Revision 0d1b5196
Added by Benoit Parmentier almost 9 years ago
| climate/research/oregon/interpolation/global_run_scalingup_assessment_part2.R | ||
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| 5 | 5 |
#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: 01/12/2016
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#MODIFIED ON: 02/01/2016
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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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#source /nobackupp6/aguzman4/climateLayers/sharedModules2/etc/environ.sh |
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# |
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#setfacl -Rmd user:aguzman4:rwx /nobackupp8/bparmen1/output_run10_1500x4500_global_analyses_pred_1992_10052015 |
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#setfacl -Rm user:aguzman4:rwx /nobackupp8/bparmen1/output_run_global_analyses_pred_12282015 |
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################################################################################################# |
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|
| ... | ... | |
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try(tb$reg <- tb$reg.x) |
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try(summary_metrics_v$year_predicted <- summary_metrics_v$year_predicted.x) |
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try(summary_metrics_v$reg <- summary_metrics_v$reg.x) |
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try(summary_metrics_v$lat <- summary_metrics_v$lat.x) |
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try(summary_metrics_v$lon <- summary_metrics_v$lon.x) |
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#tb_all <- tb |
| 225 | 228 |
#summary_metrics_v_all <- summary_metrics_v |
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|
| ... | ... | |
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shp1 <- shps_tiles[[i]] |
| 311 | 314 |
pt <- centroids_pts[[i]] |
| 312 | 315 |
if(!inherits(shp1,"try-error")){
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plot(shp1,add=T,border="blue")
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plot(shp1,add=T,border="blue",usePolypath = FALSE) #added usePolypath following error on brige and NEX
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| 314 | 317 |
#plot(pt,add=T,cex=2,pch=5) |
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label_id <- df_tile_processed$tile_id[i] |
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text(coordinates(pt)[1],coordinates(pt)[2],labels=i,cex=1.3,font=2,col=c("red"))
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text(coordinates(pt)[1],coordinates(pt)[2],labels=i,cex=1.3,font=2,col=c("red"),family="HersheySerif")
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} |
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} |
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#title(paste("Tiles ", tile_size,region_name,sep=""))
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#plot(shp1,add=T,border="blue",usePolypath = FALSE) #,add=T, |
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#plot(pt,add=T,cex=2,pch=5) |
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#label_id <- df_tile_processed$tile_id[i] |
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#text(coordinates(pt)[1],coordinates(pt)[2],labels=i,cex=1.3,font=2,col=c("red"),family="HersheySerif")
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dev.off() |
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#unique(summaty_metrics$tile_id) |
| ... | ... | |
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############### |
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### Figure 3: boxplot of average RMSE by model acrosss all tiles |
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#Ok fixed..now selection of model but should also offer an option for using both models!! so make this a function!! |
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for(i in 1:length(model_name)){ #there are two models!!
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## Figure 3a |
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res_pix <- 480 |
| ... | ... | |
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png(filename=paste("Figure3a_boxplot_overall_region_with_oultiers_",model_name[i],"_",out_suffix,".png",sep=""),
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| 384 | 391 |
width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 385 | 392 |
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boxplot(rmse~pred_mod,data=tb)#,names=tb$pred_mod) |
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title("RMSE per model over all tiles")
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#boxplot(rmse~pred_mod,data=tb)#,names=tb$pred_mod) |
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boxplot(rmse~pred_mod,data=subset(tb,tb$pred_mod==model_name[i]))#,names=tb$pred_mod) |
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title(paste("RMSE with outliers for all tiles: ",model_name[i],sep=""))
