.Rhistory

h1 <- hist(Metrics$CH)
options(rstudio.help.showDataPreview = FALSE) # Prevent bug
rm(list=ls()) # clean workspace
# setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) # Set working directory to the current file
# PLANT PARAM PATH
path_wheat <- "Wheat.xml"
# SET HERE YOUR PYTHON3 PATH
# path_python3 <- "/home/mdago/anaconda3/envs/CPB/bin/python3"
path_python3 <- "/home/mdago/anaconda3/envs/CPB2/bin/python3"
# PYTHON SCRIPTS PATH
path_CPB <- "python_files/"
# R SCRIPTS PATH
path_R_functions <- "R Functions/"
#==========================================================================
libs <- c("fmsb", "SciViews", "pander", "visreg", "readxl", "EnvStats", "FactoMineR", "factoextra", "knitr", "data.table", "reticulate", "xml2", "tidyverse", "dplyr", "plyr", "viridis", "Hmisc", "ggplot2", "gridExtra", "ggpubr","cowplot", "stats", "emmeans")
# "riot"
#==========================================================================
for(pk in libs) {
if (!requireNamespace(pk, quietly = TRUE)) {
# If not installed, install the package
install.packages(pk)
}
library(pk, character.only = T)
}
#==========================================================================
fct <- list.files(path_R_functions, pattern = ".R")
for(i in fct){
# print(paste0("R Functions/", i))
source(paste0(path_R_functions, i))
}
#==========================================================================
# source("My_Neural_Network_functions.R")
files <- list.files(path = "data/LiDAR_Metrics/", pattern = ".csv")
LiDAR_metrics <- data.frame()
for(i in files){
print(i)
date_i <- as.Date(strsplit(i, split = "_")[[1]][1], format="%Y%m%d")
file_i <- read.csv(paste0("data/LiDAR_Metrics/", i))
file_i$date <- date_i
LiDAR_metrics <- rbind(LiDAR_metrics, file_i)
}
BBCH <- data.frame(date = unique(LiDAR_metrics$date),
BBCH = c(28, 33, 36, 61, 76, 87, 90))
Days <- data.frame(date = unique(LiDAR_metrics$date),
day = as.numeric(as.Date(unique(LiDAR_metrics$date)) - as.Date("2020-11-16")))
LiDAR_metrics <- inner_join(LiDAR_metrics, BBCH, by = "date")
# Read tables
BBCH2 <- read_excel(path = "data/myCKA.xlsx", sheet = "BBCH")
Biomass <- read_excel(path = "data/myCKA.xlsx", sheet = "biomass")
BBCH_scale <- read.csv("data/BBCH_scale.csv")
coords = read.csv("test/Test.csv") # Load the csv
#==============================================================================
result_map <- ggplot(data = coords) +
geom_point(aes(x = X, y = Y, color = Z), size = 0.01) +
coord_fixed() +
scale_color_viridis() +
theme_test()
#==============================================================================
result_map
source("R Functions/Extract_Metrics.R")
Metrics <- Extract_Metrics(coords, gridsize = 20, method = "max") # Extract CH and GF
source("R Functions/Extract_Metrics.R")
Metrics <- Extract_Metrics(coords, gridsize = 20, method = "max") # Extract CH and GF
h1 <- hist(Metrics$CH)
h1
plot(h1)
h1
h1 <- hist(Metrics$CH)
ggplot(data = Metrics) +
geom_histogram(mapping = CH)
ggplot(data = Metrics) +
geom_histogram(mapping = Metrics$CH)
ggplot(data = Metrics) +
geom_histogram(aes(mapping = Metrics$CH))
ggplot(data = Metrics) +
