Fixed missing permanent jobs added

1B_Run_Full_Simulation.r

@@ -95,9 +95,10 @@ return(list(DEMO,BIRTH_DATA,c(TOTAL_POP,TOTAL_BIRTHS,TOTAL_DEATHS,TOTAL_MIGRATIO
 
 	MIGRATION_ARIMA_MODEL <- MIGRATION_ARIMA
 #DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL,OPERATOR_TOTAL,CONSTRUCTION_TOTAL,MIGRATION_MULTIPLIERS 
-CONSTRUCTION_MIGRATION  <- CONSTRUCTION_LIN_MIGRATION 
+#CONSTRUCTION_MIGRATION  <- CONSTRUCTION_LIN_MIGRATION 
-MIGRATION_MULTIPLIERS <-  INDUCED_MIGRATION_MULTIPLIERS 
+#MIGRATION_MULTIPLIERS <-  INDUCED_MIGRATION_MULTIPLIERS 
-OPERATOR_MIGRATION <- OPERATOR_LIN_MIGRATION 
+#OPERATOR_MIGRATION <- OPERATOR_LIN_MIGRATION 
+MIGRATION_ARIMA=MIGRATION_ARIMA_MODEL;OPERATOR_MIGRATION=OPERATOR_LIN_MIGRATION;CONSTRUCTION_MIGRATION=CONSTRUCTION_LIN_MIGRATION;MIGRATION_MULTIPLIERS=INDUCED_MIGRATION_MULTIPLIERS
 
 SINGLE_SIM <- function(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL,OPERATOR_MIGRATION,CONSTRUCTION_MIGRATION,MIGRATION_MULTIPLIERS ){
 	
@@ -109,11 +110,17 @@ SINGLE_SIM <- function(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL
 	CONSTRUCTION_MIGRATION <- LOCAL_WORK_ADJ(CONSTRUCTION_MIGRATION,0.41) #Assume between 41%-100% operators live in Kemmerer
 	CONSTRUCTION_MIGRATION[7] <- CONSTRUCTION_MIGRATION[7] -       sum(CONSTRUCTION_MIGRATION )
 	CONSTRUCTION_POPULATION_ADDED <- cumsum(CONSTRUCTION_MIGRATION)
-	ADDED_FROM_BASELINE <- runif(1) #The percentage of the possible growth in industries like restaurants to add compared to the Kemmerer IMPLAN model which understates possible structural growth 
+	###########TEST
-		PERMANENT_TERRAPOWER_MIGRATION <- INDUCED_SIMULATION(CONSTRUCTION_MIGRATION,OPERATOR_MIGRATION,MIGRATION_MULTIPLIERS,ADDED_FROM_BASELINE)+OPERATOR_MIGRATION
+	GROWTH_LEVEL <- sample(c("VERY_LOW","LOW","HIGH","HIGH"),1)
+if(GROWTH_LEVEL=="VERY_LOW"){ADDED_FROM_BASELINE <- 0;MIGRATION_ADJUST <- 0} else if(GROWTH_LEVEL=="LOW"){ADDED_FROM_BASELINE <- runif(1,0,0.5);MIGRATION_ADJUST <- 0}else{ADDED_FROM_BASELINE <- runif(1,0.5,1);MIGRATION_ADJUST <-(ADDED_FROM_BASELINE-0.5)/0.5}
+	MIGRATION_SHIFT <- 55*(1-MIGRATION_ADJUST)
 	
