Revision 2a9f3980
Added by Benoit Parmentier almost 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: 02/07/2016
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#MODIFIED ON: 02/10/2016
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#Version: 5 |
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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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setwd(out_dir) |
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list_outfiles <- vector("list", length=25) #collect names of output files
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list_outfiles_names <- vector("list", length=25) #collect names of output files
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list_outfiles <- vector("list", length=29) #collect names of output files
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list_outfiles_names <- vector("list", length=29) #collect names of output files
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counter_fig <- 0 #index of figure to collect outputs |
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#i <- year_predicted |
| ... | ... | |
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title(paste("RMSE with scaling for all tiles: ",model_name[i],sep=""))
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dev.off() |
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list_outfiles[[counter_fig+2]] <- paste("Figure3b_boxplot_overall_region_scaling_",model_name[i],"_",out_suffix,".png",sep="")
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counter_fig <- counter_fig +2 |
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} |
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counter_fig <- counter_fig + length(model_name) |
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#counter_fig <- counter_fig + length(model_name)
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r6 <-c("figure_3a","Boxplot overall accuracy with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[6]])
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r7 <-c("figure_3b","Boxplot overall accuracy with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[7]])
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r8 <-c("figure_3a","Boxplot overall accuracy with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[8]])
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| ... | ... | |
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################ |
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### Figure 4: plot predicted tiff for specific date per model |
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#y_var_name <-"dailyTmax" |
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#index <-244 #index corresponding to Sept 1 |
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# if (mosaic_plot==TRUE){
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# index <- 1 #index corresponding to Jan 1 |
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# date_selected <- "20100901" |
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# name_method_var <- paste(interpolation_method,"_",y_var_name,"_",sep="") |
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# |
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# pattern_str <- paste("mosaiced","_",name_method_var,"predicted",".*.",date_selected,".*.tif",sep="")
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# lf_pred_list <- list.files(pattern=pattern_str) |
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# |
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# for(i in 1:length(lf_pred_list)){
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# |
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# |
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# r_pred <- raster(lf_pred_list[i]) |
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# |
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# res_pix <- 480 |
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# col_mfrow <- 1 |
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# row_mfrow <- 1 |
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# |
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# png(filename=paste("Figure4_models_predicted_surfaces_",model_name[i],"_",name_method_var,"_",data_selected,"_",out_suffix,".png",sep=""),
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# width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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# |
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# plot(r_pred) |
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# title(paste("Mosaiced",model_name[i],name_method_var,date_selected,sep=" "))
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# dev.off() |
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# } |
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# #Plot Delta and clim... |
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# |
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# ## plotting of delta and clim for later scripts... |
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# |
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# } |
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for(i in 1:length(model_name)){
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tb_subset <- subset(tb,pred_mod==model_name[i])#mod1 is i=1, mod_kr is last |
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labels <- month.abb # abbreviated names for each month |
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## Figure 4a |
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fig_filename <- paste("Figure4a_boxplot_overall_accuracy_separated_by_month_with_outliers_",model_name[i],"_",out_suffix,".png",sep="")
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png(filename=fig_filename, |
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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boxplot(rmse~month_no,data=tb_subset,ylab=metric_name,xlab="averaged by month",axes=F)#,names=tb$pred_mod) |
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axis(1, labels = FALSE) |
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## Plot x axis labels at default tick marks |
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text(1:length(labels), par("usr")[3] - 0.25, srt = 45, adj = 1,cex=0.8,
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labels = labels, xpd = TRUE) |
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axis(2) |
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box() |
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title(paste("Overall accuracy for ", model_name[i], " by month for ",region_name,sep=""))
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#p<- bwplot(rmse~year_predicted | reg , data=tb_subset,ylim=c(0,5), |
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#main="RMSE per model and region over all tiles") |
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#print(p) |
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dev.off() |
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list_outfiles[[counter_fig+1]] <- fig_filename |
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counter_fig <- counter_fig + 1 |
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fig_filename <- paste("Figure4b_boxplot_overall_separated_by_month_scaling_",model_name[i],"_",out_suffix,".png",sep="")
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png(filename=fig_filename, |
