From fc5f3c3efbfd3ecdb424acfccd14e2647f5552fc Mon Sep 17 00:00:00 2001 From: ntthung Date: Fri, 7 May 2021 22:06:36 +0800 Subject: [PATCH] Update code for Revision 1. --- R/init.R | 53 +- ...unctions.R => input_selection_functions.R} | 85 +- data/p1-daily-Q-filled.csv | 36161 ---------------- multiproxy-mbr.Rmd | 875 +- multiproxy-mbr.html | 2559 +- results/Xrwdo.RDS | Bin 0 -> 77003 bytes results/p1merge.csv | 253 - results/p1tar.RDS | Bin 0 -> 1723 bytes results/poolDT.RDS | Bin 0 -> 1351 bytes results/pop500_gen500_lambda0.RDS | Bin 13050 -> 0 bytes results/pop500_gen500_lambda00_s100.RDS | Bin 0 -> 29395 bytes results/pop500_gen500_lambda00_s150.RDS | Bin 0 -> 26044 bytes results/pop500_gen500_lambda00_s200.RDS | Bin 0 -> 29910 bytes results/pop500_gen500_lambda00_s250.RDS | Bin 0 -> 31452 bytes results/pop500_gen500_lambda00_s300.RDS | Bin 0 -> 28873 bytes results/pop500_gen500_lambda00_s350.RDS | Bin 0 -> 29063 bytes results/pop500_gen500_lambda00_s400.RDS | Bin 0 -> 32382 bytes 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100644 results/pop500_gen500_lambda28_s150.RDS create mode 100644 results/pop500_gen500_lambda28_s200.RDS create mode 100644 results/pop500_gen500_lambda28_s250.RDS create mode 100644 results/pop500_gen500_lambda28_s300.RDS create mode 100644 results/pop500_gen500_lambda28_s350.RDS create mode 100644 results/pop500_gen500_lambda28_s400.RDS create mode 100644 results/pop500_gen500_lambda28_s450.RDS create mode 100644 results/pop500_gen500_lambda30_s100.RDS create mode 100644 results/pop500_gen500_lambda30_s150.RDS create mode 100644 results/pop500_gen500_lambda30_s200.RDS create mode 100644 results/pop500_gen500_lambda30_s250.RDS create mode 100644 results/pop500_gen500_lambda30_s300.RDS create mode 100644 results/pop500_gen500_lambda30_s350.RDS create mode 100644 results/pop500_gen500_lambda30_s400.RDS create mode 100644 results/pop500_gen500_lambda30_s450.RDS diff --git a/R/init.R b/R/init.R index c91b5da..c402d23 100644 --- a/R/init.R +++ b/R/init.R @@ -50,8 +50,6 @@ theme_set(my_theme) monthLab <- month.abb names(monthLab) <- 1:12 -lambdaLab <- function(x) sapply(x, function(v) paste0('\u03bb = ', substr(v, 1, 1))) - dark2 <- RColorBrewer::brewer.pal(3, 'Dark2')[c(2, 1, 3)] pal <- wesanderson::wes_palette('Cavalcanti1')[c(2, 1)] @@ -94,23 +92,26 @@ hinkley <- function(x, type = 2) { (mean(x, na.rm = TRUE) - median(x, na.rm = TRUE)) / scale } -roundDT <- function(DT, digits = 2, type = 'numeric') { - DT[, lapply(.SD, - function(x) { - if (is.numeric(x)) { - rd <- round(x, digits) - if (type == 'numeric') rd else sprintf(glue("%.{digits}f"), rd) - } else x - })] -} -power_set <- function(N, min.size = 1, max.size = N) { - if (max.size > N) max.size <- N - if (min.size > N) NULL else { - subsets <- foreach(k = 1:N) %dopar% combn(1:N, k, simplify = FALSE) - subsets <- unlist(subsets, recursive = FALSE) - lengths <- sapply(subsets, length) - subsets[lengths %between% c(min.size, max.size)] +roundDT <- function(DT, digits = 2, type = 'numeric') { + if (length(digits) == 1) { + DT[, lapply(.SD, + function(x) { + if (is.numeric(x)) { + rd <- round(x, digits) + if (type == 'numeric') rd else sprintf(glue("%.{digits}f"), rd) + } else x + })] + } else { + DT[, mapply(function(x, k) { + if (is.numeric(x)) { + rd <- round(x, k) + if (type == 'numeric') rd else sprintf(glue("%.{k}f"), rd) + } else x + }, + x = .SD, + k = digits, + SIMPLIFY = FALSE)] } } @@ -118,22 +119,6 @@ power_set <- function(N, min.size = 1, max.size = N) { lapplyrbind <- function(x, fun, ..., id = NULL) rbindlist(lapply(x, fun, ...), idcol = id) -# Megadroughts - -mgd <- data.table(sta = c(1756, 1790, 1876), - fin = c(1768, 1796, 1878), - name = c('Strange Parallels Drought', - 'East India Drought', - 'Victorian Great Drought')) standardize <- function(x, ...) (x - mean(x, ...)) / sd(x, ...) -boxcox_lambda <- function(x) { - bc <- MASS::boxcox(x ~ 1, plotit = FALSE) - bc$x[which.max(bc$y)] -} - -boxcox <- function(x, lambda = NULL) { - if (is.null(lambda)) lambda <- boxcox_lambda(x) - if (abs(lambda) < 0.01) log(x) else (x^lambda - 1) / lambda -} diff --git a/R/input-selection-functions.R b/R/input_selection_functions.R similarity index 68% rename from R/input-selection-functions.R rename to R/input_selection_functions.R index c86d165..a3ffb3c 100644 --- a/R/input-selection-functions.R +++ b/R/input_selection_functions.R @@ -85,14 +85,13 @@ wPCA <- function(X, rho = 1, p = 0, min.variance = 0.95, use.eigen = TRUE, retur #' @param n.site.min Minimum number of sites to be kept for each season #' @inheritParams cv_mb #' @param Xpool The input matrix of all the sites in the pool -#' @param use.robust.mean Whether the arithmetic mean (if set to FALSE) or the Tukey's biweight robust mean is used to etermine the final value from cross-validation. Default is FALSE. +#' @param use.robust.mean Whether the arithmetic mean (if set to FALSE) or the Tukey's biweight robust mean is used to determine the final value from cross-validation. Default is FALSE. #' @export cv_site_selection <- function(choice, pool, n.site.min = 5, Xpool, instQ, cv.folds, start.year, lambda = 1, log.trans, force.standardize = FALSE, use.robust.mean = FALSE) { - season <- N <- site <- NULL seasons <- levels(instQ$season) poolSubset <- pool[choice == 1L] num.targets <- length(seasons) @@ -108,7 +107,7 @@ cv_site_selection <- function(choice, pool, n.site.min = 5, pcList <- lapply(seasons, function(s) { Qa <- instQ[s] - X <- Xpool[, poolSubset[s, site]] + X <- Xpool[, poolSubset[s, site2]] PC <- wPCA(X, use.eigen = FALSE, return.matrix = TRUE) sv <- input_selection( PC[instInd, , drop = FALSE], @@ -116,8 +115,86 @@ cv_site_selection <- function(choice, pool, n.site.min = 5, PC[, sv, drop = FALSE] }) - cvFval <- cv_mb(instQ, pcList, cv.folds, start.year, lambda, log.trans, force.standardize, return.type = 'mb') + cvFval <- cv_mb(instQ, pcList, cv.folds, start.year, lambda, log.trans, force.standardize, return.type = 'fval') if (use.robust.mean) -tbrm(cvFval) else -mean(cvFval) } else -1e12 } + +# This function is a custom-made version of mbr::mb_reconstruction(), in order to extract the parameters +# In a near future this option to extract the parameters will be incorporated into mb_reconstruction() + +mb_par <- function(instQ, pc.list, start.year, lambda = 1, + log.trans = NULL, force.standardize = FALSE) { + + # Setup + years <- start.year:max(instQ$year) + instInd <- which(years %in% instQ$year) + seasons <- levels(instQ$season) + N <- length(seasons) + sInd <- 1:(N-1) + Y <- instQ$Qa + + XList <- lapply(pc.list, mbr:::prepend_ones) + XListInst <- lapply(XList, function(x) x[instInd, , drop = FALSE]) + X <- as.matrix(Matrix:::.bdiag(XList)) + XTrain <- as.matrix(Matrix:::.bdiag(XListInst)) + + # NOTES: + # Working with matrices are much faster than with data.table + # Important to keep drop = FALSE; otherwise, when there is only one PC, a vector is returned. + # Use column form so that we can use c() later + + # Calibration ------------------------------------- + + if (is.null(log.trans)) { + + if (force.standardize) { + + # Scale the observation + Y <- matrix(Y, ncol = N) + Y <- colScale(Y) + cm <- attributes(Y)[['scaled:center']] # column mean + csd <- attributes(Y)[['scaled:scale']] # column sd + Y <- c(Y) + + # Multiply X by sigma_s / sigma_q before making A + ratio <- csd / csd[N] + Xscaled <- lapply(sInd, function(k) XListInst[[k]] * ratio[k]) + A <- cbind(do.call(cbind, Xscaled), -XListInst[[N]]) + } else { + cm <- NULL + csd <- NULL + A <- cbind(do.call(cbind, XListInst[sInd]), -XListInst[[N]]) + } + + # Analytical solution when there is no transformation + XTX <- crossprod(XTrain) + XTY <- crossprod(XTrain, Y) + ATA <- crossprod(A) + beta <- solve(XTX + lambda * ATA, XTY) + + } else { # Numerical solution + + Y <- matrix(Y, ncol = N) + Y[, log.trans] <- log(Y[, log.trans]) + if (length(log.trans) < N || force.standardize) { + Y <- colScale(Y) + cm <- attributes(Y)[['scaled:center']] + csd <- attributes(Y)[['scaled:scale']] + } else { + cm <- NULL + csd <- NULL + } + Y <- c(Y) + + log.seasons <- which(log.trans < N) + log.ann <- max(log.trans) == N + + beta <- mbr:::mb_fit(XTrain, Y, lambda, cm, csd, log.seasons, log.ann, N, sInd) + } + + # Prediction ------------------------------------------ + + beta +} diff --git a/data/p1-daily-Q-filled.csv b/data/p1-daily-Q-filled.csv deleted file mode 100644 index 68e633f..0000000 --- a/data/p1-daily-Q-filled.csv +++ /dev/null @@ -1,36161 +0,0 @@ -year,day,month,Q -1921,1,Apr,14 -1921,2,Apr,14 -1921,3,Apr,13 -1921,4,Apr,13 -1921,5,Apr,13 -1921,6,Apr,13 -1921,7,Apr,12 -1921,8,Apr,12 -1921,9,Apr,11 -1921,10,Apr,11 -1921,11,Apr,11 -1921,12,Apr,10 -1921,13,Apr,11 -1921,14,Apr,11 -1921,15,Apr,11 -1921,16,Apr,10 -1921,17,Apr,10 -1921,18,Apr,12 -1921,19,Apr,16 -1921,20,Apr,16 -1921,21,Apr,16 -1921,22,Apr,16 -1921,23,Apr,16 -1921,24,Apr,16 -1921,25,Apr,16 -1921,26,Apr,13 -1921,27,Apr,13 -1921,28,Apr,13 -1921,29,Apr,13 -1921,30,Apr,13 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-1995,4,Apr,13.85 -1995,5,Apr,17.34 -1995,6,Apr,18.51 -1995,7,Apr,18.51 -1995,8,Apr,15.01 -1995,9,Apr,13.15 -1995,10,Apr,19.67 -1995,11,Apr,19.67 -1995,12,Apr,17.34 -1995,13,Apr,16.18 -1995,14,Apr,16.18 -1995,15,Apr,17.34 -1995,16,Apr,13.15 -1995,17,Apr,15.01 -1995,18,Apr,13.15 -1995,19,Apr,12.45 -1995,20,Apr,13.85 -1995,21,Apr,13.15 -1995,22,Apr,13.85 -1995,23,Apr,13.15 -1995,24,Apr,11.75 -1995,25,Apr,9.65 -1995,26,Apr,8.25 -1995,27,Apr,8.95 -1995,28,Apr,11.05 -1995,29,Apr,12.45 -1995,30,Apr,11.75 -1996,1,Apr,12.14 -1996,2,Apr,16.2 -1996,3,Apr,19.5 -1996,4,Apr,14 -1996,5,Apr,12.14 -1996,6,Apr,12.76 -1996,7,Apr,14 -1996,8,Apr,13.38 -1996,9,Apr,10.28 -1996,10,Apr,11.52 -1996,11,Apr,20.6 -1996,12,Apr,23.9 -1996,13,Apr,25 -1996,14,Apr,25 -1996,15,Apr,25 -1996,16,Apr,27.5 -1996,17,Apr,31.25 -1996,18,Apr,33.75 -1996,19,Apr,32.5 -1996,20,Apr,31.25 -1996,21,Apr,32.5 -1996,22,Apr,36.25 -1996,23,Apr,40.1 -1996,24,Apr,35 -1996,25,Apr,30 -1996,26,Apr,27.5 -1996,27,Apr,19.5 -1996,28,Apr,23.9 -1996,29,Apr,28.75 -1996,30,Apr,25 -1997,1,Apr,10.1 -1997,2,Apr,11.4 -1997,3,Apr,9.45 -1997,4,Apr,8.8 -1997,5,Apr,11.4 -1997,6,Apr,12.05 -1997,7,Apr,7.5 -1997,8,Apr,10.75 -1997,9,Apr,10.75 -1997,10,Apr,8.8 -1997,11,Apr,16.85 -1997,12,Apr,18.75 -1997,13,Apr,22.55 -1997,14,Apr,23.5 -1997,15,Apr,23.5 -1997,16,Apr,15.9 -1997,17,Apr,14 -1997,18,Apr,14.95 -1997,19,Apr,13.35 -1997,20,Apr,16.85 -1997,21,Apr,13.35 -1997,22,Apr,11.4 -1997,23,Apr,11.4 -1997,24,Apr,14.95 -1997,25,Apr,14.95 -1997,26,Apr,14.95 -1997,27,Apr,14 -1997,28,Apr,13.35 -1997,29,Apr,20.65 -1997,30,Apr,23.5 -1998,1,Apr,5.38 -1998,2,Apr,5.91 -1998,3,Apr,6.97 -1998,4,Apr,8.15 -1998,5,Apr,8.8 -1998,6,Apr,16.1 -1998,7,Apr,21.72 -1998,8,Apr,23.88 -1998,9,Apr,23.16 -1998,10,Apr,21.72 -1998,11,Apr,23.16 -1998,12,Apr,26.76 -1998,13,Apr,27.48 -1998,14,Apr,28.94 -1998,15,Apr,28.2 -1998,16,Apr,26.76 -1998,17,Apr,26.76 -1998,18,Apr,25.32 -1998,19,Apr,23.88 -1998,20,Apr,25.32 -1998,21,Apr,26.04 -1998,22,Apr,25.32 -1998,23,Apr,24.6 -1998,24,Apr,23.88 -1998,25,Apr,23.88 -1998,26,Apr,23.16 -1998,27,Apr,22.44 -1998,28,Apr,22.44 -1998,29,Apr,21 -1998,30,Apr,19.6 -1999,1,Apr,5.5 -1999,2,Apr,6.3 -1999,3,Apr,5.5 -1999,4,Apr,4.75 -1999,5,Apr,5 -1999,6,Apr,7.9 -1999,7,Apr,10.3 -1999,8,Apr,10.3 -1999,9,Apr,11.1 -1999,10,Apr,10.3 -1999,11,Apr,12.7 -1999,12,Apr,14.65 -1999,13,Apr,12.7 -1999,14,Apr,11.9 -1999,15,Apr,13.5 -1999,16,Apr,13.5 -1999,17,Apr,14.65 -1999,18,Apr,16.95 -1999,19,Apr,16.95 -1999,20,Apr,14.65 -1999,21,Apr,14.65 -1999,22,Apr,13.5 -1999,23,Apr,11.1 -1999,24,Apr,9.5 -1999,25,Apr,6.3 -1999,26,Apr,5.5 -1999,27,Apr,4.75 -1999,28,Apr,4.75 -1999,29,Apr,12.7 -1999,30,Apr,18.1 -2000,1,Apr,18.16 -2000,2,Apr,14.5 -2000,3,Apr,12.86 -2000,4,Apr,12.04 -2000,5,Apr,10.4 -2000,6,Apr,12.04 -2000,7,Apr,13.68 -2000,8,Apr,15.72 -2000,9,Apr,16.94 -2000,10,Apr,15.72 -2000,11,Apr,14.5 -2000,12,Apr,14.5 -2000,13,Apr,16.94 -2000,14,Apr,20.6 -2000,15,Apr,20.6 -2000,16,Apr,21.82 -2000,17,Apr,32.3 -2000,18,Apr,30.9 -2000,19,Apr,26.7 -2000,20,Apr,29.5 -2000,21,Apr,25.48 -2000,22,Apr,23.04 -2000,23,Apr,21.82 -2000,24,Apr,21.82 -2000,25,Apr,19.38 -2000,26,Apr,25.48 -2000,27,Apr,18.16 -2000,28,Apr,18.16 -2000,29,Apr,21.82 -2000,30,Apr,20.6 -2001,1,Apr,8.8 -2001,2,Apr,8.8 -2001,3,Apr,9.64 -2001,4,Apr,13 -2001,5,Apr,13 -2001,6,Apr,11.32 -2001,7,Apr,11.32 -2001,8,Apr,10.48 -2001,9,Apr,12.16 -2001,10,Apr,12.16 -2001,11,Apr,11.32 -2001,12,Apr,11.32 -2001,13,Apr,14.68 -2001,14,Apr,17.2 -2001,15,Apr,17.2 -2001,16,Apr,17.2 -2001,17,Apr,20.23 -2001,18,Apr,24.27 -2001,19,Apr,25.28 -2001,20,Apr,23.26 -2001,21,Apr,23.26 -2001,22,Apr,19.22 -2001,23,Apr,20.23 -2001,24,Apr,16.36 -2001,25,Apr,12.16 -2001,26,Apr,11.32 -2001,27,Apr,12.16 -2001,28,Apr,10.48 -2001,29,Apr,12.16 -2001,30,Apr,18.21 -2002,1,Apr,17 -2002,2,Apr,17 -2002,3,Apr,17 -2002,4,Apr,14.51 -2002,5,Apr,8.31 -2002,6,Apr,10.36 -2002,7,Apr,12.02 -2002,8,Apr,13.68 -2002,9,Apr,13.68 -2002,10,Apr,12.85 -2002,11,Apr,17 -2002,12,Apr,19.56 -2002,13,Apr,20.84 -2002,14,Apr,22.12 -2002,15,Apr,20.84 -2002,16,Apr,23.4 -2002,17,Apr,13.68 -2002,18,Apr,13.68 -2002,19,Apr,15.34 -2002,20,Apr,16.17 -2002,21,Apr,16.17 -2002,22,Apr,15.34 -2002,23,Apr,15.34 -2002,24,Apr,17 -2002,25,Apr,13.68 -2002,26,Apr,10.36 -2002,27,Apr,10.36 -2002,28,Apr,11.19 -2002,29,Apr,10.36 -2002,30,Apr,9.53 -2003,1,Apr,31.8 -2003,2,Apr,23.8 -2003,3,Apr,21.2 -2003,4,Apr,19.9 -2003,5,Apr,21.2 -2003,6,Apr,18.6 -2003,7,Apr,15.1 -2003,8,Apr,14.2 -2003,9,Apr,12.4 -2003,10,Apr,12.4 -2003,11,Apr,12.4 -2003,12,Apr,14.2 -2003,13,Apr,14.2 -2003,14,Apr,14.2 -2003,15,Apr,16 -2003,16,Apr,21.2 -2003,17,Apr,23.8 -2003,18,Apr,25.1 -2003,19,Apr,23.8 -2003,20,Apr,23.8 -2003,21,Apr,23.8 -2003,22,Apr,25.1 -2003,23,Apr,21.2 -2003,24,Apr,14.2 -2003,25,Apr,12.4 -2003,26,Apr,13.3 -2003,27,Apr,14.2 -2003,28,Apr,13.3 -2003,29,Apr,12.4 -2003,30,Apr,16 -2004,1,Apr,2.52 -2004,2,Apr,2.52 -2004,3,Apr,3.48 -2004,4,Apr,5.4 -2004,5,Apr,7.48 -2004,6,Apr,11.15 -2004,7,Apr,10.25 -2004,8,Apr,9.8 -2004,9,Apr,8.45 -2004,10,Apr,6.96 -2004,11,Apr,6.7 -2004,12,Apr,6.96 -2004,13,Apr,7.48 -2004,14,Apr,8.9 -2004,15,Apr,8 -2004,16,Apr,6.96 -2004,17,Apr,6.7 -2004,18,Apr,6.18 -2004,19,Apr,7.22 -2004,20,Apr,8 -2004,21,Apr,5.92 -2004,22,Apr,3.96 -2004,23,Apr,3.36 -2004,24,Apr,3.12 -2004,25,Apr,3.24 -2004,26,Apr,3 -2004,27,Apr,2.04 -2004,28,Apr,1.92 -2004,29,Apr,2.16 -2004,30,Apr,4.14 -2005,1,Apr,32.2 -2005,2,Apr,32.2 -2005,3,Apr,32.2 -2005,4,Apr,38.7 -2005,5,Apr,46.4 -2005,6,Apr,41.6 -2005,7,Apr,40 -2005,8,Apr,40 -2005,9,Apr,34.8 -2005,10,Apr,29.6 -2005,11,Apr,28.3 -2005,12,Apr,29.6 -2005,13,Apr,33.5 -2005,14,Apr,33.5 -2005,15,Apr,37.4 -2005,16,Apr,33.5 -2005,17,Apr,30.9 -2005,18,Apr,33.5 -2005,19,Apr,33.5 -2005,20,Apr,30.9 -2005,21,Apr,32.2 -2005,22,Apr,33.5 -2005,23,Apr,33.5 -2005,24,Apr,32.2 -2005,25,Apr,28.3 -2005,26,Apr,24.4 -2005,27,Apr,21.8 -2005,28,Apr,21.8 -2005,29,Apr,27 -2005,30,Apr,24.4 -2006,1,Apr,27.6 -2006,2,Apr,31.5 -2006,3,Apr,25 -2006,4,Apr,23 -2006,5,Apr,22 -2006,6,Apr,20 -2006,7,Apr,15 -2006,8,Apr,17 -2006,9,Apr,20 -2006,10,Apr,20 -2006,11,Apr,19 -2006,12,Apr,19 -2006,13,Apr,18 -2006,14,Apr,20 -2006,15,Apr,23 -2006,16,Apr,31.5 -2006,17,Apr,50.6 -2006,18,Apr,60.2 -2006,19,Apr,41.6 -2006,20,Apr,56 -2006,21,Apr,52.4 -2006,22,Apr,41.6 -2006,23,Apr,34.1 -2006,24,Apr,30.2 -2006,25,Apr,30.2 -2006,26,Apr,26.3 -2006,27,Apr,28.9 -2006,28,Apr,43.4 -2006,29,Apr,58.1 -2006,30,Apr,74.9 -2007,1,Apr,15.7 -2007,2,Apr,19.3 -2007,3,Apr,18.5 -2007,4,Apr,18.5 -2007,5,Apr,18.5 -2007,6,Apr,19.7 -2007,7,Apr,19.7 -2007,8,Apr,18.1 -2007,9,Apr,14.5 -2007,10,Apr,15.3 -2007,11,Apr,17.7 -2007,12,Apr,18.5 -2007,13,Apr,19.3 -2007,14,Apr,19.7 -2007,15,Apr,21.5 -2007,16,Apr,21.5 -2007,17,Apr,19.3 -2007,18,Apr,19.7 -2007,19,Apr,21.5 -2007,20,Apr,18.5 -2007,21,Apr,18.1 -2007,22,Apr,17.3 -2007,23,Apr,16.1 -2007,24,Apr,14.9 -2007,25,Apr,13.3 -2007,26,Apr,7.7 -2007,27,Apr,13.7 -2007,28,Apr,67.75 -2007,29,Apr,63.02 -2007,30,Apr,52 -2008,1,Apr,23.55 -2008,2,Apr,23.55 -2008,3,Apr,22.6 -2008,4,Apr,22.6 -2008,5,Apr,26.9 -2008,6,Apr,26.9 -2008,7,Apr,24.5 -2008,8,Apr,25.7 -2008,9,Apr,25.7 -2008,10,Apr,25.7 -2008,11,Apr,24.5 -2008,12,Apr,23.55 -2008,13,Apr,22.6 -2008,14,Apr,21.65 -2008,15,Apr,20.7 -2008,16,Apr,19.75 -2008,17,Apr,17.85 -2008,18,Apr,13.4 -2008,19,Apr,11.8 -2008,20,Apr,11 -2008,21,Apr,11 -2008,22,Apr,8.6 -2008,23,Apr,8.6 -2008,24,Apr,11 -2008,25,Apr,20.7 -2008,26,Apr,34.1 -2008,27,Apr,43.7 -2008,28,Apr,35.3 -2008,29,Apr,37.7 -2008,30,Apr,44.9 -2009,1,Apr,20.15 -2009,2,Apr,18.81 -2009,3,Apr,20.15 -2009,4,Apr,21.49 -2009,5,Apr,20.15 -2009,6,Apr,29.24 -2009,7,Apr,26.78 -2009,8,Apr,26.78 -2009,9,Apr,27.6 -2009,10,Apr,26.78 -2009,11,Apr,27.6 -2009,12,Apr,25.14 -2009,13,Apr,25.14 -2009,14,Apr,25.14 -2009,15,Apr,24.32 -2009,16,Apr,25.14 -2009,17,Apr,26.78 -2009,18,Apr,25.96 -2009,19,Apr,22.16 -2009,20,Apr,15.12 -2009,21,Apr,12.88 -2009,22,Apr,12.32 -2009,23,Apr,11.76 -2009,24,Apr,11.2 -2009,25,Apr,11.2 -2009,26,Apr,12.32 -2009,27,Apr,19.48 -2009,28,Apr,18.14 -2009,29,Apr,19.48 -2009,30,Apr,16.8 -2010,1,Apr,2.92 -2010,2,Apr,2.92 -2010,3,Apr,2.4 -2010,4,Apr,2.1 -2010,5,Apr,2.5 -2010,6,Apr,2.4 -2010,7,Apr,2.3 -2010,8,Apr,2.2 -2010,9,Apr,2.2 -2010,10,Apr,2.3 -2010,11,Apr,3.16 -2010,12,Apr,2.92 -2010,13,Apr,2.92 -2010,14,Apr,3.04 -2010,15,Apr,2.7 -2010,16,Apr,2.6 -2010,17,Apr,2.7 -2010,18,Apr,2.7 -2010,19,Apr,2.6 -2010,20,Apr,2.5 -2010,21,Apr,2.3 -2010,22,Apr,2.3 -2010,23,Apr,2.1 -2010,24,Apr,1.9 -2010,25,Apr,1.8 -2010,26,Apr,2.5 -2010,27,Apr,2.1 -2010,28,Apr,2.2 -2010,29,Apr,2.3 -2010,30,Apr,2.1 -2011,1,Apr,22.25 -2011,2,Apr,21.1 -2011,3,Apr,22.25 -2011,4,Apr,22.25 -2011,5,Apr,23.4 -2011,6,Apr,23.4 -2011,7,Apr,26.85 -2011,8,Apr,26.85 -2011,9,Apr,29.35 -2011,10,Apr,34.75 -2011,11,Apr,38.8 -2011,12,Apr,38.8 -2011,13,Apr,41.5 -2011,14,Apr,43.07 -2011,15,Apr,44.65 -2011,16,Apr,38.8 -2011,17,Apr,33.4 -2011,18,Apr,34.75 -2011,19,Apr,30.7 -2011,20,Apr,36.1 -2011,21,Apr,36.1 -2011,22,Apr,36.1 -2011,23,Apr,33.4 -2011,24,Apr,52.52 -2011,25,Apr,76.3 -2011,26,Apr,81.25 -2011,27,Apr,60.4 -2011,28,Apr,47.8 -2011,29,Apr,41.5 -2011,30,Apr,52.52 -2012,1,Apr,15.05 -2012,2,Apr,16 -2012,3,Apr,21.2 -2012,4,Apr,19.9 -2012,5,Apr,18.6 -2012,6,Apr,23.8 -2012,7,Apr,30.4 -2012,8,Apr,34.6 -2012,9,Apr,27.7 -2012,10,Apr,25.1 -2012,11,Apr,25.1 -2012,12,Apr,26.4 -2012,13,Apr,27.7 -2012,14,Apr,30.4 -2012,15,Apr,29 -2012,16,Apr,29 -2012,17,Apr,25.1 -2012,18,Apr,18.6 -2012,19,Apr,14.1 -2012,20,Apr,13.15 -2012,21,Apr,14.1 -2012,22,Apr,12.2 -2012,23,Apr,11.25 -2012,24,Apr,12.2 -2012,25,Apr,12.2 -2012,26,Apr,12.2 -2012,27,Apr,10.3 -2012,28,Apr,12.2 -2012,29,Apr,15.05 -2012,30,Apr,16 -2013,1,Apr,1.35 -2013,2,Apr,1.63 -2013,3,Apr,12.6 -2013,4,Apr,10.8 -2013,5,Apr,6.09 -2013,6,Apr,4.98 -2013,7,Apr,5.72 -2013,8,Apr,2.78 -2013,9,Apr,1.7 -2013,10,Apr,9 -2013,11,Apr,17.4 -2013,12,Apr,7.8 -2013,13,Apr,9 -2013,14,Apr,9.6 -2013,15,Apr,9.6 -2013,16,Apr,10.2 -2013,17,Apr,10.2 -2013,18,Apr,8.4 -2013,19,Apr,6.09 -2013,20,Apr,5.72 -2013,21,Apr,6.09 -2013,22,Apr,5.72 -2013,23,Apr,6.46 -2013,24,Apr,7.2 -2013,25,Apr,7.2 -2013,26,Apr,5.72 -2013,27,Apr,5.72 -2013,28,Apr,4.98 -2013,29,Apr,5.35 -2013,30,Apr,5.72 -2014,1,Apr,15 -2014,2,Apr,20.6 -2014,3,Apr,18.6 -2014,4,Apr,9.09 -2014,5,Apr,7.21 -2014,6,Apr,16.8 -2014,7,Apr,18.6 -2014,8,Apr,26.1 -2014,9,Apr,14.1 -2014,10,Apr,4.56 -2014,11,Apr,5.49 -2014,12,Apr,12.3 -2014,13,Apr,25 -2014,14,Apr,19.5 -2014,15,Apr,12.3 -2014,16,Apr,18.6 -2014,17,Apr,33.4 -2014,18,Apr,11.4 -2014,19,Apr,10.5 -2014,20,Apr,26.1 -2014,21,Apr,25 -2014,22,Apr,14.1 -2014,23,Apr,10.03 -2014,24,Apr,6.74 -2014,25,Apr,3.01 -2014,26,Apr,5.49 -2014,27,Apr,33.4 -2014,28,Apr,22.8 -2014,29,Apr,29.4 -2014,30,Apr,11.4 -2015,1,Apr,1.85 -2015,2,Apr,1 -2015,3,Apr,1 -2015,4,Apr,2.02 -2015,5,Apr,4.35 -2015,6,Apr,8.4 -2015,7,Apr,5.67 -2015,8,Apr,1.68 -2015,9,Apr,0.9 -2015,10,Apr,1.17 -2015,11,Apr,2.53 -2015,12,Apr,4.68 -2015,13,Apr,7.8 -2015,14,Apr,10.8 -2015,15,Apr,16.75 -2015,16,Apr,10.8 -2015,17,Apr,13.9 -2015,18,Apr,28.75 -2015,19,Apr,11.4 -2015,20,Apr,15.8 -2015,21,Apr,3.69 -2015,22,Apr,2.19 -2015,23,Apr,1.17 -2015,24,Apr,1.51 -2015,25,Apr,6.6 -2015,26,Apr,25.85 -2015,27,Apr,71.6 -2015,28,Apr,12.95 -2015,29,Apr,8.4 -2015,30,Apr,19.6 -2016,1,Apr,0.23 -2016,2,Apr,0.3 -2016,3,Apr,1.8 -2016,4,Apr,0.55 -2016,5,Apr,0.32 -2016,6,Apr,0.24 -2016,7,Apr,0.2 -2016,8,Apr,0.15 -2016,9,Apr,0.2 -2016,10,Apr,1.5 -2016,11,Apr,0.48 -2016,12,Apr,0.29 -2016,13,Apr,0.22 -2016,14,Apr,0.17 -2016,15,Apr,0.14 -2016,16,Apr,0.28 -2016,17,Apr,1.5 -2016,18,Apr,0.48 -2016,19,Apr,0.29 -2016,20,Apr,0.24 -2016,21,Apr,0.18 -2016,22,Apr,0.16 -2016,23,Apr,0.1 -2016,24,Apr,0.18 -2016,25,Apr,2.8 -2016,26,Apr,0.85 -2016,27,Apr,0.36 -2016,28,Apr,0.25 -2016,29,Apr,0.18 -2016,30,Apr,0.17 -2017,1,Apr,46 -2017,2,Apr,18 -2017,3,Apr,22.4 -2017,4,Apr,65 -2017,5,Apr,37.6 -2017,6,Apr,49.8 -2017,7,Apr,20 -2017,8,Apr,37.6 -2017,9,Apr,22.4 -2017,10,Apr,34.8 -2017,11,Apr,13 -2017,12,Apr,7 -2017,13,Apr,3.08 -2017,14,Apr,5.5 -2017,15,Apr,22.4 -2017,16,Apr,26 -2017,17,Apr,55.5 -2017,18,Apr,40.4 -2017,19,Apr,55.5 -2017,20,Apr,27.2 -2017,21,Apr,23.6 -2017,22,Apr,19 -2017,23,Apr,63.1 -2017,24,Apr,47.9 -2017,25,Apr,36.2 -2017,26,Apr,47.9 -2017,27,Apr,16 -2017,28,Apr,10 -2017,29,Apr,28.4 -2017,30,Apr,59.3 -2018,1,Apr,19.35 -2018,2,Apr,14.75 -2018,3,Apr,19.35 -2018,4,Apr,36.5 -2018,5,Apr,15.9 -2018,6,Apr,23.4 -2018,7,Apr,13.6 -2018,8,Apr,18.2 -2018,9,Apr,15.9 -2018,10,Apr,20.5 -2018,11,Apr,18.2 -2018,12,Apr,24.85 -2018,13,Apr,13.6 -2018,14,Apr,11.3 -2018,15,Apr,12.45 -2018,16,Apr,14.75 -2018,17,Apr,32.1 -2018,18,Apr,18.2 -2018,19,Apr,21.95 -2018,20,Apr,19.35 -2018,21,Apr,19.35 -2018,22,Apr,12.45 -2018,23,Apr,13.6 -2018,24,Apr,35 -2018,25,Apr,11.3 -2018,26,Apr,21.95 -2018,27,Apr,21.95 -2018,28,Apr,21.95 -2018,29,Apr,15.9 -2018,30,Apr,19.35 -2019,1,Apr,47 -2019,2,Apr,19.4 -2019,3,Apr,15.4 -2019,4,Apr,35.8 -2019,5,Apr,42.8 -2019,6,Apr,54.6 -2019,7,Apr,28.2 -2019,8,Apr,23.4 -2019,9,Apr,41.4 -2019,10,Apr,56.5 -2019,11,Apr,45.6 -2019,12,Apr,30.6 -2019,13,Apr,18.6 -2019,14,Apr,20.2 -2019,15,Apr,33 -2019,16,Apr,37.2 -2019,17,Apr,22.2 -2019,18,Apr,48.9 -2019,19,Apr,23.4 -2019,20,Apr,27 -2019,21,Apr,28.2 -2019,22,Apr,16.2 -2019,23,Apr,30.6 -2019,24,Apr,38.6 -2019,25,Apr,29.4 -2019,26,Apr,35.8 -2019,27,Apr,40 -2019,28,Apr,45.6 -2019,29,Apr,33 -2019,30,Apr,14.6 -1921,1,May,9.85 -1921,2,May,9.85 -1921,3,May,9.85 -1921,4,May,9.85 -1921,5,May,11 -1921,6,May,13 -1921,7,May,14 -1921,8,May,9.5 -1921,9,May,8.8 -1921,10,May,8.45 -1921,11,May,7.75 -1921,12,May,8.1 -1921,13,May,8.1 -1921,14,May,8.8 -1921,15,May,7.4 -1921,16,May,7.4 -1921,17,May,6.7 -1921,18,May,6 -1921,19,May,6 -1921,20,May,6.35 -1921,21,May,7.05 -1921,22,May,12 -1921,23,May,12 -1921,24,May,12 -1921,25,May,12 -1921,26,May,18 -1921,27,May,17 -1921,28,May,39 -1921,29,May,47 -1921,30,May,42 -1921,31,May,42 -1922,1,May,15 -1922,2,May,14 -1922,3,May,14 -1922,4,May,14 -1922,5,May,14 -1922,6,May,13 -1922,7,May,13 -1922,8,May,13 -1922,9,May,13 -1922,10,May,13 -1922,11,May,13 -1922,12,May,13 -1922,13,May,13 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-1995,15,May,42 -1995,16,May,42 -1995,17,May,33.6 -1995,18,May,32.25 -1995,19,May,29.55 -1995,20,May,25.5 -1995,21,May,23.17 -1995,22,May,20.84 -1995,23,May,19.67 -1995,24,May,16.18 -1995,25,May,15.01 -1995,26,May,18.51 -1995,27,May,20.84 -1995,28,May,17.34 -1995,29,May,25.5 -1995,30,May,24.33 -1995,31,May,23.17 -1996,1,May,21.7 -1996,2,May,22.8 -1996,3,May,22.8 -1996,4,May,28.75 -1996,5,May,45.3 -1996,6,May,56.5 -1996,7,May,42.7 -1996,8,May,30 -1996,9,May,26.25 -1996,10,May,21.7 -1996,11,May,20.6 -1996,12,May,18.4 -1996,13,May,17.3 -1996,14,May,17.3 -1996,15,May,16.2 -1996,16,May,17.3 -1996,17,May,20.6 -1996,18,May,17.3 -1996,19,May,18.4 -1996,20,May,17.3 -1996,21,May,16.2 -1996,22,May,16.2 -1996,23,May,23.9 -1996,24,May,32.5 -1996,25,May,44 -1996,26,May,47.9 -1996,27,May,31.25 -1996,28,May,26.25 -1996,29,May,18.4 -1996,30,May,13.38 -1996,31,May,16.2 -1997,1,May,24.55 -1997,2,May,24.55 -1997,3,May,22.55 -1997,4,May,21.6 -1997,5,May,20.65 -1997,6,May,17.8 -1997,7,May,12.05 -1997,8,May,12.7 -1997,9,May,14 -1997,10,May,16.85 -1997,11,May,21.6 -1997,12,May,25.6 -1997,13,May,25.6 -1997,14,May,23.5 -1997,15,May,22.55 -1997,16,May,21.6 -1997,17,May,22.55 -1997,18,May,18.75 -1997,19,May,12.05 -1997,20,May,12.7 -1997,21,May,13.35 -1997,22,May,12.7 -1997,23,May,15.9 -1997,24,May,18.75 -1997,25,May,19.7 -1997,26,May,26.65 -1997,27,May,35.15 -1997,28,May,37.45 -1997,29,May,31.9 -1997,30,May,23.5 -1997,31,May,17.8 -1998,1,May,21 -1998,2,May,21 -1998,3,May,21 -1998,4,May,21 -1998,5,May,23.16 -1998,6,May,24.6 -1998,7,May,26.04 -1998,8,May,26.76 -1998,9,May,21.72 -1998,10,May,19.6 -1998,11,May,18.2 -1998,12,May,21 -1998,13,May,16.75 -1998,14,May,39.15 -1998,15,May,26.7 -1998,16,May,18.65 -1998,17,May,16.75 -1998,18,May,14.85 -1998,19,May,15.8 -1998,20,May,14.85 -1998,21,May,13.9 -1998,22,May,13.9 -1998,23,May,12.95 -1998,24,May,10.64 -1998,25,May,9.96 -1998,26,May,12.95 -1998,27,May,18.65 -1998,28,May,25.4 -1998,29,May,28 -1998,30,May,19.6 -1998,31,May,16.75 -1999,1,May,14.65 -1999,2,May,11.9 -1999,3,May,9.5 -1999,4,May,6.3 -1999,5,May,7.9 -1999,6,May,7.9 -1999,7,May,7.1 -1999,8,May,7.9 -1999,9,May,22.7 -1999,10,May,37 -1999,11,May,34.6 -1999,12,May,44.68 -1999,13,May,47.24 -1999,14,May,40.84 -1999,15,May,35.8 -1999,16,May,38.28 -1999,17,May,27.4 -1999,18,May,18.1 -1999,19,May,12.7 -1999,20,May,33.4 -1999,21,May,35.8 -1999,22,May,62.6 -1999,23,May,90 -1999,24,May,100.5 -1999,25,May,93 -1999,26,May,80.2 -1999,27,May,74.66 -1999,28,May,74.66 -1999,29,May,69.3 -1999,30,May,56.2 -1999,31,May,49.8 -2000,1,May,20.6 -2000,2,May,23.04 -2000,3,May,20.6 -2000,4,May,20.6 -2000,5,May,23.04 -2000,6,May,29.5 -2000,7,May,28.1 -2000,8,May,26.7 -2000,9,May,20.6 -2000,10,May,18.16 -2000,11,May,20.6 -2000,12,May,24.26 -2000,13,May,29.5 -2000,14,May,37.9 -2000,15,May,46.42 -2000,16,May,68.05 -2000,17,May,116.2 -2000,18,May,92 -2000,19,May,66.6 -2000,20,May,98.4 -2000,21,May,121.6 -2000,22,May,87.2 -2000,23,May,62.25 -2000,24,May,62.25 -2000,25,May,57.9 -2000,26,May,49.28 -2000,27,May,47.85 -2000,28,May,46.42 -2000,29,May,50.71 -2000,30,May,43.56 -2000,31,May,37.9 -2001,1,May,17.2 -2001,2,May,16.36 -2001,3,May,13.84 -2001,4,May,17.2 -2001,5,May,20.23 -2001,6,May,19.22 -2001,7,May,19.22 -2001,8,May,21.24 -2001,9,May,18.21 -2001,10,May,17.2 -2001,11,May,19.22 -2001,12,May,27.3 -2001,13,May,28.52 -2001,14,May,27.3 -2001,15,May,30.96 -2001,16,May,32.18 -2001,17,May,37.06 -2001,18,May,43.85 -2001,19,May,39.5 -2001,20,May,37.06 -2001,21,May,46.75 -2001,22,May,75.75 -2001,23,May,58.35 -2001,24,May,39.5 -2001,25,May,32.18 -2001,26,May,30.96 -2001,27,May,69.95 -2001,28,May,127.4 -2001,29,May,102.5 -2001,30,May,71.4 -2001,31,May,61.25 -2002,1,May,7.53 -2002,2,May,8.7 -2002,3,May,11.19 -2002,4,May,9.53 -2002,5,May,8.31 -2002,6,May,12.85 -2002,7,May,12.85 -2002,8,May,13.68 -2002,9,May,12.85 -2002,10,May,12.85 -2002,11,May,12.85 -2002,12,May,14.51 -2002,13,May,23.4 -2002,14,May,48.72 -2002,15,May,43 -2002,16,May,31.12 -2002,17,May,43 -2002,18,May,81.85 -2002,19,May,78.95 -2002,20,May,83.3 -2002,21,May,70.26 -2002,22,May,53.01 -2002,23,May,39.04 -2002,24,May,33.76 -2002,25,May,29.8 -2002,26,May,28.52 -2002,27,May,35.08 -2002,28,May,65.94 -2002,29,May,48.72 -2002,30,May,41.68 -2002,31,May,37.72 -2003,1,May,31.8 -2003,2,May,41.6 -2003,3,May,38.8 -2003,4,May,36 -2003,5,May,30.4 -2003,6,May,29 -2003,7,May,25.1 -2003,8,May,17.3 -2003,9,May,17.3 -2003,10,May,14.2 -2003,11,May,14.2 -2003,12,May,15.1 -2003,13,May,22.5 -2003,14,May,26.4 -2003,15,May,34.6 -2003,16,May,33.2 -2003,17,May,29 -2003,18,May,26.4 -2003,19,May,26.4 -2003,20,May,27.7 -2003,21,May,22.5 -2003,22,May,14.2 -2003,23,May,13.3 -2003,24,May,13.3 -2003,25,May,11.5 -2003,26,May,12.4 -2003,27,May,15.1 -2003,28,May,21.2 -2003,29,May,27.7 -2003,30,May,30.4 -2003,31,May,36 -2004,1,May,5.92 -2004,2,May,5.92 -2004,3,May,7.22 -2004,4,May,9.8 -2004,5,May,11.15 -2004,6,May,10.7 -2004,7,May,10.7 -2004,8,May,14.75 -2004,9,May,16.55 -2004,10,May,10.25 -2004,11,May,7.22 -2004,12,May,6.96 -2004,13,May,6.44 -2004,14,May,5.92 -2004,15,May,5.66 -2004,16,May,6.96 -2004,17,May,8.9 -2004,18,May,7.48 -2004,19,May,6.44 -2004,20,May,8.45 -2004,21,May,46.1 -2004,22,May,70 -2004,23,May,60.55 -2004,24,May,43.5 -2004,25,May,27.02 -2004,26,May,20.3 -2004,27,May,22.54 -2004,28,May,31.5 -2004,29,May,47.4 -2004,30,May,41.1 -2004,31,May,44.8 -2005,1,May,24.4 -2005,2,May,25.7 -2005,3,May,34.8 -2005,4,May,38.7 -2005,5,May,38.7 -2005,6,May,37.4 -2005,7,May,33.5 -2005,8,May,32.2 -2005,9,May,40 -2005,10,May,40 -2005,11,May,37.4 -2005,12,May,33.5 -2005,13,May,32.2 -2005,14,May,30.9 -2005,15,May,30.9 -2005,16,May,34.8 -2005,17,May,34.8 -2005,18,May,32.2 -2005,19,May,29.6 -2005,20,May,29.6 -2005,21,May,34.8 -2005,22,May,36.1 -2005,23,May,30.9 -2005,24,May,24.4 -2005,25,May,29.6 -2005,26,May,30.9 -2005,27,May,27 -2005,28,May,25.7 -2005,29,May,25.7 -2005,30,May,30.9 -2005,31,May,33.5 -2006,1,May,70.7 -2006,2,May,54.2 -2006,3,May,39.8 -2006,4,May,31.5 -2006,5,May,30.2 -2006,6,May,27.6 -2006,7,May,26.3 -2006,8,May,34.1 -2006,9,May,41.6 -2006,10,May,38 -2006,11,May,35.4 -2006,12,May,35.4 -2006,13,May,38 -2006,14,May,34.1 -2006,15,May,112.6 -2006,16,May,88.6 -2006,17,May,70.5 -2006,18,May,57 -2006,19,May,67.5 -2006,20,May,55.5 -2006,21,May,42 -2006,22,May,43.5 -2006,23,May,48 -2006,24,May,66 -2006,25,May,87 -2006,26,May,109.4 -2006,27,May,79.5 -2006,28,May,67.5 -2006,29,May,54 -2006,30,May,43.5 -2006,31,May,63 -2007,1,May,49.4 -2007,2,May,41.6 -2007,3,May,53.57 -2007,4,May,55.15 -2007,5,May,83.5 -2007,6,May,111.98 -2007,7,May,74.05 -2007,8,May,64.6 -2007,9,May,70.9 -2007,10,May,67.75 -2007,11,May,86.85 -2007,12,May,78.77 -2007,13,May,85.17 -2007,14,May,115.33 -2007,15,May,130.8 -2007,16,May,96.9 -2007,17,May,77.2 -2007,18,May,81.93 -2007,19,May,85.17 -2007,20,May,85.17 -2007,21,May,77.2 -2007,22,May,64.6 -2007,23,May,56.72 -2007,24,May,48.1 -2007,25,May,45.5 -2007,26,May,42.9 -2007,27,May,42.9 -2007,28,May,37.85 -2007,29,May,36.7 -2007,30,May,40.3 -2007,31,May,49.4 -2008,1,May,51.7 -2008,2,May,42.5 -2008,3,May,36.5 -2008,4,May,44.9 -2008,5,May,72.5 -2008,6,May,53.3 -2008,7,May,32.9 -2008,8,May,26.9 -2008,9,May,32.9 -2008,10,May,31.7 -2008,11,May,35.3 -2008,12,May,50.1 -2008,13,May,44.9 -2008,14,May,35.3 -2008,15,May,34.1 -2008,16,May,43.7 -2008,17,May,61.3 -2008,18,May,50.1 -2008,19,May,40.1 -2008,20,May,35.3 -2008,21,May,40.1 -2008,22,May,29.3 -2008,23,May,30.5 -2008,24,May,30.5 -2008,25,May,30.5 -2008,26,May,35.3 -2008,27,May,51.7 -2008,28,May,47.3 -2008,29,May,46.1 -2008,30,May,50.1 -2008,31,May,58.1 -2009,1,May,16.8 -2009,2,May,19.48 -2009,3,May,24.32 -2009,4,May,25.14 -2009,5,May,16.24 -2009,6,May,16.24 -2009,7,May,15.68 -2009,8,May,16.8 -2009,9,May,16.8 -2009,10,May,17.47 -2009,11,May,14.56 -2009,12,May,13.44 -2009,13,May,14.56 -2009,14,May,21.49 -2009,15,May,47.4 -2009,16,May,71.35 -2009,17,May,49.85 -2009,18,May,34.94 -2009,19,May,27.6 -2009,20,May,23.5 -2009,21,May,22.83 -2009,22,May,23.5 -2009,23,May,22.16 -2009,24,May,17.47 -2009,25,May,13.44 -2009,26,May,13.44 -2009,27,May,13.44 -2009,28,May,12.88 -2009,29,May,15.68 -2009,30,May,22.83 -2009,31,May,42.5 -2010,1,May,2 -2010,2,May,2.3 -2010,3,May,2.7 -2010,4,May,2 -2010,5,May,1.24 -2010,6,May,5.6 -2010,7,May,20 -2010,8,May,18 -2010,9,May,12 -2010,10,May,11 -2010,11,May,14 -2010,12,May,16 -2010,13,May,18 -2010,14,May,19 -2010,15,May,21 -2010,16,May,21 -2010,17,May,20 -2010,18,May,19 -2010,19,May,16 -2010,20,May,18 -2010,21,May,18 -2010,22,May,20 -2010,23,May,20 -2010,24,May,15 -2010,25,May,14 -2010,26,May,15 -2010,27,May,9.8 -2010,28,May,9.2 -2010,29,May,6.8 -2010,30,May,7.4 -2010,31,May,8.6 -2011,1,May,54.1 -2011,2,May,97.75 -2011,3,May,121.3 -2011,4,May,134.9 -2011,5,May,114.5 -2011,6,May,124.7 -2011,7,May,109.4 -2011,8,May,86.2 -2011,9,May,71.43 -2011,10,May,77.95 -2011,11,May,79.6 -2011,12,May,66.7 -2011,13,May,57.25 -2011,14,May,52.52 -2011,15,May,50.95 -2011,16,May,55.67 -2011,17,May,71.43 -2011,18,May,96.1 -2011,19,May,117.9 -2011,20,May,143.7 -2011,21,May,121.3 -2011,22,May,89.5 -2011,23,May,77.95 -2011,24,May,68.28 -2011,25,May,63.55 -2011,26,May,54.1 -2011,27,May,57.25 -2011,28,May,68.28 -2011,29,May,65.12 -2011,30,May,69.85 -2011,31,May,87.85 -2012,1,May,18.6 -2012,2,May,18.6 -2012,3,May,16 -2012,4,May,16 -2012,5,May,17.3 -2012,6,May,31.8 -2012,7,May,76.6 -2012,8,May,61.2 -2012,9,May,58.4 -2012,10,May,57 -2012,11,May,44.4 -2012,12,May,30.4 -2012,13,May,26.4 -2012,14,May,22.5 -2012,15,May,22.5 -2012,16,May,26.4 -2012,17,May,26.4 -2012,18,May,23.8 -2012,19,May,22.5 -2012,20,May,21.2 -2012,21,May,22.5 -2012,22,May,23.8 -2012,23,May,22.5 -2012,24,May,19.9 -2012,25,May,18.6 -2012,26,May,22.5 -2012,27,May,25.1 -2012,28,May,23.8 -2012,29,May,26.4 -2012,30,May,27.7 -2012,31,May,34.6 -2013,1,May,4.98 -2013,2,May,5.72 -2013,3,May,6.83 -2013,4,May,9.6 -2013,5,May,7.2 -2013,6,May,4.24 -2013,7,May,5.72 -2013,8,May,5.72 -2013,9,May,3.14 -2013,10,May,3.14 -2013,11,May,6.46 -2013,12,May,7.2 -2013,13,May,6.83 -2013,14,May,5.72 -2013,15,May,4.61 -2013,16,May,7.2 -2013,17,May,10.2 -2013,18,May,9.6 -2013,19,May,7.8 -2013,20,May,5.35 -2013,21,May,3.32 -2013,22,May,6.09 -2013,23,May,3.87 -2013,24,May,2.6 -2013,25,May,3.32 -2013,26,May,3.32 -2013,27,May,2.6 -2013,28,May,2.24 -2013,29,May,2.42 -2013,30,May,2.78 -2013,31,May,3.5 -2014,1,May,15.9 -2014,2,May,11.4 -2014,3,May,23.9 -2014,4,May,50.7 -2014,5,May,46.9 -2014,6,May,28.3 -2014,7,May,19.5 -2014,8,May,16.8 -2014,9,May,15 -2014,10,May,12.3 -2014,11,May,15.9 -2014,12,May,19.5 -2014,13,May,17.7 -2014,14,May,18.6 -2014,15,May,14.1 -2014,16,May,10.03 -2014,17,May,21.7 -2014,18,May,18.6 -2014,19,May,16.8 -2014,20,May,12.3 -2014,21,May,9.56 -2014,22,May,5.8 -2014,23,May,5.8 -2014,24,May,7.21 -2014,25,May,18.6 -2014,26,May,25 -2014,27,May,19.5 -2014,28,May,21.7 -2014,29,May,25 -2014,30,May,26.1 -2014,31,May,23.9 -2015,1,May,12 -2015,2,May,9 -2015,3,May,19.6 -2015,4,May,7.2 -2015,5,May,3.36 -2015,6,May,0.95 -2015,7,May,1.85 -2015,8,May,3.69 -2015,9,May,13.9 -2015,10,May,13.9 -2015,11,May,11.4 -2015,12,May,6 -2015,13,May,8.4 -2015,14,May,6.6 -2015,15,May,3.03 -2015,16,May,2.7 -2015,17,May,3.03 -2015,18,May,9 -2015,19,May,12.95 -2015,20,May,4.02 -2015,21,May,1.34 -2015,22,May,3.69 -2015,23,May,19.6 -2015,24,May,14.85 -2015,25,May,10.2 -2015,26,May,13.9 -2015,27,May,6 -2015,28,May,8.4 -2015,29,May,5.01 -2015,30,May,5.67 -2015,31,May,9 -2016,1,May,2.2 -2016,2,May,3.8 -2016,3,May,0.8 -2016,4,May,0.44 -2016,5,May,0.36 -2016,6,May,0.32 -2016,7,May,0.3 -2016,8,May,0.5 -2016,9,May,8 -2016,10,May,0.4 -2016,11,May,0.36 -2016,12,May,0.26 -2016,13,May,0.13 -2016,14,May,0.2 -2016,15,May,1.1 -2016,16,May,0.42 -2016,17,May,0.32 -2016,18,May,0.26 -2016,19,May,0.24 -2016,20,May,0.9 -2016,21,May,1 -2016,22,May,2.6 -2016,23,May,2.6 -2016,24,May,2.8 -2016,25,May,2 -2016,26,May,0.46 -2016,27,May,0.3 -2016,28,May,0.28 -2016,29,May,0.29 -2016,30,May,0.3 -2016,31,May,0.36 -2017,1,May,36.2 -2017,2,May,46 -2017,3,May,34.8 -2017,4,May,23.6 -2017,5,May,17 -2017,6,May,26 -2017,7,May,19 -2017,8,May,61.2 -2017,9,May,33.4 -2017,10,May,34.8 -2017,11,May,8.5 -2017,12,May,6 -2017,13,May,9 -2017,14,May,65 -2017,15,May,39 -2017,16,May,36.2 -2017,17,May,36.2 -2017,18,May,100.4 -2017,19,May,75 -2017,20,May,44.6 -2017,21,May,24.8 -2017,22,May,15 -2017,23,May,15 -2017,24,May,19 -2017,25,May,17 -2017,26,May,22.4 -2017,27,May,30.8 -2017,28,May,29.6 -2017,29,May,29.6 -2017,30,May,36.2 -2017,31,May,36.2 -2018,1,May,17.05 -2018,2,May,51.65 -2018,3,May,44 -2018,4,May,24.85 -2018,5,May,17.05 -2018,6,May,14.75 -2018,7,May,14.75 -2018,8,May,10.15 -2018,9,May,7 -2018,10,May,17.05 -2018,11,May,42.5 -2018,12,May,24.85 -2018,13,May,29.2 -2018,14,May,38 -2018,15,May,33.55 -2018,16,May,27.75 -2018,17,May,27.75 -2018,18,May,19.35 -2018,19,May,21.95 -2018,20,May,26.3 -2018,21,May,32.1 -2018,22,May,35 -2018,23,May,44 -2018,24,May,68.85 -2018,25,May,68.85 -2018,26,May,45.5 -2018,27,May,45.5 -2018,28,May,59.9 -2018,29,May,51.65 -2018,30,May,58.25 -2018,31,May,54.95 -2019,1,May,24.6 -2019,2,May,34.4 -2019,3,May,30.6 -2019,4,May,28.2 -2019,5,May,16.2 -2019,6,May,10.32 -2019,7,May,19.4 -2019,8,May,24.6 -2019,9,May,23.4 -2019,10,May,21 -2019,11,May,17.8 -2019,12,May,30.6 -2019,13,May,31.8 -2019,14,May,31.8 -2019,15,May,23.4 -2019,16,May,18.6 -2019,17,May,13.8 -2019,18,May,6.3 -2019,19,May,2.43 -2019,20,May,4.15 -2019,21,May,5.44 -2019,22,May,6.97 -2019,23,May,9.65 -2019,24,May,9.65 -2019,25,May,7.64 -2019,26,May,13.8 -2019,27,May,25.8 -2019,28,May,17 -2019,29,May,15.4 -2019,30,May,22.2 -2019,31,May,23.4 -1921,1,Jun,28 -1921,2,Jun,26 -1921,3,Jun,24 -1921,4,Jun,19 -1921,5,Jun,16 -1921,6,Jun,15 -1921,7,Jun,14 -1921,8,Jun,14 -1921,9,Jun,14 -1921,10,Jun,12 -1921,11,Jun,12 -1921,12,Jun,24 -1921,13,Jun,32 -1921,14,Jun,43 -1921,15,Jun,27 -1921,16,Jun,24 -1921,17,Jun,18 -1921,18,Jun,16 -1921,19,Jun,14 -1921,20,Jun,13 -1921,21,Jun,12 -1921,22,Jun,12 -1921,23,Jun,12 -1921,24,Jun,12 -1921,25,Jun,14 -1921,26,Jun,13 -1921,27,Jun,12 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-1932,11,Jun,25.725 -1932,12,Jun,24.225 -1932,13,Jun,26.725 -1932,14,Jun,35 -1932,15,Jun,27.5 -1932,16,Jun,22.5 -1932,17,Jun,17.5 -1932,18,Jun,15.925 -1932,19,Jun,16.425 -1932,20,Jun,17.5 -1932,21,Jun,17.5 -1932,22,Jun,19 -1932,23,Jun,21.5 -1932,24,Jun,69.5 -1932,25,Jun,166.5 -1932,26,Jun,195 -1932,27,Jun,90.5 -1932,28,Jun,56 -1932,29,Jun,47 -1932,30,Jun,41.5 -1933,1,Jun,47 -1933,2,Jun,52 -1933,3,Jun,59 -1933,4,Jun,53 -1933,5,Jun,59 -1933,6,Jun,35 -1933,7,Jun,29 -1933,8,Jun,28 -1933,9,Jun,33 -1933,10,Jun,34 -1933,11,Jun,33 -1933,12,Jun,29 -1933,13,Jun,26 -1933,14,Jun,30 -1933,15,Jun,29 -1933,16,Jun,31 -1933,17,Jun,28 -1933,18,Jun,25 -1933,19,Jun,22 -1933,20,Jun,20 -1933,21,Jun,22 -1933,22,Jun,21 -1933,23,Jun,22 -1933,24,Jun,54 -1933,25,Jun,52 -1933,26,Jun,51 -1933,27,Jun,41 -1933,28,Jun,32 -1933,29,Jun,28 -1933,30,Jun,37 -1934,1,Jun,20 -1934,2,Jun,17 -1934,3,Jun,17 -1934,4,Jun,16 -1934,5,Jun,17 -1934,6,Jun,20 -1934,7,Jun,22 -1934,8,Jun,28 -1934,9,Jun,36 -1934,10,Jun,33 -1934,11,Jun,27 -1934,12,Jun,45 -1934,13,Jun,41 -1934,14,Jun,38 -1934,15,Jun,49 -1934,16,Jun,58 -1934,17,Jun,62 -1934,18,Jun,44 -1934,19,Jun,34 -1934,20,Jun,26 -1934,21,Jun,21 -1934,22,Jun,21 -1934,23,Jun,19 -1934,24,Jun,18 -1934,25,Jun,28 -1934,26,Jun,42 -1934,27,Jun,39 -1934,28,Jun,35 -1934,29,Jun,32 -1934,30,Jun,25 -1935,1,Jun,58 -1935,2,Jun,54 -1935,3,Jun,32 -1935,4,Jun,30 -1935,5,Jun,29 -1935,6,Jun,28 -1935,7,Jun,28 -1935,8,Jun,25 -1935,9,Jun,24 -1935,10,Jun,23 -1935,11,Jun,24 -1935,12,Jun,31 -1935,13,Jun,33 -1935,14,Jun,39 -1935,15,Jun,42 -1935,16,Jun,44 -1935,17,Jun,38 -1935,18,Jun,35 -1935,19,Jun,39 -1935,20,Jun,38 -1935,21,Jun,35 -1935,22,Jun,34 -1935,23,Jun,34 -1935,24,Jun,34 -1935,25,Jun,36 -1935,26,Jun,35 -1935,27,Jun,29 -1935,28,Jun,28 -1935,29,Jun,27 -1935,30,Jun,37 -1936,1,Jun,45 -1936,2,Jun,37 -1936,3,Jun,34 -1936,4,Jun,28 -1936,5,Jun,27 -1936,6,Jun,30 -1936,7,Jun,37 -1936,8,Jun,27 -1936,9,Jun,23 -1936,10,Jun,21 -1936,11,Jun,20 -1936,12,Jun,16 -1936,13,Jun,17 -1936,14,Jun,25 -1936,15,Jun,33 -1936,16,Jun,32 -1936,17,Jun,28 -1936,18,Jun,30 -1936,19,Jun,63 -1936,20,Jun,59 -1936,21,Jun,37 -1936,22,Jun,32 -1936,23,Jun,44 -1936,24,Jun,31 -1936,25,Jun,29 -1936,26,Jun,28 -1936,27,Jun,27 -1936,28,Jun,26 -1936,29,Jun,26 -1936,30,Jun,25 -1937,1,Jun,37 -1937,2,Jun,35 -1937,3,Jun,34 -1937,4,Jun,32 -1937,5,Jun,32 -1937,6,Jun,34 -1937,7,Jun,34 -1937,8,Jun,83 -1937,9,Jun,106 -1937,10,Jun,90 -1937,11,Jun,157 -1937,12,Jun,116 -1937,13,Jun,83 -1937,14,Jun,58 -1937,15,Jun,44 -1937,16,Jun,39 -1937,17,Jun,30 -1937,18,Jun,27 -1937,19,Jun,25 -1937,20,Jun,22 -1937,21,Jun,20 -1937,22,Jun,21 -1937,23,Jun,20 -1937,24,Jun,26 -1937,25,Jun,27 -1937,26,Jun,28 -1937,27,Jun,26 -1937,28,Jun,24 -1937,29,Jun,22 -1937,30,Jun,21 -1938,1,Jun,75 -1938,2,Jun,139 -1938,3,Jun,271 -1938,4,Jun,171 -1938,5,Jun,143 -1938,6,Jun,149 -1938,7,Jun,154 -1938,8,Jun,143 -1938,9,Jun,170 -1938,10,Jun,363 -1938,11,Jun,308 -1938,12,Jun,245 -1938,13,Jun,199 -1938,14,Jun,154 -1938,15,Jun,131 -1938,16,Jun,107 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-1998,28,Jun,4.14 -1998,29,Jun,4.41 -1998,30,Jun,12 -1999,1,Jun,47.24 -1999,2,Jun,37 -1999,3,Jun,34.6 -1999,4,Jun,37 -1999,5,Jun,65.28 -1999,6,Jun,74.66 -1999,7,Jun,49.8 -1999,8,Jun,35.8 -1999,9,Jun,34.6 -1999,10,Jun,37 -1999,11,Jun,35.8 -1999,12,Jun,37 -1999,13,Jun,34.6 -1999,14,Jun,34.6 -1999,15,Jun,35.8 -1999,16,Jun,53.64 -1999,17,Jun,53.64 -1999,18,Jun,43.4 -1999,19,Jun,32.2 -1999,20,Jun,25 -1999,21,Jun,20.4 -1999,22,Jun,19.25 -1999,23,Jun,18.1 -1999,24,Jun,21.55 -1999,25,Jun,25 -1999,26,Jun,31 -1999,27,Jun,33.4 -1999,28,Jun,26.2 -1999,29,Jun,23.85 -1999,30,Jun,20.4 -2000,1,Jun,37.9 -2000,2,Jun,43.56 -2000,3,Jun,56.45 -2000,4,Jun,107.2 -2000,5,Jun,123.4 -2000,6,Jun,121.6 -2000,7,Jun,116.2 -2000,8,Jun,85.6 -2000,9,Jun,70.95 -2000,10,Jun,60.8 -2000,11,Jun,56.45 -2000,12,Jun,50.71 -2000,13,Jun,43.56 -2000,14,Jun,39.3 -2000,15,Jun,37.9 -2000,16,Jun,40.7 -2000,17,Jun,40.7 -2000,18,Jun,43.56 -2000,19,Jun,47.85 -2000,20,Jun,37.9 -2000,21,Jun,37.9 -2000,22,Jun,47.85 -2000,23,Jun,43.56 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-2002,18,Jun,27.24 -2002,19,Jun,23.4 -2002,20,Jun,20.84 -2002,21,Jun,18.28 -2002,22,Jun,17 -2002,23,Jun,14.51 -2002,24,Jun,14.51 -2002,25,Jun,13.68 -2002,26,Jun,13.68 -2002,27,Jun,13.68 -2002,28,Jun,15.34 -2002,29,Jun,17 -2002,30,Jun,33.76 -2003,1,Jun,36 -2003,2,Jun,36 -2003,3,Jun,37.4 -2003,4,Jun,37.4 -2003,5,Jun,31.8 -2003,6,Jun,25.1 -2003,7,Jun,19.9 -2003,8,Jun,17.3 -2003,9,Jun,21.2 -2003,10,Jun,21.2 -2003,11,Jun,18.6 -2003,12,Jun,23.8 -2003,13,Jun,21.2 -2003,14,Jun,23.8 -2003,15,Jun,22.5 -2003,16,Jun,26.4 -2003,17,Jun,31.8 -2003,18,Jun,27.7 -2003,19,Jun,23.8 -2003,20,Jun,19.9 -2003,21,Jun,21.2 -2003,22,Jun,21.2 -2003,23,Jun,21.2 -2003,24,Jun,25.1 -2003,25,Jun,26.4 -2003,26,Jun,27.7 -2003,27,Jun,23.8 -2003,28,Jun,36 -2003,29,Jun,58.95 -2003,30,Jun,63.3 -2004,1,Jun,59.2 -2004,2,Jun,73 -2004,3,Jun,80.5 -2004,4,Jun,55.2 -2004,5,Jun,41.1 -2004,6,Jun,38.7 -2004,7,Jun,46.1 -2004,8,Jun,51.3 -2004,9,Jun,50 -2004,10,Jun,48.7 -2004,11,Jun,51.3 -2004,12,Jun,48.7 -2004,13,Jun,51.3 -2004,14,Jun,50 -2004,15,Jun,61.9 -2004,16,Jun,85 -2004,17,Jun,89.8 -2004,18,Jun,77.5 -2004,19,Jun,65.95 -2004,20,Jun,57.85 -2004,21,Jun,50 -2004,22,Jun,42.3 -2004,23,Jun,39.9 -2004,24,Jun,36.3 -2004,25,Jun,36.3 -2004,26,Jun,35.1 -2004,27,Jun,31.5 -2004,28,Jun,32.7 -2004,29,Jun,39.9 -2004,30,Jun,42.3 -2005,1,Jun,36.1 -2005,2,Jun,57.6 -2005,3,Jun,62.4 -2005,4,Jun,65.6 -2005,5,Jun,70.4 -2005,6,Jun,51.2 -2005,7,Jun,40 -2005,8,Jun,38.7 -2005,9,Jun,37.4 -2005,10,Jun,33.5 -2005,11,Jun,48 -2005,12,Jun,41.6 -2005,13,Jun,30.9 -2005,14,Jun,32.2 -2005,15,Jun,30.9 -2005,16,Jun,28.3 -2005,17,Jun,23.1 -2005,18,Jun,21.8 -2005,19,Jun,21.8 -2005,20,Jun,27 -2005,21,Jun,34.8 -2005,22,Jun,32.2 -2005,23,Jun,32.2 -2005,24,Jun,24.4 -2005,25,Jun,17.9 -2005,26,Jun,17.9 -2005,27,Jun,15.3 -2005,28,Jun,12.95 -2005,29,Jun,13.65 -2005,30,Jun,14 -2006,1,Jun,66 -2006,2,Jun,78 -2006,3,Jun,93.4 -2006,4,Jun,91.8 -2006,5,Jun,90.2 -2006,6,Jun,82.5 -2006,7,Jun,79.5 -2006,8,Jun,79.5 -2006,9,Jun,78 -2006,10,Jun,67.5 -2006,11,Jun,64.5 -2006,12,Jun,64.5 -2006,13,Jun,60 -2006,14,Jun,54 -2006,15,Jun,52.5 -2006,16,Jun,58.5 -2006,17,Jun,58.5 -2006,18,Jun,60 -2006,19,Jun,60 -2006,20,Jun,84 -2006,21,Jun,101.4 -2006,22,Jun,95 -2006,23,Jun,85.5 -2006,24,Jun,70.5 -2006,25,Jun,54 -2006,26,Jun,58.5 -2006,27,Jun,57 -2006,28,Jun,52.5 -2006,29,Jun,43.5 -2006,30,Jun,42 -2007,1,Jun,66.17 -2007,2,Jun,58.3 -2007,3,Jun,55.15 -2007,4,Jun,63.02 -2007,5,Jun,66.17 -2007,6,Jun,70.9 -2007,7,Jun,83.5 -2007,8,Jun,59.87 -2007,9,Jun,55.15 -2007,10,Jun,53.57 -2007,11,Jun,46.8 -2007,12,Jun,49.4 -2007,13,Jun,69.32 -2007,14,Jun,77.2 -2007,15,Jun,75.62 -2007,16,Jun,64.6 -2007,17,Jun,66.17 -2007,18,Jun,61.45 -2007,19,Jun,61.45 -2007,20,Jun,66.17 -2007,21,Jun,72.48 -2007,22,Jun,67.75 -2007,23,Jun,53.57 -2007,24,Jun,49.4 -2007,25,Jun,46.8 -2007,26,Jun,36.7 -2007,27,Jun,44.2 -2007,28,Jun,120.45 -2007,29,Jun,117 -2007,30,Jun,75.62 -2008,1,Jun,42.5 -2008,2,Jun,38.9 -2008,3,Jun,34.1 -2008,4,Jun,30.5 -2008,5,Jun,41.3 -2008,6,Jun,61.3 -2008,7,Jun,69.3 -2008,8,Jun,67.7 -2008,9,Jun,59.7 -2008,10,Jun,46.1 -2008,11,Jun,37.7 -2008,12,Jun,36.5 -2008,13,Jun,30.5 -2008,14,Jun,22.6 -2008,15,Jun,19.75 -2008,16,Jun,24.5 -2008,17,Jun,22.6 -2008,18,Jun,21.65 -2008,19,Jun,20.7 -2008,20,Jun,24.5 -2008,21,Jun,30.5 -2008,22,Jun,23.55 -2008,23,Jun,19.75 -2008,24,Jun,21.65 -2008,25,Jun,21.65 -2008,26,Jun,19.75 -2008,27,Jun,18.8 -2008,28,Jun,12.6 -2008,29,Jun,14.2 -2008,30,Jun,14.2 -2009,1,Jun,65.78 -2009,2,Jun,49.85 -2009,3,Jun,31.7 -2009,4,Jun,22.83 -2009,5,Jun,22.16 -2009,6,Jun,30.06 -2009,7,Jun,34.94 -2009,8,Jun,31.7 -2009,9,Jun,30.88 -2009,10,Jun,29.24 -2009,11,Jun,21.49 -2009,12,Jun,20.15 -2009,13,Jun,20.82 -2009,14,Jun,20.15 -2009,15,Jun,20.82 -2009,16,Jun,20.82 -2009,17,Jun,26.78 -2009,18,Jun,32.78 -2009,19,Jun,32.78 -2009,20,Jun,36.02 -2009,21,Jun,21.49 -2009,22,Jun,20.15 -2009,23,Jun,20.82 -2009,24,Jun,20.15 -2009,25,Jun,20.82 -2009,26,Jun,20.82 -2009,27,Jun,26.78 -2009,28,Jun,32.78 -2009,29,Jun,32.78 -2009,30,Jun,36.02 -2010,1,Jun,8.6 -2010,2,Jun,9.2 -2010,3,Jun,13 -2010,4,Jun,19 -2010,5,Jun,21 -2010,6,Jun,13 -2010,7,Jun,9.8 -2010,8,Jun,8.6 -2010,9,Jun,13 -2010,10,Jun,18 -2010,11,Jun,18 -2010,12,Jun,21 -2010,13,Jun,26.2 -2010,14,Jun,23.6 -2010,15,Jun,19 -2010,16,Jun,14 -2010,17,Jun,13 -2010,18,Jun,13 -2010,19,Jun,14 -2010,20,Jun,14 -2010,21,Jun,14 -2010,22,Jun,19 -2010,23,Jun,23.6 -2010,24,Jun,21 -2010,25,Jun,18 -2010,26,Jun,15 -2010,27,Jun,15 -2010,28,Jun,13 -2010,29,Jun,11 -2010,30,Jun,9.8 -2011,1,Jun,69.85 -2011,2,Jun,55.67 -2011,3,Jun,60.4 -2011,4,Jun,84.55 -2011,5,Jun,102.7 -2011,6,Jun,119.6 -2011,7,Jun,126.4 -2011,8,Jun,86.2 -2011,9,Jun,69.85 -2011,10,Jun,63.55 -2011,11,Jun,61.98 -2011,12,Jun,65.12 -2011,13,Jun,71.43 -2011,14,Jun,77.95 -2011,15,Jun,79.6 -2011,16,Jun,71.43 -2011,17,Jun,65.12 -2011,18,Jun,63.55 -2011,19,Jun,61.98 -2011,20,Jun,58.83 -2011,21,Jun,60.4 -2011,22,Jun,61.98 -2011,23,Jun,57.25 -2011,24,Jun,55.67 -2011,25,Jun,54.1 -2011,26,Jun,76.3 -2011,27,Jun,111.1 -2011,28,Jun,112.8 -2011,29,Jun,97.75 -2011,30,Jun,82.9 -2012,1,Jun,43 -2012,2,Jun,43 -2012,3,Jun,38.8 -2012,4,Jun,38.8 -2012,5,Jun,36 -2012,6,Jun,29 -2012,7,Jun,25.1 -2012,8,Jun,34.6 -2012,9,Jun,47.2 -2012,10,Jun,38.8 -2012,11,Jun,37.4 -2012,12,Jun,31.8 -2012,13,Jun,29 -2012,14,Jun,29 -2012,15,Jun,26.4 -2012,16,Jun,21.2 -2012,17,Jun,21.2 -2012,18,Jun,21.2 -2012,19,Jun,21.2 -2012,20,Jun,22.5 -2012,21,Jun,19.9 -2012,22,Jun,18.6 -2012,23,Jun,17.3 -2012,24,Jun,18.6 -2012,25,Jun,22.5 -2012,26,Jun,21.2 -2012,27,Jun,14.1 -2012,28,Jun,10.3 -2012,29,Jun,9.35 -2012,30,Jun,10.3 -2013,1,Jun,3.14 -2013,2,Jun,2.96 -2013,3,Jun,2.78 -2013,4,Jun,1.35 -2013,5,Jun,1.49 -2013,6,Jun,1.35 -2013,7,Jun,1.49 -2013,8,Jun,1.63 -2013,9,Jun,1.7 -2013,10,Jun,1.63 -2013,11,Jun,1.56 -2013,12,Jun,3.87 -2013,13,Jun,18.24 -2013,14,Jun,12 -2013,15,Jun,7.8 -2013,16,Jun,3.14 -2013,17,Jun,2.42 -2013,18,Jun,1.56 -2013,19,Jun,1 -2013,20,Jun,0.94 -2013,21,Jun,0.76 -2013,22,Jun,0.82 -2013,23,Jun,0.82 -2013,24,Jun,2.06 -2013,25,Jun,12 -2013,26,Jun,6.09 -2013,27,Jun,7.8 -2013,28,Jun,6.46 -2013,29,Jun,3.14 -2013,30,Jun,2.6 -2014,1,Jun,28.3 -2014,2,Jun,25 -2014,3,Jun,18.6 -2014,4,Jun,8.62 -2014,5,Jun,6.74 -2014,6,Jun,7.68 -2014,7,Jun,7.21 -2014,8,Jun,12.3 -2014,9,Jun,12.3 -2014,10,Jun,18.6 -2014,11,Jun,19.5 -2014,12,Jun,30.5 -2014,13,Jun,29.4 -2014,14,Jun,36.3 -2014,15,Jun,25 -2014,16,Jun,20.6 -2014,17,Jun,23.9 -2014,18,Jun,23.9 -2014,19,Jun,21.7 -2014,20,Jun,23.9 -2014,21,Jun,18.6 -2014,22,Jun,18.6 -2014,23,Jun,21.7 -2014,24,Jun,16.8 -2014,25,Jun,15 -2014,26,Jun,14.1 -2014,27,Jun,10.5 -2014,28,Jun,10.5 -2014,29,Jun,10.5 -2014,30,Jun,10.5 -2015,1,Jun,5.34 -2015,2,Jun,8.4 -2015,3,Jun,3.36 -2015,4,Jun,4.02 -2015,5,Jun,3.03 -2015,6,Jun,8.4 -2015,7,Jun,4.68 -2015,8,Jun,2.02 -2015,9,Jun,2.02 -2015,10,Jun,2.19 -2015,11,Jun,2.19 -2015,12,Jun,2.02 -2015,13,Jun,2.02 -2015,14,Jun,1.85 -2015,15,Jun,3.36 -2015,16,Jun,11.4 -2015,17,Jun,9 -2015,18,Jun,7.2 -2015,19,Jun,9.6 -2015,20,Jun,11.4 -2015,21,Jun,9 -2015,22,Jun,10.2 -2015,23,Jun,6.6 -2015,24,Jun,2.19 -2015,25,Jun,1.34 -2015,26,Jun,1.68 -2015,27,Jun,2.36 -2015,28,Jun,16.75 -2015,29,Jun,10.2 -2015,30,Jun,11.4 -2016,1,Jun,0.7 -2016,2,Jun,1.4 -2016,3,Jun,7.2 -2016,4,Jun,4 -2016,5,Jun,5.2 -2016,6,Jun,2 -2016,7,Jun,5.2 -2016,8,Jun,3 -2016,9,Jun,2 -2016,10,Jun,1.5 -2016,11,Jun,1.7 -2016,12,Jun,2.6 -2016,13,Jun,1 -2016,14,Jun,1 -2016,15,Jun,3.2 -2016,16,Jun,1.9 -2016,17,Jun,3.6 -2016,18,Jun,3.6 -2016,19,Jun,2.4 -2016,20,Jun,4.4 -2016,21,Jun,2.6 -2016,22,Jun,6.4 -2016,23,Jun,22 -2016,24,Jun,7.6 -2016,25,Jun,5.6 -2016,26,Jun,19.5 -2016,27,Jun,7.6 -2016,28,Jun,6.8 -2016,29,Jun,3.8 -2016,30,Jun,3 -2017,1,Jun,46 -2017,2,Jun,39 -2017,3,Jun,27.2 -2017,4,Jun,22.4 -2017,5,Jun,19 -2017,6,Jun,16 -2017,7,Jun,14 -2017,8,Jun,28.4 -2017,9,Jun,33.4 -2017,10,Jun,36.2 -2017,11,Jun,34.8 -2017,12,Jun,28.4 -2017,13,Jun,20 -2017,14,Jun,18 -2017,15,Jun,19 -2017,16,Jun,17 -2017,17,Jun,14 -2017,18,Jun,10 -2017,19,Jun,9 -2017,20,Jun,7 -2017,21,Jun,6 -2017,22,Jun,6.5 -2017,23,Jun,7.5 -2017,24,Jun,7 -2017,25,Jun,6.5 -2017,26,Jun,7 -2017,27,Jun,7 -2017,28,Jun,6.5 -2017,29,Jun,29.6 -2017,30,Jun,30.8 -2018,1,Jun,53.3 -2018,2,Jun,75.9 -2018,3,Jun,64.85 -2018,4,Jun,53.3 -2018,5,Jun,45.5 -2018,6,Jun,44 -2018,7,Jun,44 -2018,8,Jun,38 -2018,9,Jun,42.5 -2018,10,Jun,48.5 -2018,11,Jun,48.5 -2018,12,Jun,50 -2018,13,Jun,53.3 -2018,14,Jun,54.95 -2018,15,Jun,47 -2018,16,Jun,39.5 -2018,17,Jun,29.2 -2018,18,Jun,24.85 -2018,19,Jun,26.3 -2018,20,Jun,30.65 -2018,21,Jun,35 -2018,22,Jun,35 -2018,23,Jun,39.5 -2018,24,Jun,38 -2018,25,Jun,35 -2018,26,Jun,36.5 -2018,27,Jun,36.5 -2018,28,Jun,61.55 -2018,29,Jun,90 -2018,30,Jun,68.85 -2019,1,Jun,34.4 -2019,2,Jun,38.6 -2019,3,Jun,24.6 -2019,4,Jun,18.6 -2019,5,Jun,14.6 -2019,6,Jun,16.2 -2019,7,Jun,6.97 -2019,8,Jun,7.64 -2019,9,Jun,10.32 -2019,10,Jun,8.98 -2019,11,Jun,4.15 -2019,12,Jun,4.15 -2019,13,Jun,3.72 -2019,14,Jun,3.72 -2019,15,Jun,3.29 -2019,16,Jun,2.86 -2019,17,Jun,1.84 -2019,18,Jun,1.04 -2019,19,Jun,4.58 -2019,20,Jun,13 -2019,21,Jun,8.31 -2019,22,Jun,5.01 -2019,23,Jun,2.43 -2019,24,Jun,5.87 -2019,25,Jun,29.4 -2019,26,Jun,10.32 -2019,27,Jun,9.65 -2019,28,Jun,7.64 -2019,29,Jun,5.01 -2019,30,Jun,4.15 -1921,1,Jul,9.85 -1921,2,Jul,9.5 -1921,3,Jul,9.15 -1921,4,Jul,9.5 -1921,5,Jul,9.5 -1921,6,Jul,14 -1921,7,Jul,22 -1921,8,Jul,22 -1921,9,Jul,22 -1921,10,Jul,18 -1921,11,Jul,20 -1921,12,Jul,26 -1921,13,Jul,22 -1921,14,Jul,22 -1921,15,Jul,23 -1921,16,Jul,22 -1921,17,Jul,19 -1921,18,Jul,21 -1921,19,Jul,19 -1921,20,Jul,22 -1921,21,Jul,32 -1921,22,Jul,30 -1921,23,Jul,29 -1921,24,Jul,34 -1921,25,Jul,31 -1921,26,Jul,27 -1921,27,Jul,28 -1921,28,Jul,28 -1921,29,Jul,43 -1921,30,Jul,69 -1921,31,Jul,136 -1922,1,Jul,15 -1922,2,Jul,16 -1922,3,Jul,16 -1922,4,Jul,16 -1922,5,Jul,15 -1922,6,Jul,15 -1922,7,Jul,16 -1922,8,Jul,15 -1922,9,Jul,18 -1922,10,Jul,17 -1922,11,Jul,18 -1922,12,Jul,29 -1922,13,Jul,27 -1922,14,Jul,30 -1922,15,Jul,28 -1922,16,Jul,26 -1922,17,Jul,70 -1922,18,Jul,63 -1922,19,Jul,71 -1922,20,Jul,70 -1922,21,Jul,66 -1922,22,Jul,54 -1922,23,Jul,54 -1922,24,Jul,45 -1922,25,Jul,45 -1922,26,Jul,37 -1922,27,Jul,37 -1922,28,Jul,36 -1922,29,Jul,35 -1922,30,Jul,39 -1922,31,Jul,39 -1923,1,Jul,26 -1923,2,Jul,24 -1923,3,Jul,18 -1923,4,Jul,18 -1923,5,Jul,18 -1923,6,Jul,18 -1923,7,Jul,19 -1923,8,Jul,19 -1923,9,Jul,21 -1923,10,Jul,28 -1923,11,Jul,24 -1923,12,Jul,21 -1923,13,Jul,27 -1923,14,Jul,21 -1923,15,Jul,18 -1923,16,Jul,18 -1923,17,Jul,16 -1923,18,Jul,14 -1923,19,Jul,14 -1923,20,Jul,18 -1923,21,Jul,22 -1923,22,Jul,18 -1923,23,Jul,18 -1923,24,Jul,22 -1923,25,Jul,24 -1923,26,Jul,21 -1923,27,Jul,20 -1923,28,Jul,39 -1923,29,Jul,51 -1923,30,Jul,56 -1923,31,Jul,50 -1924,1,Jul,27 -1924,2,Jul,26 -1924,3,Jul,25 -1924,4,Jul,25 -1924,5,Jul,27 -1924,6,Jul,37 -1924,7,Jul,47 -1924,8,Jul,54 -1924,9,Jul,56 -1924,10,Jul,52 -1924,11,Jul,54 -1924,12,Jul,52 -1924,13,Jul,56 -1924,14,Jul,51 -1924,15,Jul,51 -1924,16,Jul,51 -1924,17,Jul,51 -1924,18,Jul,85 -1924,19,Jul,74 -1924,20,Jul,47 -1924,21,Jul,43 -1924,22,Jul,41 -1924,23,Jul,40 -1924,24,Jul,65 -1924,25,Jul,60 -1924,26,Jul,56 -1924,27,Jul,51 -1924,28,Jul,55 -1924,29,Jul,102 -1924,30,Jul,94 -1924,31,Jul,85 -1925,1,Jul,22 -1925,2,Jul,23 -1925,3,Jul,21 -1925,4,Jul,20 -1925,5,Jul,19 -1925,6,Jul,18 -1925,7,Jul,18 -1925,8,Jul,17 -1925,9,Jul,17 -1925,10,Jul,22 -1925,11,Jul,31 -1925,12,Jul,29 -1925,13,Jul,27 -1925,14,Jul,47 -1925,15,Jul,53 -1925,16,Jul,62 -1925,17,Jul,52 -1925,18,Jul,47 -1925,19,Jul,40 -1925,20,Jul,38 -1925,21,Jul,41 -1925,22,Jul,52 -1925,23,Jul,81 -1925,24,Jul,62 -1925,25,Jul,56 -1925,26,Jul,58 -1925,27,Jul,55 -1925,28,Jul,56 -1925,29,Jul,63 -1925,30,Jul,56 -1925,31,Jul,51 -1926,1,Jul,28 -1926,2,Jul,34 -1926,3,Jul,32 -1926,4,Jul,28 -1926,5,Jul,28 -1926,6,Jul,25 -1926,7,Jul,36 -1926,8,Jul,25 -1926,9,Jul,24 -1926,10,Jul,22 -1926,11,Jul,35 -1926,12,Jul,22 -1926,13,Jul,110 -1926,14,Jul,91 -1926,15,Jul,72 -1926,16,Jul,58 -1926,17,Jul,45 -1926,18,Jul,41 -1926,19,Jul,37 -1926,20,Jul,33 -1926,21,Jul,28 -1926,22,Jul,26 -1926,23,Jul,25 -1926,24,Jul,34 -1926,25,Jul,33 -1926,26,Jul,30 -1926,27,Jul,28 -1926,28,Jul,28 -1926,29,Jul,29 -1926,30,Jul,43 -1926,31,Jul,41 -1927,1,Jul,21 -1927,2,Jul,20 -1927,3,Jul,22 -1927,4,Jul,28 -1927,5,Jul,37 -1927,6,Jul,49 -1927,7,Jul,51 -1927,8,Jul,44 -1927,9,Jul,40 -1927,10,Jul,35 -1927,11,Jul,37 -1927,12,Jul,33 -1927,13,Jul,28 -1927,14,Jul,28 -1927,15,Jul,28 -1927,16,Jul,28 -1927,17,Jul,28 -1927,18,Jul,28 -1927,19,Jul,31 -1927,20,Jul,34 -1927,21,Jul,37 -1927,22,Jul,52 -1927,23,Jul,112 -1927,24,Jul,232 -1927,25,Jul,164 -1927,26,Jul,218 -1927,27,Jul,158 -1927,28,Jul,150 -1927,29,Jul,232 -1927,30,Jul,236 -1927,31,Jul,172 -1928,1,Jul,63 -1928,2,Jul,98 -1928,3,Jul,106 -1928,4,Jul,99 -1928,5,Jul,106 -1928,6,Jul,81 -1928,7,Jul,72 -1928,8,Jul,104 -1928,9,Jul,124 -1928,10,Jul,111 -1928,11,Jul,164 -1928,12,Jul,144 -1928,13,Jul,128 -1928,14,Jul,104 -1928,15,Jul,87 -1928,16,Jul,73 -1928,17,Jul,96 -1928,18,Jul,250 -1928,19,Jul,283 -1928,20,Jul,157 -1928,21,Jul,116 -1928,22,Jul,94 -1928,23,Jul,186 -1928,24,Jul,131 -1928,25,Jul,105 -1928,26,Jul,94 -1928,27,Jul,81 -1928,28,Jul,76 -1928,29,Jul,62 -1928,30,Jul,58 -1928,31,Jul,55 -1929,1,Jul,29 -1929,2,Jul,27 -1929,3,Jul,28 -1929,4,Jul,34 -1929,5,Jul,35 -1929,6,Jul,35 -1929,7,Jul,28 -1929,8,Jul,27 -1929,9,Jul,27 -1929,10,Jul,25 -1929,11,Jul,24 -1929,12,Jul,23 -1929,13,Jul,30 -1929,14,Jul,37 -1929,15,Jul,39 -1929,16,Jul,35 -1929,17,Jul,28 -1929,18,Jul,27 -1929,19,Jul,26 -1929,20,Jul,25 -1929,21,Jul,26 -1929,22,Jul,23 -1929,23,Jul,24 -1929,24,Jul,26 -1929,25,Jul,33 -1929,26,Jul,32 -1929,27,Jul,35 -1929,28,Jul,54 -1929,29,Jul,121 -1929,30,Jul,168 -1929,31,Jul,116 -1930,1,Jul,130 -1930,2,Jul,88 -1930,3,Jul,63 -1930,4,Jul,58 -1930,5,Jul,153 -1930,6,Jul,149 -1930,7,Jul,106 -1930,8,Jul,74 -1930,9,Jul,63 -1930,10,Jul,55 -1930,11,Jul,136 -1930,12,Jul,94 -1930,13,Jul,72 -1930,14,Jul,85 -1930,15,Jul,73 -1930,16,Jul,70 -1930,17,Jul,60 -1930,18,Jul,53 -1930,19,Jul,46 -1930,20,Jul,44 -1930,21,Jul,61 -1930,22,Jul,83 -1930,23,Jul,71 -1930,24,Jul,70 -1930,25,Jul,72 -1930,26,Jul,164 -1930,27,Jul,144 -1930,28,Jul,102 -1930,29,Jul,80 -1930,30,Jul,88 -1930,31,Jul,98 -1931,1,Jul,13 -1931,2,Jul,14 -1931,3,Jul,17 -1931,4,Jul,15 -1931,5,Jul,13 -1931,6,Jul,12 -1931,7,Jul,11 -1931,8,Jul,10 -1931,9,Jul,10 -1931,10,Jul,10 -1931,11,Jul,9.85 -1931,12,Jul,9.5 -1931,13,Jul,9.15 -1931,14,Jul,8.8 -1931,15,Jul,8.8 -1931,16,Jul,9.15 -1931,17,Jul,9.5 -1931,18,Jul,12 -1931,19,Jul,12 -1931,20,Jul,11 -1931,21,Jul,10 -1931,22,Jul,9.85 -1931,23,Jul,10 -1931,24,Jul,9.85 -1931,25,Jul,9.5 -1931,26,Jul,13 -1931,27,Jul,13 -1931,28,Jul,16 -1931,29,Jul,21 -1931,30,Jul,24 -1931,31,Jul,22 -1932,1,Jul,13 -1932,2,Jul,11 -1932,3,Jul,9.85 -1932,4,Jul,9.85 -1932,5,Jul,9.15 -1932,6,Jul,9.15 -1932,7,Jul,16 -1932,8,Jul,107 -1932,9,Jul,86 -1932,10,Jul,60 -1932,11,Jul,43 -1932,12,Jul,40 -1932,13,Jul,45 -1932,14,Jul,60 -1932,15,Jul,45 -1932,16,Jul,35 -1932,17,Jul,25 -1932,18,Jul,22 -1932,19,Jul,23 -1932,20,Jul,23 -1932,21,Jul,22 -1932,22,Jul,22 -1932,23,Jul,27 -1932,24,Jul,122 -1932,25,Jul,316 -1932,26,Jul,372 -1932,27,Jul,163 -1932,28,Jul,94 -1932,29,Jul,77 -1932,30,Jul,70 -1932,31,Jul,58 -1933,1,Jul,36 -1933,2,Jul,39 -1933,3,Jul,44 -1933,4,Jul,37 -1933,5,Jul,36 -1933,6,Jul,38 -1933,7,Jul,45 -1933,8,Jul,54 -1933,9,Jul,71 -1933,10,Jul,77 -1933,11,Jul,98 -1933,12,Jul,124 -1933,13,Jul,127 -1933,14,Jul,153 -1933,15,Jul,164 -1933,16,Jul,157 -1933,17,Jul,199 -1933,18,Jul,154 -1933,19,Jul,137 -1933,20,Jul,131 -1933,21,Jul,128 -1933,22,Jul,130 -1933,23,Jul,122 -1933,24,Jul,144 -1933,25,Jul,118 -1933,26,Jul,106 -1933,27,Jul,94 -1933,28,Jul,88 -1933,29,Jul,178 -1933,30,Jul,183 -1933,31,Jul,254 -1934,1,Jul,24 -1934,2,Jul,23 -1934,3,Jul,27 -1934,4,Jul,26 -1934,5,Jul,33 -1934,6,Jul,161 -1934,7,Jul,112 -1934,8,Jul,94 -1934,9,Jul,75 -1934,10,Jul,62 -1934,11,Jul,57 -1934,12,Jul,45 -1934,13,Jul,45 -1934,14,Jul,45 -1934,15,Jul,42 -1934,16,Jul,37 -1934,17,Jul,31 -1934,18,Jul,31 -1934,19,Jul,29 -1934,20,Jul,29 -1934,21,Jul,32 -1934,22,Jul,31 -1934,23,Jul,29 -1934,24,Jul,34 -1934,25,Jul,30 -1934,26,Jul,28 -1934,27,Jul,37 -1934,28,Jul,59 -1934,29,Jul,88 -1934,30,Jul,148 -1934,31,Jul,118 -1935,1,Jul,121 -1935,2,Jul,122 -1935,3,Jul,131 -1935,4,Jul,134 -1935,5,Jul,136 -1935,6,Jul,106 -1935,7,Jul,108 -1935,8,Jul,94 -1935,9,Jul,72 -1935,10,Jul,74 -1935,11,Jul,63 -1935,12,Jul,45 -1935,13,Jul,67 -1935,14,Jul,63 -1935,15,Jul,54 -1935,16,Jul,45 -1935,17,Jul,41 -1935,18,Jul,39 -1935,19,Jul,67 -1935,20,Jul,66 -1935,21,Jul,64 -1935,22,Jul,45 -1935,23,Jul,36 -1935,24,Jul,34 -1935,25,Jul,42 -1935,26,Jul,37 -1935,27,Jul,31 -1935,28,Jul,45 -1935,29,Jul,51 -1935,30,Jul,65 -1935,31,Jul,87 -1936,1,Jul,44 -1936,2,Jul,130 -1936,3,Jul,113 -1936,4,Jul,74 -1936,5,Jul,51 -1936,6,Jul,41 -1936,7,Jul,38 -1936,8,Jul,35 -1936,9,Jul,33 -1936,10,Jul,39 -1936,11,Jul,52 -1936,12,Jul,65 -1936,13,Jul,62 -1936,14,Jul,57 -1936,15,Jul,51 -1936,16,Jul,47 -1936,17,Jul,50 -1936,18,Jul,48 -1936,19,Jul,63 -1936,20,Jul,57 -1936,21,Jul,53 -1936,22,Jul,77 -1936,23,Jul,249 -1936,24,Jul,202 -1936,25,Jul,128 -1936,26,Jul,106 -1936,27,Jul,96 -1936,28,Jul,102 -1936,29,Jul,150 -1936,30,Jul,156 -1936,31,Jul,160 -1937,1,Jul,20 -1937,2,Jul,20 -1937,3,Jul,19 -1937,4,Jul,19 -1937,5,Jul,18 -1937,6,Jul,18 -1937,7,Jul,20 -1937,8,Jul,32 -1937,9,Jul,37 -1937,10,Jul,40 -1937,11,Jul,56 -1937,12,Jul,59 -1937,13,Jul,54 -1937,14,Jul,61 -1937,15,Jul,41 -1937,16,Jul,32 -1937,17,Jul,28 -1937,18,Jul,25 -1937,19,Jul,31 -1937,20,Jul,31 -1937,21,Jul,416 -1937,22,Jul,287 -1937,23,Jul,186 -1937,24,Jul,141 -1937,25,Jul,117 -1937,26,Jul,94 -1937,27,Jul,86 -1937,28,Jul,95 -1937,29,Jul,112 -1937,30,Jul,128 -1937,31,Jul,106 -1938,1,Jul,40 -1938,2,Jul,37 -1938,3,Jul,37 -1938,4,Jul,38 -1938,5,Jul,43 -1938,6,Jul,45 -1938,7,Jul,59 -1938,8,Jul,77 -1938,9,Jul,72 -1938,10,Jul,63 -1938,11,Jul,64 -1938,12,Jul,58 -1938,13,Jul,56 -1938,14,Jul,54 -1938,15,Jul,49 -1938,16,Jul,45 -1938,17,Jul,63 -1938,18,Jul,81 -1938,19,Jul,83 -1938,20,Jul,94 -1938,21,Jul,170 -1938,22,Jul,186 -1938,23,Jul,154 -1938,24,Jul,150 -1938,25,Jul,149 -1938,26,Jul,186 -1938,27,Jul,273 -1938,28,Jul,243 -1938,29,Jul,165 -1938,30,Jul,131 -1938,31,Jul,144 -1939,1,Jul,45 -1939,2,Jul,48 -1939,3,Jul,50 -1939,4,Jul,54 -1939,5,Jul,63 -1939,6,Jul,67 -1939,7,Jul,74 -1939,8,Jul,75 -1939,9,Jul,45 -1939,10,Jul,39 -1939,11,Jul,39 -1939,12,Jul,35 -1939,13,Jul,47 -1939,14,Jul,65 -1939,15,Jul,75 -1939,16,Jul,70 -1939,17,Jul,66 -1939,18,Jul,74 -1939,19,Jul,66 -1939,20,Jul,64 -1939,21,Jul,168 -1939,22,Jul,158 -1939,23,Jul,132 -1939,24,Jul,130 -1939,25,Jul,95 -1939,26,Jul,84 -1939,27,Jul,75 -1939,28,Jul,66 -1939,29,Jul,57 -1939,30,Jul,50 -1939,31,Jul,57 -1940,1,Jul,50 -1940,2,Jul,49 -1940,3,Jul,45 -1940,4,Jul,45 -1940,5,Jul,37 -1940,6,Jul,36 -1940,7,Jul,36 -1940,8,Jul,35 -1940,9,Jul,28 -1940,10,Jul,30 -1940,11,Jul,31 -1940,12,Jul,32 -1940,13,Jul,25 -1940,14,Jul,26 -1940,15,Jul,25 -1940,16,Jul,25 -1940,17,Jul,26 -1940,18,Jul,28 -1940,19,Jul,38 -1940,20,Jul,39 -1940,21,Jul,41 -1940,22,Jul,54 -1940,23,Jul,50 -1940,24,Jul,50 -1940,25,Jul,49 -1940,26,Jul,54 -1940,27,Jul,51 -1940,28,Jul,50 -1940,29,Jul,51 -1940,30,Jul,53 -1940,31,Jul,51 -1941,1,Jul,22 -1941,2,Jul,22 -1941,3,Jul,25 -1941,4,Jul,25 -1941,5,Jul,28 -1941,6,Jul,28 -1941,7,Jul,28 -1941,8,Jul,25 -1941,9,Jul,25 -1941,10,Jul,28 -1941,11,Jul,33 -1941,12,Jul,33 -1941,13,Jul,34 -1941,14,Jul,35 -1941,15,Jul,37 -1941,16,Jul,37 -1941,17,Jul,38 -1941,18,Jul,38 -1941,19,Jul,37 -1941,20,Jul,37 -1941,21,Jul,33 -1941,22,Jul,33 -1941,23,Jul,31 -1941,24,Jul,30 -1941,25,Jul,30 -1941,26,Jul,30 -1941,27,Jul,31 -1941,28,Jul,31 -1941,29,Jul,33 -1941,30,Jul,34 -1941,31,Jul,34 -1942,1,Jul,73 -1942,2,Jul,70 -1942,3,Jul,58 -1942,4,Jul,50 -1942,5,Jul,45 -1942,6,Jul,44 -1942,7,Jul,43 -1942,8,Jul,51 -1942,9,Jul,58 -1942,10,Jul,61 -1942,11,Jul,63 -1942,12,Jul,61 -1942,13,Jul,58 -1942,14,Jul,58 -1942,15,Jul,59 -1942,16,Jul,65 -1942,17,Jul,66 -1942,18,Jul,77 -1942,19,Jul,83 -1942,20,Jul,92 -1942,21,Jul,94 -1942,22,Jul,93 -1942,23,Jul,91 -1942,24,Jul,100 -1942,25,Jul,88 -1942,26,Jul,83 -1942,27,Jul,77 -1942,28,Jul,72 -1942,29,Jul,70 -1942,30,Jul,69 -1942,31,Jul,69 -1943,1,Jul,32 -1943,2,Jul,31 -1943,3,Jul,31 -1943,4,Jul,21 -1943,5,Jul,15 -1943,6,Jul,12 -1943,7,Jul,10 -1943,8,Jul,8.45 -1943,9,Jul,15 -1943,10,Jul,22 -1943,11,Jul,63 -1943,12,Jul,94 -1943,13,Jul,140 -1943,14,Jul,210 -1943,15,Jul,283 -1943,16,Jul,192 -1943,17,Jul,124 -1943,18,Jul,74 -1943,19,Jul,80 -1943,20,Jul,88 -1943,21,Jul,83 -1943,22,Jul,77 -1943,23,Jul,72 -1943,24,Jul,63 -1943,25,Jul,63 -1943,26,Jul,100 -1943,27,Jul,150 -1943,28,Jul,106 -1943,29,Jul,83 -1943,30,Jul,72 -1943,31,Jul,67 -1944,1,Jul,27 -1944,2,Jul,28 -1944,3,Jul,28 -1944,4,Jul,26 -1944,5,Jul,27 -1944,6,Jul,28 -1944,7,Jul,37 -1944,8,Jul,63 -1944,9,Jul,67 -1944,10,Jul,67 -1944,11,Jul,67 -1944,12,Jul,72 -1944,13,Jul,254 -1944,14,Jul,398 -1944,15,Jul,131 -1944,16,Jul,72 -1944,17,Jul,70 -1944,18,Jul,67 -1944,19,Jul,67 -1944,20,Jul,72 -1944,21,Jul,72 -1944,22,Jul,77 -1944,23,Jul,72 -1944,24,Jul,72 -1944,25,Jul,72 -1944,26,Jul,67 -1944,27,Jul,67 -1944,28,Jul,63 -1944,29,Jul,63 -1944,30,Jul,58 -1944,31,Jul,58 -1945,1,Jul,71 -1945,2,Jul,70 -1945,3,Jul,69 -1945,4,Jul,69 -1945,5,Jul,67 -1945,6,Jul,65 -1945,7,Jul,66 -1945,8,Jul,66 -1945,9,Jul,65 -1945,10,Jul,65 -1945,11,Jul,63 -1945,12,Jul,63 -1945,13,Jul,62 -1945,14,Jul,62 -1945,15,Jul,61 -1945,16,Jul,60 -1945,17,Jul,58 -1945,18,Jul,58 -1945,19,Jul,58 -1945,20,Jul,58 -1945,21,Jul,60 -1945,22,Jul,61 -1945,23,Jul,63 -1945,24,Jul,73 -1945,25,Jul,117 -1945,26,Jul,330 -1945,27,Jul,229 -1945,28,Jul,139 -1945,29,Jul,73 -1945,30,Jul,68 -1945,31,Jul,63 -1946,1,Jul,26 -1946,2,Jul,27 -1946,3,Jul,29 -1946,4,Jul,29 -1946,5,Jul,33 -1946,6,Jul,29 -1946,7,Jul,29 -1946,8,Jul,28 -1946,9,Jul,26 -1946,10,Jul,23 -1946,11,Jul,23 -1946,12,Jul,21 -1946,13,Jul,21 -1946,14,Jul,23 -1946,15,Jul,29 -1946,16,Jul,34 -1946,17,Jul,40 -1946,18,Jul,44 -1946,19,Jul,45 -1946,20,Jul,45 -1946,21,Jul,44 -1946,22,Jul,36 -1946,23,Jul,36 -1946,24,Jul,33 -1946,25,Jul,26 -1946,26,Jul,23 -1946,27,Jul,23 -1946,28,Jul,16 -1946,29,Jul,33 -1946,30,Jul,26 -1946,31,Jul,23 -1947,1,Jul,108 -1947,2,Jul,97 -1947,3,Jul,88 -1947,4,Jul,86 -1947,5,Jul,144 -1947,6,Jul,126 -1947,7,Jul,97 -1947,8,Jul,66 -1947,9,Jul,62 -1947,10,Jul,62 -1947,11,Jul,61 -1947,12,Jul,61 -1947,13,Jul,60 -1947,14,Jul,60 -1947,15,Jul,59 -1947,16,Jul,58 -1947,17,Jul,57 -1947,18,Jul,55 -1947,19,Jul,55 -1947,20,Jul,54 -1947,21,Jul,54 -1947,22,Jul,53 -1947,23,Jul,52 -1947,24,Jul,52 -1947,25,Jul,53 -1947,26,Jul,55 -1947,27,Jul,56 -1947,28,Jul,57 -1947,29,Jul,58 -1947,30,Jul,58 -1947,31,Jul,59 -1948,1,Jul,42 -1948,2,Jul,41 -1948,3,Jul,40 -1948,4,Jul,40 -1948,5,Jul,41 -1948,6,Jul,40 -1948,7,Jul,39 -1948,8,Jul,38 -1948,9,Jul,39 -1948,10,Jul,40 -1948,11,Jul,41 -1948,12,Jul,42 -1948,13,Jul,42 -1948,14,Jul,43 -1948,15,Jul,45 -1948,16,Jul,45 -1948,17,Jul,46 -1948,18,Jul,46 -1948,19,Jul,47 -1948,20,Jul,48 -1948,21,Jul,47 -1948,22,Jul,46 -1948,23,Jul,46 -1948,24,Jul,46 -1948,25,Jul,45 -1948,26,Jul,44 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-1978,21,Jul,69.5 -1978,22,Jul,74.6 -1978,23,Jul,104.6 -1978,24,Jul,162.2 -1978,25,Jul,167 -1978,26,Jul,155 -1978,27,Jul,122.3 -1978,28,Jul,98 -1978,29,Jul,129.2 -1978,30,Jul,203.8 -1978,31,Jul,270 -1979,1,Jul,20.8 -1979,2,Jul,19.3 -1979,3,Jul,16.5 -1979,4,Jul,14.4 -1979,5,Jul,13.7 -1979,6,Jul,11.5 -1979,7,Jul,11 -1979,8,Jul,12.5 -1979,9,Jul,15.1 -1979,10,Jul,14.4 -1979,11,Jul,13 -1979,12,Jul,15.1 -1979,13,Jul,17.2 -1979,14,Jul,20.8 -1979,15,Jul,21.6 -1979,16,Jul,18.6 -1979,17,Jul,15.8 -1979,18,Jul,14.4 -1979,19,Jul,15.1 -1979,20,Jul,15.1 -1979,21,Jul,14.4 -1979,22,Jul,13 -1979,23,Jul,12.5 -1979,24,Jul,13.7 -1979,25,Jul,11 -1979,26,Jul,8.5 -1979,27,Jul,7.4 -1979,28,Jul,7.4 -1979,29,Jul,13 -1979,30,Jul,20.8 -1979,31,Jul,19.3 -1980,1,Jul,39 -1980,2,Jul,33.2 -1980,3,Jul,27.8 -1980,4,Jul,22.5 -1980,5,Jul,19 -1980,6,Jul,16 -1980,7,Jul,14.8 -1980,8,Jul,13.6 -1980,9,Jul,11.5 -1980,10,Jul,10.5 -1980,11,Jul,11.5 -1980,12,Jul,12 -1980,13,Jul,12 -1980,14,Jul,14.2 -1980,15,Jul,16 -1980,16,Jul,13.6 -1980,17,Jul,10.5 -1980,18,Jul,8.5 -1980,19,Jul,7.4 -1980,20,Jul,7.4 -1980,21,Jul,13 -1980,22,Jul,25.3 -1980,23,Jul,81.2 -1980,24,Jul,100 -1980,25,Jul,72.8 -1980,26,Jul,68.59 -1980,27,Jul,118 -1980,28,Jul,188.39 -1980,29,Jul,234 -1980,30,Jul,210 -1980,31,Jul,158 -1981,1,Jul,30.6 -1981,2,Jul,26.1 -1981,3,Jul,23.4 -1981,4,Jul,22.49 -1981,5,Jul,21.59 -1981,6,Jul,33 -1981,7,Jul,109.85 -1981,8,Jul,269.1 -1981,9,Jul,253.15 -1981,10,Jul,121.79 -1981,11,Jul,98.35 -1981,12,Jul,96.05 -1981,13,Jul,70.9 -1981,14,Jul,54.59 -1981,15,Jul,50.2 -1981,16,Jul,43.19 -1981,17,Jul,56.19 -1981,18,Jul,117 -1981,19,Jul,102.95 -1981,20,Jul,98.35 -1981,21,Jul,157.09 -1981,22,Jul,241.6 -1981,23,Jul,267.65 -1981,24,Jul,158.39 -1981,25,Jul,129.25 -1981,26,Jul,136.75 -1981,27,Jul,130.49 -1981,28,Jul,130.49 -1981,29,Jul,145.49 -1981,30,Jul,153.19 -1981,31,Jul,175.3 -1982,1,Jul,7.6 -1982,2,Jul,8.4 -1982,3,Jul,9.2 -1982,4,Jul,9.2 -1982,5,Jul,8.8 -1982,6,Jul,8.8 -1982,7,Jul,12.1 -1982,8,Jul,15.6 -1982,9,Jul,17 -1982,10,Jul,33 -1982,11,Jul,33 -1982,12,Jul,33 -1982,13,Jul,26 -1982,14,Jul,24 -1982,15,Jul,20 -1982,16,Jul,20 -1982,17,Jul,17 -1982,18,Jul,14.2 -1982,19,Jul,12.1 -1982,20,Jul,10.7 -1982,21,Jul,13.5 -1982,22,Jul,19 -1982,23,Jul,19 -1982,24,Jul,16.3 -1982,25,Jul,14.9 -1982,26,Jul,13.5 -1982,27,Jul,12.1 -1982,28,Jul,13.5 -1982,29,Jul,14.9 -1982,30,Jul,14.9 -1982,31,Jul,15.6 -1983,1,Jul,4.55 -1983,2,Jul,4.2 -1983,3,Jul,4.55 -1983,4,Jul,3.85 -1983,5,Jul,3.5 -1983,6,Jul,3.85 -1983,7,Jul,3.05 -1983,8,Jul,2.3 -1983,9,Jul,1.8 -1983,10,Jul,1.6 -1983,11,Jul,1.3 -1983,12,Jul,1.2 -1983,13,Jul,1.1 -1983,14,Jul,1.1 -1983,15,Jul,1.2 -1983,16,Jul,1.2 -1983,17,Jul,1.4 -1983,18,Jul,1.6 -1983,19,Jul,3.5 -1983,20,Jul,12.2 -1983,21,Jul,14.6 -1983,22,Jul,14 -1983,23,Jul,12.8 -1983,24,Jul,17 -1983,25,Jul,18.6 -1983,26,Jul,17 -1983,27,Jul,14 -1983,28,Jul,11.6 -1983,29,Jul,11 -1983,30,Jul,14.6 -1983,31,Jul,16.4 -1984,1,Jul,33.3 -1984,2,Jul,29.7 -1984,3,Jul,27.9 -1984,4,Jul,26.1 -1984,5,Jul,25.2 -1984,6,Jul,27.9 -1984,7,Jul,26.1 -1984,8,Jul,24.3 -1984,9,Jul,25.2 -1984,10,Jul,32.4 -1984,11,Jul,36 -1984,12,Jul,43.2 -1984,13,Jul,45 -1984,14,Jul,50 -1984,15,Jul,53 -1984,16,Jul,58.3 -1984,17,Jul,63.8 -1984,18,Jul,62.7 -1984,19,Jul,55 -1984,20,Jul,48 -1984,21,Jul,43.2 -1984,22,Jul,38.7 -1984,23,Jul,36.9 -1984,24,Jul,36.9 -1984,25,Jul,40.5 -1984,26,Jul,38.7 -1984,27,Jul,35.1 -1984,28,Jul,31.5 -1984,29,Jul,32.4 -1984,30,Jul,48 -1984,31,Jul,61.6 -1985,1,Jul,13.7 -1985,2,Jul,11.9 -1985,3,Jul,12.8 -1985,4,Jul,12.8 -1985,5,Jul,14.6 -1985,6,Jul,12.8 -1985,7,Jul,11 -1985,8,Jul,10.25 -1985,9,Jul,10.25 -1985,10,Jul,12.8 -1985,11,Jul,11.9 -1985,12,Jul,16.4 -1985,13,Jul,24.3 -1985,14,Jul,32.23 -1985,15,Jul,41.08 -1985,16,Jul,42.55 -1985,17,Jul,32.23 -1985,18,Jul,33.7 -1985,19,Jul,28.6 -1985,20,Jul,33.7 -1985,21,Jul,32.23 -1985,22,Jul,25.37 -1985,23,Jul,25.37 -1985,24,Jul,25.37 -1985,25,Jul,42.55 -1985,26,Jul,50.91 -1985,27,Jul,41.08 -1985,28,Jul,36.65 -1985,29,Jul,33.7 -1985,30,Jul,33.7 -1985,31,Jul,55.77 -1986,1,Jul,27.3 -1986,2,Jul,24.1 -1986,3,Jul,16.9 -1986,4,Jul,13.9 -1986,5,Jul,13.2 -1986,6,Jul,12.5 -1986,7,Jul,12.5 -1986,8,Jul,18.7 -1986,9,Jul,20.5 -1986,10,Jul,15.3 -1986,11,Jul,22.3 -1986,12,Jul,79.4 -1986,13,Jul,179.93 -1986,14,Jul,84.5 -1986,15,Jul,39.35 -1986,16,Jul,28.45 -1986,17,Jul,25 -1986,18,Jul,23.2 -1986,19,Jul,21.4 -1986,20,Jul,24.1 -1986,21,Jul,52.43 -1986,22,Jul,77.7 -1986,23,Jul,70.9 -1986,24,Jul,64.15 -1986,25,Jul,65.82 -1986,26,Jul,86.3 -1986,27,Jul,91.7 -1986,28,Jul,95.3 -1986,29,Jul,74.3 -1986,30,Jul,46.48 -1986,31,Jul,39.35 -1987,1,Jul,6.4 -1987,2,Jul,7.6 -1987,3,Jul,11.7 -1987,4,Jul,9.4 -1987,5,Jul,8.8 -1987,6,Jul,7.6 -1987,7,Jul,8.2 -1987,8,Jul,11.7 -1987,9,Jul,13.4 -1987,10,Jul,10.85 -1987,11,Jul,10 -1987,12,Jul,10 -1987,13,Jul,10 -1987,14,Jul,9.4 -1987,15,Jul,7.6 -1987,16,Jul,6.4 -1987,17,Jul,4.6 -1987,18,Jul,4.6 -1987,19,Jul,5.2 -1987,20,Jul,4.6 -1987,21,Jul,6.4 -1987,22,Jul,10 -1987,23,Jul,9.4 -1987,24,Jul,10 -1987,25,Jul,28.5 -1987,26,Jul,47 -1987,27,Jul,35.1 -1987,28,Jul,21.5 -1987,29,Jul,15.95 -1987,30,Jul,10.85 -1987,31,Jul,8.8 -1988,1,Jul,51.2 -1988,2,Jul,62.4 -1988,3,Jul,65.7 -1988,4,Jul,60.8 -1988,5,Jul,57.6 -1988,6,Jul,54.4 -1988,7,Jul,48 -1988,8,Jul,46.55 -1988,9,Jul,40.75 -1988,10,Jul,31 -1988,11,Jul,34.95 -1988,12,Jul,42.2 -1988,13,Jul,74.2 -1988,14,Jul,120 -1988,15,Jul,114 -1988,16,Jul,88.6 -1988,17,Jul,74.2 -1988,18,Jul,106 -1988,19,Jul,123.75 -1988,20,Jul,122.5 -1988,21,Jul,108 -1988,22,Jul,110 -1988,23,Jul,112 -1988,24,Jul,116 -1988,25,Jul,100 -1988,26,Jul,92.4 -1988,27,Jul,81 -1988,28,Jul,81 -1988,29,Jul,74.2 -1988,30,Jul,64 -1988,31,Jul,56 -1989,1,Jul,27.1 -1989,2,Jul,23.65 -1989,3,Jul,19.8 -1989,4,Jul,18 -1989,5,Jul,16.2 -1989,6,Jul,19.8 -1989,7,Jul,37.9 -1989,8,Jul,54 -1989,9,Jul,52.6 -1989,10,Jul,39.2 -1989,11,Jul,36.6 -1989,12,Jul,52.6 -1989,13,Jul,101 -1989,14,Jul,111.2 -1989,15,Jul,112.4 -1989,16,Jul,77.6 -1989,17,Jul,52.6 -1989,18,Jul,39.2 -1989,19,Jul,39.2 -1989,20,Jul,52.6 -1989,21,Jul,61 -1989,22,Jul,52.6 -1989,23,Jul,39.2 -1989,24,Jul,31.7 -1989,25,Jul,30.55 -1989,26,Jul,44.4 -1989,27,Jul,54 -1989,28,Jul,49.8 -1989,29,Jul,39.2 -1989,30,Jul,34 -1989,31,Jul,30.55 -1990,1,Jul,29.8 -1990,2,Jul,23.1 -1990,3,Jul,18.85 -1990,4,Jul,17.35 -1990,5,Jul,21.4 -1990,6,Jul,22.25 -1990,7,Jul,22.25 -1990,8,Jul,20.55 -1990,9,Jul,18 -1990,10,Jul,16.7 -1990,11,Jul,14.75 -1990,12,Jul,14.1 -1990,13,Jul,16.05 -1990,14,Jul,21.4 -1990,15,Jul,23.1 -1990,16,Jul,23.95 -1990,17,Jul,26.5 -1990,18,Jul,26.5 -1990,19,Jul,24.8 -1990,20,Jul,23.1 -1990,21,Jul,25.65 -1990,22,Jul,27.6 -1990,23,Jul,33.1 -1990,24,Jul,37.5 -1990,25,Jul,40 -1990,26,Jul,37.5 -1990,27,Jul,33.1 -1990,28,Jul,28.7 -1990,29,Jul,25.65 -1990,30,Jul,25.65 -1990,31,Jul,47.5 -1991,1,Jul,4.8 -1991,2,Jul,4.5 -1991,3,Jul,3.16 -1991,4,Jul,2.72 -1991,5,Jul,2.28 -1991,6,Jul,2.06 -1991,7,Jul,2.72 -1991,8,Jul,2.72 -1991,9,Jul,2.72 -1991,10,Jul,3.16 -1991,11,Jul,3.38 -1991,12,Jul,6 -1991,13,Jul,7 -1991,14,Jul,5.4 -1991,15,Jul,6 -1991,16,Jul,7.4 -1991,17,Jul,9 -1991,18,Jul,12.6 -1991,19,Jul,21 -1991,20,Jul,32.8 -1991,21,Jul,32.8 -1991,22,Jul,33.6 -1991,23,Jul,32.8 -1991,24,Jul,32.8 -1991,25,Jul,36 -1991,26,Jul,34.4 -1991,27,Jul,34.4 -1991,28,Jul,36.9 -1991,29,Jul,36 -1991,30,Jul,32.8 -1991,31,Jul,32.8 -1992,1,Jul,4 -1992,2,Jul,4 -1992,3,Jul,3.88 -1992,4,Jul,4 -1992,5,Jul,4.42 -1992,6,Jul,6.2 -1992,7,Jul,5.88 -1992,8,Jul,5.26 -1992,9,Jul,5.4 -1992,10,Jul,4.7 -1992,11,Jul,4.7 -1992,12,Jul,4.98 -1992,13,Jul,4.7 -1992,14,Jul,4.56 -1992,15,Jul,8.98 -1992,16,Jul,9.88 -1992,17,Jul,9.7 -1992,18,Jul,8.44 -1992,19,Jul,7 -1992,20,Jul,6.68 -1992,21,Jul,6.04 -1992,22,Jul,5.56 -1992,23,Jul,5.72 -1992,24,Jul,18 -1992,25,Jul,44.8 -1992,26,Jul,44.8 -1992,27,Jul,47.6 -1992,28,Jul,44.8 -1992,29,Jul,37.8 -1992,30,Jul,47.6 -1992,31,Jul,56.24 -1993,1,Jul,10.6 -1993,2,Jul,12 -1993,3,Jul,10.6 -1993,4,Jul,10.6 -1993,5,Jul,11.3 -1993,6,Jul,9.2 -1993,7,Jul,7.8 -1993,8,Jul,7.8 -1993,9,Jul,14.7 -1993,10,Jul,21 -1993,11,Jul,21 -1993,12,Jul,19.2 -1993,13,Jul,22 -1993,14,Jul,22 -1993,15,Jul,31 -1993,16,Jul,37.6 -1993,17,Jul,35.4 -1993,18,Jul,33.2 -1993,19,Jul,26 -1993,20,Jul,21 -1993,21,Jul,16.5 -1993,22,Jul,14.7 -1993,23,Jul,12.9 -1993,24,Jul,13.8 -1993,25,Jul,16.5 -1993,26,Jul,15.6 -1993,27,Jul,12 -1993,28,Jul,9.2 -1993,29,Jul,9.2 -1993,30,Jul,19.2 -1993,31,Jul,21 -1994,1,Jul,56.55 -1994,2,Jul,76.9 -1994,3,Jul,76.9 -1994,4,Jul,56.55 -1994,5,Jul,40.44 -1994,6,Jul,33.82 -1994,7,Jul,32.64 -1994,8,Jul,35 -1994,9,Jul,40.44 -1994,10,Jul,40.44 -1994,11,Jul,39.08 -1994,12,Jul,33.82 -1994,13,Jul,29.1 -1994,14,Jul,24.38 -1994,15,Jul,26.74 -1994,16,Jul,31.46 -1994,17,Jul,30.28 -1994,18,Jul,27.92 -1994,19,Jul,30.28 -1994,20,Jul,33.82 -1994,21,Jul,37.72 -1994,22,Jul,33.82 -1994,23,Jul,30.28 -1994,24,Jul,27.92 -1994,25,Jul,26.74 -1994,26,Jul,25.56 -1994,27,Jul,27.92 -1994,28,Jul,39.08 -1994,29,Jul,101.2 -1994,30,Jul,122 -1994,31,Jul,94.4 -1995,1,Jul,13.85 -1995,2,Jul,12.45 -1995,3,Jul,13.15 -1995,4,Jul,11.75 -1995,5,Jul,25.5 -1995,6,Jul,43.5 -1995,7,Jul,36.3 -1995,8,Jul,30.9 -1995,9,Jul,70 -1995,10,Jul,98.6 -1995,11,Jul,82.8 -1995,12,Jul,57.2 -1995,13,Jul,37.65 -1995,14,Jul,30.9 -1995,15,Jul,29.55 -1995,16,Jul,30.9 -1995,17,Jul,40.5 -1995,18,Jul,48 -1995,19,Jul,52.5 -1995,20,Jul,62 -1995,21,Jul,60.4 -1995,22,Jul,43.5 -1995,23,Jul,39 -1995,24,Jul,32.25 -1995,25,Jul,33.6 -1995,26,Jul,71.6 -1995,27,Jul,110.5 -1995,28,Jul,91.4 -1995,29,Jul,68.4 -1995,30,Jul,63.6 -1995,31,Jul,105.3 -1996,1,Jul,42.7 -1996,2,Jul,41.4 -1996,3,Jul,40.1 -1996,4,Jul,37.5 -1996,5,Jul,36.25 -1996,6,Jul,38.8 -1996,7,Jul,45.3 -1996,8,Jul,49.2 -1996,9,Jul,61 -1996,10,Jul,47.9 -1996,11,Jul,47.9 -1996,12,Jul,42.7 -1996,13,Jul,37.5 -1996,14,Jul,42.7 -1996,15,Jul,50.5 -1996,16,Jul,47.9 -1996,17,Jul,47.9 -1996,18,Jul,46.6 -1996,19,Jul,42.7 -1996,20,Jul,41.4 -1996,21,Jul,46.6 -1996,22,Jul,50.5 -1996,23,Jul,58 -1996,24,Jul,52 -1996,25,Jul,36.25 -1996,26,Jul,26.25 -1996,27,Jul,26.25 -1996,28,Jul,37.5 -1996,29,Jul,59.5 -1996,30,Jul,61 -1996,31,Jul,56.5 -1997,1,Jul,17.8 -1997,2,Jul,15.9 -1997,3,Jul,14.95 -1997,4,Jul,12.7 -1997,5,Jul,10.75 -1997,6,Jul,12.7 -1997,7,Jul,15.9 -1997,8,Jul,15.9 -1997,9,Jul,14.95 -1997,10,Jul,14 -1997,11,Jul,14 -1997,12,Jul,14 -1997,13,Jul,12.7 -1997,14,Jul,13.35 -1997,15,Jul,15.9 -1997,16,Jul,15.9 -1997,17,Jul,16.85 -1997,18,Jul,16.85 -1997,19,Jul,16.85 -1997,20,Jul,21.6 -1997,21,Jul,26.65 -1997,22,Jul,35.15 -1997,23,Jul,54.6 -1997,24,Jul,134.85 -1997,25,Jul,151 -1997,26,Jul,116 -1997,27,Jul,97.15 -1997,28,Jul,97.15 -1997,29,Jul,108.75 -1997,30,Jul,101.5 -1997,31,Jul,85.55 -1998,1,Jul,13.9 -1998,2,Jul,15.8 -1998,3,Jul,11.32 -1998,4,Jul,15.8 -1998,5,Jul,29.3 -1998,6,Jul,34.5 -1998,7,Jul,30.6 -1998,8,Jul,24.1 -1998,9,Jul,19.6 -1998,10,Jul,21.5 -1998,11,Jul,19.6 -1998,12,Jul,19.6 -1998,13,Jul,18.65 -1998,14,Jul,15.8 -1998,15,Jul,15.8 -1998,16,Jul,14.85 -1998,17,Jul,12.95 -1998,18,Jul,14.85 -1998,19,Jul,12.95 -1998,20,Jul,11.32 -1998,21,Jul,9.96 -1998,22,Jul,9.28 -1998,23,Jul,8.6 -1998,24,Jul,10.64 -1998,25,Jul,9.96 -1998,26,Jul,12.95 -1998,27,Jul,13.9 -1998,28,Jul,14.85 -1998,29,Jul,11.32 -1998,30,Jul,9.96 -1998,31,Jul,12.95 -1999,1,Jul,16.95 -1999,2,Jul,14.65 -1999,3,Jul,12.7 -1999,4,Jul,10.3 -1999,5,Jul,10.3 -1999,6,Jul,15.8 -1999,7,Jul,16.95 -1999,8,Jul,13.5 -1999,9,Jul,12.7 -1999,10,Jul,11.9 -1999,11,Jul,11.1 -1999,12,Jul,9.5 -1999,13,Jul,9.5 -1999,14,Jul,10.3 -1999,15,Jul,9.5 -1999,16,Jul,10.3 -1999,17,Jul,13.5 -1999,18,Jul,15.8 -1999,19,Jul,16.95 -1999,20,Jul,15.8 -1999,21,Jul,12.7 -1999,22,Jul,11.1 -1999,23,Jul,11.1 -1999,24,Jul,11.9 -1999,25,Jul,11.1 -1999,26,Jul,12.7 -1999,27,Jul,13.5 -1999,28,Jul,21.55 -1999,29,Jul,31 -1999,30,Jul,40.84 -1999,31,Jul,62.6 -2000,1,Jul,47.85 -2000,2,Jul,46.42 -2000,3,Jul,39.3 -2000,4,Jul,35.1 -2000,5,Jul,32.3 -2000,6,Jul,56.45 -2000,7,Jul,82.55 -2000,8,Jul,121.6 -2000,9,Jul,92 -2000,10,Jul,63.7 -2000,11,Jul,50.71 -2000,12,Jul,59.35 -2000,13,Jul,76.75 -2000,14,Jul,69.5 -2000,15,Jul,57.9 -2000,16,Jul,53.57 -2000,17,Jul,52.14 -2000,18,Jul,60.8 -2000,19,Jul,56.45 -2000,20,Jul,50.71 -2000,21,Jul,46.42 -2000,22,Jul,43.56 -2000,23,Jul,46.42 -2000,24,Jul,52.14 -2000,25,Jul,53.57 -2000,26,Jul,56.45 -2000,27,Jul,49.28 -2000,28,Jul,42.13 -2000,29,Jul,39.3 -2000,30,Jul,50.71 -2000,31,Jul,49.28 -2001,1,Jul,26.29 -2001,2,Jul,27.3 -2001,3,Jul,23.26 -2001,4,Jul,19.22 -2001,5,Jul,18.21 -2001,6,Jul,19.22 -2001,7,Jul,19.22 -2001,8,Jul,22.25 -2001,9,Jul,33.4 -2001,10,Jul,45.3 -2001,11,Jul,43.85 -2001,12,Jul,49.65 -2001,13,Jul,43.85 -2001,14,Jul,35.84 -2001,15,Jul,34.62 -2001,16,Jul,30.96 -2001,17,Jul,29.74 -2001,18,Jul,30.96 -2001,19,Jul,29.74 -2001,20,Jul,30.96 -2001,21,Jul,40.95 -2001,22,Jul,83 -2001,23,Jul,148.2 -2001,24,Jul,149.8 -2001,25,Jul,129 -2001,26,Jul,81.55 -2001,27,Jul,58.35 -2001,28,Jul,51.1 -2001,29,Jul,46.75 -2001,30,Jul,43.85 -2001,31,Jul,39.5 -2002,1,Jul,40.36 -2002,2,Jul,33.76 -2002,3,Jul,27.24 -2002,4,Jul,22.12 -2002,5,Jul,20.84 -2002,6,Jul,22.12 -2002,7,Jul,27.24 -2002,8,Jul,37.72 -2002,9,Jul,25.96 -2002,10,Jul,28.52 -2002,11,Jul,33.76 -2002,12,Jul,32.44 -2002,13,Jul,27.24 -2002,14,Jul,24.68 -2002,15,Jul,22.12 -2002,16,Jul,20.84 -2002,17,Jul,25.96 -2002,18,Jul,29.8 -2002,19,Jul,29.8 -2002,20,Jul,28.52 -2002,21,Jul,33.76 -2002,22,Jul,36.4 -2002,23,Jul,33.76 -2002,24,Jul,29.8 -2002,25,Jul,27.24 -2002,26,Jul,20.84 -2002,27,Jul,22.12 -2002,28,Jul,27.24 -2002,29,Jul,27.24 -2002,30,Jul,25.96 -2002,31,Jul,24.68 -2003,1,Jul,57.5 -2003,2,Jul,44.45 -2003,3,Jul,31.8 -2003,4,Jul,47.35 -2003,5,Jul,51.7 -2003,6,Jul,36 -2003,7,Jul,30.4 -2003,8,Jul,27.7 -2003,9,Jul,27.7 -2003,10,Jul,30.4 -2003,11,Jul,26.4 -2003,12,Jul,25.1 -2003,13,Jul,22.5 -2003,14,Jul,25.1 -2003,15,Jul,31.8 -2003,16,Jul,27.7 -2003,17,Jul,23.8 -2003,18,Jul,18.6 -2003,19,Jul,15.1 -2003,20,Jul,15.1 -2003,21,Jul,15.1 -2003,22,Jul,16 -2003,23,Jul,16 -2003,24,Jul,22.5 -2003,25,Jul,40.2 -2003,26,Jul,45.9 -2003,27,Jul,40.2 -2003,28,Jul,29 -2003,29,Jul,26.4 -2003,30,Jul,26.4 -2003,31,Jul,33.2 -2004,1,Jul,38.7 -2004,2,Jul,36.3 -2004,3,Jul,35.1 -2004,4,Jul,37.5 -2004,5,Jul,35.1 -2004,6,Jul,32.7 -2004,7,Jul,35.1 -2004,8,Jul,39.9 -2004,9,Jul,42.3 -2004,10,Jul,41.1 -2004,11,Jul,43.5 -2004,12,Jul,50 -2004,13,Jul,76 -2004,14,Jul,96.2 -2004,15,Jul,97.8 -2004,16,Jul,79 -2004,17,Jul,59.2 -2004,18,Jul,52.6 -2004,19,Jul,47.4 -2004,20,Jul,42.3 -2004,21,Jul,41.1 -2004,22,Jul,52.6 -2004,23,Jul,93 -2004,24,Jul,129.9 -2004,25,Jul,169.2 -2004,26,Jul,206 -2004,27,Jul,206 -2004,28,Jul,172.9 -2004,29,Jul,163.8 -2004,30,Jul,156.6 -2004,31,Jul,124.8 -2005,1,Jul,12.6 -2005,2,Jul,12.25 -2005,3,Jul,12.25 -2005,4,Jul,12.6 -2005,5,Jul,15.3 -2005,6,Jul,21.8 -2005,7,Jul,30.9 -2005,8,Jul,17.9 -2005,9,Jul,15.3 -2005,10,Jul,15.3 -2005,11,Jul,15.3 -2005,12,Jul,13.3 -2005,13,Jul,30.9 -2005,14,Jul,52.8 -2005,15,Jul,57.6 -2005,16,Jul,78.8 -2005,17,Jul,102.6 -2005,18,Jul,78.8 -2005,19,Jul,54.4 -2005,20,Jul,46.4 -2005,21,Jul,102.6 -2005,22,Jul,156.75 -2005,23,Jul,137.5 -2005,24,Jul,125.25 -2005,25,Jul,165.5 -2005,26,Jul,221 -2005,27,Jul,172.5 -2005,28,Jul,137.5 -2005,29,Jul,120 -2005,30,Jul,99.2 -2005,31,Jul,82.2 -2006,1,Jul,43.5 -2006,2,Jul,36 -2006,3,Jul,36 -2006,4,Jul,36 -2006,5,Jul,33.6 -2006,6,Jul,36 -2006,7,Jul,60 -2006,8,Jul,99.8 -2006,9,Jul,73.5 -2006,10,Jul,60 -2006,11,Jul,57 -2006,12,Jul,57 -2006,13,Jul,54 -2006,14,Jul,48 -2006,15,Jul,42 -2006,16,Jul,46.5 -2006,17,Jul,52.5 -2006,18,Jul,45 -2006,19,Jul,39.6 -2006,20,Jul,51 -2006,21,Jul,66 -2006,22,Jul,63 -2006,23,Jul,60 -2006,24,Jul,48 -2006,25,Jul,78 -2006,26,Jul,109.4 -2006,27,Jul,115.8 -2006,28,Jul,159.5 -2006,29,Jul,149.4 -2006,30,Jul,101.4 -2006,31,Jul,212.2 -2007,1,Jul,66.17 -2007,2,Jul,58.3 -2007,3,Jul,52 -2007,4,Jul,44.2 -2007,5,Jul,41.6 -2007,6,Jul,34.4 -2007,7,Jul,36.7 -2007,8,Jul,44.2 -2007,9,Jul,40.3 -2007,10,Jul,34.4 -2007,11,Jul,32.1 -2007,12,Jul,29.8 -2007,13,Jul,27.5 -2007,14,Jul,29.8 -2007,15,Jul,27.5 -2007,16,Jul,30.95 -2007,17,Jul,27.5 -2007,18,Jul,27.5 -2007,19,Jul,26.6 -2007,20,Jul,27.5 -2007,21,Jul,32.1 -2007,22,Jul,36.7 -2007,23,Jul,34.4 -2007,24,Jul,42.9 -2007,25,Jul,45.5 -2007,26,Jul,53.57 -2007,27,Jul,39 -2007,28,Jul,33.25 -2007,29,Jul,34.4 -2007,30,Jul,34.4 -2007,31,Jul,30.95 -2008,1,Jul,21.65 -2008,2,Jul,28.1 -2008,3,Jul,25.7 -2008,4,Jul,28.1 -2008,5,Jul,36.5 -2008,6,Jul,36.5 -2008,7,Jul,32.9 -2008,8,Jul,30.5 -2008,9,Jul,29.3 -2008,10,Jul,34.1 -2008,11,Jul,38.9 -2008,12,Jul,36.5 -2008,13,Jul,29.3 -2008,14,Jul,21.65 -2008,15,Jul,21.65 -2008,16,Jul,20.7 -2008,17,Jul,19.75 -2008,18,Jul,20.7 -2008,19,Jul,24.5 -2008,20,Jul,28.1 -2008,21,Jul,43.7 -2008,22,Jul,51.7 -2008,23,Jul,46.1 -2008,24,Jul,51.7 -2008,25,Jul,53.3 -2008,26,Jul,51.7 -2008,27,Jul,38.9 -2008,28,Jul,40.1 -2008,29,Jul,44.9 -2008,30,Jul,43.7 -2008,31,Jul,42.5 -2009,1,Jul,20.15 -2009,2,Jul,22.16 -2009,3,Jul,23.5 -2009,4,Jul,19.48 -2009,5,Jul,20.15 -2009,6,Jul,33.86 -2009,7,Jul,71.35 -2009,8,Jul,63.33 -2009,9,Jul,44.95 -2009,10,Jul,39.26 -2009,11,Jul,31.7 -2009,12,Jul,26.78 -2009,13,Jul,24.32 -2009,14,Jul,27.6 -2009,15,Jul,30.88 -2009,16,Jul,31.7 -2009,17,Jul,29.24 -2009,18,Jul,31.7 -2009,19,Jul,36.02 -2009,20,Jul,36.02 -2009,21,Jul,31.7 -2009,22,Jul,31.7 -2009,23,Jul,27.6 -2009,24,Jul,22.83 -2009,25,Jul,19.48 -2009,26,Jul,16.8 -2009,27,Jul,15.68 -2009,28,Jul,14.56 -2009,29,Jul,16.24 -2009,30,Jul,19.48 -2009,31,Jul,22.16 -2010,1,Jul,13 -2010,2,Jul,43.1 -2010,3,Jul,40.5 -2010,4,Jul,39.2 -2010,5,Jul,31.4 -2010,6,Jul,22.3 -2010,7,Jul,23.6 -2010,8,Jul,24.9 -2010,9,Jul,26.2 -2010,10,Jul,23.6 -2010,11,Jul,17 -2010,12,Jul,16 -2010,13,Jul,14 -2010,14,Jul,11 -2010,15,Jul,19 -2010,16,Jul,24.9 -2010,17,Jul,16 -2010,18,Jul,10.4 -2010,19,Jul,12 -2010,20,Jul,23.6 -2010,21,Jul,32.7 -2010,22,Jul,24.9 -2010,23,Jul,20 -2010,24,Jul,19 -2010,25,Jul,22.3 -2010,26,Jul,26.2 -2010,27,Jul,35.3 -2010,28,Jul,64.5 -2010,29,Jul,53.5 -2010,30,Jul,54.8 -2010,31,Jul,93.2 -2011,1,Jul,71.43 -2011,2,Jul,69.85 -2011,3,Jul,77.95 -2011,4,Jul,81.25 -2011,5,Jul,74.65 -2011,6,Jul,60.4 -2011,7,Jul,55.67 -2011,8,Jul,52.52 -2011,9,Jul,50.95 -2011,10,Jul,54.1 -2011,11,Jul,66.7 -2011,12,Jul,71.43 -2011,13,Jul,60.4 -2011,14,Jul,60.4 -2011,15,Jul,96.1 -2011,16,Jul,119.6 -2011,17,Jul,140 -2011,18,Jul,126.4 -2011,19,Jul,104.35 -2011,20,Jul,99.4 -2011,21,Jul,96.1 -2011,22,Jul,96.1 -2011,23,Jul,94.45 -2011,24,Jul,94.45 -2011,25,Jul,119.6 -2011,26,Jul,119.6 -2011,27,Jul,101.05 -2011,28,Jul,82.9 -2011,29,Jul,79.6 -2011,30,Jul,77.95 -2011,31,Jul,96.1 -2012,1,Jul,9.35 -2012,2,Jul,12.2 -2012,3,Jul,14.1 -2012,4,Jul,15.05 -2012,5,Jul,17.3 -2012,6,Jul,19.9 -2012,7,Jul,23.8 -2012,8,Jul,25.1 -2012,9,Jul,25.1 -2012,10,Jul,36 -2012,11,Jul,40.2 -2012,12,Jul,25.1 -2012,13,Jul,23.8 -2012,14,Jul,27.7 -2012,15,Jul,29 -2012,16,Jul,27.7 -2012,17,Jul,23.8 -2012,18,Jul,22.5 -2012,19,Jul,27.7 -2012,20,Jul,29 -2012,21,Jul,31.8 -2012,22,Jul,27.7 -2012,23,Jul,21.2 -2012,24,Jul,29 -2012,25,Jul,58.4 -2012,26,Jul,48.6 -2012,27,Jul,31.8 -2012,28,Jul,27.7 -2012,29,Jul,26.4 -2012,30,Jul,21.2 -2012,31,Jul,21.2 -2013,1,Jul,2.6 -2013,2,Jul,1.7 -2013,3,Jul,0.88 -2013,4,Jul,1.14 -2013,5,Jul,1.28 -2013,6,Jul,4.24 -2013,7,Jul,6.09 -2013,8,Jul,5.72 -2013,9,Jul,9 -2013,10,Jul,10.8 -2013,11,Jul,15.72 -2013,12,Jul,24.08 -2013,13,Jul,17.4 -2013,14,Jul,12.6 -2013,15,Jul,12.6 -2013,16,Jul,25.32 -2013,17,Jul,21.6 -2013,18,Jul,27.8 -2013,19,Jul,15.72 -2013,20,Jul,12 -2013,21,Jul,10.2 -2013,22,Jul,11.4 -2013,23,Jul,16.56 -2013,24,Jul,19.92 -2013,25,Jul,35.9 -2013,26,Jul,17.4 -2013,27,Jul,12.6 -2013,28,Jul,11.4 -2013,29,Jul,12.6 -2013,30,Jul,19.92 -2013,31,Jul,19.92 -2014,1,Jul,10.03 -2014,2,Jul,12.3 -2014,3,Jul,13.2 -2014,4,Jul,6.74 -2014,5,Jul,7.68 -2014,6,Jul,7.21 -2014,7,Jul,8.15 -2014,8,Jul,12.3 -2014,9,Jul,21.7 -2014,10,Jul,40.65 -2014,11,Jul,43.55 -2014,12,Jul,42.1 -2014,13,Jul,46.9 -2014,14,Jul,89.9 -2014,15,Jul,65.9 -2014,16,Jul,36.3 -2014,17,Jul,29.4 -2014,18,Jul,29.4 -2014,19,Jul,33.4 -2014,20,Jul,34.85 -2014,21,Jul,33.4 -2014,22,Jul,26.1 -2014,23,Jul,23.9 -2014,24,Jul,25 -2014,25,Jul,25 -2014,26,Jul,26.1 -2014,27,Jul,56.4 -2014,28,Jul,96.8 -2014,29,Jul,83 -2014,30,Jul,56.4 -2014,31,Jul,46.9 -2015,1,Jul,2.53 -2015,2,Jul,1.68 -2015,3,Jul,0.9 -2015,4,Jul,1.34 -2015,5,Jul,9.6 -2015,6,Jul,2.7 -2015,7,Jul,0.7 -2015,8,Jul,1.17 -2015,9,Jul,3.03 -2015,10,Jul,9.6 -2015,11,Jul,24.4 -2015,12,Jul,8.4 -2015,13,Jul,2.53 -2015,14,Jul,1.85 -2015,15,Jul,2.7 -2015,16,Jul,9.6 -2015,17,Jul,4.68 -2015,18,Jul,16.75 -2015,19,Jul,41.4 -2015,20,Jul,9 -2015,21,Jul,13.9 -2015,22,Jul,12.95 -2015,23,Jul,20.55 -2015,24,Jul,21.5 -2015,25,Jul,17.7 -2015,26,Jul,9 -2015,27,Jul,10.8 -2015,28,Jul,13.9 -2015,29,Jul,16.75 -2015,30,Jul,18.65 -2015,31,Jul,16.75 -2016,1,Jul,3.4 -2016,2,Jul,9.3 -2016,3,Jul,3.4 -2016,4,Jul,3.2 -2016,5,Jul,4.4 -2016,6,Jul,2.8 -2016,7,Jul,2.6 -2016,8,Jul,5.6 -2016,9,Jul,14.5 -2016,10,Jul,18.25 -2016,11,Jul,32 -2016,12,Jul,15.75 -2016,13,Jul,8.65 -2016,14,Jul,5.6 -2016,15,Jul,4 -2016,16,Jul,3.6 -2016,17,Jul,2.8 -2016,18,Jul,2.2 -2016,19,Jul,1.9 -2016,20,Jul,1.9 -2016,21,Jul,2.4 -2016,22,Jul,9.3 -2016,23,Jul,20.75 -2016,24,Jul,66.4 -2016,25,Jul,23.25 -2016,26,Jul,12.55 -2016,27,Jul,8 -2016,28,Jul,8.65 -2016,29,Jul,4 -2016,30,Jul,50.45 -2016,31,Jul,42.5 -2017,1,Jul,26 -2017,2,Jul,23.6 -2017,3,Jul,19 -2017,4,Jul,15 -2017,5,Jul,13 -2017,6,Jul,13 -2017,7,Jul,14 -2017,8,Jul,12 -2017,9,Jul,9 -2017,10,Jul,7.5 -2017,11,Jul,28.4 -2017,12,Jul,14 -2017,13,Jul,33.4 -2017,14,Jul,77.5 -2017,15,Jul,40.4 -2017,16,Jul,36.2 -2017,17,Jul,34.8 -2017,18,Jul,85 -2017,19,Jul,143.2 -2017,20,Jul,110 -2017,21,Jul,105.8 -2017,22,Jul,127.5 -2017,23,Jul,163.3 -2017,24,Jul,200.1 -2017,25,Jul,151.4 -2017,26,Jul,135.2 -2017,27,Jul,163.3 -2017,28,Jul,129 -2017,29,Jul,100.4 -2017,30,Jul,72.5 -2017,31,Jul,55.5 -2018,1,Jul,51.65 -2018,2,Jul,44 -2018,3,Jul,38 -2018,4,Jul,32.1 -2018,5,Jul,18.2 -2018,6,Jul,12.45 -2018,7,Jul,11.3 -2018,8,Jul,12.45 -2018,9,Jul,21.95 -2018,10,Jul,18.2 -2018,11,Jul,11.3 -2018,12,Jul,14.75 -2018,13,Jul,35 -2018,14,Jul,33.55 -2018,15,Jul,35 -2018,16,Jul,18.2 -2018,17,Jul,15.9 -2018,18,Jul,39.5 -2018,19,Jul,58.25 -2018,20,Jul,59.9 -2018,21,Jul,54.95 -2018,22,Jul,47 -2018,23,Jul,45.5 -2018,24,Jul,45.5 -2018,25,Jul,41 -2018,26,Jul,51.65 -2018,27,Jul,45.5 -2018,28,Jul,39.5 -2018,29,Jul,50 -2018,30,Jul,44 -2018,31,Jul,48.5 -2019,1,Jul,3.29 -2019,2,Jul,1.84 -2019,3,Jul,1.84 -2019,4,Jul,2 -2019,5,Jul,4.15 -2019,6,Jul,17 -2019,7,Jul,12.33 -2019,8,Jul,1.52 -2019,9,Jul,1.04 -2019,10,Jul,1.52 -2019,11,Jul,1.84 -2019,12,Jul,2.86 -2019,13,Jul,8.98 -2019,14,Jul,9.65 -2019,15,Jul,5.87 -2019,16,Jul,1.2 -2019,17,Jul,0.36 -2019,18,Jul,0.72 -2019,19,Jul,1.68 -2019,20,Jul,1.04 -2019,21,Jul,1.2 -2019,22,Jul,7.64 -2019,23,Jul,8.98 -2019,24,Jul,8.31 -2019,25,Jul,17 -2019,26,Jul,17.8 -2019,27,Jul,21 -2019,28,Jul,23.4 -2019,29,Jul,16.2 -2019,30,Jul,6.97 -2019,31,Jul,8.98 -1921,1,Aug,118 -1921,2,Aug,84 -1921,3,Aug,64 -1921,4,Aug,50 -1921,5,Aug,39 -1921,6,Aug,33 -1921,7,Aug,33 -1921,8,Aug,33 -1921,9,Aug,34 -1921,10,Aug,30 -1921,11,Aug,29 -1921,12,Aug,75 -1921,13,Aug,136 -1921,14,Aug,126 -1921,15,Aug,99 -1921,16,Aug,83 -1921,17,Aug,85 -1921,18,Aug,92 -1921,19,Aug,84 -1921,20,Aug,85 -1921,21,Aug,80 -1921,22,Aug,99 -1921,23,Aug,95 -1921,24,Aug,111 -1921,25,Aug,106 -1921,26,Aug,110 -1921,27,Aug,106 -1921,28,Aug,83 -1921,29,Aug,68 -1921,30,Aug,67 -1921,31,Aug,118 -1922,1,Aug,39 -1922,2,Aug,38 -1922,3,Aug,38 -1922,4,Aug,37 -1922,5,Aug,34 -1922,6,Aug,36 -1922,7,Aug,37 -1922,8,Aug,42 -1922,9,Aug,45 -1922,10,Aug,56 -1922,11,Aug,54 -1922,12,Aug,52 -1922,13,Aug,39 -1922,14,Aug,45 -1922,15,Aug,54 -1922,16,Aug,54 -1922,17,Aug,50 -1922,18,Aug,43 -1922,19,Aug,167 -1922,20,Aug,104 -1922,21,Aug,108 -1922,22,Aug,80 -1922,23,Aug,85 -1922,24,Aug,69 -1922,25,Aug,64 -1922,26,Aug,57 -1922,27,Aug,67 -1922,28,Aug,80 -1922,29,Aug,73 -1922,30,Aug,70 -1922,31,Aug,68 -1923,1,Aug,49 -1923,2,Aug,47 -1923,3,Aug,47 -1923,4,Aug,51 -1923,5,Aug,49 -1923,6,Aug,55 -1923,7,Aug,52 -1923,8,Aug,54 -1923,9,Aug,63 -1923,10,Aug,56 -1923,11,Aug,51 -1923,12,Aug,46 -1923,13,Aug,47 -1923,14,Aug,48 -1923,15,Aug,53 -1923,16,Aug,51 -1923,17,Aug,51 -1923,18,Aug,48 -1923,19,Aug,114 -1923,20,Aug,117 -1923,21,Aug,140 -1923,22,Aug,123 -1923,23,Aug,167 -1923,24,Aug,181 -1923,25,Aug,134 -1923,26,Aug,258 -1923,27,Aug,223 -1923,28,Aug,147 -1923,29,Aug,132 -1923,30,Aug,134 -1923,31,Aug,122 -1924,1,Aug,65 -1924,2,Aug,56 -1924,3,Aug,72 -1924,4,Aug,108 -1924,5,Aug,147 -1924,6,Aug,135 -1924,7,Aug,114 -1924,8,Aug,108 -1924,9,Aug,96 -1924,10,Aug,91 -1924,11,Aug,94 -1924,12,Aug,108 -1924,13,Aug,134 -1924,14,Aug,140 -1924,15,Aug,121 -1924,16,Aug,119 -1924,17,Aug,134 -1924,18,Aug,114 -1924,19,Aug,135 -1924,20,Aug,174 -1924,21,Aug,205 -1924,22,Aug,196 -1924,23,Aug,181 -1924,24,Aug,140 -1924,25,Aug,174 -1924,26,Aug,167 -1924,27,Aug,134 -1924,28,Aug,114 -1924,29,Aug,102 -1924,30,Aug,108 -1924,31,Aug,134 -1925,1,Aug,71 -1925,2,Aug,54 -1925,3,Aug,44 -1925,4,Aug,45 -1925,5,Aug,45 -1925,6,Aug,42 -1925,7,Aug,55 -1925,8,Aug,72 -1925,9,Aug,70 -1925,10,Aug,61 -1925,11,Aug,60 -1925,12,Aug,55 -1925,13,Aug,62 -1925,14,Aug,72 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-2002,5,Aug,35.08 -2002,6,Aug,43 -2002,7,Aug,53.01 -2002,8,Aug,55.87 -2002,9,Aug,50.15 -2002,10,Aug,36.4 -2002,11,Aug,36.4 -2002,12,Aug,36.4 -2002,13,Aug,37.72 -2002,14,Aug,39.04 -2002,15,Aug,39.04 -2002,16,Aug,39.04 -2002,17,Aug,39.04 -2002,18,Aug,32.44 -2002,19,Aug,36.4 -2002,20,Aug,39.04 -2002,21,Aug,35.08 -2002,22,Aug,33.76 -2002,23,Aug,64.5 -2002,24,Aug,114.03 -2002,25,Aug,114.03 -2002,26,Aug,122.85 -2002,27,Aug,116.97 -2002,28,Aug,155.36 -2002,29,Aug,243.6 -2002,30,Aug,254.8 -2002,31,Aug,196.8 -2003,1,Aug,30.4 -2003,2,Aug,26.4 -2003,3,Aug,27.7 -2003,4,Aug,43 -2003,5,Aug,33.2 -2003,6,Aug,26.4 -2003,7,Aug,19.9 -2003,8,Aug,16 -2003,9,Aug,18.6 -2003,10,Aug,18.6 -2003,11,Aug,17.3 -2003,12,Aug,19.9 -2003,13,Aug,45.9 -2003,14,Aug,57.5 -2003,15,Aug,43 -2003,16,Aug,31.8 -2003,17,Aug,27.7 -2003,18,Aug,40.2 -2003,19,Aug,54.6 -2003,20,Aug,81 -2003,21,Aug,90 -2003,22,Aug,82.5 -2003,23,Aug,66.2 -2003,24,Aug,53.15 -2003,25,Aug,64.75 -2003,26,Aug,76.5 -2003,27,Aug,79.5 -2003,28,Aug,57.5 -2003,29,Aug,47.35 -2003,30,Aug,40.2 -2003,31,Aug,34.6 -2004,1,Aug,131.6 -2004,2,Aug,160.2 -2004,3,Aug,184.3 -2004,4,Aug,184.3 -2004,5,Aug,178.6 -2004,6,Aug,147.6 -2004,7,Aug,114.6 -2004,8,Aug,111.2 -2004,9,Aug,126.5 -2004,10,Aug,133.3 -2004,11,Aug,147.6 -2004,12,Aug,154.8 -2004,13,Aug,172.9 -2004,14,Aug,140.4 -2004,15,Aug,114.6 -2004,16,Aug,111.2 -2004,17,Aug,118 -2004,18,Aug,114.6 -2004,19,Aug,124.8 -2004,20,Aug,156.6 -2004,21,Aug,153 -2004,22,Aug,116.3 -2004,23,Aug,102.7 -2004,24,Aug,93 -2004,25,Aug,89.8 -2004,26,Aug,79 -2004,27,Aug,73 -2004,28,Aug,67.3 -2004,29,Aug,65.95 -2004,30,Aug,61.9 -2004,31,Aug,59.2 -2005,1,Aug,64 -2005,2,Aug,67.2 -2005,3,Aug,85.6 -2005,4,Aug,75.4 -2005,5,Aug,65.6 -2005,6,Aug,56 -2005,7,Aug,52.8 -2005,8,Aug,52.8 -2005,9,Aug,51.2 -2005,10,Aug,48 -2005,11,Aug,40 -2005,12,Aug,43.2 -2005,13,Aug,170.75 -2005,14,Aug,706.5 -2005,15,Aug,652.5 -2005,16,Aug,367.6 -2005,17,Aug,231.8 -2005,18,Aug,188.6 -2005,19,Aug,213.8 -2005,20,Aug,174.25 -2005,21,Aug,125.25 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-2007,16,Aug,28.65 -2007,17,Aug,52 -2007,18,Aug,44.2 -2007,19,Aug,42.9 -2007,20,Aug,37.85 -2007,21,Aug,35.55 -2007,22,Aug,40.3 -2007,23,Aug,46.8 -2007,24,Aug,61.45 -2007,25,Aug,72.48 -2007,26,Aug,78.77 -2007,27,Aug,88.52 -2007,28,Aug,75.62 -2007,29,Aug,74.05 -2007,30,Aug,75.62 -2007,31,Aug,85.17 -2008,1,Aug,40.1 -2008,2,Aug,51.7 -2008,3,Aug,116.05 -2008,4,Aug,106.18 -2008,5,Aug,74.1 -2008,6,Aug,53.3 -2008,7,Aug,41.3 -2008,8,Aug,38.9 -2008,9,Aug,77.3 -2008,10,Aug,82.47 -2008,11,Aug,67.7 -2008,12,Aug,69.3 -2008,13,Aug,94.32 -2008,14,Aug,106.18 -2008,15,Aug,130.8 -2008,16,Aug,114.08 -2008,17,Aug,84.45 -2008,18,Aug,78.9 -2008,19,Aug,82.47 -2008,20,Aug,78.9 -2008,21,Aug,75.7 -2008,22,Aug,100.25 -2008,23,Aug,102.23 -2008,24,Aug,94.32 -2008,25,Aug,92.35 -2008,26,Aug,84.45 -2008,27,Aug,67.7 -2008,28,Aug,82.47 -2008,29,Aug,96.3 -2008,30,Aug,88.4 -2008,31,Aug,70.9 -2009,1,Aug,20.82 -2009,2,Aug,16.8 -2009,3,Aug,16.8 -2009,4,Aug,16.8 -2009,5,Aug,15.68 -2009,6,Aug,14 -2009,7,Aug,15.12 -2009,8,Aug,24.32 -2009,9,Aug,26.78 -2009,10,Aug,24.32 -2009,11,Aug,18.14 -2009,12,Aug,17.47 -2009,13,Aug,22.83 -2009,14,Aug,24.32 -2009,15,Aug,30.06 -2009,16,Aug,30.88 -2009,17,Aug,25.96 -2009,18,Aug,22.83 -2009,19,Aug,22.16 -2009,20,Aug,25.96 -2009,21,Aug,23.5 -2009,22,Aug,20.15 -2009,23,Aug,21.49 -2009,24,Aug,75.7 -2009,25,Aug,115.2 -2009,26,Aug,110.4 -2009,27,Aug,65.78 -2009,28,Aug,46.17 -2009,29,Aug,39.26 -2009,30,Aug,36.02 -2009,31,Aug,48.63 -2010,1,Aug,72 -2010,2,Aug,52.2 -2010,3,Aug,58.7 -2010,4,Aug,88.5 -2010,5,Aug,91.6 -2010,6,Aug,78 -2010,7,Aug,67.5 -2010,8,Aug,70.5 -2010,9,Aug,50.9 -2010,10,Aug,49.6 -2010,11,Aug,64.5 -2010,12,Aug,129.2 -2010,13,Aug,190.3 -2010,14,Aug,221.2 -2010,15,Aug,194.1 -2010,16,Aug,127.4 -2010,17,Aug,88.5 -2010,18,Aug,85.5 -2010,19,Aug,102.8 -2010,20,Aug,96.4 -2010,21,Aug,64.5 -2010,22,Aug,129.2 -2010,23,Aug,190.3 -2010,24,Aug,221.2 -2010,25,Aug,194.1 -2010,26,Aug,127.4 -2010,27,Aug,88.5 -2010,28,Aug,85.5 -2010,29,Aug,102.8 -2010,30,Aug,96.4 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-2016,12,Aug,29.9 -2016,13,Aug,24.8 -2016,14,Aug,41.25 -2016,15,Aug,28.88 -2016,16,Aug,29.9 -2016,17,Aug,27.86 -2016,18,Aug,23.34 -2016,19,Aug,20.42 -2016,20,Aug,27.86 -2016,21,Aug,89.6 -2016,22,Aug,100.1 -2016,23,Aug,54.88 -2016,24,Aug,57.82 -2016,25,Aug,63.73 -2016,26,Aug,106.4 -2016,27,Aug,98 -2016,28,Aug,93.8 -2016,29,Aug,102.2 -2016,30,Aug,110.6 -2016,31,Aug,91.7 -2017,1,Aug,44.6 -2017,2,Aug,34.8 -2017,3,Aug,32 -2017,4,Aug,29.6 -2017,5,Aug,29.6 -2017,6,Aug,29.6 -2017,7,Aug,33.4 -2017,8,Aug,51.7 -2017,9,Aug,53.6 -2017,10,Aug,51.7 -2017,11,Aug,51.7 -2017,12,Aug,39 -2017,13,Aug,33.4 -2017,14,Aug,27.2 -2017,15,Aug,24.8 -2017,16,Aug,47.9 -2017,17,Aug,57.4 -2017,18,Aug,49.8 -2017,19,Aug,53.6 -2017,20,Aug,57.4 -2017,21,Aug,53.6 -2017,22,Aug,72.5 -2017,23,Aug,59.3 -2017,24,Aug,53.6 -2017,25,Aug,51.7 -2017,26,Aug,95.2 -2017,27,Aug,96.5 -2017,28,Aug,123 -2017,29,Aug,87.5 -2017,30,Aug,85 -2017,31,Aug,92.6 -2018,1,Aug,48.5 -2018,2,Aug,56.6 -2018,3,Aug,93.6 -2018,4,Aug,75.9 -2018,5,Aug,73.55 -2018,6,Aug,50 -2018,7,Aug,48.5 -2018,8,Aug,51.65 -2018,9,Aug,30.65 -2018,10,Aug,32.1 -2018,11,Aug,45.5 -2018,12,Aug,45.5 -2018,13,Aug,30.65 -2018,14,Aug,24.85 -2018,15,Aug,20.5 -2018,16,Aug,23.4 -2018,17,Aug,23.4 -2018,18,Aug,82.95 -2018,19,Aug,274 -2018,20,Aug,191.8 -2018,21,Aug,121.5 -2018,22,Aug,106.5 -2018,23,Aug,85.3 -2018,24,Aug,73.55 -2018,25,Aug,75.9 -2018,26,Aug,66.5 -2018,27,Aug,82.95 -2018,28,Aug,97.2 -2018,29,Aug,85.3 -2018,30,Aug,64.85 -2018,31,Aug,56.6 -2019,1,Aug,17.8 -2019,2,Aug,28.2 -2019,3,Aug,18.6 -2019,4,Aug,14.6 -2019,5,Aug,30.6 -2019,6,Aug,52.7 -2019,7,Aug,52.7 -2019,8,Aug,28.2 -2019,9,Aug,17.8 -2019,10,Aug,16.2 -2019,11,Aug,33 -2019,12,Aug,23.4 -2019,13,Aug,27 -2019,14,Aug,16.2 -2019,15,Aug,37.2 -2019,16,Aug,20.2 -2019,17,Aug,62.2 -2019,18,Aug,56.5 -2019,19,Aug,33 -2019,20,Aug,21 -2019,21,Aug,23.4 -2019,22,Aug,37.2 -2019,23,Aug,99.4 -2019,24,Aug,113.7 -2019,25,Aug,129.65 -2019,26,Aug,100.8 -2019,27,Aug,60.3 -2019,28,Aug,60.3 -2019,29,Aug,52.7 -2019,30,Aug,54.6 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-1923,1,Sep,153 -1923,2,Sep,108 -1923,3,Sep,93 -1923,4,Sep,84 -1923,5,Sep,82 -1923,6,Sep,82 -1923,7,Sep,73 -1923,8,Sep,69 -1923,9,Sep,64 -1923,10,Sep,66 -1923,11,Sep,67 -1923,12,Sep,83 -1923,13,Sep,113 -1923,14,Sep,86 -1923,15,Sep,85 -1923,16,Sep,95 -1923,17,Sep,86 -1923,18,Sep,74 -1923,19,Sep,67 -1923,20,Sep,66 -1923,21,Sep,80 -1923,22,Sep,74 -1923,23,Sep,70 -1923,24,Sep,60 -1923,25,Sep,59 -1923,26,Sep,56 -1923,27,Sep,51 -1923,28,Sep,49 -1923,29,Sep,49 -1923,30,Sep,47 -1924,1,Sep,122 -1924,2,Sep,114 -1924,3,Sep,104 -1924,4,Sep,108 -1924,5,Sep,154 -1924,6,Sep,147 -1924,7,Sep,140 -1924,8,Sep,108 -1924,9,Sep,111 -1924,10,Sep,149 -1924,11,Sep,186 -1924,12,Sep,240 -1924,13,Sep,199 -1924,14,Sep,154 -1924,15,Sep,140 -1924,16,Sep,153 -1924,17,Sep,331 -1924,18,Sep,382 -1924,19,Sep,222 -1924,20,Sep,194 -1924,21,Sep,156 -1924,22,Sep,145 -1924,23,Sep,134 -1924,24,Sep,118 -1924,25,Sep,102 -1924,26,Sep,99 -1924,27,Sep,94 -1924,28,Sep,102 -1924,29,Sep,94 -1924,30,Sep,88 -1925,1,Sep,58 -1925,2,Sep,55 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-1979,12,Sep,43.6 -1979,13,Sep,40 -1979,14,Sep,42.4 -1979,15,Sep,43.6 -1979,16,Sep,40 -1979,17,Sep,44.8 -1979,18,Sep,48.4 -1979,19,Sep,40 -1979,20,Sep,31.6 -1979,21,Sep,28 -1979,22,Sep,28 -1979,23,Sep,30.4 -1979,24,Sep,31.6 -1979,25,Sep,31.6 -1979,26,Sep,29.2 -1979,27,Sep,32.8 -1979,28,Sep,53.6 -1979,29,Sep,93 -1979,30,Sep,91.2 -1980,1,Sep,221.99 -1980,2,Sep,398.5 -1980,3,Sep,451.6 -1980,4,Sep,471.09 -1980,5,Sep,415.01 -1980,6,Sep,351.99 -1980,7,Sep,358 -1980,8,Sep,388.01 -1980,9,Sep,435.6 -1980,10,Sep,466 -1980,11,Sep,401.5 -1980,12,Sep,376 -1980,13,Sep,332.49 -1980,14,Sep,256.06 -1980,15,Sep,188.39 -1980,16,Sep,193.8 -1980,17,Sep,241.45 -1980,18,Sep,242.91 -1980,19,Sep,180.5 -1980,20,Sep,128 -1980,21,Sep,121.99 -1980,22,Sep,114.4 -1980,23,Sep,103.6 -1980,24,Sep,93.6 -1980,25,Sep,90.4 -1980,26,Sep,78.39 -1980,27,Sep,68.59 -1980,28,Sep,65.8 -1980,29,Sep,68.59 -1980,30,Sep,68.59 -1981,1,Sep,97.2 -1981,2,Sep,196.79 -1981,3,Sep,162.29 -1981,4,Sep,145.49 -1981,5,Sep,151.9 -1981,6,Sep,189.79 -1981,7,Sep,294.49 -1981,8,Sep,417 -1981,9,Sep,433 -1981,10,Sep,321.49 -1981,11,Sep,210.8 -1981,12,Sep,157.09 -1981,13,Sep,136.75 -1981,14,Sep,118.2 -1981,15,Sep,98.35 -1981,16,Sep,92.59 -1981,17,Sep,91.45 -1981,18,Sep,91.45 -1981,19,Sep,82.3 -1981,20,Sep,82.3 -1981,21,Sep,78.49 -1981,22,Sep,76.59 -1981,23,Sep,70.9 -1981,24,Sep,65.8 -1981,25,Sep,76.59 -1981,26,Sep,91.45 -1981,27,Sep,86.09 -1981,28,Sep,78.49 -1981,29,Sep,91.45 -1981,30,Sep,87.99 -1982,1,Sep,27 -1982,2,Sep,24 -1982,3,Sep,34.2 -1982,4,Sep,37.8 -1982,5,Sep,48 -1982,6,Sep,86 -1982,7,Sep,74 -1982,8,Sep,62 -1982,9,Sep,107 -1982,10,Sep,167.03 -1982,11,Sep,199.2 -1982,12,Sep,155.93 -1982,13,Sep,111.9 -1982,14,Sep,80 -1982,15,Sep,55.6 -1982,16,Sep,40.5 -1982,17,Sep,40.5 -1982,18,Sep,80 -1982,19,Sep,90 -1982,20,Sep,86 -1982,21,Sep,95.5 -1982,22,Sep,132 -1982,23,Sep,175.67 -1982,24,Sep,174.43 -1982,25,Sep,173.2 -1982,26,Sep,175.67 -1982,27,Sep,181.83 -1982,28,Sep,211.8 -1982,29,Sep,232 -1982,30,Sep,204.8 -1983,1,Sep,101.8 -1983,2,Sep,141.2 -1983,3,Sep,169.35 -1983,4,Sep,193.35 -1983,5,Sep,203.7 -1983,6,Sep,204.85 -1983,7,Sep,166.2 -1983,8,Sep,127.7 -1983,9,Sep,122.6 -1983,10,Sep,119.4 -1983,11,Sep,109.8 -1983,12,Sep,118.6 -1983,13,Sep,121.8 -1983,14,Sep,136.7 -1983,15,Sep,186.45 -1983,16,Sep,250.6 -1983,17,Sep,240.8 -1983,18,Sep,187.6 -1983,19,Sep,138.5 -1983,20,Sep,122.6 -1983,21,Sep,127.7 -1983,22,Sep,127.7 -1983,23,Sep,125.9 -1983,24,Sep,101.8 -1983,25,Sep,83.4 -1983,26,Sep,63.3 -1983,27,Sep,58.2 -1983,28,Sep,49.7 -1983,29,Sep,36.3 -1983,30,Sep,36.3 -1984,1,Sep,67.15 -1984,2,Sep,115 -1984,3,Sep,201.6 -1984,4,Sep,251.4 -1984,5,Sep,277 -1984,6,Sep,298.1 -1984,7,Sep,286.6 -1984,8,Sep,231.05 -1984,9,Sep,253 -1984,10,Sep,232.6 -1984,11,Sep,192.3 -1984,12,Sep,173.2 -1984,13,Sep,142.4 -1984,14,Sep,126.7 -1984,15,Sep,113.7 -1984,16,Sep,98.1 -1984,17,Sep,89 -1984,18,Sep,86.7 -1984,19,Sep,87.85 -1984,20,Sep,111.1 -1984,21,Sep,109.8 -1984,22,Sep,89 -1984,23,Sep,78.65 -1984,24,Sep,66 -1984,25,Sep,59.4 -1984,26,Sep,54 -1984,27,Sep,57.2 -1984,28,Sep,49 -1984,29,Sep,47 -1984,30,Sep,49 -1985,1,Sep,34.2 -1985,2,Sep,33.05 -1985,3,Sep,34.2 -1985,4,Sep,34.2 -1985,5,Sep,43.63 -1985,6,Sep,33.05 -1985,7,Sep,29.6 -1985,8,Sep,28.45 -1985,9,Sep,26.15 -1985,10,Sep,28.45 -1985,11,Sep,67.5 -1985,12,Sep,93.5 -1985,13,Sep,170.12 -1985,14,Sep,225.25 -1985,15,Sep,280 -1985,16,Sep,327.6 -1985,17,Sep,303.75 -1985,18,Sep,230.15 -1985,19,Sep,173.8 -1985,20,Sep,126.7 -1985,21,Sep,84.5 -1985,22,Sep,69.2 -1985,23,Sep,52.43 -1985,24,Sep,54.1 -1985,25,Sep,59.13 -1985,26,Sep,88.1 -1985,27,Sep,81.1 -1985,28,Sep,62.48 -1985,29,Sep,54.1 -1985,30,Sep,55.77 -1986,1,Sep,35.35 -1986,2,Sep,28.45 -1986,3,Sep,28.45 -1986,4,Sep,26.15 -1986,5,Sep,25 -1986,6,Sep,57.45 -1986,7,Sep,184.82 -1986,8,Sep,240 -1986,9,Sep,320 -1986,10,Sep,308.75 -1986,11,Sep,260 -1986,12,Sep,217.9 -1986,13,Sep,186.05 -1986,14,Sep,152 -1986,15,Sep,122.9 -1986,16,Sep,111.75 -1986,17,Sep,95.3 -1986,18,Sep,100.7 -1986,19,Sep,106.2 -1986,20,Sep,115.45 -1986,21,Sep,124.8 -1986,22,Sep,113.6 -1986,23,Sep,82.8 -1986,24,Sep,62.48 -1986,25,Sep,50.75 -1986,26,Sep,43.63 -1986,27,Sep,42.2 -1986,28,Sep,42.2 -1986,29,Sep,39.35 -1986,30,Sep,37.93 -1987,1,Sep,66.5 -1987,2,Sep,54.6 -1987,3,Sep,52 -1987,4,Sep,47 -1987,5,Sep,40.75 -1987,6,Sep,37.3 -1987,7,Sep,37.3 -1987,8,Sep,34 -1987,9,Sep,37.3 -1987,10,Sep,55.9 -1987,11,Sep,59.8 -1987,12,Sep,52 -1987,13,Sep,50.75 -1987,14,Sep,72.5 -1987,15,Sep,110.4 -1987,16,Sep,102.4 -1987,17,Sep,88 -1987,18,Sep,54.6 -1987,19,Sep,53.3 -1987,20,Sep,61.1 -1987,21,Sep,91.2 -1987,22,Sep,102.4 -1987,23,Sep,102.4 -1987,24,Sep,91.2 -1987,25,Sep,80 -1987,26,Sep,66.5 -1987,27,Sep,113.8 -1987,28,Sep,168.6 -1987,29,Sep,225 -1987,30,Sep,217.5 -1988,1,Sep,100 -1988,2,Sep,81 -1988,3,Sep,77.6 -1988,4,Sep,64 -1988,5,Sep,54.4 -1988,6,Sep,54.4 -1988,7,Sep,65.7 -1988,8,Sep,75.9 -1988,9,Sep,81 -1988,10,Sep,77.6 -1988,11,Sep,75.9 -1988,12,Sep,75.9 -1988,13,Sep,72.5 -1988,14,Sep,72.5 -1988,15,Sep,81 -1988,16,Sep,133.8 -1988,17,Sep,152 -1988,18,Sep,141.6 -1988,19,Sep,121.25 -1988,20,Sep,102 -1988,21,Sep,86.7 -1988,22,Sep,86.7 -1988,23,Sep,67.4 -1988,24,Sep,65.7 -1988,25,Sep,62.4 -1988,26,Sep,52.8 -1988,27,Sep,45.1 -1988,28,Sep,42.2 -1988,29,Sep,42.2 -1988,30,Sep,42.2 -1989,1,Sep,108.2 -1989,2,Sep,87.2 -1989,3,Sep,58.2 -1989,4,Sep,47 -1989,5,Sep,48.4 -1989,6,Sep,40.5 -1989,7,Sep,41.8 -1989,8,Sep,64 -1989,9,Sep,90.4 -1989,10,Sep,145.4 -1989,11,Sep,138.9 -1989,12,Sep,111.2 -1989,13,Sep,82.4 -1989,14,Sep,67 -1989,15,Sep,65.5 -1989,16,Sep,64 -1989,17,Sep,70 -1989,18,Sep,64 -1989,19,Sep,55.4 -1989,20,Sep,59.6 -1989,21,Sep,49.8 -1989,22,Sep,54 -1989,23,Sep,71.5 -1989,24,Sep,84 -1989,25,Sep,159.2 -1989,26,Sep,201 -1989,27,Sep,218.2 -1989,28,Sep,187.5 -1989,29,Sep,124.6 -1989,30,Sep,99.2 -1990,1,Sep,136.4 -1990,2,Sep,114.2 -1990,3,Sep,79.5 -1990,4,Sep,73.65 -1990,5,Sep,64.95 -1990,6,Sep,67.85 -1990,7,Sep,58.1 -1990,8,Sep,55.4 -1990,9,Sep,75.1 -1990,10,Sep,78 -1990,11,Sep,63.5 -1990,12,Sep,59.45 -1990,13,Sep,58.1 -1990,14,Sep,66.4 -1990,15,Sep,69.3 -1990,16,Sep,81 -1990,17,Sep,91.5 -1990,18,Sep,78 -1990,19,Sep,67.85 -1990,20,Sep,58.1 -1990,21,Sep,46.25 -1990,22,Sep,40 -1990,23,Sep,36.4 -1990,24,Sep,37.5 -1990,25,Sep,43.75 -1990,26,Sep,56.75 -1990,27,Sep,56.75 -1990,28,Sep,46.25 -1990,29,Sep,40 -1990,30,Sep,42.5 -1991,1,Sep,73.8 -1991,2,Sep,67.3 -1991,3,Sep,49 -1991,4,Sep,38.7 -1991,5,Sep,40.5 -1991,6,Sep,62.7 -1991,7,Sep,137.6 -1991,8,Sep,168 -1991,9,Sep,137.6 -1991,10,Sep,132.5 -1991,11,Sep,99 -1991,12,Sep,88.8 -1991,13,Sep,83.2 -1991,14,Sep,63.8 -1991,15,Sep,57.2 -1991,16,Sep,47 -1991,17,Sep,54 -1991,18,Sep,63.8 -1991,19,Sep,64.9 -1991,20,Sep,77.7 -1991,21,Sep,71.2 -1991,22,Sep,68.6 -1991,23,Sep,62.7 -1991,24,Sep,53 -1991,25,Sep,47 -1991,26,Sep,43.2 -1991,27,Sep,39.6 -1991,28,Sep,40.5 -1991,29,Sep,42.3 -1991,30,Sep,52 -1992,1,Sep,23.46 -1992,2,Sep,35.34 -1992,3,Sep,40.6 -1992,4,Sep,27.92 -1992,5,Sep,23.46 -1992,6,Sep,21.9 -1992,7,Sep,25.02 -1992,8,Sep,28.98 -1992,9,Sep,30.04 -1992,10,Sep,47.6 -1992,11,Sep,51.86 -1992,12,Sep,53.32 -1992,13,Sep,51.86 -1992,14,Sep,40.6 -1992,15,Sep,33.22 -1992,16,Sep,46.2 -1992,17,Sep,94.4 -1992,18,Sep,117.2 -1992,19,Sep,139.4 -1992,20,Sep,151.8 -1992,21,Sep,115.3 -1992,22,Sep,105.8 -1992,23,Sep,151.8 -1992,24,Sep,149.2 -1992,25,Sep,102 -1992,26,Sep,94.4 -1992,27,Sep,83 -1992,28,Sep,86.8 -1992,29,Sep,117.2 -1992,30,Sep,134.8 -1993,1,Sep,72.39 -1993,2,Sep,94.06 -1993,3,Sep,94.06 -1993,4,Sep,117.4 -1993,5,Sep,94.06 -1993,6,Sep,72.39 -1993,7,Sep,55.42 -1993,8,Sep,46.8 -1993,9,Sep,44.4 -1993,10,Sep,39.8 -1993,11,Sep,94.06 -1993,12,Sep,164 -1993,13,Sep,150 -1993,14,Sep,100.4 -1993,15,Sep,63.91 -1993,16,Sep,46.8 -1993,17,Sep,39.8 -1993,18,Sep,35.4 -1993,19,Sep,45.6 -1993,20,Sep,52.8 -1993,21,Sep,107.2 -1993,22,Sep,122.5 -1993,23,Sep,89.65 -1993,24,Sep,63.91 -1993,25,Sep,61.07 -1993,26,Sep,52.8 -1993,27,Sep,43.2 -1993,28,Sep,35.4 -1993,29,Sep,31 -1993,30,Sep,30 -1994,1,Sep,485.8 -1994,2,Sep,494.8 -1994,3,Sep,414 -1994,4,Sep,344.4 -1994,5,Sep,283.6 -1994,6,Sep,267.6 -1994,7,Sep,253.2 -1994,8,Sep,231 -1994,9,Sep,228 -1994,10,Sep,184.8 -1994,11,Sep,168 -1994,12,Sep,201 -1994,13,Sep,291.6 -1994,14,Sep,288.4 -1994,15,Sep,357.9 -1994,16,Sep,473.2 -1994,17,Sep,455.2 -1994,18,Sep,444.6 -1994,19,Sep,393.6 -1994,20,Sep,331.6 -1994,21,Sep,304.4 -1994,22,Sep,235.6 -1994,23,Sep,205.5 -1994,24,Sep,177.8 -1994,25,Sep,186.2 -1994,26,Sep,182 -1994,27,Sep,158.4 -1994,28,Sep,150.6 -1994,29,Sep,161 -1994,30,Sep,159.7 -1995,1,Sep,247.5 -1995,2,Sep,330 -1995,3,Sep,396.5 -1995,4,Sep,362.5 -1995,5,Sep,418.6 -1995,6,Sep,461.4 -1995,7,Sep,466.8 -1995,8,Sep,384.6 -1995,9,Sep,307.6 -1995,10,Sep,291.6 -1995,11,Sep,274 -1995,12,Sep,229.5 -1995,13,Sep,222 -1995,14,Sep,181.2 -1995,15,Sep,152.1 -1995,16,Sep,157.4 -1995,17,Sep,152.1 -1995,18,Sep,135.2 -1995,19,Sep,118.3 -1995,20,Sep,124.8 -1995,21,Sep,181.2 -1995,22,Sep,285.2 -1995,23,Sep,266 -1995,24,Sep,262.8 -1995,25,Sep,274 -1995,26,Sep,208.5 -1995,27,Sep,171.4 -1995,28,Sep,148.2 -1995,29,Sep,128.7 -1995,30,Sep,118.3 -1996,1,Sep,111.7 -1996,2,Sep,137.2 -1996,3,Sep,211.5 -1996,4,Sep,328.8 -1996,5,Sep,341.6 -1996,6,Sep,261 -1996,7,Sep,194.6 -1996,8,Sep,149.8 -1996,9,Sep,133 -1996,10,Sep,120.8 -1996,11,Sep,116.9 -1996,12,Sep,118.2 -1996,13,Sep,101.3 -1996,14,Sep,73.75 -1996,15,Sep,77.05 -1996,16,Sep,89.2 -1996,17,Sep,89.2 -1996,18,Sep,80.35 -1996,19,Sep,83.8 -1996,20,Sep,85.6 -1996,21,Sep,65.5 -1996,22,Sep,64 -1996,23,Sep,65.5 -1996,24,Sep,65.5 -1996,25,Sep,73.75 -1996,26,Sep,91 -1996,27,Sep,118.2 -1996,28,Sep,124.7 -1996,29,Sep,109.1 -1996,30,Sep,83.8 -1997,1,Sep,85.55 -1997,2,Sep,139.2 -1997,3,Sep,161.5 -1997,4,Sep,113.1 -1997,5,Sep,87 -1997,6,Sep,72.5 -1997,7,Sep,73.95 -1997,8,Sep,59.9 -1997,9,Sep,49.4 -1997,10,Sep,42.05 -1997,11,Sep,42.05 -1997,12,Sep,32.95 -1997,13,Sep,32.95 -1997,14,Sep,30.85 -1997,15,Sep,28.75 -1997,16,Sep,27.7 -1997,17,Sep,26.65 -1997,18,Sep,24.55 -1997,19,Sep,27.7 -1997,20,Sep,27.7 -1997,21,Sep,29.8 -1997,22,Sep,59.9 -1997,23,Sep,48.1 -1997,24,Sep,38.6 -1997,25,Sep,46.8 -1997,26,Sep,121.8 -1997,27,Sep,113.1 -1997,28,Sep,146.5 -1997,29,Sep,236.6 -1997,30,Sep,270.6 -1998,1,Sep,22.8 -1998,2,Sep,29.3 -1998,3,Sep,48.45 -1998,4,Sep,40.7 -1998,5,Sep,33.2 -1998,6,Sep,31.9 -1998,7,Sep,67.6 -1998,8,Sep,132.85 -1998,9,Sep,150 -1998,10,Sep,134.9 -1998,11,Sep,118.5 -1998,12,Sep,99 -1998,13,Sep,67.6 -1998,14,Sep,43.8 -1998,15,Sep,39.15 -1998,16,Sep,30.6 -1998,17,Sep,39.15 -1998,18,Sep,39.15 -1998,19,Sep,37.6 -1998,20,Sep,30.6 -1998,21,Sep,26.7 -1998,22,Sep,22.8 -1998,23,Sep,17.7 -1998,24,Sep,15.8 -1998,25,Sep,15.8 -1998,26,Sep,15.8 -1998,27,Sep,16.75 -1998,28,Sep,18.65 -1998,29,Sep,16.75 -1998,30,Sep,13.9 -1999,1,Sep,87.2 -1999,2,Sep,87.2 -1999,3,Sep,78.8 -1999,4,Sep,63.94 -1999,5,Sep,54.92 -1999,6,Sep,56.2 -1999,7,Sep,74.66 -1999,8,Sep,69.3 -1999,9,Sep,57.48 -1999,10,Sep,63.94 -1999,11,Sep,70.64 -1999,12,Sep,85.8 -1999,13,Sep,88.6 -1999,14,Sep,88.6 -1999,15,Sep,77.4 -1999,16,Sep,77.4 -1999,17,Sep,70.64 -1999,18,Sep,80.2 -1999,19,Sep,91.5 -1999,20,Sep,112.5 -1999,21,Sep,111 -1999,22,Sep,166.4 -1999,23,Sep,177.6 -1999,24,Sep,147.2 -1999,25,Sep,148.8 -1999,26,Sep,152 -1999,27,Sep,132.8 -1999,28,Sep,103.5 -1999,29,Sep,85.8 -1999,30,Sep,77.4 -2000,1,Sep,60.8 -2000,2,Sep,49.28 -2000,3,Sep,47.85 -2000,4,Sep,56.45 -2000,5,Sep,73.85 -2000,6,Sep,82.55 -2000,7,Sep,101.8 -2000,8,Sep,95.2 -2000,9,Sep,73.85 -2000,10,Sep,76.75 -2000,11,Sep,95.2 -2000,12,Sep,98.4 -2000,13,Sep,103.6 -2000,14,Sep,98.4 -2000,15,Sep,100 -2000,16,Sep,118 -2000,17,Sep,88.8 -2000,18,Sep,65.15 -2000,19,Sep,55 -2000,20,Sep,47.85 -2000,21,Sep,44.99 -2000,22,Sep,44.99 -2000,23,Sep,40.7 -2000,24,Sep,39.3 -2000,25,Sep,44.99 -2000,26,Sep,44.99 -2000,27,Sep,43.56 -2000,28,Sep,46.42 -2000,29,Sep,50.71 -2000,30,Sep,59.35 -2001,1,Sep,105.5 -2001,2,Sep,87.5 -2001,3,Sep,71.4 -2001,4,Sep,61.25 -2001,5,Sep,65.6 -2001,6,Sep,67.05 -2001,7,Sep,62.7 -2001,8,Sep,55.45 -2001,9,Sep,54 -2001,10,Sep,62.7 -2001,11,Sep,67.05 -2001,12,Sep,80.1 -2001,13,Sep,83 -2001,14,Sep,101 -2001,15,Sep,135.4 -2001,16,Sep,113 -2001,17,Sep,78.65 -2001,18,Sep,62.7 -2001,19,Sep,58.35 -2001,20,Sep,58.35 -2001,21,Sep,58.35 -2001,22,Sep,56.9 -2001,23,Sep,65.6 -2001,24,Sep,58.35 -2001,25,Sep,68.5 -2001,26,Sep,89 -2001,27,Sep,68.5 -2001,28,Sep,52.55 -2001,29,Sep,52.55 -2001,30,Sep,48.2 -2002,1,Sep,139.08 -2002,2,Sep,105.21 -2002,3,Sep,94.96 -2002,4,Sep,87.66 -2002,5,Sep,102.27 -2002,6,Sep,124.32 -2002,7,Sep,219 -2002,8,Sep,269.2 -2002,9,Sep,310.8 -2002,10,Sep,379 -2002,11,Sep,355.6 -2002,12,Sep,306 -2002,13,Sep,231 -2002,14,Sep,177.56 -2002,15,Sep,149.44 -2002,16,Sep,146.48 -2002,17,Sep,159.8 -2002,18,Sep,184.96 -2002,19,Sep,232.5 -2002,20,Sep,290 -2002,21,Sep,226.5 -2002,22,Sep,190.88 -2002,23,Sep,150.92 -2002,24,Sep,145 -2002,25,Sep,124.32 -2002,26,Sep,250 -2002,27,Sep,94.96 -2002,28,Sep,89.12 -2002,29,Sep,99.34 -2002,30,Sep,183.48 -2003,1,Sep,31.8 -2003,2,Sep,31.8 -2003,3,Sep,30.4 -2003,4,Sep,30.4 -2003,5,Sep,63.3 -2003,6,Sep,82.5 -2003,7,Sep,93 -2003,8,Sep,78 -2003,9,Sep,73.5 -2003,10,Sep,106.5 -2003,11,Sep,194.6 -2003,12,Sep,334.4 -2003,13,Sep,414 -2003,14,Sep,362.9 -2003,15,Sep,338.2 -2003,16,Sep,262.6 -2003,17,Sep,192.9 -2003,18,Sep,141.6 -2003,19,Sep,111 -2003,20,Sep,99 -2003,21,Sep,100.5 -2003,22,Sep,91.5 -2003,23,Sep,85.5 -2003,24,Sep,90 -2003,25,Sep,79.5 -2003,26,Sep,73.5 -2003,27,Sep,67.65 -2003,28,Sep,57.5 -2003,29,Sep,51.7 -2003,30,Sep,47.35 -2004,1,Sep,53.9 -2004,2,Sep,53.9 -2004,3,Sep,64.6 -2004,4,Sep,88.2 -2004,5,Sep,77.5 -2004,6,Sep,60.55 -2004,7,Sep,53.9 -2004,8,Sep,50 -2004,9,Sep,55.2 -2004,10,Sep,160.2 -2004,11,Sep,367.4 -2004,12,Sep,341 -2004,13,Sep,278 -2004,14,Sep,272 -2004,15,Sep,298 -2004,16,Sep,430 -2004,17,Sep,454 -2004,18,Sep,432.4 -2004,19,Sep,316 -2004,20,Sep,248 -2004,21,Sep,304 -2004,22,Sep,407 -2004,23,Sep,371.8 -2004,24,Sep,300 -2004,25,Sep,232 -2004,26,Sep,198 -2004,27,Sep,174.8 -2004,28,Sep,171 -2004,29,Sep,169.2 -2004,30,Sep,154.8 -2005,1,Sep,111.25 -2005,2,Sep,163.75 -2005,3,Sep,174.25 -2005,4,Sep,163.75 -2005,5,Sep,134 -2005,6,Sep,114.75 -2005,7,Sep,99.2 -2005,8,Sep,90.7 -2005,9,Sep,114.75 -2005,10,Sep,160.25 -2005,11,Sep,335.3 -2005,12,Sep,407.5 -2005,13,Sep,418.9 -2005,14,Sep,233.6 -2005,15,Sep,169 -2005,16,Sep,158.5 -2005,17,Sep,160.25 -2005,18,Sep,139.25 -2005,19,Sep,163.75 -2005,20,Sep,543 -2005,21,Sep,679.5 -2005,22,Sep,472 -2005,23,Sep,318.3 -2005,24,Sep,333.4 -2005,25,Sep,318.3 -2005,26,Sep,323.9 -2005,27,Sep,290.55 -2005,28,Sep,285 -2005,29,Sep,602.1 -2005,30,Sep,822.41 -2006,1,Sep,435.1 -2006,2,Sep,454.1 -2006,3,Sep,328.8 -2006,4,Sep,215.6 -2006,5,Sep,157.8 -2006,6,Sep,135 -2006,7,Sep,127 -2006,8,Sep,120.6 -2006,9,Sep,109.4 -2006,10,Sep,144.6 -2006,11,Sep,125.4 -2006,12,Sep,141.4 -2006,13,Sep,162.9 -2006,14,Sep,125.4 -2006,15,Sep,107.8 -2006,16,Sep,93.4 -2006,17,Sep,93.4 -2006,18,Sep,91.8 -2006,19,Sep,117.4 -2006,20,Sep,215.6 -2006,21,Sep,309 -2006,22,Sep,312.6 -2006,23,Sep,346.8 -2006,24,Sep,303.6 -2006,25,Sep,203.7 -2006,26,Sep,159.5 -2006,27,Sep,144.6 -2006,28,Sep,127 -2006,29,Sep,128.6 -2006,30,Sep,125.4 -2007,1,Sep,80.35 -2007,2,Sep,70.9 -2007,3,Sep,74.05 -2007,4,Sep,63.02 -2007,5,Sep,85.17 -2007,6,Sep,91.88 -2007,7,Sep,137.7 -2007,8,Sep,129.08 -2007,9,Sep,83.5 -2007,10,Sep,64.6 -2007,11,Sep,50.7 -2007,12,Sep,52 -2007,13,Sep,70.9 -2007,14,Sep,75.62 -2007,15,Sep,70.9 -2007,16,Sep,77.2 -2007,17,Sep,67.75 -2007,18,Sep,55.15 -2007,19,Sep,48.1 -2007,20,Sep,80.35 -2007,21,Sep,93.55 -2007,22,Sep,64.6 -2007,23,Sep,44.2 -2007,24,Sep,37.85 -2007,25,Sep,35.55 -2007,26,Sep,36.7 -2007,27,Sep,49.4 -2007,28,Sep,70.9 -2007,29,Sep,96.9 -2007,30,Sep,103.6 -2008,1,Sep,120 -2008,2,Sep,128.1 -2008,3,Sep,96.3 -2008,4,Sep,77.3 -2008,5,Sep,59.7 -2008,6,Sep,80.5 -2008,7,Sep,166.6 -2008,8,Sep,185.15 -2008,9,Sep,156.8 -2008,10,Sep,141.6 -2008,11,Sep,134.85 -2008,12,Sep,133.5 -2008,13,Sep,125.4 -2008,14,Sep,147 -2008,15,Sep,175 -2008,16,Sep,161 -2008,17,Sep,140.25 -2008,18,Sep,120 -2008,19,Sep,106.18 -2008,20,Sep,98.28 -2008,21,Sep,96.3 -2008,22,Sep,102.23 -2008,23,Sep,104.2 -2008,24,Sep,96.3 -2008,25,Sep,80.5 -2008,26,Sep,66.1 -2008,27,Sep,62.9 -2008,28,Sep,56.5 -2008,29,Sep,54.9 -2008,30,Sep,64.5 -2009,1,Sep,100.8 -2009,2,Sep,75.7 -2009,3,Sep,59.65 -2009,4,Sep,52.3 -2009,5,Sep,41.42 -2009,6,Sep,37.1 -2009,7,Sep,37.1 -2009,8,Sep,58.43 -2009,9,Sep,113.6 -2009,10,Sep,62.1 -2009,11,Sep,47.4 -2009,12,Sep,38.18 -2009,13,Sep,37.1 -2009,14,Sep,37.1 -2009,15,Sep,44.95 -2009,16,Sep,47.4 -2009,17,Sep,74.25 -2009,18,Sep,207 -2009,19,Sep,204.85 -2009,20,Sep,121.6 -2009,21,Sep,72.8 -2009,22,Sep,60.87 -2009,23,Sep,67 -2009,24,Sep,75.7 -2009,25,Sep,64.55 -2009,26,Sep,60.87 -2009,27,Sep,64.55 -2009,28,Sep,100.8 -2009,29,Sep,93.1 -2009,30,Sep,63.33 -2010,1,Sep,167.5 -2010,2,Sep,158 -2010,3,Sep,131 -2010,4,Sep,104.4 -2010,5,Sep,87 -2010,6,Sep,70.5 -2010,7,Sep,54.8 -2010,8,Sep,47 -2010,9,Sep,50.9 -2010,10,Sep,87 -2010,11,Sep,99.6 -2010,12,Sep,178.9 -2010,13,Sep,240.15 -2010,14,Sep,192.2 -2010,15,Sep,196 -2010,16,Sep,257.35 -2010,17,Sep,370.2 -2010,18,Sep,367.8 -2010,19,Sep,294.8 -2010,20,Sep,200.2 -2010,21,Sep,171.3 -2010,22,Sep,150.8 -2010,23,Sep,132.8 -2010,24,Sep,149 -2010,25,Sep,147.2 -2010,26,Sep,129.2 -2010,27,Sep,132.8 -2010,28,Sep,270.25 -2010,29,Sep,292.5 -2010,30,Sep,192.2 -2011,1,Sep,220.1 -2011,2,Sep,215.95 -2011,3,Sep,213.9 -2011,4,Sep,199.55 -2011,5,Sep,173.3 -2011,6,Sep,268.8 -2011,7,Sep,410.4 -2011,8,Sep,317.8 -2011,9,Sep,245.3 -2011,10,Sep,224.3 -2011,11,Sep,195.45 -2011,12,Sep,158.5 -2011,13,Sep,213.9 -2011,14,Sep,293 -2011,15,Sep,333.9 -2011,16,Sep,268.8 -2011,17,Sep,211.85 -2011,18,Sep,187.25 -2011,19,Sep,191.35 -2011,20,Sep,236.9 -2011,21,Sep,209.8 -2011,22,Sep,253.7 -2011,23,Sep,262.2 -2011,24,Sep,228.5 -2011,25,Sep,266.6 -2011,26,Sep,284.2 -2011,27,Sep,301.8 -2011,28,Sep,594 -2011,29,Sep,797.9 -2011,30,Sep,690.2 -2012,1,Sep,47.2 -2012,2,Sep,82.2 -2012,3,Sep,72.4 -2012,4,Sep,114.5 -2012,5,Sep,155.2 -2012,6,Sep,114.5 -2012,7,Sep,105.2 -2012,8,Sep,169.7 -2012,9,Sep,221 -2012,10,Sep,180.8 -2012,11,Sep,153.4 -2012,12,Sep,188.2 -2012,13,Sep,153.4 -2012,14,Sep,151.6 -2012,15,Sep,199.3 -2012,16,Sep,153.4 -2012,17,Sep,125.35 -2012,18,Sep,86.4 -2012,19,Sep,69.6 -2012,20,Sep,65.4 -2012,21,Sep,76.6 -2012,22,Sep,86.4 -2012,23,Sep,68.2 -2012,24,Sep,62.6 -2012,25,Sep,65.4 -2012,26,Sep,57 -2012,27,Sep,57 -2012,28,Sep,62.6 -2012,29,Sep,64 -2012,30,Sep,65.4 -2013,1,Sep,38 -2013,2,Sep,100 -2013,3,Sep,59.5 -2013,4,Sep,40.5 -2013,5,Sep,35.5 -2013,6,Sep,43 -2013,7,Sep,74.8 -2013,8,Sep,129.5 -2013,9,Sep,195 -2013,10,Sep,177 -2013,11,Sep,115.15 -2013,12,Sep,89.2 -2013,13,Sep,103.6 -2013,14,Sep,96.4 -2013,15,Sep,141.8 -2013,16,Sep,150 -2013,17,Sep,147.95 -2013,18,Sep,131.55 -2013,19,Sep,101.8 -2013,20,Sep,78.4 -2013,21,Sep,64 -2013,22,Sep,55 -2013,23,Sep,46 -2013,24,Sep,36.75 -2013,25,Sep,41.75 -2013,26,Sep,49 -2013,27,Sep,73 -2013,28,Sep,159 -2013,29,Sep,150 -2013,30,Sep,109 -2014,1,Sep,144.6 -2014,2,Sep,142 -2014,3,Sep,242.1 -2014,4,Sep,198.4 -2014,5,Sep,170.6 -2014,6,Sep,129 -2014,7,Sep,96.8 -2014,8,Sep,94.5 -2014,9,Sep,71.6 -2014,10,Sep,65.9 -2014,11,Sep,48.8 -2014,12,Sep,42.1 -2014,13,Sep,40.65 -2014,14,Sep,46.9 -2014,15,Sep,48.8 -2014,16,Sep,81.1 -2014,17,Sep,75.4 -2014,18,Sep,62.1 -2014,19,Sep,56.4 -2014,20,Sep,75.4 -2014,21,Sep,54.5 -2014,22,Sep,40.65 -2014,23,Sep,29.4 -2014,24,Sep,28.3 -2014,25,Sep,36.3 -2014,26,Sep,79.2 -2014,27,Sep,62.1 -2014,28,Sep,81.1 -2014,29,Sep,52.6 -2014,30,Sep,103.7 -2015,1,Sep,7.8 -2015,2,Sep,9 -2015,3,Sep,9.6 -2015,4,Sep,10.8 -2015,5,Sep,14.85 -2015,6,Sep,34.55 -2015,7,Sep,39.6 -2015,8,Sep,28.75 -2015,9,Sep,19.6 -2015,10,Sep,16.75 -2015,11,Sep,13.9 -2015,12,Sep,17.7 -2015,13,Sep,30.2 -2015,14,Sep,19.6 -2015,15,Sep,12.95 -2015,16,Sep,12 -2015,17,Sep,22.95 -2015,18,Sep,12.95 -2015,19,Sep,58.4 -2015,20,Sep,84.1 -2015,21,Sep,46.8 -2015,22,Sep,25.85 -2015,23,Sep,21.5 -2015,24,Sep,15.8 -2015,25,Sep,13.9 -2015,26,Sep,11.4 -2015,27,Sep,10.2 -2015,28,Sep,10.2 -2015,29,Sep,12 -2015,30,Sep,6.6 -2016,1,Sep,74.05 -2016,2,Sep,54.88 -2016,3,Sep,48.97 -2016,4,Sep,46.25 -2016,5,Sep,46.25 -2016,6,Sep,32.96 -2016,7,Sep,33.98 -2016,8,Sep,40 -2016,9,Sep,54.88 -2016,10,Sep,60.77 -2016,11,Sep,57.82 -2016,12,Sep,116.9 -2016,13,Sep,131.75 -2016,14,Sep,93.8 -2016,15,Sep,83.3 -2016,16,Sep,87.5 -2016,17,Sep,129.2 -2016,18,Sep,124.1 -2016,19,Sep,240.5 -2016,20,Sep,162.35 -2016,21,Sep,108.5 -2016,22,Sep,114.8 -2016,23,Sep,89.6 -2016,24,Sep,68.15 -2016,25,Sep,59.3 -2016,26,Sep,77 -2016,27,Sep,89.6 -2016,28,Sep,71.1 -2016,29,Sep,50.45 -2016,30,Sep,56.35 -2017,1,Sep,99.1 -2017,2,Sep,99.1 -2017,3,Sep,85 -2017,4,Sep,75 -2017,5,Sep,55.5 -2017,6,Sep,67.5 -2017,7,Sep,110 -2017,8,Sep,114.2 -2017,9,Sep,103 -2017,10,Sep,96.5 -2017,11,Sep,77.5 -2017,12,Sep,65 -2017,13,Sep,63.1 -2017,14,Sep,61.2 -2017,15,Sep,53.6 -2017,16,Sep,57.4 -2017,17,Sep,95.2 -2017,18,Sep,183 -2017,19,Sep,154.8 -2017,20,Sep,103 -2017,21,Sep,97.8 -2017,22,Sep,93.9 -2017,23,Sep,91.3 -2017,24,Sep,72.5 -2017,25,Sep,61.2 -2017,26,Sep,59.3 -2017,27,Sep,126 -2017,28,Sep,105.8 -2017,29,Sep,82.5 -2017,30,Sep,90 -2018,1,Sep,68.85 -2018,2,Sep,59.9 -2018,3,Sep,53.3 -2018,4,Sep,56.6 -2018,5,Sep,63.2 -2018,6,Sep,63.2 -2018,7,Sep,48.5 -2018,8,Sep,42.5 -2018,9,Sep,48.5 -2018,10,Sep,71.2 -2018,11,Sep,93.6 -2018,12,Sep,75.9 -2018,13,Sep,75.9 -2018,14,Sep,59.9 -2018,15,Sep,59.9 -2018,16,Sep,59.9 -2018,17,Sep,53.3 -2018,18,Sep,48.5 -2018,19,Sep,93.6 -2018,20,Sep,109.5 -2018,21,Sep,94.8 -2018,22,Sep,63.2 -2018,23,Sep,53.3 -2018,24,Sep,44 -2018,25,Sep,39.5 -2018,26,Sep,44 -2018,27,Sep,38 -2018,28,Sep,41 -2018,29,Sep,39.5 -2018,30,Sep,61.55 -2019,1,Sep,122.4 -2019,2,Sep,107.9 -2019,3,Sep,119.5 -2019,4,Sep,107.9 -2019,5,Sep,93.8 -2019,6,Sep,76 -2019,7,Sep,54.6 -2019,8,Sep,42.8 -2019,9,Sep,34.4 -2019,10,Sep,25.8 -2019,11,Sep,28.2 -2019,12,Sep,28.2 -2019,13,Sep,31.8 -2019,14,Sep,23.4 -2019,15,Sep,21 -2019,16,Sep,17 -2019,17,Sep,13.8 -2019,18,Sep,14.6 -2019,19,Sep,42.8 -2019,20,Sep,54.6 -2019,21,Sep,44.2 -2019,22,Sep,35.8 -2019,23,Sep,33 -2019,24,Sep,23.4 -2019,25,Sep,17 -2019,26,Sep,17.8 -2019,27,Sep,17.8 -2019,28,Sep,28.2 -2019,29,Sep,19.4 -2019,30,Sep,15.4 -1921,1,Oct,256 -1921,2,Oct,172 -1921,3,Oct,170 -1921,4,Oct,168 -1921,5,Oct,144 -1921,6,Oct,122 -1921,7,Oct,110 -1921,8,Oct,99 -1921,9,Oct,94 -1921,10,Oct,93 -1921,11,Oct,92 -1921,12,Oct,121 -1921,13,Oct,161 -1921,14,Oct,144 -1921,15,Oct,132 -1921,16,Oct,126 -1921,17,Oct,124 -1921,18,Oct,119 -1921,19,Oct,93 -1921,20,Oct,94 -1921,21,Oct,86 -1921,22,Oct,82 -1921,23,Oct,79 -1921,24,Oct,72 -1921,25,Oct,70 -1921,26,Oct,68 -1921,27,Oct,65 -1921,28,Oct,62 -1921,29,Oct,60 -1921,30,Oct,58 -1921,31,Oct,58 -1922,1,Oct,152 -1922,2,Oct,153 -1922,3,Oct,245 -1922,4,Oct,147 -1922,5,Oct,113 -1922,6,Oct,94 -1922,7,Oct,82 -1922,8,Oct,84 -1922,9,Oct,80 -1922,10,Oct,74 -1922,11,Oct,80 -1922,12,Oct,94 -1922,13,Oct,80 -1922,14,Oct,72 -1922,15,Oct,68 -1922,16,Oct,65 -1922,17,Oct,63 -1922,18,Oct,63 -1922,19,Oct,62 -1922,20,Oct,58 -1922,21,Oct,58 -1922,22,Oct,56 -1922,23,Oct,85 -1922,24,Oct,94 -1922,25,Oct,71 -1922,26,Oct,62 -1922,27,Oct,61 -1922,28,Oct,64 -1922,29,Oct,61 -1922,30,Oct,67 -1922,31,Oct,57 -1923,1,Oct,47 -1923,2,Oct,57 -1923,3,Oct,60 -1923,4,Oct,63 -1923,5,Oct,59 -1923,6,Oct,56 -1923,7,Oct,68 -1923,8,Oct,108 -1923,9,Oct,85 -1923,10,Oct,96 -1923,11,Oct,102 -1923,12,Oct,331 -1923,13,Oct,202 -1923,14,Oct,149 -1923,15,Oct,114 -1923,16,Oct,112 -1923,17,Oct,96 -1923,18,Oct,85 -1923,19,Oct,80 -1923,20,Oct,91 -1923,21,Oct,90 -1923,22,Oct,134 -1923,23,Oct,134 -1923,24,Oct,105 -1923,25,Oct,91 -1923,26,Oct,85 -1923,27,Oct,74 -1923,28,Oct,61 -1923,29,Oct,66 -1923,30,Oct,56 -1923,31,Oct,51 -1924,1,Oct,96 -1924,2,Oct,92 -1924,3,Oct,85 -1924,4,Oct,82 -1924,5,Oct,83 -1924,6,Oct,81 -1924,7,Oct,79 -1924,8,Oct,79 -1924,9,Oct,80 -1924,10,Oct,101 -1924,11,Oct,119 -1924,12,Oct,96 -1924,13,Oct,91 -1924,14,Oct,88 -1924,15,Oct,84 -1924,16,Oct,80 -1924,17,Oct,77 -1924,18,Oct,81 -1924,19,Oct,90 -1924,20,Oct,95 -1924,21,Oct,104 -1924,22,Oct,96 -1924,23,Oct,94 -1924,24,Oct,81 -1924,25,Oct,73 -1924,26,Oct,76 -1924,27,Oct,66 -1924,28,Oct,64 -1924,29,Oct,67 -1924,30,Oct,110 -1924,31,Oct,104 -1925,1,Oct,121 -1925,2,Oct,126 -1925,3,Oct,116 -1925,4,Oct,104 -1925,5,Oct,94 -1925,6,Oct,92 -1925,7,Oct,105 -1925,8,Oct,104 -1925,9,Oct,105 -1925,10,Oct,95 -1925,11,Oct,94 -1925,12,Oct,91 -1925,13,Oct,81 -1925,14,Oct,76 -1925,15,Oct,70 -1925,16,Oct,69 -1925,17,Oct,67 -1925,18,Oct,82 -1925,19,Oct,83 -1925,20,Oct,74 -1925,21,Oct,69 -1925,22,Oct,67 -1925,23,Oct,69 -1925,24,Oct,65 -1925,25,Oct,71 -1925,26,Oct,74 -1925,27,Oct,76 -1925,28,Oct,72 -1925,29,Oct,67 -1925,30,Oct,64 -1925,31,Oct,63 -1926,1,Oct,84 -1926,2,Oct,105 -1926,3,Oct,112 -1926,4,Oct,131 -1926,5,Oct,126 -1926,6,Oct,131 -1926,7,Oct,106 -1926,8,Oct,87 -1926,9,Oct,77 -1926,10,Oct,77 -1926,11,Oct,114 -1926,12,Oct,105 -1926,13,Oct,93 -1926,14,Oct,100 -1926,15,Oct,98 -1926,16,Oct,73 -1926,17,Oct,91 -1926,18,Oct,134 -1926,19,Oct,205 -1926,20,Oct,271 -1926,21,Oct,252 -1926,22,Oct,216 -1926,23,Oct,178 -1926,24,Oct,150 -1926,25,Oct,145 -1926,26,Oct,181 -1926,27,Oct,174 -1926,28,Oct,183 -1926,29,Oct,164 -1926,30,Oct,157 -1926,31,Oct,145 -1927,1,Oct,94 -1927,2,Oct,83 -1927,3,Oct,81 -1927,4,Oct,83 -1927,5,Oct,83 -1927,6,Oct,75 -1927,7,Oct,75 -1927,8,Oct,67 -1927,9,Oct,153 -1927,10,Oct,352 -1927,11,Oct,346 -1927,12,Oct,186 -1927,13,Oct,171 -1927,14,Oct,293 -1927,15,Oct,412 -1927,16,Oct,357 -1927,17,Oct,340 -1927,18,Oct,398 -1927,19,Oct,409 -1927,20,Oct,333 -1927,21,Oct,241 -1927,22,Oct,194 -1927,23,Oct,165 -1927,24,Oct,154 -1927,25,Oct,138 -1927,26,Oct,128 -1927,27,Oct,118 -1927,28,Oct,128 -1927,29,Oct,127 -1927,30,Oct,133 -1927,31,Oct,115 -1928,1,Oct,72 -1928,2,Oct,117 -1928,3,Oct,88 -1928,4,Oct,72 -1928,5,Oct,77 -1928,6,Oct,105 -1928,7,Oct,116 -1928,8,Oct,105 -1928,9,Oct,100 -1928,10,Oct,110 -1928,11,Oct,84 -1928,12,Oct,72 -1928,13,Oct,65 -1928,14,Oct,61 -1928,15,Oct,54 -1928,16,Oct,52 -1928,17,Oct,49 -1928,18,Oct,48 -1928,19,Oct,46 -1928,20,Oct,48 -1928,21,Oct,53 -1928,22,Oct,46 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-1983,20,Oct,159 -1983,21,Oct,140.3 -1983,22,Oct,117.8 -1983,23,Oct,94.5 -1983,24,Oct,78.8 -1983,25,Oct,59.9 -1983,26,Oct,49.7 -1983,27,Oct,44.1 -1983,28,Oct,38.9 -1983,29,Oct,36.3 -1983,30,Oct,35 -1983,31,Oct,34 -1984,1,Oct,56.1 -1984,2,Oct,71.75 -1984,3,Oct,80.95 -1984,4,Oct,72.9 -1984,5,Oct,61.6 -1984,6,Oct,53 -1984,7,Oct,61.6 -1984,8,Oct,85.55 -1984,9,Oct,109.8 -1984,10,Oct,85.55 -1984,11,Oct,75.2 -1984,12,Oct,67.15 -1984,13,Oct,71.75 -1984,14,Oct,83.25 -1984,15,Oct,79.8 -1984,16,Oct,75.2 -1984,17,Oct,72.9 -1984,18,Oct,109.8 -1984,19,Oct,124.1 -1984,20,Oct,164.8 -1984,21,Oct,170.4 -1984,22,Oct,171.8 -1984,23,Oct,280.2 -1984,24,Oct,241.9 -1984,25,Oct,174.6 -1984,26,Oct,130.6 -1984,27,Oct,124.1 -1984,28,Oct,109.8 -1984,29,Oct,102 -1984,30,Oct,96.8 -1984,31,Oct,95.5 -1985,1,Oct,98.9 -1985,2,Oct,91.7 -1985,3,Oct,74.3 -1985,4,Oct,52.43 -1985,5,Oct,42.2 -1985,6,Oct,36.5 -1985,7,Oct,33.05 -1985,8,Oct,34.2 -1985,9,Oct,31.9 -1985,10,Oct,27.3 -1985,11,Oct,26.15 -1985,12,Oct,25 -1985,13,Oct,25 -1985,14,Oct,26.15 -1985,15,Oct,29.6 -1985,16,Oct,29.6 -1985,17,Oct,29.6 -1985,18,Oct,76 -1985,19,Oct,164 -1985,20,Oct,205.65 -1985,21,Oct,181.15 -1985,22,Oct,199.53 -1985,23,Oct,167.67 -1985,24,Oct,130.5 -1985,25,Oct,97.1 -1985,26,Oct,77.7 -1985,27,Oct,72.6 -1985,28,Oct,65.82 -1985,29,Oct,59.13 -1985,30,Oct,52.43 -1985,31,Oct,52.43 -1986,1,Oct,60.8 -1986,2,Oct,77.7 -1986,3,Oct,65.82 -1986,4,Oct,55.77 -1986,5,Oct,50.75 -1986,6,Oct,50.75 -1986,7,Oct,45.05 -1986,8,Oct,34.2 -1986,9,Oct,31.9 -1986,10,Oct,42.2 -1986,11,Oct,45.05 -1986,12,Oct,47.9 -1986,13,Oct,79.4 -1986,14,Oct,69.2 -1986,15,Oct,59.13 -1986,16,Oct,65.82 -1986,17,Oct,52.43 -1986,18,Oct,37.93 -1986,19,Oct,30.75 -1986,20,Oct,27.3 -1986,21,Oct,25 -1986,22,Oct,23.2 -1986,23,Oct,22.3 -1986,24,Oct,26.15 -1986,25,Oct,31.9 -1986,26,Oct,40.77 -1986,27,Oct,46.48 -1986,28,Oct,42.2 -1986,29,Oct,42.2 -1986,30,Oct,34.2 -1986,31,Oct,31.9 -1987,1,Oct,163.6 -1987,2,Oct,134.8 -1987,3,Oct,137.2 -1987,4,Oct,113.8 -1987,5,Oct,100.8 -1987,6,Oct,77 -1987,7,Oct,68 -1987,8,Oct,62.4 -1987,9,Oct,62.4 -1987,10,Oct,62.4 -1987,11,Oct,57.2 -1987,12,Oct,55.9 -1987,13,Oct,52 -1987,14,Oct,50.75 -1987,15,Oct,47 -1987,16,Oct,57.2 -1987,17,Oct,58.5 -1987,18,Oct,53.3 -1987,19,Oct,49.5 -1987,20,Oct,44.5 -1987,21,Oct,59.8 -1987,22,Oct,52 -1987,23,Oct,49.5 -1987,24,Oct,48.25 -1987,25,Oct,49.5 -1987,26,Oct,45.75 -1987,27,Oct,42 -1987,28,Oct,45.75 -1987,29,Oct,43.25 -1987,30,Oct,39.5 -1987,31,Oct,35.1 -1988,1,Oct,48 -1988,2,Oct,56 -1988,3,Oct,49.6 -1988,4,Oct,56 -1988,5,Oct,56 -1988,6,Oct,60.8 -1988,7,Oct,59.2 -1988,8,Oct,51.2 -1988,9,Oct,48 -1988,10,Oct,49.6 -1988,11,Oct,56 -1988,12,Oct,51.2 -1988,13,Oct,46.55 -1988,14,Oct,65.7 -1988,15,Oct,108 -1988,16,Oct,127.5 -1988,17,Oct,102 -1988,18,Oct,82.9 -1988,19,Oct,120 -1988,20,Oct,161.2 -1988,21,Oct,203 -1988,22,Oct,145.5 -1988,23,Oct,102 -1988,24,Oct,84.8 -1988,25,Oct,69.1 -1988,26,Oct,62.4 -1988,27,Oct,56 -1988,28,Oct,51.2 -1988,29,Oct,48 -1988,30,Oct,51.2 -1988,31,Oct,48 -1989,1,Oct,101 -1989,2,Oct,99.2 -1989,3,Oct,113.6 -1989,4,Oct,106.4 -1989,5,Oct,95.6 -1989,6,Oct,102.8 -1989,7,Oct,142.8 -1989,8,Oct,171 -1989,9,Oct,183 -1989,10,Oct,162 -1989,11,Oct,128.5 -1989,12,Oct,113.6 -1989,13,Oct,101 -1989,14,Oct,84 -1989,15,Oct,102.8 -1989,16,Oct,140.2 -1989,17,Oct,150.8 -1989,18,Oct,144.1 -1989,19,Oct,129.8 -1989,20,Oct,142.8 -1989,21,Oct,152.2 -1989,22,Oct,135 -1989,23,Oct,113.6 -1989,24,Oct,88.8 -1989,25,Oct,68.5 -1989,26,Oct,56.8 -1989,27,Oct,55.4 -1989,28,Oct,55.4 -1989,29,Oct,56.8 -1989,30,Oct,68.5 -1989,31,Oct,67 -1990,1,Oct,38.75 -1990,2,Oct,45 -1990,3,Oct,41.25 -1990,4,Oct,36.4 -1990,5,Oct,32 -1990,6,Oct,33.1 -1990,7,Oct,34.2 -1990,8,Oct,38.75 -1990,9,Oct,51.35 -1990,10,Oct,81 -1990,11,Oct,109 -1990,12,Oct,85.5 -1990,13,Oct,62.15 -1990,14,Oct,51.35 -1990,15,Oct,41.25 -1990,16,Oct,35.3 -1990,17,Oct,32 -1990,18,Oct,29.8 -1990,19,Oct,28.7 -1990,20,Oct,26.5 -1990,21,Oct,27.6 -1990,22,Oct,51.35 -1990,23,Oct,82.5 -1990,24,Oct,85.5 -1990,25,Oct,54.05 -1990,26,Oct,48.75 -1990,27,Oct,64.95 -1990,28,Oct,50 -1990,29,Oct,40 -1990,30,Oct,37.5 -1990,31,Oct,45 -1991,1,Oct,52 -1991,2,Oct,56.1 -1991,3,Oct,58.3 -1991,4,Oct,52 -1991,5,Oct,51 -1991,6,Oct,62.7 -1991,7,Oct,63.8 -1991,8,Oct,61.6 -1991,9,Oct,43.2 -1991,10,Oct,37.8 -1991,11,Oct,34.4 -1991,12,Oct,40.5 -1991,13,Oct,36 -1991,14,Oct,41.4 -1991,15,Oct,36 -1991,16,Oct,30.4 -1991,17,Oct,28 -1991,18,Oct,28 -1991,19,Oct,33.6 -1991,20,Oct,34.4 -1991,21,Oct,35.2 -1991,22,Oct,34.4 -1991,23,Oct,31.2 -1991,24,Oct,28.8 -1991,25,Oct,29.6 -1991,26,Oct,29.6 -1991,27,Oct,28.8 -1991,28,Oct,32 -1991,29,Oct,37.8 -1991,30,Oct,35.2 -1991,31,Oct,33.6 -1992,1,Oct,123.3 -1992,2,Oct,75.8 -1992,3,Oct,60.62 -1992,4,Oct,63.54 -1992,5,Oct,60.62 -1992,6,Oct,54.78 -1992,7,Oct,46.2 -1992,8,Oct,40.6 -1992,9,Oct,33.22 -1992,10,Oct,31.1 -1992,11,Oct,30.04 -1992,12,Oct,30.04 -1992,13,Oct,32.16 -1992,14,Oct,31.1 -1992,15,Oct,40.6 -1992,16,Oct,44.8 -1992,17,Oct,66.8 -1992,18,Oct,66.8 -1992,19,Oct,59.16 -1992,20,Oct,49 -1992,21,Oct,39.2 -1992,22,Oct,34.28 -1992,23,Oct,32.16 -1992,24,Oct,32.16 -1992,25,Oct,32.16 -1992,26,Oct,40.6 -1992,27,Oct,44.8 -1992,28,Oct,40.6 -1992,29,Oct,35.34 -1992,30,Oct,31.1 -1992,31,Oct,30.04 -1993,1,Oct,42 -1993,2,Oct,117.4 -1993,3,Oct,124.2 -1993,4,Oct,127.6 -1993,5,Oct,100.4 -1993,6,Oct,68.15 -1993,7,Oct,52.8 -1993,8,Oct,43.2 -1993,9,Oct,38.7 -1993,10,Oct,36.5 -1993,11,Oct,34.3 -1993,12,Oct,43.2 -1993,13,Oct,37.6 -1993,14,Oct,32.1 -1993,15,Oct,29 -1993,16,Oct,26 -1993,17,Oct,25 -1993,18,Oct,22 -1993,19,Oct,24 -1993,20,Oct,27 -1993,21,Oct,30 -1993,22,Oct,25 -1993,23,Oct,25 -1993,24,Oct,26 -1993,25,Oct,29 -1993,26,Oct,26 -1993,27,Oct,24 -1993,28,Oct,22 -1993,29,Oct,21 -1993,30,Oct,27 -1993,31,Oct,43.2 -1994,1,Oct,153.2 -1994,2,Oct,138.9 -1994,3,Oct,131.1 -1994,4,Oct,129.8 -1994,5,Oct,132.4 -1994,6,Oct,138.9 -1994,7,Oct,137.6 -1994,8,Oct,125.9 -1994,9,Oct,116.8 -1994,10,Oct,120.7 -1994,11,Oct,119.4 -1994,12,Oct,111.6 -1994,13,Oct,107.7 -1994,14,Oct,101.2 -1994,15,Oct,96 -1994,16,Oct,88 -1994,17,Oct,84.8 -1994,18,Oct,114.2 -1994,19,Oct,123.3 -1994,20,Oct,106.4 -1994,21,Oct,116.8 -1994,22,Oct,123.3 -1994,23,Oct,102.5 -1994,24,Oct,91.2 -1994,25,Oct,83.2 -1994,26,Oct,76.9 -1994,27,Oct,73.8 -1994,28,Oct,72.25 -1994,29,Oct,76.9 -1994,30,Oct,76.9 -1994,31,Oct,72.25 -1995,1,Oct,119.6 -1995,2,Oct,150.8 -1995,3,Oct,140.4 -1995,4,Oct,136.5 -1995,5,Oct,144.3 -1995,6,Oct,170 -1995,7,Oct,207 -1995,8,Oct,225 -1995,9,Oct,195.2 -1995,10,Oct,168.6 -1995,11,Oct,145.6 -1995,12,Oct,128.7 -1995,13,Oct,118.3 -1995,14,Oct,106.6 -1995,15,Oct,98.6 -1995,16,Oct,95 -1995,17,Oct,95 -1995,18,Oct,95 -1995,19,Oct,93.2 -1995,20,Oct,78 -1995,21,Oct,70 -1995,22,Oct,65.2 -1995,23,Oct,62 -1995,24,Oct,58.8 -1995,25,Oct,60.4 -1995,26,Oct,57.2 -1995,27,Oct,55.6 -1995,28,Oct,52.5 -1995,29,Oct,52.5 -1995,30,Oct,51 -1995,31,Oct,48 -1996,1,Oct,68.8 -1996,2,Oct,58 -1996,3,Oct,53.5 -1996,4,Oct,47.9 -1996,5,Oct,47.9 -1996,6,Oct,56.5 -1996,7,Oct,56.5 -1996,8,Oct,58 -1996,9,Oct,73.75 -1996,10,Oct,91 -1996,11,Oct,134.4 -1996,12,Oct,148.4 -1996,13,Oct,113 -1996,14,Oct,87.4 -1996,15,Oct,72.1 -1996,16,Oct,58 -1996,17,Oct,50.5 -1996,18,Oct,44 -1996,19,Oct,38.8 -1996,20,Oct,53.5 -1996,21,Oct,62.5 -1996,22,Oct,55 -1996,23,Oct,46.6 -1996,24,Oct,41.4 -1996,25,Oct,38.8 -1996,26,Oct,36.25 -1996,27,Oct,37.5 -1996,28,Oct,35 -1996,29,Oct,33.75 -1996,30,Oct,31.25 -1996,31,Oct,30 -1997,1,Oct,175 -1997,2,Oct,116 -1997,3,Oct,92.8 -1997,4,Oct,73.95 -1997,5,Oct,72.5 -1997,6,Oct,127.6 -1997,7,Oct,193.2 -1997,8,Oct,221.3 -1997,9,Oct,185.5 -1997,10,Oct,139.2 -1997,11,Oct,110.2 -1997,12,Oct,88.45 -1997,13,Oct,92.8 -1997,14,Oct,131.95 -1997,15,Oct,120.35 -1997,16,Oct,85.55 -1997,17,Oct,71.1 -1997,18,Oct,61.3 -1997,19,Oct,53.3 -1997,20,Oct,48.1 -1997,21,Oct,44.35 -1997,22,Oct,42.05 -1997,23,Oct,37.45 -1997,24,Oct,32.95 -1997,25,Oct,31.9 -1997,26,Oct,34 -1997,27,Oct,40.9 -1997,28,Oct,57.2 -1997,29,Oct,46.8 -1997,30,Oct,37.45 -1997,31,Oct,32.95 -1998,1,Oct,12 -1998,2,Oct,11.32 -1998,3,Oct,10.64 -1998,4,Oct,9.28 -1998,5,Oct,7.24 -1998,6,Oct,5.88 -1998,7,Oct,5.88 -1998,8,Oct,5.2 -1998,9,Oct,4.94 -1998,10,Oct,5.2 -1998,11,Oct,10.64 -1998,12,Oct,12 -1998,13,Oct,12 -1998,14,Oct,10.64 -1998,15,Oct,9.96 -1998,16,Oct,12 -1998,17,Oct,11.32 -1998,18,Oct,12 -1998,19,Oct,15.8 -1998,20,Oct,18.65 -1998,21,Oct,17.7 -1998,22,Oct,14.85 -1998,23,Oct,12.95 -1998,24,Oct,11.32 -1998,25,Oct,7.92 -1998,26,Oct,6.56 -1998,27,Oct,5.88 -1998,28,Oct,5.88 -1998,29,Oct,5.2 -1998,30,Oct,5.2 -1998,31,Oct,5.88 -1999,1,Oct,65.28 -1999,2,Oct,56.2 -1999,3,Oct,58.76 -1999,4,Oct,70.64 -1999,5,Oct,63.94 -1999,6,Oct,51.08 -1999,7,Oct,48.52 -1999,8,Oct,45.96 -1999,9,Oct,43.4 -1999,10,Oct,48.52 -1999,11,Oct,49.8 -1999,12,Oct,44.68 -1999,13,Oct,47.24 -1999,14,Oct,43.4 -1999,15,Oct,40.84 -1999,16,Oct,40.84 -1999,17,Oct,43.4 -1999,18,Oct,49.8 -1999,19,Oct,48.52 -1999,20,Oct,40.84 -1999,21,Oct,35.8 -1999,22,Oct,35.8 -1999,23,Oct,35.8 -1999,24,Oct,34.6 -1999,25,Oct,31 -1999,26,Oct,27.4 -1999,27,Oct,31 -1999,28,Oct,34.6 -1999,29,Oct,42.12 -1999,30,Oct,47.24 -1999,31,Oct,65.28 -2000,1,Oct,55 -2000,2,Oct,68.05 -2000,3,Oct,60.8 -2000,4,Oct,52.14 -2000,5,Oct,63.7 -2000,6,Oct,87.2 -2000,7,Oct,90.4 -2000,8,Oct,79.65 -2000,9,Oct,66.6 -2000,10,Oct,60.8 -2000,11,Oct,65.15 -2000,12,Oct,59.35 -2000,13,Oct,47.85 -2000,14,Oct,43.56 -2000,15,Oct,39.3 -2000,16,Oct,44.99 -2000,17,Oct,49.28 -2000,18,Oct,53.57 -2000,19,Oct,53.57 -2000,20,Oct,42.13 -2000,21,Oct,52.14 -2000,22,Oct,42.13 -2000,23,Oct,50.71 -2000,24,Oct,62.25 -2000,25,Oct,62.25 -2000,26,Oct,56.45 -2000,27,Oct,49.28 -2000,28,Oct,43.56 -2000,29,Oct,40.7 -2000,30,Oct,78.2 -2000,31,Oct,110.8 -2001,1,Oct,42.4 -2001,2,Oct,39.5 -2001,3,Oct,49.65 -2001,4,Oct,67.05 -2001,5,Oct,65.6 -2001,6,Oct,69.95 -2001,7,Oct,59.8 -2001,8,Oct,49.65 -2001,9,Oct,39.5 -2001,10,Oct,52.55 -2001,11,Oct,59.8 -2001,12,Oct,55.45 -2001,13,Oct,55.45 -2001,14,Oct,49.65 -2001,15,Oct,45.3 -2001,16,Oct,40.95 -2001,17,Oct,39.5 -2001,18,Oct,39.5 -2001,19,Oct,37.06 -2001,20,Oct,34.62 -2001,21,Oct,32.18 -2001,22,Oct,30.96 -2001,23,Oct,28.52 -2001,24,Oct,27.3 -2001,25,Oct,28.52 -2001,26,Oct,30.96 -2001,27,Oct,34.62 -2001,28,Oct,51.1 -2001,29,Oct,102.5 -2001,30,Oct,146.6 -2001,31,Oct,140.2 -2002,1,Oct,168.68 -2002,2,Oct,165.72 -2002,3,Oct,127.26 -2002,4,Oct,127.26 -2002,5,Oct,130.2 -2002,6,Oct,116.97 -2002,7,Oct,103.74 -2002,8,Oct,84.75 -2002,9,Oct,76.05 -2002,10,Oct,68.82 -2002,11,Oct,65.94 -2002,12,Oct,61.62 -2002,13,Oct,60.18 -2002,14,Oct,55.87 -2002,15,Oct,51.58 -2002,16,Oct,47.29 -2002,17,Oct,44.43 -2002,18,Oct,50.15 -2002,19,Oct,58.74 -2002,20,Oct,55.87 -2002,21,Oct,48.72 -2002,22,Oct,41.68 -2002,23,Oct,43 -2002,24,Oct,63.06 -2002,25,Oct,65.94 -2002,26,Oct,64.5 -2002,27,Oct,86.2 -2002,28,Oct,97.88 -2002,29,Oct,139.08 -2002,30,Oct,111.09 -2002,31,Oct,86.2 -2003,1,Oct,44.45 -2003,2,Oct,61.85 -2003,3,Oct,60.4 -2003,4,Oct,54.6 -2003,5,Oct,53.15 -2003,6,Oct,47.35 -2003,7,Oct,45.9 -2003,8,Oct,44.45 -2003,9,Oct,44.45 -2003,10,Oct,41.6 -2003,11,Oct,44.45 -2003,12,Oct,38.8 -2003,13,Oct,34.6 -2003,14,Oct,31.8 -2003,15,Oct,30.4 -2003,16,Oct,33.2 -2003,17,Oct,31.8 -2003,18,Oct,31.8 -2003,19,Oct,29 -2003,20,Oct,29 -2003,21,Oct,30.4 -2003,22,Oct,30.4 -2003,23,Oct,31.8 -2003,24,Oct,33.2 -2003,25,Oct,33.2 -2003,26,Oct,31.8 -2003,27,Oct,29 -2003,28,Oct,27.7 -2003,29,Oct,25.1 -2003,30,Oct,22.5 -2003,31,Oct,29 -2004,1,Oct,138.6 -2004,2,Oct,126.5 -2004,3,Oct,123.1 -2004,4,Oct,114.6 -2004,5,Oct,109.5 -2004,6,Oct,93 -2004,7,Oct,77.5 -2004,8,Oct,73 -2004,9,Oct,67.3 -2004,10,Oct,67.3 -2004,11,Oct,67.3 -2004,12,Oct,102.7 -2004,13,Oct,109.5 -2004,14,Oct,77.5 -2004,15,Oct,70 -2004,16,Oct,63.25 -2004,17,Oct,60.55 -2004,18,Oct,59.2 -2004,19,Oct,59.2 -2004,20,Oct,57.85 -2004,21,Oct,56.5 -2004,22,Oct,52.6 -2004,23,Oct,68.65 -2004,24,Oct,61.9 -2004,25,Oct,53.9 -2004,26,Oct,51.3 -2004,27,Oct,47.4 -2004,28,Oct,46.1 -2004,29,Oct,46.1 -2004,30,Oct,44.8 -2004,31,Oct,43.5 -2005,1,Oct,679.5 -2005,2,Oct,462 -2005,3,Oct,350.5 -2005,4,Oct,322 -2005,5,Oct,288.7 -2005,6,Oct,262.8 -2005,7,Oct,240.8 -2005,8,Oct,197.6 -2005,9,Oct,149.75 -2005,10,Oct,135.75 -2005,11,Oct,121.75 -2005,12,Oct,111.25 -2005,13,Oct,106 -2005,14,Oct,113 -2005,15,Oct,107.75 -2005,16,Oct,106 -2005,17,Oct,99.2 -2005,18,Oct,94.1 -2005,19,Oct,87.3 -2005,20,Oct,83.9 -2005,21,Oct,82.2 -2005,22,Oct,77.1 -2005,23,Oct,72 -2005,24,Oct,67.2 -2005,25,Oct,67.2 -2005,26,Oct,78.8 -2005,27,Oct,72 -2005,28,Oct,67.2 -2005,29,Oct,68.8 -2005,30,Oct,97.5 -2005,31,Oct,203 -2006,1,Oct,106.2 -2006,2,Oct,104.6 -2006,3,Oct,96.6 -2006,4,Oct,107.8 -2006,5,Oct,115.8 -2006,6,Oct,115.8 -2006,7,Oct,139.8 -2006,8,Oct,173.1 -2006,9,Oct,233.4 -2006,10,Oct,357.6 -2006,11,Oct,316.2 -2006,12,Oct,262.2 -2006,13,Oct,198.6 -2006,14,Oct,164.6 -2006,15,Oct,141.4 -2006,16,Oct,133.4 -2006,17,Oct,123.8 -2006,18,Oct,122.2 -2006,19,Oct,119 -2006,20,Oct,111 -2006,21,Oct,103 -2006,22,Oct,93.4 -2006,23,Oct,93.4 -2006,24,Oct,90.2 -2006,25,Oct,87 -2006,26,Oct,84 -2006,27,Oct,76.5 -2006,28,Oct,76.5 -2006,29,Oct,82.5 -2006,30,Oct,78 -2006,31,Oct,72 -2007,1,Oct,67.75 -2007,2,Oct,49.4 -2007,3,Oct,40.3 -2007,4,Oct,36.7 -2007,5,Oct,41.6 -2007,6,Oct,80.35 -2007,7,Oct,106.95 -2007,8,Oct,105.28 -2007,9,Oct,80.35 -2007,10,Oct,77.2 -2007,11,Oct,83.5 -2007,12,Oct,83.5 -2007,13,Oct,69.32 -2007,14,Oct,66.17 -2007,15,Oct,69.32 -2007,16,Oct,80.35 -2007,17,Oct,81.93 -2007,18,Oct,78.77 -2007,19,Oct,63.02 -2007,20,Oct,55.15 -2007,21,Oct,58.3 -2007,22,Oct,56.72 -2007,23,Oct,50.7 -2007,24,Oct,42.9 -2007,25,Oct,37.85 -2007,26,Oct,35.55 -2007,27,Oct,34.4 -2007,28,Oct,32.1 -2007,29,Oct,28.65 -2007,30,Oct,27.5 -2007,31,Oct,27.5 -2008,1,Oct,61.3 -2008,2,Oct,77.3 -2008,3,Oct,86.43 -2008,4,Oct,69.3 -2008,5,Oct,66.1 -2008,6,Oct,80.5 -2008,7,Oct,77.3 -2008,8,Oct,92.35 -2008,9,Oct,74.1 -2008,10,Oct,77.3 -2008,11,Oct,84.45 -2008,12,Oct,72.5 -2008,13,Oct,66.1 -2008,14,Oct,59.7 -2008,15,Oct,43.7 -2008,16,Oct,41.3 -2008,17,Oct,37.7 -2008,18,Oct,36.5 -2008,19,Oct,34.1 -2008,20,Oct,37.7 -2008,21,Oct,51.7 -2008,22,Oct,51.7 -2008,23,Oct,84.45 -2008,24,Oct,96.3 -2008,25,Oct,86.43 -2008,26,Oct,94.32 -2008,27,Oct,108.15 -2008,28,Oct,133.5 -2008,29,Oct,133.5 -2008,30,Oct,98.28 -2008,31,Oct,78.9 -2009,1,Oct,47.4 -2009,2,Oct,42.5 -2009,3,Oct,46.17 -2009,4,Oct,63.33 -2009,5,Oct,75.7 -2009,6,Oct,62.1 -2009,7,Oct,59.65 -2009,8,Oct,51.07 -2009,9,Oct,41.42 -2009,10,Oct,37.1 -2009,11,Oct,47.4 -2009,12,Oct,42.5 -2009,13,Oct,46.17 -2009,14,Oct,63.33 -2009,15,Oct,75.7 -2009,16,Oct,62.1 -2009,17,Oct,59.65 -2009,18,Oct,51.07 -2009,19,Oct,41.42 -2009,20,Oct,37.1 -2009,21,Oct,57.2 -2009,22,Oct,51.07 -2009,23,Oct,44.95 -2009,24,Oct,49.85 -2009,25,Oct,71.35 -2009,26,Oct,54.75 -2009,27,Oct,41.42 -2009,28,Oct,33.86 -2009,29,Oct,29.24 -2009,30,Oct,27.6 -2009,31,Oct,28.42 -2010,1,Oct,127.4 -2010,2,Oct,104.4 -2010,3,Oct,90 -2010,4,Oct,76.5 -2010,5,Oct,70.5 -2010,6,Oct,67.5 -2010,7,Oct,57.4 -2010,8,Oct,67.5 -2010,9,Oct,84 -2010,10,Oct,72 -2010,11,Oct,64.5 -2010,12,Oct,60 -2010,13,Oct,60 -2010,14,Oct,64.5 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-1992,19,Dec,20.34 -1992,20,Dec,18.78 -1992,21,Dec,18 -1992,22,Dec,18 -1992,23,Dec,18 -1992,24,Dec,25.8 -1992,25,Dec,109.6 -1992,26,Dec,146.6 -1992,27,Dec,74 -1992,28,Dec,47.6 -1992,29,Dec,36.4 -1992,30,Dec,33.22 -1992,31,Dec,30.04 -1993,1,Dec,21 -1993,2,Dec,21 -1993,3,Dec,19.2 -1993,4,Dec,16.5 -1993,5,Dec,15.6 -1993,6,Dec,15.6 -1993,7,Dec,16.5 -1993,8,Dec,17.4 -1993,9,Dec,16.5 -1993,10,Dec,16.5 -1993,11,Dec,15.6 -1993,12,Dec,15.6 -1993,13,Dec,14.7 -1993,14,Dec,18.3 -1993,15,Dec,16.5 -1993,16,Dec,16.5 -1993,17,Dec,13.8 -1993,18,Dec,14.7 -1993,19,Dec,12.9 -1993,20,Dec,11.3 -1993,21,Dec,9.2 -1993,22,Dec,8.5 -1993,23,Dec,8.5 -1993,24,Dec,7.1 -1993,25,Dec,4.72 -1993,26,Dec,4.44 -1993,27,Dec,4.16 -1993,28,Dec,3.88 -1993,29,Dec,4.16 -1993,30,Dec,3.88 -1993,31,Dec,3.88 -1994,1,Dec,69.15 -1994,2,Dec,70.7 -1994,3,Dec,70.7 -1994,4,Dec,73.8 -1994,5,Dec,66.05 -1994,6,Dec,62.91 -1994,7,Dec,67.6 -1994,8,Dec,69.15 -1994,9,Dec,64.5 -1994,10,Dec,61.32 -1994,11,Dec,61.32 -1994,12,Dec,56.55 -1994,13,Dec,53.37 -1994,14,Dec,51.78 -1994,15,Dec,50.19 -1994,16,Dec,48.6 -1994,17,Dec,47.24 -1994,18,Dec,50.19 -1994,19,Dec,48.6 -1994,20,Dec,48.6 -1994,21,Dec,45.88 -1994,22,Dec,43.16 -1994,23,Dec,43.16 -1994,24,Dec,41.8 -1994,25,Dec,40.44 -1994,26,Dec,40.44 -1994,27,Dec,40.44 -1994,28,Dec,37.72 -1994,29,Dec,37.72 -1994,30,Dec,39.08 -1994,31,Dec,37.72 -1995,1,Dec,40.5 -1995,2,Dec,37.65 -1995,3,Dec,37.65 -1995,4,Dec,39 -1995,5,Dec,37.65 -1995,6,Dec,37.65 -1995,7,Dec,37.65 -1995,8,Dec,37.65 -1995,9,Dec,37.65 -1995,10,Dec,37.65 -1995,11,Dec,36.3 -1995,12,Dec,34.95 -1995,13,Dec,33.6 -1995,14,Dec,33.6 -1995,15,Dec,33.6 -1995,16,Dec,32.25 -1995,17,Dec,32.25 -1995,18,Dec,33.6 -1995,19,Dec,30.9 -1995,20,Dec,30.9 -1995,21,Dec,32.25 -1995,22,Dec,29.55 -1995,23,Dec,30.9 -1995,24,Dec,30.9 -1995,25,Dec,29.55 -1995,26,Dec,26.85 -1995,27,Dec,22.01 -1995,28,Dec,24.33 -1995,29,Dec,22.01 -1995,30,Dec,22.01 -1995,31,Dec,19.67 -1996,1,Dec,30 -1996,2,Dec,30 -1996,3,Dec,31.25 -1996,4,Dec,31.25 -1996,5,Dec,31.25 -1996,6,Dec,31.25 -1996,7,Dec,31.25 -1996,8,Dec,32.5 -1996,9,Dec,33.75 -1996,10,Dec,30 -1996,11,Dec,27.5 -1996,12,Dec,27.5 -1996,13,Dec,27.5 -1996,14,Dec,27.5 -1996,15,Dec,25 -1996,16,Dec,23.9 -1996,17,Dec,22.8 -1996,18,Dec,21.7 -1996,19,Dec,20.6 -1996,20,Dec,20.6 -1996,21,Dec,16.2 -1996,22,Dec,12.76 -1996,23,Dec,11.52 -1996,24,Dec,11.52 -1996,25,Dec,11.52 -1996,26,Dec,12.14 -1996,27,Dec,11.52 -1996,28,Dec,11.52 -1996,29,Dec,11.52 -1996,30,Dec,10.9 -1996,31,Dec,10.9 -1997,1,Dec,19.7 -1997,2,Dec,19.7 -1997,3,Dec,19.7 -1997,4,Dec,19.7 -1997,5,Dec,19.7 -1997,6,Dec,19.7 -1997,7,Dec,19.7 -1997,8,Dec,19.7 -1997,9,Dec,18.75 -1997,10,Dec,18.75 -1997,11,Dec,18.75 -1997,12,Dec,17.8 -1997,13,Dec,16.85 -1997,14,Dec,15.9 -1997,15,Dec,15.9 -1997,16,Dec,15.9 -1997,17,Dec,14.95 -1997,18,Dec,13.35 -1997,19,Dec,11.4 -1997,20,Dec,11.4 -1997,21,Dec,11.4 -1997,22,Dec,10.75 -1997,23,Dec,10.1 -1997,24,Dec,9.45 -1997,25,Dec,8.8 -1997,26,Dec,9.45 -1997,27,Dec,10.1 -1997,28,Dec,9.45 -1997,29,Dec,8.8 -1997,30,Dec,8.15 -1997,31,Dec,7.5 -1998,1,Dec,14.85 -1998,2,Dec,12.95 -1998,3,Dec,11.32 -1998,4,Dec,10.64 -1998,5,Dec,9.96 -1998,6,Dec,9.28 -1998,7,Dec,7.92 -1998,8,Dec,7.24 -1998,9,Dec,6.56 -1998,10,Dec,5.88 -1998,11,Dec,4.94 -1998,12,Dec,5.2 -1998,13,Dec,5.2 -1998,14,Dec,4.94 -1998,15,Dec,4.94 -1998,16,Dec,5.2 -1998,17,Dec,5.2 -1998,18,Dec,5.2 -1998,19,Dec,5.2 -1998,20,Dec,5.2 -1998,21,Dec,5.2 -1998,22,Dec,4.94 -1998,23,Dec,5.2 -1998,24,Dec,4.94 -1998,25,Dec,3.88 -1998,26,Dec,3.62 -1998,27,Dec,3.62 -1998,28,Dec,3.62 -1998,29,Dec,3.62 -1998,30,Dec,3.62 -1998,31,Dec,2.82 -1999,1,Dec,26.2 -1999,2,Dec,25 -1999,3,Dec,23.85 -1999,4,Dec,20.4 -1999,5,Dec,23.85 -1999,6,Dec,25 -1999,7,Dec,28.6 -1999,8,Dec,32.2 -1999,9,Dec,31 -1999,10,Dec,29.8 -1999,11,Dec,28.6 -1999,12,Dec,25 -1999,13,Dec,25 -1999,14,Dec,22.7 -1999,15,Dec,20.4 -1999,16,Dec,19.25 -1999,17,Dec,19.25 -1999,18,Dec,19.25 -1999,19,Dec,18.1 -1999,20,Dec,15.8 -1999,21,Dec,15.8 -1999,22,Dec,14.65 -1999,23,Dec,13.5 -1999,24,Dec,14.65 -1999,25,Dec,13.5 -1999,26,Dec,11.9 -1999,27,Dec,11.1 -1999,28,Dec,11.1 -1999,29,Dec,10.3 -1999,30,Dec,10.3 -1999,31,Dec,11.1 -2000,1,Dec,26.7 -2000,2,Dec,26.7 -2000,3,Dec,26.7 -2000,4,Dec,26.7 -2000,5,Dec,26.7 -2000,6,Dec,25.48 -2000,7,Dec,25.48 -2000,8,Dec,24.26 -2000,9,Dec,24.26 -2000,10,Dec,24.26 -2000,11,Dec,25.48 -2000,12,Dec,26.7 -2000,13,Dec,24.26 -2000,14,Dec,23.04 -2000,15,Dec,23.04 -2000,16,Dec,21.82 -2000,17,Dec,21.82 -2000,18,Dec,21.82 -2000,19,Dec,21.82 -2000,20,Dec,20.6 -2000,21,Dec,15.72 -2000,22,Dec,12.86 -2000,23,Dec,14.5 -2000,24,Dec,13.68 -2000,25,Dec,12.04 -2000,26,Dec,8.76 -2000,27,Dec,8.76 -2000,28,Dec,7.94 -2000,29,Dec,7.94 -2000,30,Dec,7.94 -2000,31,Dec,7.94 -2001,1,Dec,33.4 -2001,2,Dec,33.4 -2001,3,Dec,33.4 -2001,4,Dec,32.18 -2001,5,Dec,33.4 -2001,6,Dec,32.18 -2001,7,Dec,32.18 -2001,8,Dec,30.96 -2001,9,Dec,32.18 -2001,10,Dec,32.18 -2001,11,Dec,29.74 -2001,12,Dec,28.52 -2001,13,Dec,28.52 -2001,14,Dec,27.3 -2001,15,Dec,27.3 -2001,16,Dec,27.3 -2001,17,Dec,29.74 -2001,18,Dec,28.52 -2001,19,Dec,28.52 -2001,20,Dec,28.52 -2001,21,Dec,25.28 -2001,22,Dec,29.74 -2001,23,Dec,35.84 -2001,24,Dec,32.18 -2001,25,Dec,28.52 -2001,26,Dec,24.27 -2001,27,Dec,18.21 -2001,28,Dec,18.21 -2001,29,Dec,18.21 -2001,30,Dec,18.21 -2001,31,Dec,19.22 -2002,1,Dec,87.66 -2002,2,Dec,81.85 -2002,3,Dec,74.6 -2002,4,Dec,73.15 -2002,5,Dec,73.15 -2002,6,Dec,68.82 -2002,7,Dec,68.82 -2002,8,Dec,65.94 -2002,9,Dec,64.5 -2002,10,Dec,70.26 -2002,11,Dec,61.62 -2002,12,Dec,63.06 -2002,13,Dec,61.62 -2002,14,Dec,58.74 -2002,15,Dec,54.44 -2002,16,Dec,53.01 -2002,17,Dec,53.01 -2002,18,Dec,50.15 -2002,19,Dec,48.72 -2002,20,Dec,50.15 -2002,21,Dec,48.72 -2002,22,Dec,47.29 -2002,23,Dec,45.86 -2002,24,Dec,45.86 -2002,25,Dec,45.86 -2002,26,Dec,77.5 -2002,27,Dec,99.34 -2002,28,Dec,70.26 -2002,29,Dec,63.06 -2002,30,Dec,58.74 -2002,31,Dec,55.87 -2003,1,Dec,18.6 -2003,2,Dec,18.6 -2003,3,Dec,17.3 -2003,4,Dec,18.6 -2003,5,Dec,23.8 -2003,6,Dec,23.8 -2003,7,Dec,21.2 -2003,8,Dec,17.3 -2003,9,Dec,14.2 -2003,10,Dec,12.4 -2003,11,Dec,10.6 -2003,12,Dec,7.9 -2003,13,Dec,7.9 -2003,14,Dec,7.9 -2003,15,Dec,8.8 -2003,16,Dec,8.8 -2003,17,Dec,11.5 -2003,18,Dec,11.5 -2003,19,Dec,11.5 -2003,20,Dec,11.5 -2003,21,Dec,11.5 -2003,22,Dec,8.8 -2003,23,Dec,7.9 -2003,24,Dec,6.1 -2003,25,Dec,5.65 -2003,26,Dec,6.1 -2003,27,Dec,7.9 -2003,28,Dec,7.9 -2003,29,Dec,7 -2003,30,Dec,7.9 -2003,31,Dec,5.65 -2004,1,Dec,38.7 -2004,2,Dec,36.3 -2004,3,Dec,36.3 -2004,4,Dec,36.3 -2004,5,Dec,35.1 -2004,6,Dec,33.9 -2004,7,Dec,32.7 -2004,8,Dec,31.5 -2004,9,Dec,29.26 -2004,10,Dec,29.26 -2004,11,Dec,29.26 -2004,12,Dec,29.26 -2004,13,Dec,32.7 -2004,14,Dec,30.38 -2004,15,Dec,24.78 -2004,16,Dec,20.3 -2004,17,Dec,18.24 -2004,18,Dec,16.18 -2004,19,Dec,16.18 -2004,20,Dec,16.18 -2004,21,Dec,16.18 -2004,22,Dec,16.18 -2004,23,Dec,14.12 -2004,24,Dec,15.15 -2004,25,Dec,14.12 -2004,26,Dec,12.06 -2004,27,Dec,13.09 -2004,28,Dec,15.15 -2004,29,Dec,15.15 -2004,30,Dec,14.12 -2004,31,Dec,12.06 -2005,1,Dec,49.5 -2005,2,Dec,49.5 -2005,3,Dec,49.5 -2005,4,Dec,51 -2005,5,Dec,48 -2005,6,Dec,49.5 -2005,7,Dec,78.5 -2005,8,Dec,118 -2005,9,Dec,126 -2005,10,Dec,106 -2005,11,Dec,83.6 -2005,12,Dec,73.4 -2005,13,Dec,75.1 -2005,14,Dec,68.55 -2005,15,Dec,65.65 -2005,16,Dec,62.75 -2005,17,Dec,56.95 -2005,18,Dec,46.5 -2005,19,Dec,49.5 -2005,20,Dec,42 -2005,21,Dec,35 -2005,22,Dec,35 -2005,23,Dec,35 -2005,24,Dec,35 -2005,25,Dec,35 -2005,26,Dec,38.3 -2005,27,Dec,43.5 -2005,28,Dec,40.5 -2005,29,Dec,38.3 -2005,30,Dec,35 -2005,31,Dec,32.8 -2006,1,Dec,37.2 -2006,2,Dec,37.2 -2006,3,Dec,36 -2006,4,Dec,34.8 -2006,5,Dec,33.6 -2006,6,Dec,32.4 -2006,7,Dec,33.6 -2006,8,Dec,32.4 -2006,9,Dec,33.6 -2006,10,Dec,32.4 -2006,11,Dec,31.2 -2006,12,Dec,28.85 -2006,13,Dec,27.7 -2006,14,Dec,27.7 -2006,15,Dec,25.4 -2006,16,Dec,25.4 -2006,17,Dec,25.4 -2006,18,Dec,24.25 -2006,19,Dec,23.1 -2006,20,Dec,23.1 -2006,21,Dec,23.1 -2006,22,Dec,23.1 -2006,23,Dec,23.1 -2006,24,Dec,23.1 -2006,25,Dec,23.1 -2006,26,Dec,23.1 -2006,27,Dec,21.95 -2006,28,Dec,21.95 -2006,29,Dec,21.95 -2006,30,Dec,21.95 -2006,31,Dec,21.95 -2007,1,Dec,27.5 -2007,2,Dec,27.5 -2007,3,Dec,26.6 -2007,4,Dec,26.6 -2007,5,Dec,24.8 -2007,6,Dec,25.7 -2007,7,Dec,26.6 -2007,8,Dec,26.6 -2007,9,Dec,25.7 -2007,10,Dec,23.9 -2007,11,Dec,21.2 -2007,12,Dec,17.75 -2007,13,Dec,14.75 -2007,14,Dec,17.75 -2007,15,Dec,23.9 -2007,16,Dec,24.8 -2007,17,Dec,24.8 -2007,18,Dec,23 -2007,19,Dec,23 -2007,20,Dec,23 -2007,21,Dec,23.9 -2007,22,Dec,27.5 -2007,23,Dec,26.6 -2007,24,Dec,25.7 -2007,25,Dec,19.4 -2007,26,Dec,14.75 -2007,27,Dec,13.25 -2007,28,Dec,14.75 -2007,29,Dec,14.75 -2007,30,Dec,13.25 -2007,31,Dec,14 -2008,1,Dec,31.7 -2008,2,Dec,30.5 -2008,3,Dec,29.3 -2008,4,Dec,26.9 -2008,5,Dec,25.7 -2008,6,Dec,25.7 -2008,7,Dec,25.7 -2008,8,Dec,25.7 -2008,9,Dec,25.7 -2008,10,Dec,25.7 -2008,11,Dec,24.5 -2008,12,Dec,24.5 -2008,13,Dec,23.55 -2008,14,Dec,23.55 -2008,15,Dec,22.6 -2008,16,Dec,21.65 -2008,17,Dec,18.8 -2008,18,Dec,16.9 -2008,19,Dec,12.6 -2008,20,Dec,11 -2008,21,Dec,10.2 -2008,22,Dec,12.6 -2008,23,Dec,15 -2008,24,Dec,15 -2008,25,Dec,15 -2008,26,Dec,14.2 -2008,27,Dec,13.4 -2008,28,Dec,13.4 -2008,29,Dec,14.2 -2008,30,Dec,15 -2008,31,Dec,11 -2009,1,Dec,17.47 -2009,2,Dec,18.14 -2009,3,Dec,19.48 -2009,4,Dec,16.8 -2009,5,Dec,14.56 -2009,6,Dec,14 -2009,7,Dec,14 -2009,8,Dec,15.12 -2009,9,Dec,16.8 -2009,10,Dec,17.47 -2009,11,Dec,18.14 -2009,12,Dec,18.14 -2009,13,Dec,17.47 -2009,14,Dec,16.8 -2009,15,Dec,15.68 -2009,16,Dec,15.12 -2009,17,Dec,13.44 -2009,18,Dec,9.79 -2009,19,Dec,9.79 -2009,20,Dec,10.26 -2009,21,Dec,10.26 -2009,22,Dec,10.26 -2009,23,Dec,9.79 -2009,24,Dec,8.85 -2009,25,Dec,8.38 -2009,26,Dec,7.44 -2009,27,Dec,6.97 -2009,28,Dec,7.91 -2009,29,Dec,7.91 -2009,30,Dec,7.91 -2009,31,Dec,7.91 -2010,1,Dec,27.5 -2010,2,Dec,23.6 -2010,3,Dec,19 -2010,4,Dec,17 -2010,5,Dec,16 -2010,6,Dec,15 -2010,7,Dec,18 -2010,8,Dec,16 -2010,9,Dec,15 -2010,10,Dec,15 -2010,11,Dec,13 -2010,12,Dec,15 -2010,13,Dec,16 -2010,14,Dec,14 -2010,15,Dec,14 -2010,16,Dec,14 -2010,17,Dec,16 -2010,18,Dec,21 -2010,19,Dec,17 -2010,20,Dec,16 -2010,21,Dec,14 -2010,22,Dec,13 -2010,23,Dec,11 -2010,24,Dec,12 -2010,25,Dec,11 -2010,26,Dec,10.4 -2010,27,Dec,10.4 -2010,28,Dec,10.4 -2010,29,Dec,9.8 -2010,30,Dec,9.2 -2010,31,Dec,8.6 -2011,1,Dec,54.1 -2011,2,Dec,52.52 -2011,3,Dec,52.52 -2011,4,Dec,55.67 -2011,5,Dec,54.1 -2011,6,Dec,52.52 -2011,7,Dec,49.38 -2011,8,Dec,46.22 -2011,9,Dec,43.07 -2011,10,Dec,41.5 -2011,11,Dec,41.5 -2011,12,Dec,41.5 -2011,13,Dec,40.15 -2011,14,Dec,40.15 -2011,15,Dec,37.45 -2011,16,Dec,33.4 -2011,17,Dec,32.05 -2011,18,Dec,33.4 -2011,19,Dec,32.05 -2011,20,Dec,29.35 -2011,21,Dec,28 -2011,22,Dec,28 -2011,23,Dec,28 -2011,24,Dec,28 -2011,25,Dec,28 -2011,26,Dec,28 -2011,27,Dec,26.85 -2011,28,Dec,26.85 -2011,29,Dec,26.85 -2011,30,Dec,25.7 -2011,31,Dec,25.7 -2012,1,Dec,22.5 -2012,2,Dec,23.8 -2012,3,Dec,27.7 -2012,4,Dec,30.4 -2012,5,Dec,23.8 -2012,6,Dec,18.6 -2012,7,Dec,17.3 -2012,8,Dec,12.2 -2012,9,Dec,12.2 -2012,10,Dec,11.25 -2012,11,Dec,11.25 -2012,12,Dec,10.3 -2012,13,Dec,9.35 -2012,14,Dec,9.35 -2012,15,Dec,7.45 -2012,16,Dec,6.5 -2012,17,Dec,6 -2012,18,Dec,6 -2012,19,Dec,6 -2012,20,Dec,6 -2012,21,Dec,6 -2012,22,Dec,5.5 -2012,23,Dec,5.5 -2012,24,Dec,3.5 -2012,25,Dec,4.5 -2012,26,Dec,5.5 -2012,27,Dec,5 -2012,28,Dec,5.5 -2012,29,Dec,4.5 -2012,30,Dec,4.5 -2012,31,Dec,4.5 -2013,1,Dec,27.35 -2013,2,Dec,27.35 -2013,3,Dec,25.25 -2013,4,Dec,22.1 -2013,5,Dec,19.2 -2013,6,Dec,20 -2013,7,Dec,22.1 -2013,8,Dec,22.1 -2013,9,Dec,20 -2013,10,Dec,16.8 -2013,11,Dec,16.8 -2013,12,Dec,23.15 -2013,13,Dec,34.25 -2013,14,Dec,67 -2013,15,Dec,125.4 -2013,16,Dec,44.5 -2013,17,Dec,33 -2013,18,Dec,28.4 -2013,19,Dec,24.2 -2013,20,Dec,23.15 -2013,21,Dec,20 -2013,22,Dec,19.2 -2013,23,Dec,18.4 -2013,24,Dec,17.6 -2013,25,Dec,16.8 -2013,26,Dec,16 -2013,27,Dec,14.4 -2013,28,Dec,13.6 -2013,29,Dec,14.4 -2013,30,Dec,15.2 -2013,31,Dec,15.2 -2014,1,Dec,31.95 -2014,2,Dec,29.4 -2014,3,Dec,39.2 -2014,4,Dec,25 -2014,5,Dec,28.3 -2014,6,Dec,10.5 -2014,7,Dec,15.9 -2014,8,Dec,9.56 -2014,9,Dec,8.62 -2014,10,Dec,10.03 -2014,11,Dec,13.2 -2014,12,Dec,7.68 -2014,13,Dec,8.15 -2014,14,Dec,7.68 -2014,15,Dec,7.21 -2014,16,Dec,5.49 -2014,17,Dec,4.25 -2014,18,Dec,4.56 -2014,19,Dec,5.8 -2014,20,Dec,18.6 -2014,21,Dec,13.2 -2014,22,Dec,9.56 -2014,23,Dec,8.15 -2014,24,Dec,5.18 -2014,25,Dec,4.87 -2014,26,Dec,4.25 -2014,27,Dec,3.94 -2014,28,Dec,3.01 -2014,29,Dec,4.87 -2014,30,Dec,4.25 -2014,31,Dec,3.01 -2015,1,Dec,9.6 -2015,2,Dec,6 -2015,3,Dec,2.7 -2015,4,Dec,2.36 -2015,5,Dec,2.36 -2015,6,Dec,4.68 -2015,7,Dec,3.69 -2015,8,Dec,2.02 -2015,9,Dec,1.17 -2015,10,Dec,0.9 -2015,11,Dec,0.9 -2015,12,Dec,0.8 -2015,13,Dec,0.75 -2015,14,Dec,0.75 -2015,15,Dec,0.75 -2015,16,Dec,1.68 -2015,17,Dec,2.53 -2015,18,Dec,1 -2015,19,Dec,0.75 -2015,20,Dec,0.65 -2015,21,Dec,0.9 -2015,22,Dec,1.34 -2015,23,Dec,2.02 -2015,24,Dec,1 -2015,25,Dec,1.34 -2015,26,Dec,1.34 -2015,27,Dec,0.75 -2015,28,Dec,0.55 -2015,29,Dec,0.46 -2015,30,Dec,0.46 -2015,31,Dec,0.46 -2016,1,Dec,6 -2016,2,Dec,4.8 -2016,3,Dec,3.2 -2016,4,Dec,3.2 -2016,5,Dec,3.4 -2016,6,Dec,3.4 -2016,7,Dec,3.4 -2016,8,Dec,3 -2016,9,Dec,3 -2016,10,Dec,2.8 -2016,11,Dec,2.2 -2016,12,Dec,2 -2016,13,Dec,2 -2016,14,Dec,1.8 -2016,15,Dec,1.6 -2016,16,Dec,1.6 -2016,17,Dec,1.8 -2016,18,Dec,2.4 -2016,19,Dec,1.8 -2016,20,Dec,1.7 -2016,21,Dec,1.7 -2016,22,Dec,1.5 -2016,23,Dec,1.4 -2016,24,Dec,1.4 -2016,25,Dec,1.2 -2016,26,Dec,1.2 -2016,27,Dec,1.2 -2016,28,Dec,1.2 -2016,29,Dec,1.2 -2016,30,Dec,1.3 -2016,31,Dec,1.3 -2017,1,Dec,36.2 -2017,2,Dec,59.3 -2017,3,Dec,32 -2017,4,Dec,53.6 -2017,5,Dec,27.2 -2017,6,Dec,51.7 -2017,7,Dec,29.6 -2017,8,Dec,36.2 -2017,9,Dec,34.8 -2017,10,Dec,55.5 -2017,11,Dec,49.8 -2017,12,Dec,29.6 -2017,13,Dec,40.4 -2017,14,Dec,32 -2017,15,Dec,40.4 -2017,16,Dec,28.4 -2017,17,Dec,36.2 -2017,18,Dec,43.2 -2017,19,Dec,37.6 -2017,20,Dec,23.6 -2017,21,Dec,30.8 -2017,22,Dec,20 -2017,23,Dec,14 -2017,24,Dec,17 -2017,25,Dec,13 -2017,26,Dec,19 -2017,27,Dec,37.6 -2017,28,Dec,21.2 -2017,29,Dec,37.6 -2017,30,Dec,19 -2017,31,Dec,32 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-1995,16,Jan,21.26 -1995,17,Jan,19.32 -1995,18,Jan,19.32 -1995,19,Jan,20.29 -1995,20,Jan,19.32 -1995,21,Jan,19.32 -1995,22,Jan,19.32 -1995,23,Jan,18.35 -1995,24,Jan,18.35 -1995,25,Jan,17.38 -1995,26,Jan,17.38 -1995,27,Jan,16.41 -1995,28,Jan,17.38 -1995,29,Jan,16.41 -1995,30,Jan,16.41 -1995,31,Jan,16.41 -1996,1,Jan,17.34 -1996,2,Jan,17.34 -1996,3,Jan,18.51 -1996,4,Jan,17.34 -1996,5,Jan,18.51 -1996,6,Jan,17.34 -1996,7,Jan,17.34 -1996,8,Jan,17.34 -1996,9,Jan,16.18 -1996,10,Jan,13.85 -1996,11,Jan,15.01 -1996,12,Jan,12.45 -1996,13,Jan,12.45 -1996,14,Jan,12.45 -1996,15,Jan,13.15 -1996,16,Jan,12.45 -1996,17,Jan,11.75 -1996,18,Jan,11.75 -1996,19,Jan,12.45 -1996,20,Jan,11.75 -1996,21,Jan,11.75 -1996,22,Jan,11.75 -1996,23,Jan,11.75 -1996,24,Jan,11.05 -1996,25,Jan,10.35 -1996,26,Jan,10.35 -1996,27,Jan,12.45 -1996,28,Jan,23.17 -1996,29,Jan,24.33 -1996,30,Jan,20.84 -1996,31,Jan,19.67 -1997,1,Jan,10.28 -1997,2,Jan,10.28 -1997,3,Jan,11.52 -1997,4,Jan,11.52 -1997,5,Jan,10.9 -1997,6,Jan,11.52 -1997,7,Jan,11.52 -1997,8,Jan,11.52 -1997,9,Jan,12.14 -1997,10,Jan,11.52 -1997,11,Jan,10.9 -1997,12,Jan,10.9 -1997,13,Jan,11.52 -1997,14,Jan,10.9 -1997,15,Jan,9.66 -1997,16,Jan,9.04 -1997,17,Jan,9.66 -1997,18,Jan,10.28 -1997,19,Jan,10.28 -1997,20,Jan,9.66 -1997,21,Jan,9.04 -1997,22,Jan,9.04 -1997,23,Jan,9.04 -1997,24,Jan,9.04 -1997,25,Jan,10.28 -1997,26,Jan,12.14 -1997,27,Jan,12.76 -1997,28,Jan,9.66 -1997,29,Jan,7.8 -1997,30,Jan,8.42 -1997,31,Jan,8.42 -1998,1,Jan,7 -1998,2,Jan,7.5 -1998,3,Jan,7.5 -1998,4,Jan,7.5 -1998,5,Jan,7.5 -1998,6,Jan,7 -1998,7,Jan,7.5 -1998,8,Jan,7.5 -1998,9,Jan,7.5 -1998,10,Jan,7 -1998,11,Jan,7 -1998,12,Jan,7 -1998,13,Jan,6.5 -1998,14,Jan,6 -1998,15,Jan,6 -1998,16,Jan,8.15 -1998,17,Jan,8.8 -1998,18,Jan,8.8 -1998,19,Jan,8.8 -1998,20,Jan,10.1 -1998,21,Jan,10.1 -1998,22,Jan,10.1 -1998,23,Jan,9.45 -1998,24,Jan,9.45 -1998,25,Jan,9.45 -1998,26,Jan,9.45 -1998,27,Jan,8.15 -1998,28,Jan,7.5 -1998,29,Jan,7 -1998,30,Jan,7.5 -1998,31,Jan,7.5 -1999,1,Jan,2.82 -1999,2,Jan,2.42 -1999,3,Jan,2.29 -1999,4,Jan,2.29 -1999,5,Jan,2.29 -1999,6,Jan,2.02 -1999,7,Jan,1.74 -1999,8,Jan,1.74 -1999,9,Jan,1.47 -1999,10,Jan,1.34 -1999,11,Jan,1.12 -1999,12,Jan,1.12 -1999,13,Jan,1.12 -1999,14,Jan,1.12 -1999,15,Jan,1.12 -1999,16,Jan,1.2 -1999,17,Jan,1.61 -1999,18,Jan,3.09 -1999,19,Jan,4.67 -1999,20,Jan,4.67 -1999,21,Jan,7.92 -1999,22,Jan,4.94 -1999,23,Jan,4.94 -1999,24,Jan,4.94 -1999,25,Jan,3.62 -1999,26,Jan,2.56 -1999,27,Jan,2.42 -1999,28,Jan,2.42 -1999,29,Jan,2.56 -1999,30,Jan,2.56 -1999,31,Jan,2.56 -2000,1,Jan,10.3 -2000,2,Jan,11.1 -2000,3,Jan,11.1 -2000,4,Jan,7.9 -2000,5,Jan,5.5 -2000,6,Jan,7.1 -2000,7,Jan,7.1 -2000,8,Jan,7.1 -2000,9,Jan,7.1 -2000,10,Jan,6.3 -2000,11,Jan,7.1 -2000,12,Jan,7.1 -2000,13,Jan,7.1 -2000,14,Jan,10.3 -2000,15,Jan,7.1 -2000,16,Jan,6.3 -2000,17,Jan,5.5 -2000,18,Jan,6.3 -2000,19,Jan,5.5 -2000,20,Jan,5.5 -2000,21,Jan,5.5 -2000,22,Jan,6.3 -2000,23,Jan,7.9 -2000,24,Jan,8.7 -2000,25,Jan,7.9 -2000,26,Jan,7.1 -2000,27,Jan,5.5 -2000,28,Jan,5.25 -2000,29,Jan,5.5 -2000,30,Jan,5.5 -2000,31,Jan,5.25 -2001,1,Jan,8.76 -2001,2,Jan,9.58 -2001,3,Jan,8.76 -2001,4,Jan,8.76 -2001,5,Jan,9.58 -2001,6,Jan,9.58 -2001,7,Jan,9.58 -2001,8,Jan,9.58 -2001,9,Jan,8.76 -2001,10,Jan,7.94 -2001,11,Jan,7.12 -2001,12,Jan,9.58 -2001,13,Jan,9.58 -2001,14,Jan,9.58 -2001,15,Jan,10.4 -2001,16,Jan,12.86 -2001,17,Jan,12.86 -2001,18,Jan,11.22 -2001,19,Jan,12.86 -2001,20,Jan,12.86 -2001,21,Jan,12.04 -2001,22,Jan,12.04 -2001,23,Jan,11.22 -2001,24,Jan,9.58 -2001,25,Jan,8.76 -2001,26,Jan,9.58 -2001,27,Jan,8.76 -2001,28,Jan,9.58 -2001,29,Jan,9.58 -2001,30,Jan,7.12 -2001,31,Jan,6.3 -2002,1,Jan,19.22 -2002,2,Jan,17.2 -2002,3,Jan,16.36 -2002,4,Jan,18.21 -2002,5,Jan,18.21 -2002,6,Jan,18.21 -2002,7,Jan,19.22 -2002,8,Jan,18.21 -2002,9,Jan,16.36 -2002,10,Jan,15.52 -2002,11,Jan,16.36 -2002,12,Jan,16.36 -2002,13,Jan,17.2 -2002,14,Jan,18.21 -2002,15,Jan,17.2 -2002,16,Jan,16.36 -2002,17,Jan,14.68 -2002,18,Jan,13.84 -2002,19,Jan,13.84 -2002,20,Jan,13.84 -2002,21,Jan,13.84 -2002,22,Jan,12.16 -2002,23,Jan,12.16 -2002,24,Jan,12.16 -2002,25,Jan,11.32 -2002,26,Jan,11.32 -2002,27,Jan,11.32 -2002,28,Jan,12.16 -2002,29,Jan,12.16 -2002,30,Jan,11.32 -2002,31,Jan,12.16 -2003,1,Jan,54.44 -2003,2,Jan,57.3 -2003,3,Jan,58.74 -2003,4,Jan,54.44 -2003,5,Jan,51.58 -2003,6,Jan,94.96 -2003,7,Jan,90.58 -2003,8,Jan,68.82 -2003,9,Jan,60.18 -2003,10,Jan,54.44 -2003,11,Jan,51.58 -2003,12,Jan,47.29 -2003,13,Jan,45.86 -2003,14,Jan,41.68 -2003,15,Jan,40.36 -2003,16,Jan,37.72 -2003,17,Jan,37.72 -2003,18,Jan,33.76 -2003,19,Jan,33.76 -2003,20,Jan,32.44 -2003,21,Jan,27.24 -2003,22,Jan,25.96 -2003,23,Jan,24.68 -2003,24,Jan,29.8 -2003,25,Jan,28.52 -2003,26,Jan,28.52 -2003,27,Jan,28.52 -2003,28,Jan,28.52 -2003,29,Jan,27.24 -2003,30,Jan,25.96 -2003,31,Jan,28.52 -2004,1,Jan,5.2 -2004,2,Jan,5.65 -2004,3,Jan,6.1 -2004,4,Jan,6.1 -2004,5,Jan,6.55 -2004,6,Jan,6.55 -2004,7,Jan,5.65 -2004,8,Jan,5.65 -2004,9,Jan,6.1 -2004,10,Jan,9.7 -2004,11,Jan,12.4 -2004,12,Jan,14.2 -2004,13,Jan,14.2 -2004,14,Jan,12.4 -2004,15,Jan,11.5 -2004,16,Jan,10.6 -2004,17,Jan,10.6 -2004,18,Jan,8.8 -2004,19,Jan,7.9 -2004,20,Jan,7 -2004,21,Jan,5.65 -2004,22,Jan,5.2 -2004,23,Jan,5.65 -2004,24,Jan,5.65 -2004,25,Jan,6.1 -2004,26,Jan,6.55 -2004,27,Jan,6.1 -2004,28,Jan,5.2 -2004,29,Jan,5.65 -2004,30,Jan,7 -2004,31,Jan,7.9 -2005,1,Jan,11.03 -2005,2,Jan,12.06 -2005,3,Jan,11.03 -2005,4,Jan,11.03 -2005,5,Jan,13.09 -2005,6,Jan,17.21 -2005,7,Jan,17.21 -2005,8,Jan,16.18 -2005,9,Jan,15.15 -2005,10,Jan,14.12 -2005,11,Jan,12.06 -2005,12,Jan,12.06 -2005,13,Jan,13.09 -2005,14,Jan,13.09 -2005,15,Jan,13.09 -2005,16,Jan,12.06 -2005,17,Jan,11.03 -2005,18,Jan,13.09 -2005,19,Jan,14.12 -2005,20,Jan,15.15 -2005,21,Jan,16.18 -2005,22,Jan,16.18 -2005,23,Jan,18.24 -2005,24,Jan,18.24 -2005,25,Jan,17.21 -2005,26,Jan,11.03 -2005,27,Jan,10 -2005,28,Jan,11.03 -2005,29,Jan,11.03 -2005,30,Jan,12.06 -2005,31,Jan,12.06 -2006,1,Jan,36.1 -2006,2,Jan,33.9 -2006,3,Jan,32.8 -2006,4,Jan,31.7 -2006,5,Jan,30.6 -2006,6,Jan,29.5 -2006,7,Jan,29.5 -2006,8,Jan,29.5 -2006,9,Jan,28.55 -2006,10,Jan,29.5 -2006,11,Jan,26.65 -2006,12,Jan,26.65 -2006,13,Jan,25.7 -2006,14,Jan,25.7 -2006,15,Jan,25.7 -2006,16,Jan,25.7 -2006,17,Jan,23.8 -2006,18,Jan,22.85 -2006,19,Jan,25.7 -2006,20,Jan,30.6 -2006,21,Jan,31.7 -2006,22,Jan,31.7 -2006,23,Jan,31.7 -2006,24,Jan,29.5 -2006,25,Jan,26.65 -2006,26,Jan,26.65 -2006,27,Jan,14.4 -2006,28,Jan,17.2 -2006,29,Jan,22.5 -2006,30,Jan,27.5 -2006,31,Jan,23.75 -2007,1,Jan,21.95 -2007,2,Jan,20.8 -2007,3,Jan,19.65 -2007,4,Jan,19.65 -2007,5,Jan,19.65 -2007,6,Jan,18.5 -2007,7,Jan,18.5 -2007,8,Jan,18.5 -2007,9,Jan,18.5 -2007,10,Jan,17.65 -2007,11,Jan,15.95 -2007,12,Jan,15.95 -2007,13,Jan,15.95 -2007,14,Jan,15.95 -2007,15,Jan,15.95 -2007,16,Jan,15.95 -2007,17,Jan,15.1 -2007,18,Jan,15.1 -2007,19,Jan,14.25 -2007,20,Jan,14.25 -2007,21,Jan,15.1 -2007,22,Jan,15.1 -2007,23,Jan,15.1 -2007,24,Jan,15.1 -2007,25,Jan,15.1 -2007,26,Jan,14.25 -2007,27,Jan,15.1 -2007,28,Jan,15.95 -2007,29,Jan,15.95 -2007,30,Jan,5.1 -2007,31,Jan,12.5 -2008,1,Jan,11 -2008,2,Jan,10.45 -2008,3,Jan,11 -2008,4,Jan,8.25 -2008,5,Jan,8.8 -2008,6,Jan,9.35 -2008,7,Jan,9.35 -2008,8,Jan,8.8 -2008,9,Jan,7.7 -2008,10,Jan,6.6 -2008,11,Jan,6.6 -2008,12,Jan,8.25 -2008,13,Jan,7.7 -2008,14,Jan,8.25 -2008,15,Jan,8.25 -2008,16,Jan,9.35 -2008,17,Jan,14 -2008,18,Jan,14 -2008,19,Jan,13.25 -2008,20,Jan,12.5 -2008,21,Jan,13.25 -2008,22,Jan,11.75 -2008,23,Jan,10.45 -2008,24,Jan,11 -2008,25,Jan,13.25 -2008,26,Jan,17.75 -2008,27,Jan,17 -2008,28,Jan,16.25 -2008,29,Jan,14.75 -2008,30,Jan,15.5 -2008,31,Jan,16.25 -2009,1,Jan,15.95 -2009,2,Jan,15 -2009,3,Jan,15 -2009,4,Jan,11.8 -2009,5,Jan,11.8 -2009,6,Jan,9.4 -2009,7,Jan,9.4 -2009,8,Jan,9.4 -2009,9,Jan,9.4 -2009,10,Jan,9.4 -2009,11,Jan,9.4 -2009,12,Jan,8.6 -2009,13,Jan,9.4 -2009,14,Jan,11.8 -2009,15,Jan,11 -2009,16,Jan,11.8 -2009,17,Jan,11.8 -2009,18,Jan,13.4 -2009,19,Jan,12.6 -2009,20,Jan,11.8 -2009,21,Jan,11 -2009,22,Jan,10.2 -2009,23,Jan,11 -2009,24,Jan,10.2 -2009,25,Jan,11 -2009,26,Jan,9.4 -2009,27,Jan,7.8 -2009,28,Jan,7 -2009,29,Jan,6 -2009,30,Jan,6 -2009,31,Jan,5.5 -2010,1,Jan,6.5 -2010,2,Jan,6.5 -2010,3,Jan,6.5 -2010,4,Jan,6.5 -2010,5,Jan,6.5 -2010,6,Jan,6.97 -2010,7,Jan,8.85 -2010,8,Jan,10.73 -2010,9,Jan,10.26 -2010,10,Jan,7.44 -2010,11,Jan,6.5 -2010,12,Jan,6.5 -2010,13,Jan,6.5 -2010,14,Jan,6.5 -2010,15,Jan,6.5 -2010,16,Jan,6.97 -2010,17,Jan,8.85 -2010,18,Jan,10.73 -2010,19,Jan,10.26 -2010,20,Jan,7.44 -2010,21,Jan,5.42 -2010,22,Jan,6.14 -2010,23,Jan,5.78 -2010,24,Jan,6.5 -2010,25,Jan,6.5 -2010,26,Jan,6.5 -2010,27,Jan,6.5 -2010,28,Jan,4.7 -2010,29,Jan,5.06 -2010,30,Jan,5.78 -2010,31,Jan,7.44 -2011,1,Jan,8.6 -2011,2,Jan,8.6 -2011,3,Jan,9.8 -2011,4,Jan,8.6 -2011,5,Jan,9.2 -2011,6,Jan,9.2 -2011,7,Jan,8.6 -2011,8,Jan,9.2 -2011,9,Jan,7.4 -2011,10,Jan,8 -2011,11,Jan,12 -2011,12,Jan,14 -2011,13,Jan,12 -2011,14,Jan,11 -2011,15,Jan,10.4 -2011,16,Jan,9.2 -2011,17,Jan,8 -2011,18,Jan,7.4 -2011,19,Jan,8 -2011,20,Jan,10.4 -2011,21,Jan,6.2 -2011,22,Jan,5.6 -2011,23,Jan,6.2 -2011,24,Jan,6.8 -2011,25,Jan,6.8 -2011,26,Jan,6.8 -2011,27,Jan,6.2 -2011,28,Jan,5 -2011,29,Jan,5 -2011,30,Jan,9.2 -2011,31,Jan,7.4 -2012,1,Jan,25.7 -2012,2,Jan,25.7 -2012,3,Jan,25.7 -2012,4,Jan,25.7 -2012,5,Jan,25.7 -2012,6,Jan,24.55 -2012,7,Jan,24.55 -2012,8,Jan,24.55 -2012,9,Jan,24.55 -2012,10,Jan,24.55 -2012,11,Jan,24.55 -2012,12,Jan,24.55 -2012,13,Jan,24.55 -2012,14,Jan,32.05 -2012,15,Jan,52.52 -2012,16,Jan,34.75 -2012,17,Jan,26.85 -2012,18,Jan,24.55 -2012,19,Jan,24.55 -2012,20,Jan,22.25 -2012,21,Jan,21.1 -2012,22,Jan,21.1 -2012,23,Jan,21.1 -2012,24,Jan,21.1 -2012,25,Jan,19.95 -2012,26,Jan,17.65 -2012,27,Jan,19.95 -2012,28,Jan,18.8 -2012,29,Jan,18.8 -2012,30,Jan,17.65 -2012,31,Jan,18.8 -2013,1,Jan,4.5 -2013,2,Jan,6 -2013,3,Jan,6 -2013,4,Jan,4.5 -2013,5,Jan,2.5 -2013,6,Jan,1.5 -2013,7,Jan,0.9 -2013,8,Jan,1.05 -2013,9,Jan,1.35 -2013,10,Jan,1.5 -2013,11,Jan,4.5 -2013,12,Jan,10.3 -2013,13,Jan,10.3 -2013,14,Jan,10.3 -2013,15,Jan,10.3 -2013,16,Jan,6.5 -2013,17,Jan,3.5 -2013,18,Jan,1.5 -2013,19,Jan,1.2 -2013,20,Jan,1.05 -2013,21,Jan,1.2 -2013,22,Jan,1.35 -2013,23,Jan,2.5 -2013,24,Jan,3 -2013,25,Jan,3.5 -2013,26,Jan,3 -2013,27,Jan,1.5 -2013,28,Jan,1.35 -2013,29,Jan,1.5 -2013,30,Jan,9.35 -2013,31,Jan,19.9 -2014,1,Jan,15.2 -2014,2,Jan,15.2 -2014,3,Jan,15.2 -2014,4,Jan,13.6 -2014,5,Jan,13.6 -2014,6,Jan,12 -2014,7,Jan,13.6 -2014,8,Jan,15.2 -2014,9,Jan,12.8 -2014,10,Jan,14.4 -2014,11,Jan,15.2 -2014,12,Jan,20 -2014,13,Jan,17.6 -2014,14,Jan,10.9 -2014,15,Jan,9.8 -2014,16,Jan,10.35 -2014,17,Jan,11.45 -2014,18,Jan,20 -2014,19,Jan,22.1 -2014,20,Jan,19.2 -2014,21,Jan,13.6 -2014,22,Jan,11.45 -2014,23,Jan,9.25 -2014,24,Jan,9.8 -2014,25,Jan,16.8 -2014,26,Jan,19.2 -2014,27,Jan,19.2 -2014,28,Jan,10.9 -2014,29,Jan,8.15 -2014,30,Jan,8.15 -2014,31,Jan,10.35 -2015,1,Jan,3.32 -2015,2,Jan,3.01 -2015,3,Jan,3.32 -2015,4,Jan,4.25 -2015,5,Jan,4.87 -2015,6,Jan,3.94 -2015,7,Jan,2.53 -2015,8,Jan,2.36 -2015,9,Jan,17.7 -2015,10,Jan,42.1 -2015,11,Jan,19.5 -2015,12,Jan,15 -2015,13,Jan,10.5 -2015,14,Jan,6.74 -2015,15,Jan,5.18 -2015,16,Jan,3.63 -2015,17,Jan,3.01 -2015,18,Jan,3.63 -2015,19,Jan,4.25 -2015,20,Jan,4.56 -2015,21,Jan,6.27 -2015,22,Jan,20.6 -2015,23,Jan,22.8 -2015,24,Jan,16.8 -2015,25,Jan,29.4 -2015,26,Jan,29.4 -2015,27,Jan,15.9 -2015,28,Jan,8.15 -2015,29,Jan,4.87 -2015,30,Jan,4.87 -2015,31,Jan,8.62 -2016,1,Jan,0.46 -2016,2,Jan,0.46 -2016,3,Jan,0.42 -2016,4,Jan,0.34 -2016,5,Jan,0.42 -2016,6,Jan,7.2 -2016,7,Jan,2.53 -2016,8,Jan,0.46 -2016,9,Jan,0.8 -2016,10,Jan,36 -2016,11,Jan,4.02 -2016,12,Jan,2.19 -2016,13,Jan,1.85 -2016,14,Jan,1.17 -2016,15,Jan,0.8 -2016,16,Jan,2.19 -2016,17,Jan,4.35 -2016,18,Jan,0.85 -2016,19,Jan,0.46 -2016,20,Jan,0.3 -2016,21,Jan,0.26 -2016,22,Jan,0.26 -2016,23,Jan,0.85 -2016,24,Jan,2.53 -2016,25,Jan,0.7 -2016,26,Jan,0.55 -2016,27,Jan,0.8 -2016,28,Jan,6 -2016,29,Jan,16.75 -2016,30,Jan,12 -2016,31,Jan,31.65 -2017,1,Jan,1.3 -2017,2,Jan,1.5 -2017,3,Jan,2.2 -2017,4,Jan,1.7 -2017,5,Jan,1.8 -2017,6,Jan,4 -2017,7,Jan,3.6 -2017,8,Jan,4.4 -2017,9,Jan,2.2 -2017,10,Jan,6.4 -2017,11,Jan,6.8 -2017,12,Jan,8 -2017,13,Jan,3.8 -2017,14,Jan,2.4 -2017,15,Jan,1.9 -2017,16,Jan,3.6 -2017,17,Jan,3.2 -2017,18,Jan,2.6 -2017,19,Jan,1.2 -2017,20,Jan,0.9 -2017,21,Jan,0.9 -2017,22,Jan,0.85 -2017,23,Jan,0.8 -2017,24,Jan,0.9 -2017,25,Jan,0.9 -2017,26,Jan,0.9 -2017,27,Jan,0.95 -2017,28,Jan,0.9 -2017,29,Jan,0.9 -2017,30,Jan,1.4 -2017,31,Jan,1.2 -2018,1,Jan,16 -2018,2,Jan,17 -2018,3,Jan,32 -2018,4,Jan,17 -2018,5,Jan,37.6 -2018,6,Jan,13 -2018,7,Jan,14 -2018,8,Jan,15 -2018,9,Jan,18 -2018,10,Jan,12 -2018,11,Jan,18 -2018,12,Jan,15 -2018,13,Jan,11 -2018,14,Jan,11 -2018,15,Jan,13 -2018,16,Jan,9 -2018,17,Jan,8 -2018,18,Jan,19 -2018,19,Jan,19 -2018,20,Jan,6 -2018,21,Jan,16 -2018,22,Jan,26 -2018,23,Jan,11 -2018,24,Jan,9 -2018,25,Jan,6.5 -2018,26,Jan,8.5 -2018,27,Jan,8 -2018,28,Jan,7 -2018,29,Jan,9 -2018,30,Jan,7.5 -2018,31,Jan,6.5 -2019,1,Jan,33.55 -2019,2,Jan,27.75 -2019,3,Jan,26.3 -2019,4,Jan,19.35 -2019,5,Jan,15.9 -2019,6,Jan,33.55 -2019,7,Jan,23.4 -2019,8,Jan,45.5 -2019,9,Jan,54.95 -2019,10,Jan,35 -2019,11,Jan,32.1 -2019,12,Jan,15.9 -2019,13,Jan,30.65 -2019,14,Jan,17.05 -2019,15,Jan,18.2 -2019,16,Jan,20.5 -2019,17,Jan,13.6 -2019,18,Jan,35 -2019,19,Jan,18.2 -2019,20,Jan,30.65 -2019,21,Jan,26.3 -2019,22,Jan,13.6 -2019,23,Jan,8.5 -2019,24,Jan,7.5 -2019,25,Jan,19.35 -2019,26,Jan,36.5 -2019,27,Jan,13.6 -2019,28,Jan,32.1 -2019,29,Jan,26.3 -2019,30,Jan,41 -2019,31,Jan,29.2 -2020,1,Jan,31.8 -2020,2,Jan,17 -2020,3,Jan,10.99 -2020,4,Jan,6.3 -2020,5,Jan,2.86 -2020,6,Jan,1.36 -2020,7,Jan,0.88 -2020,8,Jan,0.72 -2020,9,Jan,0.72 -2020,10,Jan,1.04 -2020,11,Jan,1.04 -2020,12,Jan,1.52 -2020,13,Jan,5.44 -2020,14,Jan,5.44 -2020,15,Jan,8.31 -2020,16,Jan,3.29 -2020,17,Jan,1.04 -2020,18,Jan,0.56 -2020,19,Jan,1.2 -2020,20,Jan,4.58 -2020,21,Jan,10.99 -2020,22,Jan,16.2 -2020,23,Jan,14.6 -2020,24,Jan,8.31 -2020,25,Jan,5.01 -2020,26,Jan,8.98 -2020,27,Jan,11.66 -2020,28,Jan,6.97 -2020,29,Jan,1.2 -2020,30,Jan,0.4 -2020,31,Jan,0.36 -1922,1,Feb,24 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-2006,17,Feb,19.3 -2006,18,Feb,25 -2006,19,Feb,25 -2006,20,Feb,25 -2006,21,Feb,21.25 -2006,22,Feb,18.6 -2006,23,Feb,17.9 -2006,24,Feb,20 -2006,25,Feb,20 -2006,26,Feb,21.25 -2006,27,Feb,20 -2006,28,Feb,18.6 -2007,1,Feb,13.9 -2007,2,Feb,16.45 -2007,3,Feb,16.9 -2007,4,Feb,17.35 -2007,5,Feb,16.9 -2007,6,Feb,16.45 -2007,7,Feb,14.6 -2007,8,Feb,14.6 -2007,9,Feb,14.95 -2007,10,Feb,15.65 -2007,11,Feb,15.3 -2007,12,Feb,15.3 -2007,13,Feb,15.65 -2007,14,Feb,15.3 -2007,15,Feb,14.95 -2007,16,Feb,14.6 -2007,17,Feb,14.6 -2007,18,Feb,14.6 -2007,19,Feb,14.25 -2007,20,Feb,13.9 -2007,21,Feb,13.2 -2007,22,Feb,11.8 -2007,23,Feb,12.15 -2007,24,Feb,12.85 -2007,25,Feb,13.2 -2007,26,Feb,13.9 -2007,27,Feb,12.5 -2007,28,Feb,11.1 -2008,1,Feb,20.3 -2008,2,Feb,29.8 -2008,3,Feb,22.1 -2008,4,Feb,19.4 -2008,5,Feb,16.25 -2008,6,Feb,14 -2008,7,Feb,16.25 -2008,8,Feb,14 -2008,9,Feb,11 -2008,10,Feb,13.25 -2008,11,Feb,14 -2008,12,Feb,11.75 -2008,13,Feb,8.25 -2008,14,Feb,9.9 -2008,15,Feb,11 -2008,16,Feb,13.25 -2008,17,Feb,14.75 -2008,18,Feb,14 -2008,19,Feb,11.75 -2008,20,Feb,8.25 -2008,21,Feb,7.7 -2008,22,Feb,10.45 -2008,23,Feb,11 -2008,24,Feb,11 -2008,25,Feb,11.75 -2008,26,Feb,11 -2008,27,Feb,14.75 -2008,28,Feb,14 -2008,29,Feb,10.45 -2009,1,Feb,5 -2009,2,Feb,5 -2009,3,Feb,5.5 -2009,4,Feb,7.8 -2009,5,Feb,10.2 -2009,6,Feb,9.4 -2009,7,Feb,7.8 -2009,8,Feb,7.8 -2009,9,Feb,7.8 -2009,10,Feb,7.8 -2009,11,Feb,5.5 -2009,12,Feb,5.5 -2009,13,Feb,5.5 -2009,14,Feb,6 -2009,15,Feb,6 -2009,16,Feb,5.5 -2009,17,Feb,5.5 -2009,18,Feb,9.4 -2009,19,Feb,9.4 -2009,20,Feb,10.2 -2009,21,Feb,9.4 -2009,22,Feb,9.4 -2009,23,Feb,6.5 -2009,24,Feb,6 -2009,25,Feb,5 -2009,26,Feb,6.5 -2009,27,Feb,13.4 -2009,28,Feb,7.8 -2010,1,Feb,7.44 -2010,2,Feb,6.97 -2010,3,Feb,6.14 -2010,4,Feb,5.06 -2010,5,Feb,5.06 -2010,6,Feb,5.78 -2010,7,Feb,6.5 -2010,8,Feb,7.44 -2010,9,Feb,1.26 -2010,10,Feb,2.2 -2010,11,Feb,2.58 -2010,12,Feb,2.58 -2010,13,Feb,2.7 -2010,14,Feb,2.2 -2010,15,Feb,2.09 -2010,16,Feb,1.98 -2010,17,Feb,2.09 -2010,18,Feb,2.25 -2010,19,Feb,2.25 -2010,20,Feb,1.98 -2010,21,Feb,1.65 -2010,22,Feb,2.09 -2010,23,Feb,2.09 -2010,24,Feb,2.25 -2010,25,Feb,2.58 -2010,26,Feb,2.75 -2010,27,Feb,2.75 -2010,28,Feb,2.75 -2011,1,Feb,7.4 -2011,2,Feb,8.6 -2011,3,Feb,8.6 -2011,4,Feb,8 -2011,5,Feb,8.6 -2011,6,Feb,8 -2011,7,Feb,6.8 -2011,8,Feb,7.4 -2011,9,Feb,6.8 -2011,10,Feb,6.2 -2011,11,Feb,4.6 -2011,12,Feb,5 -2011,13,Feb,4.6 -2011,14,Feb,4.6 -2011,15,Feb,4.6 -2011,16,Feb,4.6 -2011,17,Feb,4.2 -2011,18,Feb,4.2 -2011,19,Feb,3.8 -2011,20,Feb,3.8 -2011,21,Feb,3.4 -2011,22,Feb,3.4 -2011,23,Feb,3 -2011,24,Feb,3.8 -2011,25,Feb,5.6 -2011,26,Feb,7.4 -2011,27,Feb,8 -2011,28,Feb,8.6 -2012,1,Feb,17.65 -2012,2,Feb,17.65 -2012,3,Feb,17.65 -2012,4,Feb,17.65 -2012,5,Feb,15.6 -2012,6,Feb,12.9 -2012,7,Feb,12.9 -2012,8,Feb,12.9 -2012,9,Feb,12.9 -2012,10,Feb,13.8 -2012,11,Feb,32.05 -2012,12,Feb,41.5 -2012,13,Feb,41.5 -2012,14,Feb,43.07 -2012,15,Feb,46.22 -2012,16,Feb,33.4 -2012,17,Feb,17.65 -2012,18,Feb,15.6 -2012,19,Feb,14.7 -2012,20,Feb,12.9 -2012,21,Feb,12 -2012,22,Feb,12 -2012,23,Feb,10.2 -2012,24,Feb,10.2 -2012,25,Feb,11.1 -2012,26,Feb,10.2 -2012,27,Feb,8.4 -2012,28,Feb,12.9 -2012,29,Feb,12.9 -2013,1,Feb,18.6 -2013,2,Feb,11.25 -2013,3,Feb,9.35 -2013,4,Feb,9.35 -2013,5,Feb,9.35 -2013,6,Feb,8.4 -2013,7,Feb,6.5 -2013,8,Feb,5 -2013,9,Feb,4.5 -2013,10,Feb,6 -2013,11,Feb,6 -2013,12,Feb,6.5 -2013,13,Feb,9.35 -2013,14,Feb,8.4 -2013,15,Feb,2 -2013,16,Feb,1.05 -2013,17,Feb,0.75 -2013,18,Feb,1.35 -2013,19,Feb,9.35 -2013,20,Feb,12.2 -2013,21,Feb,9.35 -2013,22,Feb,7.45 -2013,23,Feb,6.5 -2013,24,Feb,7.45 -2013,25,Feb,8.4 -2013,26,Feb,6.5 -2013,27,Feb,3.5 -2013,28,Feb,2.5 -2014,1,Feb,21.05 -2014,2,Feb,21.05 -2014,3,Feb,18.4 -2014,4,Feb,10.35 -2014,5,Feb,9.25 -2014,6,Feb,8.7 -2014,7,Feb,8.15 -2014,8,Feb,8.7 -2014,9,Feb,17.6 -2014,10,Feb,16 -2014,11,Feb,9.25 -2014,12,Feb,5.9 -2014,13,Feb,5.9 -2014,14,Feb,8.7 -2014,15,Feb,15.2 -2014,16,Feb,21.05 -2014,17,Feb,18.4 -2014,18,Feb,9.8 -2014,19,Feb,6.5 -2014,20,Feb,6.2 -2014,21,Feb,7.05 -2014,22,Feb,14.4 -2014,23,Feb,18.4 -2014,24,Feb,16.8 -2014,25,Feb,10.9 -2014,26,Feb,5 -2014,27,Feb,4.4 -2014,28,Feb,4.7 -2015,1,Feb,9.09 -2015,2,Feb,11.4 -2015,3,Feb,9.09 -2015,4,Feb,3.01 -2015,5,Feb,3.01 -2015,6,Feb,1.68 -2015,7,Feb,5.49 -2015,8,Feb,12.3 -2015,9,Feb,17.7 -2015,10,Feb,7.21 -2015,11,Feb,1.85 -2015,12,Feb,1.51 -2015,13,Feb,1.51 -2015,14,Feb,5.18 -2015,15,Feb,9.56 -2015,16,Feb,20.6 -2015,17,Feb,6.27 -2015,18,Feb,6.74 -2015,19,Feb,5.8 -2015,20,Feb,5.49 -2015,21,Feb,11.4 -2015,22,Feb,9.56 -2015,23,Feb,28.3 -2015,24,Feb,10.5 -2015,25,Feb,3.32 -2015,26,Feb,2.7 -2015,27,Feb,2.53 -2015,28,Feb,3.63 -2016,1,Feb,14.85 -2016,2,Feb,10.2 -2016,3,Feb,6 -2016,4,Feb,2.7 -2016,5,Feb,0.95 -2016,6,Feb,0.55 -2016,7,Feb,0.34 -2016,8,Feb,0.3 -2016,9,Feb,0.3 -2016,10,Feb,0.18 -2016,11,Feb,0.14 -2016,12,Feb,0.14 -2016,13,Feb,0.46 -2016,14,Feb,12.95 -2016,15,Feb,15.8 -2016,16,Feb,11.4 -2016,17,Feb,7.2 -2016,18,Feb,4.35 -2016,19,Feb,2.53 -2016,20,Feb,1.85 -2016,21,Feb,5.67 -2016,22,Feb,4.35 -2016,23,Feb,0.65 -2016,24,Feb,0.34 -2016,25,Feb,0.22 -2016,26,Feb,0.18 -2016,27,Feb,0.6 -2016,28,Feb,1 -2016,29,Feb,0.7 -2017,1,Feb,0.9 -2017,2,Feb,1.1 -2017,3,Feb,11.25 -2017,4,Feb,2 -2017,5,Feb,0.85 -2017,6,Feb,0.8 -2017,7,Feb,0.8 -2017,8,Feb,0.85 -2017,9,Feb,1.3 -2017,10,Feb,6 -2017,11,Feb,1.7 -2017,12,Feb,1.3 -2017,13,Feb,3.6 -2017,14,Feb,1 -2017,15,Feb,1.9 -2017,16,Feb,1 -2017,17,Feb,10.6 -2017,18,Feb,1.9 -2017,19,Feb,10.6 -2017,20,Feb,3.8 -2017,21,Feb,1.9 -2017,22,Feb,1.2 -2017,23,Feb,0.85 -2017,24,Feb,8.65 -2017,25,Feb,3.8 -2017,26,Feb,9.3 -2017,27,Feb,7.2 -2017,28,Feb,1 -2018,1,Feb,5 -2018,2,Feb,8.5 -2018,3,Feb,24.8 -2018,4,Feb,23.6 -2018,5,Feb,22.4 -2018,6,Feb,9.5 -2018,7,Feb,4.04 -2018,8,Feb,20 -2018,9,Feb,28.4 -2018,10,Feb,29.6 -2018,11,Feb,12 -2018,12,Feb,33.4 -2018,13,Feb,16 -2018,14,Feb,18 -2018,15,Feb,8.5 -2018,16,Feb,21.2 -2018,17,Feb,34.8 -2018,18,Feb,10 -2018,19,Feb,23.6 -2018,20,Feb,22.4 -2018,21,Feb,7.5 -2018,22,Feb,26 -2018,23,Feb,5 -2018,24,Feb,39 -2018,25,Feb,36.2 -2018,26,Feb,22.4 -2018,27,Feb,16 -2018,28,Feb,20 -2019,1,Feb,13.6 -2019,2,Feb,17.05 -2019,3,Feb,23.4 -2019,4,Feb,29.2 -2019,5,Feb,18.2 -2019,6,Feb,13.6 -2019,7,Feb,13.6 -2019,8,Feb,13.6 -2019,9,Feb,9 -2019,10,Feb,7.5 -2019,11,Feb,10.15 -2019,12,Feb,6.5 -2019,13,Feb,6 -2019,14,Feb,6.5 -2019,15,Feb,6.5 -2019,16,Feb,7 -2019,17,Feb,6 -2019,18,Feb,8.5 -2019,19,Feb,6 -2019,20,Feb,8 -2019,21,Feb,8.5 -2019,22,Feb,7.5 -2019,23,Feb,6 -2019,24,Feb,6 -2019,25,Feb,6.5 -2019,26,Feb,7 -2019,27,Feb,6.5 -2019,28,Feb,6.5 -2020,1,Feb,5.44 -2020,2,Feb,14.6 -2020,3,Feb,30.6 -2020,4,Feb,33 -2020,5,Feb,17 -2020,6,Feb,1.68 -2020,7,Feb,0.72 -2020,8,Feb,3.72 -2020,9,Feb,17.8 -2020,10,Feb,47 -2020,11,Feb,29.4 -2020,12,Feb,5.01 -2020,13,Feb,3.29 -2020,14,Feb,2.43 -2020,15,Feb,6.3 -2020,16,Feb,7.64 -2020,17,Feb,13.8 -2020,18,Feb,4.58 -2020,19,Feb,3.29 -2020,20,Feb,3.29 -2020,21,Feb,3.29 -2020,22,Feb,5.01 -2020,23,Feb,5.44 -2020,24,Feb,4.58 -2020,25,Feb,4.15 -2020,26,Feb,3.29 -2020,27,Feb,3.72 -2020,28,Feb,6.3 -2020,29,Feb,13.8 -1922,1,Mar,12 -1922,2,Mar,12 -1922,3,Mar,12 -1922,4,Mar,12 -1922,5,Mar,11 -1922,6,Mar,11 -1922,7,Mar,11 -1922,8,Mar,11 -1922,9,Mar,11 -1922,10,Mar,11 -1922,11,Mar,11 -1922,12,Mar,11 -1922,13,Mar,11 -1922,14,Mar,10 -1922,15,Mar,10 -1922,16,Mar,10 -1922,17,Mar,10 -1922,18,Mar,10 -1922,19,Mar,10 -1922,20,Mar,9.5 -1922,21,Mar,9.85 -1922,22,Mar,10 -1922,23,Mar,9.5 -1922,24,Mar,11 -1922,25,Mar,12 -1922,26,Mar,13 -1922,27,Mar,13 -1922,28,Mar,13 -1922,29,Mar,13 -1922,30,Mar,11 -1922,31,Mar,11 -1923,1,Mar,16 -1923,2,Mar,16 -1923,3,Mar,16 -1923,4,Mar,15 -1923,5,Mar,15 -1923,6,Mar,15 -1923,7,Mar,15 -1923,8,Mar,14 -1923,9,Mar,14 -1923,10,Mar,14 -1923,11,Mar,14 -1923,12,Mar,16 -1923,13,Mar,16 -1923,14,Mar,13 -1923,15,Mar,13 -1923,16,Mar,13 -1923,17,Mar,13 -1923,18,Mar,14 -1923,19,Mar,14 -1923,20,Mar,16 -1923,21,Mar,16 -1923,22,Mar,13 -1923,23,Mar,13 -1923,24,Mar,13 -1923,25,Mar,10 -1923,26,Mar,25 -1923,27,Mar,25 -1923,28,Mar,24 -1923,29,Mar,24 -1923,30,Mar,18 -1923,31,Mar,18 -1924,1,Mar,14 -1924,2,Mar,13 -1924,3,Mar,13 -1924,4,Mar,13 -1924,5,Mar,13 -1924,6,Mar,13 -1924,7,Mar,13 -1924,8,Mar,13 -1924,9,Mar,13 -1924,10,Mar,13 -1924,11,Mar,12 -1924,12,Mar,12 -1924,13,Mar,12 -1924,14,Mar,12 -1924,15,Mar,12 -1924,16,Mar,12 -1924,17,Mar,12 -1924,18,Mar,12 -1924,19,Mar,24 -1924,20,Mar,26 -1924,21,Mar,24 -1924,22,Mar,27 -1924,23,Mar,23 -1924,24,Mar,22 -1924,25,Mar,21 -1924,26,Mar,20 -1924,27,Mar,20 -1924,28,Mar,18 -1924,29,Mar,20 -1924,30,Mar,20 -1924,31,Mar,20 -1925,1,Mar,21 -1925,2,Mar,21 -1925,3,Mar,21 -1925,4,Mar,21 -1925,5,Mar,21 -1925,6,Mar,20 -1925,7,Mar,20 -1925,8,Mar,20 -1925,9,Mar,20 -1925,10,Mar,19 -1925,11,Mar,19 -1925,12,Mar,18 -1925,13,Mar,18 -1925,14,Mar,18 -1925,15,Mar,17 -1925,16,Mar,17 -1925,17,Mar,17 -1925,18,Mar,16 -1925,19,Mar,16 -1925,20,Mar,16 -1925,21,Mar,16 -1925,22,Mar,16 -1925,23,Mar,16 -1925,24,Mar,16 -1925,25,Mar,16 -1925,26,Mar,16 -1925,27,Mar,16 -1925,28,Mar,16 -1925,29,Mar,16 -1925,30,Mar,15 -1925,31,Mar,15 -1926,1,Mar,18 -1926,2,Mar,18 -1926,3,Mar,18 -1926,4,Mar,17 -1926,5,Mar,17 -1926,6,Mar,17 -1926,7,Mar,17 -1926,8,Mar,17 -1926,9,Mar,17 -1926,10,Mar,17 -1926,11,Mar,20 -1926,12,Mar,18 -1926,13,Mar,17 -1926,14,Mar,17 -1926,15,Mar,17 -1926,16,Mar,16 -1926,17,Mar,16 -1926,18,Mar,16 -1926,19,Mar,16 -1926,20,Mar,16 -1926,21,Mar,16 -1926,22,Mar,16 -1926,23,Mar,16 -1926,24,Mar,16 -1926,25,Mar,16 -1926,26,Mar,16 -1926,27,Mar,15 -1926,28,Mar,15 -1926,29,Mar,15 -1926,30,Mar,17 -1926,31,Mar,17 -1927,1,Mar,24 -1927,2,Mar,24 -1927,3,Mar,23 -1927,4,Mar,23 -1927,5,Mar,22 -1927,6,Mar,22 -1927,7,Mar,22 -1927,8,Mar,22 -1927,9,Mar,22 -1927,10,Mar,22 -1927,11,Mar,22 -1927,12,Mar,22 -1927,13,Mar,21 -1927,14,Mar,21 -1927,15,Mar,21 -1927,16,Mar,21 -1927,17,Mar,21 -1927,18,Mar,20 -1927,19,Mar,20 -1927,20,Mar,20 -1927,21,Mar,20 -1927,22,Mar,20 -1927,23,Mar,19 -1927,24,Mar,19 -1927,25,Mar,18 -1927,26,Mar,18 -1927,27,Mar,18 -1927,28,Mar,18 -1927,29,Mar,17 -1927,30,Mar,17 -1927,31,Mar,17 -1928,1,Mar,22 -1928,2,Mar,20 -1928,3,Mar,18 -1928,4,Mar,17 -1928,5,Mar,17 -1928,6,Mar,16 -1928,7,Mar,16 -1928,8,Mar,16 -1928,9,Mar,16 -1928,10,Mar,15 -1928,11,Mar,15 -1928,12,Mar,14 -1928,13,Mar,14 -1928,14,Mar,14 -1928,15,Mar,14 -1928,16,Mar,13 -1928,17,Mar,13 -1928,18,Mar,13 -1928,19,Mar,13 -1928,20,Mar,13 -1928,21,Mar,13 -1928,22,Mar,13 -1928,23,Mar,13 -1928,24,Mar,13 -1928,25,Mar,13 -1928,26,Mar,13 -1928,27,Mar,13 -1928,28,Mar,12 -1928,29,Mar,12 -1928,30,Mar,12 -1928,31,Mar,12 -1929,1,Mar,18 -1929,2,Mar,18 -1929,3,Mar,18 -1929,4,Mar,17 -1929,5,Mar,17 -1929,6,Mar,17 -1929,7,Mar,17 -1929,8,Mar,17 -1929,9,Mar,17 -1929,10,Mar,17 -1929,11,Mar,17 -1929,12,Mar,17 -1929,13,Mar,17 -1929,14,Mar,17 -1929,15,Mar,25 -1929,16,Mar,24 -1929,17,Mar,26 -1929,18,Mar,22 -1929,19,Mar,21 -1929,20,Mar,21 -1929,21,Mar,20 -1929,22,Mar,18 -1929,23,Mar,18 -1929,24,Mar,18 -1929,25,Mar,18 -1929,26,Mar,17 -1929,27,Mar,17 -1929,28,Mar,17 -1929,29,Mar,16 -1929,30,Mar,16 -1929,31,Mar,17 -1930,1,Mar,14 -1930,2,Mar,13 -1930,3,Mar,13 -1930,4,Mar,13 -1930,5,Mar,13 -1930,6,Mar,13 -1930,7,Mar,13 -1930,8,Mar,13 -1930,9,Mar,13 -1930,10,Mar,13 -1930,11,Mar,13 -1930,12,Mar,13 -1930,13,Mar,13 -1930,14,Mar,13 -1930,15,Mar,13 -1930,16,Mar,13 -1930,17,Mar,14 -1930,18,Mar,14 -1930,19,Mar,14 -1930,20,Mar,14 -1930,21,Mar,14 -1930,22,Mar,14 -1930,23,Mar,14 -1930,24,Mar,14 -1930,25,Mar,14 -1930,26,Mar,14 -1930,27,Mar,13 -1930,28,Mar,13 -1930,29,Mar,13 -1930,30,Mar,13 -1930,31,Mar,13 -1931,1,Mar,14 -1931,2,Mar,14 -1931,3,Mar,13 -1931,4,Mar,14 -1931,5,Mar,14 -1931,6,Mar,14 -1931,7,Mar,14 -1931,8,Mar,13 -1931,9,Mar,13 -1931,10,Mar,13 -1931,11,Mar,13 -1931,12,Mar,13 -1931,13,Mar,13 -1931,14,Mar,13 -1931,15,Mar,13 -1931,16,Mar,12 -1931,17,Mar,12 -1931,18,Mar,12 -1931,19,Mar,12 -1931,20,Mar,12 -1931,21,Mar,12 -1931,22,Mar,12 -1931,23,Mar,12 -1931,24,Mar,12 -1931,25,Mar,12 -1931,26,Mar,12 -1931,27,Mar,12 -1931,28,Mar,12 -1931,29,Mar,12 -1931,30,Mar,12 -1931,31,Mar,12 -1932,1,Mar,12 -1932,2,Mar,12 -1932,3,Mar,12 -1932,4,Mar,11 -1932,5,Mar,11 -1932,6,Mar,11 -1932,7,Mar,11 -1932,8,Mar,11 -1932,9,Mar,10 -1932,10,Mar,10 -1932,11,Mar,10 -1932,12,Mar,10 -1932,13,Mar,10 -1932,14,Mar,10 -1932,15,Mar,10 -1932,16,Mar,10 -1932,17,Mar,10 -1932,18,Mar,10 -1932,19,Mar,9.85 -1932,20,Mar,9.85 -1932,21,Mar,9.85 -1932,22,Mar,9.85 -1932,23,Mar,9.85 -1932,24,Mar,11 -1932,25,Mar,10 -1932,26,Mar,10 -1932,27,Mar,11 -1932,28,Mar,11 -1932,29,Mar,10 -1932,30,Mar,10 -1932,31,Mar,10 -1933,1,Mar,19 -1933,2,Mar,19 -1933,3,Mar,18 -1933,4,Mar,18 -1933,5,Mar,18 -1933,6,Mar,18 -1933,7,Mar,18 -1933,8,Mar,18 -1933,9,Mar,18 -1933,10,Mar,17 -1933,11,Mar,17 -1933,12,Mar,17 -1933,13,Mar,17 -1933,14,Mar,17 -1933,15,Mar,17 -1933,16,Mar,17 -1933,17,Mar,17 -1933,18,Mar,18 -1933,19,Mar,18 -1933,20,Mar,18 -1933,21,Mar,17 -1933,22,Mar,17 -1933,23,Mar,17 -1933,24,Mar,17 -1933,25,Mar,17 -1933,26,Mar,17 -1933,27,Mar,17 -1933,28,Mar,17 -1933,29,Mar,17 -1933,30,Mar,17 -1933,31,Mar,17 -1934,1,Mar,23 -1934,2,Mar,22 -1934,3,Mar,22 -1934,4,Mar,21 -1934,5,Mar,21 -1934,6,Mar,21 -1934,7,Mar,20 -1934,8,Mar,20 -1934,9,Mar,20 -1934,10,Mar,20 -1934,11,Mar,19 -1934,12,Mar,18 -1934,13,Mar,18 -1934,14,Mar,18 -1934,15,Mar,18 -1934,16,Mar,17 -1934,17,Mar,17 -1934,18,Mar,17 -1934,19,Mar,16 -1934,20,Mar,16 -1934,21,Mar,16 -1934,22,Mar,16 -1934,23,Mar,16 -1934,24,Mar,16 -1934,25,Mar,16 -1934,26,Mar,16 -1934,27,Mar,16 -1934,28,Mar,16 -1934,29,Mar,16 -1934,30,Mar,16 -1934,31,Mar,16 -1935,1,Mar,17 -1935,2,Mar,16 -1935,3,Mar,16 -1935,4,Mar,16 -1935,5,Mar,16 -1935,6,Mar,16 -1935,7,Mar,16 -1935,8,Mar,16 -1935,9,Mar,16 -1935,10,Mar,16 -1935,11,Mar,16 -1935,12,Mar,16 -1935,13,Mar,16 -1935,14,Mar,16 -1935,15,Mar,16 -1935,16,Mar,16 -1935,17,Mar,16 -1935,18,Mar,16 -1935,19,Mar,15 -1935,20,Mar,15 -1935,21,Mar,15 -1935,22,Mar,14 -1935,23,Mar,14 -1935,24,Mar,14 -1935,25,Mar,14 -1935,26,Mar,14 -1935,27,Mar,14 -1935,28,Mar,14 -1935,29,Mar,13 -1935,30,Mar,13 -1935,31,Mar,13 -1936,1,Mar,22 -1936,2,Mar,22 -1936,3,Mar,22 -1936,4,Mar,22 -1936,5,Mar,22 -1936,6,Mar,22 -1936,7,Mar,22 -1936,8,Mar,22 -1936,9,Mar,21 -1936,10,Mar,21 -1936,11,Mar,21 -1936,12,Mar,21 -1936,13,Mar,21 -1936,14,Mar,20 -1936,15,Mar,20 -1936,16,Mar,20 -1936,17,Mar,20 -1936,18,Mar,20 -1936,19,Mar,20 -1936,20,Mar,18 -1936,21,Mar,18 -1936,22,Mar,18 -1936,23,Mar,17 -1936,24,Mar,17 -1936,25,Mar,17 -1936,26,Mar,21 -1936,27,Mar,21 -1936,28,Mar,23 -1936,29,Mar,20 -1936,30,Mar,17 -1936,31,Mar,20 -1937,1,Mar,27 -1937,2,Mar,26 -1937,3,Mar,26 -1937,4,Mar,25 -1937,5,Mar,25 -1937,6,Mar,25 -1937,7,Mar,25 -1937,8,Mar,24 -1937,9,Mar,24 -1937,10,Mar,24 -1937,11,Mar,24 -1937,12,Mar,24 -1937,13,Mar,22 -1937,14,Mar,22 -1937,15,Mar,22 -1937,16,Mar,22 -1937,17,Mar,22 -1937,18,Mar,21 -1937,19,Mar,21 -1937,20,Mar,21 -1937,21,Mar,21 -1937,22,Mar,20 -1937,23,Mar,20 -1937,24,Mar,20 -1937,25,Mar,20 -1937,26,Mar,20 -1937,27,Mar,20 -1937,28,Mar,19 -1937,29,Mar,19 -1937,30,Mar,19 -1937,31,Mar,19 -1938,1,Mar,25 -1938,2,Mar,21 -1938,3,Mar,21 -1938,4,Mar,18 -1938,5,Mar,16 -1938,6,Mar,22 -1938,7,Mar,27 -1938,8,Mar,25 -1938,9,Mar,25 -1938,10,Mar,25 -1938,11,Mar,22 -1938,12,Mar,18 -1938,13,Mar,16 -1938,14,Mar,14 -1938,15,Mar,14 -1938,16,Mar,14 -1938,17,Mar,15 -1938,18,Mar,21 -1938,19,Mar,20 -1938,20,Mar,22 -1938,21,Mar,15 -1938,22,Mar,16 -1938,23,Mar,17 -1938,24,Mar,13 -1938,25,Mar,13 -1938,26,Mar,13 -1938,27,Mar,13 -1938,28,Mar,13 -1938,29,Mar,13 -1938,30,Mar,13 -1938,31,Mar,13 -1939,1,Mar,33 -1939,2,Mar,24 -1939,3,Mar,22 -1939,4,Mar,22 -1939,5,Mar,22 -1939,6,Mar,28 -1939,7,Mar,28 -1939,8,Mar,28 -1939,9,Mar,35 -1939,10,Mar,35 -1939,11,Mar,35 -1939,12,Mar,25 -1939,13,Mar,24 -1939,14,Mar,22 -1939,15,Mar,22 -1939,16,Mar,28 -1939,17,Mar,37 -1939,18,Mar,37 -1939,19,Mar,47 -1939,20,Mar,39 -1939,21,Mar,37 -1939,22,Mar,28 -1939,23,Mar,25 -1939,24,Mar,27 -1939,25,Mar,25 -1939,26,Mar,25 -1939,27,Mar,27 -1939,28,Mar,28 -1939,29,Mar,28 -1939,30,Mar,28 -1939,31,Mar,28 -1940,1,Mar,37 -1940,2,Mar,36 -1940,3,Mar,35 -1940,4,Mar,35 -1940,5,Mar,34 -1940,6,Mar,34 -1940,7,Mar,34 -1940,8,Mar,34 -1940,9,Mar,35 -1940,10,Mar,35 -1940,11,Mar,35 -1940,12,Mar,35 -1940,13,Mar,34 -1940,14,Mar,34 -1940,15,Mar,34 -1940,16,Mar,34 -1940,17,Mar,34 -1940,18,Mar,34 -1940,19,Mar,34 -1940,20,Mar,34 -1940,21,Mar,35 -1940,22,Mar,34 -1940,23,Mar,34 -1940,24,Mar,34 -1940,25,Mar,34 -1940,26,Mar,34 -1940,27,Mar,33 -1940,28,Mar,33 -1940,29,Mar,33 -1940,30,Mar,37 -1940,31,Mar,33 -1941,1,Mar,36 -1941,2,Mar,35 -1941,3,Mar,34 -1941,4,Mar,34 -1941,5,Mar,34 -1941,6,Mar,34 -1941,7,Mar,33 -1941,8,Mar,33 -1941,9,Mar,34 -1941,10,Mar,34 -1941,11,Mar,34 -1941,12,Mar,33 -1941,13,Mar,33 -1941,14,Mar,34 -1941,15,Mar,34 -1941,16,Mar,34 -1941,17,Mar,34 -1941,18,Mar,33 -1941,19,Mar,32 -1941,20,Mar,32 -1941,21,Mar,31 -1941,22,Mar,32 -1941,23,Mar,33 -1941,24,Mar,33 -1941,25,Mar,34 -1941,26,Mar,34 -1941,27,Mar,34 -1941,28,Mar,33 -1941,29,Mar,32 -1941,30,Mar,32 -1941,31,Mar,31 -1942,1,Mar,22 -1942,2,Mar,22 -1942,3,Mar,22 -1942,4,Mar,22 -1942,5,Mar,22 -1942,6,Mar,22 -1942,7,Mar,22 -1942,8,Mar,22 -1942,9,Mar,22 -1942,10,Mar,22 -1942,11,Mar,22 -1942,12,Mar,21 -1942,13,Mar,21 -1942,14,Mar,21 -1942,15,Mar,21 -1942,16,Mar,21 -1942,17,Mar,21 -1942,18,Mar,21 -1942,19,Mar,21 -1942,20,Mar,21 -1942,21,Mar,21 -1942,22,Mar,21 -1942,23,Mar,20 -1942,24,Mar,20 -1942,25,Mar,20 -1942,26,Mar,20 -1942,27,Mar,20 -1942,28,Mar,20 -1942,29,Mar,20 -1942,30,Mar,20 -1942,31,Mar,20 -1943,1,Mar,31 -1943,2,Mar,30 -1943,3,Mar,30 -1943,4,Mar,30 -1943,5,Mar,29 -1943,6,Mar,29 -1943,7,Mar,29 -1943,8,Mar,28 -1943,9,Mar,28 -1943,10,Mar,28 -1943,11,Mar,28 -1943,12,Mar,28 -1943,13,Mar,28 -1943,14,Mar,27 -1943,15,Mar,27 -1943,16,Mar,27 -1943,17,Mar,27 -1943,18,Mar,27 -1943,19,Mar,27 -1943,20,Mar,27 -1943,21,Mar,26 -1943,22,Mar,26 -1943,23,Mar,26 -1943,24,Mar,26 -1943,25,Mar,25 -1943,26,Mar,25 -1943,27,Mar,25 -1943,28,Mar,25 -1943,29,Mar,25 -1943,30,Mar,25 -1943,31,Mar,25 -1944,1,Mar,46 -1944,2,Mar,46 -1944,3,Mar,46 -1944,4,Mar,45 -1944,5,Mar,45 -1944,6,Mar,45 -1944,7,Mar,45 -1944,8,Mar,45 -1944,9,Mar,44 -1944,10,Mar,43 -1944,11,Mar,41 -1944,12,Mar,41 -1944,13,Mar,41 -1944,14,Mar,41 -1944,15,Mar,40 -1944,16,Mar,40 -1944,17,Mar,40 -1944,18,Mar,40 -1944,19,Mar,40 -1944,20,Mar,40 -1944,21,Mar,39 -1944,22,Mar,39 -1944,23,Mar,39 -1944,24,Mar,39 -1944,25,Mar,39 -1944,26,Mar,38 -1944,27,Mar,38 -1944,28,Mar,38 -1944,29,Mar,38 -1944,30,Mar,38 -1944,31,Mar,38 -1945,1,Mar,63 -1945,2,Mar,63 -1945,3,Mar,62 -1945,4,Mar,62 -1945,5,Mar,62 -1945,6,Mar,61 -1945,7,Mar,61 -1945,8,Mar,60 -1945,9,Mar,61 -1945,10,Mar,63 -1945,11,Mar,65 -1945,12,Mar,64 -1945,13,Mar,62 -1945,14,Mar,58 -1945,15,Mar,58 -1945,16,Mar,54 -1945,17,Mar,54 -1945,18,Mar,50 -1945,19,Mar,50 -1945,20,Mar,49 -1945,21,Mar,49 -1945,22,Mar,49 -1945,23,Mar,49 -1945,24,Mar,49 -1945,25,Mar,50 -1945,26,Mar,50 -1945,27,Mar,50 -1945,28,Mar,49 -1945,29,Mar,49 -1945,30,Mar,49 -1945,31,Mar,49 -1946,1,Mar,27 -1946,2,Mar,27 -1946,3,Mar,26 -1946,4,Mar,27 -1946,5,Mar,26 -1946,6,Mar,26 -1946,7,Mar,27 -1946,8,Mar,26 -1946,9,Mar,25 -1946,10,Mar,26 -1946,11,Mar,27 -1946,12,Mar,27 -1946,13,Mar,29 -1946,14,Mar,29 -1946,15,Mar,29 -1946,16,Mar,29 -1946,17,Mar,29 -1946,18,Mar,31 -1946,19,Mar,27 -1946,20,Mar,26 -1946,21,Mar,26 -1946,22,Mar,25 -1946,23,Mar,26 -1946,24,Mar,26 -1946,25,Mar,24 -1946,26,Mar,18 -1946,27,Mar,17 -1946,28,Mar,17 -1946,29,Mar,15 -1946,30,Mar,12 -1946,31,Mar,12 -1947,1,Mar,17 -1947,2,Mar,17 -1947,3,Mar,16 -1947,4,Mar,16 -1947,5,Mar,16 -1947,6,Mar,17 -1947,7,Mar,17 -1947,8,Mar,17 -1947,9,Mar,16 -1947,10,Mar,16 -1947,11,Mar,17 -1947,12,Mar,16 -1947,13,Mar,16 -1947,14,Mar,16 -1947,15,Mar,16 -1947,16,Mar,16 -1947,17,Mar,16 -1947,18,Mar,16 -1947,19,Mar,16 -1947,20,Mar,16 -1947,21,Mar,16 -1947,22,Mar,16 -1947,23,Mar,16 -1947,24,Mar,17 -1947,25,Mar,16 -1947,26,Mar,16 -1947,27,Mar,16 -1947,28,Mar,16 -1947,29,Mar,16 -1947,30,Mar,16 -1947,31,Mar,16 -1948,1,Mar,16 -1948,2,Mar,16 -1948,3,Mar,16 -1948,4,Mar,16 -1948,5,Mar,16 -1948,6,Mar,17 -1948,7,Mar,17 -1948,8,Mar,16 -1948,9,Mar,16 -1948,10,Mar,16 -1948,11,Mar,16 -1948,12,Mar,16 -1948,13,Mar,15 -1948,14,Mar,15 -1948,15,Mar,15 -1948,16,Mar,15 -1948,17,Mar,15 -1948,18,Mar,15 -1948,19,Mar,15 -1948,20,Mar,15 -1948,21,Mar,15 -1948,22,Mar,15 -1948,23,Mar,14 -1948,24,Mar,14 -1948,25,Mar,14 -1948,26,Mar,15 -1948,27,Mar,15 -1948,28,Mar,15 -1948,29,Mar,15 -1948,30,Mar,15 -1948,31,Mar,15 -1949,1,Mar,26 -1949,2,Mar,25 -1949,3,Mar,25 -1949,4,Mar,25 -1949,5,Mar,24 -1949,6,Mar,24 -1949,7,Mar,23 -1949,8,Mar,23 -1949,9,Mar,23 -1949,10,Mar,22 -1949,11,Mar,21 -1949,12,Mar,21 -1949,13,Mar,20 -1949,14,Mar,20 -1949,15,Mar,20 -1949,16,Mar,20 -1949,17,Mar,19 -1949,18,Mar,19 -1949,19,Mar,18 -1949,20,Mar,18 -1949,21,Mar,18 -1949,22,Mar,18 -1949,23,Mar,18 -1949,24,Mar,17 -1949,25,Mar,17 -1949,26,Mar,17 -1949,27,Mar,17 -1949,28,Mar,17 -1949,29,Mar,17 -1949,30,Mar,16 -1949,31,Mar,16 -1950,1,Mar,33 -1950,2,Mar,32 -1950,3,Mar,32 -1950,4,Mar,32 -1950,5,Mar,31 -1950,6,Mar,30 -1950,7,Mar,30 -1950,8,Mar,30 -1950,9,Mar,29 -1950,10,Mar,29 -1950,11,Mar,29 -1950,12,Mar,29 -1950,13,Mar,29 -1950,14,Mar,28 -1950,15,Mar,28 -1950,16,Mar,27 -1950,17,Mar,27 -1950,18,Mar,26 -1950,19,Mar,26 -1950,20,Mar,26 -1950,21,Mar,25 -1950,22,Mar,25 -1950,23,Mar,25 -1950,24,Mar,25 -1950,25,Mar,25 -1950,26,Mar,25 -1950,27,Mar,24 -1950,28,Mar,24 -1950,29,Mar,24 -1950,30,Mar,23 -1950,31,Mar,23 -1951,1,Mar,32 -1951,2,Mar,31 -1951,3,Mar,31 -1951,4,Mar,31 -1951,5,Mar,31 -1951,6,Mar,30 -1951,7,Mar,30 -1951,8,Mar,30 -1951,9,Mar,29 -1951,10,Mar,29 -1951,11,Mar,29 -1951,12,Mar,29 -1951,13,Mar,29 -1951,14,Mar,29 -1951,15,Mar,28 -1951,16,Mar,28 -1951,17,Mar,28 -1951,18,Mar,28 -1951,19,Mar,27 -1951,20,Mar,27 -1951,21,Mar,26 -1951,22,Mar,26 -1951,23,Mar,25 -1951,24,Mar,25 -1951,25,Mar,25 -1951,26,Mar,25 -1951,27,Mar,25 -1951,28,Mar,25 -1951,29,Mar,26 -1951,30,Mar,26 -1951,31,Mar,26 -1952,1,Mar,24 -1952,2,Mar,24 -1952,3,Mar,24 -1952,4,Mar,25 -1952,5,Mar,25 -1952,6,Mar,25 -1952,7,Mar,24 -1952,8,Mar,24 -1952,9,Mar,24 -1952,10,Mar,24 -1952,11,Mar,24 -1952,12,Mar,25 -1952,13,Mar,25 -1952,14,Mar,25 -1952,15,Mar,24 -1952,16,Mar,24 -1952,17,Mar,23 -1952,18,Mar,23 -1952,19,Mar,23 -1952,20,Mar,23 -1952,21,Mar,23 -1952,22,Mar,23 -1952,23,Mar,23 -1952,24,Mar,23 -1952,25,Mar,23 -1952,26,Mar,22 -1952,27,Mar,22 -1952,28,Mar,38 -1952,29,Mar,32 -1952,30,Mar,26 -1952,31,Mar,27 -1953,1,Mar,27 -1953,2,Mar,26 -1953,3,Mar,26 -1953,4,Mar,25 -1953,5,Mar,25 -1953,6,Mar,25 -1953,7,Mar,23 -1953,8,Mar,22 -1953,9,Mar,22 -1953,10,Mar,21 -1953,11,Mar,21 -1953,12,Mar,21 -1953,13,Mar,22 -1953,14,Mar,23 -1953,15,Mar,23 -1953,16,Mar,23 -1953,17,Mar,23 -1953,18,Mar,24 -1953,19,Mar,23 -1953,20,Mar,22 -1953,21,Mar,22 -1953,22,Mar,21 -1953,23,Mar,21 -1953,24,Mar,20 -1953,25,Mar,18 -1953,26,Mar,18 -1953,27,Mar,18 -1953,28,Mar,18 -1953,29,Mar,19 -1953,30,Mar,19 -1953,31,Mar,18 -1954,1,Mar,27 -1954,2,Mar,26 -1954,3,Mar,26 -1954,4,Mar,26 -1954,5,Mar,25 -1954,6,Mar,25 -1954,7,Mar,25 -1954,8,Mar,24 -1954,9,Mar,23 -1954,10,Mar,23 -1954,11,Mar,23 -1954,12,Mar,22 -1954,13,Mar,22 -1954,14,Mar,22 -1954,15,Mar,22 -1954,16,Mar,23 -1954,17,Mar,46 -1954,18,Mar,45 -1954,19,Mar,45 -1954,20,Mar,43 -1954,21,Mar,41 -1954,22,Mar,41 -1954,23,Mar,41 -1954,24,Mar,38 -1954,25,Mar,37 -1954,26,Mar,36 -1954,27,Mar,34 -1954,28,Mar,33 -1954,29,Mar,31 -1954,30,Mar,29 -1954,31,Mar,28 -1955,1,Mar,10 -1955,2,Mar,10 -1955,3,Mar,9.8 -1955,4,Mar,10 -1955,5,Mar,10 -1955,6,Mar,9.8 -1955,7,Mar,9.8 -1955,8,Mar,8.5 -1955,9,Mar,8.5 -1955,10,Mar,11 -1955,11,Mar,12 -1955,12,Mar,11 -1955,13,Mar,11 -1955,14,Mar,11 -1955,15,Mar,10 -1955,16,Mar,11 -1955,17,Mar,10 -1955,18,Mar,10 -1955,19,Mar,9.8 -1955,20,Mar,9.8 -1955,21,Mar,9.8 -1955,22,Mar,9.8 -1955,23,Mar,9.8 -1955,24,Mar,9.8 -1955,25,Mar,9.8 -1955,26,Mar,10 -1955,27,Mar,10 -1955,28,Mar,12 -1955,29,Mar,12 -1955,30,Mar,13 -1955,31,Mar,15 -1956,1,Mar,13 -1956,2,Mar,13 -1956,3,Mar,13 -1956,4,Mar,12 -1956,5,Mar,12 -1956,6,Mar,12 -1956,7,Mar,12 -1956,8,Mar,12 -1956,9,Mar,12 -1956,10,Mar,8.95 -1956,11,Mar,8.5 -1956,12,Mar,8.5 -1956,13,Mar,8.95 -1956,14,Mar,8.5 -1956,15,Mar,8.5 -1956,16,Mar,8.5 -1956,17,Mar,8.95 -1956,18,Mar,8.5 -1956,19,Mar,8.5 -1956,20,Mar,8.12 -1956,21,Mar,8.12 -1956,22,Mar,8.12 -1956,23,Mar,7.74 -1956,24,Mar,7.74 -1956,25,Mar,7.74 -1956,26,Mar,7.74 -1956,27,Mar,7.74 -1956,28,Mar,7.74 -1956,29,Mar,7.36 -1956,30,Mar,7.36 -1956,31,Mar,7.36 -1957,1,Mar,13 -1957,2,Mar,12 -1957,3,Mar,12 -1957,4,Mar,12 -1957,5,Mar,11 -1957,6,Mar,11 -1957,7,Mar,11 -1957,8,Mar,11 -1957,9,Mar,10 -1957,10,Mar,10 -1957,11,Mar,10 -1957,12,Mar,10 -1957,13,Mar,10 -1957,14,Mar,9.8 -1957,15,Mar,9.8 -1957,16,Mar,9.35 -1957,17,Mar,8.9 -1957,18,Mar,8.9 -1957,19,Mar,9.35 -1957,20,Mar,9.8 -1957,21,Mar,9.8 -1957,22,Mar,9.8 -1957,23,Mar,9.8 -1957,24,Mar,9.8 -1957,25,Mar,9.8 -1957,26,Mar,9.35 -1957,27,Mar,9.35 -1957,28,Mar,9.35 -1957,29,Mar,9.8 -1957,30,Mar,9.35 -1957,31,Mar,9.35 -1958,1,Mar,8 -1958,2,Mar,8 -1958,3,Mar,8 -1958,4,Mar,7.7 -1958,5,Mar,7.7 -1958,6,Mar,7.7 -1958,7,Mar,7.4 -1958,8,Mar,7.4 -1958,9,Mar,7.4 -1958,10,Mar,8 -1958,11,Mar,8.4 -1958,12,Mar,8 -1958,13,Mar,7.7 -1958,14,Mar,6.8 -1958,15,Mar,6.8 -1958,16,Mar,6.8 -1958,17,Mar,6.8 -1958,18,Mar,6.8 -1958,19,Mar,6.5 -1958,20,Mar,4.8 -1958,21,Mar,4.6 -1958,22,Mar,4.6 -1958,23,Mar,4.6 -1958,24,Mar,4.6 -1958,25,Mar,4.6 -1958,26,Mar,4.6 -1958,27,Mar,4.8 -1958,28,Mar,4.6 -1958,29,Mar,4.6 -1958,30,Mar,5 -1958,31,Mar,7.1 -1959,1,Mar,7.1 -1959,2,Mar,6.7 -1959,3,Mar,6.7 -1959,4,Mar,7.5 -1959,5,Mar,7.5 -1959,6,Mar,8 -1959,7,Mar,8 -1959,8,Mar,6.3 -1959,9,Mar,5.9 -1959,10,Mar,5.9 -1959,11,Mar,6.7 -1959,12,Mar,6.7 -1959,13,Mar,5.9 -1959,14,Mar,5.5 -1959,15,Mar,5.5 -1959,16,Mar,5.1 -1959,17,Mar,5.1 -1959,18,Mar,5.1 -1959,19,Mar,5.1 -1959,20,Mar,5.1 -1959,21,Mar,5.1 -1959,22,Mar,3.9 -1959,23,Mar,4.7 -1959,24,Mar,4.7 -1959,25,Mar,4.3 -1959,26,Mar,4.3 -1959,27,Mar,4.3 -1959,28,Mar,4.3 -1959,29,Mar,3.9 -1959,30,Mar,3.9 -1959,31,Mar,3.5 -1960,1,Mar,9.33 -1960,2,Mar,9.33 -1960,3,Mar,8.96 -1960,4,Mar,8.59 -1960,5,Mar,11 -1960,6,Mar,13 -1960,7,Mar,13 -1960,8,Mar,13 -1960,9,Mar,12 -1960,10,Mar,11 -1960,11,Mar,11 -1960,12,Mar,10 -1960,13,Mar,10 -1960,14,Mar,9.33 -1960,15,Mar,8.96 -1960,16,Mar,8.59 -1960,17,Mar,8.59 -1960,18,Mar,8.22 -1960,19,Mar,7.85 -1960,20,Mar,7.48 -1960,21,Mar,7.48 -1960,22,Mar,7.11 -1960,23,Mar,7.11 -1960,24,Mar,7.11 -1960,25,Mar,6.74 -1960,26,Mar,6.37 -1960,27,Mar,6.37 -1960,28,Mar,6 -1960,29,Mar,5.4 -1960,30,Mar,5.4 -1960,31,Mar,6.74 -1961,1,Mar,10 -1961,2,Mar,10 -1961,3,Mar,9.5 -1961,4,Mar,9.5 -1961,5,Mar,9.5 -1961,6,Mar,9 -1961,7,Mar,9 -1961,8,Mar,9 -1961,9,Mar,9 -1961,10,Mar,9 -1961,11,Mar,8.5 -1961,12,Mar,8 -1961,13,Mar,8 -1961,14,Mar,8 -1961,15,Mar,7.5 -1961,16,Mar,7.5 -1961,17,Mar,7.5 -1961,18,Mar,6.3 -1961,19,Mar,6.3 -1961,20,Mar,6.3 -1961,21,Mar,6.3 -1961,22,Mar,5.95 -1961,23,Mar,5.95 -1961,24,Mar,5.6 -1961,25,Mar,5.25 -1961,26,Mar,4.9 -1961,27,Mar,5.25 -1961,28,Mar,5.95 -1961,29,Mar,6.65 -1961,30,Mar,9.5 -1961,31,Mar,11 -1962,1,Mar,16 -1962,2,Mar,16 -1962,3,Mar,20 -1962,4,Mar,21 -1962,5,Mar,21 -1962,6,Mar,21 -1962,7,Mar,20 -1962,8,Mar,19 -1962,9,Mar,18 -1962,10,Mar,18 -1962,11,Mar,16 -1962,12,Mar,15 -1962,13,Mar,14 -1962,14,Mar,14 -1962,15,Mar,14 -1962,16,Mar,14 -1962,17,Mar,14 -1962,18,Mar,14 -1962,19,Mar,14 -1962,20,Mar,12 -1962,21,Mar,12 -1962,22,Mar,12 -1962,23,Mar,12 -1962,24,Mar,13 -1962,25,Mar,13 -1962,26,Mar,12 -1962,27,Mar,12 -1962,28,Mar,12 -1962,29,Mar,12 -1962,30,Mar,12 -1962,31,Mar,10 -1963,1,Mar,10 -1963,2,Mar,10 -1963,3,Mar,10 -1963,4,Mar,10 -1963,5,Mar,9.6 -1963,6,Mar,9.6 -1963,7,Mar,9.6 -1963,8,Mar,11 -1963,9,Mar,14 -1963,10,Mar,12 -1963,11,Mar,11 -1963,12,Mar,10 -1963,13,Mar,9.12 -1963,14,Mar,9.12 -1963,15,Mar,8.64 -1963,16,Mar,8.64 -1963,17,Mar,8.16 -1963,18,Mar,7.68 -1963,19,Mar,7.68 -1963,20,Mar,7.2 -1963,21,Mar,7.2 -1963,22,Mar,7.2 -1963,23,Mar,6.83 -1963,24,Mar,6.83 -1963,25,Mar,6.46 -1963,26,Mar,6.09 -1963,27,Mar,6.09 -1963,28,Mar,6.09 -1963,29,Mar,6.09 -1963,30,Mar,6.09 -1963,31,Mar,6.09 -1964,1,Mar,12 -1964,2,Mar,12 -1964,3,Mar,11 -1964,4,Mar,11 -1964,5,Mar,11 -1964,6,Mar,10 -1964,7,Mar,10 -1964,8,Mar,9.6 -1964,9,Mar,9.12 -1964,10,Mar,9.12 -1964,11,Mar,9.6 -1964,12,Mar,9.6 -1964,13,Mar,10 -1964,14,Mar,9.6 -1964,15,Mar,9.6 -1964,16,Mar,9.6 -1964,17,Mar,10 -1964,18,Mar,10 -1964,19,Mar,10 -1964,20,Mar,10 -1964,21,Mar,10 -1964,22,Mar,9.6 -1964,23,Mar,9.6 -1964,24,Mar,9.6 -1964,25,Mar,9.12 -1964,26,Mar,9.12 -1964,27,Mar,9.12 -1964,28,Mar,8.64 -1964,29,Mar,8.64 -1964,30,Mar,9.12 -1964,31,Mar,9.12 -1965,1,Mar,11 -1965,2,Mar,11 -1965,3,Mar,11 -1965,4,Mar,11 -1965,5,Mar,11 -1965,6,Mar,11 -1965,7,Mar,11 -1965,8,Mar,11 -1965,9,Mar,11 -1965,10,Mar,11 -1965,11,Mar,11 -1965,12,Mar,10 -1965,13,Mar,10 -1965,14,Mar,10 -1965,15,Mar,10 -1965,16,Mar,10 -1965,17,Mar,10 -1965,18,Mar,10 -1965,19,Mar,9.6 -1965,20,Mar,9.6 -1965,21,Mar,9.6 -1965,22,Mar,9.6 -1965,23,Mar,9.6 -1965,24,Mar,9.12 -1965,25,Mar,9.12 -1965,26,Mar,9.12 -1965,27,Mar,8.64 -1965,28,Mar,8.64 -1965,29,Mar,8.16 -1965,30,Mar,8.16 -1965,31,Mar,7.68 -1966,1,Mar,3.25 -1966,2,Mar,2.8 -1966,3,Mar,2.6 -1966,4,Mar,2.2 -1966,5,Mar,2 -1966,6,Mar,1.4 -1966,7,Mar,1.6 -1966,8,Mar,2 -1966,9,Mar,2.2 -1966,10,Mar,2.4 -1966,11,Mar,2.8 -1966,12,Mar,2.8 -1966,13,Mar,2.8 -1966,14,Mar,2.6 -1966,15,Mar,2 -1966,16,Mar,1.8 -1966,17,Mar,1.6 -1966,18,Mar,1.8 -1966,19,Mar,1.6 -1966,20,Mar,1.4 -1966,21,Mar,1 -1966,22,Mar,0.9 -1966,23,Mar,0.9 -1966,24,Mar,0.9 -1966,25,Mar,0.7 -1966,26,Mar,0.8 -1966,27,Mar,0.6 -1966,28,Mar,0.5 -1966,29,Mar,0.6 -1966,30,Mar,0.7 -1966,31,Mar,0.6 -1967,1,Mar,9.65 -1967,2,Mar,9.65 -1967,3,Mar,9.65 -1967,4,Mar,9.65 -1967,5,Mar,10 -1967,6,Mar,10 -1967,7,Mar,10 -1967,8,Mar,10 -1967,9,Mar,10 -1967,10,Mar,9.65 -1967,11,Mar,10 -1967,12,Mar,20 -1967,13,Mar,19 -1967,14,Mar,16 -1967,15,Mar,13 -1967,16,Mar,12 -1967,17,Mar,10 -1967,18,Mar,9.2 -1967,19,Mar,8.75 -1967,20,Mar,8.75 -1967,21,Mar,8.75 -1967,22,Mar,8.3 -1967,23,Mar,7.85 -1967,24,Mar,6.95 -1967,25,Mar,6.95 -1967,26,Mar,6.95 -1967,27,Mar,7.4 -1967,28,Mar,7.4 -1967,29,Mar,7.85 -1967,30,Mar,7.4 -1967,31,Mar,7.85 -1968,1,Mar,6.95 -1968,2,Mar,6.15 -1968,3,Mar,5.45 -1968,4,Mar,5.45 -1968,5,Mar,6.15 -1968,6,Mar,6.15 -1968,7,Mar,5.8 -1968,8,Mar,5.1 -1968,9,Mar,5.45 -1968,10,Mar,5.8 -1968,11,Mar,8.75 -1968,12,Mar,9.65 -1968,13,Mar,10 -1968,14,Mar,9.2 -1968,15,Mar,7.4 -1968,16,Mar,6.5 -1968,17,Mar,5.45 -1968,18,Mar,5.8 -1968,19,Mar,5.45 -1968,20,Mar,6.15 -1968,21,Mar,6.15 -1968,22,Mar,6.15 -1968,23,Mar,5.8 -1968,24,Mar,5.45 -1968,25,Mar,4.75 -1968,26,Mar,4.75 -1968,27,Mar,4.75 -1968,28,Mar,4.75 -1968,29,Mar,4.75 -1968,30,Mar,4.05 -1968,31,Mar,4.05 -1969,1,Mar,11 -1969,2,Mar,11 -1969,3,Mar,10 -1969,4,Mar,9.75 -1969,5,Mar,9.3 -1969,6,Mar,8.85 -1969,7,Mar,8.85 -1969,8,Mar,8.4 -1969,9,Mar,8 -1969,10,Mar,7.5 -1969,11,Mar,7.15 -1969,12,Mar,6.8 -1969,13,Mar,6.8 -1969,14,Mar,6.8 -1969,15,Mar,6.45 -1969,16,Mar,6.45 -1969,17,Mar,6.1 -1969,18,Mar,5.4 -1969,19,Mar,5.4 -1969,20,Mar,5.05 -1969,21,Mar,5.05 -1969,22,Mar,5.4 -1969,23,Mar,5.4 -1969,24,Mar,5.05 -1969,25,Mar,4.7 -1969,26,Mar,4.35 -1969,27,Mar,4.35 -1969,28,Mar,4.35 -1969,29,Mar,4 -1969,30,Mar,4 -1969,31,Mar,3.72 -1970,1,Mar,7.08 -1970,2,Mar,7.08 -1970,3,Mar,7.08 -1970,4,Mar,6 -1970,5,Mar,6 -1970,6,Mar,6 -1970,7,Mar,6 -1970,8,Mar,6.36 -1970,9,Mar,6.36 -1970,10,Mar,6.72 -1970,11,Mar,6.72 -1970,12,Mar,6.36 -1970,13,Mar,5.04 -1970,14,Mar,4.4 -1970,15,Mar,4.4 -1970,16,Mar,5.04 -1970,17,Mar,4.72 -1970,18,Mar,4.72 -1970,19,Mar,4.72 -1970,20,Mar,4.08 -1970,21,Mar,3.76 -1970,22,Mar,3.76 -1970,23,Mar,4.08 -1970,24,Mar,8.52 -1970,25,Mar,13 -1970,26,Mar,20 -1970,27,Mar,17 -1970,28,Mar,14 -1970,29,Mar,11 -1970,30,Mar,10 -1970,31,Mar,8.52 -1971,1,Mar,13 -1971,2,Mar,13 -1971,3,Mar,14 -1971,4,Mar,15 -1971,5,Mar,15 -1971,6,Mar,16 -1971,7,Mar,17 -1971,8,Mar,17 -1971,9,Mar,16 -1971,10,Mar,12 -1971,11,Mar,13 -1971,12,Mar,12 -1971,13,Mar,10 -1971,14,Mar,10 -1971,15,Mar,9.7 -1971,16,Mar,9 -1971,17,Mar,9 -1971,18,Mar,9 -1971,19,Mar,12 -1971,20,Mar,16 -1971,21,Mar,16 -1971,22,Mar,16 -1971,23,Mar,15 -1971,24,Mar,10 -1971,25,Mar,10 -1971,26,Mar,10 -1971,27,Mar,12 -1971,28,Mar,9.7 -1971,29,Mar,6.9 -1971,30,Mar,5.5 -1971,31,Mar,4.4 -1972,1,Mar,12 -1972,2,Mar,12 -1972,3,Mar,12 -1972,4,Mar,12 -1972,5,Mar,9.2 -1972,6,Mar,8.4 -1972,7,Mar,8.4 -1972,8,Mar,8.4 -1972,9,Mar,9.6 -1972,10,Mar,12 -1972,11,Mar,12 -1972,12,Mar,11 -1972,13,Mar,9.2 -1972,14,Mar,8.4 -1972,15,Mar,10 -1972,16,Mar,13 -1972,17,Mar,9.2 -1972,18,Mar,7.4 -1972,19,Mar,7.1 -1972,20,Mar,8.8 -1972,21,Mar,8.4 -1972,22,Mar,8.4 -1972,23,Mar,7.7 -1972,24,Mar,7.7 -1972,25,Mar,10 -1972,26,Mar,8.4 -1972,27,Mar,9.2 -1972,28,Mar,12 -1972,29,Mar,8.8 -1972,30,Mar,6.5 -1972,31,Mar,7.1 -1973,1,Mar,10 -1973,2,Mar,10 -1973,3,Mar,9.9 -1973,4,Mar,9.6 -1973,5,Mar,9 -1973,6,Mar,9 -1973,7,Mar,11 -1973,8,Mar,15 -1973,9,Mar,25 -1973,10,Mar,26 -1973,11,Mar,25 -1973,12,Mar,24 -1973,13,Mar,20 -1973,14,Mar,18 -1973,15,Mar,16 -1973,16,Mar,13 -1973,17,Mar,12 -1973,18,Mar,13 -1973,19,Mar,13 -1973,20,Mar,12 -1973,21,Mar,11 -1973,22,Mar,8.8 -1973,23,Mar,8.2 -1973,24,Mar,8.2 -1973,25,Mar,8.6 -1973,26,Mar,20 -1973,27,Mar,30 -1973,28,Mar,31 -1973,29,Mar,27 -1973,30,Mar,23 -1973,31,Mar,21 -1974,1,Mar,11 -1974,2,Mar,12 -1974,3,Mar,13 -1974,4,Mar,13 -1974,5,Mar,12 -1974,6,Mar,12 -1974,7,Mar,11 -1974,8,Mar,11 -1974,9,Mar,11 -1974,10,Mar,11 -1974,11,Mar,11 -1974,12,Mar,13 -1974,13,Mar,12 -1974,14,Mar,9.1 -1974,15,Mar,9.1 -1974,16,Mar,9.1 -1974,17,Mar,9.1 -1974,18,Mar,9.1 -1974,19,Mar,9.1 -1974,20,Mar,9.1 -1974,21,Mar,8 -1974,22,Mar,8 -1974,23,Mar,7.2 -1974,24,Mar,6.4 -1974,25,Mar,6.4 -1974,26,Mar,6.4 -1974,27,Mar,5.6 -1974,28,Mar,7.2 -1974,29,Mar,6.4 -1974,30,Mar,6.4 -1974,31,Mar,7.2 -1975,1,Mar,8.8 -1975,2,Mar,10 -1975,3,Mar,10 -1975,4,Mar,10 -1975,5,Mar,9.4 -1975,6,Mar,8.8 -1975,7,Mar,7.6 -1975,8,Mar,7 -1975,9,Mar,5.8 -1975,10,Mar,4.6 -1975,11,Mar,4.6 -1975,12,Mar,4.2 -1975,13,Mar,4.2 -1975,14,Mar,3.4 -1975,15,Mar,3.4 -1975,16,Mar,2.8 -1975,17,Mar,3.4 -1975,18,Mar,3 -1975,19,Mar,3 -1975,20,Mar,4.2 -1975,21,Mar,3.8 -1975,22,Mar,3.4 -1975,23,Mar,3.4 -1975,24,Mar,5 -1975,25,Mar,6.2 -1975,26,Mar,7 -1975,27,Mar,8.2 -1975,28,Mar,7 -1975,29,Mar,3.8 -1975,30,Mar,2.8 -1975,31,Mar,2.6 -1976,1,Mar,11 -1976,2,Mar,10 -1976,3,Mar,10 -1976,4,Mar,12 -1976,5,Mar,12 -1976,6,Mar,11 -1976,7,Mar,10 -1976,8,Mar,9.5 -1976,9,Mar,10 -1976,10,Mar,12 -1976,11,Mar,12 -1976,12,Mar,10 -1976,13,Mar,8 -1976,14,Mar,8.5 -1976,15,Mar,8 -1976,16,Mar,8 -1976,17,Mar,6.95 -1976,18,Mar,5.9 -1976,19,Mar,5.9 -1976,20,Mar,5.55 -1976,21,Mar,5.2 -1976,22,Mar,5.9 -1976,23,Mar,8.5 -1976,24,Mar,9 -1976,25,Mar,9 -1976,26,Mar,8 -1976,27,Mar,7.3 -1976,28,Mar,7.65 -1976,29,Mar,7.65 -1976,30,Mar,7.65 -1976,31,Mar,6.25 -1977,1,Mar,5.5 -1977,2,Mar,6 -1977,3,Mar,5.5 -1977,4,Mar,5 -1977,5,Mar,2.7 -1977,6,Mar,2.1 -1977,7,Mar,1.5 -1977,8,Mar,1.2 -1977,9,Mar,1.35 -1977,10,Mar,2.1 -1977,11,Mar,3 -1977,12,Mar,3.6 -1977,13,Mar,4.2 -1977,14,Mar,3.9 -1977,15,Mar,4.2 -1977,16,Mar,4.5 -1977,17,Mar,5 -1977,18,Mar,4.5 -1977,19,Mar,4.2 -1977,20,Mar,2.1 -1977,21,Mar,1.8 -1977,22,Mar,1.5 -1977,23,Mar,1.2 -1977,24,Mar,1.05 -1977,25,Mar,1.05 -1977,26,Mar,1.35 -1977,27,Mar,2.1 -1977,28,Mar,2.4 -1977,29,Mar,2.7 -1977,30,Mar,2.4 -1977,31,Mar,2.4 -1978,1,Mar,42 -1978,2,Mar,39 -1978,3,Mar,39 -1978,4,Mar,34 -1978,5,Mar,28 -1978,6,Mar,23 -1978,7,Mar,14 -1978,8,Mar,9 -1978,9,Mar,6.6 -1978,10,Mar,4.6 -1978,11,Mar,3.8 -1978,12,Mar,4.2 -1978,13,Mar,4.6 -1978,14,Mar,5 -1978,15,Mar,4.6 -1978,16,Mar,5 -1978,17,Mar,5.8 -1978,18,Mar,5.8 -1978,19,Mar,5.4 -1978,20,Mar,4.2 -1978,21,Mar,3.8 -1978,22,Mar,2.7 -1978,23,Mar,2.1 -1978,24,Mar,1.5 -1978,25,Mar,0.3 -1978,26,Mar,0 -1978,27,Mar,0 -1978,28,Mar,0 -1978,29,Mar,0 -1978,30,Mar,0 -1978,31,Mar,0 -1979,1,Mar,5 -1979,2,Mar,5 -1979,3,Mar,5 -1979,4,Mar,5 -1979,5,Mar,4.6 -1979,6,Mar,4.2 -1979,7,Mar,4.2 -1979,8,Mar,3.4 -1979,9,Mar,2.4 -1979,10,Mar,2.4 -1979,11,Mar,2.4 -1979,12,Mar,2.4 -1979,13,Mar,2.1 -1979,14,Mar,1.8 -1979,15,Mar,1.2 -1979,16,Mar,1.5 -1979,17,Mar,1.5 -1979,18,Mar,1.2 -1979,19,Mar,0.3 -1979,20,Mar,0 -1979,21,Mar,0 -1979,22,Mar,0 -1979,23,Mar,0 -1979,24,Mar,0 -1979,25,Mar,0 -1979,26,Mar,0 -1979,27,Mar,0 -1979,28,Mar,0 -1979,29,Mar,0 -1979,30,Mar,0 -1979,31,Mar,0 -1980,1,Mar,0.6 -1980,2,Mar,0.6 -1980,3,Mar,0.6 -1980,4,Mar,0.6 -1980,5,Mar,0.6 -1980,6,Mar,0.6 -1980,7,Mar,0.6 -1980,8,Mar,0.4 -1980,9,Mar,0.4 -1980,10,Mar,0.4 -1980,11,Mar,0.4 -1980,12,Mar,0.4 -1980,13,Mar,0.6 -1980,14,Mar,0.6 -1980,15,Mar,0.6 -1980,16,Mar,0.6 -1980,17,Mar,1 -1980,18,Mar,3.2 -1980,19,Mar,3.2 -1980,20,Mar,2.9 -1980,21,Mar,2 -1980,22,Mar,0.8 -1980,23,Mar,0.6 -1980,24,Mar,0.6 -1980,25,Mar,0.6 -1980,26,Mar,0.6 -1980,27,Mar,0.6 -1980,28,Mar,0.8 -1980,29,Mar,0.8 -1980,30,Mar,0.8 -1980,31,Mar,0.8 -1981,1,Mar,4.1 -1981,2,Mar,3.8 -1981,3,Mar,3.8 -1981,4,Mar,3.8 -1981,5,Mar,3.5 -1981,6,Mar,3.2 -1981,7,Mar,3.2 -1981,8,Mar,2.9 -1981,9,Mar,2.9 -1981,10,Mar,2.6 -1981,11,Mar,2.3 -1981,12,Mar,2.3 -1981,13,Mar,2 -1981,14,Mar,2 -1981,15,Mar,1.9 -1981,16,Mar,1.8 -1981,17,Mar,1.7 -1981,18,Mar,1.7 -1981,19,Mar,1.6 -1981,20,Mar,1.6 -1981,21,Mar,1.5 -1981,22,Mar,1.5 -1981,23,Mar,1.4 -1981,24,Mar,1.3 -1981,25,Mar,1.3 -1981,26,Mar,1.3 -1981,27,Mar,1.2 -1981,28,Mar,1.2 -1981,29,Mar,1.1 -1981,30,Mar,1 -1981,31,Mar,1 -1982,1,Mar,17.35 -1982,2,Mar,16.05 -1982,3,Mar,14.1 -1982,4,Mar,9.25 -1982,5,Mar,11.5 -1982,6,Mar,9.25 -1982,7,Mar,6.1 -1982,8,Mar,4.6 -1982,9,Mar,4.6 -1982,10,Mar,5.5 -1982,11,Mar,6.7 -1982,12,Mar,6.4 -1982,13,Mar,6.7 -1982,14,Mar,6.4 -1982,15,Mar,6.4 -1982,16,Mar,6.4 -1982,17,Mar,7 -1982,18,Mar,6.1 -1982,19,Mar,5.2 -1982,20,Mar,4.6 -1982,21,Mar,4.3 -1982,22,Mar,4.3 -1982,23,Mar,4.3 -1982,24,Mar,4.9 -1982,25,Mar,5.2 -1982,26,Mar,5.8 -1982,27,Mar,6.7 -1982,28,Mar,7.9 -1982,29,Mar,8.35 -1982,30,Mar,10.6 -1982,31,Mar,12.15 -1983,1,Mar,6 -1983,2,Mar,4.2 -1983,3,Mar,4.2 -1983,4,Mar,4.2 -1983,5,Mar,4.2 -1983,6,Mar,4.2 -1983,7,Mar,3.9 -1983,8,Mar,3.9 -1983,9,Mar,3.6 -1983,10,Mar,3.6 -1983,11,Mar,3.6 -1983,12,Mar,3 -1983,13,Mar,2.9 -1983,14,Mar,2.8 -1983,15,Mar,2.7 -1983,16,Mar,2.6 -1983,17,Mar,2.4 -1983,18,Mar,2.3 -1983,19,Mar,2.2 -1983,20,Mar,2.1 -1983,21,Mar,2 -1983,22,Mar,1.8 -1983,23,Mar,1.7 -1983,24,Mar,1.6 -1983,25,Mar,1.5 -1983,26,Mar,1.3 -1983,27,Mar,1.1 -1983,28,Mar,1 -1983,29,Mar,0.95 -1983,30,Mar,0.85 -1983,31,Mar,0.8 -1984,1,Mar,3.35 -1984,2,Mar,3.05 -1984,3,Mar,3.2 -1984,4,Mar,3.5 -1984,5,Mar,3.5 -1984,6,Mar,3.2 -1984,7,Mar,3.2 -1984,8,Mar,3.05 -1984,9,Mar,3.05 -1984,10,Mar,2.9 -1984,11,Mar,3.05 -1984,12,Mar,3.35 -1984,13,Mar,4.55 -1984,14,Mar,4.55 -1984,15,Mar,4.55 -1984,16,Mar,4.55 -1984,17,Mar,4.2 -1984,18,Mar,2.9 -1984,19,Mar,2.6 -1984,20,Mar,2.3 -1984,21,Mar,2.15 -1984,22,Mar,2 -1984,23,Mar,1.9 -1984,24,Mar,1.6 -1984,25,Mar,1.2 -1984,26,Mar,1 -1984,27,Mar,0.97 -1984,28,Mar,0.95 -1984,29,Mar,0.97 -1984,30,Mar,0.97 -1984,31,Mar,0.95 -1985,1,Mar,2.5 -1985,2,Mar,3 -1985,3,Mar,4.6 -1985,4,Mar,5.4 -1985,5,Mar,6.6 -1985,6,Mar,6.6 -1985,7,Mar,6.6 -1985,8,Mar,6.2 -1985,9,Mar,6.2 -1985,10,Mar,7 -1985,11,Mar,7 -1985,12,Mar,8.5 -1985,13,Mar,4.2 -1985,14,Mar,3.4 -1985,15,Mar,4.2 -1985,16,Mar,4.6 -1985,17,Mar,5 -1985,18,Mar,5.4 -1985,19,Mar,5.8 -1985,20,Mar,4.6 -1985,21,Mar,3.8 -1985,22,Mar,4.2 -1985,23,Mar,4.2 -1985,24,Mar,4.2 -1985,25,Mar,4.6 -1985,26,Mar,5 -1985,27,Mar,4.6 -1985,28,Mar,4.2 -1985,29,Mar,3 -1985,30,Mar,3 -1985,31,Mar,3 -1986,1,Mar,14.6 -1986,2,Mar,16 -1986,3,Mar,17.8 -1986,4,Mar,18.7 -1986,5,Mar,18.7 -1986,6,Mar,16 -1986,7,Mar,16 -1986,8,Mar,17.8 -1986,9,Mar,19.6 -1986,10,Mar,19.6 -1986,11,Mar,19.6 -1986,12,Mar,17.8 -1986,13,Mar,16 -1986,14,Mar,16 -1986,15,Mar,15.3 -1986,16,Mar,14.6 -1986,17,Mar,14.6 -1986,18,Mar,15.3 -1986,19,Mar,16 -1986,20,Mar,16 -1986,21,Mar,16 -1986,22,Mar,16.9 -1986,23,Mar,16.9 -1986,24,Mar,16 -1986,25,Mar,16 -1986,26,Mar,16 -1986,27,Mar,16 -1986,28,Mar,15.3 -1986,29,Mar,14.6 -1986,30,Mar,13.2 -1986,31,Mar,11.8 -1987,1,Mar,9.7 -1987,2,Mar,11.1 -1987,3,Mar,13.9 -1987,4,Mar,15.3 -1987,5,Mar,18.7 -1987,6,Mar,20.5 -1987,7,Mar,21.4 -1987,8,Mar,19.6 -1987,9,Mar,17.8 -1987,10,Mar,16.9 -1987,11,Mar,12.5 -1987,12,Mar,7.94 -1987,13,Mar,6.35 -1987,14,Mar,5.29 -1987,15,Mar,5.82 -1987,16,Mar,5.29 -1987,17,Mar,5.82 -1987,18,Mar,6.88 -1987,19,Mar,8.47 -1987,20,Mar,10.4 -1987,21,Mar,9 -1987,22,Mar,9.7 -1987,23,Mar,10.4 -1987,24,Mar,10.4 -1987,25,Mar,11.1 -1987,26,Mar,12.5 -1987,27,Mar,11.8 -1987,28,Mar,10.4 -1987,29,Mar,9.7 -1987,30,Mar,8.47 -1987,31,Mar,8.47 -1988,1,Mar,10.85 -1988,2,Mar,10 -1988,3,Mar,8.2 -1988,4,Mar,7 -1988,5,Mar,8.2 -1988,6,Mar,8.8 -1988,7,Mar,8.8 -1988,8,Mar,9.4 -1988,9,Mar,7 -1988,10,Mar,7 -1988,11,Mar,7 -1988,12,Mar,7 -1988,13,Mar,7 -1988,14,Mar,7 -1988,15,Mar,7 -1988,16,Mar,6.4 -1988,17,Mar,5.8 -1988,18,Mar,7 -1988,19,Mar,7 -1988,20,Mar,7 -1988,21,Mar,9.4 -1988,22,Mar,9.4 -1988,23,Mar,9.4 -1988,24,Mar,9.4 -1988,25,Mar,9.4 -1988,26,Mar,9.4 -1988,27,Mar,8.8 -1988,28,Mar,8.2 -1988,29,Mar,8.8 -1988,30,Mar,8.8 -1988,31,Mar,7.6 -1989,1,Mar,4.5 -1989,2,Mar,4.5 -1989,3,Mar,4 -1989,4,Mar,2 -1989,5,Mar,1.5 -1989,6,Mar,1.5 -1989,7,Mar,1 -1989,8,Mar,1 -1989,9,Mar,1.5 -1989,10,Mar,3 -1989,11,Mar,2.5 -1989,12,Mar,2 -1989,13,Mar,2 -1989,14,Mar,2 -1989,15,Mar,2.5 -1989,16,Mar,3.5 -1989,17,Mar,7.1 -1989,18,Mar,10.6 -1989,19,Mar,10.6 -1989,20,Mar,8.5 -1989,21,Mar,9.2 -1989,22,Mar,10.6 -1989,23,Mar,12 -1989,24,Mar,12.9 -1989,25,Mar,12 -1989,26,Mar,10.6 -1989,27,Mar,9.9 -1989,28,Mar,9.9 -1989,29,Mar,9.9 -1989,30,Mar,12.9 -1989,31,Mar,14.7 -1990,1,Mar,11.25 -1990,2,Mar,9.75 -1990,3,Mar,9 -1990,4,Mar,8.25 -1990,5,Mar,6.75 -1990,6,Mar,6 -1990,7,Mar,6.75 -1990,8,Mar,5.4 -1990,9,Mar,4.8 -1990,10,Mar,5.4 -1990,11,Mar,4.8 -1990,12,Mar,5.4 -1990,13,Mar,6.75 -1990,14,Mar,7.5 -1990,15,Mar,7.5 -1990,16,Mar,6 -1990,17,Mar,5.4 -1990,18,Mar,3.6 -1990,19,Mar,4.2 -1990,20,Mar,6.75 -1990,21,Mar,6 -1990,22,Mar,6.75 -1990,23,Mar,7.5 -1990,24,Mar,7.5 -1990,25,Mar,12.75 -1990,26,Mar,9.75 -1990,27,Mar,6 -1990,28,Mar,6 -1990,29,Mar,4.8 -1990,30,Mar,3.6 -1990,31,Mar,2.4 -1991,1,Mar,18 -1991,2,Mar,18.85 -1991,3,Mar,18 -1991,4,Mar,19.7 -1991,5,Mar,20.55 -1991,6,Mar,20.55 -1991,7,Mar,16.7 -1991,8,Mar,12.8 -1991,9,Mar,12.15 -1991,10,Mar,12.15 -1991,11,Mar,11.5 -1991,12,Mar,12.15 -1991,13,Mar,12.15 -1991,14,Mar,12.15 -1991,15,Mar,12.15 -1991,16,Mar,13.45 -1991,17,Mar,14.75 -1991,18,Mar,15.4 -1991,19,Mar,16.05 -1991,20,Mar,15.4 -1991,21,Mar,14.1 -1991,22,Mar,13.45 -1991,23,Mar,14.1 -1991,24,Mar,11.5 -1991,25,Mar,7 -1991,26,Mar,6.15 -1991,27,Mar,9.5 -1991,28,Mar,11 -1991,29,Mar,10 -1991,30,Mar,10.5 -1991,31,Mar,10.5 -1992,1,Mar,7.4 -1992,2,Mar,7.6 -1992,3,Mar,7.4 -1992,4,Mar,7.4 -1992,5,Mar,7.2 -1992,6,Mar,6.8 -1992,7,Mar,6.2 -1992,8,Mar,6 -1992,9,Mar,6 -1992,10,Mar,6.2 -1992,11,Mar,5.6 -1992,12,Mar,5.44 -1992,13,Mar,5.12 -1992,14,Mar,5.28 -1992,15,Mar,5.28 -1992,16,Mar,5.44 -1992,17,Mar,5.28 -1992,18,Mar,5.12 -1992,19,Mar,4.96 -1992,20,Mar,4.96 -1992,21,Mar,4.96 -1992,22,Mar,5.28 -1992,23,Mar,5.6 -1992,24,Mar,6 -1992,25,Mar,5.8 -1992,26,Mar,5.6 -1992,27,Mar,5.6 -1992,28,Mar,5.8 -1992,29,Mar,5.6 -1992,30,Mar,5.6 -1992,31,Mar,5.6 -1993,1,Mar,5.26 -1993,2,Mar,4.98 -1993,3,Mar,4.7 -1993,4,Mar,4.7 -1993,5,Mar,5.12 -1993,6,Mar,5.26 -1993,7,Mar,5.26 -1993,8,Mar,5.4 -1993,9,Mar,5.56 -1993,10,Mar,5.26 -1993,11,Mar,5.12 -1993,12,Mar,5.4 -1993,13,Mar,6.2 -1993,14,Mar,6.2 -1993,15,Mar,6.2 -1993,16,Mar,6.04 -1993,17,Mar,5.88 -1993,18,Mar,5.72 -1993,19,Mar,5.4 -1993,20,Mar,5.12 -1993,21,Mar,4.84 -1993,22,Mar,4.7 -1993,23,Mar,4.42 -1993,24,Mar,4.42 -1993,25,Mar,4.14 -1993,26,Mar,4.14 -1993,27,Mar,4 -1993,28,Mar,4 -1993,29,Mar,4 -1993,30,Mar,4 -1993,31,Mar,3.88 -1994,1,Mar,1.84 -1994,2,Mar,1.48 -1994,3,Mar,1.24 -1994,4,Mar,1.24 -1994,5,Mar,1.72 -1994,6,Mar,1.48 -1994,7,Mar,1.6 -1994,8,Mar,1.6 -1994,9,Mar,1.84 -1994,10,Mar,1.96 -1994,11,Mar,1.84 -1994,12,Mar,1.72 -1994,13,Mar,1.6 -1994,14,Mar,1.36 -1994,15,Mar,1.36 -1994,16,Mar,1.24 -1994,17,Mar,1.6 -1994,18,Mar,1.84 -1994,19,Mar,1.96 -1994,20,Mar,1.96 -1994,21,Mar,1.96 -1994,22,Mar,1.96 -1994,23,Mar,1.96 -1994,24,Mar,2.08 -1994,25,Mar,1.84 -1994,26,Mar,1.6 -1994,27,Mar,7.8 -1994,28,Mar,36.5 -1994,29,Mar,103.8 -1994,30,Mar,100.4 -1994,31,Mar,44.4 -1995,1,Mar,7.92 -1995,2,Mar,10.4 -1995,3,Mar,11.02 -1995,4,Mar,9.78 -1995,5,Mar,8.54 -1995,6,Mar,9.16 -1995,7,Mar,10.4 -1995,8,Mar,9.78 -1995,9,Mar,11.02 -1995,10,Mar,11.64 -1995,11,Mar,11.02 -1995,12,Mar,10.4 -1995,13,Mar,9.78 -1995,14,Mar,9.16 -1995,15,Mar,9.16 -1995,16,Mar,8.54 -1995,17,Mar,8.54 -1995,18,Mar,8.54 -1995,19,Mar,7.92 -1995,20,Mar,7.92 -1995,21,Mar,7.3 -1995,22,Mar,7.3 -1995,23,Mar,7.3 -1995,24,Mar,7.92 -1995,25,Mar,7.92 -1995,26,Mar,7.92 -1995,27,Mar,7.92 -1995,28,Mar,8.54 -1995,29,Mar,10.4 -1995,30,Mar,11.64 -1995,31,Mar,11.02 -1996,1,Mar,22.01 -1996,2,Mar,20.84 -1996,3,Mar,20.84 -1996,4,Mar,19.67 -1996,5,Mar,15.01 -1996,6,Mar,15.01 -1996,7,Mar,13.85 -1996,8,Mar,11.05 -1996,9,Mar,10.35 -1996,10,Mar,11.75 -1996,11,Mar,11.75 -1996,12,Mar,11.75 -1996,13,Mar,11.75 -1996,14,Mar,11.05 -1996,15,Mar,11.05 -1996,16,Mar,11.05 -1996,17,Mar,9.65 -1996,18,Mar,9.65 -1996,19,Mar,9.65 -1996,20,Mar,8.25 -1996,21,Mar,8.25 -1996,22,Mar,9.65 -1996,23,Mar,10.35 -1996,24,Mar,10.35 -1996,25,Mar,10.35 -1996,26,Mar,10.35 -1996,27,Mar,11.05 -1996,28,Mar,12.45 -1996,29,Mar,12.45 -1996,30,Mar,12.45 -1996,31,Mar,13.15 -1997,1,Mar,7.8 -1997,2,Mar,11.52 -1997,3,Mar,9.66 -1997,4,Mar,7.8 -1997,5,Mar,8.42 -1997,6,Mar,9.04 -1997,7,Mar,9.04 -1997,8,Mar,9.04 -1997,9,Mar,9.66 -1997,10,Mar,9.04 -1997,11,Mar,8.42 -1997,12,Mar,7.14 -1997,13,Mar,7.47 -1997,14,Mar,7.8 -1997,15,Mar,7.8 -1997,16,Mar,7.8 -1997,17,Mar,10.28 -1997,18,Mar,11.52 -1997,19,Mar,8.42 -1997,20,Mar,8.42 -1997,21,Mar,9.04 -1997,22,Mar,9.04 -1997,23,Mar,8.42 -1997,24,Mar,12.14 -1997,25,Mar,12.14 -1997,26,Mar,8.42 -1997,27,Mar,9.04 -1997,28,Mar,9.66 -1997,29,Mar,10.9 -1997,30,Mar,10.28 -1997,31,Mar,10.9 -1998,1,Mar,5.5 -1998,2,Mar,7 -1998,3,Mar,7.5 -1998,4,Mar,10.1 -1998,5,Mar,10.75 -1998,6,Mar,11.4 -1998,7,Mar,10.75 -1998,8,Mar,10.75 -1998,9,Mar,10.75 -1998,10,Mar,10.75 -1998,11,Mar,10.75 -1998,12,Mar,11.4 -1998,13,Mar,12.05 -1998,14,Mar,12.7 -1998,15,Mar,12.7 -1998,16,Mar,12.7 -1998,17,Mar,10.75 -1998,18,Mar,10.75 -1998,19,Mar,11.4 -1998,20,Mar,11.4 -1998,21,Mar,10.75 -1998,22,Mar,10.1 -1998,23,Mar,10.1 -1998,24,Mar,9.45 -1998,25,Mar,8.8 -1998,26,Mar,8.8 -1998,27,Mar,8.15 -1998,28,Mar,7.5 -1998,29,Mar,7.5 -1998,30,Mar,6.5 -1998,31,Mar,6 -1999,1,Mar,9.96 -1999,2,Mar,12.95 -1999,3,Mar,11.32 -1999,4,Mar,10.64 -1999,5,Mar,10.64 -1999,6,Mar,9.28 -1999,7,Mar,9.28 -1999,8,Mar,8.6 -1999,9,Mar,7.92 -1999,10,Mar,7.92 -1999,11,Mar,5.88 -1999,12,Mar,5.88 -1999,13,Mar,5.88 -1999,14,Mar,5.2 -1999,15,Mar,4.41 -1999,16,Mar,3.88 -1999,17,Mar,3.62 -1999,18,Mar,3.09 -1999,19,Mar,3.09 -1999,20,Mar,3.62 -1999,21,Mar,4.67 -1999,22,Mar,4.67 -1999,23,Mar,4.41 -1999,24,Mar,4.94 -1999,25,Mar,5.88 -1999,26,Mar,5.2 -1999,27,Mar,4.41 -1999,28,Mar,4.41 -1999,29,Mar,4.41 -1999,30,Mar,4.41 -1999,31,Mar,4.14 -2000,1,Mar,9.5 -2000,2,Mar,8.7 -2000,3,Mar,8.7 -2000,4,Mar,7.9 -2000,5,Mar,5.5 -2000,6,Mar,5.25 -2000,7,Mar,4.75 -2000,8,Mar,4 -2000,9,Mar,6.3 -2000,10,Mar,8.7 -2000,11,Mar,5.5 -2000,12,Mar,4 -2000,13,Mar,3.25 -2000,14,Mar,3 -2000,15,Mar,3 -2000,16,Mar,3.75 -2000,17,Mar,5 -2000,18,Mar,5.5 -2000,19,Mar,5 -2000,20,Mar,5.25 -2000,21,Mar,5.5 -2000,22,Mar,6.3 -2000,23,Mar,5.5 -2000,24,Mar,7.1 -2000,25,Mar,7.1 -2000,26,Mar,8.7 -2000,27,Mar,9.5 -2000,28,Mar,8.7 -2000,29,Mar,13.5 -2000,30,Mar,14.65 -2000,31,Mar,20.4 -2001,1,Mar,3 -2001,2,Mar,3.66 -2001,3,Mar,3.99 -2001,4,Mar,4.65 -2001,5,Mar,3.99 -2001,6,Mar,3.33 -2001,7,Mar,3 -2001,8,Mar,3.33 -2001,9,Mar,7.94 -2001,10,Mar,12.04 -2001,11,Mar,13.68 -2001,12,Mar,14.5 -2001,13,Mar,14.5 -2001,14,Mar,23.04 -2001,15,Mar,32.3 -2001,16,Mar,33.7 -2001,17,Mar,30.9 -2001,18,Mar,23.04 -2001,19,Mar,19.38 -2001,20,Mar,19.38 -2001,21,Mar,18.16 -2001,22,Mar,15.72 -2001,23,Mar,21.82 -2001,24,Mar,15.72 -2001,25,Mar,12.86 -2001,26,Mar,12.04 -2001,27,Mar,10.4 -2001,28,Mar,9.58 -2001,29,Mar,9.58 -2001,30,Mar,7.94 -2001,31,Mar,6.3 -2002,1,Mar,8.37 -2002,2,Mar,13.84 -2002,3,Mar,14.68 -2002,4,Mar,13.84 -2002,5,Mar,13 -2002,6,Mar,13.84 -2002,7,Mar,13.84 -2002,8,Mar,10.48 -2002,9,Mar,6.65 -2002,10,Mar,5.79 -2002,11,Mar,5.36 -2002,12,Mar,4.93 -2002,13,Mar,4.5 -2002,14,Mar,4.16 -2002,15,Mar,5.36 -2002,16,Mar,10.48 -2002,17,Mar,13 -2002,18,Mar,13 -2002,19,Mar,12.16 -2002,20,Mar,12.16 -2002,21,Mar,12.16 -2002,22,Mar,8.8 -2002,23,Mar,9.64 -2002,24,Mar,13.84 -2002,25,Mar,14.68 -2002,26,Mar,14.68 -2002,27,Mar,14.68 -2002,28,Mar,14.68 -2002,29,Mar,16.36 -2002,30,Mar,19.22 -2002,31,Mar,19.22 -2003,1,Mar,15.34 -2003,2,Mar,20.84 -2003,3,Mar,23.4 -2003,4,Mar,22.12 -2003,5,Mar,19.56 -2003,6,Mar,18.28 -2003,7,Mar,19.56 -2003,8,Mar,17 -2003,9,Mar,12.85 -2003,10,Mar,12.02 -2003,11,Mar,12.02 -2003,12,Mar,11.19 -2003,13,Mar,14.51 -2003,14,Mar,15.34 -2003,15,Mar,17 -2003,16,Mar,20.84 -2003,17,Mar,22.12 -2003,18,Mar,19.56 -2003,19,Mar,17 -2003,20,Mar,16.17 -2003,21,Mar,18.28 -2003,22,Mar,17 -2003,23,Mar,11.19 -2003,24,Mar,11.19 -2003,25,Mar,14.51 -2003,26,Mar,12.85 -2003,27,Mar,13.68 -2003,28,Mar,18.28 -2003,29,Mar,24.68 -2003,30,Mar,32.44 -2003,31,Mar,36.4 -2004,1,Mar,10.6 -2004,2,Mar,7.9 -2004,3,Mar,5.2 -2004,4,Mar,4.75 -2004,5,Mar,6.1 -2004,6,Mar,12.4 -2004,7,Mar,14.2 -2004,8,Mar,15.1 -2004,9,Mar,14.2 -2004,10,Mar,12.4 -2004,11,Mar,12.4 -2004,12,Mar,9.7 -2004,13,Mar,5.2 -2004,14,Mar,5.2 -2004,15,Mar,5.2 -2004,16,Mar,4.75 -2004,17,Mar,3.4 -2004,18,Mar,2.5 -2004,19,Mar,4.3 -2004,20,Mar,9.7 -2004,21,Mar,11.5 -2004,22,Mar,11.5 -2004,23,Mar,10.6 -2004,24,Mar,9.7 -2004,25,Mar,8.8 -2004,26,Mar,6.55 -2004,27,Mar,3.4 -2004,28,Mar,3.4 -2004,29,Mar,4.3 -2004,30,Mar,4.3 -2004,31,Mar,4.3 -2005,1,Mar,17.21 -2005,2,Mar,16.18 -2005,3,Mar,17.21 -2005,4,Mar,17.21 -2005,5,Mar,18.24 -2005,6,Mar,20.3 -2005,7,Mar,23.66 -2005,8,Mar,24.78 -2005,9,Mar,23.66 -2005,10,Mar,23.66 -2005,11,Mar,23.66 -2005,12,Mar,23.66 -2005,13,Mar,23.66 -2005,14,Mar,20.3 -2005,15,Mar,18.24 -2005,16,Mar,21.42 -2005,17,Mar,20.3 -2005,18,Mar,21.42 -2005,19,Mar,19.27 -2005,20,Mar,18.24 -2005,21,Mar,25.9 -2005,22,Mar,24.78 -2005,23,Mar,22.54 -2005,24,Mar,23.66 -2005,25,Mar,23.66 -2005,26,Mar,23.66 -2005,27,Mar,23.66 -2005,28,Mar,22.54 -2005,29,Mar,27.02 -2005,30,Mar,33.9 -2005,31,Mar,36.3 -2006,1,Mar,19.3 -2006,2,Mar,23.75 -2006,3,Mar,26.25 -2006,4,Mar,37.15 -2006,5,Mar,38.7 -2006,6,Mar,34.05 -2006,7,Mar,26.25 -2006,8,Mar,25 -2006,9,Mar,25 -2006,10,Mar,26.25 -2006,11,Mar,15.8 -2006,12,Mar,15.8 -2006,13,Mar,15.1 -2006,14,Mar,14.4 -2006,15,Mar,13 -2006,16,Mar,13.7 -2006,17,Mar,17.2 -2006,18,Mar,19.3 -2006,19,Mar,20 -2006,20,Mar,31.25 -2006,21,Mar,26.25 -2006,22,Mar,22.5 -2006,23,Mar,22.5 -2006,24,Mar,26.25 -2006,25,Mar,23.75 -2006,26,Mar,15.8 -2006,27,Mar,15.1 -2006,28,Mar,14.4 -2006,29,Mar,14.4 -2006,30,Mar,15.8 -2006,31,Mar,18.6 -2007,1,Mar,11.1 -2007,2,Mar,10.05 -2007,3,Mar,15.65 -2007,4,Mar,16.45 -2007,5,Mar,16.9 -2007,6,Mar,16.9 -2007,7,Mar,14.95 -2007,8,Mar,14.95 -2007,9,Mar,16 -2007,10,Mar,14.6 -2007,11,Mar,15.3 -2007,12,Mar,17.35 -2007,13,Mar,18.25 -2007,14,Mar,18.25 -2007,15,Mar,17.8 -2007,16,Mar,17.35 -2007,17,Mar,17.8 -2007,18,Mar,18.7 -2007,19,Mar,20.5 -2007,20,Mar,20.05 -2007,21,Mar,21.4 -2007,22,Mar,24.55 -2007,23,Mar,23.2 -2007,24,Mar,21.4 -2007,25,Mar,19.15 -2007,26,Mar,18.25 -2007,27,Mar,18.7 -2007,28,Mar,16.9 -2007,29,Mar,16.45 -2007,30,Mar,16 -2007,31,Mar,15.65 -2008,1,Mar,12.5 -2008,2,Mar,14 -2008,3,Mar,14.75 -2008,4,Mar,14.75 -2008,5,Mar,14 -2008,6,Mar,13.25 -2008,7,Mar,14 -2008,8,Mar,19.4 -2008,9,Mar,21.2 -2008,10,Mar,21.2 -2008,11,Mar,21.2 -2008,12,Mar,21.2 -2008,13,Mar,20.3 -2008,14,Mar,15.5 -2008,15,Mar,15.5 -2008,16,Mar,16.25 -2008,17,Mar,10.45 -2008,18,Mar,9.35 -2008,19,Mar,8.25 -2008,20,Mar,11 -2008,21,Mar,11.75 -2008,22,Mar,16.25 -2008,23,Mar,17.75 -2008,24,Mar,22.1 -2008,25,Mar,20.3 -2008,26,Mar,17.75 -2008,27,Mar,17.75 -2008,28,Mar,17 -2008,29,Mar,11.75 -2008,30,Mar,13.25 -2008,31,Mar,15.5 -2009,1,Mar,6.5 -2009,2,Mar,6.5 -2009,3,Mar,6 -2009,4,Mar,6.5 -2009,5,Mar,7 -2009,6,Mar,7 -2009,7,Mar,10.2 -2009,8,Mar,16.9 -2009,9,Mar,24.5 -2009,10,Mar,25.7 -2009,11,Mar,24.5 -2009,12,Mar,25.7 -2009,13,Mar,26.9 -2009,14,Mar,23.55 -2009,15,Mar,19.75 -2009,16,Mar,15 -2009,17,Mar,15 -2009,18,Mar,13.4 -2009,19,Mar,13.4 -2009,20,Mar,14.2 -2009,21,Mar,13.4 -2009,22,Mar,19.75 -2009,23,Mar,25.7 -2009,24,Mar,24.5 -2009,25,Mar,26.9 -2009,26,Mar,28.1 -2009,27,Mar,31.7 -2009,28,Mar,31.7 -2009,29,Mar,28.1 -2009,30,Mar,21.65 -2009,31,Mar,21.65 -2010,1,Mar,2.75 -2010,2,Mar,2.75 -2010,3,Mar,2.75 -2010,4,Mar,2.75 -2010,5,Mar,2.97 -2010,6,Mar,3.19 -2010,7,Mar,3.08 -2010,8,Mar,3.03 -2010,9,Mar,3.08 -2010,10,Mar,3.03 -2010,11,Mar,3.08 -2010,12,Mar,3.19 -2010,13,Mar,2.97 -2010,14,Mar,3.03 -2010,15,Mar,3.03 -2010,16,Mar,3.03 -2010,17,Mar,3.19 -2010,18,Mar,3.36 -2010,19,Mar,3.14 -2010,20,Mar,3.19 -2010,21,Mar,3.3 -2010,22,Mar,3.3 -2010,23,Mar,3.25 -2010,24,Mar,3.19 -2010,25,Mar,3.14 -2010,26,Mar,3.25 -2010,27,Mar,3.36 -2010,28,Mar,3.3 -2010,29,Mar,3.19 -2010,30,Mar,3.19 -2010,31,Mar,3.48 -2011,1,Mar,8.6 -2011,2,Mar,9.8 -2011,3,Mar,9.8 -2011,4,Mar,8.6 -2011,5,Mar,7.4 -2011,6,Mar,5.6 -2011,7,Mar,7.4 -2011,8,Mar,8 -2011,9,Mar,8 -2011,10,Mar,8.6 -2011,11,Mar,9.8 -2011,12,Mar,11 -2011,13,Mar,12 -2011,14,Mar,11 -2011,15,Mar,13 -2011,16,Mar,17 -2011,17,Mar,31.4 -2011,18,Mar,37.9 -2011,19,Mar,30.1 -2011,20,Mar,19 -2011,21,Mar,17 -2011,22,Mar,19 -2011,23,Mar,17 -2011,24,Mar,15 -2011,25,Mar,16 -2011,26,Mar,15 -2011,27,Mar,17 -2011,28,Mar,17 -2011,29,Mar,17 -2011,30,Mar,16 -2011,31,Mar,16 -2012,1,Mar,12.9 -2012,2,Mar,11.1 -2012,3,Mar,7.5 -2012,4,Mar,6.95 -2012,5,Mar,7.5 -2012,6,Mar,8.4 -2012,7,Mar,6.95 -2012,8,Mar,6.95 -2012,9,Mar,7.5 -2012,10,Mar,10.2 -2012,11,Mar,26.85 -2012,12,Mar,26.85 -2012,13,Mar,29.35 -2012,14,Mar,33.4 -2012,15,Mar,37.45 -2012,16,Mar,29.35 -2012,17,Mar,24.55 -2012,18,Mar,16.5 -2012,19,Mar,16.5 -2012,20,Mar,18.8 -2012,21,Mar,14.7 -2012,22,Mar,13.8 -2012,23,Mar,17.65 -2012,24,Mar,15.6 -2012,25,Mar,16.5 -2012,26,Mar,19.95 -2012,27,Mar,25.7 -2012,28,Mar,28 -2012,29,Mar,29.35 -2012,30,Mar,26.85 -2012,31,Mar,21.1 -2013,1,Mar,1.5 -2013,2,Mar,1.05 -2013,3,Mar,1.5 -2013,4,Mar,5 -2013,5,Mar,13.15 -2013,6,Mar,19.9 -2013,7,Mar,25.1 -2013,8,Mar,15.05 -2013,9,Mar,6.5 -2013,10,Mar,4 -2013,11,Mar,3 -2013,12,Mar,4 -2013,13,Mar,14.1 -2013,14,Mar,17.3 -2013,15,Mar,17.3 -2013,16,Mar,12.2 -2013,17,Mar,11.25 -2013,18,Mar,11.25 -2013,19,Mar,6.5 -2013,20,Mar,3.5 -2013,21,Mar,1.5 -2013,22,Mar,2.5 -2013,23,Mar,15.05 -2013,24,Mar,18.6 -2013,25,Mar,15.05 -2013,26,Mar,11.25 -2013,27,Mar,10.3 -2013,28,Mar,5.5 -2013,29,Mar,1.5 -2013,30,Mar,1.2 -2013,31,Mar,1.2 -2014,1,Mar,8.7 -2014,2,Mar,15.2 -2014,3,Mar,13.6 -2014,4,Mar,8.15 -2014,5,Mar,4.1 -2014,6,Mar,2.25 -2014,7,Mar,2.25 -2014,8,Mar,3.8 -2014,9,Mar,13.6 -2014,10,Mar,17.6 -2014,11,Mar,16 -2014,12,Mar,11.45 -2014,13,Mar,7.05 -2014,14,Mar,3 -2014,15,Mar,4.7 -2014,16,Mar,14.4 -2014,17,Mar,16.8 -2014,18,Mar,13.6 -2014,19,Mar,7.6 -2014,20,Mar,3.25 -2014,21,Mar,2 -2014,22,Mar,2.75 -2014,23,Mar,14.4 -2014,24,Mar,17.6 -2014,25,Mar,15.2 -2014,26,Mar,11.45 -2014,27,Mar,4.7 -2014,28,Mar,3.8 -2014,29,Mar,6.2 -2014,30,Mar,17.6 -2014,31,Mar,20 -2015,1,Mar,11.4 -2015,2,Mar,10.03 -2015,3,Mar,13.2 -2015,4,Mar,2.53 -2015,5,Mar,1.34 -2015,6,Mar,1 -2015,7,Mar,5.49 -2015,8,Mar,9.09 -2015,9,Mar,15.9 -2015,10,Mar,5.8 -2015,11,Mar,3.01 -2015,12,Mar,0.9 -2015,13,Mar,1 -2015,14,Mar,6.74 -2015,15,Mar,7.21 -2015,16,Mar,3.94 -2015,17,Mar,4.25 -2015,18,Mar,1.34 -2015,19,Mar,1.17 -2015,20,Mar,1.34 -2015,21,Mar,5.18 -2015,22,Mar,7.21 -2015,23,Mar,8.15 -2015,24,Mar,4.56 -2015,25,Mar,1.17 -2015,26,Mar,0.7 -2015,27,Mar,0.8 -2015,28,Mar,3.01 -2015,29,Mar,19.5 -2015,30,Mar,16.8 -2015,31,Mar,6.27 -2016,1,Mar,0.55 -2016,2,Mar,0.34 -2016,3,Mar,0.18 -2016,4,Mar,0.1 -2016,5,Mar,0.22 -2016,6,Mar,2.19 -2016,7,Mar,0.75 -2016,8,Mar,0.38 -2016,9,Mar,0.22 -2016,10,Mar,0.14 -2016,11,Mar,0.18 -2016,12,Mar,0.46 -2016,13,Mar,0.95 -2016,14,Mar,0.46 -2016,15,Mar,0.22 -2016,16,Mar,0.14 -2016,17,Mar,0.09 -2016,18,Mar,0.07 -2016,19,Mar,0.5 -2016,20,Mar,3.69 -2016,21,Mar,1.68 -2016,22,Mar,1 -2016,23,Mar,0.85 -2016,24,Mar,0.7 -2016,25,Mar,0.9 -2016,26,Mar,11.4 -2016,27,Mar,4.02 -2016,28,Mar,2.53 -2016,29,Mar,1 -2016,30,Mar,0.75 -2016,31,Mar,0.6 -2017,1,Mar,0.48 -2017,2,Mar,0.9 -2017,3,Mar,13.85 -2017,4,Mar,11.25 -2017,5,Mar,18.25 -2017,6,Mar,7.6 -2017,7,Mar,3.6 -2017,8,Mar,1.1 -2017,9,Mar,0.42 -2017,10,Mar,0.46 -2017,11,Mar,36.25 -2017,12,Mar,37.5 -2017,13,Mar,43.75 -2017,14,Mar,21.15 -2017,15,Mar,27.86 -2017,16,Mar,24.8 -2017,17,Mar,24.07 -2017,18,Mar,23.34 -2017,19,Mar,21.88 -2017,20,Mar,29.9 -2017,21,Mar,53.4 -2017,22,Mar,33.98 -2017,23,Mar,24.07 -2017,24,Mar,16.95 -2017,25,Mar,43.75 -2017,26,Mar,43.75 -2017,27,Mar,43.75 -2017,28,Mar,35 -2017,29,Mar,22.61 -2017,30,Mar,21.88 -2017,31,Mar,18.23 -2018,1,Mar,19 -2018,2,Mar,7 -2018,3,Mar,18 -2018,4,Mar,12 -2018,5,Mar,8 -2018,6,Mar,15 -2018,7,Mar,8 -2018,8,Mar,11 -2018,9,Mar,4.36 -2018,10,Mar,40.4 -2018,11,Mar,40.4 -2018,12,Mar,21.2 -2018,13,Mar,23.6 -2018,14,Mar,21.2 -2018,15,Mar,16 -2018,16,Mar,18 -2018,17,Mar,40.4 -2018,18,Mar,24.8 -2018,19,Mar,40.4 -2018,20,Mar,15 -2018,21,Mar,17 -2018,22,Mar,19 -2018,23,Mar,12 -2018,24,Mar,16 -2018,25,Mar,23.6 -2018,26,Mar,8.5 -2018,27,Mar,9 -2018,28,Mar,9 -2018,29,Mar,12 -2018,30,Mar,14 -2018,31,Mar,18 -2019,1,Mar,5 -2019,2,Mar,5 -2019,3,Mar,7.5 -2019,4,Mar,7.5 -2019,5,Mar,6.5 -2019,6,Mar,6 -2019,7,Mar,5 -2019,8,Mar,8 -2019,9,Mar,4 -2019,10,Mar,2.88 -2019,11,Mar,4.5 -2019,12,Mar,5.5 -2019,13,Mar,4.5 -2019,14,Mar,6.5 -2019,15,Mar,8 -2019,16,Mar,10.15 -2019,17,Mar,12.45 -2019,18,Mar,12.45 -2019,19,Mar,11.3 -2019,20,Mar,12.45 -2019,21,Mar,10.15 -2019,22,Mar,9 -2019,23,Mar,7.5 -2019,24,Mar,6 -2019,25,Mar,7.5 -2019,26,Mar,8.5 -2019,27,Mar,29.2 -2019,28,Mar,29.2 -2019,29,Mar,38 -2019,30,Mar,48.5 -2019,31,Mar,39.5 -2020,1,Mar,6.3 -2020,2,Mar,2.86 -2020,3,Mar,2 -2020,4,Mar,1.52 -2020,5,Mar,1.52 -2020,6,Mar,5.01 -2020,7,Mar,14.6 -2020,8,Mar,7.64 -2020,9,Mar,5.87 -2020,10,Mar,5.01 -2020,11,Mar,3.29 -2020,12,Mar,3.72 -2020,13,Mar,6.3 -2020,14,Mar,17.8 -2020,15,Mar,16.2 -2020,16,Mar,17.8 -2020,17,Mar,5.44 -2020,18,Mar,4.58 -2020,19,Mar,2.43 -2020,20,Mar,5.44 -2020,21,Mar,12.33 -2020,22,Mar,22.2 -2020,23,Mar,14.6 -2020,24,Mar,5.01 -2020,25,Mar,4.58 -2020,26,Mar,4.58 -2020,27,Mar,12.33 -2020,28,Mar,17.8 -2020,29,Mar,14.6 -2020,30,Mar,12.33 -2020,31,Mar,8.98 diff --git a/multiproxy-mbr.Rmd b/multiproxy-mbr.Rmd index aef2658..5a45cd7 100644 --- a/multiproxy-mbr.Rmd +++ b/multiproxy-mbr.Rmd @@ -30,15 +30,15 @@ Next, we load the necessary packages and utility functions. ```{r, message=FALSE, warning=FALSE} source('R/init.R') source('R/correlation_functions.R') -source('R/input-selection-functions.R') +source('R/input_selection_functions.R') ``` We also use some special fonts for the plots, so we need to load them as well. ```{r, echo=FALSE, eval=FALSE} if (!require(extrafont)) install.packages('extrafont') -extrafont::loadfonts() # For MAC -extrafont::loadfonts(device = 'win') # For Windows +extrafont::loadfonts(quiet = TRUE) # For MAC +extrafont::loadfonts(device = 'win', quiet = TRUE) # For Windows ``` ```{r, include=FALSE} @@ -53,11 +53,7 @@ We are now ready to proceed with the workflow. ## Tree ring proxies -### Map - -**Figure 1** was made in QGIS, but we can produce a similar map here with R. - -```{r map, fig.width=7, fig.height=6} +```{r} # Read the metadata crnMeta <- fread('data/crnMeta.csv', key = 'site') # Ring width chronologies oxiMeta <- fread('data/oxiMeta.csv', key = 'site') # Oxygen isotope @@ -74,6 +70,21 @@ siteLab <- function(s) meta[s, display] crnMeta[, species := paste0('*', species, '*')] oxiMeta[, species := paste0('*', species, '*')] +# Some other labelling functions +seasonClass <- fread('data/season_class.csv', key = 'season') +ssnLabBrief <- function(x) seasonClass[x, type] +lambdaLab <- function(x) sapply(x, function(v) paste0('\u03bb = ', v)) +dark2 <- RColorBrewer::brewer.pal(3, 'Dark2')[c(2, 1, 3)] +metricLab <- function(x) replace(x, x == 'R2', 'R\u00b2') +to_char_1dp <- function(x) sprintf("%.1f", x) +``` + + +### Map + +**Figure 1** was made in QGIS, but we can produce a similar map here with R. + +```{r map, fig.width=7, fig.height=6} # Map ggplot() + # Country borders @@ -144,7 +155,7 @@ oxiPcaImputed <- melt(id.var = 'year', variable.name = 'site', value.name = 'X') ``` -**Figure S6** +**Figure S5** ```{r, fig.width=8, fig.height=7} crnSpan <- ggplot(crnCount) + @@ -184,7 +195,7 @@ crnSpan + oxiSpan + plot_annotation(title = 'Temporal coverage') ``` -**Figure S7** +**Figure S6** ```{r, fig.width=8, fig.height=8} crnImpPlot <- ggplot() + @@ -263,7 +274,7 @@ ggplot(p1m[year %in% 1922:2016]) + panel_border('black', 0.2) ``` -**Figure S5** +**Figure 1c** ```{r, fig.width=8, fig.height=4} ggplot(p1m) + @@ -285,8 +296,6 @@ p1d[, doy := { # Ignore leap days p1d <- p1d[!(year %% 4 == 0 & doy == 60)] p1d[year %% 4 == 0 & doy > 59, doy := doy - 1] -p1DailyMean <- p1d[, .(mu = mean(Q, na.rm = TRUE)), keyby = doy] -p1d[Q <= 0, Q := p1DailyMean[doy, mu]] # Convert flow rate to volumetric (m3/s to million m3 each day) p1d[, Qv := cumsum(Q * 0.0864), by = year] @@ -338,7 +347,7 @@ annualWetSeason <- merge(d2wAnn[, .(year, doy, date)], w2dAnn[, .(year, doy, dat suffixes = c('sta', 'fin')) ``` -Plot. +**Figure S2** ```{r, fig.width=8, fig.height=7} Qtails <- p1dfy[, last(.SD), by = year @@ -417,12 +426,12 @@ p1MergeDaily <- merge( on = c('year', 'month', 'day')) p1MergeDaily[, period := fifelse(year > 1985, 'Since 1985', 'Before 1985')] -#Naturalize with quantile mapping. +#N aturalize with quantile mapping. p1MergeDaily[, Qnat := { fit <- fitQmap(.SD[period == 'Before 1985', Qobs], .SD[period == 'Before 1985', Qvic], 'RQUANT', - nlls = 30, wet.day = FALSE, nboot = 100) + nlls = 30, wet.day = 0.01, nboot = 100) doQmap(.SD[, Qvic], fit) }, by = month] @@ -467,7 +476,7 @@ natSeas[, type := fcase(type == 'Qvic', 'Uncorrected', natSeas[, season := factor(season, seasons)] ``` -Plot results. +**Figure S3** ```{r naturalization-plot, fig.width=7, fig.height=7} Qx <- seq(min(p1MonLong$Q), max(p1MonLong$Q), length.out = 1000) @@ -571,7 +580,7 @@ p1merge[, season := factor(season, seasons)] setkey(p1merge, season) # Target: 1922:2003 -p1tar <- p1merge[, .SD[1:82], by = season] +p1tar <- p1merge[year %in% 1922:2003] ## Hinkley D and transformation type p1HD <- p1merge[, .(D = round(hinkley(Qa), 2), @@ -622,14 +631,17 @@ So all targets need to be log-transformed. Bootstrapped correlations. ```{r correlation-calc} +# Core years: 1750:2003, and shift back or forth +crnLags <- -2:2 # All lags for ring width +oxiLags <- 0:2 # Positive lags only for d18O + # Input matrices -allLags <- -2:2 # Core years: 1750:2003, and shift back or forth -Xrw <- do.call(cbind, lapply(allLags, function(l) { +Xrw <- do.call(cbind, lapply(crnLags, function(l) { x <- crnMatFilled[3:256 - l, ] colnames(x) <- paste0(colnames(x), l) x })) -Xdo <- do.call(cbind, lapply(allLags, function(l) { +Xdo <- do.call(cbind, lapply(oxiLags, function(l) { x <- oxiMatFilled[3:256 - l, ] colnames(x) <- paste0(colnames(x), l) x @@ -646,29 +658,31 @@ cor_lag <- function(X, Y, l, Xyears, Yyears, alpha) { c('site', 'season'), alpha = alpha) } + set.seed(42) -rhoCrn <- lapplyrbind(allLags, function(l) { +rhoCrn <- lapplyrbind(crnLags, function(l) { DT <- cor_lag(crnMatFilled, QMat, l, 1748:2005, 1922:2003, 0.05) DT[, lag := l] DT[, alpha := 0.05][] }) -rhoOxi <- lapplyrbind(allLags, function(l) { +rhoOxi <- lapplyrbind(oxiLags, function(l) { DT <- cor_lag(oxiMatFilled, QMat, l, 1748:2005, 1922:2003, 0.05) DT[, lag := l] DT[, alpha := 0.05][] }) -dt <- CJ(s = factor(seasons, seasons), l = allLags) +dt <- CJ(s = factor(seasons, seasons), l = crnLags) dt[, sl := paste0(s, '(', l, ')')] rhoCrn[, season_lag := paste0(season, '(', lag, ')')] rhoCrn[, season_lag := factor(season_lag, levels = dt$sl)] rhoCrn[, season := factor(season, levels = seasons)] -rhoCrn[, lag := factor(lag, levels = allLags)] +rhoCrn[, lag := factor(lag, levels = crnLags)] + rhoOxi[, season_lag := paste0(season, '(', lag, ')')] rhoOxi[, season_lag := factor(season_lag, levels = dt$sl)] rhoOxi[, season := factor(season, levels = seasons)] -rhoOxi[, lag := factor(lag, levels = allLags)] +rhoOxi[, lag := factor(lag, levels =crnLags)] ``` **Figure 2** @@ -691,7 +705,7 @@ ssnLabLag <- function(x) { p1 <- ggplot(rhoCrn) + geom_hline(yintercept = 0, colour = 'gray') + geom_vline( - xintercept = c(length(allLags) + 0.5, length(allLags) * 2 + 0.5), + xintercept = c(length(crnLags) + 0.5, length(crnLags) * 2 + 0.5), colour = 'gray') + geom_linerange(aes(season_lag, ymin = low, ymax = high, alpha = signif, colour = season)) + geom_point(aes(season_lag, median, alpha = signif, colour = season)) + @@ -720,7 +734,7 @@ p1 <- ggplot(rhoCrn) + p2 <- ggplot(rhoOxi) + geom_hline(yintercept = 0, colour = 'gray') + geom_vline( - xintercept = c(length(allLags) + 0.5, length(allLags) * 2 + 0.5), + xintercept = c(length(oxiLags) + 0.5, length(oxiLags) * 2 + 0.5), colour = 'gray') + geom_linerange(aes(season_lag, ymin = low, ymax = high, alpha = signif, colour = season)) + geom_point(aes(season_lag, median, alpha = signif, colour = season)) + @@ -768,13 +782,13 @@ corOxiMAM <- grid_cor(oxi, preMAM, 'do18', 'pre', oxiMeta) ### Tree rings -**Figure S8** +**Figure S7** ```{r, fig.width=8, fig.height=7} plot_cor(corMS) ``` -**Figure S9** +**Figure S8** ```{r, fig.width=8, fig.height=7} plot_cor(corMAM, cor.limits = abs_range(corMS$rho$r)) @@ -782,7 +796,7 @@ plot_cor(corMAM, cor.limits = abs_range(corMS$rho$r)) ### Oxygen isotope -**Figure S10** +**Figure S9** ```{r, fig.width=8, fig.height=4} p1 <- plot_cor(corOxiMS) @@ -795,174 +809,275 @@ p1 + p2 + ## Site selection -The site selection script is provided in the file `site-selection-GA.R`. At the beginning of the script, you can set three parameters: `lambda`, the penalty weight; `pop`, the genetic algorithm's population size, and `gen`, the number of generations. Then you can source the script with +Number of potential inputs for each season -```{r, eval=FALSE} -source('site-selection-GA.R') +```{r} +allInputs <- rbind(rhoCrn[{signif}, .(site, lag, absRho = abs(rho0), rho = rho0, season)], + rhoOxi[{signif}, .(site, lag, absRho = abs(rho0), rho = rho0, season)]) +allInputs[, .N, by = season] ``` -or if you are using RStudio, we recommend that you submit it as a local job so that it does not freeze your RStudio. Alternatively, if you have access to a computing cluster, you can submit the script to your cluster. On a quad-core 3.4 GHz desktop with double threading (effectively 8 cores), the script took about an hour. On a 24-core 2.10 GHz computing node, it took about 20 minutes. +To speed up the search process, we only use at most 20 inputs for each season. So we take the 20 inputs with highest correlation magnitudes. This means for the wet season, we keep all 19 inputs. -In this repository, we provide two set of reconstructions, one with λ = 0, and another with λ = 1, as we presented in the paper. Both were run with `pop = 500` and `gen = 500`. +```{r} +poolDT <- allInputs[, head(.SD[order(absRho, decreasing = TRUE)], 20), by = season] +poolDT[, site2 := paste0(site, lag)] +setkey(poolDT, season) +poolDT +``` +```{r} +poolDT[, .N, by = season] +``` -## Cross-validation and final reconstruction -```{r optim-results} -# Read GA results -siteOptim_l0 <- readRDS('results/pop500_gen500_lambda0.RDS') -siteOptim_l1 <- readRDS('results/pop500_gen500_lambda1.RDS') +Now we export the necessary data for the site selection script, which should be run on a computing cluster. -# Get sites from GA results -poolDT <- rbind(rhoCrn[{signif} & abs(rho0) >= 0.2, .(site, lag), by = season], - rhoOxi[{signif} & abs(rho0) >= 0.2, .(site, lag), by = season]) -poolDT[, site2 := paste0(site, lag)] -setkey(poolDT, season) +```{r selection-prep, eval=FALSE} +saveRDS(poolDT, 'results/poolDT.RDS') +saveRDS(Xrwdo, 'results/Xrwdo.RDS') +saveRDS(p1tar, 'results/p1tar.RDS') +``` -mbPool <- rbindlist( - list( - '0.0' = poolDT[c(siteOptim_l0@solution) == 1], - '1.0' = poolDT[c(siteOptim_l1@solution) == 1] - ), - idcol = 'lambda') - -# Run cross validation to get R2, RE, and CE -lambdas = c('0.0' = '0.0', '1.0' = '1.0') -pc3seasons <- - lapply(lambdas, function(l) - lapply(seasons, function(s) { - Qa <- p1tar[s] - X <- Xrwdo[, mbPool[season == s & lambda == l, site2]] - PC <- wPCA(X, use.eigen = FALSE, return.matrix = TRUE) - sv <- input_selection(PC[173:254, ], Qa$Qa, 'leaps backward', nvmax = 8) - PC[, sv, drop = FALSE] - })) -set.seed(24) -cvFolds <- make_Z(1922:2003, nRuns = 50, frac = 0.25, contiguous = TRUE) +The site selection script is provided in the file `site-selection-GA.R`. The National Supercomputing Center in Singapore, where I ran the computations, used the PBS job management program. I provided a PBS script in this repo. If your cluster uses SLURM then you will need to replace the PBS script with your SLURM script. -cv <- lapplyrbind(lambdas, function(l) - cv_mb(p1tar, pc3seasons[[l]], cvFolds, 1750, - lambda = as.numeric(l), - log.trans = 1:3, - return.type = 'all'), - id = 'lambda') -cv[, season := factor(season, seasons)] -# Calculate the robust mean -cvRM <- cv[, - lapply(.SD, dplR::tbrm), - .SDcols = c('R2', 'RE', 'CE'), - keyby = .(lambda, season)] - -# Final reconstruction -rec <- lapplyrbind(lambdas, function(l) - mb_reconstruction(p1tar, pc3seasons[[l]], 1750, lambda = as.numeric(l), log.trans = 1:3)) +## GA outputs -rec[, season := factor(season, seasons)] -rec[, lambda := paste0(lambda, '.0')] +Read GA outputs and get the solution for each λ value: + +```{r best, rows.print=16} +# Read all GA ouptuts +fl <- list.files('results/', pattern = 'pop', full.names = TRUE) + +gaOut <- lapply(fl, function(fn) { + out <- readRDS(fn) + lambda <- as.integer(substr(fn, 29, 30)) / 10 + seed <- as.integer(substr(fn, 33, 35)) + data.table( + lambda = lambda, + seed = seed, + fval = -out@fitnessValue, + ga = c(out)) +}) %>% + rbindlist() + +# Find the best seed for each lambda value +best <- gaOut[, .SD[which.min(fval)], by = lambda] +best[, 1:3] ``` -# Results +Check GA convergence -## GA convergence +```{r, fig.width=8, fig.height=8} +par(mfrow = c(4, 4)) +best[, { + plot(ga[[1]], main = paste0('\u03bb = ', lambda)) +}, by = lambda] %>% + invisible() +``` -**Figure S11** +**Figure S10** ```{r, fig.width=7, fig.height=4} par(mfrow = c(1, 2)) plot( - siteOptim_l0, + best[lambda == 0, ga[[1]]], main = expression(lambda == 0), col = c('steelblue', NA, adjustcolor('steelblue', alpha.f = 0.1))) plot( - siteOptim_l1, - main = expression(lambda == 1), + best[lambda == 1.2, ga[[1]]], + main = expression(lambda == 1.2), col = c('steelblue', NA, adjustcolor('steelblue', alpha.f = 0.1))) ``` -## Skill scores +## Run final reconstructions and cross-validations from the selected inputs + +```{r reconst} +# Function to run reconstructions and cross-validation +reconstruct <- function(sol, lambda, pool, Xused, target, cvFolds) { + + pool <- pool[c(sol@solution) == 1] + + # Run cross validation to get R2, RE, and CE + pcaOut <- lapply(seasons, function(s) { + Qa <- target[s] + X <- Xused[, pool[season == s, site2]] + pcaModel <- prcomp(X, scale. = TRUE) + PC <- pcaModel$x + sv <- input_selection(PC[173:254, ], Qa$Qa, 'leaps backward', nvmax = 8) + list(loadings = pcaModel$rotation[, sv, drop = FALSE], + PC = PC[, sv, drop = FALSE]) + }) + + pc3s <- lapply(pcaOut, '[[', 'PC') + beta <- mb_par(target, pc3s, 1750, lambda = lambda, log.trans = 1:3) + + loadingList <- lapply(pcaOut, '[[', 'loadings') + nPCs <- sapply(loadingList, ncol) + interceptIdx <- c(1, nPCs[1] + 2, nPCs[1] + nPCs[2] + 3) + + beta2 <- beta[-interceptIdx] + loadingMat <- as.matrix(Matrix:::.bdiag(loadingList)) + weight <- loadingMat %*% beta2 + + rec <- mb_reconstruction(target, pc3s, 1750, lambda = lambda, log.trans = 1:3) + cv <- cv_mb(target, pc3s, cvFolds, 1750, + lambda = lambda, + log.trans = 1:3, + return.type = 'all') + cv[, season := factor(season, seasons)] + + list(rec = list(rec), + cv = list(cv[, -c('nRMSE', 'KGE')]), + loadings = list(loadingList), + beta = list(beta), + weight = list(weight)) +} + +# Run the function for each lambda +set.seed(24) +cvFolds <- make_Z(1922:2003, nRuns = 50, frac = 0.25, contiguous = TRUE) + +best[, c('rec', 'cv', 'loadings', 'beta', 'weight') := + reconstruct(ga[[1]], lambda, poolDT, Xrwdo, p1tar, cvFolds), by = lambda] + +# Extract reconstructions +rec <- rbindlist(best$rec) +rec[, season := factor(season, seasons)] + +# Extract CV scores +cv <- best[, cv[[1]], by = lambda] +cvLong <- melt(cv, + id.vars = c('lambda', 'rep', 'season'), + measure.vars = c('R2', 'RE', 'CE'), + variable.name = 'metric') +``` + +Check if we get the correct f-value. + +```{r, rows.print=16} +merge(cv[, .(fnew = mean(fval)), by = lambda], best[, .(lambda, fval)], by = 'lambda') +``` -### Mean skill scores +All is good. + +Check PCA loadings (**Tables S2 to S7**) ```{r} -cvRM %>% - dcast(season ~ lambda, value.var = c('R2', 'RE', 'CE')) %>% - roundDT(2) +best[lambda == 0, loadings][[1]] %>% + lapply(round, digits = 4) ``` -### Skill distribution +````{r} +best[lambda == 1.2, loadings][[1]] %>% + lapply(round, digits = 4) +``` -**Figure S12** +# Results -```{r skill-boxplot, fig.width=6, fig.height=6} -DT <- cv %>% - melt( - id.vars = c('rep', 'season', 'lambda'), - measure.vars = c('R2', 'RE', 'CE'), - variable.name = 'metric', - value.name = 'value') -hline <- DT[metric != 'R2', - .(y = 0), - by = .(season, metric)] -ggplot(DT) + - geom_hline( - aes(yintercept = y), - hline, - size = 0.3, - colour = 'firebrick') + - geom_boxplot( - aes(lambda, value, fill = season), - width = 0.5, - alpha = 0.5, - size = 0.3, - outlier.size = 0.8, - show.legend = FALSE) + +## Skill scores as a function of λ + +### Mean skill scores and Wilcoxon test + +```{r} +cvMean <- cv[, lapply(.SD, mean), .SDcols = c('R2', 'RE', 'CE'), keyby = .(lambda, season)] +cvMeanLong <- cvMean %>% melt(id.var = c('lambda', 'season'), variable.name = 'metric') + +cv0 <- cvLong[lambda == 0] + +cvp <- cvLong[, + .(p.value = { + s <- .BY$season + m <- .BY$metric + wilcox.test(value, cv0[season == s & metric == m, value])$p.value + }), + by = .(lambda, season, metric)] +cvMeanMerged <- merge(cvMeanLong, cvp, by = c('lambda', 'season', 'metric')) %>% + merge(cvMeanLong[lambda == 0, .(season, metric, v0 = value)], by = c('season', 'metric')) +``` + +**Figure 3** + +```{r, fig.width=6, fig.height=5} +ggplot(cvMeanMerged) + + geom_hline(aes(yintercept = value), cvMeanLong[lambda == 0], colour = 'gray') + + geom_linerange( + aes(lambda, ymin = value, ymax = v0, colour = season, alpha = p.value < 0.1), + size = 0.2) + + geom_point( + aes(lambda, value, colour = season, alpha = p.value < 0.1), + size = 1) + + geom_point( + aes(lambda, value, colour = season), + cvMeanLong[lambda == 0], + shape = 'triangle', + size = 2, + alpha = 1) + facet_grid( vars(metric), vars(season), - labeller = labeller( - metric = as_labeller(c(R2 = 'R\u00b2', RE = 'RE', CE = 'CE')), - season = as_labeller(ssnLabBrief)), - scales = 'free_y') + - scale_x_discrete(labels = lambdaLab) + - scale_fill_manual(values = dark2) + - labs(x = NULL, y = 'Metric value [-]') + + labeller = labeller(.rows = metricLab, .cols = ssnLabBrief), + scales = 'free') + + scale_alpha_manual( + name = 'Significantly different from \u03bb = 0', + values = c(0.4, 1)) + + scale_colour_manual(values = dark2) + + scale_x_continuous(breaks = seq(0, 3, 0.2), labels = skip_label(5)) + + guides(color = FALSE) + + labs(x= '\u03bb', y = 'Metric value [-]') + + panel_border('black', 0.2) + theme( - panel.spacing.y = unit(1.5, 'lines'), - strip.background = element_rect('gray90', NA), - axis.line.x = element_blank(), - axis.ticks.x = element_blank()) + strip.text.y = element_text(angle = 0), + legend.position = 'none') ``` -Statistically skillful? We calculate the probability of RE > 0 and of CE > 0. +### Skill distribution -```{r} -cv[, .(pRE = .SD[RE > 0, .N] / .N, - pCE = .SD[CE > 0, .N] / .N), keyby = .(lambda, season)] -``` +**Figure S11** -Model 0's wet season is not statistically skillful. +```{r, fig.width=8, fig.height=5} +ggplot(cvLong[lambda %in% round(seq(0, 3, 0.2), 1)]) + + geom_boxplot( + aes(lambda, value, group = lambda, fill = season, alpha = I(lambda / 3)), + size = 0.2, + outlier.size = 0.4, outlier.alpha = 1) + + facet_grid( + vars(metric), vars(season), + labeller = labeller(.rows = metricLab, .cols = ssnLabBrief), + scales = 'free') + + scale_x_continuous(breaks = seq(0, 3, 0.2), labels = skip_label(5)) + + scale_fill_manual(values = dark2) + + panel_border('black', 0.2) + + labs(x = '\u03bb', y = 'Metric value [-]') + + theme( + strip.text.y = element_text(angle = 0), + legend.position = 'none') +``` ## Trajectories ```{r} # Make strings to merge to plot legend -lambdaLegendDT <- cvRM[, roundDT(.SD, type = 'char') - ][, .(lab = paste(lambdaLab(.BY$lambda), R2, RE, CE, sep = ' ')), - by = .(season = as.character(season), lambda)] +lambdaLegendDT <- cvMean[, season := as.character(season) + ][, roundDT(.SD, digits = c(1, 2, 2, 2, 2), type = 'char') + ][, .(lab = paste(lambdaLab(.BY$lambda), R2, RE, CE, sep = ' ')), by = .(season, lambda)] + + lambdaLegend <- function(s) { function(x) lambdaLegendDT[season == s & lambda %in% x, lab] } +``` + +**Figure 4** + +```{r traj-plot-merged, fig.width=8, fig.height=9} # Select Model 1, apply low pass filter -recFinal <- rec[lambda == '1.0'] +recFinal <- rec[lambda == 1.2] recFinal[, season := factor(season, seasons)] recFinal[, Qlpf := dplR::pass.filt(Q, 20, 'low')] -``` - -**Figure 3** -```{r traj-plot-merged, fig.width=8, fig.height=9} p <- lapply(seasons, function(s) { - ggplot(rec[season == s & year %in% 1922:2003]) + - geom_line(aes(year, Q, colour = lambda, linetype = lambda)) + + ggplot(rec[season == s & year %in% 1922:2003 & lambda %in% c(0, 1.2)]) + + geom_line(aes(year, Q, colour = to_char_1dp(lambda), linetype = to_char_1dp(lambda))) + geom_line(aes(year, Qa), p1tar[s], colour = 'gray', size = 0.2) + scale_linetype_discrete( name = c(' R\u00b2 RE CE '), @@ -999,7 +1114,7 @@ p <- ggplot(recFinal) + aes(yintercept = Qm, colour = 'Long-term mean'), recFinal[, .(Qm = mean(Q)), by = season], size = 0.2) + - geom_line(aes(year, Q, colour = 'Reconstruction (\u03bb = 1)'), size = 0.2) + + geom_line(aes(year, Q, colour = 'Reconstruction (\u03bb = 1.2)'), size = 0.2) + geom_line(aes(year, Qlpf, colour = '20-year low-pass filter'), size = 0.3) + facet_wrap( vars(season), @@ -1014,7 +1129,7 @@ p <- ggplot(recFinal) + scale_colour_manual( name = NULL, values = c('gray', 'black', 'firebrick'), - breaks = c('Reconstruction (\u03bb = 1)', '20-year low-pass filter', 'Long-term mean'), + breaks = c('Reconstruction (\u03bb = 1.2)', '20-year low-pass filter', 'Long-term mean'), ) + labs(x = NULL, y = 'Q [million m\u00b3]') + theme( @@ -1037,24 +1152,24 @@ wrap_plots(trajPlotInst) + wrap_plots(trajPlotFull) + ## Magnitude of adjustments -**Figure S13** +**Figure S12** ```{r, fig.width=8, fig.height=6} # Amount of adjustment -adj <- rec[, dcast(.SD, season + year ~ lambda, value.var = 'Q') - ][, adj := `1.0` - `0.0`] +adj <- rec[lambda %in% c(0, 1.2), dcast(.SD, season + year ~ lambda, value.var = 'Q') + ][, adj := `1.2` - `0`] # Density of adjustment amount adjDens <- adj[, { s <- .BY$season bw <- fcase(s == 'NJ', 30, - s == 'JO', 50, - s == 'WY', 9) + s == 'JO', 60, + s == 'WY', 90) d <- density(adj, bw = bw, from = min(adj), to = max(adj)) data.table(x = d$x, y = d$y) }, by = season] -trajPlotFull2 <- ggplot(rec) + - geom_line(aes(year, Q, colour = lambda, linetype = lambda), size = 0.4) + +trajPlotFull2 <- ggplot(rec[lambda %in% c(0, 1.2)]) + + geom_line(aes(year, Q, colour = to_char_1dp(lambda), linetype = to_char_1dp(lambda)), size = 0.4) + scale_linetype_discrete( name = NULL, label = lambdaLab) + @@ -1081,31 +1196,79 @@ trajPlotFull2 + adjDensPlot + plot_layout(widths = c(3, 1)) ``` +## Flow ratio + +**Figure 5** + +```{r} +p1tarWide <- dcast(p1tar, year ~ season, value.var = 'Qa') +p1tarWide[, rQ := NJ / WY] + +deltaQ <- rec[, dcast(.SD, lambda + year ~ season, value.var = 'Q') + ][, dQ := NJ + JO - WY + ][, rQ := NJ / WY] +densEst <- deltaQ[, { + d <- density(dQ, cut = 0) + data.table(x = d$x, y = d$y) +}, by = lambda] +Qmean <- p1tar[season == 'WY', mean(Qa)] +``` + + +```{r, fig.width=5, fig.height=4} +ggplot(deltaQ[lambda == 1.2], aes(WY, rQ * 100)) + + geom_hline(yintercept = 50, colour = 'gray', size = 0.2) + + geom_point(aes(colour = 'Reconstructed'), size = 1) + + geom_point(aes(colour = 'Observed'), p1tarWide) + + geom_text( + aes(label = year), + p1tarWide[year %in% c(1971, 1973, 1975)], + size = 3, + nudge_x = 150) + + geom_text( + aes(label = year), + deltaQ[lambda == 1.2 & year == 1815], + size = 3, + nudge_x = 150) + + scale_colour_manual( + name = NULL, + values = pal) + + scale_y_continuous(breaks = seq(20, 60, 5)) + + scale_x_continuous(breaks = seq(1000, 4000, 500)) + + labs( + x = 'Annual streamflow [million m\u00b3]', + y = 'Fraction of dry season flow to annual flow [%]') + + theme( + legend.margin = margin(5, 5, 5, 5), + legend.background = element_rect(NA, 'black', 0.2), + legend.position = c(0.65, 0.9)) +``` ## Selected inputs -Extract selected inputs. +Extract selected inputs ```{r} -mbPool[, prox := fifelse(site %in% crnMeta$site, 'RW', 'OX')] -mbPool[, site.display := paste0(siteLab(site), ' ', prox, ' (', lag, ')')] -mbPool[, selected := '\u2713'] - -tickMat <- dcast(mbPool, lambda + prox + site.display ~ season, value.var = 'selected') %>% - melt( - id.var = c('lambda', 'prox', 'site.display'), - variable.name = 'season', - value.name = 'selected') -tickMat[is.na(selected), selected := ' '] -tickMat[, site.display := factor(site.display, - levels = sort(unique(site.display), decreasing = TRUE))] - -count <- tickMat[selected != ' ', .N, by = .(season, lambda)] +weightDT <- best[lambda %in% c(0, 1.2), { + DT <- poolDT[c(ga[[1]]@solution == 1)] + DT[, prox := fifelse(site %in% crnMeta$site, 'RW', 'OX')] + DT[, site.display := paste0(siteLab(site), ' ', prox, ' (', lag, ')')] + DT[, weight := weight] + DT[] +}, by = lambda] + +weightDT[, site.display := + factor(site.display, + levels = sort(unique(site.display), decreasing = TRUE))] + +weightDT[, sameSign := fifelse(weight * rho < 0, 'x', ' ')] + +count <-weightDT[, .N, by = .(season, lambda)] ``` -**Figure 4** +**Figure 6** -```{r, fig.width=6, fig.height=6, warning=FALSE} +```{r, fig.width=6, fig.height=7} yLabCol <- function(x) { sapply(x, function(xx) { ind <- grep('RW', xx) @@ -1124,129 +1287,58 @@ ssnLab2Lines <- function(l) { } } -p1 <- ggplot(tickMat[lambda == '0.0']) + - geom_text( - aes(season, site.display, label = selected, colour = prox), - family = 'Arial Unicode MS', - show.legend = FALSE, - vjust = 0.25) + - facet_wrap(vars(lambda), labeller = as_labeller(lambdaLab)) + - scale_x_discrete(labels = ssnLab2Lines('0.0')) + - scale_y_discrete(labels = yLabCol, drop = FALSE) + - scale_colour_manual(values = c('firebrick', 'black')) + - labs(x = NULL, y = NULL) + - cowplot::panel_border('black', 0.2) + - theme( - axis.text.y = element_markdown(), - axis.text.x = element_markdown(lineheight = 1.5), - strip.text = element_text(family = 'Lucida Sans Unicode', face = 'bold'), - axis.ticks = element_blank()) -p2 <- ggplot(tickMat[lambda == '1.0']) + - geom_text( - aes(season, site.display, label = selected, colour = prox), - family = 'Arial Unicode MS', - show.legend = FALSE, - vjust = 0.25) + - facet_wrap(vars(lambda), labeller = as_labeller(lambdaLab)) + - scale_x_discrete(labels = ssnLab2Lines('1.0')) + - scale_y_discrete(labels = yLabCol, drop = FALSE) + - scale_colour_manual(values = c('firebrick', 'black')) + - labs(x = NULL, y = NULL) + - cowplot::panel_border('black', 0.2) + - theme( - axis.text.y = element_blank(), - axis.text.x = element_markdown(lineheight = 1.5), - strip.text = element_text(family = 'Lucida Sans Unicode', face = 'bold'), - strip.background = element_blank(), - axis.ticks = element_blank()) +pl <- lapply(c(0, 1.2), function(l) { + ggplot(weightDT[lambda == l]) + + geom_tile(aes(season, site.display, fill = weight)) + + geom_text( + aes(season, site.display, label = sameSign), + vjust = 0.25, + colour = 'gray30') + + facet_wrap(vars(lambda), labeller = as_labeller(lambdaLab)) + + scale_x_discrete(labels = ssnLab2Lines(l), expand = c(0, 0)) + + scale_y_discrete(labels = yLabCol, drop = FALSE) + + scale_fill_distiller( + palette = 'RdBu', + direction = 1, + name = 'Weight', + limits = abs_range(weightDT$weight)) + + labs(x = NULL, y = NULL) + + cowplot::panel_border('black', 0.2) + + theme( + legend.key.height = unit(1, 'cm'), + axis.text.y = element_markdown(), + axis.text.x = element_markdown(lineheight = 1.5), + strip.text = element_text(family = 'Lucida Sans Unicode', face = 'bold'), + axis.ticks = element_blank()) +}) + +for (i in 2:length(pl)) pl[[i]] <- pl[[i]] + theme(axis.text.y = element_blank()) -p1 + p2 +wrap_plots(pl, nrow = 1, guides = 'collect') ``` ## Annual mass balance -Calculate mass difference. +**Figure 7** -```{r deltaQ} -deltaQ <- rec[, dcast(.SD, year + lambda ~ season, value.var = 'Q') - ][, dQ := NJ + JO - WY - ][, rQ := NJ / WY] +```{r} densEst <- deltaQ[, { - bw <- if (.BY$lambda == '1.0') 50 else 80 - ct <- if (.BY$lambda == '1.0') 1.5 else 1 - d <- density(dQ, bw = bw, cut = ct) + d <- density(dQ, cut = 0, bw = if (lambda == 0) 120 else 100) data.table(x = d$x, y = d$y) }, by = lambda] -``` -Probability of ΔQ within 10% mean annual flow (190 Mm3)? Range? - -```{r} -Qmean <- p1merge[season == 'WY', mean(Qa)] -deltaQ[, .(p = .SD[abs(dQ) > Qmean / 10, .N] / .N * 100, - min = min(dQ), - max = max(dQ)), - by = lambda] %>% - roundDT(0) +densEst[, yScaled := y / max(y) * 3] +densEst[, l2 := to_char_1dp(lambda)] ``` -**Figure 5** - -```{r annual dQ, fig.width=7, fig.height=7} -cols <- c('#984ea3', '#4daf4a') -p1 <- ggplot(deltaQ) + - geom_line(aes(year, WY, linetype = 'Water year', colour = 'Water year'), size = 0.4) + - geom_line(aes(year, JO + NJ, linetype = 'Dry + Wet', colour = 'Dry + Wet'), size = 0.4) + - facet_wrap( - vars(lambda), - ncol = 1, - labeller = as_labeller(lambdaLab)) + - scale_colour_manual( - name = NULL, - values = cols) + - scale_linetype_manual( - name = NULL, - values = c(2, 1)) + - labs(x = 'Year', y = 'Q [million m\u00b3]') + - theme( - legend.position = 'none', - strip.text = element_text(family = 'Lucida Sans Unicode'), - strip.background = element_rect('gray90', NA)) - -p2 <- ggplot(deltaQ) + - stat_density( - aes(y = WY, colour = 'Water year', linetype = 'Water year'), - bw = 160, - position = 'identity', - geom = 'line') + - stat_density( - aes(y = JO + NJ, colour = 'Dry + Wet', linetype = 'Dry + Wet'), - bw = 160, - position = 'identity', - geom = 'line') + - scale_x_continuous(breaks = scales::pretty_breaks(3), expand = c(0.01, 0)) + - scale_colour_manual( - name = NULL, - values = cols) + - scale_linetype_manual( - name = NULL, - values = c(2, 1)) + - facet_wrap( - vars(lambda), - ncol = 1, - labeller = as_labeller(lambdaLab)) + - labs(x = 'Density', y = NULL) + - theme( - legend.key.width = unit(0.95, 'cm'), - axis.text.y = element_blank(), - axis.ticks.y = element_blank(), - strip.text = element_text(family = 'Lucida Sans Unicode'), - strip.background = element_rect('gray90', NA)) - -p3 <- ggplot(deltaQ) + - geom_line(aes(year, dQ, colour = lambda, linetype = lambda), size = 0.4) + - scale_y_continuous(breaks = seq(-800, 800, 200), limits = abs_range(densEst$x)) + +```{r annual dQ, fig.width=8, fig.height=5} +dQlims <- abs_range(densEst[lambda %in% c(0, 1.2), x]) +p1 <- ggplot(deltaQ[lambda %in% c(0, 1.2)]) + + geom_line(aes(year, dQ, colour = as.character(lambda), linetype = as.character(lambda)), size = 0.4) + + scale_y_continuous( + breaks = seq(-800, 800, 200), + limits = dQlims) + scale_colour_manual( name = NULL, values = c('steelblue', 'darkorange'), @@ -1258,18 +1350,20 @@ p3 <- ggplot(deltaQ) + labs(x = 'Year', y = '\u0394Q [million m\u00b3]') + theme(legend.position = 'none') -p4 <- ggplot(densEst) + +p2 <- ggplot(densEst[lambda %in% c(0, 1.2)]) + geom_area( aes(x, y), - densEst[x %between% c(-Qmean / 10, Qmean / 10) & lambda == 'l=1'], + densEst[x %between% c(-Qmean / 10, Qmean / 10) & lambda == 1.2], fill = 'gray95') + - geom_line(aes(x, y, colour = lambda, linetype = lambda)) + + geom_line(aes(x, y, colour = as.character(lambda), linetype = as.character(lambda))) + scale_colour_manual( name = NULL, values = c('steelblue', 'darkorange'), labels = lambdaLab) + - scale_x_continuous(breaks = seq(-800, 800, 200), limits = abs_range(densEst$x)) + - scale_y_continuous(expand = c(0.01, 0)) + + scale_x_continuous( + breaks = seq(-1000, 1000, 200), + limits = dQlims) + + scale_y_continuous(expand = expansion(c(0, 0.1), 0), labels = skip_label(2)) + scale_linetype_manual( name = NULL, labels = lambdaLab, @@ -1282,187 +1376,42 @@ p4 <- ggplot(densEst) + axis.text.y = element_blank(), axis.ticks.y = element_blank()) -p1 + p2 + p3 + p4 + - plot_layout(ncol = 2, widths = c(2, 1), heights = c(2, 1)) -``` - -## Seasonal mass balance - -```{r seasonal dQ, fig.width=7, fig.height=7} -cols1 <- RColorBrewer::brewer.pal(9, 'Set1')[c(3, 4)] -cols2 <- RColorBrewer::brewer.pal(9, 'Set1')[c(2, 5)] +lambdas <- round(seq(0, 3, 0.2), 1) -p1 <- ggplot(deltaQ) + - geom_line(aes(year, WY - JO, linetype = 'WY - WS', colour = 'WY - WS'), size = 0.4) + - geom_line(aes(year, NJ, linetype = 'DS', colour = 'DS'), size = 0.4) + - facet_wrap( - vars(lambda), - ncol = 1, - labeller = as_labeller(lambdaLab)) + - scale_colour_manual( - name = NULL, - breaks = c('WY - WS', 'DS'), - values = cols1) + - scale_linetype_manual( - name = NULL, - breaks = c('WY - WS', 'DS'), - values = c(1, 2)) + - labs(x = 'Year', y = 'Q [million m\u00b3]') + - theme( - legend.position = 'none', - strip.text = element_text(family = 'Lucida Sans Unicode'), - strip.background = element_rect('gray90', NA)) - -p2 <- ggplot(deltaQ) + - stat_density( - aes(y = NJ, colour = 'DS', linetype = 'DS'), - bw = 100, - position = 'identity', - geom = 'line') + - stat_density( - aes(y = WY - JO, colour = 'WY - WS', linetype = 'WY - WS'), - bw = 100, - position = 'identity', - geom = 'line') + - scale_x_continuous(breaks = scales::pretty_breaks(2), expand = c(0.01, 0)) + - scale_colour_manual( - name = NULL, - breaks = c('WY - WS', 'DS'), - values = cols1) + - scale_linetype_manual( - name = NULL, - breaks = c('WY - WS', 'DS'), - values = c(1, 2)) + - facet_wrap( - vars(lambda), - ncol = 1, - labeller = as_labeller(lambdaLab)) + - labs(x = 'Density', y = NULL) + +p3 <- ggplot(densEst[lambda %in% lambdas]) + + geom_ribbon( + aes(x, + ymin = lambda * 3, + ymax = yScaled + lambda * 3, + group = rev(l2), + fill = lambda), + colour = 'gray30', + size = 0.2) + + scale_fill_distiller( + name = '\u03bb', + palette = 'Oranges', direction = 1) + + scale_y_continuous(expand = c(0, 0)) + + labs(x = '\u0394Q [million m\u00b3]') + theme( - legend.key.width = unit(0.95, 'cm'), + panel.grid.major.x = element_line('gray'), axis.text.y = element_blank(), - axis.ticks.y = element_blank(), - strip.text = element_text(family = 'Lucida Sans Unicode'), - strip.background = element_rect('gray90', NA)) + axis.line.y = element_blank(), + axis.ticks.y = element_blank()) -p3 <- ggplot(deltaQ) + - geom_line(aes(year, WY - NJ, linetype = 'WY - DS', colour = 'WY - DS'), size = 0.4) + - geom_line(aes(year, JO, linetype = 'WS', colour = 'WS'), size = 0.4) + - facet_wrap( - vars(lambda), - ncol = 1, - labeller = as_labeller(lambdaLab)) + - scale_colour_manual( - name = NULL, - breaks = c('WY - DS', 'WS'), - values = cols2) + - scale_linetype_manual( - name = NULL, - breaks = c('WY - DS', 'WS'), - values = c(1, 2)) + - labs(x = 'Year', y = 'Q [million m\u00b3]') + - theme( - legend.position = 'none', - strip.text = element_text(family = 'Lucida Sans Unicode'), - strip.background = element_rect('gray90', NA)) - -p4 <- ggplot(deltaQ) + - stat_density( - aes(y = JO, colour = 'WS', linetype = 'WS'), - bw = 170, - position = 'identity', - geom = 'line') + - stat_density( - aes(y = WY - NJ, colour = 'WY - DS', linetype = 'WY - DS'), - bw = 170, - position = 'identity', - geom = 'line') + - scale_x_continuous(breaks = scales::pretty_breaks(3), expand = c(0.01, 0)) + - scale_colour_manual( - name = NULL, - breaks = c('WY - DS', 'WS'), - values = cols2) + - scale_linetype_manual( - name = NULL, - breaks = c('WY - DS', 'WS'), - values = c(1, 2)) + - facet_wrap( - vars(lambda), - ncol = 1, - labeller = as_labeller(lambdaLab)) + - labs(x = 'Density', y = NULL) + - theme( - legend.key.width = unit(0.95, 'cm'), - axis.text.y = element_blank(), - axis.ticks.y = element_blank(), - strip.text = element_text(family = 'Lucida Sans Unicode'), - strip.background = element_rect('gray90', NA)) +p4 <- ggplot( + deltaQ[lambda %in% lambdas, .(p = .SD[abs(dQ) > Qmean / 10, .N] / .N * 100, + min = min(dQ), + max = max(dQ)), + by = lambda]) + + geom_line(aes(lambda, p)) + + geom_point(aes(lambda, p)) + + scale_x_continuous(breaks = seq(0, 3, 0.2), labels = skip_label(5)) + + scale_y_continuous(limits = c(0, 50)) + + labs(x = '\u03bb', y = '\u0394Q within 10% mean flow [%]') p1 + p2 + p3 + p4 + - plot_layout(ncol = 2, widths = c(2, 1)) -``` - -## Flow ratio - -**Figure S14** - -```{r} -p1mergeWide <- dcast(p1merge, year ~ season, value.var = 'Qa') -p1mergeWide[, rQ := NJ / WY] - -rqDensEstRec <- deltaQ[, { - bw <- if (.BY$lambda == '1.0') 0.031 else 0.033 - d <- density(rQ, bw = bw) - data.table(x = d$x, y = d$y) -}, by = lambda] - -rqDensEstRecShort <- deltaQ[year %in% 1922:2005, { - bw <- if (.BY$lambda == '1.0') 0.031 else 0.033 - d <- density(rQ, bw = bw) - data.table(x = d$x, y = d$y) -}, by = lambda] - -rqDensEstInst <- p1mergeWide[, { - d <- density(rQ, bw = 0.045) - data.table(x = d$x, y = d$y) - }] -``` - - -```{r, fig.width=7, fig.height=6} -ggplot(deltaQ[lambda == '1.0'], aes(WY, rQ * 100)) + - geom_hline(yintercept = 50, colour = 'gray', size = 0.2) + - geom_point(aes(colour = 'Reconstructed'), size = 1) + - geom_point(aes(colour = 'Observed'), p1mergeWide) + - geom_text( - aes(label = year), - rbind( - p1mergeWide[year %in% c(1971, 1973, 1975)], - p1mergeWide[order(rQ)][.N]), - size = 3, - nudge_x = 120) + - geom_text( - aes(label = year), - deltaQ[lambda == '1.0'][order(rQ)][.N], - size = 3, - nudge_x = 150) + - geom_text( - aes(label = year), - deltaQ[lambda == '1.0' & year %in% c(1815, 1819, 1879)], - size = 3, - nudge_x = 120) + - scale_colour_manual( - name = NULL, - values = pal) + - scale_y_continuous(breaks = seq(20, 60, 5)) + - scale_x_continuous(breaks = seq(1000, 4000, 500)) + - labs( - x = 'Annual streamflow [million m\u00b3]', - y = 'Fraction of dry season flow to annual flow [%]') + - theme( - legend.margin = margin(5, 5, 5, 5), - legend.background = element_rect(NA, 'black', 0.2), - legend.position = c(0.75, 0.9)) + plot_layout(ncol = 2, widths = c(2, 1), guides = 'collect') + + plot_annotation(tag_levels = 'a', tag_suffix = ')') ``` # References diff --git a/multiproxy-mbr.html b/multiproxy-mbr.html index 0dedd8a..602c900 100644 --- a/multiproxy-mbr.html +++ b/multiproxy-mbr.html @@ -165,6 +165,9 @@ summary { display: list-item; } +pre code { + padding: 0; +} @@ -303,7 +306,7 @@
-
+
@@ -332,7 +335,7 @@

