##################    Interpolation of Tmax Using Kriging  #######################################

########################### Kriging and Cokriging   ###############################################

#This script interpolates station values for the Oregon case study using Kriging and Cokring.    #

#The script uses LST monthly averages as input variables and  loads the station data             # 

#from a shape file with projection information.                                                  #

#Note that this program:                                                                         #

#1)assumes that the shape file is in the current working.                                        # 

#2)relevant variables were extracted from raster images before performing the regressions        #

#  and stored shapefile                                                                          #

#This scripts predicts tmax using autokrige, gstat and LST derived from MOD11A1.                 #

#also included and assessed using the RMSE,MAE,ME and R2 from validation dataset.                #

#TThe dates must be provided as a textfile.                                                      #

#AUTHOR: Benoit Parmentier                                                                       #

#DATE: 07/15/2012                                                                                #

#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#364--                                  #

##################################################################################################

###Loading R library and packages                                                      

#library(gtools)                                         # loading some useful tools 

library(mgcv)                                           # GAM package by Wood 2006 (version 2012)

library(sp)                                             # Spatial pacakge with class definition by Bivand et al. 2008

library(spdep)                                          # Spatial pacakge with methods and spatial stat. by Bivand et al. 2012

library(rgdal)                                          # GDAL wrapper for R, spatial utilities (Keitt et al. 2012)

library(gstat)                                          # Kriging and co-kriging by Pebesma et al. 2004

library(automap)                                        # Automated Kriging based on gstat module by Hiemstra et al. 2008

library(spgwr)

library(gpclib)

library(maptools)

library(graphics)

library(parallel)                            # Urbanek S. and Ripley B., package for multi cores & parralel processing

library(raster)

###Parameters and arguments

infile1<- "ghcn_or_tmax_covariates_06262012_OR83M.shp"             #GHCN shapefile containing variables for modeling 2010                 

#infile2<-"list_10_dates_04212012.txt"                     #List of 10 dates for the regression

infile2<-"list_365_dates_04212012.txt"

infile3<-"LST_dates_var_names.txt"                        #LST dates name

infile4<-"models_interpolation_05142012.txt"              #Interpolation model names

infile5<-"mean_day244_rescaled.rst"                       

inlistf<-"list_files_05032012.txt"                        #Stack of images containing the Covariates

path<-"/home/parmentier/Data/IPLANT_project/data_Oregon_stations_07152012"     #Jupiter LOCATION on Atlas for kriging

#path<-"H:/Data/IPLANT_project/data_Oregon_stations"                                 #Jupiter Location on XANDERS

setwd(path) 

prop<-0.3                                                                       #Proportion of testing retained for validation   

seed_number<- 100                                                               #Seed number for random sampling

models<-7                                                                       #Number of kriging model

out_prefix<-"_07192012_auto_krig_"                                              #User defined output prefix

source("krigingUK_function_07192012.R")

###STEP 1 DATA PREPARATION AND PROCESSING#####

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

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

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

CRS<-proj4string(ghcn)                       #Storing projection information (ellipsoid, datum,etc.)

mean_LST<- readGDAL(infile5)                 #Reading the whole raster in memory. This provides a grid for kriging

proj4string(mean_LST)<-CRS                   #Assigning coordinate information to prediction grid.

##Extracting the variables values from the raster files                                             

lines<-read.table(paste(path,"/",inlistf,sep=""), sep=" ")                  #Column 1 contains the names of raster files

inlistvar<-lines[,1]

inlistvar<-paste(path,"/",as.character(inlistvar),sep="")

covar_names<-as.character(lines[,2])                                         #Column two contains short names for covaraites

s_raster<- stack(inlistvar)                                                  #Creating a stack of raster images from the list of variables.

layerNames(s_raster)<-covar_names                                            #Assigning names to the raster layers

projection(s_raster)<-CRS

#stat_val<- extract(s_raster, ghcn3)                                          #Extracting values from the raster stack for every point location in coords data frame.

pos<-match("ASPECT",layerNames(s_raster)) #Find column with name "value"

r1<-raster(s_raster,layer=pos)             #Select layer from stack

pos<-match("slope",layerNames(s_raster)) #Find column with name "value"

r2<-raster(s_raster,layer=pos)             #Select layer from stack

N<-cos(r1*pi/180)

E<-sin(r1*pi/180)

Nw<-sin(r2*pi/180)*cos(r1*pi/180)   #Adding a variable to the dataframe

Ew<-sin(r2*pi/180)*sin(r1*pi/180)   #Adding variable to the dataframe.

