Created
July 26, 2014 21:03
-
-
Save alexchinco/103f458b16b495268478 to your computer and use it in GitHub Desktop.
Create figures for wavelet variance post.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| ## Prep workspace | |
| rm(list=ls()) | |
| library(foreign) | |
| library(grid) | |
| library(plyr) | |
| library(ggplot2) | |
| library(tikzDevice) | |
| library(wmtsa) | |
| print(options('tikzLatexPackages')) | |
| options(tikzLatexPackages = | |
| c("\\usepackage{tikz}\n", | |
| "\\usepackage[active,tightpage,psfixbb]{preview}\n", | |
| "\\PreviewEnvironment{pgfpicture}\n", | |
| "\\setlength\\PreviewBorder{0pt}\n", | |
| "\\usepackage{amsmath}\n", | |
| "\\usepackage{xfrac}\n" | |
| ) | |
| ) | |
| setTikzDefaults(overwrite = FALSE) | |
| print(options('tikzLatexPackages')) | |
| library(reshape) | |
| library(vars) | |
| library(scales) | |
| library(zoo) | |
| set.seed(12345) | |
| scl.str.DAT_DIR <- "~/Dropbox/research/correlation_term_structure/data/" | |
| scl.str.FIG_DIR <- "~/Dropbox/research/correlation_term_structure/figures/" | |
| ## Define simulation parameters | |
| scl.int.T_DAY <- (6.5 * 60) | |
| scl.int.DAYS <- 63 | |
| scl.int.T <- scl.int.T_DAY * scl.int.DAYS | |
| vec.int.T <- seq(1, scl.int.T) | |
| scl.flt.SIG <- 0.010 | |
| scl.flt.TET <- 0.50/scl.int.T_DAY | |
| scl.flt.SHOCK_PROB <- 0.05 | |
| ## Simulate time series | |
| vec.flt.X <- rep(0, scl.int.T) | |
| vec.flt.X[1] <- 1 | |
| vec.flt.dX <- rep(0, scl.int.T) | |
| vec.flt.SHOCK <- rep(NA, scl.int.T) | |
| vec.flt.DRAW <- runif(scl.int.DAYS, 0, 1) | |
| for (d in 1:scl.int.DAYS) { | |
| vec.int.ROWS <- seq((d-1) * scl.int.T_DAY + 1,d * scl.int.T_DAY) | |
| if (vec.flt.DRAW[d] < scl.flt.SHOCK_PROB) { | |
| scl.flt.SIGN <- runif(1, 0, 1) | |
| if (scl.flt.SIGN > 0.50) { | |
| vec.flt.SHOCK[vec.int.ROWS] <- scl.flt.TET | |
| } else { | |
| vec.flt.SHOCK[vec.int.ROWS] <- - scl.flt.TET | |
| } | |
| } else { | |
| vec.flt.SHOCK[vec.int.ROWS] <- 0 | |
| } | |
| } | |
| for (t in 2:scl.int.T) { | |
| vec.flt.dX[t] <- vec.flt.SHOCK[t] + scl.flt.SIG * rnorm(1,0,1) | |
| vec.flt.X[t] <- vec.flt.X[t-1] + vec.flt.dX[t] | |
| } | |
| mat.df.DATA <- data.frame(t = seq(1, scl.int.T), | |
| x = vec.flt.dX | |
| ) | |
| ## Create white noise process | |
| vec.flt.dX2 <- rnorm(scl.int.T, 0, sd(vec.flt.dX)) | |
| mat.df.DATA$x2 <- vec.flt.dX2 | |
| ## Plot time series | |
| if (TRUE == TRUE) { | |
| mat.df.PLOT <- data.frame(x = seq(1,scl.int.T)/scl.int.T_DAY, | |
| y = vec.flt.dX, | |
| s = vec.flt.SHOCK | |
| ) | |
| mat.df.PLOT$d <- ceiling(mat.df.PLOT$x) | |
| mat.df.PLOT <- ddply(mat.df.PLOT, c('d'), function(X)c(sum(X$y), round(mean(X$s),4))) | |
