From 92ba089f3b0d6736e12083c93fcb8b3e28487ba3 Mon Sep 17 00:00:00 2001 From: milesmedina <44986194+milesmedina@users.noreply.github.com> Date: Sun, 8 Sep 2024 17:42:27 -0400 Subject: [PATCH] small updates --- R/cumu_pyro-chl_demo.R | 37 +++++++++--------- R/cumu_pyro-max_chl-mean.R | 26 ++++++------ R/ssa_pyro_otb-ne.R | 2 +- figs/cumu_pyro-chl_demo.png | Bin 159432 -> 163944 bytes figs/cumu_pyro-max_chl-mean.png | Bin 306779 -> 306005 bytes .../ssa_pyro_plots_allsamples.png | Bin 384277 -> 0 bytes 6 files changed, 33 insertions(+), 32 deletions(-) delete mode 100644 figs/pyro_fwc_allsamples/ssa_pyro_plots_allsamples.png diff --git a/R/cumu_pyro-chl_demo.R b/R/cumu_pyro-chl_demo.R index fed728a..56b3c63 100644 --- a/R/cumu_pyro-chl_demo.R +++ b/R/cumu_pyro-chl_demo.R @@ -29,30 +29,31 @@ pyrodat <- pyro.sub |> group_by(month) |> dplyr::summarise( pyro = max(logval) ) # join pyro and chl data by month pcdat <- inner_join( pyrodat, chldat, by = 'month' ) -# Plot histogram of max pyro assoc with mean chl-a at or below 9.3 ug/L -hist( pcdat$pyro[ which(pcdat$chl <= 9.3) ], breaks = 20, freq = TRUE, +# Plot histogram of max pyro assoc with mean chl-a at or below 8.5 ug/L +hist( pcdat$pyro[ which(pcdat$chl <= 8.5) ], breaks = 20, freq = TRUE, border = rgb(1,1,1,1), col = rgb(0,0,0,0.4), - main = "", xlab = "", ylab = "", las = 1, xaxt = 'n', xlim = c(1,7) + main = "", xlab = "", ylab = "", + las = 1, xaxt = 'n', xlim = c(1,7) ) # mtext( expression(atop("(a) Monthly maximum "*italic(P.~bahamense)*" abundance", -# "associated with monthly mean chlorophyll-a \u22649.3 ug/L")), +# "associated with monthly mean chlorophyll-a \u22648.5 ug/L")), # side = 3, cex = 1.1 ) mtext( expression("(a) Monthly maximum "*italic(P.~bahamense)*" distribution"), side = 3, line = 1.5, adj = 0 ) -mtext( expression(" associated with monthly mean chlorophyll-a \u22649.3 ug/L"), +mtext( expression(" associated with monthly mean chlorophyll-a \u22648.5 ug/L"), side = 3, line = 0.5, adj = 0 ) mtext( expression(italic(P.~bahamense)*" (cells/L)"), side = 1, line = 2.5 ) -mtext( "Frequency", side = 2, line = 2 ) +mtext( "Number of samples", side = 2, line = 2 ) axis( 1, at = 1:7, labels = c( expression(10^1), expression(10^2), expression(10^3), expression(10^4), expression(10^5), expression(10^6), expression(10^7) ) ) -abline( v = pcdat$pyro[ which(pcdat$chl <= 9.3) ] |> median(), +abline( v = pcdat$pyro[ which(pcdat$chl <= 8.5) ] |> median(), col = rgb(1,0.1,0.1,0.6), lwd = 3 ) -abline( v = pcdat$pyro[ which(pcdat$chl <= 9.3) ] |> quantile(0.25), +abline( v = pcdat$pyro[ which(pcdat$chl <= 8.5) ] |> quantile(0.25), lty = 3, col = rgb(1,0.1,0.1,0.6), lwd = 3 ) -abline( v = pcdat$pyro[ which(pcdat$chl <= 9.3) ] |> quantile(0.75), +abline( v = pcdat$pyro[ which(pcdat$chl <= 8.5) ] |> quantile(0.75), lty = 3, col = rgb(1,0.1,0.1,0.6), lwd = 3 ) # Define function to summarize pyro distribution @@ -115,18 +116,18 @@ polygon( x = c( pyro_chl$min, rev(pyro_chl$max) ), polygon( x = c( pyro_chl$lwr_iqr, rev(pyro_chl$upr_iqr) ), y = c( pyro_chl$chl, rev(pyro_chl$chl) ), col = rgb(0,0.2,0.6,0.2), border = rgb(0,0,0,0) ) -segments( x0 = 0, x1 = pyro_chl$upr_iqr[which(pyro_chl$chl==9.3)], - y0 = 9.3, +segments( x0 = 0, x1 = pyro_chl$upr_iqr[which(pyro_chl$chl==8.5)], + y0 = 8.5, lty = 3, col = rgb(1,0.1,0.1,0.6), lwd = 3 ) -text( x = 2, y = 9, col = rgb(1,0.1,0.1,0.8), labels = "9.3 ug/L", pos = 3 ) -segments( x0 = pyro_chl$median[which(pyro_chl$chl==9.3)], - y0 = 0, y1 = 9.3, +text( x = 2, y = 9, col = rgb(1,0.1,0.1,0.8), labels = "8.5 ug/L", pos = 3 ) +segments( x0 = pyro_chl$median[which(pyro_chl$chl==8.5)], + y0 = 0, y1 = 8.5, lty = 1, col = rgb(1,0.1,0.1,0.6), lwd = 3 ) -segments( x0 = pyro_chl$lwr_iqr[which(pyro_chl$chl==9.3)], - y0 = 0, y1 = 9.3, +segments( x0 = pyro_chl$lwr_iqr[which(pyro_chl$chl==8.5)], + y0 = 0, y1 = 8.5, lty = 3, col = rgb(1,0.1,0.1,0.6), lwd = 3 ) -segments( x0 = pyro_chl$upr_iqr[which(pyro_chl$chl==9.3)], - y0 = 0, y1 = 9.3, +segments( x0 = pyro_chl$upr_iqr[which(pyro_chl$chl==8.5)], + y0 = 0, y1 = 8.5, lty = 3, col = rgb(1,0.1,0.1,0.6), lwd = 3 ) legend( 'topleft', bty = 'n', legend = c("Median", diff --git a/R/cumu_pyro-max_chl-mean.R b/R/cumu_pyro-max_chl-mean.R index 0b53eae..7ad5ff8 100644 --- a/R/cumu_pyro-max_chl-mean.R +++ b/R/cumu_pyro-max_chl-mean.R @@ -98,19 +98,19 @@ for( subseg in subsegs ){ polygon( x = c( pyro_chl$lwr_iqr, rev(pyro_chl$upr_iqr) ), y = c( pyro_chl$chl, rev(pyro_chl$chl) ), col = rgb(0,0.2,0.6,0.2), border = rgb(0,0,0,0) ) - segments( x0 = 0, x1 = pyro_chl$upr_iqr[which(pyro_chl$chl==9.3)], - y0 = 9.3, + segments( x0 = 0, x1 = pyro_chl$upr_iqr[which(pyro_chl$chl==8.5)], + y0 = 8.5, lty = 3, col = rgb(1,0.1,0.1,0.6), lwd = 2 ) text( x = 2, y = 9, col = rgb(1,0.1,0.1,0.8), - labels = "9.3 ug/L", pos = 3, cex = 1.3 ) - segments( x0 = pyro_chl$median[which(pyro_chl$chl==9.3)], - y0 = 0, y1 = 9.3, + labels = "8.5 ug/L", pos = 3, cex = 1.3 ) + segments( x0 = pyro_chl$median[which(pyro_chl$chl==8.5)], + y0 = 0, y1 = 8.5, lty = 1, col = rgb(1,0.1,0.1,0.6), lwd = 2 ) - segments( x0 = pyro_chl$lwr_iqr[which(pyro_chl$chl==9.3)], - y0 = 0, y1 = 9.3, + segments( x0 = pyro_chl$lwr_iqr[which(pyro_chl$chl==8.5)], + y0 = 0, y1 = 8.5, lty = 3, col = rgb(1,0.1,0.1,0.6), lwd = 2 ) - segments( x0 = pyro_chl$upr_iqr[which(pyro_chl$chl==9.3)], - y0 = 0, y1 = 9.3, + segments( x0 = pyro_chl$upr_iqr[which(pyro_chl$chl==8.5)], + y0 = 0, y1 = 8.5, lty = 3, col = rgb(1,0.1,0.1,0.6), lwd = 2 ) legend( x = 0.5, y = 26, bty = 'n', legend = c("Median", @@ -140,7 +140,7 @@ for( subseg in subsegs ){ # ) # } # // end i loop # - # # Plot annual mean chl values with 9.3 ug/L threshold + # # Plot annual mean chl values with 8.5 ug/L threshold # plot( mean ~ year, data = chl_year, type = 'l', las = 1, # ylim = c(0,50), # main = "", ylab = "", xlab = "", @@ -159,9 +159,9 @@ for( subseg in subsegs ){ # polygon( x = c( chl_year$year, rev(chl_year$year) ), # y = c( chl_year$lwr_2sd, rev(chl_year$upr_2sd) ), # col = rgb(0,0.2,0.6,0.1), border = rgb(0,0,0,0) ) - # abline( h = 9.3, lty = 2, col = rgb(1,0.2,0.1,0.7), lwd = 2 ) - # # text( x = 2022, y = 9.3, - # # col = 2, labels = "9.3 ug/L", pos = 3 ) + # abline( h = 8.5, lty = 2, col = rgb(1,0.2,0.1,0.7), lwd = 2 ) + # # text( x = 2022, y = 8.5, + # # col = 2, labels = "8.5 ug/L", pos = 3 ) # points( mean ~ year, data = chl_year ) # legend( 'topright', bty = 'n', # legend = c("Mean","\u00B11sd","\u00B12sd"), text.font = 2, cex = 1.3, diff --git a/R/ssa_pyro_otb-ne.R b/R/ssa_pyro_otb-ne.R index 0e1c64b..e6f8dfd 100644 --- a/R/ssa_pyro_otb-ne.R +++ b/R/ssa_pyro_otb-ne.R @@ -97,7 +97,7 @@ wcor(obj,groups = 1:20) |> plot() # SSA grouping ## # Specify signal component groups -grp <- list( c(1,2), c(3,4), c(7,8), c(9,10) ) +grp <- list( c(1,2), c(3,4), c(7,8) ) # SSA reconstruction ## diff --git a/figs/cumu_pyro-chl_demo.png b/figs/cumu_pyro-chl_demo.png index 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