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################## MULTI SAMPLING GAM FUSION METHOD ASSESSMENT ####################################
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############################ Merging LST and station data ##########################################
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#This script interpolates tmax values using MODIS LST and GHCND station data
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#interpolation area. It requires the text file of stations and a shape file of the study area.
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#Note that the projection for both GHCND and study area is lonlat WGS84.
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#Options to run this program are:
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#1) Multisampling: vary the porportions of hold out and use random samples for each run
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#2)Constant sampling: use the same sample over the runs
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#3)over dates: run over for example 365 dates without mulitsampling
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#4)use seed number: use seed if random samples must be repeatable
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#5)GAM fusion: possibilty of running GAM+FUSION or GAM+CAI and other options added
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#The interpolation is done first at the monthly time scale then delta surfaces are added.
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#AUTHOR: Benoit Parmentier
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#DATE: 02/26/2013
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#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#568--
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###################################################################################################
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###Loading R library and packages
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library(gtools) # loading some useful tools
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library(mgcv) # GAM package by Simon Wood
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library(sp) # Spatial pacakge with class definition by Bivand et al.
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library(spdep) # Spatial pacakge with methods and spatial stat. by Bivand et al.
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library(rgdal) # GDAL wrapper for R, spatial utilities
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library(gstat) # Kriging and co-kriging by Pebesma et al.
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library(fields) # NCAR Spatial Interpolation methods such as kriging, splines
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library(raster) # Hijmans et al. package for raster processing
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library(rasterVis)
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library(parallel) # Urbanek S. and Ripley B., package for multi cores & parralel processing
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library(reshape)
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library(plotrix)
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library(maptools)
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### Parameters and argument
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infile2<-"list_365_dates_04212012.txt"
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## output param from previous script: Database_stations_covariates_processing_function
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infile_monthly<-"monthly_covariates_ghcn_data_TMAXy2010_2010_VE_02082013.shp"
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infile_daily<-"daily_covariates_ghcn_data_TMAXy2010_2010_VE_02082013.shp"
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infile_locs<-"stations_venezuela_region_y2010_2010_VE_02082013.shp"
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infile3<-"covariates__venezuela_region__VE_01292013.tif" #this is an output from covariate script
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var<-"TMAX"
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out_prefix<-"_365d_GAM_fus5_all_lstd_02202013" #User defined output prefix
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CRS_locs_WGS84<-CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84: same as earlier
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#CRS_interp<-"+proj=lcc +lat_1=43 +lat_2=45.5 +lat_0=41.75 +lon_0=-120.5 +x_0=400000 +y_0=0 +ellps=GRS80 +units=m +no_defs";
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#infile_monthly<-"monthly_covariates_ghcn_data_TMAXy2010_2010_VE_02082013.shp" #outile4 from database_covar script
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#infile_daily<-"daily_covariates_ghcn_data_TMAXy2010_2010_VE_02082013.shp" #outfile3 from database_covar script
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#infile_locs<-"stations_venezuela_region_y2010_2010_VE_02082013.shp" #outfile2? from database covar script
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###
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if (var=="TMAX"){
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y_var_name<-"dailyTmax"
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}
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if (var=="TMIN"){
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y_var_name<-"dailyTmin"
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}
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#Input for sampling function...
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seed_number<- 100 #if seed zero then no seed?
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nb_sample<-1 #number of time random sampling must be repeated for every hold out proportion
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prop_min<-0.3 #if prop_min=prop_max and step=0 then predicitons are done for the number of dates...
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prop_max<-0.3
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step<-0
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constant<-0 #if value 1 then use the same samples as date one for the all set of dates
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#Models to run...this can be change for each run
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list_models<-c("y_var ~ s(elev_1)",
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"y_var ~ s(LST)",
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"y_var ~ s(elev_1,LST)",
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"y_var ~ s(lat) + s(lon)+ s(elev_1)",
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"y_var ~ s(lat,lon,elev_1)",
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"y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST)",
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"y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) + s(LC2)",
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"y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) + s(LC6)",
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"y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) + s(DISTOC)")
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#Choose interpolation method...
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interpolation_method<-c("gam_fusion","gam_CAI") #other otpions to be added later
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#Default name of LST avg to be matched
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lst_avg<-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")
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in_path<-"/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/input_data"
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#Create on the fly output folder...
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out_path<-"/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/output_data"
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script_path<-"/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/"
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setwd(in_path)
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source(file.path(script_path,"GAM_fusion_function_multisampling_02262013.R"))
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source(file.path(script_path,"GAM_fusion_function_multisampling_validation_metrics_02262013.R"))
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###################### START OF THE SCRIPT ########################
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################# CREATE LOG FILE #####################
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#create log file to keep track of details such as processing times and parameters.
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log_fname<-paste("R_log_t",out_prefix, ".log",sep="")
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if (file.exists(log_fname)){ #Stop the script???
