diff --git a/Taylor_Simulated_Campylobacter_environment_light_hum_min_for_rec_delay_original_modified.R b/Taylor_Simulated_Campylobacter_environment_light_hum_min_for_rec_delay_original_modified.R
deleted file mode 100644
index 4123b107946df56320c15cfc2c65c32b7e2e6371..0000000000000000000000000000000000000000
--- a/Taylor_Simulated_Campylobacter_environment_light_hum_min_for_rec_delay_original_modified.R
+++ /dev/null
@@ -1,718 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# this to calculate delay
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-n_seas<-1
-width<-14
-width_char<-paste(width)
-## Varaible file
-
-
-variable_x<-"Minimum_air_temperature"
-variable_y<-"Relative_humidity"
-variable_z<-"daylength"
-
-variable_x2<-"min_air_temp"
-variable_y2<-"humidity"
-variable_z2<-"light"
-
-
-## Varaible file
-
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
-
-dates_s<- dates_s<- seq(as.Date("1990-01-01"), as.Date("2015-12-31"), by = "day")
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
-Env_laboratory$Date<-as.Date(Env_laboratory$Date)
-
-
-
-#Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-Env_laboratory_PHE<-Env_laboratory
-#All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-#All_residents<-sum(All_residents_lab$tot)
-
-######################## include daylength ##################
-
-Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
-
-
-lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))
-colnames(lat_long_lab)<-c("PostCode","lat","long")
-Env_laboratory_int2<-merge(Env_laboratory,lat_long_lab,by="PostCode")
-Env_Campylobacter_data_int2<-merge(Env_Campylobacter_data,lat_long_lab,by="PostCode")
-#Env_laboratory_int2<-Env_laboratory_int2[,-c(16,17)]
-#names(Env_laboratory_int2)[10]<-"lat"
-#names(Env_laboratory_int2)[11]<-"long"
-#names(Env_laboratory_int2)[14]<-"Date2"
-
-daylength<-function(lat,day_year)
-{
- #Latitude measure in degrees
- P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
- Denom<-cos(lat*pi/180)*cos(P)
- Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
- D<-24-(24/pi)*acos(Numer/Denom)
- return(D)
-}
-
-latitude<-Env_laboratory_int2$lat
-day_of_the_year<-yday(as.Date(Env_laboratory_int2$Date))
-
-daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_laboratory_int2$Date),daylength_int1)
-colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-
-
-Env_laboratory<-data.frame(Env_laboratory_int2,daylength_df)
-
-### repeat for the data only ####
-
-latitude<-Env_Campylobacter_data_int2$lat
-day_of_the_year<-yday(as.Date(Env_Campylobacter_data_int2$Date))
-
-daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_Campylobacter_data_int2$Date),daylength_int1)
-colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-
-#daylength_df$lat<-as.factor(daylength_df$lat)
-#daylength_df$Date<-as.factor(daylength_df$Date)
-#Env_Lyme_data_int2$lat<-as.factor(Env_Lyme_data_int2$lat)
-#Env_Lyme_data_int2$Date<-as.factor(Env_Lyme_data_int2$Date)
-
-Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int2,daylength_df)
-
-
-
-
-########################
-
-
-
-
-
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_z2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable_z,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-