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| 388 | 396 |
dev.off() |
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list_outfiles[[counter_fig+1]] <- paste("Figure3a_boxplot_overall_region_with_oultiers_",model_name[i],"_",out_suffix,".png",sep="")
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| 390 | 398 |
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| 391 | 399 |
## Figure 3b |
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png(filename=paste("Figure3b_boxplot_overall_region_scaling_",model_name[i],"_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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boxplot(rmse~pred_mod,data=tb,ylim=c(0,5),outline=FALSE)#,names=tb$pred_mod)
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title("RMSE per model over all tiles")
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#boxplot(rmse~pred_mod,data=tb,ylim=c(0,5),outline=FALSE)#,names=tb$pred_mod) |
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boxplot(rmse~pred_mod,data=subset(tb,tb$pred_mod==model_name[i]),ylim=c(0,5),outline=FALSE)#,names=tb$pred_mod)
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#title("RMSE per model over all tiles")
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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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} |
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counter_fig <- counter_fig + length(model_name) |
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################ |
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### Figure 4: plot predicted tiff for specific date per model |
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|
| ... | ... | |
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png(filename=paste("Figure6_ac_metrics_map_centroids_tile_",model_name[i],"_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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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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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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| 496 | 504 |
p_shp <- layer(sp.polygons(reg_layer, lwd=1, col='black')) |
| 497 | 505 |
#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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list_outfiles[[counter_fig+i]] <- fig_filename |
| 510 | 518 |
} |
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counter_fig <- counter_fig+length(model_name) |
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###################### |
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### Figure 7: Number of predictions: daily and monthly |
| ... | ... | |
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sum(df_tile_processed$metrics_v)/length(df_tile_processed$metrics_v) #80 of tiles with info |
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| 524 | 532 |
#coordinates |
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#try(coordinates(summary_metrics_v) <- c("lon","lat"))
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try(coordinates(summary_metrics_v) <- c("lon.y","lat.y"))
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try(coordinates(summary_metrics_v) <- c("lon","lat"))
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#try(coordinates(summary_metrics_v) <- c("lon.y","lat.y"))
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| 527 | 535 |
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| 528 | 536 |
#threshold_missing_day <- c(367,365,300,200) |
| 529 | 537 |
|
| ... | ... | |
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| 545 | 553 |
#summary_metrics_v$n_missing <- summary_metrics_v$n == 365 |
| 546 | 554 |
#summary_metrics_v$n_missing <- summary_metrics_v$n < 365 |
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summary_metrics_v$n_missing <- summary_metrics_v$n < threshold_missing_day[i]
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summary_metrics_v$n_missing <- as.numeric(summary_metrics_v$n < threshold_missing_day[i])
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| 548 | 556 |
summary_metrics_v_subset <- subset(summary_metrics_v,model_name=="mod1") |
| 549 | 557 |
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#res_pix <- 1200 |
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res_pix <- 960 |
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col_mfrow <- 1 |
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row_mfrow <- 1 |
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fig_filename <- paste("Figure7a_ac_metrics_map_centroids_tile_",model_name[j],"_","missing_day_",threshold_missing_day[i],
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| 556 | 559 |
"_",out_suffix,".png",sep="") |
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png(filename=paste("Figure7a_ac_metrics_map_centroids_tile_",model_name[j],"_","missing_day_",threshold_missing_day[i],
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if(sum(summary_metrics_v_subset$n_missing) > 0){#then there are centroids to plot!!!
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#res_pix <- 1200 |