#r<-stack(N,E,Nw,Ew)

#rnames<-c("Northness","Eastness","Northness_w","Eastness_w")

#layerNames(r)<-rnames

#s_raster<-addLayer(s_raster, r)

#s_sgdf<-as(s_raster,"SpatialGridDataFrame") #Conversion to spatial grid data frame

xy<-coordinates(r1)  #get x and y projected coordinates...

xy_latlon<-project(xy, CRS, inv=TRUE) # find lat long for projected coordinats (or pixels...)

tmp<-raster(xy_latlon) #, ncol=ncol(r1), nrow=nrow(r1))

ncol(tmp)<-ncol(r1)

nrow(tmp)<-nrow(r1)

extent(tmp)<-extent(r)

projection(tmp)<-CRS

tmp2<-tmp

values(tmp)<-xy_latlon[,1]

values(tmp2)<-xy_latlon[,2]

r<-stack(N,E,Nw,Ew,tmp,tmp2)

rnames<-c("Northness","Eastness","Northness_w","Eastness_w", "lon","lat")

layerNames(r)<-rnames

s_raster<-addLayer(s_raster, r)

### adding var

ghcn = transform(ghcn,Northness = cos(ASPECT*pi/180)) #Adding a variable to the dataframe

ghcn = transform(ghcn,Eastness = sin(ASPECT*pi/180))  #adding variable to the dataframe.

ghcn = transform(ghcn,Northness_w = sin(slope*pi/180)*cos(ASPECT*pi/180)) #Adding a variable to the dataframe

ghcn = transform(ghcn,Eastness_w = sin(slope*pi/180)*sin(ASPECT*pi/180))  #adding variable to the dataframe.

#Remove NA for LC and CANHEIGHT

ghcn$LC1[is.na(ghcn$LC1)]<-0

ghcn$LC3[is.na(ghcn$LC3)]<-0

ghcn$CANHEIGHT[is.na(ghcn$CANHEIGHT)]<-0

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

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

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

#Model assessment: specific diagnostic/metrics for GAM

results_AIC<- matrix(1,1,models+3)  

results_GCV<- matrix(1,1,models+3)

results_DEV<- matrix(1,1,models+3)

#results_RMSE_f<- matrix(1,length(models)+3)

#Model assessment: general diagnostic/metrics 

results_RMSE <- matrix(1,1,models+3)

results_MAE <- matrix(1,1,models+3)

results_ME <- matrix(1,1,models+3)       #There are 8+1 models

results_R2 <- matrix(1,1,models+3)       #Coef. of determination for the validation dataset

results_RMSE_f<- matrix(1,1,models+3)    #RMSE fit, RMSE for the training dataset

results_MAE_f <- matrix(1,1,models+3)

#Screening for bad values: value is tmax in this case

#ghcn$value<-as.numeric(ghcn$value)

ghcn_all<-ghcn

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

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

ghcn<-ghcn_test2

#coords<- ghcn[,c('x_OR83M','y_OR83M')]

###CREATING SUBSETS BY INPUT DATES AND SAMPLING

set.seed(seed_number)                        #Using a seed number allow results based on random number to be compared...

ghcn.subsets <-lapply(dates, function(d) subset(ghcn, ghcn$date==as.numeric(d)))   #Producing a list of data frame, one data frame per date.

sampling<-vector("list",length(dates))

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

  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)

  sampling[[i]]<-ind.training

}

kriging_mod<-mclapply(1:length(dates), runKriging, mc.cores = 8)#This is the end bracket from mclapply(...) statement

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

#i<-3                                           #Date 10 is used to test kriging

## Plotting and saving diagnostic measures

accuracy_tab_fun<-function(i,f_list){

  tb<-f_list[[i]][[3]]

  return(tb)

}

tb<-kriging_mod[[1]][[3]][0,]  #empty data frame with metric table structure that can be used in rbinding...

tb_tmp<-kriging_mod #copy

for (i in 1:length(tb_tmp)){

  tmp<-tb_tmp[[i]][[3]]

  tb<-rbind(tb,tmp)

}

rm(tb_tmp)

for(i in 4:(models+3)){            # start of the for loop #1

  tb[,i]<-as.numeric(as.character(tb[,i]))  

}

tb_RMSE<-subset(tb, metric=="RMSE")

tb_MAE<-subset(tb,metric=="MAE")

tb_ME<-subset(tb,metric=="ME")

tb_R2<-subset(tb,metric=="R2")

tb_RMSE_f<-subset(tb, metric=="RMSE_f")

tb_MAE_f<-subset(tb,metric=="MAE_f")

tb_diagnostic1<-rbind(tb_RMSE,tb_MAE,tb_ME,tb_R2)

#tb_diagnostic2<-rbind(tb_,tb_MAE,tb_ME,tb_R2)

mean_RMSE<-sapply(tb_RMSE[,4:(models+3)],mean)

mean_MAE<-sapply(tb_MAE[,4:(models+3)],mean)

mean_R2<-sapply(tb_R2[,4:(models+3)],mean)

mean_ME<-sapply(tb_ME[,4:(models+3)],mean)

mean_MAE_f<-sapply(tb_MAE[,4:(models+3)],mean)

mean_RMSE_f<-sapply(tb_RMSE_f[,4:(models+3)],mean)

write.table(tb_diagnostic1, file= paste(path,"/","results2_fusion_Assessment_measure1",out_prefix,".txt",sep=""), sep=",")

write.table(tb, file= paste(path,"/","results2_fusion_Assessment_measure_all",out_prefix,".txt",sep=""), sep=",")

save(fusion_mod,file= paste(path,"/","results2_fusion_Assessment_measure_all",out_prefix,".RData",sep=""))

#### END OF SCRIPT #####