Revision ac043b28
Added by Benoit Parmentier over 9 years ago
| climate/research/oregon/interpolation/global_run_scalingup_assessment_part2.R | ||
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#Analyses, figures, tables and data are also produced in the script. |
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#AUTHOR: Benoit Parmentier |
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#CREATED ON: 03/23/2014 |
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#MODIFIED ON: 07/30/2015
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#MODIFIED ON: 09/03/2015
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#Version: 4 |
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#PROJECT: Environmental Layers project |
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#COMMENTS: analyses for run 10 global analyses,all regions 1500x4500km with additional tiles to increase overlap |
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interpolation_method <- c("gam_CAI") #PARAM2
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#out_suffix<-"run10_global_analyses_01282015" |
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#out_suffix <- "output_run10_1000x3000_global_analyses_02102015" |
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out_suffix <- "run10_1500x4500_global_analyses_pred_2010_testelev_07302015" #PARAM3
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out_dir <- "/data/project/layers/commons/NEX_data/output_run10_1500x4500_global_analyses_pred_2010_testelev_07302015" #PARAM4
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out_suffix <- "run10_1500x4500_global_analyses_pred_1992_09012015" #PARAM3
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out_dir <- "/data/project/layers/commons/NEX_data/output_run10_1500x4500_global_analyses_pred_1992_09012015" #PARAM4
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create_out_dir_param <- FALSE #PARAM 5 |
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mosaic_plot <- FALSE #PARAM6 |
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#if daily mosaics NULL then mosaicas all days of the year |
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day_to_mosaic <- c("20100829","20100830","20100831",
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"20100901","20100902","20100903") |
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day_to_mosaic <- c("19920101","19920102","19920103","19920104","19920105",
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"19920106","19920107","19920108","19920109","19920110", |
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"19920111") |
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CRS_WGS84 <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84 #CONSTANT1
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CRS_locs_WGS84<-CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84
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| ... | ... | |
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plot_region <- TRUE |
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num_cores <- 10 #PARAM 14 |
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reg_modified <- TRUE |
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region <- c("reg5") #reference region to merge if necessary #PARAM 16
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region <- c("reg4") #reference region to merge if necessary #PARAM 16
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########################## START SCRIPT ############################## |
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#lf_m_mask_reg6_1000x3000 <- mclapply(1:length(lf_m),FUN=plot_daily_mosaics,list_param=list_param_plot_daily_mosaics,mc.preschedule=FALSE,mc.cores = 10) |
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plot_screen_raster_val<-function(i,list_param){
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##USAGE### |
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#Screen raster list and produced plot as png. |
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fname <-list_param$lf_raster_fname[i] |
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screenRast <- list_param$screenRast |
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l_dates<- list_param$l_dates |
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out_dir_str <- list_param$out_dir_str |
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prefix_str <-list_param$prefix_str |
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out_suffix_str <- list_param$out_suffix_str |
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if(is.null(day_to_mosaic)){
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#idx <- seq(as.Date('2010-01-01'), as.Date('2010-12-31'), 'day')
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#idx <- seq(as.Date('20100101'), as.Date('20101231'), 'day')
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#date_l <- strptime(idx[1], "%Y%m%d") # interpolation date being processed |
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#dates_l <- format(idx, "%Y%m%d") # interpolation date being processed |
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day_to_mosaic <- dates_predicted #should be 365 days... |
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#l_dates <- day_to_mosaic |
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} |
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if(plot_region==TRUE){
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#get the files |
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l_reg_name <- unique(df_tile_processed$reg) |
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#l_reg_name <- c("reg5")
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l_reg_name <- c("reg4") #use this for the time
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#lf_mosaics_reg5 <- mixedsort(list.files(path="/data/project/layers/commons/NEX_data/output_run10_global_analyses_11302014/mosaics/reg5", |
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# pattern="CAI_TMAX_clim_month_.*_mod1_all.tif", full.names=T)) |
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lf_mosaics_reg <- vector("list",length=length(l_reg_name))
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out_dir_str <- out_dir |
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reg_name <- paste(l_reg_name[i],"_",tile_size,sep="") #make this automatic |
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#lapply() |
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list_param_plot_daily_mosaics <- list(lf_m=lf_m,reg_name=reg_name,out_dir_str=out_dir_str,out_suffix=out_suffix,l_dates=day_to_mosaic) |
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#list_param_plot_daily_mosaics <- list(lf_m=lf_m,reg_name=reg_name,out_dir_str=out_dir_str,out_suffix=out_suffix,l_dates=day_to_mosaic)
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#lf_m_mask_reg4_1500x4500 <- mclapply(1:2,FUN=plot_daily_mosaics,list_param=list_param_plot_daily_mosaics,mc.preschedule=FALSE,mc.cores = 6) |
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#lf_mosaics_mask_reg[[i]] <- lapply(1:1,FUN=plot_daily_mosaics,list_param=list_param_plot_daily_mosaics) |
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lf_raster_fname <- lf_m |
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prefix_str <- "Figure10_clim_reg4_mosaics_day_" |
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l_dates <-day_to_mosaic |
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screenRast=FALSE |
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list_param_plot_screen_raster <- list(lf_raster_fname,screenRast,l_dates,out_dir,prefix_str,out_suffix) |
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names(list_param_plot_screen_raster) <- c("lf_raster_fname","screenRast","l_dates","out_dir_str","prefix_str","out_suffix_str")
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#undebug(plot_screen_raster_val) |
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#world_m_list1<- plot_screen_raster_val(1,list_param_plot_screen_raster) |
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#world_m_list <- mclapply(11:30, list_param=list_param_plot_screen_raster, plot_screen_raster_val,mc.preschedule=FALSE,mc.cores = num_cores) #This is the end bracket from mclapply(...) statement |
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lf_mosaics_mask_reg[[i]] <- mclapply(1:length(l_dates), list_param=list_param_plot_screen_raster, plot_screen_raster_val,mc.preschedule=FALSE,mc.cores = num_cores) #This is the end bracket from mclapply(...) statement |
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lf_mosaics_mask_reg[[i]] <- mclapply(1:length(lf_m),FUN=plot_daily_mosaics,list_param=list_param_plot_daily_mosaics,mc.preschedule=FALSE,mc.cores = num_cores) |
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#lf_mosaics_mask_reg[[i]] <- mclapply(1:length(lf_m),FUN=plot_daily_mosaics,list_param=list_param_plot_daily_mosaics,mc.preschedule=FALSE,mc.cores = num_cores)
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} |
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} |
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#(subset(df_tile_processed$reg == l_reg_name[i],date) |
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for(i in 1:length(l_reg_name)){
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lf_world_pred <-list.files(path=file.path(out_dir,l_reg_name[[i]]),
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lf_world_pred<-list.files(path=file.path(out_dir,l_reg_name[[i]]), |
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pattern=paste(".tif$",sep=""),full.names=T)
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#mosaic_list_mean <- test_list |
Also available in: Unified diff
running part2 assessment 1992 Africa