| names(mat.df.PLOT) <- c("x", "y", "s") | |
| theme_set(theme_bw()) | |
| scl.str.RAW_FILE <- 'plot--why-use-wavelet-variance--daily-shocks--time-series--25jul2014' | |
| scl.str.TEX_FILE <- paste(scl.str.RAW_FILE,'.tex',sep='') | |
| scl.str.PDF_FILE <- paste(scl.str.RAW_FILE,'.pdf',sep='') | |
| scl.str.PNG_FILE <- paste(scl.str.RAW_FILE,'.png',sep='') | |
| scl.str.AUX_FILE <- paste(scl.str.RAW_FILE,'.aux',sep='') | |
| scl.str.LOG_FILE <- paste(scl.str.RAW_FILE,'.log',sep='') | |
| tikz(file = scl.str.TEX_FILE, height = 2, width = 7, standAlone=TRUE) | |
| obj.gg2.PLOT <- ggplot() | |
| obj.gg2.PLOT <- obj.gg2.PLOT + scale_colour_brewer(palette="Set1") | |
| for (r in 1:dim(mat.df.PLOT)[1]) { | |
| if (mat.df.PLOT$s[r] != 0) { | |
| obj.gg2.PLOT <- obj.gg2.PLOT + geom_vline(xintercept = mat.df.PLOT$x[r], size = 2, colour = "red", alpha = 0.25) | |
| } | |
| } | |
| obj.gg2.PLOT <- obj.gg2.PLOT + geom_path(data = mat.df.PLOT, | |
| aes(x = x, | |
| y = y | |
| ), | |
| size = 0.50 | |
| ) | |
| obj.gg2.PLOT <- obj.gg2.PLOT + xlab("$t$ $[\\text{days}]$") | |
| obj.gg2.PLOT <- obj.gg2.PLOT + ylab("$r_t$") | |
| obj.gg2.PLOT <- obj.gg2.PLOT + scale_x_continuous(breaks = c(0, 21, 42, 63)) | |
| obj.gg2.PLOT <- obj.gg2.PLOT + theme(plot.margin = unit(c(1,0.15,0.15,-0.15), "lines"), | |
| legend.position = c(0.25,0.51), | |
| legend.background = element_blank(), | |
| axis.title = element_text(size = 10), | |
| axis.text = element_text(size = 6), | |
| plot.title = element_text(vjust = 1.75), | |
| panel.grid.minor = element_blank() | |
| ) | |
| obj.gg2.PLOT <- obj.gg2.PLOT + ggtitle("Daily Time Series") | |
| print(obj.gg2.PLOT) | |
| dev.off() | |
| system(paste('lualatex', file.path(scl.str.TEX_FILE)), ignore.stdout = TRUE) | |
| system(paste('convert -density 600', file.path(scl.str.PDF_FILE), ' ', file.path(scl.str.PNG_FILE))) | |
| system(paste('mv ', scl.str.PNG_FILE, ' ', scl.str.FIG_DIR, sep = '')) | |
| system(paste('rm ', scl.str.TEX_FILE, sep = '')) | |
| system(paste('mv ', scl.str.PDF_FILE, ' ', scl.str.FIG_DIR, sep = '')) | |
| system(paste('rm ', scl.str.AUX_FILE, sep = '')) | |
| system(paste('rm ', scl.str.LOG_FILE, sep = '')) | |
| } | |
| ## Estimate wavelet coefficients | |
| obj.dwt.RESULT <- wavMODWT(vec.flt.dX, wavelet="d8", keep.series=TRUE) | |
| summary(obj.dwt.RESULT) | |
| ## Plot wavelet decomposition over time | |
| if (TRUE == TRUE) { | |
| mat.df.PLOT <- data.frame(t = seq(1,scl.int.T)/scl.int.T_DAY, | |
| d1 = as.numeric((obj.dwt.RESULT$data$d1)), | |
| d2 = as.numeric((obj.dwt.RESULT$data$d2)), | |
| d3 = as.numeric((obj.dwt.RESULT$data$d3)), | |
