# gam_daily, kriging_daily,gwr_daily,gam_CAI,gam_fusion,kriging_fusion,gwr_fusion and other options added.

#For multiple time scale methods, the interpolation is done first at the monthly time scale then delta surfaces are added.

#AUTHOR: Benoit Parmentier                                                                        

#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#568--     

#

# TO DO:

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

  stat_loc<-readOGR(dsn=dirname(infile_locs),layer=sub(".shp","",basename(infile_locs)))

  #dates2 <-readLines(file.path(in_path,dates_selected)) #dates to be predicted, now read directly from the file

  if (dates_selected==""){

    dates<-as.character(sort(unique(ghcn$date))) #dates to be predicted 

  }

  if (dates_selected!=""){

    dates<-dates_selected #dates to be predicted 

  }

  #Reading in covariate brickcan be changed...

  s_raster<-brick(infile_covariates)                   #read in the data brck

    #test <- runGAM_day_fun(1,list_param_run_prediction_gam_daily)

    method_mod_obj<-mclapply(1:length(sampling_obj$ghcn_data),list_param=list_param_run_prediction_gam_daily,runGAM_day_fun,mc.preschedule=FALSE,mc.cores = 11) #This is the end bracket from mclapply(...) statement

    save(method_mod_obj,file= file.path(out_path,paste("method_mod_obj_",interpolation_method,"_",y_var_name,out_prefix,".RData",sep="")))

    list_param_run_prediction_kriging_daily <-list(i,s_raster,covar_names,lst_avg,list_models,dst,var,y_var_name, sampling_obj,interpolation_method,out_prefix,out_path)

    names(list_param_run_prediction_kriging_daily)<-c("list_index","covar_rast","covar_names","lst_avg","list_models","dst","var","y_var_name","sampling_obj","interpolation_method","out_prefix","out_path")

    #test <- runKriging_day_fun(1,list_param_run_prediction_kriging_daily)

    #method_mod_obj<-mclapply(1:18,list_param=list_param_run_prediction_kriging_daily,runKriging_day_fun,mc.preschedule=FALSE,mc.cores = 9) #This is the end bracket from mclapply(...) statement

    save(method_mod_obj,file= file.path(out_path,paste("method_mod_obj_",interpolation_method,"_",y_var_name,out_prefix,".RData",sep="")))

    names(list_param_run_prediction_gwr_daily)<-c("list_index","covar_rast","covar_names","lst_avg","list_models","dst","var","y_var_name","sampling_obj","interpolation_method","out_prefix","out_path")

    #test <- run_interp_day_fun(1,list_param_run_prediction_gwr_daily)

    method_mod_obj<-mclapply(1:length(sampling_obj$ghcn_data),list_param=list_param_run_prediction_gwr_daily,run_interp_day_fun,mc.preschedule=FALSE,mc.cores = 11) #This is the end bracket from mclapply(...) statement

    #method_mod_obj<-mclapply(1:18,list_param=list_param_run_prediction_kriging_daily,runKriging_day_fun,mc.preschedule=FALSE,mc.cores = 9) #This is the end bracket from mclapply(...) statement

    save(method_mod_obj,file= file.path(out_path,paste("method_mod_obj_",interpolation_method,"_",y_var_name,out_prefix,".RData",sep="")))

#STAGE 5: Output analyses: assessment of results for specific dates...

#

#AUTHOR: Benoit Parmentier                                                                       

#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#363, TASK$568--   

#source(file.path(script_path,"download_and_produce_MODIS_LST_climatology_06112013.R"))

source(file.path(script_path,"covariates_production_temperatures_08052013.R"))

source(file.path(script_path,"Database_stations_covariates_processing_function_06112013.R"))

source(file.path(script_path,"results_interpolation_date_output_analyses_08052013.R"))

#source(file.path(script_path,"results_covariates_database_stations_output_analyses_04012013.R")) #to be completed

source(file.path(script_path,"sampling_script_functions_08252013.R"))

source(file.path(script_path,"GAM_fusion_function_multisampling_10042013.R")) #Includes Fusion and CAI methods

source(file.path(script_path,"GAM_fusion_function_multisampling_validation_metrics_10102013.R"))

#stages_to_run<-c(1,2,3,4,5) #May decide on antoher strategy later on...

#met_stations_outfiles_obj_file<-"met_stations_outfiles_obj_gam_CAI__365d_gam_CAI_lst_comb3_08252013.RData"

var<-"TMAX" # variable being interpolated

out_suffix_modis <-"_05302013"                       #pattern to find tiles produced previously     

#interpolation_method<-c("gam_fusion","gam_CAI","gam_daily") #other otpions to be added later

#interpolation_method<-c("gam_fusion") #other otpions to be added later

#interpolation_method<-c("kriging_fusion") #other otpions to be added later

#interpolation_method<-c("gwr_fusion") #other otpions to be added later

#interpolation_method<-c("kriging_CAI") 

#interpolation_method<-c("gam_daily") #other otpions to be added later

#interpolation_method<-c("gwr_daily") #other otpions to be added later

#out_path<-"/home/parmentier/Data/IPLANT_project/Oregon_interpolation/Oregon_03142013/output_data"

seed_number<- 100  #if seed zero then no seed?     

nb_sample<-1           #number of time random sampling must be repeated for every hold out proportion

constant<-0             #if value 1 then use the same samples as date one for the all set of dates

seed_number_month <- 100

nb_sample_month <-1           #number of time random sampling must be repeated for every hold out proportion

#dates_selected<-c("20100101","20100102","20100103","20100901") # Note that the dates set must have a specific format: yyymmdd

#dates_selected<-c("20100101","20100102","20100301","20100302","20100501","20100502","20100701","20100702","20100901","20100902","20101101","20101102")

dates_selected<-"" # if empty string then predict for the full year specified earlier

screen_data_training<- FALSE #screen training data for NA and use same input training for all models fitted

use_clim_image <- TRUE # use predicted image as a base...rather than average Tmin at the station for delta

join_daily <- FALSE # join monthly and daily station before calucating delta

#           "y_var ~ s(lat,lon) + s(LST) + ti(LST,CANHGHT)")

#Combination 5: for paper baseline=s(lat,lon)+s(elev)

#list_models<-c("y_var ~ lat*lon + elev_s")

#list_models<-c("y_var ~ s(lat,lon) + s(elev_s)")

#               "y_var ~ lat*lon + elev_s + N_w",

#               "y_var ~ lat*lon + elev_s + E_w",

#               "y_var ~ lat*lon + elev_s + LST",