####################Interpolation of Tmax for 10 dates.#####################

#This script interpolates station tmax values for the Oregon case study.It provides a mean to asssess the effect of random sampling and proportion

# of validation hold out on the RMSE.This program loads the station data from a csv file 

#and perform one type of regression:  general additive model (GAM) with different variables: 

# Lat, long, ELEV_SRTM, Eastness, Northness, DISTOC, mean_LST_monthly, Land Cover proportions.

#Note that this program:

#1)assumes that the csv file is in the current working 

#2)extract relevant variables from raster images before performing the regressions. 

#3)does not clear memory workspace at the start or end of script.

#This scripts predicts tmax using GAM and LST derived from MOD11A1.

#Interactions terms are also included and assessed using the RMSE from validation dataset.

#There are 10 dates used for the GAM interpolation. The dates must be provided as a textfile.

#Script created by Benoit Parmentier on April 25, 2012. 

###Loading r library and packages                                                      # loading the raster package

library(gtools)                                                                        # loading ...

library(mgcv)

library(sp)

library(spdep)

library(rgdal)

###Parameters and arguments

infile1<-"ghcn_or_tmax_b_04142012_OR83M.shp"

path<-"/data/computer/parmentier/Data/IPLANT_project/data_Oregon_stations"

#path<-"H:/Data/IPLANT_project/data_Oregon_stations"

setwd(path) 

infile2<-"dates_interpolation_03052012.txt"               #List of 10 dates for the regression

prop<-0.3  #Proportion of testing retained for validation   

n_runs<- 2 #Number of runs

out_prefix<-"_04252012_run30_LST"

infile3<-"LST_dates_var_names.txt"

infile4<-"models_interpolation_04032012b.txt"

#######START OF THE SCRIPT #############

###Reading the station data and setting up for models' comparison

filename<-sub(".shp","",infile1)              #Removing the extension from file.

ghcn<-readOGR(".", filename)                  #reading shapefile 

ghcn = transform(ghcn,Northness = cos(ASPECT)) #Adding a variable to the dataframe

ghcn = transform(ghcn,Eastness = sin(ASPECT))  #adding variable to the dataframe.

ghcn = transform(ghcn,Northness_w = sin(slope)*cos(ASPECT)) #Adding a variable to the dataframe

ghcn = transform(ghcn,Eastness_w = sin(slope)*sin(ASPECT))  #adding variable to the dataframe.

                                              #Note that "transform" output is a data.frame not spatial object 

#set.seed(100)

dates <-readLines(paste(path,"/",infile2, sep=""))

LST_dates <-readLines(paste(path,"/",infile3, sep=""))

models <-readLines(paste(path,"/",infile4, sep=""))

#results <- matrix(1,length(dates),14)            #This is a matrix containing the diagnostic measures from the GAM models.

results_AIC<- matrix(1,length(dates),length(models)+2)  

results_GCV<- matrix(1,length(dates),length(models)+2)

results_DEV<- matrix(1,length(dates),length(models)+2)

results_RMSE<- matrix(1,length(dates),length(models)+2)

RMSE_run<-matrix(1,length(dates),1)

cor_LST_LC1<-matrix(1,length(dates),1)      #correlation LST-LC1

cor_LST_LC3<-matrix(1,length(dates),1)      #correlation LST-LC3

cor_LST_tmax<-matrix(1,length(dates),1)    #correlation LST-tmax

#Screening for bad values

RMSE_run<-matrix(1,lenth)

results_RMSE_all<- matrix(1,length(dates)*n_runs,length(models)+3)

ghcn_all<-ghcn

ghcn_test<-subset(ghcn,ghcn$tmax>-150 & ghcn$tmax<400)

ghcn_test2<-subset(ghcn_test,ghcn_test$ELEV_SRTM>0)

ghcn<-ghcn_test2

#month_var<-c("mm_01","mm_02","mm_03","mm_04","mm_05","mm_06","mm_07","mm_08","mm_09", "mm_10", "mm_11", "mm_12")

