Revision 44f43362
Added by Benoit Parmentier over 11 years ago
| climate/research/oregon/interpolation/GAM_fusion_analysis_raster_prediction_multisampling.R | ||
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names(list_param_runClim_KGFusion)<-c("list_index","covar_rast","covar_names","lst_avg","list_models","dst","var","y_var_name","out_prefix","out_path")
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#source(file.path(script_path,"GAM_fusion_function_multisampling_03122013.R")) |
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clim_method_mod_obj<-mclapply(1:12, 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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men#test<-runClim_KGFusion(1,list_param=list_param_runClim_KGFusion)
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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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#clim_method_mod_obj<-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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#test<-runClim_KGFusion(3,list_param=list_param_runClim_KGFusion)
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save(clim_method_mod_obj,file= file.path(out_path,paste(interpolation_method,"_mod_",y_var_name,out_prefix,".RData",sep=""))) |
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list_tmp<-vector("list",length(clim_method_mod_obj))
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for (i in 1:length(clim_method_mod_obj)){
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| ... | ... | |
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#TODO : Same call for all functions!!! Replace by one "if" for all multi time scale methods... |
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if (interpolation_method=="gam_CAI" | interpolation_method=="gam_fusion"){
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#input a list:note that ghcn.subsets is not sampling_obj$data_day_ghcn |
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i<-1 |
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list_param_run_prediction_daily_deviation <-list(i,clim_yearlist,sampling_obj,dst,var,y_var_name, interpolation_method,out_prefix,out_path) |
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names(list_param_run_prediction_daily_deviation)<-c("list_index","clim_yearlist","sampling_obj","dst","var","y_var_name","interpolation_method","out_prefix","out_path")
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#test<-mclapply(1:18, runGAMFusion,list_param=list_param_runGAMFusion,mc.preschedule=FALSE,mc.cores = 9) |
| ... | ... | |
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#TODO : Same call for all functions!!! Replace by one "if" for all daily single time scale methods... |
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if (interpolation_method=="gam_daily"){
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#input a list:note that ghcn.subsets is not sampling_obj$data_day_ghcn |
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i<-1 |
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list_param_run_prediction_gam_daily <-list(i,s_raster,covar_names,lst_avg,list_models,dst,var,y_var_name, sampling_obj,interpolation_method,out_prefix,out_path) |
| 243 | 244 |
names(list_param_run_prediction_gam_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")
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#test <- runGAM_day_fun(1,list_param_run_prediction_gam_daily) |
| climate/research/oregon/interpolation/GAM_fusion_function_multisampling.R | ||
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list_out_filename[[k]]<-raster_name |
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} |
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#now predict values for raster image... |
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#now predict values for raster image...by providing fitted model list, raster brick and list of output file names
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rast_bias_list<-predict_raster_model(mod_list,s_raster,list_out_filename) |
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names(rast_bias_list)<-cname |
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#Some modles will not be predicted...remove them |
| ... | ... | |
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#### STEP 4:Adding Kriging for Climatology options |
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bias_xy<-coordinates(data_month) |
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fitbias<-Krig(bias_xy,data_month$LSTD_bias,theta=1e5) #use TPS or krige |
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mod_krtmp1<-fitbias |
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#fitbias<-Krig(bias_xy,data_month$LSTD_bias,theta=1e5) #use TPS or krige |
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fitbias<-try(Krig(bias_xy,data_month$LSTD_bias,theta=1e5)) #use TPS or krige |
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model_name<-"mod_kr" |
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if (inherits(fitbias,"Krig")){
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#Saving kriged surface in raster images |
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bias_rast<-bias_rast<-interpolate(LST,fitbias) #interpolation using function from raster package |
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data_name<-paste(var,"_bias_LST_month_",j,"_",model_name,"_",prop_month, |
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"_",run_samp,sep="") |
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raster_name_bias<-file.path(out_path,paste("fusion_",data_name,out_prefix,".tif", sep=""))
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writeRaster(bias_rast, filename=raster_name_bias,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI) |
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#now climatology layer |
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clim_rast<-LST-bias_rast |
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data_name<-paste(var,"_clim_LST_month_",j,"_",model_name,"_",prop_month, |
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"_",run_samp,sep="") |
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raster_name_clim<-file.path(out_path,paste("fusion_",data_name,out_prefix,".tif", sep=""))
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writeRaster(clim_rast, filename=raster_name_clim,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI) |
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#Adding to current objects |
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mod_list[[model_name]]<-fitbias |
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rast_bias_list[[model_name]]<-raster_name_bias |
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rast_clim_list[[model_name]]<-raster_name_clim |
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} |
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bias_rast<-interpolate(LST,fitbias) #interpolation using function from raster package |
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#Saving kriged surface in raster images |
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data_name<-paste(var,"_bias_LST_month_",j,"_",model_name,"_",prop_month, |
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"_",run_samp,sep="") |
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raster_name_bias<-file.path(out_path,paste("fusion_",data_name,out_prefix,".tif", sep=""))
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writeRaster(bias_rast, filename=raster_name_bias,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI) |
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#now climatology layer |
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clim_rast<-LST-bias_rast |
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data_name<-paste(var,"_clim_LST_month_",j,"_",model_name,"_",prop_month, |
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"_",run_samp,sep="") |
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raster_name_clim<-file.path(out_path,paste("fusion_",data_name,out_prefix,".tif", sep=""))
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writeRaster(clim_rast, filename=raster_name_clim,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI) |
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#Adding to current objects |
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mod_list[[model_name]]<-mod_krtmp1 |
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rast_bias_list[[model_name]]<-raster_name_bias |
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rast_clim_list[[model_name]]<-raster_name_clim |
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if (inherits(fitbias,"try-error")){
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#NEED TO DEAL WITH THIS!!! |
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#Adding to current objects |
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mod_list[[model_name]]<-NULL |
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rast_bias_list[[model_name]]<-NULL |
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rast_clim_list[[model_name]]<-NULL |
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} |
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#### STEP 5: Prepare object and return |
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Also available in: Unified diff
gam fusion handling errors