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added figures for paper
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@Konrad1991
Konrad1991 committed Oct 29, 2024
1 parent cb11202 commit 5412faf
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4 changes: 4 additions & 0 deletions .lintr
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linters: linters_with_defaults(
object_name_linter = NULL,
commented_code_linter = NULL
)
113 changes: 113 additions & 0 deletions Paper/.$Overview.drawio.bkp
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64 changes: 62 additions & 2 deletions Paper/DecentFitParameterVariance/FigNr1_Run_Simulations.R
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@@ -1,12 +1,72 @@
library(tsf)
library(parallel)
num_cores <- detectCores() - 1

# Figure Nr. 1
# one measured curve & 5 simulated curves (created by vastly different parameter sets)
# one measured curve & 10 curves (created by vastly different parameter sets)
# ============================================================

# Creating the 5 different simulations
# Creating the 10 different simulations
# ============================================================

# GDA new data set
# ============================================================
gda_new <- function() {
lowerBounds <- c(
kG = 100,
I0 = 0,
IHD = 0,
ID = 100
)
# NOTE: upper bounds are set due to results of preliminary runs
upperBounds <- c(
kG = 10^6,
I0 = 10^3,
IHD = 10^10,
ID = 10^6
)
additionalParameters <- c(
host = 50 * 10^-6,
guest = 292 * 10^-6,
kHD = 33000
)

# NOTE: data var column [M]
data <- readLines("../NewGDA/GDA_system_3.txt")
var <- lapply(data, function(x) {
strsplit(x, split = "\t")[[1]][1]
}) |> unlist()
signal <- lapply(data, function(x) {
strsplit(x, split = "\t")[[1]][2]
}) |> unlist()
# NOTE: Only the first dataset is used
df <- data.frame(var = var[2:25], signal = signal[2:25])
df$var <- as.numeric(df$var)
df$signal <- as.numeric(df$signal)
set.seed(1234)
seeds <- sample(1:1e6, 20)
result <- mclapply(seeds, function(seed) {
opti(
case = "gda",
lowerBounds = lowerBounds,
upperBounds = upperBounds,
path = df,
seed = seed,
ngen = 1000,
errorThreshold = 0.6,
additionalParameters = additionalParameters,
add_info = as.character(seed)
)
}, mc.cores = num_cores)
# Filter the 10 best ones
m <- lapply(result, function(x) x$metrices)
m <- Reduce(rbind, m)
result <- result[order(m[, 1])[1:10]]
save(result, file = "GDA_10_different_seeds.RData")
}
gda_new()
stop()

# GDA
# ============================================================
gda <- function() {
Expand Down
34 changes: 32 additions & 2 deletions Paper/DecentFitParameterVariance/FigNr1_Visualisation.R
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Expand Up @@ -4,6 +4,15 @@ dotsize <- 0.5
boxplot_size <- 0.5
outlier_size <- 0.5

calc_errors <- function(df) {
df <- split(df, df$seed)
lapply(df, function(x) {
signal <- x[, 2]
signalInsilico <- x[, 3]
return(sum(abs(signal - signalInsilico) / signal))
}) |> unlist()
}

sig_plot <- function(case, path, legend = FALSE) {
load(path)
seeds <- lapply(result, function(x) x$seed)
Expand All @@ -14,6 +23,20 @@ sig_plot <- function(case, path, legend = FALSE) {
})
df <- Reduce(rbind, df)

errors <- calc_errors(df)
errors_df <- data.frame(errors = errors)
p_errors <- ggplot() +
geom_boxplot(
data = errors_df,
aes(x = "", y = errors)
) +
labs(x = "", y = "rel. Error") +
theme(
axis.text = element_text(size = 5),
axis.title = element_text(size = 5),
) +
coord_flip()

df_forward_sim <- lapply(seq_len(length(result)), function(idx) {
res <- result[[idx]][[1]]
params <- result[[idx]][[2]]
Expand Down Expand Up @@ -66,6 +89,12 @@ sig_plot <- function(case, path, legend = FALSE) {
colour = guide_legend(override.aes = list(fill = NA))
)

p_signal <- plot_grid(
p_signal, p_errors,
nrow = 2,
rel_heights = c(1, 0.3)
)