| d4 = as.numeric((obj.dwt.RESULT$data$d4)), | |
| d5 = as.numeric((obj.dwt.RESULT$data$d5)), | |
| d6 = as.numeric((obj.dwt.RESULT$data$d6)), | |
| d7 = as.numeric((obj.dwt.RESULT$data$d7)), | |
| d8 = as.numeric((obj.dwt.RESULT$data$d8)), | |
| d9 = as.numeric((obj.dwt.RESULT$data$d9)), | |
| d10 = as.numeric((obj.dwt.RESULT$data$d10)), | |
| d11 = as.numeric((obj.dwt.RESULT$data$d11)), | |
| d12 = as.numeric((obj.dwt.RESULT$data$d12)), | |
| d13 = as.numeric((obj.dwt.RESULT$data$d13)), | |
| d14 = as.numeric((obj.dwt.RESULT$data$d14)) | |
| ) | |
| mat.df.PLOT <- melt(mat.df.PLOT, c("t")) | |
| names(mat.df.PLOT) <- c("t", "variable", "value") | |
| mat.df.PLOT$variable <- factor(mat.df.PLOT$variable, | |
| levels = c("d1", "d2", "d3", "d4", "d5", "d6", "d7", "d8", "d9", "d10", "d11", "d12", "d13", "d14"), | |
| labels = c("$h = 0$", "$h = 1$", "$h = 2$", "$h = 3$", "$h = 4$", "$h = 5$", "$h = 6$", "$h = 7$", "$h = 8$", "$h = 9$", "$h = 10$", "$h = 11$", "$h = 12$", "$h = 13$") | |
| ) | |
| theme_set(theme_bw()) | |
| scl.str.RAW_FILE <- 'plot--why-use-wavelet-variance--daily-shocks--wavelet-coefficients--25jul2014' | |
| scl.str.TEX_FILE <- paste(scl.str.RAW_FILE,'.tex',sep='') | |
| scl.str.PDF_FILE <- paste(scl.str.RAW_FILE,'.pdf',sep='') | |
| scl.str.PNG_FILE <- paste(scl.str.RAW_FILE,'.png',sep='') | |
| scl.str.AUX_FILE <- paste(scl.str.RAW_FILE,'.aux',sep='') | |
| scl.str.LOG_FILE <- paste(scl.str.RAW_FILE,'.log',sep='') | |
| tikz(file = scl.str.TEX_FILE, height = 6, width = 7, standAlone=TRUE) | |
| obj.gg2.PLOT <- ggplot() | |
| obj.gg2.PLOT <- obj.gg2.PLOT + scale_colour_brewer("Set1") | |
| obj.gg2.PLOT <- obj.gg2.PLOT + geom_path(data = mat.df.PLOT, | |
| aes(x = t, | |
| y = value, | |
| group = variable | |
| ), | |
| size = 0.50 | |
| ) | |
| obj.gg2.PLOT <- obj.gg2.PLOT + xlab("") | |
| obj.gg2.PLOT <- obj.gg2.PLOT + ylab("$\\langle \\theta_{h,i} \\rangle_t$") | |
| obj.gg2.PLOT <- obj.gg2.PLOT + scale_x_continuous(breaks = c(0, 21, 42, 63)) | |
| obj.gg2.PLOT <- obj.gg2.PLOT + facet_grid(variable ~ ., scales = "free_y") | |
| obj.gg2.PLOT <- obj.gg2.PLOT + theme(plot.margin = unit(c(1,0.25,-0.75,0.15), "lines"), | |
| plot.title = element_text(vjust = 1.75), | |
| legend.position = "none", | |
| axis.text = element_text(size = 6), | |
| axis.title = element_text(size = 8), | |
| panel.grid.minor = element_blank(), | |
| strip.text = element_text(size = 6) | |
| ) | |
| obj.gg2.PLOT <- obj.gg2.PLOT + ggtitle("Wavelet Coefficients") | |
| print(obj.gg2.PLOT) | |
| dev.off() | |
| system(paste('lualatex', file.path(scl.str.TEX_FILE)), ignore.stdout = TRUE) | |