-###############NOTE NEED TO USE THIS AT END TO ADJUST THE RESULTS WHILE LEAVING THE DEMOGRAPHIC MATRIX
+	##############
-	TERRA_POWER_EFFECT[1:7]	<- 	TERRA_POWER_EFFECT[1:7]+CONSTRUCTION_MIGRATION 
+	#ADDED_FROM_BASELINE <- runif(1) #The percentage of the possible growth in industries like restaurants to add compared to the Kemmerer IMPLAN model which understates possible structural growth 
+		PERMANENT_TERRAPOWER_MIGRATION <- INDUCED_SIMULATION(CONSTRUCTION_MIGRATION,OPERATOR_MIGRATION,MIGRATION_MULTIPLIERS,ADDED_FROM_BASELINE)+OPERATOR_MIGRATION
+		PERMANENT_TERRAPOWER_MIGRATION <- round(PERMANENT_TERRAPOWER_MIGRATION*2.314328) #Households per job in IMPLAN
+		###############NOTE NEED TO USE THIS AT END TO ADJUST THE RESULTS WHILE LEAVING THE DEMOGRAPHIC MATRIX
+	TERRA_POWER_EFFECT[1:7]	<- 	TERRA_POWER_EFFECT[1:7]+CONSTRUCTION_MIGRATION +PERMANENT_TERRAPOWER_MIGRATION
 	#Dry Pine Migration
 	DRY_PINE <- round(runif(1,0.15,0.45)*c(126,176,-302)*1.15)
 	DRY_PINE[3] <- DRY_PINE[3]-sum(DRY_PINE)
@@ -123,7 +130,6 @@ SINGLE_SIM <- function(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL
 	if(runif(1)>0.67){
 	  MW_CAPACITY <- runif(1,20,120)
 	  PERM_WORKERS <- MW_CAPACITY/8+30 #Seems to be a good ration 32.5 workers at 20 capacity and 45 at max this is a heurstic I came up with
-	  OTHER_PROJECT_EFFECTS_PERM
 	  DATA_CENTER_START_YEAR <- which.max(runif(YEARS_AHEAD-6))+3
 	  PERM_DATA_CENTER_OP <- PERM_WORKERS*(1+(2.515334+19.52286*ADDED_FROM_BASELINE)/40) #Add induced
 	 	 #Estimate construction workers on data center
@@ -139,18 +145,21 @@ SINGLE_SIM <- function(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL
 	  OTHER_PROJECT_EFFECTS_TEMP_POP_ADDED[(DATA_CENTER_START_YEAR+3):(DATA_CENTER_START_YEAR+5)]<-  OTHER_PROJECT_EFFECTS_TEMP_POP_ADDED[(DATA_CENTER_START_YEAR+3):(DATA_CENTER_START_YEAR+5)]+TEMP_DATA_MIGRATION
 	  OTHER_PROJECT_EFFECTS_PERM[DATA_CENTER_START_YEAR+6] <- OTHER_PROJECT_EFFECTS_PERM[DATA_CENTER_START_YEAR+3]+PERM_DATA_CENTER_OP
 	}
-	MIGRATION_SIM_VALUES <- round(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA_MODEL))+runif(1,-55,0)+TERRA_POWER_EFFECT+OTHER_PROJECT_EFFECTS_PERM)
+	MIGRATION_SIM_VALUES <- round(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA_MODEL))-MIGRATION_SHIFT+TERRA_POWER_EFFECT+OTHER_PROJECT_EFFECTS_PERM)
+	#MIGRATION_SIM_VALUES <- round(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA_MODEL))+-55+TERRA_POWER_EFFECT+OTHER_PROJECT_EFFECTS_PERM)
+	
 	#The runif applies a downshift ranging from the historic decline rate all the way to the Lincoln rate applied in the model
 	
