###----------------------------------------------------------------###
## The "bivariate local trigonometric"-example with individual phase
## adjustment, from P2_fig_04.
## This script generates a plot that combines heatmap-based plot for
## the Co- and Quad-spectra with the corresponding distance-based
## plot. The point of interest in this plot is the effect on the
## estimated local Gaussian cross-spectra as the point v varies along
## the diagonal.
#####------------------------------------------------------------#####
## In order for this script to work, it is necessary that the script
## '2_Data.R' has been used first.
## Warning: The code below assumes that '2_Data.R' was used with its
## initial arguments, i.e. an adjustment of the script that includes
## additional points might require a modification of this script.
## Note: The '..TS' value given below originates from the
## 'digest::digest'-function. This is used in order to keep track of
## the different simulations, and it is in particular used to avoid
## the re-computation of a script that has already been executed. It
## might alas be the case that this value can be influenced by the
## random number generator used during the computation, so if the
## scrips has been used without modifications and the code below
## returns an error, then it might be necessary to update the
## '..TS'-value in this script by the one created by the
## data-generating script.
#####------------------------------------------------------------#####
## Specify the packages required for this script.
library(localgaussSpec)
library(ggplot2)
library(grid)
#####------------------------------------------------------------#####
## Define the directory- and file components needed for the
## extraction of the data. The path to the main directory is given
## as a vector since '.Platform$file.sep' depends on the OS. Note
## that these values must correspond to those that are used in the
## script '2_Data.R', so any modifications there must be mirrored in
## this script.
..main_dir <- c("~", "LG_DATA_scripts", "P2_fig_04_S3.4")
..TS <- "dmt_bivariate_79e20882a99a22d412f6d372310bb0ad"
..Approx <- "Approx__1"
## Select what kind of plot that should be produced.
.plot_type <- "Cartesian" ## "Polar"
if (.plot_type == "Cartesian") {
..S_type_pair <- c("LS_c_Co", "LS_c_Quad")
.save_file_name <- "P2_fig_04.pdf" ## Co- and Quad-spectra
} else {
..S_type_pair <- c("LS_c_amplitude", "LS_c_phase")
.save_file_name <- "P2_fig_S3.3.pdf" ## Amplitude- and Phase-spectra
}
heatmap_plot_list <- list()
#####------------------------------------------------------------#####
for (..S_type in ..S_type_pair) {
## Define the 'input'-list that specifies the content of the plot.
## Some of the information in this list is redundant for the present
## plot, but it is necessary to update the plot-function before those
## parts can be removed from the list below.
input <- list(TCS_type = "S",
window = "Tukey",
Boot_Approx = "Nothing here to select",
confidence_interval = "95",
levels_Diagonal = 1L,
bw_points = "0.6",
cut = 10L,
frequency_range = c(0, 0.5),
type = "par_five",
levels_Horizontal = 100,
TS = ..TS,
S_type = ..S_type,
levels_Line = 100,
point_type = "on_diag",
Approx = ..Approx,
Vi = "Y1",
Vj = "Y2",
levels_Vertical = 100,
global_local = "local",
heatmap = TRUE,
heatmap_b_or_v = "v",
spectra_f_or_F = "f",
drop_annotation = TRUE)
#####------------------------------------------------------------#####
#####------------------------------------------------------------#####
#####------------------------------------------------------------#####
## This part deals with the heatmap-plot.
heatmap_plot <- LG_plot_helper(
main_dir = ..main_dir,
input = input,
input_curlicues = list(
NC_value = list(short_or_long_label = "short"),
title = list(
label = "Heatmap for the local Gaussian autospectrum",
element_text = list(
size = 8))))
## The present plot is given without annotations. Those must be
## extracted from the attributes, and added later on after a bit of
## tweaking. (It is possible to define everything in the
## 'input_curlicues' argument, but it is easier to experiment with
## the details by using this approach.)
annotate_heatmap <- attributes(heatmap_plot)$curlicues$text
## Add horizontal lines at the 10%, 50% and 90% percentiles, since those
## were used for the basic plots in the paper.
heatmap_plot <- heatmap_plot +
geom_hline(
yintercept = c(0.1, 0.5, 0.9),
lty = 2,
lwd = 0.2,
alpha = .5)
##--------------------------------------------------------------------
## Code only relevant for the trigonometric examples. Extract
## information about the frequencies and phase-adjustments.
alpha <- attributes(heatmap_plot)$details$fun_formals$first_dmt$alpha
phase_adjustment <- attributes(heatmap_plot)$details$fun_formals$phase_adjustment
## Add the alpha-value (frequencies) as vertical lines
heatmap_plot <- heatmap_plot +
geom_vline(
xintercept = alpha/(2*pi),
lty = 2,
lwd = 0.2,
alpha = .5)
rm(alpha)