-###################
-
-
-delta_light<-1
-delta_hum<-5
-delta_temp<-1
-delta_rain<-2
-delta_cum_rain<-2
-delta_wind<-1
-
-breaks_hum<-seq(max(min(na.omit(Env_laboratory$Relative_humidity))-10,0),max(na.omit(Env_laboratory$Relative_humidity))+10,by=delta_hum) #i
-breaks_min_temp<-seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2, max(na.omit(Env_laboratory$Minimum_air_temperature))+2,by=delta_temp)
-breaks_max_temp<-seq(min(na.omit(Env_laboratory$Maximum_air_temperature))-2, max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp)
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_mean_temp<-seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2,max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp)
-breaks_light<-seq(max(min(na.omit(Env_laboratory$daylength))-1,0),max(na.omit(Env_laboratory$daylength))+1,by=delta_light)
-
-
-i_hum_min<-max(which(breaks_hum<=min(na.omit(Env_Campylobacter_data$Relative_humidity))))
-i_hum_max<-max(which(breaks_hum<=max(na.omit(Env_Campylobacter_data$Relative_humidity))))
-
-i_min_temp_min<-max(which(breaks_min_temp<=min(na.omit(Env_Campylobacter_data$Minimum_air_temperature))))
-i_min_temp_max<-max(which(breaks_min_temp<=max(na.omit(Env_Campylobacter_data$Minimum_air_temperature))))
-
-i_max_temp_min<-max(which(breaks_max_temp<=min(na.omit(Env_Campylobacter_data$Maximum_air_temperature))))
-i_max_temp_max<-max(which(breaks_max_temp<=max(na.omit(Env_Campylobacter_data$Maximum_air_temperature))))
-
-i_rain_min<-max(which(breaks_rain<=min(na.omit(Env_Campylobacter_data$Mean_Precipitation))))
-i_rain_max<-max(which(breaks_rain<=max(na.omit(Env_Campylobacter_data$Mean_Precipitation))))
-
-i_cum_rain_min<-max(which(breaks_cum_rain<=min(na.omit(Env_Campylobacter_data$Cumul_Precipitation))))
-i_cum_rain_max<-max(which(breaks_cum_rain<=max(na.omit(Env_Campylobacter_data$Cumul_Precipitation))))
-
-i_wind_min<-max(which(breaks_wind<=min(na.omit(Env_Campylobacter_data$Mean_wind_speed))))
-i_wind_max<-max(which(breaks_wind<=max(na.omit(Env_Campylobacter_data$Mean_wind_speed))))
-
-
-i_light_min<-max(which(breaks_light<=min(na.omit(Env_Campylobacter_data$daylength))))
-i_light_max<-max(which(breaks_light<=max(na.omit(Env_Campylobacter_data$daylength))))
-
-############## from here '###################
-
-
-if (variable_z=="Maximum_air_temperature"){
-
- breaks_var<-breaks_max_temp
- i_var_min<-i_max_temp_min
- i_var_max<-i_max_temp_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Maximum_air_temperature
- Env_laboratory_var<-Env_laboratory$Maximum_air_temperature
-}
-if (variable_x=="Maximum_air_temperature"){
-
- i_var_x_min<-i_max_temp_min
- i_var_x_max<-i_max_temp_max
- breaks_var_x<-breaks_max_temp
-}
-
-if (variable_y=="Maximum_air_temperature"){
-
- i_var_y_min<-i_max_temp_min
- i_var_y_max<-i_max_temp_max
- breaks_var_y<-breaks_max_temp
-}
-
-if (variable_z=="Minimum_air_temperature"){
-
- breaks_var<-breaks_min_temp
- i_var_min<-i_min_temp_min
- i_var_max<-i_min_temp_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$min_temp
- Env_laboratory_var<-Env_laboratory$min_temp
-}
-if (variable_x=="Minimum_air_temperature"){