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res_pix <- 960 |
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col_mfrow <- 1 |
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row_mfrow <- 1 |
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png(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=""), |
| 559 | 569 |
width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 560 | 570 |
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model_name[j] |
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model_name[j]
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| 562 | 572 |
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p_shp <- layer(sp.polygons(reg_layer, lwd=1, col='black')) |
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#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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p_shp <- layer(sp.polygons(reg_layer, lwd=1, col='black'))
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#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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p1 <- p+p_shp |
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try(print(p1)) #error raised if number of missing values below a threshold does not exist |
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dev.off() |
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p1 <- p+p_shp |
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try(print(p1)) #error raised if number of missing values below a threshold does not exist |
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dev.off() |
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} |
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list_outfiles[[counter_fig+i]] <- fig_filename |
| 572 | 584 |
} |
| 573 | 585 |
counter_fig <- counter_fig+length(threshold_missing_day) #currently 4 days... |
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|
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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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| 577 | 589 |
width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| ... | ... | |
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table(tb$pred_mod) |
| 587 | 599 |
table(tb$index_d) |
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table(subset(tb,pred_mod!="mod_kr")) |
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#table(subset(tb,pred_mod!="mod_kr"))
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table(subset(tb,pred_mod=="mod1")$index_d) |
| 590 | 602 |
#aggregate() |
| 591 | 603 |
tb$predicted <- 1 |
| 592 | 604 |
test <- aggregate(predicted~pred_mod+tile_id,data=tb,sum) |
| 593 |
xyplot(predicted~pred_mod | tile_id,data=subset(as.data.frame(test), |
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pred_mod!="mod_kr"),type="h") |
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#xyplot(predicted~pred_mod | tile_id,data=subset(as.data.frame(test),
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# pred_mod!="mod_kr"),type="h")
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| 595 | 607 |
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as.character(unique(test$tile_id)) #141 tiles |
| 597 | 609 |
|
| ... | ... | |
| 628 | 640 |
##### Figure 8: Breaking down accuracy by regions!! ##### |
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|
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#summary_metrics_v <- merge(summary_metrics_v,df_tile_processed,by="tile_id") |
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##First plot with all models together |
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## Figure 8a |
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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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png(filename=paste("Figure8a_boxplot_overall_separated_by_region_with_oultiers_","_",out_suffix,".png",sep=""),
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png(filename=paste("Figure8a_boxplot_overall_separated_by_region_with_oultiers_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 639 | 652 |
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p<- bwplot(rmse~pred_mod | reg, data=tb, |
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main="RMSE per model and region over all tiles") |
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main="RMSE per model and region over all tiles with outliers")
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print(p) |
| 643 | 656 |
dev.off() |
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list_outfiles[[counter_fig+1]] <- paste("Figure8a_boxplot_overall_separated_by_region_with_oultiers_",model_name[i],"_",out_suffix,".png",sep="")
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list_outfiles[[counter_fig+1]] <- paste("Figure8a_boxplot_overall_separated_by_region_with_oultiers_",out_suffix,".png",sep="")
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| 646 | 659 |
counter_fig <- counter_fig + 1 |
| 647 | 660 |
|
| 648 | 661 |
