Revision 92a3f212
Added by Benoit Parmentier about 11 years ago
| climate/research/oregon/interpolation/GAM_fusion_function_multisampling.R | ||
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# 5)runGAMFusion <- function(i,list_param) : daily step for fusion method, perform daily prediction |
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# |
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#AUTHOR: Benoit Parmentier |
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#DATE: 08/25/2013
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#DATE: 08/30/2013
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#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#363-- |
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##Comments and TODO: |
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daily_delta_rast<-interpolate(rast_clim_month,fitdelta) #Interpolation of the bias surface... |
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#To many I/O out of swap memory on atlas |
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#Saving kriged surface in raster images |
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data_name<-paste("daily_delta_",y_var_name,"_",model_name,"_",sampling_month_dat$prop[index_m],"_",sampling_month_dat$run_samp[index_m],"_",
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sampling_dat$date[index_d],"_",sampling_dat$prop[index_d],"_",sampling_dat$run_samp[index_d],sep="") |
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daily_delta_rast<-interpolate(rast_clim_month,fitdelta) #Interpolation of the bias surface... |
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#Saving kriged surface in raster images |
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data_name<-paste("daily_delta_",y_var_name,"_",model_name,"_",sampling_month_dat$prop[index_m],"_",sampling_month_dat$run_samp[index_m],"_",
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sampling_dat$date[index_d],"_",sampling_dat$prop[index_d],"_",sampling_dat$run_samp[index_d],sep="") |
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raster_name_delta<-file.path(out_path,paste(interpolation_method,"_",var,"_",data_name,out_prefix,".tif", sep="")) |
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writeRaster(daily_delta_rast, filename=raster_name_delta,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI) |
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list_daily_delta_rast[[k]] <- raster_name_delta |
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list_daily_delta_rast[[k]] <- daily_delta_rast |
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#list_daily_delta_rast[[k]] <- raster_name_delta |
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} |
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raster_name_delta <- list_daily_delta_rast |
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#Too many I/O out of swap memory on atlas |
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#Saving kriged surface in raster images |
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delta_rast_s <-stack(list_daily_delta_rast) |
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names(delta_rast_s) <- names(daily_delta_df) |
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#Should check that all delta images have been created for every model!!! remove from list empty elements!! |
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#data_name<-paste("daily_delta_",y_var_name,"_",model_name,"_",sampling_month_dat$prop[index_m],"_",sampling_month_dat$run_samp[index_m],"_",
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# sampling_dat$date[index_d],"_",sampling_dat$prop[index_d],"_",sampling_dat$run_samp[index_d],sep="") |
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#raster_name_delta<-file.path(out_path,paste(interpolation_method,"_",var,"_",data_name,out_prefix,".tif", sep="")) |
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#writeRaster(daily_delta_rast, filename=raster_name_delta,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI) |
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data_name<-paste("daily_delta_",y_var_name,"_",sampling_month_dat$prop[index_m],"_",sampling_month_dat$run_samp[index_m],"_",
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sampling_dat$date[index_d],"_",sampling_dat$prop[index_d],"_",sampling_dat$run_samp[index_d],sep="") |
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raster_name_delta<-file.path(out_path,paste(interpolation_method,"_",var,"_",data_name,out_prefix,".tif", sep="")) |
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writeRaster(delta_rast_s, filename=raster_name_delta,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI) |
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#writeRaster(r_spat, NAflag=NA_flag_val,filename=raster_name,bylayer=TRUE,bandorder="BSQ",overwrite=TRUE) |
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#raster_name_delta <- list_daily_delta_rast |
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mod_krtmp2 <- list_mod_krtmp2 |
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} |
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# STEP 4 - Calculate daily predictions - T(day) = clim(month) + delta(day) |
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######### |
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if(use_clim_image==FALSE){
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list_daily_delta_rast <- rep(raster_name_delta,length=nlayers(rast_clim_mod)) |
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} |
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#if(use_clim_image==FALSE){
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# list_daily_delta_rast <- rep(raster_name_delta,length=nlayers(rast_clim_mod))
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#}
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#Now predict daily after having selected the relevant month |
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temp_list<-vector("list",nlayers(rast_clim_mod))
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for (k in 1:nlayers(rast_clim_mod)){
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rast_clim_month<-raster(rast_clim_list[[k]]) |
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daily_delta_rast <- raster(list_daily_delta_rast[[k]]) |
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if(use_clim_image==TRUE){
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daily_delta_rast <- list_daily_delta_rast[[k]] |
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} |
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#if use_clim_image==FALSE then daily__delta_rast already defined earlier... |
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#rast_clim_month<-raster(rast_clim_list[[k]]) |
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rast_clim_month <- subset(rast_clim_mod,k) |
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temp_predicted<-rast_clim_month + daily_delta_rast |
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data_name<-paste(y_var_name,"_predicted_",names(rast_clim_mod)[k],"_",sampling_month_dat$prop[index_m],"_",sampling_month_dat$run_samp[index_m],"_", |
| climate/research/oregon/interpolation/master_script_temp.R | ||
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#STAGE 5: Output analyses: assessment of results for specific dates... |
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# |
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#AUTHOR: Benoit Parmentier |
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#DATE: 08/25/2013
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#DATE: 08/30/2013
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#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#363, TASK$568-- |
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#met_stations_outfiles_obj_file<-"met_stations_outfiles_obj_gam_CAI__365d_gam_CAI_lst_comb3_08252013.RData" |
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var<-"TMAX" # variable being interpolated |
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out_prefix<-"_365d_gam_CAI_lst_comb3_08252013" #User defined output prefix
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out_suffix<-"_OR_08252013" #Regional suffix
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out_prefix<-"_365d_gam_CAI_lst_comb3_08302013" #User defined output prefix
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out_suffix<-"_OR_08302013" #Regional suffix
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out_suffix_modis <-"_05302013" #pattern to find tiles produced previously |
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#interpolation_method<-c("gam_fusion","gam_CAI","gam_daily") #other otpions to be added later
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#dates_selected<-c("20100101","20100102","20100301","20100302","20100501","20100502","20100701","20100702","20100901","20100902","20101101","20101102")
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dates_selected<-"" # if empty string then predict for the full year specified earlier |
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screen_data_training<-FALSE #screen training data for NA and use same input training for all models fitted |
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use_clim_image <- TRUE # use predicted image as a base...rather than average Tmin at the station for delta |
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join_daily <- FALSE # join monthly and daily station before calucating delta |
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#Models to run...this can be changed for each run |
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#LC1: Evergreen/deciduous needleleaf trees |
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list_param_raster_prediction<-list(list_param_data_prep,screen_data_training, |
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seed_number,nb_sample,step,constant,prop_minmax,dates_selected, |
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seed_number_month,nb_sample_month,step_month,constant_month,prop_minmax_month, |
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list_models,lst_avg,out_path,script_path, |
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list_models,lst_avg,out_path,script_path,use_clim_image,join_daily,
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interpolation_method) |
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names(list_param_raster_prediction)<-c("list_param_data_prep","screen_data_training",
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"seed_number","nb_sample","step","constant","prop_minmax","dates_selected", |
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"seed_number_month","nb_sample_month","step_month","constant_month","prop_minmax_month", |
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"list_models","lst_avg","out_path","script_path", |
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"list_models","lst_avg","out_path","script_path","use_clim_image","join_daily",
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"interpolation_method") |
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#debug(raster_prediction_fun) |
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raster_prediction_obj <-raster_prediction_fun(list_param_raster_prediction) |
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Also available in: Unified diff
dealing with memory swap overload, daily devation debugging following change in monthly outputs