Revision e62bc941
Added by Benoit Parmentier almost 9 years ago
| climate/research/oregon/interpolation/global_run_scalingup_assessment_part3.R | ||
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for(i in 1:length(shps_tiles)){
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shp1 <- shps_tiles[[i]] |
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pt <- centroids_pts[[i]] |
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#if(!inherits(shp1,"try-error")){
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# plot(shp1,add=T,border="blue") |
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# #plot(pt,add=T,cex=2,pch=5) |
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# label_id <- df_tile_processed$tile_id[i] |
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# text(coordinates(pt)[1],coordinates(pt)[2],labels=i,cex=1.3,font=2,col=c("red"))
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#} |
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#to be able to run on NEX set font and usePolypath, maybe add option NEX? |
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if(!inherits(shp1,"try-error")){
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plot(shp1,add=T,border="blue")
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plot(shp1,add=T,border="blue",usePolypath = FALSE) #added usePolypath following error on brige and NEX
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#plot(pt,add=T,cex=2,pch=5) |
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label_id <- df_tile_processed$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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| ... | ... | |
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#summary_metrics_v$n_missing <- summary_metrics_v$n == 365 |
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#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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summary_metrics_v_subset <- subset(summary_metrics_v,model_name=="mod1") |
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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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"_",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=""), |
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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model_name[j] |
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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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#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 |
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} |
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counter_fig <- counter_fig+length(threshold_missing_day) #currently 4 days... |
| ... | ... | |
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##################################################### |
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#### Figure 9: plotting boxplot by year and regions ########### |
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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 9a |
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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("Figure9a_boxplot_overall_separated_by_region_year_with_oultiers_",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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p<- bwplot(rmse~pred_mod | reg + year_predicted, data=tb, |
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main="RMSE per model and region over all tiles") |
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p<- bwplot(rmse~pred_mod | reg + year_predicted, data=tb, |
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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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## Figure 9b |
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png(filename=paste("Figure8b_boxplot_overall_separated_by_region_year_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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p<- bwplot(rmse~pred_mod | reg + year_predicted, data=tb,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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## Figure 9a |
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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("Figure9a_boxplot_overall_separated_by_region_year_with_oultiers_",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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#p<- bwplot(rmse~pred_mod | reg + year_predicted, data=tb, |
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# main="RMSE per model and region over all tiles") |
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#p<- bwplot(rmse~year_predicted | reg , subset(tb,tb$pred_mod==model_name[i]), |
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#main="RMSE per model and region over all tiles") |
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#p<- bwplot(rmse~year_predicted , subset(tb,tb$pred_mod==model_name[i]), |
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# main="RMSE per model and region over all tiles") |
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boxplot(rmse~year_predicted , subset(tb,tb$pred_mod==model_name[i])) |
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title(paste("RMSE with outliers and by year for all tiles: ",model_name[i],sep=""))
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#print(p) |
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dev.off() |
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## Figure 9b |
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png(filename=paste("Figure9b_boxplot_overall_separated_by_region_year_scaling_",model_name[i],"_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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#boxplot(rmse~pred_mod,data=tb,ylim=c(0,5),outline=FALSE)#,names=tb$pred_mod) |
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#title("RMSE per model over all tiles")
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#p<- bwplot(rmse~pred_mod | reg + year_predicted, data=tb,ylim=c(0,5), |
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# main="RMSE per model and region over all tiles") |
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boxplot(rmse~year_predicted,subset(tb,tb$pred_mod==model_name[i]),ylim=c(0,5),outline=FALSE) |
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title(paste("RMSE with scaling and by year for all tiles: ",model_name[i],sep=""))
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list_outfiles[[counter_fig+1]] <- paste("Figure9a_boxplot_overall_separated_by_region_year_with_oultiers_",model_name[i],"_",out_suffix,".png",sep="")
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counter_fig <- counter_fig + 1 |
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#print(p) |
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dev.off() |
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list_outfiles[[counter_fig+1]] <- paste("Figure9a_boxplot_overall_separated_by_region_year_with_oultiers_",model_name[i],"_",out_suffix,".png",sep="")
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counter_fig <- counter_fig + 1 |
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} |
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############################################################## |
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############## Prepare object to return |
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############## Collect information from assessment ########## |
| ... | ... | |
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# Browse[3]> c |
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# Error in grid.Call.graphics(L_setviewport, pvp, TRUE) : |
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# non-finite location and/or size for viewport |
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#Error in grid.Call.graphics(L_setviewport, vp, TRUE) : |
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# non-finite location and/or size for viewport |
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Also available in: Unified diff
debugging of figures and adding figure 9 to produce yearly comparison of RMSE