geom_histogram(aes(x = Metrics$CH))
ggplot(data = Metrics) +
geom_histogram(aes(x = Metrics$CH)) +
theme_bw()
ggplot(data = Metrics) +
geom_histogram(aes(x = Metrics$CH)) +
xlab("Crop Height") +
ylab("Count") +
theme_bw()
ggplot(data = Metrics) +
geom_histogram(aes(x = Metrics$CH)) +
xlab("Crop Height") +
ylab("Count") +
ggtitle("Crop Height DIstribution") +
theme_bw()
h1 <- ggplot(data = Metrics) +
geom_histogram(aes(x = Metrics$CH)) +
xlab("Crop Height") +
ylab("Count") +
ggtitle("Crop Height Distribution") +
theme_bw()
h1 <- ggplot(data = Metrics) +
geom_histogram(aes(x = Metrics$GF)) +
xlab("Gap Fraction") +
ylab("Count") +
ggtitle("Gap Fraction Distribution") +
theme_bw()
plot_grid(h1, h2, h3)
h1 <- ggplot(data = Metrics) +
geom_histogram(aes(x = Metrics$CH)) +
xlab("Crop Height") +
ylab("Count") +
ggtitle("Crop Height Distribution") +
theme_bw()
h2 <- ggplot(data = Metrics) +
geom_histogram(aes(x = Metrics$GF)) +
xlab("Gap Fraction") +
ylab("Count") +
ggtitle("Gap Fraction Distribution") +
theme_bw()
h3 <- ggplot(data = Metrics) +
geom_histogram(aes(x = Metrics$GF)) +
xlab("Density") +
ylab("Count") +
ggtitle("Denstity Distribution") +
theme_bw()
plot_grid(h1, h2, h3)
h1 <- ggplot(data = Metrics) +
geom_histogram(aes(x = CH), bins = 20) +
xlab("Crop Height") +
ylab("Count") +
ggtitle("Crop Height Distribution") +
theme_bw()
h1
h1 <- ggplot(data = Metrics) +
geom_histogram(aes(x = CH), bins = 20) +
xlab("Crop Height") +
ylab("Count") +
ggtitle("Crop Height Distribution") +
theme_bw()
h2 <- ggplot(data = Metrics) +
geom_histogram(aes(x = GF), bins = 20) +
xlab("Gap Fraction") +
ylab("Count") +
ggtitle("Gap Fraction Distribution") +
theme_bw()
h3 <- ggplot(data = Metrics) +
geom_histogram(aes(x = GF), bins = 20) +
xlab("Density") +
ylab("Count") +
ggtitle("Denstity Distribution") +
theme_bw()
plot_grid(h1, h2, h3)
Grid_density <- ggplot(data = Metrics) +
geom_tile(aes(x = X, y = Y, fill = Density)) +
scale_color_viridis() +
scale_fill_viridis() +
coord_fixed() +
theme_test()
plot_grid(Grid_density, Grid_GF, Grid_CH)
Grid_GF <- ggplot(data = Metrics) +
geom_tile(aes(x = X, y = Y, fill = GF)) +
scale_color_viridis() +
scale_fill_viridis() +
coord_fixed() +
theme_test()
Grid_CH <- ggplot(data = Metrics) +
geom_tile(aes(x = X, y = Y, fill = CH)) +
scale_color_viridis() +
scale_fill_viridis() +
coord_fixed() +
theme_test()
plot_grid(Grid_density, Grid_GF, Grid_CH)
ggplot(data = GF_Layers) +
geom_tile(aes(x = X, y = Y, fill = GF)) +
scale_color_viridis() +
scale_fill_viridis() +
facet_wrap(~layer) +
coord_fixed() +
theme_test()
biom <- ggplot(data = Biomass) +
geom_line(aes(x = Date, y = Value, color = Organ)) +
# geom_smooth(aes(x = Date, y = Value, color = Organ)) +
facet_wrap(~Type) +
theme_test()
plot_grid(biom, bbch)
options(rstudio.help.showDataPreview = FALSE) # Prevent bug
rm(list=ls()) # clean workspace
# setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) # Set working directory to the current file
# PLANT PARAM PATH
path_wheat <- "Wheat.xml"
# SET HERE YOUR PYTHON3 PATH
# path_python3 <- "/home/mdago/anaconda3/envs/CPB/bin/python3"
path_python3 <- "/home/mdago/anaconda3/envs/CPB2/bin/python3"
