Revision 75dffb8e
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: 02/03/2016
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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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year_predicted <- list_param_run_assessment_plotting$year_predicted |
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NA_value <- NA_flag_val |
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metric_name <- "rmse" #to be added to the code later... |
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##################### START SCRIPT ################# |
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####### PART 1: Read in data ######## |
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setwd(out_dir) |
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list_outfiles <- vector("list", length=25) #collect names of output files
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list_outfiles_names <- vector("list", length=25) #collect names of output files
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list_outfiles <- vector("list", length=23) #collect names of output files
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list_outfiles_names <- vector("list", length=23) #collect names of output files
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counter_fig <- 0 #index of figure to collect outputs |
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#i <- year_predicted |
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#unique(summaty_metrics$tile_id) |
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#text(lat-shp,) |
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#union(list_shp_reg_files[[1]],list_shp_reg_files[[2]]) |
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#Row used in constructing output table... |
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list_outfiles[[counter_fig+1]] <- paste("Figure1_tile_processed_region_",region_name,"_",out_suffix,".png",sep="")
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counter_fig <- counter_fig+1 |
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#this will be changed to be added to data.frame on the fly |
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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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############### |
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### Figure 2: boxplot of average accuracy by model and by tiles |
| ... | ... | |
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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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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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## Figure 2b |
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#with ylim and removing trailing... |
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for(i in 1:length(model_name)){ #there are two models!!
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| ... | ... | |
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} |
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counter_fig <- counter_fig + length(model_name) |
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#bwplot(rmse~tile_id, data=subset(tb,tb$pred_mod=="mod1")) |
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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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############### |
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### Figure 3: boxplot of average RMSE by model acrosss all tiles |
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| ... | ... | |
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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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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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################ |
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### Figure 4: plot predicted tiff for specific date per model |
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} |
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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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###################### |
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### Figure 6: plot map of average RMSE per tile at centroids |
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### Without |
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#list_df_ac_mod <- vector("list",length=length(lf_pred_list))
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list_df_ac_mod <- vector("list",length=length(model_name))
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list_df_ac_mod <- vector("list",length=2)
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for (i in 1:length(model_name)){
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| ... | ... | |
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} |
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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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###################### |
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### Figure 7: Number of predictions: daily and monthly |
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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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j<-1 #for model name 1,mod1
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j<-1 #for model name 1 |
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for(i in 1:length(threshold_missing_day)){
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#summary_metrics_v$n_missing <- summary_metrics_v$n == 365 |
| ... | ... | |
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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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#only mod1 right now |
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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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} |
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counter_fig <- counter_fig+length(threshold_missing_day) #currently 4 days... |
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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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### Potential |
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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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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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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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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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| ... | ... | |
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##### Figure 8: Breaking down accuracy by regions!! ##### |
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#summary_metrics_v <- merge(summary_metrics_v,df_tile_processed,by="tile_id") |
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################## |
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##First plot with all models together |