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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boxplot(rmse~month_no,data=tb_subset,ylim=c(0,5),outline=FALSE,ylab=metric_name, |
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xlab="averaged by month",axes=F)#,names=tb$pred_mod) |
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axis(1, labels = FALSE) |
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## Plot x axis labels at default tick marks |
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text(1:length(labels), par("usr")[3] - 0.25, srt = 45, adj = 1,cex=0.8,
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labels = labels, xpd = TRUE) |
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axis(2) |
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box() |
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title(paste("Overall accuracy for ", model_name[i], " by month for ",region_name,sep=""))
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#p<- bwplot(rmse~year_predicted | reg , data=tb_subset,ylim=c(0,5), |
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#main="RMSE per model and region over all tiles") |
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#print(p) |
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dev.off() |
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list_outfiles[[counter_fig+1]] <- fig_filename |
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counter_fig <- counter_fig + 1 |
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} |
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#counter_fig <- counter_fig + length(model_name) |
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r10 <-c("figure_4a","Boxplot overall accuracy by month with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[10]])
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r11 <-c("figure_4b","Boxplot overall accuracy by month with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[11]])
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r12 <-c("figure_4a","Boxplot overall accuracy by month with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[12]])
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r13 <-c("figure_4b","Boxplot overall accuracy by month with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[13]])
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###################### |
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### Figure 5: plot accuracy ranked |
| ... | ... | |
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title(paste("RMSE ranked by tile for ",model_name[i],sep=""))
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dev.off() |
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list_outfiles[[counter_fig+i]] <- fig_filename |
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list_outfiles[[counter_fig+1]] <- fig_filename |
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counter_fig <- counter_fig + 1 |
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} |
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counter_fig <- counter_fig + length(model_name) |
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#counter_fig <- counter_fig + length(model_name)
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r10 <-c("figure_5","Barplot of accuracy metrics ranked by tiles","mod1",metric_name,region_name,year_predicted,list_outfiles[[8]])
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r11 <-c("figure_5","Barplot of accuracy metrics ranked by tiles","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[9]])
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r14 <-c("figure_5","Barplot of accuracy metrics ranked by tiles","mod1",metric_name,region_name,year_predicted,list_outfiles[[14]])
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r15 <-c("figure_5","Barplot of accuracy metrics ranked by tiles","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[15]])
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###################### |
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### Figure 6: plot map of average RMSE per tile at centroids |
| ... | ... | |
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coordinates(ac_mod) <- ac_mod[,c("lon","lat")]
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#coordinates(ac_mod) <- ac_mod[,c("lon.x","lat.x")] #solve this later
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p_shp <- layer(sp.polygons(reg_layer, lwd=1, col='black')) |
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#p_shp <- layer(sp.polygons(reg_layer, lwd=1, col='black')) |
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p_shp <- spplot(reg_layer,"ISO3" ,col.regions=NA, col="black") #ok problem solved!! |
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| 526 | 550 |
#title("(a) Mean for 1 January")
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p <- bubble(ac_mod,"rmse",main=paste("Average RMSE per tile and by ",model_name[i]))
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| 528 | 552 |
p1 <- p+p_shp |
| ... | ... | |
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### Ranking by tile... |
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#df_ac_mod <- |
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list_df_ac_mod[[i]] <- arrange(as.data.frame(ac_mod),desc(rmse))[,c("rmse","mae","tile_id")]
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list_outfiles[[counter_fig+i]] <- fig_filename |
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list_outfiles[[counter_fig+1]] <- fig_filename |
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counter_fig <- counter_fig + 1 |
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#list_outfiles[[counter_fig+i]] <- fig_filename |
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} |
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counter_fig <- counter_fig+length(model_name) |
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#counter_fig <- counter_fig+length(model_name)
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r12 <-c("figure_6","Average accuracy metrics map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[8]])
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r13 <-c("figure_6","Average accuracy metrics map at centroids","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[9]])
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r16 <-c("figure_6","Average accuracy metrics map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[16]])
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r17 <-c("figure_6","Average accuracy metrics map at centroids","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[17]])
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| 544 | 570 |
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###################### |
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### Figure 7: Number of predictions: daily and monthly |
| ... | ... | |
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fig_filename <- paste("Figure7a_ac_metrics_map_centroids_tile_",model_name[j],"_","missing_day_",threshold_missing_day[i],
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"_",out_suffix,".png",sep="") |
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list_outfiles[[counter_fig+i]] <- fig_filename |
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if(sum(summary_metrics_v_subset$n_missing) > 0){#then there are centroids to plot!!!