| system(paste('convert -density 600', file.path(scl.str.PDF_FILE), ' ', file.path(scl.str.PNG_FILE))) | |
| system(paste('mv ', scl.str.PNG_FILE, ' ', scl.str.FIG_DIR, sep = '')) | |
| system(paste('rm ', scl.str.TEX_FILE, sep = '')) | |
| system(paste('mv ', scl.str.PDF_FILE, ' ', scl.str.FIG_DIR, sep = '')) | |
| system(paste('rm ', scl.str.AUX_FILE, sep = '')) | |
| system(paste('rm ', scl.str.LOG_FILE, sep = '')) | |
| } | |
| ## Estimate wavelet variance | |
| obj.wvl.WVAR <- wavVar(vec.flt.dX, wavelet = "d8") | |
| vec.flt.WVAR <- summary(obj.wvl.WVAR)$vmat[2,] | |
| obj.wvl.WVAR2 <- wavVar(vec.flt.dX2, wavelet = "d8") | |
| vec.flt.WVAR2 <- summary(obj.wvl.WVAR2)$vmat[2,] | |
| mat.df.WVAR <- data.frame(h = seq(0, length(vec.flt.WVAR)-1), | |
| wvar = vec.flt.WVAR, | |
| wvar2 = vec.flt.WVAR2 | |
| ) | |
| print(mat.df.WVAR) | |
| print(cbind(seq(0, length(vec.flt.WVAR)-1), mat.df.WVAR$wvar/sum(mat.df.WVAR$wvar))) | |
| summary(obj.dwt.RESULT) | |
| print(var(vec.flt.dX)) | |
| print(sum(vec.flt.WVAR)) | |
| print(sum(vec.flt.WVAR2)) | |
| names(mat.df.WVAR) <- c("h", "1", "2") | |
| mat.df.WVAR <- melt(mat.df.WVAR, c("h")) | |
| mat.df.WVAR$variable <- factor(mat.df.WVAR$variable, | |
| levels = c("1", "2"), | |
| labels = c("Return", "Noise") | |
| ) | |
| ## Plot time series | |
| if (TRUE == TRUE) { | |
| theme_set(theme_bw()) | |
| scl.str.RAW_FILE <- 'plot--why-use-wavelet-variance--daily-shocks--wavelet-variance--25jul2014' | |
| scl.str.TEX_FILE <- paste(scl.str.RAW_FILE,'.tex',sep='') | |
| scl.str.PDF_FILE <- paste(scl.str.RAW_FILE,'.pdf',sep='') | |
| scl.str.PNG_FILE <- paste(scl.str.RAW_FILE,'.png',sep='') | |
| scl.str.AUX_FILE <- paste(scl.str.RAW_FILE,'.aux',sep='') | |
| scl.str.LOG_FILE <- paste(scl.str.RAW_FILE,'.log',sep='') | |
| tikz(file = scl.str.TEX_FILE, height = 2, width = 7, standAlone=TRUE) | |
| obj.gg2.PLOT <- ggplot() | |
| obj.gg2.PLOT <- obj.gg2.PLOT + scale_colour_brewer(palette="Set1") | |
| obj.gg2.PLOT <- obj.gg2.PLOT + geom_vline(xintercept = log(scl.int.T_DAY), size = 1, colour = "black", alpha = 0.50) | |
| obj.gg2.PLOT <- obj.gg2.PLOT + geom_path(data = mat.df.WVAR, | |
| aes(x = h/log(exp(1),2), | |
| y = log(value), | |
| group = variable, | |
| colour = variable | |
| ), | |
| size = 0.50 | |
| ) | |
| obj.gg2.PLOT <- obj.gg2.PLOT + labs(colour = "") | |
| obj.gg2.PLOT <- obj.gg2.PLOT + ylab("$\\log V(h)$") | |
| obj.gg2.PLOT <- obj.gg2.PLOT + xlab("$h$ $[\\log(\\text{min})]$") | |
| ## obj.gg2.PLOT <- obj.gg2.PLOT + scale_x_date(limits = c(as.Date("1965-1-1"), as.Date("2012-12-31")), labels = date_format("%Y")) | |
