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Revision 1cd7ecf2

Added by Benoit Parmentier almost 12 years ago

GAM fusion raster prediction, transforming script in function with explicit passing and tracking of arguments

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climate/research/oregon/interpolation/GAM_fusion_analysis_raster_prediction_multisampling.R
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##################    MULTI SAMPLING GAM FUSION METHOD ASSESSMENT   ####################################
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############################ Merging LST and station data ##########################################
3 
#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.
1 
#########################    Raster prediction GAM FUSION    ####################################
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############################ Interpolation of temperature for given processing region ##########################################
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#This script interpolates temperature values using MODIS LST, covariates and GHCND station data.
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#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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#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.
13 13 
#AUTHOR: Benoit Parmentier
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#DATE: 03/05/2013
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#DATE: 03/12/2013
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#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#568--
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###################################################################################################
17 17 

  		




  ......




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#infile_locs<-"stations_venezuela_region_y2010_2010_VE_02082013.shp"


  		




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infile_covariates<-"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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#out_prefix<-"_365d_GAM_fus5_all_lstd_02202013"                #User defined output prefix should be defined in master script


  		




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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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infile_monthly<-list_outfiles$monthly_covar_ghcn_data #outile4 from database_covar script


  		




  ......




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setwd(in_path)


  		




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list_param_data_prep<-c(infile_monthly,infile_daily,infile_locs,infile_covariates,var,out_prefix,CRS_locs_WGS84)


  		




  
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#PARSING INPUTS/ARGUMENTS


  		




  
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list_param_data_prep<-c(infile_monthly,infile_daily,infile_locs,infile_covariates,covar_names,var,out_prefix,CRS_locs_WGS84)


  		




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list_param_raster_prediction<-c(list_param_data_prep,


  		




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                                seed_number,nb_sample,step,constant,prop_minmax,infile_dates,


  		




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                                list_models,lst_avg,in_path,out_path,script_path,


  		




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                                interpolation_method)


  		




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#9 parameters used in the data preparation stage and input in the current script


  		




  
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list_param_data_prep<-list_param_raster_prediction$list_param_data_prep


  		




  
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infile_monthly<-list_param_data_prep$infile_monthly


  		




  
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infile_daily<-list_param_data_prep$infile_daily


  		




  
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infile_locs<-list_param_data_prep$infile_locs


  		




  
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infile_covariates<-list_param_data_prep$infile_covariates


  		




  
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covar_names<- list_param_data_prep$covar_names


  		




  
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var<-list_param_data_prep$var


  		




  
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out_prefix<-list_param_data_prep$out_prefix


  		




  
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CRS_locs_WGS84<-list_param_data_prep$CRS_locs_WGS84


  		




  
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#6 parameters for sampling function


  		




  
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seed_number<-list_param_raster_prediction$seed_number


  		




  
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nb_sample<-list_param_raster_prediction$nb_sample


  		




  
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step<-list_param_raster_prediction$step


  		




  
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constant<-list_param_raster_prediction$constant


  		




  
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prop_minmax<-list_param_raster_prediction$prop_minmax


  		




  
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infile_dates<-list_param_raster_prediction$infile_dates


  		




  
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#6 additional parameters for monthly climatology and more


  		




  
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list_models<-list_param_raster_prediction$list_models


  		




  
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lst_avg<-list_param_raster_prediction$lst_avg


  		




  
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in_path<-list_param_raster_prediction$in_path


  		




  
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out_path<-list_param_raster_prediction$out_path


  		




  
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script_path<-list_param_raster_prediction$script_path


  		




  
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interpolation_method<-list_param_raster_prediction$interpolation_method


  		




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source(file.path(script_path,"sampling_script_functions_03052013.R"))


  		




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source(file.path(script_path,"GAM_fusion_function_multisampling_03052013.R"))


  		




  
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source(file.path(script_path,"GAM_fusion_function_multisampling_03122013.R"))


  		




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source(file.path(script_path,"GAM_fusion_function_multisampling_validation_metrics_02262013.R"))


  		




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  ......




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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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j=12


  		




  
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list_param_runClim_KGFusion<-list(j,s_raster,covar_names,list_models,dst,var,y_var_name, out_prefix)


  		




  
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names(list_param_runClim_KGFusion)<-c("list_index","covar_rast","covar_names","list_models","dst","var","y_var_name","out_prefix")


  		




  
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source(file.path(script_path,"GAM_fusion_function_multisampling_03122013.R"))


  		




  
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gamclim_fus_mod<-mclapply(1:6, list_param=list_param_runClim_KGFusion, runClim_KGFusion,mc.preschedule=FALSE,mc.cores = 6) #This is the end bracket from mclapply(...) statement


  		




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#gamclim_fus_mod<-mclapply(1:6, runClim_KGFusion,mc.preschedule=FALSE,mc.cores = 6) #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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