## End of part specific for the local trigonometric examples.
##--------------------------------------------------------------------
## Adjust the title manually (update code later on), including
## adjustments of the axes.
heatmap_plot <-
heatmap_plot +
theme(plot.title = element_text(hjust = 0.5,
vjust = 0,
size = 8,
colour = "brown")) +
annotate(geom = "text",
label = "omega",
parse = TRUE,
x = Inf,
y = -Inf,
size = 2,
hjust = "inward",
vjust = "inward") +
annotate(geom = "text",
label = "v",
parse = TRUE,
x = -Inf,
y = Inf,
size = 2,
hjust = "inward",
vjust = "inward") +
xlab(label = NULL) +
ylab(label = NULL) +
theme(axis.ticks = element_line(linewidth = 0.3),
axis.ticks.length = unit(.06, "cm"),
axis.text = element_text(size = 6))
## Add the annotations to the plot. This requires a bit of tweaking
## of the details since the result should look reasonable when the
## plots are included in a grid and then saved to file. Note that it
## is the saved file that should be inspected in order to figure out
## if the tweaking has produced a reasonable result.
## Tweak the position of the plot stamp.
annotate_heatmap$annotated$vjust[1] <- 2 * annotate_heatmap$annotated$vjust[1]
## Tweak the size of the annotated text so it looks decent after the
## grid-plot has been saved.
.scale <- 0.4
annotate_heatmap$annotated$size <-
.scale * annotate_heatmap$annotated$size
heatmap_plot <-
heatmap_plot +
eval(annotate_heatmap$annotated)
rm(.scale, annotate_heatmap)
heatmap_plot_list[[..S_type]] <- heatmap_plot
}
rm(..Approx)
#####------------------------------------------------------------#####
#####------------------------------------------------------------#####
#####------------------------------------------------------------#####
## This part deals with the distance-based plot. Note: The present
## incarnation of the code primarily aims at removing internal
## functions from the scripts, and it is thus not possible to tweak
## the annotations of this plot in the same manner used for the
## heatmap-plot.
input$heatmap <- NULL
input$heatmap_b_or_v <- NULL
input$L2_distance_plot <- TRUE
input$L2_distance_vbmL <- "v"
distance_plot <- LG_plot_helper(
main_dir = ..main_dir,
input = input,
input_curlicues = list(
NC_value = list(short_or_long_label = "short"),
limits = list(xlim = c(0, 1)),
distance_plot = list(
add_points_at_levels = c(0.10, 0.50, 0.90),
size = .7,
shape = 1,
colour = "blue"))) +
theme(axis.ticks = element_line(linewidth = 0.3),
axis.ticks.length = unit(.06, "cm"),
axis.text = element_text(size = 6))
## ## ## rm(input)
#####------------------------------------------------------------#####
#####------------------------------------------------------------#####
#####------------------------------------------------------------#####
## Create the desired grid of plots, and save this grid to disk.
## Note: It is only after having saved the result to a file, that the
## effect of the size-arguments for the text can be properly
## investigated.
.save_file <- file.path(paste(c(..main_dir, ..TS),
collapse = .Platform$file.sep),
.save_file_name)
rm(..main_dir, ..TS, .save_file_name)
## This part is used to tweak the ratios between the subplots when
## they are collected in the grid.
.x <- 25
.y <- 6
.z <- 4
.heatmap.pos.row <- 2:.y
.distance.pos.row <- .y + 1:.z
## REMINDER: Create a hybrid solution for the grid-title.
pdf(.save_file)
grid.newpage()
pushViewport(viewport(
gp = gpar("col" = "brown"),
layout = grid.layout(1+.x,6)))
for (.i in seq_along(heatmap_plot_list)) {
print(heatmap_plot_list[[.i]] +
ggtitle(label = NULL) +
theme(legend.key.width = unit(0.15, "cm"),
legend.text = element_text(size = 4.5)),
vp = viewport(
layout.pos.row = .heatmap.pos.row,
layout.pos.col = 1:2 + 2*(.i-1)))
}
rm(.i)
print(distance_plot,
vp = viewport(
layout.pos.row = .distance.pos.row,
layout.pos.col = 1:4))
.grid_text <- sprintf("Heatmap and distance plot: %s",
paste(gsub(pattern = "LS_c_",
replacement = "",
..S_type_pair),
collapse = " + "
))
grid.text(label = .grid_text,
vp = viewport(
layout.pos.row = 1,
layout.pos.col = 1:4))
dev.off()
rm(.x, .y, .z, .heatmap.pos.row, .distance.pos.row)
## Crop the resulting file. This code works on a Linux-based OS, and
## it requires that 'pdfcrop' has been installed on the system.
.crop_code <- sprintf("pdfcrop --margins 5 %s %s", .save_file, .save_file)
system(.crop_code)
rm(.crop_code, .save_file)
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