-
- i_var_x_min<-i_min_temp_min
- i_var_x_max<-i_min_temp_max
- breaks_var_x<-breaks_min_temp
-}
-
-if (variable_y=="Minimum_air_temperature"){
-
- i_var_y_min<-i_min_temp_min
- i_var_y_max<-i_min_temp_max
- breaks_var_y<-breaks_min_temp
-}
-
-if (variable_z=="Relative_humidity"){
-
- breaks_var<-breaks_hum
- i_var_min<-i_hum_min
- i_var_max<-i_hum_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Relative_humidity
- Env_laboratory_var<-Env_laboratory$Relative_humidity
-}
-if (variable_x=="Relative_humidity"){
-
- i_var_x_min<-i_hum_min
- i_var_x_max<-i_hum_max
- breaks_var_x<-breaks_hum
-}
-
-if (variable_y=="Relative_humidity"){
-
- i_var_y_min<-i_hum_min
- i_var_y_max<-i_hum_max
- breaks_var_y<-breaks_hum
-}
-
-
-if (variable_z=="mean_temp"){
-
- breaks_var<-breaks_mean_temp
- i_var_min<-i_mean_temp_min
- i_var_max<-i_mean_temp_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$mean_temp
- Env_laboratory_var<-Env_laboratory$mean_temp
-}
-if (variable_x=="mean_temp"){
-
- i_var_x_min<-i_mean_temp_min
- i_var_x_max<-i_mean_temp_max
- breaks_var_x<-breaks_mean_temp
-}
-
-if (variable_y=="mean_temp"){
-
- i_var_y_min<-i_mean_temp_min
- i_var_y_max<-i_mean_temp_max
- breaks_var_y<-breaks_mean_temp
-}
-
-if (variable_z=="Mean_Precipitation"){
-
- breaks_var<-breaks_rain
- i_var_min<-i_rain_min
- i_var_max<-i_rain_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Mean_Precipitation
- Env_laboratory_var<-Env_laboratory$Mean_Precipitation
-}
-if (variable_x=="Mean_Precipitation"){
-
- i_var_x_min<-i_rain_min
- i_var_x_max<-i_rain_max
- breaks_var_x<-breaks_rain
-}
-
-if (variable_y=="Mean_Precipitation"){
-
- i_var_y_min<-i_rain_min
- i_var_y_max<-i_rain_max
- breaks_var_y<-breaks_rain
-}
-
-if (variable_z=="Cumul_Precipitation"){
-
- breaks_var<-breaks_cum_rain
- i_var_min<-i_cum_rain_min
- i_var_max<-i_cum_rain_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Cumul_Precipitation
- Env_laboratory_var<-Env_laboratory$Cumul_Precipitation
-}
-if (variable_x=="Cumul_Precipitation"){
-
- i_var_x_min<-i_cum_rain_min
- i_var_x_max<-i_cum_rain_max
- breaks_var_x<-breaks_cum_rain
-}
-
-if (variable_y=="Cumul_Precipitation"){
-
- i_var_y_min<-i_cum_rain_min
- i_var_y_max<-i_cum_rain_max
- breaks_var_y<-breaks_cum_rain
-}
-
-
-if (variable_z=="Mean_wind_speed"){
-
- breaks_var<-breaks_wind
- i_var_min<-i_wind_min
- i_var_max<-i_wind_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Mean_wind_speed
- Env_laboratory_var<-Env_laboratory$Mean_wind_speed
-}
-if (variable_x=="Mean_wind_speed"){
-
- i_var_x_min<-i_wind_min
- i_var_x_max<-i_wind_max
- breaks_var_x<-breaks_wind
-}
-
-if (variable_y=="Mean_wind_speed"){
-
- i_var_y_min<-i_wind_min
- i_var_y_max<-i_wind_max
- breaks_var_y<-breaks_wind
-}
-
-
-
-
-if (variable_z=="daylength"){
-
- breaks_var<-breaks_light
- i_var_min<-i_light_min
- i_var_max<-i_light_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$daylength
- Env_laboratory_var<-Env_laboratory$daylength
-}
-if (variable_x=="daylength"){
-
- i_var_x_min<-i_light_min
- i_var_x_max<-i_light_max
- breaks_var_x<-breaks_light
-}
-
-if (variable_y=="daylength"){
-
- i_var_y_min<-i_light_min
- i_var_y_max<-i_light_max