# PYTHON SCRIPTS PATH
path_CPB <- "python_files/"
# R SCRIPTS PATH
path_R_functions <- "R Functions/"
#==========================================================================
libs <- c("fmsb", "SciViews", "pander", "visreg", "readxl", "EnvStats", "FactoMineR", "factoextra", "knitr", "data.table", "reticulate", "xml2", "tidyverse", "dplyr", "plyr", "viridis", "Hmisc", "ggplot2", "gridExtra", "ggpubr","cowplot", "stats", "emmeans")
# "riot"
#==========================================================================
for(pk in libs) {
if (!requireNamespace(pk, quietly = TRUE)) {
# If not installed, install the package
install.packages(pk)
}
library(pk, character.only = T)
}
#==========================================================================
fct <- list.files(path_R_functions, pattern = ".R")
for(i in fct){
# print(paste0("R Functions/", i))
source(paste0(path_R_functions, i))
}
#==========================================================================
# source("My_Neural_Network_functions.R")
files <- list.files(path = "data/LiDAR_Metrics/", pattern = ".csv")
LiDAR_metrics <- data.frame()
for(i in files){
print(i)
date_i <- as.Date(strsplit(i, split = "_")[[1]][1], format="%Y%m%d")
file_i <- read.csv(paste0("data/LiDAR_Metrics/", i))
file_i$date <- date_i
LiDAR_metrics <- rbind(LiDAR_metrics, file_i)
}
BBCH <- data.frame(date = unique(LiDAR_metrics$date),
BBCH = c(28, 33, 36, 61, 76, 87, 90))
Days <- data.frame(date = unique(LiDAR_metrics$date),
day = as.numeric(as.Date(unique(LiDAR_metrics$date)) - as.Date("2020-11-16")))
LiDAR_metrics <- inner_join(LiDAR_metrics, BBCH, by = "date")
# Read tables
BBCH2 <- read_excel(path = "data/myCKA.xlsx", sheet = "BBCH")
Biomass <- read_excel(path = "data/myCKA.xlsx", sheet = "biomass")
BBCH_scale <- read.csv("data/BBCH_scale.csv")
biom <- ggplot(data = Biomass) +
geom_line(aes(x = Date, y = Value, color = Organ)) +
# geom_smooth(aes(x = Date, y = Value, color = Organ)) +
facet_wrap(~Type) +
theme_test()
bbch <- ggplot(data = BBCH2) +
geom_line(aes(x = Date, y = BBCH)) +
theme_test()
plot_grid(biom, bbch)
plot_grid(biom, bbch, align = "v")
plot_grid(biom, bbch, align = "h")
plot_grid(biom, bbch, ncol = 1)
#==========================================================================
libs <- c("fmsb", "SciViews", "pander", "visreg", "readxl", "EnvStats", "FactoMineR", "factoextra", "knitr", "data.table", "reticulate", "xml2", "tidyverse", "dplyr", "plyr", "viridis", "Hmisc", "ggplot2", "gridExtra", "ggpubr","cowplot", "stats", "emmeans")
# "riot"
#==========================================================================
for(pk in libs) {
if (!requireNamespace(pk, quietly = TRUE)) {
# If not installed, install the package
install.packages(pk)
}
library(pk, character.only = T)
}
#==========================================================================
fct <- list.files(path_R_functions, pattern = ".R")
for(i in fct){
# print(paste0("R Functions/", i))
source(paste0(path_R_functions, i))
}
#==========================================================================