## Figure 8b |
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png(filename=paste("Figure8b_boxplot_overall_separated_by_region_scaling_","_",out_suffix,".png",sep=""),
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png(filename=paste("Figure8b_boxplot_overall_separated_by_region_scaling_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 651 | 664 |
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boxplot(rmse~pred_mod,data=tb,ylim=c(0,5),outline=FALSE)#,names=tb$pred_mod) |
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title("RMSE per model over all tiles")
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#boxplot(rmse~pred_mod,data=tb,ylim=c(0,5),outline=FALSE)#,names=tb$pred_mod)
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#title("RMSE per model over all tiles")
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| 654 | 667 |
p<- bwplot(rmse~pred_mod | reg, data=tb,ylim=c(0,5), |
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main="RMSE per model and region over all tiles") |
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main="RMSE per model and region over all tiles with scaling")
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print(p) |
| 657 | 670 |
dev.off() |
| 658 | 671 |
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| 659 | 672 |
list_outfiles[[counter_fig+1]] <- paste("Figure8b_boxplot_overall_separated_by_region_scaling_",model_name[i],"_",out_suffix,".png",sep="")
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| 660 | 673 |
counter_fig <- counter_fig + 1 |
| 661 | 674 |
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## Select mod1 only now |
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tb_subset <- subset(tb,model_name=="mod1") |
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## Figure 8c |
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##Second, plot for each model separately |
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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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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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## Figure 8c |
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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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| 669 | 685 |
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png(filename=paste("Figure8c_boxplot_overall_separated_by_region_with_oultiers_","mod1","_",out_suffix,".png",sep=""),
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fig_filename <- paste("Figure8c_boxplot_overall_separated_by_region_with_oultiers_",model_name[i],"_",out_suffix,".png",sep="")
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png(filename=fig_filename, |
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| 671 | 688 |
width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 672 | 689 |
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p<- bwplot(rmse~pred_mod | reg, data=tb_subset, |
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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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p<- bwplot(rmse~pred_mod | reg, data=tb_subset,
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main="RMSE per model and region over all tiles with outliers")
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print(p)
|
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| 693 |
dev.off()
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| 677 | 694 |
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list_outfiles[[counter_fig+1]] <- paste("Figure8c_boxplot_overall_separated_by_region_with_oultiers_",model_name[i],"_",out_suffix,".png",sep="")
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counter_fig <- counter_fig + 1 |
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list_outfiles[[counter_fig+1]] <- fig_filename
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counter_fig <- counter_fig + 1
|
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| 680 | 697 |
|
| 681 |
## Figure 8d |
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png(filename=paste("Figure8d_boxplot_overall_separated_by_region_scaling_","mod1","_",out_suffix,".png",sep=""),
|
|
| 698 |
## Figure 8d |
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fig_filename <- paste("Figure8d_boxplot_overall_separated_by_region_scaling_",model_name[i],"_",out_suffix,".png",sep="")
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png(filename=fig_filename, |
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| 683 | 701 |
width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 684 | 702 |
|
| 685 |
boxplot(rmse~pred_mod,data=tb,ylim=c(0,5),outline=FALSE)#,names=tb$pred_mod) |
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title("RMSE per model over all tiles")
|
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| 687 |
p<- bwplot(rmse~pred_mod | 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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#boxplot(rmse~pred_mod,data=tb,ylim=c(0,5),outline=FALSE)#,names=tb$pred_mod)
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#title("RMSE per model over all tiles")