| ## obj.gg2.PLOT <- obj.gg2.PLOT + scale_y_continuous(limits = c(1,12), breaks = c(2, 5, 8, 11)) | |
| obj.gg2.PLOT <- obj.gg2.PLOT + theme(plot.margin = unit(c(1,0.15,0.15,-0.10), "lines"), | |
| plot.title = element_text(vjust = 1.75), | |
| legend.position = c(0.75,0.90), | |
| legend.background = element_blank(), | |
| axis.title = element_text(size = 10), | |
| axis.text = element_text(size = 6), | |
| panel.grid.minor = element_blank() | |
| ) | |
| obj.gg2.PLOT <- obj.gg2.PLOT + ggtitle("Wavelet Variance") | |
| print(obj.gg2.PLOT) | |
| dev.off() | |
| system(paste('lualatex', file.path(scl.str.TEX_FILE)), ignore.stdout = TRUE) | |
| system(paste('convert -density 600', file.path(scl.str.PDF_FILE), ' ', file.path(scl.str.PNG_FILE))) | |
| system(paste('mv ', scl.str.PNG_FILE, ' ', scl.str.FIG_DIR, sep = '')) | |
| system(paste('rm ', scl.str.TEX_FILE, sep = '')) | |
| system(paste('mv ', scl.str.PDF_FILE, ' ', scl.str.FIG_DIR, sep = '')) | |
| system(paste('rm ', scl.str.AUX_FILE, sep = '')) | |
| system(paste('rm ', scl.str.LOG_FILE, sep = '')) | |
| } | |
| ## Estimate spectral density | |
| ## spec.ar(vec.flt.X, method = "ols") | |
| obj.stt.PS <- spec.ar(vec.flt.dX, method = "ols", order = 11, plot = FALSE) | |
| mat.df.PS <- data.frame(f = obj.stt.PS$freq, | |
| s = obj.stt.PS$spec | |
| ) | |
| obj.stt.PS2 <- spec.ar(vec.flt.dX2, method = "ols", plot = FALSE) | |
| mat.df.PS$s2 <- obj.stt.PS2$spec | |
| print(sum(mat.df.PS$s) * 1/(dim(mat.df.PS)[1] - 1)) | |
| print(sum(mat.df.PS$s2) * 1/(dim(mat.df.PS)[1] - 1)) | |
| print(var(vec.flt.dX)) | |
| names(mat.df.PS) <- c("f", "1", "2") | |
| mat.df.PS <- melt(mat.df.PS, c("f")) | |
| mat.df.PS$variable <- factor(mat.df.PS$variable, | |
| levels = c("1", "2"), | |
| labels = c("Return", "Noise") | |
| ) | |
| ## Estimate auto-correlation coefficients | |
| vec.int.LAGS <- seq(1, scl.int.T_DAY * 1.20) | |
| scl.int.LAG_MAX <- length(vec.int.LAGS) | |
| mat.df.AC <- data.frame(l = vec.int.LAGS, | |
| c = NA, | |
| c2 = NA | |
| ) | |
| vec.int.Y_ROWS <- seq(1, scl.int.T - scl.int.LAG_MAX) | |
| vec.flt.Y <- vec.flt.dX[vec.int.Y_ROWS] | |
| vec.flt.Y2 <- vec.flt.dX2[vec.int.Y_ROWS] | |
| for (l in vec.int.LAGS) { | |
| vec.int.X_ROWS <- seq(1 + l, scl.int.T - scl.int.LAG_MAX + l) | |
| obj.lm.AC <- lm(vec.flt.Y ~ vec.flt.dX[vec.int.X_ROWS]) | |
| obj.lm.AC2 <- lm(vec.flt.Y2 ~ vec.flt.dX2[vec.int.X_ROWS]) | |
| mat.df.AC[mat.df.AC$l == l, ]$c <- summary(obj.lm.AC)$coef[2,1] | |
| mat.df.AC[mat.df.AC$l == l, ]$c2 <- summary(obj.lm.AC2)$coef[2,1] | |
| } | |
| names(mat.df.AC) <- c("l", "1", "2") | |
| mat.df.AC <- melt(mat.df.AC, c("l")) | |