# source("My_Neural_Network_functions.R")
files <- list.files(path = "data/LiDAR_Metrics/", pattern = ".csv")
LiDAR_metrics <- data.frame()
for(i in files){
print(i)
date_i <- as.Date(strsplit(i, split = "_")[[1]][1], format="%Y%m%d")
file_i <- read.csv(paste0("data/LiDAR_Metrics/", i))
file_i$date <- date_i
LiDAR_metrics <- rbind(LiDAR_metrics, file_i)
}
BBCH <- data.frame(date = unique(LiDAR_metrics$date),
BBCH = c(28, 33, 36, 61, 76, 87, 90))
Days <- data.frame(date = unique(LiDAR_metrics$date),
day = as.numeric(as.Date(unique(LiDAR_metrics$date)) - as.Date("2020-11-16")))
LiDAR_metrics <- inner_join(LiDAR_metrics, BBCH, by = "date")
# Read tables
BBCH2 <- read_excel(path = "data/myCKA.xlsx", sheet = "BBCH")
Biomass <- read_excel(path = "data/myCKA.xlsx", sheet = "biomass")
BBCH_scale <- read.csv("data/BBCH_scale.csv")
biom <- ggplot(data = Biomass) +
geom_line(aes(x = Date, y = Value, color = Organ)) +
# geom_smooth(aes(x = Date, y = Value, color = Organ)) +
facet_wrap(~Type) +
ggtitle("Biomass per organ") +
theme_test()
bbch <- ggplot(data = BBCH2) +
geom_line(aes(x = Date, y = BBCH)) +
ggtitle("Devlopment Stages") +
theme_test()
plot_grid(biom, bbch, ncol = 1)
GF_grid <- ggplot(data = LiDAR_metrics,
aes(x = x-min(x), y = y-min(y), color = GF, fill = GF)) +
#geom_point(size = , shape = 15) +
geom_tile() +
coord_fixed() +
theme_test() +
scale_color_viridis() +
scale_fill_viridis() +
xlab("x") +
ylab("y") +
ggtitle("Gap Fraction") +
facet_wrap(~date)
CH_grid <- ggplot(data = LiDAR_metrics,
aes(x = x-min(x), y = y-min(y), color = Height, fill = Height)) +
# geom_point(size = 1, shape = 15) +
geom_tile() +
coord_fixed() +
theme_test() +
scale_color_viridis() +
scale_fill_viridis() +
xlab("x") +
ylab("y") +
ggtitle("Crop Height") +
facet_wrap(~date)
I_grid <- ggplot(data = LiDAR_metrics,
aes(x = x-min(x), y = y-min(y), color = Intensity, fill = Intensity)) +
# geom_point(size = 1, shape = 15) +
geom_tile() +
coord_fixed() +
theme_test() +
scale_color_viridis() +
scale_fill_viridis() +
xlab("x") +
ylab("y") +
ggtitle("Intensity") +
facet_wrap(~date)
plot_grid(GF_grid, CH_grid, I_grid)
plot_grid(GF_grid, CH_grid, I_grid, ncol =1)
grid.arrange(GF_grid, CH_grid, I_grid, ncol =1)
GF_grid <- ggplot(data = LiDAR_metrics,
aes(x = x-min(x), y = y-min(y), color = GF, fill = GF)) +
#geom_point(size = , shape = 15) +
geom_tile() +
coord_fixed() +
theme_test() +
scale_color_viridis() +
scale_fill_viridis() +
xlab("x") +
ylab("y") +
ggtitle("Gap Fraction") +
facet_wrap(~date)
CH_grid <- ggplot(data = LiDAR_metrics,
aes(x = x-min(x), y = y-min(y), color = Height, fill = Height)) +
# geom_point(size = 1, shape = 15) +
geom_tile() +
coord_fixed() +
theme_test() +
scale_color_viridis() +
scale_fill_viridis() +
xlab("x") +
ylab("y") +
ggtitle("Crop Height") +
facet_wrap(~date)
I_grid <- ggplot(data = LiDAR_metrics,
aes(x = x-min(x), y = y-min(y), color = Intensity, fill = Intensity)) +
# geom_point(size = 1, shape = 15) +
geom_tile() +
coord_fixed() +
theme_test() +
scale_color_viridis() +
scale_fill_viridis() +
xlab("x") +
ylab("y") +
ggtitle("Intensity") +
facet_wrap(~date)
# grid.arrange(GF_grid, CH_grid, I_grid, ncol =1)
GF_grid
CH_grid
I_grid