################## Kriging Multisampling method assessment  #######################################

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

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

#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.                #

#The dates must be provided as a textfile. Method is assesed using multisampling with variation  #

#of validation sample with different hold out proportions.                                       #

#AUTHOR: Benoit Parmentier                                                                       #

#DATE: 08/31/2012                                                                                #

#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...

library(reshape)

### 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"     #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<-9                                    #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<-"_08312012_365d_Kriging_multi_samp3"                                                   #User defined output prefix

setwd(path)

nb_sample<-15

prop_min<-0.1

prop_max<-0.7

step<-0.1

#source("fusion_function_07192012.R")

source("KrigingUK_function_multisampling_08312012.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

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

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

s_raster<-dropLayer(s_raster,pos)

CANHEIGHT[is.na(CANHEIGHT)]<-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,CANHEIGHT)

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

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

######  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...

nel<-length(dates)

dates_list<-vector("list",nel) #list of one row data.frame

prop_range<-(seq(from=prop_min,to=prop_max,by=step))*100

sn<-length(dates)*nb_sample*length(prop_range)

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

  d_tmp<-rep(dates[i],nb_sample*length(prop_range)) #repeating same date

  s_nb<-rep(1:nb_sample,length(prop_range))         #number of random sample per proportion

  prop_tmp<-sort(rep(prop_range, nb_sample))

  tab_run_tmp<-cbind(d_tmp,s_nb,prop_tmp)

  dates_list[[i]]<-tab_run_tmp

}

sampling_dat<-as.data.frame(do.call(rbind,dates_list))

names(sampling_dat)<-c("date","run_samp","prop")

for(i in 2:3){            # start of the for loop #1

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

}

sampling_dat$date<- as.character(sampling_dat[,1])

#ghcn.subsets <-lapply(dates, function(d) subset(ghcn, date==d)) #this creates a list of 10 or 365 subsets dataset based on dates

ghcn.subsets <-lapply(as.character(sampling_dat$date), function(d) subset(ghcn, date==d)) #this creates a list of 10 or 365 subsets dataset based on dates

sampling<-vector("list",length(ghcn.subsets))

for(i in 1:length(ghcn.subsets)){

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

  prop<-(sampling_dat$prop[i])/100

  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

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

krig_mod<-mclapply(1:length(ghcn.subsets), runKriging,mc.preschedule=FALSE,mc.cores = 8) #This is the end bracket from mclapply(...) statement

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

save(krig_mod,file= paste(path,"/","results2_krig_mod_",out_prefix,".RData",sep=""))

load("results2_krig_mod__08312012_365d_Kriging_multi_samp3.RData")

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

tb_tmp<-krig_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]))  

}

metrics<-as.character(unique(tb$metric))            #Name of accuracy metrics (RMSE,MAE etc.)

tb_metric_list<-vector("list",length(metrics))

for(i in 1:length(metrics)){            # Reorganizing information in terms of metrics 

  metric_name<-paste("tb_",metrics[i],sep="")

  tb_metric<-subset(tb, metric==metrics[i])

  tb_metric<-cbind(tb_metric,sampling_dat[,2:3])

  assign(metric_name,tb_metric)

  tb_metric_list[[i]]<-tb_metric

}

tb_diagnostic<-do.call(rbind,tb_metric_list)

tb_diagnostic[["prop"]]<-as.factor(tb_diagnostic[["prop"]])

t<-melt(tb_diagnostic,

        measure=c("mod1","mod2","mod3","mod4", "mod5", "mod6", "mod7", "mod8","mod9"), 

        id=c("dates","metric","prop"),

        na.rm=F)

avg_tb<-cast(t,metric+prop~variable,mean)

median_tb<-cast(t,metric+prop~variable,mean)

avg_tb[["prop"]]<-as.numeric(as.character(avg_tb[["prop"]]))

avg_RMSE<-subset(avg_tb,metric=="RMSE")

# Save before plotting

write.table(avg_tb, file= paste(path,"/","results2_fusion_Assessment_measure_avg_",out_prefix,".txt",sep=""), sep=",")

write.table(median_tb, file= paste(path,"/","results2_fusion_Assessment_measure_median_",out_prefix,".txt",sep=""), sep=",")

write.table(tb_diagnostic, file= paste(path,"/","results2_fusion_Assessment_measure",out_prefix,".txt",sep=""), sep=",")

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

#save(krig_mod,file= paste(path,"/","results2_krig_mod_",out_prefix,".RData",sep=""))

# get the range for the x and y axis 

X11()

xrange <- range(avg_tb$prop)

yrange <- c(0,3.6) 

# set up the plot 

plot(xrange, yrange, type="n", xlab="Proportion of hold out in %",

     ylab="RMSE" ) 

colors <- rainbow(nmodels) 

linetype <- c(1:nmodels) 

plotchar <- seq(1,1+nmodels,1)

# add lines 

for (i in 1:(nmodels)) { 

  avg_tb_RMSE <- subset(avg_tb, metric=="RMSE") 

  x<-avg_tb_RMSE[["prop"]]

  mod_name<-paste("mod",i,sep="")

  y<-avg_tb_RMSE[[mod_name]]

  lines(x, y, type="b", lwd=1.5,

        lty=1, col=colors[i], pch=plotchar[i]) 

} 

# add a title and subtitle 

title("RMSE for fusion and GAM models")

# add a legend 

legend("bottomright",legend=1:(nmodels), cex=1.2, col=colors,

       pch=plotchar, lty=linetype, title="mod")

#### END OF SCRIPT