- breaks_var_y<-breaks_light
-}
-
-
-
-
-D_eta_var_x<-function(i_var,i_var_y,i_var_x)
-{
-
-
-
-
-#var_x_loc_df$eta<-var_x_loc_df$prop*var_x_loc_df$Numb_Lab
-var_x_loc_df$eta<-var_x_loc_df$incidence*All_residents_lab[,2]*var_x_loc_df$Numb_Lab #All_residents_lab[,2] this change by postcode only
-
-if(is.na(breaks_var[i_var+1])==TRUE)
-{
- Yt0<-subset(var_x_loc_df,var_x_loc_df$daylength>=breaks_var[i_var])
-} else {
- Yt0<-subset(var_x_loc_df,var_x_loc_df$daylength>=breaks_var[i_var] & var_x_loc_df$daylength<breaks_var[i_var+1])
-}
-
-
-if(is.na(breaks_var_y[i_var_y+1])==TRUE)
-{
- Yt1<-subset(Yt0,Yt0$Relative_humidity>=breaks_var_y[i_var_y])
-} else {
- Yt1<-subset(Yt0,Yt0$Relative_humidity>=breaks_var_y[i_var_y] & Yt0$Relative_humidity<breaks_var_y[i_var_y+1])
-}
-
-
-
- if(is.na(breaks_var_x[i_var_x+1])==TRUE)
- {
- Yt2<-subset(Yt1,Yt1$Minimum_air_temperature>=breaks_var_x[i_var_x])
- } else {
- Yt2<-subset(Yt1,Yt1$Minimum_air_temperature>=breaks_var_x[i_var_x] & Yt1$Minimum_air_temperature<breaks_var_x[i_var_x+1])
- }
-
-
- if(is.na(breaks_var_x[i_var_x+2])==TRUE)
- {
- Yt3<-subset(Yt1,Yt1$Minimum_air_temperature>=breaks_var_x[i_var_x+1])
- } else {
- Yt3<-subset(Yt1,Yt1$Minimum_air_temperature>=breaks_var_x[i_var_x+1] & Yt1$Minimum_air_temperature<breaks_var_x[i_var_x+2])
- }
-
- if(length(Yt3[,1])!=0 & length(Yt2[,1])!=0){
- D_eta_D_x<- (mean(Yt3$eta- Yt2$eta))/(breaks_var_x[i_var_x+1]-breaks_var_x[i_var_x])
- } else if (length(Yt3[,1])!=0 & length(Yt2[,1])==0){
- D_eta_D_x<- 0.5*mean(Yt3$eta)/(breaks_var_x[i_var_x+1]-breaks_var_x[i_var_x])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])!=0){
- D_eta_D_x<- 0.5*mean(Yt2$eta)/(breaks_var_x[i_var_x+1]-breaks_var_x[i_var_x])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])==0){
- D_eta_D_x<-NA
- }
-
- return(D_eta_D_x)
- }
-
-
-
-
-D_eta_var_y<-function(i_var,i_var_y,i_var_x)
-{
-
-
- #var_x_loc_df$eta<-var_x_loc_df$prop*var_x_loc_df$Numb_Lab
- var_x_loc_df$eta<-var_x_loc_df$incidence*All_residents_lab[,2]*var_x_loc_df$Numb_Lab
-
-
- if(is.na(breaks_var[i_var+1])==TRUE)
- {
- Yt0<-subset(var_x_loc_df,var_x_loc_df$daylength>=breaks_var[i_var])
- } else {
- Yt0<-subset(var_x_loc_df,var_x_loc_df$daylength>=breaks_var[i_var] & var_x_loc_df$daylength<breaks_var[i_var+1])
- }
-
-
- if(is.na(breaks_var_x[i_var_x+1])==TRUE)
- {
- Yt1<-subset(Yt0,Yt0$Minimum_air_temperature>=breaks_var_x[i_var_x])
- } else {
- Yt1<-subset(Yt0,Yt0$Minimum_air_temperature>=breaks_var_x[i_var_x] & Yt0$Minimum_air_temperature<breaks_var_x[i_var_x+1])
- }
-
-
-
- if(is.na(breaks_var_y[i_var_y+1])==TRUE)
- {
- Yt2<-subset(Yt1,Yt1$Relative_humidity>=breaks_var_y[i_var_y])
- } else {
- Yt2<-subset(Yt1,Yt1$Relative_humidity>=breaks_var_y[i_var_y] & Yt1$Relative_humidity<breaks_var_y[i_var_y+1])
- }
-
-
- if(is.na(breaks_var_y[i_var_y+2])==TRUE)
- {
- Yt3<-subset(Yt1,Yt1$Relative_humidity>=breaks_var_y[i_var_y+1])
- } else {
- Yt3<-subset(Yt1,Yt1$Relative_humidity>=breaks_var_y[i_var_y+1] & Yt1$Relative_humidity<breaks_var_y[i_var_y+2])
- }
-
- if(length(Yt3[,1])!=0 & length(Yt2[,1])!=0){
- D_eta_D_y<- (mean(Yt3$eta- Yt2$eta))/(breaks_var_y[i_var_y+1]-breaks_var_y[i_var_y])
