ){ year(epcwq3$date) >= 2012 & epcwq3$subseg==subseg ), ] epcwq3.sub$month <- floor_date( epcwq3.sub$date, unit = 'month' ) - saldat <- epcwq3.sub |> group_by(month) |> summarise( sal = mean(value) ) |> as.data.frame() + saldat <- epcwq3.sub |> group_by(month) |> dplyr::summarise( sal = mean(value) ) |> as.data.frame() # pyro data (FWC) pyro.sub <- pyro[ which( pyro$subsegment==subseg & pyro$yr>=2012 ), ] pyro.sub$month <- floor_date( pyro.sub$date, unit = 'month' ) pyro.sub <- pyro.sub[ which(complete.cases(pyro.sub)), ] # pyro==NA means zero cells/L pyro.sub$logval <- log10( pyro.sub$pyro ) - pyrodat <- pyro.sub |> group_by(month) |> summarise( pyro = max(logval) ) |> as.data.frame() + pyrodat <- pyro.sub |> group_by(month) |> dplyr::summarise( pyro = max(logval) ) |> as.data.frame() # join pyro and sal data by month wqdat <- inner_join( pyrodat, saldat, by = 'month' ) @@ -62,57 +62,60 @@ for( subseg in subsegs ){ } # // end i loop # Plot pyro distn as a function of sal_max - plot( median ~ sal, data = pyro_sal, las = 1, + plot( sal ~ median, data = pyro_sal, las = 1, type = 'l', lwd = 2, col = rgb(0,0.2,0.6,0.7), - main = paste0( subseg, " OTB\nMax Pyro distn ~ Mean Salinity" ), - ylim = c(1,7), yaxt = 'n', - ylab = expression(italic(P.~bahamense)*" (cells/L)"), xlab = "" + xlim = c(1,7), xaxt = 'n', + ylab = "", xlab = "" ) - mtext( "Salinity (PSU)", side = 1, line = 2, cex = 0.7 ) - axis( 2, at = 1:7, las = 1, + mtext( paste0(subseg, " sub-segment"), + side = 3, adj = 0, line = 1 ) + mtext( expression(italic(P.~bahamense)*" (cells/L)"), + side = 1, line = 2.5, cex = 0.8 ) + mtext( "Salinity (ppt)", side = 2, line = 2, cex = 0.8 ) + axis( 1, at = 1:7, las = 1, labels = c( expression(10^1), expression(10^2), expression(10^3), expression(10^4), expression(10^5), expression(10^6), expression(10^7) ) ) - abline( h = axTicks(2), col = rgb(0,0,0,0.1) ) - abline( h = log10( c(2e0,3e0,4e0,5e0,6e0,7e0,8e0,9e0, + abline( v = axTicks(1), col = rgb(0,0,0,0.1) ) + abline( v = log10( c(2e0,3e0,4e0,5e0,6e0,7e0,8e0,9e0, 2e1,3e1,4e1,5e1,6e1,7e1,8e1,9e1, 2e2,3e2,4e2,5e2,6e2,7e2,8e2,9e2, 2e3,3e3,4e3,5e3,6e3,7e3,8e3,9e3, 2e4,3e4,4e4,5e4,6e4,7e4,8e4,9e4, 2e5,3e5,4e5,5e5,6e5,7e5,8e5,9e5, 2e6,3e6,4e6,5e6,6e6,7e6,8e6,9e6) ), col = rgb(0,0,0,0.1) ) - abline( v = min(pyro_sal$sal):max(pyro_sal$sal), col = rgb(0,0,0,0.1) ) - polygon( x = c( pyro_sal$sal, rev(pyro_sal$sal) ), - y = c( pyro_sal$min, rev(pyro_sal$max) ), + abline( h = pyro_sal$sal, col = rgb(0,0,0,0.1) ) + polygon( y = c( pyro_sal$sal, rev(pyro_sal$sal) ), + x = c( pyro_sal$min, rev(pyro_sal$max) ), col = rgb(0,0.2,0.6,0.1), border = rgb(0,0,0,0) ) - polygon( x = c( pyro_sal$sal, rev(pyro_sal$sal) ), - y = c( pyro_sal$lwr_iqr, rev(pyro_sal$upr_iqr) ), + polygon( y = c( pyro_sal$sal, rev(pyro_sal$sal) ), + x = c( pyro_sal$lwr_iqr, rev(pyro_sal$upr_iqr) ), col = rgb(0,0.2,0.6,0.2), border = rgb(0,0,0,0) ) - # segments( x0 = 9.3, - # y0 = 0, y1 = pyro_sal$upr_iqr[which(pyro_sal$sal==9.3)], - # lty = 2, col = 2 ) - # text( x = 9.3, y = pyro_sal$upr_iqr[which(pyro_sal$sal==9.3)], - # col = 2, labels = "9.3 ug/L", pos = 4, srt = 90 ) - # segments( x0 = 0, x1 = 9.3, - # y0 = pyro_sal$median[which(pyro_sal$sal==9.3)], - # lty = 2, col = 2 ) - # segments( x0 = 0, x1 = 9.3, - # y0 = pyro_sal$lwr_iqr[which(pyro_sal$sal==9.3)], - # lty = 2, col = 2 ) - # segments( x0 = 0, x1 = 9.3, - # y0 = pyro_sal$upr_iqr[which(pyro_sal$sal==9.3)], - # lty = 2, col = 2 ) - legend( 'bottomright', bty = 'n', - legend = c("Median of maxima", - "IQR of maxima", - "Range of maxima (min/max)"), text.font = 2, + legend( 'topleft', bty = 'n', + legend = c("Median", + "IQR", + "Range"), text.font = 2, text.col = c( rgb(0,0.2,0.6,0.9), rgb(0,0.2,0.6,0.5), rgb(0,0.2,0.6,0.3) ) ) - + +} # end conditional distribution loop +dev.off() + + +# Loop over sub-segments to generate annual means plots +png( "../figs/salinity_annual.png", width = 7, height = 9, units = 'in', res = 500 ) +par( mfrow=c(3,2), mar=c(4,4,3,2) ) +for( subseg in subsegs ){ # Plot annual mean salinity (with 1sd and 2sd bands) for reference + # subset data + epcwq3.sub <- epcwq3[ which( epcwq3$param=="Sal_top" & + year(epcwq3$date) >= 2000 & + epcwq3$subseg==subseg ), ] + epcwq3.sub$month <- floor_date( epcwq3.sub$date, unit = 'month' ) + saldat <- epcwq3.sub |> group_by(month) |> dplyr::summarise( sal = mean(value) ) |> as.data.frame() # initialize dataframe - sal_year <- data.frame( year = 2012:2023, + sal_year <- data.frame( year = 2000:2023, mean = NA, lwr_1sd = NA, upr_1sd = NA, @@ -121,7 +124,7 @@ for( subseg in subsegs ){ # populate rows for( i in 1:nrow(sal_year) ){ # subset sal data by year - this <- wqdat$sal[ which( year(wqdat$month)==sal_year$year[i] ) ] + this <- saldat$sal[ which( year(saldat$month)==sal_year$year[i] ) ] # add output to sal_year dataframe sal_year[i,2:6] <- data.frame( mean = mean(this), lwr_1sd = mean(this)-sd(this), @@ -130,13 +133,15 @@ for( subseg in subsegs ){ upr_2sd = mean(this)+2*sd(this) ) } # // end i loop - + # Plot annual mean sal values plot( mean ~ year, data = sal_year, type = 'l', las = 1, - main = paste0( subseg, " OTB\nAnnual mean salinity (non-zero Pyro months)" ), ylim = c(0,40), - ylab = "Salinity (PSU)", xlab = "", + ylab = "", xlab = "", lwd = 2, col = rgb(0,0.2,0.6,0.7)) + mtext( paste0(subseg, " sub-segment"), + side = 3, adj = 0, line = 1 ) + mtext( "Salinity (ppt)", side = 2, line = 2, cex = 0.8 ) abline( h = seq(0,50,5), col = rgb(0,0,0,0.1) ) abline( v = axTicks(1), col = rgb(0,0,0,0.1) ) polygon( x = c( sal_year$year, rev(sal_year$year) ), @@ -149,10 +154,9 @@ for( subseg in subsegs ){ # text( x = 2022, y = 9.3, # col = 2, labels = "9.3 ug/L", pos = 3 ) points( mean ~ year, data = sal_year ) - legend( 'topright', bty = 'n', + legend( 'bottomright', bty = 'n', legend = c("mean","\u00B11sd","\u00B12sd"), text.font = 2, text.col = c( rgb(0,0.2,0.6,0.9), rgb(0,0.2,0.6,0.5), rgb(0,0.2,0.6,0.3) ) ) - -} # end subseg loop - -dev.off() + +} # end annual means plots +dev.off() \ No newline at end of file diff --git a/R/cumu_pyro-max_temp-mean.R b/R/cumu_pyro-max_temp-mean.R index 283f656..af8939f 100644 --- a/R/cumu_pyro-max_temp-mean.R +++ b/R/cumu_pyro-max_temp-mean.R @@ -12,8 +12,8 @@ load( "../data/Pyro.Rdata") subsegs <- c("NW","NE","CW","CE","SW","SE") # Loop over sub-segments to generate plots -png( "../figs/cumu_pyro-max_temp-mean.png", width = 14, height = 9, units = 'in', res = 500 ) -par( mfrow=c(3,4), mar=c(4,4,3,2) ) +png( "../figs/cumu_pyro-max_temp-mean.png", width = 7, height = 9, units = 'in', res = 500 ) +par( mfrow=c(3,2), mar=c(4,4,3,2) ) for( subseg in subsegs ){ # Assemble data for analysis @@ -22,13 +22,13 @@ for( subseg in subsegs ){ year(epcwq3$date) >= 2012 & epcwq3$subseg==subseg ), ] epcwq3.sub$month <- floor_date( epcwq3.sub$date, unit = 'month' ) - tempdat <- epcwq3.sub |> group_by(month) |> summarise( temp = mean(value) ) |> as.data.frame() + tempdat <- epcwq3.sub |> group_by(month) |> dplyr::summarise( temp = mean(value) ) |> as.data.frame() # pyro data (FWC) pyro.sub <- pyro[ which( pyro$subsegment==subseg & pyro$yr>=2012 ), ] pyro.sub$month <- floor_date( pyro.sub$date, unit = 'month' ) pyro.sub <- pyro.sub[ which(complete.cases(pyro.sub)), ] # pyro==NA means zero cells/L pyro.sub$logval <- log10( pyro.sub$pyro ) - pyrodat <- pyro.sub |> group_by(month) |> summarise( pyro = max(logval) ) |> as.data.frame() + pyrodat <- pyro.sub |> group_by(month) |> dplyr::summarise( pyro = max(logval) ) |> as.data.frame() # join pyro and sal data by month wqdat <- inner_join( pyrodat, tempdat, by = 'month' ) @@ -49,7 +49,7 @@ for( subseg in subsegs ){ # Calculate pyro distn statistics # initiate dataframe - pyro_temp <- data.frame( temp = seq(15,45,1), + pyro_temp <- data.frame( temp = seq(15,40,1), median = NA, min = NA, lwr_iqr = NA, @@ -62,97 +62,103 @@ for( subseg in subsegs ){ } # // end i loop # Plot pyro distn as a function of temp_max - plot( median ~ temp, data = pyro_temp, las = 1, + plot( temp ~ median, data = pyro_temp, las = 1, type = 'l', lwd = 2, col = rgb(0,0.2,0.6,0.7), - main = paste0( subseg, " OTB\nMax Pyro distn ~ Mean Temperature" ), - ylim = c(1,7), yaxt = 'n', - ylab = expression(italic(P.