## looping through the dates...

#Changed this section into  a nested loop, looping through the number of models

for(j in 1:n_runs){

  ghcn.subsets <-lapply(dates, function(d) subset(ghcn, date==d)) #this creates a list of 10 subset data

  for(i in 1:length(dates)){            # start of the for loop #1

    date<-strptime(dates[i], "%Y%m%d")

    month<-strftime(date, "%m")

    LST_month<-paste("mm_",month,sep="")

    ###Regression part 1: Creating a validation dataset by creating training and testing datasets

    mod <-ghcn.subsets[[i]][,match(LST_month, names(ghcn.subsets[[i]]))]

    ghcn.subsets[[i]] = transform(ghcn.subsets[[i]],LST = mod)

    #Screening LST values

    #ghcn.subsets[[i]]<-subset(ghcn.subsets[[i]],ghcn.subsets[[i]]$LST> 258 & ghcn.subsets[[i]]$LST<313)

    n<-nrow(ghcn.subsets[[i]])

    ns<-n-round(n*prop)  #Create a sample from the data frame with 70% of the rows

    nv<-n-ns             #create a sample for validation with prop of the rows

    ind.training <- sample(nrow(ghcn.subsets[[i]]), size=ns, replace=FALSE) #This selects the index position for 70% of the rows taken randomly

    ind.testing <- setdiff(1:nrow(ghcn.subsets[[i]]), ind.training)

    data_s <- ghcn.subsets[[i]][ind.training, ]

    data_v <- ghcn.subsets[[i]][ind.testing, ]

    ####Regression part 2: GAM models

    mod1<- gam(tmax~ s(lat) + s (lon) + s (ELEV_SRTM), data=data_s)

    mod2<- gam(tmax~ s(lat,lon,ELEV_SRTM), data=data_s)

    mod3<- gam(tmax~ s(lat) + s (lon) + s (ELEV_SRTM) +  s (Northness)+ s (Eastness) + s(DISTOC), data=data_s)

    mod4<- gam(tmax~ s(lat) + s (lon) + s(ELEV_SRTM) + s(Northness) + s (Eastness) + s(DISTOC) + s(LST), data=data_s)

    mod5<- gam(tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST), data=data_s)

    mod6<- gam(tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST,LC1), data=data_s)

    mod7<- gam(tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST,LC3), data=data_s)

    ####Regression part 3: Calculating and storing diagnostic measures

    results_AIC[i,1]<- j

    results_AIC[i,1]<- dates[i]  #storing the interpolation dates in the first column

    results_AIC[i,2]<- ns        #number of stations used in the training stage

    results_AIC[i,3]<- AIC (mod1)

    results_AIC[i,4]<- AIC (mod2)

    results_AIC[i,5]<- AIC (mod3)

    results_AIC[i,6]<- AIC (mod4)

    results_AIC[i,7]<- AIC (mod5)

    results_AIC[i,8]<- AIC (mod6)

    results_AIC[i,9]<- AIC (mod7)

    results_GCV[i,1]<- dates[i]  #storing the interpolation dates in the first column

    results_GCV[i,2]<- ns        #number of stations used in the training stage

    results_GCV[i,3]<- mod1$gcv.ubre

    results_GCV[i,4]<- mod2$gcv.ubre

    results_GCV[i,5]<- mod3$gcv.ubre

    results_GCV[i,6]<- mod4$gcv.ubre

    results_GCV[i,7]<- mod5$gcv.ubre

    results_GCV[i,8]<- mod6$gcv.ubre

    results_GCV[i,9]<- mod7$gcv.ubre

    results_DEV[i,1]<- dates[i]  #storing the interpolation dates in the first column

    results_DEV[i,2]<- ns        #number of stations used in the training stage

    results_DEV[i,3]<- mod1$deviance

    results_DEV[i,4]<- mod2$deviance

    results_DEV[i,5]<- mod3$deviance

    results_DEV[i,6]<- mod4$deviance

    results_DEV[i,7]<- mod5$deviance

    results_DEV[i,8]<- mod6$deviance

    results_DEV[i,9]<- mod7$deviance

    #####VALIDATION: Prediction checking the results using the testing data########

    y_mod1<- predict(mod1, newdata=data_v, se.fit = TRUE) #Using the coeff to predict new values.