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#res_pix <- 1200 |
| ... | ... | |
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model_name[j] |
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p_shp <- layer(sp.polygons(reg_layer, lwd=1, col='black')) |
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#p_shp <- layer(sp.polygons(reg_layer, lwd=1, col='black')) |
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p_shp <- spplot(reg_layer,"ISO3" ,col.regions=NA, col="black") #ok problem solved!! |
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| 598 | 625 |
#title("(a) Mean for 1 January")
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p <- bubble(summary_metrics_v_subset,"n_missing",main=paste("Missing per tile and by ",model_name[j]," for ",
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threshold_missing_day[i])) |
| ... | ... | |
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} |
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list_outfiles[[counter_fig+i]] <- fig_filename |
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} |
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counter_fig <- counter_fig+length(threshold_missing_day) #currently 4 days... |
| 610 | 636 |
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r14 <-c("figure_7","Number of missing days threshold1 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[8]])
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r15 <-c("figure_7","Number of missing days threshold2 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[9]])
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r16 <-c("figure_7","Number of missing days threshold3 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[8]])
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r17 <-c("figure_7","Number of missing days threshold4 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[9]])
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r18 <-c("figure_7","Number of missing days threshold1 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[18]])
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r19 <-c("figure_7","Number of missing days threshold2 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[19]])
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r20 <-c("figure_7","Number of missing days threshold3 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[20]])
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r21 <-c("figure_7","Number of missing days threshold4 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[21]])
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| 615 | 641 |
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### Potential |
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png(filename=paste("Figure7b_number_daily_predictions_per_models","_",out_suffix,".png",sep=""),
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| ... | ... | |
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list_outfiles[[counter_fig+1]] <- paste("Figure7b_number_daily_predictions_per_models","_",out_suffix,".png",sep="")
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counter_fig <- counter_fig + 1 |
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r18 <-c("figure_7b","Number of daily predictions per_models","mod1",metric_name,region_name,year_predicted,list_outfiles[[9]])
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r22 <-c("figure_7b","Number of daily predictions per_models","mod1",metric_name,region_name,year_predicted,list_outfiles[[22]])
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table(tb$pred_mod) |
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table(tb$index_d) |
| ... | ... | |
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list_outfiles[[counter_fig+1]] <- paste("Figure7c_histogram_number_daily_predictions_per_models","_",out_suffix,".png",sep="")
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| 653 | 679 |
counter_fig <- counter_fig + 1 |
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r19 <-c("figure_7c","Histogram number daily predictions per models","mod1",metric_name,region_name,year_predicted,list_outfiles[[9]])
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#table(tb) |
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## Figure 7b |
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#png(filename=paste("Figure7b_number_daily_predictions_per_models","_",out_suffix,".png",sep=""),
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# width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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#xyplot(n~month | tile_id + pred_mod,data=subset(as.data.frame(tb_month_s), |
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# pred_mod!="mod_kr"),type="h") |
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#xyplot(n~month | tile_id,data=subset(as.data.frame(tb_month_s), |
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# pred_mod="mod_1"),type="h") |
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#test=subset(as.data.frame(tb_month_s),pred_mod="mod_1") |
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#table(tb_month_s$month) |
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#dev.off() |
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# |
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r23 <-c("figure_7c","Histogram number daily predictions per models","mod1",metric_name,region_name,year_predicted,list_outfiles[[23]])
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| 670 | 681 |
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########################################################## |
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##### Figure 8: Breaking down accuracy by regions!! ##### |
| ... | ... | |
| 709 | 720 |
counter_fig <- counter_fig + 1 |
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| 711 | 722 |
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r20 <-c("figure 8a","Boxplot overall accuracy by model separated by region with outliers",NA,metric_name,region_name,year_predicted,list_outfiles[[20]])
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r21 <-c("figure 8b","Boxplot overall accuracy by model separated by region with scaling",NA,metric_name,region_name,year_predicted,list_outfiles[[21]])
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r24 <-c("figure 8a","Boxplot overall accuracy by model separated by region with outliers",NA,metric_name,region_name,year_predicted,list_outfiles[[24]])