-	FINAL_REPORT_VALUES <- matrix(NA,ncol=6,nrow=YEARS_AHEAD)
+	FINAL_REPORT_VALUES <- matrix(NA,ncol=7,nrow=YEARS_AHEAD)
-	colnames(FINAL_REPORT_VALUES ) <- c("Sim_UUID","Year","Population","Births","Deaths","Net_Migration")
+	colnames(FINAL_REPORT_VALUES ) <- c("Sim_UUID","Year","Population","Births","Deaths","Net_Migration","Growth_Rate")
 	FINAL_REPORT_VALUES[,1] <- UUIDgenerate()
+	FINAL_REPORT_VALUES[,7] <- GROWTH_LEVEL
 	for(i in 1:YEARS_AHEAD){
 		C_YEAR <- ST_YEAR+i-1
 		C_RES <-SINGLE_YEAR_SIM(DEMO,BIRTH_DATA,i,SIMULATE_MORTALITY_RATE_TRENDS(),MIGRATION_SIM_VALUES[i])
 		DEMO <- C_RES[[1]]
 		BIRTH_DATA <- C_RES[[2]]
-		FINAL_REPORT_VALUES[i,-1] <- c(C_YEAR,C_RES[[3]])
+		FINAL_REPORT_VALUES[i,c(-1,-7)] <- c(C_YEAR,C_RES[[3]])
 	}
 	FINAL_REPORT_VALUES[1:7,3] <- as.numeric(FINAL_REPORT_VALUES[1:7,3])+CONSTRUCTION_POPULATION_ADDED 
 	FINAL_REPORT_VALUES[1:7,6] <- as.numeric(FINAL_REPORT_VALUES[1:7,6]) +CONSTRUCTION_MIGRATION
@@ -180,11 +189,11 @@ CONSTRUCTION_LIN_MIGRATION <- CONSTRUCTION %>% pull("Construction_Emp_Migrated")
 	MIGRATION_MATRIX <- do.call(cbind, mclapply(1:BATCH_SIZE,function(x)(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA) )),mc.cores = detectCores()-1))
 #	rownames(MIGRATION_MATRIX) <- ST_YEAR:(ST_YEAR+YEARS_AHEAD-1)
 #	colnames(MIGRATION_MATRIX) <- 1:BATCH_SIZE
-#SINGLE_SIM(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA,OPERATOR_LIN_MIGRATION,CONSTRUCTION_LIN_MIGRATION,INDUCED_MIGRATION_MULTIPLIERS)
+SINGLE_SIM(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA,OPERATOR_LIN_MIGRATION,CONSTRUCTION_LIN_MIGRATION,INDUCED_MIGRATION_MULTIPLIERS)
 	try(RES <- do.call(rbind,mclapply(1:BATCH_SIZE,function(x){SINGLE_SIM(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA,OPERATOR_LIN_MIGRATION,CONSTRUCTION_LIN_MIGRATION,INDUCED_MIGRATION_MULTIPLIERS)},mc.cores=NCORES)))
 		if(exists("RES")){
 			RES <- as.data.frame(RES) 
-			RES[,-1] <- as.numeric(as.matrix(RES[,-1]))
+			RES[,c(-1,-7)] <- as.numeric(as.matrix(RES[,c(-1,-7)]))
 			if(NEW_RES_FILE & i==1){write_csv(RES,SIM_RES_FILE)}else {write_csv(RES,SIM_RES_FILE,col_names=FALSE,append=TRUE)}
 			rm(RES)
 		}