if (!legend) {
p_signal <- p_signal + theme(legend.position = "none")
}
Expand Down Expand Up @@ -108,6 +137,7 @@ param_plot <- function(path) {
strip.text = element_text(size = 6)
)
}

empty_plot <- ggplot() +
theme_void()
size_dashes <- 0.25
Expand Down Expand Up @@ -164,10 +194,10 @@ p_param <- ggdraw(p_param) +
)

p <- plot_grid(p_signal, p_param, nrow = 2)
ggsave("../FigNr1.pdf", p,
ggsave("FigNr1.pdf", p,
width = 8, height = 8 * 2 / 3
)
ggsave("../FigNr1.png", p,
ggsave("FigNr1.png", p,
width = 8, height = 8 * 2 / 3,
bg = "white", dpi = 800
)
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80 changes: 28 additions & 52 deletions Paper/MeasurementVariance/MeasurementVariance.R
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Expand Up @@ -6,7 +6,7 @@ dba <- function() {
ID = 0
)
upperBounds <- c(
kHD = 10^7,
kHD = 10^5,
I0 = 10^5,
IHD = 10^8,
ID = 10^7
Expand All @@ -18,73 +18,50 @@ dba <- function() {
lowerBounds, upperBounds,
path = "../DBA.txt",
additionalParameters,
ngen = 1000,
num_rep = 5, num_cores = 4,
ngen = 150,
num_rep = 5, num_cores = 5,
errorThreshold = 0.6
)
}
res <- dba()
save(res, file = "dba.RData")

ida <- function() {
lowerBounds <- c(
kG = 0,
I0 = 0,
IHD = 0,
ID = 0
)
upperBounds <- c(
kG = 10^10,
I0 = 10^2,
IHD = 10^10,
ID = 10^10
)
additionalParameters <- c(
host = 4.3 * 10^-6,
dye = 6 * 10^-6,
kHD = 1.7E07
)

tsf::batch("ida",
lowerBounds, upperBounds,
path = "../IDA.txt",
additionalParameters,
ngen = 1000,
num_rep = 5, num_cores = 4,
errorThreshold = 0.6
)
}
res <- ida()
save(res, file = "ida.RData")
m <- res[[1]]$metrices
m <- Reduce(rbind, m)
print(m)
p <- res[[1]]$params
p <- Reduce(rbind, p)
p$error <- m[, 1]
print(p)

save(res, file = "dba.RData")
stop()


gda <- function() {
lowerBounds <- c(
kG = 10,
kG = 100,
I0 = 0,
IHD = 0,
ID = 0
ID = 100
)
# NOTE: upper bounds are set due to results of preliminary runs
upperBounds <- c(
kG = 10^9,
I0 = 700,
IHD = 10^9,
ID = 10^9
kG = 10^6,
I0 = 10^3,
IHD = 10^10,
ID = 10^6
)
additionalParameters <- c(
host = 103 * 10^-6,
guest = 1050 * 10^-6,
kHD = 2431.14
host = 50 * 10^-6,
guest = 292 * 10^-6,
kHD = 33000
)

tsf::batch("gda",
lowerBounds, upperBounds,
path = "../GDA.txt",
path = "../NewGDA/GDA_system_3.txt",
additionalParameters,
ngen = 1000,
num_rep = 5, num_cores = 4,
errorThreshold = 0.6
ngen = 100,
num_rep = 5, num_cores = 5,
errorThreshold = 1
)
}
res <- gda()
Expand Down Expand Up @@ -112,14 +89,13 @@ dba <- function() {
path = "../DBA.txt",
additionalParameters,
ngen = 1000,
num_rep = 5, num_cores = 4,
num_rep = 5, num_cores = 5,
errorThreshold = 0.6
)
}
res <- dba()
save(res, file = "dba.RData")


ida <- function() {
lowerBounds <- c(
kG = 0,
Expand All @@ -144,7 +120,7 @@ ida <- function() {
path = "../IDA.txt",
additionalParameters,
ngen = 1000,
num_rep = 5, num_cores = 4,
num_rep = 5, num_cores = 5,
errorThreshold = 0.6
)
}
Expand Down
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