library(tidyverse)
library(gt) #For nice color coded capacity limits table.
source("Scripts/Load_Custom_Functions/Fan_Chart_Creation_Functions.r") #Functions created to make nice graphs
	if(!exists("SAVE_RES_LOC")){SAVE_RES_LOC <- "./Results/Primary_Simulation_Results/Upper_Bound_Results/"}
	dir.create(SAVE_RES_LOC, recursive = TRUE, showWarnings = FALSE)


###Process the simulations and save the main percentile results by year
	RES <- read_csv("Results/Simulations/Kemmerer_2024_Simulation_County_Migration_Rate.csv")
	RES[,"Year"] <- RES[,"Year"]
	RES<-	RES %>% filter(!is.na(Year))
	RES <-	RES %>% filter(!is.na(Population))
	HIST <-	readRDS("Data/Cleaned_Data/Population_Data/RDS/Kemmerer_Diamondville_Population_Data.Rds") %>% filter(County=='Lincoln') %>% mutate(Percentile="Actual Population") %>% filter(Year>=1940)
	######Population
	####Fan New
POP_DATA <- GET_DATA(RES,3)
POP_PLOT <- MAKE_GRAPH(POP_DATA,COLOR='springgreen4')
POP_PLOT <- POP_PLOT+geom_line(data=HIST,aes(x=Year,y=Population),color='black',linewidth=0.75)+ scale_x_continuous(breaks = c(seq(1940, 2060, by = 10),2065))+ scale_y_continuous(breaks = seq(0, 35000, by = 500))+ggtitle("Kemmerer & Diamondville, Population Forecast")+ expand_limits( y = 0)+labs(color = "Prediction Interval",linetype="Prediction Interval",y="Population")+ theme_bw()+ theme(legend.position = "top",panel.grid.minor = element_blank())
png(paste0(SAVE_RES_LOC,"Population_Fan_Chart.png"), width = 12, height = 8, units = "in", res = 600)
POP_PLOT 
dev.off()

BIRTH_DATA <- GET_DATA(RES,4)
BIRTH_PLOT <- MAKE_GRAPH(BIRTH_DATA,COLOR='springgreen4')
BIRTH_PLOT <- BIRTH_PLOT+geom_line(data=HIST,aes(x=Year,y=Births),color='black',linewidth=0.75)+ scale_x_continuous(breaks = c(seq(2010, 2060, by = 5),2065),limits=c(2009,2065))+ scale_y_continuous(breaks = seq(0, 35000, by = 10))+ggtitle("Kemmerer & Diamondville, Birth Forecast")+ expand_limits( y = 0)+labs(color = "Prediction Interval",linetype="Prediction Interval",y="Births")+ theme_bw()+ theme(legend.position = "top",panel.grid.minor = element_blank())
png(paste0(SAVE_RES_LOC,"Birth_Fan_Chart.png"), width = 12, height = 8, units = "in", res = 600)
	BIRTH_PLOT 
dev.off()

DEATH_DATA <- GET_DATA(RES,5) %>% filter(!is.na(MIN))
DEATH_PLOT <- MAKE_GRAPH(DEATH_DATA,COLOR='springgreen4')
DEATH_PLOT <-	DEATH_PLOT+geom_line(data=HIST,aes(x=Year,y=Deaths),color='black',linewidth=0.75)+ scale_x_continuous(breaks = c(seq(2010, 2060, by = 5),2065),limits=c(2009,2065))+ scale_y_continuous(breaks = seq(0, 35000, by = 10))+ggtitle("Kemmerer & Diamondville, Mortality Forecast")+ expand_limits( y = 0)+labs(color = "Prediction Interval",linetype="Prediction Interval",y="Deaths")+ theme_bw()+ theme(legend.position = "top",panel.grid.minor = element_blank())
png(paste0(SAVE_RES_LOC,"Mortality_Fan_Chart.png"), width = 12, height = 8, units = "in", res = 600)
	DEATH_PLOT
dev.off()