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## Figure 8a |
| ... | ... | |
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print(p) |
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dev.off() |
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list_outfiles[[counter_fig+1]] <- paste("Figure8a_boxplot_overall_accuracy_by_model_separated_by_region_with_oultiers_",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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counter_fig <- counter_fig + 1 |
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## Figure 8b |
| ... | ... | |
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print(p) |
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dev.off() |
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list_outfiles[[counter_fig+1]] <- paste("Figure8b_boxplot_overall_accuracy_by_model_separated_by_region_scaling_",out_suffix,".png",sep="")
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list_outfiles[[counter_fig+1]] <- paste("Figure8b_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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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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####### |
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##Second, plot for each model separately |
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for(i in 1:length(model_name)){
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| ... | ... | |
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col_mfrow <- 1 |
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row_mfrow <- 1 |
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fig_filename <- paste("Figure8c_boxplot_overall_accuracy_separated_by_region_with_outliers_",model_name[i],"_",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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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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|
| ... | ... | |
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counter_fig <- counter_fig + 1 |
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## Figure 8d |
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fig_filename <- paste("Figure8d_boxplot_overall_accuracy_separated_by_region_scaling_",model_name[i],"_",out_suffix,".png",sep="")
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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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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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| ... | ... | |
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counter_fig <- counter_fig + 1 |
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} |
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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[[20]])
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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[[21]])
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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[[20]])
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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[[21]])
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##################################################### |
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#### Figure 9: plotting boxplot by year and regions ########### |
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| ... | ... | |
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############## Collect information from assessment ########## |
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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 |
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comments_str <- |
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c("tile processed for the region",
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"boxplot with outliers", |
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"boxplot with outliers", |
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"boxplot scaling by tiles", |
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"boxplot scaling by tiles", |
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"boxplot overall region with outliers", |
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"boxplot overall region with scaling", |
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"boxplot overall region with outliers", |
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"boxplot overall region with scaling", |
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"Barplot of accuracy metrics ranked by tile", |
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"Barplot of accuracy metrics ranked by tile", |
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"Average accuracy metrics map at centroids", |
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"Average accuracy metrics map at centroids", |
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"Number of missing day threshold1 map centroids", |
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"Number of missing day threshold2 map centroids", |
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"Number of missing day threshold3 map centroids", |
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"Number of missing day threshold4 map centroids", |
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"number_daily_predictions_per_model", |
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"histogram number_daily_predictions_per_models", |
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"boxplot overall separated by region with_outliers", |
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"boxplot overall separated by region with_scaling", |
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"boxplot overall separated by region with_outliers", |
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"boxplot overall separated by region with_scaling") |
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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? |
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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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#r15 <-c("figure_7","Number of missing days threshold2 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[15]])
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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[[16]])
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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[[17]])
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#r18 <-c("figure_7b","Number of daily predictions per_models","mod1",metric_name,region_name,year_predicted,list_outfiles[[18]])
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#r19 <-c("figure_7c","Histogram number daily predictions per models","mod1",metric_name,region_name,year_predicted,list_outfiles[[19]])
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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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#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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#Assemble all the figures description and information in a data.frame for later use |