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file.remove(log_fname)
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log_file<-file(log_fname,"w")
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}
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if (!file.exists(log_fname)){
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log_file<-file(log_fname,"w")
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}
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time1<-proc.time() #Start stop watch
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writeLines(paste("Starting script at this local Date and Time: ",as.character(Sys.time()),sep=""),
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con=log_file,sep="\n")
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writeLines("Starting script process time:",con=log_file,sep="\n")
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writeLines(as.character(time1),con=log_file,sep="\n")
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############### Reading the daily station data and other data #################
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ghcn<-readOGR(dsn=in_path,layer=sub(".shp","",infile_daily))
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CRS_interp<-proj4string(ghcn) #Storing projection information (ellipsoid, datum,etc.)
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stat_loc<-readOGR(dsn=in_path,layer=sub(".shp","",infile_locs))
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dates <-readLines(file.path(in_path,infile2)) #dates to be predicted
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#Reading of covariate brick covariates can be changed...
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rnames <-c("x","y","lon","lat","N","E","N_w","E_w","elev","slope","aspect","CANHEIGHT","DISTOC")
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lc_names<-c("LC1","LC2","LC3","LC4","LC5","LC6","LC7","LC8","LC9","LC10","LC11","LC12")
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lst_names<-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",
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"nobs_01","nobs_02","nobs_03","nobs_04","nobs_05","nobs_06","nobs_07","nobs_08",
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"nobs_09","nobs_10","nobs_11","nobs_12")
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covar_names<-c(rnames,lc_names,lst_names)
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s_raster<-brick(infile3) #read in the data brck
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names(s_raster)<-covar_names #Assigning names to the raster layers: making sure it is included in the extraction
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#Reading monthly data
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data3<-readOGR(dsn=in_path,layer=sub(".shp","",infile_monthly))
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dst_all<-data3
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dst<-data3
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### TO DO -important ###
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#Cleaning/sceerniging functions for daily stations, monthly stations and covariates?? do this at the preparation stage!!!
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##
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##### Create sampling: select training and testing sites ###
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#Make this a a function!!!!
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if (seed_number>0) {
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set.seed(seed_number) #Using a seed number allow results based on random number to be compared...
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}
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nel<-length(dates)
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dates_list<-vector("list",nel) #list of one row data.frame
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prop_range<-(seq(from=prop_min,to=prop_max,by=step))*100 #range of proportion to run
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sn<-length(dates)*nb_sample*length(prop_range) #Number of samples to run
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for(i in 1:length(dates)){
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d_tmp<-rep(dates[i],nb_sample*length(prop_range)) #repeating same date
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s_nb<-rep(1:nb_sample,length(prop_range)) #number of random sample per proportion
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prop_tmp<-sort(rep(prop_range, nb_sample))
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tab_run_tmp<-cbind(d_tmp,s_nb,prop_tmp)
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dates_list[[i]]<-tab_run_tmp
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}
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sampling_dat<-as.data.frame(do.call(rbind,dates_list))
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names(sampling_dat)<-c("date","run_samp","prop")
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for(i in 2:3){ # start of the for loop #1
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sampling_dat[,i]<-as.numeric(as.character(sampling_dat[,i]))
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}
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sampling_dat$date<- as.character(sampling_dat[,1])
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#ghcn.subsets <-lapply(dates, function(d) subset(ghcn, date==d)) #this creates a list of 10 or 365 subsets dataset based on dates
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ghcn.subsets <-lapply(as.character(sampling_dat$date), function(d) subset(ghcn, date==d)) #this creates a list of 10 or 365 subsets dataset based on dates
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#Make this a function??
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## adding choice of constant sample
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if (seed_number>0) {
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set.seed(seed_number) #Using a seed number allow results based on random number to be compared...
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}
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sampling<-vector("list",length(ghcn.subsets))
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sampling_station_id<-vector("list",length(ghcn.subsets))
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for(i in 1:length(ghcn.subsets)){
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n<-nrow(ghcn.subsets[[i]])
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prop<-(sampling_dat$prop[i])/100
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ns<-n-round(n*prop) #Create a sample from the data frame with 70% of the rows
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nv<-n-ns #create a sample for validation with prop of the rows
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ind.training <- sample(nrow(ghcn.subsets[[i]]), size=ns, replace=FALSE) #This selects the index position for 70% of the rows taken randomly
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ind.testing <- setdiff(1:nrow(ghcn.subsets[[i]]), ind.training)
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#Find the corresponding
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data_sampled<-ghcn.subsets[[i]][ind.training,] #selected the randomly sampled stations
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station_id.training<-data_sampled$station #selected id for the randomly sampled stations (115)
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#Save the information
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sampling[[i]]<-ind.training
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sampling_station_id[[i]]<- station_id.training
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}
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## Use same samples across the year...