~bahamense)*" (cells/L)"), xlab = "" + xlim = c(1,7), xaxt = 'n', + ylab = "", xlab = "" ) - mtext( "Temperature (C)", side = 1, line = 2, cex = 0.7 ) - axis( 2, at = 1:7, las = 1, + mtext( paste0(subseg, " sub-segment"), + side = 3, adj = 0, line = 1 ) + mtext( expression(italic(P.~bahamense)*" (cells/L)"), + side = 1, line = 2.5, cex = 0.8 ) + mtext( "Temperature (C)", side = 2, line = 2, cex = 0.8 ) + axis( 1, at = 1:7, las = 1, labels = c( expression(10^1), expression(10^2), expression(10^3), expression(10^4), expression(10^5), expression(10^6), expression(10^7) ) ) - abline( h = axTicks(2), col = rgb(0,0,0,0.1) ) - abline( h = log10( c(2e0,3e0,4e0,5e0,6e0,7e0,8e0,9e0, + abline( v = axTicks(1), col = rgb(0,0,0,0.1) ) + abline( v = log10( c(2e0,3e0,4e0,5e0,6e0,7e0,8e0,9e0, 2e1,3e1,4e1,5e1,6e1,7e1,8e1,9e1, 2e2,3e2,4e2,5e2,6e2,7e2,8e2,9e2, 2e3,3e3,4e3,5e3,6e3,7e3,8e3,9e3, 2e4,3e4,4e4,5e4,6e4,7e4,8e4,9e4, 2e5,3e5,4e5,5e5,6e5,7e5,8e5,9e5, 2e6,3e6,4e6,5e6,6e6,7e6,8e6,9e6) ), col = rgb(0,0,0,0.1) ) - abline( v = min(pyro_temp$temp):max(pyro_temp$temp), col = rgb(0,0,0,0.1) ) - polygon( x = c( pyro_temp$temp, rev(pyro_temp$temp) ), - y = c( pyro_temp$min, rev(pyro_temp$max) ), + abline( h = pyro_temp$temp, col = rgb(0,0,0,0.1) ) + polygon( y = c( pyro_temp$temp, rev(pyro_temp$temp) ), + x = c( pyro_temp$min, rev(pyro_temp$max) ), col = rgb(0,0.2,0.6,0.1), border = rgb(0,0,0,0) ) - polygon( x = c( pyro_temp$temp, rev(pyro_temp$temp) ), - y = c( pyro_temp$lwr_iqr, rev(pyro_temp$upr_iqr) ), - col = rgb(0,0.2,0.6,0.2), border = rgb(0,0,0,0) ) - # segments( x0 = 9.3, - # y0 = 0, y1 = pyro_temp$upr_iqr[which(pyro_temp$temp==9.3)], - # lty = 2, col = 2 ) - # text( x = 9.3, y = pyro_temp$upr_iqr[which(pyro_temp$temp==9.3)], - # col = 2, labels = "9.3 ug/L", pos = 4, srt = 90 ) - # segments( x0 = 0, x1 = 9.3, - # y0 = pyro_temp$median[which(pyro_temp$temp==9.3)], - # lty = 2, col = 2 ) - # segments( x0 = 0, x1 = 9.3, - # y0 = pyro_temp$lwr_iqr[which(pyro_temp$temp==9.3)], - # lty = 2, col = 2 ) - # segments( x0 = 0, x1 = 9.3, - # y0 = pyro_temp$upr_iqr[which(pyro_temp$temp==9.3)], - # lty = 2, col = 2 ) - legend( 'bottomright', bty = 'n', - legend = c("Median of maxima", - "IQR of maxima", - "Range of maxima (min/max)"), text.font = 2, - text.col = c( rgb(0,0.2,0.6,0.9), rgb(0,0.2,0.6,0.5), rgb(0,0.2,0.6,0.3) ) ) - - - # Plot annual mean Temperature (with 1sd and 2sd bands) for reference - # initialize dataframe - temp_year <- data.frame( year = 2012:2023, - mean = NA, - lwr_1sd = NA, - upr_1sd = NA, - lwr_2sd = NA, - upr_2sd = NA ) - # populate rows - for( i in 1:nrow(temp_year) ){ - # subset sal data by year - this <- wqdat$temp[ which( year(wqdat$month)==temp_year$year[i] ) ] - # add output to temp_year dataframe - temp_year[i,2:6] <- data.frame( mean = mean(this), - lwr_1sd = mean(this)-sd(this), - upr_1sd = mean(this)+sd(this), - lwr_2sd = mean(this)-2*sd(this), - upr_2sd = mean(this)+2*sd(this) - ) - } # // end i loop - - # Plot annual mean sal values - plot( mean ~ year, data = temp_year, type = 'l', las = 1, - main = paste0( subseg, " OTB\nAnnual mean Temperature (non-zero Pyro months)" ), - ylim = c(10,50), - ylab = "Temperature (C)", xlab = "", - lwd = 2, col = rgb(0,0.2,0.6,0.7)) - abline( h = seq(0,50,5), col = rgb(0,0,0,0.1) ) - abline( v = axTicks(1), col = rgb(0,0,0,0.1) ) - polygon( x = c( temp_year$year, rev(temp_year$year) ), - y = c( temp_year$lwr_1sd, rev(temp_year$upr_1sd) ), + polygon( y = c( pyro_temp$temp, rev(pyro_temp$temp) ), + x = c( pyro_temp$lwr_iqr, rev(pyro_temp$upr_iqr) ), col = rgb(0,0.2,0.6,0.2), border = rgb(0,0,0,0) ) - polygon( x = c( temp_year$year, rev(temp_year$year) ), - y = c( temp_year$lwr_2sd, rev(temp_year$upr_2sd) ), - col = rgb(0,0.2,0.6,0.1), border = rgb(0,0,0,0) ) - # abline( h = 9.3, lty = 2, col = 2, lwd = 1.5 ) - # text( x = 2022, y = 9.3, - # col = 2, labels = "9.3 ug/L", pos = 3 ) - points( mean ~ year, data = temp_year ) - legend( 'topright', bty = 'n', - legend = c("mean","\u00B11sd","\u00B12sd"), text.font = 2, + legend( 'topleft', bty = 'n', + legend = c("Median", + "IQR", + "Range"), text.font = 2, text.col = c( rgb(0,0.2,0.6,0.9), rgb(0,0.2,0.6,0.5), rgb(0,0.2,0.6,0.3) ) ) } # end subseg loop dev.off() + + +# Loop over sub-segments to generate annual means plots +png( "../figs/temp_annual.png", width = 7, height = 9, units = 'in', res = 500 ) +par( mfrow=c(3,2), mar=c(4,4,3,2) ) +for( subseg in subsegs ){ + + + # Plot annual mean Temperature (with 1sd and 2sd bands) for reference + # subset + epcwq3.sub <- epcwq3[ which( epcwq3$param=="Temp_top" & + year(epcwq3$date) >= 2000 & + epcwq3$subseg==subseg ), ] + epcwq3.sub$month <- floor_date( epcwq3.sub$date, unit = 'month' ) + tempdat <- epcwq3.sub |> group_by(month) |> dplyr::summarise( temp = mean(value) ) |> as.data.frame() + # initialize dataframe + temp_year <- data.frame( year = 2000:2023, + mean = NA, + lwr_1sd = NA, + upr_1sd = NA, + lwr_2sd = NA, + upr_2sd = NA ) + # populate rows + for( i in 1:nrow(temp_year) ){ + # subset sal data by year + this <- tempdat$temp[ which( year(tempdat$month)==temp_year$year[i] ) ] + # add output to temp_year dataframe + temp_year[i,2:6] <- data.frame( mean = mean(this), + lwr_1sd = mean(this)-sd(this), + upr_1sd = mean(this)+sd(this), + lwr_2sd = mean(this)-2*sd(this), + upr_2sd = mean(this)+2*sd(this) + ) + } # // end i loop + + # Plot annual mean sal values + plot( mean ~ year, data = temp_year, type = 'l', las = 1, + ylim = c(5,45), + ylab = "", xlab = "", + lwd = 2, col = rgb(0,0.2,0.6,0.7)) + mtext( paste0(subseg, " sub-segment"), + side = 3, adj = 0, line = 1 ) + mtext( "Temperature (C)", side = 2, line = 2, cex = 0.8 ) + abline( h = seq(0,50,5), col = rgb(0,0,0,0.1) ) + abline( v = axTicks(1), col = rgb(0,0,0,0.1) ) + polygon( x = c( temp_year$year, rev(temp_year$year) ), + y = c( temp_year$lwr_1sd, rev(temp_year$upr_1sd) ), + col = rgb(0,0.2,0.6,0.2), border = rgb(0,0,0,0) ) + polygon( x = c( temp_year$year, rev(temp_year$year) ), + y = c( temp_year$lwr_2sd, rev(temp_year$upr_2sd) ), + col = rgb(0,0.2,0.6,0.1), border = rgb(0,0,0,0) ) + # abline( h = 9.3, lty = 2, col = 2, lwd = 1.5 ) + # text( x = 2022, y = 9.3, + # col = 2, labels = "9.3 ug/L", pos = 3 ) + points( mean ~ year, data = temp_year ) + legend( 'topright', bty = 'n', + legend = c("mean","\u00B11sd","\u00B12sd"), text.font = 2, + text.col = c( rgb(0,0.2,0.6,0.9), rgb(0,0.2,0.6,0.5), rgb(0,0.2,0.6,0.3) ) ) + +} # end annual means plots +dev.off() \ No newline at end of file diff --git a/R/loadnorm.R b/R/loadnorm.R index 6e83212..58e18a0 100644 --- a/R/loadnorm.R +++ b/R/loadnorm.R @@ -29,7 +29,8 @@ points( tn_load ~ year, data = otbdat, # actual load cex = 1.2, lwd = 2, bg = rgb(1,1,1,1) ) mtext( "TN load (tons)", side = 2, line = 3.5, cex = 1.3 ) # ylab -mtext( "TN load rate (tons per million m3)", side = 4, line = 3.5, cex = 1.3 ) # y2 lab +mtext( expression("TN load rate (tons /"~10^6~"m3)"), + side = 4, line = 3.5, cex = 1.3 ) # y2 lab axis( 4, at = seq(0,1000,200), labels = round(seq(0,2.2,length.out=6),1), # y2 cex.lab = 1.3, cex.axis = 1.3, las = 1) @@ -41,9 +42,11 @@ points( tn_load_norm ~ year, data = otbdat, pch = 21, col = rgb(0.7,0.1,0.2,0.8) cex = 1.2, lwd = 2, bg = rgb(1,1,1,1) ) legend( 'bottomleft', horiz = FALSE, bty='n', - lty = c(1,1,2), lwd = 3, cex = 1.3, + lty = c(1,1,2), lwd = 3, cex = 1.2, col = c( rgb(0,0.1,0.4,0.8), rgb(0.7,0.1,0.2,0.8), rgb(0,0,0,0.5) ), - legend = c("Actual load","Normalized load","TMDL (1.08 tons per million m3)") ) + legend = c("Actual load","Normalized load", + expression("TMDL (486 tons/yr) = Delivery ratio (1.08 tons /"*10^6~"m3)") ) + ) # Plot hydro loads diff --git a/figs/cumu_pyro-max_sal-mean.png b/figs/cumu_pyro-max_sal-mean.png index 8734b36d3ac2f7009de6ddfbf6b2333d6664a2b6..46661b561bd21a5729bc04076755fd974c23bf48 100644 GIT binary patch literal 261979 zcmeFZXH-*N*ESjz1q1;BL5dKRrbv;Fbd+921nD9OQY6wlp~_8@4$_q>(m`r~2uN?z zJ4o+AdPzvm-toDg@BMjxoHNFE#`!XaCY$WF)?9PVa;~YEjfXIJeTEaMu!8`w6K_D+b z+*SJT)#rF>S#XknU%5F%l>dH)aI(oq|Mx=X{{4Xe)uoE^P1gT@rKJ)Ez4Y&upSCsu z@xK?4_}Kq5)PI!me-!w?hgq~RUW{opcv>|a>l$7JEp?=ydp=Gc(bCvq6G{@Lu(*f4 z)_ase2rSx!?urt0*ws^#;Ds$4H(>kF_hC>o{no&wR$`9_w65l+%yUd{rut9Y2j7nk z2-x!$)UB>4B=$kH;vJB~o1At)C;!muTqcHi#L--als%M>E^An5W@z@NvNf`AZ<=e& zctg83Dq}g5L9gU@Lm1^|aI`rmd*V7M33wBco)R%DWLeRG2=YUPTDIjuy;t*gk5S{x zaG#5@^lh5(fl~k8>g$fq;?;FiZ8nzb5G}us(A62_3qbU06B)3vEZBGxXlxTe%)*sa 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