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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) |
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df_assessment_figures_files <- as.data.frame(do.call(rbind,list_rows)) |
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names(df_assessment_figures_files) <- c("figure_no","comment","model_name","reg","metric_name","year_predicted","filename")
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r1 <-c("figure_1","tile processed for the region",NA,region_name,year_predicted,list_outfiles[[1]])
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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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r4 <-c("figure_2b","boxplot scaling by tiles","mod1",region_name,year_predicted,list_outfiles[[4]])
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r5 <-c("figure_2b","boxplot scaling by tiles","mod_kr",region_name,year_predicted,list_outfiles[[5]])
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r6 <-c("figure_3a","boxplot scaling by tiles","mod1",region_name,year_predicted,list_outfiles[[6]])
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r7 <-c("figure_3b","boxplot scaling by tiles","mod1",region_name,year_predicted,list_outfiles[[7]])
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r8 <-c("figure_3a","boxplot scaling by tiles","mod_kr",region_name,year_predicted,list_outfiles[[8]])
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| 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",
|
|
| 820 |
"figure_5", "figure_5","figure_6","figure_6","Figure_7a","Figure_7a","Figure_7a","Figure_7a","Figure_7b", |
|
| 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 |
|
| 850 |
|
|
| 851 |
|
|
| 852 |
col_reg <- rep(region_name,length(list_outfiles)) |
|
| 853 |
col_year_predicted <- rep(year_predicted,length(list_outfiles)) |
|
| 854 |
|
|
| 855 |
#This data.frame contains all the files from the assessment |
|
| 856 |
df_assessment_figures_files <- data.frame(figure_no=figure_no, |
|
| 857 |
comment = comments_str, |
|
| 858 |
model_name=col_model_name, |
|
| 859 |
reg=col_reg, |
|
| 860 |
year_predicted=col_year_predicted, |
|
| 861 |
filename=unlist(list_outfiles)) |
|
| 824 | 862 |
|
| 825 | 863 |
###Prepare files for copying back? |
| 826 | 864 |
df_assessment_figures_files_names <- file.path(out_dir,paste("df_assessment_figures_files_",region_name,"_",year_predicted,"_",out_suffix,".txt",sep=""))
|
| ... | ... | |
| 965 | 1003 |
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| 966 | 1004 |
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| 967 | 1005 |
|
| 1006 |
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|
| 1007 |
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|
| 1008 |
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|
| 1009 |
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|
| 1010 |
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|
| 1011 |
|
|
| 1012 |
|
|
| 1013 |
|
|
| 1014 |
|
|
| 1015 |
|
|
| 1016 |
|
|
| 1017 |
|
|
| 1018 |
|
|
| 1019 |
|
|
| 1020 |
|
|
| 1021 |
|
|
| 1022 |
|
|
| 1023 |
|
|
| 1024 |
|
|
| 1025 |
|
|
| 1026 |
|
|
| 1027 |
|
|
| 1028 |
|
|
| 1029 |
|
|
| 1030 |
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| 1031 |
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|
| 1032 |
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|
| 1033 |
|
|
| 1034 |
|
|
| 1035 |
#### CURRENT ERROR ON NEX |
|
| 1036 |
|
|
| 1037 |
# #comments #figure_no #region #models |
|
| 1038 |
# tile processed for the region figure_1 reg4 NA |
|
| 1039 |
# boxplot with outlier figure_2a reg4 mod1 |
|
| 1040 |
# boxplot with outlier figure_2a reg4 mod_kr |
|
| 1041 |
# boxplot scaling by tiles figure_2b reg4 mod1 |
|
| 1042 |
# boxplot scaling by tiles figure_2b reg4 mod_kr |
|
| 1043 |
# boxplot overall region with outliers figure_3a reg4 NA |
|
| 1044 |
# boxplot overall region with scaling figure_3b reg4 NA |
|
| 1045 |
# Barplot of metrics ranked by tile Figure_5 |
|
| 1046 |
# boxplot overall region with scaling figure_3b reg4 NA |
|
| 1047 |
# Barplot of metrics ranked by tile Figure_5 |
|
| 1048 |
# Barplot of metrics ranked by tile Figure_5 |
|
| 1049 |
# Average metrics map centroids Figure_6 |
|
| 1050 |
# Average metrics map centroids Figure_6 |
|
| 1051 |
# Number of missing day threshold1 map centroids Figure_7a |
|
| 1052 |
# Number of missing day threshold1 map centroids Figure_7a |
|
| 1053 |
# Number of missing day threshold1 map centroids Figure_7a |
|
| 1054 |
# Number of missing day threshold1 map centroids Figure_7a |
|
| 1055 |
# number_daily_predictions_per_model Figure_7b |
|
| 1056 |
# histogram number_daily_predictions_per_models Figure_7c |
|
| 1057 |
# boxplot_overall_separated_by_region_with_oultiers_ Figure 8a |
|
| 1058 |
# boxplot_overall_separated_by_region_with_scaling Figure 8b |
|
| 1059 |
|
|
| 1060 |
# Browse[3]> c |
|
| 1061 |
# Error in text.default(coordinates(pt)[1], coordinates(pt)[2], labels = i, : |
|
| 1062 |
# X11 font -adobe-helvetica-%s-%s-*-*-%d-*-*-*-*-*-*-*, face 2 at size 16 could not be loaded |
|
| 1063 |
# In addition: Warning message: |
|
| 1064 |
# In polypath(x = mcrds[, 1], y = mcrds[, 2], border = border, col = col, : |
|
| 1065 |
# Path drawing not available for this device |
|
| 1066 |
|
|
| 1067 |
|
|
| 1068 |
|
|
| 1069 |
# Browse[2]> for(i in 1:length(threshold_missing_day)){
|
|
| 1070 |
# + |
|
| 1071 |
# + #summary_metrics_v$n_missing <- summary_metrics_v$n == 365 |
|
| 1072 |
# + #summary_metrics_v$n_missing <- summary_metrics_v$n < 365 |
|
| 1073 |
# + summary_metrics_v$n_missing <- summary_metrics_v$n < threshold_missing_day[i] |
|
| 1074 |
# + summary_metrics_v_subset <- subset(summary_metrics_v,model_name=="mod1") |
|
| 1075 |
# + |
|
| 1076 |
# + #res_pix <- 1200 |
|
| 1077 |
# + res_pix <- 960 |
|
| 1078 |
# + |
|
| 1079 |
# + col_mfrow <- 1 |
|
| 1080 |
# + row_mfrow <- 1 |
|
| 1081 |
# + fig_filename <- paste("Figure7a_ac_metrics_map_centroids_tile_",model_name[j],"_","missing_day_",threshold_missing_day[i],
|
|
| 1082 |
# + "_",out_suffix,".png",sep="") |
|
| 1083 |
# + png(filename=paste("Figure7a_ac_metrics_map_centroids_tile_",model_name[j],"_","missing_day_",threshold_missing_day[i],
|
|
| 1084 |
# + "_",out_suffix,".png",sep=""), |
|
| 1085 |
# + width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
|
| 1086 |
# + |
|
| 1087 |
# + model_name[j] |
|
| 1088 |
# + |
|
| 1089 |
# + p_shp <- layer(sp.polygons(reg_layer, lwd=1, col='black')) |
|
| 1090 |
# + #title("(a) Mean for 1 January")
|
|
| 1091 |
# + p <- bubble(summary_metrics_v_subset,"n_missing",main=paste("Missing per tile and by ",model_name[j]," for ",
|
|
| 1092 |
# + threshold_missing_day[i])) |
|
| 1093 |
# + p1 <- p+p_shp |
|
| 1094 |
# + try(print(p1)) #error raised if number of missing values below a threshold does not exist |
|
| 1095 |
# + dev.off() |
|
| 1096 |
# + |
|
| 1097 |
# + list_outfiles[[counter_fig+i]] <- fig_filename |
|
| 1098 |
# + } |
|
| 1099 |
# debug at /nobackupp8/bparmen1/env_layers_scripts/global_run_scalingup_assessment_part2_01042016.R#272: i |
|
| 1100 |
# Browse[3]> counter_fig <- counter_fig+length(threshold_missing_day) #currently 4 days... |
|
| 1101 |
# Browse[3]> c |
|
| 1102 |
# Error in grid.Call.graphics(L_setviewport, pvp, TRUE) : |
|
| 1103 |
# non-finite location and/or size for viewport |
|
Also available in: Unified diff
modifying assessment script part2 figures creation