##################    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: 07/26/2012                                                                                #

#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(maptools)

library(graphics)

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

library(raster)

library(rasterVis)

library(fields)                              # May be used later...

### Parameters and argument

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_2_dates_04212012.txt"

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"                       #Raster or grid for the locations of predictions

#infile6<-"lst_climatology.txt"

infile6<-"LST_files_monthly_climatology.txt"

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

#path<-"/home/parmentier/Data/IPLANT_project/data_Oregon_stations_07192012_GAM"

path<-"/home/parmentier/Data/IPLANT_project/data_Oregon_stations_07152012_GWR"     #Jupiter LOCATION on Atlas for kriging

#Station location of the study area

#stat_loc<-read.table(paste(path,"/","location_study_area_OR_0602012.txt",sep=""),sep=",", header=TRUE)

#GHCN Database for 1980-2010 for study area (OR) 

#data3<-read.table(paste(path,"/","ghcn_data_TMAXy1980_2010_OR_0602012.txt",sep=""),sep=",", header=TRUE) #Not needing at this stage...

nmodels<-8                                    #number of models running

y_var_name<-"dailyTmax"                       #variable value being modeled...("value" in the GHCND database)

predval<-1                                    # if set to 1, full interpolation raster produced for the study area

prederr<-0                                    # if set to 0, no uncertain error (e.g. standard error or kriging std dev) is produced

prop<-0.3                                     #Proportion of testing retained for validation   

#prop<-0.25

seed_number<- 100                             #Seed number for random sampling

out_prefix<-"test2_07312012_365d_gwr"                                                   #User defined output prefix

setwd(path)

#source("fusion_function_07192012.R")

#source("KrigingUK_function_07262012.R")

source("GWR_function_07312012.R")

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

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.

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=""))

##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.

pos<-match("LC1",layerNames(s_raster)) #Find column with name "value"

LC1<-raster(s_raster,layer=pos)             #Select layer from stack

s_raster<-dropLayer(s_raster,pos)

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

pos<-match("LC3",layerNames(s_raster)) #Find column with name "value"

LC3<-raster(s_raster,layer=pos)             #Select layer from stack

s_raster<-dropLayer(s_raster,pos)

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

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

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

lon<-raster(xy_latlon) #Transform a matrix into a raster object ncol=ncol(r1), nrow=nrow(r1))

ncol(lon)<-ncol(r1)

nrow(lon)<-nrow(r1)

extent(lon)<-extent(r1)

projection(lon)<-CRS  #At this stage this is still an empty raster with 536 nrow and 745 ncell 

lat<-lon

values(lon)<-xy_latlon[,1]

values(lat)<-xy_latlon[,2]

r<-stack(N,E,Nw,Ew,lon,lat,LC1,LC3)

rnames<-c("Northness","Eastness","Northness_w","Eastness_w", "lon","lat","LC1","LC3")

layerNames(r)<-rnames

s_raster<-addLayer(s_raster, r)

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

####### Preparing LST stack of climatology...

#l=list.files(pattern="mean_month.*rescaled.rst")

l <-readLines(paste(path,"/",infile6, sep=""))

molst<-stack(l)  #Creating a raster stack...

#setwd(old)

molst<-molst-273.16  #K->C          #LST stack of monthly average...

idx <- seq(as.Date('2010-01-15'), as.Date('2010-12-15'), 'month')

molst <- setZ(molst, idx)

layerNames(molst) <- month.abb

####### CREATE A MASK FOR WATER AND 

pos<-match("LC10",layerNames(s_raster)) #Find column with the current month for instance mm12

LC10<-raster(s_raster,pos)

LC10_mask<-LC10

LC10_mask[is.na(LC10_mask)]<-0

LC10_mask[LC10==100]<-NA

LC10_mask[LC10_mask<100]<-1

LC10_mask[is.na(LC10_mask)]<-0

mask_land_NA<-LC10_mask

mask_land_NA[mask_land_NA==0]<-NA

breakpoints<-c(0,0.99)

colors<-c("black","red")

#plot(LC10_mask, breaks=breakpoints,col=colors)

#quartz()

plot(LC10_mask, col=colors)

# ELEV_SRTM[ELEV_SRTM==-9999]<-NA

# avl<-c(0,60000,1)

# rclmat<-matrix(avl,ncol=3,byrow=TRUE)

# ELEV_rc<-reclass(ELEV_SRTM,rclmat)

# ELEV_SRTM_m <-mask(ELEV_STRM,mask_land_NA)

# mask2<-ELEV_SRTM_m

# mask2[is.na(mask)]<-1

# s_rst_m <-mask(s_raster,mask_land_NA)   #This assigns NA to all values from LC1 that are NA in the mask_land_NA

# 

#######  Preparing tables for model assessment: specific diagnostic/metrics

#Model assessment: specific diagnostics/metrics

results_m1<- matrix(1,1,nmodels+3)    #Diagnostic metrics specific to the modeleling framework 

results_m2<- matrix(1,1,nmodels+3)

results_m3<- matrix(1,1,nmodels+3)

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

#Model assessment: general diagnostic/metrics 

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

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

results_ME <- matrix(1,1,nmodels+3)       #There are 8 models for kriging!!!

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

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

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

results_R2_f <- matrix(1,1,nmodels+3)

######### Preparing daily values for training and testing

#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')]

##Sampling: training and testing sites...

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, date==d)) #this creates a list of 10 or 365 subsets dataset based on dates

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

}

######## Prediction for the range of dates

#gwr_mod<-mclapply(1:length(dates), runGWR,mc.preschedule=FALSE,mc.cores = 8) #This is the end bracket from mclapply(...) statement

#fusion_mod357<-mclapply(357:365,runFusion, mc.cores=8)# for debugging

#test<-runKriging(1)

#test357<-mclapply(357:365,runFusion, mc.cores=8)# for debugging

gwr_mod<-mclapply(1:1, runGWR,mc.preschedule=FALSE,mc.cores = 1) #This is the end bracket from mclapply(...) statement

#test<-mclapply(357,runFusion, mc.cores=1)# for debugging

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

tb_tmp<-gwr_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:nmodels+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:(nmodels+3)],mean)

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

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

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

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

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

#Wrting metric results in textfile and model objects in .RData file

write.table(tb_diagnostic1, file= paste(path,"/","results2_kriging_Assessment_measure1",out_prefix,".txt",sep=""), sep=",")

write.table(tb, file= paste(path,"/","results2_kriging_Assessment_measure_all",out_prefix,".txt",sep=""), sep=",")

save(gwr_mod,file= paste(path,"/","results2_kriging_Assessment_measure_all",out_prefix,".RData",sep=""))

#### END OF SCRIPT