Introduction

Next, we load the necessary packages and utility functions.

source('R/init.R')
 source('R/correlation_functions.R')
-source('R/input-selection-functions.R')
+source('R/input_selection_functions.R')

We also use some special fonts for the plots, so we need to load them as well.

We are now ready to proceed with the workflow.

@@ -340,9 +343,6 @@

Introduction

Data and Preprocessing

Tree ring proxies

-
-

Map

-

Figure 1 was made in QGIS, but we can produce a similar map here with R.

# Read the metadata
 crnMeta <- fread('data/crnMeta.csv', key = 'site') # Ring width chronologies
 oxiMeta <- fread('data/oxiMeta.csv', key = 'site') # Oxygen isotope
@@ -359,163 +359,173 @@ 
Map

 crnMeta[, species := paste0('*', species, '*')]
 oxiMeta[, species := paste0('*', species, '*')]
 
-# Map
-ggplot() +
-  # Country borders
-  geom_polygon(
-    aes(long, lat, group = group), 
-    map_data('world', c('Vietnam', 'Laos', 'Cambodia', 'Thailand', 'Myanmar')),
-    fill = 'gray98', colour = 'black') +
-  # Ring width sites
-  geom_point(
-    aes(long, lat, colour = species, shape = 'Ring width'), 
-    size = 2,
-    crnMeta) +
-  # Oxygen isotope sites
-  geom_point(
-    aes(long, lat -0.25, colour = species, shape = '&delta;<sup>18</sup>O'), 
-    size = 2,
-    oxiMeta) +
-  # Map theming
-  scale_x_continuous(name = NULL, labels = pasteLong) +
-  scale_y_continuous(name = NULL, labels = pasteLat) +
-  scale_colour_brewer(name = 'Species', palette = 'Set1') +
-  scale_shape_discrete(name = 'Proxy') +
-  coord_quickmap() +
-  theme(
-    legend.key.height = unit(0.75, 'cm'),
-    legend.text = element_markdown()) +
-  panel_border('black', 0.2)
+# Some other labelling functions +seasonClass <- fread('data/season_class.csv', key = 'season') +ssnLabBrief <- function(x) seasonClass[x, type] +lambdaLab <- function(x) sapply(x, function(v) paste0('\u03bb = ', v)) +dark2 <- RColorBrewer::brewer.pal(3, 'Dark2')[c(2, 1, 3)] +metricLab <- function(x) replace(x, x == 'R2', 'R\u00b2') +to_char_1dp <- function(x) sprintf("%.1f", x)
+
+

Map

+

Figure 1 was made in QGIS, but we can produce a similar map here with R.

+
# Map
+ggplot() +
+  # Country borders
+  geom_polygon(
+    aes(long, lat, group = group), 
+    map_data('world', c('Vietnam', 'Laos', 'Cambodia', 'Thailand', 'Myanmar')),
+    fill = 'gray98', colour = 'black') +
+  # Ring width sites
+  geom_point(
+    aes(long, lat, colour = species, shape = 'Ring width'), 
+    size = 2,
+    crnMeta) +
+  # Oxygen isotope sites
+  geom_point(
+    aes(long, lat -0.25, colour = species, shape = '&delta;<sup>18</sup>O'), 
+    size = 2,
+    oxiMeta) +
+  # Map theming
+  scale_x_continuous(name = NULL, labels = pasteLong) +
+  scale_y_continuous(name = NULL, labels = pasteLat) +
+  scale_colour_brewer(name = 'Species', palette = 'Set1') +
+  scale_shape_discrete(name = 'Proxy') +
+  coord_quickmap() +
+  theme(
+    legend.key.height = unit(0.75, 'cm'),
+    legend.text = element_markdown()) +
+  panel_border('black', 0.2)

Time series

Now we read the proxy data and infill them with the package missMDA.

-
# Read data
-oxi <- fread('data/oxi.csv', key = 'site') 
-crn <- fread('data/crn.csv', key = 'site')
-
-# Count number of years in each chronology
-crnCount <- crn[year %in% 1748:2005, .(first = year[1], final = year[.N], .N), by = site
-              ][order(N, decreasing = TRUE)
-              ][, ID := .GRP, by = site]
-oxiCount <- oxi[year %in% 1748:2005, .(first = year[1], final = year[.N], .N), by = site
-              ][order(N, decreasing = TRUE)
-              ][, ID := .GRP, by = site]
-
-# Imputation
-crnWideFull   <- crn[year %in% 1748:2005, dcast(.SD, year ~ site, value.var = 'rwi')]
-crnMatFull    <- as.matrix(crnWideFull[, -'year'])
-crnImpModel   <- imputePCA(crnMatFull, ncp = 19)
-crnMatFilled  <- crnImpModel$completeObs
-crnWideFilled <- as.data.table(crnMatFilled)
-
-attributes(crnImpModel$fittedX) <- attributes(crnImpModel$completeObs)
-crnPcaImputed <-
-  as.data.table(crnImpModel$fittedX)[, year := crnWideFull$year] %>%
-  melt(id.var = 'year', variable.name = 'site', value.name = 'X')
-
-oxiWideFull   <- oxi[year %in% 1748:2005, dcast(.SD, year ~ site, value.var = 'do18')]
-oxiMatFull    <- as.matrix(oxiWideFull[, -'year'])
-oxiImpModel   <- imputePCA(oxiMatFull, ncp = 3)
-oxiMatFilled  <- oxiImpModel$completeObs
-oxiWideFilled <- as.data.table(oxiMatFilled)
-
-attributes(oxiImpModel$fittedX) <- attributes(oxiImpModel$completeObs)
-oxiPcaImputed <-
-  as.data.table(oxiImpModel$fittedX)[, year := oxiWideFull$year] %>%
-  melt(id.var = 'year', variable.name = 'site', value.name = 'X')
-

Figure S6

-
crnSpan <- ggplot(crnCount) +
-  geom_rect(
-    aes(xmin = first, xmax = final, ymin = ID - 1, ymax = ID + 0.1), 
-    fill = 'steelblue') +
-  geom_text(
-    aes(1950, ID - 0.5, label = crnLab(site)), 
-    colour = 'wheat', 
-    fontface = 'bold', 
-    size = 3.5) +
-  scale_x_continuous(expand = c(0, 0), breaks = seq(1750, 2005, 10), labels = skip_label(5)) +
-  scale_y_continuous(expand = c(0, 0)) +
-  labs(x = NULL, y = 'Number of chronologies', subtitle = 'Ring width') +
-  theme(
-    plot.subtitle = element_text(face = 'bold'),
-    panel.background = element_rect('gray95'),
-    panel.grid = element_blank())
-oxiSpan <- ggplot(oxiCount) +
-  geom_rect(
-    aes(xmin = first, xmax = final, ymin = ID - 1, ymax = ID + 0.1), fill = 'steelblue') +
-  geom_text(
-    aes(1950, ID - 0.5, label = oxiLab(site)), 
-    colour = 'wheat', 
-    fontface = 'bold', 
-    size = 3.5) +
-  scale_x_continuous(expand = c(0, 0), breaks = seq(1750, 2005, 10), labels = skip_label(5)) +
-  scale_y_continuous(expand = c(0, 0)) +
-  labs(x = NULL, y = 'Number of chronologies', subtitle = '&delta;<sup>18</sup>O') +
-  theme(
-    plot.subtitle = element_markdown(face = 'bold'),
-    panel.background = element_rect('gray95'),
-    panel.grid = element_blank())
-
-crnSpan + oxiSpan +
-  plot_layout(heights = c(5, 1.2)) +
-  plot_annotation(title = 'Temporal coverage')
+
# Read data
+oxi <- fread('data/oxi.csv', key = 'site') 
+crn <- fread('data/crn.csv', key = 'site')
+
+# Count number of years in each chronology
+crnCount <- crn[year %in% 1748:2005, .(first = year[1], final = year[.N], .N), by = site
+              ][order(N, decreasing = TRUE)
+              ][, ID := .GRP, by = site]
+oxiCount <- oxi[year %in% 1748:2005, .(first = year[1], final = year[.N], .N), by = site
+              ][order(N, decreasing = TRUE)
+              ][, ID := .GRP, by = site]
+
+# Imputation
+crnWideFull   <- crn[year %in% 1748:2005, dcast(.SD, year ~ site, value.var = 'rwi')]
+crnMatFull    <- as.matrix(crnWideFull[, -'year'])
+crnImpModel   <- imputePCA(crnMatFull, ncp = 19)
+crnMatFilled  <- crnImpModel$completeObs
+crnWideFilled <- as.data.table(crnMatFilled)
+
+attributes(crnImpModel$fittedX) <- attributes(crnImpModel$completeObs)
+crnPcaImputed <-
+  as.data.table(crnImpModel$fittedX)[, year := crnWideFull$year] %>%
+  melt(id.var = 'year', variable.name = 'site', value.name = 'X')
+
+oxiWideFull   <- oxi[year %in% 1748:2005, dcast(.SD, year ~ site, value.var = 'do18')]
+oxiMatFull    <- as.matrix(oxiWideFull[, -'year'])
+oxiImpModel   <- imputePCA(oxiMatFull, ncp = 3)
+oxiMatFilled  <- oxiImpModel$completeObs
+oxiWideFilled <- as.data.table(oxiMatFilled)
+
+attributes(oxiImpModel$fittedX) <- attributes(oxiImpModel$completeObs)
+oxiPcaImputed <-
+  as.data.table(oxiImpModel$fittedX)[, year := oxiWideFull$year] %>%
+  melt(id.var = 'year', variable.name = 'site', value.name = 'X')
+

Figure S5

+
crnSpan <- ggplot(crnCount) +
+  geom_rect(
+    aes(xmin = first, xmax = final, ymin = ID - 1, ymax = ID + 0.1), 
+    fill = 'steelblue') +
+  geom_text(
+    aes(1950, ID - 0.5, label = crnLab(site)), 
+    colour = 'wheat', 
+    fontface = 'bold', 
+    size = 3.5) +
+  scale_x_continuous(expand = c(0, 0), breaks = seq(1750, 2005, 10), labels = skip_label(5)) +
+  scale_y_continuous(expand = c(0, 0)) +
+  labs(x = NULL, y = 'Number of chronologies', subtitle = 'Ring width') +
+  theme(
+    plot.subtitle = element_text(face = 'bold'),
+    panel.background = element_rect('gray95'),
+    panel.grid = element_blank())
+oxiSpan <- ggplot(oxiCount) +
+  geom_rect(
+    aes(xmin = first, xmax = final, ymin = ID - 1, ymax = ID + 0.1), fill = 'steelblue') +
+  geom_text(
+    aes(1950, ID - 0.5, label = oxiLab(site)), 
+    colour = 'wheat', 
+    fontface = 'bold', 
+    size = 3.5) +
+  scale_x_continuous(expand = c(0, 0), breaks = seq(1750, 2005, 10), labels = skip_label(5)) +
+  scale_y_continuous(expand = c(0, 0)) +
+  labs(x = NULL, y = 'Number of chronologies', subtitle = '&delta;<sup>18</sup>O') +
+  theme(
+    plot.subtitle = element_markdown(face = 'bold'),
+    panel.background = element_rect('gray95'),
+    panel.grid = element_blank())
+
+crnSpan + oxiSpan +
+  plot_layout(heights = c(5, 1.2)) +
+  plot_annotation(title = 'Temporal coverage')

-

Figure S7

-
crnImpPlot <- ggplot() +
-  geom_line(aes(year, rwi, colour = 'RWI', linetype = 'RWI'), crn[year >= 1748]) +
-  geom_line(aes(year, X, colour = 'Imputed', linetype = 'Imputed'), crnPcaImputed) +
-  scale_colour_manual(
-    name = NULL,
-    breaks = c('RWI', 'Imputed'),
-    values = c('steelblue', 'darkorange')) +
-  scale_linetype_manual(
-    name = NULL,
-    breaks = c('RWI', 'Imputed'),
-    values = c(1, 2)) +
-  facet_wrap(
-    vars(site),
-    labeller = as_labeller(crnLab),
-    ncol = 4,
-    scales = 'free_y') +
-  labs(x = NULL, y = 'RWI') 
-
-oxiImpPlot <- ggplot() +
-  geom_line(
-    aes(year, do18, colour = '&delta;<sup>18</sup>O', linetype = '&delta;<sup>18</sup>O'), 
-    oxi[year >= 1748]) +
-  geom_line(
-    aes(year, X, colour = 'Imputed', linetype = 'Imputed'), 
-    oxiPcaImputed) +
-  scale_colour_manual(
-    name = NULL,
-    breaks = c('&delta;<sup>18</sup>O', 'Imputed'),
-    values = c('steelblue', 'darkorange')) +
-  scale_linetype_manual(
-    name = NULL,
-    breaks = c('&delta;<sup>18</sup>O', 'Imputed'),
-    values = c(1, 2)) +
-  facet_wrap(
-    vars(site),
-    labeller = as_labeller(oxiLab),
-    ncol = 4,
-    scales = 'free_y') +
-  labs(x = NULL, y = '&delta;<sup>18</sup>O') +
-  theme(
-    axis.title.y = element_markdown(),
-    legend.text = element_markdown())
-
-pl <- crnImpPlot + oxiImpPlot & 
-  theme(
-    strip.background = element_blank(),
-    strip.text = element_text(face = 'plain'),
-    legend.position = 'top',
-    legend.key.width = unit(2, 'cm'),
-    panel.border = element_rect(NA, 'black', 0.2))
-pl +
-  plot_layout(ncol = 1, heights = c(5, 1)) +
-  plot_annotation(title = 'Comparing imputed values with proxies')
+

Figure S6

+
crnImpPlot <- ggplot() +
+  geom_line(aes(year, rwi, colour = 'RWI', linetype = 'RWI'), crn[year >= 1748]) +
+  geom_line(aes(year, X, colour = 'Imputed', linetype = 'Imputed'), crnPcaImputed) +
+  scale_colour_manual(
+    name = NULL,
+    breaks = c('RWI', 'Imputed'),
+    values = c('steelblue', 'darkorange')) +
+  scale_linetype_manual(
+    name = NULL,
+    breaks = c('RWI', 'Imputed'),
+    values = c(1, 2)) +
+  facet_wrap(
+    vars(site),
+    labeller = as_labeller(crnLab),
+    ncol = 4,
+    scales = 'free_y') +
+  labs(x = NULL, y = 'RWI') 
+
+oxiImpPlot <- ggplot() +
+  geom_line(
+    aes(year, do18, colour = '&delta;<sup>18</sup>O', linetype = '&delta;<sup>18</sup>O'), 
+    oxi[year >= 1748]) +
+  geom_line(
+    aes(year, X, colour = 'Imputed', linetype = 'Imputed'), 
+    oxiPcaImputed) +
+  scale_colour_manual(
+    name = NULL,
+    breaks = c('&delta;<sup>18</sup>O', 'Imputed'),
+    values = c('steelblue', 'darkorange')) +
+  scale_linetype_manual(
+    name = NULL,
+    breaks = c('&delta;<sup>18</sup>O', 'Imputed'),
+    values = c(1, 2)) +
+  facet_wrap(
+    vars(site),
+    labeller = as_labeller(oxiLab),
+    ncol = 4,
+    scales = 'free_y') +
+  labs(x = NULL, y = '&delta;<sup>18</sup>O') +
+  theme(
+    axis.title.y = element_markdown(),
+    legend.text = element_markdown())
+
+pl <- crnImpPlot + oxiImpPlot & 
+  theme(
+    strip.background = element_blank(),
+    strip.text = element_text(face = 'plain'),
+    legend.position = 'top',
+    legend.key.width = unit(2, 'cm'),
+    panel.border = element_rect(NA, 'black', 0.2))
+pl +
+  plot_layout(ncol = 1, heights = c(5, 1)) +
+  plot_annotation(title = 'Comparing imputed values with proxies')

@@ -524,343 +534,341 @@

Streamflow

Exploring monthly streamflow

Figure S1

-
p1m <- fread('data/P1-monthly.csv')
-p1m[, Qa := sum(Qm), by = year]
-ggplot(p1m[year %in% 1922:2016]) +
-  geom_point(aes(Qa, Qm / Qa * 100), colour = 'steelblue') +
-  facet_wrap(
-    vars(month),
-    labeller = as_labeller(monthLab),
-    ncol = 4,
-    scales = 'free') +
-  labs(x = 'Annual flow [Mm\u00b3]', y = 'Monthly flow fraction [%]') +
-  panel_border('black', 0.2)
+
p1m <- fread('data/P1-monthly.csv')
+p1m[, Qa := sum(Qm), by = year]
+ggplot(p1m[year %in% 1922:2016]) +
+  geom_point(aes(Qa, Qm / Qa * 100), colour = 'steelblue') +
+  facet_wrap(
+    vars(month),
+    labeller = as_labeller(monthLab),
+    ncol = 4,
+    scales = 'free') +
+  labs(x = 'Annual flow [Mm\u00b3]', y = 'Monthly flow fraction [%]') +
+  panel_border('black', 0.2)

-

Figure S5

-
ggplot(p1m) +
-  geom_boxplot(aes(factor(month, labels = month.abb), Qm)) +
-  labs(x = NULL, y = 'Q [Mm\u00b3]')
+

Figure 1c

+
ggplot(p1m) +
+  geom_boxplot(aes(factor(month, labels = month.abb), Qm)) +
+  labs(x = NULL, y = 'Q [Mm\u00b3]')

Season delineation

Read daily data.

-
p1d <- fread('data/P1-daily-raw.csv')
-# Calculate day of the year
-p1d[, doy := {
-  date <- paste(year, month, day, sep = '-') %>% as.Date(format = c('%Y-%B-%d'))
-  yday(date)
-}]
-# Ignore leap days
-p1d <- p1d[!(year %% 4 == 0 & doy == 60)]
-p1d[year %% 4 == 0 & doy > 59, doy := doy - 1]
-p1DailyMean <- p1d[, .(mu = mean(Q, na.rm = TRUE)), keyby = doy]
-p1d[Q <= 0, Q := p1DailyMean[doy, mu]]
-
-# Convert flow rate to volumetric (m3/s to million m3 each day)
-p1d[, Qv := cumsum(Q * 0.0864), by = year] 
-p1dfy <- p1d[year %in% 1922:2016] # Full years
-setkey(p1dfy, year, doy)
+
p1d <- fread('data/P1-daily-raw.csv')
+# Calculate day of the year
+p1d[, doy := {
+  date <- paste(year, month, day, sep = '-') %>% as.Date(format = c('%Y-%B-%d'))
+  yday(date)
+}]
+# Ignore leap days
+p1d <- p1d[!(year %% 4 == 0 & doy == 60)]
+p1d[year %% 4 == 0 & doy > 59, doy := doy - 1]
+
+# Convert flow rate to volumetric (m3/s to million m3 each day)
+p1d[, Qv := cumsum(Q * 0.0864), by = year] 
+p1dfy <- p1d[year %in% 1922:2016] # Full years
+setkey(p1dfy, year, doy)

Two-phase linear regression, following Cook and Buckley (2009).

-
# Changepoint functions
-ssq_left_right <- function(x, t.range, d) {
-  idx1 <- which(t.range <= d)
-  idx2 <- which(t.range >  d)
-  t1   <- matrix(c(rep(1, length(idx1)), t.range[idx1]), ncol = 2)
-  t2   <- matrix(c(rep(1, length(idx2)), t.range[idx2]), ncol = 2) 
-  left  <- .lm.fit(t1, x[idx1])$residuals
-  right <- .lm.fit(t2, x[idx2])$residuals
-  sum(left^2) + sum(right^2)
-}
-
-# doy.range: vector of days of the year, must be contiguous
-scan_2lr <- function(DT, doy.range, scan.range, var.name) {
-  dt <- DT[doy %in% doy.range]
-  x <- dt[, get(var.name)]
-  ssq <- sapply(scan.range, ssq_left_right, x = x, t.range = doy.range)
-  data.table(doy = scan.range, ssq = ssq)
-}
-
-summary_2lr <- function(ssq) ssq[which.min(ssq)][, date := doy_to_ddMMM(doy)][]
-
-plot_2lr <- function(ssq, nudge_y = 2) {
-  changeDate <- summary_2lr(ssq)
-  ggplot(ssq, aes(doy, ssq)) +
-    geom_line(colour = 'steelblue') +
-    geom_point(data = changeDate, colour = 'steelblue') +
-    geom_text(aes(label = date), changeDate, nudge_y = nudge_y) +
-    labs(x = 'Day of year', y = 'RSS') +
-    theme_classic()
-}
-dryRange <- seq(  1, 280)
-dryScan  <- seq( 51, 270)
-wetRange <- seq(220, 365)
-wetScan  <- seq(230, 355)
-d2wAnn <- p1dfy[, summary_2lr(scan_2lr(.SD, dryRange, dryScan, 'Qv')), by = year]
-w2dAnn <- p1dfy[, summary_2lr(scan_2lr(.SD, wetRange, wetScan, 'Qv')), by = year]
-annualWetSeason <- merge(d2wAnn[, .(year, doy, date)], w2dAnn[, .(year, doy, date)],
-                         by = 'year',
-                         suffixes = c('sta', 'fin'))
-

Plot.

-
Qtails <- p1dfy[, last(.SD), by = year
-              ][order(Qv), rbind(head(.SD, 3), last(.SD, 3))]
-yearOrder <- Qtails$year
-cumuPlot <- ggplot(p1dfy) +
-  geom_line(aes(doy, Qv, group = year), na.rm = TRUE, colour = 'gray', size = 0.2) +
-  geom_line(
-    aes(doy, Qv, group = year, colour = factor(year, levels = yearOrder)), 
-    p1d[year %in% Qtails$year], 
-    size = 0.4, show.legend = FALSE) +
-  geom_text(
-    aes(366, Qv, label = year), 
-    Qtails, 
-    vjust = c(0.5, 0.5, 0.5, 0.9, 0.5, 0.5),
-    hjust = 0, 
-    size = 2.5) +
-  stat_summary(aes(doy, Qv), fun = mean, geom = 'line', na.rm = TRUE, size = 0.6) +
-  scale_x_continuous(breaks = c(1, 100, 200, 300), labels = doy_month_label) +
-  scale_colour_brewer(palette = 'RdBu') +
-  labs(x = 'Day of the year', y = 'Q [million m\u00b3]') 
-
-p1 <- ggplot(annualWetSeason) +
-  geom_linerange(aes(year, ymin = doysta, ymax = doyfin), colour = 'gray', size = 0.2) +
-  geom_line(aes(year, doysta), size = 0.2, colour = 'darkorange') +
-  geom_line(aes(year, doyfin), size = 0.2, colour = 'steelblue') +
-  geom_point(aes(year, doysta), size = 0.7, colour = 'darkorange') +
-  geom_point(aes(year, doyfin), size = 0.7, colour = 'steelblue') +
-  scale_colour_manual(name = NULL, values = c('darkgreen', 'steelblue')) +
-  scale_x_continuous(breaks = seq(1920, 2015, 5),
-                     labels = function(x) ifelse(x %% 10 == 0, x, '')) +
-  scale_y_continuous(breaks = c(110, seq(100, 300, 50)), labels = doy_month_label) +
-  labs(x = 'Year', y = 'Day of the year') +
-  theme(legend.position = 'none')
-p2 <- ggplot(annualWetSeason) +
-  geom_density(
-    aes(y = doysta), 
-    size = 0.3, 
-    colour = 'darkorange',
-    fill = 'darkorange',
-    alpha = 0.25) +
-  geom_density(
-    aes(y = doyfin), 
-    size = 0.3, 
-    colour = 'steelblue', 
-    fill = 'steelblue', 
-    alpha = 0.25) +
-  geom_hline(aes(yintercept = mean(doysta)), size = 0.2, colour = 'darkorange') +
-  geom_hline(aes(yintercept = mean(doyfin)), size = 0.2, colour = 'steelblue') +
-  scale_x_continuous(breaks = scales::pretty_breaks(2)) +
-  scale_y_continuous(breaks = c(110, seq(100, 300, 50))) +
-  labs(x = 'Density', y = NULL) +
-  theme(
-    plot.tag.position = c(-0.2, 1),
-    plot.margin = margin(l = 20),
-    axis.text.y = element_blank())
-
-delinPlot <- p1 + p2 + 
-  plot_layout(widths = c(4, 1)) 
-
-wrap_plots(cumuPlot) + wrap_plots(delinPlot) +
-  plot_layout(ncol = 1, heights = c(1.5, 1)) +
-  plot_annotation(tag_levels = 'a', tag_suffix = ')')
-

+
# Changepoint functions
+ssq_left_right <- function(x, t.range, d) {
+  idx1 <- which(t.range <= d)
+  idx2 <- which(t.range >  d)
+  t1   <- matrix(c(rep(1, length(idx1)), t.range[idx1]), ncol = 2)
+  t2   <- matrix(c(rep(1, length(idx2)), t.range[idx2]), ncol = 2) 
+  left  <- .lm.fit(t1, x[idx1])$residuals
+  right <- .lm.fit(t2, x[idx2])$residuals
+  sum(left^2) + sum(right^2)
+}
+
+# doy.range: vector of days of the year, must be contiguous
+scan_2lr <- function(DT, doy.range, scan.range, var.name) {
+  dt <- DT[doy %in% doy.range]
+  x <- dt[, get(var.name)]
+  ssq <- sapply(scan.range, ssq_left_right, x = x, t.range = doy.range)
+  data.table(doy = scan.range, ssq = ssq)
+}
+
+summary_2lr <- function(ssq) ssq[which.min(ssq)][, date := doy_to_ddMMM(doy)][]
+
+plot_2lr <- function(ssq, nudge_y = 2) {
+  changeDate <- summary_2lr(ssq)
+  ggplot(ssq, aes(doy, ssq)) +
+    geom_line(colour = 'steelblue') +
+    geom_point(data = changeDate, colour = 'steelblue') +
+    geom_text(aes(label = date), changeDate, nudge_y = nudge_y) +
+    labs(x = 'Day of year', y = 'RSS') +
+    theme_classic()
+}
+dryRange <- seq(  1, 280)
+dryScan  <- seq( 51, 270)
+wetRange <- seq(220, 365)
+wetScan  <- seq(230, 355)
+d2wAnn <- p1dfy[, summary_2lr(scan_2lr(.SD, dryRange, dryScan, 'Qv')), by = year]
+w2dAnn <- p1dfy[, summary_2lr(scan_2lr(.SD, wetRange, wetScan, 'Qv')), by = year]
+annualWetSeason <- merge(d2wAnn[, .(year, doy, date)], w2dAnn[, .(year, doy, date)],
+                         by = 'year',
+                         suffixes = c('sta', 'fin'))
+

Figure S2

+
Qtails <- p1dfy[, last(.SD), by = year
+              ][order(Qv), rbind(head(.SD, 3), last(.SD, 3))]
+yearOrder <- Qtails$year
+cumuPlot <- ggplot(p1dfy) +
+  geom_line(aes(doy, Qv, group = year), na.rm = TRUE, colour = 'gray', size = 0.2) +
+  geom_line(
+    aes(doy, Qv, group = year, colour = factor(year, levels = yearOrder)), 
+    p1d[year %in% Qtails$year], 
+    size = 0.4, show.legend = FALSE) +
+  geom_text(
+    aes(366, Qv, label = year), 
+    Qtails, 
+    vjust = c(0.5, 0.5, 0.5, 0.9, 0.5, 0.5),
+    hjust = 0, 
+    size = 2.5) +
+  stat_summary(aes(doy, Qv), fun = mean, geom = 'line', na.rm = TRUE, size = 0.6) +
+  scale_x_continuous(breaks = c(1, 100, 200, 300), labels = doy_month_label) +
+  scale_colour_brewer(palette = 'RdBu') +
+  labs(x = 'Day of the year', y = 'Q [million m\u00b3]') 
+
+p1 <- ggplot(annualWetSeason) +
+  geom_linerange(aes(year, ymin = doysta, ymax = doyfin), colour = 'gray', size = 0.2) +
+  geom_line(aes(year, doysta), size = 0.2, colour = 'darkorange') +
+  geom_line(aes(year, doyfin), size = 0.2, colour = 'steelblue') +
+  geom_point(aes(year, doysta), size = 0.7, colour = 'darkorange') +
+  geom_point(aes(year, doyfin), size = 0.7, colour = 'steelblue') +
+  scale_colour_manual(name = NULL, values = c('darkgreen', 'steelblue')) +
+  scale_x_continuous(breaks = seq(1920, 2015, 5),
+                     labels = function(x) ifelse(x %% 10 == 0, x, '')) +
+  scale_y_continuous(breaks = c(110, seq(100, 300, 50)), labels = doy_month_label) +
+  labs(x = 'Year', y = 'Day of the year') +
+  theme(legend.position = 'none')
+p2 <- ggplot(annualWetSeason) +
+  geom_density(
+    aes(y = doysta), 
+    size = 0.3, 
+    colour = 'darkorange',
+    fill = 'darkorange',
+    alpha = 0.25) +
+  geom_density(
+    aes(y = doyfin), 
+    size = 0.3, 
+    colour = 'steelblue', 
+    fill = 'steelblue', 
+    alpha = 0.25) +
+  geom_hline(aes(yintercept = mean(doysta)), size = 0.2, colour = 'darkorange') +
+  geom_hline(aes(yintercept = mean(doyfin)), size = 0.2, colour = 'steelblue') +
+  scale_x_continuous(breaks = scales::pretty_breaks(2)) +
+  scale_y_continuous(breaks = c(110, seq(100, 300, 50))) +
+  labs(x = 'Density', y = NULL) +
+  theme(
+    plot.tag.position = c(-0.2, 1),
+    plot.margin = margin(l = 20),
+    axis.text.y = element_blank())
+
+delinPlot <- p1 + p2 + 
+  plot_layout(widths = c(4, 1)) 
+
+wrap_plots(cumuPlot) + wrap_plots(delinPlot) +
+  plot_layout(ncol = 1, heights = c(1.5, 1)) +
+  plot_annotation(tag_levels = 'a', tag_suffix = ')')
+

Daily streamflow naturalization

We use robust empirical quantile mapping, following Gudmundsson et al. (2012). The procedure is implemented in the package qmap (Gudmundsson, 2016). See also Robeson et al. (2020).

-
# Merge VIC-Res simulated streamflow to daily streamflow
-p1MergeDaily <- merge(
-  p1d[year %in% 1922:2005, .(year, month = match(month, month.abb), day, Qobs = Q)],
-  fread('data/P1-VIC-Res.csv'),
-  on = c('year', 'month', 'day'))
-p1MergeDaily[, period := fifelse(year > 1985, 'Since 1985', 'Before 1985')]
-
-#Naturalize with quantile mapping.
-p1MergeDaily[, Qnat := {
-  fit <- fitQmap(.SD[period == 'Before 1985', Qobs],
-                 .SD[period == 'Before 1985', Qvic],
-                 'RQUANT', 
-                 nlls = 30, wet.day = FALSE, nboot = 100)
-  doQmap(.SD[, Qvic], fit)
-}, by = month]
-
-# Calculate monthly means
-p1Mon <- p1MergeDaily[, lapply(.SD, mean), by = .(year, month, period), .SDcols = -'day']
-
-p1MonLong <- melt(
-  p1Mon[period == 'Before 1985', -'period'], # Use data before 1985 only
-  id.var = c('year', 'month'), 
-  variable.name = 'type',
-  value.name = 'Q',
-  variable.factor = FALSE)
-
-# Calculate volumetric flow
-p1DayLong <- melt(
-  p1MergeDaily[, -'period'],
-  id.var = c('year', 'month', 'day'), 
-  variable.name = 'type',
-  value.name = 'Q',
-  variable.factor = FALSE)
-p1DayLong[, Q := Q * 3600 * 24 / 1e6]
-
-p1MonVol <- p1DayLong[, .(Q = sum(Q)), by = .(year, month, type)] 
-
-# Aggregate to seasonal flows
-
-# First, define the seasons
-names(seasons) <- seasons <- c('NJ', 'JO', 'WY')
-seasonClass <- fread('data/season_class.csv', key = 'season')
-ssnLabBrief <- function(x) seasonClass[x, type]
-
-# Get seasonal total
-natSeas <- rbindlist(list(
-  NJ = p1MonVol[, get_seasons(.SD, 'Q', 'Qa', c(11:12, 1:6), months.fwd = 11:12), by = type],
-  JO = p1MonVol[, get_seasons(.SD, 'Q', 'Qa', 7:10), by = type],
-  WY = p1MonVol[, get_seasons(.SD, 'Q', 'Qa', 1:12, months.fwd = 11:12), by = type]),
-  idcol = 'season')
-
-natSeas[, type := fcase(type == 'Qvic', 'Uncorrected',
-                        type == 'Qobs', 'Observed',
-                        type == 'Qnat', 'Bias-corrected')]
-natSeas[, season := factor(season, seasons)]
-

Plot results.

-
Qx <- seq(min(p1MonLong$Q), max(p1MonLong$Q), length.out = 1000)
-cdf <- p1MonLong[, .(Q = Qx, FQ = ecdf(Q)(Qx)), by = type]
-
-cdf[, type := fcase(type == 'Qvic', 'Uncorrected',
-                    type == 'Qobs', 'Observed',
-                    type == 'Qnat', 'Bias-corrected')]
+
# Merge VIC-Res simulated streamflow to daily streamflow
+p1MergeDaily <- merge(
+  p1d[year %in% 1922:2005, .(year, month = match(month, month.abb), day, Qobs = Q)],
+  fread('data/P1-VIC-Res.csv'),
+  on = c('year', 'month', 'day'))
+p1MergeDaily[, period := fifelse(year > 1985, 'Since 1985', 'Before 1985')]
 
-monCDFcomm <- ggplot(cdf, aes(Q, FQ, colour = type, linetype = type)) +
-  geom_line() +
-  scale_colour_manual(name = NULL, values = c('darkorange', 'black', 'gray')) +
-  scale_linetype_manual(name = NULL, values = c(1, 2, 1)) +
-  labs(x = 'Q [m\u00b3/s]', y = 'F(Q)') +
-  labs(subtitle = 'b) CDF of monthly data') 
-
-DT <- melt(p1MergeDaily[period == 'Before 1985', -'period'],
-           id.var = c('year', 'month', 'day'), 
-           variable.name = 'type',
-           value.name = 'Q',
-           variable.factor = FALSE)
-Qx <- seq(min(DT$Q), max(DT$Q), length.out = 1000)
-cdf <- DT[, .(Q = Qx, FQ = ecdf(Q)(Qx)), by = type]
-cdf[, type := fcase(type == 'Qvic', 'Uncorrected',
-                    type == 'Qobs', 'Observed',
-                    type == 'Qnat', 'Bias-corrected')]
-
-dayCDF <- ggplot(cdf, aes(Q, FQ, colour = type, linetype = type)) +
-  geom_line() +
-  scale_colour_manual(name = NULL, values = c('darkorange', 'black', 'grey')) +
-  scale_linetype_manual(name = NULL, values = c(1, 2, 1)) +
-  labs(x = 'Q [m\u00b3/s]', y = 'F(Q)') +
-  labs(subtitle = 'a) CDF of daily data')
-
-tsPlot <- ggplot(natSeas) + 
-  geom_rect(aes(xmin = 1976, xmax = 1985, ymin = -Inf, ymax = Inf), fill = 'gray97') +
-  geom_line(aes(year, Qa, colour = type, linetype = type)) +
-  facet_wrap(vars(season), scales = 'free_y', ncol = 1, labeller = as_labeller(ssnLabBrief)) +
-  scale_linetype_manual(name = NULL, values = c(1, 2, 1)) +
-  scale_colour_manual(name = NULL, values = c('darkorange', 'black', 'grey')) +
-  scale_x_continuous(
-    expand = c(0, 0),
-    breaks = seq(1975, 2005, 5)) +
-  labs(
-    subtitle = 'c) Time series',
-    x = NULL, 
-    y = 'Volumetric flow [million m\u00b3]') 
-
-layout <- '
-DDDD
-AABB
-AABB
-AABB
-AABB
-CCCC
-CCCC
-CCCC
-CCCC
-CCCC
-CCCC
-CCCC
-CCCC
-CCCC
-CCCC
-CCCC
-CCCC
-'
-
-monCDFcomm <- monCDFcomm + 
-  theme(axis.text.y = element_blank(),
-        axis.title.y = element_blank(),
-        axis.line.y = element_blank(),
-        axis.ticks.y = element_blank())
-
-pl <- dayCDF + monCDFcomm + tsPlot + guide_area() & 
-  theme(
-    strip.background = element_blank(),
-    plot.subtitle = element_text(face = 'bold'),
-    legend.key.width = unit(1.5, 'cm'), 
-    legend.position = 'top')
-pl + plot_layout(design = layout, guides = 'collect') 

-

+#N aturalize with quantile mapping.
+p1MergeDaily[, Qnat := {
+  fit <- fitQmap(.SD[period == 'Before 1985', Qobs],
+                 .SD[period == 'Before 1985', Qvic],
+                 'RQUANT', 
+                 nlls = 30, wet.day = 0.01, nboot = 100)
+  doQmap(.SD[, Qvic], fit)
+}, by = month]
+
+# Calculate monthly means
+p1Mon <- p1MergeDaily[, lapply(.SD, mean), by = .(year, month, period), .SDcols = -'day']
+
+p1MonLong <- melt(
+  p1Mon[period == 'Before 1985', -'period'], # Use data before 1985 only
+  id.var = c('year', 'month'), 
+  variable.name = 'type',
+  value.name = 'Q',
+  variable.factor = FALSE)
+
+# Calculate volumetric flow
+p1DayLong <- melt(
+  p1MergeDaily[, -'period'],
+  id.var = c('year', 'month', 'day'), 
+  variable.name = 'type',
+  value.name = 'Q',
+  variable.factor = FALSE)
+p1DayLong[, Q := Q * 3600 * 24 / 1e6]
+
+p1MonVol <- p1DayLong[, .(Q = sum(Q)), by = .(year, month, type)] 
+
+# Aggregate to seasonal flows
+
+# First, define the seasons
+names(seasons) <- seasons <- c('NJ', 'JO', 'WY')
+seasonClass <- fread('data/season_class.csv', key = 'season')
+ssnLabBrief <- function(x) seasonClass[x, type]
+
+# Get seasonal total
+natSeas <- rbindlist(list(
+  NJ = p1MonVol[, get_seasons(.SD, 'Q', 'Qa', c(11:12, 1:6), months.fwd = 11:12), by = type],
+  JO = p1MonVol[, get_seasons(.SD, 'Q', 'Qa', 7:10), by = type],
+  WY = p1MonVol[, get_seasons(.SD, 'Q', 'Qa', 1:12, months.fwd = 11:12), by = type]),
+  idcol = 'season')
+
+natSeas[, type := fcase(type == 'Qvic', 'Uncorrected',
+                        type == 'Qobs', 'Observed',
+                        type == 'Qnat', 'Bias-corrected')]
+natSeas[, season := factor(season, seasons)]
+

Figure S3

+
Qx <- seq(min(p1MonLong$Q), max(p1MonLong$Q), length.out = 1000)
+cdf <- p1MonLong[, .(Q = Qx, FQ = ecdf(Q)(Qx)), by = type]
+
+cdf[, type := fcase(type == 'Qvic', 'Uncorrected',
+                    type == 'Qobs', 'Observed',
+                    type == 'Qnat', 'Bias-corrected')]
+
+monCDFcomm <- ggplot(cdf, aes(Q, FQ, colour = type, linetype = type)) +
+  geom_line() +
+  scale_colour_manual(name = NULL, values = c('darkorange', 'black', 'gray')) +
+  scale_linetype_manual(name = NULL, values = c(1, 2, 1)) +
+  labs(x = 'Q [m\u00b3/s]', y = 'F(Q)') +
+  labs(subtitle = 'b) CDF of monthly data') 
+
+DT <- melt(p1MergeDaily[period == 'Before 1985', -'period'],
+           id.var = c('year', 'month', 'day'), 
+           variable.name = 'type',
+           value.name = 'Q',
+           variable.factor = FALSE)
+Qx <- seq(min(DT$Q), max(DT$Q), length.out = 1000)
+cdf <- DT[, .(Q = Qx, FQ = ecdf(Q)(Qx)), by = type]
+cdf[, type := fcase(type == 'Qvic', 'Uncorrected',
+                    type == 'Qobs', 'Observed',
+                    type == 'Qnat', 'Bias-corrected')]
+
+dayCDF <- ggplot(cdf, aes(Q, FQ, colour = type, linetype = type)) +
+  geom_line() +
+  scale_colour_manual(name = NULL, values = c('darkorange', 'black', 'grey')) +
+  scale_linetype_manual(name = NULL, values = c(1, 2, 1)) +
+  labs(x = 'Q [m\u00b3/s]', y = 'F(Q)') +
+  labs(subtitle = 'a) CDF of daily data')
+
+tsPlot <- ggplot(natSeas) + 
+  geom_rect(aes(xmin = 1976, xmax = 1985, ymin = -Inf, ymax = Inf), fill = 'gray97') +
+  geom_line(aes(year, Qa, colour = type, linetype = type)) +
+  facet_wrap(vars(season), scales = 'free_y', ncol = 1, labeller = as_labeller(ssnLabBrief)) +
+  scale_linetype_manual(name = NULL, values = c(1, 2, 1)) +
+  scale_colour_manual(name = NULL, values = c('darkorange', 'black', 'grey')) +
+  scale_x_continuous(
+    expand = c(0, 0),
+    breaks = seq(1975, 2005, 5)) +
+  labs(
+    subtitle = 'c) Time series',
+    x = NULL, 
+    y = 'Volumetric flow [million m\u00b3]') 
+
+layout <- '
+DDDD
+AABB
+AABB
+AABB
+AABB
+CCCC
+CCCC
+CCCC
+CCCC
+CCCC
+CCCC
+CCCC
+CCCC
+CCCC
+CCCC
+CCCC
+CCCC
+'
+
+monCDFcomm <- monCDFcomm + 
+  theme(axis.text.y = element_blank(),
+        axis.title.y = element_blank(),
+        axis.line.y = element_blank(),
+        axis.ticks.y = element_blank())
+
+pl <- dayCDF + monCDFcomm + tsPlot + guide_area() & 
+  theme(
+    strip.background = element_blank(),
+    plot.subtitle = element_text(face = 'bold'),
+    legend.key.width = unit(1.5, 'cm'), 
+    legend.position = 'top')
+pl + plot_layout(design = layout, guides = 'collect')
+

Combine flow

-
# Seasonal observed flow from 1922 to 1985
-p1s <- rbindlist(list(
-  NJ = get_seasons(p1m, 'Qm', 'Qa', c(11:12, 1:6), months.fwd = 11:12),
-  JO = get_seasons(p1m, 'Qm', 'Qa', 7:10),
-  WY = get_seasons(p1m, 'Qm', 'Qa', 1:12, months.fwd = 11:12) # New water year, Nov - Oct
-), idcol = 'season')[year %in% 1922:1984]
-p1s[, season := factor(season, levels = seasons)]
-setkey(p1s, season)
-
-# Naturalized seasonal flow from 1986 to 2005
-p1merge <- rbind(
-  p1s,
-  natSeas[type == 'Bias-corrected' & year >= 1985, -'type'])
-p1merge[, season := factor(season, seasons)]
-setkey(p1merge, season)
-
-# Target: 1922:2003
-p1tar <- p1merge[, .SD[1:82], by = season]
-
-## Hinkley D and transformation type
-p1HD <- p1merge[, .(D   = round(hinkley(Qa), 2), 
-                    Dlog = round(hinkley(log(Qa)), 2)), 
-                keyby = season
-              ][, trans := fifelse(abs(D) > abs(Dlog), 'log', 'none')]
+
# Seasonal observed flow from 1922 to 1985
+p1s <- rbindlist(list(
+  NJ = get_seasons(p1m, 'Qm', 'Qa', c(11:12, 1:6), months.fwd = 11:12),
+  JO = get_seasons(p1m, 'Qm', 'Qa', 7:10),
+  WY = get_seasons(p1m, 'Qm', 'Qa', 1:12, months.fwd = 11:12) # New water year, Nov - Oct
+), idcol = 'season')[year %in% 1922:1984]
+p1s[, season := factor(season, levels = seasons)]
+setkey(p1s, season)
+
+# Naturalized seasonal flow from 1986 to 2005
+p1merge <- rbind(
+  p1s,
+  natSeas[type == 'Bias-corrected' & year >= 1985, -'type'])
+p1merge[, season := factor(season, seasons)]
+setkey(p1merge, season)
+
+# Target: 1922:2003
+p1tar <- p1merge[year %in% 1922:2003]
+
+## Hinkley D and transformation type
+p1HD <- p1merge[, .(D   = round(hinkley(Qa), 2), 
+                    Dlog = round(hinkley(log(Qa)), 2)), 
+                keyby = season
+              ][, trans := fifelse(abs(D) > abs(Dlog), 'log', 'none')]

Table S1. Flow transformation type.

-
p1HD[]
+
p1HD[]
##    season    D  Dlog trans
-## 1:     NJ 0.14  0.02   log
+## 1:     NJ 0.13  0.01   log
 ## 2:     JO 0.15 -0.01   log
-## 3:     WY 0.12 -0.01   log
+## 3: WY 0.12 0.00 log

Figure S4

-
instDens <- p1tar[, {
-  bw <- if (.BY$season == 'NJ') 0.7 else 0.5
-  d1 <- density(standardize(Qa), bw = bw, cut = 1)
-  d2 <- density(standardize(log(Qa)), bw = bw, cut = 1)
-  c(list(x0 = d1$x, y0 = d1$y, xlog = d2$x, ylog = d2$y))
-}, by = season]
-
-ggplot(instDens) +
-  geom_line(aes(x0, y0, colour = 'No transformation')) +
-  geom_line(aes(xlog, ylog, colour = 'Log transformation')) +
-  scale_colour_manual(
-    name = NULL,
-    values = pal) +
-  facet_wrap(
-    vars(season),
-    labeller = as_labeller(ssnLabBrief),
-    scales = 'free') +
-  labs(x = 'z-score', y = 'Density') +
-  theme(
-    strip.background = element_rect('gray95', NA),
-    legend.key.width = unit(2, 'cm'),
-    legend.position = 'top')
-

+
instDens <- p1tar[, {
+  bw <- if (.BY$season == 'NJ') 0.7 else 0.5
+  d1 <- density(standardize(Qa), bw = bw, cut = 1)
+  d2 <- density(standardize(log(Qa)), bw = bw, cut = 1)
+  c(list(x0 = d1$x, y0 = d1$y, xlog = d2$x, ylog = d2$y))
+}, by = season]
+
+ggplot(instDens) +
+  geom_line(aes(x0, y0, colour = 'No transformation')) +
+  geom_line(aes(xlog, ylog, colour = 'Log transformation')) +
+  scale_colour_manual(
+    name = NULL,
+    values = pal) +
+  facet_wrap(
+    vars(season),
+    labeller = as_labeller(ssnLabBrief),
+    scales = 'free') +
+  labs(x = 'z-score', y = 'Density') +
+  theme(
+    strip.background = element_rect('gray95', NA),
+    legend.key.width = unit(2, 'cm'),
+    legend.position = 'top')
+

So all targets need to be log-transformed.

@@ -870,158 +878,163 @@

Correlation analyses

Streamflow-proxy correlations

Bootstrapped correlations.

-
# Input matrices
-allLags <- -2:2 # Core years: 1750:2003, and shift back or forth
-Xrw <- do.call(cbind, lapply(allLags, function(l) {
-  x <- crnMatFilled[3:256 - l, ]
-  colnames(x) <- paste0(colnames(x), l)
-  x
-}))
-Xdo <- do.call(cbind, lapply(allLags, function(l) {
-  x <- oxiMatFilled[3:256 - l, ]
-  colnames(x) <- paste0(colnames(x), l)
-  x
-}))
-Xrwdo <- cbind(Xrw, Xdo)
-
-# Streamflow target: 1922 to 2003
-QMat    <- log(as.matrix(dcast(p1tar, year ~ season, value.var = 'Qa')[, -'year']))
-cor_lag <- function(X, Y, l, Xyears, Yyears, alpha) {
-  ind <- which(Xyears %in% Yyears) - l
-  cor_boot(cbind(X[ind, ], Y), 
-           1:ncol(X),
-           1:ncol(Y) + ncol(X),
-           c('site', 'season'), 
-           alpha = alpha) 
-}
-set.seed(42)
-rhoCrn <- lapplyrbind(allLags, function(l) {
-    DT <- cor_lag(crnMatFilled, QMat, l, 1748:2005, 1922:2003, 0.05)
-    DT[, lag := l]
-    DT[, alpha := 0.05][]
-  })
-rhoOxi <- lapplyrbind(allLags, function(l) {
-    DT <- cor_lag(oxiMatFilled, QMat, l, 1748:2005, 1922:2003, 0.05)
-    DT[, lag := l]
-    DT[, alpha := 0.05][]
-  })
-
-dt <- CJ(s = factor(seasons, seasons), l = allLags)
-dt[, sl := paste0(s, '(', l, ')')]
-
-rhoCrn[, season_lag := paste0(season, '(', lag, ')')]
-rhoCrn[, season_lag := factor(season_lag, levels = dt$sl)]
-rhoCrn[, season := factor(season, levels = seasons)]
-rhoCrn[, lag := factor(lag, levels = allLags)]
-rhoOxi[, season_lag := paste0(season, '(', lag, ')')]
-rhoOxi[, season_lag := factor(season_lag, levels = dt$sl)]
-rhoOxi[, season := factor(season, levels = seasons)]
-rhoOxi[, lag := factor(lag, levels = allLags)]
+
# Core years: 1750:2003, and shift back or forth
+crnLags <- -2:2 # All lags for ring width
+oxiLags <- 0:2  # Positive lags only for d18O
+
+# Input matrices
+Xrw <- do.call(cbind, lapply(crnLags, function(l) {
+  x <- crnMatFilled[3:256 - l, ]
+  colnames(x) <- paste0(colnames(x), l)
+  x
+}))
+Xdo <- do.call(cbind, lapply(oxiLags, function(l) {
+  x <- oxiMatFilled[3:256 - l, ]
+  colnames(x) <- paste0(colnames(x), l)
+  x
+}))
+Xrwdo <- cbind(Xrw, Xdo)
+
+# Streamflow target: 1922 to 2003
+QMat    <- log(as.matrix(dcast(p1tar, year ~ season, value.var = 'Qa')[, -'year']))
+cor_lag <- function(X, Y, l, Xyears, Yyears, alpha) {
+  ind <- which(Xyears %in% Yyears) - l
+  cor_boot(cbind(X[ind, ], Y), 
+           1:ncol(X),
+           1:ncol(Y) + ncol(X),
+           c('site', 'season'), 
+           alpha = alpha) 
+}
+
+set.seed(42)
+rhoCrn <- lapplyrbind(crnLags, function(l) {
+    DT <- cor_lag(crnMatFilled, QMat, l, 1748:2005, 1922:2003, 0.05)
+    DT[, lag := l]
+    DT[, alpha := 0.05][]
+  })
+rhoOxi <- lapplyrbind(oxiLags, function(l) {
+    DT <- cor_lag(oxiMatFilled, QMat, l, 1748:2005, 1922:2003, 0.05)
+    DT[, lag := l]
+    DT[, alpha := 0.05][]
+  })
+
+dt <- CJ(s = factor(seasons, seasons), l = crnLags)
+dt[, sl := paste0(s, '(', l, ')')]
+
+rhoCrn[, season_lag := paste0(season, '(', lag, ')')]
+rhoCrn[, season_lag := factor(season_lag, levels = dt$sl)]
+rhoCrn[, season := factor(season, levels = seasons)]
+rhoCrn[, lag := factor(lag, levels = crnLags)]
+
+rhoOxi[, season_lag := paste0(season, '(', lag, ')')]
+rhoOxi[, season_lag := factor(season_lag, levels = dt$sl)]
+rhoOxi[, season := factor(season, levels = seasons)]
+rhoOxi[, lag := factor(lag, levels =crnLags)]

Figure 2

-
ssnLabLag <- function(x) {
-  char <- sapply(x, substr, start = 1, stop = 2)
-  yr <- sapply(x, function(xx) {
-    c1 <- substr(xx, 4, 4)
-    if (c1 == '-') c1 <- substr(xx, 4, 5)
-    c1
-  })
-  fcase(
-    char == 'NJ', paste0('<span style="color:', dark2[1], '">', yr, '</span>'),
-    char == 'JO', paste0('<span style="color:', dark2[2], '">', yr, '</span>'),
-    char == 'WY', paste0('<span style="color:', dark2[3], '">', yr, '</span>')
-  )
-}
-
-p1 <- ggplot(rhoCrn) +
-  geom_hline(yintercept = 0, colour = 'gray') +
-  geom_vline(
-    xintercept = c(length(allLags) + 0.5, length(allLags) * 2 + 0.5), 
-    colour = 'gray') +
-  geom_linerange(aes(season_lag, ymin = low, ymax = high, alpha = signif, colour = season)) +
-  geom_point(aes(season_lag, median, alpha = signif, colour = season)) +
-  scale_colour_manual(name = 'Season', values = dark2, labels = ssnLabBrief) +
-  facet_wrap(vars(site), labeller = as_labeller(crnLab), ncol = 4) +
-  scale_alpha_manual(name = 'Significance', values = c(0.25, 1)) +
-  scale_x_discrete(labels = ssnLabLag) +
-  scale_y_continuous(breaks = c(-0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6)) +
-  labs(
-    x = 'Lag [years]', y = 'Correlation [-]',
-    subtitle = 'a) Correlations between ring width and instrumental + naturalized streamflow') +
-  theme(axis.text.x = ggtext::element_markdown(family = 'Arial Narrow', size = 8),
-        axis.ticks.y = element_blank(),
-        axis.line = element_line(size = 0.1),
-        axis.ticks.x = element_line(size = 0.1),
-        legend.key.width = unit(0.1, 'cm'),
-        legend.text = element_text(margin = margin(r = 0.3, unit = 'cm')),
-        legend.title = element_text(margin = margin(r = 0.3, unit = 'cm')),
-        legend.position = 'top',
-        legend.box.spacing = unit(-0.3, 'cm'),
-        panel.grid.major.y = element_line('gray95', 0.1),
-        panel.border = element_rect(NA, 'black', 0.1),
-        strip.text = element_text(face = 'plain'),
-        strip.background = element_blank(),
-        plot.subtitle = element_text(face = 'bold'))
-p2 <- ggplot(rhoOxi) +
-  geom_hline(yintercept = 0, colour = 'gray') +
-  geom_vline(
-    xintercept = c(length(allLags) + 0.5, length(allLags) * 2 + 0.5), 
-    colour = 'gray') +
-  geom_linerange(aes(season_lag, ymin = low, ymax = high, alpha = signif, colour = season)) +
-  geom_point(aes(season_lag, median, alpha = signif, colour = season)) +
-  facet_wrap(vars(site), labeller = as_labeller(oxiLab), nrow = 1) +
-  scale_colour_manual(name = 'Season', values = dark2, labels = ssnLabBrief) +
-  scale_alpha_manual(name = 'Significance', values = c(0.25, 1)) +
-  scale_x_discrete(labels = ssnLabLag) +
-  scale_y_continuous(breaks = c(-0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6)) +
-  labs(
-    x = 'Lag [years]', y = 'Correlation [-]',
-    subtitle = 'b) Correlations between \u03b4\u00b9\u2078O and instrumental + naturalized streamflow') +
-  theme(axis.text.x = ggtext::element_markdown(family = 'Arial Narrow', size = 8),
-        axis.ticks.y = element_blank(),
-        axis.ticks.x = element_line(size = 0.1),
-        axis.line = element_line(size = 0.1),
-        panel.grid.major.y = element_line('gray95', 0.1),
-        panel.border = element_rect(NA, 'black', 0.1),
-        legend.position = 'none',
-        strip.text = element_text(face = 'plain'),
-        strip.background = element_blank(),
-        plot.subtitle = element_text(face = 'bold'))
-
-p1 / p2 + plot_layout(heights = c(6.25, 1))
-

+
ssnLabLag <- function(x) {
+  char <- sapply(x, substr, start = 1, stop = 2)
+  yr <- sapply(x, function(xx) {
+    c1 <- substr(xx, 4, 4)
+    if (c1 == '-') c1 <- substr(xx, 4, 5)
+    c1
+  })
+  fcase(
+    char == 'NJ', paste0('<span style="color:', dark2[1], '">', yr, '</span>'),
+    char == 'JO', paste0('<span style="color:', dark2[2], '">', yr, '</span>'),
+    char == 'WY', paste0('<span style="color:', dark2[3], '">', yr, '</span>')
+  )
+}
+
+p1 <- ggplot(rhoCrn) +
+  geom_hline(yintercept = 0, colour = 'gray') +
+  geom_vline(
+    xintercept = c(length(crnLags) + 0.5, length(crnLags) * 2 + 0.5), 
+    colour = 'gray') +
+  geom_linerange(aes(season_lag, ymin = low, ymax = high, alpha = signif, colour = season)) +
+  geom_point(aes(season_lag, median, alpha = signif, colour = season)) +
+  scale_colour_manual(name = 'Season', values = dark2, labels = ssnLabBrief) +
+  facet_wrap(vars(site), labeller = as_labeller(crnLab), ncol = 4) +
+  scale_alpha_manual(name = 'Significance', values = c(0.25, 1)) +
+  scale_x_discrete(labels = ssnLabLag) +
+  scale_y_continuous(breaks = c(-0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6)) +
+  labs(
+    x = 'Lag [years]', y = 'Correlation [-]',
+    subtitle = 'a) Correlations between ring width and instrumental + naturalized streamflow') +
+  theme(axis.text.x = ggtext::element_markdown(family = 'Arial Narrow', size = 8),
+        axis.ticks.y = element_blank(),
+        axis.line = element_line(size = 0.1),
+        axis.ticks.x = element_line(size = 0.1),
+        legend.key.width = unit(0.1, 'cm'),
+        legend.text = element_text(margin = margin(r = 0.3, unit = 'cm')),
+        legend.title = element_text(margin = margin(r = 0.3, unit = 'cm')),
+        legend.position = 'top',
+        legend.box.spacing = unit(-0.3, 'cm'),
+        panel.grid.major.y = element_line('gray95', 0.1),
+        panel.border = element_rect(NA, 'black', 0.1),
+        strip.text = element_text(face = 'plain'),
+        strip.background = element_blank(),
+        plot.subtitle = element_text(face = 'bold'))
+p2 <- ggplot(rhoOxi) +
+  geom_hline(yintercept = 0, colour = 'gray') +
+  geom_vline(
+    xintercept = c(length(oxiLags) + 0.5, length(oxiLags) * 2 + 0.5), 
+    colour = 'gray') +
+  geom_linerange(aes(season_lag, ymin = low, ymax = high, alpha = signif, colour = season)) +
+  geom_point(aes(season_lag, median, alpha = signif, colour = season)) +
+  facet_wrap(vars(site), labeller = as_labeller(oxiLab), nrow = 1) +
+  scale_colour_manual(name = 'Season', values = dark2, labels = ssnLabBrief) +
+  scale_alpha_manual(name = 'Significance', values = c(0.25, 1)) +
+  scale_x_discrete(labels = ssnLabLag) +
+  scale_y_continuous(breaks = c(-0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6)) +
+  labs(
+    x = 'Lag [years]', y = 'Correlation [-]',
+    subtitle = 'b) Correlations between \u03b4\u00b9\u2078O and instrumental + naturalized streamflow') +
+  theme(axis.text.x = ggtext::element_markdown(family = 'Arial Narrow', size = 8),
+        axis.ticks.y = element_blank(),
+        axis.ticks.x = element_line(size = 0.1),
+        axis.line = element_line(size = 0.1),
+        panel.grid.major.y = element_line('gray95', 0.1),
+        panel.border = element_rect(NA, 'black', 0.1),
+        legend.position = 'none',
+        strip.text = element_text(face = 'plain'),
+        strip.background = element_blank(),
+        plot.subtitle = element_text(face = 'bold'))
+
+p1 / p2 + plot_layout(heights = c(6.25, 1))
+

Proxy-climate correlations

We calculate the correlations between tree rings and CRU TS-4.04 precipitation data (Harris et al., 2020). Here we provide a subset of the CRU data set that contains only the grid points in Southeast Asia.