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| 705 |
p<- bwplot(rmse~pred_mod | reg, data=tb_subset,ylim=c(0,5),
|
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main="RMSE per model and region over all tiles with scaling")
|
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print(p)
|
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| 708 |
dev.off()
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| 691 | 709 |
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list_outfiles[[counter_fig+1]] <- paste("Figure8d_boxplot_overall_separated_by_region_scaling_",model_name[i],"_",out_suffix,".png",sep="")
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counter_fig <- counter_fig + 1 |
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list_outfiles[[counter_fig+1]] <- fig_filename
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counter_fig <- counter_fig + 1
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| 694 | 712 |
|
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} |
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|
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##################################################### |
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#### Figure 9: plotting boxplot by year and regions ########### |
| 697 | 717 |
|
| ... | ... | |
| 727 | 747 |
############## Collect information from assessment ########## |
| 728 | 748 |
|
| 729 | 749 |
# This is hard coded and can be improved later on for flexibility. It works for now... |
| 730 |
comments_str <- c("tile processed for the region",
|
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comments_str <- |
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c("tile processed for the region",
|
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| 731 | 752 |
"boxplot with outliers", |
| 732 | 753 |
"boxplot with outliers", |
| 733 | 754 |
"boxplot scaling by tiles", |
| ... | ... | |
| 751 | 772 |
"boxplot overall separated by region with_outliers", |
| 752 | 773 |
"boxplot overall separated by region with_scaling") |
| 753 | 774 |
|
| 754 |
figure_no <- c("figure_1","figure_2a","figure_2a","figure_2b","figure_2b","figure_3a","figure_3a","figure_3b","figure_3b",
|
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model_name=col_model_name, |
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reg=col_reg, |
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year_predicted=col_year_predicted, |
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filename=unlist(list_outfiles)) |
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comments_str <- |
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#Should have this at the location of the figures!!! will be done later? |
|
| 781 |
r1 <-c("figure_1","tile processed for the region",NA,region_name,year_predicted,list_outfiles[[1]])
|
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| 782 |
r2 <-c("figure_2a","boxplot with outliers","mod1",region_name,year_predicted,list_outfiles[[2]])
|
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r3 <-c("figure_2a","boxplot scaling by tiles","mod_kr",region_name,year_predicted,list_outfiles[[3]])
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|
| 784 |
r4 <-c("figure_2b","boxplot scaling by tiles","mod1",region_name,year_predicted,list_outfiles[[4]])
|
|
| 785 |
r5 <-c("figure_2b","boxplot scaling by tiles","mod_kr",region_name,year_predicted,list_outfiles[[5]])
|
|
| 786 |
r6 <-c("figure_3a","boxplot scaling by tiles","mod1",region_name,year_predicted,list_outfiles[[6]])
|
|
| 787 |
r7 <-c("figure_3b","boxplot scaling by tiles","mod1",region_name,year_predicted,list_outfiles[[7]])
|
|
| 788 |
r8 <-c("figure_3a","boxplot scaling by tiles","mod_kr",region_name,year_predicted,list_outfiles[[8]])
|
|
| 789 |
r9 <-c("figure_3b","boxplot scaling by tiles","mod_kr",region_name,year_predicted,list_outfiles[[9]])
|
|
| 790 |
|
|
| 791 |
NA,"mod1","mod_kr","mod1","mod_kr","mod1","mod_1","mod_kr","mod_kr", |
|
| 792 |
|
|
| 793 |
|
|
| 794 |
c("tile processed for the region",
|
|
| 795 |
"boxplot with outliers", |
|
| 796 |
"boxplot with outliers", |
|
| 797 |
"boxplot scaling by tiles", |
|
| 798 |
"boxplot scaling by tiles", |
|
| 799 |
"boxplot overall region with outliers", |
|
| 800 |
"boxplot overall region with scaling", |
|
| 801 |
"boxplot overall region with outliers", |
|
| 802 |
"boxplot overall region with scaling", |
|
| 803 |
"Barplot of accuracy metrics ranked by tile", |
|
| 804 |
"Barplot of accuracy metrics ranked by tile", |
|
| 805 |
"Average accuracy metrics map at centroids", |
|
| 806 |
"Average accuracy metrics map at centroids", |
|
| 807 |
"Number of missing day threshold1 map centroids", |
|
| 808 |
"Number of missing day threshold2 map centroids", |
|
| 809 |
"Number of missing day threshold3 map centroids", |
|
| 810 |
"Number of missing day threshold4 map centroids", |
|
| 811 |
"number_daily_predictions_per_model", |
|
| 812 |
"histogram number_daily_predictions_per_models", |
|
| 813 |
"boxplot overall separated by region with_outliers", |
|
| 814 |