TESTING.r

@@ -0,0 +1,124 @@
+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/Main_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_With_Data_Center_Simulation.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)
+########TEST
+SIM_START=2025
+	LAST_HISTORIC <- SIM_START-1
+	YEARS <- min(RES$Year,na.rm=TRUE):max(RES$Year,na.rm=TRUE)
+	LEVELS <- seq(0.00,1,by=0.005)
+	GROUPS <- floor(length(LEVELS)/2)
+	COL_NUM <-3
+	RES %>% tail
+#C_DATA<-	RES %>% filter(Growth_Rate=='HIGH')
+#C_DATA <-	RES %>% filter(Growth_Rate!='HIGH')
+	C_DATA <- RES
+
+nrow(RES)
+FAN_DATA <- do.call(rbind,lapply(YEARS,function(x){quantile(as.numeric(t((C_DATA %>% filter(Year==x))[,COL_NUM])),LEVELS)}))  %>% as_tibble %>% mutate(Year=YEARS)
+FAN_DATA <- FAN_DATA %>% pivot_longer(!Year,names_to="Percentile",values_to="Population") %>% group_by(Year) %>% mutate(DIFF=c(NA,diff(Population)))
+GRAPH_DATA <- FAN_DATA %>% group_by(Year)  %>% mutate(MIN_RANGE=lag(parse_number(Percentile)/100),MAX_RANGE=parse_number(Percentile)/100,MIN_POP=lag(Population),MAX_POP=Population) %>% filter(!is.na(DIFF))
+GRAPH_DATA <- GRAPH_DATA %>% mutate(MAX_POP=ifelse(MAX_RANGE!=1,MAX_POP-0.001,MAX_POP))
+	CI_80 <- GRAPH_DATA%>% filter(Percentile %in%c('10.0%','90.0%')) 	%>% group_by(Year) %>% summarize(MIN_POP=min(MIN_POP),MAX_POP=max(MAX_POP),Interval='80%')
+	CI_95 <- GRAPH_DATA%>% filter(Percentile %in%c('2.5%','97.5%'))	%>% group_by(Year) %>% summarize(MIN_POP=min(MIN_POP),MAX_POP=max(MAX_POP),Interval='95%')
+MEDIAN <- GRAPH_DATA%>% filter(Percentile %in%c('50.0%'))%>% group_by(Year) %>% summarize(MIN_POP=min(MIN_POP),MAX_POP=max(MAX_POP),Interval='Median')
+RANGE_DATA <- rbind(MEDIAN ,CI_80,CI_95)
+ggplot(GRAPH_DATA %>% filter(MIN_RANGE>=0.015,MAX_RANGE<=0.985) )+geom_ribbon(aes(group=Percentile,x=Year,ymin=MIN_POP,ymax=MAX_POP,alpha=-DIFF),fill='cadetblue')+geom_line(data=GRAPH_DATA %>% filter(Percentile=='50%'),aes(x=Year,y=MIN_POP))+geom_line(data=RANGE_DATA,aes(color=Interval,linetype=Interval,x=Year,y=MIN_POP))+geom_line(data=RANGE_DATA,aes(color=Interval,linetype=Interval,x=Year,y=MAX_POP))+scale_color_manual(values=c("grey50","grey80","black"))+scale_linetype_manual(values = c("solid","solid","dotdash"))+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())+scale_alpha(range = c(0, 1),guide="none")
+C_DATA
+
+	geom_
+
+RANGE_DATA
+
+
+QUANT <- quantile(as.numeric(t((RES %>% filter(Year==2025))[,3])),LEVELS) %>% as.numeric
+RANGE <- QUANT %>% diff
+RANGE
+RANGE <- RANGE[-1] 
+RANGE <- RANGE[-length(RANGE)]
+RANGE <- 1-RANGE/max(RANGE)
+RANGE 
+scale(RANGE)
+FAN_DATA <- do.call(rbind,lapply(YEARS,function(x){quantile(as.numeric(t((RES %>% filter(Year==x))[,COL_NUM])),LEVELS)}))  %>% as_tibble %>% mutate(Year=YEARS)
+	FAN_DATA <- 	rbind(FAN_DATA[1,],FAN_DATA)
+
+
+#######
+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_Main_Results.png"), width = 12, height = 8, units = "in", res = 600)
+POP_PLOT 
+dev.off()
+
+#Population Summary table for the key years
+KEY_YEARS <- c(2027,2030,2035,2045,2055,2065)
+KEY_YEAR_SUMMARY <- POP_DATA %>% filter(Year %in% KEY_YEARS,Group==5) %>% select(-Group)  %>% rename(CI_95_Lower=MIN,CI_95_Upper=MAX) %>% left_join(POP_DATA %>% filter(Year %in% KEY_YEARS,Group==0) %>% select(-Group)  %>% select(Year,Median=MIN)) %>% select(Year,CI_95_Lower,Median,CI_95_Upper)
+KEY_YEAR_SUMMARY <- round(KEY_YEAR_SUMMARY )
+KEY_YEAR_SUMMARY <- t(KEY_YEAR_SUMMARY )
+write.table(KEY_YEAR_SUMMARY,paste0(SAVE_RES_LOC,"Key_Year_Summary_Main_Results.csv"), sep=",",col.names=FALSE)
+
+
+BIRTH_DATA <- GET_DATA(RES,4)
+BIRTH_PLOT <- MAKE_GRAPH(BIRTH_DATA)
+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_Main_Results.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)
+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_Main_Results.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)
+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_Main_Results.png"), width = 12, height = 8, units = "in", res = 600)
+	MIGRATION_PLOT
+dev.off()
+
+#####Key year table
+
+YEARS <- c(2027,2030,2035,2045,2055,2065)
+KEY_YEAR_DATA <- RES %>% filter(Year %in% YEARS)
+ AVG_VALUES <- KEY_YEAR_DATA %>% 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_YEAR_DATA, 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.`), linewidth= 0.75)+scale_color_manual(values=c("red","black","black"))+ facet_grid(rows=vars(Year))+ scale_x_continuous(breaks = c(seq(0, 10000, by = 500)))+ 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_Main_Results.png"), width = 8, height = 12, units = "in", res = 600)
+	HISTOGRAM
+dev.off()
+#rm(KEY_YEARS)
+POP_LEVELS <- seq(2000,6000,100)
+YEARS
+if(exists("KEY_YEARS")){rm(KEY_YEARS)}
+for(i in YEARS ){
+	CURRENT_YEAR_DATA <- RES %>% filter(Year==i )  %>% pull(Population)
+	ECDF <- ecdf(CURRENT_YEAR_DATA)
+	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/Main_Results/Capacity_Table_Main_Results.html")
+system("wkhtmltopdf --disable-smart-shrinking --no-stop-slow-scripts --enable-local-file-access --page-width 99mm --page-height 328mm ./Results/Primary_Simulation_Results/Main_Results/Capacity_Table_Main_Results.html ./Results/Primary_Simulation_Results/Main_Results/Capacity_Table_Main_Results.pdf") 
+