-
pre <- readRDS('data/CRU-precip.RDS')
-preMS <- pre[month %in% 7:10, .(pre = sum(pre)), by = .(lon, lat, year)]
-preMAM <- pre[month %in% 3:5, .(pre = sum(pre)), by = .(lon, lat, year)]
-preMS.sd <- preMS[, .(sd = sd(pre)), by = .(lon, lat)][sd < 0.1]
-setkey(preMS, lon, lat)
-setkey(preMS.sd, lon, lat)
-preMS <- preMS[!preMS.sd]
-
-corMS <- grid_cor(crn, preMS, 'rwi', 'pre', crnMeta)
-corMAM <- grid_cor(crn, preMAM, 'rwi', 'pre', crnMeta)
-
-corOxiMS <- grid_cor(oxi, preMS, 'do18', 'pre', oxiMeta)
-corOxiMAM <- grid_cor(oxi, preMAM, 'do18', 'pre', oxiMeta)
+
pre <- readRDS('data/CRU-precip.RDS')
+preMS <- pre[month %in% 7:10, .(pre = sum(pre)), by = .(lon, lat, year)]
+preMAM <- pre[month %in% 3:5, .(pre = sum(pre)), by = .(lon, lat, year)]
+preMS.sd <- preMS[, .(sd = sd(pre)), by = .(lon, lat)][sd < 0.1]
+setkey(preMS, lon, lat)
+setkey(preMS.sd, lon, lat)
+preMS <- preMS[!preMS.sd]
+
+corMS <- grid_cor(crn, preMS, 'rwi', 'pre', crnMeta)
+corMAM <- grid_cor(crn, preMAM, 'rwi', 'pre', crnMeta)
+
+corOxiMS <- grid_cor(oxi, preMS, 'do18', 'pre', oxiMeta)
+corOxiMAM <- grid_cor(oxi, preMAM, 'do18', 'pre', oxiMeta)

Tree rings

-

Figure S8

-
plot_cor(corMS)
+

Figure S7

+
plot_cor(corMS)

-

Figure S9

-
plot_cor(corMAM, cor.limits = abs_range(corMS$rho$r))
+

Figure S8

+
plot_cor(corMAM, cor.limits = abs_range(corMS$rho$r))

Oxygen isotope

-

Figure S10

-
p1 <- plot_cor(corOxiMS) 
-p2 <- plot_cor(corOxiMAM, cor.limits = abs_range(corOxiMS$rho$r))
-p1 + p2 +
-  plot_layout(ncol = 1, guides = 'collect')
+

Figure S9

+
p1 <- plot_cor(corOxiMS) 
+p2 <- plot_cor(corOxiMAM, cor.limits = abs_range(corOxiMS$rho$r))
+p1 + p2 +
+  plot_layout(ncol = 1, guides = 'collect')

@@ -1030,642 +1043,730 @@

Oxygen isotope

Reconstruction

Site selection

-

The site selection script is provided in the file site-selection-GA.R. At the beginning of the script, you can set three parameters: lambda, the penalty weight; pop, the genetic algorithm’s population size, and gen, the number of generations. Then you can source the script with

-
source('site-selection-GA.R')
-

or if you are using RStudio, we recommend that you submit it as a local job so that it does not freeze your RStudio. Alternatively, if you have access to a computing cluster, you can submit the script to your cluster. On a quad-core 3.4 GHz desktop with double threading (effectively 8 cores), the script took about an hour. On a 24-core 2.10 GHz computing node, it took about 20 minutes.

-

In this repository, we provide two set of reconstructions, one with λ = 0, and another with λ = 1, as we presented in the paper. Both were run with pop = 500 and gen = 500.

+

Number of potential inputs for each season

+
allInputs <- rbind(rhoCrn[{signif}, .(site, lag, absRho = abs(rho0), rho = rho0, season)],
+                   rhoOxi[{signif}, .(site, lag, absRho = abs(rho0), rho = rho0, season)])
+allInputs[, .N, by = season]
+
##    season  N
+## 1:     NJ 31
+## 2:     JO 19
+## 3:     WY 27
+

To speed up the search process, we only use at most 20 inputs for each season. So we take the 20 inputs with highest correlation magnitudes. This means for the wet season, we keep all 19 inputs.

+
poolDT <- allInputs[, head(.SD[order(absRho, decreasing = TRUE)], 20), by = season]
+poolDT[, site2 := paste0(site, lag)]
+setkey(poolDT, season)
+poolDT
+
##     season          site lag    absRho        rho          site2
+##  1:     NJ           MCC   0 0.4273749 -0.4273749           MCC0
+##  2:     NJ          PMTK   1 0.3597523 -0.3597523          PMTK1
+##  3:     NJ      Phu Toei   0 0.3510433  0.3510433      Phu Toei0
+##  4:     NJ     Laos pine   2 0.3357694 -0.3357694     Laos pine2
+##  5:     NJ          BDFH  -1 0.3240027  0.3240027         BDFH-1
+##  6:     NJ    Chin Hills  -2 0.3110630  0.3110630   Chin Hills-2
+##  7:     NJ      Pha Taem  -1 0.3096882  0.3096882     Pha Taem-1
+##  8:     NJ      Phu Toei   1 0.3035004  0.3035004      Phu Toei1
+##  9:     NJ      Pha Taem   0 0.2899055  0.2899055      Pha Taem0
+## 10:     NJ          PMTK   0 0.2768578 -0.2768578          PMTK0
+## 11:     NJ   Phu Kradung   0 0.2649651 -0.2649651   Phu Kradung0
+## 12:     NJ           MHS  -2 0.2626475 -0.2626475          MHS-2
+## 13:     NJ      Phu Toei  -1 0.2497939  0.2497939     Phu Toei-1
+## 14:     NJ          PMFH   0 0.2410215  0.2410215          PMFH0
+## 15:     NJ Salaeng Luong   0 0.2338626 -0.2338626 Salaeng Luong0
+## 16:     NJ     Laos pine   1 0.2318004 -0.2318004     Laos pine1
+## 17:     NJ      Phu Toei   2 0.2268285  0.2268285      Phu Toei2
+## 18:     NJ           MCC   2 0.2216560 -0.2216560           MCC2
+## 19:     NJ          PMFH  -1 0.2153400  0.2153400         PMFH-1
+## 20:     NJ           MCC   1 0.2132906 -0.2132906           MCC1
+## 21:     JO          PMTK   0 0.5829340 -0.5829340          PMTK0
+## 22:     JO           MCC   0 0.4712510 -0.4712510           MCC0
+## 23:     JO           PKK   0 0.3376907 -0.3376907           PKK0
+## 24:     JO    Chin Hills  -2 0.3151751  0.3151751   Chin Hills-2
+## 25:     JO          MCFH  -1 0.3085207  0.3085207         MCFH-1
+## 26:     JO          UPML   0 0.2950661 -0.2950661          UPML0
+## 27:     JO     Laos pine   1 0.2793361 -0.2793361     Laos pine1
+## 28:     JO      Phu Toei   2 0.2692750  0.2692750      Phu Toei2
+## 29:     JO          KHDF   2 0.2539303 -0.2539303          KHDF2
+## 30:     JO          KKFH  -2 0.2194214 -0.2194214         KKFH-2
+## 31:     JO      Wat Chan  -1 0.2150527 -0.2150527     Wat Chan-1
+## 32:     JO     Laos pine  -1 0.2063185 -0.2063185    Laos pine-1
+## 33:     JO          QNFH  -2 0.1944668 -0.1944668         QNFH-2
+## 34:     JO           MHS   0 0.1879396  0.1879396           MHS0
+## 35:     JO           MHS  -2 0.1766460 -0.1766460          MHS-2
+## 36:     JO      Phu Toei   0 0.1742106  0.1742106      Phu Toei0
+## 37:     JO           MHS   2 0.1622417  0.1622417           MHS2
+## 38:     JO          UPML   1 0.1549976 -0.1549976          UPML1
+## 39:     JO Salaeng Luong   2 0.1276157  0.1276157 Salaeng Luong2
+## 40:     WY          PMTK   0 0.5513629 -0.5513629          PMTK0
+## 41:     WY           MCC   0 0.5326978 -0.5326978           MCC0
+## 42:     WY    Chin Hills  -2 0.3578758  0.3578758   Chin Hills-2
+## 43:     WY           PKK   0 0.3463292 -0.3463292           PKK0
+## 44:     WY          MCFH  -1 0.3382276  0.3382276         MCFH-1
+## 45:     WY     Laos pine   1 0.3022798 -0.3022798     Laos pine1
+## 46:     WY      Phu Toei   2 0.2862243  0.2862243      Phu Toei2
+## 47:     WY          KHDF   2 0.2837561 -0.2837561          KHDF2
+## 48:     WY          PMTK   1 0.2800732 -0.2800732          PMTK1
+## 49:     WY      Phu Toei   0 0.2768490  0.2768490      Phu Toei0
+## 50:     WY          UPML   0 0.2664733 -0.2664733          UPML0
+## 51:     WY     Laos pine   2 0.2527075 -0.2527075     Laos pine2
+## 52:     WY      Phu Toei  -1 0.2502368  0.2502368     Phu Toei-1
+## 53:     WY           MHS  -2 0.2384292 -0.2384292          MHS-2
+## 54:     WY      Phu Toei   1 0.2110871  0.2110871      Phu Toei1
+## 55:     WY          QNFH  -2 0.2044251 -0.2044251         QNFH-2
+## 56:     WY          KKFH  -2 0.2031361 -0.2031361         KKFH-2
+## 57:     WY          MCFH   0 0.2015978  0.2015978          MCFH0
+## 58:     WY           MHS   0 0.1994229  0.1994229           MHS0
+## 59:     WY          UPML   1 0.1972421 -0.1972421          UPML1
+##     season          site lag    absRho        rho          site2
+
poolDT[, .N, by = season]
+
##    season  N
+## 1:     NJ 20
+## 2:     JO 19
+## 3:     WY 20
+

Now we export the necessary data for the site selection script, which should be run on a computing cluster.

+
saveRDS(poolDT, 'results/poolDT.RDS')
+saveRDS(Xrwdo, 'results/Xrwdo.RDS')
+saveRDS(p1tar, 'results/p1tar.RDS')
+

The site selection script is provided in the file site-selection-GA.R. The National Supercomputing Center in Singapore, where I ran the computations, used the PBS job management program. I provided a PBS script in this repo. If your cluster uses SLURM then you will need to replace the PBS script with your SLURM script.

+
+
+

GA outputs

+

Read GA outputs and get the solution for each λ value:

+
# Read all GA ouptuts
+fl <- list.files('results/', pattern = 'pop', full.names = TRUE)
+
+gaOut <- lapply(fl, function(fn) {
+  out <- readRDS(fn)
+  lambda <- as.integer(substr(fn, 29, 30)) / 10
+  seed <- as.integer(substr(fn, 33, 35))
+  data.table(
+    lambda = lambda, 
+    seed = seed, 
+    fval = -out@fitnessValue,
+    ga = c(out))
+}) %>% 
+  rbindlist()
+
+# Find the best seed for each lambda value
+best <- gaOut[, .SD[which.min(fval)], by = lambda]
+best[, 1:3]
+
##     lambda seed     fval
+##  1:    0.0  300 3.679014
+##  2:    0.2  400 3.711764
+##  3:    0.4  200 3.723808
+##  4:    0.6  300 3.787130
+##  5:    0.8  400 3.793077
+##  6:    1.0  300 3.809312
+##  7:    1.2  300 3.859533
+##  8:    1.4  400 3.846040
+##  9:    1.6  200 3.892296
+## 10:    1.8  450 3.876829
+## 11:    2.0  300 3.892901
+## 12:    2.2  400 3.911734
+## 13:    2.4  400 3.949693
+## 14:    2.6  400 3.966613
+## 15:    2.8  400 3.980429
+## 16:    3.0  400 3.979361
+

Check GA convergence

+
par(mfrow = c(4, 4))
+best[, {
+  plot(ga[[1]], main = paste0('\u03bb = ', lambda))
+}, by = lambda] %>% 
+  invisible()
+

+

Figure S10

+
par(mfrow = c(1, 2))
+plot(
+  best[lambda == 0, ga[[1]]],
+  main = expression(lambda == 0), 
+  col = c('steelblue', NA, adjustcolor('steelblue', alpha.f = 0.1)))
+plot(
+  best[lambda == 1.2, ga[[1]]],
+  main = expression(lambda == 1.2), 
+  col = c('steelblue', NA, adjustcolor('steelblue', alpha.f = 0.1)))
+

-
-

Cross-validation and final reconstruction

-
# Read GA results
-siteOptim_l0 <- readRDS('results/pop500_gen500_lambda0.RDS')
-siteOptim_l1 <- readRDS('results/pop500_gen500_lambda1.RDS')
-
-# Get sites from GA results
-poolDT <- rbind(rhoCrn[{signif} & abs(rho0) >= 0.2, .(site, lag), by = season],
-                rhoOxi[{signif} & abs(rho0) >= 0.2, .(site, lag), by = season])
-poolDT[, site2 := paste0(site, lag)]
-setkey(poolDT, season)
-
-mbPool <- rbindlist(
-  list(
-    '0.0' = poolDT[c(siteOptim_l0@solution) == 1],
-    '1.0' = poolDT[c(siteOptim_l1@solution) == 1]
-  ),
-  idcol = 'lambda')
-
-# Run cross validation to get R2, RE, and CE
-lambdas = c('0.0' = '0.0', '1.0' = '1.0')
-pc3seasons <- 
-  lapply(lambdas, function(l) 
-    lapply(seasons, function(s) {
-      Qa <- p1tar[s]
-      X <- Xrwdo[, mbPool[season == s & lambda == l, site2]]
-      PC <- wPCA(X, use.eigen = FALSE, return.matrix = TRUE)
-      sv <- input_selection(PC[173:254, ], Qa$Qa, 'leaps backward', nvmax = 8)
-      PC[, sv, drop = FALSE]
-    }))
-set.seed(24)
-cvFolds <- make_Z(1922:2003, nRuns = 50, frac = 0.25, contiguous = TRUE)
-
-cv <- lapplyrbind(lambdas, function(l) 
-    cv_mb(p1tar, pc3seasons[[l]], cvFolds, 1750, 
-          lambda = as.numeric(l),
-          log.trans = 1:3,
-          return.type = 'all'),
-    id = 'lambda')
-cv[, season := factor(season, seasons)]
-# Calculate the robust mean
-cvRM <- cv[, 
-           lapply(.SD, dplR::tbrm), 
-           .SDcols = c('R2', 'RE', 'CE'), 
-           keyby = .(lambda, season)]
-
-# Final reconstruction
-rec <- lapplyrbind(lambdas, function(l) 
-  mb_reconstruction(p1tar, pc3seasons[[l]], 1750, lambda = as.numeric(l), log.trans = 1:3))
-
-rec[, season := factor(season, seasons)]
-rec[, lambda := paste0(lambda, '.0')]
+
+

Run final reconstructions and cross-validations from the selected inputs

+
# Function to run reconstructions and cross-validation
+reconstruct <- function(sol, lambda, pool, Xused, target, cvFolds) {
+  
+  pool <- pool[c(sol@solution) == 1]
+
+  # Run cross validation to get R2, RE, and CE
+  pcaOut <- lapply(seasons, function(s) {
+    Qa <- target[s]
+    X <- Xused[, pool[season == s, site2]]
+    pcaModel <- prcomp(X, scale. = TRUE)
+    PC <- pcaModel$x
+    sv <- input_selection(PC[173:254, ], Qa$Qa, 'leaps backward', nvmax = 8)
+    list(loadings = pcaModel$rotation[, sv, drop = FALSE],
+         PC = PC[, sv, drop = FALSE])
+  })
+  
+  pc3s <- lapply(pcaOut, '[[', 'PC')
+  beta <- mb_par(target, pc3s, 1750, lambda = lambda, log.trans = 1:3)
+  
+  loadingList <- lapply(pcaOut, '[[', 'loadings')
+  nPCs <- sapply(loadingList, ncol)
+  interceptIdx <- c(1, nPCs[1] + 2, nPCs[1] + nPCs[2] + 3)
+  
+  beta2 <- beta[-interceptIdx]
+  loadingMat <- as.matrix(Matrix:::.bdiag(loadingList))
+  weight <- loadingMat %*% beta2
+  
+  rec <- mb_reconstruction(target, pc3s, 1750, lambda = lambda, log.trans = 1:3)
+  cv <- cv_mb(target, pc3s, cvFolds, 1750, 
+              lambda = lambda,
+              log.trans = 1:3,
+              return.type = 'all')
+  cv[, season := factor(season, seasons)]
+  
+  list(rec = list(rec), 
+       cv = list(cv[, -c('nRMSE', 'KGE')]),
+       loadings = list(loadingList),
+       beta = list(beta),
+       weight = list(weight))
+}
+
+# Run the function for each lambda
+set.seed(24)
+cvFolds <- make_Z(1922:2003, nRuns = 50, frac = 0.25, contiguous = TRUE)
+
+best[, c('rec', 'cv', 'loadings', 'beta', 'weight') := 
+       reconstruct(ga[[1]], lambda, poolDT, Xrwdo, p1tar, cvFolds), by = lambda]
+
+# Extract reconstructions
+rec <- rbindlist(best$rec)
+rec[, season := factor(season, seasons)]
+
+# Extract CV scores
+cv <- best[, cv[[1]], by = lambda]
+cvLong <- melt(cv, 
+               id.vars = c('lambda', 'rep', 'season'),
+               measure.vars = c('R2', 'RE', 'CE'),
+               variable.name = 'metric')
+

Check if we get the correct f-value.

+
merge(cv[, .(fnew = mean(fval)), by = lambda], best[, .(lambda, fval)], by = 'lambda')
+
##     lambda     fnew     fval
+##  1:    0.0 3.679014 3.679014
+##  2:    0.2 3.711764 3.711764
+##  3:    0.4 3.723808 3.723808
+##  4:    0.6 3.787130 3.787130
+##  5:    0.8 3.793077 3.793077
+##  6:    1.0 3.809312 3.809312
+##  7:    1.2 3.859533 3.859533
+##  8:    1.4 3.846040 3.846040
+##  9:    1.6 3.892296 3.892296
+## 10:    1.8 3.876829 3.876829
+## 11:    2.0 3.892901 3.892901
+## 12:    2.2 3.911734 3.911734
+## 13:    2.4 3.949693 3.949693
+## 14:    2.6 3.966613 3.966613
+## 15:    2.8 3.980429 3.980429
+## 16:    3.0 3.979361 3.979361
+

All is good.

+

Check PCA loadings (Tables S2 to S7)

+
best[lambda == 0, loadings][[1]] %>% 
+  lapply(round, digits = 4)
+
## $NJ
+##                  PC1     PC4     PC7
+## MCC0          0.6004 -0.0098  0.1416
+## PMTK1         0.4395  0.4601 -0.2301
+## Laos pine2    0.2362  0.0037 -0.5159
+## BDFH-1       -0.4159  0.2404 -0.1157
+## Pha Taem0    -0.4144 -0.1294 -0.3543
+## Phu Kradung0  0.0711 -0.5820  0.2291
+## Laos pine1    0.1189 -0.4960  0.0222
+## Phu Toei2     0.0133  0.1571  0.4642
+## PMFH-1       -0.1630  0.3228  0.5035
+## 
+## $JO
+##                    PC1     PC8     PC9
+## PMTK0           0.5258 -0.1292  0.3164
+## MCC0            0.5345 -0.0832 -0.1001
+## Chin Hills-2   -0.3054 -0.0735  0.2416
+## Laos pine1      0.0933  0.0993  0.1851
+## Phu Toei2      -0.0502  0.4552 -0.1498
+## KHDF2           0.1351 -0.5299  0.0174
+## KKFH-2          0.1520  0.1219  0.1289
+## Wat Chan-1      0.1217  0.1455 -0.1905
+## Laos pine-1     0.0750 -0.3786  0.0573
+## MHS0           -0.1966 -0.4756 -0.1402
+## MHS2           -0.1174  0.0758 -0.1957
+## UPML1           0.4364  0.1011 -0.5137
+## Salaeng Luong2 -0.1695 -0.2274 -0.6296
+## 
+## $WY
+##                PC1     PC5    PC11
+## PMTK0       0.4988  0.0274 -0.1653
+## MCC0        0.3727  0.3141  0.7527
+## PKK0        0.4590  0.1885 -0.0440
+## Laos pine1  0.0881 -0.4189 -0.0224
+## Phu Toei2   0.0211  0.4463 -0.1495
+## KHDF2       0.1622 -0.4580  0.1957
+## Phu Toei0  -0.2788  0.1343  0.1016
+## UPML0       0.4586 -0.0155 -0.5173
+## Laos pine2  0.1389 -0.1539  0.0583
+## Phu Toei-1 -0.1759  0.3317 -0.2311
+## MHS-2       0.0591 -0.2701  0.0478
+## QNFH-2      0.0210 -0.1556  0.0575
+## MHS0       -0.1576 -0.1774 -0.0524
+
best[lambda == 1.2, loadings][[1]] %>% 
+  lapply(round, digits = 4)
+
## $NJ
+##                    PC1     PC5     PC8    PC13
+## MCC0            0.4585  0.1256 -0.2445  0.5307
+## PMTK1           0.3403  0.3327  0.0981  0.3460
+## Phu Toei0      -0.3727  0.0123 -0.4643  0.0771
+## Laos pine2      0.1038 -0.2538  0.2805  0.0374
+## BDFH-1         -0.3231  0.0935 -0.0338 -0.1368
+## Pha Taem0      -0.2665  0.1806  0.2365  0.2074
+## PMTK0           0.4115 -0.0180 -0.0441 -0.5385
+## Phu Kradung0    0.0282 -0.1732  0.6551 -0.0141
+## PMFH0          -0.2247  0.1033  0.0589 -0.0963
+## Salaeng Luong0  0.1151  0.1230 -0.2068 -0.1806
+## Laos pine1      0.0242 -0.5592 -0.1189  0.0710
+## MCC2            0.0099 -0.5288 -0.2571  0.1202
+## PMFH-1         -0.2265  0.2945 -0.0306  0.0045
+## MCC1            0.2511  0.1801 -0.1439 -0.4219
+## 
+## $JO
+##                    PC1     PC2     PC9
+## PMTK0           0.4857 -0.3972  0.1152
+## MCC0            0.4457 -0.4100 -0.2672
+## Chin Hills-2   -0.3095 -0.0068  0.4083
+## MCFH-1         -0.2420 -0.0123  0.1569
+## Laos pine1      0.1514 -0.1424  0.3354
+## KHDF2           0.0590 -0.4003  0.2424
+## KKFH-2          0.3738  0.4903 -0.0672
+## Laos pine-1     0.0923  0.0238  0.1548
+## QNFH-2          0.4251  0.4415  0.0107
+## MHS2           -0.1841 -0.2397 -0.4828
+## Salaeng Luong2 -0.1478  0.0102 -0.5395
+## 
+## $WY
+##                  PC1
+## PMTK0         0.5721
+## MCC0          0.5158
+## Chin Hills-2 -0.1898
+## MCFH-1       -0.2380
+## Laos pine1    0.2066
+## KHDF2         0.1816
+## Phu Toei0    -0.3870
+## Laos pine2    0.2584
+## MHS-2         0.1156
+## KKFH-2        0.0914

Results

-
-

GA convergence

-

Figure S11

-
par(mfrow = c(1, 2))
-plot(
-  siteOptim_l0, 
-  main = expression(lambda == 0), 
-  col = c('steelblue', NA, adjustcolor('steelblue', alpha.f = 0.1)))
-plot(
-  siteOptim_l1, 
-  main = expression(lambda == 1), 
-  col = c('steelblue', NA, adjustcolor('steelblue', alpha.f = 0.1)))
-

-
-
-

Skill scores

-
-

Mean skill scores

-
cvRM %>% 
-  dcast(season ~ lambda, value.var = c('R2', 'RE', 'CE')) %>% 
-  roundDT(2)
-
##    season R2_0.0 R2_1.0 RE_0.0 RE_1.0 CE_0.0 CE_1.0
-## 1:     NJ   0.49   0.46   0.53   0.52   0.45   0.43
-## 2:     JO   0.44   0.43   0.38   0.39   0.23   0.28
-## 3:     WY   0.46   0.46   0.49   0.49   0.40   0.39
+
+

Skill scores as a function of λ

+
+

Mean skill scores and Wilcoxon test

+
cvMean <- cv[, lapply(.SD, mean), .SDcols = c('R2', 'RE', 'CE'), keyby = .(lambda, season)]
+cvMeanLong <- cvMean %>% melt(id.var = c('lambda', 'season'), variable.name = 'metric')
+
+cv0 <- cvLong[lambda == 0]
+
+cvp <- cvLong[,
+              .(p.value = {
+                s <- .BY$season
+                m <- .BY$metric
+                wilcox.test(value, cv0[season == s & metric == m, value])$p.value
+              }),
+              by = .(lambda, season, metric)]
+cvMeanMerged <- merge(cvMeanLong, cvp, by = c('lambda', 'season', 'metric')) %>% 
+  merge(cvMeanLong[lambda == 0, .(season, metric, v0 = value)], by = c('season', 'metric'))
+

Figure 3

+
ggplot(cvMeanMerged) +
+  geom_hline(aes(yintercept = value), cvMeanLong[lambda == 0], colour = 'gray') +
+  geom_linerange(
+    aes(lambda, ymin = value, ymax = v0, colour = season, alpha = p.value < 0.1),
+    size = 0.2) +
+  geom_point(
+    aes(lambda, value, colour = season, alpha = p.value < 0.1),
+    size = 1) +
+  geom_point(
+    aes(lambda, value, colour = season), 
+    cvMeanLong[lambda == 0], 
+    shape = 'triangle', 
+    size = 2, 
+    alpha = 1) +
+  facet_grid(
+    vars(metric), vars(season), 
+    labeller = labeller(.rows = metricLab, .cols = ssnLabBrief),
+    scales = 'free') +
+  scale_alpha_manual(
+    name = 'Significantly different from \u03bb = 0',
+    values = c(0.4, 1)) +
+  scale_colour_manual(values = dark2) +
+  scale_x_continuous(breaks = seq(0, 3, 0.2), labels = skip_label(5)) +
+  guides(color = FALSE) +
+  labs(x= '\u03bb', y = 'Metric value [-]') +
+  panel_border('black', 0.2) +
+  theme(
+    strip.text.y = element_text(angle = 0),
+    legend.position = 'none')
+

Skill distribution

-

Figure S12

-
DT <- cv %>% 
-  melt(
-    id.vars = c('rep', 'season', 'lambda'),
-    measure.vars = c('R2', 'RE', 'CE'),
-    variable.name = 'metric',
-    value.name = 'value')
-hline <- DT[metric != 'R2', 
-            .(y = 0), 
-            by = .(season, metric)]
-ggplot(DT) +
-  geom_hline(
-    aes(yintercept = y), 
-    hline,
-    size = 0.3,
-    colour = 'firebrick') +
-  geom_boxplot(
-    aes(lambda, value, fill = season), 
-    width = 0.5,
-    alpha = 0.5, 
-    size = 0.3,
-    outlier.size = 0.8,
-    show.legend = FALSE) +
-  facet_grid(
-    vars(metric), vars(season), 
-    labeller = labeller(
-      metric = as_labeller(c(R2 = 'R\u00b2', RE = 'RE', CE = 'CE')),
-      season = as_labeller(ssnLabBrief)),
-    scales = 'free_y') +
-  scale_x_discrete(labels = lambdaLab) +
-  scale_fill_manual(values = dark2) +
-  labs(x = NULL, y = 'Metric value [-]') +
-  theme(
-    panel.spacing.y = unit(1.5, 'lines'),
-    strip.background = element_rect('gray90', NA),
-    axis.line.x = element_blank(), 
-    axis.ticks.x = element_blank())
-

-

Statistically skillful? We calculate the probability of RE > 0 and of CE > 0.

-
cv[, .(pRE = .SD[RE > 0, .N] / .N,
-       pCE = .SD[CE > 0, .N] / .N), keyby = .(lambda, season)]
-
##    lambda season  pRE  pCE
-## 1:    0.0     NJ 1.00 0.92
-## 2:    0.0     JO 0.88 0.72
-## 3:    0.0     WY 1.00 0.92
-## 4:    1.0     NJ 1.00 0.92
-## 5:    1.0     JO 1.00 0.94
-## 6:    1.0     WY 1.00 0.92
-

Model 0’s wet season is not statistically skillful.

+

Figure S11

+
ggplot(cvLong[lambda %in% round(seq(0, 3, 0.2), 1)]) +
+  geom_boxplot(
+    aes(lambda, value, group = lambda, fill = season, alpha = I(lambda / 3)),
+    size = 0.2,
+    outlier.size = 0.4, outlier.alpha = 1) +
+  facet_grid(
+    vars(metric), vars(season), 
+    labeller = labeller(.rows = metricLab, .cols = ssnLabBrief),
+    scales = 'free') +
+  scale_x_continuous(breaks = seq(0, 3, 0.2), labels = skip_label(5)) +
+  scale_fill_manual(values = dark2) +
+  panel_border('black', 0.2) +
+  labs(x = '\u03bb', y = 'Metric value [-]') +
+  theme(
+    strip.text.y = element_text(angle = 0),
+    legend.position = 'none')
+

Trajectories

-
# Make strings to merge to plot legend
-lambdaLegendDT <- cvRM[, roundDT(.SD, type = 'char')
-                     ][, .(lab = paste(lambdaLab(.BY$lambda), R2, RE, CE, sep = '    ')),
-                       by = .(season = as.character(season), lambda)]
-lambdaLegend <- function(s) {
-  function(x) lambdaLegendDT[season == s & lambda %in% x, lab]
-}
-
-# Select Model 1, apply low pass filter
-recFinal <- rec[lambda == '1.0']
-recFinal[, season := factor(season, seasons)]
-recFinal[, Qlpf := dplR::pass.filt(Q, 20, 'low')]
-

Figure 3

-
p <- lapply(seasons, function(s) {
-  ggplot(rec[season == s & year %in% 1922:2003]) +
-    geom_line(aes(year, Q, colour = lambda, linetype = lambda)) +
-    geom_line(aes(year, Qa), p1tar[s], colour = 'gray', size = 0.2) +
-    scale_linetype_discrete(
-      name = c('          R\u00b2     RE     CE '),
-      labels = lambdaLegend(s)) +
-    scale_colour_manual(
-      name = c('          R\u00b2     RE     CE '),
-      values = c('steelblue', 'darkorange'),
-      labels = lambdaLegend(s)) +
-    scale_x_continuous(
-      breaks = seq(1920, 2000, 10),
-      labels = skip_label(2)) +
-    labs(x = NULL, 
-         y = if (s == 'JO') 'Q [million m\u00b3]' else '') +
-    facet_wrap(
-      vars(season), 
-      strip.position = 'right',
-      labeller = as_labeller(ssnLabBrief)) +
-    theme(
-      legend.title = element_text(hjust = 1, family = 'Lucida Sans Unicode'),
-      legend.text = element_text(family = 'Lucida Sans Unicode'),
-      axis.text.x = if (s != 'WY') element_blank(), 
-      axis.ticks.x = if (s != 'WY') element_blank(), 
-      axis.line.x = if (s != 'WY') element_blank(), 
-      strip.background = element_rect('gray95', NA),
-      legend.box.margin = unit(c(0, 0, 0, -0.5), 'cm'),
-      legend.key.width = unit(1, 'cm')) 
-    
-})
-
-trajPlotInst <- wrap_plots(p) + plot_layout(ncol = 1)
-
-p <- ggplot(recFinal) +
-  geom_hline(
-    aes(yintercept = Qm, colour = 'Long-term mean'), 
-    recFinal[, .(Qm = mean(Q)), by = season],
-    size = 0.2) +
-  geom_line(aes(year, Q, colour = 'Reconstruction (\u03bb = 1)'), size = 0.2) +
-  geom_line(aes(year, Qlpf, colour = '20-year low-pass filter'), size = 0.3) +
-  facet_wrap(
-    vars(season), 
-    scales = 'free_y',
-    labeller = as_labeller(ssnLabBrief),
-    strip.position = 'right',
-    ncol = 1) +
-  scale_x_continuous(
-    breaks = seq(1750, 2000, 10), 
-    labels = skip_label(5),
-    sec.axis = dup_axis()) +
-  scale_colour_manual(
-    name = NULL,
-    values = c('gray', 'black', 'firebrick'),
-    breaks = c('Reconstruction (\u03bb = 1)', '20-year low-pass filter', 'Long-term mean'),
-  ) +
-  labs(x = NULL, y = 'Q [million m\u00b3]') +
-  theme(
-    plot.margin = margin(t = 20),
-    strip.background = element_rect('gray95', NA),
-    legend.position = 'top',
-    legend.key.width = unit(1.2, 'cm'),
-    legend.text = element_text(family = 'Lucida Sans Unicode'))
-
-trajPlotFull <- wrap_plots(p) 
-
-layout <- c(
-  patchwork::area(1, 1,  30, 19),
-  patchwork::area(31, 1, 60, 20)
-)
-
-wrap_plots(trajPlotInst) + wrap_plots(trajPlotFull) + 
-  plot_layout(design = layout)
-

+
# Make strings to merge to plot legend
+lambdaLegendDT <- cvMean[, season := as.character(season)
+                     ][, roundDT(.SD, digits = c(1, 2, 2, 2, 2), type = 'char') 
+                     ][, .(lab = paste(lambdaLab(.BY$lambda), R2, RE, CE, sep = '    ')), by = .(season, lambda)]
+
+
+lambdaLegend <- function(s) {
+  function(x) lambdaLegendDT[season == s & lambda %in% x, lab]
+}
+

Figure 4

+
# Select Model 1, apply low pass filter
+recFinal <- rec[lambda == 1.2]
+recFinal[, season := factor(season, seasons)]
+recFinal[, Qlpf := dplR::pass.filt(Q, 20, 'low')]
+
+p <- lapply(seasons, function(s) {
+  ggplot(rec[season == s & year %in% 1922:2003 & lambda %in% c(0, 1.2)]) +
+    geom_line(aes(year, Q, colour = to_char_1dp(lambda), linetype = to_char_1dp(lambda))) +
+    geom_line(aes(year, Qa), p1tar[s], colour = 'gray', size = 0.2) +
+    scale_linetype_discrete(
+      name = c('          R\u00b2     RE     CE '),
+      labels = lambdaLegend(s)) +
+    scale_colour_manual(
+      name = c('          R\u00b2     RE     CE '),
+      values = c('steelblue', 'darkorange'),
+      labels = lambdaLegend(s)) +
+    scale_x_continuous(
+      breaks = seq(1920, 2000, 10),
+      labels = skip_label(2)) +
+    labs(x = NULL, 
+         y = if (s == 'JO') 'Q [million m\u00b3]' else '') +
+    facet_wrap(
+      vars(season), 
+      strip.position = 'right',
+      labeller = as_labeller(ssnLabBrief)) +
+    theme(
+      legend.title = element_text(hjust = 1, family = 'Lucida Sans Unicode'),
+      legend.text = element_text(family = 'Lucida Sans Unicode'),
+      axis.text.x = if (s != 'WY') element_blank(), 
+      axis.ticks.x = if (s != 'WY') element_blank(), 
+      axis.line.x = if (s != 'WY') element_blank(), 
+      strip.background = element_rect('gray95', NA),
+      legend.box.margin = unit(c(0, 0, 0, -0.5), 'cm'),
+      legend.key.width = unit(1, 'cm')) 
+    
+})
+
+trajPlotInst <- wrap_plots(p) + plot_layout(ncol = 1)
+
+p <- ggplot(recFinal) +
+  geom_hline(
+    aes(yintercept = Qm, colour = 'Long-term mean'), 
+    recFinal[, .(Qm = mean(Q)), by = season],
+    size = 0.2) +
+  geom_line(aes(year, Q, colour = 'Reconstruction (\u03bb = 1.2)'), size = 0.2) +
+  geom_line(aes(year, Qlpf, colour = '20-year low-pass filter'), size = 0.3) +
+  facet_wrap(
+    vars(season), 
+    scales = 'free_y',
+    labeller = as_labeller(ssnLabBrief),
+    strip.position = 'right',
+    ncol = 1) +
+  scale_x_continuous(
+    breaks = seq(1750, 2000, 10), 
+    labels = skip_label(5),
+    sec.axis = dup_axis()) +
+  scale_colour_manual(
+    name = NULL,
+    values = c('gray', 'black', 'firebrick'),
+    breaks = c('Reconstruction (\u03bb = 1.2)', '20-year low-pass filter', 'Long-term mean'),
+  ) +
+  labs(x = NULL, y = 'Q [million m\u00b3]') +
+  theme(
+    plot.margin = margin(t = 20),
+    strip.background = element_rect('gray95', NA),
+    legend.position = 'top',
+    legend.key.width = unit(1.2, 'cm'),
+    legend.text = element_text(family = 'Lucida Sans Unicode'))
+
+trajPlotFull <- wrap_plots(p) 
+
+layout <- c(
+  patchwork::area(1, 1,  30, 19),
+  patchwork::area(31, 1, 60, 20)
+)
+
+wrap_plots(trajPlotInst) + wrap_plots(trajPlotFull) + 
+  plot_layout(design = layout)
+

Magnitude of adjustments

-

Figure S13

-
# Amount of adjustment
-adj <- rec[, dcast(.SD, season + year ~ lambda, value.var = 'Q')
-         ][, adj := `1.0` - `0.0`]
-# Density of adjustment amount
-adjDens <- adj[, {
-  s <- .BY$season
-  bw <- fcase(s == 'NJ', 30,
-              s == 'JO', 50,
-              s == 'WY', 9)
-  d <- density(adj, bw = bw, from = min(adj), to = max(adj))
-  data.table(x = d$x, y = d$y)
-}, by = season]
-
-trajPlotFull2 <- ggplot(rec) +
-  geom_line(aes(year, Q, colour = lambda, linetype = lambda), size = 0.4) +
-  scale_linetype_discrete(
-    name = NULL, 
-    label = lambdaLab) +
-  scale_colour_manual(
-    name = NULL,
-    values = c('steelblue', 'darkorange'),
-    labels = lambdaLab) +
-  labs(x = 'Year', 
-       y = 'Q [Mm\u00b3]') +
-  facet_wrap(vars(season), labeller = as_labeller(ssnLabBrief), ncol = 1, scales = 'free_y') +
-  theme(
-    legend.text = element_text(family = 'Lucida Sans Unicode'),
-    legend.position = 'top',
-    legend.key.width = unit(1.5, 'cm'),
-    strip.background = element_rect('gray95', NA))
-
-adjDensPlot <- ggplot(adjDens) +
-  geom_line(aes(x, y), size = 0.2) +
-  facet_wrap(vars(season), scales = 'free', ncol = 1, labeller = as_labeller(ssnLabBrief)) +
-  theme(strip.background = element_rect('gray95', NA)) +
-  labs(x = 'Adjustment [Mm\u00b3]', y = 'Density')
-
-trajPlotFull2 + adjDensPlot +
-  plot_layout(widths = c(3, 1))
-

+

Figure S12

+
# Amount of adjustment
+adj <- rec[lambda %in% c(0, 1.2), dcast(.SD, season + year ~ lambda, value.var = 'Q')
+         ][, adj := `1.2` - `0`] 
+# Density of adjustment amount
+adjDens <- adj[, {
+  s <- .BY$season
+  bw <- fcase(s == 'NJ', 30,
+              s == 'JO', 60,
+              s == 'WY', 90)
+  d <- density(adj, bw = bw, from = min(adj), to = max(adj))
+  data.table(x = d$x, y = d$y)
+}, by = season]
+
+trajPlotFull2 <- ggplot(rec[lambda %in% c(0, 1.2)]) +
+  geom_line(aes(year, Q, colour = to_char_1dp(lambda), linetype = to_char_1dp(lambda)), size = 0.4) +
+  scale_linetype_discrete(
+    name = NULL, 
+    label = lambdaLab) +
+  scale_colour_manual(
+    name = NULL,
+    values = c('steelblue', 'darkorange'),
+    labels = lambdaLab) +
+  labs(x = 'Year', 
+       y = 'Q [Mm\u00b3]') +
+  facet_wrap(vars(season), labeller = as_labeller(ssnLabBrief), ncol = 1, scales = 'free_y') +
+  theme(
+    legend.text = element_text(family = 'Lucida Sans Unicode'),
+    legend.position = 'top',
+    legend.key.width = unit(1.5, 'cm'),
+    strip.background = element_rect('gray95', NA))
+
+adjDensPlot <- ggplot(adjDens) +
+  geom_line(aes(x, y), size = 0.2) +
+  facet_wrap(vars(season), scales = 'free', ncol = 1, labeller = as_labeller(ssnLabBrief)) +
+  theme(strip.background = element_rect('gray95', NA)) +
+  labs(x = 'Adjustment [Mm\u00b3]', y = 'Density')
+
+trajPlotFull2 + adjDensPlot +
+  plot_layout(widths = c(3, 1))
+

+
+
+

Flow ratio

+

Figure 5

+
p1tarWide <- dcast(p1tar, year ~ season, value.var = 'Qa')
+p1tarWide[, rQ := NJ / WY]
+
+deltaQ <- rec[, dcast(.SD, lambda + year ~ season, value.var = 'Q')
+            ][, dQ := NJ + JO - WY
+            ][, rQ := NJ / WY]
+densEst <- deltaQ[, {
+  d <- density(dQ, cut = 0)
+  data.table(x = d$x, y = d$y)
+}, by = lambda]
+Qmean <- p1tar[season == 'WY', mean(Qa)]
+
ggplot(deltaQ[lambda == 1.2], aes(WY, rQ * 100)) +
+  geom_hline(yintercept = 50, colour = 'gray', size = 0.2) +
+  geom_point(aes(colour = 'Reconstructed'), size = 1) +
+  geom_point(aes(colour = 'Observed'), p1tarWide) +
+  geom_text(
+    aes(label = year), 
+    p1tarWide[year %in% c(1971, 1973, 1975)],
+    size = 3,
+    nudge_x = 150) +
+  geom_text(
+    aes(label = year), 
+    deltaQ[lambda == 1.2 & year == 1815],
+    size = 3,
+    nudge_x = 150) +
+  scale_colour_manual(
+    name = NULL,
+    values = pal) +
+  scale_y_continuous(breaks = seq(20, 60, 5)) +
+  scale_x_continuous(breaks = seq(1000, 4000, 500)) +
+  labs(
+    x = 'Annual streamflow [million m\u00b3]', 
+    y = 'Fraction of dry season flow to annual flow [%]') +
+  theme(
+    legend.margin = margin(5, 5, 5, 5),
+    legend.background = element_rect(NA, 'black', 0.2),
+    legend.position = c(0.65, 0.9))
+

Selected inputs

-

Extract selected inputs.

-
mbPool[, prox := fifelse(site %in% crnMeta$site, 'RW', 'OX')]
-mbPool[, site.display := paste0(siteLab(site), ' ', prox, ' (', lag, ')')]
-mbPool[, selected := '\u2713']
-
-tickMat <- dcast(mbPool, lambda + prox + site.display ~ season, value.var = 'selected') %>% 
-  melt(
-    id.var = c('lambda', 'prox', 'site.display'), 
-    variable.name = 'season', 
-    value.name = 'selected')
-tickMat[is.na(selected), selected := ' ']
-tickMat[, site.display := factor(site.display, 
-                                 levels = sort(unique(site.display), decreasing = TRUE))]
-
-count <- tickMat[selected != ' ', .N, by = .(season, lambda)]
-

Figure 4

-
yLabCol <- function(x) {
-  sapply(x, function(xx) {
-    ind <- grep('RW', xx)
-    if (length(ind) == 0) xx <- paste0(ind, '<span style="color:firebrick">', xx, '</span>')
-    xx
-  })
-}
-ssnLab2Lines <- function(l) {
-  n <- count[lambda == l, N]
-  function(s) {
-    out <- fcase(
-      s == 'NJ', 'Dry<br>season',
-      s == 'JO', 'Wet<br>season',
-      default = 'Water<br>year')
-    paste0(out, '<br>**', n, '**')
-  }
-} 
-
-p1 <- ggplot(tickMat[lambda == '0.0']) +
-  geom_text(
-    aes(season, site.display, label = selected, colour = prox), 
-    family = 'Arial Unicode MS',
-    show.legend = FALSE,
-    vjust = 0.25) +
-  facet_wrap(vars(lambda), labeller = as_labeller(lambdaLab)) +
-  scale_x_discrete(labels = ssnLab2Lines('0.0')) +
-  scale_y_discrete(labels = yLabCol, drop = FALSE) +
-  scale_colour_manual(values = c('firebrick', 'black')) +
-  labs(x = NULL, y = NULL) +
-  cowplot::panel_border('black', 0.2) +
-  theme(
-    axis.text.y = element_markdown(),
-    axis.text.x = element_markdown(lineheight = 1.5),
-    strip.text = element_text(family = 'Lucida Sans Unicode', face = 'bold'),
-    axis.ticks = element_blank())
-p2 <- ggplot(tickMat[lambda == '1.0']) +
-  geom_text(
-    aes(season, site.display, label = selected, colour = prox), 
-    family = 'Arial Unicode MS',
-    show.legend = FALSE,
-    vjust = 0.25) +
-  facet_wrap(vars(lambda), labeller = as_labeller(lambdaLab)) +
-  scale_x_discrete(labels = ssnLab2Lines('1.0')) +
-  scale_y_discrete(labels = yLabCol, drop = FALSE) +
-  scale_colour_manual(values = c('firebrick', 'black')) +
-  labs(x = NULL, y = NULL) +
-  cowplot::panel_border('black', 0.2) +
-  theme(
-    axis.text.y = element_blank(),
-    axis.text.x = element_markdown(lineheight = 1.5),
-    strip.text = element_text(family = 'Lucida Sans Unicode', face = 'bold'),
-    strip.background = element_blank(),
-    axis.ticks = element_blank())
-
-p1 + p2
-

+

Extract selected inputs

+
weightDT <- best[lambda %in% c(0, 1.2), {
+  DT <- poolDT[c(ga[[1]]@solution == 1)]
+  DT[, prox := fifelse(site %in% crnMeta$site, 'RW', 'OX')]
+  DT[, site.display := paste0(siteLab(site), ' ', prox, ' (', lag, ')')]
+  DT[, weight := weight]
+  DT[]
+}, by = lambda]
+
+weightDT[, site.display := 
+           factor(site.display, 
+                  levels = sort(unique(site.display), decreasing = TRUE))]
+
+weightDT[, sameSign := fifelse(weight * rho < 0, 'x', ' ')]
+
+count <-weightDT[, .N, by = .(season, lambda)]
+

Figure 6

+
yLabCol <- function(x) {
+  sapply(x, function(xx) {
+    ind <- grep('RW', xx)
+    if (length(ind) == 0) xx <- paste0(ind, '<span style="color:firebrick">', xx, '</span>')
+    xx
+  })
+}
+ssnLab2Lines <- function(l) {
+  n <- count[lambda == l, N]
+  function(s) {
+    out <- fcase(
+      s == 'NJ', 'Dry<br>season',
+      s == 'JO', 'Wet<br>season',
+      default = 'Water<br>year')
+    paste0(out, '<br>**', n, '**')
+  }
+} 
+
+pl <- lapply(c(0, 1.2), function(l) {
+  ggplot(weightDT[lambda == l]) +
+    geom_tile(aes(season, site.display, fill = weight)) +
+    geom_text(
+      aes(season, site.display, label = sameSign),
+      vjust = 0.25,
+      colour = 'gray30') +
+    facet_wrap(vars(lambda), labeller = as_labeller(lambdaLab)) +
+    scale_x_discrete(labels = ssnLab2Lines(l), expand = c(0, 0)) +
+    scale_y_discrete(labels = yLabCol, drop = FALSE) +
+    scale_fill_distiller(
+      palette = 'RdBu', 
+      direction = 1, 
+      name = 'Weight',
+      limits = abs_range(weightDT$weight)) +
+    labs(x = NULL, y = NULL) +
+    cowplot::panel_border('black', 0.2) +
+    theme(
+      legend.key.height = unit(1, 'cm'),
+      axis.text.y = element_markdown(),
+      axis.text.x = element_markdown(lineheight = 1.5),
+      strip.text = element_text(family = 'Lucida Sans Unicode', face = 'bold'),
+      axis.ticks = element_blank())
+}) 
+
+for (i in 2:length(pl)) pl[[i]] <- pl[[i]] + theme(axis.text.y = element_blank())
+
+wrap_plots(pl, nrow = 1, guides = 'collect')
+

Annual mass balance

-

Calculate mass difference.

-
deltaQ <- rec[, dcast(.SD, year + lambda ~ season, value.var = 'Q')
-            ][, dQ := NJ + JO - WY
-            ][, rQ := NJ / WY]
-densEst <- deltaQ[, {
-  bw <- if (.BY$lambda == '1.0') 50 else 80
-  ct <- if (.BY$lambda == '1.0') 1.5 else 1
-  d <- density(dQ, bw = bw, cut = ct)
-  data.table(x = d$x, y = d$y)
-}, by = lambda]
-

Probability of ΔQ within 10% mean annual flow (190 Mm3)? Range?

-
Qmean <- p1merge[season == 'WY', mean(Qa)]
-deltaQ[, .(p = .SD[abs(dQ) > Qmean / 10, .N] / .N * 100,
-           min = min(dQ), 
-           max = max(dQ)),
-       by = lambda] %>% 
-  roundDT(0)
-
##    lambda  p  min max
-## 1:    0.0 28 -640 600
-## 2:    1.0 13 -289 426
-

Figure 5

-
cols <- c('#984ea3', '#4daf4a')
-
-p1 <- ggplot(deltaQ) +
-  geom_line(aes(year, WY, linetype = 'Water year', colour = 'Water year'), size = 0.4) +
-  geom_line(aes(year, JO + NJ, linetype = 'Dry + Wet', colour = 'Dry + Wet'), size = 0.4) +
-  facet_wrap(
-    vars(lambda), 
-    ncol = 1,
-    labeller = as_labeller(lambdaLab)) +
-  scale_colour_manual(
-    name = NULL, 
-    values = cols) +
-  scale_linetype_manual(
-    name = NULL,
-    values = c(2, 1)) +
-  labs(x = 'Year', y = 'Q [million m\u00b3]') +
-  theme(
-    legend.position = 'none',
-    strip.text = element_text(family = 'Lucida Sans Unicode'),
-    strip.background = element_rect('gray90', NA))
-
-p2 <- ggplot(deltaQ) +
-  stat_density(
-    aes(y = WY, colour = 'Water year', linetype = 'Water year'), 
-    bw = 160, 
-    position = 'identity',
-    geom = 'line') +
-  stat_density(
-    aes(y = JO + NJ, colour = 'Dry + Wet', linetype = 'Dry + Wet'), 
-    bw = 160, 
-    position = 'identity',
-    geom = 'line') +
-  scale_x_continuous(breaks = scales::pretty_breaks(3), expand = c(0.01, 0)) +
-  scale_colour_manual(
-    name = NULL, 
-    values = cols) +
-  scale_linetype_manual(
-    name = NULL,
-    values = c(2, 1)) +
-  facet_wrap(
-    vars(lambda), 
-    ncol = 1,
-    labeller = as_labeller(lambdaLab)) +
-  labs(x = 'Density', y = NULL) +
-  theme(
-    legend.key.width = unit(0.95, 'cm'),
-    axis.text.y = element_blank(),
-    axis.ticks.y = element_blank(),
-    strip.text = element_text(family = 'Lucida Sans Unicode'),
-    strip.background = element_rect('gray90', NA))
-
-p3 <- ggplot(deltaQ) +
-  geom_line(aes(year, dQ, colour = lambda, linetype = lambda), size = 0.4) +
-  scale_y_continuous(breaks = seq(-800, 800, 200), limits = abs_range(densEst$x)) +
-  scale_colour_manual(
-    name = NULL,
-    values = c('steelblue', 'darkorange'),
-    labels = lambdaLab) +
-  scale_linetype_manual(
-    name = NULL,
-    labels = lambdaLab,
-    values = c(1, 2)) +
-  labs(x = 'Year', y = '\u0394Q [million m\u00b3]') +
-  theme(legend.position = 'none')
-
-p4 <- ggplot(densEst) +
-  geom_area(
-    aes(x, y), 
-    densEst[x %between% c(-Qmean / 10, Qmean / 10) & lambda == 'l=1'], 
-    fill = 'gray95') +
-  geom_line(aes(x, y, colour = lambda, linetype = lambda)) +
-  scale_colour_manual(
-    name = NULL,
-    values = c('steelblue', 'darkorange'),
-    labels = lambdaLab) +
-  scale_x_continuous(breaks = seq(-800, 800, 200), limits = abs_range(densEst$x)) +
-  scale_y_continuous(expand = c(0.01, 0)) +
-  scale_linetype_manual(
-    name = NULL,
-    labels = lambdaLab,
-    values = c(1, 2)) +
-  coord_flip() +
-  labs(x = NULL, y = 'Density') +
-  theme(
-    legend.key.width = unit(0.95, 'cm'),
-    legend.text = element_text(family = 'Lucida Sans Unicode'),
-    axis.text.y = element_blank(),
-    axis.ticks.y = element_blank())
-
-p1 + p2 + p3 + p4 +
-  plot_layout(ncol = 2, widths = c(2, 1), heights = c(2, 1))
-

-
-
-

Seasonal mass balance

-
cols1 <- RColorBrewer::brewer.pal(9, 'Set1')[c(3, 4)]
-cols2 <- RColorBrewer::brewer.pal(9, 'Set1')[c(2, 5)]
-
-p1 <- ggplot(deltaQ) +
-  geom_line(aes(year, WY - JO, linetype = 'WY - WS', colour = 'WY - WS'), size = 0.4) +
-  geom_line(aes(year, NJ, linetype = 'DS', colour = 'DS'), size = 0.4) +
-  facet_wrap(
-    vars(lambda), 
-    ncol = 1,
-    labeller = as_labeller(lambdaLab)) +
-  scale_colour_manual(
-    name = NULL, 
-    breaks = c('WY - WS', 'DS'),
-    values = cols1) +
-  scale_linetype_manual(
-    name = NULL,
-    breaks = c('WY - WS', 'DS'),
-    values = c(1, 2)) +
-  labs(x = 'Year', y = 'Q [million m\u00b3]') +
-  theme(
-    legend.position = 'none',
-    strip.text = element_text(family = 'Lucida Sans Unicode'),
-    strip.background = element_rect('gray90', NA))
-
-p2 <- ggplot(deltaQ) +
-  stat_density(
-    aes(y = NJ, colour = 'DS', linetype = 'DS'), 
-    bw = 100, 
-    position = 'identity',
-    geom = 'line') +
-  stat_density(
-    aes(y = WY - JO, colour = 'WY - WS', linetype = 'WY - WS'), 
-    bw = 100, 
-    position = 'identity',
-    geom = 'line') +
-  scale_x_continuous(breaks = scales::pretty_breaks(2), expand = c(0.01, 0)) +
-  scale_colour_manual(
-    name = NULL,
-    breaks = c('WY - WS', 'DS'),
-    values = cols1) +
-  scale_linetype_manual(
-    name = NULL,
-    breaks = c('WY - WS', 'DS'),
-    values = c(1, 2)) +
-  facet_wrap(
-    vars(lambda), 
-    ncol = 1,
-    labeller = as_labeller(lambdaLab)) +
-  labs(x = 'Density', y = NULL) +
-  theme(
-    legend.key.width = unit(0.95, 'cm'),
-    axis.text.y = element_blank(),
-    axis.ticks.y = element_blank(),
-    strip.text = element_text(family = 'Lucida Sans Unicode'),
-    strip.background = element_rect('gray90', NA))
-
-p3 <- ggplot(deltaQ) +
-  geom_line(aes(year, WY - NJ, linetype = 'WY - DS', colour = 'WY - DS'), size = 0.4) +
-  geom_line(aes(year, JO, linetype = 'WS', colour = 'WS'), size = 0.4) +
-  facet_wrap(
-    vars(lambda), 
-    ncol = 1,
-    labeller = as_labeller(lambdaLab)) +
-  scale_colour_manual(
-    name = NULL, 
-    breaks = c('WY - DS', 'WS'),
-    values = cols2) +
-  scale_linetype_manual(
-    name = NULL,
-    breaks = c('WY - DS', 'WS'),
-    values = c(1, 2)) +
-  labs(x = 'Year', y = 'Q [million m\u00b3]') +
-  theme(
-    legend.position = 'none',
-    strip.text = element_text(family = 'Lucida Sans Unicode'),
-    strip.background = element_rect('gray90', NA))
-
-p4 <- ggplot(deltaQ) +
-  stat_density(
-    aes(y = JO, colour = 'WS', linetype = 'WS'), 
-    bw = 170, 
-    position = 'identity',
-    geom = 'line') +
-  stat_density(
-    aes(y = WY - NJ, colour = 'WY - DS', linetype = 'WY - DS'), 
-    bw = 170, 
-    position = 'identity',
-    geom = 'line') +
-  scale_x_continuous(breaks = scales::pretty_breaks(3), expand = c(0.01, 0)) +
-  scale_colour_manual(
-    name = NULL, 
-    breaks = c('WY - DS', 'WS'),
-    values = cols2) +
-  scale_linetype_manual(
-    name = NULL,
-    breaks = c('WY - DS', 'WS'),
-    values = c(1, 2)) +
-  facet_wrap(
-    vars(lambda), 
-    ncol = 1,
-    labeller = as_labeller(lambdaLab)) +
-  labs(x = 'Density', y = NULL) +
-  theme(
-    legend.key.width = unit(0.95, 'cm'),
-    axis.text.y = element_blank(),
-    axis.ticks.y = element_blank(),
-    strip.text = element_text(family = 'Lucida Sans Unicode'),
-    strip.background = element_rect('gray90', NA))
-
-p1 + p2 + p3 + p4 +
-  plot_layout(ncol = 2, widths = c(2, 1))
-

-
-
-

Flow ratio

-

Figure S14

-
p1mergeWide <- dcast(p1merge, year ~ season, value.var = 'Qa')
-p1mergeWide[, rQ := NJ / WY]
-
-rqDensEstRec <- deltaQ[, {
-  bw <- if (.BY$lambda == '1.0') 0.031 else 0.033
-  d <- density(rQ, bw = bw)
-  data.table(x = d$x, y = d$y)
-}, by = lambda]
-
-rqDensEstRecShort <- deltaQ[year %in% 1922:2005, {
-  bw <- if (.BY$lambda == '1.0') 0.031 else 0.033
-  d <- density(rQ, bw = bw)
-  data.table(x = d$x, y = d$y)
-}, by = lambda]
-
-rqDensEstInst <- p1mergeWide[, {
-    d <- density(rQ, bw = 0.045)
-    data.table(x = d$x, y = d$y)
-  }]
-
ggplot(deltaQ[lambda == '1.0'], aes(WY, rQ * 100)) +
-  geom_hline(yintercept = 50, colour = 'gray', size = 0.2) +
-  geom_point(aes(colour = 'Reconstructed'), size = 1) +
-  geom_point(aes(colour = 'Observed'), p1mergeWide) +
-  geom_text(
-    aes(label = year), 
-    rbind(
-      p1mergeWide[year %in% c(1971, 1973, 1975)],
-      p1mergeWide[order(rQ)][.N]),
-    size = 3,
-    nudge_x = 120) +
-  geom_text(
-    aes(label = year),
-    deltaQ[lambda == '1.0'][order(rQ)][.N],
-    size = 3,
-    nudge_x = 150) +
-  geom_text(
-    aes(label = year), 
-    deltaQ[lambda == '1.0' & year %in% c(1815, 1819, 1879)],
-    size = 3,
-    nudge_x = 120) +
-  scale_colour_manual(
-    name = NULL,
-    values = pal) +
-  scale_y_continuous(breaks = seq(20, 60, 5)) +
-  scale_x_continuous(breaks = seq(1000, 4000, 500)) +
-  labs(
-    x = 'Annual streamflow [million m\u00b3]', 
-    y = 'Fraction of dry season flow to annual flow [%]') +
-  theme(
-    legend.margin = margin(5, 5, 5, 5),
-    legend.background = element_rect(NA, 'black', 0.2),
-    legend.position = c(0.75, 0.9))
-

+

Figure 7

+
densEst <- deltaQ[, {
+  d <- density(dQ, cut = 0, bw = if (lambda == 0) 120 else 100)
+  data.table(x = d$x, y = d$y)
+}, by = lambda]
+
+densEst[, yScaled := y / max(y) * 3]
+densEst[, l2 := to_char_1dp(lambda)]
+
dQlims <- abs_range(densEst[lambda %in% c(0, 1.2), x])
+p1 <- ggplot(deltaQ[lambda %in% c(0, 1.2)]) +
+  geom_line(aes(year, dQ, colour = as.character(lambda), linetype = as.character(lambda)), size = 0.4) +
+  scale_y_continuous(
+    breaks = seq(-800, 800, 200), 
+    limits = dQlims) +
+  scale_colour_manual(
+    name = NULL,
+    values = c('steelblue', 'darkorange'),
+    labels = lambdaLab) +
+  scale_linetype_manual(
+    name = NULL,
+    labels = lambdaLab,
+    values = c(1, 2)) +
+  labs(x = 'Year', y = '\u0394Q [million m\u00b3]') +
+  theme(legend.position = 'none')
+
+p2 <- ggplot(densEst[lambda %in% c(0, 1.2)]) +
+  geom_area(
+    aes(x, y), 
+    densEst[x %between% c(-Qmean / 10, Qmean / 10) & lambda == 1.2], 
+    fill = 'gray95') +
+  geom_line(aes(x, y, colour = as.character(lambda), linetype = as.character(lambda))) +
+  scale_colour_manual(
+    name = NULL,
+    values = c('steelblue', 'darkorange'),
+    labels = lambdaLab) +
+  scale_x_continuous(
+    breaks = seq(-1000, 1000, 200), 
+    limits = dQlims) +
+  scale_y_continuous(expand = expansion(c(0, 0.1), 0), labels = skip_label(2)) +
+  scale_linetype_manual(
+    name = NULL,
+    labels = lambdaLab,
+    values = c(1, 2)) +
+  coord_flip() +
+  labs(x = NULL, y = 'Density') +
+  theme(
+    legend.key.width = unit(0.95, 'cm'),
+    legend.text = element_text(family = 'Lucida Sans Unicode'),
+    axis.text.y = element_blank(),
+    axis.ticks.y = element_blank())
+
+lambdas <- round(seq(0, 3, 0.2), 1)
+
+p3 <- ggplot(densEst[lambda %in% lambdas]) +
+  geom_ribbon(
+    aes(x, 
+        ymin = lambda * 3, 
+        ymax = yScaled + lambda * 3, 
+        group = rev(l2),
+        fill = lambda), 
+    colour = 'gray30',
+    size = 0.2) +
+  scale_fill_distiller(
+    name = '\u03bb',
+    palette = 'Oranges', direction = 1) +
+  scale_y_continuous(expand = c(0, 0)) +
+  labs(x = '\u0394Q [million m\u00b3]') +
+  theme(
+    panel.grid.major.x = element_line('gray'),
+    axis.text.y = element_blank(),
+    axis.line.y = element_blank(),
+    axis.ticks.y = element_blank())
+
+p4 <- ggplot(
+  deltaQ[lambda %in% lambdas, .(p = .SD[abs(dQ) > Qmean / 10, .N] / .N * 100,
+           min = min(dQ), 
+           max = max(dQ)),
+       by = lambda]) +
+  geom_line(aes(lambda, p)) +
+  geom_point(aes(lambda, p)) +
+  scale_x_continuous(breaks = seq(0, 3, 0.2), labels = skip_label(5)) +
+  scale_y_continuous(limits = c(0, 50)) +
+  labs(x = '\u03bb', y = '\u0394Q within 10% mean flow [%]')
+
+p1 + p2 + p3 + p4 +
+  plot_layout(ncol = 2, widths = c(2, 1), guides = 'collect') +
+  plot_annotation(tag_levels = 'a', tag_suffix = ')')
+

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a/site-selection-GA.R +++ b/site-selection-GA.R @@ -1,145 +1,47 @@ -lambda <- 0 +args <- commandArgs(trailingOnly = TRUE) +lambdas <- seq(0, 2, 0.2) +lambda <- lambdas[as.integer(args)] + pop <- 500 gen <- 500 -source('R/init.R') -source('R/correlation_functions.R') +library(magrittr) +library(glue) +library(data.table) +library(mbr) library(GA) library(mbr) -if (!require(memoise)) install.packages('memoise') - -if (!require(mbr)) { - if (!require(remotes)) install.packages('remotes') - remotes::install_github('ntthung/mbr') -} - -# Streamflow ------------------------------ -p1merge <- fread('results/p1merge.csv', key = 'season') - -## Hinkley D and transformation type -p1HD <- p1merge[, .(D = round(hinkley(Qa), 2), - Dlog = round(hinkley(log(Qa)), 2)), - keyby = season - ][, trans := fifelse(abs(D) > abs(Dlog), 'log', 'none')] - -# Proxies ------------------------------- - -oxi <- fread('data/oxi.csv', key = 'site') -crn <- fread('data/crn.csv', key = 'site') - -crnMeta <- fread('data/crnMeta.csv', key = 'site') -oxiMeta <- fread('data/oxiMeta.csv', key = 'site') - -# In-filling -crnWideFull <- crn[year %in% 1748:2005, dcast(.SD, year ~ site, value.var = 'rwi')] -crnMatFull <- as.matrix(crnWideFull[, -'year']) -crnMatFilled <- imputePCA(crnMatFull, ncp = 19)$completeObs -crnWideFilled <- as.data.table(crnMatFilled) - -oxiWideFull <- oxi[year %in% 1748:2005, dcast(.SD, year ~ site, value.var = 'do18')] -oxiMatFull <- as.matrix(oxiWideFull[, -'year']) -oxiMatFilled <- imputePCA(oxiMatFull, ncp = 3)$completeObs -oxiWideFilled <- as.data.table(oxiMatFilled) - -# Input matrices -allLags <- -2:2 # Core years: 1750:2003, and shift back or forth -Xrw <- do.call(cbind, lapply(allLags, function(l) { - x <- crnMatFilled[3:256 - l, ] - colnames(x) <- paste0(colnames(x), l) - x -})) -Xdo <- do.call(cbind, lapply(allLags, function(l) { - x <- oxiMatFilled[3:256 - l, ] - colnames(x) <- paste0(colnames(x), l) - x -})) -Xrwdo <- cbind(Xrw, Xdo) +source('R/input_selection_functions.R') +p1tar <- readRDS('results/p1tar.RDS') +Xrwdo <- readRDS('results/Xrwdo.RDS') +poolDT <- readRDS('results/poolDT.RDS') -# Cross-validation ------------------------- set.seed(24) cvFolds <- make_Z(1922:2003, nRuns = 50, frac = 0.25, contiguous = TRUE) -# Labelling --------------------------------- -oxiLab <- function(s) oxiMeta[s, display] -crnLab <- function(s) crnMeta[s, display] +lambdaChar <- if (lambda < 1) paste0('0', lambda * 10) else as.character(lambda * 10) + +lapply(seq(100, 400), function(s) { + + siteOptim <- ga( + type = 'binary', + fitness = memoise::memoise(cv_site_selection), + pool = poolDT, + Xpool = Xrwdo, + instQ = p1tar, + cv.folds = cvFolds, + start.year = 1750, + lambda = lambda, + log.trans = 1:3, + popSize = pop, + maxiter = gen, + run = min(c(gen, 100)), + parallel = TRUE, + monitor = FALSE, + nBits = nrow(poolDT), + seed = s) + + saveRDS(siteOptim, glue('results/pop{pop}_gen{gen}_lambda{lambdaChar}_s{s}.RDS')) -lambdaLab <- function(x) sapply(x, function(v) paste0('\u03bb = ', substr(v, 3, 5))) -ssnLabBrief <- function(x) seasonClass[x, type] -names(seasons) <- seasons <- c('NJ', 'JO', 'WY') - -seasonClass <- fread('data/season_class.csv', key = 'season') - -# Streamflow: 1922 to 2003 -# Targets: 1922 to 2003 -QDry <- p1merge['NJ'][1:82] -QWet <- p1merge['JO'][1:82] -QAnn <- p1merge['WY'][1:82] -p1tar <- rbind(QDry, QWet, QAnn) -p1tar[, season := factor(season, seasons)] -setkey(p1tar, season) - -QMat <- log(as.matrix(dcast(p1tar, year ~ season, value.var = 'Qa')[, -'year'])) -cor_lag <- function(X, Y, l, Xyears, Yyears, alpha) { - ind <- which(Xyears %in% Yyears) - l - cor_boot(cbind(X[ind, ], Y), - 1:ncol(X), - 1:ncol(Y) + ncol(X), - c('site', 'season'), - alpha = alpha) -} -set.seed(42) -rhoCrn <- lapplyrbind(allLags, function(l) { - DT <- cor_lag(crnMatFilled, QMat, l, 1748:2005, 1922:2003, 0.05) - DT[, lag := l] - DT[, alpha := 0.05][] }) -rhoOxi <- lapplyrbind(allLags, function(l) { - DT <- cor_lag(oxiMatFilled, QMat, l, 1748:2005, 1922:2003, 0.05) - DT[, lag := l] - DT[, alpha := 0.05][] -}) - -dt <- CJ(s = factor(seasons, seasons), l = allLags) -dt[, sl := paste0(s, '(', l, ')')] - -rhoCrn[, season_lag := paste0(season, '(', lag, ')')] -rhoCrn[, season_lag := factor(season_lag, levels = dt$sl)] -rhoCrn[, season := factor(season, levels = seasons)] -rhoCrn[, lag := factor(lag, levels = allLags)] -rhoOxi[, season_lag := paste0(season, '(', lag, ')')] -rhoOxi[, season_lag := factor(season_lag, levels = dt$sl)] -rhoOxi[, season := factor(season, levels = seasons)] -rhoOxi[, lag := factor(lag, levels = allLags)] - -poolDT <- rbind(rhoCrn[{signif} & abs(rho0) >= 0.2, .(site, lag), by = season], - rhoOxi[{signif} & abs(rho0) >= 0.2, .(site, lag), by = season] - )[, site := paste0(site, lag)] -setkey(poolDT, season) - -Xused <- Xrwdo[, poolDT[, unique(site)]] - -lambdaChar <- fcase( - lambda == 0, '00', - lambda == 1, '10') - -siteOptim <- ga( - type = 'binary', - fitness = memoise::memoise(mbr::cv_site_selection), - pool = poolDT, - seasons = seasons, - Xpool = Xused, - instQ = p1tar, - cv.folds = cvFolds, - start.year = 1750, - lambda = lambda, - log.trans = 1:3, - num.targets = 3, - popSize = pop, - maxiter = gen, - run = min(c(gen, 100)), - parallel = TRUE, - monitor = FALSE, - nBits = nrow(poolDT)) - -saveRDS(siteOptim, glue('results/pop{pop}_gen{gen}_lambda{lambdaChar}_refactored.RDS'))