| mat.df.AC$variable <- factor(mat.df.AC$variable, | |
| levels = c("1", "2"), | |
| labels = c("Return", "Noise") | |
| ) | |
| ## Plot measures side-by-side | |
| if (TRUE == TRUE) { | |
| obj.vplayout <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y) | |
| theme_set(theme_bw()) | |
| scl.str.RAW_FILE <- 'plot--why-use-wavelet-variance--daily-shocks--analysis--25jul2014' | |
| scl.str.TEX_FILE <- paste(scl.str.RAW_FILE,'.tex',sep='') | |
| scl.str.PDF_FILE <- paste(scl.str.RAW_FILE,'.pdf',sep='') | |
| scl.str.PNG_FILE <- paste(scl.str.RAW_FILE,'.png',sep='') | |
| scl.str.AUX_FILE <- paste(scl.str.RAW_FILE,'.aux',sep='') | |
| scl.str.LOG_FILE <- paste(scl.str.RAW_FILE,'.log',sep='') | |
| tikz(file = scl.str.TEX_FILE, height = 2, width = 7, standAlone=TRUE) | |
| ## Plot 2 | |
| obj.gg2.PLOT2 <- ggplot() | |
| obj.gg2.PLOT2 <- obj.gg2.PLOT2 + scale_colour_brewer(palette="Set1") | |
| obj.gg2.PLOT2 <- obj.gg2.PLOT2 + geom_vline(xintercept = scl.int.T_DAY, size = 1, colour = "black", alpha = 0.50) | |
| obj.gg2.PLOT2 <- obj.gg2.PLOT2 + geom_path(data = mat.df.AC, | |
| aes(x = l, | |
| y = value, | |
| group = variable, | |
| colour = variable | |
| ), | |
| size = 0.50 | |
| ) | |
| obj.gg2.PLOT2 <- obj.gg2.PLOT2 + ylab("$C(\\ell)$") | |
| obj.gg2.PLOT2 <- obj.gg2.PLOT2 + xlab("$\\ell$ $[\\text{min}]$") | |
| ## obj.gg2.PLOT2 <- obj.gg2.PLOT2 + scale_x_date(limits = c(as.Date("1965-1-1"), as.Date("2012-12-31")), labels = date_format("%Y")) | |
| ## obj.gg2.PLOT2 <- obj.gg2.PLOT2 + scale_y_continuous(limits = c(1,12), breaks = c(2, 5, 8, 11)) | |
| obj.gg2.PLOT2 <- obj.gg2.PLOT2 + theme(plot.margin = unit(c(1,0.15,0.15,-0.10), "lines"), | |
| plot.title = element_text(vjust = 1.75), | |
| legend.position = "none", | |
| axis.title = element_text(size = 10), | |
| axis.text = element_text(size = 6), | |
| panel.grid.minor = element_blank() | |
| ) | |
| obj.gg2.PLOT2 <- obj.gg2.PLOT2 + ggtitle("Predictive Regression") | |
| ## Plot 3 | |
| obj.gg2.PLOT3 <- ggplot() | |
| obj.gg2.PLOT3 <- obj.gg2.PLOT3 + scale_colour_brewer(palette="Set1") | |
| obj.gg2.PLOT3 <- obj.gg2.PLOT3 + geom_vline(xintercept = log(scl.int.T_DAY), size = 1, alpha = 0.50, colour = "black") | |
| obj.gg2.PLOT3 <- obj.gg2.PLOT3 + geom_path(data = mat.df.PS, | |
| aes(x = -log(f), | |
| y = log(value), | |
| group = variable, | |
| colour = variable | |
| ), | |
| size = 0.50 | |
| ) | |
| obj.gg2.PLOT3 <- obj.gg2.PLOT3 + ylab("$\\log S(f)$") | |
| obj.gg2.PLOT3 <- obj.gg2.PLOT3 + xlab("$- \\log (f)$ $[\\log(\\text{min})]$") | |
| ## obj.gg2.PLOT3 <- obj.gg2.PLOT3 + scale_x_date(limits = c(as.Date("1965-1-1"), as.Date("2012-12-31")), labels = date_format("%Y")) | |