- } else if (length(Yt3[,1])!=0 & length(Yt2[,1])==0){
- D_eta_D_y<- 0.5*mean(Yt3$eta)/(breaks_var_y[i_var_y+1]-breaks_var_y[i_var_y])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])!=0){
- D_eta_D_y<- 0.5*mean(Yt2$eta)/(breaks_var_y[i_var_y+1]-breaks_var_y[i_var_y])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])==0){
- D_eta_D_y<-NA
- }
-
- return(D_eta_D_y)
-}
-
-
-
-
-D_eta_var<-function(i_var,i_var_y,i_var_x)
-{
-
-
-#var_x_loc_df$eta<-var_x_loc_df$prop*var_x_loc_df$Numb_Lab
-var_x_loc_df$eta<-var_x_loc_df$incidence*All_residents_lab[,2]*var_x_loc_df$Numb_Lab
-
- if(is.na(breaks_var_x[i_var_x+1])==TRUE)
- {
- Yt0<-subset(var_x_loc_df,var_x_loc_df$Minimum_air_temperature>=breaks_var_x[i_var_x])
- } else {
- Yt0<-subset(var_x_loc_df,var_x_loc_df$Minimum_air_temperature>=breaks_var_x[i_var_x] & var_x_loc_df$Minimum_air_temperature<breaks_var_x[i_var_x+1])
- }
-
-if(is.na(breaks_var_y[i_var_y+1])==TRUE)
-{
- Yt1<-subset(Yt0,Yt0$Relative_humidity>=breaks_var_y[i_var_y])
-} else {
- Yt1<-subset(Yt0,Yt0$Relative_humidity>=breaks_var_y[i_var_y] & Yt0$Relative_humidity<breaks_var_y[i_var_y+1])
-}
-
-
- if(is.na(breaks_var[i_var+1])==TRUE)
- {
- Yt2<-subset( Yt1, Yt1$daylength>=breaks_var[i_var])
- } else {
- Yt2<-subset( Yt1, Yt1$daylength>=breaks_var[i_var] & Yt1$daylength<breaks_var[i_var+1])
- }
-
-if(is.na(breaks_var[i_var+2])==TRUE)
-{
- Yt3<-subset( Yt1, Yt1$daylength>=breaks_var[i_var+1])
-} else {
- Yt3<-subset( Yt1, Yt1$Relative_humiditylight>=breaks_var[i_var+1] & Yt1$daylength<breaks_var[i_var+2])
-}
-
-
-
- if(length(Yt3[,1])!=0 & length(Yt2[,1])!=0){
- D_eta_D_v<- mean(Yt3$eta- Yt2$eta)/(breaks_var[i_var+1]-breaks_var[i_var])
- } else if (length(Yt3[,1])!=0 & length(Yt2[,1])==0){
- D_eta_D_v<- 0.5*mean(Yt3$eta)/(breaks_var[i_var+1]-breaks_var[i_var])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])!=0){
- D_eta_D_v<- 0.5*mean(Yt2$eta)/(breaks_var[i_var+1]-breaks_var[i_var])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])==0){
- D_eta_D_v<-NA
- }
-
- return(D_eta_D_v)
- }
-
-
-
-
-
-df_all_1<-c()
-#i_var<-seq(i_var_min,i_var_max)
-#i_var_x<-seq(i_var_x_min,i_var_x_max)
-
- for (j in c(1: length(All_PC_s))){
-
-var_x_loc_df<-var_x_loc_df_all2
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[j])
-
-All_residents_lab<-ddply(variable_df,~PostCode,summarise,tot=mean(residents))
-for (i_var in c(seq(i_var_min,i_var_max))){
-for (i_var_y in c(seq(i_var_y_min,i_var_y_max))){
- for (i_var_x in c(seq(i_var_x_min,i_var_x_max))){
-
-
-#df1<-data.frame(i_var,i_var_x,t(unlist(lapply(i_var,D_eta_var_x,i_var_x=i_var_x))))
-#df1<-data.frame(i_var,i_var_x,t(unlist(lapply(i_var,D_eta_var_x,i_var_x=i_var_x))))
-
-df1<-data.frame(breaks_light[i_var],breaks_hum[i_var_y],breaks_min_temp[i_var_x],D_eta_var_x(i_var,i_var_y,i_var_x),D_eta_var_y(i_var,i_var_y,i_var_x),D_eta_var(i_var,i_var_y,i_var_x),All_PC_s[j] )
-colnames(df1)<-c("daylength","Relative_humidity","Minimum_air_temperature","D_eta_D_max_temp","D_eta_D_hum","D_eta_D_light","PostCode")
-df_all_1<-rbind(df_all_1,df1)
-#mapply(D_eta_var_x,i_var, MoreArgs =list(i_var_x))
-}
-}
-
-}
-}
-
-#df_all<-ddply(df_all_1,~PostCode,summarise,tot=mean())
-df_all<-df_all_1
-