MIGRATION_DATA <- GET_DATA(RES,6) %>% filter(!is.na(MIN))
MIGRATION_PLOT <- MAKE_GRAPH(MIGRATION_DATA,COLOR='springgreen4')
MIGRATION_PLOT <- 	MIGRATION_PLOT+geom_line(data=HIST,aes(x=Year,y=Migration),color='black',linewidth=0.75)+ scale_x_continuous(breaks = c(seq(2010, 2060, by = 5),2065),limits=c(2009,2065))+ scale_y_continuous(breaks = seq(-1000, 1000, by = 50))+ggtitle("Kemmerer & Diamondville, Net Migration Forecast")+ expand_limits( y = 0)+labs(color = "Prediction Interval",linetype="Prediction Interval",y="Migration")+ theme_bw()+ theme(legend.position = "top",panel.grid.minor = element_blank())
png(paste0(SAVE_RES_LOC,"Migration_Fan_Chart.png"), width = 12, height = 8, units = "in", res = 600)
	MIGRATION_PLOT
dev.off()

#####Key year table
KEY <- RES %>% filter(Year %in% c(2029,2030,2035,2045,2055,2065))
 AVG_VALUES <- KEY %>% group_by(Year) %>% summarize(MED=median(Population),MEAN=mean(Population))
AVG_VALUES <- rbind(AVG_VALUES[,1:2]%>% rename(Value=MED) %>% mutate('Summary Stat.'="Median"),AVG_VALUES[,c(1,3)] %>% rename(Value=MEAN) %>% mutate('Summary Stat.'="Mean"))

HISTOGRAM <- ggplot(KEY, aes(x = Population,group=-Year,Color=Year,fill=Year)) + geom_histogram(alpha=0.3,bins=800)+geom_vline(data = AVG_VALUES, aes(xintercept = Value,group=`Summary Stat.`,color = `Summary Stat.`), size = 0.75)+scale_fill_gradient(low = "grey", high = "darkgreen")+scale_color_manual(values=c("red","black","black"))+ facet_grid(rows=vars(Year))+ scale_x_continuous(breaks = c(seq(0, 100000, by = 1000)))+ theme_bw()+ theme(legend.position = "top",panel.grid.minor = element_blank())+ylab("Number of Simulation")+guides(fill= guide_legend(nrow = 1))
png(paste0(SAVE_RES_LOC,"Population_Histogram.png"), width = 8, height = 12, units = "in", res = 600)
	HISTOGRAM
dev.off()

#rm(KEY_YEARS)
POP_LEVELS <- seq(2000,6000,100)
YEARS <- c(2030,2035,2045,2055,2065)
for(i in YEARS ){
	KEY <- RES %>% filter(Year==i )  %>% pull(Population)
	ECDF <- ecdf(KEY)
	ECDF_VALUES <- ECDF(POP_LEVELS)
	if(!exists("KEY_YEARS")){KEY_YEARS<- ECDF_VALUES} else{KEY_YEARS<- cbind(KEY_YEARS,ECDF_VALUES)}

}
colnames(KEY_YEARS) <-  YEARS
rownames(KEY_YEARS) <- POP_LEVELS 
PLOT_GREEN <- "forestgreen"
PLOT_YELLOW <- "yellow"
PLOT_RED <- "red"
KEY_YEARS <- KEY_YEARS%>% as.data.frame
Capacity_Risk <- KEY_YEARS%>%  gt(rownames_to_stub = TRUE,caption="Year")  %>%  data_color(    fn = scales::col_numeric( palette = c(PLOT_RED, PLOT_YELLOW, PLOT_GREEN),  domain = c(0, 1) ) ) %>%  fmt_percent( decimals = 1,  drop_trailing_zeros = FALSE) %>% tab_stubhead(label =c("Capacity"))
gtsave( data = Capacity_Risk ,  filename = "./Results/Primary_Simulation_Results/Upper_Bound_Results/Capacity_Table.html")
system("wkhtmltopdf --disable-smart-shrinking --no-stop-slow-scripts --enable-local-file-access --page-width 85mm --page-height 328mm ./Results/Primary_Simulation_Results/Upper_Bound_Results/Capacity_Table.html ./Results/Primary_Simulation_Results/Upper_Bound_Results/Capacity_Table.pdf")