    y_mod2<- predict(mod2, newdata=data_v, se.fit = TRUE)            

    y_mod3<- predict(mod3, newdata=data_v, se.fit = TRUE) 

    y_mod4<- predict(mod4, newdata=data_v, se.fit = TRUE) 

    y_mod5<- predict(mod5, newdata=data_v, se.fit = TRUE) 

    y_mod6<- predict(mod6, newdata=data_v, se.fit = TRUE)

    y_mod7<- predict(mod7, newdata=data_v, se.fit = TRUE)

    res_mod1<- data_v$tmax - y_mod1$fit #Residuals for GMA model that resembles the ANUSPLIN interpolation

    res_mod2<- data_v$tmax - y_mod2$fit   #Residuals for GAM model that resembles the PRISM interpolation                               

    res_mod3<- data_v$tmax - y_mod3$fit  

    res_mod4<- data_v$tmax - y_mod4$fit

    res_mod5<- data_v$tmax - y_mod5$fit

    res_mod6<- data_v$tmax - y_mod6$fit

    res_mod7<- data_v$tmax - y_mod7$fit

    RMSE_mod1 <- sqrt(sum(res_mod1^2)/nv)          

    RMSE_mod2 <- sqrt(sum(res_mod2^2)/nv)

    RMSE_mod3 <- sqrt(sum(res_mod3^2)/nv)

    RMSE_mod4 <- sqrt(sum(res_mod4^2)/nv)

    RMSE_mod5 <- sqrt(sum(res_mod5^2)/nv)

    RMSE_mod6 <- sqrt(sum(res_mod6^2)/nv)

    RMSE_mod7 <- sqrt(sum(res_mod7^2)/nv)

    results_RMSE[i,1]<- dates[i]  #storing the interpolation dates in the first column

    results_RMSE[i,2]<- ns        #number of stations used in the training stage

    results_RMSE[i,3]<- RMSE_mod1

    results_RMSE[i,4]<- RMSE_mod2

    results_RMSE[i,5]<- RMSE_mod3

    results_RMSE[i,6]<- RMSE_mod4

    results_RMSE[i,7]<- RMSE_mod5

    results_RMSE[i,8]<- RMSE_mod6

    results_RMSE[i,9]<- RMSE_mod7

    #Saving dataset in dataframes

    data_name<-paste("ghcn_v_",dates[[i]],sep="")

    assign(data_name,data_v)

    data_name<-paste("ghcn_s_",dates[[i]],sep="")

    assign(data_name,data_s)

    #ghcn_v<-ls(pattern="ghcn_v_")

    RMSE_run[i]<-j

    # end of the for loop #2 (nested in loop #1)

  }

  results_RMSE_all

  results_RMSEnum <-results_RMSE

  results_AICnum <-results_AIC

  mode(results_RMSEnum)<- "numeric"

  mode(results_AICnum)<- "numeric"

  # Make it numeric first

  # Now turn it into a data.frame...

  results_table_RMSE<-as.data.frame(results_RMSEnum)

  results_table_AIC<-as.data.frame(results_AICnum)

  colnames(results_table_RMSE)<-c("dates","ns","mod1", "mod2","mod3", "mod4", "mod5", "mod6", "mod7")

  colnames(results_table_AIC)<-c("dates","ns","mod1", "mod2","mod3", "mod4", "mod5", "mod6", "mod7")

  write.table(results_table_RMSE, file= paste(path,"/","results_GAM_Assessment",out_prefix,".txt",sep=""), sep=",", append=TRUE)

  #write.table(results_table_AIC, file= paste(path,"/","results_GAM_Assessment",out_prefix,".txt",sep=""),sep=",", append=TRUE)

  Print(j) #This is the run umber printed to the console.

} #end of loop 1

# End of script##########

#Selecting dates and files based on names

#cor_LST_LC<-matrix(1,10,1)

# for(i in 1:length(dates)){

#   cor_LST_LC1[i]<-cor(ghcn.subsets[[i]]$LST,ghcn.subsets[[i]]$LC1)

# }

# for(i in 1:length(dates)){

#   cor_LST_LC3[i]<-cor(ghcn.subsets[[i]]$LST,ghcn.subsets[[i]]$LC3)

# }