-time_series<-c()
-
-for (i in c(1: length(All_PC_s))){
- #for (i in c(1: 15)){
-
-
-
- #for (j in c(1: n_seas)){
-
- # n_months<-12/n_seas
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
- if (length(variable_df[,1])!=0){
-
-
-
-Delta_T<-diff(variable_df$Minimum_air_temperature)
-Delta_RH<-diff(variable_df$Relative_humidity)
-Delta_L<-diff(variable_df$daylength)
-Delta_var<-data.frame(variable_df$Date[-1],Delta_T,Delta_RH,Delta_L)
-colnames(Delta_var)<-c("dates","diff_max_temp","diff_humidity","diff_light")
-
-x<-variable_df$Relative_humidity
-y<-variable_df$Minimum_air_temperature
-z<-variable_df$daylength
-
-variable_df_1_dis<-data.frame(variable_df$Date,variable_df$Relative_humidity)
-variable_df_2_dis<-data.frame(variable_df$Date,variable_df$Minimum_air_temperature)
-variable_df_3_dis<-data.frame(variable_df$Date,variable_df$daylength)
-
-colnames(variable_df_1_dis)<-c("dates","Relative_humidity")
-colnames(variable_df_2_dis)<-c("dates","Minimum_air_temperature")
-colnames(variable_df_3_dis)<-c("dates","daylength")
-
- variable_df_1_dis$dates<-as.Date(as.character((variable_df_1_dis$dates)))
- variable_df_2_dis$dates<-as.Date(as.character((variable_df_2_dis$dates)))
- variable_df_3_dis$dates<-as.Date(as.character((variable_df_3_dis$dates)))
-
- variable_df_1_dis<-subset(variable_df_1_dis,year(variable_df_1_dis$dates)>=1990 & year(variable_df_1_dis$dates)<=2015)
- variable_df_2_dis<-subset(variable_df_2_dis,year(variable_df_2_dis$dates)>=1990 & year(variable_df_2_dis$dates)<=2015)
- variable_df_3_dis<-subset(variable_df_3_dis,year(variable_df_3_dis$dates)>=1990 & year(variable_df_3_dis$dates)<=2015)
-
- variable_df_1_dis$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
- variable_df_2_dis$Minimum_air_temperature<-(breaks_min_temp[findInterval(y, breaks_min_temp)])
- variable_df_3_dis$daylength<-(breaks_light[findInterval(z, breaks_light)])
-
-
-# var_x_loc_df<-subset(var_x_loc_df_all2,var_x_loc_df_all2$month==j)
-# var_x_loc_df$Relative_humidity<-floor(var_x_loc_df$Relative_humidity)
-# var_x_loc_df$Minimum_air_temperature<-floor(var_x_loc_df$breaks)
- #df<-subset(df_all,df_all$month==j)
- df<-subset(df_all,df_all$PostCode==All_PC_s[i])
-
- variable_df_4_dis<-merge(variable_df_1_dis,df, by="Relative_humidity")
- variable_df_5_dis<-merge(variable_df_2_dis,df, by="Minimum_air_temperature")
- variable_df_6_dis<-merge(variable_df_3_dis,df, by="daylength")
-
-
-
- variable_df_4_dis$dates<-as.Date(as.character((variable_df_4_dis$dates)))
- variable_df_4_dis<-variable_df_4_dis[order(variable_df_4_dis$dates),]
- variable_df_5_dis$dates<-as.Date(as.character((variable_df_5_dis$dates)))
- variable_df_5_dis<-variable_df_5_dis[order(variable_df_5_dis$dates),]
- variable_df_6_dis$dates<-as.Date(as.character((variable_df_6_dis$dates)))
- variable_df_6_dis<-variable_df_6_dis[order(variable_df_6_dis$dates),]
-
-
- variable_df_4_dis$dates<-as.factor(variable_df_4_dis$dates)
- variable_df_5_dis$dates<-as.factor(variable_df_5_dis$dates)
- variable_df_6_dis$dates<-as.factor(variable_df_6_dis$dates)
-
- variable_df_dis0<-merge(variable_df_4_dis,variable_df_5_dis, by=c("dates","Minimum_air_temperature","Relative_humidity","daylength") )