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if (constant==1){
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sampled<-sampling[[1]]
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data_sampled<-ghcn.subsets[[1]][sampled,] #selected the randomly sampled stations
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station_sampled<-data_sampled$station #selected id for the randomly sampled stations (115)
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list_const_sampling<-vector("list",sn)
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list_const_sampling_station_id<-vector("list",sn)
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for(i in 1:sn){
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station_id.training<-intersect(station_sampled,ghcn.subsets[[i]]$station)
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ind.training<-match(station_id.training,ghcn.subsets[[i]]$station)
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list_const_sampling[[i]]<-ind.training
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list_const_sampling_station_id[[i]]<-station_id.training
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}
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sampling<-list_const_sampling
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sampling_station_id<-list_const_sampling_station_id
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}
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#return()
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########### PREDICT FOR MONTHLY SCALE #############
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#First predict at the monthly time scale: climatology
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writeLines("Predictions at monthly scale:",con=log_file,sep="\n")
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t1<-proc.time()
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gamclim_fus_mod<-mclapply(1:12, runClim_KGFusion,mc.preschedule=FALSE,mc.cores = 6) #This is the end bracket from mclapply(...) statement
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#gamclim_fus_mod<-mclapply(1:12, runClim_KGFusion,mc.preschedule=FALSE,mc.cores = 4) #This is the end bracket from mclapply(...) statement
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save(gamclim_fus_mod,file= paste("gamclim_fus_mod",out_prefix,".RData",sep=""))
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t2<-proc.time()-t1
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writeLines(as.character(t2),con=log_file,sep="\n")
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#now get list of raster clim layers
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list_tmp<-vector("list",length(gamclim_fus_mod))
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for (i in 1:length(gamclim_fus_mod)){
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tmp<-gamclim_fus_mod[[i]]$clim
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list_tmp[[i]]<-tmp
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}
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################## PREDICT AT DAILY TIME SCALE #################
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#put together list of clim models per month...
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rast_clim_yearlist<-list_tmp
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#Second predict at the daily time scale: delta
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#gam_fus_mod<-mclapply(1:1, runGAMFusion,mc.preschedule=FALSE,mc.cores = 1) #This is the end bracket from mclapply(...) statement
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writeLines("Predictions at the daily scale:",con=log_file,sep="\n")
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t1<-proc.time()
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gam_fus_mod<-mclapply(1:length(ghcn.subsets), runGAMFusion,mc.preschedule=FALSE,mc.cores = 9) #This is the end bracket from mclapply(...) statement
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save(gam_fus_mod,file= paste("gam_fus_mod",out_prefix,".RData",sep=""))
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t2<-proc.time()-t1
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writeLines(as.character(t2),con=log_file,sep="\n")
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############### NOW RUN VALIDATION #########################
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list_tmp<-vector("list",length(gam_fus_mod))
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for (i in 1:length(gam_fus_mod)){
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tmp<-gam_fus_mod[[i]][[y_var_name]] #y_var_name is the variable predicted (tmax or tmin)
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list_tmp[[i]]<-tmp
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}
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rast_day_yearlist<-list_tmp #list of predicted images
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writeLines("Validation step:",con=log_file,sep="\n")
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t1<-proc.time()
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#calculate_accuary_metrics<-function(i)
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gam_fus_validation_mod<-mclapply(1:length(gam_fus_mod), calculate_accuracy_metrics,mc.preschedule=FALSE,mc.cores = 9) #This is the end bracket from mclapply(...) statement
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#gam_fus_validation_mod<-mclapply(1:1, calculate_accuracy_metrics,mc.preschedule=FALSE,mc.cores = 1) #This is the end bracket from mclapply(...) statement
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save(gam_fus_validation_mod,file= paste("gam_fus_validation_mod",out_prefix,".RData",sep=""))
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t2<-proc.time()-t1
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writeLines(as.character(t2),con=log_file,sep="\n")
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#################### ASSESSMENT OF PREDICTIONS: PLOTS OF ACCURACY METRICS ###########
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##Create data.frame with valiation metrics for a full year
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tb_diagnostic_v<-extract_from_list_obj(gam_fus_validation_mod,"metrics_v")
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rownames(tb_diagnostic_v)<-NULL #remove row names
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#Call function to create plots of metrics for validation dataset
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metric_names<-c("rmse","mae","me","r","m50")
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summary_metrics<-boxplot_from_tb(tb_diagnostic_v,metric_names,out_prefix)
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names(summary_metrics)<-c("avg","median")
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##Write out information concerning accuracy and predictions
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outfile<-file.path(in_path,paste("assessment_measures_",out_prefix,".txt",sep=""))
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write.table(tb_diagnostic_v,file= outfile,row.names=FALSE,sep=",")
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write.table(summary_metrics[[1]], file= outfile, append=TRUE,sep=",") #write out avg
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write.table(summary_metrics[[2]], file= outfile, append=TRUE,sep=",") #write out median
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#################### CLOSE LOG FILE #############
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#close log_file connection and add meta data
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writeLines("Finished script process time:",con=log_file,sep="\n")
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time2<-proc.time()-time1
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writeLines(as.character(time2),con=log_file,sep="\n")
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#later on add all the paramters used in the script...
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writeLines(paste("Finished script at this local Date and Time: ",as.character(Sys.time()),sep=""),
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con=log_file,sep="\n")
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writeLines("End of script",con=log_file,sep="\n")
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close(log_file)
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############################################################
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######################## END OF SCRIPT #####################
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