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r25 <-c("figure 8b","Boxplot overall accuracy by model separated by region with scaling",NA,metric_name,region_name,year_predicted,list_outfiles[[25]])
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| 714 | 725 |
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####### |
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##Second, plot for each model separately |
| ... | ... | |
| 753 | 764 |
|
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} |
| 755 | 766 |
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r22 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[22]])
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r23 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[23]])
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r24 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[24]])
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r25 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[25]])
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r26 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[26]])
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r27 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[27]])
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r28 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[28]])
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r29 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[29]])
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| 760 | 771 |
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##################################################### |
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#### Figure 9: plotting boxplot by year and regions ########### |
| ... | ... | |
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# This is hard coded and can be improved later on for flexibility. It works for now... |
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#This data.frame contains all the files from the assessment |
| 797 | 808 |
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#Should have this at the location of the figures!!! will be done later? |
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#r1 <-c("figure_1","Tiles processed for the region",NA,NA,region_name,year_predicted,list_outfiles[[1]])
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#r2 <-c("figure_2a","Boxplot of accuracy with outliers by tiles","mod1",metric_name,region_name,year_predicted,list_outfiles[[2]])
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#r3 <-c("figure_2a","boxplot of accuracy with outliers by tiles","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[3]])
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#r4 <-c("figure_2b","Boxplot of accuracy with scaling by tiles","mod1",metric_name,region_name,year_predicted,list_outfiles[[4]])
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#r5 <-c("figure_2b","Boxplot of accuracy with scaling by tiles","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[5]])
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#r6 <-c("figure_3a","Boxplot overall accuracy with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[6]])
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#r7 <-c("figure_3b","Boxplot overall accuracy with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[7]])
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#r8 <-c("figure_3a","Boxplot overall accuracy with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[8]])
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#r9 <-c("figure_3b","Boxplot overall accuracy with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[9]])
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#r10 <-c("figure_5","Barplot of accuracy metrics ranked by tiles","mod1",metric_name,region_name,year_predicted,list_outfiles[[10]])
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#r11 <-c("figure_5","Barplot of accuracy metrics ranked by tiles","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[11]])
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#r12 <-c("figure_6","Average accuracy metrics map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[12]])
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#r13 <-c("figure_6","Average accuracy metrics map at centroids","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[13]])
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#r14 <-c("figure_7","Number of missing days threshold1 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[14]])
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| 813 |
#r15 <-c("figure_7","Number of missing days threshold2 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[15]])
|
|
| 814 |
#r16 <-c("figure_7","Number of missing days threshold3 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[16]])
|
|
| 815 |
#r17 <-c("figure_7","Number of missing days threshold4 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[17]])
|
|
| 816 |
#r18 <-c("figure_7b","Number of daily predictions per_models","mod1",metric_name,region_name,year_predicted,list_outfiles[[18]])
|
|
| 817 |
#r19 <-c("figure_7c","Histogram number daily predictions per models","mod1",metric_name,region_name,year_predicted,list_outfiles[[19]])
|
|
| 818 |
#r20 <-c("figure 8a","Boxplot overall accuracy by model separated by region with outliers",NA,metric_name,region_name,year_predicted,list_outfiles[[20]])
|
|
| 819 |
#r21 <-c("figure 8b","Boxplot overall accuracy by model separated by region with scaling",NA,metric_name,region_name,year_predicted,list_outfiles[[21]])
|
|
| 820 |
#r22 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[22]])
|
|
| 821 |
#r23 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[23]])
|
|
| 822 |
#r24 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[24]])
|
|
| 823 |
#r25 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[25]])
|
|
| 824 |
|
|
| 825 | 809 |
#Assemble all the figures description and information in a data.frame for later use |
| 826 |
list_rows <-list(r1,r2,r3,r4,r5,r6,r7,r8,r9,r10,r11,r12,r13,r14,r15,r16,r17,r18,r19,r20,r21,r22,r23,r24,r25) |
|
| 810 |
list_rows <-list(r1,r2,r3,r4,r5,r6,r7,r8,r9,r10,r11,r12,r13,r14,r15,r16,r17,r18,r19,r20,r21,r22, |
|
| 811 |
r23,r24,r25,r26,r27,r28,r29) |
|
| 827 | 812 |
df_assessment_figures_files <- as.data.frame(do.call(rbind,list_rows)) |
| 828 | 813 |
names(df_assessment_figures_files) <- c("figure_no","comment","model_name","reg","metric_name","year_predicted","filename")
|
| 829 | 814 |
|
Also available in: Unified diff
debugging assessment part2 related to figures count and shapefile plotting