Revision 2cd02ebf
Added by Benoit Parmentier about 8 years ago
| climate/research/oregon/interpolation/global_product_assessment_part0.R | ||
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# |
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#AUTHOR: Benoit Parmentier |
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#CREATED ON: 10/27/2016 |
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#MODIFIED ON: 11/10/2016
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#MODIFIED ON: 11/09/2016
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#Version: 1 |
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#PROJECT: Environmental Layers project |
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#COMMENTS: |
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#source /nobackupp6/aguzman4/climateLayers/sharedModules2/etc/environ.sh |
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# |
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#setfacl -Rm u:aguzman4:rwx /nobackupp6/aguzman4/climateLayers/LST_tempSpline/ |
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#COMMIT: modifying function generate raster of number of predictions for day with missing tiles
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#COMMIT: generate raster of number of predictions for day with missing tiles |
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################################################################################################# |
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#### Step 3: combine overlap information and number of predictions by day |
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##Now loop through every day if missing then generate are raster showing map of number of prediction |
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#r_tiles_stack <- stack(list_tiles_predicted_masked)
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#names(r_tiles_stack) <- basename(in_dir_reg) #this does not work, X. is added to the name, use list instead
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r_tiles_stack <- stack(list_tiles_predicted_masked) |
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names(r_tiles_stack) <- basename(in_dir_reg)
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names(list_tiles_predicted_masked) <- basename(in_dir_reg) |
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#list_tiles_predicted_masked <- mclapply(1:length(list_tiles_predicted_m), |
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# FUN=function(i){raster(list_tiles_predicted_m[i])},
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# mc.preschedule=FALSE,mc.cores = num_cores) |
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df_missing_tiles_day <- subset(df_missing,tot_missing > 0) |
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#r_tiles_s <- r_tiles_stack |
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names_tiles <- basename(in_dir_reg) |
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generate_raster_number_of_prediction_by_day <- function(i,list_param){
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list_names_tile_coord |
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df_time_series |
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missing_tiles <- df_missing_tiles_day[i] |
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#r_tiles_s <- list_param$r_tiles_s |
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list_param$list_tiles_predicted_masked |
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r_tiles_s <- list_param$r_tiles_s |
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#df_missing_tiles_day <- subset(df_missing,tot_missing > 0) |
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#stack() ## all tiles for the day |
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#df_missing_tiles_day[,-c("tot_missing")]
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df_missing_tiles_day[,!c("tot_missing")]
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selected_col <- names(list_tiles_predicted_masked) |
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df_missing_tiles_day_subset <- subset(df_missing_tiles_day,select=selected_col) |
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df_missing_tiles_day[,-c("tot_missing")]
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#drops <- c("x","z")
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#DF[ , !(names(DF) %in% drops)] |
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selected_missing <- df_missing_tiles_day_subset[i,]==1
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selected_missing <- df_missing_tiles_day[i,]==1 |
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names(df_missing_tiles_day)[selected_missing] |
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r_day_predicted <- r_overlap_m - r_stack |
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#names(list_tiles_predicted_masked)[selected_missing] |
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list_missing_tiles_raster <- list_tiles_predicted_masked[selected_missing] |
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r_tiles_s <- stack(list_missing_tiles_raster) |
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### first sum missing |
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datasum <- stackApply(r_tiles_s, 1:nlayers(r_tiles_s), fun = sum) |
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### then substract missing tiles... |
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r_day_predicted <- r_overlap_m -datasum |
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### generate retunr object |
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returm(r_day_predicted) |
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} |
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Also available in: Unified diff
generate raster of number of predictions for day with missing tiles