"boxplot overall separated by region with_scaling", |
|
| 815 |
"boxplot overall separated by region with_outliers", |
|
| 816 |
"boxplot overall separated by region with_scaling") |
|
| 817 |
|
|
| 818 |
|
|
| 819 |
figure_no <- c("figure_1","figure_2a","figure_2a","figure_2b","figure_2b","figure_3a","figure_3b","figure_3a","figure_3b",
|
|
| 755 | 820 |
"figure_5", "figure_5","figure_6","figure_6","Figure_7a","Figure_7a","Figure_7a","Figure_7a","Figure_7b", |
| 756 |
"Figure_7c","Figure 8a","Figure 8a","Figure 8b","Figure 8b") |
|
| 821 |
"Figure_7c","Figure 8a","Figure 8b","Figure 8c","Figure 8d","Figure 8c","Figure 8d") |
|
| 822 |
|
|
| 823 |
col_model_name <- c(NA,"mod1","mod_kr","mod1","mod_kr","mod1","mod_1","mod_kr","mod_kr", |
|
| 824 |
"mod1","mod_kr","mod1","mod_kr","mod1","mod1","mod1","mod1",NA, |
|
| 825 |
NA,NA,NA,"mod1","mod1","mod_kr","mod_kr") |
|
| 826 |
|
|
| 827 |
-rw-r--r-- 1 parmentier layers 14441 Feb 2 16:06 Figure2a_boxplot_with_oultiers_by_tiles_mod1_global_analyses_overall_assessment_reg4_01272016.png |
|
| 828 |
-rw-r--r-- 1 parmentier layers 13617 Feb 2 16:06 Figure2a_boxplot_with_oultiers_by_tiles_mod_kr_global_analyses_overall_assessment_reg4_01272016.png |
|
| 829 |
-rw-r--r-- 1 parmentier layers 9638 Feb 2 16:07 Figure2b_boxplot_scaling_by_tiles_mod1_global_analyses_overall_assessment_reg4_01272016.png |
|
| 830 |
-rw-r--r-- 1 parmentier layers 9606 Feb 2 16:07 Figure2b_boxplot_scaling_by_tiles_mod_kr_global_analyses_overall_assessment_reg4_01272016.png |
|
| 831 |
-rw-r--r-- 1 parmentier layers 4925 Feb 2 16:07 Figure3a_boxplot_overall_region_with_oultiers_mod1_global_analyses_overall_assessment_reg4_01272016.png |
|
| 832 |
-rw-r--r-- 1 parmentier layers 4527 Feb 2 16:07 Figure3b_boxplot_overall_region_scaling_mod1_global_analyses_overall_assessment_reg4_01272016.png |
|
| 833 |
-rw-r--r-- 1 parmentier layers 5193 Feb 2 16:07 Figure3a_boxplot_overall_region_with_oultiers_mod_kr_global_analyses_overall_assessment_reg4_01272016.png |
|
| 834 |
-rw-r--r-- 1 parmentier layers 4522 Feb 2 16:07 Figure3b_boxplot_overall_region_scaling_mod_kr_global_analyses_overall_assessment_reg4_01272016.png |
|
| 835 |
-rw-r--r-- 1 parmentier layers 6079 Feb 2 16:07 Figure5_ac_metrics_ranked_mod1_global_analyses_overall_assessment_reg4_01272016.png |
|
| 836 |
-rw-r--r-- 1 parmentier layers 6251 Feb 2 16:07 Figure5_ac_metrics_ranked_mod_kr_global_analyses_overall_assessment_reg4_01272016.png |
|
| 837 |
-rw-r--r-- 1 parmentier layers 120492 Feb 2 16:08 Figure6_ac_metrics_map_centroids_tile_mod1_global_analyses_overall_assessment_reg4_01272016.png |
|
| 838 |
-rw-r--r-- 1 parmentier layers 120345 Feb 2 16:08 Figure6_ac_metrics_map_centroids_tile_mod_kr_global_analyses_overall_assessment_reg4_01272016.png |
|
| 839 |
-rw-r--r-- 1 parmentier layers 88938 Feb 2 16:09 Figure7a_ac_metrics_map_centroids_tile_mod1_missing_day_367_global_analyses_overall_assessment_reg4_01272016.png |
|
| 840 |
-rw-r--r-- 1 parmentier layers 89437 Feb 2 16:09 Figure7a_ac_metrics_map_centroids_tile_mod1_missing_day_365_global_analyses_overall_assessment_reg4_01272016.png |
|
| 841 |
-rw-r--r-- 1 parmentier layers 89284 Feb 2 16:10 Figure7a_ac_metrics_map_centroids_tile_mod1_missing_day_300_global_analyses_overall_assessment_reg4_01272016.png |
|
| 842 |
-rw-r--r-- 1 parmentier layers 32506 Feb 2 16:10 Figure7b_number_daily_predictions_per_models_global_analyses_overall_assessment_reg4_01272016.png |
|
| 843 |
-rw-r--r-- 1 parmentier layers 13970 Feb 2 16:10 Figure7c_histogram_number_daily_predictions_per_models_global_analyses_overall_assessment_reg4_01272016.png |
|
| 844 |
-rw-r--r-- 1 parmentier layers 12726 Feb 2 16:11 Figure8a_boxplot_overall_separated_by_region_with_oultiers__global_analyses_overall_assessment_reg4_01272016.png |
|
| 845 |
-rw-r--r-- 1 parmentier layers 12061 Feb 2 16:11 Figure8b_boxplot_overall_separated_by_region_scaling__global_analyses_overall_assessment_reg4_01272016.png |
|
| 846 |
-rw-r--r-- 1 parmentier layers 10851 Feb 2 16:11 Figure8c_boxplot_overall_separated_by_region_with_oultiers_mod1_global_analyses_overall_assessment_reg4_01272016.png |
|
| 847 |
-rw-r--r-- 1 parmentier layers 9814 Feb 2 16:11 Figure8d_boxplot_overall_separated_by_region_scaling_mod1_global_analyses_overall_assessment_reg4_01272016.png |
|
| 848 |
-rw-r--r-- 1 parmentier layers 11599 Feb 2 16:11 Figure8c_boxplot_overall_separated_by_region_with_oultiers_mod_kr_global_analyses_overall_assessment_reg4_01272016.png |
|
| 849 |
-rw-r--r-- 1 parmentier layers 9597 Feb 2 16:11 Figure8d_boxplot_overall_separated_by_region_scaling_mod_kr_global_analyses_overall_assessment_reg4_01272016.png |
|
| 757 | 850 |
|
| 758 |
col_model_name <- c(NA,"mod1","mod_kr","mod1","mod_kr","mod1","mod_kr","mod1","mod_kr","mod1","mod_kr", |
|
| 759 |
"mod1","mod_kr","mod1","mod1","mod1","mod1","mod1","mod1",NA,NA,"mod1","mod1") |
|
| 851 |
|
|
| 760 | 852 |
col_reg <- rep(region_name,length(list_outfiles)) |
| 761 | 853 |
col_year_predicted <- rep(year_predicted,length(list_outfiles)) |
| 762 | 854 |
|
Also available in: Unified diff
part2 assessment figure production debugging of errors related to shapefiles and lattice