- variable_df_dis<-merge(variable_df_dis0,variable_df_6_dis, by=c("dates","Minimum_air_temperature","Relative_humidity","daylength"))
-
- variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
- variable_df_dis$dates<-as.factor(variable_df_dis$dates)
- Delta_var$dates<-as.factor(Delta_var$dates)
- variable_df_Taylor<-merge(variable_df_dis,Delta_var, by=c("dates"))
-
- variable_df_Taylor<-variable_df_Taylor[,c(1:7,17:19)]
- colnames(variable_df_Taylor)<-c("dates",variable_x, variable_y, variable_z,"D_eta_D_max_temp","D_eta_D_hum","D_eta_D_light",
- "diff_max_temp","diff_humidity","diff_light")
-
-
- lambda_temp<-variable_df_Taylor$D_eta_D_max_temp*variable_df_Taylor$diff_max_temp
- lambda_hum<-variable_df_Taylor$D_eta_D_hum*variable_df_Taylor$diff_humidity
- lambda_light<-variable_df_Taylor$D_eta_D_light*variable_df_Taylor$diff_light
-
-
-
- time_series_1<-
- data.frame(variable_df_Taylor$dates,
- lambda_temp,lambda_hum,lambda_light,
- rep(All_PC_s[i],times=length(variable_df_Taylor$dates)),
- variable_df_Taylor$D_eta_D_max_temp,variable_df_Taylor$diff_max_temp,
- variable_df_Taylor$D_eta_D_hum,variable_df_Taylor$diff_humidity,
- variable_df_Taylor$D_eta_D_light,variable_df_Taylor$diff_light)
- colnames(time_series_1)<-c("Date","Contr_temp","Contr_hum","Contr_light",
- "Lab","delta_eta_temp","delta_temp","delta_eta_hum","delta_hum","delta_eta_light","delta_light")
- time_series<-rbind(time_series,time_series_1)
-
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
- }
-}
-
-#time_series1<-subset(time_series,time_series$Lab=="B152TG")
-#time_series1$Date<-as.Date(as.character(time_series1$Date))
-#time_series1$year<-as.factor(year(as.Date(as.character(time_series1$Date))))
-#time_series1<-subset(time_series1,time_series1$year=="2005")
-
-#time_series1<-na.omit(time_series1)
-##temp_contr<-cumsum(time_series1$Contr_temp)
-#temp_hum<-cumsum(time_series1$Contr_hum)
-#
-#df2<-data.frame(as.Date(as.character(time_series1$Date)),temp_contr,temp_hum)
-#colnames(df2)<-c("Date","contr","humidity")
-#temp_contr<-ddply(time_series1,~year,summarise,Cum_Cases=cumsum(Contr_temp))
-#time_series<-na.omit(time_series)
-#time_series$temp_contr<-cumsum(time_series$Contr_temp)
-#time_series$temp_hum<-cumsum(time_series$Contr_hum)
-#time_series$yday<-yday(time_series$Date)
-#time_series$week<-week(time_series$Date)
-#time_series$month<-month(time_series$Date)
-#time_series$Lab<-as.factor(time_series$Lab)
-
-#time_series_lab<-ddply(time_series,~Date,summarise,tot=mean(Contr_temp))
-#time_series_lab2<-ddply(time_series,~yday,summarise,tot=mean(Contr_temp))
-
-#time_series_lab<-ddply(time_series,~week,summarise,tot=mean(Contr_temp))
-#time_series_lab2<-ddply(time_series,~week,summarise,tot=mean(Contr_hum))
-
-#time_series_lab<-ddply(time_series,~month,summarise,tot=mean(Contr_temp))
-#time_series_lab2<-ddply(time_series,~month,summarise,tot=mean(Contr_hum))
-#time_series_lab2<-ddply(time_series,~Date,summarise,tot=mean(Contr_hum))
-
-write.table(time_series,paste("../../Data_Base/Cases/Taylor_contribution_Time_series_",variable_z,"_",variable_y,"_",variable_x,width_char,"_Simulated_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")