| ## obj.gg2.PLOT3 <- obj.gg2.PLOT3 + scale_y_continuous(limits = c(1,12), breaks = c(2, 5, 8, 11)) | |
| obj.gg2.PLOT3 <- obj.gg2.PLOT3 + theme(plot.margin = unit(c(1,0.15,0.15,-0.10), "lines"), | |
| plot.title = element_text(vjust = 1.75), | |
| legend.position = "none", | |
| axis.title = element_text(size = 10), | |
| axis.text = element_text(size = 6), | |
| panel.grid.minor = element_blank() | |
| ) | |
| obj.gg2.PLOT3 <- obj.gg2.PLOT3 + ggtitle("Spectral Density") | |
| ## Plot 4 | |
| obj.gg2.PLOT4 <- ggplot() | |
| obj.gg2.PLOT4 <- obj.gg2.PLOT4 + scale_colour_brewer(palette="Set1") | |
| obj.gg2.PLOT4 <- obj.gg2.PLOT4 + geom_vline(xintercept = log(scl.int.T_DAY), size = 1, alpha = 0.50, colour = "black") | |
| obj.gg2.PLOT4 <- obj.gg2.PLOT4 + geom_path(data = mat.df.WVAR, | |
| aes(x = h/log(exp(1),2), | |
| y = log(value), | |
| group = variable, | |
| colour = variable | |
| ), | |
| size = 0.50 | |
| ) | |
| obj.gg2.PLOT4 <- obj.gg2.PLOT4 + labs(colour = "") | |
| obj.gg2.PLOT4 <- obj.gg2.PLOT4 + ylab("$\\log V(h)$") | |
| obj.gg2.PLOT4 <- obj.gg2.PLOT4 + xlab("$h$ $[\\log(\\text{min})]$") | |
| ## obj.gg2.PLOT4 <- obj.gg2.PLOT4 + scale_x_date(limits = c(as.Date("1965-1-1"), as.Date("2012-12-31")), labels = date_format("%Y")) | |
| ## obj.gg2.PLOT4 <- obj.gg2.PLOT4 + scale_y_continuous(limits = c(1,12), breaks = c(2, 5, 8, 11)) | |
| obj.gg2.PLOT4 <- obj.gg2.PLOT4 + theme(plot.margin = unit(c(1,0.15,0.15,-0.10), "lines"), | |
| plot.title = element_text(vjust = 1.75), | |
| legend.position = c(0.25, 0.35), | |
| legend.background = element_blank(), | |
| axis.title = element_text(size = 10), | |
| axis.text = element_text(size = 6), | |
| panel.grid.minor = element_blank() | |
| ) | |
| obj.gg2.PLOT4 <- obj.gg2.PLOT4 + ggtitle("Wavelet Variance") | |
| ## Print results | |
| pushViewport(viewport(layout = grid.layout(1, 3))) | |
| print(obj.gg2.PLOT2, vp = obj.vplayout(1,1)) | |
| print(obj.gg2.PLOT3, vp = obj.vplayout(1,2)) | |
| print(obj.gg2.PLOT4, vp = obj.vplayout(1,3)) | |
| dev.off() | |
| system(paste('lualatex', file.path(scl.str.TEX_FILE)), ignore.stdout = TRUE) | |
| system(paste('convert -density 600', file.path(scl.str.PDF_FILE), ' ', file.path(scl.str.PNG_FILE))) | |
| system(paste('mv ', scl.str.PNG_FILE, ' ', scl.str.FIG_DIR, sep = '')) | |
| system(paste('rm ', scl.str.TEX_FILE, sep = '')) | |
| system(paste('mv ', scl.str.PDF_FILE, ' ', scl.str.FIG_DIR, sep = '')) | |
| system(paste('rm ', scl.str.AUX_FILE, sep = '')) | |
| system(paste('rm ', scl.str.LOG_FILE, sep = '')) | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment